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In California’s Salinas Valley, known as the “Salad Bowl of the World,” a push is underway to expand agriculture’s adoption of technology. Special correspondent Cat Wise reports on how such innovation is providing new opportunities for the Valley’s largely Hispanic population. Watch her full piece here: http://to.pbs.org/2gLmEga

When a porous solid retains its properties in liquid form

Known for their exceptional porosity that enables the trapping or transport of molecules, metal-organic frameworks (MOFs) take the form of a powder, which makes them difficult to format. For the first time, an international team led by scientists from the Institut de recherche de Chimie Paris (CNRS/Chimie ParisTech ), and notably involving Air Liquide, has evidenced the surprising ability of a type of MOF to retain its porous properties in the liquid and then glass state. Published on October 9, 2017 in Nature Materials website, these findings open the way towards new industrial applications.

Metal-organic frameworks (MOFs) constitute a particularly promising class of materials. Their exceptional porosity makes it possible to store and separate large quantities of gas, or to act as a catalyst for chemical reactions. However, their crystalline structure implies that they are produced in powder form, which is difficult to store and use for industrial applications. For the first time, a team of scientists from the CNRS, Chimie ParisTech, Cambridge University, Air Liquide and the ISIS (UK) and Argonne (US) synchrotrons has shown that the properties of a zeolitic MOF were unexpectedly conserved in the liquid phase (which does not generally favor porosity). Then, after cooling and solidification, the glass obtained adopted a disordered, non-crystalline structure that also retained the same properties in terms of porosity. These results will enable the shaping and use of these materials much more efficiently than in powder form.

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Balancing Time and Space in the Brain: A New Model Holds Promise for Predicting Brain Dynamics

For as long as scientists have been listening in on the activity of the brain, they have been trying to understand the source of its noisy, apparently random, activity. In the past 20 years, “balanced network theory” has emerged to explain this apparent randomness through a balance of excitation and inhibition in recurrently coupled networks of neurons. A team of scientists has extended the balanced model to provide deep and testable predictions linking brain circuits to brain activity.

Lead investigators at the University of Pittsburgh say the new model accurately explains experimental findings about the highly variable responses of neurons in the brains of living animals. On Oct. 31, their paper, “The spatial structure of correlated neuronal variability,” was published online by the journal Nature Neuroscience.

The new model provides a much richer understanding of how activity is coordinated between neurons in neural circuits. The model could be used in the future to discover neural “signatures” that predict brain activity associated with learning or disease, say the investigators.

“Normally, brain activity appears highly random and variable most of the time, which looks like a weird way to compute,” said Brent Doiron, associate professor of mathematics at Pitt, senior author on the paper, and a member of the University of Pittsburgh Brain Institute (UPBI). “To understand the mechanics of neural computation, you need to know how the dynamics of a neuronal network depends on the network’s architecture, and this latest research brings us significantly closer to achieving this goal.”

Earlier versions of the balanced network theory captured how the timing and frequency of inputs—excitatory and inhibitory—shaped the emergence of variability in neural behavior, but these models used shortcuts that were biologically unrealistic, according to Doiron.

“The original balanced model ignored the spatial dependence of wiring in the brain, but it has long been known that neuron pairs that are near one another have a higher likelihood of connecting than pairs that are separated by larger distances. Earlier models produced unrealistic behavior—either completely random activity that was unlike the brain or completely synchronized neural behavior, such as you would see in a deep seizure. You could produce nothing in between.”

In the context of this balance, neurons are in a constant state of tension. According to co-author Matthew Smith, assistant professor of ophthalmology at Pitt and a member of UPBI, “It’s like balancing on one foot on your toes. If there are small overcorrections, the result is big fluctuations in neural firing, or communication.”

The new model accounts for temporal and spatial characteristics of neural networks and the correlations in the activity between neurons—whether firing in one neuron is correlated with firing in another. The model is such a substantial improvement that the scientists could use it to predict the behavior of living neurons examined in the area of the brain that processes the visual world.

