Nuevas técnicas de análisis de imágenes de resonancia magnética para determinación de patrones de atrofia cerebral en la progresión de la enfermedad de Alzheimer

Image-based computer aided diagnosis (CAD) systems have significant potential for screening and early detection of brain diseases. In this sense, this PhD work is motivated by the development and the implementation of novel CAD systems based on several pattern recognition/classification techniques for the early detection of neurodegenerative diseases. In particular, the dissertation is focused on the analysis of the most relevant one, the Alzheimer’s disease (AD), by the use of structural magnetic resonance imaging (sMRI) techniques. The proposed CAD systems are based on several processing steps including segmentation of brain tissues (gray matter and white matter tissues), feature selection techniques such as t-test model, feature extraction techniques such as; Partial least squares (PLS), Principal Component Analysis (PCA), Independent component analysis (ICA) and Non-Negative Matrix Factorization (NNMF) techniques, and an automatic classification technique, such as the support vector machine (SVM). Most of these thesis contributions are included within the feature extraction techniques and its application to the development of automatic CAD systems for early detection of AD. The first proposed CAD system is based on the PLS approach that extracts the relevant features to characterize the AD pattern. This technique decomposes two sets of variables into the product of two matrices called scores and loadings according to a criterion of covariance maximization. In this work, these variables are those formed by the structural magnetic resonance images under study and the labels of these images. After the decomposition of these sets, the scores are used as feature vectors for the classification step. The second proposed CAD system for AD detection is based on the PCA approach, as a feature extraction technique for sMRI brain images. This approach reduces the original high-dimensional space of the brain images to a lower dimensional subspace. PCA generates a set of orthonormal basis vectors, known as Principal components (PCs), that maximizes the scatter of all the projected samples, which is equivalent to diagonalize the covariance matrix through the eigenvalue of these components. The third CAD system is based on the Independent component analysis (ICA) approach. At the beginning, a “template” image is computed as the average of healthy subject images or as the difference between normal and pathological images. Then, the ICA algorithm is applied to extract the maximally spatially independent components (ICs) revealing patterns of variation that occur in the dataset under study. The last method proposed is based on the NNMF approach, as a useful decomposition technique of multivariate data that solves the problem of finding non-negative matrices. These feature extraction techniques successfully solved the small sample size problem by obtaining only the relevant information related to AD. This process is known as a dimensionality reduction and it improves the prediction accuracy of CAD systems, specifically, in the early stage of AD. In this way, considering the fact that an early diagnosis of AD is crucial, classification experiments were performed not only to distinguish Normal Control (NC) and AD subjects but also to differentiate NC from a transitional phase between being cognitively normal and having an AD diagnosis. This later phase is called Mild Cognitive Impairment (MCI). The proposed feature extraction methods have been combined with SVM classifiers and the accuracy rates of the resulting CAD systems have been estimated by means of a sMRI database from the Alzheimer disease neuroimaging initiative (ADNI). Furthermore, a kfold-cross validation technique was applied to these systems in order to tune the classifier parameters and to estimate its performance. The obtained results demonstrate the effectiveness and the robustness of the proposed CAD systems (with accuracy value around 90%) compared to previous approaches such as the one based on Voxel-As-Features (VAF) technique.Tesis Univ. Granada. Programa Oficial de Doctorado en Tecnologías de la Información y la Comunicació

Amplitude and oscillating assessment of thermal and magnetic boundary layer flow across circular heated cylinder with heat source/sink

The effects of heat source/sink and magnetohydrodynamics on the oscillatory and periodic quantities of heat transfer and current density characteristics of viscous fluid along the magnetized and heated circular cylinder has been investigated. The governing mathematical model in terms of partial differential equations is converted into dimensionless form. The dimensionless model is again converted into steady, real and imaginary part for oscillating results. The steady, real and imaginary part is transformed into primitive form for smooth algorithm with the help of Primitive Variable Formulation (PVF). The primitive equations are reduced into system of algebraic equations by using Finite Difference Method (FDM). The heat source/sink parameter δ, the magnetic force number ξ, the buoyancy parameter λ, magneto-Prandtl factor γ, and remaining secured factors are utilized to determine computational findings of unknown quantities. Graphs are displayed for velocity, temperature distribution and magnetic profile for all pertinent parameters by FORTRAN and Teplot 360 software. The main novelty of current work is to evaluate oscillatory and periodic quantities of heat transfer and current density by using the steady solutions. It was found that prominent results in temperature distribution are deduced at each angle with the heat source effects. It was expected physically because the heat source is used as a sporting agent to compute heat transfer performance in electrically conducting fluid. The significant amplitude of oscillation in heat transfer and current density is evaluated around every position of circular cylinder. The numerical results of skin friction are compared with existing literature with excellent agreement

Alarmingly low proficiency in mathematics in Swedish education : A study of learning by Youschool, a private tutor on the web

