Incomplete Sampling Research Articles

Development of a prototype chest digital tomosynthesis (CDT) R/F system with fast image reconstruction using graphics processing unit (GPU) programming

Digital tomosynthesis offers the advantage of low radiation doses compared to conventional computed tomography (CT) by utilizing small numbers of projections (~80) acquired over a limited angular range. It produces 3D volumetric data, although there are artifacts due to incomplete sampling. Based upon these characteristics, we developed a prototype digital tomosynthesis R/F system for applications in chest imaging. Our prototype chest digital tomosynthesis (CDT) R/F system contains an X-ray tube with high power R/F pulse generator, flat-panel detector, R/F table, electromechanical radiographic subsystems including a precise motor controller, and a reconstruction server. For image reconstruction, users select between analytic and iterative reconstruction methods. Our reconstructed images of Catphan700 and LUNGMAN phantoms clearly and rapidly described the internal structures of phantoms using graphics processing unit (GPU) programming. Contrast-to-noise ratio (CNR) values of the CTP682 module of Catphan700 were higher in images using a simultaneous algebraic reconstruction technique (SART) than in those using filtered back-projection (FBP) for all materials by factors of 2.60, 3.78, 5.50, 2.30, 3.70, and 2.52 for air, lung foam, low density polyethylene (LDPE), Delrin® (acetal homopolymer resin), bone 50% (hydroxyapatite), and Teflon, respectively. Total elapsed times for producing 3D volume were 2.92s and 86.29s on average for FBP and SART (20 iterations), respectively. The times required for reconstruction were clinically feasible. Moreover, the total radiation dose from our system (5.68mGy) was lower than that of conventional chest CT scan. Consequently, our prototype tomosynthesis R/F system represents an important advance in digital tomosynthesis applications.

Structural and energy determinants in protein-RNA docking.

Deciphering the structural and energetic determinants of protein-RNA interactions harbors the potential to understand key cell processes at molecular level, such as gene expression and regulation. With this purpose, computational methods like docking aim to complement current biophysical and structural biology efforts. However, the few reported docking algorithms for protein-RNA interactions show limited predictive success rates, mainly due to incomplete sampling of the conformational space of both the protein and the RNA molecules, as well as to the difficulties of the scoring function in identifying the correct docking models. Here, we have tested the predictive value of a variety of knowledge-based and energetic scoring functions on a recently published protein-RNA docking benchmark and developed a scoring function able to efficiently discriminate docking decoys. We first performed docking calculations with the bound conformation, which allowed us to analyze the problem in optimal conditions. We found that geometry-based terms and electrostatics were the most important scoring terms, while binding propensities and desolvation were much less relevant for the scoring of protein-RNA models. This is in contrast with what we observed for protein-protein docking. The results also showed an interesting dependence of the predictive rates on the flexibility of the protein molecule, which arises from the observed higher positive charge of flexible interfaces and provides hints for future development of more efficient protein-RNA docking methods.

Annotating Protein Functional Residues by Coupling High-Throughput Fitness Profile and Homologous-Structure Analysis.

ABSTRACTIdentification and annotation of functional residues are fundamental questions in protein sequence analysis. Sequence and structure conservation provides valuable information to tackle these questions. It is, however, limited by the incomplete sampling of sequence space in natural evolution. Moreover, proteins often have multiple functions, with overlapping sequences that present challenges to accurate annotation of the exact functions of individual residues by conservation-based methods. Using the influenza A virus PB1 protein as an example, we developed a method to systematically identify and annotate functional residues. We used saturation mutagenesis and high-throughput sequencing to measure the replication capacity of single nucleotide mutations across the entire PB1 protein. After predicting protein stability upon mutations, we identified functional PB1 residues that are essential for viral replication. To further annotate the functional residues important to the canonical or noncanonical functions of viral RNA-dependent RNA polymerase (vRdRp), we performed a homologous-structure analysis with 16 different vRdRp structures. We achieved high sensitivity in annotating the known canonical polymerase functional residues. Moreover, we identified a cluster of noncanonical functional residues located in the loop region of the PB1 β-ribbon. We further demonstrated that these residues were important for PB1 protein nuclear import through the interaction with Ran-binding protein 5. In summary, we developed a systematic and sensitive method to identify and annotate functional residues that are not restrained by sequence conservation. Importantly, this method is generally applicable to other proteins about which homologous-structure information is available.

