MiR-30a-5p isoform -1|1 promotes the progression of gastric cancer by inhibiting TMEM66 and reducing intratumoral cytotoxic T cells.

Gastric cancer is histologically classified into the intestinal subtype, which forms tubular structures, and the aggressive diffuse subtype, characterized by rapid invasion and poor prognosis. The variety and quantity of miRNA isoforms between different histological subtypes of gastric cancer were unknown. Through systematic filtering, we found that more diverse miR-30a-5p isoforms was present in the diffuse subtype of gastric cancer, and was associated with patients' worse survival independent of tumor stage based on the TCGA miRNA-seq data. Among all nine isoforms of miR-30a-5p, miR-30a-5p -1|1 was more abundant than the archetype of miR-30a-5p. Higher expression of miR-30a-5p -1|1 was observed in patients with advanced tumor stage and poor survival. Furthermore, miR-30a-5p -1|1 could promote the metastasis of gastric cancer cells both in vitro and in vivo by down-regulating TMEM66. In clinical samples, decreased expression of TMEM66 was characteristic of gastric cancer, and the low level of TMEM66 correlated with deceased CD8 positive cells in the tumor microenvironment probably due to decreased cytokines production. In conclusion, the variety of miR-30a-5p isoforms correlates with worse survival in gastric cancer patients. Moreover, miR-30a-5p -1|1 could promote gastric cancer metastasis by inhibiting TMEM66 and the infiltration of intratumoral CD8 positive cells.

From lake to fisheries: Interactive effect of climate and landuse changes hit on lake fish catch?

Global warming and unpredictable nature possess a negative impact on fisheries and the daily activities of other habitats. GIS and remote sensing approach is an effective tool to determine the morphological characteristics of the lake. The present study addresses the interactive effect of climate and landuse changes hit on fish catch in lake fisheries. We used a combination of the landscape disturbance index, vulnerability index, and loss index to construct a complete ecological risk assessment framework based on the landscape structure of regional ecosystems. The results indicate an increase from around 45%-76% in the percentage of land susceptible to moderate to ecological severe risk in the landscape from 2004 to 2023. Since 1950, temperature changes have increased by 0.4%, precipitation has decreased by 6%, and water levels have decreased by 4.2%, based on the results. The results indicate that landuse, water temperature, precipitation, and water depth significantly impact the aquaculture system. The findings strongly suggest integrating possible consequences of environmental change on fish yield for governance modeling techniques to minimize their effects.

Mitochondria-associated endoplasmic reticulum membranes involve in oxidative stress-induced intestinal barrier injury and mitochondrial dysfunction under diquat exposing.

Many factors induced by environmental toxicants have made oxidative stress a risk factor for the intestinal barrier injury and growth restriction, which is serious health threat for human and livestock and induces significant economic loss. It is well-known that diquat-induced oxidative stress is implicated in the intestinal barrier injury. Although some studies have shown that mitochondria are the primary target organelle of diquat, the underlying mechanism remains incompletely understood. Recently, mitochondria-associated endoplasmic reticulum membranes (MAMs) have aroused increasing concerns among scholars, which participate in mitochondrial dynamics and signal transduction. In this study, we investigated whether MAMs involved in intestinal barrier injury and mitochondrial dysfunction induced by diquat-induced oxidative stress in piglets and porcine intestinal epithelial cells (IPEC-J2 cells). The results showed that diquat induced growth restriction and impaired intestinal barrier. The mitochondrial reactive oxygen species (ROS) was increased and mitochondrial membrane potential was decreased following diquat exposure. The ultrastructure of mitochondria and MAMs was also disturbed. Meanwhile, diquat upregulated endoplasmic reticulum stress marker protein and activated PERK pathway. Furthermore, loosening MAMs alleviated intestinal barrier injury, decrease of antioxidant enzyme activity and mitochondrial dysfunction induced by diquat in IPEC-J2 cells, while tightening MAMs exacerbated diquat-induced mitochondrial dysfunction. These results suggested that MAMs may be associated with the intestinal barrier injury and mitochondrial dysfunction induced by diquat in the jejunum of piglets.

