计算机在昆虫分类检索上的应用

笔者最近用普通386计算机,辅助进行昆虫分类检索工作,取得了一定的效果,现简报如下。根据中国经济昆虫志[1]飞虱科褐飞虱属Ninaparvata分种检索表,将昆虫的主要检索分类特征,用数据库语言Foxpro2．5,编成人机对话窗口式计算机程序。这种程序的特点是：（1）将类似两项式检索表放到计算机内,利用计算机的特性,仅将有用的信息显示出来,而无关的信息根本不出现。（2）应答速度快,一按键钮,立即显示。（3）界面友好,简单直观,在窗口下直接选择,无须打任何字符。这使得不懂计算机的人,都可用它。（4）移植性好,对于任何一个昆虫…     

自编检索表在动物分类实验教学中的应用

检索表在生物分类有着广泛的应用。在动物分类实验教学中 ,除了根据实验指导提供的分目检索表鉴定动物外 ,对学生自采、自制的一些标本 ,可让学生学会自编简单的分科检索表或由教师编制一些分种检索表进行分类鉴定。这样 ,有利于提高学生的观察分析能力 ,全面的辨认动物。自编检索表能结合当地实际进行教学 ,应用简单 ,灵活 ,效果好。1　学生自编分科检索表在昆虫分类上的应用在昆虫分类实验中 ,课前要求学生到野外或校园内采集 10个目 (不同种类 )以上的昆虫进行分类检索。对观赏园艺专业开设的“园林植物病虫害 (虫害部分 )防治”的昆虫分…     

隔指孢属分类评述及其一新种

本文报道了隔指孢属(Dactylella Grove)一新种—云南隔指孢(D. yunnanensis);并对隔指孢属的分类进行了评述;重新修订了隔指孢属的特征。列出了捕食和非捕食种类共计34种。其中包括了6个新组合,并制作了检索表。     

交互式检索表提升分类学研究与应用效率:以兰科植物为例

与传统的连续检索表相比,基于计算机等数字终端的多通道交互式检索表具有很多优势,如易于携带、实时更新、界面直观、使用方便、用户广泛、产生新数据集且便于研究以及能够推动分类学知识的普及等。本文以兰科植物为例介绍了交互式检索表的特点、程序建立和应用。兰科植物是生物多样性保育的旗舰类群,具有很强的观赏和药用价值,但中国兰科植物种类繁多,分类困难。我们在自主研发交互式检索表软件MagIden的基础上,以《中国植物志》和Flora of China的描述为依据,制作了中国兰科植物交互式电子检索表,为分类学研究者和爱好者提供了一个新的、鉴定中国兰科植物的中英文双语工具;同时,交互式检索表生成的MVSP和Bayes形态特征数据集可用于聚类分析和分支分类学研究。交互式检索表在数据积累、更新和分析利用等方面具有独特的优势,是提高传统植物分类学研究与应用效率的重要工具。     

2007年第18届国际生物学奥林匹克竞赛(2)

实验考试 2 动物形态,系统和生态学 此实验由3部分组成. 任务A解剖2个环节动物(26分) 任务B用检索表鉴定环节动物(10分) 任务C说明结构、体腔类型、生活方式和10 种蠕虫类动物的分类(27分) (总分63分) (时间共90 min) 说明:实验前,监考人将向你展示一张红色和绿色卡片以测试红绿色盲.     

安徽稻田小蜂分科检索（膜翅目：小蜂总科）

对中国安徽芜湖稻田中的小蜂总科进行了分类研究,共发现9科12属,编制了分科检索表,提供了各科主要的分类特征图。     

被子植物APG分类系统评论

随着植物分子系统学的兴起,被子植物系统发育研究取得了举世瞩目的进展。被子植物系统发育组提出了基于DNA证据的被子植物在目、科分类阶元上的分类系统,简称APG系统。本文简要概括了APG系统的主要成就:(1)验证了被子植物分类系统的可重复性和可预言性;(2)解决了一些依据形态学性状未能确定的类群的系统位置;(3)证明了将被子植物一级分类分为双子叶植物和单子叶植物的不自然性;(4)证实了单沟花粉和三沟花粉在被子植物高级分类单元划分中的重要性;(5)发现雄蕊的向心发育和离心发育在多雄蕊类群中是多次发生的,不应作为划分纲或亚纲的重要依据;(6)支持基于形态学(广义)性状划分的大多数科是自然的;(7)将一些长期认为自然的科四分五裂。同时,我们指出了尚需深入研究的几个问题:(1)如何将以分子数据建立的系统和以综合形态学证据建立的系统相协调;(2)依据APG系统的研究结果需要创立新的形态演化理论;(3)只以"单系群"作为划分科、目的依据值得商榷;(4)APG系统中一些目的分类没有可信的形态学共衍征;(5)依据APG系统需要做出一个自然系统的目、科检索表和目、科的特征集要。此外,我们对以亚洲,特别是东亚为分布中心的一些类群的系统关系或分类等级提出建议,包括八角科、芒苞草科、水青树科、火筒树科、马尾树科、七叶树科、槭树科、伯乐树科应独立为科,山茱萸科(广义)应分为山茱萸科(狭义)和蓝果树科(广义)。     

