dataesr/scientific-paragraphs-categorization huggingface dataset:
This peri-tumoral vegf-a expression that probably contributes to the formation of the mixed phenotype observed in PC3c cells, was not modulated in PC3-ERRα-induced bone lesions in vivo compared to CT. Finally, in line with our pre-clinical models, we found that VEGF-A positively correlated with ERRα expression levels in primary CRPC tumor samples from combined microarray datasets GSE1034, GSE6919 and GSE2269 (n = 140, r = 0. 364, P < 0. 0001) and in bone metastases (GSE29650, GSE41619 and GSE74685)  [34] [35] [36]  (n = 64, r = 0. 646, P < 0. 0001) (Table  1 ; Supplementary Table  S1 ). The second factor that was found upregulated by ERRα in our human preclinical models is WNT5A, a factor also known to stimulate osteoblasts and osteoclasts  [37]  and previously linked to ERRα in osteoblasts  [38].
Thus, WNT5A expression was higher in ERRαoverexpressing PC3 cells (PC3-ERRα) compared to PC3-CT clones (Figure  6A ), and down-regulating ERRα levels in PC3 cells with ERRα-SiRNA (pool of three sequences) decreased WNT5A expression compared to control sequences (Sc, Figure  6B ). WNT5A expression was also upregulated in PC3-ERRα-versus PC3-CTinduced bone lesions in vivo (Figure  6C , a versus b). XCT-790 treated PC3-ERRα cells also demonstrated a down-regulation of WNT5A expression (Figure  6D ). Stimulation of WNT5A expression by ERRα was also confirmed in PC3-ERRα-versus PC3-CT-induced SC tumors in vivo (Figure  6E ), and in PC3c-ERRα-versus PC3c-CT clones in vitro (Figure  6F ) as well as in PC3c-ERRα versus PC3c-CT-induced bone lesions in vivo (Figure  6G , a versus b). Notably, WNT5A expression also positively correlated with ERRα expression levels in bone metastases microarray datasets (GSE29650, GSE41619 and GSE74685) (n = 64, r = 0.
Correlation scores were calculated using the Pearson correlation coefficient. P-values less than 0. 05 were considered statistically significant. with ERRα expression in our meta-analysis studies of human PCa datasets. Previously, WNT11 was shown to be regulated by ERRα in PCa cells and implicated in cancer cell invasion  [43]. Our data on WNT5A and ERRα expression in both in vivo and in vitro experiments and meta-analysis of human PCa datasets reinforces the link between ERRα activity and Wnt signaling. Interestingly, analysis of circulating tumor cells (CTC) from patients had indicate that WNT5A is activated, in keeping with a role for WNT5A in cell polarization and cancer cell invasion  [44, 45, 46]. Indeed, analysis of CTCs from patients progressing on AR inhibitor compared with untreated case indicates activation of WNT5A  [46].
Meta-analysis For correlation analysis in clinical samples, gene expression data and corresponding clinical annotations for previously published datasets on normal prostate tissue, adjacent prostate tissue, primary and bone metastases were downloaded from the Gene-Expression-Omnibus (GSE6919-GSE29650-GSE32269-GSE41619-GSE21034-GSE74685) (Supplementary Table  S1 ). To obtain z-scores, normalized data were log 2 transformed and calculated by subtracting the population mean from individual expression values for each gene and then dividing the difference by the population standard deviation. Correlation scores were calculated using the Pearson correlation coefficient. P-values less than 0. 05 were considered statistically significant. Statistical significance was determined by GraphPad Prism v 5.
