Valedictorian Speech

For my English class, our final project was to draft an address to our class, as if we were the valedictorian of our year, addressing the audience at our commencement ceremony. With said ceremony looming so near on the horizon, I thought it'd be appropriate to share what I'd written.

So. Here we are. I’m sure many of you have heard commencement speeches before. They all go something along the lines of: today marks the start of a new chapter, or today is the opening of a new door. And it’s true. Today we embrace beginnings.

At this time, while we stand one step away from the future, we also reflect on our experiences, our memories, our friends. We reflect on the Friday night football games, the relief of submitting final college applications, and what seems like a thousand days in the sun in the quad during lunch. It didn’t take any time for everything to become last: our last year, our last school dance, our last day.

Be understanding. Seek understanding. I think that those two phrases sum up what we have learned, out of all the textbooks and Sparknotes and group-chats that we’ve scoured these past four years.

Be understanding.

Understand that so often we structure our lives around the next deadline or class period or even Instagram post, it becomes easy to forget to enjoy the little things -- like sitting in the sun or enjoying an old song on the radio. We so often anticipate the next moment that we forget about this one.

Understand that while today is the open door, every day is a new day. If you put your hand over your heart and feel that pulse, every beat is a new chapter. An opportunity.

Seek understanding.

Why do so few people walk across the grass of the quad even though it’s the quickest way across campus? Why do we know more about outer space than our oceans? How can you predict that perfect moment at a bonfire where everyone laughs really, really hard at a good joke? How can you balance between living in the moment and planning for the future? Ask yourself questions. Wonder. Maybe you’ll stumble upon an old memory. Or maybe a new opportunity, a new discovery.

We walked from the math building to the bungalows to the parking lot every day for four years. Individually, each day seemed monotonous, and yet -- during our senior year, I found myself lingering in the sun at lunch to watch the freshmen go to class. Thinking that one day, it’d be the last time.

So enjoy the sun. Linger. Wonder about the grass and why no one walks on it. Remember that you only have so many days left.

And if inspiration happens to strike -- whether it be for a song or a science experiment or a new goal -- understand that that is your new door. No matter how high that mountain may seem, no matter how rigorous a course-load may be, embrace that challenge. Seek meaningful work. Take that opportunity to look forward, towards the future.

Thank you, and congratulations!

The Inner Workings (Anecdotes from “Freakonomics” & “Outliers”)

A Sound of Thunder

“So inscrutable is the arrangement of causes and consequences in this world that a two-penny duty on tea, unjustly imposed in a sequestered part of it, changes the condition of all its inhabitants,” said Thomas Jefferson while reflecting on the effects of a seemingly inconsequential tea tax in colonial America, which then led to the Boston Tea Party, and, consequently, the American Revolution.

Big things have small beginnings. The avalanche effect. A multitude of things led to the separation of American from Britain, which seem only mostly clear, even in now retrospect. The cumulative effect of everything happening in our world, all at once, seems impossible to calculate.

From short stories about the unfathomable consequences of time travel (“A Sound of Thunder,” by Ray Bradbury, perhaps?) to suave explanations of chaos theory from a black leather-clad mathematician (Jurassic Park)¹, I think it’s say to safe that humans are interested in our, for the most part, inability to predict the future in complex, multifaceted systems -- like the weather, politics, and the economy.

In Freakonomics, authors Dubner and Levitt explore such convoluted relations in regards to personal motives and incentives.

In the first chapter of Freakonomics, our authors tell the story of motivation and prediction.

A common struggle for many school teachers, especially for those teaching younger grades, is dealing with parents. In particular, parents who come to pick up their students late. It makes sense: teachers are paid to work set hours, and parents who come to pick up their students late essentially force teachers to work for free out of hours.

To study this, a study was conducted in an Israeli daycare.

For the first few weeks, scientists only observed the parents coming to pick up their children. A small and consistent number of parents came late to pick up their students. After the fifth week, the researchers implemented a fine: 3 dollars for every time parents were late to pick up students. Though seemingly low, the fines accrued to nearly 400 dollars a month.

