A limitation of spatial transcriptomics technologies is that individual measurements may contain contributions from multiple cells, hindering the discovery of cell-type-specific spatial patterns of localization and expression. Here, we develop robust cell type decomposition (RCTD), a computational method that leverages cell type profiles learned from single-cell RNA-seq to decompose cell type mixtures while correcting for differences across sequencing technologies. We demonstrate the ability of RCTD to detect mixtures and identify cell types on simulated datasets. Furthermore, RCTD accurately reproduces known cell type and subtype localization patterns in Slide-seq and Visium datasets of the mouse brain. Finally, we show how RCTD's recovery of cell type localization enables the discovery of genes within a cell type whose expression depends on spatial environment. Spatial mapping of cell types with RCTD enables the spatial components of cellular identity to be defined, uncovering new principles of cellular organization in biological tissue. RCTD is publicly available as an open-source R package at https://github.com/dmcable/RCTD . <<<

There is a growing body of work that leverages features extracted viatopological data analysis to train machine learning models. While this field,sometimes known as topological machine learning (TML), has seen some notablesuccesses, an understanding of how the process of learning from topologicalfeatures differs from the process of learning from raw data is still limited.In this work, we begin to address one component of this larger issue by askingwhether a model trained with topological features learns internalrepresentations of data that are fundamentally different than those learned bya model trained with the original raw data. To quantify ``different'', weexploit two popular metrics that can be used to measure the similarity of thehidden representations of data within neural networks, neural stitching andcentered kernel alignment. From these we draw a range of conclusions about howtraining with topological features does and does not change the representationsthat a model learns. Perhaps unsurprisingly, we find that structurally, thehidden representations of models trained and evaluated on topological featuresdiffer substantially compared to those trained and evaluated on thecorresponding raw data. On the other hand, our experiments show that in somecases, these representations can be reconciled (at least to the degree requiredto solve the corresponding task) using a simple affine transformation. Weconjecture that this means that neural networks trained on raw data may extractsome limited topological features in the process of making predictions. <<<

Prashant S Emani, Jason J Liu, Declan Clarke, Matthew Jensen, Jonathan Warrell, Chirag Gupta, Ran Meng, Che Yu Lee, Siwei Xu, Cagatay Dursun, Shaoke Lou, Yuhang Chen, Zhiyuan Chu, Timur Galeev, Ahyeon Hwang, Yunyang Li, Pengyu Ni, Xiao Zhou, PsychENCODE Consortium, Trygve E Bakken, Jaroslav Bendl, Lucy Bicks, Tanima Chatterjee, Lijun Cheng, Yuyan Cheng, Yi Dai, Ziheng Duan, Mary Flaherty, John F Fullard, Michael Gancz, Diego Garrido-Martín, Sophia Gaynor-Gillett, Jennifer Grundman, Natalie Hawken, Ella Henry, Gabriel E Hoffman, Ao Huang, Yunzhe Jiang, Ting Jin, Nikolas L Jorstad, Riki Kawaguchi, Saniya Khullar, Jianyin Liu, Junhao Liu, Shuang Liu, Shaojie Ma, Michael Margolis, Samantha Mazariegos, Jill Moore, Jennifer R Moran, Eric Nguyen, Nishigandha Phalke, Milos Pjanic, Henry Pratt, Diana Quintero, Ananya S Rajagopalan, Tiernon R Riesenmy, Nicole Shedd, Manman Shi, Megan Spector, Rosemarie Terwilliger, Kyle J Travaglini, Brie Wamsley, Gaoyuan Wang, Yan Xia, Shaohua Xiao, Andrew C Yang, Suchen Zheng, Michael J Gandal, Donghoon Lee, Ed S Lein, Panos Roussos, Nenad Sestan, Zhiping Weng, Kevin P White, Hyejung Won, Matthew J Girgenti, Jing Zhang, Daifeng Wang, Daniel Geschwind, Mark Gerstein <<<

Single-cell genomics is a powerful tool for studying heterogeneous tissues such as the brain. Yet, little is understood about how genetic variants influence cell-level gene expression. Addressing this, we uniformly processed single-nuclei, multi-omics datasets into a resource comprising >2.8M nuclei from the prefrontal cortex across 388 individuals. For 28 cell types, we assessed population-level variation in expression and chromatin across gene families and drug targets. We identified >550K cell-type-specific regulatory elements and >1.4M single-cell expression-quantitative-trait loci, which we used to build cell-type regulatory and cell-to-cell communication networks. These networks manifest cellular changes in aging and neuropsychiatric disorders. We further constructed an integrative model accurately imputing single-cell expression and simulating perturbations; the model prioritized ~250 disease-risk genes and drug targets with associated cell types. <<<

