<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> <<<