Suchergebnisse

Localisation : Centre de documentation P. Bartoli, UMR LAMETA, Montpellier (S WPL 2011-01) ; Safeguarding tropical rainforests is one of the most important challenges for the future, particularly to mitigate climate change. The international community has actively sought international policy solutions to curb deforestation in tropical countries. Debt-for-nature swaps and certification of sustainable forest management have been implemented by NGOs. Some states are currently negotiating the implementation of the REDD (Reduced Emissions from Deforestation and Degradation) mechanism, a North-South financial transfer to compensate countries for avoided deforestation. However, little is known about the efficiency of these instruments. We argue that they may have a double effect: an expected direct impact on deforestation linked to the conditionalities of instruments, and an indirect impact due to their feedback effects on macroeconomic variables, affecting in turn the drivers of deforestation. The second effect is often overlooked by policy makers. The objective of the paper is to disentangle the two effects for different categories of forest countries. We conducted a panel data analysis for the period 1990-2005 and show that cluster analysis of tropical forest countries would be more relevant if it were based on relative forest endowment. On the basis of econometric results, we can recommend differentiating policy instruments according to the relative forest abundance of each country. Debt reduction programs contribute to the reduction of deforestation in all countries. Countries with abundant forests are locked in a development pathway based on overexploitation of their forests making them less responsive to incentive measures. In countries with average forest endowment, we recommend output-based REDD, whereas in countries with low forest cover, either inputbased or output-based REDD mechanisms should be efficient.

Localisation : Centre de documentation P. Bartoli, UMR LAMETA, Montpellier (S WPL 2011-01) ; Safeguarding tropical rainforests is one of the most important challenges for the future, particularly to mitigate climate change. The international community has actively sought international policy solutions to curb deforestation in tropical countries. Debt-for-nature swaps and certification of sustainable forest management have been implemented by NGOs. Some states are currently negotiating the implementation of the REDD (Reduced Emissions from Deforestation and Degradation) mechanism, a North-South financial transfer to compensate countries for avoided deforestation. However, little is known about the efficiency of these instruments. We argue that they may have a double effect: an expected direct impact on deforestation linked to the conditionalities of instruments, and an indirect impact due to their feedback effects on macroeconomic variables, affecting in turn the drivers of deforestation. The second effect is often overlooked by policy makers. The objective of the paper is to disentangle the two effects for different categories of forest countries. We conducted a panel data analysis for the period 1990-2005 and show that cluster analysis of tropical forest countries would be more relevant if it were based on relative forest endowment. On the basis of econometric results, we can recommend differentiating policy instruments according to the relative forest abundance of each country. Debt reduction programs contribute to the reduction of deforestation in all countries. Countries with abundant forests are locked in a development pathway based on overexploitation of their forests making them less responsive to incentive measures. In countries with average forest endowment, we recommend output-based REDD, whereas in countries with low forest cover, either inputbased or output-based REDD mechanisms should be efficient.

Localisation : Centre de documentation P. Bartoli, UMR LAMETA, Montpellier (S WPL 2011-01) ; Safeguarding tropical rainforests is one of the most important challenges for the future, particularly to mitigate climate change. The international community has actively sought international policy solutions to curb deforestation in tropical countries. Debt-for-nature swaps and certification of sustainable forest management have been implemented by NGOs. Some states are currently negotiating the implementation of the REDD (Reduced Emissions from Deforestation and Degradation) mechanism, a North-South financial transfer to compensate countries for avoided deforestation. However, little is known about the efficiency of these instruments. We argue that they may have a double effect: an expected direct impact on deforestation linked to the conditionalities of instruments, and an indirect impact due to their feedback effects on macroeconomic variables, affecting in turn the drivers of deforestation. The second effect is often overlooked by policy makers. The objective of the paper is to disentangle the two effects for different categories of forest countries. We conducted a panel data analysis for the period 1990-2005 and show that cluster analysis of tropical forest countries would be more relevant if it were based on relative forest endowment. On the basis of econometric results, we can recommend differentiating policy instruments according to the relative forest abundance of each country. Debt reduction programs contribute to the reduction of deforestation in all countries. Countries with abundant forests are locked in a development pathway based on overexploitation of their forests making them less responsive to incentive measures. In countries with average forest endowment, we recommend output-based REDD, whereas in countries with low forest cover, either inputbased or output-based REDD mechanisms should be efficient.

