Effect of local anaesthesia on short- and long-term pain induced by two bloodless castration methods in calves

Behavioural and cortisol responses of calves were used as indicators of pain to assess short- and long-term effects of bloodless castration methods with and without local anaesthesia. Seventy calves, aged 21-28 days, were control handled (20) or castrated using the Burdizzo (25) or rubber ring technique (25). Either 10 mL lidocaine or NaCl were distributed in both spermatic cords and the scrotal neck. The plasma cortisol response was recorded for 72 h, and behavioural and clinical traits monitored over a three month period. Local anaesthesia reduced the level of indicators of acute pain after both the Burdizzo and rubber ring techniques. It did not, however, result in a totally painless castration. As there was evidence of chronic pain lasting for several weeks after rubber ring castration, the Burdizzo method is judged to be preferable to the rubber ring technique.

NON-CHROMATOGRAPHIC PURIFICATION OF SYNTHETIC BIO-OLIGOMERS

Synthetic oligonucleotides and peptides have found wide applications in industry and academic research labs. There are ~60 peptide drugs on the market and over 500 under development. The global annual sale of peptide drugs in 2010 was estimated to be $13 billion. There are three oligonucleotide-based drugs on market; among them, the FDA newly approved Kynamro was predicted to have a $100 million annual sale. The annual sale of oligonucleotides to academic labs was estimated to be $700 million. Both bio-oligomers are mostly synthesized on automated synthesizers using solid phase synthesis technology, in which nucleoside or amino acid monomers are added sequentially until the desired full-length sequence is reached. The additions cannot be complete, which generates truncated undesired failure sequences. For almost all applications, these impurities must be removed. The most widely used method is HPLC. However, the method is slow, expensive, labor-intensive, not amendable for automation, difficult to scale up, and unsuitable for high throughput purification. It needs large capital investment, and consumes large volumes of harmful solvents. The purification costs are estimated to be more than 50% of total production costs. Other methods for bio-oligomer purification also have drawbacks, and are less favored than HPLC for most applications. To overcome the problems of known biopolymer purification technologies, we have developed two non-chromatographic purification methods. They are (1) catching failure sequences by polymerization, and (2) catching full-length sequences by polymerization. In the first method, a polymerizable group is attached to the failure sequences of the bio-oligomers during automated synthesis; purification is achieved by simply polymerizing the failure sequences into an insoluble gel and extracting full-length sequences. In the second method, a polymerizable group is attached to the full-length sequences, which are then incorporated into a polymer; impurities are removed by washing, and pure product is cleaved from polymer. These methods do not need chromatography, and all drawbacks of HPLC no longer exist. Using them, purification is achieved by simple manipulations such as shaking and extraction. Therefore, they are suitable for large scale purification of oligonucleotide and peptide drugs, and also ideal for high throughput purification, which currently has a high demand for research projects involving total gene synthesis. The dissertation will present the details about the development of the techniques. Chapter 1 will make an introduction to oligodeoxynucleotides (ODNs), their synthesis and purification. Chapter 2 will describe the detailed studies of using the catching failure sequences by polymerization method to purify ODNs. Chapter 3 will describe the further optimization of the catching failure sequences by polymerization ODN purification technology to the level of practical use. Chapter 4 will present using the catching full-length sequence by polymerization method for ODN purification using acid-cleavable linker. Chapter 5 will make an introduction to peptides, their synthesis and purification. Chapter 6 will describe the studies using the catching full-length sequence by polymerization method for peptide purification.

