scholarly journals Integrating O<sub>3</sub> influences on terrestrial processes: photosynthetic and stomatal response data available for regional and global modeling

2013 ◽  
Vol 10 (4) ◽  
pp. 6973-7012 ◽  
Author(s):  
D. Lombardozzi ◽  
J. P. Sparks ◽  
G. Bonan
Keyword(s):  
Stomatal Conductance ◽  
Radiative Forcing ◽  
Large Scale ◽  
Surface Ozone ◽  
Plant Interactions ◽  
Global Models ◽  
Response Data ◽  
Integral Role ◽  
Strong Motivation ◽  
Using Data

Abstract. Plants have a strong influence on climate by controlling the transfer of carbon dioxide and water between the biosphere and atmosphere during the processes of photosynthesis and transpiration. Chronic exposure to surface ozone (O3) differentially affects photosynthesis and transpiration because it damages stomatal conductance, the common link that controls both processes, in addition to the leaf biochemistry that only affects photosynthesis. Because of the integral role of O3 in altering plant interactions with the atmosphere, there is a strong motivation to incorporate the influence of O3 into regional and global models. However, there are currently no analyses documenting both photosynthesis and stomatal conductance responses to O3 exposure through time using a standardized O3 parameter that can be easily incorporated into models. Therefore, models often rely on photosynthesis data derived from the responses of one or a few plant species that exhibit strong negative correlations with O3 exposure to drive both rates of photosynthesis and transpiration, neglecting potential divergence between the two fluxes. Using data from the peer-reviewed literature, we have compiled photosynthetic and stomatal responses to chronic O3 exposure for all plant types with data available in the peer-reviewed literature as a standardized function of cumulative uptake of O3 (CUO), which integrates O3 flux into leaves through time. These data suggest that stomatal conductance decreases ~ 11% after chronic O3 exposure, while photosynthesis independently decreases ~ 21%. Despite the overall decrease in both variables, high variance masked any correlations between the decline in photosynthesis or stomatal conductance with increases in CUO. Though correlations with CUO are not easily generalized, existing correlations demonstrate that photosynthesis tends to be weakly but negatively correlated with CUO while stomatal conductance is more often positively correlated with CUO. Results suggest that large-scale models using data with strong negative correlations that only affect photosynthesis need to reconsider the generality of their response. Data from this analysis are now available to the scientific community and can be incorporated into global models to improve estimates of photosynthesis, global land carbon sinks, hydrology, and indirect radiative forcing that are influenced by chronic O3 exposure.

scholarly journals Integrating O<sub>3</sub> influences on terrestrial processes: photosynthetic and stomatal response data available for regional and global modeling

2013 ◽  
Vol 10 (11) ◽  
pp. 6815-6831 ◽  
Author(s):  
D. Lombardozzi ◽  
J. P. Sparks ◽  
G. Bonan
Keyword(s):  
Stomatal Conductance ◽  
Radiative Forcing ◽  
Large Scale ◽  
Surface Ozone ◽  
Plant Interactions ◽  
Global Models ◽  
Response Data ◽  
Integral Role ◽  
Strong Motivation ◽  
Using Data

Abstract. Plants have a strong influence on climate by controlling the transfer of carbon dioxide and water between the biosphere and atmosphere during the processes of photosynthesis and transpiration. Chronic exposure to surface ozone (O3) differentially affects photosynthesis and transpiration because it damages stomatal conductance, the common link that controls both processes, in addition to the leaf biochemistry that only affects photosynthesis. Because of the integral role of O3 in altering plant interactions with the atmosphere, there is a strong motivation to incorporate the influence of O3 into regional and global models. However, there are currently no analyses documenting both photosynthesis and stomatal conductance responses to O3 exposure through time using a standardized O3 parameter that can be easily incorporated into models. Therefore, models often rely on photosynthesis data derived from the responses of one or a few plant species that exhibit strong negative correlations with O3 exposure to drive both rates of photosynthesis and transpiration, neglecting potential divergence between the two fluxes. Using data from the peer-reviewed literature, we have compiled photosynthetic and stomatal responses to chronic O3 exposure for all plant types with data available in the peer-reviewed literature as a standardized function of cumulative uptake of O3 (CUO), which integrates O3 flux into leaves through time. These data suggest that stomatal conductance decreases ~11% after chronic O3 exposure, while photosynthesis independently decreases ~21%. Despite the overall decrease in both variables, high variance masked any correlations between the decline in photosynthesis or stomatal conductance with increases in CUO. Though correlations with CUO are not easily generalized, existing correlations demonstrate that photosynthesis tends to be weakly but negatively correlated with CUO while stomatal conductance is more often positively correlated with CUO. Results suggest that large-scale models using data with strong negative correlations that only affect photosynthesis need to reconsider the generality of their response. Data from this analysis are now available to the scientific community and can be incorporated into global models to improve estimates of photosynthesis, global land-carbon sinks, hydrology, and indirect radiative forcing that are influenced by chronic O3 exposure.

