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(+/?)3,4‐methylenedioxymethamphetamine (MDMA, “ecstasy”) is an abused psychostimulant that produces strong monoaminergic stimulation and whole‐body hyperthermia. MDMA‐induced thermogenesis involves activation of uncoupling proteins (UCPs), primarily a type specific to skeletal muscle (UCP‐3) and absent from the brain, although other UCP types are expressed in the brain (e.g. thalamus) and might contribute to thermogenesis. Since neuroimaging of brain temperature could provide insights into MDMA action, we measured spatial distributions of systemically administered MDMA‐induced temperature changes and dynamics in rat cortex and subcortex using a novel magnetic resonance method, Biosensor Imaging of Redundant Deviation in Shifts (BIRDS), with an exogenous temperature‐sensitive probe (thulium ion and macrocyclic chelate 1,4,7,10‐tetraazacyclododecane‐1,4,7,10‐tetramethyl‐1,4,7,10‐tetraacetate (DOTMA4?)). The MDMA‐induced temperature rise was greater in the cortex than in the subcortex (1.6 ± 0.4 °C versus 1.3 ± 0.4 °C) and occurred more rapidly (2.0 ± 0.2 °C/h versus 1.5 ± 0.2 °C/h). MDMA‐induced temperature changes and dynamics in the cortex and body were correlated, although the body temperature exceeded the cortex temperature before and after MDMA. Temperature, neuronal activity, and blood flow (CBF) were measured simultaneously in the cortex and subcortex (i.e. thalamus) to investigate possible differences of MDMA‐induced warming across brain regions. MDMA‐induced warming correlated with increases in neuronal activity and blood flow in the cortex, suggesting that the normal neurovascular response to increased neural activity was maintained. In contrast to the cortex, a biphasic relationship was seen in the subcortex (i.e. thalamus), with a decline in CBF as temperature and neural activity rose, transitioning to a rise in CBF for temperature above 37 °C, suggesting that MDMA affected CBF and neurovascular coupling differently in subcortical regions. Considering that MDMA effects on CBF and heat dissipation (as well as potential heat generation) may vary regionally, neuroprotection may require different cooling strategies. Copyright © 2015 John Wiley & Sons, Ltd.  相似文献   
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Analyzing functional species’ characteristics (species traits) that represent physiological, life history and morphological characteristics of species help understanding the impacts of various stressors on aquatic communities at field conditions. This research aimed to study the combined effects of pesticides and other environmental factors (temperature, dissolved oxygen, dissolved organic carbon, floating macrophytes cover, phosphate, nitrite, and nitrate) on the trait modality distribution of aquatic macrofauna communities. To this purpose, a field inventory was performed in a flower bulb growing area of the Netherlands with significant variation in pesticides pressures. Macrofauna community composition, water chemistry parameters and pesticide concentrations in ditches next to flower bulb fields were determined. Trait modalities of nine traits (feeding mode, respiration mode, locomotion type, resistance form, reproduction mode, life stage, voltinism, saprobity, maximum body size) likely to indicate pesticides impacts were analyzed. According to a redundancy analysis, phosphate -and not pesticides- constituted the main factor structuring the trait modality distribution of aquatic macrofauna. The functional composition could be ascribed for 2–4 % to pesticides, and for 3–11 % to phosphate. The lack of trait responses to pesticides may indicate that species may have used alternative strategies to adapt to ambient pesticides stress. Biomass of animals exhibiting trait modalities related to feeding by predation and grazing, presence of diapause form or dormancy, reproduction by free clutches and ovoviviparity, life stage of larvae and pupa, was negatively correlated to the concentration of phosphate. Hence, despite the high pesticide pollution in the area, variation in nutrient-related stressors seems to be the dominant driver of the functional composition of aquatic macrofauna assembly in agricultural ditches.  相似文献   
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Prevalence of osteoporosis is more than 50% in older adults, yet current clinical methods for diagnosis that rely on areal bone mineral density (aBMD) fail to detect most individuals who have a fragility fracture. Bone fragility can manifest in different forms, and a “one-size-fits-all” approach to diagnosis and management of osteoporosis may not be suitable. High-resolution peripheral quantitative computed tomography (HR-pQCT) provides additive information by capturing information about volumetric density and microarchitecture, but interpretation is challenging because of the complex interactions between the numerous properties measured. In this study, we propose that there are common combinations of bone properties, referred to as phenotypes, that are predisposed to different levels of fracture risk. Using HR-pQCT data from a multinational cohort (n = 5873, 71% female) between 40 and 96 years of age, we employed fuzzy c-means clustering, an unsupervised machine-learning method, to identify phenotypes of bone microarchitecture. Three clusters were identified, and using partial correlation analysis of HR-pQCT parameters, we characterized the clusters as low density, low volume, and healthy bone phenotypes. Most males were associated with the healthy bone phenotype, whereas females were more often associated with the low volume or low density bone phenotypes. Each phenotype had a significantly different cumulative hazard of major osteoporotic fracture (MOF) and of any incident osteoporotic fracture (p < 0.05). After adjustment for covariates (cohort, sex, and age), the low density followed by the low volume phenotype had the highest association with MOF (hazard ratio = 2.96 and 2.35, respectively), and significant associations were maintained when additionally adjusted for femoral neck aBMD (hazard ratio = 1.69 and 1.90, respectively). Further, within each phenotype, different imaging biomarkers of fracture were identified. These findings suggest that osteoporotic fracture risk is associated with bone phenotypes that capture key features of bone deterioration that are not distinguishable by aBMD. © 2021 American Society for Bone and Mineral Research (ASBMR).  相似文献   
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