Variable species densities are induced by volume exclusion interactions upon domain growth.

In this work we study the effect of domain growth on spatial correlations in agent populations containing multiple species. This is important as heterogenous cell populations are ubiquitous during the embryonic development of many species. We have previously shown that the long term behaviour of an agent population depends on the way in which domain growth is implemented. We extend this work to show that, depending on the way in which domain growth is implemented, different species dominate in multispecies simulations. Continuum approximations of the lattice-based model that ignore spatial correlations cannot capture this behaviour, while those that explicitly account for spatial correlations can. The results presented here show that the precise mechanism of domain growth can determine the long term behaviour of multispecies populations, and in certain circumstances, establish spatially varying species densities

The potential for circular dichroism as an additional facile and sensitive method of monitoring low-molecular-weight heparins and heparinoids

The ultraviolet circular dichroism (CD) spectra of commercial low-molecular-weight heparins, heparinoids and other anticoagulant preparations have been recorded between 180 and 260 nm. Principal component analysis of the spectra allowed their differentiation into a number of groups related to the means of their production reflecting the structural changes introduced by each process. The findings suggest that CD provides a complementary technique for the rapid analysis of heparin preparations

Correcting for the overabundance of low-mass quiescent galaxies in semi-analytic models

© 2024 The Author(s). Published by Oxford University Press on behalf of Royal Astronomical Society. This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY), https://creativecommons.org/licenses/by/4.0/We compare the l-galaxies semi-analytic model to deep observational data from the UKIDSS Ultra Deep Survey (UDS) across the redshift range 0.5 \lt; z \lt; 3. We find that the overabundance of low-mass, passive galaxies at high redshifts in the model can be attributed solely to the properties of ‘orphan’ galaxies, i.e. satellite galaxies where the simulation has lost track of the host dark matter sub-halo. We implement a simple model that boosts the star formation rates in orphan galaxies by matching them to non-orphaned satellite galaxies at a similar evolutionary stage. This straightforward change largely addresses the discrepancy in the low-mass passive fraction across all redshifts. We find that the orphan problem is somewhat alleviated by higher resolution simulations, but the preservation of a larger gas reservoir in orphans is still required to produce a better fit to the observed space density of low-mass passive galaxies. Our findings are also robust to the precise definition of the passive galaxy population. In general, considering the vastly different prescriptions used for orphans in semi-analytic models, we recommend that they are analysed separately from the resolved satellite galaxy population, particularly with JWST observations reigniting interest in the low-mass regime in which they dominate.Peer reviewe

Using approximate Bayesian computation to quantify cell-cell adhesion parameters in a cell migratory process

In this work we implement approximate Bayesian computational methods to improve the design of a wound-healing assay used to quantify cell-cell interactions. This is important as cell-cell interactions, such as adhesion and repulsion, have been shown to play a role in cell migration. Initially, we demonstrate with a model of an unrealistic experiment that we are able to identify model parameters that describe agent motility and adhesion, given we choose appropriate summary statistics for our model data. Following this, we replace our model of an unrealistic experiment with a model representative of a practically realisable experiment. We demonstrate that, given the current (and commonly used) experimental set-up, our model parameters cannot be accurately identified using approximate Bayesian computation methods. We compare new experimental designs through simulation, and show more accurate identification of model parameters is possible by expanding the size of the domain upon which the experiment is performed, as opposed to increasing the number of experimental replicates. The results presented in this work therefore describe time and cost-saving alterations for a commonly performed experiment for identifying cell motility parameters. Moreover, this work will be of interest to those concerned with performing experiments that allow for the accurate identification of parameters governing cell migratory processes, especially cell migratory processes in which cell-cell adhesion or repulsion are known to play a significant role

Prospective relationships between body weight and physical activity: an observational analysis from the NAVIGATOR study

