dataesr/scientific-paragraphs-categorization huggingface dataset:
57-fold, P <0. 01; Figure  8a ). Type I collagen immunohistochemical staining sections were performed on BM A and BM F before and after implantation in mice. Before implantation, type I collagen staining was limited to PGCAP area (Figure  8b ). After 4-week implantation, type I collagen staining was observed in biomaterial pores and in PGCAP areas corresponding to the previously described bone-like tissue zones. Furthermore, a higher type I collagen staining was observed in biomaterials when they were embedded in PGCAP as compared with the conditions in the absence of PGCAP (data not shown). Together, our results demonstrate that BM A was more efficient at promoting bone-like tissue formation and suggest that the addition of PGCAP leads to a higher stimulation of bone-like tissue formation as compared with osteogenic induction of MSCs.
Data sets We evaluated the proposed approach on the three separate data sets shown in Fig. 1:  (1) 20 hand radiographs, (2) 12 high resolution hand CTs and (3) 20 whole body CTs. Data set 1: Hand Radiographs. N = 20 hand radiographs with an average size of 460 Â 260 pixels with a resolution of 0. 423 mm/ pixel were annotated with L = 24 landmarks. The landmarks include the five finger tips, as well as the distal interphalangeal (DIP), proximal interphalangeal (PIP), metacarpophalangeal (MCP) and carpometacarpal (CMC) joints for each finger. Data set 2: Hand CTs. The 3D hand CTs have a voxel size of 0. 5 mm Â 0. 5 mm Â 0. 66 mm resulting in an average size of 256 Â 384 Â 330 voxels. They are annotated with the same 24 landmarks as the hand radiographs, with three additional landmarks placed around the carpus at the radiocarpal, radioulnar, and ulnocarpal joints, totaling in L = 27.
Data set 3: Whole body CTs. 57 landmarks were annotated for the first 20 whole body CTs from the Whole Body Morphometry Project (Mallinckrodt Institute of Radiology Washington University School of Medicine, 2010). The volumes have an average size of 512 Â 512 Â 1900 voxels, with a voxel size of 1. 3 mm Â 1. 3 mm Â 1 mm. The landmarks are distributed throughout the entire body, to be able to localize all major body parts. The landmarks in the whole body CT are depicted in Fig. 1c.
Mean-shift based interest point generation Mean-shift  (Cheng, 1995)  is a method for the density estimation and cluster analysis of a set of points in a feature space, and was employed in  Shotton et al. (2011)  on classification probabilities to obtain landmark positions. Given a d-dimensional dataset D mean-shift iteratively moves each data point d i towards the weighted mean of the data points, according to a specified kernel with bandwidth r MS around d i. The process is repeated until equilibrium is reached, i. e. when no data point is shifted anymore. The candidate positions C l for landmark l are thus estimated by employing mean-shift with a Gaussian kernel Nð0; r MS Þ on a point set obtained from P l through rejection sampling. As in the present approach, the aggregated probability mass in each cluster is assigned to the cluster center, i.
Candidate accuracy -comparison with Mean-Shift and Non-Maxima Suppression The comparison of the three different strategies evaluated in this paper for the estimation of landmark candidates is shown in Fig. 5. Two important aspects in this evaluation are the time required to perform the Random Forest classification and to solve the Markov Random Field, as these are the dominating factors. Fig. 5 (right)  shows the median localization residuals of the three landmark candidate estimation schemes Hough Regression, Mean-Shift and Non-Maxima Suppression on a leave-one-out run on the whole body CT data set, with a down-scaling factor of d = 0. 2. First, it can be observed how learnt aggregation in the Hough Regression approach (whose regressors are trained on full resolution data) is able to cope with the strongly down-sampled data used during classification.
Localization accuracy on the three data sets The results of the leave-one-out evaluation of the landmark localization is presented in Table  1  and the visualization in Fig. 4  which shows the localization accuracy for each individual landmark in the three data sets and the two compared methods. Data set 1: Hand Radiographs. For this 2D data set, both the median and mean residual are below 1 mm, at 0. 8 mm and 0. 99 mm respectively (median/mean error of 2. 00/2. 34 pixels). As we will see below, this level of accuracy is representative for the method, with the other data sets showing similar results. Fig. 4b  shows the distribution of the localization residual over the individual landmarks. The localization of the CMC joints shows slightly elevated residuals in comparison to the other landmarks.
