Stability, delivery and functions of human sperm RNAs at fertilization

Increasing attention has focused on the significance of RNA in sperm, in light of its contribution to the birth and long-term health of a child, role in sperm function and diagnostic potential. As the composition of sperm RNA is in flux, assigning specific roles to individual RNAs presents a significant challenge. For the first time RNA-seq was used to characterize the population of coding and non-coding transcripts in human sperm. Examining RNA representation as a function of multiple methods of library preparation revealed unique features indicative of very specific and stage-dependent maturation and regulation of sperm RNA, illuminating their various transitional roles. Correlation of sperm transcript abundance with epigenetic marks suggested roles for these elements in the pre- and post-fertilization genome. Several classes of non-coding RNAs including lncRNAs, CARs, pri-miRNAs, novel elements and mRNAs have been identified which, based on factors including relative abundance, integrity in sperm, available knockout data of embryonic effect and presence or absence in the unfertilized human oocyte, are likely to be essential male factors critical to early post-fertilization development. The diverse and unique attributes of sperm transcripts that were revealed provides the first detailed analysis of the biology and anticipated clinical significance of spermatozoal RNAs

CTCF binds to sites in the major histocompatibility complex that are rapidly reconfigured in response to interferon-gamma

Activation of the major histocompatibility complex (MHC) by interferon-gamma (IFN−γ) is a fundamental step in the adaptive immune response to pathogens. Here, we show that reorganization of chromatin loop domains in the MHC is evident within the first 30 min of IFN−γ treatment of fibroblasts, and that further dynamic alterations occur up to 6 h. These very rapid changes occur at genomic sites which are occupied by CTCF and are close to IFN−γ-inducible MHC genes. Early responses to IFN−γ are thus initiated independently of CIITA, the master regulator of MHC class II genes and prepare the MHC for subsequent induction of transcription

Comparative Microarray Data Mining

As a revolutionary technology, microarrays have great potential to provide genome-wide patterns of gene expression, to make accurate medical diagnosis, and to explore genetic causes underlying diseases. It is commonly believed that suitable analysis of microarray datasets can lead to achieve the above goals. While much has been done in microarray data mining, few previous studies, if any, focused on multiple datasets at the comparative level. This dissertation aims to fill this gap by developing tools and methods for set-based comparative microarray data mining. Specifically, we mine highly differentiative gene groups (HDGGs) from given datasets/classes, evaluate the concordance of datasets generated from different platforms/laboratories, investigate the impact of variability in microarray dataset on data mining results, provide tools and algorithms for the above tasks, and identify reliable invariant HDGG patterns for better understanding diseases. It is a big challenge to discover high-quality discriminating (emerging) patterns from high dimensional microarray datasets. We develop a novel feature-group selection method to help discover HDGGs, especially signature HDGGs that completely characterize some disease classes. In addition to giving insights on the diseases, better classification results are also obtained using HDGG-based classifiers compared with other existing classifiers. As microarray datasets are often generated from different platforms/laboratories, it is necessary to evaluate their concordance/consistence before they can be studied together. We provide measures and techniques to quantitatively test such concordance at the comparative level. In addition to applying measures to evaluate the degree of variability in microarray datasets, we also develop a novel algorithm called C-loocv to effectively minimize the variability. As an indicator of the utility of C-loocv, classifiers trained from C-loocv-refined datasets become more robust and predict test samples at significantly higher accuracy over classifiers trained from original datasets. Based on the variability minimization algorithm, we provide a novel strategy to mine invariant patterns from multiple datasets concerning a common disease. As a demonstration, invariant patterns are identified from two datasets concerning lung cancer; these patterns may shed light on the mechanism underlying the pathogenesis of lung cancer. Our methods are generic and can be applied to microarrays concerning any human diseases

Evaluation of Inter Laboratory and Cross Platform Concordance of DNA Microarrays through Discriminating Genes and Classifier Transferability

Microarray technology has great potential for improving our understanding of biological processes, medical conditions, and diseases. Often, microarray datasets are collected using different microarray platforms (provided by different companies) under different conditions in different laboratories. The cross-platform and cross-laboratory concordance of the microarray technology needs to be evaluated before it can be successfully and reliably applied in biological/clinical practice. New measures and techniques are proposed for comparing and evaluating the quality of microarray datasets generated from different platforms/laboratories. These measures and techniques are based on the following philosophy: the practical usefulness of the microarray technology may be confirmed if discriminating genes and classifiers, which are the focus of most, if not all, comparative investigations, discovered/trained from data collected in one lab/platform combination can be transferred to another lab/platform combination. The rationale is that the nondiscriminating genes might not be as strongly regulated as the discriminating genes, by the biological process of the tissue cells under study, and hence they may behave more randomly than the discriminating genes. Our experiment results, on microarray datasets generated from different platforms/laboratories using the reference mRNA samples in the Microarray Quality Control (MAQC) project, showed that DNA microarrays can produce highly repeatable data in a cross-platform cross-lab manner, when one focuses on the discriminating genes and classifiers. In our comparative study, we compare samples of one type against samples of another type; the methodology can be applied to situations where one compares one arbitrary class of data against another. Other findings include: (1) using three discriminating-gene/classifier-based methods to test the concordance between microarray datasets gave consistent results; (2) when noisy (nondiscriminating) genes were removed, the microarray datasets from different laboratories using common platform were found to be highly concordant, and the data generated using most of the commercial platforms studied here were also found to be concordant with each other; (3) several series of artificial datasets with known degree of difference were created, to establish a bridge between consistency rate and P-value, allowing us to estimate P-value if consistency rate between two datasets is known. Read More: http://www.worldscientific.com/doi/abs/10.1142/S021972000900401

Tea–vegetable gardens in Longsheng Nationalities Autonomous County: temporal and spatial distribution, agrobiodiversity and social–ecological values

The tea–vegetable gardens in Longsheng are particular agro-cultural landscapes constructed by the Miao, Yao and Zhuang nationalities, based on the ecological system in which they are located. Herein, we aimed to clarify the traditional agricultural practices of tea planting in Longsheng and explore the value and characteristics of tea agricultural heritage systems. A household survey among the farming communities and a quadrat investigation of different agroecosystems in the study area were conducted. The results revealed that most tea trees in the study area were 50–80 years old, and only a few ancient tea trees – older than 150 years – remained. A total of 35 vegetables belonging to 15 families were intercropped with tea trees. This agroecosystem is an artificial community in which shrubs (tea trees) form the uppermost layer, crops such as Gramineae, Araceae, Paniceae and Cucurbitaceae, make up the intermediate layer, and the bottom layer comprises low-growing crops such as Convolvulaceae and Cruciferae. Indigenous peoples have shown rich experience and high ecological wisdom in the biological combination of a complex ecological agriculture mode, with time and space configuration, and comprehensive management technology. Hence, we suggest that tea–vegetable gardens should be well documented and effectively conserved

Recent Advances on Human Crowd Simulation

International audienceHuman crowd simulation is a new technology in the virtual reality field. Since it could simulate evacuation, it has strong demands in risk assessment for public buildings. In this paper we discuss the development of the main related research topics, including semantic description for virtual environments and crowd models which generate continuum human flow. Additionally, we introduce a system named Guarder that is designed for human crowd simulation and is suit for simulating evacuation in public buildings. We also demonstrate some simulation results of Guarder to show that it could efficiently simulate evacuation in a large-scale and complex environment