171 research outputs found
Changes in carbon, nutrients and stoichiometric relations under different soil depths, plant tissues and ages in black locust plantations
No full textTo investigate influences of forest plantations on soil nutrient properties, biomass accumulation, major nutrient elements (NPK) and their stoichiometric couplings in different tissues and aged plants, and correlations between major nutrient contents in soils and in foliage of plants, 5-, 10-, 15- and 20-year-old plantations of black locust (Robinia pseudoacacia L.) and farmland were selected. Black locust plantations increased soil organic carbon (SOC) and N stocks by 23–327 and 23–119 %, respectively, in the 0–10 cm top soil layer compared to those in farmland. Soil C:N, C:P, C:K, N:P, N:K and P:K ratios were 10.1, 22.9, 0.7, 2.2, 0.7 and 0.03, respectively. These ratios were higher in the 0–10 cm soil layer than those in the 10–20 cm soil layer and increased under older plantations. Higher C contents in stem, N contents in leaf, the largest C pools in stem and N pools in root in 20-year-old plantation were observed. Correspondingly, the highest C:N, C:P and C:K and the lowest N:P and N:K ratios in stem, decreased C:N and C:P ratios in older trees were found. No strong correlations were observed between element contents in soils and in leaves of black locust trees. These results suggest that black locust plantations can increase soil nutrient concentrations, SOC and N stocks resulting in changes in element stoichiometric relations. CNPK contents and their stoichiometries vary with tissues and tree ages of black locust. No strong coupling relations exist between major nutrient element contents in the top soil and in foliage of black locust
Predicting Protein-Protein Interaction Sites From Amino Acid Sequence
No full textPredicting Protein-Protein Interaction Sites From Amino Acid Sequence Changhui Yan, Vasant Honavar and Drena Dobbs Artificial Intelligence Research Laboratory Bioinformatics and Computational Biology Graduate Program Iowa State University Ames, Iowa 50011 Corresponding author: Changhui Yan Email address of the corresponding author: [email protected] Abstract We describe an approach for computational prediction of protein-protein interaction sites using a support vector machine (SVM) classifier. Interface residues and other surface residues were extracted from 115 proteins derived from a set of 70 heterocomplexes in PDB. The SVM classifier was trained to predict whether or not a surface residue is located in the interface based on the identity of the target residue and its 10 sequence neighbors. The effectiveness of the approach was evaluated using 115 leave-one-out cross validation (jack-knife) experiments. In each experiment, an SVM classifier was trained using a set of 1250 randomly chosen interface residues and an equal number of non-interface residues from 114 of the 115 molecules. The resulting classifier was used to classify surface residues from the remaining molecule into interface and non-interface residues. The classifier in each experiment was evaluated in terms of several performance measures. In results averaged over 115 experiments, interface residues and non-interface residues were identified with relatively high specificity (71%) and sensitivity (67%), and with a correlation coefficient of 0.29 between predicted and actual class labels, indicating that the method performs substantially better than chance (zero correlation). We also investigated the classifier's performance in terms of overall interactions site recognition. In 80% of the proteins, the classifier recognized the interaction surface by identifying at least half of the interface residues, and in 98% of the proteins, at least 20% of the interface residues were correctly identified. The success of this approach was confirmed by examination of predicted interfaces in the context of the three-dimensional structures of representative complexes. This study demonstrates that an SVM classifier can be used to predict whether or not a surface residue is an interface residue using amino acid sequence information. Because surface residues can be identified based on their solvent accessible surface area (ASA), given recent progress in computational methods for predicting ASA from sequence, the approach described in this paper provides a basis for computational prediction of interaction sites in proteins for which only amino acid sequence information is available. Keywords: protein-protein interaction; interaction site prediction; interface residues; support vector machine.</p
A Critical Analysis of the Carbon Neutrality Assumption in Life Cycle Assessment of Forest Bioenergy Systems
