bluuebunny/crossref_metadata_2025_split huggingface dataset:
Norepinephrine enhances oligodendrocyte precursor cell calcium dynamics in the cerebral cortex during arousal
Authors: Lu Tsai-Yi , Hanumaihgari Priyanka , Hsu Eric T. , Agarwal Amit , Bergles Dwight E. 
Published: August 2022

AbstractOligodendrocytes are generated from a widely distributed population of progenitors that express neurotransmitter receptors, but the mechanisms that alter activity of these oligodendrocyte precursor cells (OPCs) in vivo have not been determined. We generated a novel line of transgenic mice to express membrane-anchored GCaMP6s in OPCs and used longitudinal two-photon microscopy to monitor their Ca2+ changes in the cerebral cortex of awake mice. OPCs exhibited high rates of spontaneous activity, consisting of focal, transient Ca2+ increases within their highly ramified processes. Unexpectedly, these events occurred independent of excitatory neuron activity, but were inhibited by anesthesia, sedative agents, and antagonists of noradrenergic signaling. These norepinephrine enhanced Ca2+ dynamics rapidly declined as with differentiation. Selective knockout of α1A adrenergic receptors in OPCs suppressed both spontaneous and locomotion-induced Ca2+ increases, indicating that OPCs are directly modulated by norepinephrine in vivo, providing a means to alter their dynamics and lineage progression during distinct brain states.
Classification of Human Chronotype Based on fMRI Network-Based Statistics
Authors: Mason Sophie L. , Junges Leandro , Woldman Wessel , Facer-Childs Elise R. , de Campos Brunno M. , Bagshaw Andrew P. , Terry John R. 
Published: August 2022

AbstractChronotype - the relationship between the internal circadian physiology of an individual and the external 24-hour light-dark cycle - is increasingly implicated in mental health and cognition. Individuals presenting with a late chronotype have an increased likelihood of developing depression, and can display reduced cognitive performance during the societal 9-5 day. However, the interplay between physiological rhythms and the brain networks that underpin cognition and mental health are not well understood. To address this issue, we use resting state fMRI collected from 16 people with an early chronotype and 22 people with a late chronotype to study if differentiable information about chronotype is embedded in functional brain networks. We develop a classifier utilising the Network Based-Statistic (NBS) methodology, using rigorous selection criteria to select t-statistic thresholds within the NBS approach. We find significant differences in functional networks measured in early and late chronotypes and describe conditions under which the classifier achieves 97.3% accuracy. Revealing differences in functional brain networks based on extreme chronotype suggests future avenues of research that may ultimately better characterise the relationship between internal physiology, external perturbations, brain networks and disease.
MARVEL: An integrated alternative splicing analysis platform for single-cell RNA sequencing data
Authors: Wen Wei Xiong , Mead Adam J , Thongjuea Supat 
Published: August 2022

ABSTRACTAlternative splicing is an important source of heterogeneity in gene expression between individual cells but remains an understudied area due to the paucity of computational tools to analyze splicing dynamics at single-cell resolution. Here, we present MARVEL, a comprehensive R package for single-cell splicing analysis applicable to RNA sequencing generated from the plate- and droplet-based methods. We performed extensive benchmarking of MARVEL against available tools and demonstrated its utility by analyzing iPSC differentiation into endoderm cells and cardiomyocytes. MARVEL enables systematic and integrated splicing and gene expression analysis of single cells to characterize the splicing landscape and reveal biological insights.
Weight of evidence of Y-STR matches computed with the discrete Laplace method: Impact of adding a suspect’s profile to a reference database
Authors: Andersen Mikkel Meyer , Eriksen Poul Svante , Morling Niels 
Published: August 2022