Bone Volume/Tissue Volume: (%BV/TV) includes residual trabecular and remaining cortical bone for PC3 and ACE-1 models, plus new bone formation into the bone marrow cavity for PC3c model. For the ACE-1 model, since the osteoblastic lesions is outside the cortical bone, the percent (%) of extra-new bone spicules formation (only spicules surface) with respect to total volume (trabecular, cortical and spicule) (for the ACE model) were quantified. %BV/ TV and % of extra-new bone spicules formation were carried out with CTAn (version 1. 9, Skyscan) and CTVol (version 2. 0, Skyscan) software. Dissected bones were then processed for histological and histomorphometric analysis (tumor burden-to-soft tissue volume (%TB/STV)) and TRAP-positive trabecular bone surface (Oc. S) to the total bone surface (BS) as described previously for the PC3 model  [22].
Respondents were considered to have limited patient experience if they had managed ≤10 patients/procedures. Finally, data were gathered on whether each competence was formally assessed. Additional questions on research and on the existence of a log-book were included. The complete survey can be found in Additional file 1. All questions referred to the end of the training period. Trainees were asked to answer based on the reasonable expectations of their training. Qualified rheumatologists were asked to reflect their status at the end of their training period. Data collection took place from June to December 2014.
Data analysis Descriptive statistics were used to summarize the data. Self-reported ability on each of the competences was compared between groups: respondents who received formal education vs. those who did not, respondents with limited patient experience vs. those with wider experience, respondents assessed vs. those not assessed, respondents from countries with short training vs. long training, and respondents from countries with explicit inclusion of competence in the training program vs. those without inclusion. These comparisons were performed using the Mann-Whitney U test (variables non-normally distributed). In order to investigate whether the country of training was associated with differences in self-reported ability, training methods (education, patient experience) or assessment frequency for each competence we conducted multivariable regression analysis (linear or logistic, for continuous or categorical outcomes, respectively).
Results In total, 1433 responses were received. After deleting incomplete responses (only demographic data provided) and responses outside the target population (e. g. , trainees prior to the rheumatology-specific period of the training or rheumatologists qualified over five years ago), 1,079 responses were included in the final analyses. Respondents were mostly women and were evenly distributed between trainees and certified rheumatologists (Table  1 ). The overall response rate was 24 % of the estimated target population. All 41 countries provided responses, but the response rate varied widely amongst countries from 3 % (Belarus) to 100 % (Bosnia & Herzegovina and Estonia). Thirty-six countries achieved a response rate >10 % (all except Armenia, Belarus, Germany, Italy and Ukraine). See Additional file 2 for more details.
Acknowledgements We gratefully acknowledge the help provided by  EMEUNET (Emerging EULAR Network ) and by the national rheumatology societies of all European countries in the dissemination and distribution of the survey. Members of the Working Group on Training in Rheumatology across Europe are:  Ledio Ҫollaku ,  Rheumatology, Clinic of Internal Medicine, University Hospital Center "Mother Teresa", Tirana (Albania) ;  Armine Aroyan  (Armenia),  Helga Radner  (Austria),  Anastasyia Tushina  (Belarus),  Ellen De Langhe ,  Department of Rheumatology, University Hospital Leuven, Leuven (Belgium );  Sekib Sokolovic  ( Bosnia & Herzegovina ),  Russka Shumnalieva ,  Department of Internal Medicine, Clinic of Rheumatology, Medical University-Sofia (Bulgaria );  Marko Baresic ,  Division of Clinical Immunology and Rheumatology, Department of Internal Medicine, School of Medicine ,  University Hospital Center Zagreb (Croatia) ;  Ladislav Senolt ,  Institute of Rheumatology and Department of Rheumatology, First Faculty of Medicine, Charles University in Prague (Czech Republic );  Mette Holland-Fischer ,  Department of Rheumatology, Aalborg University Hospital (Denmark );  Mart Kull  (Estonia); Antti Puolitaival,  Department of Medicine, Kuopio University Hospital, Kuopio (Finland) ;  Nino Gobejishvili (Georgia) ;  Axel Hueber ,  Department of Internal Medicine 3, University of Erlangen-Nuremberg (Germany ); Antonis Fanouriakis,  Department of Rheumatology and Clinical Immunology, "Attikon" University Hospital, Athens (Greece) ;  Paul MacMullan 10 Clinical Assistant Professor ,  Division of Rheumatology, University of Calgary, Alberta, Canada (Ireland );  Doron Rimar  (Israel);  Serena Bugatti ,  Division of Rheumatology, University of Pavia ,  IRCCS Policlinico San Matteo Foundation ,  Pavia (Italy) ;  Julija Zepa  (Latvia);  Jeanine Menassa (Lebanon) ;  Diana Karpec  (Lithuania);  Snezana Misevska-Percinkova  (Macedonia);  Karen Cassar ,  Department of Medicine, Mater Dei Hospital, Malta (Malta) ;  Elena Deseatnicova (Moldova) ;  Sander W Tas ,  Amsterdam Rheumatology  and immunology Center,  Department of Clinical Immunology & Rheumatology, Academic Medical Center/University of Amsterdam, Amsterdam (Netherlands );  Elisabeth Lie (Norway) ;  Jan Sznajd ,  Jagiellonian University Medical College , Krakow (Poland);  Florian Berghea  (Romania);  Anton Povzun ,  Scientific Research Institute of Emergency Care n.
