There is still an open debate regarding the structure forming capabilities of water at ambient conditions. To probe the presence of such inhomogeneities, we apply complex network analysis methods to a molecular dynamics simulation at room temperature. This study provides both a structural and quantitative characterization of kinetically homogeneous substates present in bulk water. We find that the conformation-space network is highly modular, and that structural properties of water molecules are spatially correlated over at least two solvation shells. From a kinetic point of view, the free energy surface is characterized by multiple heterogeneous metastable regions with different populations and marginal barriers separating them. The typical time scale of hopping between them is 200-400 fs. A scanning in temperature reveals that those substates can be stabilized either entropically or enthalpically. The latter resembles an icelike domain that extends for at least two solvation shells.
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PNAS Nexus
January 2025
Department of Engineering Sciences and Applied Mathematics, Northwestern University, Evanston, IL 60208, USA.
The COVID-19 pandemic forced a societal shift from in-person to virtual activities, including scientific conferences. As society navigates a "new normal," the question arises as to the advantages and disadvantages of these alternative modalities. We introduce two new comprehensive datasets enabling direct comparison between virtual and in-person conferences: the first, from a series of nine small conferences, encompasses over 12,000 pairs of potential scientific collaborators across five virtual and four in-person meetings on a range of scientific topics; the expressed goal of these conferences is to create novel collaborations.
View Article and Find Full Text PDFJ Biomed Phys Eng
December 2024
Medical Image and Signal Processing Research Center, School of Advanced Technologies in Medicine, Isfahan University of Medical Sciences, Isfahan, Iran.
Background: The P300 signal, an endogenous component of event-related potentials, is extracted from an electroencephalography signal and employed in Brain-computer Interface (BCI) devices.
Objective: The current study aimed to address challenges in extracting useful features from P300 components and detecting P300 through a hybrid unsupervised manner based on Convolutional Neural Network (CNN) and Long Short-term Memory (LSTM).
Material And Methods: In this cross-sectional study, CNN as a useful method for the P300 classification task emphasizes spatial characteristics of data.
Cureus
December 2024
College of Medicine, Dar Al Uloom University, Riyadh, SAU.
Background Inflight medical emergencies (IMEs) present a challenging situation due to the availability of limited medical resources and a complex cabin environment. The physicians have an ethical responsibility to aid in such situations. This study aims to assess the attitudes of Saudi physicians regarding IMEs.
View Article and Find Full Text PDFDigit Discov
December 2024
Eindhoven University of Technology, Institute for Complex Molecular Systems, Eindhoven AI Systems Institute, Dept. Biomedical Engineering Eindhoven Netherlands
Deep learning has significantly accelerated drug discovery, with 'chemical language' processing (CLP) emerging as a prominent approach. CLP approaches learn from molecular string representations (, Simplified Molecular Input Line Entry Systems [SMILES] and Self-Referencing Embedded Strings [SELFIES]) with methods akin to natural language processing. Despite their growing importance, training predictive CLP models is far from trivial, as it involves many 'bells and whistles'.
View Article and Find Full Text PDFFront Public Health
December 2024
Department of Statistics, College of Science, Bahir Dar University, Bahir Dar, Ethiopia.
Introduction: Dynamic Bayesian networks improve the modeling of complex systems by incorporating continuous probabilistic relationships between covariates that change over time. This study aimed to analyze the complex causal links contributing to child undernutrition using dynamic Bayesian network modeling, examining both the best- and worst-case scenarios. The Young Cohort of the Ethiopian Young Lives dataset from 2002-2016 was used to analyze the complex relationships among various covariates influencing child undernutrition.
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