qPCR counts of mRNA for hepatic reference gene selection in adult female Atlantic salmon from perturbation experiments under artificial temperature conditions

The dataset contains raw data (quantification cycle) for a study which determined the most suitable hepatic reference genes for normalisation of qPCR data orginating from adult (entire reproductive season) Atlantic salmon (14 days) exposed to 14 and 22 degrees C. These results will be useful for anyone wanting to study the effects of climate change/elevated temperature on reproductive physiology of fish (and perhaphs other vertebrates). In addition, a target gene (vitellogenin) has normalised using an inappropriate and an 'ideal' reference gene to demonstrate the consequences of using an unstable reference gene for normalisation. For the adult experiment, maiden and repeat adult females were held at the Salmon Enterprises of Tasmania (SALTAS) Wayatinah Hatchery (Tasmania, Australia) at ambient temperature and photoperiod in either 200 (maidens) or 50 (repeats) m3 circular tanks at stocking densities of 12-18, and 24-36 kg m-3 for maidens and repeats, respectively, until transfered to the experimental tanks.

Seawater carbonate chemistry and early development of the sea urchin Lytechinus variegatus in a laboratory experiment using artificial seawater

Land-based aquaculture facilities often utilize additional bicarbonate sources such as commercial sea salts that are designed to boost alkalinity in order to buffer seawater against reductions in pH. Despite these preventative measures, many facilities are likely to face occasional reductions in pH and corresponding reductions in carbonate saturation states due to the accumulation of metabolic waste products. We investigated the impact of reduced carbonate saturation states (Omega Ca, Omega Ar) on embryonic developmental rates, larval developmental rates, and echinoplutei skeletal morphometrics in the common edible sea urchin Lytechinus variegatus under high alkalinity conditions. Commercial artificial seawater was bubbled with a mixture of air and CO2 gas to reduce the carbonate saturation state. Rates of embryonic and larval development were significantly delayed in both the low and extreme low carbonate saturation state groups relative to the control at a given time. Although symmetry of overall skeletal body lengths was not affected, allometric relationships were significantly different between treatment groups. Larvae reared under ambient conditions had significantly greater postoral arm and overall body lengths relative to body lengths than larvae grown under extreme low carbonate saturation state conditions, indicating that extreme changes in the carbonate system affected not only developmental rates but also larval skeletal shape. Reduced rates of embryonic development and delayed and altered larval skeletal growth are likely to negatively impact larval culturing of L. variegatus in land-based, intensive culture situations where calcite and aragonite saturation states are lowered by the accumulation of metabolic waste products.

Counts and abundance of macrozoobenthic organisms settled in artificial soft sediment at Brandal, Kongsfjorden, Spitsbergen

In the Arctic the currently observed rising air temperature results in more frequent calving of icebergs. The latter are derived from tidewater glaciers. Arctic macrozoobenthic soft-sediment communities are considerably disturbed by direct hits and sediment reallocation caused by iceberg scouring. With the aim to describe the primary succession of macrozoobenthic communities following these events, scientific divers installed 28 terracotta containers in the soft-sediment off Brandal (Kongsfjorden, Svalbard, Norway) at 20 m water depth in 2002. The containers were filled with a bentonite-sand-mixture resembling the natural sediment. Samples were taken annually between 2003 and 2007. A shift from pioneering species (e.g. Cumacea: Lamprops fuscatus) towards more specialized taxa, as well as from surface-detritivores towards subsurface-detritivores was observed. This is typical for an ecological succession following the facilitation and inhibition succession model. Similarity between experimental and non-manipulated communities from 2003 was significantly highest after three years of succession. In the following years similarity decreased, probably due to elevated temperatures, which prevented the fjord-system from freezing. Some organisms numerically important in the non-manipulated community (e.g., the polychaete Dipolydora quadrilobata) did not colonies the substrate during the experiment. This suggests that the community had not fully matured within the first three years. Later, the settlement was probably impeded by consequences of warming temperatures. This demonstrates the long-lasting effects of severe disturbances on Arctic macrozoobenthic communities. Furthermore, environmental changes, such as rising temperatures coupled with enhanced food availability due to an increasing frequency of ice-free days per year, may have a stronger effect on succession than exposure time.

