生物实验数据的单因素方差分析

结合实例详细地叙述了生物学实验数据的单因素方差分析计算方法,介绍了根据计算结果如何分析组群间的差异显著性,具有较强的实用意义。     

一种从直流到超高频生物电磁学实验系统

阐述了环境对生物电磁学实验的影响和电磁计量在生物电磁学实验中的重要性。提出了一种用于从直流到超高频（ＤＣ—ＵＨＦ）段生物电磁学实验的封闭系统,计算了其中的电磁场分布,从而给出了电磁计量方法。     

"测量肺活量"实验的改进

专用的肺活量计价高也较难买到，按苏教版《生物》7年级下册所介绍的方法操作，测量和计算较麻烦且误差较大。笔者自制了一种简易肺活量计。     

提高农村中学生物实验开出率的几点做法

随着教育事业的发展,目前广大农村中学已建立一些实验中心,为生物实验教学创造了有利条件。但农村实验中心与实验室建设才起步,生物实验设备还很不完善。为了发挥实验室现有设备的作用,创造条件提高生物实验开出率,我们的几点做法是:     

生物教学实验的改进

生物学是一门建立在实验基础上的自然科学。它研究的对象很复杂,虽然书中给出的结论是经典的,需要我们学习,但是如果只依靠教师来讲解或出示一些教具演示,可能学生很难理解,也只能获得比较抽象的知识,无法体验探索学习的乐趣。所以实验在教学中显得尤为重要。必须通过人人参与实验,来培养学生学习生物的兴趣,掌握实验的基本技能,以便更好的发展他们的智力和能力。     

“显微镜使用”教法的几点改革

在大力加强生物实验教学的今天,显微镜操作与使用受到了广大师生的进一步重视。但据调查,许多初中乃至高中学生仍然不会使用显微镜,这必然会严重影响实验和课堂教学的质量。不少学生长时期内掌握不了这一基本操作技能的原因是什么?如何解决这个     

改进实验材料提高实验效果

为了全面提高学生的科学素质 ,培养适应于我国社会主义现代化建设的人才 ,普通高中生物新大纲在必修课部分安排了 2 4个实验项目 ,其中“高倍镜的使用和观察叶绿体的装片”、“观察细胞质流动”及“植物细胞的质壁分离与复原”3个实验 ,采用水绵、黑藻、苦草、眼子菜、狐尾藻和葫芦藓等植物作为实验材料 ,能显著提高实验的效果。水绵　 Spirogra,为常见绿藻 ,是由多个筒状细胞连接而成的单列不分枝的丝状体 ;竹叶眼子菜　 Pota-mogeton malainus Miq.(P.japonicus Franch.et Sav.)、小叶眼子菜　 Potamogoton pusillus L .、光叶眼子菜…     

基于数字化实验系统应用的生物实验教学分析

数字化实验系统是目前科研领域的新课题,它是一种全新的开放式的实验平台。这种平台与过去传统的实验平台相比,具有快速、准确、精密、实时等特点,在这种全新的平台上,完全实现了新课标提倡的"信息技术与各学科教学的全面联系"。本文主要分析在数字化实验系统平台上进行生物实验教学的特点,并且对这种实验教学进行一系列探讨。     

初中生物（实验本）观察能力和实验能力要求浅析

落实《九年义务教育生物教学大纲》所规定的实验、观察能力的要求,是提高生物教学质量的一个重要方面。经过两年来对生物(实验本)教材的试用,我们深深体会到,落实这两方面能力的培养,首先要分析研究教学大纲和教材的有关具体要求。现对人教社编写的两套教材中的观察能力和实验能力的要求作一些分析。     

Computational issues in mapping variation affecting susceptibility to complex disorders: the chicken and the egg

Linkage mapping strategies for complex disorders have evolved under a variety of constraints. Some of these constraints reflect the nature of complex disorders and are manifest in limitations on the kinds of data that can be collected, while others were (at least historically) strictly computational. This paper focuses on how computational issues have impacted the design of studies on complex disorders and, conversely, how our study designs have influenced the computational issues that have been addressed. We now have unprecedented computational resources, but also face unprecedented computational and methodological challenges as we move from the linkage mapping of genes influencing susceptibility to complex disorders toward the identification of the actual variation affecting susceptibility to these disorders. The near-term computational and methodological issues we must address will be profoundly influenced by the study designs of the recent past. But future study designs, as well as our investments in computational and methodological research, ought to be developed considering the computational and informatics resources we now have at hand.     

Computational exploration of metal–organic frameworks: examples of advances in crystal structure predictions and electronic structure tuning

The purpose of this article is to consider some recent developments in the area of the computational chemistry of metal–organic frameworks (MOFs), and more specifically on their crystal structure prediction and electronic structures. We intend here to illustrate how computational approaches might be powerful tool for the discovery of new families of hybrid frameworks, helping to understand their often complex energy landscapes. Also, MOFs have attracted a lot of attention due to their potential use for photocatalysis and optoelectronic, making it necessary to develop strategies to control their electronic structures. We will show how recent computational studies in this area have allowed a better understanding of their electronic properties and their potential tunability, highlighting when they have given successful guidelines for the discovery of novel MOFs with targeted properties.     

