Levels of Ambrosia pollen in the atmospheric spectra of Catalan aerobiological stations

Ambrosia pollen is known as an importantallergen in North America, and more recently in someEuropean countries. From 1989 to 1995, the Ambrosia pollen levels detected at the stationsmonitored by the Aerobiological Network of Catalonia(Xarxa Aerobiològica de Catalunya, XAC) wereinsignificant. In 1996, a considerable althoughtemporary increase in the concentration of this pollenwas detected in the atmosphere over Girona, Barcelona,Bellaterra, Manresa, and Tarragona. Most of the Ambrosia pollen collected in 1996 was concentrated ina single day. Its appearance on that day wasattributed to long range transportation, caused byunusual conditions of atmospheric circulation, fromthe Lyon region in France where the species isabundant. This is the only day where concentrations ofAmbrosia pollen that may be dangerous to humanhealth have been reached.Ambrosia coronopifolia is the most abundantspecies of the genus in Catalonia, and although rare,its expansion is favoured by the fact that it growsthrough rhizomes and sprouts easily. It is, therefore,important to monitor the growth of its population andthe release of its pollen in order to predict theappearance of pollen levels that may precipitateallergic symptoms.     

A tool for comparing different statistical methods on identifying differentially expressed genes

Background  

Many different statistical methods have been developed to deal with two group comparison microarray experiments. Most often, a substantial number of genes may be selected or not, depending on which method was actually used. Practical guidance on the application of these methods is therefore required. We developed a procedure based on bootstrap and a criterion to allow viewing and quantifying differences between method-dependent selections. We applied this procedure on three datasets that cover a range of possible sample sizes to compare three well known methods, namely: t-test, LPE and SAM.     

A generic aggregation approach to account for statistical uncertainty when combining multiple assessment results

The one-out-all-out approach (OOAO) for aggregating the assessments of single elements (e.g. species or ecosystem components) has found application in environmental polices such as the European Water Framework Directive or the Marine Strategy Framework Directive (MSFD). However, the OOAO has been challenged as being too pessimistic by making positive assessment results virtually impossible along the increasing number of aggregated elements. This study presents a generic approach, the probabilistic One-out-all-out approach, (pOOAO), to account for this issue and thereby reconciling the OOAO with probabilistic aggregation methods The pOOAO allows to determine the minimum number of elements (K_GES), which should meet their assessment benchmarks and thus should achieve a good status. By pre-setting a generic confidence level for each single assessment (e.g. 0.95) the binomial distribution can be used to obtain K_GES for any number of assessed elements. The pOOAO can also accommodate for the integration of assessments from multiple indicators within an element by adjusting the confidence level in relation to the number of integrated indicators. Depending on the generic confidence level as well as on the number of and integrated indicators and aggregated elements, the pOOAO is either consistent with the OOAO or allows for a certain number of negative assessment results, which are attributed to statistical uncertainty and error propagation. The pOOAO is consistent with the OOAO if the desired confidence level in the single assessment results is very high (>0.99) and/or the number of aggregated elements and integrated indicators is low. Through this flexibility the pOOAO could find wide application within integrated ecosystem assessments frameworks such as the MSFD, but would require to estimate the confidence level for each single assessment.     

Transect for aerobiological studies from Antarctica to Poland

While returning from the 13th Polish Scientific Expedition to Antarctica, one of the authors (M.O.) collected airborne biological samples along the transect from the South Shetland Islands to Gdynia on the Baltic Seacoast of Poland. The journey aboard the Polish ship ORP “Heweliusz”, lasted from 4 March until 13 April 1990. During this period, two samples a day, exposed 12 h each, were collected in Petri dishes. The authors made preliminary identifications of plant and animal fragments in the samples. The most important finding was the presence of propagules of lichens (fragments of thallus, isidia soredia) in all samples. The relatively small number of pollen grains in samples could be accounted for by the season of collection when few pollen grains are airborne.     

Authenticity of ancient-DNA results: a statistical approach

Although there have been several papers recommending appropriate experimental designs for ancient-DNA studies, there have been few attempts at statistical analysis. We assume that we cannot decide whether a result is authentic simply by examining the sequence (e.g., when working with humans and domestic animals). We use a maximum-likelihood approach to estimate the probability that a positive result from a sample is (either partly or entirely) an amplification of DNA that was present in the sample before the experiment began. Our method is useful in two situations. First, we can decide in advance how many samples will be needed to achieve a given level of confidence. For example, to be almost certain (95% confidence interval 0.96-1.00, maximum-likelihood estimate 1.00) that a positive result comes, at least in part, from DNA present before the experiment began, we need to analyze at least five samples and controls, even if all samples and no negative controls yield positive results. Second, we can decide how much confidence to place in results that have been obtained already, whether or not there are positive results from some controls. For example, the risk that at least one negative control yields a positive result increases with the size of the experiment, but the effects of occasional contamination are less severe in large experiments.     

