Taila Hartley, Deborah Marshall, Meryl Acker, Katharine Fooks, Meredith K Gillespie, E Magda Price, Ian D Graham, Alexandre White-Brown, Layla MacKay, Stella K Macdonald, Lauren Brady, Angela Y Hui, Joseph D Andrews, Ashfia Chowdhury, Erika Wall, Élisabeth Soubry, Grace U Ediae, Samantha Rojas, Daniel Assamad, David Dyment, Mark Tarnopolsky, Sarah L Sawyer, Caitlin Chisholm, Gabrielle Lemire, Kimberly Amburgey, Joanna Lazier, Roberto Mendoza-Londono, James J Dowling, Tugce B Balci, Christine M Armour, Priya T Bhola, Gregory Costain, Lucie Dupuis, Melissa Carter, Lauren Badalato, Julie Richer, Christie Boswell-Patterson, Peter Kannu, Dawn Cordeiro, Jodi Warman-Chardon, Gail Graham, Victoria Mok Siu, Cheryl Cytrynbaum, Alison Rusnak, Ritu B Aul, Grace Yoon, Hernan Gonorazky, Vanda McNiven, Saadet Mercimek-Andrews, Andrea Guerin, Ashish R Deshwar, Ashish Marwaha, Rosanna Weksberg, Natalya Karp, Maggie Campbell, Sarah Al-Qattan, Andrew Y Shuen, Michal Inbar-Feigenberg, Ronald Cohn, Anna Szuto, Cara Inglese, Myriam Poirier, Lauren Chad, Beth Potter, Kym M Boycott, Robin Hayeems, Care4Rare Canada Consortium <<<

PURPOSE: To evaluate the diagnostic utility of publicly funded clinical exome sequencing (ES) for patients with suspected rare genetic diseases.METHODS: We prospectively enrolled 297 probands who met eligibility criteria and received ES across 5 sites in Ontario, Canada, and extracted data from medical records and clinician surveys. Using the Fryback and Thornbury Efficacy Framework, we assessed diagnostic accuracy by examining laboratory interpretation of results and assessed diagnostic thinking by examining the clinical interpretation of results and whether clinical-molecular diagnoses would have been achieved via alternative hypothetical molecular tests.RESULTS: Laboratories reported 105 molecular diagnoses and 165 uncertain results in known and novel genes. Of these, clinicians interpreted 102 of 105 (97%) molecular diagnoses and 6 of 165 (4%) uncertain results as clinical-molecular diagnoses. The 108 clinical-molecular diagnoses were in 104 families (35% diagnostic yield). Each eligibility criteria resulted in diagnostic yields of 30% to 40%, and higher yields were achieved when >2 eligibility criteria were met (up to 45%). Hypothetical tests would have identified 61% of clinical-molecular diagnoses.CONCLUSION: We demonstrate robustness in eligibility criteria and high clinical validity of laboratory results from ES testing. The importance of ES was highlighted by the potential 40% of patients that would have gone undiagnosed without this test. <<<

Double-negative T (DNT) cells are a rare and unconventional T-lymphocyte subpopulation lacking both CD4 and CD8 markers. Their immunopathological roles and clinical relevance have yet to be elucidated. Beyond autoimmune lymphoproliferative syndrome (ALPS), these cells may also play a role in rheumatic disorders, including systemic lupus erythematosus (SLE); indeed, these two diseases share several autoimmune manifestations (including nephritis). Moreover, one of the main experimental murine models used to investigate lupus, namely the MRL/lpr mouse, is characterized by an expansion of DNT cells, which can support the production of pathogenic autoantibodies and/or modulate the immune response in this context. However, lupus murine models are not completely consistent with their human SLE counterpart, of course. In this mini review, we summarize and analyze the most relevant clinical studies investigating the DNT cell population in SLE patients. Overall, based on the present literature review and analysis, DNT cell homeostasis seems to be altered in patients with SLE. Indeed, most of the available clinical studies (which include both adults and children) reported an increased DNT cell percentage in SLE patients, especially during the active phases, even though no clear correlation with disease activity and/or inflammatory parameters has been clearly established. Well-designed, standardized, and longitudinal clinical studies focused on DNT cell population are needed, in order to further elucidate the actual contribution of these cells in SLE pathogenesis and their interactions with other immune cells (also implicated and/or altered in SLE, such as basophils), and clarify whether their expansion and/or immunophenotypic aspects may have any immunopathological relevance (and, then, represent potential disease markers and, in perspective, even therapeutic targets) or are just an unspecific epiphenomenon of autoimmunity. <<<

