dataesr/scientific-paragraphs-categorization huggingface dataset:
Sanger sequencing Primer sequences, as well as the protocol for performing the PCR, are available on the International Agency for Research on Cancer (IARC) TP53 website (http://p53. iarc. fr/ProtocolsAndTools. aspx). This PCR protocol is adaptable and can be modified based on local experience. Bidirectional sequencing analysis is the only acceptable strategy, and the chromatograms generated by Sanger sequencing should be carefully scrutinized to ensure that somatic variants present at lower allelic frequencies are not overlooked; adjusting software settings to detect germline homozygous and heterozygous variants is not sufficient. The ERIC TP53 Network provides the opportunity to analyze Sanger sequencing data via a web-based tool called GLASS  [31]. This software was purpose-built to assist with the assessment of somatic gene variations and provides a standardized variant output as recommended by the Human Genome Variation Society (HGVS).
Databases The detected variant should be checked using locus-specific databases, i. e. , either the IARC TP53 database (http://p53. ia rc. fr/TP53GeneVariations. aspx)  [41]  or the TP53 website (UMD database; http://p53. fr/) [42]. These databases compile data from peer-reviewed literature as well as general databases, and provide information about: (i) the functional impact of all possible single-nucleotide exchanges within the coding region; (ii) the variant frequencies noted in both the somatic and the germline context; and (iii) additional relevant information, including links to other resources. The TP53 website also provides a web-service tool called Seshat that is capable of managing files generated from NGS both in the vcf and bam formats. Seshat helps the user to: (i) check the variant nomenclature for consistency and generate a full description of each variant formatted according to HGVS; (ii) assess the pathogenicity of each variant according to general prediction algorithms and algorithms developed specifically for analyzing the TP53 gene; and (iii) obtain functional and structural data for each TP53 variant.
Polymorphisms and neutral variants In general, it is not recommended to include common polymorphisms and benign variants in the report to physicians. If, however, the local practice requires that these variants are detailed in the clinical report, it should be clearly indicated that the detected variant is not clinically relevant. According to the IARC database, there are six validated exonic polymorphisms within the TP53 gene; two are synonymous (c. 108G>A: p. Pro36= and c. 639A>G: p. Arg213=) and four are nonsynonymous (c. 91G>A: p. Val31Ile; c. 139C>T: p. Pro47Ser; c. 215C>G: p. Pro72Arg, and c. 1096T>G: p. Ser366Ala). The most frequent polymorphism is c. 215C>G: p. Pro72Arg, where the ancestral allele C coding for proline is less frequent in the general population than the allele G  [43]  with latitude-dependent variations.
Although the two alleles were reported to have different capabilities in inducing apoptosis and G1 arrest  [44] , studies analyzing the clinical impact of p. Pro72Arg and its associations with TP53 mutations in CLL reported inconclusive results  [45] [46] [47] [48]. Reporting of the p. Pro72Arg status is therefore not recommended due to a lack of convincing evidence with regard to prognostic or clinical relevance. Using dbSNP for filtering out polymorphisms and neutral variants is strongly discouraged as many variants listed in dbSNP exhibit loss of function and are frequently observed in cancer patients despite not being reported as pathogenic in ClinVar  [49]. More specifically, of the 100 most frequent deleterious somatic variants described in the IARC database, 65 are present in dbSNP147 and only 34 are described as being pathogenic  [41].
Synonymous variants If a synonymous variant is detected, it is important to check its predicted effect on splicing  [52]  via the IARC database or the TP53 website. For instance, synonymous variants in codon 125 (c. 375G>A and c. 375G>T) have been found in various cancers and Li-Fraumeni families and shown to affect the splicing of exon 4  [53] , therefore they are classified as pathogenic.
However, the extent of the risk posed by minor subclones harboring TP53 mutations has not been conclusively defined, and the current evidence on the poor outcome of TP53-mutated patients treated with chemoimmunotherapy in clinical trials is based on data obtained using Sanger sequencing only. Therefore, currently, the presence of minor subclonal mutations should not impact clinical decision-making. Based on current knowledge, the recommended threshold for reporting of mutations detected by NGS should reflect the Sanger-like threshold of approximately ~10% VAF. That said, bearing in mind that the 10% threshold is arbitrary, variants with 5-10% VAF can also be reported; however, always mentioning in the report that the clinical significance of TP53 mutations with VAF 5-10% is currently unknown, since we are lacking data from prospective clinical studies addressing this issue.
