Qualitative Classifications Research Articles

A model-based characterization of confined meandering rivers

In this paper we analyze the dynamics of meandering rivers that flow in geologically confined floodplains. We use a state of art meander morphodynamic model in which the floodplain erodibility can be spatially variable, and can thus be highly reduced where nearly unerodible lateral walls are simulated. We propose a hybrid empirical and model-based indicator to estimate the degree of confinement relative to the potential, unconfined meander belt. Together with commonly used meander morphometric indicators, we propose two novel planform metrics to represent the observed tendency of confined meanders (i) to develop long reaches attached to the geological boundaries, and (ii) to develop more frequent crossing of the floodplain central axis. Results reproduce realistic planforms of confined meanders, quantitatively support previously suggested qualitative classifications of confined meandering rivers, and provide a rational framework to compare meandering rivers having different degree of confinement (strong, moderate, unconfined), as well as different hydro-morphological conditions. Good agreement between model predictions and the few existing field observations support the proposed approach.

Craniocervical Morphometry in Pomeranians-Part II: Associations with Chiari-like Malformation and Syringomyelia.

The aim of Part II of this two-part study is to describe and analyze the association of various aspects and measurements related to the morphometry of the skull and craniocervical region to CM/SM status of Pomeranians, by means of computed tomography (CT) and magnetic resonance imaging (MRI). Prospectively, Pomeranians were included that underwent both CT and MRI studies of the head and cervicothoracic vertebral column. For those cases where qualitative classifications differed between observers, the experienced observer re-evaluated the studies and decided on a final classification that was used for further analysis. For quantitative measurements, the means of the observers' measurements were used for analysis. Among statistically significant differences in measurements, we found that dogs with SM had a significantly shorter clivus length based on both MRI (p = 0.01) and CT measurements (p = 0.01), and a significantly smaller caudal cranial fossa area based on both MRI (p = 0.02) and CT measurements (p = 0.02). Significant morphometrical differences were identified between dogs with or without CM/SM. The findings in this study add to those already described in other breeds and provide further insight into factors that may play a role in the pathogenesis of CM/SM in Pomeranians.

IMG-21. HOW CAN WE BEST UTILISE HISTORICAL IMAGING DATASETS FOR RESEARCH IN PAEDIATRIC NEURO-ONCOLOGY? A REVIEW OF HISTORICAL PRE-OPERATIVE IMAGING PERFORMED FOR 92 PAEDIATRIC BRAIN TUMOUR PATIENTS WITH COMPARISON TO 2021 SIOPE GUIDELINES

Abstract BACKGROUND Utilisation of historical imaging is important for studying long-term outcomes in paediatric brain tumours often pooling data across multiple centres retrospectively over many years. Disparities in MRI sequence parameters, imaging planes, fields of view and resolution limit quantitative radiomic analysis and segmentation algorithms. We review historical imaging datasets, analysing the MRI scans performed assessing suitability for modern research techniques. METHODS Retrospective review of presenting MRI scans was performed in 92 paediatric brain tumour patients between 2006-2020. Whether imaging was performed internally at our paediatric neurosciences centre or externally at their referring hospitals was recorded. Image sequences, planes and slice thickness were compared with 2021 SIOPE guidelines for paediatric brain tumour imaging. T2 signal intensities for tumour, white and grey matter were compared for scans completed in different centres. Statistical testing was performed with T-Test and Paired T-Test. RESULTS 75% of patients had all SIOPE essential sequences and 60.9% were in the recommended planes. Only 1 patient had the desired slice thickness in all sequences recommended for 1.5T scanners. 38.5% had T1 and 27.8% had post-contrast T1 in the ≤1mm slice thickness recommended for 3T scanners. Comparison between 62 internal and 30 external scans revealed statistically significant differences in T2 signal intensities for regions of interest (ROI) within tumour, white and grey matter (p<0.01). Comparison of matched ROI for 12 patients imaged internally and externally demonstrated statistically significant differences in T2 signal intensities (p<0.05). Differences persisted despite normalisation of T2 signal intensity as ratios between tumour and the weighted mean of white and grey matter (p<0.01). CONCLUSIONS This study highlights the challenges of non-standardisation of imaging resolution and acquisition parameters in historical imaging. One approach to utilise these datasets is to use qualitative classifications where human experts label imaging features and encode data for future statistical or machine learning analysis.

