Fatal tolperisone poisoning: Autopsy and toxicology findings in three suicide cases.

Tolperisone (Mydocalm(®)) is a centrally acting muscle relaxant with few sedative side effects that is used for the treatment of chronic pain conditions. We describe three cases of suicidal tolperisone poisoning in three healthy young subjects in the years 2006, 2008 and 2009. In all cases, macroscopic and microscopic autopsy findings did not reveal the cause of death. Systematic toxicological analysis (STA) including immunological tests, screening for volatile substances and blood, urine and gastric content screening by GC-MS and HPLC-DAD demonstrated the presence of tolperisone in all cases. In addition to tolperisone, only the analgesics paracetamol (acetaminophen), ibuprofen and naproxen could be detected. The blood ethanol concentrations were all lower than 0.10g/kg. Tolperisone was extracted by liquid-liquid extraction using n-chlorobutane as the extraction solvent. The quantification was performed by GC-NPD analysis of blood, urine and gastric content. Tolperisone concentrations of 7.0mg/l, 14mg/l and 19mg/l were found in the blood of the deceased. In the absence of other autopsy findings, the deaths in these three cases were finally explained as a result of lethal tolperisone ingestion. To the best of our knowledge, these three cases are the first reported cases of suicidal tolperisone poisonings.

Identificação de substâncias em análise toxicológica sistemática utilizando um sistema informatizado para cálculo de parâmetros cromatográficos e busca em bases de dados

In spite of the availability of large databases of chromatographic data on several standardized systems, one major task in systematic toxicological analysis remains, namely how to handle the experimental data and retrieve data from the large available databases in a meaningful and productive way. To achieve this purpose, our group proposed an Internet-based tool using previously published STA databases, which interlaboratorial reproducibility tests have already evaluated. The developed software has the capability to calculate corrected chromatographic parameters, after the input of data obtained with standard mixtures of calibrators, and search the databases, currently incorporating TLC, color reactions, GC and HPLC data. At the end of the process, a list with candidate substances and their similarity indexes is presented.

Estimating Local Part Thickness in Midplane Meshes for Finite Element Analysis

Within the development of motor vehicles, crash safety (e.g. occupant protection, pedestrian protection, low speed damageability), is one of the most important attributes. In order to be able to fulfill the increased requirements in the framework of shorter cycle times and rising pressure to reduce costs, car manufacturers keep intensifying the use of virtual development tools such as those in the domain of Computer Aided Engineering (CAE). For crash simulations, the explicit finite element method (FEM) is applied. The accuracy of the simulation process is highly dependent on the accuracy of the simulation model, including the midplane mesh. One of the roughest approximations typically made is the actual part thickness which, in reality, can vary locally. However, almost always a constant thickness value is defined throughout the entire part due to complexity reasons. On the other hand, for precise fracture analysis within FEM, the correct thickness consideration is one key enabler. Thus, availability of per element thickness information, which does not exist explicitly in the FEM model, can significantly contribute to an improved crash simulation quality, especially regarding fracture prediction. Even though the thickness is not explicitly available from the FEM model, it can be inferred from the original CAD geometric model through geometric calculations. This paper proposes and compares two thickness estimation algorithms based on ray tracing and nearest neighbour 3D range searches. A systematic quantitative analysis of the accuracy of both algorithms is presented, as well as a thorough identification of particular geometric arrangements under which their accuracy can be compared. These results enable the identification of each technique’s weaknesses and hint towards a new, integrated, approach to the problem that linearly combines the estimates produced by each algorithm.

A computable general equilibrium analysis of the relative price sensitivity required to induce rebound effects in response to an improvement in energy efficiency in the UK economy

In recent years there has been extensive debate in the energy economics and policy literature on the likely impacts of improvements in energy efficiency. This debate has focussed on the notion of rebound effects. Rebound effects occur when improvements in energy efficiency actually stimulate the direct and indirect demand for energy in production and/or consumption. This phenomenon occurs through the impact of the increased efficiency on the effective, or implicit, price of energy. If demand is stimulated in this way, the anticipated reduction in energy use, and the consequent environmental benefits, will be partially or possibly even more than wholly (in the case of ‘backfire’ effects) offset. A recent report published by the UK House of Lords identifies rebound effects as a plausible explanation as to why recent improvements in energy efficiency in the UK have not translated to reductions in energy demand at the macroeconomic level, but calls for empirical investigation of the factors that govern the extent of such effects. Undoubtedly the single most important conclusion of recent analysis in the UK, led by the UK Energy Research Centre (UKERC) is that the extent of rebound and backfire effects is always and everywhere an empirical issue. It is simply not possible to determine the degree of rebound and backfire from theoretical considerations alone, notwithstanding the claims of some contributors to the debate. In particular, theoretical analysis cannot rule out backfire. Nor, strictly, can theoretical considerations alone rule out the other limiting case, of zero rebound, that a narrow engineering approach would imply. In this paper we use a computable general equilibrium (CGE) framework to investigate the conditions under which rebound effects may occur in the Scottish regional and UK national economies. Previous work has suggested that rebound effects will occur even where key elasticities of substitution in production are set close to zero. Here, we carry out a systematic sensitivity analysis, where we gradually introduce relative price sensitivity into the system, focusing in particular on elasticities of substitution in production and trade parameters, in order to determine conditions under which rebound effects become a likely outcome. We find that, while there is positive pressure for rebound effects even where (direct and indirect) demand for energy is very price inelastic, this may be partially or wholly offset by negative income and disinvestment effects, which also occur in response to falling energy prices.

