RAP46 is a negative regulator of glucocorticoid receptor action and hormone-induced apoptosis

RAP46 was first identified by its ability to bind the glucocorticoid receptor. It has since been reported to bind several cellular proteins, including the anti-apoptotic protein Bcl-2, but the biological significance of these interactions is unknown. Here we show that RAP46 binds the hinge region of the glucocorticoid receptor and inhibits DNA binding and transactivation by the receptor. We further show that overexpression of RAP46 in mouse thymoma S49.1 cells inhibits glucocorticoid-induced apoptosis. Conversely, glucocorticoid-induced apoptosis and transactivation were enhanced after treating S49.1 cells with the immunosuppressant rapamycin, which down-regulates cellular levels of BAG-1, the mouse homolog of RAP46. The effect of rapamycin can, however, be overcome by overexpression of RAP46. These results together identify RAP46 as a protein that controls glucocorticoid-induced apoptosis through its negative regulatory action on the transactivation property of the glucocorticoid receptor.     

Effect of ground rubber powder and devulcanizates on the properties of natural rubber compounds

Ground rubber powder (GRP) and devulcanized rubber (Dev.R) waste through a continuous shear‐flow reaction were used as fillers for virgin natural rubber. A Horikx plot and the gel fraction proved that both main‐chain scission and crosslink scission occurred during the devulcanization. The cure characteristics, swelling behavior, crosslink density, and mechanical properties were studied. GRP and Dev.R, generated from passenger car and light truck tires, showed applicable mechanical properties in the new rubber compounds, and the compounds containing Dev.R demonstrated better properties than those containing GRP. © 2005 Wiley Periodicals, Inc. J Appl Polym Sci 97: 208–217, 2005     

Improvement of mechanical properties of rubber compounds using waste rubber/virgin rubber

Ground rubber powder (GRP) and devulcanized rubber waste obtained using the process of continuous shear flow reaction were used as both fillers and a part of rubber in virgin natural rubber (NR). The cure characteristics, swelling behavior, cross‐link density, mechanical properties, and morphology were studied in our laboratory. The GRP and devulcanized rubber, which were generated from passenger car and light truck tires, showed applicable mechanical properties in new rubber compounds, and the compounds that contained devulcanized rubber showed better properties than those with GRP. In particular, devulcanized rubber as a part of rubber obtained through a cross‐link reaction exhibited excellent mechanical properties, with a strong potential for future applications. POLYM. ENG. SCI., 45:1239–1246, 2005. © 2005 Society of Plastics Engineers     

Using k-nn and discriminant analyses to classify rain forest types in a Landsat TM image over northern Costa Rica

Conservation and land use planning in humid tropical lowland forests urgently need accurate remote sensing techniques to distinguish among floristically different forest types. We investigated the degree to which floristically and structurally defined Costa Rican lowland rain forest types can be accurately discriminated by a non-parametric k nearest neighbors (k-nn) classifier or linear discriminant analysis. Pixel values of Landsat Thematic Mapper (TM) image and Shuttle Radar Topography Mission (SRTM) elevation model extracted from segments or from 5 × 5 pixel windows were employed in the classifications. 104 field plots were classified into three floristic and one structural type of forest (regrowth forest). Three floristically defined forest types were formed through clustering the old-growth forest plots (n = 52) by their species specific importance values. An error assessment of the image classification was conducted via cross-validation and error matrices, and overall percent accuracy and Kappa scores were used as measures of accuracy. Image classification of the four forest types did not adequately distinguish two old-growth forest classes, so they were merged into a single forest class. The resulting three forest classes were most accurately classified by the k-nn classifier using segmented image data (overall accuracy 91%). The second best method, with respect to accuracy, was the k-nn with 5 × 5 pixel windows data (89% accuracy), followed by the canonical discriminant analysis using the 5 × 5 pixel window data (86%) and the segment data (82%). We conclude the k-nn classifier can accurately distinguish floristically and structurally different rain forest types. The classification accuracies were higher for the k-nn classifier than for the canonical discriminant analysis, but the differences in Kappa scores were not statistically significant. The segmentation did not increase classification accuracy in this study.     

Error compensation in machine tools: a neural network approach

For a non-idealized machine tool, each point in the workspace is associated with a tool point positioning error vector. If this error map can be determined, then it is possible to substantially improve the positioning performance of the machine by introducing suitable compensation into the control loop. This paper explores the possibility of using an artifical neural network (ANN) to compute this mapping. The training set for the ANN is obtained by mounting a physical artifact whose dimensions are precisely known in the machine's workspace. The machine, equipped with a touch trigger probe, measures the positions of features on the artifact. The difference between the machine reading and the known dimension is the machine error at that point in the workspace. Using standard modeling techniques, the kinematic error model for a CNC turning center was developed. This model was parameterized by measurement of the parametric error functions using a laser interferometer, electronic levels and a precision square. The kinematic model was then used to simulate the artifact-measuring process and develop the ANN training set. The effect of changing artifact geometry was explored and a machining operation was simulated using the ANN output to provide compensation. The results show that the ANN is capable of learning the error map of a real machine, and that ANN-based compensation can significantly reduce part-dimensional errors.     

Evaluation and comparison of estimation methods for failure rates and probabilities

An updated parametric robust empirical Bayes (PREB) estimation methodology is presented as an alternative to several two-stage Bayesian methods used to assimilate failure data from multiple units or plants. PREB is based on prior-moment matching and avoids multi-dimensional numerical integrations. The PREB method is presented for failure-truncated and time-truncated data. Erlangian and Poisson likelihoods with gamma prior are used for failure rate estimation, and Binomial data with beta prior are used for failure probability per demand estimation. Combined models and assessment uncertainties are accounted for. One objective is to compare several methods with numerical examples and show that PREB works as well if not better than the alternative more complex methods, especially in demanding problems of small samples, identical data and zero failures. False claims and misconceptions are straightened out, and practical applications in risk studies are presented.     

Numerical Analysis of Coaxial Double Gate Schottky Barrier Carbon Nanotube Field Effect Transistors

Carbon nanotube field-effect transistors (CNTFETs) have been studied in recent years as a potential alternative to CMOS devices, because of the capability of ballistic transport. The ambipolar behavior of Schottky barrier CNTFETs limits the performance of these devices. A double gate design is proposed to suppress this behavior. In this structure the first gate located near the source contact controls carrier injection and the second gate located near the drain contact suppresses parasitic carrier injection. To avoid the ambipolar behavior it is necessary that the voltage of the second gate is higher or at least equal to the drain voltage. The behavior of these devices has been studied by solving the coupled Schrödinger-Poisson equation system. We investigated the effect of the second gate voltage on the performance of the device and finally the advantages and disadvantages of these options are discussed.