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Globally, the numbers can be even worse, and the stakes often higher. “Say a person comes into the hospital in Sierra Leone with a fever and flulike symptoms,” DeRisi says. “After a few days, or a week, they die. What caused that illness? Most of the time, we never find out. Because if the cause isn’t something that we can culture and test for” — like hepatitis, or strep throat — “it basically just stays a mystery.”

It would be better, DeRisi says, to watch for rare cases of mystery illnesses in people, which often exist well before a pathogen gains traction and is able to spread.

Based on a retrospective analysis of blood samples, scientists now know that H.I.V. emerged nearly a dozen times over a century, starting in the 1920s, before it went global.

Zika was a relatively harmless illness before a single mutation, in 2013, gave the virus the ability to enter and damage brain cells.

The beauty of this approach” — running blood samples from people hospitalized all over the world through his system, known as IDseq — “is that it works even for things that we’ve never seen before, or things that we might think we’ve seen but which are actually something new.”

In this scenario, an undiscovered or completely new virus won’t trigger a match but will instead be flagged. (Even in those cases, the mystery pathogen will usually belong to a known virus family: coronaviruses, for instance, or filoviruses that cause hemorrhagic fevers like Ebola and Marburg.)

And because different types of bacteria require specific conditions in order to grow, you also need some idea of what you’re looking for in order to find it.

The same is true of genomic sequencing, which relies on “primers” designed to match different combinations of nucleotides (the building blocks of DNA and RNA).

Even looking at a slide under a microscope requires staining, which makes organisms easier to see — but the stains used to identify bacteria and parasites, for instance, aren’t the same.

The practice that DeRisi helped pioneer to skirt this problem is known as metagenomic sequencing

Unlike ordinary genomic sequencing, which tries to spell out the purified DNA of a single, known organism, metagenomic sequencing can be applied to a messy sample of just about anything — blood, mud, seawater, snot — which will often contain dozens or hundreds of different organisms, all unknown, and each with its own DNA. In order to read all the fragmented genetic material, metagenomic sequencing uses sophisticated software to stitch the pieces together by matching overlapping segments.

The assembled genomes are then compared against a vast database of all known genomic sequences — maintained by the government-run National Center for Biotechnology Information — making it possible for researchers to identify everything in the mix

Traditionally, the way that scientists have identified organisms in a sample is to culture them: Isolate a particular bacterium (or virus or parasite or fungus); grow it in a petri dish; and then examine the result under a microscope, or use genomic sequencing, to understand just what it is. But because less than 2 percent of bacteria — and even fewer viruses — can be grown in a lab, the process often reveals only a tiny fraction of what’s actually there. It’s a bit like planting 100 different kinds of seeds that you found in an old jar. One or two of those will germinate and produce a plant, but there’s no way to know what the rest might have grown into.

Such studies have revealed just how vast the microbial world is, and how little we know about it

“The selling point for researchers is: ‘Look, this technology lets you investigate what’s happening in your clinic, whether it’s kids with meningitis or something else,’” DeRisi said. “We’re not telling you what to do with it. But it’s also true that if we have enough people using this, spread out all around the world, then it does become a global network for detecting emerging pandemics

Metagenomic sequencing is especially good at what scientists call “environmental sampling”: identifying, say, every type of bacteria present in the gut microbiome, or in a teaspoon of seawater.

After the Biohub opened in 2016, one of DeRisi’s goals was to turn metagenomics from a rarefied technology used by a handful of elite universities into something that researchers around the world could benefit from

metagenomics requires enormous amounts of computing power, putting it out of reach of all but the most well-funded research labs. The tool DeRisi created, IDseq, made it possible for researchers anywhere in the world to process samples through the use of a small, off-the-shelf sequencer, much like the one DeRisi had shown me in his lab, and then upload the results to the cloud for analysis.

he’s the first to make the process so accessible, even in countries where lab supplies and training are scarce. DeRisi and his team tested the chemicals used to prepare DNA for sequencing and determined that using as little as half the recommended amount often worked fine. They also 3-D print some of the labs’ tools and replacement parts, and offer ongoing training and tech support

The metagenomic analysis itself — normally the most expensive part of the process — is provided free.