What did the researchers find?

After a few weeks, the number of parents who came late to pick up their students actually increased after the fine was implemented -- counterintuitive, I know.

Let’s go through with Dubner and Levitt.

According to them, economy is the “study of incentives: how people get what they want, or need, especially when other people want or need the same things” (16). And incentives are broken down into three main groups: economic, social, and moral.

In the case of the Israeli study, parents were offered a monetary, economic incentive to pick up their students: not paying a fine. However, the other incentives (because, presumably, you -- like me -- predicted that the parents would further refrain from picking up students late in order to avoid a fine) were not taken into consideration. In the case of the Israeli study, firstly, $3 is much cheaper than a babysitter. Secondly, the economic price paid also paid off social guilt: parents did not feel as guilty when arriving late since they paid for their mistakes.

It’s always interesting to look at success stories. To wonder how business tycoons make their wealth, how geniuses like Elon Musk and Bill Gates have their beginnings.

Canadian journalist and author Malcolm Gladwell examines such wonderings in Canadian hockey players in his book Outliers.

In Canada, the cut-off for hockey players’ birthdays is January 1. So you have to join an age group in the beginning, and are judged based on your peers in that group. Potential hockey players from  before kindergarten are grouped like this. Every year, the more talented players are selected to progress onto higher levels. Gladwell describes Canadian hockey as a meritocracy: if you’re good, you move onto better groups. Your success only relies on individual merit and ability and tenacity.

Supposedly.

A snapshot of a high league hockey game, except with the names of players replaced with their birthdays: March 11 starts around one side of the Tigers’ nest, leaving the puck for his teammate January 4, who passes it to January 22, who flips it back to March 12, who shoots at the Tigers’ goalie, April 27. April 27 blocks, but the shot’s rebounded by Vancouver’s March 6. He shoots! Defensemen February 9 and February 14 dive to block the puck while January 10 looks on helplessly.

If you’d looked at the birthdays of players in a high-end game, you’d see they all have very early birthdays.

Why is that?

Because when you have a group, if a player is born January 2 versus a player born December 31, the player in January at the age of five is more developed, mentally, physically, socially, and has every bit of advantage over a smaller almost four-year old. A coach looks at the group and sees the January player as better, chooses him for a better league. There he gets more attention, more time on the ice, more training. By the time the older kid turns six, he’ll have almost twice as much practice as his younger competitor, and therefore a higher chance of getting picked for a better team the following year. And the advantage continues, accumulates, snowballs. This is the phenomenon of relative age. Of course the kid is a decent hockey player to begin with, but he has that extra practice, that extra mentoring and support that adds up up up.

Gladwell adds in a footnote that this is an example of “self-fulfilling procephy.” Essentially, these players born in the beginning of the calendar year are told they are more talented, supported and expected to be better. They begin with a “false definition,” -- a proposition that they are more talented than their peers when really they may only have the advantage of age and development -- which leads to a new behavior, new environment, that, actually, makes “the original false conception come true.” This perpetuates a false assumption, since the prediction becomes true, though not necessarily in the way one may expect.

We all love the story of rags to riches, a “self-made man,” but the truth is, there are always factors we overlook, always small advantages that a tycoon has.

I end this post with an except from Outliers:

People don’t rise from nothing. We do owe something to parentage and patronage. The people who stand before kings may look like they did it all by themselves. But in fact they are invariably the beneficiaries of hidden advantages and extraordinary opportunities and culture legacies that allow them to learn and work hard and make sense of the world in ways others cannot. It makes a difference where and when we grew up. The culture we belong to and the legacies passed down by our forebears shape the patterns of our achievements in ways we cannot begin to imagine. It’s not enough to ask what successful people are like, in other words. It’s only by asking where they are from that we can unravel the logic behind who succeeds and who doesn’t.

