919 resultados para Model-based Categorical Sequence Clustering


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Aim The aim of this study was to assess the causal mechanisms underlying populational subdivision in Drosophila gouveai, a cactophilic species associated with xeric vegetation enclaves in eastern Brazil. A secondary aim was to investigate the genetic effects of Pleistocene climatic fluctuations on these environments. Location Dry vegetation enclaves within the limits of the Cerrado domain in eastern Brazil. Methods We determined the mitochondrial DNA haplotypes of 55 individuals (representing 12 populations) based on sequence data of a 483-bp fragment from the cytochrome c oxidase subunit II (COII) gene. Phylogenetic and coalescent analyses were used to test for the occurrence of demographic events and to infer the time of divergence amongst genetically independent groups. Results Our analyses revealed the existence of two divergent subclades (G1 and G2) plus an introgressed clade restricted to the southernmost range of D. gouveai. Subclades G1 and G2 displayed genetic footprints of range expansion and segregated geographical distributions in south-eastern and some central highland regions, east and west of the Parana River valley. Molecular dating indicated that the main demographic and diversification events occurred in the late to middle Pleistocene. Main conclusions The phylogeographical and genetic patterns observed for D. gouveai in this study are consistent with changes in the distribution of dry vegetation in eastern Brazil. All of the estimates obtained by molecular dating indicate that range expansion and isolation pre-dated the Last Glacial Maximum, occurring during the late to middle Pleistocene, and were probably triggered by climatic changes during the Pleistocene. The current patchy geographical distribution and population subdivision in D. gouveai is apparently closely linked to these past events.

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With the aim to compare the cost of treatment for rheumatoid arthritis therapy with desease-modifying antirheumatic drugs (DMARDS) for a 48-month period, were studied five different treatment stage based on clinical protocols recommended by the Brazilian Society of Rheumatology, and then five therapy cycles. The analytical model based on the Markov Analysis, considered chaces for the patient continue in some stages or change between them according with a positive effect on outcomes. Only direct costs were comprised in the analyzed data, like drugs, materials and tests used for monitoring these patients. The results of the model show that the stage in with metotrexato drug is used like monotherapy was cost-effective (R$ 113,900,00 for patient during 48 months), followed by refractory patient (R$ 1,554,483,43), those that use therapy triplicate followed by infleximable drug (R$ 1, 701, 286.76), the metotrexato intolearant patient (R$ 2,629,919,14), and final the result from that use metotrexato and infliximable in the beginning (R$ 9,292,879,31). The sensitivity analysis confirm this results, when alternate the efficacy of metotrexato and infliximabe.

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In this study, hypotheses were tested that the quality of leader-member exchanges (LMX) depends on congruity of values between leader and member. Data on negotiating latitude and personal values were gathered from 160 members of 30 work groups in Australian organizations. Factor analysis revealed 5 value dimensions: Freedom, Achievement, Mateship, Obedience, and Coping. Analyses of variance supported the hypothesis that LMX quality is higher when leaders and members share achievement and obedience values. Subsequent exploratory analysis, however, indicated that a more complex model based on compatibility of leader authority and member affiliation values may provide a more complete representation.

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A feedback model based on direct photodetection and micromaser-like atomic injection is proposed for the preservation of quantum coherence in a cavity. We show that in this way it is possible to slow down significantly the decoherence of Schrodinger cat states.

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The traditional methods employed to detect atherosclerotic lesions allow for the identification of lesions; however, they do not provide specific characterization of the lesion`s biochemistry. Currently, Raman spectroscopy techniques are widely used as a characterization method for unknown substances, which makes this technique very important for detecting atherosclerotic lesions. The spectral interpretation is based on the analysis of frequency peaks present in the signal; however, spectra obtained from the same substance can show peaks slightly different and these differences make difficult the creation of an automatic method for spectral signal analysis. This paper presents a signal analysis method based on a clustering technique that allows for the classification of spectra as well as the inference of a diagnosis about the arterial wall condition. The objective is to develop a computational tool that is able to create clusters of spectra according to the arterial wall state and, after data collection, to allow for the classification of a specific spectrum into its correct cluster.

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A previous mathematical model explaining dengue in Singapore predicted a reasonable outbreak of about 6500 cases for 2006 and a very mild outbreak with about 2000 cases for 2007. However, only 3051 cases were reported in 2006 while more than 7800 were reported in the first 44 weeks of 2007. We hypothesized that the combination of haze with other local sources of particulate matter had a significant impact on mosquito life expectancy, significantly increasing their mortality rate. To test the hypothesis a mathematical model based on the reproduction number of dengue fever and aimed at comparing the impact of several possible alternative control strategies was proposed. This model also aimed at contributing to the understanding of the causes of dengue resurgence in Singapore in the last decade. The model`s simulation demonstrated that an increase in mosquito mortality in 2006 and either a reduction in mortality or an increase in the carrying capacity of mosquitoes in 2007 explained the patterned observed in Singapore. Based on the model`s simulation we concluded that the fewer than expected number of dengue cases in Singapore in 2006 was caused by an increase in mosquito mortality due to the disproportionate haze affecting the country that year and that particularly favourable environmental conditions in 2007 propitiated mosquitoes with a lower mortality rate, which explains the greater than expected number of dengue cases in 2007. Whether our hypothesis is plausible or not should be debated further.