Report form In addition to the obligatory standard medical report content (e. g. , patient and lab identifiers, date of sampling, type of material), the report should always contain the following information: (i) the type of analysis and description of the method: methodology used, exons analyzed, LOD, and coverage in the case of NGS (median and ≥99% minimum); (ii) results and interpretation: description of the identified variant (s) according to the HGVS nomenclature, reference sequence used, type of variant (missense/truncating etc. ), effect according to the TP53 locus-specific database, frequency, and any known association with cancer; (iii) conclusion: clinical consequence of the variant and summary of the finding in the context of the current knowledge; and (iv) other optional data: VAF of the detected variant if available (estimations from Sanger sequence traces can also be informative), comparison with a previously tested sample from the same patient and, if evidenced, description of clonal evolution.
Publishing and scientific reporting in the databases It is important to distinguish between clinical reporting and reporting variants for research purposes in scientific journals. Data from publications are transferred to databases, and these databases then serve as the source of information for general use  [42, 49]. For this reason, in order to prevent incorrect entries, it is essential to follow specific rules in addition to all above-mentioned basic procedures: (i) using consistent sample and patient identifiers if the data are repeatedly published, as inconsistent identification leads to redundancy in mutation databases; (ii) including the genomic coordinate and reference genome in the variant description to avoid ambiguities; (iii) listing all variants that are found in the patient including synonymous and other benign variants  [55].
Concluding remarks In CLL, inactivation of the TP53 gene by deletion and/or mutation is strongly associated with adverse prognosis and refractoriness to chemoimmunotherapy. Detection of del (17p) and TP53 gene mutations has become an integral part in routine diagnostics and should always be performed before deciding about treatment. Analysis of TP53 exons 4-10 is a minimal requirement; however, ideally, the entire coding sequence, i. e. , exons 2-11, should be analyzed, and this can be performed by either bidirectional Sanger sequencing or NGS. NGS also allows the parallel analysis of multiple genes and is capable of identifying variants undetectable by Sanger sequencing. That notwithstanding, NGS currently faces certain technical limitations and may lead to problems with data interpretation.
Funding Supported by the IMI 2 HARMONY JU under GA No 116026, this JU receives support from the EU's H2020 R&I program and EFPIA. Further supported by the EU Horizon 2020 projects MEDGENET 692298, AEGLE 644906, projects CEITEC 2020 (LQ1601), NCMG research infrastructure (LM2015091 funded by MEYS CR), project FNBr 65269705, FM MU ROZV/24/LF/2016, DFG (SFB1074, projects B1 and B2, and EU (FIRE CLL)), and the Swedish Cancer Society and the Swedish Research Council. Publication reflects only the authors' views and the Commission is not responsible for any use that may be made of the information it contains.
Implementation There already exist freely available libraries written in C or C++ that implement HM-ACA, see e. g. HLib (http://hlib. org/), H2Lib (http://www. h2lib. org/) or Ahmed (https://github. com/xantares/ahmed). Due to license restriction we redeveloped our own implementation of HM-ACA freely available on a GitHub repository at https://github. com/xclaeys/ElastoPhi and put under Lesser Gnu Public License (LGPL). Let us briefly comment on the most remarkable parts of the code and refer to its documentation for the details. The only external library we use is Eigen (http://eigen. tuxfamily. org), which is a Free Software. The first important part is in the file cluster. hpp where the class Cluster is implemented. The constructor calls the function build, which recursively builds the cluster tree associated with a set of geometric points.
The executable MultiCompression and CompaSparse do the same but for various values of η and ε, and CompaSparse does it also for the IFPEN sparse matrix; they are the executables used to create the data postprocessed with the Python scripts of the folder postprocessing that generate the graphs of Section 3. 2. Finally, there are some visualization executables. For example, the executable VisuMesh creates a file in Gmsh (http://gmsh. info) format to visualize the mesh of the network as in Figure  1 , and the executable VisuCluster creates a file in Gmsh format to generate the images of Figures  4  and 5. Finally, the executable VisuMatrix creates the data postprocessed with the Python scripts to generate images as in Figure  6b.
