dataesr/scientific-paragraphs-categorization huggingface dataset:
94; at clustering threshold 98 %, 0. 92), IS and SLS2 for clustering thresholds lower than 0. 94 (Fig. 8b ). The transcript with lowest quality was T16H2 (0. 88 for clustering threshold above 0. 96). However, its quality was quite similar with that of the best reconstruction in current resources (0. 92 in NIPGR). Two other genes, IDI1 and STR did not display ideal reconstruction, according to the reference sequence. IDI1 was slightly better reconstructed in PMS454 and STR was better in NIPGR and PMS454. The origin of those discrepancies are unclear but might have been caused by a higher polymorphism, leading to a different reference sequence in comparison to the representative clusters obtained here. According to the quality of MIA biosynthetic gene reconstruction, we further retained the clustered dataset obtained with a sequence identity threshold of 97 %.
This threshold should be permissive enough to combine alleles differing by only few SNPs. The resulting clustered dataset, thereafter renamed CD97, was composed of a total of 534,979 clusters, 357,652 being singletons (a contig displaying no sufficient identity with other contigs) and 177,327 being real clusters, containing more than two contigs (which may originate from the same single assembly or from different single assemblies). A total of 249,423 sequences had identities (e-value < 1e-20) with sequences of the Uniprot database (Blastx), and 9,283 proteins found in this database were represented at 90 % of their length by at least one cluster in CD97. Participation of initial single assemblies in CD97 clusters was homogeneous, except for SRR122238 for which only 10 % of contigs (4. 3 % in clusters, 5.
roseus. Indeed, 25,692 had significant (e-value < 1e-20) identities with proteins of the UniprotKB database (Blastx) (Table  1 ). Fig. 7  Preparation of a consensus transcriptome using previously published RNA-seq data. Single transcriptomes were de novo assembled with Trinity using samples with paired-end design, then combined and similar contigs were subsequently clustered with CD-HIT-EST. Poor quality clusters were removed on the basis of cluster length, composition and expression (FPKM). For trying purposes, we also constructed with Trinity a transcriptome on a dataset including reads from all samples with paired-end design  To further clean CD97, all putative clusters were tested for 3 criteria: (i) length, (ii) number of contigs and (iii) expression level (sum of Fragment per Kilobase per Million of reads (FPKM) calculated on the 42 samples (19 paired-end and 23 single-end, see Additional file 3: Table  S1 ).
10d ). We expected that this procedure could reduce the loss of weakly expressed genes or rare isoforms. By fixing a minimal length of 500 bp, a number of contigs > 10 and a sum of FPKM > 50, we found that a large number of sequences (245,395) did not pass the three filters. This indicated that many clusters which size was < 500 bp have both poor representation and weak expression levels. We also found 233,752 clusters which had a sum of FPKM < 50 and contained less than 10 contigs. All sequences having a null sum of FPKM fell in this class. Out of the 543,979 clusters of CD97, a total of 485,641 sequences did not pass the filters. The resulting dataset, which contained 58,338 clusters, was retained and called CDF97. Out of these 58,338 clusters in CDF97, 49,128 sequences had a significant (e-value < 1e-20) identity within the Uni-protKB database (Blastx) (Table  1 ).
Our filtering process obviously decreased the number of potentially annotated proteins (249,423 sequences in CD97), and to a lower extent the number of full-length proteins in Uniprot represented by more 90 % of their length (9,283 in CD97, 7,110 in CDF97). However, given the large number of clusters that were removed, we considered that this loss was limited. Very rare isoforms may have been discarded in CDF97 by the filtering procedure but only if they had weak single assembly representation and low expression level. In addition, the number of full-length proteins in CDF97 was still higher than in the previously published datasets (Table  1 ). Looking for such isoforms will therefore require a more detailed examination of CD97 (non-filtered dataset). The total number of clusters in CDF97 was higher than other assemblies  (31, 450  in ccOrcae and 26,804 in PMS454) but similar to that of 59,220 in NIPGR and lower than that of mpgrCra (86,726).
Validation of the consensus FPKM-filtered-CDHIT-94 transcriptome through transcript abundance estimation The gene expression levels of SLS and T16H isoforms were determined by qPCR and compared to FPKM values calculated according to the RSEM procedure for each transcript within each assembly. As each isoform has apparent specific expression patterns (Fig. 11b ;  [22] ), the correct alignment of reads to high quality sequences should be able to yield similar results. More than 1. 1 billion reads from the 42 C. roseus samples were mapped back with Bowtie2  [63]  to current assemblies, as well as our datasets CD97 and CDF97. Substantial differences were observed in the total number of correctly aligned reads between assemblies: 81. 69 % on ccOrcae, 61. 75 % on mpgrCra, 73. 54 % on NIPGR, 61. 75 % on PMS454, 53.
