bluuebunny/crossref_metadata_2025_split huggingface dataset:
Developing a More Accurate Biomedical Literature Retrieval Method using Deep Learning and Citations in PubMed Central Full-text Articles
Authors: Lo Chun-chao , Tian Shubo , Tao Yuchuan , Hao Jie , Zhang Jinfeng 
Published: October 2021

AbstractMost queries submitted to a literature search engine can be more precisely written as sentences to give the search engine more specific information. Sentence queries should be more effective, in principle, than short queries with small numbers of keywords. Querying with full sentences is also a key step in question-answering and citation recommendation systems. Despite the considerable progress in natural language processing (NLP) in recent years, using sentence queries on current search engines does not yield satisfactory results. In this study, we developed a deep learning-based method for sentence queries, called DeepSenSe, using citation data available in full-text articles obtained from PubMed Central (PMC). A large amount of labeled data was generated from millions of matched citing sentences and cited articles, making it possible to train quality predictive models using modern deep learning techniques. A two-stage approach was designed: in the first stage we used a modified BM25 algorithm to obtain the top 1000 relevant articles; the second stage involved re-ranking the relevant articles using DeepSenSe. We tested our method using a large number of sentences extracted from real scientific articles in PMC. Our method performed substantially better than PubMed and Google Scholar for sentence queries.
NAToRA, a relatedness-pruning method to minimize the loss of dataset size in genetic and omics analyses
Authors: Leal Thiago Peixoto , Furlan Vinicius C , Gouveia Mateus Henrique , Saraiva Duarte Julia Maria , Fonseca Pablo AS , Tou Rafael , de Oliveira Scliar Marilia , de Araujo Gilderlanio Santana , Zolini Camila , Diniz Peixoto Maria Gabriela Campolina , Santos Carvalho Maria Raquel , Lima-Costa Maria Fernanda , Gilman Robert H , Tarazona-Santos Eduardo , Rodrigues Maíra Ribeiro 
Published: October 2021

AbstractGenetic and omics analyses frequently require independent observations, which is not guaranteed in real datasets. When relatedness cannot be accounted for, solutions involve removing related individuals (or observations) and, consequently, a reduction of available data. We developed a network-based relatedness-pruning method that minimizes dataset reduction while removing unwanted relationships in a dataset. It uses node degree centrality metric to identify highly connected nodes (or individuals) and implements heuristics that approximate the minimal reduction of a dataset to allow its application to large datasets. NAToRA outperformed two popular methodologies (implemented in software PLINK and KING) by showing the best combination of effective relatedness-pruning, removing all relatives while keeping the largest possible number of individuals in all datasets tested and also, with similar or lesser reduction in genetic diversity. NAToRA is freely available, both as a standalone tool that can be easily incorporated as part of a pipeline, and as a graphical web tool that allows visualization of the relatedness networks. NAToRA also accepts a variety of relationship metrics as input, which facilitates its use. We also present a genealogies simulator software used for different tests performed in the manuscript.
Structure in motion: visual motion perception as online hierarchical inference
Authors: Bill Johannes , Gershman Samuel J. , Drugowitsch Jan 
Published: October 2021

AbstractIdentifying the structure of motion relations in the environment is critical for navigation, tracking, prediction, and pursuit. Yet, little is known about the mental and neural computations that allow the visual system to infer this structure online from a volatile stream of visual information. We propose online hierarchical Bayesian inference as a principled solution for how the brain might solve this complex perceptual task. We derive an online Expectation-Maximization algorithm that explains human percepts qualitatively and quantitatively for a diverse set of stimuli, covering classical psychophysics experiments, ambiguous motion scenes, and illusory motion displays. We thereby identify normative explanations for the origin of human motion structure perception and make testable predictions for new psychophysics experiments. The proposed online hierarchical inference model furthermore affords a neural network implementation which shares properties with motion-sensitive cortical areas and motivates a novel class of experiments to reveal the neural representations of latent structure.
Rapid Assessment of the Temporal Function and Phenotypic Reversibility of Neurodevelopmental Disorder Risk Genes inC. elegans
Authors: Kepler Lexis D. , McDiarmid Troy A. , Rankin Catharine H. 
Published: October 2021

