dataesr/scientific-paragraphs-categorization huggingface dataset:
Cumulative human impacts We used a cumulative impact model following Halpern et al. [5, 25]  and Micheli et al. [21]. First, we assembled spatial datasets for n = 10 anthropogenic pressures (value D i ) (see S1 Text) and m = 11 habitats (value E j ). Secondly, all pressure layers were then log[X+1]-transformed and rescaled between 0-1 to allow direct comparisons. The sum of the different pressures per pixel was calculated. Then, cumulative impacts scores (I C ) for each 20 x 20 m pixel were calculated according to Micheli et al. [21]  and Halpern et al. [5] : I C ¼ X n i¼1 X m j¼1 D i Ã E j Ã m i;j Where D i is the value of an anthropogenic pressure at location i, E j is the presence or absence of habitat j and μ i,j is the impact weight of anthropogenic pressure I and habitat j  [5]. Like Micheli et al.
First, we assumed a linear relationship between pressure intensity and impact on habitat and ignored thus the existence of thresholds that certainly do exist. Like previous studies  [41, 42, 62] , we skipped the thresholds because there is a lack of information about them. Similarly, for the same reasons our analyses did not include eventual synergy or antagonism between pressures acting at the same place. Secondly, maps represent what we know in 2014; it is thus possible that some pressure or habitat is invisible on the maps because the information was unavailable at this time. Moreover, available data were collected during an extended period of time (four years) so they represent an average situation (besides without any seasonal variation) even if local managers easily communicate us their feed-back on the maps now available on line.
Acknowledgments We thank  Ifremer  and  Agence de l'eau RMC  for making data on pressures available and  Laetitia Gennete  for having validated and completed this database. Other data sources are cited within the supplementary file. We are grateful to  B. Halpern  for his quick answers to our Emails and to  D. Mouillot ,  V. Parravicini  and  E. Gilbert  for their advices. We thank  Sibylle Cazacu  who has improved the English version of this manuscript and  Alessio Rovere  and one anonymous reviewer for their helpful comments.
This work was essentially funded by the  Agence de l'Eau RMC  (http://www. eaurmc. fr/)and  Andromède Océanologie  (a commercial funder, http:// www. andromede-ocean. com/). Florian Holon  was supported by a PhD grant from  LabEX CeMeb  (http:// www. labexcemeb. org/)and  Andromède Océanologie. Nicolas Mouquet  was funded by  CNRS  (http://www. cnrs. fr/), Pierre Boissery by Agence de l'Eau RMC , could also help better apply and coordinate management measures at a relevant scale for biodiversity conservation. Marc Bouchoucha by Ifremer  (http://www. ifremer. fr/), Gwenaelle Delaruelle  and  Anne-Sophie Tribot  by  Andromède Océanologie  and  Julie Deter  by the  University of Montpellier  (http://www. umontpellier. fr/) and  Andromède Océanologie. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Some faults are difficult to detect endogenously by the robot, such as mechanical failures consequent to a broken connection to a power source, or software bugs in the robot controller. Alternatively, while the robot may be able to proprioceptively detect faults, the fault itself might disable the robot's communication and capability to alert other robots or a human operator. Consequently, in exogenous fault detectors, the multiplicity of robots in MRS allows the robots to robustly and explicitly detect the presence of faults in one another. Fault detection in MRS composed of a limited number of relatively complex robots has been extensively studied in the past (e. g. ,  [25, 26, 27, 28, 29] ). For instance, a high degree of fault tolerance was achieved by cooperating teams of robots utilising the ALLIANCE software architecture  [25] , consequent to a mathematical modelling of the robot's motivation to perform different tasks, and an adaptive task selection based on these motivation.
Consequently, our approach may compliment the existing endogenous models in scenarios involving temporal variations in normal behaviour (e. g. , online learning, perturbations in the environment). In such scenarios, our fault-detection algorithm does not need to be retrained, consequently saving costs and increasing the autonomy of the resultant system. In exogenous fault detection, Parker et al. [25]  proposed the ALLIANCE software architecture, wherein the mathematically modelled motivation of different robots to perform different tasks, resulted in a high degree of fault tolerance for cooperating teams of robots. Similarly, in other exogenous fault-detection models  [26, 27, 28] , an explicit and intricate inter-robot communication process was used to facilitate task allocation. However, these approaches require relatively complex and tightly-coupled robot platforms to operate successfully.
2. 8 In silico analysis of miRNAs using TCGA Both clinical and miRNA sequencing data of invasive breast cancers were downloaded from The Cancer Genome Atlas (TCGA) database. A total of 519 patients with information on ER, PR and HER2 status were selected to compare the expression profiles of four selected miRNAs (miR-10b, miR-26a, miR-146a and miR-153) in the respective tumors. 88 cases were found to have a negative ER, PR and HER2 (i. e. , triple-negative) phenotype, whereas 431 cases were positive for at least one of these receptors.
