Patient-based Literature Retrieval and Integration: A Use Case for Diabetes and arterial Hypertension

Specialized search engines such as PubMed, MedScape or Cochrane have increased dramatically the visibility of biomedical scientific results. These web-based tools allow physicians to access scientific papers instantly. However, this decisive improvement had not a proportional impact in clinical practice due to the lack of advanced search methods. Even queries highly specified for a concrete pathology frequently retrieve too many information, with publications related to patients treated by the physician beyond the scope of the results examined. In this work we present a new method to improve scientific article search using patient information. Two pathologies have been used within the project to retrieve relevant literature to patient data and to be integrated with other sources. Promising results suggest the suitability of the approach, highlighting publications dealing with patient features and facilitating literature search to physicians.

Ensemble transcript interaction networks: A case study on Alzheimer's disease

Systems biology techniques are a topic of recent interest within the neurological field. Computational intelligence (CI) addresses this holistic perspective by means of consensus or ensemble techniques ultimately capable of uncovering new and relevant findings. In this paper, we propose the application of a CI approach based on ensemble Bayesian network classifiers and multivariate feature subset selection to induce probabilistic dependences that could match or unveil biological relationships. The research focuses on the analysis of high-throughput Alzheimer's disease (AD) transcript profiling. The analysis is conducted from two perspectives. First, we compare the expression profiles of hippocampus subregion entorhinal cortex (EC) samples of AD patients and controls. Second, we use the ensemble approach to study four types of samples: EC and dentate gyrus (DG) samples from both patients and controls. Results disclose transcript interaction networks with remarkable structures and genes not directly related to AD by previous studies. The ensemble is able to identify a variety of transcripts that play key roles in other neurological pathologies. Classical statistical assessment by means of non-parametric tests confirms the relevance of the majority of the transcripts. The ensemble approach pinpoints key metabolic mechanisms that could lead to new findings in the pathogenesis and development of AD

Container Terminal Simulation. A case study: port of Valencia.

This paper is concerned with the study of non-Markovian queuing systems in container terminals. The methodology presented has been applied to analyze the ship traffic in the port of Valencia located in the Western Mediterranean. Two container terminals have been studied: the public container terminal of NOATUM and the dedicated container terminal of MSC. This paper contains the results of a simulation model based on queuing theory. The methodology presented is found to be effective in replicating realistic ship traffic operations in port as well as in conducting capacity evaluations. Thus the methodology can be used for capacity planning (long term), tactical planning (medium term) and even for the container terminal design (port enlargement purposes).

Modelling seed germination in forest tree species through survival analysis. The Pinus pinea L. case study

The direct application of existing models for seed germination may often be inadequate in the context of ecology and forestry germination experiments. This is because basic model assumptions are violated and variables available to forest managers are rarely used. In this paper, we present a method which addresses the aforementioned shortcomings. The approach is illustrated through a case study of Pinus pinea L. Our findings will also shed light on the role of germination in the general failure of natural regeneration in managed forests of this species. The presented technique consists of a mixed regression model based on survival analysis. Climate and stand covariates were tested. Data for fitting the model were gathered from a 5-year germination experiment in a mature, managed P. pinea stand in the Northern Plateau of Spain in which two different stand densities can be found. The model predictions proved to be unbiased and highly accurate when compared with the training data. Germination in P. pinea was controlled through thermal variables at stand level. At microsite level, low densities negatively affected the probability of germination. A time-lag in the response was also detected. Overall, the proposed technique provides a reliable alternative to germination modelling in ecology/forestry studies by using accessible/ suitable variables. The P. pinea case study highlights the importance of producing unbiased predictions. In this species, the occurrence and timing of germination suggest a very different regeneration strategy from that understood by forest managers until now, which may explain the high failure rate of natural regeneration in managed stands. In addition, these findings provide valuable information for the management of P. pinea under climate-change conditions.

Kinematic pattern of the drag-flick: a case study

The drag-flick is more efficient than hits or pushes when a penalty corner situation is in effect in field hockey. Previous research has studied the biomechanical pattern of the drag-flick, trying to find the cues for an optimal performance. On the other hand, some other studies have examined the most effective visual pick-up of relevant information in shots and goalkeeper anticipation. The aim of this study was to analyse the individual differences in the drag-flick pattern in order to provide relevant information for goalkeepers. One female skilled drag-flicker participated in the study. A VICON optoelectronic sy stem (Oxford Metrics, Oxford, UK) was used to capture the drag-flicks with six cameras. The results showed that the main significant differences between right and left shots (p<0.05) in the stick angles, stick minimum angular velocity and front foot-ball distance were when the front foot heel contacted the floor(T1) and at the minimum velocity of the stick, before the dragging action (T3). The findings showed that the most relevant information might be picked up at the ball-and-stick location before the dragging action.

