Automated recognition of lung diseases in CT images based on the optimum-path forest classifier

The World Health Organization estimated that around 300 million people have asthma, and 210 million people are affected by Chronic Obstructive Pulmonary Disease (COPD). Also, it is estimated that the number of deaths from COPD increased 30% in 2015 and COPD will become the third major cause of death worldwide by 2030. These statistics about lung diseases get worse when one considers fibrosis, calcifications and other diseases. For the public health system, the early and accurate diagnosis of any pulmonary disease is mandatory for effective treatments and prevention of further deaths. In this sense, this work consists in using information from lung images to identify and classify lung diseases. Two steps are required to achieve these goals: automatically extraction of representative image features of the lungs and recognition of the possible disease using a computational classifier. As to the first step, this work proposes an approach that combines Spatial Interdependence Matrix (SIM) and Visual Information Fidelity (VIF). Concerning the second step, we propose to employ a Gaussian-based distance to be used together with the optimum-path forest (OPF) classifier to classify the lungs under study as normal or with fibrosis, or even affected by COPD. Moreover, to confirm the robustness of OPF in this classification problem, we also considered Support Vector Machines and a Multilayer Perceptron Neural Network for comparison purposes. Overall, the results confirmed the good performance of the OPF configured with the Gaussian distance when applied to SIM- and VIF-based features. The performance scores achieved by the OPF classifier were as follows: average accuracy of 98.2%, total processing time of 117 microseconds in a common personal laptop, and F-score of 95.2% for the three classification classes. These results showed that OPF is a very competitive classifier, and suitable to be used for lung disease classification

Classification of induced magnetic field signals for the microstructural characterization of sigma phase in duplex stainless steels

Duplex stainless steels present excellent mechanical and corrosion resistance properties.However, when heat treated at temperatures above 600 ºC, the undesirable tertiary sigma phaseis formed. This phase presents high hardness, around 900 HV, and it is rich in chromium, thematerial toughness being compromised when the amount of this phase is not less than 4%. Thiswork aimed to develop a solution for the detection of this phase in duplex stainless steels throughthe computational classification of induced magnetic field signals. The proposed solution is based onan Optimum Path Forest classifier, which was revealed to be more robust and effective than Bayes,Artificial Neural Network and Support Vector Machine based classifiers. The induced magneticfield was produced by the interaction between an applied external field and the microstructure.Samples of the 2205 duplex stainless steel were thermal aged in order to obtain different amounts ofsigma phases (up to 18% in content). The obtained classification results were compared against theones obtained by Charpy impact energy test, amount of sigma phase, and analysis of the fracturesurface by scanning electron microscopy and X-ray diffraction. The proposed solution achieved aclassification accuracy superior to 95% and was revealed to be robust to signal noise, being thereforea valid testing tool to be used in this domain

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

Evaluation of the bibliometric scenario of the Delphi method with Brazilian affiliations

The Delphi method is a technique used to reach consensus among specialists in an area who will be able to predict demands or analyze conjunctures about strategic themes. Within this context, the present work consisted of a bibliometric evaluation performed in the Scopus database with the aid of VOSviewer software, prioritizing journals with an affiliation of Brazilian institutions and that made use of the Delphi method for the development of their research. Data collection went through validation stages, and the results obtained showed that this tool was used in several areas of knowledge, with great emphasis on health, more specifically in Medicine, Nursing, and Public health. Together, these three areas accounted for more than 60% of publications made available

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

O impacto do diagnóstico citológico de atipiasi indeterminadas no sistema público de saúde

As alterações citológicas de significado indeterminado representam uma importante limitação diagnóstica nos programas de escrutíneo de lesões cérvico-vaginais. A introdução de métodos biomoleculares, como o sistema de captura híbrida para detecção de HPV de alto risco contribui para a otimização da conduta clínica dessas pacientes, indicando colposcopia com precisão. Objetivo: avaliar o significado de lesões de significado indeterminado com relação à infecção pelo HPV, com o uso do teste de DNA para HPV com o método da captura de híbridos II. Métodos: foram estudadas amostras de 236 casos consecutivos examinados no laboratório da DIGENE-BRASIL, de pacientes com diagnóstico citológico prévio de ASCUS. As amostras foram submetidas ao teste de captura híbrida para identificação de DNA-HPV de alto e baixo riscos. Resultados: dos 236 casos analisados, 183 (77,5%) foram negativos para o teste de captura híbrida, seis (2,6%) foram positivos para HPV de baixo risco e 47 (19,9%) foram positivos para HPV de alto risco. Conclusão : as amostras positivas para HPV de baixo risco representam uma pequena e não- onsiderável minoria de casos, provavelmente, transientes. Cerca de 20% dos casos foram positivos para HPV de alto risco e deverão ser encaminhados à colposcopia e biopsia, se necessário. Esses casos representam um grande potencial de progressão para lesões cervicais.In order to optimize the morphological analysis of the cases with uncertain diagnosis, we critically analyzed the cases with Atypia of Squamous Cells of Undertemined Significance (ASCUS) in cytological samples of uterine cervix collected in conventional smears (CS) and liquidbased preparations (LBC) an to correlate the findings with Hybrid Capture II (HC2) assay and biopsy. Objective: to evaluate the meanig of undetermined cytological atypia in relation to HPV infection detected by hybrid capture II test. Methods: 97 cases taken from women examined at Perola Byignton Hospital, São Paulo, Brazil, during the year of 2002. The conventional smears were taken previously than LBC. The residual sample was placed in liquid-medium and LBC preparation with DNA-Citoliq system was performed. If at least one of the paired samples were classified as ASCUS, the pair was submitted to a guided revision in order to evaluate the type of alteration taken in account to categorized ASCUS. Results: from 97 cases studied, 14 were categorized as ASCUS by the two methods simultaneously. The others had different classification under or hyper estimated. Six cases diagnosed as squamous intraepithelial lesion (SIL) by CS were ASCUS by LBC; in contrary, 19 ASCUS by CS were SIL by LBC. Eleven ASCUS by CS were diagnosed as negative by LBC, but CS categorized 47 LBC ASCUS as negative. From the morphological parameters nuclear enlargement and coarse chromatin were regarded as ASCUS. From 68 ASCUS by LBC, 36 were HC2 positive for high risk HPV (hr-HPV) : ten of them with biopsy proven lesion. From 42 CS ASCUS, 23 were hr-HPV positive, but only 7 with histological lesion. Conclusion: our results reinforced the hypothesis that ASCUS is poorly reproducible by morphological examination by CS or LBC preparations. To add HC2 as adjunct method to ASCUS cytology can improve the routine diagnosed of the uncertain atypies

