Photometric Analysis of Recently Discovered Eclipsing Binary GSC 00008-00901

Photometric analysis of $BVR_C$ light curves of newly discovered eclipsing binary GSC 0008-00901 is presented. The orbital period is improved to 0.28948(11) days. Photometric parameters are determined, as well. The analysis yielded to conclusion that system is an over-contact binary of W UMa type with components not in thermal contact. The light curves from 2005 show the presence of a spot on the surface of one of the components, while light curves from 2006 are not affected by maculation.Comment: Accepted for publication in Astrophysics & Space Scienc

Random forest for gene selection and microarray data classification

A random forest method has been selected to perform both gene selection and classification of the microarray data. In this embedded method, the selection of smallest possible sets of genes with lowest error rates is the key factor in achieving highest classification accuracy. Hence, improved gene selection method using random forest has been proposed to obtain the smallest subset of genes as well as biggest subset of genes prior to classification. The option for biggest subset selection is done to assist researchers who intend to use the informative genes for further research. Enhanced random forest gene selection has performed better in terms of selecting the smallest subset as well as biggest subset of informative genes with lowest out of bag error rates through gene selection. Furthermore, the classification performed on the selected subset of genes using random forest has lead to lower prediction error rates compared to existing method and other similar available methods

Sprouty2 in the Dorsal Hippocampus Regulates Neurogenesis and Stress Responsiveness in Rats

Both the development and relief of stress-related psychiatric conditions such as major depression (MD) and post-traumatic stress disorder (PTSD) have been linked to neuroplastic changes in the brain. One such change involves the birth of new neurons (neurogenesis), which occurs throughout adulthood within discrete areas of the mammalian brain, including the dorsal hippocampus (HIP). Stress can trigger MD and PTSD in humans, and there is considerable evidence that it can decrease HIP neurogenesis in laboratory animals. In contrast, antidepressant treatments increase HIP neurogenesis, and their efficacy is eliminated by ablation of this process. These findings have led to the working hypothesis that HIP neurogenesis serves as a biomarker of neuroplasticity and stress resistance. Here we report that local alterations in the expression of Sprouty2 (SPRY2), an intracellular inhibitor of growth factor function, produces profound effects on both HIP neurogenesis and behaviors that reflect sensitivity to stressors. Viral vector-mediated disruption of endogenous Sprouty2 function (via a dominant negative construct) within the dorsal HIP of adult rats stimulates neurogenesis and produces signs of stress resilience including enhanced extinction of conditioned fear. Conversely, viral vector-mediated elevation of SPRY2 expression intensifies the behavioral consequences of stress. Studies of these manipulations in HIP primary cultures indicate that SPRY2 negatively regulates fibroblast growth factor-2 (FGF2), which has been previously shown to produce antidepressant- and anxiolytic-like effects via actions in the HIP. Our findings strengthen the relationship between HIP plasticity and stress responsiveness, and identify a specific intracellular pathway that could be targeted to study and treat stress-related disorders

The photometric-amplitude and mass-ratio distributions of contact binary stars

The distribution of the light-variation amplitudes, A(a), in addition to determining the number of undiscovered contact binary systems falling below photometric detection thresholds and thus lost to statistics, can serve as a tool in determination of the mass-ratio distribution, Q(q), which is very important for understanding of the evolution of contact binaries. Calculations of the expected A(a) show that it tends to converge to a mass-ratio dependent constant value for a->0. Strong dependence of A(a) on Q(q) can be used to determine the latter distribution, but the technique is limited by the presence of unresolved visual companions and by blending in crowded areas of the sky. The bright-star sample to 7.5 magnitude is too small for an application of the technique while the the Baade's Window sample from the OGLE project may suffer stronger blending; thus the present results are preliminary and illustrative only. Estimates based on the Baade's Window data from the OGLE project, for amplitudes a>0.3 mag. where the statistics appear to be complete allowing determination of Q(q) over 0.12<q<1, suggest a steep increase of Q(q) with q->0. The mass-ratio distribution can be approximated by a power law, either Q(q)~(1-q)^a1 with a1=6+/-2 or Q(q)~q^b1, with b1=-2+/-0.5, with a slight preference for the former form. Both forms must be modified by the theoretically expected cut-off caused by a tidal instability at about q_min 0.07-0.1. An expected maximum in Q(q), is expected to be mapped into a local maximum in A(a) around 0.2-0.25 mag.Comment: AASTeX5, 12 figures, 5 tables, accepted by AJ, Aug.200

A critical evaluation of network and pathway based classifiers for outcome prediction in breast cancer

Recently, several classifiers that combine primary tumor data, like gene expression data, and secondary data sources, such as protein-protein interaction networks, have been proposed for predicting outcome in breast cancer. In these approaches, new composite features are typically constructed by aggregating the expression levels of several genes. The secondary data sources are employed to guide this aggregation. Although many studies claim that these approaches improve classification performance over single gene classifiers, the gain in performance is difficult to assess. This stems mainly from the fact that different breast cancer data sets and validation procedures are employed to assess the performance. Here we address these issues by employing a large cohort of six breast cancer data sets as benchmark set and by performing an unbiased evaluation of the classification accuracies of the different approaches. Contrary to previous claims, we find that composite feature classifiers do not outperform simple single gene classifiers. We investigate the effect of (1) the number of selected features; (2) the specific gene set from which features are selected; (3) the size of the training set and (4) the heterogeneity of the data set on the performance of composite feature and single gene classifiers. Strikingly, we find that randomization of secondary data sources, which destroys all biological information in these sources, does not result in a deterioration in performance of composite feature classifiers. Finally, we show that when a proper correction for gene set size is performed, the stability of single gene sets is similar to the stability of composite feature sets. Based on these results there is currently no reason to prefer prognostic classifiers based on composite features over single gene classifiers for predicting outcome in breast cancer

