Detection of coffee fruits on tree branches using computer vision

Coffee farmers do not have efficient tools to have sufficient and reliable information on the maturation stage of coffee fruits before harvest. In this study, we propose a computer vision system to detect and classify the Coffea arabica (L.) on tree branches in three classes: unripe (green), ripe (cherry), and overripe (dry). Based on deep learning algorithms, the computer vision model YOLO (You Only Look Once), was trained on 387 images taken from coffee branches using a smartphone. The YOLOv3 and YOLOv4, and their smaller versions (tiny), were assessed for fruit detection. The YOLOv4 and YOLOv4-tiny showed better performance when compared to YOLOv3, especially when smaller network sizes are considered. The mean average precision (mAP) for a network size of 800 × 800 pixels was equal to 81 %, 79 %, 78 %, and 77 % for YOLOv4, YOLOv4-tiny, YOLOv3, and YOLOv3-tiny, respectively. Despite the similar performance, the YOLOv4 feature extractor was more robust when images had greater object densities and for the detection of unripe fruits, which are generally more difficult to detect due to the color similarity to leaves in the background, partial occlusion by leaves and fruits, and lighting effects. This study shows the potential of computer vision systems based on deep learning to guide the decision-making of coffee farmers in more objective ways.

The use of Artificial Intelligence in predicting Respiratory Syncytial Virus-inhibiting flavonoids / O uso da Inteligência Artificial na predição de flavonoides inibidores do Vírus Sincicial Respiratório

Human Respiratory Syncytial Virus (hRSV) infection results in death and hospitalization of thousands of people worldwide each year. Unfortunately, there are no vaccines or specific treatments for hRSV infections. Screening hundreds or even thousands of promising molecules is a challenge for science. We integrated biological, structural, and physicochemical properties to train and to apply the concept of artificial intelligence (AI) able to predict flavonoids with potential anti-hRSV activity. During the training and simulation steps, the AI produced results with hit rates of more than 83%. The better AIs were able to predict active or inactive flavonoids against hRSV. In the future, in vitro and/or in vivo evaluations of these flavonoids may accelerate trials for new anti-RSV drugs, reduce hospitalizations, deaths, and morbidity caused by this infection worldwide, and be used as input in these networks to determine which parameter is more important for their decision.

A infecção pelo Vírus Sincicial Respiratório Humano (hRSV) resulta na morte e hospitalização de milhares de pessoas em todo o mundo a cada ano. Infelizmente, não existem vacinas ou tratamentos específicos para tais infecções. A testagem de centenas, ou mesmo milhares, de moléculas promissoras é um desafio para a ciência. Neste trabalho, nós integramos propriedades biológicas, estruturais e físico-químicas para treinar e aplicar o conceito de inteligência artificial (IA) capaz de prever flavonoides com potencial atividade anti-hRSV. Durante as etapas de treinamento e simulação, a IA produziu resultados com taxas de acerto superiores a 83%, sendo capaz de prever flavonoides ativos ou inativos contra o hRSV. No futuro, avaliações in vitro e/ou in vivo desses flavonoides poderão acelerar os testes de novas drogas anti-RSV, reduzir hospitalizações, mortes e morbidade causadas por essa infecção. Além disso, a validação futura destes dados poderá determinar qual parâmetro tem maior peso na decisão da inteligência.

Artificial neural networks for the management of poultry industry: a simulation based on the broiler production chain / Redes neurais artificiais para o gerenciamento da indústria avícola: uma simulação baseada na cadeia de produção de frangos de corte

The aim of this study was to predict production indicators and to determine their potential economic impact on a poultry integration system using artificial neural networks (ANN) models. Forty zootechnical and production parameters from broiler breeder farms, one hatchery, broiler production flocks, and one slaughterhouse were selected as variables. The ANN models were established for four output variables: "saleable hatching", "weight at the end of week 5," "partial condemnation," and "total condemnation" and were analyzed in relation to the coefficient of multiple determination (R2), correlation coefficient (R), mean error (E), mean squared error (MSE), and root mean square error (RMSE). The production scenarios were simulated and the economic impacts were estimated. The ANN models were suitable for simulating production scenarios after validation. For "saleable hatching", incubator and egg storage period are likely to increase the financial gains. For "weight at the end of the week 5" the lineage (A) is important to increase revenues. However, broiler weight at the end of the first week may not have a significant influence. Flock sex (female) may influence the "partial condemnation" rates, while chick weight at first day may not. For "total condemnation", flock sex and type of chick may not influence condemnation rates, but mortality rates and broiler weight may have a significant impact.

