dataesr/scientific-paragraphs-categorization huggingface dataset:
Statistical analysis Statistical analyses were conducted using Statview and SPSS software, except when indicated. Data are expressed by the number of cases (qualitative variable) and as mean ± SEM or median [interquartile range] for the quantitative variable. Levels of serum lipids, FAs, LDL lipid components, body weight, BMI, and WHtR at the end of the run-in and the wash-out periods were considered as the baseline value for the following intervention period. Differences in the baseline characteristics of the groups and in the percentage of change between intervention diets were analyzed by unpaired Student's t -test for parametric variables and chi-square test for nonparametric variables. Treatment order and gender were entered in the models as an additional factor, but they were not signifi cantly different and were not considered further.
0 was used. Triple quadruple mass spectrometry (QqQ-MS) data were analyzed with LipidView version 1. 1 software (AB Sciex). Lipid identifi cation was based on their characteristic head groups and corresponding FAs against the LipidView library, which contains over 50 lipid classes, 25,000 lipid species, and more than 600 characteristic lipid fragments. Lipid quantifi cation was referred to defi ned concentrations of the internal standards added in the derivatization step [myristic-d27 acid (3,3 M), palmitic acid-1 13C (0. 3 M), and stearic-2-2-d2 acid (1. 3 M )]. For accurate plasma level quantifi cation of specifi c targeted FAs, a multiple reaction monitoring approach was performed, using the MS instrument on ionspray and positive ion operating mode at 400°C. Quantifi cation was made on the basis of an internal standard mixture added to the samples prior to the derivatization procedure (3.
The MS analysis was performed by ESI-MS/MS operating in positive ionization mode, using a method described by Hu et al. ( 27 ) , with minor changes. The declustering potential was 70 V, and the collision energy was 32 V. A specifi c scan mode was created for each lipid class (head group) with the following precursor ion (PI): PI m/z 369. 3 for CEs, PI m/z 184. 1 for PCs and SMs, NL m/z 141. 0 for PEs, NL m/z 185. 0 for PSs, and FA scans for ammonium adducts of FAs to determine the lipid FA composition. For all specifi c scan modes, the following conditions were used: unit resolution for Q1 and Q3, scan range from m/z 350 to m/z 1,200, and sample acquisition through 20 min. Lipid names and abbreviations were assigned according to LIPID MAPS nomenclature. The analytical data were processed with LipidView V1.
1D ). The subgroup with a Low LDL and High-LDL groups refer to subjects with baseline LDLc level below and above 130 mg/dl, defi ned as a borderline value for high LDL levels according the ATPIII guidelines  ( 27 ). Data are expressed in milligrams per deciliter and given as mean ± SEM. Statistical signifi cance in changes after intervention was determined by Student's t -test. of the plasma FA profi le after interventions. PCA results showed that the two principal components (PC1 and PC2) of the established PCA model explained 62 and 58%, respectively, of the total variance of the plasma FA data set. ANCOVA test of the principal components (PC1-PC5, >75% variability) revealed that global differences in plasma FA patterns were negligible. Thus, the plasma FA profi le after 4 weeks of intervention with either 3-or PhyS-supplemented milk did not differ signifi cantly from baseline.
Plasma FA profi le The plasma FA profi le is shown in supplementary Fig. 2. Plasma levels of FAs did not differ signifi cantly after run-in and wash-out periods (supplementary Table  3 ). Analysis by PCA was performed to investigate the global variation AT-DYSLP, atherosclerotic dyslipidemia was defi ned by TGL levels >150 mg/ml and HDLc levels <40 mg/dl in males or <50 mg/dl in females. Data are expressed in milligrams per deciliter and given as mean ± SEM. Statistical signifi cance in changes after intervention were determined by Student's t -test. a P < 0. 001 for differences from baseline. b The q values after FDR adjustment = 0. 075. lipid groups ( P < 0. 05; FDR adjustment: q value = 0. 075), whereas nonsignifi cant changes were induced by the 3milk. Changes relative to baseline were signifi cantly different between both interventions for all GPL groups ( Fig.
