dataesr/scientific-paragraphs-categorization huggingface dataset:
AFROweeds is available online (www. weedsbook. org/idao), as a CD-ROM  (Grard et al. , 2012)  for use offline as a program on a desktop or laptop, or as an application on smartphones or electronic tablets (currently available in the App store, Apple Inc. ). All formats, whether online, CD-ROM, or App, have a similar interface (Fig. 1 ). The identikit enables the user to select common and important morphological character states (such as flower color, leaf shape, stem shape or form) of the plant under consideration and to indicate in the pictorial multiple-choice menu what the character looks like (Fig. 2A-F ). The identikit calculates the probability of the species' identity corresponding to the combination of choices. The user can view a list of species with percentages of fit with the selected character shapes, forms, or colors, and check the most likely species (e.
g. , those with 100% fit) with the actual plant to be identified (Fig. 2G ). Clicking on the species names in the list provides access to species' pages from the database with information on botanical descriptions, ecology, biology, management, and local uses (Fig. 2H ). Users can also compare the plant being examined with field and herbarium photos or illustrations. The AFROweeds identikit currently (Sept. 2015) contains close to 200 species encountered in Africa, primarily for lowland rice. The database can also be consulted directly, hence without any identification, by clicking on 'Results' which opens the list of species alphabetically sorted.
Testing the tool, database and network by potential users We organized a workshop in Cotonou, Benin, from 24 to 26 September 2012, with 13 potential users of the tool, database and network. This test group consisted of research, development and extension staff of African NARES from 11 different countries (Benin, Burkina Faso, Côte d'Ivoire, Ghana, Kenya, Madagascar, Mali, Mozambique, Nigeria, Senegal, and Uganda). The workshop was facilitated by 8 staff members of Cirad and AfricaRice. During the workshop, we enabled the participants to work with the three productsthe identification tool, the species database, and the online networkand we facilitated discussion among them. Discussion points were whether or not the products are generally useful and desired, whether they are easy to use, and whether they generate effective and useful results.
Opinions and discussions were noted. We also started an online (bilingual) discussion on Weedsbook itself. Comments by members in general, and workshop participants in particular, were gathered (see, e. g. , Fig. 3 ). The AFROweeds identification tool was also put to test in farmer-owned rice fields. A first trial was carried out on 20 June 2012 in Ruvu, Tanzania (6°4304500 S 38°4005400 E) with a group of 7 potential users, derived from NARES of Kenya, Mozambique, Rwanda, Tanzania and Uganda. In this test, the online version of the tool was used, on an electronic tablet (iPad 3, 32 GB, Apple Macintosh) with 3G data SIM card. The tool was tested by 1-2 persons for each identification attempt and a total of 9 attempts were made, covering 8 species. For the second trial, on 25 September 2012, an encapsulated offline version of AFROweeds was installed on three electronic tablets (iPad 3, 32 GB, Apple Macintosh).
This trial was conducted in Zoungo, Ouémé valley, Benin (7°0604600 N 2°3005800 E) with the previously mentioned workshop delegates from NARES of 11 different African countries. Teams of 2-3 persons were composed in the field to practice and test the system. This test comprised 16 identification attempts, covering 12 species. In both user tests, the users randomly selected the specimen of weeds from the weed flora encountered in the farmer's rice fields. For each attempt we noted the species name, irrespective of the success of identification, the time required for each attempt (measured with stopwatches), and whether or not the identification was successful. Qualitative data derived from group discussions were ordered manually for analyses and interpretation. On quantitative data, derived from field tests, descriptive statistics were computed using MS Excel (2007).
1). Identifications were unsuccessful when the species was not yet included in the database, or when the user made an early mistake in the selection procedure. As none of the evaluators had previous experience with the tool, it is likely that with more practice the success rate would increase and the time to identification decrease. The various comments from the test users indicated that the identification tool worked well and made it easy to identify the species, especially of broad-leaved weeds which usually have more distinct and eye-catching characteristics (e. g. flower colors, leave shapes, plant morphology and habits) compared to grasses and sedges. Variations in flower color within species, however, need to be included in the tool. The identification of sedges was more difficult because there were not (yet) enough distinctive characters included in the tool.
