Association between the triglyceride glucose index and depression: a meta-analysis.

Background: Obesity and diabetes have been associated with depressive symptoms. The aim of this systematic review and meta-analysis was to evaluate the association between the triglyceride glucose index (TyG index) a novel indicator of insulin resistance (IR) and depression in the adult population. Methods: Relevant observational studies were acquired through comprehensive searches of the Medline, Web of Science, Embase, Wanfang, and China National Knowledge Internet databases. To account for heterogeneity, a random-effects model was employed to combine the findings. Additionally, multiple subgroup analyses were conducted to assess the impact of various study characteristics on the outcome. Results: The meta-analysis comprised eight datasets from six cross-sectional studies, encompassing a total of 28,973 adults. The pooled findings suggested that subjects with a high TyG index, compared to those with a low TyG index, were associated with a higher prevalence of depression (odds ratio [OR]: 1.41, 95% confidence interval (CI): 1.28-1.56, p<0.001; I2 = 19%). Sensitivity analyses, by omitting one dataset at a time, showed consistent results (OR: 1.39-1.45, p<0.05). Further subgroup analyses showed consistent results in participants aged <50 years old and in those aged ≥50 years old, in men and in women, in studies with different cutoff values for the TyG index, and in studies with different methods for the diagnosis of depression (for each subgroup difference, p>0.05). Conclusion: A high TyG index may be associated with a higher prevalence of depression in the adult population.

A novel Fas ligand plays an important role in cell apoptosis of Crassostrea hongkongensis: molecular cloning, expression profiles and functional identification of ChFasL.

Background: Apoptosis regulates normal development, homeostasis, immune tolerance and response to environmental stress by eliminating unwanted or diseased cells, and plays a key role in non-specific immunity of invertebrates. The exogenous pathway mediated by death receptors and death ligands is a very important pathway for cell apoptosis. Death ligands are mainly members of the tumour necrosis factor (TNF) family, of which FasL is an important member. The deep involvement of FasL in vertebrates cell apoptosis and immunity has been reported many times, but there is limited research on the FasL gene in shellfish, and its functional importance in oyster cell apoptosis and immunity remains unclear. Methods: The full length of ChFasL was identified and cloned based on the genome of Crassostrea hongkongensis. Quantitative PCR was used to detect the relative expression of ChFasL in different developmental stages and tissues, as well as the changes of relative expression in hemocytes after bacterial infection. The expression position of ChFasL in HEK293T cells was also located by subcellular localization, and the effect of increased recombinant protein content on the activity of reporter genes p53 and p21 was studied by dual-fluorescence reporter gene. Finally, the changes of apoptosis rate in hemocytes after ChFasL silencing was identified by RNA interference technology. Results: We identified a novel FasL gene from C. hongkongensis and named it ChFasL. We found that ChFasL has potential N-linked glycosylation site, a transmembrane domain and a TNF region, which was a typical characteristics of TNF family. ChFasL was expressed in all developmental stages of larvae and in all tissues of oysters. After stimulation by V. alginolyticus or S. haemolyticus, its relative expression in hemocytes increased significantly, suggesting that ChFasL was deeply engaged in the immune response process of C. hongkongensis to external microbial stimulation. The results of subcellular localization showed that ChFasL was mainly distributed in the cytoplasm of HEK293T cells. With the overexpression of the recombinant protein pcDNA3 1- ChFasL, the activity of p53 and p21 significantly increased, showing a positive regulatory effect. Moreover, after dsRNA successfully reduced the relative expression of ChFasL, the apoptosis rate of hemocytes was significantly lower than that the dsGFP group. Conclusion: These results comprehensively confirmed the important role of ChFasL in the apoptosis process of C. hongkongensis, which provided the basis and premise for the in-depth understanding of the immune function of apoptosis in molluscs, and also contributed to the research on the pathogenic death mechanism and disease resistance breeding of marine bivalves.

Shaping Deep Feature Space Towards Gaussian Mixture for Visual Classification.

The softmax cross-entropy loss function has been widely used to train deep models for various tasks. In this work, we propose a Gaussian mixture (GM) loss function for deep neural networks for visual classification. Unlike the softmax cross-entropy loss, our method explicitly shapes the deep feature space towards a Gaussian Mixture distribution. With a classification margin and a likelihood regularization, the GM loss facilitates both high classification performance and accurate modeling of the feature distribution. The GM loss can be readily used to distinguish the adversarial examples based on the discrepancy between feature distributions of clean and adversarial examples. Furthermore, theoretical analysis shows that a symmetric feature space can be achieved by using the GM loss, which enables the models to perform robustly against adversarial attacks. The proposed model can be implemented easily and efficiently without introducing more trainable parameters. Extensive evaluations demonstrate that the method with the GM loss performs favorably on image classification, face recognition, and detection as well as recognition of adversarial examples generated by various attacks.

Comparison of biochemical composition, nutritional quality, and metals concentrations between males and females of three different Crassostrea sp.