Abbreviations: 3D-CRT, Three-dimensional conformal radiotherapy; DAN, Dorsal attention network; DMN, Defaultmode network; ECN, Executive control network; fMRI, Functional magnetic resonance imaging; FSL, FMRIB's software library; GBM, Glioblastoma multiforme; GTV, Gross tumor volume; ICA, Independent component analysis; KPS, Karnofsky performance score; mPFC, Medial prefrontal cortex; pCC, Posterior cingulate cortex; preCG, preCentral Gyrus; RPA, Recursive partitioning analysis; rs-fMRI, resting-state fMRI; RSN, Resting-state networks; RT, Radiotherapy; SMA, Supplementary motor area; SMN, Somato motor network; WHO, World Health Organization Background Gliomas are the most common primary neoplasms of the brain  [1]  and based on World Health Organization (WHO) criteria they can be divided into low (II) and high (III and IV) grade tumors  [2].
MRI data acquisition fMRI data were acquired by means of a 3 T Philips Achieva (32-channel head coil) including anatomical sequences (T1, FLAIR, T1CE) and 3 five minute resting-state runs (FE EPI, TR/TE/flip angle = 2. 00 s/ 30 ms/77°, field of view 224x224x106. 4 mm, matrix size 2. 8×2. 8 mm, slice thickness 2. 8 mm, SENSE factor). Subjects were told to lay still and relaxed and fixate a cross shown on the screen during the acquisition. Each resting-state run consisted of 180 volumes of axial slices in an interleaved order leading to 540 volumes acquired.
Data preprocessing The data preprocessing pipeline consists of several steps performed using a combination of tools including dcm2nii  [32] , ITK-SNAP  [33] , the FMRIB's Software Library (FSL)  [34]  and custom homemade MATLAB scripts  [35]. Semiautomatic tumor segmentations were done with ITK-SNAP based on anatomical images and reviewed by an expert radiologist. Preprocessing of the functional data consisted of excluding the first two volumes of each experimental run to allow the fMRI signal to reach steady state and scrubbing of the volumes with motion related artifacts. FEAT tool was used for preprocessing (motion and slice time correction, brain extraction, 5 mm smoothing, intensity normalization) and registration to T1-weighted image (6°of freedom) and 2 mm MNI152-template (12°of freedom) were based on FSL's FLIRT and FNIRT.
Heritability (h 2 ) was defined as the ratio of additive genetic variance to the phenotype variance: h h h. These y c were used for validation of prediction accuracy. The variance components and breeding values were estimated by using the DMU average information (AI) procedure, implemented in the software DMU  (Madsen and Jensen, 2012). Reliabilities of EBV for the Brazilian validation cows and bulls were measured by squared correlations between EBV and y c for individuals in the validation data sets, divided by the average reliability of y c r y c 2 ( ) ; that is, , , r cor EBV y r v c y c 2 2 2 = ( ) where r y c 2 in validation on cows was the heritability of the trait because each cow had only one record of first lactation, and r y c 2 in validation on bulls was the average r y c 2 over 115 sires.
