Perks of Engineering

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Physicians, Depression and Burnout

When I applied for medical school, I knew I was signing up for hard work—but I had not expected this

Article by Jennifer Tsai (Scientific American)

Compared to the general population, female physicians are 2.3 times more likely to die by suicide (which overall is the third most common cause of death for women of ages 25–34). I am a medical student, and that statistic sits on my chest like a death threat.

This time last year, I found myself in a big bed with soft sheets, tucked between my friends Caitlin and Victoria, surrounded by empty ice cream containers, faces plastered in the sticky sweet syrup of cheap face masks. I remember us asking each other, how did we get here? How did we end up counting all the people around us—including us—who are depressed?

Victoria is applying to be a trauma surgeon. That’s nine years of training to learn how to pull bullets out of bodies. Caitlin wants to spend the next decade teaching her hands how to dissect seeded studs of cancer from the abdomen. Me? I’m taking a little break, trying to figure out what I want out of this life.

Together, the three of us are united in a text group known as Pu$$ySlayer69 (yes, pussy is spelled with dollar signs)—an ode to a male acquaintance’s WiFi password that we’re hoping to get emblazoned in glitter across team jackets. On this night, we were here in this bed because amidst the food porn and single girl memes, our text thread had slowly become darker and more desperate. It was filled with texts that said, “Today I feel so worthless.” Messages like, “I am crying in the bathroom between surgeries I don’t know why I just can’t do anything right.” Updates like “Another one of my patients just died. I’m so tired and sad I can’t even drive home to cry in my own bed.”

In this moment, with our arms wrapped around each other like curling vines, we checked on each other by rattling off the diagnostic criteria for depression. SIGECAPS: Sleep, Interest, Guilt, Energy, Concentration, Appetite, Psychomotor Agitation, Suicidal Ideation. We recited the words just as we learned in medical school. We laughed, because when you say it fast it sounds a bit like a song. Psychomotor agitation, suicidal ideation. We laughed so it sounded like more of a joke.

When I applied for medical school, I knew I was signing up for hard work. I knew I would have to spend countless hours studying, that my sleep cycle might never be the same. But I had not expected this.

Perhaps I shouldn’t have been so naïve. Other trainees learn of this risk from early on. In 2016, just two days after my best friend started medical school at Mt. Sinai, a woman in her fourth year jumped from the 33rd floor of the school’s dormitories. Her body was found by one of her classmates.

And bodies, when they fall, they fall heavy. I know, because one day during my surgery clerkship they dropped the stump of a 45-year-old man’s leg into my waiting arms and my first thought wasn’t oh my god I am holding somebody’s leg. It was oh my god this is heavy. I thought,someone help me.

By the time physicians reach residency, rates of depression are four times the national average. One of every three nascent physicians will experience an episode of major depression during their training, and nearly four hundred doctors commit suicide each year. More than one per day. Added together, that’s almost my entire medical school.

On this night, we took turns saving one other. We save each other at moments when we cannot save ourselves, share victories when we forgot we’ve had any. I told them about a patient I’ll call Mr. T. How he stopped eating the day they told him he had pancreatic cancer. But on my watch? I began spending my lunch breaks with him, urging him to start again. I managed to get him to eat some key lime pie; we shared wedges in his hospital bed.

Victoria’s eyes lit up when she told us she was invited to a traditional Ramadan meal with her patient’s family. Caitlin slapped her hands gleefully to each cheek like a cartoon character, reveling in in how good babies smell, especially when you deliver them with your own hands. We wielded memory and sugared ice cream spoons, embraced fear and each other, did our best to slay insecurity and doubt.

We turned this night into a midwife, helping us to be reborn.

Following greater recognition of physician burnout and depression, I have seen an increase in wellness programming at my and many other medical schools. Students now have sessions devoted to yoga and art, the opportunity to go horseback riding or participate in a modern dance class. I don’t want to diminish these efforts, but they’re speckled interpolations, not solutions. They exist once or twice a semester, on afternoons most medical students would prefer to spend studying for the next test. (There’s always a next test.) And while these attempts to dedicate more resources to personal resilience may be well-intentioned, they target individual behavior without seeking to address the stressful conditions that drive suffering in the first place.  

