Predicting the daily number of patients for allergic diseases using PM10 concentration based on spatiotemporal graph convolutional networks.

Air pollution causes and exacerbates allergic diseases including asthma, allergic rhinitis, and atopic dermatitis. Precise prediction of the number of patients afflicted with these diseases and analysis of the environmental conditions that contribute to disease outbreaks play crucial roles in the effective management of hospital services. Therefore, this study aims to predict the daily number of patients with these allergic diseases and determine the impact of particulate matter (PM10) on each disease. To analyze the spatiotemporal correlations between allergic diseases (asthma, atopic dermatitis, and allergic rhinitis) and PM10 concentrations, we propose a multi-variable spatiotemporal graph convolutional network (MST-GCN)-based disease prediction model. Data on the number of patients were collected from the National Health Insurance Service from January 2013 to December 2017, and the PM10 data were collected from Airkorea during the same period. As a result, the proposed disease prediction model showed higher performance (R2 0.87) than the other deep-learning baseline methods. The synergic effect of spatial and temporal analyses improved the prediction performance of the number of patients. The prediction accuracies for allergic rhinitis, asthma, and atopic dermatitis achieved R2 scores of 0.96, 0.92, and 0.86, respectively. In the ablation study of environmental factors, PM10 improved the prediction accuracy by 10.13%, based on the R2 score.

Methodology: non-invasive monitoring system based on standing wave ratio for detecting water content variations in plants.

BACKGROUND: Water content variation during plant growth is one of the most important monitoring parameters in plant studies. Conventional parameters (such as dry weight) are unreliable; thus, the development of rapid, accurate methods that will allow the monitoring of water content variation in live plants is necessary. In this study, we aimed to develop a non-invasive, radiofrequency-based monitoring system to rapidly and accurately detect water content variation in live plants. The changes in standing wave ratio (SWR) caused by the presence of stem water and magnetic particles in the stem water flow were used as the basis of plant monitoring systems. RESULTS: The SWR of a coil probe was used to develop a non-invasive monitoring system to detect water content variation in live plants. When water was added to the live experimental plants with or without illumination under drought conditions, noticeable SWR changes at various frequencies were observed. When a fixed frequency (1.611 GHz) was applied to a single experimental plant (Radermachera sinica), a more comprehensive monitoring, such as water content variation within the plant and the effect of illumination on water content, was achieved. CONCLUSIONS: Our study demonstrated that the SWR of a coil probe could be used as a real-time, non-invasive, non-destructive parameter for detecting water content variation and practical vital activity in live plants. Our non-invasive monitoring method based on SWR may also be applied to various plant studies.

Adsorptive removal of tetracycline from aqueous solution by maple leaf-derived biochar.

The present study evaluates the physicochemical properties of maple leaf-derived biochars (M-BCs) produced at different pyrolytic temperatures (i.e., 350, 550, and 750 °C) and their adsorptive properties for tetracycline onto M-BCs. The increase in pyrolysis temperature to produce M-BCs led to a significant increase in the biochar's hydrophobicity, surface area, and calcite (CaCO3) crystallization. The M-BC750 produced without functionalization or activation possessed a high calcite composition and a hydrophobic nature with lower O/C and H/C, hydroxyl groups (-OH) on the surface, and functional groups (i.e., O-containing) as H-bond acceptors. Among M-BCs, the M-BC750 present a highest TC adsorption capacity owing to possible mechanisms such as metal complexation, H-bonding, and hydrophobic interactions. The isotherm and kinetic models for TC adsorption followed the Freundlich models and pseudo-second-order models, respectively. M-BCs produced from the waste fallen maple leaves could be applied as low-cost environmental adsorbents for TC removal.

Correction: The anti-inflammatory effect of a glycosylation product derived from the high hydrostatic pressure enzymatic hydrolysate of a flatfish byproduct.

Correction for 'The anti-inflammatory effect of a glycosylation product derived from the high hydrostatic pressure enzymatic hydrolysate of a flatfish byproduct' by In-Hu Choe, et al., Food Funct., 2016, 7, 2557-2565.

