Probabilistic assessment of risk from the exposure to mercury in artisanal gold mining communities in Colombia

Colombia is one the largest per capita mercury polluters as a consequence of its artisanal gold mining operations, which are steadily increasing following the rising price of this metal. Compared to gravimetric separation methods and cyanidation, the concentration of gold using Hg amalgams presents several advantages: the process is less time-consuming and minimizes gold losses, and Hg is easily transported and inexpensive relative to the selling price of gold. Very often, mercury amalgamation is carried out on site by unprotected workers. During this operation large amounts of mercury are discharged to the environment and eventually reach the fresh water bodies in the vicinity where it is subjected to methylation. Additionally, as gold is released from the amalgam by heating on open charcoal furnaces in small workshops, mercury vapors are emitted and inhaled by the artisanal smelters and the general population

Effects of soluble humic acids on the uptake of heavy metals by Vetiveria zizanioides (L.) Nash in contaminated mining soils

In the past, mining wastes were left wherever they might lie in the surroundings of the mine area. Unfortunately, inactive and abandoned mines continue to pollute our environment, reason why these sites should be restored with minimum impact. Phytoextraction is an environmental-friendly and cost-effective technology less harmful than traditional methods that uses metal hyperaccumulator or at least tolerant plants to extract heavy metals from polluted soils. One disadvantage of hyperaccumulator species is their slow growth rate and low biomass production. Vetiveria zizanioides (L.) Nash, perennial species adapted to Mediterranean climate has a strong root system which can reach up to 3 m deep, is fast growing, and can survive in sites with high metal levels (Chen et al., 2004). Due to the fact that metals in abandoned mine tailings become strongly bonded to soil solids, humic acids used as chelating agents could increase metal bioavailability (Evangelou et al., 2004; Wilde et al., 2005) and thereby promote higher accumulation in the harvestable parts of the plant. The objective of this study was to examine the performance of humic acid assisted phytoextraction using Vetiveria zizanioides (L.) Nash in heavy metals contaminated soils.

Una aproximación a la predicción del valor de acciones en la bolsa de valores aplicando técnicas de Data Mining

La predicción del valor de las acciones en la bolsa de valores ha sido un tema importante en el campo de inversiones, que por varios años ha atraído tanto a académicos como a inversionistas. Esto supone que la información disponible en el pasado de la compañía que cotiza en bolsa tiene alguna implicación en el futuro del valor de la misma. Este trabajo está enfocado en ayudar a un persona u organismo que decida invertir en la bolsa de valores a través de gestión de compra o venta de acciones de una compañía a tomar decisiones respecto al tiempo de comprar o vender basado en el conocimiento obtenido de los valores históricos de las acciones de una compañía en la bolsa de valores. Esta decisión será inferida a partir de un modelo de regresión múltiple que es una de las técnicas de datamining. Para llevar conseguir esto se emplea una metodología conocida como CRISP-DM aplicada a los datos históricos de la compañía con mayor valor actual del NASDAQ.---ABSTRACT---The prediction of the value of shares in the stock market has been a major issue in the field of investments, which for several years has attracted both academics and investors. This means that the information available in the company last traded have any involvement in the future of the value of it. This work is focused on helping an investor decides to invest in the stock market through management buy or sell shares of a company to make decisions with respect to time to buy or sell based on the knowledge gained from the historic values of the shares of a company in the stock market. This decision will be inferred from a multiple regression model which is one of the techniques of data mining. To get this out a methodology known as CRISP-DM applied to historical data of the company with the highest current value of NASDAQ is used.

