Clustering U.S. 2016 presidential candidates through linguistic appraisals

Producción CientíficaThe main purpose of this paper is to cluster the United States (U.S.) 2016 presidential candidates taking the linguistic appraisals made by a random representative sample of adults living in the U.S. as our starting point. To do this, we have used the concept of ordinal proximity measure (see García-Lapresta and Pérez-Román), which allows to determine the degree of consensus in a group of agents when a set of alternatives is evaluated through non-necessarily qualitative scales.Ministerio de Economía, Industria y Competitividad (project ECO2016-77900-P

Error, reproducibility and sensitivity : a pipeline for data processing of Agilent oligonucleotide expression arrays

Background Expression microarrays are increasingly used to obtain large scale transcriptomic information on a wide range of biological samples. Nevertheless, there is still much debate on the best ways to process data, to design experiments and analyse the output. Furthermore, many of the more sophisticated mathematical approaches to data analysis in the literature remain inaccessible to much of the biological research community. In this study we examine ways of extracting and analysing a large data set obtained using the Agilent long oligonucleotide transcriptomics platform, applied to a set of human macrophage and dendritic cell samples. Results We describe and validate a series of data extraction, transformation and normalisation steps which are implemented via a new R function. Analysis of replicate normalised reference data demonstrate that intrarray variability is small (only around 2% of the mean log signal), while interarray variability from replicate array measurements has a standard deviation (SD) of around 0.5 log2 units ( 6% of mean). The common practise of working with ratios of Cy5/Cy3 signal offers little further improvement in terms of reducing error. Comparison to expression data obtained using Arabidopsis samples demonstrates that the large number of genes in each sample showing a low level of transcription reflect the real complexity of the cellular transcriptome. Multidimensional scaling is used to show that the processed data identifies an underlying structure which reflect some of the key biological variables which define the data set. This structure is robust, allowing reliable comparison of samples collected over a number of years and collected by a variety of operators. Conclusions This study outlines a robust and easily implemented pipeline for extracting, transforming normalising and visualising transcriptomic array data from Agilent expression platform. The analysis is used to obtain quantitative estimates of the SD arising from experimental (non biological) intra- and interarray variability, and for a lower threshold for determining whether an individual gene is expressed. The study provides a reliable basis for further more extensive studies of the systems biology of eukaryotic cells

Uncovering the overlapping community structure of complex networks in nature and society

Many complex systems in nature and society can be described in terms of networks capturing the intricate web of connections among the units they are made of. A key question is how to interpret the global organization of such networks as the coexistence of their structural subunits (communities) associated with more highly interconnected parts. Identifying these a priori unknown building blocks (such as functionally related proteins, industrial sectors and groups of people) is crucial to the understanding of the structural and functional properties of networks. The existing deterministic methods used for large networks find separated communities, whereas most of the actual networks are made of highly overlapping cohesive groups of nodes. Here we introduce an approach to analysing the main statistical features of the interwoven sets of overlapping communities that makes a step towards uncovering the modular structure of complex systems. After defining a set of new characteristic quantities for the statistics of communities, we apply an efficient technique for exploring overlapping communities on a large scale. We find that overlaps are significant, and the distributions we introduce reveal universal features of networks. Our studies of collaboration, word-association and protein interaction graphs show that the web of communities has non-trivial correlations and specific scaling properties.Comment: The free academic research software, CFinder, used for the publication is available at the website of the publication: http://angel.elte.hu/clusterin

Sparsest factor analysis for clustering variables: a matrix decomposition approach

We propose a new procedure for sparse factor analysis (FA) such that each variable loads only one common factor. Thus, the loading matrix has a single nonzero element in each row and zeros elsewhere. Such a loading matrix is the sparsest possible for certain number of variables and common factors. For this reason, the proposed method is named sparsest FA (SSFA). It may also be called FA-based variable clustering, since the variables loading the same common factor can be classified into a cluster. In SSFA, all model parts of FA (common factors, their correlations, loadings, unique factors, and unique variances) are treated as fixed unknown parameter matrices and their least squares function is minimized through specific data matrix decomposition. A useful feature of the algorithm is that the matrix of common factor scores is re-parameterized using QR decomposition in order to efficiently estimate factor correlations. A simulation study shows that the proposed procedure can exactly identify the true sparsest models. Real data examples demonstrate the usefulness of the variable clustering performed by SSFA

