987 resultados para Predictive testing


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A supported lanthanum gallate (LSGM) electrolyte thin-film solid oxide fuel cell with Ni-YSZ cermet anode and strontium-doped lanthanum manganite (LSM)-yttria stabilized zirconia (YSZ) composite cathode was, for the first time, fabricated and tested. The cell was prepared by an unconventional approach, in which an LSGM thin film (about 15 mum thick) was first deposited on a porous substrate such as a porous YSZ disk by a wet process and sintered at a high temperature (above 1400degrees C). NiO was then incorporated into the porous substrate by a carefully controlled impregnation process and fired at a much lower temperature. In this way, the severe reaction between LSGM and NiO at a high temperature, which is required for the full densification of LSGM film, can be avoided. A strontium-doped LaMnO3 (LSM)-YSZ composite cathode was screen printed on the surface of the LSGM film and then fired at 1250degrees C. The electrolyte resistances of the SOFC single cells fabricated by this approach are much lower compared to those of thick LSGM film supported cells. A maximum output power density of over 0.85 W/cm(2) at 800degreesC with H-2 as fuel and air as oxidant for a fabricated cell was achieved. (C) 2002 The Electrochemical Society.

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In this paper, we present an approach to discretizing multivariate continuous data while learning the structure of a graphical model. We derive the joint scoring function from the principle of predictive accuracy, which inherently ensures the optimal trade-off between goodness of fit and model complexity (including the number of discretization levels). Using the so-called finest grid implied by the data, our scoring function depends only on the number of data points in the various discretization levels. Not only can it be computed efficiently, but it is also independent of the metric used in the continuous space. Our experiments with gene expression data show that discretization plays a crucial role regarding the resulting network structure.

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In this study, an in vitro multicellular tumor spheroid model was developed using microencapsulation, and the feasibility of using the microencapsulated. multicellular tumor spheroid (MMTS) to test the effect of chemotherapeutic drugs was investigated. Human MCF-7 breast cancer cells were encapsulated in alginate-poly-L-lysine-alginate (APA) microcapsules, and a single multicellular spheroid 150 mu m in diameter was formed in the microcapsule after 5 days of cultivation. The cell morphology, proliferation, and viability of the MMTS were characterized using phase contrast microscopy, BrdU-Iabeling, MTT stain, calcein AM/ED-2 stain, and H&E stain. It demonstrated that the MMTS was viable and that the proliferating cells were mainly localized to the periphery of the cell spheroid and the apoptotic cells were in the core. The MCF-7 MMTS was treated with mitomycin C (MC) at a concentration of 0.1, 1, or 10 times that of peak plasma concentration (ppc) for up to 72 h. The cytotoxicity was demonstrated. clearly by the reduction in cell spheroid size and the decrease in cell viability. The MMTS was further used to screen the anticancer effect of chemotherapeutic drugs, treated with MC, adriamycin (ADM) and 5-fluorouracil (5-FU) at concentrations of 0.1, 1, and 10 ppc for 24, 48, and 72 h. MCF-7 monolayer culture was used as control. Similar to monolayer culture, the cell viability of MMTS was reduced after treatment with anticancer drugs. However, the inhibition rate of cell viability in MMTS was much lower than that in monolayer culture. The MMTS was more resistant to anticancer drugs than monolayer culture. The inhibition rates of cell viability were 68.1%, 45.1%, and 46.8% in MMTS and 95.1%, 86.8%, and 91.6% in monolayer culture treated with MC, ADM, and 5-FU at 10 ppc for 72 h, respectively. MC showed the strongest cytotoxicity in both MMTS and monolayer, followed by 5-FU and ADM. It demonstrated that the MMTS has the potential to be a rapid and valid in vitro model to screen chemotherapeutic drugs with a feature to mimic in vivo three-dimensional (3-D) cell growth pattern.

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Struyf, J., Dzeroski, S. Blockeel, H. and Clare, A. (2005) Hierarchical Multi-classification with Predictive Clustering Trees in Functional Genomics. In proceedings of the EPIA 2005 CMB Workshop

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Projeto de Pós-Graduação/Dissertação apresentado à Universidade Fernando Pessoa como parte dos requisitos para obtenção do grau de Mestre em Ciências Farmacêuticas

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The Transmission Control Protocol (TCP) has been the protocol of choice for many Internet applications requiring reliable connections. The design of TCP has been challenged by the extension of connections over wireless links. We ask a fundamental question: What is the basic predictive power of TCP of network state, including wireless error conditions? The goal is to improve or readily exploit this predictive power to enable TCP (or variants) to perform well in generalized network settings. To that end, we use Maximum Likelihood Ratio tests to evaluate TCP as a detector/estimator. We quantify how well network state can be estimated, given network response such as distributions of packet delays or TCP throughput that are conditioned on the type of packet loss. Using our model-based approach and extensive simulations, we demonstrate that congestion-induced losses and losses due to wireless transmission errors produce sufficiently different statistics upon which an efficient detector can be built; distributions of network loads can provide effective means for estimating packet loss type; and packet delay is a better signal of network state than short-term throughput. We demonstrate how estimation accuracy is influenced by different proportions of congestion versus wireless losses and penalties on incorrect estimation.

