ESTIMATING ANNUAL NET PRIMARY PRODUCTIVITY OF THE TALLGRASS PRAIRIE ECOSYSTEM OF THE CENTRAL GREAT PLAINS USING AVHRR NDVI

Aboveground Net Primary Productivity (ANPP) is indicative of an ecosystem's ability to capture solar energy and store it in the form of carbon (or biomass). Annual and interannual ecosystem variation in ANPP is often linked to climatic dynamics and anthropogenic influences. The Great Plains grasslands occupy over 1.5 million km2 and are a primary resource for livestock production in North America. The tallgrass prairies are the most productive of the grasslands of the region and the Flint Hills of North America represent the largest contiguous area of unplowed tallgrass prairie (1.6 million ha) (Knapp and Seastead, 1998). Measurements of ANPP are of critical importance to the proper management and understanding of climatic and anthropogenic influences on tallgrass prairie, yet accurate, detailed, and systematic measurements of ANPP over large geographic regions of this system do not exist. For these reasons, this study was conducted to investigate the use of the Normalized Difference Vegetation Index (NDVI) to model ANPP for the tallgrass prairie. Many studies have established a positive relationship between the NDVI and ANPP, but the strength of this relationship is influenced by vegetation types and can significantly vary from year-to-year depending on land use and climatic conditions. The goal of this study is to develop a robust model using the Advanced Very High Resolution Radiometer (AVHRR) biweekly NDVI values to predict tallgrass ANPP. This study was conducted using the Konza Prairie Biological Station as the primary study area with data also from the Rannells Flint Hills Prairie Preserve and other sites near Manhattan, Kansas. The dominant study period was 1989 to 2005. The optimal period for estimating ANPP using AVHRR NDVI composite datasets is prairie 30 (late July). The Tallgrass ANPP Model (TAM) explained 53% (r2 = 0.53, r = 0.73) of the year-to-year variation. Efforts to validate the TAM results were frustrated by considerable variations among existing remote sensing based ANPP model estimates and in situ clipplot measurements of peak season tallgrass production. These findings support the conclusion that ecosystem specific ANPP models are needed to improve global scale ANPP estimates. The creation of 1 km x 1 km resolution ANPP maps for a four county (~7,000 ha) for years 1989 - 2007 showed considerable variation in annual and interannual ANPP spatial patterns suggesting complex interactions among factors influencing ANPP spatially and temporally. The observed patterns on these maps would be lost using the much coarser resolution ground weather recording stations

Interaction of a non-linear gravity wave with shear flows containing a vortex layer

The interaction of a large amplitude progressing wave with a piecewise linear shear flow is described

An Exploratory Study for Perceived Advertising Value in the Relationship Between Irritation and Advertising Avoidance on the Mobile Social Platforms

This study delves deeply into advertising avoidance research and redefines the uses and gratifications theory (U&G) as divided into (a) convenience U&G, (b) content U&G, and (c) social U&G to conduct an approach to alleviate the degree of advertising avoidance on the mobile social platforms. To carefully study the forming framework of advertising avoidance, we extract the factor irritation considered to directly impact on avoidant intention induced by perceived intrusiveness and privacy concerns. As an important previous factor in advertising research, we also test the moderating effect of perceived advertising value between irritation and advertising avoidance. Findings show that ubiquity takes a negative role on mobile social platforms and tailoring also takes different roles on perceived intrusiveness and privacy concerns; unfortunately, content U&G consist of advertising informativeness and entertainment didn’t find any significant effect; in contrast with previous study, social U&G as social interaction and social integration also show some different roles but is ambiguous. However, the positive relationship of perceived intrusiveness, privacy concerns, irritation, and advertising avoidance has been confirmed again although with a pity of insignificant moderating effect of advertising value. Management issues, theoretical contributions, limitations and future study are discussed as follow

Measuring Exocytosis Rate Using Corrected Fluorescence Recovery After Photoconversion

Exocytosis plays crucial roles in regulating the distribution and function of plasma membrane (PM) and extracellular matrix proteins. However, measuring the exocytosis rate of a specific protein by conventional methods is very difficult because of exocytosis-independent trafficking such as endocytosis, which also affects membrane protein distribution. Here, we describe a novel method, corrected fluorescence recovery after photoconversion, in which exocytosis-dependent and -independent trafficking events are measured simultaneously to accurately determine exocytosis rate. In this method, the protein-of-interest is tagged with Dendra2, a green-to-red photoconvertible fluorescent protein. Following the photoconversion of PM-localized Dendra2, both the recovery of the green signal and the changes in the photoconverted red signal are measured, and the rate of exocytosis is calculated from the changing rates of these two signals

Quantification of atherosclerotic plaque volume in coronary arteries by computed tomographic angiography in subjects with and without diabetes.

