dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgements. The authors would like to thank  Mario Cachão ,  Maria Triantaphyllou , and two other anonymous reviewers for their valuable comments and suggestions to improve the quality of the paper. The authors want to thank the captain and crew of the  RV Mytilus (CSIC). This work was funded by the  EXCAPA  project ( 10MDS402013PR ) funded by  Xunta de Galicia , and the  CALIBERIA  project supported the coccolithophore analysis ( PTDC/MAR/102045/2008  from  Fundação para a Ciência e a Tecnologia -Portugal - and  COMPETE/FEDER-FCOMP-01-0124-FEDER-010599 ). Project  CGL2015-68459-P  from  Ministerio de Economía y Competitividad also contributed  to the funding of this study. B. A. was supported by a  FPU grant  ( AP2010-2559 ) of the  Ministry of Education of Spain  and by an  ETH   Zurich Postdoctoral Fellowship  from the  Swiss Federal Institute of Technology in Zurich (ETHZ).
D. Z. was funded by a postdoctoral fellowship ( Plan I2C ) from  Xunta de Galicia (Spain). F. G. was funded by  RAIA. co ( 0520_RAIA. co_1_E ). N. V. -R. was funded by  Isabel Barreto Program  ( Xunta de Galicia, Spain ). M. F. ( BI/PTDC/MAR/102045/2008/2010-016 ), C. S. ( BI/PTDC/MAR/102045/2008/2010-022 ) and C. C. ( BI/PTDC/MAR/102045/2008/2011-027 ) were supported by  CALIBERIA  project. E. S. was funded by a postdoctoral fellowship ( SFRH/BPD/111433/2015 ) from  FCT  and from  Plurianual/Estratégico project  ( UID/Multi/04326/2013 ). Edited by: Clare Woulds Reviewed by:  Mário Cachão  and four anonymous referees.
A dedicated retrospective database was at first developed and sent to all the ESTS centres interested to this project. More than 2,000 operated NETs cases have been rapidly collected, and they represented the clinical substrate of several published scientific studies. The retrospective data collection intrinsic limitations in term of patients' selection and treatment, along with the problem of possible missing data, were the reasons why the ESTS NETs working-group decided in 2015 to design and promote a new prospective database, employing the official ESTS platform. The aim of this review paper is to report the ESTS Lung NETs working-group history and to explain the architecture and use of the lung NETs databases.
Introduction: the reasons for a choice Neuroendocrine tumors (NETs) of the lung represent approximately 25% of all the neuroendocrine neoplasms of the human body  (1, 2)  The European Society of Thoracic Surgeons (ESTS) lung neuroendocrine tumors (NETs) database (AC) and high-grade large-cell neuroendocrine carcinoma (LCNC) and small-cell lung cancer (SCLC)  (3). NETs' incidence has increased in the last decades, due, probably, to an improvement in diagnostic tools (especially histology and immunohistochemistry) and awareness, as well as to the dramatic diffusion of lung cancer screening programs worldwide. Currently, NETs' incidence is reported to be up to 1. 57/1,000,000/year  (4, 5). SCLC is the most common lung NET, with a 15% of invasive primary lung cancers; LCNC accounts for approximately 8% of resected lung tumors; bronchial carcinoids (TC + AC) are about 1% to 2% of all the primary lung cancers.
A total of 172 centres worldwide replied to the questionnaire. There was a general agreement concerning the use of anatomic resections and parenchymasparing surgery, the importance of lymphadenectomy, the need for an adjuvant regimen in N + ACs and the role of surgery for local recurrences. On the other hands, controversies emerged in the use of nuclear medicine imaging, the preoperative measurement of serum markers and the follow-up timing and characteristics  (6). A specific database was then designed, and sent to all the centres that showed interest to NETs management, in order to retrospectively collect as many cases as possible, in the shortest time possible. Up to 2014, more than 2,000 operated NETs cases were collected through 17 thoracic surgery institutions worldwide (Appendix 1 shows the list of participating centres), and the database was then used for several clinical studies.
The ESTS lung NETs retrospective database From June 2014, the ESTS lung NETs retrospective database project gathered many enthusiastic responses from the ESTS community. Data about more than 2,000 patients operated between 1994 and 2012 in 17 ESTS general thoracic surgery centres were collected. Mostly of the Institutions were from Europe, but also a renowned centre from the North America contributed to the data collection. So far, ESTS neuroendocrine retrospective database still represents one of the largest series of operated lung NETs ever collected. Overall survival (OS) and cumulative incidence of recurrence (CIR) were the first endpoints analysed (Figure  2A, B ). The retrospective database demonstrated to be a mine rich of information concerning the clinical management of such rare lung tumors.