Research on machine learning hybrid framework by coupling grid-based runoff generation model and runoff process vectorization for flood forecasting.

One of the important non-engineering measures for flood forecasting and disaster reduction in watersheds is the application of machine learning flood prediction models, with Long Short-Term Memory (LSTM) being one of the most representative time series prediction models. However, the LSTM model has issues of underestimating peak flows and poor robustness in flood forecasting applications. Therefore, based on a thorough analysis of complex underlying surface attributes, this study proposes a framework for distinguishing runoff models and integrates a Grid-based Runoff Generation Model (GRGM). Simultaneously considering the time series characteristics of runoff processes, including rising, peak, and recession, a runoff process vectorization (RPV) method is proposed. In this study, a hybrid deep learning flood forecasting framework, GRGM-RPV-LSTM, is constructed by coupling the GRGM, RPV, and LSTM neural network models. Taking the Jialu River in the Zhongmu station control basin as an example, the model is validated using 18 instances of measured floods and compared with the LSTM and GRGM-LSTM models. The study shows that the GRGM model has a relative error and average coefficient of determination for simulating runoff of 8.41% and 0.976, respectively, indicating that considering the spatial distribution of runoff patterns leads to more accurate runoff calculations. Under the same lead time conditions, the GRGM-RPV-LSTM hybrid forecasting model has a Nash efficiency coefficient greater than 0.9, demonstrating better simulation performance compared to the GRGM-LSTM and LSTM models. As the lead time increases, the GRGM-RPV-LSTM model provides more accurate peak flow predictions and exhibits better robustness. The research findings can provide scientific basis for coordinated management of flood control and disaster reduction in watersheds.

Research on runoff process vectorization and integration of deep learning algorithms for flood forecasting.

Accurate multi-step ahead flood forecasting is crucial for flood prevention and mitigation efforts as well as optimizing water resource management. In this study, we propose a Runoff Process Vectorization (RPV) method and integrate it with three Deep Learning (DL) models, namely Long Short-Term Memory (LSTM), Temporal Convolutional Network (TCN), and Transformer, to develop a series of RPV-DL flood forecasting models, namely RPV-LSTM, RPV-TCN, and RPV-Transformer models. The models are evaluated using observed flood runoff data from nine typical basins in the middle Yellow River region. The key findings are as follows: Under the same lead time conditions, the RPV-DL models outperform the DL models in terms of Nash-Sutcliffe efficiency coefficient, root mean square error, and relative error for peak flows in the nine typical basins of the middle Yellow River region. Based on the comprehensive evaluation results of the train and test periods, the RPV-DL model outperforms the DL model by an average of 2.82%-22.21% in terms of NSE across nine basins, with RMSE and RE reductions of 10.86-28.81% and 36.14%-51.35%, respectively. The vectorization method significantly improves the accuracy of DL flood forecasting, and the RPV-DL models exhibit better predictive performance, particularly when the lead time is 4h-6h. When the lead time is 4-6h, the percentage improvement in NSE is 9.77%, 15.07%, and 17.94%. The RPV-TCN model shows superior performance in overcoming forecast errors among the nine basins. The research findings provide scientific evidence for flood prevention and mitigation efforts in river basins.

Study on the response analysis of LID hydrological process to rainfall pattern based on framework for dynamic simulation of urban floods.

