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  • 1
    Publication Date: 2015-08-20
    Description: It has been hypothesised that positive associations between age and levels of oxidative stress-generated damage to DNA may be related to an age-dependent decline in DNA repair activity. The objective of this study was to investigate the association between age and repair activity of oxidatively damaged DNA in peripheral blood mononuclear cells (PBMCs). We isolated PBMCs from subjects aged 18–83 years, as part of a health survey of the Danish population that focussed on lifestyle factors. The level of DNA repair activity was measured as incisions on potassium bromate-damaged DNA by the comet assay. There was an inverse association between age and DNA repair activity with a 0.65% decline in activity per year from age 18 to 83 (95% confidence interval: 0.16–1.14% per year). Univariate regression analysis also indicated inverse associations between DNA repair activity and waist-hip ratio ( P 〈 0.05) and plasma concentrations of glycosylated hemoglobin ( P = 0.07). However, multivariate regression analysis only showed an inverse association between age and DNA repair activity ( P 〈 0.05), indicating that the decline in repair activity was not mediated by metabolic risk factors. In summary, the results show an inverse association between age and DNA repair activity of oxidatively damaged DNA.
    Print ISSN: 0267-8357
    Electronic ISSN: 1464-3804
    Topics: Biology , Medicine
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  • 2
    Publication Date: 2015-08-20
    Description: Exposure to traffic-related particulate matter (PM) has been associated with increased risk of lung disease, cancer and cardiovascular disease especially in elderly and overweight subjects. The proposed mechanisms involve intracellular production of reactive oxygen species (ROS), inflammation and oxidation-induced DNA damage studied mainly in young normal-weight subjects. We performed a controlled cross-over, randomised, single-blinded, repeated-measure study where 60 healthy subjects (25 males and 35 females) with age 55–83 years and body mass index above 25kg/m 2 were exposed for 5h to either particle-filtered or sham-filtered air from a busy street with number of concentrations and PM 2.5 levels of 1800/cm 3 versus 23 000/cm 3 and 3 µg/m 3 versus 24 µg/m 3 , respectively. Peripheral blood mononuclear cells (PBMCs) were collected and assayed for production of ROS with and without ex vivo exposure to nanosized carbon black as well as expression of genes related to inflammation ( chemokine (C-C motif) ligand 2 , interleukin-8 and tumour necrosis factor ), oxidative stress response ( heme oxygenase (decycling)-1 ) and DNA repair ( oxoguanine DNA glycosylase ). DNA strand breaks and oxidised purines were assayed by the alkaline comet assay. No statistically significant differences were found for any biomarker immediately after exposure to PM from urban street air although strand breaks and oxidised purines combined were significantly associated with the particle number concentration during exposure. In conclusion, 5h of controlled exposure to PM from urban traffic did not change the gene expression related to inflammation, oxidative stress or DNA repair, ROS production or oxidatively damaged DNA in PBMCs from elderly overweight human subjects.
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    Topics: Biology , Medicine
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  • 3
    Publication Date: 2015-08-20
    Description: Ionising radiation causes free radical–mediated damage in cellular DNA. This damage is manifested as chromosomal aberrations and micronuclei (MN) in proliferating cells. Sesamol, present in sesame seeds, has the potential to scavenge free radicals; therefore, it can reduce radiation-induced cytogenetic damage in cells. The aim of this study was to investigate the radioprotective potential of sesamol in bone marrow cells of mice and related haematopoietic system against radiation-induced genotoxicity. A comparative study with melatonin was designed for assessing the radioprotective potential of sesamol. C57BL/6 mice were administered intraperitoneally with either sesamol or melatonin (10 and 20mg/kg body weight) 30min prior to 2-Gy whole-body irradiation (WBI) and sacrificed after 24h. Total chromosomal aberrations (TCA), MN and cell cycle analyses were performed using bone marrow cells. The comet assay was performed on bone marrow cells, splenocytes and lymphocytes. Blood was drawn to study haematological parameters. Prophylactic doses of sesamol (10 and 20mg/kg) in irradiated mice reduced TCA and micronucleated polychromatic erythrocyte frequency in bone marrow cells by 57% and 50%, respectively, in comparison with radiation-only groups. Sesamol-reduced radiation-induced apoptosis and facilitated cell proliferation. In the comet assay, sesamol (20mg/kg) treatment reduced radiation-induced comets (% DNA in tail) compared with radiation only ( P 〈 0.05). Sesamol also increased granulocyte populations in peripheral blood similar to melatonin. Overall, the radioprotective efficacy of sesamol was found to be similar to that of melatonin. Sesamol treatment also showed recovery of relative spleen weight at 24h of WBI. The results strongly suggest the radioprotective efficacy of sesamol in the haematopoietic system of mice.
