ALBERT

Description: Data gathering is a key operator for applications in wireless sensor networks; yet it is also a challenging problem in mobile sensor networks when considering that all nodes are mobile and the communications among them are opportunistic. This paper proposes an efficient data gathering scheme called ADG that adopts speedy mobile elements as the mobile data collector and takes advantage of the movement patterns of the network. ADG first extracts the network meta-data at initial epochs, and calculates a set of proxy nodes based on the meta-data. Data gathering is then mapped into the Proxy node Time Slot Allocation (PTSA) problem that schedules the time slots and orders, according to which the data collector could gather the maximal amount of data within a limited period. Finally, the collector follows the schedule and picks up the sensed data from the proxy nodes through one hop of message transmissions. ADG learns the period when nodes are relatively stationary, so that the collector is able to pick up the data from them during the limited data gathering period. Moreover, proxy nodes and data gathering points could also be timely updated so that the collector could adapt to the change of node movements. Extensive experimental results show that the proposed scheme outperforms other data gathering schemes on the cost of Sensors 2015, 15 23219 message transmissions and the data gathering rate, especially under the constraint of limited data gathering period.

Description: Wheezing is often treated as a crucial indicator in the diagnosis of obstructive pulmonary diseases. A rapid wheezing detection system may help physicians to monitor patients over the long-term. In this study, a portable wheezing detection system based on a field-programmable gate array (FPGA) is proposed. This system accelerates wheezing detection, and can be used as either a single-process system, or as an integrated part of another biomedical signal detection system. The system segments sound signals into 2-second units. A short-time Fourier transform was used to determine the relationship between the time and frequency components of wheezing sound data. A spectrogram was processed using 2D bilateral filtering, edge detection, multithreshold image segmentation, morphological image processing, and image labeling, to extract wheezing features according to computerized respiratory sound analysis (CORSA) standards. These features were then used to train the support vector machine (SVM) and build the classification models. The trained model was used to analyze sound data to detect wheezing. The system runs on a Xilinx Virtex-6 FPGA ML605 platform. The experimental results revealed that the system offered excellent wheezing recognition performance (0.912). The detection process can be used with a clock frequency of 51.97 MHz, and is able to perform rapid wheezing classification.

Description: IJERPH, Vol. 15, Pages 761: Source Apportionment of Polycyclic Aromatic Hydrocarbons in Sediment by the Application of Non-Negative Factor Analysis: A Case Study of Dalian Bay International Journal of Environmental Research and Public Health doi: 10.3390/ijerph15040761 Authors: Fu-Lin Tian Fa-Yun Li De-Gao Wang Yan-Jie Wang An improved method, factor analysis with non-negative constraints (FA-NNC) was adopted to apportion the sources of sediment polycyclic aromatic hydrocarbons (PAHs) in Dalian Bay, China. Cosine similarity and Monte Carlo uncertainty analysis were used to assist the FA-NNC source resolution. The results identified three sources for PAHs, which were overall traffic, diesel engine emissions and residential coal combustion. The contributions of these sources were quantified as 78 ± 4.6% from overall traffic, 12 ± 3.2% from diesel engine emissions, and 10 ± 1.9% from residential coal combustion. The results from the Monte Carlo uncertainty analysis indicated that the model was robust and convergent.

Description: Natural and engineered water systems are the main sources of Legionnaires’ disease. It is essential from a public health perspective to survey water environments for the existence of Legionella. To analyze the main serogroups, genotypes and pathogenicity of the pathogen, a stratified sampling method was adopted to collect water samples randomly from shower water, cooling tower water, and local public hot springs in Wenzhou, China. Suspected strains were isolated from concentrated water samples. Serum agglutination assay and real-time PCR (Polymerase chain reaction) were used to identify L. pneumophila. Sequence-based typing (SBT) and pulsed-field gel electrophoresis (PFGE) were used to elucidate the genetic polymorphisms in the collected isolates. The intracellular growth ability of the isolates was determined through their interaction with J774 cells and plating them onto BCYE (Buffered Charcoal Yeast Extract) agar plates. Overall, 25.56% (46/180) of water samples were Legionella-positive; fifty-two strains were isolated and two kinds of serogroups were co-detected from six water samples from 2015 to 2016. Bacterial concentrations ranged from 20 CFU/100 mL to 10,720 CFU/100 mL. In detail, the Legionella-positive rates of shower water, cooling tower water and hot springs water were 15.45%, 13.33%, and 62.5%, respectively. The main serogroups were LP1 (30.69%) and LP3 (28.85%) and all strains carried the dot gene. Among them, 52 isolates and another 10 former isolates were analyzed by PFGE. Nineteen distinct patterns were observed in 52 strains isolated from 2015 to 2016 with three patterns being observed in 10 strains isolated from 2009 to 2014. Seventy-three strains containing 52 from this study and 21 former isolates were selected for SBT analysis and divided into 25 different sequence types in 4 main clonal groups belonging to 4 homomorphic types. Ten strains were chosen to show their abilities to grow and multiply in J744 cells. Taken together, our results demonstrate a high prevalence and genetic polymorphism of Legionella in Wenzhou’s environmental water system. The investigated environmental water sources pose a potential threat to the public where intervention could help to prevent the occurrence of Legionnaires’ disease.

Description: In January 2013, severe haze events over northeastern China sparked substantial health concerns. This study explores the associations of fine particulate matter less than 2.5 μm (PM2.5) and black carbon (BC) with hospital emergency room visits (ERVs) during a haze season in Beijing. During that period, daily counts of ERVs for respiratory, cardiovascular and ocular diseases were obtained from a Level-3A hospital in Beijing from 1 December 2012 to 28 February 2013, and associations of which with PM2.5 and BC were estimated by time-stratified case-crossover analysis in single- and two-pollutant models. We found a 27.5% (95% confidence interval (CI): 13.0, 43.9%) increase in respiratory ERV (lag02), a 19.4% (95% CI: 2.5, 39.0%) increase in cardiovascular ERV (lag0), and a 12.6% (95% CI: 0.0, 26.7%) increase in ocular ERV (lag0) along with an interquartile range (IQR) increase in the PM2.5. An IQR increase of BC was associated with 27.6% (95% CI: 9.6, 48.6%) (lag02), 18.8% (95% CI: 1.4, 39.2%) (lag0) and 11.8% (95% CI: −1.4, 26.8%) (lag0) increases for changes in these same health outcomes respectively. Estimated associations were consistent after adjusting SO2 or NO2 in two-pollutant models. This study provides evidence that improving air quality and reducing haze days would greatly benefit the population health.