Neuro-angiostrongylosis in wild Black and Grey-headed flying foxes (Pteropus spp)

Objective: To identify nematodes seen in histological sections of brains of flying foxes (fruit bats) and describe the associated clinical disease and pathology. Proceedures: Gross and histological examination of brains from 86 free-living flying foxes with neurological disease was done as part of an ongoing surveillance program for Australian bat lyssavirus. Worms were recovered, or if seen in histological sections, extracted by maceration of half the brain and identified by microscopic examination. Histological archives were also reviewed. Results: There was histological evidence of angiostrongylosis in 16 of 86 recently submitted flying foxes with neurological disease and in one archival case from 1992. In 10 flying foxes, worms were definitively identified as Angiostrongylus cantonensis fifth-stage larvae. A worm fragment and third stage larvae were identified as Angiostrongylus sp, presumably A cantonensis, in a further three cases. The clinical picture was dominated by paresis, particularly of the hindlimbs, and depression, with flying foxes surviving up to 22 days in the care of wildlife volunteers. Brains containing fifthstage larvae showed a moderate to severe eosinophilic and granulomatous meningoencephalitis (n = 14), whereas there was virtually no inflammation of the brains of bats which died when infected with only smaller, third-stage larvae (n = 3). There was no histological evidence of pulmonary involvement. Conclusion: This is the first report of the recovery and identification of A cantonensis from free-living Australian wildlife. While angiostrongylosis is a common cause of paresis in flying foxes, the initial clinical course cannot be differentiated from Australian bat lyssavirus infection, and wildlife carers should be urged not to attempt to rehabilitate flying foxes with neurological disease.

Diabetic charcot neuroarthropathy of the knee: Conservative treatment options as alternatives to surgery

In contrast to the well-known Charcot neuroarthropathy (CN) of the foot, CN of the knee is hardly recognized. In a literature search, we only found five articles on total knee arthroplasty for Charcot joints (1–5). We did not find a single article dealing with alternative treatment options or the general clinical course of this disease. We started our study

Diabetic charcot neuroarthropathy of the hand: Clinical course, diagnosis, and treatment options

During the treatment of diabetic Charcot neuroarthropathy (CN) of the foot in two young patients, we discovered atypical alterations of their hands with loss of strength and paresthesia combined with atypical and nonhealing bone alterations and instability. Whereas CN of the foot is a serious and well-known complication of diabetes, CN of the hand is only mentioned in four articles (1–4).

An optimal dynamic inversion-based neuro-adaptive approach for treatment of chronic myelogenous leukemia

Combining the advanced techniques of optimal dynamic inversion and model-following neuro-adaptive control design, an innovative technique is presented to design an automatic drug administration strategy for effective treatment of chronic myelogenous leukemia (CML). A recently developed nonlinear mathematical model for cell dynamics is used to design the controller (medication dosage). First, a nominal controller is designed based on the principle of optimal dynamic inversion. This controller can treat the nominal model patients (patients who can be described by the mathematical model used here with the nominal parameter values) effectively. However, since the system parameters for a realistic model patient can be different from that of the nominal model patients, simulation studies for such patients indicate that the nominal controller is either inefficient or, worse, ineffective; i.e. the trajectory of the number of cancer cells either shows non-satisfactory transient behavior or it grows in an unstable manner. Hence, to make the drug dosage history more realistic and patient-specific, a model-following neuro-adaptive controller is augmented to the nominal controller. In this adaptive approach, a neural network trained online facilitates a new adaptive controller. The training process of the neural network is based on Lyapunov stability theory, which guarantees both stability of the cancer cell dynamics as well as boundedness of the network weights. From simulation studies, this adaptive control design approach is found to be very effective to treat the CML disease for realistic patients. Sufficient generality is retained in the mathematical developments so that the technique can be applied to other similar nonlinear control design problems as well.

A neuro-adaptive augmented optimal dynamic inversion approach for effective and efficient treatment of chronic myelogenous leukemia

Combining the advanced techniques of optimal dynamic inversion and model-following neuro-adaptive control design, an efficient technique is presented for effective treatment of chronic myelogenous leukemia (CML). A recently developed nonlinear mathematical model for cell dynamics is used for the control (medication) synthesis. First, taking a set of nominal parameters, a nominal controller is designed based on the principle of optimal dynamic inversion. This controller can treat nominal patients (patients having same nominal parameters as used for the control design) effectively. However, since the parameters of an actual patient can be different from that of the ideal patient, to make the treatment strategy more effective and efficient, a model-following neuro-adaptive controller is augmented to the nominal controller. In this approach, a neural network trained online (based on Lyapunov stability theory) facilitates a new adaptive controller, computed online. From the simulation studies, this adaptive control design approach (treatment strategy) is found to be very effective to treat the CML disease for actual patients. Sufficient generality is retained in the theoretical developments in this paper, so that the techniques presented can be applied to other similar problem as well. Note that the technique presented is computationally non-intensive and all computations can be carried out online.

