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      "body": "<html> <p>https://cdn.steemitimages.com/DQmXRMuvbpicRvEZ3gfbkk3tvzgMqECRhp2C5b1RgEymk2N/image.png</p> <p><br></p> <p><strong>A small population of brain cells deep in a memory-making region of the brain controls the production of new neurons and may have a role in common brain disorders, according to a study from scientists at the University of North Carolina School of Medicine.</strong></p> <p><br></p> <p>The scientists, who published their work in Neuron, showed that 'mossy cells' in the hippocampus regulate local stem cells to control their production of new neurons, which is important for normal learning and memory, stress response, and mood regulation. Such neurogenesis in the adult brain is disrupted in many common conditions including Alzheimer's disease, depression, anxiety, schizophrenia, traumatic brain injury, and some forms of epilepsy. Targeting mossy cells to reverse such disruption may therefore offer a new strategy for treating these conditions.</p> <p><br></p> <p>'<strong>The hope is we could manipulate even a small number of mossy cells to restore hippocampal neurogenesis and related brain functions</strong>,' said study senior author Juan Song, <code>PhD</code>, assistant professor of pharmacology and member of the UNC Neuroscience Center.</p> <p><br></p> <p>Mossy cells are themselves a type of neuron and live in the part of the hippocampus known as the dentate gyrus, where neural stem cells also reside. These mossy cells -- and the nerve fibers that input various chemical signals to them -- earned their name because their multiple connection points give them a vaguely moss-covered appearance. Their precise functions have long been debated in part because they're not easy to distinguish from other brain cells. But mossy cells are known to be important for learning and memory and unusually sensitive to stresses. They die off quickly, for example, under the low-oxygen conditions of a stroke or heart attack, or when overstimulated by seizures in temporal lobe epilepsy.</p> <p><br></p> <p>Using advanced tracing techniques on several different strains of genetically engineered lab mice, Song and colleagues labeled mossy cells in mouse hippocampi and marked their connections to other brain cells. They found that mossy cells connected to neural stem cells directly and indirectly. These mossy cells directly communicate with stem cells in the hippocampus via an excitatory pathway. The mossy cells also indirectly communicate with stem cells through nearby neurons called interneurons that then connect to stem cells via an inhibitory pathway.</p> <p><br></p> <p>Surprisingly, Song and her team discovered that mossy cells can shift the balance of their direct and indirect signaling, thus dynamically regulating neural stem cell activity. In their experiments, Song's team found that moderate mossy cell activity, for example, resulted in the dominance of indirect signaling and kept stem cells in a '<strong>quiescent</strong>' state that may lead to a low level of neurogenesis, but the stem cells could be preserved over the long term. In contrast, high mossy cell activity, which could occur when the brain is stimulated or in a diseased state, made direct signaling dominant and kept stem cells in an 'activated' state that may lead to a high level of neurogenesis. But this may risk losing stem cells over the long term.</p> <p><br></p> <p>The scientists observed that removing a portion of mossy cells caused neural stem cells to increase their activity briefly followed by a permanent loss of stem cells. The finding hints that the abnormal loss of mossy cells may be a key factor underlying many pathological conditions where neurogenesis is deficient. This suggests replacing or otherwise altering the activity of these mossy cells could be a viable treatment strategy, according to Song.</p> <p><br></p> <p>The experiments also indicated that for mossy cells, a few cells go a long way. In a key set of experiments, the researchers were able to label only about a quarter of the mossy cell population. Yet by manipulating the activity level of these few cells, the scientists observed large changes in hippocampal stem cell activity and neurogenesis.</p> <p><br></p> <p>'<em>We were surprised that this small population of mossy cells could exert such a big impact on stem cell behavior</em>,' Song said.</p> <p><br></p> <p>Song and her team will now make use of the tools and lessons developed in this study to examine what happens to mossy cells in various disease states and how they might be replaced or have their activity altered to treat those diseases.</p> <p><br></p> <p><br></p> </html>",
