Low-resolution Results Research Articles

Phylogenetic analysis of Bacillus anthracis strains from Western Siberia reveals a new genetic cluster in the global population of the species

BackgroundAnthrax is a zoonotic disease caused by the gram-positive bacterium Bacillus anthracis. The most anthrax-endemic regions of Russia are Siberia and North Caucasus. Previously, genotyping of Russian B.anthracis isolates was carried out using canSNP and MLVA data; these methods yield lower resolution results compared to whole genome SNP analysis (wgSNP). In this research, we have used wgSNP method for genotyping of 10 B.anthracis isolates, obtained during 1961–2016 in Russia on territory of Western Siberia.ResultsWe have analyzed 185 B.anthracis genomes available in GenBank database and genomes of 10 isolates obtained in this study to determine the place of Russian isolates in the global phylogeny of B.anthracis. For the studied genomes we have detected 7203 SNPs, which were used for building a phylogenetic reconstruction with Maximum Likelihood Method. Results of the phylogenetic analysis indicate that Russian strains belong to three different genetic groups. Three strains belong to genetic group “Ames”, two strains – to “STI” group. Five strains belong to the main genetic line B, and four of them form a subcluster, described for the first time, which we have named “Siberia”.ConclusionsIn this study, the data on genetic diversity of B.anthracis strains on the territory of Western Siberia is presented for the first time. As a result of complex phylogenetic analysis, the place of these isolates was determined in the global phylogenetic structure of the B.anthracis population. We describe a new cluster in the main genetic line B for the first time.

Video Inpainting by Jointly Learning Temporal Structure and Spatial Details

We present a new data-driven video inpainting method for recovering missing regions of video frames. A novel deep learning architecture is proposed which contains two subnetworks: a temporal structure inference network and a spatial detail recovering network. The temporal structure inference network is built upon a 3D fully convolutional architecture: it only learns to complete a low-resolution video volume given the expensive computational cost of 3D convolution. The low resolution result provides temporal guidance to the spatial detail recovering network, which performs imagebased inpainting with a 2D fully convolutional network to produce recovered video frames in their original resolution. Such two-step network design ensures both the spatial quality of each frame and the temporal coherence across frames. Our method jointly trains both sub-networks in an end-to-end manner. We provide qualitative and quantitative evaluation on three datasets, demonstrating that our method outperforms previous learning-based video inpainting methods.

A rock physics driven Bayesian inversion for TOC in the Fuling shale gas reservoir

Abstract Total organic content (TOC) is an important indicator in the evaluation of the organic abundance and production potential of shale reservoirs. The conventional approach to TOC estimation is to correlate TOC with inverted elastic parameters in a linearized empirical equation. However, this approach ignores complex rock physics properties (such as complex mineralogy and pore space) in the shale while generating smooth, low resolution results. This paper proposes a rock physics-based method to forward model the seismic and elastic response of the shale reservoir and then invert using a Bayesian approach to derive key parameters, such as the TOC, total porosity and the fractional volume of brittle minerals, and to delineate the sweet-spots in an unconventional shale reservoir.

Q-compensated acoustic impedance inversion of attenuated seismic data: Numerical and field-data experiments

Acoustic impedance (AI) inversion is of great interest because it extracts information regarding rock properties from seismic data and has successful applications in reservoir characterization. During wave propagation, anelastic attenuation and dispersion always occur because the subsurface is not perfectly elastic, thereby diminishing the seismic resolution. AI inversion based on the convolutional model requires that the input data be free of attenuation effects; otherwise, low-resolution results are inevitable. The intrinsic instability that occurs while compensating for the anelastic effects via inverse [Formula: see text] filtering is notorious. The gain-limit inverse [Formula: see text] filtering method cannot compensate for strongly attenuated high-frequency components. A nonstationary sparse reflectivity inversion (NSRI) method can estimate the reflectivity series from attenuated seismic data without the instability issue. Although AI is obtainable from an inverted reflectivity series through recursion, small inaccuracies in the reflectivity series can result in large perturbations in the AI result because of the cumulative effects. To address these issues, we have developed a [Formula: see text]-compensated AI inversion method that directly retrieves high-resolution AI from attenuated seismic data without prior inverse [Formula: see text] filtering based on the theory of NSRI and AI inversion. This approach circumvents the intrinsic instability of inverse [Formula: see text] filtering by integrating the [Formula: see text] filtering operator into the convolutional model and solving the inverse problem iteratively. This approach also avoids the ill-conditioned nature of the recursion scheme for transforming an inverted reflectivity series to AI. Experiments on a benchmark Marmousi2 model validate the feasibility and capabilities of our method. Applications to two field data sets verify that the inversion results generated by our approach are mostly consistent with the well logs.

