Polymorphisms in DNA-Repair Genes in a Cohort of Prostate Cancer Patients from Different Areas in Spain: Heterogeneity between Populations as a Confounding Factor in Association Studies.

BACKGROUND Differences in the distribution of genotypes between individuals of the same ethnicity are an important confounder factor commonly undervalued in typical association studies conducted in radiogenomics. OBJECTIVE To evaluate the genotypic distribution of SNPs in a wide set of Spanish prostate cancer patients for determine the homogeneity of the population and to disclose potential bias. DESIGN SETTING AND PARTICIPANTS A total of 601 prostate cancer patients from Andalusia, Basque Country, Canary and Catalonia were genotyped for 10 SNPs located in 6 different genes associated to DNA repair: XRCC1 (rs25487, rs25489, rs1799782), ERCC2 (rs13181), ERCC1 (rs11615), LIG4 (rs1805388, rs1805386), ATM (rs17503908, rs1800057) and P53 (rs1042522). The SNP genotyping was made in a Biotrove OpenArray® NT Cycler. OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS Comparisons of genotypic and allelic frequencies among populations, as well as haplotype analyses were determined using the web-based environment SNPator. Principal component analysis was made using the SnpMatrix and XSnpMatrix classes and methods implemented as an R package. Non-supervised hierarchical cluster of SNP was made using MultiExperiment Viewer. RESULTS AND LIMITATIONS We observed that genotype distribution of 4 out 10 SNPs was statistically different among the studied populations, showing the greatest differences between Andalusia and Catalonia. These observations were confirmed in cluster analysis, principal component analysis and in the differential distribution of haplotypes among the populations. Because tumor characteristics have not been taken into account, it is possible that some polymorphisms may influence tumor characteristics in the same way that it may pose a risk factor for other disease characteristics. CONCLUSION Differences in distribution of genotypes within different populations of the same ethnicity could be an important confounding factor responsible for the lack of validation of SNPs associated with radiation-induced toxicity, especially when extensive meta-analysis with subjects from different countries are carried out.

Effectiveness of two interventions based on improving patient-practitioner communication on diabetes self-management in patients with low educational level: study protocol of a clustered randomized trial in primary care.

BACKGROUND In the last decades the presence of social inequalities in diabetes care has been observed in multiple countries, including Spain. These inequalities have been at least partially attributed to differences in diabetes self-management behaviours. Communication problems during medical consultations occur more frequently to patients with a lower educational level. The purpose of this cluster randomized trial is to determine whether an intervention implemented in a General Surgery, based in improving patient-provider communication, results in a better diabetes self-management in patients with lower educational level. A secondary objective is to assess whether telephone reinforcement enhances the effect of such intervention. We report the design and implementation of this on-going study. METHODS/DESIGN The study is being conducted in a General Practice located in a deprived neighbourhood of Granada, Spain. Diabetic patients 18 years old or older with a low educational level and inadequate glycaemic control (HbA1c > 7%) were recruited. General Practitioners (GPs) were randomised to three groups: intervention A, intervention B and control group. GPs allocated to intervention groups A and B received training in communication skills and are providing graphic feedback about glycosylated haemoglobin levels. Patients whose GPs were allocated to group B are additionally receiving telephone reinforcement whereas patients from the control group are receiving usual care. The described interventions are being conducted during 7 consecutive medical visits which are scheduled every three months. The main outcome measure will be HbA1c; blood pressure, lipidemia, body mass index and waist circumference will be considered as secondary outcome measures. Statistical analysis to evaluate the effectiveness of the interventions will include multilevel regression analysis with three hierarchical levels: medical visit level, patient level and GP level. DISCUSSION The results of this study will provide new knowledge about possible strategies to promote a better diabetes self-management in a particularly vulnerable group. If effective, this low cost intervention will have the potential to be easily incorporated into routine clinical practice, contributing to decrease health inequalities in diabetic patients. TRIAL REGISTRATION Clinical Trials U.S. National Institutes of Health, NCT01849731.

