dataesr/scientific-paragraphs-categorization huggingface dataset:
The proportion of alien species, weighed by sample size (total number of species observed), was then used as a response variable in a GLMM with a binomial distribution. As explanatory variables, we considered the three groups of variables within the PAB framework as candidate explanatory variables: (1) site accessibility as a proxy for propagule pressure; (2) the first and the second PC score axes from the environmental PCA (PC1env and PC2env) and the first PC score axis from the geographic PCA (PC1geo) as descriptors for abiotic features; (3) species richness and body size as proxies for biotic features. Because our data came from sites distributed across different freshwater (lotic and lentic) systems, and richness at the site level might be influenced by habitat specific characteristics and by an uneven sampling effort within each habitat, the classification of a site as lotic or lentic was included as a random intercept in the model.
Therefore, understanding the drivers of biological invasions is crucial for the conservation and proper management of freshwater ecosystems and their biodiversity. In line with the above considerations, we proposed a comprehensive analysis of invasibility in Italian freshwater sites across a broad range of taxa. This study represents a further development of previous analyses  (Boggero et al. 2014) , and includes a larger dataset and a more formal theoretical approach within the PAB framework  (Catford et al. 2009 ). We included a large set of variables but we are aware that underlying co-varying factors that were not measured may exist. For instance, in this paper, we do not consider lake/river morphometry as while it is easy to relate the biodiversity of small rivers/lakes to their surface area, this relationship fails when considering large rivers/lakes where only two or three stretches along their perimeters/major axis are considered during monitoring.
(2014) , native richness was positively correlated to alien species occurrence. This incongruence is possibly due to the difference in datasets between the two studies. We used a much larger dataset and aggregated the data at family level, while  Boggero et al. (2014)  used different taxonomic levels. We also explored the potential influence of random effects by excluding native species richness as random slope. The obtained pattern is congruent with the results we presented. Moreover, the inclusion of native richness as random slope in our model accounted for the very large variance (Appendix 3) in this parameter across different families, thus producing a significant increase in model fit. Body size is important in many ways in both macroecology and eco-evolutionary dynamics, and may be a relevant trait in invasion biology  (Kolar and Lodge 2001; Amalfitano et al.
2015). In our study, alien species were found across the whole range of body size values, from the smallest (i. e. , Cyanobacteria and Rotifera) to the largest (i. e. , fish) organisms. Body size emerged as an important variable in the models: the likelihood of finding an alien species increased if the invading species belonged to a taxonomic group with larger body size (Figure  2 ). Within our dataset, most of the alien species were fish and other relatively large bodied taxa, whereas taxa with a smaller body size were less represented. Large taxa species are more likely to have been transported intentionally by humans because of their commercial value. Indeed, large taxa such as fish are heavily introduced for angling, and they are among the taxonomic groups showing the highest invasion rates at a global scale  (Copp et al.
Acknowledgements The  European Commission  support for the production of this publication does not constitute an endorsement of the contents. This paper reflects only the views of the authors. Neither the home academic institutions, nor the  European Commission , are responsible for any use that may be made of the information contained herein. The authors wish to thank  Alison Gourvès-Hayward  and  Aimée Johansen  for constructive feedback on an earlier version of this article.
Funding This work was supported by  DecisionShip Ahoy  project, co-funded by the  Erasmus+ programme of the European Union  (http://www. tinyurl. com/dahoyproject):[Grant Number  2017-1-FR01-KA203-037301 ];  Erasmus Mundus INternational Science Promoting Innovation  and entREpreneurship (http://www. inspiresouthafrica. eu/): [Grant Numbers  2014-0879/ 001-001-EM Action 2  and  IN15AC0178 ].
Moreover, we quantify the risk underestimation that occurs when the dependence among sea and river levels is not considered. We identify the meteorological predictors driving the river and sea levels. By incorporating such predictors into the statistical model, we extend the analysis of compound floods into the past, where data are available for predictors but not for the river and sea level stations. The paper is organized as follows. The Ravenna case study is discussed in Sect. 2. We introduce the conceptual model for compound events in Sect. 3. Pair-copula constructions, i. e. the mathematical method we use to implement the model, are introduced in Sect. 4. Based on the presented conceptual model for compound events, in Sect. 5 we develop the model for compound floods in Ravenna. Results are presented in Sect.
