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IntroductionPost-harvest processing plays a crucial role in enhancing the bioactive properties of vegetables. This study aimed to investigate the impact of post-harvest aging on parsnip’s bioactive profile and its protective effects against acrolein (Acr)-induced inflammation, a common pollutant and irritant linked to respiratory inflammation.MethodsParsnips (Pastinaca sativa L.) were aged at 60°C for up to 30 days, with extracts collected at intervals. Total phenolic content (TPC) and antioxidant capacity were assessed using DPPH assays. Key bioactive compounds, including falcarindiol, DDMP, and 5-HMF, were quantified. In vitro studies used BEAS-2B cells to evaluate anti-inflammatory effects, while in vivo tests involved treating Acr-exposed mice with aged parsnip extract to observe cytokine responses.ResultsAged parsnip extracts showed a 9.96-fold increase in TPC and a 4.25-fold increase in antioxidant capacity after 30 days. Bioactive compounds significantly increased in aged samples, especially falcarindiol and 5-HMF. In vitro, aged parsnip reduced Acr-induced TNF-α and IL-1β expression. In vivo, treated mice showed reduced bronchial inflammation, goblet cell hyperplasia, and cytokine expression compared to controls.DiscussionThese findings suggest that post-harvest aging enhances parsnip’s antioxidant and anti-inflammatory properties, highlighting its potential as a functional food ingredient for managing inflammation and respiratory health.

This study assessed the seasonal distribution and factors associated with PM2.5 in nationwide subway stations in Korea. A total of 229,644 data points collected over one year were used to determine one-day PM2.5 concentrations from 642 subway stations. The geometric mean of indoor PM2.5 concentrations was 26.6 µg/m3. PM2.5 concentrations were higher in the Seoul metropolitan area (SMA) compared to non-Seoul metropolitan areas (NSMA), with the highest level occurring in winter, followed by spring, fall, and summer, similar to the trend observed for outdoor PM2.5 concentrations. In SMA, PM2.5 concentrations were significantly associated with the operation year, number of air cleaners on platforms, and number of passengers. These findings may help in the development of national strategies for managing indoor PM2.5 concentrations in subway stations, taking into account spatial and temporal factors.

Abstract Quantum computers offer significant potential for complex system analysis, yet their application in large systems is hindered by limitations such as qubit availability and quantum hardware noise. While the variational quantum eigensolver (VQE) was proposed to address these issues, its scalability remains limited. Many efforts, including new ansätze and Hamiltonian modifications, have been made to overcome these challenges. In this work, we introduced the novel Fragment Molecular Orbital/Variational Quantum Eigensolver (FMO/VQE) algorithm. This method combines the fragment molecular orbital (FMO) approach with VQE and efficiently utilizes qubits for quantum chemistry simulations. Employing the UCCSD ansatz, the FMO/VQE achieved an absolute error of just 0.053 mHa with 8 qubits in a $${{\text{H}}}_{24}$$ H 24 system using the STO-3G basis set, and an error of 1.376 mHa with 16 qubits in a $${{\text{H}}}_{20}$$ H 20 system with the 6-31G basis set. These results indicated a significant advancement in scalability over conventional VQE, maintaining accuracy with fewer qubits. Therefore, our FMO/VQE method exemplifies how integrating fragment-based quantum chemistry with quantum algorithms can enhance scalability, facilitating more complex molecular simulations and aligning with quantum computing advancements.