For example, the bract length and the characteristics of the bulbs of the sedges needed to be more specific. However, as test users noted, even if the identification of a species does not result in a single outcome, as a result of the above-mentioned or any other constraint, the tool will still be useful in narrowing down the options and from there the user can conclude the identification by using the descriptions or photos in the database or by using field guides. There was broad consensus among participants that the tool and database should be enhanced by including (1) more species, (2) more local names of species and (3) more information on local uses (e. g. , food, fodder, medicinal, or agricultural uses) of species. Adding local names of species would facilitate discussions with farmers who know the species only by their vernacular names.
The suggestion to enhance the AFROweeds tool and database with species already covered by other tools such as Adventrop was also broadly supported by participants. During the workshop, it was also mentioned that the translation of the database into other languages such as  Portuguese and Swahili should also be considered. The tool could be expanded or improved by integrating image recognition (e. g. ,  Cope et al. , 2012; Yanikoglu et al. , 2014). A second future consideration would be to couple the identification tool with a weed management tool, such as Weed Manager  (Parsons et al. , 2009). Part of the team responsible for the development of AFROweeds is currently working on such an electronic decision support tool explicitly focusing on weed management in rice.
Discussion on the collaborative platform From the online discussion on the usefulness of Weedsbook during the workshop (Fig. 3 ), it appeared that most participants felt that the online platform Weedsbook, as an African weed science network, will help the advancement of weed science in Africa. This collaborative tool was generally found useful and efficient, as it allows the sharing of information among members and it will help agricultural change agents to update themselves on new developments, to generate new research ideas, and generally to enlarge their professional network thereby creating new opportunities for collaboration. However, language differences may hinder important discussions on the platform. The possibility of adding a translator to the platform, to give at least a rough idea of what is being discussed, was suggested.
Conclusion The AFROweeds identification tool and database is a useful instrument for agricultural change agents assisting rice farmers with weed problems in their crop. Where the user is not depending on 3G network coverage, the identification can be completed in less than seven minutes, with a success rate of at least 75%. Successful and partly successful identifications can then lead the user to factsheets with descriptions of the weed and information on weed ecology, management and local names that facilitate the communication with farmers. For further questions or information as well as for the exchange of experiences and photos, the agricultural change agent can turn to the network Weedsbook. The weed identification tool AFROweeds and the online network Weedsbook are therefore complementary.
AFROweeds and Weedsbook were positively assessed by test users and it became clear that their use should not be restricted to agricultural change agents working on rice but also serve those assisting farmers with other crops. Other recommendations were to expand the number of weed species, local names and uses, and to reach out to potential users that are less familiar with English or French as work language. As ICT products like smartphones, tablets and the internet will become increasingly common, affordable and accessible in sub-Saharan Africa, it is expected that the tool, database and network will gradually and successfully replace the field guides in book form that are currently most often used for weed identification and weed management recommendations. Unlike books, ICT products can be updated regularly.
Acknowledgements. All African AFROweeds partners are acknowledged for their contributions to the tool and database, and their participation in workshops and cooperation during the trials. We thank  Guy Manners   of Green-Ink (UK) , for proof-reading and language editing of an earlier version of the manuscript. The tools presented in this paper have benefitted from contributions by  G. Kyalo ,  K. Aloys ,  N. Ngoc ,  G. Tzelepogou  and  K. Iswaria. The  EU-ACP Science and Technology Programme  is greatfully acknowledged for funding our work under the 'African Weed of  Rice (AFROweeds)'  project (grant number  AFS/2009/219015 ). This is an output of the  CGIAR Research Program GRiSP , the  Global Rice Science Partnership.
Glucose Assay Treatment of cells for hepatocyte glucose production was performed essentially as described previously, using primary mouse hepatocytes plated in 12-well plates (1. 25×10 5 cells per well). 6, 15, 27 Glucose production was determined after a 12-hour incubation period in glucosefree DMEM (11966; Life Technologies) supplemented with 1% pen/ strep, lactate (Sigma)/pyruvate (Life Technologies; 10:1 mmol/L), and 100 nmol/L dexamethasone (dex; Merck) with or without drugs/ cytokines under investigation. At the end of the incubation period of 12 hours, 500 μl of medium was collected and glucose concentration determined by GAGO assay (glucose [glucose oxidase]; Sigma) by a modified protocol scaled down to a 96-well plate format. Each column consists of data from at least 12 wells of cells, 6 each from 2 mice.