Dataset The data used here for the contributing variables Y and the impact h are water levels at a daily resolution (daily averages of hourly measurements). We use data for the extended winter season (November-March) of the period 2009-2015. Data sources are the Italian National Institute for Environmental Protection and Research (ISPRA) for the sea, and Arpae Emilia-Romagna for rivers and impact. River data were processed in order to mask periods of low quality, i. e. those suspected of being influenced by human activities such as the use of a dam. Moreover, we applied a procedure to homogenize the data of the rivers; details are given in Appendix A. We do not filter out the astronomical tide component of the sea level, considering that the range of variation of the daily average of sea level is about 1 m, while that of the astronomical tide is about 9 cm.
To check the above, we used the astronomical tide obtained through FES2012, which is a software produced by Noveltis, Legos and the CLS Space Oceanography Division and distributed by Aviso, with support from Cnes (http://www. aviso. altimetry. fr/). Meteorological predictors were obtained from the ECMWF ERA-Interim reanalysis dataset (covering the period 1979-2015, with 0. 75 × 0. 75 • of resolution  (Dee et al. , 2011) ). Specifically, for the river predictors we use daily data (sum of 12hourly values) of total precipitation, evaporation, snowmelt and snowfall, while for the sea level predictor we use daily data (average of 6-hourly values) of sea level pressure. 3 Conceptual conditional model for compound events  Leonard et al. (2014)  define a CE as "an extreme impact that depends on multiple statistically dependent variables or events".
Details about the selection procedure of the vine (Eq. 6) are given in Appendices B2 and C, while the graphical representation of this vine is shown in Fig. B1A  (Appendix B1). As described in Sect. 3, the conditional model is based on the conditional joint pdf f Y |X (Y |X), which is decomposed via PCC. Details regarding conditional joint pdfs decomposed as C-or D-vines (including the developed algorithms for sampling from such vines) are presented in Appendix B2. Moreover, the developed routines for working with conditional vines are publicly available via the CD-VineCopulaConditional R package  (Bevacqua, 2017). More details about vines and the decompositions used for the unconditional model are given in Appendix B1. Details regarding the statistical inference of the joint pdf can be found in Appendix C.
To neglect the Monte Carlo uncertainties, i. e. the sampling uncertainties due to the model simulations, we produce long simulations. For example, to obtain the model-based return level curve, we simulate a time series h sim (t) of length equal to 200 times the length of the observed data (6 years). From this we get a time series of 1200 annual maximum values, to which we fit the GEV distribution to get the return level. Observation-based return levels are obtained by fitting a GEV to annual maximum values of h obs. The relative uncertainties are computed by propagating the parameter uncertainties of the fitted GEV distribution (more details are given at the end of Appendix D).
Meteorological predictor selection Figure  4  shows the resulting scatter plots of observed predictands (Y obs ) and selected observed predictors (X obs ). To fit the joint pdf of the conditional model, we use all time steps where data for all of the X and Y variables have been recorded. However, we calibrate the predictors of rivers and sea separately, so we use all available data for each Y variable (during the period November-March). The procedure we use to identify the meteorological predictors is shown below.
Although the model-based uncertainty in these return levels is very large (due to the shortness of the available data), for return period smaller than about 60 years it is smaller than that obtained by computing the risk directly on the observed data of the impact. We calibrate the model over the period 2009-2015, and by including meteorological predictors obtained from the ECMWF ERA-Interim reanalysis dataset, we extend the analysis of compound flooding to the full period of 1979-2015, to obtain a more robust estimation of the risk. The expected return period of the highest compound flood observed, computed over the period 1979-2015, is 19 years (the 95 % confidence interval is [3. 7, ∞] years). Moreover, we did not observe any long-term trend in risk during the period 1979-2015. Ignoring the estimated dependence between sea and river levels may lead to an underestimation of risk.
Also, for places where data show both the influence of sea and river, the measurements can be affected by human influences such as pumping stations between river and sea stations. Moreover, while compound floods involve a dependence between sea and river levels  (Leonard et al. , 2014) , places where there are stations detecting both the influence of sea and river may not present such dependence. Therefore, we argue that to obtain more in-depth knowledge of these events, it may be very useful to create an archive containing data for locations where compound floods have been recorded and eventually increase the effective number of measurements in places which are supposed to be at risk of compound floods. Code availability. The developed routines for working with conditional joint probability density functions decomposed as D-or Cvines are publicly available via the CDVineCopulaConditional R package  (Bevacqua, 2017)  (more details are given in Appendix B2).