Mining mobile apps reviews to support release planning

The mobile apps market is a tremendous success, with millions of apps downloaded and used every day by users spread all around the world. For apps’ developers, having their apps published on one of the major app stores (e.g. Google Play market) is just the beginning of the apps lifecycle. Indeed, in order to successfully compete with the other apps in the market, an app has to be updated frequently by adding new attractive features and by fixing existing bugs. Clearly, any developer interested in increasing the success of her app should try to implement features desired by the app’s users and to fix bugs affecting the user experience of many of them. A precious source of information to decide how to collect users’ opinions and wishes is represented by the reviews left by users on the store from which they downloaded the app. However, to exploit such information the app’s developer should manually read each user review and verify if it contains useful information (e.g. suggestions for new features). This is something not doable if the app receives hundreds of reviews per day, as happens for the very popular apps on the market. In this work, our aim is to provide support to mobile apps developers by proposing a novel approach exploiting data mining, natural language processing, machine learning, and clustering techniques in order to classify the user reviews on the basis of the information they contain (e.g. useless, suggestion for new features, bugs reporting). Such an approach has been empirically evaluated and made available in a web-­‐based tool publicly available to all apps’ developers. The achieved results showed that the developed tool: (i) is able to correctly categorise user reviews on the basis of their content (e.g. isolating those reporting bugs) with 78% of accuracy, (ii) produces clusters of reviews (e.g. groups together reviews indicating exactly the same bug to be fixed) that are meaningful from a developer’s point-­‐of-­‐view, and (iii) is considered useful by a software company working in the mobile apps’ development market.

Mining Abstractions in Scientific Workows

Los flujos de trabajo científicos han sido adoptados durante la última década para representar los métodos computacionales utilizados en experimentos in silico, así como para dar soporte a sus publicaciones asociadas. Dichos flujos de trabajo han demostrado ser útiles para compartir y reproducir experimentos científicos, permitiendo a investigadores visualizar, depurar y ahorrar tiempo a la hora de re-ejecutar un trabajo realizado con anterioridad. Sin embargo, los flujos de trabajo científicos pueden ser en ocasiones difíciles de entender y reutilizar. Esto es debido a impedimentos como el gran número de flujos de trabajo existentes en repositorios, su heterogeneidad o la falta generalizada de documentación y ejemplos de uso. Además, dado que normalmente es posible implementar un mismo método utilizando algoritmos o técnicas distintas, flujos de trabajo aparentemente distintos pueden estar relacionados a un determinado nivel de abstracción, basándose, por ejemplo, en su funcionalidad común. Esta tesis se centra en la reutilización de flujos de trabajo y su abstracción mediante la exploración de relaciones entre los flujos de trabajo de un repositorio y la extracción de abstracciones que podrían ayudar a la hora de reutilizar otros flujos de trabajo existentes. Para ello, se propone un modelo simple de representación de flujos de trabajo y sus ejecuciones, se analizan las abstracciones típicas que se pueden encontrar en los repositorios de flujos de trabajo, se exploran las prácticas habituales de los usuarios a la hora de reutilizar flujos de trabajo existentes y se describe un método para descubrir abstracciones útiles para usuarios, basadas en técnicas existentes de teoría de grafos. Los resultados obtenidos exponen las abstracciones y prácticas comunes de usuarios en términos de reutilización de flujos de trabajo, y muestran cómo las abstracciones que se extraen automáticamente tienen potencial para ser reutilizadas por usuarios que buscan diseñar nuevos flujos de trabajo. Abstract Scientific workflows have been adopted in the last decade to represent the computational methods used in in silico scientific experiments and their associated research products. Scientific workflows have demonstrated to be useful for sharing and reproducing scientific experiments, allowing scientists to visualize, debug and save time when re-executing previous work. However, scientific workflows may be difficult to understand and reuse. The large amount of available workflows in repositories, together with their heterogeneity and lack of documentation and usage examples may become an obstacle for a scientist aiming to reuse the work from other scientists. Furthermore, given that it is often possible to implement a method using different algorithms or techniques, seemingly disparate workflows may be related at a higher level of abstraction, based on their common functionality. In this thesis we address the issue of reusability and abstraction by exploring how workflows relate to one another in a workflow repository, mining abstractions that may be helpful for workflow reuse. In order to do so, we propose a simple model for representing and relating workflows and their executions, we analyze the typical common abstractions that can be found in workflow repositories, we explore the current practices of users regarding workflow reuse and we describe a method for discovering useful abstractions for workflows based on existing graph mining techniques. Our results expose the common abstractions and practices of users in terms of workflow reuse, and show how our proposed abstractions have potential to become useful for users designing new workflows.

