Acceleration without Disruption: DFT Software as a Service.

Density functional theory (DFT) has been a cornerstone in computational chemistry, physics, and materials science for decades, benefiting from advancements in computational power and theoretical methods. This paper introduces a novel, cloud-native application, Accelerated DFT, which offers an order of magnitude acceleration in DFT simulations. By integrating state-of-the-art cloud infrastructure and redesigning algorithms for graphic processing units (GPUs), Accelerated DFT achieves high-speed calculations without sacrificing accuracy.

Helminth Parasites of Invasive Freshwater Fish in Lithuania.

Freshwater ecosystems in Lithuania are threatened by the introduction of invasive fish species including , , , and . Data on helminth parasites of these fishes have not been comprehensively studied, with only two reports on parasites of from the Curonian Lagoon and Baltic Sea, Lithuania. We examined 278 fish individuals representing 4 invasive species from 13 waterbodies.

Electronic structure simulations in the cloud computing environment.

The transformative impact of modern computational paradigms and technologies, such as high-performance computing (HPC), quantum computing, and cloud computing, has opened up profound new opportunities for scientific simulations. Scalable computational chemistry is one beneficiary of this technological progress. The main focus of this paper is on the performance of various quantum chemical formulations, ranging from low-order methods to high-accuracy approaches, implemented in different computational chemistry packages and libraries, such as NWChem, NWChemEx, Scalable Predictive Methods for Excitations and Correlated Phenomena, ExaChem, and Fermi-Löwdin orbital self-interaction correction on Azure Quantum Elements, Microsoft's cloud services platform for scientific discovery.

Cryptic species complex shows population-dependent, rather than lineage-dependent tolerance to a neonicotinoid.

Cryptic species are rarely considered in ecotoxicology, resulting in misleading outcomes when using a single morphospecies that encompasses multiple cryptic species. This oversight contributes to the lack of reproducibility in ecotoxicological experiments and promotes unreliable extrapolations. The important question of ecological differentiation and the sensitivity of cryptic species is rarely tackled, leaving a substantial knowledge gap regarding the vulnerability of individual cryptic species within species complexes.

Accelerating Computational Materials Discovery with Machine Learning and Cloud High-Performance Computing: from Large-Scale Screening to Experimental Validation.

High-throughput computational materials discovery has promised significant acceleration of the design and discovery of new materials for many years. Despite a surge in interest and activity, the constraints imposed by large-scale computational resources present a significant bottleneck. Furthermore, examples of very large-scale computational discovery carried out through experimental validation remain scarce, especially for materials with product applicability.

Workplace violence, work-related exhaustion, and workplace cognitive failure among nurses: A cross-sectional study.

Aim: To examine the relationships between nurses' exposure to workplace violence and self-reports of workplace cognitive failure.

Design: A cross-sectional study.

Methods: An online questionnaire was administered in April 2023 to nurses in Michigan, US.

Multi-decadal improvements in the ecological quality of European rivers are not consistently reflected in biodiversity metrics.

Humans impact terrestrial, marine and freshwater ecosystems, yet many broad-scale studies have found no systematic, negative biodiversity changes (for example, decreasing abundance or taxon richness). Here we show that mixed biodiversity responses may arise because community metrics show variable responses to anthropogenic impacts across broad spatial scales. We first quantified temporal trends in anthropogenic impacts for 1,365 riverine invertebrate communities from 23 European countries, based on similarity to least-impacted reference communities.

Assessing the impact of technological change on similar occupations: Implications for employment alternatives.

Background: The fast-changing labor market highlights the need for an in-depth understanding of occupational mobility impacted by technological change. However, we lack a multidimensional classification scheme that considers similarities of occupations comprehensively, which prevents us from predicting employment trends and mobility across occupations. This study fills the gap by examining employment trends based on similarities between occupations.

The recovery of European freshwater biodiversity has come to a halt.

Owing to a long history of anthropogenic pressures, freshwater ecosystems are among the most vulnerable to biodiversity loss. Mitigation measures, including wastewater treatment and hydromorphological restoration, have aimed to improve environmental quality and foster the recovery of freshwater biodiversity. Here, using 1,816 time series of freshwater invertebrate communities collected across 22 European countries between 1968 and 2020, we quantified temporal trends in taxonomic and functional diversity and their responses to environmental pressures and gradients.

Multidecadal changes in functional diversity lag behind the recovery of taxonomic diversity.

While there has been increasing interest in how taxonomic diversity is changing over time, less is known about how long-term taxonomic changes may affect ecosystem functioning and resilience. Exploring long-term patterns of functional diversity can provide key insights into the capacity of a community to carry out ecological processes and the redundancy of species' roles. We focus on a protected freshwater system located in a national park in southeast Germany.

Employees' reactions toward COVID-19 information exposure: Insights from terror management theory and generativity theory.

As the coronavirus disease (COVID-19) has imposed significant risks to our health and affected our social and economic order, information on COVID-19 becomes readily accessible via various mass media and social media. In the current research, we aim to understand the impacts of employees' exposure to COVID-19 information on their workplace behaviors. Integrating Terror Management Theory (TMT; Becker, 1973; Greenberg et al.

A clustering-based biased Monte Carlo approach to protein titration curve prediction.

