Examining the gateway to the limbic system with diffusion tensor imaging: the perforant pathway in dementia

Current treatments for Alzheimer's disease (AD) are only able to slow the progression of mental deterioration, making early and reliable diagnosis an essential part of any promising therapeutic strategy. In the initial stages of AD, the first neuropathological alterations occur in the perforant pathway (PP), a large neuronal fiber tract located at the entrance to the limbic system. However, to date, there is no sensitive diagnostic tool for performing in vivo assessments of this structure. In the present bimodal magnetic resonance imaging (MRI) study, we examined 10 elderly controls, 10 subjects suffering from mild cognitive impairment (MCI), and 10 AD patients in order to evaluate the sensitivity of diffusion tensor imaging (DTI), a new MRI technique, for detecting changes in the PP. Furthermore, the diagnostic explanatory power of DTI data of the PP should be compared to high-resolution MRI volumetry and intervoxel coherences (COH) of the hippocampus and the entorhinal cortex, two limbic regions also involved in the pathophysiology of early AD. DTI revealed a marked decrease in COH values in the PP region of MCI (right side: 26%, left side: 29%, as compared to controls) and AD patients (right side: 37%, left side: 43%, as compared to controls). Reductions in COH values of the PP region were significantly correlated with cognitive impairment. DTI data of the PP zone were the only parameter differing significantly between control subjects and MCI patients, while the volumetric measures and the COH values of the hippocampus and the entorhinal cortex did not. DTI of medial temporal brain regions is a promising non-invasive tool for the in vivo diagnosis of the early/preclinical stages of AD.

Using Context Information to Re-architect a System

Successful software systems cope with complexity by organizing classes into packages. However, a particular organization may be neither straightforward nor obvious for a given developer. As a consequence, classes can be misplaced, leading to duplicated code and ripple effects with minor changes effecting multiple packages. We claim that contextual information is the key to rearchitecture a system. Exploiting contextual information, we propose a technique to detect misplaced classes by analyzing how client packages access the classes of a given provider package. We define locality as a measure of the degree to which classes reused by common clients appear in the same package. We then use locality to guide a simulated annealing algorithm to obtain optimal placements of classes in packages. The result is the identification of classes that are candidates for relocation. We apply the technique to three applications and validate the usefulness of our approach via developer interviews.

Observation and identification of metabolites emerging during postmortem decomposition of brain tissue by means of in situ 1H-magnetic resonance spectroscopy

Postmortem decomposition of brain tissue was investigated by (1)H-magnetic resonance spectroscopy (MRS) in a sheep head model and selected human cases. Aiming at the eventual estimation of postmortem intervals in forensic medicine, this study focuses on the characterization and identification of newly observed metabolites. In situ single-voxel (1)H-MRS at 1.5 T was complemented by multidimensional homo- and heteronuclear high-resolution NMR spectroscopy of an extract of sheep brain tissue. The inclusion of spectra of model solutions in the program LC Model confirmed the assignments in situ. The first postmortem phase was characterized mainly by changes in the concentrations of metabolites usually observed in vivo and by the appearance of previously reported decay products. About 3 days postmortem, new metabolites, including free trimethylammonium, propionate, butyrate, and iso-butyrate, started to appear in situ. Since the observed metabolites and the time course is comparable in sheep and human brain tissue, the model system seems to be appropriate.

Identification of brain structures involved in micturition with functional magnetic resonance imaging (fMRI)

OBJECTIVE: The voluntary control of micturition is believed to be integrated by complex interactions among the brainstem, subcortical areas and cortical areas. Several brain imaging studies using positron emission tomography (PET) have demonstrated that frontal brain areas, the limbic system, the pons and the premotor cortical areas were involved. However, the cortical and subcortical brain areas have not yet been precisely identified and their exact function is not yet completely understood. MATERIALS AND METHODS: This study used functional magnetic resonance imaging (fMRI) to compare brain activity during passive filling and emptying of the bladder. A cathetherism of the bladder was performed in seven healthy subjects (one man and six right-handed women). During scanning, the bladder was alternatively filled and emptied at a constant rate with bladder rincing solution. RESULTS: Comparison between passive filling of the bladder and emptying of the bladder showed an increased brain activity in the right inferior frontal gyrus, cerebellum, symmetrically in the operculum and mesial frontal. Subcortical areas were not evaluated. CONCLUSIONS: Our results suggest that several cortical brain areas are involved in the regulation of micturition.

