Danillo G Augusto, Lawton D Murdolo, Demetra S M Chatzileontiadou, Joseph J Sabatino, Tasneem Yusufali, Noah D Peyser, Xochitl Butcher, Kerry Kizer, Karoline Guthrie, Victoria W Murray, Vivian Pae, Sannidhi Sarvadhavabhatla, Fiona Beltran, Gurjot S Gill, Kara L Lynch, Cassandra Yun, Colin T Maguire, Michael J Peluso, Rebecca Hoh, Timothy J Henrich, Steven G Deeks, Michelle Davidson, Scott Lu, Sarah A Goldberg, J Daniel Kelly, Jeffrey N Martin, Cynthia A Vierra-Green, Stephen R Spellman, David J Langton, Michael J Dewar-Oldis, Corey Smith, Peter J Barnard, Sulggi Lee, Gregory M Marcus, Jeffrey E Olgin, Mark J Pletcher, Martin Maiers, Stephanie Gras, Jill A Hollenbach <<<

Studies have demonstrated that at least 20% of individuals infected with SARS-CoV-2 remain asymptomatic. Although most global efforts have focused on severe illness in COVID-19, examining asymptomatic infection provides a unique opportunity to consider early immunological features that promote rapid viral clearance. Here, postulating that variation in the human leukocyte antigen (HLA) loci may underly processes mediating asymptomatic infection, we enrolled 29,947 individuals, for whom high-resolution HLA genotyping data were available, in a smartphone-based study designed to track COVID-19 symptoms and outcomes. Our discovery cohort (n = 1,428) comprised unvaccinated individuals who reported a positive test result for SARS-CoV-2. We tested for association of five HLA loci with disease course and identified a strong association between HLA-B*15:01 and asymptomatic infection, observed in two independent cohorts. Suggesting that this genetic association is due to pre-existing T cell immunity, we show that T cells from pre-pandemic samples from individuals carrying HLA-B*15:01 were reactive to the immunodominant SARS-CoV-2 S-derived peptide NQKLIANQF. The majority of the reactive T cells displayed a memory phenotype, were highly polyfunctional and were cross-reactive to a peptide derived from seasonal coronaviruses. The crystal structure of HLA-B*15:01-peptide complexes demonstrates that the peptides NQKLIANQF and NQKLIANAF (from OC43-CoV and HKU1-CoV) share a similar ability to be stabilized and presented by HLA-B*15:01. Finally, we show that the structural similarity of the peptides underpins T cell cross-reactivity of high-affinity public T cell receptors, providing the molecular basis for HLA-B*15:01-mediated pre-existing immunity. <<<

PURPOSE: Deformable image registration (DIR) plays an important role in assisting disease diagnosis. The emergence of the Transformer enables the DIR framework to extract long-range dependencies, which relieves the limitations of intrinsic locality caused by convolution operation. However, suffering from the interference of missing or spurious connections, it is a challenging task for Transformer-based methods to capture the high-quality long-range dependencies.METHODS: In this paper, by staking the graph convolution Transformer (Graformer) layer at the bottom of the feature extraction network, we propose a Graformer-based DIR framework, named GraformerDIR. The Graformer layer is consist of the Graformer module and the Cheby-shev graph convolution module. Among them, the Graformer module is designed to capture high-quality long-range dependencies. Cheby-shev graph convolution module is employed to further enlarge the receptive field.RESULTS: The performance and generalizability of GraformerDIR have been evaluated on publicly available brain datasets including the OASIS, LPBA40, and MGH10 datasets. Compared with VoxelMorph, the GraformerDIR has obtained performance improvements of 4.6% in Dice similarity coefficient (DSC) and 0.055 mm in the average symmetric surface distance (ASD) while reducing the non-positive rate of Jacobin determinant (Npr.Jac) index about 60 times on publicly available OASIS dataset. On unseen dataset MGH10, the GraformerDIR has obtained the performance improvements of 4.1% in DSC and 0.084 mm in ASD compared with VoxelMorph, which demonstrates the GraformerDIR with better generalizability. The promising performance on the clinical cardiac dataset ACDC indicates the GraformerDIR is practicable.CONCLUSION: With the advantage of Transformer and graph convolution, the GraformerDIR has obtained comparable performance with the state-of-the-art method VoxelMorph. <<<

