Avian malaria and related haemosporidians (genera Haemoproteus, Plasmodium and Leucocytozoon) infect most clades of bird. Although these parasites are present in almost all continents, they have been irregularly studied across different geographical regions. Despite the high bird diversity in Asia, the diversity of avian haemosporidians in this region is largely unknown. Moreover, anthropogenic changes to habitats in tropical regions may have a profound impact on the overall composition of haemosporidian communities. Here we analyzed the diversity and host association of bird haemosporidians from areas with different degrees of anthropogenic disturbance in Myanmar, revealing an unexplored diversity of these parasites (27% of newly-discovered haemosporidian lineages, and 64% of new records of host–parasite assemblages) in these tropical environments. This newly discovered diversity will be valuable for detecting host range and transmission areas of haemosporidian parasites. We also found slightly higher haemosporidian prevalence and diversity in birds from paddy fields than in individuals from urban areas and hills, thus implying that human alteration of natural environments may affect the dynamics of vector-borne diseases. These outcomes provide valuable insights for biodiversity conservation management in threatened tropical ecosystems.

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Subtraction of background in a crowded scene is a crucial and challenging task of monitoring the surveillance systems. Because of the similarity between the foreground object and the background, it is known that the background detection and moving foreground objects is difficult. Most of the previous works emphasize this field but they cannot distinguish the foreground from background due to the challenges of gradual or sudden illumination changes, high-frequencies background objects of motion changes, background geometry changes and noise. After getting the foreground objects, segmentation is need to localize the objects region. Image segmentation is a useful tool in many areas, such as object recognition, image processing, medical image analysis, 3D reconstruction, etc. In order to provide a reliable foreground image, a carefully estimated background model is needed. To tackle the issues of illumination changes and motion changes, this paper establishes an effective new insight of background subtraction and segmentation that accurately detect and segment the foreground people. The scene background is investigates by a new insight, namely Mean Subtraction Background Estimation (MS), which identifies and modifies the pixels extracted from the difference of the background and the current frame. Unlike other works, the first frame is calculated by MS instead of taking the first frame as an initial background. Then, this paper make the foreground segmentation in the noisy scene by foreground detection and then localize these detected areas by analyzing various segmentation methods. Calculation experiments on the challenging public crowd counting dataset achieve the best accuracy than state-of-the-art results. This indicates the effectiveness of the proposed work.