dataesr/scientific-paragraphs-categorization huggingface dataset:
solvent atoms 488 433 No. ligands atoms 0 268 R. m. s. deviation bond 0. 007 0. 009 lengths (Å) R. m. s. deviation angles (°) 1. 174 1. 379 B average (Å 2 ) Protein (mon A/mon B) 37. 3/34. 1 54. 8/51. 1 Ligand-active site (G7) NA d 60. 4/58. 3 e Ligand-active site (GLC) NA 69. 7/NA Ligand-SBS 1 NA 69. 6/77. 0 Ligand-SBS 2 NA 84. 9/NA Solvent 34. 7 47. 3 Ramachandran plot (%) f Favored 95. 35 95. 08 Outliers 0. 13 0. 25 PDB ID 4J7R 4OKD (52)lues in parentheses refer to the highest resolution bin. b R pim. , precision-indicating merging R factor (26). c CC 1/2 , percent correlation between intensities from random half-datasets (28). dNA, not applicable. eLigand associated with monomer A/monomer B, respectively. fValues calculated using Molprobity(52).
Cohort enrolment We used data from HDSS Moramanga, in which longitudinal demographic surveillance was carried out on the population of four communities in the district of Moramanga. The HDSS Moramanga conducted a doorto-door census, and collected demographic data and data for each individual household in the two villages at a given time point (water supply, goods, ownership of animals, etc…). Children under the age of 24 months living in either of the two villages were eligible for enrolment in this study. An open cohort of children enrolled before the age of 24 months was followed up from January 2010 to May 2012. All the children were monitored until the age of 36 months. Children who moved within the study area were followed up at their new homes, whereas those who moved outside the study area were withdrawn from the study.
Bimonthly surveillance We also carried out a cross-sectional surveillance. All children included in the cohort, regardless of their history of diarrhoea, were surveyed once every two months (60 days), with the collection of a stool specimen for Campylobacter isolation. In these surveys, weight and length/ height data were also collected. This cross-sectional study provided us with data on asymptomatic Campylobacter infection. These surveillance activities were performed for the purposes of this study only.
Data analysis We analysed data from 19 th January 2010 until 31 st May 2012. Person-time at risk was calculated as the observed number of days at risk between episodes. Incidence rates were calculated by dividing the number of episodes by the number of child-years of observation. We estimated the incidence rates for diarrhoea and symptomatic infection. Children were assigned to age groups: 0 to 5 months, 6 to 11 months, 12 to 17 months, 18 to 23 months, 24 to 29 months and 30 to 36 months. This classification of age group was chosen in accordance with our hypothesis that exposure and infection risk vary with age. Anthropometric measurements were transformed into weight-for-height/length, height/length-for-age and weightfor-age Z scores based on WHO standard reference population. Stunting, wasting and underweight were defined as height/length-for-age, weight-for-height/length and weightfor-age less than -2 standard-deviations from the reference standards for children of the same age and sex, respectively  [13].
Association between infection and age At the time of enrolment The Chi-squared test was used to measure associations between Campylobacter infection and age group at the time of enrolment. If a significant result was obtained by the Chi-squared test, we assessed the strength of association between Campylobacter infection and age group at the time of enrolment by logistic regression. The unit of analysis was the individual child. Data collected at the time of enrolment were used, with age group considered as the explanatory variable and the infection status of the faecal specimen as the outcome.
During follow-up During follow-up, the association of Campylobacter infection and symptomatic Campylobacter infection with age group was assessed with a logistic mixed regression model. The outcome variable was Campylobacter infection or symptomatic Campylobacter infection status, and age group was the main explanatory variable. For the two analyses, we incorporated socioeconomic and household data collected at the time of enrolment, nutritional status data obtained during enrolment and followup as potential confounding variables. We included in the mixed models all potential confounding variables with a p-value <0. 2 in the univariate analyses. We then used backward elimination to identify confounding variables for inclusion in the final model. A Wald Chisquared test was used to assess the significance of each of the variables tested and odds ratios were calculated to quantify their effects.