Galaxy: a comprehensive approach for supporting accessible,reproducible, and transparent computational research in the life sciences

Increased reliance on computational approaches in the life sciences has revealed grave concerns about how accessible and reproducible computation-reliant results truly are. Galaxy , an open web-based platform for genomic research, addresses these problems. Galaxy automatically tracks and manages data provenance and provides support for capturing the context and intent of computational methods. Galaxy Pages are interactive, web-based documents that provide users with a medium to communicate a complete computational analysis.     

Computational studies of the extraction of visual spatial information from binocular and motion cues

This paper reviews some of the contributions that work in computational vision has made to the study of biological vision systems. We concentrate on two areas where there has been strong interaction between computational and experimental studies: the use of binocular stereo to recover the distances to surfaces in space, and the recovery of the three-dimensional shape of objects from relative motion in the image. With regard to stereo, we consider models proposed for solving the stereo correspondence problem, focussing on the way in which physical properties of the world constrain possible methods of solution. We also show how critical observations regarding human stereo vision have helped to shape these models. With regard to the recovery of structure from motion, we focus on how the constraint of object rigidity has been used in computational models of this process.     

The role of molecular modelling and simulation in the discovery and deployment of metal-organic frameworks for gas storage and separation*

ABSTRACT

Metal-organic frameworks (MOFs) are highly tuneable, extended-network, crystalline, nanoporous materials with applications in gas storage, separations, and sensing. We review how molecular models and simulations of gas adsorption in MOFs have informed the discovery of performant MOFs for methane, hydrogen, and oxygen storage, xenon, carbon dioxide, and chemical warfare agent capture, and xylene enrichment. Particularly, we highlight how large, open databases of MOF crystal structures, post-processed to enable molecular simulations, are a platform for computational materials discovery. We discuss how to orient research efforts to routinise the computational discovery of MOFs for adsorption-based engineering applications.     

How do high-level specifications of the brain relate to variational approaches?

High-level specification of how the brain represents and categorizes the causes of its sensory input allows to link "what is to be done" (perceptual task) with "how to do it" (neural network calculation). In this article, we describe how the variational framework, which encountered a large success in modeling computer vision tasks, has some interesting relationships, at a mesoscopic scale, with computational neuroscience. We focus on cortical map computations such that "what is to be done" can be represented as a variational approach, i.e., an optimization problem defined over a continuous functional space. In particular, generalizing some existing results, we show how a general variational approach can be solved by an analog neural network with a given architecture and conversely. Numerical experiments are provided as an illustration of this general framework, which is a promising framework for modeling macro-behaviors in computational neuroscience.     

Molecular dynamics simulations of membrane proteins

Membrane proteins control the traffic across cell membranes and thereby play an essential role in cell function from transport of various solutes to immune response via molecular recognition. Because it is very difficult to determine the structures of membrane proteins experimentally, computational methods have been increasingly used to study their structure and function. Here we focus on two classes of membrane proteins—ion channels and transporters—which are responsible for the generation of action potentials in nerves, muscles, and other excitable cells. We describe how computational methods have been used to construct models for these proteins and to study the transport mechanism. The main computational tool is the molecular dynamics (MD) simulation, which can be used for everything from refinement of protein structures to free energy calculations of transport processes. We illustrate with specific examples from gramicidin and potassium channels and aspartate transporters how the function of these membrane proteins can be investigated using MD simulations.     

Illuminator: increasing synergies between medicinal and computational chemists

We present Illuminator, a user-friendly web front end to computational models such as docking and 3D shape similarity calculations. Illuminator was specifically created to allow non-experts to design and submit molecules to computational chemistry programs. As such it provides a simple user interface allowing users to submit jobs starting from a 2D structure. The models provided are pre-optimized by computational chemists for each specific target. We provide an example of how Illuminator was used to prioritize the design of molecular substituents in the Anadys HCV Polymerase (NS5B) project. With 7500 submitted jobs in 1.5 years, Illuminator has allowed project teams at Anadys to accelerate the optimization of novel leads. It has also improved communication between project members and increased demand for computational drug discovery tools.     

No Longer Confidential: Estimating the Confidence of Individual Regression Predictions

Quantitative predictions in computational life sciences are often based on regression models. The advent of machine learning has led to highly accurate regression models that have gained widespread acceptance. While there are statistical methods available to estimate the global performance of regression models on a test or training dataset, it is often not clear how well this performance transfers to other datasets or how reliable an individual prediction is–a fact that often reduces a user’s trust into a computational method. In analogy to the concept of an experimental error, we sketch how estimators for individual prediction errors can be used to provide confidence intervals for individual predictions. Two novel statistical methods, named CONFINE and CONFIVE, can estimate the reliability of an individual prediction based on the local properties of nearby training data. The methods can be applied equally to linear and non-linear regression methods with very little computational overhead. We compare our confidence estimators with other existing confidence and applicability domain estimators on two biologically relevant problems (MHC–peptide binding prediction and quantitative structure-activity relationship (QSAR)). Our results suggest that the proposed confidence estimators perform comparable to or better than previously proposed estimation methods. Given a sufficient amount of training data, the estimators exhibit error estimates of high quality. In addition, we observed that the quality of estimated confidence intervals is predictable. We discuss how confidence estimation is influenced by noise, the number of features, and the dataset size. Estimating the confidence in individual prediction in terms of error intervals represents an important step from plain, non-informative predictions towards transparent and interpretable predictions that will help to improve the acceptance of computational methods in the biological community.     

More than just 'doing the math'

Two new articles show how computational tools continue to move beyond mere sequence-based bioinformatic analysis into more advanced arenas of prediction, deduction and network building.