Rank-based multi-scale entropy analysis of heart rate variability

The method of MultiScale Entropy (MSE) is an invaluable tool to quantify and compare the complexity of physiological time series at different time scales. Although MSE traditionally employs sample entropy to measure the unpredictability of each coarse-grained series, the same framework can be applied to other metrics. Here we investigate the use of a rank-based entropy measure within the MSE framework. Like in the traditional method, the series are studied in an embedding space of dimension m. The novel entropy assesses the unpredictability of the series quantifying the "amount of shuffling" that the ranks of the mutual distances between pairs of m-long vectors undergo when considering the next observation. The algorithm was tested on recordings from the Fantasia database in a time-varying fashion using non-overlapping 300-samples windows. The method was able to find statistically significant differences between young and healthy elderly subjects at 7 scales/time-windows after accounting for multiple comparisons using the Holm-Bonferroni correction. These promising results suggest the possibility of using this measure to perform a time-varying assessment of complexity with increased accuracy and temporal resolution

PO-040 Characterisation of cdk12 knocked out ovarian cancer cell lines

Introduction While cyclin-dependent kinases (CDKs) have a key role in promoting/controlling transition between the different phases of the cell cycle, transcriptional kinases, like CDK12, are mainly involved in gene transcription. CDK12 has been shown to regulate the expression of genes involved in DNA damage and to maintain genomic stability. Impairment of CDK12 activity is synergic with PARP inhibitor and cisplatin treatments in different cellular systems. We here aimed to generate ovarian cancer cell lines knocked out (KO) for CDK12 to understand its role in ovarian cancer and in response to chemotherapy. Material and methods A2780 and SKOV3 CDK12 KO clones were generated with CRISPR/Cas9 technology. Cell cycle analysis was evaluated by standard flow cytometric methods and DNA repair genes levels by Real Time PCR. Caspase 3 activity was measured to detect apoptosis with a luminescence-based assay. Cytotoxicity experiments were performed treating cells with different drug concentrations and evaluating cell survival after 72 hours by MTS assay. For in vivo studies 7.5 millions of cells were transplanted subcutaneously in nude mice and animals were monitored for tumour appearance and growth. Results and discussions We obtained 2 CDK12 KO ovarian cancer clones, A2780 KO and SKOV3 KO, out of more than 300 clones screened. The cell growth of both A2780 KO and SKOV3 KO cells is slower than the wild type (WT) cells, they have a less clonogenic ability and a tetraploid DNA content. Both CDK12 KO clones have a higher basal caspase activity than the WT cell lines, indicative of higher basal induction of apoptosis, while no increase in autophagy or senescence is observed. Both CDK12 KO clones show a decreased expression in BRCA1 and FANCD2 DNA repair genes than the WT cells. Cytotoxic experiments with anticancer agents with different mechanism of action show that both KO clones are less sensitive to ATM, CHK1 and WEE1 inhibitors treatment as compared to WT cells, while platinum and PARP inhibitors show similar cytotoxic activity in KO and WT cells. Interestingly enough, when KO clones were transplanted in nude mice, no tumour take was observed. Conclusion We were able to obtain CDK12 KO cells. We think that these models could help in disclosing new roles of CDK12 in ovarian carcinoma and may represent a useful tool to study new combination therapies for tumours with CDK12 mutations

Progress in the Neural Network Determination of Polarized Parton Distributions

We review recent progress towards a determination of a set of polarized parton distributions from a global set of deep-inelastic scattering data based on the NNPDF methodology, in analogy with the unpolarized case. This method is designed to provide a faithful and statistically sound representation of parton distributions and their uncertainties. We show how the FastKernel method provides a fast and accurate method for solving the polarized DGLAP equations. We discuss the polarized PDF parametrizations and the physical constraints which can be imposed. Preliminary results suggest that the uncertainty on polarized PDFs, most notably the gluon, has been underestimated in previous studies.Comment: 5 pages, 2 figures; to appear in the proceedings of DIS 2010, Firenz

FKBP5 DNA methylation does not mediate the association between childhood maltreatment and depression symptom severity in the Detroit Neighborhood Health Study

