Bahamas and Barbados: empirical evidence of interest rate pass-through

Includes bibliography

Automatic segmentation and classification of human intestinal parasites from microscopy images

Human intestinal parasites constitute a problem in most tropical countries, causing death or physical and mental disorders. Their diagnosis usually relies on the visual analysis of microscopy images, with error rates that may range from moderate to high. The problem has been addressed via computational image analysis, but only for a few species and images free of fecal impurities. In routine, fecal impurities are a real challenge for automatic image analysis. We have circumvented this problem by a method that can segment and classify, from bright field microscopy images with fecal impurities, the 15 most common species of protozoan cysts, helminth eggs, and larvae in Brazil. Our approach exploits ellipse matching and image foresting transform for image segmentation, multiple object descriptors and their optimum combination by genetic programming for object representation, and the optimum-path forest classifier for object recognition. The results indicate that our method is a promising approach toward the fully automation of the enteroparasitosis diagnosis. © 2012 IEEE.

Inferences of body energy reserves on conception rate of suckled Zebu beef cows subjected to timed artificial insemination followed by natural mating

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Classification of Eucalyptus urograndis hybrids under different water availability based on biometric traits

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Classification of intact açai (Euterpe oleracea Mart.) and jucara (Euterpe edulis Mart) fruits based on dry matter content by means of near infrared spectroscopy

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Development of CAN-based distributed control system for variable rate technology in agricultural machinery

The number of electronic devices connected to agricultural machinery is increasing to support new agricultural practices tasks related to the Precision Agriculture such as spatial variability mapping and Variable Rate Technology (VRT). The Distributed Control System (DCS) is a suitable solution for decentralization of the data acquisition system and the Controller Area Network (CAN) is the major trend among the embedded communications protocols for agricultural machinery and vehicles. The application of soil correctives is a typical problem in Brazil. The efficiency of this correction process is highly dependent of the inputs way at soil and the occurrence of errors affects directly the agricultural yield. To handle this problem, this paper presents the development of a CAN-based distributed control system for a VRT system of soil corrective in agricultural machinery. The VRT system is composed by a tractor-implement that applies a desired rate of inputs according to the georeferenced prescription map of the farm field to support PA (Precision Agriculture). The performance evaluation of the CAN-based VRT system was done by experimental tests and analyzing the CAN messages transmitted in the operation of the entire system. The results of the control error according to the necessity of agricultural application allow conclude that the developed VRT system is suitable for the agricultural productions reaching an acceptable response time and application error. The CAN-Based DCS solution applied in the VRT system reduced the complexity of the control system, easing the installation and maintenance. The use of VRT system allowed applying only the required inputs, increasing the efficiency operation and minimizing the environmental impact.

Reliability, Validity and Classification Accuracy of the South Oaks Gambling Screen in a Brazilian Sample

The purpose of this study was to examine the reliability, validity and classification accuracy of the South Oaks Gambling Screen (SOGS) in a sample of the Brazilian population. Participants in this study were drawn from three sources: 71 men and women from the general population interviewed at a metropolitan train station; 116 men and women encountered at a bingo venue; and 54 men and women undergoing treatment for gambling. The SOGS and a DSM-IV-based instrument were applied by trained researchers. The internal consistency of the SOGS was 0.75 according to the Cronbach`s alpha model, and construct validity was good. A significant difference among groups was demonstrated by ANOVA (F ((2.238)) = 221.3, P < 0.001). The SOGS items and DSM-IV symptoms were highly correlated (r = 0.854, P < 0.01). The SOGS also presented satisfactory psychometric properties: sensitivity (100), specificity (74.7), positive predictive rate (60.7), negative predictive rate (100) and misclassification rate (0.18). However, a cut-off score of eight improved classification accuracy and reduced the rate of false positives: sensitivity (95.4), specificity (89.8), positive predictive rate (78.5), negative predictive rate (98) and misclassification rate (0.09). Thus, the SOGS was found to be reliable and valid in the Brazilian population.

