Computerized evaluation of histological preparations of prostate tissues involves identification of tissue components such as stroma (ST), benign/normal epithelium (BN) and prostate cancer (PCa). Image classification approaches have been developed to identify and classify glandular regions in digital images of prostate tissues; however their success has been limited by difficulties in cellular segmentation and tissue heterogeneity. We hypothesized that utilizing image pixels to generate intensity histograms of hematoxylin (H) and eosin (E) stains deconvoluted from H&E images numerically captures the architectural difference between glands and stroma. In addition, we postulated that joint histograms of local binary patterns and local variance (LBPxVAR) can be used as sensitive textural features to differentiate benign/normal tissue from cancer. Here we utilized a machine learning approach comprising of a support vector machine (SVM) followed by a random forest (RF) classifier to digitally stratify prostate tissue into ST, BN and PCa areas. Two pathologists manually annotated 210 images of low- and high-grade tumors from slides that were selected from 20 radical prostatectomies and digitized at high-resolution. The 210 images were split into the training (n = 19) and test (n = 191) sets. Local intensity histograms of H and E were used to train a SVM classifier to separate ST from epithelium (BN + PCa). The performance of SVM prediction was evaluated by measuring the accuracy of delineating epithelial areas. The Jaccard J = 59.5 ± 14.6 and Rand Ri = 62.0 ± 7.5 indices reported a significantly better prediction when compared to a reference method (Chen et al., Clinical Proteomics 2013, 10:18) based on the averaged values from the test set. To distinguish BN from PCa we trained a RF classifier with LBPxVAR and local intensity histograms and obtained separate performance values for BN and PCa: JBN = 35.2 ± 24.9, OBN = 49.6 ± 32, JPCa = 49.5 ± 18.5, OPCa = 72.7 ± 14.8 and Ri = 60.6 ± 7.6 in the test set. Our pixel-based classification does not rely on the detection of lumens, which is prone to errors and has limitations in high-grade cancers and has the potential to aid in clinical studies in which the quantification of tumor content is necessary to prognosticate the course of the disease. The image data set with ground truth annotation is available for public use to stimulate further research in this area.

In pelagic species inhabiting large oceans, genetic differentiation tends to be mild and populations devoid of structure. However, large cetaceans have provided many examples of structuring. Here we investigate whether the sperm whale, a pelagic species with large population sizes and reputedly highly mobile, shows indication of structuring in the eastern North Atlantic, an ocean basin in which a single population is believed to occur. To do so, we examined stable isotope values in sequential growth layer groups of teeth from individuals sampled in Denmark and NW Spain. In each layer we measured oxygen- isotope ratios (δ18O) in the inorganic component (hydroxyapatite), and nitrogen and carbon isotope ratios (δ15N: δ13C) in the organic component (primarily collagenous). We found significant differences between Denmark and NW Spain in δ15N and δ18O values in the layer deposited at age 3, considered to be the one best representing the baseline of the breeding ground, in δ15N, δ13C and δ18O values in the period up to age 20, and in the ontogenetic variation of δ15N and δ18O values. These differences evidence that diet composition, use of habitat and/or migratory destinations are dissimilar between whales from the two regions and suggest that the North Atlantic population of sperm whales is more structured than traditionally accepted.

High-resolution three-dimensional (3-D) microscopy combined with multiplexing of fluorescent labels allows high-content analysis of large numbers of cell nuclei. The full automation of 3-D screening platforms necessitates image processing algorithms that can accurately and robustly delineate nuclei in images with little to no human intervention. Imaging-based high-content screening was originally developed as a powerful tool for drug discovery. However, cell confluency, complexity of nuclear staining as well as poor contrast between nuclei and background result in slow and unreliable 3-D image processing and therefore negatively affect the performance of studying a drug response. Here, we propose a new method, 3D-RSD, to delineate nuclei by means of 3-D radial symmetries and test it on high-resolution image data of human cancer cells treated by drugs. The nuclei detection performance was evaluated by means of manually generated ground truth from 2351 nuclei (27 confocal stacks). When compared to three other nuclei segmentation methods, 3D-RSD possessed a better true positive rate of 83.3% and F-score of 0.895+/-0.045 (p- value=0.047). Altogether, 3D-RSD is a method with a very good overall segmentation performance. Furthermore, implementation of radial symmetries offers good processing speed, and makes 3D-RSD less sensitive to staining patterns. In particular the 3D-RSG method performs well in cell lines, which are often used in imaging-based HCS platforms and are afflicted by nuclear crowding and overlaps that hinder feature extraction.

This study conducted stable isotope analysis (δ13C, δ15N, and δ34S) on the epidermis and two skeletal elements (rib and squamosal bones) of Hawaiian green turtles (Chelonia mydas) and putative diet items obtained from two neritic sites: the Kona/Kohala coast and Oahu. Turtle tissues were collected in 2018–2020 and diet samples in 2018, 2019, and 2021. The effect of body size and sampling locality on individual bulk tissue isotope values was evaluated, and stable isotope mixing models based on δ13C, δ15N, and δ34S values from those tissues and four groups of food sources were used to reconstruct diet histories of the turtles. Mixing models indicated that green turtles along the Kona/Kohala coast consumed an omnivorous diet, whereas those from Oahu had an herbivorous diet. These diet make-ups are consistent with published gut content analyses. However, mixing models using the stable isotope ratios in rib and squamosal bone failed to yield reasonable diet histories, probably due to inadequacies of the applied trophic discrimination factor (TDF), a key model parameter. These results further establish that stable isotope ratios in the epidermis can be used effectively to study green turtle diet, but also reveal that more validation—and establishment of appropriate TDFs—is needed before bone can be used reliably to assess green turtle diet.