Robustness despite uncertainty: regional climate data reveal the dominant role of humans in explaining global extinctions of Late Quaternary megafauna

Debate over the late Quaternary megafaunal extinctions has focussed on whether human colonisation or climatic changes were more important drivers of extinction, with few extinctions being unambiguously attributable to either. Most analyses have been geographically or taxonomically restricted and the few quantitative global analyses have been limited by coarse temporal resolution or overly simplified climate reconstructions or proxies. We present a global analysis of the causes of these extinctions which uses high-resolution climate reconstructions and explicitly investigates the sensitivity of our results to uncertainty in the palaeological record. Our results show that human colonisation was the dominant driver of megafaunal extinction across the world but that climatic factors were also important. We identify the geographic regions where future research is likely to have the most impact, with our models reliably predicting extinctions across most of the world, with the notable exception of mainland Asia where we fail to explain the apparently low rate of extinction found in in the fossil record. Our results are highly robust to uncertainties in the palaeological record, and our main conclusions are unlikely to change qualitatively following minor improvements or changes in the dates of extinctions and human colonisation.

Big Data and ecosystem research programmes

The size and complexity of data sets generated within ecosystem-level programmes merits their capture, curation, storage and analysis, synthesis and visualisation using Big Data approaches. This review looks at previous attempts to organise and analyse such data through the International Biological Programme and draws on the mistakes made and the lessons learned for effective Big Data approaches to current Research Councils United Kingdom (RCUK) ecosystem-level programmes, using Biodiversity and Ecosystem Service Sustainability (BESS) and Environmental Virtual Observatory Pilot (EVOp) as exemplars. The challenges raised by such data are identified, explored and suggestions are made for the two major issues of extending analyses across different spatio-temporal scales and for the effective integration of quantitative and qualitative data.

Using data insertion with the NAME model to simulate the 8 May 2010 Eyjafjallajökull volcanic ash cloud

A data insertion method, where a dispersion model is initialized from ash properties derived from a series of satellite observations, is used to model the 8 May 2010 Eyjafjallajökull volcanic ash cloud which extended from Iceland to northern Spain. We also briefly discuss the application of this method to the April 2010 phase of the Eyjafjallajökull eruption and the May 2011 Grímsvötn eruption. An advantage of this method is that very little knowledge about the eruption itself is required because some of the usual eruption source parameters are not used. The method may therefore be useful for remote volcanoes where good satellite observations of the erupted material are available, but little is known about the properties of the actual eruption. It does, however, have a number of limitations related to the quality and availability of the observations. We demonstrate that, using certain configurations, the data insertion method is able to capture the structure of a thin filament of ash extending over northern Spain that is not fully captured by other modeling methods. It also verifies well against the satellite observations according to the quantitative object-based quality metric, SAL—structure, amplitude, location, and the spatial coverage metric, Figure of Merit in Space.

Assessing species’ habitat associations from occurrence records, standardised monitoring data and expert opinion: a test with British butterflies

Accurate knowledge of species’ habitat associations is important for conservation planning and policy. Assessing habitat associations is a vital precursor to selecting appropriate indicator species for prioritising sites for conservation or assessing trends in habitat quality. However, much existing knowledge is based on qualitative expert opinion or local scale studies, and may not remain accurate across different spatial scales or geographic locations. Data from biological recording schemes have the potential to provide objective measures of habitat association, with the ability to account for spatial variation. We used data on 50 British butterfly species as a test case to investigate the correspondence of data-derived measures of habitat association with expert opinion, from two different butterfly recording schemes. One scheme collected large quantities of occurrence data (c. 3 million records) and the other, lower quantities of standardised monitoring data (c. 1400 sites). We used general linear mixed effects models to derive scores of association with broad-leaf woodland for both datasets and compared them with scores canvassed from experts. Scores derived from occurrence and abundance data both showed strongly positive correlations with expert opinion. However, only for occurrence data did these fell within the range of correlations between experts. Data-derived scores showed regional spatial variation in the strength of butterfly associations with broad-leaf woodland, with a significant latitudinal trend in 26% of species. Sub-sampling of the data suggested a mean sample size of 5000 occurrence records per species to gain an accurate estimation of habitat association, although habitat specialists are likely to be readily detected using several hundred records. Occurrence data from recording schemes can thus provide easily obtained, objective, quantitative measures of habitat association.

