A sulfotransferase specific to N-21 of gonyautoxin 2/3 from crude enzyme extraction of toxic dinoflagellate Alexandrium tamarense CI01

Sulfotransferase (ST) is the first enzyme discovered in association with paralytic shellfish poisoning (PSP) toxin biosynthesis in toxic dinoflagellates. This study investigates the ST activity in crude enzyme extraction of a toxic dinoflagellate species, Alexandrium tamarense CI01. The results show that crude enzyme can transfer a sulfate group from 3’-phosphoadenosine 5’-phosphosulfate (PAPS) to N-21 in the carbamoyl group of gonyautoxin 2/3 (GTX2/3) to produce C1/C2, but is inactive toward STX to produce GTX5. The crude enzyme is optimally active at pH 6.0 and 15°C. The activity is enhanced by Co2 , Mg2 , Mn2 and Ca2 individually, but is inhibited by Cu2 . Moreover, the activity shows no difference when various sulfur compounds are used as sulfate donors. These results demonstrate that the ST specific to GTX2/3 is present in the cells of A. tamarense CI01 and is involved in PSP toxin biosynthesis. In addition, the ST from different dinoflagellates is species-specific, which explains well the various biosynthesis pathways of the PSP toxins in toxic dinoflagellates.     

Predicting TBM penetration rate in hard rock condition: A comparative study among six XGB-based metaheuristic techniques

A reliable and accurate prediction of the tunnel boring machine(TBM) performance can assist in minimizing the relevant risks of high capital costs and in scheduling tunneling projects.This research aims to develop six hybrid models of extreme gradient boosting(XGB) which are optimized by gray wolf optimization(GWO), particle swarm optimization(PSO), social spider optimization(SSO), sine cosine algorithm(SCA), multi verse optimization(MVO) and moth flame optimization(MFO), for estimation of the TBM penetration rate(PR).To do this, a comprehensive database with 1286 data samples was established where seven parameters including the rock quality designation, the rock mass rating, Brazilian tensile strength(BTS), rock mass weathering, the uniaxial compressive strength(UCS), revolution per minute and trust force per cutter(TFC), were set as inputs and TBM PR was selected as model output.Together with the mentioned six hybrid models, four single models i.e., artificial neural network, random forest regression, XGB and support vector regression were also built to estimate TBM PR for comparison purposes.These models were designed conducting several parametric studies on their most important parameters and then, their performance capacities were assessed through the use of root mean square error, coefficient of determination, mean absolute percentage error, and a10-index.Results of this study confirmed that the best predictive model of PR goes to the PSO-XGB technique with system error of(0.1453, and 0.1325), R~2 of(0.951, and 0.951), mean absolute percentage error(4.0689, and 3.8115), and a10-index of(0.9348, and 0.9496) in training and testing phases, respectively.The developed hybrid PSO-XGB can be introduced as an accurate, powerful and applicable technique in the field of TBM performance prediction.By conducting sensitivity analysis, it was found that UCS, BTS and TFC have the deepest impacts on the TBM PR.     

周莲英同志在全省国土资源财务工作会议上的讲话

这次会议是经厅党组研究决定召开的.会议的主要任务是,总结工作,交流经验,安排部署今年的财务工作,动员全省国土资源系统广大财务工作人员进一步解放思想,更新观念,以科学发展观为统领,围绕国土资源工作中心,进一步深化财政改革,完善经济手段,增强服务意识,强化财务监管,依法使用好各项资金,不断提升财务管理水平,为全省国土资源事业的发展提供可靠的财政保障.     

山东省桓台地区土壤F的地球化学特征及其影响因素

为研究土壤氟的地球化学特征及其影响因素,选取山东省桓台地区为研究区,系统采集了70件表层土壤样品,并测试分析了其中F元素、有机碳含量、pH值等共计8项指标。绘制了元素地球化学图,掌握了该区F元素含量的空间分布特征。研究结果表明,表层土壤F含量范围为466.30~793.40 mg/kg,平均F含量为621.31 mg/kg,显著高于山东省背景值和全国背景值,具有潜在环境生态风险。研究区总面积70%的土壤F含量较高,其中F含量过剩的土壤相对集中分布在城镇附近;研究区东南部青银高速附近和北部呈零星分布的土壤中氟为适中及边缘含量水平。土壤中F含量主要受控于土壤pH、有机碳含量和施肥。该研究结果为改善研究区环境质量,防止地方性氟中毒提供了基础数据和科学依据。     

Consistent seasonal snow cover depth and duration variability over the Western Himalayas (WH)

Precipitation in solid form, i.e., snow, during winter season over the Western Himalayas (WH) leads to the build-up of seasonal snow cover. Seasonal snow cover build-up (snow cover depth and duration) largely depends on atmospheric variables such as temperature, precipitation, radiation, wind, etc. Integrated (combined) influence of atmospheric variables on seasonal snow cover gets reflected in terms of spatial and temporal variability in seasonal snow cover build-up pattern. Hence spatial and temporal variability of seasonal snow cover build-up can serve as a good indicator of climate change in high altitude mountainous regions like the WH. Consistent seasonal snow cover depth and duration, delay days and early melt days of consistent seasonal snow cover at 11 stations spread across different mountain ranges over the WH were analyzed. Mean, maximum and percentiles (25th, 50th, 75th, 90th and 95th) of consistent seasonal snow cover depth and duration show decline over the WH in the recent past 2–3 decades. Consistent seasonal snow cover is found to melt early and snow cover build-up pattern is found to show changes over the WH. Decline in consistent seasonal snow cover depth, duration and changing snow cover build-up pattern over the WH in recent decades indicate that WH has undergone considerable climate change and winter weather patterns are changing in the WH.