Validity of the CES-D for depression screening in military service members with a history of mild traumatic brain injury.

Primary Objective: To advance knowledge about the value of the Center for Epidemiologic Studies - Depression scale (CES-D) for depression screening in military service members with a history of mild traumatic brain injury (mTBI). Research Design: Retrospective data from 336 military service members with a history of mTBI were extracted from a TBI Repository at a large military medical center. Participants included in this study screened positive for mTBI in a primary care clinic or soldier readiness processing center and were enrolled in the TBI repository from November 6, 2014 to May 31, 2017. At the time of enrollment, participants completed the CES-D and their electronic medical records (EMR) were searched for diagnoses of depressive disorders. Methods and Procedures: Receiver-operating characteristic (ROC) analysis of the CES-D was used to discriminate cases with and without depression diagnoses. Main Outcomes and Results: Area under the ROC curve (AUC) was .897. Sensitivity (.824) and specificity (.826) were maximized at a cut score of 18 or greater, slightly higher than the standard cut of 16 established for civilian samples. Conclusions: Results suggest that the CES-D is a valid screening instrument for depressive disorders in military samples with a history of mTBI.

Retrospective identification of chemical warfare agents by high-temperature automatic thermal desorption-gas chromatography-mass spectrometry.

An automated thermal desorption (ATD)-gas chromatography-mass spectrometry method was developed for the analysis of selected chemical warfare (CW) agents. Suitable methods were developed for analytes of high volatility to low volatility. The less volatile CW agents required the purchase and installation of a high-temperature valve upgrade kit allowing valve temperatures of up to 260 degrees C to be reached. The limit of detection was 50 ng on the tube for most CW agents in full scan. Chloropicrin exhibited some temperature dependence, with detection limits improving as ATD temperatures were decreased below 150 degrees C. A sample storage trial was undertaken to establish the most suitable storage environment for CW agents adsorbed onto Tenax TA. Temperature and time of storage were found to influence recovery of analytes with best recoveries being observed after 1 day storage in a freezer (-12 degrees C). This method was evaluated during a trial of procedures for sampling and identification of chemical agents at Porton Down, UK. Sulfur mustard was detected downwind of a simulated exploded munition.

Extraction of thiodiglycol from soil using pressurised liquid extraction.

Thiodiglycol (TDG) is the predominant hydrolysis product of the chemical warfare agent sulfur mustard. The extraction of TDG was investigated using pressurised liquid extraction and the results compared for a variety of different solvents and soils. TDG was analysed underivatised by gas chromatography with flame photometric detection. A mixture of methanol-water (9:1), proved to be the most efficient extracting solvent for TDG at a temperature of 150 degrees C and 10 MPa.

A method for hydrolyzing and determining the amino acid compositions of picomole quantities of proteins in less than 3 hours.

A method is described for quantitatively hydrolyzing proteins in 45 min and for analyzing the hydrolysates by high-performance liquid chromatography in an additional 52 min. The alpha-amino acids were detected by the fluorescence of their omicron-phthaldialdehyde derivatives. Ten picomoles of each of the commonly occurring alpha-amino acids could be reliably determined. The method described yielded OPA-ethanethiolamino acid derivatives that were stable for 1 1/2 h and the HPLC method produced a better separation than previously published methods.

Maximum likelihood estimation of markov-process blob boundaries in noisy images.

Effective and elegant procedures have recently appeared in the published literature for determining by computer a highly variable blob boundary in a noisy image [1]-[3]. In this paper we point out that if the blob boundary is modeled as a Markov process and the additive noise is modeled as a white Gaussian noise field, then maximization of the joint likelihood of the hypothesized blob boundary and all of the image data results in roughly the same blob boundary determination as does one of the aforementioned deterministic formulations [2]. However, the formulation in this paper provides insights into and optimal parameter values for the functions involved and reveals suboptimalities in some of the formulations appearing in the literature. More generally, we agree that maximization of the joint likelihood of the hypothesized blob boundary and of the entire picture function is a fundamental approach to boundary estimation or the estimation of linear features (roads, rivers, etc.) in images, and provides a powerful mechanism for designing sequential, parallel, or other boundary estimation algorithms. The ripple filter, an advanced form of region growing, is briefly introduced and illustrates one of a number of alternative algorithms for maximizing the likelihood function. Hence, this likelihood maximization approach provides a unified view for seemingly different approaches, such as sequential boundary finding and region growing. Bounds on the accuracy of boundary estimation are readily derived with this formulation and are presented.

On optimally combining pieces of information, with application to estimating 3-d complex-object position from range data.

