Psychometric Characteristics of the ECR-RS, Structure of the Relationship between Global and Specific Attachment – Cross-Validation of the Results Using the Czech and Original Versions of the ECR-RS

Traditional Chinese medicine (TCM) formulates treatment according to body constitution (BC) differentiation. Different constitutions have specific metabolic characteristics and different susceptibility to certain diseases. This study aimed to assess theYang-Xuconstitution using a body constitution questionnaire (BCQ) and clinical blood variables. A BCQ was employed to assess the clinical manifestation ofYang-Xu. The logistic regression model was conducted to explore the relationship between BC scores and biomarkers. Leave-one-out cross-validation (LOOCV) and K-fold cross-validation were performed to evaluate the accuracy of a predictive model in practice. Decision trees (DTs) were conducted to determine the possible relationships between blood biomarkers and BC scores. According to the BCQ analysis, 49% participants without any BC were classified as healthy subjects. Among them, 130 samples were selected for further analysis and divided into two groups. One group comprised healthy subjects without any BC (68%), while subjects of the other group, named as the sub-healthy group, had three BCs (32%). Six biomarkers, CRE, TSH, HB, MONO, RBC, and LH, were found to have the greatest impact on BCQ outcomes inYang-Xusubjects. This study indicated significant biochemical differences inYang-Xusubjects, which may provide a connection between blood variables and theYang-XuBC.

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Evaluating the Association Between Psychopathy and Specific Attachment Models in Adults

Journal of Personality Disorders ◽

10.1521/pedi_2018_32_325 ◽

2019 ◽

Vol 33 (1) ◽

pp. 1-S16 ◽

Cited By ~ 1

Author(s):

Elliott Christian ◽

Martin Sellbom ◽

Ross B. Wilkinson

Keyword(s):

Peer Relationships ◽

Attachment Styles ◽

Close Relationships ◽

Self Report ◽

Future Research ◽

Current Investigation ◽

Attachment Relationships ◽

Specific Attachment ◽

The Relationship ◽

General Community

In the current investigation, we examined the association between psychopathy and attachment styles in several specific attachment relationships (i.e., romantic, mother, father, friend). Data were collected online from a combination of Australian university and general community samples (N = 729, 53.50% female) using the Expanded Levenson Self-Report Psychopathy scale (Christian & Sellbom, 2016) and a modified version of the Experiences in Close Relationships Structures (Fraley, Heffernan, Vicary, & Brumbaugh, 2011). Our results revealed that specific attachment models tend to have small to moderate associations with the components of psychopathy, but that the strength and direction of these associations tends to differ between figures, components of psychopathy, and dimension of attachment considered. Interestingly, it appeared that peer relationships (i.e., romantic, friend) tended to account for the majority of the variance in the relationship between psychopathy and general attachment styles, which may be an important avenue for future research.

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Variation of IgG N-linked glycosylation profile in diabetic retinopathy

10.21203/rs.3.rs-74884/v1 ◽

2020 ◽

Author(s):

Zhiyuan Wu ◽

Huiying Pan ◽

Di Liu ◽

Di Zhou ◽

Haibin Li ◽

...

Keyword(s):

Diabetic Retinopathy ◽

Roc Analysis ◽

Cross Validation ◽

Underlying Mechanism ◽

Brier Score ◽

Glycosylation Profile ◽

Inflammatory Status ◽

Relationship Of ◽

The Relationship ◽

Igg Glycosylation

Abstract Background: The relationship of IgG glycosylation with diabetes and diabetic nephropathy has been reported, while its role in diabetic retinopathy (DR) remained unclear. We aimed to investigate and validate the association of IgG glycosylation with DR. Methods: We analyzed the IgG N-linked glycosylation profile and identified the specific panel in the discovery population using binary logistics model. Findings were validated in the replication population. The discriminative capacity of IgG glycosylation panel was explored by ROC analysis using cross validation and Brier score. Multiple sensitive analyses were performed on the whole population. Results: 2 IgG glycans (GP15, GP20) and 2 derived traits (IGP32, IGP54) were identified and validated significantly associated with DR (P<0.05), and the adjusted OR were 0.676, 0.671, 1.770, 0.681 in combined population, respectively. The glycosylation panel achieved an average AUC of 0.67 and 0.60 in the discovery and replication population. The association was independent of blood pressure, glucose and lipids, thus improving the ROC and Brier score when the panel added. In addition, the results remained consistent when the controls were re-defined and 1:3 re-matched. Conclusions: IgG glycosylation profile reflecting a pro-inflammatory status were associated with DR. The variation of IgG glycome deserves more attention in the aggravation of diabetes and the underlying mechanism warrants further research.