The follow-up visits for a given child were not statistically independent entities. We therefore took into account the correlation between repeated observations for each individual, between multiple children living in the same household and in the same village. Thus, for the two mixed models, we added each observation for each child, household and village as a random effect. The use of a random effect made it possible to adjust for the correlation between data obtained from the same individual, the same household or the same village and to measure the variability between these data. The unit of analysis was the individual visit, with the collection of a stool sample, for investigations of the relationship between Campylobacter infection and age. For analysis of the relationship between symptomatic Campylobacter infection and age, we considered each visit at which a diarrhoeal stool sample was obtained (diarrhoeal episode) as the unit of analysis.
Recurrent episodes Survival analysis was used to assess the time to recurrent episodes of Campylobacter in children with primary infection and those with multiple infection. We used logrank tests to compare the two Kaplan-Meier survival curves. We used Cox proportional hazard models to assess the extent to which the timing of recurrent episodes could be explained by potential confounders. Time to event was estimated as the interval between the first and second episodes of infection. Data were analysed with R software version 2. 12. 1 (R Development Core  Team (2007). For all statistical tests, a p-value below 0. 05 was considered statistically significant.
In addition, technical difficulties in the isolation of Campylobacter species in developing countries, due to their fastidious growth requirements and/or the relative insensitivity of culture techniques, may influence detection rates  [29]. We do not have data on the presence of other bacterial or parasitic pathogens, thus we are therefore unable to ascertain which pathogen is the causative agent of the diarrhoeal episode. Assessing coinfections were of interest as the presence of any enteric pathogen may modulate the effect of other enteropathogens during concurrent infection to modify the clinical expression. During this study, stool samples have been stored for future testing. In this study, we followed up a small sample of children included before the age of 28 days. An epidemiological study on this birth cohort might provide a better assessment of the role of immunity in determining the occurrence of infection.
Acknowledgments We thank  Corine Vincent ,  Florian Girond ,  Renaud Lancelot  and  Dominique Bicout  for their suggestions and help, the staff of the  Epidemiology Unit  and the  Centre de Biologie Clinique of the Institut Pasteur of Madagascar. We would also like to thank the local community health workers and the people of the  Fokontany of Befotsy and Ampitambe  for their participation.
Samples were imaged with a NanoWizard II AFM (JPK Instruments AG) using Olympus silicon tips on a silicon cantilever (OMCL -160TS-R3) with a typical tip size of 7 nm, a spring constant of 26 N m ¡1 , a drive frequency of 300 §100 kHz, and a scan rate of 0. 5 -1 lines sec ¡1. Data were collected using intermittent current AFM-mode from up to 4 different samples and up to 3 different scanning sessions as 512 £ 512 or 1024 £ 1024 pixel images and processed using Gwyddion software 2. 22. All images were levelled by mean plane subtraction to correct for tilting of the sample stage and servo range errors. Each scan line was corrected for streaks, scratches, and noise related to the mica surface. Fibrillar heights were measured perpendicular to the fibrillar axis using Gwyddion's extract profile tool, but not quantified in detail.
For heteronuclear through-space transfer, chemical shift-selective ( 15 N, 13 C) and broad-band ( 1 H, 13 C)/( 1 H, 15 N) SPECIFIC-CP 72 schemes were applied. ( 13 C, 13 C) mixing was performed using PDSD-mixing times of 5, 10, 20, and 80 ms for intraresidue correlations or 80, 150, and 500 ms for sequential correlations. ( 15 N, 13 C) spectra were recorded using SPECIFIC-CP contact times between 1500 and 3500 ms followed by homonuclear PDSD-mixing of 50 to 100 ms or DREAM mixing. During indirect chemical shift evolution periods and FID acquisition, 83 kHz SPINAL decoupling  76  was applied. All spectra were recorded in form of successive one-day data sets, which were recorded and added until no further improvement of signal-to-noise ratios was visible. Data sets were processed with NMRPipe 77 by routinely using different apodisation functions, primarily shifted sine-bell squared functions, with or without linear prediction in the indirect dimension and analyzed with Sparky version 3.
Dog The full-length cDNA sequence of canine TLR4 (2709 bp) contains a 5 UTR (1-194 bp), a long ORF (195-2105 bp), a 3' UTR (2106-2687 bp) and encodes a protein of 637 amino acids  (53). Canine TLR4 is 70-77% similar to the human TLR4 nucleotide and amino acid sequences, and is highly similar to the sequences of other mammalian TLR4s. However, the canine gene lacks the two first exons found in other TLR4 genes  (42, 53). Sequence alignment confirms that this is not due to a loss of introns or a different exon sequence organization (Accession numbers: Genbank O00206 and Genbank BAB85609. 1).
