Mirazul Islam, Yilin Yang, Alan J. Simmons, Vishal M. Shah, Krushna Pavan Musale, Yanwen Xu, Naila Tasneem, Zhengyi Chen, Linh T. Trinh, Paola Molina, Marisol A. Ramirez-Solano, Iannish D. Sadien, Jinzhuang Dou, Andrea Rolong, Ken Chen, Mark A. Magnuson, Jeffrey C. Rathmell, Ian G. Macara, Douglas J. Winton, Qi Liu, Hamim Zafar, Reza Kalhor, George M. Church, Martha J. Shrubsole, Robert J. Coffey, Ken S. Lau <<<

AbstractAntigen discovery technologies have largely focused on major histocompatibility complex (MHC) class I-restricted human T cell receptors (TCRs), leaving methods for MHC class II-restricted and mouse TCR reactivities relatively undeveloped. Here we present TCR mapping of antigenic peptides (TCR-MAP), an antigen discovery method that uses a synthetic TCR-stimulated circuit in immortalized T cells to activate sortase-mediated tagging of engineered antigen-presenting cells (APCs) expressing processed peptides on MHCs. Live, tagged APCs can be directly purified for deconvolution by sequencing, enabling TCRs with unknown specificity to be queried against barcoded peptide libraries in a pooled screening context. TCR-MAP accurately captures self-reactivities or viral reactivities with high throughput and sensitivity for both MHC class I-restricted and class II-restricted TCRs. We elucidate problematic cross-reactivities of clinical TCRs targeting the cancer/testis melanoma-associated antigen A3 and discover targets of myocarditis-inciting autoreactive T cells in mice. TCR-MAP has the potential to accelerate T cell antigen discovery efforts in the context of cancer, infectious disease and autoimmunity. <<<

Large Vision-Language-Action (VLA) models, leveraging powerful pre trainedVision-Language Models (VLMs) backends, have shown promise in robotic controldue to their impressive generalization ability. However, the success comes at acost. Their reliance on VLM backends with billions of parameters leads to highcomputational costs and inference latency, limiting the testing scenarios tomainly quasi-static tasks and hindering performance in dynamic tasks requiringrapid interactions. To address these limitations, this paper proposes HiRT, aHierarchical Robot Transformer framework that enables flexible frequency andperformance trade-off. HiRT keeps VLMs running at low frequencies to capturetemporarily invariant features while enabling real-time interaction through ahigh-frequency vision-based policy guided by the slowly updated features.Experiment results in both simulation and real-world settings demonstratesignificant improvements over baseline methods. Empirically, in static tasks,we double the control frequency and achieve comparable success rates.Additionally, on novel real-world dynamic ma nipulation tasks which arechallenging for previous VLA models, HiRT improves the success rate from 48% to75%. <<<

Practical relevance: The ‘2022 AAFP/ISFM Cat Friendly Veterinary Interaction Guidelines: Approach and Handling Techniques’ (hereafter the ‘Cat Friendly Veterinary Interaction Guidelines’) support veterinary professionals with feline interactions and handling to reduce the impact of fear and other protective (negative) emotions, in so doing enhancing feline welfare and In implementing these Guidelines, team satisfaction and cat caregiver confidence in the veterinary team will increase as the result of efficient examinations, better experience, more reliable diagnostic testing and improved feline wellbeing. Veterinary professionals will learn the importance of understanding and appropriately responding to the current emotional state of the cat and tailoring each visit to the individual. Clinical challenges: Cats have evolved with emotions and behaviors that are necessary for their survival as both a predator and prey species. A clinical setting and the required examinations and procedures to meet their physical health needs can result in behavioral responses to protective emotions. Cat friendly interactions require understanding, interpreting and appropriately responding to cats’ emotional states and giving them a perceived sense of control while performing the required assessment. Evidence base: These Guidelines have been created by a Task Force of experts convened by the American Association of Feline Practitioners and the International Society of Feline Medicine, based on an extensive literature review and, where evidence is lacking, the authors’ experience. Endorsements: These Guidelines have been endorsed by a number of groups and organizations, as detailed on page 1127 and at catvets.com/interactions and icatcare.org/cat-friendly-guidelines . <<<

To train generalist robot policies, machine learning methods often require asubstantial amount of expert human teleoperation data. An ideal robot forhumans collecting data is one that closely mimics them: bimanual arms anddexterous hands. However, creating such a bimanual teleoperation system withover 50 DoF is a significant challenge. To address this, we introduce Bidex, anextremely dexterous, low-cost, low-latency and portable bimanual dexterousteleoperation system which relies on motion capture gloves and teacher arms. Wecompare Bidex to a Vision Pro teleoperation system and a SteamVR system andfind Bidex to produce better quality data for more complex tasks at a fasterrate. Additionally, we show Bidex operating a mobile bimanual robot for in thewild tasks. The robot hands (5k USD) and teleoperation system (7k USD) isreadily reproducible and can be used on many robot arms including two xArms(16k USD). Website at https://bidex-teleop.github.io/ <<<