The world is dying of loneliness, and doctors, who are worked from dusk to dawn, steeped in expectations, and challenged with grief on a daily basis, need more than grit to heal. It’s not just about self-care. If depression is so much about isolation, the antidote cannot be found alone.

Just a while ago, I stood in an atrium filled with balloons and familial fanfare and watched as Caitlin and Victoria opened their Match Day envelopes. At noon, these two women, along with a hundred other dear classmates and 30,000 eager students, discovered where they are spending the next several years of their lives as medical trainees.

I hope—oh do I hope—that the programs listed in their envelopes will recognize the ferocity of their care and the value of their hard work. I hope that they will fly to a place that treats them with kindness, one that reminds them how powerful they are on the days they feel small, and unworthy, and alone.

I am hopeful that this time next year, I will call my friends and find them wedged between a new tribe of people who support their breath. Hopeful they will find new communities and supports, new allies—new Slayers.

Light ‘relaxes’ crystal to boost solar cell efficiency

Some materials are like people. Let them relax in the sun for a little while and they perform a lot better.

A collaboration led by Rice University and Los Alamos National Laboratory found that to be the case with a perovskite compound touted as an efficient material to collect sunlight and convert it into energy.

The researchers led by Aditya Mohite, a staff scientist at Los Alamos who will soon become a professor at Rice; Wanyi Nie, also a staff scientist at Los Alamos, and lead author and Rice graduate student Hsinhan (Dave) Tsai discovered that constant illumination relaxes strain in perovskite’s crystal lattice, allowing it to uniformly expand in all directions.

Expansion aligns the material’s crystal planes and cures defects in the bulk. That in turn reduces energetic barriers at the contacts, making it easier for electrons to move through the system and deliver energy to devices.

This not only improves the power conversion efficiency of the solar cell, but also does not compromise its photostability, with negligible degradation over more than 1,500 hours of operation under continuous one-sun illumination of 100 milliwatts per cubic centimeter.

Image Credit:  Simon Prades

An AI That Knows the World Like Children Do

Artificial intelligence has staged a revival by starting to incorporate what we know about how children lear

Article by Alison Gopnik (Scientific American)

If you spend much time with children, you’re bound to wonder how young human beings can possibly learn so much so quickly. Philosophers, going all the way back to Plato, have wondered, too, but they’ve never found a satisfying answer. My five-year-old grandson, Augie, has learned about plants, animals and clocks, not to mention dinosaurs and spaceships. He also can figure out what other people want and how they think and feel. He can use that knowledge to classify what he sees and hears and make new predictions. He recently proclaimed, for example, that the newly discovered species of titanosaur on display at the American Museum of Natural History in New York City is a plant eater, so that means it really isn’t that scary.

Yet all that reaches Augie from his environment is a stream of photons hitting his retina and disturbances of air contacting his eardrums. The neural computer that sits behind his blue eyes manages somehow to start with that limited information from his senses and to end up making predictions about plant-eating titanosaurs. One lingering question is whether electronic computers can do the same.

During the past 15 years or so computer scientists and psychologists have been trying to find an answer. Children acquire a great deal of knowledge with little input from teachers or parents. Despite enormous strides in machine intelligence, even the most powerful computers still cannot learn as well as a five-year-old does.

Figuring out how the child brain actually functions—and then creating a digital version that will work as effectively—will challenge computer scientists for decades to come. But in the meantime, they are beginning to develop artificial intelligence that incorporates some of what we know about how humans learn.

THIS WAY UP

After the first burst of enthusiasm in the 1950s and 1960s, the quest for AI languished for decades. In the past few years, though, there have been striking advances, especially in the field of machine learning, and AI has become one of the hottest developments in technology. Many utopian or apocalyptic predictions have emerged about what those advances mean. They have, quite literally, been taken to presage either immortality or the end of the world, and a lot has been written about both these possibilities.

I suspect that developments in AI lead to such strong feelings because of our deep-seated fear of the almost human. The idea that creatures might bridge the gap between the human and the artificial has always been deeply disturbing, from the medieval golem to Frankenstein’s monster to Ava, the sexy robot fatale in the 2015 movie Ex Machina.