AbstractThe discrete Laplace method is recommended by multiple parties (including the International Society of Forensic Genetics, ISFG) to estimate the weight of evidence in criminal cases when a suspect’s Y-STR profile matches the crime scene Y-STR profile. Unfortunately, modelling the distribution Y-STR profiles in the database is time-consuming and requires expert knowledge. When the suspect’s Y-STR profile is added to the database, as would be the protocol in many cases, the discrete Laplace model must be recomputed. We found that the likelihood ratios with and without adding the suspect’s Y-STR profile were almost identical with 1,000 or more Y-STR profiles in the database for Y-STR profiles with 8, 12, and 17 loci. Thus, likelihood ratio calculations can be performed in seconds if a an established discrete Laplace model based on at least 1,000 Y-STR profiles is used. A match in a database with 17 Y-STR loci from at least 1,000 male individuals results in a likelihood ratio above 10,000 in approximately 94% of the cases, and above 100,000 in approximately 82% of the cases. We offer a freely available IT tool for estimating the discrete Laplace model of the STR profiles in a database and the likelihood ratio.HighlightsThe discrete Laplace method is suitable for estimating the weight of evidence of matches with 17 Y-STRs.LRs based on the discrete Laplace method are 10-100 times higher (in median) than those based on Brenner’s κ method.A database with 17 STRs from at least 1,000 males gives LRs of above 10,000 in approximately 94% of the cases and above 100,000 in approximately 82% of the cases with the discrete Laplace method.The weight of evidence of a matching Y-STR profile is computed within seconds and easily documented when a precomputed discrete Laplace model is available (an IT tool is provided).50% of all Yfiler Plus matches are between male relatives within a genetic distance of five meioses.
Widespread horizontal gene transfer between plants and their microbiota
Authors: Haimlich Shelly , Fridman Yulia , Khandal Hitaishi , Savaldi-Goldstein Sigal , Levy Asaf 
Published: August 2022

AbstractPlants host a large array of commensal bacteria that interact with the host. The growth of both bacteria and plants is often dependent on nutrients derived from the cognate partners, and the bacteria fine-tune host immunity against pathogens. This ancient interaction is common in all studied land plants and is critical for proper plant health and development. We hypothesized that the spatial vicinity and the long-term relationships between plants and their microbiota may promote or even depend on cross-kingdom horizontal gene transfer (HGT), a phenomenon that is relatively rare in nature. To test this hypothesis we analyzed the Arabidopsis thaliana genome and its extensively sequenced microbiome to detect events of horizontal transfer of full length genes that are absent from non-plant associated bacteria. Interestingly, we detected 180 unique genes that were horizontally transferred between plants and their microbiota. Genes transferred from plants to their microbiota are enriched in secreted proteins that metabolize carbohydrates, whereas microbes transferred to plants genes that are enriched in redox homeostasis functions. To validate our approach, we tested if a bacterial gene is functionally similar to its Arabidopsis homologue in planta. The Arabidopsis DET2 gene is essential for biosynthesis of the brassinosteroid phytohormones and loss-of-function of the gene leads to dwarfism. We found that expression of the DET2 homologue from Leifsonia bacteria of the Actinobacteria phylum in the Arabidopsis det2 background complements the mutant, and leads to normal plant growth. Together, these data suggest that cross-kingdom horizontal gene transfer events shape the interactions between plants and their microbiome.Significance statementWhat are the genes that shape host-microbe interactions and what are their origins are fundamental questions in molecular ecology and evolution. We explored the evolutionary mechanisms that formed Arabidopsis-microbiota interactions, as a model for host-microbe interactions. We found prevalent horizontal gene transfer, affecting 180 genes, that occurred between plants and their commensal microbiota. We propose that these genes participate in molecular mimicry between the host and its microbiome. Bacteria acquired from plants genes that primarily encode for secreted proteins that metabolize carbohydrates, thereby enabling bacteria to grow on plant-derived sugars. Additionally, we demonstrate how a bacterial gene that mimics a plant hormone biosynthesis gene can replace the plant gene function. Our results suggest that horizontal gene transfer between hosts and their microbiota is a significant and active evolutionary mechanism that contributed new traits to plants and their commensal microbiota.
Structures of human Malic Enzyme 3
Authors: Grell Tsehai A.J. , Mason Mark , Thompson Aaron A. , Gómez-Tamayo Jose Carlos , Riley Daniel , Wagner Michelle V. , Steele Ruth , Ortiz-Meoz Rodrigo F. , Wadia Jay , Shaffer Paul L. , Tresadern Gary , Sharma Sujata , Yu Xiaodi 
Published: August 2022

