dataesr/scientific-paragraphs-categorization huggingface dataset:
Non-informative genes were filtered by partitioning, and differentially expressed genes were obtained by rank product analysis. Functional analyses were carried out using the DAVID database, and module maps were constructed using the Genomica tool. Results: The functional analyses were able to discriminate between T1D and GDM patients based on genes involved in inflammation. Module maps of differentially expressed genes revealed that modulated genes: i) exhibited transcription profiles typical of macrophage and dendritic cells; ii) had been previously associated with diabetic complications by association and by meta-analysis studies, and iii) were influenced by disease duration, obesity, number of gestations, glucose serum levels and the use of medications, such as metformin. Conclusion: This is the first module map study to show the influence of epidemiological, clinical, laboratory, immunopathogenic and treatment features on the transcription profiles of T1D, T2D and GDM patients.
The analyses of gene expression signatures, however, were impaired by the presence of multiple variables associated with each type of diabetes. To circumvent these problems, here we used several bioinformatics tools to analyze demographical, clinical, laboratory, pathogenetic and treatment data against modulated genes that have been annotated by databases containing information regarding both GWAS and gene expression signatures displayed by immune cells. This type of comparison has yielded multiple informative modules that have been used to perform comprehensive maps in cancer  [41] , and here we apply these tools to diabetes.
Results Overall, a schematic heatmap with all demographic, clinical and laboratory patient features is shown in Figure  1. The global partitioning analysis of the three types of diabetes disclosed 8,469 transcripts considered as informative, which are available at www. rge. fmrp. usp. br/passos/DBF-MCL, the principal component analysis (PCA) of these genes are shown in Figure  2  and the summarized DAVID functional categories (Kegg pathways) of the main clusters are shown in Figure  3. The Venn diagrams yielded shared and specific genes after statistical analysis by rank products (T1D versus T2D, T2D versus GDM and T1D versus GDM) (Figure  4 ) as well as multiple significant summarized DAVID functional categories (Kegg pathways) (Figure  5 ). The module maps encompassing all analyses, i. e.
Comprehensive functional analysis using a module map approach To identify the influence of patient features (array or experimental sets) on gene information (gene sets), we constructed several module maps, stratifying patients according to demographic, clinical, laboratory and therapeutic characteristics. We used specialized databases associated with diabetes complications  [42] , gene clusters associated with diabetes obtained from association studies (GWAS)  [43] , and isolated immune cell types associated with the pathogenesis of diabetes  [44]. The most relevant modules are shown in Figure  6. GDM patients exhibited up-regulated genes observed in diabetes complications (including angiopathy) and in macrophages. GDM, number of gestations per patient and gestation time were associated with the induction of diabetic complications genes.
Discussion Diabetes mellitus is one of the most studied diseases, and a large amount of information is available in the public databases regarding genetic association, meta-analysis and associated complications. Few studies, however, have systematically compared the major types of diabetes in terms of gene expression profiles at the genomic level. In this study, we performed an integrative analysis of the transcriptome profiles of the major types of diabetes using several bioinformatics tools. The major innovation of this study was the construction of informative module maps integrating epidemiologic, clinical, laboratory, pathogenetic, genetic, and molecular factors implicated in diabetes to identify individual and shared features in T1D, T2D and GDM. The major finding of our global partitioning analysis that examined the differences in transcription among each diabetes patient was the cluster of genes associated with inflammation.
These macrophage genes were co-regulated with those appearing in diabetic complications such as angiopathy and retinopathy, including the IL1B and RGS1 genes, as seen in Figure  6A, B  and C. As we also observed that increased glucose levels were associated with the development of retinopathy, it is possible that the effect of high glucose levels on macrophages might be involved in the creation of GDM complications. Moreover, GDM patients exhibited gene profiles similar to those reported for obesity (data available in the Phenopedia public data banks), and the majority of patients in our study were overweight. It is also interesting to observe that obesity, two or more gestations per patient, and gestations lasting over 30 weeks exhibited the same modules of induced genes in GDM patients, as observed in Figure  6A.
