dataesr/scientific-paragraphs-categorization huggingface dataset:
Material The material consists of climatic data such as daily rainfall and temperature data for the period 1999-2018. These data come from the Ferkessédougou sugar complexes and were recorded at the Ferké 2 station. The synoptic overview of the variability of climate extremes in the study area is carried out with the RClimDex software. The software provides a data quality check to identify recording errors that may exist in the daily data  (WMO, 2009). The quality control process is based on the following principles: erroneous values of the maximum daily temperature must be replaced by -99. 9 if it is lower than the minimum daily temperature; it must not have more than 365-366 daily observations per year; -February should have no more than 28 observations in any given year; missing or negative data (for rainfall) are replaced by -99.
Methods The analysis of rainfall and temperature extremes was possible through the calculation of climate indices. Although there are many climate indices, 27 have been defined by the Expert Team on Climate Change Detection and Indices (ETCCDI). This method of analysis is commonly used to study extreme events  (Aguilar et al. , 2009; Filahi et al. , 2015; Hountondji et al. , 2011; New et al. , 2006; Santos et al. , 2010). Detailed descriptions of these indices and the RClimDex are available on the ETCCDI website (https://github. com/ ECCC-CDAS/RClimDex, last access: 19 April 2021). In this study, 8 rainfall indices and 7 temperature indices were used (Table  1 ). The choice of these indices was made to highlight extreme rainfall events and their frequency on the one hand and to assess temperature variations and their duration on the other hand.
Code availability. The Rclimdex software is freely available to the public. The manual and the software can be downloaded from the website: https://github. com/ECCC-CDAS/RClimDex, last access: 19 April 2021  (Chan, 2021). Data availability. Precipitation and temperature data are not publicly available due to the structure's research policy. However, in the framework of a research cooperation as in this study, these data can be provided free of charge.
Street links coordinates and emission flux rates are retrieved from the Vehic ular Emission Inventory (VEIN) emission model, representing the real fleet of the region. The VEIN model has an advantage to spatially represent emissions and present compatibility with MUNICH. Building height is estimated from the World Urban Database and Access Portal Tools (WUDAPT) Local Climate Zone map for SPMA. Background concentrations are obtained from the Ibirapuera air quality station located in an urban park. Finally, volatile organic compounds (VOCs) speciation is approximated using information from Sao Paulo air quality forecast emission file and non-methane hydrocarbons concentration measurements. Results show an overprediction of O 3 concentrations in both study cases. NO x concentrations are underpredicted in Pinheiros but are better simulated in Paulista Avenue.
VEIN emission model VEIN is an R package (R Core Team, 2019) to estimate vehicular emissions at a street level. VEIN imports functions from the package Spatial Features  (Pebesma, 2018) , which represent different types of geometries on space and perform geoprocessing tasks, from the data table package  (Dowle and Srinivasan, 2020)  to perform fast aggregation of databases, and from the units package  (Pebesma et al. , 2016)  to provide binding to the udunits library (https://www. unidata. ucar. edu/software/udunits/). VEIN includes a function to process vehicular flow at each street to generate activity traffic data, different emissions factors, and different sets of emissions calculation and post-processing tools  (Ibarra-Espinosa et al. , 2018). Specifically, the emissions factors are based on emissions certification tests with dynamometer measurements in laboratories  (CETESB, 2015).
Emission and street links coordinates 125 Emission rates inside the street links in VEIN model are calculated using 104 million GPS vehicles coordinates in southeast Brazil  (Ibarra-Espinosa et al. , 2019). The GPS dataset is assigned to the OpenStreetMap (2017) dataset and once traffic flow is obtained, the vehicular compositions are generated and assigned with each emission factor reported by  CETESB (2015). Emission factors are transformed into speed function, and then the average speed calculated at each street is used to obtain more representative emissions at each hour of a week. In addition, the estimation was calibrated with fuel consumption for 130   2020 ) described all details regarding the emission estimation, with the emissions dataset in g h -1 available at https://github. com/ibarraespinosa/ae1.