ABSTRACTHundreds of genes have been implicated in neurodevelopmental disorders. Previous studies have indicated that some phenotypes caused by decreased developmental function of select risk genes can be reversed by restoring gene function in adulthood. However, very few risk genes have been assessed for adult reversibility. We developed a strategy to rapidly assess the temporal requirements and phenotypic reversibility of neurodevelopmental disorder risk gene orthologs using a conditional protein degradation system and machine vision phenotypic profiling inCaenorhabditis elegans. Using this approach, we measured the effects of degrading and re- expressing orthologs of 3 neurodevelopmental risk genesEBF3, BRN3A, andDYNC1H1across 30 morphological, locomotor, sensory, and learning phenotypes at multiple timepoints throughout development. We found some degree of phenotypic reversibility was possible for each gene studied. However, the temporal requirements of gene function and degree of phenotypic reversibility varied by gene and phenotype. The data reflects the dynamic nature of gene function and the importance of using multiple time windows of degradation and re-expression to understand the many roles a gene can play over developmental time. This work also demonstrates a strategy of using a high-throughput model system to investigate temporal requirements of gene function across a large number of phenotypes to rapidly prioritize neurodevelopmental disorder genes for re-expression studies in other organisms.SUMMARY STATEMENTWe developed a strategy that combines a conditional and reversible protein degradation system with our high-throughput machine vision tracking system to assess the temporal windows of gene function and reversibility of phenotypic disruptions associated with neurodevelopmental disorder risk gene orthologs usingC. elegans. Using this approach, we assessed 3 genes (unc- 3,unc-86, anddhc-1)and found that post-embryonic rescue was possible for each gene and each phenotypic feature class assessed. Re-activation of certain genes was able to reverse multiple phenotypic disruptions late into development without inducing novel phenotypes, prioritizing them for further study.
Single Pulse Heating of Nanoparticle Array for Biological Applications
Authors: Xie Chen , Kang Peiyuan , Cazals Johan , Castelán Omar Morales , Randrianalisoa Jaona , Qin Zhenpeng 
Published: October 2021

ABSTRACTWith the ability to convert external excitation into heat, nanomaterials play an essential role in many biomedical applications. Two modes of nanoparticle (NP) array heating, nanoscale-confined heating (NCH) and macroscale-collective heating (MCH), have been found and extensively studied. Despite this, the resulting biological response at protein level remains elusive. In this study, we developed a computational model to systematically investigate the single-pulsed heating of NP array and corresponding protein denaturation/activation. We found that NCH may lead to targeted protein denaturation, however, nanoparticle heating does not lead to nanoscale selective TRPV1 channel activation. The excitation duration and NP concentration are primary factors that determine a window for targeted protein denaturation, and together with heating power, we defined quantified boundaries for targeted protein denaturation. Our results boost our understandings in the NCH and MCH under realistic physical constraints and provide a robust guidance to customize biomedical platforms with desired NP heating.
Neural knowledge assembly in humans and deep networks
Authors: Nelli Stephanie , Braun Lukas , Dumbalska Tsvetomira , Saxe Andrew , Summerfield Christopher 
Published: October 2021

AbstractHuman understanding of the world can change rapidly when new information comes to light, such as when a plot twist occurs in a work of fiction. This flexible “knowledge assembly” requires few-shot reorganisation of neural codes for relations among objects and events. However, existing computational theories are largely silent about how this could occur. Here, participants learned a transitive ordering among novel objects within two distinct contexts, before exposure to new knowledge that revealed how they were linked. BOLD signals in dorsal frontoparietal cortical areas revealed that objects were rapidly and dramatically rearranged on the neural manifold after minimal exposure to linking information. We then adapt stochastic online gradient descent to permit similar rapid knowledge assembly in a neural network model.
Short term treatment with a cocktail of rapamycin, acarbose and phenylbutyrate slows aging in mice
Authors: Jiang Zhou , Wang Juan , Imai Denise , Snider Tim , Mangalindan Ruby , Morton John , Zhu Lida , Salmon Adam B. , Wezeman Jackson , Klug Jenna , Hu Jiayi , Menon Vinal , Marka Nicholas , Neidernhofer Laura , Ladiges Warren 
Published: October 2021