Unfortunately, until the moment, there is no public data regarding the gene pattern of activated B cells. Another important finding is the similarity of T1D gene expression profiles with that observed for diabetes nephropathy (Phenopedia). As no patients in our analysis exhibited clinical nephropathy, it may be valuable to further study the particular patients exhibiting nephropathy related gene patterns. Finally, T2D patients exhibited gene expression profiles that were in disagreement with those reported in the public databases (Phenopedia) for diabetic complications (Figure  6C ). Considering that half of our T2D patients exhibited higher median glucose levels than our T1D and GDM patients, one could expect to find the induction of genes associated with diabetic complications. As the T2D patients were treated with many types of medications besides hypoglycemiants, it is possible that this intriguing and unforeseen finding may be a consequence of these treatments.
A complete file providing microarray data from all samples used in this study, as well as the numerical quantitative data and experimental conditions, is available on line at the ArrayExpress public database  [60]  through the following accession numbers: T1D (E-MEXP-3348), T2D (E-MEXP-3287) and GDM (E-MEXP-3349). These data correspond to a part of our laboratory databank, which were made publicly available and can be used for further studies and/or reanalysis.
Data quantification and normalization Data quantification and quality control were analyzed using the Feature Extraction (FE) software version 10. 7 (Agilent Technologies). Expression data were loaded into an R-environment  [61]  using the AgiND package  [62] , a tool developed by the Technologies Avancées pour le Genome et la Clinique (INSERM U1090, Marseille, France). The AgiND tool is available by request  [63]. The background adjustment was performed by subtracting median background values from the median expression values obtained by FE and then converting the results to log-scale. For each sample, any negative values observed were replaced by randomly selected small positive values. After log-transformation, quantile normalization using the normalizeQuantile function was performed.
Density based filtering and Markov clustering (DBF-MCL) and principal component analysis (PCA) The Density Based Filtering and Markov Clustering (DBF-MCL) algorithm implemented in the Rtools4TB bioconductor package  [64, 65]  was used to extract sets of co-regulated genes from our microarray dataset. DBF-MCL is a tree-step adaptive algorithm that finds genomic elements and genes located in dense areas, uses selected items to construct a graph and finally creates a partitioning graph using the Markov Clustering Algorithm (MCL). A 10% false discovery rate (FDR) was used. The principal component analysis (PCA) was performed from the informative genes identified by DBF-MCL. In this analysis, the R function prcomp  [66]  was used to evaluate PCA and the rgl package  [67]  to construct 3D graphics.
Statistical analysis Differentially expressed genes were identified using the R package RankProd  [68, 69]. Although the non-parametric rank product method does not make any assumptions about the data distribution, it can provide frequency ranking scores at each data point and is thus a robust tool for creating ranking lists. Genes were considered significantly expressed when they presented p-values and percentage of false positive predictions (PFP) smaller than 0. 001 and 0. 05, respectively. This test was used to perform paired analysis between T1D versus GDM, T2D versus GDM and T1D versus T2D. Venn diagrams illustrating up and downregulated genes in each analysis are shown in Figure  4.
Module-map construction Significant and differentially expressed genes obtained by the use of DBF-MCL and Rank Products were initially clustered using the Cluster 3. 0 and TreeView softwares  [70, 71]. Then, module maps were constructed using an ensemble of tools provided by the Genomica software  [41] , which searches for higher-order modules of gene sets and samples. Initially, this algorithm uses gene sets to annotate genes that were up-regulated (or down-regulated) by at least 1. 5-fold. The algorithm compares the modulated genes with array sets, including groups of compartmentalized genes, and organizes them into modules (module maps) discriminating variablespecific gene patterns according to patient features. It was used P value ≤ 0. 05 with false discovery ration (FDR) ≤ 0. 05.
Module map array set variables The variables used to create the experimental sets included demographic parameters (age and gender), clinical variables (disease duration, obesity, duration and number of gestations), laboratory data (serum glucose and glycated hemoglobin levels), and treatment features (use of hypoglycemiants such as insulin, metformin or other drugs). All input variables were transformed into binary data (0 or 1), according to the nature of the variable, i. e. , qualitative variables were assigned by the absence or presence of the characteristic, and quantitative variables were assigned by values below or above the mean values. The variables used included age, time of disease, gender, serum glucose and glycated hemoglobin values (Hb1Ac), use of insulin, use of metformin, use of any other type of medication, obesity, gestation time (for GDM patients) and number of gestations (for GDM patients) (Figure  1 ).