FULLY AUTOMATIC SYSTEM TO DETECT AND SEGMENT THE PROXIMAL FEMUR IN PELVIC RADIOLOGICAL IMAGES FOR LEGG-CALVE-PERTHES DISEASE

The most important outcome predictor of Legg-Calvé-Perthes disease (LCPD) is the shape of the healed femoral head. However, the deformity of the femoral head is currently evaluated by non-reproducible, categorical, and qualitative classifications. In this regard, recent advances in computer vision might provide the opportunity to automatically detect and delineate the outlines of bone in radiographic images for calculating a continuous measure of femoral head deformity. This study aimed to construct a pipeline for accurately detecting and delineating the proximal femur in radiographs of LCPD patients employing existing algorithms. To detect the proximal femur, the pretrained stateof-the-art object detection model, YOLOv5, was trained on 1580 manually annotated radiographs, validated on 338 radiographs, and tested on 338 radiographs. Additionally, 200 radiographs of shoulders and chests were added to the dataset to make the model more robust to false positives and increase generalizability. The convolutional neural network architecture, U-Net, was then employed to segment the detected proximal femur. The network was trained on 80 manually annotated radiographs using real-time data augmentation to increase the number of training images and enhance the generalizability of the segmentation model. The network was validated on 60 radiographs and tested on 60 radiographs. The object detection model achieved a mean Average Precision (mAP) of 0.998 using an Intersection over Union (IoU) threshold of 0.5, and a mAP of 0.712 over IoU thresholds of 0.5 to 0.95 on the test set. The segmentation model achieved an accuracy score of 0.912, a Dice Coefficient of 0.937, and a binary IoU score of 0.854 on the test set. The proposed fully automatic proximal femur detection and segmentation system provides a promising method for accurately detecting and delineating the proximal femoral bone contour in radiographic images, which is necessary for further image analysis.

Fully automatic system to detect and segment the proximal femur in pelvic radiographic images for Legg-Calvé-Perthes disease.

This study aimed to develop a method using computer vision techniques to accurately detect and delineate the proximal femur in radiographs of Legg-Calvé-Perthes disease (LCPD) patients. Currently, evaluating femoral head deformity, a crucial predictor of LCPD outcomes, relies on unreliable categorical and qualitative classifications. To address this limitation, we employed the pretrained object detection model YOLOv5 to detect the proximal femur on over 2000 radiographs, including images of shoulders and chests, to enhance robustness and generalizability. Subsequently, we utilized the U-Net convolutional neural network architecture for image segmentation of the proximal femur in more than 800 manually annotated images of stage IV LCPD. The results demonstrate outstanding performance, with the object detection model achieving high accuracy (mean average precision of 0.99) and the segmentation model attaining an accuracy score of 91%, dice coefficient of 0.75, and binary IoU score of 0.85 on the held-out test set. The proposed fully automatic proximal femur detection and segmentation system offers a promising approach to accurately detect and delineate the proximal femoral bone contour in radiographic images, which is essential for further image analysis in LCPD patients.Clinical significance: This study highlights the potential of computer vision techniques for enhancing the reliability ofLegg-Calvé-Perthes diseasestaging and outcome prediction.

Competitiveness of international merchandise trade: The case of Poland

The aim of the article is to present the possibilities that qualitative classifications of industries and goods introduce to the analysis of structural changes in the economy, as well as to assess changes in the international competitiveness of Polish merchandise trade in the long term. The article presents a review of the literature and the results of authors’ own research on the structure of Polish merchandise trade. The study uses Lall’s classification of goods which is relatively innovative and rarely used in the existing literature. By using this classification and the analysis of revealed comparative advantages (RCA) we also managed to assess the competitiveness of Polish exports. The research shows that since Poland’s accession to the European Union, the international competitiveness of Polish trade in terms of its technological level has changed only slightly. After the growth in the first years of Poland’s EU membership, the share of high technology products in the export structure stagnated in the 2010s and was still lower than that of medium or low technology products. Throughout the researched period, the RCA for high-tech products remained on the “foreign” side.

