If you’re even tangentially related to the tech world, you’ve probably heard an awful lot about Quantum Computing recently. But what exactly is it? Why is it useful? What is the point? And what does it mean for the future of computers for everyday people like you and me? 

What is Quantum Computing?

The basic idea behind quantum computing is that rather than having binary 1’s and 0 build the computer, you have a range of values between 0 and 1, determined by the superposition and entanglement of subatomic particles in the computer. Now this may sound scary, but it’s actually quite similar to what’s going on in your normal computer. The 1’s and 0’s you always hear about making up binary are actually just electrons. Electrons either flow through transistors (producing a 1) or they don’t (0). Computers have always relied on sub-atomic particles, we’re just changing which ones we rely on with quantum computing. 

Quantum computers were proposed in the 1980s by Richard Feynman and Yuri Manin. But it wasn’t until 1998 that Isaac Chuang at Massachusetts Institute of Technology (MIT) and Mark Kubinec of the University of California at Berkeley created the first quantum computer (2-qubit) that could be loaded with data and output a solution. These computers have been theorized for a long time, but only recently are starting to become viable tools.

What does that mean for me?

Now, before we continue, I want to be clear that in the next 10-20 years, consumers will not be using quantum computing in their homes. There are a lot of advantages we’ve found to using the range of values that quantum provides, but those advantages are not currently applicable to daily users checking emails or watching videos; what they are good for is searching databases. 

What is Quantum Computing useful for, then?

Binary-based computers are particularly bad at searching for values. In fact, most search algorithms are just special tricks to get around how bad computers are at finding values in a dataset. This is because binary values have to check each value they come across one at a time and “label” each one as not the object they’re looking for. What quantum computing allows is, rather than going one-by-one, searching all (or many of the) values at the same time. 

In a similar vein, quantum computing is quite good at modeling possible outcomes of situations because rather than having to cycle through each scenario one at a time – like a binary computer – it can process many different models at once. These advancements will be massive for many areas of scientific study by exponentially decreasing the amount of time/resources computing models takes.

Closing thoughts

If you are an average consumer like me, these advancements won’t affect you directly for many years, but I estimate we will be seeing faster scientific breakthroughs in coming years as computing time continues to decrease.

---

Sources and Extra Reading

Chuang, Isaac L.; Gershenfeld, Neil; Kubinec, Markdoi (April 1998). “Experimental Implementation of Fast Quantum Searching”.

Gibney, Elizabeth (23 October 2019). “Hello quantum world! Google publishes landmark quantum supremacy claim”.

S. J. Pauka, K. Das, R. Kalra, A. Moini, Y. Yang, M. Trainer, A. Bousquet, C. Cantaloube, N. Dick, G. C. Gardner, M. J. (2021). “A cryogenic CMOS chip for generating control signals for multiple qubits”.

Yanofsky, Noson S.; Mannucci, Mirco (2013). Quantum computing for computer scientists. Cambridge University Press. pp. 144–147, 158–169. ISBN 978-0-521-87996-5.

Within the past 200,000 years, modern humans began to appear. Though it took thousands of years to get to the Industrial Revolution, since then, technology has grown exponentially. In fact, the majority of new breakthroughs in technology have occurred within the past 250 years. Compared to the span of human existence, this time period is fractional, and it goes to show the rapid changes made in the technology sector. 

 

In 1942, the first modern digital computer was built, and it weighed over 700 pounds. This machine was called the Atanasoff–Berry computer, or ABC. This machine was built to compute calculations with up to 29 variables, and it performed one operation every 15 seconds. Fast forward only 80 years to 2022, when Apple released their new Macbook Air. This laptop barely weighs 3 pounds and can perform millions of computations per second. It has a 1080p camera and has configurable storage up to 2T. Not even at 1% of the weight of the ABC, the Macbook Air can do so much more, and only within 80 years of development too. What the ABC could do then, the Apple Watch can do now. The progress has been extremely rapid in the last few years, and that leads me to stop and wonder: Will we hit a roadblock soon? Will there be no more changes left to make? What’s next in the future of technology?

 

Every couple of years, humans make incredible progress in the technological world. In the last 2,000 years, technology has advanced exponentially. When we imagine the future, we think of robots, advanced technologies, and even a cyberpunk-esque environment. The 1985 classic film, Back to the Future, got a lot right about modern technology, but it was also quite ambitious with some predictions. For example, the flying car, hoverboard, and the sleep-inducing alpha rhythm generator are all inventions that don’t exist today. On the other hand, the power lacing shoes, drones, and biometric devices are examples of technology that do exist now. Now while some of these technologies might never exist, one can’t help but wonder, which ones will? In the 20th century, the idea of the future was very metallic, robotic, and monochromatic. Now when we imagine the future, we imagine AI, big cities, and advanced tech in every aspect of our lives. So what does the future hold for us? 

