Regulatory effects of natural products on N6-methyladenosine modification: A novel therapeutic strategy for cancer.

N6-methyladenosine (m6A) is considered to be the most common and abundant epigenetics modification in messenger RNA (mRNA) and noncoding RNA. Abnormal modification of m6A is closely related to the occurrence, development, progression, and prognosis of cancer. m6A regulators have been identified as novel targets for anticancer drugs. Natural products, a rich source of traditional anticancer drugs, have been utilized for the development of m6A-targeting drugs. Here, we review the key role of m6A modification in cancer progression and explore the prospects and structural modification mechanisms of natural products as potential drugs targeting m6A modification for cancer treatment.

Tumor mutational burden as a predictive biomarker for non-small cell lung cancer treated with immune checkpoint inhibitors of PD-1/PD-L1.

BACKGROUND: The significant clinical benefits of PD-1/PD-L1 immune checkpoint inhibitors (ICIP) in non-small cell lung cancer (NSCLC) have been widely recognized, emphasizing the urgent need for a reliable biomarker. In this study, we find the remarkable capacity of tumor mutational burden (TMB) to serve as an accessible and streamlined indicator. PATIENTS AND METHODS: We designed a retrospective cohort study, consisting of 600 NSCLC patients treated with ICIP. Association between TMB and overall survival (OS), progression-free survival (PFS), objective response rate (ORR), and disease control rate (DCR) has been explored. RESULTS: A strong positive correlation between TMB levels and OS, PFS rates, clinical benefit has been found when TMB > = 16(TMB > = 16 mutations/megabase (mut/Mb)). However, when TMB < 16, increasing TMB values did not exhibit a gradual stepwise increase in OS and PFS rates. The median months of OS in the TMB > = 16 and < 16 are 35.58, and 10.71 months respectively with average 12.39 months (p < 0.0001). The median months of PFS in the TMB > = 16 and < 16 are not-obtained, and 2.79 months respectively with an average of 3.32 months (p < 0.0001). The DCR in the TMB > = 16 and < 16 are 71.4% and 44.2% respectively with an average of 47.7% (p < 0.0001). The ORR in the TMB > = 16 and < 16 are 49.4% and 20.8% respectively with an average of 24.5% (p < 0.0001). CONCLUSION: The TMB > = 16 shows significantly associated with optimal ICIP treatment outcomes, including higher patient survival rates, delayed disease progression, and significant clinical benefits. These results present the potential of TMB as a promising biomarker candidate for NSCLC patients undergoing ICIP treatment.

Seasonal catchment memory of high mountain rivers in the Tibetan Plateau.

Rivers originating in the Tibetan Plateau are crucial to the population in Asia. However, research about quantifying seasonal catchment memory of these rivers is still limited. Here, we propose a model able to accurately estimate terrestrial water storage change (TWSC), and characterize catchment memory processes and durations using the memory curve and the influence/domination time, respectively. By investigating eight representative basins of the region, we find that the seasonal catchment memory in precipitation-dominated basins is mainly controlled by precipitation, and that in non-precipitation-dominated basins is strongly influenced by temperature. We further uncover that in precipitation-dominated basins, longer influence time corresponds to longer domination time, with the influence/domination time of approximately six/four months during monsoon season. In addition, the long-term catchment memory is observed in non-precipitation-dominated basins. Quantifying catchment memory can identify efficient lead times for seasonal streamflow forecasts and water resource management.

River ecological flow early warning forecasting using baseflow separation and machine learning in the Jiaojiang River Basin, Southeast China.

Ecological flow early warning is crucial for the rational management of watershed water resources. However, determining of accurate ecological flow threshold and choosing the appropriate forecasting model are challenging tasks. In this study, we initially developed a baseflow separation and Tennant method-based technique for calculating ecological river flow. Then an ecological flow early warning model was created using the machine learning technique based on distributed gradient enhancement framework (LightGBM). Finally, we utilized the framework of Shapley Additive Planning (SHAP) to explain how various hydrometeorological factors affect the variations in ecological flow conditions. The Jiaojiang River basin in southeast China is selected as the study area, and the hydrological stations in upstream of Baizhiao (BZA) and Shaduan (SD) are chosen for key analysis. The results of these applications show that the monthly baseflow frequency of the river ecological flow conditions of the two stations in the dry season is 20 % (7.49 m3/s) and 30 % (4.79 m3/s), respectively. The ecological flow level early warning forecasting accuracy is close to 90 % in the BZA and SD stations during dry and wet seasons. The variations of ecological flow are most affected by evaporation and base flow index. The results of this study can serve as a strong basis for the effective allocation and utilization of locally available water resources.

