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In this study, physicochemical and antioxidant properties, and storage stability (1, 3, and 7 days) of pork patties added with edible insect powders (EIP) of four species (Larvae of Tenenbrio molitor, Protaetia brevitarsis seulensis, Allomyrina dichotoma, and Gryllus bimaculatus) as meat partial substitutes were investigated. Twenty percent of each EIP was added to pork patties, and four treatments were prepared. On the other hand, two control groups were set, one with 0.1 g of ascorbic acid and the other without anything. Adding EIP decreased water content but increased protein, fat, carbohydrate, and ash contents. In addition, the use of EIP increased the water holding capacity and texture properties as well as decreased the cooking loss. However, the sensory evaluation and storage stability were negatively affected by the addition of EIP. The 2,2-diphenyl-1-picrylhydrazyl radical scavenging activity had a positive effect on storage stability. It is believed that the addition of EIP resulted in high antioxidants due to the presence of polyphenol compounds in EIP. These results indicate that EIP has great potential to be used as meat partial substitute to improve the quality improvement and antioxidant in pork patties. However, in order to improve storage stability and consumer preference, further research is needed to apply it to patties by reducing the amount of EIP or adding auxiliary ingredients.
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The abatement of micropollutants by ozonation can be accurately calculated by measuring the exposures of molecular ozone (O3) and hydroxyl radical (â¢OH) (i.e., ∫[O3]dt and ∫[â¢OH]dt). In the actual ozonation process, ∫[O3]dt values can be calculated by monitoring the O3 decay during the process. However, calculating ∫[â¢OH]dt is challenging in the field, which necessitates developing models to predict ∫[â¢OH]dt from measurable parameters. This study demonstrates the development of machine learning models to predict ∫[â¢OH]dt (the output variable) from five basic input variables (pH, dissolved organic carbon concentration, alkalinity, temperature, and O3 dose) and two optional ones (∫[O3]dt and instantaneous ozone demand, IOD). To develop the models, four different machine learning methods (random forest, support vector regression, artificial neural network, and Gaussian process regression) were employed using the input and output variables measured (or determined) in 130 different natural water samples. The results indicated that incorporating ∫[O3]dt as an input variable significantly improved the accuracy of prediction models, increasing overall R2 by 0.01-0.09, depending on the machine learning method. This suggests that ∫[O3]dt plays a crucial role as a key variable reflecting the â¢OH-yielding characteristics of dissolved organic matter. Conversely, IOD had a minimal impact on the accuracy of the prediction models. Generally, machine-learning-based prediction models outperformed those based on the response surface methodology developed as a control. Notably, models utilizing the Gaussian process regression algorithm demonstrated the highest coefficients of determination (overall R2 = 0.91-0.95) among the prediction models.
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Radical Hidroxilo , Aprendizaje Automático , Ozono , Ozono/química , Radical Hidroxilo/química , Cinética , Purificación del Agua/métodosRESUMEN
Owing to its simplicity of measurement, effluent conductivity is one of the most studied factors in evaluations of desalination performance based on the ion concentrations in various ion adsorption processes such as capacitive deionization (CDI) or battery electrode deionization (BDI). However, this simple conversion from effluent conductivity to ion concentration is often incorrect, thereby necessitating a more congruent method for performing real-time measurements of effluent ion concentrations. In this study, a random forest (RF)-based artificial intelligence (AI) model was developed to address this shortcoming. The proposed RF model showed an excellent prediction accuracy when it was first validated in predicting the effluent conductivity for both CDI (R2 = 0.86) and BDI (R2 = 0.95) data. Moreover, the RF model successfully predicted the concentration of each ion (Naâº, Kâº, Ca2âº, and Clâ») from the conductivity values. The accuracy of the ion concentration prediction was even higher than that of the effluent conductivity prediction, likely owing to the linear correlation between the input and output variables of the dataset. The effect of the sampling interval was also evaluated for conductivity and ion concentrations, and there was no significant difference up to sampling intervals of <80 s based on the error value of the model. These findings suggest that an RF model can be used to predict ion concentrations in CDI/BDI, which may be used as core indicators in evaluating desalination performance.
