Different Levels Of Resolution Research Articles

Analysis of multilevel Monte Carlo path simulation using the Milstein discretisation

The multilevel Monte Carlo path simulation method introduced by Giles ({\it Operations Research}, 56(3):607-617, 2008) exploits strong convergence properties to improve the computational complexity by combining simulations with different levels of resolution. In this paper we analyse its efficiency when using the Milstein discretisation; this has an improved order of strong convergence compared to the standard Euler-Maruyama method, and it is proved that this leads to an improved order of convergence of the variance of the multilevel estimator. Numerical results are also given for basket options to illustrate the relevance of the analysis.

Vaginal bacteriome of Nigerian women in health and disease: A study with 16S rRNA metagenomics

Introduction: The argument on what bacteria make up healthy vagina and bacterial vaginosis (BV) remain unresolved. Black women most often are placed in grade IV vaginal communities as lacking Lactobacillus-dominated microbes. We sought to determine the vaginal microbiota compositions of healthy and those with BV using 16S rRNA metagenomics methods. Materials and Methods: Twenty-eight women provided vaginal swabs for Nugent scoring. Fifteen had BV (Nugent score 7–10), whereas 13 were normal (Nugent score 0–3). DNA was extracted and 16S rRNA V4 region amplified using custom bar-coded primers prior to sequencing with MiSeq platform. Sequence reads were imported into Illumina BaseSpace Metagenomics pipeline for 16S rRNA recognition. Distribution of taxonomic categories at different levels of resolution was done using Greengenes databases. Manhattan principal component analysis was used for similarity clustering. Results: Non-BV subjects were colonized by 12 taxonomic phyla that represent 182 genera and 357 species. Overall, 23 phyla representing 388 genera and 805 species were identified in BV subjects. Firmicutes represented 95% of the sequence reads in non-BV subjects with Lactobacillus-dominated genera and Lactobacillus crispatus–dominated species, followed by Proteobacteria (3.78%), Actinobacteria (0.74%), and Bacteriodetes (0.05%). In BV subjects, Firmicutes represented 59% of the classified sequence reads, followed by Bacteroidetes (19%), Actinobacteria (15.8%), Fusobacteria (4.08%), Proteobacteria (1.48%), and Tenericutes (1.25%). Conclusion: Non-BV healthy Black African, Nigerian women had Lactobacillus genera as the predominant microbiota, contrary to published reports. The study shows that BV subjects had varying proportions of diverse bacteria similar to studies from other parts of the world.

Epitope Specific Antibodies and T Cell Receptors in the Immune Epitope Database.

The Immune Epitope Database (IEDB) is a free public resource which catalogs experiments characterizing immune epitopes. To accommodate data from next generation repertoire sequencing experiments, we recently updated how we capture and query epitope specific antibodies and T cell receptors. Specifically, we are now storing partial receptor sequences sufficient to determine CDRs and VDJ gene usage which are commonly identified by repertoire sequencing. For previously captured full length receptor sequencing data, we have calculated the corresponding CDR sequences and gene usage information using IMGT numbering and VDJ gene nomenclature format. To integrate information from receptors defined at different levels of resolution, we grouped receptors based on their host species, receptor type and CDR3 sequence. As of August 2018, we have cataloged sequence information for more than 22,510 receptors in 18,292 receptor groups, shown to bind to more than 2,241 distinct epitopes. These data are accessible as full exports and through a new dedicated query interface. The later combines the new ability to search by receptor characteristics with previously existing capability to search by epitope characteristics such as the infectious agent the epitope is derived from, or the kind of immune response involved in its recognition. We expect that this comprehensive capture of epitope specific immune receptor information will provide new insights into receptor-epitope interactions, and facilitate the development of novel tools that help in the analysis of receptor repertoire data.

Towards a molecular basis of ubiquitin signaling: A dual-scale simulation study of ubiquitin dimers.

Covalent modification of proteins by ubiquitin or ubiquitin chains is one of the most prevalent post-translational modifications in eukaryotes. Different types of ubiquitin chains are assumed to selectively signal respectively modified proteins for different fates. In support of this hypothesis, structural studies have shown that the eight possible ubiquitin dimers adopt different conformations. However, at least in some cases, these structures cannot sufficiently explain the molecular basis of the selective signaling mechanisms. This indicates that the available structures represent only a few distinct conformations within the entire conformational space adopted by a ubiquitin dimer. Here, molecular simulations on different levels of resolution can complement the structural information. We have combined exhaustive coarse grained and atomistic simulations of all eight possible ubiquitin dimers with a suitable dimensionality reduction technique and a new method to characterize protein-protein interfaces and the conformational landscape of protein conjugates. We found that ubiquitin dimers exhibit characteristic linkage type-dependent properties in solution, such as interface stability and the character of contacts between the subunits, which can be directly correlated with experimentally observed linkage-specific properties.

