Fluid dynamics alters liquid-liquid phase separation in confined aqueous two-phase systems.

Liquid-liquid phase separation is key to understanding aqueous two-phase systems (ATPS) arising throughout cell biology, medical science, and the pharmaceutical industry. Controlling the detailed morphology of phase-separating compound droplets leads to new technologies for efficient single-cell analysis, targeted drug delivery, and effective cell scaffolds for wound healing. We present a computational model of liquid-liquid phase separation relevant to recent laboratory experiments with gelatin-polyethylene glycol mixtures.

Post-Radiotherapy PET Image Outcome Prediction by Deep Learning Under Biological Model Guidance: A Feasibility Study of Oropharyngeal Cancer Application.

Purpose: To develop a method of biologically guided deep learning for post-radiation FDG-PET image outcome prediction based on pre-radiation images and radiotherapy dose information.

Methods: Based on the classic reaction-diffusion mechanism, a novel biological model was proposed using a partial differential equation that incorporates spatial radiation dose distribution as a patient-specific treatment information variable. A 7-layer encoder-decoder-based convolutional neural network (CNN) was designed and trained to learn the proposed biological model.

Extraction of Hidden Science from Nanoscale Images.

Scanning probe microscopies and spectroscopies enable investigation of surfaces and even buried interfaces down to the scale of chemical-bonding interactions, and this capability has been enhanced with the support of computational algorithms for data acquisition and image processing to explore physical, chemical, and biological phenomena. Here, we describe how scanning probe techniques have been enhanced by some of these recent algorithmic improvements. One improvement to the data acquisition algorithm is to advance beyond a simple rastering framework by using spirals at constant angular velocity then switching to constant linear velocity, which limits the piezo creep and hysteresis issues seen in traditional acquisition methods.

Suspendable Hydrogel Nanovials for Massively Parallel Single-Cell Functional Analysis and Sorting.

Techniques to analyze and sort single cells based on functional outputs, such as secreted products, have the potential to transform our understanding of cellular biology as well as accelerate the development of next-generation cell and antibody therapies. However, secreted molecules rapidly diffuse away from cells, and analysis of these products requires specialized equipment and expertise to compartmentalize individual cells and capture their secretions. Herein, we describe methods to fabricate hydrogel-based chemically functionalized microcontainers, which we call nanovials, and demonstrate their use for sorting single viable cells based on their secreted products at high-throughput using only commonly accessible laboratory infrastructure.

Mitral Valve Segmentation Using Robust Nonnegative Matrix Factorization.

Analyzing and understanding the movement of the mitral valve is of vital importance in cardiology, as the treatment and prevention of several serious heart diseases depend on it. Unfortunately, large amounts of noise as well as a highly varying image quality make the automatic tracking and segmentation of the mitral valve in two-dimensional echocardiographic videos challenging. In this paper, we present a fully automatic and unsupervised method for segmentation of the mitral valve in two-dimensional echocardiographic videos, independently of the echocardiographic view.

Capillary-Driven Rise of Well-Wetting Liquid on the Outer Surface of Cylindrical Nozzles.

Well-wetting liquids exiting small-diameter nozzles in the dripping regime can partially rise up along the outer nozzle surfaces. This is problematic for fuel injectors and other devices such as direct-contact heat and mass exchangers that incorporate arrays of nozzles to distribute liquids. We report our experimental and numerical study of the rising phenomenon for wide ranges of parameters.

Statistical energy minimization theory for systems of drop-carrier particles.

Drop-carrier particles (DCPs) are solid microparticles designed to capture uniform microscale drops of a target solution without using costly microfluidic equipment and techniques. DCPs are useful for automated and high-throughput biological assays and reactions, as well as single-cell analyses. Surface energy minimization provides a theoretical prediction for the volume distribution in pairwise droplet splitting, showing good agreement with macroscale experiments.

Experimental study of a string-based counterflow wet electrostatic precipitator for collection of fine and ultrafine particles.

Wet electrostatic precipitators (WESP) have been widely studied for collecting fine and ultrafine particles, such as diesel particulate matter (DPM), which have deleterious effects on human health. Here, we report an experimental and numerical simulation study on a novel string-based two-stage WESP. Our new design incorporates grounded vertically aligned polymer strings, along which thin films of water flow down.

Monodisperse drops templated by 3D-structured microparticles.

The ability to create uniform subnanoliter compartments using microfluidic control has enabled new approaches for analysis of single cells and molecules. However, specialized instruments or expertise has been required, slowing the adoption of these cutting-edge applications. Here, we show that three dimensional-structured microparticles with sculpted surface chemistries template uniformly sized aqueous drops when simply mixed with two immiscible fluid phases.

The challenges of modeling and forecasting the spread of COVID-19.

The coronavirus disease 2019 (COVID-19) pandemic has placed epidemic modeling at the forefront of worldwide public policy making. Nonetheless, modeling and forecasting the spread of COVID-19 remains a challenge. Here, we detail three regional-scale models for forecasting and assessing the course of the pandemic.

Impact of social distancing during COVID-19 pandemic on crime in Los Angeles and Indianapolis.

