dataesr/scientific-paragraphs-categorization huggingface dataset:
Source apportionment of PM 10 The source apportionment was performed with Positive Matrix Factorization, using the US EPA software PMF 5. 0 (US EPA, 2017), following the recommendations included in the european guideline book issued in the EU Fairmode program  (Belis et al. , 2014). However, in the environment of Alpine valleys, the local meteorology and frequent inversion layers in winter lead to strong covariations of the concentrations of many chemical species emitted from the valley bottom. Indeed, during temperature inversion in Alpine valley, pollutants are stuck into the atmospheric boundary layer (ABL) and cannot be removed by wind. Such inversion may be stable over several days. As a result, the different emission sources during that period of time add together and the dynamic from the different sources is masked.
0 to apply constraints to the factor profiles, in order to better define the sources  (Golly, 2014; Srivastava et al. , 2018; Salameh, 2015). A minimal set of constraints based on prior and external geochemical knowledge of sources fingerprints was applied: in the biomass burning factor, the contributions of levoglucosan, potassium, methoxyphenols and BC bb were increased, whereas the BC ff and HOP were set to 0, -HOP was increased in the vehicular factor. We increased the concentration of the species in the factors thanks to the "pull up maximally" option of the EPA PMF 5. 0 software (US EPA, 2017), which tried to increase the contribution of the given species to the factor. Table  1  sums up the input chemistry species and respective uncertainties used in the PMF study.
AA depletion is read continuously for 30 min from absorbance at 265 nm  (TECAN, 200). The maximum depletion rate of AA is determined by linear regression of the linear section data. For both assays, the 96-wells plate is auto shaken for 3 s before each measurement and kept at 37 • C. Three filter blanks (laboratory blanks) are included in every plate (OP AA and OP DTT) of the protocol. The average values of these blanks are then subtracted from the sample measurement of this plate. LOD value is defined as three times of the standard deviation of laboratory blanks measurements (blank filters in Gam-ble+DPPC solution). The samples were stored 3 years before they were analyzed. As mentioned in  Verma et al. (2015) , the OP activity may be impacted by such storage time. However, in a previous program (ANSES ExPOSURE, 2017), still ongoing, we have been measuring the same filter over time.
However, regular WLS do not rule out negative solutions, which should be implemented in our case, as it is not demonstrated that intrinsic OP negative values exist in the real world. Therefore, a stepwise regression is conducted. The underlying algorithm is as follows. 1. Solve the WLS problem and estimate the intrinsic OP. 2. If an intrinsic OP is negative, then set it to zero and go back to step 1. 3. Repeat until all intrinsic OPs are positive or zero. No source is discarded based on its p-value, but sources should be discarded due to negative intrinsic OP. We did not choose a direct non-negative least square approach as it would be a constraint in the model that we believe would be too strong. In addition, we can use the absence of negative coefficients as a test for the coherence of our dataset.
Data availability. The chemical, OP and PMF datasets are available on request. The Supplement related to this article is available online at https://doi. org/10. 5194/acp-18-9617-2018-supplement. Author contributions. JLJ and GU designed the experiment. AC did the OP measurement while JLB and JLJ conducted the chemical speciation. IJ and GM provided the aethalometer measurements. FC and DS ran the PMF analysis. SW, with the help of AC, developed the OP apportionment model and performed the simulations. SW prepared the manuscript with contributions from all co-authors. Competing interests. The authors declare that they have no conflict of interest. Acknowledgements. This work was funded in part by Primequal (DECOMBIO program in the Arve valley, grant ADEME 1362C0028) and by ANSES  (ExPOSURE program, grant 2016-CRD-31).
