bluuebunny/crossref_metadata_2025_split huggingface dataset:
Disease classification for whole blood DNA methylation: meta-analysis, missing values imputation, and XAI
Authors: Kalyakulina Alena , Yusipov Igor , Bacalini Maria Giulia , Franceschi Claudio , Vedunova Maria , Ivanchenko Mikhail 
Published: May 2022

AbstractBackgroundDNA methylation has a significant effect on gene expression and can be associated with various diseases. Meta-analysis of available DNA methylation datasets requires development of a specific pipeline for joint data processing.ResultsWe propose a comprehensive approach of combined DNA methylation datasets to classify controls and patients. The solution includes data harmonization, construction of machine learning classification models, dimensionality reduction of models, imputation of missing values, and explanation of model predictions by explainable artificial intelligence (XAI) algorithms. We show that harmonization can improve classification accuracy by up to 20% when preprocessing methods of the training and test datasets are different. The best accuracy results were obtained with tree ensembles, reaching above 95% for Parkinson’s disease. Dimensionality reduction can substantially decrease the number of features, without detriment to the classification accuracy. The best imputation methods achieve almost the same classification accuracy for data with missing values as for the original data. Explainable artificial intelligence approaches have allowed us to explain model predictions from both populational and individual perspectives.ConclusionsWe propose a methodologically valid and comprehensive approach to the classification of healthy individuals and patients with various diseases based on whole blood DNA methylation data using Parkinson’s disease and schizophrenia as examples. The proposed algorithm works better for the former pathology, characterized by a complex set of symptoms. It allows to solve data harmonization problems for meta-analysis of many different datasets, impute missing values, and build classification models of small dimensionality.
BigNeuron: A resource to benchmark and predict best-performing algorithms for automated reconstruction of neuronal morphology
Authors: Manubens-Gil Linus , Zhou Zhi , Chen Hanbo , Ramanathan Arvind , Liu Xiaoxiao , Liu Yufeng , Bria Alessandro , Gillette Todd , Ruan Zongcai , Yang Jian , Radojević Miroslav , Zhao Ting , Cheng Li , Qu Lei , Liu Siqi , Bouchard Kristofer E. , Gu Lin , Cai Weidong , Ji Shuiwang , Roysam Badrinath , Wang Ching-Wei , Yu Hongchuan , Sironi Amos , Iascone Daniel Maxim , Zhou Jie , Bas Erhan , Conde-Sousa Eduardo , Aguiar Paulo , Li Xiang , Li Yujie , Nanda Sumit , Wang Yuan , Muresan Leila , Fua Pascal , Ye Bing , He Hai-yan , Staiger Jochen F. , Peter Manuel , Cox Daniel N. , Simonneau Michel , Oberlaender Marcel , Jefferis Gregory , Ito Kei , Gonzalez-Bellido Paloma , Kim Jinhyun , Rubel Edwin , Cline Hollis T. , Zeng Hongkui , Nern Aljoscha , Chiang Ann-Shyn , Yao Jianhua , Roskams Jane , Livesey Rick , Stevens Janine , Liu Tianming , Dang Chinh , Guo Yike , Zhong Ning , Tourassi Georgia , Hill Sean , Hawrylycz Michael , Koch Christof , Meijering Erik , Ascoli Giorgio A. , Peng Hanchuan 
Published: May 2022

