Classification of plant communities, structure and diversity of forests in Acre, Brazil.

2016

Comparing children's self-report instruments for health-related quality of life using the International Classification of Functioning, Disability and Health for Children and Youth (ICF-CY)

Children with chronic conditions often experience a long treatment which can be complex and negatively impacts the child's well-being. In planning treatment and interventions for children with chronic conditions, it is important to measure health-related quality of life (HrQoL). HrQoL instruments are considered to be a patient-reported outcome measure (PROM) and should be used in routine practice. Purpose: The aim of this study was to compare the content dimensions of HrQoL instruments for children's self-reports using the framework of ICF-CY. Method: The sample consist of six instruments for health-related quality of life for children 5 to 18 years of age, which was used in the Swedish national quality registries for children and adolescents with chronic conditions. The following instruments were included: CHQ-CF, DCGM-37, EQ-5D-Y, KIDSCREEN-52, Kid-KINDL and PedsQL 4.0. The framework of the ICF-CY was used as the basis for the comparison. Results: There were 290 meaningful concepts identified and linked to 88 categories in the classification ICF-CY with 29 categories of the component body functions, 48 categories of the component activities and participation and 11 categories of the component environmental factors. No concept were linked to the component body structures. The comparison revealed that the items in the HrQoL instruments corresponded primarily with the domains of activities and less with environmental factors. Conclusions: In conclusion, the results confirm that ICF-CY provide a good framework for content comparisons that evaluate similarities and differences to ICF-CY categories. The results of this study revealed the need for greater consensus of content across different HrQoL instruments. To obtain a detailed description of children's HrQoL, DCGM-37 and KIDSCREEN-52 may be appropriate instruments to use that can increase the understanding of young patients' needs.

Summary of Channel Net Laws 2016-2017

Each year the South Carolina Department of Natural Resources, Marine Resources Division publishes a summary of the laws and regulations of commercial fishing in the state. This publication provides the license requirements, definitions, restrictions, and laws and regulations for channel nets.

An experimental study of gill net and trammel net 'ghost fishing' off the Algarve (southern Portugal)

Four 100 m lengths of both monofilament gill nets and trammel nets were deployed at depths between 15 and 18 m off the coast of the Algarve (south of Portugal) between April 1995 and June 1996. The nets were set on a natural rocky bottom with one end cut loose to simulate lost nets. Changes in net structure (net height, effective fishing area, movement, colonisation, wear and tear) and their catches (species, sizes, numbers, and biomass) were monitored by divers. Similar patterns were observed in all the nets, with a sharp decrease in net height and effective fishing area, and an increase in visibility within the first few weeks. Net movement was negligible except in the case of interference from other fishing gears. Catch rates were initially comparable to normally fished gill nets and trammel nets in this area, but decreased steadily over time. No sea birds, reptiles or mammals were caught in any of the 8 nets. Catches were dominated by fish (89 % by number, at least 27 species), in particular by sea breams (Sparidae) and wrasses (Labridae). Under the conditions experienced throughout the study the fishing Lifetime of a 'lost' net is between 15 and 20 wk. Based on an exponential model, we estimated that 100 m lengths of gill net and trammel net will catch 314 and 221 fish respectively over a 17 wk period. However, we consider this to be an underestimate due to high rates of predation and scavenging by octopuses, cuttlefish, moray eels, conger eels, and other fish such as the wrasse Coris julis. When the nets were surveyed in the following spring, 8 to 11 mo after being deployed, they were found to be completely destroyed or heavily colonised by algae and had become incorporated into the reef.

Genesis and classification of soils from subtropical mountain regions of Southern Brazil.

