Trans-Differentiation of Neural Stem Cells: A Therapeutic Mechanism Against the Radiation Induced Brain Damage

Radiation therapy is an indispensable therapeutic modality for various brain diseases. Though endogenous neural stem cells (NSCs) would provide regenerative potential, many patients nevertheless suffer from radiation-induced brain damage. Accordingly, we tested beneficial effects of exogenous NSC supplementation using in vivo mouse models that received whole brain irradiation. Systemic supplementation of primarily cultured mouse fetal NSCs inhibited radiation-induced brain atrophy and thereby preserved brain functions such as short-term memory. Transplanted NSCs migrated to the irradiated brain and differentiated into neurons, astrocytes, or oligodendrocytes. In addition, neurotrophic factors such as NGF were significantly increased in the brain by NSCs, indicating that both paracrine and replacement effects could be the therapeutic mechanisms of NSCs. Interestingly, NSCs also differentiated into brain endothelial cells, which was accompanied by the restoration the cerebral blood flow that was reduced from the irradiation. Inhibition of the VEGF signaling reduced the migration and trans-differentiation of NSCs. Therefore, trans-differentiation of NSCs into brain endothelial cells by the VEGF signaling and the consequential restoration of the cerebral blood flow would also be one of the therapeutic mechanisms of NSCs. In summary, our data demonstrate that exogenous NSC supplementation could prevent radiation-induced functional loss of the brain. Therefore, successful combination of brain radiation therapy and NSC supplementation would provide a highly promising therapeutic option for patients with various brain diseases

Metropolitan urban hotspots of chronic sleep deprivation: evidence from a community health survey in Gyeongbuk Province, South Korea

The geographic concentration of chronic sleep deprivation (CSD) remains largely unexplored. This paper examined the community-specific spatial pattern of the prevalence of CSD and the presence of clustered spatial hotspots among the Korean elderly population in Gyeongbuk Province, South Korea, revealing CSD hotspots and underscoring the importance of geography-focused prevention strategies. The study analysed cross-sectional data collected from 9847 elderly individuals aged 60 years and older who participated in a Korean Community Health Survey conducted in 2012. To assess the level of spatial dependence, an exploratory spatial data analysis was conducted using Global Moran’s I statistic and the local indicator of spatial association. The results revealed marked geographic variations in CSD prevalence ranging from 33.4 to 73.4%, with higher values in the metropolitan urban areas and lower in the rural areas. Almost half of the community residents [both men (44.1%) and women (53.5%)] slept 6 h or less per 24 h. The average CSD prevalence (53.6% men and 65.1% women) in the hotspots was about 13.0% higher than that in other areas (42.6% for men and 51.1% for women). To our knowledge, this is the first study to generate a CSD hotspot map that includes data on sleep deprivation across metropolitan district levels. This study demonstrates that not only is sleep deprivation distributed differentially across communities but these differences may be explained by urbanisation

Evaluating the Mutual Relationship between IPAT/Kaya Identity Index and ODIAC-Based GOSAT Fossil-Fuel CO2 Flux: Potential and Constraints in Utilizing Decomposed Variables

The IPAT/Kaya identity is the most popular index used to analyze the driving forces of individual factors on CO2 emissions. It represents the CO2 emissions as a product of factors, such as the population, gross domestic product (GDP) per capita, energy intensity of the GDP, and carbon footprint of energy. In this study, we evaluated the mutual relationship of the factors of the IPAT/Kaya identity and their decomposed variables with the fossil-fuel CO2 flux, as measured by the Greenhouse Gases Observing Satellite (GOSAT). We built two regression models to explain this flux; one using the IPAT/Kaya identity factors as the explanatory variables and the other one using their decomposed factors. The factors of the IPAT/Kaya identity have less explanatory power than their decomposed variables and comparably low correlation with the fossil-fuel CO2 flux. However, the model using the decomposed variables shows significant multicollinearity. We performed a multivariate cluster analysis for further investigating the benefits of using the decomposed variables instead of the original factors. The results of the cluster analysis showed that except for the M factor, the IPAT/Kaya identity factors are inadequate for explaining the variations in the fossil-fuel CO2 flux, whereas the decomposed variables produce reasonable clusters that can help identify the relevant drivers of this flux

Spatial Cross-Correlation of GOSAT CO₂ Concentration with Repeated Heat Wave-Induced Photosynthetic Inhibition in Europe from 2009 to 2017

In recent decades, European countries have faced repeated heat waves. Traditionally, atmospheric CO2 concentration linked to repeated heat wave-induced photosynthetic inhibition has been explored based on local-specific in-situ observations. However, previous research based on field surveys has limitations in exploring area-wide atmospheric CO2 concentrations linked to repeated heat wave-induced photosynthetic inhibition. The present study aimed to evaluate the spatial cross-correlation of Greenhouse gases Observing SATellite (GOSAT) CO2 concentrations with repeated heat wave-induced photosynthetic inhibition in Europe from 2009 to 2017 by applying geographically weighted regression (GWR). The local standardized coefficient of a fraction of photosynthetically active radiation (FPAR: −0.24) and the normalized difference vegetation index (NDVI: −0.22) indicate that photosynthetic inhibition increases atmospheric CO2 in Europe. Furthermore, from 2009 to 2017, the heat waves in Europe contributed to CO2 emissions (27.2–32.1%) induced by photosynthetic inhibition. This study provides realistic evidence to justify repeated heat wave-induced photosynthetic inhibition as a fundamental factor in mitigating carbon emissions in Europe

