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.