54 % on PMSIllu, 90. 32 % on CD97 and 89. 01 % on CDF97. To estimate abundance of clusters in CD97 and CDF97, a contig to cluster map was prepared, similarly to the procedure used in RSEM relying on a transcript to gene map. This was expected to ensure a correct estimation of expression, in particular for rare isoforms (e. g. found in only one sample) displaying slight sequence difference from the cluster representative sequence. Best hits for SLS and T16H isoforms were selected within each assembly. SLS1, SLS2, T16H1 and T16H2 sequences were respectively: Caros007144, Caros020659, Caros001600 and Caros025399 in ccOrcae; cra_locus_10318_iso_2_len_357_-ver_3, cra_locus_1389_iso_1_len_312_ver_3, cra_locus_61 84_iso_8_len_1687_ver_3 and cra_locus_6184_iso_10_len_ 1687_ver_3 in mpgrCra; Cr_TC01142 (SLS1 and SLS2), Cr_TC26727 and Cr_TC35206 in NIPGR; CROWL1VD_ rep_c387, CROWL1VD_rep_c782, CROWL1VD_rep_c13 47 (T16H1 and T16H2) in PMS454; cro.
The highest correlation was observed for CDF97 (r 2 = 0. 69; Fig. 11b ). The contig-to-cluster map used for CD97 and CDF97 (similar to the transcript to gene procedure in RSEM) apparently allowed a more precise calculation of cluster expression values. The higher correlation coefficient obtained with CD97 and CDF97 were probably due to this procedure which was expected to encompass slight Fig. 11  CDF97 performance in quality reconstruction and transcript expression level. a Current resources (A = ccOrcae, B = mpgrCra, C = NIPGR, D = PMS454, E = PMSIllu), CD97 and CDF97 were used as databases to identify homologs of MIA genes, and the resulting bitscore obtained by BLAST was compared to that of an ideal sequence (bitscore of the reference sequence against itself, i. e. bitscore ratio = 1).
Exploitation of CDF97 transcriptome for prediction of other MIA biosynthetic gene isoforms Our finding that gene isoforms may encode similar enzymes (SLS, this study; T16H  [22] ) potentially add another layer of complexity to the MIA biosynthetic pathway. To predict putative new isoforms, we therefore looked at the number of hits having a score ratio > 0. 8 within each assembly. This threshold was empirically determined as lower enough to reveal potential isoforms. According to Fig. 11a , SLS isoforms are identified in PMS454, CD97 and CDF97, as indicated by the presence of more than 1 hit for the corresponding gene. Similarly, isoforms of T16H are observable in NIPGR, CD97 and CDF97. It also appeared that our dataset CDF97 still displayed redundancy among sequences as the number of hits for some genes was still important (10HGO, HDR, HDS, SGD and SLS2).
UPLC-MS analyses All samples were centrifuged and the supernatants were stored at 4 °C prior to injection. UPLC chromatography system consisted in an ACQUITY UPLC (Waters, Milford, MA, USA). Separation was performed using a Waters Acquity HSS T3 C18 column (150 mm × 2. 1 mm, 1. 8 μm) with a flow rate of 0. 4 mL/min at 55 °C. The injection volume was 5 μL. The mobile phase consisted of solvent A (0. 1 % formic acid in water) and solvent B (0. 1 % formic acid in acetonitrile). Chromatographic separation was achieved using an 8-min linear gradient from 10 to 24 % solvent B. MS detection was performed by using a SQD mass spectrometer equipped with an electrospray ionization (ESI) source controlled by Masslynx 4. 1 software (Waters, Milford, MA). The capillary and sample cone voltages were 3,000 V and 30 V, respectively.
At the end of the amplification, a melt curve was performed to check amplification specificity. Absolute quantification of transcript copy number was performed with calibration curves and normalization with the C. roseus 40S Ribosomal protein S9 (RPS9primers qRPS9for TTACAAGTCCCTTCGGTGGT and qRPS9rev TGCTTATTCTTCATCCTCTTCATC) reference gene (Genbank accession AJ749993. 1). All amplifications were performed in triplicate and repeated at least on two independent biological repeats.
