Lifeboat Foundation

A research team from the Hefei Institutes of Physical Science of the Chinese Academy of Sciences (CAS) has developed an analysis service platform called CRISPRimmunity, which was an interactive web server for identifying important molecular events related to CRISPR and regulators of genome editing systems. The study is published in Nucleic Acids Research.

The new CRISPRimmunity platform was designed for integrated analysis and prediction of CRISPR-Cas and anti-CRISPR systems. It includes customized databases with annotations for known anti-CRISPR proteins, anti-CRISPR-associated proteins, class II CRISPR-Cas systems, CRISPR array types, HTH structural domains and mobile genetic elements. These resources allow the study of molecular events in the co-evolution of CRISPR-Cas and anti-CRISPR systems.

To improve prediction accuracy, the researchers used strategies such as homology analysis, association analysis and self-targeting in prophage regions to predict anti-CRISPR proteins. When tested on data from 99 experimentally validated Acrs and 676 non-Acrs, CRISPRimmunity achieved an accuracy of 0.997 for anti-CRISPR protein prediction.

Microbial sequence databases contain a wealth of information about enzymes and other molecules that could be adapted for biotechnology. But these databases have grown so large in recent years that they’ve become difficult to search efficiently for enzymes of interest.

Now, scientists at the Broad Institute of MIT and Harvard, the McGovern Institute for Brain Research at MIT, and the National Center for Biotechnology Information (NCBI) at the National Institutes of Health have developed a new search algorithm that has identified 188 kinds of new rare CRISPR systems in bacterial genomes, encompassing thousands of individual systems. The work appears in Science.

The algorithm, which comes from the lab of CRISPR pioneer Feng Zhang, uses big-data clustering approaches to rapidly search massive amounts of genomic data. The team used their algorithm, called Fast Locality-Sensitive Hashing-based clustering (FLSHclust) to mine three major public databases that contain data from a wide range of unusual bacteria, including ones found in coal mines, breweries, Antarctic lakes, and dog saliva.

Quantum advantage is the milestone the field of quantum computing is fervently working toward, where a quantum computer can solve problems that are beyond the reach of the most powerful non-quantum, or classical, computers.

Quantum refers to the scale of atoms and molecules where the laws of physics as we experience them break down and a different, counterintuitive set of laws apply. Quantum computers take advantage of these strange behaviors to solve problems.

Continue reading “What Is Quantum Advantage? A Quantum Computing Scientist Explains An Approaching Milestone Marking The Arrival Of Extremely Powerful Computers” »

Why it matters: While AI algorithms are seemingly everywhere, processing on the most popular platforms require powerful server GPUs to provide customers with their generative services. Arm is introducing a new dedicated chip design, set to provide AI acceleration even in the most affordable IoT devices starting next year.

The Arm Cortex-M52 is the smallest and most cost-efficient processor designed for AI acceleration applications, according to the company. This latest design from the UK-based fabless firm promises to deliver “enhanced” AI capabilities to Internet of Things (IoT) devices, as Arm states, without the need for a separate computing unit.

Paul Williamson, Arm’s SVP and general manager for the company’s IoT business, emphasized the need to bring machine learning optimized processing to “even the smallest and lowest-power” endpoint devices to fully realize the potential of AI in IoT. Despite AI’s ubiquity, Williamson noted, harnessing the “intelligence” from the vast amounts of data flowing through digital devices requires IoT appliances that are smarter and more capable.

An algorithm that can analyse hundreds of millions of genetic sequences has identified DNA-cutting genes and enzymes that are extremely rare in nature.

Light confined to an accelerating optical cavity could display a photonic counterpart of the electronic quantum Hall effect.

Place a conductor in a magnetic field and the electrical current driven by an applied voltage will not flow in a straight line but in a direction perpendicular to the electric field—a behavior known as the Hall effect [1]. Reduce the temperature to the point where the electrons manifest quantum-mechanical behavior, and the plot thickens. The conductivity (defined as the ratio between the sideways current and the voltage) exhibits discrete jumps as the magnetic field is varied—the quantum Hall effect [2]. Since electrons at low temperature and photons obey a similar wave equation [3], should we also expect a quantum Hall effect for light? This question has been bubbling under the surface for the past decade, leading to the observation of some aspects of an optical quantum Hall effect [4, 5]. But the analogy between photons and electrons remains incomplete.

An open-source advanced supercomputer algorithm predicts the patterning and dynamics of living materials, allowing for the exploration of their behaviors across space and time.

Biological materials consist of individual components, including tiny motors that transform fuel into motion. This process creates patterns of movement, leading the material to shape itself through coherent flows driven by constant energy consumption. These perpetually driven materials are called “active matter.”

The mechanics of cells and tissues can be described by active matter theory, a scientific framework to understand the shape, flows, and form of living materials. The active matter theory consists of many challenging mathematical equations.

Nov 22 (Reuters) — Ahead of OpenAI CEO Sam Altman’s four days in exile, several staff researchers sent the board of directors a letter warning of a powerful artificial intelligence discovery that they said could threaten humanity, two people familiar with the matter told Reuters.

The previously unreported letter and AI algorithm was a key development ahead of the board’s ouster of Altman, the poster child of generative AI, the two sources said. Before his triumphant return late Tuesday, more than 700 employees had threatened to quit and join backer Microsoft (MSFT.O) in solidarity with their fired leader.

The sources cited the letter as one factor among a longer list of grievances by the board that led to Altman’s firing. Reuters was unable to review a copy of the letter. The researchers who wrote the letter did not immediately respond to requests for comment.

AI has mastered some of the most complex games known to man, but models are generally tailored to solve specific kinds of challenges. A new DeepMind algorithm that can tackle a much wider variety of games could be a step towards more general AI, its creators say.

Using games as a benchmark for AI has a long pedigree. When IBM’s Deep Blue algorithm beat chess world champion Garry Kasparov in 1997, it was hailed as a milestone for the field. Similarly, when DeepMind’s AlphaGo defeated one of the world’s top Go players, Lee Sedol, in 2016, it led to a flurry of excitement about AI’s potential.

DeepMind built on this success with AlphaZero, a model that mastered a wide variety of games, including chess and shogi. But as impressive as this was, AlphaZero only worked with perfect information games where every detail of the game, other than the opponent’s intentions, is visible to both players. This includes games like Go and chess where both players can always see all the pieces on the board.