The emissions dataset presents two aspects that need to be discussed. The first one is that the regional emissions inventory, which covered southeast Brazil, might not fully represent local reality. For instance, the ratio between travel demand models (TDM) and traffic flow of GPS for the study area is 2. 22. Besides, the emission factors are average measurement of emissions certification tests, therefore, they may underestimate real-drive emissions  (Ropkins et al. , 2019). Furthermore, the real-world emission factors derived from tunnel measurements in São Paulo for NO X were 0. 3 g km -1 for light vehicles and 9. 2 g km -1 for heavy vehicles  (Pérez-Martínez et al. , 2014) , while the respective fleet-weighted CETESB (2015) emission factors are 0. 44 g km -1 and 6. 3 g km -1 , as shown on Fig.
WRF simulation Triple-nested domains are set up centered in SPMA. The mother domain has a spatial resolution of 25 km, the second 5 km, and the finest 1 km. The simulation at 1-km provides MUNICH with meteorological information. Initial and boundary conditions are retrieved from Historical Unidata Internet Data Distribution (IDD) Gridded Model Data (https://rda. ucar. edu/datasets/ds335. 0/index. html). Table  2  shows WRF configuration and Fig. 2 , the WRF domains. Longwave Radiation RRTMG  (Iacono et al. , 2008)  Shortwave Radiation RRTMG  (Iacono et al. , 2008)  PBL YSU  (Hong et al. , 2006)  Surface Layer Noah  (Tewari et al. , 2004 )).
Building height and street width Building height is retrieved from the World Urban Database and Access Portal Tools project (WUDAPT) for SPMA (Fig. 3 ). WUDAPT classifies urban areas into 17 Local Climate Zones (LCZ). These LCZ are divided into build types, which are LCZ from 1 to 10, and land cover types, which go from A to G. Each of these LCZ presents different thermal, radiative, surface cover, and geometric properties. The building height is the height of roughness elements, which is the geometric average of building heights  (Stewart and Oke, 2012). The WUDAPT file for SPMA is a raster with a spatial resolution of 120 m and was previously used in  Pellegati et al. (2019). The number of lanes is provided by the OpenStreetMap dataset, so the street width is calculated by using 3 m of lane width and by adding 1.
Discussion and conclusions Simulating air pollutants inside urban street canyons is a challenging task. It is even more difficult in cities as heterogeneous as Sao Paulo, where its urban structure is not always textbook defined. The limited number of air quality stations located inside or near urban canyons, together with the lack of information from detailed emission inventories and urban morphology data, hinder accurate air quality modeling, and consequently the air quality management. In this paper, we attempt to fill in this gap by using the MUNICH street-network model together with the VEIN vehicular emissions model. The latter provides temporal and spatially detailed emission fluxes inside the main streets and coordinates and width of the streets (i. e. , the street network). The urban morphology is completed by extracting the building height from the WUDAPT database for Sao Paulo Metropolitan Area.
(2020). Furthermore, the comparison of traffic flow between GPS and TDM data for Pinheiros area showed that TDM traffic flows are 2. 22 times higher than GPS. Hence, more representative traffic flows would also improve the emissions compilation. As a conclusion, it is important to develop new and more representative vehicular traffic flow and emission factors for Brazil. With calibrated emissions, the good performance of MUNICH in representing NO 2 concentrations in both neighborhoods and NO and NO x in Paulista Avenue urban canyon suggests that VEIN model distributes emissions spatially and temporally efficiently, which proves its potential to be used in other cities. VEIN is being continuously developed and currently offers some utilities to format emissions to the MUNICH model. On the other hand, now Google Earth allows new features as 3D view, where information on building height can be retrieved.
Acknowledgements. The authors thank  CETESB (São Paulo State Environmental Protection Agency)  for providing air pollution and meteorological data, the support from  CAPES (Coordenadoria de Aperfeiçoamento de Pessoal de Nível Superior) ,  CNPq  ( Conselho Nacional de Desenvolvimento Científico e Tecnológico) , and  FAPESP (Fundação de Amparo à Pesquisa do Estado de São Paulo , process  2016/18438-0 ), and the  Wellcome Trust  (subaward from  Yale University  to  Northeastern University , subcontract number  GR108374 ).
