dataesr/scientific-paragraphs-categorization huggingface dataset:
Comparison of LEAPs, hydrophilins, WHy domain and HSP12 amino acids usage Percentage of amino acids. Surprisingly, the Gly content (Figure  S1A ) of hydrophilins is not so important: up to 16,8%, i. e. , much less than the 34,1% for LEAP class 1 (PF00257). Hydrophilins have the highest content in Asn and Gln (Figures  S1B  & S1C ). Glu is largely more used than Asp in the case of «hydrophilins-like LEAPs» and in the same manner in those of true LEAPs and hydrophilins (Figures  S1D  & S1E ). «Hydro-Figure  3. Boxplot representation of MW/length ratio, FoldIndex, mean bulkiness and mean flexibility, mean molar fraction of buried residues and mean molar fraction of accessible residues. P1: pool 1. P2: pool 2. P3: pool 3. C8: LEAP class 8. IDP: intrinsically disordered proteins. FS: fully structured proteins.
Binary a representation of the physico-chemical properties distribution of « hydrophilins-like LEAPs » (pool 1), « control LEAPs » (pool 2), hydrophilins (pool 3), HSP12, WHy domain and LEAP class 8. +1, 0 and 21 values mean that the physico-chemical properties considered is upper, equal or lower, respectively, than either the calculated median value for the seven datasets or a definite « natural » value (see the corresponding figures).
a Values b Mean molar fraction of buried residues. c Mean molar fraction of accessible residues. d Mean transmembrane tendency. e Mean net charge at pH 7. f Mean hydrophilicity. g Grand average of hydropathy. h Mean hydrophobicity. i Intrinsically Disordered Proteins dataset. j Fully Structured proteins dataset. doi:10. 1371/journal. pone. 0109570. t001.
+1 and 21 indicate that the median value of the ratio (% amino acid considered in LEAP/% amino acid considered in Uniprot) is upper or lower than 1 (see the corresponding figures). 21 21 +1 21 +1 +1 21 +1 21 21 21 +1 21 21 +1 21 21 21 21 Pool 2 +1 21 +1 +1 21 21 21 21 +1 21 21 21 21 21 21 +1 21 +1 21 21 Pool 3 21 21 21 21 21 +1 21 21 +1 21 21 +1 21 +1 +1 +1 21 21 21 +1 WHy domain 21 21 21 21 21 +1 21 21 21 +1 21 +1 +1 21 21 21 21 +1 21 21 LEAP class 8 21 21 21 21 +1 +1 21 +1 21 +1 21 21 +1 21 21 21 21 +1 21 21 IDP b 21 21 21 +1 21 +1 +1 21 +1 21 21 21 +1 21 21 21 21 21 21 21 FS c 21 21 +1 21 21 +1 +1 21 21 21 21 +1 21 21 21 21 +1 +1 +1 +1 These data are compared to two control datasets (IDP and FS datasets). a Values b Intrinsically Disordered Proteins dataset. c Fully Structured proteins dataset.
Comparison data We used archived serologic data (EITB) from a previous population-based study in 14 of the 58 study communities to estimate the background seroprevalence. In addition, we used 111 brain CT scans taken in a previous study in Tumbes from individuals without a history of epilepsy to estimate the prevalence of NCC in the general population  [8]. The studies were conducted between 2005 and 2007.
Acknowledgments The authors wish to thank the villagers of all 58 communities involved in the studies and The Regional Directorate of Health-Tumbes. We are grateful to  A. Jara ,  R. Atto ,  P. Vilchez ,  S. Mimbela ,  C. Mendoza ,  R. Camizan ,  M. Alvarez ,  V. Benavides ,  B. Idrogo ,  L. Heras ,  E. Oliva. R. Velasquez  and  R. Gamboa , the village health personnel and all the field workers of the  Cysticercosis Working Group in Tumbes, Peru. LM wants to thank the support and guidance received from the faculty and fellow students of the program Masters in Epidemiological Research from  Universidad Peruana Cayetano Heredia  and the  U. S. Naval Medical Research Unit Six, Lima, Peru. LM wants to thank the support and guidance received from  Dr. J. R. Zunt  ( Department of Global Health, University of Washington ).
