Reference Distribution Research Articles

Normal values and distribution of ventricular global longitudinal strain in 513 healthy fetuses measured by two-dimensional speckle-tracking echocardiography: a multi-institutional cohort study.

This study aimed to determine the normal reference values and distribution of global longitudinal strain (GLS) in the right and left ventricles of healthy Japanese fetuses during pregnancy. This multi-institutional cohort study included healthy Japanese fetuses during normal pregnancies without maternal or fetal complications between 18 and 40weeks of gestation. Two-dimensional fetal echocardiographic images of the four-chamber view with a high frame rate were acquired and stored as DICOM clips. Data were collected and analyzed in a central laboratory to measure the left ventricular (LV) and right ventricular (RV) GLS using two-dimensional speckle tracking. In total, 513 fetuses were enrolled. The mean LV-GLS and RV-GLS were - 24.3% ± 3.5% and - 23.5% ± 3.7%, respectively. The magnitude of the GLS, with normal limits in both ventricles, decreased with advancing gestation. LV values were r = 0.34 (95% confidence interval, 0.27-0.42) and p < 0.0001; RV values were r = 0.33 (95% confidence interval, 0.25-0.41) and p < 0.0001. The normal values of healthy Japanese fetuses in healthy pregnancies is the first to be established by the large-scale, multi-institutional cohort study as LV-GLS of 24.3% ± 3.5% and RV-GLS of - 23.5% ± 3.7%, respectively. This can serve as a basic reference for assessing the cardiac functions in Japanese fetuses with various heart diseases.

Cross-validatory Z-Residual for Diagnosing Shared Frailty Models

Accurate model performance assessment in survival analysis is imperative for robust predictions and informed decision-making. Traditional residual diagnostic tools like martingale and deviance residuals lack a well-characterized reference distribution for censored regression, making numerical statistical tests based on these residuals challenging. Recently, the introduction of Z-residuals for diagnosing survival models addresses this limitation. However, concerns arise from conventional methods that utilize the entire dataset for both model parameter estimation and residual assessment, which may cause optimistic biases. This paper introduces cross-validatory Z-residuals as an innovative approach to address these limitations. Employing a cross-validation (CV) framework, the method systematically partitions the dataset into training and testing sets to reduce the optimistic bias. Our simulation studies demonstrate that, for goodness-of-fit tests and outlier detection, cross-validatory Z-residuals are significantly more powerful (e.g. power increased from 0.2 to 0.6). and more discriminative (e.g. AUC increased from 0.58 to 0.85) than Z-residuals without CV. We also compare the performance of Z-residuals with and without CV in identifying outliers in a real application that models the recurrence time of kidney infection patients. Our findings suggest that cross-validatory Z-residuals can identify outliers, which Z-residuals without CV fail to identify. The CV Z-residual is a more powerful tool than the No-CV Z-residual for checking survival models, particularly in goodness-of-fit tests and outlier detection. We have published a generic function, which is collected in an R package called Zresidual, for computing CV Z-residual for the output of the widely used survival R package.

Decision making using similarity to a reference distribution

Abstract Accepted by: Prof. Ali Emrouznejad In discriminant analysis (DA) and some methods of multicriteria decision analysis (MCDA), we often wish to assign a ‘candidate’ to one of two or more groups, e.g. ‘satisfactory’ or ‘unsatisfactory’. This requires specifying the characteristics of both groups. However, often we are only able or willing to specify the characteristics of one group, the reference or ‘ideal’ group. We propose a method for proceeding in this case, using the overlap index. This is shown to be the probability that a random variable could have come from a reference distribution. This article motivates and introduces the index, describes its estimation and computation, and illustrates its use with examples from discriminant analysis and MCDA. Two of these examples, one of choosing soccer players and another of appraising surgeons, involve managerial decisions. The novelty here is the introduction of ‘1-group discriminant analysis’ based on the overlap index, the application to MCDA and the methodology for the computation of the overlap index.

