In 2017, three leading vaccine researchers submitted a grant application with an ambitious goal. At the time, no one had proved a vaccine could stop even a single beta coronavirus—the notorious viral group then known to include the lethal agents of severe acute respiratory syndrome (SARS) and Middle East respiratory syndrome (MERS), as well as several causes of the common cold and many bat viruses. But these researchers wanted to develop a vaccine against them all. Grant reviewers at the National Institute of Allergy and Infectious Diseases (NIAID) deemed the plan “outstanding.” But they gave the proposal a low priority score, dooming its bid for funding.

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Fiber optic technology is the holy grail of high-speed, long-distance telecommunications. Still, with the continuing exponential growth of internet traffic, researchers are warning of a capacity crunch.


In AVS Quantum Science, researchers from the National Institute of Standards and Technology and the University of Maryland show how quantum-enhanced receivers could play a critical role in addressing this challenge. The scientists developed a method to enhance receivers based on quantum physics properties to dramatically increase network performance while significantly reducing the error bit rate (EBR) and energy consumption.


Fiber optic technology relies on receivers to detect optical signals and convert them into electrical signals. The conventional detection process, largely as a result of random light fluctuations, produces ‘shot noise,’ which decreases detection ability and increases EBR. To accommodate this problem, signals must continually be amplified as pulsating light becomes weaker along the optic cable, but there is a limit to maintaining adequate amplification when signals become barely perceptible.


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The human body is made up of nearly 40 trillion cells, of many different types. Recent advances in experimental biology have made it possible to explore the genetic material of single cells.


RAPIDS is a suite of open-source Python libraries that can speed up data science workflows using GPU acceleration. Starting from a single-cell count matrix, RAPIDS libraries can be used to perform data processing, dimensionality reduction, clustering, visualization, and comparison of cell clusters.


Several examples are inspired by the Scanpy tutorials and based upon the AnnData format. Currently, examples provide for scRNA-seq and scATAC-seq, and can be scaled up to 1 million cells. The authors also show how to create GPU-powered interactive, in-browser visualizations to explore single-cell datasets.


Dataset sizes for single-cell genomics studies are increasing, presently reaching millions of cells. With RAPIDS, it becomes easy to analyze large datasets interactively and in real time, enabling faster scientific discoveries.


Github repository is here

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An arsenal of potential treatments takes aim at proteins that are key to the virus’ life cycle.  In March 2020, as the full scope of the COVID-19 pandemic was coming into view, Jen Nwankwo and colleagues turned a pair of artificial intelligence (AI) tools against SARS-CoV-2. One newly developed AI program, called SUEDE, digitally screens all known drug-like compounds for likely activity against biomolecules thought to be involved in the disease. The other, BAGEL, predicts how to build inhibitors to known targets. The two programs searched for compounds able to block human enzymes that play essential roles in enabling the virus to infect its host cells. While SUEDE sifted through 14 billion compounds in just a few hours and spat out a hit, BAGEL created equally fast new leads.


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USC researchers harness the power of living organisms to make materials that are strong, tolerant and resilient.


Biological systems can harness their living cells for growth and regeneration, but engineering systems cannot. Until now. Qiming Wang and researchers at the USC Viterbi School of Engineering are harnessing living bacteria to create engineering materials that are strong, tolerant, and resilient. The research is published in Advanced Materials.


“The materials we are making are living and self-growing,” said Wang, the Stephen Schrank Early Career Chair in Civil and Environmental Engineering and assistant professor of civil and environmental engineering in the Sonny Astani Department of Civil and Environmental Engineering (CEE). “We have been amazed by the sophisticated microstructures of natural materials for centuries, especially after microscopes were invented to observe these tiny structures. Now we take an important step forward: We use living bacteria as a tool to directly grow amazing structures that cannot be made on our own.”


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The rate of sea-level rise in the 20th century along much of the U.S. Atlantic coast was the fastest in 2,000 years, and southern New Jersey had the fastest rates, according to a Rutgers-led study.


The global rise in sea-level from melting ice and warming oceans from 1900 to 2000 led to a rate that’s more than twice the average for the years 0 to 1800—the most significant change, according to the study in the journal Nature Communications.