Biologists often talk about the “ecology” of an organism: the tallest oak in the forest is the tallest not just because it grew from the hardiest acorn; it is the tallest also because no other trees blocked its sunlight, the soil around it was deep and rich, no rabbit chewed through its bark as a sapling, and no lumberjack cut it down before it matured. We all know that successful people come from hardy seeds. But do we know enough about the sunlight that warmed them, the soil in which they put down their roots, and the rabbits and lumberjacks they were lucky enough to avoid?

¹ Chaos theory was actually formulated by an MIT professor who attempted to simulate weather (he was a meteorologist). When inputting numbers into his system, he found varying results even though he’d submitted the same numbers twice. Upon closer inspection, the professor realized that he had rounded to a different decimal place than before, which resulted in a slightly different answer. From this insight, professor Lorenz formulated chaos theory, claiming that slight variations in initial conditions in complex systems lead to enormous consequences in the future. “Chaos: when the present determines the future, but the approximate present does not approximately determine the future.”

[https://scienceonblog.wordpress.com/2017/04/13/chaos-theory-in-jurassic-park/]

From here

Tales Which Have Neither Sense nor Reason

So, I know that I always add passages, quotes, and excerpts from one thing or another in my blog. This may or may not be annoying, but to be consistent, here’s an excerpt from The Collector, by John Fowlers. It’s often considered to be an avant-garde piece in the psychological thriller genre.

Another time G.P. It was soon after the icy douche (what he said about my work). I was restless one evening. I went round to his flat. About ten. He had his dressing-gown on.

I was just going to bed, he said.

I wanted to hear some music, i said. I’ll go away. But I didn’t.

He said, it’s late.

I said I was depressed. It had been a beastly day and Caroline had been so silly at supper.

He let me go up and made me sit on the divan and he put on some music and turned out the lights and the moon came through the window. It fell on my legs and lap through the skylight, a lovely slow silver moon. Sailing. And he sat in the armchair on the other side of the room, in the shadows.

It was the music.

The Goldberg Variations.

There was one towards the end that was very slow, very simple, very sad, but so beautiful beyond words or drawing or anything but music, beautiful there in the moonlight. Moon-music, so silvery, so far, so noble.

The two of us in that room. No past, no future. All intense deep that-time-only. A feeling that everything must end, the music, ourselves, the moon, everything. That if you get to the heart of things you find sadness for ever and ever, everywhere; but a beautiful silver sadness, like a Christ face.

Accepting the sadness. Knowing that to pretend it was all gay was treachery. Treachery to everyone sad at the moment, everyone ever sad, treachery to such music, such truth.

In all the fuss and anxiety and the shoddiness and the business of London, making a career, getting pashes, art, learning, grabbing frantically at experience, suddenly this silent silver room full of that music.

Like lying on one’s back as we did in Spain when we slept out looking up between the fig-branches into the star-corridors, the great seas and oceans of stars. Knowing what it was to be in a universe.

I cried. In silence.

At the end, he said, now can I go to bed? Gently, making fun of me a little bit, bringing me back to earth. And I went. I don’t think we said anything. I can’t remember. He had his little dry smile, he could see I was moved.

His perfect tact.

I would have gone to bed with him that night. If he had asked. If he had come and kissed me.

Not for his sake, but for being alive’s.

The first time I finished this passage, I immediately reread it, savouring the words. Then I proceeded to type out the whole passage in my Google Drive folder, which I fill with inspirational and beautiful writing. (In Fowlers’ novel, the eponymous character collects butterflies and young girls. I collect bits and pieces of interesting poetry and prose.)

I also do love to savour Bach’s Goldberg Variations -- usually Glenn Gould’s recordings. I also savour Bach’s Cello Suite 1, as I’m sure many other classical music aficionados do as well. I’m also not sure how many times I’ve listened to Bach’s Partita in D minor for violin, particularly the Chaconne (BWV 1004)¹.

In one episode of the NBC TV show Hannibal, the titular character remarks that the piano, an instrument he plays masterfully (in addition to harpsichord), has the quality of a memory.

I find that this is true.

