Detection and classification of neurodegenerative diseases: a spatially informed bayesian deep learning approach

Dissertation submitted in partial fulfilment of the requirements for the Degree of Master of Science in Geospatial TechnologiesNeurodegenerative diseases comprise a group of chronic and irreversible conditions characterized by the progressive degeneration of the structure and function of the central nervous system. The detection and classification of patients according to the underlying disease are crucial for developing oriented treatments and enriching prognosis. In this context, Magnetic resonance imaging (MRI) data can provide meaningful insights into neurodegeneration by detecting the physiological manifestations in the brain caused by the disease processes. One field of extensive clinical use of MRI is the accurate and automated classification of neurodegenerative disorders. Most studies distinguish patients from healthy subjects or stages within the same disease. Such distinction does not mirror clinical practice, as a patient may not show all symptoms, especially if the disease is in an early stage, or show, due to comorbidities, other symptoms as well. Likewise, automated classifiers are partly suited for medical diagnosis since they cannot produce probabilistic predictions nor account for uncertainty. Also, existent studies ignore the spatial heterogeneity of the brain alterations caused by neurodegenerative processes. The spatial configuration of the neuronal loss is a characteristic hallmark for each disorder. To fill these gaps, this thesis aims to develop a classification technique that incorporates uncertainty and spatial information for distinguishing four neurodegenerative diseases, Alzheimer’s disease, Mild cognitive impairment, Parkinson’s disease and Multiple Sclerosis, and healthy subjects. This technique will produce automated, contingent, and accurate predictions to support clinical diagnosis. To quantify prediction uncertainty and improve classification accuracy, this study introduces a Bayesian neural network with a spatially informed input. A convolutional neural network (CNN) is developed to identify a neurodegenerative condition based on T1weighted MRI scans from patients and healthy controls. Bayesian inference is incorporated into the CNN to measure uncertainty and produce probabilistic predictions. Also, a spatially informed MRI scan is added to the CNN to improve feature detection and classification accuracy. The Spatially informed Bayesian Neural Network (SBNN) proposed in this work demonstrates that classification accuracy can be increased up to 25% by including the spatially informed MRI scan. Furthermore, the SBNN provides robust probabilistic diagnosis that resembles clinical decision-making and accounts for atypical, numerous, and early presentations of neurodegenerative disorders

A channel model and coding for vehicle to vehicle communication based on a developed V-SCME

Over the recent years, VANET communication has attracted a lot of attention due to its potential in facilitating the implementation of 'Intelligent Transport System'. Vehicular applications need to be completely tested before deploying them in the real world. In this context, VANET simulations would be preferred in order to evaluate and validate the proposed model, these simulations are considered inexpensive compared to the real world (hardware) tests. The development of a more realistic simulation environment for VANET is critical in ensuring high performance. Any environment required for simulating VANET, needs to be more realistic and include a precise representation of vehicle movements, as well as passing signals among different vehicles. In order to achieve efficient results that reflect the reality, a high computational power during the simulation is needed which consumes a lot of time. The existing simulation tools could not simulate the exact physical conditions of the real world, so results can be viewed as unsatisfactory when compared with real world experiments. This thesis describes two approaches to improve such vehicle to vehicle communication. The first one is based on the development of an already existing approach, the Spatial Channel Model Extended (SCME) for cellular communication which is a verified, validated and well-established communication channel model. The new developed model, is called Vehicular - Spatial Channel Model Extended (V-SCME) and can be utilised for Vehicle to Vehicle communication. V-SCME is a statistical channel model which was specifically developed and configured to satisfy the requirements of the highly dynamic network topology such as vehicle to vehicle communication. V-SCME provides a precise channel coefficients library for vehicle to vehicle communication for use by the research community, so as to reduce the overall simulation time. The second approach is to apply V-BLAST (MIMO) coding which can be implemented with vehicle to vehicle communication and improve its performance over the V-SCME. The V- SCME channel model with V-BLAST coding system was used to improve vehicle to vehicle physical layer performance, which is a novel contribution. Based on analysis and simulations, it was found that the developed channel model V-SCME is a good solution to satisfy the requirements of vehicle to vehicle communication, where it has considered a lot of parameters in order to obtain more realistic results compared with the real world tests. In addition, V-BLAST (MIMO) coding with the V-SCME has shown an improvement in the bit error rate. The obtained results were intensively compared with other types of MIMO coding

Applications of MATLAB in Science and Engineering

The book consists of 24 chapters illustrating a wide range of areas where MATLAB tools are applied. These areas include mathematics, physics, chemistry and chemical engineering, mechanical engineering, biological (molecular biology) and medical sciences, communication and control systems, digital signal, image and video processing, system modeling and simulation. Many interesting problems have been included throughout the book, and its contents will be beneficial for students and professionals in wide areas of interest

