dataesr/scientific-paragraphs-categorization huggingface dataset:
We observed moderate LD of rs9268403 with three HLA-DQA1 (*0201, r2 = 0. 16; *0301, r2 = 0. 17;*0303, r2 = 0. 18), and two HLA-DQB1 (*0202, r2 = 0. 31; *0302, r2 = 0. 18) alleles. However, none of these HLA alleles provided significant association with anti-EBNA-1 antibody levels. Finally, we also made use of our sample recruited through HL probands to test the association of rs9268403 with HL in FBAT. The major allele (T) of rs9268403, which is associated with higher levels of anti-EBNA-1 antibodies, was also significantly associated (p = 0. 02) with a higher risk of HL. Several HLA variants had already been shown to be associated with HL in genome-wide association studies (GWAS)  [14]  and five were reported in the Phenotype-Genotype Integrator database (http://www. ncbi. nlm. nih. gov/gap/phegeni) (Table  S2 ).
Methods: The analysis is based on a cross-sectional survey, conducted during the 2010 high transmission season from July to September in the Nanoro Health and Demographic Surveillance Site. Following a systematic sampling technique, the survey covers 500 households with children under 5 years of age from 24 villages. In the survey, households were asked about expenditures on malaria prevention methods in the month preceding the survey. This includes expenditure on coils, indoor spraying, aerosols, repellents, herbs, cleaning of the environment and clearing of the vegetation. The data analysis was conducted with SPSS taking into account the socio-economic status (SES) of the household to examine any differences in the utilization of the prevention method and expenditure quintiles. An asset-based index, created through principal components analysis (PCA), was used to categorize the households into quintiles.
Data collection Data collection was conducted from July to September 2010. Interviews with the household respondents were conducted by field workers trained by the researchers, using questionnaires. Having obtained written informed consent, data was collected on the ownership, utilization, and expenditures of bed nets, indoor residual spraying and other malaria preventive methods such as mosquito repellents, smoke, clearing of vegetation, eating/drinking herbs, mosquito coils, cleaning the home and its surroundings, and growing plants. Information was also collected on household assets to determine their socioeconomic status. In order to validate the questionnaire, a pre-test was conducted and modifications were made to the questionnaire prior to the beginning of the main data collection. If the household head was not available, the family was asked to nominate the next most appropriate person to respond.
Data analysis The data analysis was conducted using SPSS. Differentiating by socio-economic status (SES) we examine differences in the utilization of prevention method utilization and expenditures. Data on asset ownership, water sanitation, type of house, and size of household were collected and used to generate the SES index. The selected assets were: bicycles, motorbikes, radios, television sets, cows, sheep, chicken and cellular phones. The SES is an assetindex, created using principal components analysis (PCA), was used to categorize the households into quintiles: richest (Q5), richer (Q4), poor (Q3), very poor (Q2) and poorest (Q1). Data were analyzed using tabulations, equity analysis, descriptive statistics and nonparametric tests. The equity analysis looked at the distribution of a variable among socio-economic status, i.
statistical analysis Data are presented as mean ± SEM. After testing for normality and homogeneity of variance, data were compared using parametric (one-way or two-way analysis of variance) or nonparametric tests (Kruskal-Wallis). For each para meter, except blood gas, the reference mean during the basal period is the average of the values recorded over successive 2. 5-minute intervals during the steady state period of 20 minutes. A P value ,0. 05 was regarded as statistically significant. Statistical analysis was performed by GraphPad Prism 5. 0.
Our data are well in line with this observation, and suggest that the perfusion was small in both skeletal muscle and skin. During general anesthesia, the skin is a tissue of choice to monitor because it is readily accessible, quickly vasoconstricts under shock states, and is the last to be reperfused in shock and resuscitation states. 14 Skin oxygenation can be monitored using TcPO 2 , which might better reflect PaO 2 than SaO 2. 22 Indeed, TcPO 2 is closely related to PaO 2 during anesthesia  23  and accurately reflects local ischemic suffering and global shock. 14, 24 It is also a sensitive and reliable index in the diagnosis of peripheral arterial disease. 25 The location and temperature of the electrode might affect the value of TcPO 2 recorded. 22, 26, 27 For these reasons, we consistently positioned the electrode on the internal portion of the upper thigh and the electrode was heated at 43°C, which allows for a better diffusion of O 2 , probably by shifting to the right the HbO 2 dissociation curve.
