dataesr/scientific-paragraphs-categorization huggingface dataset:
Nevertheless, regardless of the methodological differences between the two inventory approaches, both of them may use the indicators that come from the common NFI indicator set. Regarding the data about area component, significant efforts should be expanded into the collection of forest area data. While one possibility is the utilisation of landscape inventories of various sampling designs, which may provide accurate data on forest cover and fragmentation, a more traditional way is via remote sensing techniques, which are becoming an indispensable part of NFIs nowadays  (Barrett, McRoberts, Tomppo, Cienciala, & Waser, 2016). The present sets of NFI indicators represent a vital indicator fraction compared to the needs of the objective forest habitat type conservation status assessment. Although these indicators could significantly contribute to more reliable assessments, the number of indicators presented in this study is insufficient, particularly for some of the conservation status components.
Future research should thus focus on the search for indicators assisting in assessing the area and function conservation status components and for some specific low-cost indicators (e. g. , for ground vegetation). The search should be performed in collaboration with the NFI specialists, whereby an emphasis should be given to the development of measurement and observation protocols and data sources. Of significant importance should also be the exploration of statistical correlations between the indicators, as only mutually independent indicators can satisfactorily describe true components of forest habitat type conservation status.
Acknowledgements This research was largely funded by the project  DIABOLO  ( European Union 's  Horizon 2020 research and innovation programme  under grant agreement No  633464 ). Additional support came from the  Research Programme   P4 -0107 , financed by the  Slovenian Research Agency (ARRS). We would like to thank  Elena Robla ,  Roberto Vallejo , and  Vicente Sandoval  of the  Ministry of Agriculture, Fisheries and Food  for providing access to the Spanish NFI data-sets. The data for Italy were provided by the National inventory on forests and forest carbon pools  INFC2005  (www. inventarioforestale. org, www. infc. it),conducted by the  Carabinieri command  for forest protection (former National Forest Service) and scientifically and technically supported by the  CREA-Research centre  for forestry and wood (former  CRA-MPF ).
The hemichannel Panx-1, showed higher mRNA levels in DD samples than in LD samples (p = 0. 017) (Figure  3F ). Figure  3  shows gene expression levels of different genes related to purinergic metabolism or ATP release in renal samples from LD and DD. mRNA levels of CD73 (Figure  3A ) and S-adenosyl-L-homocysteine hydrolase (SAHH) (Figure  3E ) was lower in DD than in LD (p = 0. 009 and p < 0. 001, respectively), whereas we did not observe any difference in CD39 (Figure  3B ), adenylate cyclase (ADCY) (Figure  3C ), or adenosine kinase (ADK) (Figure  3D ) mRNA expression levels. The hemichannel Panx-1, showed higher mRNA levels in DD samples than in LD samples (p = 0. 017) (Figure  3F ). SAHH (E), PANX1 (F)), as determined by qPCR analysis, in renal samples from LD and DD. Data are expressed as box-and-whisker plots.
Protein Expression in Renal Biopsies from DD and LD Protein levels were measured in pre-implantation renal tissue of both DD and LD, and these data are summarized in Figure  4. It shows that the expression of the active dimer form ofA 2A R (p = 0. 004) (Figure  4A ), CD163 (p = 0. 012) (Figure  4B ), and pPKA (p = 0. 018) (Figure  4C ) proteins was significantly higher in kidney extracts from DD compared with LD samples. Levels of pCREB were also marginally higher in DD, although statistical significance was not reached (p = 0. 070) (Figure  4D ). (C) and phospho CREB (pCREB) (D) in renal samples from DD and LD. Total protein was isolated from kidney tissues, as described in Materials and Methods, and separated by SDS-PAGE. Protein expression semiquantitation performed by densitometric analysis of Western blots.
Statistics GraphPad Prism software (GraphPad Software, San Diego, CA, USA)was used to perform statistical analysis. Results are expressed as the mean ± standard deviation (lower and upper extremities) and percentages, as appropriate. Student's t-test was applied to compare means. When our data did not follow a Gaussian distribution, nonparametric tests such as the Mann-Whitney U test and Spearman correlation were used for data analysis. All p values were two-sided, and p < 0. 05 was considered significant. Spearman test significance was confirmed by p-value analysis using the Benjamini-Hochberg procedure with a criterion of <5% False Discovery Rate  [65].
