scholarly journals Nickel-Catalyzed Asymmetric Reductive Cross-Coupling of a-Chloroesters with (Hetero)Aryl Iodides

2021 ◽  
Author(s):  
Travis DeLano ◽  
Sara Dibrell ◽  
Caitlin Lacker ◽  
Adam Pancoast ◽  
kelsey poremba ◽  
...  
Keyword(s):  
Linear Regression ◽  
Regression Model ◽  
Functional Groups ◽  
Linear Regression Model ◽  
Cross Coupling ◽  
Building Blocks ◽  
Multivariate Linear Regression Model ◽  
Mild Conditions ◽  
Aryl Iodides ◽  
Reaction Proceeds

<a></a><a>An asymmetric reductive cross-coupling of alpha-chloroesters and (hetero)aryl iodides is reported. This nickel-catalyzed reaction proceeds with a chiral BiOX ligand under mild conditions, affording alpha-arylesters in good yields and enantioselectivities. The reaction is tolerant of a variety of functional groups, and the resulting products can be converted to pharmaceutically-relevant chiral building blocks</a>. A multivariate linear regression model was developed to quantitatively relate the influence of the alpha-chloroester substrate and ligand on enantioselectivity

scholarly journals Nickel-Catalyzed Asymmetric Reductive Cross-Coupling of a-Chloroesters with (Hetero)Aryl Iodides

2021 ◽  
Author(s):  
Travis DeLano ◽  
Sara Dibrell ◽  
Caitlin Lacker ◽  
Adam Pancoast ◽  
kelsey poremba ◽  
...  
Keyword(s):  
Linear Regression ◽  
Regression Model ◽  
Functional Groups ◽  
Linear Regression Model ◽  
Cross Coupling ◽  
Building Blocks ◽  
Multivariate Linear Regression Model ◽  
Mild Conditions ◽  
Aryl Iodides ◽  
Reaction Proceeds

<a></a><a>An asymmetric reductive cross-coupling of alpha-chloroesters and (hetero)aryl iodides is reported. This nickel-catalyzed reaction proceeds with a chiral BiOX ligand under mild conditions, affording alpha-arylesters in good yields and enantioselectivities. The reaction is tolerant of a variety of functional groups, and the resulting products can be converted to pharmaceutically-relevant chiral building blocks</a>. A multivariate linear regression model was developed to quantitatively relate the influence of the alpha-chloroester substrate and ligand on enantioselectivity

scholarly journals Nickel-Catalyzed Asymmetric Reductive Cross-Coupling of α-Chloroesters with (Hetero)Aryl Iodides

2021 ◽  
Author(s):  
Travis DeLano ◽  
Sara Dibrell ◽  
Caitlin R. Lacker ◽  
Adam Pancoast ◽  
Kelsey Poremba ◽  
...  
Keyword(s):  
Cross Coupling ◽  
Mild Conditions ◽  
Aryl Iodides ◽  
Reaction Proceeds

An asymmetric reductive cross-coupling of α-chloroesters and (hetero)aryl iodides is reported. This nickel-catalyzed reaction proceeds with a chiral BiOX ligand under mild conditions, affording α-arylesters in good yields and enantioselectivities....

scholarly journals Short period PM2.5 prediction based on multivariate linear regression model

PLoS ONE ◽  
2018 ◽  
Vol 13 (7) ◽  
pp. e0201011 ◽  
Author(s):  
Rui Zhao ◽  
Xinxin Gu ◽  
Bing Xue ◽  
Jianqiang Zhang ◽  
Wanxia Ren
Keyword(s):  
Linear Regression ◽  
Regression Model ◽  
Linear Regression Model ◽  
Multivariate Linear Regression ◽  
Multivariate Linear Regression Model ◽  
Short Period

scholarly journals Impact of Climate Factors in Modeling and Predicting the Transmission of Meningitis in Africa: The Case of Burkina Faso