database algorithm gene discovery functional genomics MPscan RNA interference (RNAi) is a powerful and widely used method to investigate gene function. Researchers using the model nematode Caenorhabditis elegans often use a feeding method for RNAi that involves culturing worms on a bacterial clone expressing a doublestranded RNA (dsRNA) that is intended to target a specific worm gene  (Timmons et al. 2001; Timmons and Fire 1998). Because worms can be handled robotically, screens can be automated and large numbers of clones tested in parallel  (Squiban et al. 2012). Collections of RNAi clones are available. One made by the Ahringer lab contains polymerase chain reaction (PCR)-amplified fragments of genomic DNA  (Kamath et al. 2003) , whereas the library made by the Vidal lab  (Rual et al. 2004 ) was constructed from ORFeome clones, which are derived from cDNA  (Reboul et al.
In C. elegans long dsRNAs (often. 1 kb) are used, in contrast to the short interfering RNAs (siRNA; typically 19225 bp long) used in vertebrates. Each dsRNA can thus give rise to a multitude of siRNAs, which complicates target identification. Many published studies have relied on the assignment of targets provided by the community database Wormbase  (Yook et al. 2012). This currently suffers from a number of limitations (Wormbase release WS242). The first is that target identification is based on empirical criteria. The sequence of a "primary target" is at least 95% identical with the clone insert sequence for at least 100 nucleotides  (Fievet et al. 2013) ; for "secondary targets" the definition is more than 80% identity for greater than 200 nucleotides  (Kamath and Ahringer 2003). These figures are calculated using BLAT  (Kent 2002) , which is not perfectly adapted to the task for algorithmic reasons  (Imelfort 2009).
8 and mv_B0432. 9, the current gene models may require revision since there is conflicting ORF-sequence tag (OST) evidence. The extent of the PCR product predicted by UP-TORR on the basis of mv_B0432. 8 primer sequences is indicated by the red rectangle. The reason for this erroneous prediction is not clear. "mv_" for the Ahringer or Vidal library clones, respectively) cannot be used as input to UP-TORR. It is also not well adapted to the analysis of large datasets derived from genome-wide screens. We therefore decided to construct a tool specifically for C. elegans, basing target identification on matching fragments of sequence generated in silico from the predicted inserts of RNAi clones. This is part of a collection of tools, called Clone Mapper, that also allow clone verification and sequence retrieval.
Data sources The reference genome sequence and transcript sequences (WS235 and WS240) were downloaded from ftp://ftp. wormbase. org/pub/wormbase/ species/c_elegans/sequence/. Following the Wormbase convention, transcripts corresponding to coding genes were used for the target library; those corresponding to coding genes and pseudogenes were used for the clone insert library. RNAi reagent information was extracted from the GFF3 file at ftp://ftp. wormbase. org/pub/wormbase/ species/c_elegans/gff/. Since the original ORFeome primer sequences were designed  (Reboul et al. 2001) , there have been changes in the reference sequence of the C. elegans genome, most recently for release WS235 (see http://www. wormbase. org/about/wormbase_release_WS235). For some 500 ORFeome products, the original primer sequences no longer match to the genome (K.
For close to 15% of the clones in the Vidal library, primer pairs do not match current gene models. In the example shown in Figure  1C , the primer pair mv_B0432. 8 was designed on the basis of a single gene model that existed until 2003. The predicted exons of this gene were subsequently assigned to 2 genes (B0432. 8 and B0432. 13). The insert sequence of clones like mv_B0432. 8 that do not correspond to current gene models cannot thus be readily predicted in silico and we excluded these clones. This resulted in a library of 18,405 transcripts from 13,792 genes, corresponding to 88. 2% of all ORFeome clones and 85. 9% of the Vidal collection of 11599 RNAi clones. These sequences are available via Clone Mapper (see the section A tool for clone verification).
