bluuebunny/crossref_metadata_2025_split huggingface dataset:
A sequence-based method for predicting extant fold switchers that undergo α-helix <-> β-strand transitions
Authors: Mishra Soumya , Looger Loren L. , Porter Lauren L. 
Published: January 2021

AbstractExtant fold-switching proteins remodel their secondary structures and change their functions in response to cellular stimuli, regulating biological processes and affecting human health. In spite of their biological importance, these proteins remain understudied. Few representative examples of fold switchers are available in the Protein Data Bank, and they are difficult to predict. In fact, all 96 experimentally validated examples of extant fold switchers were stumbled upon by chance. Thus, predictive methods are needed to expedite the process of discovering and characterizing more of these shapeshifting proteins. Previous approaches require a solved structure or all-atom simulations, greatly constraining their use. Here, we propose a high-throughput sequence-based method for predicting extant fold switchers that transition from α-helix in one conformation to β-strand in the other. This method leverages two previous observations: (1) α-helix <-> β-strand prediction discrepancies from JPred4 are a robust predictor of fold switching, and (2) the fold-switching regions (FSRs) of some extant fold switchers have different secondary structure propensities when expressed in isolation (isolated FSRs) than when expressed within the context of their parent protein (contextualized FSRs). Combining these two observations, we ran JPred4 on the sequences of isolated and contextualized FSRs from 14 known extant fold switchers and found α-helix <->β-strand prediction discrepancies in every case. To test the overall robustness of this finding, we randomly selected regions of proteins not expected to switch folds (single-fold proteins) and found significantly fewer α-helix <-> β-strand prediction discrepancies (p < 4.2*10−20, Kolmogorov-Smirnov test). Combining these discrepancies with the overall percentage of predicted secondary structure, we developed a classifier that often robustly identifies extant fold switchers (Matthews Correlation Coefficient of 0.70). Although this classifier had a high false negative rate (6/14), its false positive rate was very low (1/211), suggesting that it can be used to predict a subset of extant fold switchers from billions of available genomic sequences.
Impact of K-Pg Mass Extinction Event on Crocodylomorpha Inferred from Phylogeny of Extinct and Extant Taxa
Authors: Magee Andrew F. , Höhna Sebastian 
Published: January 2021

AbstractCrocodilians and their allies have survived several mass extinction events. However, the impact of the K-Pg mass extinction event on crocodylomorphs is considered as minor or non-existent although other clades of archosaurs, e.g., non-avian dinosaurs and pterosaurs, went extinct completely. Previous approaches using fossil occurrence data alone have proven inconclusive. In this paper, we take a phylogenetic approach using extant and extinct species. The time-calibrated phylogeny of extant crocodilians provides insights into the pattern of recent biodiversity changes whereas fossil occurrence data provide insights about the more ancient past. The two data sources combined into a single phylogeny with extinct and extant taxa provide a holistic view of the historical biodiversity. To utilize this combined data and to infer the impact of the K-Pg mass extinction event, we derive the likelihood function for a time-varying (episodic) serially sampled birth-death model that additionally incorporates mass extinctions and bursts of births. We implemented the likelihood function in a Bayesian framework with recently developed smoothing priors to accommodate for both abrupt and gradual changes in speciation, extinction and fossilization rates. Contrary to previous research, we find strong evidence for the K-Pg extinction event in crocodiles and their allies. This signal is robust to uncertainty in the phylogeny and the prior on the mass extinctions. Through simulated data analyses, we show that there is high power to detect this mass extinction and little risk of false positives.
A processing pipeline for image reconstructed fNIRS analysis using both MRI templates and individual anatomy
Authors: Forbes Samuel H. , Wijeakumar Sobanawartiny , Eggebrecht Adam T. , Magnotta Vincent A. , Spencer John P. 
Published: January 2021

