dataesr/scientific-paragraphs-categorization huggingface dataset:
Treatment and outcomes Therapeutic management and follow-up data were available for all 36 patients. No treatment was performed in 31 patients (86%). Interventional bronchoscopy was performed in 3 cases for tracheal stenosis. In two cases mechanical desobstruction through thermocoagulation of large lesions protruding in the tracheal lumen was performed. In the other case exhibiting a bronchoscopically estimated 80% stenosis, a tracheal stent (uncovered, Ultraflex ™ ) was placed but had to be removed because of stent breakage. The patient then underwent laser tracheobronchoplasty to stiffen the posterior tracheal wall before CO2 laser desobstruction  [10]. One patient with a history of asthma received a long-term combination of inhaled corticosteroids and bronchodilators. One COPD patient was treated with inhaled bronchodilator and antibiotics for bronchopulmonary infection.
The results were mixed in the non-BRCA population. Indeed, almost no responses were observed in the ATM, CDK12 and CHEK2 cohorts, while encouraging outcomes were reported for the smaller groups, such as PALB2, BRIP1, and RAD51B  [26]. The TRITON-2 trial generated hypotheses that suggested a better efficacy for biallelic patients, but no difference between the germinal or somatic alterations, although the populations were limited. The TRITON-3 phase III study (NCT02975934) compares rucaparib to NHT or docetaxel (physician's choice) after 1 NHT, but no previous chemotherapy, for patients with mCRPC and a deleterious mutation of BRCA1/2 or ATM. Table  1. Phase II or III trials using PARP inhibitors alone to treat prostate cancers with results. ATM: ataxia telangiectasia mutated, sBRCA: somatic deleterious mutation of BRCA.
None of the patients had HRR gene alteration. The CRR was low, around 10%, comparable to the patients without HRR alteration of the TOPARP-A study (9%), suggesting no additional effects of immunotherapy  [20]. However, even if a synergy was not observed for the CRR, pembrolizumab may prolong PARPi efficacy and longer follow-up may confirm this hypothesis. Larger phase III studies, such as the KEYLINK-010 study (NCT03834519) comparing pembrolizumab with olaparib to an NHT for previously treated mCRPC, irrespective of their HRR status, will help us explore the combination of PARPis with immunotherapy. Other combinations with chemotherapies, such as platinum, alkylating agents or topoisomerase inhibitors, or other DNA damage-response inhibitors, such as ataxia telangiectasia and rad3-related kinase (ATR) inhibitors, are under investigation  [70] [71] [72] [73].
Tools To analyse the group of radicalised adolescents, we started creating a specific grid for the study. Given the low amount of data in the scientific literature about radicalised individual characteristics in France, this grid was part of an exploratory approach. Various characteristics (e. g. , sociocultural, life history, psychopathology, behaviour, speech, commitment reasons, etc. ), identified in some studies on this topic  (10, 14, 15)  were first collected to create this grid (available in Supplementary Material). It was completed by semistructured interviews with participants and, thereafter, in collaboration with assigned educators or with additional information collected from PJJ's educational files. The grid, specific to the radicalisation phenomenon, was not completed with adolescents placed in CECs for common delinquency.
Acknowledgments: The authors would like to thank  Egle CONFORTO , responsible of the electron microscopy platform of the  LaSIE laboratory of La Rochelle University  for the technical assistance in SEM acquisition and the  Energy saving certificate program  of the  Ministry of Ecological and Solidarity Transition  "SmartReno support"  2019-2021  and the  Madur CPER FEDER program  for their support to this study.
ACKNOWLEDGMENT The authors would like to thank  Barathi Krishnamoorthy ,  Caspar van Bommel ,  Joris Meulendijks , and  Janneke Zeebregts  at  TU/e  for the technical support. The authors would also like to thank  Dr. Wim Arnold-Bik at Detect99  for performing ERD measurement,  Hans Snijders  and  Dr. Jurgen van Berkum  ( Eurofins Materials Science Netherlands B. V. ) for acquiring SIMS data, and  Dr. Dibyashree Koushik  and  Dr. Michael McMaster  ( TU/e ) for assisting the deposition of  ALD NiO. Mariadriana Creatore  acknowledges the  NWO Aspasia Program.