 

I think what we currently envision for the future is only partially accurate. Just like in Back to the Future, only some of the technologies actually came to be. While we have an idea of what the future holds, part of it is shrouded in uncertainty, and it won’t be something we’ll be able to predict. Our future technological advancements primarily depend on what we need, and that need could change with multiple factors, like climate change, overpopulation, and etc. Artificial intelligence wasn’t a larger concept until the last couple of years, so it was never in the predictions for future technologies until recently. Now that it has become such a widespread topic, we think of it as the ‘next big thing.’ So while we can have some idea about what the future holds for us, we can’t fully predict what it has in store. Maybe civilization will never have a need for flying cars, or maybe we might need to develop technology to live underwater. Who knows?

 

Sources:

https://theconversation.com/what-was-the-first-computer-122164#:~:text=The%20first%20mechanical%20computer%2C%20The,computer%20we%20all%20use%20today.&text=The%20ABC%20weighed%20over%20700,had%20small%20capacitors%20on%20it. 

https://www.vulture.com/2014/12/which-back-to-the-future-ii-tech-is-closest.html 

What does the Pythagorean Theorem have to do with machine learning? The Pythagorean Theorem is a formula to measure the hypotenuse of a triangle, which also allows us to calculate the distance between the origin and another point on a coordinate plane.

The Pythagorean Theorem can be rearranged to result in the Euclidean distance formula, to calculate the straight-line distance between any two points. The Euclidean distance metric is one of several distance metrics used in machine learning. One type of machine learning is clustering, where objects related to each other are grouped together. We can model how related objects are to each other by essentially assigning them to coordinates and calculating the distance between them; the further apart they are, the less related they are to each other. 

Objects can be related to each other in many different aspects. For example, say you have a red apple, a banana, a red pen, and a Number 2 pencil. How would you group these objects into two groups? You could say the apple and banana go together because they are both fruits, and the pen and pencil go together because they are both writing instruments. But you could also say that the apple and pen should go together because they are both red, and the banana and the pencil should go together because they are both yellow. In this case, you could group by type of object or by color. Grouping by type of object might make more sense in this example.

But what if we add in a canary to the things to be grouped? We want to make as many of the objects fit in the groups as possible, so the addition of a canary, which is yellow, would cause the grouping by color to make more sense.

In clustering, each of these objects would be a data point, and each aspect, such as type, color, size, and texture, would be a dimension of these objects. We call these different aspects features of the data. We can store this data in a matrix so that a computer can read and process the data.

It might seem that if we add more features to the data, we would be able to group the objects better. Intuitively, we think we have more choices of what to group by. However, as we add more and more features, the majority of features would be irrelevant and actually make it more difficult to group. This is called noise in our model. For example, one feature of the data set we had earlier could be “does it have a beak?” But since the canary is the only object that has a beak, that doesn’t help us cluster the objects together. When considering the distance between data points, adding features adds a dimension to our model, which increases the distance between points. Also, adding “does it have a beak?” as a feature would actually just increase the amount of data we have to store, making our algorithms run slower.

When a computer clusters data points, points within the same cluster are similar and points in different clusters are not as similar. We quantify similarity using distance metrics. There are some properties of similarity that have to hold true for clustering to make sense:

• Symmetry: The distance from A to B is equal to the distance from B to A. This makes sense because if A is similar to B, B must be similar to A. Similarity goes both ways.
• Positive Separability: The distance from X to X is equal to 0, and the distance from X to any other point does not equal 0. This rule is needed because a data point cannot be different from itself, and if data points are different, they have to have a distance between them representing that difference.
• Triangle Separability: The distance from X to Y is less than or equal to the sum of the distance from X to Z and Y to Z. This makes sense because if Y is similar to Z, and X is similar to Z, then X must be similar to Y.

Euclidean distance is the straight-line distance from point A to point B, but here’s an example of another distance metric. Manhattan distance measures around blocks, resembling the route a car in a city would take around ceilings. 

An interesting phenomenon related to distance metrics is how they lose their meaning in higher dimensions. When we add features to our dataset and increase the number of dimensions, the average distance between points increases. Think about how in one dimension when we are looking at one feature, point A and B would be on a straight line, x units apart. But in two dimensions when we added another feature, if the second feature is just as far apart as the first for points A and B respectively, A and B would be sqrt(2)*x units apart. The same pattern continues when we add more and more dimensions.

