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. <<<

Deep learning has been actively studied for time series forecasting, and the mainstream paradigm is based on the end-to-end training of neural network architectures, ranging from classical LSTM/RNNs to more recent TCNs and Transformers. Motivated by the recent success of representation learning in computer vision and natural language processing, we argue that a more promising paradigm for time series forecasting, is to first learn disentangled feature representations, followed by a simple regression fine-tuning step -- we justify such a paradigm from a causal perspective. Following this principle, we propose a new time series representation learning framework for time series forecasting named CoST, which applies contrastive learning methods to learn disentangled seasonal-trend representations. CoST comprises both time domain and frequency domain contrastive losses to learn discriminative trend and seasonal representations, respectively. Extensive experiments on real-world datasets show that CoST consistently outperforms the state-of-the-art methods by a considerable margin, achieving a 21.3% improvement in MSE on multivariate benchmarks. It is also robust to various choices of backbone encoders, as well as downstream regressors. Code is available at this https URL. <<<

The default mode network (DMN) involves interacting cortical areas, including the posterior cingulate cortex (PCC) and the retrosplenial cortex (RSC), and subcortical areas, including the medial temporal lobe (MTL). The degree of functional connectivity (FC) within the DMN, particularly between MTL and medial-parietal subsystems, relates to episodic memory (EM) processes. However, past resting-state studies investigating the link between posterior DMN-MTL FC and EM performance yielded inconsistent results, possibly reflecting heterogeneity in the degree of connectivity between MTL and specific cortical DMN regions. Animal work suggests that RSC has structural connections to both cortical DMN regions and MTL, and may thus serve as an intermediate layer that facilitates information transfer between cortical and subcortical DMNs. We studied 180 healthy old adults (aged 64-68 years), who underwent comprehensive assessment of EM, along with resting-state fMRI. We found greater FC between MTL and RSC than between MTL and the other cortical DMN regions (e.g., PCC), with the only significant association with EM observed for MTL-RSC FC. Mediational analysis showed that MTL-cortical DMN connectivity increased with RSC as a mediator. Further analysis using a graph-theoretical approach on DMN nodes revealed the highest betweenness centrality for RSC, confirming that a high proportion of short paths among DMN regions pass through RSC. Importantly, the degree of RSC mediation was associated with EM performance, suggesting that individuals with greater mediation have an EM advantage. These findings suggest that RSC forms a critical gateway between MTL and cortical DMN to support EM in older adults. <<<

Single-cell atlases promise to provide a 'missing link' between genes, diseases and therapies. By identifying the specific cell types, states, programs and contexts where disease-implicated genes act, we will understand the mechanisms of disease at the cellular and tissue levels and can use this understanding to develop powerful disease diagnostics; identify promising new drug targets; predict their efficacy, toxicity and resistance mechanisms; and empower new kinds of therapies, from cancer therapies to regenerative medicine. Here, we lay out a vision for the potential of cell atlases to impact the future of medicine, and describe how advances over the past decade have begun to realize this potential in common complex diseases, infectious diseases (including COVID-19), rare diseases and cancer. <<<

Quantum programming languages enable developers to implement algorithms for quantum computers that promise computational breakthroughs in classically intractable tasks. Programming quantum computers requires awareness of entanglement, the phenomenon in which measurement outcomes of qubits are correlated. Entanglement can determine the correctness of algorithms and suitability of programming patterns. In this work, we formalize purity as a central tool for automating reasoning about entanglement in quantum programs. A pure expression is one whose evaluation is unaffected by the measurement outcomes of qubits that it does not own, implying freedom from entanglement with any other expression in the computation. We present Twist, the first language that features a type system for sound reasoning about purity. The type system enables the developer to identify pure expressions using type annotations. Twist also features purity assertion operators that state the absence of entanglement in the output of quantum gates. To soundly check these assertions, Twist uses a combination of static analysis and runtime verification. We evaluate Twist's type system and analyses on a benchmark suite of quantum programs in simulation, demonstrating that Twist can express quantum algorithms, catch programming errors in them, and support programs that several languages disallow, while incurring runtime verification overhead of less than 3.5%. <<<

Mitochondrial dysfunction plays a central role in aging but the exact biological causes are still being determined. Here, we show that optogenetically increasing mitochondrial membrane potential during adulthood using a light-activated proton pump improves age-associated phenotypes and extends lifespan in . Our findings provide direct causal evidence that rescuing the age-related decline in mitochondrial membrane potential is sufficient to slow the rate of aging and extend healthspan and lifespan. <<<