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The majority of the traffic (bytes) flowing over the Internet today have been attributed to the Transmission Control Protocol (TCP). This strong presence of TCP has recently spurred further investigations into its congestion avoidance mechanism and its effect on the performance of short and long data transfers. At the same time, the rising interest in enhancing Internet services while keeping the implementation cost low has led to several service-differentiation proposals. In such service-differentiation architectures, much of the complexity is placed only in access routers, which classify and mark packets from different flows. Core routers can then allocate enough resources to each class of packets so as to satisfy delivery requirements, such as predictable (consistent) and fair service. In this paper, we investigate the interaction among short and long TCP flows, and how TCP service can be improved by employing a low-cost service-differentiation scheme. Through control-theoretic arguments and extensive simulations, we show the utility of isolating TCP flows into two classes based on their lifetime/size, namely one class of short flows and another of long flows. With such class-based isolation, short and long TCP flows have separate service queues at routers. This protects each class of flows from the other as they possess different characteristics, such as burstiness of arrivals/departures and congestion/sending window dynamics. We show the benefits of isolation, in terms of better predictability and fairness, over traditional shared queueing systems with both tail-drop and Random-Early-Drop (RED) packet dropping policies. The proposed class-based isolation of TCP flows has several advantages: (1) the implementation cost is low since it only requires core routers to maintain per-class (rather than per-flow) state; (2) it promises to be an effective traffic engineering tool for improved predictability and fairness for both short and long TCP flows; and (3) stringent delay requirements of short interactive transfers can be met by increasing the amount of resources allocated to the class of short flows.

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Do humans and animals learn exemplars or prototypes when they categorize objects and events in the world? How are different degrees of abstraction realized through learning by neurons in inferotemporal and prefrontal cortex? How do top-down expectations influence the course of learning? Thirty related human cognitive experiments (the 5-4 category structure) have been used to test competing views in the prototype-exemplar debate. In these experiments, during the test phase, subjects unlearn in a characteristic way items that they had learned to categorize perfectly in the training phase. Many cognitive models do not describe how an individual learns or forgets such categories through time. Adaptive Resonance Theory (ART) neural models provide such a description, and also clarify both psychological and neurobiological data. Matching of bottom-up signals with learned top-down expectations plays a key role in ART model learning. Here, an ART model is used to learn incrementally in response to 5-4 category structure stimuli. Simulation results agree with experimental data, achieving perfect categorization in training and a good match to the pattern of errors exhibited by human subjects in the testing phase. These results show how the model learns both prototypes and certain exemplars in the training phase. ART prototypes are, however, unlike the ones posited in the traditional prototype-exemplar debate. Rather, they are critical patterns of features to which a subject learns to pay attention based on past predictive success and the order in which exemplars are experienced. Perturbations of old memories by newly arriving test items generate a performance curve that closely matches the performance pattern of human subjects. The model also clarifies exemplar-based accounts of data concerning amnesia.

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In a constantly changing world, humans are adapted to alternate routinely between attending to familiar objects and testing hypotheses about novel ones. We can rapidly learn to recognize and narne novel objects without unselectively disrupting our memories of familiar ones. We can notice fine details that differentiate nearly identical objects and generalize across broad classes of dissimilar objects. This chapter describes a class of self-organizing neural network architectures--called ARTMAP-- that are capable of fast, yet stable, on-line recognition learning, hypothesis testing, and naming in response to an arbitrary stream of input patterns (Carpenter, Grossberg, Markuzon, Reynolds, and Rosen, 1992; Carpenter, Grossberg, and Reynolds, 1991). The intrinsic stability of ARTMAP allows the system to learn incrementally for an unlimited period of time. System stability properties can be traced to the structure of its learned memories, which encode clusters of attended features into its recognition categories, rather than slow averages of category inputs. The level of detail in the learned attentional focus is determined moment-by-moment, depending on predictive success: an error due to over-generalization automatically focuses attention on additional input details enough of which are learned in a new recognition category so that the predictive error will not be repeated. An ARTMAP system creates an evolving map between a variable number of learned categories that compress one feature space (e.g., visual features) to learned categories of another feature space (e.g., auditory features). Input vectors can be either binary or analog. Computational properties of the networks enable them to perform significantly better in benchmark studies than alternative machine learning, genetic algorithm, or neural network models. Some of the critical problems that challenge and constrain any such autonomous learning system will next be illustrated. Design principles that work together to solve these problems are then outlined. These principles are realized in the ARTMAP architecture, which is specified as an algorithm. Finally, ARTMAP dynamics are illustrated by means of a series of benchmark simulations.

