PenBase, the shrimp antimicrobial peptide penaeidin database: Sequence-based classification and recommended nomenclature

Antimicrobial peptides play a major role in innate immunity. The penaeidins, initially characterized from the shrimp Litopenaeus vannamei, are a family of antimicrobial peptides that appear to be expressed in all penaeid shrimps. As of recent, a large number of penaeid nucleotide sequences have been identified from a variety of penaeid shrimp species and these sequences currently reside in several databases under unique identifiers with no nomenclatural continuity. To facilitate research in this field and avoid potential confusion due to a diverse number of nomenclatural designations, we have made a systematic effort to collect, analyse, and classify all the penaeidin sequences available in every database. We have identified a common penaeidin signature and subsequently established a classification based on amino acid sequences. In order to clarify the naming process, we have introduced a 'penaeidin nomenclature' that can be applied to all extant and future penaeidins. A specialized database, PenBase, which is freely available at http://www.penbase.immunaqua.com, has been developed for the penaeidin family of antimicrobial peptides, to provide comprehensive information about their properties, diversity and nomenclature. (c) 2005 Elsevier Ltd. All rights reserved.

Evaluation of a combined wavelet and a combined principal component analysis classification system for BCG diagnostic problem

Heart disease is one of the main factor causing death in the developed countries. Over several decades, variety of electronic and computer technology have been developed to assist clinical practices for cardiac performance monitoring and heart disease diagnosis. Among these methods, Ballistocardiography (BCG) has an interesting feature that no electrodes are needed to be attached to the body during the measurement. Thus, it is provides a potential application to asses the patients heart condition in the home. In this paper, a comparison is made for two neural networks based BCG signal classification models. One system uses a principal component analysis (PCA) method, and the other a discrete wavelet transform, to reduce the input dimensionality. It is indicated that the combined wavelet transform and neural network has a more reliable performance than the combined PCA and neural network system. Moreover, the wavelet transform requires no prior knowledge of the statistical distribution of data samples and the computation complexity and training time are reduced.

Classification of jinjiang oysters Crassostrea rivularis (Gould, 1861) from China, based on morphology and phylogenetic analysis

The jinjiang oyster Crassostrea rivularis [Gould, 1861. Descriptions of Shells collected in the North Pacific Exploring Expedition under Captains Ringgold and Rodgers. Proc. Boston Soc. Nat. Hist. 8 (April) 33-40] is one of the most important and best-known oysters in China. Based on the color of its flesh, two forms of C rivularis are recognized and referred to as the "white meat" and 11 red meat" oysters. The classification of white and red forms of this species has been a subject of confusion and debate in China. To clarify the taxonomic status of the two forms of C. rivularis, we collected and analyzed oysters from five locations along China's coast using both morphological characters and DNA sequences from mitochondrial 16S rRNA and cytochrome oxidase 1, and the nuclear 28S rRNA genes. Oysters were classified as white or red forms according to their morphological characteristics and then subjected to DNA sequencing. Both morphological and DNA sequence data suggest that the red and white oysters are two separate species. Phylogenetic analysis of DNA sequences obtained in this study and existing sequences of reference species show that the red oyster is the same species as C. ariakensis Wakiya [1929. Japanese food oysters. Jpn. J. Zool. 2, 359-367.], albeit the red oysters from north and south China are genetically distinctive. The white oyster is the same species as a newly described species from Hong Kong, C. hongkongensis Lam and Morton [2003. Mitochondrial DNA and identification of a new species of Crassostrea (Bivalvia: Ostreidae) cultured for centuries in the Pearl River Delta, Hong Kong, China. Aqua. 228, 1-13]. Although the name C. rivularis has seniority over C. ariakensis and C. hongkongensis, the original description of Ostrea rivularis by Gould [1861] does not fit shell characteristics of either the red or the white oysters. We propose that the name of C. rivularis Gould [1861] should be suspended, the red oyster should take the name C. ariakensis, and the white oyster should take the name C. hongkongensis. (C) 2004 Elsevier B.V. All rights reserved.

