Visualization and exploration of high-dimensional data is a ubiquitous challenge across disciplines. Widely used techniques such as principal component analysis (PCA) aim to identify dominant trends in one dataset. However, in many settings we have datasets collected under different conditions, e.g., a treatment and a control experiment, and we are interested in visualizing and exploring patterns that are specific to one dataset. This paper proposes a method, contrastive principal component analysis (cPCA), which identifies low-dimensional structures that are enriched in a dataset relative to comparison data. In a wide variety of experiments, we demonstrate that cPCA with a background dataset enables us to visualize dataset-specific patterns missed by PCA and other standard methods. We further provide a geometric interpretation of cPCA and strong mathematical guarantees. An implementation of cPCA is publicly available, and can be used for exploratory data analysis in many applications where PCA is currently used. <<<

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. <<<

Given the challenges and complexities introduced while dealing with Dialect Arabic (DA) variations, Transformer based models, e.g., BERT, outperformed other models in dealing with the DA identification task. However, to fine-tune these models, a large corpus is required. Getting a large number high quality labeled examples for some Dialect Arabic classes is challenging and time-consuming. In this paper, we address the Dialect Arabic Identification task. We extend the transformer-based models, ARBERT and MARBERT, with unlabeled data in a generative adversarial setting using Semi-Supervised Generative Adversarial Networks (SS-GAN). Our model enabled producing high-quality embeddings for the Dialect Arabic examples and aided the model to better generalize for the downstream classification task given few labeled examples. Experimental results showed that our model reached better performance and faster convergence when only a few labeled examples are available. <<<

Wenchao Wei, Chi Chun Wong, Zhongjun Jia, Weixin Liu, Changan Liu, Fenfen Ji, Yasi Pan, Feixue Wang, Guoping Wang, Liuyang Zhao, Eagle S H Chu, Xiang Zhang, Joseph J Y Sung, Jun Yu <<<

Non-alcoholic steatohepatitis (NASH) is the severe form of non-alcoholic fatty liver disease, and is characterized by liver inflammation and fat accumulation. Dietary interventions, such as fibre, have been shown to alleviate this metabolic disorder in mice via the gut microbiota. Here, we investigated the mechanistic role of the gut microbiota in ameliorating NASH via dietary fibre in mice. Soluble fibre inulin was found to be more effective than insoluble fibre cellulose to suppress NASH progression in mice, as shown by reduced hepatic steatosis, necro-inflammation, ballooning and fibrosis. We employed stable isotope probing to trace the incorporation of C-inulin into gut bacterial genomes and metabolites during NASH progression. Shotgun metagenome sequencing revealed that the commensal Parabacteroides distasonis was enriched by C-inulin. Integration of C-inulin metagenomes and metabolomes suggested that P. distasonis used inulin to produce pentadecanoic acid, an odd-chain fatty acid, which was confirmed in vitro and in germ-free mice. P. distasonis or pentadecanoic acid was protective against NASH in mice. Mechanistically, inulin, P. distasonis or pentadecanoic acid restored gut barrier function in NASH models, which reduced serum lipopolysaccharide and liver pro-inflammatory cytokine expression. Overall this shows that gut microbiota members can use dietary fibre to generate beneficial metabolites to suppress metabolic disease. <<<

Multivariate time series data comprises various channels of variables. The multivariate forecasting models need to capture the relationship between the channels to accurately predict future values. However, recently, there has been an emergence of methods that employ the Channel Independent (CI) strategy. These methods view multivariate time series data as separate univariate time series and disregard the correlation between channels. Surprisingly, our empirical results have shown that models trained with the CI strategy outperform those trained with the Channel Dependent (CD) strategy, usually by a significant margin. Nevertheless, the reasons behind this phenomenon have not yet been thoroughly explored in the literature. This paper provides comprehensive empirical and theoretical analyses of the characteristics of multivariate time series datasets and the CI/CD strategy. Our results conclude that the CD approach has higher capacity but often lacks robustness to accurately predict distributionally drifted time series. In contrast, the CI approach trades capacity for robust prediction. Practical measures inspired by these analyses are proposed to address the capacity and robustness dilemma, including a modified CD method called Predict Residuals with Regularization (PRReg) that can surpass the CI strategy. We hope our findings can raise awareness among researchers about the characteristics of multivariate time series and inspire the construction of better forecasting models. <<<

