dataesr/scientific-paragraphs-categorization huggingface dataset:
Validation of DIDGI ® for the Digestion of Infant Formula The DIDGI ® system is a very recent one. Although several matrices (dairy, meat, fruits and vegetables, emulsions) have been submitted to digestion using the DIDGI ® system, only data obtained on the digestion of infant formulas have been published so far  (Ménard et al. 2014). In order to demonstrate that this system was physiologically-relevant, a comparison of the in vitro and in vivo digestion of an infant formula was performed. The in vivo trial was conducted on 18 piglets that were fed the infant formula for which the concentration in lipids and proteins was increased compared to a standard one, but the ratio lipids/proteins was kept constant. In parallel, in vitro gastro-intestinal digestion was performed on this enriched infant formula using the newly developed system and the extent of milk proteolysis was monitored and compared to the one obtained in vivo.
Cooperative folding The thermodynamics of GPH subunits combination has been studied by microcalorimetry and the loss of cooperative folding was observed only at high T m , i. e. , above 70°C (50). Short-term incubations (5 min) of different GPHs at temperatures between 4 and 86°C followed by sandwich ELISA to detect residual heterodimeric molecules, led to similar data  (51). The conservation or restoration, of the 3D structure at fairly high temperatures was also confirmed by measuring residual bioactivity in cell-culture assays. These data indicate that the presence of one cystine-knot in each of the two subunits together with the fastened β-subunit "seatbelt" around the α-subunit, ensure very stable tertiary quaternary structures even if it has been shown to be highly mobile by hydrogen isotope exchange of peptide protons  (35).
viasano. be). Starting with two towns in 2006, VIASANO now involves 19 towns in the French-speaking and Flemish communities of Belgium. Given the paucity of reports on the effectiveness of community-based programmes to prevent obesity, especially in young children  (23, 24) , it is important to evaluate the effects of VIASANO on the prevalence of overweight and obesity. Here we report some initial results, comparing cross-sectional data on changes in body mass index (BMI) from children in two pilot VIASANO towns situated in the French-speaking part of Belgium, with data from children in the whole of the French-speaking community of Belgium. Using this large community database as the comparison population we aimed to avoid any potential bias introduced by selection of a small number of control towns.
Measurements Data for the analysis were derived from the school health community surveillance registry (http://www. sante. cfwb. be), which records all children's weights and heights, independently measured at school by nurses working for the promotion of health in schools, a service of the French Community of Belgium. The nurses are trained to perform the measurements according to a specific measurement protocol  (25). Weight is measured in light clothes (without shoes) to the nearest 0. 1 kg using electrical digital scales (SECA, Hamburg, Germany). Standing height (without shoes) is measured to the nearest 0. 5 cm by a ruler fixed to the wall. BMI is calculated as weight in kg divided by the square of the height in m (kg/m 2 ). For the two pilot towns, the raw data (height, weight, BMI) were available, but for the whole French community, because of administrative restrictions, only data on percentages of overweight and obesity were available.
001). LOX, a gene encoding lysyl-oxidase involved in collagen cross-linking was also downregulated (FDR = 5. 7 10 -4 ). By contrast, expression of the ADIPOQ gene encoding adiponectin, another adipokine inversely related to obesity-associated insulin resistance, did not significantly vary (FDR = 0. 37). Expression of FTO, the most widely replicated gene in genome-wide association study of obesity, was not affected by surgery (FDR = 0. 99). Several gene expressions that were reported in the literature to be modified by weight loss  [7, [9] [10] [11]  were found differentially expressed in the present dataset, including ABCC6, ALDOC, B4GALT6, CCND2, CIDEA, EGFL6, ESR1, FADS1, FADS2, FASN, NQO1, SCD, SFRP4, SRPX and UCHL1. As the surgical procedure included 15 Roux-en-Y gastric bypass (RYGB) and 7 adjustable gastric banding (AGB), we tested whether differential expression was influenced by the type of procedure.
