dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgments We wish to acknowledge the following individuals who contributed significantly to the development of this document through expert panel discussion:  Louise Gresham ,  CEO, Foundation Merieux USA, Inc. , Washington DC, United States of America; Georges Thiry, Acting Director,  Dengue Vaccine Initiative  ( DVI), International Vaccine Institute, Seoul, South Korea ;  Joachim Hombach ,  Initiative for Vaccine Research (IVR) ,  WHO, Geneva, Switzerland ;  Charles Wondji ,  Liverpool School of Tropical Medicine, Liverpool, United Kingdom; Barry Beaty, Colorado State University, Fort Collins, Colorado, USA; Scott O'Neill, Monash University, Clayton, Australia; Hadyn Parry, CEO, Oxitec Ltd. , UK ;  Dawn Wesson ,  Tulane University, New Orleans, Louisiana, USA ;  Eva Harris ,  University of California Berkeley, USA ;  Roberto Barrera ,  Centers for Disease Control ,  Puerto Rico ;  Deric Cummings ,  Johns Hopkins University, Baltimore, Maryland, USA ;  Donald Sheppard ,  Brandeis University, Waltham, Massachusetts, USA ;  Gregor Devine ,  Australian Infectious Diseases Research Centre, Queensland, Australia ; and  Karen Campbell ,  San Diego State University, San Diego, California, USA.
ACKNOWLEDGMENTS. We thank  Michael R. Sussman  for technical assistance. We acknowledge the technical support of  Steven Scholzen ,  Maxime Magne ,  Maegen Howes-Podoll ,  Pich Tea , and  Gary Flores  ( University of Wisconsin-Madison ) and  Leanna Oltz  and  Kendell Welch  ( University of Wisconsin-Platteville ). The confocal microscopy was primarily performed at the  University of Wisconsin-Madison Newcomb Imaging Center  and was supported by the  National Science Foundation. This research was supported by  National Science Foundation  Grants  IOS-0701846  and  IOS-1021196  (to J. -M. A. ) and  United States Department of Agriculture-Agriculture  and  Food Research Initiative  Grant  2015-67013-22899  (to J. -M. A. and M. V. ). The work performed at the  Laboratory of Plant-Microbe Interactions (Toulouse, France)  is part of the  TULIP Laboratory of Excellence  ( ANR6106LABX-41 ).
Taste sensilla were recorded from the proboscis of 1-to 5-d-old female flies. Flies were immobilized with fine strips of tape on a pad of clay (UHU yellow patafix) to maintain the proboscis extended and to expose sensilla of the labellum. The fly's body was electrically connected to the ground through either a glass electrode containing 0. 1 mM KCl or a drop of electrocardiogram gel connected to a silver wire. Individual taste sensilla (l5, s6, and i9; see Fig. 5B-E ) were stimulated for 2 s with a capillary electrode containing the stimulus and an electrolyte. This electrolyte was either 1 mM KCl or 30 mM TCC to inhibit the activity from the water cell  (Wieczorek and Wolff, 1989). The recording electrode was connected to a taste-specific amplifier (Marion-Poll and van der Pers, 1996), further amplified 50 -100 times, bandpass filtered at 10 -2800 Hz (CyberAmp 320; Molecular Devices), and digitally sampled at 10 kHz (DT9816; Data Translation) under the control of a custom program, dbWave.
Mixed tion was the same as before, but the recording (and chemical stimulation) was limited to 2 s. The stimuli used were strychnine (1, 5, and 10 mM) "mixed" with BL or 0. 1 M sucrose. Statistical analysis. The proboscis responses (PERs and PRs) were compared with a Fisher's exact test (two tailed) using R 3. 0. 2 (R Foundation for Statistical Computing) or Statistica 10 (StatSoft). A 95% binomial confidence interval was calculated using JavaStat (http://statpages. org/confint. html). We analyzed the statistical significance of MultiCAFE consumption as in the study by  Sellier et al. (2011) , using generalized linear mixed models (GLMMs), which provide a flexible approach to correct potential problems of correlation and heteroscedasticity. To model consumption in the MultiCAFE experiment, we used a Gaussian distribution under SAS 9.