Quantitative structure of an acetate dye molecule analogue at the TiO2- acetic acid interface

The positions of atoms in and around acetate molecules at the rutile TiO2(110) interface with 0.1 M acetic acid have been determined with a precision of ±0.05 Å. Acetate is used as a surrogate for the carboxylate groups typically employed to anchor monocarboxylate dye molecules to TiO2 in dye-sensitised solar cells (DSSC). Structural analysis reveals small domains of ordered (2 x 1) acetate molecules, with substrate atoms closer to their bulk terminated positions compared to the clean UHV surface. Acetate is found in a bidentate bridge position, binding through both oxygen atoms to two five-fold titanium atoms such that the molecular plane is along the [001] azimuth. Density functional theory calculations provide adsorption geometries in excellent agreement with experiment. The availability of these structural data will improve the accuracy of charge transport models for DSSC.

Performance of the Quantitative Food Frequency Questionnaire Used in the Brazilian Center of the Prospective Study Natural History of Human Papillomavirus Infection in Men: The HIM Study

The Natural History of Human Papillomavirus (HPV) Infection in Men: The HIM Study is a prospective multi-center cohort study that, among other factors, analyzes participants` diet. A parallel cross-sectional study was designed to evaluate the validity and reproducibility of the quantitative food frequency questionnaire (QFFQ) used in the Brazilian center from the HIM Study. For this, a convenience subsample of 98 men aged 18 to 70 years from the HIM Study in Brazil answered three 54-item QFFQ and three 24-hour recall interviews, with 6-month intervals between them (data collection January to September 2007). A Bland-Altman analysis indicated that the difference between instruments was dependent on the magnitude of the intake for energy and most nutrients included in the validity analysis, with the exception of carbohydrates, fiber, polyunsaturated fat, vitamin C, and vitamin E. The correlation between the QFFQ and the 24-hour recall for the deattenuated and energy-adjusted data ranged from 0.05 (total fat) to 0.57 (calcium). For the energy and nutrients consumption included in the validity analysis, 33.5% of participants on average were correctly classified into quartiles, and the average value of 0.26 for weighted kappa shows a reasonable agreement. The intraclass correlation coefficients for all nutrients were greater than 0.40 in the reproducibility analysis. The QFFQ demonstrated good reproducibility and acceptable validity. The results support the use of this instrument in the HIM Study. J Am Diet Assoc. 2011;111:1045-1051.

Metabolic acidosis in patients with severe sepsis and septic shock: A longitudinal quantitative study

Objective: To describe the composition of metabolic acidosis in patients with severe sepsis and septic shock at intensive care unit admission and throughout the first 5 days of intensive care unit stay. Design: Prospective, observational study. Setting: Twelve-bed intensive care unit. Patients: Sixty patients with either severe sepsis or septic shock. Interventions: None. Measurements and Main Results: Data were collected until 5 days after intensive care unit admission. We studied the contribution of inorganic ion difference, lactate, albumin, phosphate, and strong ion gap to metabolic acidosis. At admission, standard base excess was -6.69 +/- 4.19 mEq/L in survivors vs. -11.63 +/- 4.87 mEq/L in nonsurvivors (p < .05); inorganic ion difference (mainly resulting from hyperchloremia) was responsible for a decrease in standard base excess by 5.64 +/- 4.96 mEq/L in survivors vs. 8.94 +/- 7.06 mEq/L in nonsurvivors (p < .05); strong ion gap was responsible for a decrease in standard base excess by 4.07 +/- 3.57 mEq/L in survivors vs. 4.92 +/- 5.55 mEq/L in nonsurvivors with a nonsignificant probability value; and lactate was responsible for a decrease in standard base excess to 1.34 +/- 2.07 mEq/L in survivors vs. 1.61 +/- 2.25 mEq/L in nonsurvivors with a nonsignificant probability value. Albumin had an important alkalinizing effect in both groups; phosphate had a minimal acid-base effect. Acidosis in survivors was corrected during the study period as a result of a decrease in lactate and strong ion gap levels, whereas nonsurvivors did not correct their metabolic acidosis. In addition to Acute Physiology and Chronic Health Evaluation 11 score and serum creatinine level, inorganic ion difference acidosis magnitude at intensive care unit admission was independently associated with a worse outcome. Conclusions: Patients with severe sepsis and septic shock exhibit a complex metabolic acidosis at intensive care unit admission, caused predominantly by hyperchloremic acidosis, which was more pronounced in nonsurvivors. Acidosis resolution in survivors was attributable to a decrease in strong ion gap and lactate levels. (Crit Care Med 2009; 37:2733-2739)

Craniometric Similarities Within and Between Human Populations in Comparison with Neutral Genetic Data

