Light emitting diode (LED) use in artificial lighting for broiler chicken production

Light emitting diode (LED) has been used in commercial poultry industry by presenting superior energy savings and providing feasibility on production process. The objective of this research was to evaluate performance and carcass yield of broiler chickens exposed to different LED colors compared with fluorescent lamps. For that, two experiments (E1 and E2) were performed and 2,646 Cobb® chickens were used. In experiment E1, male birds were exposed to 20 lux artificial lighting with red, yellow, blue, and white LED bulbs; and fluorescent bulb. In experiment E2, male and female birds were exposed to 15 lux artificial lighting with red and blue LED bulbs; and fluorescent bulb. Cumulative weight gain (kg), feed intake (kg), feed conversion, hot carcass weight (kg), carcass yield (%), and breast and thigh + drumstick yield (%) were used as response variables. Results showed no difference (p > 0.05) among treatments for performance, carcass yield, and cut yield in experiment E1. In experiment E2 there was only difference between genders (p < 0.05) and males showed higher total weight gain, feed intake, hot carcass weight and thigh + drumstick yield. Different LED color use had same effect as fluorescent lights on broiler performance and carcass yield.

Application of artificial neural networks as an alternative to volumetric water balance in drip irrigation management in watermelon crop

Precision irrigation seeks to establish strategies which achieve an efficient ratio between the volume of water used (reduction in input) and the productivity obtained (increase in production). There are several studies in the literature on strategies for achieving this efficiency, such as those dealing with the method of volumetric water balance (VWB). However, it is also of great practical and economic interest to set up versatile implementations of irrigation strategies that: (i) maintain the performance obtained with other implementations, (ii) rely on few computational resources, (iii) adapt well to field conditions, and (iv) allow easy modification of the irrigation strategy. In this study, such characteristics are achieved when using an Artificial Neural Network (ANN) to determine the period of irrigation for a watermelon crop in the Irrigation Perimeter of the Lower Acaraú, in the state of Ceará, Brazil. The Volumetric Water Balance was taken as the standard for comparing the management carried out with the proposed implementation of ANN. The statistical analysis demonstrates the effectiveness of the proposed management, which is able to replace VWB as a strategy in automation.

Análise da diversidade da microbiota intestinal de ratos submetidos à ressecção da valva ileocecal e criação de esfíncter artificial

OBJETIVO: analisar através de biologia molecular a diversidade da microbiota da junção ileocecal antes e após a ressecção da válvula ileocecal e reconstrução do trânsito com e sem a criação de "neoesfíncter". MÉTODOS: Os animais foram distribuídos em dois grupos: Grupo A (n=7) com ressecção da válvula ileocecal e anastomose ileocólica término-terminal em plano único, e Grupo B (n=7) com ressecção da válvula ileocecal e anastomose ileocólica término-terminal em plano único e confecção do esfíncter artificial. Reoperados com 20 dias coletou-se novamente conteúdo intraluminar do íleo e do cólon. Das amostras coletadas, extraiu-se DNA para reação de PCR-DGGE. Os padrões de bandas eletroforéticas , gerados na reação, foram submetidos ao programa Bionumerics para análise da similaridade e da diversidade da microbiota. RESULTADOS: a diversidade da microbiota foi maior e em mais amostras do íleo do que as do cólon. O grupo com a válvula apresentou os maiores valores e variações no cólon de 2,11 a 2,93. Em três animais de cada grupo estabeleceu-se comparação da similaridade e não se assemelharam ao controle. CONCLUSÃO: a ressecção da válvula ileocecal levou à mudanças da microbiota ileal e, com a criação de novo esfíncter, as variações foram maiores.

Evaluation of follicular lymphoid depletion in the Bursa of Fabricius: an alternative methodology using digital image analysis and artificial neural networks

Fifty Bursa of Fabricius (BF) were examined by conventional optical microscopy and digital images were acquired and processed using Matlab® 6.5 software. The Artificial Neuronal Network (ANN) was generated using Neuroshell® Classifier software and the optical and digital data were compared. The ANN was able to make a comparable classification of digital and optical scores. The use of ANN was able to classify correctly the majority of the follicles, reaching sensibility and specificity of 89% and 96%, respectively. When the follicles were scored and grouped in a binary fashion the sensibility increased to 90% and obtained the maximum value for the specificity of 92%. These results demonstrate that the use of digital image analysis and ANN is a useful tool for the pathological classification of the BF lymphoid depletion. In addition it provides objective results that allow measuring the dimension of the error in the diagnosis and classification therefore making comparison between databases feasible.