Isabel Deisenhofer, Jean-Paul Albenque, Sonia Busch, Edouard Gitenay, Stavros E. Mountantonakis, Antoine Roux, Jerome Horvilleur, Babe Bakouboula, Saumil Oza, Selim Abbey, Guillaume Theodore, Antoine Lepillier, Yves Guyomar, Francis Bessiere, Jaap Jan Smit, Theophile Mohr Durdez, Paola Milpied, Anthony Appetiti, Daniel Guerrero, Tom De Potter, Christian De Chillou, Seth Goldbarg, Atul Verma, John D. Hummel, , Jaap Jan Smit, Tom De Potter, Christian De Chillou <<<

Abstract Although pulmonary vein isolation (PVI) has become the cornerstone ablation procedure for atrial fibrillation (AF), the optimal ablation procedure for persistent and long-standing persistent AF remains elusive. Targeting spatio-temporal electrogram dispersion in a tailored procedure has been suggested as a potentially beneficial alternative to a conventional PVI-only procedure. In this multicenter, randomized, controlled, double-blind, superiority trial, patients with drug-refractory persistent AF were randomly assigned to a tailored ablation procedure targeting areas of spatio-temporal dispersion, as detected by an artificial intelligence (AI) algorithm, in addition to PVI (tailored arm, n = 187, 23% women) or to a conventional PVI-only procedure (anatomical arm, n = 183, 19% women). The primary efficacy endpoint was freedom from documented AF with or without antiarrhythmic drugs at 12 months after a single ablation procedure. Secondary endpoints included freedom from any atrial arrhythmic events, and the secondary composite safety endpoint consisted of death, cerebrovascular events, or treatment-related serious adverse events. One year post-procedure, the trial met its primary efficacy endpoint, which was achieved in 88% of patients in the tailored arm compared with 70% of patients in the anatomical arm (log-rank P < 0.0001 for superiority). However, no significant difference between arms was observed for the freedom from any atrial arrhythmia endpoint after one ablation. The safety endpoint did not differ between arms, with procedure and ablation times being twice as long in the tailored arm. These results show that AI-guided ablation of spatio-temporal dispersion areas in addition to PVI is superior to PVI alone in eliminating AF at 1-year follow-up in patients with persistent and long-standing persistent AF. Ablation of subsequent organized atrial tachycardias may be needed to maintain sinus rhythm long term. ClinicalTrials.gov identifier: NCT04702451. <<<

Zhiyi Chen, Yancheng Tang, Xuerong Liu, Wei Li, Yuanyuan Hu, Bowen Hu, Ting Xu, Rong Zhang, Lei Xia, Jing-Xuan Zhang, Zhibing Xiao, Ji Chen, Zhengzhi Feng, Yuan Zhou, Qinghua He, Jiang Qiu, Xu Lei, Hong Chen, Shaozheng Qin, Tingyong Feng <<<

Xiekui Cui, Han Yang, Charles Cai, Cooper Beaman, Xiaoyu Yang, Hongjiang Liu, Xingjie Ren, Zachary Amador, Ian R. Jones, Kathleen C. Keough, Meng Zhang, Tyler Fair, Armen Abnousi, Shreya Mishra, Zhen Ye, Ming Hu, Alex A. Pollen, Katherine S. Pollard, Yin Shen <<<

Jorge Hernández-García, Vanessa Polet Carrillo-Carrasco, Juriaan Rienstra, Keita Tanaka, Martijn de Roij, Melissa Dipp-Álvarez, Alejandra Freire-Ríos, Isidro Crespo, Roeland Boer, Willy A. M. van den Berg, Simon Lindhoud, Dolf Weijers <<<

AbstractThe Auxin Response Factors (ARFs) family of transcription factors are the central mediators of auxin-triggered transcriptional regulation. Functionally different classes of extant ARFs operate as antagonistic auxin-dependent and -independent regulators. While part of the evolutionary trajectory to the present auxin response functions has been reconstructed, it is unclear how ARFs emerged, and how early diversification led to functionally different proteins. Here, we use in silico and in vivo analyses to revisit the molecular events that led to the origin and subsequent evolution of the ARFs. We reveal the shared origin of ARFs from preexisting domains, uncovering a protein fold homologous to the ARF DNA-binding fold in a conserved eukaryotic chromatin regulator. Building on this, we reconstruct the complete evolutionary history of ARFs, including the divergence events leading to the appearance of the ARF classes and defining the main molecular targets for their functional diversification. We derive a complete evolutionary trajectory that led to the emergence of the nuclear auxin signalling pathway. <<<

