Part I

1.1 Electrostatic Phenomena

When we rub glass rods with silk, the glass rod become charged with negative charges. When we rub plastic rods with fur, the rods are charged with positive charges. After being rubbed with fur, plastic rods repel each other. However, if put the plastic rod close to a glass rod, they will attract each other. This kind of interaction between charges is called electrostatic force.

1.2 Electroscope

The electroscope is a tool to measure electrostatic force. It works by electrostatic attraction or repulsion and consists of two metallic leaves suspended from a metal hook. The leaves are inside a glass container to protect it from air currents. The leaves are connected to a metal ball which is on the top of the container. When a charged object is brought near the ball, the charges on the object cause the same charges move away toward the end of the leaves. The leaves are then spread apart due to the repulsion of same charges.

The electroscope can also quantify the amount of charge of an object. A larger charge causes the leaves to spread further apart. Electroscope can show the type of charges of two object. One can first bring an object in touch with the electroscope. This will cause the foil leaves to separate. Then, bringing the second object can be brought next to the electroscope’s post. If the two objects have same type of charges, the second contact will cause a further separation. On the contrary, if the leaves are brought closer than the two objects have different types of charge.

1.3 Benjamin Franklin’s idea

Charge produced on a rubber rod when rubbed by fur and charge produced on a glass rod when rubbed with silk have been found having different properties for a long time. They didn’t gain their names until Benjamin Franklin (1706-1790) introduced the concept “positive” and “negative” around 1750. He noticed stable fluid is present in all objects. During rubbing, they carry similar or different properties as some fluid is transferred from one object to another. Thus, he proposed that the charge on a glass rod when rubbed with silk be called positive. This concept is revolutionary, and Franklin’s model also approaches the reality more, which has electrons being transferred instead of “fluid”.

1.4 Conductors and insulators 

Conductors are materials that permit electrons to move easily from one region of the material to another. For example, electrons can move freely within a copper wire. Most metals are good conductors, while most nonmetals are insulators. Insulators can prevent charge from leaving metal wires. Most metals are conductors, while most nonmetals are insulators. Within a solid metal, one or more electrons in each atom become detached and can move freely throughout the material. In an insulator, electrons cannot move freely through the material. 

1.5 Coulomb force

Coulomb’s law is used to measure the electrostatic force. It is developed by the French scientist Charles Coulomb (1736-1806). 

The force F can be expressed as:

Were q1 and q2 indicates the charges respectively, r is the distance between the charges, and k is a constant. When the charges have the same sign, the forces are repulsive; when they have opposite signs, the forces are attractive. 

In order to measure the weak forces between two charges, Coulomb invented a torsion balance. An insulating rod is suspended at the middle of an enclosure. Two small metal balls are attached at each side of the rod. Both balls are charged. Another ball with the same charges is put close at one ball. It then causes the rod to twist. If the torque needed to create the angle of the twist is measured, the forces produced by two same charges can be measured. Besides, in order to give two balls the same charges, Coulomb placed one charged ball into contact with an uncharged ball, thus divided the charges on one ball into two equal parts. By repeating this process, he can divide charges to one-half, one-quarter and so on.

The electrostatic force has a similar functional form than the gravitational force, we can see they all have inverse-square dependence from the distance:

However, the gravitational force is always attractive. While the electric force depends on charges and can be either attractive or repulsive. For charged particles, the electrostatic force is the only force that is responsible to their interactions.

1.6 Electric field

Two positively charged objects at a distance exert a repulsion force to each other. If we place a negatively charged object instead of one positive object at the same place, the force, although still have the same magnitude, becomes repulsive. Therefore, the electrostatic force between two object depends on each object’s charges. We can define the Electrostatic field as a property generated by one charge only. This can be defined by dividing the Force F generated in between two charges by one of the charges as follows.

The electric field at some point is defined as the electric force F (experienced by a test charge q) divided by the charge q. In other words. The electric field equals to the electric force per unit charge. The object’s electric field is always present and its magnitude can vary by changing the distance from that object. If another charge q enters the field, the force it experiences is exerted by the electric filed of the original object. 

1.7 Electric Field Line

One can define electric field lines to visualize the direction of a given force or a given electric field. The electric field for a positive charge always points away from it, outward, whereas the electric field of a negative charge point towards it, or inwards.

The field direction of a single charge is the direction of the force a positive net charge experienced near the charge. We can find that the test charge is repelled by the original charge when placing near it. The test charge is attracted by the original charge. 

When placing two opposite charges together, they attract each other, so the electric field point toward the negative charge. On the left side of the negative charge, the field line is same to single negative charge’s, and on the right of the positive charge, the field line is also same to that of a single positive charge.

1.8 Electric Potential Energy

The electrostatic force is a conservative force, which means that we can define an electrostatic potential energy. This potential energy leads to the related concept of electric potential. As the change of kinetic energy equals the total work done on the object, if there is only electrostatic force act on the object, the work done by the electrostatic force equals the change in potential energy.

In a uniform electric field as shown above, , If a positive charge moves downward (in the direction of E), the work done by the electric force is positive, so U decreases.

If the charge moves in the opposite direction of E, the work done by the electric field is negative. In other words, an external force is done on the charge, causing U increases. For positive charges, moving along a positive electric field decreases their electric potential energy, while for negative charges, moving along the positive electric field increases U.

Since we usually define U=0 when r is infinite, when moving a particle from infinite far to r. Therefore,

1.9 Electric potential

Electric potential is related to electrostatic potential energy in much the same way as electric field is related to the electrostatic force. The change in electric potential is equal to the change in electrostatic potential energy per unit of positive charge.

Its unit is V or J/C. Electric potential is also called potential energy per unit charge. Like electric field, V exist at a point even if there is no charge.

Part 2

2.1 Electric Circuits 

An electric circuit is defined as a closed conducting path that allows the electric charges in the wire to move from one region to another. Within a conductor, when there is no external electric field, the charges move randomly. Instead, if there is an electric field E is established inside the conductor, charges will experience a force and start to move in one direction. The charges also experience collisions with the metal particles when they pass through conductors. In other words, the kinetic energy supplied by the electric field is dissipated to heat the conductor, which keeps the charges from moving faster.

Electric circuits must be a closed circuit instead of an open circuit or a conductor. In an open circuit, an electric field E₁ will let positive and negative charges accumulate respectively at two sides of the wire. These charges will then create an opposite electric field E₂ , which will offset E₁ and decrease the total electric field to zero.

As a result, we need a device, often called a battery, that act as an external force that can is in opposite direction to that result from E₁ and carry the positive charges from lower potential to higher potential.

Therefore, a complete circuit must consist of a wire, a conductor (or resistance), and a battery.

2.2 Electric current

In a conductor, if the charges experience an electric field, they will start moving in a direction. 

The net charge flowing through the circuit per unit time, or the rate of flow of electric charge is defined as current:

The standard unit of current is ampere, defined as 1 coulomb per second (1A=1C/s).

In different current-carrying materials, either positive or negative charges can be the moving charges. In figure 2.2, positive charges move to right, in the direction of the electric field, while negative charges move to the left, in the opposite direction. For negative charges that moves to the left, they create the same effect as the first case as the net charges on the right increases. In both case, positive charges flow to right, so we define current to be in the direction of the flow of positive charges. Current in this definition is called conventional current.

2.3 An analogy to the flow of water

In a circuit, the charges move through an external conducting path due to the electric field caused by the potential difference at the two sides of the battery. As the positive charges moves from high potential to low potential, the potential of the charges is thus decreasing. In order to maintain the current, as charges pass the battery, the battery provides a force to lift positive charges from low potential to high potential. The work done by the battery increases the potential energy of the charges.

This is analogous to water flowing in a pipe vertically, as shown in Figure 2.3. Water flows down the pipe from high gravitational potential energy to low potential energy. Then, a pump lifts the water back to its original height and increases its potential energy. While batteries provide necessary potential energy for the charges, the pump guarantees the water to flow.

2.4 Electromotive Force 

Electromotive force (emf) is the potential difference, or voltage, of a battery. It is actually not a force. Its equal to the work the battery does on every coulomb of charge that passes through it. It is, in other terms, a potential energy difference, or voltage,  ϵ or V is the symbol for emf. The unit of emf is volt (1V=1J/C)

2.5 Ohm’s Law and Resistance 

Ohm’s Law shows the proportional relationship between the current and voltage. It also shows the current not only depends to the voltage, but also on the resistance. 

The relationship was discovered experimentally by the German physicist Georg Ohm (1789-1854). In symbols:

A quantitative definition of resistance R is:

While the definition of R is:

The unit of resistance is ohm (1Ω=1V/A)

The longer the wire, the greater the resistance; the thicker the wire, the smaller the resistance. Besides, the resistance also depends on the electrical conductivity of the material. Compared with resistance of water’s flow rate in a pipe, a narrow water pipe offers more resistance; the larger cross-sectional area increases flow rate. If the pipe is stuffed with cotton, the resistance also increases. 

2.6 Series and parallel circuits

In a series circuit, the circuit elements are connected in sequence, lining up on a one after the other. In series, the current I remains the same through the path. 

The total resistance of the combination R_s is:

For each resistor, since they all have the same current, we can know their voltage respectively:

The resistance across two sides of the battery is:

Since the total resistance of a series combination is greater than any individual resistance, so connecting a bulb is series will make it glow less brightly.

2.6.2 Parallel circuits

In parallel circuits, each resistors provides an alternative path for the current. The potential difference is the same across each element.

The total current equals to the sum of the three currents in the resistors:

The current in each resistor is:

So the total resistance R is:

The total resistance of a parallel combination is less than any individual resistance. As the current in a bulb connected in parallel is independent to the adding of other resistors, connecting a bulb in parallel doesn’t change its brightness.

2.7 Electric Energy and Power

The negative and positive charges in a battery are kept at the opposite poles of the battery thanks to the chemical reactions happening inside the battery. This chemical reaction are generate by a chemical energy which keeps the positive and negative charges separated on the pole. This latter chemical energy corresponds to the electrical energy which is created within the battery and can be used through the electric circuit to move the charges from one point to another. As the charges pass resistors, the potential energy is dissipated as the charges collide with atoms in the resistors. 

The rate of transferring energy from the battery is called power:

The unit of power is watt: 1W=1J/C. The power delivered to any resistor is:

Where V_r  is the voltage across the resistor and I is the current in the resistor.

Part 3

3.1 What are magnetic poles?

Of a bar magnet, one end is called a north pole or N pole, and the other end is called a south pole or S pole. Opposite poles attract each other, and like poles repel each other. This is similar to electric interactions. We can also describe the interactions between two magnets as one sets up a magnetic field around it, and the other magnet moves in response to the magnetic field.

3.2 Is the Earth a magnet?

In early years, people sail with a compass which always points to the north. The Earth’s north is defined by the direction of the compass’s north points. As a result, Earth’s geographic north pole is like a magnet’s south pole, which attracts the compass’s north pole. The earth’s magnetic axis, though, is not parallel to its rotation axis, so the compass’s reading derivates a little from the true north. 

3.3 Magnetic field lines

While magnetic poles may be similar to electric charges, unlike isolated positive and negative charges, magnetic poles always exist in pairs. When a magnet is broken to two, each end becomes a new pole. The magnetic poles in pair are called a magnetic dipole, and an isolated magnetic pole is called magnetic monopole, which is not yet discovered.

Magnetic field lines emerge from the north pole and go into the south pole. Different from electric field lines, magnetic field lines do not end at the south pole, instead, they form a circle back to the north pole.

If we put a magnetic dipole in a magnetic field, the magnetic dipole tends to align with the magnetic field at its position due to magnetic attraction forces and repulsion forces.

3.4 An unexpected effect

The first evidence of the relationship of magnetism and electric current was discovered by Danish scientist Hans Christian Oersted. When he was giving a lecture, he surprisingly found a compass needle was deflected by a current-carrying wire. When the wire carries current, a compass placed directly over the horizontal wire deflects. As shown in figure 4, when the current is towards north, the needle deflects towards northeast. 

The electric and magnetic interactions in this case around a straight, current-carrying wire forms circular magnetic fields. The magnetic field’s direction is determined by a right-hand rule. Point the thumb of your right hand in the direction of the current. Your fingers now curling around the wire in the direction of magnetic field lines.

3.5 The magnetic force on a current-carrying wire

Since there are magnetic fields near a current, will this magnetic field exert a force to another current-carrying wire?

Ampere discovered that there is a force exerted to another current-carrying wire due to the magnetic field around the original wire. Ampere first ensured the force are not mainly caused by electrostatic effects. Then, he found out the magnetic force between two wires is proportional to the currents and inversely proportional to the distance r between the two wires.

This relationship can be expressed as:

where constant k’  is equal to   , I1 and and I2 are the current flowing through each of the wire, respectively, and l is the length of the wire.   is the force per unit length of the wire. The longer the wires, the greater the force. The force between the wires is attractive when the currents are flowing in the same direction and repulsive when the currents are flowing in the opposite directions.

3.6 The magnetic force on moving charges

By experiments, we know that the magnetic force on a moving charge is proportional to the magnitudes of the charge. When its charge doubles, the magnetic force doubles. The force is also proportional to the magnetic field strength. If the magnetic field is stronger while the charge remains the same, the force is stronger. If we have a moving charge q which moves through a magnetic field B then the magnetic force also depends on the velocity of the charge. If the charge is at rest, it experiences no magnetic forces. At last, the force is only related to the component of velocity   that is perpendicular to the field. When is parallel to the magnetic field, the force is zero.

The magnitude of magnetic force is given by: 

Sinceand are vectors, the direction of their product can be determined by a right-hand rule. First, point the index finger of your right hand in the direction of the velocity of the positive charge. Then, point the middle finger in the direction of the magnetic field. The direction of the thumb is then the direction of the magnetic force. The force on a negative charge is in the opposite direction of a positive charge moving in the same direction.

About the author

Zixuan Wang

Zixuan is currently an 11th grade student at the Shanghai Jianping High School.

The post The Differentiating Impacts of Electricity and Electromagnetism on Force and Magnetic Fields. appeared first on Exploratio Journal.

Abstract

Heart attacks, or Myocardial Infarctions (MI), lead to death of tissue due to lack of blood supply to the portion of the organ. Resulting scar tissue does not contract or function as well as healthy muscle tissue. On the other hand, stem cells have shown propensity to be guided into becoming specific cells that can be used to regenerate and repair diseased or damaged tissues in people. This paper will explore the latest research that supports using induced pluripotent stem cells for tissue reparation in cardiovascular disease.

Introduction

Over the past few decades, stem cell therapies have evolved considerably and one of their many potential applications could be to repair the scarring caused by myocardial infarctions. Myocardial Infarction (MI), which is a reduction or blockage of blood flow in the coronary arteries, commonly referred to as heart attack, is one of the leading causes of death in the United States with 805,000 people experiencing one every year (CDC). Unfortunately, out of those 805,000, 12% will die (CDC). Following MI, the inadequate blood flow to the infarcted tissue causes a severe reduction of oxygen and nutrients, leading to cardiomyocyte necrosis (reduced contractility), and therefore compromised heart function. MI does not traditionally have any treatment since once the tissue has necrotized, it can not regain its function. MI’s can only be managed with preventative measures taken to inhibit another incident. Medicines like aspirin and other anti-clotting drugs are used to keep clots from forming and causing another MI (CDC). ACE inhibitors reduce the strain on the heart by lowering blood pressure and this helps to not weaken the damaged tissues any further (NIH). Similarly, Betablockers also reduce the strain on the organ by blocking the release of stress hormones like noradrenaline and adrenaline to keep heart rate constant (NIH). All of these, however, only reduce the risk of a recurrence and do not regenerate the dead tissue, whereas a different form of therapy for the damaged tissue could bring about a brighter prospect. Stem cells can regenerate tissues suitable to one’s own body without having to use a transplant. This makes it less risky when it comes to a patient’s body rejecting the cells. Some varieties are also easily accessible, usable, and effective in their respective needs.

Out of the many varieties of stem cells, induced pluripotent stem cells, are some of the most promising to study. Induced pluripotent stem cells (iPS cells) are derived from somatic cells that are reprogrammed into iPS cells. These cells can then be made to differentiate into whatever tissue cell is needed (Shi et al, 2016). They are also important to observe because of their accessibility and high turnover rate (Krzysztof et al, 2018). In this review, we will focus on two types of stem cells: induced pluripotent stem cells, and their abilities in tissue regeneration in regards to therapies to treat the infarcted myocardium (Yoshida et al, 2017).

Induced Pluripotent Stem cells

Induced Pluripotent Stem Cells (iPSCs) are adult somatic cells that are reprogrammed into a pluripotent state. These cells are adults and unipotent, meaning they are capable of regenerating only their own specific tissue type (Tweedell, 2017). For example, an adult somatic cell in the skin could only generate skin cells. When these cells are reprogrammed into iPSCs, they become pluripotent, and are able to differentiate into any type of tissue with appropriate differentiation factors (Tweedell, 2017).

The use of iPSCs for regenerative medicine bears significant advantages. In fact, the somatic cells generally used for reprogramming are highly accessible and they are already part of the body of the person who needs them. Therefore, there is no risk of rejection when they are implanted for regenerating damaged tissues or organs (Arjmand et al, 2017). One further advantage is that they are not controversial like embryonic stem cells that are isolated from embryos while having similar properties. Generating the iPSCs is completed by taking any healthy adult somatic cells from the body and reverse engineering them into a pluripotent state where they can then differentiate into whatever cell type is needed. How this occurs is that first, the cells organize spatially and then divide into three areas. The middle section, differentiates into the middle portion of the three’s lineage and this activates certain genes.

