Diversity of antibiotic resistance genes and encoding ribosomal protection proteins gene in livestock waste polluted environment

The rapid development and increase of antibiotic resistance are global phenomena resulting from the extensive use of antibiotics in human clinics and animal feeding operations. Antibiotics can promote the occurrence of antibiotic resistance genes (ARGs), which can be transferred horizontally to humans and animals through water and the food chain. In this study, the presence and abundance of ARGs in livestock waste was monitored by quantitative PCR. A diverse set of bacteria and tetracycline resistance genes encoding ribosomal protection proteins (RPPs) from three livestock farms and a river were analyzed through denaturing gradient gel electrophoresis (DGGE). The abundance of sul(I) was 10³ to 10⁵ orders of magnitude higher than that of sul(II). Among 11 tet-ARGs, the most abundant was tet(O). The results regarding bacterial diversity indicated that the presence of antibiotics might have an evident impact on bacterial diversity at every site, particularly at the investigated swine producer. The effect of livestock waste on the bacterial diversity of soil was stronger than that of water. Furthermore, a sequencing analysis showed that tet(M) exhibited two genotypes, while the other RPPs-encoding genes exhibited at least three genotypes. This study showed that various ARGs and RPPs-encoding genes are particularly widespread among livestock.     

Development of machine learning based prediction models for hazardous properties of chemical mixtures

Lower flammability limit (LFL), upper flammability limit (UFL), auto-ignition temperature (AIT) and flash point (FP) are crucial hazardous properties for fire and explosion hazards assessment and consequence analysis. In this study, a comprehensive prediction model set was constructed by using expanded chemical mixture databases of chemical mixture hazardous properties. Machine learning based gradient boosting quantitative structure-property relationship (GB-QSPR) method is implemented for the first time to improve the model performance and prediction accuracy. The result shows that all developed models have significantly higher accuracy than other regular QSPR models, with the 5-fold cross-validation RMSE of LFL, UFL, AIT, and FP models being 1.06, 1.14, 1.08, and 1.17, respectively. All developed QSPR models can be used to estimate reliable chemical mixture hazardous properties and provide useful guidance in chemical mixture hazard assessment and consequence analysis.     

Perceptions of safety on a shared road: Driving,cycling, or walking near an autonomous vehicle

Introduction: While improved safety is a highly cited potential benefit of autonomous vehicles (AVs), at the same time a frequently cited concern is the new safety challenges that AVs introduce. The literature lacks a rigorous exploration of the safety perceptions of road users who will interact with AVs, including vulnerable road users. Addressing this gap is essential because the successful integration of AVs into transportation systems hinges on an understanding of how all road users will react to their presence. Methods: A stated preference survey of the Phoenix, Arizona, metropolitan statistical area (Phoenix MSA) was conducted in July 2018. A series of ordered probit models was estimated to analyze the survey responses and identify differences between various population groups with respect to the perceived safety of driving, cycling, and walking near AVs. Results: Greater exposure to and awareness of AVs are not uniformly associated with increases in perceived safety. Various attitudinal factors, level of AV automation, and other intrinsic and extrinsic factors are related to safety perceptions of driving, walking, and cycling near AVs. Socioeconomic and demographic characteristics, such as gender, age, income, employment, and automobile usage and ownership, have various relationships with perceived safety. Conclusions: Cycling near an AV was perceived as the least safe activity, followed by walking and then driving near an AV. Both similarities and differences were observed among the factors associated with the perceived safety of different travel alternatives. Practical Applications: Public perception will guide the development and adoption of AVs directly and indirectly. To help maintain control of public perception, transportation planners, decision makers, and other stakeholders should consider more deliberate and targeted messaging to address the concerns of different road users. In addition, more careful pilot testing and more direct attention to vulnerable road users may help avoid a backlash that could delay the rollout of this technology.     

