Cosmology with Gamma-Ray Bursts Using k-correction

In the case of Gamma-Ray Bursts with measured redshift, we can calculate the k-correction to get the fluence and energy that were actually produced in the comoving system of the GRB. To achieve this we have to use well-fitted parameters of a GRB spectrum, available in the GCN database. The output of the calculations is the comoving isotropic energy E_iso, but this is not the endpoint: this data can be useful for estimating the {\Omega}M parameter of the Universe and for making a GRB Hubble diagram using Amati's relation.Comment: 4 pages, 6 figures. Presented as a talk on the conference '7th INTEGRAL/BART Workshop 14 -18 April 2010, Karlovy Vary, Czech Republic'. Published in Acta Polytechnic

Methods for identifying high-redshift galaxy cluster candidates

Recent theories linked long gamma ray bursts (GRBs) to galaxies with rapid star formation or starburst; thus, we expect that long GRBs (LGRBs) are more frequent in midcluster galaxies where mergers and tidal interactions between gas-rich galaxies are more likely to occur. Yet there is no galaxy cluster known to be associated with LGRBs. We demonstrate that, based on deep, single-band Subaru Hyper Suprime-Cam observations, we may provide constraints on photometric redshifts of groups of galaxies. We compare three methods: cosmological approach, pseudoinverse matrix, and random forests to estimate galaxy and quasar redshifts. Comparing our results to spectroscopic redshifts of Sloan Digital Sky Survey's-detected extragalactic sources, random forests may provide the highest accuracy with as low as 17 percentage error. This is a powerful method to find clusters to place GRB host galaxies in their local environment

Mapping the Universe with Gamma-Ray Bursts

We explore large-scale cosmic structure using the spatial distribution of 542 gamma-ray bursts (GRBs) having accurately measured positions and spectroscopic redshifts. Prominent cosmological clusters are identified in both the northern and southern galactic hemispheres (avoiding extinction effects in the plane of the Milky Way) using the Bootstrap Point-Radius method. The Northern Galactic hemisphere contains a significant group of four GRBs in the redshift range 0.59 < z < 0.62 (with a Bootstrap probability of p = 0.012) along with the previously-identified Hercules-Corona Borealis GreatWall (in the revised redshift range 0.9 < z < 2.1, p = 0.017). The Southern Galactic hemisphere contains the previously-identified Giant GRB Ring (p = 0.022) along with another possible cluster of 7 - 9 GRBs at 1.179 < z < 1.444 (p = 0.031). Additionally, both the Hercules-Corona Borealis Great Wall and the Giant GRB Ring have become more prominent as the GRB sample size has grown. The approach used here underscores the potential value of GRB clustering as a probe of large-scale cosmic structure, complementary to galaxy and quasar clustering. Because of the vast scale on which GRB clustering provides valuable insights, it is important that optical GRB monitoring continue so that additional spectroscopic redshift measurements should be obtained.Comment: 11 pages, 15 figures, submitted to MNRA

Galactic foreground of gamma-ray bursts from AKARI Far-Infrared Surveyor

We demonstrate the use of the AKARI FIS All-Sky Survey maps in the study of extragalactic objects. A quick but reliable estimate of the Galactic foreground is essential for extragalactic research in general. We explored the galactic foreground and calculated hydrogen column densities using AKARI FIS and other recent all-sky survey data, and compared our results to former estimates. Our AKARI-FIS-based foreground values were then used toward gamma-ray burst (GRB) sources as input for X-ray afterglow spectrum fitting. From those fits the intrinsic column densities at the GRB sources were derived. The high-angular-resolution AKARI-FIS-based Galactic foreground hydrogen column densities are statistically very similar, but for most of the tested directions somewhat lower than previous estimates based on low-resolution data. This is due to the low filling factor of high-density enhancements in all galactic latitudes. Accordingly, our AKARI-FIS-based new intrinsic hydrogen column densities are usually higher or similar compared to the values calculated based, e.g., on the low-resolution Leiden/Argentine/Bonn survey data and listed in the Leicester database. The variation, however, is typically smaller than the error of the estimate from the fits of the X-ray afterglow spectra. There are a number of directions where the improvement of the foreground estimates resulted in an overestimate of magnitude or higher increment of the derived intrinsic hydrogen column densities. We concluded that most of the GRBs with formerly extremely low intrinsic hydrogen column densities are in fact normal, but we confirmed that GRB050233 is indeed a non- enveloped long GRB

Intelligent image-based in situ single-cell isolation

Quantifying heterogeneities within cell populations is important for many fields including cancer research and neurobiology; however, techniques to isolate individual cells are limited. Here, we describe a high-throughput, non-disruptive, and cost-effective isolation method that is capable of capturing individually targeted cells using widely available techniques. Using high-resolution microscopy, laser microcapture microscopy, image analysis, and machine learning, our technology enables scalable molecular genetic analysis of single cells, targetable by morphology or location within the sample.Peer reviewe

Pitfalls in machine learning‐based assessment of tumor‐infiltrating lymphocytes in breast cancer: a report of the international immuno‐oncology biomarker working group

