Browse Articles

Smart distributed data factory volunteer computing platform for active learning-driven molecular data acquisition

Abstract This paper presents the smart distributed data factory (SDDF), an AI-driven distributed computing platform designed to address challenges in drug discovery by creating comprehensive datasets of molecular conformations and their properties. SDDF uses volunteer computing, leveraging the processing power of personal computers worldwide to accelerate quantum chemistry (DFT) calculations. To tackle the vast chemical space and limited high-quality data, SDDF employs an ensemble of machine learning (ML) models to predict molecular properties and selectively choose the most challenging data points for further DFT calculations. The platform also generates new molecular conformations using molecular dynamics with the forces derived from these models. SDDF makes several contributions: the volunteer computing platform for DFT calculations; an active learning framework for constructing a dataset of molecular conformations; a large public dataset of diverse ENAMINE molecules with calculated energies; an ensemble of ML models for accurate energy prediction. The energy dataset was generated to validate the SDDF approach of reducing the need for extensive calculations. With its strict scaffold split, the dataset can be used for training and benchmarking energy models. By combining active learning, distributed computing, and quantum chemistry, SDDF offers a scalable, cost-effective solution for developing accurate molecular models and ultimately accelerating drug discovery.

Author Correction: BWC0977, a broad-spectrum antibacterial clinical candidate to treat multidrug resistant infections

Hepatitis A epidemics in Japan, France, and Thailand from 2007 to 2021, highlighting a post-COVID-19 decline

Regulating cellular metabolism and morphology to achieve high-yield synthesis of hyaluronan with controllable molecular weights

Response of wheat crop to water-logged conditions under different land configurations and nutrient management

CatPred: a comprehensive framework for deep learning in vitro enzyme kinetic parameters

Semantic structure preservation for accurate multi-modal glioma diagnosis

Addendum: High economic costs of reduced carbon sinks and declining biome stability in Central American forests

A novel voice in head actor critic reinforcement learning with human feedback framework for enhanced robot navigation

Abstract This work presents a novel Voice in Head (ViH) framework, that integrates Large Language Models (LLMs) and the power of semantic understanding to enhance robotic navigation and interaction within complex environments. Our system strategically combines GPT and Gemini powered LLMs as Actor and Critic components within a reinforcement learning (RL) loop for continuous learning and adaptation. ViH employs a sophisticated semantic search mechanism powered by Azure AI Search, allowing users to interact with the system through natural language queries. To ensure safety and address potential LLM limitations, the system incorporates a Reinforcement Learning with Human Feedback (RLHF) component, triggered only when necessary. This hybrid approach delivers impressive results, achieving success rates of up to 94.54%, surpassing established benchmarks. Most importantly, the ViH framework offers a modular and scalable architecture. By simply modifying the environment, the system demonstrates the potential to adapt to diverse application domains. This research provides a significant advancement in the field of cognitive robotics, paving the way for intelligent autonomous systems capable of sophisticated reasoning and decision-making in real-world scenarios bringing us one step closer to achieving Artificial General Intelligence.

Realization of a one-dimensional topological insulator in ultrathin germanene nanoribbons

Carbon dioxide removal from triethanolamine solution using living microalgae-loofah biocomposites

Abstract Nowadays, the climate change crisis is an urgent matter in which carbon dioxide (CO2) is a major greenhouse gas contributing to global warming. Amine solvents are commonly used for CO2 capture with high efficiency and absorption rates. However, solvent regeneration consumes an extensive amount of energy. One of alternative approaches is amine regeneration through microalgae. Recently, living biocomposites, intensifying traditional suspended cultivation, have been developed. With this technology, immobilizing microalgae on biocompatible materials with binder outperformed the suspended system in terms of CO2 capture rates. In this study, living microalgae-loofah biocomposites with immobilized Scenedesmus acuminatus TISTR 8457 using 5%v/v acrylic medium were tested to remove CO2 from CO2-rich triethanolamine (TEA) solutions. The test using 1 M TEA at various CO2 loading ratios (0.2, 0.4, 0.6, and 0.8 mol CO2/mol TEA) demonstrated that the biocomposites achieved CO2 removal rates 3 to 5 times higher than the suspended cell system over 28 days, with the highest removal observed at the 1 M with 0.4 mol CO2/mol TEA (4.34 ± 0.20 gCO2/gbiomass). This study triggers a new exploration of integration between biological and chemical processes that could elevate the traditional amine-based CO2 capture capabilities. Nevertheless, pilot-scale investigations are necessary to confirm the biocomposites’s efficiency.

