dataesr/scientific-paragraphs-categorization huggingface dataset:
For the second approach, we compiled species-and region-specific generalised linear mixed models (GLMMs). Species presence/absence per plot was modelled with a binomial distribution, as a function of elevation and with or without an interaction term for distance to the road (roadside/adjacent). We distinguished between species with a second degree (quadratic) and first degree (linear) binomial distribution. The former indicated a range optimum along the gradient (negative quadratic function with optimum within the sampled range, "dataset A2", see Supplementary material Appendix A Table  A2 ), the latter a linear distribution, for which the range optimum lay above or below the gradient (positive or negative linear functions, monotonically increasing or decreasing along the sampled gradient, "dataset A3", see Supplementary material Appendix A Table  A3 ).
Accepted Ar ticle 'This article is protected by copyright. All rights reserved. ' acknowledged possible differences between roads within a region by adding "road" as a random factor. Models were fitted in R with the function glmer from the package lme4  (Bates et al. 2013). The function aictab from the package AICcmodavg  (Mazerolle 2015)  was used to select the model with the best fit based on the lowest AIC value. For details on the used models and coefficients, see Supplementary material Appendix 2. There were 171 species for which the model with the best fit was a second degree function of elevation (dataset A2), of which 112 had next to this quadratic term a significant interaction between the linear elevation term and distance to the road, and 59 had an additional interaction between the quadratic elevation term and distance, hence showing a change in both optimum and amplitude (dataset A2, Supplementary material Appendix 1 Table  A2 ).
The difference between species optima was defined as the difference in elevational positions of the optimum information criteria (OPT) for roadside and adjacent plots  (ter Braak and Looman 1986, Lenoir et al. 2008) , calculated based on the coefficients from the GLMMs. Differences in range amplitudes were calculated by taking the difference between the GLMM's tolerance information criteria (AMP)  (ter Braak and Looman 1986, Lenoir et al. 2008 , see Supplementary material Appendix 2 for detailed calculations). Only those species that had an optimum elevation within the elevational range of the dataset were withhold. For 44 species, the model with the best fit included only the linear elevation term, and a significant interaction between elevation and distance (dataset A3, see Supplementary material Appendix 1 Table  A3 ).
Species origin and traits All species were marked as native or non-native for every region in which they occurred, based on data available from the MIREN-network. Any species introduced after AD 1500 within a given region was considered as non-native (N non-native = 99). To verify if changes in species ranges between roadsides and adjacent plots can be explained by differences in species' affinity for temperature or nitrogen, we used indicator values for temperature and nitrogen from the Flora Indicativa, available for a set of 184 unique species (Table  1, Landolt   2010 ). Landolt indicator values range from one to five and characterize the average air temperature during the growing period of the species ("temperature") and a species preference for soil fertility.
Accepted Ar ticle 'This article is protected by copyright. All rights reserved. ' ("nitrogen"), with a value of one in both cases meaning a low affinity for the considered factor, and five a high affinity. Landolt values were available for 85 % of the species from the European regions (NO and SW), for 94 % of the non-native species in all regions, and for 18 % of North-American native species (OR and MT) (Table  1 ). For native species from the southern hemisphere, Landolt value availability was limited (on average 6 %). This implies that our analyses based on the Landolt values will be biased towards regions with higher data availability. This bias does however not occur for non-native species and is limited to native species from the southern hemisphere. The European regions (NO and SW) had no non-native species with more than ten occurrences; the South-American datasets (AR, CLC, CLS) contained fewer native species than the other regions.
Statistical analysis Data from all three datasets (A1, A2, A3) were further tested with linear mixed models (LMMs) with species nested in region as a random factor (package nlme  (Pinheiro et al. 2013) ). First, a null model without any fixed effects was used, to test if the average optimum and amplitude (from datasets A1 and A2) differed from zero. Similar linear null models were used to analyse region-specific patterns by testing each regional dataset separately. These models were recreated for native and non-native species separately. Next, LMMs with optimum, amplitude or edge as response variables, and species origin, species nitrogen and temperature affinity and their interactions as explanatory variables were used to explore correlations for all datasets, again with species nested in region as a random factor.