2 (SAS Institute). Two-choice feeding tests were analyzed with unpaired Student's t tests with unequal variances to check for significant differences between pairs of data using Statistica 10. For electrophysiology, we analyzed the data differently according to the questions asked. To compare the firing rate of sensilla stimulated by sugar and sugar plus bitter (see Fig. 5F-H  ), we used Wilcoxon's matched pairs tests. For other binary comparisons (see Figs. 5I-K, 7 B, C, 8C,E), we used Kruskall-Wallis tests. Last, we used a one-way ANOVA with Fisher's least significant difference (LSD) post hoc analysis to evaluate the effect of strychnine on optogenetic responses (see Fig. 9 A, B ). These tests were performed using Statistica 10. To analyze dose-response curves of our electrophysiological experiments (see Figs.
4 (Figure legend continued. ) elicit spiking responses in i9 and s6 sensilla but not in l5 and l6 sensilla. F-H, Extracellular spiking activity recorded with sucrose mixed with different bitter chemicals recorded in w 1118 flies in l5 (F), s6 (G), and i9 (H) sensilla. We compared the responses to 100 mM sucrose (suc; white bar) with the responses to 100 mM sucrose mixed with a bitter chemical (black bar). The molecules tested were L-canavanine (cana), nicotine (nico), caffeine (caff), escin, lobeline (lob), strychnine (stry), and denatonium (den). All molecules were at 1 mM, except 40 mM for L-canavanine. Data are displayed as the mean and SEM (n ϭ 4 -10 as reported on the bar of each graph). The difference between the response to sucrose (first presentation) and to the mixture was determined using Wilcoxon's matched pairs tests.
These correspond, respectively, to the C-O stretch, (CH 2 ) bending and C = O stretch that are found in acetyl esters  (Gorzs as et al. , 2011). Conversely, the 1620 per cm peak, which corresponds to absorbed water  (Gorzs as et al. , 2011) , was more prominent in transgenic lines than in WT (Figure  3b ). These data are consistent with reduced acetyl ester content and an increase in adsorbed cell wall water in transgenic lines. To quantify the total acetyl content in cell walls, cell wall powder from inflorescence stems was saponified and the acetic acid released was quantified. Line D, the line most strongly expressing AnAXE1, showed an ~30% decrease in acetic acid content compared with WT, whereas other lines, with weaker transgene expression, showed no significant difference (Figure  3c ).
RNA extraction and RT-PCR Total RNA was extracted using TRIzol â Reagent (Ambion 15596-026, Foster City, CA) and treated with a DNA-free TM Kit (Ambion), and cDNA was synthesized using iScript TM (BIO-RAD Laboratories AB, Sundbyberg, Sweden). qPCR was performed using master mix from an IQ TM SYBR â Green Supermix (BIO-RAD) in a Light Cycler 480 II (Roche, Rotkreuz, Switzerland). Two reference genes, EF1 (At5 g60390) and ubiquitin (At3 g62250), were chosen for normalization using geNorm  (Vandesompele et al. , 2002). Relative expression was calculated using the ΔΔCt method with a fixed efficiency value of 2  (Hellemans et al. , 2007; Pfaffl, 2001). Primers used for RT-PCR, and efficiencies, are given in Table  S3. For semi-quantitative PCR, loading was adjusted according to ACTIN2 (At3 g18780).
Acknowledgements We thank  Markus Pauly  and  Henrik Vibe Scheller  ( University of California, Berkeley ) for Arabidopsis seeds,  Emma Master  ( Univ. of Toronto, Canada ) for discussions,  Justin Powlowski  ( Concordia University, Canada ) for the AnAXE1 clone and critical reading of the manuscript and  Arun Devadas  for help in the fermentation experiment. This work was supported by the  FP7 program RENEWALL , the  FORMAS-funded BioImprove program , VR and FORMAS grants to EJM, the  Strategic Research Council and Vinnova program Berzelius , the  Cell Wall and Carbohydrate analytical facility and Vibrational Spectrometry KBC  platforms (supported by  Bio4Energy  and  TC4F ), the  Spanish Ministry of Economy and Competitiveness (MINECO , grants  BIO2012-32910  and  PIM2010PKB-00749  to AM) and the  Glycoscience Graduate School of the University of Helsinki  (grant to S-LC).
Acknowledgements The authors are thankful for financial support from the  Slovenian Research Agency  (grants  L4-5521 ,  P4-0133 ,  V4-1417 ,), and the  Slovenian Ministry of Agriculture, Forestry and Food. The authors would also like to thank their partners from industry ( Pršutarna Lokev na Krasu , d. o. o. ) for their contribution in conducting the experiment.