O objetivo deste trabalho foi predizer os indicadores de produção e determinar o seu potencial impacto econômico em um sistema de integração utilizando as redes neurais artificiais (RNA). Quarenta parâmetros zootécnicos e de produção de granjas de matrizes e de frango de corte, um incubatório e um abatedouro foram selecionados como variáveis. Os modelos de RNA foram estabelecidos para quatro variáveis de saída ("eclosão vendável", "peso ao final da quinta semana", "condenações parciais" e "condenações totais") e foram analisados em relação ao coeficiente de determinação múltipla (R2), coeficiente de correlação (R), erro médio (E), erro quadrático médio (EQM) e raiz do erro quadrático médio (REQM). Os cenários produtivos foram simulados e os impactos foram estimados. Os modelos de RNA gerados foram adequados para simular diferentes cenários produtivos após o treinamento. Para "eclosão vendável", o modelo de incubadora e o período de incubação aumentaram os ganhos financeiros. Para "peso ao final da quinta semana", a linhagem também demonstrou influencia no retorno financeiro, o que não aconteceu com o peso ao final da primeira semana. O sexo do lote possui influência nas taxas de "condenação parcial", ao contrário do peso do frango no primeiro dia. As taxas de mortalidade e o peso do frango apresentaram influência na "condenação total", mas o sexo do lote e o tipo de pinto não tiverem influência.

Retrieval of leaf chlorophyll content in Gannan navel orange based on fusing hyperspectral vegetation indices using machine learning algorithms / Recuperação do teor de clorofila de folhas em laranja do umbigo Gannan com base na fusão de índices de vegetação hiperespectral usando algoritmos de inteligência artificial

Estimating leaf chlorophyll contents through leaf reflectance spectra is efficient and nondestructive. The literature base regarding optical indices (particularly chlorophyll indices) is wide ranging and extensive. However, it is without much consensus regarding robust indices for Gannan navel orange. To address this problem, this study investigated the performance of 19 published indices using RDS (raw data spectrum), FDS (first derivative data spectrum) and SDS (second derivative data spectrum) for the estimation of chlorophyll content in navel orange leaves. The single spectral index and combination of multiple spectral indices were compared for their accuracy in predicting chlorophyll a content (Chla), chlorophyll b content (Chlb) and total chlorophyll content (Chltot) content in navel orange leaves by using partial least square regression (PLSR), adaboost regression (AR), random forest regression (RFR), decision tree regression (DTR) and support vector machine regression (SVMR) models. Through the comparison of the above data in three datasets, the optimal modeling data set is RDS data, followed by FDS data, and the worst is SDS data. In modeling with multiple spectral indices, good results were obtained for Chla (NDVI750, NDVI800), Chlb (Datt, DD, Gitelson 2) and Chltot (Datt, DD, Gitelson2) by corresponding index combinations. Overall, we can find that the AR is also the best regression method judging by prediction performance from the results of single spectral index models and multiple spectral indices models. In comparison, result of multiple spectral indices models is better than single spectral index models in predicting Chla and Chltot content using FDS data and SDS data, respectively.

Estimar os teores de clorofila foliar através de espectros de refletância foliar é eficiente e não destrutivo, a base da literatura sobre índices ópticos (principalmente índices de clorofila) é ampla e extensa. No entanto, não há muito consenso sobre índices robustos para a laranja de Gannan. O estudo investigou o desempenho de 19 índices publicados usando RDS (espectro de dados brutos), FDS (espectro de dados de primeira derivada) e SDS (espectro de dados de segunda derivada) para a estimativa do teor de clorofila em folhas de laranja de umbigo. Os índices espectrais foram comparados quanto à sua precisão na previsão do teor de clorofila a (Chla), teor de clorofila b (Chlb) e teor de clorofila total (Chltot) em folhas de laranja de umbigo usando regressão dos mínimos quadrados parcial (PLSR), regressão adaboost (AR), modelos de regressão de floresta aleatória (RFR), regressão de árvore de decisão (DTR) e regressão de máquina de vetor de suporte (SVMR). Através da comparação dos dados acima em três conjuntos de dados, o conjunto de dados de modelagem ideal são os dados RDS, seguidos pelos dados FDS, e o pior são os dados SDS. Na modelagem com vários índices espectrais, bons resultados foram obtidos para Chla (NDVI750, NDVI800), Chlb (Datt, DD, Gitelson 2) e Chltot (Datt, DD, Gitelson2) por combinações de índices correspondentes. No geral, podemos descobrir que o AR também é o melhor método de regressão a julgar pelo desempenho de previsão dos resultados de modelos de índice espectral único e modelos de índices espectrais múltiplos. Em comparação, o resultado de modelos de índices espectrais múltiplos é melhor do que os modelos de índices espectrais únicos na previsão do conteúdo de Chla e Chltot usando dados FDS e dados SDS, respectivamente.

The embryo non-invasive pre-implantation diagnosis era: how far are we?