A Neuro-Adaptive Approach for Robust Control of High Performance Aircrafts with Parameter Inaccuracies

For high performance aircrafts, the flight control system needs to be quite effective in both assuring accurate tracking of pilot commands, while simultaneously assuring overall stability of the aircraft. In addition, the control system must also be sufficiently robust to cater to possible parameter variations. The primary aim of this paper is to enhance the robustness of the controller for a HPA using neuro-adaptive control design. Here the architecture employs a network of Gaussian Radial basis functions to adaptively compensate for the ignored system dynamics. A stable weight mechanism is determined using Lyapunov theory. The network construction and performance of the resulting controller are illustrated through simulations with a low-fidelity six –DOF model of F16 that is available in open literature.

Model-following Neuro-adaptive approach for robust control of high performance aircrafts

Based on dynamic inversion, a relatively straightforward approach is presented in this paper for nonlinear flight control design of high performance aircrafts, which does not require the normal and lateral acceleration commands to be first transferred to body rates before computing the required control inputs. This leads to substantial improvement of the tracking response. Promising results are obtained from six degree-offreedom simulation studies of F-16 aircraft, which are found to be superior as compared to an existing approach (which is also based on dynamic inversion). The new approach has two potential benefits, namely reduced oscillatory response (including elimination of non-minimum phase behavior) and reduced control magnitude. Next, a model-following neuron-adaptive design is augmented the nominal design in order to assure robust performance in the presence of parameter inaccuracies in the model. Note that in the approach the model update takes place adaptively online and hence it is philosophically similar to indirect adaptive control. However, unlike a typical indirect adaptive control approach, there is no need to update the individual parameters explicitly. Instead the inaccuracy in the system output dynamics is captured directly and then used in modifying the control. This leads to faster adaptation, which helps in stabilizing the unstable plant quicker. The robustness study from a large number of simulations shows that the adaptive design has good amount of robustness with respect to the expected parameter inaccuracies in the model.

Fast Robust Pattern Classification Algorithms for Real Time Neuro-Motor Prosthetic Applications

In this paper, we give a brief review of pattern classification algorithms based on discriminant analysis. We then apply these algorithms to classify movement direction based on multivariate local field potentials recorded from a microelectrode array in the primary motor cortex of a monkey performing a reaching task. We obtain prediction accuracies between 55% and 90% using different methods which are significantly above the chance level of 12.5%.

A Novel Anticancer Agent, 8-Methoxypyrimido4 `,5 `: 4,5]thieno(2,3-b) Quinoline-4(3H)-One Induces Neuro 2a Neuroblastoma Cell Death through p53-Dependent, Caspase-Dependent and Independent Apoptotic Pathways

Neuroblastoma is the most common cancer in infants and fourth most common cancer in children. Despite recent advances in cancer treatments, the prognosis of stage-IV neuroblastoma patients continues to be dismal which warrant new pharmacotherapy. A novel tetracyclic condensed quinoline compound, 8-methoxypyrimido 4 `,5 `: 4,5] thieno(2,3-b) quinoline-4(3H)-one (MPTQ) is a structural analogue of an anticancer drug ellipticine and has been reported to posses anticancer property. Study on MPTQ on neuroblastoma cells is very limited and mechanisms related to its cytotoxicity on neuroblastoma cells are completely unknown. Here, we evaluated the anticancer property of MPTQ on mouse neuro 2a and human SH-SY5Y neuroblastoma cells and investigated the mechanisms underlying MPTQ-mediated neuro 2a cell death. MPTQ-mediated neuro 2a and SH-SY5Y cell deaths were found to be dose and time dependent. Moreover, MPTQ induced cell death reached approximately 99.8% and 90% in neuro 2a and SH-SY5Y cells respectively. Nuclear oligonucleosomal DNA fragmentation and Terminal dUTP Nick End Labelling assays indicated MPTQ-mediated neuro 2a cell death involved apoptosis. MPTQ-mediated apoptosis is associated with increased phosphorylation of p53 at Ser15 and Ser20 which correlates with the hyperphosphorylation of Ataxia-Telangiectasia mutated protein (ATM). Immunocytochemical analysis demonstrated the increased level of Bax protein in MPTQ treated neuro 2a cells. MPTQ-mediated apoptosis is also associated with increased activation of caspase-9, -3 and -7 but not caspase-2 and -8. Furthermore, increased level of caspase-3 and cleaved Poly ( ADP Ribose) polymerase were observed in the nucleus of MPTQ treated neuro 2a cells, suggesting the involvement of caspase-dependent intrinsic but not extrinsic apoptotic pathway. Increased nuclear translocation of apoptosis inducing factor suggests additional involvement of caspase-independent apoptosis pathway in MPTQ treated neuro 2a cells. Collectively, MPTQ-induced neuro 2a cell death is mediated by ATM and p53 activation, and Bax-mediated activation of caspase-dependent and caspase-independent mitochondrial apoptosis pathways.