Classification of common oysters from North China

Oysters are commonly found on rocky shores along China's northern coast, although there is considerable confusion as to what species they are. To determine the taxonomic status of these oysters, we collected specimens from nine locations north of the Yangtze River and conducted genetic identification using DNA sequences. Fragments from three genes, mitochondrial 165 rRNA, mitochondria! cytochrome oxidase I (COI), and nuclear 285 rRNA, were sequenced in six oysters from each of the nine sites. Phylogenetic analysis of all three gene fragments clearly demonstrated that the small oysters commonly found on intertidal rocks in north China are Crassostrea gigas (Thunberg, 1793), not C. plicatula (the zhe oyster) as widely assumed. Their small size and irregular shell characteristics are reflections of the stressful intertidal environment they live in and not reliable characters for classification. Our study confirms that the oysters from Weifang, referred to as Jinjiang oysters or C. rivularis (Gould, 1861), are C. ariakensis (Wakiya, 1929). We found no evidence for the existence of C. talienwhanensis (Crosse, 1862) and other Crassostrea species in north China. Our study highlights the need for reclassifying oysters of China with molecular data.

改造型盆地油气成藏机理与油气预测－以银根－额济纳旗盆地为例

Reformed basin is a basin that underwent multiple immense reformation after the sedimentary stage, the major geologic elements of the petroleum system in the prototyped basin are destroyed to a certain extent, and their petroleum system has been reconstructed. This type of basin is frequently found in the course of exploration both home and abroad. In China, especially in the western and southern part of China, the basins in which oil explorations have been conducted are mostly reformed basins. The reformed basins from Paleozoic, Late Mesozoic to Cenozoic are widely distributed in West and South China. They are, and moreover, will be a challenge for oil and gas exploration. The conventional investigation and exploration techniques used in the slightly reconstructed basin just don't work well when facing the reformed basin. Therefore, the study on the reformed basin, especially the study on the pool-forming mechanism and reservoir prediction becomes a focus and one of difficulties for the geologists overseas and domestic. Yingen-Ejinaqi Basin is a typical case of the Late Mesozoic and Cenozoic reformed basins in China. It locates in West China and is a exploration frontier with difficulties and no break through is made for years. A comprehensive research on it will be of significance for oil and gas exploration in similar basins of China. The late research for reformed basin in China now is mainly concentrated on basin classification, formation mechanism, geologic features, and survey technique, distribution regularity of oil accumulation and its dominating factors, assessment of oil exploration prospect and target zones, etc. On the other hand, the study on the pool-forming mechanism and reservoir prediction seems insufficient in systematization, and the research is deficient in methodology and combination of qualitative and quantitative studies, as well as the application of the new theory and techniques. The current efforts are mainly directed to structures (faults), sedimentation, the relationship between reservoir evolution and oil accumulation, and some other relevant fields. However, the application of the new theory and techniques seems to be insufficient such as petroleum system, pool-forming dynamics, fluid pressure compartment, and basin simulation, etc. So is the dynamic and integrated research. As a result, incomplete knowledge and understandings derived from the research on pool-forming mechanism and reservoir prediction often do not accord with rea-lity of the basin. The study and exploration under the guidance of this knowledge will inevitably lead to errors and failure. This paper, based on the previous study of the other geologists on reformed basins, with emphasis on "wholeness or systematic, dynamic and integrated" research, presents a reverse thinking of beginning from conserved units in the basin and the combination of qualitative and quantitative study with new theory and technique by building a geological model. The paper also puts forward a new thought for studying the oil & gas accumulation and reservoir prediction , and establishes a new research system for reformed basin. It is verified by the known reservoir and oil accumulation area in the basin and has a practical value for use and reference. The new ideas and achievements in this research are as following: 1.This is the first time that the system for studying the reformed basin and its pool-forming mechanism and reservoir prediction is presented. A reverse thinking and combination of qualitative & quantitative are applied here with emphasis on "wholeness or systematic, dynamic and integrated" research, new theory, techniques & methods comprehensive use and geologic models building. 2. Identifying criterion and methods, classifying schemes, and denominating principles for the conserved units of reformed basins are presented in this paper. The geologic model of conserved units of Yingen-Ejinaqi Basin has been built. It is a practical method when combined with the traditional way for basin survey and the conserved units study. 3.The dynamic sources of basin deformation are believed to be stress, gravity and thermodynamics. The stress and gravity are key factors in basin deformation and pool forming, especially stress. Scientific proof is provided by classifying the functional type, style and range of the stress, gravity and thermodynamics. 4.The pool forming and reservoir distribution of Yingen-Ejinaqi Basin are controlled by multiple factors or geologic conditions or/and co-controlled by both of them. The qualitative and quantitative research on petroleum system and basin modeling will help us understand and determine the pool-forming period of the conserved unit (timing), the oil migrating direction (orientation), the oil accumulating region (location), the oil distributing border (bordering) and the size of oil accumulation (quantification). Thus the pool-forming and distribution zones can be predicted. 5.Three generating modes (reform-succession type, reform-destroyed type and reform-regenerating type or reform-newborn type) of pool forming for reformed basins are presented here, together with the inner relationships between basin deformation type, overlapping style and pool-forming modes. The pool-forming modes are determined by deformation type and overlapping style. Reservoir distribution will be predicted based on the modes and other concrete pool-forming conditions. 6.The evaluation methods of the conserved units and zones and the parameter selection are reliable in optimal selecting of target zones. The technical terms, new ideas and methods for the study of reformed basins, the pool-forming mechanism and reservoir prediction are presented in this paper. The concepts and terms, the identifying criterion, the denominating principles, the generating modes for pool forming, the methods of reservoir prediction, and the evaluation techniques for conserved units and zones can be used for reference in studies on the petroleum exploration of reformed basins in China and abroad. It serves as a typical example for further research of the reformed basins and the geologic regulations of oil accumulation. It has a practical value of use and reference. The future research in the field of pool-forming mechanism of the reformed basins may well be on the process simulation of pool-forming dynamics of the reformed basins. Experimental work has been conducted to simulate the processes by using quantitative and qualitative methods combined. The further study in this field calls for more efforts.