But DeRisi’s main innovation has been in streamlining and simplifying the extraordinarily complex computational side of metagenomics

IDseq is also fast, capable of doing analyses in hours that would take other systems weeks.

“What IDseq really did was to marry wet-lab work — accumulating samples, processing them, running them through a sequencer — with the bioinformatic analysis,”

“Without that, what happens in a lot of places is that the researcher will be like, ‘OK, I collected the samples!’ But because they can’t analyze them, the samples end up in the freezer. The information just gets stuck there.”

Meningitis itself isn’t a disease, just a description meaning that the tissues around the brain and spinal cord have become inflamed. In the United States, bacterial infections can cause meningitis, as can enteroviruses, mumps and herpes simplex. But a high proportion of cases have, as doctors say, no known etiology: No one knows why the patient’s brain and spinal tissues are swelling.

When Saha and her team ran the mystery meningitis samples through IDseq, though, the result was surprising. Rather than revealing a bacterial cause, as expected, a third of the samples showed signs of the chikungunya virus — specifically, a neuroinvasive strain that was thought to be extremely rare. “At first we thought, It cannot be true!” Saha recalls. “But the moment Joe and I realized it was chikungunya, I went back and looked at the other 200 samples that we had collected around the same time. And we found the virus in some of those samples as well.”

Until recently, chikungunya was a comparatively rare disease, present mostly in parts of Central and East Africa. “Then it just exploded through the Caribbean and Africa and across Southeast Asia into India and Bangladesh,” DeRisi told me. In 2011, there were zero cases of chikungunya reported in Latin America. By 2014, there were a million.

Chikungunya is a mosquito-borne virus, but when DeRisi and Saha looked at the results from IDseq, they also saw something else: a primate tetraparvovirus. Primate tetraparvoviruses are almost unknown in humans, and have been found only in certain regions. Even now, DeRisi is careful to note, it’s not clear what effect the virus has on people. “Maybe it’s dangerous, maybe it isn’t,” DeRisi says. “But I’ll tell you what: It’s now on my radar.

it reveals a landscape of potentially dangerous viruses that we would otherwise never find out about. “What we’ve been missing is that there’s an entire universe of pathogens out there that are causing disease in humans,” Imam notes, “ones that we often don’t even know exist.”

“The plan was, Let’s let researchers around the world propose studies, and we’ll choose 10 of them to start,” DeRisi recalls. “We thought we’d get, like, a couple dozen proposals, and instead we got 350.”

One study found more than 1,000 different kinds of viruses in a tiny amount of human stool; another found a million in a couple of pounds of marine sediment. And most were organisms that nobody had seen before.

“When you draw blood from someone who has a fever in Ghana, you really don’t know very much about what would normally be in their blood without fever — let alone about other kinds of contaminants in the environment. So how do you interpret the relevance of all the things you’re seeing?”

Such criticisms have led some to say that metagenomics simply isn’t suited to the infrastructure of developing countries. Along with the problem of contamination, many labs struggle to get the chemical reagents needed for sequencing, either because of the cost or because of shipping and customs holdups

we’re less likely to be caught off-guard. “With Ebola, there’s always an issue: Where’s the virus hiding before it breaks out?” DeRisi explains. “But also, once we start sampling people who are hospitalized more widely — meaning not just people in Northern California or Boston, but in Uganda, and Sierra Leone, and Indonesia — the chance of disastrous surprises will go down. We’ll start seeing what’s hidden.”

The study also found that small differences in projections for future climate conditions can yield very large differences in the economic outcomes for water scarcity.

A river basin can be considered economically robust if it is able to adapt to drought with alternative sources of water or adjust economic activity to limit usage.

The conditions that lead to these tipping points are highly variable from basin to basin, depending on a combination of local factors and global conditions

As climate change makes the physical and economic effects of water scarcity more challenging for policy makers to understand, the researchers hope their work will provide the basis for similar analyses and draw attention to the importance of expanded data collection to improve modeling and decision making.

However, for most real-world applications, measurements of materials’ properties have varying levels of fidelity, depending on the resources available.

