Explore the vast range of titles available.

Background High temperature and humidity conditions are associated with short-term elevations in the mortality rate in many United States cities. Previous research has quantified this relationship in an aggregate manner over large metropolitan areas, but within these areas the response may differ based on local-scale variability in climate, population characteristics, and socio-economic factors. Methods We compared the mortality response for 48 Zip Code Tabulation Areas (ZCTAs) comprising Philadelphia County, PA to determine if certain areas are associated with elevated risk during high heat stress conditions. A randomization test was used to identify mortality exceedances for various apparent temperature thresholds at both the city and local scale. We then sought to identify the environmental, demographic, and social factors associated with high-risk areas via principal components regression. Results Citywide mortality increases by 9.3% on days following those with apparent temperatures over 34°C observed at 7:00 p.m. local time. During these conditions, elevated mortality rates were found for 10 of the 48 ZCTAs concentrated in the west-central portion of the County. Factors related to high heat mortality risk included proximity to locally high surface temperatures, low socioeconomic status, high density residential zoning, and age. Conclusions Within the larger Philadelphia metropolitan area, there exists statistically significant fine-scale spatial variability in the mortality response to high apparent temperatures. Future heat warning systems and mitigation and intervention measures could target these high risk areas to reduce the burden of extreme weather on summertime morbidity and mortality.

Fire can induce long-lived changes to land-surface albedo, an important aspect of the Earth’s energy budget, but the temporal evolution of these anomalies is poorly understood. Due to the widespread presence of fire in Africa, this represents uncertainty in the continental energy budget, which has important implications for regional climate and hydrologic cycling. In this study, we present the first object-based accounting of albedo anomalies induced by larger (>1 km2) individual wildfires in sub-Saharan Africa. We group spatially contiguous wildfire pixels into fire objects and track the albedo anomaly for five years after the burn. We find that albedo anomalies all have the same general temporal signature: An immediate, brief period of darkening followed by persistent brightening. The strongest brightening is found in the Kalahari region while more intense and long-lived initial darkening is found in the Sahel region. The average southern hemisphere albedo anomaly is +1.50 × 10−3 in the year following wildfire, representing a statistically significant negative surface energy balance forcing on a continental scale. This study challenges an existing paradigm surrounding the physical effects of fire on the landscape. Our results suggest that models of albedo that assume a darkening and recovery to baseline are overly simplistic in almost all circumstances. Furthermore, the presumption that immediate darkening is the only meaningful effect on albedo is incorrect for the majority of the African continent.

Aim Fire is a globally important disturbance that affects nearly all vegetated biomes. Previous regional studies have suggested that the predictable seasonal pattern of a climatic time series, or seasonality, might aid in the prediction of average fire activity, but it is not known whether these findings are applicable globally. Here, we investigate how seasonality can be used to explain variations in fire activity on a global scale. Location Global, 60° S–60° N. Time period Data averaged over the period 1999–2015. Methods We describe a method to partition a periodic seasonal cycle into two seasons and define conceptually simple temporal metrics that describe spatial variability in seasonality. We explore the usefulness of these metrics in explaining global fire activity using the average monthly time series of precipitation and temperature and a flexible machine learning procedure (random forests). Results A simple model that uses only precipitation and temperature amplitude and synchrony between wet and warm seasons correctly predicts 66% of the variability in global fire activity, substantially more than a model with mean annual temperature and precipitation. A more complex model that includes all nine metrics predicts 87% of variability in global fire activity. Main conclusions This study shows that seasonality of temperature and precipitation can be used to predict multi‐year average fire activity in a globally relevant way. The mechanisms highlighted in our work could be used to improve global fire models and enhance their ability to represent the spatial patterns of fire activity. Our method might also be useful in hindcasting historical fire using reanalysis or predicting future fire regimes using coarse output from climate models.

Significant climate change in the Arctic has been observed by indigenous peoples and reported in scientific literature, but there has been little research comparing these two knowledge bases. In this study, Sami reindeer herder interviews and observational weather data were combined to provide a comprehensive description of climate changes in Northern Sweden. The interviewees described warmer winters, shorter snow seasons and cold periods, and increased temperature variability. Weather data supported three of these four observed changes; the only change not evident in the weather data was increased temperature variability. Winter temperatures increased, the number of days in cold periods was significantly reduced, and some stations displayed a 2 month-shorter snow cover season. Interviewees reported that these changes to the wintertime climate are significant, impact their identity, and threaten their livelihood. If consistency between human observations of changing weather patterns and the instrumental meteorological record is observed elsewhere, mixed methods research like this study can produce a clearer, more societally relevant understanding of how the climate is changing and the impacts of those changes on human well-being.

Extreme heat is a leading cause of weather-related mortality. Most research has considered the aggregate response of the populations of large metropolitan areas, but the focus of heat-related mortality and morbidity investigations is shifting towards a more fine-scale approach in which impacts are measured in smaller units such as postal codes. However, most existing statistical techniques to model the relationship between temperature and mortality cannot be directly applied to the intra-city scale because small sample sizes inhibit proper modelling of seasonality and long-term trends. Here we propose a time series technique based on local-scale mortality observations that can provide more reliable information about vulnerability within metropolitan areas. The method combines a generalised additive model with direct standardisation to account for changing death rates in intra-city zones. We apply the method to a 26-year time series of postal code-referenced mortality data from Philadelphia County, USA, where we find that heat-related mortality is unevenly spatially distributed. Fifteen of 46 postal codes are associated with significantly increased mortality on extreme heat days, most of which are located in the central and western portions of the county. In some cases the local death rate is more than double the county average. Identification of high-risk areas can enable targeted public health intervention and mitigation strategies.