Classification of antimicrobial resistance using artificial neural networks and the relationship of 38 genes associated with the virulence of Escherichia coli isolates from broilers

Avian pathogenic Escherichia coli (APEC) is responsible for various pathological processes in birds and is considered as one of the principal causes of morbidity and mortality, associated with economic losses to the poultry industry. The objective of this study was to demonstrate that it is possible to predict antimicrobial resistance of 256 samples (APEC) using 38 different genes responsible for virulence factors, through a computer program of artificial neural networks (ANNs). A second target was to find the relationship between (PI) pathogenicity index and resistance to 14 antibiotics by statistical analysis. The results showed that the RNAs were able to make the correct classification of the behavior of APEC samples with a range from 74.22 to 98.44%, and make it possible to predict antimicrobial resistance. The statistical analysis to assess the relationship between the pathogenic index (PI) and resistance against 14 antibiotics showed that these variables are independent, i.e. peaks in PI can happen without changing the antimicrobial resistance, or the opposite, changing the antimicrobial resistance without a change in PI.

Follicle and corpus luteum size and vascularity as predictors of fertility at the time of artificial insemination and embryo transfer in beef cattle

Abstract:Two ultrasound based fertility prediction methods were tested prior to embryo transfer (ET) and artificial insemination (AI) in cattle. Female bovines were submitted to estrous synchronization prior to ET and AI. Animals were scanned immediately before ET and AI procedure to target follicle and corpus luteum (CL) size and vascularity. In addition, inseminated animals were also scanned eleven days after insemination to target CL size and vascularity. All data was compared with fertility by using gestational diagnosis 35 days after ovulation. Prior to ET, CL vascularity showed a positive correlation with fertility, and no pregnancy occurred in animals with less than 40% of CL vascularity. Prior to AI and also eleven days after AI, no relationship with fertility was seen in all parameters analyzed (follicle and CL size and vascularity), and contrary, cows with CL vascularity greater than 70% exhibit lower fertility. In inseminated animals, follicle size and vascularity was positive related with CL size and vascularity, as shown by the presence of greater CL size and vascularity originated from follicle with also greater size and vascularity. This is the first time that ultrasound based fertility prediction methods were tested prior to ET and AI and showed an application in ET, but not in AI programs. Further studies are needed including hormone profile evaluation to improve conclusion.

Fitoplâncton (exceto Chlorophyceae) de um lago artificial urbano no Sul do Brasil

O presente estudo tem como objetivo conhecer a composição do fitoplâncton no Lago das Tartarugas, situado no Jardim Botânico da cidade de Porto Alegre, Estado do Rio Grande do Sul. As amostragens foram realizadas mensalmente, no período de junho de 2007 a maio de 2008, em uma estação em três diferentes níveis de profundidade, na zona pelágica. Um total de 49 táxons específicos e infraespecíficos pertencentes a sete classes foram registrados. Cyanobacteria apresentou maior número de táxons (35% dos táxons identificados) seguida de Bacillariophyceae (33%) e Euglenophyceae (16,3%). São apresentadas descrições, medidas e ilustrações dos táxons, assim como a distribuição dos mesmos durante o ciclo anual.

Cianobactérias planctônicas de um lago artificial urbano na cidade de Goiânia, GO

Os criatórios de peixe do estado de Goiás são inúmeros e de intensa atividade recreativa. No entanto, estudos sobre as cianobactérias nesses ambientes são escassos, fato preocupante, uma vez que é comum notar-se intensa proliferação do fitoplâncton em pesqueiros, principalmente devido a ações antrópicas. O perigo consiste na formação de florações de espécies potencialmente tóxicas, principalmente de cianobactérias. Este trabalho visa inventariar as espécies planctônicas de cianobactérias ocorrentes em um pesqueiro (lago Jaó - um lago artificial raso) da área municipal de Goiânia (GO) (16º39'13" S-49º13'26" O). As amostragens foram realizadas nos períodos de seca (2003 a 2008) e chuva (2009), quando visualmente era evidente a ocorrência de florações. Foram aferidas variáveis climatológicas, morfométricas e limnológicas. O período de seca foi representativo nos anos amostrados apresentando no máximo 50 mm de precipitação mensal em 2005. Foram registrados 31 táxons de cianobactérias pertencentes aos gêneros Dolichospermum (5 spp.), Aphanocapsa (4 spp.), Microcystis (3 spp.), Pseudanabaena (3 spp.), Radiocystis (2 spp.), Oscillatoria (2 spp.), Bacularia, Coelosphaerium, Cylindrospermopsis, Geitlerinema, Glaucospira, Limnothrix, Pannus, Phormidium, Planktolyngbya, Planktothrix, Sphaerocavum e Synechocystis, esses últimos com uma espécie cada. Nos anos de 2003 a 2005 ocorreu predomínio de florações de espécies de Dolichospermum e em 2006 predominaram espécies de Microcystis, Radiocystis e Aphanocapsa. Das espécies inventariadas neste estudo, 21 são primeiras citações para o estado de Goiás e 13 foram constadas na literatura como potencialmente tóxicas.