Database Independent Human Emotion Recognition with Meta-Cognitive Neuro-Fuzzy Inference System

Facial emotions are the most expressive way to display emotions. Many algorithms have been proposed which employ a particular set of people (usually a database) to both train and test their model. This paper focuses on the challenging task of database independent emotion recognition, which is a generalized case of subject-independent emotion recognition. The emotion recognition system employed in this work is a Meta-Cognitive Neuro-Fuzzy Inference System (McFIS). McFIS has two components, a neuro-fuzzy inference system, which is the cognitive component and a self-regulatory learning mechanism, which is the meta-cognitive component. The meta-cognitive component, monitors the knowledge in the neuro-fuzzy inference system and decides on what-to-learn, when-to-learn and how-to-learn the training samples, efficiently. For each sample, the McFIS decides whether to delete the sample without being learnt, use it to add/prune or update the network parameter or reserve it for future use. This helps the network avoid over-training and as a result improve its generalization performance over untrained databases. In this study, we extract pixel based emotion features from well-known (Japanese Female Facial Expression) JAFFE and (Taiwanese Female Expression Image) TFEID database. Two sets of experiment are conducted. First, we study the individual performance of both databases on McFIS based on 5-fold cross validation study. Next, in order to study the generalization performance, McFIS trained on JAFFE database is tested on TFEID and vice-versa. The performance The performance comparison in both experiments against SVNI classifier gives promising results.

Essays in behavioral and neuro-economics

This thesis examines foundational questions in behavioral economics—also called psychology and economics—and the neural foundations of varied sources of utility. We have three primary aims: First, to provide the field of behavioral economics with psychological theories of behavior that are derived from neuroscience and to use those theories to identify novel evidence for behavioral biases. Second, we provide neural and micro foundations of behavioral preferences that give rise to well-documented empirical phenomena in behavioral economics. Finally, we show how a deep understanding of the neural foundations of these behavioral preferences can feed back into our theories of social preferences and reference-dependent utility.

The first chapter focuses on classical conditioning and its application in identifying the psychological underpinnings of a pricing phenomenon. We return to classical conditioning again in the third chapter where we use fMRI to identify varied sources of utility—here, reference dependent versus direct utility—and cross-validate our interpretation with a conditioning experiment. The second chapter engages social preferences and, more broadly, causative utility (wherein the decision-maker derives utility from making or avoiding particular choices).

Uso de neuro-fuzzy na avaliação da suscetibilidade de escorregamento de taludes.

A Presente dissertação apresenta uma aplicação de Inteligência Computacional na área de Geotecnia, com a utilização da Técnica de Neuro-Fuzzy para indicar a suscetibilidade de escorregamento de taludes no município do Rio de Janeiro, a partir de inspeção visual. Neste trabalho, a suscetibilidade corresponde à possibilidade de ocorrência de escorregamento sem considerar os danos relacionados ao evento. Adotou-se como variável de saída a Previsão de Escorregamento (PE) com três adjetivos que correspondem a Suscetibilidades Alta, Média e Baixa. A metodologia utilizada consistiu em, inicialmente, montar um banco de dados com informações preliminares de análise de estabilidade, com a indicação dos condicionantes de escorregamento relacionados à geomorfologia, pluviosidade, capacidade de drenagem, vegetação e ocupação com seus respectivos graus de suscetibilidades de escorregamento obtidos em um conjunto de Laudos de Vistoria da Geo Rio. O banco de dados foi aplicado em um algoritmo de Neuro-Fuzzy. Diversos testes foram realizados com as alterações dos parâmetros do modelo Neuro-Fuzzy para uma combinação de fatores condicionantes de escorregamento e refinamento do banco de dados. Os testes apresentaram diminuição do erro fornecido pelo programa com o aumento de tipos de condicionantes utilizados no treinamento, o que permite inferir que o escorregamento ocorre por uma complexa relação entre diversos fatores condicionantes. O banco de dados utilizado nos testes apresenta descontinuidades nas relações entre os diversos condicionantes, ou seja, para uma mesma faixa de valores de Altura do talude, não é possível obter uma relação para todas as faixas de outro condicionante e, até mesmo, para todas as faixas da Previsão de Escorregamento. As PEs obtidas na validação do modelo tiveram seus valores próximos aos desejados somente nos conjuntos de variáveis utilizadas para o treinamento. O modelo não foi capaz de apresentar valores de suscetibilidades dentro da faixa de valores utilizados no treinamento para combinação de variáveis com pequenos ruídos, o que indica a necessidade de ampliação do banco de dados tanto quantitativamente quanto qualitativamente de modo a cobrir as descontinuidades apresentadas nas relações entre as variáveis.

