dataesr/scientific-paragraphs-categorization huggingface dataset:
With this aim, four sites were selected on a European north-south environmental gradient where reference GPP data have been collected through the eddy covariance technique  (Aubinet et al. 2000). These reference data were first used to assess the accuracy of BIOME-BGC simulations of beech GPP. Minimum temperature at the beginning of the growing season and spring-summer dryness were then tested as the meteorological factors most influential on inter-annual variations of both observed and simulated GPP. The effects of two different climate change scenarios were lastly investigated by comparing the results of statistical analyses of GPP measurements and model simulations.
Study data Daily weather data needed to initialize BIOME-BGC were obtained from a Pan-European level 1-km daily dataset produced recently by  Moreno and Hasenauer (2015)  downscaling the original E-OBS dataset  (Haylock et al. 2008). The major benefit of these downscaled data is the inclusion of the orographic effects on a local scale in the original E-OBS dataset  (Moreno and Hasenauer 2015). A daily dataset of minimum and maximum temperature and precipitation from 1950 to 2013 is now freely and publically available at ftp:// palantir. boku. ac. at/Public/ClimateData/. The reference daily weather and GPP data of the four study sites were obtained from the European Fluxes Database Cluster (http://www. europe-fluxdata. eu/home/sites-list) (see Table  1 ). The CO 2 fluxes of these sites were measured by applying the eddy covariance technique following the standard EUROFLUX methodology  (Aubinet et al.
2000; Baldocchi 2003 ). The flux data utilized in this study belong to level 4, meaning that the daily data have been gap-filled, filtered and quality checked following the database's methodology; partitioning the net CO 2 fluxes in GPP and ecosystem respiration is then done using the approach by  Reichstein et al. (2005). The analysis uses data collected during the decade 2000-2009 for all sites with the exception of Hainich, for which the reference period is 2000-2007.
(2016). Both modifications were made following the calibration exercise conducted by  Pietsch et al. (2005)  at Pan-European level. A summary of the ecophysiological parameters finally adopted is reported in Table  6. None of the selected study sites has the complete daily weather data of the 10-year study period which were required for running BIOME-BGC in a spin-up-and-go mode. Daily minimum and maximum air temperature and precipitation were therefore derived from the downscaled E-OBS dataset of  Moreno and Hasenauer (2015). Daily global solar radiation was then estimated by the mountain microclimate simulator (MT-CLIM) algorithm  (Thornton et al. 2000)  and converted into PAR through a coefficient equal to 0. 464  (Iqbal 1983). Precise information on soil depth and texture was not available for all sites.
(2012) , i. e. that while the E-OBS dataset correctly reproduces mean temperature (maximum errors around 1 °C), it variably underestimates annual rainfall (up to 30 %). The effects of these errors were assessed by linearly adjusting the E-OBS data to the common tower measurements of each study site. In the case of temperature, this was done by developing and applying site-specific linear regression equations between observed (tower) and E-OBS daily data, while the E-OBS daily rainfall data of each site were multiplied by a ratio between observed and E-OBS mean annual totals. No correction was applied to the precipitation patterns. The whole simulation experiment was then repeated using the corrected data, obtaining new GPP estimates that were re-evaluated as above against the available eddy covariance GPP data.
Analysis of the impact of expected climate changes The study concluded with an evaluation of the impact of two different emission scenarios: a medium-high non-mitigation baseline scenario (A1B) and a mitigation scenario (E1). The considered expected changes in temperature and precipitation for the whole Europe are given in Table  3 ; more details can be found in  Christensen et al. (2011). These temperature and rainfall deltas were applied to modify the daily values of the meteorological data previously used, obtaining two new datasets. Their impact on beech GPP was evaluated by two methods: -First, the temperature, precipitation and PET changes from the two scenarios were transformed into Tcor and Cws changes during the previously defined periods. The Tcor changes were, in turn, converted into GPP changes for the first part of the season using the slopes defined in the previous regression analyses.