The funding of the PhD for Samuël Weber is provided by the Ecole Normale Supérieure. The Région Auvergne Rhône-Alpes funded the PhD grant for Florie Chevrier. The Université Grenoble Alpes funded the PhD grant of Aude Calas with a Président Award. The funding of the post-doctoral position for Dalia Salameh comes from the SOURCES program (ADEME Grant 1462C0044). This study was also supported by direct funding by IGE and LCME (technician salary), the LEFE CHAT Potentiel oxydant program and the LABEX OSUG@2020 (ANR-10-LABX-56) both for funding analytical instruments. ATMO AuRA conducted all the logistical aspects of the sample collection in the field. The authors would like to thanks Lisa Fluchaire, Jean-Charles Francony, Coralie Conniès, Vincent Lucaire, and Fanny Masson for their dedicated work for the samples analyses, together with many people from Atmo AuRA for collection of samples in the field.
We indeed observed that in the task at hand, despite its simplicity, it was necessary to store the successive observations and to train the world model off-line with a shuffled presentation of the training samples (for the general scheme of the off-line training, see Algo. 1). For that reason, we created a dataset S compiling all transitions, i. e (φ t , φ t-1 , a, r) quadruplets from all tasks. When there is no predecessor of φ t by action a (as can be the case when this action would require to come through a wall), the transition is represented as (φ t , 0, a, r): those "null" transitions allow N a P networks to represent the fact that the transition does not exist. Despite its simplicity, the navigation task modeled here has some specificities: during task 5 (alternation), some states have more than one predecessor for a given action (see an example on Fig.
Acknowledgements The authors thank  O. Sigaud  for fruitful discussions, and  F. Cinotti  for proofreading. This work has received funding from the  European Unions   Horizon 2020 research and innovation programme  under grant agreement No  640891  ( DREAM Project ). This work was performed within the  Labex SMART  ( ANR-11-LABX-65 ) supported by  French  state funds managed by the  ANR  within the  Investissements d'Avenir programme  under reference  ANR-11-IDEX-0004-02.
A cost-effective approach to gather information in a smart city is to embed sensors in vehicles such as buses. To understand the limitations and opportunities of this model, it is fundamental to investigate the spatial coverage of such a network, especially in the case where only a subset of the buses have a sensing device embedded. In this paper, we propose a model to select the right subset of buses that maximizes the coverage of the city. We evaluate the model in a real scenario based on a large-scale dataset of more than 5700 buses in the city of Rio de Janeiro, Brazil. Among other findings, we observe that the fleet of buses covers approximately 5655 km of streets (approximately 47% of the streets) and show that it is possible to cover 94% of the same streets if only 18% of buses have sensing capabilities embedded.
Furthermore, sensor nodes can perform opportunistic communication with gateways, reducing the amount of necessary gateways  [4]. Finally, by leveraging existing mobility, such as that of buses, mobility is acquired with negligible costs  [5]. One aspect that we must be careful about is that applications atop a bus-based mobile wireless sensor network may have specific requirements in terms of data completeness, timeliness  [6]  and data granularity  [1]. In the case of spatial coverage, completeness and granularity are related to the knowledge of the complete area of the city and to what extent the measurements are spread through the city. Furthermore, acquiring measurements about different areas can reduce noise and uncertainties about neighboring regions  [7]. In this paper, we model the spatial coverage of a bus-based mobile wireless sensor network based on a large-scale, real-world mobility dataset in the megalopolis of Rio de Janeiro, Brazil.
• We evaluate and validate the model using a large-scale dataset from a real scenario. This paper is organized as follows. We propose our model in Section 2 and describe the case study we consider in Section 3. In Section 4, we apply our optimization model to the case study and analyze the results. We postpone the related work to Section 5 in order for the reader to gather enough background and better understand the originality of our contribution. Finally, we conclude the paper in Section 6 and point out future research directions.
Coverage Model This work falls in the context of the SensingBus initiative, which takes advantage of preexisting transport infrastructure, with minimal overhead, to add mobility to the sensors  [5]. In SensingBus, buses collect data and use the Vehicle-to-Infrastructure (V2I) paradigm to deliver the gathered data in an opportunistic fashion  [8]. SensingBus is built on a three-level architecture. At the first level, buses with embedded sensors gather data, following the IoT paradigm. Different environmental measurements are gathered and temporarily stored together with their coordinates and measurement time. At the second level, gathering points located at bus stops receive data from incoming buses and send these data to the third level, using the Internet. The second level follows the fog computing paradigm, pre-processing data before they are sent through the Internet.
