21 research outputs found

    Bonification de la démarche de planification de la conservation des milieux naturels

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    Le maintien de la biodiversitĂ© et des biens et services Ă©cologiques est essentiel pour la survie et le bien-ĂȘtre de l’homme ainsi que pour la pĂ©rennitĂ© des Ă©cosystĂšmes. De nombreuses pressions engendrĂ©es par les populations humaines viennent cependant mettre en pĂ©ril la biodiversitĂ©. Au QuĂ©bec, les efforts mis en place par le gouvernement et les instances municipales ne semblent pas suffisants pour freiner le rythme d’appauvrissement de la biodiversitĂ© du territoire. À cet Ă©gard, la dĂ©marche de planification de la conservation et l’utilisation des compĂ©tences et des outils urbanistiques par les instances municipales pour intĂ©grer la conservation dans l’amĂ©nagement de leur territoire mĂ©riteraient d’ĂȘtre amĂ©liorĂ©es. L’objectif du prĂ©sent essai est donc de bonifier la dĂ©marche de planification de la conservation pour que celle-ci assure la pĂ©rennitĂ© de la biodiversitĂ© et de l’ensemble des milieux naturels. À cet effet, une liste de critĂšres est sĂ©lectionnĂ©e, par l’entremise d’une analyse comparative, pour caractĂ©riser de maniĂšre complĂšte tous les milieux naturels. Une sĂ©rie de seuils est Ă©galement proposĂ©e afin de renseigner sur l’état des milieux naturels et de guider les dĂ©cideurs dans la planification de la conservation. Puis, une amĂ©lioration de la dĂ©marche de planification de la conservation est suggĂ©rĂ©e en proposant les Ă©tapes qui devraient ĂȘtre essentielles et en offrant des recommandations quant aux Ă©lĂ©ments Ă  considĂ©rer et ceux Ă  Ă©viter. La validitĂ© de la dĂ©marche de planification de la conservation repose sur plusieurs considĂ©rations. D’abord, il est essentiel que la dĂ©marche soit rĂ©alisĂ©e de maniĂšre rĂ©flĂ©chie et, surtout, qu’elle soit adaptĂ©e Ă  la rĂ©alitĂ© du territoire afin d’augmenter sa cohĂ©rence et son succĂšs. Dans cette optique, des objectifs clairs doivent ĂȘtre fixĂ©s dans le but d’assurer la pĂ©rennitĂ© de la biodiversitĂ© et des milieux naturels. Ces objectifs doivent donc veiller Ă  assurer la pĂ©rennitĂ©, mais aussi la reprĂ©sentativitĂ©, la complĂ©mentaritĂ©, l’intĂ©gritĂ© et la rĂ©silience des milieux naturels, ainsi qu’à conserver leurs Ă©lĂ©ments irremplaçables. Ensuite, il est recommandĂ© d’utiliser la totalitĂ© des critĂšres proposĂ©s pour caractĂ©riser et Ă©valuer la valeur des milieux naturels, car ils permettent de dĂ©crire les milieux selon l’ensemble de leurs composantes. Ceci permet d’identifier adĂ©quatement les milieux d’intĂ©rĂȘt et de planifier la conservation en consĂ©quence. Au moment de la priorisation des milieux naturels d’intĂ©rĂȘt, les particularitĂ©s de chacun devraient ĂȘtre considĂ©rĂ©es pour assurer la cohĂ©rence de la planification et l’atteinte des objectifs de conservation. L’utilisation des seuils proposĂ©s est aussi conseillĂ©e, car ils permettent de guider la dĂ©marche de planification de la conservation sur le territoire selon l’état des milieux naturels. Lorsque l’état des milieux naturels d’un territoire est prĂ©caire, la planification de la conservation devrait chercher Ă  le ramener Ă  un Ă©tat viable. En tout temps, il est important d’adopter une vision Ă  plus large Ă©chelle, au-delĂ  des limites du territoire, pour assurer une planification rĂ©flĂ©chie. De plus, une meilleure collaboration entre les MRC, les municipalitĂ©s et les acteurs de la conservation serait souhaitable afin de combiner les efforts de conservation sur un territoire. IdĂ©alement, il serait pertinent de faire du plan de conservation un outil urbanistique obligatoire et d’octroyer des compĂ©tences plus robustes aux instances municipales Ă  l’égard de la conservation sur leur territoire

    Development and validation of an instrument to measure health-related out-of-pocket costs : the cost for patients questionnaire