Cardiovascular Regeneration

As mentioned above, one of the capabilities of iPSCs is tissue regeneration, which is paramount for cardiovascular tissue regeneration. The basic process for cardiovascular tissue engineering consists in isolating somatic cells of the patient or from healthy donors, which are then reprogrammed to iPSCs. Next, the obtained iPSCs are differentiated into the specific cell type that is needed (such as cardiomyocytes, cardiac fibroblasts, or endothelial cells). The differentiated cells must be cultured in the lab to grow, and during this process they can be stimulated with chemical or physical cues to mimic the mechanical properties of the beating heart. The last step is to inject or implant the cells into the patient.

Cardiovascular tissue engineering has shown promising results in vitro and in preclinical in vivo studies. Several groups have used small animal models, including mice and rats model of myocardial infarction to assess the ability of repairing the damaged heart tissue with iPSCs-derived cardiomyocytes. An example of implantation of cardiac engineered tissues in a small animal model is provided by (Tompkins et. al. 2018), that used 3D bioprinted iPSC-derived cardiomyocytes, fibroblasts and endothelial cells to produce 3D patches that were implanted in n=6 infarcted rats.

Additionally, vivo models further demonstrate that this path of study is incredibly promising. The work of Tompkins et. al. describes small animal models where iPSCs are implanted. This article demonstrates how this work is viable in live models as they tested various species of small animals to prove efficacy. Moreover, the same study considered large animal studies and deduced that they too have promising results. More specifically, in swine models, which are known to have extremely similar cardiac structure to that of humans, these studies further the thought that using iPSCs to repair tissues is a viable solution. Kawamura et al. placed a sheet of dermal fibroblast-derived hiPSC-CMs over the infarcted area in an ischemic swine model, which produced improved cardiac performance, angiogenesis (increased number of blood vessels in the infarct), and an attenuated LV remodeling 8-weeks post implantation.

While in the lab, stimuli of stretching and current are used to help the cells mature faster and grow more resilient. This is one place of research that is continuing to challenge researchers, since they do not have years to culture mature cells and there is risk with implanting immature cells regarding their ability to adapt to the heart’s environment. However, it can and has been done, as explained above, which has drawn tremendous attention to this field of pursuit. Moreover, cardiovascular regeneration is one of the newest technologies in repairing damaged tissues in the heart. This breakthrough has made it possible to just regrow healthy and functional tissue instead of needing a transplant since it is already known that once tissue is dead from a myocardial infarction, there is no way to salvage it. As the MI damages the tissue, it makes it impossible for the original tissue to be functional, so inputting fresh, cultivated tissues open up new possibilities in life for the patient after their MI episode.

Drug Screening

During the process of drug screening, various drugs are tested on the cardiac engineered tissues to gauge safety and efficacy of the tested molecules and drugs. One of the commonly tested side effects of newly developed drugs are for drug induced arrhythmias. By testing in-vitro with iPSCs outside of a patient’s body, it is not only more convenient to do so but also safer so as to not involve a live subject (Smith et. al. 2017). Various types of cell models are used, ranging from flat, 2D monolayers to more complex 3D tissues, organ-on-a-chip models show a wide range of functionality. Each of these model types show a range from the least to most complex levels of organization in order to understand how drugs can affect the cardiomyocytes on a basic to fully vascularized level (Smith et. al. 2017 Fig. 1). This is one place of development in the field of iPSCs that would be of great benefit to the scientific community and to the general population as well. If drugs can be screened and tested within a lab without having to use in vivo models until much later in the process, it can be much more ethical and more varieties of medicines that may or may not be viewed as viable could potentially be trialed in this way due to the reduced ethics concerns.

One example of a clinical trial is in the research of Blinova et. al. which shows a personalized drug screen model that highlights how iPSCs derived from 22 healthy subjects can be grown and tested within a dish. Safety and efficacy of two drugs, dofetilide and moxifloxacin (hERG‐blocking and QT prolonging), were tested on iPSCs isolated from the peripheral blood mononuclear cells and differentiated in cardiomyocytes. There were no drug induced arrhythmia-like events observed at the studied drug concentration rate. In vivo model of testing that highlights how tissue can be grown and tested within a dish. In this trial, the researchers tested and analyzed for arrhythmias in the iPSCs. This demonstrates how various environments of the heart can be simulated in the lab and that it is necessary to do so (Blinova et. al. 2019)

Various different types of trials can be used to screen for arrhythmias in a drug screening. One method researchers historically and commonly use is the analysis of hERG channel response which is the standard procedure for in vitro preclinical trials of drug screening. While this is a method commonly used, it is not as accurate as could be desired since false positive results are frequent occurrences (Smith et. al. 2017). This is why iPSC-CMs are making headway in the field of drug screening since they offer a more accurate option. There are various tests researchers can run with in vitro models of iPSCs to represent the function of the heart more fully and effectively. Out of the multitude of options researchers now have with iPSC-CMs as an option, an example presented in the above research is that researchers measure cell contraction to observe the cardiomyocytes’ contractile function (Smith et. al. 2017).

Conclusion

After an episode of myocardial infarction, heart tissue is damaged irreversibly and the prognosis only entails either drug therapeutics or organ transplant. Cardiovascular regeneration is one of the newest technologies in regards to repairing damaged tissues in the heart. With reprogrammed iPSCs , the patient is able to have their healthy cells cultured in a lab and remediate the scarred tissue resulting from an MI episode. Furthermore, progress has also been made in labs to accommodate the new research and to screen drugs to ensure their safety with the cultured tissues when implanted in a person. All together, these breakthroughs have made it possible to regrow healthy and functioning tissue and using iPSCs could make this possibility a reality.

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About the author

Rishya Gutti

Rishya is a junior at Neuqua Valley High School. She is interested in biological sciences and is an aspiring medical student. Research programs like RISE (Research, Inquiry Skills & Experimentation) have equipped Rishya with necessary skills to conduct independent research. She is a third degree black belt in Taekwondo and has won several national titles in her age group. Rishya enjoys volunteering her time to teach mathematics to younger students and to promote mental health awareness through a non-profit organization. In her free time, you will find her reading, working out, or watching her favorite tv shows.

The post Using induced pluripotent stem cells for tissue regeneration in cardiovascular diseases appeared first on Exploratio Journal.

Introduction

According to the 2020 report of the United Nations Food and Agriculture Organization, the decades-long decrease in the prevalence of undernourishment (PoU) across the world has come to an end. In 2019, it was reported that globally, nearly 690 million people (8.9% of the world population) were undernourished. Between 2018 and 2019, the PoU increased by 0.3%, equivalent to 10 million people. [1] While a multitude of reasons are behind the rapid increase in PoU, it is agreed that human-caused climate change, world conflicts, increased urbanization, and lower global biodiversity are all major causes. As the world population expands to 9.4 billion in the coming decades, the problem of food production will be even more important [3]. 

Food production systems (FPSs) come in a variety of forms depending on their location. In developing countries, FPSs are fragmented into multiple interconnected systems and depend on smaller-scale farming operations (SSFOs). SSFOs are generally much less efficient than their larger counterparts. Addressing these inefficiencies is key to improving FPSs in developing regions. The most obvious solution is to integrate sophisticated modern agricultural practices into SSFOs; However, these practices are difficult to implement as a result of the low literacy rates of farmers in developing countries. [4] Another option is to integrate machines into farms, to automate several key processes. The combination of machines, sensors, and human oversight is known as smart farming. 

Smart farming is a new type of agricultural management utilizing various techniques to increase farm yield. Smart farming may enable us to overcome challenges related to food production demands caused by a growing population. Technologies used in smart farms vary greatly, ranging from automated weather data collectors to Unmanned Aerial Vehicles (UAVs) designed to gather topological data or water crops [2]. Interconnected devices such as those on smart farms are collectively known as the Internet of Things (IoT). Within an IoT system, data from sensors actively change the conditions in which plants are grown, resulting in increased overall productivity. For instance, an IoT system might increase water distribution to plants in the event of a drought. Within the USA, the use of IoT devices in smart farms results, on average, in a 163 dollars/day per hectare increase in farm yield. In fact, this number could even be as high as 272 dollars depending on the type of crop [5]. 

While IoT devices used in smart farms are specialized for a variety of tasks, including watering crops and gathering environmental data, they are comparable to regular IoT devices in several areas.  Previous studies have demonstrated vulnerabilities in IoT devices to cyberattacks (Distributed Denial of Service, Man in the Middle attacks, etc.) [6] Should these vulnerabilities be exploited, a smart farm stands to lose its entire crop, leading to widespread food shortage. Thus, addressing vulnerabilities in smart farming systems is of the utmost importance. 

On April 9th, 2021, the Colonial Pipeline, which transports a significant percentage of fuel for the Eastern Coast of the United States, was involved in a ransomware attack. While the ransomware was eventually removed, the shutdown of the pipeline impacted millions of consumers across the US. A similar attack on smart farms could have an even more devastating impact, as the food shortage may lead to widespread famine. In Virginia, the agricultural industry has an economic impact of approximately 70 billion dollars and is responsible for 334,000 jobs. When combined with value-added industries (Which rely on agriculture), the agricultural industry makes up nearly 10% of the state’s GDP. [7] Any disruption in the agricultural output of the state would cascade into millions of dollars worth of damage. In addition, thousands would lose their jobs leading to wide-scale unemployment. Virginia is currently building a network of smart farms through the SmartFarm Innovation Network project. As Virginia’s agriculture begins to depend more heavily on smart farming, the poor cybersecurity protocols of these farms become more of a concern. 

The aim of this literature review is to provide a brief but extensive overview of smart farming technology and potential cybersecurity vulnerabilities present in smart farms. In addition, this literature review will also suggest possible avenues to improve the security of smart farms. This document is organized as follows: Section 2 will provide a more in-depth overview of the types of IoT devices on smart farms. Section 3 will examine the potential security pitfalls present in these devices. Section 4 will provide techniques to mitigate these security risks. Finally, Section 5 will conclude the paper. 

Section 2

The role of IoT and smart technology in the agricultural industry has steadily increased over the past few decades [8]. IoT-based agricultural systems are able to be more efficient than their traditional counterparts due to a variety of reasons. In most cases, this is due to smart sensors which relay information about soil and atmospheric conditions. However, IoT technology has many more applications within a smart farm. IoT devices are routinely used for rainfall monitoring, soil nutrition management, water management, pest infection management, and crop health monitoring [9].

Section 2.1 – Soil IoT

While IoT sensors on farms vary in their uses, a majority of sensors are used to measure soil conditions on the farm. Several companies offer IoT solutions that are used to identify key soil factors such as texture, water-holding capacity, and absorption rate. Knowing this information allows farmers to stop soil erosion, densification, salinization, acidification, and pollution, which can otherwise cause thousands of dollars in damages. AgroCeres, a company specializing in IoT solutions for the agricultural industry, has released a product known as  Lab-In-A-Box. Lab-In-A-Box allows farmers to conduct hundreds of soil tests without formal training, miles away from a traditional farm. Farmers can then act on this information to improve their soil quality, resulting in a better harvest [10]. Monnit, another IoT company, provides wireless soil sensors able to connect to a central system. Data from Monnit sensors can be used in conjunction with smart pumps to actively change the amount of water given to plants based on soil moisture. 

Section 2.2 –  Weather IoT

The amount and timing of rainfall is arguably the most important factor affecting a farm’s productivity. Fluctuations in weekly or monthly rainfall levels can have a drastic impact on agricultural productivity and revenue. Therefore, predicting future weather patterns using large datasets of previous weather patterns is key to improving farm efficiency [11]. Unmanned Aerial Vehicles (UAVs) are being used for a variety of reasons in smart farms. One particularly interesting application of UAVs is to collect weather data. After collection, the data is stored on cloud servers. Next, the data can be drawn from these servers and is used for a variety of purposes. Researchers in [12] proposed a method to create a genetic algorithm (GA) to predict future weather patterns based on old data. When new data is collected by UAVs, it is fed into the GA to determine if plants need water. A sensor system is also used to check the results of the GA. Should moisture levels fall below a critical threshold, smart pumps are used to provide additional water to plants. 

Section 2.3 – Water IoT

Proper irrigation systems are an essential element in any farm. Improper distribution of water to plants, caused by a malfunctioning irrigation system, may lead to widespread crop failure. Global warming further contributes to this problem, by making water scarcer in certain regions. Therefore, identifying and solving problems in irrigation systems is of the utmost importance. IoT devices can be used to automate the process of irrigation, minimizing the risk of catastrophic failures. IoT irrigation systems come in several forms, from simple Arduino-based systems to those which implement AI technology [14]. However, in most systems, there are several key features. First, a power source is used to power the system. In most cases, this takes the form of a solar panel, but there are exceptions to this rule. Most notably, Arduino-based systems are normally powered by a combination of batteries and solar power [15]. Next, a central controller is used to obtain data regarding the environment. Should the data indicate that certain environmental variables do not fall within certain parameters, the central controller actively changes these variables through the use of smart pumps [16]. IoT systems are capable of independently performing this process multiple times a day, thereby reducing the risk of failures in irrigation systems normally caused by human error. 

Section 2.4 – Pest IoT

The productivity of several key plant species necessary for humans, such as wheat, maize, and cotton, can be severely impacted by the presence of pests. One study found that the global potential loss of crops due to pests varied from roughly 50% in wheat to more than 80% in cotton [18]. A wide variety of pesticides are used to combat this problem. However, excessive use of pesticides can damage the local environment, aid in the development of pesticide-resistant crops, and lead to several health conditions in farmers [19]. While the use of IoT in regards to pest control is limited when compared to its other uses, several studies have created potential IoT systems to deal with pest infections. Researchers at the National Taiwan University designed an IoT-based system designed to identify pest insects throughout a farm. The resulting spatial-temporal information was then used to kill these pests [20]. Another study by the Brazillian National Institute of Telecommunications expanded on this research by designing a trap that would both identify and exterminate pests. A computer vision algorithm was used to identify pests, based on images taken from an embedded system containing a camera, a GPS sensor, and motor actuators. Should the computer vision algorithm indicate that pests are present in the trap, the trap immediately kills them. [21] The potential of IoT systems in pest management is great. IoT systems may promise to reduce the amount of pesticide required to eliminate pests, thereby reducing risks to farmers while simultaneously saving large sums of money. 

As shown above, IoT devices and systems are applied in a variety of forms throughout smart farms. While not all smart farms may contain all such devices, the presence of at least one of these systems greatly increases the farm’s efficiency. However, one notable downside to intelligent IoT systems is their vulnerability to cyberattacks. Previous studies have demonstrated the ease with which bad actors are able to infiltrate IoT systems. As mentioned earlier, a malicious attack on a smart farm may have severe economic impacts due to the importance of the agricultural industry to statewide and nationwide GDP. Therefore, we will next examine the potential cybersecurity risks present in smart farm IoT systems.

Section 3

The rapid integration of IoT technology in various industries brings with it new risks in the form of novel security challenges. According to  Tawalbeh et al. (2020), improper device updates, lack of efficient and robust security protocols, user unawareness, and famous active device monitoring are among the challenges that IoT is facing. These vulnerabilities are present in not only household IoT devices, but also in smart farming systems. Attacks that take advantage of these vulnerabilities may lead to serious disruptions in the farm environment, depending on which system is breached. Attacks on smart farming systems generally fall into one of two categories: Physical, where farm machinery is disrupted, or online, where farm data is modified or deleted. The following subsections will further elaborate on these vulnerabilities. We will also examine several case studies demonstrating the effects of a successful attack on IoT systems. 

Section 3.1 – Physical Attacks

According to [23], the number of farm workers has steadily declined in the 20th century, while farm production has increased. The increased use of machinery is the cause of this discrepancy. Machinery is used for several purposes within a farm. Primary and secondary tilling of soil, harvesting, pest control, and erosion control are just some of the many applications of heavy machinery. Light machinery, on the other hand, primarily consists of drones, UAVs, and automated farm robots, and is primarily used for environmental data collection. Both heavy and light machinery can connect to IoT systems, although IoT-connected light machinery is more common. 

Previous studies have demonstrated several methods used by bad actors to wirelessly take control of a drone. This concern is further exacerbated as tutorials on how to take control of a drone are available on numerous video-sharing platforms, including YouTube [24]. A study published in the Internet of Things journal [25] found that major vulnerabilities were present in a majority of light machinery products. A summary of several of these vulnerabilities, as stated in the study, is presented below.

1. Spoofing/ Data Interference
   1. Data streamed from a drone to a central controller can be intercepted and modified. Telemetry data, crucial to maintaining the correct flight profile of a drone, is normally unencrypted. Several experiments have demonstrated the ease with which this vulnerability can be exploited, giving bad actors full control of the drone [26][27]. 
2. Malware Infection
   1. Many UAVs contain software which allow pilots to fly them from various mobile platforms . This software can be used by bad actors to inject malware payloads into the UAVs memory or the ground station itself [28]. The malware used in such an attack may vary; However, in most situations, it enables bad actors to take full control of a UAV. 

1. Prone to Wi-Fi Jamming
   1. A specific type of Distributed Denial of Service (DDoS) attack called a deauthentication attack can be performed on a UAV. Next, a bad actor may jam the UAVs intended frequency and connect it to their own. Such an attack only requires a raspberry pi to execute.

Vulnerabilities present in light machinery are different from those present in heavy machinery. While less common, IoT-connected heavy machinery still has a multitude of vulnerabilities which can be exploited by bad actors. The types of heavy machinery varies greatly on a farm. A brief overview of the types of heavy machinery commonly found on a farm is below. The IoT capability of each machine is also listed.