15 Years of Microstate Research in Schizophrenia - Where Are We? A Meta-Analysis

Schizophrenia patients show abnormalities in a broad range of task demands. Therefore, an explanation common to all these abnormalities has to be sought independently of any particular task, ideally in the brain dynamics before a task takes place or during resting state. For the neurobiological investigation of such baseline states, EEG microstate analysis is particularly well suited, because it identifies subsecond global states of stable connectivity patterns directly related to the recruitment of different types of information processing modes (e.g., integration of top-down and bottom-up information). Meanwhile, there is an accumulation of evidence that particular microstate networks are selectively affected in schizophrenia. To obtain an overall estimate of the effect size of these microstate abnormalities, we present a systematic meta-analysis over all studies available to date relating EEG microstates to schizophrenia. Results showed medium size effects for two classes of microstates, namely, a class labeled C that was found to be more frequent in schizophrenia and a class labeled D that was found to be shortened. These abnormalities may correspond to core symptoms of schizophrenia, e.g., insufficient reality testing and self-monitoring as during auditory verbal hallucinations. As interventional studies have shown that these microstate features may be systematically affected using antipsychotic drugs or neurofeedback interventions, these findings may help introducing novel diagnostic and treatment options.

Self-organizing cultured neural networks: Image analysis techniques for longitudinal tracking and modeling of the underlying network structure

Esta tesis estudia la evolución estructural de conjuntos de neuronas como la capacidad de auto-organización desde conjuntos de neuronas separadas hasta que forman una red (clusterizada) compleja. Esta tesis contribuye con el diseño e implementación de un algoritmo no supervisado de segmentación basado en grafos con un coste computacional muy bajo. Este algoritmo proporciona de forma automática la estructura completa de la red a partir de imágenes de cultivos neuronales tomadas con microscopios de fase con una resolución muy alta. La estructura de la red es representada mediante un objeto matemático (matriz) cuyos nodos representan a las neuronas o grupos de neuronas y los enlaces son las conexiones reconstruidas entre ellos. Este algoritmo extrae también otras medidas morfológicas importantes que caracterizan a las neuronas y a las neuritas. A diferencia de otros algoritmos hasta el momento, que necesitan de fluorescencia y técnicas inmunocitoquímicas, el algoritmo propuesto permite el estudio longitudinal de forma no invasiva posibilitando el estudio durante la formación de un cultivo. Además, esta tesis, estudia de forma sistemática un grupo de variables topológicas que garantizan la posibilidad de cuantificar e investigar la progresión de las características principales durante el proceso de auto-organización del cultivo. Nuestros resultados muestran la existencia de un estado concreto correspondiente a redes con configuracin small-world y la emergencia de propiedades a micro- y meso-escala de la estructura de la red. Finalmente, identificamos los procesos físicos principales que guían las transformaciones morfológicas de los cultivos y proponemos un modelo de crecimiento de red que reproduce el comportamiento cuantitativamente de las observaciones experimentales. ABSTRACT The thesis analyzes the morphological evolution of assemblies of living neurons, as they self-organize from collections of separated cells into elaborated, clustered, networks. In particular, it contributes with the design and implementation of a graph-based unsupervised segmentation algorithm, having an associated very low computational cost. The processing automatically retrieves the whole network structure from large scale phase-contrast images taken at high resolution throughout the entire life of a cultured neuronal network. The network structure is represented by a mathematical object (a matrix) in which nodes are identified neurons or neurons clusters, and links are the reconstructed connections between them. The algorithm is also able to extract any other relevant morphological information characterizing neurons and neurites. More importantly, and at variance with other segmentation methods that require fluorescence imaging from immunocyto- chemistry techniques, our measures are non invasive and entitle us to carry out a fully longitudinal analysis during the maturation of a single culture. In turn, a systematic statistical analysis of a group of topological observables grants us the possibility of quantifying and tracking the progression of the main networks characteristics during the self-organization process of the culture. Our results point to the existence of a particular state corresponding to a small-world network configuration, in which several relevant graphs micro- and meso-scale properties emerge. Finally, we identify the main physical processes taking place during the cultures morphological transformations, and embed them into a simplified growth model that quantitatively reproduces the overall set of experimental observations.