Comparison with Random Fern Regression localization Fig. 4a, c , and e shows the results of performing random ferns based localization  (Pauly et al. , 2011)  on the three data sets. In the 2D hand data set the method is able to localize the structure, with only minimal outliers, but overall the localized positions exhibit more deviation from the ground truth than in the proposed method. This is also reflected in the median/mean error of 3. 40/ 3. 76 pixels, corresponding to 1. 44/1. 59 mm. In the 3D case the localization residual increases considerably, with a median/mean error of 4. 42/7. 43 pixels, which is 2. 60/4. 16 mm. The localization error affects all parts of the structure equally, i. e. while the initial prediction of x l does predict the right parts of the image it seems to fail to do so with high enough accuracy that the local refinement can have impact.
Run times The run-times of the proposed approach for the localization on three data sets were in the order of 20 s/90 s/120 s on a 2009 8core Xeon MacPro. The implementation was performed in Matlab except for the C-based Random Forests and the loopy belief propagation MRF solver  (Andres et al. , 2010). Solving the MRF only takes <3 s, even for the whole body data set, i. e. the vast majority of runtime is spent on evaluation of the random forests. Recent advances in GPU-based Random Forests  (Sharp, 2008)  showed classification runtime for whole body CTs of about 5 s  (Criminisi et al. , 2010). By exploiting such an implementation in the two steps of low-resolution classification and subsequent refinement the runtime of the random forest evaluation of our approach could be lowered to below 5 s, resulting in a potential total runtime of less than 8 s including the time so solve the MRF.
, 2011 ) should be investigated. Their oblique splits allow for a higher adaptivity to the data and a better class separation, as well as for correlated features and random offset and scaling that occurs as noise in between observations. The learning paradigm for feature detectors may be particularly helpful for a transfer to MR image data, where such artifacts occur as a result of local and global field inhomogeneities. As a next step, we will evaluate the method on a substantially larger data set.
Acknowledgments This work was partly supported by the  European Union   FP7  ( KHRESMOI FP7-257528 ,  VISCERAL FP7-318068 ), by the  Austrian National Bank Anniversary Fund  ( BIOBONE 13468 ,  AORTAMOTION 13497 ,  FETALMORPHO 14812 ), and the  Austrian Science Fund FWF  ( PULMARCH P 22578-B19 ).
ALDH1 immunostaining Solitary fibrous tumors and haemangiopericytomas (Table  1 ) 84% of meningeal, 84. 6% of extrameningeal SFT and 85. 4% of HPC were positive with anti-ALDH1 antibody on TMA. Among HPC 75% of grade II and 93. 5% of grade III were positive. Most SFT (72%) showed intermediate positivity (10 cases, 40%) or high positivity (8 cases, 32%) while 50. 1% of HPC had high positivity (Figure  2a-b ). ALDH1 immunostaining data were statistically not different between SFT and HPC though a tendency for having a more diffuse positivity was seen in grade III HPC (p = 0. 118). Protein expression by IHC was not statistically higher in meningeal tumours than extra-meningeal tumours, unlike what was observed at the mRNA level (Figure  1 ) (p = 1 for comparing positive and negative cases and p = 0.
It is quite intriguing that the most consistent diagnostic markers for SFT and HPC, CD34 and ALDH1, are stem cell markers. These data could have histogenetic significance. The cell of origin of these tumors is unknown but is thought to be mesenchymal in nature  [1]. High ALDH1 and CD34 expression also suggests an immature undifferentiated state. In addition to its diagnostic interest, high ALDH1 expression in SFT and HPC also has therapeutic implications. It could be responsible in part for the failure of conventional chemotherapy, as has been suggested for other malignancies such as breast cancer  [29] [30] [31] , colon cancer  [32]  or medulloblastoma  [33]. Indeed, ALDH1A1 is known to metabolize and detoxify chemotherapeutic agents such as cyclophosphamide  [34]  and ifosfamide  [35]. On the other hand, ALDH1 emerges as a new therapeutic target as specific inhibitors of ALDH such as DEAB (4-diethylaminobenzaldehyde) exist  [24, 36].