It remains unclear why acute depletion of CTCF (CCCTC-binding factor) and cohesin only marginally affects expression of most genes despite substantially perturbing three-dimensional (3D) genome folding at the level of domains and structural loops. To address this conundrum, we used high-resolution Micro-C and nascent transcript profiling in mouse embryonic stem cells. We find that enhancer-promoter (E-P) interactions are largely insensitive to acute (3-h) depletion of CTCF, cohesin or WAPL. YY1 has been proposed as a structural regulator of E-P loops, but acute YY1 depletion also had minimal effects on E-P loops, transcription and 3D genome folding. Strikingly, live-cell, single-molecule imaging revealed that cohesin depletion reduced transcription factor (TF) binding to chromatin. Thus, although CTCF, cohesin, WAPL or YY1 is not required for the short-term maintenance of most E-P interactions and gene expression, our results suggest that cohesin may facilitate TFs to search for and bind their targets more efficiently. <<<

We study the problem of combining neural networks with symbolic reasoning. Recently introduced frameworks for Probabilistic Neurosymbolic Learning (PNL), such as DeepProbLog, perform exponential-time exact inference, limiting the scalability of PNL solutions. We introduce Approximate Neurosymbolic Inference (A-NeSI): a new framework for PNL that uses neural networks for scalable approximate inference. A-NeSI 1) performs approximate inference in polynomial time without changing the semantics of probabilistic logics; 2) is trained using data generated by the background knowledge; 3) can generate symbolic explanations of predictions; and 4) can guarantee the satisfaction of logical constraints at test time, which is vital in safety-critical applications. Our experiments show that A-NeSI is the first end-to-end method to scale the Multi-digit MNISTAdd benchmark to sums of 15 MNIST digits, up from 4 in competing systems. Finally, our experiments show that A-NeSI achieves explainability and safety without a penalty in performance. <<<

We present NeurASP, a simple extension of answer set programs by embracing neural networks. By treating the neural network output as the probability distribution over atomic facts in answer set programs, NeurASP provides a simple and effective way to integrate sub-symbolic and symbolic computation. We demonstrate how NeurASP can make use of a pre-trained neural network in symbolic computation and how it can improve the neural network's perception result by applying symbolic reasoning in answer set programming. Also, NeurASP can make use of ASP rules to train a neural network better so that a neural network not only learns from implicit correlations from the data but also from the explicit complex semantic constraints expressed by the rules. <<<

The fine-scale cell-free DNA fragmentation patterns in early-stage cancers are poorly understood. We developed a de novo approach to characterize the cell-free DNA fragmentation hotspots from plasma whole-genome sequencing. Hotspots are enriched in open chromatin regions, and, interestingly, 3'end of transposons. Hotspots showed global hypo-fragmentation in early-stage liver cancers and are associated with genes involved in the initiation of hepatocellular carcinoma and associated with cancer stem cells. The hotspots varied across multiple early-stage cancers and demonstrated high performance for the diagnosis and identification of tissue-of-origin in early-stage cancers. We further validated the performance with a small number of independent case-control-matched early-stage cancer samples. <<<

Daniel B Rosen, Elisabeth A Murphy, Ron S Gejman, Allyson Capili, Rachel L Friedlander, Sophie Rand, Kristen A Cagino, Shannon M Glynn, Kathy C Matthews, Jeff M Kubiak, Jim Yee, Malavika Prabhu, Laura E Riley, Yawei J Yang <<<

OBJECTIVE: To study how severity and progression of coronavirus disease (COVID-19) affect cytokine profiles in pregnant women.MATERIALS AND METHODS: 69 third-trimester, pregnant women were tested for COVID-19 infection and SARS-CoV-2 specific IgM and IgG antibodies. Patients were stratified according to SARS-CoV-2 Reverse Transcriptase-PCR (RT-PCR) status and serology (IgM and IgG) status. Cytokines G-CSF, HGF, IL-18, IL-1Ra, IL-2Ra, IL-8, and IP-10 were measured via ELISA. Retrospective chart review for COVID-19 symptoms and patient vitals was conducted, and cytokine levels were compared between SARS-CoV-2 positive and negative cohorts, by seronegative and seropositive infection, by time course since onset of infection, and according to NIH defined clinical severity.RESULTS: IL-18, IL-1Ra, and IP-10 increased in the 44 RT-PCR positive pregnant women compared to the 25 RT-PCR negative pregnant controls. Elevated cytokine levels were found in early infections, defined by positive RT-PCR and seronegative status, and higher cytokine levels were also associated with more severe disease. By IgM seroconversion, IL-8 and IP-10 returned to levels seen in uninfected patients, while IL-18 levels remained significantly elevated.CONCLUSION: Cytokine profiles of third-trimester pregnant women vary with the time course of infection and are correlated with clinical severity. <<<

