Danhong Wang, Randy L Buckner, Michael D Fox, Daphne J Holt, Avram J Holmes, Sophia Stoecklein, Georg Langs, Ruiqi Pan, Tianyi Qian, Kuncheng Li, Justin T Baker, Steven M Stufflebeam, Kai Wang, Xiaomin Wang, Bo Hong, Hesheng Liu <<<

The capacity to identify the unique functional architecture of an individual's brain is a crucial step toward personalized medicine and understanding the neural basis of variation in human cognition and behavior. Here we developed a cortical parcellation approach to accurately map functional organization at the individual level using resting-state functional magnetic resonance imaging (fMRI). A population-based functional atlas and a map of inter-individual variability were employed to guide the iterative search for functional networks in individual subjects. Functional networks mapped by this approach were highly reproducible within subjects and effectively captured the variability across subjects, including individual differences in brain lateralization. The algorithm performed well across different subject populations and data types, including task fMRI data. The approach was then validated by invasive cortical stimulation mapping in surgical patients, suggesting potential for use in clinical applications. <<<

Jae-Hyun Yang, Motoshi Hayano, Patrick T Griffin, João A Amorim, Michael S Bonkowski, John K Apostolides, Elias L Salfati, Marco Blanchette, Elizabeth M Munding, Mital Bhakta, Yap Ching Chew, Wei Guo, Xiaojing Yang, Sun Maybury-Lewis, Xiao Tian, Jaime M Ross, Giuseppe Coppotelli, Margarita V Meer, Ryan Rogers-Hammond, Daniel L Vera, Yuancheng Ryan Lu, Jeffrey W Pippin, Michael L Creswell, Zhixun Dou, Caiyue Xu, Sarah J Mitchell, Abhirup Das, Brendan L O'Connell, Sachin Thakur, Alice E Kane, Qiao Su, Yasuaki Mohri, Emi K Nishimura, Laura Schaevitz, Neha Garg, Ana-Maria Balta, Meghan A Rego, Meredith Gregory-Ksander, Tatjana C Jakobs, Lei Zhong, Hiroko Wakimoto, Jihad El Andari, Dirk Grimm, Raul Mostoslavsky, Amy J Wagers, Kazuo Tsubota, Stephen J Bonasera, Carlos M Palmeira, Jonathan G Seidman, Christine E Seidman, Norman S Wolf, Jill A Kreiling, John M Sedivy, George F Murphy, Richard E Green, Benjamin A Garcia, Shelley L Berger, Philipp Oberdoerffer, Stuart J Shankland, Vadim N Gladyshev, Bruce R Ksander, Andreas R Pfenning, Luis A Rajman, David A Sinclair <<<

All living things experience an increase in entropy, manifested as a loss of genetic and epigenetic information. In yeast, epigenetic information is lost over time due to the relocalization of chromatin-modifying proteins to DNA breaks, causing cells to lose their identity, a hallmark of yeast aging. Using a system called "ICE" (inducible changes to the epigenome), we find that the act of faithful DNA repair advances aging at physiological, cognitive, and molecular levels, including erosion of the epigenetic landscape, cellular exdifferentiation, senescence, and advancement of the DNA methylation clock, which can be reversed by OSK-mediated rejuvenation. These data are consistent with the information theory of aging, which states that a loss of epigenetic information is a reversible cause of aging. <<<

Shogo Sato, Kenneth A Dyar, Jonas T Treebak, Sara L Jepsen, Amy M Ehrlich, Stephen P Ashcroft, Kajetan Trost, Thomas Kunzke, Verena M Prade, Lewin Small, Astrid Linde Basse, Milena Schönke, Siwei Chen, Muntaha Samad, Pierre Baldi, Romain Barrès, Axel Walch, Thomas Moritz, Jens J Holst, Dominik Lutter, Juleen R Zierath, Paolo Sassone-Corsi <<<

