Novel Multitarget ACE Inhibitory Peptides from Bovine Colostrum Immunoglobulin G: Cellular Transport, Efficacy in Regulating Endothelial Dysfunction, and Network Pharmacology Studies.

In this study, we used traditional laboratory methods, bioinformatics, and cellular models to screen novel ACE inhibitory (ACEI) peptides with strong ACEI activity, moderate absorption rates, and multiple targets from bovine colostrum immunoglobulin G (IgG). The purified fraction of the compound proteinase hydrolysate of IgG showed good ACEI activity. After nano-UPLC-MS/MS identification and in silico analysis, eight peptides were synthesized and verified. Among them, SFYPDY, TSFYPDY, FSWF, WYQQVPGSGL, and GVHTFP were identified as ACEI peptides, as they exhibited strong ACEI activity (with IC50 values of 104.7, 80.0, 121.2, 39.8, and 86.3 µM, respectively). They displayed good stability in an in vitro simulated gastrointestinal digestion assay. In a Caco-2 monolayer model, SFYPDY, FSWF, and WYQQVPGSGL exhibited better absorption rates and lower IC50 values than the other peptides and were thereby identified as novel ACEI peptides. Subsequently, in a H2O2-induced endothelial dysfunction (ED) model based on HUVECs, SFYPDY, FSWF, and WYQQVPGSGL regulated ED by reducing apoptosis and ROS accumulation while upregulating NOS3 mRNA expression. Network pharmacology analysis and RT-qPCR confirmed that they regulated multiple targets. Overall, our results suggest that SFYPDY, FSWF, and WYQQVPGSGL can serve as novel multitarget ACEI peptides.

The effect of vegetable oil pre-emulsified with whey protein and pectin on physicochemical properties and microstructure of low-fat yogurt.

The effects of whey protein isolate (WPI)-pectin pre-emulsified vegetable oil on the physicochemical properties and microstructure of low-fat yogurt (LFY) were investigated by particle size distribution, water-holding capacity (WHC), texture, rheology, electron microscopy, storage stability, and sensory analysis. The vegetable oil was pre-emulsified into two types of emulsions, a mixed emulsion (ME: WPI-pectin complexes were adsorbed directly at the interface) and a bilayer emulsion (BE: Pectin was added to a previously established WPI-stabilized interface). The results showed that yogurts added with pre-emulsified vegetable oil (ME-Y, BE-Y) had significantly better quality than LFY, with better WHC, textural properties, rheological properties, and storage stability. The average particle size of ME (11.96 µm) was larger than that of BE (10.23 µm). The consistency of yogurt added with ME (ME-Y) was significantly higher than that of yogurt added with BE (BE-Y), at 2359.10 and 2181.12 g s, respectively. Meanwhile, ME-Y exhibited storage stability similar to full-fat control (FFY) and higher sensory scores. Interestingly, the WHC of BE-Y (49.03%) was higher than that of ME-Y (45.63%). In addition, WPI + Pectin-Y exhibited higher WHC (53.81%) and consistency (2518.73 g s) compared to ME-Y and BE-Y, but the particle size distribution was not uniform, and the direct addition of WPI, pectin, and oil had no positive effect on improving the rheological properties of yogurt. Overall, the addition of WPI-pectin pre-emulsified vegetable oil improved the quality of LFY. These findings are particularly relevant for the production of higher quality LFY.

A study of action difference on motor imagery based on delayed matching posture task.