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  • 4
    Publication Date: 2015-09-16
    Description: For cancer and many other complex diseases, a large number of gene signatures have been generated. In this study, we use cancer as an example and note that other diseases can be analyzed in a similar manner. For signatures generated in multiple independent studies on the same cancer type and outcome, and for signatures on different cancer types, it is of interest to evaluate their degree of overlap. Many of the existing studies simply count the number (or percentage) of overlapped genes shared by two signatures. Such an approach has serious limitations. In this study, as a demonstrating example, we consider cancer prognosis data under the Cox model. Lasso, which is representative of a large number of regularization methods, is adopted for generating gene signatures. We examine two families of measures for quantifying the degree of overlap. The first family is based on the Cox-Lasso estimates at the optimal tunings, and the second family is based on estimates across the whole solution paths. Within each family, multiple measures, which describe the overlap from different perspectives, are introduced. The analysis of TCGA (The Cancer Genome Atlas) data on five cancer types shows that the degree of overlap varies across measures, cancer types and types of (epi)genetic measurements. More investigations are needed to better describe and understand the overlaps among gene signatures.
    Print ISSN: 1467-5463
    Electronic ISSN: 1477-4054
    Topics: Biology , Computer Science
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  • 5
    Publication Date: 2015-11-20
    Description: The past two decades of microRNA (miRNA) research has solidified the role of these small non-coding RNAs as key regulators of many biological processes and promising biomarkers for disease. The concurrent development in high-throughput profiling technology has further advanced our understanding of the impact of their dysregulation on a global scale. Currently, next-generation sequencing is the platform of choice for the discovery and quantification of miRNAs. Despite this, there is no clear consensus on how the data should be preprocessed before conducting downstream analyses. Often overlooked, data preprocessing is an essential step in data analysis: the presence of unreliable features and noise can affect the conclusions drawn from downstream analyses. Using a spike-in dilution study, we evaluated the effects of several general-purpose aligners (BWA, Bowtie, Bowtie 2 and Novoalign), and normalization methods (counts-per-million, total count scaling, upper quartile scaling, Trimmed Mean of M, DESeq, linear regression, cyclic loess and quantile) with respect to the final miRNA count data distribution, variance, bias and accuracy of differential expression analysis. We make practical recommendations on the optimal preprocessing methods for the extraction and interpretation of miRNA count data from small RNA-sequencing experiments.
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  • 6
    Publication Date: 2015-11-20
    Description: Parameter estimation is a challenging computational problem in the reverse engineering of biological systems. Because advances in biotechnology have facilitated wide availability of time-series gene expression data, systematic parameter estimation of gene circuit models from such time-series mRNA data has become an important method for quantitatively dissecting the regulation of gene expression. By focusing on the modeling of gene circuits, we examine here the performance of three types of state-of-the-art parameter estimation methods: population-based methods, online methods and model-decomposition-based methods. Our results show that certain population-based methods are able to generate high-quality parameter solutions. The performance of these methods, however, is heavily dependent on the size of the parameter search space, and their computational requirements substantially increase as the size of the search space increases. In comparison, online methods and model decomposition-based methods are computationally faster alternatives and are less dependent on the size of the search space. Among other things, our results show that a hybrid approach that augments computationally fast methods with local search as a subsequent refinement procedure can substantially increase the quality of their parameter estimates to the level on par with the best solution obtained from the population-based methods while maintaining high computational speed. These suggest that such hybrid methods can be a promising alternative to the more commonly used population-based methods for parameter estimation of gene circuit models when limited prior knowledge about the underlying regulatory mechanisms makes the size of the parameter search space vastly large.
