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that are found on pain-transmitting neurons called nociceptors.

further investigate how the scorpion venom inhibits the transmission of pain, Rowe and her colleagues sequenced the Nav1.8 channel, replaced grasshopper mouse sequences with house mouse sequences, and expressed the mutant channels in a cell line to observe the effects of the genetic changes.

“a really small amount of variation—one amino acid—can actually produce these really profound physiological effects,” Rowe said. “Evolutionarily speaking, it allows this mouse to then feed on a chemically protected scorpion.”

, the rodent displayed a heightened fear response, although no foot shock had ever been given in that location. The mice eventually became conditioned to the false memory, and would freeze in Box A even in the absence of pulsing light.

“The brain mechanisms for forming and recalling the false memories are not distinguishable from those underlying genuine memories,” said Tonegawa.

hypothesized that a similar mechanism could be behind the formation of false memories in humans and said the findings underscore the problematic nature of eyewitness testimony in court proceedings.

“We hope this kind of research will reinforce the need to look at eyewitness testimony carefully,” said Tonegawa, “so that innocent people will not be incriminated.”

Tonegawa said he thinks that false memories with positive connotations likely form in the same way—a hypothesis he hopes to test with future experiments. In addition, given that false memories appear robust enough to compete with real memories, Tonegawa said he plans to investigate the factors that affect the relative strengths of the two types of memory.

Observations of predator behavior further piqued his interest. While filming and playing back his videos, he noted how cats stack their bodies behind their faces when they’re hunting. He interprets this, he said, as cats “trying to hide their heat” with their cold noses. He has also observed barn owls twisting as they swoop down, perhaps to shield their warmer parts — legs and wingpits — with cooler ones.

Maybe, he thought, “predators have to conceal their infrared to be able to catch a mouse.”

Eventually, these and other musings led Dr. Baker to place mouse hairs under a microscope. As it came into view, he felt a strong sense of familiarity. The guard hair in particular — the bristliest type of mouse hair — contained evenly-spaced bands of pigment that, to Dr. Baker, closely resembled structures that allow optical sensors to tune into specific wavelengths of light.

Thermal cameras, for instance, focus specifically on 10-micron radiation: the slice of the spectrum that most closely corresponds with heat released by living things. By measuring the stripes, Dr. Baker found they were tuned to 10 microns as well — apparently homed in on life’s most common heat signature. “That was my Eureka moment,” he said.

The researchers studied a group of brain cells in the hippocampal region of the mouse brain. They found that they could create false associations between events and environments by artificially stimulating the neurons.

A member of the MIT team, Susumu Tonegawa, commented on the significance of the research in Science  magazine's weekly podcast:Independent of what is happening around you in the outside world, humans constantly have internal activity in the brain. So just like our mouse, it is quite possible we can associate what we happen to have in our mind with a bad or good high valence, online event. So, in other words, there could be a false association of what you have in your mind rather than what is happening to you, so this is a way we believe that at least some form of strong force memory observed in humans could be made. Because our study showed that the false memories and the genuine memories are based on very similar, almost identical, brain mechanisms, it is difficult for the false memory bearer to distinguish between them. So we can study this, because we have a mouse model now.

The international team has found more than 4,000 "enhancers" in the mouse genome that appear to play a role in facial appearance.

"In the mouse embryos we can see where exactly, as the face develops, this switch turns on the gene that it controls.

"These mice looked pretty normal, but it is really hard for humans to see differences in the face of mice," explained Prof Visel.

"What this really tells us is that this particular switch also plays a role in development of the skull and can affect what exactly the skull looks like," he explained.

Understanding this could also help to reveal why and how things can go wrong as embryos develop in the womb, leading to facial birth defects.

"And they have severe implications for the kids that are affected. They affect feeding, speech, breathing, they can require extensive surgery and they have psychological implications."

Professor Visel added that scientists were just at the beginning of understanding the processes that shape the face, but their early results suggested it was an extremely complex process.

researchers were also able to activate the mitochondrial UPR via pharmacological means. Dosing worms with the antibiotic doxycyline, which inhibits bacterial and mitochondrial protein translation, also activated the mitochondrial UPR and extended worm lifespans. Rapamycin, shown to enhance longevity in mice, also extended worm lifespan and induced mitonuclear protein imbalance and the mitochondrial UPR in mouse hepatocytes.

mitochondrial ribosomal proteins are not to be trifled with. “There are a number of well-defined severe disorders in humans, including neonatal lethality, due to defects in those exact proteins,”

is beginning to cast a wider net, looking to see whether mitonuclear protein imbalance could explain longevity induced by other means, such as caloric restriction. Auwerx hopes that the work will aid in designing a drug intervention “to pump up this response via pharmacological tools.”

he’s optimistic that his team has identified a “common thread” demonstrating that longevity is not affected so much by inhibiting or stimulating mitochondria, but how the organelles “deal with proteins.”