Microplastics have been detected in various media including water, sediment, and seafood, whereas there are few studies focusing on microplastics in take-out containers. In this study, we collected take-out containers made of common polymer materials (polypropylene, PP; polystyrene, PS; polyethylene, PE; polyethylene terephthalate, PET) from five cities in China. Microplastics in the containers were analyzed after different treatments (direct flushing and flushing after immersing with hot water). Our results showed that microplastics were found in all take-out containers and abundance ranged from 3 to 29 items/container. The highest abundance occurred in PS containers with rough surface. The polymer types of some detected particles were the same as those of original containers, accounting for 30% of the total microplastics; other types included polyester, rayon, acrylic, and nylon. Treating the containers with hot water did not influence microplastic abundance. Our study indicates that microplastics in take-out containers come from atmospheric fallout and flakes from container's inner surfaces. Under slight mechanical force, loose structure and rough surface of PS containers can flake off microplastics, entering water more easily. Based on the microplastic abundance in take-out containers, people who order take-out food 4-7 times weekly may ingest 12-203 pieces of microplastics through containers. <<<

AIMS: Major depression disorder (MDD) is the single greatest cause of disability and morbidity, and affects about 10% of the population worldwide. Currently, there are no clinically useful diagnostic biomarkers that are able to confirm a diagnosis of MDD from bipolar disorder (BD) in the early depressive episode. Therefore, exploring translational biomarkers of mood disorders based on machine learning is in pressing need, though it is challenging, but with great potential to improve our understanding of these disorders.DISCUSSIONS: In this study, we review popular machine-learning methods used for brain imaging classification and predictions, and provide an overview of studies, specifically for MDD, that have used magnetic resonance imaging data to either (a) classify MDDs from controls or other mood disorders or (b) investigate treatment outcome predictors for individual patients. Finally, challenges, future directions, and potential limitations related to MDD biomarker identification are also discussed, with a goal of offering a comprehensive overview that may help readers to better understand the applications of neuroimaging data mining in depression.CONCLUSIONS: We hope such efforts may highlight the need for an urgently needed paradigm shift in treatment, to guide personalized optimal clinical care. <<<

Xin Wei, Jie Qiu, Kaicheng Yong, Jiongjiong Fan, Qi Zhang, Hua Hua, Jie Liu, Qin Wang, Kenneth M Olsen, Bin Han, Xuehui Huang <<<

Extensive allelic variation in agronomically important genes serves as the basis of rice breeding. Here, we present a comprehensive map of rice quantitative trait nucleotides (QTNs) and inferred QTN effects based on eight genome-wide association study cohorts. Population genetic analyses revealed that domestication, local adaptation and heterosis are all associated with QTN allele frequency changes. A genome navigation system, RiceNavi, was developed for QTN pyramiding and breeding route optimization, and implemented in the improvement of a widely cultivated indica variety. This work presents an efficient platform that bridges ever-increasing genomic knowledge and diverse improvement needs in rice. <<<

Deepak R. Unni, Sierra A. T. Moxon, Michael Bada, Matthew Brush, Richard Bruskiewich, Paul Clemons, Vlado Dancik, Michel Dumontier, Karamarie Fecho, Gustavo Glusman, Jennifer J. Hadlock, Nomi L. Harris, Arpita Joshi, Tim Putman, Guangrong Qin, Stephen A. Ramsey, Kent A. Shefchek, Harold Solbrig, Karthik Soman, Anne T. Thessen, Melissa A. Haendel, Chris Bizon, Christopher J. Mungall, the Biomedical Data Translator Consortium <<<

Within clinical, biomedical, and translational science, an increasing numberof projects are adopting graphs for knowledge representation. Graph-based datamodels elucidate the interconnectedness between core biomedical concepts,enable data structures to be easily updated, and support intuitive queries,visualizations, and inference algorithms. However, knowledge discovery acrossthese "knowledge graphs" (KGs) has remained difficult. Data set heterogeneityand complexity; the proliferation of ad hoc data formats; poor compliance withguidelines on findability, accessibility, interoperability, and reusability;and, in particular, the lack of a universally-accepted, open-access model forstandardization across biomedical KGs has left the task of reconciling datasources to downstream consumers. Biolink Model is an open source data modelthat can be used to formalize the relationships between data structures intranslational science. It incorporates object-oriented classification andgraph-oriented features. The core of the model is a set of hierarchical,interconnected classes (or categories) and relationships between them (orpredicates), representing biomedical entities such as gene, disease, chemical,anatomical structure, and phenotype. The model provides class and edgeattributes and associations that guide how entities should relate to oneanother. Here, we highlight the need for a standardized data model for KGs,describe Biolink Model, and compare it with other models. We demonstrate theutility of Biolink Model in various initiatives, including the Biomedical DataTranslator Consortium and the Monarch Initiative, and show how it has supportedeasier integration and interoperability of biomedical KGs, bringing togetherknowledge from multiple sources and helping to realize the goals oftranslational science. <<<

