Adequate Data Sets Research Articles

From D-RGB-based reconstruction toward a mesh deformation model for monocular reconstruction of isometric surfaces

In this paper, we address 3D reconstruction of surfaces deforming isometrically. Given that an isometric surface is represented by means of a triangular mesh and that feature/point correspondences on an image are available, the goal is to estimate the 3D positions of the mesh vertices. To perform such monocular reconstruction, a common practice is to adopt linear deformation model. We also integrate this model into a least-squares optimization. However, this model is obtained through a learning process requiring an adequate data set of possible mesh deformations. Providing this prior data is the primary goal of this work and therefore a novel reconstruction technique is proposed for a mesh overlaid across a typical isometric surface. This technique consists in the use of a range camera accompanied by a conventional camera and implements the path from the depth of the feature points to the 3D positions of the vertices through convex programming. The idea is to use the high-resolution images from the RGB camera in combination with the low-resolution depth map to enhance mesh deformation estimation. With this approach, multiple deformations of the mesh are recovered with the possibility that the resulting deformation model is simply extended to any other isometric surfaces for monocular reconstruction. Experimental results show that the proposed approach is robust to noise and generates accurate reconstructions.

IMPROVING THE RELIABILITY AND VALIDITY OF TEST DATA ADEQUACY IN PROGRAMMING ASSESSMENTS

Automatic Programming Assessment (or APA) has recently become a notable method in assisting educators of programming courses to automatically assess and grade students’ programming exercises as its counterpart; the typical manual tasks are prone to errors and lead to inconsistency. Practically, this method also provides an alternative means of reducing the educators’ workload effectively. By default, test data generation process plays an important role to perform a dynamic testing on students’ programs. Dynamic testing involves the execution of a program against different inputs or test data and the comparison of the results with the expected output, which must conform to the program specifications. In the software testing field, there have been diverse automated methods for test data generation. Unfortunately, APA rarely adopts these methods. Limited studies have attempted to integrate APA and test data generation to include more useful features and to provide a precise and thorough quality program testing. Thus, we propose a framework of test data generation known as FaSt-Gen covering both the functional and structural testing of a program for APA. Functional testing is a testing that relies on specified functional requirements and focuses the output generated in response to the selected test data and execution, Meanwhile, structural testing looks at the specific program logic to verify how it works. Overall, FaSt-Gen contributes as a means to educators of programming courses to furnish an adequate set of test data to assess students’ programming solutions regardless of having the optimal expertise in the particular knowledge of test cases design. FaSt-Gen integrates the positive and negative testing criteria or so-called reliable and valid test adequacy criteria to derive desired test data and test set schema. As for the functional testing, the integration of specification-derived test and simplified boundary value analysis techniques covering both the criteria. Path coverage criterion guides the test data selection for structural testing. The findings from the conducted controlled experiment and comparative study evaluation show that FaSt-Gen improves the reliability and validity of test data adequacy in programming assessments.

Persim 3D: Context-Driven Simulation and Modeling of Human Activities in Smart Spaces

Automated understanding and recognition of human activities and behaviors in a smart space (e.g., smart house) is of paramount importance to many critical human-centered applications. Recognized activities are the input to the pervasive computer (the smart space) which intelligently interacts with the users to maintain the application's goal be it assistance, safety, child-development, entertainment or other goals. Research in this area is fascinating but severely lacks adequate validation which often relies on datasets that contain sensory data representing the activities. Availing adequate datasets that can be used in a large variety of spaces, for different user groups, and aiming at different goals is very challenging. This is due to the prohibitive cost and the human capital needed to instrument physical spaces and to recruit human subjects to perform the activities and generate data. Simulation of human activities in smart spaces has therefore emerged as an alternative approach to bridge this deficit. Traditional event-driven approaches have been proposed. However, the complexity of human activity simulation was proved to be challenging to these initial simulation efforts. In this paper, we present Persim 3D–an alternative context-driven approach to simulating human activities capable of supporting complex activity scenarios. We present the context-activity-action nexus and show how our approach combines modeling and visualization of actions with context and activity simulation. We present the Persim 3D architecture and algorithms, and describe a detailed validation study of our approach to verify the accuracy and realism of the simulation output (datasets and visualizations) and the scalability of the human effort in using Persim 3D to simulate complex scenarios. We show positive and promising results that validate our approach.