BMDM Analysis BMDMs were harvested from culture plates using 4 mmol/L EDTA in PBS for 10 minutes at 37°C. Cells were washed in flow cytometry buffer (PBS with 2% fetal bovine serum and 1 mmol/L EDTA) and stained using the following antibodies (all BD Bioscience unless stated): F4/80 (BM8; e-bioscience), CD11c (HL3), CD206 (C068C2; Biolegend), CD69 (H1. 2F3), and CD40 (3/23). Fc block (4. 4G2) was included in all stains. Data were acquired on a LSR II flow cytometer (Becton Dickinson) and analyzed using FlowJo software (TreeStar). BMDM culture supernatants were collected after 24-hour treatment with the differentiation or activation conditions. Levels of cytokines were quantified by standard sandwich ELISA using paired antibody kits (e-bioscience).
Statistical Analyses Results in bar graphs are expressed as mean±SEM. Comparisons between groups were made by 1-way ANOVA with Dunnett or Tukey post hoc test using Prism. Differences were considered statistically significant if P<0. 05: ***P<0. 001, **P<0. 01, and *P<0. 05 unless otherwise stated. For studies on the plasma, statistical analyses of data were performed using SPSS 14. 1. ANOVA and Pearson correlation coefficients were calculated.
Taken together with the results in hepatocytes, this work suggests that metformin's anti-inflammatory actions are likely to be qualitatively different from conventional NSAIDs. We wished to establish whether the effects of metformin could be detected in humans and we started with a DM cohort. Investigating the GoDARTS patient database, we found evidence of metformin reducing subclinical inflammation as measured by NLR in patients. It is noteworthy that our findings support previous reports that metformin is capable of suppressing markers of inflammation such as high-sensitivity C-reactive protein in prediabetic individuals  42  and TNF-α in IR individuals. 43 NLR has recently been identified as a predictor of all-cause mortality and cardiovascular events,  31  whereas previous studies demonstrated a substantial beneficial effect of metformin therapy on cardiovascular outcomes.
Acknowledgments We thank  Dr Kashyap Patel  (Exeter) for demonstrating hepatocyte extraction. In addition,  Dr Andy Cassidy  and  Dr Ritu Sharma  (both Dundee) assisted set-up of real-time polymerase chain reaction. August 19, 2016  British Heart Foundation  (grant number  PG/06/143/21897 ). M. Mohan. acknowledges fellowship support from the  British Heart Foundation  (grant number  PG/14/4/30539 ). V. L. Morrison was supported by the  Ella and Georg Ehrnrooth foundation  and  D. J. K. Balfour  acknowledges funding from  Alzheimer's Research UK , grant number  ART-EXT-2010-2. C. Beall  is an  RD Lawrence Fellow  ( Diabetes UK  grant number:  13/00004647 ).
Randomized Placebo-Controlled Study: Metformin Exposure and Cytokine Levels in Nondiabetic Heart Failure Patients The anti-inflammatory effects of metformin were investigated in a randomly selected subset of patients who had participated in a double-blind, placebo-controlled study (www. clinicaltrials. gov: NCT00473876) that had evaluated the impact of metformin on in- sulin resistant (IR) and exercise capacity in nondiabetic patients with congestive heart failure. 32 Every patient had provided written informed consent before participation in this study, which was ap- proved by the East of Scotland Research Ethics Service. The subset of patients selected for this study involved 33 nondiabetic IR congestive heart failure patients (mean age, 63±7. 0 years; men, 85%; New York Heart Association class I/II/III/IV, 04/28/01/0) who were randomized to receive either 4 months of metformin (n=20; 2 g/d) or matching placebo (n=13).
