In the analysis of omics data, integrative analysis provides an effective way of pooling information across multiple datasets or multiple correlated responses, and can be more effective than single-dataset (response) analysis. Multiple families of integrative analysis methods have been proposed in the literature. The current review focuses on the penalization methods. Special attention is paid to sparse meta-analysis methods that pool summary statistics across datasets, and integrative analysis methods that pool raw data across datasets. We discuss their formulation and rationale. Beyond “standard” penalized selection, we also review contrasted penalization and Laplacian penalization which accommodate finer data structures. The computational aspects, including computational algorithms and tuning parameter selection, are examined. This review concludes with possible limitations and extensions.

Background Testicular cancer (TC) is the most common malignancy in young adult men, and in many countries the incidence rates of testicular cancer have been increasing since the middle of the twentieth century. Since disease presentation and tumor progression patterns are often heterogeneous across racial groups, there may be important racial differences in recent TC trends. Methods In this study, Surveillance, Epidemiology, and End Results (SEER) data on TC patients diagnosed between 1973 and 2015 were analyzed, including the following racial/ethnic groups: non-Hispanic whites (NHW), Hispanic whites (HW), blacks, and Asians and Pacific Islanders (API). Patient characteristics, age-adjusted incidence rates, and survival were compared across racial groups. A multivariate Cox model was used to analyze the survival data of TC patients, in order to evaluate racial differences across several relevant factors, including marital status, age group, histologic type, treatment, stage, and tumor location. Results NHWs had the highest incidence rates, followed by blacks, HWs, and APIs. There were significant survival differences among the racial groups, with NHWs having the highest survival rates and blacks having the lowest. Conclusion An analysis of SEER data showed that racial differences existed among TC patients in the United States with respect to patient characteristics, incidence, and survival. The results can be useful to stakeholders interested in reducing the burden of TC morbidity and mortality.

Enterovirus 71 (EV-A71) and coxsackievirus A16 (CV-A16) are major pathogens of hand, foot, and mouth disease (HFMD) and have been associated with consecutive outbreaks of HFMD in China over the past years. Although several other human enteroviruses (HEVs) have also acted as causative agents of HFMD, published information on their roles in the prevalence of HFMD is limited. This study was conducted to reveal the characteristics of the pathogenic spectrum and molecular epidemiology of the non-EV-71 and -CV-A16 HEVs in Anyang City, which is located in north-central China and has a population of five million. From 2011 to 2015, 2270 samples were collected from HFMD patients (3.89 ± 1.06 years of age), and 1863 HEV-positive samples, including 524 samples with 23 non-EV-71 and non-CV-A16 serotypes, were identified. Based on the nucleotide sequence of the VP1 gene, 6 common non-EV-71 and non-CV-A16 HEVs, including coxsackievirus A2, A6, A10, A14, B2, and B5, were studied to determine their phylogenies and selective pressures. Phylogenetic analyses revealed a high level of genetic divergence and a pattern of lineage replacement over time in Mainland China. Selective pressure analyses showed that purifying selection was predominant in the evolution of the VP1 gene, whereas positive selection acted on individual codons. Overall, non-EV-71 and non-CV-A16 HEVs were important constituents of the pathogenic spectrum of HFMD in Anyang City during 2011-2015. Some of these HEVs with complex and active phylogenies represent a potential threat to public health, suggesting that long-term monitoring of these pathogens should be implemented to prevent HFMD outbreaks.

Published methods for imaging and quantitatively analyzing morphological changes in neuronal axons have serious limitations because of their small sample sizes, and their time-consuming and nonobjective nature. Here we present an improved microfluidic chamber design suitable for fast and high-throughput imaging of neuronal axons. We developed the AxonQuant algorithm, which is suitable for automatic processing of axonal imaging data. This microfluidic chamber-coupled algorithm allows calculation of an ‘axonal continuity index' that quantitatively measures axonal health status in a manner independent of neuronal or axonal density. This method allows quantitative analysis of axonal morphology in an automatic and nonbiased manner. Our method will facilitate large-scale high-throughput screening for genes or therapeutic compounds for neurodegenerative diseases involving axonal damage. When combined with imaging technologies utilizing different gene markers, this method will provide new insights into the mechanistic basis for axon degeneration. Our microfluidic chamber culture-coupled AxonQuant algorithm will be widely useful for studying axonal biology and neurodegenerative disorders. © 2014 S. Karger AG, Basel