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Vertex-files for the paper "High spatial overlap but diverging age-related trajectories of cortical magnetic resonance imaging markers aiming to represent intracortical myelin and microstructure" published in Human Brain Mapping by Parent and colleagues (2023). https://doi.org/10.1002/hbm.26259 Anonymized vertex-wise files for each subject for the following cortical markers: Boundary Sharpness Coefficient (BSC) Grey-white Matter Contrast (GWC) Cortical thickness (CT) T1-weighted/T2-weighted (T1w/T2w) ratio sampled at 25% of CT in grey matter, 50% of CT in grey matter, and 25% of CT translated in the direction of superficial white matter (SWM) Each vertex file is either surface-smoothed with a 5 mm full-width half-max (FWHM) heat kernel (supplementary analyses) or a 20 mm FWHM heat kernel (main analyses). Markers are further either in raw form or residualized for mean curvature at the vertex level.

White matter hyperintensities (WMHs) are clinically significant MRI abnormalities often detected in the elderly and early stages of Alzheimer’s Disease. They are indicative of vascular pathology but represent a mixture of microstructural tissue alterations that is highly variable between individuals. To better understand these alterations, we leveraged the signal of different MRI contrasts sampled within WMHs, which have differential sensitivity to microstructural properties. Subsequently, we sought to examine the asso of these WMH signal measures to clinically-relevant measures such as cortical and global brain atrophy, cognitive function, diagnostic and demographic differences, and Alzheimer’s Disease-relevant cardiovascular risk factors.Our sample of 118 subjects was composed of healthy controls (n=30), high-risk of Alzheimer’s Disease due to familial history (n=47), mild cognitive impairment (n=32), and clinical Alzheimer’s Disease (n=9) as a means of ascertaining a spectrum of impairment. We sampled the median signal within WMHs on weighted MRI images that are commonly acquired (T1-weighted [T1w], T2-weighted [T2w], T1w/T2w ratio, Fluid-Attenuated Inversion Recovery [FLAIR]), and the relaxation times from quantitative T1 (qT1) and T2* (qT2*) images. Main analyses were performed with a periventricular/deep/superficial white matter parcellation and were repeated with a lobar white matter parcellation.We demonstrated that the correlations between WMH signal measures were variable, suggesting that they are likely influenced by different microstructural properties. We observed that the WMH qT2* and FLAIR measures displayed different age- and disease-related trends compared to normal-appearing white matter, highlighting sensitivity to WMH-specific tissue deterioration. Further, WMH qT2* particularly in periventricular and occipital white matter regions was consistently associated with several of our clinical variables of interest using both parcellation schemes in univariate analyses, and further showed high contributions to a pattern of brain variables that was associated with age and cognitive variables in multivariate Partial Least Squares Correlation analyses. qT1 and FLAIR measures showed consistent clinical relationships in multivariate analyses only, while T1w, T2w, and T1w/T2w ratio measures were not consistently associated with clinical variables.We observed that the qT2* signal was sensitive to clinically-relevant microstructural tissue alterations specific to WMHs. Combining volumetric and signal measures of WMH, particularly qT2* and to a lesser extent qT1 and FLAIR, should be considered to more precisely characterize the severity of WMHs in vivo. These findings may have implications in determining the reversibility of WMHs and potential efficacy of cardio- and cerebrovascular treatments.

This technical note describes the effects of different data reduction methods and sample sizes for neuroimaging studies in the context of canonical correlation analysis (CCA). CCA is a multivariate statistical technique which has gained increasing popularity in neuroimaging research in recent years. Here, we investigate the parcellation methods’ impact on elucidating neuroanatomical relationships (based on cortical thickness) with known risk factors related to Alzheimer’s disease risk using data from the UK Biobank. The cortical thickness values were parcellated using four common methods in neuroimaging (atlas-based parcellation, spectral clustering, principal component analysis, and independent component analysis) and results from CCA were compared. The results show that the choice of parcellation technique impacts the strength and significance of the correlation between the brain and behaviours. Principal component analysis and independent component analysis result in the strongest correlations.Additionally, we show that regardless of parcellation technique, smaller sample sizes of participants result in inflated correlation strength and significance.

