The gastrointestinal tract is continuously exposed to trillions of commensal microbes, collectively termed the microbiota, which are environmental stimuli that can direct health and disease within the host. In addition to well-established bacterial sensing pathways, microbial signals are also integrated through epigenetic modifications that calibrate the transcriptional program of host cells without altering the underlying genetic code. Microbiota-sensitive epigenetic changes include modifications to the DNA or histones, as well as regulation of non-coding RNAs. While microbiota-sensitive epigenetic mechanisms have been described in both local intestinal cells and as well in peripheral tissues, further research is required to fully decipher the complex relationship between the host and microbiota. This Review highlights current understandings of epigenetic regulation by gut microbiota and important implications of these findings in guiding therapeutic approaches to prevent or combat diseases driven by impaired microbiota-host interactions. <<<

We describe a technique for simulation and correction of the effects of an arbitrary distribution of undesired components of the static and gradient magnetic fields. This technique is applicable to direct Fourier NMR imaging. The mathematical basis and details of this technique are fully described. Computer simulation demonstrates the effectiveness of this method. <<<

Memories benefit from sleep, and sleep loss immediately following learning has a negative impact on subsequent memory storage. Several prominent hypotheses ascribe a central role to hippocampal sharp-wave ripples (SWRs), and the concurrent reactivation and replay of neuronal patterns from waking experience, in the offline memory consolidation process that occurs during sleep. However, little is known about how SWRs, reactivation, and replay are affected when animals are subjected to sleep deprivation. We performed long duration (~12 h), high-density silicon probe recordings from rat hippocampal CA1 neurons, in animals that were either sleeping or sleep deprived following exposure to a novel maze environment. We found that SWRs showed a sustained rate of activity during sleep deprivation, similar to or higher than in natural sleep, but with decreased amplitudes for the sharp-waves combined with higher frequencies for the ripples. Furthermore, while hippocampal pyramidal cells showed a log-normal distribution of firing rates during sleep, these distributions were negatively skewed with a higher mean firing rate in both pyramidal cells and interneurons during sleep deprivation. During SWRs, however, firing rates were remarkably similar between both groups. Despite the abundant quantity of SWRs and the robust firing activity during these events in both groups, we found that reactivation of neurons was either completely abolished or significantly diminished during sleep deprivation compared to sleep. Interestingly, reactivation partially rebounded upon recovery sleep, but failed to reach the levels characteristic of natural sleep. Similarly, the number of replays were significantly lower during sleep deprivation and recovery sleep compared to natural sleep. These results provide a network-level account for the negative impact of sleep loss on hippocampal function and demonstrate that sleep loss impacts memory storage by causing a dissociation between the amount of SWRs and the replays and reactivations that take place during these events. <<<

This work introduces a diffusion model for molecule generation in 3D that is equivariant to Euclidean transformations. Our E(3) Equivariant Diffusion Model (EDM) learns to denoise a diffusion process with an equivariant network that jointly operates on both continuous (atom coordinates) and categorical features (atom types). In addition, we provide a probabilistic analysis which admits likelihood computation of molecules using our model. Experimentally, the proposed method significantly outperforms previous 3D molecular generative methods regarding the quality of generated samples and efficiency at training time. <<<

Cheng Chen, Guillaume Bornet, Marcus Bintz, Gabriel Emperauger, Lucas Leclerc, Vincent S Liu, Pascal Scholl, Daniel Barredo, Johannes Hauschild, Shubhayu Chatterjee, Michael Schuler, Andreas M Läuchli, Michael P Zaletel, Thierry Lahaye, Norman Y Yao, Antoine Browaeys <<<

Spontaneous symmetry breaking underlies much of our classification of phases of matter and their associated transitions. The nature of the underlying symmetry being broken determines many of the qualitative properties of the phase; this is illustrated by the case of discrete versus continuous symmetry breaking. Indeed, in contrast to the discrete case, the breaking of a continuous symmetry leads to the emergence of gapless Goldstone modes controlling, for instance, the thermodynamic stability of the ordered phase. Here, we realize a two-dimensional dipolar XY model that shows a continuous spin-rotational symmetry using a programmable Rydberg quantum simulator. We demonstrate the adiabatic preparation of correlated low-temperature states of both the XY ferromagnet and the XY antiferromagnet. In the ferromagnetic case, we characterize the presence of a long-range XY order, a feature prohibited in the absence of long-range dipolar interaction. Our exploration of the many-body physics of XY interactions complements recent works using the Rydberg-blockade mechanism to realize Ising-type interactions showing discrete spin rotation symmetry. <<<