Soils formed in high mountainous regions in southern Brazil are characterized by great accumulation of organic matter (OM) in the surface horizons and variation in the degree of development. We hypothesized that soil properties and genesis are influenced by the interaction of parent materials and climate factors, which differ depending on the location along the altitudinal gradient. The goal of this study was to characterize and classify the soil, evaluate soil distribution, and determine the interactive effects of soil-forming factors in the subtropical mountain regions in Santa Catarina state. Soil samples were collected in areas known for wine production, for a total of 38 modal profiles. Based on morphological, physical, and chemical properties, soils were evaluated for pedogenesis and classified according to the Brazilian System of Soil Classification, with equivalent classes in the World Reference Basis (WRB). The results indicated that pedogenesis was strongly influenced by the parent material, weather, and relief. In the areas where basic effusive rocks (basalt) were observed, there was formation of extensive areas of clayey soils with reddish color and higher iron oxide contents. There was a predominance of Nitossolos Vermelhos and Háplicos (Nitisols), Latossolos Vermelhos (Ferralsols), and Cambissolos Háplicos (Cambisols), highlighting the pedogenetic processes of eluviation, illuviation of clay, and latosolization in conditions of year-long, large-volume, well-distributed rainfall and stability of land forms. In areas with acid effusive rocks (rhyodacites), medial or clayey soils were observed with lower iron oxide content, invariably acidic, and with low base content. For these soils, relief promoted substantial removal of material, resulting in intense rejuvenation, with a predominance of Cambissolos Háplicos (Cambisols) and lesser occurrence of Nitossolos Brunos (Nitisols) and Neossolos Litólicos (Leptosols). Soils formed from sedimentary rocks also tended to be more acidic, but with higher sand content, and the soils identified were Cambissolos Háplicos and Húmicos (Cambisols). Cluster analysis separated the soil profiles into three groups: the first and largest was formed by profiles originating from sedimentary rocks and rhyodacites; the second, smaller group was formed by four profiles in the Água Doce region (acidic rocks); and the third was formed by profiles derived from basalt. Discriminant analysis was effective in grouping soil classes. Thus, the study highlighted the importance of geology in the formation of soils in this landscape associated with climate and relief.

Modeling Efficient Classification as a Process of Confidence Assessment and Delegation

In visual object detection and recognition, classifiers have two interesting characteristics: accuracy and speed. Accuracy depends on the complexity of the image features and classifier decision surfaces. Speed depends on the hardware and the computational effort required to use the features and decision surfaces. When attempts to increase accuracy lead to increases in complexity and effort, it is necessary to ask how much are we willing to pay for increased accuracy. For example, if increased computational effort implies quickly diminishing returns in accuracy, then those designing inexpensive surveillance applications cannot aim for maximum accuracy at any cost. It becomes necessary to find trade-offs between accuracy and effort. We study efficient classification of images depicting real-world objects and scenes. Classification is efficient when a classifier can be controlled so that the desired trade-off between accuracy and effort (speed) is achieved and unnecessary computations are avoided on a per input basis. A framework is proposed for understanding and modeling efficient classification of images. Classification is modeled as a tree-like process. In designing the framework, it is important to recognize what is essential and to avoid structures that are narrow in applicability. Earlier frameworks are lacking in this regard. The overall contribution is two-fold. First, the framework is presented, subjected to experiments, and shown to be satisfactory. Second, certain unconventional approaches are experimented with. This allows the separation of the essential from the conventional. To determine if the framework is satisfactory, three categories of questions are identified: trade-off optimization, classifier tree organization, and rules for delegation and confidence modeling. Questions and problems related to each category are addressed and empirical results are presented. For example, related to trade-off optimization, we address the problem of computational bottlenecks that limit the range of trade-offs. We also ask if accuracy versus effort trade-offs can be controlled after training. For another example, regarding classifier tree organization, we first consider the task of organizing a tree in a problem-specific manner. We then ask if problem-specific organization is necessary.

Microstructure and mechanical properties of squeeze-cast copper

Demand for cost-effective manufacturing techniques led to the development of near-net-shape processes. Squeeze casting is one such established effort. This process enjoys the combined merits of casting and forging. Squeeze casting imparts soundness comparable to that of wrought products while maintaining isotropic nature. Aluminum alloys and zinc alloys have been successfully processed through squeeze casting, but copper and copper alloys do not seem to have been attempted. Considering the capability of squeeze casting process, it is reasonable to expect properties different from that of conventionally cast copper. This paper presents the details of a systematic investigation wherein optimum process parameters for the squeeze casting of pure copper were established. Microstructure of squeeze-cast copper has been found to be significantly different from that of conventionally cast copper, and the dendrite arm spacing is much smaller. In addition to the room temperature mechanical properties, elevated temperature properties of copper are also appreciably improved by squeeze casting.