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    Objective: The growth of healthcare spending is a major concern for insurers and governments but also for patients whose health problems may result in costs going beyond direct medical costs. To develop a comprehensive tool to measure direct and indirect costs of a health condition for patients and their families to various outpatient contexts. Methods: We conducted a content and face validation including results of a systematic review to identify the items related to direct and indirect costs for patients or their families and an online Delphi to determine the cost items to retain. We conducted a pilot test-retest with 18 naive participants and analyzed data calculating intraclass correlation and kappa coefficients. Results: An initial list of 34 items was established from the systematic review. Each round of the Delphi panel incorporated feedback from the previous round until a strong consensus was achieved. After 4 rounds of the Delphi to reach consensus on items to be included and wording, the questionnaire had a total of 32 cost items. For the test-retest, kappa coefficients ranged from 20.11 to 1.00 (median = 0.86), and intraclass correlation ranged from 20.02 to 0.99 (median = 0.62). Conclusions: A rigorous process of content and face development was implemented for the Cost for Patients Questionnaire, and this study allowed to set a list of cost elements to be considered from the patient's perspective. Additional research including a test-retest with a larger sample will be part of a subsequent validation strategy

    Detection of a novel, integrative aging process suggests complex physiological integration

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    Abstract: Many studies of aging examine biomarkers one at a time, but complex systems theory and network theory suggest that interpretations of individual markers may be context-dependent. Here, we attempted to detect underlying processes governing the levels ofmany biomarkers simultaneously by applying principal components analysis to 43 common clinical biomarkers measured longitudinally in 3694 humans from three longitudinal cohort studies on two continents (Women’s Health and Aging I & II, InCHIANTI, and the Baltimore Longitudinal Study on Aging). The first axis was associated with anemia, inflammation, and low levels of calcium and albumin. The axis structure was precisely reproduced in all three populations and in all demographic sub-populations (by sex, race, etc.); we call the process represented by the axis “integrated albunemia.” Integrated albunemia increases and accelerates with age in all populations, and predicts mortality and frailty – but not chronic disease – even after controlling for age. This suggests a role in the aging process, though causality is not yet clear. Integrated albunemia behaves more stably across populations than its component biomarkers, and thus appears to represent a higher-order physiological process emerging from the structure of underlying regulatory networks. If this is correct, detection of this process has substantial implications for physiological organizationmore generally

    Bonification de la démarche de planification de la conservation des milieux naturels

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    Le maintien de la biodiversitĂ© et des biens et services Ă©cologiques est essentiel pour la survie et le bien-ĂȘtre de l’homme ainsi que pour la pĂ©rennitĂ© des Ă©cosystĂšmes. De nombreuses pressions engendrĂ©es par les populations humaines viennent cependant mettre en pĂ©ril la biodiversitĂ©. Au QuĂ©bec, les efforts mis en place par le gouvernement et les instances municipales ne semblent pas suffisants pour freiner le rythme d’appauvrissement de la biodiversitĂ© du territoire. À cet Ă©gard, la dĂ©marche de planification de la conservation et l’utilisation des compĂ©tences et des outils urbanistiques par les instances municipales pour intĂ©grer la conservation dans l’amĂ©nagement de leur territoire mĂ©riteraient d’ĂȘtre amĂ©liorĂ©es. L’objectif du prĂ©sent essai est donc de bonifier la dĂ©marche de planification de la conservation pour que celle-ci assure la pĂ©rennitĂ© de la biodiversitĂ© et de l’ensemble des milieux naturels. À cet effet, une liste de critĂšres est sĂ©lectionnĂ©e, par l’entremise d’une analyse comparative, pour caractĂ©riser de maniĂšre complĂšte tous les milieux naturels. Une sĂ©rie de seuils est Ă©galement proposĂ©e afin de renseigner sur l’état des milieux naturels et de guider les dĂ©cideurs dans la planification de la conservation. Puis, une amĂ©lioration de la dĂ©marche de planification de la conservation est suggĂ©rĂ©e en proposant les Ă©tapes qui devraient ĂȘtre essentielles et en offrant des recommandations quant aux Ă©lĂ©ments Ă  considĂ©rer et ceux Ă  Ă©viter. La validitĂ© de la dĂ©marche de planification de la conservation repose sur plusieurs considĂ©rations. D’abord, il est essentiel que la dĂ©marche soit rĂ©alisĂ©e de maniĂšre rĂ©flĂ©chie et, surtout, qu’elle soit adaptĂ©e Ă  la rĂ©alitĂ© du territoire afin d’augmenter sa cohĂ©rence et son succĂšs. Dans cette optique, des objectifs clairs doivent ĂȘtre fixĂ©s dans le but d’assurer la pĂ©rennitĂ© de la biodiversitĂ© et des milieux naturels. Ces objectifs doivent donc veiller Ă  assurer la pĂ©rennitĂ©, mais aussi la reprĂ©sentativitĂ©, la complĂ©mentaritĂ©, l’intĂ©gritĂ© et la rĂ©silience des milieux naturels, ainsi qu’à conserver leurs Ă©lĂ©ments irremplaçables. Ensuite, il est recommandĂ© d’utiliser la totalitĂ© des critĂšres proposĂ©s pour caractĂ©riser et Ă©valuer la valeur des milieux naturels, car ils permettent de dĂ©crire les milieux selon l’ensemble de leurs composantes. Ceci permet d’identifier adĂ©quatement les milieux d’intĂ©rĂȘt et de planifier la conservation en consĂ©quence. Au moment de la priorisation des milieux naturels d’intĂ©rĂȘt, les particularitĂ©s de chacun devraient ĂȘtre considĂ©rĂ©es pour assurer la cohĂ©rence de la planification et l’atteinte des objectifs de conservation. L’utilisation des seuils proposĂ©s est aussi conseillĂ©e, car ils permettent de guider la dĂ©marche de planification de la conservation sur le territoire selon l’état des milieux naturels. Lorsque l’état des milieux naturels d’un territoire est prĂ©caire, la planification de la conservation devrait chercher Ă  le ramener Ă  un Ă©tat viable. En tout temps, il est important d’adopter une vision Ă  plus large Ă©chelle, au-delĂ  des limites du territoire, pour assurer une planification rĂ©flĂ©chie. De plus, une meilleure collaboration entre les MRC, les municipalitĂ©s et les acteurs de la conservation serait souhaitable afin de combiner les efforts de conservation sur un territoire. IdĂ©alement, il serait pertinent de faire du plan de conservation un outil urbanistique obligatoire et d’octroyer des compĂ©tences plus robustes aux instances municipales Ă  l’égard de la conservation sur leur territoire