Tissue sensitivity and response to exercise vary according to the time of day and alignment of circadian clocks, but the optimal exercise time to elicit a desired metabolic outcome is not fully defined. To understand how tissues independently and collectively respond to timed exercise, we applied a systems biology approach. We mapped and compared global metabolite responses of seven different mouse tissues and serum after an acute exercise bout performed at different times of the day. Comparative analyses of intra- and inter-tissue metabolite dynamics, including temporal profiling and blood sampling across liver and hindlimb muscles, uncovered an unbiased view of local and systemic metabolic responses to exercise unique to time of day. This comprehensive atlas of exercise metabolism provides clarity and physiological context regarding the production and distribution of canonical and novel time-dependent exerkine metabolites, such as 2-hydroxybutyrate (2-HB), and reveals insight into the health-promoting benefits of exercise on metabolism. <<<

Fetal Magnetic Resonance Imaging (MRI) is used in prenatal diagnosis and to assess early brain development. Accurate segmentation of the different brain tissues is a vital step in several brain analysis tasks, such as cortical surface reconstruction and tissue thickness measurements. Fetal MRI scans, however, are prone to motion artifacts that can affect the correctness of both manual and automatic segmentation techniques. In this paper, we propose a novel network structure that can simultaneously generate conditional atlases and predict brain tissue segmentation, called CAS-Net. The conditional atlases provide anatomical priors that can constrain the segmentation connectivity, despite the heterogeneity of intensity values caused by motion or partial volume effects. The proposed method is trained and evaluated on 253 subjects from the developing Human Connectome Project (dHCP). The results demonstrate that the proposed method can generate conditional age-specific atlas with sharp boundary and shape variance. It also segment multi-category brain tissues for fetal MRI with a high overall Dice similarity coefficient (DSC) of 85.2% for the selected 9 tissue labels. <<<

The discovery of the genetic basis for circadian rhythms has expanded our knowledge of the temporal organization of behavior and physiology. The observations that the circadian gene network is present in most living organisms from eubacteria to humans, that most cells and tissues express autonomous clocks, and that disruption of clock genes results in metabolic dysregulation have revealed interactions between metabolism and circadian rhythms at neural, molecular, and cellular levels. A major challenge remains in understanding the interplay between brain and peripheral clocks and in determining how these interactions promote energy homeostasis across the sleep-wake cycle. In this Review, we evaluate how investigation of molecular timing may create new opportunities to understand and develop therapies for obesity and diabetes. <<<

We consider the task of estimating, from observed data, a probabilistic model that is parameterized by a finite number of parameters. In particular, we are considering the situation where the model probability density function is unnormalized. That is, the model is only specified up to the partition function. The partition function normalizes a model so that it integrates to one for any choice of the parameters. However, it is often impossible to obtain it in closed form. Gibbs distributions, Markov and multi-layer networks are examples of models where analytical normalization is often impossible. Maximum likelihood estimation can then not be used without resorting to numerical approximations which are often computationally expensive. We propose here a new objective function for the estimation of both normalized and unnormalized models. The basic idea is to perform nonlinear logistic regression to discriminate between the observed data and some artificially generated noise. With this approach, the normalizing partition function can be estimated like any other parameter. We prove that the new estimation method leads to a consistent (convergent) estimator of the parameters. For large noise sample sizes, the new estimator is furthermore shown to behave like the maximum likelihood estimator. In the estimation of unnormalized models, there is a trade-off between statistical and computational performance. We show that the new method strikes a competitive trade-off in comparison to other estimation methods for unnormalized models. As an application to real data, we estimate novel two-layer models of natural image statistics with spline nonlinearities. <<<

PURPOSE: This workgroup aimed to develop an evidence-based clinical practice guideline for the use of noninvasive prenatal screening (NIPS) for pregnant individuals at general risk for fetal trisomy 21, trisomy 18, or trisomy 13 and to evaluate the utility of NIPS for other chromosomal disorders.METHODS: The NIPS Evidence-Based Guideline Work Group (n = 7) relied on the results from the recent American College of Medical Genetics and Genomics (ACMG) systematic review to form the evidentiary basis of this guideline. Workgroup members used the Grading of Recommendations Assessment, Development, and Evaluation Evidence to Decision framework to draft recommendations. The guideline underwent extensive internal and external peer review with a public comment period before approval by the ACMG Board of Directors.RESULTS: Evidence consistently demonstrated improved accuracy of NIPS compared with traditional screening methods for trisomies 21, 18, and 13 in singleton and twin gestations. Identification of rare autosomal trisomies and other microdeletion syndromes with NIPS is an emerging area of interest.CONCLUSION: ACMG strongly recommends NIPS over traditional screening methods for all pregnant patients with singleton and twin gestations for fetal trisomies 21, 18, and 13 and strongly recommends NIPS be offered to patients to screen for fetal sex chromosome aneuploidy. <<<