The start of the growing season simulated by BIOME-BGC is earlier than that of the tower observations, while the maximum peak is well reproduced. Also in this case, the slight rainfall underestimation of the E-OBS dataset causes only marginal changes in simulated GPP due to the deep soil. Figure  2d  refers to Collelongo, which is the most southern and elevated site (Table  1 ). The growing season in this forest is relatively brief (from late-spring till mid-autumn) and is not reproduced well by the model, which slightly anticipates the start and delays the end of the season. The total annual GPP observed by the tower is about 1400 g C m -2 year -1 (Fig. 3 ), while the GPP simulated by both approaches is lower, mainly due to an underestimation during the driest summer months. The corrected rainfall is around 30 % higher than the original and increases the simulated summer GPP, which, however, remains lower than that observed by the tower (MBE from -0.
63 to -0. 30 g C m -2 day -1 ). The same accuracy statistics computed on a daily basis are shown in Table  4  for the entire data series. As expected, the global agreements are always lower than those obtained using the annual averages. The model shows a variable tendency to underestimation with the exception of Hesse. The accuracies achieved using the corrected dataset are still only slightly better than those obtained using the original 1-km dataset. This last finding is fundamentally confirmed when analyzing the spatial variability of the annual GPP averages. Figure  3  shows these annual averages observed and simulated using the two rainfall datasets; the marginal underestimation obtained with the original dataset is again slightly reduced by the rainfall correction (MBE from -70. 5 to -50.
Discussion This paper investigates the possibility of simulating a major ecosystem process (GPP) for one of the most widespread European tree species, beech, using a recently produced continental daily weather dataset. This simulation, which is also a fundamental step forward towards the analysis of beech response to possible environmental changes, involves several critical aspects mostly related to the input data used and the model logic and parameter settings. These topics will be treated separately in the next two subsections, followed by a discussion on the impact of major climatic factors (i. e. rainfall and temperature) and the effect of expected climate changes.
Input data used The assessment of model performances strictly depends on the adopted model inputs, whose accuracy will be briefly discussed here. The 1-km weather dataset currently used to drive BIOME-BGC has been produced by  Moreno and Hasenauer (2015)  as a downscaled version of the original 0. 25°E-OBS dataset. Due to the well-known difficulty in extending ground point measurements over large areas  (Hofstra et al. 2009) , these meteorological data are associated with different levels of accuracy depending on the considered variable. In general, rainfall is more difficult to interpolate/extrapolate than temperature, due to its greater spatial variability and to its more irregular dependence on the main orographic and geographic factors (elevation, latitude, continentality, etc. )  (Thornton et al.
1997). This trend was confirmed for the E-OBS dataset by research conducted in Italy by  Maselli et al. (2012) , which indicated that daily temperatures are estimated quite accurately while rainfall is variably underestimated. Similar results are found in the current investigation, according to which the 1km E-OBS mean temperature errors are small for all sites while this is not the case for rainfall, which is variably underestimated (Table  2 ); the possible impact of this rainfall underestimation is discussed in the next section. Among the other meteorological drivers, radiation exerts a major effect on the simulation of GPP  (Waring and Running 2010). The accuracy of the radiation estimates obtained by MT-CLIM in Central Europe was assessed by  Thornton et al. (2000) , who found reasonable precision and accuracy also in rugged terrain conditions.
Those findings are confirmed by the current analyses versus tower measurements (data not shown), which indicate that mean radiation errors are relatively small (up to 10 %) and temporally variable. In particular, periods of underestimation and overestimation are found at each site that could be partly due to the problematic functioning and/or tuning of the sensors utilized for the ground measurements. Table  4  Accuracy statistics obtained comparing the daily tower GPP observations to the BIOME-BGC estimates driven by the original and the corrected rainfall datasets (all correlations are highly significant, P < 0. 01) As regards the other model inputs, BIOME-BGC is particularly sensitive to soil features and particularly to depth  (Chiesi et al. 2011). This can be explained by the simplified model treatment of the site water budget, which considers a unique soil layer without horizontal water displacements  (Running and Hunt 1993 ).
