硝基芳烃类污染物对水生态系统的毒理研究述评

硝基芳烃主要通过废水、粉尘、蒸气等形式污染环境,影响人体健康.从其对水生生物(包括生产者、消费者和分解者)的形态结构、生理生化、分子机制和对水生态系统的影响等方面综述了它们的生态毒害和致毒机理

硝基芳烃类污染物对水生态系统的毒理研究述评

硝基芳烃主要通过废水、粉尘、蒸气等形式污染环境,影响人体健康.从其对水生生物（包括生产者、消费者和分解者）的形态结构、生理生化、分子机制和对水生态系统的影响等方面综述了它们的生态毒害和致毒机理

硝基芳烃类污染物对水生态系统的毒理研究述评

硝基芳烃主要通过废水、粉尘、蒸气等形式污染环境,影响人体健康.从其对水生生物（包括生产者、消费者和分解者）的形态结构、生理生化、分子机制和对水生态系统的影响等方面综述了它们的生态毒害和致毒机理

CO_2电催化还原制烃类产物的研究进展

应用可再生能源驱动CO2电催化还原制备燃料,是目前清洁能源发展最具前景的方向之一。本文综述了以气态烃类（CH4、C2H4）为目标产物的CO2电催化还原的研究进展,分别介绍了电催化材料、电解质溶液以及反应机理的研究现状。指出在水溶液电解质中,电催化材料需兼具烃类产物的高选择性与电化学析氢反应的抑制能力,Cu与Cu基材料是电催化材料的首选,Cu的氧化物由于其丰富的结构特征拓宽了电催化材料优选范围。水溶液电解质的性质会显著影响CO2电催化还原产物的选择性;非水溶液或痕水溶液电解质由于可以显著抑制析氢反应,将会进一步拓宽电催化材料的优选范围。最后介绍了CO2电催化还原机理研究的现状,指出原位电化学谱学方法的应用与CO2电催化还原机理模型研究工作的开展,将成为人们深入认识以烃类为目标产物的CO2电催化还原反应的关键,并有利于指导电催化材料与电解质材料的研究开发

Stability Studies for a Membrane Electrode Assembly Type CO2 Electro-Reduction Electrolytic Cell

电化学还原CO2可实现CO2的资源化转化,是缓解因其过度排放所导致诸多环境问题的关键技术. 本文提出了一种膜电极（membrane electrode assembly,MEA）构型CO2还原电解单池的结构设计,可同步实现气体扩散阴极两侧CO2的供给与电解质液层的更新. 基于该MEA构型电解池,实验考察了电解质液层中KHCO3浓度和更新与否对氮掺杂石墨烯锚定的Ni电极表面CO2电还原制备CO的反应活性、产物分布与稳定性的影响. 结果表明,若电流密度低于5 mA·cm-2,KHCO3浓度显著影响电解电势而非产物分布. CO2还原电解单池在稳定运行中存在着“可逆”与“不可逆”两种衰减模式. 其中,阴极/电解质界面处催化剂的流失是 “不可逆”衰减形成的原因;而电解质液层中KHCO3溶液的流失导致了MEA构型CO2还原单池的“可逆”衰减,周期性更新KHCO3电解质是降低其“可逆”衰减的有效方法.Electro-catalytic reduction is an efficient way to achieve resourcable transformation of CO2, which is one of the important techniques to solve the global environmental problems originated from excessive CO2 emission. In this study, a membrane electrode assembly(MEA) type CO2 electro-reduction electrolytic cell was constucted, which enables CO2 feeding and real-time KHCO3 aqueous updating on both sides of the cathode gas diffusion electrode (GDE). By means of the electrolytic cell, effects of KHCO3 concentration and updating inside the liquid electrolytic chamber on CO2 electro-reduction activity, production distribution and stability were investigated. The experimental results suggested that the KHCO3 concentration exerted strong influence on the cell voltage rather than the production distribution for the current densities lower than 5 mA·cm-2. The performance of MEA type CO2 electro-reduction cell decayed in both “reversible” and “irreversible” ways. Catalysts leaking at the GDE/liquid electrolyte interface might be respossible for the cell “irreversible” decay. Meanwhile, th leakage of KHCO3 aqueous electrolyte arose from gas accumulation in the liquid electrolytic chamber contributed to the “reversible” degradation, which could be recovered effectively by updating the KHCO3 aqueous electrolyte.辽宁省自然科学基金项目（No.201602162）、大连理工大学GF创新基金项目（No.DUT18GF308）和国家电网公司科技项目（No.SGRI-DL-71-16-015）资助通讯作者:毛庆,黄延强E-mail:[email protected];[email protected]:MAOQing,HUANGYan-qiangE-mail:[email protected];[email protected]. 大连理工大学化工学院,辽宁 大连 116023 2. 中国科学院大连化学物理研究所,航天催化与新材料研究室,辽宁 大连 116023 3. 全球能源互联网研究院,北京 1022001. School of Chemical Engineer, Dalian University of Technology, Dalian 116024, China2. Laboratory of Aerospace Catalysts and New Materials, Dalian Institute of Chemical Physics, Chinese Academy of Science, Dalian 116023, China3. Global Energy Interconnection Research Institute, Beijing 102200, Chin