Other routines from this study are available from the authors upon request. Data availability. Sea level data of the Ravenna-Porto Corsini station were downloaded from the Italian National Institute for Environmental Protection and Research (ISPRA), and are available under the link www. mareografico. it. River data can be downloaded from Arpae Emilia-Romagna, via the link www. arpae. it/dettaglio_ generale. asp?id=3284&idlivello=1625 (the names of the used stations are S. Marco, S. Bartolo and Rasponi, where the latter is that used for the impact). Meteorological predictors were obtained from the ECMWF ERA-Interim reanalysis dataset, which is available via the link http://apps. ecmwf. int/datasets/data/interim-full-daily/ levtype=sfc/. 6 ). There are 4 trees (T 1 , T 2 , T 3 , T 4 ) and 10 edges.
, X n ), Algorithms 1 and 2 allow for the conditional sampling of (X N cond +1 ,. , X n ) given (X 1 = x cond 1 ,. , X N cond = x cond N cond ), where N cond is the number of conditioning variables. When conditioning variables are not given (N cond = 0), Algorithms 1 and 2 reduce to the special cases of Algorithms 1 and 2 shown in  Aas et al. (2009). Both routines relative to Algorithms 1 and 2 are publicly available via the CDVineCopulaConditional R package  (Bevacqua, 2017). CDVineCopulaConditional includes tools to select the best vine (based on information criteria) among those which allow for such conditional sampling, and therefore to fit the pair-copula families. Algorithm 1 Algorithm to simulate uniform variables X = (X 1 ,. , X N cond , X N cond +1 ,. , X n ) from a C-vine. Generates one sample x N cond +1 ,.
Acknowledgements. Emanuele Bevacqua received funding from the  Volkswagen Foundation 's CE: LLO  project (Az. :  88468 ), which also supported project meetings. The authors would like to thank  Arnoldo Frigessi  for hosting them, and for fruitful discussions at the  Norwegian Computing Center. Emanuele Bevacqua  would like to thank  Colin Manning  for the productive discussions, and contributions during the writing process. The authors would like to thank the anonymous reviewers for their valuable comments and suggestions which contributed to improving the quality of the paper. The data used for sea and river levels have been provided by the  Italian National Institute for Environmental Protection and Research (ISPRA)  and  Arpae Emilia-Romagna. Edited by: D. Koutsoyiannis Reviewed by:  J.
Its pharmacokinetic exposure did not significantly differ between Asian and non-Asian patients, showing a minimal food effect  (34). Additionally, data from a clinical pharmacokinetic study (NCT02163733) showed that osimertinib exposure was not affected by concurrent administration of omeprazole  (35). Thus, unlike first-and second-generation TKIs, gastric pH modifying agents can be concomitantly used with osimertinib without restrictions. A phase I/II dose-escalation study of osimertinib (AURA, NCT01802632) was carried out in patients with locally advanced or metastatic EGFR-mutated NSCLC progressing on first-or second-generation EGFR TKIs. Patients were not preselected according to T790M status  (36). The study included 253 patients who received osimertinib at five dose levels ranging from 20 to 240 mg daily and distributed between two cohorts, doseescalation and dose-expansion cohorts.
7 months [95% CI, 8. 3-13. 6] (37). The 80-mg daily dose evaluated in the phase II T790M+ extension cohort of the AURA trial (described above) was evaluated in an additional phase II "AURA2" study (NCT02094261) designed for patients with confirmed EGFR-mutant T790M+ locally advanced or metastatic NSCLC progressing on an approved EGFR TKI. A preplanned pooled analysis of both studies was performed. Among 411 patients (201 from the AURA extension and 210 from AURA2), 397 were evaluable. The ORR and DCR were 66% [95% CI, 61-71%] and 91% [95% CI, 88-94%], respectively. Median PFS was 11. 0 [95% CI, 9. 6-12. 4] months with a median duration of response of 12. 5 months [95% CI, 11. 1 months to not calculable]  (37). Osimertinib has also demonstrated activity in the first-line setting. Data from two expansion cohorts in treatment-naïve EGFR-mutated advanced NSCLC patients were recently presented.