For dataset A3, the location of the optimum (based on the slope of the linear curve with a positive slope indicating an optimum above the road fragment, and vice versa) was added as an extra fixed factor. Model simplification was done based on the lowest AIC value and significance of variables, and only the models with the best fit are shown. A correlation test was used to test consistency in species patterns between regions, both with the differences in optima and in range amplitudes. The same test was used to analyse the correlation between differences in optima and amplitudes and the difference in amount of occurrences between Accepted Ar ticle 'This article is protected by copyright. All rights reserved. ' roadsides and adjacent plots, and to test the relation between regional patterns for native species and the average nutrient affinity of a region's native species pool.
General patterns There was no general significant difference in species' elevation optima or amplitudes between roadsides and adjacent plots (LMMs, dataset A1, df = 502, optimum P = 0. 564, amplitude P = 0. 373). Range amplitudes of non-native species in dataset A1 were however on average 192 elevational meters broader in the roadsides than in the adjacent plots (Fig. 1 , Table  2 , right), which contrasted with the observations for native species, for which no general trends could be observed (Table  2 , Fig. 1A, C ). In dataset A2, differences in optima and amplitudes for non-native species with optima along the studied road fragment were not significant (Fig. 1C , LMM, df = 39 (31 species), optimum: P = 0. 579, amplitude: P = 0. 538), but the results from dataset A1 were supported by the regional trends (Fig.
1B  and 1D, Table  2 ). Indeed, for four out of the eight regions (AU, CLS, OR and MT) we observed broader range amplitudes for non-native species in the roadsides in dataset A1. In OR and MT also, the optima were higher in the roadsides, but they were lower in AU and not significant in CLS. Moreover, in dataset A2, the one region (MT) with significant trends in non-native species showed on average higher optima in the roadsides than the adjacent plots (Fig. 1D ). Native species on the other hand showed on a regional basis a trend towards lower optima in roadsides, a trend significant in three regions (dataset A1; AU, SW, NO; Fig. 1B, Table 2 ). In two regions, a smaller amplitude was recorded (AR, AU) and in SW the amplitude was broader in roadsides than adjacent plots. In dataset A2, patterns for native species were marginally significant for AU (smaller amplitude), SW (broader amplitude) and OR (higher optimum).
Location of optimum along the gradient Species were classified based on the location of their elevational optimum by integration of dataset A2 and A3 to unravel trends in range differences based on a species' location of origin that stay hidden in the general trends discussed before. The strongest range differences were observed for non-native species with an optimum below the sampled road fragments (Fig. 2 , "lowland species"). For these species, their upper range edge occured at higher elevations in the roadsides than in the adjacent plots (LMM, Estimate = 677. 1, P = 0. 021).
Temperature and nitrogen affinity Next to species origin (native or non-native), species' affinity for temperature and nitrogen also influenced how species ranges were affected by the presence of roads (Fig. 3 , Table  3 ). In non-native species of European origin (94 % of the 71 non-native species in dataset A1 and 92 % of the 37 nonnative species in dataset A2), high nitrogen affinity was correlated to higher range optima in the roadside than in the adjacent plots, while low nitrogen affinity resulted in lower range optima (Fig. 3A  and 3C). For the larger dataset A1, an additional trend related to temperature affinity was observed, with the highest difference in optima between roadsides and adjacent plots for non-native species with high indicator values for both nitrogen and temperature (upper right corner).
It is however noteworthy that non-native species with temperature affinities below 3 were not observed, so almost no non-native species with lower roadside optima were recorded. In dataset A2, only the correlation with nitrogen remained (Fig. 3C , LMM: Optima ~ 1. 78*OR N (P = 0. 06) + 0. 61*N (P = 0. 01) -0. 55*OR N *N (P = 0. 06) -1. 90 (P = 0. 02) ). Non-native species with lower temperature affinity tended to have broader range amplitudes than the more thermophilic non-natives (borderline significant in Table  3  for dataset A1). Range amplitudes did not correlate significantly with nitrogen affinity. Range optima of native species of European origin (48 % of the the 439 native species in dataset A1 and 38 % of the 125 native species in dataset A2) showed the same correlation with nitrogen as those from non-natives (Fig.
