dataesr/scientific-paragraphs-categorization huggingface dataset:
Objective indicators For the analysis of the objective indicator of behavioral performance, we logged all responses and computed the task accuracy for each task block (see Figure  5A ). Two data sets were excluded due to incompleteness. We submitted the accuracy to a 2 (stress) × 2 (workload) repeated-measures ANOVA. As for the subjective indicators of perceived arousal and effort, we found a main effect of the workload manipulation [F (1, 21) = 65. 251, p < 0. 0001, η p 2 = 0. 757] with higher accuracy for the simple 0-back task (mean = 97. 3 ± 2. 0) compared to the hard 2-back task (mean = 91. 1 ± 4. 8). As a further objective indicator, we computed skin conductance level and heart rate. Four data sets were excluded due to incompleteness. For heart rate analysis a further data set was excluded due to malfunctioning sensors.
This manipulation is comparable to the variation of the affective context of a task in real-world scenarios, for example task performance under pressure vs. normal task performance. Consequently, to create more reliable BCIs for workload detection, robust against alterations in contextual conditions, such as affective factors (emotions, moods), the training data should include data collected under the relevant contextual conditions. Zander and Jatzev (2012)  found that certain metrics might enable the identification of phases of changed contexts and therefore identify phases were additional calibration might be necessary. One could then use transfer learning  (Pan and Yang, 2010)  or other re-calibration strategies to enable an adaptation of the transfer algorithm to the new context. However, the suggested metric specifically enables the detection of LOC, which is useful for the detection of perceived LOC and subsequent reliability decrease of active BCIs when environmental and internal factors of the user change.
The grid method The grid method is one of the white-light techniques available to measure bidimensional displacement and strain fields. It consists first in depositing a crossed grid on the surface of the specimen under investigation in order to track with a camera the slight change in the grid as loading increases. In the current case, a 12bit/1,040 × 1,376 pixel SENSICAM camera connected to its companion software CamWare was employed. The grid was transferred using the procedure described in  (Piro and Grédiac 2004). The pitch of the grid was equal here to 0. 2 mm along both directions. With this technique, the in-plane displacement and strain fields are deduced from the images of the grid taken during the test by processing them with the windowed Fourier transform (WFT). This transform actually provides the phases and its derivatives of the quasi-regular marking of the surface.
Comparison between numerical and experimental results Numerical and experimental results are first compared in order to check that the measurement method provides reliable results. Various horizontal and vertical experimental displacement fields are shown in Fig. 4a, c , respectively. In this figure, note that 1 pixel = 40 μm and the crack opening is given in millimeters. They are compared with their numerical counterparts obtained with a FE calculation performed with the ABAQUS Finite Element package (see Fig. 4b, d , respectively) in 2D case. Note that these simulations are carried out within the framework of linear elasticity, so plasticity that is likely to appear at the crack tip is not taken into account. 10500 CPS4R elements were used for the model. The constitutive material is assumed to be isotropic elastic linear, with a Young's modulus equal to E = 70 GPa and a Poisson's ratio υ equal to 0.
Numerical energy release rate The finite element software cast3M has been used in order to implement the Mθ integral and obtain numerical results. The value for the Young's modulus E and the Poisson's ratio v are those already given above. Figure  15  shows the comparison between the numerical energy release rate given by the Mθ integral Eq. (  2 ), and the experimental data obtained using the compliance approach explained above Eq. (  4 ). Numerical and experimental results are in good agreement. The small difference which is observed is due to the fact that identifying the critical force inducing the specific crack length during the crack process is somewhat tricky.
• The ability to handle heterogeneous sources of data. This prerequisite ensures that the approach is able to handle all the data of the crime scene. • The availability of tools to assist investigators in the analysis and interpretation of the timeline. This requirement is motivated by the near impossibility of analysing all the data manually. In a large part of existing approaches, solutions are proposed to meet the first and the second requirements. For this purpose, automatic extractors dedicated to each source of events are used to populate a central storage system (database  [3] , ontology  [4] , etc. ). In the proposition of the ECF architecture  [3] , the use of a set of extractors to collect events and store them in a database forming the timeline is introduced. For its part, the FORE approach  [4]  uses an ontology to store a semantically richer representation of events.
