10 resultados para classification scheme
Resumo:
Quantitative examination of prostate histology offers clues in the diagnostic classification of lesions and in the prediction of response to treatment and prognosis. To facilitate the collection of quantitative data, the development of machine vision systems is necessary. This study explored the use of imaging for identifying tissue abnormalities in prostate histology. Medium-power histological scenes were recorded from whole-mount radical prostatectomy sections at × 40 objective magnification and assessed by a pathologist as exhibiting stroma, normal tissue (nonneoplastic epithelial component), or prostatic carcinoma (PCa). A machine vision system was developed that divided the scenes into subregions of 100 × 100 pixels and subjected each to image-processing techniques. Analysis of morphological characteristics allowed the identification of normal tissue. Analysis of image texture demonstrated that Haralick feature 4 was the most suitable for discriminating stroma from PCa. Using these morphological and texture measurements, it was possible to define a classification scheme for each subregion. The machine vision system is designed to integrate these classification rules and generate digital maps of tissue composition from the classification of subregions; 79.3% of subregions were correctly classified. Established classification rates have demonstrated the validity of the methodology on small scenes; a logical extension was to apply the methodology to whole slide images via scanning technology. The machine vision system is capable of classifying these images. The machine vision system developed in this project facilitates the exploration of morphological and texture characteristics in quantifying tissue composition. It also illustrates the potential of quantitative methods to provide highly discriminatory information in the automated identification of prostatic lesions using computer vision.
Resumo:
Objective: To evaluate the psychometric performance of the Child Health Questionnaire (CHQ) in children with cerebral palsy (CP).
Method: 818 parents of children with CP, aged 8–12 from nine regions of Europe completed the CHQ (parent form 50 items). Functional abilities were classified using the five-level Gross Motor Function Classification Scheme (Levels I–III as ambulant; Level IV–V as nonambulant CP).
Results: Ceiling effects were observed for a number of subscales and summary scores across all Gross Motor Function Classification System levels, whilst floor effects occurred only in the physical functioning scale (Level V CP). Reliability was satisfactory overall. Confirmatory factor analysis (CFA) revealed a seven-factor structure for the total sample of children with CP but with different factor structures for ambulant and nonambulant children.
Conclusion: The CHQ has limited applicability in children with CP, although with judicious use of certain domains for ambulant and nonambulant children can provide useful and comparable data about child health status for descriptive purposes.
Resumo:
It has been 25 years since the publication of a comprehensive review of the full spectrum of salesperformance drivers. This study takes stock of the contemporary field and synthesizes empirical evidence from the period 1982–2008. The authors revise the classification scheme for sales performance determinants devised by Walker et al. (1977) and estimate both the predictive validity of its sub-categories and the impact of a range of moderators on determinant-sales performance relationships. Based on multivariate causal model analysis, the results make two major observations: (1) Five sub-categories demonstrate significant relationships with sales performance: selling-related knowledge (ß=.28), degree of adaptiveness (ß=.27), role ambiguity (ß=-.25), cognitive aptitude (ß=.23) and work engagement (ß=.23). (2) These sub-categories are moderated by measurement method, research context, and salestype variables. The authors identify managerial implications of the results and offer suggestions for further research, including the conjecture that as the world is moving toward a knowledge-intensive economy, salespeople could be functioning as knowledge-brokers. The results seem to back this supposition and indicate how it might inspire future research in the field of personal selling.
Resumo:
The Microarray Innovations in Leukemia study assessed the clinical utility of gene expression profiling as a single test to subtype leukemias into conventional categories of myeloid and lymphoid malignancies. METHODS: The investigation was performed in 11 laboratories across three continents and included 3,334 patients. An exploratory retrospective stage I study was designed for biomarker discovery and generated whole-genome expression profiles from 2,143 patients with leukemias and myelodysplastic syndromes. The gene expression profiling-based diagnostic accuracy was further validated in a prospective second study stage of an independent cohort of 1,191 patients. RESULTS: On the basis of 2,096 samples, the stage I study achieved 92.2% classification accuracy for all 18 distinct classes investigated (median specificity of 99.7%). In a second cohort of 1,152 prospectively collected patients, a classification scheme reached 95.6% median sensitivity and 99.8% median specificity for 14 standard subtypes of acute leukemia (eight acute lymphoblastic leukemia and six acute myeloid leukemia classes, n = 693). In 29 (57%) of 51 discrepant cases, the microarray results had outperformed routine diagnostic methods. CONCLUSION: Gene expression profiling is a robust technology for the diagnosis of hematologic malignancies with high accuracy. It may complement current diagnostic algorithms and could offer a reliable platform for patients who lack access to today's state-of-the-art diagnostic work-up. Our comprehensive gene expression data set will be submitted to the public domain to foster research focusing on the molecular understanding of leukemias
Resumo:
The pharmacological classification of P2 receptors owes its origin to the pioneering efforts of Geoff Burnstock and those who followed him, research that was conducted primarily in physiological experimental systems. Over recent years, the techniques of molecular biology have been increasingly applied in the study of P2 receptors while, at the same time, advances in their pharmacological analysis have been limited by a lack of potent and selective agonist or antagonist ligands. This has resulted in a classification scheme which is largely structural in nature, with relatively little contribution from pharmacology. Our endeavours in this area have been directed towards the discovery of ligands with which the pharmacological analysis and definition of P2 receptors could be advanced, the ultimate goal being the design of therapeutic agents. This article will describe some of our experiences in this challenging but rewarding Nea. (C) 2000 Elsevier Science B.V. All rights reserved.
