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The characterization of global marine microbial taxonomic and functional diversity is a primary goal of the Global Ocean Sampling Expedition. As part of this study, 19 water samples were collected aboard the Sorcerer II sailing vessel from the southern Indian Ocean in an effort to more thoroughly understand the lifestyle strategies of the microbial inhabitants of this ultra-oligotrophic region. No investigations of whole virioplankton assemblages have been conducted on waters collected from the Indian Ocean or across multiple size fractions thus far. Therefore, the goals of this study were to examine the effect of size fractionation on viral consortia structure and function and understand the diversity and functional potential of the Indian Ocean virome. Five samples were selected for comprehensive metagenomic exploration; and sequencing was performed on the microbes captured on 3.0-, 0.8- and 0.1 µm membrane filters as well as the viral fraction (<0.1 µm). Phylogenetic approaches were also used to identify predicted proteins of viral origin in the larger fractions of data from all Indian Ocean samples, which were included in subsequent metagenomic analyses. Taxonomic profiling of viral sequences suggested that size fractionation of marine microbial communities enriches for specific groups of viruses within the different size classes and functional characterization further substantiated this observation. Functional analyses also revealed a relative enrichment for metabolic proteins of viral origin that potentially reflect the physiological condition of host cells in the Indian Ocean including those involved in nitrogen metabolism and oxidative phosphorylation. A novel classification method, MGTAXA, was used to assess virus-host relationships in the Indian Ocean by predicting the taxonomy of putative host genera, with Prochlorococcus, Acanthochlois and members of the SAR86 cluster comprising the most abundant predictions. This is the first study to holistically explore virioplankton dynamics across multiple size classes and provides unprecedented insight into virus diversity, metabolic potential and virus-host interactions.
Citation: Williamson SJ, Allen LZ, Lorenzi HA, Fadrosh DW, Brami D, Thiagarajan M, et al. (2012) Metagenomic Exploration of Viruses throughout the Indian Ocean. PLoS ONE 7(10): e42047. https://doi.org/10.1371/journal.pone.0042047
Editor: Jack Anthony Gilbert, Argonne National Laboratory, United States of America
Received: August 22, 2011; Accepted: July 2, 2012; Published: October 17, 2012
Copyright: © Williamson et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Funding: This research was supported by the Office of Science (BER), U.S. Department of Energy, Cooperative Agreement No. De-FC02-02ER63453, the Gordon and Betty Moore Foundation, the National Science Foundation award 0850256 and TeraGrid allocation DEB100001 on the Texas Advanced Computing Center Ranger. The funders had no role in the study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Competing interests: The authors have declared that no competing interests exist.
Viruses (predominantly bacteriophages) are the most abundant biological components of marine ecosystems, generally outnumbering their microbial hosts by an order of magnitude [1]. Viruses are significant agents of microbial mortality, influencing diversity, and play an integral role in marine ecosystem processes such as nutrient transformation and cycling [2]–[4]. Through interactions with microbes, viruses also influence the global flow of genes affecting host cell phenotype, niche adaptation and evolution [5]. Metagenomic investigations of viruses collected from varied marine ecosystems have provided insight into local and global viral diversity [6]–[8], genotypic distribution [9], [10], functional potential [11]–[15] and replication strategy [16], [17]. The majority of these metagenomic studies have been conducted on the viral fraction of marine samples, where a pre-filtration step (generally ranging from 0.22- to 0.45 µm) was used to physically separate viral particles from cellular organisms. Alternatively, metagenomic investigations have been conducted on virus-like sequences contained within the cellular fraction of marine samples, including surface water samples collected during the first reported phase of the Global Ocean Sampling (GOS) Expedition (termed Phase I) [12], and a depth profile collected from the HOT station ALOHA [18]. In these instances, viral sequences were identified based on their homology to known viruses. Each of these strategies has its limitations. Examinations of viruses within the cellular fraction of metagenomic data alone inevitably results in an underestimation of viral sequences due to its dependency on similarity to previously sequenced viruses and are constrained to only those viruses (or their nucleic acids) that were physically captured. Similarly, evaluation of only the viral fraction can result in a less than comprehensive picture of viral diversity and functional capacity since it excludes viruses removed through pre-filtration.
To date, no investigations of whole virioplankton assemblages have been conducted on waters collected from the Indian Ocean, however, targeted studies of cyanophage [19] and a virus infecting a heterotrophic flagellate [20] have been reported. In order to gain a more thorough understanding of the GOS Indian Ocean (GOS-IO) viromes and to better appreciate the implications of size fractionation, both the cellular and viral fractions were examined using metagenomic approaches that target dsDNA viral sequences. Virus-host associations were also evaluated using a novel classification method that predicts the taxonomy of putative microbial hosts using assembled viral metagenomic data based on polynucleotide compositional signatures described by Interpolated Context Models (ICMs) adopted from Phymm [21]. This study represents the first comparative analysis of viruses across multiple size classes from marine water samples.
Results and Discussion
Dataset characteristics and identification of viral sequences from filter fractions
Surface water samples (∼400 L) were collected from 17 sites from the tropical Indian Ocean between August and October 2005 aboard the S/V Sorcerer II. Two additional sites were sampled off the island of Zanzibar, Tanzania using alternate vessels (Figure 1; Table S1). The microbial community was pre-filtered using 20 µm mesh Nytex net and then size fractionated by serial filtration through 3.0-, 0.8-, and 0.1 µm membrane filters. The viral fraction of water samples (i.e. <0.1 µm) was concentrated and purified as described previously [22] (see Materials and Methods for details). DNA was extracted from microbial cells and viral particles as previously described [22], [23] and sequenced using a combination of Sanger and pyrosequencing technologies.
While the microbes retained within the 0.1–0.8 µm size fraction were sequenced for all samples, five independent samples were selected for comprehensive metagenomic exploration with sequencing performed on all microbial size fractions 20-0.1 µm, including the viral fraction (<0.1 µm) (Table 1; Table S2). The five samples that were comprehensively sequenced were selected based on sufficient quantity and quality of DNA across all size fractions. A total of approximately 228K Sanger and 2.2M 454 Titanium sequence reads were produced from the five viral libraries, resulting in a combined 2.7M predicted ORFs (see Materials and Methods for details on the ORF predication pipeline). The Sanger and 454 datasets had average read lengths of 989 bp and 360 bp, respectively. Despite rigorous purification of the viral concentrates (VCs) prior to metagenomic library construction, it became apparent during sequence analysis that one library, GSIOVIR110, contained non-trivial amounts of cellular contamination similar to sequences found in the larger filter data (0.1–20 µm). Therefore, this sample was not included in subsequent analyses with the exception of co-assembly with data from all size fractions.
The identification of viral sequences within the metagenomic data from larger size fractions was accomplished using a phylogenomic approach, the Automated Phylogenetic Inference System (APIS), that was designed for annotation of genomic [24] and metagenomic [25] datasets. APIS taxonomically and functionally classifies each sequence inferred from neighbor-joining phylogenetic trees (see Materials and Methods for more details); unlike previous reports using homology-based methods [12], [18]. This analysis resulted in the identification of 102,790 predicted proteins of viral origin within the larger size fractions (0.1–20 µm) (Table S2), representing 2.8% of the total predicted proteins from the Indian Ocean microbial dataset (∼3.6M total predicted proteins). This estimate is almost identical to a previous study which found that viral sequences accounted for ∼3% of GOS Phase I microbial data collected between Nova Scotia and French Polynesia [12].
Viral genotypic diversity
Viral genotypic diversity was estimated at the Indian Ocean sampling locations using the Phage Communities from Contig Spectrum (PHACCS) tool [26]. Richness, evenness and diversity estimates were obtained for each viral library using the complete set of reads generated upon sequencing. Comparisons were performed across the following categories based on the contig spectrum produced from Newbler-based assemblies of: 1) fragmented Sanger data (i.e. unmated), 2) paired-end Sanger data, 3) all 454 data, and 4) sub-sampled 454 data (Table 2, Table 3, and Table S3). Estimates of genotype richness varied widely between the Sanger and most of the 454 categories, but the relative ranking was conserved. On average, the most diverse site among all assemblies tested was GSIOVIR117, which also had the largest number of genotypes. The least diverse site, on average, was GSIOVIR122, which also had the lowest number of genotypes (Table 2 and Table 3). Evenness estimates were similar across categories with a slight increase in assemblies that utilized all of the 454 data per library rather than sub-samples (Table 2). Richness and evenness estimates, along with the most abundant genotype percentages, suggest that all sites harbored diverse viral populations with most of the diversity residing in the "long tail" of the genotype distribution.
To account for the possibility of varying genome size, diversity estimates were also obtained on the sub-sampled 454 datasets using a range of average viral genome sizes (Table 3). For these analyses, sample richness varied greatly depending on the average genome size used. The greater richness estimates (e.g. >100k genotypes) could reflect a limit in the PHACCS algorithm and likely do not reflect a relevant genome size estimation. It has been reported that the average genome size of marine viruses is typically between 50–80 kb [27], [28]. Our data suggests the same based on estimates of richness, with an interesting change in GSIOVIR117, where the richness estimates were more in line with the Sanger estimates when using an estimated genome size of 80 kb (Table 3). Lastly, as mentioned above, the relative ranking of samples with respect to diversity estimates was conserved and corresponded to the total number of predicted peptides per site. However, previous reports have suggested that there is no link between sequencing depth and the number of genotypes [26]. Tools for genome size estimations of viruses are available; however depend heavily on reference databases. Based on phylogenetic classification of ORFs from sequence reads, ∼20% of the GOS-IO data had significant similarity to a known sequence and therefore was classified (Table 1). Thus, we feel current approaches are not directly applicable as yet to these data.