Abstract The pork industry is an essential part of the global food system, providing a significant source of protein for people around the world. A major factor restraining productivity and compromising animal wellbeing in the pork industry is disease outbreaks in pigs throughout the production process: widespread outbreaks can lead to losses as high as 10% of the U.S. pig population in extreme years. In this study, we present a machine learning model to predict the emergence of infection in swine production systems throughout the production process on a daily basis, a potential precursor to outbreaks whose detection is vital for disease prevention and mitigation. We determine features that provide the most value in predicting infection, which include nearby farm density, historical test rates, piglet inventory, feed consumption during the gestation period, and wind speed and direction. We utilize these features to produce a generalizable machine learning model, evaluate the model’s ability to predict outbreaks both seven and 30 days in advance, allowing for early warning of disease infection, and evaluate our model on two swine production systems and analyze the effects of data availability and data granularity in the context of our two swine systems with different volumes of data. Our results demonstrate good ability to predict infection in both systems with a balanced accuracy of $$85.3\%$$ 85.3 % on any disease in the first system and balanced accuracies (average prediction accuracy on positive and negative samples) of $$58.5\%$$ 58.5 % , $$58.7\%$$ 58.7 % , $$72.8\%$$ 72.8 % and $$74.8\%$$ 74.8 % on porcine reproductive and respiratory syndrome, porcine epidemic diarrhea virus, influenza A virus, and Mycoplasma hyopneumoniae in the second system, respectively, using the six most important predictors in all cases. These models provide daily infection probabilities that can be used by veterinarians and other stakeholders as a benchmark to more timely support preventive and control strategies on farms.

Abstract Anomaly detection methods have a great potential to assist the detection of diseases in animal production systems. We used sequence data of Porcine Reproductive and Respiratory Syndrome (PRRS) to define the emergence of new strains at the farm level. We evaluated the performance of 24 anomaly detection methods based on machine learning, regression, time series techniques and control charts to identify outbreaks in time series of new strains and compared the best methods using different time series: PCR positives, PCR requests and laboratory requests. We introduced synthetic outbreaks of different size and calculated the probability of detection of outbreaks (POD), sensitivity (Se), probability of detection of outbreaks in the first week of appearance (POD1w) and background alarm rate (BAR). The use of time series of new strains from sequence data outperformed the other types of data but POD, Se, POD1w were only high when outbreaks were large. The methods based on Long Short-Term Memory (LSTM) and Bayesian approaches presented the best performance. Using anomaly detection methods with sequence data may help to identify the emergency of cases in multiple farms, but more work is required to improve the detection with time series of high variability. Our results suggest a promising application of sequence data for early detection of diseases at a production system level. This may provide a simple way to extract additional value from routine laboratory analysis. Next steps should include validation of this approach in different settings and with different diseases.

Underground shopping malls (USMs) are often open or semi-open environments with interconnected passageways, resulting in the sharing of indoor air. However, indoor air quality (IAQ) within these spaces can vary due to many diverse emission sources. We investigated the relationships between IAQ and geographical areas, as well as IAQ and store types, within USMs, in Korea.In 2020, we studied 10 different USMs, with a total of 128 outlets. We conducted comprehensive IAQ assessments (including measurements of fine particles (PM2.5), aldehydes, and volatile organic compounds (VOCs)) in stores, passages, and outdoor areas. The stores were categorized into three types: clothing, fashion accessories, and food services. Additionally, we measured environmental factors such as CO2 levels and presence of storefront walls.PM2.5 levels were higher outdoors, whereas aldehyde and VOC levels exhibited elevations within passages and the interior of stores than in the outdoor environment. The store-to-passage ratios for PM2.5, individual aldehydes, and VOC concentrations ranged from 1.06 to 4.93. Formaldehyde and total VOC (TVOC) concentrations were found to be elevated in clothing and fashion accessory stores, whereas PM2.5 concentrations were more prominent in food service establishments. Specific individual compounds, including propionaldehyde, hexaldehyde, benzene, n-heptane, toluene, n-octane, xylene, d-limonene, n-undecane, n-dodecane, and ethylbenzene concentrations exhibited associations with store types. Multivariate regression models demonstrated positive associations between most aldehydes and VOCs with CO2 concentrations and presence of storefront walls.This study underscored variations within USMs based on area and store type. Aldehyde and VOC concentrations were notably higher in clothing and fashion accessory stores than in food service outlets; these elevations were closely linked to CO2 levels and presence of storefront walls. These findings suggest that monitoring CO2 levels within USM stores, optimizing air-conditioning systems, and designing future stores without storefront walls can collectively contribute to an overall improvement of IAQ within USMs.

