This Guangzhou retrospective study revealed that proper use of corticosteroid in confirmed critical SARS resulted in lowered mortality and shorter hospitalization stay, and was not associated with significant secondary lower respiratory infection and other complications.
Our results revealed that factors that were associated with the ward environment and administration were important in nosocomial outbreaks of SARS. The lessons learned from this study remain very important and highly relevant to the daily operation of hospital wards if we are to prevent nosocomial outbreaks of other respiratory infections in the future.
Fugitive emissions from natural gas systems are the largest anthropogenic source of the greenhouse gas methane (CH4) in the U.S. and contribute to the risk of explosions in urban environments. Here, we report on a survey of CH4 emissions from 100 natural gas leaks in cast iron distribution mains in Metro Boston, MA. Direct measures of CH4 flux from individual leaks ranged from 4.0 - 2.3 × 10(4) g CH4•day(-1). The distribution of leak size is positively skewed, with 7% of leaks contributing 50% of total CH4 emissions measured. We identify parallels in the skewed distribution of leak size found in downstream systems with midstream and upstream stages of the gas process chain. Fixing 'superemitter' leaks will disproportionately stem greenhouse gas emissions. Fifteen percent of leaks surveyed qualified as potentially explosive (Grade 1), and we found no difference in CH4 flux between Grade 1 leaks and all remaining leaks surveyed (p = 0.24). All leaks must be addressed, as even small leaks cannot be disregarded as 'safely leaking.' Key methodological impediments to quantifying and addressing the impacts of leaking natural gas distribution infrastructure involve inconsistencies in the manner in which gas leaks are defined, detected, and classified. To address this need, we propose a two-part leak classification system that reflects both the safety and climatic impacts of natural gas leaks.
We postulate that skepticism about climate change is partially caused by the spatial heterogeneity of climate change, which exposes experiential learners to climate heuristics that differ from the global average. This hypothesis is tested by formalizing an index that measures local changes in climate using station data and comparing this index with survey-based model estimates of county-level opinion about whether global warming is happening. Results indicate that more stations exhibit cooling and warming than predicted by random chance and that spatial variations in these changes can account for spatial variations in the percentage of the population that believes that "global warming is happening." This effect is diminished in areas that have experienced more record low temperatures than record highs since 2005. Together, these results suggest that skepticism about climate change is driven partially by personal experiences; an accurate heuristic for local changes in climate identifies obstacles to communicating ongoing changes in climate to the public and how these communications might be improved.climate change | climate skepticism | experiential learning | recency weighting | local climate D espite overwhelming scientific evidence, a significant fraction of the US population does not believe that climate is changing as proxied by a general warming, (1, 2), which we term skepticism. This skepticism is likely caused by many reasons, including two psychological phenomena: climate change is hard to perceive via everyday experience, and climate change is ancillary to everyday concerns (3-6). Under these conditions, experiential learning tends to be more powerful than statistical results (4, 7-10).Here, we test the hypothesis that skepticism about climate change is partially caused by variations in the direction (warming or cooling) and magnitude of climate change over space (herein spatial heterogeneity), which expose experiential learners to climate heuristics that differ from the global average, by formalizing a simple index that measures local changes in climate and comparing this index with survey-based model estimates of countylevel opinion about whether global warming is happening (1). Beyond the predictable impact of demographic factors (11-13), our results indicate that the index for local changes in climate (which may proxy an individual's climate experience) can account for a significant fraction of county-level variations in the percentage of the population that believes that "global warming is happening." These results are tempered by our finding that belief is shaped by more recent experiences. Specifically, belief is diminished by record low temperatures since 2005. Together, these results suggest that skepticism about climate change is driven partially by personal experiences; an accurate heuristic for local changes in climate identifies obstacles to and potential solutions for communicating ongoing changes in climate to the public.Previous analyses calculate climate heuristics by comparing temperat...
A femtosecond laser-induced clean fluorescence technique was explored as a means to monitor halogenated alkanes in the atmosphere. Characteristic difluorocarbene radical (CF2) fluorescence in the UV-vis can be generated inside a femtosecond laser-induced filament for different halocarbons. We show that, due to different dissociation and excitation kinetics leading to fluorescence emission, it is possible to temporally resolve the characteristic fluorescence of CF2-containing halocarbons from that of background species, therefore enhancing the signal-to-noise ratio. Laboratory-scale experiments demonstrate the potential use of femtosecond laser-induced clean fluorescence for the remote sensing of halocarbons in the atmosphere. The combination of this detection strategy with LIDAR could allow the long-range monitoring of several atmospheric species with a single laser source, eventually leading to a better understanding of chemical and dynamic processes affecting global warming, ozone loss, tropospheric pollution, and weather prediction.
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