Exposure to childhood maltreatment increases the risk of developing mental illness later in life. Childhood maltreatment and depression have both been associated with dysregulation of the hypothalamic-pituitary-adrenal (HPA) axis—a key regulator of the body's stress response. Additionally, HPA axis dysregulation has been implicated in the etiology of a range of mental illnesses. A substantial body of work has shown history of childhood maltreatment alters DNA methylation levels within key HPA axis genes. We therefore investigated whether one of these key genes, FKBP5 mediates the relationship between childhood maltreatment and depression, and assessed FKBP5 DNA methylation and gene expression within 112 adults from the Detroit Neighborhood Health Study (DNHS). DNA methylation was assessed in 4 regions, including the upstream promoter, downstream promoter, and two glucocorticoid response elements (GREs) via pyrosequencing using whole blood derived DNA; Taqman assays measured relative RNA expression from leukocytes. Mediation analyses were conducted using sequential linear regression. Childhood maltreatment was significantly associated with depression symptom severity (FDR 0.05). Our results suggest DNA methylation does not mediate the childhood maltreatment-depression association in the DNHS

The Bjorken sum rule with Monte Carlo and Neural Network techniques

Determinations of structure functions and parton distribution functions have been recently obtained using Monte Carlo methods and neural networks as universal, unbiased interpolants for the unknown functional dependence. In this work the same methods are applied to obtain a parametrization of polarized Deep Inelastic Scattering (DIS) structure functions. The Monte Carlo approach provides a bias--free determination of the probability measure in the space of structure functions, while retaining all the information on experimental errors and correlations. In particular the error on the data is propagated into an error on the structure functions that has a clear statistical meaning. We present the application of this method to the parametrization from polarized DIS data of the photon asymmetries $A_1^p$ and $A_1^d$ from which we determine the structure functions $g_1^p(x,Q^2)$ and $g_1^d(x,Q^2)$, and discuss the possibility to extract physical parameters from these parametrizations. This work can be used as a starting point for the determination of polarized parton distributions.Comment: 24 pages, 6 figure

SNP-based pathway enrichment analysis for genome-wide association studies

<p>Abstract</p> <p>Background</p> <p>Recently we have witnessed a surge of interest in using genome-wide association studies (GWAS) to discover the genetic basis of complex diseases. Many genetic variations, mostly in the form of single nucleotide polymorphisms (SNPs), have been identified in a wide spectrum of diseases, including diabetes, cancer, and psychiatric diseases. A common theme arising from these studies is that the genetic variations discovered by GWAS can only explain a small fraction of the genetic risks associated with the complex diseases. New strategies and statistical approaches are needed to address this lack of explanation. One such approach is the pathway analysis, which considers the genetic variations underlying a biological pathway, rather than separately as in the traditional GWAS studies. A critical challenge in the pathway analysis is how to combine evidences of association over multiple SNPs within a gene and multiple genes within a pathway. Most current methods choose the most significant SNP from each gene as a representative, ignoring the joint action of multiple SNPs within a gene. This approach leads to preferential identification of genes with a greater number of SNPs.</p> <p>Results</p> <p>We describe a SNP-based pathway enrichment method for GWAS studies. The method consists of the following two main steps: 1) for a given pathway, using an adaptive truncated product statistic to identify all representative (potentially more than one) SNPs of each gene, calculating the average number of representative SNPs for the genes, then re-selecting the representative SNPs of genes in the pathway based on this number; and 2) ranking all selected SNPs by the significance of their statistical association with a trait of interest, and testing if the set of SNPs from a particular pathway is significantly enriched with high ranks using a weighted Kolmogorov-Smirnov test. We applied our method to two large genetically distinct GWAS data sets of schizophrenia, one from European-American (EA) and the other from African-American (AA). In the EA data set, we found 22 pathways with nominal P-value less than or equal to 0.001 and corresponding false discovery rate (FDR) less than 5%. In the AA data set, we found 11 pathways by controlling the same nominal P-value and FDR threshold. Interestingly, 8 of these pathways overlap with those found in the EA sample. We have implemented our method in a JAVA software package, called <it>SNP Set Enrichment Analysis </it>(SSEA), which contains a user-friendly interface and is freely available at <url>http://cbcl.ics.uci.edu/SSEA.</url></p> <p>Conclusions</p> <p>The SNP-based pathway enrichment method described here offers a new alternative approach for analysing GWAS data. By applying it to schizophrenia GWAS studies, we show that our method is able to identify statistically significant pathways, and importantly, pathways that can be replicated in large genetically distinct samples.</p

Fitting Parton Distribution Data with Multiplicative Normalization Uncertainties

We consider the generic problem of performing a global fit to many independent data sets each with a different overall multiplicative normalization uncertainty. We show that the methods in common use to treat multiplicative uncertainties lead to systematic biases. We develop a method which is unbiased, based on a self--consistent iterative procedure. We demonstrate the use of this method by applying it to the determination of parton distribution functions with the NNPDF methodology, which uses a Monte Carlo method for uncertainty estimation.Comment: 33 pages, 5 figures: published versio