I’ve played piano since elementary school. Over the years I picked up other instruments -- the recorder in third grade (I owned a plastic blue one and switched parts with my friends to make a tri-color accessory), the violin a year after that, the guitar (partially), and vocal choir -- but none seem to be so poignant as the piano to me. Perhaps because it was my first instrument, but to me, it is the definition of versatility and sentimentality. Piano introduced me to Baroque, Classical, Romantic, and jazz music; to Bach and Brahms and Coltrane and Herbie Hancock and Helene Grimaud.

But all music carries memory. It’s a kind of travelling to me. I wrote in my college apps about how writing, particularly fictional writing, transports me to different civilizations and timelines.

Music is another kind of traveling for me. Deep, magical forests engulf me when I play Stravinsky’s Firebird with my orchestra (read about that here). On piano, when playing Mendelssohn’s boat songs, I’m aboard a rocking gondola, floating through Venice’s waterways, serenaded by a gondolier. Playing traditional Vietnamese piano music for my parents brings a piece of the old country to our home here in America.

It’s impacted me monumentally; but only recently have I realized how much it’s influenced me. Since I’ve been surrounded by music -- classical, pop, Eastern, Western -- since I was young, it’s almost as though I take it for granted, as though I can’t really fathom the influence it has on my life since I can’t imagine my life without it; I’ve never had life without it.

Music.

There’s so much history, so much beauty:

From orchestra days playing Rimsky-Korsakov’s Scheherazade, which reminds me of weekend retreats, up in the mountains surrounded by my fellow orchestra members, playing music for hours in rehearsal only to return to our cabins and practice individually some more, listening to this music and imagining the story of a vizier’s daughter, a collector (hah) of stories and anecdotes, who told these enthralling, enchanting tales to keep herself alive for a thousand and one nights, an infinite, timeless desert story  --

To listening to Duke Ellington and Billie Holiday, imagining a smoky, blue club in New Orleans during the Harlem Renaissance, living in the time of a revolution! in a time of new poetry and new literature and new ideas, to spending nights and nights inside these jazz clubs listening to upright basses and seductive saxophones and knowing that that is soul music --

To learning of Helene Grimaud, a woman who fell in love with Brahms’ music at a young age, hearing her play and watching her, brim full of emotion, this indescribable, unnamable thing, thinking she -- she would just explode, because the music channeled through her, the feeling channeled through her is too much.

Imagine music back then. Imagine the symphony in the 1800s playing, and you only hear it once -- listening to music then was an active thing, an activity that required sight and sound and --

And the piano, the music, has the quality of a memory because it’s transient; it’s this beautiful thing.

Now, every day we have ways of channeling emotion, of articulating ourselves; there’s something raw and beautiful about something indescribable, something about the indefinite the indefinable, undefinable. Now we can access this emotion and feeling and quite literally time-travel anywhere we want by listening to these songs.

It’s so interesting to me: music from Brahms, over 100 years ago, played today still has so much energy, and memory, but it’s not quite the same emotion, not the same; it’s layered, so that every artists’ interpretation of this music has been woven together in this beautiful thing, this sort of Frankenstein retelling. How can we understand, relate, to music written so long ago? Because music is like emotion translated into sound: minor keys are associated with sadness, major keys with happiness.

But why? How can something “sound” happy?

Perhaps it has something to do with the way we can recognize emotion even in other languages, or how we can read body language; this universal way to connect, to sympathize, empathize.

Regardless, to me music is a large part of my life, but never one to be taken lightly.

“The piano has a quality of a memory."

- Hannibal

“Your heart will become a dusty piano in the basement of a church and she will play you when no one is looking.

Now you understand why it’s called an organ.”

