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Importance: Social isolation during outbreaks of epidemics always challenge psychological resilience profoundly. However, it still remains unclear how students’ mental health would be affected by strict on-campus quarantine, which means students stayed at dormitories or on-campus for quarantine. We analyzed the data from one of the earliest universities releasing strict on-campus quarantine to prevent the Omicron spread-out in Shanghai, to evaluate the time course of the psychological resilience in the influence of an unexpected change in the living space in university students.Objectives：The campus was locked down on 9th March. From 9th to 23rd, all students on campus were asked to stay in their dormitries, and teachers sent food supplies three times a day. From 24th March to early April, students were gradually allowed to enter the campus for necessary activities. We quantify dynamic changes in depression severity during the campus locked down.Method: All participants were undergraduate or graduate students. The survey was released on 5th, April. All survey was dropped a public link on www.wjx.cn and administered from April 5th to 7th, 2022. Following the schedule of quarantine area, we assessed depressive symptoms in SJTU students from Period 1 (two weeks before 9th, March), Period 2 (9th to 23rd, March), and Period 3 (24th March to 5th April). 274 students completed the online survey, and two incomplete data were excluded for further analysis. All these participants aged range from 18 to 25 years old and were quarantined in dormitories since 9th March 2022.Findings:A remarkable increase in depression was observed in university students after stringent quarantine. The portion of students with depression started at 9.1% before quarantine and rapidly increased to 36.1% right after quarantine. No more increase in depressive symptoms was observed with time though quarantine was ongoing, which can be predicted by the food supplement satisfaction and the duration of physical fitness per day. Interestingly, the impact of a sudden change in living space on the psychological resilence was more evident in students who have a romatic relationship than singles.

During their first year of medical residency (internship), 35% of training physicians in the United States suffer at least one depression episode. We assessed whether there is a similar increase of depression among first year residents in China, and identified predictors of depression in the two systems. 1006 residents across three cohorts (2016–2017, 2017–2018 and 2018–2019) at Shanghai Jiao Tong University and Peking Union Medical College were assessed in parallel with three cohorts of 7028 residents at 100 + US institutions. The Patient Health Questionnaire-9 (PHQ-9) depressive symptoms were measured at baseline and quarterly. Demographic, personal and residency factors were assessed as potential predictors of PHQ-9 depression scores. Similar to training interns in the US, the proportion of participants in China who met depression criteria at least once during the first year of residency increased substantially, from 9.1 to 35.1%. History of depression and symptoms at baseline were common factors significantly associated with depression during residency. By contrast, neuroticism, early family environment, female gender and not being coupled were associated with depression risk only in the US, while young age was a predictor of depression only in China. Fear of workplace violence also was a predictor in China. Long duty hours and reduced sleep duration emerged as training predictors of depression in both countries. The magnitude of depression increase and work-related drivers of depression were similar between China and the US, suggesting a need for effective system reforms in both systems.

Resident physician training is associated with a substantial increase in depression in the United States, with rates increasing from about 4% before internship to 35% at least once during the first year of residency1.Here, we sought to assess whether the rate of depression among residents in China are similar to their US counterparts and identify the common and differential predictors of depression in the two training systems. We assessed 1006 residents across three cohorts (2016-2019) at 16 affiliated hospitals of Shanghai Jiao Tong University and Peking Union Medical College. In parallel, we assessed three cohorts of 7028 residents at 100+ US institutions.At the Chinese institutions, similarly, the proportion of participants who met depression criteria increased from 9% prior to residency to 35% at least once during the first year of residency (PTo gain insight into whether differences in personal predictors between the residents in China compared to the US residents were driven more by differences between cohorts, or by training system differences, we compared US residents of East Asian descent to other US and Chinese residents. We found that for most predictors (age, Neuroticism, early family environment), US residents of East Asian descent were more similar to other US residents than to the residents training in China.Overall, the magnitude of depression increase and work-related drivers of depression were similar between China and the US, suggesting a need for system reforms, and that the types of effective reforms may be similar across the two systems.

Carbon emissions are an important environmental problem. The objective and accurate prediction of carbon emissions can serve as a reference and advance indicator for the implementation of a government’s environmental strategy. In this paper, based on a Gaussian vector basis kernel function and a global polynomial kernel function combined with the characteristics of grey prediction models, a new multi-kernel GMC(1,N) model is established that is more comprehensive and suitable for nonlinearity. It can enable more flexible improvements in prediction modelling accuracy. In this study, this new model is used to simulate the carbon dioxide emissions in Chongqing, China (one of the four municipalities under the direct control of the central government), from 2009 to 2015. Raw coal and cleaned coal, which have the most significant impact on carbon emissions, are selected to analyse the effectiveness of the model. The results show that the proposed model offers better simulation and prediction accuracy than four other models considered for comparison. Moreover, the carbon dioxide emissions of Chongqing in 2016–2020 are expected to be similar to those of the previous years. Therefore, to prevent a strong rebound of carbon emissions, it will be necessary to increase energy conservation and emission reduction efforts and to reduce energy consumption, especially coal consumption.