Recent work in imaging genetics suggests high levels of genetic overlap within cortical regions for cortical thickness (CT) and surface area (SA). We model this multivariate system of genetic relationships by applying Genomic Structural Equation Modeling (Genomic SEM) and parsimoniously define five genomic brain factors underlying both CT and SA along with a general factor capturing genetic overlap across all brain regions. We validate these factors by demonstrating the generalizability of the model to a semi-independent sample and show that the factors align with biologically and functionally relevant parcellations of the cortex. We apply Stratified Genomic SEM to identify specific categories of genes (e.g., neuronal cell types) that are disproportionately associated with pleiotropy across specific subclusters of brain regions, as indexed by the genomic factors. Finally, we examine genetic associations with psychiatric and cognitive correlates, finding that broad aspects of cognitive function are associated with a general factor for SA and that psychiatric associations are null. These analyses provide key insights into the multivariate genomic architecture of two critical features of the cerebral cortex. <<<

The development of single-cell multimodal assays provides a powerful tool for investigating multiple dimensions of cellular heterogeneity, enabling new insights into development, tissue homeostasis and disease. A key challenge in the analysis of single-cell multimodal data is to devise appropriate strategies for tying together data across different modalities. The term 'data integration' has been used to describe this task, encompassing a broad collection of approaches ranging from batch correction of individual omics datasets to association of chromatin accessibility and genetic variation with transcription. Although existing integration strategies exploit similar mathematical ideas, they typically have distinct goals and rely on different principles and assumptions. Consequently, new definitions and concepts are needed to contextualize existing methods and to enable development of new methods. <<<

Brent Brown, Vanshika Ojha, Ingo Fricke, Suhaila A Al-Sheboul, Chinua Imarogbe, Tanya Gravier, Michael Green, Lori Peterson, Ivoyl P Koutsaroff, Ayça Demir, Jonatane Andrieu, Chiuan Yee Leow, Chiuan Herng Leow <<<

The coronavirus 2019 (COVID-19) pandemic was caused by a positive sense single-stranded RNA (ssRNA) severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). However, other human coronaviruses (hCoVs) exist. Historical pandemics include smallpox and influenza, with efficacious therapeutics utilized to reduce overall disease burden through effectively targeting a competent host immune system response. The immune system is composed of primary/secondary lymphoid structures with initially eight types of immune cell types, and many other subtypes, traversing cell membranes utilizing cell signaling cascades that contribute towards clearance of pathogenic proteins. Other proteins discussed include cluster of differentiation (CD) markers, major histocompatibility complexes (MHC), pleiotropic interleukins (IL), and chemokines (CXC). The historical concepts of host immunity are the innate and adaptive immune systems. The adaptive immune system is represented by T cells, B cells, and antibodies. The innate immune system is represented by macrophages, neutrophils, dendritic cells, and the complement system. Other viruses can affect and regulate cell cycle progression for example, in cancers that include human papillomavirus (HPV: cervical carcinoma), Epstein-Barr virus (EBV: lymphoma), Hepatitis B and C (HB/HC: hepatocellular carcinoma) and human T cell Leukemia Virus-1 (T cell leukemia). Bacterial infections also increase the risk of developing cancer (e.g., ). Viral and bacterial factors can cause both morbidity and mortality alongside being transmitted within clinical and community settings through affecting a host immune response. Therefore, it is appropriate to contextualize advances in single cell sequencing in conjunction with other laboratory techniques allowing insights into immune cell characterization. These developments offer improved clarity and understanding that overlap with autoimmune conditions that could be affected by innate B cells (B1 or marginal zone cells) or adaptive T cell responses to SARS-CoV-2 infection and other pathologies. Thus, this review starts with an introduction into host respiratory infection before examining invaluable cellular messenger proteins and then individual immune cell markers. <<<

Photovoltaic hydrogen production from seawater is of great significance. Challenges of solar-driven seawater electrolysis, for example, competing among chlorine evolution reactions, chloride corrosion, and catalyst poisoning, seriously restrict the development of this technology. In this paper, we report a two-dimensional nanosheet quaternary metal hydroxide catalyst composed of Ni, Fe, Cr, and Mo elements. By in situ electrochemical activation, a partial Mo element was leached and morphologically transformed in the catalyst. The higher metal valence states and many O vacancies were obtained, providing excellent catalytic activity and corrosion resistance in overall alkaline seawater electrolysis operating at an industrial-required current density of 500 mA cm over 1000 h under 1.82 V low voltages at room temperature. The floating solar seawater splitting device shows a 20.61 ± 0.77% efficiency of solar energy to hydrogen (STH). This work demonstrates the development of efficient solar seawater electrolysis devices and potentially promotes research on clean energy conversion. <<<

The magnetoelectric effects in multiferroics have a great potential in creating next-generation memory devices. We use an alternative concept of nonvolatile memory based, on a type of nonlinear magnetoelectric effects showing a butterfly-shaped hysteresis loop. The principle is to utilize the states of the magnetoelectric coefficient, instead of magnetization, electric polarization, or resistance, to store binary information. Our experiments in a device made of the PMN-PT/Terfenol- D multiferroic heterostructure clearly demonstrate that the sign of the magnetoelectric coefficient can be repeatedly switched between positive and negative by applying electric fields, confirming the feasibility of this principle. This kind of nonvolatile memory has outstanding practical virtues such as simple structure, easy operation in writing and reading, low power, fast speed, and diverse materials available. <<<

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. <<<