Algorithms for energy-efficient adaptive wireless sensor networks

Mención Internacional en el título de doctorIn this thesis we focus on the development of energy-efficient adaptive algorithms for Wireless Sensor Networks. Its contributions can be arranged in two main lines. Firstly, we focus on the efficient management of energy resources in WSNs equipped with finite-size batteries and energy-harvesting devices. To that end, we propose a censoring scheme by which the nodes are able to decide if a message transmission is worthy or not given their energetic condition. In order to do so, we model the system using a Markov Decision Process and use this model to derive optimal policies. Later, these policies are analyzed in simplified scenarios in order to get insights of their features. Finally, using Stochastic Approximation, we develop low-complexity censoring algorithms that approximate the optimal policy, with less computational complexity and faster convergence speed than other approaches such as Q-learning. Secondly, we propose a novel diffusion scheme for adaptive distributed estimation in WSNs. This strategy, which we call Decoupled Adapt-then-Combine (D-ATC), is based on keeping an estimate that each node adapts using purely local information and then combines with the diffused estimations by other nodes in its neighborhood. Our strategy, which is specially suitable for heterogeneous networks, is theoretically analyzed using two different techniques: the classical procedure for transient analysis of adaptive systems and the energy conservation method. Later, as using different combination rules in the transient and steady-state regime is needed to obtain the best performance, we propose two adaptive rules to learn the combination coefficients that are useful for our diffusion strategy. Several experiments simulating both stationary estimation and tracking problems show that our method outperforms state-of-the-art techniques in relevant scenarios. Some of these simulations reveal the robustness of our scheme under node failures. Finally, we show that both approaches can be combined in a common setup: a WSN composed of harvesting nodes aiming to solve an adaptive distributed estimation problem. As a result, a censoring scheme is added on top of D-ATC. We show how our censoring approach helps to improve both steady-state and convergence performance of the diffusion scheme.La presente tesis se centra en el desarrollo de algoritmos adaptativos energéticamente eficientes para redes de sensores inalámbricos. Sus contribuciones se pueden englobar en dos líneas principales. Por un lado, estudiamos el problema de la gestión eficiente de recursos energéticos en redes de sensores equipadas con dispositivos de captación de energía y baterías finitas. Para ello, proponemos un esquema de censura mediante el cual, en un momento dado, un nodo es capaz de decidir si la transmisión de un mensaje merece la pena en las condiciones energéticas actuales. El sistema se modela mediante un Proceso de Decisión de Markov (Markov Decision Process, MDP) de horizonte infinito y dicho modelo nos sirve para derivar políticas óptimas de censura bajo ciertos supuestos. Después, analizamos estas políticas óptimas en escenarios simplificados para extraer intuiciones sobre las mismas. Por último, mediante técnicas de Aproximación Estocástica, desarrollamos algoritmos de censura de menor complejidad que aproximan estas políticas óptimas. Las numerosas simulaciones realizadas muestran que estas aproximaciones son competitivas, obteniendo una mayor tasa de convergencia y mejores prestaciones que otras técnicas del estado del arte como las basadas en Q-learning. Por otro lado, proponemos un nuevo esquema de difusión para estimación distribuida adaptativa. Esta estrategia, que denominamos Decoupled Adapt-then-Combine (D-ATC), se basa en mantener una estimación que cada nodo adapta con información puramente local y que posteriormente combina con las estimaciones difundidas por los demás nodos de la vecindad. Analizamos teóricamente nuestra estrategia, que es especialmente útil en redes heterogéneas, usando dos métodos diferentes: el método clásico para el análisis de régimen transitorio en sistemas adaptativos y el método de conservación de la energía. Posteriormente, y dado que para obtener el mejor rendimiento es necesario utilizar reglas de combinación diferentes en el transitorio y en régimen permanente, proponemos dos reglas adaptativas para el aprendizaje de los pesos de combinación para nuestra estrategia de difusión. La primera de ellas está basada en una aproximación de mínimos cuadrados (least-squares, LS); mientras que la segunda se basa en el algoritmo de proyecciones afines (Afifne Projection Algorithm, APA). Se han realizado numerosos experimentos tanto en escenarios estacionarios como de seguimiento que muestran cómo nuestra estrategia supera en prestaciones a otras aproximaciones del estado del arte. Algunas de estas simulaciones revelan además la robustez de nuestra estrategia ante errores en los nodos de la red. Por último, mostramos que estas dos aproximaciones son complementarias y las combinamos en mismo escenario: una red de sensores inalámbricos compuesta de nodos equipados con dispositivos de captación energética cuyo objetivo es resolver de manera distribuida y adaptativa un problema de estimación. Para ello, añadimos la capacidad de censurar mensajes a nuestro esquema D-ATC. Nuestras simulaciones muestran que la censura puede ser beneficiosa para mejorar tanto el rendimiento en régimen permanente como la tasa de convergencia en escenarios relevantes de estimación basada en difusión.This work was partially supported by the "Formación de Profesorado Universitario" fellowship from the Spanish Ministry of Education (FPU AP2010-5225).Programa Oficial de Doctorado en Multimedia y ComunicacionesPresidente: Santiago Zazo Bello.- Secretario: Miguel Lázaro Gredilla.- Vocal: Alexander Bertran

