Background: Parkinson’s disease (PD) is a progressive neurodegenerative motor disorder. However, non-motor complications frequently alter the course of the disease. A particularly disabling non-motor symptom is dementia. Methods/Design: The study is designed as a multicentre prospective, observational cohort study of about 700 PD patients aged 45–80 years with or without dementia and PD-mild cognitive impairment (MCI). The patients will be recruited in eight specialized movement disorder clinics and will be followed for 36 months. Information about the patients’ functional status will be assessed at baseline and 6-/12- month intervals. In addition, 120 patients with dementia with Lewy bodies (DLB) will be included. Well-established standardized questionnaires/tests will be applied for detailed neuropsychological assessment. In addition, patients will be asked to participate in modules including volumetric MRI, genetic parameters, and neuropsychology to detect risk factors, early diagnostic biomarkers and predictors for dementia in PD. Results: The study included 604 PD patients by March 2011; 56.3% were classified as having PD alone, with 30.6% of patients suffering from PD-MCI and 13.1% from PD with dementia. The mean age of the cohort was 68.6 ± 7.9 years, with a mean disease duration of 6.8 ± 5.4 years. There was a preponderance of patients in the earlier Hoehn and Yahr stages. Conclusion: The main aim of the study is to characterize the natural progression of cognitive impairment in PD and to identify factors which contribute to the evolution and/or progression of the cognitive impairment. To accomplish this aim we established a large cohort of PD patients without cognitive dysfunction, PD patients with MCI, and PD patients with dementia, to characterize these patients in a standardized manner, using imaging (serial structural MRI), genetic and proteomic methods in order to improve our understanding of the course of the PD process and the development of cognitive dysfunction and dementia in this disease. The inclusion of the DLB patients will start in the second quarter of 2011 in the BMBF-funded follow-up project LANDSCAPE.

Knowledge-Based Systems (KBSs) represent a relatively new programming approach and methodology that has evolved and is still evolving as an important sub-area of Artificial Intelligence (AI) research. The most prevalent application of KBSs, which emerged in recent times, has been various types of diagnosis and troubleshooting. KBS has an important role to play, particularly in fault diagnosis of process plants, which involve lot of challenges starting from commonly occurring malfunctions to rarely occurring emergency situations. The KBS approach is promising for this domain as it captures efficient problem-solving of experts, guides the human operator in rapid fault detection, explains the line of reasoning to the human operator, and supports modification and refinement of the process knowledge as experience is gained. However, most of the current KBSs in process plants are built on expert knowledge compiled in the form of production rules. These systems lack flexibility due to their process-specific nature and are unreliable when faced with unanticipated faults. Although attempts have been made to integrate knowledge based on experience and 'deep' process knowledge to overcome this lack of flexibility, very little work has been reported to make the diagnostic system flexible and usable for various plant configurations. In this thesis, we propose a hybrid knowledge framework which includes both process-specific and process-common knowledge of the structure and behavior of the domain, and a process-independent diagnostic mechanism based on causal and qualitative reasoning. This framework is flexible and allows a unified design methodology for fault diagnosis of process plants. The process-specific knowledge includes experiential knowledge about commonly occurring faults, behavioral knowledge about causal interactions among process-dependent variables, and structural knowledge about components' description and connectivity. The process-common knowledge comprises template models of various types of components commonly present in any process plant, constraints and confluences based on mass and energy balances between parameters across components. The process behavioral knowledge is qualitatively represented in the form of Signed Digraph (SDG), which is converted into a set of rules (SDGrules), added with control premises for the purpose of diagnostic reasoning. Frame-objects are used to represent the structural knowledge, while rules are used to capture experiential knowledge about common faults. An interface program viz., Knowledge Acquisition Interface (KAI) aids acquisition and conversion of (i) behavioral knowledge into a set of SDG-rules and (ii) structural knowledge and experience-based heuristic rules into a set of facts. The Diagnostic Mechanism is based on a steady state model of the process and is composed of three consecutive phases for locating a fault. The first phase is Malfunction Block Identification (MBT), which locates a malfunctioning subsystem or Malfunction Block (MB) that is responsible for causing the process malfunction. It is based on alarm data whenever violation of process parameters occurs. Once the suspected MB is identified, the second phase viz., Malfunction Parameter Identification (MPI) is invoked t o locate parameters which indicate the prime cause(s) of the fault in that MB. This is achieved by correlating various instrumentation data through causal relationships described by the SDG-rules of that MB. Finally, Malfunctioning Component Identification (MCI) phase is invoked to locate the malfunctioning component. MCI phase uses the malfunction parameter (s) obtained from previous phase and experiential and structural knowledge of that MA for this purpose. The Diagnostic Mechanism is process-independent and, therefore, is capable of adapting to various types of plant configurations. Since, the Knowledge Base and the Diagnostic Mechanism are separate, modification of either of them can be done independently. The Diagnostic Mechanism is potentially capable of investigating symptoms that have multiple or unrelated origins. It also provides explanation facility for justifying the line of diagnostic reasoning to the human operator.