Acknowledgments: We thank all the patients who agreed to participate in this study. We would like to thank  Jordi Bover  for his expert advice and assistance in manuscript review. Many thanks also to  Beatriz Bardají-de-Quixano  for her valuable advice and recommendations and to  Rick Mills  for his help in manuscript proofreading. Our nephrology research group belongs to a  Consolidated Research Catalan Group  ( AGAUR 2009/SGR-1116 ) and to REDinREN ( RD12/0021/0033 ). We are also grateful to  Sandoz Farmaceutica S. A. for its support.
Statistical analysis To investigate the cross-sectional associations of DHEA-S with subjects' characteristics and respiratory outcomes, data from ECRHS-2 and ECRHS-3 were pooled together, and we fitted multilevel models with subject ID (as a level-2 unit, to account for repeated measures) nested in the center (level-3). Associations were modeled using mixed-effects linear or Poisson with robust error variance regressions, with DHEA-S z-score included either as a continuous variable or as a categorical variable. Body mass index, smoking history, education as proxy of socioeconomic status, physical activity, and menopausal status were considered for adjustment. Age was included as precision variable because of its strong association with spirometry outcomes. Stratified analyses were performed for median age, to investigate if a different association was observed in older women (when DHEA-S is lower), and for menopausal status, as the effects of DHEA-S might change with the hormonal context.
One observes that the cyclic bandwidth corresponds to the minimum steps needed to go from one endpoint to the other endpoint of these edges either in a clockwise or counterclockwise direction on the cycle graph. First introduced to formulate a design problem in the area of ring interconnection networks  [1] , the CBP has also been found to be a relevant model for a number of additional applications including VLSI design  [2] , data structure representations  [3] , code designs  [4]  and parallel computer systems  [5]. In terms of computational complexity, the decision version of the CBP is N P-complete  [6]. Consequently, the CBP is computationally challenging for solution methods. Given the relevance of the problem, a number of studies have been proposed in the literature. A majority of early studies are of theoretical nature and focused on finding exact cyclic bandwidths for special graphs or determining lower bounds for general graphs.
Tuning of parameters In order to automatically determine the most suitable combination of input parameter values for the proposed N ILS algorithm, we have decided to employ I/F-Race, an iterated procedure based on the use of racing and Friedman's non-parametric two-way analysis of variances by ranks. It is part of the popular irace package  [26, 27]  for automatic parameter configuration. For this tuning experiment, the irace parameter controlling the maximum number of runs of the algorithm being tuned (tuning budget) was fixed to 2000. Then, a subset of 10 instances, identified as challenging for the state-ofthe-art algorithms  [15, 17] , was selected and consistently used. This subset includes certain Harwell-Boeing instances (bcsstk06, 494 bus, dwt 592, 662 bus, 685 bus, can 715 ), as well as some graphs from different standard topologies (path1000, cycle1000, mesh2D20x50, mesh3D13, tree2x9, caterpillar44, hyper-cube11 ).
Chemical composition of the alteration layers The chemical analysis of the hydrated layers has been carried out by SEM EDX at the surface of the glass plates aged at 40 °C, 85 RH % for 6 months and the results are given in Table  3. As far as possible, the analyses were performed outside the calcite and alkali carbonate salts. The pristine glasses were used as standards for the quantification. The contributions of the Pt layer and the pristine glass underneath the hydrated layer, were taken into account in the STRATAGem software by using a model of stacked layers with known thickness. The analyses totalized 91-97 wt%, the difference to 100 wt% being attributed to the contribution of protons (for about 1 wt%, according to the H 2 O content of about 10 wt%, Table  3 ) and to the porosity of the hydrated layer.
