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Every school year at the small Maine college I attended began with a lobster bake. More than a thousand bright red crustaceans, served with butter, or, for the vegetarians and the squeamish, chicken, steak, or portobello mushrooms. I ate the lobster, but suspected the squeamish had it right. It’s hard to look into the eyes of your food and not wonder what it might be like to end in a pot of boiling water.

As I ate, it turns out, biologists in the United Kingdom were answering that question. Two decades of experiments have shown lobsters, hermit crabs, and their cousins experiencing something that looks a lot like pain. Laws have been written based on those findings. Now, if you want to eat a lobster in Switzerland, you can’t boil it alive. The crustacean can only be legally cooked if it’s stunned with electricity—or knifed in the head.

But what does pain even mean to a lobster? As science journalist Ed Yong writes in his newest book, that’s a much harder question. Animals sense physical reality differently than humans, through smells, through electric fields, through currents of water, and those senses shape the very world they inhabit in ways that are fundamentally unknowable. To imagine the world of an insect crawling on a leaf “is like setting foot upon an alien planet,” he writes.

An Immense World, the Pulitzer Prize winner’s second book, is a travelogue across those planets, and a tribute to the power of human empathy. Since reading it, I found myself returning to the portraits of animal pain. Early in our conversation, I suggested to Yong that of all the animal senses, pain was the one that most people had spent time pondering. He disagreed. Often, he says, the question is boiled down to: “Do they feel it or not? In some ways, that is a very boring question to ask. The more sensible question is: what kinds of pain do they feel?”

In that way, An Immense World is not just about the minds of animals—but also the radical empathy of experts who are trying to see through their eyes.

“Scientists are people. Everyone I talked to has absolutely thought about ‘what is the world like to the creature that I study,’” Yong says. “Whenever I ask, ‘what is it like to be an electric fish, or a bat,’ they have answers, and they have interesting answers. That kind of informs the book—their speculation and feats of imagination are both vital and very much part of the story.”

“That kind of subjective, imaginative stuff is not in [scientific] papers, because it runs counter to how a lot of scientists are trained to think about their work. It’s a bit woolier and emotional and speculative. And important! But it doesn’t appear in the scientific literature very much.”

[Related: Temperature tells honey bees what time it is]

Yong delights in the ingenuity of the experiments that researchers have concocted to step into another sensory universe. Star-nosed moles filmed running their exquisitely sensitive face-tentacles over pieces of rubber; audio engineers remixing birdsong for finches and canaries; elephants freezing in response to rumbles played through buried speakers.

But much like neuroscientists have come to understand the human brain by studying what happens when a stroke kills neurons, some of the earliest insights into the sheer variety of animal senses involved mutilating them. In what Yong describes as “a series of cruel experiments,” an 18th-century Italian priest blinded bats, then tested whether they could fly. If he further deafened or gagged them, he found, they would “blunder into objects.”

Those grim experiments laid the groundwork for the studies in the mid-1900s that discovered echolocation, which opened the door for research into other senses that humans can only imagine: worlds shaped by electric fields, magnetism, or the vibrations of a leaf.

“It’s difficult when at least part of the body of knowledge that you are referring to comes through work that is hard to contemplate,” Yong says. “There are some experiments that honestly I wish had never been done. But l benefit from the knowledge gained through that. And I think probably one of the most important questions for sensory biologists right now is to sort of weigh that out. How much is it worth it?”

Courtesy of Penguin Random House

The human experience of pain comes down to two elements. The physical part is driven by nociceptors, which are nerves located all over the body that light up when cut, or crushed, or heated, or exposed to chemicals. Then there’s the conscious experience of that “nociception.” As Yong puts it, nociception is “an ancient sense” that shows up in shockingly similar ways in everything from sea slugs to people. But just because an animal registers pain signals in its brain doesn’t necessarily mean it suffers.

“A leech will writhe when pinched, but are those movements analogous to human suffering, or to an arm unconsciously pulling away from a hot pan?” Yong writes in the book. Sometimes, the answer seems to be yes. In one study from 2003, trout injected with bee venom rocked from side to side, rubbed their lips on gravel, and ignored new objects for hours, suggesting that they experienced something beyond a simple reflex to a chemical.

But because pain carries such moral weight for humans, it can be hard to imagine what it would mean to bear it differently. So Yong turns to an analogy in color vision, which is both a physical and conscious experience and works much like pain. As Yong points out, we can see the color spectrum because our neural hardware is set up to do quick arithmetic with wavelengths of light. (Not to mention how our language shapes our ability to notice fine variations in color.) A mantis shrimp, meanwhile, has four times as many types of wavelength-sensing receptors—but appears to experience the world in only 12 colors, “like a child’s coloring book,” Yong writes.

Even when animals experience pain, it might not present in familiar ways. Squid appear to experience the shock of an injury across their entire body and become hyper-sensitive to touch. Naked mole rats, on the other hand, don’t seem to register certain painful stimuli. In experiments, they didn’t react to carbon dioxide levels that would cause human eyes to sting, or when researchers injected them with acid, or when their skin made contact with capsaicin. They did, however, flinch when pinched or burned.

And so the same researchers who try to place themselves in the minds of animals find themselves inflicting pain. “A lot of the people I talked to who study how animals sense painful stimuli want to do that work to help those creatures, to inform their welfare, and how we might want to make moral ethical decisions about them,” Yong says. “But to do that, you also need to inflict pain on creatures.”

“How do you weigh up the need to get a statistically robust number of experimental subjects versus the imperative to inflict as little pain upon as few creatures as possible?” he asks.