Pupil size reveals arousal level fluctuations in human sleep

Abstract Recent animal research has revealed the intricate dynamics of arousal levels that are important for maintaining proper sleep resilience and memory consolidation. In humans, changes in arousal level are believed to be a determining characteristic of healthy and pathological sleep but tracking arousal level fluctuations has been methodologically challenging. Here we measured pupil size, an established indicator of arousal levels, by safely taping the right eye open during overnight sleep and tested whether pupil size affects cortical response to auditory stimulation. We show that pupil size dynamics change as a function of important sleep events across different temporal scales. In particular, our results show pupil size to be inversely related to the occurrence of sleep spindle clusters, a marker of sleep resilience. Additionally, we found pupil size prior to auditory stimulation to influence the evoked response, most notably in delta power, a marker of several restorative and regenerative functions of sleep. Recording pupil size dynamics provides insights into the interplay between arousal levels and sleep oscillations.

Impact of barrier width on topological insulator phase in InN/InGaN quantum wells with moderate strain

Pan-cancer multi-omic model of LINE-1 activity reveals locus heterogeneity of retrotransposition efficiency

Abstract Somatic mobilization of LINE-1 (L1) has been implicated in cancer etiology. We analyzed a recent TCGA data release comprised of nearly 5000 pan-cancer paired tumor-normal whole-genome sequencing (WGS) samples and ~9000 tumor RNA samples. We developed TotalReCall an improved algorithm and pipeline for detection of L1 retrotransposition (RT), finding high correlation between L1 expression and “RT burden” per sample. Furthermore, we mathematically model the dual regulatory roles of p53, where mutations in TP53 disrupt regulation of both L1 expression and retrotransposition. We found those with Li-Fraumeni Syndrome (LFS) heritable TP53 pathogenic and likely pathogenic variants bear similarly high L1 activity compared to matched cancers from patients without LFS, suggesting this population be considered in attempts to target L1 therapeutically. Due to improved sensitivity, we detect over 10 genes beyond TP53 whose mutations correlate with L1, including ATRX, suggesting other, potentially targetable, mechanisms underlying L1 regulation in cancer remain to be discovered.

Author Correction: Efficacy of community groups as a social prescription for senior health—insights from a natural experiment during the COVID-19 lockdown

NIR-triggering cobalt single-atom enzyme switches off-to-on for boosting the interactive dynamic effects of multimodal phototherapy

Metabolomic analysis of the intrinsic resistance mechanisms of Microtus fortis against Schistosoma japonicum infection

Observation of metal-organic interphase in Cu-based electrochemical CO2-to-ethanol conversion

Immunogenic cell death signature predicts survival and reveals the role of VEGFA + Mast cells in lung adenocarcinoma

Ligand efficacy shifts a nuclear receptor conformational ensemble between transcriptionally active and repressive states

Abstract Nuclear receptors (NRs) are thought to dynamically alternate between transcriptionally active and repressive conformations, which are stabilized upon ligand binding. Most NR ligand series exhibit limited bias, primarily consisting of transcriptionally active agonists or neutral antagonists, but not repressive inverse agonists—a limitation that restricts understanding of the functional NR conformational ensemble. Here, we report a NR ligand series for peroxisome proliferator-activated receptor gamma (PPARγ) that spans a pharmacological spectrum from repression (inverse agonism) to activation (agonism) where subtle structural modifications switch compound activity. While crystal structures provide snapshots of the fully repressive state, NMR spectroscopy and conformation-activity relationship analysis reveals that compounds within the series shift the PPARγ conformational ensemble between transcriptionally active and repressive conformations that are natively populated in the apo/ligand-free ensemble. Our findings reveal a molecular framework for minimal chemical modifications that enhance PPARγ inverse agonism and elucidate their influence on the dynamic PPARγ conformational ensemble.