Monitoring and predicting drought based on multiple indicators in an arid area, China

Droughts are one of the costliest natural disasters. Reliable drought monitoring and prediction are valuable for drought relief management. This study monitors and predicts droughts in Xinjiang, an arid area in China, based on the three drought indicators, i.e., the Standardized Precipitation Index (SPI), the Standardized Soil Moisture Index (SSMI) and the Multivariate Standardized Drought Index (MSDI). Results indicate that although these three indicators could capture severe historical drought events in the study area, the spatial coverage, persistence and severity of the droughts would vary regarding different indicators. The MSDI could best describe the overall drought conditions by incorporating the characteristics of the SPI and SSMI. For the drought prediction, the predictive skill of all indicators gradually decayed with the increasing lead time. Specifically, the SPI only showed the predictive skill at a 1-month lead time, the MSDI performed best in capturing droughts at 1- to 2-month lead times and the SSMI was accurate up to a 3-month lead time owing to its high persistence. These findings might provide scientific support for the local drought management

A Statistical Design for Testing Transgenerational Genomic Imprinting in Natural Human Populations

Genomic imprinting is a phenomenon in which the same allele is expressed differently, depending on its parental origin. Such a phenomenon, also called the parent-of-origin effect, has been recognized to play a pivotal role in embryological development and pathogenesis in many species. Here we propose a statistical design for detecting imprinted loci that control quantitative traits based on a random set of three-generation families from a natural population in humans. This design provides a pathway for characterizing the effects of imprinted genes on a complex trait or disease at different generations and testing transgenerational changes of imprinted effects. The design is integrated with population and cytogenetic principles of gene segregation and transmission from a previous generation to next. The implementation of the EM algorithm within the design framework leads to the estimation of genetic parameters that define imprinted effects. A simulation study is used to investigate the statistical properties of the model and validate its utilization. This new design, coupled with increasingly used genome-wide association studies, should have an immediate implication for studying the genetic architecture of complex traits in humans

Robust estimation of bacterial cell count from optical density

Optical density (OD) is widely used to estimate the density of cells in liquid culture, but cannot be compared between instruments without a standardized calibration protocol and is challenging to relate to actual cell count. We address this with an interlaboratory study comparing three simple, low-cost, and highly accessible OD calibration protocols across 244 laboratories, applied to eight strains of constitutive GFP-expressing E. coli. Based on our results, we recommend calibrating OD to estimated cell count using serial dilution of silica microspheres, which produces highly precise calibration (95.5% of residuals <1.2-fold), is easily assessed for quality control, also assesses instrument effective linear range, and can be combined with fluorescence calibration to obtain units of Molecules of Equivalent Fluorescein (MEFL) per cell, allowing direct comparison and data fusion with flow cytometry measurements: in our study, fluorescence per cell measurements showed only a 1.07-fold mean difference between plate reader and flow cytometry data

Support vector machines applications

Support vector machines (SVM) have both a solid mathematical background and good performance in practical applications. This book focuses on the recent advances and applications of the SVM in different areas, such as image processing, medical practice, computer vision, pattern recognition, machine learning, applied statistics, business intelligence, and artificial intelligence. The aim of this book is to create a comprehensive source on support vector machine applications, especially some recent advances

Applicability Evaluation of Multisource Satellite Precipitation Data for Hydrological Research in Arid Mountainous Areas

