scholarly journals Performance of binary prediction models in high-correlation low-dimensional settings: a comparison of methods

2022 ◽  
Vol 6 (1) ◽  
Author(s):  
Artuur M. Leeuwenberg ◽  
Maarten van Smeden ◽  
Johannes A. Langendijk ◽  
Arjen van der Schaaf ◽  
Murielle E. Mauer ◽  
...  
Keyword(s):  
Prediction Models ◽  
Strong Predictor ◽  
A Priori ◽  
Face Validity ◽  
Clinical Prediction Models ◽  
Negative Effect ◽  
The Stability ◽  
Low Dimensional ◽  
Medical Disciplines ◽  
Selection Of

Abstract Background Clinical prediction models are developed widely across medical disciplines. When predictors in such models are highly collinear, unexpected or spurious predictor-outcome associations may occur, thereby potentially reducing face-validity of the prediction model. Collinearity can be dealt with by exclusion of collinear predictors, but when there is no a priori motivation (besides collinearity) to include or exclude specific predictors, such an approach is arbitrary and possibly inappropriate. Methods We compare different methods to address collinearity, including shrinkage, dimensionality reduction, and constrained optimization. The effectiveness of these methods is illustrated via simulations. Results In the conducted simulations, no effect of collinearity was observed on predictive outcomes (AUC, R2, Intercept, Slope) across methods. However, a negative effect of collinearity on the stability of predictor selection was found, affecting all compared methods, but in particular methods that perform strong predictor selection (e.g., Lasso). Methods for which the included set of predictors remained most stable under increased collinearity were Ridge, PCLR, LAELR, and Dropout. Conclusions Based on the results, we would recommend refraining from data-driven predictor selection approaches in the presence of high collinearity, because of the increased instability of predictor selection, even in relatively high events-per-variable settings. The selection of certain predictors over others may disproportionally give the impression that included predictors have a stronger association with the outcome than excluded predictors.

scholarly journals The Types of Polysaccharide Coatings and Their Mixtures as a Factor Affecting the Stability of Bioactive Compounds and Health-Promoting Properties Expressed as the Ability to Inhibit the α-Amylase and α-Glucosidase of Chokeberry Extracts in the Microencapsulation Process

Foods ◽  
2021 ◽  
Vol 10 (9) ◽  
pp. 1994
Author(s):  
Kamil Haładyn ◽  
Karolina Tkacz ◽  
Aneta Wojdyło ◽  
Paulina Nowicka
Keyword(s):  
Antioxidant Activity ◽  
Sodium Alginate ◽  
Guar Gum ◽  
Polyphenolic Compounds ◽  
Alginate Solution ◽  
Polyphenolic Content ◽  
Negative Effect ◽  
Health Promoting ◽  
The Stability ◽  
Selection Of

This study aimed to evaluate the feasibility of microencapsulating chokeberry extract by extrusion, and assess the effects of the selected carrier substance on the contents of polyphenolic compounds, antioxidant activity, color of microspheres, and ability of microspheres to inhibit α-amylase and α-glucosidase, after 14 and 28 days of storage. The results showed that appropriate selection of the polysaccharide coating is of great importance for the proper course of the microencapsulation process, the polyphenolic content of chokeberry capsules, and their antioxidant and antidiabetic properties. The addition of guar gum to a sodium alginate solution significantly increased the stability of polyphenolic compounds in microspheres during storage, whereas the addition of chitosan had a significantly negative effect on the stability of polyphenols. The coating variant composed of sodium alginate and guar gum was also found to be the most favorable for the preservation of the antioxidant activity of the capsules. On the other hand, capsules composed of sodium alginate, guar gum, and chitosan showed the best antidiabetic properties, which is related to these tricomponent microspheres having the best α-glucosidase inhibition.

Innovative Approach to Information Search by Example of a Patent Analysis of an Important Substitution Plan

2020 ◽  
pp. 143-157
Author(s):  
Maria A. Milkova
Keyword(s):  
Information Search ◽  
Topic Modeling ◽  
Cognitive Biases ◽  
A Priori ◽  
Import Substitution ◽  
Innovative Approach ◽  
Iterative Search ◽  
Comprehensive Picture ◽  
Priori Information ◽  
Selection Of

Nowadays the process of information accumulation is so rapid that the concept of the usual iterative search requires revision. Being in the world of oversaturated information in order to comprehensively cover and analyze the problem under study, it is necessary to make high demands on the search methods. An innovative approach to search should flexibly take into account the large amount of already accumulated knowledge and a priori requirements for results. The results, in turn, should immediately provide a roadmap of the direction being studied with the possibility of as much detail as possible. The approach to search based on topic modeling, the so-called topic search, allows you to take into account all these requirements and thereby streamline the nature of working with information, increase the efficiency of knowledge production, avoid cognitive biases in the perception of information, which is important both on micro and macro level. In order to demonstrate an example of applying topic search, the article considers the task of analyzing an import substitution program based on patent data. The program includes plans for 22 industries and contains more than 1,500 products and technologies for the proposed import substitution. The use of patent search based on topic modeling allows to search immediately by the blocks of a priori information – terms of industrial plans for import substitution and at the output get a selection of relevant documents for each of the industries. This approach allows not only to provide a comprehensive picture of the effectiveness of the program as a whole, but also to visually obtain more detailed information about which groups of products and technologies have been patented.