The anti-inflammatory effect of a glycosylation product derived from the high hydrostatic pressure enzymatic hydrolysate of a flatfish byproduct.

In this study, flatfish byproducts were hydrolyzed by Protamex at high hydrostatic pressure and glycosylated with ribose to utilize the protein of flatfish byproducts as a nutraceutical. We investigated the anti-inflammatory effects of glycosylated fish byproduct protein hydrolysate (GFPH) and its anti-inflammatory mechanisms were elucidated in lipopolysaccharide (LPS)-stimulated RAW 264.7 mouse macrophage. The results showed that GFPH suppresses LPS-induced production of nitric oxide (NO) and prostaglandin E2 (PGE2) and expression of inducible nitric oxide synthase (iNOS) and cyclooxygenase-2 (COX-2) dose-dependently. The enzyme-linked immunosorbent assay (ELISA) kit clearly demonstrated that GFPH significantly reduced the production of pro-inflammatory cytokines such as, interleukin (IL)-6, interleukin (IL)-1ß and tumor necrosis factor (TNF)-α, and monocyte chemoattractant protein (MCP)-1. Moreover, GFPH reduced nuclear factor κB (NF-κB) and mitogen-activated protein kinase (MAPK) activation. These results indicate that the inhibitory effects of GFPH on LPS-induced NO and PGE2 production might be due to the suppression of the NF-κB and MAPKs signaling pathways. Therefore, these results suggest that flatfish byproducts are latent bioactive resources and GFPH may have potential as a therapeutic agent in the treatment of various inflammatory diseases.

LED light stress induced biomass and fatty acid production in microalgal biosystem, Acutodesmus obliquus.

Microbial algal system can serve as a potential source for the production of much high value bioproducts and biofuels. The quality and intensity of light are the key elements to optimize the production of algal biomass and fatty acid contents. This study presents the effect of differential LED flashing light conditions on the growth of microalgae, Acutodesmus obliquus. The induced light stress was optimized for its biomass and fatty acid content. The microalgae are exposed to various frequency of intermittent LED flashing light (blue and red lights) at three different phases in the 18 day cell growth (log, lag and stationary phase). The frequency of light flashing rate was adjusted to 120, 10, 5, 3.75, and 1 times per min. The effect of light stress on growth and fatty acids composition of A. obliquus induced an increase in algae growth and fatty acid production. Different optimal timing for light stress was subjected to elucidate the effect of light stress on algae growth and fatty acid production. The results showed an increase in the algae growth (1.2mg/L of chl a content) under light stress condition at FT10 (flashing time, 10 times per min) from the initial day (log phase) compared with the control experiment (0.4 mg/L of chl a content). However, the total fatty acids (71 mg/g) and volumetric FAME production (9.4 ml/l) level was found to be significant under FT5 (flashing time, 5 times per min), adopting flashing light from day 10 (stationary phase). TEM studies also revealed the deposition of lipid to be largest in the 18 day old cells under flashing light (FT5) condition, representing maximum accumulation of lipids bodies (up to 770 nm diameter in particle size) occupying approximately 42% of the total area of the cell.

Production of algal biomass (Chlorella vulgaris) using sediment microbial fuel cells.

In this study, a novel algal biomass production method using a sediment microbial fuel cell (SMFC) system was assessed. Under the experimental conditions, CO(2) generation from the SMFC and its rate of increase were found to be dependent on the current generated from the SMFC. However, the CH(4) production rate from the SMFC was inhibited by the generation of current. When Chlorella vulgaris was inoculated into the cathode compartment of the SMFC and current was generated under 10 Ω resistance, biomass production from the anode compartment was observed to be closely associated with the rate of current generation from the SMFC. The experimental results demonstrate that 420 mg/L of algae (dry cell weight) was produced when the current from the SMFC reached 48.5 mA/m(2). Therefore, SMFC could provide a means for producing algal biomass via CO(2) generated by the oxidation of organics upon current generation.