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BACKGROUND: Parainfluenza virus (PIV) is a significant etiological agent of acute lower respiratory tract infections (ALRIs) in infants and young children. The present study has been conducted to investigate the prevalence of recently identified respiratory viruses. METHODS: In total, 543 oropharyngeal or nasopharyngeal swab samples collected from hospitalized patients with acute respiratory symptoms (ARS) between January and December 2021 (5,653 females and 4,950 males) were tested for respiratory viruses using RT-PCR. RESULTS: At least one respiratory virus was detected by RT-PCR in 119 out of 175 samples (68%). The most frequently detected virus was human rhinovirus (HRV) (34, 6.5%), followed by human parainfluenza viruses (HPIVs) (19, 3.6%), human bocavirus (HBoV) (8, 1.5%), human adenovirus (HAdV) (7, 1.3%), and human respiratory syncytial virus (HRSV) (4, 0.8%). HPIV-3 accounted for 3.6% (19/175) of all viral pathogens and was the second most frequently detected viral pathogen in our study. HPIV-3 infections peaked in the fall (November) of 2021. Phylogenetic analysis of the coding region of the viral protein HA revealed that all 35 (100%) of 35 HPIV-infected patients were infected with HPIV-3. CONCLUSIONS: HPIV was an important causative pathogen associated with ALRI in children hospitalized in Korea in the late fall of 2021, as the social distancing rules for COVID-19 were relaxed. These findings highlight the im-portance of HPIV as a cause of ALRI.
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Infecciones del Sistema Respiratorio , Humanos , Femenino , Masculino , Lactante , Preescolar , Prevalencia , Infecciones del Sistema Respiratorio/epidemiología , Infecciones del Sistema Respiratorio/virología , Infecciones del Sistema Respiratorio/diagnóstico , Niño , República de Corea/epidemiología , Infecciones por Paramyxoviridae/epidemiología , Infecciones por Paramyxoviridae/virología , Infecciones por Paramyxoviridae/diagnóstico , AdolescenteRESUMEN
Ecklonia cava, a brown seaweed native to the East Asian coast, is known for its unique composition, including polysaccharides, polyphenols, and phlorotannins. Fucoidan is a sulfated polysaccharide widely used as a functional ingredient in foods. This study obtained crude polysaccharides (ECC_CPS) from E. cava celluclast enzymatic hydrolysate using ethanol precipitation. ECC_CPS increased cell viability during the proliferation of Hanwoo muscle satellite cells (HMSCs). The effect of ECC_CPS on the expression of proliferation-related markers was confirmed as MYF5 and MYOD expression significantly increased, whereas PAX7 expression was maintained. The evaluation of cell migration activity has a major impact on cell proliferation and differentiation, and the cell migration index significantly increased with ECC_CPS treatment (p < 0.01). This was related to the HGF/MET pathway and FAK pathway. Treatment with ECC_CPS promoted differentiation at the cell differentiation stage, thereby increasing the expression of differentiation markers, such as MYH2, MYH7, and MYOG (p < 0.001 or p < 0.01). Therefore, our findings imply that crude polysaccharide obtained from E. cava can be an additive ingredient that enhances the proliferation and differentiation of muscle satellite cells used in the manufacture of cultured meat products.
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This study investigated the antioxidant activity of radish seed oil (RSO) and its effects on the quality and storage characteristics of pork patties. To assess the antioxidant capacity of RSO, this study analyzed fatty acid composition, peroxide value (PV), and 2,2-diphenyl-1-picrylhydrazyl (DPPH) radical scavenging activity. Pork patties were manufactured with the addition of RSO-0.4%, 0.8%, 1.6%, and 2.4%-and measured in terms of proximate composition, pH, water holding capacity (WHC), cooking loss (CL), color, texture profile analysis, and a sensory evaluation. Total microbial count (TMC), volatile basic nitrogen (VBN), thiobarbituric acid reactive substances (TBARS), and PV were measured at 1, 3, and 7 days of refrigerated storage. The DPPH radical scavenging activity of RSO was found to be 75.46%. In the cases of WHC and CL, there was no significant differences observed between RSO0.4%, RSO0.8%, and positive control (PC; p>0.05). Meanwhile, RSO2.4% showed significantly lower hardness, springiness, gumminess, and chewiness than PC (p<0.05), and these values tended to decrease with the addition of increasing RSO. In terms of storage characteristics, with an increase in the amount of RSO added, TMC, VBN, TBARS, and PV all decreased; among the treatment groups, RSO2.4% showed the lowest values. In conclusion, RSO exhibits antioxidant activity, but when added in large amounts, it negatively affects the quality characteristics of patties while positively impacting their storage properties, thus necessitating a balanced consideration of both outcomes. Therefore, adding 1.6% RSO is considered to be the most appropriate level for formulations to be used in practice.
