On Automatic Diagnosis of Alzheimer's Disease based on Spontaneous Speech Analysis and Emotional Temperature

Alzheimer's disease is the most prevalent form of progressive degenerative dementia; it has a high socio-economic impact in Western countries. Therefore it is one of the most active research areas today. Alzheimer's is sometimes diagnosed by excluding other dementias, and definitive confirmation is only obtained through a post-mortem study of the brain tissue of the patient. The work presented here is part of a larger study that aims to identify novel technologies and biomarkers for early Alzheimer's disease detection, and it focuses on evaluating the suitability of a new approach for early diagnosis of Alzheimer’s disease by non-invasive methods. The purpose is to examine, in a pilot study, the potential of applying Machine Learning algorithms to speech features obtained from suspected Alzheimer sufferers in order help diagnose this disease and determine its degree of severity. Two human capabilities relevant in communication have been analyzed for feature selection: Spontaneous Speech and Emotional Response. The experimental results obtained were very satisfactory and promising for the early diagnosis and classification of Alzheimer’s disease patients.

Weakly linked binary mixtures of F = 1 87Rb Bose-Einstein condensates

We present a study of binary mixtures of Bose-Einstein condensates confined in a double-well potential within the framework of the mean field Gross-Pitaevskii (GP) equation. We re-examine both the single component and the binary mixture cases for such a potential, and we investigate what are the situations in which a simpler two-mode approach leads to an accurate description of their dynamics. We also estimate the validity of the most usual dimensionality reductions used to solve the GP equations. To this end, we compare both the semi-analytical two-mode approaches and the numerical simulations of the one-dimensional (1D) reductions with the full 3D numerical solutions of the GP equation. Our analysis provides a guide to clarify the validity of several simplified models that describe mean-field nonlinear dynamics, using an experimentally feasible binary mixture of an F = 1 spinor condensate with two of its Zeeman manifolds populated, m = ±1.

Weakly linked binary mixtures of F = 1 87Rb Bose-Einstein condensates

We present a study of binary mixtures of Bose-Einstein condensates confined in a double-well potential within the framework of the mean field Gross-Pitaevskii (GP) equation. We re-examine both the single component and the binary mixture cases for such a potential, and we investigate what are the situations in which a simpler two-mode approach leads to an accurate description of their dynamics. We also estimate the validity of the most usual dimensionality reductions used to solve the GP equations. To this end, we compare both the semi-analytical two-mode approaches and the numerical simulations of the one-dimensional (1D) reductions with the full 3D numerical solutions of the GP equation. Our analysis provides a guide to clarify the validity of several simplified models that describe mean-field nonlinear dynamics, using an experimentally feasible binary mixture of an F = 1 spinor condensate with two of its Zeeman manifolds populated, m = ±1.

Cranial neuropathies in granulomatosis with polyangiitis (Wegener's): a case-based review.

The purpose of this case-based review is to highlight cranial nerve involvement in granulomatosis with polyangiitis (Wegener's). In this disease, cranial nerve involvement may be less frequent than other neurological manifestations, but often goes unrecognized by physicians as a sign of the disease, and its prevalence and importance is likely underestimated. Awareness of this aspect of the disease is necessary to make the proper diagnosis rapidly, as it can be a major feature of a patient's presentation. We also briefly discuss the known pathogenic mechanisms, which could be important when selecting the best therapeutic option.

