bluuebunny/crossref_metadata_2025_split huggingface dataset:
The Relationship Between Suspected Adverse Drug Reactions Of HMG-Coa Reductase Inhibitors And Polypharmacology Using A National Registry Approach
Authors: Yousaf Hasan , Jones Alan M. 
Published: April 2024

AbstractAimsThe aim of this study was to explore the suspected adverse drug reaction (ADR) data of five licensed statins in the UK: atorvastatin, fluvastatin, pravastatin, rosuvastatin and simvastatin. A secondary aim was to determine if there was a link between the polypharmacological properties of the statins and their associated muscle-related side effects.MethodsThe chemical database of bioactive molecules with drug-like properties, European Molecular Biology Laboratory (ChEMBL) was used to obtain data on the pharmacological interactions of statins with human proteins. The Medicines and Healthcare products Regulatory Agency’s (MHRA) Yellow Card Scheme was used to obtain reports of suspected ADRs from 2018 to 2022. The OpenPrescribing database was used to obtain the prescribing rates for statistical interpretation.ResultsThe study found no significant difference between the statins in causing ADRs across all organ classes (X2,P> .05). Fluvastatin was found to have a higher incidence of ADRs/100,000Rxacross multiple organ classes.ConclusionNo significant difference was found between the suspected ADR incidence of the statins across all organ classes. No evidence of higher intensity statins causing more muscle symptoms than moderate intensity statins was found.
After Reaching Its Highest Levels since 1950s, Incidence of Syphilis Among US Adults Declined in 2023
Authors: Do Duy , Rodriguez Patricia J. , Gratzl Samuel , Cartwright Brianna M. Goodwin , Baker Charlotte , Stucky Nicholas L 
Published: April 2024

AbstractRecent reports showed that the incidence of syphilis in the US reached a 70-year high. Using 2019-2023 data from Truveta, this study demonstrated that while the incidence of syphilis increased from 2020 to 2022, it started to decline in 2023. The decline was driven by population subgroups that are commonly considered higher-risk for syphilis infection. Findings also highlighted growing trends among lower-risk population subgroups.
In-Flights of Outbreak Populations of Mountain Pine Beetle Alter the Local Genetic Structure of Established Populations a Decade After Range Expansion
Authors: Thompson Kirsten M. , Huber Dezene P. W. , Sperling Felix A. H. , Murray Brent W. 
Published: April 2024

AbstractMountain pine beetles began to appear at epidemic levels in Alberta, Canada, in 2006, following six years of extensive outbreaks in neighboring British Columbia. We assessed the effect of genetic MPB in-flights from the peak of the outbreak on the genetic structure of established populations of MPB and the change over time in novel regions colonized by these inflights. We used five locations sampled during the peak of the outbreak (2005/2007) and re-sampled in 2016. We performed a ddRADseq protocol to generate a SNP dataset via single-end Illumina sequencing. We detected a northern and southern genetic cluster in both sampling sets (2005/2007 and 2016) and a demographic shift in cluster assignment after ∼10 generations from south to north in two of the sites in the path of the northern outbreak. Fst values were significantly different between most sites in the same years and between the same sites at different years, with some exceptions for northern sites established by inflights. Overall, sites in the spreading path of the MPB outbreak have taken on the genetic structure of the contiguous northern outbreak except for an isolated site in Golden, BC, and in Mount Robson Provincial Park where populations are admixed between north and south. Our results suggest that range expansion during insect outbreaks can alter the genetic structure of established populations and lead to interbreeding between populations.
Selectivity of Complex Coacervation in Multi-Protein Mixtures
Authors: Ahn So Yeon , Obermeyer Allie C. 
Published: April 2024

