bluuebunny/crossref_metadata_2025_split huggingface dataset:
A literature-derived knowledge graph augments the interpretation of single cell RNA-seq datasets
Authors: Doddahonnaiah Deeksha , Lenehan Patrick , Hughes Travis , Zemmour David , Garcia-Rivera Enrique , Venkatakrishnan AJ , Chilaka Ramakrisha , Khare Apoorv , Anand Akash , Barve Rakesh , Thiagarajan Viswanathan , Soundararajan Venky 
Published: April 2021

AbstractTechnology to generate single cell RNA-sequencing (scRNA-seq) datasets and tools to annotate them have rapidly advanced in the past several years. Such tools generally rely on existing transcriptomic datasets or curated databases of cell type defining genes, while the application of scalable natural language processing (NLP) methods to enhance analysis workflows has not been adequately explored. Here we deployed an NLP framework to objectively quantify associations between a comprehensive set of over 20,000 human protein-coding genes and over 500 cell type terms across over 26 million biomedical documents. The resultant gene-cell type associations (GCAs) are significantly stronger between a curated set of matched cell type-marker pairs than the complementary set of mismatched pairs (Mann Whitney p < 6.15×10−76, r = 0.24; cohen’s D = 2.6). Building on this, we developed an augmented annotation algorithm that leverages GCAs to categorize cell clusters identified in scRNA-seq datasets, and we tested its ability to predict the cellular identity of 185 clusters in 13 datasets from human blood, pancreas, lung, liver, kidney, retina, and placenta. With the optimized settings, the true cellular identity matched the top prediction in 66% of tested clusters and was present among the top five predictions for 94% of clusters. Further, contextualization of differential expression analyses with these GCAs highlights poorly characterized markers of established cell types, such as CLIC6 and DNASE1L3 in retinal pigment epithelial cells and endothelial cells, respectively. Taken together, this study illustrates for the first time how the systematic application of a literature derived knowledge graph can expedite and enhance the annotation and interpretation of scRNA-seq data.
Individual variability in the human connectome maintains selective cross-modal consistency and shares microstructural signatures
Authors: Karahan Esin , Tait Luke , Si Ruoguang , Özkan Ayşegül , Szul Maciek , Zhang Jiaxiang 
Published: April 2021

AbstractIndividuals are different in their behavioural responses and cognitive abilities. Neural underpinnings of individual differences are largely unknown. Here, by using multimodal imaging data including diffusion MRI, functional MRI and MEG, we show the consistency of interindividual variation of connectivity across modalities. We demonstrated that regional differences in individual variability of structural and functional connectomes is characterized by higher variability in association cortices and lower variability in sensory and visual cortices. This pattern is consistent across all modalities at varying degrees as shown by significant alignment between functional and structural connectome variabilities at several clusters of brain regions. Variability in connectivity is associated with cortical myelin content and microstructural properties of connections. Our findings contribute to understanding of individual differences in functional and structural organization of brain and facilitate fingerprinting applications.
The SPARC DRC: Building a resource for the autonomic nervous system community
Authors: Osanlouy Mahyar , Bandrowski Anita , de Bono Bernard , Brooks David , Cassarà Antonio M. , Christie Richard , Ebrahimi Nazanin , Gillespie Tom , Grethe Jeffrey S. , Guercio Leonardo A. , Heal Maci , Lin Mabelle , Kuster Niels , Martone Maryann E. , Neufeld Esra , Nickerson David P. , Soltani Elias G. , Tappan Susan , Wagenaar Joost B. , Zhuang Katie , Hunter Peter J. 
Published: April 2021

ABSTRACTThe Data and Resource Center (DRC) of the NIH-funded SPARC program is developing databases, connectivity maps and simulation tools for the mammalian autonomic nervous system. The experimental data and mathematical models supplied to the DRC by the SPARC consortium are curated, annotated and semantically linked via a single knowledgebase. A data portal has been developed that allows discovery of data and models both via semantic search and via an interface that includes Google Map-like 2D flatmaps for displaying connectivity, and 3D anatomical organ scaffolds that provide a common coordinate framework for cross-species comparisons. We discuss examples that illustrate the data pipeline, which includes data upload, curation, segmentation (for image data), registration against the flatmaps and scaffolds, and finally display via the web portal, including the link to freely available online computational facilities that will enable neuromodulation hypotheses to be investigated by the autonomic neuroscience community and device manufacturers.
Soil-free pathogenicity assay ofFusarium oxysporumf.sp.vasinfectumrace 4 on Pima cotton (Gossypium barbadense) seedlings
Authors: Murty Lia D. , Shim Won Bo 
Published: April 2021

