Instance maps as an organising concept for complex experimental workflows as demonstrated for (nano)material safety research.

Nanosafety assessment, which seeks to evaluate the risks from exposure to nanoscale materials, spans materials synthesis and characterisation, exposure science, toxicology, and computational approaches, resulting in complex experimental workflows and diverse data types. Managing the data flows, with a focus on provenance (who generated the data and for what purpose) and quality (how was the data generated, using which protocol with which controls), as part of good research output management, is necessary to maximise the reuse potential and value of the data. Instance maps have been developed and evolved to visualise experimental nanosafety workflows and to bridge the gap between the theoretical principles of FAIR (Findable, Accessible, Interoperable and Re-usable) data and the everyday practice of experimental researchers.

The promise of community-driven preprints in ecology and evolution.

Publishing preprints is quickly becoming commonplace in ecology and evolutionary biology. Preprints can facilitate the rapid sharing of scientific knowledge establishing precedence and enabling feedback from the research community before peer review. Yet, significant barriers to preprint use exist, including language barriers, a lack of understanding about the benefits of preprints and a lack of diversity in the types of research outputs accepted (e.

Predicting hydrogen atom transfer energy barriers using Gaussian process regression.

Predicting reaction barriers for arbitrary configurations based on only a limited set of density functional theory (DFT) calculations would render the design of catalysts or the simulation of reactions within complex materials highly efficient. We here propose Gaussian process regression (GPR) as a method of choice if DFT calculations are limited to hundreds or thousands of barrier calculations. For the case of hydrogen atom transfer in proteins, an important reaction in chemistry and biology, we obtain a mean absolute error of 3.

Direct and indirect effects of land use on microbiomes of trap-nesting solitary bee larvae and nests.

Introduction: The global decline in biodiversity and insect populations highlights the urgent need to conserve ecosystem functions, such as plant pollination by solitary bees. Human activities, particularly agricultural intensification, pose significant threats to these essential services. Changes in land use alter resource and nest site availability, pesticide exposure and other factors impacting the richness, diversity, and health of solitary bee species.

Broad geographic dispersal is not a diversification driver for Emberizoidea.

The movement of species to new geographical areas has been proposed to be crucial for speciation. As such, dispersal has been regarded as a likely explanation for the variation in species richness among clades. The Emberizoidea are a highly diverse Oscine bird clade native to the New World that has been characterized for their ubiquitous distribution both ecologically and geographically, making this group ideal to test how biogeographical dispersal could promote speciation.

Lipidic folding pathway of α-Synuclein via a toxic oligomer.

Aggregation intermediates play a pivotal role in the assembly of amyloid fibrils, which are central to the pathogenesis of neurodegenerative diseases. The structures of filamentous intermediates and mature fibrils are now efficiently determined by single-particle cryo-electron microscopy. By contrast, smaller pre-fibrillar α-Synuclein (αS) oligomers, crucial for initiating amyloidogenesis, remain largely uncharacterized.

The population dynamics of clustered consumer-resource spatial patterns: Insights from the demographics of a Turing mechanism.

In ecology, Alan Turing's proposed activation-inhibition mechanism has been abstracted as corresponding to several ecological interaction types to explain pattern formation in ecosystems. Consumer-resource interactions have strong theoretical arguments linking them to both the Turing mechanism and pattern formation, but there is little empirical support to demonstrate these claims. Here, we connect several lines of evidence to support the proposition that consumer-resource interactions can create empirically observed spatial patterns through a mechanism similar to Turing's theory.

Estimating realized relatedness in free-ranging macaques by inferring identity-by-descent segments.

Biological relatedness is a key consideration in studies of behavior, population structure, and trait evolution. Except for parent-offspring dyads, pedigrees capture relatedness imperfectly. The number and length of identical-by-descent DNA segments (IBD) yield the most precise relatedness estimates.

MetaRange.jl: A Dynamic and Metabolic Species Range Model for Plant Species.

Process-based models for range dynamics are urgently needed due to increasing intensity of human-induced biodiversity change. Despite a few existing models that focus on demographic processes, their use remains limited compared to the widespread application of correlative approaches. This slow adoption is largely due to the challenges in calibrating biological parameters and the high computational demands for large-scale applications.

Compartment model of strategy-dependent time delays in replicator dynamics.

Real-world processes often exhibit temporal separation between actions and reactions - a characteristic frequently ignored in many modelling frameworks. Adding temporal aspects, like time delays, introduces a higher complexity of problems and leads to models that are challenging to analyse and computationally expensive to solve. In this work, we propose an intermediate solution to resolve the issue in the framework of evolutionary game theory.

Improving the antimicrobial potential of the peptide CIDEM-501 through acylation: A computational approach.

Acylation is a common method used to modify antimicrobial peptides to enhance their effectiveness. It increases the interactions between the peptide and the bacterial cell membranes. However, acylation can also reduce the selectivity of the peptides by making them more active on eukaryotic membranes, which can lead to unintended toxicity.

Preliminaries to artificial consciousness: A multidimensional heuristic approach.

