ABSTRACTPrenatal cell‐free DNA (cfDNA) screening has advanced significantly, extending beyond detecting aneuploidies to sub‐chromosomal copy number variations. However, its application for screening dominant single‐gene conditions, often caused by de novo variants, remains underutilized in the general obstetric population. This study reviews recent data and experience on prenatal cfDNA screening for dominant monogenic conditions using multiple‐gene panels, highlighting its potential to enhance early detection and management of genetic disorders. Integrating comprehensive cfDNA screening into routine prenatal care could complement current imaging techniques and standard prenatal cfDNA screening, which may overlook pre‐symptomatic fetuses with dominant monogenic conditions in early gestation. Despite promising initial results, further research is needed to confirm the clinical validity and utility of cfDNA screening for these conditions. Larger and more diverse studies are necessary to assess the broader applicability of this technology. In addition, key challenges such as access, genetic counseling, ethical considerations, and policy development need to be addressed. A comprehensive approach, including rigorous test design, informed consent, and robust counseling, is essential for the successful adoption of expanded cfDNA screening, ultimately improving clinical outcomes. <<<

Abstract<br> <br> Motivation<br> The application of constraint-based modeling to functionally analyze metagenomic data has been limited so far, partially due to the absence of suitable toolboxes.<br> <br> <br> Results<br> To address this gap, we created a comprehensive toolbox to model (i) microbe–microbe and host–microbe metabolic interactions, and (ii) microbial communities using microbial genome-scale metabolic reconstructions and metagenomic data. The Microbiome Modeling Toolbox extends the functionality of the constraint-based reconstruction and analysis toolbox.<br> <br> <br> Availability and implementation<br> The Microbiome Modeling Toolbox and the tutorials at https://git.io/microbiomeModelingToolbox.<br> <<<

Embodied AI and robotic systems increasingly depend on scalable, diverse, and physically grounded 3D content for simulation-based training and real-world deployment. While 3D generative modeling has advanced rapidly, embodied applications impose requirements far beyond visual realism: generated objects must carry kinematic structure and material properties, scenes must support interaction and task execution, and the resulting content must bridge the gap between simulation and reality. This survey presents the first survey of 3D generation for embodied AI and organizes the literature around three roles that 3D generation plays in embodied systems. In \emph{Data Generator}, 3D generation produces simulation-ready objects and assets, including articulated, physically grounded, and deformable content for downstream interaction; in \emph{Simulation Environments}, it constructs interactive and task-oriented worlds, spanning structure-aware, controllable, and agentic scene generation; and in \emph{Sim2Real Bridge}, it supports digital twin reconstruction, data augmentation, and synthetic demonstrations for downstream robot learning and real-world transfer. We also show that the field is shifting from visual realism toward interaction readiness, and we identify the main bottlenecks, including limited physical annotations, the gap between geometric quality and physical validity, fragmented evaluation, and the persistent sim-to-real divide, that must be addressed for 3D generation to become a dependable foundation for embodied intelligence. Our project page is at https://3dgen4robot.github.io. <<<

Abstract<br> Genome replication is temporally regulated during S phase, with specific genomic regions replicating at defined times in a process that is known as replication timing (RT). Based on 3D cytology in replicating nuclei, we previously proposed a model in which maize euchromatin is subdivided into subcompartments distinguished by chromatin condensation and RT. However, whether this compartmentalization reflects a general nuclear architecture that persists throughout the cell cycle was unclear. To test this model, we conducted two orthogonal assays—Hi-C for genome-wide interaction data and 3D FISH for direct visualization of chromatin organization in maize (Zea mays L.). Hi-C analyses revealed distinct patterns of early-S regions exhibited negative insulation scores with long-range contacts, whereas middle-S regions showed the opposite. Early-S regions showed the strongest correlation with epigenomic signatures of open, transcriptionally active chromatin. 3D oligo FISH painting confirmed that early-S and middle-S replicating regions occupy adjacent but largely non-overlapping nucleoplasmic sub-territories throughout interphase stages, including G1. Together, our findings redefine the maize euchromatin “A” compartment as two spatially distinct subcompartments derived from high-frequency RT transitions between early and middle S along the linear genome. These findings have implications for chromatin-templated processes and underscore the importance of RT as a defining feature of genome organization. <<<

Stock price prediction is of significant importance in quantitative investment. Existing approaches encounter two primary issues: First, they often overlook the crucial role of capturing short-term stock fluctuations for predicting high-volatility returns. Second, mainstream methods, relying on graphs or attention mechanisms, inadequately explore the temporal relationships among stocks, often blurring distinctions in their characteristics over time and the causal relationships before and after. However, the high volatility of stocks and the intricate market correlations are crucial to accurately predicting stock prices. To address these challenges, we propose a Dual-branch Framework of Fluctuation and Trend (DFT), which decomposes stocks into trend and fluctuation components. By employing a carefully design decomposition module, DFT effectively extracts short-term fluctuations and trend information from stocks while explicitly modeling temporal variations and causal correlations. Our extensive experiments demonstrate that DFT outperforms existing methods across multiple metrics, including a 300% improvement in ranking metrics and a 400% improvement in portfolio-based indicators. Through detailed experiments, we provide valuable insights into different roles of trends and fluctuations in stock price prediction. <<<