Identification of Habitat-Specific Biomes of Aquatic Fungal Communities Using a Comprehensive Nearly Full-Length 18S rRNA Dataset Enriched with Contextual Data.

Molecular diversity surveys have demonstrated that aquatic fungi are highly diverse, and that they play fundamental ecological roles in aquatic systems. Unfortunately, comparative studies of aquatic fungal communities are few and far between, due to the scarcity of adequate datasets. We combined all publicly available fungal 18S ribosomal RNA (rRNA) gene sequences with new sequence data from a marine fungi culture collection. We further enriched this dataset by adding validated contextual data. Specifically, we included data on the habitat type of the samples assigning fungal taxa to ten different habitat categories. This dataset has been created with the intention to serve as a valuable reference dataset for aquatic fungi including a phylogenetic reference tree. The combined data enabled us to infer fungal community patterns in aquatic systems. Pairwise habitat comparisons showed significant phylogenetic differences, indicating that habitat strongly affects fungal community structure. Fungal taxonomic composition differed considerably even on phylum and class level. Freshwater fungal assemblage was most different from all other habitat types and was dominated by basal fungal lineages. For most communities, phylogenetic signals indicated clustering of sequences suggesting that environmental factors were the main drivers of fungal community structure, rather than species competition. Thus, the diversification process of aquatic fungi must be highly clade specific in some cases.The combined data enabled us to infer fungal community patterns in aquatic systems. Pairwise habitat comparisons showed significant phylogenetic differences, indicating that habitat strongly affects fungal community structure. Fungal taxonomic composition differed considerably even on phylum and class level. Freshwater fungal assemblage was most different from all other habitat types and was dominated by basal fungal lineages. For most communities, phylogenetic signals indicated clustering of sequences suggesting that environmental factors were the main drivers of fungal community structure, rather than species competition. Thus, the diversification process of aquatic fungi must be highly clade specific in some cases.

Sample size determination for training cancer classifiers from microarray and RNA-seq data

The objective of many high-dimensional microarray and RNA-seq studies is to develop a classifier of cancer patients based on characteristics of their disease. The germinal center B-cell (GCB) classifier study in lymphoma and the National Cancer Institute's Director's Challenge lung (DC-lung) study are two examples. In recent years, such classifiers are often developed using regularized regression, such as the lasso. A critical question is whether a better classifier can be developed from a larger training set size and, if so, how large the training set should be. This paper examines these two questions using an existing sample size method and a novel sample size method developed here specifically for lasso logistic regression. Both methods are based on pilot data. We reexamine the lymphoma and lung cancer data sets to evaluate the sample sizes, and use resampling to assess the estimation methods. We also study application to an RNA-seq data set. We find that it is feasible to estimate sample size for regularized logistic regression if an adequate pilot data set exists. The GCB and the DC-lung data sets appear adequate, under specific assumptions. Existing human RNA-seq data sets are by and large inadequate, and cannot be used as pilot data. Pilot RNA-seq data can be simulated, and the methods in this paper can be used for sample size estimation. A MATLAB program is made available.