Sixty patients received osimertinib 80 mg (n = 30) or 160 mg (n = 30) once daily and all were evaluable. The confirmed ORR was 77% [95% CI, 64-87%] with a DCR of 98% [95% CI, 89-100%]. Median PFS was 19. 3 months [95% CI, 13. 7 to not calculable]  (38). A number of phase III trials involving osimertinib in different settings are ongoing. The phase III FLAURA trial (First-Line-AURA; NCT02296125) in EGFR-mutated treatment-naïve NSCLC patients was designed to compare osimertinib 80 mg daily vs. the current standard of care gefitinib or erlotinib. The AURA3 trial (  NCT02151981 ) is an open-label, randomized trial in the second-line setting, designed to compare osimertinib with platinum-based doublet chemotherapy in patients with EGFR T790M+ locally advanced or metastatic NSCLC. In a press release dated July 18, 2016, AstraZeneca announced that the AURA3 trial, which included more than 400 patients, had met its primary endpoint demonstrating superior PFS compared to standard platinum-based chemotherapy.
Rociletinib (CO-1686) Rociletinib is another oral, irreversible, mutant-selective inhibitor of commonly mutated forms of EGFR, including T790M, with minimal activity against wild-type EGFR in preclinical studies  (39). A phase I/II trial (TIGER-X; NCT01526928) of rociletinib was performed in patients with EGFR-mutant NSCLC with acquired resistance to first-or second-generation EGFR TKIs  (40). In the expansion (phase II) part of the study, patients with T790M+ NSCLC received rociletinib at doses of 500, 625, or 750 mg twice daily. At the time of report, 130 patients were enrolled. The MTD was not identified. The most common grade 3 AE was hyperglycemia, occurring in 20 of the 92 patients (22%) who received therapeutic doses. Among the 46 evaluable patients with T790M+, the ORR was 59% [95% CI, 45-73%].
For the 17 evaluable patients with T790M-disease, the ORR was 29% [95% CI, 8-51%] (40). In November 2015, Clovis Oncology issued a press release that contained data from a pooled analysis of TIGER-X and TIGER-2 (NCT02147990), another phase II trial examining rociletinib in second line in patients with EGFR T790M+ NSCLC progressing on at least on EGFR inhibitor. Among 325 patients, the ORR (dose range, 500-750 mg twice daily) was 30. 2% [95% CI, 25. 2-35. 5%]. The ORRs were 32% [95% CI, 25-40%] and 23% [95% CI, 14-34%] in patients receiving 625 mg (n = 170) and 500 mg (n = 79), respectively. The median duration of response for the two treatment doses was 8. 8 and 9. 1 months, respectively. Due to the different RR, an independent updated analysis was assessed in patients (intention-to-treat population) included in the TIGER-X trial confirming ORRs of 45% [95% CI, 31-60%] and 17% [95% CI, 4-41%] among patients with T790M+ and T790M-disease, respectively  (41).
Olmutinib (BI-1482694/HM61713; Olita™) Olmutinib is an oral EGFR mutant-specific TKI active against mutant EGFR isoforms, including T790M, while sparing wild-type EGFR  (42). A phase I/II trial HM-EMSI-101 (NCT01588145) was conducted to evaluate the safety, tolerability, pharmacokinetics, and preliminary activity of olmutinib in Korean patients with EGFR TKI-pretreated NSCLC  (43). Patients received olmutinib at doses ranging from 75 to 1,200 mg/day. The ORR was 58. 8% in the 34 patients who received olmutinib with a dose more than 650 mg. The most common DLTs involved gastrointestinal symptoms and increased aspartate aminotransferase, alanine aminotransferase, amylase, and lipase levels. The recommended phase II dose was 800 mg/day. In part II of the study, 76 patients with centrally confirmed T790M+ NSCLC were enrolled, 70 of whom were evaluable for response.
ASP8273 ASP8273 is another oral, irreversible TKI that inhibits the kinase activity of EGFR mutations including T790M, with limited activity against EGFR wild-type  (46). ASP8273 was further shown to suppress signaling via ERK and Akt. This agent showed activity in mutant EGFR cell lines that are resistant to other EGFR TKIs including osimertinib and rociletinib  (47). ASP8273 was evaluated in an open-label phase I/II study (NCT02192697) for safety and efficacy  (48). Thirty Japanese patients were enrolled in the phase I dose-escalation cohorts across seven dose levels (25-600 mg/day), and 15 patients were enrolled in the response expansion cohorts across four dose levels (100-400 mg/day). T790M status was 49% positive, 13% negative, and 38% unknown, respectively. Responses were observed in patients enrolled in ≥100 mg/day cohorts.