The error on the spin counting measurements was determined to be ±10% while the determination of the electron concentration had an estimated error of approximately ±60% (due to the rapid evaporation of TCE during the sample packing procedure). Powder X-ray Diffraction Measurements. Powder X-ray diffraction (PXRD) data (Figure  S2 ) were collected in high throughput transmission mode on a Panalytical Empyrean diffractometer producing Cu Kα (λ = 1. 5418 Å) radiation, equipped with an X-ray focusing mirror and PIXcel 3D detector. Pore Size and Surface Area Measurements. The materials were degassed at 120 °C for 900 min under dynamic vacuum (10 -5 bar) before analysis. Nitrogen adsorption and desorption isotherms were collected at 77. 3 K using an ASAP2420 volumetric adsorption analyzer (Micromeritics Instrument Corporation).
13nomer Feed Ratios and DNP13C MultiCP Integration of the Repeat Units within CP-CMP1, CP-CMP9, CP-CMP10, CP-CMP11, and CP-CMP15 Ratios calculated from the sum integration of all resonances of each monomer (TableS3), divided by the number of carbon atoms per monomer unit. mol. 7b02544. The raw data of each figure shown in this work are provided as a supporting dataset, available from the University of Liverpool Data Catalogue portal http://datacat. liverpool. ac. uk/451/. Details of porosity and surface area measurements, NMR experiments used, PXRD, overall sensitivity enhancement factor Σ † calculations, multiCP parameters, and additional figures (PDF) (F. B. ) E-mail: frederic. blanc@liverpool. ac. uk.
■ ACKNOWLEDGMENTS We thank  Dr. Werner E. Maas  for allowing access to the  Bruker DNP Demo Laboratories in Billerica and Wissembourg. An allocation of time at the  National EPR Facility and Service, Photon Sciences Institute, The University of Manchester , which is funded by the  EPSRC  is acknowledged, and  Mr. Adam Brookfield  is thanked for valuable technical assistance. Dr. Olivier Ouari  and  Prof. Paul Tordo  are acknowledged for providing radicals, and we thank  Dr. Ivan Sergeyev  and  Mr. Leo Tometich  for valuable technical assistance.
Meanwhile, rhizomes of the giant reed were dug locally and planted in 5 L pots to develop stems up to 1 m tall. Following the emergence of adult midges, the leaf-sheaths of these stems were smeared with a fungal conidia suspension (10 6 cfu per mL) of EGG3. Strain EGG3 was isolated by Guy Mercadier from the surface of the ovarioles of dead L. donacis females and was maintained in pure culture at the European Biological Control Laboratory collection. This strain was characterized by multi-locus sequence typing including ITS (GenBank Accession number MF627422), the Elongation factor 1 (GenBank Accession number MF627423), and the Beta Tubulin (GenBank Accession number MF627424). Then each cane was pricked (10-12 times per sheath) following  Goolsby et al. (2017a). Treated plants were placed inside 29 × 55 × 79 cm insect-proof cages where about 50 midge adults were released.
3 µM of each primer. PCR amplifications were carried out as follows: initial denaturation at 94 °C for 3 min, followed by 5 cycles of denaturation at 94 °C for 30 s, annealing at 52 °C for 30 s, plus 35 cycles of denaturation at 94 °C for 30 s, annealing at 54 °C for 30 s (ITS), or 40 cycles of denaturation at 94 °C for 30 s, annealing for 30 s at 55 °C (28S) or 54 °C (18S), elongation at 72 °C for 1 min, and final extension at 72 °C for 10 min. The purified PCR products were sequenced on both strands by Genoscreen (Genoscreen Company, Lille, France) using the PCR primers. The chromatograms were edited and aligned using Seaview software, version 4  (Gouy et al. 2010 ). The resulting consensus sequences obtained for each region or gene were Blasted in the National Center for Biotechnology Information (NCBI) database (www.