AbstractLiquid-liquid phase separation of biomolecules is increasingly recognized as relevant to various cellular functions, and complex coacervation of biomacromolecules, particularly proteins, is emerging as a key mechanism for this phenomenon. Complex coacervation is also being explored as a potential protein purification method due to its potential scalability, aqueous operation, and ability to produce a highly concentrated product. However, to date most studies of complex coacervation have evaluated the phase behavior of a binary mixture of two oppositely charged macromolecules. Therefore, a comprehensive understanding of the phase behavior of complex biological mixtures has yet to be established. To address this, a panel of engineered proteins was designed to allow for quantitative analysis of the complex coacervation of individual proteins within a multi-component mixture. The behavior of individual proteins was evaluated using a defined mixture of proteins that mimics the charge profile of theE. coliproteome. To allow for direct quantification of proteins in each phase, spectrally separated fluorescent proteins were used to construct the protein mixture. From this quantitative analysis, we observed that the coacervation behavior of individual proteins in the mixture was consistent with each other, which was distinctive from the behavior when each protein was evaluated in a single-protein system. Subtle differences in biophysical properties between the proteins became noticeable in the mixture, which allowed us to elucidate parameters for protein complex coacervation. With this understanding, we successfully designed methods to enrich a range of proteins of interest from a mixture of proteins.
Expressive modeling and fast simulation for dynamic compartments
Authors: Köster Till , Henning Philipp , Warnke Tom , Uhrmacher Adelinde 
Published: April 2024

AbstractCompartmentalization is vital for cell biological processes. The field of rule-based stochastic simulation has acknowledged this, and many tools and methods have capabilities for compartmentalization. However, mostly, this is limited to a static compartmental hierarchy and does not integrate compartmental changes. Integrating compartmental dynamics is challenging for the design of the modeling language and the simulation engine. The language should support a concise yet flexible modeling of compartmental dynamics. Our work is based on ML-Rules, a rule-based language for multi-level cell biological modeling that supports a wide variety of compartmental dynamics, whose syntax we slightly adapt. To develop an efficient simulation engine for compartmental dynamics, we combine specific data structures and new and existing algorithms and implement them in the Rust programming language. We evaluate the concept and implementation using two case studies from existing cell-biological models. The execution of these models outperforms previous simulations of ML-Rules by two orders of magnitude. Finally, we present a prototype of a WebAssembly-based implementation to allow for a low barrier of entry when exploring the language and associated models without the need for local installation.Author summaryBiochemical dynamics are constrained by and influence the dynamics of cellular compartments. Basic constraints are considered by many modeling and simulation tools, e.g., certain reactions may only occur in specific cellular compartments and at a speed influenced by the compartmental volume. However, to capture the functioning of complex compartmental dynamics such as cell proliferation or the fission or fusion of mitochondria, additional efforts are required from tool designers. These refer to how the modeler can specify these dynamics succinctly and unambiguously and how the resulting model can be executed efficiently. For modeling, we rely on ML-Rules, an expressive, formal rule-based language for modeling biochemical systems, which ships with the required features and which we only slightly adapt in our re-implementation. We design a new simulation engine that combines efficient data structures and various algorithms for efficient simulation. The achieved efficiency will enable thorough analysis, calibration, and validation of compartmental dynamics and, thus, allow the “in-silico” pursuit of research questions for which compartmental dynamics are essential. To further facilitate exploring the interplay of compartmental and non-compartmental dynamics, we exploit recent advances in web technology so that ML-Rules models can be run efficiently in the web browser.
iScore: A ML-Based Scoring Function forde novoDrug Discovery
Authors: Mahdizadeh Sayyed Jalil , Eriksson Leif A. 
Published: April 2024