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      "body": "<html> <p>Inverse problem solving isn't a new technique in classical physics, but this algorithm represents one of the first successful examples of an inverse problem solving method with quantum materials. And it could make searching for interesting physics a more streamlined and deliberate process for many scientists. More physicists are working in condensed matter than any other subfield of physics -- the rich diversity of condensed matter systems and phenomena provide ample unsolved problems to explore, from superconductivity and superfluidity to magnetism and topology. Experimentalists probe the macro-and microscopic properties of materials to observe the behavior and interactions of particles in materials under a strict set of controls. Theoretical condensed matter physicists, on the other hand, work to develop mathematical models that predict or explain the fundamental laws that govern these behaviors and interactions.</p> <p>The field of theoretical condensed matter physics has the well-earned reputation for being esoteric and difficult for the lay person to decipher, with its focus on understanding the quantum mechanics of materials. The process of writing and solving condensed matter equations is extremely intricate and meticulous. That process generally starts with a Hamiltonian -- a mathematical model that sums up the energies of all the particles in the system.</p> <p>Clark explains, 'For a typical condensed matter problem, you start with a model, which comes out as a Hamiltonian, then you solve it, and you end up with a wave function -- and you can see the properties of that wave function and see whether there is anything interesting. This algorithm inverts that process. Now, if you know the desired type of physics you would like to study, you can represent that in a wave function, and the algorithm will generate all of the Hamiltonians -- or the specific models -- for which we would get that set of properties. To be more exact, the algorithm gives us Hamiltonians with that wave function as an energy eigenstate.'</p> <p><br></p> <p><em>Clark says the algorithm gives a new way to study physical phenomena such as superconductivity.</em></p> <p>'Typically, you would guess Hamiltonians that are likely to be superconducting and then try to solve them. What this algorithm -- in theory -- will allow us to do is to write down a wave function that we know superconducts and then automatically generate all of the Hamiltonians or the specific models that give that wave function as their solution. Once you have the Hamiltonians, in some sense, that gives you all the other properties of the system -- the excitation spectrum, all the finite temperature properties.</p> <p>That requires some more steps once you have the Hamiltonian, so we didn't improve that part of the research process. But what we did, we found a way to find interesting models, interesting Hamiltonians.'</p> <p>Chertkov adds, 'There are lots of wave functions people have written down for which there are no known Hamiltonians -- maybe 50 years worth. Now we can take any of these wave functions and ask if any Hamiltonians give those as eigenstates and you may end up with one model, no models, or many. For example, we are interested in spin-liquid wave functions, highly entangled quantum states with interesting topological properties.</p> <p><br></p> <p><em>Theorists have constructed many spin-liquid wave functions, but don't know which Hamiltonians give them.</em></p> <p><br></p> <p><code>In the future, our algorithm should let us find these Hamiltonians.'</code></p> <p><br></p> <p>Clark and Chertkov tested the algorithm on wave functions related to frustrated magnetism, a topic that presents interesting physics with many open questions. Frustrated magnetism occurs in a class of materials that is insulating, so the electrons don't move around, but their spins interact. Clark explains one such wave function they tested, 'The electron spins in a frustrated magnet want to be anti-aligned, like the north and south on a magnet, but can't because they live on triangles. So we make a wave function out of a linear-superposition of all of these frustrated states and we turn the crank of this algorithm, and ask, given this wavefunction, which is an interesting quantum state on a frustrated magnet, are there</p> <p><br></p> <p><code>Hamiltonians that would give it. And we found some.'</code></p> <p><br></p> <p>Chertkov says the results of the algorithm could point experimentalists in the right direction to find interesting new physics: 'That would hopefully be one way it would be used. You pick a wave function that has some kind of physics that you care about and you see what sort of interactions can give you that sort of physics, and hopefully then the models you find through this method can be looked for in experiments. And it turns out you find many models with our method.'</p> <p>Clark sums up, 'This has inverted the part of the process where we were sort of hunting in the dark. Before, you could say, we're going to try lots of models until we find something interesting. Now you can say, this is the interesting thing we want, let's turn the crank on this algorithm and find a model that gives that.'</p> </html>",