The pursuit of artificial consciousness requires conceptual clarity to navigate its theoretical and empirical challenges. This paper introduces a composite, multilevel, and multidimensional model of consciousness as a heuristic framework to guide research in this field. Consciousness is treated as a complex phenomenon, with distinct constituents and dimensions that can be operationalized for study and for evaluating their replication.

Prediction of cccDNA dynamics in hepatitis B patients by a combination of serum surrogate markers.

Quantification of intrahepatic covalently closed circular DNA (cccDNA) is a key for evaluating an elimination of hepatitis B virus (HBV) in infected patients. However, quantifying cccDNA requires invasive methods such as a liver biopsy, which makes it impractical to access the dynamics of cccDNA in patients. Although HBV RNA and HBV core-related antigens (HBcrAg) have been proposed as surrogate markers for evaluating cccDNA activity, they do not necessarily estimate the amount of cccDNA.

RosettaHDX: Predicting antibody-antigen interaction from hydrogen-deuterium exchange mass spectrometry data.

High-throughput characterization of antibody-antigen complexes at the atomic level is critical for understanding antibody function and enabling therapeutic development. Hydrogen-deuterium exchange mass spectrometry (HDX-MS) enables rapid epitope mapping, but its data are too sparse for independent structure determination. In this study, we introduce RosettaHDX, a hybrid method that combines computational docking with differential HDX-MS data to enhance the accuracy of antibody-antigen complex models beyond what either method can achieve individually.

Nuclear pore permeability and fluid flow are modulated by its dilation state.

Changing environmental conditions necessitate rapid adaptation of cytoplasmic and nuclear volumes. We use the slime mold Dictyostelium discoideum, known for its ability to tolerate extreme changes in osmolarity, to assess which role nuclear pore complexes (NPCs) play in achieving nuclear volume adaptation and relieving mechanical stress. We capitalize on the unique properties of D.

MolecularWebXR: Multiuser discussions in chemistry and biology through immersive and inclusive augmented and virtual reality.

MolecularWebXR is a new web-based platform for education, science communication and scientific peer discussion in chemistry and biology, based on modern web-based Virtual Reality (VR) and Augmented Reality (AR). With no installs as it is all web-served, MolecularWebXR enables multiple users to simultaneously explore, communicate and discuss concepts about chemistry and biology in immersive 3D environments, by manipulating and passing around objects with their bare hands and pointing at different elements with natural hand gestures. Users may either be present in the same physical space or distributed around the world, in the latter case talking naturally with each other thanks to built-in audio.

is implicated in central nervous system, orofacial and maxillofacial anomalies.

Background: Previous studies in mouse, and zebrafish embryos show strong expression in progenitor cells of neuronal and neural crest tissues suggesting its involvement in neural crest specification. However, the role of human transcription factor activator protein 2 ( in human embryonic central nervous system (CNS), orofacial and maxillofacial development is unknown.

Methods: Through a collaborative work, exome survey was performed in families with congenital CNS, orofacial and maxillofacial anomalies.

From Bayes to Darwin: Evolutionary search as an exaptation from sampling-based Bayesian inference.

Building on the algorithmic equivalence between finite population replicator dynamics and particle filtering based approximation of Bayesian inference, we design a computational model to demonstrate the emergence of Darwinian evolution over representational units when collectives of units are selected to infer statistics of high-dimensional combinatorial environments. The non-Darwinian starting point is two units undergoing a few cycles of noisy, selection-dependent information transmission, corresponding to a serial (one comparison per cycle), non-cumulative process without heredity. Selection for accurate Bayesian inference at the collective level induces an adaptive path to the emergence of Darwinian evolution within the collectives, capable of maintaining and iteratively improving upon complex combinatorial information.

A computational model for angular velocity integration in a locust heading circuit.

Accurate navigation often requires the maintenance of a robust internal estimate of heading relative to external surroundings. We present a model for angular velocity integration in a desert locust heading circuit, applying concepts from early theoretical work on heading circuits in mammals to a novel biological context in insects. In contrast to similar models proposed for the fruit fly, this circuit model uses a single 360° heading direction representation and is updated by neuromodulatory angular velocity inputs.

Beyond-local neural information processing in neuronal networks.

While there is much knowledge about local neuronal circuitry, considerably less is known about how neuronal input is integrated and combined across neuronal networks to encode higher order brain functions. One challenge lies in the large number of complex neural interactions. Neural networks use oscillating activity for information exchange between distributed nodes.

Emergence of power law distributions in protein-protein interaction networks through study bias.

Degree distributions in protein-protein interaction (PPI) networks are believed to follow a power law (PL). However, technical and study biases affect the experimental procedures for detecting PPIs. For instance, cancer-associated proteins have received disproportional attention.

MIBiG 4.0: advancing biosynthetic gene cluster curation through global collaboration.

Specialized or secondary metabolites are small molecules of biological origin, often showing potent biological activities with applications in agriculture, engineering and medicine. Usually, the biosynthesis of these natural products is governed by sets of co-regulated and physically clustered genes known as biosynthetic gene clusters (BGCs). To share information about BGCs in a standardized and machine-readable way, the Minimum Information about a Biosynthetic Gene cluster (MIBiG) data standard and repository was initiated in 2015.