Monography of the genus Camellia or, An essay on its culture, description and classification, illustrated by two synoptical tables... /

Includes index.

Particle swarm optimization aided orthogonal forward regression for unified data modeling

We propose a unified data modeling approach that is equally applicable to supervised regression and classification applications, as well as to unsupervised probability density function estimation. A particle swarm optimization (PSO) aided orthogonal forward regression (OFR) algorithm based on leave-one-out (LOO) criteria is developed to construct parsimonious radial basis function (RBF) networks with tunable nodes. Each stage of the construction process determines the center vector and diagonal covariance matrix of one RBF node by minimizing the LOO statistics. For regression applications, the LOO criterion is chosen to be the LOO mean square error, while the LOO misclassification rate is adopted in two-class classification applications. By adopting the Parzen window estimate as the desired response, the unsupervised density estimation problem is transformed into a constrained regression problem. This PSO aided OFR algorithm for tunable-node RBF networks is capable of constructing very parsimonious RBF models that generalize well, and our analysis and experimental results demonstrate that the algorithm is computationally even simpler than the efficient regularization assisted orthogonal least square algorithm based on LOO criteria for selecting fixed-node RBF models. Another significant advantage of the proposed learning procedure is that it does not have learning hyperparameters that have to be tuned using costly cross validation. The effectiveness of the proposed PSO aided OFR construction procedure is illustrated using several examples taken from regression and classification, as well as density estimation applications.

Two-way regionalized classification of multivariate datasets and its application to the assessment of hydrodynamic dispersion

Zones of mixing between shallow groundwaters of different composition were unravelled by two-way regionalized classification, a technique based on correspondence analysis (CA), cluster analysis (ClA) and discriminant analysis (DA), aided by gridding, map-overlay and contouring tools. The shallow groundwaters are from a granitoid plutonite in the Funda o region (central Portugal). Correspondence analysis detected three natural clusters in the working dataset: 1, weathering; 2, domestic effluents; 3, fertilizers. Cluster analysis set an alternative distribution of the samples by the three clusters. Group memberships obtained by correspondence analysis and by cluster analysis were optimized by discriminant analysis, gridded memberships as follows: codes 1, 2 or 3 were used when classification by correspondence analysis and cluster analysis produced the same results; code 0 when the grid node was first assigned to cluster 1 and then to cluster 2 or vice versa (mixing between weathering and effluents); code 4 in the other cases (mixing between agriculture and the other influences). Code-3 areas were systematically surrounded by code-4 areas, an observation attributed to hydrodynamic dispersion. Accordingly, the extent of code-4 areas in two orthogonal directions was assumed proportional to the longitudinal and transverse dispersivities of local soils. The results (0.7-16.8 and 0.4-4.3 m, respectively) are acceptable at the macroscopic scale. The ratios between longitudinal and transverse dispersivities (1.2-11.1) are also in agreement with results obtained by other studies.

Feature selection by multi-objective optimisation: Application to network anomaly detection by hierarchical self-organising maps

Feature selection is an important and active issue in clustering and classification problems. By choosing an adequate feature subset, a dataset dimensionality reduction is allowed, thus contributing to decreasing the classification computational complexity, and to improving the classifier performance by avoiding redundant or irrelevant features. Although feature selection can be formally defined as an optimisation problem with only one objective, that is, the classification accuracy obtained by using the selected feature subset, in recent years, some multi-objective approaches to this problem have been proposed. These either select features that not only improve the classification accuracy, but also the generalisation capability in case of supervised classifiers, or counterbalance the bias toward lower or higher numbers of features that present some methods used to validate the clustering/classification in case of unsupervised classifiers. The main contribution of this paper is a multi-objective approach for feature selection and its application to an unsupervised clustering procedure based on Growing Hierarchical Self-Organising Maps (GHSOMs) that includes a new method for unit labelling and efficient determination of the winning unit. In the network anomaly detection problem here considered, this multi-objective approach makes it possible not only to differentiate between normal and anomalous traffic but also among different anomalies. The efficiency of our proposals has been evaluated by using the well-known DARPA/NSL-KDD datasets that contain extracted features and labelled attacks from around 2 million connections. The selected feature sets computed in our experiments provide detection rates up to 99.8% with normal traffic and up to 99.6% with anomalous traffic, as well as accuracy values up to 99.12%.

