dataesr/scientific-paragraphs-categorization huggingface dataset:
At L1, the total transverse energy summed over ECAL and HCAL was required to be greater than a given threshold (both 15 and 40 GeV thresholds were used). Only the lowest threshold trigger was prescaled. Track reconstruction for the HLT was based on the three layers of the pixel detectors, and required that the track originates within a cylindrical region centered on the nominal interaction point. This region has a length of 30 cm along the beam direction and a radius of 0. 2 cm perpendicular to it. For each event, the vertex reconstructed with the highest number of tracks was selected. The number of tracks (N online trk ) with |η| < 2. 4, p T > 0. 4 GeV/c, and a distance of closest approach of 0. 12 cm or less from this vertex was determined for each event. Data were taken with thresholds N online trk >60 or 85 (based on events selected with a L1 total energy larger than 15 GeV), and 110 (based on events selected with a L1 total energy larger than 40 GeV).
The effect is most evident in the intermediate transverse momentum region between 1 and 2 GeV/c. The near-side longrange yield obtained with the ZYAM procedure is found to be consistent with zero in the lowmultiplicity region, with an approximately linear increase with multiplicity for N offline trk  40. The new 13 TeV data presented in this Letter significantly extends the multiplicity coverage achieved by previously data at √ s = 7 TeV. Finally, a strong collision system size dependence is observed when comparing data from pp, pPb, and PbPb collisions. Comparing the pp data at √ s = 7 TeV and 13 TeV, no collision energy dependence of the near-side associated yields is observed. acknowledge the computing centers and personnel of the Worldwide LHC Computing Grid for delivering so effectively the computing infrastructure essential to our analyses.
Based on the chemical components, studies on potential environmental and health hazards as well as hazard classification data were retrieved from public databases inter alia including OECD's eChemPortal, databases at the European Chemical Agency (ECHA), US Environmental Protection Agency (EPA) Ecotox Database and hazard ratings by the GESAMP Working Group on the Evaluation of the Hazards of Harmful Substances Carried by Ships (IMO, 2012b). The hazard identification was based on legal classification of identified components. The majority of documents were assessment reports, and hazard classifications developed by the industry for the industry. In many cases no detailed scientific study reports were available. All these had in common that they were "secondary data" based on evaluations of test reports or sometimes even scientific estimations.
Can online social contacts replace the importance of real-life social connections in our pursuit of happiness? With the growing use of social network sites (SNSs), attention has been increasingly drawn to this topic. Our study empirically examines the effect of SNS use on happiness for different subgroups of young adults. More specifically, we examine whether the effect of SNSs on happiness is moderated by individual social capital, as measured in terms of frequency of social contacts and feelings of loneliness. Using Dutch data from the Longitudinal Internet Studies for the Social Sciences (LISS panel), we provide robust empirical evidence that there is, on average, no relationship between the amount of time spent on SNSs and happiness. However, we find a negative association between the numbers of hours spent on SNS and happiness for SNS users who feel socially disconnected and lonely.
Data and Variables To analyse the relationship between SNS use, individual social capital, and happiness among young adults (15-44 years), we used the Dutch Longitudinal Internet Studies for the Social Sciences (LISS) panel for the years 2012-2013. In the LISS survey, individuals report on several aspects of their life, including their happiness, Internet use, and individual social capital. Our sample included 1,339 respondents who indicated that they ever used SNSs. 3 Of these respondents, 605 individuals completed the survey in both 2012 and 2013. 4   Happiness. In our research, happiness was measured using an 11-point scale of happiness in response to the question "On the whole, how happy would you say you are?", 0 being equal to "totally unhappy" and 10 being equal to "totally happy". 5 Activity on Online Social Network Sites.
Control Variables. In our analysis, we included control variables that could confound the relationships among SNS use, individual social capital and happiness. The control variables included in the analysis were other time spent online, online gaming, trust in other people (i. e. , collective social capital), gender, age, civil 6 Please note that data were aggregated because there were few young adults in the sample who indicated that they were seeing friends very frequently or seeing friends never or only a few times per year. When re-estimating our baseline regressions (Table  3 ) using the frequency of contacts as continuous variable, the main conclusions do not change. These results are in Appendix B, Table  1. 7 The categories "Don't know" and "Not Applicable" were completed by fewer than 3.