A comparison of prediction errors clearly demonstrates the benefit of the multi-fidelity approach

The authors’ system is not restricted to materials science, but is generalizable to any problem that can be described using graph structures, such as social networks and knowledge graphs (digital frameworks that represent knowledge as concepts connected by relationships)

More research is needed to understand the scenarios for which multi-fidelity learning is most beneficial, balancing prediction accuracy with the cost of acquiring data

Duolingo has been rolling out new features—including podcasts, social interaction among users, and character-driven narratives—that aim to raise its language pragmatics as well as its addictiveness.

Where most apps really fall short, he said, is in language “pragmatics.” “That’s the learning that’s based on real-world settings—you’re in a restaurant, in an interview, waiting for a bus,” he explained. “It’s usually lost in apps.”

“In the U.S., about half of our users aren’t even really motivated to learn a language; they just want to pass the time on something besides Candy Crush,” he said.

“There are all kinds of contextual factors in language learning,” he said. “It would be hard for an app to take them all into account.”

when people think about themselves with others, they are more focused on the present, and less focused on the past or the future than when they think about themselves alone.

Time alone is reflected in people's thoughts as an opportunity to think about past experiences and future plans, to relax from the stress of social interactions, and to engage in self-selected leisure activities.

"Being alone and being with others are represented in people's minds as qualitatively different experiences, each contributing to the formation of an integrated self

For those facing current lockdowns alone, Uziel says the present findings, which highlight potential constructive effects of time alone, indicate that this could also be an opportunity for personal growth.

The new variant, called B.1.1.7

Last week, the U.K. reported a record-breaking 419,000 cases.

Studies suggest the new variant increases the transmissibility by about 50%.

Now scientists say the virus is already here in the U.S., and circulating widely-- albeit at very low levels

"A rough estimate, for across the U.S., would be a frequency of about 1 in 1000,"

In England, B.1.1.7 took about three month to take over and become the dominant strain in the outbreak.

having this new variant dominant outbreak could be very problematic, researchers say. It could fuel another surge on top of the already staggering surge the country is struggling to stop.

What's going to happen if a more contagious form starts to circulate widely, even dominate the outbreak?

Right now scientists don't believe the new variant is more deadly. But its increased transmissibility could, in the end, be even more dangerous

"Perhaps counterintuitively, I think that increased transmissibility is probably the worst of these two scenarios, because if something is more transmissible, then you just get it into a larger population,"

that each sick person could infect 1.8 people, on average.

the U.S. still has about two months to prepare for — and slow down — the variant.

Each week, more than 1.5 million people test positive for the virus across the country.

The U.S. needs to be thinking about how to minimize damage from this new variant, right now, Hodcroft says. "This is our early warning. Because by the time you have something spreading exponentially in your country, it is much harder to get it under control."

In one experiment, participants chose to either support or oppose a given sociopolitical issue. They were then presented with evidence that was conflicting, affirming, or a combination of both. In all scenarios, participants were most likely to stick with their initial decisions. Of those presented with conflicting evidence, just one in five changed their stance. Furthermore, participants who maintained their initial positions became even more confident in the superiority of their decision—a testament to how influential confirmation bias can be.

Coverage bias, in the context of politics, is a form of media bias where certain politicians or topics are disproportionately covered.

the continuity hypothesis. This framework holds that the content of dreams simply reflects what people thought, felt and did during the day — the good and the bad, the hopeful and the frightening.

People with persistent waking anxieties also tended to play out scenarios involving future work, relationships and life generally in their heads over the course of a day. Previous research has correlated this pattern to scene-shifting in dreams: the frequent changing of the setting, from indoors to outdoors, city to country, mountains to coast.

dreamers during the first phase of the pandemic recorded far more such shifts in their REM mini-dramas.

“I wasn’t expecting this, but the findings suggest to me this idea that men are mainly experiencing fear of getting sick and dying, health fears,”

Women have been weathering more secondary effects; they tend to be the ones nursing sick family members, more often than males, for instance. I suspect this is the single most likely reason that sadness is up for women in these times.”

The veneer of American exceptionalism on the battlefield still held and those who watched my games with Jabbar—mostly support personnel who likely had little interaction with Afghan officers—displayed a soft sort of jingoism in their disbelief that an Afghan might beat one of their own.

Again, it’s that soft jingoism. A belief that we can’t lose.