As we increase dimensions and distance between points increases, the points also migrate towards the edges of the space. This is because the ratio of the limits of the space to the core of the space increases as the number of dimensions increases. Think about how a square with sides of length x has a perimeter of 4x and an area of x^2. A cube, on the other hand, with sides of length x has a surface area of 6x^2 and a volume of x^3. Let’s substitute 1 as x to compare these values. 

Square perimeter = 4x = 4

Square area = x^2 = 1

Perimeter to area ratio = 4/1 = 4

Cube surface area = 6x^2 = 6

Cube volume = x^3 = 1

Surface area to volume area = 6/1 = 6

When the number of dimensions increased from 2 to 3, the ratio increased from 4 to 6. Once again, this pattern continues in higher dimensions.

Let’s visualize a model with points randomly distributed around the model space.

In one dimension, approximately 20% of points will be within the outer 10% of the space. In two dimensions, approximately 37% of points will be within the outer 10% of the space. In three dimensions, approximately 49% of points will be within the outer 10% of the space.

Continuing on to dimensions of degrees that are essentially impossible for us to visualize, approximately 60% of points in four dimensions, 68% in five dimensions, 74% in six dimensions, and 80% in seven dimensions will be within the outer 10% of the space.

This concept brings us to the titular “infinitely-dimensional orange” concept; if we had an orange with infinity dimensions and points randomly distributed across the space, all the points would be on the peel of the orange, with no points spread around in the interior.

Using more features allows each data point to become more different from each other, which makes them more sparsely distributed. This makes it harder to cluster the points accurately! In machine learning models, we address this issue using feature selection and dimensionality reduction techniques to find the most relevant features and reduce the number of dimensions we utilize to cluster data points. Not only does this reduce the processing power a computer would need to run the algorithm, but accuracy in clustering generally also increases by preventing the “infinitely-dimensional orange” phenomenon.

Reference

• https://towardsdatascience.com/the-surprising-behaviour-of-distance-metrics-in-high-dimensions-c2cb72779ea6 
• Georgia Tech CS 4641 lecture slides by Professor Roozbahani

Written by: Lauren Mellinger and Julia Raver

Every girl knows what it feels like during “that time of the month” – bad cramps, mood swings, you name it. And it seems that other girls can always tell when it’s time. There have been many studies on the correlations of menstrual cycles between best friends, teammates, or even roommates. But do menstrual cycles really sync up? 

 

[ sea-nsory details ] 

One study claims that menstrual cycles sync based on olfactory cues. So what are olfactory cues anyways? Olfaction, the sense of smell based on chemical cues in the environment, has significant effects on animal behavior (Brady et al., 2012). Specific olfactory receptors within the body react either positively or negatively to different chemical scents. This transfers specific information to olfactory centers (Ruta, 2021). 

These olfactory centers have pathways to the amygdala (which triggers a fight or flight response), lateral hypothalamus (the hunger center which controls feeding behaviors), and the insular cortex (links sensory and emotional experiences).The location where the centers deliver information determines feelings and sensations. For example, it determines when you’re hungry, if you’re swimming, or if you’re happy or sad (Russell et al., 1980). 

A study treated two groups of women on their upper lips with an alcohol-based mixture 3 times a week during a span of 4 months. One group was treated with plain alcohol and the other was treated with a mixture of alcohol and underarm perspiration from a single donor. They found that the women’s cycles treated with the donor’s perspiration scent ended up having significant menstrual cycle shifts towards the cycle of the donor (Russell et al., 1980). 

So one potential source of synching could be exposure to specific chemicals in the environment. Imagine you are constantly working out with your sports team and sweating together, it is possible that your cycles could sync due to the exposure of each other’s underarm perspiration. 

 

[ reely great roommates ]

 

Another study performed by Martha McClintock studied the cycles of undergrad roommates over the entire year. In October, the cycles were on average 8.5 days apart, but, by March, they were about 5 days apart. On the other hand, randomly matched pairs showed no synching (Strogatz, 2022). There was no science, however, behind this discovery, only physical closeness to cite. 

 

[ knot just a phase ] 

A final theory is that lunar cycles play a factor in menstrual synching. The study found that cycles lasting longer than 27 days historically had intermittent synching with both the Moon’s luminance and gravimetric cycles (Helfrich-Förster, 2021). The luminance cycle is the light intensity of the Moon in each lunar phase. The gravimetric cycle is the difference of the Moon’s pull when at different positions with respect to the Earth (Cohut, 2021). 

