Association of dietary mineral mixture with depressive symptoms: A combination of Bayesian approaches.

The relationships between mixtures of multiple minerals and depression have not been explored. Therefore, we analyzed the relationship between the mixture of nine dietary minerals [calcium (Ca), phosphorus, magnesium (Mg), iron (Fe), zinc, copper (Cu), sodium, potassium (K), and selenium (Se)] and depressive symptoms in the general population. We screened 20,342 participants from the National Health and Nutrition Examination Survey (NHANES) 2007-2018. We fitted the general linear regression, Bayesian kernel machine regression (BKMR), and Bayesian semiparametric regression models to explore associations and interactions. We obtained the relative importance of dietary minerals by calculating posterior inclusion probabilities (PIPs). The dietary intakes of minerals were obtained using the 24-h dietary recall interview, and depressive symptoms were assessed using the Patient Health Questionnaire-9 (PHQ-9). The linear analysis showed that nine minerals were negatively associated with PHQ-9 scores. The BKMR analysis showed a negative association between the dietary mineral mixture and PHQ-9 scores, with Se having the largest PIP at 1.0000, followed by K (0.7784). We also observed potential interactions between Ca and Fe, Se and Fe, and K and Mg. Among them, the interaction of Ca and Fe had the largest PIP of 0.986. In addition, the overall effect was more pronounced in females than males, and Cu's PIP (0.8376) was higher in females. Two sensitivity analyses showed that our results were robust. Our study provides a basis for formulating nutritional intervention programs for depression in the future.

Meta-DETR: Image-Level Few-Shot Detection With Inter-Class Correlation Exploitation.

Few-shot object detection has been extensively investigated by incorporating meta-learning into region-based detection frameworks. Despite its success, the said paradigm is still constrained by several factors, such as (i) low-quality region proposals for novel classes and (ii) negligence of the inter-class correlation among different classes. Such limitations hinder the generalization of base-class knowledge for the detection of novel-class objects. In this work, we design Meta-DETR, which (i) is the first image-level few-shot detector, and (ii) introduces a novel inter-class correlational meta-learning strategy to capture and leverage the correlation among different classes for robust and accurate few-shot object detection. Meta-DETR works entirely at image level without any region proposals, which circumvents the constraint of inaccurate proposals in prevalent few-shot detection frameworks. In addition, the introduced correlational meta-learning enables Meta-DETR to simultaneously attend to multiple support classes within a single feedforward, which allows to capture the inter-class correlation among different classes, thus significantly reducing the misclassification over similar classes and enhancing knowledge generalization to novel classes. Experiments over multiple few-shot object detection benchmarks show that the proposed Meta-DETR outperforms state-of-the-art methods by large margins. The implementation codes are publicly available at https://github.com/ZhangGongjie/Meta-DETR.

Association of Urinary Iodine Concentration with Depressive Symptoms among Adults: NHANES 2007-2018.

The association between iodine status and depressive symptoms has not been investigated in the general population. Therefore, we drew 8935 participants from the National Health and Nutrition Examination Survey (NHANES) 2007-2018 to explore their association. In NHANES, Inductively Coupled Plasma Dynamic Reaction Cell Mass Spectroscopy was utilized to measure urinary iodine concentration (UIC), and Patient Health Questionnaire-9 was used to assess depressive symptoms. Meanwhile, we fitted logistic regression and restricted cubic spline models. We found that high UIC was associated with a higher prevalence of depressive symptoms than the normal UIC group (OR: 1.50, 95% CI: 1.04-2.16). This association was particularly pronounced and further strengthened among females (OR: 1.90, 95% CI: 1.19-3.01) and participants aged 40-59 (OR: 1.90, 95% CI: 1.11-3.25). Moreover, we found that low UIC was associated with a high prevalence of depressive symptoms among females (OR: 1.50, 95% CI: 1.02-2.18). Moreover, the dose-response relationship between UIC and depressive symptoms presented a general trend of decreased, steady transiently, and then increased. We found that participants with high UIC had a higher prevalence of depressive symptoms than those with normal UIC. Meanwhile, we also found that females with low UIC had higher odds of reporting depressive symptoms.

The association between urinary phytoestrogens and depressive symptoms.

Present evidence outlining the association between different types of phytoestrogens and depressive symptoms in the general population is limited and contradictory. Data from the 2007-2010 National Health and Nutrition Examination Survey (NHANES) were used to examine their association. Phytoestrogens were measured in urine samples and depressive symptoms were assessed using the Patient Health Questionnaire-9 (PHQ-9). Logistic regression and restricted cubic spline models were used to evaluate associations. In one model, lignans and enterolactone were inversely associated with the prevalence of depressive symptoms. Compared with the lowest quartile (Q1), the odds ratios (ORs; 95% confidence intervals [CIs]) for participants in the highest quartile of lignans and enterolactone were 0.44 (0.27-0.72) and 0.42 (0.26-0.67) for depressive symptoms, respectively. Additionally, the dose-response relationships between urinary lignans or enterolactone and depressive symptoms showed a linear trend. Our results suggest that urinary lignans and enterolactone are inversely associated with the prevalence of depressive symptoms.

Selection of the reference genes for quantitative gene expression by RT-qPCR in the desert plant Stipagrostis pennata.

The desert pioneer plant Stipagrostis pennata plays an important role in sand fixation, wind prevention, and desert ecosystem recovery. An absence of reference genes greatly limits investigations into the regulatory mechanism by which S. pennata adapts to adverse desert environments at the molecular and genetic levels. In this study, eight candidate reference genes were identified from rhizosheath development transcriptome data from S. pennata, and their expression stability in the rhizosheaths at different development stages, in a variety of plant tissues, and under drought stress was evaluated using four procedures, including geNorm, NormFinder, BestKeeper, and RefFinder. The results showed that GAPDH and elF were the most stable reference genes under drought stress and in rhizosheath development, and ARP6 and ALDH were relatively stable in all plant tissues. In addition, elF was the most suitable reference gene for all treatments. Analysis of the consistency between the reverse transcription-quantitative PCR (RT-qPCR) and RNA sequencing data showed that the identified elF and GAPDH reference genes were stable during rhizosheath development. These results provide reliable reference genes for assuring the accuracy of RT-qPCR and offer a foundation for further investigations into the genetic responses of S. pennata to abiotic stress.