Gene expression data sets of SFTs To increase the size of the SFT series, we collected two publicly available data sets: West's set  [23]  collected from (http://www. ncbi. nlm. nih. gov/geo/query/acc. cgi? acc=GSE4305) and including 13 SFTs and 30 STS profiled using 42,000-element cDNA microarrays, and Hajdu's set  [22]  collected from (http///cbio. mskcc. org/ Public/SFT) and including 23 SFTs and 33 STS profiled using Affymetrix U133A microarrays. Whole-genome RNA expression profiles were thus available for 52 SFT/ HPC samples, including 20 meningeal cases and 32 extra-meningeal cases. However, in the study of Hajdu et al. , the meningeal cases encompassed 6 malignant SFT. Because of the rarity of this diagnosis in meningeal location some of these tumours might have the histological criteria of HPC according to the CNS WHO classification of brain tumours.
Gene expression data sets of synovial sarcomas and meningiomas To analyze ALDH1A1 mRNA expression in synovial sarcomas and meningiomas, we collected publicly available gene expression data sets. For meningiomas, we collected three data sets including a total of 161 samples: Claus's set  [40]  collected from the National Center for Biotechnology Information (NCBI)/Genbank GEO database (series entry GSE9438) and including 31 samples, Lee's set  [41]  collected from the GEO database (GSE16581) and including 68 samples, and Scheck's set collected from the GEO database (GSE4780) and including 62 samples. For synovial sarcomas, we collected four data sets including a total of 30 samples: Nakayama's set  [42]  from the GEO database (GSE6481) and including 16 cases, Detwiller's set  [43]  from the GEO database (GSE16581) and including 4 cases, West's set  [23]  collected from (http://www.
Analysis of gene expression data All gene expression data sets are listed in Additional file 2: Table  S1. RNA expression data were thus available for 52 SFT/HPC, 161 meningiomas and 30 synovial sarcomas, collected through 8 data sets. Before analysis, we first applied quantile normalization to the available processed West's data set and MAS5-processed Detwiller and Nakayama's Affymetrix data sets. For the other Affymetrix sets (raw data), we used Robust Multichip Average (RMA)  [44]  with the non-parametric quantile algorithm as normalization parameter. Quantile normalization or RMA was done in R using Bioconductor and associated packages. Normalised expression data generated from different technological platforms and laboratories were then further processed to eliminate potential bias. Processing of data included as first step the selection of genes unique and common to all sets.
We focused on ALDH1A1 and a list of 575 human housekeeping genes  [45]. Before the selection, we mapped hybridization probes across the different technological platforms (Affymetrix U133A and U133 Plus 2. 0, and Stanford cDNA microarrays). Affymetrix gene chip annotations were updated using NetAffx Annotation files (www. affymetrix. com; release from 01/12/2008). Stanford gene annotations were retrieved and updated using EntrezGene (Homo sapiens gene information db, release from 09/12/2008, ftp://ftp. ncbi. nlm. nih. gov/gene/). All probes were then mapped on the basis of their EntrezGeneID. When multiple probes were mapped to the same GeneID, the one with the highest variance in a particular dataset was selected to represent the GeneID. For redundant Affymetrix probe sets, those with an extension « _at », next « s_at », and followed by all other extensions were preferentially kept.
Randomization The random allocation sequence will be generated using a computer number generator to select random permuted blocks. The block lengths will be 2, 4, 6 and 8 and will vary randomly. The random allocation sequence will be performed using R software (version 2. 12. 1) and the additional package 'blockrand' (version 1. 2). Randomization cards will be printed out and sealed in sequentially numbered, opaque envelopes by an independent research assistant prior to the beginning of the trial. Randomization will be stratified by center. Subjects meeting the eligibility criteria at V1 will be randomly assigned to receive periodontal treatment within 15 days after V1 (immediate treatment group) or after the 3 months follow-up period (delayed treatment group (control)).
Interim or post-V4 data will be used in case of absence at V4. Assessed dental investigators will be responsible for performing a standardized periodontal probing assessment at V1 and V4. The full-mouth periodontal probing will be performed using a constant pressure probe (Florida Probe ® ; Gainesville, Fl, USA), from six sites per tooth (mesiobuccal, mid-buccal, distobuccal, mesiolingual, midlingual and distolingual). Periodontal assessment will include measurement of periodontal pocket depth (PPD) in millimeters, clinical attachment level (CAL; in millimeters) and bleeding on probing (yes/no). No radiographs will be taken. Periodontitis will be defined by the presence of at least one site with PPD ≥ 4 mm and CAL ≥ 3 mm on at least 4 different teeth. Finally, participants will self-complete questionnaires assessing QOL (HAQ and GOHAI).