This work was supported by the  Bill and Melinda Gates Foundation  (Grant  23981 ). Partial support from the  Fogarty International Center/NIH  (Training grant #  TW001140  and  R01  #  NS055627-01 ) is also acknowledged. LMM9s postgraduate training is supported by  NHLBI ,  NIH  Contract  HHSN268200900033C  ( Center of Excellence in Chronic Disease Research, CRONICAS, and Peru ). SMM is funded by  NIH  grant # NS055627-01. VCWT, FL, AEG, and RHG are members of the coordinating board of the  Cysticercosis Elimination Program in Peru. HHG is supported by a  Wellcome Trust Senior International Research Fellow in Public Health and Tropical Medicine  and is also a member of the coordinating board of the  Cysticercosis Elimination Program in Peru. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
ABSTRACT: Data from published studies and World Wide Web sources were combined to produce and test a regression model to predict Cs concentration ratios for freshwater fish species. The accuracies of predicted concentration ratios which were computed using 1) estimated species trophic levels obtained from random resampling of known food items and 2) K concentrations in the water for 207 fish from 44 species and 43 locations were tested against independent observations of ratios for 57 fish from 17 species from 25 locations. Accuracy was assessed as the percent of observed to predicted ratios within factors of 2 or 3. Conservatism, expressed as the lack of under prediction, was assessed as the percent of observed to predicted ratios that were less than 2 or less than 3. The model's median observed to predicted ratio was 1.
Although some fish, at least as adults, are clearly piscivorous (e. g. , the northern pike Esox lucius; nomenclature for common and scientific names follows fishbase. org), others are clearly non-piscivorous (e. g. , the white sucker Catostomus commersoni). Many species show varying proportions of fish and other food items in their diet, and even among nonpiscivorous fish there are differences in trophic levels and potential concentrations of Cs in their diets. These differences occur between fish that are primarily herbivorous and those that are carnivorous but feed on invertebrates. Recently, an extensive online database on the biology and ecology of freshwater and marine fish species (fishbase. org) has been developed and made freely available by the FishBase Global Information System  (Froese and Pauly, 2011).
Materials and methods Three data sources were employed in this study to produce and test models to predict C r for fish. These were: 1) the trophic level estimates from fishbase. org; 2) the data from  Rowan and Rasmussen (1994)  on fish C r and the concentrations of K and suspended sediment in the water column that were used in model construction; and 3) test data of measured C r compiled from several literature sources that were used to assess the accuracy of the predicted C r.
Trophic level data The trophic level estimates were obtained from fishbase. org where data on fish biology and ecology have been compiled for > 30,000 species from > 45,000 references and made available in various Asian and European languages. Species data may be accessed by searching either common or scientific names. Where taxonomic revisions have been made to scientific names, a search initiated using the previous name leads to the modern synonym and the appropriate data. Several alternative methods are used in fishbase. org to estimate a mean + SE tropic level, and the method employed depends on whether the available data from literature sources comprises 1) just lists of food items consumed or 2) more quantitative analyses of diet composition. Where both types of data are available, multiple estimates of trophic level may be made.
When using lists of food items consumed, trophic level is estimated using a randomized resampling of the listed items. Where more quantitative data on diet consumption is available, the trophic level is computed from the relative proportion of the food items consumed in a process similar to that used by Vander Zander and  Rasmussen (1996)  to compute trophic level effects on PCB contamination in fish. These different procedures may produce different estimates of trophic level. For example, the random resampling of 31 food items for Perca fluviatilis (European perch) indicated a mean (+ SE) trophic level of 3. 66 + 0. 58, but an analysis of diet composition for adult fish indicated a mean trophic level of 4. 35 + 0. 75. For all methods, the trophic level of the fish is computed as 1 plus the mean trophic level computed for its diet (i.
e. , a fish whose diet has a mean trophic level of 2. 5 would have a trophic level of 3. 5). These estimation methods have been shown to agree with those computed from stable isotopic ratios  (Kline and Pauly, 1988). Where data are lacking on diets, a fish's trophic level is inferred from a taxonomically related species of similar size. Because trophic level estimates computed using the random resampling procedures were available for the majority of the species involved in this study, they have been used in the models to predict C r. However, the potential effects of using the alternative estimates, such as those for P. fluviatilis, will also be discussed. These random resampling trophic levels are computed from a list of n food items, each with its own assigned trophic level, by: 1) randomly choosing one item and assigning it the largest fraction of the diet; 2) randomly selecting each of the remaining n -1 items and assigning each a successively smaller fraction of the diet; 3) computing a trophic level estimate for the diet from the sum of the product of each item's trophic level and assigned proportion in the diet; 4) repeating this random selection process for a total of 100 times; and 5) computing mean and standard error from the 100 replicates.