However, the idea that the military would have any role to play in adjudicating a U.S. election is, of itself, a chilling proposition.

Imagine a scenario where unrest around the election matches what we witnessed this summer, when the Trump administration deployed the National Guard. Except now the military hasn’t been deployed to quell those protesting social injustice, but rather to quell those who dispute the result of an election.

No democracy can survive without the assurance of a peaceful transfer of power.

Could an implicit bias in our exceptionalism take us blindly from “our city on a hill” along a winding path that leads to the lowlands of dysfunction? Are we already on that path?

Chronic poor sleep can cause a whole range of mental and physical health issues, including depression and heart disease.

In many cases, they may help the dreamer ameliorate some of their daytime anxieties. Research has found that nightmares can help some people learn to better manage stress.

In much the same way, nightmares—especially those following an upsetting event—may allow a person’s brain to relive the event and move past it, Nadorff says.

“We have the person talk through their nightmare and change it in a way that’s not threatening, and then they practice the new dream during the day using visual imagery,” Nadorff explains. This kind of daily rehearsal can help reshape the scary dream even while a person is sleeping.

This preference, which is perhaps a strong one, may have resulted in a bias to maintain the status quo. You rely on heuristics to help identify your deodorant (usually by sight) and you add it to your virtual cart and place your order. In this instance, your bias influenced your preference toward your current deodorant, and your heuristic helped you to identify it. Potential stinkiness crisis averted.

In that case, you will likely be motivated to make a purchasing decision consistent with your strong bias (i.e., look to purchase it from a different vendor, maintaining the status quo with your deodorant). Thus, in this scenario, you decide to look elsewhere.

At this step, the availability heuristic is likely to guide your decision, causing you to navigate to an alternative site that quickly comes to mind[6].

Your heuristics will help you select an alternative product that meets some criteria.

satisficing heuristic (opting for the first product that looks good enough), a similarity heuristic (opting for the product that looks closest to your current deodorant) or some other heuristic to help you select the product you decide to order.

The question, though, is often whether your biases and heuristics are aiding or inhibiting the ecological rationality of your decision, and that will vary from situation to situation.

Computers already are predominant, human life already takes place mostly within them, and people are satisfied with the results.

These devices pose numerous problems. Cost is one. Like a cheap propane gauge, a traditional bike lock is a commodity. It can be had for $10 to $15, a tenth of the price of Nokē’s connected version. Security and privacy are others. The CIA was rumored to have a back door into Samsung TVs for spying. Disturbed people have been caught speaking to children over hacked baby monitors. A botnet commandeered thousands of poorly secured internet-of-things devices to launch a massive distributed denial-of-service attack against the domain-name syste

Reliability plagues internet-connected gadgets, too. When the network is down, or the app’s service isn’t reachable, or some other software behavior gets in the way, the products often cease to function properly—or at all.

Turing guessed that machines would become most compelling when they became convincing companions, which is essentially what today’s smartphones (and smart toasters) do.

But Turing never claimed that machines could think, let alone that they might equal the human mind. Rather, he surmised that machines might be able to exhibit convincing behavior.

People choose computers as intermediaries for the sensual delight of using computers

ne such affection is the pleasure of connectivity. You don’t want to be offline. Why would you want your toaster or doorbell to suffer the same fate? Today, computational absorption is an ideal. The ultimate dream is to be online all the time, or at least connected to a computational machine of some kind.

Doorbells and cars and taxis hardly vanish in the process. Instead, they just get moved inside of computers.

“Being a computer” means something different today than in 1950, when Turing proposed the imitation game. Contra the technical prerequisites of artificial intelligence, acting like a computer often involves little more than moving bits of data around, or acting as a controller or actuator. Grill as computer, bike lock as computer, television as computer. An intermediary

Or consider doorbells once more. Forget Ring, the doorbell has already retired in favor of the computer. When my kids’ friends visit, they just text a request to come open the door. The doorbell has become computerized without even being connected to an app or to the internet. Call it “disruption” if you must, but doorbells and cars and taxis hardly vanish in the process. Instead, they just get moved inside of computers, where they can produce new affections.

The present status of intelligent machines is more powerful than any future robot apocalypse.