 

The study found that women with cycles around 29.5 days, or the period of the moon, have the highest likelihood of becoming pregnant. There was also a correlation found between the three lunar cycles – the synodic, the anomalistic, and the tropical month – and the onset of menstrual cycles. These trends however hold much stronger in ancient times. Back then there weren’t as many societal impacts like stress and nutrition on the duration and frequency of the menstrual cycle (Cohut, 2021). So studies point to a historical linkage, although it doesn’t necessarily hold true in today’s time. 

 

[ hook, line, and sink-her ]

Despite all this data and our strong desire to connect over shared pain, estrous synching is a weak phenomenon at best. Researchers have tried over and over — with roommates, best friends, and even rats — and nearly all of them come back with mixed findings. 

[ un-sync-able studies ] 

In the largest study ever taken on, data was collected from 186 women who lived in dorms together for over a year. None of the menstrual clusterings were stable or occurred more than by expected by chance (Yang & Schank, 2006).

A similar study with sorority sisters excluded those taking oral contraceptive and those with irregular cycles. Still no signs of menstrual synchrony (Wilson et al., 2003). 

Among a women’s basketball team, spending time together was actually found to be negatively related to synchrony, as was exercising. If the theory that pheromones are the source of synching cycles was true, exercising with one another should be positively associated with synchrony (Weller & Weller, 2000). 

Data from Clue, a popular period tracking app didn’t support McClintock’s conclusion either. Data from over 1500 users demonstrated that being in close proximity with another menstruator likely does not disrupt your cycle. Over 75% of pairs actually had a bigger difference in cycle start dates by the end of the study (Ray, 2022). Based on this data, your cycle would be more likely to diverge than converge with another person’s!

 

[ like rats abandoning a sinking ship ]

Observing 10 pairs of female rats, unsurprisingly, there was no effect of synchrony (Schank, 2001). Thus, the belief that there is some biological or evolutionary explanation loses its stability, too.

 

[ the synching ship of menstrual synchrony ]

From all this evidence, we can see that the popular belief of “living together, bleeding together” has pretty much been debunked.

Let’s say a typical cycle length is 28 days. The maximum that two women could be out of phase is 14 days. On average, these onsets will be 7 days apart, and half the time they should be even closer. Since the average menstrual cycle lasts 5 days, it’s not surprising that friends or roommates commonly recall experiencing overlaps, falsely confirming the myth (Strassmann, 1999). When we do see overlap, statistics says its due to coincidence supported by math rather than biology.

When you think about how much variation there is not only between menstruators but within each individual, finding proof of synchrony is virtually impossible! There are just too many obstacles in the way that reduce the potential for consistency, like birth control, energy levels, stress, weight, genetics, and age (Strassmann, 1999).

Harsh as it may sound, the studies where synchrony was found may have been plagued with methodological errors (Rudis, 2008). This should not be cause for worry though. Variability between women may be necessary and inherent to our physiology and the process of ovarian cycles (Schank, 2000). In an evolutionary context, avoiding synchrony would enhance female choice among males and reduce competition among females. Within-woman variability would also reduce the chance of males predicting the next ovulation and controlling the matings of females (Schank, 2000).

The claim that cycles dominate on phases of the moon has no leg to stand on either. No one is quite sure if they are set to the full moon, new moon, or somewhere in between.

[ to sync or swim ]

What it comes down to is a simple matter of math and time. I advise you to be careful not to mistake the ebb and flow of cycles over time as synchronization. However…

There’s no harm in bonding over the magical (and miserable) mystery that is menstruation. I think it’s a positive way to find comfort with other women who might be going through the same thing. So, what do you think?

 

 

[ sources ]

https://www.medicalnewstoday.com/articles/menstrual-cycles-and-lunar-cycles-is-there-a-link#:~:text=The%20luminance%20cycle%20refers%20to,it%20orbits%20around%20our%20planet. 