Christina V Theodoris, Ling Xiao, Anant Chopra, Mark D Chaffin, Zeina R Al Sayed, Matthew C Hill, Helene Mantineo, Elizabeth M Brydon, Zexian Zeng, X Shirley Liu, Patrick T Ellinor <<<

Mapping gene networks requires large amounts of transcriptomic data to learn the connections between genes, which impedes discoveries in settings with limited data, including rare diseases and diseases affecting clinically inaccessible tissues. Recently, transfer learning has revolutionized fields such as natural language understanding and computer vision by leveraging deep learning models pretrained on large-scale general datasets that can then be fine-tuned towards a vast array of downstream tasks with limited task-specific data. Here, we developed a context-aware, attention-based deep learning model, Geneformer, pretrained on a large-scale corpus of about 30 million single-cell transcriptomes to enable context-specific predictions in settings with limited data in network biology. During pretraining, Geneformer gained a fundamental understanding of network dynamics, encoding network hierarchy in the attention weights of the model in a completely self-supervised manner. Fine-tuning towards a diverse panel of downstream tasks relevant to chromatin and network dynamics using limited task-specific data demonstrated that Geneformer consistently boosted predictive accuracy. Applied to disease modelling with limited patient data, Geneformer identified candidate therapeutic targets for cardiomyopathy. Overall, Geneformer represents a pretrained deep learning model from which fine-tuning towards a broad range of downstream applications can be pursued to accelerate discovery of key network regulators and candidate therapeutic targets. <<<

Zi-Ning Choo, Julie M Behr, Aditya Deshpande, Kevin Hadi, Xiaotong Yao, Huasong Tian, Kaori Takai, George Zakusilo, Joel Rosiene, Arnaud Da Cruz Paula, Britta Weigelt, Jeremy Setton, Nadeem Riaz, Simon N Powell, Klaus Busam, Alexander N Shoushtari, Charlotte Ariyan, Jorge Reis-Filho, Titia de Lange, Marcin Imieliński <<<

Short-read sequencing is the workhorse of cancer genomics yet is thought to miss many structural variants (SVs), particularly large chromosomal alterations. To characterize missing SVs in short-read whole genomes, we analyzed 'loose ends'-local violations of mass balance between adjacent DNA segments. In the landscape of loose ends across 1,330 high-purity cancer whole genomes, most large (>10-kb) clonal SVs were fully resolved by short reads in the 87% of the human genome where copy number could be reliably measured. Some loose ends represent neotelomeres, which we propose as a hallmark of the alternative lengthening of telomeres phenotype. These pan-cancer findings were confirmed by long-molecule profiles of 38 breast cancer and melanoma cases. Our results indicate that aberrant homologous recombination is unlikely to drive the majority of large cancer SVs. Furthermore, analysis of mass balance in short-read whole genome data provides a surprisingly complete picture of cancer chromosomal structure. <<<

Genome-wide chromatin conformation capture assays provide formidable insights into the spatial organization of genomes. However, due to the complexity of the data structure, their integration in multi-omics workflows remains challenging. We present data structures, computational methods and visualization tools available in Bioconductor to investigate Hi-C, micro-C and other 3C-related data, in R. An online book ( https://bioconductor.org/books/OHCA/ ) further provides prospective end users with a number of workflows to process, import, analyze and visualize any type of chromosome conformation capture data. <<<

Flow-based propagation and spatiotemporal Transformer are two mainstreammechanisms in video inpainting (VI). Despite the effectiveness of thesecomponents, they still suffer from some limitations that affect theirperformance. Previous propagation-based approaches are performed separatelyeither in the image or feature domain. Global image propagation isolated fromlearning may cause spatial misalignment due to inaccurate optical flow.Moreover, memory or computational constraints limit the temporal range offeature propagation and video Transformer, preventing exploration ofcorrespondence information from distant frames. To address these issues, wepropose an improved framework, called ProPainter, which involves enhancedProPagation and an efficient Transformer. Specifically, we introducedual-domain propagation that combines the advantages of image and featurewarping, exploiting global correspondences reliably. We also propose amask-guided sparse video Transformer, which achieves high efficiency bydiscarding unnecessary and redundant tokens. With these components, ProPainteroutperforms prior arts by a large margin of 1.46 dB in PSNR while maintainingappealing efficiency. <<<