PATIENTS AND METHODS Inclusion criteria This study includes only individuals with a germline PV in the SUFU gene referred to as SUFU PV carriers. We analysed the literature to collect all patients with a SUFU PV in articles published before 1 January 2021. We contacted the authors of these publications to obtain follow-up information. In addition, through the SIOPE-HGWG, we also collected data of unpublished patients from eight different countries.
Data collection For all SUFU PV carriers, we collected data on tumours identified so far and vital status at the last follow-up. In each family, the first patient in whom the PV was identified was defined as the index patient, whether she/he had a tumour or not. All their family members, in whom the SUFU PV was identified after its identification in the index patient, were qualified as relatives. Data from French patients, currently collected in the French 'Observatory of Genetic Cancer Predisposition Syndromes in Children and Adolescents' (Observatoire des syndromes de prédisposition génétique au cancer des enfants et des adolescents, PREDCAP, IRB00003888) were merged with data obtained from each national group.
LG-R, PH and LB are in charge of the PREDCAP database. All authors contributed to the article writing, reviewing and editing. Funding LB and LG-R have been supported by la Fondation Gustave Roussy campaign: Guérir Le Cancer de l'Enfant au 21ème siècle. DGE and MJS are supported by the National Institute for Health Research (NIHR) BRC Manchester (Grant Reference Number 1215-200074). CPK and SMP have been supported by the Deutsche Kinderkrebsstiftung (DKS2019. 13).
Acknowledgements This project was additionally supported by 'la  Fondation Gustave Roussy ', the  Italian Association for Cancer Research (AIRC) , the  PedBrain Tumor  Project contributing to the  International Cancer Genome Consortium (ICGC) , funded by the  German Cancer Aid  ( 109252 ), the  German Federal Ministry of Education and Research (BMBF)  ( 01KU1201A ,  01KU1201C ) and the  BMBF.
JM-P and SKD analysed the genetic data. LG-R and LB contributed to the study design. RV, LG-R and LB provided the statistical analysis with N and A estimation method. LG-R, PH and LB are in charge of the PREDCAP database. All authors contributed to the article writing, reviewing and editing. Funding LB and LG-R have been supported by la Fondation Gustave Roussy campaign: Guérir Le Cancer de l'Enfant au 21ème siècle. DGE and MJS are supported by the National Institute for Health Research (NIHR) BRC Manchester (Grant Reference Number 1215-200074). CPK and SMP have been supported by the Deutsche Kinderkrebsstiftung (DKS2019. 13). BCC Dermatological Annually examination ► beginning at age 20 years ► earlier if previous radiotherapy Medulloblastoma Brain MRI without Every 3-4 months during the first 3 contrast agent except years then every 6 months until 5 for the first MRI years Meningioma Brain MRI Every 3-5 years ► beginning at age 30 years for patients with no previous MB ► after healing of the MB in other patients Ovarian tumour Pelvic ultrasound Every 3 years, beginning at age 5 years Cardiac fibroma Echocardiogram At the time.
g. ,  Hannart et al. , 2016). The thought experiment of imagining a world without anthropogenic forcings is called counterfactual because it did not happen in reality. In contrast, the expression factual world corresponds to today forcing conditions, i. e. with anthropogenic forcing included. Another example treated in EEA could be the case of a flood planner manager who is interested by revising 100-year return levels according to future forcing conditions. In these setups, in silico numerical experiments are needed to provide possible climate trajectories under never observed forcings, either in the past, present or future. For example, our application in Section 5 will focus on numerical outputs from the global climate model CNRM-CM5 of Météo-France. A factual dataset will correspond to a CNRM-CM5 run with all forcings (natural and anthropogenic forcings) during the years 1975-2005, mimicking today's climate.
The counterfactual data will be represented by a different CNRM-CM5 run only driven by natural forcings over the period 1850-2012, a never observed trajectory. An important step in any EEA analysis is the choice of extreme events under study. Statistically, the extreme event of interest is classically defined as an exceedance above a large threshold, say u, for some atmospheric feature. More precisely, climatologists have compared the survival probabilities p 1 = P(X > u) and p 0 = P(Z > u) where the random variable Z with cdf F takes its values in the factual world and the random variable X with cdf G represents the same atmospheric parameter but in the counterfactual word. In practice, the threshold u is set relatively to an exceptional event, say the 2003 European heatwave analysis in  Stott et al.