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Weiss and Isen have provided many supportive comments about the multi-level perspective, but also found limitations. Isen noted the importance of integrating affect, cognition, and motivation. Weiss commented similarly that the model lacked an integrating “thread.” He suggested that, to be truly multilevel, each level should constrain processes at other levels, and also provide guidance for the development of new concepts. Weiss also noted that the focus on biological processes was a strength of the model. I respond by suggesting that these very biological processes may constitute the “missing” thread. To illustrate this, I discuss some of the recent research on emotions in organizational settings, and argue that biology both constrains and guides theory at each level of the model. Based on this proposition, I revisit each of the five levels in the model, to demonstrate how this integration can be accomplished in this fashion. Finally, I address two additional points: aggregation bias, and the possibility of extending the model to include higher levels of industry and region.

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The superior cervical ganglion (SCG) in mammals varies in structure according to developmental age, body size, gender, lateral asymmetry, the size and nuclear content of neurons and the complexity and synaptic coverage of their dendritic trees. In small and medium-sized mammals, neuron number and size increase from birth to adulthood and, in phylogenetic studies, vary with body size. However, recent studies on larger animals suggest that body weight does not, in general, accurately predict neuron number. We have applied design-based stereological tools at the light-microscopic level to assess the volumetric composition of ganglia and to estimate the numbers and sizes of neurons in SCGs from rats, capybaras and horses. Using transmission electron microscopy, we have obtained design-based estimates of the surface coverage of dendrites by postsynaptic apposition zones and model-based estimates of the numbers and sizes of synaptophysin-labelled axo-dendritic synaptic disks. Linear regression analysis of log-transformed data has been undertaken in order to establish the nature of the relationships between numbers and SCG volume (V(scg)). For SCGs (five per species), the allometric relationship for neuron number (N) is N=35,067xV (scg) (0.781) and that for synapses is N=20,095,000xV (scg) (1.328) , the former being a good predictor and the latter a poor predictor of synapse number. Our findings thus reveal the nature of SCG growth in terms of its main ingredients (neurons, neuropil, blood vessels) and show that larger mammals have SCG neurons exhibiting more complex arborizations and greater numbers of axo-dendritic synapses.

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High-level microsatellite instability (AISI-H) is demonstrated in 10 to 15% of sporadic colorectal cancers and in most cancers presenting In the inherited condition hereditary nonpolyposis colorectal cancer (HNPCC). Distinction between these categories of MSI-H cancer is of clinical importance and the aim of this study was to assess clinical, pathological, and molecular features that might he discriminatory. One hundred and twelve MSI-H colorectal cancers from families fulfilling the Bethesda criteria were compared with 57 sporadic MSI-H colorectal cancers. HNPCC cancers presented at a lower age (P < 0.001) with no sporadic MSI-H cancer being diagnosed before the age of 57 years. MSI was less extensive in HNPCC cancers with 72% microsatellite markers showing band shifts compared with 87% in sporadic tumors (P < 0.001). Absent immunostaining for hMSH2 was only found in HNPCC tumors. Methylation of bMLH1 was observed in 87% of sporadic cancers but also in 55% of HNPCC tumors that showed loss of expression of hMLH1 (P = 0.02). HNPCC cancers were more frequently characterized by aberrant beta -catenin immunostaining as evidenced by nuclear positivity (P < 0.001). Aberrant p53 immunostaining was infrequent in both groups. There were no differences with respect to 5q loss of heterozygosity or codon 12 K-ras mutation, which were infrequent in both groups. Sporadic MSI-H cancers were more frequently heterogeneous (P < 0.001), poorly differentiated (P = 0.02), mucinous (P = 0.02), and proximally located (P = 0.04) than RNPCC tumors. In sporadic MSI-H cancers, contiguous adenomas were likely to be serrated whereas traditional adenomas were dominant in HNPCC. Lymphocytic infiltration was more pronounced in HNPCC but the results did not reach statistical significance. Overall, HNPCC cancers were more like common colorectal cancer in terms of morphology and expression of beta -catenin whereas sporadic MSI-H cancers displayed features consistent with a different morphogenesis. No individual feature was discriminatory for all RN-PCC cancers. However, a model based on four features was able to classify 94.5% of tumors as sporadic or HNPCC. The finding of multiple differences between sporadic and familial MSI-H colorectal cancer with respect to both genotype and phenotype is consistent with tumorigenesis through parallel evolutionary pathways and emphasizes the importance of studying the two groups separately.