SummaryMalic enzymes (ME1, ME2, and ME3) are involved in cellular energy regulation, redox homeostasis, and biosynthetic processes, through the production of pyruvate and reducing agent NAD(P)H. Recent studies have implicated the third and least well-characterized isoform, mitochondrial NADP+-dependent malic enzyme 3 (ME3), as a therapeutic target for pancreatic cancers. Here, we utilized an integrated structure approach to capture the structures of ME3 in various ligand binding states. ME3 exists as a stable tetramer and its dynamic Domain C is critical for activity. Catalytic assay results reveal that ME3 is a non-allosteric enzyme and does not require modulators for activity while structural analysis suggests that the inner stability of ME3 domain A relative to ME2 disables allostery in ME3. With structural information available for all three malic enzymes, the foundation has been laid to understand the structural and biochemical differences of these enzymes and could aid in the development of specific malic enzyme small molecule drugs.
Discovering genomic islands in unannotated bacterial genomes using sequence embedding
Authors: Banerjee Priyanka , Eulenstein Oliver , Friedberg Iddo 
Published: August 2022

MotivationGenomic islands (GEIs) are clusters of genes in bacterial genomes that are typically acquired by horizontal gene transfer. Genomic islands play a crucial role in the evolution of bacteria by helping them adapt quickly to changing environments. Specifically of interest to human health, many GEIs contain pathogenicity and antimicrobial resistance genes. Detecting GEIs is therefore an important problem in biomedical and environmental research. There have been many previous studies for computationally identifying GEIs, but most of the studies rely either on detecting differences between closely related genomes, or on annotated nucleotide sequences with predictions based on a fixed set of known features.ResultsHere we present TreasureIsland, which uses a new unsupervised representation of DNA sequences to predict GEIs. We developed a high precision boundary detection method featuring an incremental fine-tuning of GEI borders, and we evaluated the accuracy of this framework using a new comprehensive reference dataset, Benbow. We show that TreasureIsland performs competitively when compared with other GEI predictors, enabling the identification of genomic islands in unannotated and taxonomically isolated bacterial genomes.AvailabilityThe source code and the datasets used in this study are available at: https://github.com/priyamayur/GenomicIslandPredictionContactidoerg@iastate.eduSupplementary informationSupplementary Material is available at Bioinformatics online.
Does AlphaFold2 model proteins’ intracellular conformations? An experimental test using cross-linking mass spectrometry of endogenous ciliary proteins
Authors: McCafferty Caitlyn L. , Pennington Erin L. , Papoulas Ophelia , Taylor David W. , Marcotte Edward M. 
Published: August 2022

AbstractA major goal in structural biology is to understand protein assemblies in their biologically relevant states. Here, we investigate whether AlphaFold2 structure predictions match native protein conformations. We chemically cross-linked proteins in situ within intact Tetrahymena thermophila cilia and native ciliary extracts and identified 1,225 intramolecular cross-links within the 100 best-sampled proteins to provide a benchmark of distance restraints obeyed by proteins in their native assemblies. The corresponding AlphaFold2 structure predictions were highly concordant, positioning 86.2% of cross-linked residues within Cα-to-Cα distances of 30 Å, consistent with the known cross-linker length. 43% of the proteins showed no violations. Most inconsistencies occurred in low-confidence regions or between domains of the structure prediction. For basal body protein BBC118, cross-links combined with the predicted structure revealed domain packing satisfying both data. Overall, AlphaFold2 predicted biological structures with low predicted aligned error corresponded to more correct native structures. However, we observe cases where rigid body domains are oriented incorrectly, suggesting that combining structure prediction with experimental information will better reveal biologically relevant conformations.
An uncertainty-based interpretable deep learning framework for breast cancer outcomes prediction
Authors: Chai Hua , Lin Siyin , He Minfan , Yang Yuedong , OuYang Yongzhong , Zhao Huiying 
Published: August 2022

