Omnivory has been cited as an explanation for why trophic cascades are weak in many ecosystems, but empirical support for this prediction is equivocal. Compared to predators that feed only on herbivores, top omnivores-species that feed on both herbivores and primary producers-have been observed generating cascades ranging from strong to moderate, null, and negative. To gain intuition about the sensitivity of cascades to omnivory, we analyzed models describing systems with top omnivores that display either fixed or flexible diets, two foraging strategies that are supported by empirical observations. We identified regions of parameter space, wherein omnivores following a fixed foraging strategy, with herbivores and producers comprising a constant proportion of the diet, non-intuitively generate stronger cascades than predators that are otherwise demographically identical: (1) high productivity relative to herbivore mortality and (2) small discrepancies in producer vs. herbivore reward create conditions in which cascades are stronger with moderate omnivory. In contrast, flexible omnivores that attempt to optimize per capita growth rates during search never induce cascades that are stronger than the case of predators. Although we focus on simple models, the consistency of these general patterns together with prior empirical evidence suggests that omnivores should not be uniformly ruled out as agents of strong trophic cascades.

A microfluidics-based three-dimensional skin-on-chip (SoC) model is developed in this study to enable quantitative studies of transendothelial and transepithelial migration of human T lymphocytes in mimicked skin inflammatory microenvironments and to test new drug candidates. The keys results include 1) CCL20-dependent T cell transmigration is significantly inhibited by an engineered CCL20 locked dimer (CCL20LD), supporting the potential immunotherapeutic use of CCL20LD for treating skin diseases such as psoriasis; 2) transepithelial migration of T cells in response to a CXCL12 gradient mimicking T cell egress from the skin is significantly reduced by a sphingosine-1-phosphate (S1P) background, suggesting the role of S1P for T cell retention in inflamed skin tissues; and 3) T cell transmigration is induced by inflammatory cytokine stimulated epithelial cells in the SoC model. Collectively, the developed SoC model recreates a dynamic multi-cellular micro-environment that enables quantitative studies of T cell transmigration at a single cell level in response to physiological cutaneous inflammatory mediators and potential drugs.

Multiple thoracic imaging modalities have been developed to link structure to function in the diagnosis and monitoring of lung disease. Volumetric computed tomography (CT) renders three-dimensional maps of lung structures and may be combined with positron emission tomography (PET) to obtain dynamic physiological data. Magnetic resonance imaging (MRI) using ultrashort-echo time (UTE) sequences has improved signal detection from lung parenchyma; contrast agents are used to deduce airway function, ventilation-perfusion-diffusion, and mechanics. Proton MRI can measure regional ventilation-perfusion ratio. Quantitative imaging (QI)-derived endpoints have been developed to identify structure-function phenotypes, including air-blood-tissue volume partition, bronchovascular remodeling, emphysema, fibrosis, and textural patterns indicating architectural alteration. Coregistered landmarks on paired images obtained at different lung volumes are used to infer airway caliber, air trapping, gas and blood transport, compliance, and deformation. This document summarizes fundamental good practice stereological principles in QI study design and analysis; evaluates technical capabilities and limitations of common imaging modalities; and assesses major QI endpoints regarding underlying assumptions and limitations, ability to detect and stratify heterogeneous, overlapping pathophysiology, and monitor disease progression and therapeutic response, correlated with and complementary to, functional indices. The goal is to promote unbiased quantification and interpretation of in vivo imaging data, compare metrics obtained using different QI modalities to ensure accurate and reproducible metric derivation, and avoid misrepresentation of inferred physiological processes. The role of imaging-based computational modeling in advancing these goals is emphasized. Fundamental principles outlined herein are critical for all forms of QI irrespective of acquisition modality or disease entity.