Net increase in temperature-related deaths under climate change in Europe
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Climate change threatens crop diversity at low latitudes
Climate change alters the climatic suitability of croplands, likely shifting the spatial distribution and diversity of global food crop production. Analyses of future potential food crop diversity have been limited to a small number of crops. Here we project geographical shifts in the climatic niches of 30 major food crops under 1.5–4 °C global warming and assess their impact on current crop production and potential food crop diversity across global croplands. We found that in low-latitude regions, 10–31% of current production would shift outside the climatic niche even under 2 °C global warming, increasing to 20–48% under 3 °C warming. Concurrently, potential food crop diversity would decline on 52% (+2 °C) and 56% (+3 °C) of global cropland. However, potential diversity would increase in mid to high latitudes, offering opportunities for climate change adaptation. These results highlight substantial latitudinal differences in the adaptation potential and vulnerability of the global food system under global warming.
Event triggers and opinion leaders shape climate change discourse on Weibo
Understanding how real-world events and opinion leaders shape climate change discussions is vital for improving communication and policy formulation to meet global carbon mitigation goals. This study analyzed 5.3 million original posts from Weibo (2012–2022), China’s largest social media platform, to examine climate change discourse. We found five event types triggering 48 discussion peaks, including online activities, international conferences, extreme weather, domestic policies, and international news. Posts generally conveyed positive attitudes, though sentiment decreased during haze pollution and the COVID-19 pandemic. Network analysis revealed seven opinion leader groups with distinct strategies: official media and institutions emphasized political will, global initiatives, and socio-economic implications, while universities and grassroots individuals focused on scientific reality and personal actions. Celebrities and unofficial accounts often highlighted geopolitical topics, especially China-US relations. We suggest reducing fragmented echo chambers and fostering personal connections through digital media platforms to enhance public awareness.
Influence of the Covid-19 pandemic on cerebrovascular diseases in the Sao Paulo region of Brazil
The rapid spread of covid-19 overwhelmed healthcare systems. This study aimed to investigate the impact of the covid-19 pandemic on hospitalizations and hospital deaths due to cerebrovascular diseases (CVD) in São Paulo state, Brazil.
Decarbonizing urban residential communities with green hydrogen systems
Community green hydrogen systems, typically consisting of rooftop photovoltaic panels paired with hybrid hydrogen-battery storage, offer urban environments with improved access to clean, on-site energy. However, economically viable pathways for deploying hydrogen storage within urban communities remain unclear. Here we develop a bottom-up energy model linking climate, human behavior and community characteristics to assess the impacts of pathways for deploying community green hydrogen systems in North America from 2030 to 2050. We show that for the same community conditions, the cost difference between the best and worst pathways can be as high as 60%. In particular, the household centralized option emerges as the preferred pathway for most communities. Furthermore, enhancing energy storage demands within these deployment pathways can reduce system design costs up to fourfold. To achieve cost-effective urban decarbonization, the study underscores the critical role of selecting the right deployment pathway and prioritizing the integration of increased energy storage in pathway designs.
Deep learning-based image analysis in muscle histopathology using photo-realistic synthetic data
Artificial intelligence (AI), specifically Deep learning (DL), has revolutionized biomedical image analysis, but its efficacy is limited by the need for representative, high-quality large datasets with manual annotations. While latest research on synthetic data using AI-based generative models has shown promising results to tackle this problem, several challenges such as lack of interpretability and need for vast amounts of real data remain. This study aims to introduce a new approach—SYNTA—for the generation of photo-realistic synthetic biomedical image data to address the challenges associated with state-of-the art generative models and DL-based image analysis.
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