Chunhui Li, Honghui Guan, Xin Jing, Yaoyao Li, Baobao Wang, Yongxiang Li, Xuyang Liu, Dengfeng Zhang, Cheng Liu, Xiaoqing Xie, Haiyan Zhao, Yanbo Wang, Jingbao Liu, Panpan Zhang, Guanghui Hu, Guoliang Li, Suiyan Li, Dequan Sun, Xiaoming Wang, Yunsu Shi, Yanchun Song, Chengzhi Jiao, Jeffrey Ross-Ibarra, Yu Li, Tianyu Wang, Haiyang Wang <<<

Single-cross maize hybrids display superior heterosis and are produced from crossing two parental inbred lines belonging to genetically different heterotic groups. Here we assembled 1,604 historically utilized maize inbred lines belonging to various female heterotic groups (FHGs) and male heterotic groups (MHGs), and conducted phenotyping and genomic sequencing analyses. We found that the FHGs and MHGs have undergone both convergent and divergent changes for different sets of agronomic traits. Using genome-wide selection scans and association analyses, we identified a large number of candidate genes that contributed to the improvement of agronomic traits of the FHGs and MHGs. Moreover, we observed increased genetic differentiation between the FHGs and MHGs across the breeding eras, and we found a positive correlation between increasing heterozygosity levels in the differentiated genes and heterosis in hybrids. Furthermore, we validated the function of two selected genes and a differentiated gene. This study provides insights into the genomic basis of modern hybrid maize breeding. <<<

Isothermal autocatalytic DNA circuits have been proven to be versatile and powerful biocomputing platforms by virtue of their self-sustainable and self-accelerating reaction profiles, yet they are currently constrained by their complicated designs, severe signal leakages, and unclear reaction mechanisms. Herein, we developed a simpler-yet-efficient autocatalytic assembly circuit (AAC) for highly robust bioimaging in live cells and mice. The scalable and sustainable AAC system was composed of a mere catalytic DNA assembly reaction with minimal strand complexity and, upon specific stimulation, could reproduce numerous new triggers to expedite the whole reaction. Through in-depth theoretical simulations and systematic experimental demonstrations, the catalytic efficiency of these reproduced triggers was found to play a vital role in the autocatalytic profile and thus could be facilely improved to achieve more efficient and characteristic autocatalytic signal amplification. Due to its exponentially high signal amplification and minimal reaction components, our self-stacking AAC facilitated the efficient detection of trace biomolecules with low signal leakage, thus providing great clinical diagnosis and therapeutic assessment potential. <<<

Long term particulate matter (PM) exposure has been associated with an increased incidence of respiratory diseases. Here, an in vitro model was developed to study how long term diesel exhaust particle (DEP) exposure might predispose to the development of allergic reactions. Airway epithelial (16HBE) cells were exposed to low concentrations of diesel exhaust particle (DEP) for 4 days after which they were challenged with house dust mite (HDM) extract (24 h). Compared to acute exposure (24 h), 4 days DEP exposure to 16HBE cells further reduced the transepithelial electrical resistance (TEER) and increased CXCL-8 release. DEP pre-exposure aggravated HDM-induced loss of TEER, increased tracer flux across the barrier and reduced CLDN-3 expression in these 16HBE cells. HDM-induced cytokine (IL-6, CCL-22, IL-10 and CXCL-8) release was significantly increased after DEP pre-exposure. In the current study an in vitro model with long term PM exposure was presented, which might be helpful for further understanding the interplay between long term PM exposure and allergic responses. <<<

Judit Kumuthini, Michael Chimenti, Sven Nahnsen, Alexander Peltzer, Rebone Meraba, Ross McFadyen, Gordon Wells, Deanne Taylor, Mark Maienschein-Cline, Jian-Liang Li, Jyothi Thimmapuram, Radha Murthy-Karuturi, Lyndon Zass <<<