Publicly available datasets and de novo transcriptome assembly Sequencing files of C. roseus samples (project accessions: SRP035766, SRP005953, SRP017832, SRP041695 and SRP008096) were downloaded from the ftp server of the NCBI SRA database. Exhaustive description of all files is provided in Additional file 3: Table  S1. Files were converted to fastq files with the NCBI SRA toolkit (v2. 3. 4-2) and checked with FastQC (v0. 11. 2). Overall quality was good (Additional file 4: Table  S2 ) but reads from left and right sequencing of paired-end samples as well as single end samples were treated to remove aberrant fragments and adaptors with Trimmomatic v. 0. 32  [67]. Parameters were the following: Illuminaclip = 2:30:10, Leading = 3, Trailing = 3, Sliding Window = 4:15 and Minimum Length = 36.
For testing purposes, Trinity was also performed on all the reads combined from ever paired-end normalized sample, with respecting read orientation. Before processing this large number of reads, a second normalization step was performed with the same parameters as described above. Parameters for Inchworm, Chrysalis and Butterfly were defaults. All the steps were conducted on the CCSC computer grid facility (Orléans, France) using the SLURM scheduler running under on a Linux ×86_64 architecture.
Sequence alignment and annotation BLAST analyses against given databases were performed with the stand alone application v2. 29  [68]. Annotation of transcripts was performed with Blastx against the UniprotKB/Swiss-prot database and analyze of hit coverage was done with Trinity perl script analyze_blast-Plus_topHit_coverage. pl. This analysis focuses on the number of proteins which are matched at least once by sequences in a given transcriptome  [62]. Quick evaluation of transcriptome assemblies was done by setting a local BLAST server with SequenceServer (Pryiam et al. , unpublished) and analyzing candidate sequences from the MIA pathway with Blastn.
Estimation of transcript abundance Estimation of transcript abundance was performed with RSEM v1. 2. 15 after aligning reads to target transcriptome with Bowtie2  [63]  (v2. 2. 5) with default parameters for both programs and the-no-polyA option. FPKM for the CD97 and CDF97 datasets were computed after preparing respective contig-to-cluster maps (using '. clstr' file generated with CD-HIT-EST) for the rsem-prepare-reference procedure in order to get expression values reflecting abundance in all samples. This is expected to allow alignment of cultivar-specific reads to its cognate contig, while using its expression level for the whole cluster. Expression tables for CD97 and CDF97 were prepared by merging '. genes. results' files. To compare expression levels between qPCR and FPKM in other assemblies, expression levels were re-calculated using the above described procedure but without contig-to-cluster or transcript-to-gene information.
Data processing The R software (3. 1. 0, [69] ) was used with the GUI interface RStudio v0. 98. 1091 or in the command line interface for high multicore parallelization. All operations outside dedicated programs were performed with R. The Bioconductor package 'SRA. db' [70]  (v1. 22. 0) was used to retrieve sample information from the SRA. We used the 'seqinr'  [71]  package (v3. 1-3) to remove poorly represented clusters from CD97 after identification of appropriate thresholds for cluster length, abundance and contig number. Graphs for the transcriptomic analysis were generated with the 'ggplot2' package  [72]  (v1. 0. 1). Correlations between qPCR and FPKM data were calculated as the adjusted r 2 of a linear model built with the "lm" function. The correlation was established for each dataset using their own expression data (see above).
Availability of supporting data The dataset (CDF97) supporting the results of this article is available at the LabArchives, LLC, repository, with DOI number 10. 6070/H4DR2SG9 and open access at http:// dx. doi. org/10. 6070/H4DR2SG9. It is also freely available on our website http://bbv-ea2106. sciences. univ-tours. fr/. The non-filtered dataset (CD97) as well as all new assemblies described in this study are available upon request.
A transcript was considered to be annotated if it matches a protein in the database at a e-value threshold of 1e-20 b ,Reports the cumulative number of proteins in UniprotKB/Swiss-Prot matched by at least one transcript in the corresponding assembly at a given % coverage c ,Clusters in CD97 having contigs from at least ten different single assemblies Dugé de Bernonville et al. BMC Genomics (2015) 16:619.
Statistical analysis and data management After four replications, acquired data were expressed as Mean ± SEM (Arithmetical Mean ± Standard Error of the Mean). Data distribution was examined using Kolmogorov-Smirnov test. Statistical significance testing was performed by Student's t-test. SPSS 17. 0 software package was used for statistical analyses. Differences were considered significant at p<0. 05 and highly significant at p<0. 01.