Data Gathering The government of Rio de Janeiro offers an open API (Application Programming Interface) that delivers GPS data of the buses in the city  [11]. The basic information is a tuple containing the bus identification, its GPS coordinates and the timestamp of the acquisition of this information. Other elements are also available, such as the bus line identification and the bus instantaneous speed, but we do not consider them in this work. In this paper, we assume that buses follow fixed paths and that departure intervals might vary in the course of a day, but the same schedule is followed in all weekdays. We have built a script to gather data from the API during 24 h, between 0:00 h of 10 May 2017 and 0:00 of 11 May 2017, a Wednesday. The coordinates of each bus are ordered in time, resulting in a sampling of their paths, every minute.
Data Pre-Processing The gathered dataset needs to be pre-processed before serving as an input for the formulated MILP. The pre-processing consists of evaluating the path of each bus (i. e. , a set of street sections) and in estimating the length of each street section. To perform these steps, we use the API Google Snap to Roads  [12]. This API receives a list of ordered coordinates as the input, which is a sampling of the path traveled by a vehicle. It returns the most likely path followed by a vehicle according to the input coordinates, in the form of new coordinates, adjusted to the topology of the roads. The work by Ali and Dyo uses a similar approach to obtain the set of streets that can be covered by each bus route  [9]. The difference between both methods is explained in Section 5. Google Snap to Roads also associates every new coordinate to a place id, which represents a road segment.
It is important to underline that the second procedure may require a significant amount of time before completion, as it is proportional to the number of data points in the bus paths. In addition, GPS coordinates gathered from the buses are error-prone  [14]. Therefore, the first procedure aims to eliminate positions that are similar to noise or that are unnecessary. This is possible since we have detected data points indicating that some buses were parked at bus garages: those generate data points even when they were not in service. Using this filter, the positions of the same bus path are ordered, and if the distance between two points is smaller than a certain threshold, we can filter the second position out. The first position is never removed, and subsequent positions are compared only to points that are already inside the resulting dataset.
We pick 10 m as the threshold because it is a commonplace error range of commercial GPS devices  [14]. After this filtering phase, the buses have some positions removed from their paths, resulting in fewer positions to the next procedure. In case a bus has all but one position removed from its path, the bus is considered as stopped and removed from the dataset. In the course of a single minute, a bus can travel more than one street section. With this regard, simply mapping the gathered positions of a bus to the corresponding section may not reflect the complete coverage of this bus. The second step uses the API Google Snap to Roads and transforms each filtered path, obtained in the previous procedure, into a sequence of coordinates adjusted to the street topology. Google Snap to Roads also associates every adjusted position to a road segment.
In the case that an input coordinate does not fall within a street segment, Google Snap to Roads approximates the input coordinates to the nearest street segment. Therefore, the only way GPS errors can affect the path estimation is if the input coordinates are closer to some street segment that is not the previous or the next in the path. Since GPS errors are about 10 m  [14] , it is reasonable to expect that the coordinates are precise enough for Google Snap to Roads to discover the street segment where the bus was originally traveling when its position was sampled. We refer to an adjusted coordinate associated with a road segment as an adjusted position. The dataset obtained after the pre-processing is called the estimated dataset. Section 3. 3 performs an analysis of the gathered and estimated datasets.
Data Analysis The objectives of gathering and pre-processing real data from buses are to use it as the input to the problem formulated in Section 2. 2. To understand the considered scenario, it is important to perform an analysis of the data in terms of its size, before and after the preprocessing. Table  2  exhibits some attributes of the gathered and estimated datasets. The gathered data contains 5,496,878 positions, obtained from 6075 buses. The filter removes 1,384,925 positions and 328 buses (i. e. , about 25% of positions and 5% of buses are removed); thus, we finally dispose of 4,111,953 positions and 5747 buses. After these positions are applied to Google Snap to Roads, we obtain 52,250,671 estimated positions. The increase in the number of positions occurs because Google Snap to Roads estimates a smooth path for the vehicle.