All those data were meticulously reported during the working-group annual business  meetings, during the ESTS Annual Congresses, and proposals for future studies were also formally collected. Several clinical studies were published, based on the lung NETs retrospective database. One was an outcome comparison between intermediate and high-grade tumors (AC and LCNC) which, for the first time, were clinically compared  (7). The results showed that age, tumor size and sublobar resections were independent negative prognostic factors influencing ACs' OS. In LCNCs, advanced TNM stage negatively affected outcome. Furthermore, local recurrence or distant metastasis were significantly more common in LCNCs, as expected. The second study was dedicated to elaborate a prognostic model of survival for TCs, by analysing the outcome of more than 1,100 operated cases  (8).
By analysing a series of 400 operated LCNCs, an evidence of a higher OS in patients receiving adjuvant CT (adjusted HR 0. 73; 95% CI: 0. 56-0. 96, P=0. 022) was observed. Therefore, although surgery remains the mainstay of curative options in LCNCs, a combination with other treatments (e. g. , neoadjuvant or adjuvant CT ± RT) should be evaluated by future studies. Nevertheless, a retrospective data collection presents intrinsic limitations related to patients' selection and treatment bias, along with the problem of possible missing data. This was the reason why the ESTS NETs workinggroup decided to promote a new prospective database. In 2015 the ESTS Council accepted and launched the online lung NETs prospective database, employing the official ESTS platform. Tumours of the Lung Working Group decided to promote a new prospective one, using the official ESTS platform; its represented one of the first satellite databases incorporated in the ESTS Core Registry.
Stimulated by the encouraging scientific results of the retrospective dataset, in 2015 the ESTS Neuroendocrine This new format encompassed a new tool: the dashboard or Clinical Care Analysis (CCA). This resource, available for each contributor, produces a clear snapshot on the status of completeness rate of specific dataset fields, as well as the distribution of patients' characteristics in the dataset. Patients 'records are included in the following five different Electronic Medical Record data sections, further divided into different subsections:  The demographic section, in common with the "core" dataset and the satellite databases;  The "Preoperative" section, which also includes three subcategories: (I) risk factors (comorbidities, smoking history and pulmonary function evaluation); (II) medical history-(initial clinical presentation, anamnesis, possible presence of paraneoplastic syndromes, serum markers levels); (III) diagnosis and staging (pre-operative diagnosis and radiology reports; invasive diagnostic procedures);  The "Operation" section, with two subcategories: (I) group definition (data concerning type of surgery, pathological staging according to the 8 th edition of the IASLC/UICC/AJCC TNM classification system, resection status and Ki67%, immunohistochemical staining) (II) treatment [complementary treatment (CT, RT and/or biological therapy) and its intent (induction, adjuvant or palliative treatment)];  The post-operative section (early-and intermediateterm outcomes.
In particular, complication, date and outcome of discharge, possible hospital readmission and 30-90-day mortality);  The "Follow-up" section (data concerning: patient vital status and oncological outcome, presence of possible local and/or distant tumour recurrence, possible second malignancy development). The database's architecture allows to each contributor to enter the dataset in different time, as soon as patients' data become available. Actually, not all the fields had the same importance, and the missing data research and compilation represents a huge time-consuming activity. Consequently, in order to maximize the essential data collection necessary for future statistical analyses, a "minimum dataset" has been developed, according to the experience of other specific databases. T h e c o n t r i b u t i o n t o t h e E S T S p r o s p e c t i v e neuroendocrine prospective database is intensely encouraged among all the ESTS Institutions.
Conclusions The ESTS Lung NETs working-group in the last years has demonstrated to be scientifically very active. In particular, the development of a retrospective database allowed the publication of several clinical studies which highlighted outcome and prognostic factors for such rare neoplasms. The new prospective dataset is now available and its contribution is strongly encouraged among all the ESTS Institutions. Its diffusion will improve the number of collected operated cases and, therefore, this will represent an extraordinary opportunity to definitively point out biological characteristics, outcome and optimal treatment for such rare lung neoplasms.