The statement that pairs of individuals from different populations are often more genetically similar than pairs from the same population is a widespread idea inside and outside the scientific community. Witherspoon et al. [""Genetic similarities within and between human populations,"" Genetics 176:351-359 (2007)] proposed an index called the dissimilarity fraction (omega) to access in a quantitative way the validity of this statement for genetic systems. Witherspoon demonstrated that, as the number of loci increases, omega decreases to a point where, when enough sampling is available, the statement is false. In this study, we applied the dissimilarity fraction to Howells`s craniometric database to establish whether or not similar results are obtained for cranial morphological traits. Although in genetic studies thousands of loci are available, Howells`s database provides no more than 55 metric traits, making the contribution of each variable important. To cope with this limitation, we developed a routine that takes this effect into consideration when calculating. omega Contrary to what was observed for the genetic data, our results show that cranial morphology asymptotically approaches a mean omega of 0.3 and therefore supports the initial statement-that is, that individuals from the same geographic region do not form clear and discrete clusters-further questioning the idea of the existence of discrete biological clusters in the human species. Finally, by assuming that cranial morphology is under an additive polygenetic model, we can say that the population history signal of human craniometric traits presents the same resolution as a neutral genetic system dependent on no more than 20 loci.

Evaluation of Quantitative RT-PCR Using Nonamplified and Amplified RNA

Quantitative reverse-transcription polymerase chain reaction (qRT-PCR) is a standard assay in molecular medicine for gene expression analysis. Samples from incisional/needle biopsies, laser-microdissected tumor cells and other biologic sources, normally available in clinical cancer studies, generate very small amounts of RNA that are restrictive for expression analysis. As a consequence, an RNA amplification procedure is required to assess the gene expression levels of such sample types. The reproducibility and accuracy of relative gene expression data produced by sensitive methodology as qRT-PCR when cDNA converted from amplified (A) RNA is used as template has not yet been properly addressed. In this study, to properly evaluate this issue, we performed 1 round of linear RNA amplification in 2 breast cell lines (C5.2 and HB4a) and assessed the relative expression of 34 genes using cDNA converted from both nonamplified (NA) and A RNA. Relative gene expression was obtained from beta actin or glyceraldehyde 3-phosphate dehydrogenase normalized data using different dilutions of cDNA, wherein the variability and fold-change differences in the expression of the 2 methods were compared. Our data showed that 1 round of linear RNA amplification, even with suboptimal-quality RNA, is appropriate to generate reproducible and high-fidelity qRT-PCR relative expression data that have similar confidence levels as those from NA samples. The use of cDNA that is converted from both A and NA RNA in a single qRT-PCR experiment clearly creates bias in relative gene expression data.

A Framework for Exploring Multidimensional Data with 3D Projections

Visualization of high-dimensional data requires a mapping to a visual space. Whenever the goal is to preserve similarity relations a frequent strategy is to use 2D projections, which afford intuitive interactive exploration, e. g., by users locating and selecting groups and gradually drilling down to individual objects. In this paper, we propose a framework for projecting high-dimensional data to 3D visual spaces, based on a generalization of the Least-Square Projection (LSP). We compare projections to 2D and 3D visual spaces both quantitatively and through a user study considering certain exploration tasks. The quantitative analysis confirms that 3D projections outperform 2D projections in terms of precision. The user study indicates that certain tasks can be more reliably and confidently answered with 3D projections. Nonetheless, as 3D projections are displayed on 2D screens, interaction is more difficult. Therefore, we incorporate suitable interaction functionalities into a framework that supports 3D transformations, predefined optimal 2D views, coordinated 2D and 3D views, and hierarchical 3D cluster definition and exploration. For visually encoding data clusters in a 3D setup, we employ color coding of projected data points as well as four types of surface renderings. A second user study evaluates the suitability of these visual encodings. Several examples illustrate the framework`s applicability for both visual exploration of multidimensional abstract (non-spatial) data as well as the feature space of multi-variate spatial data.

Three-dimensional quantitative structure-activity relationships for a large series of potent antitubercular agents

Comparative molecular field analysis (CoMFA) studies were conducted on a series of 100 isoniazid derivatives as anti-tuberculosis agents using two receptor-independent structural data set alignment strategies: (1) rigid-body fit, and (2) pharmacophore-based. Significant cross-validated correlation coefficients were obtained (CoMFA(1), q(2) = 0,75 and CoMFA(2), q(2) = 0.74), indicating the potential of the models for untested compounds. The models were then used to predict the inhibitory potency of 20 test set compounds that were not included in the training set, and the predicted values were in good agreement with the experimental results.

