dataesr/scientific-paragraphs-categorization huggingface dataset:
The study author was contacted and advised that these data would be available from NIH. NIH were contacted but as yet these data have not been released. The mean age of study participants was reported, 65 years (SD 9) in the active group and 66 years (SD 9) in the control group. For the subgroup analysis of when treatment was started the entirety of events in this study were analysed as if all participants had started treatment more than 10 years after their menopause The mean time since menopause that treatment was started for the study population overall was 18. 2 years. For the subgroup analysis of when treatment was started, the entirety of events in this study were analysed as if all participants had started treatment more than 10 years after their menopause Participants 664 post-menopausal women (HT: n = 337; placebo: n = 327) with a mean age of 71.
The clinical events of interest were defined according to standard criteria and verified by a WEST 2001 (Continued) neurologist blinded to treatment allocation, or by objective measures of disease such as positive results on a duplex ultrasonogram or venogram for the diagnosis of VTE. All events were centrally corroborated, and sensitivity analyses undertaken to examine the effect of including only medication compliant patients in the analyses. Study publication pools results for women on unopposed and combined therapies. Vital status was confirmed for all women at the conclusion of the trial Data were not stratified according to time since menopause that treatment was started, however the mean time since menopause for the study population as a whole was 25 years. The age of each treatment group was 71 years (SD 10) for the control group and 72 years (SD 10) for the hormone group.
Three-month washout period before baseline evaluation of women using post-menopausal hormones at baseline screening. Lead-in placebo pills given for at least four weeks during screening process to establish compliance with pill taking Randomisation: Centrally randomised by permuted block algorithm Stratification: By clinical centre site and age group Allocation: By local access to remote study database Baseline equality of treatment groups: No substantive differences between study groups at baseline Blinding: All participants, clinic staff, and outcome assessors blinded, with the exception of 331 participants who were unblinded from the unopposed oestrogen arm and reassigned to combined HT arm due to change in protocol Analysis: ITT Funding Source: The National Heart, Lung, and Blood Institute.
Centrally randomised by permuted block algorithm Stratification: By clinical centre site and age group Allocation: By local access to remote study database Baseline equality of treatment groups: No substantive differences between study groups at baseline Blinding: All participants, clinic staff, and outcome assessors blinded, with the exception of 331 participants who were unblinded from the unopposed oestrogen arm and reassigned to combined HT arm due to change in protocol Analysis: ITT Funding Source: The National Heart, Lung, and Blood Institute. Wyeth-Ayerst Research provided the study medication WHI I 2002 (Continued) Participants 16,608 healthy post-menopausal women were randomised to receive either daily conjugated equine oestrogen in combination with progestin (n = 8506) or placebo (n = 8102).
Eighteen out of 19 trials reported the funding source. Only one of the trials, HERS I 1998 was exclusively funded by the pharmaceutical industry (Wyeth-Ayerst), whilst EVTET 2000 and WHISP 2006 were part funded by a grant from Novo-Nordisk Pharmaceutical. STOP IT 2001 was mainly funded by the US National Institutes of Health (NIH) but was additionally supported by Wyeth-Ayerst, Hoffman-LaRoche, Inc. Pharm. and Pharmacia and Upjohn. The study medication for ERA 2000, WHI I 2002 and WHI II 2004 was provided by Wyeth-Ayerst Research, for ESPRIT 2002 by Schering AG and for WEST 2001 by Mead Johnson laboratories. DOPS 2012 was supported by study medication from Novo Nordisk, Novartis and Leo Pharma Denmark, Greenspan 2005 by medication from Wyeth-Ayerst Laboratories and Merck Research Laboratories and WELL-HART 2003 by medication from Mead Johnson Laboratries and Pharmacia and Upjohn.
1. Boardman HM, Hartley L, Eisinga A, et al. Hormone therapy for preventing cardiovascular disease in post-menopausal women. The Cochrane database of systematic reviews. 2015;3:CD002229. 2. Veerus P, Hovi SL, Fischer K, Rahu M, Hakama M, Hemminki E. Results from the Estonian postmenopausal hormone therapy trial [ISRCTN35338757]. Maturitas. Sep 20 2006;55(2):162-173. 3. Rossouw JE, Anderson GL, Prentice RL, et al. Risks and benefits of estrogen plus progestin in healthy postmenopausal women: principal results From the Women's Health Initiative randomized controlled trial. Jama. Jul 17 2002;288(3):321-333. 4. Anderson GL, Limacher M, Assaf AR, et al. Effects of conjugated equine estrogen in postmenopausal women with hysterectomy: the Women's Health Initiative randomized controlled trial. Jama.
