bluuebunny/crossref_metadata_2025_split huggingface dataset:
Predicting cross-tissue hormone-gene relations using balanced word embeddings
Authors: Jadhav Aditya , Kumar Tarun , Raghavendra Mohit , Loganathan Tamizhini , Narayanan Manikandan 
Published: January 2021

AbstractMotivationLarge volumes of biomedical literature present an opportunity to build whole-body human models comprising both within-tissue and across-tissue interactions among genes. Current studies have mostly focused on identifying within-tissue or tissue-agnostic associations, with a heavy emphasis on associations among disease, genes and drugs. Literature mining studies that extract relations pertaining to inter-tissue communication, such as between genes and hormones, are solely missing.ResultsWe present here a first study to identify from literature the genes involved in inter-tissue signaling via a hormone in the human body. Our models BioEmbedS and BioEmbedS-TS respectively predict if a hormone-gene pair is associated or not, and whether an associated gene is involved in the hormone’s production or response. Our models are classifiers trained on word embeddings that we had carefully balanced across different strata of the training data such as across production vs. response genes of a hormone (or) well-studied vs. poorly-represented hormones in the literature. Model training and evaluation are enabled by a unified dataset called HGv1 of ground-truth associations between genes and known endocrine hormones that we had compiled. Our models not only recapitulate known gene mediators of tissue-tissue signaling (e.g., at average 70.4% accuracy for BioEmbedS), but also predicts novel genes involved in inter-tissue communication in humans. Furthermore, the species-agnostic nature of our ground-truth HGv1 data and our predictive modeling approach, demonstrated concretely using human data and generalized to mouse, hold much promise for future work on elucidating inter-tissue signaling in other multi-cellular organisms.AvailabilityProposed HGv1 dataset along with our models’ predictions, and the associated code to reproduce this work are available respectively athttps://cross-tissue-signaling.herokuapp.com/, andhttps://github.com/BIRDSgroup/BioEmbedS.Contactnmanik@cse.iitm.ac.in
Synthqa - Hierarchical Machine Learning-Based Protein Quality Assessment
Authors: Korovnik Mikhail , Hippe Kyle , Hou Jie , Si Dong , Kishaba Kiyomi , Cao Renzhi 
Published: January 2021

ABSTRACTMotivationIt has been a challenge for biologists to determine 3D shapes of proteins from a linear chain of amino acids and understand how proteins carry out life’s tasks. Experimental techniques, such as X-ray crystallography or Nuclear Magnetic Resonance, are time-consuming. This highlights the importance of computational methods for protein structure predictions. In the field of protein structure prediction, ranking the predicted protein decoys and selecting the one closest to the native structure is known as protein model quality assessment (QA), or accuracy estimation problem. Traditional QA methods don’t consider different types of features from the protein decoy, lack various features for training machine learning models, and don’t consider the relationship between features. In this research, we used multi-scale features from energy score to topology of the protein structure, and proposed a hierarchical architecture for training machine learning models to tackle the QA problem.ResultsWe introduce a new single-model QA method that incorporates multi-scale features from protein structures, utilizes the hierarchical architecture of training machine learning models, and predicts the quality of any protein decoy. Based on our experiment, the new hierarchical architecture is more accurate compared to traditional machine learning-based methods. It also considers the relationship between features and generates additional features so machine learning models can be trained more accurately. We trained our new tool, SynthQA, on the CASP dataset (CASP10 to CASP12), and validated our method on 33 targets from the latest CASP 14 dataset. The result shows that our method is comparable to other state-of-the-art single-model QA methods, and consistently outperforms each of the 14 used features.Availabilityhttps://github.com/Cao-Labs/SynthQA.gitContactcaora@plu.edu
Generalizing about GC (hypoxia): Dys- & Dat-InformaticaComment on“Germinal center B cells selectively oxidize fatty acids for energy while conducting minimal glycolysis”
Authors: Boothby Mark R. , Raybuck Ariel , Cho Sung Hoon , Stengel Kristy R. , Hiebert Scott , Li Jingxin 
Published: January 2021