Extracción de conocimientos de la diabetes tipo 1 utilizando la metodología de "Data Mining"

La diabetes mellitus es un trastorno en la metabolización de los carbohidratos, caracterizado por la nula o insuficiente segregación de insulina (hormona producida por el páncreas), como resultado del mal funcionamiento de la parte endocrina del páncreas, o de una creciente resistencia del organismo a esta hormona. Esto implica, que tras el proceso digestivo, los alimentos que ingerimos se transforman en otros compuestos químicos más pequeños mediante los tejidos exocrinos. La ausencia o poca efectividad de esta hormona polipéptida, no permite metabolizar los carbohidratos ingeridos provocando dos consecuencias: Aumento de la concentración de glucosa en sangre, ya que las células no pueden metabolizarla; consumo de ácidos grasos mediante el hígado, liberando cuerpos cetónicos para aportar la energía a las células. Esta situación expone al enfermo crónico, a una concentración de glucosa en sangre muy elevada, denominado hiperglucemia, la cual puede producir a medio o largo múltiples problemas médicos: oftalmológicos, renales, cardiovasculares, cerebrovasculares, neurológicos… La diabetes representa un gran problema de salud pública y es la enfermedad más común en los países desarrollados por varios factores como la obesidad, la vida sedentaria, que facilitan la aparición de esta enfermedad. Mediante el presente proyecto trabajaremos con los datos de experimentación clínica de pacientes con diabetes de tipo 1, enfermedad autoinmune en la que son destruidas las células beta del páncreas (productoras de insulina) resultando necesaria la administración de insulina exógena. Dicho esto, el paciente con diabetes tipo 1 deberá seguir un tratamiento con insulina administrada por la vía subcutánea, adaptado a sus necesidades metabólicas y a sus hábitos de vida. Para abordar esta situación de regulación del control metabólico del enfermo, mediante una terapia de insulina, no serviremos del proyecto “Páncreas Endocrino Artificial” (PEA), el cual consta de una bomba de infusión de insulina, un sensor continuo de glucosa, y un algoritmo de control en lazo cerrado. El objetivo principal del PEA es aportar al paciente precisión, eficacia y seguridad en cuanto a la normalización del control glucémico y reducción del riesgo de hipoglucemias. El PEA se instala mediante vía subcutánea, por lo que, el retardo introducido por la acción de la insulina, el retardo de la medida de glucosa, así como los errores introducidos por los sensores continuos de glucosa cuando, se descalibran dificultando el empleo de un algoritmo de control. Llegados a este punto debemos modelar la glucosa del paciente mediante sistemas predictivos. Un modelo, es todo aquel elemento que nos permita predecir el comportamiento de un sistema mediante la introducción de variables de entrada. De este modo lo que conseguimos, es una predicción de los estados futuros en los que se puede encontrar la glucosa del paciente, sirviéndonos de variables de entrada de insulina, ingesta y glucosa ya conocidas, por ser las sucedidas con anterioridad en el tiempo. Cuando empleamos el predictor de glucosa, utilizando parámetros obtenidos en tiempo real, el controlador es capaz de indicar el nivel futuro de la glucosa para la toma de decisones del controlador CL. Los predictores que se están empleando actualmente en el PEA no están funcionando correctamente por la cantidad de información y variables que debe de manejar. Data Mining, también referenciado como Descubrimiento del Conocimiento en Bases de Datos (Knowledge Discovery in Databases o KDD), ha sido definida como el proceso de extracción no trivial de información implícita, previamente desconocida y potencialmente útil. Todo ello, sirviéndonos las siguientes fases del proceso de extracción del conocimiento: selección de datos, pre-procesado, transformación, minería de datos, interpretación de los resultados, evaluación y obtención del conocimiento. Con todo este proceso buscamos generar un único modelo insulina glucosa que se ajuste de forma individual a cada paciente y sea capaz, al mismo tiempo, de predecir los estados futuros glucosa con cálculos en tiempo real, a través de unos parámetros introducidos. Este trabajo busca extraer la información contenida en una base de datos de pacientes diabéticos tipo 1 obtenidos a partir de la experimentación clínica. Para ello emplearemos técnicas de Data Mining. Para la consecución del objetivo implícito a este proyecto hemos procedido a implementar una interfaz gráfica que nos guía a través del proceso del KDD (con información gráfica y estadística) de cada punto del proceso. En lo que respecta a la parte de la minería de datos, nos hemos servido de la denominada herramienta de WEKA, en la que a través de Java controlamos todas sus funciones, para implementarlas por medio del programa creado. Otorgando finalmente, una mayor potencialidad al proyecto con la posibilidad de implementar el servicio de los dispositivos Android por la potencial capacidad de portar el código. Mediante estos dispositivos y lo expuesto en el proyecto se podrían implementar o incluso crear