[Related: How science came to rely on the humble lab rat]

The book’s final chapters is a close look at how the human world is encroaching on animals’ sensory lives. The focus isn’t pain, so much as the way that light from LEDs and the constant rumble of highways reshapes the worlds of species who see and hear and feel differently from us. “When we ask if animals can feel pain, we’re asking less about the animals themselves, and more about what we can do to them,” Yong writes in an earlier chapter. In other words, by focusing on pain to the exclusion of other senses, we’re left with a deeply anthropocentric view of what it means to protect nature.

As Yong writes, animals feel pain in a diversity of ways to survive the perils particular to their species. Humans can prevent some of that pain, at least, the kinds they’re responsible for—but it’s not enough. If we’re going to help species survive the Anthropocene, we need to understand the worlds they live in.

Buy An Immense World by Ed Yong here.

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Do Wild Animals Hate Being Cold In Winter?

While the weather outside may indeed get frightful this winter, a parka, knit hat, wool socks, insulated boots and maybe a roaring fire make things bearable for people who live in cold climates. But what about all the wildlife out there? Won’t they be freezing?

Anyone who’s walked their dog when temperatures are frigid knows that canines will shiver and favor a cold paw—which partly explains the boom in the pet clothing industry. But chipmunks and cardinals don’t get fashionable coats or booties.

In fact, wildlife can succumb to frostbite and hypothermia, just like people and pets. In the northern United States, the unfurred tails of opossums are a common casualty of cold exposure. Every so often an unusual cold snap in Florida results in iguanas falling from trees and manatees dying from cold stress.

Avoiding the cold is important for preserving life or limb (or, in the opossum’s case, tail) and the opportunity to reproduce. These biological imperatives mean that wildlife must be able to feel cold, in order to try to avoid the damaging effects of its extremes. Animal species have their own equivalent to what human beings experience as that unpleasant biting mixed with pins-and-needles sensation that urges us to warm up soon or suffer the consequences. In fact, the nervous system mechanisms for sensing a range of temperatures are pretty much the same among all vertebrates.

One winter challenge for warm-blooded animals, or endotherms, as they’re scientifically known, is to maintain their internal body temperature in cold conditions. Interestingly though, temperature-sensing thresholds can vary depending on physiology. For instance, a cold-blooded—that is, ectothermic—frog will sense cold starting at a lower temperature compared to a mouse. Recent research shows that hibernating mammals, like the thirteen-lined ground squirrel, don’t sense the cold until lower temperatures than endotherms that don’t hibernate.

So animals know when it’s cold, just at varying temperatures. When the mercury plummets, are wildlife suffering or just going with the icy flow?

Some animals find a protected spot to wait out the worst of it, like this chipmunk. Michael Himbeault

Slow down and check out

Torpor has energy conservation benefits for smaller-bodied wildlife in particular—think bats, songbirds and rodents. They naturally lose heat faster because the surface area of their body is large compared to their overall size. To maintain their body temperature within normal range, they must expend more energy compared to a larger-bodied animal. This is especially true for birds who maintain higher average body temperatures compared to mammals.

Unfortunately, torpor is not a perfect solution to surviving frigid conditions since it comes with trade-offs, such as a higher risk of becoming another animal’s lunch.

Adaptations that help

Unsurprisingly, animals have evolved other adaptations for weathering the winter months.

The large ears of a fennec fox would be a liability in a cold climate like where the arctic fox lives. Jonatan Pie/Unsplash and Kkonstan/Wikimedia

Wildlife species at northern latitudes tend be larger-bodied with smaller appendages than their close relatives closer to the tropics. Many animals have evolved behaviors to help them beat the cold: herding, denning, burrowing, and roosting in cavities are all good defenses. And some animals experience physiological changes as winter approaches, building fat reserves, growing thicker fur, and trapping an insulating layer of air against the skin beneath the fur or feathers.

Nature has devised other neat tricks to help various animals deal with conditions that people, for instance, would be unable to endure.

An animal standing in cold water or on ice benefits from countercurrent heat exchange (1). Warm arterial blood (2) flowing away from the heart warms up the cooler venous blood (3) heading toward the heart. Ekann, CC BY-SA

Have you ever wondered how geese can appear to stand comfortably on ice or squirrels in snow in their bare feet? The secret is the close proximity of the arteries and veins in their extremities that creates a gradient of warming and cooling. As blood from the heart travels to the toes, the warmth from the artery transfers to the vein carrying cold blood from the toes back to the heart. This countercurrent heat exchange allows the core of the body to remain warm while limiting heat loss when the extremities are cold, but not so cold that tissue damage occurs. This efficient system is used by many terrestrial and aquatic birds and mammals, and even explains how oxygen exchange occurs in the gills of fish.

Speaking of fish, how do they not freeze from the inside out in icy waters? Luckily, ice floats because water is most dense as a liquid, allowing fish to swim freely in not-quite-freezing temperatures below the solidified surface. Additionally, fish may lack the cold-sensing receptor shared by other vertebrates. They do, however, have unique enzymes that allow physiologic functions to continue at colder temperatures. In polar regions, fish even have special “antifreeze proteins” that bind to ice crystals in their blood to prevent widespread crystallization.

Another secret weapon in mammals and birds during long periods of cold exposure is brown adipose tissue or “brown fat,” which is rich in mitochondria. Even in people, these cellular structures can release energy as heat, generating warmth without the muscle contractions and energy inefficiency involved in shivering, another way the body tries to heat up. This non-shivering heat production probably explains why people in Anchorage can contentedly wear shorts and t-shirts on a 40 degrees Fahrenheit spring day.

Of course, migration can be an option—though it’s expensive in terms of energetic costs for wildlife, and financially for people who want to head closer to the equator.