But do computers really learn as well as humans? How much of the heated rhetoric points to revolutionary change, and how much is just hype? The details of how computers learn to recognize, say, a cat, a spoken word or a Japanese character can be hard to follow. But on closer inspection, the basic ideas behind machine learning are not as baffling as they first seem.

One approach tries to solve the problem by starting with the stream of photons and air vibrations that Augie, and all of us, receives—and that reaches the computer as pixels of a digital image and sound samples of an audio recording. It then tries to extract a series of patterns in the digital data that can detect and identify whole objects in the surrounding world. This so-called bottom-up approach has roots in the ideas of philosophers such as David Hume and John Stuart Mill and psychologists such as Ivan Pavlov and B. F. Skinner, among others.

In the 1980s scientists figured out a compelling and ingenious way to apply bottom-up methods to let computers hunt for meaningful patterns in data. “Connectionist,” or “neural network,” systems take inspiration from the way that neurons convert light patterns at your retina into representations of the world around you. A neural network does something similar. It uses interconnected processing elements, akin to biological cells, to transform pixels at one layer of the network into increasingly abstract representations—a nose or an entire face—as data are crunched at progressively higher layers.

Neural-network ideas have gone through a recent revival because of new techniques called deep learning—technology now being commercialized by Google, Facebook and other tech giants. The ever increasing power of computers—the exponential increase in computing capability that is captured by what is known as Moore’s law—also has a part in the new success of these systems. So does the development of enormously large data sets. With better processing capabilities and more data to crunch, connectionist systems can learn far more effectively than we might have once thought.

Over the years the AI community has seesawed between favoring these kinds of bottom-up solutions to machine learning and alternative top-down approaches. Top-down approaches leverage what a system already knows to help it learn something new. Plato, as well as so-called rationalist philosophers such as René Descartes, believed in a top-down approach to learning—and it played a big role in early AI. In the 2000s such methods also experienced their own rebirth in the form of probabilistic, or Bayesian, modeling.

Like scientists, top-down systems start out by formulating abstract and wide-ranging hypotheses about the world. The systems then make predictions about what the data should look like if those hypotheses are correct. Also like scientists, the systems then revise their hypotheses, depending on the outcome of those predictions.

NIGERIA, VIAGRA AND SPAM

Bottom-up methods are perhaps the most readily understood, so let’s consider them first. Imagine that you are trying to get your computer to separate important messages from the spam that arrives in your in-box. You might notice that spam tends to have certain distinguishing characteristics: a long list of recipient addressees, an originating address in Nigeria or Bulgaria, references to $1-million prizes or perhaps mention of Viagra. But perfectly useful messages might look the same. You don’t want to miss the announcement that you have earned a promotion or an academic award.

If you compare enough examples of spam against other types of e-mails, you might notice that only the spam tends to have qualities that combine in certain telltale ways—Nigeria, for instance, plus a promise of a $1-million prize together spell trouble. In fact, there might be some quite subtle higher-level patterns that discriminate between the spam messages and the useful ones—misspellings and IP addresses that are not at all obvious, for example. If you could detect them, you could accurately filter out the spam—without fear of missing a notice that your Viagra has shipped.

Credit: Jen Christiansen; Source: “Human-Level Concept Learning through Probabilistic Program Induction,” by Brenden M. Lake, Ruslan Salakhutdinov and Joshua B. Tenenbaum, in Science, Vol. 350; December 11, 2015 (Bayesian example)

Bottom-up machine learning can ferret out the relevant clues to solve this kind of task. To do this, a neural network must go through its own learning process. It evaluates millions of examples from huge databases, each labeled as spam or as an authentic e-mail. The computer then extracts a set of identifying features that separate spam from everything else.

hello!

that sounds quite intimidating! let’s see if i can help you out. so these are tips that have worked for me (or i’ve seen work for others based on what i’ve read/edited/etc). for science writing in general:

know your audience. how much prior knowledge of the subject do they know? what is the purpose of them reading this article? who your audience is will dictate how you write your article. in your case, it’s your professor, so they probably know a lot about the subject (or maybe not! maybe you’ll write about some niche topic they haven’t studied yet), they’re reading to grade, so they’ll be paying attention to things on the grading rubric, etc. these are all things to keep in mind when you’re prepping, outlining, drafting, and editing. 

