TFEB safeguards trophoblast syncytialization in humans and mice.

Nutrient sensing and adaptation in the placenta are essential for pregnancy viability and proper fetal growth. Our recent study demonstrated that the placenta adapts to nutrient insufficiency through mechanistic target of rapamycin (mTOR) inhibition-mediated trophoblast differentiation toward syncytiotrophoblasts (STBs), a highly specialized multinucleated trophoblast subtype mediating extensive maternal-fetal interactions. However, the underlying mechanism remains elusive. Here, we unravel the indispensable role of the mTORC1 downstream transcriptional factor TFEB in STB formation both in vitro and in vivo. TFEB deficiency significantly impaired STB differentiation in human trophoblasts and placenta organoids. Consistently, systemic or trophoblast-specific deletion of Tfeb compromised STB formation and placental vascular construction, leading to severe embryonic lethality. Mechanistically, TFEB conferred direct transcriptional activation of the fusogen ERVFRD-1 in human trophoblasts and thereby promoted STB formation, independent of its canonical function as a master regulator of the autophagy-lysosomal pathway. Moreover, we demonstrated that TFEB directed the trophoblast syncytialization response driven by mTOR complex 1 (mTORC1) signaling. TFEB expression positively correlated with the reinforced trophoblast syncytialization in human fetal growth-restricted placentas exhibiting suppressed mTORC1 activity. Our findings substantiate that the TFEB-fusogen axis ensures proper STB formation during placenta development and under nutrient stress, shedding light on TFEB as a mechanistic link between nutrient-sensing machinery and trophoblast differentiation.

Research on the chemical oxygen demand spectral inversion model in water based on IPLS-GAN-SVM hybrid algorithm.

Spectral collinearity and limited spectral datasets are the problems influencing Chemical Oxygen Demand (COD) modeling. To address the first problem and obtain optimal modeling range, the spectra are preprocessed using six methods including Standard Normal Variate, Savitzky-Golay Smoothing Filtering (SG) etc. Subsequently, the 190-350 nm spectral range is divided into 10 subintervals, and Interval Partial Least Squares (IPLS) is used to perform PLS modeling on each interval. The results indicate that it is best modeled in the 7th range (238~253 nm). The values of Mean Square Error (MSE), Mean Absolute Error (MAE) and R2score of the model without pretreatment are 1.6489, 1.0661, and 0.9942. After pretreatment, the SG is better than others, with MSE and MAE decreasing to 1.4727, 1.0318 and R2score improving to 0.9944. Using the optimal model, the predicted COD for three samples are 10.87 mg/L, 14.88 mg/L, and 19.29 mg/L. To address the problem of the small dataset, using Generative Adversarial Networks for data augmentation, three datasets are obtained for Support Vector Machine (SVM) modeling. The results indicate that, compared to the original dataset, the SVM's MSE and MAE have decreased, while its accuracy has improved by 2.88%, 11.53%, and 11.53%, and the R2score has improved by 18.07%, 17.40%, and 18.74%.

Design and implementation of a Li River water quality monitoring and analysis system based on outlier data analysis.

The detection of water quality indicators such as Temperature, pH, Turbidity, Conductivity, and TDS involves five national standard methods. Chemically based measurement techniques may generate liquid residue, causing secondary pollution. The water quality monitoring and data analysis system can effectively address the issues that conventional methods require multiple pieces of equipment and repeated measurements. This paper analyzes the distribution characteristics of the historical data from five sensors at a specific time, displays them graphically in real time, and provides an early warning of exceeding the standard; It selects four water samples from different sections of the Li River, based on the national standard method, the average measurement errors of Temperature, PH, TDS, Conductivity and Turbidity are 0.98%, 2.23%, 2.92%, 3.05% and 3.98%.;It further uses the quartile method to analyze the outlier data over 100,000 records and five historical periods are selected. Experiment results show the system is relatively stable in measuring Temperature, PH and TDS, and the proportion of outlier is 0.42%, 0.84% and 1.24%. When Turbidity and Conductivity are measured, the proportion is 3.11% and 2.92%. In the experiment of using 7 methods to fill outlier, K nearest neighbor algorithm is better than others. The analysis of data trends, outliers, means, and extreme values assists in making decisions, such as updating and maintaining equipment, addressing extreme water quality situations, and enhancing regional water quality oversight.

Application of Bioinformatics in Predicting the Efficacy of Digestive Tumour Immunotherapy Target TIM-3 and its Inhibitors.

