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ObjectivesTo report on: (a) overall myopia and high myopia prevalence, and (b) the impact of education on the spherical equivalent refractive error in children across Shanghai.DesignCross-sectional study.SettingAcross all 17 districts of Shanghai.Participants910 245 children aged 4–14 years from a school-based survey conducted between 2012 and 2013.Main outcome measuresData of children with non-cycloplegic autorefraction, visual acuity assessment and questionnaire were analysed (67%, n=6 06 476). Prevalence of myopia (≤−1.0 D) and high myopia (≤−5.0 D) was determined. We used a regression discontinuity design to determine the impact of school entry cut-off date (1 September) by comparing refractive errors at each age, for children born pre-September to post-1 September, and performed a multivariate analysis to explore risk factors associated with myopia. Data analysis was performed in 2017–2018.ResultsPrevalence rates of myopia and high myopia were 32.9% (95% CI: 32.8% to 33.1%) and 4.2% (95% CI: 4.1% to 4.2%), respectively. From 6 years of age onwards, children born pre-September were more myopic compared with those born post-1 September (ahead in school by 1 year, discontinuity at 6 years: −0.19 D (95% CI: −0.09 to −0.30 D); 14 years: −0.67 D (95% CI: −0.21 to −1.14 D)).ConclusionsOur findings suggest that myopia is associated with education, that is primarily focused on near-based activities. Efforts to reduce the burden should be directed to public awareness, reform of education and health systems.

Background Wearable smart watches provide large amount of real-time data on the environmental state of the users and are useful to determine risk factors for onset and progression of myopia. We aim to evaluate the efficacy of machine learning algorithm in differentiating indoor and outdoor locations as collected by use of smart watches. Methods Real time data on luminance, ultraviolet light levels and number of steps obtained with smart watches from dataset A: 12 adults from 8 scenes and manually recorded true locations. 70% of data was considered training set and support vector machine (SVM) algorithm generated using the variables to create a classification system. Data collected manually by the adults was the reference. The algorithm was used for predicting the location of the remaining 30% of dataset A. Accuracy was defined as the number of correct predictions divided by all. Similarly, data was corrected from dataset B: 172 children from 3 schools and 12 supervisors recorded true locations. Data collected by the supervisors was the reference. SVM model trained from dataset A was used to predict the location of dataset B for validation. Finally, we predicted the location of dataset B using the SVM model self-trained from dataset B. We repeated these three predictions with traditional univariate threshold segmentation method. Results In both datasets, SVM outperformed the univariate threshold segmentation method. In dataset A, the accuracy and AUC of SVM were 99.55% and 0.99 as compared to 95.11% and 0.95 with the univariate threshold segmentation (p < 0.01). In validation, the accuracy and AUC of SVM were 82.67% and 0.90 compared to 80.88% and 0.85 with the univariate threshold segmentation method (p < 0.01). In dataset B, the accuracy and AUC of SVM and AUC were 92.43% and 0.96 compared to 80.88% and 0.85 with the univariate threshold segmentation (p < 0.01). Conclusions Machine learning algorithm allows for discrimination of outdoor versus indoor environments with high accuracy and provides an opportunity to study and determine the role of environmental risk factors in onset and progression of myopia. The accuracy of machine learning algorithm could be improved if the model is trained with the dataset itself.

Background To explore the relationship between outdoor time and academic performance among school-aged children. Methods This study was designed as a cross-sectional study. Data were derived from a school-based prospective children myopia intervention study (STORM). Outdoor time was recorded by self-developed algorithm-validated wristwatches in real-time and calculated as the cumulative average of 10 months. The academic performance was recorded and provided by the participating schools and further standardized. Other information was collected using an online standardized questionnaire. Mixed-effects model and B-Spline method were used to investigate the association between time spent on different types of daily activity, including outdoor activity and academic performance. Results A total of 3291 children with mean age 9.25 years were included in the final analysis. Overall, outdoor time was associated with academic performance in a non-linear manner; specifically, not exceeding 2.3 h per day, outdoor time was positively associated with academic performance; exceeding 2.3 h per day, this association became non-significant. Likewise, daily sleep duration and out-of-school learning time were associated with academic performance in a non-linear manner, resulting in turning points of 11.3 and 1.4 h per day, respectively. Separate analysis showed that outdoor time and sleep duration but not out-of-school learning time were positively associated with academic performance in Chinese, mathematics and English. Conclusion Outdoor time, sleep duration and out-of-school learning time were associated with academic performance in a non-linear manner. Promotion of outdoor time may not negatively impact on academic performance. Trial registration Our study was registered in ClinicalTrials.gov (Identifier: NCT02980445).