Response to novel objects and foraging tasks by common marmoset (Callithrix Jacchus) female Pairs

Many studies have shown that environmental enrichment can significantly improve the psychological well-being of captive primates, increasing the occurrence of explorative behavior and thus reducing boredom. The response of primates to enrichment devices may be affected by many factors such as species, sex, age, personality and social context. Environmental enrichment is particularly important for social primates living in unnatural social groupings (i.e. same-sex pairs or singly housed animals), who have very few, or no, benefits from the presence of social companions in addition to all the problems related to captivity (e.g. increased inactivity). This study analyses the effects of enrichment devices (i.e. novel objects and foraging tasks) on the behavior of common marmoset (Callithrix jacchus) female pairs, a species that usually lives in family groups. It aims to determine which aspects of an enrichment device are more likely to elicit explorative behaviors, and how aggressive and stress-related behaviors are affected by its presence. Overall, the marmosets explored foraging tasks significantly longer than novel objects. The type of object, which varied in size, shape and aural responsiveness (i.e. they made a noise when the monkey touched them), did not affect the response of the monkeys, but they explored objects that were placed higher in the enclosure more than those placed lower down.Younger monkeys were more attracted to the enrichment devices than the older ones. Finally, stress-related behavior (i.e. scratching) significantly decreased when the monkeys were presented with the objects; aggressive behavior as unaffected. This study supports the importance of environmental enrichment for captive primates and shows that in marmosets its effectiveness strongly depends upon the height of the device in the enclosure and the presence of hidden food. The findings can be explained ifone considers the foraging behavior of wild common marmosets. Broader applications for the research findings are suggested in relation to enrichment

The dark side of technologies: Technostress among users of information and communication technologies

This paper tests the structure and the predictors of two psychological experiences of technostress associated with the use of information and communication technologies (ICT), i.e., technostrain (users report feelings of anxiety, fatigue, scepticism and inefficacy beliefs related to the use of technologies) and technoaddiction (users feel bad due to an excessive and compulsive use of these technologies). The study included a sample of 1072 ICT users (N = 675 nonintensive ICT users and N = 397 intensive ICT users). Results from multigroup confirmatory factor analyses among non-intensive and intensive ICT users showed, as expected, the four-factor structure of technostrain in both samples. Secondly, and also as expected, confirmatory factorial analyses revealed that technostress experiences are characterized not only by technostrain but also by an excessive and compulsive use of ICT. Moreover, multiple analyses of variance showed significant differences between non-intensive and intensive ICT users (1) in the dimensions of technostress and (2) in specific job demands and job/personal resources. Finally, linear multiple regression analyses revealed that technostrain is positively predicted by work overload, role ambiguity, emotional overload, mobbing and obstacles hindering ICT use, as well as by lack of autonomy, transformational leadership, social support, ICT use facilitators and mental competences. Work overload, role ambiguity and mobbing, as well as the lack of emotional competences, positively predict technoaddiction. Theoretical and practical implications, in addition to future research, are discussed

Patterns of primary care and mortality among patients with schizophrenia or diabetes: a cluster analysis approach to the retrospective study of healthcare utilization

Abstract Background Patients with schizophrenia have difficulty managing their medical healthcare needs, possibly resulting in delayed treatment and poor outcomes. We analyzed whether patients reduced primary care use over time, differentially by diagnosis with schizophrenia, diabetes, or both schizophrenia and diabetes. We also assessed whether such patterns of primary care use were a significant predictor of mortality over a 4-year period. Methods The Veterans Healthcare Administration (VA) is the largest integrated healthcare system in the United States. Administrative extracts of the VA's all-electronic medical records were studied. Patients over age 50 and diagnosed with schizophrenia in 2002 were age-matched 1:4 to diabetes patients. All patients were followed through 2005. Cluster analysis explored trajectories of primary care use. Proportional hazards regression modelled the impact of these primary care utilization trajectories on survival, controlling for demographic and clinical covariates. Results Patients comprised three diagnostic groups: diabetes only (n = 188,332), schizophrenia only (n = 40,109), and schizophrenia with diabetes (Scz-DM, n = 13,025). Cluster analysis revealed four distinct trajectories of primary care use: consistent over time, increasing over time, high and decreasing, low and decreasing. Patients with schizophrenia only were likely to have low-decreasing use (73% schizophrenia-only vs 54% Scz-DM vs 52% diabetes). Increasing use was least common among schizophrenia patients (4% vs 8% Scz-DM vs 7% diabetes) and was associated with improved survival. Low-decreasing primary care, compared to consistent use, was associated with shorter survival controlling for demographics and case-mix. The observational study was limited by reliance on administrative data. Conclusion Regular primary care and high levels of primary care were associated with better survival for patients with chronic illness, whether psychiatric or medical. For schizophrenia patients, with or without comorbid diabetes, primary care offers a survival benefit, suggesting that innovations in treatment retention targeting at-risk groups can offer significant promise of improving outcomes.http://deepblue.lib.umich.edu/bitstream/2027.42/78274/1/1472-6963-9-127.xmlhttp://deepblue.lib.umich.edu/bitstream/2027.42/78274/2/1472-6963-9-127.pdfPeer Reviewe