Plant height reduction in populations of triticale (X triticosecale Wittmack) by induced mutations and artificial crosses

Induced mutations by gamma radiation (0, 5, 10, 20 and 40 kR doses) and reciprocal crosses were tested as mechanisms of enhancing genetic variability for plant height in two triticale cultivars, BR4 and EMBRAPA18. The reciprocal crosses and all doses of radiation showed similar increase in genetic amplitude for this trait, being suitable for increasing variability in breeding programs. Genotypes showed different responses as the gamma ray doses were increased, expressing shorter plant height. The decision of using induced mutations or artificial crosses depends on the resources available and the selection method to be used

Automated grading of left ventricular segmental wall motion by an artificial neural network using color kinesis images

The present study describes an auxiliary tool in the diagnosis of left ventricular (LV) segmental wall motion (WM) abnormalities based on color-coded echocardiographic WM images. An artificial neural network (ANN) was developed and validated for grading LV segmental WM using data from color kinesis (CK) images, a technique developed to display the timing and magnitude of global and regional WM in real time. We evaluated 21 normal subjects and 20 patients with LVWM abnormalities revealed by two-dimensional echocardiography. CK images were obtained in two sets of viewing planes. A method was developed to analyze CK images, providing quantitation of fractional area change in each of the 16 LV segments. Two experienced observers analyzed LVWM from two-dimensional images and scored them as: 1) normal, 2) mild hypokinesia, 3) moderate hypokinesia, 4) severe hypokinesia, 5) akinesia, and 6) dyskinesia. Based on expert analysis of 10 normal subjects and 10 patients, we trained a multilayer perceptron ANN using a back-propagation algorithm to provide automated grading of LVWM, and this ANN was then tested in the remaining subjects. Excellent concordance between expert and ANN analysis was shown by ROC curve analysis, with measured area under the curve of 0.975. An excellent correlation was also obtained for global LV segmental WM index by expert and ANN analysis (R² = 0.99). In conclusion, ANN showed high accuracy for automated semi-quantitative grading of WM based on CK images. This technique can be an important aid, improving diagnostic accuracy and reducing inter-observer variability in scoring segmental LVWM.

Synergistic control of forearm based on accelerometer data and artificial neural networks

In the present study, we modeled a reaching task as a two-link mechanism. The upper arm and forearm motion trajectories during vertical arm movements were estimated from the measured angular accelerations with dual-axis accelerometers. A data set of reaching synergies from able-bodied individuals was used to train a radial basis function artificial neural network with upper arm/forearm tangential angular accelerations. The trained radial basis function artificial neural network for the specific movements predicted forearm motion from new upper arm trajectories with high correlation (mean, 0.9149-0.941). For all other movements, prediction was low (range, 0.0316-0.8302). Results suggest that the proposed algorithm is successful in generalization over similar motions and subjects. Such networks may be used as a high-level controller that could predict forearm kinematics from voluntary movements of the upper arm. This methodology is suitable for restoring the upper limb functions of individuals with motor disabilities of the forearm, but not of the upper arm. The developed control paradigm is applicable to upper-limb orthotic systems employing functional electrical stimulation. The proposed approach is of great significance particularly for humans with spinal cord injuries in a free-living environment. The implication of a measurement system with dual-axis accelerometers, developed for this study, is further seen in the evaluation of movement during the course of rehabilitation. For this purpose, training-related changes in synergies apparent from movement kinematics during rehabilitation would characterize the extent and the course of recovery. As such, a simple system using this methodology is of particular importance for stroke patients. The results underlie the important issue of upper-limb coordination.