The library of all transcripts is queried with each POS to identify matched overlapping segments (MOS). In the example shown in the lower part of the panel, a transcript (in bold) with a single difference from the clone insert sequence ( Ã ) is shown. The number of PNOs that contain at least one POS that exactly matches a given transcript is counted (MNO; here 3). An example of one matching POS for each PNO is boxed. A score is then calculated (see Materials and Methods). (C) An example of a target identified by Clone Mapper but not Wormbase. Here the RNAi clone insert sequence has multiple stretch of sequence that have perfect matches over more than 21 nt to a target transcript (upper part of the panel; sjj_Y50E8. g and ZK643. 8a), but no contiguous region of 100 nt with 95% identity (lower part of the panel; the longest stretch of identity in the selected alignment of one fragment shown here is 31 nt).
clone. Checking in this way becomes laborious when one needs to sequence-verify tens or hundreds of clones. We therefore made a BLASTN-based tool to match experimentally determined clone sequences with our in silico clone sequence libraries. It returns an output showing whether the clone is the expected one, and if not what the clone is most likely to be (Figure  3 ). This became the first tool in a suite that we have called Clone Mapper and for which we provide a web-based access via www. ciml. univ-mrs. fr/EWBANK_jonathan/ software. html. The other functionalities are described below.
Identifying potential targets of RNAi clones Given the shortcomings of current target prediction (see above; Figure  4A ), and given the known molecular basis of RNAi, we next sought to design an alternative approach based on matching short clone-derived sequences against a comprehensive collection of predicted transcript sequences. In C. elegans, dsRNA gives rise to siRNAs of different sizes (19228 bp); 22 bp is the predominant length, approximately 20% are 21mers, and ,10% are shorter than 21 bp  (Gent et al. 2010). In Clone Mapper, therefore, the clone sequence is diced in silico into fragments of a predetermined size. By default Clone Mapper uses 21mers, corresponding to. 90% of the in vivo siRNAs. Increasing the oligomer size would restrict the number of potential targets identified, whereas, as discussed below, decreasing the oligomer size would allow more potential targets to be captured, but at the probable expense of increasing the proportion of false-positives.
The number of occurrences of each oligomer within each transcript is then counted, and a score (from 0 to 100, with 100 corresponding to a high confidence target) assigned on the basis of a simple formula (Figure  4B ; see the section Materials and Methods). The method allows the identification of potential targets that would not otherwise be found (e. g. , Figure  4C ). The predicted targets (protein coding genes) for all of the Ahringer and Vidal RNAi clones in our library have been precomputed and can be retrieved by entering a clone name in Clone Mapper. Alternatively, a user can input any sequence and its potential target transcript (protein coding and/or noncoding) will be calculated de novo. Conversely, the identity of clones predicted to target a given gene, or set of genes, can be retrieved by entering the relevant identifiers in the query box under the "Find targets" rubric.
A comparative analysis of potential targets To establish on a genome-wide scale how different the transcript to RNAi clone correspondences obtained with Clone Mapper were from those reported in Wormbase, we conducted a global comparative analysis. We compared the overlap between Wormbase and Clone Mapper predictions across a range of scores for each transcript-RNAi clone pair. With regards the Vidal RNAi clones, even at the greatest scores, Clone Mapper predicted essentially all (98%) of the Wormbasepredicted clone-target pairs (Figure  5A ). The missing fraction all falls into the category of overpredicted (Figure  1C ). On the other hand, 1865 clone-target pairs not reported in Wormbase were found. Relaxing the stringency (decreasing the cut-off score from the maximum of 100) progressively increased this number; using a cut-off score of $1, there were 4482, which represents an increase of 30% over the total number of Wormbase predicted clone-target pairs (Figure  5A ).
For the Ahringer RNAi clones, when the analysis was performed with the maximum cut-off score of 100, 5825 (22. 5%) of the Wormbasepredicted clone-target pairs were not found by Clone Mapper, whereas an additional 3552 were found by Clone Mapper alone. In this case, reducing the cut-off score progressively increased both the overlap between the two sets and the number of novel clone-target pairs (Figure  5B ). With a cut-off score of $1, there were 2581 and 6539 clone-target pairs specific to Wormbase and Clone Mapper, respectively, with 23266 identified by both. This corresponded respectively to 1518, 3137 and 18664 individual RNAi clones. According to Wormbase annotations, half (49. 8%) of the 3137 RNAi clones identified by Clone Mapper as potentially targeting a novel transcript (when using a cut-off score of $1) were previously predicted to target a single gene.