AbstractAimWe demonstrate a pipeline with accompanying code to allow users to clean and prepare optode location information, prepare and standardize individual anatomical images, create the light model, run the 3D image reconstruction, and analyze data in group space.ApproachWe synthesize a combination of new and existing software packages to create a complete pipeline, from raw data to analysis.ResultsThis pipeline has been tested using both templates and individual anatomy, and on data from different fNIRS data collection systems. We show high temporal correlations between channel-based and image-based fNIRS data. In addition, we demonstrate the reliability of this pipeline with a sample dataset that included 74 children as part of a longitudinal study taking place in Scotland. We demonstrate good correspondence between data in channel space and image reconstructed data.ConclusionsThe pipeline presented here makes a unique contribution by integrating multiple tools to assemble a complete pipeline for image reconstruction in fNIRS. We highlight further issues that may be of interest to future software developers in the field.SignificanceImage reconstruction of fNIRS data is a useful technique for transforming channel-based fNIRS into a volumetric representation and managing spatial variance based on optode location. We present a novel integrated pipeline for image reconstruction of fNIRS data using either MRI templates or individual anatomy.
Variational autoencoders learn universal latent representations of metabolomics data
Authors: Gomari Daniel P. , Schweickart Annalise , Cerchietti Leandro , Paietta Elisabeth , Fernandez Hugo , Al-Amin Hassen , Suhre Karsten , Krumsiek Jan 
Published: January 2021

AbstractDimensionality reduction approaches are commonly used for the deconvolution of high-dimensional metabolomics datasets into underlying core metabolic processes. However, current state-of-the-art methods are widely incapable of detecting nonlinearities in metabolomics data. Variational Autoencoders (VAEs) are a deep learning method designed to learn nonlinear latent representations which generalize to unseen data. Here, we trained a VAE on a large-scale metabolomics population cohort of human blood samples consisting of over 4,500 individuals. We analyzed the pathway composition of the latent space using a global feature importance score, which showed that latent dimensions represent distinct cellular processes. To demonstrate model generalizability, we generated latent representations of unseen metabolomics datasets on type 2 diabetes, schizophrenia, and acute myeloid leukemia and found significant correlations with clinical patient groups. Taken together, we demonstrate for the first time that the VAE is a powerful method that learns biologically meaningful, nonlinear, and universal latent representations of metabolomics data.
The Supplementary Eye Field Tracks Cognitive Efforts
Authors: Claron Julien , Royo Julie , Arcizet Fabrice , Deffieux Thomas , Tanter Mickael , Pouget Pierre 
Published: January 2021

ABSTRACTPupil dilation is known to be an index of cognitive effort. Nevertheless, our lack of knowledge of the precise dynamics through which pupil size and activity of the medial prefrontal cortex are conjugated during cognitive tasks highlights the need for its further investigation before, during, and after changes in pupil size. Here, we tested whether pupil dynamics are related to the activity of the supplementary eye field (SEF) during a mixed pro/anti-saccade oculomotor task in two macaque monkeys. We used functional ultrasound imaging (fUS) to examine temporal changes in brain activity at the 0.1-s time scale and 0.1-mm spatial resolution in relation to behavioral performance and pupil dynamics. By combining the pupil signals and real-time imaging of NHP during cognitive tasks, we were able to infer localized CBV responses within a restricted part of the dorsomedial prefrontal cortex, referred to as the SEF, an area in which anti-saccade preparation activity is also recorded. Inversely, SEF neurovascular activity measured by fUS imaging was found to be a robust predictor of specific variations in pupil diameter over short and long time scales. Furthermore, we directly manipulated pupil diameter and CBV in the SEF using reward and cognitive effort. These results demonstrate that the SEF is an underestimated but pivotal cortical area for the monitoring and implementation of cognitive effort signals.
GAD65Cre drives reporter expression in multiple taste cell types
Authors: Larson Eric D. , Vandenbeuch Aurelie , Anderson Catherine B. , Kinnamon Sue C. 
Published: January 2021