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This article introduces a new neural network architecture, called ARTMAP, that autonomously learns to classify arbitrarily many, arbitrarily ordered vectors into recognition categories based on predictive success. This supervised learning system is built up from a pair of Adaptive Resonance Theory modules (ARTa and ARTb) that are capable of self-organizing stable recognition categories in response to arbitrary sequences of input patterns. During training trials, the ARTa module receives a stream {a^(p)} of input patterns, and ARTb receives a stream {b^(p)} of input patterns, where b^(p) is the correct prediction given a^(p). These ART modules are linked by an associative learning network and an internal controller that ensures autonomous system operation in real time. During test trials, the remaining patterns a^(p) are presented without b^(p), and their predictions at ARTb are compared with b^(p). Tested on a benchmark machine learning database in both on-line and off-line simulations, the ARTMAP system learns orders of magnitude more quickly, efficiently, and accurately than alternative algorithms, and achieves 100% accuracy after training on less than half the input patterns in the database. It achieves these properties by using an internal controller that conjointly maximizes predictive generalization and minimizes predictive error by linking predictive success to category size on a trial-by-trial basis, using only local operations. This computation increases the vigilance parameter ρa of ARTa by the minimal amount needed to correct a predictive error at ARTb· Parameter ρa calibrates the minimum confidence that ARTa must have in a category, or hypothesis, activated by an input a^(p) in order for ARTa to accept that category, rather than search for a better one through an automatically controlled process of hypothesis testing. Parameter ρa is compared with the degree of match between a^(p) and the top-down learned expectation, or prototype, that is read-out subsequent to activation of an ARTa category. Search occurs if the degree of match is less than ρa. ARTMAP is hereby a type of self-organizing expert system that calibrates the selectivity of its hypotheses based upon predictive success. As a result, rare but important events can be quickly and sharply distinguished even if they are similar to frequent events with different consequences. Between input trials ρa relaxes to a baseline vigilance pa When ρa is large, the system runs in a conservative mode, wherein predictions are made only if the system is confident of the outcome. Very few false-alarm errors then occur at any stage of learning, yet the system reaches asymptote with no loss of speed. Because ARTMAP learning is self stabilizing, it can continue learning one or more databases, without degrading its corpus of memories, until its full memory capacity is utilized.

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BP (89-A-1204); Defense Advanced Research Projects Agency (90-0083); National Science Foundation (IRI-90-00530); Air Force Office of Scientific Research (90-0175, 90-0128); Army Research Office (DAAL-03-88-K0088)

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Cream liqueurs manufactured by a one-step process, where alcohol was added before homogenisation, were more stable than those processed by a two -step process which involved addition of alcohol after homogenisation. Using the one-step process, it was possible to produce creaming-stable liqueurs by using one pass through a homogeniser (27.6 MPa) equipped with "liquid whirl" valves. Test procedures to characterise cream liqueurs and to predict shelf life were studied in detail. A turbidity test proved simple, rapid and sensitive for characterising particle size and homogenisation efficiency. Prediction of age thickening/gelation in cream liqueurs during incubation at 45 °C depended on the age of the sample when incubated. Samples that gelled at 45 °C may not do so at ambient temperature. Commercial cream liqueurs were similar in gross chemical composition, and unlike experimentally produced liqueurs, these did not exhibit either age-gelation at ambient or elevated temperatures. Solutions of commercial sodium caseinates from different sources varied in their calcium sensitivity. When incorporated into cream liqueurs, caseinates influenced the rate of viscosity increase, coalescence and, possibly, gelation during incubated storage. Mild heat and alcohol treatment modified the properties of caseinate used to stabilise non-alcoholic emulsions, while the presence of alcohol in emulsions was important in preventing clustering of globules. The response to added trisodium citrate varied. In many cases, addition of the recommended level (0.18%) did not prevent gelation. Addition of small amounts of NaOH with 0.18 % trisodium citrate before homogenisation was beneficial. The stage at which citrate was added during processing was critical to the degree of viscosity increase (as opposed to gelation) in the product during 45 °C incubation. The component responsible for age-gelation was present in the milk-solids non fat portion of the cream and variations in the creams used were important in the age-gelation phenomenon Results indicated that, in addition to possibly Ca++, the micellar casein portion of serum may play a role in gelation. The role of the low molecular weight surfactants, sodium stearoyl lactylate and monodiglycerides in preventing gelation, was influenced by the presence of trisodium citrate. Clustering of fat globules and age-gelation were inhibited when 0.18 % citrate was included. Inclusion of sodium stearoyl lactylate, but not monodiglycerides, reduced the extent of viscosity increase at 45 °C in citrate containing liqueurs.