Keyur Talsania , Tsai-wei Shen , Xiongfong Chen , Erich Jaeger , Zhipan Li , Zhong Chen , Wanqiu Chen , Bao Tran , Rebecca Kusko , Limin Wang , Andy Wing Chun Pang , Zhaowei Yang , Sulbha Choudhari , Michael Colgan , Li Tai Fang , Andrew Carroll , Jyoti Shetty , Yuliya Kriga , Oksana German , Tatyana Smirnova , Tiantain Liu , Jing Li , Ben Kellman , Karl Hong , Alex R. Hastie , Aparna Natarajan , Ali Moshrefi , Anastasiya Granat , Tiffany Truong , Robin Bombardi , Veronnica Mankinen , Daoud Meerzaman , Christopher E. Mason , Jack Collins , Eric Stahlberg , Chunlin Xiao , Charles Wang , Wenming Xiao , Yongmei Zhao <<<

Abstract Background The cancer genome is commonly altered with thousands of structural rearrangements including insertions, deletions, translocation, inversions, duplications, and copy number variations. Thus, structural variant (SV) characterization plays a paramount role in cancer target identification, oncology diagnostics, and personalized medicine. As part of the SEQC2 Consortium effort, the present study established and evaluated a consensus SV call set using a breast cancer reference cell line and matched normal control derived from the same donor, which were used in our companion benchmarking studies as reference samples. Results We systematically investigated somatic SVs in the reference cancer cell line by comparing to a matched normal cell line using multiple NGS platforms including Illumina short-read, 10X Genomics linked reads, PacBio long reads, Oxford Nanopore long reads, and high-throughput chromosome conformation capture (Hi-C). We established a consensus SV call set of a total of 1788 SVs including 717 deletions, 230 duplications, 551 insertions, 133 inversions, 146 translocations, and 11 breakends for the reference cancer cell line. To independently evaluate and cross-validate the accuracy of our consensus SV call set, we used orthogonal methods including PCR-based validation, Affymetrix arrays, Bionano optical mapping, and identification of fusion genes detected from RNA-seq. We evaluated the strengths and weaknesses of each NGS technology for SV determination, and our findings provide an actionable guide to improve cancer genome SV detection sensitivity and accuracy. Conclusions A high-confidence consensus SV call set was established for the reference cancer cell line. A large subset of the variants identified was validated by multiple orthogonal methods. <<<

Signal peptidases are vital enzymes in the protein secretion pathway. In Archaea, type I signal peptidase, responsible for the cleavage of secretory signal peptides from the majority of secreted proteins, and prepilin peptidase-like signal peptidase, responsible for processing signal peptides from prepilin-like proteins like the preflagellins and various sugar-binding proteins, have been identified. In addition, the archaeal signal peptide peptidase, responsible for degradation of signal peptides after their removal from precursor proteins, has been characterized. These enzymes seem to have a mosaic of eukaryal and bacterial characteristics, and also possess unique archaeal traits. In this review, the most current knowledge with regard to these enzymes is summarized, including their cellular function, catalytic mechanism and distribution and conservation among archaeal species. Comparisons are drawn of these enzymes to their bacterial and eukaryal counterparts, and unique archaeal features highlighted. <<<

Bingfei Yu, Quanming Shi, Julia A Belk, Kathryn E Yost, Kevin R Parker, Rui Li, Betty B Liu, Huang Huang, Daniel Lingwood, William J Greenleaf, Mark M Davis, Ansuman T Satpathy, Howard Y Chang <<<

Cells communicate with each other via receptor-ligand interactions. Here, we describe lentiviral-mediated cell entry by engineered receptor-ligand interaction (ENTER) to display ligand proteins, deliver payloads, and record receptor specificity. We optimize ENTER to decode interactions between T cell receptor (TCR)-MHC peptides, antibody-antigen, and other receptor-ligand pairs. A viral presentation strategy allows ENTER to capture interactions between B cell receptor and any antigen. We engineer ENTER to deliver genetic payloads to antigen-specific T or B cells to selectively modulate cellular behavior in mixed populations. Single-cell readout of ENTER by RNA sequencing (ENTER-seq) enables multiplexed enumeration of antigen specificities, TCR clonality, cell type, and states of individual T cells. ENTER-seq of CMV-seropositive patient blood samples reveals the viral epitopes that drive effector memory T cell differentiation and inter-clonal vs. intra-clonal phenotypic diversity targeting the same epitope. ENTER technology enables systematic discovery of receptor specificity, linkage to cell fates, and antigen-specific cargo delivery. <<<