We propose to compose dynamic tree structures that place the objects in an image into a visual context, helping visual reasoning tasks such as scene graph generation and visual Q&A. Our visual context tree model, dubbed VCTree, has two key advantages over existing structured object representations including chains and fully-connected graphs: 1) The efficient and expressive binary tree encodes the inherent parallel/hierarchical relationships among objects, e.g., "clothes" and "pants" are usually co-occur and belong to "person"; 2) the dynamic structure varies from image to image and task to task, allowing more content-/task-specific message passing among objects. To construct a VCTree, we design a score function that calculates the task-dependent validity between each object pair, and the tree is the binary version of the maximum spanning tree from the score matrix. Then, visual contexts are encoded by bidirectional TreeLSTM and decoded by task-specific models. We develop a hybrid learning procedure which integrates end-task supervised learning and the tree structure reinforcement learning, where the former's evaluation result serves as a self-critic for the latter's structure exploration. Experimental results on two benchmarks, which require reasoning over contexts: Visual Genome for scene graph generation and VQA2.0 for visual Q&A, show that VCTree outperforms state-of-the-art results while discovering interpretable visual context structures. <<<

Ming Sun, Josef Fritz, Christel Häggström, Tone Bjørge, Gabriele Nagel, Jonas Manjer, Anders Engeland, Emanuel Zitt, Bethany van Guelpen, Pär Stattin, Hanno Ulmer, Tanja Stocks <<<

BACKGROUND: Studies of obesity with or without metabolic aberrations, commonly termed metabolically unhealthy or healthy obesity, in relation to cancer risk are scarce.METHODS: We investigated body mass index (normal weight, overweight, obesity) jointly and in interaction with metabolic health status in relation to obesity-related cancer risk (n = 23 630) among 797 193 European individuals. A metabolic score comprising mid-blood pressure, plasma glucose, and triglycerides was used to define metabolically healthy and unhealthy status. Hazard ratios (HRs) and multiplicative interactions were assessed using Cox regression, and additive interactions were assessed using the relative excess risk for interaction. All statistical tests were 2-sided.RESULTS: Metabolically unhealthy obesity, with a baseline prevalence of 7%, was, compared with metabolically healthy normal weight, associated with an increased relative risk of any obesity-related cancer and of colon, rectal, pancreas, endometrial, liver, gallbladder, and renal cell cancer (P < .05), with the highest risk estimates for endometrial, liver, and renal cell cancer (HR = 2.55-3.00). Metabolically healthy obesity showed a higher relative risk for any obesity-related cancer and colon (in men), endometrial, renal cell, liver, and gallbladder cancer, though the risk relationships were weaker. There were no multiplicative interactions, but there were additive, positive interactions between body mass index and metabolic health status on obesity-related and rectal cancer among men and on endometrial cancer (P < .05).CONCLUSIONS: This study highlights that the type of metabolic obesity phenotype is important when assessing obesity-related cancer risk. In general, metabolic aberrations further increased the obesity-induced cancer risk, suggesting that obesity and metabolic aberrations are useful targets for prevention. <<<

Living organisms can produce corresponding functions by responding to external and internal stimuli, and this irritability plays a pivotal role in nature. Inspired by such natural temporal responses, the development and design of nanodevices with the ability to process time-related information could facilitate the development of molecular information processing systems. Here, we proposed a DNA finite-state machine that can dynamically respond to sequential stimuli signals. To build this state machine, a programmable allosteric strategy of DNAzyme was developed. This strategy performs the programmable control of DNAzyme conformation using a reconfigurable DNA hairpin. Based on this strategy, we first implemented a finite-state machine with two states. Through the modular design of the strategy, we further realized the finite-state machine with five states. The DNA finite-state machine endows molecular information systems with the ability of reversible logic control and order detection, which can be extended to more complex DNA computing and nanomachines to promote the development of dynamic nanotechnology. <<<