nuevas aplicaciones novedosas y muy útiles para este campo. Como conclusión del proyecto, y tras un exhaustivo análisis de los resultados obtenidos, podemos apreciar como logramos obtener el modelo insulina-glucosa de cada paciente. ABSTRACT. The diabetes mellitus is a metabolic disorder, characterized by the low or none insulin production (a hormone produced by the pancreas), as a result of the malfunctioning of the endocrine pancreas part or by an increasing resistance of the organism to this hormone. This implies that, after the digestive process, the food we consume is transformed into smaller chemical compounds, through the exocrine tissues. The absence or limited effectiveness of this polypeptide hormone, does not allow to metabolize the ingested carbohydrates provoking two consequences: Increase of the glucose concentration in blood, as the cells are unable to metabolize it; fatty acid intake through the liver, releasing ketone bodies to provide energy to the cells. This situation exposes the chronic patient to high blood glucose levels, named hyperglycemia, which may cause in the medium or long term multiple medical problems: ophthalmological, renal, cardiovascular, cerebrum-vascular, neurological … The diabetes represents a great public health problem and is the most common disease in the developed countries, by several factors such as the obesity or sedentary life, which facilitate the appearance of this disease. Through this project we will work with clinical experimentation data of patients with diabetes of type 1, autoimmune disease in which beta cells of the pancreas (producers of insulin) are destroyed resulting necessary the exogenous insulin administration. That said, the patient with diabetes type 1 will have to follow a treatment with insulin, administered by the subcutaneous route, adapted to his metabolic needs and to his life habits. To deal with this situation of metabolic control regulation of the patient, through an insulin therapy, we shall be using the “Endocrine Artificial Pancreas " (PEA), which consists of a bomb of insulin infusion, a constant glucose sensor, and a control algorithm in closed bow. The principal aim of the PEA is providing the patient precision, efficiency and safety regarding the normalization of the glycemic control and hypoglycemia risk reduction". The PEA establishes through subcutaneous route, consequently, the delay introduced by the insulin action, the delay of the glucose measure, as well as the mistakes introduced by the constant glucose sensors when, decalibrate, impede the employment of an algorithm of control. At this stage we must shape the patient glucose levels through predictive systems. A model is all that element or set of elements which will allow us to predict the behavior of a system by introducing input variables. Thus what we obtain, is a prediction of the future stages in which it is possible to find the patient glucose level, being served of input insulin, ingestion and glucose variables already known, for being the ones happened previously in the time. When we use the glucose predictor, using obtained real time parameters, the controller is capable of indicating the future level of the glucose for the decision capture CL controller. The predictors that are being used nowadays in the PEA are not working correctly for the amount of information and variables that it need to handle. Data Mining, also indexed as Knowledge Discovery in Databases or KDD, has been defined as the not trivial extraction process of implicit information, previously unknown and potentially useful. All this, using the following phases of the knowledge extraction process: selection of information, pre- processing, transformation, data mining, results interpretation, evaluation and knowledge acquisition. With all this process we seek to generate the unique insulin glucose model that adjusts individually and in a personalized way for each patient form and being capable, at the same time, of predicting the future conditions with real time calculations, across few input parameters. This project of end of grade seeks to extract the information contained in a database of type 1 diabetics patients, obtained from clinical experimentation. For it, we will use technologies of Data Mining. For the attainment of the aim implicit to this project we have proceeded to implement a graphical interface that will guide us across the process of the KDD (with graphical and statistical information) of every point of the process. Regarding the data mining part, we have been served by a tool called WEKA's tool called, in which across Java, we control all of its functions to implement them by means of the created program. Finally granting a higher potential to the project with the possibility of implementing the service for Android devices, porting the code. Through these devices and what has been exposed in the project they might help or even create new and very useful applications for this field. As a conclusion of the project, and after an exhaustive analysis of the obtained results, we can show how we achieve to obtain the insulin–glucose model for each patient.

