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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]

This study systematically analyzed the molecular mechanism and function of nuclear factor kappa B subunit 2 (NFKB2) in colorectal cancer (CRC) to investigate the potential of NFKB2 as a therapeutic target for CRC. Various experimental techniques, including RNA sequencing, proteome chip assays, and small molecule analysis, were used to obtain a deeper understanding of the regulation of NFKB2 in CRC. The results revealed that NFKB2 was upregulated in a significant proportion of patients with advanced hepatic metastasis of CRC. NFKB2 played an important role in promoting tumor growth through CD8+ T‐cell exhaustion. Moreover, NFKB2 directly interacted with signal transducer and activator of transcription 2 (STAT2), leading to increased phosphorylation of STAT2 and the upregulation of programmed death ligand 1 (PD‐L1). Applying a small molecule inhibitor of NFKB2 (Rg5) led to a reduction in PD‐L1 expression and improved response to programmed death‐1 blockade‐based immunotherapy. In conclusion, the facilitated NFKB2‐STAT2/PD‐L1 axis may suppress immune surveillance in CRC and targeting NFKB2 may enhance the efficacy of immunotherapeutic strategies. Our results provide novel insights into the molecular mechanisms underlying the contribution of NFKB2 in CRC immune escape. [ABSTRACT FROM AUTHOR]

The Gravity Recovery and Climate Experiment (GRACE) mission has monitored global total water storage anomalies (TWSA) with an unprecedented accuracy since 2002. Yet, many applications require a longer record, that is, extending prior to the GRACE period. Here, we present a new global reconstruction of long‐term (1979–2020) TWSA fields by combining machine learning with time series decomposition and statistical decomposition techniques. We find that the long‐term TWSA reconstructed from GRACE fits well with the GRACE‐FO observation over most grids (0.5° resolution) of the global land and successfully reproduces the strong El Niño signal. Comparisons to Satellite Laser Ranging solutions and to observed global mean sea level change suggest our reconstruction (doi: https://doi.org/10.5061/dryad.z612jm6bt) is more reliable than previously published products. This study provides a viable approach for both reconstructing past TWSA and filling the GRACE data gap at the global scale. Plain Language Summary: Since 2002, temporal changes of vertically integrated water storage have been detected globally with unprecedented accuracy via satellite gravimetry, as first implemented with the Gravity Recovery and Climate Experiment (GRACE) satellites. However, no similar observation system has been in place before 2002, that could have used to measure the total water storage anomalies (TWSA) with an accuracy comparable to GRACE. Several approaches, such as machine learning, have been proposed in previous studies to reconstruct past TWSA based on their empirical relationships with the climatic/hydrological drivers (e.g., precipitation and soil moisture). Different from traditional approaches, we here combine machine learning with statistical decomposition techniques to reconstruct GRACE‐like TWSA (0.5° resolution) for the period 1979–2020 over the global land. We find that the reconstructed TWSA shows strong abnormal signals during El Niño years. In addition, we compare our reconstruction to recent TWSA data obtained using Satellite Laser Ranging analysis to five satellites, that is, Lageos‐1, Lageos‐2, Ajisai, Stella, and Starlette, in the period since 1993, and to the observed global mean sea level change as validations. The comparison suggests that our reconstruction is more accurate than previously published products. We suggest our method provides a viable alternative for reconstructing long‐term TWSA. Key Points: We reconstruct Gravity Recovery and Climate Experiment‐like (GRACE) total water storage anomaly (TWSA) fields over the global land surface for the period 1979–2020TWSA reconstructed from GRACE fit well with GRACE Follow‐On data and correctly reproduce strong El Niño signalsOutside the GRACE period, our reconstruction fits better with satellite laser ranging solutions and global mean sea level than previous reconstruction [ABSTRACT FROM AUTHOR]

The Gravity Recovery and Climate Experiment (GRACE) mission ended its operation in October 2017, and the GRACE Follow‐On mission was launched only in May 2018, leading to approximately 1 year of data gap. Given that GRACE‐type observations are exclusively providing direct estimates of total water storage change (TWSC), it would be very important to bridge the gap between these two missions. Furthermore, for many climate‐related applications, it is also desirable to reconstruct TWSC prior to the GRACE period. In this study, we aim at comparing different data‐driven methods and identifying the more robust alternatives for predicting GRACE‐like gridded TWSC during the gap and reconstructing them to 1992 using climate inputs. To this end, we first develop a methodological framework to compare different methods such as the multiple linear regression (MLR), artificial neural network (ANN), and autoregressive exogenous (ARX) approaches. Second, metrics are developed to measure the robustness of the predictions. Finally, gridded TWSC within 26 regions are predicted and reconstructed using the identified methods. Test computations suggest that the correlation of predicted TWSC maps with observed ones is more than 0.3 higher than TWSC simulated by hydrological models, at the grid scale of 1° resolution. Furthermore, the reconstructed TWSC correctly reproduce the El Nino‐Southern Oscillation (ENSO) signals. In general, while MLR does not perform best in the training process, it is more robust and could thus be a viable approach both for filling the GRACE gap and for reconstructing long‐period TWSC fields globally when combined with statistical decomposition techniques. Key Points: We develop a unified methodological framework to compare viable approaches for reconstructing and predicting globally gridded GRACE fieldsPredicted total water storage change fields fit better to the observations than those simulated by hydrological modelsReconstructed total water storage change correctly reproduces a strong abnormal signal in the tropical river basin during the El Niño period [ABSTRACT FROM AUTHOR]

Background and Purpose: Sleep deprivation compromises learning and memory in both humans and animals, and can be reversed by administration of modafinil, a drug promoting wakefulness. Dysfunctional autophagy increases activation of apoptotic cascades, ultimately leading to increased neuronal death, which can be alleviated by autophagy inhibitors. This study aimed to investigate the alleviative effect and mechanism of modafinil on the excessive autophagy occurring in the hippocampus of mice with deficiency of learning and memory induced by sleep deprivation.Experimental Approach: The Morris water maze was used to assess the effects of modafinil on male C57BL/6Slac mice after 48-hr sleep deprivation. The HT-22 hippocampal neuronal cell line was also used. Nissl staining, transmission electron microscope, immunofluorescence, Western blot, transient transfection, and autophagy inducer were used to study the effect and mechanism of modafinil on hippocampal neurons with excessive autophagy and apoptosis.Key Results: Modafinil improved learning and memory in sleep-deprived mice, associated with the inhibition of excessive autophage and apoptosis and an enhanced activation of the PI3K/Akt/mTOR/P70S6K signalling pathway in hippocampal neurons. These effects of modafinil were abolished by rapamycin. In addition, modafinil suppressed the aberrant autophagy and apoptosis induced by rapamycin and reactivated PI3K/Akt/mTOR/P70S6K signals in HT-22 cells.Conclusions and Implications: These results suggested that modafinil alleviated impaired learning and memory of sleep-deprived mice potentially by suppressing excessive autophagy and apoptosis of hippocampal neurons. This novel mechanism may add to our knowledge of modafinil in the clinical treatment of impaired memory caused by sleep loss. [ABSTRACT FROM AUTHOR]