dataesr/scientific-paragraphs-categorization huggingface dataset:
Data sources We searched for SBC and modal share figures from countries with varying bicycle modal shares. In this study, SBCs are defined as falls and obstacle collisions (all crash types in which only the cyclist is involved). Other typical bicycle crash types, not included in this study, are collisions with motor vehicles, cyclists and pedestrians. We used Google Scholar, SafetyLit and Scopus to search for studies about SBCs with 'single-bicycle crash' and 'bicycle-only crash' as search terms. We contacted research organisations for grey literature and included this alongside published studies. In some cases, we also contacted authors of the publications for additional information. We also searched for literature in which bicycle modal shares are reported to allow for hypothesis testing.
Analyses A Pearson correlation is calculated between the share of SBCs and bicycle modal share to examine this relationship. ] [24]  the bicycle modal share measure used to correlate with hospitalised casualties contains data from both the Gallup study and from NTS studies. To control for possible differences we have created a control variable that equals zero for Gallup based measures and one for NTS based, that is, a dummy variable. Linear regression analysis is conducted on the share of hospitalised SBC casualties in the total number of hospitalised casualties with bicycle modal share and the control variable for bicycle modal share measurement method as independent variables. To investigate whether an increase in bicycle modal share is related to a less than proportional increase in the share of SBCs among all road crash victims, trend lines of the form SSBCT=α BMS β (ie, a power function) have been fitted to the data using SPSS.
Fatalities Data on fatalities are derived from police recorded crash statistics. On average in European countries, 17% of cyclist fatalities are killed by SBCs, but the proportion for different countries varies between 5% and 30%. 31 As a proportion of the total number of traffic fatalities (including car occupants, motorcyclists, etc), the share ranges between a few hundredths of a percent and 4%. 31 32  The large variation may result from relatively low number of cyclist fatalities in countries with low amounts of cycling.
Hospitalisations and ED treatments Tables  1  and 2  show the proportion of SBC casualties among all injured cyclists admitted to hospitals or treated at EDs. Data are derived directly from medical registrations in most studies, but some are questionnaire studies among patients. The studies vary in size: some studies based on hospital data apply to the whole population in an area. Studies based on samples tend to be smaller, focused on data provided by one or several hospitals. The sample of injured cyclists on which the proportion of SBCs is based is included as an indication of study size. The results reveal that the share of SBC victims varies between 60% and 95% of all cyclist casualties, admitted to hospital or treated at an ED. As a proportion of the total number of hospitalised traffic casualties (including car occupants, motorcyclists, etc), the share exceeds 20% in six out of 11 countries for which this proportion is known.
A design as used in this study would help, but would need to be expanded with much more detailed information on crashes and bicycle usage in age groups, gender classes, income classes and so on. Instead of the modal split variable used in this study which was derived from a survey question about people's main mode of transport for daily activities, such research requires more detailed data from NTS. Another type of research that we would like to recommend is the development and use of alternative data sources for fatally injured SBC casualties. We used police reported data for fatalities. Even though the most severe casualties are more likely to be recorded by the police, we suspect that our outcome may be an underestimate. Data from such sources are rarely reported in scientific literature, but could yield a more reliable estimate of the share of SBCs among road traffic fatalities.
They were also used for comparisons, validation and calibration of hydrological model outputs at the basin scale and over large bio-climatic regions, such as the Sahel or West Africa  [13] [14] [15] [16]. Combined with external datasets in situ, GRACE data offer the unique opportunity to estimate groundwater storage variations for lake drainage areas  [17, 18]  and large river basins  [19]  or at a regional scale  [20] , river discharges  [21] , evapotranspiration at the basin scale  [22] [23] [24]  and the water budget  [25]. They were also used to determine the specific yield  [26, 27]  and loading effects in the Sahel  [28]. In the following section, the principle of classical satellite gravimetry and the particularities of the regional method for recovering water mass variations from GRACE satellite data are presented.