structure your essay so it’s a story. just like any other essay, science articles should also have a beginning (intro and statement), a middle (paragraphs to support your statement), and an end (conclusion). usually in science essays the conclusion also serves as a discussion where the writer can finally insert their own opinions on things, and further discuss why the results in the article are important for broader implications. 

be concise. science writing is not like some other styles of writing in that it’s best to avoid fluff, unnecessary descriptions, flowery language, etc. keep it short and simple.

if you need to use jargon or abbreviations, be sure to define them (for jagon), and give the full phrasing (for abbreviations) the first time they’re used.

avoid using absolutes unless it really is an absolute. in science, hardly anything is 100% certain. that’s why phrases like these are used a lot: “the data suggest”, “x may be caused by y”, “x has been shown in controlled experiments to be caused by y”, “thus it can been hypothesized”, etc. unless it’s a law (like gravity), then we can never use an absolute to describe it, and biology especially is a lawless land. 

related, keep in mind that the word “data” is plural for “datum”. so it’s “the data are” not “the data is”. (i still catch myself making this mistake alskdfj)

like most writing, avoid the passive voice whenever possible, and keep to the same verb tense.

practice summarizing research results into concise sentences for your supporting points. it can be difficult to wade through all that jargon and data in an article, so it definitely takes practice. it’s almost like reading comprehension dialed up to 12. article titles and abstracts help with this a lot, as the authors have already summarized their main findings there. reading good examples can also help (like science news articles from reputable sources–you can see what kind of language they use to summarize key points). 

present data in an unbiased way. you may be writing an essay that argues one point over another, but that opinion should not leak into when you present study results. Ex. “60% of animals treated with ABC survived to the study endpoint, versus 10% of animals receiving the control” is unbiased, whereas “ABC had exceptional results in a study where the majority of animals treated survived” sounds pretty darn biased in favor of ABC, and may not be an accurate reporting of the results (maybe the drug actually sucked upon further studies?). Of course, if your essay statement is arguing in favor of ABC, then you can follow up your unbiased presentation of the results with something like “This study showed the efficacy of ABC in a controlled in vivo setting, and thus has promise for further studies”. 

however, “laundry-listing” data and results can make for a very boring read, so be unbiased, yes, but state things in a way where there’s a clear direction or train of thought. this part is tough; i have to admit i’m still practicing this “art” (which it definitely is). take my above example about ABC for instance. which one is easier to follow? the one i gave, or this: “Six out of 10 animals treated with ABC survived until day 42. One out of 10 animals treated with control survived until day 42.” you’ll notice this 2nd option is really stilted, boring, dry, and if the entire results section is presented like this, the reader will surely get lost as to what the point of it all is, and possibly fall asleep. so although personal bias should be avoided, there should still be direction and flow. take care to guide the reader. 

unless it’s your own opinion, every statement needs a citation. i use the free reference manager mendeley (it has a browser plug-in that adds articles you find online to its citation library, and then you can sync it with word doc and it does the citations for you). 

proofread and edit a lot (no one’s first draft is ever perfect), and if possible, send a draft to your professor or TA to look over it and see if you’re on the right track. 

phew! that’s all i can think of for now! i’m sure there’s sooo much more that goes into writing for science (and academia, in a more broad sense), but these are just a few main ones from me. 

if anyone else has something to add, please do!

NIH Lifts Ban On Research That Could Make Deadly Viruses Even Worse 

Scientists could soon resume controversial experiments on germs with the potential to cause pandemics, as government officials have decided to finally lift an unusual three-year moratorium on federal funding for the work.

The research involves three viruses — influenza, SARS, and MERS — that could kill millions if they mutated in a way that let the germs spread quickly among people.

The bird flu virus H7N9, for example, is known to have infected more than 1,500 people, and 40 percent of them died. But unlike common flu strains, this one does not spread easily among humans.

Biologists say they may need to alter these viruses in the lab to understand what genetic changes matter in starting pandemics, so they can understand the risks and get ready. But some of their past efforts to tinker with viruses have made other scientists uneasy.

