bluuebunny/crossref_metadata_2025_split huggingface dataset:
Passenger mutations confound phenotypes of SARM1-deficient mice
Authors: Uccellini Melissa B. , Bardina Susana V. , Sánchez-Aparicio Maria Teresa , White Kris M. , Hou Ying-Ju , Lim Jean K. , García-Sastre Adolfo 
Published: October 2019

AbstractThe Toll/IL-1R domain-containing adaptor protein SARM1 is expressed primarily in the brain, where it mediates axonal degeneration. Additional roles for SARM1 in a number of other processes including TLR-signaling, viral infection, chemokine expression, and expression of the proapoptotic protein XAF1 have also been described. Much of the supporting evidence for SARM1 function has been generated by comparing WT C57BL/6 (B6) mice to SARM1-deficient mice backcrossed to the B6 background. Here we show that the Sarm1 gene lies in a gene-rich region encompassing XAF1, and the MIP and MCP chemokine family loci among other genes. Because gene-targeting of SARM1-deficient strains was done with 129 ES cells and these genes are too close to segregate, they remain 129 in sequence. As this could account for phenotypes attributed to SARM1, we generated new knockout mouse strains on a pure B6 background using CRISPR. Experiments in these new strains confirmed the role of SARM1 in axonal degeneration and susceptibility to WNV infection, but not in susceptibility to VSV or LACV infection, or chemokine or Xaf1 expression. Notably, the Xaf1 gene shows sequence variation between B6 and 129, resulting in coding changes and novel splice variants. Given its known role in apoptosis, XAF1 variants may account for some phenotypes described in previously made SARM1-deficient strains. RNAseq in the new strains reveal changes in the mitochondrial electron transport chain and ribosomal proteins, suggesting possible downstream targets of SARM1. Re-evaluation of described phenotypes in these new strains will be critical for defining the function of SARM1.
Optimized data representation and convolutional neural network model for predicting tumor purity
Authors: Sun Gerald J. , Jenkins David F. , Cingolani Pablo E. , Dry Jonathan R. , Lai Zhongwu 
Published: October 2019

AbstractHere we present a machine learning model, Deep Purity (DePuty) that leverages convolutional neural networks to accurately predict tumor purity from next-generation sequencing data from clinical samples without matched normals. As input, our model utilizes SNP-based copy number and minor allele frequency data formulated as a scatterplot image. With a representation matching that used by expert human annotators, we best an existing algorithm using only ~100 manually curated samples. Our simple, data-efficient approach can serve as a straightforward alternative to traditional, more complex statistical methods, for building performant purity prediction models that enable downstream bioinformatic analysis of tumor variants and absolute copy number alterations relevant to cancer genomics.
Structural basis for adhesion G protein-coupled receptor Gpr126 function
Authors: Leon Katherine , Cunningham Rebecca L. , Riback Joshua A. , Feldman Ezra , Li Jingxian , Sosnick Tobin R. , Zhao Minglei , Monk Kelly R. , Araç Demet 
Published: October 2019

AbstractMany drugs target the extracellular regions (ECRs) of cell-surface receptors. The large and alternatively-spliced ECRs of adhesion G protein-coupled receptors (aGPCRs) have key functions in diverse biological processes including neurodevelopment, embryogenesis, and tumorigenesis. However, their structures and mechanisms of action remain unclear, hampering drug development. The aGPCR Gpr126/Adgrg6 regulates Schwann cell myelination, ear canal formation, and heart development; and GPR126 mutations cause myelination defects in human. Here, we determine the structure of the complete zebrafish Gpr126 ECR and reveal five domains including a previously-unknown domain. Strikingly, the Gpr126 ECR adopts a closed conformation that is stabilized by an alternatively spliced linker and a conserved calcium-binding site. Alternative splicing regulates ECR conformation and receptor signaling, while mutagenesis of the newly-characterized calcium-binding site abolishes Gpr126 function in vivo. These results demonstrate that Gpr126 ECR utilizes a multi-faceted dynamic approach to regulate receptor function and provide novel insights into ECR-targeted drug design.
Time distributed data analysis by Cosinor.Online application
Authors: Molcan Lubos 
Published: October 2019