Antimicrobial resistance (AMR) is arguably one of the major health and economic challenges in our society. A key aspect of tackling AMR is rapid and accurate detection of the emergence and spread of AMR in food animal production, which requires routine AMR surveillance. However, AMR detection can be expensive and time-consuming considering the growth rate of the bacteria and the most commonly used analytical procedures, such as Minimum Inhibitory Concentration (MIC) testing. To mitigate this issue, we utilized machine learning to predict the future AMR burden of bacterial pathogens. We collected pathogen and antimicrobial data from >600 farms in the United States from 2010 to 2021 to generate AMR time series data. Our prediction focused on five bacterial pathogens (Escherichia coli, Streptococcus suis, Salmonella sp., Pasteurella multocida, and Bordetella bronchiseptica). We found that Seasonal Auto-Regressive Integrated Moving Average (SARIMA) outperformed five baselines, including Auto-Regressive Moving Average (ARMA) and Auto-Regressive Integrated Moving Average (ARIMA). We hope this study provides valuable tools to predict the AMR burden not only of the pathogens assessed in this study but also of other bacterial pathogens.

Abstract Background The atmospheric particulates can be harmful to human health due to toxic substances sorbed onto particulates. Although the atmospheric particulates have been collected using different types of filters, few studies have reported background contents of major and trace element, and isotopic compositions in the blank filters used for collecting the particulates yet. Here, we first report background contents of major and trace elements, and isotopic compositions (Zn and Pb isotopes) in the blank filters. Then, we evaluate the best type of filter for elemental and isotope analyses in the particulates. Findings The contents of major elements are the lowest in the PTFE filter and become higher in the order of the Nylon, NC, and GF filters, indicating that either PTFE and/or Nylon filters are the most suitable for major element analysis in the atmospheric particulates. Likewise, the contents of trace elements are the lowest in the PTFE filter and become much higher in the order of the Nylon, NC, and GF filters, indicating that PTFE filter is the most suitable for trace element analysis in the atmospheric particulates. Otherwise, background elemental contents result in overestimating their concentrations in the atmospheric particulates. All δ66ZnJMC-Lyon values in two GF filters are within those from samples of the Chinese deserts and of the Chinese Loess Plateau. Likewise, their 206Pb/204Pb ratios are similar with those of samples from Xi’an and Beijing, indicating that the GF filter is not suitable for Zn and Pb isotope study in the atmospheric particulates. Conclusions This study suggests that the PTFE filter is the most suitable for elemental and isotope study in the atmospheric particulates and that the GF filter cannot be used for source identification in the atmospheric particulates using Zn and Pb isotopes.

Abstract Background Although many indoor public places have implemented smoke-free regulations, private homes have remained sources of tobacco smoke pollutants. This study examined differences in urinary cotinine concentrations in the Korean non-smoking adult population between living in smoking and smoke-free homes, and the relationship of urinary cotinine concentrations with socio-demographic factors in smoke-free homes. Methods Samples from 2575 non-smoking adults (≥19 years old) in the Korean National Environmental Health Survey cycle 3 (2015–2017), a representative Korean study, were used. Smoking and smoke-free homes were defined based on whether there were smokers at homes. Weighted linear regression models were used to determine urinary cotinine concentrations and identify factors associated with urinary cotinine. Results The geometric mean of urinary cotinine concentrations for non-smoking adults living in smoking homes was 2.1 μg/L (95% confidence interval [CI] = 1.8–2.4), which was significantly higher than the mean of 1.3 μg/L (95% CI = 1.2–1.4) for those living in smoke-free homes. Urinary cotinine concentrations were different significantly by home smoking status in most socio-demographic subgroups. Data from smoke-free home showed urinary cotinine concentration in adults was significantly higher in those who lived in homes with ventilation duration