AbstractTabular data, spreadsheets organized in rows and columns, are ubiquitous across scientific fields, from biomedicine to particle physics to economics and climate science1,2. The fundamental prediction task of filling in missing values of a label column based on the rest of the columns is essential for various applications as diverse as biomedical risk models, drug discovery and materials science. Although deep learning has revolutionized learning from raw data and led to numerous high-profile success stories3–5, gradient-boosted decision trees6–9 have dominated tabular data for the past 20 years. Here we present the Tabular Prior-data Fitted Network (TabPFN), a tabular foundation model that outperforms all previous methods on datasets with up to 10,000 samples by a wide margin, using substantially less training time. In 2.8 s, TabPFN outperforms an ensemble of the strongest baselines tuned for 4 h in a classification setting. As a generative transformer-based foundation model, this model also allows fine-tuning, data generation, density estimation and learning reusable embeddings. TabPFN is a learning algorithm that is itself learned across millions of synthetic datasets, demonstrating the power of this approach for algorithm development. By improving modelling abilities across diverse fields, TabPFN has the potential to accelerate scientific discovery and enhance important decision-making in various domains. <<<

Maxim E. Zaslavsky, Erin Craig, Jackson K. Michuda, Nidhi Sehgal, Nikhil Ram-Mohan, Ji-Yeun Lee, Khoa D. Nguyen, Ramona A. Hoh, Tho D. Pham, Katharina Röltgen, Brandon Lam, Ella S. Parsons, Susan R. Macwana, Wade DeJager, Elizabeth M. Drapeau, Krishna M. Roskin, Charlotte Cunningham-Rundles, M. Anthony Moody, Barton F. Haynes, Jason D. Goldman, James R. Heath, R. Sharon Chinthrajah, Kari C. Nadeau, Benjamin A. Pinsky, Catherine A. Blish, Scott E. Hensley, Kent Jensen, Everett Meyer, Imelda Balboni, Paul J. Utz, Joan T. Merrill, Joel M. Guthridge, Judith A. James, Samuel Yang, Robert Tibshirani, Anshul Kundaje, Scott D. Boyd <<<

Clinical diagnosis typically incorporates physical examination, patient history, various laboratory tests, and imaging studies but makes limited use of the human immune system’s own record of antigen exposures encoded by receptors on B cells and T cells. We analyzed immune receptor datasets from 593 individuals to develop MAchine Learning for Immunological Diagnosis, an interpretive framework to screen for multiple illnesses simultaneously or precisely test for one condition. This approach detects specific infections, autoimmune disorders, vaccine responses, and disease severity differences. Human-interpretable features of the model recapitulate known immune responses to severe acute respiratory syndrome coronavirus 2, influenza, and human immunodeficiency virus, highlight antigen-specific receptors, and reveal distinct characteristics of systemic lupus erythematosus and type-1 diabetes autoreactivity. This analysis framework has broad potential for scientific and clinical interpretation of immune responses. <<<

Eslem Ben Arous, James A. Blinkhorn, Sarah Elliott, Christopher A. Kiahtipes, Charles D. N’zi, Mark D. Bateman, Mathieu Duval, Patrick Roberts, Robert Patalano, Alexander F. Blackwood, Khady Niang, Eugénie Affoua Kouamé, Edith Lebato, Emily Hallett, Jacopo N. Cerasoni, Erin Scott, Jana Ilgner, Maria Jesús Alonso Escarza, Francois Yodé Guédé, Eleanor M. L. Scerri <<<

Abstract Humans emerged across Africa shortly before 300 thousand years ago (ka)1–3. Although this pan-African evolutionary process implicates diverse environments in the human story, the role of tropical forests remains poorly understood. Here we report a clear association between late Middle Pleistocene material culture and a wet tropical forest in southern Côte d’Ivoire, a region of present-day rainforest. Twinned optically stimulated luminescence and electron spin resonance dating methods constrain the onset of human occupations at Bété I to around 150 ka, linking them with Homo sapiens. Plant wax biomarker, stable isotope, phytolith and pollen analyses of associated sediments all point to a wet forest environment. The results represent the oldest yet known clear association between humans and this habitat type. The secure attribution of stone tool assemblages with the wet forest environment demonstrates that Africa’s forests were not a major ecological barrier for H. sapiens as early as around 150 ka. <<<