Shifting cultivation stability and change: Contrasting pathways of land use and livelihood change in Laos

Rural areas in Laos are experiencing a rapid transformation from traditional rice-based shifting cultivation systems to more permanent and diversified market-oriented cultivation systems. The consequences of these changes for local livelihoods are not well known. This study analyzes the impact of shifting cultivation change on the livelihood of rural people in six villages in three districts of northern and central Laos. Focus group discussions and household interview questionnaires were employed for data collection. The study reveals that the shifting cultivation of rice is still important in these communities, but it is being intensified as cash crops are introduced. Changes in shifting cultivation during the past ten years vary greatly between the communities studied. In the northern study sites, it is decreasing in areas with rubber expansion and increasing in areas with maize expansion, while it is stable in the central site, where sugarcane is an important cash crop. The impacts of land use change on livelihoods are also diverse. Cash crop producers hold more agricultural land than non-cash crop producers, and rubber and sugarcane producers have fewer rice shortages than non-producers. In the future, livelihood improvements in the central study site may be replicated in the northern sites, but this depends to a large extent on the economic and agricultural settings into which cash crops and other development opportunities are introduced. Moreover, the expansion of cash crops appears to counteract Lao policies aimed at replacing shifting cultivation areas with forests.

The role of cytochromes P450 and peroxidases in the detoxification of sulphonated anthraquinones by rhubarb and common sorrel plants cultivated under hydroponic conditions

Sulphonated anthraquinones are precursors of many synthetic dyes and pigments, recalcitrant to biodegradation and thus not eliminated by classical wastewater treatments. In the development of a phytotreatment to remove sulphonated aromatic compounds from dye and textile industrial effluents, it has been shown that rhubarb (Rheum rabarbarum) and common sorrel (Rumex acetosa) are the most efficient plants. Both species, producing natural anthraquinones, not only accumulate, but also transform these xenobiotic chemicals. Even if the precise biochemical mechanisms involved in the detoxification of sulphonated anthraquinones are not yet understood, they probably have cross talks with secondary metabolism, redox processes and plant energy metabolism. The aim of the present study was to investigate the possible roles of cytochrome P450 monooxygenases and peroxidases in the detoxification of several sulphonated anthraquinones. Both plant species were cultivated in a greenhouse under hydroponic conditions, with or without sulphonated anthraquinones. Plants were harvested at different times and either microsomal or cytosolic fractions were prepared. The monooxygenase activity of cytochromes P450 toward several sulphonated anthraquinones was tested using a new method based on the fluorimetric detection of oxygen consumed during cytochromes P450-catalysed reactions. The activity of cytosolic peroxidases was measured by spectrophotometry, using guaiacol as a substrate. A significant activity of cytochromes P450 was detected in rhubarb leaves, while no (rhizome) or low (petioles and roots) activity was found in other parts of the plants. An induction of this enzyme was observed at the beginning of the exposition to sulphonated anthraquinones. The results also indicated that cytochromes P450 were able to accept as substrate the five sulphonated anthraquinones, with a higher activity toward AQ-2,6-SS (0.706 nkat/mg protein) and AQ-2-S (0.720 nkat/mg protein). An activity of the cytochromes P450 was also found in the leaves of common sorrel (1.212 nkat/mg protein (AQ-2,6-SS)), but no induction of the activity occurred after the exposition to the pollutant. The activity of peroxidases increased when rhubarb was cultivated in the presence of the five sulphonated anthraquinones (0.857 nkat/mg protein). Peroxidase activity was also detected in the leaves of the common sorrel (0.055 nkat/mg protein), but in this plant, no significant difference was found between plants cultivated with and without sulphonated anthraquinones. Results indicated that the activity of cytochromes P450 and peroxidases increased in rhubarb in the presence of sulphonated anthraquinones and were involved in their detoxification mechanisms. These results suggest the existence in rhubarb and common sorrel of specific mechanisms involved in the metabolism of sulphonated anthraquinones. Further investigation should be performed to find the next steps of this detoxification pathway. Besides these promising results for the phytotreatment of sulphonated anthraquinones, it will be of high interest to develop and test, at small scale, an experimental wastewater treatment system to determine its efficiency. On the other hand, these results reinforce the idea that natural biodiversity should be better studied to use the most appropriate species for the phytotreatment of a specific pollutant.

