945 resultados para Linear Multi-step Formulae
Resumo:
Kainic acid has been used for nearly 50 years as a tool in neuroscience due to its pronounced neuroexcitatory properties. However, the significant price increase of kainic acid resulting from the disruption in the supply from its natural source, the alga Digenea Simplex, as well as inefficient synthesis of kainic acid, call for the exploration of functional mimics of kainic acid that can be synthesized in a simpler way. Aza kainoids analog could be one of them. The unsubstituted aza analog of kainoids has demonstrates its ability as an ionotropic glutamate receptor agonist and showed affinity in the chloride dependent glutamate (GluCl) binding site. This opened a question of the importance of the presence of one nitrogen or both nitrogens in the aza kainoid analogs for binding to glutamate receptors. Therefore, two different pyrrolidine analogs of kainic acid, trans -4-(carboxymethyl)pyrrolidine-3-carboxylic acid and trans -2-carboxy-3-pyrrolidineacetic acid, were synthesized through multi-step sequences. The lack of the affinity of both pyrrolidine analogs in GluCl binding site indicated that both nitrogens in aza kainoid analogs are involved in hydrogen bonding with receptors, significantly enhancing their affinity in GluCl binding site. Another potential functional mimic of kainic acid is isoxazolidine analogs of kainoids whose skeleton can be constituted directly via a 1, 3 dipolar cycloaddition as the key step. The difficulty in synthesizing N-unsubstituted isoxazolidines when applying such common protecting groups as alkyl, phenyl and benzyl groups, and the requirement of a desired enantioselectivity due to the three chiral ceneters in kainic acid, pose great challenges. Hence, several different protected nitrones were studied to establish that diphenylmethine nitrone may be a good candidate as the dipole in that the generated isoxazolidines can be deprotected in mild conditions with high yields. Our investigations also indicated that the exo/endo selectivity of the 1, 3 dipolar cycloaddition can be controlled by Lewis acids, and that the application of a directing group in dipolarophiles can accomplish a satisfied enantioselectivity. Those results demonstrated the synthesis of isoxazoldines analogs of kainic acid is very promising.
Resumo:
This study describes further validation of a previously described Peptide-mediated magnetic separation (PMS)-Phage assay, and its application to test raw cows’ milk for presence of viable Mycobacterium avium subsp. paratuberculosis (MAP). The inclusivity and exclusivity of the PMS-phage assay were initially assessed, before the 50% limit of detection (LOD50) was determined and compared with those of PMS-qPCR (targeting both IS900 and f57) and PMS-culture. These methods were then applied in parallel to test 146 individual milk samples and 22 bulk tank milk samples from Johne’s affected herds. Viable MAP were detected by the PMS-phage assay in 31 (21.2%) of 146 individual milk samples (mean plaque count of 228.1 PFU/50 ml, range 6-948 PFU/50 ml), and 13 (59.1%) of 22 bulk tank milks (mean plaque count of 136.83 PFU/50 ml, range 18-695 PFU/50 ml). In contrast, only 7 (9.1%) of 77 individual milks and 10 (45.4%) of 22 bulk tank milks tested PMS-qPCR positive, and 17 (11.6%) of 146 individual milks and 11 (50%) of 22 bulk tank milks tested PMS-culture positive. The mean 50% limits of detection (LOD50) of the PMS-phage, PMS-IS900 qPCR and PMS-f57 qPCR assays, determined by testing MAP-spiked milk, were 0.93, 135.63 and 297.35 MAP CFU/50 ml milk, respectively. Collectively, these results demonstrate that, in our laboratory, the PMS-phage assay is a sensitive and specific method to quickly detect the presence of viable MAP cells in milk. However, due to its complicated, multi-step nature, the method would not be a suitable MAP screening method for the dairy industry.