Detecting expressed genes in cell populations at the single-cell level with scGeneXpress.

Determining whether genes are expressed or not remains a challenge in single-cell RNAseq experiments due to their different expression spectra, which are influenced by genetics, the microenvironment and gene length. Current approaches for addressing this issue fail to provide a comprehensive landscape of expressed genes, since they neglect the inherent differences in the expression ranges and distributions of genes. Here, we present scGeneXpress, a method for detecting expressed genes in cell populations of single-cell RNAseq samples based on gene-specific reference distributions. We demonstrate that scGeneXpress accurately detects expressed cell markers and identity genes in 34 human and mouse tissues and can be employed to improve differential expression analysis of single-cell RNAseq data.

Zastosowanie Metody Elementów Skończonych do analizy metody określania rozmiaru buta na podstawie pomiarów pola magnetycznego

Paper presents utilization of Finite Element Method (FEM) for analysis of usefulness of shoe size assessment method, based on magnetovision measurements. Measurement method based on disturbances of the uniform magnetic field (e.g. Earth’s magnetic field) is presented. Disturbances are caused by loose material of high magnetic permeability placed inside the tested shoe, which adapts to the insides of the shoe. The disturbed magnetic field is measured by the matrix of magnetometers. The values of magnetic field are an input into inverse transformation algorithm which fits the position of matrix sensors into the reference (obtained by FEM) distribution of magnetic field. Based on the fitted parameters of sensors matrix a ratio between the measured and reference shoe size was determined. A FEM-based forward transformation is presented, which allows for generation of three-dimensional array of magnetic field distribution. Initial tests of inverse transformation were conducted successfully. Next steps consisted of tests of inverse transformation method with an inclusion of the typical measurement noise. A FEM-based forward transformation was conducted of a modified shoe model (including simplified screw model). Inverse transformation was conducted but both influence of measurement noise, as well as the presence of additional high permeability material, resulted that the presented shoe size assessment method is not suitable for practical implementation.

Identification of the Out-of-Control Variable on a Multivariate Control Chart Signals in Iron Sheet Production Process

Monitoring and diagnosis of an out of control signals in manufacturing processes has become more challenging when it involves two or more correlated variables. In any production process and in the manufacturing industry, regardless of how well designed or carefully maintained it is, a certain amount of inherent or natural variability will always exist. The natural variability is the cumulative effect of many small, essentially unavoidable causes. A process that is operating with only chance causes of variation present is said to be in statistical control. In this paper we propose a method for approaching this problem based on principal components analysis. The principal component analysis method based on the ratios form was used to investigate which of the variable(s) were responsible for the out-of-control signal. A display of matrix of scatter plots was applied to investigate the correlation between the process variables and ellipses were used an approximate control limits. The principal component scores were used as an empirical reference distribution to establish a control region for the process to detect the variables causing the out-of-control signal. These insights derived from the principal component analysis offered valuable cues for identifying potential signals of an out-of-control process. The study findings indicated distinctive patterns of variance within the data, shedding light on potential signals for an out-of-control process.

So is it better than something else? Using the results of a random-effects meta-analysis to characterize the magnitude of an effect size as a percentile.

The characterization of an effect size is best made in reference to effect sizes found in the literature. A random-effects meta-analysis is the systematic synthesis of related effects from across a literature, producing an estimate of the distribution of effects in the population. We propose using the estimated mean and variance from a random-effects meta-analysis to inform the characterization of an observed effect size. The percentile of an observed effect size within the estimated distribution of population effects can describe the magnitude of the observed effect. Because there is uncertainty in the population estimates, we propose using the prediction distribution (used frequently to estimate the prediction interval in a meta-analysis) to serve as the reference distribution when characterizing an effect size. Doing so, the percentile of an observed effect and the limits of the effect size's 95% confidence interval within the prediction distribution are calculated. With numerous meta-analyses available including various outcomes and contexts, the presented method can be useful to many researchers and practitioners. We demonstrate the application of an easy-to-use Excel worksheet to automate these percentile calculations. We follow this with a simulation study evaluating the method's performance over a range of conditions. Recommendations (and cautions) for meta-analysts and researchers conducting a single study are provided. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Research on the application of radiomics in breast cancer: A bibliometrics and visualization analysis.