Data pooling A systematic review was conducted in PubMed to identify all clinical trials carried out since 1960 with at least one AS-AQ arm in March 2014. All published antimalarial clinical trials published since 1960 were identified by the application of the key terms ((malaria OR plasmod*) AND (amodiaquine OR atovaquone OR artemisinin OR arteether OR artesunate OR artemether OR artemotil OR azithromycin OR artekin OR chloroquine OR chlorproguanil OR cycloguanil OR clindamycin OR coartem OR dapsone OR dihydroartemisinin OR duo-cotecxin OR doxycycline OR halofantrine OR lumefantrine OR lariam OR malarone OR mefloquine OR naphthoquine OR naphthoquinone OR piperaquine OR primaquine OR proguanil OR pyrimethamine OR pyronaridine OR quinidine OR quinine OR riamet OR sulphadoxine OR tetracycline OR tafenoquine)) through the PubMed library.
All research groups in the systematic review were contacted to share their data with WWARN, and those who have contributed to the WWARN data repository were also asked whether they were aware of any unpublished or ongoing clinical trials involving AS-AQ, and also asked to contribute those unpublished data if available. Individual study protocol details were available for all trials, either from the publication or as a metafile submitted with the raw data. The WWARN invited investigators to participate in this meta-analysis if their studies included: i) prospective clinical efficacy studies of the treatment of Plasmodium falciparum (either alone or mixed infections), ii) treatment with AS-AQ with a minimum of 28 days of follow-up, iii) data available on exact dosages of AS and AQ and iv) PCR genotyping results to determine whether recurrences were due to recrudescence or new infection.
Statistical analysis All statistical analyses were carried out based on an a priori statistical plan  [22] , available in Additional file 4: Text S4. The primary endpoint used in this analysis was the PCR-adjusted risk of P. falciparum recrudescence at day 28. Secondary endpoints included PCR-adjusted risk of P. falciparum recrudescence at day 42, PCR-adjusted risk of new P. falciparum infection, and parasite positivity rates (PPRs) on days 1, 2 and 3 after treatment initiation. The overall efficacy at day 28 and day 42 was computed using survival analysis [Kaplan-Meier (K-M) estimates]; comparisons of K-M survival curves were performed using log rank tests stratified by study site (using a combination of trial and study site). Gehan's test was used when K-M curves crossed. Definitions of outcome and censoring are detailed in the WWARN Clinical Module DMSAP v1.
Discussion We collated individual patient data from 43 studies of antimalarial therapy with AS-AQ, including more than 9,000 patients recruited between 1999 and 2012. The data were derived predominantly from studies conducted in sub-Saharan Africa, with a wide range of patient ages, malaria transmission intensities, drug formulations and dosing plans. Three different formulations were included, and all of them were designed to deliver a total target dose of 12 mg/kg of artesunate (AS) over three days; however, the total target dose of amodiaquine (AQ) was 30 mg/kg for FDC and coblistered NFDC regimens and 25 or 30 mg/kg for loose NFDCs. Overall, the efficacy of AS-AQ was high, but it varied with patient age, formulation and target dose. The efficacy was similar between FDC and coblistered NFDC, but significantly lower in patients treated with loose NFDCs, and lowest in those treated with an AQ target dose of 25 mg/kg.
Indeed, our results show that even though patients treated with loose NFDC-30 received the same AQ target dose (30 mg/kg) as the patients treated with FDC, the efficacy was still higher in the FDC group. The dosage of the fixed dose  Acute drug vomiting AQ dose category (mg/kg) d combination of AS-AQ was developed using a weightfor-age reference database from malaria endemic countries, to ensure optimal dosing with the pediatric formulation  [40]. This allows the FDC prescription to be based either on body weight or age, a notable advantage, as body weight often cannot be assessed easily or accurately in health facilities of many malaria endemic countries. A formulation that can be applied either by weight-or age-based criteria probably increases dosing accuracy, and the availability of different tablet strengths, including a pediatric formulation, obviates the need for tablet splitting, reduces the pill burden and potentially improves adherence  [18, 41].
AQ has previously been associated with neutropenia when taken as a prophylaxis  [57]  and when used in conjunction with antiretroviral drugs  [58]. With limited data, our analysis showed no relationship between the dose of AQ and neutropenia. However, a higher AQ dose was associated with increased risk of gastrointestinal adverse events. A dose-dependent increase in the risk of gastrointestinal adverse events was also reported with artemether-lumefantrine  [59]. Our analysis has a number of limitations. Although the search was limited to prospective clinical trials recorded in PubMed, an additional review of clinicaltrials. gov identified that out of the 36 clinical studies registered testing AS-AQ between 2000 and 2012, 28 (78%) had subsequently been published and most of them were included in the meta-analysis.