Chemical force spectroscopy (CFS) measurements The surface of S. boydii resting or germinated conidia was imaged using a NanoWizard atomic force microscope (JPK Instruments AG, Berlin, Germany) operating in intermittent contact mode under ambient conditions. A standard rectangular cantilever (Nanosensors NCL-W) was used for imaging, with a free resonance frequency of 165 kHz and a typical spring constant of about 40 N/m. The radius curvature of the tip was ~10 nm. The detailed analysis of chemical force spectroscopy images was performed using JPK Data Processing software (JPK Instruments AG). Hydrophilic and hydrophobic adhesions were obtained in ultrapure water from force-distance curves measured on the surface of both conidia and germ tubes using functionalized cantilevers. Gold-coated cantilevers (Olympus, Hambourg, Germany) with spring constants of 0.
For OrbiTrap measurements, an external calibration was used before each injection series ensuring an overall error mass accuracy below 5 ppm for the detected peptides. MS data were saved in RAW file format (Thermo Fisher Scientific) using XCalibur 2. 0. 7 with tune 2. 5. 5 SP1. Protein identification. The Proteome Discoverer 1. 2 software was used to submit MS/MS data to the translated genome of S. apiospermum IHEM 14462  [36]  completed with Sus scrofa trypsin and Elizabethkingia miricola PNGase F (10829 sequences) using the Mascot search engine (Mascot server v2. 2; http://www. matrixscience. com). Parameters were set as follows: trypsin as enzyme with one allowed miscleavage, carbamidomethylation of cysteins as fixed modification and methionine oxidation as variable modifications. Mass tolerance for MS and MS/MS was set at 10 ppm and 0.
html). For pipeline filtering of proteins the new web tool proFasta was also used (http://www. bioinformatics. nl/tools/profasta/). Functional domain analysis was performed using Interpro (http://www. ebi. ac. uk/interpro/). Protein sequence similarities were searched by using Blastp in NCBI website (http://blast. ncbi. nlm. nih. gov) against all the non-redundant protein sequences and then against species-specific databases Aspergillus fumigatus, Neurospora crassa, Magnaporthe oryzae and Saccharomyces cerevisiae. Specific websites were also used to perform Blastp analysis against Candida albicans (http://www. candidagenome. org), Colletotrichum graminicola and Colletotrichum higginsianum genomes (http://www. broadinstitute. org/annotation/genome/colletotrichum_group/ Blast. html).
0 nN) were calculated for the trunk and apex of the hyphal part of germ tubes. Characteristics of the identified cell wall proteins GPI-anchored proteins selectively released with HF-pyridine from SDS buffer washed cell wall fragments, were first analysed on SDS-PAGE. Nevertheless, extracted proteins were revealed only by silver staining. Due to the small amount of proteins, analysis of glycosylation was not performed and attention was focused on the identification of the extracted proteins by nanoLC-MS/MS. Consequently deglycosylated HF-extracts were analysed with nanoLC-MS/ MS which detected two hundred and fifty four proteins from both the conidial and germ tube extracts taken together. In silico analysis of these proteins identified 32 proteins with a signal peptide among which 20 proteins had a GPI-anchor following analysis with BigPI software (Table  1  and S1 Table).
Seven out of the 20 GPI-anchored proteins were detected in both conidial and germ tube extracts, 12 only in the germ tube extract and one only in the conidial extract. All identified GPI-anchored proteins were predicted to be glycosylated (Table  2 ), and all but one (glycine-rich protein, accession number KEZ42341. 1) had no transmembrane helices after excluding 45 N-terminal and 35 C-terminal amino acids. One transmembrane helix was found for the glycine-rich protein (accession number KEZ42341. 1). All the twenty proteins had a pI < 5 except for the glycine-rich protein whose pI was 6. 89. To analyze the serine (S)threonine (T) content of our proteins, we first considered the overall S/T content of proteins (results indicated in Table  2 ). A further analysis was performed with proFasta using additional filters (S/T content > 10%, Start position of scan 26 and End position of scan -26) to exclude the S/T content of N-and C-terminal signal sequences.
