A Molecular Phylogeny of the Tiger Beetles (Cicindelidae): Congruence of Mitochondrial and Nuclear rDNA Data Sets

As a basis for comparative studies in the family Cicindelidae (tiger beetles), a phylogenetic hypothesis for major groups was established based on molecular data, focusing on the taxa of the New World. Two markers, mitochondrial 16S rRNA and nuclear 18S rRNA, were sequenced for a total of 912 nucleotides and 30 taxa. All Cicindelidae were inferred to be a monophyletic group by the presence of a segment in the 5′ region of the 18S rDNA which is predicted to fold into a single stem-loop structure not present in the outgroup. The total number of variable and potentially informative positions was smaller in the 18S data set, but their higher internal consistency resulted in a strong phylogenetic signal. Inferred relationships were stable over a wide range of alignment parameters, but the inclusion of alignment-sensitive positions and presumptive gap characters added substantially to the resolution and overall support of the tree. Both data sets were in general congruence. The combined evidence suggests that relationships in Cicindelidae as currently recognized have to be reconsidered. No convincing evidence could be found for the traditional subdivision into two subfamilies, Cicindelinae and Collyrinae. The latter is embedded within the Cicindelinae and clearly not the sister to all other cicindelids. Most of the current tribes and subtribes, particularly in the Megacephalini, are paraphyletic groups, whereas monophyly of the large tribe Cicindelini is well supported by the molecular data.     

A Novel Test for Independence Derived from an Exact Distribution of ith Nearest Neighbours

Dependence measures and tests for independence have recently attracted a lot of attention, because they are the cornerstone of algorithms for network inference in probabilistic graphical models. Pearson''s product moment correlation coefficient is still by far the most widely used statistic yet it is largely constrained to detecting linear relationships. In this work we provide an exact formula for the th nearest neighbor distance distribution of rank-transformed data. Based on that, we propose two novel tests for independence. An implementation of these tests, together with a general benchmark framework for independence testing, are freely available as a CRAN software package (http://cran.r-project.org/web/packages/knnIndep). In this paper we have benchmarked Pearson''s correlation, Hoeffding''s , dcor, Kraskov''s estimator for mutual information, maximal information criterion and our two tests. We conclude that no particular method is generally superior to all other methods. However, dcor and Hoeffding''s are the most powerful tests for many different types of dependence.     

Improving Estimations of Spatial Distribution of Soil Respiration Using the Bayesian Maximum Entropy Algorithm and Soil Temperature as Auxiliary Data

Soil respiration inherently shows strong spatial variability. It is difficult to obtain an accurate characterization of soil respiration with an insufficient number of monitoring points. However, it is expensive and cumbersome to deploy many sensors. To solve this problem, we proposed employing the Bayesian Maximum Entropy (BME) algorithm, using soil temperature as auxiliary information, to study the spatial distribution of soil respiration. The BME algorithm used the soft data (auxiliary information) effectively to improve the estimation accuracy of the spatiotemporal distribution of soil respiration. Based on the functional relationship between soil temperature and soil respiration, the BME algorithm satisfactorily integrated soil temperature data into said spatial distribution. As a means of comparison, we also applied the Ordinary Kriging (OK) and Co-Kriging (Co-OK) methods. The results indicated that the root mean squared errors (RMSEs) and absolute values of bias for both Day 1 and Day 2 were the lowest for the BME method, thus demonstrating its higher estimation accuracy. Further, we compared the performance of the BME algorithm coupled with auxiliary information, namely soil temperature data, and the OK method without auxiliary information in the same study area for 9, 21, and 37 sampled points. The results showed that the RMSEs for the BME algorithm (0.972 and 1.193) were less than those for the OK method (1.146 and 1.539) when the number of sampled points was 9 and 37, respectively. This indicates that the former method using auxiliary information could reduce the required number of sampling points for studying spatial distribution of soil respiration. Thus, the BME algorithm, coupled with soil temperature data, can not only improve the accuracy of soil respiration spatial interpolation but can also reduce the number of sampling points.     

