87 research outputs found
Seasonal and depth-driven changes in rhodolith bed structure and associated macroalgae off Arvoredo island (southeastern Brazil)
Rhodoliths are formed by coralline red algae and can form heterogeneous substrata with high biodiversity. Here we describe a rhodolith bed at the southern limit of the known distribution of this habitat in the western Atlantic. We characterized rhodolith and macroalgal assemblages at 5, 10 and 15. m depth during summer and winter. Lithothamnion crispatum was dominant amongst the six rhodolith-forming species present. Most rhodoliths were spheroidal in shape indicating high mobility due to water movement. Rhodolith density decreased with increasing depth and during winter. Turf-forming seaweeds accounted for 60% of the biomass growing on rhodoliths. Macroalgae increased abundance and richness in the summer, but was similar between 5 and 15. m depth. They were less abundant and diverse than that recorded in rhodolith beds further north in Brazil. Both, season and depth, affected the structure of the macroalgae assemblages. We conclude that Lithothamniom is the most representative genus of Brazilian rhodolith beds. Summer is responsible for increasing the diversity and richness of macroalgae, as well as increasing rhodolith density. © 2013 Elsevier B.V
Microfluidic mixer with automated electrode switching for sensing applications
FUNDAÇÃO DE AMPARO À PESQUISA DO ESTADO DE SÃO PAULO - FAPESPCOORDENAÇÃO DE APERFEIÇOAMENTO DE PESSOAL DE NÍVEL SUPERIOR - CAPESCONSELHO NACIONAL DE DESENVOLVIMENTO CIENTÍFICO E TECNOLÓGICO - CNPQAn electronic tongue (e-tongue) is a multisensory system usually applied to complex liquid media that uses computational/statistical tools to group information generated by sensing units into recognition patterns, which allow the identification/distinctio81110FUNDAÇÃO DE AMPARO À PESQUISA DO ESTADO DE SÃO PAULO - FAPESPCOORDENAÇÃO DE APERFEIÇOAMENTO DE PESSOAL DE NÍVEL SUPERIOR - CAPESCONSELHO NACIONAL DE DESENVOLVIMENTO CIENTÍFICO E TECNOLÓGICO - CNPQFUNDAÇÃO DE AMPARO À PESQUISA DO ESTADO DE SÃO PAULO - FAPESPCOORDENAÇÃO DE APERFEIÇOAMENTO DE PESSOAL DE NÍVEL SUPERIOR - CAPESCONSELHO NACIONAL DE DESENVOLVIMENTO CIENTÍFICO E TECNOLÓGICO - CNPQ2014/50869-62015/14836-92017/06985-0Sem informaçãoSem informaçãoWe would like to thank all the staff from Additive Manufacturing Laboratory (AddLab), especially to Peter Bruno and Peter Szczesniak (MEAM/SEAS/UPenn) for all the help and support during the year that Maria L. Braunger spent as a visiting scholar at UPen
Convolutional Neural Networks for Olive Oil Classification
The analysis of the quality of olive oil is a task that is hav-ing a lot of impact
nowadays due to the large frauds that have been observed in the olive oil market. To
solve this problem we have trained a Convolutional Neural Network (CNN) to classify
701 images obtained using GC-IMS methodology (gas chromatography coupled to ion
mobil-ity spectrometry). The aim of this study is to show that Deep Learn-ing
techniques can be a great alternative to traditional oil classification methods based on
the subjectivity of the standardized sensory analy-sis according to the panel test
method, and also to novel techniques provided by the chemical field, such as
chemometric markers. This tech-nique is quite expensive since the markers are
manually extracted by an expert.