Simulation Tool and Case Study for Planning Wireless Sensor Network

In this paper, a simulation tool for assisting the deployment of wireless sensor network is introduced and simulation results are verified under a specific indoor environment. The simulation tool supports two modes: deterministic mode and stochastic mode. The deterministic mode is environment dependent in which the information of environment should be provided beforehand. Ray tracing method and deterministic propagation model are employed in order to increase the accuracy of the estimated coverage, connectivity and routing; the stochastic mode is useful for large scale random deployment without previous knowledge on geographic information. Dynamic Source Routing protocol (DSR) and Ad hoc On-Demand Distance Vector Routing protocol (AODV) are implemented in order to calculate the topology of WSN. Hence this tool gives direct view on the performance of WSN and assists users in finding the potential problems of wireless sensor network before real deployment. At the end, a case study is realized in Centro de Electronica Industrial (CEI), the simulation results on coverage, connectivity and routing are verified by the measurement.

Transductive neurofuzzy-based torque control of a milling process: results of a case study

This paper presents the design and implementation of an intelligent control system based on local neurofuzzy models of the milling process relayed through an Ehternet-based application. Its purpose is to control the spindle torque of a milling process by using an internal model control paradigm to modify the feed rate in real time. The stabilization of cutting cutting torque is especially necessary in milling processes such as high-spedd roughing of steel moulds and dies tha present minor geometric uncertainties. Thus, maintenance of the curring torque increaes the material removal rate and reduces the risk of damage due to excessive spindle vibration, a very sensitive and expensive component in all high-speed milling machines. Torque control is therefore an interesting challenge from an industrial point of view.

The Lagrangian description of aperiodic flows: a case study of the Kuroshio Current

This article reviews several recently developed Lagrangian tools and shows how their com- bined use succeeds in obtaining a detailed description of purely advective transport events in general aperiodic flows. In particular, because of the climate impact of ocean transport processes, we illustrate a 2D application on altimeter data sets over the area of the Kuroshio Current, although the proposed techniques are general and applicable to arbitrary time depen- dent aperiodic flows. The first challenge for describing transport in aperiodical time dependent flows is obtaining a representation of the phase portrait where the most relevant dynamical features may be identified. This representation is accomplished by using global Lagrangian descriptors that when applied for instance to the altimeter data sets retrieve over the ocean surface a phase portrait where the geometry of interconnected dynamical systems is visible. The phase portrait picture is essential because it evinces which transport routes are acting on the whole flow. Once these routes are roughly recognised it is possible to complete a detailed description by the direct computation of the finite time stable and unstable manifolds of special hyperbolic trajectories that act as organising centres of the flow.

Automated characterisation of intergrowth textures in mineral particles. A case study

The characterisation of mineral texture has been a major concern for process mineralogists, as liberation characteristics of the ores are intimately related to the mineralogical texture. While a great effort has been done to automatically characterise texture in unbroken ores, the characterisation of textural attributes in mineral particles is usually descriptive. However, the quantitative characterisation of texture in mineral particles is essential to improve and predict the performance of minerallurgical processes (i.e. all the processes involved in the liberation and separation of the mineral of interest) and to achieve a more accurate geometallurgical model. Driven by this necessity of achieving a more complete characterisation of textural attributes in mineral particles, a methodology has been recently developed to automatically characterise the type of intergrowth between mineral phases within particles by means of digital image analysis. In this methodology, a set ofminerallurgical indices has been developed to quantify different mineralogical features and to identify the intergrowth pattern by discriminant analysis. The paper shows the application of the methodology to the textural characterisation of chalcopyrite in the rougher concentrate of the Kansanshi copper mine (Zambia). In this sample, the variety of intergrowth patterns of chalcopyrite with the other minerals has been used to illustrate the methodology. The results obtained show that the method identifies the intergrowth type and provides quantitative information to achieve a complete and detailed mineralogical characterisation. Therefore, the use of this methodology as a routinely tool in automated mineralogy would contribute to a better understanding of the ore behaviour during liberation and separation processes.