ABSTRACTOne of the devastating early season diseases of cotton is Fusarium wilt caused byFusarium oxysporumf. sp.vasinfectum(Fov). Recent emergence of highly virulent Fov race 4 (Fov4) and its aggressiveness towardGossypium barbadense(pima) cultivars are raising significant concerns for the US cotton industry. One of the key challenges in studying Fov4 virulence and cotton Fusarium wilt pathogenesis is establishing a disease assay strategy that can help researcher overcome several technical challenges, including efficient infection and highly reproducible and consistent symptom development. Here, we have developed a small-scale, soil-free Fusarium wilt disease assay that can complement conventional assays with faster symptom development and high reproducibility in infected pima cotton seedlings. Our data showed statistically significant differences (p<0.0001) between Fov4-infected and non-infected pima cotton at 4 and 6 days post inoculation (dpi) when compared to control experiments. At 6 dpi, longitudinal observations under magnification showed Fov4 colonization in primary xylem of infected plants, which is a common symptom observed in Fov4 triggered Fusarium wilt in pima cotton. While this is an artificial assay system, this soil-free disease testing strategy can offer another strategy to supplement current assays when studying pathogen-host interaction in soil-borne diseases.
The Actin Bundling Protein Fascin Promotes Lung Cancer Growth and Metastasis by Enhancing Glycolysis and PFKFB3 Expression
Authors: Lin Shengchen , Li Yunzhan , Wang Dezhen , Huang Chongbiao , Marino David , Bollt Oana , Wu Chaodong , Taylor Matthew , Li Wei , DeNicola Gina M. , Hao Jihui , Singh Pankaj K. , Yang Shengyu 
Published: April 2021

AbstractFascin is a pro-metastasis actin bundling protein upregulated in essentially all the metastatic carcinoma. It is believed that fascin promotes cancer cell migration and invasion by facilitating membrane protrusions such as filopodia and invadopodia. Aerobic glycolysis is a key feature of cancer metabolism that provides critical intermediate metabolites for tumor growth and cell proliferation. Here we report that fascin increase glycolysis in lung cancer to promote tumor growth and metastasis. Fascin promotes glycolytic flux by increasing the expression and activities of phosphofructose kinase 1 and 2 (PFK1 and 2). The glycolytic function of fascin depends on activation of YAP1 through its canonical actin bundling activity. Fascin promotes the binding of YAP1 to a TEAD1/4 binding motif located 30 bp upstream of the PFKFB3 transcription start site to activate its transcription. Our interrogation of the TCGA database suggest that the fascin-YAP1-PFKFB3 circuit is likely conserved across different types of cancer. We further showed that the glycolytic function of fascin is essential for promotion of lung cancer growth and metastasis. Importantly, pharmacological inhibitors of fascin could be used to suppress the YAP1-PFKFB3 signaling and inhibit glycolysis in cancer cell lines, organoid cultures and xenograft metastasis models. Taken together, our data reveal an important glycolytic role of fascin in lung cancer metabolism, and suggest that pharmacological inhibitors of fascin could be used to reprogram cancer metabolism in lung cancer and potentially other cancer with fascin upregulation.
Autologous humanized mouse models of iPSC-derived tumors allow for the evaluation and modulation of cancer-immune cell interactions
Authors: Moquin-Beaudry Gaël , Benabdallah Basma , Maggiorani Damien , Le Oanh , Li Yuanyi , Colas Chloé , Raggi Claudia , Ellezam Benjamin , M’Callum Marie-Agnès , Dal Soglio Dorothée , Guimond Jean V. , Paganelli Massimiliano , Haddad Elie , Beauséjour Christian 
Published: April 2021