AbstractAccurate prediction of breast cancer outcomes is important for selecting appropriate treatment, which can prolong the survival period of the patients and improve the life quality. Recently, different deep learning-based methods are carefully designed for cancer outcomes prediction. However, the applications of these methods are still challenging due to the model interpretability. In this study, we proposed a novel multi-task deep neural network UISNet to interpret the feature importance of the prediction model by an uncertainty-based integrated gradients algorithm. Additionally, UISNet improves the prediction accuracy by introducing the prior biological pathway knowledge and utilizing the patients’ heterogeneity information. By applications to seven breast cancer public datasets, the method was shown to outperform state-of-the-art methods by achieving a 5.79% higher C-index value on average. For the identified genes based on the interpretable model, 11 out of the top 20 genes have been proved to be associated with breast cancer by literature review. The comprehensive tests indicated that our proposed method is accurate and robust to predict breast cancer outcomes, and is an effective way to identify the prognosis-related genes. The method codes are available at: https://github.com/chh171/UISNet.
Biodiversity and distribution of sea anemones (Cnidaria, Anthozoa, Actiniaria) in Peru
Authors: Durand Allison , Valdivia-Chávez Deysi , Aramayo Víctor 
Published: August 2022

AbstractDiverse and abundant sea anemones are common in shallow marine areas. Detailed biodiversity analysis in Peru are comparatively scarce. To contribute to the biodiversity inventory and distribution information of this taxa, we analyzed exhaustively the available bibliography in Peruvian waters. A total of 23 anemone species were identified, distributed in 68 localities, and grouped into 1 Order (Actiniaria), 6 Families (Actiniidae, Actinostolidae, Aiptasiidae, Isanthidae, Phelliidae and Sagartiidae) and 20 Genera. The most reported species areAnthothoe chilensis(37 references),Phymactis clematis(28),Phymanthea pluvia(27),Oulactis concinnata(18), andAntholoba achates(15). Lima is the region with the highest number of publications, followed by La Libertad, Piura, Lambayeque, and Ancash.Anthothoe chilensisoccurs in almost all the Peruvian coastal regions. On the contrary,O. concinnatahas been primarily observed in Lima, whileA. achatesoccurred only in the southern regions (Ica, Arequipa, and Moquegua). Rocky substrates (~55% records) seem to be the most suitable habitat for sea anemones in Peru, corresponding to exposed (e.g. vertical walls) and sheltered zones (e.g. rocky crevices, caves, under rock areas). Although most of the species in Peru exhibit a relatively wide spatial distribution, our results suggest that there are several regions with little or no research efforts. Despite a growing study effort over the past 30 years (>50% of biodiversity reported), the current biodiversity status for this group is still unclear. A significant effort is needed to better analyze occurrence patterns and unveil new species regarding a changing environmental scenario with human influence.
Human social learning biases in immersive virtual environments
Authors: Easter C. , Hassall C. 
Published: August 2022

AbstractSocial learning strategies describe what, when, and from whom individuals choose to learn. Evidence suggests that both humans and animals are capable of strategic social learning. However, human research generally lacks ecological and spatial realism, making it difficult to understand the importance of our use of social information in an evolutionary context. In this study, we use virtual reality to simulate three novel tasks inspired by the animal literature (Container, Route Choice and Foraging tasks) within complex, three-dimensional environments. In each experiment, combinations of demonstrators with different characteristics gave opposing solutions to the task to determine from whom participants preferentially learned. Importantly, participants were able to freely navigate the environment and attempt the task in any way they chose by using or ignoring social information. We found that participants displayed an overall bias towards learning asocially (independently) rather than socially. Asocial learning was favoured more strongly during complex tasks that spanned larger spatial scales, potentially due to the difficulties in keeping track of social information in such scenarios. When learning from others, participants displayed a bias towards learning from the majority over the minority (positive frequency-dependent social learning) and towards learning from the most successful demonstrators (payoff-based social learning), which supports the findings of previous, lab-based experiments. There was no apparent bias with respect to demonstrator dominance status, gender and body size. Our findings are the first to show a variation in the use of social learning across task and environmental complexities in humans, to carry out a comprehensive evaluation of hypothetical human learning biases, and to provide a methodological link between non-human and human social learning experiments. As demonstrated here, immersive virtual environments have great potential for research into human social evolution and we strongly encourage future research to adopt a similar approach.
Self-regulating arousal via pupil-based biofeedback
Authors: Meissner Sarah Nadine , Bächinger Marc , Kikkert Sanne , Imhof Jenny , Missura Silvia , Carro Dominguez Manuel , Wenderoth Nicole 
Published: August 2022