AbstractDisturbed biological oscillations often represent pathology and thus have a prognostic character. The most studied are 24-h (circadian) and shorter (ultradian) oscillations from them. A cosinor analysis often evaluates the presence and significance of circadian and ultradian rhythms. Skilled researchers can use MATLAB, R, Python, or other programming languages, while those less experienced often use outdated applications that require a specific operating system version or outdated add-ons. Therefore, we developed CosinorOnline, a simple web-based application coded in PHP and JavaScript to evaluate the presence and significance of different oscillations. Users can set the period length on the application’s page and insert their data. The results consist of a numerical evaluation and an adjustable plot. There is also a unique identifier to reload or immediately delete data analysis within one month. After this period, all data are automatically deleted from the app. We compared the functionality of CosinorOnline with Cosinor2 (R package) and Chronos-Fit (Windows app). The 24-h variability analysis was identical for all three applications. The evaluation of ultradian variability was the same for CosinorOnline and Cosinor2 and slightly different for Chronos-Fit. CosinorOnline and Chronos-Fit result in acrophase in units of time (decimal form), while Cosinor2 is in radians. In conclusion, CosinorOnline is a simple, easy-to-use web application to inspect time data that provides reliable results without additional installation and runs in modern web browsers. The application does not track users and aims to help users quickly decide whether their data is suitable for more profound analysis using cosinor analysis.
Can genome-based analysis be the new gold-standard for routine Salmonella serotyping?
Authors: Banerji Sangeeta , Simon Sandra , Tille Andreas , Flieger Antje 
Published: October 2019

AbstractSalmonella enterica is the second most reported bacterial cause of food-borne infections in Europe. Therefore molecular surveillance activities based on pathogen subtyping are an important measure of controlling Salmonellosis by public health agencies. In Germany, at the federal level, this work is carried out by the National Reference Center for Salmonella and other Bacterial Enteric Pathogens (NRC). With rise of next generation sequencing techniques, the NRC has introduced whole-genome-based typing methods for S. enterica in 2016. In this study we report on the feasibility of genome-based in silico serotyping in the German setting using raw sequence reads. We found that SeqSero and seven gene MLST showed 98% and 95% concordance, respectively, with classical serotyping for the here evaluated serotypes, including the most common German serotypes S. Enteritidis and S. Typhimurium as well as less frequently found serotypes. The level of concordance increased to >99% when the results of both in silico methods were combined. However, both tools exhibited misidentification of monophasic variants, in particular monophasic S. Typhimurium and therefore need to be fine-tuned for reliable detection of this epidemiologically important variant. We conclude that with adjustments Salmonella genome-based serotyping might become the new gold standard.
Factorial design as a tool to evaluate image analysis workflows systematically: its application to the filament tracing problem
Authors: Scholz Leandro Aluisio , Caznok Silveira Ana Clara , Sugai-Guérios Maura Harumi , Mitchell David Alexander 
Published: October 2019

ABSTRACTThe effect of image enhancement methods on the final result of image analysis workflows is often left out of discussions in scientific papers. In fact, before reaching a definitive enhancement workflow and its settings, there often is a great amount of pre-testing and parameter tweaking. In this work, we take the biofilament tracing problem and propose a systematic approach to testing and evaluating major image enhancement methods that are applied prior to execution of six filament tracing methods (APP, APP2, FarSIGHT Snake, NeuronStudio, Neutube and Rivulet2). We used a full factorial design of experiments to analyse five enhancement methods (deconvolution, background subtraction, pixel intensity normalization, Frangi vessel enhancement and smoothing) and the order in which they are applied, evaluating their effect on the signal-to-noise ratio, structural similarity index and geometric tracing scores of 3D images of a fungal mycelium and a synthetic neuronal tree. Our approach proved valuable as a tool to support the choice of enhancement and filament tracing workflow. For example, the use of deconvolution followed by median filtering gives the best geometric tracing scores if Neutube is used in the image of the fungal mycelium. Also, we show that FarSIGHT Snake and Neutube are the most robust filament tracing methods to changes in image quality. In addition, we reinforce the importance of extensive testing of new filament tracing methods against a broad range of image qualities and filament characteristics.
Precise regulation of the relative rates of surface area and volume synthesis in dynamic environments
Authors: Shi Handuo , Hu Yan , Huang Kerwyn Casey 
Published: October 2019

AbstractBacterial cells constantly face complex environmental changes in their natural habitats. While steady-state cell size correlates with nutrient-determined growth rate, it remains unclear how cells regulate their morphology during rapid environmental changes. Here, we systematically quantified cellular dimensions throughout passage cycles of stationary-phase cells diluted into fresh medium and grown back to saturation, and found that cells exhibit characteristic dynamics in surface area to volume ratio (SA/V). SA/V dynamics were conserved across many genetic/chemical perturbations, as well as across species and growth temperatures. We developed a model with a single fitting parameter, the time delay between surface and volume synthesis, that quantitatively explained our SA/V observations, and showed that the time delay was indeed due to differential expression of volume and surface-related genes. The first division after dilution occurred at a tightly controlled SA/V, a previously unrecognized size-control mechanism highlighting the relevance of SA/V. Finally, our time-delay model successfully predicted the quantitative changes in SA/V dynamics due to altered surface area synthesis rates or time delays from translation inhibition. Our minimal model thus provides insight into how cells regulate their morphologies through differential regulation of surface area and volume synthesis and potentiates deep understanding of the connections between growth rate and cell shape in complex environments.
Synergistic effect: a common theme in mixed-species litter decomposition
Authors: Liu Jun , Liu Xiaoyu , Song Qingni , Luan Fenggang , Wang Hui , Hu Yalin , Yang Qingpei 
Published: October 2019