ABSTRACTModeling the tumor-immune cell interactions in humanized mice is complex and limits drug development. Here, we generated easily accessible tumor models by transforming either primary skin fibroblasts or iPSC-derived cell lines injected in immune-deficient mice reconstituted with human autologous immune cells. Our results showed that either fibroblastic, hepatic or neural tumors were all efficiently infiltrated and partially or totally rejected by autologous immune cells in humanized mice. Characterization of tumor immune infiltrates revealed high expression levels of the dysfunction markers Tim3 and PD-1 in T cells and an enrichment in regulatory T cell suggesting rapid establishment of an immunosuppressive microenvironment. Inhibition of PD-1 by Nivolumab in humanized mice resulted in an increased immune cell infiltration and a slight decrease in tumor growth. We expect these versatile and accessible cancer models will facilitate preclinical studies and the evaluation of autologous cancer immunotherapies across a range of different tumor cell types.SIGNIFICANCE STATEMENTPreclinical models capable of providing an environment where human tumors are confronted with autologous immune cells are not easily accessible and limit drug development. As an alternative we generated genetically-defined tumor cell lines from primary and iPSC-derived cells for the evaluation of cancer-immune cell interactions in autologous humanized mice.
Serum CSF1 levels are elevated in patients following partial hepatectomy and acetaminophen induced acute liver failure: Reanalysis of previous data
Authors: Stutchfield BM , Starkey Lewis PJ , Wigmore SJ , Simpson K , Hume DA , Forbes SJ 
Published: April 2021

AbstractA previous study analysed serum CSF1 concentration in patients who had undergone partial hepatectomy and patients with acute liver failure (ALF) induced by acetaminophen. A correlation between low CSF1 and poor prognosis was reported in ALF. In May 2018, an allegation of research misconduct was made against the second author of the published study, D.J.Antoine, who carried out the CSF1 analysis. The current study describes the independent reanalysis of circulating CSF1 concentration in sera from the same patient cohorts. Patient serum CSF1 levels increased following partial hepatectomy. By contrast to the published report, serum CSF1 concentration was greatest in ALF patients who died compared to survivors. The results confirm the relationship between liver injury and circulating serum CSF1.
Egocentric Biases are Determined by the Precision of Self-related Predictions
Authors: Sevi Leora , Stantic Mirta , Murphy Jennifer , Coll Michel-Pierre , Catmur Caroline , Bird Geoffrey 
Published: April 2021

AbstractAccording to predictive processing theories, emotional inference involves simultaneously minimising discrepancies between predictions and sensory data relating to both one’s own and others’ states, achievable by altering either one’s own state (empathy) or perception of another’s state (egocentric bias) so they are more congruent. We tested a key hypothesis of these accounts, that predictions are weighted in inference according to their precision (inverse variance). If correct, more precise self-related predictions should bias perception of another’s emotional expression to a greater extent than less precise predictions. We manipulated predictions about upcoming own-pain (low or high magnitude) using cues that afforded either precise (a narrow range of possible magnitudes) or imprecise (a wide range) predictions. Participants judged pained facial expressions presented concurrently with own-pain to be more intense when own-pain was greater, and precise cues increased this biasing effect. Implications of conceptualising interpersonal influence in terms of predictive processing are discussed.
Oncogenic RAS activity predicts response to chemotherapy and outcome in lung adenocarcinoma
Authors: East Philip , Kelly Gavin P. , Biswas Dhruva , Marani Michaela , Hancock David C. , Creasy Todd , Sachsenmeier Kris , Swanton Charles , de Carné Trécesson Sophie , Downward Julian 
Published: April 2021

AbstractActivating mutations in the driver oncogeneKRASoccur in 32% of lung adenocarcinomas, leading to more aggressive disease and resistance to therapy in preclinical studies. However, the association betweenKRASmutational status and patient outcome or response to treatment remains unclear, likely due to additional events modulating RAS pathways. To obtain a broader measure of RAS pathway activation beyondKRASmutation only, we developed RAS84, a transcriptional signature optimised to capture RAS oncogenic activity in lung adenocarcinoma. Using RAS84 to classify lung cell lines, we show that RAS transcriptional activity outperformsKRASmutation to predict resistance to chemotherapy drugsin vitro. We report that 84% of lung adenocarcinomas show clear transcriptional evidence of RAS oncogenic activation, falling into four groups characterised by coincident mutation ofSTK11/LKB1,TP53orCDKN2A. Given that 65% of these RAS pathway active tumours do not haveKRASmutations, we find that the classifications developed when considering onlyKRASmutant tumours have significance in a much broader cohort of patients. Critically, patients in the highest RAS activity groups show adverse clinical outcome and reduced response to chemotherapy. The stratification of patients using gene expression patterns linked to oncogenic RAS signalling activity instead of genetic alterations in cancer genes could ultimately help clinical decision making.
Case based learning as a dynamic approach towards learning oral pathology
Authors: Shrestha Ashish , Marla Vinay , Rimal Jyotsna , Shrestha Sushmita , Keshwar Shashi , Zhimin Jia 
Published: April 2021