To the best of our knowledge, this is the first study to compare PHACCS-based estimations of viral diversity using sequence information produced from two different platforms (Sanger and 454), yet assembled using the same methods. The number of predicted viral genotypes (richness) in a sample was the most variable with respect to the type of data produced (Sanger mated and unmated; 454 all and sub-samples). Sub-sampling of datasets is recommended to reduce coverage and improve the quality of PHACCS estimations.
This comparison demonstrated that estimations of viral diversity using PHACCS, particularly the number of predicted genotypes, may be influenced by the nature of viral data and their subsequent assembly; yet most estimates fell within the range of previously published reports [6]–[8]. Furthermore, measurements of viral consortia diversity were underestimated since viruses retained in the larger size classes were excluded from the analysis, as they were not included in the assemblies. Subsequent taxonomic and functional assessments (discussed below) suggest that viral sequences found in the microbial (cellular) fraction are distinct compared to those generated from the viral-size fractions.
Taxonomic binning of Indian Ocean virome
Sequences originating from discrete viral fractions (VFs) and the larger fractions (LFs) were taxonomically characterized using two approaches. One approach was based on a BLASTP comparison against the NCBI non-redundant (nr) protein database, which does not include predicted protein sequences from viral metagenomes. The other was a phylogenomic approach using APIS. Examination of metagenomic data using APIS resulted in ∼20% of sequences from the VFs producing phylogentic trees, and thus taxonomic classification (Table 1). The low level of APIS classification was due to the stringency of the method (see Materials and Methods for details). The majority of predicted protein sequences from the VF were characterized as cellular using both the homology (68%) and phylogenetic-based approaches (43%) (Table 4), despite evidence that cellular contamination was not present in the viral samples (i.e. no amplification of the 16S rRNA gene during library construction; see Materials and Methods for details). A larger proportion of sequences were classified as Virus at the kingdom level through APIS (56%) than by homology comparison to the non-redundant (nr) database (32%), due to the inclusion of viral metagenomic data of diverse origin within the APIS database. As expected due to the sample processing methodology employed, viral sequences originating from the VF as well as the LF were most similar to dsDNA viruses; however, the most abundant taxonomic group varied.
According to phylogenetic characterization, the largest proportion of sequences from VF in the dsDNA virus category were most similar to sequences derived from environmental samples, including marine planktonic (∼95%) and marine sediment viral sequences (∼4%) (Table 4). Previous taxonomic characterization of marine viral metagenomes [6]–[8] suggests that the majority of viruses within these datasets are most similar to caudoviruses (i.e. tailed phages) and contain a large proportion of cyanophage-like sequences. Similarly, BLASTP-based comparisons of VF sequences against nr (NCBI) placed almost the entire dataset (∼95%) within the known Caudoviridae family, demonstrating the utility of including viral metagenomes in search databases (Table 4). Alternatively, phylogenetic characterization of LF viral sequences indicated that these were more similar to known caudoviruses (∼50%) rather than marine planktonic viruses (∼36%) suggesting inherent differences in population structure between the VF and LF (Table 4). These differences were more apparent at the family level where myovirus sequences were significantly more abundant in LF than VF, while podo- and siphovirus sequences were more abundant in the VF than LF. Overall, cyanophage-like sequences comprised a significant proportion of the Indian Ocean VF and LF caudovirus data based on BLAST and phylogenetic analyses; potentially reflective of the surplus of these sequences in reference databases. Sequences were most similar to phages infecting Prochlorococcus (P-SSM2, P-SSP7 and P-SSM4) and Synechococcus (S-PM2 and the Syn group of phages) (Table S4).
The VF and LF also differed with respect to the relative distribution of nucleocytoplasmic large DNA viruses (NCLDV), a group that includes the phycodna- and Mimi-viridae families that appear to infect eukaryotic organisms (Table 4). Although the sequenced strain of Mimivirus is known to infect amoebae [29], it has been proposed that marine Mimivirus-like sequences originate from viruses that infect a variety of marine protists including eukaryotic phytoplankton, specifically hapto- and prasinophytes [30]–[32]. Homologs to all 6 of the NCLDV families were detected in the LF and VF with different levels of relative abundance (Table 4). Overall, the LF had a larger proportion of sequences similar to the phyco- and Mimi-like viruses than VF. When restricting taxonomic evaluation to only NCLDV-related sequences, phycovirus homologs were more prevalent in the VF than the LF. The majority of VF phycovirus homologs were further classified as Chlorovirus while LF homologs were equally distributed between Chlorovirus and Ostreococcus virus OsV5 (Table 4). Mimivirus-like sequences were also more abundant in the LF than VF. The varying taxonomic distributions of viruses that likely infect bacteria (tailed phage) and eukaryotic phytoplankton (phyco- and Mimi-like viruses) between the VF and LF strongly suggests that size fractionation of marine microbial communities enriches for specific groups of viruses and significantly influences the distribution of viral families. This emphasizes the utility of sequencing across multiple size classes rather than just those designated as viral or cellular. The differential partitioning of viruses could be attributed to the morphology of the virus particle (for extracellular viruses), virus adsorption to host cells, active viral replication within infected host cells, and latent viral infection.
Functional potential of Indian Ocean viruses
Comparison of VF and LF sequences.
To examine the gene function of viruses captured in this study, ORFs derived from sequencing reads were assigned to existing protein clusters that included the published GOS data as well as reference data [33]. The GOS-IO protein dataset consisted of all ORFs from the viromes (VF and LF). Statistical analyses were used to determine if significant relationships existed between the functional repertoire of viruses within the VF and LF (Principal Component Analysis, PCA), as well as the measured oceanographic environmental parameters (Canonical Correlation Analysis, CCA). Proportional abundances for all protein clusters were calculated by library and used in each analysis. PCA indicated that viruses within these two groups were functionally divergent (Figure 2). However, no significant relationship existed between viral function and environmental factors as measured by CCA (data not shown). A significant amount of the variance was accounted for by the first two components (69%), demonstrating that size fractionation partitions viruses with different functional potentials, likely related to the similar observation found by taxonomic evaluation. A PCA biplot was also used to identify protein clusters containing viral sequences that were driving the separation of the VF and LF groups. Of particular note, two different clusters containing the same viral protein, large subunit terminase, were over-represented; cluster A predominantly contained LF sequences (LF = 2,683; VF = 1,014), and cluster B mostly consisted of VF sequences (VF = 74,812; LF = 329). Terminases are viral enzymes consisting of a small and large subunit that enable the packaging of DNA into viral proheads [34]. The small subunit is responsible for DNA recognition while the large subunit performs several functions including DNA cutting, portal vertex docking and ATPase-mediated translocation of DNA [34]. A phylogenetic tree was created from representative sequences contained in these two clusters including Indian Ocean VF and LF, GOS Phase I and publicly available reference sequences. The tree was characterized by two phylogenetically distinct groups that effectively partitioned the two terminase clusters (Figure 3). The LF group, corresponding to cluster A, contained well supported clades with Prochlorococcus and Synechococcus cyanophage reference sequences in addition to environmental viral sequences, which could represent uncultivated cyanophage. The VF group, corresponding to cluster B, was more phylogenetically diverse, characterized by multiple well-defined clades containing mixtures of reference phage and environmental viral sequences. The reference phage in the cluster B group belonged to all three of the tailed phage families rather than just described T4-like myoviruses present in the LF group, similar to the taxonomic distribution of Indian Ocean VF sequences (Table 4). Terminase-mediated packaging of DNA is a conserved mechanism among diverse linear, dsDNA containing viruses [34]. Several studies indicate that the terminase large subunits share a common ancestry with other translocating ATPases including helicases and type I and III restriction endonucleases [35], [36] and that terminase phylogeny may be predictive of the nature of the ends of virus DNA (e.g. cohesive ends) [37]. Clading of the environmental virus sequences with T4-like reference phages in the cluster A group suggests that these viruses may possess terminally redundant, circularly permuted genomes that are packaged using a T4-like headful packaging mechanism. However, it is difficult to speculate as to the packaging mechanisms of viruses in cluster B since the nature of the DNA ends for the reference phage is unclear.
Viral libraries are represented by the green circles and larger fraction libraries are represented by the blue triangles.
Selected reference sequences are colored red and green. Representative Indian Ocean VF and LF sequences are colored pink and purple respectively. GOS Phase I sequences are colored blue. Confidence values are displayed on the tree.
Pathway analyses.