GENERAL SETUP AND NON PARAMETRIC APPROACH In this work, we assume that the counterfactual and factual data samples (X 1 ,. , X m ) and (Z 1 ,. , Z n ) respectively represent iid copies of X ∼ G and Z ∼ F. In addition, the time series X i 's and Z i 's are supposed to be independent. Under these conditions, p 1,r for any integer r ≥ 2 can be rewritten as an expectation p 1,r = E (G(Z)) r-1. ( 2 ) At this stage, one can notice that there is not need to actually observe records at time r to estimate p 1,r. For any given r, we simply need to estimate the expectation defined by (2). More precisely, the natural non-parametric estimator of p 1,r can be obtained by replacing the unknown cdf G by any empirical estimator, say G m , p 1,r = 1 n n i=1 G r-1 m (Z i ), (3) where the sample sizes of the factual and counterfactual worlds are denoted n and m, respectively.
ANALYSIS OF ANNUAL DAILY TEMPERATURES RECORDS In this section, we analyze yearly maxima of daily maxima of near-surface air temperature issued from the numerical climate model CNRM-CM5 of Météo-France, a participant of the CMIP5 intercomparison project. The whole world surface is represented on a 256 × 128 grid, each cell roughy represents a 150km×150km area. Our factual dataset corresponds to the recent climatology, 1975-2005 (so n = 31), of an "all forcings" run (natural and anthropogenic forcings) spanning the preindustrial times (year 1850) to the year 2005. Our counterfactual data are issued from a second run with only natural forcings enabled over the period 1850-2012 (so m = 163). Thus, the methodology described in Section 3. 1 can be applied to both factual and counterfactual time series available at each of N = 256 × 128 = 32, 768 gridpoints.
CONCLUSION AND DISCUSSIONS To summarize our work, we propose a simple, fast and efficient approach to analyze records in EEA studies. The simplicity comes from the idea of checking if W i follows a two parameter Weibull distribution. This avoids to test if both factual and counterfactual world are EVT distributed and we completely bypass the estimation of EVT parameters for each dataset. Still, our approach does not require the assumption of the equal shape parameters in the two worlds. The "efficiency" of our approach comes from our propositions 2, 3, 4 and 5 that detail the asymptotic properties of both non-parametric and parametric of all quantities of interest. For small sample sizes n = 30, our simulation study indicates that coverage probabilities are meaningful for a wide range of setup, in particular the ones close to our application.
Therefore, assumption n/m → a on data sizes allows us to write N n = n m × 1 n n i=1   B(G(Z i )) 2G(Z i ). B(G(Z i ))   + 1 √ n n i=1   G(Z i ) -EG(Z) G 2 (Z i ) -EG 2 (Z)   + o P (1) = a. N 1,n + N 2,n + o P (1) Both terms N 1,n and N 2,n converge in distribution as n → ∞, and their limits are independent thanks to the independence of the brownian bridge B with the factual world dataset Z. On one hand, the limit in distribution of the second term N 2,n is the centered gaussian random vector with covariance matrix described after the statement of Proposition 4. On the other hand, if µ n denotes the empirical measure of the sample (G(Z 1 ),. , G(Z n )), then N 1,n = ( B(u) dµ n (u), 2uB(u) dµ n (u)) converges in distribution to the random vector V = ( 1 0 B(u) dµ(u); 1 0 2uB(u) dµ(u)) where µ is the distribution of the random variable G(Z 1 ).
the European University Institute's Migration Policy Centre (MPC) aims to fill this gap by addressing different dimensions of transnational 1  mobilities  (Recchi, 2017). In this specific sub-project, we capitalize on two of the most comprehensive data sources on transnational human movements at a global scale: 1. Data on tourism, i. e. , cross-border visits that include an overnight stay (nota bene: not necessarily for leisure), from the World Tourism Organization (UNWTO); 2. Data on cross-border air passenger traffic from Sabre, a private company that collects data directly from the airline industry. Given that their data have been collected for different purposes, both sources, taken individually, have clear limitations when used in the attempt to provide insights into global human mobility.