Simulated-annealing-based conditional simulation for the local-scale characterization of heterogeneous aquifers

Simulated-annealing-based conditional simulations provide a flexible means of quantitatively integrating diverse types of subsurface data. Although such techniques are being increasingly used in hydrocarbon reservoir characterization studies, their potential in environmental, engineering and hydrological investigations is still largely unexploited. Here, we introduce a novel simulated annealing (SA) algorithm geared towards the integration of high-resolution geophysical and hydrological data which, compared to more conventional approaches, provides significant advancements in the way that large-scale structural information in the geophysical data is accounted for. Model perturbations in the annealing procedure are made by drawing from a probability distribution for the target parameter conditioned to the geophysical data. This is the only place where geophysical information is utilized in our algorithm, which is in marked contrast to other approaches where model perturbations are made through the swapping of values in the simulation grid and agreement with soft data is enforced through a correlation coefficient constraint. Another major feature of our algorithm is the way in which available geostatistical information is utilized. Instead of constraining realizations to match a parametric target covariance model over a wide range of spatial lags, we constrain the realizations only at smaller lags where the available geophysical data cannot provide enough information. Thus we allow the larger-scale subsurface features resolved by the geophysical data to have much more due control on the output realizations. Further, since the only component of the SA objective function required in our approach is a covariance constraint at small lags, our method has improved convergence and computational efficiency over more traditional methods. Here, we present the results of applying our algorithm to the integration of porosity log and tomographic crosshole georadar data to generate stochastic realizations of the local-scale porosity structure. Our procedure is first tested on a synthetic data set, and then applied to data collected at the Boise Hydrogeophysical Research Site.

Associations between mood, anxiety or substance use disorders and inflammatory markers after adjustment for multiple covariates in a population-based study.

Inflammation is one possible mechanism underlying the associations between mental disorders and cardiovascular diseases (CVD). However, studies on mental disorders and inflammation have yielded inconsistent results and the majority did not adjust for potential confounding factors. We examined the associations of several pro-inflammatory cytokines (IL-1β, IL-6 and TNF-α) and high sensitive C-reactive protein (hsCRP) with lifetime and current mood, anxiety and substance use disorders (SUD), while adjusting for multiple covariates. The sample included 3719 subjects, randomly selected from the general population, who underwent thorough somatic and psychiatric evaluations. Psychiatric diagnoses were made with a semi-structured interview. Major depressive disorder was subtyped into "atypical", "melancholic", "combined atypical-melancholic" and "unspecified". Associations between inflammatory markers and psychiatric diagnoses were assessed using multiple linear and logistic regression models. Lifetime bipolar disorders and atypical depression were associated with increased levels of hsCRP, but not after multivariate adjustment. After multivariate adjustment, SUD remained associated with increased hsCRP levels in men (β = 0.13 (95% CI: 0.03,0.23)) but not in women. After multivariate adjustment, lifetime combined and unspecified depression were associated with decreased levels of IL-6 (β = -0.27 (-0.51,-0.02); β = -0.19 (-0.34,-0.05), respectively) and TNF-α (β = -0.16 (-0.30,-0.01); β = -0.10 (-0.19,-0.02), respectively), whereas current combined and unspecified depression were associated with decreased levels of hsCRP (β = -0.20 (-0.39,-0.02); β = -0.12 (-0.24,-0.01), respectively). Our data suggest that the significant associations between increased hsCRP levels and mood disorders are mainly attributable to the effects of comorbid disorders, medication as well as behavioral and physical CVRFs.

A connectome-based comparison of diffusion MRI schemes.

Diffusion MRI has evolved towards an important clinical diagnostic and research tool. Though clinical routine is using mainly diffusion weighted and tensor imaging approaches, Q-ball imaging and diffusion spectrum imaging techniques have become more widely available. They are frequently used in research-oriented investigations in particular those aiming at measuring brain network connectivity. In this work, we aim at assessing the dependency of connectivity measurements on various diffusion encoding schemes in combination with appropriate data modeling. We process and compare the structural connection matrices computed from several diffusion encoding schemes, including diffusion tensor imaging, q-ball imaging and high angular resolution schemes, such as diffusion spectrum imaging with a publically available processing pipeline for data reconstruction, tracking and visualization of diffusion MR imaging. The results indicate that the high angular resolution schemes maximize the number of obtained connections when applying identical processing strategies to the different diffusion schemes. Compared to the conventional diffusion tensor imaging, the added connectivity is mainly found for pathways in the 50-100mm range, corresponding to neighboring association fibers and long-range associative, striatal and commissural fiber pathways. The analysis of the major associative fiber tracts of the brain reveals striking differences between the applied diffusion schemes. More complex data modeling techniques (beyond tensor model) are recommended 1) if the tracts of interest run through large fiber crossings such as the centrum semi-ovale, or 2) if non-dominant fiber populations, e.g. the neighboring association fibers are the subject of investigation. An important finding of the study is that since the ground truth sensitivity and specificity is not known, the comparability between results arising from different strategies in data reconstruction and/or tracking becomes implausible to understand.