AbstractIntroductionOral pathology comprises of extensive teaching-learning of histopathology. Teaching histopathology is always challenging more when numerous histopathology slides are involved. There is lack of interest among the students and also face challenges to correlate with the clinical presentation. For effective learning, need for a method which is interactive and helps create clinico pathological correlation was felt. This study was designed to assess the impact and feasibility of case-based learning in routine teaching among dental students.MethodsA cross sectional study was conducted among 58 undergraduate dental students, wherein a case-based learning method was introduced in the practical classes. Thirty students were randomly selected as case based learning and remaining as conventional group. Multiple paper-based cases on oral cancer was designed and used for the case based learning group of students. A self-designed pre and posttest interventional tools along with post intervention assessment using modified essay question was designed and applied.ResultsSignificant difference in the scores between pre and post intervention questionnaire was observed among the case-based learning group (p<0.0001), however non-significant among the conventional. Similarly, significant difference in the scores of modified essay question was observed between the study groups. Majority of the students among the case-based learning group agreed that it benefitted their personal, professional and communicative skills. The students expressed their enthusiasm in learning with this method and suggested to apply it more often.DiscussionCase-based learning is an effective method and incorporation of it on a regular basis could help favor an effective learning environment.
Reference transcriptomes of porcine peripheral immune cells created through bulk and single-cell RNA sequencing
Authors: Herrera-Uribe Juber , Wiarda Jayne E. , Sivasankaran Sathesh K. , Daharsh Lance , Liu Haibo , Byrne Kristen A. , Smith Timothy P.L. , Lunney Joan K. , Loving Crystal L. , Tuggle Christopher K. 
Published: April 2021

ABSTRACTPigs are a valuable human biomedical model and an important protein source supporting global food security. The transcriptomes of peripheral blood immune cells in pigs were defined at the bulk cell-type and single cell levels. First, eight cell types were isolated in bulk from peripheral blood mononuclear cells (PBMCs) by cell sorting, representing Myeloid, NK cells and specific populations of T and B cells. Transcriptomes for each bulk population of cells were generated by RNA-seq with 10,974 expressed genes detected. Pairwise comparisons between cell types revealed specific expression, while enrichment analysis identified 1,885 to 3,591 significantly enriched genes across all 8 cell types. Gene Ontology analysis for the top 25% of significantly enriched genes (SEG) showed high enrichment of biological processes related to the nature of each cell type. Comparison of gene expression indicated highly significant correlations between pig cells and corresponding human PBMC bulk RNA-seq data available in Haemopedia. Second, higher resolution of distinct cell populations was obtained by single-cell RNA-sequencing (scRNA-seq) of PBMC. Seven PBMC samples were partitioned and sequenced that produced 28,810 single cell transcriptomes distributed across 36 clusters and classified into 13 general cell types including plasmacytoid dendritic cells (DC), conventional DCs, monocytes, B cell, conventional CD4 and CD8 αβ T cells, NK cells, and γδ T cells. Signature gene sets from the human Haemopedia data were assessed for relative enrichment in genes expressed in pig cells and integration of pig scRNA-seq with a public human scRNA-seq dataset provided further validation for similarity between human and pig data. The sorted porcine bulk RNAseq dataset informed classification of scRNA-seq PBMC populations; specifically, an integration of the datasets showed that the pig bulk RNAseq data helped define the CD4CD8 double-positive T cell populations in the scRNA-seq data. Overall, the data provides deep and well-validated transcriptomic data from sorted PBMC populations and the first single-cell transcriptomic data for porcine PBMCs. This resource will be invaluable for annotation of pig genes controlling immunogenetic traits as part of the porcine Functional Annotation of Animal Genomes (FAANG) project, as well as further study of, and development of new reagents for, porcine immunology.
Computer Vision and Deep Learning for Environment-Adaptive Control of Robotic Lower-Limb Exoskeletons
Authors: Laschowski Brokoslaw , McNally William , Wong Alexander , McPhee John 
Published: April 2021

