The Cournot–Nash model of Sections 6.1 and 6.2

1990 ◽  
pp. 267-269
Keyword(s):  
Author(s):  
Jorge Gutiérrez-Cuevas ◽  
Ana Sandoval-Rodríguez ◽  
Hugo Christian Monroy-Ramírez ◽  
Monica Vazquez-Del Mercado ◽  
Arturo Santos-García ◽  
...  

Biomedicines ◽  
2021 ◽  
Vol 9 (11) ◽  
pp. 1647
Author(s):  
Keishi Kisoh ◽  
Go Sugahara ◽  
Yuko Ogawa ◽  
Suzue Furukawa ◽  
Yuji Ishida ◽  
...  

Nonalcoholic fatty liver disease/steatohepatitis (NAFLD/NASH) is the most common liver disorder in developed countries. Although many new therapeutics for NASH are present in the drug development pipeline, there are still no approved drugs. One of the reasons that makes NASH drug development challenging is the lack of appropriate animal NASH models that resolve issues arising from inter-species differences between humans and rodents. In the present study, we developed a choline-deficient, L-amino-acid-defined, high-fat-diet (CDAHFD)-induced human NASH model using human liver chimeric mice. We demonstrated human hepatocyte injury by an elevation of plasma human alanine aminotransferase 1 in mice fed CDAHFD. Histological analysis showed that CDAHFD feeding induced similar histological changes to human NASH patients, including ballooning, inflammation, apoptosis, regeneration of human hepatocytes, and pericellular and perisinusoidal fibrosis. The chimeric mice fed CDAHFD were treated with a peroxisome-proliferator-activated receptor α/δ agonist, Elafibranor. Elafibranor ameliorated steatosis, ballooning of hepatocytes, and preserved fibrosis progression. We developed a novel humanized NASH model that can elucidate pathophysiological mechanisms and predict therapeutic efficacy in human NASH. This model will be useful in exploring new drugs and biomarkers in the early stages of human NASH.


ISFRAM 2014 ◽  
2015 ◽  
pp. 123-133 ◽  
Author(s):  
Bhabagrahi Sahoo ◽  
P. G. Saritha
Keyword(s):  

Water ◽  
2019 ◽  
Vol 11 (4) ◽  
pp. 772 ◽  
Author(s):  
Yingbing Chen ◽  
Peng Shi ◽  
Simin Qu ◽  
Xiaomin Ji ◽  
Lanlan Zhao ◽  
...  

The geomorphologic instantaneous unit hydrograph (GIUH) is an applicable approach that simulates the runoff for the ungauged basins. The nash model is an efficient tool to derive the unit hydrograph (UH), which only requires two items, including the indices n and k. Theoretically, the GIUH method describes the process of a droplet flowing from which it falls on to the basin outlet, only covering the flow concentration process. The traditional technique for flood estimation using GIUH method always uses the effective rainfall, which is empirically obtained and scant of accuracy, and then calculates the convolution of the effective rainfall and GIUH. To improve the predictive capability of the GIUH model, the Xin’anjiang (XAJ) model, which is a conceptual model with clear physical meaning, is applied to simulate the runoff yielding and the slope flow concentration, integrating with the GIUH derived based on Nash model to compute the river network flow convergence, forming a modified GIUH model for flood simulation. The average flow velocity is the key to obtain the indices k, and two methods to calculate the flow velocity were compared in this study. 10 flood events in three catchments in Fujian, China are selected to calibrate the model, and six for validation. Four criteria, including the time-to-peak error, the relative peak flow error, the relative runoff depth error, and the Nash–Sutcliff efficiency coefficient are computed for the model performance evaluation. The observed runoff value and simulated series in validation stage is also presented in the scatter plots to analyze the fitting degree. The analysis results show the modified model with a convenient calculation and a high fitting and illustrates that the model is reliable for the flood estimation and has potential for practical flood forecasting.


2012 ◽  
Vol 142 (5) ◽  
pp. S-1024-S-1025
Author(s):  
Kazuyoshi Kon ◽  
Kenichi Ikejima ◽  
Takashi Miyaji ◽  
Satoko Hosoya ◽  
Kumiko Arai ◽  
...  

2010 ◽  
Vol 24 (12) ◽  
pp. 2971-2989 ◽  
Author(s):  
Muhammad Masood Ahmad ◽  
Abdul Razzaq Ghumman ◽  
Sajjad Ahmad ◽  
Hashim Nisar Hashmi

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