Nuevos Esquemas de Visualizaciones para Mejorar la Comprensibilidad de Modelos de Data Mining

La gran cantidad de datos que se registran diariamente en los sistemas de base de datos de las organizaciones ha generado la necesidad de analizarla. Sin embargo, se enfrentan a la complejidad de procesar enormes volúmenes de datos a través de métodos tradicionales de análisis. Además, dentro de un contexto globalizado y competitivo las organizaciones se mantienen en la búsqueda constante de mejorar sus procesos, para lo cual requieren herramientas que les permitan tomar mejores decisiones. Esto implica estar mejor informado y conocer su historia digital para describir sus procesos y poder anticipar (predecir) eventos no previstos. Estos nuevos requerimientos de análisis de datos ha motivado el desarrollo creciente de proyectos de minería de datos. El proceso de minería de datos busca obtener desde un conjunto masivo de datos, modelos que permitan describir los datos o predecir nuevas instancias en el conjunto. Implica etapas de: preparación de los datos, procesamiento parcial o totalmente automatizado para identificar modelos en los datos, para luego obtener como salida patrones, relaciones o reglas. Esta salida debe significar un nuevo conocimiento para la organización, útil y comprensible para los usuarios finales, y que pueda ser integrado a los procesos para apoyar la toma de decisiones. Sin embargo, la mayor dificultad es justamente lograr que el analista de datos, que interviene en todo este proceso, pueda identificar modelos lo cual es una tarea compleja y muchas veces requiere de la experiencia, no sólo del analista de datos, sino que también del experto en el dominio del problema. Una forma de apoyar el análisis de datos, modelos y patrones es a través de su representación visual, utilizando las capacidades de percepción visual del ser humano, la cual puede detectar patrones con mayor facilidad. Bajo este enfoque, la visualización ha sido utilizada en minería datos, mayormente en el análisis descriptivo de los datos (entrada) y en la presentación de los patrones (salida), dejando limitado este paradigma para el análisis de modelos. El presente documento describe el desarrollo de la Tesis Doctoral denominada “Nuevos Esquemas de Visualizaciones para Mejorar la Comprensibilidad de Modelos de Data Mining”. Esta investigación busca aportar con un enfoque de visualización para apoyar la comprensión de modelos minería de datos, para esto propone la metáfora de modelos visualmente aumentados. ABSTRACT The large amount of data to be recorded daily in the systems database of organizations has generated the need to analyze it. However, faced with the complexity of processing huge volumes of data over traditional methods of analysis. Moreover, in a globalized and competitive environment organizations are kept constantly looking to improve their processes, which require tools that allow them to make better decisions. This involves being bettered informed and knows your digital story to describe its processes and to anticipate (predict) unanticipated events. These new requirements of data analysis, has led to the increasing development of data-mining projects. The data-mining process seeks to obtain from a massive data set, models to describe the data or predict new instances in the set. It involves steps of data preparation, partially or fully automated processing to identify patterns in the data, and then get output patterns, relationships or rules. This output must mean new knowledge for the organization, useful and understandable for end users, and can be integrated into the process to support decision-making. However, the biggest challenge is just getting the data analyst involved in this process, which can identify models is complex and often requires experience not only of the data analyst, but also the expert in the problem domain. One way to support the analysis of the data, models and patterns, is through its visual representation, i.e., using the capabilities of human visual perception, which can detect patterns easily in any context. Under this approach, the visualization has been used in data mining, mostly in exploratory data analysis (input) and the presentation of the patterns (output), leaving limited this paradigm for analyzing models. This document describes the development of the doctoral thesis entitled "New Visualizations Schemes to Improve Understandability of Data-Mining Models". This research aims to provide a visualization approach to support understanding of data mining models for this proposed metaphor visually enhanced models.

