Birth after intracytoplasmic sperm injection of ejaculated spermatozoa from a man with mosaic Klinefelter's syndrome

Aim:To report a birth after intracytoplasmic sperm injection (ICSI) of ejaculated spermatozoa from a man with mosaic Klinefelter's syndrome detected by fluorescence in situ hybridization (FISH) analysis.Methods:A 35-year- old man with a normal appearance consulted our hospital because of sterility over a 5-year period.Chromosome analysis showed low-incidence mosaic Klinefelter's syndrome.Using FISH,96 % hyperploidy of the lymphocytes was found.We examined the sex chromosome of the ejaculated spermatozoa.Using FISH,we examined 200 ejacu- lated spermatozoa and no hyperploidy was found.Results:The 33-year-old female partner of the male patient underwent an uncomplicated controlled ovarian hyperstimulation sequence using a combined recombinant-follicle stimulating hormone (rec-FSH) + human menopausal gonadotrophin (hMG) protocol,following late luteal phase pituitary down regulation.This culminated in the retrieval of seven oocytes,six of which were fertilized with ICSI. One ICSI attempt led to clinical pregnancy with a healthy baby girl.Conclusion:We report a male patient with low- incidence mosaic Klinefelter's syndrome whose ejaculated spermatozoa were identified as being haploid by FISH before ICSI,leading to the successful pregnancy of his wife and the birth of a healthy baby girl.

PMSSC:Parallelizable multi-subset based self-expressive model for subspace clustering

Subspace clustering methods which embrace a self-expressive model that represents each data point as a linear combination of other data points in the dataset provide powerful unsupervised learning techniques.However,when dealing with large datasets,representation of each data point by referring to all data points via a dictionary suffers from high computational complexity.To alleviate this issue,we introduce a parallelizable multi-subset based self-expressive model(PMS)which represents each data point by combining multiple subsets,with each consisting of only a small proportion of the samples.The adoption of PMS in subspace clustering(PMSSC)leads to computational advantages because the optimization problems decomposed over each subset are small,and can be solved efficiently in parallel.Furthermore,PMSSC is able to combine multiple self-expressive coefficient vectors obtained from subsets,which contributes to an improvement in self-expressiveness.Extensive experiments on synthetic and real-world datasets show the efficiency and effectiveness of our approach in comparison to other methods.