The menopause transition has been repeatedly associated with decreased cognitive performance and increased incidence of Alzheimer’s Disease (AD), particularly when it is induced surgically1,2or takes place at a younger age3,4. However, there are very few studies that use neuroimaging techniques to examine the effects of these variables in aggregate and in a large sample. Here, we use data from thousands of participants from the UK Biobank to assess the relationship between menopausal status, menopause type (surgical or natural), and age at menopause with cognition, AD, and neuroanatomical measures derived from magnetic resonance imaging. We find that for brain and cognitive measures, menopausal status, menopause type and age at surgical menopause do not impact the brain; but that there is a positive correlation between anatomy, cognition and age at non-surgical menopause. These results do not align with previous reports in the literature with smaller samples. However, we confirm that both early and surgical menopause are associated with a higher risk of developing AD, indicating that early and abrupt ovarian hormone deprivation might contribute to the development of the disorder.

In this paper, we aim to identify the main factors that explain the occurrence and intensity of armed conflicts in a specific region, the Middle East and North Africa. We extend the linear Bayesian Model Averaging procedure to allow for the outcome, conflict intensity, to be measured on an ordered scale. Our research led us to expand the traditional dichotomous outcome, war versus peace, into a conflict intensity measure dissociating the state of peace from four different levels of violence. We provide strong evidence that not only demographical, institutional and socio-economic but also, environmental factors must be considered when analyzing conflict intensity. Those factors being innately intertwined, we advise policy makers not to rely on a particular theory to assess policy decisions. By paying special attention to neighboring states’ characteristics, our results reveal that political economy factors, historical legacy, climate and access to natural resources are key in identifying conflict severity. Finally, we show that model averaging predictions for ordered categorical outcomes improve upon the existing out-of-sample conflict prediction techniques.JEL Codes: O11, O15, C11, C52

SummaryThere has been an immense research focus on the topic of cortical reorganization in human evolution, but much less is known regarding the reorganization of subcortical circuits which are intimate working partners of the cortex. Here, by combining advanced image analysis techniques with comparative neuroimaging data, we systematically map organizational differences in striatal, pallidal and thalamic anatomy between humans and chimpanzees. We relate interspecies differences, a proxy for evolutionary changes, to genetics and behavioral correlates. We show that highly heritable morphological measures are significantly expanded across species, in contrast to previous findings in the cortex. The identified morphological-cognitive latent variables were associated with striatal expansion, and affective latent variables were associated with more evolutionarily-conserved areas in the thalamus and globus pallidus. These findings provide new insight into the architecture of these subcortical hubs and can provide greater information on the role of these structures in health and illness.

Cortical thickness (CT), gray-white matter contrast (GWC), boundary sharpness coefficient (BSC), and T1-weighted/T2-weighted ratio (T1w/T2w) are cortical metrics derived from standard T1- and T2-weighted magnetic resonance imaging (MRI) images that are often interpreted as representing or being influenced by intracortical myelin content. However, there is little empirical evidence to justify these interpretations nor have the homologies or differences between these measures been examined. We examined differences and similarities in group mean and age-related trends with the underlying hypothesis that different measures sensitive to similar changes in underlying myelo- and microstructural processes should be highly related. We further probe their sensitivity to cellular organization using the BigBrain, a high-resolution digitized volume stemming from a whole human brain histologically stained for cell bodies with the Merker stain.The measures were generated on both the MRI-derived images of 127 healthy subjects, aged 18 to 81, and on the BigBrain volume using cortical surfaces that were generated with the CIVET 2.1.0 pipeline. Comparing MRI markers between themselves, our results revealed generally high overlap in spatial distribution (i.e., group mean), but mostly divergent age trajectories in the shape, direction, and spatial distribution of the linear age effect. Significant spatial relationships were found between the BSC and GWC and their BigBrain equivalent, as well as a correlation approaching significance between the BigBrain intensities and the T1w/T2w ratio in gray matter (GM) both sampled at half cortical depth.We conclude that the microstructural properties at the source of spatial distributions of MRI cortical markers (e.g. GM myelin) can be different from microstructural changes that affect these markers in aging. While our findings highlight a discrepancy in the interpretation of the biological underpinnings of the cortical markers, they also highlight their potential complementarity, as they are largely independent in aging. Our BigBrain results indicate a general trend of GM T1w signal and myelin being spatially related to the density of cells, which is possibly more pronounced in superficial cortical layers.Highlights–Different MRI cortical markers aim to represent myelin and microstructure–These markers show high spatial overlap, but mostly divergent age trajectories–It is unlikely that myelin changes are the source of the age effect for all markers–Trend of MRI signal being related to cell density in more superficial cortical layers

