A pragmatic approach to evaluate alternative indicators to GDP

The serious economic crisis broken out in 2008 highly stressed the limitations of GDP used as a well-being indicator and as a predictive tool for economy. This induced the need to identify new indicators able to link the economic prosperity of a country to aspects of sustainable development and externalities, both positive and negative, in the long run. The aim of this paper is to introduce a structured approach which supports the choice or the construction of alternative indicators to GDP. The starting point is the definition of what a well-being indicator actually should represent according to the Recommendations of the Stiglitz-Sen-Fitoussi Report on the measurement of economic performance and social progress. Then the paper introduces a systematic procedure for the analysis of well-being indicators. The different phases of this procedure entail the checking of indicators technical properties and their effect on the representational efficacy. Finally, some of the most representative well-being indicators drawn from the literature are compared and a detailed application example is propose

Prediction of Allogeneic Hematopoietic Stem-Cell Transplantation Mortality 100 Days After Transplantation Using a Machine Learning Algorithm: A European Group for Blood and Marrow Transplantation Acute Leukemia Working Party Retrospective Data Mining Study

PURPOSE: Allogeneic hematopoietic stem-cell transplantation (HSCT) is potentially curative for acute leukemia (AL), but carries considerable risk. Machine learning algorithms, which are part of the data mining (DM) approach, may serve for transplantation-related mortality risk prediction. PATIENTS AND METHODS: This work is a retrospective DM study on a cohort of 28,236 adult HSCT recipients from the AL registry of the European Group for Blood and Marrow Transplantation. The primary objective was prediction of overall mortality (OM) at 100 days after HSCT. Secondary objectives were estimation of nonrelapse mortality, leukemia-free survival, and overall survival at 2 years. Donor, recipient, and procedural characteristics were analyzed. The alternating decision tree machine learning algorithm was applied for model development on 70% of the data set and validated on the remaining data. RESULTS: OM prevalence at day 100 was 13.9% (n=3,936). Of the 20 variables considered, 10 were selected by the model for OM prediction, and several interactions were discovered. By using a logistic transformation function, the crude score was transformed into individual probabilities for 100-day OM (range, 3% to 68%). The model's discrimination for the primary objective performed better than the European Group for Blood and Marrow Transplantation score (area under the receiver operating characteristics curve, 0.701 v 0.646; P<.001). Calibration was excellent. Scores assigned were also predictive of secondary objectives. CONCLUSION: The alternating decision tree model provides a robust tool for risk evaluation of patients with AL before HSCT, and is available online (http://bioinfo.lnx.biu.ac.il/∼bondi/web1.html). It is presented as a continuous probabilistic score for the prediction of day 100 OM, extending prediction to 2 years. The DM method has proved useful for clinical prediction in HSCT

Gene Expression Profiling and Molecular Characterization of Antimony Resistance in Leishmania amazonensis

Leishmania are unicellular microorganisms that can be transmitted to humans by the bite of sandflies. They cause a spectrum of diseases called leishmaniasis, which are classified as neglected tropical diseases by the World Health Organization. The treatment of leishmaniasis is based on the administration of antimony-containing drugs. These drugs have been used since 1947 and still constitute the mainstay for leishmaniasis treatment in several countries. One of the problems with these compounds is the emergence of resistance. Our work seeks to understand how these parasites become resistant to the drug. We studied antimony-resistant Leishmania amazonensis mutants. We analyzed gene expression at the whole genome level in antimony-resistant parasites and identified mechanisms used by Leishmania for resistance. This work could help us in developing new strategies for treatment in endemic countries where people are unresponsive to antimony-based chemotherapy. The identification of common mechanisms among different species of resistant parasites may also contribute to the development of diagnostic kits to identify and monitor the spread of resistance

Inhibition of Effector Function but Not T Cell Activation and Increase in FoxP3 Expression in T Cells Differentiated in the Presence of PP14