As shown above, IoT is being used to advance the capabilities of several types of heavy machinery used on farms. Notably, the machinery itself is not the target of the attack. The IoT systems used on the machinery to collect data is the primary attack vector. The consequences of a disruption of heavy machinery are varied:

• Crop failure due to more or less water applied than needed (See 6th entry in table)
• Crop failure due to improper application of fertilizer due to incorrect data provided by IoT sensors (See 5th entry in table)
• Crop/Livestock loss during transport (See 4th entry in table)
• Failure to plant crops properly should a farm use automated seeding robots (See 3rd entry in table)
• Crop failure as a result of improper pesticide/herbicide application (See 2nd entry in table)

The disruption of heavy machinery through cyberattacks can have a large impact on a farm’s productivity. However, it pales in comparison to a similar attack on online assets such as IoT sensor data.

Section 3.2 – Online Attacks

Smart farms rely heavily on data collected by intelligent, IoT-connected sensors. Environmental data collected from these sensors is used to dictate the actions of other IoT-connected devices, such as smart pumps or sprayers. The following diagram illustrates an example of this relationship:

If the weather sensor, pump, or the data transferred between the devices is compromised, bad actors will have the capability of controlling the amount of water given to plants. The effects of such a compromise are disastrous: A bad actor may stop pumping water to plants while editing data to make it seem like plants are watered, leading to widespread crop failure within a matter of days. While this situation may be averted through human oversight, several smart farms proposals include little to none human supervision [[35], [36]]. 

Breaching an IoT system has been shown to be possible in a number of studies. While IoT devices designed for smart farms and IoT devices designed for private households have different uses, their vulnerabilities are similar. According to a paper by the Bulgarian Academy of Sciences [37] , an attack on an IoT system can be split into the following parts:

1. Reconnaissance – Attackers spend months researching their target, using multiple online sources. Attackers may not need to directly interact with the target during this phase. 
2. Searching for Vulnerabilities – After reconnaissance yields enough data to satisfy the attackers, they move on to the second phase. This phase primarily consists of identifying vulnerabilities in the target system. 
3. Attack – During this phase, attackers launch their attack based on target vulnerabilities identified in step 2. In several cases, a malicious payload is inserted into the target, allowing attackers to gain access to confidential data or giving them control of key systems. We provide an overview of various attack strategies below. 
4. Achieve/Maintain access – Once attackers gain control of a system, they must also ensure that their access remains in place. To this end, they cover up evidence of a breach or attack other devices connected to the primary attack vector. The longer a breach is maintained, the more information that attackers can exfiltrate. 

Attack strategies used by cybercriminals are ever changing. One research study [9] contains a detailed list of all major attack strategies used by cybercriminals to attack IoT criminals. Below is a summarized version of their findings, including only the most common attacks. However, we suggest visiting the source for more information if you are so inclined. 

With the number of attacks possible on both physical and online assets of a smart farm, properly identifying vulnerabilities in IoT systems is crucial to maintaining security. In the next section, we will discuss possible avenues towards protecting key IoT systems on smart farms.

Section 4

We have identified several key cybersecurity vulnerabilities present in smart farming systems in section 3. However, even more important is the security measures needed to patch these vulnerabilities and ensure the security of IoT systems. To this end, a list of several of the more potent vulnerabilities and methods that can be used to fix them is presented below. The following chart is not an exhaustive list of all cybersecurity vulnerabilities in IoT systems. Rather, it will address the vulnerabilities presented in the previous chart (Within section 3). A more thorough analysis is presented in Appendix A. 

Section 5

This paper is intended to provide an overview of smart farming, IoT devices used on smart farms, and potential vulnerabilities present in these IoT systems. The findings presented in this paper demonstrate that while IoT solutions may lead to an immense increase in agricultural production, they come with severe security risks. Bad actors may be able to diminish or even destroy the food supply of a country by leveraging vulnerabilities in smart farming IoT systems. 

IoT devices are not only present in agriculture. They play essential roles in a variety of industries, and can also be found in private residences. Several research studies have demonstrated the vulnerabilities in these IoT systems by breaking into them. However, as of yet, no similar study has been conducted to examine issues with smart farming IoT systems. Further research, therefore, is crucial to preventing cyberattacks on smart farming facilities. As the world population continues to increase, and as smart farming plays an ever increasing role in meeting food demands, identifying and addressing vulnerabilities in smart farming IoT systems becomes more important than ever. 

Appendix A

The following chart expands on the vulnerabilities and solutions discussed in section 3 and 4. While these types of cyberattacks are not as common, knowing their effects and how to stop them is still valuable information to protect IoT systems.

Appendix B

According to the Open Web Application Security Project foundation [44], the presence of two or more of the following conditions may indicate that a brute force attack is being attempted:

• Many failed logins from the same IP address.
• Logins with multiple usernames from the same IP address.
• Logins for a single account coming from many different IP addresses.
• Excessive usage and bandwidth consumption from a single use.
• Failed login attempts from alphabetically sequential usernames or passwords.
• Logins with a referring URL of someone’s mail or IRC client.
• Referring URLs that contain the username and password in the format <http://user:password@www.example.com/login.htm>.
• If protecting an adult website, referring URLs of known password-sharing sites
• Logins with suspicious passwords hackers commonly use, such as ownsyou (ownzyou), washere (wazhere), zealots, hacksyou, and the like.

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About the author

Tejaswi Koduru

Tejaswi is currently a Sophomore at Thomas Jefferson High School for Science and Technology in Virginia. He has studied AI to diagnose diseases in their early stages.

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Abstract

Migraine is the sixth most disabling illness in the world and the third most common (Saper, 2021). Many studies have found that migraines have numerous causes, which creates a wide variability in the effectiveness of treatments. In recent years, there has been exploration of alternative methods of treatment, such as music therapy. There have been many studies regarding music therapy and pain treatment, but very few specifically for headaches, and results have been inconclusive. The proposed research seeks to examine the extent to which predisposed beliefs about music therapy impact its efficacy when treating chronic migraines. Over the course of a six-week study, patients will be randomly assigned to one of two experimental treatment conditions whereby they will receive a combination or standard pharmaceutical treatment plus music therapy or standard pharmaceutical treatment plus therapeutic rhythmic class. Patients will also report the degree to which they perceive music therapy and other alternative treatments as effective pain-relieving measures. Patients will also report their pain symptoms weekly. It is anticipated that patients with stronger belief in the efficacy of music therapy, in particular, will experience greater reduction in pain symptoms when in the music therapy condition. The implications of an interaction between treatment efficacy beliefs and actual treatment effectiveness are discussed.

The Impact of Belief in Treatment Methodology on the Efficacy of Music Therapy in Those with Chronic Migraines

While many people have friends or family members who suffer from migraines, individuals who do not experience migraines themselves may be unaware of how chronic, unpredictable, and often mysterious migraine symptoms are. Approximately 1 billion people around the world have migraines (Pool, 2021), making migraines the third most prevalent illness in the world, along with being the 6th most disabling (Saper, 2021). Lipton et al. (2007) reported that 53.7% of those with migraines were severely impaired.  Munakata et al. (2009) found that those with episodic migraine had direct and indirect costs of $1757 per year, and those with transformed migraine had costs of $7750 per year.  Those who experience migraine–as well as their closest family and friends–are more likely to pursue knowledge regarding migraine causes and treatment (Saper, 2021), but they often find that migraines have many different causes and thus there is wide variability in types of treatment and their efficacy (Dodick & Gargus, 2008). Recently, there has been increasing interest in alternative treatments for migraine sufferers such as yoga (John et. al, 2007), risk factor modification (Schwedt, 2014), and music therapy (Diamante & Roxas, 2020). However, the degree to which such alternative treatments alleviate symptoms may be influenced by patients’ beliefs in their efficacy (e.g., Horne, 1999). Accordingly, the current research proposal seeks to examine how much predisposed beliefs about music therapy impact its efficacy, and if music therapy is actually effective in treating chronic migraines.

Music Therapy

Music therapy has been examined as a potential treatment for dementia (Vink et. al, 2003), generalized anxiety disorder (Gutiérrez & Camarena, 2015), as well as chronic migraine symptoms (Diamante & Roxas, 2020). Research on the use of music therapy as a treatment for migraines has demonstrated that it shows a significant improvement from no treatment (Oelkers-Ax, 2008). Music therapy has been shown to be effective for those seeking treatment for chronic migraine and tinnitus (Nickel et al., 2005). Additionally, Langenburg and colleagues (1995) did a case study on music therapy as treatment for someone with chronic migraines which demonstrated that it does lessen migraine symptoms. While additional studies have demonstrated similar efficacy for MT as a treatment for migraines (e.g., Diamante & Roxas, 2020), other studies have found that MT, while effective, is no more effective than other alternative treatments (Koenig, 2014). Moreover, music therapy can better improve executive functioning when it is able to provide functional support (Thaut & Hoemberg, 2017). Accordingly, we should expect music therapy to reduce migraine symptoms in patients when used in conjunction with standard treatment, but perhaps not more so than other distracting treatments. However, there are moderating factors that could influence the degree to which individual patients respond to alternative treatments such as music therapy–one of which may be their belief in the efficacy of music therapy.

Patient’s Beliefs in the Efficacy of Treatment

The ability of a patient’s belief in the efficacy of certain treatments to affect treatment outcomes has been examined in a variety of contexts. Patients’ beliefs about the type of treatment they’re receiving has been shown to impact the efficacy of that treatment (Horne, 1999). The symptoms of patients suffering from various illnesses were shown to respond more strongly to medical treatment among patients who believe more strongly in the effectiveness of traditional medicine as a treatment (Foulks et. al, 1986): the expectation of a drug’s impact on symptoms strongly influences the therapeutic efficacy of that drug (Bingel et al, 2011). Similarly, Clatworthy et. al found that patients better adhered to their treatment plan when they believed that it would work, and thus they found more benefits from their treatment (2007). In addition, a study regarding the effects of methotrexate on rheumatoid arthritis symptoms found similar results (de Thurah et. al, 2009). Studies have also demonstrated that beliefs regarding severity of the patient’s illness impact the efficacy of the treatment (Marks et al., 1986). However, to the best of our knowledge, no research has yet investigated this moderating influence regarding the efficacy of music therapy in migraine treatment. Accordingly, the current research will assign patients to two conditions of music therapy, one who has strong beliefs in the efficacy of music therapy, and one who does not. These two groups will demonstrate the relationship between belief in the efficacy of treatment and pain reduction.

Research Hypothesis: We expect that the efficacy of music therapy, in comparison to a different alternative treatment, in reducing migraine symptoms will be moderated by patients’ beliefs in music therapy’s effectiveness. Specifically, we expect that the relative efficacy of music therapy, in comparison to yoga therapy, in relieving chronic pain symptoms will be greater among individuals who already perceive music therapy as an effective treatment.

Method

Participants

Participants will be recruited from a hospital circuit, through compliant doctors asking their patients with chronic migraines. They will be given a small monetary sum, and the study will be conducted in an academic center with music facilities.  

Measures

Belief in Treatment Efficacy. Prior to being randomly assigned to a treatment condition, patients will fill out a brief survey asking them to report how effective they believe each of the following therapies would be for reducing their migraine symptoms: music therapy, rhythmic class, yoga, mindfulness meditation, traditional western medicine, and pet therapy (Appendix A). Brief descriptions of each therapy type will accompany the items. Participants will indicate their belief in the efficacy of each therapy on a 1 (“Not at All Effective”) to 7 (“Highly Effective”) Likert-type scale. The variable of interest is patients’ beliefs in the efficacy of music therapy, and patients will be told that their beliefs will not affect the treatment condition to which they are assigned. 

Treatment Condition. Patients will be randomly assigned to receive one of two treatments in addition to traditional pharmaceutical treatment: music therapy or rhythmic class This treatment manipulation is similar to one used in previous research (Gutgsell et. al 2013).  In each condition, patients will undergo their additional treatment 3 times per week. Patients in the music therapy condition will go into a dark room without distractions for 20 minutes and have a music therapist play harp pieces for them. Patients in the rhythmic class (Bozorg-Nejad et. al 2018) condition will go into a dark room without distractions for 20 minutes and will be taught how to control the various paces of the body with breathing. These conditions will last for 6 weeks.

Improvement of Symptoms. The dependent variable that will be measured is the difference in pain before treatment and after treatment using a standardized scale. Patients will rate their overall severity of their symptoms at the end of each week on a scale from 0 (“No Symptoms”) to 10 (“Emergency Treatment Necessary”) similar to measures of migraine pain used in previous research (Jensen et. al 1999). Average scores from the final three weeks will be subtracted from average scores from the first three weeks to determine the change in symptoms over the treatment period.

Procedure

 When first agreeing to participate in the study, the participants will complete an informed consent document. They will be given their monetary reimbursement of $50 for their time, and then will be separated into two groups, after being sent and completing the survey about their beliefs in the efficacy of music therapy remotely. From those two groups they will be randomly assigned to either the music therapy condition or the rhythmic class, depending on what time of day they fill out the survey. They will then go to these therapies at least three times per week for 20 minutes for six weeks. Over the course of these six weeks their pain symptoms will be monitored and recorded through their respective nurses on the standardized scale in Appendix B. 

Results

Correlational Analyses

A zero-order Pearson correlational analysis will be conducted to assess the relationship between belief in the efficacy of music therapy and improvement of pain symptoms for patients in the MT condition only. We hypothesize that, among patients in the MT condition, higher belief in MT will predict more improvement of symptoms. We expect to see a similar correlation between belief in the effectiveness of rhythmic class in the comparison condition.

T-Test

A t-test will be conducted to compare symptom improvement between the two experimental groups. We expect that without taking into account the patients’ beliefs in MT’s effectiveness that patients in the MT conditional will not show significantly more or less improvement than patients in the rhythmic class condition. 

 Moderation Analysis

To test our primary research hypothesis, a moderation analysis will be conducted using the PROCESS macro (Hayes, 2013) Model 1. Treatment condition will be added to the model as the independent variable (x), belief in the efficacy of music therapy as the moderator (w), and symptom improvement as the dependent variable (y). We predict that belief in the efficacy of MT will moderate the difference between treatment groups such that MT will be more effective than rhythmic class among participants with higher beliefs in MT’s efficacy. The conceptual model is illustrated in Figure 1.

Discussion

The proposed study seeks to explore the relationship between patients’ beliefs in the efficacy of alternative forms of treatment for chronic pain and the actual effectiveness of those treatments. In particular, this study intends to focus on music therapy (MT) as a potential treatment. Patients’ beliefs about MT, and other alternative treatments, will be measured prior to random assignment to a treatment condition that includes typical pharmaceutical treatment plus either music therapy or a comparison treatments condition. It is hypothesized that beliefs in the efficacy of MT will interact with assignment to the MT condition such that patients who perceive MT as more effective and are assigned to the MT condition will experience more pain symptom relief over the course of the study. 

Implications

Approximately 5-12% percent of people worldwide suffer from migraines (MacGregor et. al, 2003), which is millions of people. Therefore, the majority of people at least know someone who is a victim of this kind of pain. Additionally, making strides within the treatment of these migraines could vastly improve quality of life. Music therapy specifically is an instance of alternative methods, based on other facts. If the experimental hypothesis was supported, then pain treatment would be reimagined to adjust to the beliefs of the patient. To ensure the best outcomes, physicians could prescribe whichever alternative treatment an individual patient has the most positive views of in addition to pharmaceutical treatment. Additionally, results that support our hypotheses would suggest that physicians should emphasize scientific support for alternative methods to their patients. Doing so may make beliefs about such treatments more positive, which would in turn make the treatments more effective.

Limitations

This study would be done on adults, so it probably would not be as conclusive for the same study done on children or adolescents. The proposed study also does not account for potential disparities among those who have chronic migraines. This study also would not discuss the musical aspect involved in rhythm. The proposed study also does not account for the influence of those providing the treatment on the patient’s belief in efficacy. This study also only compares two types of alternative therapies, when there are many others which could have different results.

Future Directions

In future studies, there should be much deliberation regarding the control therapy and how it relates to music therapy. There could be focus on how different types of music impact the efficacy, or how it relates to what is being treated. . For instance, it has been shown that music can help those with dementia express themselves with language (Brotons & Kroger, 2000). Moreover, connecting these two things is something worth looking into, based on how music has a unique impact on the brain and has helped lessen the impacts of other neurological disorders. As far as pain management goes, it is important to consider the different ways that people can feel pain and how each experience it unique, along with the wide variety of treatment methods- beyond the ones discussed in this proposal.

References

Bingel, U., Wanigasekera, V., Wiech, K., Mhuircheartaigh, R. N., Lee, M. C., Ploner, M., & Tracey, I. (2011). The effect of treatment expectation on drug efficacy: imaging the analgesic benefit of the opioid remifentanil. Science translational medicine, 3(70), 70ra14-70ra14.

Bozorg-Nejad, M., Azizkhani, H., Ardebili, F. M., Mousavi, S. K., Manafi, F., & Hosseini, A. F. (2018). The effect of rhythmic breathing on pain of dressing change in patients with burns referred to ayatollah mousavi hospital. World journal of plastic surgery, 7(1), 51.

Brotons, M., PhD, MT-BC, Koger, S. PhD, The Impact of Music Therapy on Language Functioning in Dementia, Journal of Music Therapy, Volume 37, Issue 3, Fall 2000, Pages 183–195, https://doi.org/10.1093/jmt/37.3.183

Clatworthy, J., Bowskill, R., Rank, T., Parham, R., & Horne, R. (2007). Adherence to medication in bipolar disorder: a qualitative study exploring the role of patients’ beliefs about the condition and its treatment. Bipolar disorders, 9(6), 656-664.

de Thurah, A., Nørgaard, M., Harder, I. et al. Compliance with methotrexate treatment in patients with rheumatoid arthritis: influence of patients’ beliefs about the medicine. A prospective cohort study. Rheumatol Int 30, 1441–1448 (2010). https://doi.org/10.1007/s00296-009-1160-8

Foulks, E. F., Persons, J. B., & Merkel, R. L. (1986). The effect of patients’ beliefs about their illnesses on compliance in psychotherapy. The American Journal of Psychiatry, 143(3), 340–344. https://doi.org/10.1176/ajp.143.3.340

Gutiérrez, E. O. F., & Camarena, V. A. T. (2015). Music therapy in generalized anxiety disorder. The Arts in Psychotherapy, 44, 19-24. 

