Subcellular protein expression models for microsatellite instability in colorectal adenocarcinoma tissue images

Background New bioimaging techniques capable of visualising the co-location of numerous proteins within individual cells have been proposed to study tumour heterogeneity of neighbouring cells within the same tissue specimen. These techniques have highlighted the need to better understand the interplay between proteins in terms of their colocalisation. Results We recently proposed a cellular-level model of the healthy and cancerous colonic crypt microenvironments. Here, we extend the model to include detailed models of protein expression to generate synthetic multiplex fluorescence data. As a first step, we present models for various cell organelles learned from real immunofluorescence data from the Human Protein Atlas. Comparison between the distribution of various features obtained from the real and synthetic organelles has shown very good agreement. This has included both features that have been used as part of the model input and ones that have not been explicitly considered. We then develop models for six proteins which are important colorectal cancer biomarkers and are associated with microsatellite instability, namely MLH1, PMS2, MSH2, MSH6, P53 and PTEN. The protein models include their complex expression patterns and which cell phenotypes express them. The models have been validated by comparing distributions of real and synthesised parameters and by application of frameworks for analysing multiplex immunofluorescence image data. Conclusions The six proteins have been chosen as a case study to illustrate how the model can be used to generate synthetic multiplex immunofluorescence data. Further proteins could be included within the model in a similar manner to enable the study of a larger set of proteins of interest and their interactions. To the best of our knowledge, this is the first model for expression of multiple proteins in anatomically intact tissue, rather than within cells in culture.QNRF grant NPRP 5-1345-1-228. BBSRC and University of Warwick Institute of Advanced Study

Histopathological image analysis : a review

Over the past decade, dramatic increases in computational power and improvement in image analysis algorithms have allowed the development of powerful computer-assisted analytical approaches to radiological data. With the recent advent of whole slide digital scanners, tissue histopathology slides can now be digitized and stored in digital image form. Consequently, digitized tissue histopathology has now become amenable to the application of computerized image analysis and machine learning techniques. Analogous to the role of computer-assisted diagnosis (CAD) algorithms in medical imaging to complement the opinion of a radiologist, CAD algorithms have begun to be developed for disease detection, diagnosis, and prognosis prediction to complement the opinion of the pathologist. In this paper, we review the recent state of the art CAD technology for digitized histopathology. This paper also briefly describes the development and application of novel image analysis technology for a few specific histopathology related problems being pursued in the United States and Europe

Common Fixed Point Theorems for Four Self Maps on A Menger Space, Satisfying Common E. A. Property

In this paper, we prove common fixed point theorems for four self maps by using weak compatibility in Menger spaces. Our result extend, generalized several fixed point theorems on Menger spaces. Keywords— Common fixed points, Metric space, Menger space, weak compatible mappings and E. A. property. AMS subject classification– 47H10, 54H25

A model of the spatial tumour heterogeneity in colorectal adenocarcinoma tissue

Background There have been great advancements in the field of digital pathology. The surge in development of analytical methods for such data makes it crucial to develop benchmark synthetic datasets for objectively validating and comparing these methods. In addition, developing a spatial model of the tumour microenvironment can aid our understanding of the underpinning laws of tumour heterogeneity. Results We propose a model of the healthy and cancerous colonic crypt microenvironment. Our model is designed to generate synthetic histology image data with parameters that allow control over cancer grade, cellularity, cell overlap ratio, image resolution, and objective level. Conclusions To the best of our knowledge, ours is the first model to simulate histology image data at sub-cellular level for healthy and cancerous colon tissue, where the cells have different compartments and are organised to mimic the microenvironment of tissue in situ rather than dispersed cells in a cultured environment. Qualitative and quantitative validation has been performed on the model results demonstrating good similarity to the real data. The simulated data could be used to validate techniques such as image restoration, cell and crypt segmentation, and cancer grading.BBSRC and University of Warwick Institute of Advanced Study. QNRF grant NPRP 5-1345-1-228

Self-Path: self-supervision for classification of pathology images with limited annotations

While high-resolution pathology images lend themselves well to ‘data hungry’ deep learning algorithms, obtaining exhaustive annotations on these images for learning is a major challenge. In this paper, we propose a self-supervised convolutional neural network (CNN) frame-work to leverage unlabeled data for learning generalizable and domain invariant representations in pathology images. Our proposed framework, termed as Self-Path, employs multi-task learning where the main task is tissue classification and pretext tasks are a variety of self-supervised tasks with labels inherent to the input images.We introduce novel pathology-specific self-supervision tasks that leverage contextual, multi-resolution and semantic features in pathology images for semi-supervised learning and domain adaptation. We investigate the effectiveness of Self-Path on 3 different pathology datasets. Our results show that Self-Path with the pathology-specific pretext tasks achieves state-of-the-art performance for semi-supervised learning when small amounts of labeled data are available. Further, we show that Self-Path improves domain adaptation for histopathology image classification when there is no labeled data available for the target domain. This approach can potentially be employed for other applications in computational pathology, where annotation budget is often limited or large amount of unlabeled image data is available

