dataesr/scientific-paragraphs-categorization huggingface dataset:
Typus. india, West Bengal, Kolkata, Botanical Garden of the Ballygunge Science College campus, N22°31'37. 30" E88°21'43. 50", alt. 10. 6 m, on the base of Pterigota alata (Stercaliaceae), 28 July 2014, S. Paloi (holotype CUH AM261, ITS and LSU sequences GenBank KR872619 and KY946732, MycoBank MB819728). specimen examined. india, West Bengal, Kolkata, Ballygunge Science College campus, N22°31'37. 30" E88°21'43. 50", alt. 10. 6 m, on the base of Pterigota alata, 2 Aug. 2015, S. Paloi & A. K. Dutta, CUH AM270, ITS and LSU sequences GenBank KY450661 and KY946733. Additional.
Fibroblast ECM samples (Fig. 2e ) and FRET samples (Fig. 3 ) were acquired with the same microscope using an oil immersion 1. 45 NA 60× objective with a pixel resolution of 1024 × 1024. Images were acquired with the pinhole diameter fixed at 200 μm and pixel dwell time at 8 μs per pixel. Briefly, images were taken from fixed matrices using a 50/50 beam splitter. Samples were excited with a 488 nm laser and acceptor (Alexa-546) and donor (Alexa-488) signal was detected in two PMTs using a 12 nm detection bandwidth for donor (514-526 nm) and acceptor (566-578 nm)  25. PMT voltage for FRET imaging was kept constant at 550 V for donor and acceptor PMT. Laser transmissivity was adjusted in order to achieve high detection sensitivity while minimizing photobleaching of samples. Image analysis. Images were analyzed using Fiji-ImageJ and Matlab (MathWorks, Switzerland).
Correction factors for donor bleed through and direct activation of the acceptor used for the analysis were experimentally assessed as shown in Supplementary Fig. 5b, c. The ratios of individual channels were then calculated and displayed alongside with a color code for colorimetric illustration of the ratio range of interest. FRET acceptor channel was corrected for donor bleed through which was measured to be ~20% of the acceptor intensity (Supplementary Fig. 5b ). Further analysis such as FRET-Fn-FnBPA5/Fn ratio quantifications were carried out in MatLab. Fluorescence polarization experiments. The binding affinities of Fn to FnBPA5-Alexa-488 were determined in three independent measurements by anisotropy titrations in a Cary Eclipse Fluorescence Spectrophotometer (Agilent Technologies, Santa Clara, USA) equipped with automated polarizers.
Spatial proximity analysis. Evaluation of spatial proximity of two channels (Fig. 4b, c ) was carried out using a custom made Matlab script (MathWorks, Switzerland). High-resolution images as shown in Fig. 4b, c  were used, images were smoothed using a 2 × 2 pixel-averaging filter, saturated pixels and background below a specific threshold were furthermore excluded from the analysis. Lower threshold for each individual channel was defined to exclude unspecific signal from analysis. For each pixel in the first channel with an intensity above the lower threshold, a 5 × 5 surrounding evaluation matrix is created looking for proximal pixels in the second channel above the threshold value. In case of one or more pixels in the second channel being above the threshold, the evaluated pixel from the first channel counts as a pixel proximal to the second channel.
per view resulting in a total scanning time of 20-45 min per mouse. SPECT/CT images after kidney removal were obtained with an acquisition time of ~20 s ( 111 In-FnBPA5) and 200 s ( 111 In-scrambled FnBPA5) resulting in a total scanning time of 2. 5 h for  111  In-scrambled FnBPA5. SPECT images were reconstructed using HiSPECT software (Scivis GmbH, Goettingen, Germany). The images were reconstituted and processed with InVivoScope ® software (Bioscan Inc. , Washington DC, USA). For the biodistribution experiments, PC-3 tumor grafted mice were injected into the tail vein with 150 kBq  111  In-FnBPA5 or  111  In-scrambled FnBPA5 (2. 4 nmol, 100 µL PBS). For each peptide, groups of four mice were sacrificed at 1, 4, 24, and 96 h post injection. Blocking experiment (n = 4) was performed by injection of an excess of peptide (100 µg, 24 nmol in 100 µL PBS) directly before the administration of the corresponding radiolabeled peptide.