Moreover, our meta-analysis also included seven unpublished clinical trials that were not registered in clinicaltrials. gov. Hence our analysis has captured the majority of published data and constitutes the largest meta-analysis of AS-AQ undertaken. Furthermore there were no apparent differences in patient characteristics and outcomes between the studies included and those which were not available (Table  6  in Additional file 6: Text S6). In addition, the model estimates were robust, as a sensitivity analysis showed that the coefficients of variation for the model parameters were small and the coefficients from the final model were similar to the estimates obtained from bootstrap sampling (Table  3  and Figure  2  in Additional file 6: Text S6). Another limitation of our study was that the FDC trials were mainly conducted in West Africa and those of loose NFDC mainly in East Africa, two regions with reported varied degrees of AQ resistance  [14].
Methodology used The following features were pre-defined before the analysis of the CANONIC database: 1. ACLF was defined as a syndrome characterized by AD of cirrhosis associated to organ failure (OF) and high short-term mortality rate. 2. AD refers to the acute development of ascites, hemorrhage, encephalopathy and/or bacterial infections. 3. The sequential Organ Failure Assessment (SOFA) Scale  [12]  was the model selected for the diagnosis of organ failure because it is widely used in patients requiring intensive care treatment and is superior to the Child-Pugh and MELD scores  [13] [14] [15]  in predicting mortality in patients with cirrhosis and (OF(s)). Since components of the SOFA score (liver, renal, cerebral, coagulation, circulatory and respiratory function) do not take into account specific features of cirrhosis, the SOFA scale was modified establishing a new scale called the CLIF-SOFA score (CLIF-SOFAs) adapted for liver patients.
3A  and B ). The CLIF-C ACLFs was then tested and validated for sequential use with data obtained from patients at 48-h, between day 3 and 7 and beyond. The performance of the score improved significantly at predicting 28-day and 3-month mortality of patients when measured at 48 h compared with the measurements at the time of admission suggesting that the CLIF-C ACLF score can be updated on a daily basis taking into account the effect of interventions. Development of a scoring system for patients diagnosed as having AD but no ACLF Using a similar approach, the CLIF-C Acute Decompensation score (CLIF-C ADs) was developed for hospitalized cirrhotic patients without ACLF using the CANONIC data  [50]. The reason for undertaking this analysis was based on the hypothesis that within the no ACLF patients with AD, there is likely to be a very low risk group that should be discharged and there may be a high risk group that will progress to develop full-blown ACLF and therefore have a high mortality.
To our knowledge, compounds 1 and 2 are new natural cucurbitacins and this is the first time that a coumaroyl-derived cucurbitacin is described. However, it is worth noting that compound 1 is the acetylated derivative of the well-known glucocucurbitacin E (4). In order to verify if these compounds are not artifacts due to solvent extraction (EtOAc), LC-MS analysis of a crude extract of the leaves of C. colocynthis prepared with MeOH was undertaken. Both derivatives 1 and 2 were again easily detected in the MeOH extract thus validating their natural occurrence (Figures S21 and S22   The known cucurbitacins (Figure  1 ) were identified by comparison of their NMR data with the literature as cucurbitacin E (also named α-elaterin) (3)  [23, 34] , 2-O-β-D-glucocucurbitacin E (colocynthin, elaterinide) (4)  [22] , cucurbitacin I (elatericin B) (5)  [23, 34]  and 2-O-β-D-glucocucurbitacin I (6)  [23].
They were carried out using a Hitachi-Merck apparatus (Hitachi, Tokyo, Japan) consisting of a quaternary gradient LC pump LaChrom L-7100, a diode array detector LaChrom L-7455 and a D-7000 interface, all controlled by the D-7000 HSM software. MPLC separations were carried out with Büchi (Flawil, Switzerland) pump and columns (15 × 100 mm). Silica gel (6-35 μm, 60 Å) for MPLC columns was purchased from SDS (Peypin, France). Preparative TLC G-200 UV254 (20 × 20 cm, layer 2. 0 mm) plates were from Merck (Darmstadt, Germany). Samples were deposited with the help of a Camag (Muffenz, Switzerland) ATS4 model autosampler piloted by WinCATS 1. 3. 3 software (Camag). TLC-MS was performed using a Camag TLC-MS interface coupled with a mass spectrometer (LCQ DECA XP max, Thermo Finnigan, San Jose, CA, USA) operating in ESI positive ionization mode.