As a species, human beings have the ability to acclimate to an extent—some of us more than others—but we’re not particularly cold-adapted. Maybe that’s why it’s hard to look out the window on a frigid day and not feel bad for a squirrel hunkered down as the winter wind whips through its fur. We may never know if animals dread winter—it’s difficult to gauge their subjective experience. But wildlife do have a variety of strategies that improve their ability to withstand the cold, making sure they live to see another spring.

Bridget B. Baker is a Clinical Veterinarian and Deputy Director of the Warrior Aquatic, Translational, and Environmental Research (WATER) Lab at Wayne State University. This article was originally featured on The Conversation.

Inside The Lab That’s Growing Mushroom Computers

Upon first glance, the Unconventional Computing Laboratory looks like a regular workspace, with computers and scientific instruments lining its clean, smooth countertops. But if you look closely, the anomalies start appearing. A series of videos shared with PopSci show the weird quirks of this research: On top of the cluttered desks, there are large plastic containers with electrodes sticking out of a foam-like substance, and a massive motherboard with tiny oyster mushrooms growing on top of it.

No, this lab isn’t trying to recreate scenes from “The Last of Us.” The researchers there have been working on stuff like this for awhile: It was founded in 2001 with the belief that the computers of the coming century will be made of chemical or living systems, or wetware, that are going to work in harmony with hardware and software.

Why? Integrating these complex dynamics and system architectures into computing infrastructure could in theory allow information to be processed and analyzed in new ways. And it’s definitely an idea that has gained ground recently, as seen through experimental biology-based algorithms and prototypes of microbe sensors and kombucha circuit boards.

In other words, they’re trying to see if mushrooms can carry out computing and sensing functions.

A mushroom motherboard. Andrew Adamatzky

With fungal computers, mycelium—the branching, web-like root structure of the fungus—acts as conductors as well as the electronic components of a computer. (Remember, mushrooms are only the fruiting body of the fungus.) They can receive and send electric signals, as well as retain memory.

“I mix mycelium cultures with hemp or with wood shavings, and then place it in closed plastic boxes and allow the mycelium to colonize the substrate, so everything then looks white,” says Andrew Adamatzky, director of the Unconventional Computing Laboratory at the University of the West of England in Bristol, UK. “Then we insert electrodes and record the electrical activity of the mycelium. So, through the stimulation, it becomes electrical activity, and then we get the response.” He notes that this is the UK’s only wet lab—one where chemical, liquid, or biological matter is present—in any department of computer science.

Preparing to record dynamics of electrical resistance of hemp shaving colonized by oyster fungi. Andrew Adamatzky

The classical computers today see problems as binaries: the ones and zeros that represent the traditional approach these devices use. However, most dynamics in the real world cannot always be captured through that system. This is the reason why researchers are working on technologies like quantum computers (which could better simulate molecules) and living brain cell-based chips (which could better mimic neural networks), because they can represent and process information in different ways, utilizing a series of complex, multi-dimensional functions, and provide more precise calculations for certain problems.

Already, scientists know that mushrooms stay connected with the environment and the organisms around them using a kind of “internet” communication. You may have heard this referred to as the wood wide web. By deciphering the language fungi use to send signals through this biological network, scientists might be able to not only get insights about the state of underground ecosystems, and also tap into them to improve our own information systems.

An illustration of the fruit bodies of Cordyceps fungi. Irina Petrova Adamatzky

Mushroom computers could offer some benefits over conventional computers. Although they can’t ever match the speeds of today’s modern machines, they could be more fault tolerant (they can self-regenerate), reconfigurable (they naturally grow and evolve), and consume very little energy.

Slime molds are “intelligent,” which means that they can figure out their way around problems, like finding the shortest path through a maze without programmers giving them exact instructions or parameters about what to do. Yet, they can be controlled as well through different types of stimuli, and be used to simulate logic gates, which are the basic building blocks for circuits and electronics.

[Related: What Pong-playing brain cells can teach us about better medicine and AI]

Recording electrical potential spikes of hemp shaving colonized by oyster fungi. Andrew Adamatzky

When he had wrapped up his slime mold projects, Adamatzky wondered if anything interesting would happen if they started working with mushrooms, an organism that’s both similar to, and wildly different from, Physarum. “We found actually that mushrooms produce action potential-like spikes. The same spikes as neurons produce,” he says. “We’re the first lab to report about spiking activity of fungi measured by microelectrodes, and the first to develop fungal computing and fungal electronics.”

An example of how spiking activity can be used to make gates. Andrew Adamatzky

In the brain, neurons use spiking activities and patterns to communicate signals, and this property has been mimicked to make artificial neural networks. Mycelium does something similar. That means researchers can use the presence or absence of a spike as their zero or one, and code the different timing and spacing of the spikes that are detected to correlate to the various gates seen in computer programming language (or, and, etc). Further, if you stimulate mycelium at two separate points, then conductivity between them increases, and they communicate faster, and more reliably, allowing memory to be established. This is like how brain cells form habits.

Mycelium with different geometries can compute different logical functions, and they can map these circuits based on the electrical responses they receive from it. “If you send electrons, they will spike,” says Adamatzky. “It’s possible to implement neuromorphic circuits… We can say I’m planning to make a brain from mushrooms.”

Hemp shavings in the shaping of a brain, injected with chemicals. Andrew Adamatzky

So far, they’ve worked with oyster fungi (Pleurotus djamor), ghost fungi (Omphalotus nidiformis), bracket fungi (Ganoderma resinaceum), Enoki fungi (Flammulina velutipes), split gill fungi (Schizophyllum commune) and caterpillar fungi (Cordyceps militari).

