Understanding and distinguishing three time scale oscillations

A variety of bursting and spiking patterns arise in models for respiratory neurons [1] and other neurons associated with hormone release [2]. These models often feature quantities evolving on distinct time scales, such as fast voltage and slower ion current activation or inactivation variables. Furthermore, such systems may combine multiple interacting oscillatory mechanisms, such as intrinsic calcium oscillations together with a calcium-dependent, voltage-gated membrane potential oscillation mechanism.\ud \ud Motivated by the activity observed in these models, the goal of this project is to understand bursting dynamics in three time scale systems. We are particularly interested in knowing how much of the complication in burst patterns results from the presence of three or more timescales in the system and how much is, rather, a reflection of the models being relatively high dimensional nonlinear systems with many parameters. With this motivation, we construct a model consisting of two copies of Morris-Lecar equations with three time scales. By considering two viewpoints within the realm of geometric singular perturbation theory, we explain the mechanisms underlying the bursting dynamics of our model system, making use of computed critical manifold, superslow manifold and bifurcations. To elucidate which characteristics truly represent three time scale features, we investigate certain reductions to two time scales and the parameter dependence of solution features in the three time scale framework.\ud \ud Our results in this area may be useful for characterizing, developing models of, and analyzing models of experimental data. Moreover, they provide useful information for future modeling studies, where a determination needs to be made about how many time scales to include in a model to represent some given experimental data

Culture-Related Health Disparities in Quality of Life: Assessment of Instrument Dimensions Among Chinese

Background: Health-related quality of life (HRQoL) is one of the major focuses of primary care. However, HRQoL instruments used in China are mainly developed from Western countries. Such instruments may not cover all important health concepts valued by the Chinese as health is a culture-specific concept.Objectives: The objectives of this study are to identify culture-specific health dimensions and culture-related health disparities in primary care that are considered important by Chinese living in China.Methods: A purposive sample of 164 adult Chinese (67 healthy persons and 97 patients) were interviewed face to face. In-depth open-ended questions were asked to elicit culture-specific dimensions of quality of life in primary care settings in China.Results: Twelve health dimensions were identified. Five most frequently mentioned dimensions were: mood (N = 52, 31.71%), physical activities (N = 48, 29.27%), work (N = 40, 24.39%), diet (N = 32, 19.51%), and vitality (N = 28, 17.07%). Significantly more healthy persons reported mood (49.25 vs. 19.59%, P < 0.001), mindset (16.42 vs. 0.00%, P < 0.001), and self-care (11.94 vs. 2.06%, P = 0.016) characterizing good HRQoL, while more patients emphasized on work (4.48 vs. 38.14%, P < 0.001). Diet and vitality appeared to be culture-specific dimensions related to health among Chinese.Conclusions: To better adapt or develop HRQoL instruments for Chinese, dimensions or items regarding diet might be included and disparities in the meaning of vitality between Chinese and Western cultures should be considered

Hunting imaging biomarkers in pulmonary fibrosis: Benchmarks of the AIIB23 challenge

Airway-related quantitative imaging biomarkers are crucial for examination, diagnosis, and prognosis in pulmonary diseases. However, the manual delineation of airway structures remains prohibitively time-consuming. While significant efforts have been made towards enhancing automatic airway modelling, current public-available datasets predominantly concentrate on lung diseases with moderate morphological variations. The intricate honeycombing patterns present in the lung tissues of fibrotic lung disease patients exacerbate the challenges, often leading to various prediction errors. To address this issue, the 'Airway-Informed Quantitative CT Imaging Biomarker for Fibrotic Lung Disease 2023' (AIIB23) competition was organized in conjunction with the official 2023 International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI). The airway structures were meticulously annotated by three experienced radiologists. Competitors were encouraged to develop automatic airway segmentation models with high robustness and generalization abilities, followed by exploring the most correlated QIB of mortality prediction. A training set of 120 high-resolution computerised tomography (HRCT) scans were publicly released with expert annotations and mortality status. The online validation set incorporated 52 HRCT scans from patients with fibrotic lung disease and the offline test set included 140 cases from fibrosis and COVID-19 patients. The results have shown that the capacity of extracting airway trees from patients with fibrotic lung disease could be enhanced by introducing voxel-wise weighted general union loss and continuity loss. In addition to the competitive image biomarkers for mortality prediction, a strong airway-derived biomarker (Hazard ratio>1.5, p < 0.0001) was revealed for survival prognostication compared with existing clinical measurements, clinician assessment and AI-based biomarkers

Distinguishing cell shoving mechanisms.