New asymptotic methods are introduced that permit computationally simple Bayesian recognition and parameter estimation for many large data sets described by a combination of algebraic, geometric, and probabilistic models. The techniques introduced permit controlled decomposition of a large problem into small problems for separate parallel processing where maximum likelihood estimation or Bayesian estimation or recognition can be realized locally. These results can be combined to arrive at globally optimum estimation or recognition. The approach is applied to the maximum likelihood estimation of 3-D complex-object position. To this end, the surface of an object is modeled as a collection of patches of primitive quadrics, i.e., planar, cylindrical, and spherical patches, possibly augmented by boundary segments. The primitive surface-patch models are specified by geometric parameters, reflecting location, orientation, and dimension information. The object-position estimation is based on sets of range data points, each set associated with an object primitive. Probability density functions are introduced that model the generation of range measurement points. This entails the formulation of a noise mechanism in three-space accounting for inaccuracies in the 3-D measurements and possibly for inaccuracies in the 3-D modeling. We develop the necessary techniques for optimal local parameter estimation and primitive boundary or surface type recognition for each small patch of data, and then optimal combining of these inaccurate locally derived parameter estimates in order to arrive at roughly globally optimum object-position estimation.

Simple parallel hierarchical and relaxation algorithms for segmenting noncausal markovian random fields.

The modeling and segmentation of images by MRF's (Markov random fields) is treated. These are two-dimensional noncausal Markovian stochastic processes. Two conceptually new algorithms are presented for segmenting textured images into regions in each of which the data are modeled as one of C MRF's. The algorithms are designed to operate in real time when implemented on new parallel computer architectures that can be built with present technology. A doubly stochastic representation is used in image modeling. Here, a Gaussian MRF is used to model textures in visible light and infrared images, and an autobinary (or autoternary, etc.) MRF to model a priori information about the local geometry of textured image regions. For image segmentation, the true texture class regions are treated either as a priori completely unknown or as a realization of a binary (or ternary, etc.) MRF. In the former case, image segmentation is realized as true maximum likelihood estimation. In the latter case, it is realized as true maximum a posteriori likelihood segmentation. In addition to providing a mathematically correct means for introducing geometric structure, the autobinary (or ternary, etc.) MRF can be used in a generative mode to generate image geometries and artificial images, and such simulations constitute a very powerful tool for studying the effects of these models and the appropriate choice of model parameters. The first segmentation algorithm is hierarchical and uses a pyramid-like structure in new ways that exploit the mutual dependencies among disjoint pieces of a textured region.

Multiple-window parallel adaptive boundary finding in computer vision.

The problem considered in this paper is the estimation of highly variable object boundaries in noisy images. Boundaries may be those of a tank in an IR image, a spinal canal in a CAT scan, a cloud in a visible light image, etc. Or they may be internal to an object such as the boundary between a spherical surface and a cylindrical surface in a manufactured object. The focus of the paper is on parallel multiple-window boundary estimation algorithms. Here the image field is parti-tioned into an array of rectangular windows, and boundary finders are run simultaneously within the windows. The boundary segments found within the windows are then seamed together to obtain meaningful global boundaries. The entire procedure is treated within a maximum likelihood estimation framework that we have developed for boundary finding. Although our multiple-window estimation approach can be used with a number of local boundary finding algorithms, we concen-trate on one which is based on dynamic programming and will produce the true maximum likelihood boundary. Some theoretical considera-tions for boundary model design and boundary-finding runtime are covered. Included is the use of a low computational cost F-test for test-ing whether a window contains a boundary, and an analytical treatment which shows that use of coarse pixels with a chi-square test or an F-test improves the probability of correctly recognizing whether a boundary is present in a window.

Bayesian recognition of local 3-d shape by approximating image intensity functions with quadric polynomials.

The recognition in image data of viewed patches of spheres, cylinders, and planes in the 3-D world is discussed as a first step to complex object recognition or complex object location and orientation estimation. Accordingly, an image is partitioned into small square windows, each of which is a view of a piece of a sphere, or of a cylinder, or of a plane. Windows are processed in parallel for recognition of content. New concepts and techniques include approximations of the image within a window by 2-D quadric polynomials where each approximation is constrained by one of the hypotheses that the 3-D surface shape seen is either planar, cylindrical, or spherical; a recognizer based upon these approximations to determine whether the object patch viewed is a piece of a sphere, or a piece of a cylinder, or a piece of a plane; lowpass filtering of the image by the approximation. The shape recognition is computationally simple, and for large windows is approximately Bayesian minimum-probability-of-error recognition. These classifications are useful for many purposes. One such purpose is to enable a following processor to use an appropriate estimator to estimate shape, and orientation and location parameters for the 3-D surface seen within a window.

3-d space location and orientation parameter estimation of lambertian spheres and cylinders from a single 2-d image by fitting lines and ellipses to thresholded data.

An approach to object location and orientation estimation is discussed in which objects in 3-D space are approximated by chunks of spheres, cylinders, and planes. The surface-shape parameters of these chunks of primitive subobjects are estimated in real time from a single 2-D image assuming a Lambertian reflection model. This processing is realized by partitioning an image into small square windows and processing the windows in parallel. It is assumed that a small window views a portion of one of the spherical, cylindrical or planar chunks. The paper applies standard statistical estimators in new ways to the estimation of the 3-D shape parameters for spherical and cylindrical surfaces. Linear regression and scatter matrix eigenvalue analysis techniques are used here. The algorithms are computationally simple yet are robust and can handle noisy highly variable data.

