Contemporary Challenges and Solutions

CA18131 CP16/00163 NIS-3317 NIS-3318 decision 295741 C18/BM/12585940The human microbiome has emerged as a central research topic in human biology and biomedicine. Current microbiome studies generate high-throughput omics data across different body sites, populations, and life stages. Many of the challenges in microbiome research are similar to other high-throughput studies, the quantitative analyses need to address the heterogeneity of data, specific statistical properties, and the remarkable variation in microbiome composition across individuals and body sites. This has led to a broad spectrum of statistical and machine learning challenges that range from study design, data processing, and standardization to analysis, modeling, cross-study comparison, prediction, data science ecosystems, and reproducible reporting. Nevertheless, although many statistics and machine learning approaches and tools have been developed, new techniques are needed to deal with emerging applications and the vast heterogeneity of microbiome data. We review and discuss emerging applications of statistical and machine learning techniques in human microbiome studies and introduce the COST Action CA18131 “ML4Microbiome” that brings together microbiome researchers and machine learning experts to address current challenges such as standardization of analysis pipelines for reproducibility of data analysis results, benchmarking, improvement, or development of existing and new tools and ontologies.publishersversionpublishe

Statistical and Machine Learning Techniques in Human Microbiome Studies: Contemporary Challenges and Solutions

The human microbiome has emerged as a central research topic in human biology and biomedicine. Current microbiome studies generate high-throughput omics data across different body sites, populations, and life stages. Many of the challenges in microbiome research are similar to other high-throughput studies, the quantitative analyses need to address the heterogeneity of data, specific statistical properties, and the remarkable variation in microbiome composition across individuals and body sites. This has led to a broad spectrum of statistical and machine learning challenges that range from study design, data processing, and standardization to analysis, modeling, cross-study comparison, prediction, data science ecosystems, and reproducible reporting. Nevertheless, although many statistics and machine learning approaches and tools have been developed, new techniques are needed to deal with emerging applications and the vast heterogeneity of microbiome data. We review and discuss emerging applications of statistical and machine learning techniques in human microbiome studies and introduce the COST Action CA18131 "ML4Microbiome" that brings together microbiome researchers and machine learning experts to address current challenges such as standardization of analysis pipelines for reproducibility of data analysis results, benchmarking, improvement, or development of existing and new tools and ontologies

The Antioxidant Effect of Green Tea, Rosemary, and Their Combination on Resin Bond Strength to Bleach Tooth Structures

Objective: This study aimed to evaluate the effect of four experimental antioxidant protocols on the shear bond strength of a resin-based composite to bleach the enamel and dentin. Materials and Methods: Using extracted bovine incisors, 140 enamel/140 dentin specimens were prepared. Both enamel and dentin samples were assigned into seven groups, individually (n=20): ENC/DNC= negative control, EPC/DPC= positive control, EDR/DDR= delayed restoration, ESA/DSA= sodium ascorbate, EGT/DGT= green tea, ER/DR= rosemary and EGTR/DGTR= green tea and rosemary combination. Experimental antioxidant solutions prepared from sodium ascorbate, green tea, or rosemary extracts were applied to the bleached enamel/dentin samples in the ESA/DSA, EGT/DGT and ER/DR groups, respectively. The mixture of the green tea/ rosemary extract solutions at a 1:1 ratio was applied to the EGTR/DGTR groups to investigate possible synergistic antioxidant interaction. The shear bond strength (SBS) test was conducted at a crosshead speed of 0.5 mm/minute. Failure modes were assessed under a stereomicroscope at x40 magnification. Data were analysed statistically using Welch-ANOVA and Tamhane post-hoc tests. Results: The lowest and highest mean SBS values were obtained in the positive control groups (EPC/DPC) and negative control groups (ENC/DNC), respectively (p0.05). Synergistic antioxidant interaction could not be obtained in the green tea and rosemary combination protocol. Conclusion: Natural plant-derived antioxidants can be an alternative to synthetic sodium ascorbate and may enable immediate resin restorations of bleached tooth structures

Temporal overexpression of IL-22 and Reg3γ differentially impacts the severity of experimental autoimmune encephalomyelitis.

IL-22 is an alpha-helical cytokine which belongs to the IL-10 family of cytokines. IL-22 is produced by ROR gamma t+ innate and adaptive lymphocytes, including ILC3, gamma delta T, iNKT, Th17 and Th22 cells and some granulocytes. IL-22 receptor is expressed primarily by non-haematopoietic cells. IL-22 is critical for barrier immunity at the mucosal surfaces in the steady state and during infection. Although IL-22 knockout mice were previously shown to develop experimental autoimmune encephalomyelitis (EAE), a murine model of multiple sclerosis (MS), how temporal IL-22 manipulation in adult mice would affect EAE course has not been studied previously. In this study, we overexpressed IL-22 via hydrodynamic gene delivery or blocked it via neutralizing antibodies in C57BL/6 mice to explore the therapeutic impact of IL-22 modulation on the EAE course. IL-22 overexpression significantly decreased EAE scores and demyelination, and reduced infiltration of IFN-gamma+IL-17A+Th17 cells into the central nervous system (CNS). The neutralization of IL-22 did not alter the EAE pathology significantly. We show that IL-22-mediated protection is independent of Reg3 gamma, an epithelial cell-derived antimicrobial peptide induced by IL-22. Thus, overexpression of Reg3 gamma significantly exacerbated EAE scores, demyelination and infiltration of IFN-gamma+IL-17A+ and IL-17A+GM-CSF+Th17 cells to CNS. We also show that Reg3 gamma may inhibit IL-2-mediated STAT5 signalling and impair expansion of Treg cells in vivo and in vitro. Finally, Reg3 gamma overexpression dramatically impacted intestinal microbiota during EAE. Our results provide novel insight into the role of IL-22 and IL-22-induced antimicrobial peptide Reg3 gamma in the pathogenesis of CNS inflammation in a murine model of MS

Statistical and Machine Learning Techniques in Human Microbiome Studies: Contemporary Challenges and Solutions

The human microbiome has emerged as a central research topic in human biology and biomedicine. Current microbiome studies generate high-throughput omics data across different body sites, populations, and life stages. Many of the challenges in microbiome research are similar to other high-throughput studies, the quantitative analyses need to address the heterogeneity of data, specific statistical properties, and the remarkable variation in microbiome composition across individuals and body sites. This has led to a broad spectrum of statistical and machine learning challenges that range from study design, data processing, and standardization to analysis, modeling, cross-study comparison, prediction, data science ecosystems, and reproducible reporting. Nevertheless, although many statistics and machine learning approaches and tools have been developed, new techniques are needed to deal with emerging applications and the vast heterogeneity of microbiome data. We review and discuss emerging applications of statistical and machine learning techniques in human microbiome studies and introduce the COST Action CA18131 “ML4Microbiome” that brings together microbiome researchers and machine learning experts to address current challenges such as standardization of analysis pipelines for reproducibility of data analysis results, benchmarking, improvement, or development of existing and new tools and ontologies