The Response Regulator OmpR Negatively Controls the Expression of Genes Implicated in Tilimycin and Tilivalline Cytotoxin Production in .

toxigenic strains represent a critical health threat, mainly due to their link to antibiotic-associated hemorrhagic colitis. This serious condition results from the bacteria's ability to produce tilimycin and tilivalline cytotoxins. Our research highlights the pivotal role of OmpR, a key regulator within the EnvZ/OmpR two-component system, in controlling the virulence factors associated with .

Uniform volumetric single-cell processing for organ-scale molecular phenotyping.

Extending single-cell analysis to intact tissues while maintaining organ-scale spatial information poses a major challenge due to unequal chemical processing of densely packed cells. Here we introduce Continuous Redispersion of Volumetric Equilibrium (CuRVE) in nanoporous matrices, a framework to address this challenge. CuRVE ensures uniform processing of all cells in organ-scale tissues by perpetually maintaining dynamic equilibrium of the tissue's gradually shifting chemical environment.

Designed mosaic nanoparticles enhance cross-reactive immune responses in mice.

Nanoparticle vaccines displaying combinations of SARS-like betacoronavirus (sarbecovirus) receptor-binding domains (RBDs) could protect against SARS-CoV-2 variants and spillover of zoonotic sarbecoviruses into humans. Using a computational approach, we designed variants of SARS-CoV-2 RBDs and selected 7 natural sarbecovirus RBDs, each predicted to fold properly and abrogate antibody responses to variable epitopes. RBDs were attached to 60-mer nanoparticles to make immunogens displaying two (mosaic-2s), five (mosaic-5), or seven (mosaic-7) different RBDs for comparisons with mosaic-8b, which elicited cross-reactive antibodies and protected animals from sarbecovirus challenges.

Myelin ensheathment and drug responses of oligodendrocytes are modulated by stiffness of artificial axons.

Myelination is a key biological process wherein glial cells such as oligodendrocytes wrap myelin around neuronal axons, forming an insulative sheath that accelerates signal propagation down the axon. A major obstacle to understanding myelination is the challenge of visualizing and reproducibly quantifying this inherently three-dimensional process in vitro. To this end, we previously developed artificial axons (AAs), a biocompatible platform consisting of 3D-printed hydrogel-based axon mimics designed to more closely recapitulate the micrometer-scale diameter and sub-kilopascal mechanical stiffness of biological axons.

Automating alloy design and discovery with physics-aware multimodal multiagent AI.

The design of new alloys is a multiscale problem that requires a holistic approach that involves retrieving relevant knowledge, applying advanced computational methods, conducting experimental validations, and analyzing the results, a process that is typically slow and reserved for human experts. Machine learning can help accelerate this process, for instance, through the use of deep surrogate models that connect structural and chemical features to material properties, or vice versa. However, existing data-driven models often target specific material objectives, offering limited flexibility to integrate out-of-domain knowledge and cannot adapt to new, unforeseen challenges.

Measuring self-similarity in empirical signals to understand musical beat perception.

Experiencing music often entails the perception of a periodic beat. Despite being a widespread phenomenon across cultures, the nature and neural underpinnings of beat perception remain largely unknown. In the last decade, there has been a growing interest in developing methods to probe these processes, particularly to measure the extent to which beat-related information is contained in behavioral and neural responses.

Synthetic data in generalizable, learning-based neuroimaging.

Synthetic data have emerged as an attractive option for developing machine-learning methods in human neuroimaging, particularly in magnetic resonance imaging (MRI)-a modality where image contrast depends enormously on acquisition hardware and parameters. This retrospective paper reviews a family of recently proposed methods, based on synthetic data, for generalizable machine learning in brain MRI analysis. Central to this framework is the concept of domain randomization, which involves training neural networks on a vastly diverse array of synthetically generated images with random contrast properties.

Machine learning for antimicrobial peptide identification and design.

Artificial intelligence (AI) and machine learning (ML) models are being deployed in many domains of society and have recently reached the field of drug discovery. Given the increasing prevalence of antimicrobial resistance, as well as the challenges intrinsic to antibiotic development, there is an urgent need to accelerate the design of new antimicrobial therapies. Antimicrobial peptides (AMPs) are therapeutic agents for treating bacterial infections, but their translation into the clinic has been slow owing to toxicity, poor stability, limited cellular penetration and high cost, among other issues.

Lysosomal dysfunction and inflammatory sterol metabolism in pulmonary arterial hypertension.

Vascular inflammation regulates endothelial pathophenotypes, particularly in pulmonary arterial hypertension (PAH). Dysregulated lysosomal activity and cholesterol metabolism activate pathogenic inflammation, but their relevance to PAH is unclear. Nuclear receptor coactivator 7 () deficiency in endothelium produced an oxysterol and bile acid signature through lysosomal dysregulation, promoting endothelial pathophenotypes.

Passive infusion of an S2-Stem broadly neutralizing antibody protects against SARS-CoV-2 infection and lower airway inflammation in rhesus macaques.

The continued evolution of SARS-CoV-2 variants capable of subverting vaccine and infection-induced immunity suggests the advantage of a broadly protective vaccine against betacoronaviruses (β-CoVs). Recent studies have isolated monoclonal antibodies (mAbs) from SARS-CoV-2 recovered-vaccinated donors capable of neutralizing many variants of SARS-CoV-2 and other β-CoVs. Many of these mAbs target the conserved S2 stem region of the SARS-CoV-2 spike protein, rather than the receptor binding domain contained within S1 primarily targeted by current SARS-CoV-2 vaccines.