In the last few years, a significant amount of work has aimed to characterize maturational trajectories of cortical development. The role of pericortical microstructure putatively characterized as the gray-white matter contrast (GWC) at the pericortical gray-white matter boundary and its relationship to more traditional morphological measures of cortical morphometry has emerged as a means to examine finer grained neuroanatomical underpinnings of cortical changes. In this work, we characterize the GWC developmental trajectories in a representative sample (n = 394) of children and adolescents (~4 to ~22 years of age), with repeated scans (1–3 scans per subject, total scans n = 819). We tested whether linear, quadratic, or cubic trajectories of contrast development best described changes in GWC. A best-fit model was identified vertex-wise across the whole cortex via the Akaike Information Criterion (AIC). GWC across nearly the whole brain was found to significantly change with age. Cubic trajectories were likeliest for 63% of vertices, quadratic trajectories were likeliest for 20% of vertices, and linear trajectories were likeliest for 16% of vertices. A main effect of sex was observed in some regions, where males had a higher GWC than females. However, no sex by age interactions were found on GWC. In summary, our results suggest a progressive decrease in GWC at the pericortical boundary throughout childhood and adolescence. This work contributes to efforts seeking to characterize typical, healthy brain development and, by extension, can help elucidate aberrant developmental trajectories.

Autism spectrum disorder (ASD) is associated with atypical brain development. However, the phenotype of regionally specific increased cortical thickness observed in ASD may be driven by several independent biological processes that influence the gray/white matter boundary, such as synaptic pruning, myelination, or atypical migration. Here, we propose to use the boundary sharpness coefficient (BSC), a proxy for alterations in microstructure at the cortical gray/white matter boundary, to investigate brain differences in individuals with ASD, including factors that may influence ASD-related heterogeneity (age, sex, and intelligence quotient). Using a vertex-based meta-analysis and a large multicenter structural magnetic resonance imaging (MRI) dataset, with a total of 1136 individuals, 415 with ASD (112 female; 303 male), and 721 controls (283 female; 438 male), we observed that individuals with ASD had significantly greater BSC in the bilateral superior temporal gyrus and left inferior frontal gyrus indicating an abrupt transition (high contrast) between white matter and cortical intensities. Individuals with ASD under 18 had significantly greater BSC in the bilateral superior temporal gyrus and right postcentral gyrus; individuals with ASD over 18 had significantly increased BSC in the bilateral precuneus and superior temporal gyrus. Increases were observed in different brain regions in males and females, with larger effect sizes in females. BSC correlated with ADOS-2 Calibrated Severity Score in individuals with ASD in the right medial temporal pole. Importantly, there was a significant spatial overlap between maps of the effect of diagnosis on BSC when compared with cortical thickness. These results invite studies to use BSC as a possible new measure of cortical development in ASD and to further examine the microstructural underpinnings of BSC-related differences and their impact on measures of cortical morphology.

Autism spectrum disorder (ASD) is associated with atypical brain development. However, the phenotype of regionally specific increased cortical thickness observed in ASD may be driven by several independent biological processes that influence the gray/white matter boundary, such as synaptic pruning, myelination, or atypical migration. Here, we propose to use the boundary sharpness coefficient (BSC), a proxy for alterations in microstructure at the cortical gray/white matter boundary, to investigate brain differences in individuals with ASD, including factors that may influence ASD-related heterogeneity (age, sex, and intelligence quotient). Using a vertex-based meta-analysis and a large multi-center magnetic resonance structural imaging (MRI) dataset, with a total of 1136 individuals, 415 with ASD (112 female; 303 male) and 721 controls (283 female; 438 male), we observed that individuals with ASD had significantly greater BSC in the bilateral superior temporal gyrus and left inferior frontal gyrus indicating an abrupt transition (high contrast) between white matter and cortical intensities. Increases were observed in different brain regions in males and females, with larger effect sizes in females. Individuals with ASD under 18 had significantly greater BSC in the bilateral superior temporal gyrus and right postcentral gyrus; individuals with ASD over 18 had significantly increased BSC in the bilateral precuneus and superior temporal gyrus. BSC correlated with ADOS-2 CSS in individuals with ASD in the right medial temporal pole. Importantly, there was a significant spatial overlap between maps of the effect of diagnosis on BSC when compared to cortical thickness. These results invite studies to use BSC as a possible new measure of cortical development in ASD and to further examine the microstructural underpinnings of BSC-related differences and their impact on measures of cortical morphology.