AbstractSteadily accumulating evidence supports the concept that the outputs of immune responses are influenced by local nutrient and metabolite conditions or concentrations, as well as by the molecular programming of intermediary metabolism within immune cells. Humoral immunity and germinal center reactions are one setting in which these factors are under active investigation. Hypoxia has been highlighted as one example of how a particular nutrient is distributed in primary and secondary follicles during an antibody response, and how its sensors could impact the qualities of antibody output after immunization. Based on a bio-informatic analysis of mRNA levels in germinal center and other B cells, recently published work challenges the concept that there is any hypoxia or that it has any influence. In this perspective, we perform new analyses of published genomics data to explore potential sources of disparity and elucidate aspects of what on the surface might seem to be conflicting conclusions. In particular, the replicability and variance among data sets derived from different naïve as well as germinal center B cells are considered. The results of the investigation highlight several broader issues that merit consideration, especially at a time of heightened focus on scientific reports in the realm of immunity and antibody responses. From one finding of this re-analysis, it is proposed that a standard should be expected in which the relationship of new data sets compared to prior “fingerprints” of cell types should be reported transparently to referees and readers. In light of the strong evidence for diversity in the constituencies within germinal centers elicited by protein immunization, it also is proposed that a core practice should be to avoid overly broad conclusions about germinal centers in general when experimental systems are subject to substantial constraints imposed by technical features.
Hydroxide ions amplified by water entanglement underly the mechanism of general anesthesia
Authors: Qian Hao , Li Na , Yang Lei , Xu Younian , Chen Rong , Lu Dongshi , Zhao Ruihan , Liu Hui , Cui Nanxue , Zhou Qiao , Zhang Shihai 
Published: January 2021

AbstractIt is believed that inhaled anesthetics occupy hydrophobic pockets within target proteins, but how inhaled anesthetics with diverse shapes and sizes fit into highly structurally selective pockets is unknown. For hydroxide ions are hydrophobic, we determined whether hydroxide ions could bridge inhaled anesthetics and protein pockets. We found that small additional load of cerebral hydroxide ions decreases anesthetic potency. Multiple-water entanglement network, derived from Ising model, has a great ability to amplify ultralow changes in the cerebral hydroxide ion concentration, and consequently, amplified hydroxide ions account for neural excitability. Molecular dynamics simulations showed that inhaled anesthetics produce anesthesia by attenuating the formation of multiple-water entanglement network. This work suggests amplified hydroxide ions underlying a unified mechanism for the anesthetic action of inhaled anesthetics.
Improving antibody thermostability based on statistical analysis of sequence and structural consensus data
Authors: Jia Lei , Jain Mani , Sun Yaxiong 
Published: January 2021

AbstractThe use of Monoclonal Antibodies (MAbs) as therapeutics has been increasing over the past 30 years due to their high specificity and strong affinity towards the target. One of the major challenges towards their use as drugs is their low thermostability, which impacts both efficacy as well as manufacturing and delivery. To aid the design of thermally more stable mutants, consensus sequence-based method has been widely used. These methods typically have a success rate of about 50% with maximum melting temperature increment ranging from 10 to 32°C. In order to improve the prediction performance, we have developed a new and fast MAbs specific method by adding a 3D structural layer to the consensus sequence method. This is done by analyzing the close-by residue pairs which are conserved in more than eight hundred MAbs’ 3D structures. Major advantage of this structural level assessment is in significantly reducing the false positives by almost half from the consensus sequence method alone. In summary, combining consensus sequence and structural residue pair covariance methods, we developed an inhouse application for predicting human MAb thermostability to guide protein engineers to design more stable molecules. This application has shown success in designing MAb engineering panels in multiple biologics programs.
ACTIVA: realistic single-cell RNA-seq generation with automatic cell-type identification using introspective variational autoencoders
Authors: Heydari A. Ali , Davalos Oscar A. , Zhao Lihong , Hoyer Katrina K. , Sindi Suzanne S. 
Published: January 2021