Epstein-Barr virus BALF0/1 subverts the Caveolin and ERAD pathways to target B cell receptor complexes for degradation.

Epstein-Barr virus (EBV) establishes persistent infection, causes infectious mononucleosis, is a major trigger for multiple sclerosis and contributes to multiple cancers. Yet, knowledge remains incomplete about how the virus remodels host B cells to support lytic replication. We previously identified that EBV lytic replication results in selective depletion of plasma membrane (PM) B cell receptor (BCR) complexes, composed of immunoglobulin and the CD79A and CD79B signaling chains.

Activation and modulation of the host response to DNA damage by an integrative and conjugative element.

Mobile genetic elements help drive horizontal gene transfer and bacterial evolution. Conjugative elements and temperate bacteriophages can be stably maintained in host cells. They can alter host physiology and regulatory responses and typically carry genes that are beneficial to their hosts.

Measuring the effects of motion corruption in fetal fMRI.

Irregular and unpredictable fetal movement is the most common cause of artifacts in in utero functional magnetic resonance imaging (fMRI), affecting analysis and limiting our understanding of early functional brain development. The accurate detection of corrupted functional connectivity (FC) resulting from motion artifacts or preprocessing, instead of neural activity, is a prerequisite for reliable and valid analysis of FC and early brain development. Approaches to address this problem in adult data are of limited utility in fetal fMRI.

Ultrahigh Specific Strength by Bayesian Optimization of Carbon Nanolattices.

Nanoarchitected materials are at the frontier of metamaterial design and have set the benchmark for mechanical performance in several contemporary applications. However, traditional nanoarchitected designs with conventional topologies exhibit poor stress distributions and induce premature nodal failure. Here, using multi-objective Bayesian optimization and two-photon polymerization, optimized carbon nanolattices with an exceptional specific strength of 2.

Point-of-need diagnostics in a post-Covid world: an opportunity for paper-based microfluidics to serve during syndemics.

Zoonotic outbreaks present with unpredictable threats to human health, food production, biodiversity, national security and disrupt the global economy. The COVID-19 pandemic-caused by zoonotic coronavirus, SARS-CoV2- is the most recent upsurge of an increasing trend in outbreaks for the past 100 years. This year, emergence of avian influenza (H5N1) is a stark reminder of the need for national and international pandemic preparedness.

X-Mapper: fast and accurate sequence alignment via gapped x-mers.

Sequence alignment is foundational to many bioinformatic analyses. Many aligners start by splitting sequences into contiguous, fixed-length seeds, called k-mers. Alignment is faster with longer, unique seeds, but more accurate with shorter seeds avoiding mutations.

Global 3D model of mantle attenuation using seismic normal modes.

Seismic tomographic models based only on wave velocities have limited ability to distinguish between a thermal or compositional origin for Earth's 3D structure. Complementing wave velocities with attenuation observations can make that distinction, which is fundamental for understanding mantle convection evolution. However, global 3D attenuation models are only available for the upper mantle at present.

Extended quantum anomalous Hall states in graphene/hBN moiré superlattices.

Electrons in topological flat bands can form new topological states driven by correlation effects. The pentalayer rhombohedral graphene/hexagonal boron nitride (hBN) moiré superlattice was shown to host fractional quantum anomalous Hall effect (FQAHE) at approximately 400 mK (ref. ), triggering discussions around the underlying mechanism and role of moiré effects.

Diversity in the medical research ecosystem: a descriptive scientometric analysis of over 49 000 studies and 150 000 authors published in high-impact medical journals between 2007 and 2022.

Objectives: Health research that significantly impacts global clinical practice and policy is often published in high-impact factor (IF) medical journals. These outlets play a pivotal role in the worldwide dissemination of novel medical knowledge. However, researchers identifying as women and those affiliated with institutions in low- and middle-income countries (LMICs) have been largely under-represented in high-IF journals across multiple fields of medicine.

Defects vibrations engineering for enhancing interfacial thermal transport in polymer composites.

To push upper boundaries of thermal conductivity in polymer composites, understanding of thermal transport mechanisms is crucial. Despite extensive simulations, systematic experimental investigation on thermal transport in polymer composites is limited. To better understand thermal transport processes, we design polymer composites with perfect fillers (graphite) and defective fillers (graphite oxide), using polyvinyl alcohol (PVA) as a matrix model.

Tensor Train Optimization for Conformational Sampling of Organic Molecules.

Exploring the conformational space of molecules remains a challenge of fundamental importance to quantum chemistry: identification of relevant conformers at ambient conditions enables predictive simulations of almost arbitrary properties. Here, we propose a novel approach, called TTConf, to enable conformational sampling of large organic molecules where the combinatorial explosion of possible conformers prevents the use of a brute-force systematic conformer search. We employ tensor trains as a highly efficient dimensionality reduction algorithm, effectively reducing the scaling from exponential to polynomial.

Automated Flow Synthesis of Artificial Heme Enzymes for Enantiodivergent Biocatalysis.

The remarkable efficiency with which enzymes catalyze small-molecule reactions has driven their widespread application in organic chemistry. Here, we employ automated fast-flow solid-phase synthesis to access catalytically active full-length enzymes without restrictions on the number and structure of noncanonical amino acids incorporated. We demonstrate the total syntheses of iron-dependent myoglobin (BsMb) and sperm whale myoglobin (SwMb).

Influences of fluctuating nutrient loadings on nitrate-reducing microorganisms in rivers.

Rivers serve important functions for human society and are significantly impacted by anthropogenic nutrient inputs (e.g. organic and sulfur compounds).