bluuebunny/crossref_metadata_2025_split huggingface dataset:
Pig herd management and infection transmission dynamics: a challenge for modellers
Authors: Sicard Vianney , Picault Sébastien , Andraud Mathieu 
Published: May 2023

AbstractThe control of epidemics requires a thorough understanding of the complex interactions between pathogen transmission, disease impact, and population dynamics and management. Mechanistic epidemiological modelling is an effective way to address this issue, but handling highly structured and dynamic systems, remains challenging. We therefore developed a novel approach that combines Multi-Level Agent-Based Systems (MLABS) with spatial and temporal organization, allowing for a tuned representation of the transmission processes amongst the host population. We applied this method to model the spread of a PRRSv-like virus in pig farms, integrating the clinical consequences (conception and reproduction failures), in terms of animal husbandry practices. Results highlighted the importance to account for spatial and temporal structuring and herd management policies in epidemiological models. Indeed, disease-related abortions, inducing reassignments of sows in different batches, was shown to enhance the transmission process, favouring the persistence of the virus at the herd level. Supported by a declarative Domain-Specific Language (DSL), our approach provides flexible and powerful solutions to address the issues of on-farm epidemics and broader public health concerns. The present application, based on a simple Susceptible-Exposed-Infected-Recovered (SEIR) model, opens the way to the representation of more complex epidemiological systems, including more specific features such as maternally derived antibodies, vaccination, or dual infections, along with their respective clinical consequences on the management practices.
Ecological factors shaping the ectoparasite community assembly of the Azara’s Grass Mouse,Akodon azarae(Rodentia: Cricetidae)
Authors: Carolina Colombo Valeria , Marcela Lareschi , Daniel Monje Lucas , Raúl Antoniazzi Leandro , Serge Morand , Martín Beldomenico Pablo 
Published: May 2023

AbstractParasites are integral members of the global biodiversity. They are useful indicators of environmental stress, food web structure and diversity. Ectoparasites have the potential to transmit vector-borne diseases of public health and veterinary importance and to play an important role in the regulation and evolution of host populations. The interlinkages between hosts, parasites and the environment are complex and challenging to study, leading to controversial results. Most previous studies have been focused on one or two parasite groups, while host are often co-infected by different taxa. The present study aims to assess the influence of environmental and host traits on the entire ectoparasite community composition of the rodent Akodon azarae. A total of 278 rodents were examined and mites (Mesostigmata), lice (Phthiraptera), ticks (Ixodida) and fleas (Siphonaptera) were determined. A Multi Correspondence Analyses was performed in order to analyse interactions within the ectoparasite community and the influence of environmental and host variables on this assembly. We found that environmental variables have a stronger influence on the composition of the ectoparasite community of A. azarae than the host variables analysed. Minimum temperature was the most influential variable among the studied. In addition, we found evidence of agonistic and antagonistic interactions between ticks and mites, lice and fleas. The present study supports the hypothesis that minimum temperature play a major role in the dynamics that shape the ectoparasite community of A. azarae, probably through both direct and indirect processes. This finding becomes particularly relevant in a climate change scenario.
iDeLUCS: A deep learning interactive tool for alignment-free clustering of DNA sequences
Authors: Arias Pablo Millán , Hill Kathleen A. , Kari Lila 
Published: May 2023

AbstractSummaryWe present aninteractiveDeep Learning-based software tool for Unsupervised Clustering of DNA Sequences (iDeLUCS), that detects genomic signatures and uses them to cluster DNA sequences, without the need for sequence alignment or taxonomic identifiers.iDeLUCS is scalable and user-friendly: Its graphical user interface, with support for hardware acceleration, allows the practitioner to fine-tune the different hyper-parameters involved in the training process without requiring extensive knowledge of deep learning. The performance ofiDeLUCS was evaluated on a diverse set of datasets: several real genomic datasets from organisms in kingdoms Animalia, Protista, Fungi, Bacteria, and Archaea, three datasets of viral genomes, a dataset of simulated metagenomic reads from microbial genomes, and multiple datasets of synthetic DNA sequences. The performance ofiDeLUCS was compared to that of two classical clustering algorithms (k-means++ and GMM) and two clustering algorithms specialized in DNA sequences (MeShClust v3.0 and DeLUCS), using both intrinsic cluster evaluation metrics and external evaluation metrics. In terms of unsupervised clustering accuracy,iDeLUCS outperforms the two classical algorithms by an average of ∼ 20%, and the two specialized algorithms by an average of ∼ 12%, on the datasets of real DNA sequences analyzed. Overall, our results indicate thatiDeLUCS is a robust clustering method suitable for the clustering of large and diverse datasets of unlabelled DNA sequences.Availability and implementationiDeLUCS is available at our github repository under the terms of the MIT licence.Contactpmillana@uwaterloo.caSupplementary informationSupplementary data are available atBioinformaticsonline.
Adversarial training improves model interpretability in single-cell RNA-seq analysis
Authors: Sadria Mehrshad , Layton Anita , Bader Gary D. 
Published: May 2023

