Unsteady separation process and vorticity balance on unsteady airfoils

Low momentum fluid erupts at the unsteady separation region and forms a local shear layer at the viscous-inviscid interface. At the shear layer, the vorticity lumps into a vortex and protrudes into the inviscid region. This process initiates the separation process. The response of airfoils in unsteady free stream was investigated based on this vortex generation and convection concept. This approach enabled us to understand the complicated unsteady aerodynamics from a fundamental point of view

Isolation and characterization of stromal progenitor cells from ascites of patients with epithelial ovarian adenocarcinoma

<p>Abstract</p> <p>Background</p> <p>At least one-third of epithelial ovarian cancers are associated with the development of ascites containing heterogeneous cell populations, including tumor cells, inflammatory cells, and stromal elements. The components of ascites and their effects on the tumor cell microenvironment remain poorly understood. This study aimed to isolate and characterize stromal progenitor cells from the ascites of patients with epithelial ovarian adenocarcinoma (EOA).</p> <p>Methods</p> <p>Seventeen ascitic fluid samples and 7 fresh tissue samples were collected from 16 patients with EOA. The ascites samples were then cultured in vitro in varying conditions. Flow cytometry and immunocytochemistry were used to isolate and characterize 2 cell populations with different morphologies (epithelial type and mesenchymal type) deriving from the ascites samples. The in vitro cell culture model was established using conditional culture medium.</p> <p>Results</p> <p>The doubling times of the epithelial type and mesenchymal type cells were 36 h and 48 h, respectively, indicating faster growth of the epithelial type cells compared to the mesenchymal type cells. Cultured in vitro, these ascitic cells displayed the potential for self-renewal and long-term proliferation, and expressed the typical cancer stem/progenitor cell markers CD44^high, CD24^low, and AC133⁺. These cells also demonstrated high BMP-2, BMP4, TGF-β, Rex-1, and AC133 early gene expression, and expressed EGFR, integrin α₂β₁, CD146, and Flt-4, which are highly associated with tumorigenesis and metastasis. The epithelial type cells demonstrated higher cytokeratin 18 and E-cadherin expression than the mesenchymal type cells. The mesenchymal type cells, in contrast, demonstrated higher AC133, CD73, CD105, CD117, EGFR, integrin α₂β₁, and CD146 surface marker expression than the epithelial type cells.</p> <p>Conclusion</p> <p>The established culture system provides an in vitro model for the selection of drugs that target cancer-associated stromal progenitor cells, and for the development of ovarian cancer treatments.</p

A shallow physics-informed neural network for solving partial differential equations on surfaces

In this paper, we introduce a shallow (one-hidden-layer) physics-informed neural network for solving partial differential equations on static and evolving surfaces. For the static surface case, with the aid of level set function, the surface normal and mean curvature used in the surface differential expressions can be computed easily. So instead of imposing the normal extension constraints used in literature, we write the surface differential operators in the form of traditional Cartesian differential operators and use them in the loss function directly. We perform a series of performance study for the present methodology by solving Laplace-Beltrami equation and surface diffusion equation on complex static surfaces. With just a moderate number of neurons used in the hidden layer, we are able to attain satisfactory prediction results. Then we extend the present methodology to solve the advection-diffusion equation on an evolving surface with given velocity. To track the surface, we additionally introduce a prescribed hidden layer to enforce the topological structure of the surface and use the network to learn the homeomorphism between the surface and the prescribed topology. The proposed network structure is designed to track the surface and solve the equation simultaneously. Again, the numerical results show comparable accuracy as the static cases. As an application, we simulate the surfactant transport on the droplet surface under shear flow and obtain some physically plausible results

Homogeneous point mutation detection by quantum dot-mediated two-color fluorescence coincidence analysis

This report describes a new genotyping method capable of detecting low-abundant point mutations in a homogeneous, separation-free format. The method is based on integration of oligonucleotide ligation with a semiconductor quantum dot (QD)-mediated two-color fluorescence coincidence detection scheme. Surface-functionalized QDs are used to capture fluorophore-labeled ligation products, forming QD-oligonucleotide nanoassemblies. The presence of such nanoassemblies and thereby the genotype of the sample is determined by detecting the simultaneous emissions of QDs and fluorophores that occurs whenever a single nanoassembly flows through the femtoliter measurement volume of a confocal fluorescence detection system. The ability of this method to detect single events enables analysis of target signals with a multiple-parameter (intensities and count rates of the digitized target signals) approach to enhance assay sensitivity and specificity. We demonstrate that this new method is capable of detecting zeptomoles of targets and achieve an allele discrimination selectivity factor >10(5)