The major anxiety disorders (ANX; including generalized anxiety disorder, panic disorder and phobias) are highly prevalent, often onset early and cause substantial global disability. Although distinct in their clinical presentations, they probably represent differential expressions of a dysregulated threat-response system. Here, we present a genome-wide association meta-analysis comprising 122,341 European ancestry ANX cases and 729,881 controls. We identified 58 independent genome-wide significant risk variants and 66 genes with robust biological support. In an independent sample of 1,175,012 self-report ANX cases and 1,956,379 controls, 51 out of the 58 associations replicated. As predicted by twin studies, we found substantial genetic correlation between ANX and depression, neuroticism and other internalizing phenotypes. Follow-up analyses demonstrated enrichment in all major brain regions and highlighted GABAergic signaling as one potential mechanism implicated in ANX genetic risk. These results advance our understanding of the genetic architecture of ANX and prioritize genes for functional follow-up studies.
Extremely High Throughput (EHT) and Ultra-high reliability (UHR) are new objectives in Next-Gen Wi-Fi, i.e., Wi-Fi 7 and beyond; however, the data rate within a periodic channel sounding round is expected to significantly deteriorate under time-varying channels with Doppler effect in Downlink Multi-User Multiple-Input Multiple-Output. Therefore, Next-Gen channel sounding must carefully balance the MAC-layer CSI overhead reduction and the PHY-layer channel capacity degradation caused by the Doppler effect for data rate maximization. Despite its critical importance, the cross-layer (PHY + MAC) Wi-Fi channel sounding optimization in time-varying channels remains under-explored. This paper addresses this research gap by proposing a cross-layer optimization problem to find the optimal EHT sounding period that maximizes the average data rate by considering both MAC-layer CSI overhead and PHY-layer channel capacity degradation. This problem is then converted into an equivalent optimization problem that can be solved efficiently using our proposed model driven optimal search algorithm with proven convexity. Afterwards, we introduce a data driven Transformer-based partial CSI prediction framework to alleviate CSI staleness without introducing extra CSI overhead, which further enhances the average data rate. Through simulations, we evaluate the baseline EHT sounding protocol that always uses outdated partial CSI, and then benchmark the baseline against our proposed hybrid data and model driven approach. The numerical results demonstrate that integrating Transformer-based partial CSI prediction with the optimal channel sounding period significantly reduces CSI overhead by up to 25.2%, while increasing the average throughput by up to 30.9%.
Background Candida auris colonization screening is often performed using bilateral axilla and groin composite swabs. However, re-screening results for the same patient are often inconsistent over time, complicating interpretation of results. To address this, we designed and evaluated screening using an anterior nares and hands composite collection strategy in a cohort of patients colonized with C. auris. Methods This study included patients colonized with C. auris and cohorted on a 30-bed unit. Separate axilla and groin swabs, and anterior nares and hands swabs were obtained simultaneously at 6 different time points and tested by polymerase chain reaction (PCR) and culture. Results A total of 102 swabs (51 of each composite swab type) from 19 patients were collected. By culture, 35 (69%) axilla and groin swabs were positive compared with 45 (88%) anterior nares and hands swabs. With PCR, 41 (80%) axilla and groin swabs and 50 (98%) anterior nares and hands swabs were positive. By culture, 15 temporal inconsistencies were observed with axilla and groin as compared with one instance with anterior nares and hands. With PCR, 12 temporal inconsistencies were observed with axilla and groin compared with 2 instances with the anterior nares and hands. Conclusions We found more positives and consistent positivity for anterior nares and hands swabs by both culture and PCR, suggesting that these sources provide more reliable detection of C. auris colonization. Anterior nares and hands composite swabs provide an alternative screening approach for C. auris colonization screening that can improve test consistency.
BACKGROUND:Assessing variant-specific coronavirus disease 2019 (COVID-19) vaccine effectiveness (VE) and severity can inform public health risk assessments and decisions about vaccine composition. BA.2.86 and its descendants, including JN.1 (referred to collectively as "JN lineages"), emerged in late 2023 and exhibited substantial divergence from co-circulating XBB lineages. METHODS:We analyzed patients hospitalized with COVID-19-like illness at 26 hospitals in 20 US states admitted 18 October 2023-9 March 2024. Using a test-negative, case-control design, we estimated effectiveness of an updated 2023-2024 (monovalent XBB.1.5) COVID-19 vaccine dose against sequence-confirmed XBB and JN lineage hospitalization using logistic regression. Odds of severe outcomes, including intensive care unit (ICU) admission and invasive mechanical ventilation (IMV) or death, were compared for JN versus XBB lineage hospitalizations using logistic regression. RESULTS:A total of 585 case-patients with XBB lineages, 397 case-patients with JN lineages, and 4580 control patients were included. VE in the first 7-89 days after receipt of an updated dose was 54.2% (95% confidence interval [CI], 36.1-67.1%) against XBB lineage hospitalization and 32.7% (95% CI, 1.9-53.8%) against JN lineage hospitalization. Odds of ICU admission (adjusted odds ratio [aOR], .80; 95% CI, .46-1.38) and IMV or death (aOR, .69; 95% CI, .34-1.40) were not significantly different among JN compared with XBB lineage hospitalizations. CONCLUSIONS:Updated 2023-2024 COVID-19 vaccination provided protection against both XBB and JN lineage hospitalization, but protection against the latter may be attenuated by immune escape. Clinical severity of JN lineage hospitalizations was not higher relative to XBB.
In this paper, a secure and communication-efficient clustered federated learning (CFL) design is proposed. In our model, several base stations (BSs) with heterogeneous task-handling capabilities and multiple users with non-independent and identically distributed (non-IID) data jointly perform CFL training incorporating differential privacy (DP) techniques. Since each BS can process only a subset of the learning tasks and has limited wireless resource blocks (RBs) to allocate to users for federated learning (FL) model parameter transmission, it is necessary to jointly optimize RB allocation and user scheduling for CFL performance optimization. Meanwhile, our considered CFL method requires devices to use their limited data and FL model information to determine their task identities, which may introduce additional communication overhead. We formulate an optimization problem whose goal is to minimize the training loss of all learning tasks while considering device clustering, RB allocation, DP noise, and FL model transmission delay. To solve the problem, we propose a novel dynamic penalty function assisted value decomposed multi-agent reinforcement learning (DPVD-MARL) algorithm that enables distributed BSs to independently determine their connected users, RBs, and DP noise of the connected users but jointly minimize the training loss of all learning tasks across all BSs. Different from the existing MARL methods that assign a large penalty for invalid actions, we propose a novel penalty assignment scheme that assigns penalty depending on the number of devices that cannot meet communication constraints (e.g., delay), which can guide the MARL scheme to quickly find valid actions, thus improving the convergence speed. Simulation results show that the DPVD-MARL can improve the convergence rate by up to 20% and the ultimate accumulated rewards by 15% compared to independent Q-learning.