Life scientists are increasingly turning to high-throughput sequencing technologies in their research programs, owing to the enormous potential of these methods. In a parallel manner, the number of core facilities that provide bioinformatics support are also increasing. Notably, the generation of complex large datasets has necessitated the development of bioinformatics support core facilities that aid laboratory scientists with cost-effective and efficient data management, analysis, and interpretation. In this article, we address the challenges-related to communication, good laboratory practice, and data handling-that may be encountered in core support facilities when providing bioinformatics support, drawing on our own experiences working as support bioinformaticians on multidisciplinary research projects. Most importantly, the article proposes a list of guidelines that outline how these challenges can be preemptively avoided and effectively managed to increase the value of outputs to the end user, covering the entire research project lifecycle, including experimental design, data analysis, and management (i.e., sharing and storage). In addition, we highlight the importance of clear and transparent communication, comprehensive preparation, appropriate handling of samples and data using monitoring systems, and the employment of appropriate tools and standard operating procedures to provide effective bioinformatics support. <<<

The COVID-19 pandemic has emphasized the need for novel drug discovery process. However, the journey from conceptualizing a drug to its eventual implementation in clinical settings is a long, complex, and expensive process, with many potential points of failure. Over the past decade, a vast growth in medical information has coincided with advances in computational hardware (cloud computing, GPUs, and TPUs) and the rise of deep learning. Medical data generated from large molecular screening profiles, personal health or pathology records, and public health organizations could benefit from analysis by Artificial Intelligence (AI) approaches to speed up and prevent failures in the drug discovery pipeline. We present applications of AI at various stages of drug discovery pipelines, including the inherently computational approaches of design and prediction of a drug's likely properties. Open-source databases and AI-based software tools that facilitate drug design are discussed along with their associated problems of molecule representation, data collection, complexity, labeling, and disparities among labels. How contemporary AI methods, such as graph neural networks, reinforcement learning, and generated models, along with structure-based methods, (i.e., molecular dynamics simulations and molecular docking) can contribute to drug discovery applications and analysis of drug responses is also explored. Finally, recent developments and investments in AI-based start-up companies for biotechnology, drug design and their current progress, hopes and promotions are discussed in this article. <<<

Ales Varabyou, Markus J Sommer, Beril Erdogdu, Ida Shinder, Ilia Minkin, Kuan-Hao Chao, Sukhwan Park, Jakob Heinz, Christopher Pockrandt, Alaina Shumate, Natalia Rincon, Daniela Puiu, Martin Steinegger, Steven L Salzberg, Mihaela Pertea <<<

CHESS 3 represents an improved human gene catalog based on nearly 10,000 RNA-seq experiments across 54 body sites. It significantly improves current genome annotation by integrating the latest reference data and algorithms, machine learning techniques for noise filtering, and new protein structure prediction methods. CHESS 3 contains 41,356 genes, including 19,839 protein-coding genes and 158,377 transcripts, with 14,863 protein-coding transcripts not in other catalogs. It includes all MANE transcripts and at least one transcript for most RefSeq and GENCODE genes. On the CHM13 human genome, the CHESS 3 catalog contains an additional 129 protein-coding genes. CHESS 3 is available at http://ccb.jhu.edu/chess . <<<

Our ability to enact successful goal-directed actions involves multiple learning processes. Among these processes, implicit motor adaptation ensures that the sensorimotor system remains finely tuned in response to changes in the body and environment. Whether Parkinson's disease impacts implicit motor adaptation remains a contentious area of research: whereas multiple reports show impaired performance in this population, many others show intact performance. While there is a range of methodological differences across studies, one critical issue is that performance in many of the studies may reflect a combination of implicit adaptation and strategic re-aiming. Here, we revisited this controversy using a visuomotor task designed to isolate implicit adaptation. In two experiments, we found that adaptation in response to a wide range of visual perturbations was similar in Parkinson's disease and matched control participants. Moreover, in a meta-analysis of previously published and unpublished work, we found that the mean effect size contrasting Parkinson's disease and controls across 16 experiments involving over 200 participants was not significant. Together, these analyses indicate that implicit adaptation is preserved in Parkinson's disease, offering a fresh perspective on the role of the basal ganglia in sensorimotor learning. <<<