Self-Organizing Maps as a New Tool for Classification of Plants at Lower Hierarchical Levels

A chemotaxonomic analysis is described of a database containing various types of compounds from the Heliantheae tribe (Asteraceae) using Self-Organizing Maps (SOM). The numbers of occurrences of 9 chemical classes in different taxa of the tribe were used as variables. The study shows that SOM applied to chemical data can contribute to differentiate genera, subtribes, and groups of subtribes (subtribe branches), as well as to tribal and subtribal classifications of Heliantheae, exhibiting a high hit percentage comparable to that of an expert performance, and in agreement with the previous tribe classification proposed by Stuessy.

Falling incidence of amputations for peripheral occlusive arterial disease in western Australia between 1980 and 1992

Objectives: To assess temporal trends in the incidence of surgical procedures for peripheral occlusive arterial disease (POAD) and associated changes in outcome as measured by the rate of major lower limb amputations for POAD. Design: a retrospective descriptive population-based study was conducted of the geographically isolated population of Western Austrialia between 1980 and 1992. Methods: Vascular procedures with an accompanying diagnosis of POAD were identified in a computerised system of name-identified records of all discharges from hospital for the population. These procedures were detected using relevant codes from the International Classification of Disease and Procedures. Records of angioplasty and thrombolysis procedures were augmented by searches of hospital-based registers of invasive radiological procedures. The data for the remaining procedures were validated by a review of a random sample of medical records. Results: over the 13 years of the study, rates of major amputations fell significantly for in non-amputation vascular surgery for individuals under the age of 60. In addition, rather than an overall rise in surgery there was shift away from sympathectomy and thromboendarterectomy to angioplasty and bypass surgery. Furthermore, an increasing proportion of all major amputations had a prior attempt at arterial reconstruction. Conclusion: These observations suggest the decrease in major amputations for POAD may reflect a fall in the incidence of POAD, possibly aided by move effective surgery, rather than increased rates of vascular surgery.

Taxonomic theory and the ICF: Foundations for a unified disability athletics classification

Development of a unified classification system to replace four of the systems currently used in disability athletics (i.e., track and field) has been widely advocated. The definition and purpose of classification, underpinned by taxonomic principles and collectively endorsed by relevant disability sport organizations, have not been developed but are required for successful implementation of a unified system. It is posited that the International classification of functioning. disability, and health (ICF), published by the World Health Organization (2001), and current disability athletics systems are, fundamentally, classifications of the functioning and disability associated with health conditions and are highly interrelated. A rationale for basing a unified disability athletics system on ICF is established. Following taxonomic analysis of the current systems, the definition and purpose of a unified disability athletics classification are proposed and discussed. The proposed taxonomic framework and definitions have implications for other disability sport classification systems.

Incidence, characteristics, and predictive factors for dysphagia after pediatric traumatic brain injury

Objective: (1) To establish an incidence figure for dysphagia in a population of pediatric traumatic brain injury (TBI) cases; (2) to provide descriptive data on the admitting characteristics, patterns of resolution, and outcomes of children with and without dysphagia after TBI; and (3) to identify any factors present at admission that may predict dysphagia. Participants: A total of 1, 145 children consecutively admitted to an acute care setting for traumatic brain injury between July 1995 and July 2000. Main outcome measure: Medical parameters relating to dysphagia based on medical chart review. Results: (1) Dysphagia incidence figure of 5.3% across all pediatric head injury admissions. Incidence figures of 68% for severe TBI, 15% for moderate TBI, and only 1% for mild brain injury. (2) Statistically significant differences were found between the dysphagic and nondysphagic subgroups on the variables of length of stay, length of ventilation, Glasgow Coma Scale (GCS), computed tomography classification, duration of speech pathology intervention, supplemental feeding duration, duration until initiation of oral intake (DIOF), duration to total oral intake (DTOF), and period of time from the initiation of intake until achievement of total oral intake (DI-TOF). (3) Significant predictive factors for dysphagia included GCS < 8.5 and a ventilation period in excess of 1.5 days. Conclusion: The provision of incidence data and predictive factors for dysphagia will enable clinicians in acute care settings to allocate resources necessary to deal with the predicted number of dysphagia cases in a pediatric population, and assist in predicting patients who are at risk for dysphagia following TBI. Early detection of patients with swallowing dysfunction will be aided by these data, in turn helping to facilitate effective medical and speech pathology intervention via assisting the reduction of medical complications such as aspiration pneumonia.

