scholarly journals Sun‐induced Chl fluorescence and its importance for biophysical modeling of photosynthesis based on light reactions

2019 ◽  
Vol 223 (3) ◽  
pp. 1179-1191 ◽  
Author(s):  
Lianhong Gu ◽  
Jimei Han ◽  
Jeffrey D. Wood ◽  
Christine Y‐Y. Chang ◽  
Ying Sun
Keyword(s):  
Biophysical Modeling ◽  
Chl Fluorescence

scholarly journals TAMI-28. DIFFERENTIAL MIGRATION MECHANICS AND IMMUNE RESPONSES OF GLIOMA SUBTYPES

2020 ◽  
Vol 22 (Supplement_2) ◽  
pp. ii219-ii219
Author(s):  
Ghaidan Shamsan ◽  
Chao Liu ◽  
Brooke Braman ◽  
Susan Rathe ◽  
Aaron Sarver ◽  
...  
Keyword(s):  
Tumor Cells ◽  
Immune Cell ◽  
Molecular Subtypes ◽  
Ex Vivo ◽  
Brain Slices ◽  
Traction Force ◽  
Genetic Alterations ◽  
Sleeping Beauty ◽  
Brain Parenchyma ◽  
Biophysical Modeling

Abstract In Glioblastoma (GBM), tumor spreading is driven by tumor cells’ ability to infiltrate healthy brain parenchyma, which prevents complete surgical resection and contributes to tumor recurrence. GBM molecular subtypes, classical, proneural and mesenchymal, were shown to strongly correlate with specific genetic alterations (Mesenchymal: NF1; Classical: EGFRVIII; Proneural: PDGFRA). Here we tested the hypothesis that a key mechanistic difference between GBM molecular subtypes is that proneural cells are slow migrating and mesenchymal cells are fast migrating. Using Sleeping Beauty transposon system, immune-competent murine brain tumors were induced by SV40-LgT antigen in combination with either NRASG12V (NRAS) or PDGFB (PDGF) overexpression. Cross-species transcriptomic analysis revealed NRAS and PDGF-driven tumors correlate with human mesenchymal and proneural GBM, respectively. Similar to human GBM, CD44 expression was higher in NRAS tumors and, consistent with migration simulations of varying CD44 levels, ex vivo brain slice live imaging showed NRAS tumors cells migrate faster than PDGF tumors cells (random motility coefficient = 30µm2/hr vs. 2.5µm2/hr, p < 0.001). Consistent with CD44 function as an adhesion molecule, migration phenotype was independent of the tumor microenvironment. NRAS and human PDX/MES tumor cells were found to migrate faster and have larger cell spread area than PDGF and human PDX/PN tumors cells, respectively, in healthy mouse brain slices. Furthermore, traction force microscopy revealed NRAS tumor cells generate larger traction forces than PDGF tumors cells which further supports our theoretical mechanism driving glioma migration. Despite increased migration, NRAS cohort had better survival than PDGF which was attributed to enhanced antitumoral immune response in NRAS tumors, consistent with increased immune cell infiltration found in human mesenchymal GBM. Overall our work identified a potentially actionable difference in migration mechanics between GBM subtypes and establishes an integrated biophysical modeling and experimental approach to mechanically parameterize and simulate distinct molecular subtypes in preclinical models of cancer.

scholarly journals Using Simulation and Budget Models to Scale-Up Nitrogen Leaching from Field to Region in Canada

2001 ◽  
Vol 1 ◽  
pp. 699-706 ◽  
Author(s):  
E.C. Huffman ◽  
J.Y. Yang ◽  
S. Gameda ◽  
R. de Jong
Keyword(s):  
Dynamic Models ◽  
Scale Up ◽  
Simulation Models ◽  
Measured Data ◽  
Weather Data ◽  
Biophysical Modeling ◽  
Mineral N ◽  
Policy Model ◽  
Field Level ◽  
Time Of Year

Efforts are underway at Agriculture and Agri-Food Canada (AAFC) to develop an integrated, nationally applicable, socioeconomic/biophysical modeling capability in order to predict the environmental impacts of policy and program scenarios. This paper outlines our Decision Support System (DSS), which integrates the IROWCN (Indicator of the Risk of Water Contamination by Nitrogen) index with the agricultural policy model CRAM (Canadian Regional Agricultural Model) and presents an outline of our methodology to provide independent assessments of the IROWCN results through the use of nitrogen (N) simulation models in select, data-rich areas. Three field-level models — DSSAT, N_ABLE, and EPIC — were evaluated using local measured data. The results show that all three dynamic models can be used to simulate biomass, grain yield, and soil N dynamics at the field level; but the accuracy of the models differ, suggesting that models need to be calibrated using local measured data before they are used in Canada. Further simulation of IROWCN in a maize field using N_ABLE showed that soil-mineral N levels are highly affected by the amount of fertilizer N applied and the time of year, meaning that fertilizer and manure N applications and weather data are crucial for improving IROWCN. Methods of scaling-up simulated IROWCN from field-level to soil-landscape polygons and CRAM regions are discussed.

