dataesr/scientific-paragraphs-categorization huggingface dataset:
Observational studies in prostate cancer (PCa) have shown an increased risk of cardiovascular disease (CVD) following gonadotropin-releasing hormone (GnRH) agonists, whereas randomised-controlled trials have shown no associations. Compared to GnRH agonists, GnRH antagonists have shown less atherosclerotic effects in preclinical models. We used real-world data from five countries to investigate CVD risk following GnRH agonists and antagonists in PCa men. Data sources included cancer registries, primary and secondary healthcare databases. CVD event was defined as an incident or fatal CVD. Multivariable Cox proportional hazard models estimated hazard ratios (HRs) and 95% confidence intervals (CIs), which were pooled using randomeffects meta-analysis. Stratified analyses were conducted by history of CVD and age (75 years).
The difference in mechanism of action leads to an immediate mode of action in GnRH antagonists associated with its reduced side effects. 12 ase II and phase III studies showed no difference in terms of efficacy and baseline testosterone levels in men receiving GnRH antagonists for 1 year compared to men receiving various GnRH agonists for their PCa. 13 Comparison of the CVD safety profile in men on GnRH agonists and antagonists has yielded inconclusive results. 8, 14 though meta-analysis of observational studies  8  have shown a lower risk of CVD in men on GnRH antagonists compared to GnRH agonists, meta-analyses of randomised-controlled trials (RCTs) have shown no such associations. 14 Moreover, these studies were not designed with CVD as a primary outcome. A phase III RCT (PRONOUNCE; ClinicalTrials.
gov identifier: NCT02663908) is recruiting to compare the risk of fatal or nonfatal CVD in 900 men with PCa receiving GnRH agonist or antagonist as primary treatment. 15 Results of the PRONOUCE trial is not expected until 2021, which justifies the need for observational evidence in the interim. Since RCTs often exclude elderly participants and those with comorbidities, two most common characteristics of men with PCa receiving ADT,  16  real-world data used in observational studies may provide evidence applicable to the general PCa population. 17, 18 Only one observational study has been conducted to date directly comparing risk of CVD between GnRH agonists and GnRH antagonists. Scailteux et al showed no difference in risk of developing stroke or myocardial infarction in men with PCa receiving either treatments; however, overall CVD was not investigated as a specific outcome in the study.
| Study design and population This observational study combined data from five countries to investigate the association between GnRH agonists or antagonist (degarelix) and risk of CVD in men with PCa. All men with PCa prescribed or dispensed GnRH agonists or GnRH antagonists were included in the study. A detailed study protocol aimed to minimise heterogeneity in terms of data collection across countries by outlining exact codes for extraction of all study variables is published elsewhere. 20 ta from the United Kingdom's The Health Improvement Network (THIN) database (excluding Scotland)  21,22 (2010-2016) , National Health Service Scotland (NHSS)  23  (2010-2017), the Belgian Cancer Registry (BCR)  24 (2010-2015) , the PHARMO Database Network from the Netherlands  25   primary healthcare database (ie, general practices and community healthcare settings) and NHSS is a secondary healthcare database (ie, hospitals and outpatient clinics), BCR and PHARMO include both primary and secondary healthcare data.
[21] [22] [23] [24] [25] [26] [27]  The SNIIRAM database is a claims database combining claims from insurance plans with the National Hospital-discharge Summaries database system (PMSI). 26  As Scotland is in the United Kingdom and there may have been some overlap of men with PCa in the THIN database and Scottish NHSS database, men with PCa with a postcode in Scotland were excluded from THIN. The study period extended from 2010 to 2017 in the five countries.
f Scotland, the Netherlands and France were excluded due to low number of events for country-specific analysis. g  The United Kingdom, Scotland and the Netherlands were excluded due to low number of events for country-specific analysis. age was 73 or 74 years. Moreover, men above 75 years may be more likely to present with very advanced disease and this difference was accounted for by the analysis. 29 further sensitivity analysis was also conducted to exclude men who may have been on GnRH agonists or GnRH antagonists for less than 3 months to eliminate short-term neoadjuvant or adjuvant use. F I G U R E 1 A, All country-specific analyses were conducted using different SAS versions in Belgium (9. 4) and France (9. 4) and statistics and data (STATA) versions in the United Kingdom (14C), Scotland (  14 ) and the Netherlands (14C).
