A novel application of quantile regression for identification of biomarkers exemplified by equine cartilage microarray data

<p>Abstract</p> <p>Background</p> <p>Identification of biomarkers among thousands of genes arrayed for disease classification has been the subject of considerable research in recent years. These studies have focused on disease classification, comparing experimental groups of effected to normal patients. Related experiments can be done to identify tissue-restricted biomarkers, genes with a high level of expression in one tissue compared to other tissue types in the body.</p> <p>Results</p> <p>In this study, cartilage was compared with ten other body tissues using a two color array experimental design. Thirty-seven probe sets were identified as cartilage biomarkers. Of these, 13 (35%) have existing annotation associated with cartilage including several well-established cartilage biomarkers. These genes comprise a useful database from which novel targets for cartilage biology research can be selected. We determined cartilage specific Z-scores based on the observed M to classify genes with Z-scores ≥ 1.96 in all ten cartilage/tissue comparisons as cartilage-specific genes.</p> <p>Conclusion</p> <p>Quantile regression is a promising method for the analysis of two color array experiments that compare multiple samples in the absence of biological replicates, thereby limiting quantifiable error. We used a nonparametric approach to reveal the relationship between percentiles of M and A, where M is log₂(R/G) and A is 0.5 log₂(RG) with R representing the gene expression level in cartilage and G representing the gene expression level in one of the other 10 tissues. Then we performed linear quantile regression to identify genes with a cartilage-restricted pattern of expression.</p

Molecular modeling of temperature dependence of solubility parameters for amorphous polymers

A molecular modeling strategy is proposed to describe the temperature (T) dependence of solubility parameter (δ) for the amorphous polymers which exhibit glass-rubber transition behavior. The commercial forcefield “COMPASS” is used to support the atomistic simulations of the polymer. The temperature dependence behavior of δ for the polymer is modeled by running molecular dynamics (MD) simulation at temperatures ranging from 250 up to 650 K. Comparing the MD predicted δ value at 298 K and the glass transition temperature (Tg) of the polymer determined from δ–T curve with the experimental value confirm the accuracy of our method. The MD modeled relationship between δ and T agrees well with the previous theoretical works. We also observe the specific volume (v), cohesive energy (Ucoh), cohesive energy density (ECED) and δ shows a similar temperature dependence characteristics and a drastic change around the Tg. Meanwhile, the applications of δ and its temperature dependence property are addressed and discussed

Transition Metal Phosphorous Trisulfides as Cathode Materials in High Temperatures Batteries

The challenging environment of high temperature and high pressure on the Venus surface limit the battery options for Venus landers and surface probes. High temperature batteries employing Li alloy anodes, molten salt electrolytes and FeS cathodes were demonstrated to be resilient and operational for several days. For further improvements in performance, i.e., both specific energy and operational life, new high-capacity cathode materials are needed. Transition metal phosphorus trisulfides (TMPS3) are promising with considerably higher (2X) specific capacity, specific energy and energy density, by virtue of their ability to react with more than two lithium ions. This papers describes the assessment of these cathodes for high temperature batteries to power future Venus landers and probes. Manganese, iron, cobalt and nickel phosphorus trisulfides were synthesized and characterized by Scanning Electron Microscopy (SEM)/Energy Dispersive X-ray Spectroscopy (EDAX) and X-ray Diffraction (XRD) and tested in our high-temperature laboratory cells at 475 °C using cyclic voltammetry (CV) and galvanostatic discharges at different rates. Mn, Fe and Ni phosphorus trisulfides showed reversible behavior in cyclic voltammetric measurements. In the discharge tests, NiPS3 displayed the highest capacity out of the three metal phosphorous trisulfides tested at both C/20 and C/720 rates, with higher voltages and slightly higher capacity than FeS, followed by FePS3, while MnPS3 displayed relatively poor performance at C/20. Cathodes extracted from the discharged cells contain the transition metal (Fe, Ni or Mn) and Li2S by XRD, as expected from the reaction scheme

High-Throughput Heterogeneous Integration of Diverse Nanomaterials on a Single Chip for Sensing Applications

There is a large variety of nanomaterials each with unique electronic, optical and sensing properties. However, there is currently no paradigm for integration of different nanomaterials on a single chip in a low-cost high-throughput manner. We present a high throughput integration approach based on spatially controlled dielectrophoresis executed sequentially for each nanomaterial type to realize a scalable array of individually addressable assemblies of graphene, carbon nanotubes, metal oxide nanowires and conductive polymers on a single chip. This is a first time where such a diversity of nanomaterials has been assembled on the same layer in a single chip. The resolution of assembly can range from mesoscale to microscale and is limited only by the size and spacing of the underlying electrodes on chip used for assembly. While many applications are possible, the utility of such an array is demonstrated with an example application of a chemical sensor array for detection of volatile organic compounds below parts-per-million sensitivity