Scenario-based multi-objective optimization of reservoirs in silt-laden rivers: A case study in the Lower Yellow River.

Severe sedimentation often takes place in the river channel of silt-laden rivers, which is often mitigated through water-sediment regulation of the reservoirs. However, watersediment regulation is often competitive with other objectives of reservoirs, like water supply and hydropower generation; on the other hand, the reduction of channel sedimentation is often achieved at the expense of reservoir sedimentation, which reduces the service life of reservoirs. The Yellow River used to be the river with largest sediment transport over the world, but has experienced significant declination of runoff and sediment in recent years. This study presents a scenario-based multi-objective optimization operation model for the Xiaolangdi reservoir considering hydropower generation, reservoir sedimentation and channel sedimentation, with a generalized linear model coupled to calculate channel sedimentation based on runoff and sediment time series. A stochastic model that can reproduce both spatial correlations and low frequency attributes of the data series is adopted to generate two different scenarios based on different periods of observation and the performance of the multi-objective operation model under different scenarios is tested. The results indicate that: (1) the proposed optimization model can generate different schemes of reservoir operation and enhance operation performance; (2) the generalized linear model can well fit the relationship between daily channel sedimentation and runoff-sediment factors, but tends to overestimate the erosion efficiency after 2005; (3) the reservoir sedimentation and channel sedimentation show linear competitive relation, i.e., an average increase of 1 ton in reservoir sedimentation would result in declination of channel sedimentation from 0.455 to 0.488 tons, while the competitive relationship between hydropower generation and reservoir sedimentation is non-linear and weak; (4) the increase in the proportion of non-flood sediment load to the total sediment load makes it more difficult to prevent the reservoir from silting up.

Effectiveness of using representative subsets of global climate models in future crop yield projections.

Representative subsets of global climate models (GCMs) are often used in climate change impact studies to account for uncertainty in ensemble climate projections. However, the effectiveness of such subsets has seldom been assessed for the estimations of either the mean or the spread of the full ensembles. We assessed two different approaches that were employed to select 5 GCMs from a 20-member ensemble of GCMs from the CMIP5 ensemble for projecting canola and spring wheat yields across Canada under RCP 4.5 and 8.5 emission scenarios in the periods 2040-2069 and 2070-2099, based on crop simulation models. Averages and spreads of the simulated crop yields using the 5-GCM subsets selected by T&P and KKZ approaches were compared with the full 20-GCM ensemble. Our results showed that the 5-GCM subsets selected by the two approaches could produce full-ensemble means with a relative absolute error of 2.9-4.7% for canola and 1.5-2.2% for spring wheat, and covers 61.8-91.1% and 66.1-80.8% of the full-ensemble spread for canola and spring wheat, respectively. Our results also demonstrated that both approaches were very likely to outperform a subset of randomly selected 5 GCMs in terms of a smaller error and a larger range.

Identifying how future climate and land use/cover changes impact streamflow in Xinanjiang Basin, East China.

Climate and land use/cover changes are the main factors altering hydrological regimes. To understand the impacts of climate and land use/cover changes on streamflow within a specific catchment, it is essential to accurately quantify their changes given many possibilities. We propose an integrated framework to assess how individual and combined climate and land use/cover changes impact the streamflow of Xinanjiang Basin, in East China, in the future. Five bias-corrected and downscaled General Circulation Model (GCM) projections are used to indicate the inter-model uncertainties under three Representative Concentration Pathways (RCPs). Additionally, three land use/cover change scenarios representing a range of tradeoffs between ecological protection (EP) and urban development (UD) are projected by Cellular Automata - Markov (CA-Markov). The streamflow in 2021-2050 is then assessed using the calibrated Soil and Water Assessment Tool (SWAT) with 15 scenarios and 75 possibilities. Finally, the uncertainty and attribution of streamflow changes to climate and land use/cover changes at monthly and annual scale are analyzed. Results show that while both land use/cover change alone and combined changes project an increase in streamflow, there is a disagreement on the direction of streamflow change under climate change alone. Future streamflow may undergo a more blurred boundary between the flood and non-flood seasons, potentially easing the operation stress of Xinanjiang Reservoir for water supply or hydropower generation. We find that the impacts of climate and land use/cover changes on monthly mean streamflow are sensitive to the impermeable area (IA). The impacts of climate change are stronger than those induced by land use/cover change under EP (i.e., lower IA); and land use/cover change has a greater impact in case of UD (i.e., higher IA). However, changes in annual mean streamflow are mainly driven by land use/cover change, and climate change may decrease the influence attributed to land use/cover change.