Resumo:
There is considerable interest in hydrogen adsorption on carbon nanotubes and porous carbons as a method of storage for transport and related energy applications. This investigation has involved a systematic investigation of the role of functional groups and porous structure characteristics in determining the hydrogen adsorption characteristics of porous carbons. Suites of carbons were prepared with a wide range of nitrogen and oxygen contents and types of functional groups to investigate their effect on hydrogen adsorption. The porous structures of the carbons were characterized by nitrogen (77 K) and carbon dioxide (273 K) adsorption methods. Hydrogen adsorption isotherms were studied at 77 K and pressure up to 100 kPa. All the isotherms were Type I in the IUPAC classification scheme. Hydrogen isobars indicated that the adsorption of hydrogen is very temperature dependent with little or no hydrogen adsorption above 195 K. The isosteric enthalpies of adsorption at zero surface coverage were obtained using a virial equation, while the values at various surface coverages were obtained from the van't Hoff isochore. The values were in the range 3.9-5.2 kJ mol(-1) for the carbons studied. The thermodynamics of the adsorption process are discussed in relation to temperature limitations for hydrogen storage applications. The maximum amounts of hydrogen adsorbed correlated with the micropore volume obtained from extrapolation of the Dubinin-Radushkevich equation for carbon dioxide adsorption. Functional groups have a small detrimental effect on hydrogen adsorption, and this is related to decreased adsorbate-adsorbent and increased adsorbate-adsorbate interactions.
Resumo:
We present 65 optical spectra of the Type Ia supernova SN 2012fr, of which 33 were obtained before maximum light. At early times SN 2012fr shows clear evidence of a high-velocity feature (HVF) in the Si II 6355 line which can be cleanly decoupled from the lower velocity "photospheric" component. This Si II 6355 HVF fades by phase -5; subsequently, the photospheric component exhibits a very narrow velocity width and remains at a nearly constant velocity of v~12,000 km/s until at least 5 weeks after maximum brightness. The Ca II infrared (IR) triplet exhibits similar evidence for both a photospheric component at v~12,000 km/s with narrow line width and long velocity plateau, as well as a high-velocity component beginning at v~31,000 km/s two weeks before maximum. SN 2012fr resides on the border between the "shallow silicon" and "core-normal" subclasses in the Branch et al. (2009) classification scheme, and on the border between normal and "high-velocity" SNe Ia in the Wang et al. (2009a) system. Though it is a clear member of the "low velocity gradient" (LVG; Benetii et al., 2005) group of SNe Ia and exhibits a very slow light-curve decline, it shows key dissimilarities with the overluminous SN 1991T or SN 1999aa subclasses of SNe Ia. SN 2012fr represents a well-observed SN Ia at the luminous end of the normal SN Ia distribution, and a key transitional event between nominal spectroscopic subclasses of SNe Ia.
Resumo:
In this paper, a hardware solution for packet classification based on multi-fields is presented. The proposed scheme focuses on a new architecture based on the decomposition method. A hash circuit is used in order to reduce the memory space required for the Recursive Flow Classification (RFC) algorithm. The implementation results show that the proposed architecture achieves significant performance advantage that is comparable to that of some well-known algorithms. The solution is based on Altera Stratix III FPGA technology.
Resumo:
Network management tools must be able to monitor and analyze traffic flowing through network systems. According to the OpenFlow protocol applied in Software-Defined Networking (SDN), packets are classified into flows that are searched in flow tables. Further actions, such as packet forwarding, modification, and redirection to a group table, are made in the flow table with respect to the search results. A novel hardware solution for SDN-enabled packet classification is presented in this paper. The proposed scheme is focused on a label-based search method, achieving high flexibility in memory usage. The implemented hardware architecture provides optimal lookup performance by configuring the search algorithm and by performing fast incremental update as programmed the software controller.