This document describes a new class, itk::MorphologicalContourInterpolator, which implements a method proposed by Albu et al. in 2008. Interpolation is done by first determining correspondence between shapes on adjacent segmented slices by detecting overlaps, then aligning the corresponding shapes, generating transition sequence of one-pixel dilations and taking the median as result. Recursion is employed if the original segmented slices are separated by more than one empty slice.This class is n-dimensional, and supports inputs of 3 or more dimensions. `Slices’ are n-1-dimensional, and can be both automatically detected and manually set. The class is efficient in both memory used and execution time. It requires little memory in addition to allocation of input and output images. The implementation is multi-threaded, and processing one of the test inputs takes around 1-2 seconds on a quad-core processor.The class is tested to operate on both itk::Image and itk::RLEImage. Since all the processing is done on extracted slices, usage of itk::RLEImage for input and/or output affects performance to a limited degree.This class is implemented to ease manual segmentation in ITK-SNAP (www.itksnap.org). The class, along with test data and automated regression tests is packaged as an ITK remote module https://github.com/KitwareMedical/ITKMorphologicalContourInterpolation.

This document describes a new class, itk::RLEImage, which uses run-length encoding to reduce the memory needed for storage of label maps. This class is accompanied by all the iterators to make it a dropin replacement for itk::Image. By changing the image typedef to itk::RLEImage, many ITK image processing algorithms build without modification and with minimal performance overhead. However, it is not possible if the user code uses GetBufferPointer() or otherwise assumes a linear pixel layout.This class is implemented to reduce the memory use of ITK-SNAP (www.itksnap.org), so ITKSNAP is the base for measuring the quantitative results.The class, accompanying iterator specializations, automated regression tests, and test data are all packaged as an ITK remote module https://github.com/KitwareMedical/ITKRLEImage.

Previous analysis of mtDNA control region (CR) sequence variability for grey wolf population from BosniaH He=0.76), similar to other populations belonging to the grey wolf Dinaric-Balkan population. STRUCTURE analysis showed differentiation into two genetic clusters. These results are important for future management plans and estimation of the conservation units.

Several phenolic acids (PAs), caffeic, vanillic, syringic, p-coumaric and ferulic acid, found in Slovenian red wines were studied using gas chromatography and mass spectrometry. For isolation of the PAs from wine samples, solid phase extraction using hydrophilic modified styrene - HLB cartridges was used. The bound PAs were extracted after basic hydrolysis and o-coumaric acid was used as the internal standard. The developed method was validated and the linear concentration range for all analytes was from 1 to 100 mg L-1 with correlation coefficients above 0.999. We show that the method is repeatable (RSD<2%), recoveries were above 96%, and LOD and LOQ values were acceptable. In all of the wine samples tested, caffeic and p-coumaric acid were determined to be the predominant PAs (17-72 mg L-1), while other compounds were found in lower concentrations. Principal Component Analysis and Cluster Analysis were used to study differences between wines related towards varieties and Slovenian wine regions. The results demonstrate that variety has more influence on PAs content than wine regions in Slovenian red wines.

Aims Metaplastic breast carcinoma (MBC) is a rare subtype of breast carcinoma less responsive to conventional chemotherapy than ductal carcinoma. In molecular terms, MBCs usually cluster with triple-negative breast cancers (TNBCs), but have a worse prognosis than TNBCs. Studies investigating MBCs for specific biomarkers of therapy response are rare and limited by the methodological approaches. The aim of the present study was to characterise MBCs on a molecular level and test programmed death-ligand 1 (PD-L1) biomarker expression in MBCs for future therapeutic interventions. Methods We profiled 297 samples (MBC (n=75), TNBC (n=106), human epidermal growth factor receptor 2 (HER2)-positive breast cancers (n=32) and hormone-positive breast cancers (n=84)) by next-generation sequencing. Immunohistochemistry for PD-L1 and programmed cell death 1 (PD-1) expression was performed using automated procedures. Results The most commonly mutated genes in MBCs included TP53 (56%) and PIK3CA (23%). Pathogenic mutations in other genes, including HRAS, FBXW7, PTEN, AKT1 and SMAD4, were rare. PD-L1 expression was detected in a significantly higher proportion of MBCs (46%) than in other subtypes (6% each in hormone-positive and HER2-positive breast cancers, and 9% in TNBC, not otherwise specified, p<0.001). PD-1-positive tumour infiltrating lymphocytes (TILs) varied greatly in MBCs. Conclusions Comprehensive profiling of a large cohort of this rare subtype of breast carcinoma highlighted the predominance of TP53 mutation and increased PD-L1 expression in carcinoma cells. These results can be exploited in clinical trials using immune checkpoint inhibitors.

Analytic Hierarchy Process (AHP) is often adopted in survey-based research activities and the number of participants involved in AHP studies ranges from few experts to hundreds of interviewed people. A common goal of survey research is to collect data representative of a population and, to this end, determining sample size is essential. The question then is, what is the appropriate sample size to run AHP in a survey-based study? To the best of the authors’ knowledge, no previous study addressed the proposed research question in the field of AHP-based survey. The current study aimed to propose a simulation approach for addressing the question of appropriate sample size for AHPbased survey. The proposed approach and the related findings will be presented and discussed.