dataesr/scientific-paragraphs-categorization huggingface dataset:
2012)  with up to 2% mismatches) against a contamination database that included phiX, Univec sequences, E. coli and various contaminants detected with Kraken  (Wood and Salzberg 2014)  in more than 0. 01% of the reads (see Table  3 ). Note that our read decontamination method is stringent enough to remove real contaminants (almost exact matches with # 2% mismatches) such as phiX but does not detect similar but divergent sequences (such as different bacterial strains); these are detected in the final assembly by using BLAST or during the genome annotation. The filtered Illumina PE were trimmed to 150bp using FASTX toolkit v. 0. 0. 13 (http://hannonlab. cshl. edu/fastx_toolkit/). This was done to optimize the de Bruijn graph construction. The trimming reduced the sequencing coverage to 144. 42x (close to 120xthe ideal for a heterozygous genome) and increased the mean base quality of the reads (last cycles produce lower base qualities).
The blunt-ended double-stranded cDNA was 39adenylated and Illumina platform-compatible adaptors with unique dual indexes and unique molecular identifiers (Integrated DNA Technologies) were ligated. The ligation product was enriched with 15 PCR cycles and the final library was validated on an Agilent 2100 Bioanalyzer with the DNA 7500 assay. The libraries were sequenced on HiSeq 4000 (Illumina, Inc) with a read length of 2x76bp using HiSeq 4000 SBS kit in a fraction of a HiSeq 4000 PE Cluster kit sequencing flow cell lane generating a mean of 80 million paired end reads per sample. Image analysis, base calling and quality scoring of the run were processed using the manufacturer's software Real Time Analysis (RTA 2. 7. 7).
Genome annotation First, repeats present in the pcla4 genome assembly were annotated with RepeatMasker v4-0-6 (http://repeatmasker. org) using the repeat library specific for our assembly that was built with RepeatModeler v1. 0. 11. Repeats that were part of repetitive protein families (detected by running a BLAST search of the repeat library against Swissprot) were removed from the library before masking the genome. A first annotation of protein-coding genes was obtained by combining RNA-seq alignments, protein alignments and ab initio gene predictions on the pcla4 genome assembly. A flowchart of the annotation process is shown in Figure  5. RNA-seq reads were aligned to the genome with STAR v-2. 6. 1b (Dobin et al. 2013 ) and transcript models were subsequently generated using Stringtie v1.
0. 4  (Pertea et al. 2015 ). PASA v2. 3. 3 (Haas et al. 2008)  was used to combine the Stringtie transcript models with 534 soft coral nucleotide sequences downloaded from NCBI in January 2019. The TransDecoder program, which is part of the PASA package, was run on the PASA assemblies to detect coding regions in the transcripts. Then, the complete Stylophora pistilata proteome was downloaded from Uniprot (January 2019) and aligned to the genome using SPALN v2. 3. 1  (Gotoh 2008). Ab initio gene predictions were performed on the repeat-masked pcla4 assembly with four different programs: GeneID v1. 4  (Parra et al. 2000) , Augustus v3. 2. 3  (Stanke et al. 2006) , GlimmerHMM  (Majoros et al. 2004 ) and Genemark-ES v2. 3e  (Lomsadze et al. 2014)  with and without incorporating evidence from the RNA-seq data.
All the gene predictors except Genemark, which runs in a self-trained manner, were run with the parameters obtained by training with a set of high-quality candidate genes extracted from the Transdecoder results. Finally, all the data were combined into consensus CDS models using EvidenceModeler-1. 1. 1  (Haas et al. 2008). Additionally, UTRs and alternative splicing forms were annotated through two rounds of PASA annotation updates. The resulting transcripts were clustered into genes using shared splice sites or significant sequence overlap as criteria for designation as the same gene. Functional annotation of the annotated proteins was done using Blast2go  (Conesa et al. 2005) , which in turn ran a BLASTP  (Altschul et al. 1997 ) search against the non-redundant database (March 2019) and Interproscan  (Jones et al.
