Protein conformation from electron spin relaxation data.

Electron spin relaxation data from five ferric proteins are analyzed in terms of the fractal model of protein structures. Details of this model are presented. The results lead to a characterization of protein structures by a single parameter, the fractal dimension, d. This structural parameter is shown to determine the temperature dependence of the Raman electron spin relaxation rate, which varies as T3 + 2d. Computations of d are made using x-ray data for 17 proteins. The results range from d = 1.76 for lysozyme to d = 1.34 for ferredoxin. These values are compared with values of d obtained from the present electron spin relaxation data on five ferric proteins. Typical results are d = 1.34 +/- 0.06 from relaxation data and 1.34 +/- 0.05 from x-ray data for ferredoxin; d = 1.67 +/- 0.03 from relaxation data and 1.66 +/- 0.05 from x-ray data for ferricytochrome c. The data thus support the theoretical model. Applications of this spin resonance technique to the study of changes in protein conformation are discussed.     

Data Quality and the Comparative Method: The Case of Primate Group Size

The comparative method is frequently employed to study primate behavior and evolution. The method is used to infer adaptations, and considerable improvements have been made with respect to its implementation. Despite these advances, scant attention has been given to the nature of the data that are used in comparative analyses. This creates a potential problem as data are often compiled from studies conducted by multiple researchers, whose methods may differ, resulting in variation in data quality. In this article, we investigate the quality of data employed in studies of primate group size. Several issues concerning data quality arise when assembling data on group size. For example, data quality may be compromised if group sizes are estimated from censuses, unhabituated groups, or groups with unrecognized individuals. To mitigate these and other data quality issues, we gathered data from the literature on 23 monkeys and apes using well-defined and biologically relevant criteria for inclusion. We compare our results with those of eight published compilations of group size. Most studies did not provide details regarding the criteria for including data. We found that our group size values were uncorrelated or weakly correlated with those from three other studies and differed in a consistent fashion from those of one other study. Because conclusions derived from comparative analyses are only as accurate as the data that they use, future studies should provide details regarding data collection to ensure their reliability.     

5 S and 5.8 S ribosomal RNA sequences and protist phylogenetics

More than 100 5 S 5.8 S rRNA sequences from protists, including fungi, are known. Through a combination of quantitative treeing and special consideration of "signature' nucleotide combinations, the most significant phylogenetic implications of these data are emphasized. Also, limitations of the data for phylogenetic inferences are discussed and other significant data are brought to bear on the inferences obtained. 5 S sequences from red algae are seen as the most isolated among eukaryotics. A 5 S sequence lineage consisting of oomycetes, euglenoids, most protozoa, most slime molds and perhaps dinoflagellates and mesozoa is defined. Such a lineage is not evident from 5.8 S rRNA or cytochrome c sequence data. 5 S sequences from Ascomycota and Basidiomycota are consistent with the proposal that each is derived from a mycelial form with a haploid yeast phase and simple septal pores, probably most resembling present Taphrinales. 5 S sequences from Chytridiomycota and Zygomycota are not clearly distinct from each other and suggest that a major lineage radiation occurred in the early history of each. Qualitative biochemical data clearly supports a dichotomy between an Ascomycota-Basidiomycota lineage and a Zygomycota-Chytridiomycota lineage.     

ARTHROPOD PHYLOGENY: A COMBINED APPROACH

Abstract— Ribosomal and ubiquitin protein coding sequence data are generated from 20 arthropods and five close relatives. These molecular data are combined with morphological characters derived from the literature to approach arthropod phylogenetics from the perspective of total evidence. Trilobita were included in the analysis through morphological comparison alone. The overall data strongly support arthropod monophyly. Recent molecular analyses which have yield different results are shown to have been based on few characters, few taxa or both. The most parsimonious explanation of the data is (Annelida + (Onychophora + (Trilobita + Chelicerata) + (Crustacea + (Myriapoda + Hexapoda))))). The data are largely concordant both internally among data sets and externally with previous cladistic anatomical analyses.     

