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Biology subjects

Mahler, S.

Publications and source records attributed to Mahler, S..

2 recordsLinked to original sources

Non-invasive laser speckle imaging of extra-embryonic blood vessels in intact few-days-old avian eggs

Imaging blood vessels in early-stage avian embryos has a wide range of practical applications for developmental biology studies, drug and vaccine testing, and early sex determination. Optical imaging such as brightfield transmission imaging offers a compelling solution due to its safe non-ionizing radiation, and operational benefits. However, it comes with challenges such as eggshell opacity and light scattering. To address these, we have revisited an approach based on laser speckle contrast imaging (LSCI) and demonstrated a high quality, comprehensive and non-invasive visualization of blood vessels in few-days-old chicken eggs, with blood vessel as small as 100 {micro}m in diameter (with LSCI profile full-width-at-half-maximum of 275 {micro}m). We present its non-invasive use for monitoring blood flow, measuring the embryos heartbeat, and determining the embryos developmental stages using machine learning with 85% accuracy from stage HH15 to HH22. This method can potentially be used for non-invasive longitudinal studies of cardiovascular development and angiogenesis, as well as egg screening for the poultry industry.

bioengineering↗

Automatic structural analysis of bioinspired percolating network materials using graph theory

Mimicking numerous biological membranes and nanofiber-based tissues, there are multiple materials that are structured as percolating nanoscale networks (PPNs). They reveal unique combination of properties and the family of PNN-based composites and nanoporous materials is rapidly expanding. Their technological significance and the necessity of their structural design require a unifying approach for their structural description. However, their complex aperiodic architectures are difficult to describe using traditional methods that are tailored for crystals. A related problem is the lack of computational tools that enable one to capture and enumerate the patterns of stochastically branching fibrils that are typical for these composites. Here, we describe a conceptual methodology and a computational package, StructuralGT, to automatically produce a graph theoretical (GT) description of PNNs from various micrographs. Using nanoscale networks formed by aramid nanofibers (ANFs) as examples, we demonstrate structural analysis of PNNs with 13 GT parameters. Unlike qualitative assessments of physical features employed previously, StructuralGT allows quantitative description of the complex structural attributes of PNNs enumerating the networks morphology, connectivity, and transfer patterns. Accurate conversion and analysis of micrographs is possible for various levels of noise, contrast, focus, and magnification while a dedicated graphical user interface provides accessibility and clarity. The GT parameters are expected to be correlated to material properties of PNNs (e.g. ion transport, conductivity, stiffness) and utilized by machine learning tools for effectual materials design. Table of Content O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=172 SRC="FIGDIR/small/438877v1_ufig1.gif" ALT="Figure 1"> View larger version (144K): org.highwire.dtl.DTLVardef@1c929f0org.highwire.dtl.DTLVardef@1e07168org.highwire.dtl.DTLVardef@357e7aorg.highwire.dtl.DTLVardef@28d21_HPS_FORMAT_FIGEXP M_FIG C_FIG

bioengineering↗