Multi-level coarse-grain model of the DEM

The ability to model granular systems at the level of individual particles has largely conduced to the success of the discrete element method (DEM). At the same time, this fundamental concept hinders the use of the DEM for industrial-scale simulations as the computational cost of the method increases with the size of the system. The DEM coarse-grain (CG) model provides one means of counteracting this effect by replacing a group of original particles by a larger (pseudo) particle. The major shortcoming of this approach is that it fails to capture effects that intrinsically depend on particle size. To overcome this deficiency we have devised a novel model to efficiently combine multiple levels of coarse-graining in a single DEM simulation. While a coarse realization is used where it sufficiently represents the granular flow, the level of resolution may be increased recursively in spatially confined regions of interest. Thus, the method is able to benefit from the speedup of the coarse-grain approach and retain the details of the granular system in crucial regions. Two-way coupling between different levels of resolution is established by passing volume-averaged flow properties. We present validation data based on the comparison between the computed statistical properties of our multi-level coarse-grain (MLCG) model and the corresponding properties of the fully resolved reference system.

Simple models combining competition, defence and resource availability have broad implications in pelagic microbial food webs

In food webs, interactions between competition and defence control the partitioning of limiting resources. As a result, simple models of these interactions contain links between biogeochemistry, diversity, food web structure and ecosystem function. Working at hierarchical levels, these mechanisms also produce self-similarity and therefore suggest how complexity can be generated from repeated application of simple underlying principles. Reviewing theoretical and experimental literature relevant to the marine photic zone, we argue that there is a wide spectrum of phenomena, including single cell activity of prokaryotes, microbial biodiversity at different levels of resolution, ecosystem functioning, regional biogeochemical features and evolution at different timescales; that all can be understood as variations over a common principle, summarised in what has been termed the 'Killing-the-Winner' (KtW) motif. Considering food webs as assemblages of such motifs may thus allow for a more integrated approach to aquatic microbial ecology.

Replicate after reading: on the extraction and evocation of cultural information

Does cultural evolution happen by a process of copying or replication? And how exactly does cultural transmission compare with that paradigmatic case of replication, the copying of DNA in living cells? Theorists of cultural evolution are divided on these issues. The most important objection to the replication model has been leveled by Dan Sperber and his colleagues. Cultural transmission, they argue, is almost always reconstructive and transformative, while strict ‘replication’ can be seen as a rare limiting case at most. By means of some thought experiments and intuition pumps, I clear up some confusion about what qualifies as ‘replication’. I propose a distinction between evocation and extraction of cultural information, applying these concepts at different levels of resolution. I defend a purely abstract and information-theoretical definition of replication, while rejecting more material conceptions. In the end, even after taking Sperber’s valuable and important points on board, the notion of cultural replication remains a valid and useful one. This is fortunate, because we need it for certain explanatory projects (e.g., understanding cumulative cultural adaptations).

A unified model for human activity recognition using spatial distribution of gradients and difference of Gaussian kernel

Understanding of human action and activity from video data is growing field and received rapid importance due to surveillance, security, entertainment and personal logging. In this work, a new hybrid technique is proposed for the description of human action and activity in video sequences. The unified framework endows a robust feature vector wrapping both global and local information strengthening discriminative depiction of action recognition. Initially, entropy-based texture segmentation is used for human silhouette extraction followed by construction of average energy silhouette images (AEIs). AEIs are the 2D binary projection of human silhouette frames of the video sequences, which reduces the feature vector generation time complexity. Spatial Distribution Gradients are computed at different levels of resolution of sub-images of AEI consisting overall shape variations of human silhouette during the activity. Due to scale, rotation and translation invariant properties of STIPs, the vocabulary of DoG-based STIPs are created using vector quantization which is unique for each class of the activity. Extensive experiments are conducted to validate the performance of the proposed approach on four standard benchmarks, i.e., Weizmann, KTH, Ballet Movements, Multi-view IXMAS. Promising results are obtained when compared with the similar state of the arts, demonstrating the robustness of the proposed hybrid feature vector for different types of challenges—illumination, view variations posed by the datasets.

