In this rejoinder, we set out some of the main points that we took from the discussions of our paper "Spatial+: A novel approach to spatial confounding." The comments provided by the discussants include excellent questions and suggestions for extensions and improvements to spatial+. The discussions also highlight the growing interest in understanding spatial confounding, underpinned by the many recent contributions to the literature on this topic.
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http://dx.doi.org/10.1111/biom.13653 | DOI Listing |
Sci Rep
January 2025
Electrical and Computer Engineering Department, University of Memphis, Memphis, TN, 38152, USA.
Oral squamous cell carcinoma (OSCC) is the most common form of oral cancer, with increasing global incidence and have poor prognosis. Tumour-infiltrating lymphocytes (TILs) are recognized as a key prognostic indicator and play a vital role in OSCC grading. However, current methods for TILs quantification are based on subjective visual assessments, leading to inter-observer variability and inconsistent diagnostic reproducibility.
View Article and Find Full Text PDFEnviron Int
January 2025
Dipartimento di Geoscienze, Università di Padova, Padova, Italy.
Radon (Rn) is a radioactive gas with well-documented harmful effects; the World Health Organization has confirmed it as a cancerogenic for humans. These detrimental effects have prompted Europe to establish national reference levels to protect the exposed population. This is reflected in European directive 59/2013/EURATOM, which has been transposed into the national regulations of EU Member States.
View Article and Find Full Text PDFMed Image Anal
January 2025
Department of Computer and Data Science and Department of Biomedical Engineering, Case Western Reserve University, Cleveland, USA.
Accurate automatic polyp segmentation in colonoscopy is crucial for the prompt prevention of colorectal cancer. However, the heterogeneous nature of polyps and differences in lighting and visibility conditions present significant challenges in achieving reliable and consistent segmentation across different cases. Therefore, this study proposes a novel dynamic spectrum-driven hierarchical learning model (DSHNet), the first to specifically leverage image frequency domain information to explore region-level salience differences among and within polyps for precise segmentation.
View Article and Find Full Text PDFComput Biol Med
January 2025
University of Rwanda, Rwanda. Electronic address:
Deep learning methods have significantly improved medical image analysis, particularly in detecting COVID-19 chest X-rays. Nonetheless, these methodologies frequently inhibit some drawbacks, such as limited interpretability, extensive computational resources, and the need for extensive datasets. To tackle these issues, we introduced two novel algorithms: the Dynamic Co-Occurrence Grey Level Matrix (DC-GLM) and the Contextual Adaptation Multiscale Gabor Network (CAMSGNeT).
View Article and Find Full Text PDFPLoS Comput Biol
January 2025
Department of Anatomy and Cell Biology, Graduate School of Medical Sciences, Kyushu University, Fukuoka, Fukuoka, Japan.
Mathematical modeling has been utilized to explain biological pattern formation, but the selections of models and parameters have been made empirically. In the present study, we propose a data-driven approach to validate the applicability of mathematical models. Specifically, we developed methods to automatically select the appropriate mathematical models based on the patterns of interest and to estimate the model parameters.
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