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Abstract Concentrations of ambient particulate matter (such as PM2.5 and PM10) have come to represent a serious environmental problem worldwide, causing many deaths and economic losses. Because of the detrimental effects of PM2.5 on human health, many countries and international organizations have developed and operated regional and global short-term PM2.5 prediction systems. The short-term predictability of PM2.5 (and PM10) is determined by two main factors: the performance of the air quality model and the precision of the initial states. While specifically focusing on the latter factor, this study attempts to demonstrate how information from classical ground observation networks, a state-of-the-art geostationary (GEO) satellite sensor, and an advanced air quality modeling system can be synergistically combined to improve short-term PM2.5 predictability over South Korea. Such a synergistic combination of information can effectively overcome the major obstacle of scarcity of information, which frequently occurs in PM2.5 prediction systems using low Earth orbit (LEO) satellite-borne observations. This study first presents that the scarcity of information is mainly associated with cloud masking, sun-glint effect, and ill-location of satellite-borne data, and it then demonstrates that an advanced air quality modeling system equipped with synergistically-combined information can achieve substantially improved performances, producing enhancements of approximately 10%, 19%, 29%, and 10% in the predictability of PM2.5 over South Korea in terms of index of agreement (IOA), correlation coefficient (R), mean biases (MB), and hit rate (HR), respectively, compared to PM2.5 prediction systems using only LEO satellite-derived observations.

A CNN+LSTM (Convolutional Neural Network + Long Short-Term Memory) based deep hybrid neural network was established for the citywide daily PM2.5 prediction in South Korea. The structural hyperparameters of the CNN+LSTM model were determined through comprehensive sensitivity tests. The input features were obtained from the ground observations and GFS forecast. The performance of CNN+LSTM was evaluated by comparison with PM2.5 observations and with the 3-D CTM (three-dimensional chemistry transport model)-predicted PM2.5. The newly developed hybrid model estimated more accurate ambient levels of PM2.5 compared to the 3-D CTM. For example, the error and bias of the CNN+LSTM prediction were 1.51 and 6.46 times smaller than those by 3D-CTM simulation. In addition, based on IOA (Index of Agreement), the accuracy of CNN+LSTM prediction was 1.10–1.18 times higher than the 3-D CTM-based prediction. The importance of input features was indirectly investigated by sequential perturbing input variables. The most important meteorological and atmospheric environmental features were geopotential height and previous day PM2.5. The obstacles of the current CNN+LSTM-based PM2.5 prediction were also discussed. The promising result of this study indicates that DNN-based models can be utilized as an effective tool for air quality prediction.

Nitrous acid (HONO) is a main precursor of hydroxyl radicals (OH), which contribute to the formation of numerous secondary air pollutants in the troposphere. Despite its importance in atmospheric chemistry, HONO chemistry has not been fully incorporated into many chemical transport models (CTMs). Due to the lack of atmospheric HONO processes, CTM simulations often tend to underestimate atmospheric mixing ratios of HONO. This study was undertaken because simulations with current Community Multiscale Air Quality (CMAQ) model have a strong tendency to underestimate the HONO mixing ratio. In search of missing sources of atmospheric HONO, we attempted to sequentially incorporate the following potential HONO sources and processes into the CMAQ modeling framework: (i) gas-phase HONO reactions; (ii) traffic HONO emissions; (iii) soil HONO emissions; (iv) heterogeneous HONO production on the surfaces of aerosols; (v) heterogeneous HONO formation on tree leaf and building surfaces; (vi) photolysis reactions of particulates and deposited HNO3/nitrates called 'renoxification'. The simulation performances of the modified CMAQ models were then evaluated by comparing the modeled HONO mixing ratios with the HONO mixing ratios observed at the Olympic Park station in Seoul, South Korea. When HONO processes were fully added to the CMAQ model, average daily HONO mixing ratios increased from 0.06 ppb to 1.18 ppb. The daytime HONO mixing ratios produced from the CMAQ model run with a full account of atmospheric HONO processes were found to be in better agreement with observations than those from the original CMAQ model (CMAQv5.2.1) runs with improved statistical metrics (e.g., IOA increased from 0.59 to 0.68, while MB decreased dramatically from -0.57 ppb to -0.34 ppb). In addition, we investigated the contributions of individual atmospheric HONO processes to HONO mixing ratios, as well as the impacts of HONO atmospheric processes on the concentrations of other atmospheric species in South Korea. All these issues are also discussed in this manuscript. [ABSTRACT FROM AUTHOR]

