Adherence to antiretroviral therapy and its impact on clinical outcome in HIV-infected patients.

We analyse data on patient adherence to prescribed regimens and surrogate markers of clinical outcome for 168 human immunodeficiency virus infected patients treated with antiretroviral therapy. Data on patient adherence consisted of dose-timing measurements collected for an average of 12 months per patient via electronic monitoring of bottle opening events. We first discuss how such data can be presented to highlight suboptimal adherence patterns and between-patient differences, before introducing two novel methods by which such data can be statistically modelled. Correlations between adherence and subsequent measures of viral load and CD4+T-cell counts are then evaluated. We show that summary measures of short-term adherence, which incorporate pharmacokinetic and pharmacodynamic data on the monitored regimen, predict suboptimal trends in viral load and CD4+T-cell counts better than measures based on adherence data alone.     

Estimating subpixel surface temperatures and energy fluxes from the vegetation index-radiometric temperature relationship

Routine (i.e., daily to weekly) monitoring of surface energy fluxes, particularly evapotranspiration (ET), using satellite observations of radiometric surface temperature has not been feasible at high pixel resolution (i.e., ∼10¹-10² m) because of the low frequency in satellite coverage over the region of interest (i.e., approximately every 2 weeks). Cloud cover further reduces the number of useable observations of surface conditions resulting in high-resolution satellite imagery of a region typically being available once a month, which is not very useful for routine ET monitoring. Radiometric surface temperature observations at ∼1- to 5-km pixel resolution are available multiple times per day from several weather satellites. However, this spatial resolution is too coarse for estimating ET from individual agricultural fields or for defining variations in ET due to land cover changes. Satellite data in the visible and near-infrared wavelengths, used for computing vegetation indices, are available at resolutions an order of magnitude smaller than in the thermal-infrared, and hence provide higher resolution information on vegetation cover conditions. A number of studies have exploited the relationship between vegetation indices and radiometric surface temperature for estimating model parameters used in computing spatially distributed fluxes and available moisture. In this paper, the vegetation index-radiometric surface temperature relationship is utilized in a disaggregation procedure for estimating subpixel variation in surface temperature with aircraft imagery collected over the US Southern Great Plains. The disaggregated surface temperatures estimated by this procedure are compared to actual observations at this subpixel resolution. In addition, a remote sensing-based energy balance model is used to compare output using actual versus estimated surface temperatures over a range of pixel resolutions. From these comparisons, the utility of the surface temperature disaggregation technique appears to be most useful for estimating subpixel surface temperatures at resolutions corresponding to length scales defining agricultural field boundaries across the landscape.     

Password memorability and security: empirical results

Users rarely choose passwords that are both hard to guess and easy to remember. To determine how to help users choose good passwords, the authors performed a controlled trial of the effects of giving users different kinds of advice. Some of their results challenge the established wisdom.     

Application of structural analysis to the mechanical behaviour of the cornea.

Structural engineering analysis tools have been used to improve the understanding of the biomechanical behaviour of the cornea. The research is a multi-disciplinary collaboration between structural engineers, mathematical and numerical analysts, ophthalmologists and clinicians. Mathematical shell analysis and nonlinear finite-element modelling have been used in conjunction with laboratory experiments to study the behaviour of the cornea under different loading states and to provide improved predictions of the mechanical response to disease and injury. The initial study involved laboratory tests and mathematical back analysis to determine the corneal material properties and topography. These data were then used to facilitate the construction of accurate finite-element models that are able to reliably trace the performance of cornea upon exposure to disease, injury or elevated intra-ocular pressure. The models are being adapted to study the response to keratoconus (a disease causing loss of corneal tissue) and to tonometry procedures, which are used to measure the intra-ocular pressure. This paper introduces these efforts as examples of the application of structural engineering analysis tools and shows their potential in the field of corneal biomechanics.     

An Exploratory Examination of Student Attitudes Toward "Impaired" Peers in Clinical Psychology Training Programs.

Do psychology students view their training programs as doing enough to appropriately identify, accommodate, and possibly dismiss students who manifest significant impairment? This study reports the general findings of an exploratory survey distributed to clinical psychology graduate students. Overall, students viewed impairment as a highly sensitive and inadequately addressed issue. Students commonly reported frustration with and concern for impaired colleagues. A few respondents indicated that the terms impaired and problem student were insensitive and that greater attention needed to be given to "impaired" training contexts. Implications regarding formal standards for interpersonal functioning and programmatic supports for students are discussed. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Estimation of sensitivity coefficients of nonlinear model input parameters which have a multinormal distribution

This paper considers the estimation of sensitivity coefficients based on sequential random sampling when the input parameters of a nonlinear model are correlated and have a multinormal distribution. Due to the difficulties in generating sequential random samples for correlated model inputs and the properties of response surface models, sampling-based (simulation- and experiment-based) methods could not be used to estimate sensitivity coefficients of correlated model inputs. For this reason, an algorithm based on multi-expressions of multinormal distribution has been developed and used to generate sequential random samples for estimation of sensitivity coefficients. The multi-expression approach has very high accuracy in generating multinormal random samples. The estimated sensitivity coefficients based on sequential random samples changed when sample size changed. Most estimates converged with a sample size of 5000. Model structure mainly determined the speed of convergence. Both correlation among input parameters and model structure influenced the estimates of sensitivity coefficients. The sensitivity coefficients were compared to global partial derivatives that were computed using numerical integration.     