Acknowledgements We thank  Alain Blanc ,  Christine De Pasquale  and  Olga Gasser  at  PSI  and  Chantel Spencer  and  Dr Isabel Gerber  at  ETH  for technical support,  Dr Ingmar Schoen  for the microscopy scripts for mosaic imaging and  Dr Silvia Daniela Jaramillo  for helpful discussion. We gratefully acknowledge support from the  Scientific Center for Optical and Electron Microscopy ScopeM of ETH Zurich  and the  Swiss National Supercomputing Facility (CSCS). This research was supported by the  Swiss Competence Center for Materials Science and Technology CCMX  (Grant  0-21108-11 (VV) ), the  European Research Council  ( ERC Advanced Grant   233157  (VV) and the  Swiss National Science Foundation  (SNF Grant  CR 32I3_156931 (VV) ) and  SNF   Sinergia CRSII3_154490 (RS )), as well as from the  Wyss Center Zurich.
At least since Max Weber's famous lecture on 'Science as Vocation'  (Weber [1917 (Weber [ ] 2002)) , an awareness of the tension between two roles of the academic persona exists; the academic teacher and the professor as a public figure  (Kalleberg 2014). Leaving the 'ivory tower' and speaking to the public on current affairs is perceived by many, though not all, professors as part of their role set, or even their obligation. Others remain reluctant and practice the proverbial academic 'unworldliness'. There are no data at hand with regard to the share of professors in favor of one of the opposite interpretations of their role sets, at least not for Europe. 1  But any announcement of annual conferences or world congresses of disciplines that are part of the SSH illustrates the inclination of scholars to retrieve legitimacy by addressing important societal issues beyond the specific concerns of academic audiences.
Or, in other words: the decline of the 'traditional' or universal intellectual à la Jean-Paul Sartre and the rise of 'new' or specific intellectuals, who may be eating humble pie when raising their voice on specific topics only (see  Foucault 1984  on the distinction of universal and specific intellectuals). To indicate the more recent usage, most authors make use of the term of 'public intellectual' to designate those academics participating in public discourse and we follow this direction without further elaboration. In the following sections we provide a discussion of our research design and data collection procedure. Then our attention turns to presenting the results first in the form of a descriptive report on the properties of the 195 SSH scholars and 262 articles identified in our sample and second to further data exploration using multiple correspondence analysis (MCA).
We seek answers to questions such as: What are SSH scholars debating in the public sphere? Do academics present research findings there, in a similar way to the early history of public understanding of science? Or do they follow the traditional understanding of intellectuals speaking truth to the sovereign? Does the established distinction of experts vs. (universal) intellectuals find any resonance in the present day? Our data collection followed three methodological considerations: finding a European-wide stimulus of similar importance in every national context; concentrating on traditional newspaper media to allow for a broader range of countries to be compared; and a focus on op-ed articles, which encompass both expertise and opinions. Public intellectuals do not show any predictability concerning when they become involved in public discourse.
Counterfactually our sample of op-eds encompasses what an idealized reader concerned with European affairs and the impact of academic public intellectuals would have read during five weeks in the spring of 2014. Even though our main concern is with SSH scholars as public intellectuals, some results are based on newspaper articles as the main unit of analysis. For example, when determining which SSH discipline is most present in the public sphere, we rely on the number of articles coming from different disciplines, not the number of authors with varied disciplinary backgrounds. This provides us with the opportunity to consider multiple contributions by the same SSH author. Therefore, two datasets were established and used depending on the variables and questions dealt with. Dataset A is organized by SSH scholars who contributed newspaper articles and includes information on personal characteristics (discipline, age, gender, different forms of academic reputation).
Results We begin this section with an overview about the newspapers containing op-eds written by SSH scholars during the period of observation. From 21 newspapers in 9 EU member states we identified 262 articles (dataset B). Figure  1  shows the number of articles for the four most strongly represented SSH disciplines by newspaper. There are large differences in the number of identified contributions by SSH scholars, not only between countries, but also between news outlets within a single country. We want to emphasize that the strong representation of the Czech and French newspapers Právo, Le Monde and Libération, does not result from oversampling but from a strong inclination of the editors of these newspapers to include commentaries by SSH scholars. However, we caution against over-interpreting these differences as a direct measure of how much space a given newspaper grants to knowledge or research from the SSH more generally.
