Runnan Fang, Shihao Cai, Baixuan Li, Jialong Wu, Guangyu Li, Wenbiao Yin, Xinyu Wang, Xiaobin Wang, Liangcai Su, Zhen Zhang, Shibin Wu, Zhengwei Tao, Yong Jiang, Pengjun Xie, Fei Huang, Jingren Zhou <<<

Advanced agentic intelligence is a prerequisite for deploying Large LanguageModels in practical, real-world applications. Diverse real-world APIs demandprecise, robust function-calling intelligence, which needs agents to developthese capabilities through interaction in varied environments. The breadth offunction-calling competence is closely tied to the diversity of environments inwhich agents are trained. In this work, we scale up environments as a steptowards advancing general agentic intelligence. This gives rise to two centralchallenges: (i) how to scale environments in a principled manner, and (ii) howto effectively train agentic capabilities from experiences derived throughinteractions with these environments. To address these, we design a scalableframework that automatically constructs heterogeneous environments that arefully simulated, systematically broadening the space of function-callingscenarios. We further adapt a two-phase agent fine-tuning strategy: firstendowing agents with fundamental agentic capabilities, then specializing themfor domain-specific contexts. Extensive experiments on agentic benchmarks,tau-bench, tau2-Bench, and ACEBench, demonstrate that our trained model,AgentScaler, significantly enhances the function-calling capability of models. <<<

Giovanni Acampora, Andris Ambainis, Natalia Ares, Leonardo Banchi, Pallavi Bhardwaj, Daniele Binosi, G. Andrew D. Briggs, Tommaso Calarco, Vedran Dunjko, Jens Eisert, Olivier Ezratty, Paul Erker, Federico Fedele, Elies Gil-Fuster, Martin Gärttner, Mats Granath, Markus Heyl, Iordanis Kerenidis, Matthias Klusch, Anton Frisk Kockum, Richard Kueng, Mario Krenn, Jörg Lässig, Antonio Macaluso, Sabrina Maniscalco, Florian Marquardt, Kristel Michielsen, Gorka Muñoz-Gil, Daniel Müssig, Hendrik Poulsen Nautrup, Sophie A. Neubauer, Evert van Nieuwenburg, Roman Orus, Jörg Schmiedmayer, Markus Schmitt, Philipp Slusallek, Filippo Vicentini, Christof Weitenberg, Frank K. Wilhelm <<<

This white paper discusses and explores the various points of intersectionbetween quantum computing and artificial intelligence (AI). It describes howquantum computing could support the development of innovative AI solutions. Italso examines use cases of classical AI that can empower research anddevelopment in quantum technologies, with a focus on quantum computing andquantum sensing. The purpose of this white paper is to provide a long-termresearch agenda aimed at addressing foundational questions about how AI andquantum computing interact and benefit one another. It concludes with a set ofrecommendations and challenges, including how to orchestrate the proposedtheoretical work, align quantum AI developments with quantum hardware roadmaps,estimate both classical and quantum resources - especially with the goal ofmitigating and optimizing energy consumption - advance this emerging hybridsoftware engineering discipline, and enhance European industrialcompetitiveness while considering societal implications. <<<

Yu Guo, Murukarthick Jayakodi, Axel Himmelbach, Erez Ben-Yosef, Uri Davidovich, Michal David, Anat Hartmann-Shenkman, Mordechai Kislev, Tzion Fahima, Verena J. Schuenemann, Ella Reiter, Johannes Krause, Brian J. Steffenson, Nils Stein, Ehud Weiss, Martin Mascher <<<

Abstract Barley is one of the oldest cultivated crops, with a complex evolutionary and domestication history1. Previous studies have rejected the idea of a single origin and instead support a model of mosaic genomic ancestry2,3. With increasingly comprehensive genome data, we now ask where the haplotypes — the building blocks of this mosaic — originate, and whether all domesticated barleys share the same wild progenitors or whether certain wild populations contribute more heavily to specific lineages. To address these questions, we apply a haplotype-based approach to investigate the genetic diversity and population structure of wild and domesticated barley. We analyse whole-genome sequences from 682 genebank accessions and 23 archaeological specimens, tracing the spatiotemporal origins of haplotypes and identifying wild contributors during domestication and later gene flow events. Ancient DNA supports our genome-wide findings from modern samples. Our results suggest that a founding domesticated population emerged in the Fertile Crescent during a prolonged period of pre-domestication cultivation. A key practical insight is that the high haplotype differentiation among barley populations — arising independently, or layered on top, of selection — poses challenges for mapping adaptive loci. <<<

