Yasuaki Mohri, Jialiang Nie, Hironobu Morinaga, Tomoki Kato, Takahiro Aoto, Takashi Yamanashi, Daisuke Nanba, Hiroyuki Matsumura, Sakura Kirino, Kouji Kobiyama, Ken J. Ishii, Masahiro Hayashi, Tamio Suzuki, Takeshi Namiki, Jun Seita, Emi K. Nishimura <<<

María Bueno Álvez, Sofia Bergström, Josefin Kenrick, Emil Johansson, Mikael Åberg, Murat Akyildiz, Ozlem Altay, Hilda Sköld, Konstantinos Antonopoulos, Emmanouil Apostolakis, Yasin Hasan Balcioglu, Anna Bergström, Göran Bergström, Sophia Björkander, Suzanne Egyhazi Brage, Petter Brodin, Lynn Butler, Sara Cajander, Hanna Danielsson, Murat Dayangac, Gizem Dinler-Doganay, Levent Doğanay, Gunilla Enblad, Malin Enblad, Linn Fagerberg, Sara Falck-Jones, Anna Färnert, Mattias Forsberg, Laura Gonzalez, Anders Gummesson, Karin Gunnarsson, Iva Gunnarsson, Ulf Gyllensten, Göran Hesselager, Andreas Hober, Martin Höglund, Marie Holmqvist, Begum Horuluoglu, Rebecka Hultgren, Maria Jesus Iglesias, Helena Janols, Fredric Johansson, Anette Johnsson, Lars Klareskog, David Kotol, Inger Kull, Marika Kvarnström, Maximilian Julius Lautenbach, Ulrika Liljedahl, Henrik Lindman, Cecilia Lindskog, Miklos Lipcsey, Ingrid E Lundberg, Adil Mardinoglu, Erik Melén, Lingqi Meng, Anne-Sophie Merritt, Jan Mulder, Mai Thi-Huyen Nguyen, Jessica Nordlund, Anna Norrby-Teglund, Antonella Notarnicola, Piotr Nowak, Jacob Odeberg, Per Oksvold, Tomas Olsson, Leonid Padyukov, Karlis Pauksens, Fredrik Piehl, Elisa Pin, Fredrik Pontén, Natallia Rameika, Anton Reepalu, Joy Roy, Jochen M. Schwenk, Meltem Sen, Antti Siika, Oscar E. Simonson, Åsa Sivertsson, Tobias Sjöblom, Evelina Sjöstedt, Lovisa Skoglund, Anna Smed-Sörensen, Klara Sondén, Anders Sönnerborg, Karin Stålberg, Kristoffer Strålin, Jonas Sundén-Cullberg, Christopher Sundling, Thanadol Sutantiwanichkul, Fernanda Costa Svedman, Mattias Svensson, Elisabet Svenungsson, Tadepally Lakshmikanth, Khue Hua Tran-Minh, Hasan Türkez, Christian Unge, Per Venge, Marie Wahren-Herlenius, Jakob Woessmann, Hong Yang, Umit Haluk Yeşilkaya, Meng Yuan, Mujdat Zeybel, Cheng Zhang, Wen Zhong, Martin Zwahlen, Kalle von Feilitzen, Peter Nilsson, Fredrik Edfors, Mathias Uhlén <<<

The human blood proteome provides a holistic readout of health states through the assessment of thousands of circulating proteins. Here, we present a pan-disease resource to enable the study of diverse disease phenotypes within a harmonized proteomics dataset. By profiling protein concentrations across 59 diseases and healthy cohorts, we identified proteins associated with age, sex, and BMI, as well as disease-specific signatures. This study highlights shared and distinct protein patterns across conditions, demonstrating the power of a unified proteomics approach to uncover biological insights. The dataset, covering 8,262 individuals and up to 5,416 proteins, serves as an online resource for exploring disease-specific protein profiles and advancing precision medicine research. <<<

Thazin N. Aung, James Monkman, Jonathan Warrell, Ioannis Vathiotis, Katherine M. Bates, Niki Gavrielatou, Ioannis P. Trontzas, Chin Wee Tan, Aileen I. Fernandez, Myrto Moutafi, Ken O’ Byrne, Kurt A. Schalper, Konstantinos Syrigos, Roy S. Herbst, Arutha Kulasinghe, David L. Rimm <<<

