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Doxorubicin (DOX) is the most clinically important antibiotic in cancer treatment, but its severe cardiotoxicity and other side effects limit its clinical use. Therefore, monitoring DOX concentrations during therapy is essential to improve efficacy and reduce adverse effects. Here, we fabricated a sensitive electrochemical aptasensor for DOX detection. The sensor used gold wire as the working electrode and was modified with reduced graphene oxide (rGO)/gold nanoparticles (AuNPs) to improve the sensitivity. An aptamer was used as the recognition element for the DOX. The 5′ end of the aptamer was modified with a thiol group, and thus immobilized to the AuNPs, and the 3′ end was modified with methylene blue, which acts as the electron mediator. The combination between the aptamer and DOX would produce a binding-induced conformation, which changes the electron transfer rate, yielding a current change that correlates with the concentration of DOX. The aptasensor exhibited good linearity in the DOX concentration range of 0.3 μM to 6 μM, with a detection limit of 0.1 μM. In addition, the aptasensor was used for DOX detection in real samples and results, and showed good recovery. The proposed electrochemical aptasensor will provide a sensitive, fast, simple, and reliable new platform for detecting DOX. [ABSTRACT FROM AUTHOR]

Satellite-based precipitation products, especially those with high temporal and spatial resolution, constitute a potential alternative to sparse rain gauge networks for multidisciplinary research and applications. In this study, the validation of the 30-year Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks-Climate Data Record (PERSIANN-CDR) daily precipitation dataset was conducted over the Huai River Basin (HRB) of China. Based on daily precipitation data from 182 rain gauges, several continuous and categorical validation statistics combined with bias and error decomposition techniques were employed to quantitatively dissect the PERSIANN-CDR performance on daily, monthly, and annual scales. With and without consideration of non-rainfall data, this product reproduces adequate climatologic precipitation characteristics in the HRB, such as intra-annual cycles and spatial distributions. Bias analyses show that PERSIANN-CDR overestimates daily, monthly, and annual precipitation with a regional mean percent total bias of 11%. This is related closely to the larger positive false bias on the daily scale, while the negative non-false bias comes from a large underestimation of high percentile data despite overestimating lower percentile data. The systematic sub-component (error from high precipitation), which is independent of timescale, mainly leads to the PERSIANN-CDR total Mean-Square-Error (TMSE). Moreover, the daily TMSE is attributed to non-false error. The correlation coefficient (R) and Kling–Gupta Efficiency (KGE) respectively suggest that this product can well capture the temporal variability of precipitation and has a moderate-to-high overall performance skill in reproducing precipitation. The corresponding capabilities increase from the daily to annual scale, but decrease with the specified precipitation thresholds. Overall, the PERSIANN-CDR product has good (poor) performance in detecting daily low (high) rainfall events on the basis of Probability of Detection, and it has a False Alarm Ratio of above 50% for each precipitation threshold. The Equitable Threat Score and Heidke Skill Score both suggest that PERSIANN-CDR has a certain ability to detect precipitation between the second and eighth percentiles. According to the Hanssen–Kuipers Discriminant, this product can generally discriminate rainfall events between two thresholds. The Frequency Bias Index indicates an overestimation (underestimation) of precipitation totals in thresholds below (above) the seventh percentile. Also, continuous and categorical statistics for each month show evident intra-annual fluctuations. In brief, the comprehensive dissection of PERSIANN-CDR performance reported herein facilitates a valuable reference for decision-makers seeking to mitigate the adverse impacts of water deficit in the HRB and algorithm improvements in this product. [ABSTRACT FROM AUTHOR]