Association between anatomic variations of extrahepatic and intrahepatic bile ducts: Do look up!

Biliary anatomic variations are usually asymptomatic, but they may cause problems in diagnostic investigations and interventional and surgical procedures, increasing both their technical difficulty and their postoperative complication rates. The aim of the present study was to evaluate the prevalence of anatomic variations in the intrahepatic biliary ducts (IHBD) in relation to demographical and clinical characteristics in a large study population requiring magnetic resonance cholangiopancreatography (MRCP) for various clinical conditions. The possible association between IHBD and extrahepatic biliary ducts (EHBD) variants was then explored. From January 2017 to May 2019, 1004 patients underwent MRCP. Demographical and clinical data were collected. IHBD and EHBD anatomy were recorded and the EHBD anatomy was classified using both qualitative and quantitative classifications. The presence of a type 3 EHBD variant (an abnormal proximal cystic duct [CD] insertion) in both qualitative and quantitative classifications and an intrapancreatic CD were associated with the presence of IHBD variants at univariate analysis (p=0.008, p=0.019, and p=0.001, respectively). The presence of a posterior or medial insertion of the CD into the EHBD was a strong predictive factor of the presence of IHBD variants both at uni- and multivariate analysis (p=0.002 and p=0.003 for posterior insertion and p=0.002 and p=0.002 for medial insertion, respectively). The presence of gallstones on MRCP resulted in a strong predictor of the presence of an anatomical variant of the IHBD both at uni- and multivariate analysis (p=0.027 and p=0.046, respectively). In conclusion, the presence of a type 3 variant of the EHBD, an intrapancreatic CD and, especially, a posterior/medial CD insertion into the EHBD represent predictive factors of the concomitant presence of IHBD variants, thus radiologists must be vigilant when encountering these EHBD configurations and always remember to "look up" at the IHBD. Finally, the presence of an IHBD variant is a strong predictive factor of gallstones.

Vessel distance mapping: a novel methodology for assessing vascular‐induced cognitive resilience

AbstractBackgroundThe association between cerebral blood supply and cognition has gained increasing interest, considering the remarkable anatomical variability of the circle of Willis. Thus, qualitative classifications of the arteries contributing to the hippocampal supply has been performed in previous studies to determine whether the additional presence of vessels might translate into cognitive differences and cerebral structural changes when coexisting with vascular pathology, to the extent of constituting a cognitive reserve. Nevertheless, the promising results in these regards are not without controversy. Hence, Vessel Distance Mapping (VDM) is here introduced, in order to ascertain the correlation of VDM‐metrics with each other and with cognitive status, as well as to elucidate which metrics have the greatest impact on cognition.MethodA battery of cognitive tests was used (including ADAScog and MoCA) in 51 subjects (71 ± 8.5yrs) with (n=20) and without (n=31) cerebral small vessel disease. Their hippocampal‐related vessels were manually segmented by using high‐resolution 7T Time‐of‐Flight‐MRI. Vessel distance maps were generated by computing the distances of each voxel to its nearest vessel, obtaining 3 VDM‐metrics: global‐VDM (vessel density surrogate), VS‐VDM (vessel‐specific supply), and COMD (distance to the center of mass, reflecting vessel distribution). Hippocampal masks were used to assess the whole and subregional hippocampus. Regression models were applied for evaluating the interrelation among the metrics, and their association with cognition.ResultHigh correlations were observed for intra‐metric comparisons, as well as when comparing whole hippocampal vs. sub‐hippocampal metrics. Greater values of VDM‐metrics reflecting higher distances among vessels were associated with poorer cognitive outcomes only in subjects affected by vascular pathology (global‐VDM R²=0.7248, p=0.0054; COMD R²=0.7248, p=0.0013). Metrics revealing vascular distribution (p=0,0053) and density (p=0,0169) were the most influential on cognition, and in this context those for the whole hippocampus predominated over the subregional ones.ConclusionA mixed contribution of vessel distribution and density is proposed to confer cognitive resilience, in a way consistent with anatomical fundamentals and with previous research findings. VDM provides a new, structure‐driven approach on which to raise new questions supported by the statistical robustness of a quantitative method with potential clinical implications.