Soil texture is instead less influential, leading to only minor changes of predicted GPP. These results highlight the importance of using correct soil depth information to drive BIOME-BGC, which would be even more necessary for future climate simulations. 1-km E-OBS dataset 1-km E-OBS with corrected rainfall Site r 2 RMSE (g C m -2 day -1 ) MBE (g C m -2 day -1 ) r 2 RMSE (g C m -2 day -1 ) MBE (g C m -2 day -.
This is confirmed by the current findings, which indicate that the use of the uncorrected 1-km E-OBS rainfall estimates implies only a marginal worsening of BIOME-BGC performances. A similar reasoning applies to the possible inaccuracy in the reproduction of actual rainfall patterns by the downscaled E-OBS dataset, whose effects could not be assessed. Water availability, however, can become an important controlling factor during extremely dry summer seasons, such as that of 2003, when beech production in Europe was strongly limited by the well-known heat and drought wave  (Ciais et al. 2005; Granier et al. 2007). This is also supported by both the current observations and model simulations for all sites except Soroe, where the heat wave was less effective; in all other cases, the 2003 summer GPP was markedly lower than the multi-year average (data not shown).
Conclusions The research concerned the analysis of GPP patterns in four European beech sites representative of various environmental conditions. The following main conclusions can be drawn from the experiment: 1. The considered Pan-European 1-km daily weather dataset can drive BIOME-BGC for reproducing the tower GPP observations of the examined ecosystems with reasonable levels of accuracy. 2. The observed annual GPP of these ecosystems is primarily sensitive to air temperature around the beginning of the growing season. A secondary effect is exerted by springsummer dryness. 3. The GPPs simulated by BIOME-BGC follow a similar pattern but with a higher sensitivity to water limitation that depends mainly on soil characteristics. 4. The proposed modelling approach can approximately simulate the effects of expected temperature and rainfall increases in all examined sites, with some limits related to the critical accounting for the water factor.
Acknowledgments The authors acknowledge the E-OBS dataset from the  EU-FP6   ENSEMBLES  Project (http://ensembles-eu. metoffice. com) and the data providers in the ECA&D Project (http://eca. knmi. nl). Thanks are also due to the AFS Editors and anonymous reviewers for their precious comments on the original manuscript. The current research was partially funded by the  EU-FP7   FORMIT  Project, "FORest management strategies to enhance the  MITigation potential of European forests"  (grant agreement no. 311970 ).
University of Southern California Obesity Study  (28). All studies were approved by the Institutional Review Boards of all study centers and all participants gave written informed consent. ACT NOW was a prospective, randomized, double-blind, placebo-controlled trial to examine the effectiveness of pioglitazone in the prevention of type 2 diabetes  (29). Participants were overweight adults with impaired glucose tolerance and at least one other risk factor for type 2 diabetes who were randomized to up to 45 mg pioglitazone or matching placebo per day. Participants returned every 2 months during the first year of the study and once every 3 months thereafter for up to 48 months, with a mean and median follow-up time of 2. 3 years. The present analyses include plasma samples and matching data from the baseline visit and the latest available follow-up visit.
Study populations Clinical trial blood samples and data were obtained from participants of the Actos Now for Prevention of Diabetes (ACT NOW) study and the Risk Factors, Atherosclerosis, and Clinical Events in Diabetes (RACED) study  (29, 30). Plasma samples for in vitro studies were obtained from a subset of participants in the incubation was performed in the presence of 0. 5 mM oleic acid (Sigma-Aldrich)  (35) , and isolated apoC-III complexes containing 5 g/ml of apoC-III were used to inhibit DiI-VLDL uptake. The cell monolayers were washed twice with PBS (pH 7. 4). DiI-fluorescence was measured at an excitation/emission of 513/550 nm. The cells were then fixed with 4% paraformaldehyde for 10 min. The DiI-values were normalized by staining the cells with 4′,6-diamidino-2-phenylindole (DAPI) (1:10,000, 10 min) followed by three washes with PBS.