Clinical and biological parameters were assessed prior to surgery (T0) and at 3 months after surgery (T3). approved the clinical investigation. The study was conducted in accordance with the Helsinki Declaration and was registered in a public trials registry: Number NCT01454232. Fat mass was measured by whole-body fan-beam dual energy x-ray absorptiometry (DXA, Hologic Discovery W, software v12. 6; Hologic, Bedford, MA). Subjects were considered to have diabetes if they were taking an antidiabetic treatment or if fasting glycemia was !7 mmol/ l on at least two different occasions. To estimate the patients cardiometabolic risk, we used the thresholds established by the International Diabetes Federation (IDF, 2005) for the definition of MetS. Subjects were included in the MetS group if they had at least two of the four factors: 1) raised triglyceride levels (! 1.
RNA-seq data pre-processing Reads of bad quality were filtered out using fastq_illumina_filter. Trimming was performed using sickle with default parameters (quality threshold = 20, length threshold = 20). Orphan reads were excluded. Quality of reads was checked using FastQC. Paired-end reads were mapped to the human transcriptome using Bowtie2 v2. 1. 0 (http://bowtie-bio. sourceforge. net/ bowtie2/). The reference human transcriptome was generated from the UCSC reference genome (hg19) and the Illumina iGenome annotation file of transcripts using RSEM  [47]. Quantification of transcript abundance was performed using eXpress  [48]. The data used for analysis were the eff_counts which give the estimated number of fragments mapping to a given target adjusted for fragment and length biases  [49].
Statistical analysis After exclusion of 2 individuals failing to be sequenced because of bad quality or insufficient amount of RNA, analysis was performed in 22 subjects in the R environment (v3. 0. 2). Gene coverage was assessed using the RSeQC package (http://rseqc. sourceforge. net/). The plot showed a well-documented 3' bias due to polyA selection, but there was no clear relationship between the skewness of the coverage and the RNA quality assessed by RIN values (S9 Table, S6 Fig). We further checked by principal component analysis whether samples with low RIN may behave as outliers, but no outlier was detected. Genes with low expression levels, defined by a mean number of counts < 10 at both times, were filtered out. We also excluded a few genes annotated as micro RNAs (MIR symbol), leaving out 15,972 genes for analysis.
Parameters and their standard errors were estimated by the h-likelihood and tested by means of the t-test. To account for multiple testing, we used the FDR  [52]  and considered as significant an FDR < 0. 05. Gene Set Enrichment Analysis (GSEA). The GSEA software (v2. 0. 12) was used to investigate whether the most differentially expressed (DE) genes between T0 and T3 shared common biological functions  [15]. The analysis was performed on the list of all genes ranked either by their signed or by their absolute t-statistics obtained from the negative binomial model. Parameters were set to their default value. Gene sets tested for enrichment were taken from the MSigDB database (BioCarta, KEGG and REACTOME gene sets). The statistical significance of the enrichment score was assessed by performing 1000 permutations.
Overlap among gene sets was examined by a leading edge analysis (www. broadinstitute. org/gsea/). We considered as significant a FDR < 0. 05. Weighted Gene Coexpression Network Analysis (WGCNA). The R WGCNA package (v1. 34) was used to identify networks of coexpressed genes at T0  [18]. Prior to analysis, data were transformed using the VST transformation proposed by the DESeq package. The resulting variable was further adjusted for run, RIN and RNA concentration and the 10,000 most variable gene expressions at T0 were used for analysis. We used the adjacency matrix to specify the connection strength a ij between genes i and j (a ij = |cor(x i ,x j )| β ). The power β was fixed to 6 after applying the function pickSoftThreshold. The topological overlap measure was used to define modules of interconnected genes.
Modules were merged using the Dynamic Tree Cut function  [53]. Parameters were fixed to their default values. Spearman's rank correlation coefficients between module eigengenes and phenotypic traits were represented by a correlation heatmap. The module membership (MM) measured the correlation of a given gene expression profile with its module eigengene, genes with the highest rank of MM having strongest contribution to the module. Enrichment analysis of modules in functional GO categories was performed using the DAVID software (v6. 7)  [54]  with the list of the 15,972 genes expressed in SAT taken as reference. A FDR < 0. 05 was considered significant. For each module, the network of the 30 genes having the highest absolute MM in the module, thereafter named hubgenes  [18] , was displayed using the R function circlePlot.
