biophysical parameters
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PeerJ ◽  
2022 ◽  
Vol 10 ◽  
pp. e12804
Author(s):  
Yuanhe Yu ◽  
Xingqi Sun ◽  
Jinliang Wang ◽  
Jianpeng Zhang

Water yield is an ecosystem service that is vital to not only human life, but also sustainable development of the social economy and ecosystem. This study used annual average precipitation, potential evapotranspiration, plant available water content, soil depth, biophysical parameters, Zhang parameter, and land use/land cover (LULC) as input data for the Integrated Valuation of Ecosystem Service Tradeoffs (InVEST) model to estimate the water yield of Shangri-La City from 1974 to 2015. The spatiotemporal variations and associated factors (precipitation, evapotranspiration, LULC, and topographic factors) in water yield ecosystem services were then analyzed. The result showed that: (1) The water yield of Shangri-La City decreases from north and south to the center and showed a temporal trend from 1974 to 2015 of an initial decrease followed by an increase. Areas of higher average water yield were mainly in Hutiaoxia Town, Jinjiang Town, and Shangjiang Township. (2) Areas of importance for water yield in the study area which need to be assigned priority protection were mainly concentrated in the west of Jiantang Town, in central Xiaozhongdian Town, in central Gezan Township, in northwestern Dongwang Township, and in Hutiaoxia Town. (3) Water yield was affected by precipitation, evapotranspiration, vegetation type, and topographic factors. Water yield was positively and negatively correlated with precipitation and potential evapotranspiration, respectively. The average water yield of shrubs exceeded that of meadows and forests. Terrain factors indirectly affected the ecosystem service functions of water yield by affecting precipitation and vegetation types. The model used in this study can provide references for relevant research in similar climatic conditions.


2022 ◽  
Author(s):  
doğan çakan ◽  
sinem çiloglu ◽  
ekrem ramazan keskin

Objectives: We aimed to investigate the efficacy of locally delivered apocynin on fat graft survival in an experimental autologous fat grafting (AFG) model created in rats. Methods: Twenty-one Wistar albino male rats were included in this study. The 0.647 g mean weight grafts were harvested from the inguinal region and transferred to the nape of every rat. The subjects were randomly separated into three groups. Saline, dimethyl sulfoxide (DMSO) and apocynin, a dose of 20 mg/kg, solutions were applied once a day for 2 weeks. After 3 months, the rats were sacrificed. The evaluation of physical measurements (weight and volume) and survival rates of the grafts for volume (SRV) and weight (SRW), the viable cell count (VC) with the MTT assay, and histopathological parameters were done. Results: All biophysical parameters were found to be significantly higher in the apocynin group compared to other groups (p < .05). In the MTT test, the saline group was normalized to 100%. According to this, DMSO and apocynin groups’ means were 106% and 163%, respectively. The VC was significantly higher in the apocynin group than the other groups (p < .05). The VC was significantly higher in the DMSO group than in the saline group (p < .05). No significant difference was found in other comparisons performed according to biophysical and histopathological parameters (p > .05). Conclusion: The locally delivered apocynin decreases fat graft volume loss in an experimental AFG model. Consequently, apocynin can be used as an effective substance to increase graft survival.


Photonics ◽  
2021 ◽  
Vol 9 (1) ◽  
pp. 7
Author(s):  
Prakash Joshi ◽  
Partha Pratim Mondal

Molecular assembly in a complex cellular environment is vital for understanding underlying biological mechanisms. Biophysical parameters (such as single-molecule cluster density, cluster-area, pairwise distance, and number of molecules per cluster) related to molecular clusters directly associate with the physiological state (healthy/diseased) of a cell. Using super-resolution imaging along with powerful clustering methods (K-means, Gaussian mixture, and point clustering), we estimated these critical biophysical parameters associated with dense and sparse molecular clusters. We investigated Hemaglutinin (HA) molecules in an Influenza type A disease model. Subsequently, clustering parameters were estimated for transfected NIH3T3 cells. Investigations on test sample (randomly generated clusters) and NIH3T3 cells (expressing Dendra2-Hemaglutinin (Dendra2-HA) photoactivable molecules) show a significant disparity among the existing clustering techniques. It is observed that a single method is inadequate for estimating all relevant biophysical parameters accurately. Thus, a multimodel approach is necessary in order to characterize molecular clusters and determine critical parameters. The proposed study involving optical system development, photoactivable sample synthesis, and advanced clustering methods may facilitate a better understanding of single molecular clusters. Potential applications are in the emerging field of cell biology, biophysics, and fluorescence imaging.