An in-depth analysis of the urban flood disaster level in response to different rainfall characteristics and Low Impact Development (LID) measures is of significant importance for addressing unfavorable management conditions and implementing effective flood control measures. This study proposes a dynamic urban flood simulation framework based on the Storm Water Management Model (SWMM) and Geographic Information System (GIS) spatial analysis, incorporating an active inundation seed search algorithm. The framework is calibrated and validated using nine historical urban flood events. Subsequently, the impact of rainfall patterns on urban inundation under LID measures is analyzed based on the dynamic urban flood simulation framework. The results show that the urban flood simulation framework exhibits good applicability, with Nash-Sutcliffe Efficiency (NSE) values of 0.825 and 0.763 during the calibration and validation periods, respectively. The extent of inundation shows little variation for rainfall events with a return period greater than 20 years, and the location of flooding is minimally affected by rainfall patterns. LID measures have a decreasing effect on urban inundation control as the return period of rainfall increases, and there are variations in hydrological responses to different rainfall patterns under the same return period. For single-peak rainfall events with the same return period, the control rates of inundation volume, flow, and infiltration decrease as the rainfall peak coefficient increases, indicating a weakening effect of LID measures on flood control with increasing rainfall peak coefficient. Under the same return period conditions, LID measures exhibit the best runoff control effect for uniform rainfall, while their effectiveness is lower for double-peak rainfall events and single-peak rainfall events with an r = 0.75 coefficient. The findings of this study provide a theoretical basis for urban flood warning and management of Low Impact Development measures.

Interpretable baseflow segmentation and prediction based on numerical experiments and deep learning.

Baseflow is a crucial water source in the inland river basins of high-cold mountainous region, playing a significant role in maintaining runoff stability. It is challenging to select the most suitable baseflow separation method in data-scarce high-cold mountainous region and to evaluate effects of climate factors and underlying surface changes on baseflow variability and seasonal distribution characteristics. Here we attempt to address how meteorological factors and underlying surface changes affect baseflow using the Grey Wolf Optimizer Digital Filter Method (GWO-DFM) for rapid baseflow separation and the Long Short-Term Memory (LSTM) neural network model for baseflow prediction, clarifying interpretability of the LSTM model in baseflow forecasting. The proposed method was successfully implemented using a 63-year time series (1958-2020) of flow data from the Tai Lan River (TLR) basin in the high-cold mountainous region, along with 21 years of ERA5-land meteorological data and MODIS data (2000-2020). The results indicate that: (1) GWO-DFM can rapidly identify the optimal filtering parameters. It employs the arithmetic average of three methods, namely Chapman, Chapman-Maxwell and Eckhardt filter, as the best baseflow separation approach for the TLR basin. Additionally, the baseflow significantly increases after the second mutation of the baseflow rate. (2) Baseflow sources are mainly influenced by precipitation infiltration, glacier frozen soil layers, and seasonal ponding. (3) Solar radiation, temperature, precipitation, and NDVI are the primary factors influencing baseflow changes, with Nash-Sutcliffe efficiency coefficients exceeding 0.78 in both the LSTM model training and prediction periods. (4) Changes in baseflow are most influenced by solar radiation, temperature, and NDVI. This study systematically analyzes the changes in baseflow and response mechanisms in high-cold mountainous region, contributing to the management of water resources in mountainous basins under changing environmental conditions.

Effects of low protein diets on acid-base balance, electrolyte balance, intestinal structure, and amino acid transport in piglets.

Reducing the dietary crude protein (CP) could effectively reduce pressure on protein ingredient supplies. However, few data have been reported about the extent to which CP can be reduced and whether limiting the use of soybean meal leads to electrolyte imbalance. In this experiment, using the low protein (LP) diet [2% lower than NRC (2012)], seventy-two piglets (35 days old) were randomly divided into 2 groups with 6 replicates of 6 piglets each: CON group (CP = 18.5%) and LP group (CP = 16.5%), to investigate the effect of the LP diet on electrolyte balance, acid-base balance, intestinal structure and amino acid transport in piglets. The results revealed that the LP diet decreased the average daily gain and dietary CP digestibility, and damaged the villi structure of the small intestine. Compared with the CON diet, the potassium content decreased and the chlorine content increased in the LP diet, and similar trends were shown in piglet serum. The arterial pH, pCO2, HCO3 -, and base excess of piglets in the LP group were lower than those in the CON group, while pO2 was higher than those in the CON group. Interestingly, the LP diet significantly increased the lysine content in piglet serum and significantly decreased the levels of arginine, leucine, and glutamic acid. Furthermore, the LP diet significantly affected the expression of some amino acid transport vectors (B0AT1, EAAC1, and y+LAT1). In summary, these findings suggested that the LP diet leads to acid-base imbalance, amino acid transport disorder and amino acids imbalance in piglets, and the dietary electrolyte may be a key factor in the impact of the LP diet on piglet growth performance and intestinal health.

