dataesr/scientific-paragraphs-categorization huggingface dataset:
Knowledge based on the analysis of neighborhood To take into consideration the holistic nature of matching (i. e. , matching a feature depends on the matching of its neighbors), a Neighborhood Criterion (see the last column in Figure  3 ) may also be defined. To do that, the process should actually be slightly more complex than described in Section 4. 1. In a first iteration, the matching process is performed using some criteria, then the results are used to initialize the masses of belief of the neighborhood criterion as explained below. In a second iteration, the final matching is performed with www. josis. org the same criteria plus the neighborhood criterion. For complex cases, the process can even be reiterated again. Let us consider a feature F 1 belonging to dataset DataSet2 (the most detailed dataset) and a candidate C i belonging to dataset DataSet1 (the less detailed dataset).
According to Section 4. 1, 0 or n links are generated by the first step, i. e. , each feature of DataSet1 may be matched with 0 or n features in DataSet2. Then, for each candidate, C i , belonging to DataSet1, their n identical features defined in the first iteration are grouped into connected groups. If only one group has been identified, this group is evaluated as being sure that is to say that all features belonging to the group, including F 1, are considered to be wellmatched. Otherwise, if several groups are found neighbors of C i are analyzed to see how they are matched in the first step, and especially if corresponding features of the neighbors are connected to the groups. Four cases are distinguished (see Figure  4  for examples of these cases and last column of Figure  3  for knowledge modeling): (1) Case A: None of the neighbors of C i is matched with a neighbor of F 1 in the first iteration.
Isolated points: Presentation of the case study and typical results Experiments on isolated points have been performed on relief data extracted from two datasets of IGN-France, the French National Mapping Agency (NMA): BDTOPO c and BDCARTO c , and with the following characteristics. These datasets have different levels of detail, with BDTOPO being more detailed than the BDCARTO dataset. Features representing relief such as mountains, summits, and valleys are vague, firstly by definition (e. g. , boundary between a valley and a mountain is not perfectly defined) and secondly because the classification of objects may be confusing (e. g. , the difference between a "summit" and a "peak" may be hard to define). In addition, identical features may have different names due to linguistics or due to the use of both official names and vernacular names for the same object.
The semantics of features do not have the same level of detail in the databases. For example in the BDCARTO there are concepts, which are grouped together: e. g. , "summit, crest, hill," while in BDTOPO these same concepts are well separated. Using only the position criterion is not always efficient since the identical feature is not always the closest one. In the same way, using only the Toponym Criterion may lead semantic inconsistencies. Therefore our matching approach is based on the fusion of the three criteria (Position, Toponym, and Semantic) seems well adapted to solve this matching problem. Quantitative results of the matching process are described below. Before, Figure  5  illustrates the importance of semantic information in the matching process. Figure  5a  shows that the process did not succeed to match identical features ("l'escarpu ou pic de louesque" and "l'escarpu") when using only the name and position criteria.
Experiments on road networks: Typical results This section describes some experiments matching road networks from two datasets, BD-CARTO from IGN and MultiNet c from TomTom. These datasets have different scales, producers, and purposes. They are also highly heterogeneous. Our test area covers approximately 760km 2 in urban and rural areas. The first difference between the two datasets is scale. MultiNet is more detailed than BDCARTO. However, there are also objects such as pathways and rugged ways that are represented in the less detailed database BDCARTO but not in MultiNet. Differences in modeling and representation also exist. Each database has a specific representation of the real world according to its purpose. BDCARTO is built by the French NMA. It is used to make maps at 1:100,000 or 1:250,000 scale, and for geographical analyses at regional and departmental levels.
