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Studies examining the joint interactions and impacts of social-environmental system (SES) drivers on vegetation dynamics in Central Asia are scarce. We investigated seasonal trends and anomalies in drivers and their impacts on ecosystem structure and function (ESF). We explored the response of net primary production, evapotranspiration and normalized difference vegetation index (NDVI) to various SES drivers—climate, human influence, heat stress, water storage, and water content—and their latent relationships in Kazakhstan. We employed 13 predictor drivers from 2000 to 2016 to identify the interactions and impacts on ESF variables that reflect vegetation growth and productivity. We developed 12 models with different predictor–response variable combinations and separated them into two approaches. First, we considered the winter percent snow cover (SNOWc) and spring rainfall (P_MAM) as drivers and then as moderators in a structural equation model (SEM). SNOWc variability (SNOWc _SD ) as an SEM moderator exhibited superior model accuracy and explained the interactions between various predictor–response combinations. Winter SNOWc _SD did not have a strong direct positive influence on summer vegetation growth and productivity; however, it was an important moderator between human influence and the ESF variables. Spring rainfall had a stronger impact on ESF variability than summer rainfall. We also found strong positive feedback between soil moisture (SM) and NDVI, as well as a strong positive influence of vegetation optical depth (VOD) and terrestrial water storage (TWS) on ESF. Livestock density (LSK _D ) exhibited a strong negative influence on ESF. Our results also showed a strong positive influence of socioeconomic drivers, including crop yield per hectare (CROPh), gross domestic product per capita (GDPca), and population density (POP _D ) on vegetation productivity. Finally, we found that vegetation dynamics were more sensitive to SM, VOD, LSK _D and POP _D than climatic drivers, suggesting that water content and human influence drivers were more critical in Kazakhstan.

This paper investigates the performance of gridded rainfall datasets for precipitation detection and streamflow simulations in Indiaʼs Tungabhadra river basin. Sixteen precipitation datasets categorized under gauge-based, satellite-only, reanalysis, and gauge-adjusted datasets were compared statistically against the gridded Indian Meteorological Dataset (IMD) employing two categorical and three continuous statistical metrics. Further, the precipitation datasets’ performance in simulating streamflow was assessed by using the Soil and Water Assessment Tool (SWAT) hydrological model. Based on the statistical metrics, Asian Precipitation Highly Resolved Observational Data Integration Towards Evaluation (APHRODITE) furnished very good results in terms of detecting rainfall, followed by Climate Hazards Group Infrared Precipitation (CHIRP), National Centres for Environmental Prediction-Climate Forecast System Reanalysis (NCEP CFSR), Tropical Rainfall Measurement Mission (TRMM) 3B42 v7, Global Satellite Mapping of Precipitation Gauge Reanalysis v6 (GSMaP_Gauge_RNL), and Multisource Weighted Ensemble Precipitation (MSWEP) datasets which had good-to-moderate performances at a monthly time step. From the hydrological simulations, TRMM 3B42 v7, CHIRP, CHIRPS 0.05°, and GSMaP_Gauge_RNL v6 produced very good results with a high degree of correlation to observed streamflow, while Soil Moisture 2 Rain-Climate Change Initiative (SM2RAIN-CCI) dataset exhibited poor performance. From the extreme flow event analysis, it was observed that CHIRP, TRMM 3B42 v7, Global Precipitation Climatology Centre v7 (GPCC), and APHRODITE datasets captured more peak flow events and hence can be further implemented for extreme event analysis. Overall, we found that TRMM 3B42 v7, CHIRP, and CHIRPS 0.05° datasets performed better than other datasets and can be used for hydrological modeling and climate change studies in similar topographic and climatic watersheds in India.

UnlabelledMouse Double Minute homolog 4 (MDM4) gene up-regulation often occurs in human hepatocellular carcinoma (HCC), but the molecular mechanisms responsible for its induction remain poorly understood. Here we investigated the role of the phosphoinositide-3-kinase/v-akt murine thymoma viral oncogene homolog/mammalian target of rapamycin (PI3K/AKT/mTOR) axis in the regulation of MDM4 levels in HCC. The activity of MDM4 and the PI3K/AKT/mTOR pathway was modulated in human HCC cell lines by way of silencing and overexpression experiments. Expression of main pathway components was analyzed in an AKT mouse model and human HCCs. MDM4 inhibition resulted in growth restraint of HCC cell lines both in vitro and in vivo. Inhibition of the PI3K-AKT and/or mTOR pathways lowered MDM4 protein levels in HCC cells and reactivated p53-dependent transcription. Deubiquitination by ubiquitin-specific protease 2a and AKT-mediated phosphorylation protected MDM4 from proteasomal degradation and increased its protein stability. The eukaryotic elongation factor 1A2 (EEF1A2) was identified as an upstream inducer of PI3K supporting MDM4 stabilization. Also, we detected MDM4 protein up-regulation in an AKT mouse model and a strong correlation between the expression of EEF1A2, activated/phosphorylated AKT, and MDM4 in human HCC (each rho > 0.8, P < 0.001). Noticeably, a strong activation of this cascade was associated with shorter patient survival.ConclusionThe EEF1A2/PI3K/AKT/mTOR axis promotes the protumorigenic stabilization of the MDM4 protooncogene in human HCC by way of a posttranscriptional mechanism. The activation level of the EEF1A2/PI3K/AKT/mTOR/MDM4 axis significantly influences the survival probability of HCC patients in vivo and may thus represent a promising molecular target.