An alternative approach for estimating surface water mass densities in a region from GRACE data has been recently proposed by  [46, 47]. This new strategy is based on the optimal localization in space, instead of the best localization in spatial frequency, and leads theoretically to better spatial localization and resolution  [48]. The authors of  [47]  have shown that this regional method offers a reduction of both north-south striping due to the distribution of GRACE satellite tracks and the temporal aliasing of correction models over South America  [36]  and Australia  [49]. According to these two latter studies, regional maps present more realistic spatial and temporal patterns than the global solutions when compared to independent datasets of rainfall. The main modes of variability in South America coincide with the geographical limits of known hydrological units, such as individual groundwater layers  [36].
GINS, Géodésie par Intégrations Numériques Simultanées; GRGS, Groupe de Recherche en Géodésie Spatiale; SVD, singular value decomposition; KBRR, K-band range rate. In the first step, KBRR observations are reduced by removing the contributions of known gravitational accelerations related to large-scale mass variations (i. e. , atmosphere and ocean mass variations, polar movements, solid and oceanic tides, as well as the static gravity field of the Earth that represents 99% of the observed signals). This operation is made by iterative least squares adjustment of daily dynamical orbits using the Géodésie par Intégrations Numériques Simultanées (GINS) software  [53, 54]. Thanks to the measurements of on-board GRACE accelerometers, the effects of non-conservative forces are also removed from the KBRR observations in the orbit adjustment.
10-Day Regional GRACE Solutions for Africa Daily arcs of five-second sampled K-band range (KBR) measurements of the inter-satellite velocity have been used in the GINS software  [53, 54]  to adjust dynamical reference orbits. GRACE data are corrected from the known gravitational accelerations related to atmosphere and ocean mass redistributions, including tides and polar movements, using a priori global models. KBR rate residuals that represent mainly the continental hydrology have been converted into residual differences of potential (RDP), according to the conservation of the mechanical energy of the two GRACE vehicles versus time. Satellite tracks flying over Africa are selected, and each one is corrected from a least squares-adjusted linear trend. Following the two-step regional method explained in the previous section, time series of successive 10-day and 2° by 2° solutions of water mass change have been inverted over the whole African continent (30°W-60°E; 40°S-40°N) from RDP and, then, completed with the long wavelengths (>6700 km) of the GRGS GRACE solutions, or equivalently, the SH of degrees less than six, for the period 2003-2012.
Time Series of Altimetry-Derived Water Level of Lakes in East Africa Satellite altimetry was originally designed to provide accurate measurements of the dynamic topography of the ocean  [60]. Radar altimeters demonstrated strong capabilities to accurately estimate water levels over land and are now used for systematic monitoring of lakes  [61, 62] , large rivers  [63, 64] , wetlands and floodplains  [65]. In this study, we used the time series of water levels derived from satellite altimetry measurements made available by the Hydroweb database at Laboratoire d'Etudes en Géophysique et Océanographie Spatiales (LEGOS)-Observatoire Midi-Pyrénées (OMP)  [66]  for four large lakes of East Africa (i. e. , lakes Turkana, Victoria, Tanganyika and Malawi). All details about the processing of altimetry data and the computation of time series of water levels can be found in  [61].
TRMM 3B43 Monthly Rainfall In this study, we used the Tropical Rainfall Measuring Mission (TRMM) 3B43 product which is a combination of monthly rainfall at a spatial resolution of 0. 25° from January 1998 to December 2012, and other data sources. This dataset is obtained by combining satellite information from the passive microwave imager (TMI) and precipitation radar (PR) onboard the Tropical Rainfall Measuring Mission (TRMM), a Japan-U. S. satellite launched in November 1997, the Visible and Infrared Scanner (VIRS) onboard the Special Sensor Microwave Imager (SSM/I) and rain gauge observations. The dataset results from the merging of the TRMM 3B42-adjusted merged infrared precipitation with the monthly accumulated Climate Assessment Monitoring System or Global Precipitation Climatology Center Rain Gauge analyses  [68, 69].