It has an accuracy ranging from one to several decameters. MultiNet is built by TomTom, a private company. Its accuracy goes from five to www. josis. org twelve meters in many detailed sections, such as city street networks. The latter database focuses particularly on road and street network description for navigation applications. Thus, highways are modeled by a single polyline representing its axis in BDCARTO, and by two parallel polylines representing the axis of each single carriageway in MultiNet. Another important difference between the datasets concerns the roundabouts. In MultiNet these are represented by a set of edges, whereas in BDCARTO they are generally represented by a single node. Some results obtained by the matching process described in Section 4. 2 are illustrated. For each feature belonging to MultiNet, the most detailed dataset, the algorithm looks for candidates in BDCARTO before choosing the "best" one according to our approach based on belief theory.
In the next figures BDCARTO is displayed on the upper left side, while MultiNet is displayed on the upper right side, and both datasets as well as matching links are displayed on the bottom. Recall that for road networks the matching process is carried out in 2 steps. In a first step, the matching process is performed using Position, Semantic, Name, and Orientation criteria as described in Section 4. 2. The distances are computed between edges of roads (e. g. , for each edge to match a distance is computed between it and edge candidates). The Position Criterion is based on the Hausdorff distance between edges of roads networks. The Orientation Criterion is based on the orientation of an edge candidate compared with the edge to match. The Semantic and Name criteria are respectively based on the edge types and names.
Then, in a second step, the results of the first step are used to initialize the masses of belief of the Neighborhood Criterion. The final matching is performed with all five criteria: Position, Semantic, Name, Orientation, and Neighborhood. Figure  6  reveals the utility of using many criteria and notably the Neighborhood Criterion. Spatial context has a particular importance in the matching process of networks. Thus if the context is not taken into account i. e. , Neighborhood Criterion is not used, over-matched matching results occur, especially in urban areas. Thus, segments C1, D1 and A1, B1 are wrongly matched with A2 and B2 segments respectively. This result is due to the datasets having different scales; the segments representing streets are very close and have the same orientations.
Therefore the Semantic and Name criteria are important. Unfortunately our two datasets have heterogeneous classifications in urban areas, and name attributes are not always filled in. As a result we are faced with imprecise and insufficient semantic and name criteria. As a result the matching process without the Neighborhood Criterion is not able to distinguish between identical and non-identical features, but when a more holistic analysis is made by adding the Neighborhood Criterion, over-matched results are eliminated. Figure  6b  shows that the segments E1, D1, C1, B1, A1 are not matched any more, i. e. , no identical feature are found in the less detailed dataset. Similarly, Figure  7  illustrates the utility of the Name Criterion. In Figure  7a , the results of data matching algorithm without using the Name Criterion are shown.
Qualitative evaluation of matching results The automated matching results are compared with an interactive matching to evaluate the approach. Evaluation for isolated points is made in relation to the number of features. Five datasets (1,232 features) for five French counties are tested. For roads networks, evaluation is carried out in comparison with the total length of networks (12,725km for a 760km 2 area). The evaluation is made on behalf of the most detailed dataset and regarding to the ground truth for isolated points and an interactive matching for road networks. Two well-known indicators used in information retrieval domains to measure the performance of algorithms were applied to qualify our results. These are precision-the percentage of the matching links defined by the matching algorithm that are correct compared to an interactive matching, and recall-the percentage of the correct matching links that are actually discovered by the matching algorithm.
Concerning the sensitivity of parameters, only a few tests were carried out. The analysis consisted of varying the threshold for one criterion while the others were left unchanged. The first results have shown that sensitivity is more important for the Distance Criterion and less important for those based on semantic and names. Concerning the Distance Criterion we observed that the sensitivity is more important for thresholds corresponding to small distances. This result makes sense since datasets have different scales and different precision. Certainly, further tests should be carried out. One possibility would be to study the impact of all parameters at the same time, and with respect to knowledge about datasets such as precision, accuracy etc.
In some cases, the easiest way is to quantify the "no matched" and "no opinion," as in the case of the orientation test. Quantifying the "no matched" means "the less features that are colinear, the less matching is possible. " Quantifying "no opinion" expresses the low conviction of this criterion. In other cases, it is easier to quantify the "matched" and "not matched," such as for the position criterion where the first means "two very close features are probably identical," and the second means "two distant features in any case more than the precision of datasets, are probably not matched. " The assignment of masses of belief is an essential step in our approach. As we have noted in Section 4, curves defining the belief assignments vary from criterion to criterion. They also have different weights in the process.