Otherwise, SEM observations were performed on the edge, on a freshly fractured sample, preferentially in backscattered electron mode to visualize the chemical contrast at the interface-altered layer-pristine glass. These observations were performed in different areas of the same aged glass plate and we give an average thickness e a. Chemical compositions of the alteration layers were measured by EDX with the system Bruker QUANTAX 400, at the same time as SEM observations. Analyses were performed on the glass plate surface, outside any defect or crystal, according to the operating conditions and the specific quantification method described in ref. 14. In particular, for the quantification with the STRATAGem data-processing software, glass plates were modelled as stacks of several layers of known thickness: alteration layer/Pt or pristine glass/alteration layer/Pt when the alteration layer thickness was smaller than the analyzed depth.
ACKNOWLEDGEMENTS The authors gratefully thank  PSL University  for the funding of this work,  Marie-Hélène Chopinet ,  Sophie Papin  and  Kamila Plevacova  ( Saint-Gobain Recherche, France ) for the glass fabrication. Mélanie Moskura  ( NIMBE, CEA ) is acknowledged for her help in conducting the NMR experiments and  Nicolas Menguy  for having performed the TEM characterizations.
Flow cytometry Bodipy 493/503 (3 mg/ml) and Bodipy FL C16 (1 mM) were diluted in complete media and add to adherent cells for 30 minutes at 37 C. DAF-FM (2 mg/ml) was diluted in PBS and incubated for 30 minutes at 37 C. The cells were then washed with PBS, detached and analyzed. Before analysis, cells stained with DAF-FM were left to rest for 20 minutes, to complete de-esterification of intracellular diacetates. Surface staining was performed with the following antibodies: BV605 anti-mouse CD11c, clone N418; APC anti-mouse I-A/I-E, clone M5/114. 15. 2; BV785 anti-mouse NK1. 1, clone PK136; PE/Cy7 anti-mouse CD11b, clone M1/70; BV711 anti-mouse Ly6G, clone 1A8; PerCP/Cy5. 5 anti-mouse Ly6C, clone HK14. Samples were acquired on a LSRII flow cytometer (BD Biosciences) and data analyzed using FlowJo software.
High performance liquid chromatography Glucose and lactate were quantified in cell culture supernatant using HPLC technology (Gilson bomb system); HyperREZ XP Carbohydrate H + 8mM. Samples were filtered with a 0. 2 mm filter and the mobile phase (0. 0025M H 2 SO 4 ) was similarly filtered and degasified for 30 minutes. Each sample was analyzed using the following running protocols (sensitivity 8): 15 minutes at a constant flux of 0. 7 ml/min at 54 C for glucose and lactate detection. The peaks were detected in a refractive index detector (IOTA 2, Reagents) and integration was performed using Gilson Uniprot Software, version 5. 11.
Population Cofactors For determination of population-level influences on patterns of pathogen exposure, factors common to all persons in a given department or village were also examined. We obtained population density (no. persons/km 2 ) at the department level by dividing population size (no. inhabitants/department based on 2003 national census data, https://www. citypopulation. de/php/gabon-admin. php) by department area (km 2 ). Average household wealth and frequency of insecticide-treated mosquito net (ITN) ownership per department were obtained from the Demographic and Health Surveys program 2012 survey for Gabon  (22). Geographic displacement of households in these data remained within administrative boundaries; however, wealth and ITN data were missing for 7 departments (Appendix Table  1 ).
prevalence as the response variable, and province as a random variable to limit pseudoreplication. The inclusion of province as a random variable also enabled us to account for yearly and seasonal differences in prevalence because all departments within a given province were sampled within a single month-long field mission. To meet assumptions of normality, antibody prevalence, Plasmodium parasite prevalence, and ITN ownership frequency were arcsine squareroot transformed, population density and average wealth were log-transformed, and data points were weighted by the number of persons tested in each department. Data for the 7 departments with missing wealth and ITN data were excluded from the multiple regression model. At the individual level, we used multiple logistic regression (implemented as a generalized linear mixed effects model with binomial error distribution via the glmer function of package lme4) to test whether persons with Ebola antibodies were more or less likely than those without Ebola antibodies to also be infected with malaria parasites.