- Rudy Francisco

¹ “Our guest today is the violinist Itzhak Perlman, who is going to play the Partita in D minor, by Bach. The work ends with the great Chaconne, the best known of all Bach’s works for unaccompanied violin, and one of his most remarkable achievements.” This was the introduction given for Itzhak Perlman’s performance of the Partita at Saint John’s Smith square in London, in 1978. Acclaimed violinist Joshua Bell has said the Chaconne is “not just one of the greatest pieces of music ever written, but one of the greatest achievements of any man in history. It's a spiritually powerful piece, emotionally powerful, structurally perfect.” Composer Johannes Brahms wrote to his peer in 1877: “On one stave, for a small instrument, the man writes a whole world of the deepest thoughts and most powerful feelings. If I imagined that I could have created, even conceived the piece, I am quite certain that the excess of excitement and earth-shattering experience would have driven me out of my mind.” [Wikipedia’s article on Partita for Violin No. 2 (Bach)]

A Fairy Tale Rediscovered

As a child, one of my fondest memories was going to the library and walking through the aisles full of books, running my fingers over the spines. As a voracious reader, I consumed anything I could get my hands on. At that time I usually read novels, but the vivid illustrations from picture books begged me to open them all too often. One of my favorite picture books was The Golden Mare, the Firebird, and the Magic Ring by Ruth Sanderson, a rendition of a classic Russian folktale in rich paintings and thick pages. If all the books in the world were to disappear, this is the book I would want to save. 

I heard the tale initially from my father, who told me the fairy tale as a bedtime story, but as I stumbled upon it a second time in the library, the story was retold – this time with colorful paintings of a huntsman as he runs away from an evil Tsar, searching for Princess Vasilisa. My tiny fingers roamed over the golden fields of Imperial Russia, traced the scarlet wings of the firebird and dipped into the icy waves of the Barents Sea as I followed the huntsman on his quest. Now I could touch palpable images of a tale from so long ago. As a nine-year-old, I was entranced by the vivid colors as much as the simple plot of the fairy tale.

Eventually I moved on past picture books, delving into chaptered novels, and then hundred-paged trilogies and chronicles. But even then, I remembered the brilliant colors of The Golden Mare, the Firebird, and the Magic Ring, and in my mind, I imagined the scenes of whatever book I was reading as vividly as the images from the Firebird picture book were.

And then in my freshmen year of high school, I joined a local orchestra and there we played the Firebird suite, composed by the Russian composer Stravinsky as the score for the Firebird ballet. Here, I rediscovered the Firebird for the third time, through music. It took little effort to see the footsteps of the huntsman, tip-toeing through the forest to find the firebird as plucked bass notes filled the air, and the supremacy of the greedy Tsar as the timpani rolled and the cymbal crashed.

Now, along with the words from my father and the illustrations from the story book, I put together the Firebird as I envision it – a theatrical mélange of sights and sounds woven together to form the folktale I love so dearly. For me, the story was no longer just words in the air, but a tale engaging all of my senses.  

 Walking through the library now, I spy The Golden Mare, the Firebird, and the Magic Ring, and ten years after I heard the story orally for the first time, I thumb through the pages. As a teenager, the premise of the story is simple enough, and yet I’m still drawn to the deep colors of the firebird’s feathers, the pale folds of the princess’s dress. The story is told plainly yet beautifully, embodying the rudiments of a fairy tale.

As I read, I can hear a cascade of violins as the golden mare trots through an ecru field, the lament of an oboe as the huntsman dives into the freezing ocean. And when the huntsman finally marries the Princess, I hear Stravinsky’s trumpets ringing. As the story draws to an end, the firebird rests in the corner of the last page, waiting for the next time someone will open the book.

 

An Interdisciplinary Journey: A Brief Look at Coding and Machine-Learning in Genetics

An Interdisciplinary Journey

A Brief Look at Coding and Machine-Learning in Genetics

Melba Nuzen, Scripps Ranch High School

In today’s world of technology, the road to discovery is paved with complex, multifaceted problems. To begin looking for solutions, we must find equally complex methods to tackle these challenges.

Our road was paved in the 1950s when people first discovered DNA as the blueprint for our human systems—a design that remained unchanged throughout our lives. However, as research progressed, we discovered that our characteristics rely on more than just the nucleotides of DNA; there are certain chemical compounds and proteins that can modify the expression of DNA, collectively referred to as the epigenome.