Although originally primarily a system for functional biology, Arabidopsis thaliana has, owing to its broad geographical distribution and adaptation to diverse environments, developed into a powerful model in population genomics. Here we present chromosome-level genome assemblies of 69 accessions from a global species range. We found that genomic colinearity is very conserved, even among geographically and genetically distant accessions. Along chromosome arms, megabase-scale rearrangements are rare and typically present only in a single accession. This indicates that the karyotype is quasi-fixed and that rearrangements in chromosome arms are counter-selected. Centromeric regions display higher structural dynamics, and divergences in core centromeres account for most of the genome size variations. Pan-genome analyses uncovered 32,986 distinct gene families, 60% being present in all accessions and 40% appearing to be dispensable, including 18% private to a single accession, indicating unexplored genic diversity. These 69 new Arabidopsis thaliana genome assemblies will empower future genetic research. <<<

Experiences need to be tagged during learning for further consolidation. However, neurophysiological mechanisms that select experiences for lasting memory are not known. By combining large-scale neural recordings in mice with dimensionality reduction techniques, we observed that successive maze traversals were tracked by continuously drifting populations of neurons, providing neuronal signatures of both places visited and events encountered. When the brain state changed during reward consumption, sharp wave ripples (SPW-Rs) occurred on some trials, and their specific spike content decoded the trial blocks that surrounded them. During postexperience sleep, SPW-Rs continued to replay those trial blocks that were reactivated most frequently during waking SPW-Rs. Replay content of awake SPW-Rs may thus provide a neurophysiological tagging mechanism to select aspects of experience that are preserved and consolidated for future use. <<<

Developing anode materials with high specific capacity and cycling stability is vital for improving thin‐film lithium‐ion batteries. Thin‐film zinc oxide (ZnO) holds promise due to its high specific capacity, but it suffers from volume changes and structural stress during cycling, leading to poor battery performance. In this research, we ingeniously combined polytetrafluoroethylene (PTFE) with ZnO using a radio frequency (RF) magnetron co‐sputtering method, ensuring a strong bond in the thin‐film composite electrode. PTFE effectively reduced stress on the active material and mitigated volume change effects during Li+ ion intercalation and deintercalation. The composite thin films are thoroughly characterized using advanced techniques such as X‐ray diffraction, scanning electron microscopy, and X‐ray photoelectron spectroscopy for investigating correlations between material properties and electrochemical behaviors. Notably, the ZnO/PTFE thin‐film electrode demonstrated an impressive specific capacity of 1305 mAh g−1 (=7116 mAh cm−3) at a 0.5C rate and a remarkable capacity retention of 82% from the 1st to the 100th cycle, surpassing the bare ZnO thin film (50%). This study provides valuable insights into using binders to stabilize active materials in thin‐film batteries, enhancing battery performance. <<<

Biological aging of human organ systems reflects the interplay of age, chronic disease, lifestyle and genetic risk. Using longitudinal brain imaging and physiological phenotypes from the UK Biobank, we establish normative models of biological age for three brain and seven body systems. Here we find that an organ's biological age selectively influences the aging of other organ systems, revealing a multiorgan aging network. We report organ age profiles for 16 chronic diseases, where advanced biological aging extends from the organ of primary disease to multiple systems. Advanced body age associates with several lifestyle and environmental factors, leukocyte telomere lengths and mortality risk, and predicts survival time (area under the curve of 0.77) and premature death (area under the curve of 0.86). Our work reveals the multisystem nature of human aging in health and chronic disease. It may enable early identification of individuals at increased risk of aging-related morbidity and inform new strategies to potentially limit organ-specific aging in such individuals. <<<

