bluuebunny/crossref_metadata_2025_split huggingface dataset:
Spatial-linked alignment tool (SLAT) for aligning heterogenous slices properly
Authors: Xia Chen-Rui , Cao Zhi-Jie , Tu Xin-Ming , Gao Ge 
Published: April 2023

AbstractSpatially resolved omics technologies reveal the spatial organization of cells in various biological systems. Integrative and comparative analyses of spatial omics data depend on proper slice alignment, which should take both omics profiles and spatial context into account. Here we propose SLAT (Spatially-Linked Alignment Tool), a graph-based algorithm for efficient and effective alignment of spatial omics data. Adopting a graph adversarial matching strategy, SLAT is the first algorithm capable of aligning heterogenous spatial data across distinct technologies and modalities. Systematic benchmarks demonstrate SLAT’s superior precision, robustness, and speed vis à vis existing methods. Applications to multiple real-world datasets further show SLAT’s utility in enhancing cell-typing resolution, integrating multiple modalities for regulatory inference, and mapping fine-scale spatial-temporal changes during development. The full SLAT package is available athttps://github.com/gao-lab/SLAT.
A longitudinal resource for population neuroscience of school-age children and adolescents in China
Authors: Fan Xue-Ru , Wang Yin-Shan , Chang Da , Yang Ning , Rong Meng-Jie , Zhang Zhe , He Ye , Hou Xiaohui , Zhou Quan , Gong Zhu-Qing , Cao Li-Zhi , Dong Hao-Ming , Nie Jing-Jing , Chen Li-Zhen , Zhang Qing , Zhang Jia-Xin , Li Hui-Jie , Bao Min , Chen Antao , Chen Jing , Chen Xu , Ding Jinfeng , Dong Xue , Du Yi , Feng Chen , Feng Tingyong , Fu Xiaolan , Ge Li-Kun , Hong Bao , Hu Xiaomeng , Huang Wenjun , Jiang Chao , Li Li , Li Qi , Li Su , Liu Xun , Mo Fan , Qiu Jiang , Su Xue-Quan , Wei Gao-Xia , Wu Yiyang , Xia Haishuo , Yan Chao-Gan , Yan Zhi-Xiong , Yang Xiaohong , Zhang Wenfang , Zhao Ke , Zhu Liqi ,   Lifespan Brain Chart Consortium (LBCC),   Chinese Color Nest Consortium (CCNP), Zuo Xi-Nian 
Published: April 2023

ABSTRACTDuring the past decade, cognitive neuroscience has been calling for population diversity to address the challenge of validity and generalizability, ushering in a new era of population neuroscience. The developing Chinese Color Nest Project (devCCNP, 2013-2022), a ten-year pilot stage of the lifespan CCNP (2013-2032), is an ongoing project focusing on brain-mind development. The project aims to create and share a large-scale, longitudinal and multimodal dataset of typically developing children and adolescents (ages 6.0–17.9 at enrolment) in the Chinese population. The devCCNP houses not only phenotypes measured by demographic, biophysical, psychological and behavioural, cognitive, affective, and ocular-tracking assessments but also neurotypes measured with magnetic resonance imaging (MRI) of brain morphometry, resting-state function, naturalistic viewing function and diffusion structure. This Data Descriptor introduces the first data release of devCCNP including a total of 864 visits from 479 participants. Herein, we provided details of the experimental design, sampling strategies, and technical validation of the devCCNP resource. We demonstrate and discuss the potential of a multicohort longitudinal design to depict normative brain growth curves from the perspective of developmental population neuroscience. The devCCNP resource is shared as part of the “Chinese Data-sharing Warehouse forIn-vivoImaging Brain” in theChinese Color Nest Project (CCNP) – Lifespan Brain-Mind Development Data Community(https://www.scidb.cn/en/c/ccnp) at the Science Data Bank.Design TypesAccelerated longitudinal designBrain-mind developmentPopulation imagingBrain chartRepeated measureMeasurementsPsychological behavioursBiophysical and physical measuresIntelligence quotient measureNeuroimagingSample Characteristic - OrganismHomo sapiensSample Characteristic - EnvironmentSchool- and community-based sampleSample Characteristic - LocationChongqing and Beijing, ChinaDuration10 years (2013-2022)
PoseR - A deep learning toolbox for decoding animal behavior
Authors: Mullen Pierce N , Bowlby Beatrice , Armstrong Holly C , Zwart Maarten F 
Published: April 2023