This study investigated teaching and learning, and the classroom learning environment in which the electricity topic was taught by the regular class teacher within the prescribed Grade 9 syllabus in a Secondary School in Papua New Guinea. The study was motivated by the perceived problems students had with understanding science concepts and the lack of classroom-based studies that provide a better understanding of teaching and learning science and the influence of the classroom learning environment on students' learning. An interpretive with embedded case study was conducted in a Grade 9 class over a period of 12 weeks in which data was gathered using mixed and multiple methods. Findings of the study revealed the presence and influence of aspects of the indigenous traditional teaching and learning approach impacting on the formal modern Western oriented teaching and learning approach in this particular classroom. The study recommended that in order to maximise students' learning and understanding of science concepts in the classroom observed, cultural sensitivity should be incorporated in the pedagogy.

Introdução: Perda de memória é um dos sintomas mais comuns em pacientes nos estágios iniciais da doença de Alzheimer; esses déficits são um reflexo do envolvimento da formação do hipocampo. O BDNF tem sido relacionado com a plasticidade do hipocampo. Neste sentido, as combinações de biomarcadores, como, por exemplo, a volumetria do hipocampo, pode apresentar um maior valor preditivo para diferenciar doença de Alzheimer do envelhecimento normal em pacientes com comprometimento cognitivo leve. Objetivo: A presente tese de doutorado teve como objetivo avaliar os níveis séricos do BDNF e o volume do hipocampo em pacientes com demência devido à doença de Alzheimer, Comprometimento Cognitivo Leve (CCL) e idosos saudáveis. Métodos: Para realização do estudo foram selecionados 10 idosos saudáveis, 10 CCL e 13 pacientes com demência devido à doença de Alzheimer pelos critérios NIA-AA. Todos participantes foram submetidos a uma avaliação cognitiva. Para as análises do BDNF, foi utilizado método de ELISA e para as análises de volumetria do hipocampo as imagens foram obtidas por meio de equipamento de ressonância de 1.5T e os volumes obtidos por meio do programa NeuroQuant®. Resultados: Idosos saudáveis apresentaram níveis séricos mais elevados de BDNF do que os CCL e pacientes com demência. O grupo de pacientes com demência apresentou menor volume total do hipocampo do que os idosos saudáveis e os CCL. Não houve correlação significativa do BDNF sérico com volume do hipocampo. Conclusão: Considerando nossos resultados em conjunto (baixos níveis de BDNF nos grupos CCL e demência devido à DA e menor volume do hipocampo na demência devido à AD), podemos supor que a diminuição dos níveis de BDNF ocorre antes da lesão neuronal expressa pela redução do hipocampo. / Introduction: Memory impairment is the most common symptom in patients in the early stages of Alzheimer's disease; this deficit is a reflection of the involvement of the hippocampal formation. BDNF has been linked to the hippocampal plasticity. Combinations of biomarkers, such as the hippocampal volumetry may have higher predictive value for differentiating Alzheimer's disease from normal aging in patients with mild cognitive impairment. Objective: The objective of present thesis was to evaluate serum levels of BDNF and hippocampal volume in patients with Mild Cognitive Impairment (MCI) and dementia due to Alzheimer's disease, and healthy elderly participants. Method: Ten healthy elderly subjects, 10 MCI and 13 patients with dementia due to Alzheimer's Disease (NIA-AA criteria) were selected for the study. All participants were assessed cognitively. The ELISA method was used for BDNF analysis, and the analysis of hippocampal volumetric images were acquired with 1.5T magnetic resonance equipment and volumes obtained with NeuroQuant® program. Results: Healthy elderly had higher BDNF serum levels than MCI and dementia due to AD patients. The group of dementia patients had lower total hippocampal volume than MCI and healthy elderly participants. No significant correlation between serum BDNF and hippocampal volume was observed. Conclusion: Taking our results together (lower BDNF levels in MCI and dementia due to AD and smaller hippocampal volume in dementia due to AD) we can hypothesize that the decrease of BDNF may start before the establishment of neuronal injury expressed by the hippocampal reduction.

