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Vertical sleeve gastrectomy (VSG) is one of the most effective and durable therapies for morbid obesity and its related complications. Although bile acids (BAs) have been implicated as downstream mediators of VSG, the specific mechanisms through which BA changes contribute to the metabolic effects of VSG remain poorly understood. Here, we confirm that high fat diet-fed global farnesoid X receptor (Fxr) knockout mice are resistant to the beneficial metabolic effects of VSG. However, the beneficial effects of VSG were retained in high fat diet-fed intestine- or liver-specific Fxr knockouts, and VSG did not result in Fxr activation in the liver or intestine of control mice. Instead, VSG decreased expression of positive hepatic Fxr target genes, including the bile salt export pump (Bsep) that delivers BAs to the biliary pathway. This reduced small intestine BA levels in mice, leading to lower intestinal fat absorption. These findings were verified in sterol 27-hydroxylase (Cyp27a1) knockout mice, which exhibited low intestinal BAs and fat absorption and did not show metabolic improvements following VSG. In addition, restoring small intestinal BA levels by dietary supplementation with taurocholic acid (TCA) partially blocked the beneficial effects of VSG. Altogether, these findings suggest that reductions in intestinal BAs and lipid absorption contribute to the metabolic benefits of VSG.

Existing approaches for predicting total water storage (TWS) rely on land surface or hydrological models using meteorological forcing data. Yet, such models are more adept at predicting specific water compartments, such as soil moisture, rather than others, which consequently impedes accurately forecasting of TWS. Here we show that machine learning can be used to uncover relations between nonseasonal terms of Gravity Recovery and Climate Experiment (GRACE) derived total water storage and the preceding hydrometeorological drivers, and these relations can subsequently be used to predict water storage up to 12 months ahead, and even exceptional droughts on the basis of near real‐time observational forcing data. Validation by actual GRACE observations suggests that the method developed here has the capability to forecast trends in global land water storage for the following year. If applied in early warning systems, these predictions would better inform decision‐makers to improve current drought and water resource management. Plain Language Summary: Traditional methods for predicting short‐term/seasonal variations in land total water storages rely on hydrological models. However, these models have a drawback—they are better at predicting water stored in specific parts of the land system like soil moisture than giving an accurate forecast for the overall integrated land total water storage. In this study, we demonstrate the applicability of machine learning in uncovering relationships between the de‐season and de‐linearized terms of global water storage variability as observed by the Gravity Recovery and Climate Experiment (GRACE) satellites, and the preceding hydrometeorological variables such as sea surface temperature. These relationships can then be utilized to forecast monthly changes in land total water storage up to 1 year ahead, and even to predict exceptional drought events based on near real‐time observational forcing data. The validation by actual GRACE observations, lends further credence to the effectiveness of the method developed here, showcasing its potential to forecast trends in global land total water storage for the upcoming year. The potential applications of these predictions in early warning systems are highly promising, and we anticipate that they can assist decision‐makers in enhancing current drought and water resource management practices. Key Points: We identify a stable lag relationship between hydrometeorological variables and the GRACE derived total water storage change (TWSC)Using this stable lag relationship, we are able to forecast the global TWSC up to 1 year ahead with solely observational data as inputsOur forecasts exhibit high consistency with actual GRACE data in terms of global mean land water storage trends for the following year [ABSTRACT FROM AUTHOR]

To achieve the goal that China and Nepal jointly announce the new height of Mount Qomolangma (MQ), the campaign of Qomolangma Height Survey (QHS) was carried out from 2019 to 2020. A high precision geoid model realizing the common height datum for both sides is necessary for determining the unique height of MQ. However, high altitude and rugged topography make it extremely difficult to conduct terrestrial gravity measurements in this region, the accuracy of geoid model is restricted by terrestrial gravity data gaps. In the campaign of 2020 QHS, the airborne gravity survey was carried out over MQ and its surrounding areas, the airborne gravity data covering an area of 12,700 km2 were successfully collected. For the first time, the high precision observations of terrestrial gravity and BeiDou Navigation Satellite System (BDS) at the peak of MQ were collected. These datasets pave the way for the precise determination of the orthometric height of MQ. According to the definition of the International Height Reference System (IHRS), we developed the IHRS-based gravimetric quasigeoid model by combining the airborne and terrestrial gravity data. Validations against highly accurate GNSS leveling data at 61 benchmarks demonstrate that the accuracy of the quasigeoid model is 3.8 cm, and the addition of airborne gravity data improves the accuracy by 51.3%. Based on the IHRS, the final orthometric height of the snow surface of the peak of MQ is determined to be 8848.86 m. [ABSTRACT FROM AUTHOR]