ncbi. nlm. gov) with the Basic Alignment Search tool (BLASTn) for homology in order to identify the probable taxa in question. In case of fungi, sequences were blasted against the rRNA_typestrains/ ITS_RefSeq_Fungi  (Schoch et al. 2014). Sequences were deposited in GenBank (Table  1 ). All of the DNAs extracted in this study had previously given rise to a positive amplification band of about 700 bp for the insect host barcode using Folmer primers  (Folmer et al. 1994)  attesting to their amplifiability. In the time course of this study, we obtained voucher specimens of T. gyraloura for which DNA extraction and 18S and 28S amplifications were conducted as mentioned earlier. Following the disinfection treatment and culture, no microbial growth was observed in 40 out of 69 pupae, representing only 57.
arundinis conidia present on the A. donax leaf-sheath to start the oviposition process. The 40 pupae also were screened for the presence of parasitic nematodes using the 18S primer set. Of the 40 DNAs, 17 (42. 5%) gave rise to a single approximately 960 bp amplicon which was confirmed to belong to T. gyraloura (Table  1 ). All these 17 DNAs gave rise to a single approximately 780 bp fragment of 28S that was sequenced for species confirmation to T. gyraloura (Table  1 ). Dual infection of pupae by a fungus or a yeast and a nematode was not detected. The 18S and 28S sequences obtained in our voucher specimens of T. gyraloura represent the first reference sequences of this taxa in public databases. Our PCR screening confirmed that there is only 1 nematode species infecting the pupae, which infected 42.
Patient inclusion All consecutive patients treated with the TEFOX regimen in seven participating French centres from March 2008 to June 2015 were enrolled in our database. Follow-up data were collected until July 2017. Eligibility criteria wereas follows: (1) gastric or gastroesophageal junction (GEJ) adenocarcinoma, locally advanced or metastatic, with no possibility of curative resection as assessed by each centre's multidisciplinary staff including an experienced surgeon; (2) at least one measurable lesion; (3) SRCC according to the World HEalth Organization definition, and categorised as pure SRCC (with 100% SRC) or mixed SRCC (contingent of SRC with at least 50% SRCC but not exclusive); and (4) no previous chemotherapy for advanced disease. The study was conducted in accordance with the Declaration of Helsinki.
Background: The French West-Indies rank first for both prostate cancer incidence and mortality rates. Analyzing diagnostic and therapeutic procedures among patients with prostate cancer, using data from a population-based cancer registry, is essential for cancer surveillance and research strategies. Methods: This retrospective observational cohort study was based on data from the Martinique Cancer Registry. Records of 452 patients diagnosed with prostate cancer in 2013 were retrieved from the registry. Data extracted were: socio-demographic and clinical characteristics, circumstances of diagnosis, PSA level at diagnosis, Gleason score and risk of disease progression. Stage at diagnosis and patterns of care among prostate cancer patients were analyzed. Results: Mean age at diagnosis was 67 ± 8 years; 103 (28.
The Martinique Cancer Registry (MCR) is a population based cancer registry that collects population data in Martinique, an overseas region of France with a population of 386,486 inhabitants. The MCR is one of the only two French population-based cancer registries (PBCR) among the 30 nations and territories of the Caribbean  [14]. Between 2010 and 2014, the MCR reported 535 new cases of PCa per year with an incidence rate of 161 per 100,000 men, placing Martinique among the regions with the highest PCa rates in the world  [15, 16]. For men diagnosed during this period, 5-year survival was 97. 5% with a world-standardized mortality rate of 23. 5 per 100,000  [17]. In Martinique, there is no screening program for PCa, treatments can vary according to health care providers, patients, organizational resources and facilities.
Study population Population-based cancer registries perform continuous and exhaustive recording of all new cancer cases occurring in the population resident in a given area, regardless of where the diagnosis or the treatment takes place. The registries meet a twofold objective, namely description and surveillance of cancer risk, and secondly, research based on the analysis of the data thus collected, or by means of cross-sectional surveys  [30, 31]. Observation and follow-up of cancer cohorts are a complement to surveillance of cancer incidence, and make it possible to describe incidence and prognosis in greater detail. This was a population-based study based on data from the MCR. We included data from all patients who were diagnosed with PCa (ICD10: C61) in 2013 in private and public hospitals.