Self-organizing cultured neural networks: Image analysis techniques for longitudinal tracking and modeling of the underlying network structure (versión actualizada)

Esta tesis estudia la evolución estructural de conjuntos de neuronas como la capacidad de auto-organización desde conjuntos de neuronas separadas hasta que forman una red (clusterizada) compleja. Esta tesis contribuye con el diseño e implementación de un algoritmo no supervisado de segmentación basado en grafos con un coste computacional muy bajo. Este algoritmo proporciona de forma automática la estructura completa de la red a partir de imágenes de cultivos neuronales tomadas con microscopios de fase con una resolución muy alta. La estructura de la red es representada mediante un objeto matemático (matriz) cuyos nodos representan a las neuronas o grupos de neuronas y los enlaces son las conexiones reconstruidas entre ellos. Este algoritmo extrae también otras medidas morfológicas importantes que caracterizan a las neuronas y a las neuritas. A diferencia de otros algoritmos hasta el momento, que necesitan de fluorescencia y técnicas inmunocitoquímicas, el algoritmo propuesto permite el estudio longitudinal de forma no invasiva posibilitando el estudio durante la formación de un cultivo. Además, esta tesis, estudia de forma sistemática un grupo de variables topológicas que garantizan la posibilidad de cuantificar e investigar la progresión de las características principales durante el proceso de auto-organización del cultivo. Nuestros resultados muestran la existencia de un estado concreto correspondiente a redes con configuracin small-world y la emergencia de propiedades a micro- y meso-escala de la estructura de la red. Finalmente, identificamos los procesos físicos principales que guían las transformaciones morfológicas de los cultivos y proponemos un modelo de crecimiento de red que reproduce el comportamiento cuantitativamente de las observaciones experimentales. ABSTRACT The thesis analyzes the morphological evolution of assemblies of living neurons, as they self-organize from collections of separated cells into elaborated, clustered, networks. In particular, it contributes with the design and implementation of a graph-based unsupervised segmentation algorithm, having an associated very low computational cost. The processing automatically retrieves the whole network structure from large scale phase-contrast images taken at high resolution throughout the entire life of a cultured neuronal network. The network structure is represented by a mathematical object (a matrix) in which nodes are identified neurons or neurons clusters, and links are the reconstructed connections between them. The algorithm is also able to extract any other relevant morphological information characterizing neurons and neurites. More importantly, and at variance with other segmentation methods that require fluorescence imaging from immunocyto- chemistry techniques, our measures are non invasive and entitle us to carry out a fully longitudinal analysis during the maturation of a single culture. In turn, a systematic statistical analysis of a group of topological observables grants us the possibility of quantifying and tracking the progression of the main networks characteristics during the self-organization process of the culture. Our results point to the existence of a particular state corresponding to a small-world network configuration, in which several relevant graphs micro- and meso-scale properties emerge. Finally, we identify the main physical processes taking place during the cultures morphological transformations, and embed them into a simplified growth model that quantitatively reproduces the overall set of experimental observations.

Zeitgeist or chameleon? A quantitative analysis of CSR definitions.

Despite its increasing relevance, corporate social responsibility (CSR) remains hobbled by problems, variously charged as being chameleon, vacuous or an utterly meaningless concept. One reason is the absence of an agreed upon normative basis underpinning CSR. This is in large part due to the concept lacking a universally accepted definition. This paper explores how the concept of CSR has evolved over time drawing from 110 definitions of the construct. Using co-word analysis of definitions from 1953 to 2014, the study maps how the structure of the definitions has evolved during the field's historical development. The research uncovers the key terms underpinning the phenomenon, the centrality of these terms as well as mapping their interrelationships and evolution. The findings suggest that, despite the profusion and definitional heterogeneity over the six decades of the development of the field, there are six recurrent, enduring dimensions that underpin the CSR concept. These dimensions are economic, social, ethical, stakeholders, sustainability and voluntary. This paper makes several contributions to the academic literature. The systematic, quantitative analysis of definitions brings an objectivity that previous qualitative bibliometric analyses of CSR have lacked. The time period selected is substantially longer than previous analyses and captures the complete historical evolution of the concept. Moreover, the analysis provides the basis for the development of a new, comprehensive, yet concise, definition of CSR that captures all six of the recurring dimensions underpinning the concept.