High temperature and humidity conditions are associated with short-term elevations in the mortality rate in many United States cities. Previous research has quantified this relationship in an aggregate manner over large metropolitan areas, but within these areas the response may differ based on local-scale variability in climate, population characteristics, and socio-economic factors. We compared the mortality response for 48 Zip Code Tabulation Areas (ZCTAs) comprising Philadelphia County, PA to determine if certain areas are associated with elevated risk during high heat stress conditions. A randomization test was used to identify mortality exceedances for various apparent temperature thresholds at both the city and local scale. We then sought to identify the environmental, demographic, and social factors associated with high-risk areas via principal components regression. Citywide mortality increases by 9.3% on days following those with apparent temperatures over 34[degrees]C observed at 7:00 p.m. local time. During these conditions, elevated mortality rates were found for 10 of the 48 ZCTAs concentrated in the west-central portion of the County. Factors related to high heat mortality risk included proximity to locally high surface temperatures, low socioeconomic status, high density residential zoning, and age. Within the larger Philadelphia metropolitan area, there exists statistically significant fine-scale spatial variability in the mortality response to high apparent temperatures. Future heat warning systems and mitigation and intervention measures could target these high risk areas to reduce the burden of extreme weather on summertime morbidity and mortality.

Asprosin, a recently identified secreted hormone from adipose tissue, acts centrally to promote food intake. Asprosin is a recently discovered fasting-induced hormone that promotes hepatic glucose production. Here we demonstrate that asprosin in the circulation crosses the blood–brain barrier and directly activates orexigenic AgRP + neurons via a cAMP-dependent pathway. This signaling results in inhibition of downstream anorexigenic proopiomelanocortin (POMC)-positive neurons in a GABA-dependent manner, which then leads to appetite stimulation and a drive to accumulate adiposity and body weight. In humans, a genetic deficiency in asprosin causes a syndrome characterized by low appetite and extreme leanness; this is phenocopied by mice carrying similar mutations and can be fully rescued by asprosin. Furthermore, we found that obese humans and mice had pathologically elevated concentrations of circulating asprosin, and neutralization of asprosin in the blood with a monoclonal antibody reduced appetite and body weight in obese mice, in addition to improving their glycemic profile. Thus, in addition to performing a glucogenic function, asprosin is a centrally acting orexigenic hormone that is a potential therapeutic target in the treatment of both obesity and diabetes.

In a study involving more than 5.9 million persons in a Danish registry, the presence of a mental disorder (in 11.8% of the population) was associated with subsequent medical conditions encompassing 31 specific diagnoses, with hazard ratios that ranged from 0.82 to 3.62 and varied greatly over time.

BackgroundRecent studies showed preliminary data on associations of MRI-based imaging phenotypes of breast tumours with breast cancer molecular, genomic, and related characteristics. In this study, we present a comprehensive analysis of this relationship.MethodsWe analysed a set of 922 patients with invasive breast cancer and pre-operative MRI. The MRIs were analysed by a computer algorithm to extract 529 features of the tumour and the surrounding tissue. Machine-learning-based models based on the imaging features were trained using a portion of the data (461 patients) to predict the following molecular, genomic, and proliferation characteristics: tumour surrogate molecular subtype, oestrogen receptor, progesterone receptor and human epidermal growth factor status, as well as a tumour proliferation marker (Ki-67). Trained models were evaluated on the set of the remaining 461 patients.ResultsMultivariate models were predictive of Luminal A subtype with AUC = 0.697 (95% CI: 0.647–0.746, p < .0001), triple negative breast cancer with AUC = 0.654 (95% CI: 0.589–0.727, p < .0001), ER status with AUC = 0.649 (95% CI: 0.591–0.705, p < .001), and PR status with AUC = 0.622 (95% CI: 0.569–0.674, p < .0001). Associations between individual features and subtypes we also found.ConclusionsThere is a moderate association between tumour molecular biomarkers and algorithmically assessed imaging features.

CRISPR/Cas9 guided gene-editing is a potential therapeutic tool, however application to neurodegenerative disease models has been limited. Moreover, conventional mutation correction by gene-editing would only be relevant for the small fraction of neurodegenerative cases that are inherited. Here we introduce a CRISPR/Cas9-based strategy in cell and animal models to edit endogenous amyloid precursor protein (APP) at the extreme C-terminus and reciprocally manipulate the amyloid pathway, attenuating APP-β-cleavage and Aβ production, while up-regulating neuroprotective APP-α-cleavage. APP N-terminus and compensatory APP-homologues remain intact, with no apparent effects on neurophysiology in vitro. Robust APP-editing is seen in human iPSC-derived neurons and mouse brains with no detectable off-target effects. Our strategy likely works by limiting APP and BACE-1 approximation, and we also delineate mechanistic events that abrogates APP/BACE-1 convergence in this setting. Our work offers conceptual proof for a selective APP silencing strategy. Gene editing strategies are typically designed to correct mutant genes, but most neurodegenerative diseases are sporadic. Here the authors describe a strategy to selectively edit the C-terminus of APP and attenuate amyloid-β production, while upregulating neuroprotective α-cleavage.