Improving Automated Programming Assessments: User Experience Evaluation Using FaSt-generator

Automatic Programming Assessment (APA) has been known as a method used to automatically mark and grade students’ programming solutions. In order to realise APA as a tangible deliverable, a number of automated tools which are called Automated Programming Assessment Systems (APAS) have been developed and tested for decades. Basically, the need for decreasing the load of work among lecturers, timely feedback to students and accuracy on the grading results are the common reasons that motivate the need for APAS. In order to carry out a dynamic testing in APA, it is necessary to prepare an appropriate and adequate set of test data to judge the correctness quality of students’ programming solutions in terms of the aspects of functional and/or structural testing. Manual preparation of quality test data becomes a hard, time consuming, and feasible task in the practice of both software testing and APA. Thus, the generation of automated test data is highly desirable to alleviate the humans’ burden from performing repetitive tasks. This paper aims to describe the design, implementation and user experience when evaluating a tool developed to support APA as a test data generator that is called FaSt-generator. The tool plays an important role to furnish a test set that includes an adequate set of test data to execute both the functional and structural testing in APA. Results collected from the conducted user experience evaluation using FaSt-generator reveal that all the subjects had relatively positive opinions and greatly favour the criteria of User Perception and End-User Computing Satisfaction (EUCS).

Estimation of portfolio efficiency via DEA

Traditional DEA models and nonlinear (diversification) DEA models are often used in performance evaluation of portfolios. However, the diversification models are usually very complicated to compute except the very basic models. And the classic DEA models still need to be further justified and tested, since it is not clear whether they are over-linearised according to the diversification models. The existing studies on performance evaluation via the classic DEA models generally focus on the selection of inputs and outputs. In this work, we investigate theoretical foundation of DEA models for portfolios from a perspective of sampling portfolio. We show the classic DEA provides an effective and practical way to approximate the portfolio efficiency (PE). We further verify this approach through different portfolio models with various frictions and bounds on the market. Through the comprehensive simulations carried out in this study, we show that with adequate data sets, the classic DEA models can effectively sample portfolios to take into account sufficient diversification, and thus can be used as an effective tool in computing the PE for their performance assessments. This study can be viewed as a justification of the classic DEA performance assessments and the way to introduce other efficiency notions (like allocation efficiency, scale efficiency, etc) into assessment of portfolios.

Flow regime identification and void fraction prediction in two-phase flows based on gamma ray attenuation

Flow regime information can be used to improve measurement accuracy on gas volume fractions and as complementary information for other types of flow instrumentation in order to enhance their accuracy. In this study a method based on dual modality densitometry using artificial neural network (ANN) was presented to first identify the flow regime and then predict the void fraction in two-phase flows. The full energy peak (transmission count), photon counts of Compton edge in transmission detector and total count in the scattering detector, were chosen as the three inputs of the ANN. The stratified, homogenous and annular regimes with various void fractions were simulated by the Monte Carlo N-Particle (MCNP) code, version X, in order to obtain adequate data set used for training and testing the ANN. To validate the simulated results, several experiments were carried out in the annular regime of two-phase flow. Experimental results were in good agreement with the simulated data. The maximum difference between experimental and simulated results for the transmission, Compton edge and scattered counts, is 3.4%, 3.8% and 3.6%, respectively. By applying this method, all the three regimes were correctly distinguished and void fraction was predicted in the range of 5–95% with error of less than 1.1%.