MCA and geospatial analysis-based suitable dumping site selection for urban environmental protection: A case study of Shambu, Oromia Regional State, Ethiopia

A lack of suitable dumping sites in a town or city can have an impact on the health of the residents as well as the quality of the urban environment. There are no identified dumping sites in this study area that meet scientific or urban standards. Residents are dumping solid waste into ditches, roads, public water sources, and small streams. The solid and liquid wastes generated by residential areas, state prisons, religious areas, public markets, and business centers have a negative impact on the town. The purpose of this study is to demonstrate the novelty of using Multi-Criteria Analysis (MCA)-based geospatial analysis to select suitable dumping sites in Shambu town. Key factors for dumping site selection, such as LULC, road networks, private well locations, slope, geomorphology, geology, soil texture, drainage density, and lineament density, were confirmed as geospatial analysis criteria. In the Analytical Hierarchy Process (AHP), the importance of the key factors was weighted and prioritized, and thematic maps were created using weighted overlay analysis. The suitable dumping sites were identified using qualitative classifications such as “highly suitable” (13.84%), “moderately suitable” (7.35%), “less suitable” (30.41%), and “not suitable” (48.40%). The consistency of AHP was determined to be CI = 0.012, indicating that the weights assigned to each factor were correct. As a result, the use of geospatial and MCA analysis for dumping site suitability analysis was successful, and the findings of this study will be useful in taking action to reduce the impacts of solid waste by developing dumping plants on the identified sites.

Geography and language divergence: The case of Andic languages.

We study the correlation between phylogenetic and geographic distances for the languages of the Andic branch of the East Caucasian (Nakh-Daghestanian) language family. For several alternative phylogenies, we find that geographic distances correlate with linguistic divergence. Notably, qualitative classifications show a better fit with geography than cognacy-based phylogenies. We interpret this result as follows: The better fit may be due to implicit geographic bias in qualitative classifications. We conclude that approaches to classification other than those based on cognacy run a risk to implicitly include geography and geography-related factors as one basis of genealogical classifications.

Good Statistical Practices in Agronomy Using Categorical Data Analysis, with Alfalfa Examples Having Poisson and Binomial Underlying Distributions

Categorical data derived from qualitative classifications or countable quantitative data are common in biological scientific work and crop breeding. Categorical data analyses are important for drawing correct inferences from experiments. However, categorical data can introduce unique issues in data analysis. This paper discusses common problems arising from categorical variable analysis and modeling, demonstrates the issues or risks of misapplying analysis, and suggests approaches to address data analysis challenges using two data sets from alfalfa breeding programs. For each data set, we present several analysis methods, e.g., simple t-test, analysis of variance (ANOVA), split plot analysis, generalized linear model (glm), generalized linear mixed model (glmm) using R with R markdown, and with the standard statistical analysis software SAS/JMP. The goal is to demonstrate good analysis practices for categorical data by comparing the potential ‘bad’ analyses with better ones, avoiding too much reliance on reaching a significant p-value of 0.05, and navigating the morass of ever-increasing numbers of potential R functions. The three main aspects of this research focus on choosing the right data distribution to use, using the correct error terms for hypothesis test p-values including the right type of sum of the squares (Type I, II, and III), and proper statistical models for categorical data analysis. Our results show the importance of good statistical analysis practice to help agronomists, breeders, and other researchers apply appropriate statistical approaches to draw more accurate conclusions from their data.

Prognostic Relevance of Pancreatic Adenocarcinoma Whole-Tumor Transcriptomic Subtypes and Components.