35 However, results from our real-world data showed contradictory effects, which may be partly explained due to confounding by indication. In our methodological protocol, we used the Risk of Bias in Non-Randomised Studies of Interventions (ROBINS-I) tool to assess our study design, which emphasised three main forms of biases: misclassification of study variables, channelling or indication and unmeasured confounding. 20 We avoided misclassification bias, where possible, by following a standard protocol  20  to extract study variables from the five countries involved. Channelling or indication bias  36  occurs when a physician prescribes GnRH antagonists to men with a history of CVD based on previous evidence. 37 We were unable to avoid channelling bias in our study because data on physician preferences were not available.
In addition to no information on physician preference, heterogeneity in guidelines for prescription of GnRH antagonists in the five countries may have also affected the study results (discussed in George et al  20  ). In this large prospective cohort study, we found it difficult to fully homogenise study variables (Supplement Table  4 ). A large proportion of this complexity was attributed to the varied data sources used in our study. THIN from the United Kingdom was the most distinct database used in the study, due to its data derivation exclusively from primary healthcare settings. 21 As degarelix was a fairly new drug on the market  38  and a much smaller proportion of men were degarelix users in individual countries, there was a need to combine data from all countries in our analysis. We attempted to account for this heterogeneity in the data sources by not only setting up a standard protocol  20  but also by focusing analyses on data that was fully available in all five countries (ie, history of CVD indicator).
Moreover, further sensitivity analyses excluding the United Kingdom (Supplement Table  2 ) and a delayed start date of 3 months after GnRH initiation date (Supplement   39  Results for some subtypes of CVD were limited due to the data sources that they were obtained from. For example, the acute nature of AMI means that it is usually recorded in an acute hospital setting. 40 The United Kingdom had no AMI events for analysis because the THIN database originates from a primary healthcare setting. Therefore, further assessment of hospital registries is needed to understand the risk of developing AMI in men with PCa on GnRH analogues. A key strength of our study was the use of data from five countries that made study results applicable to the general PCa population. Moreover, the use of different types of databases (primary healthcare, secondary healthcare, cancer registries and claims databases) also ensured the inclusion of rare, adverse events that may not have been identified in an RCT.
However, our study also highlighted the challenges involved of using real-world data. Although the potential for real-world data is large in the healthcare setting, differences in data sources need supervised reconfiguration of data for real-world data to achieve its full potential. The way forward for researchers using realworld data is to combine and analyse "big data" from databases from various institutions into a single platform through projects such as the GetReal Initiative 41 and Prostate cancer dIagnOsis and treatmeNt Enhancement through the power of big data in EuRope (PIONEER),  42  which are part of the Big Data for Better Outcomes (BD4BO). 43.
ACKNOWLEDGEMENTS This work was supported by an independent research grant from  Ferring Pharmaceuticals. The authors would also like to thank all the healthcare providers and patients contributing information to the THIN database,  NHS Scotland ,  Belgian Cancer Registry , the PHARMO Database Network and SNIIRAM. The authors would also like to thank  Dr Christopher Cardwell  and  Dr Úna McMenamin  from  Queen's University Belfast, Belfast , for providing data and support with the project. This work was supported by an independent research grant from  Ferring Pharmaceuticals. The funder had no influence on the study design, analyses or interpretation of the results.
Baseline characteristics for men with prostate cancer from the five included databases in the United Kingdom (excluding Scotland), Scotland, Belgium, the Netherlands and France Men on GnRH antagonists N (%) 912 (3. 9) 2. 1 0. 8 2. 6 73. 4 9. 8 191 (20. 9) 278 (30. 5) 318 (34. 9) 125 (13. 7) 625 (68. 5) 287 (31. 5) France Men on GnRH agonists N (%) 2010 to 2013 19 641 (83. 9) 2. 2 0. 7 2. 7 74. 2 8. 6 3016 (15. 4) 6358 (32. 4) 8124 (41. 4) 2143 (10. 9) 14 011 (71. 3) 5630 (28. 7) Men on GnRH antagonists N (%) 97 (7. 6) 1. 5 1. 2 2. 2 72. 5 What's new? Prolonged use of gonadotropin-releasing hormone (GnRH) agonists has been associated with an increased risk of car-9. 6 24 (24. 7) 33 (34. 0) 26 (26. 8) 14 (14. 4) 64 (66. 0) 33 (34. 0) Netherlands Men on GnRH agonists N (%) 2010 to 2015 1187 (92.