Modeling the growth rate and lag time of different strains of Salmonella enterica and Listeria monocytogenes in ready-to-eat lettuce

The growth parameters (growth rate, mu and lag time, lambda) of three different strains each of Salmonella enterica and Listeria monocytogenes in minimally processed lettuce (MPL) and their changes as a function of temperature were modeled. MPL were packed under modified atmosphere (5% O-2, 15% CO2 and 80% N-2), stored at 7-30 degrees C and samples collected at different time intervals were enumerated for S. enterica and L monocytogenes. Growth curves and equations describing the relationship between mu and lambda as a function of temperature were constructed using the DMFit Excel add-in and through linear regression, respectively. The predicted growth parameters for the pathogens observed in this study were compared to ComBase, Pathogen modeling program (PMP) and data from the literature. High R-2 values (0.97 and 0.93) were observed for average growth curves of different strains of pathogens grown on MPL Secondary models of mu and lambda for both pathogens followed a linear trend with high R2 values (>0.90). Root mean square error (RMSE) showed that the models obtained are accurate and suitable for modeling the growth of S. enterica and L monocytogenes in MP lettuce. The current study provides growth models for these foodborne pathogens that can be used in microbial risk assessment. (C) 2011 Elsevier Ltd. All rights reserved.

The evaluation of an instrument for pediatric oncology patient classification

The objectives of this study were to assess the interrater reproducibility of the instrument to classify pediatric patients with cancer; verify the adequacy of the patient classification instrument for pediatric patients with cancer; and make a proposal for changing the instrument, thus allowing for the necessary adjustments for pediatric oncology patients. A total of 34 pediatric inpatients of a Cancer Hospital were evaluated by the teams of physicians, nurses and nursing technicians. The Kappa coefficient was used to rate the agreement between the scores, which revealed a moderate to high value in the objective classifications, and a low value in the subjective. In conclusion, the instrument is reliable and reproducible, however, it is suggested that to classify pediatric oncology patients, some items should be complemented in order to reach an outcome that is more compatible with the reality of this specific population.

Network-based high level data classification

Traditional supervised data classification considers only physical features (e. g., distance or similarity) of the input data. Here, this type of learning is called low level classification. On the other hand, the human (animal) brain performs both low and high orders of learning and it has facility in identifying patterns according to the semantic meaning of the input data. Data classification that considers not only physical attributes but also the pattern formation is, here, referred to as high level classification. In this paper, we propose a hybrid classification technique that combines both types of learning. The low level term can be implemented by any classification technique, while the high level term is realized by the extraction of features of the underlying network constructed from the input data. Thus, the former classifies the test instances by their physical features or class topologies, while the latter measures the compliance of the test instances to the pattern formation of the data. Our study shows that the proposed technique not only can realize classification according to the pattern formation, but also is able to improve the performance of traditional classification techniques. Furthermore, as the class configuration's complexity increases, such as the mixture among different classes, a larger portion of the high level term is required to get correct classification. This feature confirms that the high level classification has a special importance in complex situations of classification. Finally, we show how the proposed technique can be employed in a real-world application, where it is capable of identifying variations and distortions of handwritten digit images. As a result, it supplies an improvement in the overall pattern recognition rate.

A system for classification of time-series data from industrial non-destructive device

This work proposes a system for classification of industrial steel pieces by means of magnetic nondestructive device. The proposed classification system presents two main stages, online system stage and off-line system stage. In online stage, the system classifies inputs and saves misclassification information in order to perform posterior analyses. In the off-line optimization stage, the topology of a Probabilistic Neural Network is optimized by a Feature Selection algorithm combined with the Probabilistic Neural Network to increase the classification rate. The proposed Feature Selection algorithm searches for the signal spectrogram by combining three basic elements: a Sequential Forward Selection algorithm, a Feature Cluster Grow algorithm with classification rate gradient analysis and a Sequential Backward Selection. Also, a trash-data recycling algorithm is proposed to obtain the optimal feedback samples selected from the misclassified ones.