The rising prevalence of diabetes estimated at 3.6 million people in the UK represents a major public health and socioeconomic burden to our National Health Service. Diabetes and its associated complications are of a growing concern. Diabetes-related foot complications have been identified as the single most common cause of morbidity among diabetic patients. The complicating factor of underlying peripheral vascular disease renders the majority of diabetic foot ulcers asymptomatic until latter evidence of non-healing ulcers become evident. Therefore, preventative strategies including annual diabetic foot screening and diabetic foot care interventions facilitated through a multidisciplinary team have been implemented to enable early identification of diabetic patients at high risk of diabetic foot complications. The National Diabetes Foot Care Audit reported significant variability and deficiencies of care throughout England and Wales, with emphasis on change in the structure of healthcare provision and commissioning, improvement of patient education and availability of healthcare access, and emphasis on preventative strategies to reduce morbidities and mortality of this debilitating disease. This review article aims to summarise major risk factors contributing to the development of diabetic foot ulcers. It also considers the key evidence-based strategies towards preventing diabetic foot ulcer. We discuss tools used in risk stratification and classifications of foot ulcer. <<<

The Genome Aggregation Database (gnomAD), widely recognized as the gold-standard reference map of human genetic variation, has largely overlooked tandem repeat (TR) expansions, despite the fact that TRs constitute ∼6% of our genome and are linked to over 50 human diseases. Here, we introduce the TR-gnomAD (https://wlcb.oit.uci.edu/TRgnomAD), a biobank-scale reference of 0.86 million TRs derived from 338,963 whole-genome sequencing (WGS) samples of diverse ancestries (39.5% non-European samples). TR-gnomAD offers critical insights into ancestry-specific disease prevalence using disparities in TR unit number frequencies among ancestries. Moreover, TR-gnomAD is able to differentiate between common, presumably benign TR expansions, which are prevalent in TR-gnomAD, from those potentially pathogenic TR expansions, which are found more frequently in disease groups than within TR-gnomAD. Together, TR-gnomAD is an invaluable resource for researchers and physicians to interpret TR expansions in individuals with genetic diseases. <<<

We present MUSTACHE, a new method for multi-scale detection of chromatin loops from Hi-C and Micro-C contact maps. MUSTACHE employs scale-space theory, a technical advance in computer vision, to detect blob-shaped objects in contact maps. MUSTACHE is scalable to kilobase-resolution maps and reports loops that are highly consistent between replicates and between Hi-C and Micro-C datasets. Compared to other loop callers, such as HiCCUPS and SIP, MUSTACHE recovers a higher number of published ChIA-PET and HiChIP loops as well as loops linking promoters to regulatory elements. Overall, MUSTACHE enables an efficient and comprehensive analysis of chromatin loops. Available at: https://github.com/ay-lab/mustache . <<<

Researchers spend a great deal of time reading research papers. However, this skill is rarely taught, leading to much wasted effort. This article outlines a practical and efficient three-pass method for reading research papers. I also describe how to use this method to do a literature survey. <<<

Bolun Zhou, Wei Guo, Lei Guo, Yong Li, Zhigang Zheng, Qilin Huai, Fengwei Tan, Yin Li, Qi Xue, Jianming Ying, Liang Zhao, Shugeng Gao, Jie He <<<

Esophageal squamous cell carcinoma (ESCC) is invasive cancer and the complex mechanisms underlying carcinogenesis remain unclear. Extracellular vesicles (EVs), secreted by most cell types, serve as a critical factor in tumorigenesis via intercellular communications. Our study aims to investigate the cellular origin of EVs in ESCC, and unveil the unknown molecular and cellular mechanisms underlying cell-cell communications. Six ESCC patients were enrolled and single-cell RNA sequencing (scRNA-seq) analyses were conducted to screen different cell subpopulations. The genetic origin of EVs was tracked using the supernatant from different cellular extracts. Nanoparticle tracking analysis (NTA), western blot analysis, and transmission electron microscopy (TEM) were performed for validation. Using scRNA-seq analysis, eleven cell subpopulations were identified in ESCC. Differences in gene expression in EVs between malignant and non-malignant esophageal tissues were found. Our findings demonstrated that epithelial cells releasing EVs were the most prevalent in malignant tissues, while endothelial cells and fibroblasts releasing EVs were predominant in non-malignant tissues. Furthermore, the high levels of gene expression in EVs released from these cells were correlated significantly with a worse prognosis. Our findings revealed the genetic origin of EVs in malignant and non-malignant esophageal tissues and provided a comprehensive overview of the associated cell-cell interactions in ESCC. <<<