Patterns of Sponge Biodiversity in the Pilbara, Northwestern Australia

This study assessed the biodiversity of sponges within the Integrated Marine and Coastal Regionalisation for Australia (IMCRA) bioregions of the Pilbara using datasets amalgamated from the Western Australian Museum and the Atlas of Living Australia. The Pilbara accounts for a total of 1164 Linnean and morphospecies. A high level of “apparent endemism” was recorded with 78% of species found in only one of six bioregions, with less than 10% confirmed as widely distributed. The Ningaloo, Pilbara Nearshore and Pilbara Offshore bioregions are biodiversity hotspots (>250 species) and are recognised as having the highest conservation value, followed by North West Shelf containing 232 species. Species compositions differed between bioregions, with those that are less spatially separated sharing more species. Notably, the North West Province bioregion (110 species) exhibited the most distinct species composition, highlighting it as a unique habitat within the Pilbara. While sponge biodiversity is apparently high, incomplete sampling effort for the region was identified, with only two sampling events recorded for the Central West Transition bioregion. Furthermore, only 15% of records in the dataset are presently described (Linnean) species, highlighting the continuing need for taxonomic expertise for the conservation and management of marine biodiversity resources.

Limits on Population III star formation with the most iron-poor stars

We study the impact of star-forming minihaloes, and the initial mass function (IMF) of Population III (Pop III) stars, on the Galactic halo metallicity distribution function (MDF) and on the properties of C-enhanced and C-normal stars at [Fe/H] < −3. For our investigation we use a data-constrained merger tree model for the Milky Way formation, which has been improved to self-consistently describe the physical processes regulating star formation in minihaloes, including the poor sampling of the Pop III IMF. We find that only when star-forming minihaloes are included the low-Fe tail of the MDF is correctly reproduced, showing a plateau that is built up by C-enhanced metal-poor stars imprinted by primordial faint supernovae. The incomplete sampling of the Pop III IMF in inefficiently star-forming minihaloes (<10−3 M⊙ yr−1) strongly limits the formation of pair-instability supernovae (PISNe), with progenitor masses mPopIII = [140–260] M⊙, even when a flat Pop III IMF is assumed. Second-generation stars formed in environments polluted at >50 per cent level by PISNe are thus extremely rare, corresponding to ≈0.25 per cent of the total stellar population at [Fe/H] < −2, which is consistent with recent observations. The low-Fe tail of the MDF strongly depends on the Pop III IMF shape and mass range. Given the current statistics, we find that a flat Pop III IMF model with mPopIII = [10–300] M⊙ is disfavoured by observations. We present testable predictions for Pop III stars extending down to lower masses, with mPopIII = [0.1–300] M⊙.

Performance of maximum likelihood mixture models to estimate nursery habitat contributions to fish stocks: a case study on sea bream Sparus aurata.

BackgroundMixture models (MM) can be used to describe mixed stocks considering three sets of parameters: the total number of contributing sources, their chemical baseline signatures and their mixing proportions. When all nursery sources have been previously identified and sampled for juvenile fish to produce baseline nursery-signatures, mixing proportions are the only unknown set of parameters to be estimated from the mixed-stock data. Otherwise, the number of sources, as well as some/all nursery-signatures may need to be also estimated from the mixed-stock data. Our goal was to assess bias and uncertainty in these MM parameters when estimated using unconditional maximum likelihood approaches (ML-MM), under several incomplete sampling and nursery-signature separation scenarios.MethodsWe used a comprehensive dataset containing otolith elemental signatures of 301 juvenile Sparus aurata, sampled in three contrasting years (2008, 2010, 2011), from four distinct nursery habitats. (Mediterranean lagoons) Artificial nursery-source and mixed-stock datasets were produced considering: five different sampling scenarios where 0–4 lagoons were excluded from the nursery-source dataset and six nursery-signature separation scenarios that simulated data separated 0.5, 1.5, 2.5, 3.5, 4.5 and 5.5 standard deviations among nursery-signature centroids. Bias (BI) and uncertainty (SE) were computed to assess reliability for each of the three sets of MM parameters.ResultsBoth bias and uncertainty in mixing proportion estimates were low (BI ≤ 0.14, SE ≤ 0.06) when all nursery-sources were sampled but exhibited large variability among cohorts and increased with the number of non-sampled sources up to BI = 0.24 and SE = 0.11. Bias and variability in baseline signature estimates also increased with the number of non-sampled sources, but tended to be less biased, and more uncertain than mixing proportion ones, across all sampling scenarios (BI < 0.13, SE < 0.29). Increasing separation among nursery signatures improved reliability of mixing proportion estimates, but lead to non-linear responses in baseline signature parameters. Low uncertainty, but a consistent underestimation bias affected the estimated number of nursery sources, across all incomplete sampling scenarios.DiscussionML-MM produced reliable estimates of mixing proportions and nursery-signatures under an important range of incomplete sampling and nursery-signature separation scenarios. This method failed, however, in estimating the true number of nursery sources, reflecting a pervasive issue affecting mixture models, within and beyond the ML framework. Large differences in bias and uncertainty found among cohorts were linked to differences in separation of chemical signatures among nursery habitats. Simulation approaches, such as those presented here, could be useful to evaluate sensitivity of MM results to separation and variability in nursery-signatures for other species, habitats or cohorts.

