993 resultados para Plant selection
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Mode of access: Internet.
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v. 46, n. 2, p. 149-158, apr./jun. 2016.
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Matrix-assisted laser desorption/ionization time-of flight mass spectrometry (MALDI-TOF MS) has been widely used for the identification and classification of microorganisms based on their proteomic fingerprints. However, the use of MALDI-TOF MS in plant research has been very limited. In the present study, a first protocol is proposed for metabolic fingerprinting by MALDI-TOF MS using three different MALDI matrices with subsequent multivariate data analysis by in-house algorithms implemented in the R environment for the taxonomic classification of plants from different genera, families and orders. By merging the data acquired with different matrices, different ionization modes and using careful algorithms and parameter selection, we demonstrate that a close taxonomic classification can be achieved based on plant metabolic fingerprints, with 92% similarity to the taxonomic classifications found in literature. The present work therefore highlights the great potential of applying MALDI-TOF MS for the taxonomic classification of plants and, furthermore, provides a preliminary foundation for future research.
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The application of laser induced breakdown spectrometry (LIBS) aiming the direct analysis of plant materials is a great challenge that still needs efforts for its development and validation. In this way, a series of experimental approaches has been carried out in order to show that LIBS can be used as an alternative method to wet acid digestions based methods for analysis of agricultural and environmental samples. The large amount of information provided by LIBS spectra for these complex samples increases the difficulties for selecting the most appropriated wavelengths for each analyte. Some applications have suggested that improvements in both accuracy and precision can be achieved by the application of multivariate calibration in LIBS data when compared to the univariate regression developed with line emission intensities. In the present work, the performance of univariate and multivariate calibration, based on partial least squares regression (PLSR), was compared for analysis of pellets of plant materials made from an appropriate mixture of cryogenically ground samples with cellulose as the binding agent. The development of a specific PLSR model for each analyte and the selection of spectral regions containing only lines of the analyte of interest were the best conditions for the analysis. In this particular application, these models showed a similar performance. but PLSR seemed to be more robust due to a lower occurrence of outliers in comparison to the univariate method. Data suggests that efforts dealing with sample presentation and fitness of standards for LIBS analysis must be done in order to fulfill the boundary conditions for matrix independent development and validation. (C) 2009 Elsevier B.V. All rights reserved.
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A simultaneous optimization strategy based on a neuro-genetic approach is proposed for selection of laser induced breakdown spectroscopy operational conditions for the simultaneous determination of macronutrients (Ca, Mg and P), micro-nutrients (B, Cu, Fe, Mn and Zn), Al and Si in plant samples. A laser induced breakdown spectroscopy system equipped with a 10 Hz Q-switched Nd:YAG laser (12 ns, 532 nm, 140 mJ) and an Echelle spectrometer with intensified coupled-charge device was used. Integration time gate, delay time, amplification gain and number of pulses were optimized. Pellets of spinach leaves (NIST 1570a) were employed as laboratory samples. In order to find a model that could correlate laser induced breakdown spectroscopy operational conditions with compromised high peak areas of all elements simultaneously, a Bayesian Regularized Artificial Neural Network approach was employed. Subsequently, a genetic algorithm was applied to find optimal conditions for the neural network model, in an approach called neuro-genetic, A single laser induced breakdown spectroscopy working condition that maximizes peak areas of all elements simultaneously, was obtained with the following optimized parameters: 9.0 mu s integration time gate, 1.1 mu s delay time, 225 (a.u.) amplification gain and 30 accumulated laser pulses. The proposed approach is a useful and a suitable tool for the optimization process of such a complex analytical problem. (C) 2009 Elsevier B.V. All rights reserved.