Empirical Strategy To analyse the relationships between SNS use, individual social capital, and happiness, we used a random effects model in which we considered that observations are clustered within individuals given the longitudinal structure of the dataset; for some individuals, we had observations at two points in time  (2012 and 2013). A random effects estimator is preferred over a simple pooled linear regression (pooled OLS) because ignoring the clustering of observations within individuals can result in biased coefficients and standard errors  (Hox 2002). We prefer a random effects estimator to a fixed effects estimator because the LISS data have a limited time dimension, information is not available at both time points for all individuals, and most of the variance is between individuals rather than within individuals.
The panel was extracted from the LISS database and uses information from 3 panels of the core study: "Personality Questionnaire-LISS Core Study", "Social Integration and Leisure Questionnaire, LISS Core study", and "Demographics Questionnaire". The decrease in sample size is caused by the fact that only a limited (random) sample was asked to complete the questionnaire on online social network usage in 2013. Hence, there is indication of panel attrition bias.
Data from secondary samples within each primary period are analyzed using closed models to derive estimates of capture probability and population size. Apparent survival and temporary emigration are estimated using open models by collapsing data from the secondary periods. In general, RD estimates are more accurate and precise than those obtained through the application in sequence of closed and open models because they allow estimation of survival and abundance while accounting for temporary emigration  [45, 48, 54]. In this study, our objectives were threefold. First, we addressed different degrees of site fidelity by carefully designing the sampling periods to meet RD model assumptions. Second, we applied a RD approach to striped, common and bottlenose dolphins in the Gulf of Corinth and estimated their abundance and survival in years 2011-2015.
Capture-recapture matrices We considered two separate datasets: one for bottlenose dolphins and one for striped, common and intermediate individuals. For each dataset, we built a capture matrix (i. e. a binary table with individuals in rows and sampling occasions in columns). The entries of the matrix are 1s if an individual is detected in a sampling occasion and 0s if the individual is not detected. A "capture" was defined as a photograph of sufficient quality of an individual dolphin's distinctively marked dorsal fin, obtained during a sampling occasion. These matrices were used to estimate abundance with RD models  [46, 47]. These models are based on primary and secondary sampling occasions: the population is assumed closed between secondary occasions and open between primary occasions.
A set of 30 models composed of parameters for population size (N), apparent survival rate (S), temporary migration rates (U", U') and capture probability (p) were fitted to the data with program R  [61] , package RMark  [62] , to construct models from program MARK  [63]. The following three temporary emigration patterns were considered: 1) no temporary emigration (U" = U' = 0); 2) random temporary emigration, (U" = U') where the probability of an individual being present in the study area is not dependent on whether or not it was present in the study area in the previous sampling period; and 3) Markovian temporary emigration (U",U') where the probability of an individual being present in the study area is conditional on whether it was present in the study area before. For all three patterns of temporary emigration, we considered models where apparent survival was either constant or varying between primary occasions and capture probability was either constant, or varying with time (between secondary occasions, between primary occasions, or both).
For bottlenose dolphins we restricted the length of primary sampling occasions to 4-9 days so that movements could be negligible. A limited number of deaths might have occurred within each primary period. Because striped common and bottlenose dolphins are long-lived mammals with high survival rates  [67] , we considered as negligible the number of deaths that may have occurred within the duration of primary occasions (up to four months). Calves were all unmarked (distinctiveness category D3) and therefore they were not included in the estimation of D1 individuals. 4) Capture and survival probability do not vary among individuals. We tested these assumptions by i) fitting models incorporating heterogeneity in capture probability and ii) by running specific tests using program U-CARE  [68]  on the data pooled by primary occasions.
We used a power analysis to calculate the minimum rate of change (R) that we were able to detect with an acceptable statistical power of 0. 8  [56]  given the duration of our monitoring plan (5 yearly samples) and the precision of our estimates. Analyses were conducted using the package "fishmethods"  [72] , setting the parameters as follows: the probability of Type 1 error α = 0. 05, a linear type of change in abundance and a one-tailed test. For CR estimates the CV is expected to be proportional to the square root of abundance  [56]. We calculated the overall CV of the monitoring period by averaging the annual CVs of estimates  [53, 73].