Accepted Ar ticle 'This article is protected by copyright. All rights reserved. ' (Table  3 ). The significant interaction of species origin with temperature affinity in dataset A1 resulted in lower optima for native species with a higher temperature affinity. In dataset 2, again a positive correlation of optima with nitrogen affinity could be observed, but it was less stronge than in nonnative species (Fig. 3D , model see previous paragraph). Range amplitudes for native species were on average always broader in roadsides. Patterns for temperature and nitrogen affinity in dataset A3 were not significant and are not shown. Regional models supported the abovementioned patterns (Table  3 ), although regional datasets were seldom large enough to allow the same model complexity. Differences in optima for native species were smaller than those for non-natives in MT and OR and showed a positive correlation with nitrogen affinity in SW.
Differences in amplitudes were also larger for non-natives in AU, MT and OR, while the observed positive correlations with temperature affinity could also be observed in AU, CLS and SW. The regional differences in optima correlated significantly with the average nutrient affinity of the species recorded in that region (cor = 0. 82, t = 3. 539, df = 6, P = 0. 012), with relatively lower roadside optima in regions with on average lower nitrogen affinities and vice versa. Table  3  shows that sufficient regional data to get a significant model was available for both parameters in three regions (MT, OR, SW) and for amplitude in an additional set of two regions (AU and CLS). For CLS, origin was not significant, and the model only holds for non-native species. In SW, model results only apply to native species.
Trends in the direction of the optimum for native species varied between regions, although differences were negative in all significant cases (Fig. 1B , Table  2 ). The final pattern depended on the average nutrient affinity of the species in the regional dataset. Regions with native species with lower nitrogen affinity showed lower optima in roadsides than in the natural vegetation, and vice versa. This could either be an artefact of the limited availability of Landolt values for non-European regions or link to varying patterns of soil fertility between regions.
Figure1. Differences (in elevational meters) in range optima (x) and amplitudes (y) between roadsides and adjacent plots for dataset A1 (top) and A2 (bottom), and for the global dataset (left) and the regional subsets (right). Species-and region-specific values are marked with small dots. In A and C, non-native species are marked with orange squares, native species with blue circles, in B and D they are left black for clarity. Overall average optima and amplitude values for natives and non-natives are marked respectively with a large blue dot and an orange square (A, C) (only the range amplitude for non-native species from dataset A1 (Fig. 1A) differed significantly from zero). Regional averages (B,D) are marked with numbers, with larger fontsize for significant results. 1 = AR, Argentina, 2 = AU, Australia, 3 = SW, Switzerland, 4 = CLC, Central Chile, 5 = CLS, Southern Chile, 6 = MT, Montana, 7 = OR, Oregon, 8 = NO, Norway.
Acknowledgements We express our gratitude to the data collectors in the different MIREN-regions and acknowledge the input from two anonymous reviewers. We gratefully acknowledge the grants from the  Research Foundation -Flanders (FWO)  to  J. J. Lembrechts  and the support from the  Swedish Research Council  ( VR 2012-6252 ) to  A. Milbau ,  A. Pauchard  and  M. Nuñez.
Simulated samples Simulated samples are used to model the signal and to determine the efficiency of the signal selection. Background samples are also simulated to optimize the selection criteria. The normalization and distribution of the background events are measured from data. The signal samples are generated using the Monte Carlo (MC) event generator PYTHIA 6. 424  [39]  for a wide range of m A and tan β values, as listed in Table  1 , for the AP and the GF production mechanisms. The φ production cross sections and their corresponding uncertainties are provided by the LHC Higgs Cross Section Working Group  [16] [17] [18]. The cross sections for the GF process in the m max h scenario are obtained using the HIGLU program  [40, 41] , based on next-to-leading order (NLO) quantum chromodynamics (QCD) calculations.