Data availability. MUNICH input and output data, and scripts to generate the figures and calculations are available on GitHub (https://github. com/quishqa/MUNICH_VEIN_SP) and Zenodo (http://doi. org/10. 5281/zenodo. 4168056). MUNICH (v1. 0) is available on http://cerea. enpc. fr/munich/index. html and Zenodo (http://doi. org/10. 5281/zenodo. 4168985). VEIN can be installed from CRAN, and it is also available in Zenodo (http://doi. org/10. 5281/zenodo. 3714187). Additional information and help are available by contacting the authors.
a b s t r a c t Motor, sensory and cognitive functions rely on dynamic reshaping of functional brain networks. Tracking these rapid changes is crucial to understand information processing in the brain, but challenging due to the great variety of dimensionality reduction methods used at the network-level and the limited evaluation studies. Using Magnetoencephalography (MEG) combined with Source Separation (SS) methods, we present an integrated framework to track fast dynamics of electrophysiological brain networks. We evaluate nine SS methods applied to three independent MEG databases (N = 95) during motor and memory tasks. We report differences between these methods at the group and subject level. We seek to help researchers in choosing objectively the appropriate SS method when tracking fast reconfiguration of functional brain networks, due to its enormous benefits in cognitive and clinical neuroscience.
The existing comparative studies are mainly limited to confirming results of differences between two conditions  ( Leonardi et al. , 2013 )  or to prove that obtained results are unaffected by the method's choice  ( Miller et al. , 2016 ). However, a throughout quantitative and qualitative comparative study using both simulation-based and data-driven approaches is still missing and there is no clear consensus about the 'best' (if any) source separation or clustering method to be used to adequately tracking dFC, which is the main objective of our study. Here, we evaluate the performance of nine dimensionality reduction methods used to track functional connectivity states at both group and individual levels. This was done using simulations and three independent MEG datasets (N = 95) recorded during motor and working memory tasks (see Fig.
Dataset1: 'self-paced button press task' This dataset includes 15 healthy righthanded participants (9 male and 6 female, aged 25 ± 4 years (mean ± SD)). They were asked to press a button with the index finger of their non-dominant hand, once every 30 seconds, and should not count the time between presses. More details about this dataset can be found in  ( Kabbara et al. , 2019 ; O'Neill et al. , 2017 ).
Dataset2: 'HCP left hand movement Task' 61 healthy participants (28male and 33 female, aged 22-35) completed the MEG Motor task provided by the Human Connectome Project (HCP) (MEG-1 release)  ( Van Essen et al. , 2012 ). The corresponding experimental protocol was adapted from Buckner and colleagues  ( Buckner et al. , 2011 ; Thomas Yeo et al. , 2011 ). It was performed in two sessions of 14min each, with a small break between them. Each session consisted of 42 total blocks randomly distributed; 32 of them were partitioned into 16 hand movements blocks (8 right and 8 left), and 16-foot movements blocks (8 right and 8 left), and the remaining 10 blocks were interleaved resting/fixation blocks. Each motor effector block was preceded by a 3sec visual cue that prompts participants to either tap their left or right index and thumb fingers or squeeze their left or right toes.
Dataset3: 'sternberg working memory task' 19 healthy participants (10 male and 9 female, aged 25 ± 3 years (mean ± SD)) performed Sternberg task, in which two example visual stimuli, mainly abstract geometric shapes, were successively presented on a screen; each for 0. 6sec and separated by 1sec. Then, a maintenance period of 7sec was left before the presentation of a third probe stimulus. Consequently, subjects were asked to press a button with their right index finger only if the probe stimulus matched either of the two example stimuli and an immediate feedback will be given to show their response correctness. 30 trials were presented separated by 30sec of rest. In both datasets 1 and 3, MEG data were recorded using a 275-channel CTF MEG system at 600Hz sampling frequency and co-registered with subject-specific MRI.
Preprocessing Both datasets 1 and 3 were received already preprocessed as described in  ( O'Neill et al. , 2017 ). Briefly, bad segments produced by muscles, eye or head movement were already visually inspected and removed. For dataset 2, we used the preprocessing pipeline offered by the HCP consortium, which includes removing bad channels, segments and bad independent components from task data. Segments were retrieved from the dataset 1 in the interval  [-15; + 15sec]  relative to the button press onset, and from the dataset 3 in the interval of  [-16; + 28sec]  relative to stimulus presentation. In HCP analysis, we chose data epochs time-locked to EMG onset as we were concerned in exploring brain networks involved during movement execution. Thus, trials were segmented in [-1. 2; + 1. 2sec] relative to EMG onset.
