Handwritten Chinese text recognition (HCTR) has been an active research topic for decades. However, most previous studies solely focus on the recognition of cropped text line images, ignoring the error caused by text line detection in real-world applications. Although some approaches aimed at page-level text recognition have been proposed in recent years, they either are limited to simple layouts or require very detailed annotations including expensive line-level and even character-level bounding boxes. To this end, we propose PageNet for end-to-end weakly supervised page-level HCTR. PageNet detects and recognizes characters and predicts the reading order between them, which is more robust and flexible when dealing with complex layouts including multi-directional and curved text lines. Utilizing the proposed weakly supervised learning framework, PageNet requires only transcripts to be annotated for real data; however, it can still output detection and recognition results at both the character and line levels, avoiding the labor and cost of labeling bounding boxes of characters and text lines. Extensive experiments conducted on five datasets demonstrate the superiority of PageNet over existing weakly supervised and fully supervised page-level methods. These experimental results may spark further research beyond the realms of existing methods based on connectionist temporal classification or attention. The source code is available at https://github.com/shannanyinxiang/PageNet. <<<

A somewhat contradictory published body of evidence suggests that sex impacts severity outcomes of human leptospirosis. In this study, we used an acute animal model of disease to analyze leptospirosis in male and female hamsters infected side by side with low but increasing doses of serovar Copenhageni. We found that male hamsters were considerably more susceptible to leptospirosis, given that only 6.3% survived infection, whereas 68.7% of the females survived the same infection doses. In contrast to the females, male hamsters had high burdens of in kidney and high histopathological scores after exposure to low infection doses (∼10 bacteria). In hamsters infected with higher doses of (∼10 bacteria), differences in pathogen burdens as well as cytokine and fibrosis transcript levels in kidney were not distinct between sexes. Our results indicate that male hamsters infected with are more susceptible to severe leptospirosis after exposure to lower infectious doses than females. <<<

Tumor-derived circulating cell-free DNA (cfDNA) provides critical clues for cancer early diagnosis, yet it often suffers from low sensitivity. Here, we present a cancer early diagnosis approach using tumor fractions deciphered from circulating cfDNA methylation signatures. We show that the estimated fractions of tumor-derived cfDNA from cancer patients increase significantly as cancer progresses in two independent datasets. Employing the predicted tumor fractions, we establish a Bayesian diagnostic model in which training samples are only derived from late-stage patients and healthy individuals. When validated on early-stage patients and healthy individuals, this model exhibits a sensitivity of 86.1% for cancer early detection and an average accuracy of 76.9% for tumor localization at a specificity of 94.7%. By highlighting the potential of tumor fractions on cancer early diagnosis, our approach can be further applied to cancer screening and tumor progression monitoring. <<<

Erika Erickson, Japheth E Gado, Luisana Avilán, Felicia Bratti, Richard K Brizendine, Paul A Cox, Raj Gill, Rosie Graham, Dong-Jin Kim, Gerhard König, William E Michener, Saroj Poudel, Kelsey J Ramirez, Thomas J Shakespeare, Michael Zahn, Eric S Boyd, Christina M Payne, Jennifer L DuBois, Andrew R Pickford, Gregg T Beckham, John E McGeehan <<<

Enzymatic deconstruction of poly(ethylene terephthalate) (PET) is under intense investigation, given the ability of hydrolase enzymes to depolymerize PET to its constituent monomers near the polymer glass transition temperature. To date, reported PET hydrolases have been sourced from a relatively narrow sequence space. Here, we identify additional PET-active biocatalysts from natural diversity by using bioinformatics and machine learning to mine 74 putative thermotolerant PET hydrolases. We successfully express, purify, and assay 51 enzymes from seven distinct phylogenetic groups; observing PET hydrolysis activity on amorphous PET film from 37 enzymes in reactions spanning pH from 4.5-9.0 and temperatures from 30-70 °C. We conduct PET hydrolysis time-course reactions with the best-performing enzymes, where we observe differences in substrate selectivity as function of PET morphology. We employed X-ray crystallography and AlphaFold to examine the enzyme architectures of all 74 candidates, revealing protein folds and accessory domains not previously associated with PET deconstruction. Overall, this study expands the number and diversity of thermotolerant scaffolds for enzymatic PET deconstruction. <<<