In the presented paper, we estimate the impacts of three distinct housing policy interventions to deal with distressed and abandoned residential properties: leaving vacant buildings in their current state, remodeling/rebuilding residential structures, and razing vacant structures. We show that vacant structures negatively impact nearby housing up to 4.1%, but when demolition is followed by complete reconstruction, neighboring housing can be appreciated by as much as 14.1%. In addition, negative spillover effects from foreclosures and vacant buildings are primarily observed in low‐income and middle‐income Census tracts, whereas positive spillover effects generated by remodeling are seen in high‐income Census tracts.

Aim: Erythropoiesis-stimulating agents (ESAs) are particularly used to treat dialysis patients suffering from anemia due to renal failure. Since March 2014, ESAs have no longer been funded on top-of-diagnosis-related groups (DRGs) in French hospitals and are funded via DRGs. There are two ways to fund dialysis in French hospitals: the ‘DRG for dialysis in session’ and the ‘off-dialysis DRG’, which is not a DRG and consists in a supplement tariff specific to dialysis for patients hospitalized for another main reason than dialysis. The aim of this study is to assess the impact of this funding change on the dialysis activity and on the budget of the 37 University Public University Hospitals in Paris (10% of the hospitalizations in France). Materials & methods: A before–after study (March–September 2013 vs March–September 2014) was conducted. Medical activity data (and ESAs consumption data) were used to assess the number and costs of DRGs associated with ESAs use. As we do not have access to the whole dialysis activity over the period studied, two hypotheses were considered: the proportion of the dialysis activity was constant between the two periods (Hypothesis A); the dialysis activity was correlated to ESA consumptions delivered by hospital pharmacies to healthcare units (Hypothesis B). A budget impact analysis was also conducted taking into account the evolution of DRG costs and ESA prices. Results: The number of dialysis ‘DRG for dialysis in session’ with ESA consumption have increased by 5% (Hypothesis A) and decreased by 9% (Hypothesis B) between the two periods while the volume of ‘off-dialysis DRG’ with ESA consumption increased by 2% (Hypothesis A) and by 9% (Hypothesis B). The budget impact was -€1.02 million (Hypothesis A) and -€0.7 million (Hypothesis B) leading to a loss for our hospitals. Conclusion: There is no significant impact of the change of funding of ESAs on the hospital activity. The DRG-based payment is negative for the budget of our hospitals but is positive for the French National Health Insurance. Indeed, with the price decrease, the ESAs are more costly for hospitals (not funded on top-of-DRG), but less costly for the society.

While urban planners and transportation geographers have long emphasized the importance of social influences on individual travel behavior, many challenges remain to bridge the gap between complex conceptual frameworks and operational behavioral models. Improving the ability of models to forecast activity-travel behavior can provide greater insights into urban planning issues. This paper proposes a new model framework by evaluating how individual travel behavior is influenced by inter- and intra-household interactions. The built environment, land-use mix, and social interactions influence household member choices among different transport modes. We propose a spatial multivariate Tobit specification that allows each individual to face a set of potential destinations and transport modes and takes into consideration the travel behavior of other household members and nearby neighbors. Using the Greater Cincinnati Household Travel Survey, we analyzed more than 37,000 trips made by 1968 individuals located in Hamilton County in Cincinnati, Ohio. Results reveal that social influences and the built environment have a strong impact on the willingness to walk and to cycle.