Stability of modules was assessed by the Jackknife technique. The WGCNA algorithm was repeatedly run on the 22 subsets obtained by systematically leaving out 1 individual from the dataset and in each sub-analysis, we selected the module having the maximum overlap with each original module by means of the Jaccard index  [55]. The mean and standard deviation of the maximum Jaccard indices were calculated over the 22 subsets of n-1 individuals. In order to select the most stable modules, for each gene i we computed the number k i of times it was correctly classified in the best matching module of the different sub-analyses. We considered that a gene was steadily clustered if it was attributed to the right original module in ! 18 of the 22 sub-analyses. We repeated the same procedure by using the correlation between eigengenes instead of the Jaccard index, i.
Gene expression levels were assessed using Affymetrix Mouse Genome HT_MG-430A arrays and data were processed as previously described  [57]. To investigate the preservation of gene expression modules between HF/HS and chow diet-fed mice, we applied the same pipeline of analysis as in humans. Modules were first detected in the HF/HS group by WGCNA and the within-module correlations were then compared to those in the chow diet group.
Acknowledgments We thank  Drs. Florence Marchelli  and  Valentine Lemoine  who were involved in patient recruitment at the  Center for Research on Human Nutrition, Pitié-Salpêtrière Hospital, Paris, France , and  Michèle Guerre-Millo ,  Joan Tordjman ,  Sébastien André ,  Fabrizio Andrelli  and  Isabelle Dugail  for their critical interpretation of the results.
Acknowledgments We thank  Martine Vivier-Darrigol  ( ARS Aquitaine ),  Emmanuel Bongrain  ( Direction Départementale de la Cohésion Sociale et de la Protection des Populations Dordogne ), and  Marie-Pierre Donguy  ( Direction Générale de l'alimentation ) for their collaboration and  Patrick Rolland  and  Jet De Valk  for their comments on the manuscript.
Results In  [3] , the online Supplementary Data S3 reports that the "2-hop" method identifies HOIs with a sensitivity of 74% and a specificity of 96% for the i2b2 Obesity NLP reference set, henceforth i2b2 obesity. Our evaluation is performed on this same dataset in order to enable a comparison of our method. As such, we consider the set of terms associated to the 16 different conditions surveyed in i2b2 Obesity and retrieve from our database the corresponding concept identifiers. Given this setting, we are able to compute the sensitivity and specificity of our approach. This is presented in Table  2. The experiments have been conducted on commodity hardware running a Java implementation using a MySQL database instance, we thus consider that there are rooms for performance gains. The analysis of our method results is given in 3 dimensions: sensitivity, specificity and processing duration (not including end-user interactions).
Conclusions We have presented a novel, semi-automatic solution for defining health outcomes of interest. Our work is inspired by previous, manually-intensive work done for the purpose of text-mining clinical notes from EHRs. Our approach is supported by a cooperation between Formal Concept Analysis, to classify, infer and prune discovered concepts, and Semantic Query Expansion, to leverage the hierarchical structure of ontologies. We implemented a RESTful API and a graphical Web-based interface to illustrate the process that users would follow to browse data and refine their HOI query rapidly. First experimentations highlight positive results for the sensitivity and specificity measures. The most promising aspect of this approach consists in the the discovery of positive results not present our i2b2 Obesity NLP reference set.
Acknowledgements Publication of this article has been funded by the  FRANCE-STANFORD center for interdisciplinary studies grant. This article has been published as part of  BMC Medical Informatics  and  Decision Making Volume  15 Supplement 1, 2015: Proceedings of the ACM Eighth International Workshop on Data and  Text Mining in Biomedical Informatics. The full contents of the supplement are available online at http://www. biomedcentral. com/bmcmedinformdecismak/supplements/15/S1.
2 mm internal spacing was used as a sample holder. For each measurement, the sample holder was placed at an angle of 135 with respect to the incident beam direction. The time of data acquisition was about 8 hours. A transmission of $96% was estimated for all the investigated samples. The measured time-of-ight spectra were analyzed with the QENSH data treatment program, available at LLB, which allows, inter alia, the correction of the detector efficiency by normalization to vanadium spectra, the correction for the empty cell, the transformation of the TOF spectra into energy spectra, and the data grouping to improve the corresponding signal/noise ratio. Elastic scattering scans were performed using a high-energy resolution, wide momentum transfer backscattering spectrometer IN13 at the Institute Laue-Langevin (ILL, Grenoble, France).