Background: Our main objective is to apply bioinformatics in predicting the efficacy of digestive tumour immunotherapy target TIM-3 and its inhibitors. Methods: Our study used the gene expression omnibus (GEO) database to identify datasets associated with digestive tumours and the action of TIM-3. The GSE427729 dataset based on the GPL10192 platform. The dataset consisted of six samples of total RNA derived from TIM-3 control and knockdown RAW 264.7 cells. We used GEO2R tool to identify DEGs before performing Gene Ontology and identifying the kyoto encyclopedia of genes and genomes (KEGG) pathways. Lastly, we determined the PPI networks to identify hub genes. Results: Our study identified 57 differentially expressed genes based on an adjusted p-value of less than 0.05 and a log2 fold change of 2.0. There were 26 down-regulated genes with 31 up-regulated genes while 22, 404 genes were non-significant. The DEGs were enriched in biological pathways such as activating leukocytes, cells, and development of the immune system. Additionally, we identified four significant KEGG pathways that were implicated in digestive tumour immunotherapy and TIM-3; pathways of pancreatic cancer, NF-Kappa B signalling pathway, Toll-like receptor signalling pathway and C-type lectin receptor signalling pathway. The PPI networks identified 10 hub genes that were implicated in digestive tumour immunotherapy target TIM-3 (Myd88, Traf6, Irf7, Cdk4, Ccnd2, Mapkap1, Prr5, Mpp3, Serpinb6b and Pvrl3). Conclusion: Targeting these biological pathways, KEGG pathways, molecular functions and cellular processes can lead to novel therapeutic treatment and management in digestive tumours based on TIM-3 immunotherapy.

DIEANet: an attention model for histopathological image grading of lung adenocarcinoma based on dimensional information embedding.

Efficient and rapid auxiliary diagnosis of different grades of lung adenocarcinoma is conducive to helping doctors accelerate individualized diagnosis and treatment processes, thus improving patient prognosis. Currently, there is often a problem of large intra-class differences and small inter-class differences between pathological images of lung adenocarcinoma tissues under different grades. If attention mechanisms such as Coordinate Attention (CA) are directly used for lung adenocarcinoma grading tasks, it is prone to excessive compression of feature information and overlooking the issue of information dependency within the same dimension. Therefore, we propose a Dimension Information Embedding Attention Network (DIEANet) for the task of lung adenocarcinoma grading. Specifically, we combine different pooling methods to automatically select local regions of key growth patterns such as lung adenocarcinoma cells, enhancing the model's focus on local information. Additionally, we employ an interactive fusion approach to concentrate feature information within the same dimension and across dimensions, thereby improving model performance. Extensive experiments have shown that under the condition of maintaining equal computational expenses, the accuracy of DIEANet with ResNet34 as the backbone reaches 88.19%, with an AUC of 96.61%, MCC of 81.71%, and Kappa of 81.16%. Compared to seven other attention mechanisms, it achieves state-of-the-art objective metrics. Additionally, it aligns more closely with the visual attention of pathology experts under subjective visual assessment.

Cytochrome P450 CitCYP97B modulates carotenoid accumulation diversity by hydroxylating ß-cryptoxanthin in Citrus.

Carotenoids in plant foods provide health benefits by functioning as provitamin A. One of the vital provitamin A carotenoids, ß-cryptoxanthin, is typically plentiful in citrus fruit. However, little is known about the genetic basis of ß-cryptoxanthin accumulation in citrus. Here, we performed a widely targeted metabolomic analysis of 65 major carotenoids and carotenoid derivatives to characterize carotenoid accumulation in Citrus and determine the taxonomic profile of ß-cryptoxanthin. We used data from 81 newly sequenced representative accessions and 69 previously sequenced Citrus cultivars to reveal the genetic basis of ß-cryptoxanthin accumulation through a genome-wide association study. We identified a causal gene, CitCYP97B, which encodes a cytochrome P450 protein whose substrate and metabolic pathways in land plants were undetermined. We subsequently demonstrated that CitCYP97B functions as a novel monooxygenase that specifically hydroxylates the ß-ring of ß-cryptoxanthin in a heterologous expression system. In planta experiments provided further evidence that CitCYP97B negatively regulates ß-cryptoxanthin content. Using the sequenced Citrus accessions, we found that two critical structural cis-element variations contribute to increased expression of CitCYP97B, thereby altering ß-cryptoxanthin accumulation in fruit. Hybridization/introgression appear to have contributed to the prevalence of two cis-element variations in different Citrus types during citrus evolution. Overall, these findings extend our understanding of the regulation and diversity of carotenoid metabolism in fruit crops and provide a genetic target for production of ß-cryptoxanthin-biofortified products.

Insights into the Correlation between Microbial Community Succession and Pericarp Degradation during Pepper (Piper nigrum L.) Peeling Process via Retting.