This is due to the fact that each criterion relies on different knowledge that is more or less perfect. This flexibility is another key advantage of our approach as it allows users to precisely model different knowledge, However it may also be thought of as a drawback, as tuning the process may become fastidious. In our experiments, thresholds depend on data specifications and especially on the precision of a dataset and are thus directly set up. Moreover, in our opinion, the definition of precise thresholds is not so important for two main reason: many criteria are combined, and the curves are relatively smooth. As such only approximate thresholds are necessary. In addition, we believe that even if thresholds may be adapted to special cases, the global shapes of functions are quite general, and should be reused in many matching cases.
For future work, there are several possible solutions that could make the matching process more generic and easier for end users. One solution is to develop a method for optimizing parameters that is a compromise between matching results quality and number. A second solution is to identify thresholds and weights by data mining. Finally, a third solution is to carry out a qualitative study for each criterion in order to evaluate it. On final point to discuss is the cardinality of matching links. Our approach is "oneway," with each edge of one dataset (the most detailed) matched to one edge in the other dataset (the less detailed). It may happen that several edges of the detailed network are matched to the same edge of the other network. However, if the networks have similar scales this should not happen.
Encapsulation efficiency The amount of BSA and FITC-BSA contained in the NPs was spectrophotometrically determined directly after the extraction of the protein from the NPs. Briefly, the PLGA-BSA and PLGA-FITC-BSA NPs were treated with a solution of 2 M NaOH during 2 hours to allow degradation of the polymeric matrix and release of encapsulated BSA or FITC-BSA, respectively, from the NPs. The suspension was then centrifuged at 15,000 rpm for 15 minutes. Supernatant was collected, and the amount of protein released from the NPs was determined using a Bradford Protein Assay kit according to the manufacturer's instructions. Absorbance was measured at 595 nm by a Synergy Mx multimode microplate reader (Biotek, Winooski, VT, USA) with the Gen5 Data Analysis Software. The amount of unbound BSA or FITC-BSA found in the supernatant was also measured after each of the three washes during the synthesis of the PLGA-BSA and PLGA-FITC-BSA NPs and quantified using the Bradford Protein Assay.
As summarized in Table  1 , PLGA-BSA NPs had a zeta potential of -12. 3±2. 5 mV in water compared with -30. 8±3. 2 for unloaded PLGA NPs. In saline or RPMI, zeta potential increased to reach up to -4. 6 mV for PLGA-BSA and -2. 3 mV for PLGA NPs. To note, addition of serum in RPMI did not affect the surface charge of the NPs. The polydispersity index was 0. 041 and 0. 195 for unloaded PLGA NPs (Figure  1B ) and PLGA-BSA NPs (Figure  1C ),  respectively. The DLS results indicated a narrow size distribution and a good colloidal stability of the NPs (Figure  1B  and C ). The results were reproducible in multiple batches. The size of FITC-BSA-loaded PLGA NPs was 207 nm, and the zeta potential of the NPs in water was -18 mV (data not shown). ee% of the Bsa protein and stability of nanoparticles detected up to the end of the experiment (28 days).
cellular uptake and internalization In order to evaluate cellular uptake, THP-1 MDM cells were treated with different concentrations of PLGA-FITC-BSA NPs for 3 hours, and fluorescence intensity of the resulting labeled cells was measured by flow cytometry. Results are shown in Figure  2. Flow cytometry data demonstrated that PLGA-FITC-BSA NPs are rapidly and efficiently taken up by THP-1 MDMs (Figure  2A ). A significant dosedependent increase in fluorescence was observed from 20 up to 200 µg/mL and then increased more slowly up to the highest concentration tested, 1,000 µg/mL (Figure  2B ). We next compared the cellular uptake of PLGA-FITC-BSA with free FITC-BSA. THP-1 MDM cells were exposed to fluorescence increased up to fivefold when the protein was incorporated into PLGA NPs compared with free delivery, demonstrating that uptake of BSA is more efficient when loaded into PLGA NPs.
