Comment on tc-2022-48

Abstract. Knowledge concerning possible inhomogeneities in a data set is of key importance for any subsequent climatological analyses. Well-established relative homogenization methods developed for temperature and precipitation exist but have rarely been applied to snow-cover-related time series. We undertook a homogeneity assessment of Swiss monthly snow depth series by running and comparing the results from three well-established semi-automatic break point detection methods (ACMANT – Adapted Caussinus-Mestre Algorithm for Networks of Temperature series, Climatol – Climate Tools, and HOMER – HOMogenizaton softwarE in R). The multi-method approach allowed us to compare the different methods and to establish more robust results using a consensus of at least two change points in close proximity to each other. We investigated 184 series of various lengths between 1930 and 2021 and ranging from 200 to 2500 m a.s.l. and found 45 valid break points in 41 of the 184 series investigated, of which 71 % could be attributed to relocations or observer changes. Metadata are helpful but not sufficient for break point verification as more than 90 % of recorded events (relocation or observer change) did not lead to valid break points. Using a combined approach (two out of three methods) is highly beneficial as it increases the confidence in identified break points in contrast to any single method, with or without metadata.

ObjectivesWe investigated whether Scottish implementation of smoke-free legislation was associated with a reduction in unplanned hospitalisations or deaths (‘events’) due to respiratory tract infections (RTIs) among children.DesignInterrupted time series (ITS).Setting/participantsChildren...

To address the need for better understanding of multiphase fluid flow behavior through passive inflow control devices (PICDs), two-phase (oil-water) full-scale flow loop testing on helical and a new ICD design, "Hybrid" has been completed. The primary testing objective was to generate a comprehensive two-phase (oil-water) data set of flow performance curves for the helical ICD and the hybrid ICD. The test included a broad range of flow tests with varying viscosities, water cuts and pressures. The results are presented in an innovative manner using isobaric plots (isobars lines with trend lines for each viscosity value) and Reynolds number vs. Flow Coefficient plots, which can be used to easily compare different ICD performances. Test results confirm that for oil-water flow, a viscosity break point for the helical ICD occurs at 2 centipoise (cP) of medium oil. Below this break point the helical ICD does not promote water production in two-phase flow. Above this viscosity break point, the helical ICD exhibits a physical phenomenon where, at water cuts above 30%, the total flow increases at any given pressure drop promoting water flow. With regards to two-phase oil-water fluid flow, the hybrid ICD performs independently of viscosity for the range tested (up to 200 cP - maximum flow viscosity tested at the lab).The hybrid ICD consistently creates more resistance to water flow than to oil flow, causing total flow to gradually decrease at constant pressure as water cut increases. This effect was more apparent at higher pressure regimes. The functional break point of the hybrid ICD is determined to be above 200 cP. Introduction A comprehensive two-phase (oil-water) data set of flow performance curves for the helix inflow control devices (ICDs) and hybrid ICDs was conducted through extensive laboratory testing to identify the threshold viscosity, and to quantify single and multiphase model validity. These test results confirm that the threshold viscosity is 2 centipoise (cP). At viscosities greater than 2 cP, the test results confirm previously published findings that the helix design exhibits preferential production of water for a given pressure drop in a two-phase flow. Additionally, results verify that the helix ICD behavior exhibits correlation to the single phase model but requires a correction due to non-constant flow coefficient, and does not exhibit correlation when the fluids are multiphase, especially at higher viscosities.

Our current knowledge on snow depth trends is based almost exclusively on these non-homogenized data.Long-term observations of deposited snow are well suited as indicator of climate change. However, like all other long-term observations, they are prone to inhomogeneities that can influence and change trends if not taken into account. We investigated the effects of removing inhomogeneities in the large network of Swiss snow depth observations on trends and extreme values of commonly used snow indices, such as snow days, seasonal averages or maximum snow depth in the period 1961–2021. For this task, three homogenization methods were applied: Climatol and HOMER, which use a median based adjustment method, and interpQM, which applies quantile based adjustments. All three were run using the same break points and input data. This allowed us to investigate and quantify the effects of these methods on the homogenization results. We found that all three methods agree well on trends in seasonal average snow depth, while differences are visible for seasonal maximum snow depth and the corresponding extreme values. Here, the quantile-based method performed slightly better than the two median-based methods, as it had the smallest number of stations outside the 95 % confidence interval for 50-year return periods of maximum snow depth. These differences are mainly caused by the way the reference series are selected. The combination of a high minimum correlation (>0.7) and restrictions in vertical (<300 m) and horizontal (<100 km) distances proved to be better suited than only using correlations or distances respectively as criteria. The adjustments removed all positive trends for snow days in the original data and strengthened the negative mean trend, especially for stations >1500 m. In addition, the number of significant negative stations was increased between 7–21 %, with the strongest changes at higher snow depths.