Moreover, miR-10b was shown to be able to promote the invasion and metastasis of tumor cells through post-transcriptional regulation of HOXD10  [37]. Overall, these results indicate that miR-10b may act as an oncogenic miRNA in breast cancer cells. Nevertheless, Biagioni et al. reported that exogenous miR-10b expression reduced both Fig. 3  Proliferation assay after siBRCA1, miRNA mimic and inhibitor transfection in MDA-MB-231 and MCF7 cells. Cells were transfected with Tmock (transfection reagent only), siBRCA1, miR-146a, anti-miR-146, miR-153, miR-10b and miR-26a. After 48 h, in vitro cell proliferation was evaluated using CCK-8. The absorbance was determined at 450 nm. All experiments were performed in triplicate Fig. 4  In silico expression analysis of miRNAs using TCGA data. Clinical and miRNA expression data for breast cancer were downloaded from The Cancer Genome Atlas (TCGA) database.
These results suggest an oncogenic function of this miRNA in breast cancer. In contrast, Gao et al. reported that miR-26a could inhibit the proliferation and migration of breast cancer cells through repression of MCL-1, and that miR-26a could increase the sensitivity of breast cancer cells to paclitaxel  [38]. Others found that miR-26a can promote ovarian cancer cell proliferation by targeting ERα  [39]. We found that miR-26a significantly stimulated the proliferation of MDA-MB-231 cells and inhibited the proliferation of MCF7 cells. We also found that the average expression of miR-26a was not significantly different between triple-negative and non triple-negative breast tumors in the TCGA database. The role of miR-26a thus appears to be complex and may dependent on the tissue and/or tumor context.
We conclude that also the function of miR-153 is complex, and identifying targets of miR-153 would be an interesting means to better understand its role in cell proliferation and carcinogenesis-related processes. In conclusion, we identified miR-10b, miR-26a, miR-146a and miR-153 as potential triple-negative breast cancer biomarkers. These miRNAs may be instrumental for the future design of novel targeted treatment options of these breast cancers. This 'from bench-top to bed-side' translation remains, however, challenging  [44]  and additional research is required to better understand the function(s) of these miRNAs. Acknowledgments This research was funded by grants from the ministry of Higher Education and Scientific Research in Tunisia, the Laboratory of Biochemistry Research unit UR 12ES08 Cell Signaling and Disease, Faculty of Medicine of Monastir, the Molecular Oncology Laboratory at Center Jean Perrin in Clermont-Ferrand and the Fuda (Founding of the University of Auvergne).
Despite the widespread availability of information concerning Public Transport from different sources, it is extremely hard to have a complete picture, in particular at a national scale. Here, we integrate timetable data obtained from the United Kingdom open-data program together with timetables of domestic flights, and obtain a comprehensive snapshot of the temporal characteristics of the whole UK public transport system for a week in October 2010. In order to focus on the multimodal aspects of the system, we use a coarse graining procedure and define explicitly the coupling between different transport modes such as connections at airports, ferry docks, rail, metro, coach and bus stations. The resulting weighted, directed, temporal and multilayer network is provided in simple, commonly used formats, ensuring easy accessibility and the possibility of a straightforward use of old or specifically developed methods on this new and extensive dataset.
Background & Summary Public Transport is a fundamental service, provided in every country at various scales, and which answers to a mobility demand of a large share of the population. The quality of public transport systems directly influences the citizens' quality of life, by reducing travel-times, promoting social fairness and by improving the air quality in metropolitan areas. Increased public transportation investments also lead to a significant economic growth  [1]. The increasing complexity of these systems and the lack of proper tools for their analysis, render the managing tasks of transportation agencies harder. This is particularly true for large multi-modal systems, as single agencies often manage different separated parts of the network and both data handling and optimization have to cross hard organizational boundaries.
2  for an illustration in London). In order to perform an extensive and comprehensive analysis at both the urban and inter-urban levels, we integrate timetable information for land and water transport, obtained from a rich open-data source, the United Kingdom's National Public Transport Data Repository (NPTDR -Data Citation 1), together with the schedules of all non-stop domestic flights in the United Kingdom provided by Innovata LLC  [26]. This integration gives us the complete knowledge of British Public Transport, all modes included. We use the multilayer network framework to identify the multi-modal features of this integrated transport system, and associate to each transportation mode a separate layer. We then aggregate stops associated to different modes into single network's nodes by using a hierarchical coarse-graining procedure that integrates and corrects the grouping information provided by the National Public Transport Access Nodes (NAPTAN) scheme  [27]  used by the NPTDR.
