Identifying molecular subtypes of ovarian cancer is important. Compared to identify subtypes using single omics data, the multi-omics data analysis can utilize more information. Autoencoder has been widely used to construct lower dimensional representation for multi-omics feature integration. However, learning in the deep architectures in Autoencoder is difficult for achieving satisfied generalization performance. To solve this problem, we proposed a novel deep learning-based framework to robustly identify ovarian cancer subtypes by using denoising Autoencoder. In proposed method, the composite features of multi-omics data in the Cancer Genome Atlas were produced by denoising Autoencoder, and then the generated low-dimensional features were input into -means for clustering. At last based on the clustering results, we built the light-weighted classification model with L1-penalized logistic regression method. Furthermore, we applied the differential expression analysis and WGCNA analysis to select target genes related to molecular subtypes. We identified 34 biomarkers and 19 KEGG pathways associated with ovarian cancer. The independent test results in three GEO datasets proved the robustness of our model. The literature reviewing show 19 (56%) biomarkers and 8(42.1%) KEGG pathways identified based on the classification subtypes have been proved to be associated with ovarian cancer. The outcomes indicate that our proposed method is feasible and can provide reliable results.The independent test results in three GEO datasets proved the robustness of our model. The literature reviewing show 19 (56%) biomarkers and 8(42.1%) KEGG pathways identified based on the classification subtypes have been proved to be associated with ovarian cancer. The outcomes indicate that our proposed method is feasible and can provide reliable results. The potential correlations between chromosomal abnormalities and craniofacial malformations (CFMs) remain a challenge in prenatal diagnosis. This study aimed to evaluate 118 fetuses with CFMs by applying chromosomal microarray analysis (CMA) and G-banded chromosome analysis. Of the 118 cases in this study, 39.8% were isolated CFMs (47/118) whereas 60.2% were non-isolated CFMs (71/118). The detection rate of chromosomal abnormalities in non-isolated CFM fetuses was significantly higher than that in isolated CFM fetuses (26/71 vs. 7/47,  = 0.01). Compared to the 16 fetuses (16/104; 15.4%) with pathogenic chromosomal abnormalities detected by karyotype analysis, CMA identified a total of 33 fetuses (33/118; 28.0%) with clinically significant findings. These 33 fetuses included cases with aneuploidy abnormalities (14/118; 11.9%), microdeletion/microduplication syndromes (9/118; 7.6%), and other pathogenic copy number variations (CNVs) only (10/118; 8.5%).We further explored the CNV/phenotype correlation and found a series of clear or suspected dosage-sensitive CFM genes including , , , , , , , , , , and . These findings enrich our understanding of the potential causative CNVs and genes in CFMs. Identification of the genetic basis of CFMs contributes to our understanding of their pathogenesis and allows detailed genetic counselling.These findings enrich our understanding of the potential causative CNVs and genes in CFMs. Identification of the genetic basis of CFMs contributes to our understanding of their pathogenesis and allows detailed genetic counselling. Acute myeloid leukemia (AML) is a complex hematological disease characterized by genetic and clinical heterogeneity. The identification and understanding of chromosomal abnormalities are important for the diagnosis and management of AML patients. Compared with recurrent chromosomal translocations in AML, t(8;16)(p11.2;p13.3) can be found in any age group but is very rare and typically associated with poor prognosis. Conventional cytogenetic studies were performed among 1,824 AML patients recorded in our oncology database over the last 20years. Fluorescence in situ hybridization (FISH) was carried out to detect the translocation fusion. Array comparative genome hybridization (aCGH) was carried out to further characterize the duplication of chromosomes. We identified three AML patients with t(8;16)(p11.2;p13.3) by chromosome analysis. Two of the three patients, who harbored an additional 1q duplication, were detected by FISH and aCGH. aCGH characterized a 46.7Mb and 49.9Mb gain in chromosome 1 at band q32.1q44 separately in these two patients. One patient achieved complete remission (CR) but relapsed 3months later. The other patient never experienced CR and died 2years after diagnosis. A 1q duplication was detected in two of three AML patients with t(8;16)(p11.2;p13.3), suggesting that 1q duplication can be a recurrent event in AML patients with t(8;16). selleckchem In concert with the findings of previous studies on similar patients, our work suggests that 1q duplication may also be an unfavorable prognostic factor of the disease.A 1q duplication was detected in two of three AML patients with t(8;16)(p11.2;p13.3), suggesting that 1q duplication can be a recurrent event in AML patients with t(8;16). In concert with the findings of previous studies on similar patients, our work suggests that 1q duplication may also be an unfavorable prognostic factor of the disease. Depression is severely undertreated in Black men. This is primarily because Black men are less likely to seek traditional psychiatric treatment, have less access and more barriers to treatment, and perceive more stigma associated with treatment. Depression contributes to cardiovascular disease (CVD), and Black men have the highest rate of mortality from CVD. Resistance training (RT) can have beneficial effects on both depression and CVD. This study will be the first randomized controlled trial to test the effects of RT on depression and cardiovascular health in a sample of depressed Black men. Fifty Black men with clinically significant symptoms of depression will be randomized to either (a) a 12-week RT or (b) an attention-control group. Behavioral Activation techniques will be used to support adherence to home-based RT goals. Both groups will meet on-site twice/week during the 12-week program, and follow-up assessments will occur at the end-of-treatment and 3 months post-treatment. Qualitative interviews will be conducted after the 3-month follow-up.