The number of rare diseases is assessed between 5000 and 8000 distinct pathologies. Individually, these diseases are rare, but together they represent a major health problem at the population level. Because of the limited number of patients with certain diseases and the lack of knowledge about them, diagnosis for patients is often delayed.More than 80 % of rare diseases have a genetic origin, the vast majority of which are monogenic. The democratization of high-throughput sequencing technologies since 2005 has allowed the massive acquisition of genomic data from both healthy individuals and patients. However, establishing a diagnosis of a rare disease with current molecular biology technologies remains difficult.The industrial-scale application of exome and genome sequencing analyses associated with the constant increase in medical knowledge represents a concrete hope to bring a diagnosis to the majority of patients concerned by a rare disease suspected to be genetic.This thesis was particularly interested in the implementation of processes for constitutional genome-wide sequencing data analysis following industrial standards and bioinformatics good practices. Then, in the detection of copy number variations from exome sequencing data, but under-explored by many laboratories.Finally, this thesis has allowed the development and the initiation of collaborations still active today. This has been concretized by a study on detection of small somatic variations in an in vitro model of cancer development linked to the cellular microenvironment, the detection of structural variations using the innovative Oxford Nanopore sequencing technology and the comparison of prioritization methodologies for genetic variations using clinical descriptions based on HPO terms. ; Le nombre de maladies rares est estimé à entre 5000 et 8000 pathologies distinctes. Elles sont individuellement rares puisque par définition elles affectent moins de 1 individu sur 2000 dans la population générale, mais leur nombre les rend collectivement ...

The number of rare diseases is assessed between 5000 and 8000 distinct pathologies. Individually, these diseases are rare, but together they represent a major health problem at the population level. Because of the limited number of patients with certain diseases and the lack of knowledge about them, diagnosis for patients is often delayed.More than 80 % of rare diseases have a genetic origin, the vast majority of which are monogenic. The democratization of high-throughput sequencing technologies since 2005 has allowed the massive acquisition of genomic data from both healthy individuals and patients. However, establishing a diagnosis of a rare disease with current molecular biology technologies remains difficult.The industrial-scale application of exome and genome sequencing analyses associated with the constant increase in medical knowledge represents a concrete hope to bring a diagnosis to the majority of patients concerned by a rare disease suspected to be genetic.This thesis was particularly interested in the implementation of processes for constitutional genome-wide sequencing data analysis following industrial standards and bioinformatics good practices. Then, in the detection of copy number variations from exome sequencing data, but under-explored by many laboratories.Finally, this thesis has allowed the development and the initiation of collaborations still active today. This has been concretized by a study on detection of small somatic variations in an in vitro model of cancer development linked to the cellular microenvironment, the detection of structural variations using the innovative Oxford Nanopore sequencing technology and the comparison of prioritization methodologies for genetic variations using clinical descriptions based on HPO terms. ; Le nombre de maladies rares est estimé à entre 5000 et 8000 pathologies distinctes. Elles sont individuellement rares puisque par définition elles affectent moins de 1 individu sur 2000 dans la population générale, mais leur nombre les rend collectivement ...

The number of rare diseases is assessed between 5000 and 8000 distinct pathologies. Individually, these diseases are rare, but together they represent a major health problem at the population level. Because of the limited number of patients with certain diseases and the lack of knowledge about them, diagnosis for patients is often delayed.More than 80 % of rare diseases have a genetic origin, the vast majority of which are monogenic. The democratization of high-throughput sequencing technologies since 2005 has allowed the massive acquisition of genomic data from both healthy individuals and patients. However, establishing a diagnosis of a rare disease with current molecular biology technologies remains difficult.The industrial-scale application of exome and genome sequencing analyses associated with the constant increase in medical knowledge represents a concrete hope to bring a diagnosis to the majority of patients concerned by a rare disease suspected to be genetic.This thesis was particularly interested in the implementation of processes for constitutional genome-wide sequencing data analysis following industrial standards and bioinformatics good practices. Then, in the detection of copy number variations from exome sequencing data, but under-explored by many laboratories.Finally, this thesis has allowed the development and the initiation of collaborations still active today. This has been concretized by a study on detection of small somatic variations in an in vitro model of cancer development linked to the cellular microenvironment, the detection of structural variations using the innovative Oxford Nanopore sequencing technology and the comparison of prioritization methodologies for genetic variations using clinical descriptions based on HPO terms. ; Le nombre de maladies rares est estimé à entre 5000 et 8000 pathologies distinctes. Elles sont individuellement rares puisque par définition elles affectent moins de 1 individu sur 2000 dans la population générale, mais leur nombre les rend collectivement ...

