dataesr/scientific-paragraphs-categorization huggingface dataset:
Given an interaction network, generated by ARACNe, a (candidate) master regulator gene, and a gene signature, the MRA algorithm computes the enrichment of the signature genes in the regulon of that gene. The regulon of a TF is defined as its neighborhood in the interaction network. There are two different methods to evaluate the enrichment of the signature in the regulon. One method uses the statistical Fisher's exact test, while the other approach uses Gene Set Enrichment Analysis (GSEA). In this work, the enrichment was evaluated using the Fisher's exact test and corrected using the Benjamini and Hochberg (BH) false discovery rate (FDR) for multiple-testing. The significant TFs acting as potential master regulators in IBC and their specific regulons were then imported into the IPA software to identify the most enriched canonical pathways, the over-represented biological processes and molecular functions associated to candidate genes.
Validation cohort and tissue microarray A total of 2116 consecutive patients affected by invasive breast carcinoma were collected between January 1992 and December 2006 and included in the database of the Department of Pathology of the G. B. Rossi Hospital in Verona. Clinical data (patient's medical history, histological diagnosis, staging, treatment) were evaluated as previously published  [21]. Briefly, tumor specimens were retrospectively reviewed by pathologists to define the histological size, type and grade of the primary tumor and the histological axillary lymph node status. Clinicopathological criteria to include a patient in the IBC group (n = 39) were carried out according to Manfrin et al. [21]. They included histological diagnosis of neoplastic emboli within superficial dermal lymphatic spaces and/ or clinical signs such as diffuse erythema, "peau d'orange", edema, warmth, tenderness, breast enlargement, and diffuse induration of the breast on palpation, as described by Haagensen  [22].
Validation cohort and immunohistochemistry The presence and distribution of tissue polypeptide antigen was visualized by incubation with the specific primary antibody using Leica Bond-Max autostainer system (Milan, Italy). The complete list of primary antibodies and the corresponding experimental conditions are shown in Table  1. All immunohistochemical staining were interpreted regardless of staining intensity by three independent investigators (P. P. , A. R. and E. M. ) blinded to clinical data and laboratory results. The pattern of immunostaining was recorded according to the number of positive neoplastic cells (at least 1000 total cells were examined) and stratified into two groups: "positive expression", when more than 5% of tumor cells were positive, and "negative expression", when less than 5% were positive.
Statistical analysis Statistical analyses were performed using R and SPSS (version 15. 0) for Windows (SPSS Inc. , Chicago, Ill. ,USA). Data were reported as median or mean and standard deviation (SD), and the mean values compared using the Student's t-test, as indicated. The χ 2 test was employed to assess the association of gene/protein status and clinicopathological parameters. For NFAT5 staining pattern, the sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV) were computed. Using Kaplan-Meier method we performed survival curves and differences were estimated with the log-rank test. Results were considered statistically significant when a p ≤ 0. 05 was obtained. Correction of p-value was performed with Benjamini and Hochberg false discovery rate method.
Validation of the IBC signature Since all our network-based discovery, reported in the rest of the paper, relies on the above IBC signature, we first validated the signature by using another dataset of 41 IBC and 55 nIBC from the Translational Cancer Research Unit (TRCU) General Hospital Sint-Augustinus Antwerp-Belgium  [9]. We computed the centroid expression profile of IBC samples of the learning set and for the 566 selected probesets and evaluate its Pearson correlation, r, with each sample of the validation set. As reported in the Additional file 2: Figure  S1B , the , the resulting classification (IBC-like or nIBC-like) correlated with the actual IBC-nIBC phenotype (p-value of 1. 8 10 -4 , Student's t-test). Because of the unbalance between IBCs and nIBCs regarding the SBR tumor grade, we verified that our 566-probe set signature was not more associated with the grade than with the IBC-nIBC phenotype.
Regulatory network derived from gene expression profiles To get insight into potential transcriptional regulators of IBC phenotype, we reverse-engineered transcriptional interactions from gene expression data. Reverse-engineering was done using the ARACNe algorithm  [15]  by applying the analysis pipeline reported in Figure  1. ARACNe is an unbiased algorithm that infers direct transcriptional interactions based on the mutual information between each transcriptional regulator and its potential targets. For optimal analyses, ARACNe requires large data sets of gene expression profiles (at least 100 expression profiles) having significant endogenous (i. e. , genetic) and/or exogenous (i. e. , perturbation-induced) heterogeneity. We used the whole dataset of expression profiles reported elsewhere by some of the authors  [7]  which is ideally suited for ARACNe because it is relatively large (n = 197) and diverse.
