Stochastic Satbility and Performance Robustness of Linear Multivariable Systems

Stochastic robustness, a simple technique used to estimate the robustness of linear, time invariant systems, is applied to a single-link robot arm control system. Concepts behind stochastic stability robustness are extended to systems with estimators and to stochastic performance robustness. Stochastic performance robustness measures based on classical design specifications are introduced, and the relationship between stochastic robustness measures and control system design parameters are discussed. The application of stochastic performance robustness, and the relationship between performance objectives and design parameters are demonstrated by means of example. The results prove stochastic robustness to be a good overall robustness analysis method that can relate robustness characteristics to control system design parameters

Predicting Treatment Success in Child and Parent Therapy Among Families in Poverty

Behavior problems are prevalent in young children and those living in poverty are at increased risk for stable, high-intensity behavioral problems. Research has demonstrated that participation in child and parent therapy (CPT) programs significantly reduces problematic child behaviors while increasing positive behaviors. However, CPT programs, particularly those implemented with low-income populations, frequently report high rates of attrition (over 50%). Parental attributional style has shown some promise as a contributing factor to treatment attendance and termination in previous research. The authors examined if parental attributional style could predict treatment success in a CPT program, specifically targeting low-income urban children with behavior problems. A hierarchical logistic regression was used with a sample of 425 families to assess if parent- and child-referent attributions variables predicted treatment success over and above demographic variables and symptom severity. Parent-referent attributions, child-referent attributions, and child symptom severity were found to be significant predictors of treatment success. Results indicated that caregivers who viewed themselves as a contributing factor for their child\u27s behavior problems were significantly more likely to demonstrate treatment success. Alternatively, caregivers who viewed their child as more responsible for their own behavior problems were less likely to demonstrate treatment success. Additionally, more severe behavior problems were also predictive of treatment success. Clinical and research implications of these results are discussed

Assessment of synchrony in multiple neural spike trains using loglinear point process models

Neural spike trains, which are sequences of very brief jumps in voltage across the cell membrane, were one of the motivating applications for the development of point process methodology. Early work required the assumption of stationarity, but contemporary experiments often use time-varying stimuli and produce time-varying neural responses. More recently, many statistical methods have been developed for nonstationary neural point process data. There has also been much interest in identifying synchrony, meaning events across two or more neurons that are nearly simultaneous at the time scale of the recordings. A natural statistical approach is to discretize time, using short time bins, and to introduce loglinear models for dependency among neurons, but previous use of loglinear modeling technology has assumed stationarity. We introduce a succinct yet powerful class of time-varying loglinear models by (a) allowing individual-neuron effects (main effects) to involve time-varying intensities; (b) also allowing the individual-neuron effects to involve autocovariation effects (history effects) due to past spiking, (c) assuming excess synchrony effects (interaction effects) do not depend on history, and (d) assuming all effects vary smoothly across time.Comment: Published in at http://dx.doi.org/10.1214/10-AOAS429 the Annals of Applied Statistics (http://www.imstat.org/aoas/) by the Institute of Mathematical Statistics (http://www.imstat.org

Intravital Multiphoton Microscopy with Fluorescent Bile Salts in Rats as an In Vivo Biomarker for Hepatobiliary Transport Inhibition

The bile salt export pump (BSEP) is expressed at the canalicular domain of hepatocytes, where it mediates the elimination of monovalent bile salts into the bile. Inhibition of BSEP is considered a susceptibility factor for drug-induced liver injury that often goes undetected during nonclinical testing. Although in vitro assays exist for screening BSEP inhibition, a reliable and specific method for confirming Bsep inhibition in vivo would be a valuable follow up to a BSEP screening strategy, helping to put a translatable context around in vitro inhibition data, incorporating processes such as metabolism, protein binding, and other exposure properties that are lacking in most in vitro BSEP models. Here, we describe studies in which methods of quantitative intravital microscopy were used to identify dose-dependent effects of two known BSEP/Bsep inhibitors, 2-[4-[4-(butylcarbamoyl)-2-[(2,4-dichlorophenyl)sulfonylamino]phenoxy]-3-methoxyphenyl]acetic acid (AMG-009) and bosentan, on hepatocellular transport of the fluorescent bile salts cholylglycyl amidofluorescein and cholyl-lysyl-fluorescein in rats. Results of these studies demonstrate that the intravital microscopy approach is capable of detecting Bsep inhibition at drug doses well below those found to increase serum bile acid levels, and also indicate that basolateral efflux transporters play a significant role in preventing cytosolic accumulation of bile acids under conditions of Bsep inhibition in rats. Studies of this kind can both improve our understanding of exposures needed to inhibit Bsep in vivo and provide unique insights into drug effects in ways that can improve our ability interpret animal studies for the prediction of human drug hepatotoxicity

