Regression diagnostics with predicted residuals of linear model with improved singular value classification applied to forecast the hydrodynamic efficiency of wave energy converters

In the preliminary stages of design of the oscillating water column (OWC) type of wave energy converters (WECs), we need a reliable cost- and time-effective method to predict the hydrodynamic efficiency as a function of the design parameters. One of the cheapest approaches is to create a multiple linear regression (MLR) model using an existing data set. The problem with this approach is that the reliability of the MLR predictions depend on the validity of the regression assumptions, which are either rarely tested or tested using sub-optimal procedures. We offer a series of novel methods for assumption diagnostics that we apply in our case study for MLR prediction of the hydrodynamics efficiency of OWC WECs. Namely, we propose: a novel procedure for reliable identification of the zero singular values of a matrix; a modified algorithm for stepwise regression; a modified algorithm to detect heteroskedasticity and identify statistically significant but practically insignificant heteroscedasticity in the original model; a novel test of the validity of the nullity assumption; a modified Jarque–Bera Monte Carlo error normality test. In our case study, the deviations from the assumptions of the classical normal linear regression model were fully diagnosed and dealt with. The newly proposed algorithms based on improved singular value decomposition (SVD) of the design matrix and on predicted residuals were successfully tested with a new family of goodness-of-fit measures. We empirically investigated the correct placement of an elaborate outlier detection procedure in the overall diagnostic sequence. As a result, we constructed a reliable MLR model to predict the hydrodynamic efficiency in the preliminary stages of design. MLR is a useful tool at the preliminary stages of design and can produce highly reliable and time-effective predictions of the OWC WEC performance provided that the constructing and diagnostic procedures are modified to reflect the latest advances in statistics. The main advantage of MLR models compared to other modern black box models is that their assumptions are known and can be tested in practice, which increases the reliability of the model predictions

Evaluation of a Bayesian inference network for ligand-based virtual screening

Background Bayesian inference networks enable the computation of the probability that an event will occur. They have been used previously to rank textual documents in order of decreasing relevance to a user-defined query. Here, we modify the approach to enable a Bayesian inference network to be used for chemical similarity searching, where a database is ranked in order of decreasing probability of bioactivity. Results Bayesian inference networks were implemented using two different types of network and four different types of belief function. Experiments with the MDDR and WOMBAT databases show that a Bayesian inference network can be used to provide effective ligand-based screening, especially when the active molecules being sought have a high degree of structural homogeneity; in such cases, the network substantially out-performs a conventional, Tanimoto-based similarity searching system. However, the effectiveness of the network is much less when structurally heterogeneous sets of actives are being sought. Conclusion A Bayesian inference network provides an interesting alternative to existing tools for ligand-based virtual screening

Genome-wide association study identifies loci associated with liability to alcohol and drug dependence that is associated with variability in reward-related ventral striatum activity in African- and European-Americans.

Genetic influences on alcohol and drug dependence partially overlap, however, specific loci underlying this overlap remain unclear. We conducted a genome-wide association study (GWAS) of a phenotype representing alcohol or illicit drug dependence (ANYDEP) among 7291 European-Americans (EA; 2927 cases) and 3132 African-Americans (AA: 1315 cases) participating in the family-based Collaborative Study on the Genetics of Alcoholism. ANYDEP was heritable (h 2 in EA = 0.60, AA = 0.37). The AA GWAS identified three regions with genome-wide significant (GWS; P < 5E-08) single nucleotide polymorphisms (SNPs) on chromosomes 3 (rs34066662, rs58801820) and 13 (rs75168521, rs78886294), and an insertion-deletion on chromosome 5 (chr5:141988181). No polymorphisms reached GWS in the EA. One GWS region (chromosome 1: rs1890881) emerged from a trans-ancestral meta-analysis (EA + AA) of ANYDEP, and was attributable to alcohol dependence in both samples. Four genes (AA: CRKL, DZIP3, SBK3; EA: P2RX6) and four sets of genes were significantly enriched within biological pathways for hemostasis and signal transduction. GWS signals did not replicate in two independent samples but there was weak evidence for association between rs1890881 and alcohol intake in the UK Biobank. Among 118 AA and 481 EA individuals from the Duke Neurogenetics Study, rs75168521 and rs1890881 genotypes were associated with variability in reward-related ventral striatum activation. This study identified novel loci for substance dependence and provides preliminary evidence that these variants are also associated with individual differences in neural reward reactivity. Gene discovery efforts in non-European samples with distinct patterns of substance use may lead to the identification of novel ancestry-specific genetic markers of risk