AbstractIn the quest for acceleratingde novodrug discovery, the development of efficient and accurate scoring functions represents a fundamental challenge. This study introduces iScore, a novel machine learning (ML)-based scoring function designed to predict the binding affinity of protein-ligand complexes with remarkable speed and precision. Uniquely, iScore circumvents the conventional reliance on explicit knowledge of protein-ligand interactions and full picture of atomic contacts, instead leveraging a set of ligand and binding pocket descriptors to evaluate binding affinity. This approach avoids the inefficient and slow conformational sampling stage, thereby enabling the rapid screening of ultra-huge molecular libraries, a crucial advancement given the practically infinite dimensions of chemical space. iScore was rigorously trained and validated using the PDBbind 2020 refined set, CASF 2016, and CSAR NRC-HiQ Set1/2, employing three distinct ML methodologies: Deep Neural Network (iScore-DNN), Random Forest (iScore-RF), and eXtreme Gradient Boosting (iScore-XGB). A hybrid model, iScore-Hybrid, was subsequently developed to incorporate the strengths of these individual base learners. The hybrid model demonstrated a Pearson correlation coefficient (R) of 0.78 and a root mean square error (RMSE) of 1.23 in cross-validation, outperforming the individual base learners and establishing new benchmarks for scoring power (R= 0.814, RMSE=1.34), ranking power (ρ= 0.705), and screening power (success rate at top 10% = 73.7%).
Geometry theory of distribution shapes for autoregulatory gene circuits
Authors: Sheng Ying , Lin Genghong , Jiao Feng , Jia Chen 
Published: April 2024

AbstractIn this study, we provide a complete mathematical characterization of the phase diagram of distribution shapes in an extension of the two-state telegraph model of stochastic gene expression in the presence of positive or negative autoregulation. Using the techniques of second-order difference equations and nonlinear discrete dynamical systems, we prove that the feedback loop can only produce three shapes of steady-state protein distributions (decaying, bell-shaped, and bimodal), corresponding to three distinct parameter regions in the phase diagram. The boundaries of the three regions are characterized by two continuous curves, which can be constructed geometrically by the contour lines of a series of ratio operators. Based on the geometric structure of the phase diagram, we then provide some simple and verifiable sufficient and/or necessary conditions for the existence of the bimodal parameter region, as well as the conditions for the steady-state distribution to be decaying, bell-shaped, or bimodal. Finally, we also investigate how the phase diagram is affected by the strength of positive or negative feedback.
Strategies of resource sharing in clonal plants: A conceptual model and an example of contrasting strategies in two closely related species
Authors: Duchoslavová Jana , Jansa Jan 
Published: April 2024

AbstractBackground and AimsClonal growth helps plants to cope with environmental heterogeneity through resource integration via connecting organs. Such integration is considered to balance heterogeneity by the translocation of resources from rich to poor patches. However, such an ‘equalisation’ strategy is only one of several possible strategies, as we discuss in a brief conceptual analysis. Under certain conditions, a strategy emphasising acropetal movement and exploration of new areas or a strategy accumulating resources in older ramets may be preferred. The optimal strategy may be determined by environmental conditions, such as resource availability and level of light competition. Therefore, species from different habitats may exhibit distinct resource translocation strategies.MethodsResource translocation was compared between two closely related species from different habitats with contrasting productivity. The study examined the bidirectional translocation of carbon and nitrogen in pairs of mother and daughter ramets grown under light heterogeneity (one ramet shaded) at two developmental stages using stable-isotope labelling.Key resultsAt the early developmental stage, both species translocated resources toward daughters and the translocation was modified by shading. Later, the species of low-productivity habitats,Fragaria viridis, translocated carbon to shaded ramets, according to the ‘equalisation’ strategy. The species of high-productivity habitats,Potentilla reptans, did not support shaded older ramets. Nitrogen translocation remained mainly acropetal in both species. These findings confirmed our expectations.ConclusionsThe two studied species exhibited different translocation strategies, which may be linked to the habitat conditions experienced by each species. The ‘equalisation’ strategy may occur in habitats with lower productivity and lower light competition, than the strategy emphasising acropetal movement. The results indicate that we need to consider different possible strategies. We emphasise the importance of bidirectional tracing in translocation studies and the need for further studies to investigate the translocation patterns in species from contrasting habitats.
Does ecology shape geographical parthenogenesis? Evidence from the facultatively parthenogenetic stick insectMegacrania batesii
Authors: Miller Soleille , Wilner Daniela , Boldbataar Jigmidmaa , Bonduriansky Russell 
Published: April 2024