Randomisation: Centrally randomised by permuted block algorithm Stratification: By clinical centre site and age group Allocation: By local access to remote study database Baseline equality of treatment groups: No substantive differences between study groups at baseline Blinding: All participants, clinic staff, and outcome assessors blinded, with the exception of 331 participants who were unblinded from the unopposed oestrogen arm and reassigned to combined HT arm due to change in protocol Analysis: ITT Funding Source: The National Heart, Lung, and Blood Institute. Wyeth-Ayerst Research provided the study medication WHI I 2002 (Continued) Participants 16,608 healthy post-menopausal women were randomised to receive either daily conjugated equine oestrogen in combination with progestin (n = 8506) or placebo (n = 8102).
Power calculation: At a significance level of 5% and a power of 90% the sample size was estimated to a maximum of 240 women Data on timing of the menopause or starting treatment in relation to the menopause was not reported. The study author was emailed but no response was received. Baseline age was reported for each treatment group, control group 55. 7 (SD 5. 9) and 55. 8 (SD 7. 0) years. Events were not reported individually or according to starting treatment < 10 or > 10 years since the menopause, or age. For the subgroup analysis of when treatment was started, the entirety of events in this study were analysed as if all participants had started treatment within 10 years of their menopause Risk of bias Bias Authors' judgement Support for judgement Random sequence generation (selection Low risk Computer-generated 1:1 block randomisa- bias) tion with fixed block sizes of ten Comparator: identical placebo capsule daily Medication compliance in terms of pill counts was conducted at each follow-up visit Follow-up times: patients were followed up at three months, 12 months and 24 months Treatment adherence was not reported Loss to follow-up: zero, but 37 (23 HT and 14 placebo) women did not attend all visits due to premature termination of the study There were 33 dropouts, 10 in HT group (two wanted to be sure of being treated with oestrogen for post-menopausal symptoms, eight had adverse effects), and 23 in the placebo group(11 wantedto be sure of being treated with oestrogen for post-menopausal symptoms, 10 had adverse effects, two no reason stated) Allocation: By research pharmacist Baseline equality of treatment groups: No substantive differences between study groups at baseline Blinding: Block sizes (four, eight or 12) were randomly determined to enhance blinding.
This assumed an 18,000 sample size but trial stopped early with 26% of target Yeboah 2008 No outcomes relevant to this review Ylikorkala 1999 No outcomes relevant to this review and no control group Characteristics of studies awaiting assessment [ordered by study ID] NCT00154180 Methods Randomised placebo controlled trial Participants Recently menopausal healthy women (within 36 months of menses) Interventions 0. 45 mg oral oestrogen weekly with 200 mg cyclic oral, micronised progesterone for 12 days each month Outcomes Rate of change of carotid intimal medial thickness by ultrasound; Cognitive and Affective scores; health related quality of life Notes Sponsor: Kronos Longevity Research Institute Characteristics of ongoing studies [ordered by study ID] 4 Venous thromboembolism 10 Risk Ratio (M-H, Random, 95% CI) Subtotals only 4.
1 Hormone therapy commenced <10 years after menopause 3 9838 Risk Ratio (M-H, Random, 95% CI) 1. 74 [1. 11, 2. 73] 4. 2 Hormone therapy commenced >10 years after menopause 9 27475 Risk Ratio (M-H, Random, 95% CI) 1. 96 [1. 37, 2. 80] Analysis 1. 1. Review: Hormone therapy for preventing cardiovascular disease in post-menopausal women Comparison: 1 Hormone therapy versus placebo in primary prevention Outcome: 1 Death (all-causes) Study or subgroup HT Control Risk Ratio Weight Risk Ratio n/N n/N M-H,Random,95% CI M-H,Random,95% CI DOPS 2012 15/502 26/504 3. 5 % 0. 58 [ 0. 31, 1. 08 ] EPHT 2006 1/404 1/373 0. 2 % 0. 92 [ 0. 06, 14. 71 ] Bias (Continued) NCT00114517 ERT II 1979 3/84 7/84 0. 8 % 0. 43 [ 0. 11, 1. 60 ] Greenspan 2005 Trial name or title STOP IT 2001 Methods WHI I 2002 WHI II 2004 Participants WISDOM 2007 1/187 Early versus Late Intervention Trial with Estradiol (ELITE) 2/186 • otosclerosis 3/243 1/123 • porphyria • currently pregnant or taking contraceptive drugs in the last 12 months 0.
