Existing reinforced concrete buildings with seismically-deficient column details affect the overall behavior depending on the failure type of column. The purpose of this study is to develop and validate a machine learning-based prediction model for the column failure modes (shear, flexure-shear and flexure failure modes). For this purpose, artificial neural network (ANN), K-nearest neighbor (KNN), decision tree (DT), and random forest (RF) models were used considering previously-collected experimental data. Using four machine learning methodologies, we developed a classification learning model that can predict the column failure modes in terms of the input variables using concrete compressive strength, steel yield strength, axial load ratio, height-to-dept aspect ratio, longitudinal reinforcement ratio, and transverse reinforcement ratio. The performance of each machine learning model was compared and verified by calculating accuracy, precision, recall, F1-Score, and ROC. Based on the performance measurements of the classification model, the RF model represents the highest average value of the classification model performance measurements among the considered learning methods, and it can conservatively predict the shear failure mode. Thus, the RF model can rapidly predict the column failure modes with the simple column details. Additionally, it was demonstrated that the predicted failure modes from the selected model were exactly same as the failure mode determined from a code-defined equation (traditional method)

Hydrogels incorporated with hydrophobic motifs have received considerable attention to recapitulate the cellular microenvironments, specifically for the bio-mineralization of a 3D matrix. Introduction of hydrophobic motifs into a hydrogel often results in irregular arrangement of the motifs, and further phase separation of hydrophobic domains, but limited efforts have been made to resolve this challenge in the hydrophobically-modified hydrogel. Therefore, this study presents an advanced integrative strategy to incorporate hydrophobic domains regularly in a hydrogel by self-assembling of polymer cross-linkers, building blocks of a hydrogel. Self-assemblies between polymer cross-linkers were examined as micro-domains to incorporate hydrophobic motifs in a hydrogel. The self-assembled structures in a pre-gelled solution were confirmed with the fluorescence analysis and the hydrophobicity of a hydrogel could be tuned by incorporating the motifs in a controlled manner. Overall, the results of this study would greatly serve to tuning performance of a wide array of hydrophobically-modified hydrogels in drug delivery, cell therapies and tissue engineering.

Perilla frutense var. acuta (Lamiaceae) has been used to treat indigestion, asthma, and allergies in traditional medicine. In this study, luteolin 7-O-diglucuronide (1), apigenin 7-O-diglucuronide (2), and rosmarinic acid (3) were isolated from the leaves of P. frutescens var. acuta. Various approaches were performed to investigate the anti-inflammatory activity of the isolated compounds (1-3) and their working mechanisms. In silico docking simulation demonstrated that 1-3 would work as a PPAR-α/δ/γ agonist, and in vitro PPAR-α/δ/γ transcriptional assay showed that the Perilla extract and 3 increased PPAR-α luciferase activity [1.71 and 1.61 times of the control (PPAR-α + PPRE, p < 0.001)]. In the NF-κB luciferase assay, 1 suppressed NF-κB activity the most (56.83 % at 5 µM; 74.96 % at 10 µM; 79.86 % at 50 µM). In addition, 1 and 2 inhibited the mRNA expression of NF-κB target genes, including Il6, Mcp1, and Tnfa, at 50 µM, and 3 suppressed the genes at the mRNA level in a dose-dependent manner. We report that 1 and 2 exert anti-inflammatory effects through NF-κB inhibition, and PPAR-α/NF-κB signaling pathway is related to the anti-inflammatory activity of 3.

Quality-changed behavior of ultraviolet light absorber (UVA: UV-326) in polymers (PP, HDPE, LDPE, PLA and PS) over time during degradation was studied with an enhanced degradation method (EDM) using sulfate ion radical in seawater. EDM was employed to homogeneously degrade the entire polymer samples containing the UVA. The PP and PS samples containing 5-phr UVA films whitened rapidly due to formation of numerous grooves or crushed particles. The UVA loss rate of PS with the higher Tg was much slower. The crystalline polymers, with the exception of PS, were similar in the behavior of the change in UVA loss rate with respect to degradation time. The significant increase in initial loss rate observed during EDM degradation was due to microplasticization. A similar increase in microplasticization rate occurred with the PS, but the intermolecular interaction between UVA and PS did not result in as pronounced its increase in loss rate as with other polymers. The UVA chemical structure was not altered by EDM degradation. These results revealed that the UVA leaching from the polymer matrix was the primary cause of the loss.

Recently, two respiratory syncytial virus (RSV) vaccines based on the prefusion F (pre-F) antigen were approved in the United States. We developed and validated an enzyme-linked immunosorbent assay (ELISA)-based protocol for the practical and large-scale evaluation of RSV vaccines. Two modified pre-F proteins (DS-Cav1 and SC-TM) were produced by genetic recombination and replication using an adenoviral vector. The protocol was established by optimizing the concentrations of the coating antigen (pre-F proteins), secondary antibodies, and choice of blocking buffer. To validate the protocol, we examined its accuracy, precision, and specificity using serum samples from 150 participants across various age groups and the standard serum provided by the National Institute of Health. In the linear correlation analysis, coating concentrations of 5 and 2.5 μg/mL of DS-Cav1 and SC-TM, respectively, showed high coefficients of determination (r2 = 0.90). Concentrations of secondary antibodies (alkaline phosphatase-conjugated anti-human immunoglobulin G, diluted 1:2000) and blocking reagents (5% skim milk/PBS-T) were optimized to minimize non-specific reactions. High accuracy was observed for DS-Cav1 (r2 = 0.90) and SC-TM (r2 = 0.86). Further, both antigens showed high precision (coefficient of variation &lt; 15%). Inhibition ELISA revealed cross-reactivity of antibodies against DS-Cav1 and SC-TM but not with the attachment protein.