The epigenome can increase the production of specific proteins or turn certain genes on or off when necessary [1]. All of this occurs without altering the actual DNA code itself; epigenetic proteins instead interact with DNA. Recently, a collection of institutions have begun exploring epigenetics in the field of cancer research.

The connection between cancer and epigenetics is fairly simple: the epigenome includes proteins called transcription factors that can inhibit gene expression by blocking DNA transcription. This can stop cells from multiplying by altering gene expression—if certain genes aren’t expressed, the cell cannot divide. Modulation of transcription factors is essential to the proliferation of cancer cells, formation of tumors, and tumor metastasis to other organs, which produces secondary tumors [2]. In a study done in mice, researchers sampled various epigenomes and found a group of enhancer genes called metastatic variant enhancer loci (Met-VELs) that are frequently located near bone cancer genes [3]. The activation of these enhancer genes was required for the formation of secondary tumors, while inhibiting transcription factors that coordinated with Met-VELs interrupted metastasis. Ultimately, this decreased the growth of cancerous tumors and prevented relapse in mice.

Of course, there are many more variables to test before such research can be extended to humans. But the fundamental takeaway from this example is clear: a new scientific discovery leads to a better understanding of genetics, which inspires solutions to challenges that have major impacts on humanity.

So how do these discoveries, understandings, and solutions come about? A variety of fields, such as artificial intelligence, mathematical statistics, and computer programming are combined in careers like biostatistics and bioinformatics to address some of these challenges.

Let’s take a closer look at the previously mentioned study of bone cancer in mice. The activation of Met-VELs by transcription factors was just one of thousands of interactions found when epigenetic proteins interacted with enhancer genes. So how do we begin discovering what each protein does when it binds to its respective gene? And before we tackle that question, how do we even map out DNA strands and their epigenetic counterparts?

To sort through the billions of base-pairs in the human genome—which translates to millions of bytes of data—scientists turn to computers, or more specifically, programming languages. For example, take R, a powerful language designed for data analysis. Counting the number of nucleotides in a string of DNA would look something like this:

library(stringr)

seq1 <- “TCTTGGATCA”

count1A <- str_count(seq1, c(“A”))

count1C <- str_count(seq1, c(“C”))

count1G <- str_count(seq1, c(“G”))

count1T <- str_count(seq1, c(“T”))

Six lines of code tell the computer to read through a string of characters, seq1, and count all of the As, Cs, Gs, and Ts. Using the library stringr, and the unction str_count, this code creates four variables that hold the number of times their respective letter appears in seq1.

To compare DNA before and after a mutation, the code would resemble this:

library(stringr)

seq1 <- “TCTTGGATCA”

count1A <- str_count(seq1, c(“A”))

seq2 <- “TCATGGATCA”

count2A <- str_count(seq2, c(“A”))

if ( count1A == count2A ) {

     print(“true”)

}

This program compares two strands of DNA, seq1 and seq2. Using the process described  above, the computer generates two variables that represent the amount of “A” characters found in seq1 and seq2. Then, the code compares those two variables, returning true  if there is an equal number of “A” characters found in both sequences. This idea can be implemented for all four bases to compare much lengthier DNA strands and determine whether or not strands contain the same number of specific bases.

Of course, these are simple examples to illustrate how coding algorithms can be utilized. With a few lines of code, computers can analyze millions of strands of DNA in many types of coding languages. Now, the question to answer is how DNA interacts with proteins, and what overall effect that has on a biological system. For this complex problem, we venture off the beaten path to a more complex solution: artificial intelligence.

When AI is mentioned, images of self-driving cars and evil robots often come to mind. However, artificial intelligence can play a large role in the field of bioinformatics, particularly in genetics. Machine learning is one such application of artificial intelligence that specializes in the independent analysis of data by algorithms. This will be useful for looking at transcription factors and their roles in cell development [4].

