Simulation and Driving Factor Analysis of Satellite-Observed Terrestrial Water Storage Anomaly in the Pearl River Basin Using Deep Learning

Accurate estimation of terrestrial water storage (TWS) and understanding its driving factors are crucial for effective hydrological assessment and water resource management. The launches of the Gravity Recovery and Climate Experiment (GRACE) satellites and their successor, GRACE Follow-On (GRACE-FO), combined with deep learning algorithms, have opened new avenues for such investigations. In this study, we employed a long short-term memory (LSTM) neural network model to simulate TWS anomaly (TWSA) in the Pearl River Basin (PRB) from 2003 to 2020, using precipitation, temperature, runoff, evapotranspiration, and leaf area index (LAI) data. The performance of the LSTM model was rigorously evaluated, achieving a high average correlation coefficient (r) of 0.967 and an average Nash–Sutcliffe efficiency (NSE) coefficient of 0.912 on the testing set. To unravel the relative importance of each driving factor and assess the impact of different lead times, we employed the SHapley Additive exPlanations (SHAP) method. Our results revealed that precipitation exerted the most significant influence on TWSA in the PRB, with a one-month lead time exhibiting the greatest impact. Evapotranspiration, runoff, temperature, and LAI also played important roles, with interactive effects among these factors. Moreover, we observed an accumulation effect of precipitation and evapotranspiration on TWSA, particularly with shorter lead times. Overall, the SHAP method provides an alternative approach for the quantitative analysis of natural driving factors at the basin scale, shedding light on the natural dominant influences on TWSA in the PRB. The combination of satellite observations and deep learning techniques holds promise for advancing our understanding of TWS dynamics and enhancing water resource management strategies

Carrier frequencies, trends, and geographical distribution of hearing loss variants in China: The pooled analysis of 2,161,984 newborns

Objective: The aim of this study is to comprehensively investigate the prevalence and distribution patterns of three common genetic variants associated with hearing loss (HL) in Chinese neonatal population. Methods: Prior to June 30, 2023, an extensive search and screening process was conducted across multiple literature databases. R software was utilized for conducting meta-analyses, cartography, and correlation analyses. Results: Firstly, our study identified a total of 99 studies meeting the inclusion criteria. Notably, provinces such as Qinghai, Tibet, Jilin, and Heilongjiang lack large-scale genetic screening data for neonatal deafness. Secondly, in Chinese newborns, the carrier frequencies of GJB2 variants (c.235delC, c.299_300delAT) were 1.63 % (95 %CI 1.52 %–1.76 %) and 0.33 % (95 %CI 0.30 %–0.37 %); While SLC26A4 variants (c.919-2A > G, c.2168A > G) exhibited carrier rates of 0.95 % (95 %CI 0.86 %–1.04 %) and 0.17 % (95 %CI 0.15 %–0.19 %); Additionally, Mt 12S rRNA m.1555 A > G variant was found at a rate of 0.24 % (95 % CI 0.22 %–0.26 %). Thirdly, the mutation rate of GJB2 c.235delC was higher in the east of the Heihe-Tengchong line, whereas the mutation rate of Mt 12S rRNA m.1555 A > G variant exhibited the opposite pattern. Forthly, no significant correlation exhibited the opposite pattern of GJB2 variants, but there was a notable correlation among SLC26A4 variants. Lastly, strong regional distribution correlations were evident between mutation sites from different genes, particularly between SLC26A4 (c.919-2A > G and c.2168A > G) and GJB c.299_300delAT. Conclusions: The most prevalent deafness genes among Chinese neonates were GJB2 c.235delC variant, followed by SLC26A4 c.919-2A > G variant. These gene mutation rates exhibit significant regional distribution characteristics. Consequently, it is imperative to enhance genetic screening efforts to reduce the incidence of deafness in high-risk areas

Olanzapine-induced endoplasmic reticulum stress and inflammation in the hypothalamus were inhibited by an ER stress inhibitor 4-phenylbutyrate

Antipsychotics are the most important treatment for schizophrenia. However, antipsychotics, particularly olanzapine and clozapine, are associated with severe weight gain/obesity side-effects. Although numerous studies have been carried out to identify the exact mechanisms of antipsychotic-induced weight gain, it is still important to consider other pathways. Endoplasmic reticulum (ER) stress signaling and its associated inflammation pathway is one of the most important pathways involved in regulation of energy balance. In the present study, we examined the role of hypothalamic protein kinase R like endoplasmic reticulum kinase- eukaryotic initiation factor 2α (PERK-eIF2α) signaling and the inflammatory IkappaB kinase β- nuclear factor kappa B (IKKβ-NFκB) signaling pathway in olanzapine-induced weight gain in female rats. In this study, we found that olanzapine significantly activated PERK-eIF2α and IKKβ-NFκB signaling in SH-SY5Y cells in a dose-dependent manner. Olanzapine treatment for 8 days in rats was associated with activated PERK-eIF2α signaling and IKKβ-NFκB signaling in the hypothalamus, accompanied by increased food intake and weight gain. Co-treatment with an ER stress inhibitor, 4-phenylbutyrate (4-PBA), decreased olanzapine-induced food intake and weight gain in a dose- and time-dependent manner. Moreover, 4-PBA dose-dependently inhibited olanzapine-induced activated PERK-eIF2α and IKKβ-NFκB signaling in the hypothalamus. These results suggested that hypothalamic ER stress may play an important role in antipsychotic-induced weight gain

Knowledge-embedded spatio-temporal analysis for euploidy embryos identification in couples with chromosomal rearrangements

Abstract. Background:. The goal of the assisted reproductive treatment is to transfer one euploid blastocyst and to help infertile women giving birth one healthy neonate. Some algorithms have been used to assess the ploidy status of embryos derived from couples with normal chromosome, who subjected to preimplantation genetic testing for aneuploidy (PGT-A) treatment. However, it is currently unknown whether artificial intelligence model can be used to assess the euploidy status of blastocyst derived from populations with chromosomal rearrangement. Methods:. From February 2020 to May 2021, we collected the whole raw time-lapse videos at multiple focal planes from in vitro cultured embryos, the clinical information of couples, and the comprehensive chromosome screening results of those blastocysts that had received PGT treatment. Initially, we developed a novel deep learning model called the Attentive Multi-Focus Selection Network (AMSNet) to analyze time-lapse videos in real time and predict blastocyst formation. Building upon AMSNet, we integrated additional clinically predictive variables and created a second deep learning model, the Attentive Multi-Focus Video and Clinical Information Fusion Network (AMCFNet), to assess the euploidy status of embryos. The efficacy of the AMCFNet was further tested in embryos with parental chromosomal rearrangements. The receiver operating characteristic curve (ROC) was used to evaluate the superiority of the model. Results:. A total of 4112 embryos with complete time-lapse videos were enrolled for the blastocyst formation prediction task, and 1422 qualified blastocysts received PGT-A (n = 589) or PGT for chromosomal structural rearrangement (PGT-SR, n = 833) were enrolled for the euploidy assessment task in this study. The AMSNet model using seven focal raw time-lapse videos has the best real-time accuracy. The real-time accuracy for AMSNet to predict blastocyst formation reached above 70% on the day 2 of embryo culture, and then increased to 80% on the day 4 of embryo culture. Combing with 4 clinical features of couples, the AUC of AMCFNet with 7 focal points increased to 0.729 in blastocysts derived from couples with chromosomal rearrangement. Conclusion:. Integrating seven focal raw time-lapse images of embryos and parental clinical information, AMCFNet model have the capability of assessing euploidy status in blastocysts derived from couples with chromosomal rearrangement