Immunity and immunological surveillance for malaria elimination in tropical islands

Malaria remains one of the most significant global public health challenges. Nearly half of the world’s population remains at risk, largely in African Region. In the past decade, considerable progress has been made in the global fight to control and eliminate malaria. In some endemic countries, aggressive malaria control has reduced the malaria burden to a point where malaria elimination is becoming feasible. Nevertheless, sustained malaria control is crucial to prolong this downward trend for endemic countries. Understanding the contribution of local transmission, parasites movement, asymptomatic and sub-microscopic reservoirs can shape how active surveillances are used to pursue malaria elimination. Furthermore, a better understanding of the epidemiological effects of naturally acquired immunity against malaria is warranted to guide efforts to control or potentially eliminate the disease. In five cross-sectional surveys in Kenya conducted between 2012 and 2014 (N = 10,430), malaria prevalence (i.e. microscopy and PCR) and clinical assessments were evaluated to investigate the distribution and extent of malaria infections on islands (Mfangano, Takawiri, Kibuogi, and Ngodhe) and a mainland area (Ungoye) in Lake Victoria. Malaria prevalence varied significantly among setting; highest in the mainland, moderate in the large island, and lowest in small islands. More than 90% of infected populations were asymptomatic, and 50% of them were sub-microscopic with age-dependency for both proportions. These observations provide support for the inclusion of MDA in the area. Using the two surveys in 2012 (N = 4,112), antibody responses to P. falciparum PfAMA-1, PfMSP-119 and PfCSP were measured in order to describe transmission patterns and heterogeneity in Lake Victoria. The overall seroprevalence in Lake Victoria was 64% for PfAMA-1, 40% for PfMSP-119 and 13% for PfCSP. A clear relation between serological outcomes of PfAMA-1 and PfMSP-119 was observed with parasite prevalence and serology-derived EIR in heterogeneity in transmission. These observations collectively suggest that malaria serological measure could be an effective adjunct tool for assessing differences in transmission as well as for monitoring control and elimination in the high endemic area. Using msp1 and csp data from samples collected from 1996 to 2002, patterns of gene flow and population genetic structure of P. falciparum (N = 316) and P. vivax (N = 314) from seven sites on five islands (Gaua, Santo, Pentecost, Malakula, and Tanna) were analysed in order to understand the transmission and movement of Plasmodium parasites in Vanuatu. In general, genetic diversity was higher in P. vivax than P. falciparum from the same site. In P. vivax, high genetic diversity was likely maintained by a greater extent of gene flow among sites and islands. The results suggest that the current malaria control strategy in Vanuatu might need to be bolstered in order to control P. vivax movements across islands. To understand the impact of vector control interventions (i.e. ITNs) in Vanuatu, samples collected in 2003 (N = 231) and 2007 (N = 282) on Ambae Island were assessed for parasite infection (i.e. microscopy) and measured for antibody responses against three P. falciparum, three P. vivax and Anopheles-specific salivary gSG6 antigens. Decreases in seroprevalence were observed to all P. falciparum antigens but two of three P. vivax antigens, consistent with the pronounced decrease in parasite prevalence from 19% in 2003 to 3% in 2007. Seroprevalence to gSG6 also reduced significantly, suggesting that reduced exposure to vector bites was important to decrease in parasite prevalence. Together, decrease in both parasitological and seroepidemiological malaria metrics from 2003, and 2007 implied that reinforced vector control played a major role in the reduction of malaria transmission on Ambae Island

Advances and Applications of Dezert-Smarandache Theory (DSmT) for Information Fusion (Collected Works), Vol. 4