Root Secretion of Defense-related Proteins Is Development-dependent and Correlated with Flowering Time

Proteins found in the root exudates are thought to play a role in the interactions between plants and soil organisms. To gain a better understanding of protein secretion by roots, we conducted a systematic proteomic analysis of the root exudates of Arabidopsis thaliana at different plant developmental stages. In total, we identified 111 proteins secreted by roots, the majority of which were exuded constitutively during all stages of development. However, defense-related proteins such as chitinases, glucanases, myrosinases, and others showed enhanced secretion during flowering. Defense-impaired mutants npr1-1 and NahG showed lower levels of secretion of defense proteins at flowering compared with the wild type. The flowering-defective mutants fca-1, stm-4, and co-1 showed almost undetectable levels of defense proteins in their root exudates at similar time points. In contrast, root secretions of defense-enhanced cpr5-2 mutants showed higher levels of defense proteins. The proteomics data were positively correlated with enzymatic activity assays for defense proteins and with in silico gene expression analysis of genes specifically expressed in roots of Arabidopsis. In conclusion, our results show a clear correlation between defense-related proteins secreted by roots and flowering time.

From the portolan chart of the mediterranean to the latitude chart of the atlantic

Thesis submitted to the Instituto Superior de Estatística e Gestão de Informação da Universidade Nova de Lisboa in partial fulfillment of the requirements for the Degree of Doctor of Philosophy in Information Management – Geographic Information Systems

Remoção fotocatalítica de micropoluentes em meios porosos funcionalizados

Dissertação de mestrado integrado em Engenharia Civil

Negative rebound and disinvestment effects in response to an improvement in energy efficiency in the UK economy

This paper uses a computable general equilibrium (CGE) framework to investigate the conditions under which rebound effects may occur in response to increases in energy efficiency in the UK national economy. Previous work for the UK has suggested that rebound effects will occur even where key elasticities of substitution in production are set close to zero. The research reported in this paper involves carrying out a systematic sensitivity analysis, where relative price sensitivity is gradually introduced into the system, focusing specifically on elasticities of substitution in production and trade parameters, in order to determine conditions under which rebound effects become a likely outcome. The main result is that, while there is positive pressure for rebound effects even where (direct and indirect) demands for energy are very price inelastic, this may be partially or wholly offset by negative income, competitiveness and disinvestment effects, which also occur in response to falling energy prices. The occurrence of disinvestment effects is of particular interest. These occur where falling energy prices reduce profitability in domestic energy supply sectors, leading to a contraction in capital stock in these sectors, which may in turn lead to rebound effects that are smaller in the long run than in the short run, a result that runs contrary to the predictions of previous theoretical work in this area.

Market Familiarity and the Location of South and North MNEs

We use a systematic empirical analysis of the determinants of South-South (SS) and North-South (NS) foreign direct investment (FDI) as a canvas to explore how multinational enterprises’ (MNEs) location decisions are shaped by better acquaintance with a foreign market resulting from bilateral ties, experience of international expansion, and knowledge of how to deal with poor governance. We find that these various aspects of market familiarity, which can interact together, are important to explain and differentiate the location behaviours of South MNEs (S-MNEs) and North MNEs (N-MNEs) in developing countries.

Recurrent de novo mitochondrial DNA mutations in respiratory chain deficiency.

Starting from a cohort of 50 NADH-oxidoreductase (complex I) deficient patients, we carried out the systematic sequence analysis of all mitochondrially encoded complex I subunits (ND1 to ND6 and ND4L) in affected tissues. This approach yielded the unexpectedly high rate of 20% mutation identification in our series. Recurrent heteroplasmic mutations included two hitherto unreported (T10158C and T14487C) and three previously reported mutations (T10191C, T12706C and A13514G) in children with Leigh or Leigh-like encephalopathy. The recurrent mutations consistently involved T-->C transitions (p<10(-4)). This study supports the view that an efficient molecular screening should be based on an accurate identification of respiratory chain enzyme deficiency.

Parental substance abuse and accidental death in children.

In this report, the authors present two cases of accidental death in children of addicted parents. In the first case, the child was left unattended at home while the mother went out to buy cocaine. She was arrested and detained with no mention of the unsupervised child. The cause of death in this case was determined to be starvation and dehydration. In the second case, a child mistakenly received a methadone suppository by her father instead of an antipyretic suppository. Toxicological analysis of the femoral blood revealed methadone at a concentration of 1.2 mg/L. The cause of death was determined to be methadone intoxication. The literature is reviewed and discussed. We report these cases to illustrate the risk of harm to children from illicit drugs and prescription medications at home and because there is no mention of accidental death in children following a methadone suppository administration in the current literature.