Recent studies suggest a role for the microbiota in autism spectrum disorders (ASD), potentially arising from their role in modulating the immune system and gastrointestinal (GI) function or from gut–brain interactions dependent or independent from the immune system. GI problems such as chronic constipation and/or diarrhea are common in children with ASD, and significantly worsen their behavior and their quality of life. Here we first summarize previously published data supporting that GI dysfunction is common in individuals with ASD and the role of the microbiota in ASD. Second, by comparing with other publically available microbiome datasets, we provide some evidence that the shifted microbiota can be a result of westernization and that this shift could also be framing an altered immune system. Third, we explore the possibility that gut–brain interactions could also be a direct result of microbially produced metabolites.
Bermuda Land Snails make up a genus called Poecilozonites that is endemic to Bermuda and is extensively present in its fossil record. These snails were also integral to the creation of the theory of punctuated equilibrium. The DNA of mollusks is difficult to sequence because of a class of proteins called mucopolysaccharides that are present in high concentrations in mollusk tissue, and are not removed with standard DNA extraction methods. They inhibit Polymerase Chain Reactions (PCRs) and interfere with Next Generation Sequencing methods. This paper will discuss the DNA extraction methods that were designed to remove the inhibitory proteins that were tested on another gastropod species (Pomacea canaliculata). These were chosen because they are invasive and while they are not pulmonates, they are similar enough to Bermuda Land Snails to reliably test extraction methods. The methods that were tested included two commercially available kits: the Qiagen Blood and Tissue Kit and the Omega Biotek Mollusc Extraction Kit, and one Hexadecyltrimethylammonium Bromide (CTAB) Extraction method that was modified for use on mollusk tissue. The Blood and Tissue kit produced some DNA, the mollusk kit produced almost none, and the CTAB Extraction Method produced the highest concentrations on average, and may prove to be the most viable option for future extractions. PCRs attempted with the extracted DNA have all failed, though it is likely due to an issue with reagents. Further spectrographic analysis of the DNA from the test extractions has shown that they were successful at removing mucopolysaccharides. When the protocol is optimized, it will be used to extract DNA from the tissue from six individuals from each of the two extant species of Bermuda Land Snails. This DNA will be used in several experiments involving Next Generation Sequencing, with the goal of assembling a variety of genome data. These data will then be used to a construct reference genome for Bermuda Land Snails. The genomes generated by this project will be used in population genetic analyses between individuals of the same species, and between individuals of different species. These analyses will then be used to aid in conservation efforts for the species.
Drosophila melanogaster has been established as a model organism for investigating the developmental gene interactions. The spatio-temporal gene expression patterns of Drosophila melanogaster can be visualized by in situ hybridization and documented as digital images. Automated and efficient tools for analyzing these expression images will provide biological insights into the gene functions, interactions, and networks. To facilitate pattern recognition and comparison, many web-based resources have been created to conduct comparative analysis based on the body part keywords and the associated images. With the fast accumulation of images from high-throughput techniques, manual inspection of images will impose a serious impediment on the pace of biological discovery. It is thus imperative to design an automated system for efficient image annotation and comparison.
Results
We present a computational framework to perform anatomical keywords annotation for Drosophila gene expression images. The spatial sparse coding approach is used to represent local patches of images in comparison with the well-known bag-of-words (BoW) method. Three pooling functions including max pooling, average pooling and Sqrt (square root of mean squared statistics) pooling are employed to transform the sparse codes to image features. Based on the constructed features, we develop both an image-level scheme and a group-level scheme to tackle the key challenges in annotating Drosophila gene expression pattern images automatically. To deal with the imbalanced data distribution inherent in image annotation tasks, the undersampling method is applied together with majority vote. Results on Drosophila embryonic expression pattern images verify the efficacy of our approach.
Conclusion
In our experiment, the three pooling functions perform comparably well in feature dimension reduction. The undersampling with majority vote is shown to be effective in tackling the problem of imbalanced data. Moreover, combining sparse coding and image-level scheme leads to consistent performance improvement in keywords annotation.