Role of satellite and reanalysis precipitation products in streamflow and sediment modeling over a typical alpine and gorge region in Southwest China.

Satellite and reanalysis precipitation products, as new and complementary data sources, are attractive for hydro-meteorological applications, especially in data-sparse areas. This study evaluates the accuracy of two satellite precipitation products (TMPA 3B42V7 and PERSIANN-CDR) and one reanalysis precipitation product (NCEP-CFSR) against gauge precipitation observations with four statistical indices over the upstream of the Lancang River Basin (ULRB), Southwest China. The reliability and applicability of these precipitation products as inputs to a hydrological model (Soil and Water Assessment Tool, SWAT) for streamflow and sediment simulations are also assessed. Furthermore, we compare the spatial plots of extreme water yield (99 percentiles) and suspended sediment yield (99 percentiles) driven by the four precipitation sources, and investigate the spatial and temporal variability of water yield and suspended sediment yield over the ULRB. Results show that for direct comparisons with gauge precipitation observations, monthly TMPA 3B42V7 precipitation product performs the best at the basin scale with the smallest error and bias, and the highest correlation, followed by NCEP-CFSR, and PERSIANN-CDR. For modeling-based indirect inference, TMPA 3B42V7 presents great capability for streamflow and sediment simulations in the SWAT model on a monthly time step at the basin outlet, and PERSIANN-CDR also performs well. NCEP-CFSR shows acceptable skills in modeling sediment but unacceptable skills in modeling streamflow. Extreme water yield presents moderate spatial variability over the ULRB while extreme suspended sediment yield presents strong spatial variability. Water yield of this basin shows a decreasing trend during 1998-2008 while there is no obvious trend in suspended sediment yield in this period.

Assessing responses of hydrological processes to climate change over the southeastern Tibetan Plateau based on resampling of future climate scenarios.

With global warming, hydrological regimes in the headwater basins of the Tibetan Plateau (TP) have significantly changed. Investigating the responses of hydrological processes to climate change in TP has become more and more important to make robust strategies for water resources management. However, using just a few GCMs may constrain the uncertainty in assessment of climate impacts. Therefore, a framework is proposed in this study to generate ensemble climate change scenarios and then investigate changes of hydrological processes under climate change in the upper reaches of Yarlung Zangbo River basin (UYZR) and Lancang River basin (ULR). Firstly, the Latin Hypercube Simulation (LHS) is used to generate an ensemble of future climate change scenarios by resampling change factors of meteorological variables from 18 GCMs under emission scenarios RCP2.6 and RCP8.5. The inherent dependence structures of change factors, i.e. the correlations of change factors among 12 months for different meteorological variables, are also considered in ensembles. Secondly, the HBV hydrological model coupled with a degree-day snowmelt model is applied to explore the potential change of runoff in the future period 2041-2070. Results show that: 1) the resampling method is effective and can provide a wide ensemble of climate change scenarios. 2) Precipitation, temperature and potential evapotranspiration in the UYZR and ULR basins are expected to increase under the two scenarios, particularly under RCP8.5. 3) The total runoff also shows a moderately upward trend in two basins, both mainly due to increased precipitation. In the UYZR basin, fast runoff accounts for a larger proportion in total runoff than slow runoff, while in ULR, both almost play the same role in total runoff. Furthermore, snowmelt-induced runoff in both basins would be less and rainfall-induced runoff will probably become more important in the future.

Agricultural drought prediction using climate indices based on Support Vector Regression in Xiangjiang River basin.

Drought can have a substantial impact on the ecosystem and agriculture of the affected region and does harm to local economy. This study aims to analyze the relation between soil moisture and drought and predict agricultural drought in Xiangjiang River basin. The agriculture droughts are presented with the Precipitation-Evapotranspiration Index (SPEI). The Support Vector Regression (SVR) model incorporating climate indices is developed to predict the agricultural droughts. Analysis of climate forcing including El Niño Southern Oscillation and western Pacific subtropical high (WPSH) are carried out to select climate indices. The results show that SPEI of six months time scales (SPEI-6) represents the soil moisture better than that of three and one month time scale on drought duration, severity and peaks. The key factor that influences the agriculture drought is the Ridge Point of WPSH, which mainly controls regional temperature. The SVR model incorporating climate indices, especially ridge point of WPSH, could improve the prediction accuracy compared to that solely using drought index by 4.4% in training and 5.1% in testing measured by Nash Sutcliffe efficiency coefficient (NSE) for three month lead time. The improvement is more significant for the prediction with one month lead (15.8% in training and 27.0% in testing) than that with three months lead time. However, it needs to be cautious in selection of the input parameters, since adding redundant information could have a counter effect in attaining a better prediction.