The fourth volume on Advances and Applications of Dezert-Smarandache Theory (DSmT) for information fusion collects theoretical and applied contributions of researchers working in different fields of applications and in mathematics. The contributions (see List of Articles published in this book, at the end of the volume) have been published or presented after disseminating the third volume (2009, http://fs.unm.edu/DSmT-book3.pdf) in international conferences, seminars, workshops and journals. First Part of this book presents the theoretical advancement of DSmT, dealing with Belief functions, conditioning and deconditioning, Analytic Hierarchy Process, Decision Making, Multi-Criteria, evidence theory, combination rule, evidence distance, conflicting belief, sources of evidences with different importance and reliabilities, importance of sources, pignistic probability transformation, Qualitative reasoning under uncertainty, Imprecise belief structures, 2-Tuple linguistic label, Electre Tri Method, hierarchical proportional redistribution, basic belief assignment, subjective probability measure, Smarandache codification, neutrosophic logic, Evidence theory, outranking methods, Dempster-Shafer Theory, Bayes fusion rule, frequentist probability, mean square error, controlling factor, optimal assignment solution, data association, Transferable Belief Model, and others. More applications of DSmT have emerged in the past years since the apparition of the third book of DSmT 2009. Subsequently, the second part of this volume is about applications of DSmT in correlation with Electronic Support Measures, belief function, sensor networks, Ground Moving Target and Multiple target tracking, Vehicle-Born Improvised Explosive Device, Belief Interacting Multiple Model filter, seismic and acoustic sensor, Support Vector Machines, Alarm classification, ability of human visual system, Uncertainty Representation and Reasoning Evaluation Framework, Threat Assessment, Handwritten Signature Verification, Automatic Aircraft Recognition, Dynamic Data-Driven Application System, adjustment of secure communication trust analysis, and so on. Finally, the third part presents a List of References related with DSmT published or presented along the years since its inception in 2004, chronologically ordered

Non-covalent interactions in organotin(IV) derivatives of 5,7-ditertbutyl- and 5,7-diphenyl-1,2,4-triazolo[1,5-a]pyrimidine as recognition motifs in crystalline self- assembly and their in vitro antistaphylococcal activity

Non-covalent interactions are known to play a key role in biological compounds due to their stabilization of the tertiary and quaternary structure of proteins [1]. Ligands similar to purine rings, such as triazolo pyrimidine ones, are very versatile in their interactions with metals and can act as model systems for natural bio-inorganic compounds [2]. A considerable series (twelve novel compounds are reported) of 5,7-ditertbutyl-1,2,4-triazolo[1,5-a]pyrimidine (dbtp) and 5,7-diphenyl- 1,2,4-triazolo[1,5-a]pyrimidine (dptp) were synthesized and investigated by FT-IR and 119Sn M\uf6ssbauer in the solid state and by 1H and 13C NMR spectroscopy, in solution [3]. The X-ray crystal and molecular structures of Et2SnCl2(dbtp)2 and Ph2SnCl2(EtOH)2(dptp)2 were described, in this latter pyrimidine molecules are not directly bound to the metal center but strictly H-bonded, through N(3), to the -OH group of the ethanol moieties. The network of hydrogen bonding and aromatic interactions involving pyrimidine and phenyl rings in both complexes drives their self-assembly. Noncovalent interactions involving aromatic rings are key processes in both chemical and biological recognition, contributing to overall complex stability and forming recognition motifs. It is noteworthy that in Ph2SnCl2(EtOH)2(dptp)2 \u3c0\u2013\u3c0 stacking interactions between pairs of antiparallel triazolopyrimidine rings mimick basepair interactions physiologically occurring in DNA (Fig.1). M\uf6ssbauer spectra suggest for Et2SnCl2(dbtp)2 a distorted octahedral structure, with C-Sn-C bond angles lower than 180\ub0. The estimated angle for Et2SnCl2(dbtp)2 is virtually identical to that determined by X-ray diffraction. Ph2SnCl2(EtOH)2(dptp)2 is characterized by an essentially linear C-Sn-C fragment according to the X-ray all-trans structure. The compounds were screened for their in vitro antibacterial activity on a group of reference staphylococcal strains susceptible or resistant to methicillin and against two reference Gramnegative pathogens [4] . We tested the biological activity of all the specimen against a group of staphylococcal reference strains (S. aureus ATCC 25923, S. aureus ATCC 29213, methicillin resistant S. aureus 43866 and S. epidermidis RP62A) along with Gram-negative pathogens (P. aeruginosa ATCC9027 and E. coli ATCC25922). Ph2SnCl2(EtOH)2(dptp)2 showed good antibacterial activity with a MIC value of 5 \u3bcg mL-1 against S. aureus ATCC29213 and also resulted active against methicillin resistant S. epidermidis RP62A

Nutrition and Human Oral Health

This book contains the Nutrients Special Issue "Nutrition and Human Oral Health" edited by Dr. Kirstin Vach and Prof. Dr. Johan Woelber. It includes 18 wonderful publications that provide an outline of current scientific work in the field of nutritional dentistry

The HBP1 tumor suppressor is a negative epigenetic regulator of MYCN driven neuroblastoma through interaction with the PRC2 complex

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