Refractive index - Particle density nonequilibrium cesium plasmas probed by a multifrequency helium-neon laser

Refractive index computation for free electrons and neutral atoms in cesium plasma at helium neon laser wavelength

Computer-acquired performance map of an etched-rhenium, niobium planar diode

Computer acquired performance map of etched-rhenium, niobium planar diod

Comparison of computer-acquired performance data from several fixed spaced planar diodes

Comparison of performance data envelopes for thermionic diodes with various tungsten or rhenium emitters and niobium or molybdenum collector

Distribution of E/N and N sub e in a cross-flow electric discharge laser

The spatial distribution of the ratio of electric field to neutral gas density on a flowing gas, multiple pin-to-plane discharge was measured in a high-power, closed loop laser. The laser was operated at a pressure of 140 torr (1:7:20, CO2, N2, He) with typically a 100 meter/second velocity in the 5 x 8 x 135 centimeter discharge volume. E/N ratios ranged from 2.7 x 10 to the minus 16th power to 1.4 x 10 to the minus 16th power volts/cu cm along the discharge while the electron density ranged from 2.8 x 10 to the 10th power to 1.2 x 10 to the 10th power cm/3

Computer acquired performance data from a chemically vapor-deposited-rhenium, niobium planar diode

Performance data from a chemically vapor-deposited-rhenium, niobium thermionic converter are presented. The planar converter has a guard-ringed collector and a nominal fixed spacing of 0.25 mm (10 mils). The data were obtained by using a computerized acquisition system and are available on request to one of the authors on microfiche as individual and composite parametric current, voltage curves. The parameters are the temperatures of the emitter T sub E collector T sub C, and cesium reservoir T sub R. The composite plots have constant T sub E and varying T sub C or T sub R, or both. Current, voltage envelopes having constant T sub E with and without fixed T sub C appear in the present report. The diode was tested at increments between 1600 and 2000 K for the emitter Hohlraum, 800 to 1100 K for the collector, and 540 and 650 K for the reservoir. A total of 312 current, voltage curves were obtained in the present performance evaluation. Current, voltage envelopes from three rhenium emitter converters evaluated in the present program are also given. The data are compared at commom emitter Hohlraum temperatures

A simpler method of preprocessing MALDI-TOF MS data for differential biomarker analysis: stem cell and melanoma cancer studies

<p>Abstract</p> <p>Introduction</p> <p>Raw spectral data from matrix-assisted laser desorption/ionisation time-of-flight (MALDI-TOF) with MS profiling techniques usually contains complex information not readily providing biological insight into disease. The association of identified features within raw data to a known peptide is extremely difficult. Data preprocessing to remove uncertainty characteristics in the data is normally required before performing any further analysis. This study proposes an alternative yet simple solution to preprocess raw MALDI-TOF-MS data for identification of candidate marker ions. Two in-house MALDI-TOF-MS data sets from two different sample sources (melanoma serum and cord blood plasma) are used in our study.</p> <p>Method</p> <p>Raw MS spectral profiles were preprocessed using the proposed approach to identify peak regions in the spectra. The preprocessed data was then analysed using bespoke machine learning algorithms for data reduction and ion selection. Using the selected ions, an ANN-based predictive model was constructed to examine the predictive power of these ions for classification.</p> <p>Results</p> <p>Our model identified 10 candidate marker ions for both data sets. These ion panels achieved over 90% classification accuracy on blind validation data. Receiver operating characteristics analysis was performed and the area under the curve for melanoma and cord blood classifiers was 0.991 and 0.986, respectively.</p> <p>Conclusion</p> <p>The results suggest that our data preprocessing technique removes unwanted characteristics of the raw data, while preserving the predictive components of the data. Ion identification analysis can be carried out using MALDI-TOF-MS data with the proposed data preprocessing technique coupled with bespoke algorithms for data reduction and ion selection.</p

MTar: a computational microRNA target prediction architecture for human transcriptome

<p>Abstract</p> <p>Background</p> <p>MicroRNAs (miRNAs) play an essential task in gene regulatory networks by inhibiting the expression of target mRNAs. As their mRNA targets are genes involved in important cell functions, there is a growing interest in identifying the relationship between miRNAs and their target mRNAs. So, there is now a imperative need to develop a computational method by which we can identify the target mRNAs of existing miRNAs. Here, we proposed an efficient machine learning model to unravel the relationship between miRNAs and their target mRNAs.</p> <p>Results</p> <p>We present a novel computational architecture MTar for miRNA target prediction which reports 94.5% sensitivity and 90.5% specificity. We identified 16 positional, thermodynamic and structural parameters from the wet lab proven miRNA:mRNA pairs and MTar makes use of these parameters for miRNA target identification. It incorporates an Artificial Neural Network (ANN) verifier which is trained by wet lab proven microRNA targets. A number of hitherto unknown targets of many miRNA families were located using MTar. The method identifies all three potential miRNA targets (5' seed-only, 5' dominant, and 3' canonical) whereas the existing solutions focus on 5' complementarities alone.</p> <p>Conclusion</p> <p>MTar, an ANN based architecture for identifying functional regulatory miRNA-mRNA interaction using predicted miRNA targets. The area of target prediction has received a new momentum with the function of a thermodynamic model incorporating target accessibility. This model incorporates sixteen structural, thermodynamic and positional features of residues in miRNA: mRNA pairs were employed to select target candidates. So our novel machine learning architecture, MTar is found to be more comprehensive than the existing methods in predicting miRNA targets, especially human transcritome.</p