AbstractClosely related sexual and parthenogenetic species often show distinct distribution patterns, known as geographical parthenogenesis. These patterns, characterized by a mosaic of separate sexual and parthenogenetic populations across their natural range, can also be found in facultative parthenogens – species in which every female is capable of both sexual and parthenogenetic reproduction. The underlying mechanisms driving this phenomenon in nature remain unclear. Features of the habitat, such as differences in host plant phenotypes or niche breadth, could favour sexual or asexual reproductive modes and thus help to explain geographical parthenogenesis in natural insect populations.Megacrania batesiiis a facultatively parthenogenetic stick insect that displays geographical parthenogenesis in the wild. We aimed to explore whether sexual and parthenogenetic populations ofM. batesiidisplayed niche differentiation or variations in niche breadth that could explain the separation of the two population types. To do this, we sampled host plants from across the range ofM. batesiiand quantified phenotypic traits that might affect palatability or accessibility forM. batesii, including leaf thickness, toughness, spike size and density, height, and chemical composition. We also quantified host plant density, which could affectM. batesiidispersal. We found little evidence of phenotypic differences between host plants supporting sexual versus asexualM. batesiipopulations, and no difference in host-plant density or niche breadth between the two population types. Our results suggest that habitat parameters do not play a substantial role in shaping patterns of geographical parthenogenesis in wild populations ofM. batesii. Instead, population sex ratio variation could result from interactions between the sexes or dispersal dynamics.
AMRomics: a scalable workflow to analyze large microbial genome collection
Authors: Le Duc Quang , Nguyen Tam Thi , Nguyen Canh Hao , Ho Tho Huu , Vo Nam S. , Nguyen Trang , Nguyen Hoang Anh , Cao Minh Duc , Nguyen Son Hoang 
Published: April 2024

AbstractWhole genome analysis for microbial genomics is critical to studying and monitoring antimicrobial resistance strains. The exponential growth of microbial sequencing data necessitates a fast and scalable computational pipeline to generate the desired outputs in a timely and cost-effective manner. Recent methods have been implemented to integrate individual genomes into large collections of specific bacterial populations and are widely employed for systematic genomic surveillance. However, they do not scale well when the population expands and turnaround time remains the main issue for this type of analysis. Here, we introduce AMRomics, a minimalized microbial genomics pipeline that can work efficiently with big datasets. We use different bacterial data collections to compare AMRomics against competitive tools and show that our pipeline can generate similar results of interest but with better performance. The software is open source and is publicly available athttps://github.com/amromics/amromicsunder an MIT license.
SARM1 activation induces reversible mitochondrial dysfunction and can be prevented in human neurons by antisense oligonucleotides
Authors: Loreto Andrea , Cramb Kaitlyn M. L. , McDermott Lucy A. , Antoniou Christina , Cirilli Ilenia , Caiazza Maria Claudia , Merlini Elisa , Arthur-Farraj Peter , Mock Elliot D. , Zhao Hien T. , Bennett David L. , Orsomando Giuseppe , Coleman Michael P. , Wade-Martins Richard 
Published: April 2024

AbstractSARM1 is a key regulator of a conserved program of axon degeneration increasingly linked to human neurodegenerative diseases. Pathological SARM1 activation causes rapid NAD consumption, disrupting cellular homeostasis and leading to axon degeneration. In this study, we develop antisense oligonucleotides targeting human SARM1, demonstrating robust neuroprotection against morphological, metabolic, and mitochondrial impairment in human iPSC-derived dopamine neurons induced by the lethal neurotoxin vacor, a potent SARM1 activator. Furthermore, our findings reveal that axon fragmentation can be prevented, and mitochondrial dysfunction reversed using the NAD precursor nicotinamide, a form of vitamin B3, even after SARM1 activation has occurred, when neurons are already unhealthy. This research identifies ASOs as a promising therapeutic strategy to block SARM1, and provides an extensive characterisation and further mechanistic insights that demonstrate the reversibility of SARM1 toxicity in human neurons. It also identifies the SARM1 activator vacor as a specific and reversible neuroablative agent in human neurons.
Predicting Functional Conformational Ensembles and Binding Mechanisms of Convergent Evolution for SARS-CoV-2 Spike Omicron Variants Using AlphaFold2 Sequence Scanning Adaptations and Molecular Dynamics Simulations
Authors: Raisinghani Nishank , Alshahrani Mohammed , Gupta Grace , Xiao Sian , Tao Peng , Verkhivker Gennady 
Published: April 2024

