Prediction on the changes in potential suitable areas for mangroves along the coast of Guangxi and the threat from Spartina alterniflora invasion.

As one of the important blue carbon pools in tropical and subtropical intertidal zones, mangroves are widely distributed along the coast of Guangxi in China. To deeply explore the variations of potential suitable habitats for mangroves in China under the background of climate change, based on remote sensing interpretation data of coastal wetlands in Guangxi, global marine environment and bioclimatic environment data in 2021, we constructed a maximum entropy habitat distribution model to simulate the spatial distribution of potential suitable areas for mangroves and the invasive species, Spartina alterniflora, along the coast of Guangxi, and predicted the patterns under extreme climate change scenarios (SSP5-8.5). The results showed that the interpreted area of mangrove forests along the coastline of Guangxi was 9136.7 hm2 in 2021, while the predicted area of potential suitable habitat area was 55955.9 hm2. Current distribution area of mangroves had basically covered its potential high suitability area and nearly 10% of the moderate suitability area. The current area of S. alterniflora was 1320.4 hm2, and the predicted area of potential high suitability area was twice of current area, indicating that there was still a large proportion of high suitability area that was not occupied by S. alterniflora. The most important environmental factors driving the distribution of potential habitats in mangroves were offshore Euclidean distance (62.2%), terrain deviation index (8.7%), average sea surface temperature in the hottest season (6.1%), and seabed terrain elevation (5.6%). The contribution of geographical conditions on mangrove distribution was predominant. Under the climate change scenario (SSP5-8.5), potential suitable area for mangroves would increase by 5.3%, while that for S. alterniflora would decrease by 3.1%. The overlapping proportion of the potential suitable area for mangroves and S. alterniflora was similar under current and SSP5-8.5 scenarios, being 15.2% and 14.5%, respectively. In the future, it is necessary to strengthen the protection and ecological restoration of mangroves along the coast of Guangxi and there is great challenge for preventing further invasion of S. alterniflora.

Design, methodology, and preliminary results of the follow-up of a population-based cohort study in rural area of northern China: Handan Eye Study.

BACKGROUND: Handan Eye Study (HES), a large population-based cohort study in rural area of northern China, was one of the few studies focusing on the major eye diseases of rural Chinese population. The aim of this study was to introduce the design, methodology and to assess the data quality of the follow-up phase of HES. METHODS: All participants were recruited in Yongnian county of Handan city between 2012 and 2013. Main outcomes were measured by visual quality scales and ocular examinations. We performed the Chi-square test to make comparison of categorical data among groups, One-way analysis of variance and Kruskal-Wallis test was applied to make comparison of continuous data among groups, a post-hoc test was done to make further pairwise comparison. Inter-class correlation coefficients (ICCs) and Kappa coefficients were used to evaluate the consistency between different operators. Logistic regression was used to explore the influence factors of death, odds ratio (OR) and 95% confidence interval (CI) were used to estimate the effect size of each influence factor. RESULTS: The follow-up rate was 85.3%. Subjects were classified into three groups: the follow-up group (n = 5394), the loss to follow-up group (n = 929), and the dead group (n = 507), comparison of their baseline information was done. Compared with the other two groups, age of the dead group (66.52 ±â€Š10.31 years) was the oldest (Z = 651.293, P < 0.001), male proportion was the highest (59.0%) (χ = 42.351, P < 0.001), only 65.9% of the dead finished middle school education (Z = 205.354, P < 0.001). The marriage percentage, body mass index (BMI), best-corrected visual acuity (BCVA), and intra-ocular pressure of the dead group was the lowest either. Spherical equivalent error (SER) of the dead group was the highest. Besides, history of smoking, hypertension, diabetes, and heart disease were more common in the dead group. Multivariate analysis showed that age (OR = 1.901, 95% CI: 1.074-1.108), gender (OR = 0.317, 95% CI: 0.224-0.448), and BCVA (OR = 0.282, 95% CI: 0.158-0.503) were associated with death. While between the follow-up group and the loss to follow-up group, there was only difference on age, gender, BMI, systolic blood pressure and SER. The Cronbach coefficients of all scales used in the follow-up were ≥0.63 and the cumulative variances were ≥0.61, indicating good reliability and validity. The ICCs and Kappa coefficients between different operators were ≥0.69. CONCLUSIONS: HES has a high follow-up rate and a low risk of loss to follow-up bias. Age, gender, and BCVA are influence factors of death. Specifically, male subjects are at a higher risk of death than female, age is a risk factor of death while BCVA is a protective factor for death.

Reproducibility of optical quality parameters measured at objective and subjective best focuses in a double-pass system.