Advancements in assisted reproduction (AR) methodologies have allowed significant improvements in live birth rates of women who otherwise would not be able to conceive. One of the tools that allowed this improvement is the possibility of embryo selection based on genetic status, performed via preimplantation genetic testing (PGT). Even though the widespread use of PGT from TE biopsy helped to decrease the interval from the beginning of the AR intervention to pregnancy, especially in older patients, in AR, there are still many concerns about the application of this invasive methodology in all cycles. Therefore, recently, researchers started to study the use of cell free DNA (cfDNA) released by the blastocyst in its culture medium to perform PGT, in a method called non-invasive PGT (niPGT). The development of a niPGT would bring the diagnostics power of conventional PGT, but with the advantage of being potentially less harmful to the embryo. Its implementation in clinical practice, however, is under heavy discussion since there are many unknowns about the technique, such as the origin of the cfDNA or if this genetic material is a true representative of the actual ploidy status of the embryo. Available data indicates that there is high correspondence between results observed in TE biopsies and the ones observed from cfDNA, but these results are still contradictory and highly debatable. In the present review, the advantages and disadvantages of niPGT are presented and discussed in relation to tradition TE biopsy-based PGT. Furthermore, there are also presented some other possible non-invasive tools that could be applied in the selection of the best embryo, such as quantification of other molecules as quality biomarkers, or the use artificial intelligence (AI) to identify the best embryos based on morphological and/or morphokitetic parameters.(AU)

Combining deep learning and X-ray imaging technology to assess tomato seed quality

Traditional germination tests which assess seed quality are costly and time-consuming, mainly when performed on a large scale. In this study, we assessed the efficiency of X-ray imaging analyses in predicting the physiological quality of tomato seeds. A convolutional neural network (CNN) called mask region convolutional neural network (MaskRCNN) was also tested for its precision in adequately classifying tomato seeds into four seed quality categories. For this purpose, X-ray images were taken of seeds of 49 tomato genotypes (46 Solanum pennellii introgression lines) from two different growing seasons. Four replicates of 25 seeds for each genotype were analyzed. These seeds were further assessed for germination and seedling vigor-related traits in two independent trials. Correlation analysis revealed significant linear association between germination and image-based variables. Most genotypes differed in terms of germination and seed development performance considering the two independent trials, except LA 4046, LA 4043, and LA4047, which showed similar behavior. Our findings point out that seeds with low opacity and percentage of damaged seed tissue and high values for living tissue opacity have greater physiological quality. In short, our work confirms the reliability of X-ray imaging and deep learning methodologies in predicting the physiological quality of tomato seeds.

Morphometric descriptive report of scleral ossicle rings, by ultrasound and computed tomography, in three Testudines specimens / Relato descritivo morfobiométrico dos anéis de ossículos esclerais, por ultrassonografia e tomografia computadorizada, em três espécimes de Testudines

The scleral ossicle rings function has been related to mechanical protection, muscle fixation, support for eyeball shape and visual accommodation. There are few morphobiometric reports on these rings in different Testudines species, and we performed ultrasound (US) and computed tomography (CT) of the scleral ossicle rings in one green turtle (Chelonia mydas), one black-bellied slider (Trachemys dorbigni) and one red-footed tortoise (Chelonoidis carbonarius). The US and CT of the ossicle rings were performed for anatomical identification. The thickness, density, width, and diameters of each ring were measured. The US and CT of the scleral ossicle rings of three animals showed single and continuous circular structures, located in the anterior pole. These structures were easily observed in C. mydas, whose rings were the biggest, thickest and widest. The T. dorbigni CT presented decreased dimensions and the ossicles were the most difficult to identify. Bone density in the superior region was greater than in the inferior of each ring in all animals. Non-invasive imaging exams are good tools to study the anatomy of the ocular skeleton. The scleral ossicle rings of the three specimens presented general morphological similarities and CT enabled visualizing a greater number of details of the ring bone morphology.

Os anéis de ossículos esclerais têm sua função relacionada à proteção mecânica, fixação muscular, suporte para o formato do bulbo ocular e acomodação visual, contudo existem poucos relatos morfobiométricos sobre esses anéis em diferentes espécies de Testudines. Desta forma, foi realizada a avaliação morfobiométrica, por ultrassom (US) e tomografia computadorizada (TC), dos anéis de ossículos esclerais em uma tartaruga verde (Chelonia mydas), um tigre-d'água (Trachemys dorbigni) e um jabuti-piranga (Chelonoidis carbonarius). Foram realizadas US e TC dos anéis esclerais dos três animais para identificação anatômica, espessura, densidade, largura e diâmetros. A US e a TC dos três animais mostraram estruturas circulares únicas e contínuas, localizadas no polo anterior. Estas estruturas foram facilmente observadas na C. mydas, cujos anéis eram os maiores, mais espessos e mais largos. A TC da T. dorbigni apresentou dimensões reduzidas e os ossículos foram dificilmente identificados. A densidade óssea na região superior foi maior comparativamente a parte inferior de cada anel, em todos os animais. Exames de imagem não invasivos mostraram-se bons instrumentos para estudo do esqueleto escleral. Os anéis de ossículos esclerais dos três espécimes apresentaram semelhanças morfológicas gerais e a TC permitiu visualizar um maior número de detalhes da morfologia óssea do anel.