Our team previously defined six quantitative transcriptomic components, and a classification in five subtypes by association of these components. In this study, we compared the robustness of quantitative components and qualitative classifications from different transcriptomic profiling techniques, investigated their clinical relevance, and proposed a new prognostic model. A total of 210 patients from a multicentric cohort and 149 patients from a monocentric cohort were included in this study. RNA microarray profiles were obtained from 165 patients of the multicentric cohort. RNA sequencing (RNA-seq) profiles were obtained from all the patients. For the patients with both RNA microarray and RNA-seq profiles, the concordance in subtype assignment was partial with an 82.4% coherence rate. The correlation between the two technique projections of the six components ranged from 0.85 to 0.95, demonstrating an advantage of robustness. On the basis of the Akaike information criterion, the RNA components showed more prognostic value in univariate or multivariate models than the subtypes. Using the monocentric cohort for training, we developed a multivariate Cox regression model using all six components and clinicopathologic characteristics (node invasion and resection margins) on disease-free survival (DFS). This prognostic model was highly associated with DFS (P < 0.001). The evaluation of the model in the multicentric cohort showed significant association with DFS and overall survival (P < 0.001). We described the advantage of the prognostic value and robustness of the whole-tumor transcriptomic components than subtypes. We created and validated a new DFS-based multivariate Cox regression prognostic model, including six pancreatic adenocarcinoma transcriptomic component levels and pathologic characteristics.

MP60-08 DEEP TRANSCRIPTOMIC PROFILING OF PROSTATE CANCER WITH QUANTITATIVE MEASURES: A SPECTRA APPROACH

You have accessJournal of UrologyProstate Cancer: Markers (MP60)1 Sep 2021MP60-08 DEEP TRANSCRIPTOMIC PROFILING OF PROSTATE CANCER WITH QUANTITATIVE MEASURES: A SPECTRA APPROACH Heidi Hanson, Jacob Ambrose, Brock O'Neil, Greg Lee, Claire Leiser, Rosalie Waller, Joemy Ramsay, Michael Madsen, Rupam Das, Christopher Dechet, Brian Avery, and Nicola Camp Heidi HansonHeidi Hanson More articles by this author , Jacob AmbroseJacob Ambrose More articles by this author , Brock O'NeilBrock O'Neil More articles by this author , Greg LeeGreg Lee More articles by this author , Claire LeiserClaire Leiser More articles by this author , Rosalie WallerRosalie Waller More articles by this author , Joemy RamsayJoemy Ramsay More articles by this author , Michael MadsenMichael Madsen More articles by this author , Rupam DasRupam Das More articles by this author , Christopher DechetChristopher Dechet More articles by this author , Brian AveryBrian Avery More articles by this author , and Nicola CampNicola Camp More articles by this author View All Author Informationhttps://doi.org/10.1097/JU.0000000000002095.08AboutPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissionsReprints ShareFacebookLinked InTwitterEmail Abstract INTRODUCTION AND OBJECTIVE: Methods that embrace the complexity of prostate cancer (CaP) gene expression are necessary for accurate phenotypic characterization. We previously presented a novel agnostic computational framework, SPECTRA, to describe RNA sequencing (RNAseq) data using multiple quantitative expression variables, or transcriptomic spectra (TrS). Here, we implement this technique to derive a set of TrS variables for CaP tumors from The Cancer Genome Atlas (TCGA). We compare the identified TrS with clinical and sample characteristics, progression-free interval, and previously established CaP subtypes. METHODS: Gene-level RNAseq data were downloaded from the Genomic Data Commons (GDC) Data Portal. Data were preprocessed using the SPECTRA protocol (nsamples=480, ngenes = 10,438) and matrix factorization was used to derive quantitative measures for each TrS. Linear, logistic, multinomial logistic, and Cox regression were used to assess the relationship between TrS and age, Gleason Risk Score, tumor stage, and progression free interval. Two approaches were used to select TrS associated with demographic, clinical, and molecular features of the tumors; “hard-thresholding” (Bonferonni corrected p-value) and lasso regularization. We compare model fit statistics to determine the difference in predictive power between our TrS and previously defined molecular subtypes defined by fusion of ETS family genes. RESULTS: We identified 21 TrS in our data that together explain 65.5% of the variance in global gene expression across CaP tumors in TCGA. Many of the TrS were associated with demographic and clinical characteristics of the patients and progression-free interval, with TrS3 having the strongest associations (Figure 1). The difference in predictive power of the TrS vs the previously identified molecular subtypes was substantial, with the difference in pseudo R-square in the range of 30% on average. This suggests that more information can be gained from quantitative vs. qualitative measures of gene expression. CONCLUSIONS: This approach had substantially better model fit than traditional qualitative tumor classifications and could lead to innovative ways to understanding gene expression patterns driving individual risk, treatment response and survival. Source of Funding: 5K07CA230150-03 © 2021 by American Urological Association Education and Research, Inc.FiguresReferencesRelatedDetails Volume 206Issue Supplement 3September 2021Page: e1044-e1044 Advertisement Copyright & Permissions© 2021 by American Urological Association Education and Research, Inc.MetricsAuthor Information Heidi Hanson More articles by this author Jacob Ambrose More articles by this author Brock O'Neil More articles by this author Greg Lee More articles by this author Claire Leiser More articles by this author Rosalie Waller More articles by this author Joemy Ramsay More articles by this author Michael Madsen More articles by this author Rupam Das More articles by this author Christopher Dechet More articles by this author Brian Avery More articles by this author Nicola Camp More articles by this author Expand All Advertisement Loading ...

