Estudio epidemiológico de las patologías corneales en la especie canina en el Hospital Docente Universitario durante el periodo 2001-2011

Programa de doctorado: Clínica e investigación terapéutica.

A vulnerability analysis of the temperate forests of south central Chile A vulnerability analysis of the temperate forests of south central Chile. Biological Conservation, 122 2005: 9-21.

Areas of the landscape that are priorities for conservation should be those that are both vulnerable to threatening processes and that if lost or degraded, will result in conservation targets being compromised. While much attention is directed towards understanding the patterns of biodiversity, much less is given to determining the areas of the landscape most vulnerable to threats. We assessed the relative vulnerability of remaining areas of native forest to conversion to plantations in the ecologically significant temperate rainforest region of south central Chile. The area of the study region is 4.2 million ha and the extent of plantations is approximately 200000 ha. First, the spatial distribution of native forest conversion to plantations was determined. The variables related to the spatial distribution of this threatening process were identified through the development of a classification tree and the generation of a multivariate. spatially explicit, statistical model. The model of native forest conversion explained 43% of the deviance and the discrimination ability of the model was high. Predictions were made of where native forest conversion is likely to occur in the future. Due to patterns of climate, topography, soils and proximity to infrastructure and towns, remaining forest areas differ in their relative risk of being converted to plantations. Another factor that may increase the vulnerability of remaining native forest in a subset of the study region is the proposed construction of a highway. We found that 90% of the area of existing plantations within this region is within 2.5 km of roads. When the predictions of native forest conversion were recalculated accounting for the construction of this highway, it was found that: approximately 27000 ha of native forest had an increased probability of conversion. The areas of native forest identified to be vulnerable to conversion are outside of the existing reserve network. (C) 2004 Elsevier Ltd. All tights reserved.

Sistema de predição de alarmes em processos industriais por classificação não-supervisionada

Um sistema de predição de alarmes com a finalidade de auxiliar a implantação de uma política de manutenção preditiva industrial e de constituir-se em uma ferramenta gerencial de apoio à tomada de decisão é proposto neste trabalho. O sistema adquire leituras de diversos sensores instalados na planta, extrai suas características e avalia a saúde do equipamento. O diagnóstico e prognóstico implica a classificação das condições de operação da planta. Técnicas de árvores de regressão e classificação não-supervisionada são utilizadas neste artigo. Uma amostra das medições de 73 variáveis feitas por sensores instalados em uma usina hidrelétrica foi utilizada para testar e validar a proposta. As medições foram amostradas em um período de 15 meses.

Analysis of the claims data of a life insurance portfolio

Mestrado em Ciências Actuariais

Prespecified dose-response analysis for A Very Early Rehabilitation Trial (AVERT)

OBJECTIVE:
Our prespecified dose-response analyses of A Very Early Rehabilitation Trial (AVERT) aim to provide practical guidance for clinicians on the timing, frequency, and amount of mobilization following acute stroke.

METHODS:
Eligible patients were aged ≥18 years, had confirmed first (or recurrent) stroke, and were admitted to a stroke unit within 24 hours of stroke onset. Patients were randomized to receive very early and frequent mobilization, commencing within 24 hours, or usual care. We used regression analyses and Classification and Regression Trees (CART) to investigate the effect of timing and dose of mobilization on efficacy and safety outcomes, irrespective of assigned treatment group.

RESULTS:
A total of 2,104 patients were enrolled, of whom 2,083 (99.0%) were followed up at 3 months. We found a consistent pattern of improved odds of favorable outcome in efficacy and safety outcomes with increased daily frequency of out-of-bed sessions (odds ratio [OR] 1.13, 95% confidence interval [CI] 1.09 to 1.18, p < 0.001), keeping time to first mobilization and mobilization amount constant. Increased amount (minutes per day) of mobilization reduced the odds of a good outcome (OR 0.94, 95% CI 0.91 to 0.97, p < 0.001). Session frequency was the most important variable in the CART analysis, after prognostic variables age and baseline stroke severity.

CONCLUSION:
These data suggest that shorter, more frequent mobilization early after acute stroke is associated with greater odds of favorable outcome at 3 months when controlling for age and stroke severity.

CLASSIFICATION OF EVIDENCE:
This study provides Class III evidence that shorter, more frequent early mobilization improves the chance of regaining independence after stroke.

