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Multi-omics experiments at bulk or single-cell resolution facilitate the discovery of hypothesis-generating biomarkers for predicting response to therapy, as well as aid in uncovering mechanistic insights into cellular and microenvironmental processes. Many methods for data integration have been developed for the identification of key elements that explain or predict disease risk or other biological outcomes. The heterogeneous graph representation of multi-omics data provides an advantage for discerning patterns suitable for predictive/exploratory analysis, thus permitting the modeling of complex relationships. Graph-based approaches—including graph neural networks—potentially offer a reliable methodological toolset that can provide a tangible alternative to scientists and clinicians that seek ideas and implementation strategies in the integrated analysis of their omics sets for biomedical research. Graph-based workflows continue to push the limits of the technological envelope, and this perspective provides a focused literature review of research articles in which graph machine learning is utilized for integrated multi-omics data analyses, with several examples that demonstrate the effectiveness of graph-based approaches. [ABSTRACT FROM AUTHOR]

Purpose: Shear-wave elastography (SWE) measures tissue elasticity using ultrasound waves. This study proposes a histogram-based SWE analysis to improve breast malignancy detection. Methods: N = 22/32 (patients/tumors) benign and n = 51/64 malignant breast tumors with histological ground truth. Colored SWE heatmaps were adjusted to a 0–180 kPa scale. Normalized, 250-binned RGB histograms were used as image descriptors based on skewness and area under curve (AUC). The histogram method was compared to conventional SWE metrics, such as (1) the qualitative 5-point scale classification and (2) average stiffness (SWEavg)/maximal tumor stiffness (SWEmax) within the tumor B-mode boundaries. Results: The SWEavg and SWEmax did not discriminate malignant lesions in this database, p > 0.05, rank sum test. RGB histograms, however, differed between malignant and benign tumors, p < 0.001, Kolmogorov–Smirnoff test. The AUC analysis of histograms revealed the reduction of soft-tissue components as a significant SWE biomarker (p = 0.03, rank sum). The diagnostic accuracy of the suggested method is still low (Se = 0.30 for Se = 0.90) and a subject for improvement in future studies. Conclusions: Histogram-based SWE quantitation improved the diagnostic accuracy for malignancy compared to conventional average SWE metrics. The sensitivity is a subject for improvement in future studies. [ABSTRACT FROM AUTHOR]

Imaging spectroscopy integrates conventional imaging and spectroscopy to attain both spatial and spectral information from an object. Processing and analysis of a hypercube can be a hard task. Robust methods for hyperspectral data classification are required, insensitive to imaging deficiencies and high input dimension. Mushrooms have a thin and porous epidermal structure, and are sensitive to handling and transportation practices. Mechanical damage triggers a browning process within the tissue changing its metabolic state. The objective is to quantify different levels of physical perturbation on the mushroom pilei, using near-infrared spectral images and machine learning approaches. An ANN classifier is implemented, whose input is a small set of vectors containing representative information, and output is the set of categorical labels that correspond to different levels of mechanical vibration. For obtaining a salient dataset for classifying the images, the Harris corner detection algorithm is employed. The advantage of using interest points is to replace an exhaustive search over the entire image space by a computation over a concise set of highly informative points. A frame-based classification approach is proposed and shown to produce an increase in the classification accuracy, since feature vectors regarded as single instances may not always carry sufficient discriminant information. Comparisons with statistical features computed from wavelet coefficients showed that interest points are more suitable in assessing mechanical perturbation. Comparisons on a classifier level with support vector machines showed that ANNs perform better for the specific application, implying a connection between the classification method and the underlying learning problem. Overall, the frame-based classification scheme reduced the misclassification rate. This approach is suited for challenging classification problems where the degree of class separation is variable, i.e., assessment of mechanical damage in mushrooms. [ABSTRACT FROM AUTHOR]

