Machine learning-based ozone and PM2.5 forecasting: Application to multiple AQS sites in the Pacific Northwest.

Air quality in the Pacific Northwest (PNW) of the U.S has generally been good in recent years, but unhealthy events were observed due to wildfires in summer or wood burning in winter. The current air quality forecasting system, which uses chemical transport models (CTMs), has had difficulty forecasting these unhealthy air quality events in the PNW. We developed a machine learning (ML) based forecasting system, which consists of two components, ML1 (random forecast classifiers and multiple linear regression models) and ML2 (two-phase random forest regression model). Our previous study showed that the ML system provides reliable forecasts of O3 at a single monitoring site in Kennewick, WA. In this paper, we expand the ML forecasting system to predict both O3 in the wildfire season and PM2.5 in wildfire and cold seasons at all available monitoring sites in the PNW during 2017-2020, and evaluate our ML forecasts against the existing operational CTM-based forecasts. For O3, both ML1 and ML2 are used to achieve the best forecasts, which was the case in our previous study: ML2 performs better overall (R2 = 0.79), especially for low-O3 events, while ML1 correctly captures more high-O3 events. Compared to the CTM-based forecast, our O3 ML forecasts reduce the normalized mean bias (NMB) from 7.6 to 2.6% and normalized mean error (NME) from 18 to 12% when evaluating against the observation. For PM2.5, ML2 performs the best and thus is used for the final forecasts. Compared to the CTM-based PM2.5, ML2 clearly improves PM2.5 forecasts for both wildfire season (May to September) and cold season (November to February): ML2 reduces NMB (-27 to 7.9% for wildfire season; 3.4 to 2.2% for cold season) and NME (59 to 41% for wildfires season; 67 to 28% for cold season) significantly and captures more high-PM2.5 events correctly. Our ML air quality forecast system requires fewer computing resources and fewer input datasets, yet it provides more reliable forecasts than (if not, comparable to) the CTM-based forecast. It demonstrates that our ML system is a low-cost, reliable air quality forecasting system that can support regional/local air quality management.

Protective Effects of High-Fat Diet against Murine Colitis in Association with Leptin Signaling and Gut Microbiome.

Inflammatory bowel disease (IBD) is characterized by chronic intestinal-tract inflammation with dysregulated immune responses, which are partly attributable to dysbiosis. Given that diet plays a critical role in IBD pathogenesis and progression, we elucidated the effects of a high-fat diet (HFD) feeding on IBD development in relation to immune dysfunction and the gut microbiota. Five-week-old male C57BL/6J mice were fed either a normal diet (ND) or HFD for 14 weeks. The animals were further divided into ND, ND+ dextran sulfate sodium (DSS), HFD, and HFD+DSS treatment groups. The HFD+DSS mice exhibited lower body weight loss, lower disease activity index, longer colon length, and increased tight-junction protein expression and goblet-cell proportions compared with the ND+DSS mice. The T helper (h)1 and Th17 cell populations and pro-inflammatory cytokines involved in colitis pathogenesis were significantly more reduced in the HFD+DSS mice than in the ND+DSS mice. The HFD+DSS mice showed significantly increased serum leptin concentrations, colonic leptin receptor expression, enhanced anti-apoptotic AKT expression, and reduced pro-apoptotic MAPK and Bax expression compared with the ND+DSS mice, suggesting the involvement of the leptin-mediated pathway in intestinal epithelial cell apoptosis. The alterations in the gut-microbiota composition in the HFD+DSS group were the opposite of those in the ND+DSS group and rather similar to those of the ND group, indicating that the protective effects of HFD feeding against DSS-induced colitis are associated with changes in gut-microbiota composition. Overall, HFD feeding ameliorates DSS-induced colitis and colonic mucosal damage by reinforcing colonic barrier function and regulating immune responses in association with changes in gut-microbiota composition.

Long-Term Follow-Up of Patients with Neck Pain Associated with Ossification of the Posterior Longitudinal Ligament Treated with Integrative Complementary and Alternative Medicine: A Retrospective Analysis and Questionnaire Survey.

