dataesr/scientific-paragraphs-categorization huggingface dataset:
Compared to microarray data, similar up-and down-regulation of TI were observed in the panel of 11 genes analyzed by RT-qPCR (Figure  4A ). A difference in the range of TI was observed between the two methods that can be explained by a difference in sensibility of these methods. However, a significant and positive correlation was observed between TI obtained from RT-qPCR and microarray analyses (Figure  4B ). Similar analyses using RT-qPCR quantification of mRNA distribution within polysome profiles were performed in two additional colorectal cancer cells (Figure  4C ). We used the p53-null HCT-116 cells and the mutant p53. R273H HT-29 cells (Supplementary Figure  1 , IARC TP53 Database), to determine whether this translational regulation occurs in different colorectal cancer cell lines and whether it is dependent of p53 since several data support a role of p53 as a regulator of translational reprogramming  [28].
Genes involved in DNA replication and gene expression regulation are selectively regulated by 5-FU at the translation level To determine the main functions of genes whose recruitment whithin polysomes was altered in response to 5-FU in HCT-116 cells, we performed Gene Ontology analysis using the Functional annotation clustering analytic modules of DAVID bioinformatics resources that provides a rank classification of enriched functions based on determination of P-values and enrichment scores  [29]. Statistical enrichment analyses were performed separately on genes translationally up-and down-regulated, using the lists of 313 (TI cut-off of 1. 5, P < 0. 05) and 798 (TI cut-off of 1. 3, P < 0. 05) translationally dysregulated genes. Clusters of functional annotation common to both lists are presented in Figure  5  (for a complete list of functional annotation clustering, see Supplementary Table  3 ).
These data suggest that the early down-regulation of mir-155 in response to 5-FU treatment may contribute to the increased translation of a subset of mRNAs in colorectal cancer cells. Interestingly, Gene Ontology analysis of these mir-155 target genes whose translation was up-regulated in response to 5-FU mainly corresponded to proteins involved in transcription (Supplementary Table  5 ). (E-F ) Analysis of HIVEP2 translational regulation through its 3'UTR using luciferase reporter assays. Luciferase activities were measured to determine the role of mir-155 on HIVEP2 3'UTR-dependent translation in response to 0 or 10 μM 5-FU for 24 hrs (E). mRNA levels of Firefly and Renilla genes were analysed to verify that variation in luciferase activity shown as F-Luc/R-Luc (RLU) is not due to variation of F-Luc/R-Luc at mRNA levels (F).
Affymetrix exon-array Quality of RNA purified from polysome fractionation was verified using BioAnalyzer (Agilent) (RIN ranging from 6. 6 to 9. 3). 250 ng of total RNA was processed with the GeneChip WT Sense Target Labeling kit and hybridized to GeneChip Human Exon 1. 0 ST arrays. Affymetrix exon-array data were normalized with quantile normalization. Antigenomic probes were used to perform the background correction. Only probes targeting exons annotated from full-length cDNA were retained for analysis. Cross hybridizing probes and probes with lower signals intensity than anti-genomic background probes showing the same GC content were removed. Only probes with a DABG P-value ≤ 0. 05 in at least half of the arrays were considered for further statistical analysis. Arrays were performed in two independent replicates.
FUNDING This work was supported by  CNRS ,  INSERM ,  Université Claude Bernard Lyon 1 and Centre Léon Bérard. This project was funded by  Ligue Contre le Cancer Comité du Puy-de-Dôme  ( PPE 2016 ,  PME 2017 ). ZBI was a recipient of PhD program from Syrian government, GT was a recipient of PhD fellowships from  Ligue Nationale Contre le Cancer (LNCC)  and  Fondation ARC , FL was a recipient of PhD fellowship from  French Ministry of Research , MPE was a recipient of PhD fellowship from  Association Française contre les Myopathies (AFM).
Search strategy The MEDLINE, Scopus, EMBASE and Cochrane databases were searched for RCTs evaluating the effect of bisphosphonate or denosumab discontinuation on BMD, bone turnover and fractures. The search was carried out up to January 31, 2017. A basic search strategy was developed for MEDLINE and modified accordingly for the other search engines. The search items were: ("alendronate" OR "risedronate" OR "zoledronic acid" OR "zoledronate" OR "denosumab") AND ["discontinuation" OR "duration" OR "holiday" OR "stop" OR "withdrawal"]. The final set of abstracts was uploaded at the web version of the EndNote software (www. myendnoteweb. com), after removing duplicates. Selection of abstracts, extraction of information from fulltext articles andevaluation of risk of bias with the Newcastle-Ottawa Scale (NOS) were done independently by three researchers (PA, SAP and GM).