The focus of the optimization problem was on maximizing the spatial coverage of the network. Nevertheless, mobile sensing poses a trade-off between spatial and time coverage  [3]. Even though the area reached by each sensor grew, some areas were not sensed the whole time. To evaluate the time coverage, we count the number of times the same street section was visited by any bus equipped with a sensing node. The number of visits to a given street section influences the likelihood of detecting some event of interest in this section or determines the freshness of sensed data. Additionally, a larger number of visits also improves the efficiency of algorithms for noise reduction and value prediction  [7, 16]. To estimate the effects of the proposed optimization on the time coverage, we evaluated the number of times each street segment was visited in our dataset, for different values of the relative coverage.
The authors then formulate a MILP based on the maximum coverage problem and propose a greedy algorithm to approximate a solution. The method is tested on a dataset of London's bus routes, by applying the coordinates of the routes to a turn-by-turn API and using an additional method to identify the segments. In our work, we base our coverage model on using a slightly different definition of street section, which is not necessarily between two adjacent intersections and has a length associated with it. Additionally, we propose a different MILP formulation that takes street section lengths into account. Finally, we use the road id offered by Google Snap to Roads to identify every street section. In a previous work, we proposed SensingBus, a system where sensing nodes are coupled into buses and gateways are placed in the city to implement opportunistic data delivery  [5].
We applied the problem to real data produced from the buses of the city of Rio de Janeiro. These data were gathered as raw GPS coordinates from buses and pre-processed using Google Snap to Roads to transform the coordinates into street sections. In the last pre-processing step, we estimated the length of each street section and validated it using information from other datasets. After that, we used these data as input to the formulated MILP problem. The results obtained showed that with only 18% of the fleet of Rio de Janeiro, we can cover at least 94% of the total area, which would be covered by the whole fleet. Interestingly enough, we show that only 32 buses can cover at least 40% of the total covered by all buses. This indicates that it is possible to establish an initial service at very low cost and incrementally deploy a system that serves the whole city.
Introduction Science has an enormous impact on society; therefore, understanding and participating in science projects is important for citizens. The involvement of nonscientists in the realization and design of science projects is called citizen science. Citizen science has become more abundant during the past decade  [1]. One good example of a citizen science project is GalaxyZoo, which engages participants in classifying galaxies and has produced numerous publications-48 by 2014  [1]. This way of developing a research program is on the rise; one striking example is the game Foldit  [2] [3]. The Foldit project is an online 3D jigsaw puzzle in which players are invited to shake and wiggle the 3D structure of proteins to find their most stable conformations  [4]. Since its release in 2008, the project has brought remarkable results from a biological point of view  [2] [3] , but it was also useful to collaboratively develop new algorithms to solve a particular scientific problem  [5].
Rule 3: Enable the player to interact with scientific data Use of scientific data enables game designers to raise player interest and aim for participative data production of high scientific relevance. Intuitive interaction with the data (e. g. , through molecular simulations) enriches the learning experience. One way to allow interaction with scientific data is to derive a "single quantitative metric of success" that facilitates the transformation of a task into a game element  [23]. For instance, Phylo uses a simplified scoring function that estimates the quality of an alignment. One route to this use of data is to recycle available simulation tools. The prototypical example is Foldit, which derives from the Rosetta@home program  [24] , which in turn builds on the Rosetta software  [25].