AbstractNo sooner is an experience over than its neural memory representation begins to be strengthened and transformed through the process of memory replay. Using fMRI, we examined how memory strength manipulated through repetition during encoding modulates post-encoding replay in humans. Results revealed that repetition did not increase replay frequency in the hippocampus. However, replay in cortical regions and hippocampal-cortical coordinated replay were significantly enhanced for repeated events, suggesting that repetition accelerates the consolidation process. Interestingly, we found that replay frequency in both hippocampus and cortex modulated behavioral success on an immediate associative recognition test for the weakly encoded information, indicating a significant role for post-encoding replay in rescuing once-presented events. Together, our findings highlight the relationships of replay to stabilizing weak memories and accelerating cortical consolidation for strong memories. <<<

Simona Ceglia, Alyssa Berthelette, Kelsey Howley, Yun Li, Benedikt Mortzfeld, Shakti K Bhattarai, Nicole K H Yiew, Ying Xu, Robert Brink, Jason G Cyster, Lora V Hooper, Gwendalyn J Randolph, Vanni Bucci, Andrea Reboldi <<<

Immunoglobulin A (IgA) secretion by plasma cells, terminally differentiated B cells residing in the intestinal lamina propria, assures microbiome homeostasis and protects the host against enteric infections. Exposure to diet-derived and commensal-derived signals provides immune cells with organizing cues that instruct their effector function and dynamically shape intestinal immune responses at the mucosal barrier. Recent data have described metabolic and microbial inputs controlling T cell and innate lymphoid cell activation in the gut; however, whether IgA-secreting lamina propria plasma cells are tuned by local stimuli is completely unknown. Although antibody secretion is considered to be imprinted during B cell differentiation and therefore largely unaffected by environmental changes, a rapid modulation of IgA levels in response to intestinal fluctuations might be beneficial to the host. In the present study, we showed that dietary cholesterol absorption and commensal recognition by duodenal intestinal epithelial cells lead to the production of oxysterols, evolutionarily conserved lipids with immunomodulatory functions. Using conditional cholesterol 25-hydroxylase deleter mouse line we demonstrated that 7α,25-dihydroxycholesterol from epithelial cells is critical to restrain IgA secretion against commensal- and pathogen-derived antigens in the gut. Intestinal plasma cells sense oxysterols via the chemoattractant receptor GPR183 and couple their tissue positioning with IgA secretion. Our findings revealed a new mechanism linking dietary cholesterol and humoral immune responses centered around plasma cell localization for efficient mucosal protection. <<<

Cell-free DNA (cfDNA) in the circulating blood plasma of patients with cancer contains tumour-derived DNA sequences that can serve as biomarkers for guiding therapy, for the monitoring of drug resistance, and for the early detection of cancers. However, the analysis of cfDNA for clinical diagnostic applications remains challenging because of the low concentrations of cfDNA, and because cfDNA is fragmented into short lengths and is susceptible to chemical damage. Barcodes of unique molecular identifiers have been implemented to overcome the intrinsic errors of next-generation sequencing, which is the prevailing method for highly multiplexed cfDNA analysis. However, a number of methodological and pre-analytical factors limit the clinical sensitivity of the cfDNA-based detection of cancers from liquid biopsies. In this Review, we describe the state-of-the-art technologies for cfDNA analysis, with emphasis on multiplexing strategies, and discuss outstanding biological and technical challenges that, if addressed, would substantially improve cancer diagnostics and patient care. <<<