Model selection and abundance of marked individuals For the striped and common dolphin dataset the overall goodness of fit test was significant (χ 2 = 18. 03; p < 0. 05, df = 8). The lack of fit was mainly due to the significance of TEST 3. Sm that has no clear biological interpretation  [69]. We therefore corrected our estimates for a variance inflation factor ĉ = 18. 03/8 = 2. 25 to accommodate for the lack of fit. The best-fitting model included a constant apparent survival rate, monthly and yearly variation in capture probability, and no temporary emigration between years. No model was within 2ΔQAICc from the best-fitting model (Table  3 ). Models including individual heterogeneity of capture probability were not supported (data for ΔQAICc > 47 are in S3 Table ). Model parameter estimates are shown in Table  4.
For the bottlenose dolphin dataset, there was no sign of lack of fit (χ 2 = 1. 62, p > 0. 5, df = 7). The best-fitting model had daily variation in capture probability, constant survival, and random temporary emigration between years (Table  5 ). Six more models were within 2ΔAICc from the best-fitting model (Table  5 ), and therefore we resorted to model averaging considering the first six models in Table  6  and obtained the model-averaged estimates listed in Table  6. The model-averaged temporary emigration probability (Ƴ') was estimated to be 0. 16 (SE = 0. 13). Models with ΔAICc > 4. 99 are shown in S4 Table.
Mark ratio, total abundance and species proportions All estimates are reported with their 95% confidence interval between parentheses. For striped, common and intermediate dolphins the mark ratios are reported in Table  4. Using these proportions, the cumulative total population abundance was 1,593 (1,257-2,018) in 2011, 1,272   3. First ten models applied to the striped and common dolphin dataset, ranked by lowest QAICc, number of parameters (n par) and difference in QAICc scores (ΔAICc). QAICc weights indicate strength of evidence for a given model. S(year) = yearly variation in apparent survival; S(. ) = no variation in apparent survival; p(year. month) = yearly and monthly variation in capture probability; p(month) = monthly variation in capture probability. We applied to these cumulative estimates the estimated species proportions of the population: 0.
Temporary emigration patterns We tested a range of models on different dolphin datasets, to account for presence and absence of temporary emigration. The best models supported previous knowledge about movement  patterns of the three species in the Gulf of Corinth. For striped and common dolphins, absence of temporary emigration was the most likely scenario indicating that no movements occurred outside of the study area between primary sampling occasions. However, this result refers only to the intervals between primary sampling occasions and cannot be extended to other time frames. Random temporary emigration was instead detected for bottlenose dolphins and each individual had about a 16% probability of being outside the study area during a primary occasion. Such result is in agreement with information reported by  [26].
815 ± 0. 083  [38]. Two factors may bias our survival estimates downwards. First, the presence of subadults that have lower survival rates than adults  [67]. Second, the occurrence of transient individuals  [26]  that may cause an underestimation of apparent survival  [92]. This phenomenon was not detected by a TEST 3. SR performed on our dataset, likely due to the low power of such test with reduced sample sizes  [93].
Acknowledgments Field work in Greece was conducted under research permits issued by the  Hellenic Ministry of Environment , Energy and  Climate Change , and was coordinated by the  Tethys Research Institute until July 2011. Special thanks to OceanCare, especially  Sigrid Lu ¨ber ,  Silvia Frey ,  Vera Bu  ¨rgi and  Fabienne McLellan  who greatly helped us develop this project. Bernd and  Melany Wu  ¨rsig offered opportunities and inspiration. Paolo Ciucci  offered guidance, advice and encouragement. We are grateful to our collaborators  Mariana Ferreira Da Silveira ,  Dagmar Kna ¨bel ,  Sarah Piwetz ,  Eva Greene ,  Ana Catarina Cardoso Fonseca ,  Valeria Senigaglia ,  Annalucia Cantafaro ,  Philippa Dell ,  Riccardo Grigoletto ,  Manon Roucaute ,  Nicola Stoppelli  and all the volunteers who contributed to data collection and management.