Purpose: The interactions of neoplastic cells with each other and the microenvironment are complex. To understand intratumoral heterogeneity, subtle differences should be quantified. Main factors contributing to heterogeneity include the gradient ischemic level within neoplasms, action of microenvironment, mechanisms of intercellular transfer of genetic information, and differential mechanisms of modifications of genetic material/proteins. This may reflect on the expression of biomarkers in the context of prognosis/stratification. Hence, a rigorous approach for assessing the spatial intratumoral heterogeneity of histological biomarker expression with accuracy and reproducibility is required, since patterns in immunohistochemical images can be challenging to identify and describe. Methods: A quantitative method that is useful for characterizing complex irregular structures is lacunarity; it is a multiscale technique that exhaustively samples the image, while the decay of its index as a function of window size follows characteristic patterns for different spatial arrangements. In histological images, lacunarity provides a useful measure for the spatial organization of a biomarker when a sampling scheme is employed and relevant features are computed. The proposed approach quantifies the segmented proliferative cells and not the textural content of the histological slide, thus providing a more realistic measure of heterogeneity within the sample space of the tumor region. The aim is to investigate in whole sections of primary pancreatic neuroendocrine neoplasms (pNENs), using whole-slide imaging and image analysis, the spatial intratumoral heterogeneity of Ki-67 immunostains. Unsupervised learning is employed to verify that the approach can partition the tissue sections according to distributional heterogeneity. Results: The architectural complexity of histological images has shown that single measurements are often insufficient. Inhomogeneity of distribution depends not only on percentage content of proliferation phase but also on how the phase fills the space. Lacunarity curves demonstrate variations in the sampled image sections. Since the spatial distribution of proliferation in each case is different, the width of the curves changes too. Image sections that have smaller numerical variations in the computed features correspond to neoplasms with spatially homogeneous proliferation, while larger variations correspond to cases where proliferation shows various degrees of clumping. Grade 1 (uniform/nonuniform: 74%/26%) and grade 3 (uniform: 100%) pNENs demonstrate a more homogeneous proliferation with grade 1 neoplasms being more variant, while grade 2 tumor regions render a more diverse landscape (50%/50%). Hence, some cases show an increased degree of spatial heterogeneity comparing to others with similar grade. Whether this is a sign of different tumor biology and an association with a more benign/malignant clinical course needs to be investigated further. The extent and range of spatial heterogeneity has the potential to be evaluated as a prognostic marker. Conclusions: The association with tumor grade as well as the rationale that the methodology reflects true tumor architecture supports the technical soundness of the method. This reflects a general approach which is relevant to other solid tumors and biomarkers. Drawing upon the merits of computational biomedicine, the approach uncovers salient features for use in future studies of clinical relevance. [ABSTRACT FROM AUTHOR]

There is an increasing interest in the use of polysaccharides and proteins for the production of biodegradable films. Visible and near-infrared (VIS–NIR) spectroscopy is a reliable analytical tool for objective analyses of biological sample attributes. The objective is to investigate the potential of VIS–NIR spectroscopy as a process analytical technology for compositional characterization of biodegradable materials and correlation to their mechanical properties. Biofilms were produced by single-screw extrusion with different combinations of polybutylene adipate-co-terephthalate, whole oat flour, glycerol, magnesium stearate, and citric acid. Spectral data were recorded in the range of 400–2498 nm at 2 nm intervals. Partial least square regression was used to investigate the correlation between spectral information and mechanical properties. Results show that spectral information is influenced by the major constituent components, as they are clustered according to polybutylene adipate-co-terephthalate content. Results for regression models using the spectral information as predictor of tensile properties achieved satisfactory results, with coefficients of prediction ( R 2 C ) of 0.83, 0.88 and 0.92 (calibration models) for elongation, tensile strength, and Young's modulus, respectively. Results corroborate the correlation of NIR spectra with tensile properties, showing that NIR spectroscopy has potential as a rapid analytical technology for non-destructive assessment of the mechanical properties of the films. [ABSTRACT FROM AUTHOR]

Abstract: Tenderness is an important attribute influencing consumer opinion about the eating quality of fresh meat. Manual assessment of tenderness requires lengthy procedures with tedious sample preparations. An objective, non-destructive, and rapid technique for assessing meat tenderness is required by the meat industry. In this study, the development of partial least squares (PLS) regression models to relate near-infrared (NIR) reflectance spectra and statistical features (mean, standard deviation, norm-1 energy, norm-2 energy, average residual, and entropy) from discrete wavelet transforms (DWT) of raw porcine longissimus dorsi muscle images, to slice shear force (SSF) instrumental measurements, was investigated. The coefficient of determination (R2) of the PLS regression model was 0.63 when only spectral information from hyperspectral (HS) images was analyzed, while PLS models using DWT features extracted from computer vision (CV) images yielded coefficient of determination of 0.48. By combining them, the R2 increased to 0.75. The study has shown the potential for NIR measurements combined with wavelet features from CV images to provide better correlations with muscle tenderness. Industrial relevance: The study has shown the potential for NIR measurements combined with wavelet features from CV images to provide better correlations with muscle tenderness for the meat industry. [Copyright &y& Elsevier]

Liquid-based cytology (LBC) in conjunction with Whole-Slide Imaging (WSI) enables the objective and sensitive and quantitative evaluation of biomarkers in cytology. However, the complex three-dimensional distribution of cells on LBC slides requires manual focusing, long scanning-times, and multi-layer scanning. Here, we present a solution that overcomes these limitations in two steps: first, we make sure that focus points are only set on cells. Secondly, we check the total slide focus quality. From a first analysis we detected that superficial dust can be separated from the cell layer (thin layer of cells on the glass slide) itself. Then we analyzed 2,295 individual focus points from 51 LBC slides stained for p16 and Ki67. Using the number of edges in a focus point image, specific color values and size-inclusion filters, focus points detecting cells could be distinguished from focus points on artifacts (accuracy 98.6%). Sharpness as total focus quality of a virtual LBC slide is computed from 5 sharpness features. We trained a multi-parameter SVM classifier on 1,600 images. On an independent validation set of 3,232 cell images we achieved an accuracy of 94.8% for classifying images as focused. Our results show that single-layer scanning of LBC slides is possible and how it can be achieved. We assembled focus point analysis and sharpness classification into a fully automatic, iterative workflow, free of user intervention, which performs repetitive slide scanning as necessary. On 400 LBC slides we achieved a scanning-time of 13.9±10.1 min with 29.1±15.5 focus points. In summary, the integration of semantic focus information into whole-slide imaging allows automatic high-quality imaging of LBC slides and subsequent biomarker analysis. [ABSTRACT FROM AUTHOR]