Large-Scale Elastic Net Regularized Linear Classification SVMs and Logistic Regression

Elastic Net Regularizers have shown much promise in designing sparse classifiers for linear classification. In this work, we propose an alternating optimization approach to solve the dual problems of elastic net regularized linear classification Support Vector Machines (SVMs) and logistic regression (LR). One of the sub-problems turns out to be a simple projection. The other sub-problem can be solved using dual coordinate descent methods developed for non-sparse L2-regularized linear SVMs and LR, without altering their iteration complexity and convergence properties. Experiments on very large datasets indicate that the proposed dual coordinate descent - projection (DCD-P) methods are fast and achieve comparable generalization performance after the first pass through the data, with extremely sparse models.

Economic Valuation of Ecosystem Services: Conflicts in Classification

4 p.

Non invasive classification system of scoliosis curve types using least-squares support vector machines

Objective To determine scoliosis curve types using non invasive surface acquisition, without prior knowledge from X-ray data. Methods Classification of scoliosis deformities according to curve type is used in the clinical management of scoliotic patients. In this work, we propose a robust system that can determine the scoliosis curve type from non invasive acquisition of the 3D back surface of the patients. The 3D image of the surface of the trunk is divided into patches and local geometric descriptors characterizing the back surface are computed from each patch and constitute the features. We reduce the dimensionality by using principal component analysis and retain 53 components using an overlap criterion combined with the total variance in the observed variables. In this work, a multi-class classifier is built with least-squares support vector machines (LS-SVM). The original LS-SVM formulation was modified by weighting the positive and negative samples differently and a new kernel was designed in order to achieve a robust classifier. The proposed system is validated using data from 165 patients with different scoliosis curve types. The results of our non invasive classification were compared with those obtained by an expert using X-ray images. Results The average rate of successful classification was computed using a leave-one-out cross-validation procedure. The overall accuracy of the system was 95%. As for the correct classification rates per class, we obtained 96%, 84% and 97% for the thoracic, double major and lumbar/thoracolumbar curve types, respectively. Conclusion This study shows that it is possible to find a relationship between the internal deformity and the back surface deformity in scoliosis with machine learning methods. The proposed system uses non invasive surface acquisition, which is safe for the patient as it involves no radiation. Also, the design of a specific kernel improved classification performance.

Elastic-net prefiltering for two-class classification

A two-stage linear-in-the-parameter model construction algorithm is proposed aimed at noisy two-class classification problems. The purpose of the first stage is to produce a prefiltered signal that is used as the desired output for the second stage which constructs a sparse linear-in-the-parameter classifier. The prefiltering stage is a two-level process aimed at maximizing a model's generalization capability, in which a new elastic-net model identification algorithm using singular value decomposition is employed at the lower level, and then, two regularization parameters are optimized using a particle-swarm-optimization algorithm at the upper level by minimizing the leave-one-out (LOO) misclassification rate. It is shown that the LOO misclassification rate based on the resultant prefiltered signal can be analytically computed without splitting the data set, and the associated computational cost is minimal due to orthogonality. The second stage of sparse classifier construction is based on orthogonal forward regression with the D-optimality algorithm. Extensive simulations of this approach for noisy data sets illustrate the competitiveness of this approach to classification of noisy data problems.

Photo-electrochemical investigation of anodic oxide films on cast Ti-Mo alloys I. Anodic behaviour and effect of alloy composition

The anodic behaviour of cast Ti-Mo alloys, having different Mo contents (6-20 wt.%), was investigated in acidic and neutral aerated aqueous solutions. All sample showed a valve-metal behaviour, owing to formation and thickening of barrier-type anodic oxides displaying interference colours Growth kinetics. of passive films is influenced by both anodizing electrolyte and composition of the starting alloy. This last parameter was found to change also the solid-state properties of the films, explored by photoelectrochemical and impedance spectroscopy experiments. Thicker films (U(f) = 8 V/MSE) grown on alloys richer in Mo showed more resistive character and a photocurrent sign inversion under negative bias, that revealed an insulating character, whereas corresponding films grown on alloys with lower Mo content, as well as thinner films, behaved as n-type semiconductors. Results are discussed in terms of formation of a mixed Ti-Mo oxide phase. (C) 2008 Elsevier Ltd. All rights reserved