funded by OceanCare, a non-profit nongovernmental organization committed to ocean conservation (https://www. oceancare. org/en/). No grant number is available. OceanCare provided support in the form of salaries for co-authors GB and SB, but did not have any additional role in the study design, data collection and analysis, decision to publish, or preparation of the manuscript. The specific roles of these authors are articulated in the 'author contributions' section. Nina Luisa Santostasi  was supported by a research grant by  Sapienza University of Rome  (http://www. uniroma1. it/didattica/borse-di-studio/borse-diperfezionamento-allestero). No grant number is available. Research permits issued by the  Hellenic Ministry of Environment , Energy and  Climate Change  do not require further assessment by an animal ethics committee, and the benign research conducted for in the context of this study does not raise ethical issues.
Acknowledgments: We would like to acknowledge support from the  National Health and Medical Research Council (NHMRC) of Australia  and the  Victorian State Government Operational Infrastructure Support and Australian Government NHMRC IRIIS. Disclaimer: The opinions expressed in this paper are those of the authors and may not reflect those of their employing organizations. Piero L. Olliaro  and  John C. Reeder  are staff members of the  World Health Organization (WHO) ; the authors alone are responsible for the views expressed in this publication and it does not necessarily represent the decisions, policy, or views of their organizations.
Moreover, OTX015 was the first BET inhibitor to have shown clinical activity, with three ongoing phase I clinical trials in hematologic malignancies (NCT01713582) and solid tumors (NCT02259114, NCT02296476)  [19] [20] [21]. Recently, BET inhibitors have been shown to be active in TNBC pre-clinical models  [22] , and OTX015 demonstrated strong in vitro and in vivo synergy with the mTOR inhibitor everolimus (RAD001) in lymphoma models  [14, 23]. The current study was designed to investigate the in vitro and in vivo antitumor activity of OTX015 in TNBC cell line models as a single agent and in combination with everolimus.
001). These changes were accompanied by weakened mitochondrial activity, as reflected by a marked reduction (p-value and FDR < 0. 001) in the expression of DLAT (which codes for the E2 component of the pyruvate dehydrogenase complex) and genes with a key role in modulating mitochondrial protein levels (i. e. MRPL46 and MRPS12). Furthermore, c-MYC was not a target gene among the 20 most enriched sets with a negative normalized enrichment score (NES). OTX015 prevents BET protein-mediated recognition of acetylated lysine residues of histones, altering gene transcription patterns  [12]. A GSEA using the CMAP database indicated a strong similarity (p and FDR < 0. 001) between the GEP of OTX015 and the up-regulated and down-regulated signatures of histone deacetylase inhibitors tricostatin A (NES 3.
Indeed, JQ1-mediated blockade of this interaction suppressed tumorigenicity and stemness properties in these cells. In addition, Herrmann et al. have described the antiproliferative and pro-apoptotic effects of JQ1 in patient-derived acute myeloid leukemia cells, particularly affecting leukemia stem and progenitor niches  [44]. Together, these findings point to BET proteins as promising drug targets to address the CSC component of tumorigenesis in different malignancies. At least three other small molecule BET inhibitors are currently being evaluated in clinical trials in a range of tumor types (NCT01949883, NCT01587703, NCT01987362), highlighting the increasing interest in this class of drugs. Our pre-clinical results show the therapeutic potential of OTX015 both as a single agent as well as in combination with everolimus for the treatment of TNBC.
Drug combination studies Cells were seeded at a density of 20000 cells/ml in 96-well plates (100 µl/wells) and treated 24 h later with different concentrations of OTX015 or everolimus, or combination of both drugs for 72 h. Cells were then incubated with 0. 8 mg/ml MTT (3-[4,5-dimethylthiazol-2-yl]-2,5-diphenyltetrazolium bromide) for 2-4 hours. Cell pellets were resuspended in 0. 05 ml DMSO and absorbance was measured at 560 nm using an Infinte 200 microplate reader (Tecan Trading AG, Switzerland). OTX015 and everolimus EC 50 values were determined as previously mentioned using Prism 5. 00 software. Assays were performed in triplicate in at least three independent experiments. Results were further analyzed according to the Chou-Talalay algorithm with Compusyn software  [45]. Based on the calculated combination indexes values, < 0.