The modeling data The development of the predictive model was based on the data for freshwater fishes compiled by  Rowan and Rasmussen (1994;  Table  1 ) in conjunction with the newer estimates of trophic levels from the FishBase database. The  Rowan and Rasmussen (1994)  data included the fish's scientific name and the location of the study, the wet mass Cs concentration in the fish's whole body or muscle, the Cs concentration in the water, the K concentration in the water (µM K L -1 ) and, for some locations, the suspended sediment load (mg L -1 ) (Table  1 ). Cesium concentrations were alternatively expressed as mass for 133 Cs and Bq for 137 Cs, and no distinction was made between the C r for 133 Cs and 137 Cs. These data contained no distinction between concentrations for whole fish and those for only muscle tissue and, as a consequence, no distinction with regard to muscle vs.
whole-body made in the subsequent use of the model to predict C r in fish. Rowan and Rasmussen's (1994)  study contained data for 244 observations on more than 60 species of fish from 46 locations in freshwater systems in Europe and North America, but two sets of their data were excluded from the modeling analysis in this study. First, those species which occurred in only a single location were excluded. Second, all data from locations on the U. S. Department of Energy's Savannah River Site (hereafter, SRS) were also excluded. Studies at the SRS  (Newman and Brisbin, 1990; Whicker et al. 1990; Pinder et al. 2009 ) have consistently reported relatively small K concentrations in the water and relatively large C r for fish species. These data were removed from the modeling analysis and included in the test data to provide tests of model accuracy at the upper extremes of reported Cs concentration ratios.
The first was the data of  Smith et al. (2000)  which had previously indicated the accuracy of the  Rowan and Rasmussen (1994)  model. These data consisted of C r for 137 Cs measured for 28 instances of 6 species from 10 eastern European lakes and reservoirs and were obtained > 5 years after the Chernobyl release. These were the only test data that contained measures of suspended sediment concentrations. The second set was the temporal analysis of 137 Cs for 19 instances of 3 species from 9 European lakes following the deposition of Chernobyl releases  (Smith et al. , 2002). These temporal analyses estimated an uptake (k 1 ) and a loss rate parameter (k 2 ) for 137 Cs in fish, and the ratio of these parameters was used to estimate the steady state C r  (Smith et al. 2002; equation 4). The third set was data from the SRS which included C r measured for 137 Cs in 7 species from Pond B in the 1980s, which was 20 years since the last releases of 137 Cs into the reservoir  (Newman and Brisbin, 1990; Whicker et al.
4. The variability in trophic levels was related, in part, to phylogenetic groupings. The trophic level data included species from 16 families and 33 genera. For the seven families represented by more than one genus, variance component analyses using nested-analysis of variance procedures  (Searle, 1987)  indicated that 48% of the variation in trophic levels was due to variation among families with 38 % due to variation among genera within families. The remaining 14 % was due to variation among species within genera.
The use of alternative estimates of mean trophic level In the previous analyses, the mean trophic level for species was estimated using the method of random resampling of food items, but it was noted that alternative diet based estimates were also available for some species such as P. fluviatilis. The mean trophic level from the resampling procedure for this species was 3. 66 but an estimate of 4. 35 is also available for adult fish from diet studies (fishbase. org). Nineteen of the 57 fish in the test data sets were P. fluviatilis, and these 19 fish accounted for 9 of the 12 observed to predicted ratios > 2, which included all four of the ratios that were > 4. If the trophic level estimate of 4. 35 is used, P. fluviatilis accounts for only 5 ratios > 2 with none of these being > 4. Using the 4.
This extrapolation allows a more complete development of species information in databases to be used for predicting the environmental transfer of radionuclides into fish and other wildlife  (Yankovich et al. , 2012). Phylogenetic similarities used by  Beresford et al. (2013)  include feeding behaviors (e. g. , piscivorous), feeding locations (i. e. , benthic versus pelagic), and habitat (i. e. , lentic versus lotic), and these similarities are evaluated to produce expected relative ratios of C r between species. For example, the relative ratios for D. cepedianum and M. salmoides are 4. 3 and 8. 5, respectively  (Beresford et al. 2013 , Table  2 ). The procedures of  Beresford et al. (2013)  do not use trophic level estimates from fishbase. org, but the expected relative ratios for these species are similar to the relative trophic levels for these species of 2.