Neural networks in chemosystematic studies of asteraceae: A classification based on a dichotomic approach

This paper describes the application of artificial neural nets as an alternative and efficient method for the classification of botanical taxa based on chemical data (chemosystematics). A total of 28,000 botanical occurrences of chemical compounds isolated from the Asteraceae family were chosen from the literature, and grouped by chemical class for each species. Four tests were carried out to differentiate and classify different botanical taxa. The qualifying capacity of the artificial neural nets was dichotomically tested at different hierarchical levels of the family, such as subfamilies and groups of Heliantheae subtribes. Furthermore, two specific subtribes of the Heliantheae and two genera of one of these subtribes were also tested. In general, the artificial neural net gave rise to good results, with multiple-correlation values R > 0.90. Hence, it was possible to differentiate the dichotomic character of the botanical taxa studied.

Submucous myomas: A new presurgical classification to evaluate the viability of hysteroscopic surgical treatment - Preliminary report

STUDY OBJECTIVE: To develop a new preoperative classification of submucous myomas for evaluating the viability and the degree of difficulty of hysteroscopic myomectomy.DESIGN: Retrospective study (Canadian Task Force classification II-3)SETTING: University teaching hospitals.PATIENTS: Fifty-five patients who underwent hysteroscopic resection of submucous myomas.INTERVENTION: the possibility of total resection of the myoma, the operating time, the fluid deficit, and the frequency of any complications were considered. The myomas were classified according to the Classification of the European Society for Gynaecological Endoscopy (ESGE) and by our group's new classification (NC), which considers not only the degree of penetration of the myoma into the myometrium, but also adds in such parameters as the distance of the base of the myoma from the uterine wall, the size of the nodule (cm), and the topography of the uterine cavity. The Fisher's exact test, the Student's t test, and the analysis of variance test were used in the statistical analysis. A p value less than .05 in the two-tailed test was considered significant.MEASUREMENTS AND MAIN RESULTS: In 57 myomas, hysteroscopic surgery was considered complete. There was no significant difference among the three ESGE levels (0, 1, and 2). Using the NC, the difference between the numbers of complete surgeries was significant (p < .001) for the two levels (groups I and H). The difference between the operating times was significant for the two classifications. With respect to the fluid deficit, only the NC showed significant differences between the levels (p = .02).CONCLUSIONS: We believe that the NC gives more clues as to the difficulties of a hysteroscopic myomectomy than the standard ESGE classification. It should be stressed that the number of hysteroscopic myomectomies used in this analysis was low, and it would be interesting to evaluate the performance of the classification in a larger number of patients. (c) 2005 AAGL. All rights reserved.

Estimation of growth fraction in fine needle aspirates from non-Hodgkin's lymphoma using anti-proliferating cell nuclear antigen - Correlation with the Kiel classification

OBJECTIVE: This study was undertaken to assess whether fine needle aspirates from non-Hodgkins lymphoma (NHL) could be used for growth fraction analysis with proliferating cell nuclear antigen (PCNA) staining and if there was a relationship between the growth fraction and cytomorphologic classification according to the Kiel classification.STUDY DESIGN: the study group consisted of 40 patients with NHL diagnosed by fine needle aspiration (FNA) cytology. The cytologic classification of the lymphomas was made by two cytopathologists on May-Grunwald-Giemsa-stained slides using the Kiel classification. There were 27 cases of low and 13 of high grade lymphoma. The estimation of the growth fraction was made by PCNA immunoreactivity. The PCNA index was quantitated in smears by counting an average of 1,000 cells, and the count teas correlated with the cytomorphologic classification.RESULTS: There was It strong correlation between the PCNA index and lymphoma grading. High grade lymphomas exhibited a mean PCNA positivity of 74.0%, which was significantly higher (P <.001) than that of low grade lymphomas (17.6%).CONCLUSION: Our study showed that PCNA evalua tion is suitable for smears obtained by FNA on NHL, correlates with increasing grades of lymphoma according to the Kiel classification and may offer a method of monitoring treatment of lymphoma.