2020 ◽  
Author(s):  
Haoua Tall ◽  
Issaka Yaméogo ◽  
Ryan Novak ◽  
Lionel L Ouedraogo ◽  
Ousmane Ouedraogo ◽  
...  
Keyword(s):  
Relative Humidity ◽  
Linear Regression ◽  
Regression Model ◽  
Burkina Faso ◽  
Linear Model ◽  
Linear Regression Model ◽  
Multivariate Linear Regression ◽  
Multivariate Linear Regression Model ◽  
Climate Factors ◽  
Multivariate Linear Model

Abstract Background: Meningitis is a major cause of morbidity in the world. Previous studies showed that climate factors influence the occurrence of meningitis. A multiple linear regression model was developed to forecast meningitis cases in Burkina Faso using climate factors. However, the multivariate linear regression model based on times series data may produce fallacious results given the autocorrelation of errors. Aims: The aim of the study is to develop a model to quantify the effect of climate factors on meningitis cases, and then predict the expected weekly incidences of meningitis for each district. Data and methods: The weekly cases of meningitis come from the Ministry of Health and covers the period 2005-2017. Climate data were collected daily in 10 meteorological stations from 2005 to 2017 and were provided by the national meteorological Agency of Burkina Faso. An ARIMAX and a multivariate linear regression model were estimated separately for each district. Results: The multivariate linear model is inappropriate to model the number of meningitis cases due to autocorrelation of errors. With the ARIMAX Model, Temperature is significantly associated with an increase of meningitis cases in 3 of 10 districts, while relative humidity is significantly associated with a decrease of meningitis cases in 3 of the 10 districts. The effect of wind speed and precipitation is not significant at the 5% level in all 10 districts. The prediction of meningitis cases with 8 test observations provides an average absolute error ranging from 0.99 in Boromo and Bogandé to 7.22 in the district of Ouagadougou. Conclusion: The ARIMAX model is more appropriate than the multivariate linear model to analyze the dynamics of meningitis cases. Climatic factors such as temperature and relative humidity have a significant influence on the occurrence of meningitis in Burkina Faso; the temperature influences it positively and the relative humidity influences it negatively.

Impact of Climate Factors in Modeling and Predicting the Transmission of Meningitis in Africa: The Case of Burkina Faso

2020 ◽  
Author(s):  
Haoua Tall ◽  
Issaka Yaméogo ◽  
Ryan Novak ◽  
Lionel L Ouedraogo ◽  
Ousmane Ouedraogo ◽  
...  
Keyword(s):  
Relative Humidity ◽  
Linear Regression ◽  
Regression Model ◽  
Burkina Faso ◽  
Linear Model ◽  
Linear Regression Model ◽  
Multivariate Linear Regression ◽  
Multivariate Linear Regression Model ◽  
Climate Factors ◽  
Multivariate Linear Model

Abstract Background Meningitis is a major cause of morbidity in the world. Previous studies showed that climate factors influence the occurrence of meningitis. A multiple linear regression model was developed to forecast meningitis cases in Burkina Faso using climate factors. However, the multivariate linear regression model based on times series data may produce fallacious results given the autocorrelation of errors.Aims The aim of the study is to develop a model to quantify the effect of climate factors on meningitis cases, and then predict the expected weekly incidences of meningitis for each district.Data and methods The weekly cases of meningitis come from the Ministry of Health and covers the period 2005-2017. Climate data were collected daily in 10 meteorological stations from 2005 to 2017 and were provided by the national meteorological Agency of Burkina Faso. An ARIMAX and a multivariate linear regression model were estimated separately for each district.Results The multivariate linear model is inappropriate to model the number of meningitis cases due to autocorrelation of errors. With the ARIMAX Model, Temperature is significantly associated with an increase of meningitis cases in 3 of 10 districts, while relative humidity is significantly associated with a decrease of meningitis cases in 3 of the 10 districts. The effect of wind speed and precipitation is not significant at the 5% level in all 10 districts. The prediction of meningitis cases with 8 test observations provides an average absolute error ranging from 0.99 in Boromo and Bogandé to 7.22 in the district of Ouagadougou.Conclusion The ARIMAX model is more appropriate than the multivariate linear model to analyze the dynamics of meningitis cases. Climatic factors such as temperature and relative humidity have a significant influence on the occurrence of meningitis in Burkina Faso; the temperature influences it positively and the relative humidity influences it negatively.

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