Evaluating the Correlation between Thermal Signatures of UAV Video Stream versus Photomosaic for Urban Rooftop Solar Panels

The unmanned aerial vehicle (UAV) autopilot flight to survey urban rooftop solar panels needs a certain flight altitude at a level that can avoid obstacles such as high-rise buildings, street trees, telegraph poles, etc. For this reason, the autopilot-based thermal imaging has severe data redundancy—namely, that non-solar panel area occupies more than 99% of ground target, causing a serious lack of the thermal markers on solar panels. This study aims to explore the correlations between the thermal signatures of urban rooftop solar panels obtained from a UAV video stream and autopilot-based photomosaic. The thermal signatures of video imaging are strongly correlated (0.89–0.99) to those of autopilot-based photomosaics. Furthermore, the differences in the thermal signatures of solar panels between the video and photomosaic are aligned in the range of noise equivalent differential temperature with a 95% confidence level. The results of this study could serve as a valuable reference for employing video stream-based thermal imaging to urban rooftop solar panels

Evaluating the Correlation between Thermal Signatures of UAV Video Stream versus Photomosaic for Urban Rooftop Solar Panels

The unmanned aerial vehicle (UAV) autopilot flight to survey urban rooftop solar panels needs a certain flight altitude at a level that can avoid obstacles such as high-rise buildings, street trees, telegraph poles, etc. For this reason, the autopilot-based thermal imaging has severe data redundancy—namely, that non-solar panel area occupies more than 99% of ground target, causing a serious lack of the thermal markers on solar panels. This study aims to explore the correlations between the thermal signatures of urban rooftop solar panels obtained from a UAV video stream and autopilot-based photomosaic. The thermal signatures of video imaging are strongly correlated (0.89–0.99) to those of autopilot-based photomosaics. Furthermore, the differences in the thermal signatures of solar panels between the video and photomosaic are aligned in the range of noise equivalent differential temperature with a 95% confidence level. The results of this study could serve as a valuable reference for employing video stream-based thermal imaging to urban rooftop solar panels

Comparative Evaluation of Top-Down GOSAT XCO2 vs. Bottom-Up National Reports in the European Countries

Submitting national inventory reports (NIRs) on emissions of greenhouse gases (GHGs) is obligatory for parties of the United Nations Framework Convention on Climate Change (UNFCCC). The NIR forms the basis for monitoring individual countries’ progress on mitigating climate change. Countries prepare NIRs using the default bottom–up methodology of the Intergovernmental Panel on Climate Change (IPCC), as approved by the Kyoto protocol. We provide tangible evidence of the discrepancy between official bottom–up NIR reporting (unit: tons) versus top–down XCO2 reporting (unit: ppm) within the European continent, as measured by the Greenhouse Gases Observing Satellite (GOSAT). Bottom–up NIR (annual growth rate of CO2 emission from 2010 to 2016: −1.55%) does not show meaningful correlation (geographically weighted regression coefficient = −0.001, R2 = 0.024) to top–down GOSAT XCO2 (annual growth rate: 0.59%) in the European countries. The top five countries within the European continent on carbon emissions in NIR do not match the top five countries on GOSAT XCO2 concentrations. NIR exhibits anthropogenic carbon-generating activity within country boundaries, whereas satellite signals reveal the trans-boundary movement of natural and anthropogenic carbon. Although bottom–up NIR reporting has already gained worldwide recognition as a method to track national follow-up for treaty obligations, the single approach based on bottom–up did not present background atmospheric CO2 density derived from the air mass movement between the countries. In conclusion, we suggest an integrated measuring, reporting, and verification (MRV) approach using top–down observation in combination with bottom–up NIR that can provide sufficient countrywide objective evidence for national follow-up activities

Evaluating the Causal Relations between the Kaya Identity Index and ODIAC-Based Fossil Fuel CO2 Flux

The Kaya identity is a powerful index displaying the influence of individual carbon dioxide (CO2) sources on CO2 emissions. The sources are disaggregated into representative factors such as population, gross domestic product (GDP) per capita, energy intensity of the GDP, and carbon footprint of energy. However, the Kaya identity has limitations as it is merely an accounting equation and does not allow for an examination of the hidden causalities among the factors. Analyzing the causal relationships between the individual Kaya identity factors and their respective subcomponents is necessary to identify the real and relevant drivers of CO2 emissions. In this study we evaluated these causal relationships by conducting a parallel multiple mediation analysis, whereby we used the fossil fuel CO2 flux based on the Open-Source Data Inventory of Anthropogenic CO2 emissions (ODIAC). We found out that the indirect effects from the decomposed variables on the CO2 flux are significant. However, the Kaya identity factors show neither strong nor even significant mediating effects. This demonstrates that the influence individual Kaya identity factors have on CO2 directly emitted to the atmosphere is not primarily due to changes in their input factors, namely the decomposed variables