Implementation, interpretation, and analysis of a suboptimal boundary finding algorithm.

This paper presents a suboptimal boundary estimation algorithm for noisy images which is based upon an optimal maximum likelihood problem formulation. Both the maximum likelihood formulation and the resulting algorithm are described in detail, and computational results are given. In addition, the potential power of the likelihood formulation is demonstrated through the presentation of three simple but insightful analyses of algorithm performance. These analyses are based on a technique we have developed for comparing the accuracies of different boundary finding algorithms. This technique also helps in understanding the interplay of object shape and data models in the relative performances of boundary finders. Some of the algorithm design considerations resulting from the use of our analysis technique are new and, at first, surprising. Our technique appears to be the only one developed for comparing the accuracies of different boundary finding algorithms.

Improving the stability of algebraic curves for applications.

An algebraic curve is defined as the zero set of a polynomial in two variables. Algebraic curves are practical for modeling shapes much more complicated than conics or superquadrics. The main drawback in representing shapes by algebraic curves has been the lack of repeatability in fitting algebraic curves to data. Usually, arguments against using algebraic curves involve references to mathematicians Wilkinson (and Runge). The first goal of this article is to understand the stability issue of algebraic curve fitting. Then a fitting method based on ridge regression and restricting the representation to well behaved subsets of polynomials is proposed, and its properties are investigated. The fitting algorithm is of sufficient stability for very fast position-invariant shape recognition, position estimation, and shape tracking, based on invariants and new representations. Among appropriate applications are shape-based indexing into image databases.

Assessment and monitoring of alcohol-related problems.

Employers are increasingly requesting employees to submit to screening for alcohol-related problems (ARPs). Holistic assessment, leading to problem identification and treatment, can lead to a reduction in the number of accidents occurring in the workplace. This article argues against the practice of assessment based solely on blood levels of liver enzymes and mean corpuscular volume of erythrocytes and introduces a new diagnostic test which, if used with a full and systematic assessment, may lead to more accuracy in diagnostic interpretation.

Alcohol home detoxification: a way forward.

Alcohol detoxification within the home environment is a safe, cost-effective alternative to inpatient care and the problem drinker is more likely to complete the treatment. Yet health trusts continue to ignore this community approach for those individuals with alcohol-related problems.

Alcohol home detoxification.

Traditional inpatient treatments for alcohol-related problems often prevent the problem drinker from seeking early intervention. Inpatient treatment may be particularly difficult for women because of stigma, labelling and family commitments. An alternative, home-based program initiated in the United Kingdom (U.K.) in the late 1980s and currently being trialed by many health districts, may be of interest to Canadian nurses. In addition to improved client outcomes and cost-effectiveness, the program offers nurses a higher public and medical profile. Clinical nurse specialists in particular may want to consider whether the expanded role of their British colleagues might be appropriate for Canadian adaptation.

Helping people with alcohol problems.

Many nurses may feel reluctant to include questions on alcohol use in their general patient assessments but studies have shown that brief interventions on this subject can help people cut down on their drinking and improve their health.

Preliminary findings of cortical thickness abnormalities in blast injured service members and their relationship to clinical findings.

Though cortical abnormalities have been demonstrated in moderate and severe traumatic brain injured (TBI) patients, there have been no studies examining cortical changes following blast related mild TBI (mTBI). The purpose of this study was to determine the effects and functional relevance of blast mTBI on cortical thickness in a small cohort of carefully screened blast injured US Service Members (SM). Twelve SM with mTBI acquired through blast injury were compared to 11 demographically matched control SM without TBI. Both mTBI and control participants were active duty and had completed a combat deployment. Subjects underwent MRI examination and the T1 weighted anatomic images were processed using the FreeSurfer suite of tools. Cortical thickness maps were compared between groups and examined for relationships with time since injury (TSI). Utilizing a large database of functional imaging results (BrainMap), significant regions of interest (ROI) were used to determine the behavioral profiles most consistently associated with the specific ROI. In addition, clinical variables were examined as part of post-hoc analysis of functional relevance. Group comparisons controlling for age demonstrated several significant clusters of cortical thinning for the blast injured SM. After multiple comparisons correction (False Discovery Rate (FDR)), two left hemisphere clusters remained significant (left superior temporal (STG) and frontal (SFG) gyri). No clusters were significantly correlated with TSI after FDR correction. Behavioral analysis for the STG and SFG clusters demonstrated three significant behavioral/cognitive sub-domains, each associated with audition and language. Blast injured SMs demonstrated distinct areas of cortical thinning in the STG and SFG. These areas have been previously shown to be associated with audition and language. Post-hoc analyses of clinical records demonstrated significant abnormal audiology reports for the blast injured SM suggesting that the thinning in these ROIs might be related to injury to the external auditory system rather than direct injury to the brain from the blast. It is clear that additional replication is needed in much larger cohorts. Importantly, the combination of imaging tools and methods in this study successfully demonstrated the potential to define unique ROIs and functional correlates that can be used to design future studies.