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We examined whether selective attention, which is mainly theorized as the ability to focus on the category-relevant dimension, is a sole construct in understanding category learning. As the attention literature dissociates selective attention into focusing and filtering, we argue that filtering is another component that should be considered to fully understanding category learning. In the study, we provide an experimental paradigm that can dissociate filtering from focusing. By utilizing the paradigm along with collecting individual attention control measures, we show that filtering is related to the ability to inhibit irrelevant information. We also present that the current computational models that incorporate selective attention only as an ability to focus can not explain the results from the current study.

Introduction: Integrated crop-livestock systems (ICLS) use animals to graze crop residues or cover crops before planting fresh produce and provide ecosystem services to support organic vegetable production. However, there is a risk of foodborne pathogen transfer to fresh produce because grazing may introduce enteric foodborne pathogens into the soil via animal feces, which may subsequently be transferred to the produce. Methods: To examine the effect of cover crop use and the risk of cover crop grazing on the contamination of soil and produce by foodborne pathogens in ICLS, a three-year (2019–2021) experimental study was conducted in organically managed plots, which were assigned three different treatments (fallow without cover crop or grazing, cover crop without grazing, or cover crop with grazing by sheep) in a maize/tomato rotation. During the three years of the experiment, a total of 184 pre- and post-graze fecal samples and 96 samples of tomatoes were collected to test for foodborne pathogens (Escherichia coli O157, non-O157 Shiga toxin-producing Escherichia coli (STEC), and Listeria (L.) monocytogenes). Soil samples were collected monthly until 126–171 days after grazing (824 in total) to examine the presence of foodborne pathogens, and generic E. coli (MPN/g) was quantified to compare its persistence among the three treatments. Results and Discussion: We did not detect any foodborne pathogens from harvested tomatoes in 2020 and 2021. One non-O157 STEC positive soil sample (0.1%, 1/824) was detected in the fallow treatment, and one L. monocytogenes-positive (1.1%, 1/92) was detected from the post-graze fecal samples. When assessing proportions of generic E. coli positive and counts of generic E. coli in the soil samples using mixed effect zero-inflated negative binomial models, soil samples collected in the graze cover crop treatment plot showed significant increases in the counts of generic E. coli until 61–82 days post grazing, but no difference was observed after 96–123 days, compared to the baseline of the fallow treatment. Findings from generic E. coli counts support the use of the United States Department of Agriculture (USDA) National Organic Program (NOP) 90- or 120-day interval rule between applying raw manure and harvesting in organic farming into ICLS. Additionally, we confirmed that commercial organic compost application before cover crop seeding in the winter had no significant effect on the proportions and counts of generic E. coli in the soil of the following growing seasons. This longitudinal field trial confirmed that the effect of sheep grazing on foodborne pathogen contamination in ICLS is minimal but further studies comparing the genetic associations between fecal and soil samples would be necessary to distinguish the source of foodborne pathogen contamination.

This study raises a critical question: 'what does it mean to be smart teachers in the era of technology?'. To provide a concrete and comprehensive answer, the authors analyse multiple discourses revolving around SMART education, an education technology reform initiative in South Korea. This article critically examines the significance of SMART education as dominant discourse to shed light on the formation of smart teachers. The results show that SMART education is conceptualised as 'panacea' that can practically solve all kinds of educational problems, which leaves no choice for teachers but to accept it. The authors also analyse teachers' perceptions and practices by drawing on interviews conducted in a Korean smart city, where the SMART education was first enacted. It is argued that smart teachers are supposed to be 'adaptive' to survive by proving themselves as 'compatible'. This article concludes that it is necessary to imagine more diverse versions of smart teachers.

Abstract Sargassum horneri (S. horneri), a brown seaweed excessively proliferating along Asian coastlines, are damaging marine ecosystems. Thus, this study aimed to enhance nutritional value of S. horneri through lactic acid bacteria fermentation to increase S. horneri utilization as a functional food supplement, and consequently resolve coastal S. horneri accumulation. S. horneri supplemented fermentation was most effective with Lactiplantibacillus pentosus SH803, thus this product (F-SHWE) was used for further in vitro studies. F-SHWE normalized expressions of oxidative stress related genes NF-κB, p53, BAX, cytochrome C, caspase 9, and caspase 3, while non-fermented S. horneri (SHWE) did not, in a H2O2-induced HT-29 cell model. Moreover, in an LPS-induced HT-29 cell model, F-SHWE repaired expressions of inflammation marker genes ZO1, IL1β, IFNγ more effectively than SHWE. For further functional assessment, F-SHWE was also treated in 3T3-L1 adipocytes. As a result, F-SHWE decreased lipid accumulation, along with gene expression of adipogenesis markers PPARγ, C/EBPα, C/EBPβ, aP2, and Lpl; lipogenesis markers Lep, Akt, SREBP1, Acc, Fas; inflammation markers IFN-γ and NF-κB. Notably, gene expression of C/EBPβ, IFN-γ and NF-κB were suppressed only by F-SHWE, suggesting the enhancing effect of fermentation on obesity-related properties. Compositional analysis attributed the protective effects of F-SHWE to acetate, an organic acid significantly higher in F-SHWE than SHWE. Therefore, F-SHWE is a novel potential anti-obesity agent, providing a strategy to reduce excess S. horneri populations along marine ecosystems.

