947 resultados para association rule mining


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Airway inflammation is a key feature of bronchial asthma. In asthma management, according to international guidelines, the gold standard is anti-inflammatory treatment. Currently, only conventional procedures (i.e., symptoms, use of rescue medication, PEF-variability, and lung function tests) were used to both diagnose and evaluate the results of treatment with anti-inflammatory drugs. New methods for evaluation of degree of airway inflammation are required. Nitric oxide (NO) is a gas which is produced in the airways of healthy subjects and especially produced in asthmatic airways. Measurement of NO from the airways is possible, and NO can be measured from exhaled air. Fractional exhaled NO (FENO) is increased in asthma, and the highest concentrations are measured in asthmatic patients not treated with inhaled corticosteroids (ICS). Steroid-treated patients with asthma had levels of FENO similar to those of healthy controls. Atopic asthmatics had higher levels of FENO than did nonatopic asthmatics, indicating that level of atopy affected FENO level. Associations between FENO and bronchial hyperresponsiveness (BHR) occur in asthma. The present study demonstrated that measurement of FENO had good reproducibility, and the FENO variability was reasonable both short- and long-term in both healthy subjects and patients with respiratory symptoms or asthma. We demonstrated the upper normal limit for healthy subjects, which was 12 ppb calculated from two different healthy study populations. We showed that patients with respiratory symptoms who did not fulfil the diagnostic criteria of asthma had FENO values significantly higher than in healthy subjects, but significantly lower than in asthma patients. These findings suggest that BHR to histamine is a sensitive indicator of the effect of ICS and a valuable tool for adjustment of corticosteroid treatment in mild asthma. The findings further suggest that intermittent treatment periods of a few weeks’ duration are insufficient to provide long-term control of BHR in patients with mild persistent asthma. Moreover, during the treatment with ICS changes in BHR and changes in FENO were associated. FENO level was associated with BHR measured by a direct (histamine challenge) or indirect method (exercise challenge) in steroid-naïve symptomatic, non-smoking asthmatics. Although these associations could be found only in atopics, FENO level in nonatopic asthma was also increased. It can thus be concluded that assessment of airway inflammation by measuring FENO can be useful for clinical purposes. The methodology of FENO measurements is now validated. Especially in those patients with respiratory symptoms who did not fulfil the diagnostic criteria of asthma, FENO measurement can aid in treatment decisions. Serial measurement of FENO during treatment with ICS can be a complementary or an alternative method for evaluation in patients with asthma.

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Many developing countries are experiencing rapid expansion in mining with associated water impacts. In most cases mining expansion is outpacing the building of national capacity to ensure that sustainable water management practices are implemented. Since 2011, Australia's International Mining for Development Centre (IM4DC) has funded capacity building in such countries including a program of water projects. Five projects in particular (principally covering experiences from Peru, Colombia, Ghana, Zambia, Indonesia, Philippines and Mongolia) have provided insight into water capacity building priorities and opportunities. This paper reviews the challenges faced by water stakeholders, and proposes the associated capacity needs. The paper uses the evidence derived from the IM4DC projects to develop a set of specific capacity-building recommendations. Recommendations include: the incorporation of mine water management in engineering and environmental undergraduate courses; secondments of staff to suitable partner organisations; training to allow site staff to effectively monitor water including community impacts; leadership training to support a water stewardship culture; training of officials to support implementation of catchment management approaches; and the empowerment of communities to recognise and negotiate solutions to mine-related risks. New initiatives to fund the transfer of multi-disciplinary knowledge from nations with well-developed water management practices are called for.