Neutering (including spaying) of male and female dogs in the first year after birth has become routine in the U.S. and much of Europe, but recent research reveals that for some dog breeds, neutering may be associated with increased risks of debilitating joint disorders and some cancers, complicating pet owners' decisions on neutering. The joint disorders include hip dysplasia, cranial cruciate ligament tear or rupture, and elbow dysplasia. The cancers include lymphoma, mast cell tumor, hemangiosarcoma, and osteosarcoma. In previous studies on the Golden Retriever, Labrador Retriever and German Shepherd Dog, neutering before a year of age was associated with increased risks of one or more joint disorders, 2-4 times that of intact dogs. The increase was particularly seen with dogs neutered by 6 months of age. In female Golden Retrievers, there was an increase in one or more of the cancers followed to about 2-4 times that of intact females with neutering at any age. The goal of the present study was to expand and use the same data collection and analyses to cover an additional 29 breeds, plus three varieties of Poodles. There were major breed differences in vulnerability to neutering, both with regard to joint disorders and cancers. In most cases, the caregiver can choose the age of neutering without increasing the risks of these joint disorders or cancers. Small-dog breeds seemed to have no increased risks of joint disorders associated with neutering, and in only two small breeds (Boston Terrier and Shih Tzu) was there a significant increase in cancers. To assist pet owners and veterinarians in deciding on the age of neutering a specific dog, guidelines that avoid increasing the risks of a dog acquiring these joint disorders or cancers are laid out for neutering ages on a breed-by-breed and sex basis. <<<

ChIP-seq is a powerful method for obtaining genome-wide maps of protein-DNA interactions and epigenetic modifications. CHANCE (CHip-seq ANalytics and Confidence Estimation) is a standalone package for ChIP-seq quality control and protocol optimization. Our user-friendly graphical software quickly estimates the strength and quality of immunoprecipitations, identifies biases, compares the user's data with ENCODE's large collection of published datasets, performs multi-sample normalization, checks against quantitative PCR-validated control regions, and produces informative graphical reports. CHANCE is available at https://github.com/songlab/chance. <<<

Nicholas McGranahan, Rachel Rosenthal, Crispin T Hiley, Andrew J Rowan, Thomas B K Watkins, Gareth A Wilson, Nicolai J Birkbak, Selvaraju Veeriah, Peter Van Loo, Javier Herrero, Charles Swanton, TRACERx Consortium <<<

Immune evasion is a hallmark of cancer. Losing the ability to present neoantigens through human leukocyte antigen (HLA) loss may facilitate immune evasion. However, the polymorphic nature of the locus has precluded accurate HLA copy-number analysis. Here, we present loss of heterozygosity in human leukocyte antigen (LOHHLA), a computational tool to determine HLA allele-specific copy number from sequencing data. Using LOHHLA, we find that HLA LOH occurs in 40% of non-small-cell lung cancers (NSCLCs) and is associated with a high subclonal neoantigen burden, APOBEC-mediated mutagenesis, upregulation of cytolytic activity, and PD-L1 positivity. The focal nature of HLA LOH alterations, their subclonal frequencies, enrichment in metastatic sites, and occurrence as parallel events suggests that HLA LOH is an immune escape mechanism that is subject to strong microenvironmental selection pressures later in tumor evolution. Characterizing HLA LOH with LOHHLA refines neoantigen prediction and may have implications for our understanding of resistance mechanisms and immunotherapeutic approaches targeting neoantigens. VIDEO ABSTRACT. <<<

Luis R Lopes, Soledad Garcia-Hernández, Massimiliano Lorenzini, Marta Futema, Olga Chumakova, Dmitry Zateyshchikov, Maria Isidoro-Garcia, Eduardo Villacorta, Luis Escobar-Lopez, Pablo Garcia-Pavia, Raquel Bilbao, David Dobarro, Maria Sandin-Fuentes, Claudio Catalli, Blanca Gener Querol, Ainhoa Mezcua, Jose Garcia Pinilla, Torsten Bloch Rasmussen, Ana Ferreira-Aguar, Pablo Revilla-Martí, Maria Teresa Basurte Elorz, Alicia Bautista Paves, Juan Ramon Gimeno, Ana Virginia Figueroa, Raul Franco-Gutierrez, Maria Eugenia Fuentes-Cañamero, Marina Martinez Moreno, Martin Ortiz-Genga, Jesus Piqueras-Flores, Karina Analia Ramos, Ainars Rudzitis, Luis Ruiz-Guerrero, Ricardo Stein, Mayte Triguero-Bocharán, Luis de la Higuera, Juan Pablo Ochoa, Dad Abu-Bonsrah, Cecilia Y T Kwok, Jacob B Smith, Enzo R Porrello, Mohammed M Akhtar, Joanna Jager, Michael Ashworth, Petros Syrris, David A Elliott, Lorenzo Monserrat, Perry M Elliott <<<

