A General-Purpose Multiphase/Multispecies Model to Predict the Spread, Percutaneous Hazard, and Contact Dynamics for Nonporous and Porous Substrates and Membranes

A computational model to solve the coupled transport equations with chemical reaction and phase change for a liquid sessile droplet or the contact and spread of a sessile droplet between two approaching porous or non-porous surfaces, is developed. The model is general therefore it can be applied to toxic chemicals (contact hazard), drug delivery through porous organs and membranes, combustion processes within porous material, and liquid movements in the ground. The equation of motion and the spread of the incompressible liquid available on the primary surface for transfer into the contacting surface while reacting with other chemicals (or water) and/or the solid substrate are solved in a finite difference domain with adaptive meshing. The comparison with experimental data demonstrated the model is robust and accurate. The impact of the initial velocity on the spread topology and mass transfer into the pores is also addressed

TREX1 is expressed by microglia in normal human brain and increases in regions affected by ischemia

BACKGROUND: Mutations in the three-prime repair exonuclease 1 (TREX1) gene have been associated with neurological diseases, including Retinal Vasculopathy with Cerebral Leukoencephalopathy (RVCL). However, the endogenous expression of TREX1 in human brain has not been studied. METHODS: We produced a rabbit polyclonal antibody (pAb) to TREX1 to characterize TREX1 by Western blotting (WB) of cell lysates from normal controls and subjects carrying an RVCL frame-shift mutation. Dual staining was performed to determine cell types expressing TREX1 in human brain tissue. TREX1 distribution in human brain was further evaluated by immunohistochemical analyses of formalin-fixed, paraffin-embedded samples from normal controls and patients with RVCL and ischemic stroke. RESULTS: After validating the specificity of our anti-TREX1 rabbit pAb, WB analysis was utilized to detect the endogenous wild-type and frame-shift mutant of TREX1 in cell lysates. Dual staining in human brain tissues from patients with RVCL and normal controls localized TREX1 to a subset of microglia and macrophages. Quantification of immunohistochemical staining of the cerebral cortex revealed that TREX1 CONCLUSIONS: TREX1 is expressed by a subset of microglia in normal human brain, often in close proximity to the microvasculature, and increases in the setting of ischemic lesions. These findings suggest a role for TREX

Meniscal Regeneration: A Cause of Persisting Pain following Total Knee Arthroplasty

Many patients have persisting knee pain following total knee arthroplasty. We report the unusual case of a patient whose chronic lateral and medial knee pain were caused by entrapped regenerated meniscal tissue. This was diagnosed and successfully treated by arthroscopic debridement

Automated Analysis of Mixed Sample Raman Spectra Using Feedforward Neural Networks and One-Vs-All Decomposition

Interest in use of Raman spectrometers in many fields of analytical science has increased due to ability to nondestructively provide information about molecular structures and component materials of a mixed sample. Advancements in Raman spectrometer hardware has allowed for compact instruments to have deployment capabilities directly on interplanetary missions, flexible usage conditions requiring no sample collection/preparation, and no need for daylight radiation shielding. As the amount of science which can be collected from a Raman spectrometer in a given amount of time increases, a bottleneck will be created in data analysis which leaves a need for a faster method of spectral data classification. In this study, a framework to allow for fast automated analysis of mixed sample Raman spectral data is proposed and an implementation of this framework is tested. Analysis of mixed sample Raman spectra was achieved by implementing a model which decomposes an N-class multilabel problem into “N” single class detection problems. The model (consisting of multiple neural networks) was trained with pure sample data and was tasked with analyzing both real and theoretical mixed sample Raman data. Performance of the model is judged by its ability to detect component materials in real mixed sample data at the same level that it is able to in ideal mixed sample data (consisting of linear combinations of training data). The model’s structure, training and testing methodologies, and results will be presented.https://digitalcommons.odu.edu/engineering_batten/1002/thumbnail.jp

A General-Purpose Multiphase/Multispecies Model to Predict the Spread, Percutaneous Hazard, and Contact Dynamics for Nonporous and Porous Substrates and Membranes

