Capture and re-entrainment of microdroplets on fibers.

The capture of liquid microdroplets on fibers, webs, and surfaces is important in a range of natural and industrial processes. One such application is the fibrous filtration of aerosols. Contact angle and wetting dynamics have a significant influence on capture and re-entrainment, yet there is no comprehensive model that accounts for these properties and their influence on capture efficiency. In this study, a series of computational simulations using liquid droplets and air are carried out to investigate the influence of equilibrium and dynamic contact angles on the capture and re-entrainment of mist droplets. A range of operating conditions for droplet-fiber diameter ratios, flow velocities, and contact angles, encapsulating both super-oleophilic and super-oleophobic media, are considered. All simulations are carried out using the volume of fluid (VOF) interface capturing approach in the finite volume solver interFoam within OpenFOAM. The physics of microdroplet impacting on a fiber is discussed and three distinct regimes for the spreading of the droplet around the fiber-inertia, capillary, and stagnation pressure controlled-are identified. It was found that the classification of filtration media for any fluid system, rather broadly as philic or phobic, based on the equilibrium contact angle alone may be insufficient for two reasons: (i) the characteristics of droplet-fiber interaction, including capture or re-entrainment, differs significantly over the range of contact angles for both philic and phobic media; and more importantly (ii) equilibrium contact angle plays little role in the initial stages of the droplet-fiber interaction that predominantly dictates the fate of the droplet. On the contrary, it is the contact angle dynamics that influences the initial stages of droplet impact on fibers, while commercial filters are seldom characterized based on this property. The isolated influence of equilibrium, advancing and receding contact angles on the potential mechanisms that can result in full or partial capture or re-entrainment are highlighted. The influence of equilibrium and advancing and receding hystereses are summarized in the form of a capture-regime map that shows four distinct regimes: (i) likely capture, (ii) likely re-entrainment with minimal or no capture, (iii) receding contact angle assisted partial or full capture, and (iv) advancing contact angle inhibited partial or full re-entrainment.

Function of Human Tumor-Infiltrating Lymphocytes in Early-Stage Non-Small Cell Lung Cancer.

Cancer progression is marked by dysfunctional tumor-infiltrating lymphocytes (TIL) with high inhibitory receptor (IR) expression. Because IR blockade has led to clinical responses in some patients with non-small cell lung cancer (NSCLC), we investigated how IRs influenced CD8+ TIL function from freshly digested early-stage NSCLC tissues using a killing assay and intracellular cytokine staining after in vitro T-cell restimulation. Early-stage lung cancer TIL function was heterogeneous with only about one third of patients showing decrements in cytokine production and lytic function. TIL hypofunction did not correlate with clinical factors, coexisting immune cells (macrophages, neutrophils, or CD4+ T regulatory cells), nor with PD-1, TIGIT, TIM-3, CD39, or CTLA-4 expression. Instead, we found that the presence of the integrin αeß7 (CD103), characteristic of tissue-resident memory cells (TRM), was positively associated with cytokine production, whereas expression of the transcription factor Eomesodermin (Eomes) was negatively associated with TIL function. These data suggest that the functionality of CD8+ TILs from early-stage NSCLCs may be influenced by competition between an antitumor CD103+ TRM program and an exhaustion program marked by Eomes expression. Understanding the mechanisms of T-cell function in the progression of lung cancer may have clinical implications for immunotherapy.

Empirical Bayes conditional independence graphs for regulatory network recovery.

MOTIVATION: Computational inference methods that make use of graphical models to extract regulatory networks from gene expression data can have difficulty reconstructing dense regions of a network, a consequence of both computational complexity and unreliable parameter estimation when sample size is small. As a result, identification of hub genes is of special difficulty for these methods. METHODS: We present a new algorithm, Empirical Light Mutual Min (ELMM), for large network reconstruction that has properties well suited for recovery of graphs with high-degree nodes. ELMM reconstructs the undirected graph of a regulatory network using empirical Bayes conditional independence testing with a heuristic relaxation of independence constraints in dense areas of the graph. This relaxation allows only one gene of a pair with a putative relation to be aware of the network connection, an approach that is aimed at easing multiple testing problems associated with recovering densely connected structures. RESULTS: Using in silico data, we show that ELMM has better performance than commonly used network inference algorithms including GeneNet, ARACNE, FOCI, GENIE3 and GLASSO. We also apply ELMM to reconstruct a network among 5492 genes expressed in human lung airway epithelium of healthy non-smokers, healthy smokers and individuals with chronic obstructive pulmonary disease assayed using microarrays. The analysis identifies dense sub-networks that are consistent with known regulatory relationships in the lung airway and also suggests novel hub regulatory relationships among a number of genes that play roles in oxidative stress and secretion. AVAILABILITY AND IMPLEMENTATION: Software for running ELMM is made available at http://mezeylab.cb.bscb.cornell.edu/Software.aspx. CONTACT: ramimahdi@yahoo.com or jgm45@cornell.edu SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.