Abstract: A multistage workflow was developed for segmenting and counting murine microglias from histopathological brightfield images, in a permanent focal cerebral ischemia model. Automated counts are useful, since for the assessment of inflammatory mechanisms in ischemic stroke there is a need to quantify the brain''s responses to post-ischemia, which primarily is the rapid activation of microglial cells. Permanent middle cerebral artery occlusion was induced in murine brain tissue samples. Positive cells were quantified by immunohistochemistry for the ionized calcium-binding adaptor molecule-1 (Iba1) as the microglia marker. Microglia cells were segmented in seven sequential steps: (i) contrast boosting using quaternion operations, (ii) intensity outlier normalization, (iii) nonlocal total variation denoising, (iv) histogram specification and contrast stretching, (v) homomorphic filtering, (vi) global thresholding, and (vii) morphological filtering. Workflow counts were validated on an image subset, with ground-truth data acquired from manual counts conducted by a neuropathologist. Automated workflow matched ground-truth counts pretty well; 80–90% accuracy was achieved, as regards to time after pMCAO and correspondence to ischemic/non-ischemic tissue. [Copyright &y& Elsevier]

Abstract: Lacunarity is about quantifying the degree of spatial heterogeneity in the visual texture of imagery through the identification of the relationships between patterns and their spatial configurations in a two-dimensional setting. The computed lacunarity data can designate a mathematical index of spatial heterogeneity, therefore the corresponding feature vectors should possess the necessary inter-class statistical properties that would enable them to be used for pattern recognition purposes. The objectives of this study is to construct a supervised parsimonious classification model of binary lacunarity data–computed by Valous et al. (2009)–from pork ham slice surface images, with the aid of kernel principal component analysis (KPCA) and artificial neural networks (ANNs), using a portion of informative salient features. At first, the dimension of the initial space (510 features) was reduced by 90% in order to avoid any noise effects in the subsequent classification. Then, using KPCA, the first nineteen kernel principal components (99.04% of total variance) were extracted from the reduced feature space, and were used as input in the ANN. An adaptive feedforward multilayer perceptron (MLP) classifier was employed to obtain a suitable mapping from the input dataset. The correct classification percentages for the training, test and validation sets were 86.7%, 86.7%, and 85.0%, respectively. The results confirm that the classification performance was satisfactory. The binary lacunarity spatial metric captured relevant information that provided a good level of differentiation among pork ham slice images. [ABSTRACT FROM AUTHOR]

Abstract: The visual texture of pork ham slices reveals information about the different qualities and perceived image heterogeneity, which is encapsulated as spatial variations in geometry and spectral characteristics. Detrended Fluctuation Analysis (DFA) detects long-range correlations in nonstationary spatial sequences, by a self-similarity scaling exponent α. In the current work, the aim is to investigate the usefulness of α, using different colour channels (R, G, B, L*, a*, b*, H, S, V, and Grey), as a quantitative descriptor of visual texture in sliced ham surface patterns for the detection of long-range correlations in unidimensional spatial series of greyscale intensity pixel values at 0°, 30°, 45°, 60°, and 90° rotations. Images were acquired from three qualities of pre-sliced pork ham, typically consumed in Ireland (200 slices per quality). Results indicated that the DFA approach can be used to characterize and quantify the textural appearance of the three ham qualities, for different image orientations, with a global scaling exponent. The spatial series extracted from the ham images display long-range dependence, indicating an average behaviour around 1/f-noise. Results indicate that α has a universal character in quantifying the visual texture of ham surface intensity patterns, with no considerable crossovers that alter the behaviour of the fluctuations. Fractal correlation properties can thus be a useful metric for capturing information embedded in the visual texture of hams. [Copyright &y& Elsevier]

Abstract: Images of three qualities of pre-sliced pork and Turkey hams were evaluated for colour and textural features to characterize and classify them, and to model the ham appearance grading and preference responses of a group of consumers. A total of 26 colour features and 40 textural features were extracted for analysis. Using Mahalanobis distance and feature inter-correlation analyses, two best colour [mean of S (saturation in HSV colour space), std. deviation of b∗, which indicates blue to yellow in L∗a∗b∗ colour space] and three textural features [entropy of b∗, contrast of H (hue of HSV colour space), entropy of R (red of RGB colour space)] for pork, and three colour (mean of R, mean of H, std. deviation of a∗, which indicates green to red in L∗a∗b∗ colour space) and two textural features [contrast of B, contrast of L∗ (luminance or lightness in L∗a∗b∗ colour space)] for Turkey hams were selected as features with the highest discriminant power. High classification performances were reached for both types of hams (>99.5% for pork and >90.5% for Turkey) using the best selected features or combinations of them. In spite of the poor/fair agreement among ham consumers as determined by Kappa analysis (Kappa-value<0.4) for sensory grading (surface colour, colour uniformity, bitonality, texture appearance and acceptability), a dichotomous logistic regression model using the best image features was able to explain the variability of consumers’ responses for all sensorial attributes with accuracies higher than 74.1% for pork hams and 83.3% for Turkey hams. [Copyright &y& Elsevier]