). In Belgium, ICD-9 equivalent codes were used in cases where no ICD-10 classification was available. In the United Kingdom, the THIN database made use of already published readcodes instead of ICD codes (used in NHSS, BCR, PHARMO and SNIIRAM). 28 2. 4 | Other study variables In addition to age, we obtained information on the following covariates to better understand data heterogeneity across countries: follow-up time, year of PCa diagnosis, stage of PCa, total Gleason score, prostate-specific antigen (PSA), any prior PCa treatment, type of ADT, ADT specifics, history of CVD indicator and number of previ- ous CVD events (Table 1 and Supplement Table 1). Additional infor- mation on socio-demographic variables, such as socio-economic status (SES), civil status and ethnicity and lifestyle factors, including body mass index (BMI) and smoking, were available in THIN.
In free B-DNA, slow (microsecond-to-millisecond) motions that involve equilibrium between Watson-Crick (WC) and Hoogsteen (HG) base-pairing expand the DNA dynamic repertoire that could mediate DNA-protein assemblies. R 1ρ relaxation dispersion NMR methods are powerful tools to capture such slow conformational exchanges in solution using  13  C/ 15 N labelled DNA. Here, these approaches were applied to a dodecamer containing a TTAAA element that was assumed to facilitate nucleosome formation. NMR data and inferred exchange parameters assign HG base pairs as the minor, transient conformers specifically observed in three successive A•T base pairs forming the TAA•TTA segment. The abundance of these HG A•T base pairs can be up to 1. 2% which is high compared to what has previously been observed.
Off-resonance R 1ρ dispersion relaxation experiments: characteristics of slow conformational exchange. Off-resonance dispersion relaxation experiments performed at a single magnetic field were used to obtain thermodynamic, kinetic and structural information about conformational exchange. This type of experiment was applied to the C1′ resonances of A 7 , A 8 , A 9 and A 19 ; the four adenines for which signs of exchange arise from on-resonance dispersion relaxation experiments. Additional investigations focused on T 6 , C 4 , G 15 and G 21 , nucleotides associated to very low or null R ex (Supplementary Table  S2 ). C1′ resonances were chosen because they showed large signal to noise ratios and were subject to only a few overlaps so that data could be collected for most nucleotides of the dodecamer (Supplementary Tables  S2, S3 ).
S5 ) or compatible (on-resonance experiments, Fig. 5 ) with a conformational exchange. This unexpected situation has already been encountered  46  and may correspond to a scenario in which the typical limits of slow exchange are no longer valid; in particular, A 9 exchange rate could be faster than those of A 7 , A 8 or A 19. The results above were obtained with NMR datasets from only C1′ sugar resonances. Because of numerous resonance overlaps, off-resonance data could only be collected for C8 and C2 atoms of A 19 , which allowed the constitution of a new extended dataset, involving sugar and base atoms. The fits of this dataset were performed assuming that C1′, C8 and C2 atoms are subjected to the same conformational exchange, with one unique value for k ex and one unique value for p E. The resulting values of k ex , p E and Δω are remarkably coherent with those obtained from C1′ resonances alone (Table  3 ), confirming in particular that the population of A 19 minor conformer reaches 1.
2/1. 1%. From a methodological point of view, this approach shows that an off-resonance dataset from only one atom type is sufficient to produce reliable exchange parameters, with the obvious condition that the atom under consideration is implicated in the conformational transition of interest. The similarities between the two-state exchange characteristics of A 7 , A 8 and A 19 (Table  2 ) could be compatible with simultaneous transitions of the three nucleotides between ground and excited states, implying the transient co-existence of three successive base pair minor conformers. Thus, we completed our analyses by testing this hypothesis of synchronous motions. A collective transition implies that k ex and p E are identical for the three nucleotides and these conditions were therefore integrated as restraints in the fit calculations.
