dataesr/scientific-paragraphs-categorization huggingface dataset:
Follow up data The RENAPE registry devotes substantial efforts to engage participating physicians in the registry to minimize and monitor loss to follow-up. If the patient has returned to the facility, records are obtained and appropriate information extracted. Physicians route the record to the RENAPE Registry coordination team. If the patient has not returned to the institution, follow-up letters are usually mailed to the managing or referring physician. Letters may be sent to other physicians involved in the care of the patient. A date of last contact should be requested in the letter for the patient or contact to complete (Table  4 ).
Results and discussion Rare cancer patient registries as the RENAPE Registry represent a fundamental research tool for increasing knowledge on rare cancers by pooling data for fundamental and clinical research, epidemiological research. Patient registries and databases constitute key instruments for the development of clinical research in the field of rare cancers such RPM. They are the appropriate way to conduct research on populations and conditions that are not generally studied in clinical trials, yet are important for patients information and for clinical decision-makers  [51]  because they offer the opportunity to assess the feasibility of clinical trials, to facilitate the planning of appropriate clinical trials and to support the enrolment of patients. Because of its nonexperimental design (i.
e. , no randomization), the present registry can be used to examine the impact of physician practice behaviors on quality of care. The RENAPE Registry is based upon the voluntary participation of the centers, the percentage of institutions participating is high with all HIPEC French centers including international expert teams members of the Peritoneal Surface Oncology Group International (PSOGI). Unparticipating centers are usually low-volume centers, so that our data are likely skewed towards management practices that are mostly used in larger-volume centers. Therefore the RENAPE Registry is to be considered as a specialized patient-based registry. It doesn't meet strictly the exhaustiveness criteria required for a nationwide population-based cancer registry. To optimize data quality and exhaustiveness, experienced clinical research resources help the centers to complete the data integrity.
One of the main strength of the present registry is that detailed data are recorded to characterize patient clinical and pathological features, their multidisciplinary management and outcomes. Overall more than 500 fields have to be filled-in. The consistent longitudinal collection of patient data facilitates the creation of standards of care and dramatically improves patient outcomes even in the absence of new therapies  [52]. Data collected also allow the impact therapeutic strategies and clinical practices to be assessed. Furthermore, the RENAPE registry benefits from the many advantages offered by the EOL © application: privacy and security of the data; flexibility and scalability, simplicity and fast implementation. Therefore, the registry is always in evolution to stick to novel standard of care or classification.
The experience and multidisciplinary of active contributors allow to continuously evolve this common tool and clinical, histological items from database are upgraded. The RENAPE network organization has  gathered expert pathologists in the RENA-PATH specialized working group that participate at the process of defining consensus criteria and nomenclature for appendiceal tumors under the auspices of the PSOGI  [7, 53]. The RENA-PATH Group also leads collaborative translational research projects based on the virtual biobank linked to the Registry clinical database  [54, 55]. Biocollection is disseminated and locally stored in all associated biological resource centers (BSR). In translational collaborative studies, the RENAPE registry is used to screen patient population concerned and to provide survival data.
Conclusions The rarity of the RPM and their diagnostic uncertainties limit our understanding of its epidemiological features. There is therefore a clear need -recognized by specialists throughout Francefor a specific RPM Observational Registry. The information collected from the RENAPE Registry will improve awareness and understanding of the RPM into the incidence, prevalence, recurrence, survival and mortality rates, as well as treatment practices thereby enabling therapeutic intervention to be evaluated and ultimately optimized. The Registry will also enable clinicians to review the prognosis of different patient groups and identify long-term therapeutic benefits of therapeutic interventions. The multi-institutional nature of the project and web-based database structure permit easy data entry and provide a mechanism to scale the RENAPE Registry to a larger consortium of contributing institutions.
Study Population Patients were identified from an institutional database enrolling consecutive neuroendocrine tumor patients between 1995 and 2013. Patients provided informed consent for enrollment into the database at the time of their initial clinic visit. Consent rates for the study exceeded 95%. Of a total of 1,330 patients enrolled, we identified 440 with metastatic NET who had received treatment with single-agent SSA and were evaluable for tumor progression, based on medical record review. We identified a second cohort of 109 patients who had received treatment with everolimus and were also evaluable for progression and survival endpoints. Histologic diagnosis of neuroendocrine tumor was confirmed in the pathology department at Dana-Farber/ Brigham and Women's Cancer Center. Medical information was abstracted from patient questionnaires and the medical record.
