Life history and population dynamics of the Japanese Yam,Dioscorea japonica Thunb.

The adaptive significance of the emergence mode ofDioscorea japonica was studied with respect to initial plant size (seed, bulbil and tuber) and light intensity, using mathematical simulation based on Yokoi's (1976) model. Under 1.5% full sunlight conditions, plants emerging with only one leaf did not develop a shoot system throughout the growing period (Hori and Oshima, 1986). Simulation indicated that, for this species of plant under poor productive conditions, the optimal time for switch-over from the vegetative to reproductive growth phase to maximize the tuber weight at the end of the growing period, occurred immediately following the start of autotrophic growth. By means of shoot growth patterns, small and large size plants acquired the ability of shade tolerance and shade avoidance, respectively. Further, the life history ofD. japonica could be expressed as a flow chart based on plant size and light intensity data.     

Modeling of dynamical systems through deep learning

This review presents a modern perspective on dynamical systems in the context of current goals and open challenges. In particular, our review focuses on the key challenges of discovering dynamics from data and finding data-driven representations that make nonlinear systems amenable to linear analysis. We explore various challenges in modern dynamical systems, along with emerging techniques in data science and machine learning to tackle them. The two chief challenges are (1) nonlinear dynamics and (2) unknown or partially known dynamics. Machine learning is providing new and powerful techniques for both challenges. Dimensionality reduction methods are used for projecting dynamical methods in reduced form, and these methods perform computational efficiency on real-world data. Data-driven models drive to discover the governing equations and give laws of physics. The identification of dynamical systems through deep learning techniques succeeds in inferring physical systems. Machine learning provides advanced new and powerful algorithms for nonlinear dynamics. Advanced deep learning methods like autoencoders, recurrent neural networks, convolutional neural networks, and reinforcement learning are used in modeling of dynamical systems.     

以环境育人为理念的本科教学实验室管理模式探讨

本科教学实验室是开展本科生实验教学的主要场所,如何通过实验室的管理有效提升学生实验素养值得探索。文中主要介绍了将“6S (整理 (Seiri)、整顿 (Seiton)、清扫 (Seiso)、清洁 (Seiketsu)、素养 (Shitsuke)、安全 (Safety) 六项要素) 管理”理念引入本科教学实验室管理的应用实践与效果,特别是实验室环境管理、实验室安全管理、物品定位管理、试剂和耗材管理、学生培训和考核等方面的具体做法。通过实施“6S管理”,实验室环境得以改善,学生的实验素养得到提高,本科实验教学的质量得到改善和提高,达到环境育人的目的。这为高校教学实验室的建设和有效管理提供可推广、可操作的经验。     

疫情常态化下“发酵工程”线上线下混合式教学模式的改革与探索

新型冠状病毒肺炎疫情防控常态化下,线上线下混合式教学成为了许多高校课程采用的新型教学模式。本课程团队以学生为中心,结合传统教学法（Lecture-Based Learning,LBL）和慕课教学的优点,重新构建了“发酵工程”课程的教学过程,对课程的教学目标、教学要求、教学章节、教学考核、教学方法等进行了一系列的改革探索,并尝试了线上翻转课堂和课程思政融入课堂的教学模式,学生自主学习的积极性得以提高,促进、提升了课程的教与学效果。     

V型羊毛硫肽雷可肽的抑菌机制

【背景】雷可肽(Lexapeptide)为首例V型羊毛硫肽家族化合物,具有较好的抗革兰氏阳性菌活性,对耐甲氧西林金黄色葡萄球菌(Methicillin-Resistant Staphylococcus aureus,MRSA)和表皮葡萄球菌(Methicillin-Resistant Staphylococcus epidermidis,MRSE)的抑制作用强于广泛应用的食品防腐剂乳酸链球菌素,其对pH和高温的稳定性也优于乳酸链球菌素,具有较好的应用前景。由于抑菌机制不明确,限制了雷可肽的开发应用。【目的】探究雷可肽抑菌作用特征以及作用机制,为雷可肽开发应用奠定基础。【方法】通过菌落计数法与Mg2+试验表征雷可肽抑菌动力学曲线;采用流式细胞仪和透射电子显微镜研究雷可肽在靶细胞表面的成孔性;利用高效液相色谱与基质辅助激光解吸电离的时间飞行质谱分析雷可肽处理对革兰氏阳性菌肽聚糖前体积累的影响。【结果】雷可肽在抑菌动力学上与乳酸链球菌素没有显著差别,但在更宽的Mg2+浓度范围内仍可保持抑菌活性。雷可肽处理后的细胞具有透过荧光染料的能力,生物型透射电镜观察到细胞发生破损。此外,在雷可肽作用后的细胞中检测到肽聚糖合成的前体尿嘧啶核苷二磷酸-N-乙酰胞壁酸五肽。【结论】雷可肽能够通过抑制细胞壁肽聚糖生物合成并造成细胞损伤进而获得通透性,以此来抑制革兰氏阳性菌生长。     

基于“抗疫精神”的“基因工程”课程思政教学设计与实践

课程思政是当前高校落实“立德树人”根本任务的重要形式。在抗击新型冠状病毒肺炎疫情中,中华民族同舟共济、守望相助,共同铸就了伟大的抗疫精神,为课程思政教学提供了最生动的元素。本文对“战疫”行动中的抗疫精神进行梳理和总结,将其内涵凝练为5项内容;以新型冠状病毒的“核酸检测、疫苗研制及其变异”中蕴含的“抗疫精神”为思政载体,甄选“基因工程”课程中对应的理论内容,开展聚焦抗疫精神的“基因工程”课程思政教学设计与实践研究,利用问卷法和深度访谈法对教学实施效果进行评价,旨在引导学生在学习专业理论知识的同时,感悟课程内容中蕴含的抗疫精神,并将其内化为价值追求,潜移默化中提升学生的思想意识和道德修养,同时为生物类其他专业课程思政教学提供参考和借鉴。