William M. Schultz, Heval M. Kelli, John C. Lisko, Tina Varghese, Jia Shen, Pratik Sandesara, Arshed A. Quyyumi, Herman A. Taylor, Martha Gulati, John G. Harold, Jennifer H. Mieres, Keith C. Ferdinand, George A Mensah, Laurence S. Sperling <<<

Socioeconomic status (SES) has a measurable and significant effect on cardiovascular health. Biological, behavioral, and psychosocial risk factors prevalent in disadvantaged individuals accentuate the link between SES and cardiovascular disease (CVD). Four measures have been consistently associated with CVD in high-income countries: income level, educational attainment, employment status, and neighborhood socioeconomic factors. In addition, disparities based on sex have been shown in several studies. Interventions targeting patients with low SES have predominantly focused on modification of traditional CVD risk factors. Promising approaches are emerging that can be implemented on an individual, community, or population basis to reduce disparities in outcomes. Structured physical activity has demonstrated effectiveness in low-SES populations, and geomapping may be used to identify targets for large-scale programs. Task shifting, the redistribution of healthcare management from physician to nonphysician providers in an effort to improve access to health care, may have a role in select areas. Integration of SES into the traditional CVD risk prediction models may allow improved management of individuals with high risk, but cultural and regional differences in SES make generalized implementation challenging. Future research is required to better understand the underlying mechanisms of CVD risk that affect individuals of low SES and to determine effective interventions for patients with high risk. We review the current state of knowledge on the impact of SES on the incidence, treatment, and outcomes of CVD in high-income societies and suggest future research directions aimed at the elimination of these adverse factors, and the integration of measures of SES into the customization of cardiovascular treatment. <<<

Binjie Guo, Hanyu Zheng, Guowei Su, Ru Zhang, Haohan Jiang, Xurong Lin, Hongyan Wei, Aisheng Mo, Jie Li, Zhiyuan Qian, Zhuhao Zhang, Xiaoyuan Cheng <<<

Recent years have witnessed significant progress in reinforcement learning,especially with Zero-like paradigms, which have greatly boosted thegeneralization and reasoning abilities of large-scale language models.Nevertheless, existing frameworks are often plagued by high implementationcomplexity and poor reproducibility. To tackle these challenges, we presentAlphaZero-Edu, a lightweight, education-focused implementation built upon themathematical framework of AlphaZero. It boasts a modular architecture thatdisentangles key components, enabling transparent visualization of thealgorithmic processes. Additionally, it is optimized for resource-efficienttraining on a single NVIDIA RTX 3090 GPU and features highly parallelizedself-play data generation, achieving a 3.2-fold speedup with 8 processes. InGomoku matches, the framework has demonstrated exceptional performance,achieving a consistently high win rate against human opponents. AlphaZero-Eduhas been open-sourced at https://github.com/StarLight1212/AlphaZero_Edu,providing an accessible and practical benchmark for both academic research andindustrial applications. <<<

Progressive age-induced deterioration in the structure and function of the cardiovascular system involves cardiac hypertrophy, diastolic dysfunction, myocardial fibrosis, arterial stiffness, and endothelial dysfunction. These changes are driven by complex processes that are interconnected, such as oxidative stress, mitochondrial dysfunction, autophagy, inflammation, fibrosis, and telomere dysfunction. In recent years, the advances in research of cardiovascular aging, including the wide use of animal models of cardiovascular aging, elucidated an abundance of cell signaling pathways involved in these processes and brought into sight possible interventions, which span from pharmacological agents, such as metformin, sodium-glucose cotransporter 2-inhibitors, rapamycin, dasatinib and quercetin, to lifestyle changes. <<<

AbstractDirectional tongue movements are crucial for feeding and speech, ensuring proper food positioning for chewing and swallowing, as well as accurate sound production. While directional tuning in the arm region of the sensorimotor cortex during reaching tasks is well-studied, little is known about how 3D tongue direction is encoded in the orofacial sensorimotor cortex (OSMCx) during natural behaviors. Understanding this neural representation has important implications for rehabilitating individuals with orolingual dysfunctions. This study examines the directional tuning and population dynamics in OSMCx during naturalistic feeding and drinking, and how these are affected by sensory loss. Using biplanar videoradiography, we tracked implanted tongue markers in behaving rhesus macaques (Macaca mulatta) and simultaneously recorded 3D positional data with spiking activity from chronically implanted microelectrode arrays in primary motor (MIo) and somatosensory (SIo) areas of the orofacial cortex. In some sessions, tasks were preceded by bilateral nerve block injections to the sensory branches of the trigeminal nerve. Modulation to 3D tongue direction during feeding and drinking was found in most MIo and SIo neurons. Directional information in both individual- and population-level was higher in feeding and was more robust in MIo. Following sensory loss, alterations in tongue kinematics were accompanied by changes in directional information in MIo and SIo, manifesting as modifications in both individual neuron tuning characteristics and the broader dynamics of population-level neural activity. Overall, this study advances our understanding of how OSMCx contributes to complex, coordinated control of naturalistic tongue movements. It expands our current knowledge of orofacial control to three dimensions and demonstrates the specificity and adaptability of population activity in MIo and SIo in response to different behavioral contexts. <<<

