Multiple Experimental Modalities Research Articles

Identification of constitutive materials of bi-layer soft tissues from multimodal indentations

The personalisation of finite element models is an important problem in the biomechanical fields where subject-specific analyses are fundamental, particularly in studying soft tissue mechanics. The personalisation includes the choice of the constitutive law of the model's material, as well as the choice of the material parameters. In vivo identification of the material properties of soft tissues is challenging considering the complex behaviour of soft tissues that are, among other things, non-linear hyperelastic and heterogeneous. Hybrid experimental-numerical methods combining in vivo indentations and inverse finite element analyses are common to identify these material parameters. Yet, the uniqueness and the uncertainty of the multi-material hyperelastic model have not been evaluated. This study presents a sensitivity analysis performed on synthetic indentation data to investigate the identification uncertainties of the material parameters in a bi-material thigh phantom. Synthetic numerical data, used to replace experimental measurements, considered several measurement modalities: indenter force and displacement, stereo-camera 3D digital image correlation of the indented surface, and ultrasound B-mode images. A finite element model of the indentation was designed with either Ogden-Moerman or Mooney-Rivlin constitutive laws for both materials. The parameters' identifiability (i.e. the possibility of converging to a unique parameter set within an acceptable margin of error) was assessed with various cost functions formulated using the different synthetic data sets. The results underline the need for multiple experimental modalities to reduce the uncertainty of the identified parameters. Additionally, the experimental error can impede the identification of a unique parameter set, and the cost function depends on the constitutive law. This study highlights the need for sensitivity analyses before the design of the experimental protocol. Such studies can also be used to define the acceptable range of errors in the experimental measurement. Eventually, the impact of the evaluated uncertainty of the identified parameters should be further investigated according to the purpose of the finite element modelling.

Disentangling the effects of doping, strain and disorder in monolayer WS2 by optical spectroscopy

Monolayers of transition metal dichalcogenides (TMdC) are promising candidates for realization of a new generation of optoelectronic devices. The optical properties of these two-dimensional materials, however, vary from flake to flake, or even across individual flakes, and change over time, all of which makes control of the optoelectronic properties challenging. There are many different perturbations that can alter the optical properties, including charge doping, defects, strain, oxidation, adsorbed molecules and water intercalation. Identifying which perturbations are present is usually not straightforward and requires multiple measurements using multiple experimental modalities, which presents barriers when attempting to optimise preparation of these materials. Here, we apply high-resolution photoluminescence and differential reflectance hyperspectral imaging in situ to CVD-grown WS2 monolayers. By combining these two optical measurements and using a statistical correlation analysis we are able to disentangle three contributions modulating optoelectronic properties of these materials: electron doping, strain and disorder arising from adsorbates, substrate fluctuations and/or defects. In separating these contributions, we also observe that the B-exciton energy is less sensitive to variations in doping density than A-excitons. Our approach is not limited to monolayers of WS2 and is also applicable to other 2D materials with optical transitions, and the general approach could be extended to other characterization modalities.

Stochastic block models with multiple continuous attributes

The stochastic block model (SBM) is a probabilistic model for community structure in networks. Typically, only the adjacency matrix is used to perform SBM parameter inference. In this paper, we consider circumstances in which nodes have an associated vector of continuous attributes that are also used to learn the node-to-community assignments and corresponding SBM parameters. Our model assumes that the attributes associated with the nodes in a network’s community can be described by a common multivariate Gaussian model. In this augmented, attributed SBM, the objective is to simultaneously learn the SBM connectivity probabilities with the multivariate Gaussian parameters describing each community. While there are recent examples in the literature that combine connectivity and attribute information to inform community detection, our model is the first augmented stochastic block model to handle multiple continuous attributes. This provides the flexibility in biological data to, for example, augment connectivity information with continuous measurements from multiple experimental modalities. Because the lack of labeled network data often makes community detection results difficult to validate, we highlight the usefulness of our model for two network prediction tasks: link prediction and collaborative filtering. As a result of fitting this attributed stochastic block model, one can predict the attribute vector or connectivity patterns for a new node in the event of the complementary source of information (connectivity or attributes, respectively). We also highlight two biological examples where the attributed stochastic block model provides satisfactory performance in the link prediction and collaborative filtering tasks.

An Auto-luminescent Fluorescent BCG Whole Blood Assay to Enable Evaluation of Paediatric Mycobacterial Responses Using Minimal Blood Volumes.