The rodent at the top of this story is being studied with a technique called optogenetics, which Deisseroth pioneered.

genetically engineered the mouse so that its brain cells turn certain genes on or off when scientists shine laser light onto them. The light enters the mouse's brain through that optical fiber you see in the photo.

For example, say 20 percent of people with autism don't have Gene A, but scientists aren't sure what Gene A does. They could turn off Gene A in a mouse's brain and see what happens next. The mouse's reaction could provide a clue about what Gene A does in people and why it's missing in certain patients

The system may also play a role in symptoms of traumatic brain injur

Mice are a good model, she says, because their glymphatic systems are very similar to humans’. She and Iliff found that even months after being injured, the animals’ brains were still not clearing waste efficiently, leading to a buildup of toxic compounds, including amyloid beta. Nedergaard returns to the dishwasher analogy. “It’s like if you only use a third of the water when you turn on the machine,” she says. “You won’t get clean dishes.”

in mice, omega-3 fatty acidsimproved glymphatic functioning.

Nedergaard has shown that at least in mice, the system processes twice as much fluid during sleep as it does during wakefulness. She and her colleagues focused on amyloid beta; they found that the lymphatic system removed much more of the protein when the animals were asleep than when they were awake. She suggests that over time, sleep dysfunction may contribute to Alzheimer’s and perhaps other brain illnesses. “You only clean your brain when you’re sleeping,” she says. “This is probably an important reason that we sleep. You need time off from consciousness to do the housekeeping.”

Sleeping on your stomach is also not very effective; sleeping on your back is somewhat better, while lying on your side appears to produce the best results.

glymphatic flow is significantly decreased in the period just before a migraine. The intense pain in these headaches is caused largely by inflamed nerves in the tissue that surrounds the brain. Neuroscientists Rami Burstein and Aaron Schain, the lead authors, theorize that faulty clearance of molecular waste from the brain could trigger inflammation in these pain fibers.

other scientists have found that deep breathing significantly increases the glymphatic transport of cerebrospinal fluid into the brain.

He hypothesizes that the process involves one or more molecules released by the nervous system — perhaps small RNAs, perhaps something secreted like a hormone. But somehow those germ cells then influence the behavior of the next generation and seem to circumvent the normal need for rde-4 in the production of the small RNAs for chemotaxis in the progeny.

“I believe that today, there is not a single solid paper showing that only small RNAs are involved in epigenetic inheritance,” said Isabelle Mansuy, a neuroepigenetics researcher at the Swiss Federal Institute of Technology Zurich and the University of Zurich who studies the inheritance of trauma in humans and mice. In the mouse model she works with, she knows that small RNAs are not sufficient because if she injects small RNAs alone into fertilized mouse eggs, the resulting animals do not show the RNA-associated trait.

“If we can ingest a chemical from our environment that changes the epigenomes of the egg or sperm, why couldn’t our brain make a similar molecule that does the same thing?”

“Historically,” says Greg Blatt, the CEO of Match.com’s parent company, “relationships have been billed as ‘hard’ because, historically, commitment has been the goal. You could say online dating is simply changing people’s ideas about whether commitment itself is a life value.” Mate scarcity also plays an important role in people’s relationship decisions. “Look, if I lived in Iowa, I’d be married with four children by now,” says Blatt, a 40‑something bachelor in Manhattan. “That’s just how it is.”

“I think divorce rates will increase as life in general becomes more real-time,” says Niccolò Formai, the head of social-media marketing at Badoo, a meeting-and-dating app with about 25 million active users worldwide. “Think about the evolution of other kinds of content on the Web—stock quotes, news. The goal has always been to make it faster. The same thing will happen with meeting. It’s exhilarating to connect with new people, not to mention beneficial for reasons having nothing to do with romance. You network for a job. You find a flatmate. Over time you’ll expect that constant flow. People always said that the need for stability would keep commitment alive. But that thinking was based on a world in which you didn’t meet that many people.”

“You could say online dating allows people to get into relationships, learn things, and ultimately make a better selection,” says Gonzaga. “But you could also easily see a world in which online dating leads to people leaving relationships the moment they’re not working—an overall weakening of commitment.”

Explaining the mentality of a typical dating-site executive, Justin Parfitt, a dating entrepreneur based in San Francisco, puts the matter bluntly: “They’re thinking, Let’s keep this fucker coming back to the site as often as we can.” For instance, long after their accounts become inactive on Match.com and some other sites, lapsed users receive notifications informing them that wonderful people are browsing their profiles and are eager to chat. “Most of our users are return customers,” says Match.com’s Blatt.