AbstractThe brain’s arousal state is controlled by several neuromodulatory nuclei known to substantially influence cognition and mental well-being. Here, we investigate whether human participants can gain volitional control of their arousal state using a pupil-based biofeedback approach. Our approach inverts a mechanism suggested by previous literature that links activity of the locus coeruleus (LC), one of the key regulators of central arousal, and pupil dynamics. We show that pupil-based biofeedback enables participants to acquire volitional control of pupil size. Applying pupil self-regulation systematically modulates activity of the LC and other brainstem structures involved in arousal control. Further, it modulates cardiovascular measures such as heart rate, and behavioural and psychophysiological responses during an oddball task. We provide evidence that pupil-based biofeedback makes the brain’s arousal system accessible to volitional control, a finding that has tremendous potential for translation to behavioral and clinical applications across various domains, including stress-related and anxiety disorders.
Alcohol dependence modifies brain networks activated during abstinence and reaccess: a c-fos-based analysis in mice
Authors: Roland Alison V. , Coelho Cesar A.O. , Haun Harold L. , Gianessi Carol A. , Lopez Marcelo F. , D’Ambrosio Shannon , Machinski Samantha N. , Kroenke Christopher D. , Frankland Paul W. , Becker Howard C. , Kash Thomas L. 
Published: August 2022

AbstractHigh-level alcohol consumption causes neuroplastic changes in the brain that lead to negative affective and somatic symptoms when alcohol is withdrawn, promoting relapse drinking. We have some understanding of these plastic changes in defined brain circuits and cell types, but unbiased approaches are needed to explore broader patterns of adaptations. Here, we employed whole-brain c-fos mapping and network analysis to assess how brain-wide patterns of neuronal activity are altered during acute alcohol abstinence and reaccess in a well-characterized model of alcohol dependence. Mice underwent four cycles of chronic intermittent ethanol vapor exposure (CIE) with alternating weeks of voluntary alcohol drinking, and a subset of mice underwent forced swim stress (FSS) prior to drinking sessions to further escalate alcohol consumption. After four CIE cycles, brains were collected from mice in each group either 24 hours (abstinence) or immediately following a one-hour period of alcohol reaccess. Brains from CIE mice during acute abstinence displayed widespread neuronal activation relative to those from AIR mice, independent of FSS, and this increase in c-fos was reversed by reaccess drinking. For network analysis, mice were then classified as high or low drinkers (HD or LD). We computed Pearson correlations for all pairs of brain regions and used graph theoretical methods to identify changes in network properties associated with high-drinking behavior. Network modularity, a measure of network segregation into communities, was increased in HD mice after alcohol reaccess relative to abstinence. Within-community strength and diversity measures were computed for each region and condition, and highly coactive regions were identified. One high-diversity region, the cortical amygdala (COA), was further interrogated using a chemogenetic approach. COA silencing in CIE mice reduced voluntary drinking, validating our network analysis and indicating that this region may play an important but underappreciated role in alcohol dependence.
ATNr: Allometric trophic models in R
Authors: Gauzens Benoit , Brose Ulrich , Delmas Eva , Berti Emilio 
Published: August 2022

AbstractUnderstanding and predicting how densities of interacting species change over time has been one of the main goals of community ecology, which has become a pressing challenge in the context of global change.We present the R packageATNr, which provides an implementation of different versions of Allometric Trophic Network models (Yodzis and Innes (1992)) that simulate the biomass dynamics of trophically interacting species.Relying onC++routines, theATNrproposes an efficient and standardized implementation of the different ATNs models.By proposing a set of built in functions ready to use in a language widely used in the community of ecologists, theATNrpackage offers an easy access to ATN models.