dataesr/scientific-paragraphs-categorization huggingface dataset:
Introduction Metabolic profiling allows the identification of metabolic markers associated with diseases and supports the formulation of new pathophysiological hypothesis. One of the most employed analytical techniques for untargeted metabolomics is proton based nuclear magnetic resonance ( 1 H-NMR) spectroscopy  (Goodacre et al. 2004; Shulaev 2006 ). It provides information on molecular structures and on absolute or relative concentrations of compounds in complex mixtures. Metabolomic studies generate high-dimensional datasets requiring the use of powerful modeling techniques  (Sugimoto et al. 2012). The most commonly used modeling techniques for metabolomics profiling are partial least squares (PLS) and its derivate  (Goodpaster et al. 2010; Liland 2011; Lindon and Nicholson 2008; Trygg and Wold 2002).
However, these techniques do not perform an exhaustive exploration of the data. To overcome this limitation, we used a descriptive and easily understandable approach for metabolic profiling, which is based on a supervised rule-mining algorithm that locally and exhaustively explores the variable space of 1 H-NMR spectra datasets  (Luck et al. 2016 (Luck et al. , 2015)). Metabolic profiling of patients affected by monogenic inborn errors of metabolism is a particularly useful approach to address diagnosis/prognostic issues, and to provide insights into the pathophysiology of the disease  (Emwas et al. 2015; Mussap et al. 2013; Nicholson et al. 2012). Recently, a proof of concept of the relevance of using 1 H-NMR-based metabolic profiling to explain the recurrence of Acute Intermittent Porphyria (AIP) attacks was done  (Carichon et al.
Differences in metabolomic profiles of aAIP and PCT patients We next investigated the nature of the metabolites selected by the local model to identify the urine metabolomic profiles predictive of aAIP and PCT. Among the buckets identified with the rule-mining algorithm, some of them corresponded to metabolites involved in glucose metabolism: acetate (1. 92, 1. 96), citrate (2. 6, 2. 72), pyruvate (2. 36), glucose  (3. 48, 3. 52, 3. 56, 3. 76, 3. 92, 5. 28), and glycogen (3. 64, 5. 36, 5. 4, 5. 44) , indicating that glucose metabolism pathways are likely impacted by hepatic porphyrias. The corresponding metabolites were identified manually using the Human Metabolome Database (HMDB, http://www. hmdb. ca/). Notably, some rules characterized by the same bucket for both aAIP and PCT patients were disjoint (i.
e. , covered disjoint sub-regions of the bucket space) (Fig. 1 ). The disjoint rules indicate that aAIP patients were specifically characterized by higher concentrations of acetate, citrate and pyruvate, the inverse being observed for PCT patients. These findings illustrate that our rule-mining algorithm allows the classification of groups of patients based on the local trends (i. e. , range of values) of some variables. Fig. 1  Representation of the 1D rules for the discovery cohort dataset. Each horizontal segment corresponds to a 1D rule characterized by its variable condition: the bucket's name and the set of covered bins. We only show the rules corresponding to buckets for which rules could be generated for the two classes (i. e. , aAIP and s/fPCT). The color scale reflects the frequency of the buckets values covered by the rules over the leave-one-out splits.
aAIP patients were specifically characterized by relatively higher concentrations of acetate (p value = 0. 0004, one-way ANOVA), pyruvate (p value < 0. 0001, one-way ANOVA) and citrate (p value < 0. 0001, one-way ANOVA) (Table  1 ) compared with patients with s/fPCT. Notably, the concentrations of these metabolites were within the reference range (based on the urine concentrations collected in the HMDB database). There was no significant difference in the concentrations of acetate, pyruvate and citrate between sPCT and fPCT. Together, these results indicate that the urines of aAIP individuals are characterized by a significant increase of concentrations of glycolytic intermediates (citrate, acetate and pyruvate) compared with urines of patients with PCT.
Discussion We provide evidence that a model derived from a data mining approach based on rule generation combined with logistic regression is an innovative and straightforward tool for metabolomic profiling. Local models generate  easily interpretable rules compared to the classical PLS-DA method. In addition, these models provide information on the distribution of the groups of diseases with respect to range of the spectral values. This point is critical as it can be possible to translate this range of spectral values into of metabolite concentrations. In turn, the validation of the biological relevance of the potential biomarker identified by these models can immediately be done by measuring its concentration in independent datasets. Our approach might provide critical information on the potent metabolic disturbances associated with porphyrias.