In order to highlight the variability of our dataset, also in terms of clinical features, we report as supplementary figure (Additional file 3: Figure  S2 ) the disease free survival (DFS) curves separated by grade and tumor types showing that they are different given the grade: as expected, the survival of IBC patients was inferior to that of nIBC patients, for both grade II and grade III tumors. ARACNe inferred a network with 81,606 interactions for 1,601 TFs. Master Regulator Analysis  [10]  was then applied to score the TFs in terms of enrichment with respect to the IBC/nIBC signature. Results of MRA, for each TF, are reported in Additional file 4: Table  S2. We chose the top three enriched TFs as genes possibly related to IBC: NFAT5 (Nuclear Factor Of Activated T-Cells 5, p-value of MRA = 10 -29 , log fold change of 0.
834 and p-value = 10 -7 ), MGA (MAX Gene Associated, pvalue of MRA = 10 -35 , log fold change of 0. 73 and pvalue = 10 -10 ), and CTNNB1 (Catenin Beta-1, p-value of MRA = 10 -33 , log fold change of -1. 129 and p-value = 10 -15 ). NFAT5 and MGA are up-regulated in IBC. Figure  2A  reports the interconnected network of these three main hubs, their synergetic effect and a possible mutual "shadow" or an overlap between regulons, suggesting that these MRs might function, at least in part, cooperatively to regulate sets of genes. (Figure  2C  and D ). The figures show the distribution of the first occurrence of motifs as functions of the distance from the transcription starting site in the promoter region of each gene in the regulon (red line). Motif occurrences were detected with PWM models of NFAT5 and MGA obtained respectively from Transfac (M00935) and Human-jolma 2013 databases.
Crosstalk between NFAT5 and WNT/β-catenin signaling In IBC E-cadherin expression is maintained in the primary tumor and tumor emboli  [6] [7] [8]. Accordingly, in the validation datasets, we found that E-cadherin positivity was observed in almost all cases of IBCs (data not shown), its expression, however, was not statistically different in comparison to nIBCs (87. 2% vs. 82. 5%; p-value = 0. 698). Moreover, β-catenin positivity was prevalent in IBCs but not statistically significant when compared with nIBCs (95% vs. 82. 5%; p-value = 0. 116; Figure  4A ). IHC staining of normal breast specimens, showed that βcatenin was expressed in mammary epithelial cells and mainly restricted to the plasma membrane as well as Ecadherin (data not shown). To further characterize the relationship between WNT canonical pathway and IBC, we analyzed β-catenin expression pattern by taking into account membrane, membrane/cytoplasmic, and nuclear/ cytoplasmic localization (Figure  4D ).
Discussion Although IBC is the most aggressive form of breast cancer, very few studies have identified IBC-specific gene signatures or clinically applicable biomarkers, and therapeutic approaches are still based on clinic-pathological factors similar to those used for nIBC patients. In this study, we employed a systems biology approach with a network-based strategy to gain insight into pathways and master regulators associated with IBC pathogenesis. By applying this approach to two gene expression datasets followed by validation at the protein level, we identified NFAT5 as a novel biomarker that potentially might be developed into clinical assays to improve IBC classification into molecularly distinct phenotype. The roles of NFAT transcription factors have been extensively studied in the immune system but their impact in human cancer remains poorly understood.
These DAGs are produced in a sustained manner at endomembranes such as those of the Golgi apparatus and the endoplasmic reticulum  15. In contrast, stearoyl-arachidonyl-diacylglycerol (SADG), which is temporarily generated from phosphatidylinositol 4,5-bisphosphates at the plasma membrane upon phospholipase C activation, was not generated. Next, the location of DAG production was investigated with Daglas-pm, Daglas-em and Daglas-lum. Daglas-pm did not respond (Fig. 3E, F ). To our surprise, whilst Daglas-em also did not respond to thapsigargin (Fig. 3E, F ), the Daglas-lum emission ratio decreased and reached a plateau 20 min later (Fig. 3C-F ). These data suggest that thapsigargin-induced calcium elevation selectively resulted in lumenal DAG production under our experimental conditions. To investigate the mechanism underlying lumenal DAG production, several inhibitors were employed.