In the study, crop residue burning (CRB) emissions were estimated based on field surveys and combustion experiments to assess the impact of the CRB on particulate matter over South Korea. The estimates of CRB emissions over South Korea are 9514, 8089, 4002, 2010, 172,407, 7675, 33, and 5053 Mg year−1 for PM10, PM2.5, OC, EC, CO, NOx, SO2, and NH3, respectively. Compared with another study, our estimates in the magnitudes of CRB emissions were not significantly different. When the CRB emissions are additionally considered in the simulation, the monthly mean differences in PM2.5 (i.e., △PM2.5) were marginal between 0.07 and 0.55 μg m−3 over South Korea. Those corresponded to 0.6–4.3% in relative differences. Additionally, the △PM10 was 0.07–0.60 μg m−3 over South Korea. In the spatial and temporal aspects, the increases in PM10 and PM2.5 were high in Gyeongbuk (GB) and Gyeongnam (GN) provinces in June, October, November, and December.

Background: As the number of patients with Zika virus infection increases worldwide, nursing students who conduct clinical practice in hospitals tend to at risk of Zika virus infection. Therefore, this study was conducted to identify nursing students’ knowledge, attitudes, practices, and risk perceptions of infection prevention related to occupational exposure to Zika virus infection, and to identify correlations among the related variables. Methods: This cross-sectional study included 249 nursing students from 3 nursing colleges located in 3 Korean cities, with experience in hospital clinical practice. A questionnaire to assess the knowledge, attitudes, practices, and risk perceptions of Zika virus infection was developed through a literature review and was subjected to pilot testing and validation. Results: The level of infection-control knowledge for Zika virus was 54.5 of 100 points, and that of practice was 4.5 of 5 points. The infection-prevention attitude score was 4.4 of 5 points, and the risk-perception score was 2.3 of 5 points. Significant differences emerged in infection-prevention attitude toward Zika virus based on Zika virus vaccination intention. Additionally, practice and risk-perception scores differed significantly based on gender and Zika virus vaccination intention. Knowledge significantly correlated with attitude and attitude toward preventive behavior. Conclusion: Improvement in infection-control knowledge for Zika virus can help improve nurses’ related attitudes, which in turn could promote effective practice. Considering the characteristics of nursing students, it is necessary to develop and apply an effective and viable education program related to the prevention of Zika virus infection. Keywords: Zika, Nursing student, Knowledge, Attitude, Practice

Infantile hemangiomas (IHs) are common neoplasms composed of proliferating endothelial-like cells. Despite the relative frequency of IH and the potential severity of complications, there are currently no uniform guidelines for treatment. Although propranolol has rapidly been adopted, there is significant uncertainty and divergence of opinion regarding safety monitoring, dose escalation, and its use in PHACE syndrome (PHACE = posterior fossa, hemangioma, arterial lesions, cardiac abnormalities, eye abnormalities; a cutaneous neurovascular syndrome characterized by large, segmental hemangiomas of the head and neck along with congenital anomalies of the brain, heart, eyes and/or chest wall). A consensus conference was held on December 9, 2011. The multidisciplinary team reviewed existing data on the pharmacologic properties of propranolol and all published reports pertaining to the use of propranolol in pediatric patients. Workgroups were assigned specific topics to propose protocols on the following subjects: contraindications, special populations, pretreatment evaluation, dose escalation, and monitoring. Consensus protocols were recorded during the meeting and refined after the meeting. When appropriate, protocol clarifications and revision were made and agreed upon by the group via teleconference. Because of the absence of high-quality clinical research data, evidence-based recommendations are not possible at present. However, the team agreed on a number of recommendations that arose from a review of existing evidence, including when to treat complicated IH; contraindications and pretreatment evaluation protocols; propranolol use in PHACE syndrome; formulation, target dose, and frequency of propranolol; initiation of propranolol in infants; cardiovascular monitoring; ongoing monitoring; and prevention of hypoglycemia. Where there was considerable controversy, the more conservative approach was selected. We acknowledge that the recommendations are conservative in nature and anticipate that they will be revised as more data are made available.

Infantile hemangiomas (IH) are benign tumors of endothelial-like cells. Occurring in 4.5% of children, they are the most common tumor of childhood. The great majority of patients with IH will not need treatment, but 10% require systemic treatment. Many treatments have been described for the treatment of IH, but the Food and Drug Administration has not approved any. Over the last decade, numerous reports of successful treatment of IH with propranolol have been published. Despite its widespread use, little is known regarding the proper dosing, safety monitoring, and during of treatment or long-term outcomes for propranolol treatment of IH. Given its potential side effects, detailed education regarding proper administration of the medication as well as warning signs to watch for is necessary for parents and caretakers. Herein, we provide a parental handout that practitioners can individually tailor for use in their clinics when educating parents and caretakers about the use of propranolol for IH. Updates will also need to be made as more is learned regarding the optimal dosing and safety monitoring when using propranolol for this indication.