Farmers behaviour on mitigation and adaptation to climate change: A case study in Kenya.

Farmers in Africa are facing climate change and challenging rural livelihoods while maintaining agricultural systems that are not resilient. By 2050 the mean estimates of production of key staple crops in Africa such as maize, sorghum, millet, groundnut, and cassava are expected to decrease by between 8 and 22 percent (Schlenker and Lobell 2010). In Kenya, although projections of rainfall do not show dramatic decreases, the distribution of impacts is clearly negative for most crops. As increases in temperature will lead to increases in evapotranspiration, a potential increase in rainfall in Kenya may not offset the expected increases in agricultural water needs (Herrero et al. 2010). In order to respond to these present and future challenges, potential mitigation and adaptation options have been developed. However, implementation is not evident. In addition to their benefits in either mitigating or reducing the vulnerability of climate change effects, many of these options do not have economic costs and even provide economic benefits (e.g. savings in the consumption of energy or natural resources). Nevertheless, it is demonstrated that even when there are no biophysical, technological or economic constraints and despite their potential benefits from either the economic or environmental climate change point of view, not all farmers are willing to adopt these measures. This reflects the key role that behavioural barriers can play in the uptake of mitigation and adaptation measures.

Molecular tools to improve chestnut management: El Bierzo as a case study

The European chestnut (Castanea sativa Mill.) is a multipurpose species that has been widely cultivated around the Mediterranean basin since ancient times. New varieties were brought to the Iberian Peninsula during the Roman Empire, which coexist since then with native populations that survived the last glaciation. The relevance of chestnut cultivation has being steadily growing since the Middle Ages, until the rural decline of the past century put a stop to this trend. Forest fires and diseases were also major factors. Chestnut cultivation is gaining momentum again due to its economic (wood, fruits) and ecologic relevance, and represents currently an important asset in many rural areas of Europe. In this Thesis we apply different molecular tools to help improve current management strategies. For this study we have chosen El Bierzo (Castile and Leon, NW Spain), which has a centenary tradition of chestnut cultivation and management, and also presents several unique features from a genetic perspective (next paragraph). Moreover, its nuts are widely appreciated in Spain and abroad for their organoleptic properties. We have focused our experimental work on two major problems faced by breeders and the industry: the lack of a fine-grained genetic characterization and the need for new strategies to control blight disease. To characterize with sufficient detail the genetic diversity and structure of El Bierzo orchards, we analyzed DNA from 169 trees grafted for nut production covering the entire region. We also analyzed 62 nuts from all traditional varieties. El Bierzo constitutes an outstanding scenario to study chestnut genetics and the influence of human management because: (i) it is located at one extreme of the distribution area; (ii) it is a major glacial refuge for the native species; (iii) it has a long tradition of human management (since Roman times, at least); and (iv) its geographical setting ensures an unusual degree of genetic isolation. Thirteen microsatellite markers provided enough informativeness and discrimination power to genotype at the individual level. Together with an unexpected level of genetic variability, we found evidence of genetic structure, with three major gene pools giving rise to the current population. High levels of genetic differentiation between groups supported this organization. Interestingly, genetic structure does not match with spatial boundaries, suggesting that the exchange of material and cultivation practices have strongly influenced natural gene flow. The microsatellite markers selected for this study were also used to classify a set of 62 samples belonging to all traditional varieties. We identified several cases of synonymies and homonymies, evidencing the need to substitute traditional classification systems with new tools for genetic profiling. Management and conservation strategies should also benefit from these tools. The avenue of high-throughput sequencing technologies, combined with the development of bioinformatics tools, have paved the way to study transcriptomes without the need for a reference genome. We took advantage of RNA sequencing and de novo assembly tools to determine the transcriptional landscape of chestnut in response to blight disease. In addition, we have selected a set of candidate genes with high potential for developing resistant varieties via genetic engineering. Our results evidenced a deep transcriptional reprogramming upon fungal infection. The plant hormones ET and JA appear to orchestrate the defensive response. Interestingly, our results also suggest a role for auxins in modulating such response. Many transcription factors were identified in this work that interact with promoters of genes involved in disease resistance. Among these genes, we have conducted a functional characterization of a two major thaumatin-like proteins (TLP) that belongs to the PR5 family. Two genes encoding chestnut cotyledon TLPs have been previously characterized, termed CsTL1 and CsTL2. We substantiate here their protective role against blight disease for the first time, including in silico, in vitro and in vivo evidence. The synergy between TLPs and other antifungal proteins, particularly endo-p-1,3-glucanases, bolsters their interest for future control strategies based on biotechnological approaches.

