Boreal forest fires in 1997 and 1998: a seasonal comparison using transport model simulations and measurement data

Forest fire emissions have a strong impact on the concentrations of trace gases and aerosols in the atmosphere. In order to quantify the influence of boreal forest fire emissions on the atmospheric composition, the fire seasons of 1997 and 1998 are compared in this paper. Fire activity in 1998 was very strong, especially over Canada and Eastern Siberia, whereas it was much weaker in 1997. According to burned area estimates the burning in 1998 was more than six times as intense as in 1997. Based on hot spot locations derived from ATSR (Along Track Scanning Radiometer) data and official burned area data, fire emissions were estimated and their transport was simulated with a Lagrangian tracer transport model. Siberian and Canadian forest fire tracers were distinguished to investigate the transport of both separately. The fire emissions were transported even over intercontinental distances. Due to the El Niño induced meteorological situation, transport from Siberia to Canada was enhanced in 1998. Siberian fire emissions were transported towards Canada and contributed concentrations more than twice as high as those due to Canada's own CO emissions by fires. In 1998 both tracers arrive at higher latitudes over Europe, which is due to a higher North Atlantic Oscillation (NAO) index in 1998. The simulated emission plumes are compared to CMDL (Climate Monitoring and Diagnostics Laboratory) CO₂ and CO data, Total Ozone Mapping Spectrometer (TOMS) aerosol index (AI) data and Global Ozone Monitoring Experiment (GOME) tropospheric NO₂ and HCHO columns. All the data show clearly enhanced signals during the burning season of 1998 compared to 1997. The results of the model simulation are in good agreement with ground-based as well as satellite-based measurements

Satellite measurements of formaldehyde linked to shipping emissions

International shipping is recognized as a pollution source of growing importance, in particular in the remote marine boundary layer. Nitrogen dioxide originating from ship emissions has previously been detected in satellite measurements. This study presents the first satellite measurements of formaldehyde (HCHO) linked to shipping emissions as derived from observations made by the Global Ozone Monitoring Experiment (GOME) instrument. <br><br> We analyzed enhanced HCHO tropospheric columns from shipping emissions over the Indian Ocean between Sri Lanka and Sumatra. This region offers good conditions in term of plume detection with the GOME instrument as all ship tracks follow a single narrow track in the same east-west direction as used for the GOME pixel scanning. The HCHO signal alone is weak but could be clearly seen in the high-pass filtered data. The line of enhanced HCHO in the Indian Ocean as seen in the 7-year composite of cloud free GOME observations clearly coincides with the distinct ship track corridor from Sri Lanka to Indonesia. The observed mean HCHO column enhancement over this shipping route is about 2.0×10<sup>15</sup> molec/cm<sup>2</sup>. <br><br> Compared to the simultaneously observed NO<sub>2</sub> values over the shipping route, those of HCHO are substantially higher; also the HCHO peaks are found at larger distance from the ship routes. These findings indicate that direct emissions of HCHO or degradation of emitted NMHC cannot explain the observed enhanced HCHO values. One possible reason might be increased CH<sub>4</sub> degradation due to enhanced OH concentrations related to the ship emissions, but this source is probably too weak to fully explain the observed values. <br><br> The observed HCHO pattern also agrees qualitatively well with results from the coupled earth system model ECHAM5/MESSy applied to atmospheric chemistry (EMAC). However, the modelled HCHO values over the ship corridor are two times lower than in the GOME high-pass filtered data. This might indicate uncertainties in the satellite data and used emission inventories and/or that the in-plume chemistry taking place in the narrow path of the shipping lanes are not well represented at the rather coarse model resolution

Otimização da metodologia de embriogênese somática visando a propagação clonal de genótipos elite de cacau (Theobroma cacao L.)

bitstream/CENARGEN/23221/1/doc079.pd

Magnetotransport properties of iron microwires fabricated by focused electron beam induced autocatalytic growth

We have prepared iron microwires in a combination of focused electron beam induced deposition (FEBID) and autocatalytic growth from the iron pentacarbonyl, Fe(CO)5, precursor gas under UHV conditions. The electrical transport properties of the microwires were investigated and it was found that the temperature dependence of the longitudinal resistivity (rhoxx) shows a typical metallic behaviour with a room temperature value of about 88 micro{\Omega} cm. In order to investigate the magnetotransport properties we have measured the isothermal Hall-resistivities in the range between 4.2 K and 260 K. From these measurements positive values for the ordinary and the anomalous Hall coefficients were derived. The relation between anomalous Hall resistivity (rhoAN) and longitudinal resistivity is quadratic, rhoAN rho^2 xx, revealing an intrinsic origin of the anomalous Hall effect. Finally, at low temperature in the transversal geometry a negative magnetoresistance of about 0.2 % was measured

