通过改进实验器具促进师范院校动物学实践教学的体会

动物学是师范院校生物科学专业的基础课程之一,其教学内容包括理论教学和实践教学,而实践教学是理论联系实际、训练学生观察和动手能力的重要教学环节。因此通过引导学生改进昆虫毒瓶和昆虫展翅板等教具,既调动了学生的学习积极性,也培养了学生的科研能力和创新能力,使动物学实践教学取得了良好的教学效果。     

青藏高原山水林田湖草生态保护修复模式——以拉萨河流域为例

山水林田湖草生态保护与修复是推进生态文明建设的必然要求。青藏高原是我国生态屏障区、水源涵养区和生态脆弱区,生态地位独特、生态保护责任重大。为此,中央及地方政府高度重视青藏高原生态文明建设,积极开展山水林田湖草生态保护修复试点工程。从青藏高原山水林田湖草生态保护与修复试点的特殊性出发,以拉萨河流域为例梳理了其主要的生态保护修复工程项目,提炼了青藏高原山水林田湖草生态保护与修复的技术路线,从参与主体、整合要素、运行保障、实施目标等方面总结了青藏高原多部门跨区、多要素综合、多渠道协作、多目标耦合的山水林田湖草联动治理模式,从山、水、林、田、湖、草六大生态要素出发归纳了青藏高原生态保护修复的分类指导-精准施策的实践框架,以期对青藏高原山水林田湖草生态建设情况有一个清晰的认识,并为全国其他地区系统开展山水林田湖草生态保护修复工作提供借鉴。     

Science in Risk Assessment and Policy (SciRAP): An Online Resource for Evaluating and Reporting In Vivo (Eco)Toxicity Studies

(Eco)toxicity studies conducted according to internationally standardized test guidelines are often considered reliable by default and preferred as key evidence in regulatory risk assessment. At the same time regulatory agencies emphasize the use of all relevant (eco)toxicity data in the risk assessment process, including non-standard studies. However, there is a need to facilitate the use of such studies in regulatory risk assessment. Therefore, we propose a framework that facilitates a systematic and transparent evaluation of the reliability and relevance of (eco)toxicity in vivo studies for health and environmental risk assessment. The framework includes specific criteria to guide study evaluation, as well as a color-coding tool developed to aid the application of these criteria. In addition we provide guidance intended for researchers on how to report non-standard studies to ensure that they meet regulatory requirements. The intention of the evaluating and reporting criteria is to increase the usability of all relevant data that may fill information gaps in chemical risk assessments. The framework is publically available online, free of charge, at the Science in Risk Assessment and Policy (SciRAP) website: www.scirap.org. The aim of this article is to present the framework and resources available at the SciRAP website.     

Retrospective optimization of time-dependent fermentation control strategies using time-independent historical data

We have previously shown the usefulness of historical data for fermentation process optimization. The methodology developed includes identification of important process inputs, training of an artificial neural network (ANN) process model, and ultimately use of the ANN model with a genetic algorithm to find the optimal values of each critical process input. However, this approach ignores the time-dependent nature of the system, and therefore, does not fully utilize the available information within a database. In this work, we propose a method for incorporating time-dependent optimization into our previously developed three-step optimization routine. This is achieved by an additional step that uses a fermentation model (consisting of coupled ordinary differential equations (ODE)) to interpret important time-course features of the collected data through adjustments in model parameters. Important process variables not explicitly included in the model were then identified for each model parameter using automatic relevance determination (ARD) with Gaussian process (GP) models. The developed GP models were then combined with the fermentation model to form a hybrid neural network model that predicted the time-course activity of the cell and protein concentrations of novel fermentation conditions. A hybrid-genetic algorithm was then used in conjunction with the hybrid model to suggest optimal time-dependent control strategies. The presented method was implemented upon an E. coli fermentation database generated in our laboratory. Optimization of two different criteria (final protein yield and a simplified economic criteria) was attempted. While the overall protein yield was not increased using this methodology, we were successful in increasing a simplified economic criterion by 15% compared to what had been previously observed. These process conditions included using 35% less arabinose (the inducer) and 33% less typtone in the media and reducing the time required to reach the maximum protein concentration by 10% while producing approximately the same level of protein as the previous optimum.     

