Biodiversidade, população e economia: uma região de mata atlântica [Biodiversity, Population, and Economy: a region of atlantic forest]

Minas Gerais; Rio Doce; mata atlântica; atlantic forest; sustainable development; conservation; regional development; environment

A Central Limit Theorem for intransitive dice

Intransive dice $D^{(1)}, \ldots, D^{(\ell)}$ are dice such that $D^{(1)}$ has advantage with respect to $D^{(2)}$, dice $D^{(2)}$ has advantage with respect to $D^{(3)}$ and so on, up to $D^{(\ell)}$, which has advantage over $D^{(1)}$. In this twofold work, we present: first, (deterministic) results on existence of general intransitive dice. Second and mainly, a central limit theorem for the vector of normalized victories of a die against the next one in the list when the faces of a die are i.i.d.\ random variables and all dice are independent, but different dice may have distinct distributions associated to, as well as they may have distinct number of faces. From this central limit theorem we derive a criteria to assure that the asymptotic probability of observing intransitive dice is null, which applies for many cases, including all continuous distributions and many discrete ones.Comment: 37 pages, 3 figure

An analytical framework to assess the contribution of new technologies to societal challenges

This paper addresses the topic of impact assessment of a research project (encompassed on a Joint Activities Program) considering the major priorities established under the societal challenges defined under the Horizon 2020 programme. A methodology is proposed and demonstrated for the particular case of a project aiming the development of an electric vehicle battery charging system with novel operating modes, which was tested at a laboratory scale. Firstly, the methodology is based on literature review in order to gather meaningful information about societal challenges addressed to this technology and, secondly, questionnaires and interviews directed to the research team of this project were conducted. A SWOT (Strengths, Weaknesses, Opportunities, and Threats) analysis was derived from the data collected in the questionnaires and interviews to identify and assess the interest of the new technology and its barriers. The results show that, especially, the different operation modes for bidirectional energy transfer are a great advance comparing to other competing technologies. Moreover, the technology can contribute significantly to mitigate climate change by reducing the release of carbon dioxide emissions from the transport sector. However, since the project under analysis was tested only at laboratory, some aspects related to the software and hardware still need to be improved and the effective market uptake is still uncertain, as it is also dependent on the car manufacturers' interest.- This work is financed by the ERDF -European Regional Development Fund through the Operational Programme for Competitiveness and Internationalisation -COMPETE 2020 Programme, and by National Funds through the Portuguese funding agency, FCT -Fundacao para a Ciencia e a Tecnologia, within project SAICTPAC/0004/2015-POCI-01-0145-FEDER-016434 and project UID/CEC/00319/2019

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

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

Biodiversidade, população e economia: uma região de mata atlântica [Biodiversity, Population, and Economy: a region of atlantic forest]

Minas Gerais; Rio Doce; mata atlântica; atlantic forest; sustainable development; conservation; urban structure

The mixture of "ecstasy" and its metabolites impairs mitochondrial fusion/fission equilibrium and trafficking in hippocampal neurons, at in vivo relevant concentrations

3,4-Methylenedioxymethamphetamine (MDMA; "ecstasy") is a potentially neurotoxic recreational drug of abuse. Though the mechanisms involved are still not completely understood, formation of reactive metabolites and mitochondrial dysfunction contribute to MDMA-related neurotoxicity. Neuronal mitochondrial trafficking, and their targeting to synapses, is essential for proper neuronal function and survival, rendering neurons particularly vulnerable to mitochondrial dysfunction. Indeed, MDMAassociated disruption of Ca2+ homeostasis and ATP depletion have been described in neurons, thus suggesting possible MDMA interference on mitochondrial dynamics. In this study, we performed real-time functional experiments of mitochondrial trafficking to explore the role of in situ mitochondrial dysfunction in MDMA's neurotoxic actions. We show that the mixture of MDMA and six of its major in vivo metabolites, each compound at 10μM, impaired mitochondrial trafficking and increased the fragmentation of axonal mitochondria in cultured hippocampal neurons. Furthermore, the overexpression of mitofusin 2 (Mfn2) or dynamin-related protein 1 (Drp1) K38A constructs almost completely rescued the trafficking deficits caused by this mixture. Finally, in hippocampal neurons overexpressing a Mfn2 mutant, Mfn2 R94Q, with impaired fusion and transport properties, it was confirmed that a dysregulation of mitochondrial fission/fusion events greatly contributed to the reported trafficking phenotype. In conclusion, our study demonstrated, for the first time, that the mixture of MDMA and its metabolites, at concentrations relevant to the in vivo scenario, impaired mitochondrial trafficking and increasedmitochondrial fragmentation in hippocampal neurons, thus providing a new insight in the context of "ecstasy"-induced neuronal injury. © The Author 2014. Published by Oxford University Press on behalf of the Society of Toxicology. All rights reserved.Ministerio de Ciencia e Innovacion (MICINN), Spain (BFU2008-3980); Plan Nacional de Drogas, Spain; Fundação para a Ciência e a Tecnologia (Portugal) (FCT)

COL1A1 and miR-29b show lower expression levels during osteoblast differentiation of bone marrow stromal cells from Osteogenesis Imperfecta patients

Abstract\ud \ud Background\ud The majority of Osteogenesis Imperfecta (OI) cases are caused by mutations in one of the two genes, COL1A1 and COL1A2 encoding for the two chains that trimerize to form the procollagen 1 molecule. However, alterations in gene expression and microRNAs (miRNAs) are responsible for the regulation of cell fate determination and may be evolved in OI phenotype.\ud \ud \ud Methods\ud In this work, we analyzed the coding region and intron/exon boundaries of COL1A1 and COL1A2 genes by sequence analysis using an ABI PRISM 3130 automated sequencer and Big Dye Terminator Sequencing protocol. COL1A1 and miR-29b expression were also evaluated during the osteoblastic differentiation of mesenchymal stem cell (MSC) by qRT-PCR using an ABI7500 Sequence Detection System.\ud \ud \ud Results\ud We have identified eight novel mutations, where of four may be responsible for OI phenotype. COL1A1 and miR-29b showed lower expression values in OI type I and type III samples. Interestingly, one type III OI sample from a patient with Bruck Syndrome showed COL1A1 and miR-29b expressions alike those from normal samples.\ud \ud \ud Conclusions\ud Results suggest that the miR-29b mechanism directed to regulate collagen protein accumulation during mineralization is dependent upon the amount of COL1A1 mRNA. Taken together, results indicate that the lower levels observed in OI samples were not sufficient for the induction of miR-29b.Support for this work was provided by the Brazilian agencies FAPESP, CNPq,\ud and Center for Cell-based Therapy. We are also thankful to Cristiane Ayres\ud Ferreira and Adriana Aparecida Marques for their excellent technical\ud assistance