Aplicación de Process Mining para modelos de comportamiento en entornos de Ambient Intelligence

Esta memoria es el resultado de un proyecto cuyo objetivo ha sido realizar un análisis de la posible aplicación de técnicas relativas al Process Mining para entornos AmI (Ambient Intelligence). Dicho análisis tiene la facultad de presentar de forma clara los resultados extraídos de los procesos relativos a un caso de uso planteado, así como de aplicar dichos resultados a aplicaciones relativas a entornos AmI, como automatización de tareas o simulación social basada en agentes. Para que dicho análisis sea comprensible por el lector, se presentan detalladas explicaciones de los conceptos tratados y las técnicas empleadas. Además, se analizan exhaustivamente las dos herramientas software más utilizadas en cuanto a minería de procesos se refiere, ProM y Disco, presentando ventajas e inconvenientes de cada una, así como una comparación entre las dos. Posteriormente se ha desarrollado una metodología para el análisis de procesos con la herramienta ProM, anteriormente mencionada, explicando cuidadosamente cada uno de los pasos así como los fundamentos de los algoritmos utilizados. Por último, se han presentado las conclusiones extraídas del trabajo, así como las posibles líneas de continuación del proyecto.

Estudio de las posibilidades de aplicación de los conceptos de urban mining y landfill mining

Como la demanda de la sociedad de metal aumenta, la tasa de extracción de minerales hace lo mismo. Esto contribuye al aumento de las implicaciones ambientales en forma de emisiones y el agotamiento de los recursos naturales. El reciclaje es una fuente importante para satisfacer la demanda de metales; como mucho un 30% de la demanda de metal está cubierto por el reciclaje en algunos mercados. Otra forma de reciclaje es la práctica de Urban Mining. El presente trabajo estudia la potencialidad del Landfill Mining en los vertederos españoles. Este concepto denomina el proceso de recuperación de materiales residuales depositados en vertederos para su uso posterior como materiales secundarios y, cuando ello no es posible, para su reaprovechamiento energético. Como consecuencia esto implica el cumplimiento de un segundo objetivo: la reducción o mitigación de las emisiones de gases de efecto invernadero derivadas de la presencia de residuos en vertederos.

Antisense oligonucleotides against α1E reduce R-type calcium currents in cerebellar granule cells

Many neurons of the central nervous system display multiple high voltage-activated Ca2+ currents, pharmacologically classified as L-, N-, P-, Q-, and R-type. Of these current types, the R-type is the least understood. The leading candidate for the molecular correlate of R-type currents in cerebellar granule cells is the α1E subunit, which yields Ca2+ currents very similar to the R-type when expressed in heterologous systems. As a complementary approach, we tested whether antisense oligonucleotides against α1E could decrease the expression of R-type current in rat cerebellar granule neurons in culture. Cells were supplemented with either antisense or sense oligonucleotides and whole-cell patch clamp recordings were obtained after 6–8 days in vitro. Incubation with α1E antisense oligonucleotide caused a 52.5% decrease in the peak R-type current density, from −10 ± 0.6 picoamperes/picofarad (pA/pF) (n = 6) in the untreated controls to −4.8 ± 0.8 pA/pF (n = 11) (P < 0.01). In contrast, no significant changes in the current expression were seen in sense oligonucleotide-treated cells (−11.3 ± 3.2 pA/pF). The specificity of the α1E antisense oligonucleotides was supported by the lack of change in estimates of the P/Q current amplitude. Furthermore, antisense and sense oligonucleotides against α1A did not affect R-type current expression (−11.5 ± 1.7 and −11.7 ± 1.7 pA/pF, respectively), whereas the α1A antisense oligonucleotide significantly reduced whole cell currents under conditions in which P/Q current is dominant. Our results support the hypothesis that members of the E class of α1 subunits support the high voltage-activated R-type current in cerebellar granule cells.

