bluuebunny/crossref_metadata_2025_split huggingface dataset:
Enhanced extracellular matrix production provides protection to cell wall-deficientEscherichia coli
Authors: Crooijmans Marjolein E. , Willemse Joost J. , de Winde Johannes H. , Claessen Dennis 
Published: November 2024

AbstractEscherichia coli-induced recurrent urinary tract infections (rUTIs) present a complicated challenge within the medical field. Most first-line antibiotic treatments primarily target cell-wall synthesis, which can lead to the formation of cell wall-deficient cells. To investigate how such cells can sustain, we obtained anE. colistrain capable of efficiently proliferating without its cell wall. One of the mutations lead to enhanced expression ofrcsA, encoding an important regulator involved in responding to cell envelope stress. RNA sequencing demonstrated an upregulation of genes associated with the production of extracellular matrix components, and this increased extracellular matrix production was confirmed using various imaging techniques. Remarkably, a subsequent long-term evolution experiment on this strain revealed a further augmentation in extracellular matrix production, coinciding with an enhanced ability to withstand harsh conditions. These findings demonstrate howE. coliadapts to loss of its cell wall and that an increased synthesis of matrix constituents can compensate for the protective properties of the cell wall.SignificanceThe cell envelope is crucial for the protection ofE. coli. During bacterial infections such as urinary tract infections, antibiotics that disrupt cell wall synthesis are commonly prescribed. However, this can stimulate the formation of wall-deficient bacteria that are still able to proliferate despite the presence of these drugs. Our findings reveal that loss of the cell wall inE. coliincreases the production of extracellular matrix, a mechanism found in other unicellular organism too. This adaptation allows the bacteria to maintain their structural integrity and survive, highlighting a potential challenge in the successful treatment of bacterial infections.
The neural mechanisms of concept formation over time in music
Authors: Bonetti L. , Fernández-Rubio G. , Szabó S.A. , Carlomagno F. , Vuust P. , Kringelbach M.L. , Brattico E. 
Published: November 2024

AbstractThe interplay between abstraction, learning and memory is crucial for humans to interact with the environment. Abstraction in relation to concept formation is typically studied with Gestalt stimuli varying in their physical properties while maintaining abstract internal relations. The role of temporal integration in recognising abstract concepts has been, however, overlooked even in a sensory domain relying on temporal item succession such as audition. Here, we investigated the neural mechanisms underlying abstract concept generalisation over time using a musical adaptation of the old/new recognition paradigm. During magnetoencephalography (MEG) recordings, 108 participants categorised five-tone sequences as previously learned or novel or as musical or not. The sequences varied in pace, ranging from very fast (125 ms per sound) to very slow (5000 ms per sound). On the behavioural level, sequence duration exerted a substantial impact on the recognition and musicality judgments of the novel sequences. The differences in neural responses between memorised and novel sequences especially involved regions in the auditory cortex, medial cingulate gyrus and medial temporal lobe and were most pronounced at the highest musical pace, diminishing proportionally as the pace decreased. These findings contribute to our understanding of how the brain generalises and derives meaning from abstract concepts that unfold over time learned through the auditory system.
Dynamic Causal Modeling in Probabilistic Programming Languages
Authors: Baldy Nina , Woodman Marmaduke , Jirsa Viktor , Hashemi Meysam 
Published: November 2024