Denoising diffusion probabilistic models (DDPM) are powerful hierarchicallatent variable models with remarkable sample generation quality and trainingstability. These properties can be attributed to parameter sharing in thegenerative hierarchy, as well as a parameter-free diffusion-based inferenceprocedure. In this paper, we present Few-Shot Diffusion Models (FSDM), aframework for few-shot generation leveraging conditional DDPMs. FSDMs aretrained to adapt the generative process conditioned on a small set of imagesfrom a given class by aggregating image patch information using a set-basedVision Transformer (ViT). At test time, the model is able to generate samplesfrom previously unseen classes conditioned on as few as 5 samples from thatclass. We empirically show that FSDM can perform few-shot generation andtransfer to new datasets. We benchmark variants of our method on complex visiondatasets for few-shot learning and compare to unconditional and conditionalDDPM baselines. Additionally, we show how conditioning the model on patch-basedinput set information improves training convergence. <<<

Three-dimensional genome structure plays a pivotal role in gene regulation and cellular function. Single-cell analysis of genome architecture has been achieved using imaging and chromatin conformation capture methods such as Hi-C. To study variation in chromosome structure between different cell types, computational approaches are needed that can utilize sparse and heterogeneous single-cell Hi-C data. However, few methods exist that are able to accurately and efficiently cluster such data into constituent cell types. Here, we describe scHiCluster, a single-cell clustering algorithm for Hi-C contact matrices that is based on imputations using linear convolution and random walk. Using both simulated and real single-cell Hi-C data as benchmarks, scHiCluster significantly improves clustering accuracy when applied to low coverage datasets compared with existing methods. After imputation by scHiCluster, topologically associating domain (TAD)-like structures (TLSs) can be identified within single cells, and their consensus boundaries were enriched at the TAD boundaries observed in bulk cell Hi-C samples. In summary, scHiCluster facilitates visualization and comparison of single-cell 3D genomes. <<<

Chenming Xu, Jianli Li, Songchang Chen, Xiaoqiang Cai, Ruilin Jing, Xiaomei Qin, Dong Pan, Xin Zhao, Dongyang Ma, Xiufeng Xu, Xiaojun Liu, Can Wang, Bingxin Yang, Lanlan Zhang, Shuyuan Li, Yiyao Chen, Nina Pan, Ping Tang, Jieping Song, Nian Liu, Chen Zhang, Zhiwei Zhang, Xiang Qiu, Weiliang Lu, Chunmei Ying, Xiaotian Li, Congjian Xu, Yanlin Wang, Yanting Wu, He-Feng Huang, Jinglan Zhang <<<

AbstractCurrent non-invasive prenatal screening (NIPS) analyzes circulating fetal cell-free DNA (cfDNA) in maternal peripheral blood for selected aneuploidies or microdeletion/duplication syndromes. Many genetic disorders are refractory to NIPS largely because the maternal genetic material constitutes most of the total cfDNA present in the maternal plasma, which hinders the detection of fetus-specific genetic variants. Here, we developed an innovative sequencing method, termed coordinative allele-aware target enrichment sequencing (COATE-seq), followed by multidimensional genomic analyses of sequencing read depth, allelic fraction, and linked single nucleotide polymorphisms, to accurately separate the fetal genome from the maternal background. Analytical confounders including multiple gestations, maternal copy number variations, and absence of heterozygosity were successfully recognized and precluded for fetal variant analyses. In addition, fetus-specific genomic characteristics, including the cfDNA fragment length, meiotic error origins, meiotic recombination, and recombination breakpoints were identified which reinforced the fetal variant assessment. In 1129 qualified pregnancies tested, 54 fetal aneuploidies, 8 microdeletions/microduplications, and 8 monogenic variants were detected with 100% sensitivity and 99.3% specificity. Using the comprehensive cfDNA genomic analysis tools developed, we found that 60.3% of aneuploidy samples had aberrant meiotic recombination providing important insights into the mechanism underlying meiotic nondisjunctions. Altogether, we show that the genetic deconvolution of the fetal and maternal cfDNA enables thorough and accurate delineation of fetal genome which paves the way for the next-generation prenatal screening of essentially all types of human genetic disorders. <<<

Hex and Counter Wargames are adversarial two-player simulations of realmilitary conflicts requiring complex strategic decision-making. Unlikeclassical board games, these games feature intricate terrain/unit interactions,unit stacking, large maps of varying sizes, and simultaneous move and combatdecisions involving hundreds of units. This paper introduces a novel systemdesigned to address the strategic complexity of Hex and Counter Wargames byintegrating cutting-edge advancements in Recurrent Neural Networks withAlphaZero, a reliable modern Reinforcement Learning algorithm. The systemutilizes a new Neural Network architecture developed from existing research,incorporating innovative state and action representations tailored to thesespecific game environments. With minimal training, our solution has shownpromising results in typical scenarios, demonstrating the ability to generalizeacross different terrain and tactical situations. Additionally, we explore thesystem's potential to scale to larger map sizes. The developed system is openlyaccessible, facilitating continued research and exploration within thischallenging domain. <<<