9 research outputs found

    The Clinical Outcome Study for dysferlinopathy: An international multicenter study

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    Objective: To describe the baseline clinical and functional characteristics of an international cohort of 193 patients with dysferlinopathy. Methods: The Clinical Outcome Study for dysferlinopathy (COS) is an international multicenter study of this disease, evaluating patients with genetically confirmed dysferlinopathy over 3 years. We present a cross-sectional analysis of 193 patients derived from their baseline clinical and functional assessments. Results: There is a high degree of variability in disease onset, pattern of weakness, and rate of progression. No factor, such as mutation class, protein expression, or age at onset, accounted for this variability. Among patients with clinical diagnoses of Miyoshi myopathy or limb-girdle muscular dystrophy, clinical presentation and examination was not strikingly different. Respiratory impairment and cardiac dysfunction were observed in a minority of patients. A substantial delay in diagnosis was previously common but has been steadily reducing, suggesting increasing awareness of dysferlinopathies. Conclusions: These findings highlight crucial issues to be addressed for both optimizing clinical care and planning therapeutic trials in dysferlinopathy. This ongoing longitudinal study will provide an opportunity to further understand patterns and variability in disease progression and form the basis for trial design

    State of Wildfires 2023-2024

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    Climate change contributes to the increased frequency and intensity of wildfires globally, with significant impacts on society and the environment. However, our understanding of the global distribution of extreme fires remains skewed, primarily influenced by media coverage and regionalised research efforts. This inaugural State of Wildfires report systematically analyses fire activity worldwide, identifying extreme events from the March 2023-February 2024 fire season. We assess the causes, predictability, and attribution of these events to climate change and land use and forecast future risks under different climate scenarios. During the 2023-2024 fire season, 3.9×106 km2 burned globally, slightly below the average of previous seasons, but fire carbon (C) emissions were 16 % above average, totalling 2.4 Pg C. Global fire C emissions were increased by record emissions in Canadian boreal forests (over 9 times the average) and reduced by low emissions from African savannahs. Notable events included record-breaking fire extent and emissions in Canada, the largest recorded wildfire in the European Union (Greece), drought-driven fires in western Amazonia and northern parts of South America, and deadly fires in Hawaii (100 deaths) and Chile (131 deaths). Over 232 000 people were evacuated in Canada alone, highlighting the severity of human impact. Our analyses revealed that multiple drivers were needed to cause areas of extreme fire activity. In Canada and Greece, a combination of high fire weather and an abundance of dry fuels increased the probability of fires, whereas burned area anomalies were weaker in regions with lower fuel loads and higher direct suppression, particularly in Canada. Fire weather prediction in Canada showed a mild anomalous signal 1 to 2 months in advance, whereas events in Greece and Amazonia had shorter predictability horizons. Attribution analyses indicated that modelled anomalies in burned area were up to 40 %, 18 %, and 50 % higher due to climate change in Canada, Greece, and western Amazonia during the 2023-2024 fire season, respectively. Meanwhile, the probability of extreme fire seasons of these magnitudes has increased significantly due to anthropogenic climate change, with a 2.9-3.6-fold increase in likelihood of high fire weather in Canada and a 20.0-28.5-fold increase in Amazonia. By the end of the century, events of similar magnitude to 2023 in Canada are projected to occur 6.3-10.8 times more frequently under a medium-high emission scenario (SSP370). This report represents our first annual effort to catalogue extreme wildfire events, explain their occurrence, and predict future risks. By consolidating state-of-the-art wildfire science and delivering key insights relevant to policymakers, disaster management services, firefighting agencies, and land managers, we aim to enhance society's resilience to wildfires and promote advances in preparedness, mitigation, and adaptation. New datasets presented in this work are available from 10.5281/zenodo.11400539 (Jones et al., 2024) and 10.5281/zenodo.11420742 (Kelley et al., 2024a)

    RMM-VAE software and data

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    <p>RMM-VAE architecture for identifying weather regimes targeted to a local-scale impact variable. Software and Data to reproduce submitted paper presenting the method and applying it to identify weather regimes over the Mediterranean region.</p&gt

    The economics of climate change with endogenous preferences

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    Avoiding unmanageable climate change implies that global greenhouse gas emissions must be reduced rapidly. Carbon prices and technological development are essential to deliver such reductions. Changes in preferences, however, are rarely considered, even though other major socioeconomic transitions – such as those from reducing smoking and drink-driving – have succeeded partly because preferences have changed. This article examines the impact of climate policy-induced changes in consumers’ preferences. We show that low-carbon policies could be better designed if it is recognised that preferences can be endogenous to such policies. For instance, carbon taxes must be adjusted, if they crowd-in or -out social preferences, to achieve a given target. Further, when the urban built environment changes mobility preferences, the value of low-carbon infrastructure investments can be underestimated if such effects are ignored. Third, policy-induced changes in preferences for active travel and plant-based diets could increase the net benefits of the transition to zero emissions