The market is hugely more efficient … People expect to—and this will be increasingly the case over time—access people anywhere, anytime, based on complex search requests … Such a feeling of access affects our pursuit of love … the whole world (versus, say, the city we live in) will, increasingly, feel like the market for our partner(s). Our pickiness will probably increase.” “Above all, Internet dating has helped people of all ages realize that there’s no need to settle for a mediocre relationship.”

in general what he argues is that if you take a look at animal cognition, human too, it's computational systems. Therefore, you want to look the units of computation. Think about a Turing machine, say, which is the simplest form of computation, you have to find units that have properties like "read", "write" and "address." That's the minimal computational unit, so you got to look in the brain for those. You're never going to find them if you look for strengthening of synaptic connections or field properties, and so on. You've got to start by looking for what's there and what's working and you see that from Marr's highest level.

it's basically in the spirit of Marr's analysis. So when you're studying vision, he argues, you first ask what kind of computational tasks is the visual system carrying out. And then you look for an algorithm that might carry out those computations and finally you search for mechanisms of the kind that would make the algorithm work. Otherwise, you may never find anything.

"Good Old Fashioned AI," as it's labeled now, made strong use of formalisms in the tradition of Gottlob Frege and Bertrand Russell, mathematical logic for example, or derivatives of it, like nonmonotonic reasoning and so on. It's interesting from a history of science perspective that even very recently, these approaches have been almost wiped out from the mainstream and have been largely replaced -- in the field that calls itself AI now -- by probabilistic and statistical models. My question is, what do you think explains that shift and is it a step in the right direction?

AI and robotics got to the point where you could actually do things that were useful, so it turned to the practical applications and somewhat, maybe not abandoned, but put to the side, the more fundamental scientific questions, just caught up in the success of the technology and achieving specific goals.

The approximating unanalyzed data kind is sort of a new approach, not totally, there's things like it in the past. It's basically a new approach that has been accelerated by the existence of massive memories, very rapid processing, which enables you to do things like this that you couldn't have done by hand. But I think, myself, that it is leading subjects like computational cognitive science into a direction of maybe some practical applicability... ..in engineering? Chomsky: ...But away from understanding.

I was very skeptical about the original work. I thought it was first of all way too optimistic, it was assuming you could achieve things that required real understanding of systems that were barely understood, and you just can't get to that understanding by throwing a complicated machine at it.

if success is defined as getting a fair approximation to a mass of chaotic unanalyzed data, then it's way better to do it this way than to do it the way the physicists do, you know, no thought experiments about frictionless planes and so on and so forth. But you won't get the kind of understanding that the sciences have always been aimed at -- what you'll get at is an approximation to what's happening.

Suppose you want to predict tomorrow's weather. One way to do it is okay I'll get my statistical priors, if you like, there's a high probability that tomorrow's weather here will be the same as it was yesterday in Cleveland, so I'll stick that in, and where the sun is will have some effect, so I'll stick that in, and you get a bunch of assumptions like that, you run the experiment, you look at it over and over again, you correct it by Bayesian methods, you get better priors. You get a pretty good approximation of what tomorrow's weather is going to be. That's not what meteorologists do -- they want to understand how it's working. And these are just two different concepts of what success means, of what achievement is.

if you get more and more data, and better and better statistics, you can get a better and better approximation to some immense corpus of text, like everything in The Wall Street Journal archives -- but you learn nothing about the language.

the right approach, is to try to see if you can understand what the fundamental principles are that deal with the core properties, and recognize that in the actual usage, there's going to be a thousand other variables intervening -- kind of like what's happening outside the window, and you'll sort of tack those on later on if you want better approximations, that's a different approach.

take a concrete example of a new field in neuroscience, called Connectomics, where the goal is to find the wiring diagram of very complex organisms, find the connectivity of all the neurons in say human cerebral cortex, or mouse cortex. This approach was criticized by Sidney Brenner, who in many ways is [historically] one of the originators of the approach. Advocates of this field don't stop to ask if the wiring diagram is the right level of abstraction -- maybe it's no

if you went to MIT in the 1960s, or now, it's completely different. No matter what engineering field you're in, you learn the same basic science and mathematics. And then maybe you learn a little bit about how to apply it. But that's a very different approach. And it resulted maybe from the fact that really for the first time in history, the basic sciences, like physics, had something really to tell engineers. And besides, technologies began to change very fast, so not very much point in learning the technologies of today if it's going to be different 10 years from now. So you have to learn the fundamental science that's going to be applicable to whatever comes along next. And the same thing pretty much happened in medicine.

that's the kind of transition from something like an art, that you learn how to practice -- an analog would be trying to match some data that you don't understand, in some fashion, maybe building something that will work -- to science, what happened in the modern period, roughly Galilean science.