In this paper a mixture distribution model is extended to include spatial dependence of the autoregressive type. The resulting model nests both spatial heterogeneity and spatial dependence as special cases. A data generation process is outlined that incorporates both a finite mixture of normal distributions and spatial dependence. Whether group assignment is completely random by nature or displays some locational “pattern”, the proposed spatial-mix estimation procedure is always able to recover the true parameters. As an illustration, a basic hedonic price model is investigated that includes sub-groups of data with heterogeneous coefficients in addition to spatially clustered elements.

This paper investigates how individual activity–travel behaviors are influenced by interactions between household members in making decisions about their daily trips. Traditional discrete choice models mainly incorporate individual decision-making mechanisms ignoring the household context. In reality, each household member's travel choice often depends on the decision of the other members. We propose a new estimation method that assumes each travel decision depends on intra-household interactions. Using Bayesian estimation we develop a spatial multivariate tobit specification that allows for each individual facing a set of potential destinations to take into account the willingness to travel of other household members. Using a unique dataset containing more than 67,000 trips made by more than 3500 individuals in the area of Greater Cincinnati, we show that depending on the purpose of the trip, household members do interact and accommodate their travel decisions with respect to other household members.

Regional economists are increasingly adopting spatial analytical and spatial econometric perspectives to study questions related to local versus global spatial spillover effects. Recent studies have shown that blindly adopting traditional spatial econometric models to measure those externalities might not be relevant. Explicitly accounting on how closely related groups of economic agents such as individuals or firms interact across space can be of great benefit for researchers working at the interface of social economics and geography. Recent development in economic theory has provided new ways to incorporate a range of spillover effects into a variety of economic models (Bramoulle et al., Am Econ Rev 104:898–930, 2014). The newly redefined concept of local spillover in the socio-economic literature involves a more complex interaction structure that includes feedback effects, resulting from impact passing through neighboring observations and coming back to the original location, as typically observed for global interaction models. However, the spatial magnitude of those local externalities is more limited across space.

International audience; BackgroundTo reach the French market, a new drug requires a marketing authorization (MA) and price and reimbursement agreements. These hurdles could delay access to new and promising drugs. Since 1992, French law authorizes the use of unlicensed drugs on an exceptional and temporary basis through a compassionate-use programme, known as Temporary Authorization for Use (ATU). This programme was implemented to improve early access to drugs under development or authorized abroad. However, it is suspected to be inflationary, bypassing public bodies in charge of health technology assessment (HTA) and of pricing.ObjectiveThe aim of this study is to observe the market access after the formal licensing of drugs that went through this compassionate-use programme.MethodsWe included all ATUs that received an MA between 1 January 2005 and 30 June 2010. We first examined market access delays from these drugs using the standard administrative path. We positioned this result in relation to launch delays observed in France (for all outpatient drugs) and in other major European markets. Second, we assessed the bargaining power of a hospital purchaser after those drugs had obtained an MA by calculating the price growth rate after the approval.ResultsDuring the study period, 77 ATUs were formally licensed. The study concluded that, from the patient’s perspective, licensing and public bodies’ review time was shortened by a combined total of 36 months. The projected 11-month review time of public bodies may be longer than delays usually observed for outpatient drugs. Nonetheless, the study revealed significant benefits for French patient access based on comparable processing to launch time with those of other European countries with tight price control policies. In return, a 12 % premium, on average, is paid to pharmaceutical companies while drugs are under this status (sub-analysis on 56 drugs).ConclusionsIn many instances, the ATU programme responds to a public health need by accelerating the availability of new drugs even though this study suggests an impact of the programme on the market access of these drugs for which the standard administrative path is longer than usual. In addition, pharmaceutical companies seem to market compassionate-use drugs with a presumed benefit/risk ratio at a price that guarantees a margin for future negotiation.

This paper examines the impacts of a multi-purpose trail on residential property values in a hedonic model. Using a large housing data set in combination with street network distances, we show that proximity to trail entrances positively effects property values. Among other things, our study compares the hedonic model results from three different spatial specifications. We pay specific attention to the direct and indirect effects on residential property prices associated with potential changes in house characteristics. In addition, our study predicts property values around trail entrances using a ‘modified spatial predictive process’ approach that is well suited for capturing spatial dependence in large data sets.