Artificial neural network models for predicting 1-year mortality in elderly patients with intertrochanteric fractures in China

The mortality rate of older patients with intertrochanteric fractures has been increasing with the aging of populations in China. The purpose of this study was: 1) to develop an artificial neural network (ANN) using clinical information to predict the 1-year mortality of elderly patients with intertrochanteric fractures, and 2) to compare the ANN's predictive ability with that of logistic regression models. The ANN model was tested against actual outcomes of an intertrochanteric femoral fracture database in China. The ANN model was generated with eight clinical inputs and a single output. ANN's performance was compared with a logistic regression model created with the same inputs in terms of accuracy, sensitivity, specificity, and discriminability. The study population was composed of 2150 patients (679 males and 1471 females): 1432 in the training group and 718 new patients in the testing group. The ANN model that had eight neurons in the hidden layer had the highest accuracies among the four ANN models: 92.46 and 85.79% in both training and testing datasets, respectively. The areas under the receiver operating characteristic curves of the automatically selected ANN model for both datasets were 0.901 (95%CI=0.814-0.988) and 0.869 (95%CI=0.748-0.990), higher than the 0.745 (95%CI=0.612-0.879) and 0.728 (95%CI=0.595-0.862) of the logistic regression model. The ANN model can be used for predicting 1-year mortality in elderly patients with intertrochanteric fractures. It outperformed a logistic regression on multiple performance measures when given the same variables.

HYBRID ARTIFICIAL NEURAL NETWORK APPLIEDTO MODELING SCFE OF BASIL AND ROSEMARY OILS

This work presents the results of a Hybrid Neural Network (HNN) technique as applied to modeling SCFE curves obtained from two Brazilian vegetable matrices. A series Hybrid Neural Network was employed to estimate the parameters of the phenomenological model. A small set of SCFE data of each vegetable was used to generate an extended data set, sufficient to train the network. Afterwards, other sets of experimental data, not used in the network training, were used to validate the present approach. The series HNN correlates well the experimental data and it is shown that the predictions accomplished with this technique may be promising for SCFE purposes.

Previsão de estabilidade de óleos vegetais através da rede de inteligência artificial

Redes Neurais Artificiais são técnicas computacionais que se utilizam de um modelo matemático capaz de adquirir conhecimentos pela experiência; esse comportamento inteligente da rede provém das interações entre unidades de processamento, denominadas de neurônios artificiais. O objetivo deste trabalho foi criar uma rede neural capaz de prever a estabilidade de óleos vegetais, a partir de dados de suas composições químicas, visando um modelo para a previsão da shelf-life de óleos vegetais, tendo como parâmetros apenas dados de suas composições químicas. Os primeiros passos do processo de desenvolvimento da rede consistiram na coleta de dados relativos ao problema e sua separação em um conjunto de treinamento e outro de testes. Estes conjuntos apresentaram como variáveis dados de composição química, que incluíram os valores totais em ácidos graxos, fenóis, tocoferóis e a composição individual em ácidos graxos. O passo seguinte foi a execução do treinamento, onde o padrão de entrada apresentado à rede como parâmetro de estabilidade foi o índice de peróxido, determinado experimentalmente por um período de 16 dias de armazenagem na ausência de luz, a 65ºC. Após o treinamento foi testada a capacidade de previsão adquirida pela rede, em função do parâmetro de estabilidade adotado, mas com um novo grupo de óleos. Seguindo o teste, foi determinada a correlação linear entre os valores de estabilidade previstos pela rede e aqueles determinados experimentalmente. Com os resultados obtidos, pode-se confirmar a viabilidade de previsão da estabilidade de óleos vegetais pela rede neural, a partir de dados de sua composição química, utilizando como parâmetro de estabilidade o índice de peróxido.

An artificial neural network model for prediction of quality characteristics of apples during convective dehydration

In this study, the effects of hot-air drying conditions on color, water holding capacity, and total phenolic content of dried apple were investigated using artificial neural network as an intelligent modeling system. After that, a genetic algorithm was used to optimize the drying conditions. Apples were dried at different temperatures (40, 60, and 80 °C) and at three air flow-rates (0.5, 1, and 1.5 m/s). Applying the leave-one-out cross validation methodology, simulated and experimental data were in good agreement presenting an error < 2.4 %. Quality index optimal values were found at 62.9 °C and 1.0 m/s using genetic algorithm.