Identification of an 11-kDa FKBP12-rapamycin-binding domain within the 289-kDa FKBP12-rapamycin-associated protein and characterization of a critical serine residue.

Complexed with its intracellular receptor, FKBP12, the natural product rapamycin inhibits G1 progression of the cell cycle in a variety of mammalian cell lines and in the yeast Saccharomyces cerevisae. Previously, a mammalian protein that directly associates with FKBP12-rapamycin has been identified and its encoding gene has been cloned from both human (designated FRAP) [Brown, E.J., Albers, M.W., Shin, T.B., Ichikawa, K., Keith, C.T., Lane, W.S. & Schreiber, S.L. (1994) Nature (London) 369, 756-758] and rat (designated RAFT) [Sabatini, D.M., Erdjument-Bromage, H., Lui, M., Tempst, P. & Snyder, S.H. (1994) Cell 78, 35-43]. The full-length FRAP is a 289-kDa protein containing a putative phosphatidylinositol kinase domain. Using an in vitro transcription/translation assay method coupled with proteolysis studies, we have identified an 11-kDa FKBP12-rapamycin-binding domain within FRAP. This minimal binding domain lies N-terminal to the kinase domain and spans residues 2025-2114. In addition, we have carried out mutagenesis studies to investigate the role of Ser2035, a potential phosphorylation site for protein kinase C within this domain. We now show that the FRAP Ser2035-->Ala mutant displays similar binding affinity when compared with the wild-type protein, whereas all other mutations at this site, including mimics of phosphoserine, abolish binding, presumably due to either unfavorable steric interactions or induced conformational changes.

Identification of geostationary satellites using polarization data from unresolved images

In order to protect critical military and commercial space assets, the United States Space Surveillance Network must have the ability to positively identify and characterize all space objects. Unfortunately, positive identification and characterization of space objects is a manual and labor intensive process today since even large telescopes cannot provide resolved images of most space objects. Since resolved images of geosynchronous satellites are not technically feasible with current technology, another method of distinguishing space objects was explored that exploits the polarization signature from unresolved images. The objective of this study was to collect and analyze visible-spectrum polarization data from unresolved images of geosynchronous satellites taken over various solar phase angles. Different collection geometries were used to evaluate the polarization contribution of solar arrays, thermal control materials, antennas, and the satellite bus as the solar phase angle changed. Since materials on space objects age due to the space environment, it was postulated that their polarization signature may change enough to allow discrimination of identical satellites launched at different times. The instrumentation used in this experiment was a United States Air Force Academy (USAFA) Department of Physics system that consists of a 20-inch Ritchey-Chrétien telescope and a dual focal plane optical train fed with a polarizing beam splitter. A rigorous calibration of the system was performed that included corrections for pixel bias, dark current, and response. Additionally, the two channel polarimeter was calibrated by experimentally determining the Mueller matrix for the system and relating image intensity at the two cameras to Stokes parameters S0 and S1. After the system calibration, polarization data was collected during three nights on eight geosynchronous satellites built by various manufacturers and launched several years apart. Three pairs of the eight satellites were identical buses to determine if identical buses could be correctly differentiated. When Stokes parameters were plotted against time and solar phase angle, the data indicates that there were distinguishing features in S0 (total intensity) and S1 (linear polarization) that may lead to positive identification or classification of each satellite.

A Community Analysis of the Implementation for Household Curbside Recycling Programs: A Diversionary Method to Control Municipal Solid Waste

The Municipality of Anchorage (MOA) is required to better manage, operate and control municipal solid waste (MSW) after the Anchorage Assembly instituted a Zero Waste Policy. Two household curbside recycling programs (CRPs), pay-as-you-throw (PAYT) and single-stream, were compared and evaluated to determine an optimal municipal solid waste diversion method for households within the MOA. The analyses find: (1) a CRP must be designed from comprehensive analysis, models and data correlation that combine demographic and psychographic variables; and (2) CRPs can be easily adjusted towards community-specific goals using technology, such as Geographic Information System (GIS) and Radio Frequency Identification (RFID). Combining resources of policy-makers, businesses, and other viable actors are necessary components to produce a sustainable, economically viable curbside recycling program.

SPECTRAL DOMAIN-OPTICAL COHERENCE TOMOGRAPHY IMAGE CONTRAST AND BACKGROUND COLOR SETTINGS INFLUENCE IDENTIFICATION OF RETINAL STRUCTURES.