Recently, semantic segmentation based on deep neural network (DNN) has attracted attention as it exhibits high accuracy, and many studies have been conducted on this. However, DNN-based segmentation studies focused mainly on improving accuracy, thus greatly increasing the computational demand and memory footprint of the segmentation network. For this reason, the segmentation network requires a lot of hardware resources and power consumption, and it is difficult to be applied to an environment where they are limited, such as an embedded system. In this paper, we propose a binarized encoder-decoder network (BEDN) and a binarized deconvolution engine (BiDE) accelerating the network to realize low-power, real-time semantic segmentation. BiDE implements a binarized segmentation network with custom hardware, greatly reducing the hardware resource usage and greatly increasing the throughput of network implementation. The deconvolution used for upsampling in a segmentation network includes zero padding. In order to enable deconvolution in a binarized segmentation network that cannot express zero, we introduce zero-aware binarized deconvolution which skips padded zero activations and zero-aware batch normalization embedded binary activation considering zero-skipped convolution. The BEDN, which is a binarized segmentation network proposed to be accelerated on BiDE, has acceptable accuracy while greatly reducing the computational and memory demands of the segmentation network through full-binarization and simple structure. BEDN has a network size of 0.21 MB, and its maximum memory usage is 1.38 MB. BiDE was implemented on Xilinx ZU7EV field-programmable gate array (FPGA) to operate at 187.5 MHz. BiDE accelerated the proposed BEDN within CamVid11 images of $480\times {360}$ size at 25.89 frames per second (FPS) achieving a performance of 1.682 Tera operations per second (TOPS) and 824 Giga operations per second per watt (GOPS/W).

Porcine reproductive and respiratory syndrome is an infectious disease of pigs caused by PRRS virus (PRRSV). A modified live-attenuated vaccine has been widely used to control the spread of PRRSV and the classification of field strains is a key for a successful control and prevention. Restriction fragment length polymorphism targeting the Open reading frame 5 (ORF5) genes is widely used to classify PRRSV strains but showed unstable accuracy. Phylogenetic analysis is a powerful tool for PRRSV classification with consistent accuracy but it demands large computational power as the number of sequences gets increased. Our study aimed to apply four machine learning (ML) algorithms, random forest, k-nearest neighbor, support vector machine and multilayer perceptron, to classify field PRRSV strains into four clades using amino acid scores based on ORF5 gene sequence. Our study used amino acid sequences of ORF5 gene in 1931 field PRRSV strains collected in the US from 2012 to 2020. Phylogenetic analysis was used to labels field PRRSV strains into one of four clades: Lineage 5 or three clades in Linage 1. We measured accuracy and time consumption of classification using four ML approaches by different size of gene sequences. We found that all four ML algorithms classify a large number of field strains in a very short time (0.99 Area under curve of the Receiver of operating characteristics curve). Furthermore, the random forest approach detects a total of 4 key amino acid positions for the classification of field PRRSV strains into four clades. Our finding will provide an insightful idea to develop a rapid and accurate classification model using genetic information, which also enables us to handle large genome datasets in real time or semi-real time for data-driven decision-making and more timely surveillance.

Abstract Background We examined the indoor air quality (IAQ) perceptions of workers and their relationships with the symptoms of sick-building syndrome (SBS) and store types in underground shopping centers. Methods In 2017, 314 store workers in nine underground shopping centers in Seoul, Korea, were assessed. The all participants’ stores were partially opened onto a passage. Using questionnaires, they were asked about their demographics, job characteristics, 16 SBS symptoms, and seven IAQ perceptions. The SBS symptoms were categorized as skin, eye irritation, respiratory, or general. An IAQ perception score was calculated by adding the number of positive responses to each type of IAQ and categorized into three levels. Results The prevalence of SBS symptom groups in the previous month was 43.6% for skin symptoms, 62.4% for eye irritation symptoms, 65.6% for respiratory symptoms, and 64.7% for general symptoms. Participants who perceived IAQ were more likely to have SBS symptoms (odds ratio: 1.81–7.84). The type of store employing the workers was associated with several IAQ perceptions. Subjects who worked in clothing and fashion-accessory stores were more likely to have high IAQ perception scores than those who worked in food services. Conclusions About half of the store workers in underground shopping centers had experienced SBS symptom groups in the previous month. The SBS symptom groups were associated with almost all IAQ perceptions. Because IAQ perception was associated with store type, studies of IAQ in working areas might need to improve store workers’ health in underground shopping centers.