Gametogenesis can significantly affect the biochemical composition of oysters, but little research on the difference between sexes. Therefore, we conducted the first in-depth study on the composition differences between males and females of three different Crassostrea sp.. The results showed that females had higher glycogen, lipid, Cu and Zn contents than males, while males had higher protein and taurine contents than females at maturity, which might be related to special meiosis pattern of eggs and less energy was required for female gametogenesis. In addition, both males and females had well-balanced essential amino acid compositions. The omega-3: omega-6 (n-3: n-6) ratio of males was significantly higher than that of females, indicating that the nutritional quality of males was higher. These results provide a reliable and refined theoretical and research basis for revealing the nutritional quality, extracting beneficial ingredients, and developing functional food of Crassostrea sp., and provide data support for the sex-regulated breeding of oysters.

The Microbiome Structure of a Rice-Crayfish Integrated Breeding Model and Its Association with Crayfish Growth and Water Quality.

The rice-crayfish (RC) integrated breeding model is an important and special agricultural ecosystem that provides a unique ecological environment for exploring the microbial diversity, composition, and functional capacity. To date, little is known about the effect of the breeding model on microbiome assembly and breeding model-specific microbiome composition and the association of the microbiome with water quality and crayfish growth. In the present study, we assessed the taxonomic shifts in gut and water microbiomes and their associations with water quality and crayfish growth in the RC and crayfish monoculture (CM) breeding models across six time points of rice growth, including seedling (a), tillering and jointing (b), blooming (c), filling (d), fruiting (e), and rotting of rice residues (f). Dominant bacterial phyla, such as Proteobacteria, Actinobacteria, Bacteroidetes, and Firmicutes, were detected in both gut and water microbiomes across breeding models. Notably, the diversity and structure of the gut and water microbiomes significantly (P < 0.001) differed between the RC and CM models, with higher microbial diversity being noted in the RC model than in the CM model. The taxa enriched in the RC model included Bacillus sp., Streptomyces sp., Lactobacillus sp., Prevotella sp., Rhodobacter sp., Bifidobacterium sp., Akkermansia sp., and Lactococcus sp., some of which are potentially beneficial to animals. However, opportunistic pathogens, such as Citrobacter sp. and Aeromonas sp., were depleted in the RC model. Furthermore, in the RC model, the enriched taxa that formed complex cooccurrence networks showed a significant positive correlation with water quality and crayfish growth, whereas the depleted taxa showed a significant negative correlation with water quality and crayfish growth. These results suggest that the RC model has a better microbiome composition and that RC model-specific microbes could play important roles in improving crayfish growth and water quality. IMPORTANCE The present study comprehensively compared two different breeding models in terms of their microbiome composition and the associations of the microbiomes with crayfish growth and water quality. RC model-specific microbiome composition was identified; these microbes were found to have a positive association with water quality and crayfish growth. These results provide valuable information for guiding microbial isolation and culture and for potentially harnessing the power of the microbiome to improve crayfish production and health and water quality.

Image Captioning with End-to-end Attribute Detection and Subsequent Attributes Prediction.

Semantic attention has been shown to be effective in improving the performance of image captioning. The core of semantic attention based methods is to drive the model to attend to semantically important words, or attributes. In previous works, the attribute detector and the captioning network are usually independent, leading to the insufficient usage of the semantic information. Also, all the detected attributes, no matter whether they are appropriate for the linguistic context at the current step, are attended to through the whole caption generation process. This may sometimes disrupt the captioning model to attend to incorrect visual concepts. To solve these problems, we introduce two end-to-end trainable modules to closely couple attribute detection with image captioning as well as prompt the effective uses of attributes by predicting appropriate attributes at each time step. The multimodal attribute detector (MAD) module improves the attribute detection accuracy by using not only the image features but also the word embedding of attributes already existing in most captioning models. MAD models the similarity between the semantics of attributes and the image object features to facilitate accurate detection. The subsequent attribute predictor (SAP) module dynamically predicts a concise attribute subset at each time step to mitigate the diversity of image attributes. Compared to previous attribute based methods, our approach enhances the explainability in how the attributes affect the generated words and achieves a state-of-the-art single model performance of 128.8 CIDEr-D on the MSCOCO dataset. Extensive experiments on the MSCOCO dataset show that our proposal actually improves the performances in both image captioning and attribute detection simultaneously. The codes are available at: https://github.com/ RubickH/Image-Captioning-with-MAD-and-SAP.

Automatic Timed Up-and-Go Sub-Task Segmentation for Parkinson's Disease Patients Using Video-Based Activity Classification.

The timed up-and-go (TUG) test has been widely accepted as a standard assessment for measuring the basic functional mobility of patients with Parkinson's disease. Several basic mobility sub-tasks "Sit," "Sit-to-Stand," "Walk," "Turn," "Walk-Back," and "Sit-Back" are included in a TUG test. It has been shown that the time costs of these sub-tasks are useful clinical parameters for the assessment of Parkinson's disease. Several automatic methods have been proposed to segment and time these sub-tasks in a TUG test. However, these methods usually require either well-controlled environments for the TUG video recording or information from special devices, such as wearable inertial sensors, ambient sensors, or depth cameras. In this paper, an automatic TUG sub-task segmentation method using video-based activity classification is proposed and validated in a study with 24 Parkinson's disease patients. Videos used in this paper are recorded in semi-controlled environments with various backgrounds. The state-of-the-art deep learning-base 2-D human pose estimation technologies are used for feature extraction. A support vector machine and a long short-term memory network are then used for the activity classification and the subtask segmentation. Our method can be used to automatically acquire clinical parameters for the assessment of Parkinson's disease using TUG videos-only, leading to the possibility of remote monitoring of the patients' condition.