Purpose: Integrative Korean medicine treatment (KMT) is a conservative treatment approach for the ossification of the posterior longitudinal ligament (OPLL) in Korea; nonetheless, relevant studies focusing on KMT for OPLL are lacking. A multicenter retrospective analysis of patient medical records and a questionnaire survey were conducted to investigate the effectiveness of integrative KMT in patients with OPLL treated for neck pain. Patients and Methods: A total of 78 inpatients radiologically diagnosed with OPLL and treated for neck pain at four Korean medicine hospitals from April 1, 2016, to December 31, 2019, were enrolled. The primary index was an improvement in the numeric rating scale (NRS) score for neck pain, whereas the secondary outcome indices were improvements in the NRS score for arm pain, neck disability index (NDI) score, and EuroQol 5-dimension 5-level (EQ-5D-5L) score. Results: At discharge, the NRS score for neck pain, NRS score for arm pain, and NDI score decreased by 2.47 (95% confidence interval [CI], -2.81 to -2.14), 1.32 (95% CI, -1.73 to -0.91), and 16.02 (95% CI, -18.89 to -13.15), respectively, as compared with the scores at admission (p < 0.001). The EQ-5D-5L score increased by 0.12 (95% CI, 0.09 to 0.16) as compared with the score at admission (p < 0.001). This trend was also evident during follow-up. With respect to Patient Global Impression of Change evaluation, 33 (61.1%) patients claimed to have very much improved, whereas 17 (31.5%) patients reported to have much improved. Conclusion: Inpatients with OPLL who received integrative KMT showed improvements in neck pain, arm pain, the NDI, and quality of life, which were retained throughout the follow-up period.

Development of a Machine Learning Approach for Local-Scale Ozone Forecasting: Application to Kennewick, WA.

Chemical transport models (CTMs) are widely used for air quality forecasts, but these models require large computational resources and often suffer from a systematic bias that leads to missed poor air pollution events. For example, a CTM-based operational forecasting system for air quality over the Pacific Northwest, called AIRPACT, uses over 100 processors for several hours to provide 48-h forecasts daily, but struggles to capture unhealthy O3 episodes during the summer and early fall, especially over Kennewick, WA. This research developed machine learning (ML) based O3 forecasts for Kennewick, WA to demonstrate an improved forecast capability. We used the 2017-2020 simulated meteorology and O3 observation data from Kennewick as training datasets. The meteorology datasets are from the Weather Research and Forecasting (WRF) meteorological model forecasts produced daily by the University of Washington. Our ozone forecasting system consists of two ML models, ML1 and ML2, to improve predictability: ML1 uses the random forest (RF) classifier and multiple linear regression (MLR) models, and ML2 uses a two-phase RF regression model with best-fit weighting factors. To avoid overfitting, we evaluate the ML forecasting system with the 10-time, 10-fold, and walk-forward cross-validation analysis. Compared to AIRPACT, ML1 improved forecast skill for high-O3 events and captured 5 out of 10 unhealthy O3 events, while AIRPACT and ML2 missed all the unhealthy events. ML2 showed better forecast skill for less elevated-O3 events. Based on this result, we set up our ML modeling framework to use ML1 for high-O3 events and ML2 for less elevated O3 events. Since May 2019, the ML modeling framework has been used to produce daily 72-h O3 forecasts and has provided forecasts via the web for clean air agency and public use: http://ozonematters.com/. Compared to the testing period, the operational forecasting period has not had unhealthy O3 events. Nevertheless, the ML modeling framework demonstrated a reliable forecasting capability at a selected location with much less computational resources. The ML system uses a single processor for minutes compared to the CTM-based forecasting system using more than 100 processors for hours.

Complex population dynamics in a spatial microbial ecosystem with Physarum polycephalum.

This research addresses the interactions between the unicellular slime mold Physarum polycephalum and a red yeast in a spatial ecosystem over week-long imaging experiments. An inverse relationship between the growth rates of both species is shown, where P. polycephalum has positive growth when the red yeast has a negative growth rate and vice versa. The data also captures successional and oscillatory dynamics between both species. An advanced image analysis methodology for semantic segmentation is used to quantify population density over time, for all components of the ecosystem. We suggest that P. polycephalum is capable of exhibiting a sustainable feeding strategy by depositing a nutritive slime trail, allowing yeast to serve as a periodic food source. This opens a new direction of P. polycephalum research, where the population dynamics of spatial ecosystems can be readily quantified and complex ecological dynamics can be studied.