In this work, we developed an efficient approach to compute ensemble averages in systems with pairwise-additive energetic interactions between the entities. Methods involving full enumeration of the configuration space result in exponential complexity. Sampling methods such as Markov Chain Monte Carlo (MCMC) algorithms have been proposed to tackle the exponential complexity of these problems; however, in certain scenarios where significant energetic coupling exists between the entities, the efficiency of the such algorithms can be diminished.

Long-term data reveal unimodal responses of ground beetle abundance to precipitation and land use but no changes in taxonomic and functional diversity.

While much of global biodiversity is undoubtedly under threat, the responses of ecological communities to changing climate, land use intensification, and long-term changes in both taxonomic and functional diversity over time, has still not been fully explored for many taxonomic groups, especially invertebrates. We compiled time series of ground beetles covering the past two decades from 40 sites located in five regions across Germany. We calculated site-based trends for 21 community metrics representing taxonomic and functional diversity of ground beetles, activity density (a proxy for abundance), and activity densities of functional groups.

Workplace Interventions in Response to COVID-19: an Occupational Health Psychology Perspective.

The outbreak of COVID-19 has imposed significant threats to individuals' physical health and has substantially changed the socioeconomic order and the nature of our work and life all over the world. To guide organizations to design effective workplace interventions to mitigate the negative impacts of COVID-19, we take the occupational health psychology (OHP) perspective to propose a framework that highlights important areas for organizations to intervene in order to better protect workers' physical health and safety and to promote workers' psychological well-being. Specifically, we integrate the prevention-based public health model with the Total Worker Health (TWH) and OHP-based approaches to propose a comprehensive set of primary, secondary, and tertiary interventions that target different groups of employees with varied exposure risks to the new coronavirus.

Toward Quantum Computing for High-Energy Excited States in Molecular Systems: Quantum Phase Estimations of Core-Level States.

This paper explores the utility of the quantum phase estimation (QPE) algorithm in calculating high-energy excited states characterized by the promotion of electrons occupying core-level shells. These states have been intensively studied over the last few decades, especially in supporting the experimental effort at light sources. Results obtained with QPE are compared with various high-accuracy many-body techniques developed to describe core-level states.

Recovery from air pollution and subsequent acidification masks the effects of climate change on a freshwater macroinvertebrate community.

Freshwater ecosystems are dynamic, complex systems with a multitude of physical and ecological processes and stressors which drive fluctuations on the community-level. Disentangling the effects of different processes and stressors is challenging due to their interconnected nature. However, as protected areas (i.

Data-driven molecular modeling with the generalized Langevin equation.

The complexity of molecular dynamics simulations necessitates dimension reduction and coarse-graining techniques to enable tractable computation. The generalized Langevin equation (GLE) describes coarse-grained dynamics in reduced dimensions. In spite of playing a crucial role in non-equilibrium dynamics, the memory kernel of the GLE is often ignored because it is difficult to characterize and expensive to solve.

A data-driven framework for sparsity-enhanced surrogates with arbitrary mutually dependent randomness.

The challenge of quantifying uncertainty propagation in real-world systems is rooted in the high-dimensionality of the stochastic input and the frequent lack of explicit knowledge of its probability distribution. Traditional approaches show limitations for such problems, especially when the size of the training data is limited. To address these difficulties, we have developed a general framework of constructing surrogate models on spaces of stochastic input with arbitrary probability measure irrespective of the mutual dependencies between individual components of the random inputs and the analytical form.

Visualizing biomolecular electrostatics in virtual reality with UnityMol-APBS.

Virtual reality is a powerful tool with the ability to immerse a user within a completely external environment. This immersion is particularly useful when visualizing and analyzing interactions between small organic molecules, molecular inorganic complexes, and biomolecular systems such as redox proteins and enzymes. A common tool used in the biomedical community to analyze such interactions is the Adaptive Poisson-Boltzmann Solver (APBS) software, which was developed to solve the equations of continuum electrostatics for large biomolecular assemblages.

Atomic Radius and Charge Parameter Uncertainty in Biomolecular Solvation Energy Calculations.

Atomic radii and charges are two major parameters used in implicit solvent electrostatics and energy calculations. The optimization problem for charges and radii is underdetermined, leading to uncertainty in the values of these parameters and in the results of solvation energy calculations using these parameters. This paper presents a new method for quantifying this uncertainty in implicit solvation calculations of small molecules using surrogate models based on generalized polynomial chaos (gPC) expansions.

Improvements to the APBS biomolecular solvation software suite.

The Adaptive Poisson-Boltzmann Solver (APBS) software was developed to solve the equations of continuum electrostatics for large biomolecular assemblages that have provided impact in the study of a broad range of chemical, biological, and biomedical applications. APBS addresses the three key technology challenges for understanding solvation and electrostatics in biomedical applications: accurate and efficient models for biomolecular solvation and electrostatics, robust and scalable software for applying those theories to biomolecular systems, and mechanisms for sharing and analyzing biomolecular electrostatics data in the scientific community. To address new research applications and advancing computational capabilities, we have continually updated APBS and its suite of accompanying software since its release in 2001.

Spermine Condenses DNA, but Not RNA Duplexes.

Interactions between the polyamine spermine and nucleic acids drive important cellular processes. Spermine condenses DNA and some RNAs, such as poly(rA):poly(rU). A large fraction of the spermine present in cells is bound to RNA but apparently does not condense it.