Hematopoietic chimerism after allogeneic stem cell transplantation: a comparison of quantitative analysis by automated DNA sizing and fluorescent in situ hybridization

BACKGROUND: Allogeneic hematopoietic stem cell transplantation (allo-HSCT) is performed mainly in patients with high-risk or advanced hematologic malignancies and congenital or acquired aplastic anemias. In the context of the significant risk of graft failure after allo-HSCT from alternative donors and the risk of relapse in recipients transplanted for malignancy, the precise monitoring of posttransplant hematopoietic chimerism is of utmost interest. Useful molecular methods for chimerism quantification after allogeneic transplantation, aimed at distinguishing precisely between donor's and recipient's cells, are PCR-based analyses of polymorphic DNA markers. Such analyses can be performed regardless of donor's and recipient's sex. Additionally, in patients after sex-mismatched allo-HSCT, fluorescent in situ hybridization (FISH) can be applied. METHODS: We compared different techniques for analysis of posttransplant chimerism, namely FISH and PCR-based molecular methods with automated detection of fluorescent products in an ALFExpress DNA Sequencer (Pharmacia) or ABI 310 Genetic Analyzer (PE). We used Spearman correlation test. RESULTS: We have found high correlation between results obtained from the PCR/ALF Express and PCR/ABI 310 Genetic Analyzer. Lower, but still positive correlations were found between results of FISH technique and results obtained using automated DNA sizing technology. CONCLUSIONS: All the methods applied enable a rapid and accurate detection of post-HSCT chimerism

Velocity Field Estimation on Density-Driven Solute Transport With a Convolutional Neural Network

Recent advances in machine learning open new opportunities to gain deeper insight into hydrological systems, where some relevant system quantities remain difficult to measure. We use deep learning methods trained on numerical simulations of the physical processes to explore the possibilities of closing the information gap of missing system quantities. As an illustrative example we study the estimation of velocity fields in numerical and laboratory experiments of density-driven solute transport. Using high-resolution observations of the solute concentration distribution, we demonstrate the capability of the method to structurally incorporate the representation of the physical processes. Velocity field estimation for synthetic data for both variable and uniform concentration boundary conditions showed equal results. This capability is remarkable because only the latter was employed for training the network. Applying the method to measured concentration distributions of density-driven solute transport in a Hele-Shaw cell makes the velocity field assessable in the experiment. This assessability of the velocity field even holds for regions with negligible solute concentration between the density fingers, where the velocity field is otherwise inaccessible

Velocity Field Estimation on Density-Driven Solute Transport With a Convolutional Neural Network [Dataset]

This data set accompanies the manuscript ‘Velocity Field Estimation on Density-Driven Solute Transport With a Convolutional Neural Network’. Concentration fields are stored as portable pixel maps (.ppm) and flow fields are stored in the Middlebury .flo file format (http://vision.middlebury.edu/flow/code/flow-code/README.txt).<br