Novel topological descriptors for analyzing biological networks

<p>Abstract</p> <p>Background</p> <p>Topological descriptors, other graph measures, and in a broader sense, graph-theoretical methods, have been proven as powerful tools to perform biological network analysis. However, the majority of the developed descriptors and graph-theoretical methods does not have the ability to take vertex- and edge-labels into account, e.g., atom- and bond-types when considering molecular graphs. Indeed, this feature is important to characterize biological networks more meaningfully instead of only considering pure topological information.</p> <p>Results</p> <p>In this paper, we put the emphasis on analyzing a special type of biological networks, namely bio-chemical structures. First, we derive entropic measures to calculate the information content of vertex- and edge-labeled graphs and investigate some useful properties thereof. Second, we apply the mentioned measures combined with other well-known descriptors to supervised machine learning methods for predicting Ames mutagenicity. Moreover, we investigate the influence of our topological descriptors - measures for only unlabeled vs. measures for labeled graphs - on the prediction performance of the underlying graph classification problem.</p> <p>Conclusions</p> <p>Our study demonstrates that the application of entropic measures to molecules representing graphs is useful to characterize such structures meaningfully. For instance, we have found that if one extends the measures for determining the structural information content of unlabeled graphs to labeled graphs, the uniqueness of the resulting indices is higher. Because measures to structurally characterize labeled graphs are clearly underrepresented so far, the further development of such methods might be valuable and fruitful for solving problems within biological network analysis.</p

Measurements of 181Ta(n,2n)180Ta reaction cross-section at the neutron energy of 14.78 MeV

The cross-section of the 181Ta(n,2n)180Ta reaction has been measured with respect to the 197Au(n,2n)196Au monitor reaction at the incident neutron energy of 14.78± 0.20 MeV, using neutron activation analysis and off-line γ-ray spectrometric technique. The present measurement has been done at the energy where discrepant measured results are available in the EXFOR data library. The result has been compared with evaluated data libraries JEFF-3.3 and ENDF/B-VII.1. The present result has also been supported by theoretical predictions of nuclear model code TALYS1.8 and TALYS-1.9. The uncertainty and the correlations among the measured cross-section has been studied using co-variance analysis

Chemical and chemometric methods for halal authentication of gelatin: an overview

The issue of food authenticity has become a concern among religious adherents, particularly Muslims, due to the possible presence of nonhalal ingredients in foods as well as other commercial products. One of the nonhalal ingredients that commonly found in food and pharmaceutical products is gelatin which extracted from porcine source. Bovine and fish gelatin are also becoming the main commercial sources of gelatin. However, unclear information and labeling regarding the actual sources of gelatin in food and pharmaceutical products have become the main concern in halal authenticity issue since porcine consumption is prohibited for Muslims. Hence, numerous analytical methods involving chemical and chemometric analysis have been developed to identify the sources of gelatin. Chemical analysis techniques such as biochemical, chromatography, electrophoretic, and spectroscopic are usually combined with chemometric and mathematical methods such as principal component analysis, cluster, discriminant, and Fourier transform analysis for the gelatin classification. A sample result from Fourier transform infrared spectroscopy analysis, which combines Fourier transform and spectroscopic technique, is included in this paper. This paper presents an overview of chemical and chemometric methods involved in identification of different types of gelatin, which is important for halal authentication purposes

Measurement of (n,γ) reaction cross section of 186W-isotope at neutron energy of 20.02±0.58 MeV

392-396The cross-section of 186W(n,&gamma;)187W reaction has been measured at an average neutron energy of 20.02&plusmn;0.58 MeV by using activation technique. The 27Al(n,&alpha;)24Na and 115In(n,n&acute;)115mIn reactions have been used for absolute neutron flux measurement. Theoretically the reaction cross-sections have been calculated by using the TALYS-1.9 code. The results from the present work and the EXFOR based literature data have been compared with the evaluated data and calculated data from TALYS-1.9 code

Measurement of (n,γ) reaction cross section of 186W-isotope at neutron energy of 20.02±0.58 MeV