Driver's Fatigue is one important factor leading to traffic accident. So, how to detect driver's fatigue status accurately is critical to avoid traffic injuries and deaths. In the driving environment of vehicles, there are higher requirements for the real-time performance and reliability. The traditional fatigue judging methods are sensitive to the light fluctuation and have lower real-time performance. This paper improves eye location method and presents a fatigue judgment rules to meet the demanding of the automobile driving environment. In the whole process, we take inter-frame information to define eye region to improve the eyes location method based on AdaBoost. Then, we combine skin color segmentation and eyelids ellipse fitting method to judge the eye state. In the last, we design a fatigue recognition rules based on rough set which combines closing eyes rate, average eyes blinking time and consecutive eye closure frames together. These methods can achieve better real-time performance. The experimental results show this method can meet with the requirements of driving environment and improves fatigue judgment accuracy rate.

Background Streptococcus pneumoniae infections are a major cause of global morbidity and mortality, and the emergence of antibiotic-resistant Streptococcus pneumoniae strains has been increasingly reported. This study provides up-to-date information on bacterial serotype distribution and drug resistance from S. pneumoniae clinical isolates that could guide prevention and treatment strategies for pneumococcal disease in China. Methods A total of 94 S. pneumoniae isolates were collected from outpatients and inpatients at one Chinese hospital from 2011–2013. Drug susceptibility and resistance was determined by minimum inhibitory concentrations (MICs). Capsular serotypes were identified by the quellung reaction test and multiplex polymerase chain reaction. Results Fifteen serotypes were identified among the 94 S. pneumoniae clinical isolates that were collected. Prevalent serotypes were 19F (42.6 %), 19A (8.5 %), 3 (8.5 %), and 6B (7.4 %). Potential immunization coverage rates for the 7-, 10- and 13-valent pneumococcal polysaccharide conjugate vaccines were 59.6, 62.6, and 79.6 %, respectively. Resistance rates to tetracycline, erythromycin, and trimethoprim/sulfamethoxazole were 91.2, 80.2 and 63.8 %, respectively. Resistance rates to penicillin, amoxicillin, ceftriaxone, and cefotaxime were 47.3, 34.1, 19.8, and 18.7 %, respectively. In almost all cases, antimicrobial resistance of the S. pneumoniae isolates in patients five years or younger was higher than isolates collected from patients aged 51 years or older. Conclusion Prevalent serotypes among the 94 S. pneumoniae clinical isolates were 19F, 19A, 3, and 6B. The 13-valent pneumococcal polysaccharide conjugate vaccine covered the majority of the serotypes identified in this sample. Drug resistance varied among different serotypes and age groups. Clinical precautions should be taken to avoid the development of multidrug resistance in this potential human pathogen. Electronic supplementary material The online version of this article (doi:10.1186/s12879-015-1042-5) contains supplementary material, which is available to authorized users.

In real-time control systems, multicore microcontrollers have been more and more widely applied. The workload balance and the responding time of ready tasks are most important factors for real-time multicore embedded systems. In this article, we focus on real-time requirement of multicore processors with shared memory to present one multicore task scheduling model and method based on multi-master model, in which each core can manage the task scheduling and resource allocation like a master once tasks are ready, and declines the response time of ready tasks to be scheduled. And then, based on this model, we propose one multi-master multicore scheduling policy and algorithm to reduce scheduling switch count and to achieve better load-balance. In the last, we have implemented a multi-master scheduling prototype to demonstrate that the approach is promising. This experiments show this model can achieve some benefits for real-time performance and load-balance.

List of primers used for S. pneumonia serotype deduction ( http://www.cdc.gov/streplab/pcr.html ). Table S2. Distribution of serotypes in different specimens. Table S3. Distribution of different serotypes in different age groups. Table S4. The serotypes distribution and coverage rates of vaccines of the 94 isolates of S.pneumoniae. Table S5. The serotype distribution and coverage rates of vaccines of S.pneumoniae isolates from children and elderly patients. Table S6. The antibiotic susceptibility of 94 strains of S.pneumoniae. (PDF 140 kb)

Vilfredo Pareto noted in 1906 that 80% of the land in Italy was owned by 20% of the population – and that the ratio seemed to apply to many countries, many time periods, and many economies. But is it more than a useful rule of thumb? Pareto also observed that 20% of the pea pods in his garden contained 80% of the peas. His ratio seems to pop up everywhere. Fred J. Rispoli, Suhua Zeng, Tim Green and Jennifer Higbie try telling it to the birds.