dataesr/scientific-paragraphs-categorization huggingface dataset:
Population-based cancer registries (PBCR) participate in epidemiological surveillance and in the evaluation of cancer types by enabling analysis of incidence and survival data over time. The aim of this study was to examine overall survival (OS) in patients with colorectal cancer (CRC) by analyzing data from the Martinique population-based cancer registry between 1993 and 2012. All colorectal cancer cases diagnosed in Martinique between 1993 and 2012 were included. Characteristics of CRC patients were analyzed according to age subgroups, namely: <50 years, 50 to 74 years and over 75 years. We recorded the following socio-demographic and clinical variables: year of diagnosis, age at diagnosis, sex, histology, zone of residence, and subsite of the cancer. Incidence of malignant neoplasms of the colon and rectum (ICD-10 C18-21) was extracted from the Martinique Cancer Registry database.
Introduction Population-based cancer registries (PBCR) participate in epidemiological surveillance and in the evaluation of cancer types by enabling analysis of incidence and survival data over time. Continuous, exhaustive recording of all cancer diagnoses, studies of prognostic factors are performed to evaluate the efficacy of management by the healthcare system. For the Caribbean zone, data are sparse concerning countries that do not have a PBCR, and are based primarily on estimations and projections. [1, 2] artinique is an administrative region of France comprising a single Department, and situated in the West Indies in the Caribbean ocean. Martinique has many unique features, such as its outlying geographical location and the fact that it is an island. The socio-cultural and demographic context is also specific to this region, with the result that multiple factors need to be taken into account when considering the determinants of health in the Martinique population.
Population and design We performed a retrospective study on all incident cases of invasive colorectal cancer diagnosed in Martinique between January 1, 1993 and December 31, 2012. 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,  [13]  the French FRANCIM network, and the European Network of Cancer Registries. Registry procedures were approved by the French National authority for the protection of privacy and personal data.
Data collection Data were extracted anonymously using the International Classification of Diseases version 10 (ICD-10) codes and the International Classification of Diseases for Oncology. [14] Population data were available from the French census bureau for the study period. Characteristics of CRC patients were analyzed according to age subgroups, namely: <50 years, 50 to 74 years and over 75 years. We recorded the following socio-demographic and clinical variables: year of diagnosis, age at diagnosis, sex, histology, zone of residence and subsite of the cancer. Incidence of malignant neoplasms of the colon and rectum (ICD-10 C18-21) was extracted from the Martinique Cancer Registry database. Stage at diagnosis (localized: stage I-II, regional: stage III and metastatic stage: stage IV) was also analyzed for the 2008 to 2012 period.
Data regarding CRC screening were obtained from the French Public Health Agency. Indicators for participation in the screening program were calculated by sliding 2-year periods, reflecting the bi-annual frequency of invitations to participate in the program. The reference population for the calculation of participation rates was based on the population size projections from the French National Institute of Statistics and Economic Studies (Institut national de la statistique et des études économiques, INSEE)  [15]  to ensure homogeneity of estimations across Departments. [12] ital status (dead/alive) was obtained according to a standardized procedure, namely by consulting the National Directory for the Identification of Natural Persons (Répertoire national d'identification des personnes physiques, RNIPP maintained by the INSEE), after also consulting the death registries in the town hall of the locality where the person lived, and of the locality where the person was born, and medical files were also used to update information.
Missing data on vital status were analyzed. In Martinique, there are no Death Certificate Only registrations, because reliable data regarding deaths for patients residing in Martinique are obtained from the French epidemiological center on medical causes of death from the French National Institute of Health and Medical Research (CépiDc, Inserm: http:// www. cepidc. inserm. fr/site4/), ensuring completeness of death information. In view of the high quality of the database of the Martinique Cancer Registry, the registry data are published regularly at an international scale through the International Agency for Research on Cancer (Cancer incidence in Five Continents) since 1981.
Variables with a P value <. 20 in the univariate analysis were included in the multivariable analysis. A P value <. 05 was considered statistically significant. All analyses were performed using SAS version 9. 2. (SAS Institute Inc. , Cary, NC). According to French legislation, cancer data were previously rendered anonymous with codes. The Martinique Cancer Registry database was approved by the French National authority for the protection of privacy and personal data (Commission Nationale Informatique et Libertés, CNIL No. 987 001). Additional approval from ethical committees was not required since our study did not involve direct patient contact.
The attendant reduction in the detection of aggressive stage cancers could be explained by earlier identification of these patients, and increased awareness among the general public. The impact of organized screening on OS is, however, more difficult to interpret, due to the low number of deaths in each subgroup of stage at diagnosis. The limitation of this study includes limitation in the available data regarding screening process, pattern of care of colorectal cancer, treatments performed, and reports of multidisciplinary meetings. We have added the data available based on the variables that are generally recorded as standard in international cancer registries. The results focus mainly on the stage at the time of diagnosis, which is a major predictive factor for the therapeutic strategy.