Distance Metric Between 3D Models and 3D Images for Recognition and Classification

Similarity measurements between 3D objects and 2D images are useful for the tasks of object recognition and classification. We distinguish between two types of similarity metrics: metrics computed in image-space (image metrics) and metrics computed in transformation-space (transformation metrics). Existing methods typically use image and the nearest view of the object. Example for such a measure is the Euclidean distance between feature points in the image and corresponding points in the nearest view. (Computing this measure is equivalent to solving the exterior orientation calibration problem.) In this paper we introduce a different type of metrics: transformation metrics. These metrics penalize for the deformatoins applied to the object to produce the observed image. We present a transformation metric that optimally penalizes for "affine deformations" under weak-perspective. A closed-form solution, together with the nearest view according to this metric, are derived. The metric is shown to be equivalent to the Euclidean image metric, in the sense that they bound each other from both above and below. For Euclidean image metric we offier a sub-optimal closed-form solution and an iterative scheme to compute the exact solution.

Some Extensions of the K-Means Algorithm for Image Segmentation and Pattern Classification

In this paper we present some extensions to the k-means algorithm for vector quantization that permit its efficient use in image segmentation and pattern classification tasks. It is shown that by introducing state variables that correspond to certain statistics of the dynamic behavior of the algorithm, it is possible to find the representative centers fo the lower dimensional maniforlds that define the boundaries between classes, for clouds of multi-dimensional, mult-class data; this permits one, for example, to find class boundaries directly from sparse data (e.g., in image segmentation tasks) or to efficiently place centers for pattern classification (e.g., with local Gaussian classifiers). The same state variables can be used to define algorithms for determining adaptively the optimal number of centers for clouds of data with space-varying density. Some examples of the applicatin of these extensions are also given.

Gait Dynamics for Recognition and Classification

This paper describes a representation of the dynamics of human walking action for the purpose of person identification and classification by gait appearance. Our gait representation is based on simple features such as moments extracted from video silhouettes of human walking motion. We claim that our gait dynamics representation is rich enough for the task of recognition and classification. The use of our feature representation is demonstrated in the task of person recognition from video sequences of orthogonal views of people walking. We demonstrate the accuracy of recognition on gait video sequences collected over different days and times, and under varying lighting environments. In addition, preliminary results are shown on gender classification using our gait dynamics features.

Multiple Resolution Image Classification

Binary image classifiction is a problem that has received much attention in recent years. In this paper we evaluate a selection of popular techniques in an effort to find a feature set/ classifier combination which generalizes well to full resolution image data. We then apply that system to images at one-half through one-sixteenth resolution, and consider the corresponding error rates. In addition, we further observe generalization performance as it depends on the number of training images, and lastly, compare the system's best error rates to that of a human performing an identical classification task given teh same set of test images.