Statistical Analysis For the purposes of this study, progression was defined as evidence of radiologic disease progression (based on clinical or radiology report; Response Evaluation Criteria In Solid Tumors [RECIST] progression was not required) or evidence of clinical progression as documented in the medical record. To further capture progression events that may not have been captured in the medical record, initiation of a new therapy was also considered a progression event. PFS was defined as time from the start of SSA treatment until progression or death, whichever event came first. Patients were censored for progression-free survival at the date of their last available follow-up visit. OS was defined as the time from SSA initiation to the date of death from any cause. Survival data were obtained from the medical record or, if not available, from the Social Security Death Index.
Associations Between Disease Progression Endpoints and Overall Survival in Patients Receiving Treatment with Everolimus To explore whether we would find similar associations between disease progression endpoints and treatment with other agents, we identified 109 patients in our database who had received treatment with the mTOR inhibitor everolimus. Within this cohort, the median age was 55 years; 56 patients were male, 97% had well differentiated tumors, 25% had neuroendocrine tumors of the small intestine, 50% had neuroendocrine tumors of the  With a median follow-up time of 3. 25 years, there were 66 progression events and 62 deaths. The median PFS for patients receiving everolimus was 0. 8 years (9. 6 months), and the median OS was 3. 1 years (Table  3 ). The calculated Kendall tau correlation between PFS and OS was 0.
Interestingly, however, a recent study, which evaluated associations between disease progression endpoints and OS in patients with NETs, did in fact demonstrate an association between median time to tumor progression and median OS  [24]. This analysis included a range of studies and included data from 22 trials comprising more than 2,500 patients. A potential limitation of this study, however, is that the analysis was based on overall trial results rather than analysis of individual patient data. The current study, in contrast, is based on analysis of patientlevel data. Additionally, the prior study evaluated both large, randomized studies and single-arm studies of novel therapies, many of which had been performed in a late-stage, treatment-refractory setting. The inclusion of late-stage studies may have reduced the influence of postprogression therapy on the PFS/OS association in this cohort.
ACKNOWLEDGMENTS We are grateful for support from the  Gitta and Saul Kurlat Fund for Neuroendocrine Tumor Research, Jane Dybowski Fund for Neuroendocrine Cancer, McIntyre Family Fund for Neuroendocrine Tumor Research, Lipson Family Fund, Goldhirsh-Yellin Foundation Fund for Neuroendocrine Tumor Research , the  Murphy Family Fund for Carcinoid Tumor Research , and the  Caring for Carcinoid Foundation. This study was funded in part with a grant from  Ipsen Pharmaceuticals. This work was also supported by  U. S. National Institutes of Health  Grant  R01 CA151532  to M. H. K. The abstract was submitted for presentation and accepted for www. TheOncologist. comO c AlphaMed Press 2017  Ter-Minassian , Zhang, Brooks et al.
Different kinds of information available The data collected so far for Riau pertain mainly to the technical management of oil palm plantations. CIFOR is also a source of significant data, albeit with a dominant interest in forests and conservation issues. Thus, there seems to be a lack of social and economic analysis of the impact of the palm oil sector. In comparison, for Jambi, there are ample resources pertaining to the social and economic aspects of the development of this sector. In-depth analyses of changing landscapes and of the internal dynamics between actors in the palm oil sector are available. This report aims to provide an inventory of palm oil production systems in two provinces of Indonesia. Production systems are driven by several factors. The type of business model adopted defines many aspects of plot management.
Results Completed surveys were received from 167 laboratories. The submitted data was collated and summarised. The survey showed that some form of ctDNA plasma testing for EGFR, KRAS and NRAS was used in the majority of responding laboratories (151/167, 90%) but that only 62 (37%) laboratories currently perform diagnostic plasma ctDNA testing (Fig. 1 ). A further 56 laboratories (34%) have plasma ctDNA test methodologies in the development phase (Fig. 1 ). During 2015, 46 diagnostic laboratories tested fewer than 100 samples, while 9 tested more than 101 samples (4 did not respond). The most frequently used method for plasma ctDNA testing was next-generation sequencing (NGS), used by 27% of laboratories. The most commonly used testing platform was Ion PGM System®/Thermo Fisher Scientific (Fig.