MotivationSingle-cell RNA sequencing (scRNAseq) technologies allow for measurements of gene expression at a single-cell resolution. This provides researchers with a tremendous advantage for detecting heterogeneity, delineating cellular maps, or identifying rare subpopulations. However, a critical complication remains the low number of single-cell observations due to limitations by the rarity of a subpopulation, tissue degradation, or cost. This absence of sufficient data may cause inaccuracy or irreproducibility of downstream analysis. In this work, we present ACTIVA (Automated Cell-Type-informed Introspective Variational Autoencoder): a novel framework for generating realistic synthetic data using a single-stream adversarial variational autoencoder conditioned with cell-type information. Within a single framework, ACTIVA can generate data representative of the entire population, or specific subpopulations on demand, as opposed to two separate models (such as scGAN and cscGAN). Data generation and augmentation with ACTIVA can enhance scRNAseq pipelines and analysis, such as benchmarking new algorithms, studying the accuracy of classifiers, and detecting marker genes. ACTIVA will facilitate analysis of smaller datasets, potentially reducing the number of patients and animals necessary in initial studies.ResultsWe train and evaluate models on multiple public scRNAseq datasets. In comparison to GAN-based models (scGAN and cscGAN), we demonstrate that ACTIVA generates cells that are more realistic and harder for classifiers to identify as synthetic, which also have better pair-wise correlations between genes. We show that data augmentation with ACTIVA significantly improves the classification of rare subtypes (more than 45% improvement compared to not augmenting and 4% better than cscGAN) all while reducing training time by an order of magnitude in comparison to both models.Availability of data and codeLinks to raw, pre- and post-processed data, source code and tutorials are available at https://github.com/SindiLab.Supplementary informationSupplementary material can be found as a separate file with the same pre-print submission.
Multiple Monkey Pose Estimation Using OpenPose
Authors: Blanco Negrete Salvador , Labuguen Rollyn , Matsumoto Jumpei , Go Yasuhiro , Inoue Ken-ichi , Shibata Tomohiro 
Published: January 2021

AbstractThis paper proposes a system for pose estimation on monkeys, in diverse and challenging scenarios, and under complex social interactions by using OpenPose. In comparison to most animals used for research, Monkeys present additional difficulties for pose estimation. Multiple degrees of freedom, unique complex postures, intricated social interactions, among others. Our monkey OpenPose trained model is robust against these difficulties. It achieves similar performance as in human pose estimation models, and it can run in Realtime.
Mentorship as a strategy to improve research ability of students and young researchers in Africa: an exploratory study and initial findings on the CORE Africa Research Mentorship Scheme
Authors: Ngongalah Lem , Rawlings Ngwa Niba , Musisi James , Awanchiri Kimonia , Akwah Emmanuella , Ngeh Etienne , Ssemwanga Andrew 
Published: January 2021

AbstractMentorship provides an opportunity to support skill development, improve research ability, promote interest in research and offer career advice. The need for research mentorship in Africa is well-recognised. However, there is scarce literature on the development of such programmes and their potential impacts on students and young researchers in Africa (SYRIA). This study documents the development of the CORE Africa Research Mentorship Scheme (CARMS), and reports the outcomes and challenges experienced over a two-year period, from 2018 to 2020.41 mentors and mentees from Cameroon, Uganda and Nigeria participated in the programme. Mentors were based in Africa and the UK, while mentees were all based in Africa. Mentees gained knowledge and skills in various research areas including proposal writing, research methods, data analysis, report writing and research publication. Nine mentees gained their first research publication through the CARMS and three successfully completed their first grant proposal. Three mentees were supported by their mentor to develop PhD research proposals and five others gained skills that helped them secure paid research jobs. Eleven mentees have ‘graduated’ from the programme, seven of which are currently enrolled as trainee mentors. Mentees appreciated the opportunity to improve on their research skills and felt that the programme gave them a ‘safe’ environment to freely express their concerns.Main challenges encountered were: difficulty finding suitable mentors, communication barriers, poorly defined mentorship goals, dealing with mentee’s lack of knowledge/experience of ‘basic’ research concepts and funding limitations. This programme had several positive impacts on the knowledge and skills of mentees and demonstrates the importance of mentorship in research capacity strengthening. Sustaining such programmes requires investments in training and development, to ensure that mentees are continuously and adequately supported.
Engineering of a fluorescent chemogenetic reporter with tunable color for advanced live-cell imaging
Authors: Benaissa Hela , Ounoughi Karim , Aujard Isabelle , Fischer Evelyne , Goïame Rosette , Nguyen Julie , Tebo Alison G. , Li Chenge , Saux Thomas Le , Danglot Lydia , Pietrancosta Nicolas , Morin Xavier , Jullien Ludovic , Gautier Arnaud 
Published: January 2021