MultiMap: A Tool to Automatically Extract and Analyse Spatial Microscopic Data From Large Stacks of Confocal Microscopy Images.

The development of 3D visualization and reconstruction methods to analyse microscopic structures at different levels of resolutions is of great importance to define brain microorganization and connectivity. MultiMap is a new tool that allows the visualization, 3D segmentation and quantification of fluorescent structures selectively in the neuropil from large stacks of confocal microscopy images. The major contribution of this tool is the posibility to easily navigate and create regions of interest of any shape and size within a large brain area that will be automatically 3D segmented and quantified to determine the density of puncta in the neuropil. As a proof of concept, we focused on the analysis of glutamatergic and GABAergic presynaptic axon terminals in the mouse hippocampal region to demonstrate its use as a tool to provide putative excitatory and inhibitory synaptic maps. The segmentation and quantification method has been validated over expert labeled images of the mouse hippocampus and over two benchmark datasets, obtaining comparable results to the expert detections.

Hamiltonian adaptive resolution molecular dynamics simulation of infrared dielectric functions of liquids

Hamiltonian adaptive resolution scheme (H-AdResS), which allows to simulate materials by treating different domains of the system at different levels of resolution, is a recently proposed atomistic/coarse-grained multiscale model. In this work, a scheme to calculate the dielectric functions of liquids on account of H-AdResS is presented. In the proposed H-AdResS dielectric-function calculation scheme (DielectFunctCalS), the corrected molecular dipole moments are calculated by multiplying molecular dipole moment by the weighting fraction of the molecular mapping point. As the widths of all-atom and hybrid regions show different degrees of influence on the dielectric functions, a prefactor is multiplied to eliminate the effects of all-atom and hybrid region widths. Since one goal of using the H-AdResS method is to reduce computational costs, widths of the all-atom region and the hybrid region can be reduced considering that the coarse-grained simulation is much more timesaving compared to atomistic simulation. Liquid water and ethanol are taken as test cases to validate the DielectFunctCalS. The H-AdResS DielectFunctCalS results are in good agreement with all-atom molecular dynamics simulations. The accuracy of the H-AdResS results, together with all-atom molecular dynamics results, depends heavily on the choice of the force field and force field parameters. The H-AdResS DielectFunctCalS allows us to calculate the dielectric functions of macromolecule systems with high efficiency and makes the dielectric function calculations of large biomolecular systems possible.

Kinetic models of hematopoietic differentiation.

As cell and molecular biology is becoming increasingly quantitative, there is an upsurge of interest in mechanistic modeling at different levels of resolution. Such models mostly concern kinetics and include gene and protein interactions as well as cell population dynamics. The final goal of these models is to provide experimental predictions, which is now taking on. However, even without matured predictions, kinetic models serve the purpose of compressing a plurality of experimental results into something that can empower the data interpretation, and importantly, suggesting new experiments by turning “knobs” in silico. Once formulated, kinetic models can be executed in terms of molecular rate equations for concentrations or by stochastic simulations when only a limited number of copies are involved. Developmental processes, in particular those of stem and progenitor cell commitments, are not only topical but also particularly suitable for kinetic modeling due to the finite number of key genes involved in cellular decisions. Stem and progenitor cell commitment processes have been subject to intense experimental studies over the last decade with some emphasis on embryonic and hematopoietic stem cells. Gene and protein interactions governing these processes can be modeled by binary Boolean rules or by continuous‐valued models with interactions set by binding strengths. Conceptual insights along with tested predictions have emerged from such kinetic models. Here we review kinetic modeling efforts applied to stem cell developmental systems with focus on hematopoiesis. We highlight the future challenges including multi‐scale models integrating cell dynamical and transcriptional models.This article is categorized under: Models of Systems Properties and Processes > Mechanistic ModelsDevelopmental Biology > Stem Cell Biology and Regeneration

Estimating individualized exposure impacts from ambient ozone levels: A synthetic information approach

There is ample evidence that short-term ozone exposure is associated with increased respiratory symptoms. Many studies, however, aggregate the population, activities, or concentration levels of the pollutant across space and/or time, failing to capture critical variations in the exposure levels. We couple spatiotemporal air quality estimates of ozone with a synthetic information model of the Houston Metropolitan Area, allowing us to attach exposure levels to individuals based on exact times, geo-locations, and microenvironments of activities. Several scenarios of the model are run at different levels of resolution. When we maintain the spatiotemporal resolution of the data, the proportion of the population that experiences sharp increases in short-term exposure increases substantially. This can be particularly important if experienced by sensitive populations given the increased risk for adverse health effects. We find that individuals in the same zip code, neighborhood, and even household have varying levels of exposure.