scholarly journals Characterizing sources of high surface ozone events in the southwestern U.S. with intensive field measurements and two global models

2019 ◽  
Author(s):  
Li Zhang ◽  
Meiyun Lin ◽  
Andrew O. Langford ◽  
Larry W. Horowitz ◽  
Christoph J. Senff ◽  
...  
Keyword(s):  
Large Scale ◽  
Las Vegas ◽  
Surface Ozone ◽  
Field Measurements ◽  
Ambient Air ◽  
Daily Maximum ◽  
Global Models ◽  
High Background ◽  
Stratospheric Intrusions ◽  
Surface O3

Abstract. The detection and attribution of high background ozone (O3) events in the southwestern U.S. is challenging but relevant to the effective implementation of the lowered National Ambient Air Quality Standard (NAAQS; 70 ppbv). Here we leverage intensive field measurements from the Fires, Asian, and Stratospheric TransportLas Vegas Ozone Study (FAST-LVOS) in MayJune 2017, alongside high-resolution simulations with two global models (GFDL-AM4 and GEOS-Chem), to pinpoint the sources of O3 during high-O3 events. We show stratospheric influence on four out of the ten events with daily maximum 8-hour average (MDA8) surface O3 above 65 ppbv in the greater Las Vegas region. While O3 produced from regional anthropogenic emissions dominates pollution in the Las Vegas Valley, stratospheric intrusions can mix with regional pollution to push surface O3 above 70 ppbv. GFDL-AM4 captures the key characteristics of deep stratospheric intrusions consistent with ozonesondes, lidar profiles, and co-located measurements of O3, CO, and water vapor at Angel Peak, whereas GEOS-Chem has difficulty simulating the observed features and underestimates observed O3 by ~ 20 ppbv at the surface. The two models also differ substantially during a wildfire event, with GEOS-Chem estimating ~ 15 ppbv greater O3, in better agreement with lidar observations. At the surface, the two models bracket the observed MDA8 O3 values during the wildfire event. Both models capture the large-scale transport of Asian pollution, but neither resolves some fine-scale pollution plumes, as evidenced from aerosol backscatter, aircraft, and satellite measurements. U.S. background O3 estimates from the two models differ by 5 ppbv on average and up to 15 ppbv episodically. Our multi-model approach tied closely to observational analysis yields process insights, suggesting that elevated background O3 may pose challenges to achieving a potentially lower NAAQS level (e.g., 65 ppbv) in the southwestern U.S.

Student Perceptions of Community: A Different Perspective

NASPA Journal ◽  
1998 ◽  
Vol 35 (4) ◽  
Author(s):  
Jackie Clark ◽  
Joan Hirt
Keyword(s):  
Student Perceptions ◽  
Large Scale ◽  
Research University ◽  
Small Scale ◽  
Social Environments ◽  
Common Assumption ◽  
Small Communities ◽  
Public Research University ◽  
The Common ◽  
Using Data

The creation of small communities has been proposed as a way of enhancing the educational experience of students at large institutions. Using data from a survey of students living in large and small residences at a public research university, this study does not support the common assumption that small-scale social environments are more conducive to positive community life than large-scale social environments.

scholarly journals Modeling Fused Filament Fabrication using Artificial Neural Networks

2021 ◽  
Author(s):  
Paul Oehlmann ◽  
Paul Osswald ◽  
Juan Camilo Blanco ◽  
Martin Friedrich ◽  
Dominik Rietzel ◽  
...  
Keyword(s):  
Real Time ◽  
Large Scale ◽  
Cost Effective ◽  
Print Quality ◽  
Ann Model ◽  
Production Environment ◽  
Fused Filament Fabrication ◽  
Artificial Neural ◽  
Using Data ◽  
Artificial Neural Network Ann

AbstractWith industries pushing towards digitalized production, adaption to expectations and increasing requirements for modern applications, has brought additive manufacturing (AM) to the forefront of Industry 4.0. In fact, AM is a main accelerator for digital production with its possibilities in structural design, such as topology optimization, production flexibility, customization, product development, to name a few. Fused Filament Fabrication (FFF) is a widespread and practical tool for rapid prototyping that also demonstrates the importance of AM technologies through its accessibility to the general public by creating cost effective desktop solutions. An increasing integration of systems in an intelligent production environment also enables the generation of large-scale data to be used for process monitoring and process control. Deep learning as a form of artificial intelligence (AI) and more specifically, a method of machine learning (ML) is ideal for handling big data. This study uses a trained artificial neural network (ANN) model as a digital shadow to predict the force within the nozzle of an FFF printer using filament speed and nozzle temperatures as input data. After the ANN model was tested using data from a theoretical model it was implemented to predict the behavior using real-time printer data. For this purpose, an FFF printer was equipped with sensors that collect real time printer data during the printing process. The ANN model reflected the kinematics of melting and flow predicted by models currently available for various speeds of printing. The model allows for a deeper understanding of the influencing process parameters which ultimately results in the determination of the optimum combination of process speed and print quality.