36 s and a 90-degree pulse length of 8 μs. Data points (32K) were collected during 64 scans with a spectral width of 20 ppm. The preprocessing of the urine 1 H-NMR spectra was performed with MestReNova 8. 0 software. A line-broadening factor of 0. 3 Hz prior to Fourier transformation was applied. The spectra were then phased, baseline corrected, and referenced to TSP. Each 1 H-NMR spectrum was reduced by an equidistant binning method from 0. 24 to 10. 00 ppm with a bin width of 0. 04 ppm to limit misalignment problems. The spectral regions corresponding to urea (5. 52-6. 04 ppm) and water (4. 28-5. 24 ppm) were deleted to remove variability because of the suppression of water. Then, as mean of normalization and prerequisite to our method we discretized the 210 remaining buckets with a 10 quantile-based binning for the main analysis (i.
e. , aAIP vs. PCT) and with a 5 quantile-based binning for the secondary analysis (i. e. , sPCT vs. fPCT). Two NMR spectra datasets were obtained from the discovery cohort: one with 52 individuals and 210 buckets for main analysis (i. e. , aAIP vs. PCT) and one with 21 individuals and 210 buckets for the secondary analysis (i. e. , sPCT vs. fPCT). One NMR spectra dataset was obtained from the validation cohort containing 21 individuals and 210 buckets for the secondary analysis (i. e. , sPCT vs. fPCT). Finally, the resulting matrices were centered and scaled to unit variance for the PLS-DA. The dataset before normalization containing the two cohorts is available in supplementary information (S1 Dataset).
This set of selected variables generated by the global approach was next used to perform the local approach, which is based on a rule-mining algorithm that locally and exhaustively explores the variable space (see Algorithm 1). The objective is to highlight local phenomena characterized by subgroups of subjects. These relationships are under the form of 1-dimensional rules (1D rules) where variable condition corresponds to a range of values of a given variable observed in the dataset. To identify the most discriminative rules, we used as a rule quality measure the z-score and the rule modality size. The z-score tests whether the proportions of subjects with the target modality in the rule and in the entire dataset are different. We selected rules with a z-score > 1. 96 corresponding to a confidence level of 95%.
Evaluation and comparison of our approach with PLS-DA The predictive performance was evaluated using a leaveone-out cross validation and the F1 score with a permutation test of 2000 runs. A p value < 0. 05 was considered to be significant. As a means of another validation and in order to prove the consistency of the results found with our methods, we run a PLS-DA on the same dataset as for the rule-mining based approach [Scikit-learn project implementation  (Pedregosa et al. 2011) ]. For the PLS-DA, the evaluation procedure was the same as for the rule-mining approach (i. e. , the predictive performance of the models was evaluated using the F1-score with a permutation test of 2000 runs). A p value below 0. 05 was considered to be significant.
Whole Genome Resequencing and Mutation Prediction Clone Analysis Two gat-positive clones (revertant) isolated from the second colonization (day 6 of population 2. 15) were sequenced to test for the presence of additional mutations, besides the one reverting the gat-phenotype. Two independent colonies were used to inoculate 10 mL of LB and incubated at 37 C with  Sousa et al. doi:10. 1093/molbev/msx221 MBE agitation for DNA extraction (following a previously described protocol;  Wilson 2001). The DNA library construction and sequencing was carried out at the IGC sequencing facility. Each sample was paired-end sequenced on an Illumina Mi Seq Benchtop Sequencer. Standard procedures produced data sets of Illumina paired-end 250 bp read pairs. Genome sequencing data have been deposited in the NCBI Read Archive, https://www.
ncbi. nlm. nih. gov/bioproject/ PRJNA295680 (last accessed August 18, 2017). Mutations were identified using the BRESEQ pipeline  (Deatherage and Barrick 2014). To detect potential duplication events, we used ssaha2  (Ning et al. 2001 ) with the paired end information. This is a stringent analysis that maps reads only to their unique match (with <3 mismatches) on the reference genome. Sequence coverage along the genome was assessed with a 250 bp window and corrected for GC% composition by normalizing by the mean coverage of regions with the same GC%. We then looked for regions with high differences (>1. 4) in coverage. Large deletions were identified based on the absence of coverage. For additional verification of mutations predicted by BRESEQ, we also used the software IGV (version 2. 1)  (Robinson et al.