Microarray hybridization and data analysis Messenger RNA (mRNA) and microRNA (miRNA) expression profiling was performed using Agilent microarrays (Agilent Technologies, Santa Clara, CA, USA) according to the manufacturer's instructions. The mRNA and miRNA data discussed in this publication have been deposited in the National Center for Biotechnology Information Gene Expression Omnibus (GEO)  [13, 14]  and are accessible under GEO series accession number [GEO:GSE57705] (http://www. ncbi. nlm. nih. gov/ geo/query/acc. cgi?acc=GSE57705).
Epithelial markers such as cytokeratin 19, CLDN1, CLDN3, and CLDN4 showed lower expression in SSEA4-positive cells, whereas mesenchymal indicators such as fibronectin, vitronectin, ZEB1, and ZEB2 were upregulated (Additional file 10: Figure  S6 A ). In contrast, published stem cell markers were not consistently regulated among the SSEA4-positive and SSEA4-negative cell fractions (Additional file 10: Figure  S6 C ). As SSEA4 is a glycolipid epitope, its expression cannot be monitored directly by transcriptome profiling. SSEA3, the direct precursor of SSEA4, showed no increased signal intensity in SSEA4-positive cells when measured with anti-SSEA3 antibody in all analyzed models (data not shown). This indicates that SSEA4 enrichment regulation may be due to increased SSEA3-to-SSEA4 conversion or to increased SSEA4 catabolism in SSEA4-negative cells.
However, no difference was observed in the expression of genes coding for enzymes involved in degradation of SSEA4 or of ST3GAL2, the enzyme catalyzing this final step of SSEA4 synthesis  [22]. In expression analysis of the miRNA data set, we identified 166 miRNAs more than twofold overrepresented and 68 miRNAs more than twofold underrepresented in SSEA4-positive versus SSEA4-negative cells among all analyzed tumor models (Additional file 9: Table  S3 ). Hierarchical clustering of the Pearson correlation coefficients of our mRNA and miRNA expression datasets showed a higher correlation of SSEA4-positive versus SSEA4-negative phenotype in the miRNA rather than mRNA data set (Fig. 4c ). No miRNAs were significantly upregulated, and 18 miRNAs were significantly downregulated (p < 0. 05 by paired t test), in SSEA4positive cells (Fig.
ST3GAL2 is a highly significant predictive and prognostic marker in breast cancer patients Given the functional connection between SSEA4 and ST3GAL2, we evaluated the clinical value of ST3GAL2 in a large, publicly available clinical microarray database of breast tumors from 2977 patients  [15]. Highly significant differences (p < 0. 01) trending toward a poorer outcome for patients expressing higher levels of ST3GAL2 within the estrogen receptor-negative (ER -) or ER -/progesterone receptor-negative (PR -) subset of patients were found, independent of the treatment (Fig. 7a ). When we focused on patients treated with chemotherapy, we observed a highly significant reduction of relapse-free survival independent from the tumor subtype (p < 0. 01, HR 1. 91) in ER - patients (p < 0. 01, HR 2. 97) and in ER -/PR -patients (p < 0.
8a-c ). Analysis of mesenchyme-specific (FN1, SNAI1, ZEB2) and epithelial (CLDN3) genes by quantitative real-time polymerase chain reaction revealed that ovarian cancer primary cells with high SSEA4 expression showed higher expression of mesenchyme-specific genes and lower expression of the epithelium-specific ones (Fig. 8a  and b ). In light of these results, we evaluated whether ST3GAL2 expression could be associated with patient prognosis in ovarian cancer. Using the whole dataset of ovarian tumors from 1464 patients  [16] , a significant difference trending toward a worse clinical outcome was observed with respect to progressionfree survival (p < 0. 05) as well as postprogression survival (p < 0. 05) (Fig. 8e ). When we assessed patients treated with chemotherapy, the level of significance was also strongly increased for both endpoints (p < 0.
The predictive value of ST3GAL2 was evaluated using a large, publicly available clinical microarray database based on breast tumors from 2977 patients  [15]. Estrogen receptor-negative (ER -) patients and ER -/progesterone receptor -negative (PR -) patients displayed highly significant differences (p < 0. 01) trending toward a poorer prognosis for patients expressing higher levels of ST3GAL2 (a). When we focused on patients treated with chemotherapy, we observed a highly significant reduction of relapse-free survival independent of the tumor subtype [p < 0. 01, hazard ratio (HR) 1. 91] in ER -patients (p < 0. 01, HR 2. 97) and in double-negative patients (p < 0. 01, HR 3. 08) among patients expressing high levels of ST3GAL2 (b). Also, when we applied distant metastasis-free survival as a primary endpoint, we found that patients treated with chemotherapy had a worse prognosis when expressing high levels of ST3GAL2 (c).