    Treatise on the Conflict of Laws

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    <p>Loading importance is calculated as the loading divided by the sum of the absolute values of all loadings. These values are ordered from high (red, on bottom) to low (magenta, on top) for the first 20 loadings; remaining loadings are grouped together as “Other” in white. Accordingly, neutrophils have the strongest loading, then AST, then lymphocytes, etc. The order and colors are derived from the full analysis combining the three data sets (left column, top-left panel “All”) and applied to all other columns in the figure. Stability of loadings is indicated by conservation of loading heights across bars. For each panel, the loadings for the full data set are at left. Numbers indicate subset sample sizes. For all panels except BLSA, the 43-variable set is used; for BLSA there was insufficient sample size to perform PCA on subsets with 43 variables, so the 34-variable analysis is presented.</p

    Biomarker loading order and stability for PCA1 across datasets and subsets.

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    <p>Loading importance is calculated as the loading divided by the sum of the absolute values of all loadings. These values are ordered from high (red, on bottom) to low (magenta, on top) for the first 20 loadings; remaining loadings are grouped together as “Other” in white. Accordingly, hemoglobin has the strongest loading, then hematocrit, then albumin, etc. The order and colors are derived from the full analysis combining the first visits of individuals in all three datasets (top-left panel, left column, “All”) and applied to all other columns in the figure. Stability of loadings is indicated by conservation of loading heights across bars. (For an example of unstable loadings, see <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0116489#pone.0116489.g005" target="_blank">Fig. 5</a>.) For each panel, the loadings for the full dataset are at left. Numbers indicate subset sample sizes. For all panels except BLSA, the 43-variable set is used; for BLSA there was insufficient sample size to perform PCA on subsets with 43 variables, so the 34-variable analysis is presented.</p

    Age trajectories of PCA1 in the three data sets, based on Bayesian mixed models.

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    <p>(a) 34-variable data set; (b) 43-variable data set; (c) ages 20–50 only. In (a) and (b), BLSA and InCHIANTI are based on fixed quadratic models with a random (individual) intercept, while WHAS is based on a fixed linear model with a random intercept. Linear models with random intercept were used in (c). Credibility intervals are based on calculating, independently for each age, which of the 1000 iterations’ trajectories were in the 2.5<sup>th</sup> and 97.5<sup>th</sup> percentiles. Note that the better fit of the linear model for WHAS appears to be due to the more limited age range for this dataset.</p

    Age-adjusted biomarker loading order and stability for PCA1 across data sets and subsets.

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    <p>Loading importance is calculated as the loading divided by the sum of the absolute values of all loadings. These values are ordered from high (red, on bottom) to low (magenta, on top) for the first 20 loadings; remaining loadings are grouped together as “Other” in white. Accordingly, hemoglobin has the strongest loading, then hematocrit, then albumin, etc. The order and colors are derived from the full analysis combining the first visits of individuals in all three data sets (top-left panel, left column, “All”) and applied to all other columns in the figure. Stability of loadings is indicated by conservation of loading heights across bars. For each panel, the loadings for the full data set are at left. Numbers indicate subset sample sizes. For all panels except BLSA, the 43-variable set is used; for BLSA there was insufficient sample size to perform PCA on subsets with 43 variables, so the 34-variable analysis is presented.</p
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