AbstractFor predictive computational models to be considered reliable in crucial areas such as biology and medicine, it is essential for them to be accurate, robust, and interpretable. A sufficiently robust model should not have its output affected significantly by a slight change in the input. Also, these models should be able to explain how a decision is made. Efforts have been made to improve the robustness and interpretability of these models as independent challenges, however, the effect of robustness and interpretability on each other is poorly understood. Here, we show that predicting cell type based on single-cell RNA-seq data is more robust by adversarially training a deep learning model. Surprisingly, we find this also leads to improved model interpretability, as measured by identifying genes important for classification. We believe that adversarial training will be generally useful to improve deep learning robustness and interpretability, thereby facilitating biological discovery.
Model Agnostic Semi-Supervised Meta-Learning Elucidates Understudied Out-of-distribution Molecular Interactions
Authors: Wu You , Xie Li , Liu Yang , Xie Lei 
Published: May 2023

AbstractMany biological problems are understudied due to experimental limitations and human biases. Although deep learning is promising in accelerating scientific discovery, its power compromises when applied to problems with scarcely labeled data and data distribution shifts. We developed a semi-supervised meta learning framework Meta Model Agnostic Pseudo Label Learning (MMAPLE) to address these challenges by effectively exploring out-of-distribution (OOD) unlabeled data when transfer learning fails. The power of MMAPLE is demonstrated in multiple applications: predicting OOD drug-target interactions, hidden human metabolite-enzyme interactions, and understudied interspecies microbiome metabolite-human receptor interactions, where chemicals or proteins in unseen data are dramatically different from those in training data. MMAPLE achieves 11% to 242% improvement in the prediction-recall on multiple OOD benchmarks over baseline models. Using MMAPLE, we reveal novel interspecies metaboliteprotein interactions that are validated by bioactivity assays and fill in missing links in microbiome-human interactions. MMAPLE is a general framework to explore previously unrecognized biological domains beyond the reach of present experimental and computational techniques.
Polyene-based colouration preserved in 12 million-year-old gastropod shells
Authors: Wolkenstein Klaus , Schmidt Burkhard C. , Harzhauser Mathias 
Published: May 2023

AbstractPolyene pigments represent a major class of pigments in present-day organisms. Their occurrence in fossils has been frequently discussed, but to date no spectroscopic evidence was found. Here, we usein situRaman microspectroscopy to examine the chemistry of exceptionally well-preserved gastropod shells with colour preservation from the Middle Miocene of the Vienna Basin (Austria, Hungary). Raman signals indicative for the presence of intact, i.e. non-hydrogenated polyene pigments were obtained from the fossil gastropods, thus revealing the first record of intact polyenes in fossils. The observed Raman values are in good agreement with those of unmethylated (non-carotenoid) polyenes. Fossil polyene pigments were detected in representatives of the superfamily Cerithioidea, but not in representatives of other gastropod families with colour preservation found at the same localities. Our results show that Raman spectroscopy represents a valuable tool for the non-destructive screening of rare fossils with colour preservation for the occurrence of polyene pigments.
Electrical stimulation of the dorsal motor nucleus of the vagus regulates inflammation without affecting the heart rate
Authors: Falvey Aidan , Palandira Santhoshi P. , Chavan Sangeeta S. , Brines Michael , Tracey Kevin J. , Pavlov Valentin A. 
Published: May 2023

