Search Results (13)

This study examined the effects of different forage sources on the ruminal bacteriome, growth performance, and carcass characteristics of Hanwoo steers during the fattening stage. In Korea, where high-concentrate feeding is common, selecting suitable forage is crucial for sustainable beef production. Fifteen 23-month-old Hanwoo steers, weighing an average of 679.27 ± 43.60 kg, were fed the following five different forage sources: oat hay (OAT), rye silage (RYE), Italian ryegrass (IRS), barley forage (BAR), and rice straw silage (RSS), alongside 1.5 kg of dry matter concentrate daily for five months. Carcass traits were evaluated post-slaughter, and rumen fluid samples were analyzed using full-length 16S rRNA gene sequencing to determine the bacteriome composition. The forage source significantly affected the alpha-diversity indices and bacteriome biomarkers linked to the feed efficiency and ruminal fermentation. Differences in the backfat thickness and meat yield index were noted, with alpha-diversity indices correlating with carcass traits. The phylum Planctomycetota, especially the family Thermoguttaceae, was linked to nitrogen fixation in high-protein diets like IRS, while the genus Limimorpha emerged as a biomarker for the meat yield. These findings highlight the importance of forage selection during late fattening to optimize beef production, considering diet and bacteriome shifts. Full article

Lowering blood cholesterol levels is crucial for reducing the risk of cardiovascular disease in patients with familial hypercholesterolemia. To develop Perilla frutescens (L.) Britt. leaves as a functional food with a cholesterol-lowering effect, in this study, we collected P. frutescens (L.) Britt. leaves from different regions of China and Republic of Korea. On the basis of the extraction yield (all components; g/kg), we selected P. frutescens (L.) Britt. leaves from Hebei Province, China with an extract yield of 60.9 g/kg. After evaluating different concentrations of ethanol/water solvent for P. frutescens (L.) Britt. leaves, with luteolin 7-glucuronide as the indicator component, we selected a 30% ethanol/water solvent with a high luteolin 7-glucuronide content of 0.548 mg/g in Perilla. frutescens (L.) Britt. leaves. Subsequently, we evaluated the cholesterol-lowering effects of P. frutescens (L.) Britt. leaf extract and luteolin 7-glucuronide by detecting total cholesterol in HepG2 cells. The 30% ethanol extract lowered cholesterol levels significantly by downregulating 3-hydroxy-3-methyl-glutaryl-coenzyme A reductase expression. This suggests that P. frutescens (L.) Britt leaves have significant health benefits and can be explored as a potentially promising food additive for the prevention of hypercholesterolemia-related diseases. Full article

Detecting anomalies in the Brake Operating Unit (BOU) braking system of metro trains is very important for trains’ reliability and safety. However, current periodic maintenance and inspection cannot detect anomalies at an early stage. In addition, constructing a stable and accurate anomaly detection system is a very challenging task. Hence, in this work, we propose a method for detecting anomalies of BOU on metro vehicles using a one-class long short-term memory (LSTM) autoencoder. First, we extracted brake cylinder (BC) pressure data from the BOU data since one of the anomaly cases of metro trains is that BC pressure relief time is delayed by 4 s. After that, extracted BC pressure data is split into subsequences which are fed into our proposed one-class LSTM autoencoder which consists of two LSTM blocks (encoder and decoder). The one-class LSTM autoencoder is trained using training data which only consists of normal subsequences. To detect anomalies from test data that contain abnormal subsequences, the mean absolute error (MAE) for each subsequence is calculated. When the error is larger than a predefined threshold which was set to the maximum value of MAE in the training (normal) dataset, we can declare that example an anomaly. We conducted the experiments with the BOU data of metro trains in Korea. Experimental results show that our proposed method can detect anomalies of the BOU data well. Full article

Brain tumor classification plays an important role in clinical diagnosis and effective treatment. In this work, we propose a method for brain tumor classification using an ensemble of deep features and machine learning classifiers. In our proposed framework, we adopt the concept of transfer learning and uses several pre-trained deep convolutional neural networks to extract deep features from brain magnetic resonance (MR) images. The extracted deep features are then evaluated by several machine learning classifiers. The top three deep features which perform well on several machine learning classifiers are selected and concatenated as an ensemble of deep features which is then fed into several machine learning classifiers to predict the final output. To evaluate the different kinds of pre-trained models as a deep feature extractor, machine learning classifiers, and the effectiveness of an ensemble of deep feature for brain tumor classification, we use three different brain magnetic resonance imaging (MRI) datasets that are openly accessible from the web. Experimental results demonstrate that an ensemble of deep features can help improving performance significantly, and in most cases, support vector machine (SVM) with radial basis function (RBF) kernel outperforms other machine learning classifiers, especially for large datasets. Full article