Protein clusters containing viral sequences originating from the VF and LF were also categorized in the context of KEGG pathways, with the addition of a virus structure category, in order to assess the functional potential of Indian Ocean virioplankton. The majority of protein sequences from VF and LF were not mapped to a pathway and remained uncharacterized (VF = ∼80%; LF = ∼37%). This level of functional novelty was not unexpected due to high abundance of hypothetical proteins in each category (VF = ∼68%; LF = ∼50%). Smaller proportions of data were considered poorly characterized (VF = ∼3%; LF = ∼5%) or were not specific to a particular pathway (VF = ∼3%; LF = ∼8%). The remaining sequences were mapped primarily to the Virus Structure, Metabolism and Genetic Information Processing categories (Figure 4, Table S5). A heatmap of functional categories (Figure S1) further demonstrated the partitioning of VF and LF sequences as observed through PCA with differential clustering of the VF and LF. The vast majority of VF (∼79%) and LF (∼80%) sequences within the genetic information processing pathway were categorized as putative DNA replication, recombination and repair proteins (Table S5).
The inset pie charts represent the breakdown of the Metabolism super-pathway (top) and Energy metabolism pathway (bottom). The percentages of viral sequences attributed to the Energy metabolism pathway are indicated on the Metabolism pie charts.
The largest proportion of viral sequences within the Metabolism pathway was attributed to energy metabolism, with a slight enrichment in the VF (Figure 4, Table S5). A relatively small proportion of these sequences from the VF and LF were classified into lower categories including nitrogen metabolism and oxidative phosphorylation. Although nitrogen metabolism genes have been previously documented in viral metagenomes created from a variety of environmental settings [11], the exact nature of the genes contributing to this pathway was unclear. Several studies have demonstrated that viruses infecting eukaryotic phytoplankton and zooplankton (likely to be retained in the LF) carry genes of either host or bacterial (i.e prey) origin [38]–[45]. However, the majority of these genes are involved with lipid, carbohydrate and protein metabolism and polyamine biosynthesis rather than nitrogen metabolism or oxidative phosphorylation [46]–[48]. Only VF sequences possessed enzyme commission (EC) numbers that could be linked directly to the KEGG nitrogen reduction and fixation pathway and these were examined in more detail. The majority of VF sequences within the nitrogen metabolism category were annotated as glutamate synthase (n = 98), which together with glutamine synthetase, comprise the GS-GOGAT pathway. This pathway facilitates the process of ammonium assimilation in phytoplankton [49] and is dependent on the availability of nitrogen compounds in the environment. The Indian Ocean is considered an oligotrophic water mass with very low concentrations of available nitrogen [50], and nitrogen concentrations measured in our samples were indeed indicative of a nitrogen-limited environment (Table S1). The presence of glutamate synthase genes suggest that viruses may play a role in nitrogen modulation and assimilation during the infection of host cells. Proteins involved in oxidative phosphorylation (OP) pathway were much more abundant than photosynthesis-related proteins, comprising ∼30% of VF and ∼11% of LF sequences within the energy metabolism category (Figure 3, Table S5); with 466 VF and 25 LF sequences possessing EC numbers. NADH dehydrogenase I subunit and inorganic diphosphatase were represented in both the VF (n = 255 and 53 respectively) and LF (n = 7 and 18 respectively) while the cbb3-type cytochrome C oxidase subunit I was only detected in the VF (n = 158). To the best of our knowledge, this is the first report of viral cytochrome C oxidase and inorganic diphosphatase genes in the marine environment. NADH dehydrogenase and cytochrome C oxidase are both components of the electron transport chain in bacteria, which is ultimately used to produce ATP. Viral type I NADH dehydrogenase genes were first reported by Alperovitch-Lavy and colleagues [51] and were detected through a combined analysis of GOS microbial scaffolds and long PCR amplification of viral fractions collected from the Pacific Line Islands [52]. Interestingly, the viral NADH dehydrogenase genes were co-localized on viral scaffolds (and amplicons) containing photosystem I and II genes suggesting that cyanophage encode this complex. A subsequent search of GOS scaffolds by Sharon and coworkers (2011) for viral auxiliary metabolic genes also revealed the presence of viral Type I NADH dehydrogenase subunits putatively involved in cyclic electron flow around PSI and respiration during viral infection. Again, these genes were attributed to cyanophage since the majority of scaffolds containing viral auxiliary genes that were examined in this study appeared to be related to know cyanophages [15]. It's possible that the viral NADH dehydrogenase genes observed in this study are of cyanophage origin due to the abundance of cyanophage- like sequences in the Indian Ocean data. However, the abundance of virus-SAR86 host predictions (discussed later) coupled with the presence of viral cbb3-type cytochrome C oxidases, which are only found in proteobacteria, suggests that viruses that infect heterotrophic bacteria may also be the source of these genes. The enzyme inorganic diphosphatase catalyzes the conversion of diphosphate (Ppi) to phosphate (Pi), which is needed for the production of ATP. Out of the three OP enzymes, inorganic diphosphatase was more evenly distributed between the VF and LF suggesting that a diverse group of viruses may carry this gene. If the viral version of inorganic diphosphatase is expressed and functional during infection, viruses could potentially contribute to host ATP production. This process could temporarily prolong the lifespan of the host and increase replication efficiency, analogous to viral NADH dehydrogenase and PS genes. An alternative hypothesis is that viral inorganic diphosphatase is used to produce Pi for incorporation into viral nucleic acids. Phosphate concentration in the marine environment is thought to influence virus production due to their inherently high nucleic acid to protein ratio [52]. The ability to influence the availability of phosphate during infection could maximize nucleic acid biosynthesis. Furthermore, a variety of phosphorous metabolism genes have been detected in the genomes of cultivated viruses that infect heterotrophic bacteria, cyanophage genomes [53], [54], as well as numerous viral metagenomes [11], [12], [55], suggesting that viruses have developed multiple strategies to address phosphate-limiting conditions. It is now well known that cyanophages carry photosynthesis (PS) related genes, including those associated with photosystems I and II [14], [56]–[59], and the presence of viral PS genes has been documented in numerous marine metagenomic studies [7], [12], [13], [15], [18]. However, only a small proportion of VF sequences (0.42%) could be mapped to proteins involved in photosynthesis based on KEGG classification of protein clusters. A direct BLAST analysis of VF and LF sequences using PSI and PSII genes collected from cyanophage genomes (PSII) as well as Prochlorococcus and Synechococcus (PSI) (Table S6) did reveal the presence of additional viral PS genes. The PSII genes psbA and psbD (total = 6,877) far outnumbered the PSI genes that were previously noted in the marine environment including psaA, psaB, psaC, psaD psaE and psaK (total = 371) (Table S7). Viral PSI genes were also noted in previous analyses of GOS microbial metagenomic data including 17 samples collected from the Indian Ocean [14], [15]. It is hypothesized that viral PSI components may facilitate electron donation from alternative sources other than plastocyanin to the PSI of their hosts, thereby increasing ATP generation for replication [14]. The discrepancy in the abundance of viral PSII versus PSI genes in the Indian Ocean data suggests that cyanophage may benefit more from carrying PSII genes, which have been shown to supplement photosynthesis in culture [60], [61].
Carbonic anhydrase (CA) enzymes were also present in the viral data (VF = 24; LF = 1). CA is responsible for catalyzing the conversion of carbonic acid to CO2 and is utilized by a diverse group of marine phytoplankton as part of their inorganic carbon concentration mechanism (CCM) to support photosynthetic carbon fixation [62]–[64]. Again, this is the first report of viral CAs in the marine environment to the best of our knowledge. Together with the PSI and PSII genes, the presence of these viral CAs suggests that the repertoire of viral-encoded photosynthesis-related genes is broader than previously recognized.
Identification of potential virus-host relationships
There is a growing body of evidence based on analyses of fully sequenced viral genomes that phage tend to mimic the polynucleotide sequence composition of their hosts [65]–[68]. On this evidence, we developed the first classification method to predict the taxonomy of bacterial hosts for uncharacterized viral metagenomic sequences that does not rely on homology or sequence alignment; rather it compares sequence composition between viruses and bacteria. Briefly, our MGTAXA method involves three steps [69]: (I) trains one Glimmer Interpolated Context Model (ICM) [70] for every taxonomic node represented by at least one available bacterial reference sequence; (II) scores each metagenomic viral sequence against all models; and (III) picks the model with the highest score as representing the taxonomy of the putative host. This follows the approach of the Phymm bacterial classifier [21] where it was used to assign taxonomy to bacterial metagenomic sequences. The benchmarking of this classification scheme on temperate phages resulted in 97% accuracy at the phylum level and 89% at the genus level of the predicted host taxonomy with 2% rejection rate of the testing samples, and on all phages regardless of replication strategy - in 76% accuracy at the phylum level and 50% at the genus level with 6% rejection rate (see Methods S1, for detailed method description and benchmarking protocol). If the method were selecting candidate hosts from all bacterial genomes in RefSeq in an unbiased way, a randomly generated genus-level assignment would have an average accuracy of 0.2%.
Two approaches were taken to predict the bacterial hosts: 1) direct assignment – the ICMs trained on RefSeq (NCBI) genomes and 2) a transitive assignment – the hosts were predicted using ICMs trained on large metagenomic bacterial scaffolds (>100 kb) from the cellular fraction, to which a bacterial taxonomy was in turn assigned based on scoring against RefSeq ICMs (see Methods S1, for details on cellular fraction contig selection and taxonomic assignment). The essence of the transitive assignment process is that it restricts the model set to the bacterial genomes that are most abundant in a given metagenome (at least those that assembled into large scaffolds) before using that model set for host assignment. The value of the transitive method is that it recruits viral contigs to microbial scaffolds obtained from the same environment. On the other hand, depending on the dynamics of the phage-host system as well as sequence variability, the hosts for the assembled viruses might not necessarily be the best assembled microbes. In that case, the direct assignments can still be more accurate and informative. Thus, we consider these two approaches to function synergistically in the designation of virus∶host classifications.