Generative pretrained models have achieved remarkable success in various domains such as language and computer vision. Specifically, the combination of large-scale diverse datasets and pretrained transformers has emerged as a promising approach for developing foundation models. Drawing parallels between language and cellular biology (in which texts comprise words; similarly, cells are defined by genes), our study probes the applicability of foundation models to advance cellular biology and genetic research. Using burgeoning single-cell sequencing data, we have constructed a foundation model for single-cell biology, scGPT, based on a generative pretrained transformer across a repository of over 33 million cells. Our findings illustrate that scGPT effectively distills critical biological insights concerning genes and cells. Through further adaptation of transfer learning, scGPT can be optimized to achieve superior performance across diverse downstream applications. This includes tasks such as cell type annotation, multi-batch integration, multi-omic integration, perturbation response prediction and gene network inference. <<<

Xi Cheng, Haroon Popal, Huanqing Wang, Renfen Hu, Yinyin Zang, Mingzhe Zhang, Mark Allen Thornton, Huajian Cai, Yanchao Bi, James Reilly, Ingrid R. Olson, Yin Wang <<<

A defining characteristic of social complexity in Homo sapiens is the diversity of our relationships. We build various types of connections with people in families, schools, workplaces, neighborhoods, and online communities. How do we make sense of such complex systems of human relationships? By using natural language processing, online surveys, laboratory cognitive tasks, and computational modelling on diverse modern cultures across the world (n = 20,425) and ancient cultures across 3,000 years of history, we discovered a universal representational space of relationship concepts, comprised of five principal dimensions (formality, activeness, valence, exchange, equality) and three core categories (hostile, public and private relationships). Our work reveals the fundamental cognitive constructs and cultural principles of relationship knowledge and advances our understanding of human sociality. <<<

Topological Data Analysis is a recent and fast growing field providing a setof new topological and geometric tools to infer relevant features for possiblycomplex data. This paper is a brief introduction, through a few selectedtopics, to basic fundamental and practical aspects of \tda\ for non experts. <<<

Ruichen Li, Haotian Ye, Du Jiang, Xuelan Wen, Chuwei Wang, Zhe Li, Xiang Li, Di He, Ji Chen, Weiluo Ren, Liwei Wang <<<

PURPOSE: To develop and demonstrate the efficacy of a novel head-and-neck multimodality image registration technique using deep-learning-based cross-modality synthesis.METHODS AND MATERIALS: Twenty-five head-and-neck patients received magnetic resonance (MR) and computed tomography (CT) (CTaligned ) scans on the same day with the same immobilization. Fivefold cross validation was used with all of the MR-CT pairs to train a neural network to generate synthetic CTs from MR images. Twenty-four of 25 patients also had a separate CT without immobilization (CTnon-aligned ) and were used for testing. CTnon-aligned 's were deformed to the synthetic CT, and compared to CTnon-aligned registered to MR. The same registrations were performed from MR to CTnon-aligned and from synthetic CT to CTnon-aligned . All registrations used B-splines for modeling the deformation, and mutual information for the objective. Results were evaluated using the 95% Hausdorff distance among spinal cord contours, landmark error, inverse consistency, and Jacobian determinant of the estimated deformation fields.RESULTS: When large initial rigid misalignment is present, registering CT to MRI-derived synthetic CT aligns the cord better than a direct registration. The average landmark error decreased from 9.8 ± 3.1 mm in MR→CTnon-aligned to 6.0 ± 2.1 mm in CTsynth →CTnon-aligned deformable registrations. In the CT to MR direction, the landmark error decreased from 10.0 ± 4.3 mm in CTnon-aligned →MR deformable registrations to 6.6 ± 2.0 mm in CTnon-aligned →CTsynth deformable registrations. The Jacobian determinant had an average value of 0.98. The proposed method also demonstrated improved inverse consistency over the direct method.CONCLUSIONS: We showed that using a deep learning-derived synthetic CT in lieu of an MR for MR→CT and CT→MR deformable registration offers superior results to direct multimodal registration. <<<

Mathias Munschauer, Celina T Nguyen, Klara Sirokman, Christina R Hartigan, Larson Hogstrom, Jesse M Engreitz, Jacob C Ulirsch, Charles P Fulco, Vidya Subramanian, Jenny Chen, Monica Schenone, Mitchell Guttman, Steven A Carr, Eric S Lander <<<