We consider the strategic behavior of secondary users (SUs) in a cognitive radio system where SUs opportunistically share a single primary user (PU) band over a coverage area. The service of an SU can be interrupted by a PU in a preemptive manner, and the interrupted SU may abandon the system or wait until the PU band is sensed available. In the latter case, if spectrum sensing errors occur, they will cause misdetections and false alarms which impact the system’s performance heavily. In this paper, we model this problem as a retrial queueing system with server breakdowns and recoveries in which the interrupted SUs are treated as retrial customers. They will retry for using the PU band after some period of time due to interruptions or misdetections. The arrival of a PU during service of an SU is modeled as a server breakdown, and the recovery time is equivalent to the service time of this PU. We focus on the behavior of arriving SUs who can make decisions on whether to join the system or to balk based on a natural cost structure and the delays caused by PUs’ interruptions, which can be studied as a non-cooperative game. The equilibrium and optimal strategies of SUs are both derived. Furthermore, to bridge the gap between the individually and socially optimal strategies, a novel strategy of imposing an admission fee on SUs to join the retrial group is proposed. Finally, some numerical examples are presented to show the effect of several key parameters on the system performance.

Abstract BACKGROUND Local delivery of temozolomide (TMZ) through polymers is superior to oral administration in a rodent glioma model. OBJECTIVE We hypothesized that the observed clinical synergy of orally administered TMZ and carmustine (BCNU) wafers would translate into even greater effectiveness with the local delivery of BCNU and TMZ and the addition of radiotherapy in animal models of malignant glioma. METHODS TMZ and BCNU were incorporated into biodegradable polymers that were implanted in F344 rats bearing established intracranial tumors. We used 2 different rodent glioma models: the 9L gliosarcoma and the F98 glioma. RESULTS In the 9L rodent glioma model, groups treated with the combination of local TMZ, local BCNU, and radiation therapy (XRT) had 75% long-term survivors (defined as animals alive 120 days after tumor implantation), which was superior to the combination of local TMZ and local BCNU (median survival, 95 days; long-term survival, 25%) and the combination of oral TMZ, local BCNU, and XRT (median survival, 62 days; long-term survival, 12.5%). To simulate the effect of this treatment in chemoresistant gliomas, a second rodent model was used with the F98 glioma, a cell line relatively resistant to alkylating agents. F98 glioma cells express high levels of alkyltransferase, an enzyme that deactivates alkylating agents and is the major mechanism of resistance of gliomas. The triple therapy showed a significant improvement in survival when compared with controls (P = .0004), BCNU (P = .0043), oral TMZ (P = .0026), local TMZ (P = .0105), and the combinations of either BCNU and XRT (P = .0378) or oral TMZ and BCNU (P = .0154). CONCLUSION The survival of tumor-bearing animals in the 9L and F98 glioma models was improved with the local delivery of BCNU and TMZ combined with XRT when compared with either treatment alone or oral TMZ, local BCNU, and XRT.

An analytic model for cluster-based wireless sensor network (WSN) is developed for traffic flow analysis and network performance evaluation in this paper. The traffic flow path is modeled by a number of tandem linked parallel-queues with single-server and finite capacity, where channels of control data and message data are distinguished. Traffic of the tandem-cluster path is analyzed by dividing it into individual clusters. Through development of a stochastic model for this model, the explicit results of several important quality-of-service (QoS) metrics, such as the data blocking probability (DBP), successful data delivery rate (DDR), network throughput, source-to-destination delay (SDD) for both control and message traffic flows, and the network lifetime, of the proposed WSN are derived. A dynamic traffic allocation algorithm aiming to maintain the QoS of the whole WSN and two energy/consumption distribution schemes aiming to optimize the WSN's energy utilization are further proposed, respectively. The accuracy of the analytic model and QoS evaluation, as well as the effectiveness of the proposed algorithm and schemes are also validated through numerical analysis and simulation.

The objective of the present study was to develop the selection criteria of proton signals for the determination of scutellarin using quantitative nuclear magnetic resonance (qNMR), which is the main bioactive compound in breviscapine preparations for the treatment of cerebrovascular disease. The methyl singlet signal of 3-(trimethylsilyl)propionic-2,2,3,3-d4 acid sodium salt was selected as the internal standard for quantification. The molar concentration of scutellarin was determined by employing different proton signals. To obtain optimum proton signals for the quantification, different combinations of proton signals were investigated according to two selection criteria: the recovery rate of qNMR method and quantitative results compared with those obtained with ultra-performance liquid chromatography. As a result, the chemical shift of H-2′ and H-6′ at δ 7.88 was demonstrated as the most suitable signal with excellent linearity range, precision, and recovery for determining scutellarin in breviscapine preparations from different manufacturers, batch numbers, and dosage forms. Hierarchical cluster analysis was employed to evaluate the determination results. The results demonstrated that the selection criteria of proton signals established in this work were reliable for the qNMR study of scutellarin in breviscapine preparations.