Motivated by in vitro time-lapse images of ovarian cancer spheroids inducing mesothelial cell clearance, the traditional agent-based model of cell migration, based on simple volume exclusion, was extended to include the possibility that a cell seeking to move into an occupied location may push the resident cell, and any cells neighbouring it, out of the way to occupy that location. In traditional discrete models of motile cells with volume exclusion such a move would be aborted. We introduce a new shoving mechanism which allows cells to choose the direction to shove cells that expends the least amount of shoving effort (to account for the likely resistance of cells to being pushed). We call this motility rule 'smart shoving'. We examine whether agent-based simulations of different shoving mechanisms can be distinguished on the basis of single realisations and averages over many realisations. We emphasise the difficulty in distinguishing cell mechanisms from cellular automata simulations based on snap-shots of cell distributions, site-occupancy averages and the evolution of the number of cells of each species averaged over many realisations. This difficulty suggests the need for higher resolution cell tracking

Invasion of an occupied domain from the horizontal midline (displayed in Fig 2(b)).

<p>Green gentlemen cells are present with density <i>c</i>_<i>G</i> = 0.6. Red cells enter the domain at a rate of four attempts per time-step. The continuous curves denotes simple shovers, and broken curves denotes smart shovers. Site occupancies, obtained from the agent–based model, are averaged over 300 independent realisations at times <i>t</i> = 50, 100, 250. Arrows show increasing time.</p

Single realisations of the agent–based model describing invasion of an occupied domain from three separate locations (displayed in Fig 2).

<p>Green resident cells are gentlemen in each scenario (with initial uniform concentration <i>c</i>_<i>G</i> = 0.6). Red cells enter the domain at a rate of four attempts per time-step. Simulations are displayed at times <i>t</i> = 50, 250. The first column represents gentlemen invaders, the middle column represents simple shover invaders and the third column represents smart shover invaders.</p

Schematic of the three invasion regions considered.

<p>Invasion regions are displayed in red, whereas a background of resident cells is displayed in green. When considering invasion of red cells into an empty domain, the uniform concentration of the green cells is <i>c</i>_<i>G</i> = 0. When considering invasion of red cells into a non-empty domain, the uniform concentration of the green cells is taken to be <i>c</i>_<i>G</i> = 0.6, unless otherwise stated. The domain has dimensions <i>L</i>_<i>x</i> × <i>L</i>_<i>y</i> = 200 × 20. In a) the red cells enter the lattice from a 2D band in the middle region of the lattice defined by coordinates <i>x</i> ∈ [90, 109] and <i>y</i> ∈ [1, 20]. In b) the red cells enter the lattice from the horizontal midline of the lattice defined by coordinates <i>x</i> ∈ [90, 109] and <i>y</i> = 10. In c) the red cells enter the lattice from upper horizontal boundary of the lattice defined by coordinates <i>x</i> ∈ [90, 109] and <i>y</i> = 20. Homogenous vertical and reflecting horizontal boundary conditions are implemented in all simulations of the agent–based model. In all simulations, invasion of red cells occurs at a rate of 4 attempts per time-step.</p

Invasion of an occupied domain from the 2D band (displayed in Fig 2(a)).

<p>Green gentlemen cells are present with density <i>c</i>_<i>G</i> = 0.6. Red cells enter the domain at a rate of four attempts per time-step. Site occupancies, obtained from the agent–based model, are averaged over 300 independent realisations at times <i>t</i> = 50, 100, 250. The continuous curves denotes simple shovers, and broken curves denotes smart shovers. Arrows show increasing time.</p

Invasion of an occupied domain from the upper horizontal (displayed in Fig 2(c)).

<p>Green gentlemen cells are present with density <i>c</i>_<i>G</i> = 0.6. Red cells enter the domain at a rate of four attempts per time-step. The continuous curves denotes simple shovers, and broken curves denotes smart shovers. Site occupancies, obtained from the agent–based model, are averaged over 300 independent realisations at times <i>t</i> = 50, 100, 250. Arrows show increasing time.</p