Persons not otherwise involved in the study prepared the test items in sachets and assigned codes (A to F) to the study groups and were not blinded. The people who administered the test items to the cats, and assessed the feed consumption and acceptability of the test items, could only identify the groups according to the codes. The investigators responsible for clinical examinations were fully blinded as they had no access to the study codes and were kept out of sight of the medicated food. At no stage in the study was it necessary to reveal the blinding code to blinded personnel. The blinding code was revealed only after the database had been locked.
Statistics All analyses and calculations were performed using SAS Version 8. 2 (SAS Institute Inc, Cary, NC, USA). Unless stated, data are presented as mean and SD or median and range. Statistical significance was declared with two-sided P values <0. 05. With the exception of the acceptability scores, all efficacy variables were analyzed using repeated measures analysis of covariance (RMANCOVA). In the primary analysis, the model included baseline as covariate and treatment, day and treatment x day interaction as effects. In a secondary analysis, percentage of food consumed was included as an additional covariate. In the event of a significant overall treatment effect in the RMANCOVA analysis, pair-wise group comparisons were determined from the RMANCOVA model with no correction for multiple comparisons (in order not to inflate the type II error).
A trend analysis of dose of Lenziaren â for each variable was included in the RMANCOVA analysis. The dose-response relation of Lenziaren â on serum and urine phosphate was also evaluated with the standard Hill model (sigmoidal curve). However, in the few cases where convergence of the model was achieved, the slope estimate was not significantly different from zero (data not shown). Acceptability was assessed as the time for the cat to consume their daily feed ration. Groups were compared using a Mann-Whitney U-test separately in the predose (study day À7 to À1) and postdose (study day 0 to +6) periods. Clinical chemistry, haematology and urine variables were evaluated using the change from baseline (from study day À1 to days +7, +14, +21 and +28) and frequencies of values outside the reference range for the laboratory.
ACKNOWLEDGMENTS The authors thank the following employees of  Clinvet International Ltd  for technical assistance in the study:  Mrs A. Bubb ,  Dr C. Cordel ,  Mr C. B. Erasmus ,  Ms S. Nieuwenhuizen ,  Mr A. Roets ,  Dr Y. Roodt ,  Dr A. van Wyk ,  Sr L. van Zyl  and  Mr R. A. Zweigers. We also thank  Ms E. M. Barsch  of  Pathcare Veterinary Laboratory  for the clinical chemistry and haematology analyses.
Spiked formulae were also analysed using the HPLC-FLD method (Table  2 ); in this case, recoveries were in the range 92-102%. The precision of the HPLC-FLD method applied to GOS ingredients was determined by analysing each ingredient in duplicate on six different days on the same instrument by the same operator. The data from these experiments were used to determine the relative standard deviation under repeatability conditions (RSD (r)) and the relative standard deviation under intermediate reproducibility conditions (RSD (iR)); results are shown in Table  3. The same data were also used to determine the total dietary fibre (TDF) content of the GOS ingredients by excluding the GOS disaccharides, and the precision of those measurements was also determined (Table  4 ). For GOS analysis, the robust RSD (r) is in the range 0.
Cichlid OR repertoires A comprehensive search of the genome sequences of five fishes belonging to the cichlid family (Oreochromis niloticus, Pundamilia nyererei, Haplochromis (Astatotilapia) burtoni, Neolaprologus brichardi, Mitriaclima zebra) was undertaken in order to identify their OR gene repertoires. First, we retrieved 183 fish OR sequences from the literature  [17, 18]  to construct a query set for TBLASTN searches of each cichlid genome sequence determined by the BROAD Institute. This search, performed with a cut-off of 1e -50 , identified 820 candidates OR genes distributed over 733 contigs. These candidate genes were checked by TBLASTN against a set of 247 (Additional file 1) non-OR GPCRs to eliminate false positives. The remaining candidate genes were checked with TBLASTX against the fish protein database (NCBI, taxiD: 7898).