Estimating shifts in diversification rates based on higher-level phylogenies

Macroevolutionary studies recently shifted from only reconstructing the past state, i.e. the species phylogeny, to also infer the past speciation and extinction dynamics that gave rise to the phylogeny. Methods for estimating diversification dynamics are sensitive towards incomplete species sampling. We introduce a method to estimate time-dependent diversification rates from phylogenies where clades of a particular age are represented by only one sampled species. A popular example of this type of data is phylogenies on the genus- or family-level, i.e. phylogenies where one species per genus or family is included. We conduct a simulation study to validate our method in a maximum-likelihood framework. Further, this method has already been introduced into the Bayesian package MrBayes, which led to new insights into the evolution of Hymenoptera.

Imaging complement by phase-plate cryo-electron tomography from initiation to pore formation.

Phase plates in cryo-electron tomography (cryoET) improve contrast, increasing the ability to discern separate molecules and molecular complexes in dense biomolecular environments. Here, we applied this new technology to the activation of the human complement system. Binding of C1 to antigen-antibody complexes initiates a cascade of proteolytic events that deposits molecules onto adjacent surfaces and terminates with the formation of membrane-attack-complex (MAC) pores in the targeted membranes. We imaged steps in this process using a Volta phase plate mounted on a Titan Krios equipped with a Falcon-II direct electron detector. The data show patches of single-layer antibodies on the surface and C1 bound to antibody platforms, with ca. ∼4% of instances where C1r and C1s proteases have dissociated from C1, and potentially instances of C1 transiently interacting with its substrate C4 or product C4b. Next, extensive deposition of C4b and C3b molecules is apparent, although individual molecules cannot always be properly distinguished with the current methods. Observations of MAC pores include formation of both single and composite pores, and instances of potential soluble-MAC dissociation upon failure of membrane insertion. Overall, application of the Volta phase plate cryoET markedly improved the contrast in the tomograms, which allowed for individual components to be more readily interpreted. However, variability in the phase shift induced by the phase-plate during the course of an experiment, together with incomplete sampling during tomogram acquisition, limited the interpretability of the resulting tomograms. Our studies exemplify the potential in studying molecular processes with complex spatial topologies by phase-plate cryoET.

Zero-effort projection for sensory data reconstruction in wireless sensor networks

Compressive sensing is a promising technique for data gathering in large-scale wireless sensor networks. Existing compressive sensing–based data gathering techniques still follow sampling than compression paradigm. In this article, we proposed a random sampling zero-encoding data gathering scheme for wireless sensor networks, which exploits virtual Gaussian energy diffusion model to obtain sampling and compression data gathering. Our proposed data gathering model not only can make simultaneous sampling and compression but also do not need to assign projection matrix to each sensor node. Our scheme can efficiently resolve two types of sensor networks’ data gathering problems: recover missing sensory data and extend monitoring field using incomplete random sampling. Extensive experimental results show that our proposed random sampling zero-encoding data gathering model has good performance for reconstructing the sensory data in wireless sensor networks.

Generalizing the Network Scale-Up Method: A New Estimator for the Size of Hidden Populations.