Competition is commonly reflected in aggressive interactions among groupmates as individuals try to attain or maintain higher social ranks that can offer them better access to critical resources. In this study, we investigate the factors that can shift competitive incentives against higher- or lower-ranking groupmates, that is, more or less powerful individuals. We use a long-term behavioural data set on five wild groups of the two gorilla species starting in 1998, and we show that most aggression is directed from higher- to lower-ranking adult females close in rank, highlighting rank-reinforcement incentives. Yet, females directed 42% of aggression to higher-ranking females than themselves. Females targeted groupmates of higher rank with increasing number of males in the group, suggesting that males might buffer female–female aggression risk. Contrarily, they targeted females of lower rank with increasing number of females in the group, potentially because this is a low-risk option that females prefer when they have access to a larger pool of competitors to choose from. Lactating and pregnant females, especially those in the latest stage of pregnancy, targeted groupmates of higher rank than the groupmates that cycling females targeted, suggesting that energetic needs may motivate females to risk confrontation with more powerful rivals. Our study provides critical insights into the evolution of competitive behaviour, showing that aggression heuristics, the simple rules that animals use to guide their aggressive interactions, are not merely species-specific but also dependent on the conditions that populations and individuals experience. <<<

Daphne J. Smits, Christophe Debuy, Alice S. Brooks, Rachel Schot, Federico Ferraro, Dmitrijs Rots, Arjan Bouman, Virginie J. M. Verhoeven, Laura Donker Kaat, Sarina G. Kant, Yolande van Bever, Serwet Demirdas, Shimriet Zeidler, Marieke F. van Dooren, Stephany H. Donze, Lies H. Hoefsloot, Marjon A. van Slegtenhorst, Martina Wilke, Frank Sleutels, Mark Drost, Hennie T. Brüggenwirth, Rick van Minkelen, Anne Goverde, Janna A. Hol, Ingrid M. B. H. van de Laar, Yvette van Ierland, Anneke Kievit, Vyne van der Schoot, Kyra E. Stuurman, Grazia M. S. Mancini, Marja W. Wessels, Tjakko J. van Ham, Tjitske Kleefstra, Tahsin Stefan Barakat <<<

IntroductionChewing has been reported to enhance cognitive function through the increase in cerebral blood flow. However, the mechanisms linking cerebral blood flow increase to metabolic changes in the brain affecting cognition remain unclear. We hypothesized that glutathione (GSH) plays a pivotal role in these mechanisms. Therefore, this study aimed to investigate changes in brain GSH levels following chewing and their association with cognitive function in healthy young adults.MethodsA total of 52 university students were recruited, and the Korean version of the Repeatable Battery for the Assessment of Neuropsychological Status was used for the neurocognitive evaluations. Brain GSH levels following chewing gum or wood blocks were measured using MEscher-GArwood Point RESolved Spectroscopy (MEGA-PRESS) sequence, and their relevance to neurocognitive evaluation results was investigated.ResultsChewing significantly increased brain GSH concentration, particularly in the wood-chewing group compared to the gum-chewing group, as observed in the anterior cingulate cortex. Furthermore, the rise in GSH concentration in the wood-chewing group was positively correlated with memory function.ConclusionChewing moderately hard material elevates brain antioxidant levels such as GSH, potentially influencing cognitive function. <<<

Jing Zhang, Ian R. Humphreys, Jimin Pei, Jinuk Kim, Chulwon Choi, Rongqing Yuan, Jesse Durham, Siqi Liu, Hee-Jung Choi, Minkyung Baek, David Baker, Qian Cong <<<

Protein-protein interactions (PPI) are essential for biological function. Coevolutionary analysis and deep learning (DL) based protein structure prediction have enabled comprehensive PPI identification in bacteria and yeast, but these approaches have had limited success for the more complex human proteome. We overcame this challenge by enhancing the coevolutionary signals with 7-fold deeper multiple sequence alignments harvested from 30 petabytes of unassembled genomic data and developing a new DL network trained on augmented datasets of domain-domain interactions from 200 million predicted protein structures. We systematically screened 200 million human protein pairs and predicted 17,849 interactions with an expected precision of 90%, of which 3,631 interactions were not identified in previous experimental screens. Three-dimensional models of these predicted interactions provide numerous hypotheses about protein function and mechanisms of human diseases. <<<

Jeu Park, Do Hoon Lee, Seokjin Ham, Jiyoung Oh, Jung-Ran Noh, Yun Kyung Lee, Yoon Jeong Park, Gung Lee, Sang Mun Han, Ji Seul Han, Ye Young Kim, Yong Geun Jeon, Han Nahmgoong, Kyung Cheul Shin, Sung Min Kim, Sung Hee Choi, Chul-Ho Lee, Jiyoung Park, Tae Young Roh, Sun Kim, Jae Bum Kim <<<