A Modular Mind? A Test Using Individual Data from Seven Primate Species

It has long been debated whether the mind consists of specialized and independently evolving modules, or whether and to what extent a general factor accounts for the variance in performance across different cognitive domains. In this study, we used a hierarchical Bayesian model to re-analyse individual level data collected on seven primate species (chimpanzees, bonobos, orangutans, gorillas, spider monkeys, brown capuchin monkeys and long-tailed macaques) across 17 tasks within four domains (inhibition, memory, transposition and support). Our modelling approach evidenced the existence of both a domain-specific factor and a species factor, each accounting for the same amount (17%) of the observed variance. In contrast, inter-individual differences played a minimal role. These results support the hypothesis that the mind of primates is (at least partially) modular, with domain-specific cognitive skills undergoing different evolutionary pressures in different species in response to specific ecological and social demands.     

Advantages and Limitations of Anticipating Laboratory Test Results from Regression- and Tree-Based Rules Derived from Electronic Health-Record Data

Laboratory testing is the single highest-volume medical activity, making it useful to ask how well one can anticipate whether a given test result will be high, low, or within the reference interval (“normal”). We analyzed 10 years of electronic health records—a total of 69.4 million blood tests—to see how well standard rule-mining techniques can anticipate test results based on patient age and gender, recent diagnoses, and recent laboratory test results. We evaluated rules according to their positive and negative predictive value (PPV and NPV) and area under the receiver-operator characteristic curve (ROC AUCs). Using a stringent cutoff of PPV and/or NPV≥0.95, standard techniques yield few rules for sendout tests but several for in-house tests, mostly for repeat laboratory tests that are part of the complete blood count and basic metabolic panel. Most rules were clinically and pathophysiologically plausible, and several seemed clinically useful for informing pre-test probability of a given result. But overall, rules were unlikely to be able to function as a general substitute for actually ordering a test. Improving laboratory utilization will likely require different input data and/or alternative methods.     

A Quantitative Test of the Neutral Theory Using Pooled Allozyme Data

Neutral theory predicts a positive correlation between the amount of polymorphism within species and evolutionary rate. Previous tests of this prediction using both allozyme and DNA data have led to conflicting conclusions about the influence of selection and mutation drift. It is argued here that quantitative conclusions about the adequacy of neutral theory can be obtained by analyzing genetic data pooled from many sources. Using this approach, a large database containing information on allozyme variation in over 1500 species is used to examine the relationship between heterozygosity and genetic distance. The results provide support for the hypothesis that a major percentage of protein variation can be explained by variation in neutral mutation rate, and a minor percentage by strong selection.     

A Congruence Study of Molecular and Morphological Data for Eutherian Mammals

The four orders of eutherian mammals which are traditionally placed in the superorder Archonta [Chiroptera (microbats and megabats), Dermoptera (flying lemurs), Primates (primates), and Scandentia (tree shrews)] are among the best-studied taxa of their infraclass from both the molecular and morphological perspectives. Nevertheless, the ordinal relationships of archontans remain unresolved. While morphological studies favor their monophyly, molecular investigations do not. To evaluate these opposing conclusions, parsimony analyses were conducted with three separate sets of DNA sequences from both the nuclear and mitochondrial genomes and one file of morphological data for archontans and other eutherian mammals. Statistical tests of character support and ordinal branching pattern differences documented that the three sets of DNA sequences and their results were homogeneous and congruent, thereby allowing for the combination of these data into one large matrix for further phylogenetic analysis. In contrast, these same tests revealed that the combined sequence and morphological files and their topologies were in strong conflict. Archontan monophyly was supported by the morphological evidence, but this arrangement was strongly rejected by the combined DNA sequences that favored instead a grouping of Dermoptera, Primates, and Scandentia with Lagomorpha (rabbits) and Rodentia (rodents). Resolution of these significant differences will require further evaluations about the homologies and evolutionary properties of the molecular and morphological characters and about the appropriateness of the chosen phylogenetic methods, as well as the incorporation of new comparative data from both sources.     

Assessing the Importance of Social Factors in Bird Song Learning: A Test Using Computer-Simulated Tutors

We used a mixed live/synthetic tutoring design to investigate whether the social factors of eavesdropping on adult singing interactions and/or direct interaction with a tutor would influence song learning in song sparrows ( Melospiza melodia ). Males were brought into the laboratory at 4–5 d-old, hand-raised and then tutored by two pairs of adult song sparrows in June and July From January through March of the next year, subjects received tutoring from computer simulations of two of the original live tutors. The first, non-interactive, model simulated one of the earlier tutors singing 'naturalistic' bouts of song with no interaction with the subject. The second, interactive, model simulated a different early tutor that behaved similarly to the non-interactive model, but synchronized its singing with the subject, and tried to match the subject's song. Subjects learned relatively more from their interactive late tutor and his early partner, and showed no tendency to learn more from their late than their early-only tutors. These results support the eavesdropping hypothesis, and also suggest that direct interaction with the tutor is a relevant social factor.     