ABSTRACT<br> <br> Antibodies are central mediators of the adaptive immune response, and they are powerful research tools and therapeutics. Antibody discovery requires substantial experimental effort, such as immunization campaigns or<br> <i>in vitro</i><br> library screening. Predicting antibody-antigen binding<br> <i>a priori</i><br> remains challenging. However, recent machine learning methods raise the possibility of<br> <i>in silico</i><br> antibody discovery, bypassing or reducing initial experimental bottlenecks. Here, we report a virtual screen using AlphaFold-Multimer (AF-M) that prospectively identified nanobody binders to MRGPRX2, a G protein-coupled receptor (GPCR) and therapeutic target for the treatment of pseudoallergic inflammation and itch. Using previously reported nanobody-GPCR structures, we identified a set of AF-M outputs that effectively discriminate between interacting and non-interacting nanobody-GPCR pairs. We used these outputs to perform a prospective<br> <i>in silico</i><br> screen, identified nanobodies that bind MRGPRX2 with high affinity, and confirmed activity in signaling and functional cellular assays. Our results provide a proof of concept for fully computational antibody discovery pipelines that can circumvent laboratory experiments.<br> <<<

<br> Despite rising concerns about sycophancy—excessive agreement or flattery from artificial intelligence (AI) systems—little is known about its prevalence or consequences. We show that sycophancy is widespread and harmful. Across 11 state-of-the-art models, AI affirmed users’ actions 49% more often than humans, even when queries involved deception, illegality, or other harms. In three preregistered experiments (<br> N</i><br> = 2405), even a single interaction with sycophantic AI reduced participants’ willingness to take responsibility and repair interpersonal conflicts, while increasing their conviction that they were right. Despite distorting judgment, sycophantic models were trusted and preferred. This creates perverse incentives for sycophancy to persist: The very feature that causes harm also drives engagement. Our findings underscore the need for design, evaluation, and accountability mechanisms to protect user well-being.<br> <<<

<br> Background<br> Insomnia is one of the most common non-motor comorbidities of Parkinson’s disease (PD) and often before the onset of motor symptoms. Identifying the molecular mechanisms of insomnia may facilitate the early diagnosis of PD and contribute to therapeutic development.<br> <br> <br> Methods<br> <br> Five human PD substantia nigra (SN) bulk-seq datasets (GSE20141, GSE7621, GSE20164, GSE20163, and GSE20333), with an insomnia-related gene list, were acquired from GEO and Genecard databases. First, the integration of GSE20141 and GSE7621 was analyzed to identify insomnia-related DEGs using limma and the WGCNA framework. GSE20164 and GSE20163 combination were used as a training set for insomnia-related hub gene recognition. Furthermore, the aforementioned four datasets, along with an independent validation set (GSE20333), were cross-validated for insomnia-related diagnostic model construction. The human PD-SN single-cell profile (GSE140231) was utilized for exploring the mechanisms underlying the heterogeneity of insomnia-related hub genes in spatial and temporal contexts. Furthermore, a cutting-edge artificial intelligence (AI)-driven framework (DrugRefLector) and molecular docking techniques was used to identify an optimal agent for the treatment of PD based on the GSE20164 and GSE20163 integrated dataset. Finally, an<br> <i>in vitro</i><br> q-RT-PCR experiment was conducted to estimate the targeted gene expression.<br> <br> <br> <br> Results<br> TAZ (WWTR1) is associated with the increased expression of insomnia-related diagnostic markers linked to PD pathogenesis, mainly in neurons, and has excellent predictive performance for PD diagnosis. Furthermore, BRD-K97481123 can be considered as a potential therapeutic agent for the treatment of PD by targeting TAZ.<br> <br> <br> Conclusion<br> By integrating AI pipelines and multi-omics, our study first traced TAZ mechanisms in PD pathogenesis and elaborated on TAZ’s predictive and druggable potential for PD patients.<br> <<<

<br> Sequential movements rely on two information sources: external sensory cues and internal memory representations. Although often both sources jointly drive sequential behavior, previous research has primarily examined them in isolation. To address this, we trained participants (<br> <i>n</i><br> = 26, 15F) to perform sequences of rapid finger presses in response to numerical cues. Sensory influence was measured by varying the number of visible cues, and memory influence was determined by comparing repeating and random sequences. Early in learning, participants integrated sensory and memory information: repeating sequences were performed more quickly when more cues were visible. After learning, when repeating sequences were predictable with certainty, participants relied solely on memory and ignored sensory cues. However, when this certainty was manipulated by introducing occasional violations within repeating sequences, participants reverted to integrating memory with sensory cues. We propose a computational model that successfully predicted both speed and accuracy of individual presses. Critically, this model relied on the assumption that multiple movements are planned independently of each other. This independence assumption was then validated by examining response patterns to isolated violations in repeating sequences. Finally, we provide evidence into how sequence memories can be flexibly deactivated and reactivated in response to these violations. Together, these results reveal how brain dynamically integrates sensory and memory information to produce sequences of movements.<br> <<<