Regional hybrid energy system (RHES) is an effective way to accommodate clean energy and reduce its uncertainty. It is also an important development direction for energy market reform, playing an important role in the field of energy supply and demand. Based on electricity price, thermal price, and the relationship between load supply and demand, the paper proposes an optimal scheduling model for regional energy systems to promote clean energy consumption. Firstly, the typical structure and system modules of the integrated regional energy system are introduced in detail, and the two subsystem operating models of electricity and heating are given. Secondly, a multi‐objective function that combines the economics and stability of the system is constructed. The model maximizes the economic benefits and minimizes the net load fluctuations on the basis of maximizing the consumption of clean energy. The multi‐objective singularity is realized by fuzzy membership function. The decision‐making risk caused by the uncertainty of clean energy output is analyzed by introducing robust optimization theory. Finally, the effectiveness of the proposed model is verified based on a region in northwestern China. The results show that the proposed multi‐objective model can combine different optimization demands together, maximize the economic characteristics of clean energy while properly controlling risks, and provide reasonable support for decision makers to develop optimal operation plans. [ABSTRACT FROM AUTHOR]

Reference evapotranspiration (ET0) is a crucial parameter for hydrological modeling, land-atmospheric interaction investigations and agricultural irrigation management. This study investigated changes in ET0 and attributed those changes to climate variations in a coastal area (Zhejiang province) of China by a numerical experiment method. The results indicated that annual ET0 increased significantly (p < 0.05) at a rate of 1.58 mm·year-1 from 1973 to 2013, which was mainly caused by an obvious increase in ET0 in spring. Air temperature and water vapor pressure deficits increased significantly (p < 0.05) at rates of 0.04 °C·year-1 and 0.005 kPa·year-1, respectively, at an annual time scale during the study period, while wind speed and solar radiation decreased significantly (p < 0.05) at rates of -0.01 m/s·year-1 and -3.94 MJ·m-2·day-1·year-1, respectively. The contributions of changes in air temperature, wind speed, water vapor pressure deficits and solar radiation to increases in ET0 were 0.39, -0.56, 2.62 and -0.61 mm·year-1, respectively. The decrease in wind speed and solar radiation negatively affected the increase in ET0, which was offset by the positive effects of the air temperature and water vapor pressure deficits increase, where the water vapor pressure deficits was the dominant factor in increasing ET0 in the coastal area. Moreover, the impact of topography on ET0 was further discussed. ET0 changes at plain stations were approximately 5.4 times those at hill stations, which may be due to the impact of a large water body and the augment of surface roughness from intense human activities in the well-developed plain area. The results are helpful for investigating spatial and temporal changes in the evaporative demand for well-developed regions under energy-limited conditions. [ABSTRACT FROM AUTHOR]

Despite numerous assessments of satellite-based and reanalysis precipitation across the globe, few studies have been conducted based on the precipitation linear trend (LT), particularly during daytime and nighttime, when there are different precipitation mechanisms. Herein, we first examine LTs for the whole day (LTwd), daytime (LTd), and nighttime (LTn) over mainland China (MC) in 2003–2017, with sub-daily observations from a dense rain gauge network. For MC and ten Water Resources Regions (WRRs), annual and seasonal LTwd, LTd, and LTn were generally positive but with evident regional differences. Subsequently, annual and seasonal LTs derived from six satellite-based and six reanalysis popular precipitation products were evaluated using metrics of correlation coefficient (CC), bias, root-mean-square-error (RMSE), and sign accuracy. Finally, metric-based optimal products (OPs) were identified for MC and each WRR. Values of each metric for annual and seasonal LTwd, LTd, or LTn differ among products; meanwhile, for any single product, performance varied by season and time of day. Correspondingly, the metric-based OPs varied among regions and seasons, and between daytime and nighttime, but were mainly characterized by OPs of Tropical Rainfall Measuring Mission (TRMM) 3B42, ECMWF Reanalysis (ERA)-Interim, and Modern Era Reanalysis for Research and Applications (MERRA)-2. In particular, the CC-based (RMSE-based) OPs in southern and northern WRRs were generally TRMM3B42 and MERRA-2, respectively. These findings imply that to investigate precipitation change and obtain robust related conclusions using precipitation products, comprehensive evaluations are necessary, due to variation in performance within one year, one day and among regions for different products. Additionally, our study facilitates a valuable reference for product users seeking reliable precipitation estimates to examine precipitation change across MC, and an insight (i.e., capacity in detecting LTs, including daytime and nighttime) for developers improving algorithms. [ABSTRACT FROM AUTHOR]