g-C_3N_4/C@Bi_2MoO_6复合光催化剂的制备及其光催化活性

通过水热合成法和液相沉积法制备g-C_3N_4/C@Bi_2MoO_6复合光催化剂,并采用X射线衍射、扫描电子显微镜、氮气吸脱附、紫外-可见漫反射等技术对其进行表征。研究了可见光下g-C_3N_4/C@Bi_2MoO_6催化降解罗丹明B(Rh B)的影响因素,并对其光催化反应机理进行初步探讨。实验结果表明:g-C_3N_4掺杂量为60%(w)时g-C_3N_4/C@Bi_2MoO_6的光催化活性最高;在60%g-C_3N_4/C@Bi_2MoO_6的投加量为1.00 g/L、初始Rh B质量浓度为2.50 mg/L、可见光照射150 min的条件下,Rh B的降解率达到97.90%;在g-C_3N_4/C@Bi_2MoO_6光催化降解体系中,h~+和·O_2~-是主要活性物种。     

微波强化CWPO工艺CuO_x-CeO_2/GAC催化剂的制备及其对二甲亚砜的降解

以颗粒活性炭(GAC)为载体、铜为活性组分、铈为助剂组分、草酸钠为沉淀剂,采用浸渍焙烧法制得CuO_x-CeO_2/GAC催化剂。以H_2O_2为氧化剂,微波强化催化湿式过氧化氢氧化(CWPO)处理二甲亚砜(DMSO)初始质量浓度为1 000 mg/L的废水,处理3 min后DMSO去除率达93.8%。催化剂第7次使用时DMSO去除率仍保持在75%以上。初始废水pH在3~9范围内,DMSO去除率均在85%以上。助剂Ce的加入提高了催化剂表面活性组分的分散性和稳定性,使催化剂的活性稳定性和使用寿命显著提高。     

Structural equation model analysis for the evaluation of overall driving performance: A driving simulator study focusing on driver distraction

Objective: The present research relies on 2 main objectives. The first is to investigate whether latent model analysis through a structural equation model can be implemented on driving simulator data in order to define an unobserved driving performance variable. Subsequently, the second objective is to investigate and quantify the effect of several risk factors including distraction sources, driver characteristics, and road and traffic environment on the overall driving performance and not in independent driving performance measures.

Methods: For the scope of the present research, 95 participants from all age groups were asked to drive under different types of distraction (conversation with passenger, cell phone use) in urban and rural road environments with low and high traffic volume in a driving simulator experiment. Then, in the framework of the statistical analysis, a correlation table is presented investigating any of a broad class of statistical relationships between driving simulator measures and a structural equation model is developed in which overall driving performance is estimated as a latent variable based on several individual driving simulator measures.

Results: Results confirm the suitability of the structural equation model and indicate that the selection of the specific performance measures that define overall performance should be guided by a rule of representativeness between the selected variables. Moreover, results indicate that conversation with the passenger was not found to have a statistically significant effect, indicating that drivers do not change their performance while conversing with a passenger compared to undistracted driving. On the other hand, results support the hypothesis that cell phone use has a negative effect on driving performance. Furthermore, regarding driver characteristics, age, gender, and experience all have a significant effect on driving performance, indicating that driver-related characteristics play the most crucial role in overall driving performance.

Conclusions: The findings of this study allow a new approach to the investigation of driving behavior in driving simulator experiments and in general. By the successful implementation of the structural equation model, driving behavior can be assessed in terms of overall performance and not through individual performance measures, which allows an important scientific step forward from piecemeal analyses to a sound combined analysis of the interrelationship between several risk factors and overall driving performance.     