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      "body": "<html> <p>Inverse problem solving isn't a new technique in classical physics, but this algorithm represents one of the first successful examples of an inverse problem solving method with quantum materials. And it could make searching for interesting physics a more streamlined and deliberate process for many scientists. More physicists are working in condensed matter than any other subfield of physics -- the rich diversity of condensed matter systems and phenomena provide ample unsolved problems to explore, from superconductivity and superfluidity to magnetism and topology. Experimentalists probe the macro-and microscopic properties of materials to observe the behavior and interactions of particles in materials under a strict set of controls. Theoretical condensed matter physicists, on the other hand, work to develop mathematical models that predict or explain the fundamental laws that govern these behaviors and interactions.</p> <p>The field of theoretical condensed matter physics has the well-earned reputation for being esoteric and difficult for the lay person to decipher, with its focus on understanding the quantum mechanics of materials. The process of writing and solving condensed matter equations is extremely intricate and meticulous. That process generally starts with a Hamiltonian -- a mathematical model that sums up the energies of all the particles in the system.</p> <p>Clark explains, 'For a typical condensed matter problem, you start with a model, which comes out as a Hamiltonian, then you solve it, and you end up with a wave function -- and you can see the properties of that wave function and see whether there is anything interesting. This algorithm inverts that process. Now, if you know the desired type of physics you would like to study, you can represent that in a wave function, and the algorithm will generate all of the Hamiltonians -- or the specific models -- for which we would get that set of properties. To be more exact, the algorithm gives us Hamiltonians with that wave function as an energy eigenstate.'</p> <p><br></p> <p><em>Clark says the algorithm gives a new way to study physical phenomena such as superconductivity.</em></p> <p>'Typically, you would guess Hamiltonians that are likely to be superconducting and then try to solve them. What this algorithm -- in theory -- will allow us to do is to write down a wave function that we know superconducts and then automatically generate all of the Hamiltonians or the specific models that give that wave function as their solution. Once you have the Hamiltonians, in some sense, that gives you all the other properties of the system -- the excitation spectrum, all the finite temperature properties.</p> <p>That requires some more steps once you have the Hamiltonian, so we didn't improve that part of the research process. But what we did, we found a way to find interesting models, interesting Hamiltonians.'</p> <p>Chertkov adds, 'There are lots of wave functions people have written down for which there are no known Hamiltonians -- maybe 50 years worth. Now we can take any of these wave functions and ask if any Hamiltonians give those as eigenstates and you may end up with one model, no models, or many. For example, we are interested in spin-liquid wave functions, highly entangled quantum states with interesting topological properties.</p> <p><br></p> <p><em>Theorists have constructed many spin-liquid wave functions, but don't know which Hamiltonians give them.</em></p> <p><br></p> <p><code>In the future, our algorithm should let us find these Hamiltonians.'</code></p> <p><br></p> <p>Clark and Chertkov tested the algorithm on wave functions related to frustrated magnetism, a topic that presents interesting physics with many open questions. Frustrated magnetism occurs in a class of materials that is insulating, so the electrons don't move around, but their spins interact. Clark explains one such wave function they tested, 'The electron spins in a frustrated magnet want to be anti-aligned, like the north and south on a magnet, but can't because they live on triangles. So we make a wave function out of a linear-superposition of all of these frustrated states and we turn the crank of this algorithm, and ask, given this wavefunction, which is an interesting quantum state on a frustrated magnet, are there</p> <p><br></p> <p><code>Hamiltonians that would give it. And we found some.'</code></p> <p><br></p> <p>Chertkov says the results of the algorithm could point experimentalists in the right direction to find interesting new physics: 'That would hopefully be one way it would be used. You pick a wave function that has some kind of physics that you care about and you see what sort of interactions can give you that sort of physics, and hopefully then the models you find through this method can be looked for in experiments. And it turns out you find many models with our method.'</p> <p>Clark sums up, 'This has inverted the part of the process where we were sort of hunting in the dark. Before, you could say, we're going to try lots of models until we find something interesting. Now you can say, this is the interesting thing we want, let's turn the crank on this algorithm and find a model that gives that.'</p> </html>",
