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Accurate high-spatial-resolution precipitation is significantly important in hydrological and meteorological modelling, especially in rain-gauge-sparse areas. Some methods and strategies have been applied for satellite-based precipitation downscaling, residual correction and precipitation calibration. However, which downscaling scheme can provide reliable high-resolution precipitation efficiently remains unanswered. To address this issue, this study aimed to present a framework combining the machine learning downscaling algorithm and post-process procedures. Firstly, four ML-based models, namely support vector regression, random forest, spatial random forest (SRF) and eXtreme gradient boosting (XGBoost), were tested for downscaling and compared with conventional downscaling methods. Then, the effectiveness of the residual correction process using ordinary Kriging and the calibration process using the geographical difference analysis (GDA) method was investigated. The results showed that the ML-based methods had better performance than the conventional regression and interpolation approaches. The SRF and XGBoost outperformed others in generating accurate precipitation estimation with a high resolution. The GDA calibration process significantly improved the downscaled results. However, the residual correction process decreased the downscaling performance of the ML-based models. Combining the SRF or XGBoost downscaling algorithm with the GDA calibration method could be a promising downscaling scheme for precipitation data. The scheme could be used to generate high-resolution precipitation, especially in areas urgently requiring data, which would benefit regional water resource management and hydrological disaster prevention.

Timely and reliable estimation of regional crop yield is a vital component of food security assessment, especially in developing regions. The traditional crop forecasting methods need ample time and labor to collect and process field data to release official yield reports. Satellite remote sensing data is considered a cost-effective and accurate way of predicting crop yield at pixel-level. In this study, maximum Enhanced Vegetation Index (EVI) during the crop-growing season was integrated with Machine Learning Regression (MLR) models to estimate wheat and rice yields in Pakistan's Punjab province. Five MLR models were compared using a fivefold cross-validation method for their predictive accuracy. The study results revealed that the regression model based on the Gaussian process outperformed over other models. The best performing model attained coefficient of determination (R 2 ), Root Mean Square Error (RMSE, t/ha), and Mean Absolute Error (MAE, t/ha) of 0.75, 0.281, and 0.236 for wheat; 0.68, 0.112, and 0.091 for rice, respectively. The proposed method made it feasible to predict wheat and rice 6-8 weeks before the harvest. The early prediction of crop yield and its spatial distribution in the region can help formulate efficient agricultural policies for sustainable social, environmental, and economic progress.

Suspended particulate matter (SPM) is one of the dominant water constituents in inland and coastal waters, and SPM concnetration (CSPM) is a key parameter describing water quality. This study, using in-situ spectral and CSPM measurements as well as Sentinel 2 Multispectral Imager (MSI) images, aimed to develop CSPM retrieval models and further to estimate the CSPM values of Poyang Lake, China. Sixty-eight in-situ hyperspectral measurements and relative spectral response function were applied to simulate Sentinel 2 MIS spectra. Thirty-four samples were used to calibrate and the left samples were used to validate CSPM retrieval models, respectively. The developed models were then applied to two Sentinel 2 MSI images captured in wet and dry seasons, and the derived CSPM values were compared with those derived from MODIS B1 (λ = 645 nm). Results showed that the Sentinel 2 MSI B4–B8b models achieved acceptable to high fitting accuracies, which explained 81–93% of the variation of CSPM. The validation results also showed the reliability of these six models, and the estimated CSPM explained 77–93% of the variation of measured CSPM with the mean absolute percentage error (MAPE) ranging from 36.87% to 21.54%. Among those, a model based on B7 (λ = 783 nm) appeared to be the most accurate one. The Sentinel 2 MSI-derived CSPM values were generally consistent in spatial distribution and magnitude with those derived from MODIS. The CSPM derived from Sentinel 2 MSI B7 showed the highest consistency with MODIS on 15 August 2016, while the Sentinel 2 MSI B4 (λ = 665 nm) produced the highest consistency with MODIS on 2 April 2017. Overall, this study demonstrated the applicability of Sentinel 2 MSI for CSPM retrieval in Poyang Lake, and the Sentinel 2 MSI B4 and B7 are recommended for low and high loadings of SPM, respectively.

The vulnerable ecosystem of the arid and semiarid region in Central Asia is sensitive to precipitation variations. Long-term changes of the seasonal precipitation can reveal the evolution rules of the precipitation climate. Therefore, in this study, the changes of the seasonal precipitation over Central Asia have been analyzed during the last century (1901–2013) based on the latest global monthly precipitation dataset Global Precipitation Climatology Centre (GPCC) Full Data Reanalysis Version 7, as well as their relations with El Niño- Southern Oscillation (ENSO). Results show that the precipitation in Central Asia is mainly concentrated in spring and summer seasons, especially in spring. For the whole study period, increasing trends were found in spring and winter, while decreasing trends were detected in summer and fall. Inter-annual signals with 3–7 years multi-periods were derived to explain the dominant components for seasonal precipitation variability. In terms of the dominant spatial pattern, Empirical orthogonal function (EOF) results show that the spatial distribution of EOF-1 mode in summer is different from those of the other seasons during 1901–2013. Moreover, significant ENSO-associated changes in precipitation are evident during the fall, winter, spring, and absent during summer. The lagged associations between ENSO and seasonal precipitation are also obtained in Central Asia. The ENSO-based composite analyses show that these water vapor fluxes of spring, fall and winter precipitation are mainly generated in Indian and North Atlantic Oceans during El Niño. The enhanced westerlies strengthen the western water vapor path for Central Asia, thereby causing a rainy winter.

