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BackgroundAs two different kinds of mental disorders, anxiety disorder and depressive disorder could probably coexist in one with the proceeding of the illness. However, there are few studies on how to prevent and treat the coexistence of anxiety and depressive disorders.ObjectiveTo discuss the relationship between anxiety and depressive disorders, and the moderating effect of body mass index (BMI) in their relationship.MethodsBy use of simple random sampling, 86 outpatients and inpatients with anxiety disorders were selected from the First Affiliated Hospital of Nanchang University during June 1st to August, 31st, 2021. A self-made demographic questionnaire was used to obtain the demographic information. The Hamilton Anxiety Rating Scale was used to assess the anxiety level. The Hamilton Rating Scale for Depression was used to assess the depression level. Pearson correlation analyses were performed to assess the relationship between anxiety and depression disorders, and that between BMI and anxiety or depression disorder. Hierarchical regression analysis was adopted to explore the moderating effect of BMI on the relationship between anxiety and depressive disorders.ResultsEighty eligible cases were also diagnosed with depression. The anxiety prevalence was significantly increased with depression prevalence (r=0.70, P0.05) . BMI also had no significant linear correlation with depression prevalence (r=0.14, P>0.05) . BMI moderated the relationship between anxiety and depression disorders significantly (β=-0.16, P

To address the challenges of excessive model parameters and low detection accuracy in printed circuit board (PCB) defect detection, this paper proposes a novel PCB defect detection model based on the improved RTDETR (Real-Time Detection, Embedding and Tracking) method, named MFAD-RTDETR. Specifically, the proposed model introduces the designed Detail Feature Retainer (DFR) into the original RTDETR backbone to capture and retain local details. Subsequently, based on the Mamba architecture, the Visual State Space (VSS) module is integrated to enhance global attention while reducing the original quadratic complexity to a linear level. Furthermore, by exploiting the deformable attention mechanism, which dynamically adjusts reference points, the model achieves precise localization of target defects and improves the accuracy of the transformer in complex visual tasks. Meanwhile, a receptive field synthesis mechanism is incorporated to enrich multi-scale semantic information and reduce parameter complexity. In addition, the scheme proposes a novel Multi-frequency Aggregation and Diffusion feature composite paradigm (MFAD-feature composite paradigm), which consists of the Aggregation Diffusion Fusion (ADF) module and the Refiner Feature Composition (RFC) module. It aims to strengthen features with fine-grained awareness while preserving a certain level of global attention. Finally, the Wise IoU (WIoU) dynamic nonmonotonic focusing mechanism is used to reduce competition among high-quality anchor boxes and mitigate the effects of the harmful gradients from low-quality examples, thereby concentrating on anchor boxes of average quality to promote the overall performance of the detector. Extensive experiments are conducted on the PCB defect dataset released by Peking University to validate the effectiveness of the proposed model. The experimental results show that our approach achieves the 97.0% and 51.0% performance in mean Average Precision (mAP)@0.5 and mAP@0.5:0.95, respectively, which significantly outperforms the original RTDETR. Moreover, the model reduces the number of parameters by approximately 18.2% compared to the original RTDETR. [ABSTRACT FROM AUTHOR]

BACKGROUND:Widespread brain atrophy in alcohol-dependent individuals (ALC) has been consistently documented in pathological and magnetic resonance imaging (MRI) studies. Longitudinal MRI studies have shown that the regional brain volume losses in ALC are partially reversible during abstinence from alcohol. The goal of this study was to determine volume reductions in cortical and subcortical regions functionally important to substance use behavior and their changes during short-term (1 week to 1 month) and long-term abstinence (1 to 7 months) from alcohol. The regions of interest (ROIs) were as follows: anterior cingulate cortex (ACC), dorsolateral prefrontal cortex (DLPFC), orbitofrontal cortex (OFC), insula, amygdala, and hippocampus. METHODS:A total of 85 unique ALC were assessed at 1 week (n = 65), 1 month (n = 82), and 7 months (n = 36) of abstinence. In addition, 17 light/nondrinking healthy controls (CON) were assessed at baseline and follow-up over a 10-month interval. Regional brain volumes were derived from FreeSurfer. Cross-sectional statistical analyses using 1-way analysis of variance or Fisher's exact test were applied to identify group differences. Longitudinal statistical analyses using linear mixed models were applied to identify regional volume increases and nonlinear volume recovery trajectories. RESULTS:We demonstrated significant regional volume reductions in ACC, DLPFC, and hippocampus. Older age was associated with smaller DLPFC and OFC, and higher average monthly drinking over 1 year prior to study was associated with smaller OFC. We also demonstrated significant volume increases of all ROIs except amygdala in ALC and significant nonlinear volume recovery trajectories of DLPFC, OFC, and insula. CONCLUSIONS:Results showed significant volume reductions in key regions of the executive control, salience, and emotion networks in ALC at entry into treatment and significant volume increases during short-term and long-term abstinence that were nonlinear over the entire abstinence period for the DLPFC, OFC, and insula. This gray matter plasticity during alcohol abstinence may have important neurobiological and neurocognitive implications in ALC, and it may contribute to an individual's ability to maintain abstinence from alcohol at different phases.