Resumo:
Thesis (Ph.D.)--University of Washington, 2016-08
Resumo:
Thesis (Ph.D.)--University of Washington, 2016-08
Resumo:
Thesis (Ph.D.)--University of Washington, 2016-08
Resumo:
Structuring integrated social-ecological systems (SES) research remains a core challenge for achieving sustainability. Numerous concepts and frameworks exist, but there is a lack of mutual learning and orientation of knowledge between them. We focus on two approaches in particular: the ecosystem services concept and Elinor Ostrom’s diagnostic SES framework. We analyze the strengths and weaknesses of each and discuss their potential for mutual learning. We use knowledge types in sustainability research as a boundary object to compare the contributions of each approach. Sustainability research is conceptualized as a multi-step knowledge generation process that includes system, target, and transformative knowledge. A case study of the Southern California spiny lobster fishery is used to comparatively demonstrate how each approach contributes a different lens and knowledge when applied to the same case. We draw on this case example in our discussion to highlight potential interlinkages and areas for mutual learning. We intend for this analysis to facilitate a broader discussion that can further integrate SES research across its diverse communities.
Resumo:
This dissertation contains four essays that all share a common purpose: developing new methodologies to exploit the potential of high-frequency data for the measurement, modeling and forecasting of financial assets volatility and correlations. The first two chapters provide useful tools for univariate applications while the last two chapters develop multivariate methodologies. In chapter 1, we introduce a new class of univariate volatility models named FloGARCH models. FloGARCH models provide a parsimonious joint model for low frequency returns and realized measures, and are sufficiently flexible to capture long memory as well as asymmetries related to leverage effects. We analyze the performances of the models in a realistic numerical study and on the basis of a data set composed of 65 equities. Using more than 10 years of high-frequency transactions, we document significant statistical gains related to the FloGARCH models in terms of in-sample fit, out-of-sample fit and forecasting accuracy compared to classical and Realized GARCH models. In chapter 2, using 12 years of high-frequency transactions for 55 U.S. stocks, we argue that combining low-frequency exogenous economic indicators with high-frequency financial data improves the ability of conditionally heteroskedastic models to forecast the volatility of returns, their full multi-step ahead conditional distribution and the multi-period Value-at-Risk. Using a refined version of the Realized LGARCH model allowing for time-varying intercept and implemented with realized kernels, we document that nominal corporate profits and term spreads have strong long-run predictive ability and generate accurate risk measures forecasts over long-horizon. The results are based on several loss functions and tests, including the Model Confidence Set. Chapter 3 is a joint work with David Veredas. We study the class of disentangled realized estimators for the integrated covariance matrix of Brownian semimartingales with finite activity jumps. These estimators separate correlations and volatilities. We analyze different combinations of quantile- and median-based realized volatilities, and four estimators of realized correlations with three synchronization schemes. Their finite sample properties are studied under four data generating processes, in presence, or not, of microstructure noise, and under synchronous and asynchronous trading. The main finding is that the pre-averaged version of disentangled estimators based on Gaussian ranks (for the correlations) and median deviations (for the volatilities) provide a precise, computationally efficient, and easy alternative to measure integrated covariances on the basis of noisy and asynchronous prices. Along these lines, a minimum variance portfolio application shows the superiority of this disentangled realized estimator in terms of numerous performance metrics. Chapter 4 is co-authored with Niels S. Hansen, Asger Lunde and Kasper V. Olesen, all affiliated with CREATES at Aarhus University. We propose to use the Realized Beta GARCH model to exploit the potential of high-frequency data in commodity markets. The model produces high quality forecasts of pairwise correlations between commodities which can be used to construct a composite covariance matrix. We evaluate the quality of this matrix in a portfolio context and compare it to models used in the industry. We demonstrate significant economic gains in a realistic setting including short selling constraints and transaction costs.
Resumo:
The protein lysate array is an emerging technology for quantifying the protein concentration ratios in multiple biological samples. It is gaining popularity, and has the potential to answer questions about post-translational modifications and protein pathway relationships. Statistical inference for a parametric quantification procedure has been inadequately addressed in the literature, mainly due to two challenges: the increasing dimension of the parameter space and the need to account for dependence in the data. Each chapter of this thesis addresses one of these issues. In Chapter 1, an introduction to the protein lysate array quantification is presented, followed by the motivations and goals for this thesis work. In Chapter 2, we develop a multi-step procedure for the Sigmoidal models, ensuring consistent estimation of the concentration level with full asymptotic efficiency. The results obtained in this chapter justify inferential procedures based on large-sample approximations. Simulation studies and real data analysis are used to illustrate the performance of the proposed method in finite-samples. The multi-step procedure is simpler in both theory and computation than the single-step least squares method that has been used in current practice. In Chapter 3, we introduce a new model to account for the dependence structure of the errors by a nonlinear mixed effects model. We consider a method to approximate the maximum likelihood estimator of all the parameters. Using the simulation studies on various error structures, we show that for data with non-i.i.d. errors the proposed method leads to more accurate estimates and better confidence intervals than the existing single-step least squares method.