When stimulated with VEGF the endothelial cells proliferate and migrate through the matrix to form tubule-like structures. The extent of tubule formation was quantified after 11 days as de-scribed elsewhere. [14] Treatment with 25 nm and 50 nm concentrations of 16 f and 17 f almost completely inhibits the formation of tubule-like structures (Figure  2 ). Quantification carried  [a] Results are the mean of three determinations. [b] Not determined. [c] Data for 18 c and 19 c are taken from the literature. [8] ble  3. out by calculating total pixel length reflects the lack of tubule formation. Both phenol 16 f and its sulfamate 17 f are highly potent. GI With these excellent in vitro data in hand, we decided to explore further the microtubule disruptor activity in particular of 16 f-g and 17 f-g alongside the established potent agent combretastatin A-4 (CA-4), currently in clinical trials.
Computational methods: The Schrçdinger software running under Maestro version 9. 2. 112 was used for all computational work. The crystal structure of 17 f solved in this work was used. One molecule was taken from this structure and run through a brief geometry optimisation procedure. Both dihedral angles were manually and independently adjusted in steps of 308 and the energy of the conformers was calculated. Compounds 16 g and 17 g were built by altering the crystal structure of 17 f to yield the desired structure which was then run through a brief geometry optimisation procedure. Conformers were generated and molecular energies calculated as described above. 5-Hydroxy-6-methoxyindan-1-one (4): 5,6-Dimethoxy-indan-1-one (25. 93 g, 135. 0 mmol) was placed in a 1 L round-bottom flask. Piperidine (200 mL) and H 2 O (50 mL) were added and the reaction mixture was stirred at 140 8C for 8 days.
Acknowledgements We thank  Ms. Alison Smith  for technical support and the  NCI DTP  for providing in vitro screening resources. This work was supported by  Sterix Ltd. , a member of the IPSEN Group, and in part by the  Wellcome Trust  (Program Grant  082837  to B. V. L. P. ). B. V. L. P. is a  Wellcome Trust Senior Investigator (Grant   101010 ).
Acknowledgments This article is both dedicated, in memoriam, and profoundly indebted to Imma. This research received no specific grant from any funding agency in the public, commercial, or notfor-profit sectors. However, the article-processing charge was defrayed by a grant from the  Banco de Instrumentos del Centro de Investigación Biomédica en Red de Salud Mental (CIBERSAM). An early draft of some of the ideas incorporated in this article was presented at the  Encuentro FIPSE sobre Investigación de la Perspectiva Social del VIH/ SIDA (FIPSE Research Meeting on Social Perspectives on HIV/AIDS) (Valencia, Spain ,  November  [ 26][27][28] 2006 ), and appeared in Tratado SET de trastornos adictivos ( SET Handbook of Addictive Disorders ). 135.
Reads pre-processing and annotation The generated sequences were screened to remove the exact and nearly identical duplicates using the CD HIT 454 program  [15] , available under the CAMERA 2. 0 web portal  [16]. All screened viromes are publicly available on the Metavir web-server (Table  1 )  [17]. A BLASTN search against the non-redundant NCBI database (E,1e 205 ) was performed. Reads having no significant hits were classified as ''unknown reads'', whereas those with significant similarity to sequences stored in the NCBI database were classified as ''known reads''.
Estimation of viral genotype abundances The GAAS (Genome relative Abundance and Average Size) program  [18]  was used to estimate the relative abundance of each viral genotype present in the metagenomes. Briefly, GAAS performs a BLASTX search against the Viral Refseq database and normalizes the number of reads matching one viral genotype to the genome length of that viral genotype. The BLASTX search was performed with an E-value of 1e 205.
Assembly and contig analysis Read assemblies were performed for each sample using the GS De Novo Assembler (Roche Applied Science, Inc, Branford, CT), an application especially suited for the analysis of the 454 Life Sciences Genome Sequencer FLX-generated data. We chose a minimum overlap length of 35 bp and a minimum overlap identity of 98%. Only contigs longer than 400 bp were kept for further analyses. We classified as ''large contigs'' those spanning more than 1,500 bp. Contigs were classified as known if they had a significant hit in a BLASTN search against the non-redundant NCBI database (E-value,1e 205 ), otherwise they were classified as unknown. Contigs were then annotated by a BLASTX search against the non-redundant NCBI database (E-value,1e 205 ).