The method was subsequently applied to all 5 Kb or longer contigs assembled from the Indian Ocean 454 viral libraries. Figure 5 demonstrates the results of MGTAXA predictions of putative host taxonomy for the viruses present in the Indian Ocean metagenomic dataset. We found that even when all RefSeq bacterial genomes were used for host prediction (i.e. direct assignment) there was a preferential selection of microbial taxa that are indigenous to the marine environment including Prochlorococcus and Idiomarina. Assignments to NCBI unclassified Gammaproteobacteria (which encompasses diverse marine isolates including the uncultivated SAR86 cluster), and to a lesser degree, the SAR86 cluster itself were also prevalent [71]. This behavior further supports the validity of our host prediction method already demonstrated on the compiled benchmark. The transitive assignments redistributed the number of assigned viral reads per contig towards fewer overall bacterial genera, generally focusing on those that demonstrated the highest abundance in microbial metagenomic data (Table S8) such as the SAR86 cluster (119 scaffolds) and Rhodobacterales HTCC2255 (33 scaffolds). This result was expected by the design of the transitive assignment methodology. However, this trend was not universal, as demonstrated by the number of Prochlorococcus assignments for which 38 bacterial scaffolds were assigned (Figure 5; Table S8). The viral contigs that were assigned to these putative host taxa by the direct method were largely reassigned to several different bacterial genera by the transitive method (Table S9). Transitive assignments to Candidatus Pelagibacter and the SAR11 cluster were also sparse despite the prevalence of bacterial scaffolds attributed to these organisms (87 and 33 respectively) (Table S8). The top predicted host for Indian Ocean viral assemblages using transitive methodology was the SAR86 cluster. The SAR86 cluster encompasses a group of uncultivated, proteorhodopsin-containing Gammaproteobacteria [72] and can comprise a significant portion of the microbial communities in various marine environments [73], [74]. Since the bacterial members of the SAR86 cluster remain wild, no phages infective for these organisms have been reported. However, it is unlikely that these microbes are completely resistant to viral infection. Indeed, our results indicate otherwise and suggest that SAR86 virus-host interactions prevailed in the tropical Indian Ocean at the time of sampling. Due to the lack of specific SAR86 virus isolates and their corresponding genomic information, none of our viral metagenomic data could be specifically assigned to these putative viruses through our taxonomic analyses. This limitation further highlights the benefit of the homology-independent predictions of host taxonomy by revealing previously unknown, yet potentially significant virus-host relationships. Cyanobacteria within the Acaryochloris genus became the second most abundant predicted host for viruses in the Indian Ocean through transitive assignment despite the fact that only 10 bacterial scaffolds were assigned to this organism. Acaryochloris exists as either a free-living organism or as a symbiont of higher organisms (including macroalgae and ascidians) and is unique among the cyanobacteria since its main photosynthetic pigment is Chld rather than Chla [75], [76]. Two Acaryochloris phage have also been isolated and their genomes sequenced and recently described by Chan and coworkers [77] who documented the unique presence of mitochondrial DNA polymerase. The authors of this study found homologs of this phage-encoded gene in the GOS microbial data, including the Indian Ocean, further suggesting that these cyanophage (and their hosts) are present in this environment. It's possible that these viruses were more abundant in the Indian Ocean at the time of sampling than indicated by our analyses with related sequences not receiving a definitive taxonomic assignment as discussed previously.
The area of each bar is proportional to the total number of viral reads contained in the contigs assigned to a particular host genus. Direct assignments are colored green and transitive assignments are colored blue. Direct assignments were made by assigning viral contigs to the host models built on NCBI RefSeq genomes. Transitive assignments were made by assigning viral contigs to models built on large bacterial contigs, and then assigning taxonomy to these bacterial contigs (as described in the text). Twenty top taxa in pairs of green and blue are shown according to the maximum count from either direct or transitive assignment method, respectively. If no bar is shown for one of the methods, the count was zero.
This is the first study to examine the Indian Ocean virome using holistic metagenomic approaches. Since our analyses were not constrained to the "viral fraction" of samples, we were able to gain a much more comprehensive understanding of virus diversity, total gene complement and functional potential as well as virus-host relationships. Significant taxonomic differences were evident between viruses represented in the VF versus the LF. An enrichment of cyano-myoviruses and viruses that likely infect eukaryotic phytoplankton or heterotrophic protists was found in the LF; while podo- and siphoviruses were prevalent in the VF. Similarly, notable differences in functional potential were evident by the distribution of abundances within metabolic pathways. The presence of putative viral genes potentially involved in nitrogen metabolism, carbon fixation and oxidative phosphorylation suggests that viruses infecting autotrophic and heterotrophic microbes may influence host cell physiology through diverse mechanisms in the Indian Ocean. Predicted virus-host relationships suggest that members of the SAR86 cluster and the cyanobacteria Acaryochloris and Procholorococcus represent the dominant hosts for viruses in the Indian Ocean, providing insight into the types of viruses that putatively possess these metabolic capabilities.
Materials and Methods
Sample and metadata collection & size fractionation
Surface water samples (∼400 L) were collected from 17 sites from the tropical Indian Ocean between August and October, 2005 aboard the S/V Sorcerer II. Two additional sites, GS148 and GS149 (∼200 L), were sampled off the island of Zanzibar, Tanzania using alternate vessels. All field studies conducted within the EEZ of foreign nations received Marine Research Permits as required under the U.N. Convention on Law of the Sea, and as required, separate agreements to access genetic resources. The locations were not privately owned or protected in any way and the field studies did not involve endangered or protected species. A YSI (model 6600) was used to measure the physical environmental parameters including water temperature, salinity, dissolved oxygen and sample depth. Sub-samples were collected for dissolved nutrient analyses as described previously [23] and were processed by the Virginia Institute of Marine Sciences (VIMS) Analytical Service Center. The microbial community was first passed through a 20 µm Nytex pre-filter and then size fractionated by serial filtration through 3.0 µm, 0.8 µm and 0.1 µm membrane filters (Pall Life Sciences, East Hills, NY). Filters were preserved and stored as described previously [23].
Virus concentration & purification
The viral fraction of water samples (i.e. <0.1 µm) was concentrated using tangential flow filtration (TFF) as described previously [22]. Viral concentrates (VCs) were cryo-preserved through the addition of glycerol (10% final concentration) and frozen at −20°C. VCs were transferred to and stored at −80°C upon return to the J. Craig Venter Institute (JCVI) until further processed. VCs were further concentrated, treated with nuclease to remove free DNA and pelleted through a sucrose cushion as previously described [22]. To check for cellular contamination a 16S rRNA gene PCR was used with positive (E. coli cells) and negative (DEPC water) samples. Gel electrophoresis was performed on 2 µl (of 50 ul total reaction volume) on a 0.8% agarose gel stained with SybrGold (Invitrogen). If no discernible 16S rRNA gene product (∼1500 bp) was visualized the samples were further processed through DNA extraction.
DNA extraction, library construction, sequencing and post-processing of data
Methods describing DNA extraction from filters, construction of clone libraries, template preparation, and automated cycle sequencing can be found in Rusch et al., 2007. Viral DNA was extracted from purified VCs and modified linker amplified shotgun libraries (LASLs) were constructed as described previously [22]. In addition to Sanger libraries, 454 Titanium libraries were prepared from amplified viral DNA (LASLs) and pyrosequenced at JCVI. Briefly, genomic DNA was fragmented and size-selected to a range of 500–800 bp. Linkers were ligated to DNA fragments for use as priming sites during subsequent amplification reactions. Three replicate amplification reactions were completed using 15 total cycles to reduce biases and amplification of potential cellular contamination from kit reagents. Adaptors were ligated onto the fragments and used as priming sites for emulsion PCR. Amplified samples were purified using AMPure beads to remove small DNA fragments and sequencing was performed using the 454 GS FLX Titanium sequencing platform. Viral metagenomic sequences were trimmed of any linker sequence left over during LASL production. Additionally, artificial replicates were screened for and removed from all 454 data using an approach described by Gomez-Alvarez (2009) [78].
Identification of viral sequences in the filter fractions of data & taxonomic profiling
The Automated Phylogenetic Inference System (APIS) was used for the taxonomic classification of viral predicted proteins as well as the identification of viral proteins within the larger size classes of data [25]. APIS automated the process of calculating sequence similarity, alignment, and phylogenetic inference for each protein in a given dataset. Each predicted protein was compared to an in-house curated database (phyloDB), which consists of proteins from complete (or nearly complete) genomes and selected Sanger-sequenced viral metagenomes, using BLASTP. Full-length sequences of significant BLASTP hits were retrieved and then a multiple alignment was generated using MUSCLE. From this alignment, a neighbor-joining tree was produced using QuickTree [79] to determine the phylogenetic placement of the query sequence by comparing the taxonomic classification of the sequence(s) that clade with the query. If the taxonomic information differed among these clading sequences, this was noted and the classification of the query was limited to the higher taxonomic ranks where they were in agreement. To identify viral sequences from the larger size class (0.1–20 µm) of organisms, all proteins classified via APIS as viral at the Kingdom level were used in further analyses.
