Estudo da síntese da zeólita MCM-71

A new kind of material, denominated MCM-71, was synthesized and characterized by several complementary techniques: X Ray Diffractometry, textural analysis by nitrogen adsorption, Scanning electronic microscopy and infrared spectroscopy. MCM-71 zeolite was successfully synthesized by hydrothermal synthesis in the presence of triethanolamine. Mordenite phase as impurity was not detected, otherwise quartz was observed. The MCM-71 sample obtained presented a BET surface area of 20 m²/g in the as synthesized form and of 85 m²/g in protonic form. By SEM was observed crystals with rectangular shape with average size of 2 x 0,2 x 0,05 µm and this crystals were agglomerated in spherical particles with average diameter between 14 and 24 µm

PIK3CA, HRAS and PTEN in human papillomavirus positive oropharyngeal squamous cell carcinoma

Background: Recent genomic evidence suggests frequent phosphatidylinositide 3-kinase (PI3K) pathway activation in human papillomavirus (HPV) positive oropharyngeal squamous cell carcinoma. Mutations/amplification of the gene encoding p110α catalytic subunit of phosphoinositide 3-kinase (PIK3CA), loss of phosphatase and tensin homolog (PTEN) and HRAS mutations are known to activate PI3K pathway. Methods and results: PIK3CA mutations were identified by Sanger sequencing in 23 of 75 (31%) HPV-positive oropharyngeal carcinomas, including exon 9 (p.E545K [n = 10] and p.E542K [n = 5]) or exon 20 (p.H1047Y, n = 2) mutations. Five rare and one novel (p.R537Q) PIK3CA mutations were identified. HRAS mutation (p.Q61L) was detected in 1 of 62 tested cases. PIK3CA amplification by fluorescence in situ hybridization (FISH) was identified in 4 cases (4/21, 20%), while PTEN loss was seen in 7 (7/21, 33%) cases (chromosome 10 monosomy [n = 4], homozygous deletion [n = 3]). Conclusions: Overall, genetic alterations that likely lead to PI3K pathway activation were identified in 34 of 75 cases (45%) and did not correlate with disease specific survival. These findings offer a molecular rationale for therapeutic targeting of PI3K pathway in patients with HPV-positive oropharyngeal carcinoma. © 2013 Chiosea et al.; licensee BioMed Central Ltd

Ecological patterns and conservation opportunities with carbon credits in Brazil nut groves: a study-case in the Southeast Amazon.

BACKGROUND: Brazil Nuts (BN) tree is a species of high importance in Amazon region. Their continuous use by traditional communities is often related to disturbances that lead to larger degraded areas where this species is commonly found ("BN groves"). Here we aimed to explore the ecological patterns of BN groves vegetation and its relationship with BN trees and evaluate their potential as a source of carbon credits. We sampled 15 circular plots, with Brazilian Nut trees as the center (focal trees) and collected morphometric data from the focal trees. Additionally, we evaluated fruit production for a period of 5 years to obtain annual measurements, which were used as a proxy of the anthropic impact associated with the collection process. Through analysis of the data, we: i) examined the effects of BN trees on the adjacent vegetation; ii) quantified the potential amount of carbon credits in the adjacent vegetation and in the focal trees by converting carbon stock to equivalent CO2. RESULTS: The adjacent vegetation structure was influenced by the size of BN trees (focal trees). No important effects of BN trees on the adjacent vegetation floristic composition and functional attributes were found. Additionally, we found that Brazilian Nut groves possess a significant potential for carbon credits that could be leveraged in the future carbon credit market. CONCLUSION: The study highlights the potential for carbon credit generation in Brazil nut groves in the Southeast Amazon as a means of supporting conservation and restoration efforts in these environments

Crowd-11: A Dataset for Fine Grained Crowd Behaviour Analysis

Conference of 30th IEEE Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2017 ; Conference Date: 21 July 2017 Through 26 July 2017; Conference Code:130113International audienceCrowd behaviour analysis is a challenging task in computer vision, mainly due to the high complexity of the interactions between groups and individuals. This task is particularly crucial given the magnitude of manual monitoring required for effective crowd management. Within this context, a key challenge is to conceive a highly generic, fine and context-independent characterisation of crowd behaviours. Since current datasets answer only partially to this problem, a new dataset is generated, with a total of 11 crowd motion patterns and over 6000 video clips with an average length of 100 frames per sequence. We establish the first baseline of crowd characterisation with an extensive evaluation on shallow and deep methods. This characterisation is expected to be useful in multiple crowd analysis circumstances, we present a new deep architecture for crowd characterisation and demonstrate its application in the context of anomaly classification

Crowd Behavior Analysis Using Local Mid-Level Visual Descriptors

International audienceCrowd behavior analysis has recently emerged as an increasingly important and dedicated problem for crowd monitoring and management in the visual surveillance community. In particular, it is receiving a lot of attention to detect potentially dangerous situations and to prevent overcrowdedness. In this paper, we propose to quantify crowd properties by a rich set of visual descriptors. The calculation of these descriptors is realized through a novel spatio-temporal model of the crowd. It consists of modeling time-varying dynamics of the crowd using local feature tracks. It also involves a Delaunay triangulation to approximate neighborhood interactions. In total, the crowd is represented as an evolving graph, where the nodes correspond to the tracklets. From this graph, various mid-level representations are extracted to determine the ongoing crowd behaviors. In particular, the effectiveness of the proposed visual descriptors is demonstrated within three applications: crowd video classification, anomaly detection, and violence detection in crowds. The obtained results on videos from different data sets prove the relevance of these visual descriptors to crowd behavior analysis. In addition, by means of comparisons to other existing methods, we demonstrate that the proposed descriptors outperform the state-of-the-art methods with a significant margin using the most challenging data sets

An Unsupervised Learning Based Aprroach for Unexpected Event Detection

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Tracking of Objects of Interest in a Sequence of Images

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Video Surveillance

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Robot Companion, an intelligent interactive robot coworker for the Industry 5.0

International audienceTo overcome the limitations of the so-called Industry 4.0 focusing on mass production and full automation, a novel paradigm was recently introduced, namely Industry 5.0, which aims at an increased collaboration between humans and machines, and particularly robots, instead of replacing the former with the latter. This challenge requires novel interactive intelligent robots able to perform complex tasks easily and efficiently and to collaborate on the fly with humans whenever required, be it for training or working. In this work, the Robot Companion, a novel demonstrator of this paradigm, is introduced. It combines robotics, Artificial Intelligence, software engineering and embedded systems technologies, and targets industrial assembly tasks. First tests show that this robot can efficiently assemble a representative gear system autonomously or in collaboration with human operators

An intelligent robotics modular architecture for easy adaptation to novel tasks and applications

International audienceIndustrial robots significantly contributed to the increase of quality and productivity in the industry. Still, their deployment and use remain complex and expensive, limiting their main market to mass production in large factories. This article introduces an intelligent robotics framework intended to solve this issue. It relies on a four-layer modular architecture associating a components-agnostic orchestrator coordinating software modules accessed through a standard middleware, and different hardware running the required functions. This architecture is implemented for performing various tasks in autonomy or in collaboration with a human operator, the different components being turned on and adapted on-demand according to the use-case requirements. We illustrate the proposed concept on four robotic sequences: the assembly of a representative gear unit with one arm, the same application with two robots, the Robothon® Grand Challenge and the insertion of deformable objects in a rail