The Impact of Competitive Quasi-Market on Service Delivery in Benue State University, Makurdi Nigeria

GIS Business ◽  
2019 ◽  
Vol 14 (4) ◽  
pp. 85-98
Author(s):  
Idoko Peter
Keyword(s):  
Service Delivery ◽  
A Priori ◽  
Least Square ◽  
Secondary Source ◽  
State University ◽  
Ordinary Least Square ◽  
Negative Effect ◽  
The Impact ◽  
The Relationship

This research the impact of competitive quasi market on service delivery in Benue State University, Makurdi Nigeria. Both primary and secondary source of data and information were used for the study and questionnaire was used to extract information from the purposively selected respondents. The population for this study is one hundred and seventy three (173) administrative staff of Benue State University selected at random. The statistical tools employed was the classical ordinary least square (OLS) and the probability value of the estimates was used to tests hypotheses of the study. The result of the study indicates that a positive relationship exist between Competitive quasi marketing in Benue State University, Makurdi Nigeria (CQM) and Transparency in the service delivery (TRSP) and the relationship is statistically significant (p<0.05). Competitive quasi marketing (CQM) has a negative effect on Observe Competence in Benue State University, Makurdi Nigeria (OBCP) and the relationship is not statistically significant (p>0.05). Competitive quasi marketing (CQM) has a positive effect on Innovation in Benue State University, Makurdi Nigeria (INVO) and the relationship is statistically significant (p<0.05) and in line with a priori expectation. This means that a unit increases in Competitive quasi marketing (CQM) will result to a corresponding increase in innovation in Benue State University, Makurdi Nigeria (INVO) by a margin of 22.5%. It was concluded that government monopoly in the provision of certain types of services has greatly affected the quality of service experience in the institution. It was recommended among others that the stakeholders in the market has to be transparent so that the system will be productive to serve the society effectively

Evaluation of the Modified Naranjo Criteria for Assessing Causal Attribution of Clinical Outcome to Homeopathic Intervention as Presented in Case Reports

Homeopathy ◽  
2020 ◽  
Vol 109 (04) ◽  
pp. 191-197
Author(s):  
Chetna Deep Lamba ◽  
Vishwa Kumar Gupta ◽  
Robbert van Haselen ◽  
Lex Rutten ◽  
Nidhi Mahajan ◽  
...  
Keyword(s):  
Clinical Outcome ◽  
Causal Relationship ◽  
Clinical Case ◽  
Assessment Tool ◽  
Causal Attribution ◽  
Case Reports ◽  
Face Validity ◽  
Rater Agreement ◽  
The Face ◽  
Selection Of

Abstract Objectives The objective of this study was to establish the reliability and content validity of the “Modified Naranjo Criteria for Homeopathy—Causal Attribution Inventory” as a tool for attributing a causal relationship between the homeopathic intervention and outcome in clinical case reports. Methods Purposive sampling was adopted for the selection of information-rich case reports using pre-defined criteria. Eligible case reports had to fulfil a minimum of nine items of the CARE Clinical Case Reporting Guideline checklist and a minimum of three of the homeopathic HOM-CASE CARE extension items. The Modified Naranjo Criteria for Homeopathy Inventory consists of 10 domains. Inter-rater agreement in the scoring of these domains was determined by calculating the percentage agreement and kappa (κ) values. A κ greater than 0.4, indicating fair agreement between raters, in conjunction with the absence of concerns regarding the face validity, was taken to indicate the validity of a given domain. Each domain was assessed by four raters for the selected case reports. Results Sixty case reports met the inclusion criteria. Inter-rater agreement/concordance per domain was “perfect” for domains 1 (100%, κ = 1.00) and 2 (100%, κ = 1.00); “almost perfect” for domain 8 (97.5%, κ = 0.86); “substantial” for domains 3 (96.7%, κ = 0.80) and 5 (91.1%, κ = 0.70); “moderate” for domains 4 (83.3%, κ = 0.60), 7 (67.8%, κ = 0.46) and 9 (99.2%, κ = 0.50); and “fair” for domain 10 (56.1%, κ = 0.38). For domains 6A (46.7%, κ = 0.03) and 6B (50.3%, κ = 0.18), there was “slight agreement” only. Thus, the validity of the Modified Naranjo Criteria for Homeopathy tool was established for each of its domains, except for the two that pertain to direction of cure (domains 6A and 6B). Conclusion The Modified Naranjo Criteria for Homeopathy—Causal Attribution Inventory was identified as a valid tool for assessing the likelihood of a causal relationship between a homeopathic intervention and clinical outcome. Improved wordings for several criteria have been proposed for the assessment tool, under the new acronym “MONARCH”. Further assessment of two MONARCH domains is required.