We propose an efficient design of Transformer-based models for multivariatetime series forecasting and self-supervised representation learning. It isbased on two key components: (i) segmentation of time series intosubseries-level patches which are served as input tokens to Transformer; (ii)channel-independence where each channel contains a single univariate timeseries that shares the same embedding and Transformer weights across all theseries. Patching design naturally has three-fold benefit: local semanticinformation is retained in the embedding; computation and memory usage of theattention maps are quadratically reduced given the same look-back window; andthe model can attend longer history. Our channel-independent patch time seriesTransformer (PatchTST) can improve the long-term forecasting accuracysignificantly when compared with that of SOTA Transformer-based models. We alsoapply our model to self-supervised pre-training tasks and attain excellentfine-tuning performance, which outperforms supervised training on largedatasets. Transferring of masked pre-trained representation on one dataset toothers also produces SOTA forecasting accuracy. Code is available at:https://github.com/yuqinie98/PatchTST. <<<

Abstract Collagen structure in biological tissues imparts its intrinsic physical properties by the formation of several covalent crosslinks. For the first time, two major crosslinks in the skin dihydroxylysinonorleucine (HLNL) and histidinohydroxymerodesmosine (HHMD), were isotopically labelled and then analysed by liquid-chromatography high-resolution accurate-mass mass spectrometry (LC-HRMS) and small-angle neutron scattering (SANS). The isotopic labelling followed by LC-HRMS confirmed the presence of one imino group in both HLNL and HHMD, making them more susceptible to degrade at low pH. The structural changes in collagen due to extreme changes in the pH and chrome tanning were highlighted by the SANS contrast variation between isotopic labelled and unlabelled crosslinks. This provided a better understanding of the interaction of natural crosslinks with the chromium sulphate in collagen suggesting that the development of a benign crosslinking method can help retain the intrinsic physical properties of the leather. This analytical method can also be applied to study artificial crosslinking in other collagenous tissues for biomedical applications. Graphical abstract <<<

Nanopore direct RNA sequencing (DRS) provides the direct access to native RNA strands with full-length information, shedding light on rich qualitative and quantitative properties of gene expression profiles. Here with NanoTrans, we present an integrated computational framework that comprehensively covers all major DRS-based application scopes, including isoform clustering and quantification, poly(A) tail length estimation, RNA modification profiling, and fusion gene detection. In addition to its merit in providing such a streamlined one-stop solution, NanoTrans also shines in its workflow-orientated modular design, batch processing capability, all-in-one tabular and graphic report output, as well as automatic installation and configuration supports. Finally, by applying NanoTrans to real DRS datasets of yeast, Arabidopsis, as well as human embryonic kidney and cancer cell lines, we further demonstrated its utility, effectiveness, and efficacy across a wide range of DRS-based application settings. <<<

When observers have prior knowledge about the likely outcome of their perceptual decisions, they exhibit robust behavioural biases in reaction time and choice accuracy. Computational modelling typically attributes these effects to strategic adjustments in the criterion amount of evidence required to commit to a choice alternative - usually implemented by a starting point shift - but recent work suggests that expectations may also fundamentally bias the encoding of the sensory evidence itself. Here, we recorded neural activity with EEG while participants performed a contrast discrimination task with valid, invalid, or neutral probabilistic cues across multiple testing sessions. We measured sensory evidence encoding via contrast-dependent steady-state visual-evoked potentials (SSVEP), while a read-out of criterion adjustments was provided by effector-selective mu-beta band activity over motor cortex. In keeping with prior modelling and neural recording studies, cues evoked substantial biases in motor preparation consistent with criterion adjustments, but we additionally found that the cues produced a significant modulation of the SSVEP during evidence presentation. While motor preparation adjustments were observed in the earliest trials, the sensory-level effects only emerged with extended task exposure. Our results suggest that, in addition to strategic adjustments to the decision process, probabilistic information can also induce subtle biases in the encoding of the evidence itself. <<<

Curriculum learning and imitation learning have been leveraged extensively inthe robotics domain. However, minimal research has been done on leveragingthese ideas on control tasks over highly stochastic time-series data. Here, wetheoretically and empirically explore these approaches in a representativecontrol task over complex time-series data. We implement the fundamental ideasof curriculum learning via data augmentation, while imitation learning isimplemented via policy distillation from an oracle. Our findings reveal thatcurriculum learning should be considered a novel direction in improvingcontrol-task performance over complex time-series. Our ample random-seedout-sample empirics and ablation studies are highly encouraging for curriculumlearning for time-series control. These findings are especially encouraging aswe tune all overlapping hyperparameters on the baseline -- giving an advantageto the baseline. On the other hand, we find that imitation learning should beused with caution. <<<