936 resultados para MODEL ANALYSIS


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Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

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This study was designed to present the feasibility of an in vivo image-guided percutaneous cryoablation of the porcine vertebral body. Methods The institutional animal care committee approved this study. Cone-beam computed tomography (CBCT)-guided vertebral cryoablations (n = 22) were performed in eight pigs with short, 2-min, single or double-freezing protocols. Protective measures to nerves included dioxide carbon (CO2) epidural injections and spinal canal temperature monitoring. Clinical, radiological, and pathological data with light (n = 20) or transmission electron (n = 2) microscopic analyses were evaluated after 6 days of clinical follow-up and euthanasia. Results CBCT/fluoroscopic-guided transpedicular vertebral body cryoprobe positioning and CO2 epidural injection were successful in all procedures. No major complications were observed in seven animals (87.5 %, n = 8). A minor complication was observed in one pig (12.5 %, n = 1). Logistic regression model analysis showed the cryoprobe-spinal canal (Cp-Sc) distance as the most efficient parameter to categorize spinal canal temperatures lower than 19 °C (p<0.004), with a significant Pearson’s correlation test (p < 0.041) between the Cp-Sc distance and the lowest spinal canal temperatures. Ablation zones encompassed pedicles and the posterior wall of the vertebral bodies with an inflammatory rim, although no inflammatory infiltrate was depicted in the surrounding neural structures at light microscopy. Ultrastructural analyses evidenced myelin sheath disruption in some large nerve fibers, although neurological deficits were not observed. Conclusions CBCT-guided vertebral cryoablation of the porcine spine is feasible under a combination of a short freezing protocol and protective measures to the surrounding nerves. Ultrastructural analyses may be helpful assess the early modifications of the nerve fibers.

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The aim of the work is to conduct a finite element model analysis on a small – size concrete beam and on a full size concrete beam internally reinforced with BFRP exposed at elevated temperatures. Experimental tests performed at Kingston University have been used to compare the results from the numerical analysis for the small – size concrete beam. Once the behavior of the small – size beam at room temperature is investigated and switching to the heating phase reinforced beams are tested at 100°C, 200°C and 300°C in loaded condition. The aim of the finite element analysis is to reflect the three – point bending test adopted into the oven during the exposure of the beam at room temperature and at elevated temperatures. Performance and deformability of reinforced beams are straightly correlated to the material properties and a wide analysis on elastic modulus and coefficient of thermal expansion is given in this work. Develop a good correlation between the numerical model and the experimental test is the main objective of the analysis on the small – size concrete beam, for both modelling the aim is also to estimate which is the deterioration of the material properties due to the heating process and the influence of different parameters on the final result. The focus of the full – size modelling which involved the last part of this work is to evaluate the effect of elevated temperatures, the material deterioration and the deflection trend on a reinforced beam characterized by a different size. A comparison between the results from different modelling has been developed.

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UPTAKE AND METABOLISM OF 5’-AMP IN THE ERYTHROCYTE PLAY KEY ROLES IN THE 5’-AMP INDUCED MODEL OF DEEP HYPOMETABOLISM Publication No. ________ Isadora Susan Daniels, B.A. Supervisory Professor: Cheng Chi Lee, Ph.D. Mechanisms that initiate and control the natural hypometabolic states of mammals are poorly understood. The laboratory developed a model of deep hypometabolism (DH) initiated by uptake of 5’-adenosine monophosphate (5’-AMP) into erythrocytes. Mice enter DH when given a high dose of 5’-AMP and the body cools readily. Influx of 5’-AMP appears to inhibit thermoregulatory control. In a 15°C environment, mice injected with 5’-AMP (0.5 mg/gw) enter a Phase I response in which oxygen consumption (VO2) drops rapidly to 1/3rd of euthermic levels. The Phase I response appears independent of body temperature (Tb). This is followed by gradual body temperature decline that correlates with VO2 decline, called Phase II response. Within 90 minutes, mouse Tb approaches 15°C, and VO2 is 1/10th of normal. Mice can remain several hours in this state, before gradually and safely recovering. The DH state translates to other mammalian species. Our studies show uptake and metabolism of 5’-AMP in erythrocytes causes biochemical changes that initiate DH. Increased AMP shifts the adenylate equilibrium toward ADP formation, consequently decreasing intracellular ATP. In turn, glycolysis slows, indicated by increased glucose and decreased lactate. 2,3-bisphosphoglycerate levels rise, allosterically reducing oxygen affinity for hemoglobin, and deoxyhemoglobin rises. Less oxygen transport to tissues likely triggers the DH model. The major intracellular pathway for AMP catabolism is catalyzed by AMP deaminase (AMPD). Multiple AMPD isozymes are expressed in various tissues, but erythrocytes only have AMPD3. Mice lacking AMPD3 were created to study control of the DH model, specifically in erythrocytes. Telemetric measurements demonstrate lower Tb and difficulty maintaining Tb under moderate metabolic stress. A more dramatic response to lower dose of 5’-AMP suggests AMPD activity in the erythrocyte plays an important role in control of the DH model. Analysis of adenylates in erythrocyte lysate shows 3-fold higher levels of ATP and ADP but similar AMP levels to wild-type. Taken together, results indicate alterations in energy status of erythrocytes can induce a hypometabolic state. AMPD3 control of AMP catabolism is important in controlling the DH model. Genetically reducing AMP catabolism in erythrocytes causes a phenotype of lower Tb and compromised ability to maintain temperature homeostasis.

