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Sorghum is a versatile crop used for energy production, human and animal feedings, and raw material for industry. The grain sorghum is the fifth most important cereal worldwide, and Brazil is one of the top 10 sorghum producers. In the last 40 years, the sorghum grain yield in Brazil has increased to 32.70 kg ha-1 year -1. Although this clear evolution, much remains to be done, and sorghum breeders in Brazil still face several challenges. This review discusses the main characteristics of sorghum genetics and breeding, aiming for sorghum improvement for food, fodder, feed, and fuel uses. Herein will be highlighted essential topics related to the genetic control of the main interest traits; conventional sorghum breeding; the development of sorghum lines and hybrids; the use of male-sterility in sorghum breeding; the implication of genotypes-by-environments interaction in sorghum, the use of genome-wide association studies, and genomic prediction to maximize the efficiency of the sorghum breeding programs.

Tropical maize production represents 49% of the global maize harvested area. In terms of productivity, the annual increase rate in tropical regions is twice as higher as the increase rate in temperate regions. The increase in maize grain yield was expressive in the last 41 years in Brazil. Such achievement was possible due to the joint efforts of many areas of science, especially, plant breeding. Although maize breeding success, many are the challenges faced by the maize breeders to develop high-yielding maize hybrids. Tropical conditions provide more intense challenges in terms of pests and diseases pressures, and above all, water deficit conditions. In addition, the constant increase of breeding programs costs requires the development and application of new techniques to improve the genetic gain per time unit, increasing the maize breeding programs’ efficiency. This review discusses the main challenges faced by maize breeding programs under tropical conditions, highlighting topics related to the development of maize hybrids for first and second growing seasons; breeding for biotic and abiotic stresses conditions; specialty maize breeding; use of transgenic in maize; and genomic tools to maximize the efficiency of maize breeding programs under tropical conditions.

The chemical treatment of corn seeds is widely adopted for protecting crops at the early stages. However, some molecules interfere in the quality of seeds, especially when stored, by accelerating their deterioration. The objective of this work was to investigate the influence of insecticides on the enzymatic expression and quality of corn seeds subjected to storage. Seeds of the hybrids 2B647PW and SHS4070 were subjected to four different chemical treatments (standard treatment, standard + clothianidin, standard + thiamethoxam, and standard + fipronil) and stored for different periods (0, 3, 6, and 9 months). The quality evaluation included the germination test, first germination count, cold test, and health test. The expression of the enzymes α-amylase, esterase, superoxide dismutase, catalase, and alcohol dehydrogenase were determined by the gel electrophoresis technique. The insecticides clothianidin, thiamethoxam, and fipronil reduce the physiological quality of the seeds of hybrid 2B647PW after nine months of storage. The treatment of seeds with the insecticides clothianidin, thiamethoxam, and fipronil reduces the expression of α-amylase, superoxide dismutase, and catalase enzymes after nine months of storage. The enzymes α-amylase, superoxide dismutase, catalase, and alcohol dehydrogenase are good quality markers for hybrid corn seeds. Resumo: O tratamento químico de sementes de milho é prática amplamente adotada para proteção das lavouras. No entanto, algumas moléculas interferem na qualidade das sementes, sobretudo quando armazenadas, acelerando sua deterioração. O objetivo deste trabalho foi investigar a influência de inseticidas sobre a expressão enzimática e qualidade de sementes de milho submetidas ao armazenamento. Sementes dos híbridos 2B647PW e SHS4070 foram submetidas a quatro diferentes tratamentos químicos (tratamento padrão, padrão + clotianidina, padrão + tiametoxam e padrão + fipronil) e armazenadas por diferentes períodos (0, 3, 6 e 9 meses). Os testes de qualidade incluíram a primeira contagem de germinação, germinação, teste frio sem solo e teste de sanidade. A expressão das enzimas α-amilase, esterase, superóxido dismutase, catalase e álcool desidrogenase foram determinadas pela técnica de eletroforese em gel. Concluiu-se que os inseticidas clotianidina, tiametoxam e fipronil reduzem a qualidade fisiológica das sementes do híbrido 2B647PW aos nove meses de armazenamento. O tratamento das sementes com os inseticidas clotianidina, tiametoxam e fipronil reduz a expressão das enzimas α-amilase, superóxido dismutase e catalase aos nove meses de armazenamento. As enzimas α-amilase, superóxido dismutase, catalase e álcool desidrogenase são bons marcadores da qualidade de sementes híbridas de milho.