Applications of livestock monitoring devices and machine learning algorithms in animal production and reproduction: an overview

Some sectors of animal production and reproduction have shown great technological advances due to the development of research areas such as Precision Livestock Farming (PLF). PLF is an innovative approach that allows animals to be monitored, through the adoption of cutting-edge technologies that continuously collect real-time data by combining the use of sensors with advanced algorithms to provide decision tools for farmers. Artificial Intelligence (AI) is a field that merges computer science and large datasets to create expert systems that are able to generate predictions and classifications similarly to human intelligence. In a simplified manner, Machine Learning (ML) is a branch of AI, and can be considered as a broader field that encompasses Deep Learning (DL, a Neural Network formed by at least three layers), generating a hierarchy of subsets formed by AI, ML and DL, respectively. Both ML and DL provide innovative methods for analyzing data, especially beneficial for large datasets commonly found in livestock-related activities. These approaches enable the extraction of valuable insights to address issues related to behavior, health, reproduction, production, and the environment, facilitating informed decision-making. In order to create the referred technologies, studies generally go through five steps involving data processing: acquisition, transferring, storage, analysis and delivery of results. Although the data collection and analysis steps are usually thoroughly reported by the scientific community, a good execution of each step is essential to achieve good and credible results, which impacts the degree of acceptance of the proposed technologies in real life practical circumstances. In this context, the present work aims to describe an overview of the current implementations of ML/DL in livestock reproduction and production, as well to identify potential challenges and critical points in each of the five steps mentioned, which can affect results and application of AI techniques by farmers in practical situations.(AU)

Artificial intelligence applied to enzymatic hydrolysis of lactose: improving the control of industrial processes / Inteligência artificial aplicada a hidrólise enzimática da lactose: melhorando o controle dos processos industriais

Lactose is the main carbohydrate in milk, and its absorption occurs via enzymatic hydrolysis, generating glucose and galactose. Lactose intolerance is the reduction of intestinal hydrolysis capacity due to hypolactasia, which results in the need to consume dairy foods with low levels of this carbohydrate. ß-galactosidase enzymes are used in dairy industries to hydrolyze lactose, thereby allowing intolerant consumers access to dairy products without the negative health implications. Alternative and official analytical methods are used to quantify the carbohydrate content resulting from enzymatic hydrolysis. The objective of this study was to evaluate the enzymatic hydrolysis of two distinct industrial enzymes produced by the microorganisms Bacillus licheniformis and Kluyveromyces lactis using three analytical methods: enzymatic method, cryoscopy, and high performance liquid chromatography (HPLC) using artificial intelligence to improve the control of the industrial processes. After adding the enzymes to skim milk, time kinetics was performed by collecting samples at time 0, every 10 min for 1 h, and every 30 min until the end of 5 h of hydrolysis. In 97% of the cases, a decrease in lactose concentration was observed by HPLC, followed by the deepening of the cryoscopic point. Glucose measurements by absorbance and HPLC quantification were correlated (r = 0.79; p < 0.01) but not concordant (p < 0.01). It was concluded that by means of artificial intelligence, it was possible to indirectly estimate lactose concentration using an algorithm that associates cryoscopy and glucose concentration.(AU)

O principal carboidrato do leite é a lactose e a sua absorção ocorre devido à hidrólise enzimática, gerando glicose e galactose. A intolerância à lactose é a redução da capacidade de hidrólise intestinal devido à hipolactasia, gerando a necessidade do consumo de alimentos lácteos com baixo teor deste carboidrato. As enzimas ß-galactosidase são utilizadas nas indústrias de laticínios para hidrolisar a lactose, proporcionando ao consumidor intolerante a possibilidade de ingerir os produtos lácteos sem prejuízos à saúde. Para quantificar o conteúdo de carboidratos resultante da hidrólise enzimática, são utilizados métodos analíticos alternativos e oficiais. O objetivo deste estudo foi avaliar a hidrólise enzimática de duas enzimas industriais distintas produzidas pelos microrganismos Bacillus licheniformis e Kluyveromyces lactis, por meio de três métodos analíticos: método enzimático, crioscopia e HPLC. A inteligência artificial foi utilizada para melhorar o controle dos processos industriais. Após a adição das enzimas ao leite desnatado, foi realizada a cinética de tempo coletando as amostras no tempo 0, a cada 10 minutos, até completar 1 hora de reação, e a cada 30 minutos até serem atingidas 5 horas de reação de hidrólise. Em 97% dos casos, a diminuição da concentração de lactose por HPLC acompanhou o aprofundamento do ponto crioscópico. As medições de glicose por absorbância e HPLC foram correlacionadas (r = 0,79; p < 0,01), mas não concordantes (p < 0,01). Concluiu-se que, por meio da inteligência artificial, é possível estimar indiretamente a concentração de lactose a partir de um algoritmo que associa a crioscopia e a concentração de glicose.(AU)

Genomic prediction of leaf rust resistance to Arabica coffee using machine learning algorithms

Genomic selection (GS) emphasizes the simultaneous prediction of the genetic effects of thousands of scattered markers over the genome. Several statistical methodologies have been used in GS for the prediction of genetic merit. In general, such methodologies require certain assumptions about the data, such as the normality of the distribution of phenotypic values. To circumvent the non-normality of phenotypic values, the literature suggests the use of Bayesian Generalized Linear Regression (GBLASSO). Another alternative is the models based on machine learning, represented by methodologies such as Artificial Neural Networks (ANN), Decision Trees (DT) and related possible refinements such as Bagging, Random Forest and Boosting. This study aimed to use DT and its refinements for predicting resistance to orange rust in Arabica coffee. Additionally, DT and its refinements were used to identify the importance of markers related to the characteristic of interest. The results were compared with those from GBLASSO and ANN. Data on coffee rust resistance of 245 Arabica coffee plants genotyped for 137 markers were used. The DT refinements presented equal or inferior values of Apparent Error Rate compared to those obtained by DT, GBLASSO, and ANN. Moreover, DT refinements were able to identify important markers for the characteristic of interest. Out of 14 of the most important markers analyzed in each methodology, 9.3 markers on average were in regions of quantitative trait loci (QTLs) related to resistance to disease listed in the literature.