Metabolism of sulphonated anthraquinones in rhubarb, maize and celery: the role of cytochromes P450 and peroxidases

Sulphonated anthraquinones are precursors of many synthetic dyes and pigments, recalcitrant to biodegradation, and thus contaminating many industrial effluents and rivers. In the development of a phytotreatment to remove sulphonated aromatic compounds, rhubarb (Rheum rhaponticum), a plant producing natural anthraquinones, as well as maize (Zea mays) and celery (Apium graveolens), plants not producing anthraquinones, were tested for their ability to metabolise these xenobiotics. Plants were cultivated under hydroponic conditions, with or without sulphonated anthraquinones, and were harvested at different times. Either microsomal or cytosolic fractions were prepared. The monooxygenase activity of cytochromes P450 towards several sulphonated anthraquinones was tested using a new method based on the fluorimetric detection of oxygen consumed during cytochromes P450-catalysed reactions. The activity of cytosolic peroxidases was measured by spectrophotometry, using guaiacol as a substrate. Results indicated that the activity of cytochromes P450 and peroxidases significantly increased in rhubarb plants cultivated in the presence of sulphonated anthraquinones. A higher activity of cytochromes P450 was also detected in maize and celery exposed to the pollutants. In these two plants, a peroxidase activity was also detected, but without a clear difference between the control plants and the plants exposed to the organic contaminants. This research demonstrated the existence in rhubarb, maize and celery of biochemical mechanisms involved in the metabolism and detoxification of sulphonated anthraquinones. Taken together, results confirmed that rhubarb might be the most appropriate plant for the phytotreatment of these organic pollutants.

Synthesis and testing of photoaffinity probes for the site-selectivechemical modifications of the 5-HT3 receptor

The 5-HT3 receptor (5-HT3R) is an important ion channel responsible for the transmission of nerve impulses in the central nervous system.1 It is difficult to characterize transmembrane dynamic receptors with classical structural biology approaches like crystallization and x-ray. The use of photoaffinity probes is an alternative approach to identify regions in the protein that are important for the binding of small molecules. Therefore we synthesized a small library of photoaffinity probes by conjugating photophores via various linkers to granisetron which is a known antagonist of the 5-HT3R. We were able to obtain several compounds with diverse linker lengths and different photolabile moieties that show nanomolar binding affinities for the orthosteric binding site. Furthermore we established a stable h5-HT3R expressing cell line and a purification protocol to yield the receptor in a high purity. Currently we are investigating the photo crosslinking of these ligands with the 5-HT3R.

Landfast sea-ice and platelet-layer thickness and conductivity of Atka Bay, Antarctica, December 2012

Ice shelves strongly impact coastal Antarctic sea-ice and the associated ecosystem through the formation of a sub-sea-ice platelet layer. Although progress has been made in determining and understanding its spatio-temporal variability based on point measurements, an investigation of this phenomenon on a larger scale remains a challenge due to logistical constraints and a lack of suitable methodology. In this study, we applied a laterally-constrained Marquardt-Levenberg inversion to a unique multi-frequency electromagnetic (EM) induction sounding dataset obtained on the landfast sea ice of Atka Bay, eastern Weddell Sea, in 2012. In addition to consistent fast-ice thickness and -conductivities along > 100 km transects; we present the first comprehensive, high resolution platelet-layer thickness and -conductivity dataset recorded on Antarctic sea ice. The reliability of the algorithm was confirmed by using synthetic data, and the inverted platelet-layer thicknesses agreed within the data uncertainty to drill-hole measurements. Ice-volume fractions were calculated from platelet-layer conductivities, revealing that an older and thicker platelet layer is denser and more compacted than a loosely attached, young platelet layer. The overall platelet-layer volume below Atka Bay fast ice suggests that the contribution of ocean/ice-shelf interaction to sea-ice volume in this region is even higher than previously thought. This study also implies that multi-frequency EM induction sounding is an effective approach in determining platelet layer volume on a larger scale than previously feasible. When applied to airborne multi-frequency EM, this method could provide a step towards an Antarctic-wide quantification of ocean/ice-shelf interaction.

