Mild hypothermia reduces lipopolysaccharide-induced microglial activation via down-regulation of Tent5c.

Microglial activation is a critical factor in the pathogenesis and progression of neuroinflammatory diseases. Mild hypothermia, known for its neuroprotective properties, has been shown to alleviate microglial activation. In this study, we explore the differentially expressed (DE) mRNAs and long non-coding RNAs (lncRNAs) in BV-2 microglial cells under different conditions: normal temperature (CN), mild hypothermia (YT), normal temperature with lipopolysaccharide (LPS), and mild hypothermia with LPS (LPS + YT). Venn analysis revealed 119 DE mRNAs that were down-regulated in the LPS + YT vs LPS comparison but up-regulated in the CN vs LPS comparison, primarily enriched in Gene Ontology terms related to immune and inflammatory responses. Furthermore, through Venn analysis of YT vs CN and LPS + YT vs LPS comparisons, we identified 178 DE mRNAs and 432 DE lncRNAs. Among these transcripts, we validated the expression of Tent5c at the protein and mRNA levels. Additionally, siRNA-knockdown of Tent5c attenuated the expression of pro-inflammatory genes (TNF-α, IL-1ß, Agrn, and Fpr2), cellular morphological changes, NLRP3 and p-P65 protein levels, immunofluorescence staining of p-P65 and number of cells with ASC-speck induced by LPS. Furthermore, Tent5c overexpression further potentiated the aforementioned indicators in the context of mild hypothermia with LPS treatment. Collectively, our findings highlight the significant role of Tent5c down-regulation in mediating the anti-inflammatory effects of mild hypothermia.

Unveiling Endophytic Bacterial Community Structures of Different Rice Cultivars Grown in a Cadmium-Contaminated Paddy Field.

Endophytic bacteria play potentially important roles in the processes of plant adaptation to the environment. Understanding the composition and dynamics of endophytic bacterial communities under heavy metal (HM) stress can reveal their impacts on host development and stress tolerance. In this study, we investigated root endophytic bacterial communities of different rice cultivars grown in a cadmium (Cd)-contaminated paddy field. These rice cultivars are classified into low (RBQ, 728B, and NX1B) and high (BB and S95B) levels of Cd-accumulating capacity. Our metagenomic analysis targeting 16S rRNA gene sequence data reveals that Proteobacteria, Firmicutes, Actinobacteria, Acidobacteria, Bacteroidetes, and Spirochaetes are predominant root endophytic bacterial phyla of the five rice cultivars that we studied. Principal coordinate analysis shows that the developmental stage of rice governs a larger source of variation in the bacterial communities compared to that of any specific rice cultivar or of the root Cd content. Endophytic bacterial communities during the reproductive stage of rice form a more highly interconnected network and exhibit higher operational taxonomic unit numbers, diversities, and abundance than those during the vegetative stage. Forty-five genera are significantly correlated with Cd content in rice root, notably including positive-correlating Geobacter and Haliangium; and negative-correlating Pseudomonas and Streptacidiphilus. Furthermore, Phylogenetic Investigation of Communities by Reconstruction of Unobserved States analysis shows that functional pathways, such as biosynthesis of siderophore and type II polyketide products, are significantly enhanced during the reproductive stage compared to those during the vegetative stage under Cd stress. The isolated endophytic bacteria from the Cd-contaminated rice roots display high Cd resistance and multiple traits that may promote plant growth, suggesting their potential application in alleviating HM stress on plants. This study describes in detail for the first time the assemblage of the bacterial endophytomes of rice roots under Cd stress and may provide insights into the interactions among endophytes, plants, and HM contamination.

DECODE: Deep Confidence Network for Robust Image Classification.

Recent years have witnessed the success of deep convolutional neural networks for image classification and many related tasks. It should be pointed out that the existing training strategies assume that there is a clean dataset for model learning. In elaborately constructed benchmark datasets, deep network has yielded promising performance under the assumption. However, in real-world applications, it is burdensome and expensive to collect sufficient clean training samples. On the other hand, collecting noisy labeled samples is very economical and practical, especially with the rapidly increasing amount of visual data in the web. Unfortunately, the accuracy of current deep models may drop dramatically even with 5%-10% label noise. Therefore, enabling label noise resistant classification has become a crucial issue in the data driven deep learning approaches. In this paper, we propose a DEep COnfiDEnce network (DECODE) to address this issue. In particular, based on the distribution of mislabeled data, we adopt a confidence evaluation module that is able to determine the confidence that a sample is mislabeled. With the confidence, we further use a weighting strategy to assign different weights to different samples so that the model pays less attention to low confidence data, which is more likely to be noise. In this way, the deep model is more robust to label noise. DECODE is designed to be general, such that it can be easily combined with existing studies. We conduct extensive experiments on several datasets, and the results validate that DECODE can improve the accuracy of deep models trained with noisy data.