A. Extraction Layer The Extraction Layer aims to extract information from footprints contained in heterogeneous sources. 1) Extraction: During an investigation, many sources (which are all potential input data for the extraction layer) may be used to get information about user's activity. To deal with all these sources, the extraction layer is composed of several parsers (part A1 of Figure  1 ), each one dedicated to a unique source of footprints. The use of multiple and dedicated parsers allows to take into account specificity of each source while allowing to handle heterogeneous sources. It should be noted that some sources are more difficult to treat than others. Indeed, structured sources such as databases or XML files are easy to handle with appropriate parsers. In another hand, sources such as instant messaging histories or social networks require complex algorithms involving natural language processing or pictures require image processing algorithms to access and understand the content of them.
A description of relevant information that can be extracted from sources is given below. First, the activities of a user on a system can be studied using several sources. Operating systems record a lot of information about events occurring on a machine. From a machine running the Windows operating system for example, it is possible to get information about user's and software's activities using events logs (which record information about various kind of events such as session login, start/stop service or software, error occurred during the execution of a program, installation of a new software, etc. ), system and software configuration In complement to OS footprints, information about user's activities are also available in logs of software which are rich source of footprints. For example, antivirus logs contain information about exploits and malicious software detected on the computer.
Server logs such as Apache logs or Microsoft IIS logs can be used to get information about query sent to the server. In their part, files found on a crime scene allows to answer multiple purposes during an investigation by using their contents and metadata (which allow to know how and when a file was produced and who created it. Third, the behaviour of a user on the web can be studied using information contained in files or databases used by browsers to work. First of all, browsers footprints can be used to know user's interests and activities by studying the user's browsing history (the user's visits and at the time of the visits), bookmarks and forms filled by the user (e. g. search field, registration form, etc. ). Regarding contents of fields however, the highly dependent semantics of data makes its usage difficult (for example, data entered into the field of a search engine gives information about a user's interests, while a field of a registry form (e.
For example, a mapping rule can be created to identify footprints related to the creation of bookmark by web browsers. When such a footprint is identified (antecedents of the rule satisfied), then the rule orders the population tool to create a new object (representing the bookmark) and a new event representing the creation of this object as consequences. All the knowledge extracted must be federated into a unique knowledge model. The knowledge model used in our approach is briefly presented in Section III. This model is implemented in our architecture using an ontology which is itself stored in a triple store (a triple store is a database dedicated for storing knowledge in the form of triplets <subject, predicate, object>). The combination of the ontology and the triple store enables to provide a framework for knowledge representation (mapping rules populate semantic concepts whose meaning is defined in the ontology) and ensuring that the architecture is able to support the processing of large volumes of data.
Flow Cytometry Analysis Analysis of cell surface OAcGD2-and GD2-expression on tumor cells was performed by indirect immunofluorescence measured by flow cytometry. Briefly, cells were incubated with either mAb c. 8B6, ch14. 18 or Rituximab at 10 mg/mL for 30 min at 4uC. Antibody binding was detected by incubation with a fluorescein-isothiocyanate-conjugated-labeled F(ab') 2 fragment of goat anti-human IgG (Jackson Immunoresearch, Soham, UK) for 30 min at 4uC. Cell fluorescence was analyzed using a FACSCalibur flow cytometer (BD Biosciences, San Jose, CA, USA) and CellQuestPro software (BD Biosciences).
Radioiodination and Immunoreactivity of Radiolabeled Antibodies Monoclonal antibodies (30 mg in 50 ml PBS) were labeled with iodine-125 (PerkinElmer, Billerica, MA) using the iodogen method and were purified by gel filtration chromatography on a PD10 column (Sephadex G25, GE Healthcare, Uppsala, Sweden). Immunoreactivity was determined by binding assays of 10 ng of either 125 I-c. 8B6 or 125 I-ch14. 18 with serially diluted NXS2 cell suspension concentrations for 2 hours at 4uC. Cell-bound radioactivity was then separated from free antibody by centrifugation through a dibutylphthalate oil cushion in microfuge tubes. Cell pellets and supernatant activities were then separately measured using a gamma counter (Wallac, Finland). The binding data were analyzed with the Equilibrium Expert software  [21]  according to a one-site equilibrium binding equation and the percentage of binding was determined by measuring the radioactivity bound to cells.
