Impact characterization of linear resonant actuator (LRA) is studied experimentally by newly developed drop tester, which can control various experimental uncertainty such as rotational moment, air resistance, secondary impact and so on. The feasibility of this test apparatus was verified by comparison with free fall test. By utilizing a high-speed camera and measuring the vibrational displacement of spring material, the impact behavior was captured and the damping ratio of the system was defined. Based on the above processes, the finite element model was established and the experimental and analytical results were successfully correlated. Finally, the damage of the system from impact loading can be expected by developed model and as a result, this research can improve the impact reliability of LRA.

The maturity of tomato fruit is normally characterized by external color and it is often difficult to know when fruit have achieved commercial maturity or become over-mature. The internal structure of tomato fruit change during development and this study investigates the utility of nondestructive measurement of tomato fruit structure as a function of maturity using magnetic resonance imaging (MRI). The objective of this work is to use analysis of internal tomato fruit structural measurements to characterize maturity. Intact cherry tomato fruit were harvested at six different maturity stages. At each stage of maturity, the internal structure of the fruit was measured using a series of 2D magnetic resonance (MR) images. Qualitative and quantitative image analyses were performed to correlate internal fruit structure with maturity. Internal structural changes observed in the pericarp region of the tomato fruit are highly correlated with fruit maturity. MR image information combined with classical analysis techniques provides a more complete understanding of structure and physicochemical changes in tomato fruit during maturation. This study demonstrates that MRI is a useful analytical tool to characterize internal changes in agricultural produce as the produce matures. This technique can be applied to almost any agricultural produce to monitor internal physical changes due to external impact, maturity stage, variation in climate, storage time and condition or other factor impacting quality.

The objective of this study was to determine the effects of supplementing L-isoleucine (L-Ile) on milk protein synthesis, using an immortalized bovine mammary epithelial (MAC-T) cell line. In this case, the cells were treated with 0, 0.3, 0.6, 0.9, 1.2 and 1.5 mM of supplemental Isoleucine (Ile), and the most efficient time for protein synthesis for each amino acid was determined by measuring the cell, medium and total protein at 0, 24, 48, 72 and 96 h. Confirmatory tests showed that 48h incubation time and 0.6 mM dosage of L-Ile are considered as the optimal time and dosage. The mechanism of milk protein synthesis was elucidated through proteomics analysis to clarify the metabolic pathway. When the L-Ile was supplemented, extracellular protein (medium protein) reached a peak at 48h, whereas in the case of the intracellular cell protein, it was shown to have reached to its peak at 24h in all L-Ile dosage treatments. In total, it is noted that there were 63 upregulated and 52 downregulated proteins. The results of the protein pathway analysis showed that the L-Ile group stimulated insulin/IGF pathway-mitogen activated protein kinase kinase/MAP kinase cascade, insulin/IGF pathway-protein kinase B signaling cascade, p53 pathway, de novo purine biosynthesis, Wnt signaling pathway, glycolysis, pentose phosphate pathway, and ATP synthesis which are pathways involved and related to protein and energy metabolism. Together, these results demonstrate that L-Ile supplementation was effective in stimulating β-casein synthesis by stimulating genes and pathways which are significantly related to protein and energy metabolism.

We assessed the growth performance, physiological traits, and gene expressions in steers fed with dietary rumen-protected L-tryptophan (RPT) under cold environment. Eight Korean native steers were assigned to two dietary groups, no RPT (Control) and RPT (0.1% RPT supplementation on a dry matter basis), 6 wks. Maximum and minimum temperatures throughout the experiment were 6.7°C and -7.0°C, respectively. Supplementation of 0.1% RPT to a total mixed ration did not increase body weight but had positive effects of elevating average daily gain (ADG) and reducing the feed conversion ratio (FCR) at day 27 and 48. Metabolic parameter showed higher glucose level (at day 27) in the 0.1% RPT group compared to the control group. Real-time PCR analysis showed no significant differences in the expression of muscle (MYF6, MyoD, and Desmin) metabolism genes between the two groups, whereas the expression of fat (PPARγ, C/EBPα, and FABP4) metabolism genes was lower in the 0.1% RPT group than in the control group. Thus, we demonstrate that long-term (6 wks) dietary supplementation of 0.1% RPT was beneficial owing to enhanced growth performance by increasing ADG and glucose level, decreasing FCR, and maintaining homeostasis in immune responses in beef steers during cold environment.

Little is known about the effect of statin use in lung cancer development in idiopathic pulmonary fibrosis (IPF). We analyzed the database of the National Health Insurance Service to further investigate the clinical impacts of statin on lung cancer development and overall survival (OS) in IPF patients. The analysis included 9,182 individuals diagnosed with IPF, of which 3,372 (36.7%) were statin users. Compared to statin non-users, the time from diagnosis of IPF to lung cancer development and OS were longer in statin users in IPF patients. In Cox proportional hazard regression models, higher statin compliance, statin use, and being female had an inverse association with lung cancer risk, while older age at diagnosis of IPF and smoking history were associated with higher risk of lung cancer in IPF patients. For OS, statin use, female sex, higher exercise frequency, and diabetes were associated with longer survival. In contrast, older age at diagnosis of IPF and smoking history were associated with shorter OS in IPF patients. These data from a large population indicate that statin had an independent protective association with lung cancer development and mortality in IPF patients.