BACKGROUND: Deep learning is a promising way to improve health care. Image-processing medical disciplines, such as pathology, are expected to be transformed by deep learning. The first clinically applicable deep-learning diagnostic support tools are already available in cancer pathology, and their number is increasing. However, data on the environmental sustainability of these tools are scarce. We aimed to conduct an environmental-sustainability analysis of a theoretical implementation of deep learning in patient-care pathology.METHODS: For this modelling study, we first assembled and calculated relevant data and parameters of a digital-pathology workflow. Data were breast and prostate specimens from the university clinic at the Institute of Pathology of the Rheinisch-Westfälische Technische Hochschule Aachen (Aachen, Germany), for which commercially available deep learning was already available. Only specimens collected between Jan 1 and Dec 31, 2019 were used, to omit potential biases due to the COVID-19 pandemic. Our final selection was based on 2 representative weeks outside holidays, covering different types of specimens. To calculate carbon dioxide (CO2) or CO2 equivalent (CO2 eq) emissions of deep learning in pathology, we gathered relevant data for exact numbers and sizes of whole-slide images (WSIs), which were generated by scanning histopathology samples of prostate and breast specimens. We also evaluated different data input scenarios (including all slide tiles, only tiles containing tissue, or only tiles containing regions of interest). To convert estimated energy consumption from kWh to CO2 eq, we used the internet protocol address of the computational server and the Electricity Maps database to obtain information on the sources of the local electricity grid (ie, renewable vs non-renewable), and estimated the number of trees and proportion of the local and world's forests needed to sequester the CO2 eq emissions. We calculated the computational requirements and CO2 eq emissions of 30 deep-learning models that varied in task and size. The first scenario represented the use of one commercially available deep-learning model for one task in one case (1-task), the second scenario considered two deep-learning models for two tasks per case (2-task), the third scenario represented a future, potentially automated workflow that could handle 7 tasks per case (7-task), and the fourth scenario represented the use of a single potential, large, computer-vision model that could conduct multiple tasks (multitask). We also compared the performance (ie, accuracy) and CO2 eq emissions of different deep-learning models for the classification of renal cell carcinoma on WSIs, also from Rheinisch-Westfälische Technische Hochschule Aachen. We also tested other approaches to reducing CO2 eq emissions, including model pruning and an alternative method for histopathology analysis (pathomics).FINDINGS: The pathology database contained 35 552 specimens (237 179 slides), 6420 of which were prostate specimens (10 115 slides) and 11 801 of which were breast specimens (19 763 slides). We selected and subsequently digitised 140 slides from eight breast-cancer cases and 223 slides from five prostate-cancer cases. Applying large deep-learning models on all WSI tiles of prostate and breast pathology cases would result in yearly CO2 eq emissions of 7·65 metric tons (t; 95% CI 7·62-7·68) with the use of a single deep-learning model per case; yearly CO2 eq emissions were up to 100·56 t (100·21-100·99) with the use of seven deep-learning models per case. CO2 eq emissions for different deep-learning model scenarios, data inputs, and deep-learning model sizes for all slides varied from 3·61 t (3·59-3·63) to 2795·30 t (1177·51-6482·13. For the estimated number of overall pathology cases worldwide, the yearly CO2 eq emissions varied, reaching up to 16 megatons (Mt) of CO2 eq, requiring up to 86 590 km2 (0·22%) of world forest to sequester the CO2 eq emissions. Use of the 7-task scenario and small deep-learning models on slides containing tissue only could substantially reduce CO2 eq emissions worldwide by up to 141 times (0·1 Mt, 95% CI 0·1-0·1). Considering the local environment in Aachen, Germany, the maximum CO2 eq emission from the use of deep learning in digital pathology only would require 32·8% (95% CI 13·8-76·6) of the local forest to sequester the CO2 eq emissions. A single pathomics run on a tissue could provide information that was comparable to or even better than the output of multitask deep-learning models, but with 147 times reduced CO2 eq emissions.INTERPRETATION: Our findings suggest that widespread use of deep learning in pathology might have considerable global-warming potential. The medical community, policy decision makers, and the public should be aware of this potential and encourage the use of CO2 eq emissions reduction strategies where possible.FUNDING: German Research Foundation, European Research Council, German Federal Ministry of Education and Research, Health, Economic Affairs and Climate Action, and the Innovation Fund of the Federal Joint Committee. <<<

Bin Fu, Jiaoyang Liao, Shuanghong Chen, Wei Li, Qiudao Wang, Jian Hu, Fei Yang, Shenlin Hsiao, Yanhong Jiang, Liren Wang, Fangping Chen, Yuanjin Zhang, Xin Wang, Dali Li, Mingyao Liu, Yuxuan Wu <<<