Breast cancer is the most prevalent form of cancer worldwide. Therefore, improved disease detection has emerged as a focal point in clinical studies. At the forefront of innovation, radiomics has the capability to extract comprehensive insights from medical images, ultimately enhancing the accuracy of diagnostic procedures. There has been rapid growth in the field of radiomics research on breast cancer in the past few years. We explored pertinent research articles in the Web of Science Core Collection database to gain a thorough understanding of breast cancer radiomics. We used CiteSpace to conduct a bibliometric analysis of the annual distribution of different nations, institutions, journals, authors, keywords, and references in the field of breast cancer radiomics. GraphPad Prism software was used to examine and graph yearly and country-specific trends and the proportions of publications. The tools utilized for the visualization of science mapping included CiteSpace and VOSviewer. Of the 891 publications, most were original articles (731, 91.09%) and a few were reviews (160, 8.91%). Most academic research has been published in China and the United States. The study centers predominantly consisted of major academic institutions, such as Fudan University and the Chinese Academy of Sciences, with some of their members being prominent figures in the field. Pinker, Katja has published the largest number of research papers. The majority of these studies have been published in medical journals focusing on radiology and oncology in recent years. In the realm of cutting-edge medical research, the top two keywords, magnetic resonance imaging and machine learning stand at the forefront as current areas of intense focus. Breast cancer radiomics is advancing rapidly, presenting numerous opportunities and obstacles. Our study of the literature in this academic area aimed to pinpoint the primary themes addressed in the studies and anticipate prospective avenues for research.

Subject-independent auditory spatial attention detection based on brain topology modeling and feature distribution alignment

Auditory spatial attention detection (ASAD) seeks to determine which speaker in a surround sound field a listener is focusing on based on the one’s brain biosignals. Although existing studies have achieved ASAD from a single-trial electroencephalogram (EEG), the huge inter-subject variability makes them generally perform poorly in cross-subject scenarios. Besides, most ASAD methods do not take full advantage of topological relationships between EEG channels, which are crucial for high-quality ASAD. Recently, some advanced studies have introduced graph-based brain topology modeling into ASAD, but how to calculate edge weights in a graph to better capture actual brain connectivity is worthy of further investigation. To address these issues, we propose a new ASAD method in this paper. First, we model a multi-channel EEG segment as a graph, where differential entropy serves as the node feature, and a static adjacency matrix is generated based on inter-channel mutual information to quantify brain functional connectivity. Then, different subjects’ EEG graphs are encoded into a shared embedding space through a total variation graph neural network. Meanwhile, feature distribution alignment based on multi-kernel maximum mean discrepancy is adopted to learn subject-invariant patterns. Note that we align EEG embeddings of different subjects to reference distributions rather than align them to each other for the purpose of privacy preservation. A series of experiments on open datasets demonstrate that the proposed model outperforms state-of-the-art ASAD models in cross-subject scenarios with relatively low computational complexity, and feature distribution alignment improves the generalizability of the proposed model to a new subject.

Extended fiducial inference: toward an automated process of statistical inference

Abstract While fiducial inference was widely considered a big blunder by R.A. Fisher, the goal he initially set—‘inferring the uncertainty of model parameters on the basis of observations’—has been continually pursued by many statisticians. To this end, we develop a new statistical inference method called extended Fiducial inference (EFI). The new method achieves the goal of fiducial inference by leveraging advanced statistical computing techniques while remaining scalable for big data. Extended Fiducial inference involves jointly imputing random errors realized in observations using stochastic gradient Markov chain Monte Carlo and estimating the inverse function using a sparse deep neural network (DNN). The consistency of the sparse DNN estimator ensures that the uncertainty embedded in observations is properly propagated to model parameters through the estimated inverse function, thereby validating downstream statistical inference. Compared to frequentist and Bayesian methods, EFI offers significant advantages in parameter estimation and hypothesis testing. Specifically, EFI provides higher fidelity in parameter estimation, especially when outliers are present in the observations; and eliminates the need for theoretical reference distributions in hypothesis testing, thereby automating the statistical inference process. Extended Fiducial inference also provides an innovative framework for semisupervised learning.