Accurate weight measurement is pivotal for monitoring the growth and well-being of cattle. However, the conventional weighing process, which involves physically placing cattle on scales, is labor-intensive and distressing for the animals. Hence, the development of automated cattle weight prediction techniques assumes critical significance. This study proposes a weight prediction approach for Korean cattle using 3D segmentation-based feature extraction and regression machine learning techniques from incomplete 3D shapes acquired from real farm environments. In the initial phase, we generated mesh data of 3D Korean cattle shapes using a multiple-camera system. Subsequently, deep learning-based 3D segmentation with the PointNet network model was employed to segment two dominant parts of the cattle. From these segmented parts, three crucial dimensions of Korean cattle were extracted. Finally, we implemented five regression machine learning models (CatBoost regression, LightGBM, Polynomial regression, Random Forest regression, and XGBoost regression) for weight prediction. To validate our approach, we captured 270 Korean cattle in various poses, totaling 1190 poses of 270 cattle. The best result was achieved with mean absolute error (MAE) of 25.2 kg and mean absolute percent error (MAPE) of 5.81% using the random forest regression model.

This study was conducted to investigate the effect of supplementing different types of methionine (L and D-type) and its precursor (HMBi) on milk protein synthesis using immortalized bovine mammary epithelial cell line (MAC-T Cell); D-methionine (D-Met), L-methionine (L-met) and 2-hydroxy-4-methylthiobutanoic acid I (HMBi), an isopropyl ester of the hydroxy analogue precursor of methionine. The underlying mechanism of milk protein synthesis by adding D- and L-type amino acid as well as HMBi was elucidated through omics analysis to verify the metabolism pathway. Results showed that HMBi group showed the highest beta casein mRNA expression levels compared to D- and L-Met groups and highest medium protein although not different with the L-Met treatment. The observed upregulated (>2 protein expression vs. control) and downregulated (<0.5 protein expression vs. control) proteins in L-Met, D-Met and HMBi treated groups were: 39, 77; 46, 68; and 40, 78, respectively. Interestingly, based on protein pathway analysis, L-Met treated group stimulated the ATP synthesis, PI3 kinase and pyruvate metabolism. On the other hand, the D-Met group stimulated fructose-galactose metabolism, glycolysis pathway, PI3 kinase and pyruvate metabolism. And lastly HMBi-treated group stimulated pentose phosphate pathway and glycolysis pathway. Metabolite analysis revealed that L-Met treated group resulted in the increase in 11 metabolites. On the other hand, D-Met treated group showed increase in 7 metabolites and decreased of uridine monophosphate (UMP). HMBi supplementation caused increases of 3 metabolites and decreased of UMP and N-acetyl-L-glutamate Addition of different isoforms of Met stimulated the production of intermediate metabolites for energy production. Addition of L-Met stimulated the production of energy metabolites such as pyruvate, malate, and fumarate, well-known as intermediates of Krebs cycle. On the other hand, HMBi supplementation resulted in increases of energy metabolite glucose 1-phosphate and 6-phosphogluconate. Results showed that HMBi-treated group exhibited highest expression of β-casein mRNA expression by stimulating proteins and metabolites as well as protein and metabolic pathways involved in protein and energy synthesis. As a result, HMBi-treated group resulted in highest protein concentration but not significantly different with L-Met. Both the D- and L-isoforms has considerably the same medium protein concentration and β-casein mRNA expression higher than the control. So, D- and L-Met isoforms can be used alternatively without any significant change in protein synthesis efficiency in bovine mammary epithelial cells.

The study was done to determine the effect of supplementing different forms of L-methionine (L-Met) and acetate on protein synthesis in immortalized bovine mammary epithelial cell line (MAC-T Cell): Control, L-Met, conjugated L-Met and acetate (CMA), and non-conjugated L-Met and Acetate (NMA). Protein synthesis mechanism was determined by omics method. NMA group had the highest protein content in the media and β-casein mRNA expression levels (P < 0.05). The number of upregulated and downregulated proteins observed were 77 and 62 in CMA group and 50 and 81 in NMA group from 448 proteins, respectively (P < 0.05). NMA and CMA treatments stimulated pathways related to protein and energy metabolism (P < 0.05). Metabolomic analysis also revealed CMA and NMA treatments resulted in increases of several metabolites (P < 0.05). In conclusion, NMA treatment increased protein concentration and expression level of β-casein mRNA in MAC-T cells compared to CMA.

This study presents a novel approach for automating the body condition scoring of dairy cows, leveraging advancements in 3D imaging technology and deep neural networks. The primary objective was to design and implement a system capable of accurately and efficiently assessing the body condition of dairy cows, a critical metric in livestock management for optimizing health and productivity. To achieve this, a 3D camera was employed to capture detailed point cloud data, reconstructing the three-dimensional morphology of individual cows. The obtained data were then fed into a deep neural network, specifically tailored for the task of ranking body condition. The neural network was trained on a diverse dataset of annotated body condition score representing varying degrees of body condition, ensuring robust performance across different physiological states. The results demonstrate the efficacy of the proposed system in automatically and objectively scoring the body condition of dairy cows. The automated process not only expedites the assessment but also reduces the subjectivity associated with manual scoring methods. This innovative approach holds promise for improving the efficiency of dairy farm management by providing timely and accurate body condition assessments. The integration of 3D imaging and deep learning techniques paves the way for future advancements in precision livestock farming, contributing to enhanced animal welfare, optimized production, and sustainable agriculture practices.