Gene editing to disrupt the GATA1-binding site at the +58 BCL11A erythroid enhancer could induce γ-globin expression, which is a promising therapeutic strategy to alleviate β-hemoglobinopathy caused by HBB gene mutation. In the present study, we report the preliminary results of an ongoing phase 1/2 trial (NCT04211480) evaluating safety and efficacy of gene editing therapy in children with blood transfusion-dependent β-thalassemia (TDT). We transplanted BCL11A enhancer-edited, autologous, hematopoietic stem and progenitor cells into two children, one carrying the β/β genotype, classified as the most severe type of TDT. Primary endpoints included engraftment, overall survival and incidence of adverse events (AEs). Both patients were clinically well with multilineage engraftment, and all AEs to date were considered unrelated to gene editing and resolved after treatment. Secondary endpoints included achieving transfusion independence, editing rate in bone marrow cells and change in hemoglobin (Hb) concentration. Both patients achieved transfusion independence for >18 months after treatment, and their Hb increased from 8.2 and 10.8 g dl at screening to 15.0 and 14.0 g dl at the last visit, respectively, with 85.46% and 89.48% editing persistence in bone marrow cells. Exploratory analysis of single-cell transcriptome and indel patterns in edited peripheral blood mononuclear cells showed no notable side effects of the therapy. <<<

Yusheng Cai, Muzhao Xiong, Zijuan Xin, Chengyu Liu, Jie Ren, Xiying Yang, Jinghui Lei, Wei Li, Feifei Liu, Qun Chu, Yiyuan Zhang, Jian Yin, Yanxia Ye, Dingyi Liu, Yanling Fan, Shuhui Sun, Yaobin Jing, Qian Zhao, Liyun Zhao, Shanshan Che, Yandong Zheng, Haoteng Yan, Shuai Ma, Si Wang, Juan Carlos Izpisua Belmonte, Jing Qu, Weiqi Zhang, Guang-Hui Liu <<<

Regeneration across tissues and organs exhibits significant variation throughout the body and undergoes a progressive decline with age. To decode the relationships between aging and regenerative capacity, we conducted a comprehensive single-cell transcriptome analysis of regeneration in eight tissues from young and aged mice. We employed diverse analytical models to study tissue regeneration and unveiled the intricate cellular and molecular mechanisms underlying the attenuated regenerative processes observed in aged tissues. Specifically, we identified compromised stem cell mobility and inadequate angiogenesis as prominent contributors to this age-associated decline in regenerative capacity. Moreover, we discovered a unique subset of Arg1 macrophages that were activated in young tissues but suppressed in aged regenerating tissues, suggesting their important role in age-related immune response disparities during regeneration. This study provides a comprehensive single-cell resource for identifying potential targets for interventions aimed at enhancing regenerative outcomes in the aging population. <<<

Within the context of screening tests, it is important to avoid misconceptions about sensitivity, specificity, and predictive values. In this article, therefore, foundations are first established concerning these metrics along with the first of several aspects of pliability that should be recognized in relation to those metrics. Clarification is then provided about the definitions of sensitivity, specificity, and predictive values and why researchers and clinicians can misunderstand and misrepresent them. Arguments are made that sensitivity and specificity should usually be applied only in the context of describing a screening test's attributes relative to a reference standard; that predictive values are more appropriate and informative in actual screening contexts, but that sensitivity and specificity can be used for screening decisions about individual people if they are extremely high; that predictive values need not always be high and might be used to advantage by adjusting the sensitivity and specificity of screening tests; that, in screening contexts, researchers should provide information about all four metrics and how they were derived; and that, where necessary, consumers of health research should have the skills to interpret those metrics effectively for maximum benefit to clients and the healthcare system. <<<

Lian Narunsky-Haziza, Gregory D. Sepich-Poore, Ilana Livyatan, Omer Asraf, Cameron Martino, Deborah Nejman, Nancy Gavert, Jason E. Stajich, Guy Amit, Antonio González, Stephen Wandro, Gili Perry, Ruthie Ariel, Arnon Meltser, Justin P. Shaffer, Qiyun Zhu, Nora Balint-Lahat, Iris Barshack, Maya Dadiani, Einav N. Gal-Yam, Sandip Pravin Patel, Amir Bashan, Austin D. Swafford, Yitzhak Pilpel, Rob Knight, Ravid Straussman <<<