Objectives: While bidirectional relationships exist between body weight and physical activity, direction of causality remains uncertain and previous studies have been limited by self-reported activity or weight and small sample size. We investigated the prospective relationships between weight and physical activity. Design: Observational analysis of data from the Nateglinide And Valsartan in Impaired Glucose Tolerance Outcomes Research (NAVIGATOR) study, a double-blinded randomised clinical trial of nateglinide and valsartan, respectively. Setting Multinational study of 9306 participants. Participants: Participants with biochemically confirmed impaired glucose tolerance had annual measurements of both weight and step count using research grade pedometers, worn for 7 days consecutively. Along with randomisation to valsartan or placebo plus nateglinide or placebo, participants took part in a lifestyle modification programme. Outcome measures: Longitudinal regression using weight as response value and physical activity as predictor value was conducted, adjusted for baseline covariates. Analysis was then repeated with physical activity as response value and weight as predictor value. Only participants with a response value preceded by at least three annual response values were included. Results: Adequate data were available for 2811 (30%) of NAVIGATOR participants. Previous weight (χ2=16.8; p<0.0001), but not change in weight (χ2=0.1; p=0.71) was inversely associated with subsequent step count, indicating lower subsequent levels of physical activity in heavier individuals. Change in step count (χ2=5.9; p=0.02) but not previous step count (χ2=0.9; p=0.34) was inversely associated with subsequent weight. However, in the context of trajectories already established for weight (χ2 for previous weight measurements 747.3; p<0.0001) and physical activity (χ2 for previous step count 432.6; p<0.0001), these effects were of limited clinical importance. Conclusions: While a prospective bidirectional relationship was observed between weight and physical activity, the magnitude of any effect was very small in the context of natural trajectories already established for these variables

Bootstrapping Conversational Agents With Weak Supervision

Many conversational agents in the market today follow a standard bot development framework which requires training intent classifiers to recognize user input. The need to create a proper set of training examples is often the bottleneck in the development process. In many occasions agent developers have access to historical chat logs that can provide a good quantity as well as coverage of training examples. However, the cost of labeling them with tens to hundreds of intents often prohibits taking full advantage of these chat logs. In this paper, we present a framework called \textit{search, label, and propagate} (SLP) for bootstrapping intents from existing chat logs using weak supervision. The framework reduces hours to days of labeling effort down to minutes of work by using a search engine to find examples, then relies on a data programming approach to automatically expand the labels. We report on a user study that shows positive user feedback for this new approach to build conversational agents, and demonstrates the effectiveness of using data programming for auto-labeling. While the system is developed for training conversational agents, the framework has broader application in significantly reducing labeling effort for training text classifiers.Comment: 6 pages, 3 figures, 1 table, Accepted for publication in IAAI 201

The effect of domain growth on spatial correlations

Mathematical models describing cell movement and proliferation are important tools in developmental biology research. In this work we present methods to include the effects of domain growth on the evolution of spatial correlations between agent locations in a continuum approximation of a one-dimensional lattice-based model of cell motility and proliferation. This is important as the inclusion of spatial correlations in continuum models of cell motility and proliferation without domain growth has previously been shown to be essential for their accuracy in certain scenarios. We include the effect of spatial correlations by deriving a system of ordinary differential equations that describe the expected evolution of individual and pair density functions for agents on a growing domain. We then demonstrate how to simplify this system of ordinary differential equations by using an appropriate approximation. This simplification allows domain growth to be included in models describing the evolution of spatial correlations between agents in a tractable manner

Variable species densities are induced by volume exclusion interactions upon domain growth.

In this work we study the effect of domain growth on spatial correlations in agent populations containing multiple species. This is important as heterogenous cell populations are ubiquitous during the embryonic development of many species. We have previously shown that the long term behaviour of an agent population depends on the way in which domain growth is implemented. We extend this work to show that, depending on the way in which domain growth is implemented, different species dominate in multispecies simulations. Continuum approximations of the lattice-based model that ignore spatial correlations cannot capture this behaviour, while those that explicitly account for spatial correlations can. The results presented here show that the precise mechanism of domain growth can determine the long term behaviour of multispecies populations, and in certain circumstances, establish spatially varying species densities

How domain growth is implemented determines the long-term behavior of a cell population through its effect on spatial correlations

Domain growth plays an important role in many biological systems, and so the inclusion of domain growth in models of these biological systems is important to understanding how these systems function. In this work we present methods to include the effects of domain growth on the evolution of spatial correlations in a continuum approximation of a lattice-based model of cell motility and proliferation. We show that, depending on the way in which domain growth is implemented, different steady-state densities are predicted for an agent population. Furthermore, we demonstrate that the way in which domain growth is implemented can result in the evolution of the agent density depending on the size of the domain. Continuum approximations that ignore spatial correlations cannot capture these behaviors, while those that account for spatial correlations do. These results will be of interest to researchers in developmental biology, as they suggest that the nature of domain growth can determine the characteristics of cell populations