2015). 16 S rRNA gene amplification and sequencing was carried out at the Gene Expression Unit from Instituto Gulbenkian de Ciência, following the service protocol. For each sample, the V4 region of the 16 S rRNA gene was amplified in triplicate, using the primer pair F515/R806, under the following PCR cycling conditions: 94 C for 3 min, 35 cycles of 94 C for 60 s, 50 C for 60 s, and 72 C for 105 s, with an extension step of 72 C for 10 min  (Caporaso et al. 2011; Caporaso et al. 2012). Samples were then pair-end sequenced on an Illumina MiSeq Benchtop Sequencer, following Illumina recommendations. QIIME  (Caporaso et al. 2010 ) was used to analyze the 16S rRNA sequences by first quality filtering the samples with a phred quality threshold of 20 (with script split_libraries_fastq) and then OTU picking by assigning operational taxonomic units at 97% similarity against the Greengenes database (DeSantis et al.
2006); using script pick_open_reference_otus with default settings, but enabling reverse strand matches). The OTU table generated by the latter script was then filtered in two steps. First, with script remove_low_confidence_OTUs, from the Microbiome helper repository  (Comeau et al. 2017) , to remove potentially spurious OTUs. Second, to remove contaminants  (Salter et al. 2014 ) identified in the negative control, we filtered out any OTUs that were present in both the negative control and two or less samples at a frequency <0. 01. The OTUs that were kept in the final OTU table and that were shared between negative control and samples had a maximum frequency of 0. 004 in the negative control, were present at a frequency of >0. 01 in at least five samples and had an average frequency in samples that was at least one order of magnitude higher than the observed in the negative control.
The OTU table obtained after these two steps was then used for the analysis below. To understand the clustering pattern of the mouse gut microbiota in each experiment, we used the script jackknifed_beta_diversity to build rarefied OTU tables (at 75% of the minimum read depth in each experiment, as advised on QIIME's website). This built 100 rarefied OTU tables, computed weighted UniFrac  (Lozupone and Knight 2005)  and generated a consensus tree (per experiment) with jackknife support of tree nodes. Consensus trees were then plotted with R package ggtree together with the OTU relative abundance  (Yu et al. 2017). To calculate alpha diversity, the OTU tables for each experiment were rarefied 100 times to even depth (multiple_r-arefactions_even_depth), alpha diversity was estimated and collated (scripts alpha_diversity and collate_alpha), and the mean for each sample-metric combination was calculated (plotted in supplementary figs.
S5 and S11, Supplementary Material online). To test whether there was an effect of cluster (second colonization) or galactitol concentration (galactitol supplementation experiment), we used linear mixed effects models with mouse as a random effect. For the second colonization, UPGMA clustering identified two main clusters (fig. 6 ). Thus, we then used linear discriminant analysis effect size (LefSe) to identify if there were any taxa that were differentially abundant between the two clusters  (Segata et al. 2011). The input data for LefSe (supplementary fig. S10 , Supplementary Material online) was obtained after filtering the 97% OTUs for those that were present in at least six samples (i. e. , at least three mice) and at a frequency that was above 0. 5%, which corresponds to $100 reads. Furthermore, for the galactitol supplementation experiment, we used the R package phyloseq (McMurdie and Holmes 2013) to compute the following beta-diversity metrics: Jaccard index, Bray-Curtis, unweighted and weighted UniFrac.
Acknowledgments We thank  Karina Xavier ,  Luis Teixeira ,  Ivo Chelo ,  Armand Leroy ,  Christian Schlo ¨tterer ,  Michael Lassig  and  Jan Engelstaedter  for discussions throughout this work. We thank  Donald Court  for kindly providing strain XTL298. We thank the  Gene Expression, Flow Cytometry and Bioinformatics Units of the Instituto Gulbenkian de Ciência  for technical support. This research received funding from the  European Research Council (ERC) :  ERC-StG-ECOADAPT ;  University of Cologne-Instituto Gulbenkian de Ciência , under SFB of  DFG. The authors have no conflict of interest to report. The sequence data are available from https://www. ncbi. nlm. nih. gov/bioproject/PRJNA295680 (last accessed August 18, 2017).