For Germany, the female representation of SSH professors is also considerably higher, although still unbalanced at about 30% (Destatis 2016: 121, 126). Posner (2001)  argues that SSH academics engage in public intellectual work only after acquiring a solid reputation within their discipline. The impact and relationship of 'scientific capital'  (Bourdieu 1988 ) and/or public prestigerecognition beyond their field of specializationon public interventions by SSH scholars remains unexplored for Europe. Are public interventions characteristic only of academics who score high on pure scientific capital and/or on public prestige? There are no published data available offering measurement of the reputation of scholars acting as public intellectuals. Therefore, we had to create our own measure and gathered two distinct datasets.
As an indication of academic reputation (or scientific capital), we calculated each SSH scholar's Hirsch-Index (2005). 14 As a proxy for public prestige, we counted the number of languages in which Wikipedia biographies are available for each author. Cf. the Policy Brief 'More information for scholars and more data for researchers are needed', https://ec. europa. eu/research/social-sciences/pdf/policy_briefs/policy-brief-interco-032016. pdf and forthcoming INTERCO publications.
5 IQR (interquartile range) of the lower quartile and highest data point still within 1. 5 IQR of the upper quartile. It is noteworthy that the results presented in Figure  2  differ considerably from the overall discipline-specific academic reputation as measured by the Scholarometer. The scholars from the sample analyzed in this paper differ greatly from the averages for their disciplines at large. 17 Top scoring SSH scholars in Figure  2  belong to the category of 'old men' -Jürgen  Habermas (1929 -), Joseph Stiglitz (1943 -), Anthony Giddens (1938 -), Zygmunt Bauman (1925 -2017 ), Ulrich Beck (1944 -2015) -followed by the first woman on rank 9, the Hungarian philosopher Ágnes Heller  (1929-). With regard to their combined reputational ranking, three of the first five SSH scholars are sociologists.
In the MCA, we also included whether an SSH scholar contributed mostly to domestic affairs, international politics or EU politics. This information is based on all registered authors (dataset A). Figures  4  and 5  present the results of the MCA. The horizontal axis in Figure  4  is determined first and foremost by four variables: h-index (22. 8%), number of Wikipedia entries (20. 3%), the country in which the newspaper is published (17. 4%) and commentary in foreign newspapers (15. 7%). These variables explain about 76% of the variance of the first axis. The 12 categories retained for the interpretation of this axis (in italics in Table  A2  in the appendix) add up to about 81% of the variance. On the west side of the plane are situated: wiki: 11-19 entries (15. 2%), hindex >29 (15. 2%), foreign (commentary) (13.
There is no indication of CH 3 I cluster formation in the TOF mass spectrum, probably due to the low CH 3 I (X) concentration in the gas mixture at the expansion stage. To investigate the nature of the well of the CH 3 I 2+ precursor ion C-I bond at our laser intensities, ab initio computations employing the Molpro2010 package have been carried out. The 46 inner electrons of the iodine atom, were accounted for by a relativistic effective core potential and the valence 5s5p electron were described with a contracted Gaussian basis set (9s9p3d2f 1g)/[5s5p3d2f 2g]. To account for the interactions involving the carbon and/or the hydrogen atoms, an all-electron Gaussian basis set aug-cc-pVTZ was used. The state-average complete active space self-consistent field (SA-CASSCF) method was utilized to generate optimized zero-order wave functions.
Moreover an ideal ion image is expected to be symmetric with respect to the vertical axis, which is assumed also to cross the image position of the parent ion. The minute disturbances to a perfect four-fold symmetry of the raw ion images are due to image errors which originate from imperfect ion optics, from an imperfect alignment of the polarization vector along the vertical direction and/or from an imperfect homogenous gain over the surface of the dual MCP. In the present experiment much care was taken to align the polarization vector of the intense laser field along the vertical direction, which resulted in a nearly fourfold symmetric ion image. All observed ion images, shown in the left column of Figure  2  are almost identical to the four-fold symmetrical ones in the central column. The panels in the right column of Figure  2  display the Abel inverted cylindrical symmetric recoil velocity distribution of the I q+ fragments with respect to the polarization vector, as obtained with the "Davis62" computer code  [47].