For heterologous competition experiments, 125 I-mAb c. 8B6 or ch14. 18 competed with increasing concentrations of unlabeled ch14. 18 and c. 8B6, respectively. After 2 h at 4uC, cell-bound radioactivity was separated from free antibody by centrifugation through a dibutylphthalate oil cushion in microfuge tubes. Cell pellet and supernatant activities were then separately measured using a gamma counter (Wallac, Finland). Competition binding curves were plotted as uncorrected bound/total (B/T) as a function of competing antibody concentration and were analyzed with the Equilibrium Expert software according to two-site (GD2 and OAcGD2) equilibrium binding for each antibody. All binding data were analysed simultaneously using the software package taking into account the immunoreactive fraction of the labelled antibodies determined as described above.
Complement Dependent Cytotoxicity (CDC) Aliquots of NXS2 cells (1610 4 ) were incubated with 80 mL of antibody solution at various concentrations for 2 h at 37uC, human serum (20 ml) was added to provide complement. Cytotoxicity was determined within the tumor cell population after addition of the viability probe propidium iodide (PI) using a FACSCalibur flow cytometer (BD Biosciences, San Jose, CA, USA) and CellQuestPro software (BD Biosciences). The percentage of specific lysis was calculated as: 1006 (non viable PI + tumor cells)/(non viable PI + tumor cells+viable tumor cells).
Antibody-Dependent Cell-mediated Cytotoxicity (ADCC) An ADCC assay was performed as reported previously  [22]. Tumor cells were labeled with membrane dye PKH-26 (Sigma Aldrich, St. Louis, MO, USA) according to the manufacturer's instructions. Aliquots of the labeled cells (1610 4 cells/100 ml) and were incubated with 50 mL of antibodies in 96-well microtiter plates. The human NK-92-RFccIII+ cells were used as effector cells  [23]. Fifty mL of NK-92-RFccIII+ cells at the indicated effector-to-target ratio (E/T) were added to the tumor cells and incubated for 24 hours at 37uC. Cell death within the PKH-26 + target cell population was then assessed by the addition of TO-PRO-3 iodide (TP3) (Life Technologies, Grand Island, NY, USA). The double-positive TP3 + , PKH26 + dead target cell population was detected by flow cytometry analysis using a FACSCalibur flow cytometer (BD Biosciences) in conjunction with CellQuestPro software (BD Biosciences).
Such a simultaneous analysis of binding data obtained under a variety of conditions is made possible by the Equilibrium Expert software  [21]  which runs as an add-in to MicrosoftH Excel. A partition function, involving the concentrations of the different free molecular species and of the different complexes present in the experiment, is used to describe the experimental system. Binding parameters are estimated by non-linear least square fitting of experimental measurements with user-defined weighting of the experimental data. As shown in Table  1 , the estimate of the average number of OAcGD2 binding sites was 0. 560. 2610 6 molecules per NXS2 cell whereas the average number of GD2 binding sites was 1. 060. 1610 6 per NXS2 in accordance with results obtained with other techniques. The Kd value of mAb c.
After incubation, cell death within the PKH-26+ target cell population was detected by the addition of the viability probe TP3. As shown in Fig. 3B , ADCC was observed with either chimeric mAb c. 8B6 or ch14. 18 on the NXS2 cells. Specific lysis achieved maximum values of 19. 261. 3% for mAb ch14. 18 and 11. 561. 5% for chimeric mAb c. 8B6. The differences in ADCC between mAbs c. 8B6 and ch14. 18 were statistically significant (p,0. 01). The ADCC activity of mAb c. 8B6 was 2. 8-fold weaker than mAb ch14. 18 when their EC 50 values were compared, with an EC 50 value for mAb ch14. 18 of 0. 64 mg/ml, and an EC 50 value for mAb c. 8B6 of 1. 81 mg/ml (data not shown). Maximum capacity of lysis by NK-92-RFccIII+ was determined to be 64. 764. 8% with the NK-sensitive mouse T cell lymphoma YAC-1 as a target cell (data not shown).