AbstractBiocompatible fluorescent reporters with spectral properties spanning the entire visible spectrum are indispensable tools for imaging the biochemistry of living cells and organisms in real time. Here, we present the engineering of a fluorescent chemogenetic reporter with tunable optical and spectral properties. A collection of live-cell compatible fluorogenic chromophores with various electronic properties enables to generate bimolecular fluorescent assemblies that cover the visible spectrum from blue to red using a single protein tag engineered and optimized by directed evolution and rational design. We showed that the ability to tune the fluorescence color and properties through simple molecular modulation provides an unprecedent experimental versatility for imaging proteins in live cells, including delicate cultured hippocampal neurons, and in multicellular organisms. The ability to tune the spectral properties and fluorescence performance enables to match the spectral specifications and requirements of the most advanced imaging techniques, and allowed us to achieve efficient stimulated emission depletion (STED) nanoscopy of fusion proteins in live cells and live primary cultured neurons.
Wild to domesticates: genomes of edible diploid bananas hold traces of several undefined genepools
Authors: Sardos Julie , Breton Catherine , Perrier Xavier , Van Den Houwe Ines , Paofa Janet , Rouard Mathieu , Roux Nicolas 
Published: January 2021

AbstractThis study is an unprecedent exploration of the diversity of 226 diploid bananas genotyped with restriction-site-associated DNA sequencing data (RADseq) to clarify the processes that led to the creation of edible diploid AA bananas. This wide set included 72 seedy bananas, mostlyM. acuminatafrom different genepools, and 154 edible, i.e. parthenocarpic and sterile, AA accessions obtained from genebanks and recent collecting missions. We highlighted the geographic organisation of the diversity of edible AAs and confirmed the admixed nature of many and further conducted introgressions tests within AAs from South East Asia and New Guinea. Lastly, taking advantage of the presence of an important number ofM. acuminatassp.banksii(22) and of AA from Papua New Guinea (76) in the set, we investigated the patterns of differentiation between wild and cultivated bananas seemingly belonging to the same genepool. We discovered a few cultivated AAs that may be of pure origins both in South-East Asia and in New Guinea. We also detected two undefined parental genepools in South East Asia for which regions of origin could be Thailand and a region between north Borneo and the Philippines, respectively. Finally, we suggest the existence of a third genepool in New Guinea island that might be a source population for both edible AAs and the localM. acuminatassp.banksii.
Midkine-a regulates the formation of a fibrotic scar during zebrafish heart regeneration
Authors: Grivas Dimitrios , González-Rajal Álvaro , la Pompa José Luis de 
Published: January 2021

AbstractThe adult zebrafish heart regenerates after injury, unlike the hearts of mammals. Heart cryoinjury triggers the formation of a fibrotic scar that gradually degrades, leading to regeneration. Midkine-a (Mdka) is a multifunctional cytokine that is activated after cardiac injury. Here, we investigated the role ofmdkain zebrafish heart regeneration. We show thatmdkaexpression is strongly induced at 1-day post cryoinjury (dpci) throughout the epicardium, whereas by 7 dpci expression has become restricted to epicardial cells covering the injured area. To study the role ofmdkain heart regeneration, we generatedmdka-knock out (KO) zebrafish strains. Analysis of injured hearts showed that loss ofmdkadecreased endothelial cell proliferation and resulted in a blockade of heart regeneration characterized by retention of a collagenous scar. Transcriptional analysis revealed increases in collagen transcription and TGFβ signalling activity. These results reveal a critical role formdkain fibrosis regulation during heart regeneration.
Tracing back variations in archaeal cell division to protein domain architectures
Authors: Frohn Béla , Härtel Tobias , Cox Jürgen , Schwille Petra 
Published: January 2021