Face Recognition Using Multiresolution Hybrid Kekre-DCT Wavelet Transform Features with Multiclass ECOC Framework

Error Correcting Output Codes (ECOC) framework is efficient approach for multiclass classification problems by reducing them to binary classifiers. In this paper Face recognition system is implemented by extracting features using novel multiresolution hybrid wavelet transform approach. This approach combines two orthogonal transform like Kekre and DCT transform using Kronceker product to generate hybrid wavelet transform which gives different levels of resolution. The generated transform also uses energy compaction technique to obtain dimensionality reduction. The extracted features are then passed for classification via ECOC approach where binary classifiers are coded using Sparse random coding design and decoded using Hamming distance. The final model predicts the class labels. The best accuracy of 99.67% is obtained at 99% of energy threshold.

NMR in structure-based drug design.

NMR spectroscopy is a powerful technique that can provide valuable structural information for drug discovery endeavors. Here, we discuss the strengths (and limitations) of NMR applications to structure-based drug discovery, highlighting the different levels of resolution and throughput obtainable. Additionally, the emerging field of paramagnetic NMR in drug discovery and recent developments in approaches to speed up and automate protein-observed NMR data collection and analysis are discussed.

An integrated system for modeling tree and stand survival

Traditionally, separate models have been used to predict the number of trees per unit area (stand-level survival) and the survival probability of an individual tree (tree-level survival) at a certain age. This study investigated the development of integrated systems in which survival models at different levels of resolution are related in a mathematical structure. Two approaches for modeling tree and stand survival were considered: deriving a stand-level survival model from a tree-level survival model (approach 1) and deriving a tree survival model from a stand survival model (approach 2). Both approaches rely on finding a tree diameter that yields a tree survival probability equal to the stand-level survival probability. The tree and stand survival models from either approach are conceptually compatible with each other but not numerically compatible. Parameters of these models can be estimated either sequentially or simultaneously. Results indicated that approach 2, with parameters estimated sequentially (first from the stand survival model and then from the derived tree survival model), performed best in predicting both tree- and stand-level survival. Although disaggregation did not help improve prediction of tree-level survival, this method can be used when numerical consistency between stand and tree survival is desired.

Effects of Coarse Graining and Saturation of Hydrocarbon Chains on Structure and Dynamics of Simulated Lipid Molecules

Molecular dynamics simulations are used extensively to study the processes on biological membranes. The simulations can be conducted at different levels of resolution: all atom (AA), where all atomistic details are provided; united atom (UA), where hydrogen atoms are treated inseparably of corresponding heavy atoms; and coarse grained (CG), where atoms are grouped into larger particles. Here, we study the behavior of model bilayers consisting of saturated and unsaturated lipids DOPC, SOPC, OSPC and DSPC in simulations performed using all atom CHARMM36 and coarse grained Martini force fields. Using principal components analysis, we show that the structural and dynamical properties of the lipids are similar, both in AA and CG simulations, although the unsaturated molecules are more dynamic and favor more extended conformations. We find that CG simulations capture 75 to 100% of the major collective motions, overestimate short range ordering, result in more flexible molecules and 5–7 fold faster sampling. We expect that the results reported here will be useful for comprehensive quantitative comparisons of simulations conducted at different resolution levels and for further development and improvement of CG force fields.

Discovering variable fractional orders of advection–dispersion equations from field data using multi-fidelity Bayesian optimization

The fractional advection–dispersion equation (FADE) can describe accurately the solute transport in groundwater but its fractional order has to be determined a priori. Here, we employ multi-fidelity Bayesian optimization to obtain the fractional order under various conditions, and we obtain more accurate results compared to previously published data. Moreover, the present method is very efficient as we use different levels of resolution to construct a stochastic surrogate model and quantify its uncertainty. We consider two different problem set ups. In the first set up, we obtain variable fractional orders of one-dimensional FADE, considering both synthetic and field data. In the second set up, we identify constant fractional orders of two-dimensional FADE using synthetic data. We employ multi-resolution simulations using two-level and three-level Gaussian process regression models to construct the surrogates.