The cells were imaged at 25 °C on a Carl Zeiss Axiovert 200 microscope with a cooled CCD camera CoolSNAP HQ (Roper Scientific Inc. Tucson, AZ) controlled by the MetaFluor software (Universal Imaging, West Chester, PA). The exposure time at 440 ± 10. 5 nm excitation was 100 ms and 300 ms for Venus and Cerulean, respectively. Fluorescence images were obtained through 480 ± 15 nm and 535 ± 13 nm filters with a 40x oil immersion objective (Carl Zeiss, Jena, Germany). The images in this study were displayed in intensity modulated display (IMD) mode, in which the intensity of the fluorescence is considered to make the images displayed with Cerulean/Venus ratio images. Cell staining. All manipulations were performed at room temperature. MDCK cells grown on coverslips were washed with phosphate buffered saline (PBS) and then fixed for 20 min with 3% paraformaldehyde in PBS.
Acknowledgements We thank  S. Kwok  for valuable comments. Digda was provided by  Prof. M. Matsuda  through  Prof. E. Kiyokawa. We are also grateful to  R. Ishitsuka ,  K. Iwamoto  for critical reading of the manuscript. This work was supported by  Integrated Lipidology Program of RIKEN ,  Grant-in Aid for Scientific Research 25293015  (to T. K) from the  Ministry of Education, Culture, Sports, Science and Technology of Japan  and  Naito Foundation.
Immunoblotting Immunoblotting was performed following standard Western or slot blot protocols. Antibodies were selected by Western blot for their suitability to slot blot analyses  (Guedj et al. , 2012; Souchet et al. , 2014)  (Supplementary Table  2 ). Digitized images of immunoblots were obtained using a LAS-3000 imaging system (Fuji Photo Film Co. Ltd. ), and densitometry measurements were collected with an image analyzer (UnScan It software, Silk Scientific Inc. ). Quantification of total proteins after Ponceau-S coloration was used as an internal control.
Statistical Analysis For comparisons between groups analyzed by two, TG/WT, TG treated/WT, TG treated/TG, t-tests were performed. All graphs were plotted as mean ± SEM. Data were considered significant when p ≤ 0. 05: in Tables 1-6 a color code was used with red and green for significant increase and decrease respectively. A p-value of 0. 06-0. 10 was considered to indicate a strong statistical tendency due to the small sample size: in Tables 1-6 tendancy to an increase was coded in pink and tendancy to a decrease was coded in pale green. Behavioral analyses were performed using the Mann-Whitney test as the non-normality of data precluded the use of parametric statistics (e. g. , analysis of variance). All statistical analyses were performed using GraphPad6 software package.
These results were further confirmed through the verification of the over-expression of two proteins involved in these mechanisms, C-reactive protein (CRP) and orosomucoid 1 (ORM1), in 126 S2 HAT patients suffering from either the chronic or the acute form of HAT. Both proteins were significantly increased (p < 0. 0001) in the CSF of rhodesiense HAT patients. These findings contribute in better understanding the pathophysiological mechanisms of late stage HAT caused by T. b. gambiense or T. b. rhodesiense and pave the way for further investigations on the clinical significance of CRP and ORM1. Mass spectrometry data are available via ProteomeXchange (identifier PXD001082).
Protein identification and quantification Protein identification was done using EasyProt platform v2. 3  [27]. Peak lists were generated from raw data using ReadW software. After peak list generation, the CID and HCD spectra were merged for simultaneous identification and quantification  [26]. Peptide spectral matches were searched against Swiss-Prot/ UniProt database (Version 13 June 2012, 536 0 489 entries), by choosing Homo sapiens taxonomy. All other searching parameters were set as follows: (i) oxidized methionines as variable modifications; (ii) carbamidomethylation of cysteines, TMT sixplex amino-termini and TMT six-plex lysines as fixed modifications; (iii) trypsin was selected as the digestion enzyme allowing only 1 missed-cleavage, (iv) only peptides with a minimum of 6 residues were selected for identification; (v) the precursor ion tolerance was set to 10 ppm.