Gutgsell, K. J., Schluchter, M., Margevicius, S., DeGolia, P. A., McLaughlin, B., Harris, M., … & Wiencek, C. (2013). Music therapy reduces pain in palliative care patients: a randomized controlled trial. Journal of pain and symptom management, 45(5), 822-831.

Horne, R. (1999). Patients’ beliefs about treatment: The hidden determinant of treatment outcome?[Editorial]. Journal of Psychosomatic Research, 47(6), 491–495. https://doi.org/10.1016/S0022-3999(99)00058-6

Jensen, M. P., Turner, J. A., Romano, J. M., & Fisher, L. D. (1999). Comparative reliability and validity of chronic pain intensity measures. Pain, 83(2), 157-162.

John, P., Sharma, N., Sharma, C.M. and Kankane, A. (2007), Effectiveness of Yoga Therapy in the Treatment of Migraine Without Aura: A Randomized Controlled Trial. Headache: The Journal of Head and Face Pain, 47: 654-661

Koenig J. Music therapy in the treatment of primary headache disorders. OA Alternative Medicine 2014 Jan 18;2(1):1

Langenberg, M., Frommer, J., & Tress, W. (1995). Music therapy single case research–a qualitative approach. Psychotherapie, Psychosomatik, medizinische Psychologie, 45(12), 418-426.

MacGregor, E. A., Brandes, J., & Eikermann, A. (2003). Migraine prevalence and treatment patterns: the global Migraine and Zolmitriptan Evaluation survey. Headache: The Journal of Head and Face Pain, 43(1), 19-26.

Lipton, R. B., Bigal, M. E., Diamond, M., Freitag, F., Reed, M. L., & Stewart, W. F. (2007). Migraine prevalence, disease burden, and the need for preventive therapy. Neurology, 68(5), 343-349.

Marks, G., Richardson, J. L., Graham, J. W., & Levine, A. (1986). Role of health locus of control beliefs and expectations of treatment efficacy in adjustment to cancer. Journal of personality and social psychology, 51(2), 443.

Munakata, J., Hazard, E., Serrano, D., Klingman, D., Rupnow, M. F., Tierce, J., … & Lipton, R. B. (2009). Economic burden of transformed migraine: results from the American Migraine Prevalence and Prevention (AMPP) Study. Headache: The Journal of Head and Face Pain, 49(4), 498-508.

Oelkers-Ax, R., Leins, A., Parzer, P., Hillecke, T., Bolay, H. V., Fischer, J., … & Resch, F. (2008). Butterbur root extract and music therapy in the prevention of childhood migraine: an explorative study. European Journal of Pain, 12(3), 301-313.

Thaut, M. H. (2010). Neurologic music therapy in cognitive rehabilitation. Music Perception, 27(4), 281-285.

Vink, A. C., Bruinsma, M. S., & Scholten, R. J. (2003). Music therapy for people with dementia. Cochrane database of systematic reviews, (4).

Appendix A

Questionnaire for measuring belief in efficacy of music therapy.

How confident are you in the ability of pet therapy to treat your pain?

  1                   2                 3                   4                  5                 6                 7

Not at all                                                                                                  Extremely 

confident                                                                                                  confident

How confident are you in the ability of rhythmic classes to treat your pain?

  1                   2                 3                   4                  5                 6                 7

Not at all                                                                                                  Extremely 

confident                                                                                                  confident

How confident are you in the ability of yoga to treat your pain?

  1                   2                 3                   4                  5                 6                 7

Not at all                                                                                                  Extremely 

confident                                                                                                  confident

How confident are you in the ability of music therapy to treat your pain?

  1                   2                 3                   4                  5                 6                 7

Not at all                                                                                                  Extremely 

confident                                                                                                  confident

How confident are you in the ability of traditional western medicine to treat your pain?

  1                   2                 3                   4                  5                 6                 7

Not at all                                                                                                  Extremely 

confident                                                                                                  confident

How confident are you in the ability of mindfulness meditation to treat your pain?

  1                   2                 3                   4                  5                 6                 7

Not at all                                                                                                  Extremely 

confident                                                                                                  confident

Appendix B

Measurement of pain

How would you rate your pain symptoms this week??

  1               2              3                 4              5              6              7                8               9            10

No symptoms                                                                                                                                                                                                                                                                                                    

About the author

Karishma Kulshrestha

Karishma is currently a Senior at the Thomas Worthington High School in Ohio. She has a strong passion for people, and her extraverted nature is where this seed was planted. As someone with chronic migraines herself, she has seen a lot of the medical systems from the patient perspective which has allowed her to think about things others might not normally. Karishma is in marching band and was a drum major this year. She also plays volleyball, and started a ski club at her school.

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Abstract

The ability of stem cells to self-renew and form different mature cells expands the possibilities of application in cell therapy, such as tissue reorganization in regenerative medicine, drug detection, and treatment of neurodegenerative diseases. In addition to stem cells found in embryos, several adult organs and tissues also have stem cell niches in an undifferentiated state. In the central nervous system of adult mammals, neurogenesis occurs in two areas: the subventricular zone and the dentate gyrus of the hippocampus. The different nervous systems originate from adult neural stem cells, which can self-renew or differentiate into astrocytes, oligodendrocytes, or neurons that respond to specific stimuli. The regulation of the fate of neural stem cells is a finely controlled process that relies on a complex regulatory network that extends from the epigenetics to the translational level and involves components of the extracellular matrix. Therefore, a better understanding of the mechanisms by which neurogenesis is induced, regulated, and maintained will provide clues for the development of new strategies for neurodegenerative treatment. In this review, we focus on the regulatory mechanisms of transcription factors, microRNAs, and components of the extracellular matrix in neuronal differentiation.

1. Introduction 

Neocortical circuits consist of profoundly interconnected excitatory glutamatergic and inhibitory GABAergic neurons, which are produced from unmistakable pools of RGCs. During embryo development, the excitatory neurons are formed from RGCs confined in the ventricular zone of the dorsal telencephalon and relocate radially toward the pial surface in a back to the front way (spiral movement). Then again, inhibitory neurons fundamentally begin from the ventral telencephalon and relocate digressively into the neocortex (distracting movement). Regardless of such unique formative starting points, both excitatory and inhibitory neurons go through the multipolar stage with a few minor cycles in the neocortex before axon augmentation. Then, at that point, they go through dynamic morphological changes to start axon development, in particular, neuronal polarization. Sakakibara and Hatanaka () assessed the consecutive occasions in polarization cycles of both excitatory and inhibitory neurons, and they talked about the hidden atomic instruments. At the multipolar stage, the excitatory neurons fleetingly utilize a multipolar relocation mode, to be specific movement with no fixed heading, in the subventricular and middle of the road zones. Then, at that point, they receive a bipolar shape during neuronal polarization and relocate rapidly toward the pial surface along with RGC measures, which is called headway mode.

Many types of molecules are involved in these powerful changes in neuron morphology and migration. The small GTP binding protein plays an important role in regulating the development of the cortex and the formation of neurons. The Rnd protein, “abnormal” relatives of Rho, was displayed in neuronal movement, and their ascending and descending pathways were discussed. Many cytoplasmic protein elements, including parts of the cytoskeleton, are managed through phosphorylation and dephosphorylation measures. Ohshima focused on protein kinases, including CDK5 and JNK, and examined their paperwork on cytoskeletal associations during multipolar bipolar progression and extended movement. OhtakaMaruyama and Okado comprehensively summarized the atomic pathways involved in these formation cycles and emphasized the importance of subplate neurons in steering events and the development of the six-layer neocortex design. 

 Pluripotent stem cells are usually derived from embryonic tissue. At least three different types of mammalian pluripotent stem cells have been identified: embryos or cancer cells (CE), embryonic stem cells (ES) derived from the inner cell mass of blastocysts, and embryonic germ cells (EG) derived from post-implantation embryos. In the early 1990s, several groups reported the existence of a subset of stem cells found in the central nervous system (CNS). These cells form the brains of fetal mice or mice that grow in culture and show an almost unlimited lifespan. However, compared to embryonic stem cells, their differentiation potential is more limited and they mainly produce three main cell types of the central nervous system: neurons, astrocytes, and oligodendrocytes, hence the name NSC. These cells have also been isolated from the adult central nervous system, although it is not clear whether these dividing cells are truly pluripotent or whether their fate becomes more restricted during development. For the purposes of this review, NSCs are defined as nerve cells that have the potential to self-renew and generate all the different types of cells in the nervous system after differentiation. 

2. Method

Neuronal differentiation is an early event in mammalian embryogenesis, occurring shortly after germ layer differentiation. The organization of the central nervous system is derived from a well-defined neuroectoderm, the neural plate, which is located in the dorsal midline of the embryo. It appears that neural plaque is produced by signals that locally inhibit or avoid inducing non-neural differentiation. Examples of such signals come from bone morphogenetic protein (BMP) and other molecules of the transforming growth factor-beta (TGF beta) superfamily, which direct epidermal differentiation when gastrulation

Neuronal fate is inhibited by BMP. In the body, several molecules that promote neuronal differentiation, such as noggin, follistatin, and chordates, are antagonists of BMP. Although noggin antagonizes BMP signaling, it is not necessary for the induction of early neurogenesis, because knockout mice are normal at embryonic day 8.5 (although they die at birth). These data indicate that other BMP antagonists can compensate without Noggin expression and illustrate the concept of redundant signaling pathways during embryonic development. The role of BMP is further regulated by the presence of two ubiquitously expressed BMP receptors, BMPR1A and BMPR1B, which do not appear until the 9th day of the embryo. The expression of BMPR1A induces the expression of BMPR1B, and this process is inhibited by the sonic hedgehog. The further development of the neural lamina into mature cells of the central nervous system is clearly and precisely regulated by spatial and temporal differentiation patterns. The growth and proliferation of cells in the early neural plate eventually lead to the closure of the developing nerve sulcus and the formation of a hollow neural tube. The neural tube cavity produces the ventricular system and the epithelial layer contains stem cells that will produce neurons and glial cells of the central nervous system. One of the central problems in developmental neurobiology is the mechanism by which a simple neuroepithelium (only one cell thick) can produce the various cell types that make up the mammalian central nervous system. Currently, there is a large number of studies that have determined the internal factors and external soluble signals that affect this regional pattern and specific neural differentiation. An example of this invertebrate neurogenesis is the transmission of dorsal and ventral patterns of opposing soluble signals: sonic hedgehog (Shh) and BMP antagonists, chordin and noggin, are secreted from the bottom plate, while other signals are emitted from the top plate. Form a gradient. Signal concentration. The precise concentration and ratio of each signal in the neural tube is critical to the development of specific neuronal phenotypes at different points along the gradient. For example, there is a concentration-dependent induction of model genes in progenitor cells encoding homeodomain transcription factors. These differences in expression patterns produce neuronal clusters with different division and differentiation patterns (and ultimately different phenotypes) along the dorsal-ventral axis of the plaque. In humans, the neural tube is formed during the third and fourth weeks of pregnancy. Initially, the neuroepithelial lining consisted of a single layer of neural stem cells with similar morphology. These cells then divide symmetrically to enrich the pool of NSCs or divide asymmetrically to produce a more differentiated progeny from which neurons and mature cells of the glial mass line develop. Although the BMP family of molecules may be involved again, the signals that determine symmetric or asymmetric division are not yet fully understood. 

Retroviral marker studies have been used to identify dividing cells in the ventricular region. Approximately 48% of the labeled cells remained in the colonies in the ventricular zone, indicating self-renewal at this site. In humans and other mammals, the active proliferation of such progenitor cells is likely to be balanced by apoptosis to maintain a stable population, but the exact mechanism has not been determined. As development continues, neurons migrate, partly guided by radially oriented glial processes, and the size of the ventricular area decreases. Neural stem cells are still attached to the basal layer. It has been shown that these cells can divide asymmetrically, and the more differentiated offspring migrate from the ventricular area to the overlying cortex. By obtaining specific phenotypic markers, these cells can divide further and distinguish them from neural stem cells. During CNS development, the temporal pattern causes oligodendrocytes to generate neuronal cell types earlier. In the spinal cord, these two cell populations appear to be produced from a common precursor, and the final decision of fate depends on external signals and specific patterns of transcriptional activation. There are two main types of transcription factors that can determine the fate of neurons or the glia. These are homeodomain factors, an example is NKx2.2, and the basic helix loop helix family of transcription factors, including Olig1 and Olig2. The expression of Olig transcription factors is regulated by external signals (such as Shh), and their expression has been shown to clearly define cells as oligodendrocytes. However, in mice, Olig1 and Olig2 are expressed from E9, long before the presence of oligodendrocyte precursors. At this early stage, Olig2 is known to be the core of neuronal development in specific areas of the ventral spinal cord and is found in cells that can track the fate of motor neurons. The importance of Olig2 in neurodevelopment has been demonstrated in functional gain experiments. As development proceeded, the expression of Olig1 and Olig2 persisted and began to overlap with the homeodomain transcription factor NKx2.2. These doubly positive progenitor cells migrate from the ventral midline and mature into oligodendrocytes. Olig1 and Olig2 have been developed in double-mutant mice. These animals lack oligodendrocytes and also have a considerable loss of motor neurons. In the Olig1 / Olig2 double mutant, the offspring of stem cells in the pMN region of the developing spinal cord generally develop into motor neurons, then oligodendrocytes, but form V2 interneurons and then astrocytes. These results indicate that the expression of the combination of transcription factors determines the fate of the stem cell bank in the developing embryo. However, this does not rule out the existence of more restrictive dividing cell populations. The expression of proglia transcription factors can be regulated by cell surface receptors such as gaps. The jagged1 (notched ligand) signal of neurons has been shown to inhibit the phenotype of oligodendrocytes. Presumably, when the number of neurons is sufficient, jagged1 is down-regulated, and a signal from the original oligodendrocytes (one of which may increase electrical activity) triggers myelination. Once the neural precursor determines the fate of the oligodendrocyte lineage, the last step of honey-forming cell formation needs to exist.

3. Conclusion

All in all, corticogenesis from mouse ESCs shows a setup collection of parent-of-beginning articulation and DNA methylation of engraved loci. This model could be utilized dependably to unwind the atomic components engaged with choosing the communicated parental allele with regard to engraving during the cortical turn of events. Our discoveries likewise give support to utilize ESCs to demonstrate cortical turn of events and for drug screening. The in vitro corticogenesis framework could be an integral asset to pinpoint sedates that de-curb the quieted parental allele in specific mind sicknesses related to irritated IG articulation or to gauge the effect of ecotoxic compounds on the epigenetic marks and the advancement of cortical cells. The cerebral cortex creates through the organized age of many neuronal subtypes, yet the systems included stay hazy. Here we show that mouse undeveloped undifferentiated organisms, refined with no morphogen except for within the sight of a sonic hedgehog inhibitor, summarize in vitro the significant achievements of cortical turn of events, prompting the successive age of a different collection of neurons that show the most notable elements of veritable cortical pyramidal neurons. When joined into the cerebral cortex, these neurons foster examples of axonal projections relating to a wide scope of cortical layers, yet in addition to exceptionally explicit cortical regions, specifically visual and limbic regions, subsequently showing that the character of a cortical region can be determined with no impact from the mind. The disclosure of natural corticogenesis reveals new insight into the instruments of neuronal particular and opens new roads for the demonstrating and treatment of neural disorder.

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About the author

Harsheel Dhruva

Harsheel is currently a Senior at Irvington High School in Fremont, California. Growing up in the Bay Area, he is very interested in the natural sciences and the potential of discovery from advancing technology. He is pursuing studies in Neuroscience and Biotechnology in hope of pioneering his own research in the future and create technology to improve the human condition. Outside of his study, Harsheel loves completing challenging physical activity or listening to music whenever he gets the chance, and is always happy to read something new.

The post Exploring Corticogenesis: Pluripotent Stem Cells into Cerebral Operations appeared first on Exploratio Journal.

Introduction

“If agriculture is to continue to feed the world, it needs to become more like manufacturing”, says Geoffrey Carrand fortunately, that is already beginning to happen.¹Advances in technology are key to the future of agriculture as farmers strive to feed the world with limited natural resources.² In Tamil Nadu state, located in South India, Agriculture is the greatest overriding sector of the state’s economy and nearly 70% of states population is based on agriculture. The agriculture in Tamil Nadu has executed a good performance over the years with the help of efficient farmers who are both receptive and responsive to the technological development announced in the agricultural sector of the state.³ Innovations for small agricultural operations can significantly increase profit margins by minimizing the need for manual labour with automation, expediting machinery commands with remote and real-time monitoring, and allowing farmers to utilize resources more efficiently with preventative maintenance and environmental prediction. Mass embracement of these technology advancements in agriculture will allow small land holding farmers to achieve higher potential for profit, and higher yields on the upfront investments.⁴I live in Tamil Nadu and our family own an agricultural field and that interests me to investigate this study, for which I researched and collected primary and secondary data regarding the topic.  The technologies that are currently reshaping the agriculture of Tamil Nadu state are detailed below.