Abstract Background In a multi-unit housing (MUH) complex, secondhand smoke (SHS) can pass from one living space to another. The aim of this study was to determine the prevalence of SHS incursion, and to establish the relationship between SHS incursion and socio-demographic and built environmental factors in MUH in Korea. Methods A population-based sample of 2600 residents (aged ≥19 years) living in MUH from across the city of Seoul, Korea, was obtained through a web-based selection panel. The residents completed a questionnaire detailing socio-demographic factors, smoking status, frequency of SHS incursion, and built environmental factors. The presence of a personal smoke-free home rule was determined by residents declaring that no one smoked inside the home. Results Of the 2600 participants, non-smoking residents who lived in homes with a personal smoke-free rule were selected for further analysis (n = 1784). In the previous 12 months, 74.7% of residents had experienced SHS incursion ≥1 times. A multivariate ordinal logistic regression analysis indicated that residents who spent more time at home, lived with children, supported the implementation of smoke-free regulations in MUH, lived in small homes, lived in homes with natural ventilation provided by opening a front door or the windows and front door, and lived in homes with more frequent natural ventilation were more likely to report SHS incursion into their homes. Conclusions The majority of the non-smoking residents experienced SHS incursion, even with a personal smoke-free rule in their homes. A smoke-free policy in MUH is needed to protect residents from SHS exposure when they are at home.

Abstract Miniaturized microfluidic biosensors have recently been advanced for portable point-of-care diagnostics by integrating lab-on-a-chip technology and electrochemical analysis. However, the design of a small, integrated, and reliable biosensor for multiple and simultaneous electrochemical analyses in a single device remains a challenge. Here, we present a simultaneous microfluidic electrochemical biosensing system to detect multiple biomarkers of pulmonary hypertension diseases in a single device. The miniaturized biosensor, which is composed of five chambers, is precisely and individually controlled using in-house-built pneumatic microvalves to manipulate the flow pathway. Each chamber is connected to an electrochemical sensor designed to detect four different biomarkers plus a reference control. Our design allows for loading of multiple reagents for simultaneous analyses. On the basis of the developed microfluidic electrochemical sensor system, we successfully detected four well-defined pulmonary hypertension-associated biomarkers, namely, fibrinogen, adiponectin, low-density lipoprotein, and 8-isoprostane. This novel approach offers a new platform for a rapid, miniaturized, and sensitive diagnostic sensor in a single device for various human diseases.

The purpose of this study was to evaluate the relationship between the use of certain household products (HPs) at home and atopic dermatitis (AD) in adults. The study was conducted on 1,500 households in Seoul, South Korea. We obtained information on general characteristics, the use of HPs, and AD through an online panel survey in July 2018. HPs were selected as 23 kinds of products with associated risks that are management targets of the Ministry of Environment. The international study of asthma and allergies in childhood was used for the AD questionnaires. Logistic regression analysis was used to identify AD affected by the use of HPs. Average number of used HPs was 13.44 out of 23, and average usage frequency of HPs was 3.52 times a month at home in Seoul for the last 1 year. Compared with subjects with a low number of HPs used (reference), subjects with a high number of used HPs (4th quartile) were more likely to have lifetime diagnosis of AD (odds ratio (OR) = 1.77, 95% confidence interval (CI); 1.23-2.54), symptoms of AD in the last 12 months (OR = 2.66, 95% CI; 1.92-3.70), and treatment of AD in the last 12 months (OR = 2.37, 95% CI; 1.48-3.80). Compared with subjects with a low HPs usage frequency (reference), subjects with a high HPs usage frequency (4th quartile) were more likely to have lifetime diagnosis of AD (OR = 1.88, 95% CI; 1.31-2.70), symptoms of AD in the last 12 months (OR = 2.14, 95% CI; 1.54-2.96), and treatment of AD in the last 12 months (OR = 2.23, 95% CI; 1.39-3.60). Therefore, the use of HPs was significantly associated with AD. The findings of this study might be useful as basic data for managing allergic diseases and establishing preventive measures.