CeO2/TiO2纳米管催化剂脱硝性能研究

利用废气中本身含有的CO催化还原烟气中的NO_x,可以实现以废治废。采用TiO₂纳米管负载CeO₂,制备CeO₂/TiO₂纳米管催化剂,并对其进行了SEM表征及影响因素实验。实验结果表明：在n（Ce）∶n（Ti）=3∶7、焙烧温度500 ℃、焙烧时间3 h时制备的CeO₂/TiO₂纳米管催化剂形貌较好,表面颗粒分布相对均匀;反应温度400~600 ℃时NO脱除率达98%;该催化剂具有一定的抗氧性能;当n（SO₂）∶n（NO）=（1∶2）~（2∶1）时,NO脱除率仍然在95%以上。     

CePO4-TiO2固溶体的NH3-SCR活性

采用共沉淀法合成了不同Ce与Ti摩尔比的CePO₄-TiO₂复合材料,利用XRD、TEM、NH₃-TPD和H₂-TPR技术对其物理化学性质进行了表征,并对其NH₃-SCR活性进行了评价。实验结果表明：随着CePO₄含量的增加,催化剂的活性提高;当Ce与Ti的摩尔比为8:2时催化剂的活性最高,230 ℃的NO转化率达到100%,且具有更广的温度窗口,优于纯CePO₄。表征结果显示,CePO₄与TiO₂形成固溶体后,颗粒尺寸较为均匀,强酸位的数量增多,氧化还原性能提升,从而提高了催化剂的SCR反应活性,同时改善了其抗水抗硫性能。     

含氮生物降解促进剂对土壤吸附正十六烷的影响

选用正十六烷模拟润滑油,通过振荡平衡实验,考察了两种含氮脂肪酸型生物降解促进剂（甲基二乙醇胺油酸酯（MDEAO）和油酸二乙醇酰胺（ODEA））对水土体系中正十六烷吸附行为的影响。实验结果表明：正十六烷在水土体系中的有机质标化分配系数与土壤种类无关,与生物降解促进剂种类有关;土壤对正十六烷的吸附过程更符合Freundlich等温吸附模型;MDEAO的临界胶束浓度为2.0 mg/L,对土壤吸附正十六烷有一定的促进作用,ODEA的临界胶束浓度为0.7 mg/L,形成的胶束对正十六烷的增溶效果明显,促进了正十六烷在土壤中的解吸。     

A Bayesian hierarchical approach to quantifying stakeholder attitudes toward conservation in the presence of reporting error

Stakeholder support is vital for achieving conservation success, yet there are few reliable mechanisms to monitor stakeholder attitudes toward conservation. Approaches used to assess attitudes rarely account for bias arising from reporting error, which can lead to falsely reporting a positive attitude toward conservation (false-positive error) or not reporting a positive attitude when the respondent has a positive attitude toward conservation (false-negative error). Borrowing from developments in applied conservation science, we used a Bayesian hierarchical model to quantify stakeholder attitudes as the probability of having a positive attitude toward wildlife notionally (or in abstract terms) and at localized scales while accounting for reporting error. We compared estimates from our model, Likert scores, and naïve estimates (i.e., proportion of respondents reporting a positive attitude in at least 1 question that was only susceptible to false-negative error) with true stakeholder attitudes through simulations. We then applied the model in a survey of tea estate staff on their attitudes toward Asian elephants (Elephas maximus) in the Kaziranga–Karbi Anglong landscape of northeast India. In simulations, Bayesian model estimates of stakeholder attitudes toward wildlife were less biased than naïve estimates or Likert scores. After accounting for reporting errors, we estimated the probability of having a positive attitude toward elephants notionally as 0.85 in the Kaziranga landscape, whereas the proportion of respondents who had positive attitudes toward elephants at a localized scale was 0.50. In comparison, without accounting for reporting errors, naïve estimates of proportions of respondents with positive attitudes toward elephants were 0.69 and 0.23 notionally and at local scales, respectively. False (positive and negative) reporting probabilities were consistently not 0 (0.22–0.68). Regular and reliable assessment of stakeholder attitudes–combined with inference on drivers of positive attitudes–can help assess the success of initiatives aimed at facilitating human behavioral change and inform conservation decision making.