• Research dataclose
• 2018-2022close
• Open Access close
• IE close
• figshare close

Supplementary Material for 'Dairy streptococcal cell wall and exopolysaccharide genome diversity', as published in Microbial Genomics.

Additional file 2: Table S1. N-glycans and features from secreted glycoproteins from all cell lines.

Additional file 4: Supplemental Figure 3. Dual administration of low-dose allogeneic MSCs does not significantly alter the frequency of mononuclear phagocytes or activated dendritic cells in the spleen.

We propose adaptive incremental mixture Markov chain Monte Carlo (AIMM), a novel approach to sample from challenging probability distributions defined on a general state-space. While adaptive MCMC methods usually update a parametric proposal kernel with a global rule, AIMM locally adapts a semiparametric kernel. AIMM is based on an independent Metropolis–Hastings proposal distribution which takes the form of a finite mixture of Gaussian distributions. Central to this approach is the idea that the proposal distribution adapts to the target by locally adding a mixture component when the discrepancy between the proposal mixture and the target is deemed to be too large. As a result, the number of components in the mixture proposal is not fixed in advance. Theoretically, we prove that there exists a stochastic process that can be made arbitrarily close to AIMM and that converges to the correct target distribution. We also illustrate that it performs well in practice in a variety of challenging situations, including high-dimensional and multimodal target distributions. Finally, the methodology is successfully applied to two real data examples, including the Bayesian inference of a semiparametric regression model for the Boston Housing dataset. Supplementary materials for this article are available online.

This dataset contains key characteristics about the data described in the Data Descriptor Lake Ohrid’s tephrochronological dataset reveals 1.36 Ma of Mediterranean explosive volcanic activity. Contents: 1. human readable metadata summary table in CSV format 2. machine readable metadata file in JSON format

Additional file 1. Supplementary Tables.

Additional file 4: Table S3. P-values for siRNA knock down experiments (Fig. 6)

Additional file 3: Table S2. Detailed assignments of N-glycans from cell glycoproteins of A2780 cell line using UPLC and exoglycosidase digestions.

Variable importance, interaction measures, and partial dependence plots are important summaries in the interpretation of statistical and machine learning models. In this article, we describe new visualization techniques for exploring these model summaries. We construct heatmap and graph-based displays showing variable importance and interaction jointly, which are carefully designed to highlight important aspects of the fit. We describe a new matrix-type layout showing all single and bivariate partial dependence plots, and an alternative layout based on graph Eulerians focusing on key subsets. Our new visualizations are model-agnostic and are applicable to regression and classification supervised learning settings. They enhance interpretation even in situations where the number of variables is large. Our R package vivid (variable importance and variable interaction displays) provides an implementation. Supplementary files for this article are available online.

Additional file 5: Supplemental Figure 4. C57BL/6 MSCs polarize M0 and skew M1-like macrophages towards an-anti-inflammatory, M2-like phenotype after co-culture.