Abstract Background and Hypothesis Stress sensitivity may represent an important target for resilience-promoting, preventive interventions. Resilience Training (RT) is a 4-session, group-based behavioral intervention, focusing on mindfulness-based skills, that leads to reductions in psychopathology. To investigate the mechanisms of RT, the current study tested whether acquiring the skills taught in RT leads to decreases in psychopathology via reductions in one manifestation of stress sensitivity, emotion reactivity, and associated changes in hippocampal-frontal connectivity. Study Design An open trial of RT was conducted in 103 non-help-seeking young adults with mild-to-moderate psychotic experiences (PEs) and/or symptoms of depression. Transdiagnostic symptoms, emotion reactivity, and mindfulness-related skills were measured, and, in a subset of participants (n = 41), resting-state, functional magnetic resonance imaging (fMRI) data were collected, before and after completion of RT. Study Results Replicating and extending findings of prior studies of RT, significant increases in mindfulness-related RT skills and significant decreases in transdiagnostic symptoms and emotion reactivity, as well as changes in hippocampal-frontal functional connectivity, were observed following RT (all P < .02). Mediation analyses revealed that associations between the acquisition of the RT skills and decreases in symptoms (P < .006) were fully mediated by the decrease in emotion reactivity, which was also correlated with a significant pre-to-post increase in hippocampal-dorsolateral prefrontal cortex connectivity (P = .032). Conclusions RT may lead to improvements in mental health by increasing the capacity to manage day-to-day stress. Future randomized controlled trials with extended follow-up can determine whether such improvements decrease the likelihood of developing disabling mental illnesses in transdiagnostically at-risk individuals. <<<

Yuri Alexeev, Marwa H. Farag, Taylor L. Patti, Mark E. Wolf, Natalia Ares, Alán Aspuru-Guzik, Simon C. Benjamin, Zhenyu Cai, Zohim Chandani, Federico Fedele, Nicholas Harrigan, Jin-Sung Kim, Elica Kyoseva, Justin G. Lietz, Tom Lubowe, Alexander McCaskey, Roger G. Melko, Kouhei Nakaji, Alberto Peruzzo, Sam Stanwyck, Norm M. Tubman, Hanrui Wang, Timothy Costa <<<

Artificial intelligence (AI) advancements over the past few years have had anunprecedented and revolutionary impact across everyday application areas. Itssignificance also extends to technical challenges within science andengineering, including the nascent field of quantum computing (QC). Thecounterintuitive nature and high-dimensional mathematics of QC make it a primecandidate for AI's data-driven learning capabilities, and in fact, many of QC'sbiggest scaling challenges may ultimately rest on developments in AI. However,bringing leading techniques from AI to QC requires drawing on disparateexpertise from arguably two of the most advanced and esoteric areas of computerscience. Here we aim to encourage this cross-pollination by reviewing howstate-of-the-art AI techniques are already advancing challenges across thehardware and software stack needed to develop useful QC - from device design toapplications. We then close by examining its future opportunities and obstaclesin this space. <<<

Michael H. Kramer, Qiang Zhang, Robert Sprung, Ryan B. Day, Petra Erdmann-Gilmore, Yang Li, Ziheng Xu, Nichole M. Helton, Daniel R. George, Yiling Mi, Peter Westervelt, Jacqueline E. Payton, Sai M. Ramakrishnan, Christopher A. Miller, Daniel C. Link, John F. DiPersio, Matthew J. Walter, R. Reid Townsend, Timothy J. Ley <<<