We propose to remedy these systematic biases by combining and adjusting the two data sources, thereby producing more reliable estimates of cross-country human mobility globally. We describe the merging of these sources also as a possible precedent for similar endeavors for other types of country-to-country flows (like migration). In the following sections, we firstly make general remarks about the composition of transnational mobility data in the two baseline sources and give an overview of the procedures followed to combine them (Sect. 9. 2). We then describe these procedures in more detail in Sect. 9. 3. Section 9. 4 highlights some findings derived from the first explorations of the newly created dataset. In the conclusion (Sect. 9. 5), we outline some pending limitations, advocate the use of this novel dataset to study transnational human mobility empirically in social science research and describe a set of general lessons from our project that might prove useful for other researchers embarking on similar endeavors.
e. , they register tourist arrivals in destination countries, but not tourists returning to their countries of origin. 3 Air traffic statistics include NR A a!b þ R A a!b , i. e. , air passengers only. 4 Thus, both datasets are suboptimal as they systematically exclude R L a!b. Despite their differences, we expect the two datasets to be strongly correlated, because they share the same core component: NR A a!b. They should diverge only when R A a!b and/or NR L a!b are large and/or not correlated. The original UNWTO tourist files, however, also record residents of b going from b to a with all transportation means, that is R A b!a and R L b!a. If we imagine that these people return to their country of residence in the same year of their outbound travel, we can count them as part of R A a!b and R L a!b.
e. , they are registered). Unregistered or illegal border crossings are in fact left out by default from tourism and air traffic statistics, and, as a consequence, from our estimates. 3 For a small number of receiving countries, returning residents are reported in the original UNWTO data, however without indication of where they are returning from. Thus, this information cannot be used in research interested in obtaining country-to-country flow estimates. estimates through this assumption (see also Sect. 9. 4). Conceptually, migration (be it voluntary or involuntary) is excluded from our estimates, even though we cannot rule out that some 'visitors' may overstay their travels and thus become migrants. More on this issue will be explored in the Conclusions (Sect. 9. 5). We therefore revise the original UNWTO tourism data to build a yearly matrix of tourists/visitors travelling from a to b that also includes (returning) travellers from b who moved to a: T revised a!b ¼ NR A a!b þ NR L a!b þ R A b!a þ R L b!a Hereafter, we will call this the GMP-revised tourism data  [1].
Its creation is described in detail in Sect. 9. 3. 1. As for the air passenger data, which we use in its KCMD-revised form [2] (see explanation below), we assume that they tend to be lower than the revised tourism data  [1]  because travelers also move by other means of transportation. However, [1] and [2] should converge progressively as the distance between origin and destination increases, given that air travel tends to become the exclusive means of transportation at long distances. This distance-mediated relationship between [1] and [2] leads us to transform the air passenger data. We compute an estimate of transnational mobility [3] that adjusts [2] by a factor that accounts for the distance between countries. The formal procedure to estimate [3] is described in Sect. 9. 3. 3. In a final step, we combine the two revised sources, [1] and [3], to create an integrated dataset on global transnational mobility.
As we hold that both [1] and [3] tend to underestimate actual mobility flows, our final estimate is always the largest of the two when we have both information-that is, either  [1]  or  [3]. When we lack [3], we take [1], and vice versa. Figure  9. 1 provides an overview of this procedure. The individual steps are described in more detail in the following sections. The resulting final dataset covers 196 sender and receiving countries, generating a matrix of 38,220 cases (i. e. , country pairs) per year. For the entire 2011-2016 period, about 9. 5 billion trips (approx. 61%) are ultimately derived from [1] and 6 billion trips (approx. 38%) from  [3]. Overall, 12. 0% of cells are empty, which can mean either a total absence of transnational mobility between these countries (most likely in the case of pairs of small and distant nations) or missing data.
Building the Dataset In the following subsections, we outline in more detail how we handled the raw data and proceeded toward the production of the final Global Transnational Mobility Dataset. We first describe the creation of the GMP-revised tourism data (Sect. 9. 3. 1). Second, we bring the KCMD-revised air passenger trend data in (Sect. 9. 3. 2). Third, we introduce the correction factor that adjusts the latter source, taking geographic distance into account (Sect. 9. 3. 3). Finally, we describe the merging and finalization of the dataset (Sect. 9. 3. 4).