Ensemble-Based Computational Approach Discriminates Functional Activity of p53 Cancer and Rescue Mutants

The tumor suppressor protein p53 can lose its function upon single-point missense mutations in the core DNA-binding domain (“cancer mutants”). Activity can be restored by second-site suppressor mutations (“rescue mutants”). This paper relates the functional activity of p53 cancer and rescue mutants to their overall molecular dynamics (MD), without focusing on local structural details. A novel global measure of protein flexibility for the p53 core DNA-binding domain, the number of clusters at a certain RMSD cutoff, was computed by clustering over 0.7 µs of explicitly solvated all-atom MD simulations. For wild-type p53 and a sample of p53 cancer or rescue mutants, the number of clusters was a good predictor of in vivo p53 functional activity in cell-based assays. This number-of-clusters (NOC) metric was strongly correlated (r2 = 0.77) with reported values of experimentally measured ΔΔG protein thermodynamic stability. Interpreting the number of clusters as a measure of protein flexibility: (i) p53 cancer mutants were more flexible than wild-type protein, (ii) second-site rescue mutations decreased the flexibility of cancer mutants, and (iii) negative controls of non-rescue second-site mutants did not. This new method reflects the overall stability of the p53 core domain and can discriminate which second-site mutations restore activity to p53 cancer mutants

The VEGF -634G>C promoter polymorphism is associated with risk of gastric cancer

<p>Abstract</p> <p>Background</p> <p>Both TGF-β1 and VEGF play a critic role in the multiple-step process of tumorgenesis of gastric cancer. Single nucleotide polymorphisms (SNPs) of the <it>TGFB1 </it>and <it>VEGF </it>genes have been associated with risk and progression of many cancers. In this study, we investigated the association between potentially functional SNPs of these two genes and risk of gastric cancer in a US population.</p> <p>Methods</p> <p>The risk associated with genotypes and haplotypes of four <it>TGFB1 </it>SNPs and four <it>VEGF </it>SNPs were determined by multivariate logistic regression analysis in 171 patients with gastric cancer and 353 cancer-free controls frequency-matched by age, sex and ethnicity.</p> <p>Results</p> <p>Compared with the <it>VEGF</it>-634GG genotype, the -634CG genotype and the combined -634CG+CC genotypes were associated with a significantly elevated risk of gastric cancer (adjusted OR = 1.88, 95% CI = 1.24-2.86 and adjusted OR = 1.56, 95% CI = 1.07-2.27, respectively). However, none of other <it>TGFB1 </it>and <it>VEGF </it>SNPs was associated with risk of gastric cancer.</p> <p>Conclusion</p> <p>Our data suggested that the <it>VEGF</it>-634G>C SNP may be a marker for susceptibility to gastric cancer, and this finding needs to be validated in larger studies.</p

Predicting Positive p53 Cancer Rescue Regions Using Most Informative Positive (MIP) Active Learning