AIMS: The aim of this study was to determine the frequency of heterozygous truncating ALPK3 variants (ALPK3tv) in patients with hypertrophic cardiomyopathy (HCM) and confirm their pathogenicity using burden testing in independent cohorts and family co-segregation studies.METHODS AND RESULTS: In a discovery cohort of 770 index patients with HCM, 12 (1.56%) were heterozygous for ALPK3tv [odds ratio(OR) 16.11, 95% confidence interval (CI) 7.94-30.02, P = 8.05e-11] compared to the Genome Aggregation Database (gnomAD) population. In a validation cohort of 2047 HCM probands, 32 (1.56%) carried heterozygous ALPK3tv (OR 16.17, 95% CI 10.31-24.87, P < 2.2e-16, compared to gnomAD). Combined logarithm of odds score in seven families with ALPK3tv was 2.99. In comparison with a cohort of genotyped patients with HCM (n = 1679) with and without pathogenic sarcomere gene variants (SP+ and SP-), ALPK3tv carriers had a higher prevalence of apical/concentric patterns of hypertrophy (60%, P < 0.001) and of a short PR interval (10%, P = 0.009). Age at diagnosis and maximum left ventricular wall thickness were similar to SP- and left ventricular systolic impairment (6%) and non-sustained ventricular tachycardia (31%) at baseline similar to SP+. After 5.3 ± 5.7 years, 4 (9%) patients with ALPK3tv died of heart failure or had cardiac transplantation (log-rank P = 0.012 vs. SP- and P = 0.425 vs. SP+). Imaging and histopathology showed extensive myocardial fibrosis and myocyte vacuolation.CONCLUSIONS: Heterozygous ALPK3tv are pathogenic and segregate with a characteristic HCM phenotype. <<<

Recent visual question answering (VQA) frameworks employ different combinations of attention techniques to derive a correct answer. Attention techniques in vision-language tasks have mostly achieved success through the improvement of local features for both modalities. Attention as a concept is heavily established by human cognition mechanism. Different combinations of attention techniques are not well proven as a means of human cognition. Neural networks were originally inspired by the structure of the human brain. Many researchers have recently resorted to frameworks that resemble the human brain, and their models have achieved high performance. To this end, we aim to consider a framework that utilizes human biological and psychological concepts to achieve a good understanding of vision and language modalities. In this view, we introduce a hierarchical reasoning based on a perception action cycle (HIPA) framework to tackle VQA tasks. It integrates the reasoning process of multi-modalities with the perception action cycle (PAC), which explains the learning mechanism of humans about the surrounding world. It comprehends the visual modality through three phases of reasoning: object-level attention, organization, and interpretation. It comprehends the language modality through word-level attention, interpretation, and conditioning. Subsequently, vision and language modalities are interpreted dependently in a cyclic and hierarchical way throughout the entire framework. For further assessment of the visual and language features, we argue that image-question pairs of the same answer ought to have similar visual and language features eventually. As a result, we conduct visual and language feature evaluation experiments using metrics such as standard deviation of cosine similarity and Manhattan distance. We show that employing PAC in our framework improves the standard deviation compared with other VQA frameworks. For further assessment, we also test the novel proposed HIPA on the visual relationship detection (VRD) tasks. The proposed method achieves the state-of-the-art results on the TDIUC and VRD datasets and obtains competitive results on the VQA 2.0 dataset. <<<