KCMD-revised air passenger trend data [2] Only [1] is available Our first source, the UNWTO tourism data, was obtained by the Global Mobilities Project (GMP) of the EUI's Migration Policy Centre (MPC) from the UNWTO as a set of files containing yearly flows from 1995 to 2016 for a global set of countries and territories worldwide (UNWTO, 2015). 5 While the harmonization and collection of national statistics on travels is part of the UNWTO mission, its online data are highly aggregated (see: https://www. unwto. org/unwto-tourism-dashboard, consulted December 18th, 2019). Therefore, we drew on the original country data kindly provided upon request by this organization. This dataset consists of 219 distinct files, one per receiving country/territory. To create a unified, standardized, and usable dataset (hereafter the GMP-revised tourism data), we took the following steps: Step 1: Prioritizing the different UNWTO operationalizations of 'arrivals' The country-to-country flow data on arrivals is reported in eight different categories in the UNWTO data (see Table  9.
2 in the Appendix). The UNWTO defines arrivals-and describes its sources-as follows: Arrivals data measure the flows of international visitors to the country of reference: each arrival corresponds to one inbound tourism trip. If a person visits several countries during the course of a single trip, his/her arrival in each country is recorded separately. In an accounting period, arrivals are not necessarily equal to the number of persons travelling (when a person visits the same country several times a year, each trip by the same person is counted as a separate arrival). Arrivals data should correspond to inbound visitors by including both tourists and sameday non-resident visitors. All other types of travelers (such as border, seasonal and other short-term workers, long-term students and others) should be excluded, as they do not qualify as visitors.
Data are obtained from different sources: administrative records (immigration, traffic counts, and other possible types of controls), border surveys or a mix of them. If data are obtained from accommodation surveys, the number of guests is used as estimate of arrival figures; consequently, in this case, breakdowns by regions, main purpose of the trip, modes of transport used or forms of organization of the trip are based on complementary visitor surveys. (UNWTO, 2015, p. 9). To include as many cases as possible in the unified dataset, we use all eight 'arrivals' categories, in the order of preference shown in Table  9. 2 in the Appendix. Step 2: Creating a unified dataset We then created a unified dataset that contains the relevant country-to-country flow data for all cases for which this information was available.
6 In doing so, we exclude several 'odd' sender categories, such as 'other countries of the world', which cannot readily be included in a country-to-country flow matrix. Details about this procedure and its consequences are described in  Recchi et al. (2019a, Appendix). Step 3: Adding returning residents In line with the considerations made in Sect. 9. 2, we add the returning residents R A b!a þ R L b!a , to the incoming non-residents NR A a!b þ NR L a!b to obtain a more complete picture of human mobility across borders. In doing so, we effectively double the number of trips in the tourism dataset. Furthermore, the matrix becomes symmetric, i. e. , mobility flows are now, by necessity, the same in both directions (T revised a!b ¼ T revised b!a Þ. Note that information is only added up if it was available in both directions.
Bringing in the KCMD-Revised Air Passenger Trend Data [2] The second source is a dataset on global air passenger traffic collected by a private travel industry company,  Sabre (2020). The dataset contains information on the total number of passengers flying between any two airports worldwide, regardless of whether the flights are direct or indirect. Here, we draw on a simplified and reduced version created by researchers at the European Commission's Knowledge Centre on Migration and Democracy (KCMD) that represents the yearly trend between countries (henceforth KCMD-revised air passenger trend data  [2] ). This version was generated through a time-series decomposition that dissects the raw overall air passenger flow between two countries into a trend component, a seasonal component, and a residual component  (Gabrielli et al.
, 2019). In the KCMD-revised air passenger trend data [2] used here, the monthly trend data is aggregated to yearly averages. The data is available for the years 2011 to 2016. We merge the two datasets [1] and [2] using ISO 3166-1 alpha-3 country codes. In line with the considerations made in Sect. 9. 2, we hypothesize: (a) [1] to be on average larger than [2], as it includes both air passengers and land/ water travellers; (b) [1] and [2] to be highly correlated, since many travellers use flights to cross borders; (c) [1] and [2] to be more strongly correlated as the distance between country pairs increases, since people are more likely to use air transportation at longer distances. All three hypotheses hold empirically. As expected, tourism figures based on  [1] , where cross-border trips are reported with all transportation means, tend to be higher than air passenger figures based on [2], which report journeys that take place with flight transportation only.