AbstractThe actions of animals provide a window into how their minds work. Recent advances in deep learning are providing powerful approaches to recognize patterns of animal movement from video recordings, including markerless pose estimation models. However, tools to efficiently parse coordinates of animal position and pose into meaningful semantic behavioral labels are lacking. Here, we present PoseRecognition (PoseR), a behavioral decoder leveraging state- of-the-art action recognition models using spatio-temporal graph convolutional networks. We show that it can be used to decode animal behavior quickly and accurately from pose estimations, using zebrafish larvae and mice as model organisms. PoseR can be accessed using a Napari plugin, which facilitates efficient behavioral extraction, annotation, model training and deployment. We have simplified the workflow of behavioral analysis after pose estimation, transforming coordinates of animal position and pose into meaningful semantic behavioral labels, using methods designed for fast and accurate behavioral extraction, annotation, model training and deployment. Furthermore, we contribute a novel method for unsupervised clustering of behaviors and provide open-source access to our zebrafish datasets and models. The design of our tool ensures scalability and versatility for use across multiple species and contexts, improving the efficiency of behavioral analysis across fields.
Salt corrections for RNA secondary structures in the ViennaRNA package
Authors: Yao Hua-Ting , Lorenz Ronny , Hofacker Ivo L. , Stadler Peter F. 
Published: April 2023

AbstractBackgroundRNA features a highly negatively charged phosphate backbone that attracts a of cloud counter-ions that reduce the electrostatic repulsion in a concentration dependent manner. Ion concentrations thus have a large influence on folding and stability of RNA structures. Despite their well-documented effects, salt effects are not handled by currently available secondary stucture prediction algorithms. Combining Debye-Hückel potentials for line charges and Manning’s counter-ion condensation theory, Einertet al. [Biophys. J.100: 2745-2753 (2011)] modeled the energetic effects contributions monovalent cations on loops and helices.ResultsThe model of Einertet al. is adapted to match the structure of the dynamic programming recursion of RNA secondary structure prediction algorithms. An empirical term describing the dependence salt dependence of the duplex initiation energy is added to improve co-folding predictions for two or more RNA strands. The slightly modified model is implemented in theViennaRNApackage in such way that only the energy parameters but not the algorithmic structure is affected. A comparison with data from the literature show that predicted free energies and melting temperatures are in reasonable agreement with experiments.ConclusionThe new feature in theViennaRNApackage makes it possible to study effects of salt concentrations on RNA folding in a systematic manner. Strictly speaking, the model pertains only to mono-valent cations, and thus covers the most important parameter, i.e., the NaCl concentration. It remains a question for future research to what extent unspecific effects of bi- and tri-valent cations can be approximated in a similar manner.AvailabilityCorrections for the concentration of monovalent cations are available in theViennaRNApackage starting from version 2.6.0.
Analysis of Differential Gene Expression and Core Canonical Pathways involved in the Epithelial to Mesenchymal Transition of Triple Negative Breast Cancer Cells by Ingenuity Pathway Analysis
Authors: Bhattacharya Santanu , Ghosh Somiranjan , Banerjee Hirendra 
Published: April 2023

ABSTRACTTriple Negative Breast Cancer (TNBC) is a malignant form of cancer with very high mortality and morbidity. Epithelial to Mesenchymal Transition (EMT) is the most common pathophysiological change observed in cancer cells of epithelial origin that promotes metastasis, drug resistance and cancer stem cell formation. Since the information regarding differential gene expression in TNBC cells and cell signaling events leading to EMT is limited, this investigation was done by comparing transcriptomic data generated by RNA isolation and sequencing of a EMT model TNBC cell line in comparison to regular TNBC cells. RNA sequencing and Ingenuity Pathway Software Analysis (IPA) of the transcriptomic data revealed several upregulated and downregulated gene expressions along with novel core canonical pathways including Sirtuin signaling, Oxidative Phosphorylation and Mitochondrial dysfunction events involved in EMT changes of the TNBC cells.
Applying an interpretable machine learning approach to assess intraspecific trait variation under landscape-scale population differentiation
Authors: Majumder Sambadi , Mason Chase M. 
Published: April 2023