OR repertoires are usually classified into classes, families and subfamilies according to the percentages of AA identity shared by the different ORs. In this study, we used the same 40% and 60% AA identity thresholds as proposed by Glusman et al. [19]. Each cichlid From a larger set of OR sequences retrieved from ENSEMBL and GENBANK, we characterized a subset of true OR genes by multiple alignment of AA sequences, phylogenic tree construction and BLAST analysis. DNA samples used by the BROAD institute to determine the genomic sequences were for each species extracted from a single fish with 2 N chromosomes. contains ORs from 24 to 26 families and between 47 and 51 subfamilies. There are, however, only 56 subfamilies in total indicating that most subfamilies are common to the five cichlids (Figure  1 , Additional files 2 and 4, Table  3 ).
Conserved amino-acid motifs ORs are GPCRs that belong to the rodhopsin subfamily. They are characterized by a number of AA patterns of which the MAYDRY motif in the internal loop 2 is the most characteristic. These patterns have often been used for mining whole genome sequences for OR identification  [12]. We used the MEME program  [31]  to search for the five best motifs for each of the five cichlids and D. rerio. The first four motifs identified for each fish are very similar in both sequence and position between fishes (Figure  2 ). They are also not very different from those identified in OR mammals, despite the substantial distance from any common ancestor  [13, 14, 32, 33]. However, no clear fifth pattern was identified for the fish ORs. ORs are transmembrane proteins. Although no signal peptide has been identified in their sequences using Polyphobius  [34] , an N-glycosylation site, Asn N Ser/ Thr has been detected in all ORs identified to date  [18].
The greatest numbers of pairs or triplets were found between H. burtoni, M. zebra and P. nyererei, in agreement with their closer phylogenetic relatedness. In O. niloticus 7 pairs, 1 triplet and 2 quadruplets of paralogous genes were identified consistent with this repertoire having undergone a higher level of duplication. The list of genes is shown in Additional file 5. exon boundaries while maintaining the reading frames open through the junction of the adjacent exons. Interestingly, these two motifs are similar to those found at mammalian gene exon/intron boundaries  [40]. Most of these donor/acceptor sites were also predicted by the FSPLICE program  [41]  with the FISH model weight matrix (data not shown). ii. Introns were in nearly the same positions in all the ORs (Table  7 ): By comparing the gene nucleotide sequences and the amino acid sequences, we inferred the intron position relative to the 2D OR structure (Additional file 10) and noticed a nearly fixed position (Table  7 ).
In 27 of the 31 OR genes with one intron interrupting the coding frame, the intron is in phase 0 and in phase 2 for the remaining; also for 26, the intron is within the sequence encoding the MAYDRY motif in the first internal loop. Similarly, of the 11 OR genes with two introns within the coding sequence, first intron is in the sequence encoding the extracellular part in five, and in the sequence encoding internal loop 2 in a further five. All 11 OR genes with two introns have their distal intron in the sequence encoding external loop 2. iii. BLASTX analysis. All proteins identified by TBLASTN search were subjected to a BLASTX search against the non-redundant NCBI protein database. Only proteins giving a strong hit with OR proteins, and no hits or a meaningless hit with other GPCRs, were kept as true ORs.
iv. Phylogenetic analysis. The AA sequences of 507 cichlid ORs and of 247 non OR class A (Additional file 1) GPCRs identified in GenBank were aligned with MAFFT, and a tree constructed with PHYML and drawn with FigTree [42] (Figure  5 ). All ORs, with one or several coding exons, clearly form a separate branch from the non OR class A GPCRs; this argues for them being true OR genes and not  dN/dS ratios were calculated for each pair of OR genes identified in the 14 families with 4 or more genes (panel a). The numbers of OR pairs per family with a dN/dS ratio above 1 are indicated in panel b. For those in which dS was 0, the dN/dS was arbitrarily given the value >10. In panel c, dN/dS ratios of pairs of paralogous genes (columns 2 and 6) were compared with the ratios of pairs of orthologous genes (columns 4 and 8).
See Additional file 9 for the genes of each of these two groups and a complete alignment of their sequences. It was due to this belief that several authors mining genome sequences restricted their search to long ORF or eliminated short ORF afterwards  [18]. Definitive proof of their status as OR will be provided by functional studies, implying RNA transcription analysis of olfactory epithelium and identification, at least for some of their ligands. Obviously, such studies are out of the scope of the present paper. There are several arguments, in particular the results of the BLAST analysis and their phylogenetic positions (arguments 3 and 4 developed in the Results section) strongly indicate these multi-coding exon genes code actual ORs. Functional studies are required for a conclusive demonstration that these sequences are indeed active OR genes.
