Simulations of Attosecond Metallization in Quartz and Diamond Probed with Inner-Shell Transient Absorption Spectroscopy.

When dielectrics are hit with intense infrared (IR) laser pulses, transient metalization can occur. The initial attosecond dynamics behind this metallization are not entirely understood. Therefore, simulations are needed to understand this process and to help interpret experimental observations of it, such as with attosecond transient absorption (ATA).

Evaluating the antibacterial activity of engineered phage ФEcSw endolysin against multidrug-resistant Escherichia coli strain Sw1.

Objective: The emergence of bacteriophage-encoded endolysins hold significant promise as novel antibacterial agents, particularly against the growing threat of antibiotic-resistant bacteria. Therefore, we investigated the phage ФEcSw endolysin to enhance the lytic activity against multi-drug-resistant Escherichia coli Sw1 through site-directed mutagenesis (SDM) guided by in silico identification of critical residues.

Methods: A computational analysis was conducted to elucidate the protein folding pattern, identify the active domains, and recognize critical residues of ФEcSw endolysin.

Generalized representative structures for atomistic systems.

A new method is presented to generate atomic structures that reproduce the essential characteristics of arbitrary material systems, phases, or ensembles. Previous methods allow one to reproduce the essential characteristics (e.g.

Machine Learning Techniques to Infer Protein Structure and Function from Sequences: A Comprehensive Review.

The elucidation of protein structure and function plays a pivotal role in understanding biological processes and facilitating drug discovery. With the exponential growth of protein sequence data, machine learning techniques have emerged as powerful tools for predicting protein characteristics from sequences alone. This review provides a comprehensive overview of the importance and application of machine learning in inferring protein structure and function.

Statistical mechanics of stochastic quantum control: d-adic Rényi circuits.

The dynamics of quantum information in many-body systems with large onsite Hilbert space dimension admits an enlightening description in terms of effective statistical mechanics models. Motivated by this fact, we reveal a connection between three separate models: the classically chaotic d-adic Rényi map with stochastic control, a quantum analog of this map for qudits, and a Potts model on a random graph. The classical model and its quantum analog share a transition between chaotic and controlled phases, driven by a randomly applied control map that attempts to order the system.

Assessing dimensions of thought disorder with large language models: The tradeoff of accuracy and consistency.

Natural Language Processing (NLP) methods have shown promise for the assessment of formal thought disorder, a hallmark feature of schizophrenia in which disturbances to the structure, organization, or coherence of thought can manifest as disordered or incoherent speech. We investigated the suitability of modern Large Language Models (LLMs - e.g.

Mapping and Characterization of Local Structures of CsPbBr.

Inorganic perovskite CsPbBr is a promising material for optoelectronic applications and high-energy radiation detection due to its excellent photophysical properties, high carrier mobility, large carrier diffusion length, and higher stability than organic perovskite materials. Understanding phase transitions at the atomic level is crucial for optimizing its applications. Here, we employ experimental characterizations and molecular dynamics simulations to study the phase transitions in CsPbBr as a function of temperature.

Enrichment using speech latencies improves treatment effect size in a clinical trial of bipolar depression.

Clinical trials in depression lack objective measures. Speech latencies are an objective measure of psychomotor slowing with face validity and empirical support. 'Turn latency' is the response time between speakers.

Insights into core-mantle differentiation from bulk Earth melt simulations.

The earth is thought to have gone through complex physicochemical changes during the accretion and magma ocean stages. To better understand this evolution process at the fundamental level, we investigate the behavior of a bulk earth melt system by simulating the composition FeMgSiO (in wt%) at high pressure. A deep neural network potential trained by first-principles data can enable accurate molecular dynamics simulation of large supercells that greatly enhances sampling for reliable evaluation of elemental partitioning.

Evaluating speech latencies during structured psychiatric interviews as an automated objective measure of psychomotor slowing.

We sought to derive an objective measure of psychomotor slowing from speech analytics during a psychiatric interview to avoid potential burden of dedicated neurophysiological testing. Speech latency, which reflects response time between speakers, shows promise from the literature. Speech data was obtained from 274 subjects with a diagnosis of bipolar I depression enrolled in a randomized, doubleblind, 6-week phase 2 clinical trial.

Out of time order correlation of the Hubbard model with random local disorder.

The out-of-time-order correlator (OTOC) serves as a powerful tool for investigating quantum information spreading and chaos in complex systems. We present a method employing non-equilibrium dynamical mean-field theory and coherent potential approximation combined with diagrammatic perturbation on the Schwinger-Keldysh contour to calculate the OTOC for correlated fermionic systems subjected to both random disorder and electron interaction. Our key finding is that random disorder enhances the OTOC decay in the Hubbard model for the metallic phase in the weakly interacting limit.