ABSTRACTIn taste buds, Type I cells represent the majority of cells (50-60%) and primarily have a glial-like function in taste buds. However, recent studies suggest that they have additional sensory and signaling functions including amiloride-sensitive salt transduction, oxytocin modulation of taste, and substance P mediated GABA release. Nonetheless, the overall function of Type I cells in transduction and signaling remains unclear, primarily because of the lack of a reliable reporter for this cell type. GAD65 expression is specific to Type I taste cells and GAD65 has been used as a Cre driver to study Type I cells in salt taste transduction. To test the specificity of transgene-driven expression, we crossed GAD65Cre mice with floxed tdTomato and Channelrhodopsin (ChR2) lines and examined the progeny with immunochemistry, chorda tympani recording, and calcium imaging. We report that while many tdTomato+ taste cells express NTPDase2, a specific marker of Type I cells, we see expression of tdTomato in both Gustducin and SNAP25 positive taste cells. We also see ChR2 in cells just outside the fungiform taste buds. Chorda tympani recordings in the GAD65Cre/ChR2 mice show large responses to blue light, larger than any response to standard taste stimuli. Further, several isolated tdTomato positive taste cells responded to KCl depolarization with increases in intracellular calcium, indicating the presence of voltage-gated calcium channels. Taken together, these data suggest that GAD65Cre mice drive expression in multiple taste cell types and thus cannot be considered a reliable reporter of Type I cell function.
Reconstructing tumor trajectories during therapy through integration of multiple measurement modalities
Authors: Griffiths Jason I. , Chen Jinfeng , Winblad Onalisa , O’Dea Anne , Sharma Priyanka , Trivedi Meghna , Kalinsky Kevin , Wisinski Kari B. , O’Regan Ruth , Makhoul Issam , Yuan Yuan , Spring Laura M. , Bardia Aditya , Jahanzeb Mohammad , Adler Frederick R. , Cohen Adam L. , Bild Andrea H. , Khan Qamar J. 
Published: January 2021

AbstractBackgroundAccurately determining changes in tumor size during therapy is essential to evaluating response or progression. However, individual imaging methodologies often poorly reflect pathologic response and long-term treatment efficacy in patients with estrogen receptor positive (ER+) early-stage breast cancer. Mathematical models that measure tumor progression over time by integrating diverse imaging and tumor measurement modalities are not currently used but could increase accuracy in measuring response and provide biological insights into cancer evolution.MethodsFor ER+ breast cancer patients enrolled on a neoadjuvant clinical trial, we reconstructed their tumor size trajectories during therapy by combining all available information on tumor size, including different imaging modalities, physical examinations and pathological assessment data. Tumor trajectories during six months of treatment were generated, using a Gaussian process and the most probable trajectories were evaluated, based on clinical data, using measurement models that account for biases and differences in precision between tumor measurement methods, such as MRI, ultrasound and mammograms.ResultsReconstruction of tumor trajectories during treatment identified five distinct patterns of tumor size changes, including rebound growth not evident from any single modality. These results increase specificity to distinguish innate or acquired resistance compared to using any single measurement alone. The speed of therapeutic response and extent of subsequent rebound tumor growth quantify sensitivity or resistance in this patient population.ConclusionsTumor trajectory reconstruction integrating multiple modalities of tumor measurement accurately describes tumor progression on therapy and reveals various patterns of patient responses. Mathematical models can integrate diverse response assessments and account for biases in tumor measurement, thereby providing insights into the timing and rate at which resistance emerges.
The role of mental simulation in primate physical inference abilities
Authors: Rajalingham Rishi , Piccato Aida , Jazayeri Mehrdad 
Published: January 2021

AbstractPrimates can richly parse sensory inputs to infer latent information, and adjust their behavior accordingly. It has been hypothesized that such flexible inferences are aided by simulations of internal models of the external world. However, evidence supporting this hypothesis has been based on behavioral models that do not emulate neural computations. Here, we test this hypothesis by directly comparing the behavior of humans and monkeys in a ball interception task to that of recurrent neural network (RNN) models with or without the capacity to “simulate” the underlying latent variables. Humans and monkeys had strikingly similar behavioral patterns suggesting common underlying neural computations. Comparison between primates and a large class of RNNs revealed that only RNNs that were optimized to simulate the position of the ball were able to accurately capture key features of the behavior such as systematic biases in the inference process. These results are consistent with the hypothesis that primates use mental simulation to make flexible inferences. Moreover, our work highlights a general strategy for using model neural systems to test computational hypotheses of higher brain function.
Auto-qPCR: A Python-based web app for automated and reproducible analysis of qPCR data
Authors: Maussion Gilles , Thomas Rhalena A. , Demirova Iveta , Gu Gracia , Cai Eddie , Chen Carol X.-Q , Abdian Narges , Strauss Theodore J.P. , Kelaï Sabah , Nauleau-Javaudin Angela , Beitel Lenore K. , Ramoz Nicolas , Gorwood Philip , Durcan Thomas M. 
Published: January 2021