Resumo:
The work presented herein focused on the automation of coordination-driven self assembly, exploring methods that allow syntheses to be followed more closely while forming new ligands, as part of the fundamental study of the digitization of chemical synthesis and discovery. Whilst the control and understanding of the principle of pre-organization and self-sorting under non-equilibrium conditions remains a key goal, a clear gap has been identified in the absence of approaches that can permit fast screening and real-time observation of the reaction process under different conditions. A firm emphasis was thus placed on the realization of an autonomous chemical robot, which can not only monitor and manipulate coordination chemistry in real-time, but can also allow the exploration of a large chemical parameter space defined by the ligand building blocks and the metal to coordinate. The self-assembly of imine ligands with copper and nickel cations has been studied in a multi-step approach using a self-built flow system capable of automatically controlling the liquid-handling and collecting data in real-time using a benchtop MS and NMR spectrometer. This study led to the identification of a transient Cu(I) species in situ which allows for the formation of dimeric and trimeric carbonato bridged Cu(II) assemblies. Furthermore, new Ni(II) complexes and more remarkably also a new binuclear Cu(I) complex, which usually requires long and laborious inert conditions, could be isolated. The study was then expanded to the autonomous optimization of the ligand synthesis by enabling feedback control on the chemical system via benchtop NMR. The synthesis of new polydentate ligands has emerged as a result of the study aiming to enhance the complexity of the chemical system to accelerate the discovery of new complexes. This type of ligand consists of 1-pyridinyl-4-imino-1,2,3-triazole units, which can coordinate with different metal salts. The studies to test for the CuAAC synthesis via microwave lead to the discovery of four new Cu complexes, one of them being a coordination polymer obtained from a solvent dependent crystallization technique. With the goal of easier integration into an automated system, copper tubing has been exploited as the chemical reactor for the synthesis of this ligand, as it efficiently enhances the rate of the triazole formation and consequently promotes the formation of the full ligand in high yields within two hours. Lastly, the digitization of coordination-driven self-assembly has been realized for the first time using an in-house autonomous chemical robot, herein named the ‘Finder’. The chemical parameter space to explore was defined by the selection of six variables, which consist of the ligand precursors necessary to form complex ligands (aldehydes, alkineamines and azides), of the metal salt solutions and of other reaction parameters – duration, temperature and reagent volumes. The platform was assembled using rounded bottom flasks, flow syringe pumps, copper tubing, as an active reactor, and in-line analytics – a pH meter probe, a UV-vis flow cell and a benchtop MS. The control over the system was then obtained with an algorithm capable of autonomously focusing the experiments on the most reactive region (by avoiding areas of low interest) of the chemical parameter space to explore. This study led to interesting observations, such as metal exchange phenomena, and also to the autonomous discovery of self assembled structures in solution and solid state – such as 1-pyridinyl-4-imino-1,2,3-triazole based Fe complexes and two helicates based on the same ligand coordination motif.
Resumo:
Object recognition has long been a core problem in computer vision. To improve object spatial support and speed up object localization for object recognition, generating high-quality category-independent object proposals as the input for object recognition system has drawn attention recently. Given an image, we generate a limited number of high-quality and category-independent object proposals in advance and used as inputs for many computer vision tasks. We present an efficient dictionary-based model for image classification task. We further extend the work to a discriminative dictionary learning method for tensor sparse coding. In the first part, a multi-scale greedy-based object proposal generation approach is presented. Based on the multi-scale nature of objects in images, our approach is built on top of a hierarchical segmentation. We first identify the representative and diverse exemplar clusters within each scale. Object proposals are obtained by selecting a subset from the multi-scale segment pool via maximizing a submodular objective function, which consists of a weighted coverage term, a single-scale diversity term and a multi-scale reward term. The weighted coverage term forces the selected set of object proposals to be representative and compact; the single-scale diversity term encourages choosing segments from different exemplar clusters so that they will cover as many object patterns as possible; the multi-scale reward term encourages the selected proposals to be discriminative and selected from multiple layers generated by the hierarchical image segmentation. The experimental results on the Berkeley Segmentation Dataset and PASCAL VOC2012 segmentation dataset demonstrate the accuracy and efficiency of our object proposal model. Additionally, we validate our object proposals in simultaneous segmentation and detection and outperform the state-of-art performance. To classify the object in the image, we design a discriminative, structural low-rank framework for image classification. We use a supervised learning method to construct a discriminative and reconstructive dictionary. By introducing an ideal regularization term, we perform low-rank matrix recovery for contaminated training data from all categories simultaneously without losing structural information. A discriminative low-rank representation for images with respect to the constructed dictionary is obtained. With semantic structure information and strong identification capability, this representation is good for classification tasks even using a simple linear multi-classifier.
