Prevalence of Parainfluenza Viruses in Seoul, 2021 - 2022.

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.

Quantitative differential analysis of norovirus outbreak samples using RT-ddPCR.

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.

Efficient Ray Tracing of Large 3D Scenes for Mobile Distributed Computing Environments.

Cluster computing has attracted much attention as an effective way of solving large-scale problems. However, only a few attempts have been made to explore mobile computing clusters that can be easily built using commodity smartphones and tablets. To investigate the possibility of mobile cluster-based rendering of large datasets, we developed a mobile GPU ray tracer that renders nontrivial 3D scenes with many millions of triangles at an interactive frame rate on a small-scale mobile cluster. To cope with the limited processing power and memory space, we first present an effective 3D scene representation scheme suitable for mobile GPU rendering. Then, to avoid performance impairment caused by the high latency and low bandwidth of mobile networks, we propose using a static load balancing strategy, which we found to be more appropriate for the vulnerable mobile clustering environment than a dynamic strategy. Our mobile distributed rendering system achieved a few frames per second when ray tracing 1024 × 1024 images, using only 16 low-end smartphones, for large 3D scenes, some with more than 10 million triangles. Through a conceptual demonstration, we also show that the presented rendering scheme can be effectively explored for augmenting real scene images, captured or perceived by augmented and mixed reality devices, with high quality ray-traced images.

Prediction of Oxidant Exposures and Micropollutant Abatement during Ozonation Using a Machine Learning Method.

Oxidation of micropollutants (MPs) by ozonation proceeds via the reactions with molecular ozone (O3) and hydroxyl radicals (•OH). To predict MP abatement during ozonation, a model that can accurately predict oxidant exposures (i.e., ∫0t[O3]dt⁢ and⁢ ∫0t[O•H]dt) needs to be developed. This study demonstrates machine learning models based on the random forest (RF) algorithm to output oxidant exposures from water quality parameters (input variables) that include pH, alkalinity, dissolved organic carbon concentration, and fluorescence excitation-emission matrix (FEEM) data (to characterize organic matter). To develop the models, 60 different samples of natural waters and wastewater effluents were collected and characterized, and the oxidant exposures in each sample were determined at a specific O3 dose (2.5 mg/L). Four RF models were developed depending on how FEEM data were utilized (i.e., one model free of FEEM data, and three other models that used FEEM data of different resolutions). The regression performance and Akaike information criterion (AIC) were evaluated for each model. The models using high-resolution FEEM data generally exhibited high prediction accuracy with reasonable AIC values, implying that organic matter characteristics quantified by FEEM can be important factors to improve the accuracy of the prediction model. The developed models can be applied to predict the abatement of MPs in drinking water and wastewater ozonation processes and to optimize the O3 dose for the intended removal of target MPs. The machine learning models using higher-resolution FEEM data offer more accurate prediction by better calculating the complex nonlinear relationship between organic characteristics and oxidant exposures.

Perioral Ruler in Routine Esthetic Surgery: Convenient and Exact.

Lip and perioral surgery, like any other esthetic surgery, requires an exact and convenient measurement tool to ensure reliable and reproducible outcomes. Although three-dimensional measuring equipment has proven its effectiveness in measuring facial parameters over the past two decades, it has some drawbacks, including high cost, long scanning times, and non-portability. Thus, digital photography remains the standard tool of measurement in esthetic surgery to date. Many authors have presented evaluation and measurement methods using digital photography in combination with different tools. However, there are no specific tools for the perioral region. Therefore, we devised a specific ruler for perioral measurements. The ruler has differently colored lines for length and angle measurements and a reference point for correct positioning. It can be used in preoperative consultation, intraoperative orientation, and postoperative evaluation.Level of Evidence IV This journal requires that authors assign a level of evidence to each article. For a full description of these Evidence-Based Medicine ratings, please refer to the Table of Contents or the online Instructions to Authors www.springer.com/00266.

Arctic Primary Aerosol Production Strongly Influenced by Riverine Organic Matter.

The sources of primary and secondary aerosols in the Arctic are still poorly known. A number of surface seawater samples-with varying degrees of Arctic riverine and sea ice influences-were used in a sea spray generation chamber to test them for their potential to produce sea spray aerosols (SSA) and cloud condensation nuclei (CCN). Our interdisciplinary data showed that both sea salt and organic matter (OM) significantly influenced the SSA production. The number concentration of SSA in the coastal samples was negatively correlated with salinity and positively correlated with a number of OM tracers, including dissolved and chromophoric organic carbon (DOC, CDOM), marine microgels and chlorophyll a (Chl-a) but not for viral and bacterial abundances; indicating that OM of riverine origin enhances primary aerosol production. When all samples were considered, transparent exopolymer particles (TEP) were found to be the best indicator correlating positively with the ratio number concentration of SSA/salinity. CCN efficiency was not observed to differ between the SSA from the various samples, despite differences in organic characteristics. It is suggested that the large amount of freshwater from river runoff have a substantial impact on primary aerosols production mechanisms, possibly affecting the cloud radiative forcing.

Similarity Graph-Based Camera Tracking for Effective 3D Geometry Reconstruction with Mobile RGB-D Camera.