Identificació de paisatges funcionals mitjançant anàlisi cluster

El terme paisatge i les seves aplicacions són cada dia més utilitzats per les administracions i altres entitats com a eina de gestió del territori. Aprofitant la gran quantitat de dades en bases compatibles amb SIG (Sistemes d’Informació Geogràfica) existents a Catalunya s’ha desenvolupat una síntesi cartogràfica on s’identifiquen els Paisatges Funcionals (PF) de Catalunya, concepte que fa referència al comportament fisico-ecològic del terreny a partir de variables topogràfiques i climàtiques convenientment transformades i agregades. S’ha utilitzat un mètode semiautomàtic i iteratiu de classificació no supervisada (clustering) que permet la creació d’una llegenda jeràrquica o nivells de generalització. S’ha obtingut com a resultat el Mapa de Paisatges Funcionals de Catalunya (MPFC) amb una llegenda de 26 categories de paisatges i 5 nivells de generalització amb una resolució espacial de 180 m. Paral·lelament, s’han realitzat validacions indirectes sobre el mapa obtingut a partir dels coneixements naturalistes i la cartografia existent, així com també d’un mapa d’incertesa (aplicant lògica difusa) que aporten informació de la fiabilitat de la classificació realitzada. Els Paisatges Funcionals obtinguts permeten relacionar zones de condicions topo-climàtiques homogènies i dividir el territori en zones caracteritzades ambientalment i no políticament amb la intenció que sigui d’utilitat a l’hora de millorar la gestió dels recursos naturals i la planificació d’actuacions humanes.

Hierarchic species-area relationships and the management of forest habitat islands in intensive farmland

Habitat loss and fragmentation due to land use changes are major threats to biodiversity in forest ecosystems, and they are expected to have important impacts on many taxa and at various spatial scales. Species richness and area relationships (SARs) have been used to assess species diversity patterns and drivers, and thereby in the establishment of conservation and management strategies. Here we propose a hierarchical approach to achieve deeper insights on SARs in small forest islets in intensive farmland and to address the impacts of decreasing naturalness on such relationships. In the intensive dairy landscapes of Northwest Portugal, where small forest stands (dominated by pines, eucalypts or both) represent semi-natural habitat islands, 50 small forest stands were selected and surveyed for vascular plant diversity. A hierarchical analytical framework was devised to determine species richness and inter- and intra-patch SARs for the whole set of forest patches (general patterns) and for each type of forest (specific patterns). Differences in SARs for distinct groups were also tested by considering subsets of species (native, alien, woody, and herbaceous). Overall, values for species richness were confirmed to be different between forest patches exhibiting different levels of naturalness. Whereas higher values of plant diversity were found in pine stands, higher values for alien species were observed in eucalypt stands. Total area of forest (inter-patch SAR) was found not to have a significant impact on species richness for any of the targeted groups of species. However, significant intra-patch SARs were obtained for all groups of species and forest types. A hierarchical approach was successfully applied to scrutinise SARs along a gradient of forest naturalness in intensively managed landscapes. Dominant canopy tree and management intensity were found to reflect differently on distinct species groups as well as to compensate for increasing stand area, buffering SARs among patches, but not within patches. Thus, the maintenance of small semi-natural patches dominated by pines, under extensive practices of forest management, will promote native plant diversity while at the same time contributing to limit the expansion of problematic alien invasive species.

A microRNA Signature Associated with Early Recurrence in Breast Cancer.

