Preliminary shotgun glycoproteome analysis of N-glycosylation sites in serum proteins of ricin-intoxicated rats

In this study, the variations of serum glycoproteins after exposure to ricin toxin (RT) in Wistar albino rats are reported. For glycopeptide enrichment, Microcon YM-3 centrifugal filter device capture and micro-liquid chromatography (LC)–electrospray ionization (ESI)–tandem mass spectrometric (MS/MS) analysis were used; 74 trypsin-digested proteins were identified in the control group, and 58 in the RT-intoxication group. Additionally, 33 N-glycosides and 14 glycoproteins were identified in the control, and 50 and 21 in the RT-intoxication group.     

The influence of caste,class and gender in surviving multiple disasters: A case study from Orissa,India

Sociological and anthropological studies in India reveal that caste, class and gender in everyday life are both rigid and dynamic, but little is known about how they influence the survival mechanisms of women during ‘multiple disasters’, nor about how women negotiate with these structural mores to meet their cultural and biological needs. This is explored through the experiences of 12 women-headed households from different social castes in Orissa, India. Multiple disasters or disasters that occur in ‘one specific place’ (such as floods, cyclone and drought) are regular events in coastal parts of the state of Orissa. The super-cyclone of 1999, two floods of 2001 and 2003 and drought of 2000 and 2002 form the case study. Participant observation, in-depth interviews and documentary evidence complement the fieldwork. The findings suggest a complex interplay of caste, class and gender in surviving the multiple disasters including structural mutability under the purview of social organization. In doing so, women demonstrated their individual and collective agencies in order to meet their cultural and biological needs under severe crisis. This research stresses that gender and disaster studies must include a consideration of caste and class for effective disaster management and social vulnerability reduction.     

Linking the stressors and stressing the linkages: Human—environment vulnerability and brownfield redevelopment in New York City

This paper suggests that the mitigation of one hazard—soil contamination—can unintentionally affect vulnerabilities and perceived vulnerabilities to additional stressors in the local human—environment system through a study of brownfield redevelopment in New York City. This study employs a Vulnerability Scoping Diagram (VSD) approach to identify components that contribute to vulnerabilities and perceived vulnerabilities in the local community, based on the thematic analysis of 55 interviews with residents from four neighbourhoods with brownfield redevelopment activities in New York City. This analysis of resident observations and perceptions of post-redevelopment hazard conditions indicates how mitigating vulnerability to one urban hazard—soil contamination—has the potential to affect vulnerabilities and perceived vulnerabilities to additional hazards like flooding and air pollution because of the complex linkages among multiple stressors. A causal model of vulnerability to the unintended impacts of brownfield redevelopment is subsequently developed to further demonstrate the interactive linkages among exposure, sensitivity and adaptive capacity to multiple stressors. This study also provides measures that stakeholders can monitor and evaluate over time to track the socio-spatial and environmental implications of brownfield redevelopment and subsequent changes in the local human—environment system.     

基于GIS的矿井瓦斯赋存规律多影响因素智能分析技术

为了提高矿井瓦斯赋存规律分析的准确性和智能化程度,提出了多因素共同作用下瓦斯原始数据筛选和瓦斯赋存规律分析的一种智能化技术。介绍了如何利用地理信息（GIS）技术建立瓦斯地质空间数据模型,结合计算机编程实现了对大量复杂的原始数据的智能分析、处理和筛选。并实现了瓦斯赋存规律多种主控因素情况下多元回归的自动智能化分析,进而使得瓦斯赋存规律模型可以在取得新的原始数据后进行动态、智能的更新。为煤矿工作人员快速、准确的掌握地矿井瓦斯赋存规律提供了先进的手段和工具。     

基于遗传算法的支持向量机预测有机物自燃点的研究

根据定量构效关系(QSPR)原理,研究自燃点(AIT)与其分子结构间的内在定量关系。以265种有机化合物作为样本集,随机选择238种作为训练集,27种作为测试集,用遗传算法(GA)进行变量选择,分别建立多元线性回归(MLR)模型和支持向量机(SVM)模型研究有机物的自燃点与其分子结构间的关系。通过分析,发现造成模型预测效果不佳的原因是试验数据本身存在问题。通过对2个模型的比较,结果为GA-SVM模型明显优于GA-MLR模型,说明自燃点与其分子结构间具有很强的非线性关系。     