Using network theory to identify the causes of disease outbreaks of unknown origin.

The identification of undiagnosed disease outbreaks is critical for mobilizing efforts to prevent widespread transmission of novel virulent pathogens. Recent developments in online surveillance systems allow for the rapid communication of the earliest reports of emerging infectious diseases and tracking of their spread. The efficacy of these programs, however, is inhibited by the anecdotal nature of informal reporting and uncertainty of pathogen identity in the early stages of emergence. We developed theory to connect disease outbreaks of known aetiology in a network using an array of properties including symptoms, seasonality and case-fatality ratio. We tested the method with 125 reports of outbreaks of 10 known infectious diseases causing encephalitis in South Asia, and showed that different diseases frequently form distinct clusters within the networks. The approach correctly identified unknown disease outbreaks with an average sensitivity of 76 per cent and specificity of 88 per cent. Outbreaks of some diseases, such as Nipah virus encephalitis, were well identified (sensitivity = 100%, positive predictive values = 80%), whereas others (e.g. Chandipura encephalitis) were more difficult to distinguish. These results suggest that unknown outbreaks in resource-poor settings could be evaluated in real time, potentially leading to more rapid responses and reducing the risk of an outbreak becoming a pandemic

Clustering and forecasting of dissolved oxygen concentration on a river basin

The aim of this contribution is to combine statistical methodologies to geographically classify homogeneous groups of water quality monitoring sites based on similarities in the temporal dynamics of the dissolved oxygen (DO) concentration, in order to obtain accurate forecasts of this quality variable. Our methodology intends to classify the water quality monitoring sites into spatial homogeneous groups, based on the DO concentration, which has been selected and considered relevant to characterize the water quality. We apply clustering techniques based on Kullback Information, measures that are obtained in the state space modelling process. For each homogeneous group of water quality monitoring sites we model the DO concentration using linear and state space models, which incorporate tendency and seasonality components in different ways. Both approaches are compared by the mean squared error (MSE) of forecasts

The role of rigidity in adaptive and maladaptive families assessed by FACES IV: the points of view of adolescents

Previous studies using Olson’s Circumplex Model and FACES IV, the self-report assessing family functioning, did not clarify the role of rigidity, a dimension of this model. Rigidity emerged as ambiguous: it was considered either as a functional or as a dysfunctional dimension. Building upon the results of previous studies, we provided a contribution intended to disambiguate the role of rigidity considering adolescents’ perceptions and using a non-a priori classification analysis. 320 Italian adolescents (13–21 years) participated in this study and responded to a questionnaire containing scales of the study variables. A latent class analysis was performed to identify the association of rigidity with the other dimensions of Olson’s model and with indicators of adaptive family functioning in adolescence: parental monitoring and family satisfaction. We found six clusters corresponding to family typologies and having different levels of functioning. Rigidity emerged as adaptive in the typologies named rigidly balanced and flexibly oscillating; it was associated with positive dimensions of family functioning, i.e. flexibility, cohesion, parental monitoring, and high levels of family satisfaction. Differently, when rigidity was associated with disengagement, low cohesion and flexibility, and lack of parental supervision, emerged as maladaptive. This was the case of two typologies: the rigidly disengaged and the chaotically disengaged. Adolescents of these families reported the lowest levels of satisfaction. In the two last typologies, the flexibly chaotic and the cohesively disorganized, rigidity indicated a mid-range functionality as these families were characterized by emotional connectedness but lack of containment. Clinical implications are discussed