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Maize (Zea mays L.) is a very important cereal to world-wide economy which is also true for Brazil, particularly in the South region. Grain yield and plant height have been chosen as important criteria by breeders and farmers from Santa Catarina State (SC), Brazil. The objective of this work was to estimate genetic-statistic parameters associated with genetic gain for grain yield and plant height, in the first cycle of convergent-divergent half-sib selection in a maize population (MPA1) cultivated by farmers within the municipality of Anchieta (SC). Three experiments were carried out in different small farms at Anchieta using low external agronomic inputs; each experiment represented independent samples of half-sib families, which were evaluated in randomized complete blocks with three replications per location. Significant differences among half-sib families were observed for both variables in all experiments. The expected responses to truncated selection of the 25% better families in each experiment were 5.1, 5.8 and 5.2% for reducing plant height and 3.9, 5.7 and 5.0% for increasing grain yield, respectively. The magnitudes of genetic-statistic parameters estimated evidenced that the composite population MPA1 exhibits enough genetic variability to be used in cyclical process of recurrent selection. There were evidences that the genetic structure of the base population MPA1, as indicated by its genetic variability, may lead to expressive changes in the traits under selection, even under low selection pressure.
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Causal inference methods - mainly path analysis and structural equation modeling - offer plant physiologists information about cause-and-effect relationships among plant traits. Recently, an unusual approach to causal inference through stepwise variable selection has been proposed and used in various works on plant physiology. The approach should not be considered correct from a biological point of view. Here, it is explained why stepwise variable selection should not be used for causal inference, and shown what strange conclusions can be drawn based upon the former analysis when one aims to interpret cause-and-effect relationships among plant traits.
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The rhizosphere constitutes a complex niche that may be exploited by a wide variety of bacteria. Bacterium-plant interactions in this niche can be influenced by factors such as the expression of heterologous genes in the plant. The objective of this work was to describe the bacterial communities associated with the rhizosphere and rhizoplane regions of tobacco plants, and to compare communities from transgenic tobacco lines (CAB1, CAB2 and TRP) with those found in wild-type (WT) plants. Samples were collected at two stages of plant development, the vegetative and flowering stages (1 and 3 months after germination). The diversity of the culturable microbial community was assessed by isolation and further characterization of isolates by amplified ribosomal RNA gene restriction analysis (ARDRA) and 16S rRNA sequencing. These analyses revealed the presence of fairly common rhizosphere organisms with the main groups Alphaproteobacteria, Betaproteobacteria, Actinobacteria and Bacilli. Analysis of the total bacterial communities using PCR-DGGE (denaturing gradient gel electrophoresis) revealed that shifts in bacterial communities occurred during early plant development, but the reestablishment of original community structure was observed over time. The effects were smaller in rhizosphere than in rhizoplane samples, where selection of specific bacterial groups by the different plant lines was demonstrated. Clustering patterns and principal components analysis (PCA) were used to distinguish the plant lines according to the fingerprint of their associated bacterial communities. Bands differentially detected in plant lines were found to be affiliated with the genera Pantoea, Bacillus and Burkholderia in WT, CAB and TRP plants, respectively. The data revealed that, although rhizosphere/rhizoplane microbial communities can be affected by the cultivation of transgenic plants, soil resilience may be able to restore the original bacterial diversity after one cycle of plant cultivation.
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The present work had as purpose to evaluate some characteristics of papaya trees (Carica papaya L.), Golden cultivar, obtained trough plant mass selection, regarding plant and fruit quality in the first months of production. The samples were evaluated in a commercial crop at: 0, 20, 40, 70, 130, 180, 230, 260, 280, 310 and 340 days after the planting (DAP) and the first fruits were harvested at 230 DAP. The results showed the low height (199cm in 340 DAP) and low first flowering`s heigth (71cm), which is important to facilitate the harvest process. The plants presented good yield with high number of leafs (allowing a great area of fruit cover) and about 60 fruits per plant. The fruits kept similar features to cv. Golden. The fruit`s fresh weight ranged from 302.4 to 467.5g, which is in the range of the Brazilian market. The pulp thickness was 2.35cm, which is a feature of great economic interest. The pulp thickness showed close relation with climatic factors, and great variations of temperature and precipitation accelerated the pulp loss of thickness.