The Financial Rate of Profit: What Is It, and How Has It Behaved in the United States?

This work contributes to the almost nonexistent literature on the profit rate of the financial sector. It updates the single study to include financial variables to cover the past decade, compares this profit rate to the (almost unpublished) Weisskopf and NIPA financial profit rates, compares the financial and nonfinancial sector rates, and details the procedure to construct the profit rate in the financial sector including relevant financial variables which capitalists consider to make profit-rate decisions. JEL Classification: B50, E11

Stratification and Prognostic Relevance of Jass's Molecular Classification of Colorectal Cancer

Background: The current proposed model of colorectal tumorigenesis is based primarily on CpG island methylator phenotype (CIMP), microsatellite instability (MSI), KRAS, BRAF, and methylation status of 0-6-Methylguanine DNA Methyltransferase (MGMT) and classifies tumors into five subgroups. The aim of this study is to validate this molecular classification and test its prognostic relevance. Methods: Three hundred two patients were included in this study. Molecular analysis was performed for five CIMP-related promoters (CRABP1, MLH1, p16INK4a, CACNA1G, NEUROG1), MGMT, MSI, KRAS, and BRAF. Methylation in at least 4 promoters or in one to three promoters was considered CIMP-high and CIMP-low (CIMP-H/L), respectively. Results: CIMP-H, CIMP-L, and CIMP-negative were found in 7.1, 43, and 49.9% cases, respectively. One hundred twenty-three tumors (41%) could not be classified into any one of the proposed molecular subgroups, including 107 CIMP-L, 14 CIMP-H, and two CIMP-negative cases. The 10 year survival rate for CIMP-high patients [22.6% (95%CI: 7-43)] was significantly lower than for CIMP-L or CIMP-negative (p = 0.0295). Only the combined analysis of BRAF and CIMP (negative versus L/H) led to distinct prognostic subgroups. Conclusion: Although CIMP status has an effect on outcome, our results underline the need for standardized definitions of low- and high-level CIMP, which clearly hinders an effective prognostic and molecular classification of colorectal cancer.

Medication errors in a swiss cardiovascular surgery department: a cross-sectional study based on a novel medication error report method

The purpose of this study was (1) to determine frequency and type of medication errors (MEs), (2) to assess the number of MEs prevented by registered nurses, (3) to assess the consequences of ME for patients, and (4) to compare the number of MEs reported by a newly developed medication error self-reporting tool to the number reported by the traditional incident reporting system. We conducted a cross-sectional study on ME in the Cardiovascular Surgery Department of Bern University Hospital in Switzerland. Eligible registered nurses (n = 119) involving in the medication process were included. Data on ME were collected using an investigator-developed medication error self reporting tool (MESRT) that asked about the occurrence and characteristics of ME. Registered nurses were instructed to complete a MESRT at the end of each shift even if there was no ME. All MESRTs were completed anonymously. During the one-month study period, a total of 987 MESRTs were returned. Of the 987 completed MESRTs, 288 (29%) indicated that there had been an ME. Registered nurses reported preventing 49 (5%) MEs. Overall, eight (2.8%) MEs had patient consequences. The high response rate suggests that this new method may be a very effective approach to detect, report, and describe ME in hospitals.

Evaluating Prediction Rules for t-Year Survivors With Censored Regression Models

Suppose that we are interested in establishing simple, but reliable rules for predicting future t-year survivors via censored regression models. In this article, we present inference procedures for evaluating such binary classification rules based on various prediction precision measures quantified by the overall misclassification rate, sensitivity and specificity, and positive and negative predictive values. Specifically, under various working models we derive consistent estimators for the above measures via substitution and cross validation estimation procedures. Furthermore, we provide large sample approximations to the distributions of these nonsmooth estimators without assuming that the working model is correctly specified. Confidence intervals, for example, for the difference of the precision measures between two competing rules can then be constructed. All the proposals are illustrated with two real examples and their finite sample properties are evaluated via a simulation study.