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The past few years have witnessed an exponential increase in studies trying to identify molecular markers in patients with breast tumours that might predict for the success or failure of hormonal therapy or chemotherapy. HER2, a tyrosine kinase membrane receptor of the epidermal growth factor receptor family, has been the most widely studied marker in this respect. This paper attempts to critically review to what extent HER2 may improve 'treatment individualisation' for the breast cancer patient. Copyright (C) 2000.

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Knowing one's HIV status is particularly important in the setting of recent tuberculosis (TB) exposure. Blood tests for assessment of tuberculosis infection, such as the QuantiFERON Gold in-tube test (QFT; Cellestis Limited, Carnegie, Victoria, Australia), offer the possibility of simultaneous screening for TB and HIV with a single blood draw. We performed a cross-sectional analysis of all contacts to a highly infectious TB case in a large meatpacking factory. Twenty-two percent were foreign-born and 73% were black. Contacts were tested with both tuberculin skin testing (TST) and QFT. HIV testing was offered on an opt-out basis. Persons with TST >or=10 mm, positive QFT, and/or positive HIV test were offered latent TB treatment. Three hundred twenty-six contacts were screened: TST results were available for 266 people and an additional 24 reported a prior positive TST for a total of 290 persons with any TST result (89.0%). Adequate QFT specimens were obtained for 312 (95.7%) of persons. Thirty-two persons had QFT results but did not return for TST reading. Twenty-two percent met the criteria for latent TB infection. Eighty-eight percent accepted HIV testing. Two (0.7%) were HIV seropositive; both individuals were already aware of their HIV status, but one had stopped care a year previously. None of the HIV-seropositive persons had latent TB, but all were offered latent TB treatment per standard guidelines. This demonstrates that opt-out HIV testing combined with QFT in a large TB contact investigation was feasible and useful. HIV testing was also widely accepted. Pairing QFT with opt-out HIV testing should be strongly considered when possible.

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BACKGROUND: Optimally, expanded HIV testing programs should reduce barriers to testing while attracting new and high-risk testers. We assessed barriers to testing and HIV risk among clients participating in mobile voluntary counseling and testing (MVCT) campaigns in four rural villages in the Kilimanjaro Region of Tanzania. METHODS: Between December 2007 and April 2008, 878 MVCT participants and 506 randomly selected community residents who did not access MVCT were surveyed. Gender-specific logistic regression models were used to describe differences in socioeconomic characteristics, HIV exposure risk, testing histories, HIV related stigma, and attitudes toward testing between MVCT participants and community residents who did not access MVCT. Gender-specific logistic regression models were used to describe differences in socioeconomic characteristics, HIV exposure risk, testing histories, HIV related stigma, and attitudes toward testing, between the two groups. RESULTS: MVCT clients reported greater HIV exposure risk (OR 1.20 [1.04 to 1.38] for males; OR 1.11 [1.03 to 1.19] for females). Female MVCT clients were more likely to report low household expenditures (OR 1.47 [1.04 to 2.05]), male clients reported higher rates of unstable income sources (OR 1.99 [1.22 to 3.24]). First-time testers were more likely than non-testers to cite distance to testing sites as a reason for not having previously tested (OR 2.17 [1.05 to 4.48] for males; OR 5.95 [2.85 to 12.45] for females). HIV-related stigma, fears of testing or test disclosure, and not being able to leave work were strongly associated with non-participation in MVCT (ORs from 0.11 to 0.84). CONCLUSIONS: MVCT attracted clients with increased exposure risk and fewer economic resources; HIV related stigma and testing-related fears remained barriers to testing. MVCT did not disproportionately attract either first-time or frequent repeat testers. Educational campaigns to reduce stigma and fears of testing could improve the effectiveness of MVCT in attracting new and high-risk populations.