2014 ) to detect protein domains on the annotated proteins. Finally, the annotation of non-coding RNAs (ncRNAs) was performed as follows. First, the program cmsearch (v1. 1)  (Cui et al. 2016)  which is part of the Infernal package (Nawrocki and Eddy 2013) was run against the RFAM  (Nawrocki et al. 2015)  database of RNA families (v12. 0). Also, tRNAscan-SE (v1. 23)  (Lowe and Eddy 1997)  was run in order to detect the transfer RNA genes present in the genome assembly. PASA-assemblies longer than 200bp that had not been annotated as protein-coding and not overlapped by more than 80% by a small ncRNA were incorporated into the ncRNA annotation as long-noncoding RNAs (lncRNAs). Via the functional annotation, we were able to detect the presence of bacterial and fungal genes, suggesting the presence of some residual contamination in the assembly (pcla4).
Therefore, we removed potential contaminant sequence by combining as criteria for retention the gene functional annotation, the mean GC content and the presence of expression and P. clavata-specific repeats for each scaffold. As a result of this decontamination process, 2,322 scaffolds were removed from the assembly as they belonged mainly to Aspergillus, Endozoicomonas or other bacteria (pcla5, Table  S3 ). Finally, after separating the mitochondrial genome and collapsing haplotypes with Purge Haplotigs, the annotation of the resulting assembly (pcla8) was updated by removing the genes present in the discarded scaffolds. The gene completeness of the pcla8 assembly (i. e. , free of contaminants, mitochondrion and redundant haplotigs) was estimated with BUSCO v3  (Simão et al. 2015)  using the metazoa database (metazoa_odb9) of 978 conserved genes.
Genome-wide heterozygosity (SNVs) Re-sequencing at sufficient depth (. 20x) allows the extraction of valuable genome-wide information from a single diploid sample by simply re-mapping against the reference genome and calling variants. Although adaptor removal and quality trimming are not recommended for MaSuRCA, they are strictly necessary before variant calling. Therefore, we detected and trimmed Illumina adaptor sequences and performed quality trimming of the PE400 library. For this purpose, we used the Trim Galore! wrapper script (http:// www. bioinformatics. babraham. ac. uk/projects/trim_galore/) with -q 10 and then we filtered out contaminated reads, as described above (Table  3 ). Finally, all these reads were mapped against a reference genome that included pcla8 and chrMT using BWA MEM with option -M.
For variant calling, we used GATK 3. (dbSNP). Specifically, we did not perform base quality score recalibration, as the model normally degrades the base qualities in the absence of known variant sites. First, the BWA alignment was screened for duplicates using MarkDuplicates of PICARD v1. 6 (https://broadinstitute. github. io/picard/). Then, we identified the callable sites per sample using the GATK's CallableLoci tool, with options-minBaseQuality 10-minMappingQuality 20, to be in concordance with the default value for these parameters in HaplotypeCaller (see below). The actual variant calling was performed using the HaplotypeCaller but restricting it to callable sites and with options: -dt NONE -rf BadCigar-never_trim_vcf_format_ field -ploidy 2-min_base_quality_score 10-standard_min_confidence_ threshold_for_calling 30-emitRefConfidence GVCF and-GVCFGQBands at Genotype  Qualities 15, 20, 25, 30, 35, 40, 45, 50, 55, 60, 65, 70 , 80 and 99.
Genome size and complexity We show the results of two GenomeScope analyses and compare them with the empirical data available for the phylum Cnidaria. GenomeScope fits the 21-mer and the 57-mer profiles to a diploid mixture model  respectively). This method estimates the haploid genome size to be between 610. 08 and 700. 17 Mb (Table  2 ). The analysis also suggests that the genome possess an appreciable amount of heterozygosity (0. 89-1. 59%) and high repetitiveness (42. 09-56. 42% of the genome is likely to be repeated) (Figures  2, 3  and S3b ). The genome size of cnidarians shows large variations, ranging from just 224. 92 Mb in Nematostella vectensis (T. R. Gregory, unpublished data) to the 3. 48 Gb estimated for Agalma elegans  (Adachi et al. 2017). However, the mean cnidarian genome size is 838.
