Newly recorded species of the genus Isohypsibius (Tardigrada;Hypsibiidae) from China

This paper reports two newly recorded species, lsohypsibius lunulatus Iharos, 1966 and lsohypsibiusprosostomus Thulin, 1928, of the genus lsohypsibius （Tardigrada; Hypsibiidae） from China. The specimens of lsohysibius lunulatus were collected from Taibai Mt （34°18′N, 107°42′E） at 2,500 m a.s.1, and those oflsohypsibius prosostomus from Taibai Mt （34°10′N, 107°35′E） at 2,000 m above sea level. All specimens are deposited at the College of Life Sciences, Shaanxi Normal University, China. A key to the Chinese species of lsohypsibius was also given.

Video learning based image classification method for object recognition

Automatic image classification is the first step toward semantic understanding of an object in the computer vision area.The key challenge of problem for accurate object recognition is the ability to extract the robust features from various viewpoint images and rapidly calculate similarity between features in the image database or video stream.In order to solve these problems,an effective and rapid image classification method was presented for the object recognition based on the video learning technique.The optical-flow and RANSAC algorithm were used to acquire scene images from each video sequence.After the selection of scene images,the local maximum points on comer of object around local area were found using the Harris comer detection algorithm and the several attributes from local block around each feature point were calculated by using scale invariant feature transform （SIFT） for extracting local descriptor.Finally,the extracted local descriptor was learned to the three-dimensional pyramid match kernel.Experimental results show that our method can extract features in various multi-viewpoint images from query video and calculate a similarity between a query image and images in the database.

Essential Oil Composition of Four Endemic Tanacetum L. （Asteraceae） Taxa from Turkey and a Chemotaxonomic Approach

In this study the essential oil components aerial parts of Tanacetum heterotomum （Bornm.） Grierson, T. zahlbruckneri （Nab.） Griersson, T. densum （Lab.） Schultz Bip. subsp, amani Heywood and T. cadmeum （Boiss.） Heywood subsp, orientale were examined by HS-SPME/GC-MS technique. Thirty six, thirty nine, forty and forty five constituents were determined representing 88.9%, 90.1%, 90.8% and 91.5% of the oil, respectively. The main compounds of studied Tanacetum L. taxa; borneol, α-pinene, 1,8-cineole, β-pinene, camphor, germacrene D, spathulenol are determined. Studied Tanacetum taxa showed congruency with the discription in Flora of Turkey as morphological properties; on the contrary essential oil composition were detected very quiet diverse infrageneric level. Chemotypes of Tanacetum L. taxa were reported as borneol, germacrene D, spathulenol, α-pinene, 1,8-cineole, β-pinene and camphor. The results obtained from this study were discussed in terms of chemotaxonomy and natural products.

Variable Sets principle and method for flood classification

Flood classification is an effective way to improve flood forecasting accuracy. According to the opposite unity mathematical theorem in Variable Sets theory, this paper proposes a Variable Sets principle and method for flood classification, which is based on the mathematical theorem of dialectics basic laws. This newly proposed method explores a novel way to analyze and solve engineering problems by utilizing a dialectical thinking. In this paper, the Tuwei River basin, located in the Yellow River tributary, is taken as an example for flood classification. The results obtained in this study reveal the problems in a previous method—Set Pair Analysis classification method. The variable sets method is proven to be theoretically rigorous, computationally simple. The classification results are objective, accurate and consistent with the actual situations. This study demonstrates the significant importance of using a scientifically sound method in solving engineering problems.

Hybridization and the species problem in conservation

Most biologists recognize the ＂species phenomenon＂ as a real pattern in nature： Biodiversity is characterized by dis- continuities between recognizable groups classified as species. Many conservation laws focus on preventing species extinction. However, species are not fixed. Discontinuities evolve gradually and sometimes disappear. Exactly how to define particular spe- cies is not always obvious. Hybridization between taxonomic species reminds us that species classification is not a perfect repre- sentation of nature. Classification is a model that is very useful, but not adequate in all cases. Conservationists often confront questions about how to apply species-based laws when hybridization confounds classification. Development of sophisticated techniques and nuanced interpretation of data in the basic study of species and speciation has exposed the need for deeper educa- tion in genetics and evolution for applied conservationists and decision makers. Here we offer a brief perspective on hybridiza- tion and the species problem in conservation. Our intended audience is conservation practitioners and decision-makers more than geneticists and evolutionary biologists. We wish to emphasize that the goals and premises of legislative classification are not identical to those of scientific classification. Sometimes legal classification is required when the best available science indicates that discrete classification is not an adequate model for the case. Establishing legal status and level of protection for hybrids and hybrid populations means choosing from a range of scientifically valid alternatives. Although we should not abandon species-based approaches to conservation, we must recognize their limitations and work to clarify the roles of science and values in ethical and legal decisions [Current Zoology 61 （1）： 206-216, 2015].