Poppy Z Grimes, Brittany L Mitchell, Katherine N Thompson, Qingkun Deng, Xueyi Shen, Jareth C Wolfe, Jodi T Thomas, Robyn E Wootton, Daniel E Adkins, Saranya Arirangan, Elham Assary, Chris Chatzinakos, Charlotte A Dennison, Swathi Hassan Gangaraju, Andreas Jangmo, Yeongmi Jeong, Siim Kurvits, Qingqin S Li, Ehsan Motazedi, Joonas Naamanka, Thuy-Dung Nguyen, Ilja M Nolte, Vanessa K Ota, Joelle A Pasman, Mina Shahisavandi, Amy Shakeshaft, John R Shorter, Chloe Slaney, Martin Tesli, Carol A Wang, Uxue Zubizarreta-Arruti, , , , Silvia Alemany, Ole A Andreassen, Helga Ask, Sintia I Belangero, Rosa Bosch, Gerome Breen, Rodrigo A Bressan, Alfonso Buil, Enda M Byrne, Miquel Casas, William E Copeland, Thalia C Eley, Laurie J Hannigan, Catharina A Hartman, Alexandra Havdahl, Ian B Hickie, Golam M Khandaker, Kelli Lehto, Hermine Maes, Nicholas G Martin, Alexander Neumann, Albertine J Oldehinkel, Pedro M Pan, Hong Pan, Craig E Pennell, Roseann E Peterson, Alina Rodriguez, Giovanni A Salum, Tanja GM Vrijkotte, Robbee Wedow, Andrew JO Whitehouse, Anita Thapar, Henrik Larsson, Christel M Middeldorp, Andrew McIntosh, Mark J Adams, Yi Lu, Heather C Whalley, Alex SF Kwong <<<

Adolescent depression is a heritable psychiatric condition with rising global prevalence and severe long-term outcomes, yet its biological underpinnings remain poorly understood. We conducted the first genome-wide association study of adolescent-onset depression, comprising 102,428 cases (diagnosis or clinical symptom thresholds) and 286,911 controls, including diverse ancestries. Cross-ancestry meta-analysis identified 52 independent variants across 17 loci; European-only analysis found 61 variants at 29 loci, with a SNP-based heritability of 9.8%. Comparative analyses revealed two genes unique to adolescent-onset versus lifetime depression, enriched in neuronal subtypes, and two genes as potential drug repurposing targets. Polygenic scores were associated with adolescent-onset depression across ancestries, persistent depression trajectories, more severe outcomes, as well as reduced cortical volume, surface area and white matter integrity. Genetic correlation and Mendelian randomisation analyses support shared genetic liability and causal links with early puberty and modifiable health and behavioural risk factors. These findings uncover novel genetic loci and refine biological pathways underlying adolescent-onset depression, revealing age-specific mechanisms and early intervention opportunities. <<<

Li Ping Tang, Li Ming Zhai, Jiming Li, Yue Gao, Qiu Li Ma, Rui Li, Qing Fei Liu, Wen Jie Zhang, Wang Jinsong Yao, Bangbang Mu, Chao Qin, Xin Tian, Rahul Shaw, Keke Xia, Jian Xu, Ying Hua Su, Xian Sheng Zhang <<<

It is increasingly clear that an extraordinarily diverse range of clinically important conditions—including infections, vaccinations, autoimmune diseases, transplants, transfusion reactions, aging, and cancers—leave telltale signatures in the millions of V(D)J-rearranged antibody and T cell receptor [TR per the Human Genome Organization (HUGO) nomenclature but more commonly known as TCR] genes collectively expressed by a person’s B cells (antibodies) and T cells. We refer to these as the immunome. Because of its diversity and complexity, the immunome provides singular opportunities for advancing personalized medicine by serving as the substrate for a highly multiplexed, near-universal blood test. Here we discuss some of these opportunities, the current state of immunome-based diagnostics, and highlight some of the challenges involved. We conclude with a call to clinicians, researchers, and others to join efforts with the Adaptive Immune Receptor Repertoire Community (AIRR-C) to realize the diagnostic potential of the immunome. <<<

Abstract Waddington’s epigenetic landscape has long served as a conceptual framework for understanding cell fate decisions. The landscape’s geometry encodes the molecular mechanisms that guide the gene expression profiles of uncommitted cells toward terminally differentiated cell types. In this study, we demonstrate that applying the concept of intrinsic dimension to single-cell transcriptomic data can effectively capture trends in expression trajectories, supporting this framework. This approach allows us to define a robust cell potency score without relying on prior biological information. By analyzing an extensive collection of datasets from various species, experimental protocols, and differentiation processes, we validate our method and successfully reproduce established hierarchies of cell type potency. Our work provides a direct link between geometric properties of single-cell expression profiles and the level of differentiation of a cell population. <<<