https://modern-cynic.org/2021/01/12/corporate-rats-desert-a-sinking-ship/ 

https://doi.org/10.1080/23293691.2016.1150132

https://doi.org/10.1126/sciadv.abe1358 

https://www.gynecajournalcu.org/blog-articles/staff-writing/do-periods-actually-sync.html

https://www.empowher.com/media/reference/true-or-false-women-who-live-together-tend-have-synchronized-menstrual-periods

https://doi.org/10.1016/0091-3057(80)90020-9 

https://www.rockefeller.edu/news/30838-study-reveals-smell-receptors-work 

https://www.sciencedirect.com/science/article/pii/S0306453000000299

https://www.sciencedirect.com/science/article/pii/S0031938400003954

ttps://academic.oup.com/humrep/article/14/3/579/632869?login=true

https://ieeexplore.ieee.org/abstract/document/638513 

https://modernfertility.com/blog/cycles/

https://www.sciencedirect.com/science/article/pii/030645309500007B

https://doi.org/10.1016/S0306-4530(01)00102-0

https://www.sciencedirect.com/science/article/pii/030645309190021K

https://doi.org/10.1007/s12110-006-1005-z

https://doi.org/10.1007/s12110-006-1004-0 

Do you remember those early days of the Covid-19 Pandemic? Schools were shut down. Zoom meetings were the norm. Graduation parties, birthdays, and weddings all felt like another era. But, worst of all, the number of people who succumbed to this virus and lost their lives was just horrific. More people died from Covid than during 9/11 or World War 2. Those people were brothers, sisters, mothers, fathers, aunts, and uncles. Each of them meant something special to someone else. We put our hope in the scientists and researchers, and don’t get me wrong they were amazing! But every step of the way, the virus stymied and defied even the best scientists. Scientists initially thought that once vaccines came along, our relationship with the virus would be significantly different. They thought that by summer 2021, life would return entirely to normal and that Covid would be a thing of the past. However, the virus turned out to be much more formidable than many of them imagined, and in some ways, even now, the world does not have a good handle on this virus, and we are still living by its command. There is no ability to clearly tell what is going to come next. Some suspect that there will be another winter wave, but that the impact on hospitals will be significantly lower than the last two years. Others think that there will not be a winter surge at all. The most pessimistic of experts, though, suggest that hospitals could be in for a tough few months as we head into the holidays. But, if you want to hear the even more sobering news folks, keep this in mind. This will not be the last pandemic that shakes up the world like this. In this post, I hope to inform you about why we should not be complacent into thinking that this was a one time tragedy, how we keep making ourselves more and more prone to pandemics, and what we might be able to do to better be prepared.

So you might be wondering, what type of virus is fit to become a pandemic? When do we know that we’ve got a pandemic on our hands? To put it simply, a pandemic is a worldwide epidemic. An epidemic is when there is the exponential growth of a disease in a certain country. A pandemic is when a disease has been able to grow exponentially in multiple countries. This concept of exponential growth is key. There have been so many diseases that have spread to multiple countries. SARS spread to about 30 countries. Ebola spread to 7 countries outside of the continent of Africa. But do you know what the difference was? Do you know what the saving grace was? Countries were quickly able to get a handle on who was carrying the disease and who was sick, so they could isolate them before they were able to spread it to anyone else. You can think of diseases like SARS and Ebola like the villian, Lord Voldemort from Harry Potter. Voldemort was cruel and terrible, but he made it clear from day one that he hated Harry, and that he would do whatever it took to kill him. SARS and Ebola were like that as well. They were extremely nasty illnesses, but it was extremely clear when someone had it because their peak viral load or the time that they would be most likely to transmit the infection to other people would be when they were having symptoms. Covid-19; however, was completely different. Most of the spread occurred when people did not even know that they had the virus. There were people walking around, who were positive that felt perfectly fine. For god sakes, some of them did not even have a runny nose! This raises the question, how do you fight something when you do not even know what you are fighting? How do you fight off an enemy, when you do not even know that that enemy exists? This is precisely where the problem laid. By the time, health authorities figured out that a person had the virus, he/she had already passed it to 4 other people, and those 4 people had passed it to 4 other people, and so on. You can quickly see how 1 infection can become a 100 and a 1000 in just the course of a few days. Plus, to add to the issue, no one had any preexisting immunity to this virus. So, we were essentially facing an invisible enemy without even any armor. Plus, Covid was also deceptive, where, even when a person had symptoms, those symptoms coincided with other diseases that we have learned to accept as part of our daily existence. All this created the perfect storm for a devastating global pandemic. So, to recap, there are really three things that increase the risk that a virus will have the capability to cause a pandemic. First of all, it has the capability to spread in a way that cannot be easily detected/kept up with. Second of all, the symptoms that it causes are not easily distinguishable from other diseases. But, third, and most importantly, no one in the population has any form of immunity to it.

