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Urban residents are suffering from serious thermal stress resulting from urban heat island and global warming. Investigators have explored several methods to address this issue. Vegetation, especially trees, were found to play a vital part in urban environments. It produces a cooling effect by reducing temperature and radiation levels. To find thermal performances of trees in detail, this study physically measured two urban parks during August 2019 in a satellite city of China regarding their thermal environments relevant to tree planting indices. There were three planting indices used, sky view factor, leaf area index and enclosure area. Through associating them with thermal indicators by linear regression, all of the indices were confirmed to have significant thermal effects. Every 0.1 increase in sky view factor resulted in an increase of 1°C air temperature, 0.16 m/s air velocity, 40 W/m2 solar radiation level and 1.6°C mean radiant temperature. Same effects were found in nearly 0.4 leaf area index decrease and approximately 20 m2 enclosure area increase. These results provide very optimistic directions for future urban forestry planning and landscaping. [ABSTRACT FROM AUTHOR]

• Whole-exome sequencing is an effective clinical aid for the diagnosis of retinitis pigmentosa. • In Chinese patients with retinitis pigmentosa, the hotspot genes were USH2A , CYP4V2 and RPGR. • Sixteen novel variants were detected that had not been reported previously. • Minigene assays validated that two novel splicing variants probably destroyed the RNA splicing. The study aimed to screen for the causative variants in Chinese patients with suspected retinitis pigmentosa (RP). A cohort of 75 unrelated Chinese patients with a clinical diagnosis of RP and their available family members were enrolled in this study. Genomic DNA of all subjects was extracted and whole-exome sequencing (WES) was applied. Candidate variants were identified, and minigene assays were conducted to evaluate the pathogenicity of novel splicing variants. Totally, the diagnostic yield was 44 % (33/75) and 16 novel variants that had not been reported previously were found. Among the genetically solved 33 cases, 31 patients were identified as carrying causative variants of RP and 2 patients carried pathogenic variants implicated in other retinal diseases. USH2A , CYP4V2 , and RPGR were the most common causative genes, accounting for about half of the genetically solved cases. Moreover, minigene assays validated that the novel splicing variants were detrimental. Additionally, 9 patients carried a single deleterious heterozygous variant in 6 genes with autosomal recessive hereditary patterns, and no corresponding copy number variants (CNVs) was detected. The findings of this study revealed the genetic landscape of RP in China and provided guidance for clinicians. [ABSTRACT FROM AUTHOR]

• A detection approach for late-autumn shoots of litchi tree based on UAV remote sensing is provided. • A remote sensing dataset of late-autumn shoots of litchi tree is constructed. • A deep learning model based on the combination of YOLOv5 and Swin-transformer is proposed. • The detection accuracy of late-autumn shoots in remote sensing images is improved. • It will provide technical support for other researchers on late-autumn shoots and orchards manager. Litchi is one of the most common economic fruits in southern China, however, the growth of late-autumn shoots of litchi hinders flower bud differentiation and reduces yield of fruit. The early identification of the late-autumn shoots is of great significance for orchard management to control shoots and then increase fruit yield. At present, the identification of late-autumn shoots still relies on manual methods, which is not suitable for smart orchard management in a large area due to low recognition efficiency and high subjectivity. Therefore, a convenient, fast and cost-effective method is urgently needed. In response to this problem, the paper proposes a method based on the combination of unmanned aerial vehicle (UAV) remote sensing and object detection algorithm to detect late-autumn shoots. For this purpose, a remote sensing dataset of late-autumn shoots of litchi is first constructed by UAV. An improved YOLOv5 algorithm called YOLOv5-SBiC is then developed for late-autumn shoots identification. In the YOLOv5-SBiC algorithm, the transformer module is introduced to speed up the convergence of the network and improve detection accuracy, the attention mechanism module is employed to help the model extracting details, and BiFPN is used to better solve the multi-scale problem in detecting and then improve the recognition effect of small-sized objects. In addition, CIOU is selected as the loss function of bounding boxes regression to achieve high-precision localization of the boxes. The test results demonstrate that the recognition accuracy of YOLOv5-SBiC reaches a relatively high value of 79.6%, which is 4.0% higher than that (75.6%) of the original YOLOv5 algorithm and 15.9% higher than that (63.7%) of the pure transformer algorithm. It's also demonstrated that YOLOv5-SBiC is more competitive than the mainstream target detection algorithms in the dataset of late-autumn shoots. [ABSTRACT FROM AUTHOR]

During June 2-8, 2009, an outbreak of influenza A pandemic (H1N1) 2009 occurred among 31 members of a tour group in China. To identify the mode of transmission and risk factors, we conducted a retrospective cohort investigation. The index case-patient was a female tourist from the United States. Secondary cases developed in 9 (30%) tour group members who had talked with the index case-patient and in 1 airline passenger (not a tour group member) who had sat within 2 rows of her. None of the 14 tour group members who had not talked with the index case-patient became ill. This outbreak was apparently caused by droplet transmission during coughing or talking. That airborne transmission was not a factor is supported by lack of secondary cases among fellow bus and air travelers. Our findings highlight the need to prevent transmission by droplets and fomites during a pandemic.