Conflicting phylogenetic signals in genomic data of the coffee family (Rubiaceae)

Reconstructions of phylogenetic relationships in the flowering plant family Rubiaceae have up until now relied heavily on single‐ or multi‐gene data, primarily from the plastid compartment. With the availability of cost‐ and time‐efficient techniques for generating complete genome sequences, the opportunity arises to resolve some of the relationships that, up until now, have proven problematic. Here, we contribute new data from complete 58 plastid genome sequences, representing 55 of the currently 65 recognized tribes of the Rubiaceae. Also contributed are new data from the nuclear rDNA cistrons for corresponding taxa. Phylogenetic analyses are conducted on two plastid data sets, one including data from the protein coding genes only, and a second where protein coding data are combined with non‐coding regions, and on a nuclear rDNA data set. Our results clearly show that simply adopting a “more characters” approach does not resolve the relationships in the Rubiaceae. More importantly, we identify conflicting phylogenetic signals in the data. Analyses of the same plastid data, treated as nucleotides or as codon‐degenerated data, resolve and support conflicting topologies in the subfamily Cinchonoideae. As these analyses use the same data, we interpret the conflict to result from erroneous assumptions in the models used to reconstruct our phylogenies. Conflicting signals are also identified in the analyses of the plastid versus the nuclear rDNA data sets. These analyses use data from different genomic compartments, with different inheritance patterns, and we interpret the conflicts as representing “real” conflicts, reflecting biological processes of the past.     

The energetic equivalence rule rejected because of a potentially common sampling error: evidence from carabid beetles

Abundance data from pitfall traps are widely used to estimate the relationship between beetle body size and abundance. Such data probably overestimate densities of large bodied species and may overestimate slopes of size‐abundance relationships. Here, we test this idea by comparing size‐abundance patterns found using data from pitfall trapping with those found with data from a quantitative method of estimating abundance, quadrat sampling. We use data from a total of 47 communities. As expected, slopes of size‐abundance relationships are significantly more positive when estimated using data from pitfall traps compared to when using quadrat sampling data. This was seen when looking across different communities, within communities sampled by both methods and when focusing on the set of species found by both methods within a community. These results were also generally found regardless of method of analysis, which were done using regression with species values as independent data points and using the independent contrast method, and with slopes estimated using ordinary least square regression or the structural relation. Most important, slopes of size‐abundance relationships based on data from pitfall traps were on average significantly more positive than ?0.75 on log–log scales, and thus inconsistent with the energetic equivalence rule. Slopes based on quadrat sampling, on the other hand, were on average not significantly different from ?0.75. The rejection of the energetic equivalence rule based on data from pitfall traps here is therefore a sampling artefact. Similar problems may apply to abundance data from virtually all insect trapping methods, and should make us consider re‐examining many of the size‐abundance patterns documented so far. As a large proportion of all animal species are insects, and traps are widely used to estimate abundance, this is a potentially important problem for our general understanding of the relationship between species body size and abundance.     

Using multiresponse data to estimate biokinetic parameters

Biokinetic parameters are usually calculated from slopes and intercepts taken from plots of experimental data. One response at an item is plotted and used for parameter estimation. Aside from problems that may be caused by transformations made when the data are plotted, this approach has the weakness of not using all the data simultaneously when there is more than one response. This paper shows how multiresponse biological data can be handled to get parameter estimates that are much more precise than those obtained using conventional methods.     

Detection and normalization of biases present in spotted cDNA microarray data: a composite method addressing dye, intensity-dependent, spatially-dependent, and print-order biases.

Microarrays are often used to identify target genes that trigger specific diseases, to elucidate the mechanisms of drug effects, and to check SNPs. However, data from microarray experiments are well known to contain biases resulting from the experimental protocols. Therefore, in order to elucidate biological knowledge from the data, systematic biases arising from their protocols must be removed prior to any data analysis. To remove these biases, many normalization methods are used by researchers. However, not all biases are eliminated from the microarray data because not all types of errors from experimental protocols are known. In this paper, we report an effective way of removing various types of biases by treating each microarray dataset independently to detect biases present in the dataset. After the biases contained in each dataset were identified, a combination of normalization methods specifically made for each dataset was applied to remove biases one at a time.     