CCS and its transitions by SOS rules In our CCS formalisation, the type "β Label" ('b or β is the type variable for actions) accounts for visible actions, divided into input and output actions, defined by HOL's Datatype package: The type "β Action" is the union of all visible actions, plus invisible action τ (now based on HOL's option theory). The cardinality of "β Action" (and therefore of all CCS types built on top of it) depends on the choice (or type-instantiation) of β. The type "(α, β) CCS", accounting for the CCS syntax3, is then defined inductively: ('a or α is 2https://github. com/HOL-Theorem-Prover/HOL/tree/master/examples/CCS 3The order of type variables α and β is irrelevant. Our choice is aligned with other CCS literals. CCS(h, k) is the CCS subcalculus which can use at most h constants and k actions.
Direct estimates of the anthropogenic CO 2 absorption in the sea surface layers are difficult due to the large natural variability driven by physical and biological phenomena. Bates et al. (2014)  have extracted the trend from the large variability based on the analysis of a long time series (monthly or seasonal sampling). For the global surface ocean,  Lauvset et al. (2015)  have used the Surface Ocean CO 2 Atlas (SOCAT) database  (Bakker et al. , 2014)  combined with an interpolation method. Estimates of anthropogenic storage in the Mediterranean Sea differ by about a factor of 2  (Huertas et al. , 2009; Touratier and Goyet, 2009). In addition to the anthropogenic signal, oceanic DIC can also be the signature of a strong inter-annual variability. In the North Atlantic, for instance,  McKinley et al.
(2011)  have shown that the long-term trend emerges only after more than 25 years because of natural variability. A high-frequency sampling of the seawater carbon chemistry at the air-water interface over extended periods of time is useful in assessing the trends and variability of DIC. In Published by Copernicus Publications on behalf of the European Geosciences Union. this paper we analyse two 3-year time series of hourly fugacity of CO 2 , (f CO 2 ), measured with autonomous CARIOCA sensors  (Copin-Montégut et al. , 2004; Merlivat and Brault, 1995 ) in 1995 -1997 and 2013 -2015  at two nearby locations in the northwestern Mediterranean Sea (Fig. 1 ). Using measured f CO 2 , temperature (T ) and salinity (S), we derive the other variables of the carbonate system (pH and DIC). The experimental setting is first described, and the recent data obtained over the 2013-2015 period are presented.
The BOUSSOLE and DYFAMED sites Data collection was carried out at the BOUSSOLE site (43 • 22 N, 7 • 54 E) in 2013-2015  (Antoine et al. , 2006 (Antoine et al. , , 2008) )  and at the DYFAMED site (43 • 25 N, 7 • 52 E) in 1995-1997  (Marty et al. , 2002). These sites are 3 nautical miles apart, both located in the Ligurian Sea, one of the basins of the northwestern Mediterranean Sea (Fig. 1 ). The water depth is ∼ 2400 m. The prevailing ocean currents are usually weak (< 20 cm s -1 ), because these sites are in the central area of the cyclonic circulation that characterises the Ligurian Sea. The two sites surrounded by the permanent geostrophic Ligurian frontal jet flow are protected from coastal inputs  (Antoine et al. , 2008; Heimbürger et al. , 2013; Millot, 1999). Monthly cruises are carried out at the same location.
The BOUSSOLE mooring (2013-2015) time series Temperature and f CO 2 were measured from February 2013 to February 2016. All seasons were well represented, with missing data only in May-July 2013. For some periods, simultaneous measurements were made at the 3 and 10 m depths (Fig. 2a-c ). The range of temperature (Fig. 2a ) extends from 13 • C in winter up to 27 • C in summer, followed by progressive cooling in fall. The coldest temperature, 13 • C, results from the winter vertical mixing with the deeper Levantine Intermediate Water (LIW) marked by extremes in temperature and salinity  (Copin-Montégut and Bégovic, 2002). Temperature provides the main control of the seasonality of f CO 2 , from 350 µatm to more than 550 µatm in summer 2013 (Fig. 2b ). The fugacity of CO 2 in seawater is a function of temperature, DIC, alkalinity, salinity and dissolved nutrients.