In this study, more accurate information on the levels of aerosol optical depth (AOD) was calculated from the assimilation of the modeled AOD based on the optimal interpolation method. Additionally, more realistic levels of surface particulate matters over the Arctic were estimated using the assimilated AOD based on the linear relationship between the particulate matters and AODs. In comparison to the MODIS observation, the assimilated AOD was much improved compared with the modeled AOD (e.g., increase in correlation coefficients from −0.15–0.26 to 0.17–0.76 over the Arctic). The newly inferred monthly averages of PM10 and PM2.5 for April–September 2008 were 2.18–3.70 μg m−3 and 0.85–1.68 μg m−3 over the Arctic, respectively. These corresponded to an increase of 140–180%, compared with the modeled PMs. In comparison to in-situ observation, the inferred PMs showed better performances than those from the simulations, particularly at Hyytiala station. Therefore, combining the model simulation and data assimilation provided more accurate concentrations of AOD, PM10, and PM2.5 than those only calculated from the model simulations.

We have previously reported that the application of a DC field can adjust the position and/or bandwidth of the selective reflection notch in polymer-stabilized cholesteric liquid crystals (PSCLCs). The proposed mechanism of these electro-optic (EO) response is ion-facilitated electromechanical deformation of the polymer stabilizing network. Accordingly, the concentration of ions trapped within the polymer network should considerably influence the EO response of PSCLC. Our prior studies have indicated that photoinitiators can increase ion density in PSCLC by an order of magnitude. Here, we isolate the contribution of ionic impurities associated with liquid crystal monomers (LCMs) by utilizing initiator-less polymerization. PSCLCs prepared with LCM with low ion concentration show bandwidth broadening of the reflection band whereas PSCLCs prepared with LCM with high ion concentration exhibit a red shifting tuning of the reflection band. The extent of the tuning or bandwidth broadening of the CLC reflection band depends on the concentration of LCMs and the chirality of the LCM.

Concentrations of ambient particulate matter (such as PM2.5 and PM10) have come to represent a serious environmental problem worldwide, causing many deaths and economic losses. Because of the detrimental effects of PM2.5 on human health, many countries and international organizations have developed and operated regional and global short-term PM2.5 prediction systems. The short-term predictability of PM2.5 (and PM10) is determined by two main factors: the performance of the air quality model and the precision of the initial states. While specifically focusing on the latter factor, this study attempts to demonstrate how information from classical ground observation networks, a state-of-the-art geostationary (GEO) satellite sensor, and an advanced air quality modeling system can be synergistically combined to improve short-term PM2.5 predictability over South Korea. Such a synergistic combination of information can effectively overcome the major obstacle of scarcity of information, which frequently occurs in PM2.5 prediction systems using low Earth orbit (LEO) satellite-borne observations. This study first presents that the scarcity of information is mainly associated with cloud masking, sun-glint effect, and ill-location of satellite-borne data, and it then demonstrates that an advanced air quality modeling system equipped with synergistically-combined information can achieve substantially improved performances, producing enhancements of approximately 10%, 19%, 29%, and 10% in the predictability of PM2.5 over South Korea in terms of index of agreement (IOA), correlation coefficient (R), mean biases (MB), and hit rate (HR), respectively, compared to PM2.5 prediction systems using only LEO satellite-derived observations. [ABSTRACT FROM AUTHOR]

The study analyzed temporal variations of Ozone Monitoring Instrument (OMI)-observed NO2 columns, interregional correlation, and comparison between NO2 columns and NOx emissions during the period from 2006 to 2015. Regarding the trend of the NO2 columns, the linear lines were classified into four groups: (1) ‘upward and downward’ over six defined geographic regions in central-east Asia; (2) ‘downward’ over Guangzhou, Japan, and Taiwan; (3) ‘stagnant’ over South Korea; and (4) ‘upward’ over North Korea, Mongolia, Qinghai, and Northwestern Pacific ocean. In particular, the levels of NO2 columns in 2015 returned to those in 2006 over most of the polluted regions in China. Quantitatively, their relative changes in 2015 compared to 2006 were approximately 10%. From the interregional correlation analysis, it was found that unlike positive relationships between the polluted areas, the different variations of monthly NO2 columns led to negative relationships in Mongolia and Qinghai. Regarding the comparison between NO2 columns and NOx emission, the NOx emissions from the Copernicus Atmosphere Monitoring Service (CAMS) and Clean Air Policy Support System (CAPSS) inventories did not follow the year-to-year variations of NO2 columns over the polluted regions. In addition, the weekly effect was only clearly shown in South Korea, Japan, and Taiwan, indicating that the amounts of NOx emissions are significantly contributed to by the transportation sector.