Abstract Non-small cell lung cancer (NSCLC) shows variable responses to immunotherapy, highlighting the need for biomarkers to guide patient selection. We applied a spatial multi-omics approach to 234 advanced NSCLC patients treated with programmed death 1-based immunotherapy across three cohorts to identify biomarkers associated with outcome. Spatial proteomics (n = 67) and spatial compartment-based transcriptomics (n = 131) enabled profiling of the tumor immune microenvironment (TIME). Using spatial proteomics, we identified a resistance cell-type signature including proliferating tumor cells, granulocytes, vessels (hazard ratio (HR) = 3.8, P = 0.004) and a response signature, including M1/M2 macrophages and CD4 T cells (HR = 0.4, P = 0.019). We then generated a cell-to-gene resistance signature using spatial transcriptomics, which was predictive of poor outcomes (HR = 5.3, 2.2, 1.7 across Yale, University of Queensland and University of Athens cohorts), while a cell-to-gene response signature predicted favorable outcomes (HR = 0.22, 0.38 and 0.56, respectively). This framework enables robust TIME modeling and identifies biomarkers to support precision immunotherapy in NSCLC. <<<

Ziqi Dong, Niek Wit, Aastha Agarwal, Adam James Reid, Dnyanesh Dubal, Sina Beier, Krishnaa T. Mahbubani, Kourosh Saeb-Parsy, Jelle van den Ameele, James A. Nathan, Emma L. Rawlins <<<

Yu Chen, Zhixi Chen, Hao Wang, Zhen Cui, Kai-Le Li, Zhiwei Song, Lingjiang Chen, Xiaoxiang Sun, Xiaoyu Xu, Yixue Zhang, Li Tan, Jian Yuan, Rong Tan, Min-Hua Luo, Fang-Lin Sun, Haipeng Liu, Ying Jiang, Zhiyong Mao <<<

Efficient DNA repair might make possible the longevity of naked mole-rats. However, whether they have distinctive mechanisms to optimize functions of DNA repair suppressors is unclear. We find that naked mole-rat cyclic guanosine monophosphate–adenosine monophosphate synthase (cGAS) lacks the suppressive function of human or mouse homologs in homologous recombination repair through the alteration of four amino acids during evolution. The changes enable cGAS to retain chromatin longer upon DNA damage by weakening TRIM41-mediated ubiquitination and interaction with the segregase P97. Prolonged chromatin binding of cGAS enhanced the interaction between repair factors FANCI and RAD50 to facilitate RAD50 recruitment to damage sites, thereby potentiating homologous recombination repair. Moreover, the four amino acids mediate the function of cGAS in antagonizing cellular and tissue aging and extending life span. Manipulating cGAS might therefore constitute a mechanism for life-span extension. <<<

AbstractThe hippocampal cognitive map supports navigation towards, or away from, salient locations in familiar environments1. Although much is known about how the hippocampus encodes location in world-centred coordinates, how it supports flexible navigation is less well understood. We recorded CA1 place cells while rats navigated to a goal on the honeycomb maze2. The maze tests navigation via direct and indirect paths to the goal and allows the directionality of place cells to be assessed at each choice point. Place fields showed strong directional polarization characterized by vector fields that converged to sinks distributed throughout the environment. The distribution of these ‘convergence sinks’ (ConSinks) was centred near the goal location and the population vector field converged on the goal, providing a strong navigational signal. Changing the goal location led to movement of ConSinks and vector fields towards the new goal. The honeycomb maze allows independent assessment of spatial representation and spatial action in place cell activity and shows how the latter relates to the former. The results suggest that the hippocampus creates a vector-based model to support flexible navigation, allowing animals to select optimal paths to destinations from any location in the environment. <<<

Procrastination is considered a result of failed self-regulation. However, could experiencing a sense of successful self-discipline help to boost motivation and reduce procrastination? To explore this question, two studies were conducted to investigate the relationship between the sense of self-discipline, autonomous motivation, and procrastination. Results showed that trait sense of self-discipline negatively predicted general procrastination (Study 1); self-discipline primed participants procrastinated less than the control group (Study 2); autonomous motivation mediated the relationship between sense of self-discipline and procrastination (Study 1 and Study 2). These findings suggest that cultivating a sense of self-discipline can have positive effects on both autonomous motivation and procrastination, and provide useful guidance for interventions aimed at reducing procrastination. <<<

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 <<<