    State of Wildfires 2023–2024

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    International audienceClimate change contributes to the increased frequency and intensity of wildfires globally, with significant impacts on society and the environment. However, our understanding of the global distribution of extreme fires remains skewed, primarily influenced by media coverage and regionalised research efforts. This inaugural State of Wildfires report systematically analyses fire activity worldwide, identifying extreme events from the March 2023–February 2024 fire season. We assess the causes, predictability, and attribution of these events to climate change and land use and forecast future risks under different climate scenarios. During the 2023–2024 fire season, 3.9×106 km2 burned globally, slightly below the average of previous seasons, but fire carbon (C) emissions were 16 % above average, totalling 2.4 Pg C. Global fire C emissions were increased by record emissions in Canadian boreal forests (over 9 times the average) and reduced by low emissions from African savannahs. Notable events included record-breaking fire extent and emissions in Canada, the largest recorded wildfire in the European Union (Greece), drought-driven fires in western Amazonia and northern parts of South America, and deadly fires in Hawaii (100 deaths) and Chile (131 deaths). Over 232 000 people were evacuated in Canada alone, highlighting the severity of human impact. Our analyses revealed that multiple drivers were needed to cause areas of extreme fire activity. In Canada and Greece, a combination of high fire weather and an abundance of dry fuels increased the probability of fires, whereas burned area anomalies were weaker in regions with lower fuel loads and higher direct suppression, particularly in Canada. Fire weather prediction in Canada showed a mild anomalous signal 1 to 2 months in advance, whereas events in Greece and Amazonia had shorter predictability horizons. Attribution analyses indicated that modelled anomalies in burned area were up to 40 %, 18 %, and 50 % higher due to climate change in Canada, Greece, and western Amazonia during the 2023–2024 fire season, respectively. Meanwhile, the probability of extreme fire seasons of these magnitudes has increased significantly due to anthropogenic climate change, with a 2.9–3.6-fold increase in likelihood of high fire weather in Canada and a 20.0–28.5-fold increase in Amazonia. By the end of the century, events of similar magnitude to 2023 in Canada are projected to occur 6.3–10.8 times more frequently under a medium–high emission scenario (SSP370). This report represents our first annual effort to catalogue extreme wildfire events, explain their occurrence, and predict future risks. By consolidating state-of-the-art wildfire science and delivering key insights relevant to policymakers, disaster management services, firefighting agencies, and land managers, we aim to enhance society's resilience to wildfires and promote advances in preparedness, mitigation, and adaptation. New datasets presented in this work are available from https://doi.org/10.5281/zenodo.11400539 (Jones et al., 2024) and https://doi.org/10.5281/zenodo.11420742 (Kelley et al., 2024a)

    Muscle MRI in patients with dysferlinopathy: pattern recognition and implications for clinical trials.

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    International audienceBackground and objective Dysferlinopathies are a group of muscle disorders caused by mutations in the DYSF gene. Previous muscle imaging studies describe a selective pattern of muscle involvement in smaller patient cohorts, but a large imaging study across the entire spectrum of the dysferlinopathies had not been performed and previous imaging findings were not correlated with functional tests.Methods We present cross-sectional T1-weighted muscle MRI data from 182 patients with genetically confirmed dysferlinopathies. We have analysed the pattern of muscles involved in the disease using hierarchical analysis and presented it as heatmaps. Results of the MRI scans have been correlated with relevant functional tests for each region of the body analysed.Results In 181 of the 182 patients scanned, we observed muscle pathology on T1-weighted images, with the gastrocnemius medialis and the soleus being the most commonly affected muscles. A similar pattern of involvement was identified in most patients regardless of their clinical presentation. Increased muscle pathology on MRI correlated positively with disease duration and functional impairment.Conclusions The information generated by this study is of high diagnostic value and important for clinical trial development. We have been able to describe a pattern that can be considered as characteristic of dysferlinopathy. We have defined the natural history of the disease from a radiological point of view. These results enabled the identification of the most relevant regions of interest for quantitative MRI in longitudinal studies, such as clinical trials

    Muscle MRI in patients with dysferlinopathy : pattern recognition and implications for clinical trials

    No full text
    Altres ajuts: The estimatedDysferlinopathies are a group of muscle disorders caused by mutations in the DYSF gene. Previous muscle imaging studies describe a selective pattern of muscle involvement in smaller patient cohorts, but a large imaging study across the entire spectrum of the dysferlinopathies had not been performed and previous imaging findings were not correlated with functional tests. We present cross-sectional T1-weighted muscle MRI data from 182 patients with genetically confirmed dysferlinopathies. We have analysed the pattern of muscles involved in the disease using hierarchical analysis and presented it as heatmaps. Results of the MRI scans have been correlated with relevant functional tests for each region of the body analysed. In 181 of the 182 patients scanned, we observed muscle pathology on T1-weighted images, with the gastrocnemius medialis and the soleus being the most commonly affected muscles. A similar pattern of involvement was identified in most patients regardless of their clinical presentation. Increased muscle pathology on MRI correlated positively with disease duration and functional impairment. The information generated by this study is of high diagnostic value and important for clinical trial development. We have been able to describe a pattern that can be considered as characteristic of dysferlinopathy. We have defined the natural history of the disease from a radiological point of view. These results enabled the identification of the most relevant regions of interest for quantitative MRI in longitudinal studies, such as clinical trials
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