PRECISION FARMING

Precision Farming helps farmers to generate data with the help of sensors and analyse that information to take intelligent and quick decisions.⁵ It is the application of modern information technologies to provide, process and analyse multisource data of high spatial and temporal resolution for decision making and operations in the management of crop production⁶  and helps in changing the socio-economic status of farmers.⁷ 

CASE STUDY   

Mr.Rajamani, a farmer in Coimbatore district, Tamil Nadu, follows the precision farming in his field, where he broadcasted the coriander seeds in between the turmeric crop, small onion and chilly as intercrops, red gram as border crop and irrigated the field through drip system. He harvested the coriander in 30 – 35 days, onion in 70 days, chillies on 90th day, red gram on 250th day and turmeric fingers on 275 days after sowing. He acquired yield of 7 tonnes of turmeric fingers and 13 tonnes of onion, 2 tonnes of green and dry chilies and 50 kg red gram in one hectare of land. He sold turmeric fingers at the rate of Rs. 135 / kg, onion at Rs. 20/ kg, chillies at Rs. 12/ kg, red gram at Rs. 100/ kg, tender coriander leaves Rs.4/kg and coriander seed at Rs. 15/ kg. He invested Rs. 3,35,400 for cultivation practices and attained a high profit of Rs. 9,66,000 per hectare and this was possible since he shifted from conventional farming to precision farming.⁸

ORGANIC FARMING

This process involves the use of biological materials, avoiding synthetic substances to maintain soil fertility and ecological balance thereby minimizing pollution and wastage. It relies on ecological processes, biodiversity and cycles adapted to local conditions, rather than the use of inputs with adverse effects and care for the larger environment and conservation of natural habitats and wildlife.⁹

CASE STUDY

Organic farming is the in-thing now in Thanjavur district, the rice bowl of Tamil Nadu, where one hundred farmers are successful in practising organic farming, for crops like- banana, maize and paddy. Mr.Kulandaisamy, a progressive farmer has raised Rasthali and Robust variety of bananas adopting organic farming methods. With respect to Rasthali, a bunch has five to six hands (hands means “seeppu” in Tamil language), instead of three to four hands, which are normally seen in ordinary cultivation and a single bunch weighs 20 kgs and fetches Rs.200. And the Robust variety of banana has 12 to 15 hands in a bunch and the bunch weighs 30 to 35 kgs, which are popularly sold in Tiruchirappalli city (my native), market. Fertiliser used by the farmer was composed using organic matters, neem, and pancha kavya prepared using cow’s urine and cow dung. The farmer has cultivated maize, paddy and Vanila (a profit-oriented venture), using organic farming.¹⁰

DRONES 

 Drones help farmers optimize the use of inputs such as seeds, fertilizers, water, and pesticides more efficiently. This allows timely protection of crops from pests, saves time for crop scouting, reduces overall cost in farm production, and secures high yield, increasing the farm profitability. ¹¹Initially used for chemical spraying, today drones are a great tool used to assess different aspects of plant health, weeds, and assets.

CASE STUDY

Tamil Nadu Agricultural University has developed drones, that could carry 15kg agricultural input, that helps to spray pesticides, insecticides and herbicides to protect crops, with a capability of covering one hectare in five minutes and a maximum of three hectares in 15 minutes in a single flight. It could cover large areas in a short time, so that pests could be destroyed across massive tracts of land before they could spread. However, drifting due to varying wind speed is a risk. During windy days it may not be a good choice, says the officials. ¹² And in future, usage of a drone would eliminate dependence on the already scarce farm labour. 

IOT BASED SMART FARMING

In IoT-based smart farming, a system is built for monitoring the crop field with the help of sensors and automating the irrigation system. The farmers can monitor the field conditions from anywhere.In terms of environmental issues, IoT-based smart farming can provide great benefits including more efficient water usage, or optimization of inputs and treatments. ¹³

CASE STUDY

A team of college students from Salem city, Tamil Nadu, have come into the spotlight recently for their Internet-of-Things (IoT) based software solution that seeks to give a push to smart farming, and this crop guidance software is a set of three applications that enable remote monitoring of pest control, automated watering and growth. The objective is to bring in smart farming solutions that allow farmers to produce maximum yields with minimum resources such as water, fertiliser and seeds, and reduce wastage or losses. One of the applications of their IoT-based comprehensive solution is the Plant Growth Monitoring System, which uses colour sensors to check the growth of the plants and sprays fertiliser as and when needed. Another application with an additional WiFi module identifies pest attacks and sprays pesticide precisely on the affected parts of the agricultural land. Another application, the Automatic Plant Watering System, uses a moisture sensor and utilises water resource judiciously as it triggers watering based on the moisture level of the soil. The systems work automatically- for example- in the watering system when the sufficient water level is reached, it will switch off on its own. This makes it easier for farmers and makes the process seamless. As the applications are IoT-based, they also have a feature that provides weather conditions for proper crop growth to the farmers. ¹⁴

MOBILE APPS 

There are mobile applications that provide latest agricultural information about trends, equipment, technologies and methods being used, help identify pests and diseases, provide real-time data about weather, early warnings about storms, local markets offering best prices, seeds, fertilizers etc. In addition, farmers can also interact and get guidance from agriculture experts across the country via the apps. These apps help in providing market information, facilitating market links, providing access to extension services, farm related information etc.¹⁵

CASE STUDY

About 4,91,811 users of Uzhavan App, launched by Tamil Nadu Agricultural Ministry, educate farmers about soil quality, seeds and fertilizers. FarmMOJO app provides real time solutions for aquaculture, including shrimp and fish farming and this app records data like pH balance, ammonia and nitrate levels and water quality. Tumaini app allows farmers, cultivating banana, to scan plants and detects symptoms on any part of the crop with an accuracy rate of 98%.¹⁶

HYDROPONICS

Hydroponics is a way to skip the soil and grow crops directly in nutrient-rich water, that gives higher yield with fewer resources. ¹⁷

CASE STUDY

According to Mr.Srinivasan, a resident of Chennai city, perform commercial hydroponics by growing crops like spinach, kale, and lettuce. With nearly 250 plants across 50 square feet, Rahul Dhoka runs Acqua Farms in Chennai city. The rate of crop growth is around 30 to 40 per cent faster and have a 30 to 40 per cent higher yield, than soil-based agriculture, with a less cost of production ie-lettuce costs Rs 15 to 17 per kg, and basil costs Rs 20 to 25 per kg. ¹⁸

SOIL MOISTURE INDICATOR

Soil moisture indicator has sensors to detect the soil moisture content for proper development of plants and notify the user when the soil gets too dry or too wet.¹⁹ This device works based on the principle that electrical conductivity of the soil is directly proportional to soil moisture or soil electrical resistance is indirectly proportional to soil moisture content.^20.  It is suitable for different types of soils and can be used in nurseries, farms, potted plants, etc.²¹

CASE STUDY

The Soil Moisture Indicator, a handy and user-friendly electronic moisture–indicating device, was first launched at Sugarcane Breeding Institute at Coimbatore city, Tamil Nadu, which is used for the efficient irrigation management practices such as irrigation-scheduling, particularly in sugarcane fields, that helps the farmers in deciding when to irrigate their fields and as a result there would be considerable saving of irrigation water. The sensor rods of the device need to be inserted into the soil to a required depth to assess the soil moisture, which is indicated by glowing LEDs and the device is suitable for use in agricultural farms as well as in potted plants.²²

RFID TECHNOLOGY

Radio Frequency Identification Technology offers monitoring systems, which protects the crops from pests and wireless sensors can be used to monitor cattle. It uses dedicated software and hardware to monitor livestock management.²³  It supports Animal population trackingand Animal data base monitoring.²⁴

CASE STUDY

“Mr. Vijaykumar, Perambalur district, Tamil Nadu, relies on computerisation to monitor the dairy along with the 60 staff who stay on the farm. Each animal has a blue-collar tag with a unique microchip number. “Of the 368 cows here, 160 are used for milking and the microchip helps us not just to track each cow’s health and daily milk output, but also to decide if it can be selected for breeding in future.²⁵

CONCLUSION:

The agriculture technologies have changed almost all the domains of farming from sowing to harvesting. These technologies are continued to evolve and invent new innovations that act as catalyst to ameliorate farmers’ life by increasing incoming and providing the access to research stations and agro-scientists.  The use of technology can make farmers feel more empowered and enable them to adopt required measures in needful time. It has potential capabilities to transform agriculture into a better prospect to get aware of climatic change and appropriate use of limited natural resources in agricultural land.²⁶ However technologies like hydroponics; drone farming; use of mobile applications and IOT based devices had get tremendous success in Tamil Nadu. Many government organizations and private bodies are working on to give a clear view about new technologies to the farmers of Tamil Nadu, like robotic farming, chromosomal technique and application of remote sensing etc… which are practiced globally.

REFERENCES

1. https://www.economist.com/technology-quarterly/2016-06-09/factory-fresh
2. https://www.raconteur.net/sustainability/top-5-tech-innovations-in-agriculture/
3. https://www.agrifarming.in/agriculture-farming-in-tamil-nadu-cultivation-practices
4. https://www.aeris.com/news/post/five-iot-applications-that-are-reshaping-agriculture-technology/
5. https://www.biz4intellia.com/blog/5-applications-of-iot-in-agriculture/
6. https://www.sciencedirect.com/topics/earth-and-planetary-sciences/precision-agriculture
7. https://www.downtoearth.org.in/blog/agriculture/why-farmers-today-need-to-take-up-precision-farming-64659
8. https://icar.org.in/node/8077
9. https://www.conserve-energy-future.com/organic-farming-benefits.php
10. https://agritech.tnau.ac.in/org_farm/orgfarm_success%20stories.html
11. https://blog.agrivi.com/post/powerful-role-of-drones-in-agriculture_april2018
12. https://timesofindia.indiatimes.com/city/coimbatore/reaping-rich-dividends-with-drones/articleshow/71382745.cms
13. https://www.iotforall.com/iot-applications-in-agriculture
14. https://www.edexlive.com/campus/2020/sep/29/tn-students-develop-smart-farming-solutions-for-automatedpest-control-and-plant-watering-14868.html
15. https://www.manage.gov.in/publications/edigest/dec2017.pdf
16. https://m.timesofindia.com/city/chennai/click-cure-tech-helps-farmers-reap-a-bounty/amp_articleshow/71284085.cms
17. https://www.verticalroots.com/the-what-and-why-of-hydroponic-farming/
18. https://www.dtnext.in/News/City/2019/12/10052809/1202790/Hydroponic-farming-helps-Chennaiites-grow-greens-at-.vpf
19. https://link.springer.com/chapter/10.1007/978-981-15-5113-0_55
20. https://sugarcane.icar.gov.in/index.php/en/home/1157-soil-moisture-indicator
21. http://www.techsourcesolutions.in/manufacturing/soil-moisture-indicator/
22. http://sugarcane100.blogspot.com/2016/01/soil-moisture-indicator-launched.html?m=1
23. https://agritechsupport.com/agriculture-technology/radio-frequency-identification-technology-in-agriculture/
24. https://academic.oup.com/jas/article-abstract/97/Supplement_2/1/5541113
25. https://www.thehindu.com/sci-tech/agriculture/data-analytics-helps-an-integrated-farm-in-arumbavur-village-perambalur-district-to-produce-additive-free-cows-milk/article26888613.ece
26. https://www.ijcmas.com/10-2-2021/Pradeep%20Kumar%20Singh,%20et%20al.pdf

About the author

Harnishya Palanichamy

Harnishya is currently in Grade 10 at the Hebron School, Ootacamund, Tamil Nadu, India. She’s passionate about the field of computer science, particularly the coding languages, Java and JavaScript. She enjoys coding and researching about AI. She started her coding journey by coding games in JavaScript, and she also has experience with robotics; being in the school robotics club. In the future, she wants to develop her coding knowledge by creating more complex apps.

The post Technologies Reshaping Agriculture in Tamil Nadu, India appeared first on Exploratio Journal.

Abstract 

Sleep has been the center of discussion for scientists over nearly the past century, with compelling evidence that show sleep influences memory consolidation. Nervous systems have specific structures related to distinctive functions, which serves to transform short-term memories into long-term memories, with synapses playing a crucial role. Tests on electrophysiology markers of synaptic efficacy adds proof to the synaptic homeostasis (SHY) hypothesis. During sleep, spontaneous activity renormalizes net synaptic strength and restores cellular homeostasis. Sleep stages and rhythms correspond to 5 types of brain waves respectively, and there are interplays between sleep rhythms and memory consolidation. Explicit memories (associated with neocortex and amygdala), reported by traditional studies, are strengthened during SWS sleep, whereas implicit memory (associated with basal ganglia and cerebellum) mechanisms have been also demonstrated in recent research to be recruited during NREM sleep periods. Experiments have provided evidence that show how individuals reproduce knowledge they learned after retention of several hours after intervals of sleep or wakefulness respectively. Data analysis show that sleep promotes memory consolidation consistently; effects are extremely significant in the situation with declarative memories (syllables).  Further research done recently have also come up with the result of brain reactivation during sleep.

1. Introduction

The basics of memory formation involve three stages: encoding, storage, and retrieval. Encoding represents the transformation of sensory inputs into short-term and long-term memories, storage is defined as to maintaining the encoded sensory information over time, whereas retrieval refers to how people get access to the actual memories stored in their brain. Different mechanisms, as well as different brain areas, are triggered when humans are forming and storing memories. For example, short-term memory involves mainly the prefrontal cortex, while long term memory involves the hippocampus, neocortex and amygdala (explicit memory) or the basal ganglia and cerebellum (implicit memory). Explicit memories, which are episodic events and semantic information, together with implicit memories, which involve motor memories, make up long-term memories. 

Memory consolidation is the rehearsal of earlier memories resulting in the increasing number of synapses, swelling amount of neurotransmitter released, and enlarging number of receptors on postsynaptic membrane. By consolidation, short-term memories can develop into long-term memories. Memory consolidation is rather significant. It is reported that students on average are able to grasp only 25% of the lecture they are listening to even carefully. The left 75% requires the mechanism of memory consolidation to be in the storehouse where it should be. Hence, teachers are supposed to assign homework and give feedbacks. This can be named as early consolidation practice which is later strengthened by quizzes and examinations.

Over the last decades, scientists have gained compelling evidence that sleep enhances the formation of long-term memories. Traditional theories proposed a passive role for sleep supporting memories by sheltering them from external stimuli, however recent research have uncovered an active role for sleep during memory consolidation. Traditional studies emphasized the role of rapid-eye-movement (REM) sleep, present papers have revealed that slow-wave-sleep (SWS) is also significant (Rasch B, Born J. 2013). Initially, scientists emphasize that sleeping provides shelter for the brain’s memory storage from external stimuli or interferences; yet later researchers put forward that sleep actively involves in system consolidation of memories. In this review, we will summarize about the correlations of sleep and memory consolidation. 

1. Neuron physiology
2. Neuronal structures and functions 

Neurons (also called nerve cells or neurones) are the fundamental units of the brain and nervous system. They are responsible for receiving input from external world, sending commands to the muscles, and relaying or integrating electrical signals. Most of a neuron’s organelles are in the cell body, with the largest karyoplasmic ratio of approximately 1:1 among all the other cell types.

 A useful model is to consider the structure of a neuron as a tree, consisting of three major parts: an axon (tree root), dendrites (tree branches), and a cell body (tree trunk). Long and slender, axons are projections of nerve cells that conducts action potential away from the cell body. The primary function of axons is to transmit signals to target cells. Axons fall into two categories: myelinated and unmyelinated. Myelinated axons are surrounded by segments of a fatty layer of insulating glial cells, namely Schwann cells (in the CNS) and oligodendrocytes (in the PNS). Vacant space in myelin sheath is known as nodes of Ranvier, where action potentials jump along. Due to their lengths, axons can be divided into branches of telodendrions, whose ends are called axon terminals. Axon terminals, as pre-synaptic axons, contain vesicles of neurotransmitters for the relay of electrical signals in the synapses. The post-synaptic dendrites can be easily distinguished by its similar appearances to lushy but short branches. Dendrites receives signals from axons and carries the action potential towards the soma. Dendrite spines are small protrusions that increase the receptive properties of dendrites to isolate signal specificity. Implication of dendrite spines can be seen in long-term potentiation. In other words, the ability of dendritic development plays a crucial role in memory formation. The cell body, the soma, holds nucleus responsible for production of proteins.  

The site in the nervous system where signals are relayed is called synapses. As signals can be described as chemical and electrical, so as synapses. In chemical synapses, the electrical signals must first trigger the release of neurotransmitters and next the second messenger or be again converted into electrical signals. While in electrical synapses, currents past through gap junctions directly by inducing voltage changes in the postsynaptic cells. Without the multiple steps used in chemical synapses, electrical ones are special for their rapidity (Cirelli C. 2013).

When not functioning, a neuron has a rest potential of 60-70 mV across its membrane, with the interior more negatively-charged. Opening or closing ion channels in the membrane lead to changes in the potentials. Hyperpolarization is when the membrane potential becomes more negative, with depolarization the opposite. Opening of channels that let potassium cations out or chlorine anions in may results in hyperpolarization. On the other hand, opening of channels that let sodium cations in will cause depolarization. When multiple depolarizing inputs happen at the same time, a large enough depolarization may ultimately lead to an action potential. An action potential is an all-or-none event, which means the stimulus must thrust the threshold of negative 55 mV. At this threshold, sodium cations influxes to increase the potential up to 40mV. Next, influxes stop and potassium cations rush out, causing a rapid fall towards normal resting state. Yet the potassium cations keep flowing out, resulting in a period of undershoot, where even larger potentials are needed to induce another action potential. Eventually, potassium outfluxes stop and the potential returns to -60 to -70 mV, ready for the next cycle. (Reece, J. B., et al, 2011)

1.12 Electrophysiology of synapses during wakefulness and sleep

Electrophysiological markers of synaptic efficacy have been tested in recent decades. The slope of the early response evoked by electrical stimulation delivered within a cell is a classical electrophysiological measure of synaptic strength, with a steeper slope indicating higher synaptic efficiency. A recent study utilized transcranial magnetic stimulation to evoke a response in humans’ frontal cortex. Results have shown that the slope of early responses increased gradually over the period of 18 hours of consisting awareness, and then recover to basic levels after one night of sleep (Cirelli, Chiara,2013). The explanation for such phenomenon has been proposed. The synaptic homeostasis hypothesis (SHY) proposes that sleep is the price the brain pays for plasticity. During a waking episode, learning statistical regularities about the current environment requires strengthening connections throughout the brain. This increases cellular needs for energy and supplies, decreases signal-to-noise ratios, and saturates learning. During sleep, spontaneous activity renormalizes net synaptic strength and restores cellular homeostasis. Activity-dependent down-selection of synapses can also explain the benefits of sleep on memory acquisition, consolidation, and integration (Tononi, G, et al, 2014).