Assessment of clinical and therapeutic characteristics A complete medical chart review of the past history and physical examinations were systematically recorded at inclusion. The following additional clinical data were collected at baseline, including date of birth, gender, disease duration, age at onset, disease location and behavior according to the Montreal classification, concomitant perianal disease, extra-intestinal manifestation, smoking history, previous history of IBD-related surgery. Prior drug-response history (e. g. , steroid dependency and/or resistance, as previously defined by the European Crohn's and Colitis Organization (ECCO)) was also recorded by reviewing the medical charts. The presence of at least one of the following composite criteria in the IBD-related past medical history was defined as severe disease course: -For patients with CD: uncontrolled active disease requiring adjunction of anti-TNF after failure with conventional immunosuppressant's, two or more CDrelated previous surgeries or bowel resection longer than 70 cm, concomitant active perianal disease with complex fistulas or spread bowel disease.
Serum analysis All patients and healthy controls provided a venous blood sample at inclusion. Serum was rapidly transported to the laboratory, separated from blood by centrifugation and kept frozen at -80°C until use. Serum concentrations of anti-GM-CSF Abs were quantified by enzyme-linked immunosorbent assay (ELISA) with Elabscience® kit. Clinical data analyses and serologic assessments were performed in a blinded manner without knowledge of the patients' diagnosis and medical history. Briefly, serum samples were diluted 16000 fold and incubated on microtiter plates coated with specific antigens at 37°C. The unbound antibodies were removed. Then detection antibodies were added to the wells and the plates were incubated at 37°C. The wells were then washed by washing buffer for 3 times and then 100 µl of HRP conjugate was added to each well.
Baseline clinical, molecular, and imaging data MGMT_MET was assessed by methylation-specific PCR and isocitrate dehydrogenase 1 (IDH1) mutations by immunohistochemistry. Information was obtained on the date of progression, treatment after progression, and date of death or last control. Tumor imaging characteristics (size, location and eloquence)  [15]  were evaluated on axial sections on pre-surgical MRIs that were available to all radiologists through a shared platform. (See Table  1  for explanation. ).
Literature search criteria We performed an NCBI Pubmed search for articles published in English before March 2017 to identify publications on Pseudoprogression or Radionecrosis. Using the key words "pseudoprogression AND glioblastoma'' and "radionecrosis AND glioblastoma'', we had 770 and 220 hits, respectively. We then selected publications reporting data on patients and reviews that focused on "imaging", "MGMT", "IDH mutations", "surgery", "clinical characteristics", "clinical outcome", and "differential diagnosis". Finally, we selected 130 publications for review and cited the ones most relevant to our study in our manuscript.
Acknowledgments This work was supported by  Fundació La Marató   TV3  (Project  665/C/2013 ) (http://www. ccma. cat/tv3/marato/projectes-financats/2012/231/). The MRI imaging platform is supported by the  citizens' initiative "Montgat en Acció ". No grant number is applicable. The funding sources had no role in the study design; in the collection, analysis and interpretation of data; in the writing of the report; or in the decision to submit the article for publication. The authors thank the biobanks of participating institutions:  Fundació Institut Mar d'Investigacions Mèdiques, Institut d'Investigacions Biomèdiques August Pi i Sunyer.
Disruption of the human SHANK3 gene can cause several neuropsychiatric disease entities including Phelan-McDermid syndrome, autism spectrum disorder (ASD), and intellectual disability. Although, a wide array of neurobiological studies strongly supports a major role for SHANK3 in organizing the post-synaptic protein scaffold, the molecular processes at synapses of individuals harboring SHANK3 mutations are still far from being understood. In this study, we biochemically isolated the post-synaptic density (PSD) fraction from striatum and hippocampus of adult Shank3 11 -/-mutant mice and performed ion-mobility enhanced data-independent label-free LC-MS/MS to obtain the corresponding PSD proteomes (Data are available via ProteomeXchange with identifier PXD005192). This unbiased approach to identify molecular disturbances at Shank3 mutant PSDs revealed hitherto unknown brain region specific alterations including a striatal decrease of several molecules encoded by ASD susceptibility genes such as the serine/threonine kinase Cdkl5 and the potassium channel K Ca 1.
