Os gêneros Fasciolaria Lamarck, 1799 e Leucozonia Gray, 1847 no nordeste brasileiro (Mollusca: Gastropoda: Fasciolariidae)

The genera Fasciolaria Lamarck, 1799 and Leucozonia Gray, 1847 are represented in Northeastern Brazil by three species. Fasciolaria aurantiaca Lamarck, 1816; Leucozonia ocellata (Gmelin, 1791) and Leucozonia nassa (Gmelin, 1791). The three species are described and illustrated. An identification key for all the above mentioned taxa is included, together with some ecological data. The anatomy and radula of Fasciolaria aurantiaca and Leucozonia nassa are described and illustrated. Polimorfism in Fasciolaria aurantiaca and Leucozonia nassa is discussed.

Identification of novel functional TBP-binding sites and general factor repertoires.

Our current knowledge of the general factor requirement in transcription by the three mammalian RNA polymerases is based on a small number of model promoters. Here, we present a comprehensive chromatin immunoprecipitation (ChIP)-on-chip analysis for 28 transcription factors on a large set of known and novel TATA-binding protein (TBP)-binding sites experimentally identified via ChIP cloning. A large fraction of identified TBP-binding sites is located in introns or lacks a gene/mRNA annotation and is found to direct transcription. Integrated analysis of the ChIP-on-chip data and functional studies revealed that TAF12 hitherto regarded as RNA polymerase II (RNAP II)-specific was found to be also involved in RNAP I transcription. Distinct profiles for general transcription factors and TAF-containing complexes were uncovered for RNAP II promoters located in CpG and non-CpG islands suggesting distinct transcription initiation pathways. Our study broadens the spectrum of general transcription factor function and uncovers a plethora of novel, functional TBP-binding sites in the human genome.

Parameter identification in time series models

Time series analysis can be categorized into three different approaches: classical, Box-Jenkins, and State space. Classical approach makes a basement for the analysis and Box-Jenkins approach is an improvement of the classical approach and deals with stationary time series. State space approach allows time variant factors and covers up a broader area of time series analysis. This thesis focuses on parameter identifiablity of different parameter estimation methods such as LSQ, Yule-Walker, MLE which are used in the above time series analysis approaches. Also the Kalman filter method and smoothing techniques are integrated with the state space approach and MLE method to estimate parameters allowing them to change over time. Parameter estimation is carried out by repeating estimation and integrating with MCMC and inspect how well different estimation methods can identify the optimal model parameters. Identification is performed in probabilistic and general senses and compare the results in order to study and represent identifiability more informative way.

Global transcriptome analysis and identification of differentially expressed genes after infection of Fragaria vesca with powdery mildew (Podosphaera aphanis)

Background: Podosphaera aphanis, the causal agent of strawberry powdery mildew causes significant economic loss worldwide. Methods: We used the diploid strawberry species Fragaria vesca as a model to study plant pathogen interactions. RNA-seq was employed to generate a transcriptome dataset from two accessions, F. vesca ssp. vesca Hawaii 4 (HW) and F. vesca f. semperflorens Yellow Wonder 5AF7 (YW) at 1 d (1 DAI) and 8 d (8 DAI) after infection. Results: Of the total reads identified about 999 million (92%) mapped to the F. vesca genome. These transcripts were derived from a total of 23,470 and 23,464 genes in HW and YW, respectively from the three time points (control, 1 and 8 DAI). Analysis identified 1,567, 1,846 and 1,145 up-regulated genes between control and 1 DAI, control and 8 DAI, and 1 and 8 DAI, respectively in HW. Similarly, 1,336, 1,619 and 968 genes were up-regulated in YW. Also 646, 1,098 and 624 down-regulated genes were identified in HW, while 571, 754 and 627 genes were down-regulated in YW between all three time points, respectively. Conclusion: Investigation of differentially expressed genes (log2 fold changes �5) between control and 1 DAI in both HW and YW identified a large number of genes related to secondary metabolism, signal transduction; transcriptional regulation and disease resistance were highly expressed. These included flavonoid 3´-monooxygenase, peroxidase 15, glucan endo-1,3-β-glucosidase 2, receptor-like kinases, transcription factors, germin-like proteins, F-box proteins, NB-ARC and NBS-LRR proteins. This is the first application of RNA-seq to any pathogen interaction in strawberry

Characterization of Caulobacter crescentus response to low temperature and identification of genes involved in freezing resistance