InversionNet: A real-time and accurate full waveform inversion with convolutional neural network

With data proliferation in all geosciences domains, machine learning and data analytics are emerging as important research areas in geosciences. Full-waveform inversion has been an important tool to infer the subsurface based on geophysical measurements. However, solving full-waveform inversion can be challenging. The existing computational methods for solving acoustic full-waveform inversion are not only computationally expensive but also yields low-resolution results because of the ill-posedness and cycle skipping issues of full-waveform inversion. To resolve those issues, we employ machine-learning techniques to solve full-waveform inversion in this work. In particular, we build a convolutional neural network to model the correspondence from data to velocity structures. Our numerical examples using synthetic reflection data show that our new methods much improve the accuracy of the velocity inversion. Furthermore, the computational time for inverting the seismic data is significantly reduced.

Stochastic Gabor reflectivity and acoustic impedance inversion

To delineate subsurface lithology to estimate petrophysical properties of a reservoir, it is possible to use acoustic impedance (AI) which is the result of seismic inversion. To change amplitude to AI, removal of wavelet effects from the seismic signal in order to get a reflection series, and subsequently transforming those reflections to AI, is vital. To carry out seismic inversion correctly it is important to not assume that the seismic signal is stationary. However, all stationary deconvolution methods are designed following that assumption. To increase temporal resolution and interpretation ability, amplitude compensation and phase correction are inevitable. Those are pitfalls of stationary reflectivity inversion. Although stationary reflectivity inversion methods are trying to estimate reflectivity series, because of incorrect assumptions their estimations will not be correct, but may be useful. Trying to convert those reflection series to AI, also merging with the low frequency initial model, can help us. The aim of this study was to apply non-stationary deconvolution to eliminate time variant wavelet effects from the signal and to convert the estimated reflection series to the absolute AI by getting bias from well logs. To carry out this aim, stochastic Gabor inversion in the time domain was used. The Gabor transform derived the signal's time–frequency analysis and estimated wavelet properties from different windows. Dealing with different time windows gave an ability to create a time-variant kernel matrix, which was used to remove matrix effects from seismic data. The result was a reflection series that does not follow the stationary assumption. The subsequent step was to convert those reflections to AI using well information. Synthetic and real data sets were used to show the ability of the introduced method. The results highlight that the time cost to get seismic inversion is negligible related to general Gabor inversion in the frequency domain. Also, obtaining bias could help the method to estimate reliable AI. To justify the effect of random noise on deterministic and stochastic inversion results, a stationary noisy trace with signal-to-noise ratio equal to 2 was used. The results highlight the inability of deterministic inversion in dealing with a noisy data set even using a high number of regularization parameters. Also, despite the low level of signal, stochastic Gabor inversion not only can estimate correctly the wavelet's properties but also, because of bias from well logs, the inversion result is very close to the real AI. Comparing deterministic and introduced inversion results on a real data set shows that low resolution results, especially in the deeper parts of seismic sections using deterministic inversion, creates significant reliability problems for seismic prospects, but this pitfall is solved completely using stochastic Gabor inversion. The estimated AI using Gabor inversion in the time domain is much better and faster than general Gabor inversion in the frequency domain. This is due to the extra number of windows required to analyze the time–frequency information and also the amount of temporal increment between windows. In contrast, stochastic Gabor inversion can estimate trustable physical properties close to the real characteristics. Applying to a real data set could give an ability to detect the direction of volcanic intrusion and the ability of lithology distribution delineation along the fan. Comparing the inversion results highlights the efficiency of stochastic Gabor inversion to delineate lateral lithology changes because of the improved frequency content and zero phasing of the final inversion volume.

A New Electron Microscopic Method to Observe the Distribution of Phosphatidylinositol 3,4-bisphosphate.