We develop a general space–time filter applied to panel data models in order to control for heterogeneity as well as both time and spatial dependence. Treatment of initial period observations is analyzed when the number of time periods is small. A second issue relates to a restriction implied by the filter specification on the space–time cross-product term that can greatly simplify interpretation of model estimates as well as the estimation procedure. An applied illustration of the method is provided using a Solow growth model. The application shows that the theoretical restriction implied for the cross-product term in our space–time filter specification is consistent with this particular dynamic space–time panel data set.

Regional growth models often emphasize the importance of research and development activities leading to technological progress. The role of knowledge production and spatiotemporal spillover effects is investigated using a space-time panel data set covering 49 US states over the period 1994–2005. The aim is to test for the existence of regional knowledge spillovers in the context of a space-time dynamic suggested by the knowledge production function. A space-time specification is set forth that can be applied to panel data models that include random effects. We compare alternative models that have been proposed in the panel data literature to provide a better understanding of how new ideas diffuse across space and time. The results indicate that the space-time panel data set is consistent with the presence of strong spatiotemporal regional spillovers of knowledge. The empirical findings are interpreted in light of the existing theoretical and empirical literature on endogenous growth.

We provide a Bayesian spatial Markov Chain Monte Carlo model composition (MC3) analysis of growth rates in European regional patenting activity. Based on theoretical models on innovation and growth, we identify a large set of candidate explanatory variables that characterize regional stocks of knowledge, including: human resources devoted to innovative activity, scientific and technical capabilities, public and private investments, government policies, as well as regional industry structure, and indicators of regional technology gaps that reflect distance from the technological frontier. Our analysis shows that accommodating spatial dependence and heterogeneity leads to different conclusions regarding factors important for technological transfer and knowledge spillovers.

Precipitation, atmospheric and in-cloud icing affect wind turbine operation in various ways, including measurement and control errors, power losses, mechanical and electrical failures and safety hazard. Anti-icing and de-icing strategies are used to minimize these effects. Many active and passive methods are in development but few are available on the market. Active heating of blades is the most tested, used and reliable way to prevent icing effects. It is used in parallel with passive hydrophobic coating to lower energy consumption. Precise icing evaluation of the site should be done during the assessment phase to evaluate the necessity and benefits of installing an anti-icing and/or a de-icing system. This evaluation shall continue during operation in order to optimize production and avoid component failure related to icing events. Multiple anemometry in combination with relative humidity measurements is a cheap and reliable icing detection method during assessment while the use of ice sensors and the power curve method is recommended during operation.

A space–time filter is set forth for spatial panel data situations that include random effects. We propose a general spatial dynamic specification that encompasses several spatiotemporal models previously used in the panel data literature. We apply the model to the case of highway induced travel demand. The theory of induced travel demand asserts that increased highway capacity will induce growth in traffic for a number of reasons. Our model allows us to quantify the spatial spillover impacts of increased highway capacity at one location in the network on travel times in neighboring locations and in future time periods.

D’apres les modeles de la nouvelle economie geographique, le caractere limite de la diffusion des connaissances represente un des principaux determinants de la localisation des activites innovantes. En s’appuyant sur la problematique des externalites dynamiques presentee par Glaeser et al. (1992), de nombreux travaux econometriques recents tentent d’evaluer les effets de la diffusion des connaissances sur la localisation des activites innovantes. Les externalites de connaissance etant favorisees par la proximite a la fois geographique et technologique, nous etudions differentes structures de dependance spatiale integrant l’indice technologique de Jaffe (1986) et le temps de transport entre regions europeennes. Les resultats mettent en avant l’importance de l’information concernant la qualite des infrastructures de communication ainsi que de la proximite technologique dans l’explication de la polarisation des activites innovantes en Europe.

