Association between eicosapentaenoic acid consumption and the risk of depressive symptoms in US adults: Analyses from NHANES 2005-2018.

BACKGROUND: This study examines the relationship between eicosapentaenoic acid (EPA) intake from food and depression. EPA, an Omega-3 fatty acid commonly found in fish and seafood, has garnered attention for its potential role in depression prevention and treatment. METHODS: We selected 30,976 participants from the National Health and Nutrition Examination Survey (NHANES) conducted between 2005 and 2018. Depressive symptoms were diagnosed using the Patient Health Questionnaire (PHQ-9). EPA intake was assessed through dietary evaluation. Logistic regression and restricted cubic spline regression (RCS) were employed to assess the correlation between EPA and depressive symptom. RESULTS: The prevalence of depressive symptoms was 7.3 %. Participants with depressive symptoms exhibited lower EPA intake from food compared to non-depressed individuals. This negative association with depressive symptoms persisted even after accounting for various potential influencing factors (e.g., age, gender, body mass index, total energy intake, comorbidities). Notably, EPA demonstrated a nonlinear association with depressive symptoms, particularly in females. CONCLUSIONS: This study emphasizes a significant negative correlation between EPA consumption and depressive symptoms, particularly in females. This suggests that maintaining a rich EPA diet may play a role in depression prevention and treatment.

WiFi Indoor Location Based on Area Segmentation.

Indoor positioning is the basic requirement of future positioning services, and high-precision, low-cost indoor positioning algorithms are the key technology to achieve this goal. Different from outdoor maps, indoor data has the characteristic of uneven distribution and close correlation. In areas with low data density, in order to achieve a high-precision positioning effect, the positioning time will be correspondingly longer, but this is not necessary. The instability of WiFi leads to the introduction of noise when collecting data, which reduces the overall performance of the positioning system, so denoising is very necessary. For the above problems, a positioning system using the DBSCAN algorithm to segment regions and realize regionalized positioning is proposed. DBSCAN algorithm not only divides the dataset into core points and edge points, but also divides part of the data into noise points to achieve the effect of denoising. In the core part, the dimensionality of the data is reduced by using stacking auto-encoders (SAE), and the localization task is accomplished by using a deep neural network (DNN) with an adaptive learning rate. At the edge points, the random forest (RF) algorithm is used to complete the localization task. Finally, the proposed architecture is verified on the UJIIndoorLoc dataset. The experimental results show that our positioning accuracy does not exceed 1.5 m with a probability of less than 87.2% at the edge point, and the time is only 32 ms; the positioning accuracy does not exceed 1.5 m with a probability of less than 98.8% at the core point. Compared with indoor positioning algorithms such as multi-layer perceptron and K Nearest Neighbors (KNN), good results have been achieved.