Module map gene set variables Immune cell specific gene sets were obtained by reanalyzing raw data from the ImmGen project  [44, 72]  (the detailed procedure is provided as Additional file 1). Disease-related gene sets were obtained from GWAS integrator  [43]  and Phenopedia  [42]. GWAS integrator is a compilation of genes offered by GWAS catalogs and databases such as HapMap, SNAP and HugeNavigator  [73]. All of the information about genes related to T1D and T2D were considered for use in this study. Phenopedia is an available database about genetic association studies and meta-analysis summarized in the Human Genome Epidemiology (HuGE) encyclopedia  [74]. Lists of genes can be obtained about specific diseases or related complications  [75]. The available lists of genes related to diabetes mellitus include those associated with pre-diabetic status, diabetes, type 1 diabetes, type 2 diabetes, gestational diabetes, diabetic complications, obesity, angiopathy, ketoacidosis, nephropathy, neuropathy and retinopathy.
Functional analysis All clusters of co-regulated genes were analyzed by functional analysis using the Database for Annotation, Visualization, and Integrated Discovery (DAVID) version 6. 7  [76]. This approach was used to identify significant biological processes and pathways represented by the differentially expressed genes  [77]. A biological process or pathway was considered to be significant if it contained a minimum of three genes per category featuring score values less than 0. 05 including the Benjamini-Hochberg correction. Moreover, we selected Kyoto Encyclopedia of Genes and Genomes (KEGG) provided by DAVID analysis  [76]  to explore biological pathways.
Confirmation by real-time PCR Relevant and differentially expressed genes obtained using the microarray data were confirmed using qRT-PCR (Table  1 ). The sequences of selected genes were retrieved from the NCBI GenBank database  [78]. All major alternative transcripts were considered to design the primers, and the Primer3 web tool [79] was used to select pairs of oligonucleotide primers spanning at the exon/exon junction. An optimal melting temperature of 60°C was standardized for all genes. Transcriptional expression levels were determined using SYBR Green (Life Technologies), in a StepOne Real Time PCR System (Applied Biosystems). The experiments were done in duplicates. The GAPDH and HPRT1 genes were used as constitutive genes and the selection of the best genes for qRT analysis was done using the Housekeeper software  [80].
Acknowledgments This study was funded by the  Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP -(FAPESP  # 2008/56594-8 ,  FAPESP  # 2010/05622-1 ,  FAPESP  # 210/00932-2 ,  FAPESP  # 2010/12069-7 ) and  Conselho Nacional de Desenvolvimento Científico e Tecnológico  (CNPq #  563731/2010-9 ), We thank  Dr. Pascal Rihet  and the members of  TAGC/INSERM-U1090, Marseille, France  for the important suggestions. We also thank the  ImmGen  project members for making available the immune cells raw microarray data.
Experimental database The material studied in this paper is an aluminum-silicon alloy widely used in the automotive industry, A319, without heat treatment and obtained by a LFC process. Its chemical composition is given in Table  1. The exposure of this alloy at high temperature (above 150 °C) leads to a modification of its microstructure and mechanical properties as already discussed in  [28, 29]. Let us remark that the TMF of engine parts, defined as the initiation of visible cracks often occurs in regions of high temperature. In these regions microstructure and fatigue resistance are altered by the long term high temperature exposure, also denoted as ''overaging''. Moreover, over-aging corresponds to a stabilization of both mechanical properties and microstructure under a long term exposure of the material to high temperature.
In the present case, over-aging corresponds to an exposure of 200 h at 250 °C. As a consequence TMF criteria discussed next are built from experiments done on over-aged materials. The thermomechanical fatigue behavior is estimated from strain-controlled Low Cycle Fatigue tests on over-aged specimens. The experimental database considered here consists of 35 tests, performed with a strain ratio R e ¼À1. Temperatures, strain and number of cycles to failure ranges are detailed in Table  2. All tests were conducted with a mechanical strain rate of _ e ¼ 10 À3 s À1.