Climatic Aptitude Evaluation for Grapevine Cultivation in Pão de Açúcar, Alagoas

The grapes production in Brazil is comprised in southeastern and southern regions and also in the semi-arid Pernambuco. Environmental, climatic and even human factors influence on quality of grape production, which are sensitive to weather changes. In Alagoas State, a pilot project was carried out for Municipalities of Rio Largo, Pão de Açúcar and Delmiro Gouveia between 2013 to 2015 years; but the results were incipient. This work evaluated the climatic aptitude for grapevine cultivation for municipality of Pão de Açúcar. For climate characterization, three indices of the Geoviticure Multicriteria Climatic Classification System (MCC) were adopted: Heliothermic (HI), Cold Night (CI) and Dryness (DI), considering different cycles during the year. The Zuluaga Index (IZ) was also used to evaluate the risk of incidence of fungal diseases of the vine, especially in relation at mildew incidence (Plasmoparaviticola), a major disease in humid regions. According on CI, DI and ZI indices, the municipality of Pão de Açúcar presented climatic aptitude for vines production with highest quality potential between August to January months, classified as preferential for all indexes analyzed

Programming of thermoelectric generation systems based on a heuristic composition of ant colonies

a b s t r a c t Studies related to biologically inspired optimization techniques, which are used for daily operational scheduling of thermoelectric generation systems, indicate that combinations of biologically inspired computation methods together with other optimization techniques have an important role to play in obtaining the best solutions in the shortest amount of processing time. Following this line of research, this article uses a methodology based on optimization by an ant colony to minimize the daily scheduling cost of thermoelectric units. The proposed model uses a Sensitivity Matrix (SM) based on the information provided by the Lagrange multipliers to improve the biologically inspired search process. Thus, a percentage of the individuals in the colony use this information in the evolutionary process of the colony. The results achieved through the simulations indicate that the use of the SM results in quality solutions with a reduced number of individuals

LPS Induces mTORC1 and mTORC2 Activation During Monocyte Adhesion

Monocyte adhesion is a crucial step in transmigration and can be induced by lipopolysaccharide (LPS). Here, we studied the role of mammalian target of rapamycin (mTOR) complexes, mTORC1 and mTORC2, and PKC in this process. We used THP-1 cells, a human monocytic cell line, to investigate monocyte adhesion under static and flow conditions. We observed that 1.0 μg/mL LPS increased PI3K/mTORC2 pathway and PKC activity after 1 h of incubation. WYE-354 10−6 M (mTORC2/mTORC1 inhibitor) and 10−6 M wortmannin avoided monocyte adhesion in culture plates. In addition, WYE also blocked LPS-induced CD11a expression. Interestingly, rapamycin and WYE-354 blocked both LPS-induced monocyte adhesion in a cell monolayer and actin cytoskeleton rearrangement, confirming mTORC1 involvement in this process. Once activated, PKC activates mTORC1/S6K pathway in a similar effect observed to LPS. Activation of the mTORC1/S6K pathway was attenuated by 10−6 M U0126, an MEK/ERK inhibitor, and 10−6 M calphostin C, a PKC inhibitor, indicating that the MEK/ERK/TSC2 axis acts as a mediator. In agreement, 80 nM PMA (a PKC activator) mimicked the effect of LPS on the activation of the MEK/ERK/TSC2/mTORC1/S6K pathway, monocyte adhesion to ECV cells and actin cytoskeleton rearrangement. Our findings show that LPS induces activation of mTOR complexes. This signaling pathway led to integrin expression and cytoskeleton rearrangement resulting in monocyte adhesion. These results describe a new molecular mechanism involved in monocyte adhesion in immune-based diseases