Excel file containing additional data too large to fit in a PDF, CUT&RUN–RNAseq merge analyses.

IntroductionBreast cancer continues to be the most common malignancy and the leading cause of cancer-related deaths in Ethiopia. The poor prognosis and high mortality rate of breast cancer patients in the country are largely caused by late-stage diagnosis. Hence, understanding the epidemiology of late-stage diagnosis is essential to address this important problem. However, previous reports in Ethiopia indicated inconsistent findings. Therefore, this literature review was conducted to generate dependable evidence by summarizing the prevalence and determinants of late-stage diagnosis among breast cancer patients in Ethiopia.MethodsPertinent articles were retrieved by systematically searching on major electronic databases and gray literature. Data were extracted into an Excel spreadsheet and analyzed using the STATA 17 statistical software. The pooled estimates were summarized using the random effect meta-analysis model. Heterogeneity and small study effect were evaluated using the I2 statistics and Egger’s regression test in conjunction with the funnel plot, respectively. Meta-regression, sub-group analysis, and sensitivity analysis were also employed. Protocol registration number: CRD42024496237.ResultsThe pooled prevalence of late-stage diagnosis after combining reports of 24 studies with 8,677 participants was 65.85 (95% CI: 58.38, 73.32). Residence (adjusted OR: 1.92; 95% CI: 1.45, 2.53), patient delay at their first presentation (adjusted OR: 2.65; 95% CI: 1.56, 4.49), traditional medicine use (adjusted OR: 2.54; 95% CI: 1.89, 3.41), and breast self-examination practice (adjusted OR: 0.28; 95% CI: 0.09, 0.88) were significant determinants of late-stage diagnosis.ConclusionTwo-thirds of breast cancer patients in Ethiopia were diagnosed at an advanced stage. Residence, delay in the first presentation, traditional medicine use, and breast self-examination practice were significantly associated with late-stage diagnosis. Public education about breast cancer and its early detection techniques is crucial to reduce mortality and improve the survival of patients. Besides, improving access to cancer screening services is useful to tackle the disease at its curable stages.

Attached is the Cobble App in Matlab developed by Erin Bray, for calculation of cobble shape parameters as reported in Bray et al "Influence of particle lithology, size, and angularity on rates and products of bedload wear: an experimental study" (In Review).

For the Cobble App to work, install Matlab version 2022b; Add the Image Processing Toolbox; Add the Computer Vision System Toolbox.Photo files must be saved in grayscale (no RGB embedded when saving photo files). Files of photos can be saved as .tif or .tiff (both should work in the cobble app) All extraneous white edges/borders or dots in files need to be removed (there were some stray white specks in the background of one of the photo files that was reduced to 75% resolution). The Photo ID string column such as "P1_A1_N_PT" needs to be consistently formatted, with no extra spaces or extra characters, in both the Excel spreadsheet and in the photo file names, with no changes to the file string name even if you reduce the photo resolution to 75%. To use the merge functionality within the Cobble App, which pairs image-based shape parameters with corresponding handheld measurements of mass, diameter of each particle, the Excel spreadsheet needs to always have the identical number of columns and name of columns.

This data is sourced from the Census 2011 and shows the population and population density by council area. Raw data sourced from http://www.scotlandscensus.gov.uk/en/censusresults/downloadablefiles.html and then manipulated in excel to merge a number of tables. The resulting data was joined to a shapefile of Scottish Council areas from sharegeo (http://www.sharegeo.ac.uk/handle/10672/305). Both sources should be attributed as the sources of the base data. GIS vector data. This dataset was first accessioned in the EDINA ShareGeo Open repository on 2012-12-19 and migrated to Edinburgh DataShare on 2017-02-21.

Excel spreadsheet containing the underlying numerical data for Figs 1C, 2C, 2D, 4B, 4C, 5A, 5B, S11, S12 and S14.