Semi-supervised subspace clustering and applications to neuroscience

Machine learning techniques are used for extracting valuable knowledge from data. Nowa¬days, these techniques are becoming even more important due to the evolution in data ac¬quisition and storage, which is leading to data with different characteristics that must be exploited. Therefore, advances in data collection must be accompanied with advances in machine learning techniques to solve new challenges that might arise, on both academic and real applications. There are several machine learning techniques depending on both data characteristics and purpose. Unsupervised classification or clustering is one of the most known techniques when data lack of supervision (unlabeled data) and the aim is to discover data groups (clusters) according to their similarity. On the other hand, supervised classification needs data with supervision (labeled data) and its aim is to make predictions about labels of new data. The presence of data labels is a very important characteristic that guides not only the learning task but also other related tasks such as validation. When only some of the available data are labeled whereas the others remain unlabeled (partially labeled data), neither clustering nor supervised classification can be used. This scenario, which is becoming common nowadays because of labeling process ignorance or cost, is tackled with semi-supervised learning techniques. This thesis focuses on the branch of semi-supervised learning closest to clustering, i.e., to discover clusters using available labels as support to guide and improve the clustering process. Another important data characteristic, different from the presence of data labels, is the relevance or not of data features. Data are characterized by features, but it is possible that not all of them are relevant, or equally relevant, for the learning process. A recent clustering tendency, related to data relevance and called subspace clustering, claims that different clusters might be described by different feature subsets. This differs from traditional solutions to data relevance problem, where a single feature subset (usually the complete set of original features) is found and used to perform the clustering process. The proximity of this work to clustering leads to the first goal of this thesis. As commented above, clustering validation is a difficult task due to the absence of data labels. Although there are many indices that can be used to assess the quality of clustering solutions, these validations depend on clustering algorithms and data characteristics. Hence, in the first goal three known clustering algorithms are used to cluster data with outliers and noise, to critically study how some of the most known validation indices behave. The main goal of this work is however to combine semi-supervised clustering with subspace clustering to obtain clustering solutions that can be correctly validated by using either known indices or expert opinions. Two different algorithms are proposed from different points of view to discover clusters characterized by different subspaces. For the first algorithm, available data labels are used for searching for subspaces firstly, before searching for clusters. This algorithm assigns each instance to only one cluster (hard clustering) and is based on mapping known labels to subspaces using supervised classification techniques. Subspaces are then used to find clusters using traditional clustering techniques. The second algorithm uses available data labels to search for subspaces and clusters at the same time in an iterative process. This algorithm assigns each instance to each cluster based on a membership probability (soft clustering) and is based on integrating known labels and the search for subspaces into a model-based clustering approach. The different proposals are tested using different real and synthetic databases, and comparisons to other methods are also included when appropriate. Finally, as an example of real and current application, different machine learning tech¬niques, including one of the proposals of this work (the most sophisticated one) are applied to a task of one of the most challenging biological problems nowadays, the human brain model¬ing. Specifically, expert neuroscientists do not agree with a neuron classification for the brain cortex, which makes impossible not only any modeling attempt but also the day-to-day work without a common way to name neurons. Therefore, machine learning techniques may help to get an accepted solution to this problem, which can be an important milestone for future research in neuroscience. Resumen Las técnicas de aprendizaje automático se usan para extraer información valiosa de datos. Hoy en día, la importancia de estas técnicas está siendo incluso mayor, debido a que la evolución en la adquisición y almacenamiento de datos está llevando a datos con diferentes