Agreement Between Trend-Based and Qualitative Analysis of the Retinal Nerve Fiber Layer Thickness for Glaucoma Progression on Spectral-Domain Optical Coherence Tomography.

IntroductionTo evaluate the agreement between trend-based analysis and qualitative assessment of the retinal nerve fiber layer (RNFL) thickness for glaucomatous progression on spectral-domain optical coherence tomography (SDOCT).MethodsRetrospective review of 190 eyes from 103 patients with glaucoma or suspected glaucoma that underwent SDOCT imaging during four consecutive clinic visits. Trend-based progression was characterized by a significantly negative slope. Progression by qualitative analysis was determined by review of raw SDOCT B-scans.ResultsThe slope was significantly greater in those with progression than without progression for both trend-based and qualitative analysis (p < 0.001). However, the qualitative grading classified a significantly greater proportion of eyes as progressing compared to trend-based analysis in both the superotemporal (ST) (23.2% vs. 10.5%, p = 0.001) and inferotemporal (IT) RNFL (27.4% vs 8.4%, p < 0.001). The trend-based and qualitative classifications of progression showed poor agreement in both the ST (kappa = 0.0135) and IT RNFL (kappa = 0.1222). The agreement between trend-based and qualitative analysis was lower for eyes with artifacts (ST = 58.11%; IT = 68.7%) than those without artifacts (ST = 80.2%; IT = 74.8%). Moreover, among eyes with artifacts, there was no significant difference in slope between those qualitatively categorized as progressing versus not progressing (p > 0.05).ConclusionsPoor agreement was found between a trend-based and qualitative analysis of change in RNFL on SDOCT. Careful qualitative review of SDOCT imaging may identify specific areas of glaucoma progression not captured by trend-based methods, especially in the presence of artifacts. Such an approach may also prove useful for detecting glaucoma progression in a clinical setting when there are few data points available.

RUSLE Model Based Annual Soil Loss Quantification for Soil Erosion Protection: A Case of Fincha Catchment, Ethiopia

In this study, Revised Universal Soil Loss Equation (RUSLE) model and Geographic Information System (GIS) platforms were successfully applied to quantify the annual soil loss for the protection of soil erosion in Fincha catchment, Ethiopia. The key physical factors such as rainfall erosivity ( R-factor), soil erodibility ( K-factor), topographic condition (LS-factor), cover management ( C-factor), and support practice ( P-factor) were prepared in GIS environment from rainfall, soil, Digital Elevation Model (DEM), Land use/Land cover (LULC) respectively. The RUSLE equation was used in raster calculator of ArcGIS spatial tool analyst. The individual map of the derived factors was multiplied in the raster calculator and an average annual soil loss ranges from 0.0 to 76.5 t ha−1 yr−1 was estimated. The estimated annual soil loss was categorized based on the qualitative and quantitative classifications as Very Low (0–15 t ha−1 yr−1), Low (15–45 t ha−1 yr−1), Moderate (45–75 t ha−1 yr−1), and High (&gt;75 t ha−1 yr−1). It was found from the generated soil erosion severity map that about 45% of the catchment area was vulnerable to the erosion with an annual soil loss of (&gt;75 t ha−1 yr−1), and this demonstrates that the erosion reduction actions are immediately required to ensure the sustainable soil resources in the study area. The soil erosion severity map generated based on RUSLE model and GIS platforms have a paramount role to alert all stakeholders in controlling the effects of the erosion. The results of the RUSLE model can also be further considered along with the catchment for practical soil loss protection practices.