For example, the DAS28 assessment will be performed in the clinical center where participants are usually followed for their RA treatment. Third, investigators will be trained that keeping participants in the trial until the end is important, regardless of whether they continue to receive the assigned treatment. This information will also be conveyed to study participants. Finally, missing outcome data will be handled using estimating-equation methods  [42, 43]. Complete cases will be weighted by the inverse of an estimate of the probability being observed. Efficacy of periodontal treatment will be assessed, as will the correlation between clinical response to periodontal treatment and DAS28 scores. The proportion of subjects who have one or more AEs in the two groups will be compared by Fisher's exact test.
Acknowledgements This work was sponsored by the  University Hospital of Toulouse  for regulatory and ethics submission. This work is supported by a grant from the  Clinical Research Hospital Program of the French Ministry of Health  (PHRC inter-regional 2010: Etude  ESPERA -Service d'Odontologie CHU de Toulouse , PHRC Inter-r égional 2010 study number  10 046 08 ). We particularly thank  Dr. Marie-Elise Llau  ( DRC-CHU de Toulouse ) and  Susan Becker  for proof-reading.
Clinical data collection comprised a standardized interview process inquiring for crisis frequency and medication intake frequency during the last three months and acute and prophylactic antimigraine drugs currently used. Patients from the EM and MOH groups were given a headache diary, recording days with headache and acute headache medication intake during the follow-up period. All subjects answered self-administered questionnaires. In order to assess depression, the Beck Depression Inventory ((BDI-13 items)  [7]  was used. Anxiety was assessed using the State Trait Anxiety Inventory (STAI-state)  [8]. Catastrophizing was assessed using the Pain Catastrophizing Scale (PCS)  [9]. The Barratt Impulsiveness Scale (BIS-11)  [10]  is a self-report measure of impulsive personality traits. The Medication Dependence Questionnaire for Headache sufferers (MDQ-H)  [11]  is a rating scale assessing dependence to medication in headache patients.
Then, all subjects underwent IGT  [12] , a computerised task which is a test aiming to detect decision-making impairment. It is a test where the subject is confronted with a decision that involves a conflict between an immediate reward and a long term negative consequence. The lower the score achieved, the poorer the decision-making performance is. Subjects from the MOH group underwent either an inpatient or outpatient withdrawal procedure. MOH and EM groups were re-examined at 12 months from inclusion. Patients from the MOH group were classified in a binary way as relapse or non relapse depending on whether they fulfilled ICHD-II criteria for MOH at month twelve visit. Statistical analysis was performed using SPSS version 17. 0 software. Descriptive analysis was performed and the differences between groups were tested using chi square and a one-way analysis of variance when appropriate.
The significance level was fixed at 0. 05. Statistical analyses were conducted using R software (Foundation for Statistical Computing, Vienna, Austria) version 2. 7. 1, with the additional package series (version 0. 10-16) and laercio (version 1. 0-0). Infrared spectra were recorded on a Mattson infrared spectrometer (Genesis II spectrometer for Mattson) by diffuse reflectance on a diamond ATR. The dentin slices were placed on the diamond face with no further sample preparation. Spectra were registered from 500 to 4000 cm -1 with 32 scans and a resolution of 4 cm -1. Small sample chips weighting 15 mg were used for the thermogravimetric analysis of water uptake and loss. The samples were placed in platinum holders and analyzed with an SDT Q600 from TA instruments (Guyancourt, France).
Expression data were analyzed by the RMA (Robust Multichip Average) method in R using Bioconductor and associated packages  [25]. RMA performed the background adjustment, the quantile normalization and finally the summarization of 11 oligonucleotides per gene. Raw data of the 16 SFT samples that we have hybridised are publicly available in a MIAME format in the ArrayExpress database (accession number: E-MTAB-1361).
Public Gene Expression Data Sets To increase the size of the SFT series and include STS samples as controls, we collected three publicly available data sets: West's set  [23]  collected from (http://microarray-pubs. stanford. edu/tmaportal/DTF_SFTbreast) and including 13 SFTs (surgical specimen; disease stage not available) and 30 STS profiled using 42,000-element cDNA microarrays, Hajdu's set  [22]  collected from (http///cbio. mskcc. org/Public/SFT) and including 23 SFTs (surgical specimen representing 9 primary tumors, 4 local relapses and 10 metastatic relapses) and 33 STSs profiled using Affymetrix U133A microarrays, and Barretina's set  [26]  collected from NCBI GEO database (GSE21124) and including 149 STSs profiled using U133A microarrays. Both expression and histoclinical (Table  S1 ) data were collected.
