Applications of Machine Learning in Human Microbiome Studies: A Review on Feature Selection, Biomarker Identification, Disease Prediction and Treatment

The number of microbiome-related studies has notably increased the availability of data on human microbiome composition and function. These studies provide the essential material to deeply explore host-microbiome associations and their relation to the development and progression of various complex diseases. Improved data-analytical tools are needed to exploit all information from these biological datasets, taking into account the peculiarities of microbiome data, i.e., compositional, heterogeneous and sparse nature of these datasets. The possibility of predicting host-phenotypes based on taxonomy-informed feature selection to establish an association between microbiome and predict disease states is beneficial for personalized medicine. In this regard, machine learning (ML) provides new insights into the development of models that can be used to predict outputs, such as classification and prediction in microbiology, infer host phenotypes to predict diseases and use microbial communities to stratify patients by their characterization of state-specific microbial signatures. Here we review the state-of-the-art ML methods and respective software applied in human microbiome studies, performed as part of the COST Action ML4Microbiome activities. This scoping review focuses on the application of ML in microbiome studies related to association and clinical use for diagnostics, prognostics, and therapeutics. Although the data presented here is more related to the bacterial community, many algorithms could be applied in general, regardless of the feature type. This literature and software review covering this broad topic is aligned with the scoping review methodology. The manual identification of data sources has been complemented with: (1) automated publication search through digital libraries of the three major publishers using natural language processing (NLP) Toolkit, and (2) an automated identification of relevant software repositories on GitHub and ranking of the related research papers relying on learning to rank approach.This study was supported by COST Action CA18131 “Statistical and machine learning techniques in human microbiome studies”. Estonian Research Council grant PRG548 (JT). Spanish State Research Agency Juan de la Cierva Grant IJC2019-042188-I (LM-Z). EO was founded and OA was supported by Estonian Research Council grant PUT 1371 and EMBO Installation grant 3573. AG was supported by Statutory Research project of the Department of Computer Networks and Systems

Catalyzing Transcriptomics Research in Cardiovascular Disease : The CardioRNA COST Action CA17129

Cardiovascular disease (CVD) remains the leading cause of death worldwide and, despite continuous advances, better diagnostic and prognostic tools, as well as therapy, are needed. The human transcriptome, which is the set of all RNA produced in a cell, is much more complex than previously thought and the lack of dialogue between researchers and industrials and consensus on guidelines to generate data make it harder to compare and reproduce results. This European Cooperation in Science and Technology (COST) Action aims to accelerate the understanding of transcriptomics in CVD and further the translation of experimental data into usable applications to improve personalized medicine in this field by creating an interdisciplinary network. It aims to provide opportunities for collaboration between stakeholders from complementary backgrounds, allowing the functions of different RNAs and their interactions to be more rapidly deciphered in the cardiovascular context for translation into the clinic, thus fostering personalized medicine and meeting a current public health challenge. Thus, this Action will advance studies on cardiovascular transcriptomics, generate innovative projects, and consolidate the leadership of European research groups in the field.COST (European Cooperation in Science and Technology) is a funding organization for research and innovation networks (www.cost.eu)

LES, RANS and combined simulation of impinging flows and heat transfer

This thesis reports on a numerical study of a round, isothermal turbulent jet of incompressible fluid, impinging normally on a flat wall at a different temperature. The aim was to generate detailed information about the ime-dependent three-dimensional velocity and temperature field, and, based on this, to extract statistically averaged flow and turbulence properties, as well as to identify and analyze the dominant vortical structures, their evolution and thermal signature on the target wall. The main body of the thesis deals with LES studies using the dynamic subgrid-scale model, of flow and heat transfer in a round impinging jet at Re=20000 and orifice-to-plate distance h/D=2. The LES were performed using the in-house unstructured finite-volume computational code T-FlowS. Prior to the jet simulations, the computational code and its features (the numerical schemes and solver, boundary conditions, mesh generation and refinement, implementation of the dynamic sub-grid scale model into an unstructured code) as well as structure identification and interpretation, were tested in the simulation of a plane channel and a pipe flow, the latter with heat transfer. Because a round impinging jet contains several flow regions, each featured by different flow physics (free jet expansion, impingement, jet deflection with strong acceleration, radially spreading and decelerating wall jet), several mesh refinements (with up to 10 million mesh cells), and different conditions at the free inflow boundary of the computational domain were explored until satisfactory agreement with the available experimental results was achieved. The final results, believed to be credibly accurate, were processed to extract the mean flow and turbulence statistics, budgets of the transport equations for the Reynolds stresses and turbulent-heat-flux components, as well as to analyze the time dynamics of the vortical structure and its relation with the instantaneous and averaged wall heat transfer. The LES confirmed some of the experimentally detected features such as a dip and a second maximum in the Nusselt number and negative production of turbulence kinetic energy in the stagnation region, but also revealed some other interesting phenomena such as strong oscillation of the stagnation point and the unsteady flow separation at the onset of wall-jet formation. These events were identified as the main cause of the Nu-number nonuniformity, and were linked to the ring vortices generated in the jet shear layer, and their asymmetric break-up prior or after the impingement. The second focus of the thesis was the study of the feasibility of combining the LES and RANS approaches into a hybrid method. The goal was to provide the time resolved three-dimensional solutions of the velocity and temperature field (though associated with the larger turbulence scales only) while using the mesh size typical of that used in off-wall LES or in the RANS computation. Two directions were pursued in parallel: zonal approaches with predefined RANS and LES zones, and seamless methods with a single statistical model serving both as the RANS- and as the LES sub-grid scale model. Prior to hybrid simulations, several RANS models were tested in computation of several generic flows with heat transfer, aimed at examining their suitability to serve as the near-wall RANS model in hybridization with LES. In order to verify the hybrid approaches, simulations of the plane channel flow at a range of Reynolds numbers have been carried out with four different hybrid models. Two of the hybrid models tested were subsequently applied to simulate the round impinging jet having the same Re number and configuration as in the LES study, but using much coarser grid ($\approx$ 1.6 million). While the hybrid simulations yielded satisfactory results in plane channel flows, their performance in the impinging jet - though superior to the conventional LES when using the same (coarse) mesh, was not fully satisfactory. Several critical issues have been detected requiring further testing that was beyond the scope of this thesis, thus preventing the final conclusions to be drawn. Based on this research, some directions for further research both in hybrid LES/RANS and in conventional LES have been proposed.Applied Science

High-frequency rotary oscillations control of flow around cylinder at Re = 1.4 x 10⁵

We perform a series of URANS simulations of the flow over a rotary oscillating cylinder at Re = 1.4 105 to study the possible reduction of the drag and lift forces acting on the body when the rigid wall is rotary oscillating around the axis of symmetry. Two parameters of the external forcing are varied, i.e. the amplitude of rotation and the frequency. We find that the high-frequency and relatively high-amplitude forcing leads to the drag reduction of 78% compared to the non-rotating case. The oscillations (rms) of drag and lift coefficients are also significantly reduced.ChemE/Transport Phenomen