Then, as functional connectivity was proved to be frequency-dependent  ( Baker et al. , 2014 ; Hipp et al. , 2012 ) , each dataset was preprocessed in its appropriate frequency band actively involved in the corresponding cognitive task. While beta band  was used for self-paced and HCP left hand motor task, working memory data was filtered in a broader band  as it is has been shown to involve multiple frequency bands, according to previous studies  ( Brookes et al. , 2012 ; O'Neill et al. , 2017 ). After these preprocessing steps, an average of 34, 150 and 29 per subject were kept from dataset 1, 2 and 3, respectively.
Dipole orientation was determined using a non-linear search for optimum 'signal to noise ratio' (SNR)  ( Sekihara and Nagarajan, 2008 ). Following this, we estimated the functional connectivity by computing the amplitude envelope correlations (using Hilbert transformation) between all ROIs  ( Brookes et al. , 2012 ; Hipp et al. , 2012 ). In order to avoid spurious estimates of functional connectivity, we performed leakage correction on the reconstructed sources signals. We used the multivariate approach based on symmetric orthogonalisation proposed by  ( Brookes et al. , 2012 ; Colclough et al. , 2015 )  for datasets 1 and 3, while pair-wise orthogonalization  ( Brookes et al. , 2016 ; Tewarie et al. , 2019b )  was applied to dataset 2 due to the short time period of the task. Fig. 1. Illustration of the investigation structure for each of the three task-related paradigms.
A. The fundamental processing pipeline applied on each subject data from sensor-level (using non-invasive MEG technique) to cortical-level (using beamforming as the inverse problem solution) to dynamic functional connectivity computation (S-dFC) (using the sliding window approach). B. Concatenation of S-dFC of all subjects along time axis to form a group data referred to as G-dFC, C. Comparative analysis between nine different source separation (SS) methods (six variants of ICA, PCA, NMF and Kmeans) applied on both group-level (X group ) and subject-level (X subj ) data in order to derive k dominant task-related spatiotemporal components (the mixing matrices represent brain spatial maps while the extracted sources represent corresponding temporal weights fluctuations).
Dynamic functional connectivity analysis (dFC) To estimate the dynamic functional brain networks, we adopted the widely used approach of sliding windows for datasets1 and 3. To this end, a time window of length 6sec with 0. 5sec was used for datasets 1 and 3 as applied by  ( O'Neill et al. , 2017 ). Concerning the dataset 2 (HCP dataset), the fast time scale of the task imposes a very small time window width that may be too noisy to extract meaningful information  ( Liuzzi et al. , 2019 ). Thus, we avoided to apply the sliding window approach, and used instead the high temporal resolution version of amplitude envelope correlation metric; the 'Instanteneous Amplitude Correlation' (IAC) already validated in a recent work for the same dataset  ( Tewarie et al. , 2019b ). As a result, we obtained, for each subject trial, a 'dynamic functional connectivity (dFC)' matrix of dimension  [NxNxT] , where T refers to the number of windows for datasets 1 and 3, and number of total time samples for dataset 2 (T = 49, 1221 and 75 for datasets 1, 2 and 3 respectively).
Next, due to symmetry, we unfolded this matrix into a 2-D [Nx(N -1)/2 × T] matrix by removing the redundant connections in each time window. Then, the mean of each row of this matrix is subtracted from the data. Finally, all subjects' trials dFC were concatenated along the temporal dimension. We defined this matrix as a 'Group dynamic functional connectivity matrix (G-dFC)', denoted 'X'. Note that for dataset 2, we averaged connectivity matrices of all trials relative to each subject  ( Zhu et al. , 2020 )  due to memory limitation in Matlab regarding high dimensional data of the temporally concatenated 'sample-by-sample' dFC of all subject's trials.
Technical details about these approaches can be found in the Supplementary Materials. 2. 2. 5. 1. 2. Selection of significant components. Among the NC opt extracted components, identifying those that reflect genuine brain activity related to the task is critical. In this paper, we followed a testing procedure adopted by  ( O'Neill et al. , 2017 )  and previously described in  ( Hunt et al. , 2012 ; Winkler et al. , 2014 )  to determine significant components modulated by the tasks. The testing relies on the construction of empirical null distribution based on a 'sign flipping' permutation approach. Therefore, a component was considered significant if, at any time point, the corresponding time signal, averaged over trials, fell outside a threshold defined at 0. 05 with corrections. For all datasets, 2tailed distribution was allowed, and Bonferroni corrections were applied for multiple comparisons across the NCopt components and across tem-poral degree of freedom.