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The aim of study was to investigate proliferation and differentiation capacities of Hanwoo myosatellite cells for the development of Hanwoo cell cultures. From P1 to P19, the number of live cells decreased and the cell size increased. It was confirmed that the PAX7 mRNA was higher in P3 than P6 and P9 (p < 0.05). The maximum differentiation score was measured from P1 to P12. The maximum differentiation score maintained high from P1 to P10. Immunostaining was performed for both P1 and P10 cells to investigate differentiation characteristics. And there were no significant differences in differentiation characteristics between P1 and P10 cells. MYOG mRNA was low, whereas C-FOS mRNA was high (p < 0.05) in the late passage. Myosin and Tom20 protein also showed low values in the late passage (p < 0.05). In conclusion, our results suggest that it is appropriate to use P1 to P10 for the production of cultured meat using Hanwoo muscle cells. If cell culture meat production is performed without differentiation, the passage range may increase further. These results provide basic essential data required for further development of Hanwoo cell cultures, which could provide a valuable source of protein for human populations in the future.
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Células Satélite del Músculo Esquelético , Humanos , Diferenciación Celular/genética , Carne , ARN Mensajero , Proliferación CelularRESUMEN
To improve culture efficiency of Hanwoo myosatellite cells, these cells were cultured at different temperatures. Hanwoo myosatellite cells were compared with C2C12 cells to observe proliferation and differentiation at culture temperatures of 37°C and 39°C and determine the possibility of using them as cultured meat. Immunofluorescence staining using Pax7 and Hoechst, both cells cultured at 37°C proliferated better than cultured at 39°C (p < 0.05). When differentiated cells were stained with myosin and Hoechst, there was no significant difference in myotube thickness and Fusion index (p > 0.05). In Western blotting analysis, Hanwoo myosatellite cells were no significant difference in the expression of myosin between cells differentiated at the two temperatures (p > 0.05). C2C12 cells were no significant difference in the expression of myosin between cells differentiated at the two temperatures (p > 0.05). In reverse transcription and quantitative polymerase chain reaction (RT-qPCR) analysis, Hanwoo myosatellite cells cultured at 39°C had significantly (p < 0.05) higher expression levels of MyHC, MYF6, and MB than those cultured at 37°C. C2C12 cells cultured at 39°C showed significantly (p < 0.05) higher expression levels of MYOG and MB than those cultured at 37°C. To increase culture efficiency of Hanwoo myosatellite cells, proliferating at 37°C and differentiating at 39°C are appropriate. Since results of temperature differences of Hanwoo myosatellite cells were similar to those of C2C12 cells, they could be used as a reference for producing cultured meat using Hanwoo satellite cells.
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Radiative cooling has attracted tremendous interest as it can tackle global warming by saving energy consumption in heating, ventilation, and air conditioning (HVAC) in buildings. Polymer materials play an important role in radiative cooling owing to their high infrared emissivity. Along this line, numerous studies on optically optimized geometries were carried out to enhance the selective wavelength absorption for high infrared emissivity; however, the polymer material itself relatively was not investigated and optimized enough. Herein, we investigate the infrared radiation (IR) absorption coefficient of various polymer types, and introduce a new concept of radiative-cooling composites. By dispersing the IR scattering medium in a polymer matrix, IR can be effectively scattered and attenuated by the polymer matrix. Indium tin oxide was utilized as the IR scattering medium in a cellulose acetate polymer matrix in this report. The window film was made with this composite and showed an effective cooling performance by outdoor thermal evaluation. This composite opens a new venue to endow materials with enhanced radiative-cooling property regardless of the polymer types.