Elastic conformity in Hertzian contacts

The study of elastic conformity within rough surface contacts is presented in terms of a conformity parameter derived as a function of normal load, elastic-modulus and the geometry of asperities within the interface. The parameter can be used in conjunction with the profile structure function to determine the wavelength of features which will elastically conform, and those remaining, which will appear to the contact as roughness. Experimental confirmation of the parameter's ability to define the minimum wavelength of surface asperity conformity is shown through optical studies of the contact interface created when laser-milled steel balls are pressed against a chromium plated glass disc under various loads. Practical applications demonstrated include the parameter's ability to identify surface features responsible for generating vibration in rolling bearings by an elastic percussion mechanism, and to explain interactions between surface roughness structure and the apparent ehd film-thickness generated during rolling contact. The parameter is also shown to reduce to the well known plasticity index when conditions are allowed to approach the elastic limit.     

Comparison of spherical and truncated cone geometries for single abrasive-grain cutting

The purpose of the present work was to compare the cutting action of two different abrasive-grain geometries using experimental observations and a validated finite element model. A spherical tool was used to approximate a dull abrasive grain while a truncated cone tool was used to approximate an abrasive grain with a well defined cutting edge. The selected geometries were chosen to represent extreme cases in order to bracket the cutting action of a range of cutting geometries. The results showed that both tools produced similar normal and tangential forces per unit width up to a depth of cut of approximately 3 μm. The improved cutting geometry of the truncated cone tool caused the normal force per unit width to decrease and the tangential force per unit width to increase in relation to the spherical tool. The truncated cone tool was shown to experimentally and numerically be more efficient based on the reduced pile-up heights and improved stress distributions. It was also shown that both geometries converged towards the same specific energy to displace material at suitably large depths of cut, which suggests that there is a minimum specific energy obtainable for a given workpiece material that is independent of the grain geometry. However, specific energies to remove material were higher for the spherical tool as compared to the truncated cone tool. Analysis of the energy components of the finite element model showed that frictional energy contributions were high with the spherical tool and low with the truncated cone tool. Finally, it was found that both tools required approximately the same energy to shear a chip from a workpiece when friction was subtracted from the specific energy for material removal.     

FFT regression and cross-noise reduction for comparing images in remote sensing

In many remote sensing studies it is desired to quantify the functional relationship between images of a given target that were acquired by different sensors. Such comparisons are problematic because when the pixel values of one image are plotted versus the other, the 'cross-noise' is quite high. Typically, the correlation coefficient is quite low, even when the compared images look alike. Nevertheless, we can try to quantify the functional relationship between two images by a suitable regression model function Y = f ( X ), while choosing one of them as 'the reference' Y and using the other one as a 'predictor' X . The underlying assumption of classical regression is that Y is absolutely correct while X is erroneous. Thus, the objective is to fit X to Y by choosing the parameters of Y = f ( X ), which minimize the 'residuals' ( “ - Y ). When comparing images in remote sensing this objective is not valid because Y itself is error prone. The alternative FFT regression method presented herein comprises a two-stage sensor fusion approach, whereby the initially low correlation between X and Y is increased and the residuals are dramatically decreased. First, pairwise image transforms are applied to X and Y whereby the correlation coefficient is increased, e.g. from roughly 0.4 to about 0.8-0.85. A predicted image Y fft is then derived by least squares minimization between the amplitude matrices of X and Y, via the 2D FFT. In the second stage, there are two options: For one time predictions, the phase matrix of Y is combined with the amplitude matrix of Y fft, whereby an improved predicted image Y plock is formed. Usually, the residuals of Y plock versus Y are about half of the values of Y fft versus Y . For long term predictions, the phase matrix of a 'field mask' is combined with the amplitude matrices of the reference image Y and the predicted image Y fft . The field mask is a binary image of a pre-selected region of interest in X and Y . The resultant images Y pref and Y pred are modified versions of Y and Y fft respectively. The residuals of Y pred versus Y pref are even lower than the residuals of Y plock versus Y . Images Y pref and Y pred represent a close consensus of two independent imaging methods which view the same target. The practical utility of FFT regression is demonstrated by examples wherein remotely sensed NDVI images X are used for predicting yield distributions in agricultural fields. Reference yield maps Y were derived by yield monitors which measure the flow rate of the crop while it is being harvested. The 2D FFT transforms, as well as other mathematical operations in this paper were performed in the 'MATLAB' environment.