Avec le soutien du Comité pour la science ouverte (Ministère de l'Enseignement supérieur, de la Recherche et de l'Innovation) ; The guide on the opening of research data (or Open Data) is intended to support the employees of educational institutions and research organisations in adopting a reasonable approach to opening research data. It is the result of the study of an inter-organisation working group led by the National Institute for Agricultural Research (INRA). It is not an exhaustive guide and is intended to for information purposes alone. It attempts to answer the most common questions such employees may be faced with such as whether it is a voluntary approach or is imposed by regulations and whether it corresponds with their institution's own objectives. The legal framework is cited if it exists. However, the readers are made aware that the legal landscape on this subject is continually shifting and that they need to refer their institution's Open Data policy. ; Le guide sur l'ouverture des données de recherche (ou Open Data) a pour vocation d'accompagner les agents des établissements concernés (établissements d'enseignement et organismes de recherche) dans une démarche d'ouverture raisonnée des données de recherche. Il est issu des réflexions d'un groupe de travail inter-organismes animé par l'INRA. Il ne prétend pas à l'exhaustivité ; il est fourni uniquement à titre d'information. Il tente de répondre aux questions les plus courantes auxquelles ils pourront être confrontés que cette démarche soit volontaire et réponde aux objectifs de l'établissement ou qu'elle soit imposée par la réglementation. Le cadre légal est cité, lorsqu'il existe. L'attention du lecteur est toutefois attirée sur le paysage très mouvant du droit sur ce sujet et sur la nécessité de se référer à la politique de son établissement en matière d'Open Data.

Avec le soutien du Comité pour la science ouverte (Ministère de l'Enseignement supérieur, de la Recherche et de l'Innovation) ; The guide on the opening of research data (or Open Data) is intended to support the employees of educational institutions and research organisations in adopting a reasonable approach to opening research data. It is the result of the study of an inter-organisation working group led by the National Institute for Agricultural Research (INRA). It is not an exhaustive guide and is intended to for information purposes alone. It attempts to answer the most common questions such employees may be faced with such as whether it is a voluntary approach or is imposed by regulations and whether it corresponds with their institution's own objectives. The legal framework is cited if it exists. However, the readers are made aware that the legal landscape on this subject is continually shifting and that they need to refer their institution's Open Data policy. ; Le guide sur l'ouverture des données de recherche (ou Open Data) a pour vocation d'accompagner les agents des établissements concernés (établissements d'enseignement et organismes de recherche) dans une démarche d'ouverture raisonnée des données de recherche. Il est issu des réflexions d'un groupe de travail inter-organismes animé par l'INRA. Il ne prétend pas à l'exhaustivité ; il est fourni uniquement à titre d'information. Il tente de répondre aux questions les plus courantes auxquelles ils pourront être confrontés que cette démarche soit volontaire et réponde aux objectifs de l'établissement ou qu'elle soit imposée par la réglementation. Le cadre légal est cité, lorsqu'il existe. L'attention du lecteur est toutefois attirée sur le paysage très mouvant du droit sur ce sujet et sur la nécessité de se référer à la politique de son établissement en matière d'Open Data.

This paper compares the relative productive efficiencies of four models of primary care service delivery using the data envelopment analysis method on 130 primary care practices in Ontario, Canada. A quality-controlled measure of output and two input scenarios are employed: one with full-time-equivalent labour inputs and the other with total expenditures. Regression analysis controls for the mix of patients in the practice population. Overall, we find that community health centres fare the worst when it comes to relative efficiency scores.

Extracting public opinion by analyzing Big Social data has grown substantially due to its interactive nature, in real time. In fact, our actions on social media generate digital traces that are closely related to our personal lives and can be used to accompany major events by analysing peoples' behavior. It is in this context that we are particularly interested in Big Data analysis methods. The volume of these daily-generated traces increases exponentially creating massive loads of information, known as big data. Such important volume of information cannot be stored nor dealt with using the conventional tools, and so new tools have emerged to help us cope with the big data challenges. For this, the aim of the first part of this manuscript is to go through the pros and cons of these tools, compare their respective performances and highlight some of its interrelated applications such as health, marketing and politics. Also, we introduce the general context of big data, Hadoop and its different distributions. We provide a comprehensive overview of big data tools and their related applications.The main contribution of this PHD thesis is to propose a generic analysis approach to automatically detect trends on given topics from big social data. Indeed, given a very small set of manually annotated hashtags, the proposed approach transfers information from hashtags known sentiments (positive or negative) to individual words. The resulting lexical resource is a large-scale lexicon of polarity whose efficiency is measured against different tasks of sentiment analysis. The comparison of our method with different paradigms in literature confirms the impact of our method to design accurate sentiment analysis systems. Indeed, our model reaches an overall accuracy of 90.21%, significantly exceeding the current models on social sentiment analysis. ; Extraire l'opinion publique en analysant les Big Social data a connu un essor considérable en raison de leur nature interactive, en temps réel. En effet, les données issues des ...