Physiological and Biochemical Analysis of Somaclones Derived from Leaf Explants of Lathyrus sativus

Low ODAP somaclones have been evaluated for physiological and biochemical parameters especially in relation to attributes that lead to increased biomass production. All the somaclones during development had substantially lower ODAP content in leaves as compared to parent P24. Considerable variation was observed in relation to leaf width, leaf length, internodal length and leaf area. Somaclone Bio L12 had the highest whereas parent P24 and Bi0164 had the least leaf area. Harvest index was the highest and biomass production was the lowest in the Bio 164. Bio L08 gave the highest seed yield. Photosynthetic rates were also higher in Bio L12, although no significant positive correlation was observed in leaf photosynthesis and seed yield. The differences in physiolpgical and biochemical parameters indicate the possibility of development of high yielding genotypes. The results in present investigation show differences in photosynthetic rate, leaf characteristics, seed yield and ODAP content among somalones and parent. Somaclones with extremely low ODAP content with variability in leaf morphology and photosynthetic rate is indicative of variation induced during plant tissue culture.     

Using Combined Diagnostic Test Results to Hindcast Trends of Infection from Cross-Sectional Data

Infectious disease surveillance is key to limiting the consequences from infectious pathogens and maintaining animal and public health. Following the detection of a disease outbreak, a response in proportion to the severity of the outbreak is required. It is thus critical to obtain accurate information concerning the origin of the outbreak and its forward trajectory. However, there is often a lack of situational awareness that may lead to over- or under-reaction. There is a widening range of tests available for detecting pathogens, with typically different temporal characteristics, e.g. in terms of when peak test response occurs relative to time of exposure. We have developed a statistical framework that combines response level data from multiple diagnostic tests and is able to ‘hindcast’ (infer the historical trend of) an infectious disease epidemic. Assuming diagnostic test data from a cross-sectional sample of individuals infected with a pathogen during an outbreak, we use a Bayesian Markov Chain Monte Carlo (MCMC) approach to estimate time of exposure, and the overall epidemic trend in the population prior to the time of sampling. We evaluate the performance of this statistical framework on simulated data from epidemic trend curves and show that we can recover the parameter values of those trends. We also apply the framework to epidemic trend curves taken from two historical outbreaks: a bluetongue outbreak in cattle, and a whooping cough outbreak in humans. Together, these results show that hindcasting can estimate the time since infection for individuals and provide accurate estimates of epidemic trends, and can be used to distinguish whether an outbreak is increasing or past its peak. We conclude that if temporal characteristics of diagnostics are known, it is possible to recover epidemic trends of both human and animal pathogens from cross-sectional data collected at a single point in time.     

Assessing Species Distribution Using Google Street View: A Pilot Study with the Pine Processionary Moth

Mapping species spatial distribution using spatial inference and prediction requires a lot of data. Occurrence data are generally not easily available from the literature and are very time-consuming to collect in the field. For that reason, we designed a survey to explore to which extent large-scale databases such as Google maps and Google street view could be used to derive valid occurrence data. We worked with the Pine Processionary Moth (PPM) Thaumetopoea pityocampa because the larvae of that moth build silk nests that are easily visible. The presence of the species at one location can therefore be inferred from visual records derived from the panoramic views available from Google street view. We designed a standardized procedure allowing evaluating the presence of the PPM on a sampling grid covering the landscape under study. The outputs were compared to field data. We investigated two landscapes using grids of different extent and mesh size. Data derived from Google street view were highly similar to field data in the large-scale analysis based on a square grid with a mesh of 16 km (96% of matching records). Using a 2 km mesh size led to a strong divergence between field and Google-derived data (46% of matching records). We conclude that Google database might provide useful occurrence data for mapping the distribution of species which presence can be visually evaluated such as the PPM. However, the accuracy of the output strongly depends on the spatial scales considered and on the sampling grid used. Other factors such as the coverage of Google street view network with regards to sampling grid size and the spatial distribution of host trees with regards to road network may also be determinant.     

