Comparative analysis of vaginal microbiota sampling using 16S rRNA gene analysis

Background Molecular methods such as next-generation sequencing are actively being employed to characterize the vaginal microbiota in health and disease. Previous studies have focused on characterizing the biological variation in the microbiota, and less is known about how factors related to sampling contribute to the results. Our aim was to investigate the impact of a sampling device and anatomical sampling site on the quantitative and qualitative outcomes relevant for vaginal microbiota research. We sampled 10 Finnish women representing diverse clinical characteristics with flocked swabs, the Evalyn (R) self-sampling device, sterile plastic spatulas and a cervical brush that were used to collect samples from fornix, vaginal wall and cervix. Samples were compared on DNA and protein yield, bacterial load, and microbiota diversity and species composition based on Illumina MiSeq sequencing of the 16S rRNA gene. We quantified the relative contributions of sampling variables versus intrinsic variables in the overall microbiota variation, and evaluated the microbiota profiles using several commonly employed metrics such as alpha and beta diversity as well as abundance of major bacterial genera and species. Results The total DNA yield was strongly dependent on the sampling device and to a lesser extent on the anatomical site of sampling. The sampling strategy did not affect the protein yield or the bacterial load. All tested sampling methods produced highly comparable microbiota profiles based on MiSeq sequencing. The sampling method explained only 2% (p-value = 0.89) of the overall microbiota variation, markedly surpassed by intrinsic factors such as clinical status (microscopy for bacterial vaginosis 53%, p = 0.0001), bleeding (19%, p = 0.0001), and the variation between subjects (11%, p-value 0.0001). Conclusions The results indicate that different sampling strategies yield comparable vaginal microbiota composition and diversity. Hence, past and future vaginal microbiota studies employing different sampling strategies should be comparable in the absence of other technical confounders. The Evalyn (R) self-sampling device performed equally well compared to samples taken by a clinician, and hence offers a good-quality microbiota sample without the need for a gynecological examination. The amount of collected sample as well as the DNA and protein yield varied across the sampling techniques, which may have practical implications for study design.Peer reviewe

Group Factor Analysis

Factor analysis provides linear factors that describe relationships between individual variables of a data set. We extend this classical formulation into linear factors that describe relationships between groups of variables, where each group represents either a set of related variables or a data set. The model also naturally extends canonical correlation analysis to more than two sets, in a way that is more flexible than previous extensions. Our solution is formulated as variational inference of a latent variable model with structural sparsity, and it consists of two hierarchical levels: The higher level models the relationships between the groups, whereas the lower models the observed variables given the higher level. We show that the resulting solution solves the group factor analysis problem accurately, outperforming alternative factor analysis based solutions as well as more straightforward implementations of group factor analysis. The method is demonstrated on two life science data sets, one on brain activation and the other on systems biology, illustrating its applicability to the analysis of different types of high-dimensional data sources

The 15th International CDIO Conference: Proceedings – Full Papers

We discuss a conceptual thesis structure model and visual tool for enhancing the writing process in the context of an engineering Master’s thesis. Our model is based on visualizing the thesis as a series of funnels that adjust the writing focus to the desired scope in each individual chapter. At the end of the thesis, the focus is widened back into the original topic area with a reflection on how the solutions proposed in the thesis have impacted or potentially will impact the field. Using our model gives students the opportunity to write a good Master’s thesis in various engineering disciplines. In our experience, the Focus Funnel approach has been very useful and effective, resulting in an overall improvement in the quality of engineering Master’s theses in our degree program.</p

Vaginal Microbiota Composition Correlates Between Pap Smear Microscopy and Next Generation Sequencing and Associates to Socioeconomic Status

Recent research on vaginal microbiota relies on high throughput sequencing while microscopic methods have a long history in clinical use. We investigated the correspondence between microscopic findings of Pap smears and the vaginal microbiota composition determined by next generation sequencing among 50 asymptomatic women. Both methods produced coherent results regarding the distinction between Lactobacillus-dominant versus mixed microbiota, reassuring gynaecologists for the use of Pap smear or wet mount microscopy for rapid evaluation of vaginal bacteria as part of diagnosis. Cytologic findings identified women with bacterial vaginosis and revealed that cytolysis of vaginal epithelial cells is associated to Lactobacillus crispatus-dominated microbiota. Education and socio-economic status were associated to the vaginal microbiota variation. Our results highlight the importance of including socio-economic status as a co-factor in future vaginal microbiota studies.Peer reviewe