Automatic coronary calcium scoring in chest CT using a deep neural network in direct comparison with non-contrast cardiac CT: A validation study

PurposeTo evaluate deep-learning based calcium quantification on Chest CT scans compared with manual evaluation, and to enable interpretation in terms of the traditional Agatston score on dedicated Cardiac CT. MethodsAutomated calcium quantification was performed using a combination of deep-learning convolution neural networks with a ResNet-architecture for image features and a fully connected neural network for spatial coordinate features. Calcifications were identified automatically, after which the algorithm automatically excluded all non-coronary calcifications using coronary probability maps and aortic segmentation. The algorithm was first trained on cardiac-CTs and refined on non-triggered chest-CTs. This study used on 95 patients (cohort 1), who underwent both dedicated calcium scoring and chest-CT acquisitions using the Agatston score as reference standard and 168 patients (cohort 2) who underwent chest-CT only using qualitative expert assessment for external validation. Results from the deep-learning model were compared to Agatston-scores(cardiac-CTs) and manually determined calcium volumes(chest-CTs) and risk classifications. ResultsIn cohort 1, the Agatston score and AI determined calcium volume shows high correlation with a correlation coefficient of 0.921(p < 0.001) and R2 of 0.91. According to the Agatston categories, a total of 67(70 %) were correctly classified with a sensitivity of 91 % and specificity of 92 % in detecting presence of coronary calcifications. Manual determined calcium volume on chest-CT showed excellent correlation with the AI volumes with a correlation coefficient of 0.923(p < 0.001) and R2 of 0.96, no significant difference was found (p = 0.247). According to qualitative risk classifications in cohort 2, 138(82 %) cases were correctly classified with a k-coefficient of 0.74, representing good agreement. All wrongly classified scans (30(18 %)) were attributed to an adjacent category. ConclusionArtificial intelligence based calcium quantification on chest-CTs shows good correlation compared to reference standards. Fully automating this process may reduce evaluation time and potentially optimize clinical calcium scoring without additional acquisitions.

Defining the dance: quantification and classification of endoplasmic reticulum dynamics.

The availability of quantification methods for subcellular organelle dynamic analysis has increased rapidly over the last 20 years. The application of these techniques to contiguous subcellular structures that exhibit dynamic remodelling over a range of scales and orientations is challenging, as quantification of 'movement' rarely corresponds to traditional, qualitative classifications of types of organelle movement. The plant endoplasmic reticulum represents a particular challenge for dynamic quantification as it itself is an entirely contiguous organelle that is in a constant state of flux and gross remodelling, controlled by the actinomyosin cytoskeleton.

Features and psychometric properties of the Montreal Cognitive Assessment: Review and proposal of a process-based approach version (MoCA-PA)

The current study presents a rapid review of the psychometric features of the standard Montreal Cognitive Assessment (MoCA), and the proposal for a modified version of the test, informed by the methodology of the Boston Process Approach to neuropsychological assessment. In order to aid the process of identification of the primary underlying neurocognitive mechanism responsible for defective test performance, the MoCA-Process-Based Approach (MoCA-PA) adds complementary or satellite test conditions in some of its subtests, includes “new” qualitative indices to capture the cognitive processes involved in each cognitive task, and incorporates new qualitative classifications of error subtypes. It provides concurrent assessment of multiple cognitive processes within each task, without significantly increasing administration time or placing significant additional burden upon the respondent. We present preliminary results obtained from an initial sample of 45 community-dwelling older adults attending a University program for seniors. Results suggest the usefulness of additional indices in providing additional information on cognitive deterioration that may be overlooked with the only consideration of quantitative scores. Future research will aim to collect normative data for different clinical populations using the newly developed indices in order to determine the validity and clinical utility of the relatively novel qualitative process-based methods used in the MoCA-PA.