A computational model to solve the coupled transport equations with chemical reaction and phase change for a liquid sessile droplet or the contact and spread of a sessile droplet between two approaching porous or non-porous surfaces, is developed. The model is general therefore it can be applied to toxic chemicals (contact hazard), drug delivery through porous organs and membranes, combustion processes within porous material, and liquid movements in the ground. The equation of motion and the spread of the incompressible liquid available on the primary surface for transfer into the contacting surface while reacting with other chemicals (or water) and/or the solid substrate are solved in a finite difference domain with adaptive meshing. The comparison with experimental data demonstrated the model is robust and accurate. The impact of the initial velocity on the spread topology and mass transfer into the pores is also addressed

In vivo measurement of skin microrelief using photometricstereo in the presence of interreflections

This paper proposes and describes an implementation of a novel photometric stereo based technique for in vivo assessment of three-dimensional (3D) skin topographyin the presence of interreflections. The proposed method illuminates skin with red, green, and blue colored lights and uses the resulting variation in surface gradients tomitigate the effects of interreflections. Experiments were carried out on Caucasian, Asian and African American subjects to demonstrate the accuracy of our methodand to validate the measurements produced by our system. Our method produced significant improvement in 3D surface reconstruction for all Caucasian, Asian and African American skin types. The results also illustrate the differences in recovered skin topography due to non-diffuse Bidirectional reflectance distribution function(BRDF) for each color illumination used, which also concur with the existing multispectral BRDF data available for skin

A case report of a septic hip secondary to a psoas abscess

Psoas abscess was first described by Mynter in 1881. Though rare, its prevalence is increasing with advances in radiology and an increasing ability to accurately diagnose the condition. The symptoms of a psoas abscess can be insidious and nonspecific, and patients often present with a limp, fever, weight loss, and flank or abdominal pain

Quantifying flood model accuracy under varying surface complexities

This is the final version. Available on open access from Elsevier via the DOI in this recordData availability: Data will be made available on request.Open Access experimental datasets used in this paper are available at https://zenodo.org/communities/floodinteract/Floods in urban areas which feature interactions between piped and surface networks are hydraulically complex. Further, obtaining in situ calibration data, although necessary for robust simulations, can be very challenging. The aim of this research is to evaluate the performance of a commonly used deterministic 1D-2D flood model, calibrated using low resolution data, against a higher resolution dataset containing flows, depths and velocity fields; which are replicated from an experimental scale model water facility. Calibration of the numerical model was conducted using a lower resolution dataset, which consisted of a simple rectangular profile. The model was then evaluated against a dataset that was higher in spatial resolution and more complex in geometry (a street profile containing parking spaces). The findings show that when the model increased in scenario complexity model performance was reduced, though most of the simulation error was < 10% (NRMSE). Similarly, there was more error in the validated model that was higher in spatial resolution than lower. This was due to calibration not being stringent enough when conducted in a lower spatial resolution. However, overall the work shows the potential for the use of low-resolution datasets for model calibration.Engineering and Physical Sciences Research Council (EPSRC

Combining semi-automated image analysis techniques with machine learning algorithms to accelerate large scale genetic studies

Background: Genetic analyses of plant root system development require large datasets of extracted architectural traits. To quantify such traits from images of root systems, researchers often have to choose between automated tools (that are prone to error and extract only a limited number of architectural traits) or semi-automated ones (that are highly time consuming). Findings: We trained a Random Forest algorithm to infer architectural traits from automatically-extracted image descriptors. The training was performed on a subset of the dataset, then applied to its entirety. This strategy allowed us to (i) decrease the image analysis time by 73% and (ii) extract meaningful architectural traits based on image descriptors. We also show that these traits are sufficient to identify Quantitative Trait Loci that had previously been discovered using a semi-automated method. Conclusions: We have shown that combining semi-automated image analysis with machine learning algorithms has the power to increase the throughput in large scale root studies. We expect that such an approach will enable the quantification of more complex root systems for genetic studies. We also believe that our approach could be extended to other area of plant phenotyping