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The MIRAGE-Shanghai experiment was designed to characterize the factors controlling regional air pollution near a Chinese megacity (Shanghai) and was conducted during September 2009. This paper provides information on the measurements conducted for this study. In order to have some deep analysis of the measurements, a regional chemical/dynamical model (version 3 of Weather Research and Forecasting Chemical model – WRF-Chemv3) is applied for this study. The model results are intensively compared with the measurements to evaluate the model capability for calculating air pollutants in the Shanghai region, especially the chemical species related to ozone formation. The results show that the model is able to calculate the general distributions (the level and the variability) of air pollutants in the Shanghai region, and the differences between the model calculation and the measurement are mostly smaller than 30%, except the calculations of HONO (nitrous acid) at PD (Pudong) and CO (carbon monoxide) at DT (Dongtan).

The main scientific focus is the study of ozone chemical formation not only in the urban area, but also on a regional scale of the surrounding area of Shanghai. The results show that during the experiment period, the ozone photochemical formation was strongly under the VOC (volatile organic compound)-limited condition in the urban area of Shanghai. Moreover, the VOC-limited condition occurred not only in the city, but also in the larger regional area. There was a continuous enhancement of ozone concentrations in the downwind of the megacity of Shanghai, resulting in a significant enhancement of ozone concentrations in a very large regional area in the surrounding region of Shanghai. The sensitivity study of the model suggests that there is a threshold value for switching from VOC-limited condition to NO_x (nitric oxide and nitrogen dioxide)-limited condition. The threshold value is strongly dependent on the emission ratio of NO_x / VOCs. When the ratio is about 0.4, the Shanghai region is under a strong VOC-limited condition over the regional scale. In contrast, when the ratio is reduced to about 0.1, the Shanghai region is under a strong NO_x-limited condition. The estimated threshold value (on the regional scale) for switching from VOC-limited to NO_x-limited condition ranges from 0.1 to 0.2. This result has important implications for ozone production in this region and will facilitate the development of effective O₃ control strategies in the Shanghai region

Ischemia and reperfusion injury (IRI) is an inevitable event in conventional organ transplant procedure and is associated with significant mortality and morbidity post-transplantation. We hypothesize that IRI is avoidable if the blood supply for the organ is not stopped, thus resulting in optimal transplant outcomes. Here we described the first case of a novel procedure called ischemia-free organ transplantation (IFOT) for patients with end-stage liver disease. The liver graft with severe macrovesicular steatosis was donated from a 25-year-old man. The recipient was a 51-year-old man with decompensated liver cirrhosis and hepatocellular carcinoma. The graft was procured, preserved, and implanted under continuous normothermic machine perfusion. The recipient did not suffer post-reperfusion syndrome or vasoplegia after revascularization of the allograft. The liver function test and histological study revealed minimal hepatocyte, biliary epithelium and vascular endothelium injury during preservation and post-transplantation. The inflammatory cytokine levels were much lower in IFOT than those in conventional procedure. Key pathways involved in IRI were not activated after allograft revascularization. No rejection, or vascular or biliary complications occurred. The patient was discharged on day 18 post-transplantation. This marks the first case of IFOT in humans, offering opportunities to optimize transplant outcomes and maximize donor organ utilization.

Abstract:In this paper, we tackle air quality forecasting by using machine learning approaches to predict the hourly concentration of air pollutants (e.g., ozone, particle matter (PM 2.5 ) and sulfur dioxide). Machine learning, as one of the most popular techniques, is able to efficiently train a model on big data by using large-scale optimization algorithms. Although there exist some works applying machine learning to air quality prediction, most of the prior studies are restricted to several-year data and simply train standard regression models (linear or nonlinear) to predict the hourly air pollution concentration. In this work, we propose refined models to predict the hourly air pollution concentration on the basis of meteorological data of previous days by formulating the prediction over 24 h as a multi-task learning (MTL) problem. This enables us to select a good model with different regularization techniques. We propose a useful regularization by enforcing the prediction models of consecutive hours to be close to each other and compare it with several typical regularizations for MTL, including standard Frobenius norm regularization, nuclear norm regularization, and 2,1 -norm regularization. Our experiments have showed that the proposed parameter-reducing formulations and consecutive-hour-related regularizations achieve better performance than existing standard regression models and existing regularizations.

Female patients with sepsis have better clinical outcomes than male patients in terms of mortality and length of hospitalization and ICU stay.This is an open access article distributed under the terms of the Creative Commons Attribution-Non Commercial-No Derivatives License 4.0 (CCBY-NC-ND), where it is permissible to download and share the work provided it is properly cited. The work cannot be changed in any way or used commercially without permission from the journal. http://creativecommons.org/licenses/by-nc-nd/4.0.

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Open Access. This is an open access article distributed under the terms of the CC-BY License.