AbstractPharmaceutical intervention of aging requires targeting multiple pathways, thus there is rationale to test combinations of drugs each targeting different but overlapping processes. In order to determine if combining drugs previously shown to improve lifespan would have greater impact than any individuyal drug, a diet containing rapamycin at 14 ppm, acarbose at 1000 ppm, and phenylbutyrate at 1000 ppm was fed to 20-month-old C57BL/6 and HET3 4-way cross mice of both sexes for three months. Mice fed the cocktail diet showed a strain and gender-dependent phenotype consistent with healthy aging including decreased body fat and blood glucose, improved cognition, and increased grip strength and walking ability compared to mice fed individual drug or control diets. A cocktail diet containing ½ dosing of each compound was overall less effective than the full dose. The composite age-related lesion score of heart, lungs, liver and kidney was decreased in mice fed the cocktail diet compared to mice fed individual drug or control diets suggesting an interactive advantage of the three drugs. Senescence and inflammatory cytokine levels in kidneys from mice fed the cocktail diet were lower than in kidneys from mice fed control diet, and consistent with low expression levels in kidneys from young untreated mice, suggesting the cocktail diet delayed aging partly by senolytic and anti-inflammatory effects.
Multi-species mechanistic model of functional response provides new insights into predator-mediated interactions in a vertebrate community
Authors: Beardsell Andréanne , Gravel Dominique , Clermont Jeanne , Berteaux Dominique , Gauthier Gilles , Bêty Joël 
Published: October 2021

1.AbstractPrey handling processes are considered a key driver of short-term positive indirect effects between prey sharing the same predator. However, a growing body of research indicates that predators are rarely limited by such processes in the wild. Density-dependent changes in predator foraging be-havior can also generate positive indirect effects but they are rarely included as explicit functions of prey densities in functional response models. With the aim of untangling proximate drivers of species interactions in natural communities and improve our ability to quantify interaction strength, we extended the Holling multi-species model by including density-dependent changes in predator foraging behavior. Our model, based on species traits and behavior, was inspired by the verte-brate community of the arctic tundra, where the main predator (the arctic fox) is an active forager feeding primarily on cyclic small rodent (lemming) populations and eggs of various tundra-nesting bird species. Short-term positive indirect effects of lemmings on birds have been documented over the circumpolar Arctic but the underlying proximate mechanisms remain poorly known. We used a unique data set, containing high-frequency GPS tracking, accelerometer, behavioral, and experimental data to parameterize the multi-species model, and a 15-year time series of prey densities and bird nesting success to evaluate inter-action strength between species. Our results showed that: (i) prey handling processes play a minor role in our system and (ii) density-dependent changes in predator foraging behavior can be the proximate drivers of a predominant predator-mediated interaction observed in the arctic tundra. Mechanisms outlined in our study have been little studied and may play a significant role in natural systems. We hope that our study will provide a useful starting point to build mechanistic models of predation, and we think that our approach could conceivably be applied to a broad range of food webs.
ImmuNet: A Segmentation-Free Machine Learning Pipeline for Immune Landscape Phenotyping in Tumors by Muliplex Imaging
Authors: Sultan Shabaz , Gorris Mark A. J. , Martynova Evgenia , van der Woude Lieke L. , Buytenhuijs Franka , van Wilpe Sandra , Verrijp Kiek , Figdor Carl G. , de Vries I. Jolanda M. , Textor Johannes 
Published: October 2021