Ramya Undamatla, Olayemi G Fagunloye, Jeffrey Chen, Lia R Edmunds, Anjana Murali, Amanda Mills, Bingxian Xie, Martha M Pangburn, Ian Sipula, Gregory Gibson, Claudette St Croix, Michael J Jurczak <<<

Nonalcoholic fatty liver disease (NAFLD) encompasses a spectrum of pathologies that includes steatosis, steatohepatitis (NASH) and fibrosis and is strongly associated with insulin resistance and type 2 diabetes. Changes in mitochondrial function are implicated in the pathogenesis of NAFLD, particularly in the transition from steatosis to NASH. Mitophagy is a mitochondrial quality control mechanism that allows for the selective removal of damaged mitochondria from the cell via the autophagy pathway. While past work demonstrated a negative association between liver fat content and rates of mitophagy, when changes in mitophagy occur during the pathogenesis of NAFLD and whether such changes contribute to the primary endpoints associated with the disease are currently poorly defined. We therefore undertook the studies described here to establish when alterations in mitophagy occur during the pathogenesis of NAFLD, as well as to determine the effects of genetic inhibition of mitophagy via conditional deletion of a key mitophagy regulator, PARKIN, on the development of steatosis, insulin resistance, inflammation and fibrosis. We find that loss of mitophagy occurs early in the pathogenesis of NAFLD and that loss of PARKIN hastens the onset but not severity of key NAFLD disease features. These observations suggest that loss of mitochondrial quality control in response to nutritional stress may contribute to mitochondrial dysfunction and the pathogenesis of NAFLD. <<<

Recent work in cognitive and systems neuroscience has suggested that the hippocampus might support planning, imagination, and navigation by forming cognitive maps that capture the abstract structure of physical spaces, tasks, and situations. Navigation involves disambiguating similar contexts, and the planning and execution of a sequence of decisions to reach a goal. Here, we examine hippocampal activity patterns in humans during a goal-directed navigation task to investigate how contextual and goal information are incorporated in the construction and execution of navigational plans. During planning, hippocampal pattern similarity is enhanced across routes that share a context and a goal. During navigation, we observe prospective activation in the hippocampus that reflects the retrieval of pattern information related to a key-decision point. These results suggest that, rather than simply representing overlapping associations or state transitions, hippocampal activity patterns are shaped by context and goals. <<<

Andrew J. Stout , Miles J. Arnett , Kristin Chai , Tina Guo , Lishu Liao , Addison B. Mirliani , Miriam L. Rittenberg , Michelle Shub , Eugene C. White , John S. K. Yuen , Xiaoli Zhang , David L. Kaplan <<<

For cultured meat to succeed at scale, muscle cells from food-relevant species must be expanded in vitro in a rapid and reliable manner to produce millions of metric tons of biomass annually. Toward this goal, genetically immortalized cells offer substantial benefits over primary cells, including rapid growth, escape from cellular senescence, and consistent starting cell populations for production. Here, we develop genetically immortalized bovine satellite cells (iBSCs) via constitutive expression of bovine Telomerase reverse transcriptase (TERT) and Cyclin-dependent kinase 4 (CDK4). These cells achieve over 120 doublings at the time of publication and maintain their capacity for myogenic differentiation. They therefore offer a valuable tool to the field, enabling further research and development to advance cultured meat. <<<