The Worst Jobs In Science

Crisp sea air isn’t a match for the stink of stomach juices and half-digested squid, but marine biologist Michelle Staudinger doesn’t mind. When boats come in from fishing tournaments up and down the East Coast, she’s waiting on the docks, asking anglers to let her clean their catch for free in exchange for the stomach contents. On any given weekend, she says, “I’m usually elbow-deep into one or another of the East Coast’s pelagic fishes. I get a lot of compliments on how fast I can gut a tuna.” At University of Massachusetts Amherst, Staudinger surveys coastal fish and marine mammals to evaluate the predator-prey relationship over time. Besides relying on fishermen to deliver species that live far offshore, she waits for some animals, such as dwarf sperm whales, to wash up dead. She once necropsied a whale that had been shipped on a flatbed truck from Florida to Massachusetts. “Oh, yeah, it’s disgusting,” she says. “But we now have baseline data for looking at how ecology is changing as the impacts of climate change grow.”. Illustration by Peter and Maria Hoey

Fish Gutter

At University of Massachusetts Amherst, Staudinger surveys coastal fish and marine mammals to evaluate the predator-prey relationship over time. Besides relying on fishermen to deliver species that live far offshore, she waits for some animals, such as dwarf sperm whales, to wash up dead. She once necropsied a whale that had been shipped on a flatbed truck from Florida to Massachusetts. “Oh, yeah, it’s disgusting,” she says. “But we now have baseline data for looking at how ecology is changing as the impacts of climate change grow.”

Sobriety Tester

Can people accurately estimate their own blood alcohol level when inebriated? That’s the question Loyola Marymount University researchers set out to answer. To get data, they sent then–psychology student Greg Wisenberg into frat parties and late-night pizza places near various southern California universities. Once there, he had to quiz revelers on their blood alcohol level and actually measure it with a breathalyzer. Not surprisingly, reactions around the kegs were mixed. “People were suspicious,” Wisenberg says. “Like, ‘What are you doing here? Why aren’t you drunk too?’” The results showed that drinkers fall into two groups: Those who were slightly buzzed consistently overestimated their intoxication, while those who were loaded grossly underestimated it. If the researchers can elucidate how partiers perceive inebriation, others could use the information to teach safety and intervention. A noble goal, but cold comfort for Wisenberg, who suffered insults and even half-serious physical threats. To one particularly boisterous group, he had to say, “I’m working now. I’m not going to fight you, but you can take my survey!”

Sobriety Tester

The results showed that drinkers fall into two groups: Those who were slightly buzzed consistently overestimated their intoxication, while those who were loaded grossly underestimated it. If the researchers can elucidate how partiers perceive inebriation, others could use the information to teach safety and intervention. A noble goal, but cold comfort for Wisenberg, who suffered insults and even half-serious physical threats. To one particularly boisterous group, he had to say, “I’m working now. I’m not going to fight you, but you can take my survey!”

Fatberg Flusher

Dead Sea Sampler

As John Selker began installing sensors in the Dead Sea, a tourist perished after taking a gulp of water. “It turns out the salt is so intensely concentrated that it’s hazardous,” says Selker, a hydrological engineer at Oregon State University. The infamous lake is covered with a thick, highly saline layer of 97°F water, and it’s getting saltier as it evaporates. Selker’s task was to figure out whether the surface exchanges water with the cooler layer far below. He eventually found that it does—information that could be used to better manage the lake. But first he learned a different lesson: Anything left in the Dead Sea eventually grows heavy with salt and sinks. After Selker began deploying more than a million dollars worth of fiber-optic cable, his computer-powered buoy foundered to the bottom. Then his team had to face the treacherous task of recovering it. “I was scared,” he says. “This is a fluid that burns like acid if you get it in your eyes. We had no life preservers, we were standing on little planks over the surface, and a helicopter was flying in to the beach to take away the dead guy.”

Dead Sea Sampler

The infamous lake is covered with a thick, highly saline layer of 97°F water, and it’s getting saltier as it evaporates. Selker’s task was to figure out whether the surface exchanges water with the cooler layer far below. He eventually found that it does—information that could be used to better manage the lake. But first he learned a different lesson: Anything left in the Dead Sea eventually grows heavy with salt and sinks.

After Selker began deploying more than a million dollars worth of fiber-optic cable, his computer-powered buoy foundered to the bottom. Then his team had to face the treacherous task of recovering it. “I was scared,” he says. “This is a fluid that burns like acid if you get it in your eyes. We had no life preservers, we were standing on little planks over the surface, and a helicopter was flying in to the beach to take away the dead guy.”

Queasiness Generator

Former Greek Navy officer Panagiotis Matsangas got his Ph.D. in nausea. Now a scientist for the Naval Postgraduate School in Monterey, California, his job is to convince test subjects to sit in a special motorized chair that heaves side to side for an hour, while they try to solve cognitive tests through virtual-reality goggles. “People often don’t want to participate when they learn too much about my studies beforehand,” Matsangas says. Inducing even mild motion sickness, he’s found, causes a person’s multitasking ability and cognitive performance to fall, which could impact everything from Navy staffing policies to ship design. But when Matsangas tries to butter up subjects by explaining the importance of the tests, it doesn’t always work, he says. “Some of them feel really unwell.”

Queasiness Generator

Inducing even mild motion sickness, he’s found, causes a person’s multitasking ability and cognitive performance to fall, which could impact everything from Navy staffing policies to ship design. But when Matsangas tries to butter up subjects by explaining the importance of the tests, it doesn’t always work, he says. “Some of them feel really unwell.”

Troll Hunter

If you’ve ever had a little too much fun editing Wikipedia, you may have been part of information scientist Madelyn Rose Sanfilippo’s research. Whoever changed Grover Cleveland’s page to say he had “mad beat boxing skills” is an example of the light-hearted form of trolling Sanfilippo researches. But her work at Indiana University has also sent Sanfilippo into the ugliest corners of the Internet. People will join memorial pages for the deceased, for example, just to mock the public mourning. “You can’t really sit there and read for extensive periods of time because it becomes overwhelming,” Sanfilippo says. There’s also a professional hazard to maintaining close proximity with trolls, who see an information scientist as bait—she often receives insulting and offensive emails. “It’s mostly poking fun at my credibility as a researcher,” says Sanfilippo. Despite the harassment, she thinks the work is only getting more important. “With trolling’s increasing prevalence,” she says, “it’s important to understand how to mitigate the impacts.”