AbstractTissue specimens taken from primary tumors or metastases contain important information for diagnosis and treat-ment of cancer patients. Multiplex imaging allowsin situvisualization of heterogeneous cell populations, such as immune cells, in tissue samples. Most image processing pipelines first segment cell boundaries and then measure marker expression to assign cell phenotypes. In dense tissue environments, this segmentation-first approach can be inaccurate due to segmentation errors or overlapping cells. Here we introduce the machine learning pipeline “ImmuNet” that identifies positions and phenotypes of cells without segmenting them. ImmuNet is easy to train: human annotators only need to click on an immune cell and score its expression of each marker. This approach al-lowed us to annotate 34,458 cells. We show that ImmuNet consistently outperforms a state-of-the-art segmentation-based pipeline for multiplex immunohistochemistry analysis across tissue types, cell types and tissue densities, achieving error rates below 5-10% on challenging detection and phenotyping tasks. We externally validate Im-muNet results by comparing them to flow cytometric measurements from the same tissue. In summary, ImmuNet is an effective, simpler alternative to segmentation-based approaches when only cell positions and phenotypes, but not their shapes, are required for downstream analyses. Thus, ImmuNet helps researchers to analyze multiplex tissue images more easily and accurately.
Two structurally different oomycete lipophilic MAMPs induce distinctive plant immune responses
Authors: Monjil Mohammad Shahjahan , Kato Hiroaki , Ota Satomi , Matsuda Kentaro , Suzuki Natsumi , Tenhiro Shiho , Tatsumi Ayane , Pring Sreynich , Miura Atsushi , Camagna Maurizio , Suzuki Takamasa , Tanaka Aiko , Terauchi Ryohei , Sato Ikuo , Chiba Sotaro , Kawakita Kazuhito , Ojika Makoto , Takemoto Daigo 
Published: October 2021

AbstractPlants recognize a variety of external signals and induce appropriate mechanisms to increase their tolerance to biotic and abiotic stresses. Precise recognition of attacking pathogens and induction of effective resistance mechanisms are critical functions for plant survival. Some molecular patterns unique to a certain group of microbes, microbe-associated molecular patterns (MAMPs), are sensed by plant cells as non-self molecules via pattern recognition receptors. While MAMPs of bacterial and fungal origin have been identified, reports on oomycete MAMPs are relatively limited. This study aimed to identify MAMPs from an oomycete pathogenPhytophthora infestans, the causal agent of potato late blight. Using reactive oxygen species (ROS) production and phytoalexin production in potato as markers, two structurally different groups of elicitors, namely ceramides and diacylglycerols were identified.P. infestansceramides (Pi-Cer A, B and D) induced ROS production, while diacylglycerol (Pi-DAG A and B), containing eicosapentaenoic acid (EPA) as a substructure, induced phytoalexins production in potato. The molecular patterns in Pi-Cers and Pi-DAGs essential for defense induction were identified as 9-methyl-4,8-sphingadienine (9Me-Spd) and 5,8,11,14-tetraene-type fatty acid (5,8,11,14-TEFA), respectively. These structures are not found in plants, but in oomycetes and fungi, indicating that they are microbe molecular patterns recognized by plants. When Arabidopsis was treated with Pi-Cer D and EPA, partially overlapping but different sets of genes were induced. Furthermore, expression of some genes is upregulated only after the simultaneous treatment with Pi-Cer D and EPA, indicating that plants combine the signals from simultaneously recognized MAMPs to adapt the defense response to pathogens.
Distribution of multi-unit pitch responses recorded intracranially from human auditory cortex
Authors: Berger Joel I , Gander Phillip E , Kikuchi Yukiko , Kumar Sukhbinder , Kovach Christopher , Oya Hiroyuki , Kawasaki Hiroto , Howard Matthew A , Griffiths Timothy D 
Published: October 2021