Unbiasedness of EBV was assessed by looking at the slope of the regression of y c on EBV. Furthermore, because the 115 Brazilian bulls were born from 1974 to 2004 and their 17,323 daughters were born from 1982 to 2011, genetic trend due to selection could inflate the correlation between EBV and y c. Therefore, for the 115 Brazilian bulls and their 17,323 daughters, EBV and y c were corrected for genetic trend by a regression on birth year, and then the reliabilities and regression coefficients were calculated by using the corrected EBV and corrected y c. The calculation of reliability and regression coefficient was based on the data pooled over the 5-fold validation sets. Table  2  shows the estimated genetic correlations between Brazilian and Nordic (Nordic and French) populations based on different models.
DATA REDUCTION The BCG at the centre of PKS 0745-191 was observed by ALMA in Cycle 1 with single pointings to cover the CO(1-0) line at 104. 53 GHz in band 3 and the CO(3-2) line at 313. 56 GHz in band 7 (ID = 2012. 1. 00837. S; PI McNamara). The observations were centred on the nucleus of PKS. 97, J2000) and the HPBW of the primary beam was 60 arcsec in band 3 and 20 arcsec in band 7. The data were taken in two 8 minute observations in band 3 on 2014 April 26 and 27 and in a 25 minute observation in band 7 on 2014 August 19. The observations utilised 32 -36 antennas with baselines of 20 -560 m at CO(1-0) and a more extended configuration with baselines of 34 -1100 m at CO(3-2). The frequency division correlator mode was used with a 1. 875 GHz bandwidth and frequency resolution of 488. 3 kHz (1.
Gas distribution in the central galaxy The CO(1-0) and CO(3-2) rotational transition lines were both detected and imaged at the centre of the BCG in PKS 0745-191. The continuum-subtracted total spectral line profiles were extracted from a 6 arcsec × 6 arcsec region for CO(1-0) and a 4 arcsec × 4 arcsec region for CO(3-2) and are shown in Fig. 1. Larger regions produce consistent total fluxes but significantly noisier spectra. Each spectrum was fitted with a single Gaussian component using the package MPFIT  (Markwardt 2009 ) and the best fit results, corrected for primary beam response and instrumental broadening, are shown in Table  1. The CO(1-0) and CO(3-2) total spectra are consistent with single velocity components blueshifted to -30 to -50 km s -1 and have a comparable FWHM of 200 -230 km s -1.
Line ratio The CO(3-2)/CO(1-0) line ratio was calculated for each of the three filaments and a region centred on the nucleus. The CO(3-2) dataset was convolved with a 2D Gaussian to match the CO(1-0) synthesized beam and the integrated intensities (in K km s -1 ) were determined from spectral fitting to each dataset using matching CO(1-0) beam-sized regions. The line ratios for the BCG centre, N and SE filaments were consistent within the error with CO(3-2)/CO(1-0)= 0. 81 ± 0. 05, 0. 8 ± 0. 1 and 0. 9 ± 0. 1, respectively. The line ratio measured for the SW filament was significantly lower at CO(3-2)/CO(1-0)= 0. 53 ± 0. 07. The FWHM measured in each of the four regions is consistent within the errors for both the CO(1-0) and the matched resolution CO(3-2) observation. Assuming that the CO emission is optically thick and the gas is thermalized, the line ratios indicate an excitation temperature of 20 -30 K.
However, no increase in the velocity dispersion is observed either along the SW filament or by comparison with the other filaments. and NAOJ. The scientific results reported in this article are based on data obtained from the Chandra Data Archive. This publication makes use of data products from the Two Micron All Sky Survey, which is a joint project of the University of Massachusetts and the Infrared Processing and Analysis Center/California Institute of Technology, funded by the National Aeronautics and Space Administration and the National Science Foundation.
