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The use of antibiotics in the food industry is avoided due to the increase of antibiotic-resistant bacteria. Therefore, the bacteriophage is emerging as an alternative agent. Here, we characterized the Salmonella Enteritidis phage PBSE191 and applied it to a polyvinyl alcohol (PVA) film. Transmission electron microscopic analysis revealed that it belonged to the Caudoviricetes class, with an icosahedral head and flexible tails. The phage showed rapid and strong lytic activity within 1 h. It was active against a broad range of Salmonella isolates, including six serotypes. In 25 min, 99 % of the initial population was adsorbed to the bacterial cell surface. The phage was also applied to 10 % (w/v) PVA films and coatings, which were then characterized in terms of phage stability and antibacterial performance, both in vitro and in foods. The phage remained stable in the 10 % (w/v) PVA solution containing 20 % (w/w, based on PVA weight) sorbitol (PVAS20), indicating that the phage was stable under dry conditions and strongly released in the polymer. Furthermore, significant bacterial cell reduction (2.0 × 10

Purpose: The purpose of this study was to investigate the availability of software for rehabilitation with the Kinect sensor by presenting an efficient algorithm based on machine learning when classifying the motion data of the PNF pattern if the subjects were wearing a patient gown. Methods: The motion data of the PNF pattern for upper extremities were collected by Kinect sensor. The data were obtained from 8 nor mal university students without the limitation of upper extremities. The subjects, wearing a T-shirt, performed the PNF patterns, D1 and D2 flexion, extensions, 30 times; the same protocol was repeated while wearing a patient gown to compare the classification perfor mance of algorithms. For comparison of performance, we chose four algorithms, Naive Bayes Classifier, C4.5, Multilayer Perceptron, and Hidden Markov Model. The motion data for wearing a T-shirt were used for the training set, and 10 fold cross-validation test was per formed. The motion data for wearing a gown were used for the test set. Results: The results showed that all of the algorithms performed well with 10 fold cross-validation test. However, when classifying the data with a hospital gown, Hidden Markov model (HMM) was the best algorithm for classifying the motion of PNF. Conclusion: We showed that HMM is the most efficient algorithm that could handle the sequence data related to time. Thus, we sug gested that the algorithm which considered the sequence of motion, such as HMM, would be selected when developing software for re habilitation which required determining the correctness of the motion.

It has been claimed that wearable devices are useful for healthcare applications by providing functionalities such as idle alerts, pedometer, heart-rate measurement, and calorie calculation. However, these functionalities have the limitations of providing only passive assistance. In order to prompt users to do physical activities, we developed a prototype application for active assistance, which works on smartwatch devices. It guides users to stretch their arms periodically during their daily lives. For effective guidance, we integrated motion recognition and gamification elements. We performed a user study to confirm the usefulness of our approach.

Incidence of AKI in hospitalized patients with cancer is increasing, but there have been few studies on AKI in patients with cancer. We conducted a retrospective cohort study in a South Korean tertiary care hospital. A total of 2211 consecutive patients (without cancer 61.5%; with cancer 38.5%) were included over a 140-month period. Predictors of all-cause death were examined using the Kaplan-Meier method and the Cox proportional hazards model. The main contributing factors of AKI were sepsis (31.1%) and ischemia (52.7%). AKI was multifactorial in 78% of patients with cancer and in 71% of patients without cancer. Hospital mortality rates were higher in patients with cancer (42.8%) than in patients without cancer (22.5%) (p = 0.014). In multivariate analyses, diabetes mellitus (DM) and cancer diagnosis were associated with hospital mortality. Cancer diagnosis was independently associated with mortality [odds ratio = 3.010 (95% confidence interval, 2.340-3.873), p = 0.001]. Kaplan-Meier analysis revealed that subjects with DM and cancer (n = 146) had lower survival rates than subjects with DM and without cancer (n = 687) (log rank test, p = 0.001). The presence of DM and cancer was independently associated with mortality in AKI patients both with and without cancer. Studies are warranted to determine whether proactive measures may limit AKI and improve outcomes.

In a recent decade, the GPS has been introduced to directly measure the displacements of super long-span bridges. However, due to its high cost, it is not appropriate to apply a number of GPS receivers in a super-long span bridge in order to measure the displacements through the overall bridge. From the limited point-based measurements of the displacement, a mode shape model for the bridge maintenance gets more complicated and includes errors due to the assumption in the displacements other than the measured points. This study considers digital cameras as the supplementary approach for the displacement measurements to address the limitation of GPS receivers. The objective of this study is to theoretically investigate the feasibility to use digital cameras with GPS receivers to develop a more compelling mode shape model for the super long-span bridge maintenance. From a case study, it was found that digital cameras can provide a sufficient measuring width with a moderate resolution to measure the displacements of a super long-span bridge.