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1. The past 15 years has seen the emergence of a new field of neuroscience research based primarily on how the immune system and the central nervous system can interact. A notable example of this interaction occurs when peripheral inflammation, infection or tissue injury activates the hypothalamic- pituitary-adrenal axis (HPA). 2. During such assaults, immune cells release the pro- inflammatory cytokines interleukin (IL)-1, IL-6 and tumour necrosis factor-alpha into the general circulation. 3. These cytokines are believed to act as mediators for HPA axis activation. However, physical limitations of cytokines impede their movement across the blood-brain barrier and, consequently, it has been unclear as to precisely how and where IL-1beta signals cross into the brain to trigger HPA axis activation. 4. Evidence from recent anatomical and functional studies suggests two neuronal networks may be involved in triggering HPA axis activity in response to circulating cytokines. These are catecholamine cells of the medulla oblongata and the circumventricular organs (CVO). 5. The present paper examines the role of CVO in generating HPA axis responses to pro-inflammatory cytokines and culminates with a proposed model based on cytokine signalling primarily involving the area postrema and catecholamine cells in the ventrolateral and dorsal medulla.

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Map algebra is a data model and simple functional notation to study the distribution and patterns of spatial phenomena. It uses a uniform representation of space as discrete grids, which are organized into layers. This paper discusses extensions to map algebra to handle neighborhood operations with a new data type called a template. Templates provide general windowing operations on grids to enable spatial models for cellular automata, mathematical morphology, and local spatial statistics. A programming language for map algebra that incorporates templates and special processing constructs is described. The programming language is called MapScript. Example program scripts are presented to perform diverse and interesting neighborhood analysis for descriptive, model-based and processed-based analysis.

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The present paper addresses two major concerns that were identified when developing neural network based prediction models and which can limit their wider applicability in the industry. The first problem is that it appears neural network models are not readily available to a corrosion engineer. Therefore the first part of this paper describes a neural network model of CO2 corrosion which was created using a standard commercial software package and simple modelling strategies. It was found that such a model was able to capture practically all of the trends noticed in the experimental data with acceptable accuracy. This exercise has proven that a corrosion engineer could readily develop a neural network model such as the one described below for any problem at hand, given that sufficient experimental data exist. This applies even in the cases when the understanding of the underlying processes is poor. The second problem arises from cases when all the required inputs for a model are not known or can be estimated with a limited degree of accuracy. It seems advantageous to have models that can take as input a range rather than a single value. One such model, based on the so-called Monte Carlo approach, is presented. A number of comparisons are shown which have illustrated how a corrosion engineer might use this approach to rapidly test the sensitivity of a model to the uncertainities associated with the input parameters. (C) 2001 Elsevier Science Ltd. All rights reserved.

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In this paper, we present the results of a qualitative study of subordinate perceptions of leaders. The study represents a preliminary test of a model based on Affective Events Theory, which posits that leaders who are seen to be effective shape the affective events that determine employees' attitudes and behaviours in the workplace. Within this framework, we argue that effective leaders ameliorate employees' hassles by providing frequent, small emotional uplifts. The resulting positive affective states are then proposed to lead to more positive employee attitudes and behaviours, and more positive regard for the leader. Importantly, leaders who demonstrate these ameliorating behaviours are likely to require high levels of emotional intelligence, defined in terms of the ability to recognise, understand, and manage emotions in self and others. To investigate this model, we conducted interviews and focus groups with 10 leaders and 24 employees. Results confirmed that these processes do indeed exist in the workplace. In particular, leaders who were seen by employees to provide continuous small emotional uplifts were consistently held to be the most effective. Study participants were especially affected by negative events (or hassles). Leaders who failed to deal with hassles or, worse still, were the source of hassles, were consistently seen to be less effective. We conclude with a discussion of implications for practicing managers, and suggest that our exploratory findings provide justification for emotional intelligence training as a means to improve leader perceptions and effectiveness. [Abstract from author]

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This paper discusses a document discovery tool based on Conceptual Clustering by Formal Concept Analysis. The program allows users to navigate e-mail using a visual lattice metaphor rather than a tree. It implements a virtual. le structure over e-mail where files and entire directories can appear in multiple positions. The content and shape of the lattice formed by the conceptual ontology can assist in e-mail discovery. The system described provides more flexibility in retrieving stored e-mails than what is normally available in e-mail clients. The paper discusses how conceptual ontologies can leverage traditional document retrieval systems and aid knowledge discovery in document collections.

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Abstract — The analytical methods based on evaluation models of interactive systems were proposed as an alternative to user testing in the last stages of the software development due to its costs. However, the use of isolated behavioral models of the system limits the results of the analytical methods. An example of these limitations relates to the fact that they are unable to identify implementation issues that will impact on usability. With the introduction of model-based testing we are enable to test if the implemented software meets the specified model. This paper presents an model-based approach for test cases generation from the static analysis of source code.