The network scale-up method enables researchers to estimate the size of hidden populations, such as drug injectors and sex workers, using sampled social network data. The basic scale-up estimator offers advantages over other size estimation techniques, but it depends on problematic modeling assumptions. We propose a new generalized scale-up estimator that can be used in settings with non-random social mixing and imperfect awareness about membership in the hidden population. Further, the new estimator can be used when data are collected via complex sample designs and from incomplete sampling frames. However, the generalized scale-up estimator also requires data from two samples: one from the frame population and one from the hidden population. In some situations these data from the hidden population can be collected by adding a small number of questions to already planned studies. For other situations, we develop interpretable adjustment factors that can be applied to the basic scale-up estimator. We conclude with practical recommendations for the design and analysis of future studies.

Deploying temporary networks for upscaling of sparse network stations

Soil observations networks at the national scale play an integral role in hydrologic modeling, drought assessment, agricultural decision support, and our ability to understand climate change. Understanding soil moisture variability is necessary to apply these measurements to model calibration, business and consumer applications, or even human health issues. The installation of soil moisture sensors as sparse, national networks is necessitated by limited financial resources. However, this results in the incomplete sampling of the local heterogeneity of soil type, vegetation cover, topography, and the fine spatial distribution of precipitation events. To this end, temporary networks can be installed in the areas surrounding a permanent installation within a sparse network. The temporary networks deployed in this study provide a more representative average at the 3km and 9km scales, localized about the permanent gauge. The value of such temporary networks is demonstrated at test sites in Millbrook, New York and Crossville, Tennessee. The capacity of a single U.S. Climate Reference Network (USCRN) sensor set to approximate the average of a temporary network at the 3km and 9km scales using a simple linear scaling function is tested. The capacity of a temporary network to provide reliable estimates with diminishing numbers of sensors, the temporal stability of those networks, and ultimately, the relationship of the variability of those networks to soil moisture conditions at the permanent sensor are investigated. In this manner, this work demonstrates the single-season installation of a temporary network as a mechanism to characterize the soil moisture variability at a permanent gauge within a sparse network.

Intratumoral and Intertumoral Genomic Heterogeneity of Multifocal Localized Prostate Cancer Impacts Molecular Classifications and Genomic Prognosticators

BackgroundNext-generation sequencing is revealing genomic heterogeneity in localized prostate cancer (CaP). Incomplete sampling of CaP multiclonality has limited the implications for molecular subtyping, stratification, and systemic treatment. ObjectiveTo determine the impact of genomic and transcriptomic diversity within and among intraprostatic CaP foci on CaP molecular taxonomy, predictors of progression, and actionable therapeutic targets. Design, setting, and participantsFour consecutive patients with clinically localized National Comprehensive Cancer Network intermediate- or high-risk CaP who did not receive neoadjuvant therapy underwent radical prostatectomy at Roswell Park Cancer Institute in June–July 2014. Presurgical information on CaP content and a customized tissue procurement procedure were used to isolate nonmicroscopic and noncontiguous CaP foci in radical prostatectomy specimens. Three cores were obtained from the index lesion and one core from smaller lesions. RNA and DNA were extracted simultaneously from 26 cores with ≥90% CaP content and analyzed using whole-exome sequencing, single-nucleotide polymorphism arrays, and RNA sequencing. Outcome measurements and statistical analysisSomatic mutations, copy number alternations, gene expression, gene fusions, and phylogeny were defined. The impact of genomic alterations on CaP molecular classification, gene sets measured in Oncotype DX, Prolaris, and Decipher assays, and androgen receptor activity among CaP cores was determined. Results and limitationsThere was considerable variability in genomic alterations among CaP cores, and between RNA- and DNA-based platforms. Heterogeneity was found in molecular grouping of individual CaP foci and the activity of gene sets underlying the assays for risk stratification and androgen receptor activity, and was validated in independent genomic data sets. Determination of the implications for clinical decision-making requires follow-up studies. ConclusionsGenomic make-up varies widely among CaP foci, so care should be taken when making treatment decisions based on a single biopsy or index lesions. Patient summaryWe examined the molecular composition of individual cancers in a patient's prostate. We found a lot of genetic diversity among these cancers, and concluded that information from a single cancer biopsy is not sufficient to guide treatment decisions.