Ana Mikolic, Tasha Klotz, Penelope Brasher, Keith Yeates, Ana-Maria Vranceanu, Karen D Kendall, Deborah L Snell, Chantel T Debert, Mark Bayley, William Panenka, Molly Cairncross, Cindy Hunt, Matthew Burke, Maria Carmela Tartaglia, Noah Silverberg <<<

IntroductionPersistent symptoms after mild traumatic brain injury (mTBI) negatively affect daily functioning and quality of life. Fear avoidance behaviour, a coping style in which people avoid or escape from activities or situations that they expect will exacerbate their symptoms, maybe a particularly potent and modifiable risk factor for chronic disability after mTBI. This study will evaluate the efficacy of graded exposure therapy (GET) for reducing persistent symptoms following mTBI, with two primary aims: (1) To determine whether GET is more effective than usual care; (2) to identify for whom GET is the most effective treatment option, by evaluating whether baseline fear avoidance moderates differences between GET and an active comparator (prescribed aerobic exercise). Our findings will guide evidence-based care after mTBI and enable better matching of mTBI patients to treatments.Methods and analysisWe will conduct a multisite randomised controlled trial with three arms. Participants (n=220) will be recruited from concussion clinics and emergency departments in three Canadian provinces and randomly assigned (1:2:2 ratio) to receive enhanced usual care, GET or prescribed aerobic exercise. The outcome assessment will occur remotely 14–18 weeks following baseline assessment, after completing the 12-week treatment phase. The primary outcome will be symptom severity (Rivermead Post-concussion Symptoms Questionnaire).Ethics and disseminationInformed consent will be obtained from all participants. All study procedures were approved by the local research ethics boards (University of British Columbia Clinical Research Ethics Board, University of Calgary Conjoint Health Research Ethics Board, University Health Network Research Ethics Board—Panel D). Operational approvals were obtained for Vancouver Coastal Health Research Institute and Provincial Health Services Authority. If GET proves effective, we will disseminate the GET treatment manual and present instructional workshops for clinicians.Trial registration numberClinicalTrials.gov #NCT05365776 <<<

Abstract Background Noninvasive prenatal testing (NIPT) is increasingly used to screen for fetal chromosomal aneuploidy by analyzing cell-free DNA (cfDNA) in peripheral maternal blood. The method provides an opportunity for early detection of large genetic abnormalities without an increased risk of miscarriage due to invasive procedures. Commercial applications for use at clinical laboratories often take advantage of DNA sequencing technologies and include the bioinformatic workup of the sequence data. The interpretation of the test results and the clinical report writing, however, remains the responsibility of the diagnostic laboratory. In order to facilitate this step, we developed NIPTviewer, a web-based application to visualize and guide the interpretation of NIPT data results. Results NIPTviewer has a database functionality to store the NIPT results and a web interface for user interaction and visualization. The application has been implemented as part of a novel analysis pipeline for NIPT in a diagnostic laboratory at Uppsala University Hospital. The validation data set included 84 previously analyzed plasma samples with known results regarding chromosomes 13, 18, 21, X and Y. They were sequenced in six different experiments, uploaded to NIPTviewer and assigned to a clinical laboratory geneticist for interpretation. The results of all previously analyzed samples were replicated. Conclusion NIPTviewer facilitates NIPT results interpretation and has been implemented as part of a NIPT analysis routine that was accredited by the national accreditation body for Sweden (Swedac). <<<

Vision-language-action (VLA) models have emerged as the next generation ofmodels in robotics. However, despite leveraging powerful pre-trainedVision-Language Models (VLMs), existing end-to-end VLA systems often lose keycapabilities during fine-tuning as the model adapts to specific robotic tasks.We argue that a generalizable VLA model should retain and expand upon the VLM'score competencies: 1) Open-world embodied reasoning - the VLA should inheritthe knowledge from VLM, i.e., recognize anything that the VLM can recognize,capable of solving math problems, possessing visual-spatial intelligence, 2)Reasoning following - effectively translating the open-world reasoning intoactionable steps for the robot. In this work, we introduce ChatVLA-2, a novelmixture-of-expert VLA model coupled with a specialized three-stage trainingpipeline designed to preserve the VLM's original strengths while enablingactionable reasoning. To validate our approach, we design a math-matching taskwherein a robot interprets math problems written on a whiteboard and pickscorresponding number cards from a table to solve equations. Remarkably, ourmethod exhibits exceptional mathematical reasoning and OCR capabilities,despite these abilities not being explicitly trained within the VLA.Furthermore, we demonstrate that the VLA possesses strong spatial reasoningskills, enabling it to interpret novel directional instructions involvingpreviously unseen objects. Overall, our method showcases reasoning andcomprehension abilities that significantly surpass state-of-the-art imitationlearning methods such as OpenVLA, DexVLA, and pi-zero. This work represents asubstantial advancement toward developing truly generalizable roboticfoundation models endowed with robust reasoning capacities. <<<