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This study sought to assess the extent to which the entry characteristics of students in a graduate-entry medical programme predict the subsequent development of clinical reasoning ability. Subjects comprised 290 students voluntarily recruited from three successive cohorts of the University of Queensland's MBBS Programme. Clinical reasoning was measured once a year over a period of three years using two methods, a set of 10 Clinical Reasoning Problems (CRPs) and the Diagnostic Thinking Inventory (DTI). Data on gender, age at entry into the programme, nature of primary degree, scores on selection criteria (written examination plus interview) and academic performance in the first two years of the programme were recorded for each student, and their association with clinical reasoning skill analysed using univariate and multivariate analysis. Univariate analysis indicated significant associations between CRP score, gender and primary degree with a significant but small association between DTI and interview score. Stage of progression through the programme was also an important predictor of performance on both indicators. Subsequent multivariate analysis suggested that female gender is a positive predictor of CRP score independently of the nature of a subject's primary degree and stage of progression through the programme, although these latter two variables are interdependent. Positive predictors of clinical reasoning skill are stage of progression through the MBBS programme, female gender and interview score. Although the nature of a student's primary degree is important in the early years of the programme, evidence suggests that by graduation differences between students' clinical reasoning skill due to this factor have been resolved.

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The evolutionary success of beetles and numerous other terrestrial insects is generally attributed to co-radiation with flowering plants but most studies have focused on herbivorous or pollinating insects. Non-herbivores represent a significant proportion of beetle diversity yet potential factors that influence their diversification have been largely unexamined. In the present study, we examine the factors driving diversification within the Scarabaeidae, a speciose beetle family with a range of both herbivorous and non-herbivorous ecologies. In particular, it has been long debated whether the key event in the evolution of dung beetles (Scarabaeidae: Scarabaeinae) was an adaptation to feeding on dinosaur or mammalian dung. Here we present molecular evidence to show that the origin of dung beetles occurred in the middle of the Cretaceous, likely in association with dinosaur dung, but more surprisingly the timing is consistent with the rise of the angiosperms. We hypothesize that the switch in dinosaur diet to incorporate more nutritious and less fibrous angiosperm foliage provided a palatable dung source that ultimately created a new niche for diversification. Given the well-accepted mass extinction of non-avian dinosaurs at the Cretaceous-Paleogene boundary, we examine a potential co-extinction of dung beetles due to the loss of an important evolutionary resource, i.e., dinosaur dung. The biogeography of dung beetles is also examined to explore the previously proposed "out of Africa" hypothesis. Given the inferred age of Scarabaeinae as originating in the Lower Cretaceous, the major radiation of dung feeders prior to the Cenomanian, and the early divergence of both African and Gondwanan lineages, we hypothesise that that faunal exchange between Africa and Gondwanaland occurred during the earliest evolution of the Scarabaeinae. Therefore we propose that both Gondwanan vicariance and dispersal of African lineages is responsible for present day distribution of scarabaeine dung beetles and provide examples.

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Peroxisome proliferator activated receptor-gamma 2 (PPARG2) is a nuclear hormone receptor of ligand-dependent ranscription factor involved in adipogenesis and a molecular target of the insulin sensitizers thiazolidinediones. We addressed the question of whether the 3 variants (-1279G/A, Pro12Ala, and His478His) in the PPARG2 gene are associated with type 2 diabetes mellitus and its related traits in a South Indian population. The study subjects (1000 type 2 diabetes mellitus and 1000 normal glucose-tolerant subjects) were chosen randomly from the Chennai Urban Rural Epidemiology Study, an ongoing population-based study in southern India. The variants were screened by single-stranded conformational variant, direct sequencing, and restriction fragment length polymorphism. Linkage disequilibrium was estimated from the estimates of haplotypic frequencies. The -1279G/A, Pro12Ala, and His478His variants of the PPARG2 gene were not associated with type 2 diabetes mellitus. However, the 2-loci analyses showed that, in the presence of Pro/Pro genotype of the Pro12Ala variant, the -1279G/A promoter variant showed increased susceptibility to type 2 diabetes mellitus (odds ratio, 2.092; 95% confidence interval, 1.22-3.59; P = .008), whereas in the presence of 12Ala allele, the -1279G/A showed a protective effect against type 2 diabetes mellitus (odds ratio, 0.270; 95% confidence interval, 0.15-0.49; P < .0001). The 3-loci haplotype analysis showed that the A-Ala-T (-1279G/A-Pro12Ala-His478His) haplotype was associated with a reduced risk of type 2 diabetes mellitus (P < .0001). Although our data indicate that the PPARG2 gene variants, independently, have no association with type 2 diabetes mellitus, the 2-loci genotype analysis involving -1279G/A and Pro12Ala variants and the 3-loci haplotype analysis have shown a significant association with type 2 diabetes mellitus in this South Indian population. (C) 2010 Elsevier Inc. All rights reserved.