Enormous waves of technological innovations over the past several years, marked by the advances in AI technologies, are profoundly reshaping the industry and the society. However, down the road, a key challenge awaits us, that is, our capability of meeting rapidly-growing scenario-specific demands is severely limited by the cost of acquiring a commensurate amount of training data. This difficult situation is in essence due to limitations of the mainstream learning paradigm: we need to train a new model for each new scenario, based on a large quantity of well-annotated data and commonly from scratch. In tackling this fundamental problem, we move beyond and develop a new learning paradigm named INTERN. By learning with supervisory signals from multiple sources in multiple stages, the model being trained will develop strong generalizability. We evaluate our model on 26 well-known datasets that cover four categories of tasks in computer vision. In most cases, our models, adapted with only 10% of the training data in the target domain, outperform the counterparts trained with the full set of data, often by a significant margin. This is an important step towards a promising prospect where such a model with general vision capability can dramatically reduce our reliance on data, thus expediting the adoption of AI technologies. Furthermore, revolving around our new paradigm, we also introduce a new data system, a new architecture, and a new benchmark, which, together, form a general vision ecosystem to support its future development in an open and inclusive manner. See project website at this https URL . <<<

In neuroimaging, cortical surface atlases play a fundamental role for spatial normalization, analysis, visualization, and comparison of results across individuals and different studies. However, existing cortical surface atlases created for adults are not suitable for infant brains during the first two postnatal years, which is the most dynamic period of postnatal structural and functional development of the highly-folded cerebral cortex. Therefore, spatiotemporal cortical surface atlases for infant brains are highly desired yet still lacking for accurate mapping of early dynamic brain development. To bridge this significant gap, leveraging our infant-dedicated computational pipeline for cortical surface-based analysis and the unique longitudinal infant MRI dataset acquired in our research center, in this paper, we construct the first spatiotemporal (4D) high-definition cortical surface atlases for the dynamic developing infant cortical structures at seven time points, including 1, 3, 6, 9, 12, 18, and 24 months of age, based on 202 serial MRI scans from 35 healthy infants. For this purpose, we develop a novel method to ensure the longitudinal consistency and unbiasedness to any specific subject and age in our 4D infant cortical surface atlases. Specifically, we first compute the within-subject mean cortical folding by unbiased groupwise registration of longitudinal cortical surfaces of each infant. Then we establish longitudinally-consistent and unbiased inter-subject cortical correspondences by groupwise registration of the geometric features of within-subject mean cortical folding across all infants. Our 4D surface atlases capture both longitudinally-consistent dynamic mean shape changes and the individual variability of cortical folding during early brain development. Experimental results on two independent infant MRI datasets show that using our 4D infant cortical surface atlases as templates leads to significantly improved accuracy for spatial normalization of cortical surfaces across infant individuals, in comparison to the infant surface atlases constructed without longitudinal consistency and also the FreeSurfer adult surface atlas. Moreover, based on our 4D infant surface atlases, for the first time, we reveal the spatially-detailed, region-specific correlation patterns of the dynamic cortical developmental trajectories between different cortical regions during early brain development. <<<

Motivation: Molecular subtypes of cancers and autoimmune disease, defined by transcriptomic profiling, have provided insight into disease pathogenesis, molecular heterogeneity and therapeutic responses. However, technical biases inherent to different gene expression profiling platforms present a unique problem when analyzing data generated from different studies. Currently, there is a lack of effective methods designed to eliminate platform-based bias. We present a method to normalize and classify RNA-seq data using machine learning classifiers trained on DNA microarray data and molecular subtypes in two datasets: breast invasive carcinoma (BRCA) and colorectal cancer (CRC).Results: Multiple analyses show that feature specific quantile normalization (FSQN) successfully removes platform-based bias from RNA-seq data, regardless of feature scaling or machine learning algorithm. We achieve up to 98% accuracy for BRCA data and 97% accuracy for CRC data in assigning molecular subtypes to RNA-seq data normalized using FSQN and a support vector machine trained exclusively on DNA microarray data. We find that maximum accuracy was achieved when normalizing RNA-seq datasets that contain at least 25 samples. FSQN allows comparison of RNA-seq data to existing DNA microarray datasets. Using these techniques, we can successfully leverage information from existing gene expression data in new analyses despite different platforms used for gene expression profiling.Availability and implementation: FSQN has been submitted as an R package to CRAN. All code used for this study is available on Github (https://github.com/jenniferfranks/FSQN).Contact: michael.l.whitfield@dartmouth.edu.Supplementary information: Supplementary data are available at Bioinformatics online. <<<

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 <<<

A 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. <<<