AbstractWe have developed a deep-scale proteome and phosphoproteome database from 44 representative acute myeloid leukemia (AML) patients from the LAML TCGA dataset and 6 healthy bone marrow–derived controls. After confirming data quality, we orthogonally validated several previously undescribed features of AML revealed by the proteomic data. We identified examples of posttranscriptionally regulated proteins both globally (ie, in all AML samples) and also in patients with recurrent AML driver mutations. For example, samples with IDH1/2 mutations displayed elevated levels of the 2-oxoglutarate–dependent histone demethylases KDM4A/B/C, despite no changes in messenger RNA levels for these genes; we confirmed this finding in vitro. In samples with NPMc mutations, we identified several nuclear importins with posttranscriptionally increased protein abundance and showed that they interact with NPMc but not wild-type NPM1. We identified 2 cell surface proteins (CD180 and MRC1/CD206) expressed on AML blasts of many patients (but not healthy CD34+ stem/progenitor cells) that could represent novel targets for immunologic therapies and confirmed these targets via flow cytometry. Finally, we detected nearly 30 000 phosphosites in these samples; globally, AML samples were associated with the abnormal phosphorylation of specific residues in PTPN11, STAT3, AKT1, and PRKCD. FLT3-TKD samples were associated with increased phosphorylation of activating tyrosines on the cytoplasmic Src-family tyrosine kinases FGR and HCK and related signaling proteins. PML-RARA–initiated AML samples displayed a unique phosphorylation signature, and TP53-mutant samples showed abundant phosphorylation of serine-183 on TP53 itself. This publicly available database will serve as a foundation for further investigations of protein dysregulation in AML pathogenesis. <<<

How can we teach humanoids to climb staircases and sit on chairs using thesurrounding environment context? Arguably, the simplest way is to just showthem-casually capture a human motion video and feed it to humanoids. Weintroduce VIDEOMIMIC, a real-to-sim-to-real pipeline that mines everydayvideos, jointly reconstructs the humans and the environment, and produceswhole-body control policies for humanoid robots that perform the correspondingskills. We demonstrate the results of our pipeline on real humanoid robots,showing robust, repeatable contextual control such as staircase ascents anddescents, sitting and standing from chairs and benches, as well as otherdynamic whole-body skills-all from a single policy, conditioned on theenvironment and global root commands. VIDEOMIMIC offers a scalable path towardsteaching humanoids to operate in diverse real-world environments. <<<

AbstractDNA methylation is a ubiquitous chromatin feature, present in 25% of cytosines in the maize genome, but variation and evolution of the methylation landscape during maize domestication remain largely unknown. Here, we leverage whole-genome sequencing (WGS) and whole-genome bisulfite sequencing (WGBS) data on populations of modern maize, landrace, and teosinte (Zea mays ssp. parviglumis) to estimate epimutation rates and selection coefficients. We find weak evidence for direct selection on DNA methylation in any context, but thousands of differentially methylated regions (DMRs) are identified population-wide that are correlated with recent selection. For two trait-associated DMRs, vgt1-DMR and tb1-DMR, HiChIP data indicate that the interactive loops between DMRs and respective downstream genes are present in B73, a modern maize line, but absent in teosinte. Our results enable a better understanding of the evolutionary forces acting on patterns of DNA methylation and suggest a role of methylation variation in adaptive evolution. <<<

Dong Ju Lee, Yuju Jeon, Jung-Pil Lee, Lanshuang Zhang, Ki Hwan Koh, Feng Li, Anthony U. Mu, Junlin Wu, Yu-Ting Chen, Seamus McNulty, Wei Tang, Marta Vicencio, Dapeng Xu, Jiyoung Kim, Zheng Chen <<<

Abstract The dry-process is a sustainable and promising fabrication method for all-solid-state batteries by eliminating solvents. However, a pragmatic fabrication design for thin and robust solid-state electrolyte (SSE) layers has not been established. Herein, we report a dry-process approach that enhances mechanical stability of SSE layers from film fabrication to cell operation. By co-rolling thick SSE and positive electrode feeds, a uniform, thin SSE layer (50 µm) and a high loading positive electrode layer (5 mAh cm−2) with high active material ratio (80 wt%) are simultaneously achieved. This SSE-positive electrode integrated film exhibits enhanced physical properties and cyclability (> 80% retention after 500 cycles) at low stack pressure (2 MPa) compared to the freestanding counterparts, attributed to reinforced and intimate SSE-positive electrode interface constructed during co-rolling process. Additionally, an all-solid-state pouch cell with high stack-level specific energy (310 Wh kg−1) and energy density (805 Wh L−1) operating at 30 °C and 5 MPa is demonstrated. <<<