1. Correlations between sleep and memory consolidation
2. Sleep stages and brain waves

As measured by eletroencephalography (EEG), the brain’s electrical activity is determined by the presence of brain waves, which fall into 5 categories: Gamma, Beta, Alpha, Theta and Delta waves. Gamma waves, are small-amplitude high-frequency (25-140 Hz) ones related to hyper brain activity, such as problem solving and high-concentration tasks. Beta waves (14-25 HZ) possess both lower amplitude and frequency traits and are generated when the brain is busily engaged in activities and conversation. Alpha waves (7.5-13 Hz); Theta waves (3.5-7.5 Hz) and Delta waves (3Hz or below) are all associated with rest, sleep and dreaming activities. 

During the approximately 90-minute-long sleep cycle, non-REM sleep first appears and rapid-eye-movement sleep (REM) follows. The sleep cycle has four stages, that are characterized by the presence of different brain waves. Stage 1 represents the transition step between wakefulness and sleep, in which the brain generates Alpha waves.  The following stage 2 is where Theta waves, K-Complex and sleep spindle waves alternate as responses and inhibition towards outside stimuli. While progressing to stage 3 and 4 sleep, people are drifting down into deeper sleep, and the brain emits Delta waves. Finally, people fall into stage 5 sleep, or REM sleep, presenting bran waves similar to those of an awake person. In this phase, dreaming occurs and the skeletal muscles experience atonia, and are unable to move. Cycles of slow wave and REM sleep alternate at night, with the slow wave sleep becoming less deep and the REM periods more prolonged until awakening.

2. Consolidation during SWS and REM 

Memory consolidation happens during slow wave sleep and REM phase. Slow wave sleep (SWS) periods appear to strengthen explicit memories evidenced by the observations on increased SWS after intensive episodic learning (Acosta MT, 2019). Recent studies on implicit memories during sleep have also demonstrated the recruitment of implicit learning mechanisms during NREM sleep, suggesting that memory consolidation may be optimized when taking into account sleep stages and sleep rhythms (Born J, et al, 2012). 

During slow wave sleep, the experienced episodes are stored temporarily in the hippocampus and are then redistributed in a long-term store in the neocortex. The waves generated in this process are hippocampal ripples, thalamocortical spindles and slow waves (delta waves). Originating in the medial temporal lobe, hippocampal ripples have a frequency of 100-300 Hz. Thalamocortical spindles are typical waves for stage 2 and SWS sleep, with a frequency of 10-15 Hz (Gage et al, 2018).  Spindles reaching the neocortex likely act to prime respective neuronal networks, e.g., by stimulating Ca²⁺ influx for subsequent synaptic plastic processes. Thus, memory information carried in single troughs of spindle oscillations maybe particularly effective in changing synaptic connections underlying the long-term storage of the information in the respective neocortical networks (Born et al, 2012). The third key SWS oscillation are the slow waves (0.5 – 4Hz). Slow waves are considered to reflect the brain’s up-and-down states, which is critical for memory redistribution, i.e., the encoding of information. The more information is encoded during wakefulness the higher amplitude of the slow oscillations is generated over respective cortical areas during succeeding SWS（Schonauer, et al. 2014).  A major function of the slow oscillations is that they temporally group neuronal activity into hyperpolarizing down-states during which neurons are globally silent and succeeding depolarizing up-states during which neuronal firing is increased to wake-like levels (Steriade et al. 2006). As previously described, the hippocampus, thalamus, and the cortex combine to form the complex neural symphony that corresponds to a key aspect of human SWS: memory stabilization and consolidation.

3. Effects of sleep on memory consolidation

3.1 Performance of individual subjects on the LGT-3 total score

Primary research has found that sleep consistently promotes overall declarative memory consolidation. Forty healthy adults aged 18–30 y, and 17 healthy adults aged 24–55 y with extensive meditation experience participated in the 3 experiments (Schonauer et al, 2014). In Experiment 1, subjects learned Turkish in the afternoon followed by a 2-h sleep, and after a 3-h retention they were tested. Results shown people performing far better after sleep than after wakefulness. There was only one exception that shown better results after an interval of wakefulness. In Experiment 2, volunteers acquired knowledge in the morning, after which they took a 2-h sleep and were tested after 8-h retention. Statistical analysis comported with results from experiment 1, with exception of 2 of 18 subjects (Schonauer et al, 2014).  In Experiment 3, procedures were same with Experiment 1 expect for the three states (sleep/active awake/meditation) subjects were kept respectively to compare. Subjects performed better on the test when there were only 3 h between learning and testing than when encoding and testing were spaced 8 h apart. There was still one exception out of 18 subjects. Conclusions were drawn: sleep does improve the consolidation of declarative memories while such effect is reserved only to sleep, and does not happen in the case of wakefulness. 

3.2 Significant improvements on syllable memories 

Two OS, L. R. Hodell and J. S. McGraw served as subjects and were required to learn meaningless syllables in series of ten by reading aloud, with exposure at the rate of 0.7 sec (Jenkins et al. 2021). The series were repeated by the subjects until they completely remembered the syllables. The experiment was carried out at night, when subjects go to bed after learning. They were separately waked by Ebbinghaus, the scientist, to give reproduction of the syllables. Analysis of data shown superiority of the reproductions after intervals of sleep. This superiority became more and more prominent as the length of the intervals elongated (Jenkins et al. 2021).

3.3 Improve memory by protection or reactivation of the brain during sleep

Sleep after learning leads to superior recall of syllables after the 1-, 2-, 4-, and 8-h retention interval, compared with wake intervals of the same length. Two subjects (H. and Mc.) participated in this classic study by Jenkins and Dallenbach. For each data point, each participant completed 6–8 trials, with the different retention intervals performed in random order (Rash et al. 2013). A time dependency of the effects of sleep on memory formation is indicated by studies showing stronger effects for sleep occurring shortly after learning than for sleep at a later time. For example, sleep occurring within 3-h after learning vocabulary was more beneficial than sleep delayed by more than 10 h. Furthermore, recall of word pairs after 24-h was better when sleep occurred immediately after learning than after a day of wakefulness.Whereas initially it was commonly assumed that sleep improves memory in a passive manner, by protecting it from being overwritten by interfering external stimulus inputs, the current theorizing assumes an active consolidation of memories that is specifically established during sleep, and basically originates from the reactivation of newly encoded memory representations (Rash et al. 2013). Maquet and colleagues (Maquet et al. 2000) led first experiments to mark out the activation of brain in learning using PET. Although there have been plenty of current studies on the evidence of reactivation of the brain using PET, fMRI, and EEG, higher resolution images will be needed to determine when and where reactivations occur. 

Conclusions

There’s no deny that sleep is fundamental for memory formation, as proved by studies down recently, which showcased significant enhancements in implicit memories and episodic learning in SWS and REM respectively, syllable learnings being the most evident.  However, there’s still great needs for higher resolution images to further decipher the exact mechanisms of latest discovered reactivations during sleeping.  

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About the author

Ashley Qian

Ashley is currently a Junior at the Wuxi Big Bridge Academy.

The post Brief Review of Sleeping to Enhance Memory Consolidation appeared first on Exploratio Journal.

Abstract 

How did the lockdown and social media use affect the body image, eating habits, self-esteem, and mental health of adolescent girls? This paper looks at the impacts the lockdown had on social media usage, body image, and eating/exercise habits. Research shows that adolescent girls found themselves with lower self-esteem, depression, anxiety, and disordered eating habits during and after the pandemic. These changes combined with increased social media use that typically promotes unrealistic body expectations and unhealthy eating and exercise habits lead to an overall decrease in young girls’ self-esteem and body image. Based on these findings, we can formulate ways to help everyone with these unhealthy habits if we ever go into lockdown again in the future and even in our normal daily lives as well. 

On March 12th of last year, schools began shutting down saying that they would be closed for the next two weeks out of precaution against COVID-19. Those two weeks quickly turned into three, and then four, and eventually they turned into months. Those months in quarantine were incredibly tough on everyone. Because everything closed down and everyone was stuck inside, peoples’ schedules changed drastically. For Katy, a freshman in high school on the dance team, covid was incredibly difficult. Halfway through her freshman year, her school shut down because of the pandemic. She was forced to do both school online as well as her dance classes. Because she spent most of her time at home, she found herself looking at social media much more often than before. She also began feeling insecure about falling behind on her goals for dance because of the pandemic, and as a result, she followed lots of fitness and dance influencers to try to learn and improve from them. Unfortunately, she only felt more insecure about how she looked and her abilities because those influencers did not post realistic habits. Those influencers she followed promoted unhealthy eating and exercise habits to “be a better dancer”, which led Katy to adopt those habits. Katy continued to feel bad about herself as she decreased the amount she ate and increased the amount she exercised. Katy now struggles with worsened body image and disordered eating habits that negatively impact her physical and mental health. 

How did the pandemic impact our lives? During the COVID-19 lockdown, a survey of U.S. social media users found that 29.7 percent of respondents were using social media for 1-2 hours additional hours per day. This significant increase was due to the extra time spent at home during the lockdown. Everyone’s normal lives were disrupted. Students were forced to do virtual school, many adults were forced to work from home, and many others even lost their jobs due to the pandemic. The uncertainty and confusion of the pandemic led to significant amounts of stress, anxiety, and depression for everyone. 

According to researchers at the University of Michigan conducting a survey from 977 parents of teens, they reported that 1 in 3 girls (aged 13-19) experienced new or worsening anxiety. Additionally, more parents of adolescent girls observed increased symptoms of depression, anxiety, and worry in their kids than parents of adolescent boys. The increase of social media usage also contributed to comparison and unrealistic expectations that worsened people’s self-esteem and body image, which often led to an increase in disordered eating and exercise habits for many. 

Specifically for adolescent girls, the comparison between before and after the pandemic shows the negative impact it had on adolescent girls’ mental health. According to the American Academy of Child and Adolescent Psychiatry in 2018, as many as 10 in 100 adolescent girls had an eating disorder. According to the Anxiety and Depression Association of America, around 25% of teenage girls displayed depressive symptoms. Of the total teenage population taking antidepressants, nearly two-thirds of them are adolescent girls. Therefore, a significant percentage of adolescent girls did have mental health issues and eating disorders in 2018, before the pandemic. Later studies prove that these issues worsened for many with the pandemic. Because their schedules were disrupted and they had more time at home, many adolescent girls adopted or increased their social media usage. Due to the pandemic lockdown and increased social media usage, the eating and exercise habits and in adolescent girls worsened. We can see how worsened eating and exercise habits would impact the mental health and self-esteem of adolescent girls. 

How social media usage affects the body image of young girls?

A research review that examined social media and body image concerns examined that increased social media use leads to more negative body image in young men and women. It specifically shows that appearance comparisons are the direct and important link between the usage of social media and negative body image. 

A research report examined the data from different studies. It showed the correlation between the usage of Facebook and the internalization of thin-ideal media. The study also reports that spending more time on Facebook/Myspace is associated with higher levels of body dissatisfaction and thin-idealization in adolescent girls (ages 14-22). Finally, it showed that elevated appearance exposure, such as posting or viewing, on Facebook was associated with more body dissatisfaction and thin-idealization amongst female high school students as well. 

From this data, we can conclude that heavier usage and interaction on social media such as Facebook is linked to much higher body dissatisfaction. Due to the increase of global social media and internet usage during the COVID-19 pandemic lockdown, we can see how the increase negatively impacted young girls. On many social media platforms, many users tend to edit, photoshop, or alter their photos in order to achieve an “ideal photo”. This is because of the abundant access to photoshop and editing apps online. This allows influencers and other social media public figures to alter their photos to make themselves look more “thin”, “attractive”, or “likable”. Too often, young girls compare themselves to unrealistic social media photos that they are convinced are the norm, and as a result, they feel depressed and dissatisfied when they don’t fit into those norms. Additionally, because young girls are at such a vulnerable age where societal influences can have a strong impact on them for life, these factors are already leaving a destructive impact that will last a long time. 

A study that surveyed 144 girls between the ages of 14-18 years old in the Netherlands and the impact normal and retouched Instagram photos had on their body image proved that the manipulated Instagram photos directly led to lower body image, specifically in those with higher social comparison tendencies. The manipulated photos were also rated more positively than the normal ones. 

This study randomly exposed participants to either 10 original Instagram photos or 10 manipulated photos. Afterward, the participants completed a survey containing various questions regarding their own self-esteem and their opinions about the photos they saw. Regarding the participants’ self-esteem, the study showed that on a scale from 0-6, girls with higher social comparison tendency that were shown manipulated photos had the lowest body image of 3.7. The data shows that 63 participants showed a lower tendency to make social comparisons, and 81 showed a higher tendency to make social comparisons. The participants also said in the survey that they could identify the manipulated photos better than they could identify the original photos. 

From this data, we can conclude that exposure to digitally manipulated photos in adolescent girls, especially those with higher social comparison tendencies, will lead to lower body image in the girls. This is because the participants rate the manipulated photos in the study “more desirable” than the natural photos. Additionally, the data also proves that the majority of the participants could tell that the manipulated photos were manipulated. This shows that the participants are aware of the photos manipulation, yet they still have the tendency to compare themselves to the fake photos. Because manipulated photos are so commonly seen in social media, and the media in general, those with higher social comparison tendencies naturally continue to compare themselves and alter their own images in order to fit into what is deemed “desirable”. As a result, the girls are left stuck with lower body image, which has led and will lead to unhealthy responses, such as depression, over-exercising, and/or disordered eating. 

How did the pandemic impact the eating and exercise habits of adolescent girls?

A study done in Australia compared those with eating disorders to the general population regarding their eating and exercise habits during the pandemic. This study launched a survey on April 1, 2020, in Australia to 5,469 participants, 180 of those self-reporting previous eating disorder history, to determine the changes in eating and exercise behaviors in people during the COVID-19 pandemic lockdown. Between both groups, the groups with the pre-existing eating disorders and the group without pre-existing eating disorders, both groups showed an increase in disordered eating during the lockdown period. The eating disorder group showed an increase in pre-existing behaviors, and the regular group developed disordered eating habits as well. The data is divided into three main groups, the general population, eating disorder, and the anorexia nervosa subgroup.

In total, all three categories reported an overall increase in restricting habits amongst those with previous eating disorders and a slight increase in those without them. For binging, the eating disorder group and general population group both showed an overall increase in bingeing of 35%, while the anorexia nervosa subgroup reported 21% of the participants with an increase in binging habits. Exercise trends are similar for all three groups, there is a significant percentage of the population showing an increase, no difference, and less exercise. For the eating habits reports, none of the categories showed a significant percentage of the population reporting a decrease (<14%), but all of the exercising categories did. 

This study proves that due to the lockdown, those with previous eating disorders increased their disordered eating habits. Additionally, those without previous eating disorders developed disordered eating habits. There could be many reasons for this. For many, the additional time spent in quarantine left them with the time to think about eating/restricting and act on their thoughts. Another factor was the unstable and rapid changes throughout the pandemic. No one knew what the world was going to look like in a few weeks, let alone a few months, and this uncertainty led to lots of stress for many. Another factor of the pandemic was the financial and emotional uncertainty. Many feared losing jobs, housing, food, schooling, and loved ones due to COVID-19 as well. This additional stress may have led to bad coping mechanisms, like obsessing over eating, and exercise habits for the need to feel control over their lives. 

A study conducted in the UK explored how the COVID-19 lockdown influences the eating habits and body image of adolescent girls. 

There are five different categories for the survey data, and participants would rank how well they thought they fit with the categories on a scale of “strongly disagree” to “strongly agree”. The participants were divided into three groups for each category, no (previous) diagnosis, other diagnoses, and eating disorder diagnosis. The first set of data from the survey responses is regarding “difficulty to regulate/control eating”. According to the data, a majority of all three groups said that they agree with the statement. The second category was “more preoccupied with food and eating”. The majority of all three groups also said that they agree with this statement. The next two categories are “exercising more” and “thinking about exercise more”. A significant percentage of each of the groups said that they agree and strongly agree with both statements. The last category is “more concerned about the way I look”. A significant percentage of all three groups said they agree with this statement as well. 

Overall, the data shows that the participants with an alternate diagnosis or an eating disorder had greater difficulty with their relationships with eating, exercise, and body image. At the same time, the people that had no previous mental or ED-related diagnosis reported an increase in negative relationships with eating and exercise during the lockdown. This proves that the lockdown did have a directly negative effect on many different types of people, those with and without a mental diagnosis. While everyone was on lockdown stuck in their homes, many people had lots of spare time on their hands. Specifically for those with an eating disorder/other mental diagnoses, they obsessed over food, exercise, and body image more than they would have when they were busy with their normal lives. Those with pre-existing eating disorders dealt with increased disordered eating habits, more so than the general population and even those with other mental disorders. 

The rapid and unreliable changes in our lifestyles also contributed to increased anxiety amongst many. The fear of losing one’s job, loved ones, or one’s own life during the lockdown was incredibly stressful, which oftentimes led to harmful coping habits like disordered eating or over-exercising. 