SummaryThe Endosomal Sorting Complex Required for Transport (ESCRT) system is a multi-protein machinery that is involved in cell division of both Eukaryotes and Archaea. This spread across super-phyla suggests that a precursor ESCRT machinery existed already at an evolutionary early stage of life, making it a promising candidate for the (re)construction of a minimal cell division machinery. There are, however, only few experimental data about ESCRT machineries in Archaea, due to high technical challenges in cultivation and microscopy. Here, we analyse the proteins of archaeal ESCRT machineries bioinformatically on a domain level, to enable mechanistical comparison without such challenging experiments. First, we infer that there are at least three different cell division mechanisms utilizing ESCRT proteins in archaea, probably similar in their constriction mechanisms but different in membrane tethering. Second, we show that ESCRT proteins in Asgard archaea are highly similar to eukaryotic ESCRT proteins, strengthening the recently developed idea that all Eukaryotes descended from archaea. Third, we reconstruct a plausible evolutionary development of ESCRT machineries and suggest that a simple ESCRT-based constriction machinery existed in the last archaeal common ancestor. These findings not only give very interesting insights into the evolution of cell division in Archaea and Eukaryotes, but also reveal new research avenues by suggesting hypothesis-driven experiments for both, cell biology and bottom-up synthetic biology.
The stem cell-specific protein TRIM71 inhibits maturation and activity of the pro-differentiation miRNA let-7 via two independent molecular mechanisms
Authors: Torres-Fernández Lucia A. , Mitschka Sibylle , Ulas Thomas , Dahm Kilian , Becker Matthias , Händler Kristian , Windhausen Julia , Schultze Joachim L. , Kolanus Waldemar 
Published: January 2021

AbstractThe stem cell-specific RNA-binding protein TRIM71/LIN-41 was the first identified target of the pro-differentiation and tumor suppressor miRNA let-7. TRIM71 has essential functions in embryonic development and a proposed oncogenic role in several cancer types, such as hepatocellular carcinoma. Here, we show that TRIM71 regulates let-7 expression and activity via two independent mechanisms. On the one hand, TRIM71 enhances pre-let-7 degradation through its direct interaction with LIN28 and TUT4, thereby inhibiting let-7 maturation and indirectly promoting the stabilization of let-7 targets. On the other hand, TRIM71 represses the activity of mature let-7 via its RNA-dependent interaction with the RNA-Induced Silencing Complex (RISC) effector protein AGO2. We found that TRIM71 directly binds and stabilizes let-7 targets, suggesting that let-7 activity inhibition occurs on active RISCs. MiRNA enrichment analysis of several transcriptomic datasets from mouse embryonic stem cells and human hepatocellular carcinoma cells suggests that these let-7 regulatory mechanisms shape transcriptomic changes during developmental and oncogenic processes. Altogether, our work reveals a novel role for TRIM71 as a miRNA repressor and sheds light on the precise mechanistic dual regulation of let-7.
Development of first linkage map forSilphium integrifolium(Asteraceae) enables identification of sporophytic self-incompatibility locus
Authors: Price John H. , Raduski Andrew R. , Brandvain Yaniv , Van Tassel David L. , Smith Kevin P. 
Published: January 2021

SUMMARYSilphium integrifolium(Asteraceae) has been identified as a candidate for domestication as a perennial oilseed crop and has a sporophytic self-incompatibility system—the genetic basis of which is not well understood in theAsteraceae. To address this gap, we sought to map the genomic location of the self-recognition locus (S-locus) in this species.We used a biparental population and genotyping-by-sequencing to create the first genetic linkage map for this species. Then we developed a novel crossing scheme and set of analysis methods in order to infer S-locus genotypes for a subset of these individuals, allowing us to map the trait. Finally, we identified potential gene candidates using synteny analysis with the annual sunflower (Helianthus annuus) genome.Our linkage map contains 198 SNP markers and resolved into the correct number of linkage groups. We were able to successfully map the S-locus and identify several potential gene candidates in the sunflower syntenic region.Our method is effective and efficient, allowed us to map theS. integrifoliumS-locus using fewer resources than previous studies, and could be readily be applied to other species. Our best gene candidate appears to be worthy of future work inS. integrifoliumand otherAsteraceaespecies.