Evaluation of the colorimetric VITEK 2 card for identification of gram-negative nonfermentative rods: comparison to 16S rRNA gene sequencing

Ninety strains of a collection of well-identified clinical isolates of gram-negative nonfermentative rods collected over a period of 5 years were evaluated using the new colorimetric VITEK 2 card. The VITEK 2 colorimetric system identified 53 (59%) of the isolates to the species level and 9 (10%) to the genus level; 28 (31%) isolates were misidentified. An algorithm combining the colorimetric VITEK 2 card and 16S rRNA gene sequencing for adequate identification of gram-negative nonfermentative rods was developed. According to this algorithm, any identification by the colorimetric VITEK 2 card other than Achromobacter xylosoxidans, Acinetobacter sp., Burkholderia cepacia complex, Pseudomonas aeruginosa, and Stenotrophomonas maltophilia should be subjected to 16S rRNA gene sequencing when accurate identification of nonfermentative rods is of concern.

Population pharmacokinetics of sevoflurane in conjunction with the AnaConDa: toward target-controlled infusion of volatiles into the breathing system

BACKGROUND: The Anesthetic Conserving Device (AnaConDa) uncouples delivery of a volatile anesthetic (VA) from fresh gas flow (FGF) using a continuous infusion of liquid volatile into a modified heat-moisture exchanger capable of adsorbing VA during expiration and releasing adsorbed VA during inspiration. It combines the simplicity and responsiveness of high FGF with low agent expenditures. We performed in vitro characterization of the device before developing a population pharmacokinetic model for sevoflurane administration with the AnaConDa, and retrospectively testing its performance (internal validation). MATERIALS AND METHODS: Eighteen females and 20 males, aged 31-87, BMI 20-38, were included. The end-tidal concentrations were varied and recorded together with the VA infusion rates into the device, ventilation and demographic data. The concentration-time course of sevoflurane was described using linear differential equations, and the most suitable structural model and typical parameter values were identified. The individual pharmacokinetic parameters were obtained and tested for covariate relationships. Prediction errors were calculated. RESULTS: In vitro studies assessed the contribution of the device to the pharmacokinetic model. In vivo, the sevoflurane concentration-time courses on the patient side of the AnaConDa were adequately described with a two-compartment model. The population median absolute prediction error was 27% (interquartile range 13-45%). CONCLUSION: The predictive performance of the two-compartment model was similar to that of models accepted for TCI administration of intravenous anesthetics, supporting open-loop administration of sevoflurane with the AnaConDa. Further studies will focus on prospective testing and external validation of the model implemented in a target-controlled infusion device.

Detection of complex point targets with distributed assets in a MIMO radar system

The report explores the problem of detecting complex point target models in a MIMO radar system. A complex point target is a mathematical and statistical model for a radar target that is not resolved in space, but exhibits varying complex reflectivity across the different bistatic view angles. The complex reflectivity can be modeled as a complex stochastic process whose index set is the set of all the bistatic view angles, and the parameters of the stochastic process follow from an analysis of a target model comprising a number of ideal point scatterers randomly located within some radius of the targets center of mass. The proposed complex point targets may be applicable to statistical inference in multistatic or MIMO radar system. Six different target models are summarized here – three 2-dimensional (Gaussian, Uniform Square, and Uniform Circle) and three 3-dimensional (Gaussian, Uniform Cube, and Uniform Sphere). They are assumed to have different distributions on the location of the point scatterers within the target. We develop data models for the received signals from such targets in the MIMO radar system with distributed assets and partially correlated signals, and consider the resulting detection problem which reduces to the familiar Gauss-Gauss detection problem. We illustrate that the target parameter and transmit signal have an influence on the detector performance through target extent and the SNR respectively. A series of the receiver operator characteristic (ROC) curves are generated to notice the impact on the detector for varying SNR. Kullback–Leibler (KL) divergence is applied to obtain the approximate mean difference between density functions the scatterers assume inside the target models to show the change in the performance of the detector with target extent of the point scatterers.