Objectives This study compared the scientific publication speeds of Korean medical journals before and during the coronavirus disease 2019 (COVID-19) era. Methods We analyzed 2,064 papers from 43 international Korean medical journals, selecting 12 papers annually from 2019 to 2022. We assessed publication speed indicators, including the time from submission to revision and from submission to publication. Additionally, we examined variations in publication speed based on journal and paper characteristics, including whether the studies were related to COVID-19. Results Among the 43 journals analyzed, 39.5% disclosed the peer review duration from submission to the first decision, and 11.6% reported their acceptance rates. The average time from submission to acceptance was 127.0 days in 2019, 126.1 days in 2020, 124.6 days in 2021, and 126.4 days in 2022. For COVID-19-related studies, the average time from submission to revision was 61.4 days, compared to 105.1 days for non-COVID-19 studies; from submission to acceptance, it was 87.4 days for COVID-19-related studies and 127.1 days for non-COVID-19 studies. All indicators for COVID-19-related studies showed shorter durations than those for non-COVID-19 studies, and the proportion of studies accepted within 30 or 60 days was significantly higher for COVID-19-related studies. Conclusions This study investigated the publication speed of Korean international medical journals before and during the COVID-19 pandemic. The pandemic influenced journals’ review and publication processes, potentially impacting the quality of academic papers. These findings provide insights into publication speeds during the COVID-19 era, suggesting that journals should focus on maintaining the integrity of their publication and review processes.

Abstract Purpose To develop a deep learning (DL) model for generating automated regions of interest (ROIs) on 99mTc-diethylenetriamine pentaacetic acid (DTPA) renal scans for glomerular filtration rate (GFR) measurement. Methods Manually-drawn ROIs retrieved from a Picture Archiving and Communications System were used as ground-truth (GT) labels. A two-dimensional U-Net convolutional neural network architecture with multichannel input was trained to generate DL ROIs. The agreement between GFR values from GT and DL ROIs was evaluated using Lin’s concordance correlation coefficient (CCC) and slope coefficients for linear regression analyses. Bias and 95% limits of agreement (LOA) were assessed using Bland-Altman plots. Results A total of 24,364 scans (12,822 patients) were included. Excellent concordance between GT and DL GFR was found for left (CCC 0.982, 95% confidence interval [CI] 0.981–0.982; slope 1.004, 95% CI 1.003–1.004), right (CCC 0.969, 95% CI 0.968–0.969; slope 0.954, 95% CI 0.953–0.955) and both kidneys (CCC 0.978, 95% CI 0.978–0.979; slope 0.979, 95% CI 0.978–0.979). Bland-Altman analysis revealed minimal bias between GT and DL GFR, with mean differences of − 0.2 (95% LOA − 4.4–4.0), 1.4 (95% LOA − 3.5–6.3) and 1.2 (95% LOA − 6.5–8.8) mL/min/1.73 m² for left, right and both kidneys, respectively. Notably, 19,960 scans (81.9%) showed an absolute difference in GFR of less than 5 mL/min/1.73 m². Conclusion Our DL model exhibited excellent performance in the generation of ROIs on 99mTc-DTPA renal scans. This automated approach could potentially reduce manual effort and enhance the precision of GFR measurement in clinical practice.

Streptococcus suis is a zoonotic pathogen that affects pigs and humans when they handle pigs or eat undercooked pork products. Globally, an outbreak of infection occurred in China in 2005, [...]

Purpose We assessed the clinical relevance of waist-height ratio (WHtR) as an indicator of cardiometabolic risk and body fat mass measured by dual-energy x-ray absorptiometry (DXA) among Korean children and adolescents. Methods Data from 1,661 children and adolescents aged 10–18 years who participated in the Korea National Health and Nutrition Examination Survey were analyzed. Unadjusted Pearson correlation, age- and sex-adjusted Pearson correlation, and multiple linear regression analyses were performed to investigate the relationships between WHtR standard deviation score (SDS) and cardiometabolic risk factors, as well as DXA-assessed parameters. Results WHtR SDS was correlated with cardiometabolic risk factors, including systolic blood pressure, glucose, total cholesterol, high-density lipoprotein cholesterol, triglyceride, and low-density lipoprotein cholesterol, as well as DXA-assessed parameters such as lean mass SDS, fat mass SDS, and fat mass percentage SDS in both whole body and trunk using an adjusted Pearson correlation analyses among all participants (p