Neural-symbolic computing (NeSy), which pursues the integration of the symbolic and statistical paradigms of cognition, has been an active research area of Artificial Intelligence (AI) for many years. As NeSy shows promise of reconciling the advantages of reasoning and interpretability of symbolic representation and robust learning in neural networks, it may serve as a catalyst for the next generation of AI. In the present paper, we provide a systematic overview of the important and recent developments of research on NeSy AI. Firstly, we introduce study history of this area, covering early work and foundations. We further discuss background concepts and identify key driving factors behind the development of NeSy. Afterward, we categorize recent landmark approaches along several main characteristics that underline this research paradigm, including neural-symbolic integration, knowledge representation, knowledge embedding, and functionality. Then, we briefly discuss the successful application of modern NeSy approaches in several domains. Finally, we identify the open problems together with potential future research directions. This survey is expected to help new researchers enter this rapidly-developing field and accelerate progress towards data-and knowledge-driven AI. <<<

The tumor microenvironment is a dynamic cellular milieu that interacts with cancer cells and promotes tumor progression and metastasis. However, the specific mechanisms by which the tumor microenvironment impacts cancer cells' behaviors remain poorly understood. In this study, enriched cancer-associated fibroblasts (CAFs) were observed in tumor tissues isolated from epidermal growth factor receptor tyrosine kinase inhibitors (EGFR TKIs) resistant non-small cell lung cancer (NSCLC) patients. CAFs isolated from tumor tissues were capable of producing tryptophan metabolite kynurenine (Kyn), which significantly increased the proliferation and EGFR TKIs resistance of NSCLC cells. In this study, it was further observed that the activation of tryptophan 2,3-dioxygenase (TDO) in CAFs, resulted in the enhanced capability of tryptophan metabolism in them compared to normal fibroblasts. As a result, Kyn produced by CAFs facilitated the up-regulation of Aryl Hydrocarbon Receptor (AhR) signals in NSCLC, thereby resulting in the downstream ATK and ERK signaling pathways activation. Finally, inhibition of AhR signals efficiently prevented tumor growth and development of EGFR TKIs resistance, eventually improved the outcome of EGFR TKIs, and described a promising therapeutic strategy for NSCLC. <<<

The medial (MEC) and lateral entorhinal cortex (LEC), widely studied in rodents, are well defined and characterized. In humans, however, the exact locations of their homologues remain uncertain. Previous functional magnetic resonance imaging (fMRI) studies have subdivided the human EC into posteromedial (pmEC) and anterolateral (alEC) parts, but uncertainty remains about the choice of imaging modality and seed regions, in particular in light of a substantial revision of the classical model of EC connectivity based on novel insights from rodent anatomy. Here, we used structural, not functional imaging, namely diffusion tensor imaging (DTI) and probabilistic tractography to segment the human EC based on differential connectivity to other brain regions known to project selectively to MEC or LEC. We defined MEC as more strongly connected with presubiculum and retrosplenial cortex (RSC), and LEC as more strongly connected with distal CA1 and proximal subiculum (dCA1pSub) and lateral orbitofrontal cortex (OFC). Although our DTI segmentation had a larger medial-lateral component than in the previous fMRI studies, our results show that the human MEC and LEC homologues have a border oriented both towards the posterior-anterior and medial-lateral axes, supporting the differentiation between pmEC and alEC. <<<

Image compression is a fundamental research field and many well-known compression standards have been developed for many decades. Recently, learned compression methods exhibit a fast development trend with promising results. However, there is still a performance gap between learned compression algorithms and reigning compression standards, especially in terms of widely used PSNR metric. In this paper, we explore the remaining redundancy of recent learned compression algorithms. We have found accurate entropy models for rate estimation largely affect the optimization of network parameters and thus affect the rate-distortion performance. Therefore, in this paper, we propose to use discretized Gaussian Mixture Likelihoods to parameterize the distributions of latent codes, which can achieve a more accurate and flexible entropy model. Besides, we take advantage of recent attention modules and incorporate them into network architecture to enhance the performance. Experimental results demonstrate our proposed method achieves a state-of-the-art performance compared to existing learned compression methods on both Kodak and high-resolution datasets. To our knowledge our approach is the first work to achieve comparable performance with latest compression standard Versatile Video Coding (VVC) regarding PSNR. More importantly, our approach generates more visually pleasant results when optimized by MS-SSIM. This project page is at this https URL this https URL <<<