ABSTRACTBigNeuron is an open community bench-testing platform combining the expertise of neuroscientists and computer scientists toward the goal of setting open standards for accurate and fast automatic neuron reconstruction. The project gathered a diverse set of image volumes across several species representative of the data obtained in most neuroscience laboratories interested in neuron reconstruction. Here we report generated gold standard manual annotations for a selected subset of the available imaging datasets and quantified reconstruction quality for 35 automatic reconstruction algorithms. Together with image quality features, the data were pooled in an interactive web application that allows users and developers to perform principal component analysis, t-distributed stochastic neighbor embedding, correlation and clustering, visualization of imaging and reconstruction data, and benchmarking of automatic reconstruction algorithms in user-defined data subsets. Our results show that image quality metrics explain most of the variance in the data, followed by neuromorphological features related to neuron size. By benchmarking automatic reconstruction algorithms, we observed that diverse algorithms can provide complementary information toward obtaining accurate results and developed a novel algorithm to iteratively combine methods and generate consensus reconstructions. The consensus trees obtained provide estimates of the neuron structure ground truth that typically outperform single algorithms. Finally, to aid users in predicting the most accurate automatic reconstruction results without manual annotations for comparison, we used support vector machine regression to predict reconstruction quality given an image volume and a set of automatic reconstructions.
A computational model of fractal interface formation in bacterial biofilms
Authors: Brooks Caelan , Yao Meiyi , McCool Jake T. , Gillman Alan , Süel Gürol M. , Mugler Andrew , Larkin Joseph W. 
Published: May 2022

ABSTRACTBacterial colonies benefit from cellular heterogeneity, with cells differentiating into diverse states of physiology and gene expression. As colonies grow, such cells in distinct states arrange into spatial patterns. To uncover the functional role of these emergent patterns, we must understand how they arise from cellular growth, phenotypic inheritance, and mechanical interactions among cells. Here we present a simple, agent-based model to predict patterns formed by motile and extracellular matrix-producing cells in developing populations ofBacillus subtilisbacteria. By incorporating phenotypic inheritance, differential mechanical interactions of the two cell types, and the escape of peripheral motile cells, our model predicts the emergence of a pattern: matrix cells surround a fractal-like population of interior motile cells. We find that, while some properties of the emergent motile-matrix interface depend on the initial cellular arrangement, the distribution of motile cells at large radii are a product solely of the model’s growth mechanism. Using a box-counting analysis, we find that the interface of the two phenotypes exhibits a fractal dimension that increases as biofilms grow but eventually reaches a maximum as the thickness of the peripheral layer of matrix exceeds the capacity of the inner cells to grow and push it out of the way. By systematically varying the model parameters, we find correlations between the fractal dimension of the phenotypic interface and the radial growth of the motile cells within simulated biofilms. We validate several of our findings using experimental data from aB. subtilisbiofilm grown in a microfluidic device. Our model demonstrates how colony-level phenotypes emerge from single cell-level interactions and cells modifying their own environment.
OPUS-Mut: studying the effect of protein mutation through side-chain modeling
Authors: Xu Gang , Wang Qinghua , Ma Jianpeng 
Published: May 2022

AbstractPredicting the effect of protein mutation is crucial in many applications such as protein design, protein evolution, and genetic disease analysis. Structurally, the mutation is basically the replacement of the side chain of a particular residue. Therefore, accurate side-chain modeling is useful in studying the effect of mutation. Here, we propose a computational method, namely OPUS-Mut, which significantly outperforms other backbone-dependent side-chain modeling methods including our previous method OPUS-Rota4. We evaluate OPUS-Mut by four case studies on Myoglobin, p53, HIV-1 protease, and T4 lysozyme. The results show that the predicted structures of side chains of different mutants are consistent well with their experimentally determined results. In addition, when the residues with significant structural shifts upon the mutation are considered, it is found that the extent of the predicted structural shift of these affected residues can be correlated reasonably well with the functional changes of the mutant measured by experiments. OPUS-Mut can also help one to identify the harmful and benign mutations, and thus may guide the construction of a protein with relatively low sequence homology but with similar structure.
Computer-aided diagnosis of reflectance confocal images to differentiate between lentigo maligna (LM) and atypical intraepidermal melanocytic proliferation (AIMP)
Authors: Mandal Ankita , Priyam Siddhaant , Chan Hsien Herbert , Gouveia Bruna Melhoranse , Guitera Pascale , Song Yang , Baker Matthew Arthur Barrington , Vafaee Fatemeh 
Published: May 2022

