微生物修复返溶铬污染场地的研究进展

目前,我国历史遗留铬渣堆场多数采用湿法解毒工艺进行处理,但大量化学药剂的添加不仅增加了成本,引入了污染物,而且随时间的延长铬渣中的Cr(Ⅵ) 源源不断的返溶,场地出现返黄现象,形成二次污染。为了持久稳定的修复铬渣,研究人员提出用微生物修复技术处理湿法解毒后铬渣中Cr(Ⅵ) 的返溶。文中综述了国内外微生物修复铬渣污染场地的研究进展,首先简述了铬渣的危害、处理现状及传统的铬污染修复技术,并以湿法解毒铬污染为例,重点揭示了处理后铬渣中Cr(Ⅵ) 的返溶机理,由此可知湿法解毒后的二次污染不可避免。随后详述了微生物修复Cr(Ⅵ) 过程中生物还原、生物吸附和生物矿化三大作用机理,并阐述了铬污染场地修复过程中微生物物种的响应及群落结构的演替,最后,总结了微生物修复铬渣的研究进展并展望了未来的研究方向。     

非编码RNA与病毒性心肌炎

病毒性心肌炎(Viral myocarditis,VMC)是一种由病毒感染所引起的以心肌细胞炎症为特征的疾病。由于病毒性心肌炎的发病机制尚未完全研究清楚,因此该病的诊断及治疗对于临床医生来说仍具有极大的挑战性。非编码RNAs (Non-coding RNAs,ncRNAs)是一类不具有编码蛋白质功能的RNA,越来越多的研究表明ncRNAs参与到调控VMC的发生和发展过程中,这可能成为VMC的治疗或诊断的新研究靶点。文中对近3年来关于ncRNAs在VMC的发病机制及诊断中可能发挥的作用进行了综述。     

应用型本科高校围绕解决生物工程专业复杂工程问题能力培养的课程体系思考与构建

准确把握专业复杂工程问题的特征与内涵是设置专业毕业要求、构建课程体系、设计教学内容的重要前提。文中通过讨论生物产业的复杂工程问题特征,挖掘长三角地区生物产业对于本科层次人才的需求,总结岗位典型任务和要求,阐述了典型任务中包含的复杂工程问题的内涵。在此基础上构建了多阶段培养解决生物工程专业复杂工程问题能力的课程体系。该课程体系结合产教深度融合的医药生物技术学院、教师科研反哺教学项目建设、课程团队与一流课程建设、覆盖全员的学生专业社团建设等多种措施,更好地支撑了解决复杂工程问题能力的培养。     

链球菌病类活疫苗活菌计数参考品的研制

研制链球菌病类活疫苗活菌计数参考品,可以更加科学地评价活菌计数结果的准确性和有效性。首先,制备了一批链球菌病类活疫苗活菌计数参考品,对其物理性状、纯粹性、真空度、剩余水分进行检验,并对其均一性、运输稳定性、热稳定性进行测定,另组织3家单位通过协作标定的方式对参考品活菌数进行赋值,用协作标定法统计参考品在12个月内的保存期。参考品的性状检查、纯粹检验、真空度测定和剩余水分测定结果均符合《中国兽药典》的规定;均一性试验结果显示,参考品计数结果的变异系数小于10%,均一性良好;运输稳定性试验证明,参考品在夏季和冬季用泡沫盒加冰袋的方式运输3日内数值仍能保持稳定;加速热稳定性试验验证,参考品在–20 ℃条件下保存3个月、4 ℃条件下保存21 d内均可活菌数稳定;通过协作标定,统计出参考品活菌数的赋值范围为 (8.5–12.1)×107 CFU/支;保存期试验结果证实,参考品在–70 ℃以下保存一年内活菌数可维持稳定状态。链球菌病类活疫苗活菌计数参考品,不仅可以为链球菌病类活疫苗的活菌计数实验提供参照物,而且可以用于评价马丁琼脂培养基的质量,为兽用生物制品质量控制提供保障。     

新工科背景下生物反应工程课程的多元化教学模式 改革与探索

生物反应工程作为一门理论性与应用性都很强的专业课程,在生物工程等相关专业的课程设置中处于桥梁和纽带地位,对新型应用型工科人才的培养发挥着重要的作用。但由于该课程中公式等抽象理论知识过多,导致学生学习效率十分低下。因此,为了适应新工科教育背景下对创新型人才培养的需求,提高学生的学习兴趣和积极性,并培养学生的自主学习等创新能力,教学团队在课程教学中通过引入虚拟仿真技术、开展微课教学、采用案例式教学模式、利用科研平台等多元化方式,对该课程的教学模式、方法和手段尝试改革和探索,取得了一定的教学效果,并就此进行了一些探讨,以期能为相关课程的教学改革提供一些思路和启示。     

Characterization of a G protein α subunit encoded gene from the dimorphic fungus-Tremella fuciformis

Antonie van Leeuwenhoek - Tremella fuciformis is a dimorphic fungus which can undertake the reversible transition between yeast and pseudohypha forms. G protein α subunit (Gα) carries...     

Microbulbifer hainanensis sp. nov., a moderately halopilic bacterium isolated from mangrove sediment

Antonie van Leeuwenhoek - A new bacterium was successfully isolated from a mangrove sediment sample in Haikou City, Hainan Province, China. The organism is a Gram-negative, rod-shaped, non-motile...     

Description and genome analysis of Microvirga antarctica sp. nov., a novel pink-pigmented psychrotolerant bacterium isolated from Antarctic soil

Antonie van Leeuwenhoek - A novel pink-pigmented bacterium, designated strain 3D7T, was isolated during an investigation of potential psychrotolerant species from Antarctic soil. Cells of the...     

Exploiting nonsystematic covariate monitoring to broaden the scope of evidence about the causal effects of adaptive treatment strategies

In studies based on electronic health records (EHR), the frequency of covariate monitoring can vary by covariate type, across patients, and over time, which can limit the generalizability of inferences about the effects of adaptive treatment strategies. In addition, monitoring is a health intervention in itself with costs and benefits, and stakeholders may be interested in the effect of monitoring when adopting adaptive treatment strategies. This paper demonstrates how to exploit nonsystematic covariate monitoring in EHR‐based studies to both improve the generalizability of causal inferences and to evaluate the health impact of monitoring when evaluating adaptive treatment strategies. Using a real world, EHR‐based, comparative effectiveness research (CER) study of patients with type II diabetes mellitus, we illustrate how the evaluation of joint dynamic treatment and static monitoring interventions can improve CER evidence and describe two alternate estimation approaches based on inverse probability weighting (IPW). First, we demonstrate the poor performance of the standard estimator of the effects of joint treatment‐monitoring interventions, due to a large decrease in data support and concerns over finite‐sample bias from near‐violations of the positivity assumption (PA) for the monitoring process. Second, we detail an alternate IPW estimator using a no direct effect assumption. We demonstrate that this estimator can improve efficiency but at the potential cost of increase in bias from violations of the PA for the treatment process.