scholarly journals Periodic Fluctuations in the Incidence of Gastrointestinal Cancer

2021 ◽  
Vol 11 ◽  
Author(s):  
Mun-Gan Rhyu ◽  
Jung-Hwan Oh ◽  
Tae Ho Kim ◽  
Joon-Sung Kim ◽  
Young A Rhyu ◽  
...  
Keyword(s):  
Colorectal Cancer ◽  
Gastric Cancer ◽  
Stem Cells ◽  
Linear Regression ◽  
Stochastic Model ◽  
Cancer Incidence ◽  
Linear Regression Model ◽  
Regression Models ◽  
Log Linear ◽  
Fluctuation Model

PurposeNative stem cells can be periodically replaced during short and long epigenetic intervals. Cancer-prone new stem cells might bring about periodic (non-stochastic) carcinogenic events rather than stochastic events. We investigated the epigenetic non-stochastic carcinogenesis by analyzing regular fluctuations in lifelong cancer incidence.Materials and MethodsKorean National Cancer Screening Program data were collected between 2009 and 2016. Non-linear and log-linear regression models were applied to comparatively evaluate non-stochastic and stochastic increases in cancer incidence. Prediction performances of regression models were measured by calculating the coefficient of determination, R2.ResultsThe incidence of gastric and colorectal cancers fluctuated regularly during both short (8 years) and long (20 years) intervals in the non-linear regression model and increased stochastically in the log-linear regression model. In comparison between the 20-year interval fluctuation model and the stochastic model, R2 values were higher in the 20-year interval fluctuation model of men with gastric cancer (0.975 vs. 0.956), and in the stochastic model of men with colorectal cancer (0.862 vs. 0.877) and women with gastric cancer (0.837 vs. 0.890) and colorectal cancer (0.773 vs. 0.809). Men with gastric cancer showed a high R2 value (0.973) in the 8-year interval fluctuation model as well.ConclusionLifelong incidence of gastrointestinal cancer tended to fluctuate during short and long intervals, especially in men with gastric cancer, suggesting the influence of an epigenetic schedule.

Estimation in Dynamic Linear Regression Models with Infinite Variance Errors

1993 ◽  
Vol 9 (4) ◽  
pp. 570-588 ◽  
Author(s):  
Keith Knight
Keyword(s):  
Asymptotic Behavior ◽  
Linear Regression ◽  
Regression Model ◽  
Linear Regression Model ◽  
Regression Models ◽  
Infinite Variance ◽  
Linear Regression Models ◽  
Asymptotically Normal ◽  
Second Moments ◽  
True Values

This paper considers the asymptotic behavior of M-estimates in a dynamic linear regression model where the errors have infinite second moments but the exogenous regressors satisfy the standard assumptions. It is shown that under certain conditions, the estimates of the parameters corresponding to the exogenous regressors are asymptotically normal and converge to the true values at the standard n−½ rate.

scholarly journals Brazilian Articles in Top-Tier Dental Journals and Influence of International Collaboration on Citation Rates

2019 ◽  
Vol 30 (4) ◽  
pp. 307-316 ◽  
Author(s):  
Ana Paula R Gonçalves ◽  
Bruna L Porto ◽  
Bruna Rodolfo ◽  
Clovis M Faggion Jr ◽  
Bernardo A. Agostini ◽  
...  
Keyword(s):  
Linear Regression ◽  
Regression Model ◽  
International Collaboration ◽  
Linear Regression Model ◽  
Regression Models ◽  
Dental Materials ◽  
Original Research ◽  
Linear Regression Models ◽  
Article Type ◽  
Multilevel Linear Regression

Abstract This study investigated the presence of co-authorship from Brazil in articles published in top-tier dental journals and analyzed the influence of international collaboration, article type (original research or review), and funding on citation rates. Articles published between 2015 and 2017 in 38 selected journals from 14 dental subareas were screened in Scopus. Bibliographic information, citation counts, and funding details were recorded for all articles (N=15619). Collaboration with other top-10 publishing countries in dentistry was registered. Annual citations averages (ACA) were calculated. A linear regression model assessed differences in ACA between subareas. Multilevel linear regression models evaluated the influence of article type, funding, and presence of international collaboration in ACA. Brazil was a frequent co-author of articles published in the period (top 3: USA=25.5%; Brazil=13.8%; Germany=9.2%) and the country with most publications in two subareas. The subjects with the biggest share of Brazil are Operative Dentistry/Cariology, Dental Materials, and Endodontics. Brazil was second in total citations, but fifth in citation averages per article. From the total of 2155 articles co-authored by Brazil, 74.8% had no co-authorship from other top-10 publishing countries. USA (17.8%), Italy (4.2%), and UK (3.2%) were the main co-author countries, but the main collaboration country varied between subjects. Implantology and Dental Materials were the subjects with most international co-authorship. Review articles and articles with international collaboration were associated with increased citation rates, whereas the presence of study funding did not influence the citations.