scholarly journals Nebula: ultra-efficient mapping-free structural variant genotyper

2021 ◽  
Author(s):  
Parsoa Khorsand ◽  
Fereydoun Hormozdiari
Keyword(s):  
Large Scale ◽  
Structural Variants ◽  
Sequencing Technologies ◽  
Generic Framework ◽  
Common Genetic Variants ◽  
Order Of Magnitude ◽  
Complex Events ◽  
Comparable Accuracy ◽  
Using Data ◽  
Computational Resources

Abstract Large scale catalogs of common genetic variants (including indels and structural variants) are being created using data from second and third generation whole-genome sequencing technologies. However, the genotyping of these variants in newly sequenced samples is a nontrivial task that requires extensive computational resources. Furthermore, current approaches are mostly limited to only specific types of variants and are generally prone to various errors and ambiguities when genotyping complex events. We are proposing an ultra-efficient approach for genotyping any type of structural variation that is not limited by the shortcomings and complexities of current mapping-based approaches. Our method Nebula utilizes the changes in the count of k-mers to predict the genotype of structural variants. We have shown that not only Nebula is an order of magnitude faster than mapping based approaches for genotyping structural variants, but also has comparable accuracy to state-of-the-art approaches. Furthermore, Nebula is a generic framework not limited to any specific type of event. Nebula is publicly available at https://github.com/Parsoa/Nebula.

scholarly journals Assisting scalable diagnosis automatically via CT images in the combat against COVID-19

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Bohan Liu ◽  
Pan Liu ◽  
Lutao Dai ◽  
Yanlin Yang ◽  
Peng Xie ◽  
...  
Keyword(s):  
Large Scale ◽  
Area Under The Curve ◽  
Independent Set ◽  
Reference Method ◽  
Ct Images ◽  
Loss Of Life ◽  
Case Identification ◽  
Reverse Transcription Pcr ◽  
Using Data

AbstractThe pandemic of Coronavirus Disease 2019 (COVID-19) is causing enormous loss of life globally. Prompt case identification is critical. The reference method is the real-time reverse transcription PCR (RT-PCR) assay, whose limitations may curb its prompt large-scale application. COVID-19 manifests with chest computed tomography (CT) abnormalities, some even before the onset of symptoms. We tested the hypothesis that the application of deep learning (DL) to 3D CT images could help identify COVID-19 infections. Using data from 920 COVID-19 and 1,073 non-COVID-19 pneumonia patients, we developed a modified DenseNet-264 model, COVIDNet, to classify CT images to either class. When tested on an independent set of 233 COVID-19 and 289 non-COVID-19 pneumonia patients, COVIDNet achieved an accuracy rate of 94.3% and an area under the curve of 0.98. As of March 23, 2020, the COVIDNet system had been used 11,966 times with a sensitivity of 91.12% and a specificity of 88.50% in six hospitals with PCR confirmation. Application of DL to CT images may improve both efficiency and capacity of case detection and long-term surveillance.

scholarly journals Integrative cBioPortal Analysis Revealed Molecular Mechanisms That Regulate EGFR-PI3K-AKT-mTOR Pathway in Diffuse Gliomas of the Brain

Cancers ◽  
2021 ◽  
Vol 13 (13) ◽  
pp. 3247
Author(s):  
Petar Brlek ◽  
Anja Kafka ◽  
Anja Bukovac ◽  
Nives Pećina-Šlaus
Keyword(s):  
Large Scale ◽  
Molecular Mechanisms ◽  
In Silico Analysis ◽  
Total Sample ◽  
Mtor Signaling ◽  
Biological Behavior ◽  
Mrna Transcription ◽  
Diffuse Gliomas ◽  
New Treatment ◽  
Using Data