características que deben ser explotadas. Por lo tanto, los avances en la recolección de datos deben ir ligados a avances en las técnicas de aprendizaje automático para resolver nuevos retos que pueden aparecer, tanto en aplicaciones académicas como reales. Existen varias técnicas de aprendizaje automático dependiendo de las características de los datos y del propósito. La clasificación no supervisada o clustering es una de las técnicas más conocidas cuando los datos carecen de supervisión (datos sin etiqueta), siendo el objetivo descubrir nuevos grupos (agrupaciones) dependiendo de la similitud de los datos. Por otra parte, la clasificación supervisada necesita datos con supervisión (datos etiquetados) y su objetivo es realizar predicciones sobre las etiquetas de nuevos datos. La presencia de las etiquetas es una característica muy importante que guía no solo el aprendizaje sino también otras tareas relacionadas como la validación. Cuando solo algunos de los datos disponibles están etiquetados, mientras que el resto permanece sin etiqueta (datos parcialmente etiquetados), ni el clustering ni la clasificación supervisada se pueden utilizar. Este escenario, que está llegando a ser común hoy en día debido a la ignorancia o el coste del proceso de etiquetado, es abordado utilizando técnicas de aprendizaje semi-supervisadas. Esta tesis trata la rama del aprendizaje semi-supervisado más cercana al clustering, es decir, descubrir agrupaciones utilizando las etiquetas disponibles como apoyo para guiar y mejorar el proceso de clustering. Otra característica importante de los datos, distinta de la presencia de etiquetas, es la relevancia o no de los atributos de los datos. Los datos se caracterizan por atributos, pero es posible que no todos ellos sean relevantes, o igualmente relevantes, para el proceso de aprendizaje. Una tendencia reciente en clustering, relacionada con la relevancia de los datos y llamada clustering en subespacios, afirma que agrupaciones diferentes pueden estar descritas por subconjuntos de atributos diferentes. Esto difiere de las soluciones tradicionales para el problema de la relevancia de los datos, en las que se busca un único subconjunto de atributos (normalmente el conjunto original de atributos) y se utiliza para realizar el proceso de clustering. La cercanía de este trabajo con el clustering lleva al primer objetivo de la tesis. Como se ha comentado previamente, la validación en clustering es una tarea difícil debido a la ausencia de etiquetas. Aunque existen muchos índices que pueden usarse para evaluar la calidad de las soluciones de clustering, estas validaciones dependen de los algoritmos de clustering utilizados y de las características de los datos. Por lo tanto, en el primer objetivo tres conocidos algoritmos se usan para agrupar datos con valores atípicos y ruido para estudiar de forma crítica cómo se comportan algunos de los índices de validación más conocidos. El objetivo principal de este trabajo sin embargo es combinar clustering semi-supervisado con clustering en subespacios para obtener soluciones de clustering que puedan ser validadas de forma correcta utilizando índices conocidos u opiniones expertas. Se proponen dos algoritmos desde dos puntos de vista diferentes para descubrir agrupaciones caracterizadas por diferentes subespacios. Para el primer algoritmo, las etiquetas disponibles se usan para bus¬car en primer lugar los subespacios antes de buscar las agrupaciones. Este algoritmo asigna cada instancia a un único cluster (hard clustering) y se basa en mapear las etiquetas cono-cidas a subespacios utilizando técnicas de clasificación supervisada. El segundo algoritmo utiliza las etiquetas disponibles para buscar de forma simultánea los subespacios y las agru¬paciones en un proceso iterativo. Este algoritmo asigna cada instancia a cada cluster con una probabilidad de pertenencia (soft clustering) y se basa en integrar las etiquetas conocidas y la búsqueda en subespacios dentro de clustering basado en modelos. Las propuestas son probadas utilizando diferentes bases de datos reales y sintéticas, incluyendo comparaciones con otros métodos cuando resulten apropiadas. Finalmente, a modo de ejemplo de una aplicación real y actual, se aplican diferentes técnicas de aprendizaje automático, incluyendo una de las propuestas de este trabajo (la más sofisticada) a una tarea de uno de los problemas biológicos más desafiantes hoy en día, el modelado del cerebro humano. Específicamente, expertos neurocientíficos no se ponen de acuerdo en una clasificación de neuronas para la corteza cerebral, lo que imposibilita no sólo cualquier intento de modelado sino también el trabajo del día a día al no tener una forma estándar de llamar a las neuronas. Por lo tanto, las técnicas de aprendizaje automático pueden ayudar a conseguir una solución aceptada para este problema, lo cual puede ser un importante hito para investigaciones futuras en neurociencia.