Background and Objectives Cardiovascular health is monitored by many different approaches in order to diagnose and prevent critical patient conditions. The wide used of Photoplethysmography in medicine can facilitate heart status monitoring by heart rate variability (HRV) calculations extracted from blood volume variation measurements. The purpose of this work is to develop a computational process which relies on the evaluation of HRV as a prognostic marker in the stratification of the clinical heart conditions: Idiopathic Dilated Cardiomyopathy (IDC), Chagas Cardiomyopathy (CC) and Ischemic Cardiomyopathy (IC). Methods The database created for the study was developed with the assistance of Dr. Carlos Alberto Studart Gomes Hospital in the city of Fortaleza, CE. The detection of cardiac anomaly is analyzed by computational intelligence and pattern recognition algorithms. The classifiers used are the Multilayer Perceptron (MLP), Self-Organizing Map (SOM), Kernel kmeans (Kk-means), and k-Nearest Neighbors (k-NN). Results The results indicate SOM and Kk-means classifiers are the most suitable algorithms to be applied because they hold the highest true rates (Acc = 85.44% to 100%) and sensitivity (Se = 92.85% to 100%). Conclusions Among the heart conditions considered in this paper, Chagas Cardiomyopathy analysis presented the best results for any algorithm because the differences in the patterns of this disease are easier to identify due to arrhythmogenic characteristics. IDC and IC have similar statistical variables versus healthy subjects, which may complicate the pattern recognition performed by classifiers. The combination of extracted PPG signal features with machine learning may result in a high stratification performance and be of assistance to doctors for early diagnosis of disease.

CAPES - Coordenação de Aperfeiçoamento de Pessoal de Nível Superior O eletrocardiograma (ECG) é considerado um dos métodos mais importantes de assistência diagnóstica no domínio cardiovascular clínico em vista ao crescente número de cardiopatias. Com a expansão da aquisição de exames ECG, fez-se necessário o desenvolvimento de algoritmos para automatizar a análise do sinal ECG, reduzindo o tempo de laudo pelos especialistas. Neste contexto, o presente trabalho propõe um método de detecção automática dos picos R baseado em vetorcardiograma para traçados curtos de 12 derivações. Esse algoritmo, denominado Vectordet, considera as limitações de um sistema de saúde, tais como excesso de ruídos, diversidades patológicas e recursos computacionais limitados. Esse cenário clínico é representado pelo banco de dados de derivações padrões do St. Petersburg Institute of Cardiological Technics 12-lead Arrhythmia Database. O desempenho do Vectordet é comparado com as referências Pan-Tompkins e Wavedet, algoritmos de detecção de pico R de derivação a derivação. Perante a distinta capacidade de detecção em cada derivação dessas duas referências, uma regra de múltiplas derivações, denominada Single Lead Rule, foi avaliada para ponderar a detecção de diferentes entradas. A avaliação da performance dos algoritmos se restringe as métricas de sensibilidade (Se), preditividade positiva (P+) e tempo de processamento (Tp), apesar de serem apresentados o erro RMS, a especificidade e a acurácia. Assim, os resultados obtidos para o algoritmo proposto são Se = 99,30%, P+ = 99,15% e Tp = 0,001 s por exame de 10 s. Com base nos algoritmos de referência, o Vectordet assegura detecção de picos R com desempenho estatisticamente superior ao Wavedet e custo computacional 25 e 3700 vezes mais rápido do que Pan-Tompkins e Wavedet, respectivamente. The electrocardiogram (ECG) is considered one of the most important diagnosis assistance methods in the cardiovascular clinical area. With the expansion of the ECG exam acquisition, it has become necessary to develop algorithms for automating the ECG signal analysis, reducing the medical report time. In this context, the objective of this research is the development of an algorithm for automatic detection of R peaks on short 12-leads ECG excerpt based on vectorcardiogram. This algorithm, named Vectordet, considers the health systems limitations, which are the excessive noise level, diverse pathologies and limited computational resources. This case scenario is represented by St. Petersburg Institute of Cardiological Technics 12-lead Arrhythmia Database. The Vectordet performance is compared with the references Pan-Tompkins and Wavedet, which are single lead R peak detection algorithms. Before the distinct capacity in each lead detection of these two references, a multiple leads rule, called Single Lead Rule, was studied for weighting different input detections. The algorithms performances evaluation is restricted to the metrics of sensitivity (Se), positive predictivity (P+) and processing time (Tp), although it is presented the RMS error, specificity and accuracy. Thereby, the results obtained in the proposed algorithm are Se = 99.30%, P+ = 99.15% and a Tp = 0.001 s for each 10-s exam. From the reference algorithms, Vectordet ensures an R peak detection with a performance statistically superior to Wavedet and a computational cost 25 and 3700 times faster than Pan-Tompkins and Wavedet, respectively.