Discussion Current practice for plasma ctDNA testing in CRC and NSCLC tumour diagnostic testing was examined by the survey. The laboratories offering plasma ctDNA testing were  those which already provide tissue-based molecular pathology services. Most survey participants (86. 8%) offered diagnostic testing of EGFR, KRAS and NRAS from formalin-fixed paraffin-embedded (FFPE) tissue and participated in EQA for solid tumours. Of these, 78% participated in EQA assessment for NSCLC and 68% for CRC, so these laboratories already have experience in molecular technologies and understand how to interpret and report results. The data suggest that no single, definitive technology for the analysis of plasma ctDNA has yet emerged. The methods currently used are a mixture of commercial and locally developed assays.
Acknowledgements We would like to thank the sponsors who supported the plasma ctDNA pilot of IQN Path, the initial step of which is presented here by the survey results. Sponsors include Amgen, AstraZeneca ,  Merck KGA, Sysmex Inostics, Boehringer Ingelheim ,  Roche  and  Thermo Fisher Scientific. Funding IQN Path  provided administrative support for the project. Compliance with ethical standards This research does not contain any studies involving human participants or animals performed by any of the authors. Human subjects were not used; therefore, formal consent was not required.
Acknowledgments Elodier Barbier  had a  Doctoral fellowship  financed by the  Conseil Regional de Bourgogne Franche Comteand FEADER. This work was also supported by  The Conseil Departmental de la Cote d'Or , the  Laboratoire Departemental d'Analyse de la Cote d'Or. The authors wish to thank  Dr. Eric Gueneau  (Director of the  Laboratoire Departemental de la Cote d'Or)  and  Pr. Catherine Neuwirth  ( Bacteriology Department, University Hospital Dijon ) for their invaluable help and discussions.
d 18 O values are all negative but show a quite widespread distribution from -1 to -12% VPDB. Nonetheless, two main groups may be distinguished (Fig. 6 ). The first, with less 18 O-depleted values mostly falling between -2 and -4% VPDB, corresponds to Upper Triassic replacement dolomites and dolomite cements. The second group of values, basically comprised between -6 and -10% VPDB, includes all the other samples (dolomite and calcite veins, calcite cements and de-dolomite), which largely overlap each other. Samples of the first group (both finely and coarsely crystalline dolomites) show the least 18 O-depleted values of the whole dataset which, however, are lower than those reported in present day settings where dolomite is forming and ranges from 0 to ?4% VPDB  (Tucker and Wright 1990). The study of fluid inclusions indeed provides temperature constraints to these dolomites, with formation temperatures averaging 120-130 °C (Fig.
Acknowledgements The authors thank the two reviewers,  M. Lopez-Horgue  and  P. Nievergelt , and the Journal Editor  S. Schmid  for a thorough revision of the text and the suggestions, which improved the manuscript. The research was financed by  Petrobras S. A. and  N. Incerpi  et al. Universita Italo-Francese/UniversiteFranco-Italienne (Bando Vinci 2014 ,  C2-190  to NicoloIncerpi).
The Particle Physics Masterclasses The Particle Physics Masterclasses mostly take place in schools and last between 4 to 6 hours. The facilitators of these events are in most cases young particle physicists, e. g. PhD or Master students. During a Masterclass the scientists give an introduction into particle physics research, e. g. in the "Standard Model of Particle Physics", how the research community works together, which questions should be answered by the actual research, etc. Afterwards the young participants get an introduction how to visually identify particles from their traces in the detector. After an introductory exercise the participants make own measurements with original data from CERN. The students work in pairs to classify 50 to 100 events into various categories. Then the results of the groups are combined and discussed.
Introduction Since the beginning of the industrial revolution, human activities such as fossil fuel burning, cement production and land use change have led to the increase of greenhouse gase concentrations in the atmosphere, altering the radiative balance of the Earth system and changing the climate of our planet  (IPCC, 2014). Current emissions of carbon dioxide (CO 2 ) exceed 10 Pg C yr -1 of which about half remains in the atmosphere (Le  Quéré et al. , 2016). The remainder is estimated to be taken up in roughly equal shares by the land and the ocean. In past decades, the magnitude of the ocean carbon sink was mainly estimated from global ocean biogeochemistry models and atmospheric inverse models but the recent increase in oceanic CO 2 measurements and the creation of the Surface Ocean CO 2 Atlas (SOCAT) database  (Bakker et al.