BackgroundDiabetes mellitus (DM) is considered a cardiovascular risk factor. The aim of this study was to analyze the prevalence and volume of coronary artery plaque in patients with diabetes mellitus (DM) vs. those without DM.MethodsThis study recruited consecutive patients who underwent coronary computed tomography (CT) angiography (CCTA) between October 2016 and November 2017. Personal information including conventional cardiovascular risk factors was collected. Plaque phenotypes were automatically calculated for volume of different component. The volume of different plaque was compared between DM patients and those without DM.ResultsAmong 6381 patients, 931 (14.59%) were diagnosed with DM. The prevalence of plaque in DM subjects was higher compared with nondiabetic group significantly (48.34% vs. 33.01%, χ = 81.84, P < 0.001). DM was a significant risk factor for the prevalence of plaque in a multivariate model (odds ratio [OR] = 1.465, 95% CI: 1.258-1.706, P < 0.001). The volume of total plaque and any plaque subtypes in the DM subjects was greater than those in nondiabetic patients significantly (P < 0.001).ConclusionThe coronary artery atherosclerotic plaques were significantly higher in diabetic patients than those in non-diabetic patients

Plant high-throughput phenotyping using photogrammetry and 3D modeling techniques

Doctor of PhilosophyAgronomyKevin PriceStephen M. WelchPlant phenotyping has been studied for decades for understanding the relationship between plant genotype, phenotype, and the surrounding environment. Improved accuracy and efficiency in plant phenotyping is a critical factor in expediting plant breeding and the selection process. In the past, plant phenotypic traits were extracted using invasive and destructive sampling methods and manual measurements, which were time-consuming, labor-intensive, and cost-inefficient. More importantly, the accuracy and consistency of manual methods can be highly variable. In recent years, however, photogrammetry and 3D modeling techniques have been introduced to extract plant phenotypic traits, but no cost-efficient methods using these two techniques have yet been developed for large-scale plant phenotyping studies. High-throughput 3D modeling techniques in plant biology and agriculture are still in the developmental stages, but it is believed that the temporal and spatial resolutions of these systems are well matched to many plant phenotyping needs. Such technology can be used to help rapid phenotypic trait extraction aid crop genotype selection, leading to improvements in crop yield. In this study, we introduce an automated high-throughput phenotyping pipeline using affordable imaging systems, image processing, and 3D reconstruction algorithms to build 2D mosaicked orthophotos and 3D plant models. Chamber-based and ground-level field implementations can be used to measure phenotypic traits such as leaf length, rosette area in 2D and 3D, plant nastic movement, and diurnal cycles. Our automated pipeline has cross-platform capabilities and a degree of instrument independence, making it suitable for various situations

ARMOR: A Model-based Framework for Improving Arbitrary Baseline Policies with Offline Data

We propose a new model-based offline RL framework, called Adversarial Models for Offline Reinforcement Learning (ARMOR), which can robustly learn policies to improve upon an arbitrary baseline policy regardless of data coverage. Based on the concept of relative pessimism, ARMOR is designed to optimize for the worst-case relative performance when facing uncertainty. In theory, we prove that the learned policy of ARMOR never degrades the performance of the baseline policy with any admissible hyperparameter, and can learn to compete with the best policy within data coverage when the hyperparameter is well tuned, and the baseline policy is supported by the data. Such a robust policy improvement property makes ARMOR especially suitable for building real-world learning systems, because in practice ensuring no performance degradation is imperative before considering any benefit learning can bring

Interpretation of unsaturated soil behaviour in the stress-saturation space. II: Constitutive relationships and validations

Based on the equations for volume change and saturation variation proposed in the companion paper [37], an alternative constitutive framework is presented for interpreting coupled hydro-mechanical behaviour for unsaturated soils. In this new framework, all constitutive laws are built in the space of stress vs. degree of saturation. Suction is not involved explicitly in the constitutive model for unsaturated soils. The loading-collapse yield surface is derived based on the proposed volume change equation in the plane of the effective degree of saturation and the Bishop effective stress. The proposed volume change equation and the corresponding yield surface are generalised to three-dimensional stress states by incorporating with the Modified Cam-clay model, following the same procedure introduced in the Sheng–Fredlund–Gens (SFG) model. The basic properties and performance of the proposed constitutive model are then illustrated through numerical examples with various drying/wetting/loading paths. Finally, the proposed model is validated against a variety of experimental data including drained and undrained tests, isotropic and triaxial tests and reconstituted and compacted soils