AbstractUnderstanding the intricate dynamics of brain activities necessitates models that incorporate causality and nonlinearity. Dynamic Causal Modelling (DCM) presents a statistical framework that embraces causal relationships among brain regions and their responses to experimental manipulations, such as stimulation. In this study, we perform Bayesian inference on a neurobiologically plausible generative model that simulates event-related potentials observed in magne-to/encephalography data. This translates into probabilistic inference of latent and observed states of a system driven by input stimuli, described by a set of nonlinear ordinary differential equations (ODEs) and potentially correlated parameters. We provide a guideline for reliable inference in the presence of multimodality, which arises from parameter degeneracy, ultimately enhancing the predictive accuracy of neural dynamics. Solutions include optimizing the hyperparameters, leveraging initialization with prior information, and employing weighted stacking based on predictive accuracy. Moreover, we implement the inference and conduct comprehensive model comparison in several probabilistic programming languages to streamline the process and benchmark their efficiency. Our investigation shows that model inversion in DCM extends beyond variational approximation frameworks, demonstrating the effectiveness of gradient-based Markov Chain Monte Carlo methods. We illustrate the accuracy and efficiency of posterior estimation using a self-tuning variant of Hamiltonian Monte Carlo and the automatic Laplace approximation, effectively addressing parameter degeneracy challenges. This technical endeavor holds the potential to advance the inversion of state-space ODE models, and contribute to neuro-science research and applications in neuroimaging through automatic DCM.
Key gut microbiota components and functions in an aquatic keystone species across diets assessed by metaproteomics
Authors: Dumas Thibaut , Pible Olivier , Grenga Lucia , Esposti Davide Degli , Delorme Nicolas , Geffard Olivier , Chaumot Arnaud , Armengaud Jean 
Published: November 2024

AbstractThe gut microbiota plays a crucial role in maintaining host fitness and modulating contaminant toxicity-related responses. However, information on how the gut microbiota of sentinel species responds to environmental factors is limited. In this study, we characterized the gut microbial community and its functions under normal, contaminant-free conditions by examining the effects of different diets over a 10-day period (alder leaf, carrot, spinach, and protein-rich granules) on the amphipodGammarus fossarum, commonly used in bioassays for ecotoxicity assessment of contaminated rivers. Metaproteomic analysis of intestine samples enabled taxonomic characterization of the gut microbiota from this millimetric animal, assignment of biological functions to each microbial entity, and functional analysis of host proteins. The most abundant microbes detected in the gut belong to 37 bacterial and 5 fungal genera. Functional analyses of host and microbial proteins revealed complementary metabolic activities, allowing the degradation of complex polysaccharides such as cellulose and chitin. Diet was found to shape microbial community structure, with foodborne microorganisms strongly influencing structural changes during short-term feeding in amphipods. These microorganisms remained viable post-ingestion and contributed to food digestion. Functional stability was maintained across different diets, although the protein-rich granules diet induced functional shifts in both the host and its microbiota, reflecting their adaptation to a novel nutrient source. Finally, we identified a core microbiota driving key gut functions, less affected by dietary variations. These findings are significant for future ecotoxicological and biomonitoring investigations, leveraging the microbiomes of these sentinel animals as pivotal targets.
Inhibition and Updating Share Common Resources: Evidence from Drift Diffusion Model
Authors: Sun Yuhong , Lin Yaohui , Han Shangfeng 
Published: November 2024

AbstractInhibition and updating are fundamental cognitive functions in humans, yet the nature of their relationship—whether shared or distinct—remains ambiguous. This study investigates the relationship between inhibition and updating and examines whether this relationship is altered in individuals with trait anxiety. A novel paradigm that combines N-back and congruent/incongruent Stroop tasks were utilized to explore these issues by employing Signal Detection Theory (SDT) and the hierarchical drift diffusion model (HDDM) methods. The results revealed that participants exhibited longer response times and higher error rates in conditions requiring both inhibition and updating compared to those requiring updating alone. SDT analysis indicated lower discriminability, while HDDM analysis showed slower drift rates and longer non-decision times. These findings suggest that inhibition consumes cognitive resources, leading to worse updating performance, implying that both functions may rely on shared resources. In the anxiety group, higher trait anxiety scores correlated with increased response times and decreased accuracy. The drift rate fully mediated the effect of trait anxiety on accuracy, suggesting that the speed of effective information accumulation is a key mechanism underlying the abnormal functioning of inhibition and updating in individuals with anxiety. Overall, the results elucidate the relationship between these fundamental cognitive functions, supporting the notion that inhibition and updating share cognitive resources. Furthermore, this study provides evidence and insights into the cognitive abnormalities observed in individuals with anxiety.
Are resting-state networks the brain’s cognitive atoms? Differential dynamic reconfiguration of functional brain networks across tasks and at rest
Authors: Jung JeYoung , Lambon Ralph Matthew A. 
Published: November 2024