scholarly journals Incorporating radiomics into clinical trials: expert consensus on considerations for data-driven compared to biologically driven quantitative biomarkers

2021 ◽  
Author(s):  
Laure Fournier ◽  
Lena Costaridou ◽  
Luc Bidaut ◽  
Nicolas Michoux ◽  
Frederic E. Lecouvet ◽  
...  
Keyword(s):  
Clinical Trials ◽  
Quantitative Imaging ◽  
A Priori ◽  
External Validation ◽  
Data Driven ◽  
Imaging Biomarkers ◽  
Clinical Validation ◽  
Biological Processes ◽  
Imaging Data ◽  
Selection Of

Abstract Existing quantitative imaging biomarkers (QIBs) are associated with known biological tissue characteristics and follow a well-understood path of technical, biological and clinical validation before incorporation into clinical trials. In radiomics, novel data-driven processes extract numerous visually imperceptible statistical features from the imaging data with no a priori assumptions on their correlation with biological processes. The selection of relevant features (radiomic signature) and incorporation into clinical trials therefore requires additional considerations to ensure meaningful imaging endpoints. Also, the number of radiomic features tested means that power calculations would result in sample sizes impossible to achieve within clinical trials. This article examines how the process of standardising and validating data-driven imaging biomarkers differs from those based on biological associations. Radiomic signatures are best developed initially on datasets that represent diversity of acquisition protocols as well as diversity of disease and of normal findings, rather than within clinical trials with standardised and optimised protocols as this would risk the selection of radiomic features being linked to the imaging process rather than the pathology. Normalisation through discretisation and feature harmonisation are essential pre-processing steps. Biological correlation may be performed after the technical and clinical validity of a radiomic signature is established, but is not mandatory. Feature selection may be part of discovery within a radiomics-specific trial or represent exploratory endpoints within an established trial; a previously validated radiomic signature may even be used as a primary/secondary endpoint, particularly if associations are demonstrated with specific biological processes and pathways being targeted within clinical trials. Key Points • Data-driven processes like radiomics risk false discoveries due to high-dimensionality of the dataset compared to sample size, making adequate diversity of the data, cross-validation and external validation essential to mitigate the risks of spurious associations and overfitting. • Use of radiomic signatures within clinical trials requires multistep standardisation of image acquisition, image analysis and data mining processes. • Biological correlation may be established after clinical validation but is not mandatory.

scholarly journals Spatio-temporal selection of reference genes in the two congeneric species of Glycyrrhiza

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Yuping Li ◽  
Xiaoju Liang ◽  
Xuguo Zhou ◽  
Yu An ◽  
Ming Li ◽  
...  
Keyword(s):  
Gene Expression ◽  
Developmental Stage ◽  
Reference Genes ◽  
Developmental Stages ◽  
Individual Factors ◽  
Congeneric Species ◽  
Spatio Temporal ◽  
The Stability ◽  
Different Tissues ◽  
Selection Of

AbstractGlycyrrhiza, a genus of perennial medicinal herbs, has been traditionally used to treat human diseases, including respiratory disorders. Functional analysis of genes involved in the synthesis, accumulation, and degradation of bioactive compounds in these medicinal plants requires accurate measurement of their expression profiles. Reverse transcription quantitative real-time PCR (RT-qPCR) is a primary tool, which requires stably expressed reference genes to serve as the internal references to normalize the target gene expression. In this study, the stability of 14 candidate reference genes from the two congeneric species G. uralensis and G. inflata, including ACT, CAC, CYP, DNAJ, DREB, EF1, RAN, TIF1, TUB, UBC2, ABCC2, COPS3, CS, R3HDM2, were evaluated across different tissues and throughout various developmental stages. More importantly, we investigated the impact of interactions between tissue and developmental stage on the performance of candidate reference genes. Four algorithms, including geNorm, NormFinder, BestKeeper, and Delta Ct, were used to analyze the expression stability and RefFinder, a comprehensive software, provided the final recommendation. Based on previous research and our preliminary data, we hypothesized that internal references for spatio-temporal gene expression are different from the reference genes suited for individual factors. In G. uralensis, the top three most stable reference genes across different tissues were R3HDM2, CAC and TUB, while CAC, CYP and ABCC2 were most suited for different developmental stages. CAC is the only candidate recommended for both biotic factors, which is reflected in the stability ranking for the spatio (tissue)-temporal (developmental stage) interactions (CAC, R3HDM2 and DNAJ). Similarly, in G. inflata, COPS3, R3HDM2 and DREB were selected for tissues, while RAN, COPS3 and CS were recommended for developmental stages. For the tissue-developmental stage interactions, COPS3, DREB and ABCC2 were the most suited reference genes. In both species, only one of the top three candidates was shared between the individual factors and their interactions, specifically, CAC in G. uralensis and COPS3 in G. inflata, which supports our overarching hypothesis. In summary, spatio-temporal selection of reference genes not only lays the foundation for functional genomics research in Glycyrrhiza, but also facilitates these traditional medicinal herbs to reach/maximize their pharmaceutical potential.

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