Prediction of metabolic syndrome and its associated risk factors in patients with chronic kidney disease using machine learning techniques.

Introduction: Chronic kidney disease (CKD) and metabolic syndrome (MS) are recognized as public health problems which are related to overweight and cardiometabolic factors. The aim of this study was to develop a model to predict MS in people with CKD.

Methods: This was a prospective cross-sectional study of patients from a reference center in São Luís, MA, Brazil.

EvoImp: Multiple Imputation of Multi-label Classification data with a genetic algorithm.

Missing data is a prevalent problem that requires attention, as most data analysis techniques are unable to handle it. This is particularly critical in Multi-Label Classification (MLC), where only a few studies have investigated missing data in this application domain. MLC differs from Single-Label Classification (SLC) by allowing an instance to be associated with multiple classes.

Monopole directional antenna bioinspired in elliptical leaf with golden ratio for WLAN and 4G applications.

In this work, it is proposed the development a new monopole directional antenna, bioinspired in elliptical leaf, with cut by golden ratio, for 4G band application, by the use of the technique of the cut of the radiating element for the increasing of the antenna perimeter, being the first work to use this technique in a bioinspired antenna, promotes resonance frequency turned, and reconfiguring of the antenna parameters as bandwidth, radiation pattern and gain, with the use of the reflector near to the group plane, without the insertion of active devices as the pin diode or change in radiating element. The shape antenna is generated by Gielis formula, built in FR4 substrate, with cuts calculated by golden ratio. To compare the results of the bioinspired monopole on the elliptical sheet, a square-shaped monopole antenna was designed, simulated and measured, the structures were designed in the MATLAB software version 2015(b) and the simulations were performed in the Ansys software version 2016.

Diagnostic Classification of Cases of Canine Leishmaniasis Using Machine Learning.

Proposal techniques that reduce financial costs in the diagnosis and treatment of animal diseases are welcome. This work uses some machine learning techniques to classify whether or not cases of canine visceral leishmaniasis are present by physical examinations. For validation of the method, four machine learning models were chosen: K-nearest neighbor, Naïve Bayes, support vector machine and logistic regression models.

Phenotypic Characterization of Chronic Kidney Patients Through Hierarchical Clustering.

Chronic kidney disease is a major public health problem around the world and this disease early diagnosis is still a great challenge as it is asymptomatic in its early stages. Thus, in order to identify variables capable of assisting CKD diagnosis and monitoring, machine learning techniques and statistical analysis use has shown itself to be extremely promising. For this work, unsupervised machine learning, statistical analysis techniques and discriminant analysis were used.

Autofocus Entropy Repositioning Method Bioinspired in the Magnetic Field Memory of the Bees Applied to Pollination.

In this paper, a bioinspired method in the magnetic field memory of the bees, applied in a rover of precision pollination, is presented. The method calculates sharpness features by entropy and variance of the Laplacian of images segmented by color in the HSV system in real-time. A complementary positioning method based on area feature extraction between active markers was developed, analyzing color characteristics, noise, and vibrations of the probe in time and frequency, through the lateral image of the probe.

EFFECT OF CHRONIC INGESTION OF WINE ON THE GLYCEMIC, LIPID AND BODY WEIGHT HOMEOSTASIS IN MICE.

Background: The health benefits associated with moderate wine consumption, as with ethanol and phenolic compounds, include different mechanisms still little understandable.

Aim: Evaluate glycemic and weight variations, and the deposit of triglycerides, cholesterol and liver glycogen with red wine consumption.

Methods: 60 ApoE knockout mice were divided into three groups of 20: Wine Group (WG), Ethanol Group (EG) and Water Group (WAG).