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  • 7
    Publication Date: 2015-11-20
    Description: Human housekeeping genes are often confused with essential human genes, and several studies regard both types of genes as having the same level of evolutionary conservation. However, this is not necessarily the case. To clarify this, we compared the differences between human housekeeping genes and essential human genes with respect to four aspects: the evolutionary rate (dN/dS), protein sequence identity, single-nucleotide polymorphism (SNP) density and level of linkage disequilibrium (LD). The results showed that housekeeping genes had lower evolutionary rates, higher sequence identities, lower SNP densities and higher levels of LD compared with essential genes. Together, these findings indicate that housekeeping and essential genes are two distinct types of genes, and that housekeeping genes have a higher level of evolutionary conservation. Therefore, we suggest that researchers should pay careful attention to the distinctions between housekeeping genes and essential genes. Moreover, it is still controversial whether we should substitute human orthologs of mouse essential genes for human essential genes. Therefore, we compared the evolutionary features between human orthologs of mouse essential genes and human housekeeping genes and we got inconsistent results in long-term and short-term evolutionary characteristics implying the irrationality of simply replacing human essential genes with human orthologs of mouse essential genes.
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  • 8
    Publication Date: 2015-11-20
    Description: Whole-genome search of genes is an essential approach to dissecting complex traits, but a marginal one-single-nucleotide polymorphism (SNP)/one-phenotype regression analysis widely used in current genome-wide association studies fails to estimate the net and cumulative effects of SNPs and reveal the developmental pattern of interplay between genes and traits. Here we describe a computational framework, which we refer to as two-side high-dimensional genome-wide association studies (2HiGWAS), to associate an ultrahigh dimension of SNPs with a high dimension of developmental trajectories measured across time and space. The model is implemented with a dual dimension-reduction procedure for both predictors and responses to select a sparse but full set of significant loci from an extremely large pool of SNPs and estimate their net time-varying effects on trait development. The model can not only help geneticists to precisely identify an entire set of genes underlying complex traits but also allow them to elucidate a global picture of how genes control developmental and dynamic processes of trait formation. We investigated the statistical properties of the model via extensive simulation studies. With the increasing availability of GWAS in various organisms, 2HiGWAS will have important implications for genetic studies of developmental compelx traits.
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  • 9
    Publication Date: 2015-11-20
    Description: Three principal approaches have been proposed for inferring the set of transcripts expressed in RNA samples using RNA-seq. The simplest approach uses curated annotations, which assumes the transcripts in a sample are a subset of the transcripts listed in a curated database. A more ambitious method involves aligning reads to a reference genome and using the alignments to infer the transcript structures, possibly with the aid of a curated transcript database. The most challenging approach is to assemble reads into putative transcripts de novo without the aid of reference data. We have systematically assessed the properties of these three approaches through a simulation study. We have found that the sensitivity of computational transcript set estimation is severely limited. Computational approaches (both genome-guided and de novo assembly) produce a large number of artefacts, which are assigned large expression estimates and absorb a substantial proportion of the signal when performing expression analysis. The approach using curated annotations shows good expression correlation even when the annotations are incomplete. Furthermore, any incorrect transcripts present in a curated set do not absorb much signal, so it is preferable to have a curation set with high sensitivity than high precision. Software to simulate transcript sets, expression values and sequence reads under a wider range of parameter values and to compare sensitivity, precision and signal-to-noise ratios of different methods is freely available online ( https://github.com/boboppie/RSSS ) and can be expanded by interested parties to include methods other than the exemplars presented in this article.
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  • 10
    Publication Date: 2015-11-20
    Description: Significant efforts have been made recently to improve data throughput and data quality in screening technologies related to drug design. The modern pharmaceutical industry relies heavily on high-throughput screening (HTS) and high-content screening (HCS) technologies, which include small molecule, complementary DNA (cDNA) and RNA interference (RNAi) types of screening. Data generated by these screening technologies are subject to several environmental and procedural systematic biases, which introduce errors into the hit identification process. We first review systematic biases typical of HTS and HCS screens. We highlight that study design issues and the way in which data are generated are crucial for providing unbiased screening results. Considering various data sets, including the publicly available ChemBank data, we assess the rates of systematic bias in experimental HTS by using plate-specific and assay-specific error detection tests. We describe main data normalization and correction techniques and introduce a general data preprocessing protocol. This protocol can be recommended for academic and industrial researchers involved in the analysis of current or next-generation HTS data.
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