Systematic uncertainties The following sources of systematic uncertainties are taken into account, and the impact of one standard deviation change is reported in terms of a variation in the nominal signal efficiency defined in Section 5. The limited number of simulated events introduces an uncertainty in the signal selection efficiency that is at most 2. 0%. The muon trigger, reconstruction, identification, and isolation efficiencies are determined from data using a tag-and-probe technique  [38]. The uncertainty in the trigger efficiency correction is 0. 5%, whereas 1. 0% is assigned to the combination of uncertainties in muon reconstruction and identification, as well as on isolation efficiencies. A systematic uncertainty in the pileup multiplicity is evaluated by changing the total cross section for inelastic pp collisions in simulation.
Acknowledgments We congratulate our colleagues in the CERN accelerator departments for the excellent performance of the LHC and thank the technical and administrative staffs at  CERN  and at other  CMS institutes  for their contributions to the success of the CMS effort. In addition, we gratefully acknowledge the computing centres and personnel of the Worldwide LHC Computing Grid for delivering so effectively the computing infrastructure essential to our analyses. Finally, we acknowledge the enduring support for the construction and operation of the LHC and the CMS detector provided by the following funding agencies:  BMWFW  and  FWF (Austria) ;  FNRS  and  FWO (Belgium) ;  CNPq ,  CAPES ,  FAPERJ , and  FAPESP (Brazil) ;  MES (Bulgaria) ;  CERN ;  CAS, MoST , and  NSFC (China) ;  COLCIENCIAS (Colombia) ;  MSES  and  CSF (Croatia) ;  RPF (Cyprus) ;  MoER ,  ERC IUT  and  ERDF (Estonia) ;  Academy of Finland ,  MEC , and  HIP (Finland) ;  CEA  and  CNRS/IN2P3  (France);  BMBF ,  DFG , and  HGF (Germany) ;  GSRT (Greece) ;  OTKA  and  NIH (Hungary) ;  DAE  and  DST (India) ;  IPM (Iran) ;  SFI (Ireland) ;  INFN (Italy) ;  NRF  and  WCU (Republic of Korea) ;  LAS (Lithuania) ;  MOE  and  UM (Malaysia) ;  CINVESTAV ,  CONACYT ,  SEP , and  UASLP-FAI (Mexico) ;  MBIE (New Zealand );  PAEC (Pakistan) ;  MSHE  and  NSC (Poland) ;  FCT (Portugal) ;  JINR (Dubna) ;  MON, RosAtom, RAS  and  RFBR (Russia) ;  MESTD (Serbia) ;  SEIDI  and  CPAN (Spain) ;  Swiss Funding Agencies (Switzerland) ;  MST (Taipei) ;  ThEPCenter, IPST, STAR  and  NSTDA (Thailand) ;  TUBITAK  and  TAEK (Turkey) ;  NASU  and  SFFR (Ukraine) ;  STFC  ( United Kingdom );  DOE  and  NSF (USA).
Study design and sample Data were drawn from the 2010 French national sexual and reproductive health survey, FECOND, comprising 8475 individuals aged 15 to 49 years residing in France. Participants were selected following a two-stage probability sampling method. Phone numbers (including both landline and cell-phones) were generated using random digit dialling. One individual per phone number was selected for participation. The refusal rate was estimated at 20%  [22]. After verbal consent, participants responded to a 40min telephone questionnaire. The FECOND study was approved by the French Commission Nationale de l'Informatique et des Libertés and the current secondary analysis was approved by the Bloomberg School of Public Health Institutional Review Board at Johns Hopkins University. The present analysis was restricted to respondents aged 15 to 24 years (n = 2309) who reported ever having had sexual intercourse (n = 944 females and n = 731 males).
Acknowledgements We thank all women and men who participated in the FECOND survey. Funding This work was supported by a grant from the  French Ministry of Health , a grant from the  French National Agency of Research  (# ANR-08-BLAN-0286-01 ), and funding from  National Institute of Health and Medical Research (INSERM)  and the  National Institute for Demographic Research (INED). Caroline Moreau  is also supported by the  William Robertson  endowment funds and  Robert Blum  through the  William H. Gates Sr. endowment.