AbstractLitter decomposition plays a key role in ecosystem nutrients cycling, yet, to date science is lacking a comprehensive understanding of the non-additive effect in mixing litter decomposition.In order to fill that gap, we compiled 69 individual studies for the purpose of performing two sub-meta-analyses on the non-additive effect.Our results show that a significantly synergistic effect occurs at global scale with the average increase by 2-4% in litter mixture decomposition; In particular, low-quality litter in mixture shows a significantly synergistic effect, while no significant change is observed with high-quality species. Additionally, the synergistic effect turns into the antagonistic effect when soil fauna is absent or litter decomposition enters into humus-near stage. In contrast to temperate and tropical areas, studies in frigid area also show a significantly antagonistic effect.Our meta-analysis provides a systematic evaluation of the non-additive effect in decomposition mixed litters, which is critical for understanding and improving the carbon forecasts and nutrient dynamics.
Understanding patterns of HIV multi-drug resistance through models of temporal and spatial drug heterogeneity
Authors: Feder Alison F. , Harper Kristin , Brumme Chanson J. , Pennings Pleuni S. 
Published: October 2019

1AbstractTriple-drug therapies have transformed HIV from a fatal condition to a chronic one. These therapies should prevent HIV drug resistance evolution, because one or more drugs suppress any partially-resistant viruses. In practice, such therapies drastically reduced, but did not eliminate, resistance evolution. In this article, we reanalyze published data from an evolutionary perspective and demonstrate several intriguing patterns about HIV resistance evolution - resistance evolves (1) even after years on successful therapy, (2) sequentially, often via one mutation at a time and (3) in a partially predictable order. We describe how these observations might emerge under two models of HIV drugs varying in space or time. Despite decades of work in this area, much opportunity remains to create models with realistic parameters for three drugs, and to match model outcomes to resistance rates and genetic patterns from patients on triple-drug therapy. Further, lessons from HIV may inform other systems.
Pegasus, a small extracellular peptide regulating the short-range diffusion of Wingless
Authors: Magny Emile G , Pueyo Jose I , Bishop Sarah A , Aguilar-Hidalgo Daniel , Couso Juan Pablo 
Published: October 2019

AbstractSmall Open Reading Frames (smORFs) coding for peptides of less than 100 amino-acids are emerging as a fundamental and pervasive gene class, found in the hundreds of thousands in metazoan genomes. Even though some of these genes are annotated, their function, if any, remains unknown. Here we characterize the function of a smORF encoding a short 80 aa peptide,pegasus(peg), which facilitates Wg diffusion during the development of theDrosophilawing imaginal disc. During wing development, Wg has sequential functions, and in the later stages, whenpegis strongly expressed, it patterns the presumptive wing margin. A reduction in Wg protein secretion at this stage produces effects ranging from total abolition of the wing margin to partial loss of bristles and reduction of proneural gene expression. Here we show that the Peg peptide enhances the short-range of Wg diffusion in this context, in order to produce a proper wing margin. We show that CRISPR/Cas9-mediated mutations ofpegasusgenerate wing margin phenotypes, and changes in target gene expression, consistent with a role in Wg signalling. We find that Peg is secreted, and that it co-localizes and co-immunoprecipitates with Wg, suggesting that Peg directly binds Wg in order to enhance its signalling, and our data from fixed andin-vivoWg gradient measurements supports a model in which this enhancement occurs by increasing diffusion of extracellular Wg. Our results unveil a new element in the regulation of the Wg signalling pathway, and shed light on the functional consequences of the miss-regulation of Wg diffusion in this developmental context, while also reminding us of the functional diversity, and relevance of small open reading frame genes.
Feature selection for the classification of fall-risk in older subjects: a combinational approach using static force-plate measures
Authors: Reilly D’Ó’ 
Published: October 2019