Even though interest in developing the Arctic region is increasing continuously, the standard procedure to be used to analyze the station-keeping performance of a floater considering ice loads has not been established yet. In this paper, the effectiveness of heading control with a dynamic positioning system is analyzed to evaluate the improvement of the performance of the station-keeping system in the ice conditions. Complex environmental loads with ice-induced forces were generated and applied to a ship type floater with dynamic positioning and mooring systems. Three-hour time-domain simulations were conducted for the two different station-keeping systems with mooring only and mooring with a dynamic positioning system. Position offsets and mooring line tensions for the two scenarios were compared with maximum values and most probable maxima (MPM) values. The results of the simulation showed that the heading control can reduce 8.2% of MPM values for the mooring lines and improve the station-keeping performance by about 16.3%. The validity of the station-keeping system that was designed was confirmed, and it is expected that the specification of mooring lines can be relaxed with the heading control. Full article

A study was conducted to determine the effects of lactic acid bacteria (LAB) on nutritive value and in vitro rumen digestibility of maize and rice straw silages. Two identical experiments were carried out for each of the two silages. A total of five treatments were used for each experiment: (1) negative control (NC); (2) positive control (PC); (3) Lactobacillus plantarum (LPL); (4) L. paracasei (LPA); and (5) L. acidophilus (LA). Each treatment was then divided into four ensiling periods: 3, 7, 20, and 40 days with three replications. The LPL treatment had significantly higher dry matter (DM), lower ammonia-N, and a lower number of fungi on maize silage after 40 days (p < 0.05). On the other hand, the LA treatment increased DM and CP content, reduced NDF and ADF contents compared to NC, and also produced more lactic acid compared to the other LAB-treated rice straw silages. Results of the in vitro rumen fermentation of maize silages showed no significant differences in DMD after LAB inoculation. However, higher DMD and ruminal ammonia-N were shown by rice straw ensiled with L. acidophilus. In conclusion, silage additives, which could improve the ensiling process of maize and rice straw, appeared to be different and substrate specific. Full article

Deep neural networks have demonstrated impressive results in various cognitive tasks such as object detection and image classification. This paper describes a neuromorphic computing system that is designed from the ground up for energy-efficient evaluation of deep neural networks. The computing system consists of a non-conventional compiler, a neuromorphic hardware architecture, and a space-efficient microarchitecture that leverages existing integrated circuit design methodologies. The compiler takes a trained, feedforward network as input, compresses the weights linearly, and generates a time delay neural network reducing the number of connections significantly. The connections and units in the simplified network are mapped to silicon synapses and neurons. We demonstrate an implementation of the neuromorphic computing system based on a field-programmable gate array that performs image classification on the hand-wirtten 0 to 9 digits MNIST dataset with 99.37% accuracy consuming only 93uJ per image. For image classification on the colour images in 10 classes CIFAR-10 dataset, it achieves 83.43% accuracy at more than 11× higher energy-efficiency compared to a recent field-programmable gate array (FPGA)-based accelerator. Full article

In recent years, deep learning models have been used successfully in almost every field including both industry and academia, especially for computer vision tasks. However, these models are huge in size, with millions (and billions) of parameters, and thus cannot be deployed on the systems and devices with limited resources (e.g., embedded systems and mobile phones). To tackle this, several techniques on model compression and acceleration have been proposed. As a representative type of them, knowledge distillation suggests a way to effectively learn a small student model from large teacher model(s). It has attracted increasing attention since it showed its promising performance. In the work, we propose an ensemble model that combines feature-based, response-based, and relation-based lightweight knowledge distillation models for simple image classification tasks. In our knowledge distillation framework, we use ResNet−20 as a student network and ResNet−110 as a teacher network. Experimental results demonstrate that our proposed ensemble model outperforms other knowledge distillation models as well as the large teacher model for image classification tasks, with less computational power than the teacher model. Full article

Crossbar-based neuromorphic computing to accelerate neural networks is a popular alternative to conventional von Neumann computing systems. It is also referred as processing-in-memory and in-situ analog computing. The crossbars have a fixed number of synapses per neuron and it is necessary to decompose neurons to map networks onto the crossbars. This paper proposes the k-spare decomposition algorithm that can trade off the predictive performance against the neuron usage during the mapping. The proposed algorithm performs a two-level hierarchical decomposition. In the first global decomposition, it decomposes the neural network such that each crossbar has k spare neurons. These neurons are used to improve the accuracy of the partially mapped network in the subsequent local decomposition. Our experimental results using modern convolutional neural networks show that the proposed method can improve the accuracy substantially within about 10% extra neurons. Full article