AbstractCell cycle structures vary significantly across cell types, which exhibit distinct phase compositions. Asynchronous DNA replication and dynamic cellular characteristics during the cell cycle result in considerable heterogeneity in DNA dosage, chromatin accessibility, methylation, and expression. Nonetheless, the consequences of cell cycle disruption in the interpretation of multi‐omics data remain unclear. Here, we systematically assessed the influence of distinct cell phase structures on the interpretation of omics features in proliferating cells, and proposed solutions for each omics dataset. For copy number variation (CNV) calling, asynchronous replication timing (RT) interference induces false CNVs in cells with high S‐phase ratio (SPR), which are significantly decreased following replication timing domain (RTD) correction. Similar noise is observed in the chromatin accessibility data. Moreover, for DNA methylation and transcriptomic analyses, cell cycle‐sorted data outperformed direct comparison in elucidating the biological features of compared cells. Additionally, we established an integrated pipeline to identify differentially expressed genes (DEGs) after cell cycle phasing. Consequently, our study demonstrated extensive cell‐cycle heterogeneity, warranting consideration in future studies involving cells with diverse cell‐cycle structures. RTD correction or phase‐specific comparison could reduce the influence of cell cycle composition on the analysis of the differences observed between stem and differentiated cells. <<<

Michael Kosicki, Boyang Zhang, Vivian Hecht, Anusri Pampari, Laura E. Cook, Neil Slaven, Jennifer A. Akiyama, Ingrid Plajzer-Frick, Catherine S. Novak, Momoe Kato, Stella Tran, Riana D. Hunter, Kianna von Maydell, Sarah Barton, Erik Beckman, Yiwen Zhu, Diane E. Dickel, Anshul Kundaje, Axel Visel, Len A. Pennacchio <<<

Abstract All contemporary Eurasians trace most of their ancestry to a small population that dispersed out of Africa about 50,000 years ago (ka)1–9. By contrast, fossil evidence attests to earlier migrations out of Africa10–15. These lines of evidence can only be reconciled if early dispersals made little to no genetic contribution to the later, major wave. A key question therefore concerns what factors facilitated the successful later dispersal that led to long-term settlement beyond Africa. Here we show that a notable expansion in human niche breadth within Africa precedes this later dispersal. We assembled a pan-African database of chronometrically dated archaeological sites and used species distribution models (SDMs) to quantify changes in the bioclimatic niche over the past 120,000 years. We found that the human niche began to expand substantially from 70 ka and that this expansion was driven by humans increasing their use of diverse habitat types, from forests to arid deserts. Thus, humans dispersing out of Africa after 50 ka were equipped with a distinctive ecological flexibility among hominins as they encountered climatically challenging habitats, providing a key mechanism for their adaptive success. <<<

Akanksha Jain, Gilles Gut, Fátima Sanchis-Calleja, Reto Tschannen, Zhisong He, Nicolas Luginbühl, Fides Zenk, Antonius Chrisnandy, Simon Streib, Christoph Harmel, Ryoko Okamoto, Malgorzata Santel, Makiko Seimiya, René Holtackers, Juliane K. Rohland, Sophie Martina Johanna Jansen, Matthias P. Lutolf, J. Gray Camp, Barbara Treutlein <<<

Abstract Brain organoids enable the mechanistic study of human brain development and provide opportunities to explore self-organization in unconstrained developmental systems1–3. Here we establish long-term, live light-sheet microscopy on unguided brain organoids generated from fluorescently labelled human induced pluripotent stem cells, which enables tracking of tissue morphology, cell behaviours and subcellular features over weeks of organoid development4. We provide a novel dual-channel, multi-mosaic and multi-protein labelling strategy combined with a computational demultiplexing approach to enable simultaneous quantification of distinct subcellular features during organoid development. We track actin, tubulin, plasma membrane, nucleus and nuclear envelope dynamics, and quantify cell morphometric and alignment changes during tissue-state transitions including neuroepithelial induction, maturation, lumenization and brain regionalization. On the basis of imaging and single-cell transcriptome modalities, we find that lumenal expansion and cell morphotype composition within the developing neuroepithelium are associated with modulation of gene expression programs involving extracellular matrix pathway regulators and mechanosensing. We show that an extrinsically provided matrix enhances lumen expansion as well as telencephalon formation, and unguided organoids grown in the absence of an extrinsic matrix have altered morphologies with increased neural crest and caudalized tissue identity. Matrix-induced regional guidance and lumen morphogenesis are linked to the WNT and Hippo (YAP1) signalling pathways, including spatially restricted induction of the WNT ligand secretion mediator (WLS) that marks the earliest emergence of non-telencephalic brain regions. Together, our work provides an inroad into studying human brain morphodynamics and supports a view that matrix-linked mechanosensing dynamics have a central role during brain regionalization. <<<