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The motivation behind the fusion of Intrusion Detection Systems was the realization that with the increasing traffic and increasing complexity of attacks, none of the present day stand-alone Intrusion Detection Systems can meet the high demand for a very high detection rate and an extremely low false positive rate. Multi-sensor fusion can be used to meet these requirements by a refinement of the combined response of different Intrusion Detection Systems. In this paper, we show the design technique of sensor fusion to best utilize the useful response from multiple sensors by an appropriate adjustment of the fusion threshold. The threshold is generally chosen according to the past experiences or by an expert system. In this paper, we show that the choice of the threshold bounds according to the Chebyshev inequality principle performs better. This approach also helps to solve the problem of scalability and has the advantage of failsafe capability. This paper theoretically models the fusion of Intrusion Detection Systems for the purpose of proving the improvement in performance, supplemented with the empirical evaluation. The combination of complementary sensors is shown to detect more attacks than the individual components. Since the individual sensors chosen detect sufficiently different attacks, their result can be merged for improved performance. The combination is done in different ways like (i) taking all the alarms from each system and avoiding duplications, (ii) taking alarms from each system by fixing threshold bounds, and (iii) rule-based fusion with a priori knowledge of the individual sensor performance. A number of evaluation metrics are used, and the results indicate that there is an overall enhancement in the performance of the combined detector using sensor fusion incorporating the threshold bounds and significantly better performance using simple rule-based fusion.

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Classification of large datasets is a challenging task in Data Mining. In the current work, we propose a novel method that compresses the data and classifies the test data directly in its compressed form. The work forms a hybrid learning approach integrating the activities of data abstraction, frequent item generation, compression, classification and use of rough sets.

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Classification of large datasets is a challenging task in Data Mining. In the current work, we propose a novel method that compresses the data and classifies the test data directly in its compressed form. The work forms a hybrid learning approach integrating the activities of data abstraction, frequent item generation, compression, classification and use of rough sets.

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Automatic identification of software faults has enormous practical significance. This requires characterizing program execution behavior and the use of appropriate data mining techniques on the chosen representation. In this paper, we use the sequence of system calls to characterize program execution. The data mining tasks addressed are learning to map system call streams to fault labels and automatic identification of fault causes. Spectrum kernels and SVM are used for the former while latent semantic analysis is used for the latter The techniques are demonstrated for the intrusion dataset containing system call traces. The results show that kernel techniques are as accurate as the best available results but are faster by orders of magnitude. We also show that latent semantic indexing is capable of revealing fault-specific features.

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We present a search for associated production of the standard model (SM) Higgs boson and a $Z$ boson where the $Z$ boson decays to two leptons and the Higgs decays to a pair of $b$ quarks in $p\bar{p}$ collisions at the Fermilab Tevatron. We use event probabilities based on SM matrix elements to construct a likelihood function of the Higgs content of the data sample. In a CDF data sample corresponding to an integrated luminosity of 2.7 fb$^{-1}$ we see no evidence of a Higgs boson with a mass between 100 GeV$/c^2$ and 150 GeV$/c^2$. We set 95% confidence level (C.L.) upper limits on the cross-section for $ZH$ production as a function of the Higgs boson mass $m_H$; the limit is 8.2 times the SM prediction at $m_H = 115$ GeV$/c^2$.