Diffuse gliomas are a heterogeneous group of tumors with aggressive biological behavior and a lack of effective treatment methods. Despite new molecular findings, the differences between pathohistological types still require better understanding. In this in silico analysis, we investigated AKT1, AKT2, AKT3, CHUK, GSK3β, EGFR, PTEN, and PIK3AP1 as participants of EGFR-PI3K-AKT-mTOR signaling using data from the publicly available cBioPortal platform. Integrative large-scale analyses investigated changes in copy number aberrations (CNA), methylation, mRNA transcription and protein expression within 751 samples of diffuse astrocytomas, anaplastic astrocytomas and glioblastomas. The study showed a significant percentage of CNA in PTEN (76%), PIK3AP1 and CHUK (75% each), EGFR (74%), AKT2 (39%), AKT1 (32%), AKT3 (19%) and GSK3β (18%) in the total sample. Comprehensive statistical analyses show how genomics and epigenomics affect the expression of examined genes differently across various pathohistological types and grades, suggesting that genes AKT3, CHUK and PTEN behave like tumor suppressors, while AKT1, AKT2, EGFR, and PIK3AP1 show oncogenic behavior and are involved in enhanced activity of the EGFR-PI3K-AKT-mTOR signaling pathway. Our findings contribute to the knowledge of the molecular differences between pathohistological types and ultimately offer the possibility of new treatment targets and personalized therapies in patients with diffuse gliomas.

scholarly journals The effects of physical restructuring on the socioeconomic status of neighbourhoods: Selective migration and upgrading

Urban Studies ◽  
2018 ◽  
Vol 56 (8) ◽  
pp. 1647-1663
Author(s):  
Merle Zwiers ◽  
Maarten van Ham ◽  
Reinout Kleinhans
Keyword(s):  
Large Scale ◽  
Spillover Effects ◽  
High Income ◽  
Direct Effects ◽  
Urban Restructuring ◽  
Deprived Neighbourhoods ◽  
New Construction ◽  
Using Data ◽  
Main Instrument ◽  
Dutch Cities

In the last few decades, many governments have implemented urban restructuring programmes with the main goal of combating a variety of socioeconomic problems in deprived neighbourhoods. The main instrument of restructuring has been housing diversification and tenure mixing. The demolition of low-quality (social) housing and the construction of owner-occupied or private rented dwellings was expected to change the population composition of deprived neighbourhoods through the in-migration of middle- and high-income households. Many studies have been critical with regard to the success of such policies in actually upgrading neighbourhoods. Using data from the 31 largest Dutch cities for the 1999 to 2013 period, this study contributes to the literature by investigating the effects of large-scale demolition and new construction on neighbourhood income developments on a low spatial scale. We use propensity score matching to isolate the direct effects of policy by comparing restructured neighbourhoods with a set of control neighbourhoods with low demolition rates, but with similar socioeconomic characteristics. The results indicate that large-scale demolition leads to socioeconomic upgrading of deprived neighbourhoods as a result of attracting and maintaining middle- and high-income households. We find no evidence of spillover effects to nearby neighbourhoods, suggesting that physical restructuring only has very local effects.

scholarly journals Relationship between Shortwave Cloud Radiative Forcing and Local Meteorological Variables Compared in Observations and Several Global Climate Models

2006 ◽  
Vol 19 (17) ◽  
pp. 4344-4359 ◽  
Author(s):  
Markus Stowasser ◽  
Kevin Hamilton
Keyword(s):  
Relative Humidity ◽  
Vertical Velocity ◽  
Radiative Forcing ◽  
Large Scale ◽  
Climate Models ◽  
Grid Point ◽  
Global Climate Models ◽  
Coupled Models ◽  
Cloud Radiative Forcing ◽  
Cloud Forcing

Abstract The relations between local monthly mean shortwave cloud radiative forcing and aspects of the resolved-scale meteorological fields are investigated in hindcast simulations performed with 12 of the global coupled models included in the model intercomparison conducted as part of the preparation for Intergovernmental Panel on Climate Change (IPCC) Fourth Assessment Report (AR4). In particular, the connection of the cloud forcing over tropical and subtropical ocean areas with resolved midtropospheric vertical velocity and with lower-level relative humidity are investigated and compared among the models. The model results are also compared with observational determinations of the same relationships using satellite data for the cloud forcing and global reanalysis products for the vertical velocity and humidity fields. In the analysis the geographical variability in the long-term mean among all grid points and the interannual variability of the monthly mean at each grid point are considered separately. The shortwave cloud radiative feedback (SWCRF) plays a crucial role in determining the predicted response to large-scale climate forcing (such as from increased greenhouse gas concentrations), and it is thus important to test how the cloud representations in current climate models respond to unforced variability. Overall there is considerable variation among the results for the various models, and all models show some substantial differences from the comparable observed results. The most notable deficiency is a weak representation of the cloud radiative response to variations in vertical velocity in cases of strong ascending or strong descending motions. While the models generally perform better in regimes with only modest upward or downward motions, even in these regimes there is considerable variation among the models in the dependence of SWCRF on vertical velocity. The largest differences between models and observations when SWCRF values are stratified by relative humidity are found in either very moist or very dry regimes. Thus, the largest errors in the model simulations of cloud forcing are prone to be in the western Pacific warm pool area, which is characterized by very moist strong upward currents, and in the rather dry regions where the flow is dominated by descending mean motions.

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