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This study focuses on the potential capability of numerous machine learning models, namely CatBoost, GradientBoosting, HistGradientBoosting, ExtraTrees, XGBoost, DecisionTree, Bagging, light gradient boosting machine (LGBM), GaussianProcess, artificial neural network (ANN), and light long short-term memory (LightLSTM). These models were investigated to predict the photocatalytic degradation of malachite green from wastewater using various NM-BiFeO3 composites. A comprehensive databank of 1200 data points was generated under various experimental conditions. The ten input variables selected were the catalyst type, reaction time, light intensity, initial concentration, catalyst loading, solution pH, humic acid concentration, anions, surface area, and pore volume of various photocatalysts. The MG dye degradation efficiency was selected as the output variable. An evaluation of the performance metrics suggested that the CatBoost model, with the highest test coefficient of determination (0.99) and lowest mean absolute error (0.64) and root-mean-square error (1.34), outperformed all other models. The CatBoost model showed that the photocatalytic reaction conditions were more important than the material properties. The modeling results suggested that the optimized process conditions were a light intensity of 105 W, catalyst loading of 1.5 g/L, initial MG dye concentration of 5 mg/L and solution pH of 7. Finally, the implications and drawbacks of the current study were stated in detail.
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Bismuto , Aguas Residuales , Sustancias Húmicas , Aprendizaje AutomáticoRESUMEN
To effectively evaluate the performance of capacitive deionization (CDI), an electrochemical ion separation technology, it is necessary to accurately estimate the number of ions removed (effluent concentration) according to energy consumption. Herein, we propose and evaluate a deep learning model for predicting the effluent concentration of a CDI process. The developed deep learning model exhibited excellent prediction accuracy for both constant current and constant voltage modes (R2 ≥ 0.968), and the accuracy increased with the data size. This model was based on the open-source language, Python, and the code has since been distributed with proper instructions for general use. Owing to the nature of the data-oriented deep learning model, the findings of this study are not only applicable to conventional CDI but also to various types of CDI (membrane CDI, flow CDI, faradaic CDI, etc.). Therefore, by referring to the examples shown in this study, we hope that this open-source deep learning code will be widely used in CDI research.
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Aprendizaje Profundo , Purificación del Agua , Electrodos , Iones , ElectricidadRESUMEN
There are several factors that affect the welfare and meat quality of pigs during pre-slaughter transport. Among various factors, the effects of weather conditions and loading density were studied. A total of 3,726 finishing pigs were allotted to one of nine groups arranged in a 3 × 3 factorial design according to the weather conditions (low temperature [LT], under 10°C; normal temperature [NT], 10°C-24°C; high temperature [HT], upper 24°C), and loading density (low density [LD], upper 0.43 m2/100 kg; normal density [ND], 0.37-0.43 m2/100 kg; high density [HD], under 0.37 m2/100 kg). Each treatment group follow as: LTLD, LTND, LTHD, NTLD, NTND, NTHD, HTLD, HTND, HTHD. In terms of carcass composition, pigs had the highest carcass weight and backfat thickness at LT. Comparing the HD transport to the ND transport, the meat quality indicated a lower pH and more drip loss. The incidence rate of pale, soft, exudative (PSE) pork was high in the order of the HD, LD, and the ND transport (20%, 9%, and 2%, respectively). The HT transport showed the lowest pH and greatest L* value under the given weather conditions. Pigs transported under the HTHD and LTLD conditions had the greatest rates of PSE pork (40% and 20%, respectively). Pigs exposed to HD transport had the shortest laying time and the highest overplap behavior. The LDLT transport pigs had a shorter laying time than the LDNT and LDHT transport pigs. In conclusion, too high or too low density transport is generally not excellent for meat quality or animal welfare, however it is preferable to transport at a slightly low density at high temperature and at a slightly high density at low temperature.