Fourier rebinning and consistency equations for time-of-flight PET planograms

Due to the unique geometry, dual-panel PET scanners have many advantages in dedicated breast imaging and on-board imaging applications since the compact scanners can be combined with other imaging and treatment modalities. The major challenges of dual-panel PET imaging are the limited-angle problem and data truncation, which can cause artifacts due to incomplete data sampling. The time-of-flight (TOF) information can be a promising solution to reduce these artifacts. The TOF planogram is the native data format for dual-panel TOF PET scanners, and the non-TOF planogram is the 3D extension of linogram. The TOF planograms is five-dimensional while the objects are three-dimensional, and there are two degrees of redundancy. In this paper, we derive consistency equations and Fourier-based rebinning algorithms to provide a complete understanding of the rich structure of the fully 3D TOF planograms. We first derive two consistency equations and John’s equation for 3D TOF planograms. By taking the Fourier transforms, we obtain two Fourier consistency equations (FCEs) and the Fourier–John equation (FJE), which are the duals of the consistency equations and John’s equation, respectively. We then solve the FCEs and FJE using the method of characteristics. The two degrees of entangled redundancy of the 3D TOF data can be explicitly elicited and exploited by the solutions along the characteristic curves. As the special cases of the general solutions, we obtain Fourier rebinning and consistency equations (FORCEs), and thus we obtain a complete scheme to convert among different types of PET planograms: 3D TOF, 3D non-TOF, 2D TOF and 2D non-TOF planograms. The FORCEs can be used as Fourier-based rebinning algorithms for TOF-PET data reduction, inverse rebinnings for designing fast projectors, or consistency conditions for estimating missing data. As a byproduct, we show the two consistency equations are necessary and sufficient for 3D TOF planograms. Finally, we give numerical examples of implementation of a fast 2D TOF planogram projector and Fourier-based rebinning for a 2D TOF planograms using the FORCEs to show the efficacy of the Fourier-based solutions.

Methodological congruence in phylogenomic analyses with morphological support for teiid lizards (Sauria: Teiidae)

A well-known issue in phylogenetics is discordance among gene trees, species trees, morphology, and other data types. Gene-tree discordance is often caused by incomplete lineage sorting, lateral gene transfer, and gene duplication. Multispecies-coalescent methods can account for incomplete lineage sorting and are believed by many to be more accurate than concatenation. However, simulation studies and empirical data have demonstrated that concatenation and species tree methods often recover similar topologies. We use three popular methods of phylogenetic reconstruction (one concatenation, two species tree) to evaluate relationships within Teiidae. These lizards are distributed across the United States to Argentina and the West Indies, and their classification has been controversial due to incomplete sampling and the discordance among various character types (chromosomes, DNA, musculature, osteology, etc.) used to reconstruct phylogenetic relationships. Recent morphological and molecular analyses of the group resurrected three genera and created five new genera to resolve non-monophyly in three historically ill-defined genera: Ameiva, Cnemidophorus, and Tupinambis. Here, we assess the phylogenetic relationships of the Teiidae using “next-generation” anchored-phylogenomics sequencing. Our final alignment includes 316 loci (488,656bp DNA) for 244 individuals (56 species of teiids, representing all currently recognized genera) and all three methods (ExaML, MP-EST, and ASTRAL-II) recovered essentially identical topologies. Our results are basically in agreement with recent results from morphology and smaller molecular datasets, showing support for monophyly of the eight new genera. Interestingly, even with hundreds of loci, the relationships among some genera in Tupinambinae remain ambiguous (i.e. low nodal support for the position of Salvator and Dracaena).