ACKNOWLEDGMENTS We thank the  MERCES consortium , and particularly the members of the Work Package 3 (WP3-Restoration of coastal shallow hard bottoms and mesophotic habitats). We also would like to thank  Dr T. Bakran-Petricioli  ( University of Zagreb ) for her logistical support for the samples used in the thermotolerance experiment and  Donat Petricioli  and  Mirko Belosevic  for assistance in the field. We are grateful to the staff of  Experimental Aquarium Facilities at the Institute of Marine Sciences ;  E. Martinez  and  M. Delgado , for their technical support. We thank  Oxford Nanopore Technologies  for assistance with preparing and sequencing the libraries. We acknowledge the funding support of the  European Union 's  Horizon 2020 research and innovation program  under grant agreement No  689518  ( MERCES ) and the  Strategic Funding   UID/Multi/04423/2013  through national funds provided by  FCT -Foundation for Science and Technology and European Regional Development Fund (ERDF) , in the framework of the programme PT2020.
JBL is funded by an assistant researcher contract framework of the  RD Unit -UID/Multi/ 04423/2019 -Interdisciplinary Centre of Marine and Environmental Research - financed by the  European Regional Development Fund (ERDF)  through  COMPETE2020 -Operational Program for Competitiveness and Internationalisation (POCI)  and national funds through FCT/MCTES (PIDDAC). AA was partially supported by the  FCT  project  PTDC/CTA-AMB/31774/2017  ( POCI-01-0145-FEDER/031774/2017 ). DGG was supported by an  FPU grant  ( FPU15/05457 ) from the  Spanish Ministry of Education. We acknowledge support of the  Spanish Ministry of Science and Innovation  to the EMBL partnership, the  Centro de.
Material and Methods Study design and study population. The ALS registry Swabia has been described previously in detail  [15] [16] [17]. In brief, it is a population-based clinical-epidemiological registry with the aim to collect data on all newly diagnosed ALS cases in Swabia, a defined geographic region with approximately 8. 4 million inhabitants in the South-West of Germany. All reported ALS cases were defined by the diagnosis of possible, probable or definite ALS according to the revised El Escorial criteria by an experienced neurologist. Notifications of patients with suspected ALS were tracked and evaluated during the clinical course by the registry. Patients prospectively registered between October 01, 2010 and June 30, 2014 were asked to provide informed consent to participate in a population-based case-control study.
Laboratory measurements. According to a common standard protocol for cases and controls, blood samples were transported in cooled containers to the study center. Serum was obtained by centrifugation for 10 min at 2000 RPM × g and 4 °C (Heraeus Multifuge 3 S-R, Fa. Thermofischer). Blood specimens were transferred into 0. 5-1. 0 ml aliquots with screw tops on the same day and stored at -80° Celsius until further analysis. IGF-1 (ng/ mL) was measured in serum (human IGF-1 Quantikine, R&D Systems). The lower detection limit of IGF-1 in this assay was approximately 0. 026 ng/mL. The inter-assay coefficient of variation (CV) was 12. 5%. Further details can be found in the product data sheet of the manufacturer. All laboratory analyses were performed in blinded fashion at the Biomarker Laboratory of the Department of Internal Medicine II-Cardiology, Ulm University Medical Center.