This will probably involve analysis of RNA transcription in olfactory epithelium and identification, at least for some of them, of their ligands. Such studies are beyond the scope of the present paper. Nevertheless, our work provides several arguments, in particular the results of the BLAST analysis and the phylogenetic positions of the sequences ((iii) and (iv) in the Results section), strongly indicating that these multi-coding exon genes indeed encode true ORs. We performed a TBLASTN search to determine whether these multi-coding exon genes are specific to the cichlids or whether they had been overlooked during the mining of other fish genomes. We searched the OR fish repertoires in NCBI and ENSEMBL databases with a set of cichlid multi-exon OR gene sequences. We also inspected, one by one, the AA and gene sequences of the medaka, stickleback and zebrafish OR genes in the ENSEMBL database.
Methods The sequences of the five cichlid genomes were determined by the BROAD Institute using DNA samples  prepared from a single double-haploid individual of each species, except in the case of M. zebra, which was caught in the wild. (http://www. broadinstitute. org). A dataset of 143 zebrafish ORs and 40 takifugu ORs  [17, 18]  was used as bait for exhaustive TBLASTN searches (http:// blast. ncbi. nlm. nih. gov/Blast. cgi). Candidate genes were then compared to a negative dataset of 247 non-OR and non-TAAR animal GPCRs retrieved from the NCBI and ENSEMBL databases (Additional file 1). TBLASTN results were filtered with a homemade python script so that candidate OR sequences conformed to the following rules: (1) one or more matches with the positive dataset and (2) no match with the negative dataset using an e-value cut-off of 1e -50.
Selected candidates were re-checked using both BLASTX and BLASTP against the fish protein database (NCBI, taxiD: 7898) using default parameters with a cut-off of 1. e-100. All genes were manually collected, biocurated and translated into protein sequences using Geneious software 6. 1  [52]. Incomplete OR genes found at the ends of contigs were annotated as "edges" whereas incomplete   OR genes found inside contigs were considered to be "fragments". Genes with disruptive frameshifts or stop codons were annotated as pseudogenes. For spliced OR genes, predicted sequences and splice sites were manually corrected on the basis of multiple alignment using MAFFT 7  [22]  and also by using FSPLICE  [41]. The list and sequences of complete cichlid OR genes (spliced or not spliced), pseudogenes, edges and fragments are available as supplementary information (Additional file 2).
brichardi, H. burtoni, P. nyererei, O. niloticus and M. zebra respectively) then "OR", then p for pseudogene, e for edge or f for fragment followed by a letter or the family and three digits to designate the gene itself. For example, BRIORe. E041 designates the edge OR gene 041 belonging to family E. Ratios of non-synonymous to synonymous nucleotide substitutions (ω = dN/dS) were calculated with the method of Nei-Gojobori as modified by Zhang et al. [26]  using Perl and python scripts to automate the whole process. These ratios were calculated for both the entire proteins and different subregions (i. e. individual transmembrane domains or loop regions). Conserved motifs were identified in predicted OR protein sequences with the online program Multiple Expectation Maximization for Motif Elicitation (MEME) online program v.
3 MgSO 4 , 2 CaCl 2 , 26 NaHCO 3 , 1. 25 NaH 2 PO 4 , 15 glucose, pH 7. 6 and 305 mOsm. The epithelia were further immersed in PBS containing 4% paraformaldehyde and 0. 2% glutaraldehyde for 1 h at room temperature. They were subsequently washed in PBS and mounted using DAKO mounting medium between a slide and a coverslip. The septal epithelia were visualized using a Olympus BX51WI microscope (10ϫ objective; Olympus UPLFLN10x; numerical aperture, 0. 30) coupled to a Olympus DP72 camera with a 4140 ϫ 3096 resolution. GFPcontaining neurons were observed using a GFP filter after excitation through a BP460-490 filter with light from a mercury HBO 100W Olympus lamp. Images covering the entire epithelium were taken and the whole tissue was reconstituted using the MosaicJ plugins of ImageJ software (National Institutes of Health) and the neuronal density was calculated as the ratio of the number of GFP-containing neurons against the total olfactory sensory epithelium surface.