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Realistic time frames in which management decisions are made often preclude the completion of the detailed analyses necessary for conservation planning. Under these circumstances, efficient alternatives may assist in approximating the results of more thorough studies that require extensive resources and time. We outline a set of concepts and formulas that may be used in lieu of detailed population viability analyses and habitat modeling exercises to estimate the protected areas required to provide desirable conservation outcomes for a suite of threatened plant species. We used expert judgment of parameters and assessment of a population size that results in a specified quasiextinction risk based on simple dynamic models The area required to support a population of this size is adjusted to take into account deterministic and stochastic human influences, including small-scale disturbance deterministic trends such as habitat loss, and changes in population density through processes such as predation and competition. We set targets for different disturbance regimes and geographic regions. We applied our methods to Banksia cuneata, Boronia keysii, and Parsonsia dorrigoensis, resulting in target areas for conservation of 1102, 733, and 1084 ha, respectively. These results provide guidance on target areas and priorities for conservation strategies.
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The role of physiological understanding in improving the efficiency of breeding programs is examined largely from the perspective of conventional breeding programs. Impact of physiological research to date on breeding programs, and the nature of that research, was assessed from (i) responses to a questionnaire distributed to plant breeders and physiologists, and (ii) a survey of literature abstracts. Ways to better utilise physiological understanding for improving breeding programs are suggested, together with possible constraints to delivering beneficial outcomes. Responses from the questionnaire indicated a general view that the contribution by crop physiology to date has been modest. However, most of those surveyed expected the contribution to be larger in the next 20 years. Some constraints to progress perceived by breeders and physiologists were highlighted. The survey of literature abstracts indicated that from a plant breeding perspective, much physiological research is not progressing further than making suggestions about possible approaches to selection. There was limited evidence in the literature of objective comparison of such suggestions with existing methodology, or of development and application of these within active breeding programs. It is argued in this paper that the development of outputs from physiological research for breeding requires a good understanding of the breeding program(s) being serviced and factors affecting its performance. Simple quantitative genetic models, or at least the ideas they represent, should be considered in conducting physiological research and in envisaging and evaluating outputs. The key steps of a generalised breeding program are outlined, and the potential pathways for physiological understanding to impact on these steps are discussed. Impact on breeding programs may arise through (i) better choice of environments in which to conduct selection trials, (ii) identification of selection criteria and traits for focused introgression programs, and (iii) identifying traits for indirect selection criteria as an adjunct to criteria already used. While many breeders and physiologists apparently recognise that physiological understanding may have a major role in the first area, there appears to be relatively Little research activity targeting this issue, and a corresponding bias, arguably unjustified, toward examining traits for indirect selection. Furthermore, research on traits aimed at crop improvement is often deficient because key genetic parameters, such as genetic variation in relevant breeding populations and genetic (as opposed to phenotypic) correlations with yield or other characters of economic importance, are not properly considered in the research. Some areas requiring special attention for successfully interfacing physiology research with breeding are discussed. These include (i) the need to work with relevant genetic populations, (ii) close integration of the physiological research with an active breeding program, and (iii) the dangers of a pre-defined or narrow focus in the physiological research.
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Plant transformation is now a core research tool in plant biology and a practical tool for cultivar improvement. There are verified methods for stable introduction of novel genes into the nuclear genomes of over 120 diverse plant species. This review examines the criteria to verify plant transformation; the biological and practical requirements for transformation systems; the integration of tissue culture, gene transfer, selection, and transgene expression strategies to achieve transformation in recalcitrant species; and other constraints to plant transformation including regulatory environment, public perceptions, intellectual property, and economics. Because the costs of screening populations showing diverse genetic changes can far exceed the costs of transformation, it is important to distinguish absolute and useful transformation efficiencies. The major technical challenge facing plant transformation biology is the development of methods and constructs to produce a high proportion of plants showing predictable transgene expression without collateral genetic damage. This will require answers to a series of biological and technical questions, some of which are defined.
Linking biophysical and genetic models to integrate physiology, molecular biology and plant breeding