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Background: Soybean milk by-product (SMBP) is a potential alternative feed ingredient in swine diets due to its high protein content. However, information on energy and nutritional values of SMBP used as swine feed ingredient is limited. Objective: To estimate energy values and protein digestibility of SMBP in pigs based on in vitro assays. Methods: Four SMBP samples were obtained from 3 soybean milk-producing facilities. In vitro total tract disappearance (IVTTD) and in vitro ileal disappearance (IVID) of dry matter (DM) in the SMBP samples were determined. In vitro ileal disappearance of crude protein was determined by analyzing crude protein content in undigested residues after determining IVID of DM. Digestible and metabolizable energy of SMBP were estimated using gross energy, IVTTD of DM, and prediction equations. Results: Sample 4 had greater IVTTD of DM than that of sample 3 (97.7 vs. 94.4%, p

Cell therapies for age-related macular degeneration (AMD) treatment have been developed by integrating hydrogel-based biomaterials. Until now, cell activity has been observed only in terms of the modulus of the hydrogel. In addition, cell behavior has only been observed in the 2D environment of the hydrogel and the 3D matrix. As time-dependent stress relaxation is considered a significant mechanical cue for the control of cellular activities, it is important to optimize hydrogels for retinal tissue engineering (TE) by applying this viewpoint. Herein, a gellan Gum (GG)/Hyaluronic acid (HA) hydrogel was fabricated using a facile physical crosslinking method. The physicochemical and mechanical properties were controlled by forming a different composition of GG and HA. The characterization was performed by conducting a mass swelling study, a sol fraction study, a weight loss test, a viscosity test, an injection force study, a compression test, and a stress relaxation analysis. The biological activity of the cells encapsulated in 3D constructs was evaluated by conducting a morphological study, a proliferation test, a live/dead analysis, histology, immunofluorescence staining, and a gene expression study to determine the most appropriate material for retinal TE biomaterial. Hydrogels with moderate amounts of HA showed improved physicochemical and mechanical properties suitable for injection into the retina. Moreover, the time-dependent stress relaxation property of the GG/HA hydrogel was enhanced when the appropriate amount of HA was loaded. In addition, the cellular compatibility of the GG/HA hydrogel in in vitro experiments was significantly improved in the fast-relaxing hydrogel. Overall, these results demonstrate the remarkable potential of GG/HA hydrogel as an injectable hydrogel for retinal TE and the importance of the stress relaxation property when designing retinal TE hydrogels. Therefore, we believe that GG/HA hydrogel is a prospective candidate for retinal TE biomaterial.

A new development process for the noise, vibration, and harshness (NVH) of a vehicle is presented using data analysis and machine learning with long-term NVH driving data. The process includes exploratory data analysis (EDA), variable importance analysis, correlation analysis, sensitivity analysis, and development target selection. In this paper, to dramatically reduce the development period and cost related to vehicle NVH, we propose a technique that can accurately identify the precise connectivity and relationship between vehicle systems and NVH factors. This new technique uses whole big data and reflects the nonlinearity of dynamic characteristics, which was not considered in existing methods, and no data are discarded. Through the proposed method, it is possible to quickly find areas that need improvement through correlation analysis and variable importance analysis, understand how much room noise increases when the NVH level of the system changes through sensitivity analysis, and reduce vehicle development time by improving efficiency. The method could be used in the development process and the validation of other deep learning and machine learning models. It could be an essential step in applying artificial intelligence, big data, and data analysis in the vehicle and mobility industry as a future vehicle development process.

Background: Soybean milk by-product (SMBP) is a potential alternative feed ingredient in swine diets due to its high protein content. However, information on energy and nutritional values of SMBP used as swine feed ingredient is limited. Objective: To estimate energy values and protein digestibility of SMBP in pigs based on in vitro assays. Methods: Four SMBP samples were obtained from 3 soybean milk-producing facilities. In vitro total tract disappearance (IVTTD) and in vitro ileal disappearance (IVID) of dry matter (DM) in the SMBP samples were determined. In vitro ileal disappearance of crude protein was determined by analyzing crude protein content in undigested residues after determining IVID of DM. Digestible and metabolizable energy of SMBP were estimated using gross energy, IVTTD of DM, and prediction equations. Results: Sample 4 had greater IVTTD of DM than that of sample 3 (97.7 vs. 94.4%, p<0.05), whereas IVID of DM in sample 4 was lower compared with sample 1 (53.5 vs. 65.0%, p<0.05). In vitro ileal disappearance of crude protein in sample 2 was greater than that in sample 1 and 3 (92.6 vs. 90.6 and 90.1%; p<0.05). The estimated metabolizable energy of SMBP ranged from 4,311 to 4,619 kcal/kg as-is basis and the value of sample 3 was the least (p<0.05) among SMBP samples. Conclusion: Energy values and protein digestibility should be determined before using SMBP in swine diets. [ABSTRACT FROM AUTHOR]