VIS3COS

We study the impact of local density and stellar mass on the structure and morphology of approximately 500 quiescent and star-forming galaxies from the VIMOS Spectroscopic Survey of a Superstructure in COSMOS (VIS3COS). We perform bulge-to-disc decomposition of the surface brightness profiles and find ∼41 ± 3% of &gt; 1010 M⊙ galaxies to be best fitted with two components. We complement our analysis with non-parametric morphological measurements and qualitative visual classifications. We find that both galaxy structure and morphology depend on stellar mass and environment for our sample as a whole. We only find an impact of the environment on galaxy size for galaxies more massive than 1011 M⊙. We find higher Sérsic indices (n) and bulge-to-total ratios (B/T) in high-density regions when compared to low-density counterparts at similar stellar masses. We also find that galaxies with higher stellar mass have steeper light profiles (high n, B/T) compared to galaxies with lower stellar mass. Using visual classifications, we find a morphology–density relation at z ∼ 0.84 for galaxies more massive than 1010 M⊙, with elliptical galaxies being dominant at high-density regions and disc galaxies more common in low-density regions. However, when splitting the sample into colour–colour-selected star-forming and quiescent sub-populations, there are no statistically significant differences between low- and high-density regions. We find that quiescent galaxies are smaller, have higher Sérsic indices (for single profiles, around n ∼ 4), and higher bulge-to-total light ratios (for decomposed profiles, around B/T ∼ 0.5) when compared to star-forming counterparts (n ∼ 1 and B/T ∼ 0.3, for single and double profiles, respectively). We confirm these trends with non-parametric quantities, finding quiescent galaxies to be smoother (lower asymmetry, lower M20) and to have most of their light over smaller areas (higher concentration and Gini coefficient) than star-forming galaxies. Overall, we find a stronger dependence of structure and morphology on stellar mass than on local density and these relations are strongly correlated with the quenching fraction. The change in average structure or morphology corresponds to a change in the relative fractions of blue disc-like galaxies and red elliptical galaxies with stellar mass and environment. We hypothesise that the processes responsible for the quenching of star formation must also affect the galaxy morphology on similar timescales.

Using rubrics as alternative self-assessment technique of project

In this paper, we investigate the effectiveness of employing rubric as a self-assessment technique of the project method, which covers a wide range of knowledge, skills and abilities in a variety of learning objects and activities. The assessment rubric is defined as a descriptive rating guide, which consists of specific pre-defined performance criteria (Petropoulou, Kasimatis&amp;Retalis, 2015). It is an alternative assessment technique, which produces a valid evaluation through a process of determining attainment based on pre-defined qualitative classifications of specific criteria (Mitchel&amp;Crawford, 1995).&#x0D; In this study, the assessment rubric was used as a self-assessment technique in the implementation of a project entitled "Utopia and Reality", conducted in ASPETE (School of Pedagogical and Technological Education) during the year 2017-2018.The students of two undergraduate departments of ASPETE (Civil Engineering Educators and Mechanical Engineering Educators) participated in the study. The rubric that was employed in the study included the following criteria (each with pre-determined quality levels; Kasimatis&amp;Papanikolaou, 2012):Quality and Content completeness, Scientific validity, Structure and Organization, Form and Presentation.The quantitative analysis of the data revealed statistically significantly differences in the students’ self-assessment levels (as identified by the rubric) with respect to the students’ gender and the department of study. Moreover, the tool allowed the students to evaluate the end product of their efforts, thus allowing for their realizing the usefulness of the rubric as a self-assessment tool.&#x0D; &#x0D; Keywords: self-assessment, project, rubric, assessment criteria, reflection