Yihan Jiang, Yuanyuan Chen, Lijiang Wei, Farui Liu, Zeqing Zheng, Zong Zhang, Zheng Li, Yingying Tang, Jijun Wang, Qing Xie, Chuanxin M. Niu, Chaozhe Zhu <<<

Despite recent progress in general purpose robotics, robot policies still lagfar behind basic human capabilities in the real world. Humans interactconstantly with the physical world, yet this rich data resource remains largelyuntapped in robot learning. We propose EgoZero, a minimal system that learnsrobust manipulation policies from human demonstrations captured with ProjectAria smart glasses, $\textbf{and zero robot data}$. EgoZero enables: (1)extraction of complete, robot-executable actions from in-the-wild, egocentric,human demonstrations, (2) compression of human visual observations intomorphology-agnostic state representations, and (3) closed-loop policy learningthat generalizes morphologically, spatially, and semantically. We deployEgoZero policies on a gripper Franka Panda robot and demonstrate zero-shottransfer with 70% success rate over 7 manipulation tasks and only 20 minutes ofdata collection per task. Our results suggest that in-the-wild human data canserve as a scalable foundation for real-world robot learning - paving the waytoward a future of abundant, diverse, and naturalistic training data forrobots. Code and videos are available at https://egozero-robot.github.io. <<<

Many real-world applications require the prediction of long sequencetime-series, such as electricity consumption planning. Long sequencetime-series forecasting (LSTF) demands a high prediction capacity of the model,which is the ability to capture precise long-range dependency coupling betweenoutput and input efficiently. Recent studies have shown the potential ofTransformer to increase the prediction capacity. However, there are severalsevere issues with Transformer that prevent it from being directly applicableto LSTF, including quadratic time complexity, high memory usage, and inherentlimitation of the encoder-decoder architecture. To address these issues, wedesign an efficient transformer-based model for LSTF, named Informer, withthree distinctive characteristics: (i) a $ProbSparse$ self-attention mechanism,which achieves $O(L \log L)$ in time complexity and memory usage, and hascomparable performance on sequences' dependency alignment. (ii) theself-attention distilling highlights dominating attention by halving cascadinglayer input, and efficiently handles extreme long input sequences. (iii) thegenerative style decoder, while conceptually simple, predicts the longtime-series sequences at one forward operation rather than a step-by-step way,which drastically improves the inference speed of long-sequence predictions.Extensive experiments on four large-scale datasets demonstrate that Informersignificantly outperforms existing methods and provides a new solution to theLSTF problem. <<<

Gil Zimran, Michal Shpilman, Eve Hobson, Yasuko Kamisugi, Amichai Baichman-Kass, Hong Zhang, Rafa Ruiz-Partida, María R. González-Bermúdez, Matan Azar, Erez Feuer, Maayan Gal, Jorge Lozano-Juste, Jan de Vries, Andrew C. Cuming, Assaf Mosquna, Yufei Sun <<<

The multicellular coordination that underlies tissue homeostasis and disease progression is of fundamental interest. However, how diverse cell types are organized within tissue niches for cohesive functioning remains largely unknown. Here we systematically characterized cross-tissue coordinated cellular modules in healthy tissues, uncovering their spatiotemporal dynamics and phenotypic associations, and examined their rewiring in cancer. We first compiled a comprehensive single-cell transcriptomic atlas from 35 human tissues, revealing substantial inter-tissue variability in cellular composition. By leveraging covariance in cellular abundance, we identified 12 cellular modules with distinct cellular compositions, tissue prevalences and spatial organizations, and demonstrated coordinated intercellular communication within cellular modules using in situ spatial and in vivo perturbation data. Among them, two immune cellular modules in the spleen showed contrasting chronological dynamics with ageing. Analysis of multicellular changes in the breast revealed a menopausal trajectory associated with fibroblast dynamics. Furthermore, interrogation across cancer types uncovered simultaneous rewiring of two types of multicellular ecosystem during tumour progression, including the loss of tissue-specific healthy organization and the emergence of a convergent cancerous ecosystem. These findings reveal fundamental organizing principles of multicellular ecosystems in health and cancer, laying a foundation for further investigations into tissue-level functional coordination across diverse contexts. <<<

