BackgroundIn recent years in China, the tense physician-patient relationship has been an outstanding problem. Empathy is one of the fundamental factors enhancing the therapeutic effects of physician-patient relationships and is significantly associated with clinical and academic performance among students.MethodsThis cross-sectional study used the JSPE-S (The Student Version of the Jefferson Scale of Physician Empathy) to assess 902 medical students from 1st year to 4th year at China Medical University. The reliability of the questionnaire was assessed by Cronbach’s alpha coefficient. We performed an exploratory factor analysis to evaluate the construct validity of the JSPE-S. Group comparisons of empathy scores were conducted via the t-test and one-way ANOVA. Statistic analysis was performed by SPSS 13.0.ResultsThe Cronbach’s alpha coefficient was 0.83. The three factors emerging in the factor analysis of the JSPE-S are “perspective taking”, “compassionate care” and “ability to stand in patients’ shoes”, which accounted for 48.00%. The mean empathy score was 109.60. The empathy score of medical students had significant differences between different genders (p < 0.05) and academic year level (p < 0.05).ConclusionsThis study provided support for the validity and reliability of the Chinese translated version of the JSPE-S for medical students. Early exposure to clinical training and a curriculum for professional competencies help to enhance the empathy of medical students. We suggest that the curriculum within Chinese medical schools include more teaching on empathy and communicational skills.

Abstract. While the elevated ambient levels of particulate matters with aerodynamic diameter of 2.5 µm or less (PM2.5) are alleviated largely with the implementation of effective emission control measures, an opposite trend with a rapid increase has been seen in surface ozone (O3) in the North China Plain (NCP) region over the past several years. It is critical to determine the real culprit causing such a large increase in surface O3. In this study, 7-year surface observations and satellite retrieval data are analyzed to determine the long-term change in surface O3 as well as driving factors. Results indicate that anthropogenic emission control strategies and changes in aerosol concentrations as well as aerosol optical properties such as single-scattering albedo (SSA) are the most important factors driving such a large increase in surface O3. Numerical simulations with the National Center for Atmospheric Research (NCAR) Master Mechanism (MM) model suggest that reduction of O3 precursor emissions and aerosol radiative effect accounted for 45 % and 23 % of the total change in surface O3 in summertime during 2013–2019, respectively. Planetary boundary layer (PBL) height with an increase of 0.21 km and surface air temperature with an increase of 2.1 ∘C contributed 18 % and 12 % to the total change in surface O3, respectively. The combined effect of these factors was responsible for the rest of the change. Decrease in SSA or strengthened absorption property of aerosols may offset the impact of aerosol optical depth (AOD) reduction on surface O3 substantially. While the MM model enables quantification of an individual factor's percentage contributions, it requires further refinement with aerosol chemistry included in the future investigation. The study indicates an important role of aerosol radiative effect in development of more effective emission control strategies on reduction of ambient levels of O3 as well as alleviation of national air quality standard exceedance events.

Improving the speed and accuracy of chlorophyll (Ch1) content prediction in different light areas of apple trees is a central priority for understanding the growth response to light intensity and in turn increasing the primary production of apples. In vitro assessment by wet chemical extraction is the standard method for leaf chlorophyll determination. This measurement is expensive, laborious, and time-consuming. Over the years, alternative methods-both rapid and nondestructive-were explored, and many vegetation indices (VIs) were developed to retrieve Ch1 content at the canopy level from meter-to decameter-scale reflectance observations, which have lower accuracy due to the possible confounding influence of the canopy structure. Thus, the spatially continuous distribution of Ch1 content in different light areas within an apple tree canopy remains unresolved. Therefore, the objective of this study is to develop methods for Ch1 content estimation in areas of different light intensity by using 3D models with color characteristics acquired by a 3D laser scanner with centimeter spatial resolution. Firstly, to research relative light intensity (RLI), canopies were scanned with a FARO Focus3D 120 laser scanner on a calm day without strong light intensity and then divided into 180 cube units for each canopy according to actual division methods in three-dimensional spaces based on distance information. Meanwhile, four different types of RLI were defined as 0-30%, 30-60%, 60-85%, and 85-100%, respectively, according to the actual division method for tree canopies. Secondly, Ch1 content in the 180 cubic units of each apple tree was measured by a leaf chlorophyll meter (soil and plant analyzer development, SPAD). Then, color characteristics were extracted from each cubic area of the 3D model and calculated by two color variables, which could be regarded as effective indicators of Ch1 content in field crop areas. Finally, to address the complexity and fuzziness of relationships between the color characteristics and Ch1 content of apple tree canopies (which could not be expressed by an accurate mathematical model), a three-layer artificial neural network (ANN) was constructed as a predictive model to find Ch1 content in different light areas in apple tree canopies. The results indicated that the mean highest and mean lowest value of Ch1 content distributed in 60-85% and 0-30% of RLI areas, respectively, and that there was no significant difference between adjacent RLI areas. Additionally, color characteristics changed regularly as the RLI rose within canopies. Moreover, the prediction of Ch1 content was strongly correlated with those of actual measurements (R = 0.9755) by the SPAD leaf chlorophyll meter. In summary, the color characteristics in 3D apple tree canopies combined with ANN technology could be used as a potential rapid technique for predicting Ch1 content in different areas of light in apple tree canopies.

Geometric three-dimensional (3D) reconstruction has emerged as a powerful tool for plant phenotyping and plant breeding. Although laser scanning is one of the most intensely used sensing techniques for 3D reconstruction projects, it still has many limitations, such as the high investment cost. To overcome such limitations, in the present study, a low-cost, novel, and efficient imaging system consisting of a red-green-blue (RGB) camera and a photonic mixer detector (PMD) was developed, and its usability for plant phenotyping was demonstrated via a 3D reconstruction of a soybean plant that contains color information. To reconstruct soybean canopies, a density-based spatial clustering of applications with noise (DBSCAN) algorithm was used to extract canopy information from the raw 3D point cloud. Principal component analysis (PCA) and iterative closest point (ICP) algorithms were then used to register the multisource images for the 3D reconstruction of a soybean plant from both the side and top views. We then assessed phenotypic traits such as plant height and the greenness index based on the deviations of test samples. The results showed that compared with manual measurements, the side view-based assessments yielded a determination coefficient (R2) of 0.9890 for the estimation of soybean height and a R2 of 0.6059 for the estimation of soybean canopy greenness index; the top view-based assessment yielded a R2 of 0.9936 for the estimation of soybean height and a R2 of 0.8864 for the estimation of soybean canopy greenness. Together, the results indicated that an assembled 3D imaging device applying the algorithms developed in this study could be used as a reliable and robust platform for plant phenotyping, and potentially for automated and high-throughput applications under both natural light and indoor conditions.