Somatic mutations in cancer cells can generate tumour-specific neoepitopes, which are recognized by autologous T cells in the host. As neoepitopes are not subject to central immune tolerance and are not expressed in healthy tissues, they are attractive targets for therapeutic cancer vaccines. Because the vast majority of cancer mutations are unique to the individual patient, harnessing the full potential of this rich source of targets requires individualized treatment approaches. Many computational algorithms and machine-learning tools have been developed to identify mutations in sequence data, to prioritize those that are more likely to be recognized by T cells and to design tailored vaccines for every patient. In this Review, we fill the gaps between the understanding of basic mechanisms of T cell recognition of neoantigens and the computational approaches for discovery of somatic mutations and neoantigen prediction for cancer immunotherapy. We present a new classification of neoantigens, distinguishing between guarding, restrained and ignored neoantigens, based on how they confer proficient antitumour immunity in a given clinical context. Such context-based differentiation will contribute to a framework that connects neoantigen biology to the clinical setting and medical peculiarities of cancer, and will enable future neoantigen-based therapies to provide greater clinical benefit. <<<

Linn Sofie Sæther , Thor Ueland , Beathe Haatveit , Luigi Angelo Maglanoc , Attila Szabo , Srdjan Djurovic , Pål Aukrust , Daniel Roelfs , Christine Mohn , Monica Bettina Elkjaer Greenwood Ormerod , Trine Vik Lagerberg , Nils Eiel Steen , Ingrid Melle , Ole Andreas Andreassen , Torill Ueland <<<

AbstractA potential relationship between dysregulation of immune/inflammatory pathways and cognitive impairment has been suggested in severe mental illnesses (SMI), such as schizophrenia (SZ) and bipolar (BD) spectrum disorders. However, multivariate relationships between peripheral inflammatory/immune-related markers and cognitive domains are unclear, and many studies do not account for inter-individual variance in both cognitive functioning and inflammatory/immune status. This study aimed to investigate covariance patterns between inflammatory/immune-related markers and cognitive domains and further elucidate heterogeneity in a large SMI and healthy control (HC) cohort (SZ = 343, BD = 289, HC = 770). We applied canonical correlation analysis (CCA) to identify modes of maximum covariation between a comprehensive selection of cognitive domains and inflammatory/immune markers. We found that poor verbal learning and psychomotor processing speed was associated with higher levels of interleukin-18 system cytokines and beta defensin 2, reflecting enhanced activation of innate immunity, a pattern augmented in SMI compared to HC. Applying hierarchical clustering on covariance patterns identified by the CCA revealed a high cognition—low immune dysregulation subgroup with predominantly HC (24% SZ, 45% BD, 74% HC) and a low cognition—high immune dysregulation subgroup predominantly consisting of SMI patients (76% SZ, 55% BD, 26% HC). These subgroups differed in IQ, years of education, age, CRP, BMI (all groups), level of functioning, symptoms and defined daily dose (DDD) of antipsychotics (SMI cohort). Our findings suggest a link between cognitive impairment and innate immune dysregulation in a subset of individuals with severe mental illness. <<<

Transfer learning enables the transfer of knowledge gained while learning to perform one task (source) to a related but different task (target), hence addressing the expense of data acquisition and labelling, potential computational power limitations and dataset distribution mismatches. We propose a new transfer learning framework for task-specific learning (functional regression in partial differential equations) under conditional shift based on the deep operator network (DeepONet). Task-specific operator learning is accomplished by fine-tuning task-specific layers of the target DeepONet using a hybrid loss function that allows for the matching of individual target samples while also preserving the global properties of the conditional distribution of the target data. Inspired by conditional embedding operator theory, we minimize the statistical distance between labelled target data and the surrogate prediction on unlabelled target data by embedding conditional distributions onto a reproducing kernel Hilbert space. We demonstrate the advantages of our approach for various transfer learning scenarios involving nonlinear partial differential equations under diverse conditions due to shifts in the geometric domain and model dynamics. Our transfer learning framework enables fast and efficient learning of heterogeneous tasks despite considerable differences between the source and target domains. <<<