Seismicity and seismic structure at Okmok Volcano, Alaska

Okmok volcano is an active volcanic caldera located on the northeastern portion of Umnak Island in the Aleutian arc, with recent eruptions in 1997 and 2008. The Okmok area had ~900 locatable earthquakes between 2003 and June 2008, and an additional ~600 earthquakes from the beginning of the 2008 eruption to mid 2009, providing an adequate dataset for seismic tomography. To image the seismic velocity structure of Okmok, we apply waveform cross-correlation using bispectrum verification and double-difference tomography to a subset of these earthquakes. We also perform P-wave attenuation tomography using a spectral decay technique. We examine the spatio-temporal characteristics of seismicity in the opening sequence of the 2008 eruption to investigate the path of magma migration during the establishment of a new eruptive vent. We also incorporate the new earthquake relocations and three-dimensional (3D) velocity model with first-motion polarities to compute focal mechanisms for selected events in the 2008 pre-eruptive and eruptive periods.Through these techniques we obtain precise relocations, a well-constrained 3D P-wave velocity model, and a marginally resolved S-wave velocity model. We image a main low Vp and Vs anomaly directly under the caldera consisting of a shallow zone at 0–2km depth connected to a larger deeper zone that extends to about 6km depth. We find that areas of low Qp are concentrated in the central to southwestern portion of the caldera and correspond fairly well with areas of low Vp. We interpret the deeper part of the low velocity anomaly (4–6km depth) beneath the caldera as a magma body. This is consistent with results from ambient noise tomography and suggests that previous estimates of depth to Okmok's magma chamber based only on geodetic data may be too shallow. The distribution of events preceding the 2008 eruption suggest that a combination of overpressure in the zone surrounding the magma chamber and the introduction of new material from below were jointly responsible for the explosive eruption. Magma escaping from the top of the main magma chamber likely reacted with both a smaller shallow pod of magma and groundwater on its way up below the Cone D area. The earthquakes in the 2008 pre-eruptive and eruptive periods are found to have a mixture of strike-slip, oblique normal, and oblique thrust mechanisms, with a dominant P-axis orientation that is nearly perpendicular to the regional tectonic stress. This may indicate that the stresses related to magmatic activity locally dominated regional tectonic forces during this time period.

Feasibility of single-beat full-volume capture real-time three-dimensional echocardiography for quantification of right ventricular volume: Validation by cardiac magnetic resonance imaging

The lack of an accurate noninvasive method for assessing right ventricular (RV) volume and function has been a major deficiency of two-dimensional (2D) echocardiography. The aim of our study was to test the feasibility of single-beat full-volume capture with real-time three-dimensional echo (3DE) imaging system for the evaluation of RV volumes and function validated by cardiac magnetic resonance imaging (CMRI). Sixty-one subjects (16 normal subjects, 20 patients with hypertension, 16 patients with pulmonary heart disease and 9 patients with coronary heart disease) were studied. RV volume and function assessments using 3DE were compared with manual tracing with CMRI as the reference method. Fifty-nine of 61 patients (96.7%; 36 male, mean age, 62 ± 15 years) had adequate three-dimensional echocardiographic data sets for analysis. The mean RV end diastolic volume (EDV) was 105 ± 38 ml, end-systolic volume (ESV) was 60 ± 30 and RV ejection fraction (EF) was 44 ± 11% by CMRI; and EDV 103 ± 38 ml, ESV 60 ± 28 ml and RV EF 41 ± 13% by 3DE. The correlations and agreements between measurements estimated by two methods were acceptable. RV volumes and function can be analyzed with 3DE software in most of subjects with or without heart diseases, which is able to be estimated with single-beat full-volume capture with real-time 3DE compared with CMRI.

An adaptive neural fuzzy network clothing comfort evaluation model and application in digital home

With the development of digital TV, especially 3D technologies, some applications in the digital home industry have been reported to meet and even exceed customer expectation in the market place for enterprise. In order to evaluate the clothing comfort performance in the digital e-commerce service, the clothing comfort model is very important. Considered the physical, physiological and psychological factors, a theoretical clothing comfort evaluation model is presented based on the adaptive fuzzy neural network in this paper. According to the characters of clothing comfort, Fuzzy Neural Networks (FNN) can provide a very human machine knowledge representation scheme friendly for acquiring, representing and using the knowledge of the domain expert. This is a significant advantage for clothing comfort evaluation where the exact system transfer functions cannot be well modeled and adequate training data sets are not available. The experiment results shown that there has the same prediction trend about the experiment result and simulation result. This clothing comfort evaluation model is used as a component in the smart clothing function user experience system.

