Optimal Proposal Particle Filters for Detecting Anomalies and Manoeuvres from Two Line Element Data

Detecting anomalous behaviour of satellites is an important goal within the broader task of space situational awareness. The Two Line Element (TLE) data published by NORAD is the only widely-available, comprehensive source of data for satellite orbits. We present here a filtering approach for detecting anomalies in satellite orbits from TLE data. Optimal proposal particle filters are deployed to track the state of the satellites' orbits. New TLEs that are unlikely given our belief of the current orbital state are designated as anomalies. The change in the orbits over time is modelled using the SGP4 model with some adaptations. A model uncertainty is derived to handle the errors in SGP4 around singularities in the orbital elements. The proposed techniques are evaluated on a set of 15 satellites for which ground truth is available and the particle filters are shown to be superior at detecting the subtle in-track and cross-track manoeuvres in the simulated dataset, as well as providing a measure of uncertainty of detections

Identification of a prognostic signature in colorectal cancer using combinatorial algorithm-driven analysis

Acknowledgements The colorectal cancer microarray was provided by the NHS Grampian Biorepository and the majority of the immunostaining was performed in the Grampian Biorepository laboratory (www.biorepository.nhsgrampian.org/). The antibodies were developed in collaboration with Vertebrate Antibodies Ltd (https://vertebrateantibodies.com/)Peer reviewedPublisher PD

A means of assessing deep learning-based detection of ICOS protein expression in colon cancer.

Biomarkers identify patient response to therapy. The potential immune‐checkpoint bi-omarker, Inducible T‐cell COStimulator (ICOS), expressed on regulating T‐cell activation and involved in adaptive immune responses, is of great interest. We have previously shown that open-source software for digital pathology image analysis can be used to detect and quantify ICOS using cell detection algorithms based on traditional image processing techniques. Currently, artificial intelligence (AI) based on deep learning methods is significantly impacting the domain of digital pa-thology, including the quantification of biomarkers. In this study, we propose a general AI‐based workflow for applying deep learning to the problem of cell segmentation/detection in IHC slides as a basis for quantifying nuclear staining biomarkers, such as ICOS. It consists of two main parts: a simplified but robust annotation process, and cell segmentation/detection models. This results in an optimised annotation process with a new user‐friendly tool that can interact with1 other open‐source software and assists pathologists and scientists in creating and exporting data for deep learning. We present a set of architectures for cell‐based segmentation/detection to quantify and analyse the trade‐offs between them, proving to be more accurate and less time consuming than traditional methods. This approach can identify the best tool to deliver the prognostic significance of ICOS protein expression

Subcellular Epithelial HMGB1 Expression Is Associated with Colorectal Neoplastic Progression, Male Sex, Mismatch Repair Protein Expression, Lymph Node Positivity, and an ‘Immune Cold’ Phenotype Associated with Poor Survival

Acknowledgments: The authors would like to thank NHS Grampian Biorepository, in particular Joan Wilson, Victoria Morrison, Kristine Nellany, and Nadine Hay, for their assistance in preparing tissue for this project. The authors also thank Tasneem O Atezia and Christina A Christopoulou for their contribution to this project during their time in the McLean laboratory. The laboratory work was instigated when M.H.M., R.J.P. and D.P.B. were based at the Institute of Medical Sciences, University of Aberdeen. Funding This work was funded by project grants from NHS Grampian Endowments and Friends of Anchor (https://www.friendsofanchor.org, charity no. SC025332). Within the McLean laboratory at the University of Aberdeen, SH received a Medical Research Scotland Summer Studentship, and AH received an Aberdeen Summer Research Studentship (University of Aberdeen).Peer reviewedPublisher PD

ICOSeg: real-time ICOS protein expression segmentation from immunohistochemistry slides using a lightweight conv-transformer network.

In this article, we propose ICOSeg, a lightweight deep learning model that accurately segments the immune-checkpoint biomarker, Inducible T-cell COStimulator (ICOS) protein in colon cancer from immunohistochemistry (IHC) slide patches. The proposed model relies on the MobileViT network that includes two main components: convolutional neural network (CNN) layers for extracting spatial features; and a transformer block for capturing a global feature representation from IHC patch images. The ICOSeg uses an encoder and decoder sub-network. The encoder extracts the positive cell's salient features (i.e., shape, texture, intensity, and margin), and the decoder reconstructs important features into segmentation maps. To improve the model generalization capabilities, we adopted a channel attention mechanism that added to the bottleneck of the encoder layer. This approach highlighted the most relevant cell structures by discriminating between the targeted cell and background tissues. We performed extensive experiments on our in-house dataset. The experimental results confirm that the proposed model achieves more significant results against state-of-the-art methods, together with an 8× reduction in parameters

The adaptive immune and immune checkpoint landscape of neoadjuvant treated esophageal adenocarcinoma using digital pathology quantitation

Acknowledgments The samples used in this research were received from the Northern Ireland Biobank, which has ethical approval to use de-identified tissue samples from the Belfast Health and Social Care Tissue Pathology archive (REC:11/NI/0013). The Northern Ireland Molecular Pathology Laboratory, was responsible for construction of tissue microarrays, slide staining, and scanning. We are grateful to the NVIDIA Corporation for supporting our research via the GPU Grant Program for researchers. The research leading to these results has also received funding from Invest Northern Ireland. The authors thank Mr. Ken Arthur for the construction of the original tissue microarrays used in this study. Funding This study was funded by a CRUK Accelerator Grant (A20256) to JJ and MST. CRUK had no role in the study design, collection, analysis, and interpretation of the data or in the writing of the report.Peer reviewedPublisher PD