Experimental Studies and Dynamics Modeling Analysis of the Swimming and Diving of Whirligig Beetles (Coleoptera: Gyrinidae)

Whirligig beetles (Coleoptera, Gyrinidae) can fly through the air, swiftly swim on the surface of water, and quickly dive across the air-water interface. The propulsive efficiency of the species is believed to be one of the highest measured for a thrust generating apparatus within the animal kingdom. The goals of this research were to understand the distinctive biological mechanisms that allow the beetles to swim and dive, while searching for potential bio-inspired robotics applications. Through static and dynamic measurements obtained using a combination of microscopy and high-speed imaging, parameters associated with the morphology and beating kinematics of the whirligig beetle''s legs in swimming and diving were obtained. Using data obtained from these experiments, dynamics models of both swimming and diving were developed. Through analysis of simulations conducted using these models it was possible to determine several key principles associated with the swimming and diving processes. First, we determined that curved swimming trajectories were more energy efficient than linear trajectories, which explains why they are more often observed in nature. Second, we concluded that the hind legs were able to propel the beetle farther than the middle legs, and also that the hind legs were able to generate a larger angular velocity than the middle legs. However, analysis of circular swimming trajectories showed that the middle legs were important in maintaining stable trajectories, and thus were necessary for steering. Finally, we discovered that in order for the beetle to transition from swimming to diving, the legs must change the plane in which they beat, which provides the force required to alter the tilt angle of the body necessary to break the surface tension of water. We have further examined how the principles learned from this study may be applied to the design of bio-inspired swimming/diving robots.     

Site Distribution at the Edge of the Palaeolithic World: A Nutritional Niche Approach

This paper presents data from the English Channel area of Britain and Northern France on the spatial distribution of Lower to early Middle Palaeolithic pre-MIS5 interglacial sites which are used to test the contention that the pattern of the richest sites is a real archaeological distribution and not of taphonomic origin. These sites show a marked concentration in the middle-lower reaches of river valleys with most being upstream of, but close to, estimated interglacial tidal limits. A plant and animal database derived from Middle-Late Pleistocene sites in the region is used to estimate the potentially edible foods and their distribution in the typically undulating landscape of the region. This is then converted into the potential availability of macronutrients (proteins, carbohydrates, fats) and selected micronutrients. The floodplain is shown to be the optimum location in the nutritional landscape (nutriscape). In addition to both absolute and seasonal macronutrient advantages the floodplains could have provided foods rich in key micronutrients, which are linked to better health, the maintenance of fertility and minimization of infant mortality. Such places may have been seen as ‘good (or healthy) places’ explaining the high number of artefacts accumulated by repeated visitation over long periods of time and possible occupation. The distribution of these sites reflects the richest aquatic and wetland successional habitats along valley floors. Such locations would have provided foods rich in a wide range of nutrients, importantly including those in short supply at these latitudes. When combined with other benefits, the high nutrient diversity made these locations the optimal niche in northwest European mixed temperate woodland environments. It is argued here that the use of these nutritionally advantageous locations as nodal or central points facilitated a healthy variant of the Palaeolithic diet which permitted habitation at the edge of these hominins’ range.     

Comparison of Protein Structures in Solution Using Local Conformations Derived from NMR Data: Application to Cytochrome c

Abstract

Structural comparisons of proteins in solution are often required to examine structure-functional relationships, study structural effects of mutations or distinguish between various forms of the same molecule under different conditions. A nuclear magnetic resonance (NMR) based probabilistic strategy is presented and used to study the structural differences between the two redox states of cytochrome C in solution. A probabilistic approach is employed to calculate the main chain conformations of horse ferro- and ferricytochrome C in solution, based on the published sequential d connectivity data. Conformational differences between the two oxidation states of horse cytochrome C in solution are found to be statistically significant. The largest changes in conformation are at residues Lys27, Thr28, Leu32, Gln42, Thr47, Tyr48, Thr49, Glu69, Lys72, Met80, Phe82, Ile85 and Lys86, all of which are close to the heme (within 14 Å of the heme iron in the high resolution Xray structure of tuna cytochrome c). We suggest that these conformational changes may modulate local dipole moments and hence influence the interactions of cytochrome C with its physiological redox partners during the electron transfer process. The oxidation state dependent conformational differences are found to be much greater in solution than in the crystalline state, and the solution and crystal structures differ significantly in regions close to the heme. These results suggest that the highly charged nature of cytochrome C makes this protein particularly sensitive to the ionic strength of its environment and leads to differences between crystal and solution structures in the same oxidation state. In such cases, crystal structures must be used with caution for modeling molecular interactions in vivo. More generally, this analysis indicates that the determination of accurate local conformations based on nmr data can provide useful information about structure-functional aspects of proteins in solution.     