Detailed urban landuse information plays a fundamental role in smart city management. A sufficient sample size has been identified as a very crucial pre-request in machine learning algorithms for urban landuse classification. However, it is often difficult to recognize and label landuse categories from remote sensing images alone. Alternatively, field investigation is time-consuming with a high demand in human resources and monetary cost. Therefore, previous studies on urban landuse classification have often relied on a small size of labeled samples with very uneven spatial distribution. This study aims to explore the effectiveness of a semi-supervised classification framework with multi-source data for detailed urban landuse classification with a few labeled samples. A disagreement-based semi-supervised learning approach, the Co-Forest, was employed and compared with traditional supervised methods (e.g., random forest and XGBoost). Multi-source geospatial data were utilized including optical and nighttime light remote sensing and geospatial big data, which present the physical and socio-economic features of landuse categories. Taking urban landuse classification in Shenzhen City as a case, results show that the classification accuracy of the semi-supervised method are generally on par with that of traditional supervised methods, and less labeled samples are needed to achieve a comparable result under different training set ratios. Given a small sample size, the accuracy tends to be stable with training samples no less than 5% in total. Our results also indicate that the classification accuracy by using multi-source data is significantly higher than that with any single data source being applied. Among these data, map POI and high-resolution optical remote sensing data make larger contributions on the classification, followed by mobile data and nighttime light remote sensing data.

Therapeutic targeting of KRAS -mutant colorectal cancer (CRC) is an unmet need. Here, we show that Proprotein Convertase Subtilisin/Kexin type 9 (PSCK9) promotes APC/KRAS-mutant CRC and is a therapeutic target. Using CRC patient cohorts, isogenic cell lines and transgenic mice, we identify that de novo cholesterol biosynthesis is induced in APC / KRAS mutant CRC, accompanied by increased geranylgeranyl diphosphate (GGPP)─a metabolite necessary for KRAS activation. PCSK9 is the top up-regulated cholesterol-related gene. PCSK9 depletion represses APC / KRAS -mutant CRC cell growth in vitro and in vivo, whereas PCSK9 overexpression induces oncogenesis. Mechanistically, PCSK9 reduces cholesterol uptake but induces cholesterol de novo biosynthesis and GGPP accumulation. GGPP is a pivotal metabolite downstream of PCSK9 by activating KRAS/MEK/ERK signaling. PCSK9 inhibitors suppress growth of APC/KRAS-mutant CRC cells, organoids and xenografts, especially in combination with simvastatin. PCSK9 overexpression predicts poor survival of APC / KRAS -mutant CRC patients. Together, cholesterol homeostasis regulator PCSK9 promotes APC / KRAS -mutant CRC via GGPP-KRAS/MEK/ERK axis and is a therapeutic target. Looking for metabolic-associated vulnerabilities is a promising approach for therapeutic intervention in KRAS-mutant colorectal cancer. Here, the authors show that the cholesterol-uptake regulator PCSK9 drives tumourigenesis and is a therapeutic target in KRASmutant colorectal cancer.

Helicobacter pylori , particularly cytotoxin-associated gene A (CagA)-positive strains, plays a key role in the progression of gastric cancer (GC). Ferroptosis, associated with lethal lipid peroxidation, has emerged to play an important role in malignant and infectious diseases, but the role of CagA in ferroptosis in cancer cells has not been determined. Here, we report that CagA confers GC cells sensitivity to ferroptosis both in vitro and in vivo. Mechanistically, CagA promotes the synthesis of polyunsaturated ether phospholipids (PUFA-ePLs), which is mediated by increased expression of alkylglycerone phosphate synthase ( AGPS ) and 1-acylglycerol-3-phosphate O-acyltransferase 3 ( AGPAT3 ), leading to susceptibility to ferroptosis. This susceptibility is mediated by activation of the MEK/ERK/SRF pathway. SRF is a crucial transcription factor that increases AGPS transcription by binding to the AGPS promoter region. Moreover, the results demonstrated that CagA-positive cells are more sensitive to apatinib than are CagA-negative cells, suggesting that detecting the H. pylori CagA status may aid patient stratification for treatment with apatinib. Unlocking ferroptosis: CagA’s role in gastric cancer cells Gastric cancer, a major cause of cancer death globally, is often associated with chronic infection by a bacterium named Helicobacter pylori. A specific strain of H. pylori that produces a protein named CagA is particularly harmful, as it can cause severe stomach diseases, including cancer. However, the connection between CagA and a kind of cell death known as ferroptosis in gastric cancer cells was unclear. In this research, scientists discovered that CagA boosts the production of certain fats in cells, making them more prone to ferroptosis. The study was an experiment involving both cell cultures and animal models. The researchers concluded that targeting the pathway influenced by CagA could be a new strategy for treating gastric cancer, especially in patients infected with CagA-positive H. pylori strains. This summary was initially drafted using artificial intelligence, then revised and fact-checked by the author.