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A multi-model analysis of Atlantic multidecadal variability is performed with the following aims: to investigate the similarities to observations; to assess the strength and relative importance of the different elements of the mechanism proposed by Delworth et al. (J Clim 6:1993–2011, 1993) (hereafter D93) among coupled general circulation models (CGCMs); and to relate model differences to mean systematic error. The analysis is performed with long control simulations from ten CGCMs, with lengths ranging between 500 and 3600 years. In most models the variations of sea surface temperature (SST) averaged over North Atlantic show considerable power on multidecadal time scales, but with different periodicity. The SST variations are largest in the mid-latitude region, consistent with the short instrumental record. Despite large differences in model configurations, we find quite some consistency among the models in terms of processes. In eight of the ten models the mid-latitude SST variations are significantly correlated with fluctuations in the Atlantic meridional overturning circulation (AMOC), suggesting a link to northward heat transport changes. Consistent with this link, the three models with the weakest AMOC have the largest cold SST bias in the North Atlantic. There is no linear relationship on decadal timescales between AMOC and North Atlantic Oscillation in the models. Analysis of the key elements of the D93 mechanisms revealed the following: Most models present strong evidence that high-latitude winter mixing precede AMOC changes. However, the regions of wintertime convection differ among models. In most models salinity-induced density anomalies in the convective region tend to lead AMOC, while temperature-induced density anomalies lead AMOC only in one model. However, analysis shows that salinity may play an overly important role in most models, because of cold temperature biases in their relevant convective regions. In most models subpolar gyre variations tend to lead AMOC changes, and this relation is strong in more than half of the models.

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Objective. The goal of this study is to characterize the current workforce of CIHs, the lengths of professional practice careers of the past and current CIHs.^ Methods. This is a secondary data analysis of data compiled from all of the nearly 50 annual roster listings of the American Board of Industrial Hygiene (ABIH) for Certified Industrial Hygienists active in each year since 1960. Survival analysis was performed as a technique to measure the primary outcome of interest. The technique which was involved in this study was the Kaplan-Meier method for estimating the survival function.^ Study subjects: The population to be studied is all Certified Industrial Hygienists (CIHs). A CIH is defined by the ABIH as an individual who has achieved the minimum requirements for education, working experience and through examination, has demonstrated a minimum level of knowledge and competency in the prevention of occupational illnesses. ^ Results. A Cox-proportional hazards model analysis was performed by different start-time cohorts of CIHs. In this model we chose cohort 1 as the reference cohort. The estimated relative risk of the event (defined as retirement, or absent from 5 consecutive years of listing) occurred for CIHs for cohorts 2,3,4,5 relative to cohort 1 is 0.385, 0.214, 0.234, 0.299 relatively. The result show that cohort 2 (CIHs issued from 1970-1980) has the lowest hazard ratio which indicates the lowest retirement rate.^ Conclusion. The manpower of CIHs (still actively practicing up to the end of 2009) increased tremendously starting in 1980 and grew into a plateau in recent decades. This indicates that the supply and demand of the profession may have reached equilibrium. More demographic information and variables are needed to actually predict the future number of CIHs needed. ^

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Innovations in the current interconnected world of organizations have lead to a focus on business models as a fundamental statement of direction and identity. Although industry transformations generally emanate from technological changes, recent examples suggest they may also be due to the introduction of new business models. In the past, different types of airline business models could be clearly separated from each other. However, this has changed in recent years partly due to the concentration process and partly to reaction caused by competitive pressure. At least it can be concluded that in future the distinction of different business models will remain less clear. To advance the use of business models as a concept, it is essential to be able to compare and perform analyses to identify the business models that may have the highest potential. This can essentially contribute to understanding the synergies and incompatibilities in the case of two airlines that are going in for a merger. This is illustrated by the example of Swiss Air-Lufthansa merger analysis. The idea is to develop quantitative methods and tools for comparing and analyzing Aeronautical/Airline business models. The paper identifies available methods of comparing airline business models and lays the ground work for a quantitative model of comparing airline business models. This can be a useful tool for business model analysis when two airlines are merged