Introdução: Perda de memória é um dos sintomas mais comuns em pacientes nos estágios iniciais da doença de Alzheimer; esses déficits são um reflexo do envolvimento da formação do hipocampo. O BDNF tem sido relacionado com a plasticidade do hipocampo. Neste sentido, as combinações de biomarcadores, como, por exemplo, a volumetria do hipocampo, pode apresentar um maior valor preditivo para diferenciar doença de Alzheimer do envelhecimento normal em pacientes com comprometimento cognitivo leve. Objetivo: A presente tese de doutorado teve como objetivo avaliar os níveis séricos do BDNF e o volume do hipocampo em pacientes com demência devido à doença de Alzheimer, Comprometimento Cognitivo Leve (CCL) e idosos saudáveis. Métodos: Para realização do estudo foram selecionados 10 idosos saudáveis, 10 CCL e 13 pacientes com demência devido à doença de Alzheimer pelos critérios NIA-AA. Todos participantes foram submetidos a uma avaliação cognitiva. Para as análises do BDNF, foi utilizado método de ELISA e para as análises de volumetria do hipocampo as imagens foram obtidas por meio de equipamento de ressonância de 1.5T e os volumes obtidos por meio do programa NeuroQuant®. Resultados: Idosos saudáveis apresentaram níveis séricos mais elevados de BDNF do que os CCL e pacientes com demência. O grupo de pacientes com demência apresentou menor volume total do hipocampo do que os idosos saudáveis e os CCL. Não houve correlação significativa do BDNF sérico com volume do hipocampo. Conclusão: Considerando nossos resultados em conjunto (baixos níveis de BDNF nos grupos CCL e demência devido à DA e menor volume do hipocampo na demência devido à AD), podemos supor que a diminuição dos níveis de BDNF ocorre antes da lesão neuronal expressa pela redução do hipocampo. / Introduction: Memory impairment is the most common symptom in patients in the early stages of Alzheimer's disease; this deficit is a reflection of the involvement of the hippocampal formation. BDNF has been linked to the hippocampal plasticity. Combinations of biomarkers, such as the hippocampal volumetry may have higher predictive value for differentiating Alzheimer's disease from normal aging in patients with mild cognitive impairment. Objective: The objective of present thesis was to evaluate serum levels of BDNF and hippocampal volume in patients with Mild Cognitive Impairment (MCI) and dementia due to Alzheimer's disease, and healthy elderly participants. Method: Ten healthy elderly subjects, 10 MCI and 13 patients with dementia due to Alzheimer's Disease (NIA-AA criteria) were selected for the study. All participants were assessed cognitively. The ELISA method was used for BDNF analysis, and the analysis of hippocampal volumetric images were acquired with 1.5T magnetic resonance equipment and volumes obtained with NeuroQuant® program. Results: Healthy elderly had higher BDNF serum levels than MCI and dementia due to AD patients. The group of dementia patients had lower total hippocampal volume than MCI and healthy elderly participants. No significant correlation between serum BDNF and hippocampal volume was observed. Conclusion: Taking our results together (lower BDNF levels in MCI and dementia due to AD and smaller hippocampal volume in dementia due to AD) we can hypothesize that the decrease of BDNF may start before the establishment of neuronal injury expressed by the hippocampal reduction.