Acknowledgements We thank the  Ilonka Kraft-Oberbeck ,  Ines Dobias  and  Nicola Lämmle  for their excellent field work,  Gerlinde Trischler  for expert technical assistance and  Gertrud Feike ,  Sarah Enderle , and  Birgit Och  for excellent data management and technical support. We thank the members of the  ALS Registry Swabia Study group :  Alber B. ,  Klinikum Günzburg ,  Department of Neurology. Andres F. , Kreiskliniken Reutlingen, Department of Neurology. Arnold G. , Klinikum Sindelfingen-Böblingen, Department of Neurology. Asshauer I. , Klinikum Friedrichshafen, Department of Psychiatry and Psychotherapy. Baezner H. , Bürgerhospital Stuttgart, Department of Neurology. Baier H. , ZFP Südwürttemberg Weissenau, Department of Epileptology. Beattie J. , Ostalb-Klinikum Aalen, Department of Neurology.
Determination of Individual Ellagitannin Composition The individual ellagitannin composition was also determined. The equipment used for this analysis was a Thermo-Finnigan Surveyor HPLC system composed of a Surveyor PDA plus detector, a Surveyor autosampler, and Surveyor a quaternary pump controlled by Xcalibur data treatment system. This HPLC system was also coupled to a Thermo-Finnigan LCQ Advantage spectrometer equipped with an ion trap mass analyzer. The electrospray ionization mass spectrometry detection was realized in negative ion mode with the below optimized parameters: capillary temperature 400 • C, capillary voltage 3 V, nebulizer gas flow 1. 75 L/min, desolvation gas flow 1 L/min, and spray voltage 5 kV. The analyses were accomplished in duplicate on a 250 × 4. 6 mm, 5 µm Lichrospher 100 RP 18 column.
NMR Analysis All 1D and 2D NMR experiments were carried out with a Bruker Avance 600 MHz NMR spectrometer (Bruker, Wissembourg, France) operating at 600. 3 MHz and equipped with a 5 mm TXI probe. Data were processed using TOPSPIN software version 3. 2 (Bruker Biospin, Billerica, MA, USA). All NMR spectra were acquired in methanol-d4/D2O 1:1 (v/v). Sodium [3-methylsilyl2,2 ,3,3 -2H4] propionate (TSP-d4) served as an internal reference for proton chemical shifts. NMR experiments were recorded at 293 K. Molecule assignments were achieved by two-dimensional 1H-1H COSY, 1H-1H TOCSY, 1H-1H ROESY, 1H-13C HSQC, and 1H-13C HMBC experiments. All 2D experiments were carried out with 2048 data points × 400 increments and a spectral width of 8417 Hz and 33,209 Hz in proton and carbon dimension, respectively, and 1.
Isolation of Ellagitannin Thermal Products Semipreparative HPLC was realized with an Agilent system having a quaternary pump (1260 Infinity), a compartment thermostat column (1290 Infinity), a sample injector (1260 Standard Autosampler), and a diode array detector (1260 DAD VL+). Chromatographic separation was carried out on a Prontosil column C18 column (250 × 8 mm, 5 µm, Metrohm France, Paris, France). UV detection was set at 280 nm. The mobile phases were A (H 2 O) and B (MeOH), each solvent containing 0. 025% of TFA. The flow rate was set at 2. 5 mL/min, and eluent B varied as follows: 1-3% during 25 min; 3-100% during 10 min, 100% during 5 min, 100 to 0% during 2 min, and 0% during 2 min. The injection volume was 200 µL. The data obtained were analyzed by LC Open Lab. Once ellagitannins compounds were isolated, they were characterized by LC-MS/MS and 1D/2D-NMR spectroscopy.
Moreover, this manuscript summarizes available evidence and provides guidance to bridge past and future research approaches. of incident malignancy compared to the general population,  9  and incident cancer in HF was associated with a 56% excess adjusted mortality risk. In a subsequent study, the same investigators retrospectively evaluated 1081 first myocardial infarction (MI) survivors and observed that patients who developed HF within 30 days of MI had a 71% higher incidence of cancer compared to those without HF. 10 These observations were confirmed by a Danish HF cohort study reporting a higher risk of cancer over a 4. 5-year follow-up period in patients with HF, also even after excluding all cancers that occurred within a year of HF diagnosis. Discrepancies among the outcomes of numerous cohorts are a clear drawback, and high-quality data are urgently required.