Principal Component Analysis of the GRACE Datasets Principal component analysis (PCA), or discrete Karhunen-Loève transform, consists of decomposing the time series of water mass maps (i. e. , maps of regional water mass over Africa) into main space and time "modes" that are projections onto orthogonal directions (or the principal axis) of variability  [72]  (e. g. , see  [73]  for the computational aspects). Before PCA decomposition, the time series of the regional solutions has been corrected from the dominant seasonal oscillation related to each surface element (see Figure  5 ) using 13-month window averaging.
Residual six-month and even multi-year oscillations appear for temporal modes of JPL. As for the first mode of variability, similarities between PCA modes of GRACE and TRMM rainfall exist. The second PCA mode of the TRMM data is characterized by important amplitudes of precipitation in the southern tropics, in particular over the Congo Basin. This strong signal also appears in the second modes of GRACE datasets, but it is less visible in the case of GRGS solutions. The temporal mode of the rainfall data is shifted; it occurs ~6 months sooner than the GRACE solutions (Figure  8 ). The third mode of rainfall is related to the African monsoon, as its main signature is located in the Sub-Saharan band, as for the spatial PCA modes of GRACE solutions (Figure  9 ).
Time Series of TWS Averaged over African River Drainage Basins The masks used in this study come from the five-minute, 1/2° and 1° datasets of the continental watersheds and river networks for use in regional and global hydrologic and climate system modeling studies  [75]. The 1° dataset is used for the drainage regions and the river watersheds, while the 0. 5° dataset is used to define the drainage regions around the lakes. The coordinates of the drainage basin limits for each lake are obtained using the lake boundaries and the drainage network at 1/2°. Time series of water mass for the main drainage basins of Africa over 2004-2012 (see Figure  1 ) have been computed as masked averages versus time and then converted into mm of equivalent sea level (ESL) profiles. In this latter operation, the water volume variations (i.
Subsurface Water Storage Changes by Combining Regional Solutions and Radar Altimeter Data Time series of the subsurface waters (i. e. , soil plus ground waters) were estimated by removing altimetry-based water levels of the main African lake, or reservoir waters, from the TWS measured by GRACE. As altimetry-based water levels from  [78]  have a temporal resolution of one month, the ten-day regional solutions were consequently averaged on monthly time periods before computing the mere difference. Anomalies of these residual subsurface waters, including groundwater, were estimated at a monthly time-scale as the difference between TWS and surface storage over the common period of availability of two datasets (i. e. , mid-2003-2012). These anomalies are presented in Figure  13  for the largest East African lakes: Turkana, Victoria, Tanganyika and Malawi.
The time variations of the surface reservoir exhibit a steep decrease from 2004 to the beginning of 2006, followed by a significant increase up to mid-2009, whereas they are lower for TWS (Figure  13d ). These results demonstrate the strong capacities of multi-satellite observations to monitor quantitatively the changes in the storage of the surface and sub-surface reservoirs associated with climate variability and human activities at a regional scale. These remotely-sensed datasets are likely to have huge importance in regions where in situ data are sparse, while they have huge importance in terms of water supply for dense human populations. In the case of Lake Victoria, it directly supports 30 million people in terms of a freshwater supply  [8]  and indirectly another 340 million people along the Nile Basin  [82] , being the source of the White Nile.
Conclusions In this paper, we present 10-day regional solutions of water mass change over Africa for the period 2003-2012, revealing the dominant seasonal and African monsoon signals (±250 mm EWH). Principal component analysis (PCA) of the GRACE datasets provided the main modes of variability of the surface water mass. Temporal and spatial patterns are consistent for the regional and global solutions. However, the regional solutions offer a better geographical localization of hydrological structures, while global solutions remain affected by aliasing errors (i. e. , north-south striping). This is probably due to the benefit of details brought by GRACE short tracks into the regional solution, instead of considering band-limited spherical harmonics defined on the entire Earth. Monitoring water supply by using these regional solutions enables us to confirm the long-term drought of the NWSAS aquifer and even to reveal the sudden water loss occurring in early 2008.