Results A total of 4,272 volunteers from 210 villages were enrolled in the study. Among those sampled, we obtained data on both malaria status and Ebola antibodies from 4,170 persons: 2,199 (52. 7%) female and 1,971 (47. 8%) male participants, 16-90 (median 49) years of age. These data showed that across Gabon, 2,190 (52. 5%) persons were infected with >1 species of Plasmodium, 638 (15. 3%) were positive for ZEBOV-specific IgG, and an overabundance of 425 (10. 2%) were in both categories (Figure  1 ; χ 2 = 59. 4, df = 1, p<0. 0001). Because of missing data, we analyzed individuallevel risk factors for exposure to both pathogens on a subset of 3,912 persons (Table ; Appendix  Table 1). At the population level, we found a striking positive correlation between the geographic distributions of Ebola virus exposure and Plasmodium parasite infection, measured as the prevalence of each across administrative departments (Figure  2 ; Spearman rank correlation coefficient ρ = 0.
TOAR took advantage of the IAGOS (In-Service Aircraft for a Global Observing System) database  (6) , which contains ozone observations from more than 60,000 commercial aircraft flights worldwide, to quantify ozone trends above five regions of the Northern Hemisphere (northeastern United States, Germany, India, Southeast Asia, and Northeast China/Korea) during 1994-2014. Relatively weak ozone increases (<7% decade -1 ) occurred above northeastern United States and Germany but not at all tropospheric levels and not in all seasons. However, ozone increased in all seasons and at most levels above the three Asian regions (4 to 70% decade -1 ). An independent analysis of IAGOS observations reported widespread upper tropospheric ozone increases above several regions in the northern midlatitudes for the period 1994-2013 (4 to 5% decade -1 ), but the study did not address the tropics or the lower or mid-troposphere (MT)  (7).
IAGOS data The IAGOS program is a European Research Infrastructure that has measured ozone worldwide since 1994, using instruments onboard commercial aircraft of internationally operating airlines  (6). Ozone is measured on all flights using a dual-beam ultraviolet absorption monitor (time resolution of 4 s) with an accuracy estimated at about ±(2 nmol mol -1 + 2%)  (9, 10). Because most IAGOS aircraft have belonged to airlines based in Europe since the program's inception, Western Europe is the program's most frequently sampled region of the world. Above northwestern Europe (0°E to 15°E, 47°N to 55°N) IAGOS aircraft measured 34,600 ozone profiles between 1994 and 2016, which are approximately four profiles per day. IAGOS aircraft takeoff and land at any time of day and data from all hours of the day are used in this analysis.
Any ozone observation with a pressure value less than the lower bound of the tropopause layer was removed from the dataset. The 11 study regions (Fig. 1 ) were chosen because they were the only regions available with frequent sampling in both the early  (1994) (1995) (1996) (1997) (1998) (1999) (2000) (2001) (2002) (2003) (2004)  and late periods (2011-2016) between 1994 and 2016, which allows the calculation of ozone changes over periods spanning two decades (see table  S1 ). For the years 2005-2010, several regions have large data gaps, and therefore, we did not include these years when comparing all regions. However, the ozone trend values (see the "Trend calculation" section) are based on all available ozone profiles, and they are not dependent on the choice of the early and late periods.
The length of these time series (>20 years) is advantageous because they are long enough to largely overcome the influence of internal climate variability, such as El Niño-Southern Oscillation  (12) , on long-term ozone trends  (13). However, we cannot rule out some impact from internal climate variability on the observed trends. Several studies over the past 20 years have examined the regional representativeness of IAGOS observations  (4, 9, (14) (15) (16) (17)  and have established that the data compare well to regional surface and free tropospheric ozonesonde records and that the data are not biased. For example, a recent study showed that IAGOS observations above Europe are representative of regional-scale surface ozone observation across Western Europe  (15). They evaluated IAGOS ozone observations against rural ozone monitoring sites at low and high elevations across a broad region of Western Europe.
They concluded that the IAGOS data are not biased: "Based on 11 years of data  (2002) (2003) (2004) (2005) (2006) (2007) (2008) (2009) (2010) (2011) (2012) , this study thus demonstrates that IAGOS observations in the lowest troposphere can be used as a complement to surface stations to study the air quality in/around the agglomeration, providing important information on the vertical distribution of pollution. " The strong winds and the high meteorological variability in the MT and upper troposphere (UT), coupled with ozone's long lifetime [several weeks in the FT (1)] relative to transport time scales, also make IAGOS observations representative of the FT. There is no obvious influence from aircraft pollution in the flight corridors that span broad regions of the Northern Hemisphere and are also routinely sampled by ozonesondes and satellites.