Acknowledgments The authors thank  FRANCIM Network ,  Santé Publique France ,  INCa ,  AMREC ,  CHU Martinique  and all those who contributed to the recording of cancer data in the registries: the  Oncology Hematology 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.
Much like in protein-protein networks, interpreting microbial association networks also relies on literature-curated gold standard databases  (Li et al. , 2016) , although such references are woefully incomplete for most planktonic groups  (Poelen et al. , 2014). As part of the recent Tara Oceans expedition  (Karsenti et al. , 2011; Bork et al. , 2015) , determinants of community structure in global ocean plankton communities were assessed using co-occurrence networks  (Lima-Mendez et al. , 2015) , based on the abundance of viruses, bacteria, metazoans and algae across 68 Tara Oceans stations in two depth layers in the photic zone (Annexe A). Pairwise links between species were computed based on how frequently they were found to co-occur in similar samples (positive correlations) or, on the contrary, if the presence of one organism negatively correlated with the presence of another (negative correlations).
Diatoms are segregators in the open ocean Co-occurrence amongst different micro-organisms in the plankton has recently been investigated at a large scale using data from Tara Oceans by Lima-Mendez et al, 2015. The resulting interactome, denoted the Tara Oceans Interactome, represents species (nodes), connected by links (edges) that represent either positive or negative associations. Positive associations should be understood as two organisms that are often abundant in the same sample whereas negative associations emerge if the presence of one organism negatively correlates with the presence of another. Due to the potential impact of environmental drivers on the co-occurrence of two organisms, the Tara Oceans Interactome also provides insight into the effects of abiotic factors on pairwise correlations.
A skewed knowledge about diatom biotic associations To review current knowledge about diatom interactions we generated an online open access database (DOI:10. 5281/zenodo. 2619533) that assembled the queryable knowledge in the literature about diatom associations from both marine and freshwater habitats, and is synchronized with GloBI, a global effort to map biological interactions  (Poelen et al. , 2014). A total of 1,533 associations from over 500 analyzed papers involving 83 unique genera of diatoms and 588 unique genera of other partners are reported here and made available through the GloBi database, illustrating a diversity of association types such as predation, symbiosis, allelopathy, parasitism, and epibiosis, as well as the diversity of partners involved in the associations, including both prokaryotes and eukaryotes, micro-and macro-organisms (Figure  6.
a). We noted that 58% (883 out of 1,533) of the interactions are labelled "eatenBy" ("Predation" in Figure  6. a) and involve mainly insects (267 interactions; 30% of diatom predators) and crustaceans (15% of diatom predators). Cases of epibiosis, representing approximately 10% of the literature database, were largely dominated by epiphytic. CC-BY-NC-ND 4. 0 International license a certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under The copyright holder for this preprint (which was not this version posted July 17, 2019. ; https://doi. org/10. 1101/704353 doi: bioRxiv preprint diatoms living on plants (40% of epibionts) and epizoic diatoms living on copepods (9% of epibionts). Parasitic and photosymbiotic interactions, although known to have significant ecological implications at the individual host level as well as at the community composition scale  (Van Veen et al.
, 2008) , represented only 15% of the literature database for a total of 219 interactions, involving principally diatom associations with radiolarians and cyanobacteria. Interactions involving bacteria represent 72 associations (4. 8 % of the literature database). The distribution of habitats amongst the studied diatoms reveals a singular pattern: the majority of diatom interactions in the literature are represented by a handful of freshwater diatoms, whereas many marine species are reported in just a small number of interactions (Figure  S5 ). In terms of partners involved (detailed in Figure  S6 ), one third are represented by insects feeding upon diatoms in streams, and crustaceans feeding upon diatoms in both marine and freshwater environments. Other principal partners are plants, upon which diatoms attach as "epiphytes," such as Posidonia (seagrass), Potamogeton (pondweed), Ruppia (ditchgrass) and Thalassia (seagrass).
Overlapping empirical evidence from data-driven results reveals gaps in knowledge and extends it to the global ocean In an effort to improve edge annotation in the co-occurrence network, the literature database presented here was used. The occurrence of a specific genera in the literature was compared to its occurrence in the Tara Oceans Interactome (Table  S8 ). On average, the co-occurrence network revealed many more potential links between species than has been reported in the literature (Figure  6  Of 1,533 literature-based interactions, 178 could potentially be found in the Tara Oceans Interactome, as both partners had a representative barcode in the Tara Oceans database. A total of 33 literature-based interactions (18. 5% of the literature associations) were recovered in the network at the genus level, representing a total of 289 interactions from the interactome and 209 different barcodes.