Acknowledgments We are grateful to  Jana Ciomperlik ,  Janina Kuhl ,  Nadine Chelius ,  Petra Kussmann ,  Tanja Töpfer ,  Jens Gaiser ,  Alena Böttcher ,  Sophie Banis ,  Erwan Selingue , and  Vanessa Yvonnet  for excellent technical assistance. Furthermore, we thank  Yvonne Diener  for supplying us with the CD133 siRNAs and for support in designing the knockdown experiments. In addition, we are grateful to  Michail Knauel  for help with the microarray data analysis. This work was supported by the  Eurocancer Stem Cell Training Network (EuroCSCTraining)  of the  European Union  ( Innovative Training Networks FP7 Program ;  Marie Skłodowska-Curie Actions  grant  264361 ) and by the  Dietmar Hopp Foundation  (to ATr).
Data The sampled animals came from 5 pig breeds: 24 Duroc (DU), 33 Landrace (LR), 41 Large White dam line (LWF), 10 Large White sire line (LWM) and 39 Piétrain (PI). The two Large White lines derived from a common and recent ancestor, and were specialized on "male" traits (like conformation) for LWM or on "female" traits (like maternal behaviour) for LWF. All animals were males (castrates) except Piétrain (females). They were reared in 10 different contemporary groups, and slaughtered around 100 kg as in  [22]. From these animals, 147 post-mortem Longissimus dorsi muscle samples were analyzed using the porcine Agilent 44 K v1 microarray. A total of 12,358 probes were detected and considered as expressed in muscle in the conditions of this experiment and after normalization. From these 12,358 probes, 9,055 (73 %) were mapped on the porcine genome (Sscrofa10.
Functional annotation Tables  1  and 2  underline the large number of gene lists to be analysed for biological interpretation. The functional analysis was undertaken in a sequential manner. First, the objective was to evaluate the impact of the sex effect of the Piétrain breed. While it would have been possible to restrict the functional analysis to autosomal genes, excluding important genes such as IGF2 (absent from the current assembly) may give unreliable results. Therefore, the list of the 1,374 DE probes from the union of the global and pairwise comparisons restricted to autosomes was compared to the same list plus the 61 DE probes localised on chromosomes X and Y (Additional file 2). The free GeneCodis software  [27]  was used and the comparison was restricted to KEGG pathways, which are a collection of manually drawn pathway maps representing our knowledge on the molecular interaction and reaction networks.
NLRC5 is an intracellular receptor involved in innate immune sensing; it is induced by interferons in case of pathogen infection  [54, 55]. It is also involved in the regulation of kinase activity and NF-kappaB transcription factor activity (Biological Process Ontology from GeneCodis). The three other genes have mechanistic nuclear roles. RAD1 codes a cell cycle checkpoint protein required for cell cycle arrest and DNA damage repair. DHX33 (DEAH (Asp-Glu-Ala-His) box polypeptide 33) is identified to code a protein with an important role in rRNA transcription and cell proliferation. CPNE2 (copine II) codes a calcium-dependent membrane-binding protein that may regulate molecular events at the interface of the cell membrane and cytoplasm. Only one gene, PRKRIP1 (PRKR interacting protein 1 (IL11 inducible)) gene was over-expressed in Large White dam line.
RNA concentrations were determined using a Nano-Drop® ND-1000 spectrophotometer (NanoDrop Technologies, Wilmington, DE). cRNA labeled with fluorescent Cyanine 3-CTP was used for hybridization at 65 °C for 17 h onto porcine oligo microarray slides, according to the manufacturer's recommendations (Low Input Quick Amp Labeling with Low Input QuickAmp Cy3 labeling kit One-color and One-Color Microarray-Based Gene Expression Analysis; Agilent Technologies). Hybridized microarray slides were scanned with scanner Genepix 4000B (AXON INSTRUMENTS) at 5-μm resolution. The scanned images were analyzed numerically using Agilent Feature Extraction Software version 9. 5. 3. 1. (Agilent Technologies).
Microarray data: normalization The whole set of 45,220 spots were analysed on 181 chips (one sample per chip, a limited number of animals giving 2 to 3 samples). One chip was removed because of overall bad quality. Each spot on each chip was allocated a weight of 0 or 1 depending on various criteria of spot's quality and signal to background level. On average, half of the spots per chip had a 0 weight. Only spots with a weight of 1 for at least 70 % of the chips were kept for further analysis, leading to 12,358 spots. All intensity data were log transformed, and the term "data" or "intensity" referred to log (natural logarithm) transformed data throughout the manuscript. The effect of the hybridization day was removed by subtracting the mean intensity of the hybridization day. Then the mean of each chip was subtracted to make all chips comparable.