Automation of shear-wave splitting parameter determination of local earthquakes at Yellowstone : application as indicator of crustal stress and temporal variation

Shear-wave splitting can be a useful technique for determining crustal stress fields in volcanic settings and temporal variations associated with activity. Splitting parameters were determined for a subset of local earthquakes recorded from 2000-2010 at Yellowstone. Analysis was automated using an unsupervised cluster analysis technique to determine optimum splitting parameters from 270 analysis windows for each event. Six stations clearly exhibit preferential fast polarization values sub-orthogonal to the direction of minimum horizontal compression. Yellowstone deformation results in a local crustal stress field differing from the regional field dominated by NE-SW extension, and fast directions reflect this difference rotating around the caldera maintaining perpendicularity to the rim. One station exhibits temporal variations concordant with identified periods of caldera subsidence and uplift. From splitting measurements, we calculated a crustal anisotropy of ~17-23% and crack density ~0.12-0.17 possibly resulting from stress-aligned fluid filled microcracks in the upper crust and an active hydrothermal system.

Parameter estimation for transformer modeling

Large Power transformers, an aging and vulnerable part of our energy infrastructure, are at choke points in the grid and are key to reliability and security. Damage or destruction due to vandalism, misoperation, or other unexpected events is of great concern, given replacement costs upward of $2M and lead time of 12 months. Transient overvoltages can cause great damage and there is much interest in improving computer simulation models to correctly predict and avoid the consequences. EMTP (the Electromagnetic Transients Program) has been developed for computer simulation of power system transients. Component models for most equipment have been developed and benchmarked. Power transformers would appear to be simple. However, due to their nonlinear and frequency-dependent behaviors, they can be one of the most complex system components to model. It is imperative that the applied models be appropriate for the range of frequencies and excitation levels that the system experiences. Thus, transformer modeling is not a mature field and newer improved models must be made available. In this work, improved topologically-correct duality-based models are developed for three-phase autotransformers having five-legged, three-legged, and shell-form cores. The main problem in the implementation of detailed models is the lack of complete and reliable data, as no international standard suggests how to measure and calculate parameters. Therefore, parameter estimation methods are developed here to determine the parameters of a given model in cases where available information is incomplete. The transformer nameplate data is required and relative physical dimensions of the core are estimated. The models include a separate representation of each segment of the core, including hysteresis of the core, λ-i saturation characteristic, capacitive effects, and frequency dependency of winding resistance and core loss. Steady-state excitation, and de-energization and re-energization transients are simulated and compared with an earlier-developed BCTRAN-based model. Black start energization cases are also simulated as a means of model evaluation and compared with actual event records. The simulated results using the model developed here are reasonable and more correct than those of the BCTRAN-based model. Simulation accuracy is dependent on the accuracy of the equipment model and its parameters. This work is significant in that it advances existing parameter estimation methods in cases where the available data and measurements are incomplete. The accuracy of EMTP simulation for power systems including three-phase autotransformers is thus enhanced. Theoretical results obtained from this work provide a sound foundation for development of transformer parameter estimation methods using engineering optimization. In addition, it should be possible to refine which information and measurement data are necessary for complete duality-based transformer models. To further refine and develop the models and transformer parameter estimation methods developed here, iterative full-scale laboratory tests using high-voltage and high-power three-phase transformer would be helpful.

IDENTIFICATION OF GENES CONTROLLING BIOLOGICAL PROCESSES AND PATHWAYS THROUGH STATISTICAL ANALYSIS AND NETWORK RECONSTRUCTION