AUTOFLY-Aid: Flight Deck Automation Support with Dynamic 4D Trajectory Management for Responsive and Adaptive Airborne Collision Avoidance

AUTOFLY-Aid Project aims to develop and demonstrate novel automation support algorithms and tools to the flight crew for flight critical collision avoidance using “dynamic 4D trajectory management”. The automation support system is envisioned to improve the primary shortcomings of TCAS, and to aid the pilot through add-on avionics/head-up displays and reality augmentation devices in dynamically evolving collision avoidance scenarios. The main theoretical innovative and novel concepts to be developed by AUTOFLY-Aid project are a) design and development of the mathematical models of the full composite airspace picture from the flight deck’s perspective, as seen/measured/informed by the aircraft flying in SESAR 2020, b) design and development of a dynamic trajectory planning algorithm that can generate at real-time (on the order of seconds) flyable (i.e. dynamically and performance-wise feasible) alternative trajectories across the evolving stochastic composite airspace picture (which includes new conflicts, blunder risks, terrain and weather limitations) and c) development and testing of the Collision Avoidance Automation Support System on a Boeing 737 NG FNPT II Flight Simulator with synthetic vision and reality augmentation while providing the flight crew with quantified and visual understanding of collision risks in terms of time and directions and countermeasures.

The potential of organic fertilizers and water management to reduce N2O emissions in Mediterranean climate cropping systems. A review.

Environmental problems related to the use of synthetic fertilizers and to organic waste management have led to increased interest in the use of organic materials as an alternative source of nutrients for crops, but this is also associated with N2O emissions. There has been an increasing amount of research into the effects of using different types of fertilization on N2O emissions under Mediterranean climatic conditions, but the findings have sometimes been rather contradictory. Available information also suggests that water management could exert a high influence on N2O emissions. In this context, we have reviewed the current scientific knowledge, including an analysis of the effect of fertilizer type and water management on direct N2O emissions. A meta-analysis of compliant reviewed experiments revealed significantly lower N2O emissions for organic as opposed to synthetic fertilizers (23% reduction). When organic materials were segregated in solid and liquid, only solid organic fertilizer emissions were significantly lower than those of synthetic fertilizers (28% reduction in cumulative emissions). The EF is similar to the IPCC factor in conventionally irrigated systems (0.98% N2O-N N applied−1), but one order of magnitude lower in rainfed systems (0.08%). Drip irrigation produces intermediate emission levels (0.66%). Differences are driven by Mediterranean agro-climatic characteristics, which include low soil organic matter (SOM) content and a distinctive rainfall and temperature pattern. Interactions between environmental and management factors and the microbial processes involved in N2O emissions are discussed in detail. Indirect emissions have not been fully accounted for, but when organic fertilizers are applied at similar N rates to synthetic fertilizers, they generally make smaller contributions to the leached NO3− pool. The most promising practices for reducing N2O through organic fertilization include: (i) minimizing water applications; (ii) minimizing bare soil; (iii) improving waste management; and (iv) tightening N cycling through N immobilization. The mitigation potential may be limited by: (i) residual effect; (ii) the long-term effects of fertilizers on SOM; (iii) lower yield-scaled performance; and (iv) total N availability from organic sources. Knowledge gaps identified in the review included: (i) insufficient sampling periods; (ii) high background emissions; (iii) the need to provide N2O EF and yield-scaled EF; (iv) the need for more research on specific cropping systems; and (v) the need for full GHG balances. In conclusion, the available information suggests a potential of organic fertilizers and water-saving practices to mitigate N2O emissions under Mediterranean climatic conditions, although further research is needed before it can be regarded as fully proven, understood and developed.

Integrated studies for the evaluation of conservation treatments on stone material from archaeological sites. Application to the case of Mérida (Spain)