It has been a wellestablished discipline for several decades  (Gimenes et al. , 2014). TEL is a large domain for research and practice, including e-learning, mobile learning, and Learning Management System (LMS). An LMS is the framework that handles all aspects of the learning process. An LMS is also the concrete infrastructure that delivers and manages instructional content, identifies and assesses individual and organizational learning or training goals, tracks the progress towards meeting those goals, and collects and presents data for supervising the learning process of organization as a whole  (Szabo and Flesher, 2002). LMSs support the use of standards for describing the learning objects, packaging them into larger content and learning units (such as lessons and courses), and applying various instructional design strategies and techniques  (Jovanovic et al.
, 2007). Nowadays, LMSs are not restricted to distant learning only. Teachers use them for blended learning which combines traditional face-to-face learning with computer supported learning  (Graham, 2005). LMSs have created remarkable opportunities for higher education to expand the educational process beyond the traditional classroom to include geographically dispersed students  (Brito et al. , 2014). The research work presented in this paper is a continuity of other former works in our lab  (Oubahssi et al. , 2010)   (Abdallah et al. , 2008)  by proposing a new implementation approach of learning situations and pedagogical scenarios. It takes place into the context of the GraphiT project (Graphical Visual Instructional Design Languages for Teachers). Its main goal is to study the possibilities and limits about the pedagogical expressiveness of operationalizable languages to specify future leaning scenarios that could be fully deployed and automatically set up upon an existing LMS.
(Abdallah et al. , 2008)  applies his proposal on the Moodle platform. All these presented works and many others  (Drira et al. , 2012)  propose a meta-model to formalize LMS instructional language but to our knowledge, there is no proposition that focuses on identifying an explicit process or method to formalize it. The next section emphasizes on the importance and the utility of defining an explicit method to formalize LMS instructional languages.
Technical Analysis The second step of the process concerns the technical analysis  (Abedmouleh et al. , 2012). Several technical analyses are possible: databases, source code, courses backup/restore, etc. During this step, the main source of information for identifying the instructional design language is the LMS database. The other technical analyses will be used during the confrontation step. This analysis consists in specifying a reduced Conceptual Data Model from the one available by LMS providers if it exists. In our approach, the database analysis has to be restricted to the tables/columns in relation to instructional design data. The main obstacle is to identify these data. Information from the micro-HMI analysis could be useful to achieve this goal. This technical analysis consists in (1) looking over all database tables in order to sketch a first draft of the model, (2) focusing on tables embedding elements in relation to instructional design concepts.
Moodle 2. 4 offers 7 resources (Book, Page, Label, IMS content package, File, Folder, and URL) and 13 activities (Forum, Database, Glossary, Assignment, Lesson, Quiz, Workshop, SCORM package, External tool, Choice, Survey, Wiki, and Feedback). In figure  2 , we present only one resource (Label), and 5 activities (Survey, Chat, Workshop, Quiz, and Forum) for clarity reasons. In the page specification of each concept, attributes are divided into different parts. For example, for the Chat activity, its fields are divided into 4 parts named: general, common module settings, restrict access, and activity completion. These parts names are presented in the macro-HMI model. Note that there are only two types of relationships within this model: composition relationship and inheritance relationship.
Application of the Factorization on Moodle After the macro-HMI analysis, we applied the factorization process. We noticed that all activities/resources had the common attributes: "commonModuleSettings", "restrictAccess", and "activityCompletion". So we moved these attributes to the Activity/Resource class. All activities had the common attribute "general" according to the macro-HMI model, that's why we created a class called "Activity" and we moved the attribute "general" into it. Some Moodle activities could have outcomes like Chat activity, Workshop, and Quiz. We added in the macro model a class named "ActivityWithOutcomes". This class had "outcomes" as an attribute. We noticed that some activities with outcomes could be graded. Therefore, we added the class "GradedActi-vityWithOutcomes".
Technical Analysis The technical analysis consists in analyzing the Moodle database. Our goal is to identify the Moodle instruction design language from a technical viewpoint to approve the relevant of specific data for this language. This analysis consists in specifying the reduced Conceptual Data Model for Moodle in relation with the instruction design. We have reviewed all Moodle database tables. Titles semantic analysis of tables and fields allows to (1) gather the tables related to the ID, and (2) ignores those related to technical specifications (users' management, learners' tracking…). Then we studied dependences and relations between database tables. The generated Conceptual Data Model is based on reverse engineering rules. Foreign keys enable the specification of required multiplicities.