Convergence and divergence in spherical harmonic series of the gravitational field generated by high‐resolution planetary topography—A case study for the Moon

AbstractTheoretically, spherical harmonic (SH) series expansions of the external gravitational potential are guaranteed to converge outside the Brillouin sphere enclosing all field‐generating masses. Inside that sphere, the series may be convergent or may be divergent. The series convergence behavior is a highly unstable quantity that is little studied for high‐resolution mass distributions. Here we shed light on the behavior of SH series expansions of the gravitational potential of the Moon. We present a set of systematic numerical experiments where the gravity field generated by the topographic masses is forward‐modeled in spherical harmonics and with numerical integration techniques at various heights and different levels of resolution, increasing from harmonic degree 90 to 2160 (~61 to 2.5 km scales). The numerical integration is free from any divergence issues and therefore suitable to reliably assess convergence versus divergence of the SH series. Our experiments provide unprecedented detailed insights into the divergence issue. We show that the SH gravity field of degree‐180 topography is convergent anywhere in free space. When the resolution of the topographic mass model is increased to degree 360, divergence starts to affect very high degree gravity signals over regions deep inside the Brillouin sphere. For degree 2160 topography/gravity models, severe divergence (with several 1000 mGal amplitudes) prohibits accurate gravity modeling over most of the topography. As a key result, we formulate a new hypothesis to predict divergence: if the potential degree variances show a minimum, then the SH series expansions diverge somewhere inside the Brillouin sphere and modeling of the internal potential becomes relevant.

A multi-resolution HEALPix data structure for spherically mapped point data.

Data describing entities with locations that are points on a sphere are described as spherically mapped. Several data structures designed for spherically mapped data have been developed. One of them, known as Hierarchical Equal Area iso-Latitude Pixelization (HEALPix), partitions the sphere into twelve diamond-shaped equal-area base cells and then recursively subdivides each cell into four diamond-shaped subcells, continuing to the desired level of resolution. Twelve quadtrees, one associated with each base cell, store the data records associated with that cell and its subcells.HEALPix has been used successfully for numerous applications, notably including cosmic microwave background data analysis. However, for applications involving sparse point data HEALPix has possible drawbacks, including inefficient memory utilization, overwriting of proximate points, and return of spurious points for certain queries.A multi-resolution variant of HEALPix specifically optimized for sparse point data was developed. The new data structure allows different areas of the sphere to be subdivided at different levels of resolution. It combines HEALPix positive features with the advantages of multi-resolution, including reduced memory requirements and improved query performance.An implementation of the new Multi-Resolution HEALPix (MRH) data structure was tested using spherically mapped data from four different scientific applications (warhead fragmentation trajectories, weather station locations, galaxy locations, and synthetic locations). Four types of range queries were applied to each data structure for each dataset. Compared to HEALPix, MRH used two to four orders of magnitude less memory for the same data, and on average its queries executed 72% faster.

As Simple as Possible but not Simpler: On the Reliability of Protein Coarse-Grained Models

Mechanical unfolding of a single domain of loop-truncated superoxide dismutase protein has been simulated via force spectroscopy techniques with both all-atom (AA) models and several coarse-grained models having different levels of resolution: A Gō model containing all heavy atoms in the protein (HA-Gō), the associative memory, water mediated, structure and energy model (AWSEM) which has 3 interaction sites per amino acid, and a Gō model containing only one interaction site per amino acid at the Cα position (Cα-Gō). To systematically compare results across models, the scales of time, energy, and force were suitably renormalized in each model. TM alignment, native contact, and clustering analysis show that all models consistently predict a similar single pathway unfolding mechanism for early force- induced unfolding events, but these models diverge in their predictions for late stage unfolding events when the protein is more significantly disordered. When the protein is about half-unfolded, the unfolding pathways of the AA, HA-Gō, Cα-Gō models bifurcate repeatedly to multiple branches. The AWSEM model has a single dominant unfolding pathway over the whole range of unfolding, in contrast to all other models. However, the AWSEM pathway has the most structural similarity to the AA model at high nativeness, but the least structural similarity to the AA model at low nativeness. In comparison to the AA model, the sequence of native contact breakage is best predicted by the HA-Gō model.