BACKGROUND: Transcriptional regulation is a key aspect of environmental stress responses. Heat stress induces transcriptional memory, i.e., sustained induction or enhanced re-induction of transcription, that allows plants to respond more efficiently to a recurrent HS. In light of more frequent temperature extremes due to climate change, improving heat tolerance in crop plants is an important breeding goal. However, not all heat stress-inducible genes show transcriptional memory, and it is unclear what distinguishes memory from non-memory genes. To address this issue and understand the genome and epigenome architecture of transcriptional memory after heat stress, we identify the global target genes of two key memory heat shock transcription factors, HSFA2 and HSFA3, using time course ChIP-seq.RESULTS: HSFA2 and HSFA3 show near identical binding patterns. In vitro and in vivo binding strength is highly correlated, indicating the importance of DNA sequence elements. In particular, genes with transcriptional memory are strongly enriched for a tripartite heat shock element, and are hallmarked by several features: low expression levels in the absence of heat stress, accessible chromatin environment, and heat stress-induced enrichment of H3K4 trimethylation. These results are confirmed by an orthogonal transcriptomic data set using both de novo clustering and an established definition of memory genes.CONCLUSIONS: Our findings provide an integrated view of HSF-dependent transcriptional memory and shed light on its sequence and chromatin determinants, enabling the prediction and engineering of genes with transcriptional memory behavior. <<<

BACKGROUND: Cervical cancer is currently estimated to be the fourth most common cancer among women worldwide and the leading cause of cancer-related deaths in some of the world's poorest countries. C/EBPβ has tumor suppressor effects because it is necessary for oncogene-induced senescence. However, C/EBPβ also has an oncogenic role. The specific role of C/EBPβ in cervical cancer as a tumor suppressor or oncoprotein is unclear.OBJECTIVE: To explore the role of the C/EBPβ protein in cervical tumorigenesis and progression.METHODS: Quantitative RT-PCR was used to analyze C/EBPβ (15 cervical cancer tissue samples and 15 corresponding normal cervical tissue samples), miR-661, and MTA1 mRNA expression in clinical samples (10 cervical cancer tissue samples and 10 corresponding normal cervical tissue samples). Immunohistochemistry was used to analyze C/EBPβ (381 clinical samples), Ki67 (80 clinical samples) and PCNA ( 60 clinical samples) protein expression. MALDI-TOF MassARRAY was used to analyze C/EBPβ gene methylation (13 cervical cancer tissues and 13 corresponding normal cervical tissues). Cell proliferation was analyzed by CCK-8 in cervical cancer cell lines. Western blotting and immunohistochemistry were performed to detect C/EBPβ protein expression levels, and mRNA expression was analyzed by quantitative RT-PCR analysis. Flow cytometry was performed to measure cell cycle distribution and cell apoptosis. Colony formation, Transwell, cell invasion, and wound healing assays were performed to detect cell migration and invasion.RESULTS: C/EBPβ protein expression was significantly reduced in cervical cancer tissues compared with cervicitis tissues (P < 0.01). Ki67 protein and PCNA protein expression levels were significantly higher in cervical cancer tissues compared with cervicitis tissues. The rate of C/EBPβ gene promoter methylation of CpG12, 13, 14 and CpG19 in cervical cancer tissues was significantly increased compared with normal cervical tissue (P < 0.05). In addition, C/EBPβ was overexpressed in cervical cancer cells and this overexpression inhibited cell proliferation, migration, invasion, arrested cells in S phase, and promoted apoptosis.CONCLUSIONS: We have demonstrated that C/EBPβ decreased in cervical cancer tissues and overexpression of the C/EBPβ gene in cervical cancer cells could inhibit proliferation, invasion and migration. <<<