Confrontation and Formalization The micro-HMI analysis and the technical analysis have specified two Moodle instructional design models according to two different viewpoints. In this step, we are interested in the confrontation of these models to formalize Moodle instructional design language. This step is very important in our process. We think that the use of only one analysis method presents many negative points. For example, the micro-HMI model depends directly on the Moodle analyst competence. This means the possibility lack of pedagogical attributes. Similarly, the technical analysis is not an easy task. Many data structures are not explicitly reported when creating the database. From the 2 models comparison, we notice that every element/ attribute existing in the micro-HMI model is certainly presented in the technical model.
But some elements exist in the technical model without being present in the micro-HMI model. That is why we refer to the PHP source code analysis of Moodle to verify the presence of these elements. Figure  3  (including corrections in red) shows an extract of Moodle final model. Corrections in red present the confrontation result of the two models. For example thanks to the technical analysis, we found that every section has an order. This attribute has not been detected by the micro-HMI model. The code source analysis confirms the presence of this attribute. The attribute "SectionOrder" is presented in the final HMI model. The confrontation phase allows also rectifying information on the micro-HMI model. Figure  4  shows an example about relationship verification between the "GradeCondition" class and the "Activity/Resource" class.
Based on the micro-HMI analysis, the "GradeCondition" class refers to the abstract class "Activity/Resource" while the same class refers to a graded activity in the technical model. The code source analysis of Moodle conditionlib. php file confirms that the grade condition refers to a graded activity. That is why the reference relationship is between the two classes "GradeCondition" and "GradedActivityWithOutcomes" in the final model. The final model resulting from the confrontation phase formalizes the Moodle instructional design language.
CONCLUSIONS In this paper, we present a meta-model-based approach and method for identifying and formalizing LMS languages. We apply our proposed method on the Moodle platform. The meta-model will be used as a basis for the development of the external editor by using a Model Driven Engineering tooling like EMF-GMF. It will guide and generate most of the final code for the editor. We have then been able to propose a graphical and external editor communicating with the system thanks to a dedicated API developed and integrated to the LMS. It will offer more user-friendly and soundly computer artefacts when development is freed from the technological choices related to the initial design of the TEL system considered. This will facilitate the use of LMS and allow to teachers and pedagogical engineers (service information technology and communication for education) of becoming more familiar with the specific design upon this LMS.
26-1. 26i and coating refractive index 1. 50 + 0i, and the uncertainty of the derived D p /D c due to particle geometry is < 6 %  (Liu et al. , 2015). The chemical composition of non-refractory PM1 was measured by an Aerodyne C-ToF aerosol mass spectrometer (AMS). A detailed description of the instrument can be found elsewhere  (Drewnick et al. , 2005). A time-and compositiondependent collection efficiency (CE) was applied to the data based on the algorithm by  Middlebrook et al. (2012). It is noted that most atmospheric BC particles contain nonabsorbing material as a result of co-emission of either the particulate matter or its precursors. This additional material will be observed by the AMS. BC particles with little associated non-absorbing material may bounce and result in a reduced CE but the fraction of the non-refractory mass mixed with BC is small relative to the total non-refractory mass, and is therefore unlikely to affect the reported AMS mass loadings.
HYSPLIT backward trajectories The HYSPLIT 4. 0 model  (Draxler and Hess, 1998)  back trajectories were initiated from the latitude, longitude and altitude of the aircraft every 30 s along the flight path and calculated 14 days backward in time. Horizontal and vertical wind fields for trajectory calculations were provided by the 1 • × 1 • , 3-hourly GDAS1 reanalysis meteorology (Global Data Assimilation System; NOAA Air Resources Laboratory, Boulder, CO, USA). This also allows retrieval of potential temperature (θ ) and precipitation rate along each trajectory path. The reported meteorological information extracted along the trajectory path is averaged over all of the trajectories along the flight track for the targeting time period.