Phosphatidylinositol 3,4-bisphosphate [PtdIns(3,4)P2] is a phosphoinositide that plays important roles in signal transduction, endocytosis, and cell migration among others. The intracellular distribution of PtdIns(3,4)P2 has mainly been studied by observing the distribution of GFP-tagged PtdIns(3,4)P2-binding protein domains in live cells and by labeling with anti-PtdIns(3,4)P2 antibody in fixed cell samples, but these methods only offer low spatial resolution results and may have pitfalls. In the present study, we developed an electron microscopic method to observe the PtdIns(3,4)P2 distribution using the SDS-treated freeze-fracture replica labeling method. The recombinant GST-tagged pleckstrin homology (PH) domain of TAPP1 was used as the binding probe, and its binding to PtdIns(3,4)P2 in the freeze-fracture replica was confirmed by using liposomes containing different phosphoinositides and by the lack of labeling by a mutant probe, in which one amino acid in the PH domain was substituted. The method was applied to NIH3T3 cell samples and showed that the increase of PtdIns(3,4)P2 in cells treated with hydrogen peroxide occurs in the cytoplasmic leaflet of the plasma membrane, except in the caveolar membrane. The present method can define the distribution of PtdIns(3,4)P2 at a high spatial resolution and will facilitate our understanding of the physiological function of this less studied phosphoinositide.

Dolphin: a tool for automatic targeted metabolite profiling using 1D and 2D (1)H-NMR data.

One of the main challenges in nuclear magnetic resonance (NMR) metabolomics is to obtain valuable metabolic information from large datasets of raw NMR spectra in a high throughput, automatic, and reproducible way. To date, established software packages used to match and quantify metabolites in NMR spectra remain mostly manually operated, leading to low resolution results and subject to inconsistencies not attributable to the NMR technique itself. Here, we introduce a new software package, called Dolphin, able to automatically quantify a set of target metabolites in multiple sample measurements using an approach based on 1D and 2D NMR techniques to overcome the inherent limitations of 1D (1)H-NMR spectra in metabolomics. Dolphin takes advantage of the 2D J-resolved NMR spectroscopy signal dispersion to avoid inconsistencies in signal position detection, enhancing the reliability and confidence in metabolite matching. Furthermore, in order to improve accuracy in quantification, Dolphin uses 2D NMR spectra to obtain additional information on all neighboring signals surrounding the target metabolite. We have compared the targeted profiling results of Dolphin, recorded from standard biological mixtures, with those of two well established approaches in NMR metabolomics. Overall, Dolphin produced more accurate results with the added advantage of being a fully automated and high throughput processing package.

OR06: GROUP SPECIFIC, UNAMBIGUOUS FULL-LENGTH GENE HLA CLASS I TYPING BY SANGER SEQUENCING: A ROBUST SSBT STRATEGY FOR TYPING AND A GOLDEN REFERENCE FOR NEXT GENERATION SEQUENCING APPROACHES

Classically, sequence base typing (SBT) of HLA is performed using sequences from exons 2, 3 and 4 for HLA class I and exons 2 and 3 for HLA class II. This partial analysis of HLA genes usually leads to allele and genotype ambiguities, which causes complications in the assignment of alleles. Consequently, the current IMGT database lacks complete sequences for more than 90% of all alleles. We developed and validated a group specific Sanger sequencing approach that results in an unambiguous typing result for HLA class I alleles. This approach provides full length gene sequences and enables not only an unambiguous typing of alleles, but also allows for filling sequence gaps in the unknown allele sequences of the IMGT/HLA database. The method is based on group specific, full-length amplification of HLA class I alleles using any low resolution results. The alleles were separated into 8 different groups for HLA-A, 15 for HLA-B and 13 for HLA-C. Depending on the location of the group-specific primers, amplicons varied in length between 2.9 and 3.2 kb for HLA-A, 2.8 and 3.8 kb for HLA-B and 2.8 and 3.3 kb for HLA-C. The amplicon is sequenced using generic sequencing primers, suitable for all amplified groups. This approach is routinely applicable for high resolution typing of clinical samples and the identification of allele specific full length gene sequences, with some additional sequence primers. Currently we confirmed the complete allele sequences of HLA-A∗02:01:01:01, A∗03:01:01:01, A31:01:02, B∗08:01:01, B∗15:01:01:01, B∗40:01:01, B∗51:01:01, C∗04:01:01:01, C∗05:01:01:01, C∗07:01:01:01 and C∗12:02:02, and identified full length sequences of partially known (e.g. HLA-A∗74:11) and novel alleles (e.g. HLA-A∗23:38 N, B∗35:241 or C∗02:69). Moreover, this full-length group specific approach can serve as THE reference for next generation sequencing approaches where new polymorphism in introns and all exons, previously unknown in the IMGT/HLA database, are identified.