Direct, age- and gender-specific reference intervals: applying a modified M-estimator of the Yeo-Johnson transformation to clinical real-world data

Abstract Objectives Reference intervals for the general clinical practice are expected to cover non-pathological values, but also reflect the underlying biological variation present in age- and gender-specific patient populations. Reference intervals can be inferred from routine patient data measured in high capacity using parametric approaches. Stratified reference distributions are obtained which may be transformed to normality via e.g. a Yeo-Johnson transformation. The estimation of the optimal transformation parameter for Yeo-Johnson through maximum likelihood can be highly influenced by the presence of outlying observations, resulting in biased reference interval estimates. Methods To reduce the influence of outlying observations on parametric reference interval estimation, a reweighted M-estimator approach for the Yeo-Johnson (YJ) transformation was utilised to achieve central normality in stratified reference populations for a variety of laboratory test results. The reweighted M-estimator for the YJ transformation offers a robust parametric approach to infer relevant reference intervals. Results The proposed method showcases robustness up to 15 % of outliers present in routine patient data, highlighting the applicability of the reweighted M-estimator in laboratory medicine. Furthermore, reference intervals are personalised based on the patients’ age and gender for a variety of analytes from routine patient data collected in a tertiary hospital, robustly reducing the dimensionality of the data for more data-driven approaches. Conclusions The method shows the advantages for estimating reference intervals directly and parametrically from routine patient data in order to provide expected reference ranges. This approach to locally inferred reference intervals allows a more nuanced comparison of patients’ test results.

Differential image motion in astrometric observations with very large seeing-limited telescopes

We investigate how to quantitatively model the observed differential image motion (DIM) in relative astrometric observations. As a test bed we used differential astrometric observations from the FORS2 camera of the Very Large Telescope (VLT) obtained during 2010--2019 under several programs of observations of southern brown dwarfs . The measured image motion was compared to models that decompose atmospheric turbulence in frequency space and translate the vertical turbulence profile into DIM amplitude. This approach accounts for the spatial filtering by the telescope's entrance pupil and the observation parameters (field size, zenith angle, reference star brightness and distribution, and exposure time), and it aggregates that information into a newly defined metric integral term. We demonstrate excellent agreement (within 1&lt;!PCT!&gt;) between the model parameters derived from the DIM variance and determined by the observations. For a 30 s exposure of a typical 1 field close to the Galactic plane, image motion limits astrometric precision to sim 60 mu as when sixth-order transformation polynomial is applicable. We confirm that the measured image motion variance is well described by Kolmogorov-type turbulence with exponent 11/3 dependence on the field size at effective altitudes of 16--18 km, where the best part of the DIM is generated. Extrapolation to observations with extremely large telescopes enables the estimation of the astrometric precision limit for seeing-limited observations of sim 5 mu as, which has a variety of exciting scientific applications.

How Do Quantitative Videofluoroscopy Measures Differ Between People With Amyotrophic Lateral Sclerosis and Age-Matched Healthy Adults?