NASA's Potential Contributions for Remediation of Retention Ponds Using Solar Ultraviolet Radiation and Photocatalysis

This Candidate Solution uses NASA Earth science research on atmospheric ozone and aerosols data (1) to help improve the prediction capabilities of water runoff models that are used to estimate runoff pollution from retention ponds, and (2) to understand the pollutant removal contribution and potential of photocatalytically coated materials that could be used in these ponds. Models (the EPA's SWMM and the USGS SLAMM) exist that estimate the release of pollutants into the environment from storm-water-related retention pond runoff. UV irradiance data acquired from the satellite mission Aura and from the OMI Surface UV algorithm will be incorporated into these models to enhance their capabilities, not only by increasing the general understanding of retention pond function (both the efficacy and efficiency) but additionally by adding photocatalytic materials to these retention ponds, augmenting their performance. State and local officials who run pollution protection programs could then develop and implement photocatalytic technologies for water pollution control in retention ponds and use them in conjunction with existing runoff models. More effective decisions about water pollution protection programs could be made, the persistence and toxicity of waste generated could be minimized, and subsequently our natural water resources would be improved. This Candidate Solution is in alignment with the Water Management and Public Health National Applications

False discovery rate regression: an application to neural synchrony detection in primary visual cortex

Many approaches for multiple testing begin with the assumption that all tests in a given study should be combined into a global false-discovery-rate analysis. But this may be inappropriate for many of today's large-scale screening problems, where auxiliary information about each test is often available, and where a combined analysis can lead to poorly calibrated error rates within different subsets of the experiment. To address this issue, we introduce an approach called false-discovery-rate regression that directly uses this auxiliary information to inform the outcome of each test. The method can be motivated by a two-groups model in which covariates are allowed to influence the local false discovery rate, or equivalently, the posterior probability that a given observation is a signal. This poses many subtle issues at the interface between inference and computation, and we investigate several variations of the overall approach. Simulation evidence suggests that: (1) when covariate effects are present, FDR regression improves power for a fixed false-discovery rate; and (2) when covariate effects are absent, the method is robust, in the sense that it does not lead to inflated error rates. We apply the method to neural recordings from primary visual cortex. The goal is to detect pairs of neurons that exhibit fine-time-scale interactions, in the sense that they fire together more often than expected due to chance. Our method detects roughly 50% more synchronous pairs versus a standard FDR-controlling analysis. The companion R package FDRreg implements all methods described in the paper

Temporal Variability of Upper-level Winds at the Eastern Range, Western Range and Wallops Flight Facility

Space launch vehicles incorporate upper-level wind profiles to determine wind effects on the vehicle and for a commit to launch decision. These assessments incorporate wind profiles measured hours prior to launch and may not represent the actual wind the vehicle will fly through. Uncertainty in the upper-level winds over the time period between the assessment and launch can be mitigated by a statistical analysis of wind change over time periods of interest using historical data from the launch range. Five sets of temporal wind pairs at various times (.75, 1.5, 2, 3 and 4-hrs) at the Eastern Range, Western Range and Wallops Flight Facility were developed for use in upper-level wind assessments. Database development procedures as well as statistical analysis of temporal wind variability at each launch range will be presented

Synthesis and Structure of a Tungsten Dimer with a W-W Bond Order of 3.5

Reaction of W2(mhp)4, where mhp is the anion of 2-hydroxy-6- methylpyridine, with AICI3 in refluxing diglyme provides the new paramagnetic compound W2Cl2(mhp)3 • CH2CI2 in 67 percent yield after recrystallization from dichloromethane. The source of the oxidant for oxidation of tungsten from W(2.0) to W(2.5) was not identified. Orange-brown crystals of the new compound belong to the noncentric orthorhombic space group P2I2424 with a = 13.150(6), b = 21.24(1) and c = 8.738(4) Å, Z = 4. Refinement of the structure provided R(RW) = 0.058(0.080) and a W-W bond distance of 2.214(2) Å, 0.053 Å longer than that in W2(mhp)4 • CH2CI2. The W-W bond has a bond order of 3.5 corresponding to the bonding orbital population a27i481. Strong bands arising from W-Cl stretching modes occur at 321 and 289 cm-1 in the IR spectra, and a weak band at 782 nm (e ~ 600 M_1cm_1) in the UV-visible spectrum may correspond to the 8 —> 8 electronic transition. The EPR spectrum shows a strong, nearly symmetric, absorption with g = 1.842. Cyclic voltammetry in dichloromethane reveals a quasi-reversible reduction at Ey2 = —0.7 V vs. SSCE and an irreversible oxidation at +0.3 V