Ordinal regression model and the linear regression model were superior to the logistic regression models

2006 ◽  
Vol 59 (5) ◽  
pp. 448-456 ◽  
Author(s):  
Colleen M. Norris ◽  
William A. Ghali ◽  
L. Duncan Saunders ◽  
Rollin Brant ◽  
Diane Galbraith ◽  
...  
Keyword(s):  
Logistic Regression ◽  
Linear Regression ◽  
Regression Model ◽  
Linear Regression Model ◽  
Regression Models ◽  
Ordinal Regression ◽  
Logistic Regression Models ◽  
Ordinal Regression Model

scholarly journals Evaluating predictive capabilities in Industry 4.0 framework using Regression Models

2021 ◽  
Author(s):  
Shibajyoti Banerjee
Keyword(s):  
Linear Regression ◽  
Regression Model ◽  
Linear Regression Model ◽  
Industry 4.0 ◽  
Regression Models ◽  
Machine Performance

Observing decline in machine performance using a Linear Regression model<br>

scholarly journals Significant non-linearity in nitrous oxide chamber data and its effect on calculated annual emissions

2009 ◽  
Vol 6 (1) ◽  
pp. 115-141 ◽  
Author(s):  
P. C. Stolk ◽  
C. M. J. Jacobs ◽  
E. J. Moors ◽  
A. Hensen ◽  
G. L. Velthof ◽  
...  
Keyword(s):  
Nitrous Oxide ◽  
Linear Regression ◽  
Regression Model ◽  
Linear Regression Model ◽  
Regression Models ◽  
Goodness Of Fit ◽  
Surface Fluxes ◽  
Linear Regression Models ◽  
Open Questions ◽  
Non Linear

Abstract. Chambers are widely used to measure surface fluxes of nitrous oxide (N2O). Usually linear regression is used to calculate the fluxes from the chamber data. Non-linearity in the chamber data can result in an underestimation of the flux. Non-linear regression models are available for these data, but are not commonly used. In this study we compared the fit of linear and non-linear regression models to determine significant non-linearity in the chamber data. We assessed the influence of this significant non-linearity on the annual fluxes. For a two year dataset from an automatic chamber we calculated the fluxes with linear and non-linear regression methods. Based on the fit of the methods 32% of the data was defined significant non-linear. Significant non-linearity was not recognized by the goodness of fit of the linear regression alone. Using non-linear regression for these data and linear regression for the rest, increases the annual flux with 21% to 53% compared to the flux determined from linear regression alone. We suggest that differences this large are due to leakage through the soil. Macropores or a coarse textured soil can add to fast leakage from the chamber. Yet, also for chambers without leakage non-linearity in the chamber data is unavoidable, due to feedback from the increasing concentration in the chamber. To prevent a possibly small, but systematic underestimation of the flux, we recommend comparing the fit of a linear regression model with a non-linear regression model. The non-linear regression model should be used if the fit is significantly better. Open questions are how macropores affect chamber measurements and how optimization of chamber design can prevent this.

scholarly journals Chemical Oxygen Demand Can Be Converted to Gross Energy for Food Items Using a Linear Regression Model

2020 ◽  
Author(s):  
Taylor L Davis ◽  
Blake Dirks ◽  
Elvis A Carnero ◽  
Karen D Corbin ◽  
Jonathon Krakoff ◽  
...  
Keyword(s):  
Linear Regression ◽  
Regression Model ◽  
Chemical Oxygen Demand ◽  
Linear Regression Model ◽  
Regression Models ◽  
Oxygen Demand ◽  
Dry Weight ◽  
Food Items ◽  
Gross Energy ◽  
Sample Set

ABSTRACT Background Human and microbial metabolism are distinct disciplines. Terminology, metrics, and methodologies have been developed separately. Therefore, combining the 2 fields to study energetic processes simultaneously is difficult. Objectives When developing a mechanistic framework describing gut microbiome and human metabolism interactions, energy values of food and digestive materials that use consistent and compatible metrics are required. As an initial step toward this goal, we developed and validated a model to convert between chemical oxygen demand (COD) and gross energy (${E_g}$) for &gt;100 food items and ingredients. Methods We developed linear regression models to relate (and be able to convert between) theoretical gross energy (${E_g}^{\prime}$) and chemical oxygen demand (COD′); the latter is a measure of electron equivalents in the food's carbon. We developed an overall regression model for the food items as a whole and separate regression models for the carbohydrate, protein, and fat components. The models were validated using a sample set of computed ${E_g}^{\prime}$ and COD′ values, an experimental sample set using measured ${E_g}$ and COD values, and robust statistical methods. Results The overall linear regression model and the carbohydrate, protein, and fat regression models accurately converted between COD and ${E_g}$, and the component models had smaller error. Because the ratios of COD per gram dry weight were greatest for fats and smallest for carbohydrates, foods with a high fat content also had higher ${E_g}$ values in terms of kcal · g dry weight−1. Conclusion Our models make it possible to analyze human and microbial energetic processes in concert using a single unit of measure, which fills an important need in the food–nutrition–metabolism–microbiome field. In addition, measuring COD and using the regressions to calculate ${E_g}$ can be used instead of measuring ${E_g}$ directly using bomb calorimetry, which saves time and money.

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