En rad företag som erbjuder läxhjälp har uppstått sedan Rutavdraget för läxhjälp utökades till att gälla för gymnasieelever. Youschool är ett av dessa företag. Det som utmärker Youschool är att de erbjuder läxhjälp där elever och lärare kan kommunicera med varandra både med penna och ljud i realtid via nätet. De tillhandahåller virtuella lektioner med en lärare på 2 till 4 elever åt gången, där en dokumentkamera är det verktyg som utgör grunden i kommunikationstekniken. Eleverna lägger exempelvis sitt skrivblock under sina dokumentkameror och så kan de som är med på lektionen följa varandras resonemang,eftersom de hela tiden kan se vad alla gör med sina pennor. Matematik 2b är en kurs främst för elever som läser på Samhällsvetenskapsprogrammet eller på Ekonomiprogrammet, två högskoleförberedande gymnasieprogram. Statistik för resultaten på de nationella proven i kursen, från totalundersökningar i Sverige, visar att en hög andel elever får underkänt betyg på provet. Denna studie är kvalitativ och utgörs av semistrukturerade telefonintervjuer med elever som läser Matematik 2b och som använder Youschool som läxhjälp samt av observationer från en skärminspelad lektion på Youschool där eleverna jobbar med ett för studien tillrättalagt material som testar deras kunskaper om funktionsbegreppet. Syftet är att undersöka om och i så fall hur läroverktyget Youschool kan utgöra ett stöd i elevers kunskapsutveckling i Matematik 2b. Kursens kunskapskrav kopplade till matematiska förmågor hos eleverna och van Hieles tankenivåer är de analysverktyg som används i diskussionen av resultaten. Lärarens roll som stöttande och utmanande framträder som viktig för att upprätthålla elevernas motivation till att arbeta under lektionerna på Youschool. Vidare kan eventuellt antydas att eleverna tränas i vissa matematiska förmågor mer än andra, och att elever önskar fler uppgifter som stimulerar deras tänkande på de högre av van Hiele-nivåerna. Tekniken som å ena sidan möjliggör undervisningen på Youschool kanbehöva utvecklas eftersom den å andra sidan ofta strular.There are a growing number of companies in Sweden that provide private tutoring to students in upper secondary school, one of these companies is Youschool. They distinguished themselves from the others by having a tutor communicating with a student via Internet–using pencil and sound in real time. Youschool provide virtual classes with two to four students at each time lead by one teacher using a“document camera”as the main communication equipment. The students put their notebooks under their document cameras and are thereafter able to demonstrate their solutions and follow each other’s. They can literally follow each stroke of each other’s pencils. Matematik 2b is a mathematics unit in the Swedish upper secondary school mainly taken by students in the Business Management and Economics Programme and the Humanities Programme, both theoretical programmes preparing students for university studies. Statistics based upon the Swedish national examinations each year shows that a great number of students fail the tests in this unit. This is a qualitative study based on semi structured telephone interviews of students taking the Matematik 2b unit, and who are using Youschool as private tutoring, as well as observations of a screen filmed class where students practiced solving mathematic problems. The purpose of the study is to research whether Youschool is supportive in studying mathematics or not. In the discussion section of this study, both the curriculum of the unit and the van Hiele-levels are referred to when analysing the findings. The results point out the importance of a supportive and challenging tutor to help students to keep their motivation up during classes in Youschool. Furthermore, some mathematics skills might be better practiced using Youschool than others, therefore students wish to exercise further mathematic problems to stimulate thinking on the higher van Hiele-levels. However, the technology that is supposed to enable learning by Youschool might sometimes be the one thing to hinder a student from learning. Problems with the technology therefore impose Youschool to update their systems to affirm effective learning

Intrasubject subcortical quantitative referencing to boost MRI sensitivity to Parkinson's disease

International audienceSeveral postmortem studies have shown iron accumulation in the substantia nigra of Parkinson's disease patients. Iron concentration can be estimated via MRI-R2∗ mapping. To assess the changes in R2∗ occurring in Parkinson's disease patients compared to controls, a multicentre transversal study was carried out on a large cohort of Parkinson's disease patients (n = 163) with matched controls (n = 82). In this study, 44 patients and 11 controls were removed due to motion artefacts, 21 patient and 6 controls to preserve matching. Thus, 98 patients and 65 age and sex-matched healthy subjects were selected with enough image quality. The study was conducted on patients with early to late stage Parkinson's disease. The images were acquired at 3Tesla in 12 clinical centres. R2∗ values were measured in subcortical regions of interest (substantia nigra, red nucleus, striatum, globus pallidus externus and globus pallidus internus) contralateral (dominant side) and ipsilateral (non dominant side) to the most clinically affected hemibody. As the observed inter-subject R2∗ variability was significantly higher than the disease effect, an original strategy (intrasubject subcortical quantitative referencing, ISQR) was developed using the measurement of R2∗ in the red nucleus as an intra-subject reference. R2∗ values significantly increased in Parkinson's disease patients when compared with controls; in the substantia nigra (SN) in the dominant side (D) and in the non dominant side (ND), respectively (PSN_D and PSN_ND < 0.0001). After stratification into four subgroups according to the disease duration, no significant R2∗ difference was found in all regions of interest when comparing Parkinson's disease subgroups. By applying our ISQR strategy, R2(ISQR)∗ values significantly increased in the substantia nigra (PSN_D and PSN_ND < 0.0001) when comparing all Parkinson's disease patients to controls. R2(ISQR)∗ values in the substantia nigra significantly increased with the disease duration (PSN_D = 0.01; PSN_ND = 0.03) as well as the severity of the disease (Hoehn & Yahr scale <2 and ≥ 2, PSN_D = 0.02). Additionally, correlations between R2(ISQR)∗ and clinical features, mainly related to the severity of the disease, were found. Our results support the use of ISQR to reduce variations not directly related to Parkinson's disease, supporting the concept that ISQR strategy is useful for the evaluation of Parkinson's disease