Flow cytometry analysis cannot determine if the PLGA NPs are inside the cells or simply on the cell surface. Therefore, to further investigate whether the NPs was indeed taken up and internalized within the cells, we performed confocal laser scanning microscopy studies. The data highlighted in Figure  4  are representative images of live cells incubated with PLGA-FITC-BSA NPs or free FITC-BSA for 3 hours. While in untreated control cultures (Figure  4A ), no fluorescence was detected. In cells treated with PLGA-FITC-BSA NPs, fluorescence intensity increased with the concentration of NPs (Figure  4C-F ). At the lowest dose tested, 20 µg/mL, a high level of cellular uptake was already observed (Figure  4C ), and at 100 µg/mL, almost all cells have had taken up the BSA-loaded PLGA NPs (Figure  4D ).
We also observed an increase when cells were treated with BSA alone at equivalent amount found in the PLGA NPs but to a lesser extent than the PLGA-BSA NPs (data not shown). This might be explained in part by the fact that cells were exposed to higher concentrations of BSA when it was delivered by PLGA NPs compared with free BSA in solution, confirming again higher delivery of BSA when the protein is encapsulated. More experiments are needed though to confirm this. 0] [61]  Because PMNs are key players in innate immunity and inflammation,  62, 63  it is important to evaluate the effects of NPs on the response and functions of human neutrophils. Therefore, in this study, we wanted to further investigate the toxicological profile of PLGA NPs by evaluating their effects on human PMNs. To do so, we performed in vitro apoptosis neutrophil assays.
Acknowledgments This work was supported by the  Public Health Agency of Canada  and the  National Research Council of Canada. Denis Girard  was supported partly by  Natural Sciences and Engineering Research Council of Canada (NSERC)  and  Institut de recherche Robert-Sauvé  en santé et en sécurité du travail (IRSST). David Gonçalves was supported by a  PhD NSERC scholarship award. AS Guedj has received a scholarship from the  Université Montpellier II  for her research training in Canada. We are grateful to  Michèle Bergeron  and  Xuefen Yang  for their technical help with the flow cytometry analysis and  Sylvie Faucher  for her help with the luminex bead assay.
Background: Trauma is the most common cause of mortality among people between the ages of 1 and 45 years, costing Canadians 19. 8 billion dollars a year (2004 data), yet half of all patients with major traumatic injuries do not receive evidence-based care, and significant regional variation in the quality of care across Canada exists. Accordingly, our goal is to lead a research project in which stakeholders themselves will adapt evidence-based trauma care knowledge tools to their own varied institutional contexts and cultures. We will do this by developing and assessing the combined impact of WikiTrauma, a free collaborative database of clinical decision support tools, and Wiki101, a training course teaching participants how to use WikiTrauma. WikiTrauma has the potential to ensure that all stakeholders (eg, patients, clinicians, and decision makers) can all contribute to, and benefit from, evidence-based clinical knowledge about trauma care that is tailored to their own needs and clinical setting.
What Are the Proposed Methods for Protecting Against Sources of Bias? Although blinding of the participants and randomization are not feasible in this small trial, we will mobilize all efforts to minimize any other sources of bias. All data collectors (medical archivists) will be blinded to the allocation group. Throughout our study, we will prevent contamination by protecting Wiki101 and WikiTrauma by password and note any potential competing intervention. We will also identify any professional working in more than one participating trauma center to consider the impact of this potential source of bias. Wiki101 will be a standardized online training program. We will encourage all participants to complete all 12 months of the study. To minimize a potential Hawthorne effect, our control group will receive an invitation to consult the INESSS website at the beginning of the study.