AbstractIt is increasingly popular to utilise functional connectivity (FC) analyses of resting-state functional magnetic resonance imaging (fMRI) to characterize human functional brain networks and to use the emergent resting-state networks (RSNs) in basic and clinical neuroscience. Often, they are treated as ‘atomic’ building blocks that underpin human cognition. However, the true function of these RSNs, as well as the relationship between intrinsic and task-evoked functional brain networks, are complex and incompletely characterized. Here, we investigated the functional characteristics of the intrinsic and extrinsic networks using resting-state and task fMRI. Independent component analysis (ICA) was used to estimate spatiotemporal functional networks during tasks and at rest, and to compare the spatiotemporal properties of each network. While there was some spatial correspondence between the RSNs and task-evoked networks, our results demonstrated that the task-evoked functional networks were different from the RSNs in task-relatedness as well as spatial topology. Furthermore, the degree of topological differences between the RSNs and task-evoked networks was modulated by a given task. Comparison between the RSNs and task-evoked networks showed that tasks reconfigure the RSNs by changing FC with various brain regions specific to the task condition. Our findings indicate that the brain does not maintain an “invariant intrinsic” network architecture when it engages in a task. Instead, the tasks reconfigure the network architectures, thereby accommodating specific computational/representational task requirements through flexible interactions between demand-specific regions. Thus, the results suggest that task fMRI is required to understand the full repertoire of the brain’s functional architecture.Significant StatementResting-state networks (RSNs) could offer a critical foundation for understanding the brain’s intrinsic organization. However, the functional nature of these intrinsic networks, and their relationship to those activated during specific tasks, are complex and not fully understood. We undertook a comprehensive examination of the functional attributes of intrinsic and extrinsic networks. Although we observed some spatial congruence between RSNs and task-evoked networks, we found fundamental differences in their task-relatedness and spatial topology. These findings highlight the dynamic nature of the brain’s functional networks, which adapt to specific task demands through flexible interactions among task-specific regions. Thus, task fMRI is essential for a comprehensive understanding of how the human brain dynamically reconfigures its functional architecture in response to external demands, providing valuable insights into the cognition and behaviour.
Astrocytic modulation of population encoding in mouse visual cortex via GABA transporter 3 revealed by multiplexed CRISPR/Cas9 gene editing
Authors: Park Jiho , Sipe Grayson O. , Tang Xin , Ojha Prachi , Fernandes Giselle , Leow Yi Ning , Zhang Caroline , Osako Yuma , Natesan Arundhati , Drummond Gabrielle T. , Jaenisch Rudolf , Sur Mriganka 
Published: November 2024

SummaryAstrocytes, which are increasingly recognized as pivotal constituents of brain circuits governing a wide range of functions, express GABA transporter 3 (Gat3), an astrocyte-specific GABA transporter responsible for maintenance of extra-synaptic GABA levels. Here, we examined the functional role of Gat3 in astrocyte-mediated modulation of neuronal activity and information encoding. First, we developed a multiplexed CRISPR construct applicable for effective genetic ablation of Gat3 in the visual cortex of adult mice. Usingin vivotwo-photon calcium imaging of visual cortex neurons in Gat3 knockout mice, we observed changes in spontaneous and visually driven single neuronal response properties such as response magnitudes and trial-to-trial variability. Gat3 knockout exerted a pronounced influence on population-level neuronal activity, altering the response dynamics of neuronal populations and impairing their ability to accurately represent stimulus information. These findings demonstrate that Gat3 in astrocytes profoundly shapes the sensory information encoding capacity of neurons and networks within the visual cortex.
Archaic Adaptive Introgression in Modern Human Reproductive Genes
Authors: Kendall Christopher , Nooranikhojasteh Amin , Parra Esteban , Schillaci Michael A. , Viola Bence 
Published: November 2024

