850 resultados para Model Identification
Finite mixture regression model with random effects: application to neonatal hospital length of stay
Resumo:
A two-component mixture regression model that allows simultaneously for heterogeneity and dependency among observations is proposed. By specifying random effects explicitly in the linear predictor of the mixture probability and the mixture components, parameter estimation is achieved by maximising the corresponding best linear unbiased prediction type log-likelihood. Approximate residual maximum likelihood estimates are obtained via an EM algorithm in the manner of generalised linear mixed model (GLMM). The method can be extended to a g-component mixture regression model with the component density from the exponential family, leading to the development of the class of finite mixture GLMM. For illustration, the method is applied to analyse neonatal length of stay (LOS). It is shown that identification of pertinent factors that influence hospital LOS can provide important information for health care planning and resource allocation. (C) 2002 Elsevier Science B.V. All rights reserved.
Resumo:
This paper develops an Internet geographical information system (GIS) and spatial model application that provides socio-economic information and exploratory spatial data analysis for local government authorities (LGAs) in Queensland, Australia. The application aims to improve the means by which large quantities of data may be analysed, manipulated and displayed in order to highlight trends and patterns as well as provide performance benchmarking that is readily understandable and easily accessible for decision-makers. Measures of attribute similarity and spatial proximity are combined in a clustering model with a spatial autocorrelation index for exploratory spatial data analysis to support the identification of spatial patterns of change. Analysis of socio-economic changes in Queensland is presented. The results demonstrate the usefulness and potential appeal of the Internet GIS applications as a tool to inform the process of regional analysis, planning and policy.
Resumo:
A family of Golgi-localised molecules was recently described in animals and fungi possessing extensive coiled regions and a short (similar to40 residues) conserved C-terminal domain, called the GRIP domain, which is responsible for their location to this organelle. Using the model plant Arabidopsis thaliana, we identified a gene (AtGRIP) encoding a putative GRIP protein. We demonstrated that the C-terminal domain from AtGRIP functions as a Golgi-targeting sequence in plant cells. Localisation studies in living cells expressing the AtGRIP fused to a DsRed2 fluorescent probe, showed extensive co-location with the Golgi marker alpha-mannosidase I in transformed tobacco protoplasts. GRIP-like sequences were also found in genomic databases of rice, maize, wheat and alfalfa, suggesting that this domain may be a useful Golgi marker for immunolocalisation studies. Despite low sequence identity amongst GRIP domains, the plant GRIP sequence was able to target to the Golgi of mammalian cells. Taken together, these data indicate that GRIP domain proteins might be implicated in a targeting mechanism that is conserved amongst eukaryotes.
Resumo:
Given that an important functional attribute of stem cells in vivo is their ability to sustain tissue regeneration, we set out to establish a simple and easy technique to assess this property from candidate populations of human keratinocyte stem cells in an in vivo setting. Keratinocytes were inoculated into devitalized rat tracheas and transplanted subcutaneously into SCID mice, and the epithelial lining regenerated characterized to establish the validity of this heterotypic model. Furthermore, the rate and quality of epidermal tissue reconstitution obtained from freshly isolated unfractionated vs. keratinocyte stem cell-enriched populations was tested as a function of (a) cell numbers inoculated; and (b) the inclusion of irradiated support keratinocytes and dermal cells. Rapid and sustained epidermal tissue regeneration from small numbers of freshly isolated human keratinocyte stem cells validates the utilization of this simple and reliable model system to assay for enrichment of epidermal tissue-reconstituting cells.
Resumo:
In this study, we examined the relationship between transformational/transactional leadership perceptions and organizational identification and further explored the moderating role of individual difference variables, such as separateness-connectedness self-schema, and positive and negative affectivity. Data from 502 services employees indicated significant positive effects of transformational and transactional leadership perceptions on organizational identification. Regarding the moderating role of individual differences, our data showed that the positive relationship of transformational leadership and organizational identification was stronger for individuals of low positive affectivity as well as for employees of high negative affectivity. In addition, results indicated that transactional leadership had a stronger positive effect on organizational identification for individuals characterized by a connected self-schema. (c) 2005 Elsevier Inc. All rights reserved.
