Understanding the mechanism of cadmium (Cd) accumulation in plants is important to help reduce its potential toxicity to both plants and humans through dietary and environmental exposure. Here, we report on a study to uncover the genetic basis underlying natural variation in Cd accumulation in a world-wide collection of 349 wild collected Arabidopsis thaliana accessions. We identified a 4-fold variation (0.5–2 µg Cd g−1 dry weight) in leaf Cd accumulation when these accessions were grown in a controlled common garden. By combining genome-wide association mapping, linkage mapping in an experimental F2 population, and transgenic complementation, we reveal that HMA3 is the sole major locus responsible for the variation in leaf Cd accumulation we observe in this diverse population of A. thaliana accessions. Analysis of the predicted amino acid sequence of HMA3 from 149 A. thaliana accessions reveals the existence of 10 major natural protein haplotypes. Association of these haplotypes with leaf Cd accumulation and genetics complementation experiments indicate that 5 of these haplotypes are active and 5 are inactive, and that elevated leaf Cd accumulation is associated with the reduced function of HMA3 caused by a nonsense mutation and polymorphisms that change two specific amino acids.

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ResumoEste artigo objetiva analisar trabalhos publicados sobre o modelo UTAUT nos anais dos três principais eventos brasileiros da área de Gestão de Tecnologia da Informação (CONTECSI, ENANPAD e ENADI), no período de 2011 a 2015. Pretende contribuir para um maior aprofundamento acerca da compreensão, por meio da teoria, da intenção comportamental no sentido da aceitação e uso de tecnologia da informação (TI), especialmente em um contexto cujo impacto da TI na vida das pessoas é crescente e dinâmico. Foi realizada a indexação dos anais dos três eventos citados, no período proposto, através do software Copernic Desktop Search (versão 5.1.0), realizando, posteriormente, uma filtragem pelo termo "utaut". Os resultados evidenciam que a produção de artigos sobre UTAUT no Brasil nas referidas condições aponta para uma maior utilização nas áreas de educação e comércio, apresentando, ainda, combinação com outras teorias e modelos como forma de atender os objetivos propostos pelos autores. (CONTECSI, ENANPAD and ENADI) Palavras-chave: Adoção de Tecnologia da Informação; UTAUT; produção científica. Abstract This paper aims to analyze published works about the UTAUT model in the annals of the three main Brazilian events in the area of Information Technology Management

Even though social networks can provide free space for discussing ideas, people can also use them to propagate hate speech and, given the amount of written material in such networks, it becomes necessary to rely on automatic methods for identifying this problem. In this work, we set out to verify the use of some classic Machine Learning algorithms for the task of hate speech detection in tweets written in Portuguese, by testing four different models (SVM, MLP, Logistic Regression and Naïve Bayes) with different configurations. Results show that these algorithms produce better results (in terms of micro-averaged F1 score) than the LSTM used for benchmark, being also competitive to other results by the related literature

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Oral epithelial dysplasia is a common precancerous lesion type that can be graded as mild, moderate and severe. Although not all oral epithelial dysplasia become cancer over time, this premalignant condition has a significant rate of progressing to cancer and the early treatment has been shown to be considerably more successful. The diagnosis and distinctions between mild, moderate, and severe grades are made by pathologists through a complex and time-consuming process where some cytological features, including nuclear shape, are analysed. The use of computer-aided diagnosis can be applied as a tool to aid and enhance the pathologist decisions. Recently, deep learning based methods are earning more and more attention and have been successfully applied to nuclei segmentation problems in several scenarios. In this paper, we evaluated the impact of different color spaces transformations for automated nuclei segmentation on histological images of oral dysplastic tissues using fully convolutional neural networks (CNN). The CNN were trained using different color spaces from a dataset of tongue images from mice diagnosed with oral epithelial dysplasia. The CIE L*a*b* color space transformation achieved the best averaged accuracy over all analyzed color space configurations (88.2%). The results show that the chrominance information, or the color values, does not play the most significant role for nuclei segmentation purpose on a mice tongue histopathological images dataset.

This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/).

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