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Ion transport in a recently demonstrated promising soft matter solid plastic-polymer electrolyte is discussed here in the context of solvent dynamics and ion association. The plastic-polymer composite electrolytes display liquid-like ionic conductivity in the solid state,compliable mechanical strength (similar to 1 MPa), and wide electrochemical voltage stability (>= 5 V). Polyacrylonitrile (PAN) dispersed in lithium perchlorate (LiClO4)-succinonitrile (SN) was chosen as the model system for the study (abbreviated LiClO4-SN:PAN). Systematic observation of various mid-infrared isomer and ion association bands as a function of temperature and polyme concentration shows an effective increase in trans conformer concentration along with free Li+ ion concentration. This strongly supports the view that enhancement in LiClO4-SN:PAN ionic conductivity over the neat plastic electrolyte (LiClO4-SN) is due to both increase in charge mobility and concentration. The ionic conductivity and infrared spectroscopy studies are supported by Brillouin light scattering. For the LiClO4-SN:PAN composites, a peak at 17 GHz was observed in addition to the normal trans-gauche isomerism (as in neat SN) at 12 GHz. The fast process is attributed to increased dynamics of those SN molecules whose energy barrier of transition from gauche to trans has reduced under influences induced by the changes in temperature and polymer concentration. The observations from ionic conductivity, spectroscopy, and light scattering studies were further supplemented by temperature dependent nuclear magnetic resonance H-1 and Li-7 line width measurements.

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We present a search for the Higgs boson in the process $q\bar{q} \to ZH \to \ell^+\ell^- b\bar{b}$. The analysis uses an integrated luminosity of 1 fb$^{-1}$ of $p\bar{p}$ collisions produced at $\sqrt{s} =$ 1.96 TeV and accumulated by the upgraded Collider Detector at Fermilab (CDF II). We employ artificial neural networks both to correct jets mismeasured in the calorimeter, and to distinguish the signal kinematic distributions from those of the background. We see no evidence for Higgs boson production, and set 95% CL upper limits on $\sigma_{ZH} \cdot {\cal B}(H \to b\bar{b}$), ranging from 1.5 pb to 1.2 pb for a Higgs boson mass ($m_H$) of 110 to 150 GeV/$c^2$.

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We present a search for the technicolor particles $\rho_{T}$ and $\pi_{T}$ in the process $p\bar{p} \to \rho_{T} \to W\pi_{T}$ at a center of mass energy of $\sqrt{s}=1.96 \mathrm{TeV}$. The search uses a data sample corresponding to approximately $1.9 \mathrm{fb}^{-1}$ of integrated luminosity accumulated by the CDF II detector at the Fermilab Tevatron. The event signature we consider is $W\to \ell\nu$ and $\pi_{T} \to b\bar{b}, b\bar{c}$ or $b\bar{u}$ depending on the $\pi_{T}$ charge. We select events with a single high-$p_T$ electron or muon, large missing transverse energy, and two jets. Jets corresponding to bottom quarks are identified with multiple $b$-tagging algorithms. The observed number of events and the invariant mass distributions are consistent with the standard model background expectations, and we exclude a region at 95% confidence level in the $\rho_T$-$\pi_T$ mass plane. As a result, a large fraction of the region $m(\rho_T) = 180$ - $250 \mathrm{GeV}/c^2$ and $m(\pi_T) = 95$ - $145 \mathrm{GeV}/c^2$ is excluded.

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We present a search for standard model Higgs boson production in association with a W boson in proton-antiproton collisions at a center of mass energy of 1.96 TeV. The search employs data collected with the CDF II detector that correspond to an integrated luminosity of approximately 1.9 inverse fb. We select events consistent with a signature of a single charged lepton, missing transverse energy, and two jets. Jets corresponding to bottom quarks are identified with a secondary vertex tagging method, a jet probability tagging method, and a neural network filter. We use kinematic information in an artificial neural network to improve discrimination between signal and background compared to previous analyses. The observed number of events and the neural network output distributions are consistent with the standard model background expectations, and we set 95% confidence level upper limits on the production cross section times branching fraction ranging from 1.2 to 1.1 pb or 7.5 to 102 times the standard model expectation for Higgs boson masses from 110 to $150 GeV/c^2, respectively.