The range of values obtained from the different scenarios therefore reflects the sensitivity of estimated global OC burial to the key input parameters. To build predictive models of OC burial, the watershed of each lake and reservoir in our data set was identified through the WWF HydroBASINS tool (http://www. hydrosheds. org) and their watershed characteristics were extracted (Methods section). Geostatistical models to predict OC burial rates in the world's COSCATs were developed using stepwise multiple linear regression (MLR), including as potential explanatory variables the watershed characteristics, system type (lake or reservoir) and system area. The MLR indicated (Supplementary Table  1 ) that the areal proportion of cropland, temperature and runoff in the catchments have positive influence on burial, while lake/reservoir area and average slope of terrain in the catchments have negative influence.
No model of acceptable predictive power could be fitted to lakes and reservoirs in >15 °C COSCATs, probably because of the scarcity of data in the tropics (n = 34). These two methods (A, all and S, split) were then used in combination with two different inventories of global inland water area. The first was adapted from the GLWD data set (Global Lakes and Wetlands Database  19  for lakes and reservoirs by Raymond et al. 15  ) and utilizes size distribution relationships from the literature to statistically estimate the area and abundance of small lakes and reservoirs (inventory R). The second inventory is based on high-resolution satellite images and utilizes algorithms for inland water body detection (GLOWABO data set, ref. 20  , inventory G) (Supplementary Fig. 4 ). Based on the scaling methods (A, S) and lake area inventories (R, G) described here, we produced the global modern OC burial scenarios AR, AG, SR, SG (Fig.
Methods OC burial data compilation. Our data set was composed of 475 observations of modern (i. e. , the last ~150 years) OC burial rates, mainly derived from 210 Pb-and 137 Cs-dated cores, in 403 different lakes and reservoirs. Our analysis relied exclusively on whole-system OC burial rates (i. e. , a single spatially resolved rate for each system), thus we treated the observations as follows. If data on whole-system OC burial from the same system were available in different publications, we used the average value. If OC burial corrected for sediment focusing was estimated at two different sub-basins of a lake, we considered the average rate of the different zones as the whole-system OC burial. If data come from a single core sampled in the deepest zone of a system, we applied a correction for sediment focusing, which is the tendency for preferential deposition of sediment in the deepest part of a lake  40.
cgiar-csi. org). Land cover data were derived from maps of 1000-m resolution (Global Land Cover Project, GLC2000), made available by the European Commission's science and knowledge service, including 23 land cover classes (http://forobs. jrc. ec. europa. eu/products/glc2000/ glc2000. php). Data on annual temperature, soil OC and net primary productivity were obtained from the Atlas of the Biosphere (https://nelson. wisc. edu/sage/dataand-models/atlas/). Finally, runoff data were derived from UNH/GRDC Composite Runoff Fields (http://www. grdc. sr. unh. edu). These geographic analyzes were possible for systems within the HydroBasins tool coverage (below 60°northern latitude) and for which geographic coordinates were available. All geographic analyzes were performed in the software package ArcGIS 10.
15  and inventory G, GLOWABO, from ref. 20. Both data sets were clustered by COSCAT zones and we calculated the total area of water bodies for each of the following size classes-≥0. 001-0. 1 km 2 , >0. 1-1 km 2 , >1-10 km 2 , >10-100 km 2 , >100 km 2. Since inventory R lists lakes and reservoirs separately, we used its lake/reservoir ratio from each size class in each COSCAT to estimate reservoir area in inventory G, which does not identify water bodies as either lakes or reservoirs. Upscaling. Global OC burial was estimated after modeling OC burial in each of the world's COSCATs, using the MLR predictive equations and the total area of lakes and reservoirs in each COSCAT. In order to account for surface area (one of the MLR predictors), we used the median surface area of each size class range (e.
Acknowledgements We thank  N. Barros  for the valuable discussions. The research leading to these results has received funding from the  European Research Council  under the European Union's  Seventh Framework Programme  ( FP7/2007-2013) / ERC  grant agreement  n°336642  to  S. S. Additional  financial support from the  Swedish Research Council (VR)  and the  Swedish Research Council for Environment (Formas)  to S. S. and from the  Knut and Alice Wallenberg Foundation  (grant  KAW 2013. 0091 ) to L. J. T. is acknowledged.