AbstractBackgroundThe vagus nerve plays an important role in neuroimmune interactions and in the regulation of inflammation. A major source of efferent vagus nerve fibers that contribute to the regulation of inflammation is the brainstem dorsal motor nucleus of the vagus (DMN) as recently shown using optogenetics. In contrast to optogenetics, electrical neuromodulation has broad therapeutic implications, but the anti-inflammatory efficacy of electrical DMN stimulation (eDMNS) was not previously investigated. Here, we examined the effects of eDMNS on heart rate (HR) and cytokine levels in murine endotoxemia as well as the cecal ligation and puncture (CLP) model of sepsis.MethodsAnesthetized male 8–10-week-old C57BL/6 mice on a stereotaxic frame were subjected to eDMNS using a concentric bipolar electrode inserted into the left or right DMN or sham stimulation. eDMNS (50, 250 or 500 μA and 30 Hz, for 1 min) was performed and HR recorded. In endotoxemia experiments, sham or eDMNS utilizing 250 μA or 50 μA was performed for 5 mins and was followed by LPS (0.5 mg/kg) i.p. administration. eDMNS was also applied in mice with cervical unilateral vagotomy or sham operation. In CLP experiments sham or left eDMNS was performed immediately post CLP. Cytokines and corticosterone were analyzed 90 mins after LPS administration or 24h after CLP. CLP survival was monitored for 14 days.ResultsEither left or right eDMNS at 250 μA and 500 μA decreased HR, compared with pre- and post-stimulation. This effect was not observed at 50 μA. Left side eDMNS at 50 μA, compared with sham stimulation, significantly decreased serum and splenic levels of the pro-inflammatory cytokine TNF and increased serum levels of the anti-inflammatory cytokine IL-10 during endotoxemia. The anti-inflammatory effect of eDMNS was abrogated in mice with unilateral vagotomy and were not associated with serum corticosterone alterations. Right side eDMNS suppressed serum TNF levels but had no effects on serum IL-10 and on splenic cytokines. In mice with CLP, left side eDMNS suppressed serum TNF and IL-6, as well as splenic IL-6 and increased splenic IL-10 and significantly improved the survival rate of CLP mice.ConclusionsFor the first time we show that a regimen of eDMNS which does not cause bradycardia alleviates LPS-induced inflammation and these effects require an intact vagus nerve and are not associated with corticosteroid alterations. eDMNS also decreases inflammation and improves survival in a model of polymicrobial sepsis. These findings are of interest for further studies exploring bioelectronic anti-inflammatory approaches targeting the brainstem DMN.
In silicoidentification of switching nodes in metabolic networks
Authors: Mairet Francis 
Published: May 2023

AbstractCells modulate their metabolism according to environmental conditions. A major challenge to better understand metabolic regulation is to identify, from the hundreds or thousands of molecules, the key metabolites where the re-orientation of fluxes occurs. Here, a method called ISIS (forIn SilicoIdentification of Switches) is proposed to locate these nodes in a metabolic network, based on the analysis of a set of flux vectors (obtained e.g. by parsimonious flux balance analysis with different inputs). A metabolite is considered as a switch if the fluxes at this point are redirected in a different way when conditions change. The soundness of ISIS is shown with four case studies, using both core and genome-scale metabolic networks ofEscherichia coli, Saccharomyces cerevisiaeand the diatomPhaeodactylum tricornutum. Through these examples, we show that ISIS can identify hot-spots where fluxes are reoriented. Additionally, switch metabolites are deeply involved in post-translational modification of proteins, showing their importance in cellular regulation. InP. tricornutum, we show that Erythrose 4-phosphate is an important switch metabolite for mixotrophy suggesting the importance of this metabolite in the non-oxidative pentose phosphate pathway to orchestrate the flux variations between glycolysis, the Calvin cycle and the oxidative pentose phosphate pathway when the trophic mode changes. Finally, a comparison between ISIS and reporter metabolites identified with transcriptomic data confirms the key role of metabolites such as L-glutamate or L-aspartate in the yeast response to nitrogen input variation. Overall, ISIS opens up new possibilities for studying cellular metabolism and regulation, as well as potentially for developing metabolic engineering.
Expansion microscopy resolves the 3D thylakoid structure
Authors: Bos Peter R. , Berentsen Jarne , Wientjes Emilie 
Published: May 2023