7. A three-level trigger system selects events to be read out. The measurement uses data collected with the ATLAS experiment during the 2012 data-taking period. Only runs with stable proton beams colliding at √ s = 8 TeV are used in which all relevant detector components were functional. This data sample corresponds to an integrated luminosity of 20. 3 fb -1 determined with an uncertainty of ±1. 9% and derived from beam-separation scans performed in November 2012  [16]. The analysis relies on event simulation to correct the measured event yields for experimental effects and for the study of background processes. Different simulated event samples are used to model the signal from the individual production mechanisms: q q → W + W -events are simulated using the Powheg 1. 0 generator  [17] [18] [19] [20] [21] , which is interfaced to Pythia 8.
170  [22] ; for the non-resonant gg-induced WW signal the gg2ww program (version 3. 1. 3) [23]  is employed and interfaced to Herwig 6. 5/Jimmy 4. 31  [24, 25] ; resonant WW * production via a Higgs boson with a mass of m H = 125 GeV is modelled using Powheg+Pythia 8. 170. The three event samples are simulated using the CT10 NLO  [26]  parton distribution function (PDF). Photon radiation is modelled using Photos  [27]. The parameter tune used for the underlying event is AU2  [28]. The event samples are normalised to a cross section times branching ratio of 5. 58 pb (q q → W + W -[1]), 0. 153 pb (non-resonant gg → W + W -  [23] ) and 0. 435 pb (gg → H → W + W -  [4] ). The sum of these contributions corresponds to a total WW cross-section of 58. 7 +4. 2 -3. 8 pb where the uncertainties are due to scale and PDF uncertainties in the cross section calculations.
5% and are dominated by differences in simulated tt event samples produced with Powheg and MC@NLO, and uncertainties in the Wt production cross section. Statistical uncertainties from the limited size of the control samples in data and simulation introduce an uncertainty of ±3. 5%, resulting in an overall precision in the estimated top-quark background yield of ±5. 2%. The estimation of the remaining background processes closely follows the methodology described in Ref. [14]. Data-driven estimates of the yields of W+jets and multijet production are determined in an event sample in data that is selected with relaxed identification and isolation criteria for the leptons. The composition of this event sample with genuine and misidentified leptons can be inferred using the probabilities of genuine and misidentified leptons selected with the relaxed criteria to satisfy the nominal lepton selection criteria.
The transverse momentum distribution of the selected jet after the final event selection is shown in Figure  1(b) , where data is shown together with the simulated WW signal events and the estimated background yields. Good agreement between the data and the estimated yields is observed for the selected WW+1-jet candidate sample. 1 : Summary of the event yields in the selected WW+1-jet events observed in data and estimated from signal and background contributions. The estimated event yields for the WW signal are determined from simulated event samples which are scaled to a total cross section of 58. 7 +4. 2 -3. 8 pb. The estimated yields from diboson production are determined from simulated event samples whereas the yields of all other backgrounds are estimated using datadriven methods. The statistical and systematic uncertainties are shown separately.
1b ). At the micrometer scale, SEM micrographs show cubic shape γ' precipitates with an average size close to 200 nm homogeneously distributed in the γ matrix (Fig. 1c ). In order to simplify the x-ray data analysis and to dissociate physical mechanisms in such materials, this microstructure sensibility differs from those employed in aircraft engines (5-20 µm grain sizes and three populations of γ' precipitates  [8] ). Three 6. 22 mm diameter fatigue test specimen were then machined. One was used as a reference. One was subjected to shotpeening, one to shot-peening and interrupted fatigue testing. The peening operation was done using an ultrasonic technique with 3 mm-diameter 100Cr6 steel shots during 1080s. The fatigue test was performed at imposed deformation (Δε/2 = 0. 5 %, R ε = 0) at 450°C during 300 cycles.
Data processing Due the highly coherent interfaces (lattice mismatch < 10 -3 ), fundamental reflections (h, k, l with the same parity) correspond to the convolution of the intensities belonging to the γ and γ' phase whereas superstructure reflections (h, k, l with different parities) correspond only to the contribution of the γ' phase intensities. Laue patterns provide thus quantitative information about the average alloy (γ + γ') and the γ' phase. Data analysis has been performed using the XMAS  [11]  and LaueTool  [12]  softwares. The main output of the calculations is the coordinates of the reciprocal lattice vectors in a given system of coordinates (sample, laboratory) for each crystallite founded in the Laue pattern. This is then used to determine the crystal orientation, its unit cell parameters and finally deviatoric strain and stress components.