Data products We use a 21 year observationally based global monthly gridded sea surface pCO 2 product covering the 1991 through 2011 period  (Landschützer et al. , 2015a). This period was chosen to cover the overlapping temporal extent of the four wind products selected for this study. For our analysis, we create a climatological monthly mean F CO 2 estimate from our gridded pCO 2 fields over this period and atmospheric partial pressures of CO 2 (pCO 2,air ) calculated from the NOAA Marine Boundary Layer reference product at 100 % humidity  (Dickson et al. , 2007). The pCO 2 fields are based on measurements of the Surface Ocean CO 2 Atlas version 2 (SOCATv2) dataset  (Bakker et al. , 2014)  using a two-step artificial neuronal network  (Landschützer et al. , 2015a)  to generate continuous monthly 1 • × 1 • resolution pCO 2 maps for the global ocean excluding the Arctic Ocean, coastal regions and marginal seas.
A more detailed description of the method and its extensive evaluation can be found in  Landschützer et al. (2013 Landschützer et al. ( , 2014 Landschützer et al. ( , 2016)). Four global wind speed datasets are used to evaluate the sensitivity of F CO 2 to the choice of one wind product over the other. The four data products selected are the most widely used in the literature: Cross-Calibrated Multi-Platform Ocean Surface Wind Vector 3. 0 (CCMP;  Atlas et al. , 2011) , the global atmospheric reanalysis ERA-Interim (ERA;  Dee et al. , 2011) , the NCEP/NCAR reanalysis 1 (NCEP1;  Kalnay et al. , 1996)  and the NCEP/DOE AMIP-II Reanalysis (NCEP2;  Kanamitsu et al. , 2002). The latter is an update of NCEP1, using an improved forecast model and data assimilation system  (Kanamitsu et al. , 2002).
To achieve the same 1 • × 1 • spatial resolution for the wind field as that of pCO 2 , a cells aggregation is performed for CCMP and ERA that have finer spatial resolutions (0. 25 • × 0. 25 • ). This aggregation generates a 1 • × 1 • grid by performing surface weighted averages of all the wind speed values comprised in each 1 • × 1 • cell. The original spatial resolution of both NCEP1 and NCEP2 is a global T62 Gaussian grid (i. e. , 192 longitudes equally spread and 94 latitudes unequally spread) and is translated into a continuous 1 • × 1 • data field using a two-dimensional spline interpolation. The original spatial resolutions of the four wind speed products are summarized in Table  1. Their temporal resolution is the same (6 h) and much finer than the one of pCO 2. Therefore, centered monthly mean for the wind speed ( U 10 ) and its second moment ( U 2 10 ) are calculated to match the temporal resolution of the pCO 2 data.
The use of U 10 2 accounts for the variance of wind speed in the k estimates. In what follows, F CO 2 calculated with these different wind datasets are referred to as F CO 2-CCMP , F CO 2-ERA , F CO 2-NCEP1 and F CO 2-NCEP2. All of the calculations are performed using the CO 2 solubility (K 0 ) product calculated by  Landschützer et al. (2015a)  following  Weiss (1974) , the sea ice fraction from  Rayner et al. (2003)  and the sea surface temperature (SST) from NOAA OI SST V2 (daily 0. 25 • × 0. 25 • resolution;  Reynolds et al. , 2007). We transformed the original SST data to monthly mean values at 1 • spatial resolution following the same procedure as that used for the CCMP wind data. The Schmidt number was calculated using the transformed SST field and the equation proposed by  Wanninkhof (2014).
F CO 2 uncertainty arising from the choice of wind product The zonal mean air-sea F CO 2 (mol C m -2 yr -1 ) calculated using the four wind speed datasets is illustrated in Fig. 2a. The four mean latitudinal profiles reveal strong qualitative similarities that reflect both the latitudinal U 10 (Fig. 2b ) and pCO 2 distributions (Fig. 2c ). In the Northern Hemisphere, high latitudes (> 40 • N) act as strong CO 2 sinks while the tropics are close to neutral and a narrow latitudinal band around the Equator is a moderate CO 2 source. In the Southern Hemisphere, a strong CO 2 sink can be observed at around 40 • S while the Southern Ocean further south is quasi neutral. In quantitative terms, however, substantial differences between profiles can be observed, especially in the equatorialand mid-latitudes as a result of differences in the applied wind speed products.