Dysphagia is a leading cause of morbidity in people with amyotrophic lateral sclerosis (PwALS). Previous videofluoroscopic swallowing studies (VFSS) in PwALS do not account for the influence of senescence. We aimed to compare swallowing in PwALS and an age- and sex-matched control group using healthy reference data to define typical and atypical values. We conducted retrospective analysis of VFSS data from 19 PwALS (10 male, Mage = 63 years, range: 47-82) compared to control data from a cohort of healthy adults. Participants swallowed 20% w/v liquid barium from thin to extremely thick consistency. Blinded duplicate VFSS analysis using the ASPEKT (Analysis of Swallowing Physiology: Events, Kinematics and Timing) method yielded descriptive statistics for 16 quantitative VFSS parameters by consistency. Mann-Whitney U tests were used to identify significant cohort differences. Additionally, the frequencies of atypical values (in the 25% tails of the reference distribution) were tabulated by cohort and compared using odds ratios. PwALS showed increased frequencies of multiple swallows per bolus, incomplete laryngeal vestibule closure, and reduced hyoid speed across consistencies. By contrast, similar frequencies of atypical values for pharyngeal constriction and residue in both cohorts suggest that age-related changes may contribute to the presence of these features in PwALS. This analysis builds on previous descriptions of swallowing pathophysiology in amyotrophic lateral sclerosis (ALS) by clarifying the extent to which aging may account for some of the atypical findings seen in this patient population. Longitudinal studies are recommended to further differentiate the effects of ALS from age-related changes in swallowing over the course of disease progression.

Searching for QPOs in BATSE short gamma-ray bursts based on narrowband and broadband features

Gamma-ray bursts (GRBs), especially short GRBs, are often considered potential candidates for exhibiting kilohertz quasi-periodic oscillations (QPOs) due to their origin from binary mergers. It has already been discovered that two bursts exhibit QPOs. While systematic searches for QPOs in GRBs typically concentrate on the kilohertz range, there has been no comprehensive exploration in the hundred-hertz range. In this study, we systematically conducted QPO searches on all BATSE short burst data within the 0-1000 Hz range. Using nested significance tests, we observed that the reference distributions for different GRBs are quite similar. This observation prompted us to analyze the data by selectively focusing on those with larger statistical values, obviating the need to iterate through all the data and significantly reducing computational workload. Ultimately, our findings did not reveal any compelling evidence for QPOs, which may suggest that the GRB jet has lost the early merging memory.

Kernel-based cumulative sum and differential cumulative sum approaches for resilient fault detection in LVDC microgrids

The energy industry is rapidly shifting from conventional to smart grid systems, involving advancements in microgrids and power management systems. However, achieving desired outcomes with the existing grid setup poses significant challenges. To address the above issues, adopting a Low Voltage Direct Current (LVDC) distribution system provides successful operational advantages. The primary objective of this work is to improve the system's security by developing a more resilient fault detection scheme. The presented work has proposed a Kernel-based Cumulative Sum (K-CUSUM) and Kernel-based Differential Cumulative Sum (K-DCUSUM) technique which is designed to identify changes in data under fewer assumptions. Instead of using the classical Maximum Mean Discrepancy (MMD) technique, the K-CUSUM and K-DCUSUM methods use a non-parametric MMD testing framework to evaluate incoming data by comparing it to reference distribution samples. Further, a comprehensive analysis is undertaken on an LVDC test system, considering fault scenarios involving pole-to-pole and pole-to-ground configurations. This research encompassed the determination of fault location, evaluation of fault resistance, examination of noise introduction, and an exploration of the influence of photovoltaic (PV) penetration. Moreover, the proposed approach is tested for a standard LVDC Microgrid. The results are compared with other contemporary techniques and recent literature.

The Terminator: An Integration of Inner and Outer Approximations for Solving Wasserstein Distributionally Robust Chance Constrained Programs via Variable Fixing