A second search against the UniProt/TrEMBL database (Version 11 December 2013, 49 0 453 0 151 entries), choosing the Trypanosomatidae taxonomy (ID5654) was performed, in order to check for the presence of parasite proteins. All other settings were kept as reported above. For all identified human proteins, relative protein quantification was obtained with Isobar quantification tool (version 1. 76)  [28]  embedded in EasyProt. Only peptides specific for a unique entry in the job were taken into account for protein quantification, and a unique quantified peptide was considered sufficient to obtain protein quantification. The protein ratio T. b. rhodesiense/T. b. gambiense was computed according to the tagging design, i. e. 129 + 130 + 131/126 + 127 + 128, after isotopic purity correction (according to the algorithm given by the manufacturer) and Isobar default normalization  [28].
Gene ontology and pathway analysis Significant differentially expressed CSF human proteins were submitted to gene ontology (GO) analysis to evaluate their association with specific biological processes (BP) GO terms. The analysis was performed with bioCompendium, available at http:// biocompendium. embl. de. The whole list of identified and quantified CSF human proteins was further investigated with Ingenuity 1 Pathway Analysis -IPA (Ingenuity 1 Systems, Redwood City, CA, USA, http://www. ingenuity. com) [31] [32] [33]. Briefly, a list containing the IDs of each identified and quantified protein, the corresponding fold-change for the rhodesiense/gambiense ratio, as well as the quantification ratio pvalue and sample p-value generated with Isobar, were uploaded in the IPA analysis tool. A "Core Analysis" was then performed to highlight important biological pathways associated to the dataset.
The following settings were applied: Ingenuity knowledge base (genes + endogenous chemicals) was the reference set, both direct and indirect relationships were included and no filter at the level of the cut-off or p-value on experimentally observed data were applied to keep all the entries for the analysis. Finally, human was selected as the species. By using the canonical pathway tool of IPA Ingenuity, the proteins of the experimental dataset have been displayed within well-established signalling or metabolic pathways. For each pathway, the following parameters were calculated: p-value, indicating the association between a specific pathway and the experimental dataset; and ratio, indicating the percentage of genes in a pathway also found in the experimental dataset. This ratio was computed by dividing the number of genes belonging to a specific pathway found in the experimental data set, by the total number of genes of that pathway.
Statistical analyses Statistical analyses were performed using IBM SPSS v20. 0 (IBM, NY, USA). All statistical tests were two-tailed and the p-value for significance was set to 0. 05. Comparisons between two un-paired groups were done using the Mann-Whitney U test. Comparisons between more than two unpaired groups were done with the Kruskal-Wallis test followed by post-test paired comparisons. When multiple comparisons were done, the Bonferroni's correction was applied, multiplying the p-value by the number of comparisons performed. Box plot graphs were obtained with Aabel v2. 4. 2 (Gigawiz Ltd. Co. , Tulsa, OK, USA).
Gene ontology and pathway analyses Among the 11 differentially expressed proteins, 8 were successfully associated to four BP GO terms. Two of these terms were significantly represented (adjusted p-value <0. 05) among the 8 proteins: the acute inflammatory response (GO: 0002526) and the humoral immune response (GO: 0006959) (Supplementary Fig. S1 ). Interestingly, the acute inflammatory response involved 7 out of the 8 proteins over-expressed in rhodesiense samples compared to gambiense samples. To go deeper into the obtained data and to investigate whether specific pathways were represented in the dataset, an analysis with IPA Ingenuity was performed. Among the 222 entries uploaded, 198 were successfully mapped against the Ingenuity knowledge database and thus used for the subsequent analyses.
By applying the 1E-04 p-value threshold, 13 pathways were selected as significantly represented in our dataset, with the most represented being the "acute phase response signalling" (p-value = 1. 09E-44), the "liver X receptors/retinoid X receptors (LXR/RXR) activation" (p-value = 1. 05E-39) and the "complement system" (p-value = 3. 05 E-25) (Table  3  and Supplementary Table  S4 ). The first of these pathways, the acute phase response signalling, had 42 of its total 173 genes (24%) represented in our dataset. Of these 42 entries, 30 had a positive fold change (i. e. !1. 0) for the rhodesiense/ gambiense ratio and comprised 6 of the significantly overexpressed proteins (Table  3 ). The second most represented pathway was the LXR/RXR activation. Among the 34 proteins of our dataset involved in this pathway, 22 had a positive fold-change, including 6 significantly expressed proteins.
