ICGE: an R package for detecting relevant clusters and atypical units in gene expression

Background: Gene expression technologies have opened up new ways to diagnose and treat cancer and other diseases. Clustering algorithms are a useful approach with which to analyze genome expression data. They attempt to partition the genes into groups exhibiting similar patterns of variation in expression level. An important problem associated with gene classification is to discern whether the clustering process can find a relevant partition as well as the identification of new genes classes. There are two key aspects to classification: the estimation of the number of clusters, and the decision as to whether a new unit (gene, tumor sample ... ) belongs to one of these previously identified clusters or to a new group. Results: ICGE is a user-friendly R package which provides many functions related to this problem: identify the number of clusters using mixed variables, usually found by applied biomedical researchers; detect whether the data have a cluster structure; identify whether a new unit belongs to one of the pre-identified clusters or to a novel group, and classify new units into the corresponding cluster. The functions in the ICGE package are accompanied by help files and easy examples to facilitate its use. Conclusions: We demonstrate the utility of ICGE by analyzing simulated and real data sets. The results show that ICGE could be very useful to a broad research community.

Subtypes of Patients Experiencing Exacerbations of COPD and Associations with Outcomes

Chronic obstructive pulmonary disease (COPD) is a complex and heterogeneous condition characterized by occasional exacerbations. Identifying clinical subtypes among patients experiencing COPD exacerbations (ECOPD) could help better understand the pathophysiologic mechanisms involved in exacerbations, establish different strategies of treatment, and improve the process of care and patient prognosis. The objective of this study was to identify subtypes of ECOPD patients attending emergency departments using clinical variables and to validate the results using several outcomes. We evaluated data collected as part of the IRYSS-COPD prospective cohort study conducted in 16 hospitals in Spain. Variables collected from ECOPD patients attending one of the emergency departments included arterial blood gases, presence of comorbidities, previous COPD treatment, baseline severity of COPD, and previous hospitalizations for ECOPD. Patient subtypes were identified by combining results from multiple correspondence analysis and cluster analysis. Results were validated using key outcomes of ECOPD evolution. Four ECOPD subtypes were identified based on the severity of the current exacerbation and general health status (largely a function of comorbidities): subtype A (n = 934), neither high comorbidity nor severe exacerbation; subtype B (n = 682), moderate comorbidities; subtype C (n = 562), severe comorbidities related to mortality; and subtype D (n = 309), very severe process of exacerbation, significantly related to mortality and admission to an intensive care unit. Subtype D experienced the highest rate of mortality, admission to an intensive care unit and need for noninvasive mechanical ventilation, followed by subtype C. Subtypes A and B were primarily related to other serious complications. Hospitalization rate was more than 50% for all the subtypes, although significantly higher for subtypes C and D than for subtypes A and B. These results could help identify characteristics to categorize ECOPD patients for more appropriate care, and help test interventions and treatments in subgroups with poor evolution and outcomes.

An enactive and dynamical systems theory account of dyadic relationships

Many social relationships are a locus of struggle and suffering, either at the individual or interactional level. In this paper we explore why this is the case and suggest a modeling approach for dyadic interactions and the well-being of the participants. To this end we bring together an enactive approach to self with dynamical systems theory. Our basic assumption is that the quality of any social interaction or relationship fundamentally depends on the nature and constitution of the individuals engaged in these interactions. From an enactive perspective the self is conceived as an embodied and socially enacted autonomous system striving to maintain an identity. This striving involves a basic two-fold goal: the ability to exist as an individual in one's own right, while also being open to and affected by others. In terms of dynamical systems theory one can thus consider the individual self as a self-other organized system represented by a phase space spanned by the dimensions of distinction and participation, where attractors can be defined. Based on two everyday examples of dyadic relationship we propose a simple model of relationship dynamics, in which struggle or well-being in the dyad is analyzed in terms of movements of dyadic states that are in tension or in harmony with individually developed attractors. Our model predicts that relationships can be sustained when the dyad develops a new joint attractor toward which dyadic states tend to move, and well-being when this attractor is in balance with the individuals' attractors. We outline how this can inspire research on psychotherapy. The psychotherapy process itself provides a setting that supports clients to become aware how they fare with regards to the two-fold norm of distinction and participation and develop, through active engagement between client (or couple) and therapist, strategies to co-negotiate their self-organization.