New circumscription of the genus Gamochaeta (Asteraceae, Gnaphalieae) inferred from nuclear and plastid DNA sequences

Gamochaeta (tribe Gnaphalieae, Asteraceae) is composed of ca. 60 species primarily distributed in tropical and subtropical America. Within the tribe Gnaphalieae, the genus is characterized by capitula arranged in spikes or head-like clusters, few hermaphroditic central florets, truncate style branches with apical sweeping trichomes, pappus bristles connate at the base into a ring falling as a unit, and achenes with globose twin trichomes. Previous molecular phylogenetic studies have suggested the paraphyly of the genus, but have not provided a basis for redefining generic limits due to incomplete taxon sampling. To address this problem, DNA sequences from the plastid (trnL-F) and nuclear (ETS and ITS) genomes were analyzed from a broad taxon sample representing the full range of morphological variation known in the genus. Our results affirm that Gamochaeta is paraphyletic as presently circumscribed. Two clades can be recognized: one clade that includes the majority of the species currently assigned to Gamochaeta and a second clade that includes Gamochaetopsis, Stuckertiella and seven species of Gamochaeta. We present here a new circumscription of Gamochaeta, including two new combinations, Gamochaeta alpina and Gamochaeta peregrina, and the resurrection of Gamochaeta capitata. Our results also show Omalotheca supina, O. norvegica and O. sylvatica, which were placed by some authors in Gamochaeta or in Gnaphalium, form a monophyletic group distantly related to both genera.

Fungal Associates of the Xylosandrus compactus (Coleoptera: Curculionidae, Scolytinae) Are Spatially Segregated on the Insect Body.

Studies of symbioses have traditionally focused on explaining one-to-one interactions between organisms. In reality, symbioses are often much more dynamic. They can involve many interacting members, and change depending on context. In studies of the ambrosia symbiosis-the mutualism between wood borer beetles and fungi-two variables have introduced uncertainty when explaining interactions: imprecise symbiont identification, and disregard for anatomical complexity of the insects. The black twig borer, Xylosandrus compactus Eichhoff, is a globally invasive ambrosia beetle that infests >200 plant species. Despite many studies on this beetle, reports of its primary symbionts are conflicting. We sampled adult X. compactus and infested plant material in central Florida to characterize the fungal symbiont community using dilution series, beetle partitioning, and DNA-based identification. X. compactus was consistently associated with two fungal taxa, Fusarium spp. and Ambrosiella xylebori Multivariate analyses revealed that A. xylebori was strongly associated with the beetle mycangium while Fusarium spp. were associated with the abdomen and external surfaces. The Fusarium spp. carried by X. compactus are not members of the Ambrosia Fusarium Clade, and are probably not mutualists. Fungal community composition of the mycangium was less variable than external body surfaces, thus providing a more consistent fungal inoculum. This is the first report of spatial partitioning as a mechanism for maintenance of a multimember ambrosia fungus community. Our results provide an explanation for discrepancies among previous reports, and suggest that conflicting results are not due to differences in symbiont communities, but due to inconsistent and incomplete sampling.

IMAGE-BASED AIRBORNE LiDAR POINT CLOUD ENCODING FOR 3D BUILDING MODEL RETRIEVAL

Abstract. With the development of Web 2.0 and cyber city modeling, an increasing number of 3D models have been available on web-based model-sharing platforms with many applications such as navigation, urban planning, and virtual reality. Based on the concept of data reuse, a 3D model retrieval system is proposed to retrieve building models similar to a user-specified query. The basic idea behind this system is to reuse these existing 3D building models instead of reconstruction from point clouds. To efficiently retrieve models, the models in databases are compactly encoded by using a shape descriptor generally. However, most of the geometric descriptors in related works are applied to polygonal models. In this study, the input query of the model retrieval system is a point cloud acquired by Light Detection and Ranging (LiDAR) systems because of the efficient scene scanning and spatial information collection. Using Point clouds with sparse, noisy, and incomplete sampling as input queries is more difficult than that by using 3D models. Because that the building roof is more informative than other parts in the airborne LiDAR point cloud, an image-based approach is proposed to encode both point clouds from input queries and 3D models in databases. The main goal of data encoding is that the models in the database and input point clouds can be consistently encoded. Firstly, top-view depth images of buildings are generated to represent the geometry surface of a building roof. Secondly, geometric features are extracted from depth images based on height, edge and plane of building. Finally, descriptors can be extracted by spatial histograms and used in 3D model retrieval system. For data retrieval, the models are retrieved by matching the encoding coefficients of point clouds and building models. In experiments, a database including about 900,000 3D models collected from the Internet is used for evaluation of data retrieval. The results of the proposed method show a clear superiority over related methods.