Many protein engineering problems involve finding mutations that produce proteins with a particular function. Computational active learning is an attractive approach to discover desired biological activities. Traditional active learning techniques have been optimized to iteratively improve classifier accuracy, not to quickly discover biologically significant results. We report here a novel active learning technique, Most Informative Positive (MIP), which is tailored to biological problems because it seeks novel and informative positive results. MIP active learning differs from traditional active learning methods in two ways: (1) it preferentially seeks Positive (functionally active) examples; and (2) it may be effectively extended to select gene regions suitable for high throughput combinatorial mutagenesis. We applied MIP to discover mutations in the tumor suppressor protein p53 that reactivate mutated p53 found in human cancers. This is an important biomedical goal because p53 mutants have been implicated in half of all human cancers, and restoring active p53 in tumors leads to tumor regression. MIP found Positive (cancer rescue) p53 mutants in silico using 33% fewer experiments than traditional non-MIP active learning, with only a minor decrease in classifier accuracy. Applying MIP to in vivo experimentation yielded immediate Positive results. Ten different p53 mutations found in human cancers were paired in silico with all possible single amino acid rescue mutations, from which MIP was used to select a Positive Region predicted to be enriched for p53 cancer rescue mutants. In vivo assays showed that the predicted Positive Region: (1) had significantly more (p<0.01) new strong cancer rescue mutants than control regions (Negative, and non-MIP active learning); (2) had slightly more new strong cancer rescue mutants than an Expert region selected for purely biological considerations; and (3) rescued for the first time the previously unrescuable p53 cancer mutant P152L

Multistep Ion Channel Remodeling and Lethal Arrhythmia Precede Heart Failure in a Mouse Model of Inherited Dilated Cardiomyopathy

Background: Patients with inherited dilated cardiomyopathy (DCM) frequently die with severe heart failure (HF) or die suddenly with arrhythmias, although these symptoms are not always observed at birth. It remains unclear how and when HF and arrhythmogenic changes develop in these DCM mutation carriers. In order to address this issue, properties of the myocardium and underlying gene expressions were studied using a knock-in mouse model of human inherited DCM caused by a deletion mutation DK210 in cardiac troponinT. Methodology/Principal Findings: By 1 month, DCM mice had already enlarged hearts, but showed no symptoms of HF and a much lower mortality than at 2 months or later. At around 2 months, some would die suddenly with no clear symptoms of HF, whereas at 3 months, many of the survivors showed evident symptoms of HF. In isolated left ventricular myocardium (LV) from 2 month-mice, spontaneous activity frequently occurred and action potential duration (APD) was prolonged. Transient outward (Ito) and ultrarapid delayed rectifier K + (IKur) currents were significantly reduced in DCM myocytes. Correspondingly, down-regulation of Kv4.2, Kv1.5 and KChIP2 was evident in mRNA and protein levels. In LVs at 3-months, more frequent spontaneous activity, greater prolongation of APD and further down-regulation in above K + channels were observed. At 1 month, in contrast, infrequent spontaneous activity and down-regulation of Kv4.2, but not Kv1.5 or KChIP2, were observed

Specific Syndecan-1 Domains Regulate Mesenchymal Tumor Cell Adhesion, Motility and Migration