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Consistency and overfitting of multi-omics methods on experimental data

Briefings in Bioinformatics ◽

10.1093/bib/bbz070 ◽

2019 ◽

Vol 21 (4) ◽

pp. 1277-1284 ◽

Cited By ~ 5

Author(s):

Sean D McCabe ◽

Dan-Yu Lin ◽

Michael I Love

Keyword(s):

Experimental Data ◽

Factor Analysis ◽

Canonical Correlation ◽

Cross Validation ◽

Small Sample Size ◽

R Package ◽

Small Sample ◽

Ready Availability ◽

Variation Explained ◽

The Relationship

Abstract Knowledge on the relationship between different biological modalities (RNA, chromatin, etc.) can help further our understanding of the processes through which biological components interact. The ready availability of multi-omics datasets has led to the development of numerous methods for identifying sources of common variation across biological modalities. However, evaluation of the performance of these methods, in terms of consistency, has been difficult because most methods are unsupervised. We present a comparison of sparse multiple canonical correlation analysis (Sparse mCCA), angle-based joint and individual variation explained (AJIVE) and multi-omics factor analysis (MOFA) using a cross-validation approach to assess overfitting and consistency. Both large and small-sample datasets were used to evaluate performance, and a permuted null dataset was used to identify overfitting through the application of our framework and approach. In the large-sample setting, we found that all methods demonstrated consistency and lack of overfitting; however, in the small-sample size setting, AJIVE provided the most stable results. We provide an R package so that our framework and approach can be applied to evaluate other methods and datasets.

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Mentalization, specific attachment, and relational satisfaction from the intrapsychic and interpersonal perspectives

Polish Psychological Bulletin ◽

10.1515/ppb-2015-0046 ◽

2015 ◽

Vol 46 (3) ◽

pp. 393-400

Author(s):

Dominika Górska

Keyword(s):

Internal Representation ◽

Mental States ◽

Attachment Anxiety ◽

Relational Satisfaction ◽

Heterosexual Couples ◽

Specific Attachment ◽

Negative Effect ◽

Relationship Structures ◽

Relationship Assessment ◽

The Relationship

Abstract Mentalization is a process of social cognition that involves making inferences about one’s own behavior and the behavior of other people on the basis of unobservable mental states. Particularly in psychodynamic approaches, mentalization is conceptualized in the context of activation of internal representation of emotional relationship. In this study, we checked whether mentalization constitutes a predictor of relational satisfaction in the context of one’s own and the partner’s specific attachment. The research sample was composed of 32 heterosexual couples living together for at least two years. Three tools were applied: the Mental State Task, Experiences in Close Relationships-Relationship Structures, and the Relationship Assessment Scale. The results indicate a strong negative effect of one’s own attachment anxiety and avoidance, and a strong effect of the partner’s attachment anxiety on the relationship satisfaction for both men and women. In women, the incremental validity of mentalization in predicting satisfaction in the relationship beyond the attachment of their partners has been observed. The results are discussed in the context of the intrapsychic and interpersonal aspects of mentalization and attachment.

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Model Regresi Nonparametrik dengan Pendekatan Spline (Studi Kasus: Berat Badan Lahir Rendah di Rumah Sakit Ibu dan Anak Siti Fatimah Makassar)

Journal of Mathematics, Computations, and Statistics ◽

10.35580/jmathcos.v2i1.12460 ◽

2020 ◽

Vol 2 (1) ◽

pp. 70

Author(s):

Wahidah Sanusi ◽

Rahmat Syam ◽

Rabiatul Adawiyah

Keyword(s):

Birth Weight ◽

Low Birth Weight ◽

Nonparametric Regression ◽

Gestational Age ◽

Cross Validation ◽

Predictor Variable ◽

Coefficient Of Determination ◽

Generalized Cross Validation ◽

Child Hospital ◽

The Relationship

Pendekatan nonparametrik merupakan suatu pendekatan yang digunakan apabila bentuk hubungan antara variabel respon dan variabel prediktornya tidak diketahui atau tidak adanya informasi mengenai bentuk fungsi regresinya. Spline merupakan suatu teknik yang dilakukan untuk mengestimasi parameter dalam regresi nonparametrik. Penelitian ini bertujuan untuk mengetahui model hubungan antara berat badan lahir rendah dan faktor-faktor yang mempengaruhi berdasarkan model spline. Faktor-faktor tersebut adalah usia ibu, usia kehamilan, dan jarak kehamilan. Data tersebut diperoleh dari rumah sakit ibu dan anak siti Fatimah Makassar tahun 2017. Dimana untuk mendapatkan model spline terbaik langkah awal yang dilakukan adalah menentukan knot dengan nilai Generalized Cross Validation (GCV) yang minimum. Berdasarkan penelitian yang telah dilakukan, dua variabel dinyatakan berpengaruh terhadap berat badan lahir rendah yaitu usia ibu, dan usia kehamilan. Model regresi nonparametrik dengan pendekatan Spline yang terbentuk memiliki koefisien determinasi sebesar 78,19%, serta nilai GCV dengan tiga titik knot yaitu 0.0117.Kata kunci: Regresi Nonparametrik, Spline, Berat Badan Lahir Rendah, Generalized Cross Validation The non-parametric approach is an approach that is used if the form of the relationship between the response variable and the predictor variable is unknown or the absence of information about the shapes of regression functions. The Spline is a technique performed to estimate the parameters in the nonparametric regression. This study aims to determine the model of the relationship between low birth weight and the factors that affect the based on the spline model. Such factors are maternal age, gestational age, and pregnancy distance. The Data is obtained from the mother and child hospital siti Fatimah Makassar 2017. Where to get a spline model best the initial step is to determine the knots with the value of the Generalized Cross Validation (GCV) which is a minimum. Based on the research that has been done, the two variables stated effect against low birth weight, namely age of mother, and gestational age. Nonparametric regression Model with the approach of the Spline that is formed has a coefficient of determination of 78.19 to%, as well as the value of the GCV with a three-point knot that is 0.0117.Keyword : Nonparametric Regression, Spline, Low Birth Weight, Generalized Cross Validation