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There has been an increased demand for characterizing user access patterns using web mining techniques since the informative knowledge extracted from web server log files can not only offer benefits for web site structure improvement but also for better understanding of user navigational behavior. In this paper, we present a web usage mining method, which utilize web user usage and page linkage information to capture user access pattern based on Probabilistic Latent Semantic Analysis (PLSA) model. A specific probabilistic model analysis algorithm, EM algorithm, is applied to the integrated usage data to infer the latent semantic factors as well as generate user session clusters for revealing user access patterns. Experiments have been conducted on real world data set to validate the effectiveness of the proposed approach. The results have shown that the presented method is capable of characterizing the latent semantic factors and generating user profile in terms of weighted page vectors, which may reflect the common access interest exhibited by users among same session cluster.

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Component-based development (CBD) has become an important emerging topic in the software engineering field. It promises long-sought-after benefits such as increased software reuse, reduced development time to market and, hence, reduced software production cost. Despite the huge potential, the lack of reasoning support and development environment of component modeling and verification may hinder its development. Methods and tools that can support component model analysis are highly appreciated by industry. Such a tool support should be fully automated as well as efficient. At the same time, the reasoning tool should scale up well as it may need to handle hundreds or even thousands of components that a modern software system may have. Furthermore, a distributed environment that can effectively manage and compose components is also desirable. In this paper, we present an approach to the modeling and verification of a newly proposed component model using Semantic Web languages and their reasoning tools. We use the Web Ontology Language and the Semantic Web Rule Language to precisely capture the inter-relationships and constraints among the entities in a component model. Semantic Web reasoning tools are deployed to perform automated analysis support of the component models. Moreover, we also proposed a service-oriented architecture (SOA)-based semantic web environment for CBD. The adoption of Semantic Web services and SOA make our component environment more reusable, scalable, dynamic and adaptive.

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This research examined the factors contributing to the performance of online grocers prior to, and following, the 2000 dot.com collapse. The primary goals were to assess the relationship between a company’s business model(s) and its performance in the online grocery channel and to determine if there were other company and/or market related factors that could account for company performance. ^ To assess the primary goals, a case based theory building process was utilized. A three-way cross-case analysis comprising Peapod, GroceryWorks, and Tesco examined the common profit components, the structural category (e.g., pure-play, partnership, and hybrid) profit components, and the idiosyncratic profit components related to each specific company. ^ Based on the analysis, it was determined that online grocery store business models could be represented at three distinct, but hierarchically, related levels. The first level was termed the core model and represented the basic profit structure that all online grocers needed in order to conduct operations. The next model level was termed the structural model and represented the profit structure associated with the specific business model configuration (i.e., pure-play, partnership, hybrid). The last model level was termed the augmented model and represented the company’s business model when idiosyncratic profit components were included. In relation to the five company related factors, scalability, rate of expansion, and the automation level were potential candidates for helping to explain online grocer performance. In addition, all the market structure related factors were deemed possible candidates for helping to explain online grocer performance. ^ The study concluded by positing an alternative hypothesis concerning the performance of online grocers. Prior to this study, the prevailing wisdom was that the business models were the primary cause of online grocer performance. However, based on the core model analysis, it was hypothesized that the customer relationship activities (i.e., advertising, promotions, and loyalty program tie-ins) were the real drivers of online grocer performance. ^

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This research examined the factors contributing to the performance of online grocers prior to, and following, the 2000 dot.com collapse. The primary goals were to assess the relationship between a company’s business model(s) and its performance in the online grocery channel and to determine if there were other company and/or market related factors that could account for company performance. To assess the primary goals, a case based theory building process was utilized. A three-way cross-case analysis comprising Peapod, GroceryWorks, and Tesco examined the common profit components, the structural category (e.g., pure-play, partnership, and hybrid) profit components, and the idiosyncratic profit components related to each specific company. Based on the analysis, it was determined that online grocery store business models could be represented at three distinct, but hierarchically, related levels. The first level was termed the core model and represented the basic profit structure that all online grocers needed in order to conduct operations. The next model level was termed the structural model and represented the profit structure associated with the specific business model configuration (i.e., pure-play, partnership, hybrid). The last model level was termed the augmented model and represented the company’s business model when idiosyncratic profit components were included. In relation to the five company related factors, scalability, rate of expansion, and the automation level were potential candidates for helping to explain online grocer performance. In addition, all the market structure related factors were deemed possible candidates for helping to explain online grocer performance. The study concluded by positing an alternative hypothesis concerning the performance of online grocers. Prior to this study, the prevailing wisdom was that the business models were the primary cause of online grocer performance. However, based on the core model analysis, it was hypothesized that the customer relationship activities (i.e., advertising, promotions, and loyalty program tie-ins) were the real drivers of online grocer performance.