Acknowledgments The authors thank the  Foundation for Innovative New Diagnostics (FIND) , for financial support in sample collection in Uganda, D. R. C. and Angola; and the  Belgian Directorate General for Development Cooperation , for financial support in patients' sample collection in D. R. C. Part of the specimen collection in Malawi was supported by a grant from the  European Union  [ FP6-2004-INCO-DEV-3 032334 ; NEUROTRYP]. The authors also acknowledge the  PRIDE team  for their support for MS data deposition to ProteomeXchange Consortium (identifier  PXD001082 ).
Ethics and funding This research was approved by the Ethical Research Committee of the University of Bordeaux III and was registered with an approved ICMJE clinical trial registry: NCT01464073 ClinicalTrials. gov. https://clinicaltrials. gov/ct2/show/NCT01464073. This research was supported by grants of La Réunion island authorities (CPER 2012) and by the European Research and Development Fund  (POCT FEDER 2007 -2013). The sponsors played no role in determining the study design, data collection, analysis and interpretation, or writing of the report. The corresponding author had full access to all the data and had the final responsibility for the decision to submit for publication.
Randomization After inclusion, subjects were randomly assigned to one of the three groups: G1 (LIPOXmax); G2 (60% VO2max); G3 (GPP at-home, 30 minutes minimum per day at a moderate intensity). Randomization was stratified on baseline BMI status defined as follows: stratum 1: 27. 0 BMI < 30. 0 kg/m², stratum 2: BMI ! 30. 0 kg/m². The two lists of randomization were provided by the statistician of the Centre for Clinical Investigation (AF) before starting the research, using Ralloc program of Stata v10. 0 1 (randomization by block with equal block size = 3, and balanced allocation ratio 1:1:1). The statistician gave the individual intervention's assignment to the research coordinator who informed the participant.
Repeated ANOVA measures allowed estimating time effect (M0-M3-M5), group effect (G1-G2-G3) and the interaction time Ã group. Wilcoxon rank-sum test was used to analyse the differences between G1 and G2. The primary outcome and the other five-month changes were analyzed between M5 and M0 within the group of subjects for whom these data were available. The longitudinal evolution of metabolic parameters was analyzed for the subjects for whom the M0, M3 and M5 data were complete. Missing observations were excluded. Except for the aforementioned post hoc analysis, the significance level was set to 5%. Analyses were carried out using SAS 1 version 9. 2 (SAS Institute Inc. , Cary, NC, USA).
Changes in metabolic characteristics Out of the 109 subjects assessed at M5, the analyses of metabolic characteristics were performed for the 103 subjects who had a complete longitudinal follow-up (M0, M3 and M5). These data are summarized in Table  4. Post hoc analyses have been performed to compare groups two by two (Tables  4  and 5 ). Total cholesterol, triglycerides and FPG levels were stable over time. HDL-C/LDL-C ratio improved in the three groups throughout the intervention with different patterns (rapid but slight and transient response in G1, increasing response with time in G2 and G3). HbA1c decreased over time without significant inter-group differences. Glucose control improved throughout the intervention, the percentage of insulin-resistant women (HOMA-IR index > 2. 5) falling from 80% to 60% between M0 and M5.
Procedure and ethics We collected data between October, 2011, and February, 2012, and invited participants via local managers, phone calls, posters, and other printed materials, and visits to FHS facilities. The questionnaires were answered collectively, during working hours, in accordance with every FHS timetable, for the purpose of causing minimum disturbance to the service, and with flexibility to fit working patterns. Participants signed an informed consent form. The Ethical Committee of the Federal University of Sao Paulo (UNIFESP) approved the study protocol.
Characterization methods The morphology of films and NRs was studied using scanning electron microscopy (SEM) with an ULTRA-55 field emission SEM (Carl Zeiss SMT AG) working at an electron beam energy of 15 keV. Alternatively, a Supra 40 field emission SEM was also used (electron beam energy of 3 keV). Analysis of the rod size distribution was made with the ImageJ software  [28]. The crystalline structure of the NRs was determined by X-Ray Diffraction analysis (XRD, Siemens D5000 with Cu-K a radiation and a graphite monochromator). Transmittances of the films were measured with a UVevis spectrophotometer (Agilent 8453). Cathodoluminescence was carried out in a FEI Quanta SEM equipped with a liquid He cryogenic stage in order to cool down the sample from room temperature to 5 K. The luminescence of the sample under electron irradiation was collected through a parabolic mirror into the entrance slit of a Jobin-Yvon HR spectrometer.