Low-cost nature-inspired deep learning system for PM2.5 forecast over Delhi, India.

Air quality has a tremendous impact on India's health and prosperity. Air quality models are crucial tools for surveying and projecting air pollution episodes, which can be used to issue health advisories to take action ahead of time. Short-term increases in air pollution trigger many adverse health events; a fast, efficient, cost-effective, and reliable air quality prediction model would aid in minimizing the effect on health and prosperity. Deterministic models, on the other hand, are less robust in predicting the pollutant series since it is non-stationary and non-linear. Atmospheric chemistry models are computationally expensive and often rely on outdated emissions information. We propose a deep learning model in this study that integrates neural networks, fuzzy inference systems, and wavelet transforms to predict the most prominent air pollutant affecting Delhi, India i.e., PM2.5 (particulate matter of aerodynamic diameter less than or equal to 2.5 µm). We have included the main aspects of air quality models in this research i.e., less computational time (7 min approximately using I5-1035G1, 1.19 GHz processor), less resource-intensive (dependent only on the pollutant lagged values), and high spatial resolution (1 km) for forecasting air quality three days ahead. The model predictions show a significant correlation coefficient lying in [0.96,0.98], [0.86,0.93], and [0.82,0.91] with Central Pollution Control Board (CPCB) monitored data at various sites in Delhi for one, two, and three days of forecast respectively.

High rate of discordance between clinical and autopsy diagnoses in blood and marrow transplantation.

We analyzed autopsies performed in a Canadian blood and marrow transplantation (BMT) program. We aimed to assess variables that predict the performance of an autopsy, whether rates of autopsy are changing, and the rate of discordance between clinical and autopsy diagnoses. All deceased adult patients from January 1990 to December 2004 were reviewed. Autopsy rates were compared to a large teaching hospital. Of 476 myeloablative BMT patients, 225 died and 48 (27%) underwent autopsy. Autopsy was more likely in patients dying: <100 days post-BMT, in the intensive care unit, after allografting, and on weekends. Autopsy rates among BMT patients declined during the three time periods (1990-1994, 1995-1999, 2000-2004). The autopsy rate at the teaching hospital showed a similar downward temporal trend. Major and minor disagreements at autopsy were present in 16 (34%) and 14 (30%) of cases, respectively. There was no change in discordance rates over time. Thus, despite advances in diagnostic procedures, high levels of disagreement between clinical and autopsy diagnoses for BMT patients persist as autopsy rates decline. We recommend that the autopsy regains its role as a valuable investigation. This may become especially relevant in an era where patients with medical comorbidities are undergoing reduced-intensity BMT.