To be noted, the extracted protein in this study was different from the one previously identified Lima et al. [67]  in S. apiospermum (KEZ41328. 1) that did not contain a signal peptide, a transmembrane helix, or a GPI anchor. Twelve GPI-anchored proteins were only found in germ tube extracts and among these, four proteins belonged to known GPI-anchored protein families implicated in cell wall biosynthesis activities, one to CFEM (Common in Fungal Extracellular Membrane)-domain containing proteins, one to cerato-platanin proteins and the rest had no known functions. The first group of proteins contained a protein similar to Crhp proteins (KEZ42985. 1) that are suggested to be involved in the linkage of cell wall polysaccharides β(1-6)glucan and β(1-3)glucan to chitin  [68] [69] [70]. Crhp proteins are classified in the glycoside hydrolases 16 family (GH16) of the CAZy Database (http://www.
Acknowledgments The authors would like to thank  Dr. Emmanuelle Com  and the  Proteomics Core Facility Biogenouest, IRSET-Inserm   U1085  ( Rennes, France ) for the assistance to identify proteins in our extracts and the thoughtful discussions for the protein analysis. The authors also acknowledge  Dr. Patrick Saulnier  ( L'UNAM Université, Université d'Angers, INSERM U646, France ) for the use of the  Zetasizer Nano ZS.
We performed backwards-stepwise regression as a mean for model simplification. We started by including both female treatment and egg assay and their interaction, and proceeded to remove non-significant terms. Comparisons of fits of the different models are provided in S3 Text. Differences between each female treatment and egg assay were highlighted by estimating the degree of overlap of the 95% CI, following the recommendations of  [48]. The difference was considered significant when the 95% CI did not overlap on more than half of their length (p<0. 05) [49]. The confidence intervals are represented on the bar plots of Figs  1  and 3. For Figs  2  and 4 , they are represented with the estimates of the models (package ggplot2) in S2 and S3 Figs, respectively. All the data were analysed using R software  [49].
Since the 1960s, research has dealt with agricultural intensification (AI) as a solution to ensure global food security. Recently, sustainable intensification (SI) has increasingly been used to describe those agricultural and farming systems that ensure adequate ecosystem service provision. Studies differ in terms of the application scales and methodologies, thus we aim to summarize the main findings from the literature on how AI and SI are assessed, from the farm to global levels. Our literature review is based on 7865 papers selected from the Web of Science database and analysed using CorText software. A further selection of 105 relevant papers was used for an in-depth full-text analysis on: i) farming systems studied; ii) related ecosystem services; iii) indicators of intensity; and iv) temporal and spatial scales of analysis.
g. , land use science, food planning and ecosystem services) and to make an analysis on a farmland to global scale. A subsequent qualitative and statistical analysis on the full texts of selected papers highlighted the interactions among variables indirectly related to AI to identify major research gaps and recommendations. In order to systematically review the obtained papers' database and highlight their main relevant concepts, we applied an analysis grid containing the following criteria: i) the main declared topic related to AI research; ii) the literature definition used for identifying AI concept; iii) and the methods applied to evaluate AI.
Materials and methods This paper involves two main steps. Research papers were selected in both steps according to the PRISMA (preferred reporting items for systematic reviews and meta-analysis) flowchart (Figure  1 )  (Liberati et al. , 2009). In the first step, we provided an overview of how the concept of AI has been used in the literature. We reviewed international studies on AI from the first publications in 1975 until now, and from the international bibliographic database Web of Science (WoS). The papers obtained were analysed using the bibliometric software CorText  (Tancoigne et al. , 2014). Software CorText is a platform dedicated to the cleaning and treatment of large textual corpuses with the aim of synthesising and analysing big data, whether structured or unstructured  (IFRIS, 2014).
Bibliometric analysis The bibliometric analysis was performed using the CorText software  (IFRIS, 2014) , which enabled us to upload data sets and run the different analytical process in order to perform lexical analysis and mapping the structure and the dynamics of the corpus. The common procedure is specified as follows: i) calculation of the frequency of occurrence of each term; ii) normalisation of these occurrence and co-occurrence measurements as proximity measurements to link the nodes  (Tancoigne et al. , 2014). CorText manager recommends choosing direct measures for heterogeneous network like chi-squared test, which only takes into account the raw co-occurrence number between two nodes. The dataset is analysed through a lexical extraction of the title and abstract from the selected papers, which supply key information on the most co-common topics.
Data collection was carried out using keywords including various synonyms and combinations of the concept. A systematic search in the WoS database (main keywords: agricultural intensification OR land use intensity OR agricultural intensity), combined with others [sustainability OR ecosystem services OR land use modelling (Table  1 ); Timespan: 1975-2014; Search language: English], yielded 7865 publications that were retrieved and imported as a corpus to the CorText Manager Software for data analysis. The resulting map based on a keywords analysis identified the most relevant terms, the dynamics across the links and nodes, the interactions between them and the distribution along with their weight.
