The transformation of the nuclear nanoarchitecture in human field carcinogenesis.

Morphological alterations of the nuclear texture are a hallmark of carcinogenesis. At later stages of disease, these changes are well characterized and detectable by light microscopy. Evidence suggests that similar albeit nanoscopic alterations develop at the predysplastic stages of carcinogenesis. Using the novel optical technique partial wave spectroscopic microscopy, we identified profound changes in the nanoscale chromatin topology in microscopically normal tissue as a common event in the field carcinogenesis of many cancers. In particular, higher-order chromatin structure at supranucleosomal length scales (20-200 nm) becomes exceedingly heterogeneous, a measure we quantify using the disorder strength (Ld ) of the spatial arrangement of chromatin density. Here, we review partial wave spectroscopic nanocytology clinical studies and the technology's promise as an early cancer screening technology.

Skeletal light-scattering accelerates bleaching response in reef-building corals.

BACKGROUND: At the forefront of ecosystems adversely affected by climate change, coral reefs are sensitive to anomalously high temperatures which disassociate (bleaching) photosynthetic symbionts (Symbiodinium) from coral hosts and cause increasingly frequent and severe mass mortality events. Susceptibility to bleaching and mortality is variable among corals, and is determined by unknown proportions of environmental history and the synergy of Symbiodinium- and coral-specific properties. Symbiodinium live within host tissues overlaying the coral skeleton, which increases light availability through multiple light-scattering, forming one of the most efficient biological collectors of solar radiation. Light-transport in the upper ~200 µm layer of corals skeletons (measured as 'microscopic' reduced-scattering coefficient, µ'(S,m)), has been identified as a determinant of excess light increase during bleaching and is therefore a potential determinant of the differential rate and severity of bleaching response among coral species. RESULTS: Here we experimentally demonstrate (in ten coral species) that, under thermal stress alone or combined thermal and light stress, low-µ'(S,m) corals bleach at higher rate and severity than high-µ'(S,m) corals and the Symbiodinium associated with low-µ'(S,m) corals experience twice the decrease in photochemical efficiency. We further modelled the light absorbed by Symbiodinium due to skeletal-scattering and show that the estimated skeleton-dependent light absorbed by Symbiodinium (per unit of photosynthetic pigment) and the temporal rate of increase in absorbed light during bleaching are several fold higher in low-µ'(S,m) corals. CONCLUSIONS: While symbionts associated with low-[Formula: see text] corals receive less total light from the skeleton, they experience a higher rate of light increase once bleaching is initiated and absorbing bodies are lost; further precipitating the bleaching response. Because microscopic skeletal light-scattering is a robust predictor of light-dependent bleaching among the corals assessed here, this work establishes µ'(S,m) as one of the key determinants of differential bleaching response.

Automated Cell Selection Using Support Vector Machine for Application to Spectral Nanocytology.

Partial wave spectroscopy (PWS) enables quantification of the statistical properties of cell structures at the nanoscale, which has been used to identify patients harboring premalignant tumors by interrogating easily accessible sites distant from location of the lesion. Due to its high sensitivity, cells that are well preserved need to be selected from the smear images for further analysis. To date, such cell selection has been done manually. This is time-consuming, is labor-intensive, is vulnerable to bias, and has considerable inter- and intraoperator variability. In this study, we developed a classification scheme to identify and remove the corrupted cells or debris that are of no diagnostic value from raw smear images. The slide of smear sample is digitized by acquiring and stitching low-magnification transmission. Objects are then extracted from these images through segmentation algorithms. A training-set is created by manually classifying objects as suitable or unsuitable. A feature-set is created by quantifying a large number of features for each object. The training-set and feature-set are used to train a selection algorithm using Support Vector Machine (SVM) classifiers. We show that the selection algorithm achieves an error rate of 93% with a sensitivity of 95%.