0 M/1. 2 M interface and incubated with buffer C (0. 32 M sucrose, 12 mM Tris, pH 8. 1, 1% Triton X-100). After centrifugation at 32 800 × g, the PSD pellet was collected (PSD) and solubilized in H 2 O. Equal amounts of 10 µg from each sample were separated via SDS-PAGE and subsequently blotted on Nitrocellulose membranes according to standard protocols. Incubation with the primary antibody was followed by incubation with an HRPconjugated secondary antibody (Dako). Signals were visualized with the Pierce ECL Western Blotting Substrate and further detected with the MicroChemi 4. 2 machine. Signals were quantified with Gelanalyzer software 1.
After separation of peptides, the column was rinsed with 90% mobile phase B, followed by a re-equilibration step at initial conditions (1% mobile phase B) resulting in a total run time of 120 min. [Glu1]-fibrinopeptide was used as lock mass at 100 fmol/µl. All samples were analyzed in three technical replicates. NanoESI-MS analysis of tryptic peptides on the Waters Synapt G2-S system was performed in positive V-mode with a resolving power of at least 25 000 FWHM (full width half maximum). The instrument was equipped with a NanoLockSpray source and the lock mass channel was sampled every 30 s. LC-MS data were collected using ion mobility enhanced MS E  (Silva et al. , 2005; Geromanos et al. , 2012). Precursor ion information was collected in low-energy MS mode applying a constant collision energy of 4 eV.
Raw Data Processing and Data Analysis Initial signal processing of continuum LC-IMS-MS E data and subsequent database search were performed using vendor software ProteinLynx Global SERVER (PLGS, version 3. 02, Waters). For protein and peptide identification, data were searched against a custom compiled database containing UniProtKB/Swiss-Prot entries of the mouse reference proteome (UniProtKB release 2014_02, 16 780 entries). Sequence information for enolase 1 (Saccharomyces cerevisiae) as well as for common contaminants (i. e. , human keratins, porcine trypsin) was added to the databases. Following criteria were applied for database search: (i) trypsin as digestion enzyme, (ii) up to two missed cleavages per peptide, (iii) a minimum peptide length of six amino acids, (iv) carbamidomethyl cysteine as fixed and (v) methionine oxidation as variable modification.
The false discovery rate (FDR) for peptide and protein identification was assessed searching a reverse database and set to 0. 01 for database search in PLGS. Label-free quantification analysis was performed using the ISOQuant software tool  (Distler et al. , 2014a (Distler et al. , , 2016)). Briefly, the analysis included retention time alignment, exact mass retention time (EMRT) and IMS clustering as well as data normalization and protein homology filtering. Settings and algorithms have been described in detail in previous protocols  (Distler et al. , 2014a (Distler et al. , , 2016)). The peptide-level FDR for cluster annotation was set to 0. 01 within ISOQuant. For further quantitative analysis, only proteins identified by at least two peptides with a minimum length of six amino acids were considered.
Additionally, to be included in the final dataset, a protein had to be identified in at least four biological replicates in at least one condition (i. e. , either in WT striatum, Shank3 11 -/-mutant striatum, WT hippocampus or Shank3 11 -/-mutant hippocampus). For each protein, absolute in-sample amounts were estimated using TOP3 quantification  (Silva et al. , 2006). Based on the quantitative information derived from the TOP3 approach, we calculated the relative amount of each protein with respect to the sum over all detected proteins [ppm: parts per million (w/w) of total protein]. The mass spectrometry proteomics data have been deposited to the ProteomeXchange Consortium 2  via the PRIDE partner repository  (Vizcaino et al. , 2014 (Vizcaino et al. , , 2016) )  with the dataset identifier PXD005192.
Statistical analysis of the data was conducted using Student's t-test with Bonferroni correction for multiple hypothesis testing to largely exclude potential false-positive hits. As this is a very conservative and stringent approach, Bonferroni adjusted p-values of p ≤ 0. 05 were considered statistically significant to not exclude proteins that are actually displaying differences between WT and Shank3 11 -/-mutants. In addition, for each protein we calculated the log-transformed ratio of its average amount in WT mice divided by its average amount in Shank3 11 -/-mutant mice for hippocampus and striatum. To be included in the list of regulated proteins, proteins had to be statistically significant (p ≤ 0. 05) and display a log 2 ratio of at least ±0. 24 between WT and Shank3 11 -/-mutant mice.