Assessment of honey bee cells using deep learning

Temporal assessment of honey bee colony strength is required for different applications in many research projects. This task often requires counting the number of cells with brood and food reserves multiple times a year from images taken in the apiary. There are thousands of cells in each frame, which makes manual counting a time-consuming and tedious activity. Thus, the assessment of frames has been frequently been performed in the apiary in an approximate way by using methods such as the Liebefeld. The automation of this process using modern imaging processing techniques represents a major advance. The objective of this work was to develop a software capable of extracting each cell from frame images, classify its content and display the results to the researcher in a simple way. The cells’ contents display a high variation of patterns which added to light variation make their classification by software a challenging endeavor. To address this challenge, we used Deep Neural Networks (DNNs) for image processing. DNNs are known by achieving the state-of-art in many fields of study including image classification, because they can learn features that best describe the content being classified, such as the interior of frame cells. Our DNN model was trained with over 60,000 manually labeled images whose cells were classified into seven classes: egg, larvae, capped larvae, honey, nectar, pollen, and empty. Our contribution is an end-to-end software capable of doing automatic background removal, cell detection, and classification of its content based on an input image. With this software the researcher is able to achieve an average accuracy of 94% over all classes and get better results compared with approximation methods and previous techniques that used handmade features like color and texture.This research was funded through the 2013-2014 BiodivERsA/FACCE-JPJ joint call for research proposals,witht he national funders FCT (Portugal), CNRS (France), and MEC (Spain).info:eu-repo/semantics/publishedVersio

Multifunctional flexible optical waveguide sensor: on the bioinspiration for ultrasensitive sensors development

This paper presents the development of a bioinspired multifunctional flexible optical sensor (BioMFOS) as an ultrasensitive tool for force (intensity and location) and orientation sensing. The sensor structure is bioinspired in orb webs, which are multifunctional devices for prey capturing and vibration transmission. The multifunctional feature of the structure is achieved by using transparent resins that present both mechanical and optical properties for structural integrity and strain/deflection transmission as well as the optical signal transmission properties with core/cladding configuration of a waveguide. In this case, photocurable and polydimethylsiloxane (PDMS) resins are used for the core and cladding, respectively. The optical transmission, tensile tests, and dynamic mechanical analysis are performed in the resins and show the possibility of light transmission at the visible wavelength range in conjunction with high flexibility and a dynamic range up to 150 Hz, suitable for wearable applications. The BioMFOS has small dimensions (around 2 cm) and lightweight (0.8 g), making it suitable for wearable application and clothing integration. Characterization tests are performed in the structure by means of applying forces at different locations of the structure. The results show an ultra-high sensitivity and resolution, where forces in the μN range can be detected and the location of the applied force can also be detected with a sub-millimeter spatial resolution. Then, the BioMFOS is tested on the orientation detection in 3D plane, where a correlation coefficient higher than 0.9 is obtained when compared with a gold-standard inertial measurement unit (IMU). Furthermore, the device also shows its capabilities on the movement analysis and classification in two protocols: finger position detection (with the BioMFOS positioned on the top of the hand) and trunk orientation assessment (with the sensor integrated on the clothing). In both cases, the sensor is able of classifying the movement, especially when analyzed in conjunction with preprocessing and clustering techniques. As another wearable application, the respiratory rate is successfully estimated with the BioMFOS integrated into the clothing. Thus, the proposed multifunctional device opens new avenues for novel bioinspired photonic devices and can be used in many applications of biomedical, biomechanics, and micro/nanotechnology

Automatic detection and classification of honey bee comb cells using deep learning

In a scenario of worldwide honey bee decline, assessing colony strength is becoming increasingly important for sustainable beekeeping. Temporal counts of number of comb cells with brood and food reserves offers researchers data for multiple applications, such as modelling colony dynamics, and beekeepers information on colony strength, an indicator of colony health and honey yield. Counting cells manually in comb images is labour intensive, tedious, and prone to error. Herein, we developed a free software, named DeepBee©, capable of automatically detecting cells in comb images and classifying their contents into seven classes. By distinguishing cells occupied by eggs, larvae, capped brood, pollen, nectar, honey, and other, DeepBee© allows an unprecedented level of accuracy in cell classification. Using Circle Hough Transform and the semantic segmentation technique, we obtained a cell detection rate of 98.7%, which is 16.2% higher than the best result found in the literature. For classification of comb cells, we trained and evaluated thirteen different convolutional neural network (CNN) architectures, including: DenseNet (121, 169 and 201); InceptionResNetV2; InceptionV3; MobileNet; MobileNetV2; NasNet; NasNetMobile; ResNet50; VGG (16 and 19) and Xception. MobileNet revealed to be the best compromise between training cost, with ~9 s for processing all cells in a comb image, and accuracy, with an F1-Score of 94.3%. We show the technical details to build a complete pipeline for classifying and counting comb cells and we made the CNN models, source code, and datasets publicly available. With this effort, we hope to have expanded the frontier of apicultural precision analysis by providing a tool with high performance and source codes to foster improvement by third parties (https://github.com/AvsThiago/DeepBeesource).This research was developed in the framework of the project “BeeHope - Honeybee conservation centers in Western Europe: an innovative strategy using sustainable beekeeping to reduce honeybee decline”, funded through the 2013-2014 BiodivERsA/FACCE-JPI Joint call for research proposals, with the national funders FCT (Portugal), CNRS (France), and MEC (Spain).info:eu-repo/semantics/publishedVersio