ABSTRACTPremiseHere we demonstrate the application of interpretable machine learning methods to investigate intraspecific functional trait divergence using diverse genotypes of the wide-ranging sunflowerHelianthus annuusoccupying populations across contrasting ecoregions - the Great Plains versus the North American Deserts.MethodsRecursive feature elimination was applied to functional trait data from the HeliantHome database, followed by the application of Boruta to detect traits most predictive of ecoregion. Random Forest and Gradient Boosting Machine classifiers were then trained and validated, with results visualized using accumulated local effects plots.Key ResultsThe most ecoregion-predictive functional traits span categories of leaf economics, plant architecture, reproductive phenology, and floral and seed morphology. Relative to the Great Plains, genotypes from the North American Deserts exhibit shorter stature, fewer leaves, higher leaf nitrogen, and longer average length of phyllaries.ConclusionsThis approach readily identifies traits predictive of ecoregion origin, and thus functional traits most likely to be responsible for contrasting ecological strategies across the landscape. This type of approach can be used to parse large plant trait datasets in a wide range of contexts, including explicitly testing the applicability of interspecific paradigms at intraspecific scales.
Inhibition of serotonin biosynthesis in neuroendocrine neoplasm suppresses tumor growthin vivo
Authors: Tow Dane H. , Tran Catherine G. , Borbon Luis C. , Ridder Maclain , Li Guiying , Kaemmer Courtney A. , Abusada Ellen , Mahalingam Aswanth Harish , Sadanandam Anguraj , Chandrasekaran Chandrikha , Dillon Joseph , Spitz Douglas R. , Quelle Dawn E. , Chan Carlos H.F. , Bellizzi Andrew , Howe James R. , Ear Po Hien 
Published: April 2023

AbstractSmall bowel neuroendocrine tumors (SBNETs) originate from enterochromaffin cells in the intestine which synthesize and secrete serotonin. SBNETs express high levels of tryptophan hydroxylase 1 (Tph1), a key enzyme in serotonin biosynthesis. Patients with high serotonin level may develop carcinoid syndrome, which can be treated with somatostatin analogues and the Tph1 inhibitor telotristat ethyl in severe cases. Although the active drug telotristat can efficiently reduce serotonin levels, its effect on tumor growth is unclear. This study determined the effect of serotonin inhibition on tumor cell growthin vitroandin vivo. The levels of Tph1 in various neuroendocrine neoplasms (NENs) were determined and the biological effects of Tph1 inhibitionin vitroandin vivousing genetic and pharmacologic approaches was tested. Gene and protein expression analyses were performed on patient tumors and cancer cell lines. shRNAs targetingTPH1were used to create stable knockdown in BON cells. Control and knockdown lines were assessed for their growth ratesin vitroandin vivo, angiogenesis potential, serotonin levels, endothelial cell tube formation, tumor weight, and tumor vascularity.TPH1is highly expressed in SBNETs and many cancer types.TPH1knockdown cells and telotristat treated cells showed similar growth rates as control cellsin vitro. However,TPH1knockdown cells formed smaller tumorsin vivoand tumors were less vascularized. Although Tph1 inhibition with telotristat showed no effect on tumor cell growthin vitro, Tph1 inhibition reduced tumor formationin vivo. Serotonin inhibition in combination with other therapies is a promising new avenue for targeting metabolic vulnerabilities in NENs.
Prediction of virus-host associations using protein language models and multiple instance learning
Authors: Liu Dan , Young Francesca , Robertson David L , Yuan Ke 
Published: April 2023