Comparing empirical knowledge and data-driven association networks reveal understudied genera such as Leptocylindrus and Actinocyclus, and those that are not even present in the literature, such as Proboscia and Haslea (Figure  6. b,c). However, Proboscia is a homotypic synonym of Rhizosolenia that is found in the interactome, which illustrates the consequences of non-universal taxonomic denominations on diversity analysis. While 18. 5% of the literature database was recovered in the interactome, it explained only 6. 5% of the 4,369 edges composing the diatom network. The gap between the 20% of diatom-bacteria interactions in the Tara Oceans Interactome. CC-BY-NC-ND 4. 0 International license a certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity.
It is made available under The copyright holder for this preprint (which was not this version posted July 17, 2019. ; https://doi. org/10. 1101/704353 doi: bioRxiv preprint compared to only 4. 8% for diatom-bacteria associations described in the literature highlights how little we know about host-associated microbiomes at this stage. Most of the experimental studies focus on symbiosis with diazotrophs and roseobacters, and antibacterial activity of Skeletonema against bacterial pathogens. In many ways, this high proportion of unmatched interactions should be regarded as the "Unknown" proportion of microbial diversity emerging from metabarcoding surveys. Part of it is truly unknown and new, part of it is due to biases in data gathering and processing, and part of it is due to the lack of an extensive reference database.
Literature Data Base. Literature was screened until November 2017 to look for all ecological interactions involving diatoms to establish the current state of knowledge regarding the diatom interactome, both in marine and freshwater environments and is made available at the DOI: 10. 5281/zenodo. 2619533. It is designed to be completed by external contributions. Diatom ecological interactions as defined in this paper are a very large group of associations, characterized by (i) the. CC-BY-NC-ND 4. 0 International license a certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under The copyright holder for this preprint (which was not this version posted July 17, 2019. ; https://doi. org/10. 1101/704353 doi: bioRxiv preprint nature of the association defined by the ecological interaction or the mechanism (predation, symbiosis, mutualism, competition, epibiosis), (ii) the diatom involved, and (iii) the partners of the interaction.
The protocol to build the list of literature-based interactions was the following (i) collect publications involving diatom associations using (a) the Web of Science query TITLE: (diatom*) AND TOPIC : (symbio* OR competition OR parasit* OR predat* OR epiphyte OR allelopathy OR epibiont OR mutualism) ; (b) Eutils tools to mine Pubmed and extract /eutils. ncbi. nlm. nih. gov/entrez/eutils/esearch. fcgi?= "Bacillariophyta") function from the RGlobi package (Poelen et al. , 2014), the most recent and extensive automated database of biotic interactions; and (d) personal mining from other publication browsers and input from experts (ii) extract when relevant the partners of the interactions based on the title and on the abstract for Web of Science, Pubmed and personal references and normalize the label of the interaction based on Globi nomenclature (iii) display KRONA plot with Type of Interaction / Partner Class / Diatom genus / Partner genus_species (Figure 6a).
Cases of episammic (sand) and epipelon (mud) interactions were not considered as they involved association with non-living surfaces. Comparison of literature interactions and diatom interactome. All partner genera interacting with diatoms based on the literature were searched for in the Tara Oceans dataset based on the lineage of the barcode. For each barcode that had a match,. CC-BY-NC-ND 4. 0 International license a certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under.
Determination of Monophosphate Nucleotides The qualitative analysis of monophosphate nucleotides was performed as reported by Yamaoka et al. [19]. LC-MS was performed under the following conditions: instrument, Waters UPLC™ and system with an electrospray ionization (ESI) interface and a quadrupole mass detection system (Waters, Milford, MA, USA); software, MassLynx TM ; column, ACQUITY UPLC™ Column HSS T3 (2. 1 mm ID and 100 mm length, 1. 8-µm particle size) (Waters, Milford, MA, USA). The ESI source was operated at 120 • C with a desolvation temperature of 450 • C, 800 L/h desolvation gas flow rate, and a capillary voltage set at 3. 5 kV. The cone voltage was 30 V and collision energies 30 eV. Integration and quantitation were performed using the Waters MassLynx TM software. Detection of these compounds was performed in the negative ionization.
Determination of Free Amino Acids Milled freeze-dried samples were used for free amino acid determination. Ultra-performance liquid chromatography coupled with photodiode array detector and electrospray ionization-mass spectrometry (UPLC-PDA-ESI-MS) analysis was carried out on a Waters ACQUITY (Waters Corporation, Milford, U. S. A. ). We used parameters of detection as detailed by  Roucher et al. [20]  and Armenta et al. [21]. The ESI source was operated at 120 • C with a desolvation temperature of 450 • C, 800 L/h desolvation gas flow rate, and a capillary voltage set at 3. 5 kV. The cone voltage varied from 20-35 V and collision energies within 10 to 40 eV, depending on the free amino acid investigated. Integration and quantitation were performed using the Waters MassLynx TM software. Detection of these compounds was performed in the positive ionization.