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Background: Understanding transcriptional regulation by genome-wide microarray studies can contribute to unravel complex relationships between genes. Attempts to standardize the annotation of microarray data include the Minimum Information About a Microarray Experiment (MIAME) recommendations, the MAGE-ML format for data interchange, and the use of controlled vocabularies or ontologies. The existing software systems for microarray data analysis implement the mentioned standards only partially and are often hard to use and extend. Integration of genomic annotation data and other sources of external knowledge using open standards is therefore a key requirement for future integrated analysis systems. Results: The EMMA 2 software has been designed to resolve shortcomings with respect to full MAGE-ML and ontology support and makes use of modern data integration techniques. We present a software system that features comprehensive data analysis functions for spotted arrays, and for the most common synthesized oligo arrays such as Agilent, Affymetrix and NimbleGen. The system is based on the full MAGE object model. Analysis functionality is based on R and Bioconductor packages and can make use of a compute cluster for distributed services. Conclusion: Our model-driven approach for automatically implementing a full MAGE object model provides high flexibility and compatibility. Data integration via SOAP-based web-services is advantageous in a distributed client-server environment as the collaborative analysis of microarray data is gaining more and more relevance in international research consortia. The adequacy of the EMMA 2 software design and implementation has been proven by its application in many distributed functional genomics projects. Its scalability makes the current architecture suited for extensions towards future transcriptomics methods based on high-throughput sequencing approaches which have much higher computational requirements than microarrays.

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There is an increasing rate of papillary thyroid carcinomas that may never progress to cause symptoms or death. Predicting outcome and determining tumour aggressiveness could help diminish the number of patients submitted to aggressive treatments. We aimed to evaluate whether markers of the immune system response and of tumour-associated inflammation could predict outcome of differentiated thyroid cancer (DTC) patients. Retrospective cohort study. We studied 399 consecutive patients, including 325 papillary and 74 follicular thyroid carcinomas. Immune cell markers were evaluated using immunohistochemistry, including tumour-associated macrophages (CD68) and subsets of tumour-infiltrating lymphocytes (TIL), such as CD3, CD4, CD8, CD16, CD20, CD45RO, GRANZYME B, CD69 and CD25. We also investigated the expression of cyclooxygenase 2 (COX2) in tumour cells and the presence of concurrent lymphocytic infiltration characterizing chronic thyroiditis. Concurrent lymphocytic infiltration characterizing chronic thyroiditis was observed in 29% of the cases. Among all the immunological parameters evaluated, only the enrichment of CD8+ lymphocytes (P = 0·001) and expression of COX2 (P =0·01) were associated with recurrence. A multivariate model analysis identified CD8+ TIL/COX2 as independent risk factor for recurrence. A multivariate analysis using Cox's proportional-hazards model adjusted for the presence of concurrent chronic thyroiditis demonstrated that the presence of concurrent chronic thyroiditis had no effect on prognostic prediction mediated by CD8+ TIL and COX2. In conclusion, we suggest the use of a relatively simple pathology tool to help select cases that may benefit of a more aggressive approach sparing the majority of patients from unnecessary procedures.

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Resource specialisation, although a fundamental component of ecological theory, is employed in disparate ways. Most definitions derive from simple counts of resource species. We build on recent advances in ecophylogenetics and null model analysis to propose a concept of specialisation that comprises affinities among resources as well as their co-occurrence with consumers. In the distance-based specialisation index (DSI), specialisation is measured as relatedness (phylogenetic or otherwise) of resources, scaled by the null expectation of random use of locally available resources. Thus, specialists use significantly clustered sets of resources, whereas generalists use over-dispersed resources. Intermediate species are classed as indiscriminate consumers. The effectiveness of this approach was assessed with differentially restricted null models, applied to a data set of 168 herbivorous insect species and their hosts. Incorporation of plant relatedness and relative abundance greatly improved specialisation measures compared to taxon counts or simpler null models, which overestimate the fraction of specialists, a problem compounded by insufficient sampling effort. This framework disambiguates the concept of specialisation with an explicit measure applicable to any mode of affinity among resource classes, and is also linked to ecological and evolutionary processes. This will enable a more rigorous deployment of ecological specialisation in empirical and theoretical studies.