Construction of intensity-duration-frequency (IDF) curves for precipitation with annual maxima data in Rwanda, Central Africa

Abstract. Detailed probabilistic information on the intensity of precipitation in Central Africa is highly needed in order to cope with the risk analysis of natural hazards. In the mountainous areas of Rwanda land slides frequently occur and might cause a heavy toll in human lives. The establishment of Intensity-Duration-Frequency curves for precipitation in Central Africa remains a difficult task as adequate long-term data sets for short aggregation times are usually not available. In 1962 recording raingauges were installed at several stations in Rwanda. According to the climatological procedures in use at that time in Congo, Rwanda and Burundi, maximum monthly and annual precipitation depths for fixed-time durations of 15, 30, 45, 60 and 120 min were determined from the rainfall charts. The data set is completed by the monthly and annual daily precipitation extremes from the non-recording raingauge at the stations. The authors used the dataset to establish the IDF-curves for precipitation at 3 stations in Rwanda having more than 20 yr of operation. The fixed-hour intervals of multiple 15 min require the use of a technique converting data from fixed-time intervals into data of arbitrary starting intervals. Therefore, the van Montfort technique was used.

Early Prenatal Diagnosis of Orofacial Clefts: Evaluation of the Retronasal Triangle Using a New Three-Dimensional Reslicing Technique

Background: The role of three-dimensional ultrasound in the prenatal diagnosis of orofacial clefts is increasing in recent time. Materials and Methods: We obtained three-dimensional data sets of the fetal face from 100 low-risk and 50 high-risk first-trimester fetuses to evaluate the offline reformatting accuracy of the retronasal triangle (RNT) using a novel reslicing technique. Results: Adequate volume data sets for offline analysis were captured in 98% of cases. The RNT view was reformatted in 96% of cases by offline analysis. The secondary palate could be assessed in 93% by offline analysis and an abnormal RNT in the coronal plane was detected in 2 cases of lethal aneuploidy. The false-positive rate was 1.33% in the two studied groups. Conclusion: Abnormal RNT seems to be a valuable ultrasound marker for the early diagnosis of facial clefting. This novel reslicing technique has proven to be easy, fast and accurate, which suggest that this technology could be included in daily practice and integrated with other applications such as multiplanar mode and volume NT. Prospective studies are needed to confirm these promising results and to demonstrate if these diagnostic armamentarium tools will lead to a diagnostic enhancement of cleft lip and palate in early pregnancy.

Importance of the Quality of the Discharge Report in the Management of a Surgical Clinical Unit

BackgroundThe discharge report is a basic document at the end of a care process, and is a key element in the coding process, since its correct wording, reliability and completeness are factors used to determine the hospital production. Materials and methodsFrom a hypothesis based on the analysis of the consistency between the discharge report and data collected from the routine clinical notes during admission, we should be able to re-code all those mis-coded, thus placing them in a more appropriate diagnosis-related group (DRG). A total of 24 patient outliers were analysed for the correct filling in of the type and reason for admission, personal history, medication, anamnesis, primary and secondary diagnosis, surgical procedure, outcome, number of diagnostic and procedures cited, concordance between discharge report and history and recoding of the DRG. ResultsFrom a total of 24 episodes, 6 had precise and valid reports, 4 were valid but not precise enough, 9 were insufficient, and 5 were clearly invalid. The recoded DRG after the documentation review was not significantly different, according to the Wilcoxon test, being changed in only 5 cases (P=.680). ConclusionQuality in discharge reports depends on an adequate minimum data set (MDS) in concordance with the source documentation during admission. Discordance can change the DRG, despite it not being significantly different in our series. Self-audit of discharge reports allows quality improvements to be developed along with a reduction in information mistakes.

How much data is enough? The importance of morphological sampling interval and duration for calibration of empirical shoreline models

The ability to robustly predict future shoreline position under the influence of changing waves and sea-level rise is a key challenge to scientists and engineers alike. While extrapolating a linear trend out in time is a common baseline approach, the recent development of a number of empirical shoreline models allows the prediction of storm and annual-scale variability as well. The largest constraint in applying these models is the availability of high quality, adequate duration data sets in order to calibrate model free parameters. This contribution outlines several such models and discusses the monitoring programs required to calibrate and hindcast shoreline change from 1 to 10years at two distinct beach types: a storm-dominated site and the second exhibiting a large seasonal variability. The seasonally-dominated site required longer data sets but was less sensitive to sampling interval, while the storm-dominated site converged on shorter, more frequently sampled data sets. In general, calibration based on a single year of observed shorelines resulted in a large range of model skill and was not considered robust. Monitoring programs of at least two years, with shorelines sampled at dt≤30days were sufficient to determine initial estimates of calibration coefficients and hindcast short-term (1–5years) shoreline variability. In the presence of unresolved model processes and noise, hindcasting longer (5+ years) data sets required longer (5+ years) calibration data sets, particularly when sampling intervals exceeded 60days.