AbstractThe light-harvesting reactions of photosynthesis take place on the thylakoid membrane inside chloroplasts. The thylakoid membrane is folded into appressed membranes, the grana, and non-appressed membranes that interconnect the grana, the stroma lamellae. This folding is essential for the correct functioning of photosynthesis. Electron microscopy and atomic force microscopy are commonly used to study the thylakoid membrane, but these techniques have limitations in visualizing a complete chloroplast and its organization. To overcome this limitation, we applied expansion microscopy (ExM) on isolated chloroplasts. ExM is a technique that involves physically expanding a sample in a swellable hydrogel to enhance the spatial resolution of fluorescence microscopy. Using all-protein staining, we have visualized the 3D structure of spinach thylakoids with a high level of detail. We were able to resolve stroma lamellae that were 60 nm apart and observe their helical wrapping around the grana. Furthermore, we accurately measured the dimensions of grana from top-views of chloroplasts, which allow for precise determination of the grana diameter. Ultimately, we constructed a 3D model of a complete chloroplast, which provides a foundation for structure-based modeling of photosynthetic adaptations. Our results demonstrate that ExM is a fast and reliable technique for studying thylakoid organization with a high level of detail.
Particulate matter composition drives differential molecular and morphological responses in lung epithelial cells
Authors: Engels Sean M. , Kamat Pratik , Pafilis G. Stavros , Li Yukang , Agrawal Anshika , Haller Daniel J. , Phillip Jude M. , Contreras Lydia M. 
Published: May 2023

AbstractParticulate matter (PM) is a ubiquitous component of indoor and outdoor air pollution that is epidemiologically linked to many human pulmonary diseases. PM has many emission sources, making it challenging to understand the biological effects of exposure due to the high variance in chemical composition. However, the effects of compositionally unique particulate matter mixtures on cells have not been analyzed using both biophysical and biomolecular approaches. Here, we show that in a human bronchial epithelial cell model (BEAS-2B), exposure to three chemically distinct PM mixtures drives unique cell viability patterns, transcriptional remodeling, and the emergence of distinct morphological subtypes. Specifically, PM mixtures modulate cell viability and DNA damage responses and induce the remodeling of gene expression associated with cell morphology, extracellular matrix organization and structure, and cellular motility. Profiling cellular responses showed that cell morphologies change in a PM composition-dependent manner. Lastly, we observed that particulate matter mixtures with high contents of heavy metals, such as cadmium and lead, induced larger drops in viability, increased DNA damage, and drove a redistribution among morphological subtypes. Our results demonstrate that quantitative measurement of cellular morphology provides a robust approach to gauge the effects of environmental stressors on biological systems and determine cellular susceptibilities to pollution.
Changes in Seasonal Footwear Elicited Alterations in Gait Kinematics but Not Stability
Authors: Garrah Sydney N. , Best Aaron N. , Wu Amy R. 
Published: May 2023

AbstractDuring daily walking, humans might contend with various perturbations from slippery surfaces in the winter to uneven sidewalks in the summer. Inertial sensors enable investigations of how humans maintain balance under these natural conditions, but conducting these outdoor studies has practical considerations that might influence study results, such as the selection of footwear under different weather conditions. Our study investigates the effects of winter and summer shoe types on gait patterns, specifically whether different shoe types induce changes in gait stability measures under the same walking environment. Twelve healthy adult participants walked indoors with winter and summer shoes while their gait kinematics were recorded using an inertial sensor-based motion capture system. Spatiotemporal measures, body kinematics, stability measures (minimum margin of stability and local divergence exponent), and stepping regressions were calculated to evaluate differences between walking in summer and winter shoes. Statistical significance was determined by paired t-tests. Varying shoe types altered spatiotemporal and kinematic measures, such as increased stride time and stance time while wearing winter shoes, but increased step width and reliance on stepping were the only stability-related changes found. Our study provides insights into the influence of footwear for inertial sensor-based gait studies in real environments, aiding the analysis and interpretation of those studies to augment our understanding of natural stability behavior.
13C tracer analysis reveals the landscape of metabolic checkpoints in human CD8+T cell differentiation
Authors: Kirchmair Alexander , Nemati Niloofar , Lamberti Giorgia , Trefny Marcel P. , Krogsdam Anne , Siller Anita , Hörtnagl Paul , Schumacher Petra , Sopper Sieghart , Sandbichler Adolf M. , Zippelius Alfred , Ghesquière Bart , Trajanoski Zlatko 
Published: May 2023