ACKNOWLEDGEMENTS HRR and ACF acknowledge support from  ERC  Advanced Grant Feedback 340442. BRM acknowledges support from the  Natural Sciences and Engineering Council of Canada  and the  Canadian Space Agency Space Science Enhancement Program. PEJN was partly supported by  NASA  contract  NAS8-03060. ACE acknowledges support from  STFC  grant  ST/L00075X/1. GRT acknowledges support from  Einstein Postdoctoral Fellowship Award Number   PF-150128 , issued by the Chandra X-ray Observatory Center, which is operated by the  Smithsonian Astrophysical Observatory  for and on behalf of  NASA  under contract  NAS8-03060. We thank the reviewer for their thorough reading of the paper and encouraging comments. This paper makes use of the following ALMA data:  ADS/JAO. ALMA 2012. 1. 00837. S. ALMA is a partnership of  ESO  (representing its member states),  NSF (USA)  and  NINS (Japan) , together with  NRC (Canada) ,  NSC  and  ASIAA (Taiwan) , and  KASI (Republic of Korea) , in cooperation with the  Republic of Chile.
1 cycle (Applied Biosystems, Foster City, CA). The electropherograms obtained were assembled and corrected using ChromasPro software (Technelysium Pty, Queensland, Australia). Detection of pathogens. To test for the presence of pathogens, qPCRs were performed using previously reported primers and probes targeting the 16S rRNA gene of Borrelia  [29] , the ITS intergenic spacer of Bartonella  [8] , the rpoB gene of Acinetobacter  [9] , the ompB gene of Rickettsia prowazekii and the pla gene of Yersinia pestis  [27]. All qPCRs were performed using a CFX96 TMREAL-Time System C1000 Thermal Cycler (Bio-Rad Laboratories) and the Eurogentec Master Mix Probe PCR kit (Eurogentec). Prevention of DNA contamination. In order to ensure that no contamination by modern DNA would interfere with the results, all pre-PCR and post-PCR procedures were performed respectively in a separate, clean room, free of louse DNA under a hood with air-capture, using autoclaved and UV treated material.
Extractions and PCR amplification blanks were used as negative controls, in order to detect possible contamination by external DNA. No positive control was included in any experimental steps in order to minimize potential contamination. No amplifications were detected among the negative controls throughout the study. Data Analysis MEGA 6. 06 was used for phylogenetic analyses and tree reconstruction with 500 bootstrap replicates using the maximum likelihood method under Kimura's 2-parameter with complete deletion  [30]  For comparison, the ancient DNA sequences obtained in this study were combined with the 30 cytb haplotypes reported by Drali et al. [31]. This dataset was complemented with newly available sequences in GenBank, after being assigned to haplotypes using DnaSP v5. 10  [32]. Ancient lice sequences from 10,000 and 4,000 year-old Peruvian and Chilean mummies, respectively, were also included  [17, 26].
Finally, a dataset that consisted of 49 haplotypes was created in which two are shared between contemporary and ancient lice. These haplotypes span 40 geographic locations (countries) on five continents (S1 Table ). The novel haplotypes identified were deposited in GenBank under the following accession numbers: KX249763-KX249775. In order to investigate the possible relationships among haplotypes, the median-joining (MJ) network using the Bandelt method was constructed using the NETWORK4. 6 program (www. fluxus-engineering. com/sharenet. htm) [33]. For A. baumannii, the nucleotide sequences obtained in this study were aligned with the reference sequences available in public databases (GenBank) and a phylogenetic tree was also constructed using the maximum likelihood method under Kimura's 2-parameter model implemented in MEGA 6.