Background Drug use has rarely been studied in multiple system atrophy (MSA) while such patients receive many treatments based on weak evidence. Objective To analyze drug use from the database of the French MSA Reference Center, and to compare it with data from patients with Parkinson disease (PD). Methods Medication of 147 MSA and 180 age-and sexmatched PD patients was analyzed. Motor and autonomic symptoms were explored in MSA patients by the SCOPA-Autonomic and Unified MSA Rating Scale (UMSARS). Results MSA and PD patients received a mean of five different drugs. MSA patients were more frequently exposed to laxatives, antidiabetic medications, antihypotensives, muscarinic antagonists, alpha-adrenergic blockers, and antidepressants. Levodopa consumption was less in MSA-C (cerebellar) patients compared with MSA-P (parkinsonian) and PD patients.
Sample Data of 147 consecutive patients that were recorded between 2008 and 2011 in the Registry of the French Reference Center for MSA were analyzed. The French MSA Reference Center was created in 2007 by the French Ministry of Health  [4]  to facilitate the management of patients suffering from such a disorder and to facilitate research in this field  [5]. A prospective database including demographics, clinical characteristics, and treatments of all patients referred to and followed up by the reference center was created and is available. All patients gave informed written consent before entering the database. Data include an evaluation with the Unified MSA Rating Scale (UMSARS)  [6]  and the SCOPA-Autonomic scale  [7, 8]. MSA patients were classified as MSA-P (P for parkinsonian) or MSA-C (C for cerebellar) subtype and ''probable'' or ''possible'' MSA according to current consensus criteria  [9].
To better characterize drug use in MSA, a comparison with PD was performed, as this disorder is better studied and usually drugs with acknowledged efficacy and safety are used. Data on PD patients were obtained from another database  [10]. PD patients were managed within the same movement disorders units and recruited in the same period. PD patients were selected to match by age and sex the MSA sample. Patients were included if they fulfilled the UK PD Society Brain Bank criteria for PD  [11]  and had neither undergone neurosurgical interventions for PD nor cognitive impairment preventing data collection in a reliable manner. All patients gave informed consent allowing their data to be registered in the databases used in this study.
There is nearly no information about drug prescription and consumption in this disease, apart from small anecdotal retrospective reports and a recent survey reporting neuropsychiatric drug consumption in a sample of European patients  [16]. Our data were collected systematically, in almost 150 consecutive patients fulfilling international diagnostic criteria for the diagnosis of MSA recruited prospectively, which conforms to a unique dataset. There are however some methodological limitations with this study: (1) our data were restricted to French centers, (2) measures of disease severity and autonomic function were used as proxy indicators in a cross-sectional analysis, but no assessment of consequences and causality could be performed, thus making possible some bias, (3) no measures of drug efficacy and safety were available, (4) comorbidities were not registered, (  5 ) the control group of PD patients was extracted from another survey that had been conducted at the same time by the same centers and investigators, but had not been specifically pre-planned to serve as a comparative group, and (  6 ) the sample size only allowed the analysis of drugs consumed by more than 5 % of the studied patients, and between-group differences \10 % may have been missed because of insufficient power.
This large sample is well adapted to the building of predictive models  (Wisz et al. , 2008). Point counts were stratified according to the agricultural landscapes' forest cover gradient (from 0 to 100% of forest cover, mean ¼ 23%). The dominance of open spaces was linked to the fact that this is an agricultural area with a dispersion of small wooded fragments. Furthermore, because of the spatial resolution of the Spot 4 satellite image used (20 m), the smallest wooded elements and some hedgerows were under-represented. We continued to use this map dating from 1998 with this resolution because we wanted to be as synchronous as possible with the bird data dating from the 90s. We checked that the forest cover in the study area had remained stable between 1990 and 2000, corresponding to the sampling period  (Guyon et al.