A machine learning pipeline for discriminant pathways identification

Motivation: Identifying the molecular pathways more prone to disruption during a pathological process is a key task in network medicine and, more in general, in systems biology. Results: In this work we propose a pipeline that couples a machine learning solution for molecular profiling with a recent network comparison method. The pipeline can identify changes occurring between specific sub-modules of networks built in a case-control biomarker study, discriminating key groups of genes whose interactions are modified by an underlying condition. The proposal is independent from the classification algorithm used. Three applications on genomewide data are presented regarding children susceptibility to air pollution and two neurodegenerative diseases: Parkinson's and Alzheimer's. Availability: Details about the software used for the experiments discussed in this paper are provided in the Appendix

Managing affect in learners' questions in undergraduate science

This is the author's accepted manuscript. The final published article is available from the link below. Copyright @ 2012 Society for Research into Higher Education.This article aims to position students' classroom questioning within the literature surrounding affect and its impact on learning. The article consists of two main sections. First, the act of questioning is discussed in order to highlight how affect shapes the process of questioning, and a four-part genesis to question-asking that we call CARE is described: the construction, asking, reception and evaluation of a learner's question. This work is contextualised through studies in science education and through our work with university students in undergraduate chemistry, although conducted in the firm belief that it has more general application. The second section focuses on teaching strategies to encourage and manage learners' questions, based here upon the conviction that university students in this case learn through questioning, and that an inquiry-based environment promotes better learning than a simple ‘transmission’ setting. Seven teaching strategies developed from the authors' work are described, where university teachers ‘scaffold’ learning through supporting learners' questions, and working with these to structure and organise the content and the shape of their teaching. The article concludes with a summary of the main issues, highlighting the impact of the affective dimension of learning through questioning, and a discussion of the implications for future research

Reverse Engineering Gene Networks with ANN: Variability in Network Inference Algorithms

Motivation :Reconstructing the topology of a gene regulatory network is one of the key tasks in systems biology. Despite of the wide variety of proposed methods, very little work has been dedicated to the assessment of their stability properties. Here we present a methodical comparison of the performance of a novel method (RegnANN) for gene network inference based on multilayer perceptrons with three reference algorithms (ARACNE, CLR, KELLER), focussing our analysis on the prediction variability induced by both the network intrinsic structure and the available data. Results: The extensive evaluation on both synthetic data and a selection of gene modules of "Escherichia coli" indicates that all the algorithms suffer of instability and variability issues with regards to the reconstruction of the topology of the network. This instability makes objectively very hard the task of establishing which method performs best. Nevertheless, RegnANN shows MCC scores that compare very favorably with all the other inference methods tested. Availability: The software for the RegnANN inference algorithm is distributed under GPL3 and it is available at the corresponding author home page (http://mpba.fbk.eu/grimaldi/regnann-supmat

Inferring gene regression networks with model trees

<p>Abstract</p> <p>Background</p> <p>Novel strategies are required in order to handle the huge amount of data produced by microarray technologies. To infer gene regulatory networks, the first step is to find direct regulatory relationships between genes building the so-called gene co-expression networks. They are typically generated using correlation statistics as pairwise similarity measures. Correlation-based methods are very useful in order to determine whether two genes have a strong global similarity but do not detect local similarities.</p> <p>Results</p> <p>We propose model trees as a method to identify gene interaction networks. While correlation-based methods analyze each pair of genes, in our approach we generate a single regression tree for each gene from the remaining genes. Finally, a graph from all the relationships among output and input genes is built taking into account whether the pair of genes is statistically significant. For this reason we apply a statistical procedure to control the false discovery rate. The performance of our approach, named R<smcaps>EG</smcaps>N<smcaps>ET</smcaps>, is experimentally tested on two well-known data sets: <it>Saccharomyces Cerevisiae </it>and E.coli data set. First, the biological coherence of the results are tested. Second the E.coli transcriptional network (in the Regulon database) is used as control to compare the results to that of a correlation-based method. This experiment shows that R<smcaps>EG</smcaps>N<smcaps>ET</smcaps> performs more accurately at detecting true gene associations than the Pearson and Spearman zeroth and first-order correlation-based methods.</p> <p>Conclusions</p> <p>R<smcaps>EG</smcaps>N<smcaps>ET</smcaps> generates gene association networks from gene expression data, and differs from correlation-based methods in that the relationship between one gene and others is calculated simultaneously. Model trees are very useful techniques to estimate the numerical values for the target genes by linear regression functions. They are very often more precise than linear regression models because they can add just different linear regressions to separate areas of the search space favoring to infer localized similarities over a more global similarity. Furthermore, experimental results show the good performance of R<smcaps>EG</smcaps>N<smcaps>ET</smcaps>.</p