Resumo:
The service of a critical infrastructure, such as a municipal wastewater treatment plant (MWWTP), is taken for granted until a flood or another low frequency, high consequence crisis brings its fragility to attention. The unique aspects of the MWWTP call for a method to quantify the flood stage-duration-frequency relationship. By developing a bivariate joint distribution model of flood stage and duration, this study adds a second dimension, time, into flood risk studies. A new parameter, inter-event time, is developed to further illustrate the effect of event separation on the frequency assessment. The method is tested on riverine, estuary and tidal sites in the Mid-Atlantic region. Equipment damage functions are characterized by linear and step damage models. The Expected Annual Damage (EAD) of the underground equipment is further estimated by the parametric joint distribution model, which is a function of both flood stage and duration, demonstrating the application of the bivariate model in risk assessment. Flood likelihood may alter due to climate change. A sensitivity analysis method is developed to assess future flood risk by estimating flood frequency under conditions of higher sea level and stream flow response to increased precipitation intensity. Scenarios based on steady and unsteady flow analysis are generated for current climate, future climate within this century, and future climate beyond this century, consistent with the WWTP planning horizons. The spatial extent of flood risk is visualized by inundation mapping and GIS-Assisted Risk Register (GARR). This research will help the stakeholders of the critical infrastructure be aware of the flood risk, vulnerability, and the inherent uncertainty.
Resumo:
Background Flatfish metamorphosis denotes the extraordinary transformation of a symmetric pelagic larva into an asymmetric benthic juvenile. Metamorphosis in vertebrates is driven by thyroid hormones (THs), but how they orchestrate the cellular, morphological and functional modifications associated with maturation to juvenile/adult states in flatfish is an enigma. Since THs act via thyroid receptors that are ligand activated transcription factors, we hypothesized that the maturation of tissues during metamorphosis should be preceded by significant modifications in the transcriptome. Targeting the unique metamorphosis of flatfish and taking advantage of the large size of Atlantic halibut (Hippoglossus hippoglossus) larvae, we determined the molecular basis of TH action using RNA sequencing. Results De novo assembly of sequences for larval head, skin and gastrointestinal tract (GI-tract) yielded 90,676, 65,530 and 38,426 contigs, respectively. More than 57 % of the assembled sequences were successfully annotated using a multi-step Blast approach. A unique set of biological processes and candidate genes were identified specifically associated with changes in morphology and function of the head, skin and GI-tract. Transcriptome dynamics during metamorphosis were mapped with SOLiD sequencing of whole larvae and revealed greater than 8,000 differentially expressed (DE) genes significantly (p < 0.05) up- or down-regulated in comparison with the juvenile stage. Candidate transcripts quantified by SOLiD and qPCR analysis were significantly (r = 0.843; p < 0.05) correlated. The majority (98 %) of DE genes during metamorphosis were not TH-responsive. TH-responsive transcripts clustered into 6 groups based on their expression pattern during metamorphosis and the majority of the 145 DE TH-responsive genes were down-regulated. Conclusions A transcriptome resource has been generated for metamorphosing Atlantic halibut and over 8,000 DE transcripts per stage were identified. Unique sets of biological processes and candidate genes were associated with changes in the head, skin and GI-tract during metamorphosis. A small proportion of DE transcripts were TH-responsive, suggesting that they trigger gene networks, signalling cascades and transcription factors, leading to the overt changes in tissue occurring during metamorphosis.