it turns out that there actually are neural circuits which are reacting to particular kinds of rhythm, which happen to show up in language, like syllable length and so on. And there's some evidence that that's one of the first things that the infant brain is seeking -- rhythmic structures. And going back to Gallistel and Marr, its got some computational system inside which is saying "okay, here's what I do with these things" and say, by nine months, the typical infant has rejected -- eliminated from its repertoire -- the phonetic distinctions that aren't used in its own language.

people like Shimon Ullman discovered some pretty remarkable things like the rigidity principle. You're not going to find that by statistical analysis of data. But he did find it by carefully designed experiments. Then you look for the neurophysiology, and see if you can find something there that carries out these computations. I think it's the same in language, the same in studying our arithmetical capacity, planning, almost anything you look at. Just trying to deal with the unanalyzed chaotic data is unlikely to get you anywhere, just like as it wouldn't have gotten Galileo anywhere.

with regard to cognitive science, we're kind of pre-Galilean, just beginning to open up the subject

You can invent a world -- I don't think it's our world -- but you can invent a world in which nothing happens except random changes in objects and selection on the basis of external forces. I don't think that's the way our world works, I don't think it's the way any biologist thinks it is. There are all kind of ways in which natural law imposes channels within which selection can take place, and some things can happen and other things don't happen. Plenty of things that go on in the biology in organisms aren't like this. So take the first step, meiosis. Why do cells split into spheres and not cubes? It's not random mutation and natural selection; it's a law of physics. There's no reason to think that laws of physics stop there, they work all the way through. Well, they constrain the biology, sure. Chomsky: Okay, well then it's not just random mutation and selection. It's random mutation, selection, and everything that matters, like laws of physics.

What I think is valuable is the history of science. I think we learn a lot of things from the history of science that can be very valuable to the emerging sciences. Particularly when we realize that in say, the emerging cognitive sciences, we really are in a kind of pre-Galilean stage. We don't know wh

at we're looking for anymore than Galileo did, and there's a lot to learn from that.

Reality-directed imagination, for its part, endeavours to re-create, in the intellectual realm, actions and events that have existed or have taken place, which we may have plenty or partial information about.

Reality-directed imagination is thus a means to retain a solid sense of reality rather than to submerge into the everlasting landscape of fantasy. We imagine what was and try to afford it life.  

Without reality-directed imagination, on the other hand, the study of history would be well-nigh impossible.

By resorting to reality-directed imagination we are able intellectually to disconnect ourselves from the present; to visualize, like a landscape gradually making its appearance as we move backwards in time, the setting in which an event occurred or the personal features of an individual we follow. We are able emotionally to connect ourselves to the prevailing conditions or to a person's thoughts.

In the study of history we make use of reality-directed imagination as we depict in our minds the characters of individuals or the nature of events. We even try to fill the gaps by resorting to our imagination ever vigilant not to lose sight of reality as it was. In other words, we attempt to imagine the unknown by resorting to the known.

Without imagination as a study-device, the learning of history becomes well-nigh impossible, for the information furnished to us is rendered unintelligible. We are unable to relate to it in any meaningful manner. We assess it in a mechanical way, devoid of image, sound and feel. Our attempt to understand it leads to a dead-end for we cannot leap forward from the stale fact before us and relate it to other facts beyond it.

Without imagination we cannot compare, distinguish and separate; we cannot know the difference between the particular and the general. In order to study history we need to avoid the mechanical, on the one hand, and the fantastic, on the other. In other words, we ought to eschew both lack of imagination and fantasy-directed imagination; the first does not allow us to proceed forward while the latter leads us to the realm of the unreal.

Whatever else, it is sure to unnerve most of us.

Scientists, for example, should never create a Sheef that feels pain.

Scientists began grappling with the ethics of lab-raised embryos more than four decades ago.

In 1979, a federal advisory board recommended that the cutoff should be 14 days.

The embryonic cells develop into three types, called germ layers. Each of those germ layers goes on to produce all the body’s tissues and organs.

This triggered communication by the cells, and they organized themselves into the arrangement found in an early mouse embryo.

Even if ethicists do manage to agree on certain limits, Paul S. Knoepfler, a stem cell biologist at the University of California, Davis, wondered how easy it would be for scientists to know if they had crossed them.

Spotting a primitive streak is easy. Determining whether a collection of neurons connected to other tissues in a dish can feel pain is not.

Research on differentiated kidney cells, particularly podocytes that can be cultured in the lab, may hold some answers. Although there are a few human- and mouse-derived cell lines that potentially differentiate into functional podocytes, there is a need for better cell lines and systems that mimic the in vivo situation more closely

"The experiments are elegant and seem to show that in the mouse sperm production can be achieved when only two genes from the Y-chromosomes are present.

"Whilst this is of limited use in understanding human fertility, this kind of work is important if we are to unravel to complexities of how genes control fertility."

Bennett said his father was able to design his own death by ordering his son not to fly home. He wanted control and dignity.