形象检索法与形象检索表

形象检索法是采用典型的外部形态特征,辅以图片检索,对种及种以下单元进行分类的方法。该方法仅使用典型的外部形态特征对不同分类等级的多歧对应或非对应性状编制检索表,对鉴定所涉及的特征或名词术语辅以简练准确的文字以及典型特征的照片或图片予以说明。主要为非专业人员快速检索而设计。对野生动物的执法、管理、保护教育及知识普及具有理论和实践指导意义。     

食蚜蝇科卵壳超微形态初探

本文应用扫描电镜研究了24种食蚜蝇(双翅目,食蚜蝇科)卵壳的超微形态.描述了所研究种类卵的形态并编列了检索表.研究结果表明卵的受精孔形态是和卵壳花纹同等重要的分类特征.文中讨论了受精孔形态和卵表花纹在食蚜蝇卵分类中的重要性以及造成卵壳背、腹面花纹差异的原因.     

红火蚁及其重要近似种的鉴别

介绍了红火蚁及其重要近似种的分类特征和分布,以及火蚁属中国已知种类,还提供了上述种类的鉴别检索表。     

A text and image analysis workflow using citizen science data to extract relevant social media records: Combining red kite observations from Flickr,eBird and iNaturalist

There is an urgent need to develop new methods to monitor the state of the environment. One potential approach is to use new data sources, such as User-Generated Content, to augment existing approaches. However, to date, studies typically focus on a single date source and modality. We take a new approach, using citizen science records recording sightings of red kites (Milvus milvus) to train and validate a Convolutional Neural Network (CNN) capable of identifying images containing red kites. This CNN is integrated in a sequential workflow which also uses an off-the-shelf bird classifier and text metadata to retrieve observations of red kites in the Chilterns, England. Our workflow reduces an initial set of more than 600,000 images to just 3065 candidate images. Manual inspection of these images shows that our approach has a precision of 0.658. A workflow using only text identifies 14% less images than that including image content analysis, and by combining image and text classifiers we achieve almost perfect precision of 0.992. Images retrieved from social media records complement those recorded by citizen scientists spatially and temporally, and our workflow is sufficiently generic that it can easily be transferred to other species.     

Growth of Wolinella succinogenes with polysulphide as terminal acceptor of phosphorylative electron transport

Polysulphide was formed according to reaction (1), when tetrathionate was (1) $${\text{S}}_4 {\text{O}}_6^{2 - } + {\text{HS}}^ - \to 2{\text{S}}_2 {\text{O}}_3^{2 - } + {\text{S(O)}} + {\text{H}}^ + $$ added to an anaerobic buffer (pH 8.5) containing excess sulphide. S(O) denotes the zero oxidation state sulphur in the polysulphide mixture S _infn ^sup2- . The addition of formate to the polysulphide solution in the presence of Wolinella succinogenes caused the reduction of polysulphide according to reaction (2). The bacteria grew in a medium containing formate and sulphide, (2) $${\text{HCO}}_2^ - + {\text{S(O)}} + {\text{H}}2{\text{O}} \to {\text{HCO}}_3^ - + {\text{HS}}^ - + {\text{H}}^ + $$ when tetrathionate was continuously added. The cell density increased proportional to reaction (3) which represents the sum of reactions (1) and (3) $${\text{HCO}}_2^ - + {\text{S}}_{\text{4}} {\text{O}}_6^{2 - } + {\text{H}}2{\text{O}} \to {\text{HCO}}_3^ - + 2{\text{S}}_{\text{2}} {\text{O}}_3^{2 - } + 2{\text{H}}^ + $$ (2). The cell yield per mol formate was nearly the same as during growth on formate and elemental sulphur, while the velocity of growth was greater. The specific activities of polysulphide reduction by formate measured with bacteria grown with tetrathionate or with elemental sulphur were consistent with the growth parameters. The results suggest that W. succinogenes grow at the expense of formate oxidation by polysulphide and that polysulphide is an intermediate during growth on formate and elemental sulphur.     

Electronic mail.