La simulation des écoulements diphasiques rencontrés dans les moteurs à combustion interne (MCI) est de grande importance pour la prédiction de la performance des moteurs et des émissions polluantes. L’injection directe du carburant liquide à l’intérieur de la chambre de combustion génère loin de l’injecteur un brouillard de gouttes polydisperses, communément appelé spray. Du point de vue de la modélisation, l’émergence des méthodes Eulériennes pour la description du spray est considérée prometteuse par la communauté scientifique. De plus, la prise en compte de la distribution en taille des gouttes par les approches Eulériennes, de manière peu coûteuse en temps de calcul, n’est plus considérée comme un verrou depuis le développement de la méthode Eulerian Multi Size Moment (EMSM). Afin d’envisager la simulation de configurations réalistes de MCI, ce travail de thèse propose de modéliser les interactions turbulentes two-way entre le spray polydisperse évaporant et la phase gazeuse environnante par la méthode EMSM. Dans le contexte du formalisme Arbitrary Lagrangian Eulerian (ALE) dédiée au traitement du maillage mobile, les termes sources présents dans le modèle diphasique sont traités séparément des autres contributions. Le système d’équations est fermé à l’aide d’une technique de reconstruction par maximisation d’entropie (ME), originellement introduite pour EMSM. Une nouvelle stratégie de résolution a été développée pour garantir la stabilité numérique aux échelles de temps très rapides introduites par les transferts de masse, quantité de mouvement et énergie, tout en respectant la condition de réalisabilité associée à la préservation de l’espace des moments d’ordre ´élevé. A l’aide des simulations académiques, la stabilité et la précision de la méthode ont été étudiées aussi bien pour des lois d’évaporation constantes que dépendantes du temps. Tous ces développements ont été intégrés dans le code industriel IFP-C3D dédié aux écoulements compressibles et réactifs. Dans le contexte de la simulation en 2-D de l’injection directe, les résultats se sont avérés très encourageants comme en témoignent les comparaisons qualitatives et quantitatives de la méthode Eulerienne à la simulation Lagrangienne de référence des gouttes. De plus, les simulations en 3-D effectuées dans une configuration typique de chambre de combustion et des conditions d’injection réalistes ont donné lieu à des résultats qualitativement très satisfaisants. Afin de prendre en compte la modélisation de la turbulence, une extension moyennée, au sens de Reynolds, des équations du modèle diphasique two-way est dérivée, un soin particulier étant apporté aux fermetures des corrélations turbulentes. La répartition de l’énergie dans le spray ainsi que les interactions turbulentes entre les phases ont été étudiées dans des cas tests homogènes. Ces derniers donnent un aperçu intéressant sur la physique sous-jacente dans les MCI. Cette nouvelle approche RANS diphasique est maintenant prête à être employée pour les simulations d’application de MCI. / The ability to simulate two-phase flows is of crucial importance for the prediction of internal combustion engine (ICE) performance and pollutant emissions. The direct injection of the liquid fuel inside the combustion chamber generates a cloud of polydisperse droplets, called spray, far downstream of the injector. From the modeling point of view, the emergence of Eulerian techniques for the spray description is considered promising by the scientific community. Moreover, the bottleneck issue for Eulerian methods of capturing the droplet size distribution with a reasonable computational cost, has been successfully tackled through the development of Eulerian Multi Size Moment (EMSM) method. Towards realistic ICE applications, the present PhD work addresses the modeling of two-way turbulent interactions between the polydisperse spray and its surrounding gas-phase through EMSM method. Following to the moving mesh formalism ArbitraryLagrangian Eulerian (ALE), the source terms arising in the two-phase model have been treated separately from other contributions. The equation system is closed through the maximum entropy (ME) reconstruction technique originally introduced for EMSM. A new resolution strategy is developed in order to guarantee the numerical stability under veryfast time scales related to mass, momentum and energy transfers, while preserving the realizability condition associated to the set of high order moments. From the academic point of view, both the accuracy and the stability have been deeply investigated under both constant and time dependent evaporation laws. All these developments have beenintegrated in the industrial software IFP-C3D dedicated to compressible reactive flows. In the context of 2-D injection simulations, very encouraging quantitative and qualitative results have been obtained as compared to the reference Lagrangian simulation of droplets. Moreover, simulations conducted under a typical 3-D configuration of a combustion chamber and realistic injection conditions have given rise to fruitful achievements. Within the framework of industrial turbulence modeling, a Reynolds averaged (RA) extension of the two-way coupling equations is derived, providing appropriate closures for turbulent correlations. The correct energy partitions inside the spray and turbulent interactions between phases have been demonstrated through homogeneous test-cases. The latter cases gave also some significant insights on underlying physics in ICE. This new RA approach is now ready for ICE application simulations.

Background: Parkinson’s disease (PD) is a progressive neurodegenerative motor disorder. However, non-motor complications frequently alter the course of the disease. A particularly disabling non-motor symptom is dementia. Methods/Design: The study is designed as a multicentre prospective, observational cohort study of about 700 PD patients aged 45–80 years with or without dementia and PD-mild cognitive impairment (MCI). The patients will be recruited in eight specialized movement disorder clinics and will be followed for 36 months. Information about the patients’ functional status will be assessed at baseline and 6-/12- month intervals. In addition, 120 patients with dementia with Lewy bodies (DLB) will be included. Well-established standardized questionnaires/tests will be applied for detailed neuropsychological assessment. In addition, patients will be asked to participate in modules including volumetric MRI, genetic parameters, and neuropsychology to detect risk factors, early diagnostic biomarkers and predictors for dementia in PD. Results: The study included 604 PD patients by March 2011; 56.3% were classified as having PD alone, with 30.6% of patients suffering from PD-MCI and 13.1% from PD with dementia. The mean age of the cohort was 68.6 ± 7.9 years, with a mean disease duration of 6.8 ± 5.4 years. There was a preponderance of patients in the earlier Hoehn and Yahr stages. Conclusion: The main aim of the study is to characterize the natural progression of cognitive impairment in PD and to identify factors which contribute to the evolution and/or progression of the cognitive impairment. To accomplish this aim we established a large cohort of PD patients without cognitive dysfunction, PD patients with MCI, and PD patients with dementia, to characterize these patients in a standardized manner, using imaging (serial structural MRI), genetic and proteomic methods in order to improve our understanding of the course of the PD process and the development of cognitive dysfunction and dementia in this disease. The inclusion of the DLB patients will start in the second quarter of 2011 in the BMBF-funded follow-up project LANDSCAPE.