AbstractLentigo maligna (LM), a form of melanoma in situ that predominantly affects sun-exposed areas such as the face, has an ill-defined clinical border and has a high rate of recurrence. Atypical Intraepidermal Melanocytic Proliferation (AIMP) is a term used to describe the melanocytic proliferation of an uncertain malignant potential. Clinically and histologically, AIMP can be difficult to distinguish from LM, and indeed AIMP may in some cases progress to LM. Reflectance Confocal Microscopy (RCM) is often used to investigate these lesions non-invasively, however, RCM is often not readily available nor is the associated expertise for RCM image interpretation. Here, we demonstrate machine learning architectures that can correctly classify lesions between LM and AIMP on stacks of RCM images. Overall, our methods showcase the potential for computer-aided diagnosis in dermatology, which in conjunction with the remote acquisition, can expand the range of diagnostic tools in the community.
Bacterial strain-dependent dissociation of cell recruitment and cell-to-cell spread in earlyM. tuberculosisinfection
Authors: Zha B. Shoshana , Desvignes Ludovic , Fergus Tawania J. , Cornelius Amber , Cheng Tan-Yun , Moody D. Branch , Ernst Joel D. 
Published: May 2022

ABSTRACTIn the initial stage of respiratory infection,Mycobacterium tuberculosistraverses from alveolar macrophages to phenotypically diverse monocyte-derived phagocytes and neutrophils in the lung parenchyma. Here, we compare thein vivokinetics of early bacterial growth and cell-to-cell spread of two strains ofM. tuberculosis: a lineage 2 strain, 4334, and the widely studied lineage 4 strain H37Rv. Using flow cytometry, live cell sorting of phenotypic subsets, and quantitation of bacteria in cells of the distinct subsets, we found that 4334 induces less leukocyte influx into the lungs but demonstrates earlier population expansion and cell-to-cell spread. The earlier spread of 4334 to recruited cells, including monocyte-derived dendritic cells, is accompanied by earlier and greater magnitude of CD4+T cell activation. The results provide evidence that strain-specific differences in interactions with lung leukocytes can shape adaptive immune responsesin vivo.IMPORTANCETuberculosis is a leading infectious disease killer world-wide and is caused byMycobacterium tuberculosis. After exposure toM. tuberculosis,outcomes range from apparent elimination to active disease. Early innate immune responses may contribute to differences in outcomes, yet it is not known how bacterial strains alter the early dynamics of innate immune and T cell responses. We infected mice with distinct strains ofM. tuberculosisand discovered striking differences in innate cellular recruitment, cell- to-cell spread of bacteria in the lungs, and kinetics of initiation of antigen-specific CD4 T cell responses. We also found thatM. tuberculosiscan spread beyond alveolar macrophages even before a large influx of inflammatory cells. These results provide evidence that distinct strains ofM. tuberculosiscan exhibit differential kinetics in cell-to- cell spread which is not directly linked to early recruitment of phagocytes but is subsequently linked to adaptive immune responses.
Fermented foods restructure gut microbiota and promote immune regulation via microbial metabolites
Authors: Spencer Sean Paul , Silva Evelyn Giselle Lemus , Caffery Elisa Benedetti , Carter Matthew Merrill , Culver Rebecca Neal , Wang Min , Gellman Rebecca Hope , Wastyk Hannah Constance , Higginbottom Steven Kyle , Sonnenburg Justin Laine 
Published: May 2022