SynerGNet: A Graph Neural Network Model to Predict Anticancer Drug Synergy.

Drug combination therapy shows promise in cancer treatment by addressing drug resistance, reducing toxicity, and enhancing therapeutic efficacy. However, the intricate and dynamic nature of biological systems makes identifying potential synergistic drugs a costly and time-consuming endeavor. To facilitate the development of combination therapy, techniques employing artificial intelligence have emerged as a transformative solution, providing a sophisticated avenue for advancing existing therapeutic approaches.

Augmented drug combination dataset to improve the performance of machine learning models predicting synergistic anticancer effects.

Combination therapy has gained popularity in cancer treatment as it enhances the treatment efficacy and overcomes drug resistance. Although machine learning (ML) techniques have become an indispensable tool for discovering new drug combinations, the data on drug combination therapy currently available may be insufficient to build high-precision models. We developed a data augmentation protocol to unbiasedly scale up the existing anti-cancer drug synergy dataset.

Tracking Charge Migration with Frequency-Matched Strobo-Spectroscopy.

We present frequency-matched strobo-spectroscopy (FMSS) of charge migration (CM) in bromobutadiyne, simulated with time-dependent density functional theory. CM + FMSS is a pump-probe scheme that uses a frequency-matched high harmonic generation (HHG)-driving laser as an independent probe step, following the creation of a localized hole on the bromine atom that induces CM dynamics. We show that the delay-dependent harmonic yield tracks the phase of the CM dynamics through its sensitivity to the amount of electron density on the bromine end of the molecule.

Unlocking the Potential of Kinase Targets in Cancer: Insights from CancerOmicsNet, an AI-Driven Approach to Drug Response Prediction in Cancer.

Deregulated protein kinases are crucial in promoting cancer cell proliferation and driving malignant cell signaling. Although these kinases are essential targets for cancer therapy due to their involvement in cell development and proliferation, only a small part of the human kinome has been targeted by drugs. A comprehensive scoring system is needed to evaluate and prioritize clinically relevant kinases.

Measurement and Feedback Driven Entanglement Transition in the Probabilistic Control of Chaos.

We uncover a dynamical entanglement transition in a monitored quantum system that is heralded by a local order parameter. Classically, chaotic systems can be stochastically controlled onto unstable periodic orbits and exhibit controlled and uncontrolled phases as a function of the rate at which the control is applied. We show that such control transitions persist in open quantum systems where control is implemented with local measurements and unitary feedback.

Effect of gamma irradiation on the physical properties of MoS monolayer.

Two-dimensional transition metal dichalcogenides (2D-TMDs) have been proposed as novel optoelectronic materials for space applications due to their relatively light weight. MoS has been shown to have excellent semiconducting and photonic properties. Although the strong interaction of ionizing gamma radiation with bulk materials has been demonstrated, understanding its effect on atomically thin materials has scarcely been investigated.

Computational Materials Design for Ceramic Nuclear Waste Forms Using Machine Learning, First-Principles Calculations, and Kinetics Rate Theory.

Ceramic waste forms are designed to immobilize radionuclides for permanent disposal in geological repositories. One of the principal criteria for the effective incorporation of waste elements is their compatibility with the host material. In terms of performance under environmental conditions, the resistance of the waste forms to degradation over long periods of time is a critical concern when they are exposed to natural environments.

Current- and Field-Induced Topology in Twisted Nodal Superconductors.

We show that interlayer current induces topological superconductivity in twisted bilayers of nodal superconductors. A bulk gap opens and achieves its maximum near a "magic" twist angle θ_{MA}. Chiral edge modes lead to a quantized thermal Hall effect at low temperatures.

Application of the variational autoencoder to detect the critical points of the anisotropic Ising model.

We generalize the previous study on the application of variational autoencoders to the two-dimensional Ising model to a system with anisotropy. Due to the self-duality property of the system, the critical points can be located exactly for the entire range of anisotropic coupling. This presents an excellent test bed for the validity of using a variational autoencoder to characterize an anisotropic classical model.

Global food insecurity and famine from reduced crop, marine fishery and livestock production due to climate disruption from nuclear war soot injection.

Atmospheric soot loadings from nuclear weapon detonation would cause disruptions to the Earth's climate, limiting terrestrial and aquatic food production. Here, we use climate, crop and fishery models to estimate the impacts arising from six scenarios of stratospheric soot injection, predicting the total food calories available in each nation post-war after stored food is consumed. In quantifying impacts away from target areas, we demonstrate that soot injections larger than 5 Tg would lead to mass food shortages, and livestock and aquatic food production would be unable to compensate for reduced crop output, in almost all countries.