Clinical question 3: Who is a candidate for treatment re-initiation? Reassessment after bisphosphonate or denosumab discontinuation should include clinical evaluation to check for the presence of new fractures and risk factors for fractures  [42, 45]. BMD measurement by DEXA can be considered at this point. Analysis of the FLEX study data has shown that this is not important for patients who have obtained a significant BMD gain. For example, for a patient with a T-score around -2, DEXA could even be postponed for almost five years, as it is unlikely to change management  [50]. Again, the decision should be individualized, taking into consideration each patient's specific characteristics. Measurement of BTM, especially resorption markers, such as serum CTX, has been also proposed  [2]. If any new fracture is present or BMD has decreased (femur neck T-score ≤ -2.
49 nM) within 10 min of a 17 nM Fe(II) spike addition, yet other methods still observed Fe(II) concentrations of 2. 7 to 3. 7 nM 30 min later. k 1 also diverged accordingly with the ferrozine derived value 4% of that derived from luminol without pre-concentration. These apparent inconsistencies suggest that some inter-dataset differences in measured Fe(II) oxidation rates in natural waters may be attributable to differences in the analytical methods used rather than arising solely from substantial shifts in Fe(II) speciation.
From an energetic perspective, Fe(II) is expected to be more bioavailable than Fe(III)  (Sunda et al. , 2001). Furthermore, regardless of whether dissolved Fe(II) is actually more bioavailable than dissolved Fe(III) to specific cellular uptake systems, redox cycling in oxic waters is always expected to have the net effect of maintaining Fe in more labile phases  (Croot et al. , 2001; Emmenegger et al. , 2001)  due to the enhanced solubility of Fe(II) relative to Fe(III). The impracticality of creating stable transient-species reference materials, means that uncertainties remain about how comparable and how accurate existing Fe(II) data are. This is especially the case at nanomolar Fe(II) concentrations (which can occur, for example, in coastal waters, estuaries and OMZs) where multiple methods are available, each with different potential interferences and analytical constraints.
The dissolved O 2 concentration was only known after the completion of each method comparison experiment. For each spiked method comparison experiment (1-8, Table  1 ) the calculated Fe(II) oxidation rate constant (assumed to be first order with respect to [Fe(II)]) was derived from  Millero et al. (1987)  using measured salinity, temperature, dissolved O 2 , pH (Table  2 ) and the corresponding values for K w and I. The expected Fe(II) oxidation rate was also estimated from  Roy and Wells (2011)  using the two surface (5 m depth) oxidation rate constants reported (at 25 • C and pH 8. 0) from Ocean Station Papa (North Pacific). These apparent rate constants were adjusted for dissolved O 2 as per equations 1 and 2 (originally assumed to be saturated, now presented for measured dissolved O 2 concentrations, Table  2 ); but not for the discrepancies in salinity, temperature or pH as these were small and the relationships with k not derived in the original dataset.
(2016). Conversion of pH from carbonate parameters determined at 25 • C, to pH free values at the experimental temperatures was done using CO2SYS in MATLAB  (van Heuven et al. , 2011). A humic/fulvic organic stock solution was prepared by adding 50 mg Suwannee River humic acid standard I and 50 mg Suwannee River fulvic acid standard II (International Humic Substances Society, IHSS, used as received) to 20 mL de-ionized water (Milli-Q,18. 2 M •cm) and then allowed to stand for 2 days at 6 • C in the dark without filtration. Co(II) and V(IV) interferences, with luminol A and B, were tested by spiking Co(II) and V(IV) into the seawater matrix used for method comparison experiments 7 and 8 (Table  1 ) after Fe(II) concentration fell below the detection limit of both methods. Calculated spikes of 0.
CONCLUSIONS The almost universal adaptation of a luminol based method for measuring Fe(II) in seawater, largely because of its sensitivity and the possibility to achieve low picomolar detection limits, means that it should be logistically feasible to standardize a precise definition of "Fe(II)". Thus, even if Fe(II) was measured with unknown accuracy and not all interfering species or method artifacts were identified, Fe(II) data in specified environments would at least be internally consistent and precision could be improved. Such standardization has however yet to occur as at present there are methodological differences reported in sample handling, preservation and analysis techniques. Fortunately, whilst some uncertainties remain with respect to interfering species, it seems that different luminol FIA techniques produce similar Fe(II) concentrations and apparent oxidation rates in a simple seawater matrix.