7 × 10 4. This contrasts with 2013 which showed the lowest number of fire counts (1. 4 × 10 4 ). Therefore, whilst 2008 does indeed show the importance of biomass burning in the Arctic, Asian pollution sources make a substantial contribution to Arctic BC during many spring seasons. Figure  12b  shows the changes in anthropogenic emissions of BC between 2005 and 2010 as described in the ECLIPSE database. There is a marked increase in BC emissions over this period of between 20-30 % and these increases are projected to continue in the coming decades. A number of studies point out the scavenging efficiency of BC represents a major uncertainty in model prediction of BC in the Arctic (e. g. J. Liu et al. , 2011; Hodnebrog et al. , 2014; Myhre and Samset, 2015) , and these studies have shown that models could underestimate the BC loadings in the Arctic during the cold season by a factor of up to 10 due to a possible underestimation of the BC lifetime.
Acknowledgements. This work was supported by the  Natural Environment Research Council (NERC)  through the  ACCACIA  ( NE/I028696/1 ) and  GASSP  ( NE/J023515/1 ) projects. The BAe-146-301 Atmospheric Research Aircraft was flown by  Directflight Ltd  and managed by the  Facility for Airborne Atmospheric Measurements (FAAM) , a joint entity of  NERC  and the  Met Office. Boris Quennehen acknowledges the European Union  for funding the  FP7 ECLIPSE project  (grant agreement no. 282688 ) as well as the IPSL CICLAD/CLIMSERV mesocenter for providing computing resources. Edited by:  L. M. Russell.
Acknowledgments. The authors thank the subjects, their families, and communities for actively participating in both studies; field staff in  Niakhar Institut de Recherche pour le Développement ,  Unité  de recherche sur les maladies infectieuses et tropicales émergentes, Senegal;  Medical Research Council Basse Station ,  The Gambia ;  Centre pour le Développement des Vaccins Bamako , Mali; and  Shirdi Sai Baba Rural Hospital, Vadu Budruk, India , for the valuable work with their communities. The authors also thank the laboratories at the  Public Health England (formerly Health Protection Agency)  and the  US Centers for Disease Control and Prevention  ( Julie Chatt-Soroka  and  Jeffrey Martin ) for serum bactericidal antibody and enzyme-linked immunosorbent assay testing, respectively. Disclaimers.
Errors and omissions excepted, the names of proprietary products are distinguished by initial capital letters. Financial support. These studies were funded by the  Meningitis Vaccine Project (PATH)  through a grant from the  Bill & Melinda Gates Foundation (BMGF). Supplement sponsorship. This article appears as part of the supplement "The Meningitis Vaccine Project: The  Development, Licensure, Introduction and Impact of a New Group A Meningococcal Conjugate Vaccine for Africa ," sponsored by the  Meningitis Vaccine Project  through a grant from the  Bill & Melinda Gates Foundation. Potential conflicts of interest. All authors: No reported conflicts. All authors have submitted the  ICMJE Form for Disclosure of Potential Conflicts of Interest. Conflicts that the editors consider relevant to the content of the manuscript have been disclosed.
5061/dryad. 3gn80). Teams completed at least three surveys for at least three locations at each reef, including these habitat types: lagoon (1-3 m in depth), reef flat (1-3 m), crest (~5 m), slope (7-10 m) or bommie fields (7-12 m). All required permits for scientific surveys were obtained from the Permits section of the Great Barrier Reef Marine Park Authority. Surveys were undertaken on snorkel and SCUBA. Survey data were collated in 50 km segments from 150 km north to 350 km south of the track (10 segments). Within these segments, the percentage of surveys that recorded each of the five damage levels was calculated. To estimate the area of coral reef affected at each damage level, the proportion of each level of cyclone damage observed during the surveys was extrapolated to the known reef area within each of the ten 50 km segments.
Estimates were then produced of the percentage of the total reef area within the Marine Park (24,839 km 2 ) impacted by TC Yasi at each damage category level. Data were aggregated at the scale of individual reefs to identify the percentage of surveys at each reef for which structural damage was observed. Damage levels for surveys within each 50 km segment were also averaged and a custom coded bootstrap routine with R 2. 15. 3 (R Development Core Team, www. R-project. org) was used to generate 95% confidence intervals using resampling with replacement (10,000 times) of the ten 50 km segments. This method generates estimates of error while accounting for unequal effort among the 10 segments and pools all data (no discrimination between habitat or shelf position). A permutational analysis of variance (PermANOVA) was also used to test the effects of three fixed factors on the mean level of damage experienced: side of track (two levels: north/ south of the eye), shelf position (two levels: mid-shelf and outer-shelf), and habitat (5 levels: lagoon, reef flat, crest, slope and bommie fields).