The clinical significance of the tumor-immune interaction in breast cancer (BC) has been well established, and tumor-infiltrating lymphocytes (TILs) have emerged as a predictive and prognostic biomarker for patients with triple-negative (estrogen receptor, progesterone receptor, and HER2 negative) breast cancer (TNBC) and HER2-positive breast cancer. How computational assessment of TILs can complement manual TIL-assessment in trial- and daily practices is currently debated and still unclear. Recent efforts to use machine learning (ML) for the automated evaluation of TILs show promising results. We review state-of-the-art approaches and identify pitfalls and challenges by studying the root cause of ML discordances in comparison to manual TILs quantification. We categorize our findings into four main topics; (i) technical slide issues, (ii) ML and image analysis aspects, (iii) data challenges, and (iv) validation issues. The main reason for discordant assessments is the inclusion of false-positive areas or cells identified by performance on certain tissue patterns, or design choices in the computational implementation. To aid the adoption of ML in TILs assessment, we provide an in-depth discussion of ML and image analysis including validation issues that need to be considered before reliable computational reporting of TILs can be incorporated into the trial- and routine clinical management of patients with TNBC

Image-based multiplex immune profiling of cancer tissues : translational implications. A report of the International Immuno-oncology Biomarker Working Group on Breast Cancer

Recent advances in the field of immuno-oncology have brought transformative changes in the management of cancer patients. The immune profile of tumours has been found to have key value in predicting disease prognosis and treatment response in various cancers. Multiplex immunohistochemistry and immunofluorescence have emerged as potent tools for the simultaneous detection of multiple protein biomarkers in a single tissue section, thereby expanding opportunities for molecular and immune profiling while preserving tissue samples. By establishing the phenotype of individual tumour cells when distributed within a mixed cell population, the identification of clinically relevant biomarkers with high-throughput multiplex immunophenotyping of tumour samples has great potential to guide appropriate treatment choices. Moreover, the emergence of novel multi-marker imaging approaches can now provide unprecedented insights into the tumour microenvironment, including the potential interplay between various cell types. However, there are significant challenges to widespread integration of these technologies in daily research and clinical practice. This review addresses the challenges and potential solutions within a structured framework of action from a regulatory and clinical trial perspective. New developments within the field of immunophenotyping using multiplexed tissue imaging platforms and associated digital pathology are also described, with a specific focus on translational implications across different subtypes of cancer.Gilead Breast Cancer Research Grant; Breast Cancer Research Foundation; Susan G Komen Leadership; Interne Fondsen KU Leuven/Internal Funds KU Leuven; Swedish Society for Medical Research; Swedish Breast Cancer Association; Cancer Research Program; US Department of Defense; Mayo Clinic Breast Cancer; Marie Sklodowska Curie; NHMRC; National Institutes of Health; Cancer Research UK; Japan Society for the Promotion of Science; Horizon 2020 European Union Research and Innovation Programme National Cancer Institute; National Heart, Lung and Blood Institute; National Institute of Biomedical Imaging and Bioengineering; VA Merit Review Award; US Department of Veterans Affairs Biomedical Laboratory Research Breast Cancer Research Program; Prostate Cancer Research Program; Lung Cancer Research Program; Kidney Precision Medicine Project (KPMP) Glue Grant; EPSRC; Melbourne Research Scholarship; Peter MacCallum Cancer Centre; KWF Kankerbestrijding; Dutch Ministry of Health, Welfare and Sport the Breast Cancer Research Foundation; Agence Nationale de la Recherche; Q-Life; National Breast Cancer Foundation of Australia; National Health and Medical Council of Australia; All-Island Cancer Research Institute; Irish Cancer Society; Science Foundation Ireland Investigator Programme; Science Foundation Ireland Strategic Partnership Programme. Open access funding provided by IReL.https://pathsocjournals.onlinelibrary.wiley.com/journal/10969896hj2024ImmunologySDG-03:Good heatlh and well-bein

Spatial analyses of immune cell infiltration in cancer : current methods and future directions. A report of the International Immuno-Oncology Biomarker Working Group on Breast Cancer

Modern histologic imaging platforms coupled with machine learning methods have provided new opportunities to map the spatial distribution of immune cells in the tumor microenvironment. However, there exists no standardized method for describing or analyzing spatial immune cell data, and most reported spatial analyses are rudimentary. In this review, we provide an overview of two approaches for reporting and analyzing spatial data (raster versus vector-based). We then provide a compendium of spatial immune cell metrics that have been reported in the literature, summarizing prognostic associations in the context of a variety of cancers. We conclude by discussing two well-described clinical biomarkers, the breast cancer stromal tumor infiltrating lymphocytes score and the colon cancer Immunoscore, and describe investigative opportunities to improve clinical utility of these spatial biomarkers. © 2023 The Pathological Society of Great Britain and Ireland.http://www.thejournalofpathology.com/hj2024ImmunologySDG-03:Good heatlh and well-bein

Image-based multiplex immune profiling of cancer tissues: translational implications. A report of the International Immuno-oncology Biomarker Working Group on Breast Cancer

Recent advances in the field of immuno-oncology have brought transformative changes in the management of cancer patients. The immune profile of tumours has been found to have key value in predicting disease prognosis and treatment response in various cancers. Multiplex immunohistochemistry and immunofluorescence have emerged as potent tools for the simultaneous detection of multiple protein biomarkers in a single tissue section, thereby expanding opportunities for molecular and immune profiling while preserving tissue samples. By establishing the phenotype of individual tumour cells when distributed within a mixed cell population, the identification of clinically relevant biomarkers with high-throughput multiplex immunophenotyping of tumour samples has great potential to guide appropriate treatment choices. Moreover, the emergence of novel multi-marker imaging approaches can now provide unprecedented insights into the tumour microenvironment, including the potential interplay between various cell types. However, there are significant challenges to widespread integration of these technologies in daily research and clinical practice. This review addresses the challenges and potential solutions within a structured framework of action from a regulatory and clinical trial perspective. New developments within the field of immunophenotyping using multiplexed tissue imaging platforms and associated digital pathology are also described, with a specific focus on translational implications across different subtypes of cancer