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. <<<

Yongping Zhang, Shuting Jiang, Fuhong He, Yuanyuan Tian, Haiyang Hu, Li Gao, Lin Zhang, Aili Chen, Yixin Hu, Liyan Fan, Chun Yang, Bi Zhou, Dan Liu, Zihan Zhou, Yanxun Su, Lei Qin, Yi Wang, Hailong He, Jun Lu, Peifang Xiao, Shaoyan Hu, Qian-Fei Wang <<<

Abstract Background Cancer patients can achieve dramatic responses to chemotherapy yet retain resistant tumor cells, which ultimately results in relapse. Although xenograft model studies have identified several cellular and molecular features that are associated with chemoresistance in acute myeloid leukemia (AML), to what extent AML patients exhibit these properties remains largely unknown. Results We apply single-cell RNA sequencing to paired pre- and post-chemotherapy whole bone marrow samples obtained from 13 pediatric AML patients who had achieved disease remission, and distinguish AML clusters from normal cells based on their unique transcriptomic profiles. Approximately 50% of leukemic stem and progenitor populations actively express leukemia stem cell (LSC) and oxidative phosphorylation (OXPHOS) signatures, respectively. These clusters have a higher chance of tolerating therapy and exhibit an enhanced metabolic program in response to treatment. Interestingly, the transmembrane receptor CD69 is highly expressed in chemoresistant hematopoietic stem cell (HSC)-like populations (named the CD69+ HSC-like subpopulation). Furthermore, overexpression of CD69 results in suppression of the mTOR signaling pathway and promotion of cell quiescence and adhesion in vitro. Finally, the presence of CD69+ HSC-like cells is associated with unfavorable genetic mutations, the persistence of residual tumor cells in chemotherapy, and poor outcomes in independent pediatric and adult public AML cohorts. Conclusions Our analysis reveals leukemia stem cell and OXPHOS as two major chemoresistant features in human AML patients. CD69 may serve as a potential biomarker in defining a subpopulation of chemoresistant leukemia stem cells. These findings have important implications for targeting residual chemo-surviving AML cells. <<<

Alexander Haglund, Verena Zuber, Maya Abouzeid, Yifei Yang, Jeong Hun Ko, Liv Wiemann, Maria Otero-Jimenez, Louwai Muhammed, Rahel Feleke, Alexi Nott, James D. Mills, Liisi Laaniste, Djordje O. Gveric, Daniel Clode, Ann C. Babtie, Susanna Pagni, Ravishankara Bellampalli, Alyma Somani, Karina McDade, Jasper J. Anink, Lucia Mesarosova, Nurun Fancy, Nanet Willumsen, Amy Smith, Johanna Jackson, Javier Alegre-Abarrategui, Eleonora Aronica, Paul M. Matthews, Maria Thom, Sanjay M. Sisodiya, Prashant K. Srivastava, Dheeraj Malhotra, Julien Bryois, Leonardo Bottolo, Michael R. Johnson <<<

Abstract Gene expression quantitative trait loci are widely used to infer relationships between genes and central nervous system (CNS) phenotypes; however, the effect of brain disease on these inferences is unclear. Using 2,348,438 single-nuclei profiles from 391 disease-case and control brains, we report 13,939 genes whose expression correlated with genetic variation, of which 16.7–40.8% (depending on cell type) showed disease-dependent allelic effects. Across 501 colocalizations for 30 CNS traits, 23.6% had a disease dependency, even after adjusting for disease status. To estimate the unconfounded effect of genes on outcomes, we repeated the analysis using nondiseased brains (n = 183) and reported an additional 91 colocalizations not present in the larger mixed disease and control dataset, demonstrating enhanced interpretation of disease-associated variants. Principled implementation of single-cell Mendelian randomization in control-only brains identified 140 putatively causal gene–trait associations, of which 11 were replicated in the UK Biobank, prioritizing candidate peripheral biomarkers predictive of CNS outcomes. <<<