In the original authors list there was a mistake with the surname of Professor Alessia Flammini. The corrected authors list is:

The increase of air surface temperature at global scale is a fact with values around 0.85 °C since the late nineteen century. Nevertheless, the increase is not equally distributed all over the world, varying from one region to others. Thus, it becomes interesting to study the evolution of temperature indices for a certain area in order to analyse the existence of climatic trend in it. In this work, monthly temperature time series from two Mediterranean areas are used: the Umbria region in Italy, and the Guadalquivir Valley in southern Spain. For the available stations, six temperature indices (three annual and three monthly) of mean, average maximum and average minimum temperature have been obtained, and the existence of trends has been studied by applying the non-parametric Mann–Kendall test. Both regions show a general increase in all temperature indices, being the pattern of the trends clearer in Spain than in Italy. The Italian area is the only one at which some negative trends are detected. The presence of break points in the temperature series has been also studied by using the non-parametric Pettit test and the parametric standard normal homogeneity test (SNHT), most of which may be due to natural phenomena.

Numerical simulation of surf–swash zone motions and turbulent flow

This paper presents a non-heuristic and control parameter independent dominant point detection method. It is based on the suppression of break points and provides a more balanced performance than the recent break point suppression methods. It is made non-heuristic using an analytical error bound for digitizing a line segment. It provides a good combination of compression ratio and error metrics by avoiding both over-fitting and under-fitting.

This paper presents an algorithm to capture outline of bitmap characters. In order to obtain outline, a cubic function with one shape parameter is used. Genetic algorithm is applied to find the value of shape parameter. The procedure of capturing outline consists of the steps: boundary detection, detection of corner points, break points and fitting the curve.

In the present study, several conventional methods to detect methicillin-resistant Staphylococcus aureus (MRSA) were compared with polymerase chain reaction (PCR) detection of mecA gene-positive isolates. Cefoxitin E-test was also evaluated as a possible phenotypic method of MRSA detection. Oxacillin agar screen and PBP2' latex agglutination methods were found to be more sensitive than oxacillin and cefoxitin disk-diffusion methods. Cefoxitin disk diffusion was found to be the most specific. A combination of oxacillin agar screening with cefoxitin disk diffusion, or oxacillin disk diffusion with PBP2', improved sensitivity and specificity. Cefoxitin E-test with the current break points had low sensitivity and specificity (33.3% and 75%, respectively) for the detection of MRSA. However, changing the break points to <or= 4 microg/ml and to >or= 6 microg/ml for sensitive and resistant, respectively, greatly improved both. Changing the 30-microg cefoxitin disk-diffusion break points to <or= 21 mm for resistant slightly improved sensitivity but had no effect on specificity. It was therefore concluded that the use of more than one screening method is necessary to detect all MRSA isolates in clinical settings.

This article develops an asymmetric volatility model that takes into consideration the structural breaks in the volatility process. Break points and other parameters of the model are estimated using MCMC and Gibbs sampling techniques. Models with different number of break points are compared using the Bayes factor and BIC. We provide a formal test and hence a new procedure for Bayes factor computation to choose between models with different number of breaks. The procedure is illustrated using simulated as well as real data sets. The analysis shows an evidence to the fact that the financial crisis in the market from the first week of September 2008 has caused a significant break in the structure of the return series of two major NYSE indices viz., S & P 500 and Dow Jones. Analysis of the USD/EURO exchange rate data also shows an evidence of structural break around the same time.