The developmental processes and functions of an organism are controlled by the genes and the proteins that are derived from these genes. The identification of key genes and the reconstruction of gene networks can provide a model to help us understand the regulatory mechanisms for the initiation and progression of biological processes or functional abnormalities (e.g. diseases) in living organisms. In this dissertation, I have developed statistical methods to identify the genes and transcription factors (TFs) involved in biological processes, constructed their regulatory networks, and also evaluated some existing association methods to find robust methods for coexpression analyses. Two kinds of data sets were used for this work: genotype data and gene expression microarray data. On the basis of these data sets, this dissertation has two major parts, together forming six chapters. The first part deals with developing association methods for rare variants using genotype data (chapter 4 and 5). The second part deals with developing and/or evaluating statistical methods to identify genes and TFs involved in biological processes, and construction of their regulatory networks using gene expression data (chapter 2, 3, and 6). For the first part, I have developed two methods to find the groupwise association of rare variants with given diseases or traits. The first method is based on kernel machine learning and can be applied to both quantitative as well as qualitative traits. Simulation results showed that the proposed method has improved power over the existing weighted sum method (WS) in most settings. The second method uses multiple phenotypes to select a few top significant genes. It then finds the association of each gene with each phenotype while controlling the population stratification by adjusting the data for ancestry using principal components. This method was applied to GAW 17 data and was able to find several disease risk genes. For the second part, I have worked on three problems. First problem involved evaluation of eight gene association methods. A very comprehensive comparison of these methods with further analysis clearly demonstrates the distinct and common performance of these eight gene association methods. For the second problem, an algorithm named the bottom-up graphical Gaussian model was developed to identify the TFs that regulate pathway genes and reconstruct their hierarchical regulatory networks. This algorithm has produced very significant results and it is the first report to produce such hierarchical networks for these pathways. The third problem dealt with developing another algorithm called the top-down graphical Gaussian model that identifies the network governed by a specific TF. The network produced by the algorithm is proven to be of very high accuracy.

Vertebroplasty: experimental characterization of polymethylmethacrylate bone cement spreading as a function of viscosity, bone porosity, and flow rate

STUDY DESIGN: This is an experimental study on an artificial vertebra model and human cadaveric spine. OBJECTIVE: Characterization of polymethylmethacrylate (PMMA) bone cement distribution in the vertebral body as a function of cement viscosity, bone porosity, and injection speed. Identification of relevant parameters for improved cement flow predictability and leak prevention in vertebroplasty. SUMMARY OF BACKGROUND DATA: Vertebroplasty is an efficient procedure to treat vertebral fractures and stabilize osteoporotic bone in the spine. Severe complications result from bone cement leakage into the spinal canal or the vascular system. Cement viscosity has been identified as an important parameter for leak prevention but the influence of bone structure and injection speed remain obscure. METHODS: An artificial vertebra model based on open porous aluminum foam was used to simulate bone of known porosity. Fifty-six vertebroplasties with 4 different starting viscosity levels and 2 different injection speeds were performed on artificial vertebrae of 3 different porosities. A validation on a human cadaveric spine was executed. The experiments were radiographically monitored and the shape of the cement clouds quantitatively described with the 2 indicators circularity and mean cement spreading distance. RESULTS: An increase in circularity and a decrease in mean cement spreading distance was observed with increasing viscosity, with the most striking change occurring between 50 and 100 Pas. Larger pores resulted in significantly reduced circularity and increased mean cement spreading distance whereas the effect of injection speed on the 2 indicators was not significant. CONCLUSION: Viscosity is the key factor for reducing the risk of PMMA cement leakage and it should be adapted to the degree of osteoporosis encountered in each patient. It may be advisable to opt for a higher starting viscosity but to inject the material at a faster rate.

The Staining Effect of the Hydrochloric Acid-Chromate Trioxide Solution on the Minerals of the Chalcocite-Stibnite-Galena Ternary System.

The object of this work has been to devise a method by which the different phases in the chalcocite-stibnite-galena ternary system may be identified. As the mineralogists have no precise methods for the identification of these phases, a hydrochloric acid-chromate trioxide staining solution was employed.

MODEL UPDATING AND STRUCTURAL HEALTH MONITORING OF HORIZONTAL AXIS WIND TURBINES VIA ADVANCED SPINNING FINITE ELEMENTS AND STOCHASTIC SUBSPACE IDENTIFICATION METHODS

