Kernel-based machine learning protocol for predicting DNA-binding proteins

DNA-binding proteins (DNA-BPs) play a pivotal role in various intra- and extra-cellular activities ranging from DNA replication to gene expression control. Attempts have been made to identify DNA-BPs based on their sequence and structural information with moderate accuracy. Here we develop a machine learning protocol for the prediction of DNA-BPs where the classifier is Support Vector Machines (SVMs). Information used for classification is derived from characteristics that include surface and overall composition, overall charge and positive potential patches on the protein surface. In total 121 DNA-BPs and 238 non-binding proteins are used to build and evaluate the protocol. In self-consistency, accuracy value of 100% has been achieved. For cross-validation (CV) optimization over entire dataset, we report an accuracy of 90%. Using leave 1-pair holdout evaluation, the accuracy of 86.3% has been achieved. When we restrict the dataset to less than 20% sequence identity amongst the proteins, the holdout accuracy is achieved at 85.8%. Furthermore, seven DNA-BPs with unbounded structures are all correctly predicted. The current performances are better than results published previously. The higher accuracy value achieved here originates from two factors: the ability of the SVM to handle features that demonstrate a wide range of discriminatory power and, a different definition of the positive patch. Since our protocol does not lean on sequence or structural homology, it can be used to identify or predict proteins with DNA-binding function(s) regardless of their homology to the known ones

An Investigation of Degradation of Mechanical Behaviour of Prestressing Strands Subjected to Chloride Attacking

Corrosion of reinforcing and prestressing steel due to chloride contamination is one of the primary causes of deterioration of concrete structures. A review of published literatures shows that the research on the deterioration of mechanical properties of reinforcing steel is more than that on prestressing strands, even though the corrosion of prestressing strands may trigger structural collapse without warnings due to higher stress levels in the steel. This paper aims to investigate the degradation of mechanical behaviour of corroded prestressing strands. Details of a comprehensive experiment designed to examine the mechanical behaviour of corroded prestressing strands in concrete structural members are presented. A micromechanical damage model for failure mechanism of corroded prestressing strands is proposed, and a model for damage factor is derived. Based on these models, a constitutive model for corroded prestressing strands is developed and verified with test results. It is found in the paper that both the strength and ductility of corroded prestressing strands decrease with the increase of corrosion and that the hemispherical model for the pit shape is more appropriate for the prediction of strength reduction of corroded prestressing strands. The paper concludes that the constitutive model developed in the paper can be used to predict the mechanical behaviour of corroded prestressing strands accurately, paving the way for the assessment of corrosion-induced flexural failure of prestressed concrete structures

N-acetylcysteine effectively alleviates systemic lupus erythematosus in mice via regulation of oxidative stress

Purpose: To study the influence of N-acetylcysteine (NAC) on systemic lupus  erythematosus (SLE) mice, and the mechanism(s) involved. Methods: Fourteen MRL/lpr SLE mice aged 5 weeks (mean weight = 20.35 ± 2.12 g) were divided into two 7-mouse groups: SLE (control) and treatment groups. The control group comprised healthy female SPF-grade C57BL/6 mice (n = 7). The treatment group mice received intraperitoneal injection of NAC at a dose of 250 mg/kg daily for 8 weeks. The serum levels of malondialdehyde (MDA) and nitric oxide (NO), and activities of glutathione peroxidase (GPx) and superoxide dismutase (SOD), were assayed using standard methods. The level of urine protein and activity of anti-double stranded (ds) DNA antibody were determined using their respective enzyme-linked assay (ELISA) kits. Results: The spleens of mice in SLE mice were significantly enlarged, relative to control mice, but they were reduced significantly by NAC (p < 0.05). N-Acetylcysteine (NAC) also significantly reduced the serum levels of MDA and NO in SLE mice, but significantly  increased the serum activities of superoxide dismutase and GPx. Moreover, urine protein concentration and activity of anti-dsDNA antibody in SLE mice significantly increased, but reduced significantly by NAC treatment (p < 0.05). Conclusion: These results suggest that NAC effectively alleviates SLE in mice via regulation of oxidative stress. Thus, NAC has the potentials for development into a therapy for the management of SLE. Keywords: Anti-dsDNA antibodies, Antioxidant enzymes, N-acetylcysteine, Oxidative stress, Systemic lupus erythematosu

A Novel Joint Gene Set Analysis Framework Improves Identification of Enriched Pathways in Cross Disease Transcriptomic Analysis

Motivation: Gene set enrichment analysis is a widely accepted expression analysis tool which aims at detecting coordinated expression change within a pre-defined gene sets rather than individual genes. The benefit of gene set analysis over individual differentially expressed (DE) gene analysis includes more reproducible and interpretable results and detecting small but consistent change among gene set which could not be detected by DE gene analysis. There have been many successful gene set analysis applications in human diseases. However, when the sample size of a disease study is small and no other public data sets of the same disease are available, it will lead to lack of power to detect pathways of importance to the disease.Results: We have developed a novel joint gene set analysis statistical framework which aims at improving the power of identifying enriched gene sets through integrating multiple similar disease data sets. Through comprehensive simulation studies, we demonstrated that our proposed frameworks obtained much better AUC scores than single data set analysis and another meta-analysis method in identification of enriched pathways. When applied to two real data sets, the proposed framework could retain the enriched gene sets identified by single data set analysis and exclusively obtained up to 200% more disease-related gene sets demonstrating the improved identification power through information shared between similar diseases. We expect that the proposed framework would enable researchers to better explore public data sets when the sample size of their study is limited

Effects of fatty acid activation on photosynthetic production of fatty acid-based biofuels in Synechocystis sp. PCC6803

<p>Abstract</p> <p>Background</p> <p>Direct conversion of solar energy and carbon dioxide to drop in fuel molecules in a single biological system can be achieved from fatty acid-based biofuels such as fatty alcohols and alkanes. These molecules have similar properties to fossil fuels but can be produced by photosynthetic cyanobacteria.</p> <p>Results</p> <p><it>Synechocystis </it>sp. PCC6803 mutant strains containing either overexpression or deletion of the <it>slr1609 </it>gene, which encodes an acyl-ACP synthetase (AAS), have been constructed. The complete segregation and deletion in all mutant strains was confirmed by PCR analysis. Blocking fatty acid activation by deleting <it>slr1609 </it>gene in wild-type <it>Synechocystis </it>sp. PCC6803 led to a doubling of the amount of free fatty acids and a decrease of alkane production by up to 90 percent. Overexpression of <it>slr1609 </it>gene in the wild-type <it>Synechocystis </it>sp. PCC6803 had no effect on the production of either free fatty acids or alkanes. Overexpression or deletion of <it>slr1609 </it>gene in the <it>Synechocystis </it>sp. PCC6803 mutant strain with the capability of making fatty alcohols by genetically introducing fatty acyl-CoA reductase respectively enhanced or reduced fatty alcohol production by 60 percent.</p> <p>Conclusions</p> <p>Fatty acid activation functionalized by the <it>slr1609 </it>gene is metabolically crucial for biosynthesis of fatty acid derivatives in <it>Synechocystis </it>sp. PCC6803. It is necessary but not sufficient for efficient production of alkanes. Fatty alcohol production can be significantly improved by the overexpression of <it>slr1609 </it>gene.</p