Ting Li, Yue-Tao Tan, Yan-Xing Chen, Xiao-Jun Zheng, Wen Wang, Kun Liao, Hai-Yu Mo, Junzhong Lin, Wei Yang, Hai-Long Piao, Rui-Hua Xu, Huai-Qiang Ju <<<

OBJECTIVE: Methionine metabolism is involved in a myriad of cellular functions, including methylation reactions and redox maintenance. Nevertheless, it remains unclear whether methionine metabolism, RNA methylation and antitumour immunity are molecularly intertwined.DESIGN: The antitumour immunity effect of methionine-restricted diet (MRD) feeding was assessed in murine models. The mechanisms of methionine and YTH domain-containing family protein 1 (YTHDF1) in tumour immune escape were determined in vitro and in vivo. The synergistic effects of MRD or YTHDF1 depletion with PD-1 blockade were also investigated.RESULTS: We found that dietary methionine restriction reduced tumour growth and enhanced antitumour immunity by increasing the number and cytotoxicity of tumour-infiltrating CD8+ T cells in different mouse models. Mechanistically, the S-adenosylmethionine derived from methionine metabolism promoted the N6-methyladenosine (m6A) methylation and translation of immune checkpoints, including PD-L1 and V-domain Ig suppressor of T cell activation (VISTA), in tumour cells. Furthermore, MRD or m6A-specific binding protein YTHDF1 depletion inhibited tumour growth by restoring the infiltration of CD8+ T cells, and synergised with PD-1 blockade for better tumour control. Clinically, YTHDF1 expression correlated with poor prognosis and immunotherapy outcomes for cancer patients.CONCLUSIONS: Methionine and YTHDF1 play a critical role in anticancer immunity through regulating the functions of T cells. Targeting methionine metabolism or YTHDF1 could be a potential new strategy for cancer immunotherapy. <<<

Diffusion models have emerged as a powerful new family of deep generative models with record-breaking performance in many applications, including image synthesis, video generation, and molecule design. In this survey, we provide an overview of the rapidly expanding body of work on diffusion models, categorizing the research into three key areas: efficient sampling, improved likelihood estimation, and handling data with special structures. We also discuss the potential for combining diffusion models with other generative models for enhanced results. We further review the wide-ranging applications of diffusion models in fields spanning from computer vision, natural language processing, temporal data modeling, to interdisciplinary applications in other scientific disciplines. This survey aims to provide a contextualized, in-depth look at the state of diffusion models, identifying the key areas of focus and pointing to potential areas for further exploration. Github: this https URL. <<<

Traditional deformable registration techniques achieve impressive results and offer a rigorous theoretical treatment, but are computationally intensive since they solve an optimization problem for each image pair. Recently, learning-based methods have facilitated fast registration by learning spatial deformation functions. However, these approaches use restricted deformation models, require supervised labels, or do not guarantee a diffeomorphic (topology-preserving) registration. Furthermore, learning-based registration tools have not been derived from a probabilistic framework that can offer uncertainty estimates. In this paper, we present a probabilistic generative model and derive an unsupervised learning-based inference algorithm that makes use of recent developments in convolutional neural networks (CNNs). We demonstrate our method on a 3D brain registration task, and provide an empirical analysis of the algorithm. Our approach results in state of the art accuracy and very fast runtimes, while providing diffeomorphic guarantees and uncertainty estimates. Our implementation is available online at http://voxelmorph.csail.mit.edu. <<<