Trend calculation For each IAGOS region and for 15 pressure surfaces spaced every 50 hPa from 950 to 200 hPa, we first calculated monthly mean ozone values and used these monthly means to construct a mean 1994-2016 seasonal cycle. This mean seasonal cycle was then used to deseasonalize the individual IAGOS observations on the same pressure level. The linear trend was then calculated using all available ozone profiles between September 1994 and December 2016 (1998-2016 for the Persian Gulf) using the quantile regression method  (31). The deseasonalization step reduces the impact of autocorrelation; the remaining autocorrelation can be accounted for in the quantile regression method, but it requires an aggregation of profiles into the monthly time scale (e. g. , the autocorrelation structure cannot be imposed on data with multiple measurements on the same time scale).
However, this aggregation would substantially reduce the sample size and prevents us from accurately estimating extreme quantiles; therefore, this additional step to further reduce autocorrelation was not taken. Quantile regression is known for its tolerance to a greater number of outliers because it is based on rank values of the sample distribution rather than mean values, and it is considered to be a robust approach when the record may contain many intermittent missing values  (32) (33) (34). As the temporal distribution of IAGOS profiles in a given region can be highly intermittent, the quantile regression method is well suited for this dataset. Each trend is reported as the linear ozone rate of change over the period 1994-2016 in units of nmol mol -1 decade -1. Each trend value is accompanied by its P value (table  S1 ), which is the probability under a specified statistical model that a statistical summary of the data would be equal to or more extreme than its observed value  (35).
Acknowledgment: We acknowledge the support of the  European Commission, Airbus , and the  Airlines (Lufthansa, Air France ,  Austrian ,  Air Namibia ,  Cathay Pacific ,  Iberia ,  China Airlines , and  Hawaiian Airlines ) that carry the MOZAIC or IAGOS equipment and perform the maintenance since 1994. Funding: A. G. , O. R. C. , K. -L. C. , and I. B. were supported by the  Cooperative Institute for Research in Environmental Sciences, University of Colorado, Boulder.
Most scenarios from integrated assessment models (IAMs) that project greenhouse gas emissions include the use of bioenergy as a means to reduce CO 2 emissions or even to achieve negative emissions (together with CCS -carbon capture and storage). The potential amount of CO 2 that can be removed from the atmosphere depends, among others, on the yields of bioenergy crops, the land available to grow these crops and the efficiency with which CO 2 produced by combustion is captured. While bioenergy crop yields can be simulated by models, estimates of the spatial distribution of bioenergy yields under current technology based on a large number of observations are currently lacking. In this study, a random-forest (RF) algorithm is used to upscale a bioenergy yield dataset of 3963 observations covering Miscanthus, switchgrass, eucalypt, poplar and willow using climatic and soil conditions as explanatory variables.
bioenergy crop yields can be used to provide bioenergy yields for IAMs, to evaluate land surface models or to identify the most suitable lands for future bioenergy crop plantations. The 0. 5 • × 0. 5 • global maps for yields of different bioenergy crops and the best crop and for the best crop composition generated from this study can be download from https://doi. org/10. 5281/zenodo. 3274254 (Li, 2019).
Reasons for the agricultural land abandonment may include degraded soil quality, low crop price, or environmental and ecological protection  (Kang et al. , 2013; Tang et al. , 2010). The biomass yields of bioenergy crops on marginal lands or in future land-use scenarios simulated by IAMs are often estimated from crop yield datasets with limited observations (e. g. ,  Cai et al. , 2011; Havlík et al. , 2011; Kyle et al. , 2011; Tang et al. , 2010)  or using a meta-analysis of experimental data extracted from scientific papers  (Laurent et al. , 2015). These approaches largely oversimplify the spatial variability in climatic conditions and soil properties. Alternatively, yields of bioenergy crops can be simulated by specific bioenergy crop models (e. g. ,  Hastings et al. , 2009; Miguez et al.