Resumo:
Aluminum alloyed with small atomic fractions of Sc, Zr, and Hf has been shown to exhibit high temperature microstructural stability that may improve high temperature mechanical behavior. These quaternary alloys were designed using thermodynamic modeling to increase the volume fraction of precipitated tri-aluminide phases to improve thermal stability. When aged during a multi-step, isochronal heat treatment, two compositions showed a secondary room-temperature hardness peak up to 700 MPa at 450°C. Elevated temperature hardness profiles also indicated an increase in hardness from 200-300°C, attributed to the precipitation of Al3Sc, however, no secondary hardness response was observed from the Al3Zr or Al3Hf phases in this alloy.
Resumo:
Kainic acid has been used for nearly 50 years as a tool in neuroscience due to its pronounced neuroexcitatory properties. However, the significant price increase of kainic acid resulting from the disruption in the supply from its natural source, the alga Digenea Simplex, as well as inefficient synthesis of kainic acid, call for the exploration of functional mimics of kainic acid that can be synthesized in a simpler way. Aza kainoids analog could be one of them. The unsubstituted aza analog of kainoids has demonstrates its ability as an ionotropic glutamate receptor agonist and showed affinity in the chloride dependent glutamate (GluCl) binding site. This opened a question of the importance of the presence of one nitrogen or both nitrogens in the aza kainoid analogs for binding to glutamate receptors. Therefore, two different pyrrolidine analogs of kainic acid, trans-4-(carboxymethyl)pyrrolidine-3-carboxylic acid and trans-2-carboxy-3-pyrrolidineacetic acid, were synthesized through multi-step sequences. The lack of the affinity of both pyrrolidine analogs in GluCl binding site indicated that both nitrogens in aza kainoid analogs are involved in hydrogen bonding with receptors, significantly enhancing their affinity in GluCl binding site. Another potential functional mimic of kainic acid is isoxazolidine analogs of kainoids whose skeleton can be constituted directly via a 1, 3 dipolar cycloaddition as the key step. The difficulty in synthesizing N-unsubstituted isoxazolidines when applying such common protecting groups as alkyl, phenyl and benzyl groups, and the requirement of a desired enantioselectivity due to the three chiral ceneters in kainic acid, pose great challenges. Hence, several different protected nitrones were studied to establish that diphenylmethine nitrone may be a good candidate as the dipole in that the generated isoxazolidines can be deprotected in mild conditions with high yields. Our investigations also indicated that the exo/endo selectivity of the 1, 3 dipolar cycloaddition can be controlled by Lewis acids, and that the application of a directing group in dipolarophiles can accomplish a satisfied enantioselectivity. Those results demonstrated the synthesis of isoxazoldines analogs of kainic acid is very promising.
Resumo:
Pure hydrogen production from methane is a multi-step process run on a large scale for economic reasons. However, hydrogen can be produced in a one-pot continuous process for small scale applications, namely Low Temperature Steam Reforming. Here, Steam Reforming is carried out in a reactor whose walls are composed by a membrane selective toward hydrogen. Pd is the most used membrane material due to its high permeability and selectivity. However, Pd deteriorates at temperatures higher than 500°C, thus the operative temperature of the reaction has to be lowered. However, the employment of a membrane reactor may allow to give high yields thanks to hydrogen removal, which shifts the reaction toward the products. Moreover, pure hydrogen is produced. This work is concentrated on the synthesis of a catalytic system and the investigation of its performances in different processes, namely oxy-reforming, steam reforming and water gas shift, to find appropriate conditions for hydrogen production in a catalytic membrane reactor. The catalyst supports were CeZr and Zr oxides synthesized by microemulsion, impregnated with different noble metals. Pt, Rh and PtRh based catalysts were tested in the oxy reforming process at 500°C, where Rh on CeZr gave the most interesting results. On the opposite, the best performances in low temperature steam reforming were obtained with Rh impregnated on Zr oxide. This catalyst was selected to perform low temperature steam reforming in a Pd membrane reactor. The hydrogen removal given by the membrane allowed to increase the methane conversion over the equilibrium of a classical fixed bed reactor thanks to an equilibrium shift effect. High hydrogen production and recoveries were also obtained, and no other compound permeated through the membrane which proved to be hydrogen selective.