No full textThis study presents a critical analysis regarding the assumption of carbon neutrality in life cycle assessment (LCA) models aimed at assessing climate change impacts of bioenergy usage. We identified a complex of problems in the carbon neutrality assumption, especially regarding bioenergy derived from forest residues. In this study, we summarized several issues related to carbon neutral assumptions, with particular emphasis on possible carbon accounting errors at the product level. We analyzed errors in estimating emissions in the supply chain, direct and indirect emissions due to forest residue extraction, biogenic CO2 emission from biomass combustion for energy, and other effects related to forest residue extraction. Various modeling approaches are discussed in detail. We concluded that there is a need to correct accounting errors when estimating climate change impacts and proposed possible remedies. To accurately assess climate change impacts of bioenergy use, greater efforts are required to improve forest carbon cycle modeling, especially to identify and correct pitfalls associated with LCA accounting, forest residue extraction effects on forest fire risk and biodiversity. Uncertainties in accounting carbon emissions in LCA are also highlighted, and associated risks are discussed.The accepted manuscript in pdf format is listed with the files at the bottom of this page. The presentation of the authors' names and (or) special characters in the title of the manuscript may differ slightly between what is listed on this page and what is listed in the pdf file of the accepted manuscript; that in the pdf file of the accepted manuscript is what was submitted by the author
Study on numerical simulation of asymmetric structure aluminum profile extrusion based on ALE method
No full textPt-AlGaN/GaN HEMT-sensor layout optimization for enhancement of hydrogen detection
No full textThis paper reports on the layout optimization of Pt-AlGaN/GaN HEMT-sensors for enhancing hydrogen sensor performance. Sensors with gate width and length ratios Wg/Lg from 0.25 to 10 were designed, fabricated and tested for the detection of hydrogen gas at 200 °C. Sensitivity, sensing current variation and transient response are directly related to the sensor gate electrode Wg/Lg ratio. The obtained results demonstrated a 217 % increase in sensitivity and 4630 % increase in sensing current variation at 500 ppm H2 for a Wg/Lg from 0.25 to 10. In addition, the detection limit was lowered to 5 ppm. Transient characteristics demonstrated faster sensor response to H2, but slower recovery rates with increasing ratio.Accepted author manuscriptElectronic Components, Technology and Material
System development and clinical applications of the handheld PA/US dual mode imaging system
No full textAs an emerging technology, photoacoustic (PA) imaging (PAI) has gained lots of progresses in the past two decades. By combing optical contrast with ultrasound detection, PAI can maintain high spatial resolution in deeper regions. Furthermore, with the aid of multispectral imaging, PAI can reveal functional information such as oxygenation saturation (SO2), which is closely related to tumor malignancy. With the above advantages, nowadays PAI has gained lots of progresses in clinically translatable research. These research shows that PAI has the potential to complement existing imaging techniques such as ultrasound (US) imaging for cancer detection, disease evaluation and prognosis monitoring.
This thesis mainly discusses the clinical application of PAI, including system design and clinical research. Particularly, it presents several studies including the development of PA/US imaging system and the clinical evaluation of PA/US imaging in diagnosis of superficial cancer. The main research results and the major novelties of this thesis include:
1) The collaborative development of the PA/US imaging system for clinical studies, where the author was mainly responsible for the design of optical path and the trigger method between the laser and the US system.
2) The design of optical fluence compensation strategy based on clinical US structural imaging. This strategy first identifies the tissue type based on US structural imaging, and then utilizes the known optical absorption and scattering parameters to simulate optical fluence map in the imaging region. The simulated optical fluence map can then be used for correction of original PA images. The effectiveness of this strategy has been verified based on clinical PAI data. The proposed method can improve the accuracy of quantification PAI and restore PA signals in deeper regions.
3) The collaborative study of PA/US imaging on thyroid nodules with the self-developed 2D PA/US handheld imaging system. By comparing PAI results with color Doppler flow imaging (CDFI) results from 10 thyroid nodules, we found that PAI can reveal more abundant vessels than CDFI, and can thus provide valuable information in diagnosis of thyroid cancer.
4) The design of the method for quantification analysis of 3D PA/US imaging on breast cancer. Specifically, this method first calculates the ellipse enclosing the tumor region with the minimum volume and then automatically segments the tumor regions and tumor surrounding regions. Next, the volumetric mean SO2 of tumor regions and tumor surrounding regions were calculated for quantification analysis. Our results demonstrate that quantification analysis of 3D functional PA/US imaging on breast cancers has the potential to improve the specificity in diagnosis of breast cancer.