Such shifts are usually interpreted as being due to the presence of more BI conformers  47, 48. Examining high-resolution X-ray structures of free DNA suggests that another backbone alteration could also occur on α/γ angles, as briefly previously mentioned  27. Indeed, structures containing exclusively HG pairings (PDB codes 1GQU and 1RSB, of 2. 5 and 2. 2 Å of resolution, respectively) enclose TpA junctions adopting unusual α/γ:g+ /g-angles, instead of the canonical g-/g+ configuration. Unusual α/γ angles are infrequent in WC B-DNA X-ray structures  49, 50  because they are energetically very costly to generate  50. Nevertheless, as rare as they are, these atypical α/γ conformations are mainly encountered in ApA steps or A•T-rich contexts (Supplementary Table  S4 ). So, the potential ability of A•T-rich patches to adopt unusual α/γ conformations could be a factor promoting the emergence of HG base pairs.
This protocol implemented using homebased Python and R scripts enables to derive the errors associated to each exchange parameter according to a strategy described previously  67. Method 2 used the "Bloch-McConnell Numerical Simulator" (BMNS) developed by Al-Hashimi's group 3 , which is exclusively based on a two-state exchange model. The calculations were performed with the same constraints on the R 2 relaxation rates than for the fits described above. (2) R 1ρ = R 1 cos 2 θ + R 2 sin 2 θ + sin  2.
The mutation will be produced later on, in the sequencing step, affecting only some reads and not all of them, thus creating discrepancies in the same UMI group (Figure  1B ). With the growing number of variant calling tools, it has become hard to choose the right tool adapted to a certain experiment. Data simulation can play an important role for testing different tools on a dataset that we have control on, a control that we don't have on real biological data. Many short reads simulators exist at the moment. These tools are publicly available for researchers and allow them to test their algorithms on a simulated dataset, a dataset in which variants are inserted at different frequencies and at different positions. The usage of the reads simulators enable having a very accurate benchmarking of each variant calling tool ability.
Software input UMI-Gen requires a minimum of three parameters at execution: a list of control BAM/SAM samples, the BED file with the coordinates of the targeted genomic regions and a reference genome FASTA file with BWA index files. UMI-Gen can also accept a fourth optional file under the PILEUP format. In fact, when running UMI-Gen on control samples, a PILEUP file is automatically produced. This file contains the A, C, G and T average counts at each position for all the control samples. This file can be given to UMI-Gen at execution time and will allow the software to reload the pileup generated during the last analysis instead of regenerating it. This will allow the user to gain some significant time since the pileup generation is the most time-consuming step.
Software output Once all are inserted, UMI-Gen will generate the two FASTQ files (R1 and R2). It will then call BWA  (Li and Durbin (2009) ) to do the alignment, a step that will produce a BAM file. SAMtools  (Li et al. (2009) ) is finally called to create the BAM's index file and convert the BAM into SAM. All five files are generated in the desired output directory. In addition, UMI-Gen generates a binary PILEUP file that corresponds to the average pileup dumped. This file can be used to skip the pileup regeneration and load the pileup directly if the analysis was already done on the same control samples.
Implementation Launching UMI-Gen's workflow (Figure  4 ) is handled by a main Python script that controls many Python3 modules. In order to achieve better overall performance, Cython was used to compile all Python modules. UMI-Gen requires for the tools BWA and SAMtools to be installed on the PC/server: BWA is called for the alignment step and SAMtools for converting, sorting and indexing the generated BAM files. Our tool can be executed through a UNIX/Linux command line interface. In total, UMI-Gen can accept 20 parameters at execution. Managing these parameters allows the user to have full control over his simulated data. A list of all the parameters and thresholds is available in Table  S2.
Our main objective was to develop a UMI-tagged reads simulator that is fast, accurate and reliable. UMI-Gen is able to estimate the background error noise of a given control dataset and then reproduce it accurately in the produced reads. Doing so, it allows to mimic the sequencer's background noise of a real sequencing experiment. We also showed that our simulator is able to accurately insert variants if provided with a list of variants with exact locations and their corresponding frequencies. In our tests, we were able to insert mutations as low as 0. 1% but theoretically, we can go as low as we want provided that the depth of the produced sample is accordingly increased. Moreover, in our variant callers comparison, SiNVICT did a somewhat good job detecting the 8 of the added variants and went as low as 5%.