How Will the Outcome Measures Be Measured at Follow-Up? We will measure quarterly composite performance scores from the Quebec Trauma Registry for all 57 adult trauma centers. Data in the Quebec Trauma Registry is routinely collected in all Quebec trauma centers every 3 months. The composite performance score is calculated as the average of 15 other indicators routinely collected in the Quebec Trauma Registry  [4]. This score has good discrimination, construct validity, criterion predictive validity, and forecasting properties  [120]. Mortality rates, complication rates (for delirium, pneumonia, and deep venous thrombosis), length of stay, and FIM will also be measured on a quarterly basis for all 57 trauma centers from routinely collected data in the Quebec Trauma Registry. The intention to use WikiTrauma will be measured by a validated questionnaire  [40].
Data Analysis Segmented regression will be used to measure, statistically, the changes in level and slope in the postintervention period compared to the preintervention period  [119]. Thus, we will present a regression model with different intercept and slope coefficients for the pre-and postintervention time periods. We will compare the changes in quality of care measured at our four intervention trauma centers to the changes in quality of care measured at the other 53 trauma centers where no experimental intervention occurred. During the implementation period of WikiTrauma, we will continue to measure the impact on the quality of care. However, we will only proceed to compare the change in slope in the postintervention period once WikiTrauma has been fully implemented. We will use a Durbin-Watson test to verify the presence of autocorrelation and use an autoregressive error model to correct for this serial correlation.
Study Duration This project is planned to last 18 months. We have planned 3 months to implement the trial, including ethics approval in the four designated trauma centers and for delivering Wiki101 to the four local trauma committees. We will analyze all retrospective data obtained from the Quebec Trauma Registry in the first 3 months of our study and every 3 months thereafter for a total of 12 months. The last 3 months will be used to prepare our datasets, conduct our various analyses, and write our final report.
Acknowledgments The authors would like to thank the following agencies for funding this project: the  Canadian Medical Protective Association, Fonds de recherche du Québec-Santé  ( Career Scientist Award ,  24856  and Establishment of young researchers-Junior 1 grant,  24856 ), the  Canadian Institutes of Health Research  ( Planning Grant ,  RN201023 -309271 ), and the  CSSS Alphonse-Desjardins-Centre hospitalier affilié universitaire (CHAU) de Lévis. The authors would also like to thank the members of the  Canadian Critical Care Trials Group  for supporting the development of this protocol and  Dr Shane English  for having peer-reviewed our manuscript. The authors would also like to thank  Louisa Blair  and  Sandra Owens  for editing the manuscript. In addition, the authors would like to thank  Marie Robert  from the  Fondation NeuroTrauma Marie-Robert ,  François Belleau ,  Jacob Orlowitz ,  Patrice Di Marcantonio ,  Mathieu Vézina ,  Marcel Rheault ,  Sylvain Croteau ,  Jean-Luc Morin ,  Saileth Ramirez ,  Yves Daigle ,  Susie Gagnon ,  Claudine Blanchet ,  Amina Belcaïd ,  Charles Lacroix ,  Jean-Michel Garro ,  Amélie Bujold ,  Rémi Blanchette ,  Jean-Daniel Boutin ,  Dr Richard Boisvert ,  Dr Nelson Piché  and  Dr Stéphane Panic  for participating in the development of this project.
Acknowledgements. The BLLAST field experiment was made possible thanks to the contribution of several institutions and organisations: INSU-CNRS (Institut National des Sciences de l'Univers, Centre national de la Recherche Scientifique, LEFE-IMAGO program), Météo-France, Observatoire Midi-Pyrénées (University of Toulouse), EUFAR (EUropean Facility for Airborne Research) BLLATE-1&2, COST ES0802 (European Cooperation in the field of Scientific and Technical) and the Spanish MINECO projects CGL2009-08609, CGL2012-37416-C04-03 and CGL2011-13477-E. The field experiment would not have taken place without the significant contribution of all participating European and American research groups. The Piper Aztec research airplane is operated by SAFIRE, which is a unit supported by INSU-CNRS, Météo-France and the French Spatial Agency (CNES).