Entre crânios analógicos e imagens digitais: alguns antecedentes históricos e culturais das tecnologias de neuro-imageamento

Nos últimos anos, temos nos deparado com a difusão maciça e a popularização crescente de descrições biológicas para aspectos outrora pensados como mentais, sociais, ou relacionais. Visível em diversas arenas leigas e científicas, esta tendência freqüentemente elege o cérebro como o órgão privilegiado da sua atenção. A cada semana é divulgada uma nova localização cerebral correlacionada os mais variados aspectos comportamentais e ou de personalidade. Acompanhando este movimento, é notável o esforço intelectual e financeiro despendido nos últimos anos no campo da saúde mental, no sentido de fazer avançar pesquisas cujo foco central é a descoberta das bases neurobiológicas dos transtornos mentais. Esta tendência apontaria na direção de uma fusão entre a psiquiatria e a neurologia em uma disciplina única, de teor fisicalista, chamada por alguns de cerebrologia. Dentre os acontecimentos que serviram de alicerce para a legitimação e a popularização desta tendência, o desenvolvimento nas últimas décadas de novas técnicas e tecnologias de visualização médica, como a tomografia por emissão de pósitrons (PET scan) e a ressonância magnética funcional (fMRI), foi fundamental. Elas permitiram a construção de imagens das mais diversas categorias nosográficas construídas no campo psiquiátrico, veiculando tacitamente uma série de pressupostos e promessas. Malgrado o imaginário cultural sustentado por estas tecnologias e todo o esforço despendido nas últimas décadas no sentido de se tentar localizar os marcadores biológicos dos transtornos psiquiátricos, não há, até o presente momento, nenhum resultado conclusivo que autorize o diagnóstico por imagem de nosografias como a esquizofrenia, a depressão, e muito menos o jogo patológico. Apesar de todo o alarde midiático e dos montantes milionários direcionados para pesquisas nesta área, os resultados concretos obtidos até agora não estão livres das mais ferozes controvérsias. Entretanto, ainda que estejamos muito longe da construção de mapas precisos para as perturbações mentais é espantoso o poder de convencimento que as neuro-imagens comportam na atualidade. Os scans são exibidos como verdades visuais, ou fatos acerca das pessoas e do mundo, numa proporção muito superior aos dados que apresentam. Alguns críticos chamam este aspecto de neurorealismo, ou de retórica da auto-evidência. A intenção deste trabalho é problematizar o poder persuasivo que as neuro-imagens detém na contemporaneidade, especialmente quando utilizadas com a finalidade diagnóstica no campo da saúde mental. Se estas imagens transmitem uma ideia de neutralidade, transparência imediata e auto-evidência, este trabalho almeja inseri-las num contexto sócio-histórico, a partir do qual puderam adquirir sentido, familiaridade e valor de verdade. O ponto de partida é o de que elas estão localizadas no cruzamento de dois movimentos históricos distintos: o das ilustrações médicas, em sua relação com a produção de conhecimento objetivo; e o das pesquisas acerca da localização no córtex cerebral de comportamentos complexos e traços de personalidade. Além de estabelecer algumas condições históricas de possibilidade para a emergência de um neo-localizacionismo cerebral, mediado pelas novas tecnologias de imageamento, pretende-se enfatizar algumas descontinuidades com projetos anteriores e marcar a influência do contexto cultural da atualidade para o sucesso e poder persuasivo deste tipo de tecnologia.

Neuro-Fuzzy genetic system for selection of construction project managers

Choosing a project manager for a construction project—particularly, large projects—is a critical project decision. The selection process involves different criteria and should be in accordance with company policies and project specifications. Traditionally, potential candidates are interviewed and the most qualified are selected in compliance with company priorities and project conditions. Precise computing models that could take various candidates’ information into consideration and then pinpoint the most qualified person with a high degree of accuracy would be beneficial. On the basis of the opinions of experienced construction company managers, this paper, through presenting a fuzzy system, identifies the important criteria in selecting a project manager. The proposed fuzzy system is based on IF-THEN rules; a genetic algorithm improves the overall accuracy as well as the functions used by the fuzzy system to make initial estimates of the cluster centers for fuzzy c-means clustering. Moreover, a back-propagation neutral network method was used to train the system. The optimal measures of the inference parameters were identified by calculating the system’s output error and propagating this error within the system. After specifying the system parameters, the membership function parameters—which by means of clustering and projection were approximated—were tuned with the genetic algorithm. Results from this system in selecting project managers show its high capability in making high-quality personnel predictions