Troll Hunter

There’s also a professional hazard to maintaining close proximity with trolls, who see an information scientist as bait—she often receives insulting and offensive emails. “It’s mostly poking fun at my credibility as a researcher,” says Sanfilippo. Despite the harassment, she thinks the work is only getting more important. “With trolling’s increasing prevalence,” she says, “it’s important to understand how to mitigate the impacts.”

Wriggling With Parasites

Christopher Schmitt, a primatologist at the University of California, Berkeley, is currently researching the role of genomics in primates’ weight gain.

I was studying wild lowland woolly monkeys in Amazonian Ecuador, and one day I was running in a downpour when I accidentally fell down on a trail and stuck my hand in some jaguar scat. Then I made the mistake of scratching some old tick bites, introducing a type of hookworm. These worms don’t have the enzymes to digest the dermis and enter the bloodstream, so they crawl around under the skin leaving raised trails. They itched so bad, it felt like my bones were on fire. A few weeks later, in an effort to help, a Quechua guide had me grind up leaves, spit in them, and rub the paste on my skin. The next morning, I woke up with black chemical burns all over. Finally, I went five hours by boat and truck to the closest medical facility to get a simple anti-parasitic medicine. But it was worth it. Because of that trip, I was able to publish an important paper about monkeys’ social groupings.”

Robot Teacher

Scientists have long sought to create the ultimate social robot, a personable machine like C-3PO. But for artificial intelligence to react to our emotions, someone’s first got to train bots to recognize them—which is Michel Valstar’s job. A computer scientist at the University of Nottingham in England, Valstar spends his days creating a database of faces showing anger, disgust, fear, and happiness. “Computers are so literal,” he says. “They have to be fed every possible situation and taught the context.” First, Valstar recruits human subjects to make expressions for a camera. To capture real anguish, for example, he asked a group of chronic back pain sufferers to repeatedly perform difficult stretches. Then he annotates the footage, a task that takes several hours per minute of video. “It’s the kind of work that turns you into a zombie,” he says, requiring close attention to detail but promising endless monotony. With the help of buckets of coffee, Valstar has now built a record so comprehensive it will be used in the new field of behaviomedics—training robots to spot changes in patients caused by medical conditions like pain or depression.

Robot Teacher

First, Valstar recruits human subjects to make expressions for a camera. To capture real anguish, for example, he asked a group of chronic back pain sufferers to repeatedly perform difficult stretches. Then he annotates the footage, a task that takes several hours per minute of video. “It’s the kind of work that turns you into a zombie,” he says, requiring close attention to detail but promising endless monotony. With the help of buckets of coffee, Valstar has now built a record so comprehensive it will be used in the new field of behaviomedics—training robots to spot changes in patients caused by medical conditions like pain or depression.

Rat Exerciser

After graduate school, Marc Kubasak set an ambitious goal: Find a way for paralyzed people to walk. But to help people, first he had to help rats. Kubasak took paralyzed animals, transplanted glial cells from the olfactory bulb in the brain to the injured area, and then retrained them to walk. That involved sewing little vests to suspend the rodents from a robotic arm. Then, he says, “I had to make little circles with my fingers moving the rats’ legs on a treadmill for five to 12 hours a day, five days a week.” He walked 40 rats, clocking 2,500 hours over the course of a year. In the middle of the study, Kubasak developed a rodent allergy: His airways closed up and his hand swelled to the size of a catcher’s mitt. He was rushed to the ER. Eventually, he had to work in a total-body suit, complete with battery-powered respirator. But Kubasak persevered. Most of his rats walked again. And this year, doctors at Wroclaw Medical University in Poland and University College London translated Kubasak’s procedure to a man whose spine was damaged in a knife attack.

Rat Exerciser

In the middle of the study, Kubasak developed a rodent allergy: His airways closed up and his hand swelled to the size of a catcher’s mitt. He was rushed to the ER. Eventually, he had to work in a total-body suit, complete with battery-powered respirator. But Kubasak persevered. Most of his rats walked again. And this year, doctors at Wroclaw Medical University in Poland and University College London translated Kubasak’s procedure to a man whose spine was damaged in a knife attack.

This article was originally published in the February 2023 issue of Popular Science, under the title “The Worst Jobs In Science”.

The Data Science Behind Ipl

This article was published as a part of the Data Science Blogathon.

Introduction

IPL, I am sure that, things like fun, entertainment, and sports have come to your mind. I am sure that you never relate science and education with IPL, but even you must be surprised to know that science play a big role in things like IPL. How a winning IPL team can be formed by spending the least possible through Data Science. Let’s find out in today’s article.

In this article, I will explain the data science behind IPL and introduce an interesting career option behind it. Let’s start with the basic “What is Data🤔?”.

What is Data?

Data is basically information about anything. For example the no. of fruits on a tree or the flavor of your ice cream or even the no. of stars in the universe or how much % of peoples like the government. All of this is nothing but data. There is an immense amount of data all around us in our lives, but simply having data around us is of no use to us. It is important for us to know what data is useful and what data should analyze and how be recognizing patterns, we can make use of that data. Let’s think, What are we going to do by counting the no. of leaves on the tree? What use would that be😅? It is useless data and is of no use to us. But the % of peoples that favor the government is useful data. It would be useful in politics. It can help the government understand what they should change and how they can transform themselves. This data would come in handy during the elections but it isn’t sufficient to simply record that data if you don’t analyze it, compare it, and improve it. The recording, studying, and observing data and then using it to arrive at the decision, is called Data Science.