An energy resolution of 9 meV FWHM, corresponding to an incident neutron wavelength of 2. 23 Å, was achieved in the Q range 0. 2-4. 6 ÅÀ1. A standard, indium sealed, at aluminium cell with a thickness of 0. 3 mm was used as a sample holder for all the examined samples. The temperatures explored ranged from 130 to 310 K, and the acquisition time varied between 1 and 2 hours depending on the type of nanosponge. The acquired data were corrected in order to take into account the incident ux, cell scattering, self-shielding, and detector responses. The elastic intensity of each sample relative to a given temperature was then normalized with respect to the corresponding lowest measured temperature (T ¼ 20 K). A transmission of about 90% was measured for b-CDPMA12, 88% for b-CDPMA14 and 88. 5% for b-CDPMA18.
Acknowledgements This article is a part of dissertation research of Mustofa which was conducted in 1999 under the supervision of  Prof. Michèle Mallié  and  Prof. Alexis Valentin ,  University of Monpellier I, France. Authors are also grateful to  Prof. Noerhajati Soeripto , Director of  Central for Tropical Medicine, Faculty of Medicine, Gadjah Mada University Yogyakarta  who provided opportunity to  Mustofa  to take doctoral program in France.
Background: Transcriptome sequencing offers a great resource for the study of non-model plants such as Catharanthus roseus, which produces valuable monoterpenoid indole alkaloids (MIAs) via a complex biosynthetic pathway whose characterization is still undergoing. Transcriptome databases dedicated to this plant were recently developed by several consortia to uncover new biosynthetic genes. However, the identification of missing steps in MIA biosynthesis based on these large datasets may be limited by the erroneous assembly of close transcripts and isoforms, even with the multiple available transcriptomes. Results: Secologanin synthases (SLS) are P450 enzymes that catalyze an unusual ring-opening reaction of loganin in the biosynthesis of the MIA precursor secologanin. We report here the identification and characterization in C.
roseus of a new isoform of SLS, SLS2, sharing 97 % nucleotide sequence identity with the previously characterized SLS1. We also discovered that both isoforms further oxidize secologanin into secoxyloganin. SLS2 had however a different expression profile, being the major isoform in aerial organs that constitute the main site of MIA accumulation. Unfortunately, we were unable to find a current C. roseus transcriptome database containing simultaneously well reconstructed sequences of SLS isoforms and accurate expression levels. After a pair of close mRNA encoding tabersonine 16-hydroxylase (T16H1 and T16H2), this is the second example of improperly assembled transcripts from the MIA pathway in the public transcriptome databases. To construct a more complete transcriptome resource for C. roseus, we re-processed previously published transcriptome data by combining new single assemblies.
Care was particularly taken during clustering and filtering steps to remove redundant contigs but not transcripts encoding potential isoforms by monitoring quality reconstruction of MIA genes and specific SLS and T16H isoforms. The new consensus transcriptome allowed a precise estimation of abundance of SLS and T16H isoforms, similar to qPCR measurements. Conclusions: The C. roseus consensus transcriptome can now be used for characterization of new genes of the MIA pathway. Furthermore, additional isoforms of genes encoding distinct MIA biosynthetic enzymes isoforms could be predicted suggesting the existence of a higher level of complexity in the synthesis of MIA, raising the question of the evolutionary events behind what seems like redundancy.
In such a case, mixing samples is expected to increase the possibility to find lowly expressed genes and isoforms, due to the diversity of reads sequenced from diverse tissues/experimental conditions, for instance. However, combining libraries prepared from different sources, such as plant cultivars, may also generate more potential isoforms due to genetic polymorphisms. In the present study, we built a new consensus transcriptome for C. roseus using already published data. We generated assemblies for every available sample to take advantage of the diversity of tissues/experimental conditions, combined them and tested different thresholds to cluster homologous contigs. Special attention was taken to reduce the redundancy without affecting transcript quality. Optimization of this consensus assembly was performed by monitoring reconstruction quality of all MIA biosynthetic genes, with a particular emphasis on the two previously described T16H isoforms  [22]  and on a newly identified SLS isoform whose functional validation is also depicted.