Recurrent breast cancer occurring after the initial treatment is associated with poor outcome. A bimodal relapse pattern after surgery for primary tumor has been described with peaks of early and late recurrence occurring at about 2 and 5 years, respectively. Although several clinical and pathological features have been used to discriminate between low- and high-risk patients, the identification of molecular biomarkers with prognostic value remains an unmet need in the current management of breast cancer. Using microarray-based technology, we have performed a microRNA expression analysis in 71 primary breast tumors from patients that either remained disease-free at 5 years post-surgery (group A) or developed early (group B) or late (group C) recurrence. Unsupervised hierarchical clustering of microRNA expression data segregated tumors in two groups, mainly corresponding to patients with early recurrence and those with no recurrence. Microarray data analysis and RT-qPCR validation led to the identification of a set of 5 microRNAs (the 5-miRNA signature) differentially expressed between these two groups: miR-149, miR-10a, miR-20b, miR-30a-3p and miR-342-5p. All five microRNAs were down-regulated in tumors from patients with early recurrence. We show here that the 5-miRNA signature defines a high-risk group of patients with shorter relapse-free survival and has predictive value to discriminate non-relapsing versus early-relapsing patients (AUC = 0.993, p-value<0.05). Network analysis based on miRNA-target interactions curated by public databases suggests that down-regulation of the 5-miRNA signature in the subset of early-relapsing tumors would result in an overall increased proliferative and angiogenic capacity. In summary, we have identified a set of recurrence-related microRNAs with potential prognostic value to identify patients who will likely develop metastasis early after primary breast surgery.

Clustering algorithms for anti-money laundering using graph theory and social network analysis

HEMOLIA (a project under European community’s 7th framework programme) is a new generation Anti-Money Laundering (AML) intelligent multi-agent alert and investigation system which in addition to the traditional financial data makes extensive use of modern society’s huge telecom data source, thereby opening up a new dimension of capabilities to all Money Laundering fighters (FIUs, LEAs) and Financial Institutes (Banks, Insurance Companies, etc.). This Master-Thesis project is done at AIA, one of the partners for the HEMOLIA project in Barcelona. The objective of this thesis is to find the clusters in a network drawn by using the financial data. An extensive literature survey has been carried out and several standard algorithms related to networks have been studied and implemented. The clustering problem is a NP-hard problem and several algorithms like K-Means and Hierarchical clustering are being implemented for studying several problems relating to sociology, evolution, anthropology etc. However, these algorithms have certain drawbacks which make them very difficult to implement. The thesis suggests (a) a possible improvement to the K-Means algorithm, (b) a novel approach to the clustering problem using the Genetic Algorithms and (c) a new algorithm for finding the cluster of a node using the Genetic Algorithm.

A nuclear ribosomal DNA pseudogene in triatomines opens a new research field of fundamental and applied implications in Chagas disease

A pseudogene, designated as "ps(5.8S+ITS-2)", paralogous to the 5.8S gene and internal transcribed spacer (ITS)-2 of the nuclear ribosomal DNA (rDNA), has been recently found in many triatomine species distributed throughout North America, Central America and northern South America. Among characteristics used as criteria for pseudogene verification, secondary structures and free energy are highlighted, showing a lower fit between minimum free energy, partition function and centroid structures, although in given cases the fit only appeared to be slightly lower. The unique characteristics of "ps(5.8S+ITS-2)" as a processed or retrotransposed pseudogenic unit of the ghost type are reviewed, with emphasis on its potential functionality compared to the functionality of genes and spacers of the normal rDNA operon. Besides the technical problem of the risk for erroneous sequence results, the usefulness of "ps(5.8S+ITS-2)" for specimen classification, phylogenetic analyses and systematic/taxonomic studies should be highlighted, based on consistence and retention index values, which in pseudogenic sequence trees were higher than in functional sequence trees. Additionally, intraindividual, interpopulational and interspecific differences in pseudogene amount and the fact that it is a pseudogene in the nuclear rDNA suggests a potential relationships with fitness, behaviour and adaptability of triatomine vectors and consequently its potential utility in Chagas disease epidemiology and control.