Using Data Science, we interpret any data and derive useful information from it, and use it in our decision-making process. It is possible to use Data Science in any aspect of life.

In cricket and IPL, Data Science is used in a somewhat unique and interesting manner. In 2008, IPL came, which completely revolutionized the cricket world, because before IPL never had such an immense amount of money invested into cricket. Considering the auction 2023, totaled ~400 crore INR spent on the players. So much money is being spent in IPL. Data Collection and Data Analysis in IPL has breached the next level, because as IPL spending lot of money on players, it has become necessary for IPL teams to find out that, “Should they spend on a particular player or not?” or “How valuable is the player going to be for the team?”

How should they judge in detail, “Which player should they buy and which one they shouldn’t it?”, “How much money should be spent on which player?” or “What are the values of the different players?”.

You will not believe that, but IPL teams have started hiring proper companies who are experts in such Data Analysis. Performance Analytics Companies that analyze how good players are, and develop strategies for that players. These Data Analysis companies analyze data about players in detail to understand who is good at what aspect. In IPL a metric that they use, is MVPI or The Most Valuable Player Index, which is a weighted composite score of the different attributes of a player.

Let’s see some of the Batsman Metrics :

I. Hard-hitting Ability: How many sixes and fours a batsman scores, the following equation is used.

Hard-hitting Ability = (Fours + Sixes) / Ball played by batsman

How many fours and sizes has batsman hit in his IPL career divided by the no. of ball he played. This calculates the hard-hitting ability of the players.

II. Finishing Ability: Not out innings divided by the total innings played.

Finishing Ability = Not out innings / Total innings played.

III. Consistency of Player: Total Run / No. of times out.

IV. Running b/w the wickets: (Total run – (Fours + Sixes)) / (total ball played – boundry balls).

If this fourth metric is better in batsman than the hard-hitting metrics, then you can easily guess that he is not good at hitting boundaries but is good at getting singles, twos, and threes on other balls.

Similarly, some Bowling Metrics are :

I. Economy: Run scored / (No. of ball bowled by bowler / 6).

II. Wicket taking ability: No. of balls bowled / Wicket taken.

III. Consistency: Run conceded / Wicket taken.

IV. Crucial Wicket Taking Ability: No. of times four or five-wicket taken / No. of inning played.

This whole data help us to understand the weak and the strong area of different players, whether a player is good at hitting boundaries or at the running between the wickets, whether a bowler performs better against left-handed batsman or right-handed batsman, whether a batsman perform better against spinner or fast bowlers. Analysis can also work out in “What Stadium and in which weather does a player performs better?”

In one interview, Virender Sehwag encapsulated the importance of Data Science very nicely. He said that “Every game you play, they will record your good performance, your bad performance, you played against which bowler, you scored against which team and which bowler, and the whole data will easily show you that you are good against Pakistan but you’re not good performed against Bangladesh, you’re good against South Africa but you’re not good against England. In 2003 when our computer analytics guy come in and he showed me videos and different kinds of data analysis, I got amazed!!!😲”.

During the auctioning of players, the IPL teams that do not have a lot of money would definitely want to know whether the player that they are buying is worth the money, they spent for their team or not. Because more often it happens that the most expensive player in IPL auction is not the top-performing player of IPL always. The best example of this would be the first season of IPL that is 2008 where Rajasthan Royals had lifted the trophy and Rajasthan Royals was one of the cheapest teams in that season. It means that the money that they had spent on the players was way lesser than what others teams had spent. It was one the cheapest team, but they still won the IPL😎. Check out the IPL 2008 auction list below.

Auctioning of players and forming teams is not just one area where Data Science is used, after this Machine Learning techniques are also used to predict the match results. Different models are created with the help of programming and computers in which, inputs like the position of a player, location of the match, the weather of the day, etc are all added as variables and on the basis of previous matches, these models predict the future results of the matches. If you provide the data input of the previous matches, such as the venues of the matches as well as teams that played, players that were present as well as the type of players that were present, then in the future it could be predicted the result of the matches presently being played.

Obviously, it will not be 100% accurate but it could be quite useful. Programming languages like ‘Python’ and libraries like ‘Pandas’, ‘Matplotlib’, and ‘Seaborn’ are used for data preprocessing and data analysis.

Some Interesting Analysis⚡

I. One of the analyses that analyzed that IPL matches between 2008 – 2023, reveals that Eden Garden and M Chinnaswamy Stadium are the best venues for chasing the score, so if a match is being held in either of these two venues and a team wins the toss, fielding would be a better option. Let’s do the same analysis on IPL matches. You can download the dataset from here. Here we are using IPL Matches 2008-2023 dataset.

Dataset Description: It contains a total of 17 columns. Let’s take a look at them.

Attribute Information :

city

date

player_of_match

venue

neutral_venue

team1

team2

toss_winner

toss_decision

winner

result

result_margin

eliminator

method

umpire1

umpire2

In the code section, I will directly show the main part of the code. To know detailed descriptions you can directly download the Jupyter Notebook.