A Generative Shape Compositional Framework to Synthesise Populations of Virtual Chimaeras

Generating virtual populations of anatomy that capture sufficient variability while remaining plausible is essential for conducting in-silico trials of medical devices. However, not all anatomical shapes of interest are always available for each individual in a population. Hence, missing/partially-overlapping anatomical information is often available across individuals in a population. We introduce a generative shape model for complex anatomical structures, learnable from datasets of unpaired datasets. The proposed generative model can synthesise complete whole complex shape assemblies coined virtual chimaeras, as opposed to natural human chimaeras. We applied this framework to build virtual chimaeras from databases of whole-heart shape assemblies that each contribute samples for heart substructures. Specifically, we propose a generative shape compositional framework which comprises two components - a part-aware generative shape model which captures the variability in shape observed for each structure of interest in the training population; and a spatial composition network which assembles/composes the structures synthesised by the former into multi-part shape assemblies (viz. virtual chimaeras). We also propose a novel self supervised learning scheme that enables the spatial composition network to be trained with partially overlapping data and weak labels. We trained and validated our approach using shapes of cardiac structures derived from cardiac magnetic resonance images available in the UK Biobank. Our approach significantly outperforms a PCA-based shape model (trained with complete data) in terms of generalisability and specificity. This demonstrates the superiority of the proposed approach as the synthesised cardiac virtual populations are more plausible and capture a greater degree of variability in shape than those generated by the PCA-based shape model

Bayesian Group Factor Analysis

We introduce a factor analysis model that summarizes the dependencies between observed variable groups, instead of dependencies between individual variables as standard factor analysis does. A group may correspond to one view of the same set of objects, one of many data sets tied by co-occurrence, or a set of alternative variables collected from statistics tables to measure one property of interest. We show that by assuming group-wise sparse factors, active in a subset of the sets, the variation can be decomposed into factors explaining relationships between the sets and factors explaining away set-specific variation. We formulate the assumptions in a Bayesian model which provides the factors, and apply the model to two data analysis tasks, in neuroimaging and chemical systems biology.Comment: 9 pages, 5 figure

A Generative Shape Compositional Framework: Towards Representative Populations of Virtual Heart Chimaeras

Generating virtual populations of anatomy that capture sufficient variability while remaining plausible is essential for conducting in-silico trials of medical devices. However, not all anatomical shapes of interest are always available for each individual in a population. Hence, missing/partially-overlapping anatomical information is often available across individuals in a population. We introduce a generative shape model for complex anatomical structures, learnable from datasets of unpaired datasets. The proposed generative model can synthesise complete whole complex shape assemblies coined virtual chimaeras, as opposed to natural human chimaeras. We applied this framework to build virtual chimaeras from databases of whole-heart shape assemblies that each contribute samples for heart substructures. Specifically, we propose a generative shape compositional framework which comprises two components - a part-aware generative shape model which captures the variability in shape observed for each structure of interest in the training population; and a spatial composition network which assembles/composes the structures synthesised by the former into multi-part shape assemblies (viz. virtual chimaeras). We also propose a novel self supervised learning scheme that enables the spatial composition network to be trained with partially overlapping data and weak labels. We trained and validated our approach using shapes of cardiac structures derived from cardiac magnetic resonance images available in the UK Biobank. Our approach significantly outperforms a PCA-based shape model (trained with complete data) in terms of generalisability and specificity. This demonstrates the superiority of the proposed approach as the synthesised cardiac virtual populations are more plausible and capture a greater degree of variability in shape than those generated by the PCA-based shape model.Comment: 15 pages, 4 figure

Role of shared identity and agency trust in online voting among Finnish citizens

This study examined the impact of shared identity and agency trust, governmental vs. third party, on Finnish citizens' intention to vote online. Using the integrated model of shared identity and trust as a theoretical lens, a within-subject quasi-experiment was conducted to understand the impact of agency trust on intention to vote online. The model was tested using data from 248 Finnish citizens using PLS-SEM. We found that citizens’ perceptions of shared identity with online voting agencies significantly contribute to agency trust. This trust in agencies, then directly and indirectly through perceived usefulness, affects online voting intention. Perceived usefulness directly and perceived ease of use indirectly increase the intention to vote online. However, the perceived usefulness of online voting is contingent upon the voting administering agency being the government. This study contributes to the understanding of agency trust in online voting adoption in the Finnish context and highlights the role of shared identity in building citizen trust in online voting. It also emphasizes the effect of voting agency type on the perceived usefulness of online voting