Assuming you read that, you’re probably wondering, “Okay, I understand that certain viruses are capable of causing pandemics, but how do these powerful viruses even make it to the human population in the first place? How are they even spread to humans in the first place? Where do these viruses originate from? I can understand the curiosity. It seems mysterious how a virus that did not even exist before 2019 suddenly made its way to humans. However, I’m here to tell you that the answer is not as mysterious as you think. The answer to that is simple! Covid likely came from an animal reservoir. You might be thinking, “Wait, how? We don’t even have that much contact with animals on a daily basis?” What you have to remember, though, is that it only talks one source of contact. All it takes is one unlucky encounter, and the virus can jump to humans. Given the wrong circumstances, it is possible, and then once that human spreads it to another human, you have begun the stage of community transmission. In fact, recent research has even shown that there are multiple stages of zoonotic transmission, where in each stage, the viruses involved not only get better in transmitting from animals to humans, but also get better equipped to stay in the human population. For instance, research by Brian Pike and others suggests that there are about five stages of zoonotic transmission. According to them, viruses that are in stage 1 are solely present in humans, while diseases viruses that are in stage 5 are solely present in animals. Put another way, stage 1, a virus is solely present in animals. When that virus has gained the ability to jump to a human, it is in stage 2. However, that still does not mean that it is time to be concerned yet. Viruses at stage 2 are still not guaranteed to cause a pandemic because they are not able to transmit readily between humans. They have just been passed on from an animal reservoir. When you reach stage 3 and 4; however, things are much more concerning. At stage 3, the virus is not only present in humans, but it has actually gained the ability to spread from one human to another. Put another way, it has become fit enough to spread between humans. Stage 4 takes it up a notch because the frequency of this human to human transmission only increases, as the virus transmits more and more periodically from person to person, without even the involvement of an animal host. For instance, if you think about the Influenza virus, it is a stage 4 virus because it is transmitted significantly on a periodic basis during the winter between humans, and there are no animal reservoirs involved (Pike et al., 2010). Think about it, you do not get the Flu from an animal! You get it from another person. Additionally, this other fact comes to mind. Viruses that are in stage 4 are more and more likely to mutate. This also helps to explain how the Covid Pandemic kickstarted itself. Initially, Covid may not have been the most fit. However, by continuously spreading from person to person, it reached a point where it essentially begun to learn how to become smarter and more fit so ask to skirt any developed immunity and infect more people at once. Viruses obviously cannot mutate to obtain an advantage if they are not able to replicate, which also sheds insight as to why stage 3 and 4 viruses are significantly more dangerous and more prone to cause pandemics.

You have probably reached the point, where you’re thinking to yourself, Sachit, this is great information, but how is any of this important? How is any of this remotely optimistic or positive? Why am I telling you all this information? What I am here to tell you is that this information, while stressful and terrible, is actually extremely positive. Given that so many pandemics start when a virus is transmitted from an animal to a human, this also shows that we are not helpless and that there are many ways that we can help to prevent future pandemics. Many of them unsurprisingly come down to changing the way our relationship with the environment works. For instance, according to research done by Brian Pike, HIV became such a significant problem for the human population because of the significant amount of primate hunting and butchering that was going on in the early 20th century. Perhaps, even more surprisingly, the researchers found that there were a group of viruses that had not even been previously found to have had the ability to infect humans that humans had antibodies too. For instance, one of the most noteworthy facts that the researchers found was that there were individuals, living a Cameroonian village who had antibodies to the Simian virus. This is usually a virus that only affects monkeys, so the fact that it had obtained the ability to affect humans was astounding (Pike et al., 2010). SARS was thought to have arose from bats in a wet market that were captured. The H1N1 Pandemic, also known as Swine Flu was thought to originate because different flu viruses, typically only found in pigs essentially recombined to form a super fit strain that could infect humans. Guess how humans came into contact with pigs? Well, the answer is that it was the same way in individuals in the Cameroonian village came into contact with monkeys. It was through hunting them and butchering them. Sometimes, though, contact may not even come through hunting and capturing a given species. Contact may occur in circumstances in which an individual does not have any control. For instance, Lassa Fever is a devastating disease that spreads between humans who are in close contact with rats. Hence, many individuals who are in a situation where they live in crowded, congregate housing conditions are at significantly increased risk of catching a disease like Lassa Fever (Pike et al., 2010).