AIM: To evaluate intra-session repeatability and reproducibility of optical quality parameters measured at objective and subjective best focuses in a double-pass system. METHODS: Thirty Chinese healthy adults (19 to 40 years old) meeting our inclusion criterion were enrolled in the study. After a basic eye examination, two methods of optical quality measurement, based on subjective and objective best focuses were performed using the Optical Quality Analysis System (OQAS) with an artificial pupil diameter of 4.0 mm. RESULTS: With each method, three consecutive measurements of the following parameters: the modulation transfer function cutoff frequency (MTFcutoff), the Strehl(2D) ratio, the OQAS values (OVs) at contrasts of 100%, 20%, 9% and the objective scatter index (OSI) were performed by an experienced examiner. The repeatability of each method was evaluated by the repeatability limit (RL) and the coefficient of repeatability (COR). Reproducibility of the two methods was evaluated by intra-class correlation coefficient (ICC) and the 95% limits of agreement (Bland and Altman analysis). Thirty subjects, seven females and twenty three males, of whom 15 right eyes and 15 left eyes were selected randomly for recruitment in the study. The RLs (percentage) for the six parameters measured at objective focus and subjective focus ranged from 8.44% to 15.13% and 10.85% to 16.26%, respectively. The CORs for the two measurement methods ranged from 8.27% to 14.83% and 10.63% to 15.93%, respectively. With regard to reproducibility, the ICCs for the six parameters of OQAS ranged from 0.024 to 0.276. The 95% limits of agreement obtained for the six parameters (in comparison of the two methods) ranged from -0.57 to 42.18 (MTFcutoff), -0.01 to 0.23 (Strehl(2D) ratio), -0.02 to 1.40 (OV100%), -0.10 to 1.75 (OV20%), -0.14 to 1.80 (OV9%) and -1.46 to 0.18 (OSI). CONCLUSION: Measurements provided by OQAS with either method showed a good repeatability. However, the results obtained from the two different measurement methods showed a poor reproducibility. These findings suggest that it might be best to evaluate patients' optical quality by OQAS using the best focus as chosen automatically by the instrument.

A novel RPGR gene mutation in a Chinese family with X-linked dominant retinitis pigmentosa.

OBJECTIVE: To report a novel mutation in RPGR gene in a Chinese family with X-linked dominant retinitis pigmentosa(XLRP). METHODS: Genetic linkage analysis was performed on the known genetic loci for XLRP with a panel of polymorphic markers, then the mutations were identified by single-strand conformation polymorphism(SSCP) and direct sequencing. RESULTS: Significant two-point Lod score was generated using marker DXS8025 (Zmax=2.4, theta =0). The disease gene locus was confined to Xp21.1 with further analysis of genetic linkage and haplotype. Mutations screening of RPGR gene in this family revealed a GA deletion at ORF15+483-484 which caused the open reading frameshift. This novel mutation co-segregated with the affected members of the pedigree, but it was not present in the unaffected relatives. CONCLUSION: The above finding expands the spectrum of RPGR mutations causing XLRP in Chinese family and is useful for further genetic consultation and genetic diagnosis.

[Prevalence of low vision and blindness in defined populations in rural and urban areas in Beijing].

OBJECTIVE: To investigate the prevalence and causes of low vision and blindness in Beijing residents aged 40 and over. METHODS: 4,451 residents aged 40 and over in 3 rural communities and 5 urban communities in Beijing underwent eye examination, including examination of distant and near visual acuity (VA), best corrected distant and near VA, pinhole VA, and visual field, slit lamp biomicroscopy, and dilated ocular examination in the form of in-home survey by defined population-based sampling. The medical history was surveyed too. The data were analyzed based on the criteria of the World Health Organization. RESULTS: The general prevalence rates of low vision and blindness were 0.99% (95% CI: 0.70-1.28) and 0.39% (95% CI: 0.21-0.57) respectively. The prevalence rate of low vision in females was 1.45%, 2.23 times that of males (0.65%) (OR: 1.97, 95% CI: 1.00-3.95). The prevalence rate of low vision of rural residents was 1.76%, 2.89 times that of urban residents (0.61%) (OR: 2.93, 95% CI: 1.43-6.11). The prevalence rate of blindness in females was 0.64% and 0.37% in males. The prevalence rate of blindness of rural residents was 1.06%, 2.04 times that of the urban residents (0.52%) (OR: 3.77, 95% CI: 1.41-10.62). The 3 major causes of blindness were cataract (37.50%), glaucoma (29.20%), and high myopic macular degeneration (8.30%). The prevalence of blindness increased with age. CONCLUSION: The prevalence rates of low vision and blindness are higher in the rural areas. Cataract, glaucoma, and high myopic macular degeneration are the major causes of blindness. The prevalence of low vision and blindness are influenced by age, sex; area, health care level, educational level, and environmental factors.