According to a BLASTN search (E-value,1e-05) against the non-redundant NCBI database, between 0. 31% and 23. 18% of the reads had no significant similarity to known sequences and were thus classified as ''unknown'' (Fig. 1 ). GAAS  [18]  was used to more accurately estimate the relative abundance of each viral genotype (Fig. 2 ). Because longer genomes have higher probabilities of being sequenced in the shotgun approach, they are overrepresented in terms of number of sequences in the metagenome. To avoid this bias, GAAS normalizes the number of sequences associated with a viral genotype to its genome length. Relative abundances estimated by GAAS were synthesized and presented at the viral family level except for all bacteriophagerelated sequences, which were grouped together. Reads assembly was performed on each metagenome and a total of 2,000 contigs were obtained, including 240 large contigs (longer than 1,500 bp).
Human herpesvirus 3 and papillomaviruses co-infection As expected, the positive control showed high abundance of Herpesviridae (6,674 reads) and, in particular, reads matching human herpesvirus 3 (HHV3). Indeed, 5,990 reads related to HHV3 were recovered (BLASTX against the Viral Refseq Genomes Database, E-value,1e-05). After mapping of the reads onto the reference HHV3 genome, 98% (122,502 bp) of the genome was reconstructed (Figure  S1 ) with an average coverage depth of 18. 93 reads. In addition to HHV3, 173 reads matching viruses from the Papillomaviridae family were recovered and 11 contigs could be generated after de novo assembly (Table  2 ). Contig annotation retrieved 4 different types of human papillomaviruses (HPVs): HPV isolate 915 F 06 002 KN1, HPV type 50, HPV type 80, and HPV type 12 (Table  2 ).
Major and minor capsid proteins as well as replication and regulatory proteins were encoded by the contigs (BLASTX against the non-redundant NCBI database, E-value,1e-05). One contig (contig 37, 1196 bp long) was found to harbor two different ORFs matching the E2 and L2 genes of HPV type 50 (BLASTX against the nonredundant NCBI database, E-value,1e-05). Both ORFs were incomplete, but their position on the contig was consistent with that of the E2 and L2 genes on the HPV type 50 genome (Figure  3 ). More than 50% of each of the four HPV genomes could be reconstructed by mapping (Figure  S2  and Table  S4 ), and the subsets of reads that mapped onto each reference genome were mutually exclusive, supporting the presence of multiple different genotypes of papillomaviruses. All recovered HPV genotypes were HPVs first isolated from human skin under healthy (HPV type 80, HPV isolate 915 F 06 002 KN1) or disease (HPV type 50, HPV type 12) conditions  [24] [25] [26] [27].
Variable Anelloviridae genotypes were detected in 5 patients According to GAAS analysis, six metagenomes (P1, P2, P3, P6, P7 and negative control N2) contained Anelloviridae (57. 04%, 68. 76%, 26. 05%, 60. 23%, 98. 59% and 41. 5%, respectively) (Figure  2 ) and, in particular, torque teno viruses (TTVs) (49. 6%, 66. 97%, 26. 05%, 60. 23%, 98. 59% and 38. 14%, respectively). In total for Anelloviridae (torque teno viruses and others), we recovered 20 reads for P1, 108 for P2, 3 for P3, 56 for P6, 1628 for P7 and 17 for negative control N2 (BLASTX search against the nonredundant NCBI database, E-value,1e 205 , Table  S5 ). Among the recovered reads, there were reads matching unclassified Anelloviridae as well as reads matching Alfatorqueviruses, Betatorqueviruses and Gammatorqueviruses. More than 70% of several TTV genomes could be reconstructed by mapping reads from samples P2, P6 and P7.
These genomes corresponded to the TTV clone Saf-09 in sample P2 (Figure  S3 ) and the TTV strain SIA109 and TTV 24 clone Saa-01 in sample P6 (Figure  S4 ). In addition, we were able to fully reconstruct the genome of three TTV isolates (torque teno virus TTV-HD14 h, torque teno virus isolate TTVyon-LC011 and torque teno virus isolate JT41F), the genome of TTV 29 and that of Micro TTV isolate microTTV-HD14. 2 in sample P7 (Figure  S5 ). Several contigs were also assembled de novo and annotated as TTVs (BLASTx against the non-redundant NCBI database, E-value,1e 205 ) (Table  S6 ). The presence of contig 127, contig 129 and contig 64 was verified by standard PCR in sample P7, which, according to in silico analyses, had the greatest abundance of TTVs. PCRs targeting contig 127 and contig 64 showed non-specific amplification, while PCR targeting contig 129 yielded only a specific PCR product that was further verified by Sanger sequencing.