Background/Aims: CTLA-4 polymorphisms are associated with susceptibility to various cancers, but the results are often conflicting. Hence, we performed a comprehensive meta-analysis to quantitatively investigate the association between CTLA-4 polymorphisms (rs231775, rs4553808，rs5742909, rs3087243 or rs733618) and cancer risk. Methods: Data were collected from PubMed and Web of Science. A total of 67 case-control studies were selected for quantitative analysis. Stata (Version 12) software was used to calculate the odds ratio (OR) and 95% confidence interval (CI) to evaluate the strength of the associations. Subgroup meta-analysis was conducted based on ethnicity and cancer type. Heterogeneity tests, sensitivity analysis, and publication bias assessments were also performed. Results: rs231775, rs4553808 and rs5742909 but not rs3087243 and rs733618 were significantly related to cancer risk. In analyses stratified by ethnicity, both rs231775 and rs4553808 were significant susceptibility polymorphisms in an Asian population but not in a Caucasian population. Moreover, there were stronger associations between the rs231775 polymorphism and increased risk of bone, breast, liver, head and neck and pancreatic cancers. Additionally, rs4553808 was associated with significantly increased susceptibility to breast cancer and head and neck cancer. Conclusion: rs231775, rs4553808 and rs5742909 may be used as predictive genetic biomarkers for cancer predisposition. Combined detection of CTLA-4 SNPs could be a useful tool for prediction of cancer susceptibility in clinical practice.

Registration of large-scale optical images with airborne LiDAR data is the basis of the integration of photogrammetry and LiDAR. However, geometric misalignments still exist between some aerial optical images and airborne LiDAR point clouds. To eliminate such misalignments, we extended a method for registering close-range optical images with terrestrial LiDAR data to a variety of large-scale aerial optical images and airborne LiDAR data. The fundamental principle is to minimize the distances from the photogrammetric matching points to the terrestrial LiDAR data surface. Except for the satisfactory efficiency of about 79 s per 6732 × 8984 image, the experimental results also show that the unit weighted root mean square (RMS) of the image points is able to reach a sub-pixel level (0.45 to 0.62 pixel), and the actual horizontal and vertical accuracy can be greatly improved to a high level of 1/4–1/2 (0.17–0.27 m) and 1/8–1/4 (0.10–0.15 m) of the average LiDAR point distance respectively. Finally, the method is proved to be more accurate, feasible, efficient, and practical in variety of large-scale aerial optical image and LiDAR data.

Coral reefs are vital to marine biodiversity but are increasingly threatened by global climate change and human activities, leading to significant declines in live coral cover (LCC). Monitoring LCC is crucial for assessing the health of coral reef ecosystems and understanding their degradation and recovery. Traditional methods for estimating LCC, such as the manual interpretation of underwater survey videos, are labor-intensive and time-consuming, limiting their scalability for large-scale ecological monitoring. To overcome these challenges, this study introduces an innovative deep learning-based approach that utilizes semantic segmentation to automatically interpret LCC from underwater videos. That is, we enhanced PSPNet for live coral segmentation by incorporating channel and spatial attention mechanisms, along with pixel shuffle modules. Experimental results demonstrated that the proposed model achieved a mean Intersection over Union (mIoU) of 89.51% and a mean Pixel Accuracy (mPA) of 94.47%, showcasing superior accuracy in estimating LCC compared to traditional methods. Moreover, comparisons indicated that the proposed model aligns more closely with manual interpretations than other models, with an mean absolute error of 4.17%, compared to 5.89% for the original PSPNet, 6.03% for Deeplab v3+, 7.12% for U-Net, and 6.45% for HRNet, suggesting higher precision in LCC estimation. By automating the estimation of LCC, this deep learning-based approach can greatly enhance efficiency, thereby contributing significantly to global conservation efforts by enabling more scalable and efficient monitoring and management of coral reef ecosystems.

Live coral cover (LCC) is regarded as the most efficient indicator of coral reef health. However, LCCs are usually sampled with standardized transect or photo quadrat techniques in field, which are incomplete and labour-intensive. To overcome such difficulties, we study a model to transfer the pixels of multispectral satellite images to quantitative LCCs. The idea is to extend band ratio-based (BR) indices to a novel index constructed using the ratio of different linear combinations (RDLC) of band reflectance and water depths. On the basis of field surveyed LCCs, an empirical process is further proposed to solve the unknown parameters of this RDLC. This approach provides new thinking for designing LCC-sensitive indices for given multispectral satellite images. The experimental results on Weizhou Island and Palmyra Atoll demonstrate that the method is effective and feasible, where the mean relative errors (MREs) are improved from 45 to 56% for BRs to 23–29% for RDLCs for Weizhou Island.

Coral reefs are important as they can help to maintain ecological balance, biological resources, and species diversity on earth. However, they are globally threatened by human activities and climate change. As live coral cover (LCC) is regarded as an important measure of the health of coral reefs, analysis on LCC change associated with environmental parameters, such as chlorophyll-a concentration (Chl-a), sea surface temperature (SST), and photosynthetically active radiation (PAR), is of great value. Research on this front would help us comprehend the changes in coral reefs induced by human activities and global changes. Instead of using spasmodically in-field-measured environmental parameters, in this study, we chose to combine the successive Chl-a, SST, and PAR products of the Moderate Resolution Imaging Spectroradiometer (MODIS) with historic LCC records to establish an empirical relationship using nonlinear optimization. Thereafter, the established relationship was further used to discuss some possible developments of LCCs. According to the experiments, we concluded that the degradation of the LCC around Weizhou Island may be mainly caused by human-activity-caused eutrophication. Besides, we also showed that even if the Chl-a and the PAR can keep constant with current average levels, the corals around Weizhou Island may still be in a risk of disappearing between 2120–2140 as the SST continues to rise.

Coral reefs in the South China Sea (SCS) are critical for regional marine biodiversity and ecosystem services but face escalating threats from climate change and anthropogenic stressors. However, a holistic evaluation of habitat suitability spanning the distinct environmental gradients from low-latitude deep-water atolls to high-latitude marginal reefs remains limited. This study utilized high-resolution remote sensing data and the MaxEnt (Maximum Entropy) model combined with Principal Component Analysis (PCA) to systematically map potential habitat suitability and elucidate the multi-scale environmental drivers shaping the realized niche of SCS corals. The results revealed significant spatial heterogeneity characterized by a distinct “High South, Low North” latitudinal gradient, with Unsuitable areas dominating 85.5% of the study region, followed by Marginally Suitable habitats at 5.0%, while the northern Nansha Islands were identified as the core distribution area with the highest suitability and continuity. Minimum Phosphate (Min. Phos.) concentration and Sea Surface Temperature (SST) were identified as the core environmental factors determining the spatial distribution of coral reefs in the South China Sea. The optimal environmental ranges were identified as: SST between 28.52 °C and 29.41 °C, water depth shallower than 34 m, extremely low phosphate (0–0.005 mmol/m³), and low cumulative thermal stress (DHW < 0.83 °C-weeks). Crucially, PCA further quantified two potential climate refugia: low-latitude thermal refugia in the southern Nansha Islands, characterized by high environmental stability, and high-latitude marginal refugia in the Beibu Gulf, which offer physical buffering against warming, while necessitating targeted efforts to mitigate the risks of habitat degradation and eutrophication driven by intensifying anthropogenic activities These findings challenge the traditional conservation view relying solely on high-latitude migration, advocating for a climate-resilient spatial planning strategy that prioritizes strict protection of southern biodiversity source banks while enhancing the connectivity of northern marginal stepping stones.