Dirk A. Lamprecht, Richard J. Wall, Annelies Leemans, Barry Truebody, Joke Sprangers, Patricia Fiogbe, Cadi Davies, Jennefer Wetzel, Stijn Daems, William Pearson, Vanessa Pillay, Samantha Saylock, M. Daniel Ricketts, Ellie Davis, Adam Huff, Tsehai Grell, Shiming Lin, Michelle Gerber, Ann Vos, John Dallow, Sam J. Willcocks, Christine Roubert, Stéphanie Sans, Amandine Desorme, Nicolas Chappat, Aurélie Ray, Mariana Pereira Moraes, Tracy Washington, Hope D’Erasmo, Pavankumar Sancheti, Melissa Everaerts, Mario Monshouwer, Jorge Esquivias, Gerald Larrouy-Maumus, Ruxandra Draghia Akli, Helen Fletcher, Alexander S. Pym, Bree B. Aldridge, Jansy P. Sarathy, Kathleen W. Clancy, Bart Stoops, Neeraj Dhar, Adrie J. C. Steyn, Paul Jackson, Clara Aguilar-Pérez, Anil Koul <<<

Yasir Sohail, Chongle Zhang, Dezhen Xue, Jinyu Zhang, Dongdong Zhang, Shaohua Gao, Yang Yang, Xiaoxuan Fan, Hang Zhang, Gang Liu, Jun Sun, En Ma <<<

Hao Huang, Nora R. Balzer, Lea Seep, Iva Splichalova, Nelli Blank-Stein, Maria Francesca Viola, Eliana Franco Taveras, Kerim Acil, Diana Fink, Franzisca Petrovic, Nikola Makdissi, Seyhmus Bayar, Katharina Mauel, Carolin Radwaniak, Jelena Zurkovic, Amir H. Kayvanjoo, Klaus Wunderling, Malin Jessen, Mohamed H. Yaghmour, Lukas Kenner, Thomas Ulas, Stephan Grein, Joachim L. Schultze, Charlotte L. Scott, Martin Guilliams, Zhaoyuan Liu, Florent Ginhoux, Marc D. Beyer, Christoph Thiele, Felix Meissner, Jan Hasenauer, Dagmar Wachten, Elvira Mass <<<

Ying Zhang, Pourya Naderi Yeganeh, Haiwei Zhang, Simon Yuan Wang, Zhouyihan Li, Bowen Gu, Dian-Jang Lee, Zhibin Zhang, Athanasios Ploumakis, Ming Shi, Hao Wu, Eric Lieberman Greer, Winston Hide, Judy Lieberman <<<

Saro Passaro, Gabriele Corso, Jeremy Wohlwend, Mateo Reveiz, Stephan Thaler, Vignesh Ram Somnath, Noah Getz, Tally Portnoi, Julien Roy, Hannes Stark, David Kwabi-Addo, Dominique Beaini, Tommi Jaakkola, Regina Barzilay <<<

AbstractAccurately modeling biomolecular interactions is a central challenge in modern biology. While recent advances, such as AlphaFold3 and Boltz-1, have substantially improved our ability to predict biomolecular complex structures, these models still fall short in predicting binding affinity, a critical property underlying molecular function and therapeutic efficacy. Here, we present Boltz-2, a new structural biology foundation model that exhibits strong performance for both structure and affinity prediction. Boltz-2 introduces controllability features including experimental method conditioning, distance constraints, and multi-chain template integration for structure prediction, and is, to our knowledge, the first AI model to approach the performance of free-energy perturbation (FEP) methods in estimating small molecule–protein binding affinity. Crucially, it achieves strong correlation with experimental readouts on many benchmarks, while being at least 1000×more computationally efficient than FEP. By coupling Boltz-2 with a generative model for small molecules, we demonstrate an effective workflow to find diverse, synthesizable, high-affinity binders, as estimated by absolute FEP simulations on the TYK2 target. To foster broad adoption and further innovation at the intersection of machine learning and biology, we are releasing Boltz-2 weights, inference, and training code1under a permissive open license, providing a robust and extensible foundation for both academic and industrial research. <<<