Assessing Water Erosion Processes in Degraded Area Using Unmanned Aerial Vehicle Imagery

The use of Unmanned Aerial Vehicles (UAVs) and Structure from Motion (SfM) techniques can contribute to increase the accessibility, accuracy, and resolution of Digital Elevation Models (DEMs) used for soil erosion monitoring. This study aimed to evaluate the use of four DEMs obtained over a year to monitor erosion processes in an erosion-degraded area, with occurrence of rill and gully erosions, and its correlation with accumulated rainfall during the studied period. The DEMs of Geomorphic Change Detection (GCD) of horizontal and vertical resolutions of 0.10 and 0.06 m were obtained. It was possible to detect events of erosion and deposition volumes of the order of 2 m3, with a volumetric error of ~50 %, in rills and gullies in the initial stage denominated R and GS-I, respectively. Events of the order of 100 m3, with a volumetric error around 14 % were found for advanced gullies, a segment denominated GS-II. In the three studied erosion situations, the deposition volume increased with the accumulated rainfall. The segments R and GS-I presented an inverse relationship between erosion volume and accumulated rainfall during the studied period. This behaviour can be explained by the dynamics of the deposition and erosion volumes during the erosion process. In the GS-II segment, erosion and deposition volumes were proportional and a direct relation with the cumulative rainfall over the studied period and a low percentage of volumetric error were found

Underexpression of MMP-2 and its Regulators, TIMP2, MT1-MMP and IL-8, is Associated with Prostate Cancer

Objective: Extracellular matrix homeostasis is strictly maintained by a coordinated balance between the expression of metalloproteinases (MMPs) and their regulators. The purpose of this study was to investigate whether MMP-2 and its specific regulators, TIMP-2, MT1-MMP and IL-8, are expressed in a reproducible, specific pattern and if the profiles are related to prognosis and clinical outcome of prostate cancer (PCa). Materials and Methods: MMP-2, TIMP-2, MT1-MMP and IL-8 expression levels were analyzed by quantitative real-time polymerase chain reaction (qRT-PCR) in freshly frozen malignant and benign tissue specimens collected from 79 patients with clinically localized PCa who underwent radical prostatectomies. The control group consisted of 11 patients with benign prostate hyperplasia (BPH). The expression profile of the MMP-2 and its regulators were compared using Gleason scores, pathological stage, pre-operative PSA levels and the final outcome of the PCa. Results: The analysis of 79 specimens of PCa revealed that MMP-2, TIMP-2, MT1-MMP and IL-8 were underexpressed at 60.0%, 72.2%, 62.0% and 65.8%, respectively, in malignant prostatic tissue in relation to BPH samples. Considering the prognostic parameters, we demonstrated that high Gleason score tumors (>= 7) over-expressed MMP-2 (p = 0.048) and TIMP-2 (p = 0.021), compared to low Gleason score tumors (< 7). Conclusion: We have demonstrated that MMP-2 and its regulators are underexpressed in PCa. Alternatively, overexpression of MMP-2 and TIMP-2 was related to higher Gleason score tumors. We postulate that alterations in metalloproteinase expression may be important in the control of tissue homeostasis related to prostate carcinogenesis and tumor behavior.FAPESP (Fundacao de Amparo a Pesquisa do Estado de Sao Paulo) [2009/50368-9]Fundacao de Amparo a Pesquisa do Estado de Sao Paulo (FAPESP

INVENTÁRIO E DIAGNÓSTICO DA ARBORIZAÇÃO URBANA VIÁRIA DE RIO BRANCO, AC

O presente trabalho foi desenvolvido dentro do perímetro urbano da cidade de Rio BrancoAC, localizada entre as coordenadas geográficas de 9°58’29’’ de latitude sul e 67°48’36’’ de longitude oeste. Teve como objetivo geral o levantamento e diagnóstico da arborização viária. A metodologia estatística utilizada foi definida tomando-se como unidade amostral o quarteirão. Encontrou-se pouquíssimos espécimes nas calçadas dos quarteirões amostrados, totalizando 292 indivíduos distribuídos por 39 espécies, sendo 11 nativas e 28 exóticas. A média por quarteirão foi de 1,83 árvores, e por quilômetro de calçada foi de 4,57 árvores. Concluiu-se que o número de árvores existentes nas calçadas foi muito pequeno, tendo-se como referência 100 árvores por quilômetro de calçada como ideal. A maioria das espécies encontrada era exótica (78,57%), a despeito da cidade encontrar-se numa região com uma das maiores diversidades de espécies arbóreas do planeta. Quanto ao estado físico, a copa normal foi predominante, exceto na região central. As recomendações indicadas foram primeiramente de se elaborar um plano de arborização urbana para o município, contendo referências técnicas para escolha das espécies, técnicas de manejo e programa de educação ambiental