In our trace-based system, we define one sensor that labels timestamps, and records what situation is chosen to run every time the decision is made. Our TBS tracks important properties during the execution, and then save them as traces composed of two objects: i) the current state of the application (all of the recorded properties) and ii) the completed situation (situation's identifier). Then we transform these two objects into the following format: the state is a set of properties represented by a vector and the executed situation is transformed into a character string, as shown hereby: trace = pro 1 , pro 2 ,. , pro m , situation _ name We now have a simple TBS that supports the data for our proposed preselecting strategy in the next section. The collected traces are analyzed to predict what are the possible situations according to the current state.
We used this information to build a database for our prediction model. To define the prediction model we started with a training phase. We carried out all the records from the TBS and applied the approach presented in the previous section. Then we have evaluated the performances of the obtained predicting model by using the Weka software  [20]. In the Table  II , we summarized the correct rate and the needed time to compute Accepted in IEEE International Conference on Control, Decision and Information Technologies, Metz, November 3-5 2014 the prediction model using the 4 methods mentioned in Section III above. We can see that the performance of kNN technique is the lowest. And there is no great difference between the three other methods while the computation time for of SVM and Neural Network is longer than for the Naïve Bayes.
ACKNOWLEDGMENTS This work was supported by grants from the  Canadian Institutes of Health Research  ( MOP115042  to TA,  MOP77686  to VP and  MOP243919  to MP), the  US National Institutes of Health  ( R01DK58096  to VP),  Marie Curie Foundation (CIG NeuROSenS  to XF)  PCIG09-GA-2011-293738  and  Société Francophone du Diabète  (to TA and XF). TA was supported by a salary award from  Fonds de Recherche Québec-Santé (FRQS). VP holds the  Canada Research Chair in Diabetes and Pancreatic Beta-Cell Function. MP holds the Canada Research Chair in Diabetes and Metabolism. We thank the  Rodent Metabolic Phenotyping core and Biology and Imaging core of CRCHUM.
The mutation was also present, in the heterozygous state, in one clinically unaffected sibling and in both unaffected parents, and was absent from the other two unaffected siblings. It was not found in 120 Algerian normal hearing control individuals or in the Exome Variant Server database. EPS8 is an F-actin capping and bundling protein. Mutant mice lacking EPS8 (Eps8 -/-mice), which is present in the hair bundle, the sensory antenna of the auditory sensory cells that operate the mechano-electrical transduction, are also profoundly deaf and have abnormally short hair bundle stereocilia. Conclusion: This new DFNB form is likely to arise from abnormal hair bundles resulting in compromised detection of physiological sound pressures.
Both parents and the three other siblings did not show any sign of hearing impairment, and audiometric tests carried out on the parents and one of the siblings revealed an absence of hearing threshold elevation for all frequencies tested. We first sought mutations in the genes most frequently implicated in DFNB: OTOF and MYO15A. No mutations of these genes were found in the two patients. We therefore carried out whole-exome sequencing, with pooled genomic DNA from the two affected children, as previously described  [7]. We excluded variants present in the dBSNP132, 1000 genomes, and HapMap databases, with a prevalence greater than 0. 01%. As the parents were consanguineous, we hypothesized that the causal mutation would be present in the homozygous state in the patients. We first searched for sequence variants of the coding and non-coding exons and of splice sites in the known DFNB and DFNA (autosomal dominant nonsyndromic deafness) genes.
No such variants were found (none were detected in the heterozygous state either). We then focused on nonsense, missense, splice site and frame-shifting mutations in all genes, by seeking insertions/deletions and SNPs. By using the same criteria as above, the number of sequence variants found decreased from 6268 to 0 for insertions/deletions (total number of insertion/deletion variants found in this pooled DNA), and from 74007 to 2 for SNPs. One of these biallelic variants was a conservative missense mutation, c. 1110A > C (p. E730D), in BNC2 (gene ID: 54796, NM_01763), predicted to be nonpathogenic by PolyPhen-2, SIFT, and Mutation Taster software. The other was a nonsense mutation, c. 88C > T (p. Gln30*), in exon 3 of EPS8 (gene ID: 2059, NM_004447. 5). An analysis of the segregation of this mutation, based on sequencing of the corresponding DNA fragments in the parents, the two affected children and three unaffected siblings, confirmed the biallelic mutation in the two deaf children and showed that the mutation was present in the heterozygous state in both parents and one sibling, but not in the other two siblings (Figure  1B, D ).