Porosities distribution In this study, 248 pores were detected using the proposed procedure and their Feret diameter or apogee of an equivalent ellipse has been measured. In  [30] , it has been shown that the pore sizes match a log-normal distribution function expressed as: p a ¼ 1 ar ffiffiffiffiffiffi ffi 2p p exp À 1 2 lnðaÞÀ l r ! 2 2 4 3 5 ð1Þ where a is the pore size. l and r denote the mean value and the standard deviation of the distribution, which can be computed for a discrete series of porosities a i ; i ¼ 1; n using the formulas: l ¼ 1 n X n i¼1 lnða i Þ r ¼ ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi ffi 1 n X n i¼1 ðlnða i ÞÀ lÞ 2 r ð2Þ A common method to quantify the adequacy with an chosen distribution function is to build a Quantiles-Quantiles plot to compare the observed data set with the model.
(  5 )). These parameters can now be identified from a series of n experimental data pairs: ðiÞ f ; / ðiÞ Þji ¼ 1; ng and the identification can be performed using different methods. Two particular ones have been chosen here: (i) maximizing likelihood: ðm; kÞ¼argmax P n i¼1 p F N ðiÞ f ; / ðiÞ ð13Þ which is one of the standard techniques to identify probability density functions from experimental data  [35, 37]. This optimization allows accessing the values of the m and k parameters without any a priori choice on the criterion form since it is only a maximization of the probability of occurrence of the experimental database. We chose therefore to represent the criterion function -referred as the lifetime line -as the line connecting either the median number of cycles to failure or the maximum probability value for p F to a given fatigue parameter /.
Results and discussion A series of fatigue criteria have been identified from an experimental TMF database on over-aged specimens. The criteria have been identified using all possible choices between the discussed options: (i) pore size measurement: Feret diameter or apogee of equivalent ellipse; (ii) probability distribution: lognormal or exponential; (iii) optimization method: maximum likelihood or least-square minimization. Each of the identified criteria is represented by the same probability distribution given in Eq. (  5 ) but with varying material parameter values for m and k. The identified parameters and the corresponding choices in the method are displayed in Table  5. The results expressed in terms of estimated versus experimental fatigue lifetime (see Figs. 8 or 9 for example) show that the initial choice of probability density function for the pore-size (exponential or lognormal) has a negligible impact on the final result.
However, even in this case, 10 results out of 35 (29%) lied outside the relevance interval as displayed in Fig. 8. The criterion built as the locus of maximum probability provides unsatisfactory results as 18 points lie outside the relevance interval (52%) as displayed in Fig. 9. Moreover, as it can be seen in Fig. 12 , that these two identified criteria are poorly positioned with respect to the experimental points. We can state that they have a strong tendency to overestimate the lifetime for high values of / and underestimate it for lower values of /. This can be justify, by the a relatively small number for a precise statistical answer (database containing only 35 elements). As seen in Fig. 10 , we observe maximum probability values higher for high values of / than for low values of / of the fatigue parameter.
Acknowledgements The authors would like to thank  David Balloy ,  Jean-Yves Dauphin  and  Jean-Charles Tissier  at the  Ecole Centrale de Lille, France , for their help in SEM analysis and fruitful discussion during this work;  Alexandra Marie-Louise  ( PSA Peugeot-Citroen ) for providing the experimental database. This work was supported by  PSA Peugeot-Citroen  and has received the financial support of the  French Minister of Research and Higher Education  and of the project  ANR-12-RMNP-0011.
Data analysis Raw DNA and RNA sequence was processed into reads using CASAVA software (Illumina). DNA was aligned to the human reference genome (build 36) using Illumina's ELAND2 pipeline, followed by somatic mutation analysis to identify single-nucleotide variants (SNVs), indels, and copy number variants (CNVs; Fig. S2 ) as described previ-  transcript is the predominant form of CTLA4 RNA expressed in the examined tissues. This is despite the fact that ~40% of the tumor is normal infiltrate, which is expected to express wild-type CTLA4.
T G A G C A A A A T G G T G A G G A G T A A G A CTLA4 exon 3 CD28 exon 4 L S K M V R S K 590 595 600 605 610 T G A G C A A A A T G G T G A G G A G T A A G A CTLA4 exon 3 CD28 exon 4 L S K M V R S K Thigh Thigh Back Back ously  (Craig et al. 2013). For RNA-seq analysis, reads were aligned to the human genome using Tophat 1. 2. 0  (Trapnell et al. 2009 ) followed by transcript assembly/ abundance estimate, and differential expression analysis with Cufflinks 0. 9. 3  (Trapnell et al. 2010). Sequencing data can be accessed through NCBI's (National Center for Biotechnology Information) dbGaP (database of Genotypes and Phenotypes) under accession number phs000773. v1. p1.
