A novel representation of RNA secondary structure based on element-contact graphs

<p>Abstract</p> <p>Background</p> <p>Depending on their specific structures, noncoding RNAs (ncRNAs) play important roles in many biological processes. Interest in developing new topological indices based on RNA graphs has been revived in recent years, as such indices can be used to compare, identify and classify RNAs. Although the topological indices presented before characterize the main topological features of RNA secondary structures, information on RNA structural details is ignored to some degree. Therefore, it is necessity to identify topological features with low degeneracy based on complete and fine-grained RNA graphical representations.</p> <p>Results</p> <p>In this study, we present a complete and fine scheme for RNA graph representation as a new basis for constructing RNA topological indices. We propose a combination of three vertex-weighted element-contact graphs (ECGs) to describe the RNA element details and their adjacent patterns in RNA secondary structure. Both the stem and loop topologies are encoded completely in the ECGs. The relationship among the three typical topological index families defined by their ECGs and RNA secondary structures was investigated from a dataset of 6,305 ncRNAs. The applicability of topological indices is illustrated by three application case studies. Based on the applied small dataset, we find that the topological indices can distinguish true pre-miRNAs from pseudo pre-miRNAs with about 96% accuracy, and can cluster known types of ncRNAs with about 98% accuracy, respectively.</p> <p>Conclusion</p> <p>The results indicate that the topological indices can characterize the details of RNA structures and may have a potential role in identifying and classifying ncRNAs. Moreover, these indices may lead to a new approach for discovering novel ncRNAs. However, further research is needed to fully resolve the challenging problem of predicting and classifying noncoding RNAs.</p

Dynamics of Chemical Degradation in Water Using Photocatalytic Reactions in an Ultraviolet Light Emitting Diode Reactor

This work examined ultraviolet (UV) light emitting diodes (LED) and hydrogen peroxide in an advanced oxidation process in support of a USAF installation net zero water initiative. A UV LED reactor was used for degradation of soluble organic chemicals. There were linear relationships between input drive current, optical output power, and first order degradation rate constants. When drive current was varied, first order degradation rates depended on chemical identities and the drive current. When molar peroxide ratios were varied, kinetic profiles revealed peroxide-limited or radical-scavenged phenomena. Molar absorptivity helped explain the complexity of chemical removal profiles. Degradation kinetics were used to compare fit of molecular descriptors from published quantitative structure property relationship (QSPR) models. A novel QSPR model was built using zero point energy and molar absorptivity as predictors. Finally, a systems architecture was used to describe a net zero water program and proposed areas for UV LED reactor integration. Facility-level wastewater treatment was found to be the most feasible near-term application

Characterization of Jet Fuel Combustion Emissions During a C-130 Aeromedical Evacuation Engines Running Onload

The purpose of this research was to characterize jet fuel combustion emissions (JFCE) in an occupational setting. Prior research demonstrated that aircraft emit hazardous species, especially at engine start-up and ground idle. Complaints of eye, nose, and throat irritation from occupational exposures near aircraft exist. In this study JFCE were tested during an aeromedical evacuation engines running patient onload (ERO) on a C-130 Hercules at the 179th Airlift Wing, Mansfield-Lahm Air National Guard. Ultrafine particles, VOC, formaldehyde, carbon monoxide (CO), sulfuric acid, and metals were sampled simultaneously in approximate crew and patient breathing zones. Testing methods were portable condensation particle counters (CPC), polycarbonate filters (PC) and thermophoretic samplers (TPS) for electron microscopy, MultiRae® gas monitors, EPA methods TO-17 and TO-11, and NIOSH methods N0600, N7908, N7300. Ultrafine particulate matter, VOC including EPA HAPs, formaldehyde, CO, and unburned jet fuel were detected. Particles were dominated by soot that was predominantly carbonaceous with trace oxygen, sulfur and few metals in concentrations up to 3.4E+06 particles/cc. Particle size distributions were varied with most sizes less than 100 nanometers (nm). Particle morphology was highly irregular. VOC were detected in ppb, and formaldehyde in ppm. Additive or synergistic effects are suspected and may intensify irritation. Health implications from inhaling nano-sized soot particles are inconclusive

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

An Improved QSPR Modeling of Hydrocarbon Dipole Moments

Dipole moments of hydrocarbons are not an easy property to model with conventional 2D descriptors. A comparison of the performance of the most commonly used sets of topological descriptors is presented, each set containing descriptors derived from the regular and Detour distance matrix, Electrotopological State Indices, and the basic number of atoms of each type and bonds. Data were taken on a representative set of 35 hydrocarbon dipole moments previously reported and the classical multivariable regression analysis for establishing the models is employed