Model-based machine learning to identify clinical relevance in a high-resolution simulation of sepsis and trauma

Introduction: Sepsis is a devastating, costly, and complicated disease. It represents the summation of varied host immune responses in a clinical and physiological diagnosis. Despite extensive research, there is no current mediator-directed therapy, nor a biomarker panel able to categorize disease severity or reliably predict outcome. Although still distant from direct clinical translation, dynamic computational and mathematical models of acute systemic inflammation and sepsis are being developed. Although computationally intensive to run and calibrate, agent-based models (ABMs) are one type of model well suited for this. New analytical methods to efficiently extract knowledge from ABMs are needed. Specifically, machine-learning techniques are a promising option to augment the model development process such that parameterization and calibration are performed intelligently and efficiently. Methods: We used the Keras framework to train an Artificial Neural Network (ANN) for the purpose of identifying critical biological tipping points at which an in silico patient would heal naturally or require intervention in the Innate Immune Response Agent-Based Model (IIRABM). This ANN, determines simulated patient “survival” from cytokine state based on their overall resilience and the pathogenicity of any active infections experienced by the patient, defined by microbial invasiveness, toxigenesis, and environmental toxicity. These tipping points were gathered from previously generated datasets of simulated sweeps of the 4 IIRABM initializing parameters. Results: Using mean squared error as our loss function, we report an accuracy of greater than 85% with inclusion of 20% of the training set. This accuracy was independently validated on withheld runs. We note that there is some amount of error that is inherent to this process as the determination of the tipping points is a computation which converges monotonically to the true value as a function of the number of stochastic replicates used to determine the point. Conclusion: Our method of regression of these critical points represents an alternative to traditional parameter-sweeping or sensitivity analysis techniques. Essentially, the ANN computes the boundaries of the clinically relevant space as a function of the model’s parameterization, eliminating the need for a brute-force exploration of model parameter space. In doing so, we demonstrate the successful development of this ANN which will allows for an efficient exploration of model parameter space

A Model of Human Categorization and Similarity Based Upon Category Theory

Categorization and the judgement of similarity are fundamental in cognition. We propose that these and other activities are based upon an underlying structure of knowledge, or concept representation, in the brain. Further, we propose that this structure can be represented mathematically in a declarative form via category theory, the mathematical theory of structure. We test the resulting mathematical model in an experiment in which human subjects provide judgements of similarity for pairs of line drawings using a numerical scale to represent degrees of similarity. The resulting numerical similarities are compared with those derived from the category-theoretic model by comparing diagrams. The diagrams represent distributed concept structures underlying the line drawings. To compare with a more conventional analysis technique, we also compare the human judgements with those provided by a two-dimensional feature space model equipped with a distance metric for the line drawings. The results are equally favorable for both models. Because of this and the putative explanatory power of the category-theoretic model, we propose that this model is worthy of further exploration as a mathematical model for cognitive science

Development of the automation and control of a microgravity experiment by means of LabVIEW

This master thesis is part of an experimental project carried out at the Space Exploration Laboratory of the Universitat Politècnica de Catalunya, by a five master¿s students team, under the guidance of Ricard Gonzalez Cinca, to study the use of acoustic waves, for heat transfer enhancement, in a gas medium and with the innovation of been done under microgravity conditions, which have never been done before. The objective of the experiment is to obtain new knowledge in this field, which might help Scientifics develop a new version of a thermal control system. The students had the opportunity to experiment under microgravity conditions, by being selected for the Drop Your Thesis Programme, provided by ESA academy, allowing the students to develop the experiment at the Space Exploration Laboratory and test it in the ZARM drop tower. The experiment was divided into different subsystems: mechanical, software, electronics, mathematical simulations and testing. Each subsystem was developed by one of the masters students. This master thesis is focused on the software part of the experiment, designed using LabVIEW from National Instruments. This document is divided into three main parts, starting with the introduction to the experiment, which explains the experiment background information, as well as the Your Thesis Programme, then it continues with the introduction of the experiment software platform, LabVIEW. The thesis finalises with an explanation of the experiment control software developed

Communitarian mathematics education: walking into boundaries

In this article, we outline some of the challenges involved in the construction of a communitarian mathematics education within the realms of the Urban Boundaries Project. The Urban Boundaries Project has been innovative in the way it congregates the critical development of basic needs of three distinct communities with an Ethnomathematics Posture (MESQUITA; RESTIVO; D’AMBROSIO, 2011), where concepts such as critical participation, ethnomathematics, violence, and urban boundaries, are discussed through political philosophy. This posture allows us to share the local history of the empowerment, autonomy, and wisdom of the three communities’ situationality involved in the project, i.e., their knowledge of their political space. The local history is shared through the systematization of some content collected with critical ethnographical registration. To put it in Lacanian terms, we seek to symbolize a set of life-world experiences that have been historically neglected in mathematics education research, as well as in educational sciences more generally. As a result, this paper instigates the exploration of “disturbing” choices in the mathematical education culture

