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. <<<

The topology of the functional brain network has been associated with several neuropsychiatric conditions. However, little is known about its genetic underpinnings, and whether this overlaps with brain structure and neuropsychiatric conditions. Hence, we conducted genome-wide association study across six graph metrics of the macroscale functional networks at global, hemispheric and regional levels and their hemispheric asymmetry in 54,030 individuals. We identified seven experiment-wide significant loci and prioritised ten candidate genes. Both global graph metrics and hemispheric asymmetry are modestly heritable, but phenotypically and genetically form two clusters. Furthermore, cortical macro- and microstructure are causally related to global graph metrics and asymmetry, respectively, suggesting a dual structural constraint on functional network organisation. Finally, amongst twelve common neuropsychiatric conditions, only autism was genetically correlated with graph metrics of the functional network, supporting phenotypic case-control differences in functional connectivity. Overall, our results suggest different genetic axes shaping different aspects of brain functional topology and demonstrate shared biology with brain structure and autism. <<<

Precision medicine is a personalized medical model based on the individual’s genome, phenotype, and lifestyle that provides tailored treatment plans for patients. In this context, tumor organoids, a 3-dimensional preclinical model based on patient-derived tumor cell self-organization, combined with digital analysis methods, such as high-throughput sequencing and image processing technology, can be used to analyze the genome, transcriptome, and cellular heterogeneity of tumors, so as to accurately track and assess the growth process, genetic characteristics, and drug responsiveness of tumor organoids, thereby facilitating the implementation of precision medicine. This interdisciplinary approach is expected to promote the innovation of cancer diagnosis and enhance personalized treatment. In this review, the characteristics and culture methods of tumor organoids are summarized, and the application of multi-omics, such as bioinformatics and artificial intelligence, and the digital methods of organoids in precision medicine research are discussed. Finally, this review explores the main causes and potential solutions for the bottleneck in the clinical translation of digital tumor organoids, proposes the prospects of multidisciplinary cooperation and clinical transformation to narrow the gap between laboratory and clinical settings, and provides references for research and development in this field. <<<

How the prefrontal cortex contributes to working memory remains controversial, as theories differ in their emphasis on its role in storing memories versus controlling their content. To adjudicate between these competing ideas, we tested how perturbations to the human (both sexes) lateral prefrontal cortex impact the storage and control aspects of working memory during a task that requires human subjects to allocate resources to memory items based on their behavioral priority. Our computational model made a strong prediction that disruption of this control process would counterintuitively improve memory for low-priority items. Remarkably, transcranial magnetic stimulation of retinotopically-defined superior precentral sulcus, but not intraparietal sulcus, unbalanced the prioritization of resources, improving memory for low-priority items as predicted by the model. Therefore, these results provide direct causal support for models in which the prefrontal cortex controls the allocation of resources that support working memory, rather than simply storing the features of memoranda. <<<

Elisabetta Schiavello, Veronica Biassoni, Giovanna Gattuso, Marta Podda, Stefano Chiaravalli, Francesco Barretta, Manila Antonelli, Loris De Cecco, Emilia Pecori, Lorenza Gandola, Maura Massimino <<<

Introduction: The H3K27M-mutant diffuse midline glioma (DMG) was first included in the World Health Organization (WHO) Classification of central nervous system (CNS) tumors in 2016, and confirmed in its fifth edition. The biological behavior and dismal prognosis of this tumor resemble diffuse intrinsic pontine gliomas (DIPG). Homogeneously-treated series are rarely reported. Methods: From 2016 onwards, we treated patients with DMG with radiotherapy and concomitant/adjuvant nimotuzumab/vinorelbine, plus re-irradiation at relapse, as already done for DIPG. Results: We treated nine patients, seven females, with a median age at diagnosis of 13 years. Tumor sites were: thalamic in five cases, pontocerebellar in two, pineal in one, and paratrigonal with nodular/leptomeningeal dissemination in one. Three patients were biopsied, and six had partial tumor resections. Central pathological review was always performed. The median time to local progression was 12.7 months, and the median overall survival was 17.8 months. Six patients died of tumor progression, one of cerebral bleeding at progression. Two were alive, one in continuous remission, the other after relapsing, at 38.6 and 46.3 months after diagnosis. Progression-free survival was 33.3% at one year. Overall survival was 88.9%, 33.3% and 22.2% at 1, 2 and 3 years, respectively. Conclusions: This is a small series of homogeneously-treated DMG patients. The results obtained are comparable with those of DIPG patients. Given the phenotypically- and molecularly-defined setting of DMG and severe outcome in this orphan population, they should be treated and included in registries and protocols of DIPG. <<<

Sujing Yuan, Renqiang Sun, Hao Shi, Nicole M. Chapman, Haoran Hu, Cliff Guy, Sherri Rankin, Anil KC, Gustavo Palacios, Xiaoxi Meng, Xiang Sun, Peipei Zhou, Xiaoyang Yang, Stephen Gottschalk, Hongbo Chi <<<

Zedao Liu, Zhenlin Yang, Junqi Wu, Wenjie Zhang, Yuxuan Sun, Chao Zhang, Guangyu Bai, Li Yang, Hongtao Fan, Yawen Chen, Lei Zhang, Benyuan Jiang, Xiaoyan Liu, Xiaoshi Ma, Wei Tang, Chang Liu, Yang Qu, Lixu Yan, Deping Zhao, Yilong Wu, Shun He, Long Xu, Lishan Peng, Xiaowei Chen, Bolun Zhou, Liang Zhao, Zhangyi Zhao, Fengwei Tan, Wanting Zhang, Dingcheng Yi, Xiangjie Li, Qianqian Gao, Guangjian Zhang, Yongjie Wang, Minglei Yang, Honghao Fu, Yongjun Guo, Xueda Hu, Qingyuan Cai, Lu Qi, Yufei Bo, Hui Peng, Zhigang Tian, Yunlang She, Chang Zou, Linnan Zhu, Sijin Cheng, Yi Zhang, Wenzhao Zhong, Chang Chen, Shugeng Gao, Zemin Zhang <<<

Anti-PD-(L)1 treatment is standard for non-small cell lung cancer (NSCLC), but patients show variable responses to the same regimen. The tumor immune microenvironment (TIME) is associated with immunotherapy response, yet the heterogeneous underlying therapeutic outcomes remain underexplored. We applied single-cell RNA and TCR sequencing (scRNA/TCR-seq) to analyze surgical tumor samples from 234 NSCLC patients post-neoadjuvant chemo-immunotherapy. Analyses revealed five distinct TIME subtypes with varying major pathological response (MPR) rates. MPR patients had elevated levels of FGFBP2 NK/NK-like T cells, memory B cells, or effector T cells, while non-MPR patients showed higher CCR8 Tregs. T cell clonal expansion analyses unveiled heterogeneity in non-MPR patients, marked by varying expansions of Tex-relevant cells and CCR8 Tregs. Precursor exhausted T cells (Texp cells) correlated with recurrence-free survival, identifying a patient subgroup with reduced recurrence risk despite lack of MPR. Our study dissects TIME heterogeneity in response to chemoimmunotherapy, offering insights for NSCLC management. <<<