Predicting smear negative pulmonary tuberculosis with classification trees and logistic regression: a cross-sectional study

Abstract Background Smear negative pulmonary tuberculosis (SNPT) accounts for 30% of pulmonary tuberculosis cases reported yearly in Brazil. This study aimed to develop a prediction model for SNPT for outpatients in areas with scarce resources. Methods The study enrolled 551 patients with clinical-radiological suspicion of SNPT, in Rio de Janeiro, Brazil. The original data was divided into two equivalent samples for generation and validation of the prediction models. Symptoms, physical signs and chest X-rays were used for constructing logistic regression and classification and regression tree models. From the logistic regression, we generated a clinical and radiological prediction score. The area under the receiver operator characteristic curve, sensitivity, and specificity were used to evaluate the model's performance in both generation and validation samples. Results It was possible to generate predictive models for SNPT with sensitivity ranging from 64% to 71% and specificity ranging from 58% to 76%. Conclusion The results suggest that those models might be useful as screening tools for estimating the risk of SNPT, optimizing the utilization of more expensive tests, and avoiding costs of unnecessary anti-tuberculosis treatment. Those models might be cost-effective tools in a health care network with hierarchical distribution of scarce resources.

Fault diagnosis of induction motor based on decision trees and adaptive neuro-fuzzy inference

This paper presents a fault diagnosis method based on adaptive neuro-fuzzy inference system (ANFIS) in combination with decision trees. Classification and regression tree (CART) which is one of the decision tree methods is used as a feature selection procedure to select pertinent features from data set. The crisp rules obtained from the decision tree are then converted to fuzzy if-then rules that are employed to identify the structure of ANFIS classifier. The hybrid of back-propagation and least squares algorithm are utilized to tune the parameters of the membership functions. In order to evaluate the proposed algorithm, the data sets obtained from vibration signals and current signals of the induction motors are used. The results indicate that the CART–ANFIS model has potential for fault diagnosis of induction motors.

Anger, preoccupied attachment and domain disorganisation in borderline personality disorder

Emotional dysregulation and attachment insecurity have been reported in borderline personality disorder (BPD). Domain disorganization, evidenced in poor regulation of emotions and behaviors in relation to the demands of different social domains, may be a distinguishing feature of BPD. Understanding the interplay between these factors may be critical for identifying interacting processes in BPD and potential subtypes of BPD. Therefore, we examined the joint and interactive effects of anger, preoccupied attachment, and domain disorganization on BPD traits in a clinical sample of 128 psychiatric patients. The results suggest that these factors contribute to BPD both independently and in interaction, even when controlling for other personality disorder traits and Axis I symptoms. In regression analyses, the interaction between anger and domain disorganization predicted BPD traits. In recursive partitioning analyses, two possible paths to BPD were identified: high anger combined with high domain disorganization and low anger combined with preoccupied attachment. These results may suggest possible subtypes of BPD or possible mechanisms by which BPD traits are established and maintained.

Application and evaluation of classification trees for screening unwanted plants

Risk assessment systems for introduced species are being developed and applied globally, but methods for rigorously evaluating them are still in their infancy. We explore classification and regression tree models as an alternative to the current Australian Weed Risk Assessment system, and demonstrate how the performance of screening tests for unwanted alien species may be quantitatively compared using receiver operating characteristic (ROC) curve analysis. The optimal classification tree model for predicting weediness included just four out of a possible 44 attributes of introduced plants examined, namely: (i) intentional human dispersal of propagules; (ii) evidence of naturalization beyond native range; (iii) evidence of being a weed elsewhere; and (iv) a high level of domestication. Intentional human dispersal of propagules in combination with evidence of naturalization beyond a plants native range led to the strongest prediction of weediness. A high level of domestication in combination with no evidence of naturalization mitigated the likelihood of an introduced plant becoming a weed resulting from intentional human dispersal of propagules. Unlikely intentional human dispersal of propagules combined with no evidence of being a weed elsewhere led to the lowest predicted probability of weediness. The failure to include intrinsic plant attributes in the model suggests that either these attributes are not useful general predictors of weediness, or data and analysis were inadequate to elucidate the underlying relationship(s). This concurs with the historical pessimism that we will ever be able to accurately predict invasive plants. Given the apparent importance of propagule pressure (the number of individuals of an species released), future attempts at evaluating screening model performance for identifying unwanted plants need to account for propagule pressure when collating and/or analysing datasets. The classification tree had a cross-validated sensitivity of 93.6% and specificity of 36.7%. Based on the area under the ROC curve, the performance of the classification tree in correctly classifying plants as weeds or non-weeds was slightly inferior (Area under ROC curve = 0.83 +/- 0.021 (+/- SE)) to that of the current risk assessment system in use (Area under ROC curve = 0.89 +/- 0.018 (+/- SE)), although requires many fewer questions to be answered.