After developing the model, the scientists examined data from the living visual cortex and found that their model accurately predicted the behavior of neurons based on how far apart they were. The activity of nearby neuron pairs was strongly correlated. At an intermediate distance, pairs of neurons were anticorrelated (When one responded more, the other responded less.), and at greater distances still they were independent.

“This model will help us to better understand how the brain computes information because it’s a big step forward in describing how network structure determines network variability,” said Doiron. “Any serious theory of brain computation must take into account the noise in the code. A shift in neuronal variability accompanies important cognitive functions, such as attention and learning, as well as being a signature of devastating pathologies like Parkinson’s disease and epilepsy.”

While the scientists examined the visual cortex, they believe their model could be used to predict activity in other parts of the brain, such as areas that process auditory or olfactory cues, for example. And they believe that the model generalizes to the brains of all mammals. In fact, the team found that a neural signature predicted by their model appeared in the visual cortex of living mice studied by another team of investigators.

“A hallmark of the computational approach that Doiron and Smith are taking is that its goal is to infer general principles of brain function that can be broadly applied to many scenarios. Remarkably, we still don’t have things like the laws of gravity for understanding the brain, but this is an important step for providing good theories in neuroscience that will allow us to make sense of the explosion of new experimental data that can now be collected,” said Nathan Urban, associate director of UPBI.

Man dies. Come from darkness, into darkness he returns, and is reabsorbed, without a trace left, into the illimitable void of time.

Leonid Andreyev. (via drunk-on-books)

waaavess

New theory explains how beta waves arise in the brain

Beta rhythms, or waves of brain activity with an approximately 20 Hz frequency, accompany vital fundamental behaviors such as attention, sensation and motion and are associated with some disorders such as Parkinson’s disease. Scientists have debated how the spontaneous waves emerge, and they have not yet determined whether the waves are just a byproduct of activity, or play a causal role in brain functions. Now in a new paper led by Brown University neuroscientists, they have a specific new mechanistic explanation of beta waves to consider.

The new theory, presented in the Proceedings of the National Academy of Sciences, is the product of several lines of evidence: external brainwave readings from human subjects, sophisticated computational simulations and detailed electrical recordings from two mammalian model organisms.

“A first step to understanding beta’s causal role in behavior or pathology, and how to manipulate it for optimal function, is to understand where it comes from at the cellular and circuit level,” said corresponding author Stephanie Jones, research associate professor of neuroscience at Brown University. “Our study combined several techniques to address this question and proposed a novel mechanism for spontaneous neocortical beta. This discovery suggests several possible mechanisms through which beta may impact function.”

Making waves

The team started by using external magnetoencephalography (MEG) sensors to observe beta waves in the human somatosensory cortex, which processes sense of touch, and the inferior frontal cortex, which is associated with higher cognition.

They closely analyzed the beta waves, finding they lasted at most a mere 150 milliseconds and had a characteristic wave shape, featuring a large, steep valley in the middle of the wave.

The question from there was what neural activity in the cortex could produce such waves. The team attempted to recreate the waves using a computer model of a cortical circuitry, made up of a multilayered cortical column that contained multiple cell types across different layers. Importantly, the model was designed to include a cell type called pyramidal neurons, whose activity is thought to dominate the human MEG recordings.

They found that they could closely replicate the shape of the beta waves in the model by delivering two kinds of excitatory synaptic stimulation to distinct layers in the cortical columns of cells: one that was weak and broad in duration to the lower layers, contacting spiny dendrites on the pyramidal neurons close to the cell body; and another that was stronger and briefer, lasting 50 milliseconds (i.e., one beta period), to the upper layers, contacting dendrites farther away from the cell body. The strong distal drive created the valley in the waveform that determined the beta frequency.

Meanwhile they tried to model other hypotheses about how beta waves emerge, but found those unsuccessful.