ACKNOWLEDGMENTS  D. Polyviou  ( University of Southampton ) is thanked for H 2 O 2 measurements,  E. Skjoldal  ( University of Bergen ) for O 2 measurements,  E. Reggiani  ( NIVA ) for carbonate measurements and  J. Gallego-Urrea  ( University of Gothenburg ) for assistance with CO2SYS. M. Rijkenberg  ( NIOZ ) is thanked for recommendations on calibration methods for Fe(II) FIA. Labview software for operating the luminol A FIA system was designed by  P. Croot ,  M. Heller ,  C. Neill , and  W. King. Linear regression (and standard errors) were calculated in SigmaPlot.
FUNDING Analytical work in Crete was funded by the  European Commission (OCEAN-CERTAIN ,  FP7-ENV-2013-6. 1-1 ;no:  603773 ). MH, MG, and EA gratefully acknowledge funding from the  Collaborative Research Centre 754  ( SFB 754 ) " Climate-Biogeochemistry Interactions in the Tropical Ocean " funded by the  German Research Foundation (DFG).
Data collection Data are collected on a computerized database (FileMaker Pro). Patient's characteristics such as age at surgery, Charnley classification  [9] , aetiology, BMI and status (normal, overweight, obese, morbid obesity) functional evaluation, range of motion, surgical details, implant characteristics, pre, post op X-ray analysis, complications and functional outcomes are recorded. Among the 1030 New Wave TKA TM implanted from 2002 to 2015 by the single senior surgeon, we have identified 100 cemented New Wave TKA TM implanted from 2003 to 2005 and 100 cementless New Wave TKA TM implanted from 2004 to 2006. All these cases were primary replacement. We have excluded TKA after high tibia osteotomy (HTO), revision cases (uni or total knee revisions) or in patients born before 1921 (Figure  4 ).
A shell model based estimate for βγ and βp ratios requires the knowledge of B(M1) and B(E2) strengths from resonance states to valence and core excited states, and of spectroscopic factors from resonance states to 95 Pd daughter states. To determine these, simplified shell model calculations have been performed in the gds model space, these allow one-particle one-hole excitations (i. e. t π = t ν = 1 truncation), including simultaneous proton and neutron excitations, across the N=Z=50 closed shells. In the case of the hole-hole (h-h) configurations, which are dominated by the g 9/2 valence space, the interaction was taken from f pg calculations  [4, 5]. Whilst for the cross shell particlehole (p-h) configurations, two body matrix elements from the SNA interaction in the OXBASH program package were employed, which are taken from the H7B potential  [29].
Study design and patients UNCOVER-1 (NCT01474512) and UNCOVER-2 (NCT01597245) are multicentre, phase 3, randomized, double-blind, placebocontrolled trials. 12, 13 Patients ≥18 years with chronic plaque psoriasis ≥6 months prior to randomization were eligible. 12, 13 At screening and baseline, eligible patients had a static Physician's Global Assessment (sPGA) score ≥3; Psoriasis Area and Severity Index (PASI) ≥12; and ≥10% affected body surface area. Trials were compliant with applicable guidelines. Ethical review boards approved protocols and informed consent forms. Signed informed consent was obtained prior to study-related procedures.
14 The physician-reported sPGA rated lesions as follows: 0 = clear, 1 = minimal, 2 = mild, 3 = moderate; 4 = severe, 5 = very severe. The Itch NRS is a patient-reported outcome whereby patients rate their itch on a numeric scale of 0 (no itch) to 10 (worst itch imaginable) in a 24-h period. 15 mong continuously treated patients and withdrawn patients, response rates were summarized through the maintenance period (weeks 12-60). In addition, mean PASI and Itch NRS were calculated for these groups for the induction period and maintenance periods (weeks 0-60). Patients in the continuously treated group who experienced disease worsening continued to receive IXEQ4W (open label) per protocol, and, therefore, data for these patients were analysed in the continuously treated group through Week 60 irrespective of having prior sPGA ≥3.