Binary proposal for assessing quality of Open Access Institutional Repositories : the case of Spanish repositories

The reason for this study is to propose a new quantitative approach on how to assess the quality of Open Access University Institutional Repositories. The results of this new approach are tested in the Spanish University Repositories. The assessment method is based in a binary codification of a proposal of features that objectively describes the repositories. The purposes of this method are assessing the quality and an almost automatically system for updating the data of the characteristics. First of all a database was created with the 38 Spanish institutional repositories. The variables of analysis are presented and explained either if they are coming from bibliography or are a set of new variables. Among the characteristics analyzed are the features of the software, the services of the repository, the features of the information system, the Internet visibility and the licenses of use. Results from Spanish universities ARE provided as a practical example of the assessment and for having a picture of the state of the development of the open access movement in Spain.

Cost information in engineering design -Potentials and limitations of activity-based costing

The purpose of this thesis is to analyse activity-based costing (ABC) and possible modified versions ofit in engineering design context. The design engineers need cost information attheir decision-making level and the cost information should also have a strong future orientation. These demands are high because traditional management accounting has concentrated on the direct actual costs of the products. However, cost accounting has progressed as ABC was introduced late 1980s and adopted widely bycompanies in the 1990s. The ABC has been a success, but it has gained also criticism. In some cases the ambitious ABC systems have become too complex to build,use and update. This study can be called an action-oriented case study with some normative features. In this thesis theoretical concepts are assessed and allowed to unfold gradually through interaction with data from three cases. The theoretical starting points are ABC and theory of engineering design process (chapter2). Concepts and research results from these theoretical approaches are summarized in two hypotheses (chapter 2.3). The hypotheses are analysed with two cases (chapter 3). After the two case analyses, the ABC part is extended to cover alsoother modern cost accounting methods, e.g. process costing and feature costing (chapter 4.1). The ideas from this second theoretical part are operationalized with the third case (chapter 4.2). The knowledge from the theory and three cases is summarized in the created framework (chapter 4.3). With the created frameworkit is possible to analyse ABC and its modifications in the engineering design context. The framework collects the factors that guide the choice of the costing method to be used in engineering design. It also illuminates the contents of various ABC-related costing methods. However, the framework needs to be further tested. On the basis of the three cases it can be said that ABC should be used cautiously when formulating cost information for engineering design. It is suitable when the manufacturing can be considered simple, or when the design engineers are not cost conscious, and in the beginning of the design process when doing adaptive or variant design. If the design engineers need cost information for the embodiment or detailed design, or if manufacturing can be considered complex, or when design engineers are cost conscious, the ABC has to be always evaluated critically.