Genomic prediction of leaf rust resistance to Arabica coffee using machine learning algorithms

Genomic selection (GS) emphasizes the simultaneous prediction of the genetic effects of thousands of scattered markers over the genome. Several statistical methodologies have been used in GS for the prediction of genetic merit. In general, such methodologies require certain assumptions about the data, such as the normality of the distribution of phenotypic values. To circumvent the non-normality of phenotypic values, the literature suggests the use of Bayesian Generalized Linear Regression (GBLASSO). Another alternative is the models based on machine learning, represented by methodologies such as Artificial Neural Networks (ANN), Decision Trees (DT) and related possible refinements such as Bagging, Random Forest and Boosting. This study aimed to use DT and its refinements for predicting resistance to orange rust in Arabica coffee. Additionally, DT and its refinements were used to identify the importance of markers related to the characteristic of interest. The results were compared with those from GBLASSO and ANN. Data on coffee rust resistance of 245 Arabica coffee plants genotyped for 137 markers were used. The DT refinements presented equal or inferior values of Apparent Error Rate compared to those obtained by DT, GBLASSO, and ANN. Moreover, DT refinements were able to identify important markers for the characteristic of interest. Out of 14 of the most important markers analyzed in each methodology, 9.3 markers on average were in regions of quantitative trait loci (QTLs) related to resistance to disease listed in the literature.(AU)

Computational classification of animals for a highway detection system / Classificação computacional de animais para sistema de detecção nas rodovias

Vehicle-animal collisions represent a serious problem in roadway infrastructure. To avoid these roadway collisions, different mitigation systems have been applied in various regions of the world. In this article, a system for detecting animals on highways is presented using computer vision and machine learning algorithms. The models were trained to classify two groups of animals: capybaras and donkeys. Two variants of the convolutional neural network called Yolo (You only look once) were used, Yolov4 and Yolov4-tiny (a lighter version of the network). The training was carried out using pre-trained models. Detection tests were performed on 147 images. The accuracy results obtained were 84.87% and 79.87% for Yolov4 and Yolov4-tiny, respectively. The proposed system has the potential to improve road safety by reducing or preventing accidents with animals.(AU)

As colisões entre veículos e animais representam um sério problema na infraestrutura rodoviária. Para evitar tais acidentes, medidas mitigatórias têm sido aplicadas em diferentes regiões do mundo. Neste projeto é apresentado um sistema de detecção de animais em rodovias utilizando visão computacional e algoritmo de aprendizado de máquina. Os modelos foram treinados para classificar dois grupos de animais: capivaras e equídeos. Foram utilizadas duas variantes da rede neural convolucional chamada Yolo (você só vê uma vez) ­ Yolov4 e Yolov4-tiny (versão mais leve da rede) ­ e o treinamento foi realizado a partir de modelos pré-treinados. Testes de detecção foram realizados em 147 imagens e os resultados de precisão obtidos foram de 84,87% e 79,87% para Yolov4 e Yolov4-tiny, respectivamente. O sistema proposto tem o potencial de melhorar a segurança rodoviária reduzindo ou prevenindo acidentes com animais.(AU)

Computational classification of animals for a highway detection system / Classificação computacional de animais para sistema de detecção nas rodovias

Vehicle-animal collisions represent a serious problem in roadway infrastructure. To avoid these roadway collisions, different mitigation systems have been applied in various regions of the world. In this article, a system for detecting animals on highways is presented using computer vision and machine learning algorithms. The models were trained to classify two groups of animals: capybaras and donkeys. Two variants of the convolutional neural network called Yolo (You only look once) were used, Yolov4 and Yolov4-tiny (a lighter version of the network). The training was carried out using pre-trained models. Detection tests were performed on 147 images. The accuracy results obtained were 84.87% and 79.87% for Yolov4 and Yolov4-tiny, respectively. The proposed system has the potential to improve road safety by reducing or preventing accidents with animals.(AU)

As colisões entre veículos e animais representam um sério problema na infraestrutura rodoviária. Para evitar tais acidentes, medidas mitigatórias têm sido aplicadas em diferentes regiões do mundo. Neste projeto é apresentado um sistema de detecção de animais em rodovias utilizando visão computacional e algoritmo de aprendizado de máquina. Os modelos foram treinados para classificar dois grupos de animais: capivaras e equídeos. Foram utilizadas duas variantes da rede neural convolucional chamada Yolo (você só vê uma vez) ­ Yolov4 e Yolov4-tiny (versão mais leve da rede) ­ e o treinamento foi realizado a partir de modelos pré-treinados. Testes de detecção foram realizados em 147 imagens e os resultados de precisão obtidos foram de 84,87% e 79,87% para Yolov4 e Yolov4-tiny, respectivamente. O sistema proposto tem o potencial de melhorar a segurança rodoviária reduzindo ou prevenindo acidentes com animais.(AU)