This mutation was not present in 120 Algerian control individuals or in the Exome Variant Server database. The mutation is expected to result either in an abortive protein truncated at amino acid position 29 or in no protein at all due to nonsense mediated mRNA decay  [9]. It is therefore functionally equivalent to the knock-out of the orthologous gene in Eps8 -/-mice, which are also profoundly deaf  [10, 11]. We thus concluded that this mutation is responsible for the profound congenital deafness in the patients. The 39 remaining DFNB families were also investigated for the presence of mutations in EPS8, and we did not find any. EPS8 is a 822 amino acids protein composed of a pleckstrin homology (PH) domain, a SRC Homology 3 (SH3) domain and a F-actin binding domain (Figure  1E ). The Cterminal region has significant sequence homology with the sterile-motif (SAM) domains  [12].
MATERIAL For our recordings, we used the following sensors: ElectroEncephaloGram (EEG, 28 active electrodes in a 10/20 system without T7, T8, Fp1, and Fp2), ElectroCardioGram (ECG, two active electrodes), facial ElectroMyoGram (EMG, two active electrodes), ElectroOculoGram (EOG, four active electrodes), breath belt (SleepSense), pulse (g. PULSEsensor), and a galvanic skin response sensor (g. GSRsensor). All sensors were connected and amplified with three synchronized g. USBAmp amplifiers (g. tec, Austria). The workload task was designed in the Presentation software (Neurobehavioral Systems, www. neurobs. com/presentation) and EEG signals were recorded and visually inspected with Open ViBE  (Renard et al. , 2010). Figure  1  shows a participant sitting fully-wired in the experimental environment.
Behavioral data To investigate the effects of the experimental manipulations on behavior, we calculated the performance per block based on the number of true positive (TP), true negative (TN), false negative (FN), and false positive (FP) responses resulting from the button presses within the n-back task (left click for targets, right click for non-targets) using the following equation: Per f = (TP+TN) (TP+TN+FP+FN). As for ratings, we analyzed the data in a 2 (stress) × 2 (workload) ANOVA.
Physiological data Physiological responses were analyzed with respect to heart rate (HR) and galvanic skin response (GSR). Before applying statistical methods, the GSR data was pre-processed by extracting the mean GSR value (μS) for each block and then averaging these values over blocks as described above. The ECG signal was band-pass filtered between 5 and 200 Hz, applying a notch-filter 48-52 Hz to reduce power line noise, before mean HR for each of the blocks was extracted. As for the former analyses, we analyzed the data with a 2 (stress) × 2 (workload) ANOVA. We are reporting data as significant if p < 0. 05 and as trend if p < 0. 1. For all ANOVAs partial eta squared values (η p 2 ) are calculated as a measure of effect size.
The goal of our work is to design a generic workload-level estimator, usable in practice, i. e. , that can work across different affective contexts (here, different psychosocial stress levels). To do so, we performed different evaluations to estimate (1) the general performance of our system, independently of the affective context; (2) how it behaves within a given affective context; (3) how it behaves across different affective contexts, i. e. , can a workloadlevel estimator calibrated on data from a given affective context (e. g. , a relaxed condition) be used to estimate workload in another affective context (e. g. , a stressful condition), (4) if effects of time can explain across-context classification performance loss, and (5) whether calibrating our system with data from different affective contexts makes the system better or worse, even if used in a single affective context.
Different sub-parts of the data were thus used for training and testing within our CV scheme, in particular: 1. General performance estimation: This is the overall evaluation, in which we used all the data, from both affective contexts, i. e. , with EEG trials from both the relaxed and the stressful conditions. Therefore, within each fold of the CV, 20 blocks (i. e. , 900 trials) were available for training, and 4 blocks (i. e. , 180 trials) were available for testing. The number of trials from each workload-level (high/low) and each psychosocial stress (relaxed/stressful) was balanced in both the training and testing set. 2. Within affective context performance estimation: This evaluation assessed the performance of our system when calibrated on a single affective context and tested on the same affective context.