BACterial Hosts for production of Bioactive phenolics from bERRY fruits (BacHBerry) was a 3-year project funded by the Seventh Framework Programme (FP7) of the European Union that ran between November 2013 and October 2016. The overall aim of the project was to establish a sustainable and economically-feasible strategy for the production This article is written by the BacHBerry consortium (www. bachberry.
g. roots, leaves or fruits) via complex Ugni d - ? - ? - - Aristotelia f - ? ? - - - The presence of a compound is noted with ? whereas the absence is noted with a Phenol explorer database (http://phenol-explorer. eu), b  (Chaovanalikit et al. 2004 ), c (Zheng et al. 2011), d (Ruiz et al. 2010), e  (Wang et al. 2016b ), f  (Fredes et al. 2014)  downstream processing  (Wang et al. 2016a). The BacHBerry consortium decided to address these challenges and set the following objectives: (1) to systematically analyze the phenolic contents in the large berry germplasm collections available from consortium members, (2) to establish a publicly-available database of transcriptomic and metabolic data obtained from berry bioprospecting within, as well as outside of the project, (3) to discover novel bioactivities in berry extracts against a range of human pathologies, such as Alzheimer's disease and Amyotrophic Lateral Sclerosis (ALS), by high-throughput screening with subsequent identification of functional biomolecules, (4) to identify the corresponding biosynthetic genes and perform functional characterization of the respective gene products, (5) to assay a selection of the biosynthetic genes for functionality in Gram-positive bacterial hosts and use those to construct bacterial cell factories for phenolic production, (6) to improve the production efficiency further by introducing modifications to the host metabolic networks predicted via rational design or computational tools developed within the project, (7) to design and optimize costeffective food-grade methods for extraction of phenolic compounds from bacterial fermentation broth, and (8) to optimize fermentation conditions and subsequently upscale the production to pilot plant levels.
Berry extract metabolite profiling Fruit samples from a total of 112 species/genotypes were collected, extracted, and subsequently subjected to liquid-chromatography mass-spectrometry (LC-MS) metabolomic analysis (see details in Supplementary Materials S1). An untargeted method was used to generate a total of 1890 mass spectral features (1506 and 384 in positive and negative modes, respectively), which were integrated to generate the dataset used for statistical analysis. Principal component analysis (PCA) was used as a multivariate statistical analysis tool to identify those species, which had the most distinct phytochemical profiles (Fig. 2a, b ). Using the four principal components it was possible to select the species/genotypes, which represented the broadest phenolic diversity (Table  2 ).
cerevisiae  (Tohge et al. 2005; Luo et al. 2007; Pandey et al. 2016; Wang et al. 2016a). Berry bioactives identified within the project had special decorations, and those where the genes encoding the decorating activities had not been previously identified, required further research. In a recent work, the transcriptome of the commercial blueberry (Vaccinium corymbosum) was studied using RNA sequencing technology (RNA-seq) in order to discover putative genes related to antioxidant production using next-generation sequencing (NGS;  Li et al. 2012). Similarly, we aimed to identify decorating enzymes and regulators of the biosynthetic pathways of berry bioactives by obtaining transcriptome sequences of target species at different stages of fruit development. This information was used to construct a database of transcriptome sequences for a broad range of berry species, especially those identified as target species containing the promising bioactive molecules.
Sequencing of selected berries at different stages of maturation was also conducted. The data have provided significant resources not only for understanding phenolic biosynthesis in berries, but also for investigating commonalities in genes expressed in berries (Table  3 ). Multi-layer comparisons of the metabolomics and transcriptomics data of developmental stages, and targeted, as well as, non-targeted searches of candidate transcripts were performed to elucidate genes involved in specific phenolics biosynthetic pathways. Analyses of expression of these genes from different developmental stages of selected berry species were performed with the expVIP tool  (Borrill et al. 2016). These data were used to search for genes encoding putative decorating enzymes based on the list of molecules of potential interest as bioactives and having special decorations in target berry species.