AbstractPredicting virus-host associations is essential to determine the specific host species that viruses interact with, and discover if new viruses infect humans and animals. Currently, the host of the majority of viruses is unknown, particularly in microbiomes. To address this challenge, we introduce EvoMIL, a deep learning method that predicts the host species for viruses from viral sequences only. It also identifies important viral proteins that significantly contribute to host prediction. The method combines a pre-trained large protein language model (ESM) and attention-based multiple instance learning to allow protein-orientated predictions. Our results show that protein embeddings capture stronger predictive signals than sequence composition features, including amino acids, physiochemical properties, and DNA k-mers. In multi-host prediction tasks, EvoMIL achieves median F1 score improvements of 8.6%, 12.3%, and 4.1% in prokaryotic hosts, and 0.5%, 1.8% and 3% in eukaryotic hosts. EvoMIL binary classifiers achieve impressive AUC over 0.95 for all prokaryotic and range from roughly 0.8 to 0.9 for eukaryotic hosts. Furthermore, EvoMIL estimates the importance of single proteins in the prediction task and maps them to an embedding landscape of all viral proteins, where proteins with similar functions are distinctly clustered together, highlighting the ability of EvoMIL to capture key proteins in virus-host specificity.Author summaryBeing able to predict which viruses can infect which host species, and identifying the specific proteins that are involved in these interactions, are fundamental tasks in virology. Traditional methods for predicting these interactions rely on common manual features among proteins, overlooking the structure of the protein ”language” encoded in individual proteins. We have developed a novel method that combines a protein language model and multiple instance learning to allow host prediction directly from protein sequences, without the need to extract manual features. This method significantly improved prediction accuracy and revealed key proteins involved in virus-host interactions.
The Chromatin Landscape of Pathogenic Transcriptional Cell States in Rheumatoid Arthritis
Authors: Weinand Kathryn , Sakaue Saori , Nathan Aparna , Jonsson Anna Helena , Zhang Fan , Watts Gerald F. M. , Zhu Zhu ,   Accelerating Medicines Partnership Program: 5 Rheumatoid Arthritis and Systemic Lupus Erythematosus (AMP RA/SLE) Network, Rao Deepak A. , Anolik Jennifer H. , Brenner Michael B. , Donlin Laura T. , Wei Kevin , Raychaudhuri Soumya 
Published: April 2023

AbstractSynovial tissue inflammation is the hallmark of rheumatoid arthritis (RA). Recent work has identified prominent pathogenic cell states in inflamed RA synovial tissue, such as T peripheral helper cells; however, the epigenetic regulation of these states has yet to be defined. We measured genome-wide open chromatin at single cell resolution from 30 synovial tissue samples, including 12 samples with transcriptional data in multimodal experiments. We identified 24 chromatin classes and predicted their associated transcription factors, including aCD8+GZMK+ class associated with EOMES and a lining fibroblast class associated with AP-1. By integrating an RA tissue transcriptional atlas, we found that the chromatin classes represented ‘superstates’ corresponding to multiple transcriptional cell states. Finally, we demonstrated the utility of this RA tissue chromatin atlas through the associations between disease phenotypes and chromatin class abundance as well as the nomination of classes mediating the effects of putatively causal RA genetic variants.
Pan-cancer analysis of the ion permeome reveals functional regulators of glioblastoma aggression
Authors: Bahcheli Alexander T. , Min Hyun-Kee , Bayati Masroor , Dong Weifan , Fortuna Alexander , Zhao Hongyu , Dzneladze Irakli , Chan Jade , Chen Xin , Guevara-Hoyer Kissy , Dirks Peter B. , Huang Xi , Reimand Jüri 
Published: April 2023