Malignant mesothelioma is an asbestos induced cancer that is difficult to diagnose. Several studies have combined biomarkers to improve mesothelioma diagnosis, but with moderate success, and there is a need for new mesothelioma biomarkers. The tumour is often resistant to treatment and most patients will survive less than a year. An indicator of patient survival is the tumours growth pattern, which in turn is influenced by expressed proteoglycans. In this thesis work, we aim to improve the possibilities to diagnose malignant mesothelioma by combining biomarkers and by identifying new ones. We also investigate tumour driving mechanisms with focus on one of these suggested biomarkers, the cell-bound proteoglycan syndecan-1. We were able to construct a diagnostic two-step model based on biomarkers in patient material. By implementing a cut-off level and thereafter focusing on unresolved patients we combined hyaluronan and N-ERC/mesothelin (paper I), which significantly increased the diagnostic accuracy for malignant mesothelioma. To further improve diagnosis, we used mass spectrometry to find new biomarkers. We identified and validated galectin-1, which was excellent in discriminating mesotheliomas from adenocarcinomas (paper II). In the same study, we were also the first to describe aldo-keto reductase 1B10 as a novel prognostic mesothelioma biomarker. Syndecan-1 has been indicated as a marker for carcinomas. In paper I we describe how higher levels of syndecan-1 indicate the presence of a carcinoma over a mesothelioma. This was verified in paper II when syndecan-1 was identified as downregulated in fluids from mesothelioma patients compared to lung cancer patients. Paper III and paper IV focus on this proteoglycan. Malignant cell lines transfected with syndecan-1 and various truncated forms of syndecan-1 affected adhesion and migration, which are key features of cancer invasion (paper III). The results showed a domain- and cell type specific effect on the cells’ motility. Regulating syndecan-1 levels and analysing the global gene expression of mesothelioma cells made it evident that this proteoglycan has a strong influence on transforming growth factor β signalling and several growth factor pathways (paper IV). Links to cell migration and proliferation were furthermore identified, along with glycosaminoglycan modifying enzymes. These results can shed light on the complex role of syndecan-1 in invasion and growth of malignant mesenchymal cells. Taken together, this thesis work describes a complement to conventional mesothelioma diagnosis and identifies novel biomarkers. Furthermore, the potential biomarker syndecan-1 was shown to have an effect on cell motility and proliferation. These results increase our understanding of this aggressive malignancy

Stimulation of osteogenic differentiation in human osteoprogenitor cells by pulsed electromagnetic fields: an in vitro study

Background: Although pulsed electromagnetic field (PEMF) stimulation may be clinically beneficial during fracture healing and for a wide range of bone disorders, there is still debate on its working mechanism. Mesenchymal stem cells are likely mediators facilitating the observed clinical effects of PEMF. Here, we performed in vitro experiments to investigate the effect of PEMF stimulation on human bone marrow-derived stromal cell (BMSC) metabolism and, specifically, whether PEMF can stimulate their osteogenic differentiation. Methods: BMSCs derived from four different donors were cultured in osteogenic medium, with the PEMF treated group being continuously exposed to a 15 Hz, 1 Gauss EM field, consisting of 5-millisecond bursts with 5-microsecond pulses. On culture day 1, 5, 9, and 14, cells were collected for biochemical analysis (DNA amount, alkaline phosphatase activity, calcium deposition), expression of various osteoblast-relevant genes and activation of extracellular signal-regulated kinase (ERK) signaling. Differences between treated and control groups were analyzed using the Wilcoxon signed rank test, and considered significant when p < 0.05. Results: Biochemical analysis revealed significant, differentiation stage-dependent, PEMF-induced differences: PEMF increased mineralization at day 9 and 14, without altering alkaline phosphatase activity. Cell proliferation, as measured by DNA amounts, was not affected by PEMF until day 14. Here, DNA content stagnated in PEMF treated group, resulting in less DNA compared to control. Quantitative RT-PCR revealed that during early culture, up to day 9, PEMF treatment increased mRNA levels of bone morphogenetic protein 2, transforming growth factor-beta 1, osteoprotegerin, matrix metalloproteinase-1 and-3, osteocalcin, and bone sialoprotein. In contrast, receptor activator of NF-B ligand expression was primarily stimulated on day 14. ERK1/2 phosphorylation was not affected by PEMF stimulation. Conclusions: PEMF exposure of differentiating human BMSCs enhanced mineralization and seemed to induce differentiation at the expense of proliferation. The osteogenic stimulus of PEMF was confirmed by the up-regulation of several osteogenic marker genes in the PEMF treated group, which preceded the deposition of mineral itself. These findings indicate that PEMF can directly stimulate osteoprogenitor cells towards osteogenic differentiation. This supports the theory that PEMF treatment may recruit these cells to facilitate an osteogenic response in vivo. © 2010 Jansen et al; licensee BioMed Central Ltd