Abstract: The quaternionic singular value decomposition is a technique to decompose a quaternion matrix (representation of a colour image) into quaternion singular vector and singular value component matrices exposing useful properties. The objective of this study was to use a small portion of uncorrelated singular values, as robust features for the classification of sliced pork ham images, using a supervised artificial neural network classifier. Images were acquired from four qualities of sliced cooked pork ham typically consumed in Ireland (90 slices per quality), having similar appearances. Mahalanobis distances and Pearson product moment correlations were used for feature selection. Six highly discriminating features were used as input to train the neural network. An adaptive feedforward multilayer perceptron classifier was employed to obtain a suitable mapping from the input dataset. The overall correct classification performance for the training, validation and test set were 90.3%, 94.4%, and 86.1%, respectively. The results confirm that the classification performance was satisfactory. Extracting the most informative features led to the recognition of a set of different but visually quite similar textural patterns based on quaternionic singular values. [Copyright &y& Elsevier]

Abstract: Textural patterns are often complex, exhibit scale-dependent changes in structure and are difficult to identify and describe. The lacunarity morphometric uses multiscale windowing to measure the scale dependency of spatial heterogeneity. In the current work, the objectives are to investigate the usefulness of lacunarity, using different colour scales (R,G,B, L ∗, a ∗, b ∗, and Gray), as a quantitative descriptor of visual texture in sliced ham surfaces, and compare these results with the binary approach developed by . Images were acquired from three qualities of sliced pork ham, typically consumed in Ireland (200slices/quality). Lacunarity plots reveal important textural content information that corresponds to degree of spatial heterogeneity of intensities and level of self-similar behaviour. The results of intensity lacunarity suggest that window sizes up to 10pixels may be adequate enough to cover textural features and produce meaningful results. Once the box size is larger than 10, lacunarity curves either converge or display atypical behaviour and then decay. Investigation confirmed that both binary and intensity lacunarity approaches are useful quantitative descriptors of visual texture in sliced ham images. Moreover, potential future research directions were suggested for computing lacunarity in colour images directly. [Copyright &y& Elsevier]

The production of hams follows processes in which the characteristics of raw materials and processing conditions influence the quality attributes. The increased use of non-contact evaluation methods leads to a better understanding of the materials and processes involved, resulting in meat products that are safer with improved quality. To satisfy the increased awareness and greater expectation of consumers, it is necessary to improve quality control methodologies. Imaging and spectroscopy are proven technologies that can provide useful information regarding ham quality and the effects of the processing regime. The aim of this review is to communicate perspectives and aspects, relating to imaging, spectroscopic and colorimetric techniques on the subject of non-contact quality evaluation and control of hams. Moreover, several promising non-invasive techniques and accompanying technological advances are also presented that have the potential to be robust and efficient. Imaging and spectroscopic technologies continue to change at a rapid pace, and the developed techniques can mature into industrial applications with the right integration framework. [Copyright &y& Elsevier]

Abstract: Fat-connective tissue size distribution (FSD) in hams is a fundamental physical property for its quality assessment. FSD is related to the sensory properties such as texture, taste, quality of raw meat and visual appearance. In this paper we present a tool to carry out the multifractal analysis (MFA) of two-dimensional binary images of pre-sliced pork hams through the calculation of the f(α)-spectra, Rényi (Dq ) dimensions, and associated statistical regressions and parameters. The application is presented for the structural characterization of FSD in three qualities of pork hams (high yield, medium yield and premium quality hams) using image sections of 512×512pixels2 with a spatial resolution of 0.102mm/pixel. MFA was carried out using the method of moments in the optimized box size range of 32–512pixels for all the ham images using powers of 2, and estimating the probability distribution for moments ranging from −10< q <10 in steps of 0.5. The experimental results suggest that MFA has a discriminating effect among the three types of ham using the maximum entropy and correlation dimension D 2. This investigation revealed the usefulness of the MFA dimensions as quantitative descriptors of texture analysis and pattern distributions of FSD in pre-sliced ham images. [Copyright &y& Elsevier]

Abstract: An important goal for characterization of texture appearance is the quantification of spatial patterns. The objective was to investigate the potential usefulness of two fractal metrics based on fast Fourier transform and gliding box lacunarity as descriptors of visual texture in ham slices. Images were acquired from three qualities of sliced pork ham, typically consumed in Ireland (200slices/quality). Unexpected characteristics in textural pattern were revealed; the values of fractal dimension were larger for the smoothest surface. Alternatively, the decreasing trend of the power spectrum intercept towards the smoother premium quality ham showed that it correlates well with the overall magnitude of visual roughness. The results of lacunarity suggest that it has a discriminating power among the three ham qualities and its behaviour resembles the one of an exponential decay function. Results showed that Fourier analysis dimension, power spectrum intercept and lacunarity are important fractal parameters and useful quantitative descriptors that capture information embedded in the spatial structure of the underlying image texture of hams. [Copyright &y& Elsevier]