Patients in the withdrawal group who experienced disease worsening at one visit received open-label IXEQ4W. These patients were considered non-responders at all subsequent visits; data for these patients were not analysed in the withdrawal group after the time of relapse. Among patients who were withdrawn and retreated, the time of sPGA ≥3 was considered retreatment Week 0. Response rates were summarized over time from retreatment Week 0 through retreatment Week 24. sPGA 0,1 response rates were calculated for all patients who experienced disease worsening (sPGA ≥3). PASI 75, PASI 90 and PASI 100 response rates were calculated for patients who had sPGA ≥3 and did not have a PASI 75 at the time of sPGA ≥3; the baseline PASI was used in the calculations. Mean PASI and Itch NRS were calculated for 24 weeks of retreatment.
Although the maintenance period ended at Week 60, additional data from the long-term extension period were used up to and including Week 84. This allowed analysis of 24 weeks of retreatment for some additional patients who had an sPGA ≥3 after Week 36. At the time of this database lock, all patients completed Week 60. Not all retreated patients had the opportunity to receive 24 weeks of retreatment. Adverse events (AEs) were classified based on the Medical Dictionary for Regulatory Activities version 18. 0. A treatmentemergent AE (TEAE) was defined as an event that first occurred or worsened in severity in the maintenance period; all events starting or worsening in the relapse period were counted in the retreatment period and were not included in the maintenance period. Exposure-adjusted incidence rates (IRs) for TEAEs report the number of unique patients with a specific event per 100 patient-years of exposure; this calculation uses the sum of all patients' time (in 100 years) of exposure during the treatment period.
Statistical analyses All patients were analysed per assigned treatment in UNCOVER-1 and UNCOVER-2, including the re-assignment of IXEQ4W to relapsed patients. Categorical efficacy measures were compared using Cochran-Mantel-Haenszel (CMH) tests stratified by study. Categorical variables were analysed using observed data as well as using non-responder imputation (NRI), which imputes missing values as non-responders for both the In UNCOVER-1 and UNCOVER-2, the 12-week induction period was followed by a 48-week randomized withdrawal (maintenance) period (weeks 12-60), whereby ixekizumab responders at Week 12 (sPGA = 0 or 1) were rerandomized to placebo (withdrawal group), IXEQ4W (continuously treated groups) or ixekizumab 80 mg every 12 weeks (IXEQ12W; not included in analyses). Patients in the withdrawal group who experienced disease worsening (sPGA ≥3 [relapse]) during the maintenance period received open-label IXEQ4W (retreatment group).
Although the maintenance period ended at Week 60, additional data from the long-term extension period were used up to and including Week 84 to allow for 24 weeks of retreatment. At the time of this database lock, all patients completed Week 60. Not all retreated patients had the opportunity to receive 24 weeks of retreatment. ETN, etanercept; IXE Q2W, ixekizumab 80 mg every 2 weeks; IXE Q4W, ixekizumab 80 mg every 4 weeks; IXEQ12W, ixekizumab 80 mg every continuously treated and interrupted patients, for Week 60 comparisons. Continuous PASI measures were analysed using an analysis of covariance model on observed values with main effects for treatment and study, and baseline as a covariate. The median time to relapse is the simple arithmetic median time of relapse for each patient from the start of maintenance to first relapse.
5%) of patients who withdrew from the recommended dose (IXEQ2W) and restarted treatment upon relapse (sPGA ≥3). Neutralizing antibodies were detected in two patients during retreatment in this group and in no continuously treated patients. There were no meaningful differences in % PASI improvement upon retreatment in patients with or without treatment-emergent ADA (Fig. 5 ). 66. 5% and 70. 7% (observed) of patients recaptured an sPGA 0,1 at 12 and 24 weeks of retreatment, respectively. It is important to highlight that the data presented here are only partially represented in the analysis of retreatment efficacy within the US Prescribing Instructions. 17 In the latter analysis, the recapture rate was assessed based on the proportion of patients who recaptured an sPGA 0,1 at any time point within 12 weeks after receiving at least one dose of retreatment, and included data to Week 60.
Figure1Study Diagram. The study diagram is shown. In UNCOVER-1 and UNCOVER-2, the 12-week induction period was followed by a 48-week randomized withdrawal (maintenance) period (weeks 12-60), whereby ixekizumab responders at Week 12 (sPGA = 0 or 1) were rerandomized to placebo (withdrawal group), IXEQ4W (continuously treated groups) or ixekizumab 80 mg every 12 weeks (IXEQ12W; not included in analyses). Patients in the withdrawal group who experienced disease worsening (sPGA ≥3 [relapse]) during the maintenance period received open-label IXEQ4W (retreatment group). Although the maintenance period ended at Week 60, additional data from the long-term extension period were used up to and including Week 84 to allow for 24 weeks of retreatment. At the time of this database lock, all patients completed Week 60.
