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. <<<

When holding visual information temporarily in working memory (WM), the neural representation of the memorandum is distributed across various cortical regions, including visual and frontal cortices. However, the role of stimulus representation in visual and frontal cortices during WM has been controversial. Here, we tested the hypothesis that stimulus representation persists in the frontal cortex to facilitate flexible control demands in WM. During functional MRI, participants flexibly switched between simple WM maintenance of visual stimulus or more complex rule-based categorization of maintained stimulus on a trial-by-trial basis. Our results demonstrated enhanced stimulus representation in the frontal cortex that tracked demands for active WM control and enhanced stimulus representation in the visual cortex that tracked demands for precise WM maintenance. This differential frontal stimulus representation traded off with the newly-generated category representation with varying control demands. Simulation using multi-module recurrent neural networks replicated human neural patterns when stimulus information was preserved for network readout. Altogether, these findings help reconcile the long-standing debate in WM research, and provide empirical and computational evidence that flexible stimulus representation in the frontal cortex during WM serves as a potential neural coding scheme to accommodate the ever-changing environment. <<<

Ruth Horn, Angeliki Kerasidou, Jennifer Merchant, , Mark Bale, Natalie Banner, James Buchanan, Anne Cambon-Thomsen, Herve Chneiweiss, Lene Cividanes, Angus Clarke, Janneke M. L. Kuiper, Celine Lewis, Anneke Lucassen, Elizabeth Ormondroyd, Michael Parker, Eva Winkler <<<

Heidi L. Rehm, Joseph T. Alaimo, Swaroop Aradhya, Pinar Bayrak-Toydemir, Hunter Best, Rhonda Brandon, Jillian G. Buchan, Elizabeth C. Chao, Elaine Chen, Jacob Clifford, Ana S.A. Cohen, Laura K. Conlin, Soma Das, Kyle W. Davis, Daniela del Gaudio, Florencia Del Viso, Christina DiVincenzo, Marcia Eisenberg, Lucia Guidugli, Monia B. Hammer, Steven M. Harrison, Kathryn E. Hatchell, Lindsay Havens Dyer, Lily U. Hoang, James M. Holt, Vaidehi Jobanputra, Izabela D. Karbassi, Hutton M. Kearney, Melissa A. Kelly, Jacob M. Kelly, Michelle L. Kluge, Timothy Komala, Paul Kruszka, Lynette Lau, Matthew S. Lebo, Christian R. Marshall, Dianalee McKnight, Kirsty McWalter, Yan Meng, Narasimhan Nagan, Christian S. Neckelmann, Nir Neerman, Zhiyv Niu, Vitoria K. Paolillo, Sarah A. Paolucci, Denise Perry, Tina Pesaran, Kelly Radtke, Kristen J. Rasmussen, Kyle Retterer, Carol J. Saunders, Elizabeth Spiteri, Christine Stanley, Anna Szuto, Ryan J. Taft, Isabelle Thiffault, Brittany C. Thomas, Amanda Thomas-Wilson, Erin Thorpe, Timothy J. Tidwell, Meghan C. Towne, Hana Zouk, Christian Marshall, Linyan Meng, Vaidehi Jobanputra, Ryan Taft, Euan Ashley, Ghunwa Nakouzi, Wei Shen, Stephen Kingsmore, Heidi Rehm <<<

This paper introduces a novel trajectory planner for autonomous robots,specifically designed to enhance navigation by incorporating dynamic obstacleavoidance within the Robot Operating System 2 (ROS2) and Navigation 2 (Nav2)framework. The proposed method utilizes Model Predictive Control (MPC) with afocus on handling the uncertainties associated with the movement prediction ofdynamic obstacles. Unlike existing Nav2 trajectory planners which primarilydeal with static obstacles or react to the current position of dynamicobstacles, this planner predicts future obstacle positions using a stochasticVector Auto-Regressive Model (VAR). The obstacles' future positions arerepresented by probability distributions, and collision avoidance is achievedthrough constraints based on the Mahalanobis distance, ensuring the robotavoids regions where obstacles are likely to be. This approach considers therobot's kinodynamic constraints, enabling it to track a reference path whileadapting to real-time changes in the environment. The paper details theimplementation, including obstacle prediction, tracking, and the constructionof feasible sets for MPC. Simulation results in a Gazebo environmentdemonstrate the effectiveness of this method in scenarios where robots mustnavigate around each other, showing improved collision avoidance capabilities. <<<