1,721,491 research outputs found
Weird facets of ribosome synthesis in stressfully chilled\ud bacteria
No full textLolita Piersimoni, Mara Giangrossi, Claudio O. Gualerzi & Cynthia L. Pon\ud
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Laboratory of Genetics, Department of Biology MCA, University of Camerino, 62032 Camerino (MC), Italy\ud
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We present data indicating that an imbalance of initiation factors/ribosome is generated by a cold-shock-induced decrease of the rate of assembly of ribosomal subunits accompanied by an increased expression of initiation factors. The rRNA synthesized after cold shock (a 37°C→10°C temperature-downshift), unlike that synthesized during exponential growth at 37°C, is transcribed mainly from the P2 promoters, which become activated by stress. Furthermore, our data indicate that both synthesis and maturation of rRNA are substantially retarded after temperature downshift. The amount of rRNA synthesized de novo during and after cold-adaptation represents a fairly large amount of total RNA. The fate of the rRNA synthesized during cold-shock is that of-ending up primarily in the active ribosomes present in the polysomes, while the majority of the rRNA whose synthesis had started immediately before or after the stress ultimately ends up in presumably inactive 70S monomers.\ud
With respect to ribosomal proteins (r-proteins), we show that after cold-shock five r-proteins (S2, S3, S5, S6 and S19) give rise to two distinct spots in 2D-gel electrophoresis. The appearance of new spots for proteins S2, S3, S5 and S6 is due to the lack of some post translational modifications as determined by mass spectrometry analysis. In addition, the data indicate the presence of post translational modifications so far unknown.\ud
We also demonstrate that cold-shock does not affect the stoichiometry of the r-proteins within the 30S ribosomal subunits. The analysis of non-ribosomal proteins associated with the peak of 30S ribosomal subunits (which probably contains precursors of the 50S subunits) becomes specifically enriched in a number of proteins which are or might be involved in assembly and/or maturation of the ribosomal particles and translation. For example, among the initiation factors, IF2 is the only cold-shock induced initiation factor whose level increases in association with the 30S ribosomal subunit and whose function in cold-shock is actually not known. These findings open new perspectives towards the identification of a cold-shock role for this and other proteins. \ud
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Supported by a MIUR PRIN 2007 Grant to CLP\u
Multivariate boundaries of a self representing stratum of large units in agricultural survey design
No full text"In business surveys in general, and in multipurpose agricultural surveys in particular, the problem of designing a sample from a list frame usually consists of two different aspects. The first is concerned with the choice of a rule for stratifying the population when several size variables are available and the second is devoted to sample size determination and sample allocation to a given set of strata. The main property that is required of the sample design is that it delivers a specified level of precision for a set of variables of interest using as few sampling units as possible. This article examines how this can be achieved via a basic partition into two strata, one completely enumerated and the other sampled, defined in such a way as to achieve both these objectives. The procedure was used to design the Italian Milk Products Monthly Survey on the basis of a set of auxiliary variables obtained from an annual census of the same target population. Given the combinatorial optimization nature of the problem, the authors use stochastic relaxation theory, and in particular, they use simulated annealing because of its flexibility. Their results indicate that in this situation the multivariate partition obtained by using this random search strategy is a suitable solution as it permits identification of boundaries of any shape. Furthermore, numerical comparisons between sampling designs obtained by using these procedures and some simple extensions of univariate stratification rules are made. The gain from using the proposed strategy is nontrivial as it achieves the required precision using a sample size that is notably smaller than that required by simple extensions to univariate stratification rules." (author's abstract
A distance correlation index of spatial dependence for compositional data
No full textGeographical data in economic, social or environmental sciences are usually recorded as compositions, i.e. relative frequencies, and a common inquiring problem concerns the analysis of these data over different geographical regions. In the present paper we define a new statistical test to verify spatial dependence of such geographical distributions based on distance correlation, a recently introduced measure of dependence between random vectors. The proposed index computes the non-linear spatial distance between distributions and can be applied on compositional frequency distributions. Simulations and an application on Italian electoral data are presented, to illustrate the capabilities of the proposed test to detect spatial dependence. © 2019 The Author(s). Papers in Regional Science © 2019 RSA
A spatially balanced design with probability function proportional to the within sample distance
No full textThe units observed in a biological, agricultural, and environmental survey are often randomly selected from a finite population whose main feature is to be geo-referenced thus its spatial distribution should be used as essential information in designing the sample. In particular our interest is focused on probability samples that are well spread over the population in every dimension which in recent literature are defined as spatially balanced samples. To approach the problem we used the within sample distance as the summary index of the spatial distribution of a random selection criterion. Moreover numerical comparisons are made between the relative efficiency, measured with respect to the simple random sampling, of the suggested design and some other classical solutions as the Generalized Random Tessellation Stratified (GRTS) design used by the US Environmental Protection Agency (EPA) and other balanced or spatially balanced selection procedures as the Spatially Correlated Poisson Sampling (SCPS), the balanced sampling (CUBE), and the Local Pivotal method (LPM). These experiments on real and simulated data show that the design based on the within sample distance selects samples with a better spatial balance thus gives estimates with a lower sampling error than those obtained by using the other methods. The suggested method is very flexible to the introduction of stratification and coordination of samples and, even if in its nature it is computationally intensive, it is shown to be a suitable solution even when dealing with high sampling rates and large population frames where the main problem arises from the size of the distance matrix
Spbsampling: An R Package for Spatially Balanced Sampling
No full textThe basic idea underpinning the theory of spatially balanced sampling is that units closer to each other provide less information about a target of inference than units farther apart. Therefore, it should be desirable to select a sample well spread over the population of interest, or a spatially balanced sample. This situation is easily understood in, among many others, environmental, geological, biological, and agricultural surveys, where usually the main feature of the population is to be geo-referenced. Since traditional sampling designs generally do not exploit the spatial features and since it is desirable to take into account the information regarding spatial dependence, several sampling designs have been developed in order to achieve this objective. In this paper, we present the R package Spbsampling, which provides functions in order to perform three specific sampling designs that pursue the aforementioned purpose. In particular, these sampling designs achieve spatially balanced samples using a summary index of the distance matrix. In this sense, the applicability of the package is much wider, as a distance matrix can be defined for units according to variables different than geographical coordinates
Spatial auto‐correlation and auto‐regressive models estimation from sample survey data
No full textMaximum likelihood estimation of the model parameters for a spatial population based on data collected from a survey sample is usually straightforward when sampling and non-response are both non-informative, since the model can then usually be fitted using the available sample data, and no allowance is necessary for the fact that only a part of the population has been observed. Although for many regression models this naive strategy yields consistent estimates, this is not the case for some models, such as spatial auto-regressive models. In this paper, we show that for a broad class of such models, a maximum marginal likelihood approach that uses both sample and population data leads to more efficient estimates since it uses spatial information from sampled as well as non-sampled units. Extensive simulation experiments based on two well-known data sets are used to assess the impact of the spatial sampling design, the auto-correlation parameter and the sample size on the performance of this approach. When compared to some widely used methods that use only sample data, the results from these experiments show that the maximum marginal likelihood approach is much more precise
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