Male factor infertility affects one-sixth of couples worldwide, and non-obstructive azoospermia (NOA) is one of the most severe forms. Our previous genome-wide association study (GWAS) identified three susceptibility loci for NOA in Han Chinese men. Here we test promising associations in an extended three-stage validation using 3,608 NOA cases and 5,909 controls to identify additional risk loci. We find strong evidence of three NOA susceptibility loci (Po5.0 Â 10 À 8 ) at 6p21.32 (rs7194, P ¼ 3.76 Â 10 À 19 ), 10q25.3 (rs7099208, P ¼ 6.41 Â 10 À 14 ) and 6p12.2 (rs13206743, P ¼ 3.69 Â 10 À 8 ), as well as one locus approaching genome-wide significance at 1q42.13 (rs3000811, P ¼ 7.26 Â 10 À 8 ). In addition, we investigate the phenotypic effect of the related gene (gek, orthologous to CDC42BPA) at 1q42.13 on male fertility using a Drosophila model. These results advance our understanding of the genetic susceptibility to NOA and provide insights into its pathogenic mechanism.

Anomaly detection on attributed networks aims at finding nodes whose patterns deviate significantly from the majority of reference nodes, which is pervasive in many applications such as network intrusion detection and social spammer detection. However, most existing methods neglect the complex cross-modality interactions between network structure and node attribute. In this paper, we propose a deep joint representation learning framework for anomaly detection through a dual autoencoder (AnomalyDAE), which captures the complex interactions between network structure and node attribute for high-quality embeddings. Specifically, Anoma-lyDAE consists of a structure autoencoder and an attribute autoencoder to learn both node embedding and attribute embedding jointly in latent space. Moreover, attention mechanism is employed in structure encoder to learn the importance between a node and its neighbors for an effective capturing of structure pattern, which is important to anomaly detection. Besides, by taking both the node embedding and attribute embedding as inputs of attribute decoder, the cross-modality interactions between network structure and node attribute are learned during the reconstruction of node attribute. Finally, anomalies can be detected by measuring the reconstruction errors of nodes from both the structure and attribute perspectives. Extensive experiments on real-world datasets demonstrate the effectiveness of the proposed method.

BackgroundHepatocellular carcinoma (HCC) is frequently preceded by hepatitis virus infection or alcohol abuse. Genetic backgrounds may increase susceptibility to HCC from these exposures.MethodsMitochondrial DNA (mtDNA) of peripheral blood, tumor, and/or adjacent non-tumor tissue from 49 hepatitis B virus-related and 11 alcohol-related HCC patients, and from 38 controls without HCC were examined for single nucleotide polymorphisms (SNPs) and mutations in the D-Loop region.ResultsSingle nucleotide polymorphisms (SNPs) in the D-loop region of mt DNA were examined in HCC patients. Individual SNPs, namely the 16266C/T, 16293A/G, 16299A/G, 16303G/A, 242C/T, 368A/G, and 462C/T minor alleles, were associated with increased risk for alcohol- HCC, and the 523A/del was associated with increased risks of both HCC types. The mitochondrial haplotypes under the M haplogroup with a defining 489C polymorphism were detected in 27 (55.1%) of HBV-HCCand 8 (72.7%) of alcohol- HCC patients, and in 15 (39.5%) of controls. Frequencies of the 489T/152T, 489T/523A, and 489T/525C haplotypes were significantly reduced in HBV-HCC patients compared with controls. In contrast, the haplotypes of 489C with 152T, 249A, 309C, 523Del, or 525Del associated significantly with increase of alcohol-HCC risk. Mutations in the D-Loop region were detected in 5 adjacent non-tumor tissues and increased in cancer stage (21 of 49 HBV-HCC and 4 of 11 alcohol- HCC, p < 0.002).ConclusionsIn sum, mitochondrial haplotypes may differentially predispose patients to HBV-HCC and alcohol-HCC. Mutations of the mitochondrial D-Loop sequence may relate to HCC development.

Digital pathology and microscope image analysis is widely used in comprehensive studies of cell morphology. Identification and analysis of leukocytes in blood smear images, acquired from bright field microscope, are vital for diagnosing many diseases such as hepatitis, leukaemia and acquired immune deficiency syndrome (AIDS). The major challenge for robust and accurate identification and segmentation of leukocyte in blood smear images lays in the large variations of cell appearance such as size, colour and shape of cells, the adhesion between leukocytes (white blood cells, WBCs) and erythrocytes (red blood cells, RBCs), and the emergence of substantial dyeing impurities in blood smear images. In this paper, an end‐to‐end leukocyte localization and segmentation method is proposed, named LeukocyteMask, in which pixel‐level prior information is utilized for supervisor training of a deep convolutional neural network, which is then employed to locate the region of interests (ROI) of leukocyte, and finally segmentation mask of leukocyte is obtained based on the extracted ROI by forward propagation of the network. Experimental results validate the effectiveness of the propose method and both the quantitative and qualitative comparisons with existing methods indicate that LeukocyteMask achieves a state‐of‐the‐art performance for the segmentation of leukocyte in terms of robustness and accuracy .

In conventional in vitro fertilization (IVF), complete failure of fertilization occurs in 5% to 15% of treatments. Although the causes may be unclear, sperm defects appear to be the major contributor. However, a convincing test is not yet available that can predict the risk of fertilization failure. In this study, we found that germinal angiotensin-converting enzyme (gACE) (also called testicular ACE) was undetectable in sperm from patients who had total fertilization failure (TFF) and lower fertilization rates (LFRs) by IVF based on Western blot and indirect immunofluorescence analyses. Additionally, almost all of the patients without gACE on sperm (23 of 25) manifested a TT genotype of the rs4316 single-nucleotide polymorphism of ACE. Overall, our results indicate that the absence of gACE expression is responsible for TFF and LFRs by IVF. The rs4316 polymorphism of ACE might be associated with infertility in those patients. We conclude that sperm lacking gACE may be recognized before commencing IVF and that the patients may be directed instead to consider intracytoplasmic sperm injection.