Baobao Wang, Mei Hou, Junpeng Shi, Lixia Ku, Wei Song, Chunhui Li, Qiang Ning, Xin Li, Changyu Li, Binbin Zhao, Ruyang Zhang, Hua Xu, Zhijing Bai, Zhanchao Xia, Hai Wang, Dexin Kong, Hongbin Wei, Yifeng Jing, Zhouyan Dai, Hu Hailing Wang, Xinyu Zhu, Chunhui Li, Xuan Sun, Shuaishuai Wang, Wen Yao, Gege Hou, Zhi Qi, He Dai, Xuming Li, Hongkun Zheng, Zuxin Zhang, Yu Li, Tianyu Wang, Taijiao Jiang, Zhaoman Wan, Yanhui Chen, Jiuran Zhao, Jinsheng Lai, Haiyang Wang <<<

Hybrid maize displays superior heterosis and contributes over 30% of total worldwide cereal production. However, the molecular mechanisms of heterosis remain obscure. Here we show that structural variants (SVs) between the parental lines have a predominant role underpinning maize heterosis. De novo assembly and analyses of 12 maize founder inbred lines (FILs) reveal abundant genetic variations among these FILs and, through expression quantitative trait loci and association analyses, we identify several SVs contributing to genomic and phenotypic differentiations of various heterotic groups. Using a set of 91 diallel-cross F hybrids, we found strong positive correlations between better-parent heterosis of the F hybrids and the numbers of SVs between the parental lines, providing concrete genomic support for a prevalent role of genetic complementation underlying heterosis. Further, we document evidence that SVs in both ZAR1 and ZmACO2 contribute to yield heterosis in an overdominance fashion. Our results should promote genomics-based breeding of hybrid maize. <<<

DNA methylation is one of the most commonly studied epigenetic marks, due to its role in disease and development. Illumina methylation arrays have been extensively used to measure methylation across the human genome. Methylation array analysis has primarily focused on preprocessing, normalization, and identification of differentially methylated CpGs and regions. GOmeth and GOregion are new methods for performing unbiased gene set testing following differential methylation analysis. Benchmarking analyses demonstrate GOmeth outperforms other approaches, and GOregion is the first method for gene set testing of differentially methylated regions. Both methods are publicly available in the missMethyl Bioconductor R package. <<<

We propose BrainNetCNN, a convolutional neural network (CNN) framework to predict clinical neurodevelopmental outcomes from brain networks. In contrast to the spatially local convolutions done in traditional image-based CNNs, our BrainNetCNN is composed of novel edge-to-edge, edge-to-node and node-to-graph convolutional filters that leverage the topological locality of structural brain networks. We apply the BrainNetCNN framework to predict cognitive and motor developmental outcome scores from structural brain networks of infants born preterm. Diffusion tensor images (DTI) of preterm infants, acquired between 27 and 46 weeks gestational age, were used to construct a dataset of structural brain connectivity networks. We first demonstrate the predictive capabilities of BrainNetCNN on synthetic phantom networks with simulated injury patterns and added noise. BrainNetCNN outperforms a fully connected neural-network with the same number of model parameters on both phantoms with focal and diffuse injury patterns. We then apply our method to the task of joint prediction of Bayley-III cognitive and motor scores, assessed at 18 months of age, adjusted for prematurity. We show that our BrainNetCNN framework outperforms a variety of other methods on the same data. Furthermore, BrainNetCNN is able to identify an infant's postmenstrual age to within about 2 weeks. Finally, we explore the high-level features learned by BrainNetCNN by visualizing the importance of each connection in the brain with respect to predicting the outcome scores. These findings are then discussed in the context of the anatomy and function of the developing preterm infant brain. <<<

RNA-binding proteins (RBPs) are effectors and regulators of posttranscriptional gene regulation (PTGR). RBPs regulate stability, maturation, and turnover of all RNAs, often binding thousands of targets at many sites. The importance of RBPs is underscored by their dysregulation or mutations causing a variety of developmental and neurological diseases. This chapter globally discusses human RBPs and provides a brief introduction to their identification and RNA targets. We review RBPs based on common structural RNA-binding domains, study their evolutionary conservation and expression, and summarize disease associations of different RBP classes. <<<