Tongue carcinoma: tumor volume measurement

Microarray technology is a recent development in experimental molecular biology which can produce quantitative expression measurements for thousands of genes in a single, cellular mRNA sample. These many gene expression measurements form a composite profile of the sample, which can be used to differentiate samples from different classes such as tissue types or treatments. However, for the gene expression profile data obtained in a specific comparison, most likely only some of the genes will, be differentially expressed between the classes, while many other genes have similar expression levels. Selecting a list of informative differential genes from these data is important for microarray data analysis. In this paper, we describe a framework for selecting informative genes, called ranking and combination analysis (RAC), which combines various existing informative gene selection methods. We conducted experiments using three data sets and six existing feature selection methods. The results show that the RAC framework is a robust and efficient approach to identify informative gene for microarray data. The combination approach on two selecting methods almost always performed better than the less efficient individual, and in many cases, better than both. More significantly, when considering all three data sets together, the combination approach, on average, outperforms each individual feature selection method. All of these indicate that RCA might be a viable and feasible approach for the microarray gene expression analysis.

Purpose:This study compared three methods of acoustic algorithm-supported extraction and analysis of vocal tremor properties (i.e., rate, extent, and regularity of intensity level and fundamental frequency modulation): (a) visual perception and manual data extraction, (b) semi-automated data extraction, and (c) fully automated data extraction.Method:Forty-five midvowel sustained [a:] and [i:] audio recordings were collected as part of a scientific project to learn about the physiologic substrates of vocal tremor. This convenience data set contained vowels with a representative variety in vocal tremor severity. First, the vocal tremor properties in intensity level and fundamental frequency tracks were visually inspected and manually measured using Praat software. Second, the vocal tremor properties were determined using two Praat scripts: automated with the script of Maryn et al. (2019) and semi-automated with an adjusted version of this script to enable the user to intervene with the signal processing. The reliability of manual vocal tremor property measurement was assessed using the intraclass correlation coefficient. The properties as measured with the two scripts (automated vs. semi-automated) were compared with the manually determined properties using correlation and diagnostic accuracy statistical methods.Results:With intraclass correlation coefficients between .770 and .914, the reliability of the manual method was acceptable. The semi-automated method correlated with manual property measures better and was more accurate in diagnosing vocal tremor than the automated method.Discussion:Manual acoustic measurement of vocal tremor properties can be laborious and time-consuming. Automated or semi-automated acoustic methods may improve efficiency in vocal tremor property measurement in clinical as well as research settings. Although both Praat script-supported methods in this study yielded acceptable validity with the manual data measurements as a referent, the semi-automated method showed the best outcomes.Supplemental Material:https://doi.org/10.23641/asha.28873088

Spinal cord segmentation is a developing area of research intended to aid the processing and interpretation of advanced magnetic resonance imaging (MRI). For example, high resolution three-dimensional volumes can be segmented to provide a measurement of spinal cord atrophy. Spinal cord segmentation is difficult due to the variety of MRI contrasts and the variation in human anatomy. In this study we propose a new method of spinal cord segmentation based on one-dimensional template matching and provide several metrics that can be used to compare with other segmentation methods. A set of ground-truth data from 10 subjects was manually-segmented by two different raters. These ground truth data formed the basis of the segmentation algorithm. A user was required to manually initialize the spinal cord center-line on new images, taking less than one minute. Template matching was used to segment the new cord and a refined center line was calculated based on multiple centroids within the segmentation. Arc distances down the spinal cord and cross-sectional areas were calculated. Inter-rater validation was performed by comparing two manual raters (n = 10). Semi-automatic validation was performed by comparing the two manual raters to the semi-automatic method (n = 10). Comparing the semi-automatic method to one of the raters yielded a Dice coefficient of 0.91 +/- 0.02 for ten subjects, a mean distance between spinal cord center lines of 0.32 +/- 0.08 mm, and a Hausdorff distance of 1.82 +/- 0.33 mm. The absolute variation in cross-sectional area was comparable for the semi-automatic method versus manual segmentation when compared to inter-rater manual segmentation. The results demonstrate that this novel segmentation method performs as well as a manual rater for most segmentation metrics. It offers a new approach to study spinal cord disease and to quantitatively track changes within the spinal cord in an individual case and across cohorts of subjects.