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

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

Pitfalls in assessing stromal tumor infiltrating lymphocytes (sTILs) in breast cancer

Abstract: Stromal tumor-infiltrating lymphocytes (sTILs) are important prognostic and predictive biomarkers in triple-negative (TNBC) and HER2-positive breast cancer. Incorporating sTILs into clinical practice necessitates reproducible assessment. Previously developed standardized scoring guidelines have been widely embraced by the clinical and research communities. We evaluated sources of variability in sTIL assessment by pathologists in three previous sTIL ring studies. We identify common challenges and evaluate impact of discrepancies on outcome estimates in early TNBC using a newly-developed prognostic tool. Discordant sTIL assessment is driven by heterogeneity in lymphocyte distribution. Additional factors include: technical slide-related issues; scoring outside the tumor boundary; tumors with minimal assessable stroma; including lymphocytes associated with other structures; and including other inflammatory cells. Small variations in sTIL assessment modestly alter risk estimation in early TNBC but have the potential to affect treatment selection if cutpoints are employed. Scoring and averaging multiple areas, as well as use of reference images, improve consistency of sTIL evaluation. Moreover, to assist in avoiding the pitfalls identified in this analysis, we developed an educational resource available at www.tilsinbreastcancer.org/pitfalls

Report on computational assessment of Tumor Infiltrating Lymphocytes from the International Immuno-Oncology Biomarker Working Group

Funder: U.S. Department of Health & Human Services | NIH | National Cancer Institute (NCI)Funder: National Center for Research Resources under award number 1 C06 RR12463-01, VA Merit Review Award IBX004121A from the United States Department of Veterans Affairs Biomedical Laboratory Research and Development Service, the DOD Prostate Cancer Idea Development Award (W81XWH-15-1-0558), the DOD Lung Cancer Investigator-Initiated Translational Research Award (W81XWH-18-1-0440), the DOD Peer Reviewed Cancer Research Program (W81XWH-16-1-0329), the Ohio Third Frontier Technology Validation Fund, the Wallace H. Coulter Foundation Program in the Department of Biomedical Engineering and the Clinical and Translational Science Award Program (CTSA) at Case Western Reserve University.Funder: Susan G Komen Foundation (CCR CCR18547966) and a Young Investigator Grant from the Breast Cancer Alliance.Funder: The Canadian Cancer SocietyFunder: Breast Cancer Research Foundation (BCRF), Grant No. 17-194Abstract: Assessment of tumor-infiltrating lymphocytes (TILs) is increasingly recognized as an integral part of the prognostic workflow in triple-negative (TNBC) and HER2-positive breast cancer, as well as many other solid tumors. This recognition has come about thanks to standardized visual reporting guidelines, which helped to reduce inter-reader variability. Now, there are ripe opportunities to employ computational methods that extract spatio-morphologic predictive features, enabling computer-aided diagnostics. We detail the benefits of computational TILs assessment, the readiness of TILs scoring for computational assessment, and outline considerations for overcoming key barriers to clinical translation in this arena. Specifically, we discuss: 1. ensuring computational workflows closely capture visual guidelines and standards; 2. challenges and thoughts standards for assessment of algorithms including training, preanalytical, analytical, and clinical validation; 3. perspectives on how to realize the potential of machine learning models and to overcome the perceptual and practical limits of visual scoring

Application of a risk-management framework for integration of stromal tumor-infiltrating lymphocytes in clinical trials

Funder: Breast Cancer Research Foundation (BCRF); doi: https://doi.org/10.13039/100001006Abstract: Stromal tumor-infiltrating lymphocytes (sTILs) are a potential predictive biomarker for immunotherapy response in metastatic triple-negative breast cancer (TNBC). To incorporate sTILs into clinical trials and diagnostics, reliable assessment is essential. In this review, we propose a new concept, namely the implementation of a risk-management framework that enables the use of sTILs as a stratification factor in clinical trials. We present the design of a biomarker risk-mitigation workflow that can be applied to any biomarker incorporation in clinical trials. We demonstrate the implementation of this concept using sTILs as an integral biomarker in a single-center phase II immunotherapy trial for metastatic TNBC (TONIC trial, NCT02499367), using this workflow to mitigate risks of suboptimal inclusion of sTILs in this specific trial. In this review, we demonstrate that a web-based scoring platform can mitigate potential risk factors when including sTILs in clinical trials, and we argue that this framework can be applied for any future biomarker-driven clinical trial setting

Fractional Factorial Design and Desirability Function-Based Approach in Spice Paprika Processing Technology to Improve Extractable Colour Stability

The storage-stability of extractable colour in paprika powder is strongly influenced by the processing steps. The purpose of this research work was to decrease the degradation rate of extractable colour of paprika powders to 3-4 ASTA units/month and increase the shelf-life. Fractional factorial experimental design and desirability function-based approach was applied to moderate the adverse effect of the key-importance processing steps on colour agent content. Photochemical-accelerated shelf-life test was used for the empirically-based quality improvement. Post-harvest ripening, drying, sorting (seed content), milling and additives (paprika seed oil, tocopherol extract) were identified as important for the degradation rate of extractable colour. The colour stability was very sensitive to the milling and drying intensity, while proper setup of other processing steps compensated the adverse effect of drying and milling parameters. The supplementation with cold-pressed spice paprika seed oil was demonstrated as a natural way of colour stabilization. At the highest desirability value (0.986) the rate constant of accelerated colour stability test (k) and shelf-life time (θS[100ASTA]) were −0.494 ASTA units/day and 2326 days, respectively. Alternative factor level settings enabled taking into account processing-tradition and efficiency expectation. In these cases, desirability values and the predicted shelf-life were 0.5-0.8 and 712-918 days, respectively. Validation study showed that the real observed rate constant and shelf-life values met the predicted values and their 95% confidence interval