Let’s load the libraries:

import pandas as pd import numpy as np import matplotlib.pyplot as plt import seaborn as sns %matplotlib inline

Read the dataset:

Let’s remove the useless columns from the dataset:

df.drop(labels = ['id', 'date', 'player_of_match', 'neutral_venue', 'result', 'result_margin', 'eliminator', 'method','umpire1', 'umpire2'], axis = 1,inplace = True)

Let’s analyze the better option after winning the toss:

match_win_target = match_loss_target = match_win_chassing = match_loss_chassing = 0 for i in range(len(df)) : if df.toss_decision.iloc[i] == 'bat' : # target diya if df.toss_winner.iloc[i] == df.winner.iloc[i] : match_win_target += 1 else : match_loss_target += 1 else : # target chase kiya or Fielding li if df.toss_winner.iloc[i] == df.winner.iloc[i] : match_win_chassing += 1 else : match_loss_chassing += 1 print('{} times captain choose batting option and win the match.'.format(match_win_target)) print('{} times captain choose batting option but loose the match.'.format(match_loss_target)) print('{} times captain choose fielding option and win the match.'.format(match_win_chassing)) print('{} times captain choose fielding option but loose the match.'.format(match_loss_chassing))

Let’s create a specific column and describe, how the team wins the match(by giving the target or by chasing the score):

for i in range(len(df)) : if df.toss_decision.iloc[i] == 'bat' : if df.toss_winner.iloc[i] == df.winner.iloc[i] : # captain choose batting option and win the match then it will count as target. df['target'].iloc[i] = 1 else : # captain choose batting option and loose the match then it will count as chasing. df['chase'].iloc[i] = 1 else : if df.toss_winner.iloc[i] == df.winner.iloc[i] : # captain choose fielding option and win the match then it will count in chasing. df['chase'].iloc[i] = 1 else : # captain choose fielding option and loose the match then it will count in target. df['target'].iloc[i] = 1

Let’s extract some more useful information from the data:

targetlist = [] chaselist = [] for i in top15_stadium : print('Analysis on "{} Stadium"'.format(i)) x = np.sum(df[df.venue1 == i].target) y = np.sum(df[df.venue1 == i].chase) print(x, 'times team gave good target and win the match.') print(y, 'times team easily chase the score and win the match.') targetlist.append(x) chaselist.append(y) print()

Let’s visualize the above data for better understanding:

top15_stadium = ['Eden Gardens, Kolkata', 'Feroz Shah Kotla, Delhi', 'Wankhede Stadium, Mumbai', 'Rajiv Gandhi International Stadium, Uppal, Hyderabad', 'M Chinnaswamy Stadium, Bangalore', 'MA Chidambaram Stadium, Chepauk, Chennai', 'Sawai Mansingh Stadium, Jaipur', 'Punjab Cricket Association Stadium, Mohali, Chandigarh', 'Dubai International Cricket Stadium, Dubai', 'Sheikh Zayed Stadium, Abu Dhabi','Maharashtra Cricket Association Stadium, Pune', 'Punjab Cricket Association IS Bindra Stadium, Mohali, Chandigarh', 'Sharjah Cricket Stadium, Sharjah', 'Dr DY Patil Sports Academy, Mumbai', 'Subrata Roy Sahara Stadium, Pune'] data = {'target': [30, 34, 36, 27, 26, 35, 15, 15, 19, 13, 7, 9, 7, 7, 11], 'chase': [47, 39, 37, 37, 37, 22, 32, 20, 14, 16, 14, 12, 11, 10, 6]} df1 = pd.DataFrame(data,columns=['target', 'chase'], index = top15_stadium) df1.plot.barh(figsize = (15,10)) plt.style.use('seaborn-bright') plt.title('Top-15 Stadiums') plt.ylabel('Stadiums') plt.xlabel('No. of Matches Win') plt.xticks(np.arange(0, 54, 3)) plt.show()

The above plot reveals that “How many times team give good target or easily chase the target in the particular stadium.” Let’s look at the horizontal bar of “Eden Garden, Kolkata” stadium, this bar reveals that more than 45 times easily chase the score and win the match and approx 30 times the team gave good target and win the match. From this, we can easily conclude that this stadium is better for chasing the score, so if a match is being held in this venue and a team wins the toss, fielding would be a better option. Similarly, we can easily analyze the whole plot.

Let’s convert the above data in terms of % for better understanding:

target1 = [] chase1 = [] for i in top15_stadium : print(i) x = np.sum(df[df.venue1 == i].target) y = np.sum(df[df.venue1 == i].chase) total = x + y t = ((x / total) * 100) c = ((y / total) * 100) target1.append(round(t, 2)) chase1.append(round(c, 2)) print('{:.2f}% probablity that if you choose to bat, then you will win the match.'.format((x / total) * 100)) print('{:.2f}% probability that if you choose to field, then you will win the match.'.format((y / total) * 100)) print()

Let’s visualize the above data for better understanding:

top15_stadium = ['Eden Gardens, Kolkata', 'Feroz Shah Kotla, Delhi', 'Wankhede Stadium, Mumbai', 'Rajiv Gandhi International Stadium, Uppal, Hyderabad', 'M Chinnaswamy Stadium, Bangalore', 'MA Chidambaram Stadium, Chepauk, Chennai', 'Sawai Mansingh Stadium, Jaipur', 'Punjab Cricket Association Stadium, Mohali, Chandigarh', 'Dubai International Cricket Stadium, Dubai', 'Sheikh Zayed Stadium, Abu Dhabi','Maharashtra Cricket Association Stadium, Pune', 'Punjab Cricket Association IS Bindra Stadium, Mohali, Chandigarh', 'Sharjah Cricket Stadium, Sharjah', 'Dr DY Patil Sports Academy, Mumbai', 'Subrata Roy Sahara Stadium, Pune'] data = {'Bat_first': target1, 'Field_first': chase1} df2 = pd.DataFrame(data,columns=['Bat_first', 'Field_first'], index = top15_stadium) df2 df2.plot.barh(figsize = (15,10)) plt.style.use('seaborn-bright') plt.title('Top-15 Stadiums') plt.ylabel('Stadiums') plt.xlabel('Probability to win') plt.xticks(np.arange(0, 75, 3)) plt.show()

Here the above plot reveals that “What is the probability of winning if you choose to bat first or field first in top-15 stadiums.” Let’s look at the horizontal bar of “Subrata Roy Sahara Stadium, Pune”, this bar reveals that in this stadium if you choose to bat first after winning the coin toss then more than 63% of chances that you will win that match, on the other hand, if you choose to field first then there is only 35% chance that you will win the match.