Background: A broad, holistic approach was performed among informal waste collectors (IWCs) in Korea to understand their complex multidimensional health and safety problems. Methods: In the quantitative study, a survey of IWCs was conducted at four junk shops in Gangbuk-gu, Seoul, and survey data were used to calculate age-standardized prevalence rates based on comparisons with the general population in Korea. A qualitative study was also performed to provide more details on IWCs’ occupational and musculoskeletal injuries and depression. Results: In the quantitative study, the age-standardized prevalence rate (aSPR) of occupational injury was higher than that of the general standard population (aSPR: 10.42, 95% confidence interval (CI) 5.19–18.64) and that of blue-collar workers (aSPR: 4.65, 95% CI 2.32–8.32). Regarding musculoskeletal problems, compared to employed populations, the aSPRs of shoulder pain (aSPR: 2.63, 95% CI 1.60–4.06), wrist pain (aSPR: 3.33, 95% CI 1.33–6.86), knee pain (aSPR: 1.51, 95% CI 1.01–2.17), and ankle pain (aSPR: 3.54, 95% CI 1.14–8.26) were higher. Regarding psychological problems, depression (aSPR: 2.55, 95% CI 1.27–4.56) and suicidal or self-harm ideation (aSPR: 2.09, 95% CI 1.11–3.58) were higher compared to general populations. Through the qualitative study and case study on muscular problems, more details on the work environment problems of IWCs were obtained. Conclusions: IWCs are exposed to various occupational hazards and lack proper protection. They show a high prevalence of occupational injury, musculoskeletal disease, and depression. Full article

Due to the rare occurrence of young-onset bladder cancer (YBC), its genomic characteristics remain largely unknown. Twenty-nine biopsy-proven YBC cases were collected using a nation-wide search for bladder cancer diagnosed at 20 years or younger. Whole exome sequencing and RNA sequencing were carried out in 21 and 11 cases, respectively, and compared with those of adult bladder cancer (ABC) cases obtained from public databases. Almost all YBCs were low grade, non-invasive papillary tumors. YBC had a low mutation burden and less complex copy number alterations. All cases harbored putative driver mutations. Mutations were most commonly found in HRAS (10 cases), with a preference for exon 5. FGFR3 gene fusions were noted with various partner genes (7 cases). The alterations on HRAS and FGFR3 occurred in a mutually exclusive manner. Others included KRAS mutations (2 cases), chromosomes 4p and 10q arm-level deletions (1 case), and ERCC2 mutation (1 case). There were no point mutations in TP53 and FGFR3. The gene expression profiles of YBC were similar to those of the ABC group with good prognosis. None of the YBCs and ABCs with YBC-like mutations showed progression to muscle-invasive tumors. Our results suggest that bladder cancer with YBC-like mutations represents an indolent bladder tumor, regardless of age. Full article

The leaves of Perilla frutescens var. acuta (PFA) are commonly used as a traditional medicine in Korea, Japan, and China. We previously showed that PFA attenuates eye fatigue by improving visual accommodation through a clinical study. However, detailed mechanisms and chemical compounds have not been studied. In this study, we analyzed the active compounds in an aqueous extract of PFA involved in ciliary muscle relaxation in vitro and in vivo. NMR and MS analyses showed that the PFA extract contained mainly luteolin-7-O-diglucuronide and apigenin-7-O-diglucuronide. The composition after freeze-drying and spray-drying was similar. Freeze-dried PFA (50 µg/mL, 100 µg/mL, and 200 µg/mL) increased nitric oxide and cGMP levels in ciliary muscle cells isolated from the eyes of rats. [Ca²⁺]i decreased in a dose-dependent manner. Furthermore, Sprague-Dawley rats treated with freeze-dried PFA (200 mg/kg, orally) showed significantly increased cGMP levels compared with the control group and irradiated with white light. Our results suggest that PFA extract has the potential to reduce eye fatigue by relaxing ciliary muscles. Full article

The fruit of Stauntonia hexaphylla is commonly used as a traditional anthelmintic in Korea, Japan, and China. However, its anti-inflammatory activity and the underlying mechanisms have not been studied systematically. In the present study, we examined the anti-inflammatory activities of an aqueous extract of S. hexaphylla fruit (SHF) in lipopolysaccharide (LPS)-activated RAW 264.7 cells. The SHF extract contained anti-inflammatory compounds, such as neochlorogenic acid, chlorogenic acid, and cryptochlorogenic acid. The extract inhibited protein levels of inducible nitric oxide synthase and the activity of cyclooxygenase enzyme, with concomitant reductions in the production of nitric oxide and prostaglandin E₂ in LPS-activated RAW 264.7 cells. Additionally, the SHF extract reduced the production of pro-inflammatory cytokines, including tumor necrosis factor-α, interleukin (IL)-1β, and IL-6. The SHF extract attenuated LPS-induced nuclear factor-κB (NF-κB) activation by decreasing the phosphorylation of its inhibitor, IκBα. Furthermore, the SHF extract showed a significant anti-inflammatory effect in vivo by reducing the volume of carrageenan-induced paw edema in rats. Our results suggest that the SHF extract exerts potential anti-inflammatory properties against LPS-activated RAW 254.7 cells, and in an animal model of inflammation. Full article