Results are calculated globally (numbers in bold) and regionally (minimum and maximum relative difference between all RECCAP regions results). Note that the RECCAP regions 2 and 3 (Equatorial Pacific, EP) have been merged. C * represents F CO 2 calculated with a quadratic k relationship where c is recalibrated for each wind product to fit a global average k value of 16 cm h -1 for the period of our study  Along the tropics (  23 • N and S) and in the high latitudes, the ocean behaves as a sink for CO 2 , with the notable exception of the coastal regions. Figure  3  shows that the F CO 2 calculated for each 1 • × 1 • cell using NCEP2 is larger than that obtained using NCEP1 over 87 % of the oceanic surface area. In addition, computing the flux with NCEP1 leads to the lowest F CO 2 for 52 % of the oceanic surface area compared to all other wind datasets.
, 2010 Laruelle et al. , , 2014)). It should be noted, however, that the spatial extent of our pCO 2 data product does not include the near coastal zone and thus only partly cover these areas. Comparing the air-sea CO 2 exchange using all climatological mean wind products, we find that CCMP (global wind average of 7. 5 m s -1 from 1991 through 2011, which is close to that calculated by  Wanninkhof, 2014 , for the period 1990-2009 of 7. 3 m s -1 ) leads to a slightly more intense CO 2 exchange between 40 • S to 40 • N and in the Arctic region (> 60 • N) than F CO 2-ERA and F CO 2-NCEP1 (Fig. 2a ). The differences between the median F CO 2 fields generated using ERA and NCEP1 are very small (< 0. 1 mol C m -2 yr -1 ) and either wind product can yield the most intense F CO 2 from one region to the other.
Hence, the relative percentage difference of regions 2 and 3 will be expressed in one region only. Most of the variability results from the use of the NCEP2 dataset, especially in regions of high wind speeds. Excluding NCEP2, the relative variability in F CO 2 drops below 10 % for all regions, except in the Equatorial Pacific (regions 2 and 3). Globally, the 21 year average oceanic CO 2 flux calculated with the different wind products varies between -1. 30 and -1. 38 Pg C yr -1 using ERA, NCEP1 and CCMP in conjunction with the formulation of k proposed by  Wanninkhof (2014, Table 4 ). These estimates are thus consistent with each other, but fall in the low end of the range of global estimates published for the global oceanic CO 2 uptake  (Gruber et al. , 2009; Takahashi et al. , 2009; Wanninkhof et al.
, 2013b). This result can partly be explained by the absence of the Arctic Ocean and coastal regions in our pCO 2 climatology  (Landschützer et al. , 2014). Using the NCEP2 dataset and the  Wanninkhof (2014)  formulation, the F CO 2 increases significantly to -1. 84 Pg C yr -1. In relative terms, this represents a difference of about 29 % in the global ocean CO 2 uptake estimate across all wind products (Table  3 ). Thus, even with the use of the same k -U 10 equation, the choice of wind products can lead to significantly different global and regional F CO 2 estimates. 3. 2 F CO 2 uncertainty arising from global k -U 10 parameterizations and wind products In this section, we constrain the uncertainty in air-sea CO 2 fluxes associated to the use of all published k -U 10 parameterizations derived from 14 C bomb inventories and the hybrid formulation of  Wanninkhof et al.
(2009; Tables A1 and 4 ). We also report estimates obtained with the Wanninkhof (1992) formulation in these tables, but exclude it from our analysis as it is now accepted that this parameterization is outdated  (Wanninkhof, 2014). Globally, we find that the 21 year mean ocean uptake of CO 2 averaged across all quadratic formulations and wind speed datasets is -1. 52 Pg C yr -1 (Table  4 ), in agreement but again on the lower end of previous estimates  (Gruber et al. , 2009; Takahashi et al. , 2009; Wanninkhof et al. , 2013b). However, the range is significant and varies from -1. 30 to -1. 98 Pg C yr -1. This range is even larger (-1. 19 to -1. 98 Pg C yr -1 ) when the hybrid formulation is also included in the analysis. The 40 % global F CO 2 relative uncertainty mainly stems from the use of the NCEP2 wind product (Table  3 ).