Partitioning of persistent organic pollutants (POPs) between human serum and breast milk : a literature review

The literature was reviewed to assess the relationship between the lipid adjusted concentration in human serum and breast milk (expressed as the serum/milk ratio) of a broad range of POPs in paired samples. Thirteen studies were identified, including seven studies that reported serum/milk ratios for polychlorinated dibenzo-dioxins and -furans (PCDD/Fs), ten for polychlorinated biphenyls (PCBs), five for polybrominated diphenyl ethers (PBDEs), and five for organochlorine pesticides (OCPs). Mean serum/milk ratios ranged between 0.7 and 25 depending on the compound and congener. For PCDD/Fs, PCBs and PBDEs, a clear trend of increasing mean serum/milk ratio by increasing molar volume, hydrophobicity and number of halogen substitutes was observed. The mean serum/milk ratios reported by the 13 studies summarized here will aid comparison between human POPs exposure studies using either serum or milk samples. More studies are needed to allow a valid comparison between data obtained from analysis of breast milk and serum samples for a broader range of POPs. Furthermore such studies may shed light on compound specific factors as well as other determinants that may affect the partitioning and partition kinetics of POPs between serum and breast milk.

Recursive pathways to marginal likelihood estimation with prior-sensitivity analysis

We investigate the utility to computational Bayesian analyses of a particular family of recursive marginal likelihood estimators characterized by the (equivalent) algorithms known as "biased sampling" or "reverse logistic regression" in the statistics literature and "the density of states" in physics. Through a pair of numerical examples (including mixture modeling of the well-known galaxy dataset) we highlight the remarkable diversity of sampling schemes amenable to such recursive normalization, as well as the notable efficiency of the resulting pseudo-mixture distributions for gauging prior-sensitivity in the Bayesian model selection context. Our key theoretical contributions are to introduce a novel heuristic ("thermodynamic integration via importance sampling") for qualifying the role of the bridging sequence in this procedure, and to reveal various connections between these recursive estimators and the nested sampling technique.

Spatially explicit perceptions of ecosystem services and land cover change in forested regions of Borneo

Spatially explicit information on local perceptions of ecosystem services is needed to inform land use planning within rapidly changing landscapes. In this paper we spatially modelled local people's use and perceptions of benefits from forest ecosystem services in Borneo, from interviews of 1837 people in 185 villages. Questions related to provisioning, cultural/spiritual, regulating and supporting ecosystem services derived from forest, and attitudes towards forest conversion. We used boosted regression trees (BRTs) to combine interview data with social and environmental predictors to understand spatial variation of perceptions across Borneo. Our results show that people use a variety of products from intact and highly degraded forests. Perceptions of benefits from forests were strongest: in human-altered forest landscapes for cultural and spiritual benefits; in human-altered and intact forests landscapes for health benefits; intact forest for direct health benefits, such as medicinal plants; and in regions with little forest and extensive plantations, for environmental benefits, such as climatic impacts from deforestation. Forest clearing for small scale agriculture was predicted to be widely supported yet less so for large-scale agriculture. Understanding perceptions of rural communities in dynamic, multi-use landscapes is important where people are often directly affected by the decline in ecosystem services.

Eliciting and encoding expert knowledge on variable selection into classical or Bayesian species distribution models