A study of personality in children aged 8-12 years: Comparing self- and partents' ratings

This study was designed to investigate personality development with children aged 8 to 12. For this purpose, Children's self-perceptions were compared to parent's ratings. 506 children and their parents completed a selection of 38 questions from the Hierarchical Personality Inventory for Children (HiPIC). Results showed an age-related increase in the structural congruence of children's ratings compared to parents' ratings and a highly significant increase in the reliabilities of both parents' and children's assessments. The mean correlation between the children's self-descriptions and parents' ratings were higher for Conscientiousness and Imagination than for Extraversion, Benevolence and Emotional Stability and significantly increased with the children's age. Mean-levels decreased with age for Imagination in parents' ratings and for Benevolence, Conscientiousness, and Imagination, in children's ratings. This study showed that personality development from 8 to 12 years goes along with an increase in the agreement between the children's self-perceptions and the parents' perceptions of the children's personality.

Clustering compositional data trajectories

Our essay aims at studying suitable statistical methods for the clustering ofcompositional data in situations where observations are constituted by trajectories ofcompositional data, that is, by sequences of composition measurements along a domain.Observed trajectories are known as “functional data” and several methods have beenproposed for their analysis.In particular, methods for clustering functional data, known as Functional ClusterAnalysis (FCA), have been applied by practitioners and scientists in many fields. To ourknowledge, FCA techniques have not been extended to cope with the problem ofclustering compositional data trajectories. In order to extend FCA techniques to theanalysis of compositional data, FCA clustering techniques have to be adapted by using asuitable compositional algebra.The present work centres on the following question: given a sample of compositionaldata trajectories, how can we formulate a segmentation procedure giving homogeneousclasses? To address this problem we follow the steps described below.First of all we adapt the well-known spline smoothing techniques in order to cope withthe smoothing of compositional data trajectories. In fact, an observed curve can bethought of as the sum of a smooth part plus some noise due to measurement errors.Spline smoothing techniques are used to isolate the smooth part of the trajectory:clustering algorithms are then applied to these smooth curves.The second step consists in building suitable metrics for measuring the dissimilaritybetween trajectories: we propose a metric that accounts for difference in both shape andlevel, and a metric accounting for differences in shape only.A simulation study is performed in order to evaluate the proposed methodologies, usingboth hierarchical and partitional clustering algorithm. The quality of the obtained resultsis assessed by means of several indices

Possible solution of some essential zero problems in compositional data analysis

One of the tantalising remaining problems in compositional data analysis lies in how to deal with data sets in which there are components which are essential zeros. By anessential zero we mean a component which is truly zero, not something recorded as zero simply because the experimental design or the measuring instrument has not been sufficiently sensitive to detect a trace of the part. Such essential zeros occur inmany compositional situations, such as household budget patterns, time budgets,palaeontological zonation studies, ecological abundance studies. Devices such as nonzero replacement and amalgamation are almost invariably ad hoc and unsuccessful insuch situations. From consideration of such examples it seems sensible to build up amodel in two stages, the first determining where the zeros will occur and the secondhow the unit available is distributed among the non-zero parts. In this paper we suggest two such models, an independent binomial conditional logistic normal model and a hierarchical dependent binomial conditional logistic normal model. The compositional data in such modelling consist of an incidence matrix and a conditional compositional matrix. Interesting statistical problems arise, such as the question of estimability of parameters, the nature of the computational process for the estimation of both the incidence and compositional parameters caused by the complexity of the subcompositional structure, the formation of meaningful hypotheses, and the devising of suitable testing methodology within a lattice of such essential zero-compositional hypotheses. The methodology is illustrated by application to both simulated and real compositional data

