Vehicular particulate matter emissions in road tunnels in Sao Paulo, Brazil

In the metropolitan area of Sao Paulo, Brazil, ozone and particulate matter ( PM) are the air pollutants that pose the greatest threat to air quality, since the PM and the ozone precursors ( nitrogen oxides and volatile organic compounds) are the main source of air pollution from vehicular emissions. Vehicular emissions can be measured inside road tunnels, and those measurements can provide information about emission factors of in-use vehicles. Emission factors are used to estimate vehicular emissions and are described as the amount of species emitted per vehicle distance driven or per volume of fuel consumed. This study presents emission factor data for fine particles, coarse particles, inhalable particulate matter and black carbon, as well as size distribution data for inhalable particulate matter, as measured in March and May of 2004, respectively, in the Janio Quadros and Maria Maluf road tunnels, both located in Sao Paulo. The Janio Quadros tunnel carries mainly light-duty vehicles, whereas the Maria Maluf tunnel carries light-duty and heavy-duty vehicles. In the Janio Quadros tunnel, the estimated light-duty vehicle emission factors for the trace elements copper and bromine were 261 and 220 mu g km(-1), respectively, and 16, 197, 127 and 92 mg km(-1), respectively, for black carbon, inhalable particulate matter, coarse particles and fine particles. The mean contribution of heavy-duty vehicles to the emissions of black carbon, inhalable particulate matter, coarse particles and fine particles was, respectively 29, 4, 6 and 6 times higher than that of light-duty vehicles. The inhalable particulate matter emission factor for heavy-duty vehicles was 1.2 times higher than that found during dynamometer testing. In general, the particle emissions in Sao Paulo tunnels are higher than those found in other cities of the world.

Trypanosome Prereplication Machinery Contains a Single Functional Orc1/Cdc6 Protein, Which Is Typical of Archaea

In unicellular eukaryotes, such as Saccharomyces cerevisiae, and in multicellular organisms, the replication origin is recognized by the heterohexamer origin recognition complex (ORC) containing six proteins, Orc1 to Orc6, while in members of the domain Archaea, the replication origin is recognized by just one protein, Orc1/Cdc6; the sequence of Orc1/Cdc6 is highly related to those of Orc1 and Cdc6. Similar to Archaea, trypanosomatid genomes contain only one gene encoding a protein named Orc1. Since trypanosome Orc1 is also homologous to Cdc6, in this study we named the Orc1 protein from trypanosomes Orc1/Cdc6. Here we show that the recombinant Orc1/Cdc6 from Trypanosoma cruzi (TcOrc1/Cdc6) and from Trypanosoma brucei (TbOrc1/Cdc6) present ATPase activity, typical of prereplication machinery components. Also, TcOrc1/Cdc6 and TbOrc1/Cdc6 replaced yeast Cdc6 but not Orc1 in a phenotypic complementation assay. The induction of Orc1/Cdc6 silencing by RNA interference in T. brucei resulted in enucleated cells, strongly suggesting the involvement of Orc1/Cdc6 in DNA replication. Orc1/Cdc6 is expressed during the entire cell cycle in the nuclei of trypanosomes, remaining associated with chromatin in all stages of the cell cycle. These results allowed us to conclude that Orc1/Cdc6 is indeed a member of the trypanosome prereplication machinery and point out that trypanosomes carry a prereplication machinery that is less complex than other eukaryotes and closer to archaea.

A Road Following Approach Using Artificial Neural Networks Combinations

Navigation is a broad topic that has been receiving considerable attention from the mobile robotic community over the years. In order to execute autonomous driving in outdoor urban environments it is necessary to identify parts of the terrain that can be traversed and parts that should be avoided. This paper describes an analyses of terrain identification based on different visual information using a MLP artificial neural network and combining responses of many classifiers. Experimental tests using a vehicle and a video camera have been conducted in real scenarios to evaluate the proposed approach.