Free-living bacteria must respond to a wide range of temperature changes, and have developed specific mechanisms to survive in extreme environments. In this work we describe a remarkable resistance of mesophilic bacterium Caulobacter crescentus to several cycles of freezing at -80 degrees C, which was able to grow at low temperatures. Exponentially growing cells and late stationary-phase cells presented higher freezing resistance at both -20 and -80 degrees C than early stationary-phase cells. Cryotolerance was observed when log-phase cultures grown at 30 degrees C were preincubated at 5, 15 or 20 degrees C before freezing at -20 degrees C. A transposon library was screened to identify mutants sensitive to freezing at -80 degrees C and three strains presenting < 10% survival were isolated. Identification of genes disrupted in each mutant showed that they encoded an AddA family DNA helicase, a DEAD/DEAH box RNA helicase and a putative RND (resistance, nodulation, cell division) efflux system component. These strains showed longer generation times than wild-type cells when growing at 15 degrees C, with the RNA helicase mutant presenting a severe growth defect. These analyses suggest that the singular intrinsic resistance to freezing of C. crescentus is in fact a consequence of several independent traits, especially the maintenance of a proper degree of supercoiling of nucleic acids.

Fractal dimension applied to plant identification

This article discusses methods to identify plants by analysing leaf complexity based on estimating their fractal dimension. Leaves were analyzed according to the complexity of their internal and external shapes. A computational program was developed to process, analyze and extract the features of leaf images, thereby allowing for automatic plant identification. Results are presented from two experiments, the first to identify plant species from the Brazilian Atlantic forest and Brazilian Cerrado scrublands, using fifty leaf samples from ten different species, and the second to identify four different species from genus Passiflora, using twenty leaf samples for each class. A comparison is made of two methods to estimate fractal dimension (box-counting and multiscale Minkowski). The results are discussed to determine the best approach to analyze shape complexity based on the performance of the technique, when estimating fractal dimension and identifying plants. (C) 2008 Elsevier Inc. All rights reserved.

Identification of Mechanical Systems through Kautz Filter

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Measurement of the t(t)over-bar production cross section in pp collisions at 7 TeV in lepton plus jets events using b-quark jet identification

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Performance of tau-lepton reconstruction and identification in CMS

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Identification of structural damage in flexible structures using system norm and neural networks

Nowadays there is great interest in damage identification using non destructive tests. Predictive maintenance is one of the most important techniques that are based on analysis of vibrations and it consists basically of monitoring the condition of structures or machines. A complete procedure should be able to detect the damage, to foresee the probable time of occurrence and to diagnosis the type of fault in order to plan the maintenance operation in a convenient form and occasion. In practical problems, it is frequent the necessity of getting the solution of non linear equations. These processes have been studied for a long time due to its great utility. Among the methods, there are different approaches, as for instance numerical methods (classic), intelligent methods (artificial neural networks), evolutions methods (genetic algorithms), and others. The characterization of damages, for better agreement, can be classified by levels. A new one uses seven levels of classification: detect the existence of the damage; detect and locate the damage; detect, locate and quantify the damages; predict the equipment's working life; auto-diagnoses; control for auto structural repair; and system of simultaneous control and monitoring. The neural networks are computational models or systems for information processing that, in a general way, can be thought as a device black box that accepts an input and produces an output. Artificial neural nets (ANN) are based on the biological neural nets and possess habilities for identification of functions and classification of standards. In this paper a methodology for structural damages location is presented. This procedure can be divided on two phases. The first one uses norms of systems to localize the damage positions. The second one uses ANN to quantify the severity of the damage. The paper concludes with a numerical application in a beam like structure with five cases of structural damages with different levels of severities. The results show the applicability of the presented methodology. A great advantage is the possibility of to apply this approach for identification of simultaneous damages.

Identification and filtering of uncharacteristic noise in the CMS hadron calorimeter

Commissioning studies of the CMS hadron calorimeter have identified sporadic uncharacteristic noise and a small number of malfunctioning calorimeter channels. Algorithms have been developed to identify and address these problems in the data. The methods have been tested on cosmic ray muon data, calorimeter noise data, and single beam data collected with CMS in 2008. The noise rejection algorithms can be applied to LHC collision data at the trigger level or in the offline analysis. The application of the algorithms at the trigger level is shown to remove 90% of noise events with fake missing transverse energy above 100 GeV, which is sufficient for the CMS physics trigger operation. © 2010 IOP Publishing Ltd and SISSA.

Taxonomia de Micronycteris Gray, 1866 (Chiroptera, Phyllostomidae) do Brasil

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Estudo taxonômico de Leptogium (ACH.) S.F. Gray (Collemataceae, fungos liquenizados)

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Identification of b-quark jets with the CMS experiment

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)