The analyzed data includes instances belonging to two different crops, the first
covers the years 2014–2015 and the second 2015–2016. Both har-vests have instances
classified in the three categories of existing oil, extra virgin olive oil (EVOO), virgin
olive oil (VOO) and lampante olive oil (LOO). The aim of this study is to demonstrate
that Deep Learning techniques in combination with chemical techniques are a good
alterna-tive to the panel test method, implying even better accuracy than results
obtained in previous wor
A investigação enquanto prática de deliberação curricular: o caso do projecto ICR
Este artigo refere-se a um projecto de investigação curricular colaborativa que
nasceu da conjugação entre, por um lado, a preocupação de alguns professores do
ensino básico com o desinteresse manifestado por determinados alunos em relação à escola e ao currículo e, por outro, o interesse de alguns professores universitários em estudar questões de relevância curricular. Dessa conjugação resultou a assunção de uma dimensão investigativa na prática profissional dos referidos professores do ensino básico, concretizada num projecto de investigação-acção colaborativa conduzido por uma equipa de quatro docentes universitários e dez docentes do ensino básico (todos os ciclos), que têm estudado a problemática do reconhecimento (ou não), por parte dos alunos, da relevância das aprendizagens escolares. A recolha de dados tem sido feita em sucessivos ciclos de investigação-acção, com a duração de um ano escolar cada, principalmente através do registo sistemático de manifestações de desinteresse (por parte dos alunos em relação ao currículo) observadas nas aulas e de entrevistas aos alunos, conduzidas pelos seus professores. Os dados têm sido analisados pelos próprios professores do ensino básico, com o apoio dos docentes universitários, e sujeitos a interpretações individuais e de equipa, sendo essas interpretações inspiradoras de novas estratégias de ensino, que são continuamente monitorizadas e revistas. Os processos já amadurecidos e os resultados já gerados sugerem que, apesar da existência de algumas dificuldades, é possível desenvolver nas escolas do ensino básico práticas de gestão curricular que integrem uma componente de
investigação.ABSTRACT: This article describes a project of collaborative research on curriculum, which
was created by a team that includes (1) elementary school teachers worried about the
lack of interest shown by some of their students with regard to the school and the
curriculum, and (2) university professors interested in studying issues of curriculum
relevance. This partnership has contributed to an increased use of research in classrooms by those elementary school teachers, through an action research project focused on students’ acknowledgment of the relevance of what they learn in school. Data has been collected in successive cycles of action research, mainly through classroom field notes that provide evidence of given students’ lack of interest with
regard to the curriculum, and through interviews conducted by the teachers. Each action research cycle is one school year long. Data have been analyzed by the elementary school teachers, sometimes with support from the university professors, and interpreted both individually and collectively. Such interpretation of data inspires
teachers in designing new teaching strategies, which are continuously monitored and
reviewed. The processes that have already been consolidated and the results that have already been generated by this project suggest that, despite some difficulties, it is possible to develop curricula in elementary schools in ways that entail research
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Proteomic and functional analysis identifies galectin-1 as a novel regulatory component of the cytotoxic granule machinery
Secretory granules released by cytotoxic T lymphocytes (CTLs) are powerful weapons against intracellular microbes and tumor cells. Despite significant progress, there is still limited information on the molecular mechanisms implicated in target-driven degranulation, effector cell survival and composition and structure of the lytic granules. Here, using a proteomic approach we identified a panel of putative cytotoxic granule proteins, including some already known granule constituents and novel proteins that contribute to regulate the CTL lytic machinery. Particularly, we identified galectin-1 (Gal1), an endogenous immune regulatory lectin, as an integral component of the secretory granule machinery and unveil the unexpected function of this lectin in regulating CTL killing activity. Mechanistic studies revealed the ability of Gal1 to control the non-secretory lytic pathway by influencing Fas–Fas ligand interactions. This study offers new insights on the composition of the cytotoxic granule machinery, highlighting the dynamic cross talk between secretory and non-secretory pathways in controlling CTL lytic function
Rhodolith Beds Are Major CaCO3 Bio-Factories in the Tropical South West Atlantic
Rhodoliths are nodules of non-geniculate coralline algae that occur in shallow waters (<150 m depth) subjected to episodic disturbance. Rhodolith beds stand with kelp beds, seagrass meadows, and coralline algal reefs as one of the world's four largest macrophyte-dominated benthic communities. Geographic distribution of rhodolith beds is discontinuous, with large concentrations off Japan, Australia and the Gulf of California, as well as in the Mediterranean, North Atlantic, eastern Caribbean and Brazil. Although there are major gaps in terms of seabed habitat mapping, the largest rhodolith beds are purported to occur off Brazil, where these communities are recorded across a wide latitudinal range (2°N - 27°S). To quantify their extent, we carried out an inter-reefal seabed habitat survey on the Abrolhos Shelf (16°50′ - 19°45′S) off eastern Brazil, and confirmed the most expansive and contiguous rhodolith bed in the world, covering about 20,900 km2. Distribution, extent, composition and structure of this bed were assessed with side scan sonar, remotely operated vehicles, and SCUBA. The mean rate of CaCO3 production was estimated from in situ growth assays at 1.07 kg m−2 yr−1, with a total production rate of 0.025 Gt yr−1, comparable to those of the world's largest biogenic CaCO3 deposits. These gigantic rhodolith beds, of areal extent equivalent to the Great Barrier Reef, Australia, are a critical, yet poorly understood component of the tropical South Atlantic Ocean. Based on the relatively high vulnerability of coralline algae to ocean acidification, these beds are likely to experience a profound restructuring in the coming decades
Computational modeling with spiking neural networks
This chapter reviews recent developments in the area of spiking neural networks (SNN) and summarizes the main contributions to this research field. We give background information about the functioning of biological neurons, discuss the most important mathematical neural models along with neural encoding techniques, learning algorithms, and applications of spiking neurons. As a specific application, the functioning of the evolving spiking neural network (eSNN) classification method is presented in detail and the principles of numerous eSNN based applications are highlighted and discussed
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