A statistical approach to the classification of biological cells from their diffraction patterns.

An effective method of graphically representing complex biological data is presented, and a statistical classification technique is outlined including an example involving exfoliated cervical cells.     

A statistical test for comparing several nucleotide sequences

A statistical test useful for the simultaneous comparison of nucleotide sequences is presented. A likelihood ratio test is derived, examples demonstrated, and tables of critical values are provided.     

A statistical approach to finding overlooked genetic associations

Background  

Complexity and noise in expression quantitative trait loci (eQTL) studies make it difficult to distinguish potential regulatory relationships among the many interactions. The predominant method of identifying eQTLs finds associations that are significant at a genome-wide level. The vast number of statistical tests carried out on these data make false negatives very likely. Corrections for multiple testing error render genome-wide eQTL techniques unable to detect modest regulatory effects.     

A statistical approach to the theory of the central nervous system

A “probabilistic” rather than a “deterministic” approach to the theory of neural nets is developed. Neural nets are characterized by certain parameters which give the probability distributions of different kinds of synaptic connections throughout the net. Given a “state” of the net (i.e., the distribution of firing neurons) at a given moment, an equation for the state at the next moment of quantized time is deduced. Certain very special cases involving constant distributions are solved. A necessary condition for a steady state is deduced in terms of an integral equation, in general non-linear.     

Simultaneous evaluation of genotoxicity data from different sources: a multivariate statistical approach.

A great deal of information on short-term mutagenicity assays presently exists, having been generated through individual as well as large comparative programs. The comparative programs have often examined the same tests, but with different sets of chemicals; this then gives rise to the problem of how to identify the information which is common to the different data bases, i.e., the general properties of the assays. This paper continues previous analyses of this subject, and describes a general approach by which different and heterogeneous data bases can be compared to each other. The results relative to 4 assays (Salmonella typhimurium gene mutation, mouse lymphoma L5178Y cell gene mutation, chromosomal aberrations in CHO cells, and SCEs in CHO cells) in 4 different data bases were studied. Factor analysis was used to model the different pieces of information. The analysis demonstrated a concordance between the indications of the U.S. National Toxicology Program and the International Program for the Evaluation of Short-Term Tests for Carcinogens, whereas the results of Gene-Tox and the International Program for Chemical Safety turned out to be biased, to different degrees, by their specific aims and characteristics. Moreover, the general properties--independent of the specific data bases--of the 4 assays were highlighted, and the similarities between the performances of the assays were given a quantitative measure.     

A systematic statistical linear modeling approach to oligonucleotide array experiments

We outline and describe steps for a statistically rigorous approach to analyzing probe-level Affymetrix GeneChip data. The approach employs classical linear mixed models and operates on a gene-by-gene basis. Forgoing any attempts at gene presence or absence calls, the method simultaneously considers the data across all chips in an experiment. Primary output includes precise estimates of fold change (some as low as 1.1), their statistical significance, and measures of array and probe variability. The method can accommodate complex experiments involving many kinds of treatments and can test for their effects at the probe level. Furthermore, mismatch probe data can be incorporated in different ways or ignored altogether. Data from an ionizing radiation experiment on human cell lines illustrate the key concepts.     

A statistical light scattering approach to separating fast and slow dynamics

Light scattering is a powerful technique to study the structural and dynamical properties of biomolecular systems or other soft materials such as polymeric solutions and blends or gels. An important application of this technique is the study of the kinetics of formation of supramolecular structures. However, in such cases, the system under study is rapidly changing, and consequently the integration time for each measurement is limited. In order to overcome this difficulty, a statistical approach has been developed based on the analysis of the scattered light intensity distribution (Manno et al. 2006, 2004). Indeed the intensity distribution depends upon the ratio between the integration time of each measurement and the coherence time of scattered radiation. This method has been applied to protein aggregation (Manno et al. 2006) and to sol-gel transition (Manno et al. 2004), to obtain information on the heterogeneity of morphological and dynamical features during such processes. In the present work, we accurately test the validity of this approach by analyzing the statistical properties of the light scattered by a model system: a solution of polystyrene spherical macromolecules of different sizes. Each molecular size is related to a given diffusion coefficient and to a given coherence time of the scattered intensity. The effect of changing the experimental integration time is systematically investigated. A mixture of particles of two different sizes is also analyzed to test the validity and robustness of the model based on the convolution of a gaussian with an exponential distribution. Proceedings of the XVIII Congress of the Italian Society of Pure and Applied Biophysics (SIBPA), Palermo, Sicily, September 2006.     