Nazartinib (EGF816) Nazartinib is a novel, irreversible mutant-selective EGFR inhibitor that specifically targets both EGFR-activating mutations (L858R, Del19) and the resistant T790M mutation, while sparing wild-type EGFR  (50). NCT02108964 (EGF816X2101) is a phase I/II first-in-human study of nazartinib in patients with EGFR-mutated locally advanced or metastatic NSCLC. Updated results from the phase I dose-escalation part were recently presented. Patients were assigned to receive once-daily nazartinib with doses ranging from 75 to 350 mg. At the cutoff date of January 29, 2016, 152 patients had been treated across seven cohorts  (51). Among them, 147 patients were evaluable for response. The confirmed ORR was 46. 9% [95% CI, 38. 7-55. 3%] and the DCR was 87. 1% [95% CI, 80. 6-92. 0%]. The estimated median PFS across all dose levels was 9.
7 months [95% CI, 7. 3-11. 1]. Among 69 patients with confirmed responses at the cutoff date, the estimated median duration of response was 9. 5 months [95% CI, 9. 2-14. 7]. The most common toxicities (all grades) were rash (54%), diarrhea (37%), and pruritus (34%). Interestingly, the rashes observed in the study tended to have a different pattern, location, and histology than those seen with other EGFR TKIs that target wild-type EGFR. Diarrhea was the most common grade 3/4 AE (16%), and of note, both incidence of diarrhea and rash tended to increase with increasing nazartinib doses  (51). The phase II part, performed in six cohorts, is ongoing (Figure  1 ). In addition, the drug is being investigated in association with INC280, a specific MET inhibitor (based on the potential escape pathway for third-generation EGFR TKIs) in an ongoing phase Ib/II trial in patients with advanced EGFR mutant NSCLC (NCT02335944), and with nivolumab, an anti-PD-1 monoclonal antibody in a phase II trial in EGFR mutant/ T790M+ NSCLC patients who have progressed on first-line EGFR TKI (NCT02323126).
Avitinib (AC0010) Avitinib is another new-generation inhibitor of EGFR that, like the abovementioned agents, targets EGFR-activating mutations overcoming T790M-induced mutation with limited activity against wild-type EGFR. Clinical trials were initiated in China and the United States in parallel using avitinib as second-line therapy in NSCLC patients progressing on first-generation EGFR TKIs and who have acquired the gatekeeper T790M mutation. Two trials evaluating the safety, tolerability, pharmacokinetics, and antitumor activity of avitinib are ongoing; a phase I/II trial (NCT02274337) in advanced NSCLC patients progressing on prior therapy with an EGFR TKI agent and a phase I trial (NCT02330367) designed to determine the MTD and/or recommended phase 2 dose in previously treated mutant EGFR NSCLC patients with a T790M resistant mutation.
HS-10296 HS-10296 is a small molecule inhibitor of EGFR-activating mutations and T790M-resistant mutation with limited activity against wild-type EGFR. An open-label, multicenter, phase I/II trial of HS-10296 with dose escalation, dose expansion, and extension cohorts in locally advanced or metastatic NSCLC patients who have progressed following prior therapy with an EGFR TKI agent is currently recruiting participants (NCT02981108).
CNS activity was confirmed in the AURA study phase II extension cohort (NCT01802632) and the AURA2 phase II study (NCT02094261)  (56). The phase I BLOOM trial (NCT02228369) was designed to assess for the first time the safety, tolerability, pharmacokinetics, and preliminary antitumor activity of two third-generation EGFR TKIs, orsimetinib and AZD3759. AZD3759 was the first EGFR TKI primarily designed to effectively across the blood-brain barrier to tackle CNS metastases in patients with EGFR mutant NSCLC  (57, 58). The osimertinib cohort of the trial included 21 Asian patients with advanced or metastatic NSCLC harboring the L858R mutation (n = 13) or an exon 19 deletion (n = 9), and a confirmed diagnosis of leptomeningeal metastasis by positive cerebrospinal fluid cytology. At study entry, the T790M mutation was detected in cerebrospinal fluid in two patients and in plasma in six.