ABSTRACTIon channels, transporters, and other ion-permeating proteins, collectively comprising the ion permeome (IP), are common drug targets. However, their roles in cancer are understudied. Our integrative pan-cancer analysis shows that IP genes display highly-elevated expression patterns in subsets of cancer samples significantly more often than expected transcriptome-wide. To enable target identification, we identified 410 survival-associated IP genes in 29 cancer types using a machine learning approach. Notably,GJB2andSCN9Ashow prominent expression in neoplastic cells and associate with poor prognosis in glioblastoma (GBM), the most common and aggressive brain cancer.GJB2orSCN9Aknockdown in patient-derived GBM cells induces transcriptome-wide changes involving neural projection and proliferation pathways, impairs cell viability and tumor sphere formation, mitigates tunneling nanotube formation, and extends the survival of GBM-bearing mice. Thus, aberrant activation of IP genes appears as a pan-cancer feature of tumor heterogeneity that can be exploited for mechanistic insights and therapy development.
The Rous sarcoma virus Gag polyprotein forms biomolecular condensates driven by intrinsically-disordered regions
Authors: Maldonado Rebecca Kaddis , Rice Breanna L. , Lambert Gregory S. , Sudol Malgorzata , Flanagan John M. , Parent Leslie J. 
Published: April 2023

AbstractBiomolecular condensates (BMCs) play important roles in cellular structures including transcription factories, splicing speckles, and nucleoli. BMCs bring together proteins and other macromolecules, selectively concentrating them so that specific reactions can occur without interference from the surrounding environment. BMCs are often made up of proteins that contain intrinsically disordered regions (IDRs), form phase-separated spherical puncta, form liquid-like droplets that undergo fusion and fission, contain molecules that are mobile, and are disrupted with phase-dissolving drugs such as 1,6-hexanediol. In addition to cellular proteins, many viruses, including influenza A, SARS-CoV-2, and human immunodeficiency virus type 1 (HIV-1) encode proteins that undergo phase separation and rely on BMC formation for replication. In prior studies of the retrovirus Rous sarcoma virus (RSV), we observed that the Gag protein forms discrete spherical puncta in the nucleus, cytoplasm, and at the plasma membrane that co-localize with viral RNA and host factors, raising the possibility that RSV Gag forms BMCs that participate in the virion intracellular assembly pathway. In our current studies, we found that Gag contains IDRs in the N-terminal (MAp2p10) and C-terminal (NC) regions of the protein and fulfills many criteria of BMCs. Although the role of BMC formation in RSV assembly requires further study, our results suggest the biophysical properties of condensates are required for the formation of Gag complexes in the nucleus and the cohesion of these complexes as they traffic through the nuclear pore, into the cytoplasm, and to the plasma membrane, where the final assembly and release of virus particles occurs.
Perceptual Transitions between Object Rigidity & Non-rigidity: Competition and cooperation between motion-energy, feature-tracking and shape-based priors
Authors: Maruya Akihito , Zaidi Qasim 
Published: April 2023

AbstractWhy do moving objects appear rigid when projected retinal images are deformed non-rigidly? We used rotating rigid objects that can appear rigid or non-rigid to test whether shape features contribute to rigidity perception. When two circular rings were rigidly linked at an angle and jointly rotated at moderate speeds, observers reported that the rings wobbled and were not linked rigidly but rigid rotation was reported at slow speeds. When gaps, paint or vertices were added, the rings appeared rigidly rotating even at moderate speeds. At high speeds, all configurations appeared non-rigid. Salient features thus contribute to rigidity at slow and moderate speeds, but not at high speeds. Simulated responses of arrays of motion-energy cells showed that motion flow vectors are predominantly orthogonal to the contours of the rings, not parallel to the rotation direction. A convolutional neural network trained to distinguish flow patterns for wobbling versus rotation, gave a high probability of wobbling for the motion-energy flows. However, the CNN gave high probabilities of rotation for motion flows generated by tracking features with arrays of MT pattern-motion cells and corner detectors. In addition, circular rings can appear to spin and roll despite the absence of any sensory evidence, and this illusion is prevented by vertices, gaps, and painted segments, showing the effects of rotational symmetry and shape. Combining CNN outputs that give greater weight to motion energy at fast speeds and to feature tracking at slow, with the shape-based priors for wobbling and rolling, explained rigid and nonrigid percepts across shapes and speeds (R2=0.95). The results demonstrate how cooperation and competition between different neuronal classes leads to specific states of visual perception and to transitions between the states.