As discussed below, the choice of cut-off is necessarily arbitrary, but our results, taken together with bioinformatic and experimental investigation of on-and off-target effects  (Rual et al. 2007; Zhou et al. 2014) , suggest that Clone Mapper can identify a substantial number of novel targets. We also calculated the number of protein-coding genes targeted by the combined set of Vidal and Ahringer RNAi clones. Using the arbitrary cut-off score of $1, the entire set of clones is predicted to target a total of 19,120 of the 20,540 protein coding genes (93. 1%; WS240). This figure only increases marginally, to 19,595 (95. 4%), when the cut-off score is reduced to include all targets (Figure  5C ). To evaluate the potential impact of these differences in prediction, we compared the list of putative targets in four published data sets with those obtained with Clone Mapper.
DISCUSSION With Clone Mapper, we have attempted to satisfy several unmet needs for C. elegans researchers using RNAi. In addition to the central function of identifying potential targets for RNAi clones, it offers tools for clone verification and for the retrieval of RNAi clone and transcript sequences. Clone Mapper complements the tools already available in Wormbase and the web-based tool UP-TORR  (Hu et al. 2013). It can be used in conjunction with Wormbase Converter  (Engelmann et al. 2011 ) (also available via http://www. ciml. univ-mrs. fr/EWBANK_jonathan/software. html) to reanalyze published RNAi datasets. As with any resource, there are certain intrinsic and extrinsic limitations. A total of 1490 Vidal clones that are present in the physical library were purportedly amplified using primers that are not compatible with current gene models.
When the clone insert sequence is known, it can be used as the input to Clone Mapper, thus avoiding this problem. Within Clone Mapper, the length used to search for possible matches between clone insert and target transcript can be defined by the user, with a minimum of 6 bp, so that it can be used to identify potential seed regions for miRNAs  (Grosswendt et al. 2014)  in complete C. elegans transcripts. It can equally be increased to ensure specificity. The minimal length of sequence identity required to obtain efficient knock-down of green fluorescent protein expression in C. elegans has been experimentally determined to be $23 bp  (Parrish et al. 2000). It has also been reported that to observe an efficient RNAi effect, the length may vary from 30 to 50 nucleotides  (Rual et al. 2007).
Indeed, siRNAs generated from a diced primary target (secondary siRNAs) can knock-down mRNAs that are not a direct target of siRNA derived from an RNAi clone  (Zhou et al. 2014). We did not implement this level of target identification as part of the tool, but users can search for these indirect hits by inputting the sequence of any target transcript into the de novo target prediction utility that is available within Clone Mapper. The modular architecture of Clone Mapper also allows users to choose the best reagent for specifically knocking down a given gene. The identity of clones predicted to target a given gene, or set of genes, can be retrieved. Then one can check the number of off-target genes predicted for each clone, to identify the most specific clone. Finally, an in silico reanalysis of selected published RNAi datasets identified new target genes.
ACKNOWLEDGMENTS We thank the bioinformatics platforms of the CIML and the  Laboratoire d'Informatique Fondamentale de Marseille  for providing computing resources, database, and web servers. We are grateful to system administrators  Manuel Bertrand  and  Kai Poutrain  for their support and input. We thank the staff at Wormbase, particularly  Todd Harris ,  Chris Grove , and  Kevin Howe  for their invaluable help,  R. Mako Saito  and  R. Pukkila-Worley  for providing clone lists and  Ian Hope  and  Jérôme Reboul  for comments. This work was funded by institutional grants from  INSERM  and  CNRS , and a program grant from the  ANR  ( ANR-12-BSV3-0001-01  to J. J. E. ).
Knee extensor strength at 6 weeks after surgery was higher (95% CI -0. 38 to 0. 61,  = 0. 73) in the MV group. No trends for differences between the groups were observed in knee kinematics, TUG, WOMAC, or step count. Our results suggest a short term advantage in the first 6 weeks after surgery of the MV approach over the MP approach, but a larger study is required to confirm these findings. This trial is registered with NCT056445.