How Global Is the Global Biodiversity Information Facility?

There is a concerted global effort to digitize biodiversity occurrence data from herbarium and museum collections that together offer an unparalleled archive of life on Earth over the past few centuries. The Global Biodiversity Information Facility provides the largest single gateway to these data. Since 2004 it has provided a single point of access to specimen data from databases of biological surveys and collections. Biologists now have rapid access to more than 120 million observations, for use in many biological analyses. We investigate the quality and coverage of data digitally available, from the perspective of a biologist seeking distribution data for spatial analysis on a global scale. We present an example of automatic verification of geographic data using distributions from the International Legume Database and Information Service to test empirically, issues of geographic coverage and accuracy. There are over 1/2 million records covering 31% of all Legume species, and 84% of these records pass geographic validation. These data are not yet a global biodiversity resource for all species, or all countries. A user will encounter many biases and gaps in these data which should be understood before data are used or analyzed. The data are notably deficient in many of the world's biodiversity hotspots. The deficiencies in data coverage can be resolved by an increased application of resources to digitize and publish data throughout these most diverse regions. But in the push to provide ever more data online, we should not forget that consistent data quality is of paramount importance if the data are to be useful in capturing a meaningful picture of life on Earth.     

Direct maximum parsimony phylogeny reconstruction from genotype data

Background  

Maximum parsimony phylogenetic tree reconstruction from genetic variation data is a fundamental problem in computational genetics with many practical applications in population genetics, whole genome analysis, and the search for genetic predictors of disease. Efficient methods are available for reconstruction of maximum parsimony trees from haplotype data, but such data are difficult to determine directly for autosomal DNA. Data more commonly is available in the form of genotypes, which consist of conflated combinations of pairs of haplotypes from homologous chromosomes. Currently, there are no general algorithms for the direct reconstruction of maximum parsimony phylogenies from genotype data. Hence phylogenetic applications for autosomal data must therefore rely on other methods for first computationally inferring haplotypes from genotypes.     

Detecting regulatory mechanisms in endocrine time series measurements

The regulatory mechanisms underlying pulsatile secretion are complex, especially as it is partly controlled by other hormones and the combined action of multiple agents. Regulatory relations between hormones are not directly observable but may be deduced from time series measurements of plasma hormone concentrations. Variation in plasma hormone levels are the resultant of secretion and clearance from the circulation. A strategy is proposed to extract inhibition, activation, thresholds and circadian synchronicity from concentration data, using particular association methods. Time delayed associations between hormone concentrations and/or extracted secretion pulse profiles reveal the information on regulatory mechanisms. The above mentioned regulatory mechanisms are illustrated with simulated data. Additionally, data from a lean cohort of healthy control subjects is used to illustrate activation (ACTH and cortisol) and circadian synchronicity (ACTH and TSH) in real data. The simulation and the real data both consist of 145 equidistant samples per individual, matching a 24-hr time span with 10 minute intervals. The results of the simulation and the real data are in concordance.     

Nonparametric estimation of a delay distribution based on left-censored and right-truncated data

This paper proposes a nonparametric method for estimating a delay distribution based on left-censored and right-truncated data. A variance-covariance estimator is provided. The method is applied to the Australian AIDS data in which some data are left censored and some data are not left censored. This situation arises with AIDS case-reporting data in Australia because reporting delays were recorded only from November 1990 rather than from the beginning of the epidemic there. It is shown that inclusion of the left-censored data, as opposed to analyzing only the uncensored data, improves the precision of the estimate.     

Learning about human population history from ancient and modern genomes

Genome-wide data, both from SNP arrays and from complete genome sequencing, are becoming increasingly abundant and are now even available from extinct hominins. These data are providing new insights into population history; in particular, when combined with model-based analytical approaches, genome-wide data allow direct testing of hypotheses about population history. For example, genome-wide data from both contemporary populations and extinct hominins strongly support a single dispersal of modern humans from Africa, followed by two archaic admixture events: one with Neanderthals somewhere outside Africa and a second with Denisovans that (so far) has only been detected in New Guinea. These new developments promise to reveal new stories about human population history, without having to resort to storytelling.     