The detrimental impact on both human health and global climate of atmospheric particular matter (PM) is now well-established. Among the various classifications of PM, a significant portion is comprised of secondary organic aerosol (SOA). Despite its importance, there are still much uncertainty regarding the formation and evolution of SOA in the atmosphere, beginning with the oxidation of organic gases that leads to semi-volatile and low volatility products. The need to further improve the current knowledge SOA is made apparent by the observed large discrepancy between model predictions and field measurements of SOA. Proposed explanations behind the orders of magnitude underprediction of ambient SOA levels by state-of-the-art airshed models include: missing particle-forming oxidized organic products, unidentified SOA precursor emissions, and issues related to the fundamentals of current SOA partition theory, all of which are considered in this study to develop corresponding improvements to the latest airshed models. The model used in this study is the UCI-CIT airshed model, and the improvement scenario tests are set in the urban region of South Coast Air Basin of California. Recent chamber results have shown that the original implementation of alkane-derived SOA provided an underestimate for what was likely to be occurring in urban atmospheres. Thus, the original chemical mechanism is revised to include higher generation products of medium- and long-chain alkanes that can contribute to SOA in this study. Primary organic aerosol (POA) has been identified to be able to evaporate with dilution; therefore, test cases are developed that treat fractions of POA as semi-volatile, a source of SOA, rather than nonvolatile. While current atmospheric models assume that SOA are liquids into which semi-VOCs undergo equilibrium partitioning and grow the particles, recent laboratory and field experiments have shown otherwise. Hence, a new kinetics-driven partition theory is developed and analyzed against the original formulations. The results from the expanded chemical mechanism to include higher-generation products of alkane in the atmosphere shows that only the tetrahydrofurans will contribute to SOA and those contributions are only a small fraction compared to other SOA sources in the model, contrary to the prediction made based on chamber experiments and box models. In the tests for redistribution of POA as gas-phase parent VOCs sources, POA decreased with no commensurate increase in SOA. This is essentially due to the fact that the amount of mass that the POA can contribute is a small fraction of that already in the gas-phase parent VOC pool. Finally, using the newly developed kinetically determined SOA growth mechanism, to achieve the same level of predicted SOA levels as the original equilibrium approach requires 40–50% of SOA parent species to be allocated to the particle phase. The new formulation of SOA partition behavior based on kinetics will require the measurement of new input data and the corresponding parameterization for models in the future. The implication of this new approach should demand wider attention from the community.

Secondary organic aerosol (SOA) comprises a significant portion of atmospheric particular matter (PM). The impact of PM on both human health and global climate has long been recognized. Despite its importance, there are still many unanswered questions regarding the formation and evolution of SOA in the atmosphere. This study uses a modeling approach to understand the preferred partitioning behavior of SOA species into aqueous or organic condensed phases. More specifically, this work uses statistical analyses of approximately 24,000 data values for each variable from a state-of-the-art 3-D airshed model. Spatial and temporal distributions of fractions of SOA residing in the aqueous phase (fAQ) in the South Coast Air Basin of California are presented. Typical values of fAQ within the basin near the surface range from 5 to 80%. Results show that the distribution of fAQ values is inversely proportional to the total SOA loading. Further analysis accounting for various meteorological parameters indicates that large fAQ values are the results of aqueous-phase SOA insensitivity to the ambient conditions; while organic-phase SOA concentrations are dramatically reduced under unfavorable SOA formation conditions, aqueous-phase SOA level remains relatively unchanged, thus increasing fAQ at low SOA loading. Diurnal variations of fAQ near the surface are also observed: it tends to be larger during daytime hours than nighttime hours. When examining the vertical gradient of fAQ, largest values are found at heights above the surface layer. In summary, one must consider SOA in both organic and aqueous phases for proper regional and global SOA budget estimation.

Non-invasive deep-tissue calcium imaging of live mammals with high sensitivity and resolution is challenging owing to light scattering experienced by traditional calcium ion (Ca2+) indicators with excitation and emission wavelengths within 400-750 nm. Here, we report near-infrared II (NIR-II) calcium imaging beyond 1000 nm by exploring a natural protein derived from a bacterium (Thermochromatium tepidum) living in a calcium carbonate-rich environment. This highly photostable fluorescent protein enables NIR-II imaging of intracellular Ca2+ responses to stimulant drugs in cultured mammalian cells with sensitivity comparable to that of visible Ca2+ indicators. We achieve in vivo NIR-II imaging of Ca2+ transients in response to two different tumor treatment strategies in intact tumors with high sensitivity, resolution, and contrast, opening the possibility of non-invasive deep-tissue calcium imaging for assessing treatment efficacy longitudinally.