The development of chemotherapies for glioblastoma is hindered by their limited bioavailability and toxicity on normal brain function. To overcome these limitations, we investigated the structure‐dependent activity of heptamethine cyanine dyes (HMCD), a group of tumour‐specific and BBB permeable near‐infrared fluorescent dyes, in both commercial (U87MG) and patient‐derived GBM cell lines. HMCD analogues with strongly ionisable sulphonic acid groups were not taken up by patient‐derived GBM cells, but were taken up by the U87MG cell line. HMCD uptake relies on a combination of transporter uptake through organic anion‐transporting polypeptides (OATPs) and endocytosis into GBM cells. The uptake of HMCDs was not affected by p‐glycoprotein efflux in GBM cells. Finally, we demonstrate structure‐dependent cytotoxic activity at high concentrations (EC50: 1–100 μM), likely due to mitochondrial damage‐induced apoptosis. An in vivo orthotopic glioblastoma model highlights tumour‐specific accumulation of our lead HMCD, MHI‐148, for up to 7 days following a single intraperitoneal injection. These studies suggest that strongly ionisable groups like sulphonic acids hamper the cellular uptake of HMCDs in patient‐derived GBM cell lines, highlighting cell line‐specific differences in HMCD uptake. We envisage these findings will help in the design and structural modifications of HMCDs for drug‐delivery applications for glioblastoma. [ABSTRACT FROM AUTHOR]

A CNN+LSTM (Convolutional Neural Network + Long Short-Term Memory) based deep hybrid neural network was established for the citywide daily PM2.5 prediction in South Korea. The structural hyperparameters of the CNN+LSTM model were determined through comprehensive sensitivity tests. The input features were obtained from the ground observations and GFS forecast. The performance of CNN+LSTM was evaluated by comparison with PM2.5 observations and with the 3-D CTM (three-dimensional chemistry transport model)-predicted PM2.5. The newly developed hybrid model estimated more accurate ambient levels of PM2.5 compared to the 3-D CTM. For example, the error and bias of the CNN+LSTM prediction were 1.51 and 6.46 times smaller than those by 3D-CTM simulation. In addition, based on IOA (Index of Agreement), the accuracy of CNN+LSTM prediction was 1.10–1.18 times higher than the 3-D CTM-based prediction. The importance of input features was indirectly investigated by sequential perturbing input variables. The most important meteorological and atmospheric environmental features were geopotential height and previous day PM2.5. The obstacles of the current CNN+LSTM-based PM2.5 prediction were also discussed. The promising result of this study indicates that DNN-based models can be utilized as an effective tool for air quality prediction. [ABSTRACT FROM AUTHOR]

Background: Despite higher risks associated with prostate cancer, young African American men are poorly represented in prostate-specific antigen (PSA) trials, which limits proper evidence-based guidance. We evaluated the impact of PSA screening, alongside primary care provider utilization, on prostate cancer outcomes for these patients.Methods: We identified African American men aged 40-55 years, diagnosed with prostate cancer between 2004 and 2017 within the Veterans Health Administration. Inverse probability of treatment-weighted propensity scores were used in multivariable models to assess PSA screening on PSA levels higher than 20, Gleason score of 8 or higher, and metastatic disease at diagnosis. Lead-time adjusted Fine-Gray regression evaluated PSA screening on prostate cancer-specific mortality (PCSM), with noncancer death as competing events. All statistical tests were 2-sided.Results: The cohort included 4726 patients. Mean age was 51.8 years, with 84-month median follow-up. There were 1057 (22.4%) with no PSA screening prior to diagnosis. Compared with no screening, PSA screening was associated with statistically significantly reduced odds of PSA levels higher than 20 (odds ratio [OR] = 0.56, 95% confidence interval [CI] = 0.49 to 0.63; P < .001), Gleason score of 8 or higher (OR = 0.78, 95% CI = 0.69 to 0.88; P < .001), and metastatic disease at diagnosis (OR = 0.50, 95% CI = 0.39 to 0.64; P < .001), and decreased PCSM (subdistribution hazard ratio = 0.52, 95% CI = 0.36 to 0.76; P  < .001). Primary care provider visits displayed similar effects.Conclusions: Among young African American men diagnosed with prostate cancer, PSA screening was associated with statistically significantly lower risk of PSA levels higher than 20, Gleason score of 8 or higher, and metastatic disease at diagnosis and statistically significantly reduced risk of PCSM. However, the retrospective design limits precise estimation of screening effects. Prospective studies are needed to validate these findings. [ABSTRACT FROM AUTHOR]