Data collection Data were recorded in the PBCR database of Martinique in strict conformity with the international standards laid down by the International Agency for Research on Cancer, the French FRANCIM network, and the European Network of Cancer Registries. Quality control of all recorded cases in the MCR was performed in accordance with international guidelines for cancer registries. The following variables were recorded and retrieved for all cases: date of diagnosis, patient's age, symptoms at diagnosis and complementary imaging exams performed. Total blood prostate-specific antigen (PSA) levels at the time of diagnosis were grouped into five categories: 1) < 4 ng/mL, 2) 4. 01-10 ng/mL, 3) 10. 01-20 ng/mL, 4) 20. 01-100 ng/mL, 5) > 100 ng/Ml. Clinical stage at diagnosis was classified into localized (T1N0M0 -T2N0M0 and T3aN0M0) versus locally advanced (T3b/T4N0M0) and regional/metastatic group (N+/M+) based on the TNM classification, 7th edition (2010).
Statistical analysis Patient characteristics are described as mean ± standard deviation for quantitative variables, and number (percentage) for qualitative variables. Comparisons were performed using the Student t and Chi square or Fisher's exact tests, as appropriate. Missing data were analysed for each variable. We analyzed socio-demographic characteristics (age at diagnosis and area of residence at the time of diagnosis) and clinical factors such as symptoms at diagnosis, diagnostic procedures, clinical stage at diagnosis and therapeutic management. Patients were classified into three age groups (< 65 years, 65-74 years and ≥ 75 years) to enable analysis of variations in clinical characteristics according to age. All analyses were performed using SAS version 9. 2. (SAS Institute Inc. , Cary, NC, USA).
Among the limitations of our study, it should be noted that we had difficulty accessing data about the stage of disease at the time of diagnosis. Indeed, the cancer registrars may have difficulty extracting the full TNM code from clinical records, if this has not been explicitly recorded by the clinicians or pathologists. Furthermore, regarding access to data concerning the treatment administered or dispensed, treatment administered in-hospital can be identified from data sources already used in the registry (e. g. medical informatics databases from the national social security system). However, in our study, we did not have data regarding treatment performed more than 2 years after diagnosis, and this represents a potential limitation. For patients who travel outside of Martinique for treatment, data on treatment were reported in the medical records.
Conclusions This study from the Martinique Cancer Registry provides the most comprehensive clinical data regarding the quality of diagnosis and management of patients with PCa in Martinique. The diagnostic and therapeutic management was found to be in line with international recommendations, except for PET CT imaging. These findings will contribute to identifying public health challenges in the management of PCa in this geographic area. Genomic studies on PCa patients could help to better understand clinical factors associated with high incidence of prostate cancer in Martinique and help develop better treatment strategies. The future installation of a PET-CT scan in Martinique will enable better staging of prostate cancers and optimized therapeutic follow-up of this disease. Finally, the results of our study will contribute to improving epidemiological knowledge of cancer patterns in ultraperipheral geographic regions, and help to develop surveillance tools and raise awareness of health states in these populations.
Acknowledgments The authors thank the  FRANCIM Network ,  Santé Publique France ,  INCa ,  International Agency for Research on Cancer , and all those who contributed to the recording of cancer data in the registries: the  Oncology Haematology Urology Pathology Division of the University Hospital of Martinique , the laboratories and departments of anatomy, cytology, and pathology; the departments of medical informatics of the public and private hospitals; the local offices of the national social security service; and general practitioners and specialists. We thank  Fiona Ecarnot, MSc  ( EA3920 ,  University Hospital Besancon, France ) for editorial assistance.
Data processing After sequencing, all reads were subjected to quality control and trimming with Trimmomatic v0. 25, to remove Illumina sequencing adapters and low-quality reads  [35]. After quality control, the ribosomal and bacterial contaminants were removed with SortMeRNA v2. 0  [36] , which takes as input the files containing all high-quality reads and multiple rRNA database files (16S and 23S for bacteria and archae and 18S and 28S for eukarya) to sort apart in two files rRNA and rejected reads (corresponding to mRNA).