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【Objective】The present paper aimed to realize the genetic resources and genetic relationship of Henan Big-tail sheep, Small-tail Han sheep and Yuxi Fat-tail sheep.【Method】The genetic diversity of mitochondrion（ mt DNA） D-loop and genetic differentiation of Henan sheep breeds were analyzed in the present study.【Result】50 haplotypes were identified in total,23 in Small-tail Han sheep,25 in Yuxi Fattail sheep, and three in Henan Big-tail sheep. The haplotype diversity were 0. 975,0. 936 and 0. 667,the nucleotide diversity were 0. 0230,0. 0129 and 0. 0270 in Small-tail Han sheep, Yuxi Fat-tail sheep and Henan Big-tail sheep, showing abundant genetic diversity of D-loop sequence in Small-tail Han and Yuxi Fat-tail sheep. The phylogenetic tree gathered these three sheep breeds into three lineages（ A-C）,the three breeds exsisted in all three lineages. The genetic distance of Small-tail Han sheep and Yuxi Fat-tail sheep was the farest（ 0. 0303）. The genetic distance of Henan Big-tail sheep and Small-tail sheep was the nearest（ 0. 0260）. The neutrality test demonstrated that Small-tail Han sheep and Yuxi Fat-tail sheep maintained equilibrium（ P >0. 1）,while Henan Big-tail sheep undergone bottleneck effect（ P <0. 05） in recent decades. The network result showed three maternal origins of Henan sheep breeds. 【Conclusion 】Three Henan sheep breeds showed high genetic diversity. The genetic distances reach the standard of subspecies difference. Small-tail sheep was farthest to Yuxi Fat-tail sheep and nearest to Henan Big-tail sheep. Henan sheep breeds have three maternal origins. The study will provide theoretical basis for the study of genetic relationship and population history, and the protection of genetic resources for Henan sheep breeds. [ABSTRACT FROM AUTHOR]

Copyright of Experimental Technology & Management is the property of Experimental Technology & Management Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The traditional Chinese wolfberry classification usually adopts manual grading in terms of the wolfberry characterizing of size, color, surface defects, and so on. It is a time-consuming and inefficient work. Fortunately, machine vision provides an efficient and fast way to improve the classification efficiency and accuracy. During the process of wolfberry classification by machine vision, the first and important task is to segment wolfberry particles from the image, and then classify them into different grades according to their characteristics. However, the accuracy of wolfberry image segmentation process is often hindered by a number of constraints including noise, inhomogeneous intensity, complex adherent and overlapped particles, which easily cause the decline of segmentation accuracy, and subsequently affect the wolfberry classification effect. For the purpose to improve the accuracy and efficiency of wolfberry image segmentation, a method for efficient segmentation of adherent wolfberries based on morphological multi-scale reconstruction and concave points matching is hereby proposed. Firstly, the component of the original color image is extracted to partially remove the shadow noise around or inside the wolfberries, and then the red component image is reconstructed by morphological multi-scale mixture opening-closing reconstruction operation can effectively retain the interesting contour edge of wolfberry particles and eliminate the irregular details, the influence of wolfberries edge contours blur and location offset on the subsequent classification will be greatly reduced. The binary regions of wolfberries are extracted from the reconstructed image by the method of maximum between-cluster variance, and the holes in the interior of wolfberries are filled by morphological filling operator. In the filled binary image, there are 2 kinds of wolfberries. One kind consists of single non-adherent wolfberries particles, and can be extracted by morphological area opening operation without further processing. The other kind mainly contains adherent or overlapped wolfberries particles, and needs to further segment, so 8-neighborhood tracking algorithm is used to extract the edge of single pixel contours of the adherent wolfberries. Taking into account that the shape of wolfberry is ellipsoid, the concave points usually locate in the edges where they are touched or overlapped with each other. Therefore the circular template is used to detect these edge concave points. For the incorrect concave point's pairs matched by the shortest Euclidean distance as fitting conditions, they can be modified according to the unequal pixel distance between the middle point of the connecting line and the boundary point since the length-to-width ratio of the wolfberry is obvious. When all the concave points' pairs of the adherent wolfberries are confirmed, adherent wolfberries are segmented. The final segmentation results are the combination of single non-adherent and adherent or overlapped wolfberries. the simulation results show that this method can achieve more accurate segmentation results and lower over-segmentation rate compared with the methods of mark-controlled watershed, direct concave points matching, and watershed combined with concave point segmentation, and is especially suitable for the segmentation of adherent wolfberries. The highest accurate segmentation rate is above 96% while over-segmentation rate less than 2%. [ABSTRACT FROM AUTHOR]

Objective To investigate the storage conditions for plasma samples of pepsinogen (PG) and gastrin-17 (G-17). Methods A total of 180 plasma samples were collected to detect PG-Ⅰ, PG-Ⅱand G-17 as the first day′s con-sequence (the control group) by ELISA double antibody sandwich method. The remaining samples were divided into group 1 and group 2 with 90 samples in each. Each group was equally divided into the non-separated group with upper layer which was plasma after centrifugation, the separated group with only plasma after centrifugation and the stabilizer group with plasma and stabilizer, 30 cases in each group. Every sample in each group was packaged into 6 pipes which were separately stored at room temperature, 4℃and -20℃, and we took the samples from group 1 to detect PG-Ⅰand PG-Ⅱ, or G-17 in the group 2 at the same time for 7 days. The concentrations of PG-Ⅰ, PG-Ⅱand G-17 from non-separated, separated and stabilizer groups in the different storage temperatures at different time were compared with the consequences of the first day, and the days of P>0.05 was taken as the maintain time of stability. Results In the non-separated group, the plasma PG-Ⅰ, PG-Ⅱand G-17 maintained the levels for 2 days, 7 days and 1 day at room temperature; 5 days, 7days and 3 days at 4 ℃;and 4 days, 3 days and 2 days at -20 ℃. In the separated group, the plasma PG-Ⅰ, PG-Ⅱ and G-17 could maintain the levels for 7 days, 6 days and 2 days at room temperature; 5 days, 7days and 5 days at 4 ℃; and 5 days, 7 days and 6 days at -20℃. The plasma PG-Ⅰ, PG-Ⅱand G-17 in the stabilizer group could maintain the levels for 7 days, 7 days and 7 days at room temperature; 5 days, 6 days and 4 days at 4 ℃;and 7 days, 5 days and 4 days at-20 ℃. Conclusion In 7 days of storage for clinic detection, PG and G-17 detection samples are recommended to storage in low temperature after centrifugation, and are recommended to preserve at room temperature if we add stabilizer. [ABSTRACT FROM AUTHOR]