A BLAST-based approach was used, which is part of the Viral Metagenome Annotation Pipeline (VMGAP) [80]. Environmental sequences were compared against NCBI AllGroup.niaa database (BLASTP) using an e-value of < = 1e−10 and identity > = 50%, the top hit was noted and taxonomic information transferred to each metagenomic protein.
Functional characterization of viral sequences
ORF prediction and viral annotation.
Open reading frames were predicted as described previously [81], and were based on a combination of naïve 6-frame translations and MetaGeneAnnotator [82], an ab initio gene finder program. The predicted protein coding sequences were then annotated using the Viral Metagenome Annotation Pipeline (VMGAP), described by Lorenzi and coworkers [80].
Annotation of protein clusters and KEGG classification.
ORFs predicted on 454 sequence reads were mapped to protein clusters as follows. RPS-BLAST was first used to compare each protein sequence against a database of Position Specific Scoring Matrices (PSSM) representing clusters containing more than 20 proteins. Then, proteins were assigned to clusters based on the PSSM that gave the highest bit score with an e-value ≤1×10−3. For those protein sequences that did not produce any significant hit against the PSSM database, a BLASTP search was conducted against a database of proteins belonging to clusters containing fewer than 20 members. Proteins were then assigned to clusters based on the hit having the lowest e-value with at least 40% identity and 70% coverage. Protein sequences that did not result in any significant hit remained unassigned.
Annotation was performed on all protein clusters [33] from the individual predicted protein annotations within each cluster. All protein annotations were counted within, and across, all clusters and an uncorrected p-value was calculated using Fisher's exact test indicating the probability of random association of each annotation with each cluster. The annotation, description, coverage and p-value were given for each of the 3 best annotations. For clusters that did not receive annotation by this method, a different strategy was taken based on additional searches and relative ranking of the results (Methods S1, for details). To bin protein clusters into KEGG classes, all cluster names having a match that was 100% identical to any KEGG class definition were assigned to the corresponding three levels of classification from the KEGG Pathway Database. When more than one possible classification class were available at a particular level, all classes representing <75% of that level were labeled "unspecific". Clusters without any direct KEGG association were examined for the presence of specific keywords and then classified following the same three-level classification. Otherwise, clusters were binned as unclassified for each level.
Large subunit terminase phylogeny.
Environmental sequences and references were retrieved from two proteins clusters, both containing putative genes encoding terminase enzymes. The average length of all sequences was calculated and sequences below 40% of the average were removed. Non-viral GOS Phase I sequences were reduced through clustering and only the representative sequence used in subsequent steps. Existing hidden Markov model (HMM) profiles, including fragment HMMs, obtained in cluster annotation or peptide annotation were identified and the HMM that accounted for the majority of viral sequences was selected for alignment using HMMER (http://hmmer.org/). The multiple alignments were processed through a gap-filtering step to remove sequences that contain 60% or more gaps. Phylogenetic trees were constructed using the program FastTree [83] and processed using Archaeopteryx (http://phylosoft.org/archaeopteryx/).
Statistical analyses
Principal Component Analysis (PCA) and Canonical Component Analysis (CCA) were performed using the R statistical program. For PCA, library proportional abundances of viral sequences for each protein cluster were calculated and used to build the centered matrix. For CCA, oceanographic metadata was included to assess correlation between these and protein cluster proportional abundances.
Metagenomic assembly
The Sanger and 454 data from each viral library were assembled independently using the Newbler GS De Novo Assembler, version 2.3, with a minimum identity threshold of 86%. In addition to virus-specific assemblies, a comprehensive global assembly was also conducted on all Sanger reads produced from Phase I of GOS and all Indian Ocean size fractions as described by Rusch et al. 2007 using the Celera Assembler version 5.3 with a minimum identity threshold of 86%. A minimum overlap length of 40 bp was used for all assemblies.
Genotype diversity estimates
A local command line version of the PHACCS program [26] was used to estimate viral genotypic diversity. The 454 datasets were randomly sub-sampled 8–10 times prior to assembly to approximate the same amount of base pairs as the corresponding Sanger datasets and the resulting contig spectrums were used as input. The average genome size used was 50 Kbp for all, except where the 454-subsampling used additional genome sizes to assess the effect on diversity estimates, and the minimum overlap was 40 bp. Averages and standard deviations were calculated in Excel.
Virus-host classification
As this is the first report of an alignment-independent method that aims to predict the virus∶host relationships in the metagenomic datasets, detailed methodology and benchmarking is reported in Methods S1. Briefly, sequence composition similarities were used to bin viral contigs with 1) bacterial hosts from RefSeq and 2) the cellular fraction metagenomic sequences corresponding to this dataset. Hosts were described at the genus taxonomic level; if the lowest level reached for assignment was order or higher, the viral contigs were labeled as 'unassigned'. The implementation parallelized for a distributed computing cluster is available as part of our open-source software package MGTAXA [69].
Accession numbers
All Sanger-generated viral data was submitted to the NCBI Trace Archives. These include the raw reads from viral metagenomes as well as the predicted protein sequences extracted from the larger size fractions of data. All 454-generated viral data was submitted to the NCBI Short Read Archive (SRA), corresponding to accession numbers SRX096024, SRX096023, SRX096025, and SRX096299. The microbial and viral assemblies used for the analysis of virus-host classification were submitted under NCBI's Project ID 19733.
Supporting Information
Methods S1.
Additional methods including, i) Alternative cluster annotation, ii) Identification of viral photosynthesis genes, iii) Classification of Virus∶Host relationships.
Figure S1.
Heatmap showing delineation of viral sequences from large vs. viral fraction. Abundance of sequences from each site within selected functional groups at the level-2 classification of Gene Ontology (GO) was used to generate heatmap. Hierarchical clustering of sites indicates a grouping of large (blue) versus viral (green) fraction. Functional groups are color-coded as follows: cellular processes (purple), cellular processes and signaling (blue), environmental information processing (green), genetic information processing (yellow), metabolism (orange), phage structure (red).
Table S1.
Indian Ocean sample characteristics and oceanographic metadata.
Table S2.
Viral sequences retrieved from the larger size classes.
Table S3.
Assembly statistics for Indian Ocean viral metagenomic libraries.
Table S4.
Top five most abundant cyanophage homologs based on taxonomic assignments.
Table S5.
Functional characterization of VF and LF viral sequences by KEGG category.
Table S6.
List of reference sequences and genomes used to identify Indian Ocean viral photosystem genes.
Table S7.
Viral photosystem genes present in the Indian Ocean metagenomic data.
Table S8.
GOS Bacterial Scaffold Taxonomic Assignments based on MGTAXA.
Table S9.
Re-distribution of viral contigs to bacterial taxa from direct to transitive assignments.
Table S10.
NCBI RefSeq prokaryotic genomes used for building reference models.
Table S11.
Benchmarking accuracy for predicting the host taxonomy.
Table S12.
Benchmarking set of virus∶host pairs compiled from NCBI RefSeq.
Table S13.
Summary counts for the benchmark virus∶host pairs in Table S12.
We would like to deeply thank Jeff Hoffman for sample and metadata collection and processing, Cynthia Andrews-Pfannkoch for DNA extraction from filters and construction of metagenomic clone libraries, Johannes Goll for his assistance with METAREP; Jeff Hoover for his assistance with the viral metagenomic annotation pipeline; Harald Kattnig for his contribution to phylogenetic tree building, Doug Rusch for his contribution to selecting bacterial scaffolds for MGTAXA host classification and Jasmine Pollard for her assistance with figure preparation.
Author Contributions
Conceived and designed the experiments: SJW JCV. Performed the experiments: SJW LZA DWF. Analyzed the data: SJW LZA HAL MT DB JPM AT SY. Contributed reagents/materials/analysis tools: SJW LZA HAL JPM AT SY. Wrote the paper: SJW LZA HAL AT SY.
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text:
For my song "Lost Universe", you'll see it just by scrolling down a bit near the middle in the music section. I was pretty stoked.
Thought it kinda cool. I like to pretend I'm famous sometimes just becuz it pulls up all my websites when you punch "Josh Hunsaker" into google and sometimes I get stuff like this (it's rare, so I never get a dangerous ego boost lucky for me).
Just had to gloat for a bit…ah, feel better now. I'm good. Direct link to song:
Damn that is AWESOME Josh! Well done man! Damn! Seriously cool track dude! Found myself on the IMDB by doing the same – googling oneself my be vain, but its a great way to find some uses of your music!
Congrats for being frontpaged on NG
I was frontpaged also several times, once was my "Freedom Cry".
and also my "Good Morning America" was here on AJ as "track of the day".
Anyway, I wish you that this is only the start and your tracks will be featured as "track of the day", you'll be in the "Author Salute" article and constantly on NG frontpage
Yeah, sorohan, I kept hoping they would feature a track of mine here so I could get that extra cool paw!! lol. I like how some people have like 50 paws below their avatar. Reminds me of the equivalent of being a highly decorated soldier or something.
I've been really sick/lazy lately so I haven't submitted much new stuff to aj but hopefully come this weekend I can start uploading again…
I thought it was funny the first time I googled myself and it turned up with like 30,000 hits. I had my first glimpse of being 'famous' then, haha. Maybe if I pretend hard enough it will become reality, something like "fake it till you make it", that would be nice
Well, to get yourself known, you should collaborate with as much people you can.
I'm talking everything here, flash games & movies on NG, YouTube vids, live concerts, theater plays, loops over friends websites and after awhile you'll get more than 30,000 hits on Google
That would be neat to get that some day. Great job guys!