The Internet of Things (IoT) provides smart solutions for future urban communities to address key benefits with the least human intercession. A smart home offers the necessary capabilities to promote efficiency and sustainability to a resident with their healthcare-related, social, and emotional needs. In particular, it provides an opportunity to assess the functional health ability of the elderly or individuals with cognitive impairment in performing daily life activities. This work proposes an approach named Cognitive Assessment of Smart Home Resident (CA-SHR) to measure the ability of smart home residents in executing simple to complex activities of daily living using pre-defined scores assigned by a neuropsychologist. CA-SHR also measures the quality of tasks performed by the participants using supervised classification. Furthermore, CA-SHR provides a temporal feature analysis to estimate if the temporal features help to detect impaired individuals effectively. The goal of this study is to detect cognitively impaired individuals in their early stages. CA-SHR assess the health condition of individuals through significant features and improving the representation of dementia patients. For the classification of individuals into healthy, Mild Cognitive Impaired (MCI), and dementia categories, we use ensemble AdaBoost. This results in improving the reliability of the CA-SHR through the correct assignment of labels to the smart home resident compared with existing techniques.

One particular challenge of brain connectomics deals with inferring differences in the brain due to diseases such as Alzheimer's. More specifically, structural connectomics aims at investigating the connectivity between regions in the brain based on the distribution of neuronal fibers. The first step in generating structural connectomes is to perform tractography reconstruction on diffusion MRI (dMRI) data, to extract the most likely pathways of neural fibers. However, current tractography reconstruction algorithms suffer from having high sensitivity and low specificity. Thus, the following steps  of creating, analyzing and deriving graphs metrics from connectivity maps based on tractography impair the reliable assessment of structural connectivity. A promising method to improve tractography and subsequent structural connectomes is to apply tractogram filtering methods. In this study, the impact of tractogram filtering on structural connectomics and derived graph measures of subjects with mild cognitive impairment (MCI), specifically using spherical-deconvolution informed filtering of tractograms (SIFT), is experimentally examined. Moreover, the study also aims at inferring the effects of tractogram filtering in machine-learning based classification of the aforementioned structural connectomes. The pipeline in this experimental setup uses registration tools from FSL, tractography tools from MRTrix3Tissue as well as Keras for classification. The results from the given experiments show, that graph measures such as nodestrength and betweenness centrality are altered for the individual nodes. This leads to new connectomes with nodes, which are more important after tractogram filtering. This effect was also seen in connectomes weighted by fractional anisotropy (FA), mean diffusivity (MD) and radial diffusivity (RD). Moreover, structural connectomes based on filtered tractograms yield a higher classification performance. The best classification performance was reached with 88.65% on raw connectomes. Limiting factors in this experimental setup are identified as the small number of subjects at hand and computation time and the errors introduced by image registration and tractography parameterization.

Background: Mild Cognitive Impairment (MCI) is seen as a state between normal aging and dementia, with patients having an increased risk of developing Alzheimer’s disease (AD) and other sorts of dementia. MCI has been linked to a change in gut microbiota which impacts the microbiota-gut-brain axis (MGBA), consequently affecting neurological functions. A way of altering microbiota and thereby promoting cognitive health is through the administration of prebiotics, probiotics, and synbiotics. Aim: This systematic literature review aims to assess the impact of prebiotics, probiotics, and synbiotics on MCI by compiling existing data on the matter. Methods: Three databases - Web of Science, Cochrane, and PubMed - were searched and articles were included based on the following inclusion criteria: (1) randomized clinical trials (RCTs), (2) conducted on adults evaluated with MCI during the study, (3) including a prebiotic, probiotic, or synbiotic intervention of any kind, (4) comparing the intervention with a placebo or control group, (5) written in English, (6) reporting the main outcome of cognitive function using any neuropsychological evaluation test. Results: Five studies were included in the final selection. These studies showed that cognitive function improved after probiotic intervention, significantly affecting several cognitive domains: attention, calculation, orientation in time, and delayed memory. Two studies showed that subjects with low cognitive scores at baseline benefited more from probiotic supplementation compared to high-scoring subjects. Conclusions: Probiotics appear to improve cognition in MCI subjects; however, further research is needed to conclude the effects of prebiotics and synbiotics.