OBJECTIVE: To explore and analyze the expression of eukaryotic translation elongation factor 1 alpha 2 (eEF1A2) gene in cervical cancer tissues, its relationship with patient survival, gene mutations, and changes in copy number in cervical cancer and chronic cervicitis tissues.METHODS: The expression of the eEF1A2 gene in cervical cancer and its relationship with patient survival were analyzed using gene expression profile interactive analysis. Changes in eEF1A2 expression in cervical cancer tissues were analyzed using cBioPortal, a portal for cancer genomics analysis. The eEF1A2 copy number in cervical cancer tissues and chronic cervicitis tissues was determined by real-time fluorescence quantitative polymerase chain reaction. The relationship between the expression of eEF1A2 protein and the clinical stage, pathological grade, and patient survival of cervical cancer was analyzed by the database: The Human Protein Atlas, an integrated repository portal for tumor-immune system interactions.RESULTS: Gene expression profile interactive analysis database analysis showed no significant differences in the expression of eEF1A2 between cervical cancer and normal cervical tissues (P > .05). The eEF1A2 gene expression level was not correlated with the survival of cervical cancer patients (P > .05). Analysis of the cBioPortal database showed that 18 of 297 cervical cancer patients had eEF1A2 gene changes, including missense mutation, splice mutation, amplification, and messenger RNA increase. There was no significant difference in eEF1A2 gene copy number between cervical cancer and chronic cervicitis (P > .05). The Human Protein Atlas and an integrated repository portal for tumor-immune system interactions database analysis of immunohistochemical data showed that eEF1A2 protein expression was no significant difference in clinical stage, pathological grade and patient survival of cervical cancer (P > .05).CONCLUSION: The eEF1A2 gene was mutated in cervical cancer tissues. The eEF1A2 gene copy number was not associated with changes in the expression of the eEF1A2 gene in cervical cancer tissues. <<<

We present our novel software, nanomonsv, for detecting somatic structural variations (SVs) using tumor and matched control long-read sequencing data with a single-base resolution. The current version of nanomonsv includes two detection modules, Canonical SV module, and Single breakend SV module. Using tumor/control paired long-read sequencing data from three cancer and their matched lymphoblastoid lines, we demonstrate that Canonical SV module can identify somatic SVs that can be captured by short-read technologies with higher precision and recall than existing methods. In addition, we have developed a workflow to classify mobile element insertions while elucidating their in-depth properties, such as 5' truncations, internal inversions, as well as source sites for 3' transductions. Furthermore, Single breakend SV module enables the detection of complex SVs that can only be identified by long-reads, such as SVs involving highly-repetitive centromeric sequences, and LINE1- and virus-mediated rearrangements. In summary, our approaches applied to cancer long-read sequencing data can reveal various features of somatic SVs and will lead to a better understanding of mutational processes and functional consequences of somatic SVs. <<<

Jorge D Martin-Rufino, Nicole Castano, Michael Pang, Emanuelle I Grody, Samantha Joubran, Alexis Caulier, Lara Wahlster, Tongqing Li, Xiaojie Qiu, Anna Maria Riera-Escandell, Gregory A Newby, Aziz Al'Khafaji, Santosh Chaudhary, Susan Black, Chen Weng, Glen Munson, David R Liu, Marcin W Wlodarski, Kacie Sims, Jamie H Oakley, Ross M Fasano, Ramnik J Xavier, Eric S Lander, Daryl E Klein, Vijay G Sankaran <<<

Systematic evaluation of the impact of genetic variants is critical for the study and treatment of human physiology and disease. While specific mutations can be introduced by genome engineering, we still lack scalable approaches that are applicable to the important setting of primary cells, such as blood and immune cells. Here, we describe the development of massively parallel base-editing screens in human hematopoietic stem and progenitor cells. Such approaches enable functional screens for variant effects across any hematopoietic differentiation state. Moreover, they allow for rich phenotyping through single-cell RNA sequencing readouts and separately for characterization of editing outcomes through pooled single-cell genotyping. We efficiently design improved leukemia immunotherapy approaches, comprehensively identify non-coding variants modulating fetal hemoglobin expression, define mechanisms regulating hematopoietic differentiation, and probe the pathogenicity of uncharacterized disease-associated variants. These strategies will advance effective and high-throughput variant-to-function mapping in human hematopoiesis to identify the causes of diverse diseases. <<<