环境规制影响资源型城市绿色全要素生产率的途径与政策建议

环境规制对资源型城市绿色全要素生产率是否具有推动作用,通过何种路径驱动以及是否存在城市类型异质性有待进一步理清。本文选取2004—2019年中国113座资源型城市的面板数据,利用非径向、非角度的SBM模型与GML指数对资源型城市绿色全要素生产率进行测算,随后通过构建多重中介效应模型实证检验了科技创新、外商直接投资和产业结构优化在环境规制影响绿色全要素生产率的传导机制及其在城市类型间的异质性。研究表明：（1）从全样本回归结果看,环境规制对资源型城市绿色全要素生产率的作用系数显著为正,产业结构优化和外商直接投资均发挥了显著的正向中介效应,而科技创新表现为间接抑制作用;在非资源型城市中,三类中介效应均不显著。（2）类型异质性检验显示,科技创新在成长型、成熟型城市中发挥了正向中介效应,在再生型城市中表现为间接抑制作用;外商直接投资在衰退型城市中的中介效应值为正,而在再生型城市中表现为负向中介效应;产业结构优化在成长型与成熟型城市中发挥了正向中介效应。（3）对比中介效应结果表明,全样本回归时,产业结构优化、外商直接投资和科技创新的中介效应梯次递减,产业结构优化的中介效应占比接近80%,环境规制的...     

多模式集成方法在安徽地区PM2.5预报中的应用研究

分别采用算术平均、权重平均、多元线性回归和神经网络的集成方法,对3种空气质量模式在安徽地区2017年2月-2018年2月PM_2.5预报结果进行集成释用.结果表明：各模式和订正产品的预报值与实况值之间均能达到显著相关,相较于WRF-Chem,多元线性回归的均方根误差（RMSE）下降了21.7%,归一化平均偏差（NMB）下降了6%,且在16个地市中NMB均下降至-25%~25%之间;从不同时次的预报效果来看,在3个代表性城市（淮北、合肥和芜湖）,多元线性回归均能大幅度降低RMSE和NMB,但从时间和空间效果来看,其对于提升预报值与实况值之间的相关性方面,略差于权重平均的集成方法;多元线性回归方法对于重污染天气PM_2.5预报评分（TS）最高,为0.46.该方法能较为有效地提升不同模式的预报效果,可为重污染天气预报预警提供参考.     

多孔球三相流化床冷态试验的研究

对多孔球为填料的三相流化床进行冷态试验研究,研究不同工况下气体空塔流速、填料静止高度、喷淋密度等因素对流化床阻力特性和脱硫效率的影响.试验表明,用多孔球替代空心球做填料,不仅可以消除空心球在流化过程中出现的问题,还可以适当提高脱硫效率.     

Detection and quantitation of trace levels of low molecular weight halocarbons (LMHs) associated with commercial sodium humate

Examination of vapor above commercial sodium humate and above 10wt% aqueous humate solution by gas chromatography (GC) revealed the presence of chloroform and trichloroethylene. Further examination of the aqueous humate by the purge and trap procedure confirmed the association of chloroform and trichloroethylene with the humate. GC/mass spectrometry analyses of methylene chloride extract of the humate established the association of LMHs with the humate. Improved headspace capillary GC technique showed that the humate contained not only chloroform, trichloroethylene but several other LMHs in the range 190 to 4000ng·kg^‐1.     

A framework for allocating conservation resources among multiple threats and actions

Land managers decide how to allocate resources among multiple threats that can be addressed through multiple possible actions. Additionally, these actions vary in feasibility, effectiveness, and cost. We sought to provide a way to optimize resource allocation to address multiple threats when multiple management options are available, including mutually exclusive options. Formulating the decision as a combinatorial optimization problem, our framework takes as inputs the expected impact and cost of each threat for each action (including do nothing) and for each overall budget identifies the optimal action to take for each threat. We compared the optimal solution to an easy to calculate greedy algorithm approximation and a variety of plausible ranking schemes. We applied the framework to management of multiple introduced plant species in Australian alpine areas. We developed a model of invasion to predict the expected impact in 50 years for each species-action combination that accounted for each species’ current invasion state (absent, localized, widespread); arrival probability; spread rate; impact, if present, of each species; and management effectiveness of each species-action combination. We found that the recommended action for a threat changed with budget; there was no single optimal management action for each species; and considering more than one candidate action can substantially increase the management plan's overall efficiency. The approximate solution (solution ranked by marginal cost-effectiveness) performed well when the budget matched the cost of the prioritized actions, indicating that this approach would be effective if the budget was set as part of the prioritization process. The ranking schemes varied in performance, and achieving a close to optimal solution was not guaranteed. Global sensitivity analysis revealed a threat's expected impact and, to a lesser extent, management effectiveness were the most influential parameters, emphasizing the need to focus research and monitoring efforts on their quantification.