Jinghui Lei, Zijuan Xin, Ning Liu, Taixin Ning, Ying Jing, Yicheng Qiao, Zan He, Mengmeng Jiang, Yuanhan Yang, Zhiyi Zhang, Liyun Zhao, Jingyi Li, Dongliang Lv, Yupeng Yan, Hui Zhang, Lingling Xiao, Baohu Zhang, Haoyan Huang, Shuhui Sun, Fangshuo Zheng, Xiaoyu Jiang, Huifen Lu, Xueda Dong, Shasha Yue, Chencan Ma, Jichen Shuai, Zhejun Ji, Feifei Liu, Yanxia Ye, Kaowen Yan, Qinchao Hu, Gang Xu, Qian Zhao, Ruochen Wu, Yusheng Cai, Yanling Fan, Yaobin Jing, Qiaoran Wang, Pradeep Reddy, Xiaoyong Lu, Zikai Zheng, Beibei Liu, Amin Haghani, Shuai Ma, Keiichiro Suzuki, Concepcion Rodriguez Esteban, Jiayin Yang, Moshi Song, Steve Horvath, Weiqi Zhang, Wei Li, Andy Peng Xiang, Lan Zhu, Xiaobing Fu, Guoguang Zhao, Juan Carlos Izpisua Belmonte, Jing Qu, Si Wang, Guang-Hui Liu <<<

目的:通过生物信息学方法,利用R语言编程进行数据挖掘,寻找宫颈癌中有意义的基因。方法:从TCGA数据库下载宫颈癌患者临床信息以及RNA-seq数据。使用R语言limma包进行差异表达分析,筛选差异表达基因。使用STRING数据库和Cytoscape软件进行蛋白互作网络分析。使用R语言clusterProfiler包通过KEGG数据库,GO数据库进行信号通路富集分析。使用数据库GEPIA2对宫颈癌正…… <<<

Juan L. Gomez, Christopher J. Magnus, Jordi Bonaventura, Oscar Solis, Fallon P. Curry, Marjorie R. Levinstein, Reece C. Budinich, Meghan L. Carlton, Emilya N. Ventriglia, Sherry Lam, Le Wang, Ingrid Schoenborn, William Dunne, Michael Michaelides, Scott M. Sternson <<<

Abstract Chemical feedback is ubiquitous in physiology but is challenging to study without perturbing basal functions. One example is addictive drugs, which elicit a positive-feedback cycle of drug-seeking and ingestion by acting on the brain to increase dopamine signalling1–3. However, interfering with this process by altering basal dopamine also adversely affects learning, movement, attention and wakefulness4. Here, inspired by physiological control systems, we developed a highly selective synthetic physiology approach to interfere with the positive-feedback cycle of addiction by installing a cocaine-dependent opposing signalling process into this body–brain signalling loop. We used protein engineering to create cocaine-gated ion channels that are selective for cocaine over other drugs and endogenous molecules. Expression of an excitatory cocaine-gated channel in the rat lateral habenula, a brain region that is normally inhibited by cocaine, suppressed cocaine self-administration without affecting food motivation. This artificial cocaine-activated chemogenetic process reduced the cocaine-induced extracellular dopamine rise in the nucleus accumbens. Our results show that cocaine chemogenetics is a selective approach for countering drug reinforcement by clamping dopamine release in the presence of cocaine. In the future, chemogenetic receptors could be developed for additional addictive drugs or hormones and metabolites, which would facilitate efforts to probe their neural circuit mechanisms using a synthetic physiology approach. As these chemogenetic ion channels are specific for cocaine over natural rewards, they may also offer a route towards gene therapies for cocaine addiction. <<<

Iain S. Forrest, Ha My T. Vy, Ghislain Rocheleau, Daniel M. Jordan, Ben O. Petrazzini, Girish N. Nadkarni, Judy H. Cho, Mythily Ganapathi, Kuan-Lin Huang, Wendy K. Chung, Ron Do <<<

Accurate variant penetrance estimation is crucial for precision medicine. We constructed machine learning (ML) models for 10 diseases using 1,347,298 participants with electronic health records, then applied them to an independent cohort with linked exome data. Resulting probabilities were used to evaluate ML penetrance of 1648 rare variants in 31 autosomal dominant disease-predisposition genes. ML penetrance was variable across variant classes, but highest for pathogenic and loss-of-function variants, and was associated with clinical outcomes and functional data. Compared with conventional case-versus-control approaches, ML penetrance provided refined quantitative estimates and aided the interpretation of variants of uncertain significance and loss-of-function variants by delineating clinical trajectories over time. By leveraging ML and deep phenotyping, we present a scalable approach to accurately quantify disease risk of variants. <<<