The quality of species distribution models (SDMs) relies to a large degree on the quality of the input data, from bioclimatic indices to environmental and habitat descriptors (Austin, 2002). Recent reviews of SDM techniques, have sought to optimize predictive performance e.g. Elith et al., 2006. In general SDMs employ one of three approaches to variable selection. The simplest approach relies on the expert to select the variables, as in environmental niche models Nix, 1986 or a generalized linear model without variable selection (Miller and Franklin, 2002). A second approach explicitly incorporates variable selection into model fitting, which allows examination of particular combinations of variables. Examples include generalized linear or additive models with variable selection (Hastie et al. 2002); or classification trees with complexity or model based pruning (Breiman et al., 1984, Zeileis, 2008). A third approach uses model averaging, to summarize the overall contribution of a variable, without considering particular combinations. Examples include neural networks, boosted or bagged regression trees and Maximum Entropy as compared in Elith et al. 2006. Typically, users of SDMs will either consider a small number of variable sets, via the first approach, or else supply all of the candidate variables (often numbering more than a hundred) to the second or third approaches. Bayesian SDMs exist, with several methods for eliciting and encoding priors on model parameters (see review in Low Choy et al. 2010). However few methods have been published for informative variable selection; one example is Bayesian trees (O’Leary 2008). Here we report an elicitation protocol that helps makes explicit a priori expert judgements on the quality of candidate variables. This protocol can be flexibly applied to any of the three approaches to variable selection, described above, Bayesian or otherwise. We demonstrate how this information can be obtained then used to guide variable selection in classical or machine learning SDMs, or to define priors within Bayesian SDMs.

Interactions between transgenic trees and mycorrhizal and pathogenic fungi

The development of biotechnology techniques in plant breeding and the new commercial applications have raised public and scientific concerns about the safety of genetically modified (GM) crops and trees. To find out the feasibility of these new technologies in the breeding of commercially important Finnish hardwood species and to estimate the ecological risks of the produced transgenic plants, the experiments of this study have been conducted as a part of a larger project focusing on the risk assessment of GM-trees. Transgenic Betula pendula and Populus trees were produced via Agrobacterium mediated transformation. Stilbene synthase (STS) gene from pine (Pinus sylvestris) and chitinase gene from sugar beet (Beta vulgaris) were transferred to (hybrid) aspen and birch, respectively, to improve disease resistance against fungal pathogens. To modify lignin biosynthesis, a 4-coumarate:coenzyme A ligase (4CL) gene fragment in antisense orientation was introduced into two birch clones. In in vitro test, one transgenic aspen line expressing pine STS gene showed increased resistance to decay fungus Phellinus tremulae. In the field, chitinase transgenic birch lines were more susceptible to leaf spot (Pyrenopeziza betulicola) than the non-transgenic control clone while the resistance against birch rust (Melampsoridium betulinum) was improved. No changes in the content or composition of lignin were detected in the 4CL antisense birch lines. In order to evaluate the ecological effects of the produced GM trees on non-target organisms, an in vitro mycorrhiza experiment with Paxillus involutus and a decomposition experiment in the field were performed. The expression of a transgenic chitinase did not disturb the establishment of mycorrhizal symbiosis between birch and P. involutus in vitro. 4CL antisense transformed birch lines showed retarded root growth but were able to form normal ectomycorrhizal associations with the mycorrhizal fungus in vitro. 4CL lines also showed normal litter decomposition. Unexpected growth reductions resulting from the gene transformation were observed in chitinase transgenic and 4CL antisense birch lines. These results indicate that genetic engineering can provide a tool in increasing disease resistance in Finnish tree species. More extensive data with several ectomycorrhizal species is needed to evaluate the consequences of transgene expression on beneficial plant-fungus symbioses. The potential pleiotropic effects of the transgene should also be taken into account when considering the safety of transgenic trees.

Potential Distribution and Risk Assessment of an Invasive Plant Species: A Case Study of Hymenachne amplexicaulis in Australia

Given the limited resources available for weed management, a strategic approach is required to give the best bang for your buck. The current study incorporates: (1) a model ensemble approach to identify areas of uncertainty and commonality regarding a species invasive potential, (2) current distribution of the invaded species, and (3) connectivity of systems to identify target regions and focus efforts for more effective management. Uncertainty in the prediction of suitable habitat for H. amplexicaulis (study species) in Australia was addressed in an ensemble-forecasting approach to compare distributional scenarios from four models (CLIMATCH; CLIMEX; boosted regression trees [BRT]; maximum entropy [Maxent]). Models were built using subsets of occurrence and environmental data. Catchment risk was determined through incorporating habitat suitability, the current abundance and distribution of H. amplexicaulis, and catchment connectivity. Our results indicate geographic differences between predictions of different approaches. Despite these differences a number of catchments in northern, central, and southern Australia were identified as high risk of invasion or further spread by all models suggesting they should be given priority for the management of H. amplexicaulis. The study also highlighted the utility of ensemble approaches in indentifying areas of uncertainty and commonality regarding the species invasive potential.