Introduction: Understanding protective human immunity against mycobacteria is critical to developing and evaluating new vaccines against tuberculosis. Children are the most susceptible population to infection, disease, and death from tuberculosis, but also have the strongest evidence of BCG-inducible protection. Limited amounts of blood can be obtained for research purposes in paediatrics and therefore there is a need for high-yield, low-volume, human immunology assays.Methods: We transformed BCG Danish with plasmids encoding luciferase full operon derived from Photorhabdus luminescens together with Green Fluorescent Protein and antibiotic selection markers. We characterised the luminescent and fluorescent properties of this recombinant BCG strain (BCG-GFP-LuxFO) using a luminometer and flow cytometry and developed a paediatric whole blood in vitro infection model.Results: Luminescence of BCG-GFP-LuxFO correlated with optical density (Spearman Rank Correlation coefficient r = 0.985, p < 0.0001) and colony forming units (CFUs) in liquid culture medium (r = 0.971, p < 0.0001). Fluorescence of BCG-GFP-LuxFO in paediatric whole blood was confirmed by flow cytometry in granulocytes and monocytes 1 h following infection. Luminescence of BCG-GFP-LuxFO in whole blood corresponded with CFUs (r = 0.7123, p < 0.0001).Conclusion: The BCG-GFP-LuxFO assay requires 225 μL whole blood per sample, from which serial luminescence measurements can be obtained, together with biochemical analysis of supernatants and cellular assay applications using its fluorescent properties. This offers the opportunity to study human-mycobacterial interactions using multiple experimental modalities with only minimal blood volumes. It is therefore a valuable method for investigating paediatric immunity to tuberculosis.

Estimability Analysis and Optimal Design in Dynamic Multi-scale Models of Cardiac Electrophysiology.

We present an applied approach to optimal experimental design and estimability analysis for mechanistic models of cardiac electrophysiology, by extending and improving on existing computational and graphical methods. These models are 'multi-scale' in the sense that the modeled phenomena occur over multiple spatio-temporal scales (e.g., single cell vs. whole heart). As a consequence, empirical observations of multi-scale phenomena often require multiple distinct experimental procedures. We discuss the use of conventional optimal design criteria (e.g., D-optimality) in combining experimental observations across multiple scales and multiple experimental modalities. In addition, we present an improved 'sensitivity plot' - a graphical assessment of parameter estimability - that overcomes a well-known limitation in this context. These techniques are demonstrated using a working Hodgkin-Huxley cell model and three simulated experimental procedures: single cell stimulation, action potential propagation, and voltage clamp. In light of these assessments, we discuss two model modifications that improve parameter estimability, and show that the choice of optimality criterion has a profound effect on the contribution of each experiment.

Salivary cortisol and soluble tumor necrosis factor‐α receptor II responses to multiple experimental modalities of acute pain

The present study compared cortisol and soluble tumor necrosis factor-α receptor II (sTNFαRII) responses provoked by cold pressor, hot water, ischemic, and neutral water (i.e., room temperature) modalities. Oral fluid samples were collected before, immediately after, and during recovery to assess physiological responses. From baseline, the cold pressor, but not hot water or ischemic modalities, produced a significant time-dependent elevation in cortisol, whereas cortisol significantly decreased for the neutral water task. When compared to baseline, the cold pressor, hot water, and ischemic modalities were associated with decreased sTNFαRII responses over time. The sTNFαRII response to neutral water initially decreased but returned to approximate baseline levels. Pain ratings were positively associated with cortisol increase from baseline and the overall cortisol response was negatively associated with the overall sTNFαRII response.

MHC-I prediction using a combination of T cell epitopes and MHC-I binding peptides

We propose a novel learning method that combines multiple experimental modalities to improve the MHC Class-I binding prediction. Multiple experimental modalities are often accessible in the context of a binding problem. Such modalities can provide different labels of data, such as binary classifications, affinity measurements, or direct estimations of the binding profile. Current machine learning algorithms usually focus on a given label type. We here present a novel Multi-Label Vector Optimization (MLVO) formalism to produce classifiers based on the simultaneous optimization of multiple labels. Within this methodology, all label types are combined into a single constrained quadratic dual optimization problem. We apply the MLVO to MHC class-I epitope prediction. We combine affinity measurements (IC50/EC50), binary classifications of epitopes as T cell activators and existing algorithms. The multi-label vector optimization algorithms produce classifiers significantly better than the ones resulting from any of its components. These matrix based classifiers are better or equivalent to the existing state of the art MHC-I epitope prediction tools in the studied alleles.