Functional MRI (fMRI) studies have traditionally relied on intersubject normalization based on global brain morphology, which cannot establish proper functional correspondence between subjects due to substantial intersubject variability in functional organization. Here, we reliably identified a set of discrete, homologous functional regions in individuals to improve intersubject alignment of fMRI data. These functional regions demonstrated marked intersubject variability in size, position, and connectivity. We found that previously reported intersubject variability in functional connectivity maps could be partially explained by variability in size and position of the functional regions. Importantly, individual differences in network topography are associated with individual differences in task-evoked activations, suggesting that these individually specified regions may serve as the "localizer" to improve the alignment of task-fMRI data. We demonstrated that aligning task-fMRI data using the regions derived from resting state fMRI may lead to increased statistical power of task-fMRI analyses. In addition, resting state functional connectivity among these homologous regions is able to capture the idiosyncrasies of subjects and better predict fluid intelligence (gF) than connectivity measures derived from group-level brain atlases. Critically, we showed that not only the connectivity but also the size and position of functional regions are related to human behavior. Collectively, these findings suggest that identifying homologous functional regions across individuals can benefit a wide range of studies in the investigation of connectivity, task activation, and brain-behavior associations. <<<

Annika Frede, Paulo Czarnewski, Gustavo Monasterio, Kumar P Tripathi, David A Bejarano, Ricardo O Ramirez Flores, Chiara Sorini, Ludvig Larsson, Xinxin Luo, Laura Geerlings, Claudio Novella-Rausell, Chiara Zagami, Raoul Kuiper, Rodrigo A Morales, Francisca Castillo, Matthew Hunt, Livia Lacerda Mariano, Yue O O Hu, Camilla Engblom, Ana-Maria Lennon-Duménil, Romy Mittenzwei, Astrid M Westendorf, Nadine Hövelmeyer, Joakim Lundeberg, Julio Saez-Rodriguez, Andreas Schlitzer, Srustidhar Das, Eduardo J Villablanca <<<

Therapeutic promotion of intestinal regeneration holds great promise, but defining the cellular mechanisms that influence tissue regeneration remains an unmet challenge. To gain insight into the process of mucosal healing, we longitudinally examined the immune cell composition during intestinal damage and regeneration. B cells were the dominant cell type in the healing colon, and single-cell RNA sequencing (scRNA-seq) revealed expansion of an IFN-induced B cell subset during experimental mucosal healing that predominantly located in damaged areas and associated with colitis severity. B cell depletion accelerated recovery upon injury, decreased epithelial ulceration, and enhanced gene expression programs associated with tissue remodeling. scRNA-seq from the epithelial and stromal compartments combined with spatial transcriptomics and multiplex immunostaining showed that B cells decreased interactions between stromal and epithelial cells during mucosal healing. Activated B cells disrupted the epithelial-stromal cross talk required for organoid survival. Thus, B cell expansion during injury impairs epithelial-stromal cell interactions required for mucosal healing, with implications for the treatment of IBD. <<<

Long-acting injectables are considered one of the most promising therapeutic strategies for the treatment of chronic diseases as they can afford improved therapeutic efficacy, safety, and patient compliance. The use of polymer materials in such a drug formulation strategy can offer unparalleled diversity owing to the ability to synthesize materials with a wide range of properties. However, the interplay between multiple parameters, including the physicochemical properties of the drug and polymer, make it very difficult to intuitively predict the performance of these systems. This necessitates the development and characterization of a wide array of formulation candidates through extensive and time-consuming in vitro experimentation. Machine learning is enabling leap-step advances in a number of fields including drug discovery and materials science. The current study takes a critical step towards data-driven drug formulation development with an emphasis on long-acting injectables. Here we show that machine learning algorithms can be used to predict experimental drug release from these advanced drug delivery systems. We also demonstrate that these trained models can be used to guide the design of new long acting injectables. The implementation of the described data-driven approach has the potential to reduce the time and cost associated with drug formulation development. <<<