Background and purposeWith extensive research efforts in place to address the clinical relevance of cerebral microbleeds (CMBs), there remains a need for fast and accurate methods to detect and quantify CMB burden. Although some computer-aided detection algorithms have been proposed in the literature with high sensitivity, their specificity remains consistently poor. More sophisticated machine learning methods appear to be promising in their ability to minimize false positives (FP) through high-level feature extraction and the discrimination of hard-mimics. To achieve superior performance, these methods require sizable amounts of precisely labelled training data. Here we present a user-guided tool for semi-automated CMB detection and volume segmentation, offering high specificity for routine use and FP labelling capabilities to ease and expedite the process of generating labelled training data.Materials and methodsExisting computer-aided detection methods reported by our group were extended to include fully-automated segmentation and user-guided CMB classification with FP labelling. The algorithm's performance was evaluated on a test set of ten patients exhibiting radiotherapy-induced CMBs on MR images.ResultsThe initial algorithm's base sensitivity was maintained at 86.7%. FP's were reduced to inter-rater variations and segmentation results were in 98% agreement with ground truth labelling. There was an approximate 5-fold reduction in the time users spent evaluating CMB burden with the algorithm versus without computer aid. The Intra-class Correlation Coefficient for inter-rater agreement was 0.97 CI[0.92,0.99].ConclusionsThis development serves as a valuable tool for routine evaluation of CMB burden and data labelling to improve CMB classification with machine learning. The algorithm is available to the public on GitHub (https://github.com/LupoLab-UCSF/CMB_labeler).

PurposeFamilial cerebral cavernous malformation (CCM) patients present with multiple lesions that can grow both in number and size over time and are reliably detected on susceptibility-weighted imaging (SWI). Manual counting of lesions is arduous and subject to high variability. We aimed to develop an automated algorithm for counting CCM microbleeds (lesions

Using data from three automatic weather stations (LGB69, Eagle and Dome A) from distinctly different climatological zones along the CHINARE (Chinese National Antarctic Research Expedition) traverse route from Zhongshan Station to Dome A, we investigated the characteristics of meteorological conditions and subsurface heat conduction. Spatial analysis indicated decreasing trends in air temperature, relative humidity and wind speed from the coastal katabatic wind zone to the inland plateau region, and air temperatures clearly showed a strong daily variability in winter, suggesting the effect from the fluctuation in the Antarctic atmospheric system. We also analyzed the optimal response time of the 1 and 3 m depth snow temperatures to the 0.1 m depth snow temperature for each site under clear/overcast and day/night situations. This showed an important enhancement to the heat transfer from shortwave radiation penetration. Using an iterative optimization method, we estimated the subsurface heat conduction variations along the transect. This was ~3–5 W m–2. Multiple maxima in daily mean subsurface fluxes were found in winter, with a typical value above 2 W m–2, while a single minimum value under –2 W m–2 was found in summer. On an annual scale, a larger mean loss of subsurface heat conduction was observed in the inland plateau compared to in the coastal katabatic area. Finally, we discussed the possible influences of turbulent and radiant transport on the vertical heat response and confirmed the wind enhancement on the growth of thermal conductivity. This preliminary study provides a brief perspective and an important reference for studying subsurface heat conduction in inland areas of Antarctica. [ABSTRACT FROM AUTHOR]