Background: T-helper polarization of naïve T cells is determined by a complex mechanism that involves many factors, eventually leading to activation of Th1, Th2, or Th17 responses or alternatively the generation of regulatory T cells. Placental Protein 14 (PP14) is a 28 kDa glycoprotein highly secreted in early pregnancy that is able to desensitize T cell receptor (TCR) signaling and modulate T cell activation. Methodology/Principal Findings: Prolonged antigen-specific stimulation of T cells in the presence of PP14 resulted in an impaired secretion of IFN-c, IL-5 and IL-17 upon restimulation, although the cells proliferated and expressed activation markers. Furthermore, the generation of regulatory CD4 + CD25 high Foxp3 + T cells was induced in the presence of PP14, in both antigen-specific as well as polyclonal stimulation. In accordance with previous reports, we found that the induction of FoxP3 expression by PP14 is accompanied by down regulation of the PI3K-mTOR signaling pathway. Conclusions/Significance: These data suggest that PP14 arrests T cells in a unique activated state that is not accompanied with the acquisition of effector function, together with promoting the generation of regulatory T cells. Taken together, our results may elucidate the role of PP14 in supporting immune tolerance in pregnancy by reducing T cell effector function

A discounting framework for regulatory impact analysis

This article presents a methodology designed to facilitate the systematic comparison of alternative discounting procedures for the costs and benefits of industrial regulatory activity. A discounting framework developed by Bradford is adapted for use in the context of industry regulation. Within this framework, the choice among the various discounting procedures is reduced to a choice between assumptions about various economic and financial parameter values. As an illustration of the way in which the framework can be applied, the article includes an examination of the validity of parameter assumptions implied by the discounting approach currently used by regulatory agencies in their analyses of regulations affecting the motor vehicle industry. Several hypothetical programs are analyzed to demonstrate the broad differences in program treatment that might be expected if this current discounting approach were replaced by procedures generated within the framework from more reasonable parameter assumptions. Sensitivity of the benefit/cost calculations to uncertainty about underlying parameters is also briefly discussed.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/45445/1/11077_2004_Article_BF00149750.pd

Transcriptional and genomic parallels between the monoxenous parasite Herpetomonas muscarum and Leishmania

Trypanosomatid parasites are causative agents of important human and animal diseases such as sleeping sickness and leishmaniasis. Most trypanosomatids are transmitted to their mammalian hosts by insects, often belonging to Diptera (or true flies). These are called dixenous trypanosomatids since they infect two different hosts, in contrast to those that infect just insects (monoxenous). However, it is still unclear whether dixenous and monoxenous trypanosomatids interact similarly with their insect host, as fly-monoxenous trypanosomatid interaction systems are rarely reported and under-studied–despite being common in nature. Here we present the genome of monoxenous trypanosomatid Herpetomonas muscarum and discuss its transcriptome during in vitro culture and during infection of its natural insect host Drosophila melanogaster. The H. muscarum genome is broadly syntenic with that of human parasite Leishmania major. We also found strong similarities between the H. muscarum transcriptome during fruit fly infection, and those of Leishmania during sand fly infections. Overall this suggests Drosophila-Herpetomonas is a suitable model for less accessible insect-trypanosomatid host-parasite systems such as sand fly-Leishmania

Drug Resistance in Eukaryotic Microorganisms

Eukaryotic microbial pathogens are major contributors to illness and death globally. Although much of their impact can be controlled by drug therapy as with prokaryotic microorganisms, the emergence of drug resistance has threatened these treatment efforts. Here, we discuss the challenges posed by eukaryotic microbial pathogens and how these are similar to, or differ from, the challenges of prokaryotic antibiotic resistance. The therapies used for several major eukaryotic microorganisms are then detailed, and the mechanisms that they have evolved to overcome these therapies are described. The rapid emergence of resistance and the restricted pipeline of new drug therapies pose considerable risks to global health and are particularly acute in the developing world. Nonetheless, we detail how the integration of new technology, biological understanding, epidemiology and evolutionary analysis can help sustain existing therapies, anticipate the emergence of resistance or optimize the deployment of new therapies