Statistical analyses Depending on data distribution, differences in apoC-III measures were tested by Wilcoxon rank-sum test for two categories, and by Kruskal-Wallis test for three or more categories. Paired Student's t-test or Wilcoxon signed rank test was used to determine differences from baseline within each treatment group. Relationships between continuous variables were evaluated using Spearman correlations. General linear models were used to test the relationships between apoC-III proteoforms and metabolic variables after adjustment for potential covariates, including age, gender, BMI, ethnicity, and use of lipid-lowering therapy. Data were log 10 transformed if not normally distributed. All longitudinal analyses were adjusted for treatment assignment. Cox proportional hazards regression was used to determine the association between apoC-III proteoform measures and development of cardiovascular events in RACED in the models adjusted for treatment assignment.
To control for cardiovascular risk, a United Kingdom Prospective Diabetes Study (UKPDS) risk engine coefficient representing a composite index of traditional cardiovascular risk factors [age, hemoglobin A1c (HbA1c), sex, blood pressure, race/ethnicity, smoking, and total and HDL cholesterol] was used. The interaction of apoC-III proteoform measures with the randomized treatment assignment was also tested in the Cox proportional hazard model along with the main effects, with P < 0. 15 as threshold for stratified analyses. Statistical analyses were performed using SAS Institute software (version 9. 2; Cary, NC). P < 0. 05 was considered statistically significant.
They conclude that energy consumption and financial development increase CO 2 emissions. Rafindadi, Yusof, Zaman, Kyophilavong, and Akhmat (2014)  employ the regression by the ordinary least square on panel data and the fixed effects model on panel data regression by the least squares method of two courses to study the causal relationship between pollution and economic activity indicators in the Asia-Pacific countries for the period 1975-2012. In their study, pollution is measured by CO 2 emissions and economic indicators are measured by GDP, the production of water, the added value of natural resources, and energy consumption. They find the existence of a positive and significant relationship between CO 2 emissions and GDP. Also, they conclude that energy consumption affects positively pollution.
Data This study is elaborated to investigate empirically the causal linkage between economic aggregates (economic growth, energy consumption, financial development, and trade) and environmental pollution in the Asian countries during the period 1991-2013. We utilize yearly panel data for a sample composed by eight Asian countries (China, India, Thailand, Japan, Malaysia, Singapore, Indonesia, and South Korea). 1990 1992 1994 1996 1998 2000 2002 2004 2006 2008 2010   11,4 1990 1992 1994 1996 1998 2000 2002 2004 2006 2008 2010   1990 1992 1994 1996 1998 2000 2002 2004 2006 2008 2010   1990 1992 1994 1996 1998 2000 2002 2004 2006 2008 2010    Following to the two statistics of skewness (asymmetry) and kurtosis (leptokurtic), we can conclude that all variables utilized in this paper are characterized by non-normal distribution.
Study Instrument and Data Collection. A 27-item selfadministered patient questionnaire provided 94 self-care practices and free text options in 11 categories: over-thecounter medication, specific foods or drinks, herbal teas, alcoholic drinks, special self-prepared recipes, pastilles or drops, something for the nose, inhalation, throat gargle/spray, items applied externally, and extras at home. In addition, the following patient characteristics were requested: age, gender, health insurance status, number of school years, pills taken daily, and expenditures for the last common cold. To assure random samples at all sites, the questionnaires were distributed to consecutive patients independent of their reason for physician consultation. Inclusion criteria were the following: being ≥18 years, being able to understand the questionnaire, or being in attendance of someone providing assistance.
Discussion This is the first study on self-care for common cold with data from several European countries. According to the COCO data, European primary care patients can consistently be considered as high users of self-care for common colds. Prevalence of application modes showed similar patterns across countries, while the number of items on country level varied markedly: the modes "foodstuffs," "intestinal absorption," and "extras at home" were the most frequently used modes in 10 countries, with "foodstuffs" representing the most frequently used one across all countries. Of the large spectrum of 527 practices reported, 19 self-care practices rank among the top five across countries. Age standardization was used to account for differences in ages between sites and countries. This study was not without limitations.
