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Dedicated to ending the HIV epidemic — dr. moupali das, MD, MPH, executive director, HIV clinical research, gilead sciences.

Dr. Moupali Das, MD, MPH, is Executive Director, HIV Clinical Research, in the Virology Therapeutic Area, at Gilead Sciences (https://www.gilead.com/), where she leads the pre-exposure prophylaxis (PrEP) clinical drug development program, including evaluating the safety and efficacy of a long-acting, twice yearly, subcutaneous injection being studied for HIV prevention. Her responsibilities also include expanding the populations who may benefit from PrEP.

Dr. Das has led high-performing teams in academic medicine, public health, implementation science, and cross-functionally in drug development. She has successfully helped develop, implement, and evaluate how to better test, link to care, increase virologic suppression, and improve quality of life for people with HIV, and to prevent HIV in those who may benefit from PrEP.

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Experts consider glass nanoparticles kept inside extreme vacuum layers as potential platforms for examining the quantum world’s limits. However, a question in the field of quantum theory remains unanswered: at which size does an object start being described by quantum physics laws rather than classical physics laws?

Achieving Quantum-State Cooling in More Than One Direction Is Challenging

SciTechDaily reports that a research team attempted to precisely answer the question through the ERC-Synergy project Q-Xtreme. The team comprised Lukas Novotny from ETH Zurich, Markus Aspelmeyer from the University of Vienna, Oriol Romero-Isart from the University of Innsbruck, and Romain Quidant from Zurich.

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Computer models are an important tool for studying how the brain makes and stores memories and other types of complex information. But creating such models is a tricky business. Somehow, a symphony of signals—both biochemical and electrical—and a tangle of connections between neurons and other cell types creates the hardware for memories to take hold. Yet because neuroscientists don’t fully understand the underlying biology of the brain, encoding the process into a computer model in order to study it further has been a challenge.

Now, researchers at the Okinawa Institute of Science and Technology (OIST) have altered a commonly used computer model of called a Hopfield network in a way that improves performance by taking inspiration from biology. They found that not only does the new network better reflect how neurons and other cells wire up in the , it can also hold dramatically more memories.

The complexity added to the network is what makes it more realistic, says Thomas Burns, a Ph.D. student in the group of Professor Tomoki Fukai, who heads OIST’s Neural Coding and Brain Computing Unit. “Why would biology have all this complexity? Memory capacity might be a reason,” Mr. Burns says.

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National University of Singapore (NUS) pharmaceutical scientists have developed synthetic peptide nanonets for treating infections by bacteria strains resistant to last-resort antibiotics.

In nature, trap-and-kill is a common immune defense mechanism employed by various species, including humans. In response to the presence of pathogens, peptides are released from host cells and they promptly self-assemble in solution to form cross-linked nanonets, which then entrap the bacteria and render them more vulnerable to antimicrobial components.

Several research groups have explored synthetic biomimetics of nanonets as an avenue for addressing the global healthcare challenge of widespread . However, most prominent studies in the field only yielded disjointed short nanofibrils restricted to the bacterial surfaces and are incapable of physically immobilizing the bacteria. Additionally, these designs were lacking in control over the initiation of the self-assembly process.

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Optical computing has been gaining wide interest for machine learning applications because of the massive parallelism and bandwidth of optics. Diffractive networks provide one such computing paradigm based on the transformation of the input light as it diffracts through a set of spatially-engineered surfaces, performing computation at the speed of light propagation without requiring any external power apart from the input light beam. Among numerous other applications, diffractive networks have been demonstrated to perform all-optical classification of input objects.

Researchers at the University of California, Los Angeles (UCLA), led by Professor Aydogan Ozcan, have introduced a “time-lapse” scheme to significantly improve the accuracy of diffractive optical networks on complex input objects. The findings are published in the journal Advanced Intelligent Systems.

In this scheme, the object and/or the diffractive network are moved relative to each other during the exposure of the output detectors. Such a “time-lapse” scheme has previously been used to achieve super-resolution imaging, for example, in , by capturing multiple images of a scene with lateral movements of the camera.

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