Rehabilitación de Molduras Huecas de Piedra Artificial

En las décadas de 1930, 1940 y 1950 se utilizó con cierta profusión en las fachadas españolas la solución de molduras horizontales resueltas con elementos huecos prefabricados de piedra artificial. Con el paso del tiempo, dichas molduras han sufrido procesos de desprendimiento debido a la entrada de agua por el tablero superior, con la consiguiente corrosión y rotura de los alambres de anclaje, por lo que requieren una reparación. Se describe un caso de rehabilitación mediante reanclado desde el exterior de las diferentes molduras de una fachada de un edificio singular en Madrid, con varillas roscadas de acero inoxidable y resina epoxi de adherencia, así como la introducción de juntas de dilatación con el objeto de reducir las variaciones dimensionales del conjunto.

Aplicación de técnicas de inteligencia artificial a la predicción de contaminantes atmosféricos

Abstract Air pollution is a big threat and a phenomenon that has a specific impact on human health, in addition, changes that occur in the chemical composition of the atmosphere can change the weather and cause acid rain or ozone destruction. Those are phenomena of global importance. The World Health Organization (WHO) considerates air pollution as one of the most important global priorities. Salamanca, Gto., Mexico has been ranked as one of the most polluted cities in this country. The industry of the area led to a major economic development and rapid population growth in the second half of the twentieth century. The impact in the air quality is important and significant efforts have been made to measure the concentrations of pollutants. The main pollution sources are locally based plants in the chemical and power generation sectors. The registered concerning pollutants are Sulphur Dioxide (SO2) and particles on the order of ∼10 micrometers or less (PM10). The prediction in the concentration of those pollutants can be a powerful tool in order to take preventive measures such as the reduction of emissions and alerting the affected population. In this PhD thesis we propose a model to predict concentrations of pollutants SO2 and PM10 for each monitoring booth in the Atmospheric Monitoring Network Salamanca (REDMAS - for its spanish acronym). The proposed models consider the use of meteorological variables as factors influencing the concentration of pollutants. The information used along this work is the current real data from REDMAS. In the proposed model, Artificial Neural Networks (ANN) combined with clustering algorithms are used. The type of ANN used is the Multilayer Perceptron with a hidden layer, using separate structures for the prediction of each pollutant. The meteorological variables used for prediction were: Wind Direction (WD), wind speed (WS), Temperature (T) and relative humidity (RH). Clustering algorithms, K-means and Fuzzy C-means, are used to find relationships between air pollutants and weather variables under consideration, which are added as input of the RNA. Those relationships provide information to the ANN in order to obtain the prediction of the pollutants. The results of the model proposed in this work are compared with the results of a multivariate linear regression and multilayer perceptron neural network. The evaluation of the prediction is calculated with the mean absolute error, the root mean square error, the correlation coefficient and the index of agreement. The results show the importance of meteorological variables in the prediction of the concentration of the pollutants SO2 and PM10 in the city of Salamanca, Gto., Mexico. The results show that the proposed model perform better than multivariate linear regression and multilayer perceptron neural network. The models implemented for each monitoring booth have the ability to make predictions of air quality that can be used in a system of real-time forecasting and human health impact analysis. Among the main results of the development of this thesis we can cite: A model based on artificial neural network combined with clustering algorithms for prediction with a hour ahead of the concentration of each pollutant (SO2 and PM10) is proposed. A different model was designed for each pollutant and for each of the three monitoring booths of the REDMAS. A model to predict the average of pollutant concentration in the next 24 hours of pollutants SO2 and PM10 is proposed, based on artificial neural network combined with clustering algorithms. Model was designed for each booth of the REDMAS and each pollutant separately. Resumen La contaminación atmosférica es una amenaza aguda, constituye un fenómeno que tiene particular incidencia sobre la salud del hombre. Los cambios que se producen en la composición química de la atmósfera pueden