Why would anyone ever choose a solution that doesn’t involve computers, when computers are available? Propane tanks and bike locks are still edge cases, but ordinary digital services work similarly: The services people seek out are the ones that allow them to use computers to do things—from finding information to hailing a cab to ordering takeout. This is a feat of aesthetics as much as it is one of business. People choose computers as intermediaries for the sensual delight of using computers, not just as practical, efficient means for solving problems.

This is not where anyone thought computing would end up. Early dystopic scenarios cautioned that the computer could become a bureaucrat or a fascist, reducing human behavior to the predetermined capacities of a dumb machine. Or else, that obsessive computer use would be deadening, sucking humans into narcotic detachment.Those fears persist to some extent, partly because they have been somewhat realized. But they have also been inverted. Being away from them now feels deadening, rather than being attached to them without end. And thus, the actions computers take become self-referential: to turn more and more things into computers to prolong that connection.

But the real present status of intelligent machines is both humdrum and more powerful than any future robot apocalypse. Turing is often called the father of AI, but he only implied that machines might become compelling enough to inspire interaction. That hardly counts as intelligence, artificial or real. It’s also far easier to achieve. Computers already have persuaded people to move their lives inside of them. The machines didn’t need to make people immortal, or promise to serve their every whim, or to threaten to destroy them absent assent. They just needed to become a sufficient part of everything human beings do such that they can’t—or won’t—imagine doing those things without them.

. The real threat of computers isn’t that they might overtake and destroy humanity with their future power and intelligence. It’s that they might remain just as ordinary and impotent as they are today, and yet overtake us anyway.

Compartmentalization – the person relegates different ideas and images to specific mental frames and keeps boundaries between them

At the other end of the continuum is a very destructive process that can result in violence.”

For years, USPS has struggled to stay afloat with the emergence of technology threatening their bottom line, but coronavirus has further strained their financial situation as mail volume has declined. The Postal Service has warned that it could be insolvent by June, the House Oversight Committee said this week.

"The Postal Service remains concerned that this measure will be insufficient to enable the Postal Service to withstand the significant downturn in our business that could directly result from the pandemic," said USPS spokesman David Partenheimer. "Under a worst case scenario, such downturn could result in the Postal Service having insufficient liquidity to continue operations."

Dr. Byrne and her colleagues measured risky decision-making by presenting people with a gambling scenario. They could choose between two bets: One offered a guaranteed amount of money, while the other offered the possibility of a larger amount of money but also the possibility of receiving no cash. A different exercise measured people’s preference for immediate versus delayed rewards: Participants could choose a certain amount of money now, or a larger amount later.

Study subjects also reported Covid-19 precautions they had taken in their daily lives, including masking and social distancing.

with Covid-19, people don’t feel sick immediately after an exposure so the benefits of wearing a mask, social distancing or getting vaccinated aren’t immediately apparent. “You don’t see the lives you potentially save,” she says

“People generally are more motivated by immediate gratification or immediate benefits rather than long-term benefits, even when the long-term benefits are much greater,

Research has also found being extroverted or introverted affects how people make decisions about Covid-19 precautions. A recent study of more than 8,500 people in Japan published in the journal PLOS One in October 2020 found that those who scored high on a scale of extraversion were 7% less likely to wear masks in public and avoid large gatherings, among other precautions.

The study also found that people who scored high on a measure of conscientiousness—valuing hard work and achievement—were 31% more likely to follow Covid-19 public-health precautions.

Scientists believe that a person’s propensity to take risks is partly genetic and partly the result of early life experiences

Certain negative childhood experiences including physical, emotional or sexual abuse, parental divorce, or living with someone who was depressed or abused drugs or alcohol are linked to risky behavior in adulthood like smoking and drinking heavily, other research has found.

Studies of twins have generally found that about 30% of the difference in individual risk tolerance is genetic

And scientists have discovered that the brains of people who are more willing to take risks look different than those of people who are more cautious.

ambling task had differences in the structure and function of the amygdala, a part of the brain involved in detecting threats, and the prefrontal cortex, a region involved in executive

Even people who have the same information and a similar perception of the risks may make different decisions because of the ways they interpret the information. When public-health officials talk about breakthrough infections in vaccinated individuals being rare, for example, “rare means different things” to different people