Detecting un-authorized genetically modified organisms (GMOs) and derived materials

Genetically modified plants, in the following referred to as genetically modified organisms or GMOs, have been commercially grown for almost two decades. In 2010 approximately 10% of the total global crop acreage was planted with GMOs (James, 2011). More than 30 countries have been growing commercial GMOs, and many more have performed field trials. Although the majority of commercial GMOs both in terms of acreage and specific events belong to the four species: soybean, maize, cotton and rapeseed, there are another 20 + species where GMOs are commercialized or in the pipeline for commercialization. The number of GMOs cultivated in field trials or for commercial production has constantly increased during this time period. So have the number of species, the number of countries involved, the diversity of novel (added) genetic elements and the global trade. All of these factors contribute to the increasing complexity of detecting and correctly identifying GMO derived material. Many jurisdictions, including the European Union (EU), legally distinguish between authorized (and therefore legal) and un-authorized (and therefore illegal) GMOs. Information about the developments, field trials, authorizations, cultivation, trade and observations made in the official GMO control laboratories in different countries around the world is often limited, despite several attempts such as the OECD BioTrack for voluntary dissemination of data. This lack of information inevitably makes it challenging to detect and identify GMOs, especially the un-authorized GMOs. The present paper reviews the state of the art technologies and approaches in light of coverage, practicability, sensitivity and limitations. Emphasis is put on exemplifying practical detection of un-authorized GMOs. Although this paper has a European (EU) bias when examples are given, the contents have global relevance.     

新工科背景下生物工程五层次实践教学体系的构建

强化实践教学，提升创新能力是新工科建设的关键任务之一。文中结合天津大学化工学院生物工程专业培养方案的修订内容，不断改进实践教学体系和课程内容，建立了基础实验、综合实验、课程设计、科研实践、实习实训等五层次的教学改革体系，为培养具有实践能力和创新精神的卓越创新人才，提出了创新的教学改革模式，为新工科建设提出了新的设想和新的方案。     

基于文献计量的城市空间结构与生态环境关联性研究

以Web of Science核心合集中1999-2020年期间发表的城市空间结构与生态环境关联性的文献为研究样本，运用文献计量的方法，主要借助VOSviewer和Citespace软件，厘清该领域研究的主要特征与进展。研究结果表明：城市空间结构与生态环境关联性研究的文献总量上呈现波动上升的趋势，尤其在近五年的波动骤增态势十分显著，成为地理学、城乡规划学、环境与生态学等学科交叉融合研究的重要方向之一；研究内容主要聚焦于城市空间结构与大气环境的关联性机理，具体涉及温室气体排放、空气污染和城市热岛效应，同时水环境、土壤环境以及生物多样性等方面也越来越受到重视而逐步成为研究热点。未来可围绕构建两者关联性框架和评估体系、面向生态环境问题的城市空间结构优化设计、两者发展变化的模拟和预测等核心问题展开研究。     

Present status of sentinel lymph node biopsy in cervical cancer

Cervical cancer is the fourth most common cancer in women, and seventh overall. This disease represents a medical, economic and social burden. In early FIGO stage patients (IA, IB1 and IIA1), nodal involvement is the most important prognostic factor. Imaging evaluation of nodal metastasis is of limited value. In order to determine lymph node involvement, allow loco-regional control of the disease, define the need for adjuvant radiotherapy and improve survival, standard surgery for early disease is radical hysterectomy with systematic pelvic lymphadenectomy. However, this surgical treatment has risks and complications: longer operative time, larger blood loss, neurovascular or ureteral injury, lower-limb lymphedema, symptomatic lymphocysts, hydronephrosis. A method that allows to define the presence of regional metastasis with less morbidity and equal or greater precision is particularly relevant. The use of the sentinel lymph node biopsy is intended to reach that purpose. The present study reviews recent literature on the role of sentinel lymph node biopsy in cervical cancer, analyzing its indications and contraindications, injection and detection techniques, tracers used, surgical and pathological approaches and its applicability in up-to-date clinical practice.     

Distinguishing three subtypes of hematopoietic cells based on gene expression profiles using a support vector machine

Hematopoiesis is a complicated process involving a series of biological sub-processes that lead to the formation of various blood components. A widely accepted model of early hematopoiesis proceeds from long-term hematopoietic stem cells (LT-HSCs) to multipotent progenitors (MPPs) and then to lineage-committed progenitors. However, the molecular mechanisms of early hematopoiesis have not been fully characterized. In this study, we applied a computational strategy to identify the gene expression signatures distinguishing three types of closely related hematopoietic cells collected in recent studies: (1) hematopoietic stem cell/multipotent progenitor cells; (2) LT-HSCs; and (3) hematopoietic progenitor cells. Each cell in these cell types was represented by its gene expression profile among a total number of 20,475 genes. The expression features were analyzed by a Monte-Carlo Feature Selection (MCFS) method, resulting in a feature list. Then, the incremental feature selection (IFS) and a support vector machine (SVM) optimized with a sequential minimum optimization (SMO) algorithm were employed to access the optimal classifier with the highest Matthews correlation coefficient (MCC) value of 0.889, in which 6698 features were used to represent cells. In addition, through an updated program of MCFS method, seventeen decision rules can be obtained, which can classify the three cell types with an overall accuracy of 0.812. Using a literature review, both the rules and the top features used for building the optimal classifier were confirmed to be commonly used or potential biological markers for distinguishing the three cell types of HSPCs. This article is part of a Special Issue entitled: Accelerating Precision Medicine through Genetic and Genomic Big Data Analysis edited by Yudong Cai & Tao Huang.