, 1999;  European forest map available for these two 2 dates and  Pekkarinen et al. , 2009 ; <http://forest. jrc. ec. europa. eu/forest-mapping/forest-cover-map). The bird presenceeabsence data were taken from 20-min point counts carried out each sampling year in the month of May between 6 and 11 a. m. , during the peak of vocal activity, in the absence of heavy wind and rain. Counting only began 3 min after arrival at the point in order to limit the disturbances caused by the observer to the detection of individuals. We selected 10 species (Table  1 ) on the basis of their preference along the forest to open landscape gradient (see  Balent and Courtiade, 1992) , which made it possible to choose species associated with open, forest, and intermediate habitats. All the selected species have an occurrence of more than 20 (Table  2 ) which is sufficient to avoid problems of modelling rare species  (Stockwell and Peterson, 2002).
Calculation of the forest influence index at different smoothing radii For each point count, we calculated the NFC, varying between 0 and 1, from the area of all the forest patches present in a given radius, weighting it in inverse proportion to the distance of each forest patch to the calculation point by means of a decreasing exponential function  (Lauga and Joachim, 1992). This is a continuous variable that can be calculated at every point of the territory  (Lauga et al. , 1996). The NFC is calculated using a binary classification of a Spot 4 image taken from a satellite in 1998. Each pixel (20 Â 20 m) takes the value 1 or 0 depending on whether or not the pixel is wooded. A layer with the 1064 georeferenced point counts is superimposed to the classified image. We obtained these maps using the Idrisi software after a supervised image classification.
Acknowledgements The authors thank Huw ap  Thomas (Axtrad)  for thorough editorial advice in English. This research was supported through a  DIREN Midi-Pyrénées grant  " Impact  of introducing woods in an agricultural landscape on that landscape's biodiversity: developing a management-aid model", an  INRA -Midi-Pyrénées Region PSDR3 grant CHAPAY  "Land use -land cover changes and landscape dynamics", a  National Research Agency  grant  BIODIVAGRIM  ( ANR-07-BDIV-002 ) "Conserving biodiversity in agro-ecosystems: a spatially explicit landscape modelling approach", and an  INRA   postdoctoral fellowship  to  Céline Pelosi. We thank  Laurent Raison  for organising the field campaigns and the ornithologists who carried out the point counts,  Philippe Caniot ,  Sylvain Frémeaux ,  Jean Joachim  and  Laurent Raison.
We study the production of scalar and pseudoscalar Higgs bosons via gluon fusion and bottomquark annihilation in the MSSM. Relying on the NNLO-QCD calculation implemented in the public code SusHi, we provide precise predictions for the Higgs-production cross section in six benchmark scenarios compatible with the LHC searches. We also provide a detailed discussion of the sources of theoretical uncertainty in our calculation. We examine the dependence of the cross section on the renormalization and factorization scales, on the precise definition of the Higgs-bottom coupling and on the choice of PDFs, as well as the uncertainties associated to our incomplete knowledge of the SUSY contributions through NNLO. In particular, a potentially large uncertainty originates from uncomputed higher-order QCD corrections to the bottom-quark contributions to gluon fusion.
The code SusHi  [53] , on the other hand, computes the cross section for Higgs-boson production in both gluon fusion and bottom-quark annihilation, in the SM and in the MSSM. In the case of gluon fusion, SusHi includes the exact results of ref. [9]  for the NLO-QCD contributions of two-loop diagrams with top and bottom quarks, and the approximate results of refs. [28, 31, 34]  and refs. [31, 32]  for the NLO-QCD contributions of two-loop diagrams with stop and sbottom squarks, respectively. The NLO-QCD contributions of one-loop diagrams with emission of an additional parton are taken from ref. [33]. The NNLO-QCD contributions from diagrams with top quarks are included via a call to ggh@nnlo, and the corresponding contributions from diagrams with stop squarks are estimated following ref. [36].