Similarly, the software UnityMol  [26]  and BioSpring  [27]  were used to create DocMolecules. DocMolecules uses Protein Data Bank (PDB) structures as input for biological targets and molecular models for drugs. Use of force field terms such as nonbonded interactions and precomputed electrostatic fields drives feedback loops in the game. When generating data, the considerations presented in "Ten Simple Rules for Effective Online Outreach"  [28]  apply, especially Rule #8 ("Collect and assess data"). An interesting aspect of CSGs, in which the data are generated by volunteers, is whether the data are made available. There is a general tendency to increase the availability of the data. For example, metrics measured in the game, and largely used for the development of the game itself, can be made open  [29].
The availability of the data generated by the players paves the way for another change in perspective for the participants. In addition to receiving the data they generated predigested by professional scientists, players can also access untreated information that allows them to analyze these data. A striking example is the resolution of a previously unsolved crystal structure by scientists thanks to a Foldit-generated model  [30]. Furthermore, the source code of some CSGs has been publicly released to foster the collective development of these platforms (e. g. , Phylo, Mark2Cure).
The polyA+ fraction was purified and randomly fragmented, converted to doublestranded cDNA and processed through subsequent enzymatic treatments of end-repair, dA-tailing, and ligation to adapters as in Illumina's "TruSeq Stranded mRNA Sample Preparation Part # 15031047 Rev. D" kit. The adapter-ligated library was completed by PCR with Illumina PE primers. The resulting purified cDNA library was applied to an Illumina flow cell for cluster generation and sequenced on an Illumina HiSeq2500 by following the manufacturer's protocols. Image analysis, per-cycle base calling, and quality score assignment were performed with Illumina Real Time Analysis software. Conversion of Illumina BCL files to bam format was performed with the Illumina2bam tool (Wellcome Trust Sanger Institute-NPG). Unaligned single read sequences are provided in BAM files.
statistic analysis A logistic regression model was fitted to the lymphocyte subpopulations data to determine the relative importance of each population as a predictor of response for the variables of interest (NEDA3, NEDA-4, MRI activity, clinical activity, and CDP). The model was fitted with the rstanarm package  (13). The mean of each posterior distribution was used to create the heatmap with the pheatmap package  (14). Four variables from the first approach were selected to first construct a two-dimensional data representation by nonparametric multidimensional scaling (Seuclidean method) and then to visualize differences between responder and NR subgroups. Spearman correlations (rho) for pairs of subpopulations were assessed at baseline and 6 months.
Bioinformatics analysis To assess the effect of fingolimod on PBMCs from MS patients, the differentially expressed genes (DEGs) of samples before and after 6 months of treatment were analyzed. In samples collected at 6 months, DEGs between responder and NR patients were determined to analyze the differential effect of fingolimod according to the clinical response. To search for possible predictor genes of a clinical response, we analyzed DEGs between responder and NR patients using samples that were collected before treatment. The sequencing quality was analyzed with FastQC  (15). Reads were aligned to the human genome (GRch38/hg38) (16) using STAR  (17)  and Samtools 0. 1. 19. 0  (18) , and transcript assembly, abundance estimation and differential expression were calculated with DESeq2  (19).
The estimated p-value was corrected to account for multiple hypothesis testing using Benjamini and Hochberg false discovery rate (FDR) adjustment. Genes with an FDR less than or equal to 0. 05 were considered differentially expressed. In addition, we conducted a gene set enrichment analysis (GSEA), a publicly available computational method that employs pre-defined sets of genes to identify statistically significant differences between two biological conditions. This method can detect the upregulation or downregulation of recognized cellular pathways and biological processes under experimental conditions. GSEA was performed using GSEA software v2. 2. 1 obtained from the Broad Institute (20) and following the developer's protocol  (21). The previously obtained DEGs were ranked according to their t-statistics.
Validation of Model The expression levels by qPCR of genes used in the model building were measured on the pretreatment samples obtained from the remaining 30 patients who were not used for the analysis of RNA-seq. All of them have available data on the clinical evolution up to 2 years (NEDA-4) and results of cytometry experiments before treatment. To evaluate out-of-sample prediction, we computed the exact leave-one-out cross-validation  (25).