Sea surface salinity changes The mean value of salinity and the standard error of the mean computed from 56 samples taken at BOUSSOLE in 2013-2015 are respectively 38. 19 and 0. 02. In 1998-1999, ship measurements of surface salinity were made during monthly cruises at the DYFAMED site  (Copin-Montégut et al. , 2004). The mean salinity and the standard error of the mean of this set of 19 data are respectively 38. 21 and 0. 03. Thus, there is no significant salinity change between the two decades. 3. 3 Decadal changes in f CO 2 @13 3. 3. 1 Time series of f CO 2 @13 in  1995-1997 and 2013-2015  The two time series of high-frequency data were analysed in order to quantify the change in f CO 2 @13 2 decades apart at the sea surface. To account for the inter-annual seasonal variability as well as irregular sampling, we performed an analysis of the change in f CO 2 @13 as a function of SST (Fig.
3a  and b ). For the 2013-2015 data set, we excluded summer data measured at 10 m depth as they were not representative of the surface mixed layer due to a strong stratification. Much larger f CO 2 @13 values are observed at low temperatures than at high temperatures, the decrease being similar and strongly nonlinear for the two studied periods. As described in Sect. 3. 1, large values at low temperatures result from mixing with enriched deep waters during winter, and low values for 26-28 • C temperatures occur at the end of summer after the biological drawdown of carbon. An increase in f CO 2 @13 between the two periods is evident across the range of temperatures.
At a coastal site 50 km away from DYFAMED,  Kapsenberg et al. (2017)  have measured an increase in alkalinity unrelated to salinity over the period from 2007 to 2015. They attribute it to changes in freshwater inputs from land. However, based on data from  Coppola et al. (2016) , alkalinity in the upper 50 m at DY-FAMED did not change significantly from 2007 to 2014 (3. 204 µmol kg -1 , P = 0. 0794, r 2 = 0. 08). Thus, we cannot conclude whether the difference observed at DYFAMED and BOUSSOLE between the two periods is real or an artifact of measurement techniques. As a sensitivity test, we compute the expected changes in DIC and pH from 1995-1997 to 2013-2015 for a mean alkalinity increase in 10 µmol kg -1. We obtain annual changes of dDIC = +0. 46 µmol kg -1 yr -1 and dpH = -0. 0001 pH unit yr -1 , which are well below errors estimated in Sect.
Acknowledgements. Seawater samples were analysed for DIC and Alk by the SNAPO-CO 2 at LOCEAN in Paris. The CO2Sys toolbox of Pierrot et al. (2006) has been used for the calculations of DIC and pH. The adaptation of CARIOCA sensors to high pressure has been supported by the  BIO-optics and CARbon EXperiment (BIOCAREX)  project, funded by the  Agence Nationale de la Recherche  ( ANR, Paris ). We are grateful for the helpful comments from  Gilles Reverdin  and the reviewers of the manuscript. Many thanks to  Laurent Coppola , who kindly provided additional MLD data at Dyfamed. Edited by: Jack Middelburg Reviewed by: two anonymous referees.
We restricted the analyses to only SNPs that had a minor allele frequency (MAF) of at least 1%. For genotyped SNPs, those present only on platform V1, or in chromosome Y and mitochondrial chromosomes were excluded due to small sample sizes and unreliable genotype calling respectively. Next, using trio data from all research participants in the 23andMe dataset, where available, SNPs that failed a parent offspring transmission test were excluded. For imputed SNPs, we excluded SNPs with average r 2 < 0. 5 or minimum r 2 < 0. 3 in any imputation batch, as well as SNPs that had strong evidence of an imputation batch effect. The batch effect test is an F test from an ANOVA of the SNP dosages against a factor representing imputation batch; we excluded results with P < 10 -50.
Genomic inflation factor, heritability, and functional enrichment: We used Linkage Disequilibrium Score regression coefficient (LDSR) to calculate genomic inflation due to population stratification  44  (https://github. com/bulik/ldsc). Heritability and genetic correlation was performed using extended methods in LDSR  45. Difference in heritability between males and females was quantified using  46  : ܼ ൌ ݄ ௦ ଶ െ ݄ ௦ ଶ ටܵ‫ܧ‬ ௦ ଶ ‫ܧܵ‬ ௦ ଶ where Z is the Z score for the difference in heritability for a trait, (h 2 males -h 2 females ) is the difference SNP heritability estimate in males and females, and SE is the standard errors for heritability. Two-tailed P-values were calculated, and reported as significant if P < 0. 05. This We identified enrichment in genomic functional elements for the traits by partitioning heritability performed in LDSR  47.