Supervised localimage feature detection

This thesis is about detection of local image features. The research topic belongs to the wider area of object detection, which is a machine vision and pattern recognition problem where an object must be detected (located) in an image. State-of-the-art object detection methods often divide the problem into separate interest point detection and local image description steps, but in this thesis a different technique is used, leading to higher quality image features which enable more precise localization. Instead of using interest point detection the landmark positions are marked manually. Therefore, the quality of the image features is not limited by the interest point detection phase and the learning of image features is simplified. The approach combines both interest point detection and local description into one phase for detection. Computational efficiency of the descriptor is therefore important, leaving out many of the commonly used descriptors as unsuitably heavy. Multiresolution Gabor features has been the main descriptor in this thesis and improving their efficiency is a significant part. Actual image features are formed from descriptors by using a classifierwhich can then recognize similar looking patches in new images. The main classifier is based on Gaussian mixture models. Classifiers are used in one-class classifier configuration where there are only positive training samples without explicit background class. The local image feature detection method has been tested with two freely available face detection databases and a proprietary license plate database. The localization performance was very good in these experiments. Other applications applying the same under-lying techniques are also presented, including object categorization and fault detection.

Evidence for chronic low-grade systemic inflammation in individuals with agoraphobia from a population-based prospective study.

BACKGROUND: Anxiety disorders have been linked to an increased risk of incident coronary heart disease in which inflammation plays a key pathogenic role. To date, no studies have looked at the association between proinflammatory markers and agoraphobia. METHODS: In a random Swiss population sample of 2890 persons (35-67 years, 53% women), we diagnosed a total of 124 individuals (4.3%) with agoraphobia using a validated semi-structured psychiatric interview. We also assessed socioeconomic status, traditional cardiovascular risk factors (i.e., body mass index, hypertension, blood glucose levels, total cholesterol/high-density lipoprotein-cholesterol ratio), and health behaviors (i.e., smoking, alcohol consumption, and physical activity), and other major psychiatric diseases (other anxiety disorders, major depressive disorder, drug dependence) which were treated as covariates in linear regression models. Circulating levels of inflammatory markers, statistically controlled for the baseline demographic and health-related measures, were determined at a mean follow-up of 5.5 ± 0.4 years (range 4.7 - 8.5). RESULTS: Individuals with agoraphobia had significantly higher follow-up levels of C-reactive protein (p = 0.007) and tumor-necrosis-factor-α (p = 0.042) as well as lower levels of the cardioprotective marker adiponectin (p = 0.032) than their non-agoraphobic counterparts. Follow-up levels of interleukin (IL)-1β and IL-6 did not significantly differ between the two groups. CONCLUSIONS: Our results suggest an increase in chronic low-grade inflammation in agoraphobia over time. Such a mechanism might link agoraphobia with an increased risk of atherosclerosis and coronary heart disease, and needs to be tested in longitudinal studies.

Design and implementation of FPGA-based LQ control of active magnetic bearings

The need for high performance, high precision, and energy saving in rotating machinery demands an alternative solution to traditional bearings. Because of the contactless operation principle, the rotating machines employing active magnetic bearings (AMBs) provide many advantages over the traditional ones. The advantages such as contamination-free operation, low maintenance costs, high rotational speeds, low parasitic losses, programmable stiffness and damping, and vibration insulation come at expense of high cost, and complex technical solution. All these properties make the use of AMBs appropriate primarily for specific and highly demanding applications. High performance and high precision control requires model-based control methods and accurate models of the flexible rotor. In turn, complex models lead to high-order controllers and feature considerable computational burden. Fortunately, in the last few years the advancements in signal processing devices provide new perspective on the real-time control of AMBs. The design and the real-time digital implementation of the high-order LQ controllers, which focus on fast execution times, are the subjects of this work. In particular, the control design and implementation in the field programmable gate array (FPGA) circuits are investigated. The optimal design is guided by the physical constraints of the system for selecting the optimal weighting matrices. The plant model is complemented by augmenting appropriate disturbance models. The compensation of the force-field nonlinearities is proposed for decreasing the uncertainty of the actuator. A disturbance-observer-based unbalance compensation for canceling the magnetic force vibrations or vibrations in the measured positions is presented. The theoretical studies are verified by the practical experiments utilizing a custom-built laboratory test rig. The test rig uses a prototyping control platform developed in the scope of this work. To sum up, the work makes a step in the direction of an embedded single-chip FPGA-based controller of AMBs.