White pepper, used both as a seasoning in people's daily diets and as a medicinal herb, is typically produced by removing the pericarp of green pepper through the retting process. However, the mechanism of the retting process for peeling remains unclear. Therefore, this study aimed to investigate the changes in physicochemical factors, microbial community succession effects, and metabolites of the pepper pericarp during the pepper peeling process. The findings indicated that pre-treatment involving physical friction before the retting process effectively reduced the production time of white pepper. During the retting process, the pectinase activity increased, leading to a decrease in the pectin content in the pepper pericarp. There was a significant correlation observed between the changes in pH, pectin content, and peeling rate and the Shannon diversity index of bacteria and fungi. Prevotella, Lactococcus, and Candida were the dominant microbial genera during the retting. The functional predictions suggested that the monosaccharides degraded from the pepper pericarp could have been utilized by microbes through sugar metabolism pathways. Metabolomic analysis showed that the metabolic pathways of carbohydrates and amino acids were the main pathways altered during the pepper peeling process. The verification experiment demonstrated that the degradation of pectin into galacturonic acid by polygalacturonase was identified as the key enzyme in shortening the pepper peeling time. The structure of the pepper pericarp collapsed after losing the support of pectin, as revealed by scanning electron microscopy. These results suggest that the decomposition of the pepper pericarp was driven by key microbiota. The succession of microbial communities was influenced by the metabolites of the pepper pericarp during retting. These findings provide new insights into the retting process and serve as an important reference for the industrial production of white pepper.

Supershear triggering and cascading fault ruptures of the 2023 Kahramanmaras, Türkiye, earthquake doublet.

On 6 February 2023, two large earthquakes (moment magnitude 7.8 and 7.6) shocked a vast area of southeastern Türkiye and northern Syria, leading to heavy casualties and economic loss. To investigate the rupture process over multiple fault segments, we performed a comprehensive analysis of local seismic and geodetic data and determined supershear ruptures on the initial branch and the Pazarcik and Erkenek segments and subshear ruptures on the Amanos segment of event 1. The bilateral rupture of event 2 also presents distinct sub- and supershear velocities. The dynamic stress of the branch fault rupture triggered the Pazarcik segment initial rupture at a point 9 kilometers west of the junction of these two faults, boosting the supershear rupture of the Pazarcik segment of the main fault. The geometry and prestress level of multiple segments controlled the rupture behaviors and influenced the ground shaking intensity.

Psychosocial interventions for suicidal and self-injurious-related behaviors among adolescents: a systematic review and meta-analysis of Chinese practices.

Background: Suicidal and self-injurious-related behaviors (SSIRBs) are a serious public health challenge in China. However, a comprehensive systematic review of psychosocial interventions for SSIRBs among Chinese adolescents has not been performed. To fill this gap, this systematic review and meta-analysis aimed to examine psychosocial interventions for SSIRBs among Chinese adolescents. Methods: Eight international (PubMed, EMBASE, Cochrane Library, ScienceDirect, Clinical Trial, CINAHL, PsycINFO, and Web of Science) and four Chinese (Wanfang, SinoMed, CEPS, and CNKI) databases were searched from inception to 31 January 2023. Data extraction and quality assessment were independently conducted by two groups of researchers. Qualitative synthesis and meta-analysis were both used. Results: The initial search yielded 16,872 titles. Of the 649 full texts reviewed, 19 intervention articles focusing on SSIRBs met the inclusion criteria. Thirteen out of the 19 included studies involved cognitive-behavioral therapy (CBT). Seven non-suicidal self-injury (NSSI) studies assessing self-injurious behaviors were included (six short-term studies and three long-term studies). Compared with long-term interventions [-1.30 (95% CI: -1.84, -0.76)], short-term psychosocial interventions had a higher standardized mean difference (SMD) value [1.86 (95% CI: -2.72, -0.99)]. Meta-regression showed an inverse relationship between the treatment response and sample size (slope = 0.068, Z = 2.914, p = 0.004) and proportion of females (slope = 1.096, Z = 5.848, p < 0.001). Subgroup analyses showed that compared with the "less than 1 month" group [-0.494 (-0.783, -0.205)], in the "immediate postintervention" group, the pooled estimate was significantly lower [-2.800 (-4.050, -1.550), p < 0.001]. Conclusion: Our review systematically summarized the key characteristics and effectiveness of existing psychosocial interventions for SSIRBs among Chinese adolescents. Short-term psychosocial interventions for NSSI were significantly effective in reducing self-injurious behavior scores, especially in the immediate postintervention period. More favorable treatment responses could be observed in both male and small samples.