Wind energy has been one of the most growing sectors of the nation’s renewable energy portfolio for the past decade, and the same tendency is being projected for the upcoming years given the aggressive governmental policies for the reduction of fossil fuel dependency. Great technological expectation and outstanding commercial penetration has shown the so called Horizontal Axis Wind Turbines (HAWT) technologies. Given its great acceptance, size evolution of wind turbines over time has increased exponentially. However, safety and economical concerns have emerged as a result of the newly design tendencies for massive scale wind turbine structures presenting high slenderness ratios and complex shapes, typically located in remote areas (e.g. offshore wind farms). In this regard, safety operation requires not only having first-hand information regarding actual structural dynamic conditions under aerodynamic action, but also a deep understanding of the environmental factors in which these multibody rotating structures operate. Given the cyclo-stochastic patterns of the wind loading exerting pressure on a HAWT, a probabilistic framework is appropriate to characterize the risk of failure in terms of resistance and serviceability conditions, at any given time. Furthermore, sources of uncertainty such as material imperfections, buffeting and flutter, aeroelastic damping, gyroscopic effects, turbulence, among others, have pleaded for the use of a more sophisticated mathematical framework that could properly handle all these sources of indetermination. The attainable modeling complexity that arises as a result of these characterizations demands a data-driven experimental validation methodology to calibrate and corroborate the model. For this aim, System Identification (SI) techniques offer a spectrum of well-established numerical methods appropriated for stationary, deterministic, and data-driven numerical schemes, capable of predicting actual dynamic states (eigenrealizations) of traditional time-invariant dynamic systems. As a consequence, it is proposed a modified data-driven SI metric based on the so called Subspace Realization Theory, now adapted for stochastic non-stationary and timevarying systems, as is the case of HAWT’s complex aerodynamics. Simultaneously, this investigation explores the characterization of the turbine loading and response envelopes for critical failure modes of the structural components the wind turbine is made of. In the long run, both aerodynamic framework (theoretical model) and system identification (experimental model) will be merged in a numerical engine formulated as a search algorithm for model updating, also known as Adaptive Simulated Annealing (ASA) process. This iterative engine is based on a set of function minimizations computed by a metric called Modal Assurance Criterion (MAC). In summary, the Thesis is composed of four major parts: (1) development of an analytical aerodynamic framework that predicts interacted wind-structure stochastic loads on wind turbine components; (2) development of a novel tapered-swept-corved Spinning Finite Element (SFE) that includes dampedgyroscopic effects and axial-flexural-torsional coupling; (3) a novel data-driven structural health monitoring (SHM) algorithm via stochastic subspace identification methods; and (4) a numerical search (optimization) engine based on ASA and MAC capable of updating the SFE aerodynamic model.

The European baseline series in 10 European Countries, 2005/2006--results of the European Surveillance System on Contact Allergies (ESSCA)

BACKGROUND: Continual surveillance based on patch test results has proved useful for the identification of contact allergy. OBJECTIVES: To provide a current view on the spectrum of contact allergy to important sensitizers across Europe. PATIENTS/METHODS: Clinical and patch test data of 19 793 patients patch tested in 2005/2006 in the 31 participating departments from 10 European countries (the European Surveillance System on Contact Allergies' (ESSCA) www.essca-dc.org) were descriptively analysed, aggregated to four European regions. RESULTS: Nickel sulfate remains the most common allergen with standardized prevalences ranging from 19.7% (central Europe) to 24.4% (southern Europe). While a number of allergens shows limited variation across the four regions, such as Myroxylon pereirae (5.3-6.8%), cobalt chloride (6.2-8.8%) or thiuram mix (1.7-2.4%), the differences observed with other allergens may hint on underlying differences in exposures, for example: dichromate 2.4% in the UK (west) versus 4.5-5.9% in the remaining EU regions, methylchloroisothiazolinone/methylisothiazolinone 4.1% in the South versus 2.1-2.7% in the remaining regions. CONCLUSIONS: Notwithstanding residual methodological variation (affecting at least some 'difficult' allergens) tackled by ongoing efforts for standardization, a comparative analysis as presented provides (i) a broad overview on contact allergy frequencies and (ii) interesting starting points for further, in-depth investigation.

On the use of textural features for writer identification in old handwritten music scores

Writer identification consists in determining the writer of a piece of handwriting from a set of writers. In this paper we present a system for writer identification in old handwritten music scores which uses only music notation to determine the author. The steps of the proposed system are the following. First of all, the music sheet is preprocessed for obtaining a music score without the staff lines. Afterwards, four different methods for generating texture images from music symbols are applied. Every approach uses a different spatial variation when combining the music symbols to generate the textures. Finally, Gabor filters and Grey-scale Co-ocurrence matrices are used to obtain the features. The classification is performed using a k-NN classifier based on Euclidean distance. The proposed method has been tested on a database of old music scores from the 17th to 19th centuries, achieving encouraging identification rates.