Wage differentials between permanent and temporary workers: Firm and occupational segregation

Published as an article in: Moneda y Crédito (2004), 219, pp.: 43-68.

Scenario Cluster Lagrangian Decomposition in two stage stochastic mixed 0-1 optimization

In this paper we introduce four scenario Cluster based Lagrangian Decomposition (CLD) procedures for obtaining strong lower bounds to the (optimal) solution value of two-stage stochastic mixed 0-1 problems. At each iteration of the Lagrangian based procedures, the traditional aim consists of obtaining the solution value of the corresponding Lagrangian dual via solving scenario submodels once the nonanticipativity constraints have been dualized. Instead of considering a splitting variable representation over the set of scenarios, we propose to decompose the model into a set of scenario clusters. We compare the computational performance of the four Lagrange multiplier updating procedures, namely the Subgradient Method, the Volume Algorithm, the Progressive Hedging Algorithm and the Dynamic Constrained Cutting Plane scheme for different numbers of scenario clusters and different dimensions of the original problem. Our computational experience shows that the CLD bound and its computational effort depend on the number of scenario clusters to consider. In any case, our results show that the CLD procedures outperform the traditional LD scheme for single scenarios both in the quality of the bounds and computational effort. All the procedures have been implemented in a C++ experimental code. A broad computational experience is reported on a test of randomly generated instances by using the MIP solvers COIN-OR and CPLEX for the auxiliary mixed 0-1 cluster submodels, this last solver within the open source engine COIN-OR. We also give computational evidence of the model tightening effect that the preprocessing techniques, cut generation and appending and parallel computing tools have in stochastic integer optimization. Finally, we have observed that the plain use of both solvers does not provide the optimal solution of the instances included in the testbed with which we have experimented but for two toy instances in affordable elapsed time. On the other hand the proposed procedures provide strong lower bounds (or the same solution value) in a considerably shorter elapsed time for the quasi-optimal solution obtained by other means for the original stochastic problem.

Is the Current Regulation of the VIII Division European Anchovy Optimal?

This paper sets out to assess the workability of the regulation currently in force in the European anchovy fishery of the VIII division. Particular attention is paid to the importance of the institutional regime in the allocation of natural resources. The study uses a bio-economic approach and takes into account the fact that, not only the European Union and the individual countries involved, but also some of the resource users or appropriators intervene in its management. In order to compare the effectiveness of the rules which, at the various levels, have been set up to restrict exploitation of the resource, the anchovy fishery is simulated in two extreme situations: open access and sole ownership. The results obtained by effective management will then be contrasted with those obtained from the maximum and zero profit objectives related with the two above-mentioned scenarios. Thus, if the real data come close to those derived from the sole ownership model it will have to be acknowledged that the rules at present in force are optimal. If, on the other hand, the situation more closely approach the results obtained from the open access model, we will endeavour in our conclusions to provide suggestions for economic policy measures that might improve the situation in the fishery.

Rhythm Analysis during Cardiopulmonary Resuscitation: Past, Present, and Future

Survival from out-of-hospital cardiac arrest depends largely on two factors: early cardiopulmonary resuscitation (CPR) and early defibrillation. CPR must be interrupted for a reliable automated rhythm analysis because chest compressions induce artifacts in the ECG. Unfortunately, interrupting CPR adversely affects survival. In the last twenty years, research has been focused on designing methods for analysis of ECG during chest compressions. Most approaches are based either on adaptive filters to remove the CPR artifact or on robust algorithms which directly diagnose the corrupted ECG. In general, all the methods report low specificity values when tested on short ECG segments, but how to evaluate the real impact on CPR delivery of continuous rhythm analysis during CPR is still unknown. Recently, researchers have proposed a new methodology to measure this impact. Moreover, new strategies for fast rhythm analysis during ventilation pauses or high-specificity algorithms have been reported. Our objective is to present a thorough review of the field as the starting point for these late developments and to underline the open questions and future lines of research to be explored in the following years.