So, you might be thinking the solution is to just prevent human contact with animals, and that that will solve the problem. However, I want to tell you that that view fails to look at the full picture. There are other factors that make pandemics more likely to occur, other than just what I talked below. Activities that we do as humans, such as cut down trees may not seem harmful to us, but they kill species, and impact the diversity of our environment. This makes it much more likely for the perfect storm to occur for a virus to cause a pandemic. Have any of you seen the movie Contagion? Do you remember the ending scene where trees were cut down in the Macao forest, causing bats to fly out in droves? Pigs were exposed to the feces of these bats, causing them to become infected with the virus, and then when humans decided to butcher the pigs, they were exposed. It seems like an extremely far-fetched event, and it seems like something that would be unlikely to happen. However, the things that humans did ended up making something that was far-fetched all the more set to happen. If you don’t believe in movies, I can even give you a similar example of something eerily similar that happened in real life. In 1998, deforestation occurred, which caused a full livestock of pig to be exposed to fruit bats. Once a group of farm workers in Malaysia were exposed to these specific pigs, that was it. The situation was finished! (Pike et al., 2010).

Reading all this, I hope you understand, when you ask the question, who is responsible for a pandemic, there is no one answer to that question. No single person or group of individuals is responsible for a pandemic happening.  Instead, what I hope you have gained from this post is the understanding that there are truly things that we can do to not only reduce the risk of another pandemic occurring, but ultimately make the impact of the next one much lower. For instance, trying to encourage communities that participate in hunting a lot to use best hygiene practices, such as cleaning injuries, scratches, and bites with soap can go a long way. Additionally, if someone does have an injury, avoiding contact with an animal via hunting or butchering can also go a significantly long way (Pike et al.,2010). Similarly, not chopping trees and putting the health of the environment over the health of corporations likely will help to serve us in the long – term. However, it is understood that not every cause is preventable. In the cases where contact between animals and humans is not preventable, such as in congregate settings, there is no easy solution. However, perhaps maybe some sort of genomic surveillance can be instituted to monitor what types of viruses are most likely to spread in such populations on a daily basis. Maybe vaccines can be formulated and administered ahead of time, and given to these populations based on which viruses are most likely to spread in a given community at a given time. At the very least, what needs to be understood is that doing nothing is not going to get us anywhere. Pretending like we, in some way, as humans did not contribute to the Covid – 19 Pandemic and that it was out of our control is ridiculous. There are practices that we can institute right now that will reduce the likelihood that this will happen again. What those things are are not entirely clear, yet, but if we come out of this crisis as sitting ducks, we are making ourselves ground zero for an even more horrible pandemic in the future. Most of all, I hope this post equips you with information as to what you can do as an individual to make the next pandemic less likely because as much as we don’t like to hear it, all of us are at fault in some way or another! We need to stop pretending like we have control over Mother Nature. Mother Nature has control over us!

References:

Pike, B. L., Saylors, K. E., Fair, J. N., LeBreton, M., Tamoufe, U., Djoko, C. F., Rimoin, A. W., & Wolfe, N. D. (2010). The origin and prevention of pandemics. Clinical Infectious Diseases, 50(12), 1636–1640. https://doi.org/10.1086/652860

 

 

After Roe v. Wade was overturned this past summer, I saw a spark of online discussions about access to birth control, as well as misinformation online on how different contraceptive methods work. Plan B has many misconceptions surrounding it, and seeing as the misuse of the pill can result in life-altering (and life-creating) consequences, it’s critical that people who use it understand how it works.

Plan B is known as a “morning after” pill to prevent pregnancy after unprotected intercourse or failed contraceptive methods (which would be Plan A). As opposed to abortion pills (that we can think of as Plan C), which terminate a preganancy that has already started, Plan B prevents a pregnancy from beginning by preventing the process of ovulation, fertilization, and implantation that causes a pregnancy. Compared to other forms of birth control pills, Plan B is only used as an emergency treatment after intercourse instead of being taken regularly, and is available over the counter instead of by prescription.

An Overview of How Pregnancy Occurs

Before talking about how Plan B prevents a pregnancy, we have to cover how pregnancy actually happens. Let’s put on our scientist thinking-caps and see if we can notice any details about the process that might be helpful to develop a way of preventing it.

Ovulation is when a mature egg is released from an ovary into a fallopian tube where the egg moves towards the uterus. The egg will move around the fallopian tube for 12-24 hours. Also during ovulation, the uterus is prepared for a potential pregnancy by the inner layer becoming spongy and thick, the ideal environment for a fertilized egg to implant.

There are a few hormones involved in the ovarian and menstrual cycles: FSH (follicle-stimulating hormone), LH (luteinizing hormone), estrogen, and progesterone. Estrogen causes FSH and LH to spike right before ovulation, and the high levels of LH trigger ovulation. After ovulation, progesterone levels rise and inhibit the development of another egg (a mystery tool that will help us later!) to give the released egg time to travel through the fallopian tubes and get fertilized.