In recent decades archaeological sites have been subject of many interventions. The application of conservation treatments, such us consolidation and protection ones by means of using, for instance, synthetic resins or organosilicic compounds, has been demonstrated inadequate in many cases, and even harmful for the heritage materials [1]. Evaluation studies should be a mandatory task, ideally before and after the intervention, but both tasks are complex and unusual in the case of archaeological heritage. Moreover, there is a general lack of knowledge in the mid and long term effects of these treatments, and how to act when these have resulted in deterioration of the original material. Remains of Roman Augusta Emerita, located in Merida (Spain), have gone through many interventions since the first archaeological campaign, in 1910. Some of them have demonstrated already to be harmful [2], others, more recent, must be evaluated in order to determine its effectiveness and durability, considering that many of these treatments are currently still applied. For this purpose a range of parameters has been measured such as color, surface hardness and roughness, mechanical or hydric properties, porosity, etc. on the original material (granite, marble and mortars mainly), and then the transformations of those same parameters analyzed after treatment, both in situ, in places where a intervention is documented, and in the laboratory, in samples. The study is being conducted both in the laboratory (Petrophysics Laboratory within IGEO) and in situ, on selected archaeological sites of Mérida (Theater and House of Mitreo). The comparison of results in untreated and treated areas of the site, and in treated-untreated samples, allows the distinction of variables that affect the interaction between products and stone material, issues such us effectiveness and durability of treatment and its validation or dismissal.

Formalisation and experiences of R2RML-based SPARQL to SQL query translation using Morph

R2RML is used to specify transformations of data available in relational databases into materialised or virtual RDF datasets. SPARQL queries evaluated against virtual datasets are translated into SQL queries according to the R2RML mappings, so that they can be evaluated over the underlying relational database engines. In this paper we describe an extension of a well-known algorithm for SPARQL to SQL translation, originally formalised for RDBMS-backed triple stores, that takes into account R2RML mappings. We present the result of our implementation using queries from a synthetic benchmark and from three real use cases, and show that SPARQL queries can be in general evaluated as fast as the SQL queries that would have been generated by SQL experts if no R2RML mappings had been used.

Methods and Techniques for the Generation and Eficient Exploitation of RDB2RDF Mappings

RDB to RDF Mapping Language (R2RML) es una recomendación del W3C que permite especificar reglas para transformar bases de datos relacionales a RDF. Estos datos en RDF se pueden materializar y almacenar en un sistema gestor de tripletas RDF (normalmente conocidos con el nombre triple store), en el cual se pueden evaluar consultas SPARQL. Sin embargo, hay casos en los cuales la materialización no es adecuada o posible, por ejemplo, cuando la base de datos se actualiza frecuentemente. En estos casos, lo mejor es considerar los datos en RDF como datos virtuales, de tal manera que las consultas SPARQL anteriormente mencionadas se traduzcan a consultas SQL que se pueden evaluar sobre los sistemas gestores de bases de datos relacionales (SGBD) originales. Para esta traducción se tienen en cuenta los mapeos R2RML. La primera parte de esta tesis se centra en la traducción de consultas. Se propone una formalización de la traducción de SPARQL a SQL utilizando mapeos R2RML. Además se proponen varias técnicas de optimización para generar consultas SQL que son más eficientes cuando son evaluadas en sistemas gestores de bases de datos relacionales. Este enfoque se evalúa mediante un benchmark sintético y varios casos reales. Otra recomendación relacionada con R2RML es la conocida como Direct Mapping (DM), que establece reglas fijas para la transformación de datos relacionales a RDF. A pesar de que ambas recomendaciones se publicaron al mismo tiempo, en septiembre de 2012, todavía no se ha realizado un estudio formal sobre la relación entre ellas. Por tanto, la segunda parte de esta tesis se centra en el estudio de la relación entre R2RML y DM. Se divide este estudio en dos partes: de R2RML a DM, y de DM a R2RML. En el primer caso, se estudia un fragmento de R2RML que tiene la misma expresividad que DM. En el segundo caso, se representan las reglas de DM como mapeos R2RML, y también se añade la semántica implícita (relaciones de subclase, 1-N y M-N) que se puede encontrar codificada en la base de datos. Esta tesis muestra que es posible usar R2RML en casos reales, sin necesidad de realizar materializaciones de los datos, puesto que las consultas SQL generadas son suficientemente eficientes cuando son evaluadas en el sistema gestor de base de datos relacional. Asimismo, esta tesis profundiza en el entendimiento de la relación existente entre las dos recomendaciones del W3C, algo que no había sido estudiado con anterioridad. ABSTRACT. RDB to RDF Mapping Language (R2RML) is a W3C recommendation that allows specifying rules for transforming relational databases into RDF. This RDF data can be materialized and stored in a triple store, so that SPARQL queries can be evaluated by the triple store. However, there are several cases where materialization is not adequate or possible, for example, if the underlying relational database is updated frequently. In those cases, RDF data is better kept virtual, and hence SPARQL queries over it have to be translated into SQL queries to the underlying relational database system considering that the translation process has to take into account the specified R2RML mappings. The first part of this thesis focuses on query translation. We discuss the formalization of the translation from SPARQL to SQL queries that takes into account R2RML mappings. Furthermore, we propose several optimization techniques so that the translation procedure generates SQL queries that can be evaluated more efficiently over the underlying databases. We evaluate our approach using a synthetic benchmark and several real cases, and show positive results that we obtained. Direct Mapping (DM) is another W3C recommendation for the generation of RDF data from relational databases. While R2RML allows users to specify their own transformation rules, DM establishes fixed transformation rules. Although both recommendations were published at the same time, September 2012, there has not been any study regarding the relationship between them. The second part of this thesis focuses on the study of the relationship between R2RML and DM. We divide this study into two directions: from R2RML to DM, and from DM to R2RML. From R2RML to DM, we study a fragment of R2RML having the same expressive power than DM. From DM to R2RML, we represent DM transformation rules as R2RML mappings, and also add the implicit semantics encoded in databases, such as subclass, 1-N and N-N relationships. This thesis shows that by formalizing and optimizing R2RML-based SPARQL to SQL query translation, it is possible to use R2RML engines in real cases as the resulting SQL is efficient enough to be evaluated by the underlying relational databases. In addition to that, this thesis facilitates the understanding of bidirectional relationship between the two W3C recommendations, something that had not been studied before.