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      "body": "<html> <p>Inverse problem solving isn't a new technique in classical physics, but this algorithm represents one of the first successful examples of an inverse problem solving method with quantum materials. And it could make searching for interesting physics a more streamlined and deliberate process for many scientists. More physicists are working in condensed matter than any other subfield of physics -- the rich diversity of condensed matter systems and phenomena provide ample unsolved problems to explore, from superconductivity and superfluidity to magnetism and topology. Experimentalists probe the macro-and microscopic properties of materials to observe the behavior and interactions of particles in materials under a strict set of controls. Theoretical condensed matter physicists, on the other hand, work to develop mathematical models that predict or explain the fundamental laws that govern these behaviors and interactions.</p> <p>The field of theoretical condensed matter physics has the well-earned reputation for being esoteric and difficult for the lay person to decipher, with its focus on understanding the quantum mechanics of materials. The process of writing and solving condensed matter equations is extremely intricate and meticulous. That process generally starts with a Hamiltonian -- a mathematical model that sums up the energies of all the particles in the system.</p> <p>Clark explains, 'For a typical condensed matter problem, you start with a model, which comes out as a Hamiltonian, then you solve it, and you end up with a wave function -- and you can see the properties of that wave function and see whether there is anything interesting. This algorithm inverts that process. Now, if you know the desired type of physics you would like to study, you can represent that in a wave function, and the algorithm will generate all of the Hamiltonians -- or the specific models -- for which we would get that set of properties. To be more exact, the algorithm gives us Hamiltonians with that wave function as an energy eigenstate.'</p> <p><br></p> <p><em>Clark says the algorithm gives a new way to study physical phenomena such as superconductivity.</em></p> <p>'Typically, you would guess Hamiltonians that are likely to be superconducting and then try to solve them. What this algorithm -- in theory -- will allow us to do is to write down a wave function that we know superconducts and then automatically generate all of the Hamiltonians or the specific models that give that wave function as their solution. Once you have the Hamiltonians, in some sense, that gives you all the other properties of the system -- the excitation spectrum, all the finite temperature properties.</p> <p>That requires some more steps once you have the Hamiltonian, so we didn't improve that part of the research process. But what we did, we found a way to find interesting models, interesting Hamiltonians.'</p> <p>Chertkov adds, 'There are lots of wave functions people have written down for which there are no known Hamiltonians -- maybe 50 years worth. Now we can take any of these wave functions and ask if any Hamiltonians give those as eigenstates and you may end up with one model, no models, or many. For example, we are interested in spin-liquid wave functions, highly entangled quantum states with interesting topological properties.</p> <p><br></p> <p><em>Theorists have constructed many spin-liquid wave functions, but don't know which Hamiltonians give them.</em></p> <p><br></p> <p><code>In the future, our algorithm should let us find these Hamiltonians.'</code></p> <p><br></p> <p>Clark and Chertkov tested the algorithm on wave functions related to frustrated magnetism, a topic that presents interesting physics with many open questions. Frustrated magnetism occurs in a class of materials that is insulating, so the electrons don't move around, but their spins interact. Clark explains one such wave function they tested, 'The electron spins in a frustrated magnet want to be anti-aligned, like the north and south on a magnet, but can't because they live on triangles. So we make a wave function out of a linear-superposition of all of these frustrated states and we turn the crank of this algorithm, and ask, given this wavefunction, which is an interesting quantum state on a frustrated magnet, are there</p> <p><br></p> <p><code>Hamiltonians that would give it. And we found some.'</code></p> <p><br></p> <p>Chertkov says the results of the algorithm could point experimentalists in the right direction to find interesting new physics: 'That would hopefully be one way it would be used. You pick a wave function that has some kind of physics that you care about and you see what sort of interactions can give you that sort of physics, and hopefully then the models you find through this method can be looked for in experiments. And it turns out you find many models with our method.'</p> <p>Clark sums up, 'This has inverted the part of the process where we were sort of hunting in the dark. Before, you could say, we're going to try lots of models until we find something interesting. Now you can say, this is the interesting thing we want, let's turn the crank on this algorithm and find a model that gives that.'</p> </html>",
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