Acknowledgments We thank  Keira Cheetham ,  Russell Grocock , and  Mark Ross  of  Illumina  for independent point mutation analysis,  Marc Laurent ,  Suneer Jain , and  David Bentley  ( Illumina Clinical Services Laboratory ) for genomic sequencing advice,  David Marwil  ( Lexington, KY ) for guidance in the patient's case, and  Paula Oliver  ( Children's Hospital of Philadelphia ) for insightful discussions about NDFIP1 mechanisms.
3%), suggesting that ATM deficiency attenuates short-term apoptosis, concordant with the findings on cleaved PARP (Figure  6a ) and our previous findings. 19, 29 gure  4  Cell cycle phase distribution changes after IR treatment. Ctrl and A-T cells at various developmental stages (primary fibroblasts, hiPSCs, hNPCs, D30 postmitotic neurons) were treated with 5 Gy IR, collected after 24 h, and analyzed for DNA content by flow cytometry (a). In b, Ctrl fibroblasts, hiPSCs and hNPCs were treated with 5 Gy IR, collected at the indicated times and analyzed by western blot. b-Actin was used as a loading control Treatment of A-T and Ctrl neuronal cells with genotoxic and oxidative agents. As genotoxic agents and oxidative stress may similarly affect ATM-deficient neurons, we investigated the viability of Ctrl and A-T post-mitotic neurons (D30) upon treatment with compounds that work through different mechanisms: camptothecin (CPT), which inhibits Topoisomerase I (Top1) and traps Top1-DNA covalent complexes (Top1-ccs);  30  trabectedin, which blocks transcription factor activity;  31  Paraquat, which induces ROS through depolarization of the inner mitochondrial membrane,  32  and    7a  and b ), a finding concordant with previous data  33  and with the results in Figure  6b  showing that these cells have the same ability to resolve SSBs.
Electrophysiology analysis. Electrophysiological recordings were done as reported  18, 57  at room temperature (22-25 1C) using a Multiclamp 700 A patch clamp amplifier and pClamp 10. 2 software (Molecular Devices, Sunnyvale, CA, USA). Recordings usually started 5 min after the rupture of the membrane patch, to allow intracellular dialysis with the pipette solution. External bath solution consisted of 129 mM NaCl, 1. 25 mM NaH 2 PO 4 , 35 mM glucose, 1. 8 mM MgSO 2 , 1. 6 mM CaCl 2 , 3 mM KCl and 10 mM HEPES, pH 7. 4 with NaOH. The internal pipette solution consisted of 120 mM K gluconate, 15 mM KCl, 2 mM MgCl 2 , 0. 2 mM EGTA, 10 mM HEPES, 20 mM P-creatine, 2 mM ATP-Na 2 , 0. 2 mM GTP-Na 2 and 0. 1 mM Leupeptine, pH 7. 2 with KOH. Cell capacitance and series resistance errors were carefully compensated (B85%) throughout the experiment.
Cell cycle phase analysis. Cells at different developmental stages were irradiated with 5 Gy IR, incubated for 24 h and then fixed with 70% ethanol, washed, treated PBS-RNAse A, at 37 1C for 30 min and finally stained with propidium iodide. Cells were analyzed with a FACSCalibur flow cytofluorimeter instrument fitted with a Cell Quest software package (Becton Dickinson, Sunnyvale, CA, USA). In vitro BER assay. Whole-cell extracts for BER assays were prepared from 15 Â 10 6 cells that were harvested, resuspended in 400 ml buffer I (10 mM Tris pH 7. 8, 200 mM KCl) and then lysed by adding an equal volume of Buffer II (10 mM Tris pH 7. 8, 200 mM KCl, 2 mM EDTA, 40% glycerol, 0. 2% Nonidet-P40, 2 mM DTT and protease inhibitors). After stirring for 1 h at 4 1C and centrifugation, the supernatant was aliquoted and stored at À 80 1C.