AbstractModern humans and archaic hominins, namely Denisovans and Neanderthals, have been demonstrated to have a long history of admixture. Specifically, some of these admixture events have been adaptive and allowed modern humans to adapt to their new environments outside of Africa. Little research has been done on the impact of archaic introgression on genes associated with reproduction. In this study we report evidence of putative adaptive introgression of 118 genes within modern humans that have been previously associated with reproduction in mice or modern humans. From these genes we identified 11 archaic core haplotypes, three that have been positively selected. Additionally, we found that 327 archaic alleles have genome-wide significance for a variety of traits and 308 of these variants were discovered to be eQTLs regulating 176 genes. We highlight that 81% of the archaic eQTLs overlapping a core haplotype region regulate genes expressed in ovaries, prostate, testes, and vagina compared to other tissues. We also found that several of the putatively adaptively introgressed genes in our results are enriched in developmental and cancer pathways. Further, some of these genes have been associated with embryo development and reproductive-inhibiting phenotypes like preeclampsia. Lastly, we found that archaic alleles overlapping an introgressed segment on chromosome 2 are protective against prostate cancer. Taken together, our results describe how archaic haplotypes, when introduced into a modern human background, may be important in regulating development across the lifespan of an individual.
Modulation of learning-dependent plasticity in the attention network
Authors: Contò F. , Ellena G. , Edwards G. , Battelli L. 
Published: November 2024

AbstractNeuromodulatory interventions are gaining interest for their potential to enhance key cognitive functions, including attention and learning. However, their effects on cortical activity during training remains limited. Our previous work demonstrated that bilateral transcranial random noise stimulation (tRNS) over the intraparietal sulcus (IPS), coupled with a multi-session attention-based perceptual learning increased functional connectivity within the dorsal-ventral attention network at rest (DVAN, Contò et al., 2021). In this follow-up study we examined how the same multi-session stimulation protocol modulates BOLD signal in the DVAN during two attention tasks. Our findings show that tRNS targeting the IPS selectively enhanced BOLD responses during a spatial, but not a temporal task, indicating task-specific neural effects. Conversely, BOLD responses in the DVAN significantly decreased after sham. Control analyses within the default mode network (DMN) showed no significant changes, indicating the network specific effects of tRNS. The region-specific nature of these effects suggests that tRNS-induced modulation is constrained to key network nodes, enhancing the DVAN’s cortical response in spatial tasks, a response that instead decreases after sham and has no benefit on behavior. This selectivity of the cortical response aligns with theories of neural efficiency, indicating that tRNS may prevent natural downregulation in task-relevant areas and promotes sustained plasticity. Our results highlight the potential of tRNS in augmenting cognitive training, providing a powerful tool for the precise modulation of attention networks in both healthy individuals and clinical population.
Expression variability following gene duplication facilitates gene retention
Authors: Cai Haoran , Des Marais David L. 
Published: November 2024

AbstractGenome analyses reveal that gene duplication in eukaryotes is pervasive, providing a primary source for the emergence of new genes. The mechanisms governing the retention of duplicated genes, particularly in the early stages post-duplication, remain ambiguous. Patterns of divergence between duplicated genes vary, leading to biases in the functions of genes retained following duplication. For example, genes that arise from tandem duplication tend to be involved in envi- ronmental responses. However, the mechanisms that cause such functional biases remain elusive. Here, to better understand the mechanisms and factors determining gene retention, we study a frequently overlooked molecular feature —expression variability—as measured by within-line expression variation, termed variability. We find that, on average, genes with duplicates exhibit higher expression variability than singletons. We further find that such elevated expression variability for small-scale duplications (SSDs) is likely driven by the immediate increase in expression variability following duplication events. These findings suggest a potential overarch- ing mechanism that facilitates gene expression divergence, functional gains of environmental responses, and duplicate retention following SSDs.
Modelling the Complex Smoking Exposure History in Assessment of Pan-Cancer Risk
Authors: Liu Wei , Chen Ya-Ting , Tao Baiwenrui , Lv Ying , Zhang Yan-Xi , Ren Hui-Ying , Zhang Yu-Ting , Fan Yu-Ping , Li Meng-Han , Shi Ya-Xin , Wang Shi-Yuan , Chen Bing-Wei , van Osch Frits , Zeegers Maurice P. , Qin Qi-Rong , Wesselius Anke , Yu Evan Yi-Wen 
Published: November 2024