The apparent inconsistencies are explained by differences in cancer and HF diagnoses, guidelines, and strategies. Another reason could be the small sample sizes, 9,10 short follow-up period,  [9] [10] [11]  lack of adjustment for smoking status and HF severity,  11  availability of only self-reported data, poor cancer adjudication in HF databases, or limited data obtained in women. 12 It should be pointed out that most evidence originates from associations identified in retrospective analyses. This has inherent limitations in that causality is not guaranteed and that retrospective analyses are hampered by their original design, generally under powered toward specific cancer outcomes.
Safety of heart failure treatments and medical radiology The safety of HF treatments with regard to cancer incidence is still a subject of investigation. Several studies demonstrated a higher lung cancer incidence among patients treated with angiotensin-converting enzyme inhibitors, especially in individuals treated for more than 5 years,  79  and a dose-response relationship between hydrochlorothiazide and both basal and squamous cell carcinoma. 80 But data from a large cohort study could not link cancer prevalence to angiotensin receptor blocker (ARB) treatment, although in subgroup analysis a significant association between ARB and cancers in male genital organs was reported. 81 Large randomized clinical trials with irbesartan, valsartan, and losartan did not show any increase in the overall or site-specific cancer prevalence in patients associated with ARB use.
A recent registry-based cohort study investigated the association of congenital heart disease (CHD) with the risk of developing cancer. 138 The authors found that by the age of 41 years, one out of 50 patients with CHD developed cancer. They also reported a twofold higher risk of cancer in children and young adults with CHD compared to healthy matched controls. A long-term follow-up study evaluated cancer incidence in patients with chronic HF from the Danish registries. The cancer incidence rates were higher in all age groups. However, older HF patients (≥80 years) had a lower incidence rate than the HF patients of the age group between 70 to 79 years. 11 Also, data from a cohort of peripartum cardiomyopathy patients from Germany and Sweden reported a strikingly higher cancer incidence among (very young) women with peripartum cardiomyopathy compared to age-matched controls (20-50 years).
5 Harmonising national CV and cancer registries is one path to pursue as exemplified by the Virtual Cardio-Oncology Research Initiative (VICORI) in the UK. VICORI created a national linked data resource between the English National Cancer Registration and Analysis Service and the six national CV audits, and will link the datasets using unique identifiers such as NHS numbers to track hospital admission data and mortality for patients in both cancer and CV registries. Based on these outcomes, systematic screening for cancer should be considered for risk stratification in young predisposed patients, which allows early prevention and optimal management. Similar studies are of pivotal clinical significance as HF and cancer are not limited to a specific age group. Overall, the risk of new cancers is similar or slightly higher than the risk of new HF (with an average of 5-10 per 1000 person per year for both cancer and HF).
139 Consequently, the answers to many unsolved questions in HF-cancer interactions will come from large registries or cohorts of patients (estimated to optimally be >100 000 general comers or >10 000 patients presenting with one or the other condition). However, cancer registries usually report CV mortality, but no cardiac morbidity parameters. 140 Community-based databases, such as health data from the Rochester epidemiological data, have been used to describe a higher risk of new cancer in patients HF  9  or after MI. 10  Similarly, national health insurance registries can offer an appropriate setting to decipher HF-cancer interactions. 11, 141 he second relates to the collection of relevant parameters to better phenotype HF in cancer patients and reciprocally cancer in HF patients. 142 In most clinical studies, both conditions are mutually exclusive, thus hampering specific investigations on HF-cancer interactions.
