a b s t r a c tProstaglandin F2a (PGF) treatment is routinely used in the reproductive management of mares to induce luteolysis and allow a subsequent return to estrus. The objective of this retrospective study was to assess the effect of follicle size at the time of administration of cloprostenol on interval to subsequent ovulation. A secondary objective was to determine the incidence of hemorrhagic anovulatory follicle (HAF) formation after PGF administration. Reproductive records of 275 mares monitored over a total of 520 estrous cycles were evaluated. All mares received a single intramuscular dose of 250 mg of the synthetic PGF analog cloprostenol sodium between days 5 and 12 after ovulation. The average interval from PGF to ovulation was 8.4 AE 2.5 days. The interval from PGF administration to subsequent ovulation was inversely proportional to the diameter of the largest follicle at the time of treatment. Administration of cloprostenol to mares with a large (!35 mm in diameter) diestrous follicle resulted in one of three outcomesdovulation within 48 hours (13.4%) with variable uterine edema, ovulation after 48 hours usually accompanied by the presence of uterine edema (73.1%), or regression without ovulation followed by emergence and eventual ovulation of a new dominant follicle (13.4%). There was no effect of mare age or season on interval from PGF to ovulation. The overall incidence of HAF development after PGF administration in this study was low (2.5%).

A 3D object can be recovered from scanned point data, which requires accurate estimating normal directions of the object surface from the cloud data. Many point cloud processing algorithms rely on the accurate normal as input to generate an accurate 3D surface model. The neighborhood of a data point in its smooth region can be well approximated by a plane. However, the neighborhood of a feature point employed for the normal estimation is isotropic which would enclose points belonging to different surface patches across the sharp feature. In this paper, isotropic neighborhoods are segmented to search anisotropic neighborhoods for the accurate normal estimation. Normals and candidate feature points are first estimated by the principal component analysis (PCA) method. Neighborhoods of the feature point are then mapped into a Gaussian image. A k-means clustering algorithm is then used for the Gaussian image to identify an anisotropic sub-neighborhood for the data point. The normal of the candidate feature point is finally estimated by the anisotropic neighborhood with the PCA method. The proposed method can accurately estimate normal directions while preserving sharp features of the object surface. Applications have demonstrated the effectiveness of the proposed method.

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Clustering analysis is one of the most important techniques in point cloud processing, such as registration, segmentation, and outlier detection. However, most of the existing clustering algorithms exhibit a low computational efficiency with the high demand for computational resources, especially for large data processing. Sometimes, clusters and outliers are inseparable, especially for those point clouds with outliers. Most of the cluster-based algorithms can well identify cluster outliers but sparse outliers. We develop a novel clustering method, called spatial neighborhood connected region labeling. The method defines spatial connectivity criterion, finds points connections based on the connectivity criterion among the k-nearest neighborhood region and classifies connected points to the same cluster. Our method can accurately and quickly classify datasets using only one parameter k. Comparing with K-means, hierarchical clustering and density-based spatial clustering of applications with noise methods, our method provides better accuracy using less computational time for data clustering. For applications in the outlier detection of the point cloud, our method can identify not only cluster outliers, but also sparse outliers. More accurate detection results are achieved compared to the state-of-art outlier detection methods, such as local outlier factor and density-based spatial clustering of applications with noise.

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