Resumo:
This study tested the utility of a stress and coping model of employee adjustment to a merger Two hundred and twenty employees completed both questionnaires (Time 1: 3 months after merger implementation; Time 2: 2 years later). Structural equation modeling analyses revealed that positive event characteristics predicted greater appraisals of self-efficacy and less stress at Time 1. Self-efficacy, in turn, predicted greater use of problem-focused coping at Time 2, whereas stress predicted a greater use of problem-focused and avoidance coping. Finally, problem-focused coping predicted higher levels of job satisfaction and identification with the merged organization (Time 2), whereas avoidance coping predicted lower identification.
Resumo:
A new approach to identify multivariable Hammerstein systems is proposed in this paper. By using cardinal cubic spline functions to model the static nonlinearities, the proposed method is effective in modelling processes with hard and/or coupled nonlinearities. With an appropriate transformation, the nonlinear models are parameterized such that the nonlinear identification problem is converted into a linear one. The persistently exciting condition for the transformed input is derived to ensure the estimates are consistent with the true system. A simulation study is performed to demonstrate the effectiveness of the proposed method compared with the existing approaches based on polynomials. (C) 2006 Elsevier Ltd. All rights reserved.
Resumo:
Motivation: The clustering of gene profiles across some experimental conditions of interest contributes significantly to the elucidation of unknown gene function, the validation of gene discoveries and the interpretation of biological processes. However, this clustering problem is not straightforward as the profiles of the genes are not all independently distributed and the expression levels may have been obtained from an experimental design involving replicated arrays. Ignoring the dependence between the gene profiles and the structure of the replicated data can result in important sources of variability in the experiments being overlooked in the analysis, with the consequent possibility of misleading inferences being made. We propose a random-effects model that provides a unified approach to the clustering of genes with correlated expression levels measured in a wide variety of experimental situations. Our model is an extension of the normal mixture model to account for the correlations between the gene profiles and to enable covariate information to be incorporated into the clustering process. Hence the model is applicable to longitudinal studies with or without replication, for example, time-course experiments by using time as a covariate, and to cross-sectional experiments by using categorical covariates to represent the different experimental classes. Results: We show that our random-effects model can be fitted by maximum likelihood via the EM algorithm for which the E(expectation) and M(maximization) steps can be implemented in closed form. Hence our model can be fitted deterministically without the need for time-consuming Monte Carlo approximations. The effectiveness of our model-based procedure for the clustering of correlated gene profiles is demonstrated on three real datasets, representing typical microarray experimental designs, covering time-course, repeated-measurement and cross-sectional data. In these examples, relevant clusters of the genes are obtained, which are supported by existing gene-function annotation. A synthetic dataset is considered too.
Resumo:
Purple acid phosphatases are a family of binuclear metallohydrolases that have been identified in plants, animals and fungi. Only one isoform of similar to 35 kDa has been isolated from animals, where it is associated with bone resorption and microbial killing through its phosphatase activity, and hydroxyl radical production, respectively. Using the sensitive PSI-BLAST search method, sequences representing new purple acid phosphatase-like proteins have been identified in mammals, insects and nematodes. These new putative isoforms are closely related to the similar to 55 kDa purple acid phosphatase characterized from plants. Secondary structure prediction of the new human isoform further confirms its similarity to a purple acid phosphatase from the red kidney bean. A structural model for the human enzyme was constructed based on the red kidney bean purple acid phosphatase structure. This model shows that the catalytic centre observed in other purple acid phosphatases is also present in this new isoform. These observations suggest that the sequences identified in this study represent a novel subfamily of plant-like purple acid phosphatases in animals and humans. (c) 2006 Elsevier B.V. All rights reserved.
