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. <<<

Hang Yang, Guowei Wu, Yaoxin Li, Xiaoyu Xu, Jing Cong, Haoshu Xu, Yiyao Ma, Yang Li, Runsen Chen, Adam Pines, Ting Xu, Valerie J. Sydnor, Theodore D. Satterthwaite, Zaixu Cui <<<

The human cerebral cortex exhibits intricate interareal functional synchronization at the macroscale, with substantial individual variability in these functional connections. However, the spatial organization of functional connectivity (FC) variability across the human connectome edges and its significance in cognitive development remain unclear. Here, we identified a connectional axis in the edge-level FC variability. The variability declined continuously along this axis from within-network to between-network connections and from the edges linking association networks to those linking the sensorimotor and association networks. This connectional axis of functional variability is associated with spatial pattern of structural connectivity variability. Moreover, the connectional variability axis evolves in youth with an flatter axis slope. We also observed that the slope of the connectional variability axis was positively related to the performance in the higher-order cognition. Together, our results reveal a connectional axis in functional variability that is linked with structural connectome variability, refines during development, and is relevant to cognition. <<<

Marina Terekhova, Amanda Swain, Pavla Bohacova, Ekaterina Aladyeva, Laura Arthur, Anwesha Laha, Denis A. Mogilenko, Samantha Burdess, Vladimir Sukhov, Denis Kleverov, Barbora Echalar, Petr Tsurinov, Roman Chernyatchik, Kamila Husarcikova, Maxim N. Artyomov <<<

Wei Liu, Jun Li, Yitao Tang, Yining Zhao, Chaozhong Liu, Meiyi Song, Zhenlin Ju, Shwetha V. Kumar, Yiling Lu, Rehan Akbani, Gordon B. Mills, Han Liang <<<

Creativity is core to being human. Generative artificial intelligence (AI)—including powerful large language models (LLMs)—holds promise for humans to be more creative by offering new ideas, or less creative by anchoring on generative AI ideas. We study the causal impact of generative AI ideas on the production of short stories in an online experiment where some writers obtained story ideas from an LLM. We find that access to generative AI ideas causes stories to be evaluated as more creative, better written, and more enjoyable, especially among less creative writers. However, generative AI–enabled stories are more similar to each other than stories by humans alone. These results point to an increase in individual creativity at the risk of losing collective novelty. This dynamic resembles a social dilemma: With generative AI, writers are individually better off, but collectively a narrower scope of novel content is produced. Our results have implications for researchers, policy-makers, and practitioners interested in bolstering creativity. <<<

Generative AI technologies have been deployed in many places, such as(multimodal) large language models and vision generative models. Theirremarkable performance should be attributed to massive training data andemergent reasoning abilities. However, the models would memorize and generatesensitive, biased, or dangerous information originated from the training dataespecially those from web crawl. New machine unlearning (MU) techniques arebeing developed to reduce or eliminate undesirable knowledge and its effectsfrom the models, because those that were designed for traditionalclassification tasks could not be applied for Generative AI. We offer acomprehensive survey on many things about MU in Generative AI, such as a newproblem formulation, evaluation methods, and a structured discussion on theadvantages and limitations of different kinds of MU techniques. It alsopresents several critical challenges and promising directions in MU research. Acurated list of readings can be found:https://github.com/franciscoliu/GenAI-MU-Reading. <<<

Anna Lauko, Samuel J. Pellock, Kiera H. Sumida, Ivan Anishchenko, David Juergens, Woody Ahern, Jihun Jeung, Alexander F. Shida, Andrew Hunt, Indrek Kalvet, Christoffer Norn, Ian R. Humphreys, Cooper Jamieson, Rohith Krishna, Yakov Kipnis, Alex Kang, Evans Brackenbrough, Asim K. Bera, Banumathi Sankaran, K. N. Houk, David Baker <<<

The design of enzymes with complex active sites that mediate multistep reactions remains an outstanding challenge. With serine hydrolases as a model system, we combined the generative capabilities of RFdiffusion with an ensemble generation method for assessing active site preorganization at each step in the reaction to design enzymes starting from minimal active site descriptions. Experimental characterization revealed catalytic efficiencies ( k cat / K m ) up to 2.2 × 10 5 M −1 s −1 and crystal structures that closely match the design models (Cα root mean square deviations <1 angstrom). Selection for structural compatibility across the reaction coordinate enabled identification of new catalysts remove with five different folds distinct from those of natural serine hydrolases. Our de novo approach provides insight into the geometric basis of catalysis and a roadmap for designing enzymes that catalyze multistep transformations. <<<