Genetic diversity of a brazilian wine grape germplasm collection based on morphoagronomic traits

The objectives of this study were to evaluate the performance of cultivars, to quantify the variability and to estimate the genetic distances of 66 wine grape accessions in the Grape Germplasm Bank of the EMBRAPA Semi-Arid, in Juazeiro, BA, Brazil, through the characterization of discrete and continuous phenotypic variables. Multivariate statistics, such as, principal components, Tocher's optimization procedure, and the graphic of the distance, were efficient in grouping more similar genotypes, according to their phenotypic characteristics. There was no agreement in the formation of groups between continuous and discrete morpho-agronomic traits, when Tocher's optimization procedure was used. Discrete variables allowed the separation of Vitis vinifera and hybrids in different groups. Significant positive correlations were observed between weight, length and width of bunches, and a negative correlation between titratable acidity and TSS/TTA. The major part (84.12%) of the total variation present in the original data was explained by the four principal components. The results revealed little variability between wine grape accessions in the Grape Germplasm Bank of Embrapa Semi-Arid.

Aspirin and statin use and the subsequent development of depression in men and women: Results from a longitudinal population-based study.

OBJECTIVE: Low-grade chronic inflammation is one potential mechanism underlying the well-established association between major depressive disorder (MDD) and increased cardiovascular morbidity. Both aspirin and statins have anti-inflammatory properties, which may contribute to their preventive effect on cardiovascular diseases. Previous studies on the potentially preventive effect of these drugs on depression have provided inconsistent results. The aim of the present paper was to assess the prospective association between regular aspirin or statin use and the incidence of MDD. METHOD: This prospective cohort study included 1631 subjects (43.6% women, mean age 51.7 years), randomly selected from the general population of an urban area. Subjects underwent a thorough physical evaluation as well as semi-structured interviews investigating DSM-IV mental disorders at baseline and follow-up (mean duration 5.2 years). Analyses were adjusted for a wide array of potential confounders. RESULTS: Our main finding was that regular aspirin or statin use at baseline did not reduce the incidence of MDD during follow-up, regardless of sex or age (hazard ratios, aspirin: 1.19; 95%CI, 0.68-2.08; and statins: 1.25; 95%CI, 0.73-2.14; respectively). LIMITATIONS: Our study is not a randomized clinical trial and could not adjust for all potential confounding factors, information on aspirin or statin use was collected only for the 6 months prior to the evaluations, and the sample was restricted to subjects between 35 and 66 years of age. CONCLUSION: Our data do not support a large scale preventive treatment of depression using aspirin or statins in subjects aged from 35 to 66 years from the community.

Growth and production of irrigated vitória pineapple grown in semi-arid conditions

This study aimed to evaluate the growth characteristics of irrigated Vitória pineapple plants grown in semi-arid conditions and determine its developmental stages based on those characteristics. It was used a randomized block design with four replicates. The experimental treatments were: plant harvest at 270, 330, 390, 450, 510, 570, 690, 750, and 810 days after planting (DAP). The following variables were determined: plant height, stem diameter, D-leaf length, D-leaf fresh and dry mass, biomass production of plants and plant parts (organs), and vegetative biomass. Five phenological stages are proposed based on vegetative biomass production: < 20% biomass production (V1); 21-40% (V2); 41-60% (V3); 61-80% (V4); and > 80% (V5). The maximum growth rate for plant height, D-leaf length, and stem diameter was observed at the end of the phenological stage V1 (390-411 DAP), and at the end of stage V5 these plant traits had average values of 106, 82, and 7 cm, respectively. The maximum biomass accumulation rates were observed at stages V4 and V5, resulting in a final fruit yield and total fresh biomass of 72 t ha-1 and 326 t ha-1, respectively. Finally, we estimated that 80% of the accumulated biomass may remain in the field after fruit and slip harvest, and could be incorporated as plant residue into the soil.