Global Satellite Mapping of Precipitation (GSMaP), Climate Hazards Group InfraRed Preconception with Station data (CHIRPS), Tropical Rain Measurement Mission Multisatellite Precipitation Analysis (TRMM 3B42 V7) and Rainfall Estimation from Soil Moisture Observations (SM2RAIN) are satellite precipitation products with high applicability, but their applicability in hydrological research in arid mountainous areas is not clear. Based on precipitation and runoff data, this study evaluated the applicability of each product to hydrological research in a typical mountainous basin (the Qaraqash River basin) in an arid region by using two methods: a statistical index and a hydrological model (Soil and Water Assessment Tool, SWAT). Simulation results were evaluated by Nash efficiency coefficient (NS), relative error (PBIAS) and determination coefficient (R2). The results show that: (1) The spatial distributions of precipitation estimated by these four products in the Qaraqash River basin are significantly different, and the multi-year average annual precipitation of GSMaP is 97.11 mm, which is the closest to the weather station interpolation results. (2) On the annual and monthly scales, GSMaP has the highest correlation (R ≥ 0.82) with the observed precipitation and the smallest relative error (BIAS < 6%). On the seasonal scale, the inversion accuracy of GSMaP in spring, summer and autumn is significantly higher than other products. In winter, all four sets of products perform poorly in estimating the actual precipitation. (3) Monthly runoff simulations based on SM2RAIN and GSMaP show good fitting (R2 > 0.6). In daily runoff simulation, GSMaP has the greatest ability to reproduce runoff changes. The study provides a reference for the optimization of precipitation image data and hydrological simulation in data-scarce areas

Coordinated Planning of Electricity/gas/storage Distribution Network Based on LSTM and Demand Response

This paper presents a collaborative planning method of an electricity-gas-storage regional integrated energy system based on LSTM neural network and demand response. First, the LSTM Neural network is used for load forecasting, and the energy hub structure of the electric gas storage system is established. Then, the mathematical models of power storage, gas storage, electric network topology, gas network topology, and P2G are established to minimize the expansion cost of the electricity-gas-storage system, and the collaborative planning of energy storage, power lines, and natural gas pipelines is proposed based on the existing electric gas coupling integrated energy system. The original model which is difficult to solve is transformed into a mixed-integer linear programming model by introducing auxiliary variables, and the CPLEX solver is called to solve it. Finally, the economic advantages of collaborative planning of electricity-gas-storage system are verified by an example, and the connection of power storage and gas storage can reduce system pressure and optimize equipment selection

Genome-Wide Identification and Analysis of the GRAS Transcription Factor Gene Family in <i>Theobroma cacao</i>

GRAS genes exist widely and play vital roles in various physiological processes in plants. In this study, to identify Theobroma cacao (T. cacao) GRAS genes involved in environmental stress and phytohormones, we conducted a genome-wide analysis of the GRAS gene family in T. cacao. A total of 46 GRAS genes of T. cacao were identified. Chromosomal distribution analysis showed that all the TcGRAS genes were evenly distributed on ten chromosomes. Phylogenetic relationships revealed that GRAS proteins could be divided into twelve subfamilies (HAM: 6, LISCL: 10, LAS: 1, SCL4/7: 1, SCR: 4, DLT: 1, SCL3: 3, DELLA: 4, SHR: 5, PAT1: 6, UN1: 1, UN2: 4). Of the T. cacao GRAS genes, all contained the GRAS domain or GRAS superfamily domain. Subcellular localization analysis predicted that TcGRAS proteins were located in the nucleus, chloroplast, and endomembrane system. Gene duplication analysis showed that there were two pairs of tandem repeats and six pairs of fragment duplications, which may account for the rapid expansion in T. cacao. In addition, we also predicted the physicochemical properties and cis-acting elements. The analysis of GO annotation predicted that the TcGRAS genes were involved in many biological processes. This study highlights the evolution, diversity, and characterization of the GRAS genes in T. cacao and provides the first comprehensive analysis of this gene family in the cacao genome

S and N codoped graphene quantum dots decorated on (001)TiO2 to boost surface reaction for photocatalytic hydrogen production

Titanium dioxide (TiO2) composites are popular photocatalysts for hydrogen production from water. Here we report a composite consisting of S and N co-doped graphene quantum dots (SN-GQDs) with (001)TiO2 for photocatalytic hydrogen production. In addition to traditional advantages like light absorption promotion and charge transfer improvement, the as-prepared SN-GQDs/(001)TiO2 composite could significantly enhance the surface reaction by promotion of the formation of hydroxyl radicals, which results in the increase of the hydrogen production activity by 40 times. This work reveals the importance of surface reaction promotion by GQDs for hydrogen production, which will help researchers understand relevant systems comprehensively