Clinical classification versus semiquantification with adapted reference values for 123I-FP-CIT SPECT in a nuclear medicine department

Semi quantification (SQ) in DaTScan® studies is broadly used in clinic daily basis, however there is a suspicious about its discriminative capability, and concordance with the diagnostic classification performed by the physician. Aim: Evaluate the discriminate capability of an adapted database and reference's values of healthy controls for the Dopamine Transporters (DAT) with 123I–FP-IT named DBRV adapted to Nuclear Medicine Department's protocol and population of Infanta Cristina's Hospital, and its concordance with the physician classification.

Evaluation of the World Health Organization 2009 classification of dengue severity in autopsied individuals, during the epidemics of 2011 and 2012 in Brazil

Abstract: INTRODUCTION: The dengue classification proposed by the World Health Organization (WHO) in 2009 is considered more sensitive than the classification proposed by the WHO in 1997. However, no study has assessed the ability of the WHO 2009 classification to identify dengue deaths among autopsied individuals suspected of having dengue. In the present study, we evaluated the ability of the WHO 2009 classification to identify dengue deaths among autopsied individuals suspected of having dengue in Northeast Brazil, where the disease is endemic. METHODS: This retrospective study included 121 autopsied individuals suspected of having dengue in Northeast Brazil during the epidemics of 2011 and 2012. All the autopsied individuals included in this study were confirmed to have dengue based on the findings of laboratory examinations. RESULTS: The median age of the autopsied individuals was 34 years (range, 1 month to 93 years), and 54.5% of the individuals were males. According to the WHO 1997 classification, 9.1% (11/121) of the cases were classified as dengue hemorrhagic fever (DHF) and 3.3% (4/121) as dengue shock syndrome. The remaining 87.6% (106/121) of the cases were classified as dengue with complications. According to the 2009 classification, 100% (121/121) of the cases were classified as severe dengue. The absence of plasma leakage (58.5%) and platelet counts <100,000/mm3 (47.2%) were the most frequent reasons for the inability to classify cases as DHF. CONCLUSIONS: The WHO 2009 classification is more sensitive than the WHO 1997 classification for identifying dengue deaths among autopsied individuals suspected of having dengue.

Bayesian classification criterion for forensic multivariate data

This study presents a classification criteria for two-class Cannabis seedlings. As the cultivation of drug type cannabis is forbidden in Switzerland, law enforcement authorities regularly ask laboratories to determine cannabis plant's chemotype from seized material in order to ascertain that the plantation is legal or not. In this study, the classification analysis is based on data obtained from the relative proportion of three major leaf compounds measured by gas-chromatography interfaced with mass spectrometry (GC-MS). The aim is to discriminate between drug type (illegal) and fiber type (legal) cannabis at an early stage of the growth. A Bayesian procedure is proposed: a Bayes factor is computed and classification is performed on the basis of the decision maker specifications (i.e. prior probability distributions on cannabis type and consequences of classification measured by losses). Classification rates are computed with two statistical models and results are compared. Sensitivity analysis is then performed to analyze the robustness of classification criteria.

Hypertensive portal colopathy in schistosomiasis mansoni: proposal for a classification

Portal hypertension is a frequent complication of chronic liver disease, detected not only in schistosomiasis, but also in cirrhosis of any etiology. Vascular alterations in the colonic mucosa are a potential source for acute or chronic bleeding and have been observed in patients with portal hypertension. The purpose of this prospective study was to describe and propose a classification for the vascular alterations of portal hypertension in the colonic mucosa among patients with hepatosplenic schistosomiasis mansoni. One or more alterations of portal colopathy were observed in all patients and they were classified according to their intensity, obeying the classification proposed by the authors. Portal colopathy is an important finding in hepatosplenic schistosomiasis and might be the cause of lower gastrointestinal bleeding in patients with severe portal hypertension.