Material to system-level benchmarking of CMOS-integrated RRAM with ultra-fast switching for low power on-chip learning.

Analog hardware-based training provides a promising solution to developing state-of-the-art power-hungry artificial intelligence models. Non-volatile memory hardware such as resistive random access memory (RRAM) has the potential to provide a low power alternative. The training accuracy of analog hardware depends on RRAM switching properties including the number of discrete conductance states and conductance variability.

A synergistic future for AI and ecology.

Research in both ecology and AI strives for predictive understanding of complex systems, where nonlinearities arise from multidimensional interactions and feedbacks across multiple scales. After a century of independent, asynchronous advances in computational and ecological research, we foresee a critical need for intentional synergy to meet current societal challenges against the backdrop of global change. These challenges include understanding the unpredictability of systems-level phenomena and resilience dynamics on a rapidly changing planet.

A biological sequence comparison algorithm using quantum computers.

Genetic information is encoded as linear sequences of nucleotides, represented by letters ranging from thousands to billions. Differences between sequences are identified through comparative approaches like sequence analysis, where variations can occur at the individual nucleotide level or collectively due to various phenomena such as recombination or deletion. Detecting these sequence differences is vital for understanding biology and medicine, but the complexity and size of genomic data require substantial classical computing power.

An analog-AI chip for energy-efficient speech recognition and transcription.

Models of artificial intelligence (AI) that have billions of parameters can achieve high accuracy across a range of tasks, but they exacerbate the poor energy efficiency of conventional general-purpose processors, such as graphics processing units or central processing units. Analog in-memory computing (analog-AI) can provide better energy efficiency by performing matrix-vector multiplications in parallel on 'memory tiles'. However, analog-AI has yet to demonstrate software-equivalent (SW) accuracy on models that require many such tiles and efficient communication of neural-network activations between the tiles.

Role of masks in mitigating viral spread on networks.

Masks have remained an important mitigation strategy in the fight against COVID-19 due to their ability to prevent the transmission of respiratory droplets between individuals. In this work, we provide a comprehensive quantitative analysis of the impact of mask-wearing. To this end, we propose a novel agent-based model of viral spread on networks where agents may either wear no mask or wear one of several types of masks with different properties (e.

r-BRICS - A Revised BRICS Module That Breaks Ring Structures and Carbon Chains.

Molecular fragmentation has been frequently used for machine learning, molecular modeling, and drug discovery studies. However, the current molecular fragmentation tools often lead to large fragments that are useful to limited tasks. Specifically, long aliphatic chains, certain connected ring structures, fused rings, as well as various nitrogen-containing molecular entities often remain intact when using BRICS.

Ab initio quantum chemistry with neural-network wavefunctions.

Deep learning methods outperform human capabilities in pattern recognition and data processing problems and now have an increasingly important role in scientific discovery. A key application of machine learning in molecular science is to learn potential energy surfaces or force fields from ab initio solutions of the electronic Schrödinger equation using data sets obtained with density functional theory, coupled cluster or other quantum chemistry (QC) methods. In this Review, we discuss a complementary approach using machine learning to aid the direct solution of QC problems from first principles.

Quantum-enhanced Markov chain Monte Carlo.

Quantum computers promise to solve certain computational problems much faster than classical computers. However, current quantum processors are limited by their modest size and appreciable error rates. Recent efforts to demonstrate quantum speedups have therefore focused on problems that are both classically hard and naturally suited to current quantum hardware, such as sampling from complicated-although not explicitly useful-probability distributions.

An end-to-end deep learning framework for translating mass spectra to de-novo molecules.

Elucidating the structure of a chemical compound is a fundamental task in chemistry with applications in multiple domains including drug discovery, precision medicine, and biomarker discovery. The common practice for elucidating the structure of a compound is to obtain a mass spectrum and subsequently retrieve its structure from spectral databases. However, these methods fail for novel molecules that are not present in the reference database.

Accelerating drug target inhibitor discovery with a deep generative foundation model.

Inhibitor discovery for emerging drug-target proteins is challenging, especially when target structure or active molecules are unknown. Here, we experimentally validate the broad utility of a deep generative framework trained at-scale on protein sequences, small molecules, and their mutual interactions-unbiased toward any specific target. We performed a protein sequence-conditioned sampling on the generative foundation model to design small-molecule inhibitors for two dissimilar targets: the spike protein receptor-binding domain (RBD) and the main protease from SARS-CoV-2.

Evidence for the utility of quantum computing before fault tolerance.

Quantum computing promises to offer substantial speed-ups over its classical counterpart for certain problems. However, the greatest impediment to realizing its full potential is noise that is inherent to these systems. The widely accepted solution to this challenge is the implementation of fault-tolerant quantum circuits, which is out of reach for current processors.

Spreading processes with mutations over multilayer networks.