2021 ◽  
Author(s):  
D.A. Pinotsis ◽  
S. Fitzgerald ◽  
C. See ◽  
A. Sementsova ◽  
A. S. Widge

AbstractA major difficulty with treating psychiatric disorders is their heterogeneity: different neural causes can lead to the same phenotype. To address this, we propose describing the underlying pathophysiology in terms of interpretable, biophysical parameters of a neural model derived from the electroencephalogram. We analyzed data from a small patient cohort of patients with depression and controls. We constructed biophysical models that describe neural dynamics in a cortical network activated during a task that is used to assess depression state. We show that biophysical model parameters are biomarkers, that is, variables that allow subtyping of depression at a biological level. They yield a low dimensional, interpretable feature space that allowed description of differences between individual patients with depressive symptoms. They capture internal heterogeneity/variance of depression state and achieve significantly better classification than commonly used EEG features. Our work is a proof of concept that a combination of biophysical models and machine learning may outperform earlier approaches based on classical statistics and raw brain data.


Author(s):  
Alan Cézar Bezerra ◽  
Jhon Lennon Bezerra da Silva ◽  
Geber Barbosa de Albuquerque Moura ◽  
Pabrício Marcos Oliveira Lopes ◽  
Cristina Rodrigues Nascimento ◽  
...  

Author(s):  
K. V. Ticman ◽  
S. G. Salmo III ◽  
K. E. Cabello ◽  
M. Q. Germentil ◽  
D. M. Burgos ◽  
...  

Abstract. The mangrove forests of Lawaan-Balangiga in Eastern Samar lost significant cover due to the Typhoon Haiyan that struck the region in 2013. The mangroves in the area have since shown signs of recovery in terms of growth and spatial coverage, but these widely varied with locations. This study aims to further examine the status of recovery of mangroves across different locations by analysing the time series trends of selected vegetation and moisture indices: Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI), Modified Soil Adjusted Vegetation Index (MSAVI), and Normalized Difference Moisture Index (NDMI). These indices were extracted from Landsat 8 surface reflectance images, spanning 2014 to 2020, using Google Earth Engine (GEE). The time series analyses showed similar NDVI, MSAVI and NDMI values and trends after the 2013 typhoon event. The trend slopes also indicated high correlation (0.91 – 1.00) between and among the indices, with NDVI having the highest correlation with MSAVI (∼1.00). The study was able to corroborate the previous study on mangroves in Lawaan-Balangiga, by presenting positive trend results in the identified recovered areas. These trends, however, would still have to be validated by collecting and comparing biophysical parameters in the field. The next step of the research would be to identify the factors that contribute to the varying rates of recovery in the areas and to evaluate how this can affect the carbon sequestration rates of recovering mangroves.


2021 ◽  
Author(s):  
Christopher J. Nunn ◽  
Sidhartha Goyal

Eukaryotic cells contain numerous copies of mitochondrial DNA (mtDNA), allowing for the coexistence of mutant and wild-type mtDNA in individual cells. The fate of mutant mtDNA depends on their relative replicative fitness within cells and the resulting cellular fitness within populations of cells. Yet the dynamics of the generation of mutant mtDNA and features that inform their fitness remain unaddressed. Here we utilize long read single-molecule sequencing to track mtDNA mutational trajectories in Saccharomyces cerevisiae. We show a previously unseen pattern that constrains subsequent excision events in mtDNA fragmentation. We also provide evidence for the generation of rare and contentious non-periodic mtDNA structures that lead to persistent diversity within individual cells. Finally, we show that measurements of relative fitness of mtDNA fit a phenomenological model that highlights important biophysical parameters governing mtDNA fitness. Altogether, our study provides techniques and insights into the dynamics of large structural changes in genomes that may be applicable in more complex organisms.


2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Kamila S. Bożek ◽  
Krystyna Żuk-Gołaszewska ◽  
Anna Bochenek ◽  
Janusz Gołaszewski ◽  
Hazem M. Kalaji

AbstractHow agricultural ecosystems adapt to climate change is one of the most important issues facing agronomists at the turn of the century. Understanding agricultural ecosystem responses requires assessing the relative shift in climatic constraints on crop production at regional scales such as the temperate zone. In this work we propose an approach to modeling the growth, development and yield of Triticum durum Desf. under the climatic conditions of north-eastern Poland. The model implements 13 non-measurable parameters, including climate conditions, agronomic factors, physiological processes, biophysical parameters, yield components and biological yield (latent variables), which are described by 33 measurable predictors as well as grain and straw yield (manifest variables). The agronomic factors latent variable was correlated with nitrogen fertilization and sowing density, and biological yield was correlated with grain yield and straw yield. An analysis of the model parameters revealed that a one unit increase in agronomic factors increased biological yield by 0.575. In turn, biological yield was most effectively determined by climate conditions (score of 60–62) and biophysical parameters (score of 60–67) in the 2nd node detectable stage and at the end of heading. The modeled configuration of latent and manifest variables was responsible for less than 70% of potential biological yield, which indicates that the growth and development of durum wheat in north-eastern Europe can be further optimized to achieve high and stable yields. The proposed model accounts for local climate conditions and physiological processes in plants, and it can be implemented to optimize agronomic practices in the cultivation of durum wheat and, consequently, to expand the area under T. durum to regions with a temperate climate.


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