Multiresolution representation for curves based on ternary subdivision schemes

Subdivision offers a way to increase the resolution of models, while reverse subdivision possesses the opposite ability. Combining the two theories could realize the multiresolution (MR) representation of models. Based on two ternary subdivision schemes, we present the trial and refined filters and an algorithm to realize MR representation for curves, which has some difference compared with the work relating to binary schemes. And the filters yield biorthogonal wavelet systems which are the underlying theory fundament of curves MR. By experiments and numerical calculations, we demonstrate that by using the ternary methods one can accomplish the MR representation for curves and the low-resolution results obtained by reverse subdivision can approximate the original curves well. Besides, ternary methods need smaller number of decomposition times than binary methods to get low-resolution results at similar levels of resolution for the same original curve.

Fast detection of facial wrinkles based on Gabor features using image morphology and geometric constraints

Facial wrinkles are important features of aging human skin which can be incorporated in several image-based applications related to aging. Facial wrinkles are 3D features of skin and appear as subtle discontinuities or cracks in surrounding skin texture. However, facial wrinkles can easily be masked by illumination/acquisition conditions in 2D images due to the specific nature of skin surface texture and its reflective properties. Existing approaches to image-based analysis of aging skin are based on the analysis of wrinkles as texture and not as curvilinear discontinuity/crack features. Previously, we proposed a stochastic approach based on Marked Point Processes (MPP) to localize facial wrinkles as curves. In this paper, we present a fast deterministic algorithm based on Gabor filters and image morphology to improve localization results. We propose image features based on Gabor filter bank to highlight the subtle curvilinear discontinuities in skin texture caused by wrinkles. Then, image morphology is used to incorporate geometric constraints to localize curvilinear shapes of wrinkles at image sites of large Gabor filter responses. Experiments are conducted on two sets of low and high resolution images and results are compared with those of MPP modeling. Experiments show that the proposed algorithm not only is significantly faster than MPP-based approach but also provides visually better results.

Characterization and Space–Time Downscaling of the Inundation Extent over the Inner Niger Delta Using GIEMS and MODIS Data

Abstract The objective in this work is to develop downscaling methodologies to obtain a long time record of inundation extent at high spatial resolution based on the existing low spatial resolution results of the Global Inundation Extent from Multi-Satellites (GIEMS) dataset. In semiarid regions, high-spatial-resolution a priori information can be provided by visible and infrared observations from the Moderate Resolution Imaging Spectroradiometer (MODIS). The study concentrates on the Inner Niger Delta where MODIS-derived inundation extent has been estimated at a 500-m resolution. The space–time variability is first analyzed using a principal component analysis (PCA). This is particularly effective to understand the inundation variability, interpolate in time, or fill in missing values. Two innovative methods are developed (linear regression and matrix inversion) both based on the PCA representation. These GIEMS downscaling techniques have been calibrated using the 500-m MODIS data. The downscaled fields show the expected space–time behaviors from MODIS. A 20-yr dataset of the inundation extent at 500 m is derived from this analysis for the Inner Niger Delta. The methods are very general and may be applied to many basins and to other variables than inundation, provided enough a priori high-spatial-resolution information is available. The derived high-spatial-resolution dataset will be used in the framework of the Surface Water Ocean Topography (SWOT) mission to develop and test the instrument simulator as well as to select the calibration validation sites (with high space–time inundation variability). In addition, once SWOT observations are available, the downscaled methodology will be calibrated on them in order to downscale the GIEMS datasets and to extend the SWOT benefits back in time to 1993.

Picornavirus uncoating intermediate captured in atomic detail.

It remains largely mysterious how the genomes of non-enveloped eukaryotic viruses are transferred across a membrane into the host cell. Picornaviruses are simple models for such viruses, and initiate this uncoating process through particle expansion, which reveals channels through which internal capsid proteins and the viral genome presumably exit the particle, although this has not been clearly seen until now. Here we present the atomic structure of an uncoating intermediate for the major human picornavirus pathogen CAV16, which reveals VP1 partly extruded from the capsid, poised to embed in the host membrane. Together with previous low-resolution results, we are able to propose a detailed hypothesis for the ordered egress of the internal proteins, using two distinct sets of channels through the capsid, and suggest a structural link to the condensed RNA within the particle, which may be involved in triggering RNA release.