AbstractFermented foods are ancient and ubiquitous, thought to be consumed in nearly every culture over the last 10,000 years and as part of the hominin diet for millions of years. A growing body of evidence supports their potential health benefits, but the mechanistic basis of their effects on the gut microbiome and host immunity remain to be elucidated. Fermented foods are diverse, each representing a complex mixture of food, microbes, and metabolites creating a significant challenge to disentangle the effects of individual components. Herein, we further define the chemical signature of individual fermented foods to categorize them based on the primary metabolic end-products of fermentation. Using mouse models, we find that fermented foods have both microbiome directed as well as differential host directed effects that correspond to their metabolite composition. Fermented food brine drink shows site-specific restructuring of the gut microbiome and promotion of tolerogenic barrier immunity; fractionation of the brine to examine the effects of the microbe-free, metabolite rich supernatant shows similar activity. Lactate, the main metabolite of lactic acid fermentation and the major metabolite within the brine drink, when administered in water, fuels a trans-kingdom metabolic network to selectively promote the growth of Akkermansia muciniphila. in the small intestine, while promoting immune tolerance via an increase in microbiota-dependent Regulatory T-cells. These findings suggest that the beneficial effects of fermented food consumption can be mediated by microbial metabolites within fermented foods, independent of microbial content, and highlight the importance of further defining the diverse chemical landscape of fermented foods to inform their potential health benefits and therapeutic use.
Recombinant Origin and Interspecies Transmission of a HERV-K(HML-2)-related Primate Retrovirus With a Novel RNA Transport Element
Authors: Williams Zachary H. , Gaucherand Lea , Lee Derek C. , Mostafa Salwa Mohd , Phelan James , Coffin John M. , Johnson Welkin E. 
Published: May 2022

ABSTRACTHERV-K(HML-2), the youngest clade of human endogenous retroviruses (HERVs), includes many intact or nearly intact proviruses, but no replication competent HML-2 proviruses have been identified in humans. HML-2-related proviruses are present in other primates, including rhesus macaques, but the extent and timing of HML-2 activity in macaques remains unclear. We have identified 145 HML-2-like proviruses in rhesus macaques, including a clade of young, rhesus-specific insertions. Age estimates, intact ORFs, and insertional polymorphism of these insertions are consistent with recent or ongoing infectious activity in macaques. 108 of the proviruses form a clade characterized by an ~750 bp sequence between env and the 3’ LTR, derived from an ancient recombination with a HERV-K(HML-8)-related virus. This clade is found in Old World monkeys (OWM), but not great apes, suggesting it originated after the ape/OWM split. We identified similar proviruses in white-cheeked gibbons; the gibbon insertions cluster within the OWM recombinant clade, suggesting interspecies transmission from OWM to gibbons. The LTRs of the youngest proviruses have deletions in U3, which disrupt the Rec Response Element (RcRE), required for nuclear export of unspliced viral RNA. We show that the HML-8 derived region functions as a Rec-independent constitutive transport element (CTE), indicating the ancestral Rec-RcRE export system was replaced by a CTE mechanism.
Gene mapping methodology powered by induced genome rearrangements
Authors: Yone Hideyuki , Kono Hiromitsu , Hirai Hayato , Ohta Kunihiro 
Published: May 2022

AbstractPhenotypic variation occurs through genome rearrangements and mutations in certain responsible genes; however, systematic gene identification methodologies based on genome rearrangements have not been fully established. Here, we explored the loci responsible for the given phenotype using the TAQing system and compared it with a conventional mutagenesis-based method. Two yeast strains with different genetic backgrounds and flocculation phenotypes were fused and genomic rearrangements were induced by transient DNA breaks. Then, selection pressure was applied and multiple mutants were generated, showing different flocculation abilities. We also raised mutants with altered cohesiveness due to spontaneous mutations during long-term recursive passages of haploid strains without TAQing treatment. Comparative genomic analysis of the TAQed mutants revealed three chromosomal regions harboring pivotal flocculation genes, whereas conventional mutagenesis generated a more diverse list of candidate loci after prolonged selection. The combined use of these approaches will accelerate the identification of genes involved in complex phenotypes.
Near Infrared Diffuse In Vivo Flow Cytometry
Authors: Pace Joshua , Ivich Fernando , Marple Eric , Niedre Mark 
Published: May 2022