Electronic mail (email) has many advantages over other forms of communication: it is easy to use, free of charge, fast, and delivers information in a digital format. As a text only medium, email is usually less formal in style than conventional correspondence and may contain acronyms and other features, such as smileys, that are peculiar to the Internet. Email client programs that run on your own microcomputer render email powerful and easy to use. With suitable encoding methods, email can be used to send any kind of computer file, including pictures, sounds, programs, and movies. Numerous biomedical electronic mailing lists and other Internet services are accessible by email.     

Lights,Camera…Citizen Science: Assessing the Effectiveness of Smartphone-Based Video Training in Invasive Plant Identification

The rapid growth and increasing popularity of smartphone technology is putting sophisticated data-collection tools in the hands of more and more citizens. This has exciting implications for the expanding field of citizen science. With smartphone-based applications (apps), it is now increasingly practical to remotely acquire high quality citizen-submitted data at a fraction of the cost of a traditional study. Yet, one impediment to citizen science projects is the question of how to train participants. The traditional “in-person” training model, while effective, can be cost prohibitive as the spatial scale of a project increases. To explore possible solutions, we analyze three training models: 1) in-person, 2) app-based video, and 3) app-based text/images in the context of invasive plant identification in Massachusetts. Encouragingly, we find that participants who received video training were as successful at invasive plant identification as those trained in-person, while those receiving just text/images were less successful. This finding has implications for a variety of citizen science projects that need alternative methods to effectively train participants when in-person training is impractical.     

Accurate Construction of Photoactivated Localization Microscopy (PALM) Images for Quantitative Measurements

Localization-based superresolution microscopy techniques such as Photoactivated Localization Microscopy (PALM) and Stochastic Optical Reconstruction Microscopy (STORM) have allowed investigations of cellular structures with unprecedented optical resolutions. One major obstacle to interpreting superresolution images, however, is the overcounting of molecule numbers caused by fluorophore photoblinking. Using both experimental and simulated images, we determined the effects of photoblinking on the accurate reconstruction of superresolution images and on quantitative measurements of structural dimension and molecule density made from those images. We found that structural dimension and relative density measurements can be made reliably from images that contain photoblinking-related overcounting, but accurate absolute density measurements, and consequently faithful representations of molecule counts and positions in cellular structures, require the application of a clustering algorithm to group localizations that originate from the same molecule. We analyzed how applying a simple algorithm with different clustering thresholds (t_Thresh and d_Thresh) affects the accuracy of reconstructed images, and developed an easy method to select optimal thresholds. We also identified an empirical criterion to evaluate whether an imaging condition is appropriate for accurate superresolution image reconstruction with the clustering algorithm. Both the threshold selection method and imaging condition criterion are easy to implement within existing PALM clustering algorithms and experimental conditions. The main advantage of our method is that it generates a superresolution image and molecule position list that faithfully represents molecule counts and positions within a cellular structure, rather than only summarizing structural properties into ensemble parameters. This feature makes it particularly useful for cellular structures of heterogeneous densities and irregular geometries, and allows a variety of quantitative measurements tailored to specific needs of different biological systems.     

Integrating image data into biomedical text categorization

Categorization of biomedical articles is a central task for supporting various curation efforts. It can also form the basis for effective biomedical text mining. Automatic text classification in the biomedical domain is thus an active research area. Contests organized by the KDD Cup (2002) and the TREC Genomics track (since 2003) defined several annotation tasks that involved document classification, and provided training and test data sets. So far, these efforts focused on analyzing only the text content of documents. However, as was noted in the KDD'02 text mining contest-where figure-captions proved to be an invaluable feature for identifying documents of interest-images often provide curators with critical information. We examine the possibility of using information derived directly from image data, and of integrating it with text-based classification, for biomedical document categorization. We present a method for obtaining features from images and for using them-both alone and in combination with text-to perform the triage task introduced in the TREC Genomics track 2004. The task was to determine which documents are relevant to a given annotation task performed by the Mouse Genome Database curators. We show preliminary results, demonstrating that the method has a strong potential to enhance and complement traditional text-based categorization methods.     

Guide to the Internet. The world wide web.

The world wide web provides a uniform, user friendly interface to the Internet. Web pages can contain text and pictures and are interconnected by hypertext links. The addresses of web pages are recorded as uniform resource locators (URLs), transmitted by hypertext transfer protocol (HTTP), and written in hypertext markup language (HTML). Programs that allow you to use the web are available for most operating systems. Powerful on line search engines make it relatively easy to find information on the web. Browsing through the web--"net surfing"--is both easy and enjoyable. Contributing to the web is not difficult, and the web opens up new possibilities for electronic publishing and electronic journals.     