Within the subfield of machine learning, there are two general methods for addressing problems: supervised and unsupervised learning. As the name suggests, supervised learning teaches the machine how to analyze data by inputting annotated data points to train the machine to recognize an expected output. In the case of epigenetics, this means training and testing a machine learning model to recognize enhancer genes by inputting a series of known enhancer genes and non-enhancer genes; this way, the model can make an educated guess as to whether or not a new piece of data is an enhancer gene or not [5]. If we give our model examples of DNA that contain transcription start sites (TSS) as well as DNA that does not contain TSSs, the algorithm will theoretically be able to recognize a pattern and then find TSSs itself.

Figure 1: Supervised Learning in recognizing transcription start sites in DNA [6]

In regards to epigenetics, the model would sift through megabytes of DNA to pick out notable genes of interest, allowing for more time to be allocated toward concentrating on analyzing how the DNA interacts with transcription factors [6].

On the other hand, unsupervised learning comes into play when it’s preferable to avoid giving a model pre-determined labels or groups. An application of this type of learning could be determining the functions and effects of specific transcription initiation complexes. Given enhancers and their respective proteins along with their impact on associated functions, a machine learning model can group proteins together based on similar effects. This occurs in one of two ways: generative or discriminative modelling. The former type of modelling groups data based on similar characteristics, whereas the latter draws a boundary between data points [6]. When dealing with unknown variables, such as the functions of proteins, discriminative modeling is used more often, since scientists have few predetermined groups to classify proteins into.

Figure 2: Unsupervised Learning in grouping data [6]

With these methods, the machine can then conclude that a certain group of enhancers and their epigenetic counterparts halt the proliferation of cancer cells, as seen in Met-VELs.

Though our journey, filled with pit-stops at various science disciplines, took us on a winding and tangled road, the combination of coding, machine-learning, and genetics has led us to a fascinating discovery full of potential. But this explanation covers only the basics of such a revelation; in reality, studying epigenetics and cancer cells is just one application of the interdisciplinary study. At the moment, combining fields of interest is the road leading us toward the future. Our journey will continue as we synthesize a variety of concepts to take on complex, ever-diversifying problems and explore new solutions that will impact humanity for years to come.

References

[1] Epigenomics Fact Sheet. National Human Genome Research Institute website. https://www.genome.gov/27532724/epigenomics-fact-sheet/. Accessed February 8, 2018.

[2] Davis CP. Understanding Cancer: Metastasis, Stages of Cancer, and More. OnHealth. https://www.onhealth.com/content/1/cancer_types_treatments. Accessed February 8, 2018.

[3] Researchers Inhibit Cancer Metastases via Novel Steps - Blocking Action of Gene Enhancers Halts Spread of Tumor Cells. Case Western Reserve University School of Medicine website. http://casemed.case.edu/cwrumed360/news-releases/release.cfm?news_id=1026&news_category=8. Accessed February 12, 2018.

[4] Marr B. What Is The Difference Between Artificial Intelligence And Machine Learning? Forbes. https://www.forbes.com/sites/bernardmarr/2016/12/06/what-is-the-difference-between-artificial-intelligence-and-machine-learning/#4dc79f282742. Published September 15, 2017. Accessed February 18, 2018.

[5] Marr B. Supervised V Unsupervised Machine Learning -- What's The Difference? Forbes. https://www.forbes.com/sites/bernardmarr/2017/03/16/supervised-v-unsupervised-machine-learning-whats-the-difference/#5d786d6a485d. Published March 16, 2017. Accessed February 18, 2018.

[6] Libbrecht MW, Noble WS. Machine learning applications in genetics and genomics. Nature Reviews Genetics. 2015;16(6):321-332. doi:10.1038/nrg3920.

Deep Creek Hot Springs Hike and the History of Mathematics (Review of “How Not to Be Wrong”)

In my high school calculus class, many students find it difficult to remain perfectly attentive throughout each 45 minute lecture, taking notes on each problem while also engaging in class to answer questions or perhaps question the teacher. I am, of course, no exception to this rule.

In what I’ve learned from my 13 years in the American public education system (I did not, by the way, take any common core classes), the math education goes something like this: teacher lectures and does a few example problems, you the student independently do homework, review answers, take a test with slightly different questions, and then proceed to forget the majority of whatever you’ve just learned.