Ethical approval was granted from the local hospital trust and the University Research Ethics Committees. All participants gave informed consent, signing a consent form before participating in the study. The study has been registered with ClinicalTrials. gov (NCT056445). 2. 1. Surgical Protocols. Knee arthroplasties were performed using either the medial parapatellar approach or midvastus approach. A standard 15 cm skin incision was used in all cases so that it was not possible for the patient or the research assessor to detect which approach had been used. PFC Sigma cruciate retaining arthroplasty (DePuy International Ltd, Leeds, UK) implants were used with cement and the patella was not resurfaced. All patients had surgery under spinal anaesthesia supplemented by a femoral nerve block. All femoral nerve blocks were undertaken with a nerve stimulator and 20 mL of 0.
Statistical Analysis. An independent -test or the Mann-Whitney  test for nonparametric data was used to compare differences between the two groups before surgery. Differences between the two groups were analysed using repeated measures two-way analysis of variance (ANOVA). To account for possible differences between the groups at baseline preoperative measures were used as covariant. Level of statistically significant difference was set at  < 0. 05. Cohen's effect size  was calculated to inform sample size for future, appropriately powered, larger scale trials. Effect sizes were defined to be medium for values for Cohen's  of more than 0. 3 but less than 0. 5, good for values of 0. 5 and greater but less than 0. 8, and large for values of 0. 8 and greater  [17]. Statistical calculations were performed using Statistical Package for Social Sciences (SPSS), version 19.
Conjugation of DNA through a type IV secretion system (T4SS) drives horizontal gene transfer. Yet little is known on the diversity of these nanomachines. We previously found that T4SS can be divided in eight classes based on the phylogeny of the only ubiquitous protein of T4SS (VirB4). Here, we use an ab initio approach to identify protein families systemat- ically and specifically associated with VirB4 in each class. We built profiles for these proteins and used them to scan 2262 genomes for the presence of T4SS. Our analysis led to the identification of thousands of occurrences of 116 protein families for a total of 1623 T4SS. Importantly, we could identify almost always in our profiles the essential genes of wellstudied T4SS. This allowed us to build a database with the largest number of T4SS described to date.
Yet these taxa encode homologs of VirB4 and VirD4 in their mobile genetic elements suggesting the presence of T4SS-mediated conjugation  (8). Here, we took advantage of our previous dataset of VirB4 homologs to characterize all MPF classes. The repertoires, diversity and evolution of relaxases have been recently reported  (29). Therefore, we focused our attention on T4SS. To establish the repertoire of protein families typical of each class of T4SS, we detected the families of genes systematically associated and co-localized with VirB4 in the eight MPF classes. With these families we built protein profiles that we used to detect and classify T4SS. This resulted in a much more complete database of T4SS than those currently available. Using profile-profile alignments we identified distant homologies between protein families of different MPF classes thereby providing the first large-scale systematic analysis of protein homology between all different MPF classes.
Data Data on complete prokaryotic chromosomes and plasmids were taken from GenBank Refseq (ftp://ftp. ncbi. nih. gov/genomes/Bacteria/, last accessed February 2013). The dataset of 2262 complete prokaryotic genomes comprised 2393 chromosomes and 1813 plasmids. We used the annotations of the GenBank files, having removed all pseudogenes and proteins with inner stop codons. Some protein profiles were taken and improved from our previous work  (8).
With these seven datasets of proteins (one for each class, except for MPF T for which we already had the protein profiles) we performed all-versus-all BLASTP and used the scores to build protein families. We made protein alignments of these families, and kept the ones that give hits within the class. Thumbnail: scheme of the virB system. and used the output to build protein clusters using SiLiX (30) (identity ≥30% and overlap >50%). We carried out a multiple alignment of the proteins in each cluster with more than five proteins using MUSCLE (31) (default parameters). We used the multiple alignments to build phylogenetic trees using PHYML  (32). With these two pieces of evidence we removed the very few cases of extreme divergence, the proteins that were too short and the proteins that were too long (typically false positives, fusions or fissions of proteins motivated by sequencing errors or pseudogenization).