A statistical approach to estimate soil ciliate diversity and distribution based on data from five continents

A total of 359 soil samples collected from five continents (Africa, Asia, Australia, Europe and South America) were investigated for the presence/absence of soil ciliate species. Merging records by species identity, we have compiled a master data list (species by sample matrix). In the list, a total of 964 soil ciliate species (644 described and 320 undescribed) are recorded. The species distributions within the 359 samples and across the five continents are examined. The frequency distribution of the species over samples is used for global diversity estimation. A statistical ACE (abundance‐based coverage estimation) model which links observed data to unseen species is applied to assess regional and global soil ciliate species richness. The model, whose reliability was tested by its power to predict the number of new species in additional samples from Africa, may resolve the controversial issue on global species diversity of soil ciliates. Although an accurate point estimate is not feasible due to severe undersampling, the statistical model enables us to obtain a minimum regional diversity and global species diversity. A consistent finding over all five continents is that at least half of the species diversity is still undiscovered. Our model also yields a global soil ciliate diversity of at least 1900 species, that is, doubles the number of currently known species, and thus diversity is relatively high. This is consistent with the finding of Foissner, who used a probability‐based method. Soil ciliate distributions between continent pairs are analyzed by adjusted abundance‐based similarity/overlap indices. These new indices account for the effect of unseen species and also reduce the bias generated by undersampling. The adjusted abundance‐based Jaccard (or Sørensen) index shows that there is about 30% (18% for Sørensen) dis‐similarity between any two continents, supporting the moderate endemicity model. The results are discussed with respect to protist species distribution, that is, whether they are cosmopolitan or of restricted distribution.     

A statistical approach to study the interactive effects of process parameters on succinic acid production from Bacteroides fragilis

A statistical approach response surface methodology (RSM) was used to study the production of succinic acid from Bacteroides fragilis. The most influential parameters for succinic acid production obtained through one-at-a-time method were glucose, tryptone, sodium carbonate, inoculum size and incubation period. These resulted in the production of 5.4gL(-1) of succinic acid in 48h from B. fragilis under anaerobic conditions. Based on these results, a statistical method, face-centered central composite design (FCCCD) falling under RSM was employed for further enhancing the succinic acid production and to monitor the interactive effect of these parameters, which resulted in a more than 2-fold increase in yield (12.5gL(-1) in 24h). The analysis of variance (ANOVA) showed the adequacy of the model and the verification experiments confirmed its validity. On subsequent scale-up in a 10-L bioreactor using conditions optimized through RSM, 20.0gL(-1) of succinic acid was obtained in 24h. This clearly indicated that the model stood valid even on large scale. Thus, the statistical optimization strategy led to an approximately 4-fold increase in the yield of succinic acid. This is the first report on the use of FCCCD to improve succinic acid production from B. fragilis. The present study provides useful information about the regulation of succinic acid synthesis through manipulation of various physiochemical parameters.     

A multivariate statistical approach to Centaurea classification using essential oil composition data of some species from Turkey

The composition of the essential oils of ten Centaurea species from Turkey, Centaurea aladaghensis, C. antiochia var. prealta, C. antitauri, C. babylonica, C. balsamita, C. cheirolepidoides, C. deflexa, C. iconiensis, C. lanigera, C. ptosimopappoides have been studied. Multivariate statistical analyses (Principal Component Analysis, Multidimensional Scaling, Hierarchical Cluster Analysis) applied to GC-MS data, seem to be very useful to investigate and establish the natural taxonomic delimitation of this very difficult genus. The groupings resulted independent from the ecological similarities (i.e. plants that live in the same habitats or share similar morphological characteristics), so it seems that the environment has no influence on the biosynthesis of the volatiles of these plants.     

A graph-theoretic approach to comparing and integrating genetic, physical and sequence-based maps

For many species, multiple maps are available, often constructed independently by different research groups using different sets of markers and different source material. Integration of these maps provides a higher density of markers and greater genome coverage than is possible using a single study. In this article, we describe a novel approach to comparing and integrating maps by using abstract graphs. A map is modeled as a directed graph in which nodes represent mapped markers and edges define the order of adjacent markers. Independently constructed graphs representing corresponding maps from different studies are merged on the basis of their common loci. Absence of a path between two nodes indicates that their order is undetermined. A cycle indicates inconsistency among the mapping studies with regard to the order of the loci involved. The integrated graph thus produced represents a complete picture of all of the mapping studies that comprise it, including all of the ambiguities and inconsistencies among them. The objective of this representation is to guide additional research aimed at interpreting these ambiguities and inconsistencies in locus order rather than presenting a "consensus order" that ignores these problems.