De novo assembly and annotation of a transcriptome during xylogenesis in Pinus canariensis Chr. Sm. ex DC

Nowadays, there is a great amount of genomic and transcriptomic data available about forest species, including ambitious projects looking for complete sequencing and annotation of different gymnosperm genomes [1, 2]. Pinus canariensis is an endemic conifer of the Canary Islands with re-sprouting capability and resilience against fire and mechanical damage, as result of an adaptation to volcanic environments. Additionally, this species has a high proportion of axial parenchyma compared with other conifers, and this tissue connects with radial parenchyma allowing transport of reserves. The most internal tracheids stop accumulating water [3], and get filled of resins and polyphenols synthesized by the axial parenchyma; this is the so-called ?torch-heartwood? [4], which avoids decay. This wood achieves very high prices due to its particular resistance to rot. These features make P. canariensis an interesting model species for the analysis of these developmental processes in conifers. In this study we aim to perform a complete transcriptome annotation during xylogenesis in Pinus canariensis, using next-generation sequencing (NGS) -Roche 454 pyrosequencing-, in order to provide a genomic resource for further analysis, including expression profiling and the identification of candidate genes for important adaptive features.

DRC1, DNA replication and checkpoint protein 1, functions with DPB11 to control DNA replication and the S-phase checkpoint in Saccharomyces cerevisiae

In addition to DNA polymerase complexes, DNA replication requires the coordinate action of a series of proteins, including regulators Cdc28/Clb and Dbf4/Cdc7 kinases, Orcs, Mcms, Cdc6, Cdc45, and Dpb11. Of these, Dpb11, an essential BRCT repeat protein, has remained particularly enigmatic. The Schizosaccharomyces pombe homolog of DPB11, cut5, has been implicated in the DNA replication checkpoint as has the POL2 gene with which DPB11 genetically interacts. Here we describe a gene, DRC1, isolated as a dosage suppressor of dpb11–1. DRC1 is an essential cell cycle-regulated gene required for DNA replication. We show that both Dpb11 and Drc1 are required for the S-phase checkpoint, including the proper activation of the Rad53 kinase in response to DNA damage and replication blocks. Dpb11 is the second BRCT-repeat protein shown to control Rad53 function, possibly indicating a general function for this class of proteins. DRC1 and DPB11 show synthetic lethality and reciprocal dosage suppression. The Drc1 and Dpb11 proteins physically associate and function together to coordinate DNA replication and the cell cycle.