International audience; This paper estimates the effects of knowledge spillovers on patent growth rates across 335 European regions over the 1989-1999 period. We propose a dynamic model based on an innovation production function. A Bayesian approach is used to take into account area-specific innovation and spatial spillovers. The estimation of the model proceeds via Markov Chain Monte Carlo simulation. The results show significant positive and negative spatial effects on innovative activity. The model allows for a rich spatial specification, which we illustrate by incorporating transport proximity measured by transportation time between regions to augment the typical spatial proximity measure of connectivity between regions. Doing this produces more pronounced spatial spillovers that exhibit a more polarized spatial pattern than a model relying on spatial proximity alone

1. The goal of this study was to investigate the effects of the delayed pharmacological preconditioning produced by an adenosine A(1)-receptor agonist (A(1)-DPC) against ventricular arrhythmias induced by ischaemia and reperfusion, compared to those of ischaemia-induced delayed preconditioning (I-DPC). 2. Eighty-nine instrumented conscious rabbits underwent a 2 consecutive days protocol. On day 1, rabbits were randomly divided into four groups: 'Control' (saline, i.v.), 'I-DPC' (six 4-min coronary artery occlusion/4-min reperfusion cycles), 'A(1)-DPC(100)' (N(6)-cyclopentyladenosine, 100 microg kg(-1), i.v.), and 'A(1)-DPC(400)' (N(6)-cyclopentyladenosine, 400 microg kg(-1), i.v.). On day 2, i.e., 24 h later, the incidence and severity of ventricular arrhythmias during a 30-min coronary artery occlusion and subsequent reperfusion were analysed in all animals, using an arrhythmia score. 3. I-DPC, A(1)-DPC(100) and A(1)-DPC(400) significantly reduced the infarct size (34+/-5, 42+/-3 and 43+/-7% of the area at risk, respectively) as compared to Control (55+/-3% of the area at risk). 4. During both ischaemia and reperfusion, neither the incidence nor the severity of ventricular arrhythmias were altered by A(1)-DPC(100), A(1)-DPC(400) or I-DPC as compared to Control. 5. Thus, despite reduction of infarct size induced by delayed preconditioning, A(1)-DPC as well as I-DPC failed to exert any anti-arrhythmic effect in the conscious rabbit model of ischaemia-reperfusion.

This article examines the growth performance of Africa and the Middle East for the period 1990-2005. It employs a Bayesian model-averaging method that constructs estimates as a weighted average of spatial autoregressive estimates for every possible combination of included variables. One of the results is that the inclusion of spatial dependences has a direct impact on the determinants of growth in Africa and the Middle East. A main contribution of this article is to consider geographical effects in a more flexible way, which allows an enriched comprehension of the role of institutional factors in explaining low economic development.

Economic Research Forum Working paper n°490; International audience; This paper examines Africa's and Middle East's growth performance for the period 1990- 2005. It employs a Bayesian Model Averaging method that constructs estimates as a weighted average of Spatial Autoregressive estimates for every possible combination of included variables. One of the results of the paper is that the inclusion of spatial dependencies has a direct impact on the determinants of growth in Africa and Middle-East. Indeed, the methodology used in the paper offers an interesting response to the institution/geography debate on the explanation of growth and development. In particular, our methodology allows a selection of the institutional variables that count to explain low development since the geographical variables are partially integrated in the spatial dependence effect.

A space-time filter structure is introduced that can be used to accommodate dependence across space and time in the error components of panel data models that contain random effects. This general specification encompasses several more specific space-time structures that have been used recently in the panel data literature. Markov Chain Monte Carlo methods are set forth for estimating the model which allow simple treatment of initial period observations as endogenous or exogenous. Performance of the approach is demonstrated using both Monte Carlo experiments and an applied illustration.