Resumo:
Calibration of a groundwater model requires that hydraulic properties be estimated throughout a model domain. This generally constitutes an underdetermined inverse problem, for which a Solution can only be found when some kind of regularization device is included in the inversion process. Inclusion of regularization in the calibration process can be implicit, for example through the use of zones of constant parameter value, or explicit, for example through solution of a constrained minimization problem in which parameters are made to respect preferred values, or preferred relationships, to the degree necessary for a unique solution to be obtained. The cost of uniqueness is this: no matter which regularization methodology is employed, the inevitable consequence of its use is a loss of detail in the calibrated field. This, ill turn, can lead to erroneous predictions made by a model that is ostensibly well calibrated. Information made available as a by-product of the regularized inversion process allows the reasons for this loss of detail to be better understood. In particular, it is easily demonstrated that the estimated value for an hydraulic property at any point within a model domain is, in fact, a weighted average of the true hydraulic property over a much larger area. This averaging process causes loss of resolution in the estimated field. Where hydraulic conductivity is the hydraulic property being estimated, high averaging weights exist in areas that are strategically disposed with respect to measurement wells, while other areas may contribute very little to the estimated hydraulic conductivity at any point within the model domain, this possibly making the detection of hydraulic conductivity anomalies in these latter areas almost impossible. A study of the post-calibration parameter field covariance matrix allows further insights into the loss of system detail incurred through the calibration process to be gained. A comparison of pre- and post-calibration parameter covariance matrices shows that the latter often possess a much smaller spectral bandwidth than the former. It is also demonstrated that, as all inevitable consequence of the fact that a calibrated model cannot replicate every detail of the true system, model-to-measurement residuals can show a high degree of spatial correlation, a fact which must be taken into account when assessing these residuals either qualitatively, or quantitatively in the exploration of model predictive uncertainty. These principles are demonstrated using a synthetic case in which spatial parameter definition is based oil pilot points, and calibration is Implemented using both zones of piecewise constancy and constrained minimization regularization. (C) 2005 Elsevier Ltd. All rights reserved.
Resumo:
The medically significant genus Chlamydia is a class of obligate intracellular bacterial pathogens that replicate within vacuoles in host eukaryotic cells termed inclusions. Chlamydia's developmental cycle involves two forms; an infectious extracellular form, known as an elementary body (EB), and a non-infectious form, known as the reticulate body (RB), that replicates inside the vacuoles of the host cells. The RB surface is covered in projections that are in intimate contact with the inclusion membrane. Late in the developmental cycle, these reticulate bodies differentiate into the elementary body form. In this paper, we present a hypothesis for the modulation of these developmental events involving the contact-dependent type III secretion (TTS) system. TTS surface projections mediate intimate contact between the RB and the inclusion membrane. Below a certain number of projections, detachment of the RB provides a signal for late differentiation of RB into EB. We use data and develop a mathematical model investigating this hypothesis. If the hypothesis proves to be accurate, then we have shown that increasing the number of inclusions per host cell will increase the number of infectious progeny EB until some optimal number of inclusions. For more inclusions than this optimum, the infectious yield is reduced because of spatial restrictions. We also predict that a reduction in the number of projections on the surface of the RB (and as early as possible during development) will significantly reduce the burst size of infectious EB particles. Many of the results predicted by the model can be tested experimentally and may lead to the identification of potential targets for drug design. © Society for Mathematical Biology 2006.
Resumo:
Recent research on teacher stress in primary schools (e.g. Leonard, Bourke & Schofield, 1999) has shown that higher levels of teacher exhaustion are associated with higher levels of student satisfaction. This paper seeks to explain this surprising finding by considering a construct discussed widely in the organisational literature known as extra-role or organisational citizenship behaviour (OCB). Teacher OCB may include extra efforts to make lessons enjoyable and interesting, organising extra-curricular activities and spending personal time talking with students. The proposed model of analysis also draws on literature relating to job burnout (Maslach, 1982), which generally suggests that the three components of chronic occupational stress - exhaustion, depersonalisation and reduced accomplishment - occur together. However, this paper proposes that although teachers who engage in more OCB experience more exhaustion, they may simultaneously increase their feelings of personal accomplishment and work identification, which may in turn help to avert burnout. It is argued that only with this particular set of job attitudes are the effects of exhaustion caused by high levels of OCB sufficiently buffered to avoid job burnout, and thus positively affect students' quality of school life. The development and piloting of an instrument to measure teachers' OCB will be discussed. The preliminary findings reported herein are part of a larger ongoing study investigating the consequences of stress and OCB in primary school teachers.