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Computer simulation of Cerebral Arteriovenous Malformation—validation analysis of hemodynamics parameters

PeerJ ◽

10.7717/peerj.2724 ◽

2016 ◽

Vol 5 ◽

pp. e2724 ◽

Cited By ~ 1

Author(s):

Y. Kiran Kumar ◽

Shashi Bhushan Mehta ◽

Manjunath Ramachandra

Keyword(s):

Arteriovenous Malformation ◽

Cross Validation ◽

Univariate Analysis ◽

Vessel Diameter ◽

Cerebral Arteriovenous Malformation ◽

Mean Flow ◽

Exponential Regression ◽

Hemodynamic Assessment ◽

Clinical Measurements ◽

The Relationship

ProblemThe purpose of this work is to provide some validation methods for evaluating the hemodynamic assessment of Cerebral Arteriovenous Malformation (CAVM). This article emphasizes the importance of validating noninvasive measurements for CAVM patients, which are designed using lumped models for complex vessel structure.MethodsThe validation of the hemodynamics assessment is based on invasive clinical measurements and cross-validation techniques with the Philips proprietary validated software’s Qflow and 2D Perfursion.ResultsThe modeling results are validated for 30 CAVM patients for 150 vessel locations. Mean flow, diameter, and pressure were compared between modeling results and with clinical/cross validation measurements, using an independent two-tailed Studentttest. Exponential regression analysis was used to assess the relationship between blood flow, vessel diameter, and pressure between them. Univariate analysis is used to assess the relationship between vessel diameter, vessel cross-sectional area, AVM volume, AVM pressure, and AVM flow results were performed with linear or exponential regression.DiscussionModeling results were compared with clinical measurements from vessel locations of cerebral regions. Also, the model is cross validated with Philips proprietary validated software’s Qflow and 2D Perfursion. Our results shows that modeling results and clinical results are nearly matching with a small deviation.ConclusionIn this article, we have validated our modeling results with clinical measurements. The new approach for cross-validation is proposed by demonstrating the accuracy of our results with a validated product in a clinical environment.

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Application of nonparametric regression to groundwater level prediction

Canadian Journal of Civil Engineering ◽

10.1139/l91-073 ◽

1991 ◽

Vol 18 (4) ◽

pp. 600-606 ◽

Cited By ~ 17

Author(s):

Kaz Adamowski ◽

W. Feluch

Keyword(s):

Nonparametric Regression ◽

Groundwater Level ◽

Cross Validation ◽

Accurate Prediction ◽

Nonparametric Model ◽

Nonparametric Regression Model ◽

Groundwater Level Fluctuations ◽

The Relationship ◽

Sample Experiment ◽

Validation Stage

A new nonparametric regression model is proposed to investigate the relationship between groundwater level fluctuations and streamflow time series observations. The developed nonparametric model does not force the relationship between variables into a rigidly defined class (i.e., linear regression) and is capable of inferring complicated relationships. The results from the analysis indicate that the nonparametric method gives more accurate prediction results than those obtained from parametric regression. A split-sample experiment shows that nonparametric regression gives accurate prediction (extrapolation) results at the validation stage. Key words: nonparametric regression, cross-validation method, groundwater level, streamflow.

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Ratio and Difference Estimation in Multi-Criteria Decision Analysis

19th Design Automation Conference: Volume 2 — Design Optimization; Geometric Modeling and Tolerance Analysis; Mechanism Synthesis and Analysis; Decomposition and Design Optimization ◽

10.1115/detc1993-0416 ◽

1993 ◽

Author(s):

F. A. Lootsma

Keyword(s):

Analytic Hierarchy Process ◽

Cross Validation ◽

Pairwise Comparison ◽

Comparison Method ◽

Analytic Hierarchy ◽

Basic Assumptions ◽

Common Basis ◽

Hierarchy Process ◽

The Relationship ◽

Relative Preferences

Abstract Since decisions are invariably made within a given context, we model relative preferences as ratios of increments or decrements in an interval on the axis of desirability. Next, we sort the ratio magnitudes into a small number of categories, represented by numerical values on a geometric scale. We explain why the Analytic Hierarchy Process (AHP), typically based on a pairwise-comparison method, is concerned with category judgement of ratio magnitudes, whereas the Simple Multi-Attribute Rating Technique (SMART) essentially uses the orders of magnitude of these ratios. This provides a common basis for the analysis of the methods in question and for a cross-validation of their results. The present paper summarizes the basic assumptions and the relationship between the two methods.

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