Universal Seesaw Mass Matrix Model with an S_3 Symmetry

Stimulated by the phenomenological success of the universal seesaw mass matrix model, where the mass terms for quarks and leptons f_i (i=1,2,3) and hypothetical super-heavy fermions F_i are given by \bar{f}_L m_L F_R +\bar{F}_L m_R f_R + \bar{F}_L M_F F_R + h.c. and the form of M_F is democratic on the bases on which m_L and m_R are diagonal, the following model is discussed: The mass terms M_F are invariant under the permutation symmetry S_3, and the mass terms m_L and m_R are generated by breaking the S_3 symmetry spontaneously. The model leads to an interesting relation for the charged lepton masses.Comment: 8 pages + 1 table, latex, no figures, references adde

Evolution of the Yukawa coupling constants and seesaw operators in the universal seesaw model

The general features of the evolution of the Yukawa coupling constants and seesaw operators in the universal seesaw model with det M_F=0 are investigated. Especially, it is checked whether the model causes bursts of Yukawa coupling constants, because in the model not only the magnitude of the Yukawa coupling constant (Y_L^u)_{33} in the up-quark sector but also that of (Y_L^d)_{33} in the down-quark sector is of the order of one, i.e., (Y_L^u)_{33} \sim (Y_L^d)_{33} \sim 1. The requirement that the model should be calculable perturbatively puts some constraints on the values of the intermediate mass scales and tan\beta (in the SUSY model).Comment: 21 pages, RevTex, 10 figure

Self-Dual Non-Abelian Vector Multiplet in Three Dimensions

We present an N=1 supersymmetric non-Abelian compensator formulation for a vector multiplet in three-dimensions. Our total field content is the off-shell vector multiplet (A_\mu{}^I, \lambda^I) with the off-shell scalar multiplet (\phi^I, \chi^I; F^I) both in the adjoint representation of an arbitrary non-Abelian gauge group. This system is reduced to a supersymmetric sigma-model on a group manifold, in the zero-coupling limit. Based on this result, we formulate a 'self-dual' non-Abelian vector multiplet in three-dimensions. By an appropriate identification of parameters, the mass of the self-dual vector multiplet is quantized. Additionally, we also show that the self-dual non-Abelian vector multiplet can be coupled to supersymmetric Dirac-Born-Infeld action. These results are further reformulated in superspace to get a clear overall picture.Comment: 14 pages, no figure

A Unified Description of Quark and Lepton Mass Matrices in a Universal Seesaw Model

In the democratic universal seesaw model, the mass matrices are given by \bar{f}_L m_L F_R + \bar{F}_L m_R f_R + \bar{F}_L M_F F_R (f: quarks and leptons; F: hypothetical heavy fermions), m_L and m_R are universal for up- and down-fermions, and M_F has a structure ({\bf 1}+ b_f X) (b_f is a flavour-dependent parameter, and X is a democratic matrix). The model can successfully explain the quark masses and CKM mixing parameters in terms of the charged lepton masses by adjusting only one parameter, b_f. However, so far, the model has not been able to give the observed bimaximal mixing for the neutrino sector. In the present paper, we consider that M_F in the quark sectors are still "fully" democratic, while M_F in the lepton sectors are partially democratic. Then, the revised model can reasonably give a nearly bimaximal mixing without spoiling the previous success in the quark sectors.Comment: 7 pages, no figur

Cell Maps Representation For Lung Adenocarcinoma Growth Patterns Classification In Whole Slide Images

Lung adenocarcinoma is a morphologically heterogeneous disease, characterized by five primary histologic growth patterns. The quantity of these patterns can be related to tumor behavior and has a significant impact on patient prognosis. In this work, we propose a novel machine learning pipeline capable of classifying tissue tiles into one of the five patterns or as non-tumor, with an Area Under the Receiver Operating Characteristic Curve (AUCROC) score of 0.97. Our model's strength lies in its comprehensive consideration of cellular spatial patterns, where it first generates cell maps from Hematoxylin and Eosin (H&E) whole slide images (WSIs), which are then fed into a convolutional neural network classification model. Exploiting these cell maps provides the model with robust generalizability to new data, achieving approximately 30% higher accuracy on unseen test-sets compared to current state of the art approaches. The insights derived from our model can be used to predict prognosis, enhancing patient outcomes