23-OR

Aim Our aim was to identify a novel DRB1 ∗ 09 allele not detected by PCR-SSO in an NMDP donor. Methods Luminex-based SSO, SSP, and SBT were used to determine the HLA typing. Results The following typing was established using Luminex based technology: HLA-A ∗ 02:xx, 02:xx; B ∗ 07:xx, 45:xx; C ∗ 07:xx, 16:xx; DRB1 ∗ 13:xx, - ;DQB1 ∗ 03:xx, 05:xx. To investigate the unusual DR-DQ associations, high resolution PCR-SBT was performed (SeCore, Life Technologies). Low resolution results were confirmed and high resolution Class II results were: HLA-DRB1 ∗ 13:02, -; DQB1 ∗ 03:03, 05:01. Further testing included DRB1 low resolution PCR-SSP (Life Technologies and Olerup). The results obtained from both kits indicated the presence of an allele from the DRB1 ∗ 09 group. Next, PCR-SBT was performed using AlleleSeqr (Celera). This typing yielded HLA-DRB1 ∗ 09:xx, 13:02. A one nucleotide difference in exon 2 at position 112 was detected. This resulted in an A to C substitution. To confirm the DRB1 ∗ 09 allele as potentially novel, PCR-SBT was set up using SBTexcellerator (Qiagen). This approach allowed for the separate sequencing of the DRB1 ∗ 09 and DRB1 ∗ 13 alleles. The mutation occurred in the DRB1 ∗ 09 allele. This base substitution resulted in an amino acid change from a lysine to a glutamine at codon 9. The substitution is found on the pleated floor of the HLA protein, affecting its structure and potentially influencing peptide binding and presentation [Fig. 1]. Download full-size image Conclusions The sequence of this new allele is in the process of being submitted to the IMGT/HLA database for an official allele assignment. This demonstrates the need for HLA labs to utilize multiple methods and typing kits to achieve the most comprehensive results.

Continuous-scale mathematical morphology-based optimal scale band demodulation of impulsive feature for bearing defect diagnosis

Integer-scale structuring element is usually used in the traditional mathematical morphology (MM) for signal processing. When applied for impulsive feature demodulation of vibration signal of rolling element bearings, the integer-scale MM (ISMM) may lead to low resolution result and thus undermines its defect diagnosis capability. For this reason, this paper proposes a continuous-scale MM (CSMM) scheme by interpolation and re-sampling to improve scale resolution for more reliable fault signature extraction. Based on the frequency domain kurtosis criterion, a narrowband merging operation is employed to locate the optimal scale band that best reflects the impulsive feature from the CSMM analysis results. The demodulated components in the optimal scale band are employed to detect the existence of the bearing fault. The proposed optimal CSMM demodulation technique is evaluated using both simulated and experimental bearing vibration signals. The results show that, the CSMM is capable of generating demodulation signals with higher resolution, and the optimal scale band demodulation based on the CSMM can reliably extract impulsive features for bearing defect diagnosis.

STIS optical spectroscopy of the lobes of CRL 618

Context. Many proto-planetary nebulae show collimated structures sometimes showing multiple outflows.Aims. We present the results of new optical spectroscopic observations (both medium (with a dispersion of 0.56 A pixel-1 ) and low (2.73 and 4.92 A pixel-1 for the G430L and G750L gratings) spectral resolution) of the lobes of the proto-planetary nebula CRL 618 obtained with the Space Telescope Imaging Spectrograph on board of the Hubble Space Telescope. Methods. We analyse the density structure and the excitation conditions of the lobes of CRL 618. The spectra obtained at medium spectral resolution (~50 km s-1 ) allow us to quote the fraction of unscattered (intrinsic) Hα emission. We have also obtained de-reddened emission line ratios of several features from the low spectral resolution spectra.Results. We find that most of the analysed line ratios are reproduced by planar shocks moving through a dense medium (with pre-shock densities ~104 cm-3 ) with shock velocities from 30 to 40 km s-1 (except the [O III]/Hβ line ratios which require shock velocities of 80 to 90 km s-1 ). We find that the [S II]-weighted ionization fraction ranges from 0.015 to 0.06. The total densities derived from the electron density and the ionization fraction are ~105 to 106 cm-3 . Conclusions. We conclude that the spectra of the lobes of CRL 618 can be margially reproduced by steady plane–parallel shock models for shock velocities which are significantly lower than the velocities at which the jet moves outwards (~200 km s-1 ). These results are consistent with the predictions of a jet with a variable ejection velocity. The mirror-symmetry, the luminosity asymmetry between both lobes and the ejection velocity variability suggest that its central source may host a binary system.