Directing gene expression to cerebellar granule cells using γ-aminobutyric acid type A receptor α6 subunit transgenes

Expression of the γ-aminobutyric acid type A receptor α6 subunit gene is restricted to differentiated granule cells of the cerebellum and cochlear nucleus. The mechanisms underlying this limited expression are unknown. Here we have characterized the expression of a series of α6-based transgenes in adult mouse brain. A DNA fragment containing a 1-kb portion upstream of the start site(s), together with exons 1–8, can direct high-level cerebellar granule cell-specific reporter gene expression. Thus powerful granule cell-specific determinants reside within the 5′ half of the α6 subunit gene body. This intron-containing transgene appears to lack the cochlear nucleus regulatory elements. It therefore provides a cassette to deliver gene products solely to adult cerebellar granule cells.

Activation of a caspase 3-related cysteine protease is required for glutamate-mediated apoptosis of cultured cerebellar granule neurons

Neurotoxicity induced by overstimulation of N-methyl-d-aspartate (NMDA) receptors is due, in part, to a sustained rise in intracellular Ca2+; however, little is known about the ensuing intracellular events that ultimately result in cell death. Here we show that overstimulation of NMDA receptors by relatively low concentrations of glutamate induces apoptosis of cultured cerebellar granule neurons (CGNs) and that CGNs do not require new RNA or protein synthesis. Glutamate-induced apoptosis of CGNs is, however, associated with a concentration- and time-dependent activation of the interleukin 1β-converting enzyme (ICE)/CED-3-related protease, CPP32/Yama/apopain (now designated caspase 3). Further, the time course of caspase 3 activation after glutamate exposure of CGNs parallels the development of apoptosis. Moreover, glutamate-induced apoptosis of CGNs is almost completely blocked by the selective cell permeable tetrapeptide inhibitor of caspase 3, Ac-DEVD-CHO but not by the ICE (caspase 1) inhibitor, Ac-YVAD-CHO. Western blots of cytosolic extracts from glutamate-exposed CGNs reveal both cleavage of the caspase 3 substrate, poly(ADP-ribose) polymerase, as well as proteolytic processing of pro-caspase 3 to active subunits. Our data demonstrate that glutamate-induced apoptosis of CGNs is mediated by a posttranslational activation of the ICE/CED-3-related cysteine protease caspase 3.

Defective γ-aminobutyric acid type B receptor-activated inwardly rectifying K+ currents in cerebellar granule cells isolated from weaver and Girk2 null mutant mice

Stimulation of inhibitory neurotransmitter receptors, such as γ-aminobutyric acid type B (GABAB) receptors, activates G protein-gated inwardly rectifying K+ channels (GIRK) which, in turn, influence membrane excitability. Seizure activity has been reported in a Girk2 null mutant mouse lacking GIRK2 channels but showing normal cerebellar development as well as in the weaver mouse, which has mutated GIRK2 channels and shows abnormal development. To understand how the function of GIRK2 channels differs in these two mutant mice, we compared the G protein-activated inwardly rectifying K+ currents in cerebellar granule cells isolated from Girk2 null mutant and weaver mutant mice with those from wild-type mice. Activation of GABAB receptors in wild-type granule cells induced an inwardly rectifying K+ current, which was sensitive to pertussis toxin and inhibited by external Ba2+ ions. The amplitude of the GABAB receptor-activated current was severely attenuated in granule cells isolated from both weaver and Girk2 null mutant mice. By contrast, the G protein-gated inwardly rectifying current and possibly the agonist-independent basal current appeared to be less selective for K+ ions in weaver but not Girk2 null mutant granule cells. Our results support the hypothesis that a nonselective current leads to the weaver phenotype. The loss of GABAB receptor-activated GIRK current appears coincident with the absence of GIRK2 channel protein and the reduction of GIRK1 channel protein in the Girk2 null mutant mouse, suggesting that GABAB receptors couple to heteromultimers composed of GIRK1 and GIRK2 channel subunits.