Methodology for estimating building integrated photovoltaics electricity production under shadowing conditions and case study

Building integrated photovoltaic (BIPV) systems are a relevant application of photovoltaics. In countries belonging to the International Energy Agency countries, 24% of total installed PV power corresponds to BIPV systems. Electricity losses caused by shadows over the PV generator have a significant impact on the performance of BIPV systems, being the major source of electricity losses. This paper presents a methodology to estimate electricity produced by BIPV systems which incorporates a model for shading losses. The proposed methodology has been validated on a one year study with real data from two similar PV systems placed on the south façade of a building belonging to the Technical University of Madrid. This study has covered all weather conditions: clear, partially overcast and fully overcast sky. Results of this study are shown at different time scales, resulting that the errors committed by the best performing model are below 1% and 3% in annual and daily electricity estimation. The use of models which account for the reduced performance at low irradiance levels also improves the estimation of generated electricity.

Building resilience to water scarcity in Southern Spain: A case study of rice farming in Doñana protected wetlands

Agricultural water management needs to evolve in view of increased water scarcity, especially when farming and natural protected areas are closely linked. In the study site of Don?ana (southern Spain), water is shared by rice producers and a world heritage biodiversity ecosystem. Our aim is to contribute to defining adaptation strategies that may build resilience to increasing water scarcity and minimize water conflicts among agricultural and natural systems. The analytical framework links a participatory process with quantitative methods to prioritize the adaptation options. Bottom-up proposed adaptation measures are evaluated by a multi-criteria analysis (MCA) that includes both socioeconomic criteria and criteria of the ecosystem services affected by the adaptation options. Criteria weights are estimated by three different methods?analytic hierarchy process, Likert scale and equal weights?that are then compared. Finally, scores from an MCA are input into an optimization model used to determine the optimal land-use distribution in order to maximize utility and land-use diversification according to different scenarios of funds and water availability. While our results show a spectrum of perceptions of priorities among stakeholders, there is one overriding theme that is to define a way to restore part of the rice fields to natural wetlands. These results hold true under the current climate scenario and evenmore so under an increased water scarcity scenario.

Structuring research methods and data with the research object model: genomics workflows as a case study

Background: One of the main challenges for biomedical research lies in the computer-assisted integrative study of large and increasingly complex combinations of data in order to understand molecular mechanisms. The preservation of the materials and methods of such computational experiments with clear annotations is essential for understanding an experiment, and this is increasingly recognized in the bioinformatics community. Our assumption is that offering means of digital, structured aggregation and annotation of the objects of an experiment will provide necessary meta-data for a scientist to understand and recreate the results of an experiment. To support this we explored a model for the semantic description of a workflow-centric Research Object (RO), where an RO is defined as a resource that aggregates other resources, e.g., datasets, software, spreadsheets, text, etc. We applied this model to a case study where we analysed human metabolite variation by workflows. Results: We present the application of the workflow-centric RO model for our bioinformatics case study. Three workflows were produced following recently defined Best Practices for workflow design. By modelling the experiment as an RO, we were able to automatically query the experiment and answer questions such as “which particular data was input to a particular workflow to test a particular hypothesis?”, and “which particular conclusions were drawn from a particular workflow?”. Conclusions: Applying a workflow-centric RO model to aggregate and annotate the resources used in a bioinformatics experiment, allowed us to retrieve the conclusions of the experiment in the context of the driving hypothesis, the executed workflows and their input data. The RO model is an extendable reference model that can be used by other systems as well.

Social tags and linked data for ontology development: a case study in the financial domain

We describe a domain ontology development approach that extracts domain terms from folksonomies and enrich them with data and vocabularies from the Linked Open Data cloud. As a result, we obtain lightweight domain ontologies that combine the emergent knowledge of social tagging systems with formal knowledge from Ontologies. In order to illustrate the feasibility of our approach, we have produced an ontology in the financial domain from tags available in Delicious, using DBpedia, OpenCyc and UMBEL as additional knowledge sources.