Gene expression profiling RNA was extracted as described above and hybridized on HumanHT-12-v4 Expression BeadChip (Illumina ® , USA) microarrays. Gene expression values were processed and analyzed as previously reported  [14]. Changes in expression level were considered statistically significant when the absolute value of logarithm of foldchange (|LogFC|) was ≥ 0. 5 and the adjusted p-value (q) was ≤ 0. 05. Functional gene set analysis was performed using gene set enrichment analysis (GSEA) with the complete MSigDB v5. 0 database and drug signatures created from the Connectivity Map database (CMAP). In addition, gene set expression values were computed performing a 'gene set variation analysis' (GSVA). In these studies, a p-value ≤ 0. 05 and a false discovery rate (FDR) < 25% were considered statistically significant.
Raw data will be available at the National Center for Information Gene Expression Omnibus (https://www. ncbi. nlm. nih. gov/geo/) database. Access code GSE79721. Mice were subcutaneously injected in the right flank with 10 × 10 6 MDA-MB-231 cells. When average tumor weight was ≈130 mg, mice were randomized (nine animals/group) to one of the following experimental groups: vehicle (for OTX15, water, twice daily, oral; for everolimus vehicle, 5% Tween-80/5% polyethylene glycol 400, thrice weekly, intraperitoneal); 50 mg/kg OTX015, twice daily, oral; 2 mg/kg everolimus, thrice weekly, intraperitoneal; 50 mg/kg OTX015 + 2 mg/kg everolimus, according to the single agent dosing schedules. In the experiment with paclitaxel, mice were randomized (eight animals/group) to vehicle (cremophor:ethanol 1:1, then diluted 1:5 with saline; once weekly, intravenous) or 0.
Statistical analyses Graphical results are plotted as mean ± standard error of the mean (SEM) of at least three independent experiments. Statistical analysis was performed by oneway ANOVA followed by a Student-Newman-Keuls (SNK) a posteriori test, Student t-test for independent samples with equal or unequal variances, as appropriate, with InfoStat software (Argentina), or by two-way ANOVA followed by a Bonferroni a posteriori test employing Prism 5. 0 software. Transformation of the variable to obtain homoscedasticity was applied before the ANOVA tests if required. A p-value ≤ 0. 05 was considered statistically significant.
g. Chiesi et al. 2005; Powell et al. 2010). While each of these tools provides information on a specific aspect, only simulation models can be applied in a prognostic way to hypothesize the effects of global environmental changes at various spatial and temporal scales  (Turner et al. 2006; Chiesi et al. 2011; Huber et al. 2013). One of these models is the biogeochemical BIOME-BGC which has been successfully applied in a large number of investigations all over the world (e. g. Law et al. 2004; Ichii et al. 2005; Chiesi et al. 2007; Tatarinov and Cienciala 2009; Ueyama et al. 2009; Pötzelsberger et al. 2015). The use of this model for regional-scale studies requires the availability of spatially extended daily weather datasets with a suitable resolution, typically around 1 km  (Hofstra et al.
2009). While these datasets have been produced for the conterminous USA  (Thornton et al. 1997) , up to now this has not been the case for the continent of Europe, where the existing long-term daily weather datasets have lower spatial resolution. For example, the dataset of minimum and maximum temperature and rainfall released by  Haylock et al. (2008)  (E-OBS) has a grid size of 0. 25°(about 30 km). Concerning only the Italian national territory, a first attempt was made by  Maselli et al. (2012)  to downscale the E-OBS dataset to 1-km resolution. More recently, this work has been extended by  Moreno and Hasenauer (2015)  to perform a similar downscaling for the whole of Europe. The objective of the current paper is to assess the capability of BIOME-BGC to simulate beech gross primary production (GPP) using this spatially extended 1-km E-OBS dataset under present and expected climatic conditions.