The role of self-esteem and emotion regulation in the associations between childhood trauma and mental health in adulthood: a moderated mediation model.

BACKGROUND: High levels of childhood trauma (CT) have been observed in adults with mental health problems. Herein, we investigated whether self-esteem (SE) and emotion regulation strategies (cognitive reappraisal (CR) and expressive suppression (ES)) affect the association between CT and mental health in adulthood, including depression and anxiety symptoms. METHODS: We performed a cross-sectional study of 6057 individuals (39.99% women, median age = 34 y), recruited across China via the internet, who completed the Patient Health Questionnaire-9 (PHQ-9), Generalized Anxiety Disorder-7 (GAD-7), Childhood Trauma Questionnaire (CTQ), Self-esteem Scale (SES), and Emotion Regulation Questionnaire (ERQ). Multivariate linear regression analysis and bias-corrected percentile bootstrap methodologies were used to assess the mediating effect of SE, and hierarchical regression analysis and subgroup approach were performed to examine the moderating effects of emotion regulation strategies. RESULTS: After controlling for age and sex, we found that (1) SE mediated the associations between CT and depression symptoms in adulthood (indirect effect = 0.05, 95% confidence interval [CI]: 0.04-0.05, 36.2% mediated), and CT and anxiety symptoms in adulthood (indirect effect = 0.03, 95% CI: 0.03-0.04, 32.0% mediated); (2) CR moderated the association between CT and SE; and (3) ES moderated the association between of CT and mental health in adulthood via SE, and such that both the CT-SE and SE-mental health pathways were stronger when ES is high rather than low, resulting the indirect effect was stronger for high ES than for low ES. CONCLUSIONS: These findings suggested that SE plays a partially mediating role in the association between CT and mental health in adulthood. Furthermore, ES aggravated the negative effect of CT on mental health in adulthood via SE. Interventions such as emotional expression training may help reduce the detrimental effects of CT on mental health. TRIAL REGISTRATION: The study was registered on http://www.chictr.org.cn/index.aspx and the registration number was ChiCTR2200059155.

BK-SWMM flood simulation framework is being proposed for urban storm flood modeling based on uncertainty parameter crowdsourcing data from a single functional region.

In recent years, urban flood disasters caused by sudden heavy rains have become increasingly severe, posing a serious threat to urban public infrastructure and the life and property safety of residents. Rapid simulation and prediction of urban rain-flood events can provide timely decision-making reference for urban flood control and disaster reduction. The complex and arduous calibration process of urban rain-flood models has been identified as a major obstacle affecting the efficiency and accuracy of simulation and prediction. This study proposes a multi-scale urban rain-flood model rapid construction method framework, BK-SWMM, focusing on urban rain-flood model parameters and based on the basic architecture of Storm Water Management Model (SWMM). The framework comprises two main components: 1) constructing a SWMM uncertainty parameter sample crowdsourcing dataset and coupling Bayesian Information Criterion (BIC) and K-means clustering machine learning algorithm to discover clustering patterns of SWMM model uncertainty parameters in urban functional areas; 2) coupling BIC and K-means with SWMM model to form BK-SWMM flood simulation framework. The applicability of the proposed framework is validated by modeling three different spatial scales in the study regions based on observed rainfall-runoff data. The research findings indicate that the distribution pattern of uncertainty parameters, such as depression storage, surface Manning coefficient, infiltration rate, and attenuation coefficient. The distribution patterns of these seven parameters in urban functional zones indicate that the values are highest in the Industrial and Commercial Areas (ICA), followed by Residential Areas (RA), and lowest in Public Areas (PA). All three spatial scales' REQ, NSEQ, and RD2 indices were superior to the SWMM and less than 10%, greater than 0.80, and greater than 0.85, respectively. However, when the study area's geographical scale expands, the simulation's accuracy will decline. Further research is required on the scale dependency of urban storm flood models.