Abstract: This paper presents a novel and non-destructive approach to the appearance characterization and classification of commercial pork, turkey and chicken ham slices. Ham slice images were modelled using directional fractal () dimensions and a minimum distance classifier was adopted to perform the classification task. Also, the role of different colour spaces and the resolution level of the images on DF analysis were investigated. This approach was applied to 480 wafer thin ham slices from four types of hams (120 slices per type): i.e., pork (cooked and smoked), turkey (smoked) and chicken (roasted). DF features were extracted from digitalized intensity images in greyscale, and R, G, B, L ∗, a ∗, b ∗, H, S, and V colour components for three image resolution levels (100%, 50%, and 25%). Simulation results show that in spite of the complexity and high variability in colour and texture appearance, the modelling of ham slice images with DF dimensions allows the capture of differentiating textural features between the four commercial ham types. Independent DF features entail better discrimination than that using the average of four directions. However, DF dimensions reveal a high sensitivity to colour channel, orientation and image resolution for the fractal analysis. The classification accuracy using six DF dimension features () was 93.9% for training data and 82.2% for testing data. [Copyright &y& Elsevier]

Abstract: Due to the high variability and complex colour distribution in meats and meat products, the colour signal calibration of any computer vision system used for colour quality evaluations, represents an essential condition for objective and consistent analyses. This paper compares two methods for CIE colour characterization using a computer vision system (CVS) based on digital photography; namely the polynomial transform procedure and the transform proposed by the sRGB standard. Also, it presents a procedure for evaluating the colour appearance and presence of pores and fat-connective tissue on pre-sliced hams made from pork, turkey and chicken. Our results showed high precision, in colour matching, for device characterization when the polynomial transform was used to match the CIE tristimulus values in comparison with the sRGB standard approach as indicated by their values. The [3×20] polynomial transfer matrix yielded a modelling accuracy averaging below 2.2 units. Using the sRGB transform, high variability was appreciated among the computed (8.8±4.2). The calibrated laboratory CVS, implemented with a low-cost digital camera, exhibited reproducible colour signals in a wide range of colours capable of pinpointing regions-of-interest and allowed the extraction of quantitative information from the overall ham slice surface with high accuracy. The extracted colour and morphological features showed potential for characterizing the appearance of ham slice surfaces. CVS is a tool that can objectively specify colour and appearance properties of non-uniformly coloured commercial ham slices. [Copyright &y& Elsevier]

The tumor microenvironment is an interacting heterogeneous collection of cancer cells, resident as well as infiltrating host cells, secreted factors, and extracellular matrix proteins. With the growing importance of immunotherapies, it has become crucial to be able to characterize the composition and the functional orientation of the microenvironment. The development of novel computational image analysis methodologies may enable the robust quantification and localization of immune and related biomarker-expressing cells within the microenvironment. The aim of the review is to concisely highlight a selection of current and significant contributions pertinent to methodological advances coupled with biomedical or translational applications. A further aim is to concisely present computational advances that, to our knowledge, have currently very limited use for the assessment of the microenvironment but have the potential to enhance image analysis pipelines; on this basis, an example is shown for the detection and segmentation of cells of the microenvironment using a published pipeline and a public dataset. Finally, a general proposal is presented on the conceptual design of automation-optimized computational image analysis workflows in the biomedical and clinical domain. [ABSTRACT FROM AUTHOR]

Papaya grading is performed manually which may lead to misclassifications, resulting in fruit boxes with different maturity stages. The objective is to predict the ripening of the papaya fruit using digital imaging and random forests. A series of physical/chemical analyses are carried out and true maturity stage is derived from pulp firmness measurements. Imaging and image analysis provides hand-crafted color features computed from the peel and random decision forests are implemented to predict ripening stage. More specifically, a total of 114 samples from 57 fruits are used for the experiments, and classified into three stages of maturity. After image acquisition and analysis, twenty-one hand-crafted color features (comprising seven groups) that have low computational cost are extracted and evaluated. Random forests with two datasets (cross-validation and prediction set) are employed for the experiments. Concerning all image features, 94.3% classification performance is obtained over the cross-validation set. The prediction set obtained 94.7% misclassifying only a single sample. For the group comparisons, the normalized mean of the RGB (red, green, blue) color space achieved better performance (78.1%). Essentially, the technique can mature into an industrial application with the right integration framework. [ABSTRACT FROM AUTHOR]

The homology groups of a topological space provide us with information about its connectivity and the number and type of holes in it. This type of information can find practical applications in describing the intrinsic structure of an image, as well as in identifying equivalence classes in collections of images. When computing homological characteristics, the existence and strength of the relationships between each pair of points in the topological space are studied. The practical use of this approach begins by building a topological space from the image, in which the computation of the homology groups can be carried out in a feasible time. Once the homological properties are obtained, what follows is the task of translating such information into operations such as image segmentation. This work presents a technique for denoising persistent diagrams and reconstructing the shape of segmented objects using the remaining classes on the diagram. A case study for the segmentation of cell nuclei in histological images is used for demonstration purposes. With this approach: a) topological denoising is achieved by aggregating trivial classes on the persistence diagram, and b) a growing seed algorithm uses the information obtained during the construction of the persistence diagram for the reconstruction of the segmented cell structures. [ABSTRACT FROM AUTHOR]