Title of the clinical trial Intervention(s) Outcome measures Phase Identifier. Clinical Research on Treatment of Propranolol Tumour size I NCT03245554 Gastrointestinal Cancer in the Preoperative by Propranolol Hydrochlorothiazide and Risk of Skin Hydrochlorothiazide Non-melanoma skin cancer N/A NCT04334824 Cancer ACEi Melanoma skin cancer Clinical Study of Propranolol Propranolol Overall response rate II NCT04005365 Combined With Neoadjuvant Chemotherapy in Gastric Cancer Colorectal Metastasis Prevention Propranolol etodolac 5-year disease-free-survival II NCT03919461 International Trial 2 Placebo Biomarkers in extracted tumour tissue samples assessing pro-and anti-metastatic processes Biomarkers in blood samples assessing pro-and anti-metastatic processes Number of patients with treatment-related adverse events Depression, anxiety, global distress Fatigue Efficacy of Chemopreventive Agents Aspirin Disease-free survival N/A NCT04245644 on Disease-free and Overall Beta-blockers Overall survival Survival in Patients With Metformin Pancreatic Ductal ACEi Adenocarcinoma: The CAOS Statins Study Propranolol Hydrochloride in Laboratory biomarker CREB phosphorylation II NCT03152786 Treating Patients With Prostate analysis BAD phosphorylation Cancer Undergoing Surgery Propranolol Hydrochloride Distress score Questionnaire administration Levels of transcripts that reflect Survey administration ADRB2/PKA activation Plasma catecholamine levels (including epinephrine) Plasma propranolol levels Self-perceived stress MELABLOCK: A Clinical Trial on the Propranolol Effect of propranolol on overall survival II/III NCT02962947 Efficacy and Safety of Propranolol Placebo for melanoma patients in stage II/IIIA 80 mg in Melanoma Patients (T2, N0 or N1, M0) Effect of propranolol on disease-free survival for melanoma patients in stage II/IIIA Effect of propranolol on specific mortality for melanoma patients in stage II/IIIA Effect of propranolol on long-term safety in melanoma patients in stage II/IIIA Beta Adrenergic Receptor Blockade Carvedilol Change in biomarkers in prostate biopsy II NCT02944201 as a Novel Therapy for Patients compared to prostatectomy tissues With Adenocarcinoma of the Change in serum PSA Prostate Anti-Cancer Effects of Carvedilol Carvedilol Survival curve of overall survival I NCT03980249 With Standard Treatment in Survival curve of progression-free Glioblastoma and Response of survival Peripheral Glioma Circulating Quantify circulating tumour cells Tumour Cells © 2020 The Authors.
Acknowledgements The pSi has been provided by  ST Microelectronics. We thank  A. Grichine  for access to the confocal microscope and for excellent technical assistance,  P. Marche  for the access to Victor and Perkin-Elmer spectrofluorometers. This work was conducted within the  IBFC program  that was financially supported by the  Investissements d'Avenir program  ( ANR-10-NANO-03-01 ,  2012-2016 ) The authors wish also to acknowledge financial assistance from the  LabEx ARCANE  ( ANR-11-LABX-0003-01 ), the  Institute Carnot PolyNat at Grenoble , and the  ICMG Chemistry Nanobio Platform, Grenoble  for providing facilities (PCN-ICMG).
Acknowledgements We thank  Cyril Herry  ( Neurocentre Magendie ) for the development of optogenetic tools in our laboratory. We thank all of the personnel of the  Animal Facility of the Neurocentre Magendie  for mouse care. This work was supported by  Centre National de la Recherche Scientifique ,  Institut National de la Santé et de la Recherche Médicale, Fondation pour la Recherche sur le Cerveau ,  Conseil Régional d'Aquitaine, Ministère de l'Enseignement supérieur et de la Recherche, University of Bordeaux  and the  Agence Nationale pour la Recherche Grant PTSDmemo  ( ANR-14-CE13-0026-01 ), as well as an  EquipEx Grant OptoPath  ( ANR-10-EQX-008-1 ).