Authors propose, in this study, a Spatial Multinomial Probit model to examine the determinants of land use change, at the parcel level, in the French Département du Rhones from 1992 to 2003. It is based on an economic model that assumes that landowners have a choice between 4 land use categories for a given parcel at a given date: (1) agricultural, (2) forest, (3) urban and (4) no use. Each landowner compares costs and benefits associated with each parcel and each class and chooses the optimal land use to maximize his/her profit. The Spatial Multinomial Probit model allows for covariates and spatial dependence, and we use these features to explore the relative importance of factors that drive landowners to choose a specific land use category. The findings suggest the presence of spatial patterns, implying that the decision to choose land use for a given parcel of land is strongly influenced by non-observed factors in neighboring parcels.; Les auteurs proposent, dans cette étude, un modèle Probit Multinomial Spatial pour examiner les déterminants de l'utilisation des terres, à l’échelle de la parcelle, dans le département du Rhônes français de 1992 à 2003. Il est basé sur un modèle économique qui suppose que les propriétaires ont le choix entre 4 catégories d'utilisation des terres pour une parcelle donnée à une date donnée: (1) l'agriculture, (2) forêt, (3) et urbain (4) sans usage. Chaque propriétaire terrien compare les coûts et les avantages liés à chaque parcelle et de chaque catégorie d’usage et choisit la meilleure utilisation des terres afin de maximiser ses profits. Le modèle Probit Spatial multinomial permet de modéliser la dépendance spatiale, et les auteurs utilisent ces fonctions pour étudier l'importance relative des facteurs non observables qui poussent les propriétaires à choisir une catégorie d'utilisation des sols. Les résultats suggèrent la présence de structures spatiales, ce qui implique que la décision de choisir l'utilisation des terres pour une parcelle de terrain est fortement influencée par des facteurs non observée dans les parcelles voisines.

We extend the literature on Bayesian model comparison for ordinary least-squares regression models to include spatial autoregressive and spatial error models. Our focus is on comparing models that consist of different matrices of explanatory variables. A Markov Chain Monte Carlo model composition methodology labelled MC to the third by Madigan and York (1995) is developed for two types of spatial econometric models that are frequently used in the literature. The methodology deals with cases where the number of possible models based on different combinations of candidate explanatory variables is large enough that calculation of posterior probabilities for all models is difficult or infeasible. Estimates and inferences are produced by averaging over models using the posterior model probabilities as weights, a procedure known as Bayesian model averaging. We illustrate the methods using a spatial econometric model of origin-destination population migration flows between the 48 US States and District of Columbia during the 1990 to 2000 period.

This study investigates the pattern of knowledge spillovers arising from patent activity between European regions. A Bayesian hierarchical model is developed that specifies region-specific latent effects parameters modeled using a connectivity structure between regions that can reflect geographical proximity in conjunction with technological and other types of proximity. This approach exploits the fact that interregional relationships may exhibit industry-specific technological linkages or transportation network linkages, which is in contrast to traditional studies relying exclusively on geographical proximity. We also allow for both symmetric and asymmetric knowledge spillovers between regions, and for heterogeneity across the regional sample. A series of formal Bayesian model comparisons provides support for a model based on technological proximity combined with spatial proximity, asymmetric knowledge spillovers, and heterogeneity in the disturbances. Estimates of region-specific latent effects parameters structured in this fashion are produced by the model and used to draw inferences regarding the character of knowledge spillovers across the regions. The method is illustrated using sample data on patent activity covering 323 regions in nine European countries. Copyright © 2008 John Wiley & Sons, Ltd.

Late preconditioning (PC) against myocardial stunning develops after coronary artery occlusion (CAO) at rest and subsequent reperfusion. We investigated whether late PC occurs after exercise-induced ischemia (high-flow ischemia) in dogs. A circumflex coronary artery stenosis (by using occluders) was set up before the onset of treadmill exercise in nine chronically instrumented dogs to suppress exercise-induced increase in mean coronary blood flow velocity (CBFV, Doppler) without simultaneously affecting left ventricular (LV) wall thickening (Wth) at rest. Two similar exercises were performed 24 h apart. On day 1, LV Wth was reduced by 84 ± 5% ( P < 0.01), and exercise-induced increases in transmural myocardial blood flow (MBF, fluorescent microspheres) in the ischemic zone were blunted. LV Wth was depressed throughout the first 10 h and returned to its baseline value after 24 h. On day 2, changes in LV Wth and MBF were similar as was the time course for LV Wth recovery, indicating lack of late PC. Also, CBFV responses to acetylcholine, nitroglycerin, and reactive hyperemia (20-s CAO) were not significantly different on days 1 and 2. Similar results were obtained in a subgroup of four additional dogs with more severe stenosis during exercise. Late PC against myocardial stunning was confirmed to occur in a model of 10-min CAO followed by coronary artery reperfusion (CAR) in another four dogs. Thus in contrast with CAO at rest followed by CAR, severe myocardial ischemia in coronary flow-limited exercising dogs does not induce late PC against myocardial stunning.