8 (European Americans) and 3. 2 (African Americans) items were reported on average  [34]. Turning to self-medication, 62% used pharmaceutical products, which is higher than the 52% reported in the British survey ( = 4,327)  [13]  and the 44% reported in Estonian pharmacy customers ( = 300) who used a combination of herbal products/medicinal plants and self-medication  [33]. Based on a review of the literature, we identified 21 self-care items in the COCO data that are discussed as possibly being beneficial for cold symptoms in adults; the majority of items lack an assessment for effectiveness or were shown to be ineffective. Of the five most frequently reported items (utilization of about 40% each), that is, "water," "honey," "paracetamol," "oranges/orange juice," and "staying in bed," only paracetamol is evidence-based.
Following growth kinetics Projection images of the tissue grown in the pores were taken twice a week by phase contrast microscopy (Nikon Digital sight DS 2Mv). Because the amount of tissue produced in the three-dimensional pores could not be determined on such images, the evolution of the PTA, i. e. the difference between rsif. royalsocietypublishing. org J. R. Soc. Interface 13: 20160136 the initial and the current pore areas (PA), was used to estimate tissue growth kinetics. PTAðtÞ ¼ PAðd4Þ À PAðtÞ ð 4:1Þ PAs were measured manually by using the program NIS-ELEMENTS v. 3. 10 (Nikon). For each condition of culture, kinetics was followed in three scaffolds containing three pores, i. e. n ¼ 9. Statistics of the PTA curves were analysed using SIGMAPLOT v. 12. 3.
After 35 days of growth, the formation of thick collagen fibres is rare in the treated samples compared with the control condition and a hole in the collagen network suggests that the ECM produced in such conditions cannot bear the tension imposed by the surrounding cells (i,j, arrow). The collagenase treatment suddenly increased the projected tissue area (n), whereas the deprivation of ASC did not significantly (two-way ANOVA) alter tissue growth (o). (Scale bar, 100 mm. ) Error bars in (l -o) indicate the standard deviation, (n ¼ 9) for each dataset.
Current research directions also include the use of multiscale modelling, which aims at understanding the functioning of complete organs and organisms as the result of the dynamic interplay in time and space of molecules, cells and tissues. Such integrative efforts are supported by translational EC programmes to generate robust data infrastructure and exchange standards for the different omics technologies (e. g. , Coordination of Standards in Metabolomics (COSMOS, http://www. cosmos-fp7. eu/) or the HUPO Proteomics Standards Initiative (PSI, http://www. psidev. info/) and data integration and modelling itself developed by the COMBINE community (http://co. mbine. org)). These initiatives are already creating a strong basis for new avenues in biomedical and pharmaceutical research. Examples include the Virtual Human Heart, the Virtual Liver Network (http://www.
General skills that students should have acquired in a Systems Biology educational programme It is essential to define the competences that students should have acquired in a Systems Biology educational programme; on the one hand for the students to better anticipate potential career paths after having completed education, and for potential employers in academia and industry to match candidate skills with their needs. It may be difficult to define those skills and competences down to details of, for instance, types of modelling approaches and experimental techniques, simply because there are too many. Therefore, we suggest 'higher level' skills, that rather reflect the interdisciplinary systems approach and its way of thinking and operating. We suggest that graduates from Systems Biology Master's programme should have acquired the following skills: • An understanding of the type of biological, medical or bioengineering questions that can be approached by integrating experimental data sets with mathematical modelling.
Scientific programming Programming: e. g. , Matlab or Mathematica, Python, Perl, Java, R, C and C++ Tools for genome-scale metabolic models, kinetic modelling (stochastic and deterministic), network analysis: e. g. (iv) experimental design, measurement, analysis, interpretation and knowledge generation. Each area is broken down into specific topics and the relevance in which it should be taught. modelling. It, therefore, may be necessary to introduce students before or at the beginning of the programme to basic principles in biology or mathematics. Table  2  provides a prerequisite for a Systems Biology graduate programme summarizing main concepts that are necessary to 'level-up' students in order to successfully complete the programme. In our experience, the majority of students entering master's programmes in Systems Biology are coming from either experimental or theoretical background.