PURPOSE To evaluate image contrast and color setting on assessment of retinal structures and morphology in spectral-domain optical coherence tomography. METHODS Two hundred and forty-eight Spectralis spectral-domain optical coherence tomography B-scans of 62 patients were analyzed by 4 readers. B-scans were extracted in 4 settings: W + N = white background with black image at normal contrast 9; W + H = white background with black image at maximum contrast 16; B + N = black background with white image at normal contrast 12; B + H = black background with white image at maximum contrast 16. Readers analyzed the images to identify morphologic features. Interreader correlation was calculated. Differences between Fleiss-kappa correlation coefficients were examined using bootstrap method. Any setting with significantly higher correlation coefficient was deemed superior for evaluating specific features. RESULTS Correlation coefficients differed among settings. No single setting was superior for all respective spectral-domain optical coherence tomography parameters (P = 0.3773). Some variables showed no differences among settings. Hard exudates and subretinal fluid were best seen with B + H (κ = 0.46, P = 0.0237 and κ = 0.78, P = 0.002). Microaneurysms were best seen with W + N (κ = 0.56, P = 0.025). Vitreomacular interface, enhanced transmission signal, and epiretinal membrane were best identified using all color/contrast settings together (κ = 0.44, P = 0.042, κ = 0.57, P = 0.01, and κ = 0.62, P ≤ 0.0001). CONCLUSION Contrast and background affect the evaluation of retinal structures on spectral-domain optical coherence tomography images. No single setting was superior for all features, though certain changes were best seen with specific settings.

Predator species identification from saliva at kill sites with limited remains

Thesis (Master's)--University of Washington, 2016-06

Method for detection of respiratory viruses in the sputa of patients with cystic fibrosis

Since the role of respiratory viruses in lung exacerbations of patients with cystic fibrosis has been hampered by the difficulty of detecting viruses in viscous sputum specimens, a multiplex reverse transcriptase PCR (RT-PCR) assay combined with colorimetric amplicon detection was tested for the identification of seven common respiratory viruses in the sputa of cystic fibrosis patients. Of 52 sputa from 38 patients, 12 (23%) samples from 12 patients were positive for a respiratory virus (4 for influenza B, 3 for parainfluenza 1, 3 for influenza A and 2 for respiratory syncytial virus). These results suggest that the RT-PCR method carried out on sputum may provide a convenient means of investigating the role of virus infection in lung exacerbations of cystic fibrosis patients.

Paradoxical association between smoking and olfactory identification in psychosis versus controls

Objective: Deficits in olfactory identification have been demonstrated in patients with schizophrenia. This study examined the interaction between smoking and olfactory identification in patients with psychotic disorders versus well controls. Method: Olfactory identification was assessed in three groups of subjects using the University of Pennsylvania Smell Identification Test (UPSIT). Sixteen patients with affective psychoses, 22 patients with nonaffective psychoses, and 21 well controls were tested. Results: There was a significant interaction between diagnostic classification (patient or control) and smoking. Patients who were smokers scored higher on the UPSIT than non-smokers, while controls who were smokers scored lower than non-smokers. Conclusions: Smoking may have a 'normalising' effect on olfactory identification in some patients with psychosis. Further studies are needed to examine the relationship between psychosis, olfactory identification and the effects of nicotine.

Re-admission to intensive care: Identification of risk factors

Background and Purpose. The re-admission of patients to intensive care is associated with increased morbidity, mortality, loss of morale for patients and family, and increased health costs. The aim of the present study was to identify factors which place patients at a higher risk of re-admission to intensive care. Method. A prospective study of patients who were re-admitted to a 22-bed tertiary level intensive care facility within a 12-month period. Data were kept on every patient re-admitted to intensive care, including standard demographic data, initial admission diagnosis, co-morbidities, re-admission diagnosis, mobility on discharge, secretions, airway, chest X-ray, PaCO2, PaO2, PaO2/FiO2and time of discharge. Subjects included 74 patients who had been re-admitted to intensive care in a 12-month period and a comparison group of patients who were not re-admitted to intensive care. A cross-tabs procedure was initially used to estimate maximum likelihood. Significant factors with an value of 65 years (p

Identification of MIMO Hammerstein systems using cardinal spline functions

A new approach to identify multivariable Hammerstein systems is proposed in this paper. By using cardinal cubic spline functions to model the static nonlinearities, the proposed method is effective in modelling processes with hard and/or coupled nonlinearities. With an appropriate transformation, the nonlinear models are parameterized such that the nonlinear identification problem is converted into a linear one. The persistently exciting condition for the transformed input is derived to ensure the estimates are consistent with the true system. A simulation study is performed to demonstrate the effectiveness of the proposed method compared with the existing approaches based on polynomials. (C) 2006 Elsevier Ltd. All rights reserved.

Beyond the sphere of the English facial approximation literature: Ramifications of German papers on western method concepts

In the English literature, facial approximation methods have been commonly classified into three types: Russian, American, or Combination. These categorizations are based on the protocols used, for example, whether methods use average soft-tissue depths (American methods) or require face muscle construction (Russian methods). However, literature searches outside the usual realm of English publications reveal key papers that demonstrate that the Russian category above has been founded on distorted views. In reality, Russian methods are based on limited face muscle construction, with heavy reliance on modified average soft-tissue depths. A closer inspection of the American method also reveals inconsistencies with the recognized classification scheme. This investigation thus demonstrates that all major methods of facial approximation depend on both face anatomy and average soft-tissue depths, rendering common method classification schemes redundant. The best way forward appears to be for practitioners to describe the methods they use (including the weight each one gives to average soft-tissue depths and deep face tissue construction) without placing them in any categorical classificatory group or giving them an ambiguous name. The state of this situation may need to be reviewed in the future in light of new research results and paradigms.