Multi-class Latent Concept Pooling for computer-aided endoscopy diagnosis

Successful computer-aided diagnosis systems typically rely on training datasets containing sufficient and richly annotated images. However, detailed image annotation is often time consuming and subjective, especially for medical images, which becomes the bottleneck for the collection of large datasets and then building computer-aided diagnosis systems. In this article, we design a novel computer-aided endoscopy diagnosis system to deal with the multi-classification problem of electronic endoscopy medical records (EEMRs) containing sets of frames, while labels of EEMRs can be mined from the corresponding text records using an automatic text-matching strategy without human special labeling. With unambiguous EEMR labels and ambiguous frame labels, we propose a simple but effective pooling scheme called Multi-class Latent Concept Pooling, which learns a codebook from EEMRs with different classes step by step and encodes EEMRs based on a soft weighting strategy. In our method, a computer-aided diagnosis system can be extended to new unseen classes with ease and applied to the standard single-instance classification problem even though detailed annotated images are unavailable. In order to validate our system, we collect 1,889 EEMRs with more than 59K frames and successfully mine labels for 348 of them. The experimental results show that our proposed system significantly outperforms the state-of-the-art methods. Moreover, we apply the learned latent concept codebook to detect the abnormalities in endoscopy images and compare it with a supervised learning classifier, and the evaluation shows that our codebook learning method can effectively extract the true prototypes related to different classes from the ambiguous data

Multi-class Latent Concept Pooling for computer-aided endoscopy diagnosis

Successful computer-aided diagnosis systems typically rely on training datasets containing sufficient and richly annotated images. However, detailed image annotation is often time consuming and subjective, especially for medical images, which becomes the bottleneck for the collection of large datasets and then building computer-aided diagnosis systems. In this article, we design a novel computer-aided endoscopy diagnosis system to deal with the multi-classification problem of electronic endoscopy medical records (EEMRs) containing sets of frames, while labels of EEMRs can be mined from the corresponding text records using an automatic text-matching strategy without human special labeling. With unambiguous EEMR labels and ambiguous frame labels, we propose a simple but effective pooling scheme called Multi-class Latent Concept Pooling, which learns a codebook from EEMRs with different classes step by step and encodes EEMRs based on a soft weighting strategy. In our method, a computer-aided diagnosis system can be extended to new unseen classes with ease and applied to the standard single-instance classification problem even though detailed annotated images are unavailable. In order to validate our system, we collect 1,889 EEMRs with more than 59K frames and successfully mine labels for 348 of them. The experimental results show that our proposed system significantly outperforms the state-of-the-art methods. Moreover, we apply the learned latent concept codebook to detect the abnormalities in endoscopy images and compare it with a supervised learning classifier, and the evaluation shows that our codebook learning method can effectively extract the true prototypes related to different classes from the ambiguous data

绿色农业新技术集成研究与示范

一、该项目针对农业生产中食品安全和环境污染问题,开展了S-诱抗素、新奥霉素、壳寡糖、棉铃虫病毒杀虫剂与昆虫病原线虫生物制剂、功能性堆肥及其浸提液工业化生产等的试验和示范,立项准确,针对性强,意义重大。 二、项目研究出S-诱抗素等生物制剂及其生产工艺、工厂化技术;研究开发出昆虫病原线虫活体繁殖技术,实现了工厂化生产;研究开发了两种功能性堆肥及浸提液,提出了“功能性堆肥+秸秆生物反应堆+堆肥浸提液+S-诱抗素等生物制剂”健康、安全设施蔬菜生产模式;在宁夏实现了地上、地下,土壤、作物生物制剂联防技术体系,为低耗、高效、安全、健康农产品生产开辟了新途径。 三、在S-诱抗素、新奥霉素高产菌株的生产工艺,昆虫病原线虫活体繁殖工厂化生产方面取得了新突破;在S-诱抗素、新奥霉素、壳寡糖、棉铃虫病毒杀虫剂、功能性堆肥及其浸提液集成应用控制作物病虫害等方面有创新。研究成果达到了国内先进水平,S-诱抗素、新奥霉素高产菌株的生产工艺研究达到国际领先。 四、项目执行期间,在宁夏15个市县建立核心试验基地14个,示范推广点40个,累计推广面积17万亩,新增效益9600万元。获得发明专利4项,实用新型专利1项,制定地方标准5项,专著1部,发表论文19篇(其中SCI收录6篇)。培训农技人员300人次,农民4700多人次