Multiple reports have highlighted the importance of the local immunological cellular composition (i.e. the density of effector T cells and macrophage polarization state) in predicting clinical outcome in advanced metastatic stage of colorectal cancer. However, in spite of the general association between a high effector T cell density and improved outcome, our recent work has revealed a specific lymphocyte-driven cancer cell-supporting signal. Indeed, lymphocyte-derived CCL5 supports CCR5-positive tumor cell proliferation and thereby fosters tumor growth in metastatic liver lesions. Upon systematic analysis of CCR5 expression by tumor cells using immunohistochemistry, we observed that the intensity of CCR5 increases with primary tumor size and peaks in T4 tumors. In liver metastases however, though CCR5 expression intensity is globally heightened compared to primary tumors, alterations in the expression patterns appear, leading to "patchiness" of the stain. CCR5 patchiness is, therefore, a signature of liver metastases in our cohort (n = 97 specimens) and relates to globally decreased expression intensity, but does not influence the extent of the response to CCR5 inhibitor Maraviroc in patients. Moreover, CCR5 patchiness relates to a poor immune landscape characterized by a low cytotoxic-to-regulatory T cell ratio at the invasive margin and enriched cellular and molecular markers of macrophage M2 polarization. Finally, because higher numbers of PD-1- and CTLA-4-positive cells surround tumors with patchy CCR5 expression, one can speculate that these tumors potentially respond to immune checkpoint blockade. This hypothesis is corroborated by the prolonged disease-free survival and disease-specific survival observed in patients with low gene expression of CCR5 in metastases from two publically available cohorts. These observations highlight the complex role of the CCL5-CCR5 axis in CRC metastatic progression and warrant further investigations. [ABSTRACT FROM AUTHOR]

Background: For virtually every patient with colorectal cancer (CRC), hematoxylin-eosin (HE)-stained tissue slides are available. These images contain quantitative information, which is not routinely used to objectively extract prognostic biomarkers. In the present study, we investigated whether deep convolutional neural networks (CNNs) can extract prognosticators directly from these widely available images.Methods and Findings: We hand-delineated single-tissue regions in 86 CRC tissue slides, yielding more than 100,000 HE image patches, and used these to train a CNN by transfer learning, reaching a nine-class accuracy of >94% in an independent data set of 7,180 images from 25 CRC patients. With this tool, we performed automated tissue decomposition of representative multitissue HE images from 862 HE slides in 500 stage I-IV CRC patients in the The Cancer Genome Atlas (TCGA) cohort, a large international multicenter collection of CRC tissue. Based on the output neuron activations in the CNN, we calculated a "deep stroma score," which was an independent prognostic factor for overall survival (OS) in a multivariable Cox proportional hazard model (hazard ratio [HR] with 95% confidence interval [CI]: 1.99 [1.27-3.12], p = 0.0028), while in the same cohort, manual quantification of stromal areas and a gene expression signature of cancer-associated fibroblasts (CAFs) were only prognostic in specific tumor stages. We validated these findings in an independent cohort of 409 stage I-IV CRC patients from the "Darmkrebs: Chancen der Verhütung durch Screening" (DACHS) study who were recruited between 2003 and 2007 in multiple institutions in Germany. Again, the score was an independent prognostic factor for OS (HR 1.63 [1.14-2.33], p = 0.008), CRC-specific OS (HR 2.29 [1.5-3.48], p = 0.0004), and relapse-free survival (RFS; HR 1.92 [1.34-2.76], p = 0.0004). A prospective validation is required before this biomarker can be implemented in clinical workflows.Conclusions: In our retrospective study, we show that a CNN can assess the human tumor microenvironment and predict prognosis directly from histopathological images. [ABSTRACT FROM AUTHOR]

Background: Biliary tract cancers (BTCs) have a poor prognosis. BTCs are characterized by a prominent desmoplastic reaction which possibly contributes to the aggressive phenotype of this tumor. The desmoplastic reaction includes excessive production and deposition of extracellular matrix proteins such as periostin, secreted protein acidic and rich in cysteine (SPARC), thrombospondin-1, as well as accumulation of α-smooth muscle actin-positive cancer-associated fibroblasts and immune cells, secreting growth factors and cytokines including transforming growth factor (TGF)-β. In the present study, we investigated the expression of SPARC in BTC as well as its possible regulation by TGF-β. Methods: Expression levels of Sparc,TGF-β1 and its receptor ALK5 were evaluated by quantitative real-time PCR in 6 biliary tract cell lines as well as 1 immortalized cholangiocyte cell line (MMNK-1). RNAs from tumor samples of 7 biliary tract cancer patients were analyzed for expression of Sparc, TGF-β type II receptor (TbRII) as well as Twist and ZO-1. MMNK-1 cells were stimulated with TGF-β for 24 h, and Sparc,ZO-1 and E-Cadherin expressions were determined. The presence of SPARC protein was analyzed by immunohistochemistry in tumor specimens from 10 patients. Results: When comparing basal Sparc transcript levels in diverse BTC cell lines to MMNK-1 cells, we found that it was strongly downregulated in all cancer cell lines. The remaining expression levels were higher in highly differentiated cell lines (CCSW1, MZChA1, MZChA2 and TFK-1) than in less differentiated and undifferentiated ones (BDC, SKChA1). Expression of Sparc in BTC patient samples showed a significant positive correlation with expression of the epithelial marker ZO-1. In contrast, the mesenchymal marker Twist and the TbRII showed a trend of negative correlation with expression of Sparc in these samples. TGF-β exposure significantly downregulated Sparc expression in MMNK-1 cholangiocytes in vitro in parallel to downregulation of epithelial markers (E-Cadherin and ZO-1). Finally, SPARC immunostaining was performed in 10 patient samples, and the correlation between absence of SPARC and survival times was analyzed. Conclusions: These data imply that a decrease in SPARC expression is correlated with dedifferentiation of BTC cells resulting in enhanced EMT being possibly mediated by TGF-β. Thereby SPARC levels might be a marker for individual prognosis of a patient, and strategies aiming at inhibition of SPARC downregulation might have potential for new future therapies. [ABSTRACT FROM AUTHOR]