From this, we can easily conclude that this stadium is better for giving the target to the opposition team, so if a match is being held in this venue and a team wins the toss, batting would be a better option. Similarly, we can easily analyze the whole plot. For more details, you can directly download the jupyter notebook.

II. Another analysis took into account the batting average and strike rate of all the IPL players, and concluded that all the players below the age of 35 had a batting avg. of 24.51 and an avg. strike rate of 126.84 and on the other hand players above 35 years had an avg. strike rate of 112.1 much lesser and batting avg. of 21.34. This show that younger player should be preferred if a team has to improve its performance.

The final conclusion🤩

You might wonder with all the data we are analyzing, how much of this data and its analysis is actually useful? and How much of it is random? Previously we talk about which stadium is better for chasing, and what will happen in what kind of weather. It could be that all of these are random things. It could be coincident that, it was easier to chase and win in that particular stadium. I am sure that this question definitely pops up in your mind. It is a legitimate one because, in a lot of data analysis, randomness is also taken into account. If there is randomness, it could be put into the algorithms so that it also be accounted for while analyzing the data. And this generates even more accurate results.

Mind-Blowing Fact🤯 : In fact, when Kolkata Knight Riders won the trophy in 2014 the Auction Analysis of SAP got a lot of credit for their victory. Kolkata Knight Riders had hired that SAP Data Analysis Company to analyze the data and to explain in detail “What kind of team should be formed, Which player should be sent, where, when, and what should the strategies be?”. On the basis, of this analysis, KKR finally won the trophy.

Let’s Wrap Up!

But in the end, I will only like to say that this is all a game of probabilities, you can definitely increase your chances of winning the match by taking all those things into account but there’s never a 100% guarantee. Because after all, the IPL players are human beings😁, not machines.

Golden Words😁❕

If you really enjoyed it, don’t forget to share it with your friends. If you have any query don’t hesitate to leave a response below. You can also connect me on LinkedIn. And finally, … it doesn’t go without saying😉…

Thanks for reading!

-ronyl

The media shown in this article are not owned by Analytics Vidhya and is used at the Author’s discretion.

Zuckerberg: Facebook Wants To Take The Pain Out Of Building Apps

Facebook is seen as a website for connecting people. Now the company also wants to make it easier for outside developers to build their apps and connect them with users, by providing back-end hosting tools.

On Thursday the social network held its first Parse Developer Day, a conference geared toward third-party app developers. Parse is a cloud-service company that provides a hosted back-end infrastructure to make it easier for developers to build their mobile apps. Facebook announced its acquisition of Parse in April.

Facebook already provides software tools to let outside developers plug into the social network’s data with Facebook Platform. But that service is focused more on the distribution of apps, Facebook CEO Mark Zuckerberg said during a surprise appearance at the conference in San Francisco.

“We want to do more than that,” he said. What Facebook is trying to do with Parse, he said, is to give developers new tools to build and grow their apps.

In other words, Facebook wants developers to focus less on things like managing servers, maintaining connections to other services and building push notifications, and instead focus more on the user’s front-end experience.

Parse addresses those issues, Zuckerberg said, by eliminating the pain for developers and letting them just focus on building a great app.

During a keynote address, Parse CEO Ilya Sukhar likened the company’s goals to applying for a driver’s license, but not wanting to stand in line at the Department of Motor Vehicles to fill out all of the paperwork.

“You just want to get out there and drive,” he said.

The goal has big implications not just for developers but for Facebook. The social network claims to have more than 1 billion active users, and already lets those people log into certain apps through Facebook, giving the app permission to access their list of friends and other profile information.

But by getting into the business of selling application development tools, Facebook wants to support an even tighter bond between apps and the site.

Simplifying apps development

To provide more ways to simplify the app development process, some new products were unveiled by Parse’s Sukhar during Thursday’s event, which drew more than 600 attendees.

The two biggest new products are Background Jobs and Parse Analytics, he said. Background Jobs is designed to let developers schedule tasks into their apps, such as messages or notifications, by sending simple code to Parse’s servers. Prior to that product’s launch, developers could not run arbitrary code on the company’s servers, Sukhar said.

Parse Analytics, meanwhile, will let developers monitor various activities within their apps to see what’s working and what’s not. With the feature, developers can see, for instance, whether their app has more action on Android or iOS, and whether certain demographics are spending more money within the app than others.

Developers could use work-arounds to gather some of this data before, Sukhar said, but now they can get it all in one place, visualized on a dashboard.

Parse also announced Thursday a partnership with the Unity gaming development platform, to make it easier for games developers to build their apps on iOS, Android and the Web using Parse.

Some developers have questions about how Parse’s services might continue to change now that the company is owned by Facebook. Andres Le Roux of chúng tôi wondered in an interview at the conference whether there might be tighter synergies between Facebook Platform and Parse in the months to come.

Another looming question is whether Parse will let developers continue to build their apps for Twitter just as easily as they can for Facebook. During a meeting with the media in May following the acquisition, Parse’s Sukhar said that Twitter with Parse would not be going away.

More than 100,000 apps have been built using Parse, Sukhar reported Thursday. The company’s wide range of clients includes Showtime, eBay, Warner Brothers and Zynga.

Competitors in the cloud hosting space include Amazon’s S3 cloud computing storage service, Google’s Cloud Platform and Firebase.

But Parse sees its biggest competitor as people who build apps themselves, Sukhar said in May.

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