Predicting the Phenotypic Values of Physiological Traits Using SNP Genotype and Gene Expression Data in Mice

Predicting phenotypes using genome-wide genetic variation and gene expression data is useful in several fields, such as human biology and medicine, as well as in crop and livestock breeding. However, for phenotype prediction using gene expression data for mammals, studies remain scarce, as the available data on gene expression profiling are currently limited. By integrating a few sources of relevant data that are available in mice, this study investigated the accuracy of phenotype prediction for several physiological traits. Gene expression data from two tissues as well as single nucleotide polymorphisms (SNPs) were used. For the studied traits, the variance of the effects of the expression levels was more likely to differ among the genes than were the effects of SNPs. For the glucose concentration, the total cholesterol amount, and the total tidal volume, the accuracy by cross validation tended to be higher when the gene expression data rather than the SNP genotype data were used, and a statistically significant increase in the accuracy was obtained when the gene expression data from the liver were used alone or jointly with the SNP genotype data. For these traits, there were no additional gains in accuracy from using the gene expression data of both the liver and lung compared to that of individual use. The accuracy of prediction using genes that were selected differently was examined; the use of genes with a higher tissue specificity tended to result in an accuracy that was similar to or greater than that associated with the use of all of the available genes for traits such as the glucose concentration and total cholesterol amount. Although relatively few animals were evaluated, the current results suggest that gene expression levels could be used as explanatory variables. However, further studies are essential to confirm our findings using additional animal samples.     

A Penalized-Likelihood Method to Estimate the Distribution of Selection Coefficients from Phylogenetic Data

We develop a maximum penalized-likelihood (MPL) method to estimate the fitnesses of amino acids and the distribution of selection coefficients (S = 2Ns) in protein-coding genes from phylogenetic data. This improves on a previous maximum-likelihood method. Various penalty functions are used to penalize extreme estimates of the fitnesses, thus correcting overfitting by the previous method. Using a combination of computer simulation and real data analysis, we evaluate the effect of the various penalties on the estimation of the fitnesses and the distribution of S. We show the new method regularizes the estimates of the fitnesses for small, relatively uninformative data sets, but it can still recover the large proportion of deleterious mutations when present in simulated data. Computer simulations indicate that as the number of taxa in the phylogeny or the level of sequence divergence increases, the distribution of S can be more accurately estimated. Furthermore, the strength of the penalty can be varied to study how informative a particular data set is about the distribution of S. We analyze three protein-coding genes (the chloroplast rubisco protein, mammal mitochondrial proteins, and an influenza virus polymerase) and show the new method recovers a large proportion of deleterious mutations in these data, even under strong penalties, confirming the distribution of S is bimodal in these real data. We recommend the use of the new MPL approach for the estimation of the distribution of S in species phylogenies of protein-coding genes.     

A Physiological Approach to the Analysis of Crop Growth Data. II. Growth of Stylosanthes humilis

Crops of three ecotypes of the pasture legume Stylosanthes humiliswere grown during different lengths of growing season at Katherine,N T, Australia The ecotypes had contrasting flowering behaviour,and the interaction between their relative periods of vegetativeand reproductive growth and the length of the growing seasonwas studied A preliminary analysis, employing the techniques of traditionalgrowth analysis, was made of the data. The results were equivocaland a physiological approach to the analysis of the data wastaken From this second analysis it was concluded that the mainphysiological differences in the growth of the crops was theirflowering date and that differences in forage yield, and theyield of reproductive parts, at the end of the growing seasoncould be attributed to this factor No other consistent significantdifferences in the physiological behaviour of the crops wereobserved crop growth, mathematical model, Stylosanthes humilis, legume