Bone morphogenetic protein (BMP)-9, a member of the TGFβ-family of cytokines, is believed to be mainly produced in the liver. The serum levels of BMP-9 were reported to be reduced in newly diagnosed diabetic patients and BMP-9 overexpression ameliorated steatosis in the high fat diet-induced obesity mouse model. Furthermore, injection of BMP-9 in mice enhanced expression of fibroblast growth factor (FGF)21. However, whether BMP-9 also regulates the expression of the related FGF19 is not clear. Because both FGF21 and 19 were described to protect the liver from steatosis, we have further investigated the role of BMP-9 in this context. We first analyzed BMP-9 levels in the serum of streptozotocin (STZ)-induced diabetic rats (a model of type I diabetes) and confirmed that BMP-9 serum levels decrease during diabetes. Microarray analyses of RNA samples from hepatic and intestinal tissue from BMP-9 KO- and wild-type mice (C57/Bl6 background) pointed to basal expression of BMP-9 in both organs and revealed a down-regulation of hepatic Fgf21 and intestinal Fgf19 in the KO mice. Next, we analyzed BMP-9 levels in a cohort of obese patients with or without diabetes. Serum BMP-9 levels did not correlate with diabetes, but hepatic BMP-9 mRNA expression negatively correlated with steatosis in those patients that did not yet develop diabetes. Likewise, hepatic BMP-9 expression also negatively correlated with serum LPS levels. In situ hybridization analyses confirmed intestinal BMP-9 expression. Intestinal (but not hepatic) BMP-9 mRNA levels were decreased with diabetes and positively correlated with intestinal E-Cadherin expression. In vitro studies using organoids demonstrated that BMP-9 directly induces FGF19 in gut but not hepatocyte organoids, whereas no evidence of a direct induction of hepatic FGF21 by BMP-9 was found. Consistent with the in vitro data, a correlation between intestinal BMP-9 and FGF19 mRNA expression was seen in the patients' samples. In summary, our data confirm that BMP-9 is involved in diabetes development in humans and in the control of the FGF-axis. More importantly, our data imply that not only hepatic but also intestinal BMP-9 associates with diabetes and steatosis development and controls FGF19 expression. The data support the conclusion that increased levels of BMP-9 would most likely be beneficial under pre-steatotic conditions, making supplementation of BMP-9 an interesting new approach for future therapies aiming at prevention of the development of a metabolic syndrome and liver steatosis. • BMP-9 is not only produced in the liver but also in the intestine. • Intestinal BMP-9 is correlated with diabetes and with intestinal E-Cadherin (= marker for intestinal barrier function). • Intestinal rather than hepatic BMP-9 seems to be involved in protecting the liver by locally upregulating FGF19 expression. • Results from mouse-models have been at least partially contradictory, the core of the data provided now are derived from patient samples. [ABSTRACT FROM AUTHOR]

On a broader scale, T cell density and localization in colorectal cancer liver metastases have prognostic and predictive implications. As T cell distribution at higher resolutions has not been fully investigated, a detailed resolution analysis of T cell distribution was performed. Patient tissues were divided into 10 µm distance classes between the tumor border and adjacent normal liver. Thereby, distinct density patterns of T cell localization in relation to the malignant tissue could be detected. At a distance of 20 to 30 µm to the tumor, a decrease of CD3 T cells is common. Within this area, cytotoxic Granzyme B and CD8+T cells were found to be significantly reduced as well as CD163 macrophages were increased and identified to be in close contact with T cells. Our data suggests a physical or functional border within this region. Survival analysis revealed improved overall survival in patients with high T cells numbers at the direct tumor border. Interestingly, the decreased T cells in the 20 to 30 µm region were also found to be significantly associated with improved survival. Consequently, the detailed localization of T cells, despite blockade, could be associated with improved clinical outcome. The high-resolution analysis represents new insights into relevant heterogenous T cell distributions especially related to clinical responses. As the paradoxical observation of localization-dependent prognostic relevance of T cell densities is only detectable by detailed spatial analyses, this investigation of spatial profiles at higher resolutions is suggested as a new biomarker for survival and response to therapies. [ABSTRACT FROM AUTHOR]