Colonic epithelial cathelicidin (LL-37) expression intensity is associated with progression of colorectal cancer and presence of CD8+ T cell infiltrate

Colorectal cancer (CRC) remains a leading cause of cancer mortality. Here, we define the colonic epithelial expression of cathelicidin (LL-37) in CRC. Cathelicidin exerts pleotropic effects including anti-microbial and immunoregulatory functions. Genetic knockout of cathelicidin led to increased size and number of colorectal tumours in the azoxymethane-induced murine model of CRC. We aimed to translate this to human disease. The expression of LL-37 in a large (n = 650) fully characterised cohort of treatment-naïve primary human colorectal tumours and 50 matched normal mucosa samples with associated clinical and pathological data (patient age, gender, tumour site, tumour stage [UICC], presence or absence of extra-mural vascular invasion, tumour differentiation, mismatch repair protein status, and survival to 18 years) was assessed by immunohistochemistry. The biological consequences of LL-37 expression on the epithelial barrier and immune cell phenotype were assessed using targeted quantitative PCR gene expression of epithelial permeability (CLDN2, CLDN4, OCLN, CDH1, and TJP1) and cytokine (IL-1β, IL-18, IL-33, IL-10, IL-22, and IL-27) genes in a human colon organoid model, and CD3+ , CD4+ , and CD8+ lymphocyte phenotyping by immunohistochemistry, respectively. Our data reveal that loss of cathelicidin is associated with human CRC progression, with a switch in expression intensity an early feature of CRC. LL-37 expression intensity is associated with CD8+ T cell infiltrate, influenced by tumour characteristics including mismatch repair protein status. There was no effect on epithelial barrier gene expression. These data offer novel insights into the contribution of LL-37 to the pathogenesis of CRC and as a therapeutic molecule

Talin mechanosensitivity is modulated by a direct interaction with cyclin-dependent kinase-1

Talin (TLN1) is a mechanosensitive component of adhesion complexes that directly couples integrins to the actin cytoskeleton. In response to force, talin undergoes switch-like behavior of its multiple rod domains that modulate interactions with its binding partners. Cyclin-dependent kinase-1 (CDK1) is a key regulator of the cell cycle, exerting its effects through synchronized phosphorylation of a large number of protein targets. CDK1 activity maintains adhesion during interphase, and its inhibition is a prerequisite for the tightly choreographed changes in cell shape and adhesion that are required for successful mitosis. Using a combination of biochemical, structural, and cell biological approaches, we demonstrate a direct interaction between talin and CDK1 that occurs at sites of integrin-mediated adhesion. Mutagenesis demonstrated that CDK1 contains a functional talin-binding LD motif, and the binding site within talin was pinpointed to helical bundle R8. Talin also contains a consensus CDK1 phosphorylation motif centered on S1589, a site shown to be phosphorylated by CDK1 in vitro. A phosphomimetic mutant of this site within talin lowered the binding affinity of the cytoskeletal adaptor KANK and weakened the response of this region to force as measured by single molecule stretching, potentially altering downstream mechanotransduction pathways. The direct binding of the master cell cycle regulator CDK1 to the primary integrin effector talin represents a coupling of cell proliferation and cell adhesion machineries and thereby indicates a mechanism by which the microenvironment can control cell division in multicellular organisms

A Case Matched Gender Comparison Transcriptomic Screen Identifies eIF4E and eIF5 as Potential Prognostic and Tractable Biomarkers in Male Breast Cancer

Purpose: Breast cancer (BC) affects both genders, but is understudied in men. Although still rare, male BC is being diagnosed more frequently. Treatments are wholly informed by clinical studies conducted in women, based on assumptions that underlying biology is similar. Experimental design: A transcriptomic investigation of male and female BC was performed, confirming transcriptomic data in silico. Biomarkers were immunohistochemically assessed in 697 MBCs (n=477, training; n=220, validation set) and quantified in pre- and post-treatment samples from a male BC patient receiving Everolimus and PI3K/mTOR inhibitor. Results: Gender-specific gene expression patterns were identified. eIF transcripts were up-regulated in MBC. eIF4E and eIF5 were negatively prognostic for overall survival alone (Log rank; p=0.013; HR=1.77, 1.12-2.8 and p=0.035; HR=1.68, 1.03-2.74, respectively), or when co-expressed (p=0.01; HR=2.66, 1.26-5.63), confirmed in the validation set. This remained upon multivariate Cox regression analysis (eIF4E p=0.016; HR 2.38 (1.18-4.8), eIF5 p=0.022; HR 2.55 (1.14-5.7); co-expression p=0.001; HR=7.04 (2.22-22.26)). Marked reduction in eIF4E and eIF5 expression was seen post BEZ235/Everolimus, with extended survival. Conclusions: Translational initiation pathway inhibition could be of clinical utility in male BC patients overexpressing eIF4E and eIF5. With mTOR inhibitors which target this pathway now in the clinic, these biomarkers may represent new targets for therapeutic intervention, although further independent validation is required