Oesophageal pleural fistula presenting with Parvimonas micra infection causing cervical and brain abscesses.

Parvimonas micra (P. micra) infections causing spinal cord compression are extremely rare. We report an occult oesophageal pleural fistula presenting with spinal epidural and brain abscesses resulting in severe neurological deficits caused by P. micra. Molecular detection proved to be instrumental in identifying the causative pathogen. Essential management with decompression, drainage, antibiotics and fistula repair lead to a good outcome

Air and water pollution over time and industries with stochastic dominance

We employ a stochastic dominance (SD) approach to analyze the components that contribute to environmental degradation over time. The variables include countries\u2019 greenhouse gas (GHG) emissions and water pollution. Our approach is based on pair-wise SD tests. First, we study the dynamic progress of each separate variable over time, from 1990 to 2005, within 5-year horizons. Then, pair-wise SD tests are used to study the major industry contributors to the overall GHG emissions and water pollution at any given time, to uncover the industry which contributes the most to total emissions and water pollution. While CO2 emissions increased in the first order SD sense over 15 years, water pollution increased in a second-order SD sense. Electricity and heat production were the major contributors to the CO2 emissions, while the food industry gradually became the major water polluting industry over time. SD sense over 15 years, water pollution increased in a second-order SD sense. Electricity and heat production were the major contributors to the CO2 emissions, while the food industry gradually

Why is Pollution from U.S. Manufacturing Declining? The Roles of Trade, Regulation, Productivity, and Preferences

Between 1990 and 2008, emissions of the most common air pollutants from U.S. manufacturing fell by 60 percent, even as real U.S. manufacturing output grew substantially. This paper develops a quantitative model to explain how changes in trade, environmental regulation, productivity, and consumer preferences have contributed to these reductions in pollution emissions. We estimate the model’s key parameters using administrative data on plant-level production and pollution decisions. We then combine these estimates with detailed historical data to provide a model-driven decomposition of the causes of the observed pollution changes. Finally, we compare the model-driven decomposition to a statistical decomposition. The model and data suggest three findings. First, the fall in pollution emissions is due to decreasing pollution per unit output within narrowly defined products, rather than to changes in the types of products produced or changes to the total quantity of manufacturing output. Second, the implicit pollution tax that rationalizes firm production and abatement behavior more than doubled between 1990 and 2008. Third, environmental regulation explains 75 percent or more of the observed reduction in pollution emissions from manufacturing

A Novel Non-Parametric Spatiotemporal Scan Statistic: An Application to Detect Disease Outbreaks

The majority of the widely used scan statistics are based on distributional assumptions. Contrary to the existing methods, with a new perspective in clustering, the Mann-Whitney Scan Statistic was introduced to detect clusters in continuous data indexed by time or space, without any distributional assumptions or parameters to set up. We propose an extension of the Mann-Whitney Scan Statistic that can be applied to spatiotemporal data based on spatiotemporal distance measure. This novel scan statistic is distribution-free and seems to be powerful against parametric spatiotemporal scan statistics. The results are applicable in a wide variety of spatiotemporal domains, including epidemiology, socioeconomic analysis and climate sciences, irrespective of continuous or discrete data

A Novel Non-Parametric Spatiotemporal Scan Statistic: An Application to Detect Disease Outbreaks

The majority of the widely used scan statistics are based on distributional assumptions. Contrary to the existing methods, with a new perspective in clustering, the Mann-Whitney Scan Statistic was introduced to detect clusters in continuous data indexed by time or space, without any distributional assumptions or parameters to set up. We propose an extension of the Mann-Whitney Scan Statistic that can be applied to spatiotemporal data based on spatiotemporal distance measure. This novel scan statistic is distribution-free and seems to be powerful against parametric spatiotemporal scan statistics. The results are applicable in a wide variety of spatiotemporal domains, including epidemiology, socioeconomic analysis and climate sciences, irrespective of continuous or discrete data

Guarantee loans for urban agriculture in Gampaha, Sri Lanka

Agricultural development towards security of food, nutrition and livelihood is high on the political agenda in Sri Lanka. Recently, national priorities have included the development of food-secure and resilient cities; in this regard, the Western Province has been a forerunner, having commenced its urban agriculture campaign as early as 2000