With a model of what to look for, the team then tested it by looking for a real biological correlate of it in two animal models. The team analyzed measurements in the cortex of mice and rhesus macaques and found direct confirmation that this kind of stimulation and response occurred across the cortical layers in the animal models.

“The ultimate test of the model predictions is to record the electrical signals inside the brain,” Jones said. “These recordings supported our model predictions.”

Beta in the brain

Neither the computer models nor the measurements traced the source of the excitatory synaptic stimulations that drive the pyramidal neurons to produce the beta waves, but Jones and her co-authors posit that they likely come from the thalamus, deeper in the brain. Projections from the thalamus happen to be in exactly the right places needed to deliver signals to the right positions on the dendrites of pyramidal neurons in the cortex. The thalamus is also known to send out bursts of activity that last 50 milliseconds, as predicted by their theory.

With a new biophysical theory of how the waves emerge, the researchers hope the field can now investigate whether beta rhythms affect or merely reflect behavior and disease. Jones’s team in collaboration with Professor of Neuroscience Christopher Moore at Brown is now testing predictions from the theory that beta may decrease sensory or motor information processing functions in the brain. New hypotheses are that the inputs that create beta may also stimulate inhibitory neurons in the top layers of the cortex, or that they may may saturate the activity of the pyramidal neurons, thereby reducing their ability to process information; or that the thalamic bursts that give rise to beta occupy the thalamus to the point where it doesn’t pass information along to the cortex.

Figuring this out could lead to new therapies based on manipulating beta, Jones said.

“An active and growing field of neuroscience research is trying to manipulate brain rhythms for optimal function with stimulation techniques,” she said. “We hope that our novel finding on the neural origin of beta will help guide research to manipulate beta, and possibly other rhythms, for improved function in sensorimotor pathologies.”

Sketching out magnetism with electricity

In a proof-of-concept study published in Nature Physics, researchers drew magnetic squares in a nonmagnetic material with an electrified pen and then “read” this magnetic doodle with X-rays.

The experiment demonstrated that magnetic properties can be created and annihilated in a nonmagnetic material with precise application of an electric field – something long sought by scientists looking for a better way to store and retrieve information on hard drives and other magnetic memory devices. The research took place at the Department of Energy’s SLAC National Accelerator Laboratory and the Korea Advanced Institute of Science and Technology.

“The important thing is that it’s reversible. Changing the voltage of the applied electric field demagnetizes the material again,” said Hendrik Ohldag, a co-author on the paper and scientist at the lab’s Stanford Synchrotron Radiation Lightsource (SSRL), a DOE Office of Science User Facility.

“That means this technique could be used to design new types of memory storage devices with additional layers of information that can be turned on and off with an electric field, rather than the magnetic fields used today,” Ohldag said. “This would allow more targeted control, and would be less likely to cause unwanted effects in surrounding magnetic areas.”

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Sixty Symbols has a great new video explaining the laboratory set-up for demoing a Kelvin-Helmholtz instability. You can see a close-up from the demo above. Here the pink liquid is fresh water and the blue is slightly denser salt water. When the tank holding them is tipped, the lighter fresh water flows upward while the salt water flows down. This creates a big velocity gradient and lots of shear at the interface between them. The situation is unstable, meaning that any slight waviness that forms between the two layers will grow (exponentially, in this case). Note that for several long seconds, it seems like nothing is happening. That’s when any perturbations in the system are too small for us to see. But because the instability causes those perturbations to grow at an exponential rate, we see the interface go from a slight waviness to a complete mess in only a couple of seconds. The Kelvin-Helmholtz instability is incredibly common in nature, appearing in clouds, ocean waves, other planets’ atmospheres, and even in galaxy clusters! (Image and video credit: Sixty Symbols)

Flowers

My Neighbor Totoro | Tonari no Totoro (1988, Japan)

Director: Hayao Miyazaki Cinematographer: Mark Henley