Resumo:
L’atrofia ottica dominante (ADOA) è una malattia mitocondriale caratterizzata da difetti visivi, che si manifestano durante l’infanzia, causati da progressiva degenerazione delle cellule gangliari della retina (RGC). ADOA è una malattia genetica associata, nella maggior parte dei casi, a mutazioni nel gene OPA1 che codifica per la GTPasi mitocondriale OPA1, appartenente alla famiglia delle dinamine, principalmente coinvolta nel processo di fusione mitocondriale e nel mantenimento del mtDNA. Finora sono state identificate più di 300 mutazioni patologiche nel gene OPA1. Circa il 50% di queste sono mutazioni missenso, localizzate nel dominio GTPasico, che si pensa agiscano come dominanti negative. Questa classe di mutazioni è associata ad una sindrome più grave nota come “ADOA-plus”. Nel lievito Saccharomyces cerevisiae MGM1 è l’ortologo del gene OPA1: nonostante i due geni abbiano domini funzionali identici le sequenze amminoacidiche sono scarsamente conservate. Questo costituisce una limitazione all’uso del lievito per lo studio e la validazione di mutazioni patologiche nel gene OPA1, infatti solo poche sostituzioni possono essere introdotte e studiate nelle corrispettive posizioni del gene di lievito. Per superare questo ostacolo è stato pertanto costruito un nuovo modello di S. cerevisiae, contenente il gene chimerico MGM1/OPA1, in grado di complementare i difetti OXPHOS del mutante mgm1Δ. Questo gene di fusione contiene una larga parte di sequenza corrispondente al gene OPA1, nella quale è stato inserito un set di nuove mutazioni trovate in pazienti affetti da ADOA e ADOA-plus. La patogenicità di queste mutazioni è stata validata sia caratterizzando i difetti fenotipici associati agli alleli mutati, sia la loro dominanza/recessività nel modello di lievito. A tutt’oggi non è stato identificato alcun trattamento farmacologico per la cura di ADOA e ADOA-plus. Per questa ragione abbiamo utilizzato il nostro modello di lievito per la ricerca di molecole che agiscono come soppressori chimici, ossia composti in grado di ripristinare i difetti fenotipici indotti da mutazioni nel gene OPA1. Attraverso uno screening fenotipico high throughput sono state testate due differenti librerie di composti chimici. Questo approccio, noto con il nome di drug discovery, ha permesso l’identificazione di 23 potenziali molecole attive.
Resumo:
Research on diversity in teams and organizations has revealed ambiguous results regarding the effects of group composition on workgroup performance. The categorization—elaboration model (van Knippenberg et al., 2004) accounts for this variety and proposes two different underlying processes. On the one hand diversity may bring about intergroup bias which leads to less group identification, which in turn is followed by more conflict and decreased workgroup performance. On the other hand, the information processing approach proposes positive effects of diversity because of a more elaborate processing of information brought about by a wider pool and variety of perspectives in more diverse groups. We propose that the former process is contingent on individual team members' beliefs that diversity is good or bad for achieving the team's aims. We predict that the relationship between subjective diversity and identification is more positive in ethnically diverse project teams when group members hold beliefs that are pro-diversity. Results of two longitudinal studies involving postgraduate students working in project teams confirm this hypothesis. Analyses further reveal that group identification is positively related to students' desire to stay in their groups and to their information elaboration. Finally, we found evidence for the expected moderated mediation model with indirect effects of subjective diversity on elaboration and the desire to stay, mediated through group identification, moderated by diversity beliefs.
Resumo:
In data envelopment analysis (DEA), operating units are compared on their outputs relative to their inputs. The identification of an appropriate input-output set is of decisive significance if assessment of the relative performance of the units is not to be biased. This paper reports on a novel approach used for identifying a suitable input-output set for assessing central administrative services at universities. A computer-supported group support system was used with an advisory board to enable the analysts to extract information pertaining to the boundaries of the unit of assessment and the corresponding input-output variables. The approach provides for a more comprehensive and less inhibited discussion of input-output variables to inform the DEA model. © 2005 Operational Research Society Ltd. All rights reserved.