subset: commoncrawl


text:
NORA Manufacturing Sector Strategic Goals
R012317 - 021H: Hearing Hazard Associated with Industrial Noise Exposure (2317)Start Date: 5/15/2008
End Date: 5/14/2012
Principal Investigator (PI)Name: Viji Potula
Organization: NIOSH
Sub-Unit: OEP
Funded By: NIOSH
Primary Goal Addressed4.0
Secondary Goal Addressed
Attributed to Manufacturing
Project Description
Short Summary
For a given SPL and spectrum of a noise any realistic combination of the three 'primary variables' that yield the same value of ß(t) will produce the same hearing loss. If this hypothesis can be shown to be true then the evaluation of industrial noises for hearing conservation purposes will be greatly simplified by using an energy metric and an appropriate ß(t) value. If not true, then in addition to energy and ß(t) the above three histograms will need to be measured and evaluated and noise exposure criteria will of necessity be more complex. The data gathered from the proposed experimental design will serve as a guide in the latter case.
The primary question that will be resolved by this experimental design is: In order to estimate hearing loss from prolonged complex noise exposure, do we need to know something about the 'primary variables' that define a complex noise or is ß(t) a sufficiently encompassing metric? This data is a necessary prerequisite to the application of ß(t) to industrial hearing conservation/noise measurement protocols.
The equal energy hypothesis (EEH) is the basis of our current noise exposure criteria. Application of the EEH in many industrial environments severely underestimates hearing loss. For the past two funding cycles we have been studying, in an animal model, the effects that the statistical properties of a complex noise exposure, as embodied in the single number metric, the kurtosis, have on hearing. The kurtosis [(ßt)], defined as the ratio of the fourth-order central moment to the squared second-order moment of the amplitude distribution, has been shown to be important in identifying and quantifying the hazard to hearing from complex industrial noise environments. The primary aim of the earlier work was to develop an approach to measuring noise that would predict the hearing loss that will result from long-term industrial exposures. This proposal, which is a continuation of this effort, has a single specific aim: To develop an understanding of how the variables that define a complex noise affect hearing loss for a fixed value of the kurtosis. In our previous work we varied parameters such as (i) the peak and (ii) interval histograms and (iii) duration of the impact noise transients in order to produce, at a constant energy, a broad range of ß(t) over which the EEH and hearing trauma could be studied. An extension of the work, and one that is necessary to establish guidelines for using (t) in noise assessment and measurement practice, is to understand how the above three `primary variables' [(i), (ii), and (iii)] affect hearing trauma at a constant ß(t). Considering that for a given spectral distribution of energy, there is virtually an infinite number of different noise exposures that can have the same ß(t), the question that we are asking is: In order to estimate hearing loss from prolonged exposure, to what extent do we need to know something about the `primary variables' that define the complex noise or is the kurtosis a sufficiently encompassing metric? All planned exposures will be of equal energy and will be structured to model some of the essential features of an idealized workweek lasting three weeks. [Long-term exposures often yield results that differ from those found from shorter acute exposures.] Exposures will be nonGaussian, interrupted and intermittent. For three values of the kurtosis, ß(t) = 10, 25 and 50, we will establish the relation among: (i) the three primary variables (peak, interval and impact duration histograms) that reflect the basic statistical properties of a complex noise; (ii) the kurtosis; and (iii) the noise-induced hearing loss as established by hearing thresholds and sensory cell loss. These results will complement the results generated over the previous two funding cycles by evaluating the sensitivity of ß(t) to the primary variables that define a complex noise. Such data is a necessary prerequisite to the application of ß(t) to industrial hearing conservation/noise measurement protocols. This data will also contribute to our continuing evaluation, application and understanding of the EEH. Project Narrative: The equal energy hypothesis (EEH) is the basis of our current noise exposure criteria. Application of the EEH in many industrial environments severely underestimates hearing loss. The proposed research is aimed at developing a new noise measurement and evaluation strategy. That will predict the potential of an industrial noise to produce hearing loss.
There is only one specific aim to the proposed work: to demonstrate whether or not noise-induced trauma can be estimated by knowing only the exposure energy and the kurtosis, ß(t), regardless of the value of the variables that determine ß(t).
- Page last reviewed: July 22, 2015
- Page last updated: July 6, 2015
- Content source:
- National Institute for Occupational Safety and Health (NIOSH) Office of the Director


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I think (or, more to the point, I hope) that it's not a bug at all, but allergies that have me snorting and gasping and feeling generally unwell. I'm a walking snot factory at the moment (lovely, huh?) and am finding that all the time I spend hanging upside down in yoga practices (this being a Yoga National Guard weekend) doesn't help matters in the least.
I'm certainly functional and haven't yet approached miserable; I'm more annoyed than anything else. I'm trying to tough it out during the day time – I neti in the morning and am trying to stay well-hydrated all day, and I'm doing my best to ignore the goopy nose and wildly itchy ears (MAN! If I didn't know how dangerous it is to stick things in one's ears, I'd be rooting around in there with anything that would fit! The itchy is insane!). I'll take something for the symptoms at night, both because lying down exacerbates the effects and because most allergy medicines make me stupid and unfit for anything but sleep.
The funny bit is that we're not even in full-blown allergy season yet. Yes, things are starting to bloom (and, it would seem, that I'm sensitive to whatever is blooming now) but the REAL pollen attack is still several weeks off. Just you wait; I'll take a picture of my black car all done up in a yellowish-green dust so you can see for yourself. It amazes me every year. I don't think I'm allergic to whatever that pollen is, though, so my hope is that I'll dry up whenever whatever it is that's sprouting now finishes its run.
Happy Saturday, Everyone!