AbstractIn this study, we combined AlphaFold-based approaches for atomistic modeling of multiple protein states and microsecond molecular simulations to accurately characterize conformational ensembles and binding mechanisms of convergent evolution for the SARS-CoV-2 Spike Omicron variants BA.1, BA.2, BA.2.75, BA.3, BA.4/BA.5 and BQ.1.1. We employed and validated several different adaptations of the AlphaFold methodology for modeling of conformational ensembles including the introduced randomized full sequence scanning for manipulation of sequence variations to systematically explore conformational dynamics of Omicron Spike protein complexes with the ACE2 receptor. Microsecond atomistic molecular dynamic simulations provide a detailed characterization of the conformational landscapes and thermodynamic stability of the Omicron variant complexes. By integrating the predictions of conformational ensembles from different AlphaFold adaptations and applying statistical confidence metrics we can expand characterization of the conformational ensembles and identify functional protein conformations that determine the equilibrium dynamics for the Omicron Spike complexes with the ACE2. Conformational ensembles of the Omicron RBD-ACE2 complexes obtained using AlphaFold-based approaches for modeling protein states and molecular dynamics simulations are employed for accurate comparative prediction of the binding energetics revealing an excellent agreement with the experimental data. In particular, the results demonstrated that AlphaFold-generated extended conformational ensembles can produce accurate binding energies for the Omicron RBD-ACE2 complexes. The results of this study suggested complementarities and potential synergies between AlphaFold predictions of protein conformational ensembles and molecular dynamics simulations showing that integrating information from both methods can potentially yield a more adequate characterization of the conformational landscapes for the Omicron RBD-ACE2 complexes. This study provides insights in the interplay between conformational dynamics and binding, showing that evolution of Omicron variants through acquisition of convergent mutational sites may leverage conformational adaptability and dynamic couplings between key binding energy hotspots to optimize ACE2 binding affinity and enable immune evasion.
Unveiling the Core Functional Networks of Cognition: An Ontology-Guided Machine Learning Approach
Authors: Wu Guowei , Cui Zaixu , Wang Xiuyi , Du Yi 
Published: April 2024

AbstractDeciphering the functional architecture that underpins diverse cognitive functions is fundamental quest in neuroscience. In this study, we employed an innovative machine learning framework that integrated cognitive ontology with functional connectivity analysis to identify brain networks essential for cognition. We identified a core assembly of functional connectomes, primarily located within the association cortex, which showed superior predictive performance compared to two conventional methods widely employed in previous research across various cognitive domains. Our approach achieved a mean prediction accuracy of 0.13 across 16 cognitive tasks, including working memory, reading comprehension, and sustained attention, outperforming the traditional methods’ accuracy of 0.08. In contrast, our method showed limited predictive power for sensory, motor, and emotional functions, with a mean prediction accuracy of 0.03 across 9 relevant tasks, slightly lower than the traditional methods’ accuracy of 0.04. These cognitive connectomes were further characterized by distinctive patterns of resting-state functional connectivity, structural connectivity via white matter tracts, and gene expression, highlighting their neurogenetic underpinnings. Our findings reveal a domain-general functional network fingerprint that pivotal to cognition, offering a novel computational approach to explore the neural foundations of cognitive abilities.