We present a novel approach aimed at enhancing the efficacy of solving both regular and distributionally robust chance constrained programs using an empirical reference distribution. In general, these programs can be reformulated as mixed-integer programs (MIPs) by introducing binary variables for each scenario, indicating whether a scenario should be satisfied. Whereas existing methods have focused predominantly on either inner or outer approximations, this paper bridges this gap by studying a scheme that effectively combines these approximations via variable fixing. By checking the restricted outer approximations and comparing them with the inner approximations, we derive optimality cuts that can notably reduce the number of binary variables by effectively setting them to either one or zero. We conduct a theoretical analysis of variable fixing techniques, deriving an asymptotic closed-form expression. This expression quantifies the proportion of binary variables that should be optimally fixed to zero. Our empirical results showcase the advantages of our approach in terms of both computational efficiency and solution quality. Notably, we solve all the tested instances from literature to optimality, signifying the robustness and effectiveness of our proposed approach. History: Accepted by Andrea Lodi/Design &amp; Analysis of Algorithms — Discrete. Funding: This work was supported by Office of Naval Research [N00014-24-1-2066]; Division of Civil, Mechanical and Manufacturing Innovation [2246414]. Supplemental Material: The software that supports the findings of this study is available within the paper and its Supplemental Information ( https://pubsonline.informs.org/doi/suppl/10.1287/ijoc.2023.0299 ) as well as from the IJOC GitHub software repository ( https://github.com/INFORMSJoC/2023.0299 ). The complete IJOC Software and Data Repository is available at https://informsjoc.github.io/ .

MVComp toolbox: MultiVariate Comparisons of brain MRI features accounting for common information across measures

Introduction Multivariate approaches have recently gained in popularity to address the physiological unspecificity of neuroimaging measures and to better characterize the complexity of biological processes underlying behavior. However, commonly used approaches are biased by the intrinsic associations between variables, or they are computationally expensive and may be more complicated to implement than standard univariate approaches. Here, we propose using the Mahalanobis distance (D2), an individual-level measure of deviation relative to a reference distribution that accounts for covariance between measures. To facilitate its use, we introduce an open-source python-based tool for computing D2 relative to a reference group or within a single individual: the MultiVariate Comparison (MVComp) toolbox (https://github.com/neuralabc/mvcomp (https://github.com/neuralabc/mvcomp)). The toolbox allows different levels of analysis (i.e., group- or subject-level), resolutions (e.g., voxel-wise, ROI-wise) and dimensions considered (e.g., combining MRI measures or WM tracts). Several example cases are presented to showcase the wide range of possible applications of MVComp and to demonstrate the functionality of the toolbox. Methods &amp; Results The D2 framework was applied to the assessment of white matter (WM) microstructure at 1) the group-level, where D2 can be computed between a subject and a reference group to yield an individualized measure of deviation. We observed that clustering applied to D2 in the corpus callosum yields parcellations that highly resemble known topography based on neuroanatomy, suggesting that D2 provides an integrative index that meaningfully reflects the underlying microstructure. 2) At the subject level, D2 was computed between voxels to obtain a measure of (dis)similarity. The loadings of each MRI measure (i.e., its relative contribution to D2) were then extracted in voxels of interest to showcase a useful option of the MVComp toolbox. These relative contributions can provide important insights into the physiological underpinnings of differences observed. Discussion Integrative multivariate models are crucial to expand our understanding of the complex brain-behavior relationships and the multiple factors underlying disease development and progression. Our toolbox facilitates the implementation of a useful multivariate method, making it more widely accessible.

Investigation of Variability of Flaw Strength Distributions on Brittle SiC Ceramic

The present paper investigates flaw strength distributions established using various flexural tests on batches of SiC bar test specimens, namely four-point bending as well as three-point bending tests with different span lengths. Flaw strength is provided by the elemental stress operating on the critical flaw at the fracture of a test specimen. Fracture-inducing flaws and their locations are identified using fractography. A single population of pores was found to dominate the fracture. The construction of diagrams of p-quantile vs. elemental strengths was aimed at assessing the Gaussian nature of flaw strengths. Then, empirical cumulative distributions of strengths were constructed using the normal distribution function. The Weibull distributions of strengths are then compared to the normal reference distributions. The parameters of the Weibull cumulative probability distributions are estimated using maximum likelihood and moment methods. The cumulative distributions of flexural strengths for the different bending tests are predicted from the flaw strength density function using the elemental strength model, and from the cumulative distribution of flexural strength using the Weibull function. Flaw strength distributions that include the weaker flaws that are potentially present in larger test pieces are extrapolated using the p-quantile diagrams. Implications are discussed regarding the pertinence of an intrinsically representative flaw strength distribution, considering failure predictions. Finally, the influence of the characteristics of fracture-inducing flaw populations expressed in terms of flaw strength interval, size, dispersion, heterogeneity, and reproducibility with volume change is examined.