DeepSeek-AI, Aixin Liu, Bei Feng, Bin Wang, Bingxuan Wang, Bo Liu, Chenggang Zhao, Chengqi Dengr, Chong Ruan, Damai Dai, Daya Guo, Dejian Yang, Deli Chen, Dongjie Ji, Erhang Li, Fangyun Lin, Fuli Luo, Guangbo Hao, Guanting Chen, Guowei Li, H. Zhang, Hanwei Xu, Hao Yang, Haowei Zhang, Honghui Ding, Huajian Xin, Huazuo Gao, Hui Li, Hui Qu, J. L. Cai, Jian Liang, Jianzhong Guo, Jiaqi Ni, Jiashi Li, Jin Chen, Jingyang Yuan, Junjie Qiu, Junxiao Song, Kai Dong, Kaige Gao, Kang Guan, Lean Wang, Lecong Zhang, Lei Xu, Leyi Xia, Liang Zhao, Liyue Zhang, Meng Li, Miaojun Wang, Mingchuan Zhang, Minghua Zhang, Minghui Tang, Mingming Li, Ning Tian, Panpan Huang, Peiyi Wang, Peng Zhang, Qihao Zhu, Qinyu Chen, Qiushi Du, R. J. Chen, R. L. Jin, Ruiqi Ge, Ruizhe Pan, Runxin Xu, Ruyi Chen, S. S. Li, Shanghao Lu, Shangyan Zhou, Shanhuang Chen, Shaoqing Wu, Shengfeng Ye, Shirong Ma, Shiyu Wang, Shuang Zhou, Shuiping Yu, Shunfeng Zhou, Size Zheng, T. Wang, Tian Pei, Tian Yuan, Tianyu Sun, W. L. Xiao, Wangding Zeng, Wei An, Wen Liu, Wenfeng Liang, Wenjun Gao, Wentao Zhang, X. Q. Li, Xiangyue Jin, Xianzu Wang, Xiao Bi, Xiaodong Liu, Xiaohan Wang, Xiaojin Shen, Xiaokang Chen, Xiaosha Chen, Xiaotao Nie, Xiaowen Sun, Xiaoxiang Wang, Xin Liu, Xin Xie, Xingkai Yu, Xinnan Song, Xinyi Zhou, Xinyu Yang, Xuan Lu, Xuecheng Su, Y. Wu, Y. K. Li, Y. X. Wei, Y. X. Zhu, Yanhong Xu, Yanping Huang, Yao Li, Yao Zhao, Yaofeng Sun, Yaohui Li, Yaohui Wang, Yi Zheng, Yichao Zhang, Yiliang Xiong, Yilong Zhao, Ying He, Ying Tang, Yishi Piao, Yixin Dong, Yixuan Tan, Yiyuan Liu, Yongji Wang, Yongqiang Guo, Yuchen Zhu, Yuduan Wang, Yuheng Zou, Yukun Zha, Yunxian Ma, Yuting Yan, Yuxiang You, Yuxuan Liu, Z. Z. Ren, Zehui Ren, Zhangli Sha, Zhe Fu, Zhen Huang, Zhen Zhang, Zhenda Xie, Zhewen Hao, Zhihong Shao, Zhiniu Wen, Zhipeng Xu, Zhongyu Zhang, Zhuoshu Li, Zihan Wang, Zihui Gu, Zilin Li, Ziwei Xie <<<

We present DeepSeek-V2, a strong Mixture-of-Experts (MoE) language modelcharacterized by economical training and efficient inference. It comprises 236Btotal parameters, of which 21B are activated for each token, and supports acontext length of 128K tokens. DeepSeek-V2 adopts innovative architecturesincluding Multi-head Latent Attention (MLA) and DeepSeekMoE. MLA guaranteesefficient inference through significantly compressing the Key-Value (KV) cacheinto a latent vector, while DeepSeekMoE enables training strong models at aneconomical cost through sparse computation. Compared with DeepSeek 67B,DeepSeek-V2 achieves significantly stronger performance, and meanwhile saves42.5% of training costs, reduces the KV cache by 93.3%, and boosts the maximumgeneration throughput to 5.76 times. We pretrain DeepSeek-V2 on a high-qualityand multi-source corpus consisting of 8.1T tokens, and further performSupervised Fine-Tuning (SFT) and Reinforcement Learning (RL) to fully unlockits potential. Evaluation results show that, even with only 21B activatedparameters, DeepSeek-V2 and its chat versions still achieve top-tierperformance among open-source models. <<<

