The pseudo-compartment method for coupling PDE and compartment-based models of diffusion

Spatial reaction-diffusion models have been employed to describe many emergent phenomena in biological systems. The modelling technique most commonly adopted in the literature implements systems of partial differential equations (PDEs), which assumes there are sufficient densities of particles that a continuum approximation is valid. However, due to recent advances in computational power, the simulation, and therefore postulation, of computationally intensive individual-based models has become a popular way to investigate the effects of noise in reaction-diffusion systems in which regions of low copy numbers exist. The stochastic models with which we shall be concerned in this manuscript are referred to as `compartment-based'. These models are characterised by a discretisation of the computational domain into a grid/lattice of `compartments'. Within each compartment particles are assumed to be well-mixed and are permitted to react with other particles within their compartment or to transfer between neighbouring compartments. We develop two hybrid algorithms in which a PDE is coupled to a compartment-based model. Rather than attempting to balance average fluxes, our algorithms answer a more fundamental question: `how are individual particles transported between the vastly different model descriptions?' First, we present an algorithm derived by carefully re-defining the continuous PDE concentration as a probability distribution. Whilst this first algorithm shows strong convergence to analytic solutions of test problems, it can be cumbersome to simulate. Our second algorithm is a simplified and more efficient implementation of the first, it is derived in the continuum limit over the PDE region alone. We test our hybrid methods for functionality and accuracy in a variety of different scenarios by comparing the averaged simulations to analytic solutions of PDEs for mean concentrations.Comment: MAIN - 24 pages, 10 figures, 1 supplementary file - 3 pages, 2 figure

Generation of Busulfan Chimeric Mice for the Analysis of T Cell Population Dynamics

This protocol was developed to generate chimeric mice in which T lymphocytes could be stratified by age on the basis of congenic marker expression. The conditioning drug busulfan is used to ablate host haematopoietic stem cells while leaving the peripheral immune system intact. Busulfan treatment is followed by bone marrow transplantation (BMT), with T-cell depleted donor bone marrow bearing a different congenic marker (CD45.2) to that of the host mouse (CD45.1). New cell production post-BMT can thus be tracked by measuring the fraction of CD45.2^{+} cells over time within a population of interest (Hogan et al., 2015; Gossel et al., 2017)

Research and applications: Artificial intelligence

The program is reported for developing techniques in artificial intelligence and their application to the control of mobile automatons for carrying out tasks autonomously. Visual scene analysis, short-term problem solving, and long-term problem solving are discussed along with the PDP-15 simulator, LISP-FORTRAN-MACRO interface, resolution strategies, and cost effectiveness

CVD of CrO2: towards a lower temperature deposition process

We report on the synthesis of highly oriented a-axis CrO2 films onto (0001) sapphire by atmospheric pressure CVD from CrO3 precursor, at growth temperatures down to 330 degree Celsius, i.e. close to 70 degrees lower than in published data for the same chemical system. The films keep the high quality magnetic behaviour as those deposited at higher temperature, which can be looked as a promising result in view of their use with thermally sensitive materials, e.g. narrow band gap semiconductors.Comment: 13 pages, 4 figure

Improving the Generalizability of Depression Detection by Leveraging Clinical Questionnaires

Automated methods have been widely used to identify and analyze mental health conditions (e.g., depression) from various sources of information, including social media. Yet, deployment of such models in real-world healthcare applications faces challenges including poor out-of-domain generalization and lack of trust in black box models. In this work, we propose approaches for depression detection that are constrained to different degrees by the presence of symptoms described in PHQ9, a questionnaire used by clinicians in the depression screening process. In dataset-transfer experiments on three social media datasets, we find that grounding the model in PHQ9's symptoms substantially improves its ability to generalize to out-of-distribution data compared to a standard BERT-based approach. Furthermore, this approach can still perform competitively on in-domain data. These results and our qualitative analyses suggest that grounding model predictions in clinically-relevant symptoms can improve generalizability while producing a model that is easier to inspect

Improving the Generalizability of Depression Detection by Leveraging Clinical Questionnaires

Automated methods have been widely used to identify and analyze mental healthconditions (e.g., depression) from various sources of information, includingsocial media. Yet, deployment of such models in real-world healthcareapplications faces challenges including poor out-of-domain generalization andlack of trust in black box models. In this work, we propose approaches fordepression detection that are constrained to different degrees by the presenceof symptoms described in PHQ9, a questionnaire used by clinicians in thedepression screening process. In dataset-transfer experiments on three socialmedia datasets, we find that grounding the model in PHQ9's symptomssubstantially improves its ability to generalize to out-of-distribution datacompared to a standard BERT-based approach. Furthermore, this approach canstill perform competitively on in-domain data. These results and ourqualitative analyses suggest that grounding model predictions inclinically-relevant symptoms can improve generalizability while producing amodel that is easier to inspect.<br

Subsonic aerodynamic and flutter characteristics of several wings calculated by the SOUSSA P1.1 panel method

The SOUSSA (steady, oscillatory, and unsteady subsonic and supersonic aerodynamics) program is the computational implementation of a general potential flow analysis (by the Green's function method) that can generate pressure distributions on complete aircraft having arbitrary shapes, motions and deformations. Some applications of the initial release version of this program to several wings in steady and oscillatory motion, including flutter are presented. The results are validated by comparisons with other calculations and experiments. Experiences in using the program as well as some recent improvements are described

Ranking and clustering of nodes in networks with smart teleportation

Random teleportation is a necessary evil for ranking and clustering directed networks based on random walks. Teleportation enables ergodic solutions, but the solutions must necessarily depend on the exact implementation and parametrization of the teleportation. For example, in the commonly used PageRank algorithm, the teleportation rate must trade off a heavily biased solution with a uniform solution. Here we show that teleportation to links rather than nodes enables a much smoother trade-off and effectively more robust results. We also show that, by not recording the teleportation steps of the random walker, we can further reduce the effect of teleportation with dramatic effects on clustering.Comment: 10 pages, 7 figure