Abstract Background The objectives of this study were to determine urinary cotinine concentrations in non-smoking residents of smoke-free homes and to establish the relationship of urinary cotinine with housing type and other socio-demographic and secondhand smoke (SHS) exposure factors. Methods We used data from the Korean National Environmental Health Survey I (2009–2011). The study included 814 non-smoking adult residents living in apartments, attached, and detached housing. Residents who lived with smokers were excluded. Urinary cotinine concentration was used as a biomarker for SHS exposure. The factors associated with urinary cotinine levels in non-smoking residents were determined using multivariate regression analysis. Results Urinary cotinine was detected in 88 % of the 814 non-smoking residents of smoke-free homes. The urinary cotinine concentrations of residents living in attached [1.18 ng/mg creatinine (Cr)] and detached housing (1.23 ng/mg Cr) were significantly higher than those of residents who lived in apartments (0.69 ng/mg Cr). Urinary cotinine concentrations were significantly higher in residents who were men, those with a household income ≤1000 USD/month, those who were former smokers with >1 year and ≤1 year of not smoking, and those who experienced SHS odor every day. In the multivariate regression analysis, housing type, sex, former smoking status, and frequency of experiencing SHS odor were associated with urinary cotinine concentrations (R 2 = 0.14). Conclusions The majority of non-smoking residents of smoke-free homes had detectable urinary cotinine. Housing type, sex, former smoking status, and frequency of experiencing SHS odor were predictors for urinary cotinine concentrations in the study participants.

Objectives Bedding in childcare centers (CCCs) can hold house dust mite (HDM) allergens. This study examined whether HDM allergen levels can be reduced through the distribution of an educational newsletter on bedding control to parents of CCC children in Korea. Methods All 38 CCCs were measured for Der 1 (sum of Der f 1 and Der p 1) concentrations on classroom floors and bedding before the intervention. Educational newsletters on children’s bedding control were sent to 21 CCCs by mail, and teachers were asked to distribute the newsletters to the parents of the children (intervention group). The remaining 17 CCCs were not sent newsletters (control group). The measurement of Der 1 concentrations in 38 CCCs was repeated after the intervention. Dust samples were collected with a vacuum cleaner and analyzed using enzyme-linked immunosorbent assay methods. Results The Der 1 concentrations on the bedding were significantly higher than those on the floors in 38 CCCs at baseline (p

Objectives Antimicrobial chemicals are used in a variety of household and personal care products. Exposure to antimicrobial household products has been hypothesized to lead to allergic diseases in children. Methods We investigated antimicrobial household product exposure and allergic symptoms in Korean children. An antimicrobial exposure (AE) score was derived. To examine the symptoms of allergic diseases (current wheeze, current rhinitis, and current eczema) in the past 12 months, we used a questionnaire based on the core module of the International Study of Asthma and Allergies in Children. Complete data for the analysis were available for 25,805 of the 35,590 (72.5%) children. Results The prevalence of current allergic diseases was as follows: wheeze, 5.6%; allergic rhinitis, 32.6%; and eczema, 17.7%. The mean (standard deviation) AE score was 14.3 (9.3) (range: 0-40). Compared with subjects with a low AE score (reference), subjects with a high AE score (fourth quartile) were more likely to have symptoms of wheezing and allergic rhinitis (adjusted odds ratio [aOR] for wheezing 1.24, 95% confidence interval [CI], 1.05-1.45, p for trend=0.24; aOR for allergic rhinitis 1.30, 95% CI, 1.20-1.40, p