The independent effects of each factor and their possible interactions and all post-hoc pairwise comparisons across levels were calculated using PERMANOVA+. This analysis is based on a Bray-Curtis similarity matrix, 9999 permutations of the raw data under a reduced model, and Type III (partial) sums-of-squares. Recovery surveys were conducted between January and December 2013 (24-35 months after TC Yasi) at reefs from 150 km north to 150 km south of the cyclone track (the area where damage was most severe). The recovery surveys were conducted in the same manner as the impact assessments at a sub-set of the reefs surveyed in 2011 (see blue locations in Fig. 2 ). The number of reefs re-surveyed in 2013 in each of the 50 km segments is as follows (150 kmn = 3, 100-3, 50-3, -50-4, -100-3, -150-2, -200-1).
POLARCAT observations POLARCAT is a consortium of tropospheric chemistry experiments performed during the IPY 2008  (Law et al. , 2014). A wealth of data on tropospheric ozone and its photochemical precursors were obtained through the depth of the Arctic troposphere during spring and summer. These observations provide an opportunity to evaluate model representations of processes controlling tropospheric ozone in imported pollutant layers above the surface. The NASA Arctic Research of the Composition of the Troposphere from Aircraft and Satellites (ARCTAS) mission  (Jacob et al. , 2010)  was grouped into three parts, ARCTAS-A, ARCTAS-B and ARCTAS-CARB. Three research aircraft took part in this campaign with a slightly different goal for each mission. ARCTAS-A and ARCTAS-B targeted mid-latitude pollution layers and wildfire plumes, respectively, transported to the Arctic.
SMHI-MATCH SMHI-MATCH (Multiple-scale Atmospheric Transport and Chemistry Modeling System) is an offline CTM developed at the Swedish Meteorological and Hydrological Institute  (Robertson et al. , 1999). SMHI-MATCH can be run on both global and regional domains but for the POLMIP model runs were performed for the 20-90 • N region. The chemical scheme in MATCH considers 61 species using 130 chemical reactions and is based on  Simpson (1992)  but with extended isoprene chemistry and updated reactions and reaction rates. Information about the implementation of the chemical scheme can be found in  Andersson et al. (2007) , where evaluation of standard simulations for the European domain is also given. ERA-Interim reanalysis data from ECMWF were used to drive SMHI-MATCH for the years 2007 and 2008.
The 6-hourly data (3-hourly for precipitation) were extracted from the ECMWF archives on a 0. 75 • × 0. 75 • rotated latitude-longitude grid. The original data had 60 levels, but only the 35 lowest levels reaching to about 16 km in the Arctic were used in SMHI-MATCH. Monthly average results for 2007 and 2008 from global model runs using MOZART at ECMWF in the MACC (Monitoring Atmospheric Chemical Composition) project were used as both upper and 20 • N chemical boundary conditions. In addition to the standard daily POLMIP emissions, NO emissions from lightning were included using monthly data from the GEIAv1 data set, which has an annual global total of 12. 2 Tg N yr -1. DMS (dimethylsulfide) emissions were simulated using monthly DMS ocean concentrations and the flux parameterization from  Lana et al.
Surface network ethane and propane The NOAA Global Monitoring Division/INSTAAR network of surface sites provides weekly observations of light NMHCs around the globe  (Helmig et al. , 2009). The model results for ethane and propane are compared to the data over a range of northern mid-to high latitudes in Fig. 11. Monthly mean model output is used and the nearest grid point (longitude, latitude, altitude) selected for each site. All models (except GEOS-Chem, which used higher ethane emissions and has lower OH concentrations) significantly underestimate the winter-spring observations, indicating the POLMIP emissions are much too low for both C 2 H 6 and C 3 H 8 , consistent with the conclusion that CO emissions are too low (as discussed in Sect. 5. 4 and in  Monks et al. , 2014).