scholarly journals Mechanistic Modeling of Microtopographic Impacts on CO 2 and CH 4 Fluxes in an Alaskan Tundra Ecosystem Using the CLM‐Microbe Model

2019 ◽  
Vol 11 (12) ◽  
pp. 4288-4304
Author(s):  
Yihui Wang ◽  
Fengming Yuan ◽  
Fenghui Yuan ◽  
Baohua Gu ◽  
Melanie S. Hahn ◽  
...  
Keyword(s):  
Mechanistic Modeling ◽  
Tundra Ecosystem ◽  
Alaskan Tundra

Seasonal Movement of Nutrients in Plants of Differing Growth Form in an Alaskan Tundra Ecosystem: Implications for Herbivory

1980 ◽  
Vol 68 (1) ◽  
pp. 189 ◽  
Author(s):  
F. Stuart Chapin III ◽  
Douglas A. Johnson ◽  
Jay D. McKendrick
Keyword(s):  
Growth Form ◽  
Seasonal Movement ◽  
Tundra Ecosystem ◽  
Alaskan Tundra

scholarly journals An Integrated Approach of Mechanistic-Modeling and Machine-Learning for Thickness Optimization of Frozen Microwaveable Foods

Foods ◽  
2021 ◽  
Vol 10 (4) ◽  
pp. 763
Author(s):  
Ran Yang ◽  
Zhenbo Wang ◽  
Jiajia Chen
Keyword(s):  
Machine Learning ◽  
Learning Algorithm ◽  
Food Product ◽  
Integrated Approach ◽  
Mechanistic Modeling ◽  
Bayesian Optimization ◽  
Initial Training ◽  
Thickness Optimization ◽  
Heating Uniformity ◽  
Food Design

Mechanistic-modeling has been a useful tool to help food scientists in understanding complicated microwave-food interactions, but it cannot be directly used by the food developers for food design due to its resource-intensive characteristic. This study developed and validated an integrated approach that coupled mechanistic-modeling and machine-learning to achieve efficient food product design (thickness optimization) with better heating uniformity. The mechanistic-modeling that incorporated electromagnetics and heat transfer was previously developed and validated extensively and was used directly in this study. A Bayesian optimization machine-learning algorithm was developed and integrated with the mechanistic-modeling. The integrated approach was validated by comparing the optimization performance with a parametric sweep approach, which is solely based on mechanistic-modeling. The results showed that the integrated approach had the capability and robustness to optimize the thickness of different-shape products using different initial training datasets with higher efficiency (45.9% to 62.1% improvement) than the parametric sweep approach. Three rectangular-shape trays with one optimized thickness (1.56 cm) and two non-optimized thicknesses (1.20 and 2.00 cm) were 3-D printed and used in microwave heating experiments, which confirmed the feasibility of the integrated approach in thickness optimization. The integrated approach can be further developed and extended as a platform to efficiently design complicated microwavable foods with multiple-parameter optimization.

Biotic validation of small open-top chambers in a tundra ecosystem

2000 ◽  
Vol 6 (7) ◽  
pp. 835-842 ◽  
Author(s):  
Robert D. Hollister ◽  
Patrick J. Webber
Keyword(s):  
Open Top Chambers ◽  
Tundra Ecosystem

scholarly journals Combining Mechanistic Modeling and Raman Spectroscopy for Monitoring Antibody Chromatographic Purification

Processes ◽  
2019 ◽  
Vol 7 (10) ◽  
pp. 683 ◽  
Author(s):  
Feidl ◽  
Garbellini ◽  
Luna ◽  
Vogg ◽  
Souquet ◽  
...  
Keyword(s):  
Experimental Data ◽  
Raman Spectroscopy ◽  
Estimation Procedure ◽  
Mechanistic Modeling ◽  
Spectroscopic Techniques ◽  
Chromatographic Purification ◽  
On Line ◽  
Filter Approach ◽  
Underlying Mechanisms ◽  
Combining Information

Chromatography is widely used in biotherapeutics manufacturing, and the corresponding underlying mechanisms are well understood. To enable process control and automation, spectroscopic techniques are very convenient as on-line sensors, but their application is often limited by their sensitivity. In this work, we investigate the implementation of Raman spectroscopy to monitor monoclonal antibody (mAb) breakthrough (BT) curves in chromatographic operations with a low titer harvest. A state estimation procedure is developed by combining information coming from a lumped kinetic model (LKM) and a Raman analyzer in the frame of an extended Kalman filter approach (EKF). A comparison with suitable experimental data shows that this approach allows for the obtainment of reliable estimates of antibody concentrations with reduced noise and increased robustness.

Comparison of Radiocesium in Alaskan Tundra Plants, 1961 and 1971

1975 ◽  
Vol 7 (3) ◽  
pp. 285
Author(s):  
W. H. Rickard ◽  
J. D. Hedlund ◽  
H. A. Sweany
Keyword(s):  
Alaskan Tundra

scholarly journals Accelerated Repurposing and Drug Development of Pulmonary Hypertension Therapies for COVID-19 Treatment Using an AI-Integrated Biosimulation Platform

Molecules ◽  
2021 ◽  
Vol 26 (7) ◽  
pp. 1912
Author(s):  
Kaushik Chakravarty ◽  
Victor G. Antontsev ◽  
Maksim Khotimchenko ◽  
Nilesh Gupta ◽  
Aditya Jagarapu ◽  
...  
Keyword(s):  
Pulmonary Hypertension ◽  
Renin Angiotensin System ◽  
Mechanistic Modeling ◽  
Channel Blockers ◽  
Angiotensin System ◽  
In Silico Modeling ◽  
Site Of Action ◽  
Line Of Therapy

The COVID-19 pandemic has reached over 100 million worldwide. Due to the multi-targeted nature of the virus, it is clear that drugs providing anti-COVID-19 effects need to be developed at an accelerated rate, and a combinatorial approach may stand to be more successful than a single drug therapy. Among several targets and pathways that are under investigation, the renin-angiotensin system (RAS) and specifically angiotensin-converting enzyme (ACE), and Ca2+-mediated SARS-CoV-2 cellular entry and replication are noteworthy. A combination of ACE inhibitors and calcium channel blockers (CCBs), a critical line of therapy for pulmonary hypertension, has shown therapeutic relevance in COVID-19 when investigated independently. To that end, we conducted in silico modeling using BIOiSIM, an AI-integrated mechanistic modeling platform by utilizing known preclinical in vitro and in vivo datasets to accurately simulate systemic therapy disposition and site-of-action penetration of the CCBs and ACEi compounds to tissues implicated in COVID-19 pathogenesis.

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