is the total number of nodes in S-dFC network, and   denotes the total number of nodes in G-dFC network. All networks were 70% thresholded. Lower values of AD indicate stronger spatial similarity between S-dFC and G-dFC networks. (2) Correlation Signals (CS) for 'Temporal Similarity': 𝐶𝑆 ( 𝑇 𝑆, 𝑇 𝐺 ) = ∑ 𝑠 ∑ 𝑔 ( 𝑇 𝑆 𝑠𝑔 -𝑇 𝑆 )( 𝑇 𝐺 𝑠𝑔 -𝑇 𝐺 ) √ ( ∑ 𝑠 ∑ 𝑔 ( 𝑇 𝑆 𝑠𝑔 -𝑇 𝑆 ) 2 )( ∑ 𝑠 ∑ 𝑔 ( 𝑇 𝐺 𝑠𝑔 -𝑇 𝐺 ) 2 ) 𝑠 ∈ [ 1 , 𝐿 𝑠 ] ; 𝑔 ∈ [ 1 , 𝐿 𝐺 ] (3) Where   is the temporal signal of each S-dFC component of length   and   represents temporal signals of G-dFC significant component of length. Higher values of CS reveal stronger temporal similarity between S-dFC and G-dFC signals. We perform this analysis on each subject among the 15 subjects of the MEG dataset 1 (Motor task). Therefore, for each method, we counted the number of subjects that show satisfactory results performance in the context of S-dFC, based on the previously explained measures.
Results are shown in Fig. 2  for the three empirical datasets. Hereinafter, we set NC to the computed NC opt value relative to each method and task. Results of different SS methods applied on empirical data are illustrated in Figs. 3 4 5. In each Fig. , we presented only the components that demonstrated significant task modulation based on the applied null distribution approach (described in the methods section). The networks were thresholded only for visualization purpose (70% for dataset 1 and 3, 85% for dataset 2). Corresponding dynamic reconfiguration of each significant network were plotted together. The temporal fluctuations represent component time signals averaged over trials and subjects.
, revealing an oscillatory temporal activation of motor component for all SS methods. All NC opt extracted components with corresponding null distribution are shown in Supplementary Fig. S5  for an example of ICA-JADE method. In this Fig. , 'Mot' refers to sensorimotor network and 'Aux' refers to all others 'non-sensorimotor' networks. An interactive version of ICA-JADE results can be found on our github https://github. com/judytabbal/dynbrainSS. git using rotatable MATLAB Figs. Reproducing these results is also possible/available using the MATLAB interface on github. this network's connectivity was previously demonstrated  ( O'Neill et al. , 2017 ). During the same period, we can notice a drop-in connectivity in 'Sens' network revealed only by NMF method. At [10-12sec] period, many networks seem to be significantly modulated among all methods: (1) 'Vis' is re-activated at the probe stimulus presentation in all methods except for PSAUD, (2) 'Sens' shows significant increase with FastICA method.
Discussion In this study, we have evaluated the robustness of the most popular SS methods applied to extract the main brain networks fluctuating during time in order to help researchers make a rational choice (if any) among the multitude of available methods. Specifically, nine algorithms have been compared using simulated data (see supplementary materials) and three independent MEG datasets (N = 95) recorded during motor and memory tasks. The discrepancy in the datasets size and behavioral tasks performed allows testing SS methods performance on different scenarios. As the evoked responses (analyzed here) last for hundreds of milliseconds, we conducted our comparative analysis based on MEG datasets to benefit from the excellent temporal resolution of this technique. However, the same pipeline study can be applied in task-related fMRI context.
Still, the executed time of these algorithms is sensible to dataset's features  (size, complexity, type…). Other metrics such as the number of floating-point operations (FLOPs) required for the algorithm completion could be also tested. We also suggest for future studies to explore other data simulation approaches that build the desired ground-truth brain states based on more realistic modeling (using Neural Mass for instance), however, this may introduce the effect of other parameters in the comparison (forward problem, inverse solution…). Besides simulation approach, some studies attempt to consider fMRI data as a ground truth to quantify and compare SS methods performance in the context of M/EEG studies  ( Colclough et al. , 2016 ; J et al. , 2020 ). The method's performance were evaluated on three real MEG datasets already published and tested by previous studies  ( Casorso et al.