Diffusion-weighted magnetic resonance imaging (DW-MRI) is a non-invasive way of imaging white matter tracts in the human brain. DW-MRIs are usually acquired using echo-planar imaging (EPI) with high gradient fields, which could introduce severe geometric distortions that interfere with further analyses. Most tools for correcting distortion require two minimally weighted DW-MRI images (B0) acquired with different phase-encoding directions, and they can take hours to process per subject. Since a great amount of diffusion data are only acquired with a single phase-encoding direction, the application of existing approaches is limited. We propose a deep learning-based registration approach to correct distortion using only the B0 acquired from a single phase-encoding direction. Specifically, we register undistorted T1-weighted images and distorted B0 to remove the distortion through a deep learning model. We apply a differentiable mutual information loss during training to improve inter-modality alignment. Experiments on the Human Connectome Project dataset show the proposed method outperforms SyN and VoxelMorph on several metrics, and only takes a few seconds to process one subject. <<<

Bobae Yang, Sueun Kim, Woong-Jae Jung, Kyungwoo Kim, Sugyung Kim, Yong-Jin Kim, Tae-Gyun Kim, Eun-Chong Lee, Jung-Sik Joo, Chae Gyu Park, Sumin Oh, Kyung Hyun Yoo, Hyoung-Pyo Kim <<<

Dendritic cells are antigen-presenting cells orchestrating innate and adaptive immunity. The crucial role of transcription factors and histone modifications in the transcriptional regulation of dendritic cells has been extensively studied. However, it is not been well understood whether and how three-dimensional chromatin folding controls gene expression in dendritic cells. Here we demonstrate that activation of bone marrow-derived dendritic cells induces extensive reprogramming of chromatin looping as well as enhancer activity, both of which are implicated in the dynamic changes in gene expression. Interestingly, depletion of CTCF attenuates GM-CSF-mediated JAK2/STAT5 signaling, resulting in defective NF-κB activation. Moreover, CTCF is necessary for establishing NF-κB-dependent chromatin interactions and maximal expression of pro-inflammatory cytokines, which prime Th1 and Th17 cell differentiation. Collectively, our study provides mechanistic insights into how three-dimensional enhancer networks control gene expression during bone marrow-derived dendritic cells activation, and offers an integrative view of the complex activities of CTCF in the inflammatory response of bone marrow-derived dendritic cells. <<<

Anna Fowler, Shazia Mahamdallie, Elise Ruark, Sheila Seal, Emma Ramsay, Matthew Clarke, Imran Uddin, Harriet Wylie, Ann Strydom, Gerton Lunter, Nazneen Rahman <<<

Targeted next generation sequencing (NGS) panels are increasingly being used in clinical genomics to increase capacity, throughput and affordability of gene testing. Identifying whole exon deletions or duplications (termed exon copy number variants, 'exon CNVs') in exon-targeted NGS panels has proved challenging, particularly for single exon CNVs. We developed a tool for the Detection of Exon Copy Number variants (DECoN), which is optimised for analysis of exon-targeted NGS panels in the clinical setting. We evaluated DECoN performance using 96 samples with independently validated exon CNV data. We performed simulations to evaluate DECoN detection performance of single exon CNVs and to evaluate performance using different coverage levels and sample numbers. Finally, we implemented DECoN in a clinical laboratory that tests and with the TruSight Cancer Panel (TSCP). We used DECoN to analyse 1,919 samples, validating exon CNV detections by multiplex ligation-dependent probe amplification (MLPA). In the evaluation set, DECoN achieved 100% sensitivity and 99% specificity for BRCA exon CNVs, including identification of 8 single exon CNVs. DECoN also identified 14/15 exon CNVs in 8 other genes. Simulations of all possible BRCA single exon CNVs gave a mean sensitivity of 98% for deletions and 95% for duplications. DECoN performance remained excellent with different levels of coverage and sample numbers; sensitivity and specificity was >98% with the typical NGS run parameters. In the clinical pipeline, DECoN automatically analyses pools of 48 samples at a time, taking 24 minutes per pool, on average. DECoN detected 24 BRCA exon CNVs, of which 23 were confirmed by MLPA, giving a false discovery rate of 4%. Specificity was 99.7%. DECoN is a fast, accurate, exon CNV detection tool readily implementable in research and clinical NGS pipelines. It has high sensitivity and specificity and acceptable false discovery rate. DECoN is freely available at www.icr.ac.uk/decon. <<<