A Consistency Test of Spectroscopic Gravities for Late-Type Stars

Chemical analyses of late-type stars are usually carried out following the classical recipe: LTE line formation and homogeneous, plane-parallel, flux-constant, and LTE model atmospheres. We review different results in the literature that have suggested significant inconsistencies in the spectroscopic analyses, pointing out the difficulties in deriving independent estimates of the stellar fundamental parameters and hence,detecting systematic errors. The trigonometric parallaxes measured by the HIPPARCOS mission provide accurate appraisals of the stellar surface gravity for nearby stars, which are used here to check the gravities obtained from the photospheric iron ionization balance. We find an approximate agreement for stars in the metallicity range -1 <= [Fe/H] <= 0, but the comparison shows that the differences between the spectroscopic and trigonometric gravities decrease towards lower metallicities for more metal-deficient dwarfs (-2.5 <= [Fe/H] <= -1.0), which casts a shadow upon the abundance analyses for extreme metal-poor stars that make use of the ionization equilibrium to constrain the gravity. The comparison with the strong-line gravities derived by Edvardsson (1988) and Fuhrmann (1998a) confirms that this method provides systematically larger gravities than the ionization balance. The strong-line gravities get closer to the physical ones for the stars analyzed by Fuhrmann, but they are even further away than the iron ionization gravities for the stars of lower gravities in Edvardsson's sample. The confrontation of the deviations of the iron ionization gravities in metal-poor stars reported here with departures from the excitation balance found in the literature, show that they are likely to be induced by the same physical mechanism(s).Comment: AAS LaTeX v4.0, 35 pages, 10 PostScript files; to appear in The Astrophysical Journa

Incorporating topological information for predicting robust cancer subnetwork markers in human protein-protein interaction network

BACKGROUND: Discovering robust markers for cancer prognosis based on gene expression data is an important yet challenging problem in translational bioinformatics. By integrating additional information in biological pathways or a protein-protein interaction (PPI) network, we can find better biomarkers that lead to more accurate and reproducible prognostic predictions. In fact, recent studies have shown that, “modular markers,” that integrate multiple genes with potential interactions can improve disease classification and also provide better understanding of the disease mechanisms. RESULTS: In this work, we propose a novel algorithm for finding robust and effective subnetwork markers that can accurately predict cancer prognosis. To simultaneously discover multiple synergistic subnetwork markers in a human PPI network, we build on our previous work that uses affinity propagation, an efficient clustering algorithm based on a message-passing scheme. Using affinity propagation, we identify potential subnetwork markers that consist of discriminative genes that display coherent expression patterns and whose protein products are closely located on the PPI network. Furthermore, we incorporate the topological information from the PPI network to evaluate the potential of a given set of proteins to be involved in a functional module. Primarily, we adopt widely made assumptions that densely connected subnetworks may likely be potential functional modules and that proteins that are not directly connected but interact with similar sets of other proteins may share similar functionalities. CONCLUSIONS: Incorporating topological attributes based on these assumptions can enhance the prediction of potential subnetwork markers. We evaluate the performance of the proposed subnetwork marker identification method by performing classification experiments using multiple independent breast cancer gene expression datasets and PPI networks. We show that our method leads to the discovery of robust subnetwork markers that can improve cancer classification. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s12859-016-1224-1) contains supplementary material, which is available to authorized users

Validation of a novel numerical model to predict regionalized blood flow in the coronary arteries

Aims Ischaemic heart disease results from insufficient coronary blood flow. Direct measurement of absolute flow (mL/min) is feasible, but has not entered routine clinical practice in most catheterization laboratories. Interventional cardiologists, therefore, rely on surrogate markers of flow. Recently, we described a computational fluid dynamics (CFD) method for predicting flow that differentiates inlet, side branch, and outlet flows during angiography. In the current study, we evaluate a new method that regionalizes flow along the length of the artery. Methods and results Three-dimensional coronary anatomy was reconstructed from angiograms from 20 patients with chronic coronary syndrome. All flows were computed using CFD by applying the pressure gradient to the reconstructed geometry. Side branch flow was modelled as a porous wall boundary. Side branch flow magnitude was based on morphometric scaling laws with two models: a homogeneous model with flow loss along the entire arterial length; and a regionalized model with flow proportional to local taper. Flow results were validated against invasive measurements of flow by continuous infusion thermodilution (Coroventis™, Abbott). Both methods quantified flow relative to the invasive measures: homogeneous (r 0.47, P 0.006; zero bias; 95% CI −168 to +168 mL/min); regionalized method (r 0.43, P 0.013; zero bias; 95% CI −175 to +175 mL/min). Conclusion During angiography and pressure wire assessment, coronary flow can now be regionalized and differentiated at the inlet, outlet, and side branches. The effect of epicardial disease on agreement suggests the model may be best targeted at cases with a stenosis close to side branches