A few days ago, my class began delving deeper into the realm of limits and sequences. At one point in the lecture, my teacher interrupted to say something like this: all your life, you’ve been taught to do math, empirically. You may not have understood why to proceed a certain way when solving a test or understood which formula to use, but you begin learning some of these reasonings as you graduate from class to class.

Which I guess, makes sense.

But a while ago, someone had recommended to me a book entitled How Not to Be Wrong: the Power of Mathematical Thinking by Jordan Ellenberg. I had read the introduction (only, yeah I know, I know) and wrote a review of the novel’s beginning anecdote, which can be found here.

Several months later, I decide to take a hike outside of San Bernardino, to a hot spring site in a place called Deep Creek Canyon.

The hike and subsequent adventure were actually wonderful: it felt so good to enjoy the absolute, irrevocable silence of the canyon -- no noise, traffic, or pollution -- so quiet you could only hear the blood rushing in your ears.

The springs themselves were a delight as well! After about an hour hike down into a canyon, we waded across a freezing stream to have lunch on some sun-drenched rocks overlooking the cold river. Afterwards, we alternated between the just-on-this-side-of-uncomfortable hot springs and the freezing creek, not unlike Japanese onsen. Someone had set up a slackline across the freezing river, which we took turns attempting to cross as well.

Where does the math come in? you might ask. Well, we used Newton’s Law of Cooling to calculate the rate at which our body temperature dropped after plunging into the mountaintop snow-cold water. Just kidding.

Hardly any math was used that day: we relished in the torpor of a hidden getaway in the middle of the desert, sprawling in the drowsy hot springs before feeling the adrenaline paramount to a freediver’s thrill at submerging in our icy creek.

But -- there was a lull in the middle of the day wherein my companion pulled out a new copy of a Cicero book, and I my iPhone, finally beginning to read Ellenberg again.

It didn’t feel like math.

Ellenberg explains one of the basic ideas in calculus: limits. He begins with explaining how early mathematicians found the number pi -- anecdotal style.

It’s not a strange concept to me -- irrational numbers. Pi, Euler’s number, and the like. But discovering irrational numbers? Basic human intuition understands the concept of whole numbers relatively easily. One toga, two goats, three laurel wreaths, four stuffed grape leaves. Half of a stuffed grape leaf may be easy to imagine. But 3.1415 grape leaves?

When graphing curves and parabolas in math, if you zoom in close enough to any curve, it resembles a line. In statistics, this method of representing data, known as linear regression, often simplifies trends when viewing data with a microscope -- that is, looking at only small chunks of data.

The legend Archimedes decided to find the area of a circle by inscribing other shapes, like squares, pentagons, and octagons, inside of a circle. The area of the polygon contained with the circle represents the area of a circle the more sides that polygon has.

For example, the area of an octagon within a circle better resembles the circle’s area than a square within a that same circle. Archimedes theorized that a circle was simply a polygon, and its area could be calculated by calculating the area of a polygon whose numbers of sides approaches infinity. Here’s a catchy slogan Ellenberg presents: “straight locally, curved globally.”

Math has this wonderful, complex, and quirky history seemingly irreconcilable with what I’ve learned in class, at least up until this year.

This year, I’ve had the pleasure of encountering several elegant equations, such as this one:

Euler’s equation uses two of the most irrational numbers, e and pi, as well as the imaginary i and 1 and 0.

It seems counterintuitive to read about math, but I think the introduction of mathematical ideas through anecdotes -- such as how Cauchy, a professor who both infuriated and inspired coworkers and students alike when he taught his freshmen students his own cutting edge take on calculus when the curriculum nearly the opposite -- makes math more approachable, less of a tool you use out of necessity and more of a concept you develop and harness as your own. It’s a living thing with its own history and archaic notions, an art form if you look at it a certain way as well. I’m sure many more complicated equations relating to earth and space are equally as deserving of awe. But for now, I’ll continue reading on.

Until then!