Categorizing biomedicine images using novel image features and sparse coding representation

Background

Images embedded in biomedical publications carry rich information that often concisely summarize key hypotheses adopted, methods employed, or results obtained in a published study. Therefore, they offer valuable clues for understanding main content in a biomedical publication. Prior studies have pointed out the potential of mining images embedded in biomedical publications for automatically understanding and retrieving such images' associated source documents. Within the broad area of biomedical image processing, categorizing biomedical images is a fundamental step for building many advanced image analysis, retrieval, and mining applications. Similar to any automatic categorization effort, discriminative image features can provide the most crucial aid in the process.

Method

We observe that many images embedded in biomedical publications carry versatile annotation text. Based on the locations of and the spatial relationships between these text elements in an image, we thus propose some novel image features for image categorization purpose, which quantitatively characterize the spatial positions and distributions of text elements inside a biomedical image. We further adopt a sparse coding representation (SCR) based technique to categorize images embedded in biomedical publications by leveraging our newly proposed image features.

Results

we randomly selected 990 images of the JPG format for use in our experiments where 310 images were used as training samples and the rest were used as the testing cases. We first segmented 310 sample images following the our proposed procedure. This step produced a total of 1035 sub-images. We then manually labeled all these sub-images according to the two-level hierarchical image taxonomy proposed by [1]. Among our annotation results, 316 are microscopy images, 126 are gel electrophoresis images, 135 are line charts, 156 are bar charts, 52 are spot charts, 25 are tables, 70 are flow charts, and the remaining 155 images are of the type "others". A serial of experimental results are obtained. Firstly, each image categorizing results is presented, and next image categorizing performance indexes such as precision, recall, F-score, are all listed. Different features which include conventional image features and our proposed novel features indicate different categorizing performance, and the results are demonstrated. Thirdly, we conduct an accuracy comparison between support vector machine classification method and our proposed sparse representation classification method. At last, our proposed approach is compared with three peer classification method and experimental results verify our impressively improved performance.

Conclusions

Compared with conventional image features that do not exploit characteristics regarding text positions and distributions inside images embedded in biomedical publications, our proposed image features coupled with the SR based representation model exhibit superior performance for classifying biomedical images as demonstrated in our comparative benchmark study.

     

Optimized Negative Staining: a High-throughput Protocol for Examining Small and Asymmetric Protein Structure by Electron Microscopy

Structural determination of proteins is rather challenging for proteins with molecular masses between 40 - 200 kDa. Considering that more than half of natural proteins have a molecular mass between 40 - 200 kDa^1,2, a robust and high-throughput method with a nanometer resolution capability is needed. Negative staining (NS) electron microscopy (EM) is an easy, rapid, and qualitative approach which has frequently been used in research laboratories to examine protein structure and protein-protein interactions. Unfortunately, conventional NS protocols often generate structural artifacts on proteins, especially with lipoproteins that usually form presenting rouleaux artifacts. By using images of lipoproteins from cryo-electron microscopy (cryo-EM) as a standard, the key parameters in NS specimen preparation conditions were recently screened and reported as the optimized NS protocol (OpNS), a modified conventional NS protocol ³ . Artifacts like rouleaux can be greatly limited by OpNS, additionally providing high contrast along with reasonably high‐resolution (near 1 nm) images of small and asymmetric proteins. These high-resolution and high contrast images are even favorable for an individual protein (a single object, no average) 3D reconstruction, such as a 160 kDa antibody, through the method of electron tomography^4,5. Moreover, OpNS can be a high‐throughput tool to examine hundreds of samples of small proteins. For example, the previously published mechanism of 53 kDa cholesteryl ester transfer protein (CETP) involved the screening and imaging of hundreds of samples ⁶. Considering cryo-EM rarely successfully images proteins less than 200 kDa has yet to publish any study involving screening over one hundred sample conditions, it is fair to call OpNS a high-throughput method for studying small proteins. Hopefully the OpNS protocol presented here can be a useful tool to push the boundaries of EM and accelerate EM studies into small protein structure, dynamics and mechanisms.     

Inference of Euler angles for single-particle analysis by means of evolutionary algorithms

Single-particle analysis is one of the methods for structural studies of protein and macromolecules; it requires advanced image analysis of electron micrographics. Reconstructing three-dimensional (3D) structure from microscope images is not an easy analysis because of the low image resolution of images and lack of the directional information of images in 3D structure. To improve the resolution, different projections are aligned, classified, and averaged. Inferring the orientations of these images is so difficult that the task of reconstructing 3D structures depends upon the experience of researchers. But recently, a method to reconstruct 3D structures was automatically devised. In this paper, we propose a new method for determining Euler angles of projections by applying genetic algorithms. We empirically show that the proposed approach has improved the previous one in terms of computational time and acquired precision.