In this paper, we present a novel approach for reconstructing 3D geometry from a stream of images captured by a consumer-grade mobile RGB-D sensor. In contrast to previous real-time online approaches that process each incoming image in acquisition order, we show that applying a carefully selected order of (possibly a subset of) frames for pose estimation enables the performance of robust 3D reconstruction while automatically filtering out error-prone images. Our algorithm first organizes the input frames into a weighted graph called the similarity graph. A maximum spanning tree is then found in the graph, and its traversal determines the frames and their processing order. The basic algorithm is then extended by locally repairing the original spanning tree and merging disconnected tree components, if they exist, as much as possible, enhancing the result of 3D reconstruction. The capability of our method to generate a less error-prone stream from an input RGB-D stream may also be effectively combined with more sophisticated state-of-the-art techniques, which further increases their effectiveness in 3D reconstruction.

Endoscopic foreign body removal in the upper gastrointestinal tract: risk factors predicting conversion to surgery.

BACKGROUND/AIMS: Most foreign bodies of the upper gastrointestinal tract (UGIT) are successfully removed by endoscopic techniques without complications. However, some require conversion to surgery due to failure of endoscopic removal. The aim of this study was to analyze the risk factors predicting the need to convert to surgery after inability to endoscopically remove the foreign body. PATIENTS AND METHODS: The medical records of 885 patients treated between January 2006 and March 2014 for suspected foreign bodies in the UGIT were retrospectively reviewed. Patient characteristics, the type of foreign bodies, clinical outcomes, and risk factors predicting the conversion to surgery were analyzed. RESULTS: While endoscopic removal was successful in 94.7% (665/702) of the patients, the remaining 5.3% (37/702) needed conversion to surgery. There were no procedure-related deaths. According to the multivariate logistic regression analyses, older age (>70 years, p = 0.004), location (upper esophagus, p = 0.001), larger size (maximal diameter > 30 mm, p = 0.005), and longer impaction time (>40 h, p < 0.001) were significant risk factors predicting conversion to surgery due to inability to remove the foreign body endoscopically. CONCLUSIONS: Most of the foreign bodies in the UGIT were successfully removed by endoscopic techniques. However, surgical removal might need to be considered in patients with age >70 years, and those with foreign bodies in the upper esophagus, maximal diameter >30 mm, and impaction time >40 h, due to the possible high failure rate of endoscopic removal.

The bone integration effects of platelet-rich fibrin by removal torque of titanium screw in rabbit tibia.

Platelet-rich fibrin (PRF) is nowadays often used in various fields, but no removal torque studies have yet been done to evaluate the effects of the platelet-rich fibrin on the bone integration at the initial healing period. An experimental study have been performed in rabbits to evaluate whether the complete PRF clots can accelerate the bone integration of implants at the initial healing period after creating bone defects in tibias. The effect of the complete PRF clots on bone integration was studied in two rabbit groups, 4-week group (group A) and 6-week group (group B) after preparing bony defects. Artificial bony defects were prepared in the tibias of rabbits. The complete PRF clots was applied to the defects in the experimental group, whereas the defects were unfilled in the control group. Four weeks later, machined implants were installed into the rabbit tibias (group A). Six weeks after installation, the removal torque from nine rabbits was measured to examine the bone healing effect of PRF. In another rabbits (group B), 6 weeks after preparing bony defects, installation was performed, and another 6 weeks after installation, the removal torque from nine rabbits was measured. The authors found a positive significant effect of the complete PRF clots on bone integration (higher bone density) in group A installed at 4 weeks after preparing bony defects (p = 0.008; t-test), but not in group B installed at 6 weeks after preparing bony defects (p = 0.677).

Prediction of hydroxyl radical exposure during ozonation using different machine learning methods with ozone decay kinetic parameters.

The abatement of micropollutants by ozonation can be accurately calculated by measuring the exposures of molecular ozone (O3) and hydroxyl radical (•OH) (i.e., ∫[O3]dt and ∫[•OH]dt). In the actual ozonation process, ∫[O3]dt values can be calculated by monitoring the O3 decay during the process. However, calculating ∫[•OH]dt is challenging in the field, which necessitates developing models to predict ∫[•OH]dt from measurable parameters. This study demonstrates the development of machine learning models to predict ∫[•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 •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.

Decoupling ion concentrations from effluent conductivity profiles in capacitive and battery electrode deionizations using an artificial intelligence model.

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 (Na⁺, K⁺, Ca2⁺, and Cl⁻) 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.

Antioxidant Activity of Radish Seed Oil and the Quality and Storage Characteristics of Pork Patties with Added Radish Seed Oil.

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.

Effect of Crude Polysaccharides from Ecklonia cava Hydrolysate on Cell Proliferation and Differentiation of Hanwoo Muscle Stem Cells for Cultured Meat Production.

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.

Effects of Edible Insect Powders as Meat Partial Substitute on Physicochemical Properties and Storage Stability of Pork Patties.

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.

Radiative-Cooling Composites with Enhanced Infrared Emissivity by Structural Infrared Scattering through Indium Tin Oxide Nanoparticles in a Polymer Matrix.

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.

An open-source deep learning model for predicting effluent concentration in capacitive deionization.

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.

Investigating proliferation and differentiation capacities of Hanwoo steer myosatellite cells at different passages for developing cell-cultured meat.

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.

Machine learning approaches to predict the photocatalytic performance of bismuth ferrite-based materials in the removal of malachite green.

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.

Culturing characteristics of Hanwoo myosatellite cells and C2C12 cells incubated at 37°C and 39°C for cultured meat.

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.

Effect of loading density and weather conditions on animal welfare and meat quality of slaughter pigs.

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.