Mg/Ca and Sr/Ca ratios of ostracod valves from living species of Lake Geneva

The Mg/Ca and Sr/Ca ratios of living ostracods belonging to 15 different species and sampled monthly over a one year-cycle at five sites (2, 5, 13, 33, and 70 m water depths) in western Lake Geneva (Switzerland) are compared to the oxygen and carbon isotope compositions measured on the same samples as well as to the temperature and chemical composition of the water (δ18OH2O, δ13CDIC, Mg/CaH2O, and Sr/CaH2O) at the time of ostracod calcification. The results indicate that trace element incorporation varied at the species level, mainly because of the ecological and biological differences between the different species (life-cycle, (micro-)habitat preference, biomineralisation processes) and the control thereof on trace element incorporation of the ostracods. In littoral zones, the Mg/Ca and Sr/Ca of ostracod valves increase as temperature and Mg/Ca and Sr/Ca of water increase during spring and summer, hence reflecting mainly seasonal variations. However, given that for Lake Geneva the Mg/Ca and Sr/Ca of water also vary with temperature, it is not possible to distinguish the effects of temperature from those of changes in chemical composition of water on the trace element content in ostracod valves. Results support that both water temperature and water Mg/Ca and Sr/Ca ratios control the final trace element content of Cyprididae valves. In contrast, the trace element content of species living in deeper zones of the basin is influenced by variations in the chemical composition of the pore water for the infaunal species. Trace element content measured for these specimens cannot, therefore, be used to reconstruct the compositions of the water lake bottom. In addition, incorporation of Mg and Sr into the shell differs from one family, sub-family, or even species to the other. This suggests that the distinctive Mg and Sr partition coefcients for the analysed taxa result from different valve calcification strategies that may be phylogenetic.

Decision Support Methods for Global Optomization

Immobile location-allocation (LA) problems is a type of LA problem that consists in determining the service each facility should offer in order to optimize some criterion (like the global demand), given the positions of the facilities and the customers. Due to the complexity of the problem, i.e. it is a combinatorial problem (where is the number of possible services and the number of facilities) with a non-convex search space with several sub-optimums, traditional methods cannot be applied directly to optimize this problem. Thus we proposed the use of clustering analysis to convert the initial problem into several smaller sub-problems. By this way, we presented and analyzed the suitability of some clustering methods to partition the commented LA problem. Then we explored the use of some metaheuristic techniques such as genetic algorithms, simulated annealing or cuckoo search in order to solve the sub-problems after the clustering analysis

Modeling of hybrid systems by piecewise decomposition

Piecewise linear models systems arise as mathematical models of systems in many practical applications, often from linearization for nonlinear systems. There are two main approaches of dealing with these systems according to their continuous or discrete-time aspects. We propose an approach which is based on the state transformation, more particularly the partition of the phase portrait in different regions where each subregion is modeled as a two-dimensional linear time invariant system. Then the Takagi-Sugeno model, which is a combination of local model is calculated. The simulation results show that the Alpha partition is well-suited for dealing with such a system

Drivers of human resource management competences development in Brazilian multinational subsidiaries: a multilevel research

This article aims to verify the factors associated with the development of human resource management (HRM) competences in foreign subsidiaries of Brazilian multinationals. These competences are essential in that they allow foreign units to adopt HRM practices that are consistent with the countries or markets in which they operate. A multilevel research was conducted, involving headquarters and subsidiaries of major Brazilian companies; the empirical analysis employed hierarchical linear modelling. Despite the recurrent debate on global standardisation versus local adaptation, it was identified that the integration of international HRM policies (addressing simultaneously global guidelines and local response) may stimulate competences development. In addition, interaction in external networks in the host country may enhance the development of HRM competences in the subsidiaries. However, specific cultural factors of the company may inhibit development activity in units abroad.

Solving Large Location-Allocation problems by Clustering and Simulated Annealing

Globalization involves several facility location problems that need to be handled at large scale. Location Allocation (LA) is a combinatorial problem in which the distance among points in the data space matter. Precisely, taking advantage of the distance property of the domain we exploit the capability of clustering techniques to partition the data space in order to convert an initial large LA problem into several simpler LA problems. Particularly, our motivation problem involves a huge geographical area that can be partitioned under overall conditions. We present different types of clustering techniques and then we perform a cluster analysis over our dataset in order to partition it. After that, we solve the LA problem applying simulated annealing algorithm to the clustered and non-clustered data in order to work out how profitable is the clustering and which of the presented methods is the most suitable