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Capacitive deionization (CDI) is an alternative desalination technology that uses electrochemical ion separation. Although several attempts have been made to maximize the energy efficiency and productivity of CDI with conventional control methods, it is difficult to optimize the CDI processes because of the complex correlation between the operational conditions and the composition of feed water. To address these challenges, we applied deep reinforcement learning (DRL) to automatically control the membrane capacitive deionization (MCDI) process, which is one of the representative CDI processes, to accomplish high energy efficiency while desalinating water. In the DRL model, the numerical model is combined as the environment that provides states according to the actions. The feed water conditions, that is, the input state of the DRL, were assumed to have a random salt concentration and constant foulant concentration. The model was constructed to minimize energy consumption and maximize desalted water volume per cycle. After training of 1,000 episodes, the DRL model achieved a 22.07% reduction in specific energy consumption (from 0.054 to 0.042 kWh m-3) and 11.60% increase in water desalted water volume per cycle (from 1.96×10-5 to 2.19×10-5 m3), achieving the desired degree of desalination, compared to the first episode. This improved performance was because the trained model selected the optimized operating conditions of current, voltage, and the number and intensity of flushing. Furthermore, it was possible to train the model depending on demand by modifying the reward function of the DRL model. The fundamental principle described in this study for applying the DRL model in MCDI operations can be the cornerstone of a fully automated water desalination process.
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Purificación del Agua , Purificación del Agua/métodos , Electrodos , Cloruro de Sodio , Agua , AutomatizaciónRESUMEN
Enhancing engineering efficiency and reducing operating costs are permanent subjects that face all engineers over the world. To effectively improve the performance of filtration systems, it is necessary to determine an optimal operating condition beyond conventional methods of periodic and empirical operation. Herein, this paper proposes an effective approach to finding an optimal operating strategy using deep reinforcement learning (DRL), particularly for an ultrafiltration (UF) system. Deep learning was developed to represent the UF system utilizing a long-short term memory and provided an environment for DRL. DRL was designed to control three actions; operating pressure, cleaning time, and cleaning concentration. Ultimately, DRL proposed the UF system to actively change the operating pressure and cleaning conditions over time toward better water productivity and operating efficiency. DRL denoted â¼20.9% of specific energy consumption can be reduced by increasing average water flux (39.5-43.7 L m-2 h-1) and reducing operating pressure (0.617-0.540 bar). Moreover, the optimal action of DRL was reasonable to achieve better performance beyond the conventional operation. Crucially, this study demonstrated that due to the nature of DRL, the approach is tractable for engineering systems that have structurally complex relationships among operating conditions and resultants.
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Ultrafiltración , Purificación del Agua , Filtración , Humanos , Membranas Artificiales , Ultrafiltración/métodos , Agua , Purificación del Agua/métodosRESUMEN
In 2016, the westerly quasi-biennial oscillation (WQBO) in the equatorial stratosphere was unprecedentedly disrupted by westward forcing near 40 hPa; this was followed by another disruption in 2020. Strong extratropical Rossby waves propagating toward the tropics were considered the main cause of the disruptions, but why the zonal wind is reversed only in the middle of the WQBO remains unclear. Here, we show that strong westerly winds in the equatorial lower stratosphere (70 to 100 hPa) help to disrupt the WQBO by hindering the wind reversal at its base. They also help equatorial westward waves propagate further upward, increasing the negative forcing at around 40 hPa that drives the QBO disruptions. Tropical westerly winds have been increasing in the past and are projected to increase in a warmer climate. These background wind changes may allow more frequent QBO disruptions in the future, leading to less predictability in atmospheric weather and climate systems.
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Among future food problems, the demand for meat is expected to increase rapidly, but the production efficiency of meat, which is a protein source, is very low compared to other foods. To address this problem, research on the development and production of cultured meat as an alternative meat source using muscle stem cells in vitro has recently been undertaken. Many studies have been conducted on myosatellite cells for medical purposes, but studies on alternative meat production are rare. In vitro cell culture mimics the in vivo environment for cell growth. The satellite cell niche is closer to hypoxic (2% O2) than normoxic (20% O2) conditions. The aim of this study was to investigate the efficient oxygen conditions of myosatellite cell cultures for the production of cultured meat. The bovine satellite cell counts and mRNA (Pax7, Myf5 and HIF1α) levels were higher in hypoxia than normoxia (p < 0.05). Through Hoechst-positive nuclei counts, and expression of Pax7, MyoD and myosin protein by immunofluorescence, it was confirmed that muscle cells performed normal proliferation and differentiation. Myoblast fusion was higher under hypoxic conditions (p < 0.05), and the myotube diameters were also thicker (p < 0.05). In the myotube, the number of cells was high in hypoxia, and the expression of the total protein amounts, differentiation marker mRNA (myogenin, myosin and TOM20), and protein markers (myosin and TOM20) was also high. The study results demonstrated that the proliferation and differentiation of bovine myosatellite cells were promoted more highly under hypoxic conditions than under normoxic conditions. Therefore, hypoxic cultures that promote the proliferation and differentiation of bovine myosatellite cells may be an important factor in the development of cultured meat.