Pre-processing of expression data included as first step the selection of genes unique and common to the three data sets. We mapped hybridization probes across the different technological platforms (Affymetrix U133A and U133 Plus 2. 0, and Stanford cDNA microarrays). Affymetrix gene chips annotations were updated using NetAffx Annotation files (www. affymetrix. com; release from 01/12/2008). Stanford gene annotations were retrieved and updated using EntrezGene (Homo sapiens gene information db, release from 09/12/2008, ftp://ftp. ncbi. nlm. nih. gov/gene/). All probes were then mapped based on their EntrezGeneID. When multiple probes were mapped to the same GeneID, the one with the highest variance in a particular data set was selected to represent the GeneID. For redundant Affymetrix probe sets, those with an extension « _at », next « s_at », and followed by all other extensions were preferentially kept.
This step retained a total of 10,138 unique genes common to the three series. The second step was the normalization of the three combined data sets for these 10,138 genes using Distance Weighted Discrimination (DWD)  [27]. Analysis of this pooled set was then unsupervised and supervised. Unsupervised analysis was done using hierarchical clustering in the learning set. Before analysis, data were log 2 -transformed. Clustering was done using the Cluster program  [28]  with data median-centered on genes, Pearson correlation as similarity metrics and centroid linkage clustering. Results were displayed using TreeView program  [28]. The robustness of tumor clusters was estimated by the AU (Approximately unbiased) p-values provided by multiscale bootstrap resampling in the R-package pvclust  [29] , larger the p-values, more robust the clusters.
The optimal threshold for classification was determined in the learning set using ROC curve (defined as the cut-off that maximizes the ROC area), and was then applied to the validation set (West's series here). To help in the interpretation, the lists of differential genes were interrogated using the Ingenuity Pathway Analysis (IPA) software (version 5. 5. 1-1002; Ingenuity Systems, Rewood City, CA) and the GO (Gene Ontology) database.
Cluster II (N = 111) included only genetically-complex STSs, with leimyosarcomas (left branch, cluster IIa) being more homogeneous than dedifferentiated liposarcomas, pleiomorphic liposarcomas, and malignant fibrous histiocytomas (right branch, cluster IIb). The most heterogeneous STSss were the dedifferentiated liposarcomas and the pleiomorphic liposarcomas, which were scattered across the two clusters, intermixed with other STS subtypes. The pvclust package defined the robustness of tumor clusters (data not shown). As expected, all SFTs and all genetically-simple STSs, except GISTs (93% robust cluster), were included in 100% robust clusters. By contrast, only a part of genetically-complex STSs (from 26 to 66% according to the histological type) were included in robust clusters (AU p-value $95%).
Discussion To our knowledge, only one high-throughput molecular study in literature has compared the gene expression profiles of SFTs (N = 23) and STSs (N = 34)  [22] ; it revealed insights potentially relevant at the clinical level. Unfortunately, the series size impeded to test the robustness of differential expression profiles in an independent validation set, and no protein analysis was reported. In this context, our present analysis, which pools our own data set and 3 publicly available data sets, is the largest one analyzing the genome-wide transcriptional differences between SFTs and STSs and between relevant subgroups of SFT. We analyzed 52 SFTs and 212 STSs. This allowed the definition of independent learning and validation sets. Furthermore, the differential expression of some genes was confirmed at the protein level using IHC in a larger series of 93 SFTs and 752 STSs.
Of course, before any clinical testing, functional experiments are warranted to determine whether the overexpression of these ''druggable'' genes in SFTs represents a ''passenger'' or a ''driver'' alteration. Histone deacetylases may be interesting candidates. HDACs are transcriptional corepressors, whose mutations and/or overepression have been reported in several cancers, making them new important therapeutic targets. Clinical trials with HDAC inhibitors are ongoing in sarcomas (NCT01112384: SB039; NCT00918489: vorinostat; NCT00878800: belinostat). On the basis of our observation, analysis of patients with SFT is awaited. Our other supervised analyses compared expression profiles of SFT subgroups defined upon anatomical location and mitotic index. Meningeal SFTs have long been considered as different from pleural or extra-pleural SFTs.