Morphological and physiological changes during maturation of okra seeds evaluated through image analysis

This study was performed to associate specific morphological parameters, defined by X-ray images, with seed performance of okra (Abelmoschus esculentus (L.) Moench) during maturation. Fruits of cultivar Santa Cruz 47 at different developmental stages were collected at five-day intervals (from 5 to 65 days after anthesis) and water content, dry matter, germination and vigor were determined in seeds extracted immediately after each harvest or after temporary storage for seven days. X-ray tests were also performed after each harvest and the images were analyzed by ImageJ® software to produce data of aspect ratio (relation between major and minor axes of the ellipse surrounding the seed perimeter) and percentage of free space area in the inner seed cavity. Physiological maturity (maximum accumulation of dry matter) was reached at 30 days after anthesis (DAA), when seed water content was 56.6 %. Seed germination and vigor increased during maturation, achieving the maximum at 50 DAA. Seeds showed morphological changes during maturation, becoming more spherical (aspect ratio near 1.0); at the same time, the free space available in the inner cavity of the seed decreased. This parameter can be successfully used as a marker of physiological maturity when values equal or lower than 5 % are reached.

Morphological and physiological changes during maturation of okra seeds evaluated through image analysis

This study was performed to associate specific morphological parameters, defined by X-ray images, with seed performance of okra (Abelmoschus esculentus (L.) Moench) during maturation. Fruits of cultivar Santa Cruz 47 at different developmental stages were collected at five-day intervals (from 5 to 65 days after anthesis) and water content, dry matter, germination and vigor were determined in seeds extracted immediately after each harvest or after temporary storage for seven days. X-ray tests were also performed after each harvest and the images were analyzed by ImageJ® software to produce data of aspect ratio (relation between major and minor axes of the ellipse surrounding the seed perimeter) and percentage of free space area in the inner seed cavity. Physiological maturity (maximum accumulation of dry matter) was reached at 30 days after anthesis (DAA), when seed water content was 56.6 %. Seed germination and vigor increased during maturation, achieving the maximum at 50 DAA. Seeds showed morphological changes during maturation, becoming more spherical (aspect ratio near 1.0); at the same time, the free space available in the inner cavity of the seed decreased. This parameter can be successfully used as a marker of physiological maturity when values equal or lower than 5 % are reached.(AU)

Three-dimensional laparoscopic Roux-en-Y gastric bypass with totally hand-sewn anastomoses for morbid obesity. A single center experience

Purpose To assess the impact of three-dimensional (3D) vision use on operative time (OT) in laparoscopic Roux-en-Y gastric bypass (LRYGB) with hand-sewn anastomoses. Methods We analyzed a prospectively collected database of patients who underwent LRYGB. We included all patients operated on with either 2D or 3D vision. Demographics and clinical characteristics, operative time, hospital stay and 30-day postoperative complications were collected for all patients and analyzed. Results During the study time, out of 143 patients who underwent LRYGB for morbid obesity, 111 were considered eligible. Seventy-eight patients were operated with 2D vision and 33 patients with 3D vision. Demographics and clinical characteristics were not different among groups. Mean OT was 203±51 and 167±32 minutes in the 2D and 3D groups respectively (p<0.001). Multivariate analyses showed that increasing age and BMI were independently related to prolonged OT, while 3D vision (OR 6.675, 95% CI 2.380-24.752, p<0.001) was strongly associated with shorter OT. Conclusions The use of 3D vision in LRYGB significantly reduced the OT, though intra- and postoperative complication rates and the length of hospital stay were not affected. Despite its limitations, our study supports the value of 3D vision laparoscopy in bariatric surgery.(AU)

Prediction of Girolando cattle weight by means of body measurements extracted from images

The objective with this study was to analyze the body measurements of Girolando cattle, as well as measurements extracted from their images, to generate a model to understand which measures further explain the cattle body weight. Therefore, the experiment physically measured 34 Girolando cattle (two males and 32 females), for the following traits: heart girth (HGP), circumference of the abdomen, body length, occipito-ischial length, wither height, and hip height. In addition, images of the dorsum and the body lateral area of these animals allowed measurements of hip width (HWI), body length, tail distance to the neck, dorsum area (DAI), dorsum perimeter, wither height, hip height, body lateral area, perimeter of the lateral area, and rib height. The measurements extracted from the images were subjected to the stepwise regression method and regression-based machine learning algorithms. The HGp was the physical measure with stronger positive correlation with respect to body weight. In the stepwise method, the final model generated R² of 0.70 and RMSE of 42.52 kg and the equation. The linear regression and SVM algorithms obtained the best results, followed by discretization regression with random forests. The set of rules presented in this study can be recommended for estimating body weight in Girolando cattle, at a correlation coefficient of 0.71, by measurements of hip width and dorsum area, both extracted from cattle images.(AU)