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Células Satélite del Músculo Esquelético , Animales , Bélgica , Bovinos , Diferenciación Celular , Proliferación Celular , Células Cultivadas , Hipoxia/metabolismo , Carne , Fibras Musculares Esqueléticas , Proteína MioD/genética , Proteína MioD/metabolismo , Proteína MioD/farmacología , Oxígeno/metabolismo , ARN Mensajero/metabolismo , Células Satélite del Músculo Esquelético/metabolismoRESUMEN
This study aimed to determine whether fermented soy sauce has a mutually synergistic effect on the quality and storage properties of pork patties, and to investigate the effects on the availability and physicochemical properties of various taste ingredients of soy sauce, a traditional Korean food ingredient. The experimental groups were as follows: Control (-): No additives; Control (+): 0.1% ascorbic acid; T1: 1% fermented soy sauce; T2: 3% fermented soy sauce; T3: 5% fermented soy sauce. No significant difference was detected in moisture, protein, and fat among the various treatment groups; however, ash content and water holding capacity increased and texture properties improved with the concentration of fermented soy sauce. The addition of fermented soy sauce during refrigerated storage for 10 days showed a positive effect on the storage properties. The peroxide value, content of thiobarbituric acid reactive substances and total phenolics, and 2,2-diphenyl-1-picrylhydrazyl free radical scavenging activity differed significantly in pork patties with different treatments and storage intervals. The effect of fermented soy sauce on the overall quality and storage properties of pork patties during refrigerated storage is relatively unknown. These findings demonstrate that the addition of fermented soy sauce improves the quality properties and antioxidant activity of pork patties.
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Noroviruses cause acute gastroenteritis with symptoms of diarrhoea and vomiting, and their high infectivity allows outbreaks to readily occur. Quickly identifying and isolating potential contaminants is an effective method to prevent the spread of outbreaks. A total of 376 samples collected from nine outbreaks were categorized as either patient, asymptomatic individual, cook or environmental samples, according to the source of contamination. Using real-time PCR and sequencing analysis, norovirus GII genotypes were detected in 34·9% of samples from patients, 19·2% from asymptomatic individuals, 2·4% from the environment and 1·4% from cooks. Our findings showed contrasting results in samples categories quantified based on the limit of blank and detection limit by reverse transcription droplet digital PCR, which is a more sensitive testing method than real-time-PCR.
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Infecciones por Caliciviridae , Gastroenteritis , Norovirus , Infecciones por Caliciviridae/epidemiología , Brotes de Enfermedades , Heces , Gastroenteritis/epidemiología , Genotipo , Humanos , Norovirus/genética , ARN Viral/genética , Reacción en Cadena en Tiempo Real de la Polimerasa/métodosRESUMEN
It is impossible to know the amount of pork primal cut by pig carcass grade which is determined only by carcass weight and backfat thickness in the Korean Pig Carcass System. The aim of this study was to investigate the correlation between the pig carcass grade and the amount of pork primal cut estimated with AutoFom III. A total of 419,321 Landrace, Yorkshire, and Duroc (LYD) pigs were graded with the Korean Pig Carcass Grade System. Amounts of belly, neck, loin, tenderloin, spare ribs, shoulder, and ham were estimated with AutoFom III. Regression equations for seven primal cuts according to each grade were derived. There were significant differences among the three carcass grades due to heteroscedasticity variance (p < 0.0001). Three regression equations were derived from AutoFom III estimation of primal cuts according to carcass grades. The coefficient of determination of the regression equation was 0.941 for grade 1+, 0.982 for grade 1, and 0.993 for grade 2. Regression equations obtained from this study are suitable for AutoFom III software, a useful tool for the analysis of each pig carcass grade in the Korean Pig Carcass Grade System. The high reliability of predicting the amount of primal cut with AutoFom III is advantageous for the management of slaughterhouses to optimize their product sorting in Korea.