The fine-scale insights of existing cropland trends and their nexus with agrometeorological parameters are of paramount importance in assessing future food security risks and analyzing adaptation options under climate change. This study has analyzed the seasonal cropland trends in the Indus River Plain (IRP), using multi-year remote sensing data. A combination of Sen’s slope estimator and Mann–Kendall test was used to quantify the existing cropland trends. A correlation analysis between enhanced vegetation index (EVI) and 9 agrometeorological parameters, derived from reanalysis and remote sensing data, was conducted to study the region’s cropland-climate nexus. The seasonal trend analysis revealed that more than 50% of cropland in IRP improved significantly from the year 2003 to 2018. The lower reaches of the IRP had the highest fraction of cropland, showing a significant decreasing trend during the study period. The nexus analysis showed a strong correlation of EVI with the evaporative stress index (ESI) during the water-stressed crop season. Simultaneously, it exhibited substantial nexus of EVI with actual evapotranspiration (AET) during high soil moisture crop season. Temperature and solar radiation had a negative linkage with EVI response. In contrast, a positive correlation of rainfall with EVI trends was spatially limited to the IRP’s upstream areas. The relative humidity had a spatially broad positive correlation with EVI compare to other direct climatic parameters. The study concluded that positive and sustainable growth in IRP croplands could be achieved through effective agriculture policies to address spatiotemporal AET anomalies.

KRAS is an important tumor intrinsic factor driving immune suppression in colorectal cancer (CRC). In this study, we demonstrate that SLC25A22 underlies mutant KRAS-induced immune suppression in CRC. In immunocompetent male mice and humanized male mice models, SLC25A22 knockout inhibits KRAS-mutant CRC tumor growth with reduced myeloid derived suppressor cells (MDSC) but increased CD8 + T-cells, implying the reversion of mutant KRAS-driven immunosuppression. Mechanistically, we find that SLC25A22 plays a central role in promoting asparagine, which binds and activates SRC phosphorylation. Asparagine-mediated SRC promotes ERK/ETS2 signaling, which drives CXCL1 transcription. Secreted CXCL1 functions as a chemoattractant for MDSC via CXCR2, leading to an immunosuppressive microenvironment. Targeting SLC25A22 or asparagine impairs KRAS-induced MDSC infiltration in CRC. Finally, we demonstrate that the targeting of SLC25A22 in combination with anti-PD1 therapy synergizes to inhibit MDSC and activate CD8 + T cells to suppress KRAS-mutant CRC growth in vivo. We thus identify a metabolic pathway that drives immunosuppression in KRAS-mutant CRC. KRAS mutations are associated with an immunosuppressive microenvironment in patients with colorectal cancer (CRC). Here the authors show that knockout of SLC25A22, a mitochondrial glutamate carrier, reverts KRAS-mediated immunosuppression in preclinical CRC models, by suppressing CXCL1 production and impairing MDSC recruitment.

BackgroundTumourigenesis in right-sided and left-sided colons demonstrated distinct features.ObjectiveWe aimed to characterise the differences between the left-sided and right-sided adenomas (ADs) representing the early stage of colonic tumourigenesis.DesignSingle-cell and spatial transcriptomic datasets were analysed to reveal alterations between right-sided and left-sided colon ADs. Cells, animal experiments and clinical specimens were used to verify the results.ResultsSingle-cell analysis revealed that in right-sided ADs, there was a significant reduction of goblet cells, and these goblet cells were dysfunctional with attenuated mucin biosynthesis and defective antigen presentation. An impairment of the mucus barrier led to biofilm formation in crypts and subsequent bacteria invasion into right-sided ADs. The regions spatially surrounding the crypts with biofilm occupation underwent an inflammatory response by lipopolysaccharide (LPS) and an apoptosis process, as revealed by spatial transcriptomics. A distinct S100A11+ epithelial cell population in the right-sided ADs was identified, and its expression level was induced by bacterial LPS and peptidoglycan. S100A11 expression facilitated tumour growth in syngeneic immunocompetent mice with increased myeloid-derived suppressor cells (MDSC) but reduced cytotoxic CD8+ T cells. Targeting S100A11 with well-tolerated antagonists of its receptor for advanced glycation end product (RAGE) (Azeliragon) significantly impaired tumour growth and MDSC infiltration, thereby boosting the efficacy of anti-programmed cell death protein 1 therapy in colon cancer.ConclusionOur findings unravelled that dysfunctional goblet cells and consequential bacterial translocation activated the S100A11-RAGE axis in right-sided colon ADs, which recruits MDSCs to promote immune evasion. Targeting this axis by Azeliragon improves the efficacy of immunotherapy in colon cancer.