Three-Dimensional Quantitative Structure-Activity Relationship Study of Antitumor 2-Formylpyridine Thiosemicarbazones Derivatives as Inhibitors of Ribonucleotide Reductase

In order to extend previous SAR and QSAR studies, 3D-QSAR analysis has been performed using CoMFA and CoMSIA approaches applied to a set of 39 alpha-(N)-heterocyclic carboxaldehydes thiosemicarbazones with their inhibitory activity values (IC(50)) evaluated against ribonucleotide reductase (RNR) of H.Ep.-2 cells (human epidermoid carcinoma), taken from selected literature. Both rigid and field alignment methods, taking the unsubstituted 2-formylpyridine thiosemicarbazone in its syn conformation as template, have been used to generate multiple predictive CoMFA and CoMSIA models derived from training sets and validated with the corresponding test sets. Acceptable predictive correlation coefficients (Q(cv)(2) from 0.360 to 0.609 for CoMFA and Q(cv)(2) from 0.394 to 0.580 for CoMSIA models) with high fitted correlation coefficients (r` from 0.881 to 0.981 for CoMFA and r(2) from 0.938 to 0.993 for CoMSIA models) and low standard errors (s from 0.135 to 0.383 for CoMFA and s from 0.098 to 0.240 for CoMSIA models) were obtained. More precise CoMFA and CoMSIA models have been derived considering the subset of thiosemicarbazones (TSC) substituted only at 5-position of the pyridine ring (n=22). Reasonable predictive correlation coefficients (Q(cv)(2) from 0.486 to 0.683 for CoMFA and Q(cv)(2) from 0.565 to 0.791 for CoMSIA models) with high fitted correlation coefficients (r(2) from 0.896 to 0.997 for CoMFA and r(2) from 0.991 to 0.998 for CoMSIA models) and very low standard errors (s from 0.040 to 0.179 for CoMFA and s from 0.029 to 0.068 for CoMSIA models) were obtained. The stability of each CoMFA and CoMSIA models was further assessed by performing bootstrapping analysis. For the two sets the generated CoMSIA models showed, in general, better statistics than the corresponding CoMFA models. The analysis of CoMFA and CoMSIA contour maps suggest that a hydrogen bond acceptor near the nitrogen of the pyridine ring can enhance inhibitory activity values. This observation agrees with literature data, which suggests that the nitrogen pyridine lone pairs can complex with the iron ion leading to species that inhibits RNR. The derived CoMFA and CoMSIA models contribute to understand the structural features of this class of TSC as antitumor agents in terms of steric, electrostatic, hydrophobic and hydrogen bond donor and hydrogen bond acceptor fields as well as to the rational design of this key enzyme inhibitors.

qtl.outbred: Interfacing outbred line cross data with the R/qtl mapping software

Background qtl.outbred is an extendible interface in the statistical environment, R, for combining quantitative trait loci (QTL) mapping tools. It is built as an umbrella package that enables outbred genotype probabilities to be calculated and/or imported into the software package R/qtl. Findings Using qtl.outbred, the genotype probabilities from outbred line cross data can be calculated by interfacing with a new and efficient algorithm developed for analyzing arbitrarily large datasets (included in the package) or imported from other sources such as the web-based tool, GridQTL. Conclusion qtl.outbred will improve the speed for calculating probabilities and the ability to analyse large future datasets. This package enables the user to analyse outbred line cross data accurately, but with similar effort than inbred line cross data.

Novel Statistical Methods in Quantitative Genetics : Modeling Genetic Variance for Quantitative Trait Loci Mapping and Genomic Evaluation

This thesis develops and evaluates statistical methods for different types of genetic analyses, including quantitative trait loci (QTL) analysis, genome-wide association study (GWAS), and genomic evaluation. The main contribution of the thesis is to provide novel insights in modeling genetic variance, especially via random effects models. In variance component QTL analysis, a full likelihood model accounting for uncertainty in the identity-by-descent (IBD) matrix was developed. It was found to be able to correctly adjust the bias in genetic variance component estimation and gain power in QTL mapping in terms of precision.  Double hierarchical generalized linear models, and a non-iterative simplified version, were implemented and applied to fit data of an entire genome. These whole genome models were shown to have good performance in both QTL mapping and genomic prediction. A re-analysis of a publicly available GWAS data set identified significant loci in Arabidopsis that control phenotypic variance instead of mean, which validated the idea of variance-controlling genes.  The works in the thesis are accompanied by R packages available online, including a general statistical tool for fitting random effects models (hglm), an efficient generalized ridge regression for high-dimensional data (bigRR), a double-layer mixed model for genomic data analysis (iQTL), a stochastic IBD matrix calculator (MCIBD), a computational interface for QTL mapping (qtl.outbred), and a GWAS analysis tool for mapping variance-controlling loci (vGWAS).