Acknowledgments This work benefited from the support of  the  project  REMEDDIES ANR-13-BS09-016  of the  French National Research Agency (ANR). The experiments were performed on beamline BM32 at the  European Synchrotron Radiation Facility (ESRF), Grenoble, France. We are grateful to Local Contact at the  ESRF  for providing assistance in using beamline BM32.
The DBS technique  [48]  yields measurements of the propagation velocity of turbulent structures, v turb , and density fluctuation levels. The TORBEAM beam tracing code  [49]  is used with the reconstructed magnetic equilibrium from EFIT and density profiles from a profile reflectometer  [50]  (averaged over the period of time the E r profile is obtained) as inputs to determine the local scattering position and wave number. Since v turb ¼ v E×B þ v ph , if the phase velocity of the turbulence v ph is small, then the measured velocity is dominated by the v E×B drift and E r can be inferred. One would expect the linear phase velocity to be on the order of the diamagnetic velocity, but could be in either the ion or electron direction depending on the nature of the turbulence. The DBS measurements are obtained at low wave number, k ⊥ ≈ 2.
Within the project, the evaluation carried out was established in order to find out whether (1) participants in the project engaged with the general objectives of the CP2PKE, and discussed and redefined these objectives to suit their specific context and culture; (2) participants were able to interpret the concepts and the tools used to carry out the CP2PKE through their own culturally specific understanding, and whether they critically engaged with these concepts and tools, and proposed their views and interpretation on them, leading to a redefinition of concepts and tools in a locally owned way; (3) participants engaged with their wider community to help in the presentation, discussion, and implementation of the North Rupununi best practices; and (4) the North Rupununi best practices had an impact on host communities (as a source of inspiration for developing their own In addition, we collected quantitative and qualitative data about participants, the community, the characteristics of each trip, and the teams facilitating the exchange to understand contextual factors that could influence the impact of the CP2PKE.
In the case of the peer-to-peer interviews, this was where small groups of participants (2-4) discussed their experiences of the CP2PKE process, with one of them taking down notes of the highlights of the discussion. At the community level, we analyzed 115 individual questionnaires and the discussion transcript from one focus group. At the consultant level, 12 reports and evaluations were analyzed. Finally, 15 end-of-trip COBRA team group evaluations, as well as 17 reflective diaries, were also used as data to better understand the impact and success factors of the exchange. Both the consultant reports and COBRA team evaluations provided additional observations on the experiences of both the community participants and the wider community. These data were organized into individual participant and community responses, and then coded to elicit themes related to the transferability of concepts and techniques, and the impact of implementation of best practices.
Acknowledgments: We would like to especially thank the COBRA participants and the communities of the  North Rupununi (Guyana), Kwamalasamutu (Suriname), Kavanayén (Venezuela), Katoonarib (Guyana), Laguna Colorada (Colombia) ,  Maturuca (Brazil) , and  Antecuma Pata  ( French Guiana ) for their enthusiastic participation in this research. We would also like to thank the following practitioners and academics for helping facilitate the exchanges and for their insightful inputs into the process:  Rachelle Bong A Jan  and  Gwendolyn Smith of Attune Development, Suriname ;  Bibiana Bilbao  and  Adriana Millan  of the  Universidad Simón Bolívar, Venezuela ;  Vincenzo Lauriola  of the  Instituto Nacional de Pesquisas da Amazônia, Brazil ;  Jorge Restrepo  and  Sandra Aristizabal  of the  United Nations Development Programme, Colombia ;  Aurélia Stefani  of the  Université des Antilles et de la Guyane, Institut Pasteur, French Guiana; Claire Couly of the Parc Amazonien de Guyane, French Guiana ; and  Nicholas Fredericks  and  Faye Fredericks  of the  South Central People's Development Association, Guyana.