Using 3D computed tomography in the anatomical description of the eye and the vestibulocochlear organ of a blue-and-yellow macaw (Ara ararauna Linnaeus, 1758) and of a toucan (Ramphastos toco Statius Muller, 1776) / Usando a tomografia computadorizada 3D na descrição anatômica do olho e do órgão vestibulococlear de arara-canindé (Ara araraunaLinnaeus, 1758) e tucano (Ramphastos tocoStatius Muller, 1776)

The aim of this study was to evaluate the use of Computed Tomography to study the anatomy of the eye and the vestibulocochlear organ of the wild birds. For this purpose, formaldehyde-embalmed specimens of a toucan and of a blue-and-yellow macaw were submitted to a whole-body scan by a 64 slice-Multidetector CT yielding 0,7mm-thick transversally oriented images. These were reconstructed by specific software that produced additional images in dorsal, transversal, and sagittal planes, as well as three-dimensional images, which were obtained by two techniques: Maximum Intensity Projection and Volume Rendering. Our study found that the eye bulbs in the orbit occupy a proportionally large space in the skull, highlighting the important role that vision plays in these animals. CT provided gross anatomic information about the size and shape of the eye, such as lenses and scleral rings of these birds. Regarding the vestibulocochlear organ, CT was less likely to identify the inner ear structures, especially the ones of the membranous labyrinth. The bony semicircular canals were clearly seen and in the middle ear, the columella was identified. Our results demonstrate that the vestibulocochlear organ of birds is less complex than that of mammals, although, as expected, the semicircular canals are very well developed, being adapted to the accurate balance present in these animals.

O objetivo deste estudo foi avaliar o uso da Tomografia Computadorizada para estudar a anatomia do olho e do órgão vestibulococlearde aves silvestres. Para tanto, espécimes de um tucano e de uma arara-canindé embalsamadas com formaldeído foram escaneados através dotomógrafo Multislice-64 canais produzindo imagens orientadas transversalmente com 0,7mm de espessura. Estas foram reconstruídas por um software específico que produziu imagens adicionais nos planos dorsal, transversal e sagital, além de imagens tridimensionais, obtidas por duas técnicas: Projeção de Intensidade Máxima e Renderização Volumétrica. Descobriu-se que os bulbos oculares na órbita ocupam um espaço proporcionalmente grande no crânio, destacando o importante papel que a visão desempenha nesses animais. A TC forneceu informações anatômicas sobre o tamanho e a forma do olho, bem como de lentes e anéis esclerais dessas aves. Em relação ao órgão vestibulococlear, a TC teve menor desempenho ao identificar as estruturas da orelha interna, principalmente as do labirinto membranoso. Os canais ósseos semicirculares foram vistos claramente e, na orelha média, a columela foi identificada. Os resultados demonstram que o órgão vestibulococlear das aves é menos complexo que o dos mamíferos, embora, como esperado, os canais semicirculares sejam muito bem desenvolvidos, estando adaptados para opreciso equilíbrio presente nesses animais. A TC pode ser usada como uma boa técnica para avaliar as estruturas do olho e da orelha nessas aves e pode ser útil para estudá-las in vivoquanto às condições patológicas ou àscomparações entre diferentes espécies.

Using 3D computed tomography in the anatomical description of the eye and the vestibulocochlear organ of a blue-and-yellow macaw (Ara ararauna Linnaeus, 1758) and of a toucan (Ramphastos toco Statius Muller, 1776) / Usando a tomografia computadorizada 3D na descrição anatômica do olho e do órgão vestibulococlear de arara-canindé (Ara araraunaLinnaeus, 1758) e tucano (Ramphastos tocoStatius Muller, 1776)

The aim of this study was to evaluate the use of Computed Tomography to study the anatomy of the eye and the vestibulocochlear organ of the wild birds. For this purpose, formaldehyde-embalmed specimens of a toucan and of a blue-and-yellow macaw were submitted to a whole-body scan by a 64 slice-Multidetector CT yielding 0,7mm-thick transversally oriented images. These were reconstructed by specific software that produced additional images in dorsal, transversal, and sagittal planes, as well as three-dimensional images, which were obtained by two techniques: Maximum Intensity Projection and Volume Rendering. Our study found that the eye bulbs in the orbit occupy a proportionally large space in the skull, highlighting the important role that vision plays in these animals. CT provided gross anatomic information about the size and shape of the eye, such as lenses and scleral rings of these birds. Regarding the vestibulocochlear organ, CT was less likely to identify the inner ear structures, especially the ones of the membranous labyrinth. The bony semicircular canals were clearly seen and in the middle ear, the columella was identified. Our results demonstrate that the vestibulocochlear organ of birds is less complex than that of mammals, although, as expected, the semicircular canals are very well developed, being adapted to the accurate balance present in these animals.(AU)