05. Partitioned heritability analyses were conducted to identify enrichment using LDSR. Genetic correlations: LDSR was also used to calculate genetic correlations. We restricted our analyses to only the non-stratified GWAS dataset due to the unavailability of sex-stratified GWAS data in the phenotypes investigated. We calculated initial genetic correlations using LD Hub  50  for schizophrenia  51  , bipolar disorder, major depressive disorder, depressive symptoms, educational attainment (years of schooling. ), NEO-Openness to experience, NEO-Conscientiousness, subjective wellbeing, and neuroticism. For anorexia nervosa  52  , and ADHD  53  , we used the data available from the PGC webpage (https://www. med. unc. edu/pgc/results-and-downloads) to conduct genetic correlation analyses as these are in larger samples and, consequently, have greater statistical power than the datasets available on LD Hub.
For autism, we used summary statistics from the PGC-iPSYCH meta-analysis  54  , details of which are provided in the Supplementary Note. We also conducted genetic correlation for extraversion  55  separately as the data was unavailable on LD Hub. For the anorexia nervosa, autism and the extraversion analyses, the North West European LD scores were used and the intercepts were not constrained as the extent of participant overlap was unknown. We report significant lists if they Bonferroni corrected P < 0. 05, which we acknowledge is conservative. For anorexia nervosa and autism, we also conducted genetic correlation analyses using the sex-stratified EQ dataset due to the significant sex-differences observed in these conditions. We correct for these using Bonferroni correction, and report significant correlations at P < 0.
Sex differences Sex differences in empathy  62  may reflect genetic as well as non-genetic factors (such as prenatal steroid hormones, and postnatal learning)  63. In our dataset, there was a significant female advantage on the EQ (P < 2x10  -16  Cohen's d = 0. 65) (Figure  2 ). To investigate the biological basis for the sex-difference observed in the traits, we first tested the heritability of the sex-stratified GWAS analyses for the EQ. Our analyses revealed no significance difference between the heritability in the males-only and the females-only datasets (P = 0. 48 for male-female difference in the EQ) (Figure  2  and Supplementary Table  5 ). Additionally, there was a high genetic-correlation between the males-only and females-only GWAS (r g = 0. 82±0. 16, P = 2. 34x10  -7  ), indicating a high degree of similarity in the genetic architecture of the traits in males and females.
Genetic correlations To investigate how the EQ correlates with psychiatric conditions, psychological traits and educational attainment, we performed genetic correlation (Methods) with six psychiatric conditions (autism, ADHD, anorexia nervosa, bipolar disorder, depression (major depressive disorder and the larger depressive symptoms dataset) and schizophrenia), six psychological traits (NEO-extraversion, NEO-openness to experience, NEO-conscientiousness, neuroticism, and subjective wellbeing), and educational attainment (a proxy measure of IQ, measured using years of schooling) (Supplementary Table  9 ). With psychiatric conditions, three genetic correlations were significant following Bonferroni correction: EQ-autism (r g = -0. 27 ± 0. 07, P = 1. 63x10 -4 ), EQ-schizophrenia (r g = 0. 19±0.
39±0. 14; P = 8. 8x10 -03 ) (Figure  3 ). All three correlations were in the predicted direction as studies have identified a positive phenotypic correlation between all three traits and the EQ  31, 65. We previously reported a small positive correlation between the EQ and the Eyes Test (r g = 0. 18±0. 06; P = 0. 007)  34  , mirroring previous reported estimates of phenotypic correlation in the general population  35  and estimates in our database from 916 neurotypical adults (r = 0. 11±0. 032; P = 0. 003, Pearson correlation).
Our results suggest that genetic variants associated with self-reported empathy slightly increase the risk for schizophrenia and anorexia nervosa; the latter remained significant even after using the females-only EQ dataset. A previous study  34  identified a significant genetic correlation between cognitive empathy (measured using the Eyes Test  76  ) and anorexia nervosa, underscoring the importance of empathy as a genetic risk factor in anorexia nervosa. However, both cognitive empathy and anorexia nervosa are positively correlated with educational attainment  34, 77  , and it is possible that the correlation between cognitive empathy and anorexia nervosa may be mediated by educational attainment. Here, self-reported empathy is not genetically correlated with educational attainment, which allows us to conclude that empathy indeed contributes to genetic risk for anorexia nervosa.