Chemotactic Peptide-induced Changes of Intermediate Filament Organization in Neutrophils during Granule Secretion: Role of Cyclic Guanosine Monophosphate

In neutrophils activated to secrete with formyl-methionyl-leucyl-phenylalanine, intermediate filaments are phosphorylated transiently by cyclic guanosine monophosphate (cGMP)-dependent protein kinase (G-kinase). cGMP regulation of vimentin organization was investigated. During granule secretion, cGMP levels were elevated and intermediate filaments were transiently assembled at the pericortex to areas devoid of granules and microfilaments. Microtubule and microfilament inhibitors affected intermediate filament organization, granule secretion, and cGMP levels. Cytochalasin D and nocodazole caused intermediate filaments to assemble at the nucleus, rather than at the pericortex. cGMP levels were elevated in neutrophils by both inhibitors; however, with cytochalasin D, cGMP was elevated earlier and granule secretion was excessive. Nocodazole did not affect normal cGMP elevations, but specific granule secretion was delayed. LY83583, a guanylyl cyclase antagonist, inhibited granule secretion and intermediate filament organization, but not microtubule or microfilament organization. Intermediate filament assembly at the pericortex and secretion were partially restored by 8-bromo-cGMP in LY83583-treated neutrophils, suggesting that cGMP regulates these functions. G-kinase directly induced intermediate filament assembly in situ, and protein phosphatase 1 disassembled filaments. However, in intact cells stimulated with formyl-methionyl-leucyl-phenylalanine, intermediate filament assembly is focal and transient, suggesting that vimentin phosphorylation is compartmentalized. We propose that, in addition to changes in microfilament and microtubule organization, granule secretion is also accompanied by changes in intermediate filament organization, and that cGMP regulates vimentin filament organization via activation of G-kinase.

Distinct Molecular Events during Secretory Granule Biogenesis Revealed by Sensitivities to Brefeldin A

The biogenesis of peptide hormone secretory granules involves a series of sorting, modification, and trafficking steps that initiate in the trans-Golgi and trans-Golgi network (TGN). To investigate their temporal order and interrelationships, we have developed a pulse–chase protocol that follows the synthesis and packaging of a sulfated hormone, pro-opiomelanocortin (POMC). In AtT-20 cells, sulfate is incorporated into POMC predominantly on N-linked endoglycosidase H-resistant oligosaccharides. Subcellular fractionation and pharmacological studies confirm that this sulfation occurs at the trans-Golgi/TGN. Subsequent to sulfation, POMC undergoes a number of molecular events before final storage in dense-core granules. The first step involves the transfer of POMC from the sulfation compartment to a processing compartment (immature secretory granules, ISGs): Inhibiting export of pulse-labeled POMC by brefeldin A (BFA) or a 20°C block prevents its proteolytic conversion to mature adrenocorticotropic hormone. Proteolytic cleavage products were found in vesicular fractions corresponding to ISGs, suggesting that the processing machinery is not appreciably activated until POMC exits the sulfation compartment. A large portion of the labeled hormone is secreted from ISGs as incompletely processed intermediates. This unregulated secretory process occurs only during a limited time window: Granules that have matured for 2 to 3 h exhibit very little unregulated release, as evidenced by the efficient storage of the 15-kDa N-terminal fragment that is generated by a relatively late cleavage event within the maturing granule. The second step of granule biogenesis thus involves two maturation events: proteolytic activation of POMC in ISGs and a transition of the organelle from a state of high unregulated release to one that favors intracellular storage. By using BFA, we show that the two processes occurring in ISGs may be uncoupled: although the unregulated secretion from ISGs is impaired by BFA, proteolytic processing of POMC within this organelle proceeds unaffected. The finding that BFA impairs constitutive secretion from both the TGN and ISGs also suggests that these secretory processes may be related in mechanism. Finally, our data indicate that the unusually high levels of unregulated secretion often associated with endocrine tumors may result, at least in part, from inefficient storage of secretory products at the level of ISGs.