Low-resolution color-based visual tracking with state-space model identification

A novel tracking method is proposed to resolve the poor performance of color-based tracker in low-resolution vision. The proposed method integrates vector autoregression (VAR) with a conceptual frame of state-space model (SSM) to achieve an appropriate model that clearly describes the relation between high-resolution tracking results (states) and corresponding low-resolution tracking results (observations). Here, the parameters of SSM are calculated by the maximum likelihood (ML) estimator to optimize the SSM and minimize its model error. By using the Kalman filter, known as an effective filter of SSM, to estimate the states of the tracked object from its incomplete observations, it is observed that the estimated states are closer to their actual values than their observations or estimates by other unoptimized SSMs. Therefore, the proposed method can be used to improve low-resolution tracking results. Moreover, it can decrease computational complexity and save on processing time.

A wavelet-based adaptive method for determining eigenstates of electronic systems

The possibilities for reducing the necessary computation power in wavelet-based electronic structure calculations are studied. The expansion of the expectation values of energy operators, the integrals of basis functions are mostly system-independent, consequently it is not necessary to compute them in each calculations. Fixed building blocks, such as a parameterized expansion of the nuclear and electron–electron cusp can reduce the amount of necessary calculation. An algorithm for local expansion refinement is also given. It is possible to determine the significant expansion coefficients of a high resolution level without solving the Schrodinger equation using only lower resolution results.

High resolution entry and exit Monte Carlo dose calculations from a linear accelerator 6 MV beam under the influence of transverse magnetic fields

A current concern with 6 MV transverse field MRI-linac hybrid systems is the predicted increases in skin dose (both the entry and exit sides) caused by the effects of the magnetic field on secondary electrons. In this work high resolution GEANT4 Monte Carlo simulations have been performed at the beam central axis in the entry and exit regions of a water phantom to predict surface (0 microm depth) and skin (70 microm depth) doses when placed in such a hybrid system. A 30 x 30 x 20 cm3 water phantom with 10 microm thick voxels has been simulated by being irradiated perpendicularly with a 6 MV photon beam (Varian 2100C) of sizes of 5 x 5, 10 x 10, 15 x 15, and 20 x 20 cm2. Uniform transverse magnetic fields of 0.2, 0.75, 1.5, and 3 T with varying thickness above the phantom have been investigated. Simulations with and without lepton contamination have been performed. In the entry region the high resolution scoring has yielded unexpected surface and skin doses. There is a small amount of nonpurged air-generated lepton contamination that originates immediately above the phantom surface and delivers its dose over very short longitudinal distances in the entry region. At 0.2 T the surface and skin doses are not accurately predicted using lepton-contamination-free simulations and extrapolated lower resolution scoring. Lepton-free simulations are up to 7% of Dmax lower than simulations with leptons. However, compared to 0 T, entry skin dose is reduced at 0.2 and 0.75 T but increases to 28%-31% of Dmax at 3 T. For skin doses at the central axis in the exit region, high resolution scoring shows relative increases of 38%-106%, depending on the magnetic field strength and field size. These values are also up to 20% higher than lower resolution results. The shape of the exit dose profiles varies unpredictably and so extrapolation of low resolution data is insufficient. In order to achieve accurate Monte Carlo skin dosimetry in a transverse field MRI-linac system, the authors recommend using high resolution scoring. In systems of 0.2 T the inclusion of air-generated lepton contamination is also recommended.

Parasites alter the topology of a stream food web across seasons

Relatively few published food webs have included parasites, and in this study we examined the animal community in a stream across eight contiguous seasons to test how inclusion of helminth parasites alters the topology or structure of the food web. Food webs constructed for each season and analyzed using common binary matrix measures show that species richness, linkage density, and the number of observed and possible links increased when parasites were included as individual species nodes. With parasite-parasite and predator-parasite links omitted, measures of community complexity, such as connectance (C), generally increased over multiple seasons. However, relative nestedness (n*) decreased when parasites were included, which may be a result of low resolution of basal resources inflating specialist-to-specialist links. Overall, adding parasites resulted in moderate changes in food web measures when compared to those of four other published food webs representing different ecosystems. In addition, including parasites in the food web revealed consistent pathways of energy flow, and the association of parasite life histories along these pathways suggest stable evolutionary groups of interacting species within the community.