IMAGE-BASED AIRBORNE LiDAR POINT CLOUD ENCODING FOR 3D BUILDING MODEL RETRIEVAL

With the development of Web 2.0 and cyber city modeling, an increasing number of 3D models have been available on web-based model-sharing platforms with many applications such as navigation, urban planning, and virtual reality. Based on the concept of data reuse, a 3D model retrieval system is proposed to retrieve building models similar to a user-specified query. The basic idea behind this system is to reuse these existing 3D building models instead of reconstruction from point clouds. To efficiently retrieve models, the models in databases are compactly encoded by using a shape descriptor generally. However, most of the geometric descriptors in related works are applied to polygonal models. In this study, the input query of the model retrieval system is a point cloud acquired by Light Detection and Ranging (LiDAR) systems because of the efficient scene scanning and spatial information collection. Using Point clouds with sparse, noisy, and incomplete sampling as input queries is more difficult than that by using 3D models. Because that the building roof is more informative than other parts in the airborne LiDAR point cloud, an image-based approach is proposed to encode both point clouds from input queries and 3D models in databases. The main goal of data encoding is that the models in the database and input point clouds can be consistently encoded. Firstly, top-view depth images of buildings are generated to represent the geometry surface of a building roof. Secondly, geometric features are extracted from depth images based on height, edge and plane of building. Finally, descriptors can be extracted by spatial histograms and used in 3D model retrieval system. For data retrieval, the models are retrieved by matching the encoding coefficients of point clouds and building models. In experiments, a database including about 900,000 3D models collected from the Internet is used for evaluation of data retrieval. The results of the proposed method show a clear superiority over related methods.

Investigating the timing of origin and evolutionary processes shaping regional species diversity: Insights from simulated data and neotropical butterfly diversification rates.

Different diversification scenarios have been proposed to explain the origin of extant biodiversity. However, most existing meta-analyses of time-calibrated phylogenies rely on approaches that do not quantitatively test alternative diversification processes. Here, I highlight the shortcomings of using species divergence ranks, which is a method widely used in meta-analyses. Divergence ranks consist of categorizing cladogenetic events to certain periods of time, typically to either Pleistocene or to pre-Pleistocene ages. This approach has been claimed to shed light on the origin of most extant species and the timing and dynamics of diversification in any biogeographical region. However, interpretations drawn from such method often confound two fundamental questions in macroevolutionary studies, tempo (timing of evolutionary rate shifts) and mode ("how" and "why" of speciation). By using simulated phylogenies under four diversification scenarios, constant-rate, diversity-dependence, high extinction, and high speciation rates in the Pleistocene, I showed that interpretations based on species divergence ranks might have been seriously misleading. Future meta-analyses of dated phylogenies need to be aware of the impacts of incomplete taxonomic sampling, tree topology, and divergence time uncertainties, as well as they might be benefited by including quantitative tests of alternative diversification models that acknowledge extinction and diversity dependence.

Estimating species-area relationships by modeling abundance and frequency subject to incomplete sampling.

Models and data used to describe species–area relationships confound sampling with ecological process as they fail to acknowledge that estimates of species richness arise due to sampling. This compromises our ability to make ecological inferences from and about species–area relationships. We develop and illustrate hierarchical community models of abundance and frequency to estimate species richness. The models we propose separate sampling from ecological processes by explicitly accounting for the fact that sampled patches are seldom completely covered by sampling plots and that individuals present in the sampling plots are imperfectly detected. We propose a multispecies abundance model in which community assembly is treated as the summation of an ensemble of species‐level Poisson processes and estimate patch‐level species richness as a derived parameter. We use sampling process models appropriate for specific survey methods. We propose a multispecies frequency model that treats the number of plots in which a species occurs as a binomial process. We illustrate these models using data collected in surveys of early‐successional bird species and plants in young forest plantation patches. Results indicate that only mature forest plant species deviated from the constant density hypothesis, but the null model suggested that the deviations were too small to alter the form of species–area relationships. Nevertheless, results from simulations clearly show that the aggregate pattern of individual species density–area relationships and occurrence probability–area relationships can alter the form of species–area relationships. The plant community model estimated that only half of the species present in the regional species pool were encountered during the survey. The modeling framework we propose explicitly accounts for sampling processes so that ecological processes can be examined free of sampling artefacts. Our modeling approach is extensible and could be applied to a variety of study designs and allows the inclusion of additional environmental covariates.