In Chapter 1, the mechanism of PAI and its recent clinical translational progresses are first reviewed. Next, in Chapter 2, the development of a clinical PA/US imaging system based on handheld US imaging is presented. The imaging ability of the newly developed system was verified with the phantom study as well as the in vivo study. After then, in Chapter 3, the optical fluence compensation method for handheld PA/US imaging based on tissue structural information provided in US imaging is introduced. The clinical result demonstrates that important tissue structures such as vessels in deeper tissue can be restored after fluence compensation based on the proposed method. Chapter 4 and Chapter 5 introduce the clinical studies based on the self-developed PA/US imaging systems, including 2D PA/US imaging of thyroid nodules, 3D PA/US imaging of breast cancer, and quantification analysis of the imaging results. The quantification analysis demonstrates that 3D functional PA/US imaging has the potential to improve the diagnosis specificity of breast cancer.
In Chapter 6, the author presented the research conducted in Georgia Institute of Technology. In this chapter, the laser induced surface acoustic waves (SAWs) and acoustic radiation force (ARF) induced SAWs were compared. The results demonstrate that laser-induced SAW imaging is able to perform stiffness evaluation and has the potential to provide higher spatial resolution.
In Chapter 7, the author summarized the major novelties in this thesis and discussed research directions in the future work.Ph.D
Product innovation in emerging market-based international joint ventures: An organizational ecology perspective
No full textThis study investigates product innovation in market-seeking international joint ventures (IJVs) in a large emerging market, and tests two sets of hypotheses: organizational orientation and environmental adaptation. Drawing on organizational ecology theory, we suggest that IJVs’ product innovation can be understood as a consequence of organizational orientation defined by IJV contract specifications and as a subsequent response to major contingencies of the local environment. Empirically, we analyzed a longitudinal dataset consisting of 3555 IJVs in China from 1999 to 2003, and found strong support for both sets of hypotheses. Product innovation in IJVs is positively associated with initial conditions such as balanced ownership structure, state partnership, and project size; IJVs are more innovative when they operate in an industry with a faster pace of innovation or a higher level of foreign direct investment legitimization, and where they are located in a region with greater agglomerated innovative activities. Journal of International Business Studies (2008) 39, 1114–1132. doi:10.1057/jibs.2008.51
An Adaptive Control of Fractional-Order Nonlinear Uncertain Systems with Input Saturation
No full textThis paper develops a fractional-order adaptive fuzzy backstepping control scheme for incommensurate fractional-order nonlinear uncertain systems with external disturbances and input saturation. Based on backstepping algorithm, the fuzzy logic system is used to approximate the unknown nonlinear uncertainties in each step of the backstepping, and the fractional-order parameters update laws for fuzzy logic system, unknown parameters, and the external disturbances are proposed. With the aids of the frequency distributed model of fractional integrator for the fractional-order systems in the procedure of controller design, the stability of the closed-loop system is established. To verify the effectiveness and robustness of the proposed controller, two simulation examples are demonstrated at last
Adaptive Neural Network Control of a Class of Fractional Order Uncertain Nonlinear MIMO Systems with Input Constraints
No full textAn adaptive backstepping control scheme for a class of incommensurate fractional order uncertain nonlinear multiple-input multiple-output (MIMO) systems subjected to constraints is discussed in this paper, which ensures the convergence of tracking errors even with dead-zone and saturation nonlinearities in the controller input. Combined with backstepping and adaptive technique, the unknown nonlinear uncertainties are approximated by the radial basis function neural network (RBF NN) in each step of the backstepping procedure. Frequency distributed model of a fractional integrator and Lyapunov stability theory are used for ensuring asymptotic stability of the overall closed-loop system under input dead-zone and saturation. Moreover, the parameter update laws with incommensurate fractional order are used in the controller to compensate unknown nonlinearities. Two simulation results are presented at the end to ensure the efficacy of the proposed scheme
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