AbstractModelling complex smoking histories, with more comprehensive and flexible methods, to show what profile of smoking behavior is associated with the risk of different cancers remains poorly understood. This study aims to provide insight into the association between complex smoking exposure history and pan-cancer risk by modelling both smoking intensity and duration in a large-scale prospective cohort. Here, we used data including a total of 0.5 million with cancer incidences of 12 smoking-related cancers. To jointly interpret the effects of intensity and duration of smoking, we modelled excess relative risks (ERRs)/pack-year isolating the intensity effects for fixed total pack-years, thus enabling the smoking risk comparison for total exposure delivered at low intensity (for long duration) and at a high intensity (for short duration). The pattern observed from the ERR model indicated that for a fixed number of pack-years, low intensity/long duration or high intensity/short duration is associated with a different greater increase in cancer risk. Those findings were extended to an increase of time since smoking cessation (TSC) showing a reduction of ERR/pack-year for most cancers. Moreover, individuals with favorable lifestyle behaviors, such as regular physical activity and healthy dietary intakes, were shown to have lower ERRs/pack-year, compared to those with unfavorable lifestyle behaviors. Overall, this study systematically evaluates and demonstrates that for pan-cancer risks, smoking patterns are varied, while reducing exposure history to a single metric such as pack-years was too restrictive. Therefore, cancer screening guidelines should consider detailed smoking patterns, including intensity, duration, and cessation, for more precise prevention strategies.Highlights (Key context and significance)Distinct cancer risk patterns emerge based on smoking exposure beyond equal pack-years: smoking duration is a stronger risk factor for some cancers, while smoking intensity dominates for others.Time since smoking cessation (TSC) significantly lowers cancer risk: former smokers experience substantial reductions in risk for most cancers within the first 20 years after quitting.Favorable lifestyle behaviors mitigate cancer risks: individuals with regular physical activity and healthy diets show lower excess relative risks (ERRs) for most cancers, compared to those with unhealthy habits.Tailored cancer screening based on smoking behavior: cancer screening guidelines should consider detailed smoking patterns, including intensity, duration, and cessation, for more precise prevention strategies.
AI coupled to pharmacometric modelling to tailor malaria and tuberculosis treatment in Africa
Authors: Turon Gemma , Mulubwa Mwila , Montaner Anna , Njoroge Mathew , Chibale Kelly , Duran-Frigola Miquel 
Published: November 2024

AbstractAfrica’s vast genetic diversity poses challenges for optimising drug treatments in the continent, which is exacerbated by the fact that drug discovery and development efforts have historically been performed outside Africa. This has led to suboptimal therapeutic outcomes in African populations and overall scarcity of relevant pharmacogenetic data, including characteristic genotypes as well as drugs prescribed in the continent to treat infectious diseases. Here, we propose a general approach to identify drug-gene pairs with potential pharmacogenetic interest. Furthermore, we delve deeper into the analysis of malaria and tuberculosis therapies, many of which remain uncharacterised from a pharmacogenetic perspective. Our pipeline leverages artificial intelligence and the latest advances in knowledge embedding techniques to exploit currently available biomedical data and subsequently prioritise pharmacogenes for each drug. Predicted pharmacogenes are then incorporated into pharmacometric modelling to hypothesise which ones might be of clinical interest, and which dose adjustments could be made to provide better treatment outcomes for the African population.