cambiar el clima, producir lluvia ácida o destruir el ozono, fenómenos todos ellos de una gran importancia global. La Organización Mundial de la Salud (OMS) considera la contaminación atmosférica como una de las más importantes prioridades mundiales. Salamanca, Gto., México; ha sido catalogada como una de las ciudades más contaminadas en este país. La industria de la zona propició un importante desarrollo económico y un crecimiento acelerado de la población en la segunda mitad del siglo XX. Las afectaciones en el aire son graves y se han hecho importantes esfuerzos por medir las concentraciones de los contaminantes. Las principales fuentes de contaminación son fuentes fijas como industrias químicas y de generación eléctrica. Los contaminantes que se han registrado como preocupantes son el Bióxido de Azufre (SO2) y las Partículas Menores a 10 micrómetros (PM10). La predicción de las concentraciones de estos contaminantes puede ser una potente herramienta que permita tomar medidas preventivas como reducción de emisiones a la atmósfera y alertar a la población afectada. En la presente tesis doctoral se propone un modelo de predicción de concentraci ón de los contaminantes más críticos SO2 y PM10 para cada caseta de monitorización de la Red de Monitorización Atmosférica de Salamanca (REDMAS). Los modelos propuestos plantean el uso de las variables meteorol ógicas como factores que influyen en la concentración de los contaminantes. La información utilizada durante el desarrollo de este trabajo corresponde a datos reales obtenidos de la REDMAS. En el Modelo Propuesto (MP) se aplican Redes Neuronales Artificiales (RNA) combinadas con algoritmos de agrupamiento. La RNA utilizada es el Perceptrón Multicapa con una capa oculta, utilizando estructuras independientes para la predicción de cada contaminante. Las variables meteorológicas disponibles para realizar la predicción fueron: Dirección de Viento (DV), Velocidad de Viento (VV), Temperatura (T) y Humedad Relativa (HR). Los algoritmos de agrupamiento K-means y Fuzzy C-means son utilizados para encontrar relaciones existentes entre los contaminantes atmosféricos en estudio y las variables meteorológicas. Dichas relaciones aportan información a las RNA para obtener la predicción de los contaminantes, la cual es agregada como entrada de las RNA. Los resultados del modelo propuesto en este trabajo son comparados con los resultados de una Regresión Lineal Multivariable (RLM) y un Perceptrón Multicapa (MLP). La evaluación de la predicción se realiza con el Error Medio Absoluto, la Raíz del Error Cuadrático Medio, el coeficiente de correlación y el índice de acuerdo. Los resultados obtenidos muestran la importancia de las variables meteorológicas en la predicción de la concentración de los contaminantes SO2 y PM10 en la ciudad de Salamanca, Gto., México. Los resultados muestran que el MP predice mejor la concentración de los contaminantes SO2 y PM10 que los modelos RLM y MLP. Los modelos implementados para cada caseta de monitorizaci ón tienen la capacidad para realizar predicciones de calidad del aire, estos modelos pueden ser implementados en un sistema que permita realizar la predicción en tiempo real y analizar el impacto en la salud de la población. Entre los principales resultados obtenidos del desarrollo de esta tesis podemos citar: Se propone un modelo basado en una red neuronal artificial combinado con algoritmos de agrupamiento para la predicción con una hora de anticipaci ón de la concentración de cada contaminante (SO2 y PM10). Se diseñó un modelo diferente para cada contaminante y para cada una de las tres casetas de monitorización de la REDMAS. Se propone un modelo de predicción del promedio de la concentración de las próximas 24 horas de los contaminantes SO2 y PM10, basado en una red neuronal artificial combinado con algoritmos de agrupamiento. Se diseñó un modelo para cada caseta de monitorización de la REDMAS y para cada contaminante por separado.

Artificial Neural Nets with Interaction of Afferents

The aim is to obtain computationally more powerful, neuro physiologically founded, artificial neurons and neural nets. Artificial Neural Nets (ANN) of the Perceptron type evolved from the original proposal by McCulloch an Pitts classical paper [1]. Essentially, they keep the computing structure of a linear machine followed by a non linear operation. The McCulloch-Pitts formal neuron (which was never considered by the author’s to be models of real neurons) consists of the simplest case of a linear computation of the inputs followed by a threshold. Networks of one layer cannot compute anylogical function of the inputs, but only those which are linearly separable. Thus, the simple exclusive OR (contrast detector) function of two inputs requires two layers of formal neurons