Atypicality index as an add-on to combined first-trimester screening for chromosomal aberrations.

To compute a set of atypicality indices based on combined first-trimester screening (cFTS) markers and second-trimester estimated fetal weight (EFW), and to demonstrate their potential in identifying pregnancies at reduced or increased risk of chromosomal aberrations following a low-risk cFTS result. The atypicality index quantifies the unusualness of an individual set of measurements relative to a reference distribution and can be computed from any variables or measurements available. A score of 0% on the atypicality index represents the most typical profiles, while a score of 100% indicates the highest level of atypicality. From the Danish Fetal Medicine Database, we retrieved data on all pregnant women seen for cFTS in the Central Denmark Region between January 2008 and December 2018. All pregnancies with a cytogenetic or molecular analysis obtained prenatally, postnatally or following pregnancy loss or termination were identified. A first-trimester atypicality index (AcFTS) was computed based on nuchal translucency (NT) thickness, maternal serum free β-human chorionic gonadotropin (β-hCG) and pregnancy-associated plasma protein-A (PAPP-A). Furthermore, a second-trimester index (AcFTS + EFW) was computed from cFTS markers and EFW from a routine second-trimester anomaly scan. All pregnancies were stratified into subgroups based on their atypicality levels and their cFTS risk estimates. The risk of chromosomal aberrations in each subgroup was then compared with the overall prevalence, and a graphical presentation of the multivariate measurement profiles was developed. We retrieved data on 145 955 singleton pregnancies, of which 9824 (6.7%) were genetically examined. Overall, 1 in 122 (0.82% (95% CI, 0.77-0.87%)) of all pregnancies seen for cFTS were affected by a fetal chromosomal aberration, and in screen-negative pregnancies (cFTS trisomy 21 risk < 1 in 100 and/or trisomy 18/13 risk < 1 in 50), 0.41% (95% CI, 0.38-0.44%) were affected. In screen-negative pregnancies with a typical first-trimester profile (AcFTS < 80%), the risk of chromosomal aberrations was significantly reduced (0.28%) compared with the overall risk. The risk of chromosomal aberrations increased with higher atypicality index to 0.49% (AcFTS [80-90%)), 1.52% (AcFTS [90-99%)) and 4.44% (AcFTS ≥ 99%) and was significantly increased in the two most atypical subgroups. The same applied for the second-trimester atypicality index, with risks of chromosomal aberrations of 0.76% and 4.16% in the two most atypical subgroups (AcFTS + EFW [90-99%) and AcFTS + EFW ≥ 99%, respectively). As an add-on to cFTS, the atypicality index identifies women with typical measurement profiles, which may provide reassurance, whereas atypical profiles may warrant specialist referral and further investigation. In pregnancies identified as low risk on cFTS but with a highly atypical distribution of NT, PAPP-A and β-hCG, the risk of a chromosomal aberration is substantially increased. The atypicality index optimizes the interpretation of pre-existing prenatal screening profiles and is not limited to cFTS markers or EFW. © 2023 International Society of Ultrasound in Obstetrics and Gynecology.

DEAMBULAÇÕES GEOGRÁFICAS PELA LITERATURA DE CORDEL EM PORTUGAL

Literatura de Cordel (Chapbooks), more than a genre, is a form of circulation among the people of various literary genres, of oral tradition or not. The aim is to analyze, in the Portuguese case, the geographical distribution of publishing locations, themes, and references to its circulation. The catalogs of the National Museum of Archaeology and the collection of Arnaldo Saraiva will be used as examples. We will try to find areas of occurrence and possible explanations for the distribution found.