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I conceive that the insertion, et cujus voluntas est immutabilis, should stand after et affectum mentis, not before. If God does not accept the heart's affection for its own sake, what does he accept for its own sake that man can give? Moreover a special title of God is that of 'searcher of hearts'.
Of God and His Creatures: 3.119


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Search 1,551 Cape May jobs from job sites, newspapers, associations and company career pages.
The Cape May, NJ job market is weak compared to the rest of the US. Over the last year, job postings in Cape May, NJ have declined by 41% relative to a national decline of 32%.
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Food, entertainment, shopping, local traditions - where is it all happening in Cape May?
What jobs are on the rise in Cape May?
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What companies are fueling growth in Cape May? Why are they a great employer?
What are the seasons like in Cape May? How do Cape May dwellers cope?
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Lone Star Audio Fest 2012
I believe this is the eighth year for this local show and for me this was the best of these I've attended. Local turnout was good and several rooms were approaching standards I've come to expect at larger shows.
Ground floor, GR Research, Dodd Audio, DB audio Lab and PI audio group
Raven Audio was also on the ground floor, very nice set up here with Raven Electronics and GR Research line array speakers.
Next up was the Legacy room
Stereo Clarity, a popular room, crowed with interested listeners during the day I visited.
Second floor, Paolo Audio. Beautiful workmanship especially the electronics
Affirm Audio (previously MaxxHorn) showed off their latest speaker using KR Audio electronics.
Bob Spence always delivers top class fit and finish on Affirm speakers
This next room is about hanging out and having fun. Not only was it the official Vinyl room offering free LP cleaning with a new Audio Desk, there was three great fresh draft beers offered by Barrett Tillman. Barrett is the official brewmeister / audiophile of Dallas.
Dave Slagle tweaking the system
As advertised, vinyl playback
There was a drawing, door prizes offered. View is from the balcony showing part of the crowd in attendance that day.
Soundfield Audio up next, I was impressed by the bookshelf size monitors here. Kef passive plus active subwoofer within the same cabinet.
I did not have time to return and hear the larger design, one of several open baffle designs at this show.
Overall room view of smaller and larger Soundfield speakers
The smaller of the two speakers is one I would consider for myself, I liked its balance, resolution and bass considering the tiny box.
Room 215, Melody here and looking good
Room 220, The Black Hole, Enter At Your Own Risk.
(I went in anyway)
Room 223 Rick Schultz, cable builder and audiophile transplant from Canada,
Room 225, Brines Acoustics
Next is Crescendo systems, room 228 huge electrostatic panels
Brads House of Hi Fi room 229
Van Zyl Audio
Manzanita Audio Solutions, open baffle speakers
Room 211 Odd Watt
Up next, Element cables and speaker, room 327
Room 330, Polk Audio, Unison Research and Black Cat
Room 305 Apex, cable pro, Syclotron Audio,
Seijin Audio Junction
DIY room by Audio Fred. (We regret to inform you that audio snobs are not allowed in this room.) Quote posted on the door.
Room 321 Raven Audio
Room 221, π (Pi) Speakers
This is a small local show but appears to be growing each year. This appeared to be the largest and most enthusiastic of all the LSAF shows since it began.
I'm looking forward to attending next year and invite those who live close enough to attend to make plans, it's fun, free and friendly in a relaxed atmosphere.


text: i missed out on last year's iphone release. and for a whole year used a blackberry curve. SUCKS! Very unstable, slow, and low battery-life. this coming month, im going to buy an iphone3G. going to go with a friend..who else is going??? btw: do i need to camp out?? or do you think they'll have enough to cover hundreds??


text: Regional Management Corp. (NYSE: RM), a diversified specialty consumer finance company, announced today that its Chief Executive Officer Thomas Fortin will be presenting at the Stephens Spring Investment Conference on Tuesday, June 5, 2012 at the Palace Hotel in New York City. The presentation will begin at 1:00 PM Eastern time. Investors and interested parties may listen to the webcast of the presentation by visiting Regional Management's website at www.RegionalManagement.com under the Recent Events section at the appropriate time. About Regional Management Corp. Regional Management Corp. (NYSE: RM) is a diversified specialty consumer finance company providing a broad array of loan products primarily to customers with limited access to consumer credit from banks, thrifts, credit card companies and other traditional lenders. Regional Management began operations in 1987 with four branches in South Carolina and has expanded its branch network to 194 locations with over 164,000 active accounts across South Carolina, Texas, North Carolina, Tennessee, Alabama and Oklahoma as of March 31, 2012. Each of its loan products is secured, structured on a fixed rate, fixed term basis with fully amortizing equal monthly installment payments and is repayable at any time without penalty. Regional Management's loans are sourced through its multiple channel platform, including in its branches, through direct mail campaigns, independent and franchise automobile dealerships, online credit application networks, furniture and appliance retailers and its consumer website. For more information, please visit http://www.RegionalManagement.com.


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Condos Kicks Off "Transparency Tour"
10/12/11 12:44PM By Jane Lindholm, Samantha Fields
| Download MP3
Secretary of State Jim Condos kicks off his 12-stop "transparency tour" across the state this week. The goal is to educate state and town employees and anyone else who's interested about Vermont's public records and open meetings laws.
He spoke with VPR's Jane Lindholm about the tour.


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Crimping Master
Last Activity: 07-18-2017 01:30 PM
About Me
- About ktfabian2
- Biography
- Mother of 2, youngest, 21, just came back from Iraq. Oldest, 24, in school. Love Cats
- Location
- In the great Northeast where we have four seasons!
- Interests
- scrapbooking, cardmaking, reading, sewing
- Occupation
- unemployed due to back injury 16 yrs ago
- Tracy
Proud Mom of Iraq War Vet, Please Support Our Troops!
I support Operation Write Home and the Danville, PA Ronald McDonald House
- Tracy


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Alive Natural Sound
The Black Keys 'The Big Come Up'
Alive Natural Sound
The Black Keys 'The Big Come Up'
This Ohio duo indulges in Gories-style minimalism, with a heavier dose of the blues.
Guitarist Dan Auerbach and drummer Patrick Carney sound like the garage is as far as they'll ever get. Where the White Stripes reminded everyone that alt rock could still contain graceful '60s hooks and prettiness, the Black Keys, with their heroic cover of 'She Said, She Said," drag the Beatles back to the Cavern". Spin.
"Akron has spawned the most compelling two-piece, hyper-primitive, bluesbased rock band of the last five years. This is one of the five best records of 2002, and bass players everywhere should continue to grow nervous" Village voice.
1. Busted
2. Do The Rump
3. I'll Be Your Man
4. Countdown
5. The Breaks
6. Run Me Down
7. She Said, She Said (Alternate Version)
8. Heavy Soul (Alternate Version)
9. Yearnin' (Alternate Version)
10. No Fun (Vinyl Only)
11. Them Eyes
12. Leavin' Trunk
13. Brooklyn Bound
14. 240 Years Before Your Time


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For the nine months to 30 September 2007, Gazprom made a pre-tax profit of 611bn roubles ($25bn; £13bn), down from 623bn roubles a year earlier.
Its sales rose to 1.7 trillion roubles from 1.6 trillion a year earlier.
Gazprom supplies a quarter of Europe's gas, with much of the gas being piped via Ukraine.
Ukraine row
Earlier this week, Russia and Ukraine reached a deal over Kiev's $1.5bn unpaid bill to Gazprom.
Gazprom had earlier threatened to switch off supplies if the money was not paid.
The spat was just the latest dispute between Russia and Ukraine over gas exports.
A previous row in the winter of 2005/2006 saw a reduction in Gazprom's supplies to western Europe, as a number of pipelines pass through Ukraine.
Gazprom has also had similar disputes with Belarus and Georgia.
What are these?
Most Popular Now | 56,514 people are reading stories on the site right now.";