Identification of neutral biochemical network models from time series data

<p>Abstract</p> <p>Background</p> <p>The major difficulty in modeling biological systems from multivariate time series is the identification of parameter sets that endow a model with dynamical behaviors sufficiently similar to the experimental data. Directly related to this parameter estimation issue is the task of identifying the structure and regulation of ill-characterized systems. Both tasks are simplified if the mathematical model is canonical, <it>i.e</it>., if it is constructed according to strict guidelines.</p> <p>Results</p> <p>In this report, we propose a method for the identification of admissible parameter sets of canonical S-systems from biological time series. The method is based on a Monte Carlo process that is combined with an improved version of our previous parameter optimization algorithm. The method maps the parameter space into the network space, which characterizes the connectivity among components, by creating an ensemble of decoupled S-system models that imitate the dynamical behavior of the time series with sufficient accuracy. The concept of sloppiness is revisited in the context of these S-system models with an exploration not only of different parameter sets that produce similar dynamical behaviors but also different network topologies that yield dynamical similarity.</p> <p>Conclusion</p> <p>The proposed parameter estimation methodology was applied to actual time series data from the glycolytic pathway of the bacterium <it>Lactococcus lactis </it>and led to ensembles of models with different network topologies. In parallel, the parameter optimization algorithm was applied to the same dynamical data upon imposing a pre-specified network topology derived from prior biological knowledge, and the results from both strategies were compared. The results suggest that the proposed method may serve as a powerful exploration tool for testing hypotheses and the design of new experiments.</p

The discipline of Natural Design

If we define design work as those cognitive and practical things to which designers give their valuable effort, then our Natural Design framework allows the recording and replaying of design work. Natural Design provides a meta-structural framework that has developed through our observations of engineering design in safety and mission critical industries, such as aircraft design. Our previous work has produced parametrisable models of design work for software intensive systems, and we now look to make an initial assessment of our natural design framework for its fit to the more creative design practices. In this paper we briefly sketch the framework and subsequently attempt to locate ‘creativity’ in it. We find that, although there are good strong hooks for what the designer does, we are forced to find a role for the community of the designer in the creative process in our framework, something that was only implicit in our previous work. Keywords: Natural design; Engineering design; Creativity</p

Computational interaction techniques for 3D selection, manipulation and navigation in immersive VR

3D interaction provides a natural interplay for HCI. Many techniques involving diverse sets of hardware and software components have been proposed, which has generated an explosion of Interaction Techniques (ITes), Interactive Tasks (ITas) and input devices, increasing thus the heterogeneity of tools in 3D User Interfaces (3DUIs). Moreover, most of those techniques are based on general formulations that fail in fully exploiting human capabilities for interaction. This is because while 3D interaction enables naturalness, it also produces complexity and limitations when using 3DUIs. In this thesis, we aim to generate approaches that better exploit the high potential human capabilities for interaction by combining human factors, mathematical formalizations and computational methods. Our approach is focussed on the exploration of the close coupling between specific ITes and ITas while addressing common issues of 3D interactions. We specifically focused on the stages of interaction within Basic Interaction Tasks (BITas) i.e., data input, manipulation, navigation and selection. Common limitations of these tasks are: (1) the complexity of mapping generation for input devices, (2) fatigue in mid-air object manipulation, (3) space constraints in VR navigation; and (4) low accuracy in 3D mid-air selection. Along with two chapters of introduction and background, this thesis presents five main works. Chapter 3 focusses on the design of mid-air gesture mappings based on human tacit knowledge. Chapter 4 presents a solution to address user fatigue in mid-air object manipulation. Chapter 5 is focused on addressing space limitations in VR navigation. Chapter 6 describes an analysis and a correction method to address Drift effects involved in scale-adaptive VR navigation; and Chapter 7 presents a hybrid technique 3D/2D that allows for precise selection of virtual objects in highly dense environments (e.g., point clouds). Finally, we conclude discussing how the contributions obtained from this exploration, provide techniques and guidelines to design more natural 3DUIs

The Possibilities of the Parabola

The present study details an investigation into the provision of opportunities for secondary school students to develop an understanding of visual aesthetics by manipulating a single mathematical curve: the parabola. The study documents the establishment of a robust cross-curricular relationship between Mathematics and Visual Design by providing a pedagogical model for learning that relies on the conduit between the two subject areas being explicitly linked. Although the central concept is the marriage of disparate themes, it was operationalised by the development and delivery of activities sequenced to grow appreciation and understanding of the links between unrelated curricula. While this account aims to foster cross-curricular discourse and action, the product output simultaneously provided avenues for the presentation and exhibition of student work; a gallery of which is included in the study. Documenting the inter-disciplinary approach to learning and teaching has resulted in an exploration of the complexities we employ to discover meaning in a range of contexts not singularly reliant on art or language. A convergence is presented in that mathematical rules unite with the rules of art and design in the attempt to project new concepts into new situations where a space for originality exists. Here, the students have been encouraged to imagine new, effective ways of bringing ideas to form (Richmond, 2009). Naturally, developing explicit appreciation/action situations required critical and creative thinking to coincide with lateral and literal approaches to gaining knowledge and understanding of aesthetics. The study presents a reflexive account of the delivery of coursework entitled The Possibilities of the Parabola, from concept to completion