Ion condensation on charged patterned surfaces

We study ion condensation onto a patterned surface of alternating charges. The competition between self-energy and ion-surface interactions leads to the formation of ionic crystalline structures at low temperatures. We consider different arrangements of underlying ionic crystals, including single ion adsorption, as well as the formation of dipoles at the interface between charged domains. Molecular dynamic simulation illustrates existence of single and mixed phases. Our results contribute to understanding pattern recognition, and molecular separation and synthesis near patterned surfaces.Comment: 3 figure

Efficiency of Finding Muon Track Trigger Primitives in CMS Cathode Strip Chambers

In the CMS Experiment, muon detection in the forward direction is accomplished by cathode strip chambers~(CSC). These detectors identify muons, provide a fast muon trigger, and give a precise measurement of the muon trajectory. There are 468 six-plane CSCs in the system. The efficiency of finding muon trigger primitives (muon track segments) was studied using~36 CMS CSCs and cosmic ray muons during the Magnet Test and Cosmic Challenge~(MTCC) exercise conducted by the~CMS experiment in~2006. In contrast to earlier studies that used muon beams to illuminate a very small chamber area ($< \! 0.01$~m$^2$), results presented in this paper were obtained by many installed CSCs operating {\em in situ} over an area of $\approx \! 23$~m$^2$ as a part of the~CMS experiment. The efficiency of finding 2-dimensional trigger primitives within 6-layer chambers was found to be~$99.93 \pm 0.03\%$. These segments, found by the CSC electronics within $800$~ns after the passing of a muon through the chambers, are the input information for the Level-1 muon trigger and, also, are a necessary condition for chambers to be read out by the Data Acquisition System

Association Between the Size and 3D CT-Based Radiomic Features of Breast Cancer Hepatic Metastasis

PurposeTo evaluate the effect of the anatomic size on 3D radiomic imaging features of the breast cancer hepatic metastases.Materials and methodsCT scans of 81 liver metastases from 54 patients with breast cancer were evaluated. Ten most common 3D radiomic features from the histogram and gray level co-occurrence matrix (GLCM) categories were calculated for the hepatic metastases (HM) and compared to normal liver (NL). The effect of size was evaluated by using linear mixed-effects regression models. The effect of size on different radiomic features was analyzed for both liver lesions and background liver.ResultsThree-dimensional radiomic features from GLCM demonstrate an important size dependence. The texture-feature size dependence was found to be different among feature categories and between the HM and NL, thus demonstrating a discriminatory power for the tissue type. Significant difference in the slope was found for GLCM homogeneity (NL slope = 0.004, slope difference 95% confidence interval [CI] 0.06-0.1, p &lt;0.001), contrast (NL slope = 45, slope difference 95% CI 205-305, p &lt;0.001), correlation (NL slope = 0.04, slope difference 95% CI 0.11-0.21, p &lt;0.001), and dissimilarity (NL slope = 0.7, slope difference 95% CI 3.6-5.4, p &lt;0.001). The GLCM energy (NL slope = 0.002, slope difference 95% CI -0.0005 to -0.0003, p &lt;0.007), and entropy (NL slope = 1.49, slope difference 95% CI 0.07-0.52, p &lt;0.009) exhibited size-dependence for both NL and HM, although demonstrating a difference in the slope between themselves.ConclusionRadiomic features of breast cancer hepatic metastasis exhibited significant correlation with tumor size. This finding demonstrates the complex behavior of imaging features and the need to include feature-specific properties into radiomic models

The Brain Tumor Segmentation (BraTS-METS) Challenge 2023: Brain Metastasis Segmentation on Pre-treatment MRI

Clinical monitoring of metastatic disease to the brain can be a laborious and timeconsuming process, especially in cases involving multiple metastases when the assessment is performed manually. The Response Assessment in Neuro-Oncology Brain Metastases (RANO-BM) guideline, which utilizes the unidimensional longest diameter, is commonly used in clinical and research settings to evaluate response to therapy in patients with brain metastases. However, accurate volumetric assessment of the lesion and surrounding peri-lesional edema holds significant importance in clinical decision-making and can greatly enhance outcome prediction. The unique challenge in performing segmentations of brain metastases lies in their common occurrence as small lesions. Detection and segmentation of lesions that are smaller than 10 mm in size has not demonstrated high accuracy in prior publications. The brain metastases challenge sets itself apart from previously conducted MICCAI challenges on glioma segmentation due to the significant variability in lesion size. Unlike gliomas, which tend to be larger on presentation scans, brain metastases exhibit a wide range of sizes and tend to include small lesions. We hope that the BraTS-METS dataset and challenge will advance the field of automated brain metastasis detection and segmentation