Caihua Sang, Qiang Liu, Yiwei Lai, Shijun Xia, Ruhong Jiang, Songnan Li, Qi Guo, Qifan Li, Mingyang Gao, Xueyuan Guo, Lihong Huang, Nian Liu, Chenxi Jiang, Song Zuo, Xiaoxia Liu, Mengmeng Li, Weili Ge, Shangming Song, Lianghua Chen, Shuanglun Xie, Jiangang Zou, Ke Chen, Xiangfei Liu, Hesheng Hu, Xinhua Wang, Jinlin Zhang, Zhaojun Wang, Chi Wang, Liu He, Chao Jiang, Ribo Tang, Ning Zhou, Yunlong Wang, Deyong Long, Xin Du, Chenyang Jiang, Laurent Macle, Jianzeng Dong, Changsheng Ma, , Wei Wang, Xin Zhao, Changyi Li, Zhuo Liang, Xu Li, Xiangyi Kong, Wenli Dai, Yufeng Wang, Lu Yu, Xueyan Ding, Hui Cheng, Jianwei Lin, Pei Zhang, Yaxun Sun, Xiaofeng Hou, Yao Wang, Yumei Xue, Rong Bai, Jing Du <<<

ImportanceSuccess rates of pulmonary vein isolation (PVI) are modest for persistent atrial fibrillation (AF). Additional linear ablation beyond PVI has not been proved superior to PVI alone in randomized trials. Ethanol infusion of the vein of Marshall (EIVOM) facilitates ablation at the mitral isthmus and may lead to improved effectiveness of a linear ablation strategy.ObjectiveTo determine whether linear ablation with radiofrequency energy combined with EIVOM added to PVI improves sinus rhythm maintenance compared with PVI alone in patients with persistent AF.Design, Setting, and ParticipantsThe PROMPT-AF trial is an investigator-initiated, multicenter, open-label, randomized trial involving 12 tertiary hospitals in China. A total of 498 patients aged 18 to 80 years, with AF persisting for more than 3 months, undergoing first-time AF ablation, were enrolled and randomized from August 27, 2021, to July 16, 2023.InterventionsPatients were randomized to undergo PVI alone or PVI plus EIVOM and linear ablation (intervention). The latter group first underwent EIVOM, followed by PVI and linear ablation of the left atrial roof, mitral isthmus, and cavotricuspid isthmus.Main Outcomes and MeasuresThe primary end point was freedom from any documented atrial arrhythmias lasting more than 30 seconds, without the use of antiarrhythmic drugs within 12 months. Secondary outcomes included freedom from atrial arrhythmia recurrence, AF, atrial arrhythmia recurrence after multiple procedures, and documented atrial tachycardia or atrial flutter with or without antiarrhythmic drugs; AF burden; and improvement in quality of life. Patients were monitored with wearable single-lead electrocardiographic (ECG) patches, worn for 24 hours a week, supplemented by symptom-triggered ECGs and Holter monitoring.ResultsAmong 498 randomized patients, 495 (99.4%) were included in the primary analysis (mean age, 61.1 years [SD, 9.7] years, 361 male [72.9%]). After 12 months, 174 of 246 patients (70.7%) assigned to undergo PVI plus EIVOM and linear ablation and 153 of 249 patients (61.5%) assigned to undergo PVI alone remained free from atrial arrhythmias without taking antiarrhythmic drugs (hazard ratio, 0.73; 95% CI, 0.54-0.99, P = .045). The intervention effect was consistent across all prespecified subgroups. The comparison of secondary outcomes did not demonstrate significant results.ConclusionAmong patients with persistent AF, linear ablation combined with EIVOM in addition to PVI significantly improved freedom from atrial arrhythmias within 12 months compared with PVI alone.Trial RegistrationClinicalTrials.gov Identifier: NCT04497376 <<<

NP-hard problems regularly come up in video games, with interestingconnections to real-world problems. In the game Minecraft, players placetorches on the ground to light up dark areas. Placing them in a way thatminimizes the total number of torches to save resources is far from trivial. Inthis paper, we use Quantum Computing to approach this problem. To this end, wederive a QUBO formulation of the torch placement problem, which we uncover tobe very similar to another NP-hard problem. We employ a solution strategy thatinvolves learning Lagrangian weights in an iterative process, adding to theever growing toolbox of QUBO formulations. Finally, we perform experiments onreal quantum hardware using real game data to demonstrate that our approachyields good torch placements. <<<

Mohammed Toufiq, Darawan Rinchai, Eleonore Bettacchioli, Basirudeen Syed Ahamed Kabeer, Taushif Khan, Bishesh Subba, Olivia White, Marina Yurieva, Joshy George, Noemie Jourde-Chiche, Laurent Chiche, Karolina Palucka, Damien Chaussabel <<<