The human genome contains thousands of long non-coding RNAs, but specific biological functions and biochemical mechanisms have been discovered for only about a dozen. A specific long non-coding RNA-non-coding RNA activated by DNA damage (NORAD)-has recently been shown to be required for maintaining genomic stability, but its molecular mechanism is unknown. Here we combine RNA antisense purification and quantitative mass spectrometry to identify proteins that directly interact with NORAD in living cells. We show that NORAD interacts with proteins involved in DNA replication and repair in steady-state cells and localizes to the nucleus upon stimulation with replication stress or DNA damage. In particular, NORAD interacts with RBMX, a component of the DNA-damage response, and contains the strongest RBMX-binding site in the transcriptome. We demonstrate that NORAD controls the ability of RBMX to assemble a ribonucleoprotein complex-which we term NORAD-activated ribonucleoprotein complex 1 (NARC1)-that contains the known suppressors of genomic instability topoisomerase I (TOP1), ALYREF and the PRPF19-CDC5L complex. Cells depleted for NORAD or RBMX display an increased frequency of chromosome segregation defects, reduced replication-fork velocity and altered cell-cycle progression-which represent phenotypes that are mechanistically linked to TOP1 and PRPF19-CDC5L function. Expression of NORAD in trans can rescue defects caused by NORAD depletion, but rescue is significantly impaired when the RBMX-binding site in NORAD is deleted. Our results demonstrate that the interaction between NORAD and RBMX is important for NORAD function, and that NORAD is required for the assembly of the previously unknown topoisomerase complex NARC1, which contributes to maintaining genomic stability. In addition, we uncover a previously unknown function for long non-coding RNAs in modulating the ability of an RNA-binding protein to assemble a higher-order ribonucleoprotein complex. <<<

Temporal binding has been understood as an illusion in timing judgment. When an action triggers an outcome (e.g. a sound) after a brief delay, the action is reported to occur later than if the outcome does not occur, and the outcome is reported to occur earlier than a similar outcome not caused by an action. We show here that an attention mechanism underlies the seeming illusion of timing judgment. In one method, participants watch a rotating clock hand and report event times by noting the clock hand position when the event occurs. We find that visual spatial attention is critically involved in shaping event time reports made in this way. This occurs because action and outcome events result in shifts of attention around the clock rim, thereby biasing the perceived location of the clock hand. Using a probe detection task to measure attention, we show a difference in the distribution of visual spatial attention between a single-event condition (sound only or action only) and a two-event agency condition (action plus sound). Participants accordingly report the timing of the same event (the sound or the action) differently in the two conditions: spatial attentional shifts masquerading as temporal binding. Furthermore, computational modeling based on the attention measure can reproduce the temporal binding effect. Studies that use time judgment as an implicit marker of voluntary agency should first discount the artefactual changes in event timing reports that actually reflect differences in spatial attention. The study also has important implications for related results in mental chronometry obtained with the clock-like method since Wundt, as attention may well be a critical confounding factor in the interpretation of these studies. <<<

Both IL-17A and IL-22 share cellular sources and signaling pathways. They have synergistic action on epithelial cells to stimulate their production of antimicrobial peptides which are protective against infections. However, both interleukins may contribute to ARDS pathology if their production is not controlled. This study aimed to investigate serum levels of IL-17A and IL-22 in relation to the disease outcome in patients with SARS-CoV-2. Serum IL-17A and IL-22 were measured by ELISA in 40 patients with SARS-CoV-2, aged between 2 months and 16 years, (18 had COVID-19 and 22 had multisystem inflammatory syndrome in children "MIS-C") in comparison to 48 age- and sex-matched healthy control children. Patients with COVID-19 and MIS-C had significantly higher serum IL-17A and IL-22 levels than healthy control children (P < 0.001). Increased serum IL-17A and IL-22 levels were found in all patients. Elevated CRP and serum ferritin levels were found in 90% of these patients. Lymphopenia, neutrophilia, neutropenia, thrombocytopenia and elevated ALT, LDH and D-dimer were found in 45%, 42.5 %, 2.5%, 30%, 32.5%, 82.5%, and 65%, respectively of these patients. There were non-significant differences between patients who recovered and those who died or had a residual illness in serum levels of IL-17A, IL-22 and the routine inflammatory markers of COVID-19. In conclusions, serum IL-17A and IL-22 levels were up-regulated in all patients with COVID-19 and MIS-C. Levels of serum IL-17A, IL-22 and the routine inflammatory markers of COVID-19 were not correlated with the disease outcome. Our conclusions are limited by the sample size. <<<