Governments have implemented social distancing measures to address the ongoing COVID-19 pandemic. The measures include instructions that individuals maintain social distance when in public, school closures, limitations on gatherings and business operations, and instructions to remain at home. Social distancing may have an impact on the volume and distribution of crime.

Hyperspectral Image Unmixing With Endmember Bundles and Group Sparsity Inducing Mixed Norms.

Hyperspectral images provide much more information than conventional imaging techniques, allowing a precise identification of the materials in the observed scene, but because of the limited spatial resolution, the observations are usually mixtures of the contributions of several materials. The spectral unmixing problem aims at recovering the spectra of the pure materials of the scene (endmembers), along with their proportions (abundances) in each pixel. In order to deal with the intra-class variability of the materials and the induced spectral variability of the endmembers, several spectra per material, constituting endmember bundles, can be considered.

Using insurance claims to predict and improve hospitalizations and biologics use in members with inflammatory bowel diseases.

Objective: Inflammatory Bowel Disease (IBD) is an inflammatory disorder of the gastrointestinal tract that can necessitate hospitalization and the use of expensive biologics. Models predicting these interventions may improve patient quality of life and reduce expenditures.

Materials And Methods: We used insurance claims from 2011 to 2013 to predict IBD-related hospitalizations and the initiation of biologics.

Mapping Buried Hydrogen-Bonding Networks.

We map buried hydrogen-bonding networks within self-assembled monolayers of 3-mercapto-N-nonylpropionamide on Au{111}. The contributing interactions include the buried S-Au bonds at the substrate surface and the buried plane of linear networks of hydrogen bonds. Both are simultaneously mapped with submolecular resolution, in addition to the exposed interface, to determine the orientations of molecular segments and directional bonding.

Growth and containment of a hierarchical criminal network.

We model the hierarchical evolution of an organized criminal network via antagonistic recruitment and pursuit processes. Within the recruitment phase, a criminal kingpin enlists new members into the network, who in turn seek out other affiliates. New recruits are linked to established criminals according to a probability distribution that depends on the current network structure.

Multiclass Data Segmentation Using Diffuse Interface Methods on Graphs.

We present two graph-based algorithms for multiclass segmentation of high-dimensional data on graphs. The algorithms use a diffuse interface model based on the Ginzburg-Landau functional, related to total variation and graph cuts. A multiclass extension is introduced using the Gibbs simplex, with the functional's double-well potential modified to handle the multiclass case.

Height drift correction in non-raster atomic force microscopy.

We propose a novel method to detect and correct drift in non-raster scanning probe microscopy. In conventional raster scanning drift is usually corrected by subtracting a fitted polynomial from each scan line, but sample tilt or large topographic features can result in severe artifacts. Our method uses self-intersecting scan paths to distinguish drift from topographic features.

Improved accuracy and speed in scanning probe microscopy by image reconstruction from non-gridded position sensor data.

Scanning probe microscopy (SPM) has facilitated many scientific discoveries utilizing its strengths of spatial resolution, non-destructive characterization and realistic in situ environments. However, accurate spatial data are required for quantitative applications but this is challenging for SPM especially when imaging at higher frame rates. We present a new operation mode for scanning probe microscopy that uses advanced image processing techniques to render accurate images based on position sensor data.

Stability of ring patterns arising from two-dimensional particle interactions.

Pairwise particle interactions arise in diverse physical systems ranging from insect swarms to self-assembly of nanoparticles. In the presence of long-range attraction and short-range repulsion, such systems can exhibit bound states. We use linear stability analysis of a ring equilibrium to classify the morphology of patterns in two dimensions.

Dissipation and displacement of hotspots in reaction-diffusion models of crime.

The mechanisms driving the nucleation, spread, and dissipation of crime hotspots are poorly understood. As a consequence, the ability of law enforcement agencies to use mapped crime patterns to design crime prevention strategies is severely hampered. We also lack robust expectations about how different policing interventions should impact crime.

A wavelet-laplace variational technique for image deconvolution and inpainting.

We construct a new variational method for blind deconvolution of images and inpainting, motivated by recent PDE-based techniques involving the Ginzburg-Landau functional, but using more localized wavelet-based methods. We present results for both binary and grayscale images. Comparable speeds are achieved with better sharpness of edges in the reconstruction.

A study of EWOD-driven droplets by PIV investigation.

Despite the recent interest in droplet-based microfluidics using electrowetting-on-dielectric (EWOD), fundamental understanding of the fluid dynamics remains limited to two-dimensional (2D) reduction of the Navier-Stokes equation. Experimental data are in dire need to verify the predictions and advance the field. We report an investigation of the flow inside droplets actuated by EWOD in air using micro particle image velocimetry (micro-PIV).

Inpainting of binary images using the Cahn-Hilliard equation.

Image inpainting is the filling in of missing or damaged regions of images using information from surrounding areas. We outline here the use of a model for binary inpainting based on the Cahn-Hilliard equation, which allows for fast, efficient inpainting of degraded text, as well as super-resolution of high contrast images.

A nonlocal continuum model for biological aggregation.

We construct a continuum model for biological aggregations in which individuals experience long-range social attraction and short-range dispersal. For the case of one spatial dimension, we study the steady states analytically and numerically. There exist strongly nonlinear states with compact support and steep edges that correspond to localized biological aggregations, or clumps.