He  is a consultant for/received clinical research grants from  Baxalta. He received honoraria from  Wyeth. He serves as a scientific officer for AbbVie and Anacor. He is on the steering committee of  AbbVie, Celgene ,  Eli Lilly and Company ,  Forward Pharma ,  Janssen ,  Kyowa Hakko Kirin ,  Merck Sharp and Dohme ,  Novartis ,  Pfizer ,  Regeneron , and  Merck-Serono. Eli Lilly and Company. He is an investigator and consultant for  Novartis ,  Janssen ,  Celgene ,  Pfizer ,  Amgen ,  Sun Pharma ,  Merck ,  AbbVie  and  Boehringer Ingelheim. M. Augustin  scientific advisory board of  Eli Lilly and Company, Celgene, TREVI, Sanofi-Aventis, Creabilis, Chugai, Opko, TREVI, Creabilis ,  Pfizer. He serves as a clinical investigator for Allergan and  Genentech  and is funded by  GSK/Stiefel and LEO Foundation.
ACKNOWLEDGMENTS We would like to thank all the families agreeing to participate to research works and the members of national networks on CMS for fruitful discussions. This work is supported by  INSERM ,  CNRS ,  Sorbonne universités UPMC-Paris 06 ,  Association Francaise contre les Myopathies (AFM) -Téléthon  (research grants  AFM16573  and  AFM20030 ), and  Investissements d'avenir  ( ANR-10-IAIHU-06 ) to SN, and the  Medical Research Council  as part of the  MRC Centre for Neuromuscular Diseases  (reference  G1002274 , grant  ID 98482 ), the  Wellcome Trust   Pathfinder Award , and by the  European Union Seventh Framework Programme  ( FP7/2007-2013)  under grant agreement no. 305444  ( RD-Connect ) and no. 2012-305121  (NeurOmics) to HL.
Epidemiology and clinical presentation Analyses from different registry data estimate the general increase in cancer incidence in kidney transplant recipients to be two-to three-fold compared with the general population  [3, 4, [16] [17] [18] [19] [20] [21] [22] [23] [24] [25]. Estimates of cancer incidence obtained from different registries differ widely, suggesting that data quality is problematic. This was confirmed in a recent study by Yanik et al. [26] , who compared cancer diagnoses collected in the Scientific Registry of Transplant Recipients (SRTR) database with 15 linked cancer registries for colorectal, liver, lung, breast, prostate and kidney cancers, melanoma and non-Hodgkin lymphoma (NHL). They concluded that SRTR cancer data were strikingly incomplete, as only 36. 8% of cancers were both registered in the SRTR database and cancer registries, whereas 47.
5% of cancers were only documented in cancer registries and 15. 7% were only documented in the SRTR database  [26]. The estimated sensitivity for identifying cancer was only 52. 5% for the SRTR and 84. 3% for cancer registries  [26]. Data from the USA concerning 175 732 solid organ transplant recipients (58. 4% kidney transplant recipients) during the period 1987-2008 showed that the standardized incidence ratio (SIR) for cancer overall was 2. 1 (95% CI 2. 06-2. 14) higher compared with the general population, with an excess absolute risk of 719 cancer cases per 100 000 person-years  [3]. The majority of the patients included in these studies were kidney transplant recipients  [27]. It is important to note that this increase is not uniform for all cancer types; some cancers are not increased following kidney transplantation, e.
In an analysis of the Collaborative Transplant Study (CTS) database, the incidence and impact of malignant lymphoma after solid organ transplantation in 195 938 solid organ transplant recipients (145 104 cadaveric kidney transplant recipients) between 1985 and 2001 were studied  [30]. Over the 10year observation period, the risk for malignant lymphoma in renal transplant recipients was 11. 8-fold higher compared with a matched non-transplanted population, and most lymphomas occurred in the first post-transplant year  [30]. Recent data suggest that from 2005 to 2010, the 5-year incidence of posttransplant lymphoproliferative disease (PTLD) in adult kidney transplant recipients has remained stable  [31]. There was, however, a substantial decline in PTLD rates for paediatric recipients reported in patients transplanted from 2002 to 2012 compared with those transplanted from 2000 to 2009  [31].