AbstractQuantifying changes in DNA and RNA levels is essential in numerous molecular biology protocols. Quantitative real time PCR (qPCR) techniques have evolved to become commonplace, however, data analysis includes many time-consuming and cumbersome steps, which can lead to mistakes and misinterpretation of data. To address these bottlenecks, we have developed an open-source Python software to automate processing of result spreadsheets from qPCR machines, employing calculations usually performed manually. Auto-qPCR is a tool that saves time when computing qPCR data, helping to ensure reproducibility of qPCR experiment analyses. Our web-based app (https://auto-q-pcr.com/) is easy to use and does not require programming knowledge or software installation. Using Auto-qPCR, we provide examples of data treatment, display and statistical analyses for four different data processing modes within one program: (1) DNA quantification to identify genomic deletion or duplication events; (2) assessment of gene expression levels using an absolute model, and relative quantification (3) with or (4) without a reference sample. Our open access Auto-qPCR software saves the time of manual data analysis and provides a more systematic workflow, minimizing the risk of errors. Our program constitutes a new tool that can be incorporated into bioinformatic and molecular biology pipelines in clinical and research labs.
Global analysis of expression, maturation and subcellular localization of mouse liver transcriptome identifies novel sex-biased and TCPOBOP-responsive long non-coding RNAs
Authors: Goldfarb Christine N. , Waxman David J. 
Published: January 2021

AbstractWhile nuclear transcription and RNA processing and localization are well established for protein coding genes (PCGs), these processes are poorly understood for lncRNAs. Here, we characterize global patterns of transcript expression, maturation and localization for mouse liver RNA, including more than 15,000 lncRNAs. PolyA-selected liver RNA was isolated and sequenced from four subcellular fractions (chromatin, nucleoplasm, total nucleus, and cytoplasm), and from the chromatin-bound fraction without polyA selection. Transcript processing, determined from normalized intronic to exonic sequence read density ratios, progressively increased for PCG transcripts in going from the chromatin-bound fraction to the nucleoplasm and then on to the cytoplasm. Transcript maturation was similar for lncRNAs in the chromatin fraction, but was significantly lower in the nucleoplasm and cytoplasm. LncRNAs were 11-fold more likely to be significantly enriched in the nucleus than cytoplasm, and 100-fold more likely to be significantly chromatin-bound than nucleoplasmic. Sequencing chromatin-bound RNA greatly increased the sensitivity for detecting lowly expressed lncRNAs and enabled us to discover and localize hundreds of novel regulated liver lncRNAs, including lncRNAs showing sex-biased expression or responsiveness to a xenobiotic agonist ligand of constitutive androstane receptor (Nr1i3). Integration of our findings with prior studies and lncRNA annotations identified candidate regulatory lncRNAs for a variety of hepatic functions based on gene co-localization within topologically associating domains or transcription divergent or antisense to PCGs associated with pathways linked to hepatic physiology and diseases.
Determine independent gut microbiota-diseases association by eliminating the effects of human lifestyle factors
Authors: Wang Xin , Yang Yuqing , Li Jianchu , Jiang Rui , Chen Ting , Zhu Congmin 
Published: January 2021