If semen is released into the vagina during intercourse, sperm cells can swim into the cervix towards the fallopian tubes. If it encounters an egg, the egg and sperm will join together in a process called fertilization. This is what we are trying to prevent. Sperm can stay active in the uterus and fallopian tubes for up to six days after it is released. Therefore, fertilization can occur up to six days after intercourse. If fertilization does not occur, the thick lining of the uterus is released during menstruation, and progesterone levels fall to allow for another ovulation.

A fertilized egg will move into the uterus while dividing into a cluster of cells called a blastocyst. The blastocyst will reach the uterus 3-4 days after fertilization and will stay in the uterus for 2-3 days. If the blastocyst attaches to the lining of the uterus, this is called implantation and signifies conception. The process of implantation takes 3-4 days. If a blastocyst does not implant in the uterus, it is released out of the vagina during menstruation, along with the thick lining of the uterus.

How the Pill Works

The Plan B pill is 1.5 milligrams of levonorgestrel, a synthetic form of progesterone (remember her?), and is currently sold over the counter at drugstores. The person at risk of an unwanted pregnancy takes one pill. Compared to other forms of birth control pills which are taken regularly, Plan B is a one-dose pill that is only taken once, after intercourse within 72 hours. The pill works using three main methods: delaying ovulation, preventing fertilization, and preventing implantation.

Plan B delays ovulation to prevent the mature egg from even encountering sperm. This happens by the synthetic progesterone inhibiting the spike of FSH and LH, and therefore preventing ovulation. This is the primary way that the pill is effective.

If the egg has already been released into the fallopian tubes, Plan B can still be effective by preventing fertilization. The pill prevents fertilization by slowing down the movement of both the egg and sperm by triggering the growth of cervical mucus, to reduce the probability that the two will meet to fertilize the egg. 

Finally, Plan B also claims to prevent implantation by thinning the lining of the uterus so that a fertilized egg will be unable to attach to the uterine lining. However, scientific studies have not proven this effect.

These three methods prevent conception from occurring, as opposed to an abortion pill (our Plan C), which would terminate a pregnancy that has already started by causing a miscarriage.

This is why Plan B needs to be taken within 72 hours, and as soon as possible for maximum effectiveness, because it will not terminate a pregnancy and will only target the factors that cause a pregnancy to prevent one from occurring in the first place.

Effectiveness of Plan B

The Plan B pill of 1.5 milligrams of levonorgestrel is only effective for those under 165 pounds. The options for those over 165 pounds include a copper IUD, which reduces the movement of sperm to prevent fertilization, and the ella morning after pill, which functions similarly to Plan B but contains 30 milligrams of ulipristal and is only available by prescription. IUDs that are implemented within 5 days of intercourse are 99% effective, whereas the ella pill is 98% effective.

Plan B is proven to be 87% effective at preventing pregnancy when taken within 72 hours of intercourse for those under 165 pounds. The sooner the pill is taken, the more effective it is, since the pill has the best chance of working when ovulation has not occurred yet. Effectiveness decreases if ovulation has occurred and decreases further if fertilization has occurred. Compared to other forms of contraception, Plan B is less effective. 

Cost and Accessibility

Other birth control pills need to be taken before intercourse regularly and other methods of birth control need to be implemented prior to intercourse, whereas Plan B is taken after intercourse if no birth control methods were used or other forms of contraception failed. Plan B can cost $50-$60 whereas generic levonorgestrel pills range from $15-$40. 

Plan B was previously only available by prescription, but was approved by the FDA to be over the counter for ages 18+ in 2006 and then expanded to all ages in 2013.

References

• planb.ca/en/how-plan-b-works/
• npr.org/sections/health-shots/2012/02/06/146358069/the-morning-after-pill-how-it-works-and-who-uses-it
• plannedparenthood.org/learn/pregnancy/how-pregnancy-happens#:~:text=The%20sperm%20and%20uterus%20work,an%20egg%2C%20it’s%20called%20fertilization.
• plannedparenthood.org/learn/birth-control/iud
• plannedparenthood.org/learn/morning-after-pill-emergency-contraception/whats-plan-b-morning-after-pill
• webmd.com/sex/birth-control/plan-b 
• britannica.com/science/levonorgestrel
• organismalbio.biosci.gatech.edu/growth-and-reproduction/animal-reproduction-ii-reproductive-structure-and-function/