The segmentation of cell nuclei is an important step towards the automated analysis of histological images. The presence of a large number of nuclei in whole-slide images necessitates methods that are computationally tractable in addition to being effective. In this work, a method is developed for the robust segmentation of cell nuclei in histological images based on the principles of persistent homology. More specifically, an abstract simplicial homology approach for image segmentation is established. Essentially, the approach deals with the persistence of disconnected sets in the image, thus identifying salient regions that express patterns of persistence. By introducing an image representation based on topological features, the task of segmentation is less dependent on variations of color or texture. This results in a novel approach that generalizes well and provides stable performance. The method conceptualizes regions of interest (cell nuclei) pertinent to their topological features in a successful manner. The time cost of the proposed approach is lower-bounded by an almost linear behavior and upper-bounded by O ( n 2 ) in a worst-case scenario. Time complexity matches a quasilinear behavior which is O ( n 1 + ɛ ) for ε < 1. Images acquired from histological sections of liver tissue are used as a case study to demonstrate the effectiveness of the approach. The histological landscape consists of hepatocytes and non-parenchymal cells. The accuracy of the proposed methodology is verified against an automated workflow created by the output of a conventional filter bank (validated by experts) and the supervised training of a random forest classifier. The results are obtained on a per-object basis. The proposed workflow successfully detected both hepatocyte and non-parenchymal cell nuclei with an accuracy of 84.6%, and hepatocyte cell nuclei only with an accuracy of 86.2%. A public histological dataset with supplied ground-truth data is also used for evaluating the performance of the proposed approach (accuracy: 94.5%). Further validations are carried out with a publicly available dataset and ground-truth data from the Gland Segmentation in Colon Histology Images Challenge (GlaS) contest. The proposed method is useful for obtaining unsupervised robust initial segmentations that can be further integrated in image/data processing and management pipelines. The development of a fully automated system supporting a human expert provides tangible benefits in the context of clinical decision-making. [ABSTRACT FROM AUTHOR]

Cellular and humoral inflammations play important roles in ischemic brain injury. The effectiveness of immunomodulatory therapies may critically depend on the chosen experimental model. Our purpose was to compare the post-ischemic neuroinflammation among murine permanent and transient middle cerebral artery occlusion ( MCAO) models. Permanent MCAO was induced by transtemporal electrocoagulation and 30 minutes or 90 minutes transient MCAO was induced by intraluminal filament in C57BL/6 mice. Infiltration of leukocyte subpopulations was quantified by immunohistochemistry and fluorescence-activated cell sorting. Cerebral cytokine and adhesion molecule expression was measured by real-time polymerase chain reaction ( RT-PCR). Neutrophil infiltration was noted at 24 h after transient MCAO, but did not further increase until 5 days in the permanent MCAO model. Few T cells were observed in both MCAO models at 24 h, but permanent MCAO demonstrated much more infiltrating T cells at 5 days. Pronounced microglial activation was evident at 24 h and 5 days after permanent but not after transient MCAO. The number of invading NK cells and expression of MHCII on CD11b+ cells did not differ among the three groups. Five days after MCAO, the expression of IL-1, TNF-α and IFN-γ and of the adhesion molecules ICAM-1 and VCAM-1 was significantly higher in the permanent than in the transient MCAO groups. Cellular and humoral inflammation differs substantially among commonly used MCAO models. Neuroinflammation is more pronounced after permanent electrocoagulatory MCAO compared with 30 minutes and 90 minutes filament- MCAO. [ABSTRACT FROM AUTHOR]

Abstract: A method to discriminate between various grades of pork and turkey ham was developed using colour and wavelet texture features. Image analysis methods originally developed for predicting the palatability of beef were applied to rapidly identify the ham grade. With high quality digital images of 50–94 slices per ham it was possible to identify the greyscale that best expressed the differences between the various ham grades. The best 10 discriminating image features were then found with a genetic algorithm. Using the best 10 image features, simple linear discriminant analysis models produced 100% correct classifications for both pork and turkey on both calibration and validation sets. [Copyright &y& Elsevier]

Cancer immunotherapy has fundamentally changed the landscape of oncology in recent years and significant resources are invested into immunotherapy research. It is in the interests of researchers and clinicians to identify promising and less promising trends in this field in order to rationally allocate resources. This requires a quantitative large-scale analysis of cancer immunotherapy related databases. We developed a novel tool for text mining, statistical analysis and data visualization of scientific literature data. We used this tool to analyze 72002 cancer immunotherapy publications and 1469 clinical trials from public databases. All source codes are available under an open access license. The contribution of specific topics within the cancer immunotherapy field has markedly shifted over the years. We show that the focus is moving from cell-based therapy and vaccination towards checkpoint inhibitors, with these trends reaching statistical significance. Rapidly growing subfields include the combination of chemotherapy with checkpoint blockade. Translational studies have shifted from hematological and skin neoplasms to gastrointestinal and lung cancer and from tumor antigens and angiogenesis to tumor stroma and apoptosis. This work highlights the importance of unbiased large-scale database mining to assess trends in cancer research and cancer immunotherapy in particular. Researchers, clinicians and funding agencies should be aware of quantitative trends in the immunotherapy field, allocate resources to the most promising areas and find new approaches for currently immature topics. [ABSTRACT FROM AUTHOR]