We describe a procedure for designing proteins with backbones produced by varying the parameters in the Crick coiled coil-generating equations. Combinatorial design calculations identify low-energy sequences for alternative helix supercoil arrangements, and the helices in the lowest-energy arrangements are connected by loop building. We design an antiparallel monomeric untwisted three-helix bundle with 80-residue helices, an antiparallel monomeric right-handed four-helix bundle, and a pentameric parallel left-handed five-helix bundle. The designed proteins are extremely stable (extrapolated ΔGfold > 60 kilocalories per mole), and their crystal structures are close to those of the design models with nearly identical core packing between the helices. The approach enables the custom design of hyperstable proteins with fine-tuned geometries for a wide range of applications. <<<

Julien Perolat, Bart De Vylder, Daniel Hennes, Eugene Tarassov, Florian Strub, Vincent de Boer, Paul Muller, Jerome T Connor, Neil Burch, Thomas Anthony, Stephen McAleer, Romuald Elie, Sarah H Cen, Zhe Wang, Audrunas Gruslys, Aleksandra Malysheva, Mina Khan, Sherjil Ozair, Finbarr Timbers, Toby Pohlen, Tom Eccles, Mark Rowland, Marc Lanctot, Jean-Baptiste Lespiau, Bilal Piot, Shayegan Omidshafiei, Edward Lockhart, Laurent Sifre, Nathalie Beauguerlange, Remi Munos, David Silver, Satinder Singh, Demis Hassabis, Karl Tuyls <<<

We introduce DeepNash, an autonomous agent that plays the imperfect information game Stratego at a human expert level. Stratego is one of the few iconic board games that artificial intelligence (AI) has not yet mastered. It is a game characterized by a twin challenge: It requires long-term strategic thinking as in chess, but it also requires dealing with imperfect information as in poker. The technique underpinning DeepNash uses a game-theoretic, model-free deep reinforcement learning method, without search, that learns to master Stratego through self-play from scratch. DeepNash beat existing state-of-the-art AI methods in Stratego and achieved a year-to-date (2022) and all-time top-three ranking on the Gravon games platform, competing with human expert players. <<<

The pervasive uncertainty and dynamic nature of real-world environments present significant challenges for the widespread implementation of machine-driven Intelligent Decision-Making (IDM) systems. Consequently, IDM should possess the ability to continuously acquire new skills and effectively generalize across a broad range of applications. The advancement of Artificial General Intelligence (AGI) that transcends task and application boundaries is critical for enhancing IDM. Recent studies have extensively investigated the Transformer neural architecture as a foundational model for various tasks, including computer vision, natural language processing, and reinforcement learning. We propose that a Foundation Decision Model (FDM) can be developed by formulating diverse decision-making tasks as sequence decoding tasks using the Transformer architecture, offering a promising solution for expanding IDM applications in complex real-world situations. In this paper, we discuss the efficiency and generalization improvements offered by a foundation decision model for IDM and explore its potential applications in multi-agent game AI, production scheduling, and robotics tasks. Lastly, we present a case study demonstrating our FDM implementation, DigitalBrain (DB1) with 1.3 billion parameters, achieving human-level performance in 870 tasks, such as text generation, image captioning, video game playing, robotic control, and traveling salesman problems. As a foundation decision model, DB1 represents an initial step toward more autonomous and efficient real-world IDM applications. <<<