AbstractIntroductionFeature selection prevents over-fitting in predictive models. This study aimed to present an effective feature selection method that leads to a reliable classification of fall-risk in older subjects using static force-platform data across four conditions only.Method528 features were generated from a publicly available dataset of force-plate signals from 45 low-risk and 28 high-risk subjects. Subjects were classified as high- or low-risk if they recorded ≥1 falls in the prior 12 months and/or were rated as high-risk on the FES. The feature selection protocol included SVM-RFE, GA and ReliefF and finally SAFE. Several machine-learning models were then used to evaluate classification performance.Results67 features were identified after the three-fold process which was further reduced to 18 features after SAFE. The MLP achieved the highest average classification accuracy of 80%. All classification models evaluating this final subset displayed high variance across all performance metrics, especially in terms of sensitivity to high-risk subjects.InterpretationAn optimal feature set of static force-plate measures was insufficient in creating a reliable classifier of fall-risk. This was due potentially to the limited information about fall-risk that could be provided by such measures leading to under-fitting/over-fitting being unavoidable and appeared to be centered around an insensitivity to high-risk subjects.ConclusionStatic stability measures have shown some usability in fall-risk classification however feature sets limited to such measures are inadequately sensitive to high-risk subjects. The utilized feature selection methods demonstrated their ability to identify relevant stability measures and could be used successfully on dynamic measures.
A history-derived reward prediction error signal in ventral pallidum
Authors: Ottenheimer David J. , Bari Bilal A. , Sutlief Elissa , Fraser Kurt M. , Kim Tabitha H. , Richard Jocelyn M. , Cohen Jeremiah Y. , Janak Patricia H. 
Published: October 2019

ABSTRACTLearning from past interactions with the environment is critical for adaptive behavior. Within the framework of reinforcement learning, the nervous system builds expectations about future reward by computing reward prediction errors (RPEs), the difference between actual and predicted rewards. Correlates of RPEs have been observed in the midbrain dopamine system, which is thought to locally compute this important variable in service of learning. However, the extent to which RPE signals may be computed upstream of the dopamine system is largely unknown. Here, we quantify history-based RPE signals in the ventral pallidum (VP), an input region to the midbrain dopamine system implicated in reward-seeking behavior. We trained rats to associate cues with future delivery of reward and fit computational models to predict individual neuron firing rates at the time of reward delivery. We found that a subset of VP neurons encoded RPEs and did so more robustly than nucleus accumbens, an input to VP. VP RPEs predicted trial-by-trial task engagement, and optogenetic inhibition of VP reduced subsequent task-related reward seeking. Consistent with reinforcement learning, activity of VP RPE cells adapted when rewards were delivered in blocks. We further found that history- and cue-based RPEs were largely separate across the VP neural population. The presence of behaviorally-instructive RPE signals in the VP suggests a pivotal role for this region in value-based computations.
Chipper: Open-source software for semi-automated segmentation and analysis of birdsong and other natural sounds
Authors: Searfoss Abigail M. , Pino James C. , Creanza Nicole 
Published: October 2019

AbstractAudio recording devices have changed significantly over the last 50 years, making large datasets of recordings of natural sounds, such as birdsong, easier to obtain. This increase in digital recordings necessitates an increase in high-throughput methods of analysis for researchers. Specifically, there is a need in the community for open-source methods that are tailored to recordings of varying qualities and from multiple species collected in nature.We developed Chipper, a Python-based software to semi-automate both the segmentation of acoustic signals and the subsequent analysis of their frequencies and durations. For avian recordings, we provide widgets to best determine appropriate thresholds for noise and syllable similarity, which aid in calculating note measurements and determining syntax. In addition, we generated a set of synthetic songs with various levels of background noise to test Chipper’s accuracy, repeatability, and reproducibility.Chipper provides an effective way to quickly generate reproducible estimates of birdsong features. The cross-platform graphical user interface allows the user to adjust parameters and visualize the resulting spectrogram and signal segmentation, providing a simplified method for analyzing field recordings.Chipper streamlines the processing of audio recordings with multiple user-friendly tools and is optimized for multiple species and varying recording qualities. Ultimately, Chipper supports the use of citizen-science data and increases the feasibility of large-scale multi-species birdsong studies.
Spatial preferences account for inter-animal variability during the continual learning of a dynamic cognitive task
Authors: Kastner David B. , Miller Eric A. , Yang Zhounan , Roumis Demetris K. , Liu Daniel F. , Frank Loren M. , Dayan Peter 
Published: October 2019

AbstractIndividual animals perform tasks in different ways, yet the nature and origin of that variability is poorly understood. In the context of spatial memory tasks, variability is often interpreted as resulting from differences in memory ability, but the validity of this interpretation is seldom tested since we lack a systematic approach for identifying and understanding factors that make one animal’s behavior different than another. Here we identify such factors in the context of spatial alternation in rats, a task often described as relying solely on memory of past choices. We combine hypothesis-driven behavioral design and reinforcement learning modeling to identify spatial preferences that, when combined with memory, support learning of a spatial alternation task. Identifying these preferences allows us to capture differences among animals, including differences in overall learning ability. Our results show that to understand the complexity of behavior requires quantitative accounts of the preferences of each animal.