Spiny burs of Castanea mollissima Blume (Chinese chestnut) are usually discarded as industrial waste during post-harvesting processing. The objective of this study was to establish an extraction and isolation procedure for tannins from chestnut burs, and to assess their potential antioxidant activity. Aqueous ethanol solution was used as extraction solvent, and HPD 100 macroporous resin column was applied for isolation. The influence of solvent concentration in the extraction and elution process on extraction yield, tannins and polyphenols content, as well as antioxidant potential, including DPPH and ABTS radical scavenging ability, reducing power ability and cellular antioxidant ability were assessed. In both the extraction and isolation process, 50% aqueous ethanol led to superior total tannins and polyphenols content as well as significantly higher antioxidant activity. In addition, the antioxidant activity and the total tannins content in extracts and fractions had a positive linear correlation, and the predominant components responsible for antioxidant activities were characterized as hydrolysable tannins. To the best of our knowledge, this is the first report on the enrichment of tannins from burs of C. mollissim using macroporous resin chromatography, and to assess the cellular antioxidant activity of them.

The extensive use of copper and booster biocides in antifouling (AF) paints has raised environmental concerns and the need to develop new AF agents. In the present study, 18 alkaloids derived from terrestrial plants were initially evaluated for AF activity using laboratory bioassays with the bryozoan Bugula neritina and the barnacle Balanus albicostatus. The results showed that 4 of the 18 alkaloids were effective in inhibiting larval settlement of B. neritina, with an EC range of 6.18 to 43.11 μM, and 15 of the 18 alkaloids inhibited larval settlement of B. albicostatus, with EC values ranging from 1.18 to 67.58 μM. Field trials that incorporated five alkaloids respectively into paints with 20% w/w indicated an in situ AF efficiency of evodiamine, strychnine, camptothecin (CPT), and cepharanthine, with the most potent compound being CPT, which also exhibited stronger AF efficiency than the commercial antifoulants cuprous oxide and zinc pyrithione in the field over a period of 12 months. Further field trials with different CPT concentrations (0.1 to 20% w/w) in the paints suggested a concentration-dependent AF performance in the natural environment, and the effective concentrations to significantly inhibit settlement of biofoulers in the field were ≥ 0.5% w/w (the efficiency of 0.5% w/w lasted for 2 months). Moreover, CPT toxicity against the crustacean Artemia salina, the planktonic microalgae Phaeodactylum tricornutum and Isochrysis galbana, was examined. The results showed that 24 h LC of CPT against A. salina was 20.75 μM, and 96 h EC (growth inhibition) values of CPT to P. tricornutum and I. galbana were 55.81 and 6.29 μM, respectively, indicating that CPT was comparatively less toxic than several commercial antifoulants previously reported. Our results suggest the novel potential application of CPT as an antifoulant.

We consider quantum decoherence and Landauer’s principle in qubit-cavity quantum field theory (QFT) interaction, treating the qubit as the system and cavity QFT as the environment. In particular, we investigate the changes that occur in the system with a pure initial state and environment during the decoherence process, with or without energy dissipation, and compare the results with the case in which the initial state of the system is a mixed state and thus decoherence is absent. When we choose an interaction Hamiltonian such that the energy and coherence of the system change simultaneously, the population change of the system and the energy change are the same when the initial state is mixed. However, the decoherence terms increase the von Neumann entropy of the system. In this case the energy change and decoherence of the system are not independent physical processes. The decoherence process maintains unitarity. On the other hand, if the interaction Hamiltonian does not change the energy of the system, there is only the decoherence effect. The environment will be a distribution in the basis of the displaced number state and always increases the energy. Landauer’s principle is satisfied in both cases.

Background A mobile app generates passive data, such as GPS data traces, without any direct involvement from the user. These passive data have transformed the manner of traditional assessments that require active participation from the user. Passive data collection is one of the most important core techniques for mobile health development because it may promote user retention, which is a unique characteristic of a software medical device. Objective The primary aim of this study was to quantify user retention for the “Staff Hours” app using survival analysis. The secondary aim was to compare user retention between passive data and active data, as well as factors associated with the survival rates of user retention. Methods We developed an app called “Staff Hours” to automatically calculate users’ work hours through GPS data (passive data). “Staff Hours” not only continuously collects these passive data but also sends an 11-item mental health survey to users monthly (active data). We applied survival analysis to compare user retention in the collection of passive and active data among 342 office workers from the “Staff Hours” database. We also compared user retention on Android and iOS platforms and examined the moderators of user retention. Results A total of 342 volunteers (224 men; mean age 33.8 years, SD 7.0 years) were included in this study. Passive data had higher user retention than active data (P=.011). In addition, user retention for passive data collected via Android devices was higher than that for iOS devices (P=.015). Trainee physicians had higher user retention for the collection of active data than trainees from other occupations, whereas no significant differences between these two groups were observed for the collection of passive data (P=.700). Conclusions Our findings demonstrated that passive data collected via Android devices had the best user retention for this app that records GPS-based work hours.

Background There are numerous mobile apps for tracking work hours, but only a few of them record work hours automatically instead of relying on manual logging. No apps have been customized for medical staff, whose work schedules are highly complicated as they have both regular hours and on-call duties. Objective The specific aims of this study were to (1) identify the Staff Hours app users’ GPS-defined work hours, (2) examine the overtime work hours from the app-recorded total work hours and the participants’ self-reported scheduled work hours, and (3) compare these app-recorded total work hours among different occupations. Methods We developed an app, Staff Hours, to automatically calculate a user’s work hours via GPS background data. Users can enter their scheduled hours, including regular hours and on-call duties. The app automatically generates overtime reports by comparing the app-recorded total work hours with the user-defined scheduled hours. A total of 183 volunteers (60 females and 123 males; mean age 32.98 years, SD 6.74) were included in this study. Most of the participants (162/183, 88.5%) were medical staff, and their positions were resident physicians (n=89), visiting staff (n=38), medical students (n=10), registered nurses (n=25), and non–health care professionals (non-HCPs; n=21). Results The total work hours (mean 55.69 hours, SD 21.34) of the 183 participants were significantly higher than their scheduled work hours (mean 50.67 hours, SD 21.44; P=.01). Medical staff had significantly longer total work hours (mean 57.01 hours, SD 21.20) than non-HCPs (mean 45.48 hours, SD 20.08; P=.02). Residents (mean 60.38 hours, SD 18.67) had significantly longer work hours than visiting staff (mean 51.42 hours, SD 20.33; P=.03) and non-HCPs (mean 45.48 hours, SD 20.08; P=.004). Conclusions Staff Hours is the first automatic GPS location–based app designed for medical staff to track work hours and calculate overtime. For medical staff, this app could keep complete and accurate records of work hours in real time, reduce bias, and allow for better complying with labor regulations.