Transplantation of bFGF-expressing neural stem cells promotes cell migration and functional recovery in rat brain after transient ischemic stroke.

Cerebrovascular disease such as stroke is one of the most common diseases in the aging population, and neural stem cells (NSCs) transplantation may provide an alternative therapy for cerebral ischemia. However, a hostile microenvironment in the ischemic brain offers is challenging for the survival of the transplanted cells. Considering the neuroprotective role of basic fibroblast growth factor (bFGF), the present study investigated whether bFGF gene-modified NSCs could improve the neurological function deficit after transient middle cerebral artery occlusion (MCAO) in adult male Sprague-Dawley rats. These rats were intravenously injected with modified NSCs (5×106/200 µL) or vehicle 24 h after MCAO. Histological analysis was performed on days 7 and 28 after tMCAO. The survival, migration, proliferation, and differentiation of the transplanted modified C17.2 cells in the brain were improved. In addition, the intravenous infusion of NSCs and bFGF gene-modified C17.2 cells improved the functional recovery as compared to the control. Furthermore, bFGF promoted the C17.2 cell growth, survival, and differentiation into mature neurons within the infarct region. These data suggested that bFGF gene-modified NSCs have the potential to be a therapeutic agent in brain ischemia.

[Simultaneous remediation of Cr (VI) and p-NCB by nanosacle iron].

Nanoscale Fe and Ni/Fe, which were prepared by chemical deposition, were utilized as catalyst for remediation of Cr(VI) and pNCB in contaminated water. The interactions between Cr( VI) and p-NCB in contaminated water during the simultaneous remediation process were analyzed. It is demonstrated from the experiment that p-NCB can be degradated into p-CAN by nanoscale iron, but cannot exhibit the effect of dechlorination, and that there is a competitive relationship between Cr( VI) and p-NCB in the remediation process. The nanoscale Nil Fe bimetals could be applied in simultaneous remediation of p-NCB with Cr( VI) and give rise to a good remediation efficiency, where the products are only Cr(III) and p-CAN without any intermediate products. It was found that the conditions of higher Ni(II) concentration can promote the degradation rate of p-NCB. The optimum Ni/Fe ratio is 1:50. Whereas, the higher concentrations of Cr(VI) and p-NCB will lead to the lower degradation rate. Under the condition that concentration of Cr (VI) was 20 mg/L, the corresponding maximum dechlorination of p-NCB was 43.0%; under the condition that concentration of p-NCB was 40 mg/L, the corresponding maximum removal efficiency of Cr(VI) was 71.4%.

[Water environmental simulation of main contaminations in the Qiantang River at low water period].

Technical framework for water environment simulation of contaminations is established based on visualization and a spatial environmental model is built. The main two contaminations, namely NH: -N and TP, are simulated on the platform of MapInfo and Delft3D in the Qiantang River at the low water period, to analyze its space-time diversity. For NH4+ -N, the measured values are 0.19 mg/L and 0.66 mg/L larger than simulated values at the Lanjiang River mouth and the Yanlingwu, 0.16 mg/L, 0.54 mg/L and 0.49 mg/L smaller at the Zhaixi, the Yushan and the Yuanpu. For TP, the measured values are 0.13 mg/L and 0.14 mg/L higher than simulated values at the Meicheng Water facility and Yanlingwu. However, the measure values are slightly lower than simulated ones at Zhaixi, Yushan, Puyang River mouth and Yuanpu, the trend of which accords with actual situation. The results indicate that the contaminations of the Qiantang Reach mostly come from the Lanjiang River, the Fuchun River and the Puyang River on the upstream, among which the Lanjiang River and the Puyang River have a very high concentration of polluted materials, which means bad water quality, and influence the water downstream. The Lanjiang River becomes the chief contaminative source in the Fuchun River. When the discharge from the Xin'an River Dam is small, the recirculation region may be formed and makes part of the Xin'an Reach contaminated. Otherwise, when the discharge is large, the water quality in the Fuchun River is apparently improved. And the Puyang River, which brings the contaminations from the upstream, along with the polluted water let into it from the industries along the reach, has significant impacts on the water quality in Qiantang Reach.