scholarly journals Searching for the neurite density with diffusion MRI: Challenges for biophysical modeling

2019 ◽  
Vol 40 (8) ◽  
pp. 2529-2545 ◽  
Author(s):  
Björn Lampinen ◽  
Filip Szczepankiewicz ◽  
Mikael Novén ◽  
Danielle Westen ◽  
Oskar Hansson ◽  
...  
Keyword(s):  
Diffusion Mri ◽  
Biophysical Modeling ◽  
Neurite Density

Biophysical Modeling of Fragment Length Distributions of DNA Plasmids after X and Heavy-Ion Irradiation Analyzed by Atomic Force Microscopy

2008 ◽  
Vol 169 (6) ◽  
pp. 649-659 ◽  
Author(s):  
Thilo Elsässer ◽  
Stephan Brons ◽  
Katarzyna Psonka ◽  
Michael Scholz ◽  
Ewa Gudowska-Nowak ◽  
...  
Keyword(s):  
Atomic Force Microscopy ◽  
Fragment Length ◽  
Ion Irradiation ◽  
Heavy Ion ◽  
Biophysical Modeling ◽  
Force Microscopy ◽  
Heavy Ion Irradiation ◽  
Atomic Force

scholarly journals Potential for rapid genetic adaptation to warming in a Great Barrier Reef coral

2017 ◽  
Author(s):  
Mikhail V. Matz ◽  
Eric A. Treml ◽  
Galina V. Aglyamova ◽  
Madeleine J. H. van Oppen ◽  
Line K. Bay
Keyword(s):  
Genetic Variation ◽  
Great Barrier Reef ◽  
Coral Cover ◽  
Reef Coral ◽  
Biophysical Model ◽  
Detectable Effect ◽  
Genetic Adaptation ◽  
Barrier Reef ◽  
Biophysical Modeling ◽  
Adaptive Genetic Variation

AbstractCan genetic adaptation in reef-building corals keep pace with the current rate of sea surface warming? Here we combine population genomic, biophysical modeling, and evolutionary simulations to predict future adaptation of the common coralAcropora milleporaon the Great Barrier Reef. Loss of coral cover in recent decades did not yet have detectable effect on genetic diversity in our species. Genomic analysis of migration patterns closely matched the biophysical model of larval dispersal in favoring the spread of existing heat-tolerant alleles from lower to higher latitudes. Given these conditions we find that standing genetic variation could be sufficient to fuel rapid adaptation ofA. milleporato warming for the next 100-200 years, although random thermal anomalies would drive increasingly severe mortality episodes. However, this adaptation will inevitably cease unless the warming is slowed down, since no realistic mutation rate could replenish adaptive genetic variation fast enough.

scholarly journals Lobster Fishery Connectivity and Management in Indonesia Waters

2021 ◽  
Vol 3 (1) ◽  
Author(s):  
Waluyo Waluyo ◽  
Taslim Arifin
Keyword(s):  
Genetic Structure ◽  
Fishery Management ◽  
Marine Organism ◽  
Molecular Genetic ◽  
Three Dimensional ◽  
Geographical Information ◽  
Biophysical Model ◽  
Complex Life Cycle ◽  
Biophysical Modeling ◽  
The Individual

The distribution of lobsters in Indonesia waters is very wide, even lobster species in Indonesia are also scattered in the tropical waters of the western Pacific Ocean, the Indian Ocean, Africa to Japanese waters.Indonesia waters are divided into 11 (eleven) Fishery Management Zone (FMZ). Lobsters in Indonesia may come from various water areas, both national and regional waters zone, it’s called the sink population, its spread is influenced by the movement of the current. Lobster seed is nurtured by nature through oceancurrents from Australia, East Indonesia, Japan, then back to Australia. Lobsters have a complex life cycle,where adult lobsters inhabit coral reefs as a place to lay eggs, then hatch into planktonic larvae, and grow up in open seas and carry out diurnal and ontogenetic vertical migrations before returning to nurseries in shallow coastal areas and reefs. coral, as well as habitat by the type of species. Literature research had used at leasttwo methodologies to estimate the distribution and connection sensitivity matrices of marine organism larvae.The two most common approaches are using genetic markers and numerical biophysical modeling. Thus, this research uses molecular genetic techniques to explain the genetic structure of lobster populations using a biophysical model approach that can explain the genetic structure of lobsters, as well as the distribution base on regional oceanographic synthesis data and lobster biology known in Indonesia waters. This model has four components, namely: 1) a benthic module based on a Geographical Information System (GIS) which is a lobster habitat in the spawning and recruitment process, 2) a physical oceanography module containing daily velocity in the form of a three-dimensional hydrodynamic model, 3) a larva biology module that describes larval life history characteristics, and 4) a Lagrangian Stochastic module that tracks the individual trajectories of larvae.

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