On Tuesday, the Department of Health and Human Services released a new framework for making decisions about funding research that has the potential to create a new pandemic strain.

“We have a responsibility to ensure that research with infectious agents is conducted responsibly, and that we consider the potential biosafety and biosecurity risks associated with such research,” said Francis Collins, director of the National Institutes of Health, in a statement.

Surely I’m not the first one stating this, thinking about how many of us had been inactive for months, maybe years. I observed how the dashboard went from bubbling, never ending photosets of pens-cups of coffe-fancy notes, to the same repetitive masterposts reblogged ad nauseam.

The famous bloggers from then are still famous now, probably even more. They keep the community updated with new resources centered around planning and organisation, which I confess I’ve used many times. So this is not a drag. They are, no doubt, the content creators. But does it mean that the community is growing or getting more creative? Being helpful? From my point of view, no way.

The studyblr phenomenom was really a thing back in 2014.  Lots of individuals, esentially women, shared their progress as they prepared for their A levels, GCSEs and applications. Inspiring, hard-working individuals who were motivated to discuss their experiences in school and uni, willingly to show how studying can be fun and, yes, aesthetic. Because all about social media is about aesthetic and followers, especially Tumblr and Instagram. The difference from this to other fandoms was the support from this community. It was seemingly non-toxic.

I myself reblogged many times aesthetically pleasing photos, as many of us. Beautiful ultrabooks with Momentum backgrounds, warm lightning through the window. A mess of strategically placed notes, handwritten and colourful, messing on the table. Next the succulent there was a delicious latte and a set of Zebra pastel markers ready to be used. At first I was in awe, trying to emulate these inspiring lifestyles that screamed academic success. Hermione Granger and Rory Gilmore were the epitome of the perfect student and the role model of every stuyblr to be. Nonetheless, already then people was suspicious that the perfect and inspiring studyblr world was not that perfect.

A few posts started to circulate concerning the increasing obsession towards artistic notes and studyspo. For those who aren’t familiar with the term, studyspo refers to the meaningless beautiful photos I detailed before. Both were inspiring and pretty, but didn’t add any valuable information and overloaded dahsboards. There existed the notion that to be a succesful user you should hoard a variety of expensive stationery items, along with neat handwritten notes. Bullet journals where more like an artist notebook, far from the minimalist and simple view that its creator wanted. Definitely, a materialistic feeling surrounded the studyblr posts and it was troubling part of the community.

The problem got bigger, indeed. Masterposts borned as an effective way to collect different instructional posts regarding the same topic, such as languages, mathematics, basic health advice and so on. However, they started to spread like studyspo and clogged again the dahsboards with the same posts, on and on. Masterposts had a good reputation in the community and were easy to create, so users used it to grow their audience. The result? What we have now.

The community was cracking. The tags once useful were flooded with porn and unrelated topics, as the popularity of the studyblr phenomenom drew the attention of press and mass media. The appblr and high school students had grown up and lacked time to renovate the content flow, as they moved to university. Although the community was raising their voice against perfectionism, the portrait of the high-achieving, sleep deprived student was still the trend. Tumblr decreased in popularity and Instagram seemed a better option to gain a solid fanbase, because, you know, who doesn’t have an insta?

We can say, as 2017 is ending, that studyblr is no longer what it used to be. It has plateaud and it’s trapped into old clichés. Is that what we should expect from the possibly most educated community of Tumblr? The stagnation? The lack of creativity? The classism?

Being back here, after all this time, it’s like staring down the ruins of an ancient civilization. A nostalgic mist floats around. But, be that as it may, some of my favourites are still active. Is that a light of hope? If it is, it may be flickering, almost extingishing. It is our turn to change what we don’t like, what it is outdated.