Inferring the dynamics of a spatial epidemic from time-series data

Spatial interactions are key determinants in the dynamics of many epidemiological and ecological systems; therefore it is important to use spatio-temporal models to estimate essential parameters. However, spatially-explicit data sets are rarely available; moreover, fitting spatially-explicit models to such data can be technically demanding and computationally intensive. Thus non-spatial models are often used to estimate parameters from temporal data. We introduce a method for fitting models to temporal data in order to estimate parameters which characterise spatial epidemics. The method uses semi-spatial models and pair approximation to take explicit account of spatial clustering of disease without requiring spatial data. The approach is demonstrated for data from experiments with plant populations invaded by a common soilborne fungus, Rhizoctonia solani. Model inferences concerning the number of sources of disease and primary and secondary infections are tested against independent measures from spatio-temporal data. The applicability of the method to a wide range of host-pathogen systems is discussed.     

Linear rank tests for interval-censored data with application to PCB levels in adipose tissue of transformer repair workers

Linear rank statistics are described for testing for differences between groups when the data are interval-censored. The statistics are closely related to those described by Prentice (1978, Biometrika 65, 167-179) for right-censored data. Problems in calculating the statistics are discussed and several approaches to computation including estimation of the efficient rank scores are described. Results from a small simulation study are presented. The methods are applied to data from a study relating tissue levels of PCBs to occupational exposure.     

Phylogenetic resolution within the tribe Episcieae (Gesneriaceae): congruence of ITS and NDHF sequences from parsimony and maximum-likelihood analyses

Generic relationships within Episcieae were assessed using ITS and ndhF sequences. Previous analyses of this tribe have focussed only on ndhF data and have excluded two genera, Rhoogeton and Oerstedina, which are included in this analysis. Data were analyzed using both parsimony and maximum-likelihood methods. Results from partition homogeneity tests imply that the two data sets are significantly incongruent, but when Rhoogeton is removed from the analysis, the data sets are not significantly different. The combined data sets reveal greater strength of relationships within the tribe with the exception of the position of Rhoogeton. Poorly or unresolved relationships based exclusively on ndhF data are more fully resolved with ITS data. These resolved clades include the monophyly of the genera Columnea and Paradrymonia and the sister-group relationship of Nematanthus and Codonanthe. A closer affinity between Neomortonia nummularia and N. rosea than has previously been seen is apparent from these data, although these two species are not monophyletic in any tree. Lastly, Capanea appears to be a member of Gloxinieae, although C. grandiflora remains within Episcieae. Evolution of fruit type, epiphytic habit, and presence of tubers is re-examined with the new data presented here.     

A test for linkage and association in general pedigrees: the pedigree disequilibrium test

Family-based tests of linkage disequilibrium typically are based on nuclear-family data including affected individuals and their parents or their unaffected siblings. A limitation of such tests is that they generally are not valid tests of association when data from related nuclear families from larger pedigrees are used. Standard methods require selection of a single nuclear family from any extended pedigrees when testing for linkage disequilibrium. Often data are available for larger pedigrees, and it would be desirable to have a valid test of linkage disequilibrium that can use all potentially informative data. In this study, we present the pedigree disequilibrium test (PDT) for analysis of linkage disequilibrium in general pedigrees. The PDT can use data from related nuclear families from extended pedigrees and is valid even when there is population substructure. Using computer simulations, we demonstrated validity of the test when the asymptotic distribution is used to assess the significance, and examined statistical power. Power simulations demonstrate that, when extended pedigree data are available, substantial gains in power can be attained by use of the PDT rather than existing methods that use only a subset of the data. Furthermore, the PDT remains more powerful even when there is misclassification of unaffected individuals. Our simulations suggest that there may be advantages to using the PDT even if the data consist of independent families without extended family information. Thus, the PDT provides a general test of linkage disequilibrium that can be widely applied to different data structures.