Honeybees (Apis Mellifera) perform an essential role in the ecosystem and economy through pollination of insect-pollinated plants, but their population is declining. Many causes of honeybees' decline are likely to be influenced by the microbiome which is thought to play an important role in bees and is particularly susceptible to infection and pesticides. However, there has been no systematic review or meta-analysis on honeybee microbiome data. Therefore, we conducted the first systematic meta-analysis of 16S-rRNA data to address this gap in the literature. Four studies were in a usable format - accounting for 336 honeybee's worth of data - the largest such dataset to the best of our knowledge. We analysed these datasets in QIIME2 and visualised the results in R-studio. For the first time, we conducted a multi-study evaluation of the core and rare bee microbiome and confirmed previous compositional microbiome data. We established that Snodgrassella, Lactobacillus, Bifidobacterium, Fructobacillus and Saccaribacter form part of the core microbiome and identify 251 rare bacterial genera. Additional components of the core microbiome were likely obscured by incomplete classification. Future studies should refine and add to our existing dataset to produce a more conclusive and high-resolution portrait of the honeybee microbiome. Furthermore, we emphasise the need for an actively curated dataset and enforcement of data sharing standards.
In light microscopy, eyepiece graticules are commonly used to gauge the size of objects at the micron scale. While this is a relatively simple tool to use, not all microscopes possess this feature. Furthermore, calibrating an eyepiece graticule with a stage micrometer can be time-consuming, particularly for inexperienced microscopists. Similarly, calculating the size of individual objects may also take some time. We present an open-source program to determine the size of objects under a microscope using Python and OpenCV. Taking photos of a stage micrometer under a microscope, we identify gradations on the micrometer and calculate the distance between lines on the micrometer in pixels. From this, we can infer the size of objects from bright-field microscopy images. We believe this will improve access to quantitative microscopy techniques and increase the speed at which samples may be analyzed by light microscopy. Future studies may aim to integrate this with machine learning for object identification.
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