1. A good (but inexpensive) calculator. It doesn’t need sophisticated features or graphics. When solving a problem you’re told to write the whole process step by step, like in Mathematics or Introduction to Physics, so interesting functions like integrals or matrix operations are just for checking (if you’re allowed to use them, in all my Math related subjects I wasn’t). Don’t misinterpret this: these two are quite useful in later subjects, such as Hydraulic Engines, Heat Transfer or Kinetics. But you can have one with those characteristics for less than 20€, which is the case of my Casio fx991es PLUS.2. Notebooks: light, minimalistic. One for each subject. Nothing fancy. After all, your handwriting is going to be a mess after thousands of hours of practice; the notes you started a few weeks ago will remain unfinished. It is homework, not your Midori traveler’s. It is vital, however, that they’re comfortable for you. Spiral binding ones are really worth the investment, as those with thicker paper, specially if you like rollerball and gel pens.3. Pens and mechanical pencils. I’m not a huge fan of them, but mechanical pencils are clean, cheap and refillable. They come with softer and harder leads. Depending on the purpose, some would be a better choice for you. They’re specially relevant for technical drawings, because of their consistent line. In regards to pens, I love the comfort of rollerballs, but they’re not for you if you’re left handed. However, this is a trial and error process. Try different brands and types until you find The One, just remember that it shouldn’t -necessarily- be pricey. 4. A good backpack: FUNCTIONAL AND COMFORTABLE. There a THOUSANDS of beautiful backpacks so PLEASE look for COMFORT. You’re carrying a third of your weight in books, so preferable to be high durable (nylon for example) and with a large main compartment. Molded back panels and (extra?) padded shoulder straps are important too, if not more.

I wanted to ask if its true that you cant learn to love to code.

Of course no! You can learn to love to code. But loving it doesn’t mean it’s going to be easy. But it’s not impossible. You have to practice, read lots of examples, ask for help and learn the syntaxis of your language.

The point of programming is that you need both practice and theory. Theory is really important. How can you code if you don’t know how classes, loops, conditions, variables, constants, and so on, work? How can you code if you don’t understand the problem? Practice is the way for excellence, so your brain gets used to the syntax, the structure, detection of error and you can focus on the algorithms (the set of rules that precisely defines a sequence of operations).

So yes, you can learn to love to code. You just have to work for it.

I prefer learning something new at first in person.

That’s cool! Everyone has its own way for learning. Presencial classes are super cool because your pace is set by a teacher, you can personally ask them questions or share your views with the rest of the class. And you can enjoy self-learning too! Just try till you found the one that fits you.

How do i know if i truly love it?

Oh darling, this is like life. Sometimes you are up, and then down. You’ll often hate it because there’s a window with bugs and error messages and you don’t understand why. You’ll stand for hours in front of the screen (or the wall) thinking how to solve a problem. You’ll be sleeping and, in the middle of the night, open your eyes and laptop and start typing the solution. It’s hard, but it’s worth it. It’s an amazing feeling that you can create something that was in your mind. For example, you can create and app, a simple program for calculus, a web, even artificial intelligence! There are plenty of possibilities. Everbody doesn’t like the same, but for knowing what you really like, you must try.

I’ve talked to some of mine, because it came up. My advanced programming teacher, who seemed to be on top of everything and super smart, told me that he had failed Operating Systems 5 times, and passed the sixth with the minimum passing grade required.

I’ve had several teachers tell me classes that were nightmarish, teachers that were horrible, which was funny, because some of those teachers of my teachers were also mine. I’ve had one of my teachers (my supervisor) actually tell me that when he was in his 4th year, he was disenchanted with his degree, and wondering if he had even picked the correct degree, because he wasn’t sure he liked it anymore.

And as for grades… most of my teachers had good grades, but the highest average grade for any of my teachers was an 8.2/8.3 over 10. Very impressive, but still, not perfect.

Teachers are people too, and while in computer science it looks like many were already acquianted with computers and programming, it doesn’t make them geniuses.

Also, most of my teachers had to take an extra year to finish their studies. Needing more time is nothing to be ashamed of, and I think that having struggled with classes, and having failed exams, gives you a better background to empathise with your future students, because chances are, they’re struggling the way you struggled. And if you present yourself honestly and say “Hey, I was like you once, I’ve failed, but I kept working hard and here I am”, then you might have a student who, just like you, thought teachers were supossed to be amazing with excellent grades, and now seeing they can be just like them, they might feel inspired to continue to become a teacher.

You can become the inspiration you would need now to someone in the future, and I think that would be wonderful.