NMRExtractor is a large language model-powered pipeline that automatically extracts experimental NMR data from massive open-access publications, resulting in the construction of NMRBank—the largest open-access NMR dataset available to date. <<<

Ying Cheng, Zhuying Gao, Tiantian Zhang, Yuhua Wang, Xueqin Xie, Guoqiang Han, Yashu Li, Rong Yin, Yilin Chen, Peipei Wang, Jin Hu, Tong Zhang, Chengli Guo, Jihua Chai, Jing Wang, Manman Cui, Kexin Gao, Weidong Liu, Shuxin Yao, Pengbo Lu, Ziyan Cao, Yanbing Zheng, Jiwei Chang, Zheming Liu, Qibin Song, Weiming Li, Fuling Zhou, Haojian Zhang <<<

Bi-Wen Wang, Qian W. L. Zhang, Yi Chen, Wenhao Zhao, Yu Liu, Guo-Qiang Tang, Hong-Xia Ma, Bin Su, Hejiu Hui, John W. Delano, Fu-Yuan Wu, Xian-Hua Li, Yuyang He, Qiu-Li Li <<<

There is extensive geologic evidence of ancient volcanic activity on the Moon, but it is unclear how long that volcanism persisted. Magma fountains produce volcanic glasses, which have previously been found in samples of the Moon’s surface. We investigated ~3000 glass beads in lunar soil samples collected by the Chang’e-5 mission and identified three as having a volcanic origin on the basis of their textures, chemical compositions, and sulfur isotopes. Uranium-lead dating of the three volcanic glass beads shows that they formed 123 ± 15 million years ago. We measured high abundances of rare earth elements and thorium in these volcanic glass beads, which could indicate that such recent volcanism was related to local enrichment of heat-generating elements in the mantle sources of the magma. <<<

Abstract Background With the COVID-19 outbreak, an increasing number of individuals are concerned about their health, particularly their immune status. However, as of now, there is no available algorithm that effectively assesses the immune status of normal, healthy individuals. In response to this, a new score-based method is proposed that utilizes complete blood cell counts (CBC) to provide early warning of disease risks, such as COVID-19. Methods First, data on immune-related CBC measurements from 16,715 healthy individuals were collected. Then, a three-platform model was developed to normalize the data, and a Gaussian mixture model was optimized with expectation maximization (EM-GMM) to cluster the immune status of healthy individuals. Based on the results, Random Forest (RF), Light Gradient Boosting Machine (LightGBM) and Extreme Gradient Boosting (XGBoost) were used to determine the correlation of each CBC index with the immune status. Consequently, a weighted sum model was constructed to calculate a continuous immunity score, enabling the evaluation of immune status. Results The results demonstrated a significant negative correlation between the immunity score and the age of healthy individuals, thereby validating the effectiveness of the proposed method. In addition, a nonlinear polynomial regression model was developed to depict this trend. By comparing an individual’s immune status with the reference value corresponding to their age, their immune status can be evaluated. Conclusion In summary, this study has established a novel model for evaluating the immune status of healthy individuals, providing a good approach for early detection of abnormal immune status in healthy individuals. It is helpful in early warning of the risk of infectious diseases and of significant importance. <<<

Shangqian Xie, Darren Hagen, Gabrielle M. Becker, Kimberly M. Davenport, Katie A. Shira, Morgan R. Stegemiller, Jacob W. Thorne, Sarem Khilji, Denise Konetchy, Patricia Villamediana, Brenda M. Murdoch, Stephanie D. McKay <<<

Shuang Zhou, Meng Li, Pengcheng Wang, Chenghao Guo, Jinxin Zhang, Xi Luo, Yu-Chen Fan, En-Qiang Chen, Xingshun Qi, Jinjun Chen, Lechi Ye, Hai-Yang Yuan, Wen-Bing Yin, Kai Wang, Ming-Hua Zheng, Yanli Pang, Jie Qiao, Changtao Jiang <<<

The gut microbiota is known to be associated with a variety of human metabolic diseases, including metabolic dysfunction–associated steatohepatitis (MASH). Fungi are increasingly recognized as important members of this community; however, the role of fungal symbionts in metabolic diseases is unknown. We have systematically isolated and characterized gut fungi, identifying Fusarium foetens as an intestinal symbiotic filamentous fungus in mice. F. foetens reverses MASH progression in mouse models through an intestinal ceramide synthetase 6 (CerS6)–ceramide axis. Moreover, we identified FF-C1, a secondary metabolite from F. foetens , as a CerS6 inhibitor that has an endogenous protective effect on MASH progression. <<<