AbstractRobotic exoskeletons require human control and decision making to switch between different locomotion modes, which can be inconvenient and cognitively demanding. To support the development of automated locomotion mode recognition systems (i.e., high-level controllers), we designed an environment recognition system using computer vision and deep learning. We collected over 5.6 million images of indoor and outdoor real-world walking environments using a wearable camera system, of which ~923,000 images were annotated using a 12-class hierarchical labelling architecture (called the ExoNet database). We then trained and tested the EfficientNetB0 convolutional neural network, designed for efficiency using neural architecture search, to predict the different walking environments. Our environment recognition system achieved ~73% image classification accuracy. While these preliminary results benchmark Efficient-NetB0 on the ExoNet database, further research is needed to compare different image classification algorithms to develop an accurate and real-time environment-adaptive locomotion mode recognition system for robotic exoskeleton control.
Common ALS/FTD risk variants inUNC13Aexacerbate its cryptic splicing and loss upon TDP-43 mislocalization
Authors: Brown Anna-Leigh , Wilkins Oscar G. , Keuss Matthew J. , Hill Sarah E. , Zanovello Matteo , Lee Weaverly Colleen , Lee Flora C.Y. , Masino Laura , Qi Yue A. , Bryce-Smith Sam , Bampton Alexander , Gatt Ariana , Phatnani Hemali ,   NYGC ALS Consortium, Schiavo Giampietro , Fisher Elizabeth M.C. , Raj Towfique , Secrier Maria , Lashley Tammaryn , Ule Jernej , Buratti Emanuele , Humphrey Jack , Ward Michael E. , Fratta Pietro 
Published: April 2021

AbstractVariants within theUNC13Agene have long been known to increase risk of amyotrophic lateral sclerosis (ALS) and frontotemporal dementia (FTD), two related neurodegenerative diseases defined by mislocalization of the RNA-binding protein TDP-43. Here, we show that TDP-43 depletion induces robust inclusion of a cryptic exon (CE) withinUNC13A, a critical synaptic gene, resulting in nonsense-mediated decay and protein loss. Strikingly, two common polymorphisms strongly associated with ALS/FTD risk directly alter TDP-43 binding within the CE or downstream intron, increasing CE inclusion in cultured cells and in patient brains. Our findings, which are the first to demonstrate a genetic link specifically between loss of TDP-43 nuclear function and disease, reveal both the mechanism by whichUNC13Avariants exacerbate the effects of decreased nuclear TDP-43 function, and provide a promising therapeutic target for TDP-43 proteinopathies.One-Sentence SummaryShared ALS/FTD risk variants increase the sensitivity of a cryptic exon in the synaptic geneUNC13Ato TDP-43 depletion.
The Alzheimer susceptibility geneBIN1induces isoform-dependent neurotoxicity through early endosome defects
Authors: Lambert Erwan , Saha Orthis , Landeira Bruna Soares , de Farias Ana Raquel Melo , Hermant Xavier , Carrier Arnaud , Pelletier Alexandre , Davoine Lindsay , Dupont Cloé , Amouyel Philippe , Bonnefond Amélie , Lafont Frank , Adelfettah Farida , Verstreken Patrik , Chapuis Julien , Barois Nicolas , Delahaye Fabien , Dermaut Bart , Lambert Jean-Charles , Costa Marcos R. , Dourlen Pierre 
Published: April 2021

SUMMARYTheBridging Integrator 1(BIN1) gene is a major susceptibility gene for Alzheimer’s disease (AD). Deciphering its pathophysiological role is challenging due to its numerous isoforms. Here we observed in Drosophila that human BIN1 isoform1 (BIN1iso1) overexpression, contrary to BIN1iso8 and BIN1iso9, induced an accumulation of endosomal vesicles and neurodegeneration. Systematic search for endosome regulators able to prevent BIN1iso1-induced neurodegeneration indicated that a defect at the early endosome level is responsible for the neurodegeneration. In human induced neurons (hiNs) and cerebral organoids,BIN1knock-out resulted in the narrowing of early endosomes. This phenotype was rescued by BIN1iso1 but not BIN1iso9 expression. Finally, BIN1iso1 overexpression also led to an increase in the size of early endosomes and neurodegeneration in hiNs. Altogether, our data demonstrate that the AD susceptibility geneBIN1, and especially BIN1iso1, contributes to early-endosome size deregulation, which is an early pathophysiological hallmark of AD pathology.