Additionally, during the lockdown, there was an overall trend of increased social media usage. This is understandable, given that the main form of communication while in lockdown was through technology/social media. However, the concentrated exposure to misleading/glamorized images may have led people to partake in over-exercising and disordered eating habits which overall, may have worsened their mental and physical health. 

Moving forward, there are many things we can do to help those struggling with disordered eating, body image, and exercise issues if we go back into lockdown again, or even if we don’t. We can help make social media a more safe and real place for everyone. Reminding ourselves that we should not compare ourselves to social media photos is really important. The reality is, we don’t know where these photos came from, they could be photoshopped, edited, and/or posed to look “better”. Additionally, choosing to follow influencers and content creators on social media that don’t promote harmful eating and exercise habits, and blocking/reporting those that do is a great and simple way to prevent being exposed to these harmful ideas. 

If we ever go back into a lockdown again, we cannot control the outside world, but we can control how it impacts us. Trying to cope with tough events using healthier coping mechanisms, such as exercising normally or going for walks outside, is a great way to deal with any stress or anxiety. It is important to look out for ourselves and others during these difficult times, and we must remind ourselves that it is okay to not be functioning at our best because these are not normal circumstances. 

There are many factors that may have contributed to adolescent girls developing/worsening their habits during the lockdown. These factors include dramatic lifestyle changes, stress, previous eating disorders, and mental health issues, excessive social media usage, and additional time spent being stuck inside. All of these factors may have contributed to low self-esteem, body image, eating disorders, and poor mental health in adolescent girls, and many are still dealing with the lasting effects of these issues today. 

References

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Daalmans, S., Kleemans, M., Anschütz, D., & Carbaat, I. (2016, December 15). Picture perfect: The direct effect of manipulated instagram photos on body image in adolescent girls. Taylor & Francis Online. https://www.tandfonline.com/doi/full/10.1080/15213269.2016.1257392.  

Phillipou, A., Meyer, D., Neill, E., Tan, E. J., Toh, W. L., Rheenen, T. E. V., & Rossell, S. L. (2020, June 1). Eating and exercise behaviors in eating disorders and the general population during THE COVID‐19 pandemic in Australia: Initial results from the COLLATE project. Wiley Online Library. https://onlinelibrary.wiley.com/doi/full/10.1002/eat.23317.   

April 20, 2021 | P. (2021, April 28). How to spot teen depression during covid. Scripps Health. https://www.scripps.org/news_items/5319-teen-depression-during-covid-19-pandemic-what-to-look-for.   

Robertson, M., Duffy, F., Newman, E., Prieto Bravo, C., Ates, H. H., & Sharpe, H. (2021). Exploring changes in body image, eating and exercise during the COVID-19 lockdown: A UK survey. Appetite, 159, 105062. https://doi.org/10.1016/j.appet.2020.105062 

About the author

Grace Julian

Grace is currently a Junior at the Episcopal Academy. She enjoys learning about history, psychology, and political science, helps run her school’s political blog, and is a leader of the community service executive board. In her free time, she enjoys playing water polo and reading.

The post Examining the effects of the COVID-19 lockdown on adolescent girls appeared first on Exploratio Journal.

Abstract

As an innovative way to target B cell malignancies, the chimeric antigen receptors (CARs) therapy uses patient’s cells and reengineers them with a T cell receptor, enabling them to identify CD-19 antigens and eliminate tumors. However, the problem of cytotoxicity and tumor relapse exists within the CARs therapies. To address these limitations, a new generation of CARs, T cell receptor fusion constructs (TRuCs), to treat cancer diseases have been developed. Unlike CAR-T cells, which do not integrate anti-CD19 as part of T cell receptor (TCR), TRuC-T cells incorporates fusion constructs into functional T cell receptors. Using quantitative techniques such as Fluorescence-activated cell sorting (FACS) and Blue native PAGE, researchers have reported that TRuC-T cells exhibit higher levels of activated signaling proteins compared with CAR-T cells. In both vitro and in vivo models, TRuC-T cells showed a higher efficacy than CAR-T cells in killing lymphoma and leukemia tumors. They also showed a significantly lower level of cytokine release and lower risk of tumor relapse after the treatment than CARs. As a new generation of cancer treatments, TRuC-T cells carry the prospect of treating diseases in an efficacious way with fewer side effects.

Keywords: cancer cells, tumor cells, the chimeric antigen receptors (CARs), T cell receptor fusion constructs (TRuCs), 

Introduction

Cancer, a disease that involves abnormal cell growth through uncontrolled mitosis and with the potential to invade to other parts of the body, is one of the most deleterious and dangerous threats to people’s physical health. Propagating in all parts of the human body such as the lymph system as regular cells need to divide through mitosis, cancer cells cause detrimental effects by keeping old cells from dying and interfering with daughter cell functioning. In 2018, there was an estimated 18.1 million new cancer cases diagnosed and 9.5 million cancer deaths around the globe (National Cancer Institute, 2020). Indeed, the number of cases and deaths are expected to rise to 29.5 million and 16.4 million by 2040, which call for a strong need for immunologists and biochemists to work out a better therapeutic solution. In August 2017, CAR (chimeric antigen receptor) therapy, which reprograms the immune system by engineering the T-cell receptor and targeting CD19 on the surface of cancer cells, first demonstrated impressive response to aggressive lymphomas. However, it has been accompanied by seriously harmful side effects such as cytokine release syndrome (CRS) and neurotoxicity. A considerable proportion of patients treated with CARs also relapsed.

Here, I discuss the development of the TRuC-T cells (TCR Fusion Construct T cells), their unique design and their high efficacy in the treatment of B cell malignancies. TRuC-T cells entered the clinic in 2018, killing tumor cells more efficaciously than CAR-T cells but with a significantly lower level of cytokine production. In addition, they exhibit potent anti-tumor activity in both liquid and solid cancer models, while the response seen in solid tumors from CAR-T cell therapy are less robust. This paper will discuss the effect of TRuC-T cells on cancer therapy in the following parts: TCR design, TCR composition, TCR efficacy in vitro, TCR activation, and TCR efficacy in vivo. The research on TRuC-T cells has a prominent significance in exploring current ways to cure cancer diseases and contributing to the improvement of people’s health all over the world.

Design of TRuC

T cell receptor fusion constructs effectively attach anti-CD19 to TCR complexes enabling them to recognize tumor surface antigens (Baeuerle, 2019). As shown in figure 1, there are four essential parts in TRuCs: EF1α, anti-CD19, TCR subunits, and GFP, between which linker and signal peptides connect individual domains. EF1α acts as a transcriptional promotor to enhance TRuC protein production. Anti-CD19 comprises two low and high domains which target antigen CD19 and induce immune response in the body. They are interconnected with G4S (G-quadruplex secondary structures) that allows fusion protein flexibility and also allows domains to come together. In TCR subunits, ECD, TM, and ICD respectively represent extracellular domain, transmembrane domain, and intracellular domain, all of which contributes to the activation of T cells in response to CD19. T2A is the cleavage site where GFP is cleaved and detected as a sign of successful TRuC activation. 

To test TRuC assembly on individual TCR subunits, anti-CD19 was attached to each TCR subunit. There are five different kinds of TRuC: TCRα, TCRβ, CD3γ, CD3δ, and CD3ε. “All subunits are type I membrane proteins but CD3ζ have extracellular immunoglobulin (Ig) domains” (Baeuerle, 2019). Changes in the arrangement of anti-CD19 can regulate the activity of TCR not only in surface expression but also in function. Of note, two scFv (single-chain variable fragment) are fused to CD3ε while only one scFv is fused to other units in TCR, which affect the efficacy of TRuCs and will be discussed in the following sections. 

The researchers use the technology FACS (Fluorescence-activated cell sorting) to show the expression of all five TRuC variants, and of 28ζ CAR, BBζ CAR, and linked GFP. It measures the shape and size of the cell and quantifies surface molecules and fluorescence. The y-axis represents anti-F(ab’)2 antibody recognizing the murine scFv framework. The x-axis represents GFP, which makes sure that the engineered genes work and indicates whether the TRuC protein is at the cell surface. Higher GFP amounts indicates that the engineered TRuC DNA constructs are being expressed in destination cells. As shown in figure 1c, the ε-TRuC exhibits the highest proportion 83% of T cells with anti-F(ab’)2 and GFP among five TRuCs. The other four vary greatly between 9% and 57%, which reflects the differences in efficiency of different scFv fused onto the TCR. As the second generation of CAR-T cells, BBζ CAR express a higher proportion on the T cell surface than any individual TRuC. Yet 28ζ CAR exhibit a lower expression than most TRuCs. 

Consistent with FACS analysis, ε-TRuC and BBζ CAR have the highest F(ab’)2 mean fluorescent intensity compared with other signaling units. However, a big difference in MFI occurs in two individual donors despite the fact that both of them are inserted into the same T cell receptor. This means that there are discrepancies between individuals, so TRuCs and CARs will have different expression levels in different individuals. Another point worth noting is the vector control in this graph: without the scFv units fused to the TRuC or CAR, the F(ab’)2 MFI are still in a high amount, which reveals that the signaling units TCRα, TCRβ, CD3γ, CD3δ may not be helpful to enhance the mean fluorescent intensity. Additionally, the scale of the data is a bit unclear because there are far more numbers between 1000 and 10000 than that between 10 and 100. It is inappropriate to put them together as causing confusion.

TCR composition

After the incorporation of TRuCs, cells were lysed and TCR complexes were separated by Blue native PAGE. They were stained using an anti-CD3ζ antibody. “0” denotes the natural TCR complex, “1” and “2” denote TCR complexes with one or two TRuCs, respectively (Baeuerle, 2019). Blue native PAGE is a technique used for isolation of protein complexes and measurement of native protein masses without protein complex denaturation. In Figure 2a, the y-axis represents the measure of molecular weight or mass in kDa (kilodalton). Some are natural T cell receptor, while others are engineered TRuC receptor. The minimum mass of protein of natural TCR complex is 440 kDa. There is a direct correlation between the quantity of protein and how dark it is. According to the extent of stain darkness, the TCR complex with two α-TRuCs has the highest molecular mass with protein complexes measuring 660 kDa. Following close are TCR complexes with ε-TRuCs and β-TRuCs. Interestingly, natural vector control has a higher amount of protein than the cell with δ-TRuCs. 

Researchers then used immunoprecipitation to show the amount of individual TRuC proteins in TCRs. “They were immunopurified using the anti-F(ab’)2 antibody, and then separated by reducing SDS-PAGE. (Co)-purified proteins were detected using the described antibodies by western blot” (Baeuerle, 2019). Different from figure 2a, TRuC variants here have similar amounts of protein and use different specific antibodies. For α-TRuC, they use corresponding anti-TCRα to show its molecular size 95 kDa. For β-TRuC, they use anti-TCRβ and identify its size as 80 kDa. For 28ζ and BBζ CAR-T cells, they both use anti-CD3ζ to show it size at 72 kDa. The overall result is that each TRuC and CAR shows its expected molecular size.  

What researchers do next is to test the function of the T-cell receptor, which determines their sensitivity to different amounts of peptides. The higher mean fluorescent intensity it has, the more sensitive the T cell is. If TRuC or CAR T cells are too sensitive, they will be activated without any stimulation and be always on, which will finally lead to the self-destruction of the T cell. As shown in figure 2c, the MFI increases as the H-A peptide amount increases, indicating that they are becoming more active. It is worth noting that both of the CARs are easily activated, probably due to its strong tonic signaling. Also, the ε-TRuC exhibits equal mean fluorescent intensity with the vector control, meaning that TRuCs do not interfere with normal TCR function, while CARs impair a lot through increasing sensitivity.

TCR efficacy in vitro  

To test the TRuC and CAR efficacy, researchers use two approaches: one was by percent lysis of luciferase-expressing Nalm6 cells after 24 h; the other was by means of an impedance-based assay using CD19-expressing HeLa cells to study the kinetics of cell lysis (Baeuerle, 2019). Luciferase is a bioluminescent enzyme, which in this case serves as reporter of successful lysis. Fig. 3a represents the tumor cell lysis of luciferase-expressing Nalm6 cells. The three blue dots are the biological replicates and means of the lysis percent. Error bars are also included in the graph. It is easy to see that there are stark differences in the lysis percent between vector control and two TCRs. The vector control only lyses about ten percent of tumor cells, while the most effective TCR, BBζ CAR-T cells, exhibits 65% tumor cell lysis. Among the TRuCs, the ε-TRuC demonstrates the highest tumor cell lysis, with 60% cell lysis. α-TRuC’s efficacy in tumor cell lysis is less robust than the other TRuC subunits, with 25% lysis.

As mentioned in the previous paragraph, this is the other approach that uses CD19-expressing HeLa cells to study cell lysis. HeLa cells are tumor cells, which are represented on the y-axis as normalized cells. After T cells were added, the tumor cell index decreased quickly and finally declined to zero after 120 hours. The fact that normalized cell index increased for vector control and cells without T cells insertion demonstrates the effectiveness of TRuC and CAR to lyse tumor cells. Among all TCRs, the most efficacious was BBζ CAR-T cells, in accordance with the result in tumor cell lysis using luciferase. The one with slowest rate of decreasing normalized cell index was β-TRuC.

Researchers selected three TCRs, ε-TRuC, 28ζ CAR, BBζ CAR, to examine the donor variability in tumor cell killing. It is clearly shown in fig.3c that donor variability only slightly affected the efficacy of TCRs in decreasing normalized cell index. All three patients’ normalized cell index successfully declined to zero after 120 hours. The cytokine release after the T cell adding and treatment is discussed in the next part. 

Accompanied with T-cell treatment are cytokine production and secretion. One of the most harmful side effects of CAR-T cells is the cytokine release syndrome (CRS), which is further illustrated in these graphs. Compared with TRuCs, CARs release far more IL-2, IFNγ, IL-4, IL-13, and TNFα cytokines. Specifically for IL-2, when no TCRs are added, no cytokine release was observed. After the incorporation of γ-TRuC and ε-TRuC, there are approximately 4000 to 5000 pg IL-2 release, while after 28ζ CAR was inserted into the cell, the cytokine release was about 22000 pg. Thus, smaller amount of cytokine release is one of the advantages TRuC receptors have over CAR-T cell receptors, which prevents patients from suffering from neurotoxicity and other diseases.

TCR activation and signaling

The researchers then investigated on the activation of T-cell receptors by quantifying the amounts of CD69⁺ and CD25⁺ using FACS. The distribution of these two positive cell surface molecules, shown in fig. 4a, corresponds to the percentages of T-cell activation. ε-TRuC had the most significant amount of signaling among all TRuCs, displaying similar activation levels to BBζ CAR. Both ε-TRuC and BBζ CAR had 65% of CD69⁺ and CD25⁺ signaling, while that of α, β, δ-TRuC were much lower, exhibiting activation between 20 to 30%. Overall, both TRuCs and CARs showed less expression of CD69⁺ upon activation, but comparatively higher expression of CD25⁺, possibly due to their different TCR units. 

Researchers then looked for the phosphorylation of intracellular signaling proteins and found that both CAR-T and TRuC-T cells induce intracellular signaling protein phosphorylation, but to different extents. They co-cultured T cells with CD19+ Raji cells at a 10:1 effector-to-target ratio for 30 min (Baeuerle, 2019). The phosphorylation of intracellular signaling proteins upon activation induced a change in charge, in turn causing a conformational change. The y-axis represents mean fluorescent intensity, which was used for detecting the amount of signaling per cell. For phosphorylation of CD3ε, the TRuCs displayed more than double the phosphorylation than the CARs. While for phosphorylation of LAT (Linker for activation of T cells) was an order of magnitude lesser, the signaling of TRuCs and CARs was measured and indicated that intercellular signaling is activated in both TRuCs and CARs upon stimulation.

Using FACS again, the graph shows T cells in GFP and phosphor-CD3ζ after 5 days expansion in the presence of IL-2 and anti-CD3/anti-CD28-coupled Dynabeads (Baeuerle, 2019). ε-TRuC had low levels of activated phosphor-CD3ζ, while the two CARs had slightly higher activation, while all their GFP amounts displayed 46.7%. In summary, TRuC-T cells and CAR-T cells had differing intracellular activation and signaling events. 

TCR efficacy in vivo

To test the anti-tumor activity of TCRs in liquid tumor model, researchers subcutaneously inoculated lymphoma and leukemia cells into mice. After the tumor cells began to grow in mice, they injected them with TRuC-T cells and CAR-T cells to treat them and test the efficacy of killing tumor cells. There was also a group of non-transduced mice used as a negative control. In NT group, the tumor volume increased in an exponential way: it nearly quadrupled after 20 days and kept increasing as the study progressed. However, the situation turned way much better when T cells were present. For ε-TRuC, when 1×10⁸ T cells were injected, the tumor volume kept close to zero and no tumor could be detected at the end of the study. While when 1×10⁸ CAR-T cells were injected into the mice, the tumor volume increased slowly. Thus, TRuC-T cells are more effective than CAR-T cells at killing tumor cells in vivo. Another advantage TRuC-T cells displayed was that the survival rates of mice after injecting them were almost 100%, while 20 to 30% of mice injected with CARs die after 20-30 days, highlighting the cytotoxic effects of CAR-T cell therapies. Also, as the number of injected T cells was decreased, the effectiveness of tumor cell elimination decreased as well: when 5×10⁴ T cells were injected, neither TRuC nor CAR could reduce the tumor volume over 40 days. 

Immunocompromised NSG mice were injected with 5 × 10⁵ Raji-LUC cells into the tail vein of mice 5 days prior to treatment with 1 × 10⁷ non-transduced or engineered T cells (Baeuerle, 2019). For ε-TRuC, the luminescence rose, then decreased, and finally stayed constant, corresponding with its tumor volume in fig. 5a. The two CAR-T cells’ luminescence increases more than with TRuC: although it decreased at first, it then relapsed quickly, meaning that the tumor grew post treatment in CAR-treated mice. 