Full-text analysis From our 105 papers sample, the items searched for were: i) the case study location and the spatial level of the analysis; ii) the methods applied and the presence of thresholds in the evaluation of the sustainability of AI/LUI. We also collected data on the ecosystem services, the farming systems or land uses in which AI was analysed and the indicators taken into consideration. The set of variables (ecosystem services, farming systems and indicators) were coded and consisted of binary and discrete variables within the database. These variables were calculated through frequency and descriptive analyses and points of significance for both ecosystem services and indicators at different scales. Statistical methods enhanced the understanding of the data and were used for comparisons.
, 2002; Niedertscheider and Erb, 2014). Many studies do not consider the large diversity of other crops or other kinds of farming systems that are also critically important sources of food  (Stoate et al. , 2009). For instance,  Temme and Verburg (2011)  proposed combining European databases to build land-use intensity maps using separate methodologies for arable land and grassland. A few papers analyse AI in more complex agricultural systems or heterogeneous/mix farming systems in local areas (Figure  4B ).
Discussion and conclusions We have presented a review of the indicators of AI, based on a sample of papers published in international databases (1975-2014) that dealt with AI from farmland to global levels. Wide differences were found on how to address this phenomenon. Despite several methods for measuring AI and sustainability, our review revealed some important research gaps: -The farming systems or land use systems analysed in agricultural intensity oriented studies. We observed that the analysis of agricultural intensification/intensity is conducted on large-scale land use or crop land, in particular monocultural crop systems, whereas there is a lack of studies focused on more complex and heterogeneous systems, e. g. , polycultural systems or periurban farming systems. This gap is also reflected by the level of analysis at which these studies are generally performed: global and farm scales are the most common, missing out local and regional studies.
Antibiotic resistance profiles and the genes responsible for these properties, including those encoding efflux pumps of the RND family are well documented in a clinical context. Nevertheless, little is known in the environmental context. In this study, we tried to fill this gap by sequencing the whole genomes of three soil originating strains with known antibiotic resistance profiles, two of them showing higher antibiotic resistance levels than reference clinical MDR strains. These genomic data combined with those available in the public archives have constituted a good database for the implementation of a comparative genomic survey of the antibiotic resistance determinants of S. maltophilia to try deciphering the origin of efflux pumps associated with MDR phenotypes among environmental strains of S.
The genome sequences were obtained from the NCBI (http://www. ncbi. nlm. nih. gov/) and are referenced with the accession numbers showed in the table 1. The clinical reference strain K279a  (Avison et al. 2001 ) and the environmental reference strain R551-3  (Taghavi et al. 2008)  were kindly provided by Dr Matthew B. Avison and Dr Daniel van der Lelie, respectively.
Antibiotic Resistance Test The in vitro antimicrobial resistances of the three newly sequenced S. maltophilia strains and the two reference strains K279a and R551-3 were determined using the Vitek2 system with a NO93 card dedicated to nonfermenting Gram-negative bacteria (bioMerieux, Marcy l'Etoile, France) according to manufacturer instructions. Minimal inhibitory concentrations (MIC) of 18 antibiotics were determined. MIC results were analyzed by the AESTM (Advanced Expert System) software incorporated in this system. The disk diffusion method was also used for strains BurA1 and BurE1. The phenotype for aminoside resistance was compared with that of the reference Strains for which the antibiotic resistance profile is not described in the references but presence of numerous antibiotic resistance genes are mentioned.
The 454 reads were first de novo assembled using Newbler 2. 6 (Roche) with an estimated average coverage of 13-fold. Hiseq reads were then mapped on the de novo assembly using BWA-MEM software  (Li and Durbin 2009). The mapping of the Illumina reads raised the estimated coverage to 2,500-fold. BurE1 and PierC1 whole genomes were sequenced at the University of Lyon (France) by the DTAMB/Biofidal structure using a Roche 454 GS Junior sequencer. For each strain, the number of reads was 186,793 and 160,103, respectively, with an average read lengths of 434 and 446 bp. The estimated average coverage was 18-fold and 15-fold, respectively. For the three strains, the large contigs (size > 500 bp) were reordered relative to the genome sequence of the reference strain K279a using the Mauve Contig Mover  (Rissman et al.