Operational bias correction for PM2.5 using the AIRPACT air quality forecast system in the Pacific Northwest.

A bias correction scheme based on a Kalman filter (KF) method has been developed and implemented for the AIRPACT air quality forecast system which operates daily for the Pacific Northwest. The KF method was used to correct hourly rolling 24-h average PM2.5 concentrations forecast at each monitoring site within the AIRPACT domain and the corrected forecasts were evaluated using observed daily PM2.5 24-h average concentrations from 2017 to 2018. The evaluation showed that the KF method reduced mean daily bias from approximately -50% to ±6% on a monthly averaged basis, and the corrected results also exhibited much smaller mean absolute errors typically less than 20%. These improvements were also apparent for the top 10 worst PM2.5 days during the 2017-2018 test period, including months with intensive wildfire events. Significant differences in AIRPACT performance among urban, suburban, and rural monitoring sites were greatly reduced in the KF bias correction forecasts. The daily 24-h average bias corrections for each monitoring site were interpolated to model grid points using three different interpolation schemes: cubic spline, Gaussian Kriging, and linear Kriging. The interpolated results were more accurate than the original AIRPACT forecasts, and both Kriging methods were better than the cubic spline method. The Gaussian method yielded smaller mean biases and the linear method yielded smaller absolute errors. The KF bias correction method has been implemented operationally using both Kriging interpolation methods for routine output on the AIRPACT website (http://lar.wsu.edu/airpact). This method is relatively easy to implement, but very effective to improve air quality forecast performance.Implications: Current chemical transport models, including CMAQ, used for air quality forecasting can have large errors and uncertainties in simulated PM2.5 concentrations. In this paper, we describe a relatively simple bias correction scheme applied to the AIRPACT air quality forecast system for the Pacific Northwest. The bias correction yields much more accurate and reliable PM2.5 results compared to the normal forecast system. As such, the operational bias corrected forecasts will provide a much better basis for daily air quality management by agencies within the region. The bias corrected results also highlight issues to guide further improvements to the normal forecast system.

Cloning, expression, and characterization of thermophilic L-asparaginase from Thermococcus kodakarensis KOD1.

The present study demonstrates cloning, expression, and characterization of hyperthermostable L-asparaginase from Thermococcus kodakarensis KOD1 in Escherichia coli BLR(DE3). The recombinant 6× His-tagged protein L-asparaginase from T. kodakarensis (TkAsn), was purified to homogeneity by heat treatment followed by affinity chromatography using a nickel-nitrilotriacetic acid (Ni-NTA) column. The molecular mass of the purified enzyme was found to be approximately 37 kDa by sodium dodecyl sulfate-polyacrylamide gel electrophoresis (SDS-PAGE). The enzymatic properties, such as optimum temperature and pH, were 90 °C and 8.0, respectively. Its appearent Km , Vmax , and Kcat values were 2.6 mM, 1121 µmol min(-1) mg(-1) , and 694 S(-1) , respectively. The enzyme displayed high thermal stability at optimum temperature with an insignificant loss in enzymatic activity, retaining almost 90% of its activity over a time period of 32 h. The relative activity of the enzyme was significantly inhibited by the supplementation of Cu(2+) and Ni(2+) ions, while moderately inhibited by other ions. In contrast, Mg(2+) ions enhanced the relative activity compared to the control. The acrylamide contents in baked dough were reduced to sixty percent after treatment with recombinant TkAsn as compared to the untreated control. Results of the present study revealed that the enzyme was highly active at broader range of temperatures and pH, which reflect the potential of recombinant TkAsn in the food processing industry. In addition, the high thermal stability of the enzyme may facilitates its handling, storage, and transportation.

Two-dimensional alignment of imogolite on a solid surface.

Surface modified imogolite fiber, hydrated aluminium silicate that has the shape of a rigid hollow cylinder, was aligned with consistent nano spacing and was visualized by scanning tunneling microscopy.