AbstractBackgroundFeature selection is a critical step for translating advances afforded by systems-scale molecular profiling into actionable clinical insights. While data-driven methods are commonly utilized for selecting candidate genes, knowledge-driven methods must contend with the challenge of efficiently sifting through extensive volumes of biomedical information. This work aimed to assess the utility of large language models (LLMs) for knowledge-driven gene prioritization and selection.MethodsIn this proof of concept, we focused on 11 blood transcriptional modules associated with an Erythroid cells signature. We evaluated four leading LLMs across multiple tasks. Next, we established a workflow leveraging LLMs. The steps consisted of: (1) Selecting one of the 11 modules; (2) Identifying functional convergences among constituent genes using the LLMs; (3) Scoring candidate genes across six criteria capturing the gene’s biological and clinical relevance; (4) Prioritizing candidate genes and summarizing justifications; (5) Fact-checking justifications and identifying supporting references; (6) Selecting a top candidate gene based on validated scoring justifications; and (7) Factoring in transcriptome profiling data to finalize the selection of the top candidate gene.ResultsOf the four LLMs evaluated, OpenAI's GPT-4 and Anthropic's Claude demonstrated the best performance and were chosen for the implementation of the candidate gene prioritization and selection workflow. This workflow was run in parallel for each of the 11 erythroid cell modules by participants in a data mining workshop. Module M9.2 served as an illustrative use case. The 30 candidate genes forming this module were assessed, and the top five scoring genes were identified as BCL2L1, ALAS2, SLC4A1, CA1, and FECH. Researchers carefully fact-checked the summarized scoring justifications, after which the LLMs were prompted to select a top candidate based on this information. GPT-4 initially chose BCL2L1, while Claude selected ALAS2. When transcriptional profiling data from three reference datasets were provided for additional context, GPT-4 revised its initial choice to ALAS2, whereas Claude reaffirmed its original selection for this module.ConclusionsTaken together, our findings highlight the ability of LLMs to prioritize candidate genes with minimal human intervention. This suggests the potential of this technology to boost productivity, especially for tasks that require leveraging extensive biomedical knowledge. <<<

AbstractCancer diagnosis and therapeutics (theranostics) based on the tumor microenvironment (TME) and biomarkers has been an emerging approach for precision medicine. DNA nanotechnology dynamically controls the self‐assembly of DNA molecules at the nanometer scale to construct intelligent DNA chemical reaction systems. The DNA logic circuit is a particularly emerging approach for computing within the DNA chemical systems. DNA logic circuits can sensitively respond to tumor‐specific markers and the TME through logic operations and signal amplification, to generate detectable signals or to release anti‐cancer agents. In this review, the fundamental concepts of DNA logic circuits are clarified, the basic modules in the circuit are summarized, and how this advanced nano‐assembly circuit responds to tumor‐related molecules, how to perform logic operations, to realize signal amplification, and selectively release drugs through discussing over 30 application examples, are demonstrated. This review shows that DNA logic circuits have powerful logic judgment and signal amplification functions in improving the specificity and sensitivity of cancer diagnosis and making cancer treatment controllable. In the future, researchers are expected to overcome the existing shortcomings of DNA logic circuits and design smarter DNA devices with better biocompatibility and stability, which will further promote the development of cancer theranostics. <<<

Abstract The societal shift toward greater gender equality has led to increased variability in people’s gender role attitudes, or the belief that men and women should occupy distinct family roles (i.e. men as breadwinners and women as homemakers). Existing evidence on the association between gender role attitudes and relationship well-being remains inconclusive with mixed findings, likely because past research has not adequately considered the direction and degree of (in)congruencies between partners within the relationship. Using longitudinal samples of 1,327 couples from the United States and 5,856 couples from Germany tracked over 2 and 13 years, respectively, we employed dyadic response surface analysis to examine how different patterns of partner (in)congruencies in gender role attitudes predict relationship well-being in mixed-gender relationships. The results showed that, for US men and German men and women, the direction of incongruence between partners’ gender role attitudes mattered: relationship satisfaction was higher when men adopted more egalitarian attitudes than women (or conversely, when women adopted more traditional attitudes than men) compared with the reverse. Relationship satisfaction was also higher when both partners showed congruence in extreme gender role attitudes (either strongly traditional or egalitarian) than when either partner endorsed more neutral attitudes. US women reported higher relationship satisfaction only when either partner endorsed more egalitarian attitudes. Although past research emphasizes the benefits of partner similarity for relationship well-being, our findings highlight the importance of both similarity and complementarity in gender role attitudes, potentially subject to cultural and contextual factors. <<<