This experiment was conducted to evaluate the bioavailability of different organic selenium (Se) products in finishing pigs. A total of 48 growing pigs, average body weight 47.6 kg±0.05, were allotted to four different treatments in a randomized complete block (RCB) design in three replicates with four pigs per pen. Three different organic Se products, Se-enriched yeast (treatments A and B) and Se-proteinate (treatment C), were used in conjunction with a basal diet with no added Se as a control treatment. In growing period, pigs were fed the same diet but finishing pigs were fed each treatment diet containing organic Se products for 6 weeks. During the experimental period, feed intake and body weight were measured and blood samples were collected to determine the Se concentration. At the end of this experiment, 3 pigs per treatment were killed and various tissues (loin, liver, kidney, pancreas and spleen) were collected to analyze the Se concentration. The body weight, and average daily feed intake (ADFI) were similar among treatments, but the average daily gain (ADG) was increased on Se-proteinate treatment (p<0.01) and gain-to-feed ratio (G/F ratio) was improved on Se yeast B or Se-proteinate treatment (p<0.01). The tissue Se content was also increased when pigs were fed organic Se sources, and Se was retained efficiently in loin (p<0.01) and kidney (p<0.05) when Se yeast B was provided. The serum Se concentration was increased when organic Se was provided and was higher when pigs were fed Se-proteinate (p<0.01); subsequently liver Se was also higher on Se-proteinate treatment than other treatments. The Se yeast A treatment did not show any increment of Se concentration both in serum and tissues. This result demonstrated that Se retention and bioavailability in finishing pigs were varied by Se products although organic sources were provided. Consequently, each organic Se product should be evaluated before it is used as a supplement in animal feed.
Significance Communicating in ways that motivate engagement in social distancing remains a critical global public health priority during the COVID-19 pandemic. This study tested motivational qualities of messages about social distancing (those that promoted choice and agency vs. those that were forceful and shaming) in 25,718 people in 89 countries. The autonomy-supportive message decreased feelings of defying social distancing recommendations relative to the controlling message, and the controlling message increased controlled motivation, a less effective form of motivation, relative to no message. Message type did not impact intentions to socially distance, but people’s existing motivations were related to intentions. Findings were generalizable across a geographically diverse sample and may inform public health communication strategies in this and future global health emergencies.
The regression equation of the American College of Sports Medicine (ACSM) was a preferred method for estimating maximal oxygen consumption (VO2max). Recently, a more precise equation from the fitness registry and the importance of exercise national database (FRIEND) for healthy people was developed. This study compared VO2max estimated by the ACSM and FRIEND equations to VO2max directly measured in coronary artery disease (CAD) patients. Overall, 293 CAD patients who participated in cardiac rehabilitation between June 2015 and December 2018 were analyzed. Directly measured VO2max values were compared to the ACSM and FRIEND equations. The directly measured VO2max was significantly different from the estimated VO2max by ACSM equation (24.16 vs. 28.7 mL/kg/min, p < 0.001), which was overestimated by 20% in men and 16% in women. However, there was no statistically significant difference between the directly measured VO2max and the estimated VO2max by the FRIEND equation. (24.16 vs. 24.15 mL/kg/min, p = 0.986). In CAD patients, the estimated VO2max from the ACSM equation was significantly higher than the directly measured VO2max. In addition, estimated cardiorespiratory fitness (CRF) by the FRIEND equation showed similar results with directly measured CRF. As a result, the FRIEND equation can predict CRF more accurately than the ACSM.
This study aimed to evaluate quality changes and generation of sulfur volatiles of different chicken parts (whole-chicken, breast, and leg), according to the storage period (1, 3, 5, 7, and 9 days). All samples eventually exceeded 6 log CFU/mL of total viable counts (TVC) after storage for 9 days (whole chicken), 7 days (breast), and 5 days (leg). pH of the whole chicken, breast, and leg significantly increased with the storage period. The volatile basic nitrogen (VBN) value of whole chicken was 16.70 mg% on day 9, whereas that of the breast and leg exceeded 20 mg% on day 9. The thiobarbituric acid reactive substance (TBARS) values of the whole chicken and leg exceeded 0.30 mg MDA/kg on the 9th and 7th day, respectively, but the breast had not exceeded 0.30 mg MDA/kg even after 9 days. The amounts of sulfur volatiles from spoiled chicken samples, including hydrogen sulfide (HS), methyl mercaptan (MM), dimethyl disulfide (DMDS) and dimethyl sulfide (DMS), increased during storage. In particular, among these four compounds, DMS increased most markedly. When TVC exceeded 7 log CFU/g, the amount of generated DMS increased rapidly. Thus, the presence of DMS was characteristics of potential spoilage bacteria on chilled chicken; therefore, it could be adopted as a potential indicator to monitor freshness based on whether the number of microorganisms on chicken has reached the level of spoilage.
With the introduction of smart phones, the augmented reality became popular and is increasingly drawing attention. The augmented reality in the mobile devices is becoming an individual area to study. Many applications of the augmented reality have been studied, but there are just a few studies on its combination with artificial intelligence in games. In this study, an artificial intelligence algorithm was proposed, which dynamically converts the state of the 3D agent in the augmented reality environment using the 3-Axis acceleration sensor in the smart phone. To control the state of the agent to which the artificial intelligence is applied, users used to directly enter the data or use markers to detect them. The critical values, which were determined via test, were given to the acceleration sensor to ensure accurate state conversion. In this paper, makerless tracking technology was used to implement the augmented reality, and the state of the agent was dynamically converted using the 3-Axis acceleration seonsor.
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