Data sources We use two different Danish register data sets that can be linked to each other thanks to their common firm identifiers. Both data sources are administered by Statistics Denmark, and together, they provide data for the time period 1995-2005. The master data set is the "Integrated Database for Labor Market Research" (henceforth IDA) database, a longitudinal employer-employee register that contains annual information for each individual employed in the recorded population of Danish firms during the period 1980-2005 (see Parrotta and Pozzoli 2012 for further details on the variables that are included in IDA). In our final data set, we include individuals (i) who are 18 to 60 years old, (ii) who have stable occupations (i. e. , students, trainees and part-time employees are disregarded), (iii) who have positive labor income and (iv) who belong to neither the top nor the bottom percentile of the earning distribution.
In addition, transitions that may have resulted from mergers or acquisitions, i. e. , transitions in which more than half of an enterprise's workforce moves to the same arrival firm, are not considered as job-to-job labor flows. The retrieved information is then aggregated at the firm level to obtain data regarding firm size, partial/total foreign ownership, whether the firm includes more than one establishment (plant) and detailed workforce composition characteristics -labor diversity, among others. 3 he second data source, REGNSKAB, provides the following information about the firms' business accounts: value added (calculated as the difference between total sales and the costs of intermediate goods), materials (intermediate goods), capital (fixed assets) and related industry. 4 All of the companies in the final sample that were used in the empirical analysis have at least 10 employees and are not in one of the following sectors: i) agriculture, fishing and quarrying; ii) electricity, gas and water supply and iii) public services.
The results from this additional exercise show that arrival firms benefit more in terms of the acquired knowledge from intra-industry worker flows than from inter-industry ones, as the estimated coefficient of our knowledge transfer measure for within-industry labor mobility flows is statistically larger than the estimated coefficient for between-industry flows. This may indicate that knowledge transfers can more easily yield productivity gains when they originate with  18  Information on patent applications is drawn from the database of patent applications sent to the European Patent Office (EPO) by Danish firms. Access to these data has been made possible by the Center for Economic and Business Research (CEBR), an independent research center affiliated with the Copenhagen Business School (CBS).
The data set covers a period of 28 years (1978-2005) and allows us to account for approximately 3,000 applicants, corresponding to nearly 2,500 unique firms. More details concerning the construction and composition of the data set can be found in Kaiser et al. (2015). 19 Total factor productivity is estimated separately by a 2-digit industry level by using ACF. co-workers who are employed in similar environments and core businesses. As in Stoyanov and Zubanov (2012), we therefore find that the knowledge introduced to firms by newly hired workers is mostly industry specific. All in all, these results for the different types of sending firms underpin the relevance of considering the channel of knowledge transfers to explain the main findings reported in the previous section. The results also allow us to safely dismiss the idea that the new hires may benefit the arrival firms exclusively when they originate from highly productive, innovative and internationalized firms, from employers that pay more (typically larger companies) or from firms with a large endowment of human capital due to a highly educated workforce, as the estimated coefficient for our knowledge transfer variable also remains positive and statistically significant when we don't consider those categories of firms.
ANTIANGIOGENIC AGENTS Over the past decade, numerous clinical trials involving novel agents in patients with NSCLC who progressed on first-line therapy reported modest improvements in progression-free survival (PFS) but no significant improvements in overall survival (OS). These include vandetanib (ZODIAC, ZEAL and ZEST trials),  [15] [16] [17]  aflibercept (VITAL),  18  bevacizumab (BeTa)  19  and sunitinib (SUN1087)  20 21  as monotherapy, or in combination with chemotherapy (docetaxel or pemetrexed) or erlotinib. In 2014, two antiangiogenic agents were approved for patients with advanced NSCLC after first-line chemotherapy. The results of the LUME-Lung 1 trial (NCT00805194, study 1199. 13) first led to European Union (EU) approval of nintedanib (Vargatef; Boehringer Ingelheim, Ingelheim, Germany), in combination with docetaxel, for the treatment of patients with locally advanced, metastatic or locally recurrent NSCLC of adenocarcinoma histology after first-line chemotherapy.