AbstractSignificanceDiffuse in vivo Flow Cytometry (DiFC) is an emerging technique for enumerating rare fluorescently labeled circulating cells non-invasively in the bloodstream. Thus far we have reported red and blue-green versions of DiFC. Use of near-infrared (NIR) fluorescent light would in principle allow use of DiFC in deeper tissues and would be compatible with emerging NIR fluorescence molecular contrast agents.AimIn this work, we describe the design of a NIR-DiFC instrument and demonstrate its use in optical flow phantoms in vitro and in mice in vivo.ApproachWe developed an improved optical fiber probe design for efficient collection of fluorescence from individual circulating cells, and efficient rejection of instrument autofluorescence. We built a NIR-DiFC instrument. We tested this with NIR fluorescent microspheres and cell lines labeled with OTL38 fluorescence contrast agent in a flow phantom model. We also tested NIR-DiFC in nude mice injected intravenously with OTL38-labeled L1210A cells.ResultsNIR-DiFC allowed detection of CTCs in flow phantoms with mean signal to noise ratios (SNRs) of 19 to 32 dB. In mice, fluorescently-labeled CTCs were detectable with mean SNR of 26 dB. NIR-DiFC also exhibited orders significantly lower autofluorescence and false-alarm rates than blue-green DiFC.ConclusionsNIR-DiFC allows use of emerging NIR contrast agents. This work could pave the way for future use of NIR-DiFC in humans.
A User-Friendly Guide to Using Distance Measures to Compare Time Series in Ecology
Authors: Dove Shawn , Böhm Monika , Freeman Robin , Jellesmark Sean , Murrell David 
Published: May 2022

AbstractTime series are a critical component of ecological analysis, used to track changes in biotic and abiotic variables. Information can be extracted from the properties of time series for tasks such as classification, clustering, prediction, and anomaly detection. These common tasks in ecological research rely on the notion of (dis-) similarity which can be determined by using distance measures. A plethora of distance measures have been described in the scientific literature, but many of them have not been introduced to ecologists. Furthermore, little is known about how to select appropriate distance measures and the properties they focus on for time-series related tasks.Here we describe 16 potentially desirable properties of distance measures, test 42 distance measures for each property, and present an objective method to select appropriate distance measures for any task and ecological dataset. We then demonstrate our selection method by applying it to a set of real-world data on breeding bird populations in the UK. We also discuss ways to overcome some of the difficulties involved in using distance measures to compare time series.Our real-world population trends exhibit a common challenge for time series comparison: a high level of stochasticity. We demonstrate two different ways of overcoming this challenge, first by selecting distance measures with properties that make them well-suited to comparing noisy time series, and second by applying a smoothing algorithm before selecting appropriate distance measures. In both cases, the distance measures chosen through our selection method are not only fit-for-purpose but are consistent in their rankings of the population trends.The results of our study should lead to an improved understanding of, and greater scope for, the use of distance measures for comparing time series within ecology, and allow for the answering of new ecological questions.
Rapid modeling of experimental molecular kinetics with simple electronic circuits instead of with complex differential equations
Authors: Deng Yijie , Beahm Douglas Raymond , Ran Xinping , Riley Tanner G , Sarpeshkar Rahul 
Published: May 2022

AbstractKinetic modeling has relied on using a tedious number of mathematical equations to describe molecular kinetics in interacting reactions. The long list of differential equations with associated abstract variables and parameters inevitably hinders readers’ easy understanding of the models. However, the mathematical equations describing the kinetics of biochemical reactions can be exactly mapped to the dynamics of voltages and currents in simple electronic circuits wherein voltages represent molecular concentrations and currents represent molecular fluxes. For example, we theoretically derive and experimentally verify accurate circuit models for Michaelis-Menten kinetics. Then, we show that such circuit models can be scaled via simple wiring among circuit motifs to represent more and arbitrarily complex reactions. Hence, we can directly map reaction networks to equivalent circuit schematics in a rapid, quantitatively accurate, and intuitive fashion without needing mathematical equations. We verify experimentally that these circuit models are quantitatively accurate. Examples include i) different mechanisms of competitive, noncompetitive, uncompetitive, and mixed enzyme inhibition, important for understanding pharmacokinetics; ii) product-feedback inhibition, common in biochemistry; iii) reversible reactions, important in many metabolic pathways; and iv) translation and transcription dynamics in a cell-free system, which brings insight into the functioning of all gene-protein networks. We envision that circuit modeling and simulation could become a powerful scientific communication language and tool for quantitative studies of kinetics in biology.