A good linearity of intensity signals were observed between all samples. One animal was hybridized 3 times, and 32 individuals were sampled and hybridized twice. A very good adequacy was observed between samples from the same individual, with an average correlation of 0. 98. Then, for each individual, only the sample with the best mean quality was kept. Finally, 147 chips representing 147 individuals were available for deeper statistical analysis. The normalized microarray dataset have been deposited in the NCBI Gene Expression Omnibus GSE56011.
Differential analysis A linear mixed model was fitted for each spot, with breed as fixed effect and contemporary groups as random effect, with the lme function of the R package nlme. The significance of the breed effect on gene expression was evaluated with an F-type test (R function anova). A False Discovery Rate (FDR,  [69] ) correction was performed on raw p-values (R function multtest). In the same linear mixed model, comparisons of each pair of breed were tested, with a FDR correction thereafter. In all cases, a threshold of 0. 1 % has been applied except for the comparison between LWM and LWF (5 % threshold). This stringent threshold was chosen to ease the functional analysis; no enrichment can be highlighted with too many genes. The fold change (FC) for any pair of breeds was calculated as the exponential of the difference between mean expression level in breed 1 and mean expression level in breed 2.
Breed prediction A Random Forest (RF) analysis was performed in order to predict the breed of each animal on the basis of its transcriptomic profile, using the random Forest package of the R software  [70]. A preliminary RF analysis was used to eliminate the 70 % less important genes to avoid too much noise. On the basis of the remaining 30 %, i. e. 3,336 spots, a second RF analysis was performed with 5,000 trees (the other parameters were the default ones). A sparse Partial Least Square -Discriminant Analysis (sPLS-DA) was conducted following  [30] , using the mixOmics R package (http://www. mixOmics. org).
Porcine Gene Expression Microarray (G2514F, V1: 020109 Agilent Technologies; GEO accession number GPL10162) used in this work was previously described by the manufacturer. The 43,803 porcine probes sourced from UniGene (Release 33, Feb 2008), RefSeq (Release 27, Jan 2008), TIGR (Release 12, Jun 2006) using Agilent 60-mer SurePrint technology.
Acknowledgements This work is part of the  DéLiSus  project funded by  BIOPORC  ( ADN, Choice Genetics France, Gene + and Nucléus breeding organizations and IFIP) and French ANR  ( ANR-07-GANI-001 ). We thank the animal and DNA providers (BIOPORC). F. R. acknowledges financial support from  Région Midi-Pyrénées and Département de Génétique Animale of INRA. The microarray hybridizations and image analysis have been realized at the Biochip Platform http://biopuce. insatoulouse. fr/. The microarray annotation was performed by  SIGENAE computer group (Système d'information du  projet d'analyse des génomes des animaux d'élevage, http://www. sigenae. org). We are grateful to  Zhilei Li ,  Marine Dumont ,  Agnès Solomiac ,  Laurence Souchu  (students of the  Institut National des Sciences Appliquées de Toulouse, France)  for an earlier contribution in the statistical analysis, and  David Robelin ,  Bertrand Servin  and  Nathalie Villa-Vialaneix  for their help.
The 4 x 44 K 44 Porcine Gene Expression Microarray (G2514F, V1: 020109 Agilent Technologies; GEO accession number GPL10162) used in this work was previously described by the manufacturer. The 43,803 porcine probes sourced from UniGene (Release 33, Feb 2008), RefSeq (Release 27, Jan 2008), TIGR (Release 12, Jun 2006) using Agilent 60-mer SurePrint technology.
Comparison of field data and model outputs The aim of this section is to provide a detailed comparison between observed and modeled meteorological variables including aeolian snow mass fluxes. The model performances are assessed using the efficiency statistical test (E) proposed by  Nash and Sutcliffe (1970) : E = 1 -(RMSE/s) 2 , ( 7 ) where s is the standard deviation of the observations and RMSE is the root mean squared error of the simulated variable. An efficiency index of 1 means a perfect simulation (RMSE = 0) and a value of 0 or less means that the model is no better than a minimalist model whose output constantly equals the mean value of the modeled variable over the time period concerned. Wind speed and relative humidity were compared at a height of 2 m above the surface. Simulation data were extracted from the nearest grid point to the AWSS concerned.