Determination of Polyphenolic Compounds The analysis of polyphenolic compounds were determined by the Folin-Ciocalteu method  [22]. An aliquot (100 µL) of extract was mixed with 2000 µL of distilled water and 200 µL of Folin-Ciocalteu phenol reagent. Two hundred microliters of sodium carbonate solution (200 g/L) was added to the mixture. The mixture was incubated at 20 • C for 1 h in darkness. The absorbance was read at 765 nm on a UV-VIS spectrophotometer (Shimadzu UV-2401 PC, Kyoto, Japan). Solutions of gallic acid from 0 to 500 mg/L were measured with the same procedure, for the creation of the calibration curve. All data were obtained in triplicate. The results were expressed as g GAE/100 g of d. w.
The search generated 15 articles that included seven from high-income countries (Canada, USA, Taiwan, United Kingdom and Italy)  (Wolstenholme and Whynes, 1998; Ricciardi et al. , 2009; Ferrandina et al. , 2010; Subramanian et al. , 2010; Tang et al. , 2010; Cromwell et al. , 2016; Pendrith et al. , 2016)  and eight manuscripts from middle-and low-income countries (Morocco, Ethiopia, Thailand, Mexico, China and Colombia)  (Borja Aburto et al. , 2005; Berraho et al. , 2012; Sanchez-Roman et al. , 2012; Shi et al. , 2012; Termrungruanglert et al. , 2012; Hailu and Mariam, 2013; Cheikh et al. , 2016; Gamboa and Murillo, 2016). Most studies included an appropriate description of the sources of information that were used (e. g. , administrative databases, chart review, interviews with medical and administrative staff, etc.
). Although the sources of information were provided in the majority of the articles, there was no data on the quantity of goods and services consumed. In six of the published reports, a panel of experts estimated the quantity of medical goods and services used to treat a typical case of CC. In most studies, the unitary prices used to generate cost estimates were obtained from the administrative personnel of local hospitals or by clinicians. In these cases, the authors did not make any adjustments to market prices when estimating costs. For example, a study of a public system in Taiwan relied on claims and payment information from the database of the national system of medical insurance  (Tang et al. , 2010). A report from Canada provided a detailed description of the different procedures and data sources used to estimate the costs to treat CC  (Pendrith et al.
Materials and Methods A retrospective observational study was conducted to collect data on the use of resources to treat CC patients at the OHMC21. Published treatment guidelines were used to identify the costs of services and resources involved in the treatment of a patient with CC (General Health Council (Mexico), 2017). Costs were determined using information obtained from the patients' medical charts, which was reviewed and extracted using a data collection tool that was designed for this purpose. Treatment dates were defined based on the first and last appointments with a specialist that were registered in the patients' medical charts. A sample of medical charts was identified based on the list of CC patients and the date they received health care was established by consulting several sources.
The patients' charts were also reviewed to identify all documentation (medical notes, tests orders, test results, inpatient and outpatient formats, nurse registries, etc. ) that could provide information regarding the CC treatment they received from January 1, 2010 to December 31, 2014. After the required information was identified, a questionnaire was filled out for each patient, which was later entered into a database in Microsoft Access version 2010. The study protocol was approved by the IMSS National Committee of Scientific Research and the Ethics Committee. A patient's medical chart was included only if it contained all the required information and met the following inclusion criteria: 1) a confirmed CC diagnosis during the study period, 2) an established period of treatment, and 3) a description of the CC treatment received (e.
In high-income countries, analyses were mainly conducted using statistical models that were constructed with cost information from administrative and epidemiological databases  (Wolstenholme and Whynes, 1998; Ferrandina et al. , 2010; Subramanian et al. , 2010; Tang et al. , 2010; Cromwell et al. , 2016; Pendrith et al. , 2016). The advantage of this approach is that data are collected in a systematic and consistent way. Contrarily, in middle-and low-income countries, the cost analyses are more frequently carried out through the review of medical records, information provided by a panel of experts, a treatment algorithm, or micro-costing techniques  (Sanchez-Roman et al. , 2012; Shi et al. , 2012; Hailu and Mariam, 2013; Gamboa and Murillo, 2016). However, these methods may result in greater errors that lead to incorrect cost estimates.
An advantage of cost analyses that rely on administrative and epidemiological databases is that the information generated on the use of resources at the patient level, does not require a certain set of conditions nor resources for the design, collection, and management of the data. Another relevant issue in estimating the costs of treatment is the extent to which unitary prices depict the actual economic costs of resources. Some experts have recommended using market prices for cost analyses  (Drummond et al. , 2005). The 15 reports identified in the literature review mostly used prices provided by clinicians or hospital administrators who have access to financial information. In many of the studies, the researchers did not make any adjustments to account for market prices when estimating costs  (Tang et al.