ABSTRACTHuman lifestyle and physiological variables on human disease risk have been revealed to be mediated by gut microbiota. Low concordance between many case-control studies for detecting disease-associated microbe existed and it is likely due to the limited sample size and the population-wide bias in human lifestyle and physiological variables. To infer association between whole gut microbiota and diseases accurately, we propose to build machine learning models by including both human variables and gut microbiota based on the American Gut Project data, the largest known publicly available human gut bacterial microbiota dataset. When the model's performance with both gut microbiota and human variables is better than the model with just human variables, the independent association of gut microbiota with the disease will be confirmed. We found that gut microbes showed different association strengths with different diseases. Adding gut microbiota into human variables enhanced the association strengths with inflammatory bowel disease (IBD) and unhealthy status; showed no effect on association strengths with Diabetes and IBS; reduced the association strengths with small intestinal bacterial overgrowth,C. difficileinfection, lactose intolerance, cardiovascular disease and mental disorders. Our results suggested that although gut microbiota was reported to be associated with many diseases, a considerable proportion of these associations may be spurious. We also proposed a list of microbes as biomarkers to classify IBD and unhealthy status, and validated them by reference to previously published research.IMPORTANCEwe reexamined the association between gut microbiota and multiple diseases via machine learning models on a large-scale dataset, and by considering the effect of human variables ignored by previous studies, truly independent microbiota-disease associations were estimated. We found gut microbiota is associated independently with IBD and overall health of human, but more evidence is needed to judge associations between microbiota and other diseases. Further functional investigations of our reported disease-related microbes will improve understanding of the molecular mechanism of human diseases.
SARS-CoV-2 viral load distribution reveals that viral loads increase with age: a retrospective cross-sectional cohort study
Authors: Euser Sjoerd , Aronson Sem , Manders Irene , van Lelyveld Steven , Herpers Bjorn , Sinnige Jan , Kalpoe Jayant , van Gemeren Claudia , Snijders Dominic , Jansen Ruud , Stekhoven Sophie Schuurmans , van Houten Marlies , Lede Ivar , Stuart James Cohen , Megelink Fred Slijkerman , Kapteijns Erik , den Boer Jeroen , Sanders Elisabeth , Wagemakers Alex , Souverein Dennis 
Published: January 2021

ABSTRACTObjectiveTo describe the SARS-CoV-2 viral load distribution in different patient groups and age categories.MethodsAll SARS-CoV-2 RT-PCR results from nasopharyngeal (NP) and oropharyngeal (OP) swabs (first PCR from unique patients only) that were collected between January 1 and December 1, 2020, predominantly in the Public Health Services regions Kennemerland and Hollands Noorden, province of North Holland, the Netherlands were included in this study. Swabs were derived from patients with respiratory symptoms who were presented at the general practitioner (GP), hospital, or hospital health care workers (HCWs) of four regional hospitals, nursing home residents and HCWs of multiple nursing homes, and in majority (>75%) from Public Health testing facilities of the two Public Health Services. SARS-CoV-2 PCR crossing point (Cp) values were used to estimate viral loads (higher Cp-values indicate lower viral loads).ResultsIn total, 278.455 unique patients were tested of whom 9·1% (n=25.374) were SARS-CoV-2 positive. As there were differences in viral load distribution between tested populations, further analyses focused on PCRs performed by public health services (n=211.914) where sampling and inclusion were uniform. These data reveal a clear relation between age and SARS-CoV-2 viral load, with especially children aged<12 years showing lower viral loads than shown in adults (β: −0·03, 95CI% −0·03 to −0·02, p<0·001), independent of sex and/or symptom duration. Interestingly, the median Cp-values between the oldest (>79 years) and youngest (<12 years) population differed by over 4 PCR cycles, suggesting approximately a 16-fold difference in viral load. In addition, the proportion of children aged <12 years with a low load (Cp-value >30) was significantly higher compared to the other patients (31·1% vs. 17·2%, p-value<0.001).ConclusionWe observed that in patients tested by Public Health Services, SARS-CoV2 viral load increases significantly with age. Previous studies suggest that young children (<12 years) play a limited role in SARS-CoV-2 transmission. Currently, the relation between viral load and infectivity is not yet well understood, and further studies should elucidate whether the lower viral load in children is indeed related to their suggested limited role in SARS-CoV-2 transmission. Moreover, as rapid antigen tests are less sensitive than PCR, these results suggest that SARS-CoV-2 antigen tests could have lower sensitivity in children than in adults.