Deep convolutional neural networks (DCNNs) trained for face identification can rival and even exceed human-level performance. The ways in which the internal face representations in DCNNs relate to human cognitive representations and brain activity are not well understood. Nearly all previous studies focused on static face image processing with rapid display times and ignored the processing of naturalistic, dynamic information. To address this gap, we developed the largest naturalistic dynamic face stimulus set in human neuroimaging research (700+ naturalistic video clips of unfamiliar faces). We used this naturalistic dataset to compare representational geometries estimated from DCNNs, behavioral responses, and brain responses. We found that DCNN representational geometries were consistent across architectures, cognitive representational geometries were consistent across raters in a behavioral arrangement task, and neural representational geometries in face areas were consistent across brains. Representational geometries in late, fully connected DCNN layers, which are optimized for individuation, were much more weakly correlated with cognitive and neural geometries than were geometries in late-intermediate layers. The late-intermediate face-DCNN layers successfully matched cognitive representational geometries, as measured with a behavioral arrangement task that primarily reflected categorical attributes, and correlated with neural representational geometries in known face-selective topographies. Our study suggests that current DCNNs successfully capture neural cognitive processes for categorical attributes of faces but less accurately capture individuation and dynamic features. <<<

Yingjie Bian, Wei Li, Daniel M Kremer, Peter Sajjakulnukit, Shasha Li, Joel Crespo, Zeribe C Nwosu, Li Zhang, Arkadiusz Czerwonka, Anna Pawłowska, Houjun Xia, Jing Li, Peng Liao, Jiali Yu, Linda Vatan, Wojciech Szeliga, Shuang Wei, Sara Grove, J Rebecca Liu, Karen McLean, Marcin Cieslik, Arul M Chinnaiyan, Witold Zgodziński, Grzegorz Wallner, Iwona Wertel, Karolina Okła, Ilona Kryczek, Costas A Lyssiotis, Weiping Zou <<<

Abnormal epigenetic patterns correlate with effector T cell malfunction in tumours, but the cause of this link is unknown. Here we show that tumour cells disrupt methionine metabolism in CD8 T cells, thereby lowering intracellular levels of methionine and the methyl donor S-adenosylmethionine (SAM) and resulting in loss of dimethylation at lysine 79 of histone H3 (H3K79me2). Loss of H3K79me2 led to low expression of STAT5 and impaired T cell immunity. Mechanistically, tumour cells avidly consumed methionine and outcompeted T cells for methionine by expressing high levels of the methionine transporter SLC43A2. Genetic and biochemical inhibition of tumour SLC43A2 restored H3K79me2 in T cells, thereby boosting spontaneous and checkpoint-induced tumour immunity. Moreover, methionine supplementation improved the expression of H3K79me2 and STAT5 in T cells, and this was accompanied by increased T cell immunity in tumour-bearing mice and patients with colon cancer. Clinically, tumour SLC43A2 correlated negatively with T cell histone methylation and functional gene signatures. Our results identify a mechanistic connection between methionine metabolism, histone patterns, and T cell immunity in the tumour microenvironment. Thus, cancer methionine consumption is an immune evasion mechanism, and targeting cancer methionine signalling may provide an immunotherapeutic approach. <<<

We propose an effective denoising diffusion model for generatinghigh-resolution images (e.g., 1024$\times$512), trained on small-size imagepatches (e.g., 64$\times$64). We name our algorithm Patch-DM, in which a newfeature collage strategy is designed to avoid the boundary artifact whensynthesizing large-size images. Feature collage systematically crops andcombines partial features of the neighboring patches to predict the features ofa shifted image patch, allowing the seamless generation of the entire image dueto the overlap in the patch feature space. Patch-DM produces high-quality imagesynthesis results on our newly collected dataset of nature images(1024$\times$512), as well as on standard benchmarks of smaller sizes(256$\times$256), including LSUN-Bedroom, LSUN-Church, and FFHQ. We compare ourmethod with previous patch-based generation methods and achievestate-of-the-art FID scores on all four datasets. Further, Patch-DM alsoreduces memory complexity compared to the classic diffusion models. <<<