Results and discussion Identification and characterization of a second SLS isoform While amplifying the coding sequence of SLS (CYP71A1, Genbank accession number L10081)  [14] , sequencing of the PCR products revealed the presence of a second putative isoform exhibiting 96 % identity with the original SLS isoform. Interrogation of the C. roseus transcriptomic databases (Medicinal Plant Genomics Resource, CathaCyc/Orcae and Phytometasyn) led to the identification of identical but partial sequences confirming thus the existence of this new SLS sequence that has been recently deposited to Genbank under accession number KF415117. The corresponding P450 also displayed a high level of identity (97 %) with the first SLS isoform (Additional file 1: Figure  S1 ) suggesting that it could also catalyze the oxidative ring cleavage of loganin to produce secologanin.
While T16H1 and T16H2 have distinct organ specific-roles in MIA biosynthesis, IS4 and IS5 display somewhat redundant functions. To gain insight into the respective involvement of the two SLS isoforms, SLS1 and SLS2 gene expression was measured in the main C. roseus organs (Fig. 5 ). SLS2 expression was detected in all the tested organs and reached maxima in those directly associated with MIA biosynthesis including roots, flower buds and leaves. By contrast, SLS1 transcripts were barely detectable in all organs except in roots where expression was three-fold lower than SLS2. It is interesting to note that SLS1 was initially characterized from a cell suspension culture cDNA library  [15]. Therefore, these results suggest that SLS1 and SLS2 can contribute concomitantly to secologanin biosynthesis in roots while SLS2 can be the prominent isoform of secologanin biosynthesis in the aerial parts of the plant.
The corresponding datasets will be thereafter named mpgrCra, PMSIllu (Illumina reads), PMS454 (454 reads), ccOrcae and NIPGR, respectively (Additional file 3: Table  S1 ). Our analysis revealed that correct reconstruction of MIA genes was not systematic. Reference sequences of MIA genes available on NCBI were blasted against those assemblies and the bitscore of best hit was compared to that of an ideal reconstruction, i. e. the bitscore of the reference sequence against itself. On the whole, quality of reconstruction was quite unequal between assemblies (Fig. 6 ). PMS454 and ccOrcae assemblies displayed the best sequences while PMSIllu was of weaker quality (see for example 10HGO, 16OMT, CMK, HDS, IDI1 and IO). NIPGR and mpgrCra assemblies were quite similar in content, probably due to the construction design of the NIPGR assembly (independent libraries assembled and mixed with mpgrCra before filtering).
Preparation of a consensus transcriptome for C. roseus The quality of reconstruction of MIA biosynthetic genes in single assemblies suggests that raw resources might contain enough information to construct a consensus transcriptome since most of genes displayed a good reconstruction (>0. 8) in at least one sample (Fig. 6 ). Because samples were sequenced at different depth, it may be possible that partial transcripts were also reconstructed in single assemblies. Therefore, two strategies based on the combination of samples were then tested to correctly assemble isoforms and MIA biosynthetic genes (Fig. 7 ): we first tried to combine all reads and generate a new assembly, while, in the second approach, the individual transcriptomes were combined and the resulting dataset clustered (using CD-HIT-EST) and subsequently filtered (to ensure the removal of clusters with weak representation by reads and in single assemblies).
A total of 3,145,245 contigs from single assemblies were then combined. This allowed combining very high quality transcripts within one new assembly which however, contained an evident redundancy due to the merging procedure. Indeed, the resulting large dataset is expected to cover a large number of isoforms. These isoforms may be real transcripts such as isoforms of SLS and T16H that have to be differentiated, or alleles of different cultivars, which should be integrated into a reference sequence. Running CD-HIT-EST with different sequence identity thresholds succeeded in combining contigs into clusters (Fig. 8a ). This algorithm clusters similar sequences and uses one of them as a representative one. A weak decrease in sequence quality was observed with lower identity thresholds for 16OMT (bitscore/ideal bitscore in non-clustered dataset, 0.