Abstract Background Many cases of deep vein thrombosis (DVT) are diagnosed in the emergency department, and abbreviated lower extremity venous point-of-care ultrasound (POCUS) has already shown an accuracy comparable to that of specialists. This study aimed to identify the learning curve necessary for emergency medicine (EM) residents to achieve expertise-level accuracy in diagnosing DVT through a 3-point lower extremity venous POCUS. Methods This prospective study was conducted at an emergency department between May 2021 and October 2022. Four EM residents underwent a one-hour POCUS training session and performed DVT assessments in participants with DVT symptoms or confirmed pulmonary embolism. POCUS was performed at three proximal lower extremity sites to evaluate the thrombi presence and vein compressibility, with results validated by specialized radiology ultrasound. Cumulative sum (CUSUM) and the Bush and Mosteller models were used to analyze the learning curve, while generalized estimating equations were used to identify factors affecting diagnostic accuracy. Results 91 POCUS scans were conducted in 49 patients, resulting in 22% DVT confirmed by specialized venous ultrasound. In the CUSUM analysis, all four EM residents attained a 90% success rate at the common femoral vein, whereas only half achieved this rate when all three sites were considered. According to Bush and Mosteller models, 13–18 cases are required to attain 90–95% diagnostic accuracy. After 10–16 cases, the examination time for each resident decreased, and a 20% increase in examiner confidence was linked to a 2.506-fold increase in the DVT diagnosis accuracy. Conclusion EM residents generally required 13–18 cases for 90–95% DVT diagnostic accuracy, but proficiency varied among individuals, particularly requiring more cases for regions outside the common femoral vein.

Swarm robot systems are an important application of autonomous unmanned surface vehicles on water surfaces. For monitoring natural environments and conducting security activities within a certain range using a surface vehicle, the swarm robot system is more efficient than the operation of a single object as the former can reduce cost and save time. It is necessary to detect adjacent surface obstacles robustly to operate a cluster of unmanned surface vehicles. For this purpose, a LiDAR (light detection and ranging) sensor is used as it can simultaneously obtain 3D information for all directions, relatively robustly and accurately, irrespective of the surrounding environmental conditions. Although the GPS (global-positioning-system) error range exists, obtaining measurements of the surface-vessel position can still ensure stability during platoon maneuvering. In this study, a three-layer convolutional neural network is applied to classify types of surface vehicles. The aim of this approach is to redefine the sparse 3D point cloud data as 2D image data with a connotative meaning and subsequently utilize this transformed data for object classification purposes. Hence, we have proposed a descriptor that converts the 3D point cloud data into 2D image data. To use this descriptor effectively, it is necessary to perform a clustering operation that separates the point clouds for each object. We developed voxel-based clustering for the point cloud clustering. Furthermore, using the descriptor, 3D point cloud data can be converted into a 2D feature image, and the converted 2D image is provided as an input value to the network. We intend to verify the validity of the proposed 3D point cloud feature descriptor by using experimental data in the simulator. Furthermore, we explore the feasibility of real-time object classification within this framework.

Abstract In this work, we study the end-of-the-world (EOW) branes anchored to the boundaries of BCFT $${}_2$$ 2 dual to the BTZ black hole. First, we explore the thermodynamics of the boundary system consisting of the conformal boundary and two EOW branes. This thermodynamics is extended by the tension appearing as the effective cosmological constant of JT black holes on the EOW branes. The tension contribution is identified with the shadow entropy equivalent to the boundary entropy of the BCFT $${}_2$$ 2 . The thermodynamics of the JT black holes and the bulk of BCFT $${}_2$$ 2 can be combined into a novel grafted thermodynamics based on the first law. Second, we focus on the observer’s view of the EOW branes by lowering the temperature. We show that the EOW branes generate a scale called “reefs” inside the horizon. This scale also appears in the grafted thermodynamics. At high temperatures, observers on the EOW branes see their respective event horizons. The reef starts to grow relatively to the horizon size at the temperature, $$T_{grow}$$ T grow . As the temperature cools down the reef area fills the entire interior of the JT black holes at the temperature $$T_{out}$$ T out . Then, the observers recognize their horizons disappear and see the large density of the energy flux. At this temperature, the two JT regions become causally connected. This connected spacetime has two asymptotic $$AdS_2$$ A d S 2 boundaries with a conformal matter. Also, we comment on the grafted thermodynamics to higher dimensions in Appendix B.

Various X-ray techniques are employed to investigate specimens in diverse fields. Generally, scattering and absorption/emission processes occur due to the interaction of X-rays with matter. The output signals from these processes contain structural information and the electronic structure of specimens, respectively. The combination of complementary X-ray techniques improves the understanding of complex systems holistically. In this context, we introduce a multiplex imaging instrument that can collect small-/wide-angle X-ray diffraction and X-ray emission spectra simultaneously to investigate morphological information with nanoscale resolution, crystal arrangement at the atomic scale and the electronic structure of specimens.