AbstractThe perception of pitch requires the abstraction of stimulus properties related to the spectrotemporal structure of sound. Previous studies utilizing both animal electrophysiology and human imaging have indicated the presence of a center for pitch representation in the auditory cortex. Recent data from our own group - examining local field potentials (LFPs) in humans - indicate more widely distributed pitch-associated responses within the auditory cortex (Gander et al., 2019). To probe this with greater spatial resolution, we examined multi-unit activity related to three different auditory stimuli, in seven epilepsy patients who were implanted with high-impedance electrodes in auditory cortex for the clinical purpose of localizing seizures. The stimuli were regular-interval noise (RIN) with a pitch strength that is related to the temporal regularity, and pitch value determined by repetition rate, and harmonic complexes with missing fundamentals. We demonstrated increases in spiking activity in 69 of 104 (66%) responsive multiunit activity in auditory cortex due to pitch-associated stimuli. Importantly, these responses were distributed across the entire extent of Heschl’s gyrus (HG), in both primary and non-primary areas, rather than isolated to a specific region, and this finding was evident regardless of the stimulus presented. These findings are the first multi-unit pitch responses recorded from humans, and align with a recent study in macaques (Kikuchi et al., 2019) demonstrating that both local field potential and unit responses to pitch-inducing stimuli are distributed throughout auditory cortex.Significance StatementThe perception of pitch is a fundamental acoustic attribute that is mediated by the auditory system. Despite its importance, there is still debate as to the precise areas responsible for its encoding, which may be due to differences in the recording measures and choices of stimuli used in previous studies. Here, we present the first study to measure multi-unit pitch responses in the auditory cortices of intracranially-implanted humans. Importantly, we demonstrate reliable responses to three different pitch-inducing paradigms that are distributed throughout Heschl’s gyrus, rather than being localized to a particular region. These data provide a bridge across animal and human studies, and aid in our understanding of the processing of a critical attribute of acoustic stimuli.
Generating accurate 3D gaze vectors using synchronized eye tracking and motion capture
Authors: Stone Scott A. , Boser Quinn A. , Dawson T. Riley , Vette Albert H. , Hebert Jacqueline S. , Pilarski Patrick M. , Chapman Craig S. 
Published: October 2021

AbstractAssessing gaze behaviour during real-world tasks is difficult; dynamic bodies moving through dynamic worlds make gaze analysis difficult. Current approaches involve laborious coding of pupil positions. In settings where motion capture and mobile eye tracking are used concurrently in naturalistic tasks, it is critical that data collection be simple, efficient, and systematic. One solution is to combine eye tracking with motion capture to generate 3D gaze vectors. When combined with tracked or known object locations, 3D gaze vector generation can be automated. Here we use combined eye and motion capture and explore how linear regression models generate accurate 3D gaze vectors. We compare spatial accuracy of models derived from four short calibration routines across three pupil data inputs: the efficacy of calibration routines were assessed, a validation task requiring short fixations on taskrelevant locations, and a naturalistic object interaction task to bridge the gap between laboratory and “in the wild” studies. Further, we generated and compared models using spherical and cartesian coordinate systems and monocular (Left or Right) or binocular data. All calibration routines performed similarly, with the best performance (i.e., sub-centimetre errors) coming from the naturalistic task trials when the participant is looking at an object in front of them. We found that spherical coordinate systems generate the most accurate gaze vectors with no differences in accuracy when using monocular or binocular data. Overall, we recommend one-minute calibration routines using binocular pupil data combined with a spherical world coordinate system to produce the highest quality gaze vectors.
In-vitro cellular reprogramming to model gonad development and its disorders
Authors: Gonen Nitzan , Eozenou Caroline , Mitter Richard , Bernardo Andreia , Chervova Almira , Frachon Emmanuel , Commère Pierre-Henri , Mazen Inas , Gobaa Samy , McElreavey Kenneth , Lovell-Badge Robin , Bashamboo Anu 
Published: October 2021

AbstractDuring embryonic development, mutually antagonistic signaling cascades determine the fate of the bipotential gonad towards a testicular or ovarian identity. Errors in this process result in human Disorders of Sex Development (DSDs), where there is discordance between chromosomal, gonadal, and anatomical sex. The absence of an appropriate, accessible in-vitro system is a major obstacle in understanding mechanisms of sex-determination/DSDs. Here, we describe protocols for differentiation of mouse and human pluripotent cells towards gonadal progenitors. Transcriptomic analysis reveals that the in-vitro-derived murine gonadal cells are equivalent to E11.5 in-vivo progenitors. Using similar conditions, Sertoli-like cells derived from 46,XY human induced pluripotent stem cells (hiPSCs) exhibit sustained expression of testis-specific genes, secrete AMH, migrate and form tubular structures. The cells derived from a 46,XY DSD female hiPSCs, carrying a NR5A1 variant, show aberrant gene expression and absence of tubule formation. CRISPR/Cas9-mediated correction of the variant rescued the phenotype. This is a robust tool to understand mechanisms of sex-determination and model DSDs.