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Zaccai, J., Ince, P. and Brayne, C. (2006) Population-based neuropathological studies of dementia: design, methods and areas of investigation – a systematic review. BMC Neurology, 6 (2). ISSN 1471-2377
Prospective population-based neuropathological studies have a special place in dementia research which is under emphasised.
A systematic review of the methods of population-based neuropathological studies of dementia was carried out. These studies were assessed in relation to their representativeness of underlying populations and the clinical, neuropsychological and neuropathological approaches adopted.
Six studies were found to be true population-based neuropathological studies of dementia in the older people: the Hisayama study (Japan); Vantaa 85+ study (Finland); CC75C study (Cambridge, UK); CFAS (multicentre, UK); Cache County study (Utah, USA); HAAS (Hawaï, USA). These differ in the core characteristics of their populations. The studies used standardised neuropathological methods which facilitate analyses on: clinicopathological associations and confirmation of diagnosis, assessing the validity of hierarchical models of neuropathological lesion burden; investigating the associations between neuropathological burden and risk factors including genetic factors. Examples of findings are given although there is too little overlap in the areas investigated amongst these studies to form the basis of a systematic review of the results.
Clinicopathological studies based on true population samples can provide unique insights in dementia. Individually they are limited in power and scope; together they represent a powerful source to translate findings from laboratory to populations.
|Copyright, Publisher and Additional Information:
|© 2006 Zaccai et al; licensee BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
|The University of Sheffield
|Academic Units:
|The University of Sheffield > Faculty of Medicine, Dentistry and Health (Sheffield) > School of Medicine (Sheffield)
|Depositing User:
|Repository Officer
|Date Deposited:
|14 Mar 2006
|Last Modified:
|05 Jun 2014 06:37
|Published Version:
|Identification Number:


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Hardly anything is more important than children's education. Children's Music Foundation (CMF) has been serving young children by offering a fully-developed music program that can be easily used in classrooms. It gives useful tools to music teachers while it can be successfully and easily used by teachers who are not trained in music. This is important because in a financial crunch, music is often eliminated from school. How sad! What I like about the CMF's program, First Note, is that it does more than bringing music education to classrooms. It brings fun. It teaches children how fun it is to learn although they may not be aware they are learning a lot more than singing and dancing. Music does a lot for brain development in any age but particularly young age. In a long run, children who learned early on that learning is fun stay in school. CMF is helping schools keep music in classrooms and children have fun in learning!
Previous Stories
The Children's Music Foundation is an organization committed to the education of young students through music. The program is well developed and, most importantly, FUN for the kids in the classroom.
The "First Note" program uses music as an educational tools and standars-based. It can be taught by all classroom teachers with or without specific music background. The program comes with all of the teaching tools and a mentor to fully support teachers.
It is quite heartwarning to see young students being engaged in learning and having fun in the classroom. I have chosen the Children's Music Foundation to be the organization that receives a significant portion of my annual giving.
The Children's Music Foundation is helping to keep music alive in the public schools. The curriculum they have developed is effective and extremely scalable.
the mission and purpose of CMF is overwhelmingly compelling. the product they have created to help young people learn music from an early age is setting the standard. And it all started here in Seattle!
CMF is a fabulous organization helping to bring music into the lives of young children, particularly those who would have no exposure otherwise!
Childrens Music Foundation is bringing professional and fun music instruction to schools. Music make a difference in a child's life and makes a difference in the world. In these days of reduced spending the Childrens Music Foundation fills an important niche.
I found this Nonprofit to be an excellent resource for music education. I highly recommend the Childrens Music Foundation.
The creative and lean team at Children's Music Foundation has developed a wonderful tool for arts/music literacy that virtually ANY teacher can use--in any age-appropriate classroom. The organization has a clear-eyed view of the need for teaching tools to be both user-friendly and effective, and the "First Note" program hits this note beautifully. Children's Music Foundation is an entrepreneurial, social venture nonprofit that means business. Donation dollars will be "turned" time and again, as the organization's business model promises a diverse and steady income stream from both contributed revenue in addition to curriculum sales to schools and districts.


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Cluster 2 Progress & Pics
We started building our new cluster this week.
"Cluster 2" ("C2") promises increased performance in several respects, as well as more capacity for customer growth.
Equipment started arriving this week and the system started coming together on Thursday.
The pics below show some of the components in the data center warehouse and others racked up in a new and improved cabinet design that has better airflow and access from all sides.
Existing customers will not be affected in any negative way while we build and test the new cluster.
Palettes of servers!
Disks and SAN units upon arrival.
New cabinets are a definite improvement, especially in terms of access.
Lots of storage!
Smaller cabinet waiting for a particular customer.


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2004 318dse N47 temporary loss of power
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Join Date: Nov 2012
Location: Ireland
Posts: 1
My Ride: 2004 318dse
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2004 318dse N47 temporary loss of power
Everything works well in all conditions , when suddenly as I was going up a hill I lost all power, accelerator pedal fully depressed and no response. I went around again and went up the same hill , everything was fine. In the morning when I started up the engine, blue smoke blew out the exhaust, after about 30 seconds of driving the blue smoke stopped, everything is fine and no problems at all.Then I stopped and the engine ran on idle rpm for about 10-15 minutes and the blue smoke blew out the exhaust again.
2 days later , got the car serviced in a BMW Garage and replaced the air filter and breather filter.The blue smoke problem has been fixed. The other problem seem to have been resolved until it happened again while going 100km/h on level road and the same loss of power happened for a small amount of time.
Any suggestions? I need this fixed urgently as it could be dangerous when overtaking other drivers. Servicing said there was no problem regarding the engine, but I am still unsure.Thank you in advance.
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(CNSNews.com) – U.S. taxpayers spend at least $65 billion on health care waste and fraud, according to the government's own numbers and even more according to independent studies, even as the federal purview over health care expands under Obamacare.
On Wednesday, the House Energy and Commerce Committee will hold a hearing into Medicare and Medicaid fraud and ways to combat the problem.
In fiscal year 2010, the Centers for Medicare and Medicaid Services estimated it made more than $65 billion in "improper payments." Improper payments are defined as Medicare and Medicaid payments that should not have been made or were made in an incorrect amount, according to a memo by the committee.
The background memo for the hearing states, "The true annual cost of health care fraud and abuse to the Federal government is not known." However, it could be more costly than the government states, even according to more left-leaning sources.
The controversial Donald Berwick, who was recess-appointed in 2010 to be the administrator of the Center for Medicare and Medicaid Services and who has advocated for health care rationing, participated in a study earlier this year on health care fraud.
Fraud and abuse made up $98 billion of Medicare and Medicaid spending in fiscal year 2011, according to a study by Berwick and RAND Corporation assistant policy analyst Andrew D. Hackbarth.
"The opportunity is immense. In just 6 categories of waste—overtreatment, failures of care coordination, failures in execution of care processes, administrative complexity, pricing failures, and fraud and abuse—the sum of the lowest available estimates exceeds 20 percent of total health care expenditures," said the report by Berwick and Hackbarth published in the Journal of the American Medical Association in April.
A separate study in September by the Institute for Medicine found that in fiscal year 2009 about $750 billion was wasted on unnecessary services and heavy administrative costs that would not be categorized as fraud, but rather as an unnecessary expense.
"By one estimate, almost 75,000 needless deaths could have been averted in 2005 if every state had delivered care on par with the best performing state," the Institute of Medicine study said. "Current waste diverts resources; the committee estimates $750 billion in unnecessary health spending in 2009 alone."
CMS has been putting fraud prevention strategies in place, according to a Government
Accountability Office report from April, but the GAO said the agency had not completed other anti-fraud actions. CMS was supposed to extend the requirement for surety bonds to high-risk providers.
Surety bonds allow CMS to recover money even if it turns out that fraud was involved in the submission of claims, but the agency did not do this, according to the GAO.
"GAO's previous work found persistent weaknesses in Medicare's enrollment standards and procedures that increased the risk of enrolling entities intent on defrauding the program," the April report said. "CMS has strengthened provider enrollment—for example, in February 2011, CMS designated three levels of risk—high, moderate, and limited—with different screening procedures for categories of providers at each level."
The GAO report continues, "Having mechanisms in place to resolve vulnerabilities that lead to erroneous payments is critical to effective program management and could help address fraud.
Such vulnerabilities are service- or system-specific weaknesses that can lead to payment errors— for example, providers receiving multiple payments as a result of incorrect coding. GAO has previously identified weaknesses in this process, which resulted in vulnerabilities being left unaddressed."
Witnesses scheduled to testify at the hearing are Louis Saccoccio, CEO of the National Health Care Anti-Fraud Association; Michael Terzich, senior vice president of global sales and marketing for Zebra Technologies; Alanna Lavelle, director of investigations for Wellpoint; Dan Olson, director of fraud prevention for Health Information Designs and Neville Pattinson, senior vice president with Gemalto, Inc. on behalf of Secure ID Coalition.