Groundwater potential maps are crucial tools for effectively managing water resources, particularly in agriculturally focused countries such as Vietnam. However, creating these maps is a challenging task that requires reliable data and methods. In this study, we integrated the Split‐Point Sampling and Node Attribute Subsampling Classifier (SPAARC) with the Bagging (B), MultiBoostAB (MBAB), and Random Subspace (RSS) ensemble learning techniques and developed three ensemble models: B‐SPAARC, MBAB‐SPAARC, and RSS‐SPAARC. We selected 13 geoenvironmental factors based on their availability, relevance, and association with groundwater potential in the Sesan River basin of Vietnam. We assessed the models' performance using various metrics such as area under the curve (AUC), accuracy, sensitivity, specificity, and RMSE. The findings indicated that the ensemble models performed better than the single SPAARC model in mapping groundwater potential. The MBAB‐SPAARC model demonstrated the highest accuracy with an AUC value of 0.891, followed by B‐SPAARC (AUC = 0.844), RSS‐SPAARC (AUC = 0.871), and the single SPAARC (AUC = 0.853) models. The results also highlighted that elevation, rainfall, land use/cover, and altitude were the most significant factors for mapping groundwater potential in the Sesan River basin. The innovative ensemble models and reliable potential maps developed in this study assist water resource managers in planning water usage based on the benefits and costs for various users and in devising sustainable strategies for using, protecting, and managing groundwater. Key Points: Groundwater potential mapping was carried out at the Central Highlands of VietnamHybrid Machine learning Models based on Naïve Bayes Tree were developed and usedResults showed that the proposal models are potential and accurate tools [ABSTRACT FROM AUTHOR]

The fruit and vegetable classification problem is an inseparable branch in the field of image recognition. GoogLeNet provides a more ideal solution for the fruit and vegetable classification problem. We use the GoogLeNet network to classify apples, lemons, oranges, pomegranates, tomatoes, and colored peppers. Through experiments, we obtained the training accuracy of GoogLeNet as 96.88%, the testing accuracy as 96%, and the training speed as 11.38 sheets/second. The recognition accuracy of this model can meet the basic recognition requirements, but the training speed is low. Therefore, we decided to optimize GoogLeNet to significantly improve the training speed and further enhance the recognition accuracy of GoogLeNet. We reduced the number of convolutional kernels of GoogLeNet and adjusted the structure of Inception, which reduced the number of parameters of GoogLeNet by nearly 48% and increased the training speed of GoogLeNet from 11.38 to 33.68 sheets/second. To further improve its recognition accuracy, we tried two methods: 1) introducing a new activation function Swish; 2) between convolutional layers, we introduced DropBlock layer; these two methods improved the testing accuracy of GoogLeNet by 2%. Finally, we introduce AlexNet, VGGNet, ResNet18, DenseNet121, and Inception-ResNet to compare with our optimized GoogLeNet. By comparison, we found that our model has incredible performance in ACC, AUC, FPS, Recall, etc.

UnlabelledVertical sleeve gastrectomy (VSG) is one of the most commonly performed clinical bariatric surgeries used for the remission of obesity and diabetes. However, the precise molecular mechanism by which VSG exerts its beneficial effects remains elusive. We report that the membrane-bound G protein-coupled bile acid receptor, GPBAR-1 (also known as TGR5), is required to mediate the effects of anti-obesity, anti-hyperglycemia, and improvements of fatty liver of VSG in mice. In the absence of TGR5, the beneficial metabolic effects of VSG in mice are lost. Moreover, we found that the expression of TGR5 increased significantly after VSG, and VSG alters both BA levels and composition in mice, resulting in enhancement of TGR5 signaling in the ileum and brown adipose tissues, concomitant with improved glucose control and increased energy expenditure.ConclusionOur study elucidates a novel underlying mechanism by which VSG achieves its postoperative therapeutic effects through enhanced TGR5 signaling. (Hepatology 2016;64:760-773).

Pyrrolizidine alkaloids (PAs) are among the most hepatotoxic natural products that produce irreversible injury to humans via the consumption of herbal medicine and honey, and through tea preparation. Toxicity and death caused by PA exposure have been reported worldwide. Metabolomics and genomics provide scientific and systematic views of a living organism and have become powerful techniques for toxicology research. In this study, senecionine hepatotoxicity on rats was determined via a combination of metabolomic and genomic analyses. From the global analysis generated from two omics data, the compromised bile acid homeostasis in vivo was innovatively demonstrated and confirmed. Serum profiling of bile acids was altered with significantly elevated conjugated bile acids after senecionine exposure, which was in accordance with toxicity. Similarly, the hepatic mRNA levels of several key genes associated with bile acid metabolism were significantly changed. This process included cholesterol 7-α hydroxylase, bile acid CoA-amino acid N-acetyltransferase, sodium taurocholate cotransporting polypeptide, organic anion-transporting polypeptides, and multidrug-resistance-associated protein 3. In conclusion, a cross-omics study provides a comprehensive analysis method for studying the toxicity caused by senecionine, which is a hepatotoxic PA. Moreover, the change in bile acid metabolism and the respective transporters may provide a new PA toxicity mechanism.