Nevertheless, the mutations that we found in both haplotypes A55 and A56 are synonymous, i. e. resulting in no amino acid changes, compared to the A5 haplotype. The sequences of these two novel haplotypes were deposited in GenBank under accession number: KX232678-KX232679. Within the clade B ancient head lice, all eight sequences (all from comb A) belonged to the modern haplotype B36, which is the most common within the B-haplogroup (74% of the 184 analyzed haplogroup B human lice). This haplotype occupied a central position in the MJ network, and is found in Africa, Europe and America, as well as in pre-Columbian Chilean mummies.
Detection of Pathogens The qPCR targeting the rpoB gene of Acinetobacter was positive for four samples (4/24) of ancient lice DNA tested (34<Ct<36) from comb B. The qPCR products generated were directly sequenced. The four obtained sequences of the partial rpoB gene (182-bp) were 100% identical to one another and were identified as A. baumannii on the basis of a BLAST search. These sequences had 180 of 182 base positions in common (98. 9% identity) with a reference strain of A. baumannii (GenBank accession number CP012952) and 181 of 182 base positions in common (99. 4% similarity) with A. baumannii isolated from modern human head lice Borrelia spp. , Bartonella spp. , Rickettsia prowazekii and Y. pestis DNA could not be identified in any of the 24 ancient head lice specimens tested.
Acknowledgments We would like to thank  Dr. Orit Shamir  and  Dr. Naama Sukenik  from the  Israel Antiquities Authority , as well as  Professor Simcha Lev-Yadun  from  Haifa University , for their help in identifying and allowing us to examine the ancient louse combs for louse remains. We also thank  IHU Méditerranée Infection  for financially supporting the study.
The labeled products were purified, hybridized and washed according to Agilent protocols. The results of 8X60k, 2X400k and 244k kits were analyzed by Agilent CytoGenomics v. 2. 7 and Agilent DNA Analytics 4. 0 softwares, using default analysis methods-CGH v2 with the ADM-2 aberration and ADM1 aberration algorithms, respectively.
Next-Generation Sequencing (NGS) For Whole Exome Sequencing (WES), genomic DNA, library and sequencing were performed as previously described  [8]. A custom NGS panel of 37 genes involved in retinal dystrophies, including the CHM gene, was developed as follows: a total of 588 target regions (1,472 amplicons) were entered into DesignStudio software (Illumina). Once the oligonucleotide probes were synthesized, libraries were constructed and indexed by PCR using common primers from the TruSeq Amplicon Index Kit (Illumina). Finally, libraries were normalized and pooled, prior to sequencing on a NextSeq500 system (Illumina).
In vitro prenylation assay At confluence, the patient's fibroblasts cultured in a 6-well plate, were washed in cold PBS, scraped in PBS containing antiproteases and pelleted at 3000g for 5 min. The pellet was resuspended in cold, freshly prepared degassed prenylation/lysis buffer and in vitro prenylation was performed as described  [9]. Western blot detection was performed using enhanced chemiluminescence system ECL (Life Technologies). The amount of biotinylated Rab proteins was then quantified by scanning densitometry using the software Image J and expressed as a function of the β-actin signal. Experiments were performed in triplicate. Due to small sample sizes, 2x2 comparisons were performed using a non-parametric Mann-Whitney test.
Complete or partial deletions of CHM, found in 13 families (36%) in total, as well as nonsense (13 families; 36%), were the most frequent mutations identified in our cohort. Among the deletions, exon 9 was the most recurrent one (3 families; 8%). From the families carrying nonsense mutations, c. 877C>T (p. Arg293 Ã ) was the most frequent one, having been found in 4 families (11%) from diverse origins: Spain, Portugal and Poland. The frameshift c. 525_526delAG (p. Lys178Argfs Ã 5) mutation was found both in one Spanish and in one Portuguese family (Table  1 ). The nucleotide substitution c. 49G>T, reported for the first time in this study, was not present in our in-house exome variant database nor in any public database. It was predicted to produce the substitution p. Gly17Cys, indicated as putatively pathogenic by the SIFT, Poplyphen-2 and Mutation Taster softwares.