The recordings were made with a sampling rate of 25 kHz using the MC Rack software (Multi Channel Systems MCS GmbH). The analysis of recordings from 256-MEAs was performed using Python 3. 6. Recordings were bandpassfiltered (400-5000 Hz, Butterworth 2nd order), and spikes were detected as threshold crossing of 5 times the standard deviation of the filtered signal with a pause time of 1. 5 ms. Light-induced RGCs activation recorded by an electrode was quantified for each light intensity using a two-tailed, paired t-test comparing the detected spikes during the 20 repetitions of light-ON (350 ms of light flash) or light-OFF (350 ms after the light shut-off) versus the spontaneous activity recorded before light onset. Only electrodes with a statistically significant difference (p < 0. 01 and t-statistic > 2) were considered light-activated.
Under photopic conditions, the interval between light flashes was fixed at 1 s. ERG signals were amplified and band-filtered between 0. 3 and 1000 Hz with an amplifier (CP511 AC amplifier). Electrical signals were digitized at 20 kHz with a power laboratory data acquisition board (AD Instruments). Under dark adaptation, scotopic threshold responses (STR) were recorded to light flashes of -4 log cd•s•m -2 ; rod responses were recorded to light flashes of -2 cd•s•m -2 , and mixed responses were recorded in response to light flashes of 1. 5 log cd•s•m -2. Oscillatory potential (OP) was isolated using white flashes of 1. 5 log cd•s•m -2 in a recording frequency range of 100-10,000 Hz. Under light adaptation, cone-mediated responses to light flashes of 2 log cd•s•m -2 on a rod-saturating background of 30 cd•m -2 were recorded.
Whole transcriptome sequencing and data analysis 2000-5000 frozen sorted-cells were lysed in ~10 µl of lysis buffer, and cDNA synthesis was performed using the SMART-Seq v4 Ultra Low Input RNA Kit (catalog 634888, Takara Bio). First-strand cDNA synthesis was performed using 20-50% of the input, and was followed by full-length double-strand cDNA amplification using 17 PCR cycles. The quality of the resulting cDNA was validated using Bioanalyzer and High Sensitivity DNA Kit (Agilent), and Qubit dsDNA HS fluorometric quantification (Thermo-Fisher Scientific). NGS libraries were prepared using 150 pg of cDNA input and the Nextera XT DNA Library Preparation Kit (catalog FC-131-1024, Illumina) with 11 cycles of PCR. Libraries were sequenced as single reads (75 bp read length) on a NextSeq500 (Illumina) with a depth of >20 million reads.
Library preparation and sequencing procedures were performed by the same individual, and a design to minimize technical batch effects. Data quality of raw RNA-seq reads in FASTQ files was assessed using ReadQC (ngs-bits version 2018_04) to identify potential sequencing cycles with low average quality and base distribution bias. Reads were preprocessed with Skewer (version 0. 2. 2) and aligned using STAR (version 2. 5. 4a), allowing spliced read alignment to the mouse reference genome (Ensembl Mus musculus GRCm38). Alignment quality was analyzed using MappingQC (ngs-bits version 2018_04) and visually inspected with Broad Integrative Genome Viewer (version 2. 4. 0). Based on the genome annotation ITAG (Ensembl 75), normalized read counts for all genes were obtained using subread (version 1. 6.
0) and edgeR (version 3. 28. 0). Raw counts data were processed using iDEP, an integrated web application for RNA-seq data analysis  [28]. To provide access to RNAseq data, we generated a BioJupies notebook  [29]  link providing an interactive and visual analysis of all the data (https://amp. pharm. mssm. edu/biojupies/notebook/LjknTl51J). Sequencing data are deposited on GEO with the accession number GSE141601. For differential gene expression (DEG) analysis, gene counts were filtered to only retain genes with a value > 1 cpm (count per million), in at least two samples within at least one group (control or treated), leaving around 14,400 genes for determination of differential expression in each of the pair-wise comparisons between experimental groups. Differentially expressed genes between treated and control groups were identified using the two-tailed permutation FDR-based Student's t test (FDR < 0.
05 and 250 randomizations). Only transcripts coding for protein sequences were retained for pathway analysis. Quantitative gene expression data was submitted and integrated into the PaintOmics 3 data analysis platform  [30, 31] , in order to identify trends in pathway modulation following the TSA treatment. A stable link is provided to access and visualize the results (http://www. paintomics. org/?jobID=4CIbSr1eA4).