The study for the first time looked at the phenomena that contributed to sea-level change over 2,000 years at six sites along the coast (in Connecticut, New York City, New Jersey and North Carolina), using a sea-level budget. A budget enhances understanding of the processes driving sea-level change. The processes are global, regional (including geological, such as land subsidence) and local, such as groundwater withdrawal.


“Having a thorough understanding of sea-level change at sites over the long-term is imperative for regional and local planning and responding to future sea-level rise,” said lead author Jennifer S. Walker, a postdoctoral associate in the Department of Earth and Planetary Sciences in the School of Arts and Sciences at Rutgers University-New Brunswick. “By learning how different processes vary over time and contribute to sea-level change, we can more accurately estimate future contributions at specific sites.”


Sea-level rise stemming from climate change threatens to permanently inundate low-lying islands, cities and lands. It also heightens their vulnerability to flooding and damage from coastal and other storms.

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In traditional electronics, separate chips process and store data, wasting energy as they toss data back and forth over what engineers call a “memory wall.” New algorithms combine several energy-efficient hybrid chips to create the illusion of one mega–AI chip.


Smartwatches and other battery-powered electronics would be even smarter if they could run AI algorithms. But efforts to build AI-capable chips for mobile devices have so far hit a wall – the so-called “memory wall” that separates data processing and memory chips that must work together to meet the massive and continually growing computational demands imposed by AI.


Hardware and software innovations give eight chips the illusion that they’re one mega-chip working together to run AI. “Transactions between processors and memory can consume 95 percent of the energy needed to do machine learning and AI, and that severely limits battery life,” said computer scientist Subhasish Mitra, senior author of a new study published in Nature Electronics.


Now, a team that includes Stanford computer scientist Mary Wootters and electrical engineer H.-S. Philip Wong has designed a system that can run AI tasks faster, and with less energy, by harnessing eight hybrid chips, each with its own data processor built right next to its own memory storage. This recent paper builds on the team’s prior development of a new memory technology, called RRAM, that stores data even when power is switched off – like flash memory – only faster and more energy efficiently. Their RRAM advance enabled the Stanford researchers to develop an earlier generation of hybrid chips that worked alone.


Their latest design incorporates a critical new element: algorithms that meld the eight, separate hybrid chips into one energy-efficient AI-processing engine.

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Neanderthal fossils from a cave in Belgium believed to belong to the last survivors of their species ever discovered in Europe are thousands of years older than once thought, a new study said.


Previous radiocarbon dating of the remains from the Spy Cave yielded ages as recent as approximately 24,000 years ago, but the new testing pushes the clock back to between 44,200 to 40,600 years ago. The research appeared in the Proceedings of the National Academy of Sciences and was carried out by a team from Belgium, Britain and Germany.


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Scientists have recovered DNA from mammoth fossils found in Siberian permafrost that are more than a million years old. This DNA—the oldest genomic evidence recovered to date—illuminates the evolutionary history of woolly mammoths and Columbian mammoths. It also raises the prospect of recovering DNA from other organisms this ancient—including extinct members of the human family.


Ever since the recovery of two short DNA sequences from a recently extinct zebra subspecies known as the quagga in 1984, researchers have been working to get ever larger amounts of DNA from ever older remains. Advances in ancient DNA extraction and sequencing methods eventually brought to light genomes of creatures from deeper time, including cave bears and Neandertals.


In 2013, investigators announced that they had retrieved DNA from a 700,000-year-old horse fossil—by far the oldest genomic data ever obtained. But as astonishingly old as that genetic material was, some experts predicted that sequenceable DNA should survive more than a million years in fossils preserved in frozen environments.


The new findings, published today in Nature, bear that prediction out. Tom van der Valk and Love Dalén of the Center for Paleogenetics in Stockholm and their colleagues obtained DNA from molar teeth belonging to three mammoths from different time periods. Mammoth species can be distinguished on the basis of dental characteristics. One tooth, discovered in deposits thought to be around 700,000 years old, looked like that of an early woolly mammoth, Mammuthus primigenius. The other two teeth—one dated to around one million years ago and the other to 1.2 million years ago or more—resembled molars of the steppe mammoth, Mammuthus trogontherii.

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