GPNN: Power studies and applications of a neural network method for detecting gene-gene interactions in studies of human disease

Background The identification and characterization of genes that influence the risk of common, complex multifactorial disease primarily through interactions with other genes and environmental factors remains a statistical and computational challenge in genetic epidemiology. We have previously introduced a genetic programming optimized neural network (GPNN) as a method for optimizing the architecture of a neural network to improve the identification of gene combinations associated with disease risk. The goal of this study was to evaluate the power of GPNN for identifying high-order gene-gene interactions. We were also interested in applying GPNN to a real data analysis in Parkinson's disease. Results We show that GPNN has high power to detect even relatively small genetic effects (2–3% heritability) in simulated data models involving two and three locus interactions. The limits of detection were reached under conditions with very small heritability (

Identification and molecular modeling of a novel, plant-like, human purple acid phosphatase

Purple acid phosphatases are a family of binuclear metallohydrolases that have been identified in plants, animals and fungi. Only one isoform of similar to 35 kDa has been isolated from animals, where it is associated with bone resorption and microbial killing through its phosphatase activity, and hydroxyl radical production, respectively. Using the sensitive PSI-BLAST search method, sequences representing new purple acid phosphatase-like proteins have been identified in mammals, insects and nematodes. These new putative isoforms are closely related to the similar to 55 kDa purple acid phosphatase characterized from plants. Secondary structure prediction of the new human isoform further confirms its similarity to a purple acid phosphatase from the red kidney bean. A structural model for the human enzyme was constructed based on the red kidney bean purple acid phosphatase structure. This model shows that the catalytic centre observed in other purple acid phosphatases is also present in this new isoform. These observations suggest that the sequences identified in this study represent a novel subfamily of plant-like purple acid phosphatases in animals and humans. (c) 2006 Elsevier B.V. All rights reserved.

Use of drought response index for identification of drought tolerant genotypes in rainfed lowland rice

Drought is a major constraint for rice production in the rainfed lowlands in Southeast Asia and Eastern India. The breeding programs for tainted lowland rice in these regions focus on adaptation to a range of drought conditions. However, a method of selection of drought tolerant genotypes has not been established and is considered to be one of the constraints faced by rice breeders. Drought response index (DRI) is based on grain yield adjusted for variation in potential yield and flowering date, and has been used recently, but its consistency among drought environments and hence its usefulness is not certain. In order to establish a selection method and subsequently to identify donor parents for drought resistance breeding, a series of experiments with 15 contrasting genotypes was conducted under well-watered and managed drought conditions at two sites for 5 years in Cambodia. Water level in the field was recorded and used to estimate the relative water level (WLREL) around flowering as an index of the severity of water deficit at the time of flowering for each entry. This was used to determine if DRI or yield reduction was due to drought tolerance or related to the amount of available water at flowering, i.e. drought escape. Grain yield reduction due to drought ranged from 12 to 46%. The drought occurred mainly during the reproductive phase, while four experiments had water stress from the early vegetative stage. There was significant variation for water availability around flowering among the nine experiments and this was associated with variation in mean yield reduction. Genotypic variation in DRI was consistent among most experiments, and genotypic mean DRI ranged from -0.54 to 0.47 (LSD 5% = 0.47). Genotypic variation in DRI was not related to WLREL around flowering in the nine environments. It is concluded that selection for DRI under drought conditions would allow breeders to identify donor lines with high drought tolerance as an important component of breeding better adapted varieties for the rainfed lowlands; two genotypes were identified with high DRI and low yield reduction and were subsequently used in the breeding program in Cambodia. (c) 2006 Elsevier B.V. All rights reserved.

GPNN: Power studies and applications of a neural network method for detecting gene-gene interactions in studies of human disease

Background: The identification and characterization of genes that influence the risk of common, complex multifactorial disease primarily through interactions with other genes and environmental factors remains a statistical and computational challenge in genetic epidemiology. We have previously introduced a genetic programming optimized neural network (GPNN) as a method for optimizing the architecture of a neural network to improve the identification of gene combinations associated with disease risk. The goal of this study was to evaluate the power of GPNN for identifying high-order gene-gene interactions. We were also interested in applying GPNN to a real data analysis in Parkinson's disease. Results: We show that GPNN has high power to detect even relatively small genetic effects (2-3% heritability) in simulated data models involving two and three locus interactions. The limits of detection were reached under conditions with very small heritability (