Planktonic foraminifera stable isotopes and water column structure: Disentangling ecological signals

Differential carbon and oxygen stable isotope (δ13C and δ18O) fractionation between planktonic foraminifera test calcite and sea water related to ecology and life stage confound the potential for reconstructing palaeo-water column temperature and carbon gradients. Multi-species analysis and strict selection of test sizes are useful methods for identifying these fractionation processes, also known as ‘vital effects’, in fossil taxa. However, there are a limited number of species with adequate size-controlled data sets, needed for ground truthing the approach in the modern. Here we report δ13C and δ18O measurements made on twelve species of modern planktonic foraminifera across a range of fourteen tightly constrained size windows from a tropical Indian Ocean core top sample. This data set includes more test size windows per species, especially from the smallest (identifiable) test size-classes, and a wider range of species than previously attempted. We use the size controlled δ18O calcite trajectories to infer depth habitats and calculate species-specific calcification temperatures. The temperatures are then used to constrain species-specific calcification depths along the modern vertical temperature profile in the western tropical Indian Ocean. By overlaying the per species δ13C calcite trajectories on local water column δ13CDIC profiles, we estimate if and when (i.e. at which test sizes) the planktonic foraminifera species investigated approach ambient δ13CDIC values. The profiling shows significant size-controlled δ13C deviation from seawater values in all species at some life/growth stage, which we attribute to (i) metabolic fractionation in tests < 150–300 μm (juveniles of all species and small adults), and; (ii) photosymbiont fractionation, affecting large tests (>~ 300 μm) of mixed layer photosymbiotic taxa. For most species there is a size-window where these effects appear to be at a minimum, and/or in balance. Exceptions are Globigerinita glutinata, a small (< 200 μm) surface living species, Globigerina bulloides, which is highly opportunistic, and deep living Globorotalia tumida and Globorotaloides hexagonus, the latter two species being affected by various unexplained δ13C vital effects. Use of our refined guidelines for test-size selection should improve the potential for making realistic reconstructions of water column δ13CDIC in a modern tropical stratified setting and potentially in the distant geological past when there are no living analogues present.

Importancia de la calidad del informe de alta en la gestión de una unidad clínica quirúrgica

BackgroundThe discharge report is a basic document at the end of a care process, and is a key element in the coding process, since its correct wording, reliability and completeness are factors used to determine the hospital production. Material and methodsFrom a hypothesis based on the analysis of the consistency between the discharge report and data collected from the routine clinical notes during admission, we should be able to re-code all those mis-coded, thus placing them in a more appropriate diagnosis-related group (DRG). A total of 24 patient outliers were analysed for the correct filling in of the type and reason for admission, personal history, medication, anamnesis, primary and secondary diagnosis, sugical procedure, outcome, number of diagnostic and procedures cited, concordance between discharge report and history and recoding of the DRG. ResultsFrom a total of 24 episodes, 6 had precise and valid reports, 4 were valid but not precise enough, 9 were insufficient, and 5 were clearly invalid. The recoded DRG after the documentation review was not significantly different, according to the Wilcoxon test, being changed in only 5 cases (P = .680). ConclusionQuality in discharge reports depends on an adequate minimum data set (MDS) in concordance with the source documentation during admission. Discordance can change the DRG, despite it not being significantly different in our series. Self-audit of discharge reports allows quality improvements to be developed along with a reduction in information mistakes.