O objetivo deste estudo foi avaliar o uso da Tomografia Computadorizada para estudar a anatomia do olho e do órgão vestibulococlearde aves silvestres. Para tanto, espécimes de um tucano e de uma arara-canindé embalsamadas com formaldeído foram escaneados através dotomógrafo Multislice-64 canais produzindo imagens orientadas transversalmente com 0,7mm de espessura. Estas foram reconstruídas por um software específico que produziu imagens adicionais nos planos dorsal, transversal e sagital, além de imagens tridimensionais, obtidas por duas técnicas: Projeção de Intensidade Máxima e Renderização Volumétrica. Descobriu-se que os bulbos oculares na órbita ocupam um espaço proporcionalmente grande no crânio, destacando o importante papel que a visão desempenha nesses animais. A TC forneceu informações anatômicas sobre o tamanho e a forma do olho, bem como de lentes e anéis esclerais dessas aves. Em relação ao órgão vestibulococlear, a TC teve menor desempenho ao identificar as estruturas da orelha interna, principalmente as do labirinto membranoso. Os canais ósseos semicirculares foram vistos claramente e, na orelha média, a columela foi identificada. Os resultados demonstram que o órgão vestibulococlear das aves é menos complexo que o dos mamíferos, embora, como esperado, os canais semicirculares sejam muito bem desenvolvidos, estando adaptados para opreciso equilíbrio presente nesses animais. A TC pode ser usada como uma boa técnica para avaliar as estruturas do olho e da orelha nessas aves e pode ser útil para estudá-las in vivoquanto às condições patológicas ou àscomparações entre diferentes espécies.(AU)

Macroscopic morphological aspects, ecometry and rebound tonometry of the eye bulb in sloth (Bradypus variegatus) / Aspectos morfológicos macroscópicos, ecometria e tonometria de rebote do bulbo ocular em bicho-preguiça (Bradypus variegatus)

The field related to the visual system of wild animals is deeply scarce. Settling anatomical and physiological parameters for these animals is still a descriptive vision for Bradypus variegatus (Schinz, 1825). Thus, our research aimed to determine patterns of normal eye for this species. For this purpose, eight eye bulbs were dissected from the carcasses obtained by natural death, and then performed an overview of ocular anatomical. Rebound tonometry (RBT) and ocular B-mode ultrasonography were also applied for eight eyes in four animals from "Parque Estadual Dois Irmãos", situated in the city of Recife, state of Pernambuco (PE), to estimate the intraocular pressure and ocular ecobiometry. The ocular morphology of sloth is similar as described for other species, however, with some peculiarities. They present a third eyelid emerging in the nasal region of the inferior conjunctival sac and retina and also contain little differentiated blood vessels. Medium the intraocular pressure (IOP) was 4.25mmHg with no difference for both eyes. Ultrasonography of ocular anatomy is also similar regarding other species. Ecobiometric patterns were evaluated to determine the anterior chamber depth, lens width, vitreous chamber depth, and axial length (AL) of ocular globe and the averaged as shown 0.63±1.11mm, 3.73±0.24mm, 6.15±0.41mm, 3.70±0.27mm, and 8.48±0.22mm, respectively. There was no difference between the right and left eyes. The RBT and ocular B-mode ultrasonography are fast exams and easy for animal testing. This study contributed to the characterization of ocular anatomy as well as settling medium values of IOP and intraocular measures; however, further research on physiology and histology is necessary to better understand the visual function of the species.(AU)

O campo de estudo relacionado ao sistema visual de animais silvestres é muito escasso. Estabelecer parâmetros anatômicos e fisiológicos para estes animais ainda está restrito a uma visão descritiva, assim ocorre em Bradypus variegatus (Schinz, 1825). Diante deste fato, objetivou-se com este estudo determinar padrões de normalidade oftálmica nesta espécie. Para isto foram dissecados oito bulbos oculares de cadáveres obtidos por morte natural e realizada a descrição anatômica ocular. Além disso, foram realizadas tonometria de rebote (TonoVet®) e ultrassonografia em modo B em oito olhos de quatro animais provenientes do Parque Estadual Dois Irmãos, Recife/PE, para avaliação da pressão intraocular e realização da ecobiometria ocular. A anatomia ocular do bicho-preguiça é semelhante à descrita para outras espécies com algumas particularidades. Apresentam uma terceira pálpebra emergindo na região nasal do saco conjuntival inferior e retina com vasos sanguíneos pouco diferenciados. A pressão intraocular média foi de 4,25mmHg não havendo diferença entre os olhos direito e esquerdo. A anatomia ocular ultrassonográfica é semelhante à encontrada para outras espécies. Os padrões ecobiométricos obtidos foram: profundidade da câmara anterior, espessura do cristalino, diâmetro do cristalino, profundidade da câmara vítrea e comprimento axial do bubo ocular com tamanhos médios de 0,63±1,11mm, 3,73±0,24mm, 6,15±0,41mm, 3,70±0,27mm e 8,48±0,22mm, respectivamente. Não houve diferença entre os olhos direito e esquerdo. A tonometria de rebote e a ultrassonografia ocular em modo B são exames de rápida e fácil execução, sendo bem tolerados pelos animais. Este estudo contribuiu para a caracterização anatômica ocular e para o estabelecimento de valores médios da pressão intraocular e das medidas intraoculares, no entanto são necessárias outras pesquisas na área da fisiologia e histologia para melhor compreensão da função visual da espécie.(AU)