Objective. Motor imagery (MI)-based brain-computer interfaces (BCIs) provide an additional control pathway for people by decoding the intention of action imagination. The way people imagine greatly affects MI-BCI performance. Action itself is one of the factors that influence the way people imagine. Whether the different actions cause a difference in the MI performance is unknown. What is more important is how to manifest this action difference in the process of imagery, which has the potential to guide people to use their individualized actions to imagine more effectively.Approach.To explore action differences, this study proposes a novel paradigm named as action observation based delayed matching posture task. Ten subjects are required to observe, memorize, match, and imagine three types of actions (cutting, grasping and writing) given by visual images or videos, to accomplish the phases of encoding, retrieval and reinforcement of MI. Event-related potential (ERP), MI features, and classification accuracy of the left or the right hand are used to evaluate the effect of the action difference on the MI difference.Main results.Action differences cause different feature distributions, resulting in that the accuracy with high event-related (de)synchronization (ERD/ERS) is 27.75% higher than the ones with low ERD/ERS (p< 0.05), which indicates that the action difference has impact on the MI difference and the BCI performance. In addition, significant differences in the ERP amplitudes exists among the three actions: the amplitude of P300-N200 potential reaches 9.28µV of grasping, 5.64µV and 5.25µV higher than the cutting and the writing, respectively (p< 0.05).Significance.The ERP amplitudes derived from the supplementary motor area shows positive correlation to the MI classification accuracy, implying that the ERP might be an index of the MI performance when the people is faced with action selection. This study demonstrates that the MI difference is related to the action difference, and can be manifested by the ERP, which is important for improving MI training by selecting suitable action; the relationship between the ERP and the MI provides a novel index to find the suitable action to set up an individualized BCI and improve the performance further.

Particulate matter emissions associated with marcellus shale drilling waste disposal and transport.

This study models emissions quantities and neighboring exposure concentrations of six airborne pollutants, including PM10, PM2.5, crystalline silica, arsenic, uranium, and barium, which resulted from the disposal of Marcellus shale drill cuttings waste during the 2011-2017 period. Using these predicted exposures, this study evaluates current setback distances required in Pennsylvania from waste facilities. For potential residents living at the perimeter of the current setback distance, 274 m (900 ft), a waste disposal rate of 612.4 metric tons per day at landfills (the 99th percentile in record) does not result in exceedances of the exposure limits for any of the six investigated pollutants. However, the current setback distance can result in exceedance with respect to the 24-hr daily concentration standards for PM10 and PM2.5 established in the National Air Ambient Quality Standards (NAAQS), if daily waste disposal rate surpasses 900 metric tons per day. Dry depositions of barium-containing and uranium-containing particulate matter should not be a danger to public health based on these results. To investigate the air quality impacts of waste transportation and the potential for reductions, this article describes an optimization of landfill locations in Pennsylvania indicating the potential benefits in reduced environmental health hazard level possible by decreasing the distance traveled by waste disposal trucks. This strategy could reduce annual emissions of PM10 and PM2.5 by a mean of 64% and reduce the expected number of annual fatal accidents by nearly half, and should be considered a potential risk management goal in the long run. Therefore, policy to limit or encourage reduction of distances traveled by waste removal trucks and manage setback distances as a function of delivered waste quantities is merited. Implications This study shows the necessity of reviewing current setback distance required in Pennsylvania, which might not ensure 24-hr mean PM10 and PM2.5 levels below the values stated in National Ambient Air Quality Standards for the residents living at the perimeter. Furthermore, this study also reveals potential tremendous benefits from optimizing location of landfills accepting drill cuttings within Pennsylvania, with PM10 and PM2.5 emission, total distance traveled shrinking, and number of fatal accidents shrinking by nearly half.

A TrAdaBoost Method for Detecting Multiple Subjects' N200 and P300 Potentials Based on Cross-Validation and an Adaptive Threshold.

Traditional training methods need to collect a large amount of data for every subject to train a subject-specific classifier, which causes subjects fatigue and training burden. This study proposes a novel training method, TrAdaBoost based on cross-validation and an adaptive threshold (CV-T-TAB), to reduce the amount of data required for training by selecting and combining multiple subjects' classifiers that perform well on a new subject to train a classifier. This method adopts cross-validation to extend the amount of the new subject's training data and sets an adaptive threshold to select the optimal combination of the classifiers. Twenty-five subjects participated in the N200- and P300-based brain-computer interface. The study compares CV-T-TAB to five traditional training methods by testing them on the training of a support vector machine. The accuracy, information transfer rate, area under the curve, recall and precision are used to evaluate the performances under nine conditions with different amounts of data. CV-T-TAB outperforms the other methods and retains a high accuracy even when the amount of data is reduced to one-third of the original amount. The results imply that CV-T-TAB is effective in improving the performance of a subject-specific classifier with a small amount of data by adopting multiple subjects' classifiers, which reduces the training cost.