A key scientific challenge during the outbreak of novel infectious diseases is to predict how the course of the epidemic changes under countermeasures that limit interaction in the population. Most epidemiological models do not consider the role of mutations and heterogeneity in the type of contact events. However, pathogens have the capacity to mutate in response to changing environments, especially caused by the increase in population immunity to existing strains, and the emergence of new pathogen strains poses a continued threat to public health.

Molecular basis of differential HLA class I-restricted T cell recognition of a highly networked HIV peptide.

Cytotoxic-T-lymphocyte (CTL) mediated control of HIV-1 is enhanced by targeting highly networked epitopes in complex with human-leukocyte-antigen-class-I (HLA-I). However, the extent to which the presenting HLA allele contributes to this process is unknown. Here we examine the CTL response to QW9, a highly networked epitope presented by the disease-protective HLA-B57 and disease-neutral HLA-B53.

A Small Step Toward Generalizability: Training a Machine Learning Scoring Function for Structure-Based Virtual Screening.

Over the past few years, many machine learning-based scoring functions for predicting the binding of small molecules to proteins have been developed. Their objective is to approximate the distribution which takes two molecules as input and outputs the energy of their interaction. Only a scoring function that accounts for the interatomic interactions involved in binding can accurately predict binding affinity on unseen molecules.

A mutagenesis study of autoantigen optimization for potential T1D vaccine design.

A previously reported autoreactive antigen, termed the X-idiotype, isolated from a unique cell population in Type 1 diabetes (T1D) patients, was found to stimulate their CD4+ T cells. This antigen was previously determined to bind more favorably than insulin and its mimic (insulin superagonist) to HLA-DQ8, supporting its strong role in CD4+ T cell activation. In this work, we probed HLA-X-idiotype-TCR binding and designed enhanced-reactive pHLA-TCR antigens using an in silico mutagenesis approach which we functionally validated by cell proliferation assays and flow cytometry.

Impact of the COVID-19 pandemic on treatment for mental health needs: a perspective on service use patterns and expenditures from commercial medical claims data.

Objective: To examine changes in use patterns, cost of healthcare services before and after the outbreak of the COVID-19 pandemic, and their impacts on expenditures for patients receiving treatment for depression, anxiety, eating disorders, and substance use.

Methods: This cross-sectional study employed statistical tests to analyze claims in MarketScan® Commercial Database in March 2020-February 2021 and quarterly from March 2020 to August 2021, compared to respective pre-pandemic periods. The analysis is based on medical episodes created by the Merative™ Medical Episode Grouper (MEG).

Topology and redescriptions detect multiple alternative biological pathways from clinical phenotypes.

Biological pathways play a crucial role in the properties of diseases and are important in drug discovery. Identifying the logical relationships among distinctive phenotypic clusters could reveal possible connections to the underlying pathways. However, this process is challenging since clinical phenotypes are often available through unstructured electronic health records.

Ethical Implications of the Use of Language Analysis Technologies for the Diagnosis and Prediction of Psychiatric Disorders.

Recent developments in artificial intelligence technologies have come to a point where machine learning algorithms can infer mental status based on someone's photos and texts posted on social media. More than that, these algorithms are able to predict, with a reasonable degree of accuracy, future mental illness. They potentially represent an important advance in mental health care for preventive and early diagnosis initiatives, and for aiding professionals in the follow-up and prognosis of their patients.

Roughness of Molecular Property Landscapes and Its Impact on Modellability.

In molecular discovery and drug design, structure-property relationships and activity landscapes are often qualitatively or quantitatively analyzed to guide the navigation of chemical space. The roughness (or smoothness) of these molecular property landscapes is one of their most studied geometric attributes, as it can characterize the presence of activity cliffs, with rougher landscapes generally expected to pose tougher optimization challenges. Here, we introduce a general, quantitative measure for describing the roughness of molecular property landscapes.

On the Choice of Active Site Sequences for Kinase-Ligand Affinity Prediction.

Recent work showed that active site rather than full-protein-sequence information improves predictive performance in kinase-ligand binding affinity prediction. To refine the notion of an "active site", we here propose and compare multiple definitions. We report significant evidence that our novel definition is superior to previous definitions and better models of ATP-noncompetitive inhibitors.

On-chip scalable highly pure and indistinguishable single-photon sources in ordered arrays: Path to quantum optical circuits.

Realization of quantum optical circuits is at the heart of quantum photonic information processing. A long-standing obstacle, however, has been the absence of a suitable platform of single photon sources (SPSs). Such SPSs need to be in spatially ordered arrays and produce, on-demand, highly pure, and indistinguishable single photons with sufficiently uniform emission characteristics to enable controlled interference between photons from distinct sources underpinning functional quantum optical networks.

Cross-Border Transmissions of the Delta Substrain AY.29 During Tokyo Olympic and Paralympic Games.

Tokyo Olympic and Paralympic Games, postponed for the COVID-19 pandemic, were finally held in the summer of 2021. Just before the games, the Alpha variant was being replaced with the more contagious Delta variant. AY.