Contribution to the Investigation of Motorcyclists’ Speed Prediction Equations for Two-Lane Rural Roads

The calculation of speed prediction equations has been the subject of numerous researches in the past. The majority of them present models to predict free-flow speed in terms of the road geometry at the curved road sections and more specifically in terms of the radiuses of the curves. Common characteristic is that none of them approaches the speed behavior of motorcycles since they are excluded from the datasets of the various studies. Instead, the models usually predict operating speed for other vehicle types such as passenger cars, vans, pickups and trucks. The present paper aims to cover this gap by developing speed prediction equations for motorcycles. For this purpose a new methodology is proposed while field measurements were carried out in order to obtain an adequate dataset of free-flow speeds along the curved sections of three different two lane rural roads. The aforementioned field measurements were conducted by two participants incorporating various road conditions (e.g. light conditions, experience level, familiarity with the routes). The ultimate target was the development of speed prediction equations by calculating the optimum regression curves between the curve radius’ and the corresponding velocities for the different road conditions. The research revealed that the proposed methodology could be used as a very useful tool to investigate motorcyclists’ behavior at curved road sections. Moreover it was feasible to draw conclusions correlating the speed adjustment with the various driving conditions.

The use of survey data to study migration–environment relationships in developing countries: alternative approaches to data collection

Growing interest in the environmental aspects of migration is not matched by research on their interrelationships, due partly to the lack of adequate data sets on the two together. Focusing on the microlevel, we describe the data required to effectively investigate these interrelationships. Data sources are discussed, be collected, focusing on household surveys and remote sensing. The main section of the paper describes three alternative approaches to data collection: (a) using existing population and environmental data from different sources, illustrated by Burkina Faso; (b) adding questions to a survey developed for another purpose, illustrated for Guatemala using a DHS survey; and (c) designing a new survey specifically to collect both migration and environmental data to investigate interrelationships, illustrated by Ecuador. Methods used and summary findings are described, followed by a discussion of their advantages and limitations. We conclude with recommendations as to effective use of each approach as research on migration-environment linkages moves forward.

Phylogenomic analyses support the position of turtles as the sister group of birds and crocodiles (Archosauria).

BackgroundThe morphological peculiarities of turtles have, for a long time, impeded their accurate placement in the phylogeny of amniotes. Molecular data used to address this major evolutionary question have so far been limited to a handful of markers and/or taxa. These studies have supported conflicting topologies, positioning turtles as either the sister group to all other reptiles, to lepidosaurs (tuatara, lizards and snakes), to archosaurs (birds and crocodiles), or to crocodilians. Genome-scale data have been shown to be useful in resolving other debated phylogenies, but no such adequate dataset is yet available for amniotes.ResultsIn this study, we used next-generation sequencing to obtain seven new transcriptomes from the blood, liver, or jaws of four turtles, a caiman, a lizard, and a lungfish. We used a phylogenomic dataset based on 248 nuclear genes (187,026 nucleotide sites) for 16 vertebrate taxa to resolve the origins of turtles. Maximum likelihood and Bayesian concatenation analyses and species tree approaches performed under the most realistic models of the nucleotide and amino acid substitution processes unambiguously support turtles as a sister group to birds and crocodiles. The use of more simplistic models of nucleotide substitution for both concatenation and species tree reconstruction methods leads to the artefactual grouping of turtles and crocodiles, most likely because of substitution saturation at third codon positions. Relaxed molecular clock methods estimate the divergence between turtles and archosaurs around 255 million years ago. The most recent common ancestor of living turtles, corresponding to the split between Pleurodira and Cryptodira, is estimated to have occurred around 157 million years ago, in the Upper Jurassic period. This is a more recent estimate than previously reported, and questions the interpretation of controversial Lower Jurassic fossils as being part of the extant turtles radiation.ConclusionsThese results provide a phylogenetic framework and timescale with which to interpret the evolution of the peculiar morphological, developmental, and molecular features of turtles within the amniotes.