In the end, researchers wanted to investigate the effectiveness of TRuC-T cells against solid tumor models. They focused on specific domain for BCMA (B cell maturation antigen) and IL13Rα2 (interleukin-13 receptor α2). As shown in fig. 6a, almost 100% of tumor cells are lysed and the tumor growth is inhibited no matter what kind of TRuC was fused with BCMA. For HeLa-CD19, the TRuCs had a low effect in tumor cell lysis, having small difference with the vector control.

RMPI 8226 is another multiple myeloma cell line, which is used here to test its anti-tumor activity. ε-TRuC, γ-TRuC, and β-TRuC successfully reduced tumor volume to zero after 40 days. However, the experiment cannot assure that the tumor may relapse after these 40 days.

Researchers lysed U251 glioblastoma cells using IL-13Rα2-specific ε-TRuC-T cells at various effector-to-target ratios. When the effector-to-target ratio was larger, the percentage of tumor lysis tended to increase. In fig.6a, the effector-to-target ratio was 1:1 and had near 100% tumor cell lysis. By contrast, when the ratio was 1:1 in IL-13Rα2, the tumor cell lysis was only 35%. This shows that IL-13Rα2-specific TRuC cells are less efficient than BCMA TRuC cells in inducing tumor cell lysis, despite having similar effects on tumor growth.

As TRuC-T cells were shown to have efficacious tumor cell lysis effects and anti-tumor activity, they also released cytokines IL-2 and IFN- γ. Comparatively, the cytokine IFN- γ was released at higher levels than that of IL-2, about 20 times, but the unit is pg/mL, so the amount was actually lower than that released by CAR-T cells. The question of relapse is answered in the graph on the right: after 40 days, the tumor volume kept constant at zero and the trend suggested that their effects would be continual rather than only for a period of days,

Conclusion

TRuC-T cells are a generation of more effective CAR-T cell therapies that use fusion constructs to incorporate anti-CD19 into functional T cell receptors, as opposed to using chimeric T cell receptors. They are effective in vitro, as well as in vivo towards both liquid and solid tumors. TRuCs induce roughly half the amount of cytokine release compared to CAR-T cells and present a new generation of engineered T-cell receptor anti-cancer therapies, with promising efficacy and a lower risk of cytokine release syndrome (CRS). The research on TRuC-T cells plays a significant role in exploring current ways to combat cancer diseases improving people’s health all over the globe.

Bibliography

“Cancer Statistics.” National Cancer Institute, www.cancer.gov/about-cancer/understanding/statistics.

Baeuerle, Patrick A., et al. “Synthetic TRuC receptors engaging the complete T cell receptor for potent anti-tumor response.” Nature communications 10.1 (2019): 1-12.

About the author

Yicheng (Ethan) Ding

Yicheng is strongly interested in biology, especially molecular biology related to human bodies, such as proteins, DNA, and tumors. This inspiration is obtained partly due to the COVID-19 pandemic, which raises our concern and his want to improve human disease treatments. In his spare time, Ethan usually plays table tennis and plays basketball with friends, as well as watching TV programs such as the Big Bang theory.

The post The effect of T-cell receptors on cancer therapy appeared first on Exploratio Journal.

1.Introduction

Lightning rods, mostly made of copper, is a structure that protects buildings from being damaged by attracting flashes through electric-magnetic force and guide the current to the ground. After learning a bit about electricity and experiencing a night of thunder and lightning, I intend to explore how lightning rods work. Therefore, in this presentation, I will first introduce the historical research on lightning rods, and then explain how lightning rods work in general using electrostatic principles and some easy-to-understand analogies. I will then write a program to calculate the effective range of the lightning rod based on the Monte Carlo technique and finally propose a lightning protection solution in conjunction with the 3D street view.

2. The History of Lightning Rod

2.1 Franklin Kite Experiment

In 1746, Franklin turned his home into an electrical laboratory after occasionally discovering the electrical experiments of other scientists, and in a letter he described receiving an electric shock as “a numbing sensation from the beginning to the end”.

In 1747, thanks to Franklin’s discoveries, people stopped using glassy and resinous to describe electricity. They began to use positive and negative electricity.

In 1749, Franklin began to make analogies between lightning and batteries, and from then on lightning became palpable. He explained by analogy the bifurcation in lightning, the color of the lightning, and the deafening sound, and was determined to prove that lightning and electricity were directly related. in 1750, he began to focus his research on the protective devices for lightning. This was man’s first step toward the lightning rod

Fifteen years later, Franklin’s close friend recorded in his diary Franklin’s famous kite experiment. He took the risk of using a kite to try to get up close and personal with lightning. He even tied a key to the kite in order to attract an electrical charge. Even though the string of the kite was already made of insulating silk, this was still a very risky act considering the strength of the lightning bolt could even make the insulator relatively conductive. The conclusion of this experiment was that the key was seen to receive the electric charge brought by the lightning, and Franklin thus proved that lightning is electricity.

2.2 Tip Lightning Rod or Round-end Ones?

Almost simultaneously with the kite experiment, Franklin realized the fact that iron needles can conduct electricity, and tried to integrate this into the “lightning rod” invention. In his diary, he envisioned, “Could there be a way to protect people from sudden lightning strikes by inserting thin needles directly into clouds and pulling the electricity out of them before the lightning strikes the ground?”

Franklin focused on elevating the tip of the lightning rod, while Benjamin Wilson, a member of the Royal Court circle of George III, believed that the pointed lightning rod would attract lightning (and this property remained unchanged and became the main principle of the modern lightning rod) and was not as safe as the round-headed lightning rod. Most scholars at the time also supported Benjamin Wilson’s view, so much so that this eventually turned into a political showdown, with proponents of Franklin’s lightning rod being falsely accused of “trying to establish their own political group in England. The war between science and politics officially ended when an East India Company was struck by lightning, and Franklin’s spiked design is still used today.

2.3 Three primary Modern lightning rods

2.3.1 Early Streamline Emmision (ESE)

ESE systems are more similar to conventional lightning rods. They are designed to trigger early initiation of upward flow, which increases the effective protection range. This discharge trigger increases the probability of triggering a “streamline” discharge at or near the tip of the rod as the ionized “leader” approaches. 

2.3.2 Charge Transfer System (CTS)

The CTS is characterized by its designated protection zone. It is the only system that deters lightning strikes, rather than encouraging them. CTS technology is based on existing physical and mathematical principles. The CTS collects the induced charge from the thunderstorm clouds in the area and transfers it to the surrounding air via an ionizer, thereby reducing the electric field strength in the protected area.  The resulting reduction in the potential difference between the site and the clouds inhibits the formation of upward currents and thus reduces electric shocks.

2.3.3 Dissipation Array System (DAS)

DAS is a special type of CTS. Based on the “protected area” of CTS, DAS can completely isolate a facility from direct lightning strikes during a thunderstorm by releasing the induced charge within the protected area to 55% of its pre-installation level in relation to its surroundings. When the electric field is reduced, the upward current does not get enough energy, and it is the connection of the upward and downward currents that is required for lightning to occur. Without energy, the connection cannot be made, so lightning cannot be generated.

3. The working principle of the lightning rod

3.1 Electron distribution

3.1.1 Electrons in the earth’s crust

Before asking how lightning rods come into use, let’s first examine the function of electrons that makes lightning occurs. Before we go into how lightning rods work, let’s take a look at how electrons work to cause lightning. To begin with, the ground’s surface is made up of positive charges because the dipole cloud produces an electric field that forces electrons to flow to the earth’s core. The earth’s crust is plainly devoid of negatively charged electrons, resulting in a positively charged ground. Colors have been employed to depict the phase cancellation process, with yellow indicating negative charges and blue representing positive charges. The green hue created by combining blue and yellow is neutral, but the absence of either color gives it a bluish/yellowish appearance.

The positive charge upon the ground produces an electric field between the earth and the clouds, resulting in a negative charge covering the bottom of the clouds. And this electric field can reach 400,000 volts, creating a powerful electric field that lingers in the atmosphere. The procedure of positive and negative charge exchange in the clouds is essentially like an ion engine that repels the negative charge of the entire planet to the opposite side, according to the principle that different charges attract and the same charges repel.

We all know that when the electric field’s dipole reaches a particular level, clouds unleash lightning, which neutralizes the charge at the cloud’s bottom compared to the ground, and then the clouds repeat the process to rebuild the potential difference in the form of an exponential equation. Here’s a visual representation of this.

The time it takes to recharge is known as the resetting time, and we use it to determine the power of the ion pump described before, for which we have data of around 5 seconds. I=Q/T. When the experiment is replaced, the result is a cloud with a charge of -20C and a resetting time of 5 seconds.

Despite the fact that 4 amps may appear to be a little quantity, comparable to double the current of a mobile phone charger (2A) or the current used in street lights (4A), it will inflict a great deal of damage due to the fact that it is released from a very small hole.

3.1.2 Electrons in clouds

Clouds that carries lightning consists soft hail particles and ice particles. Soft hails has more weight than ice particles, therefore they fall to the bottom during a thunderstorm while the small crystals were uplifted to the top. This falling process allowed negatively charged hails stay at the bottom(6-8km) and positively charged ice floats to the upper part of the cloud to 10km.

3.1.3 Generation of lightning

There are three major hypothesis about how lightning comes into place. 

• The electric field inside a stormy cloud is far higher that what has been calibrated. 

• Lightning is created via hydrometeors, which means water particles in the cloud
• Energetic runaway electrons initiate the lightning.

 Because the overactive electrons (hypothesis 3) in the above figure are hydrometeors (hypothesis 2), ice crystals and water droplets traveling through the cloud, and the situation presented by hypothesis 2 usually boosts the electric field strength by a large margin, due to the equation of Coulomb’s law, these three points are actually interconnected.

It can be found that the electric field strength is inversely proportional to the square of the distance. Thus overactive electrons are in between many electrons of different charges, causing a huge electric field and thus the birth of lightning.

Most lightning is intra-cloud lightning, while lightning that occurs outside of clouds is divided into four main types, two from the ground to the thunderclouds, which are beyond the scope of this report, and two from the thunderclouds to the ground. One of them is downward lightning negatively-charged leader caused by a negative charge at the bottom of the cloud and a positive charge for activation, and the other is downward lightning positively-charged leader caused by a negative charge leading from the positive charge at the top of the cloud connected to the ground charged leader.

3.2 Lightning propagation methods

The negative step leader, as its name suggests, will extend the length of the leader channel by step propagation. In the study of lightning pathways, early studies based on photography were skewed because some of the steps were too tiny to be seen with the human eye. The multiple-station dE/dt technique was utilized by J. Howard, M.A. Uman, C. Biagi, D. Hill, V.A. Rakov, and D.M. Jordan in 2011 to localize each step. When Step brings the lightning to the ground, the length charge is around 10^-3C/m, and the earth sends a return stroke to contact with it, resulting in lightning. TThis generally happens in the conductor nearest to the elevation, since lightning, like an item in an automated pathfinding system, will want to walk on the side with the least “resistance,” that is, the side with the quickest potential reduction movement. Because the charges at the ground and at the bottom of the cloud are generally different, the ground attracts the leader in this scenario.

3.3 How do conductors work?

 A conductor is a substance that electrons are relatively free to move compared to the insulators, which is the property needed to create the lightning rod–any net charge resides on the surface because ρ=0 inside a conductor according to Gauss’s law. So that negative charges are attaching on the surface of the lightning rod, making it easy to be strike. The reason is, usually a ground is conductive and there are negative charges throughout the ground. When you put a conductor such as a metal on the ground, the electrons of the ground moved to the metal, and the protons in the metal moved to the ground until the metal and the ground are equipotential and the metal and the ground can be regard as a system because of the formula E = -⊽ V , Where E represents the electric field and it equals the negative product of divergence of electric potential. When the system is a closed loop the divergence is 0 so that there is no electric field and electrons are static again. The metal can then be seen as a whole with the ground. This reasoning is also valid for conductive buildings and lightning rods, which become more vulnerable to lightning strikes as if they were a mountain range raised on the natural landscape.

What will the lightning rods do to the lightning that it had intercepted? When lightning occurs, the lightning rod can attract the discharge channel of lightning, so that the lightning current flows from the lightning rod into the earth’s land, avoiding huge currents to cause damage to buildings, equipment, trees or injury to people or animals that happen to walk above the ground.

4. The effective area of lightning rods.

4.1 Monte Carlo Technique

Abhay Srivastava calculated the protection of the lightning rods by applying a mathematic model conducting rod using Monte Carlo technique. It is a computer simulated model that randomized the distribution of lightning strokes. It assumes a concave lateral surface of the cone, using the concept of striking distance  in Golde’s formula, where d_s means the striking distance, A=10 and σ = .65  are constants.

 As the graph suggests, it initialized the sky with height k, the starting point of the lightning as h0, which will randomly stepping down towards the ground until it sees a postive charged object in its detecting sphere, which is assigned H_n. H_n-1 and H_n-2 are the last two steps from the striking point, and each point in this graph is given a three dimension coordinate.

The author assumes that the cube is 100*100*1000, and that a Cumulonimbus Cloud capable of storing lightning has a height of 500-16,000 meters.

The lightning will begin at a random location at the maximum height,1000m.

int[][] origin=new int[Math.random()*101][Math.random()*101];

In 5% to 80% range of the striking distance it generate some random variables to select the step length of leader.。Then it designate two angels  of the spherical polar coordinates: the inclination angle α lying between π/2  and 3π/2 and the azimuthal angle β lying between 0 and 2π. 

The mathematic formula is as follow. Iteration in Java should be used to infer where the lightning will strike. The loop terminates when the program determines that the lightning leader has reached the monitoring range, which is simulated as a sphere of radius 20m.

/*

*Precondition: it checks every step of the lightening

*Postcondition: true means that the lightning has successfully been 
*intercepted, and therefore will not be accounted as lightenings that have 

*caused damage.

*/

Public boolean inRegion(Object leader,int r){

if(Math.sqrt(Math.pow(leader.getx()-rod.getx(),2)+Math.pow(leader.gety()-rod.gety(),2)+Math.pow(leader.getz()-rod.getz(),2)<=r)}

//Euclidean distance

return true;

return false;

}

Then comes the main program for generating the path, written according to the following mathematical equation.

public int[][] stepProcess(int[][] before,Object leader,int r){

int[][] after=int[][] before;

I=leader.getx();

j=leader.getz();

K=leader.gety();

while(k!=0&&!inRegion(leader,r)){

i=i+r*Math.sin( )*Math.cos(β);

j=j+r*Math.sin( )*Math.sin(β);

k=k+r*Math.sin( );

}

if(inRegion(leader,r)){

return before;

}

else{

after[i][j]=after[i][j]+1;

return after;

}

//the higher the number in the array is, the more dangerous is the area

}

After running the program, I tested 1000 times, assuming 10 strikes per year in this area, which would be all the lightning this virtual area has suffered in 100 years, and drew a graph of the conclusions drawn, where the yellow area represents relative safety(have been struck once), green represents absolute safety, and red represents danger(more than once).

 4.2 Real Life Application

In the 3D street view of Gaudet Map, I intercepted a dense map of high-rise buildings of about 1000*1000 and used the model for the simulation of lightning rod placement. It is assumed that all the buildings need protection, but we can ignore the open space. Here are the before-after graph of the map. When lightning rods were applied in that area, it meant to make sure every building to stay in the green or yellow circle of fig 12/13, which take the height of the lightning rods, r, as a variable and execute the program.

5. Reference List

1.“Modern Lightning Protection Lightning Rods with Lightning Eliminators.” Edited by LEC By admin, LEC, 19 Sept. 2018, www.lightningprotection.com/lightning-rods-are-old-new-lightning-protection-part-3/. 

2. J. Howard, M.A. Uman, C. Biagi, D. Hill, V.A. Rakov, D.M. Jordan, Measured close lightning leader-step electric-field-derivative waveforms, J. Geophys. 

Res. 116 (2011) http://dx.doi.org/10.1029//2010JD015249. 

3. E.P. Krider, C.D. Weidman, R.C. Noggle, The electric field produced by lightning stepped leaders, J. Geophys. Res. 82 (1977) 951–960.

4. Srivastava, Abhay, and Mrinal Mishra. “Lightning Modeling And Protection Zone Of Conducting Rod Using Monte Carlo Technique – ScienceDirect.” Lightning Modeling And Protection Zone Of Conducting Rod Using Monte Carlo Technique – ScienceDirect, Www.sciencedirect.com, 13 June. 2013, https://www.sciencedirect.com/science/article/pii/S0307904X13003478?via%3Dihub.

“Franklin’s Lightning Rod | The Franklin Institute.” The Franklin Institute, Www.fi.edu, 8 March. 2014, https://www.fi.edu/history-resources/franklins-lightning-rod.

5. Godwin, Ian . “Franklin Letter To King Fans Flames Of Lightning Debate › News In Science (ABC Science).” Franklin Letter To King Fans Flames Of Lightning Debate › News In Science (ABC Science), Www.abc.net.au, 26 March. 2003, https://www.abc.net.au/science/articles/2003/03/26/816484.htm.

6. M. Vargas, H. Torres. On the development of a lightning leader model for tortuous or branched channels – Part II: model description

7. J. Electrostat., 66 (2008), pp. 489-495

8. M.A. Uman, The Lightning Discharge, Academic Press, London, 1987, 376 pages, revised paperback edition, Dover, New York, 2001. 

9. K. Berger, Blitzstrom-Parameter von Aufwärtsblitzen, Bull. Schweiz. Elektrotech. Ver. 69 (1978) 353–360.

About the author

Douyun (Winnie) Shi

Winnie is a Physics learner at the Starriver Bilingual School in Shanghai, China.

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