The cross-section of 186W(n,γ)187W reaction has been measured at an average neutron energy of 20.02±0.58 MeV by using activation technique. The 27Al(n,α)24Na and 115In(n,n´)115mIn reactions have been used for absolute neutron flux measurement. Theoretically the reaction cross-sections have been calculated by using the TALYS-1.9 code. The results from the present work and the EXFOR based literature data have been compared with the evaluated data and calculated data from TALYS-1.9 code

Decision tree supported substructure prediction of metabolites from GC-MS profiles

Gas chromatography coupled to mass spectrometry (GC-MS) is one of the most widespread routine technologies applied to the large scale screening and discovery of novel metabolic biomarkers. However, currently the majority of mass spectral tags (MSTs) remains unidentified due to the lack of authenticated pure reference substances required for compound identification by GC-MS. Here, we accessed the information on reference compounds stored in the Golm Metabolome Database (GMD) to apply supervised machine learning approaches to the classification and identification of unidentified MSTs without relying on library searches. Non-annotated MSTs with mass spectral and retention index (RI) information together with data of already identified metabolites and reference substances have been archived in the GMD. Structural feature extraction was applied to sub-divide the metabolite space contained in the GMD and to define the prediction target classes. Decision tree (DT)-based prediction of the most frequent substructures based on mass spectral features and RI information is demonstrated to result in highly sensitive and specific detections of sub-structures contained in the compounds. The underlying set of DTs can be inspected by the user and are made available for batch processing via SOAP (Simple Object Access Protocol)-based web services. The GMD mass spectral library with the integrated DTs is freely accessible for non-commercial use at http://gmd.mpimp-golm.mpg.de/. All matching and structure search functionalities are available as SOAP-based web services. A XML + HTTP interface, which follows Representational State Transfer (REST) principles, facilitates read-only access to data base entities

Identification of a novel Leucine-rich repeat protein and candidate PP1 regulatory subunit expressed in developing spermatids

<p>Abstract</p> <p>Background</p> <p>Spermatogenesis is comprised of a series of highly regulated developmental changes that transform the precursor germ cell into a highly specialized spermatozoon. The last phase of spermatogenesis, termed spermiogenesis, involves dramatic morphological change including formation of the acrosome, elongation and condensation of the nucleus, formation of the flagella, and disposal of unnecessary cytoplasm. A prominent cytoskeletal component of the developing spermatid is the manchette, a unique microtubular structure that surrounds the nucleus of the developing spermatid and is thought to assist in both the reshaping of the nucleus and redistribution of spermatid cytoplasm. Although the molecular motor KIFC1 has been shown to associate with the manchette, its precise role in function of the manchette and the identity of its testis specific protein partners are unknown. The purpose of this study was to identify proteins in the testis that interact with KIFC1 using a yeast 2 hybrid screen of a testis cDNA library.</p> <p>Results</p> <p>Thirty percent of the interacting clones identified in our screen contain an identical cDNA encoding a 40 kD protein. This interacting protein has 4 leucine-rich repeats in its amino terminal half and is expressed primarily in the testis; therefore we have named this protein testis leucine-rich repeat protein or TLRR. TLRR was also found to associate tightly with the KIFC1 targeting domain using affinity chromatography. In addition to the leucine-rich repeats, TLRR contains a consensus-binding site for protein phosphatase-1 (PP1). Immunocytochemistry using a TLRR specific antibody demonstrates that this protein is found near the manchette of developing spermatids.</p> <p>Conclusion</p> <p>We have identified a previously uncharacterized leucine-rich repeat protein that is expressed abundantly in the testis and associates with the manchette of developing spermatids, possibly through its interaction with the KIFC1 molecular motor. TLRR is homologous to a class of regulatory subunits for PP1, a central phosphatase in the reversible phosphorylation of proteins that is key to modulation of many intracellular processes. TLRR may serve to target this important signaling molecule near the nucleus of developing spermatids in order to control the cellular rearrangements of spermiogenesis.</p