AbstractNaïve T cells remain in an actively maintained state of quiescence until activation by antigenic signals, upon which they start proliferation and generation of effector cells to initiate a functional immune response. Metabolic reprogramming is essential to meet the biosynthetic demands of the differentiation process, and failure to do so can promote the development of hypofunctional exhausted T cells. Here we used13C metabolomics and transcriptomics to study the metabolic dynamics of CD8+T cells in their complete course of differentiation from naïve over stem-like memory to effector cells. The quiescence of naïve T cells was evident in a profound suppression of glucose oxidation and a decreased expression ofENO1, downstream of which no glycolytic flux was detectable. Moreover, TCA cycle activity was low in naïve T cells and associated with a downregulation of SDH subunits. Upon stimulation and exit from quiescence, the initiation of cell growth and proliferation was accompanied by differential expression of T cell regulatory genes and metabolic reprogramming towards aerobic glycolysis with high rates of nutrient uptake, respiration and lactate production. High flux in anabolic pathways imposed a strain on NADH homeostasis, which coincided with engagement of the proline cycle for mitochondrial redox shuttling. With acquisition of effector functions, cells increasingly relied on glycolysis as opposed to oxidative phosphorylation, which paradoxically was not linked to changes in mitochondrial abundance. We further investigated the metabolic phenotype of exhausted T cells, finding that decreased effector function concurred with a reduction in mitochondrial metabolism, glycolysis and amino acid import, and an upregulation of suppressive and quiescence-associated genes, includingTXNIPandKLF2. Thus, these results identify multiple features critical for the metabolic reprogramming that supports quiescence, proliferation and effector function of CD8+T cells during differentiation. Further, an impairment of the same processes in exhaustion suggests that targeting these control points may be useful for both modulation of differentiation and prevention of exhaustion.
Energy Guided Diffusion for Generating Neurally Exciting Images
Authors: Pierzchlewicz Paweł A. , Willeke Konstantin F. , Nix Arne F. , Elumalai Pavithra , Restivo Kelli , Shinn Tori , Nealley Cate , Rodriguez Gabrielle , Patel Saumil , Franke Katrin , Tolias Andreas S. , Sinz Fabian H. 
Published: May 2023

AbstractIn recent years, most exciting inputs (MEIs) synthesized from encoding models of neuronal activity have become an established method to study tuning properties of biological and artificial visual systems. However, as we move up the visual hierarchy, the complexity of neuronal computations increases. Consequently, it becomes more challenging to model neuronal activity, requiring more complex models. In this study, we introduce a new attention readout for a convolutional data-driven core for neurons in macaque V4 that outperforms the state-of-the-art task-driven ResNet model in predicting neuronal responses. However, as the predictive network becomes deeper and more complex, synthesizing MEIs via straightforward gradient ascent (GA) can struggle to produce qualitatively good results and overfit to idiosyncrasies of a more complex model, potentially decreasing the MEI’s model-to-brain transferability. To solve this problem, we propose a diffusion-based method for generating MEIs via Energy Guidance (EGG). We show that for models of macaque V4, EGG generates single neuron MEIs that generalize better across architectures than the state-of-the-art GA while preserving the within-architectures activation and requiring 4.7x less compute time. Furthermore, EGG diffusion can be used to generate other neurally exciting images, like most exciting natural images that are on par with a selection of highly activating natural images, or image reconstructions that generalize better across architectures. Finally, EGG is simple to implement, requires no retraining of the diffusion model, and can easily be generalized to provide other characterizations of the visual system, such as invariances. Thus EGG provides a general and flexible framework to study coding properties of the visual system in the context of natural images.1