Original datasources Land and water Public Transport timetables are provided by the NPTDR (Data Citation 1) under Open Government licence. Snapshots of every journey are recorded for all services running in Great Britain (i. e. England, Scotland and Wales) during a full week in October for years ranging between 2004 and 2011. We choose the year 2010 as a working dataset as it was the most recent, fully consistent one (some more recent timetables are not updated for some areas in 2011). The raw files contain the information available in the travel-lines' web sites and call-centres during the selected week. For road transport, transportation agencies take into account the average traffic conditions at different hours and days in the design of timetables, and these data then implicitly contain the effects of congestion.
The timetable information is provided within a resolution of the minute which we will therefore use in our dataset. In the NPTDR data, the modes covered and identified are: bus, coach, (national) rail, ferry and metro (which includes underground, tram, light rail and non-national rail trains). All routes are originally referenced to stops defined using the NaPTAN scheme  [27]. In this scheme, every UK rail or metro station, coach terminal, airport, ferry terminal, bus stop or taxi rank is associated to at least one Stop Point. Each stop point is geo-referenced, has a detailed toponym and is identified by an ID denominated ATCOcode. For all bus stops, which represent the large majority of stops, the first three characters of the ATCOcodes can be linked to the Administrative Areas. In order to get a complete picture for all transportation modes, we use detailed schedules of all non-stop UK domestic flights, provided by Innovata LLC  [26]  for the week of 18-24 October 2010 (Innovata kindly accepted to grant us the rights of sharing these timetables and licensing this derived dataset as Open Data).
Timetables correction The original NPTDR data are not free from errors. For this reason, the timetables underwent a data-cleaning process: • A specific error was found in the Rail timetables. For some reasons, some unrecorded times were substituted with a '00000000' string, which also represents a correct stop time at midnight. When an error happens at the arrival at a station B following station A, the passage time at B is estimated by adding to the passage time at A the average of the travel times recorded for all other correct A-B trips. This solution solved the problem almost entirely. • Further inconsistent stop times have been corrected by temporal interpolation whenever possible. This temporal interpolation assumes for instance that the travel-time (when known) t AC = t C -t A between A and C in a A-B-C trajectory has to be split in t AC = t AB + t BC where t AB /t BC = d AB /d BC , where d IJ is the euclidean distance between I and J.
Coarse graining procedure Stop points are then organized in Stop Areas representing facilities (Airports, Bus/Metro/Coach/Railway Stations) or possible interchange points. Also Stop Areas are geo-referenced and identified by an ATCOcode (characterized by the character 'G' at position four in the code). These Stop Areas have been taken as a basis for defining a multilayer network from the timetable data. Probably because of the bottom-up data entry process used for NaPTAN, the Stop Areas were not homogeneously defined. In particular, the spatial size can vary significantly and in some cases groups of stops can cover a distance of order 10 kilometres or more. In order to define consistently the nodes in the multilayer network where the inter-layer edges represent the inter-modal connections, we define inter-modal exchange nodes by correcting inconsistent NaPTAN Stop Areas using as a reference parameter a walking distance wd = 500 m, used here as the maximal distance allowed to reach a bus stop by walking.
We focused on bus stops as we identified them as the main source of errors, and we therefore removed bus stops from groups when they were out of the walking distance range and included them when they were within that range. Finally, the procedure aimed to maintain the hierarchy defined by the NaPTAN scheme: Air ¿ Ferry ¿ Rail ¿ Metro ¿ Coach ¿ Bus and in the coarse graining we took into account this ranking where Airports are the most important locations and Bus stops the less important. In more detail, this data-cleaning and aggregation procedure follows the steps listed hereafter: xiii) We assign to each Area a representative Point, chosen at random between those with the higher rank; xiv) We assign an areacode to each Area which corresponds to the first 3 digits of the ATCOcode of the majority of its bus stops.
Defining the network The Areas defined by the procedure described above comprise nodes connecting the different layers of the multilayer network. The distance used for computing inter-layer weight is then computed as the average distance between all Points of the first layer and all Points of the second layer within these Areas. This walking time is obtained from this distance using a standard walking speed of 5 km/h and converted in minutes. Time is described by an integer in our dataset and in order to avoid occasional divide-by-zero errors, we added 1 minute of minimal connection time to these connection times, which may reflect the time needed for getting off the vehicle, finding the vehicle of the following ride and getting on that second vehicle. We also took into account the 2 hours time requested for airport screening procedures, check-ins and luggage retrieval.
These 2 hours of connection times are added to any connection towards the Air layer and 30 minutes are added to all connections from the Air layer. Both the NAPTAN and the Innovata data use a format where each route is considered as active during certain days of the weeks. We prefer to define the temporal networks by translating directly this information into the existence or not of links between two nodes at a given moment measured in minutes from the 00:00 of Monday. This choice does not optimize memory space but allows for an extremely straightforward reading of the data. Not all studies need the temporal network information, and we therefore defined a weighted static network, where we associate to each edge a weight given by the minimal travel-time recorded during the considered week. We impose this minimal travel-time to be at least of one minute, again to avoid divide-by-zero errors for times defined with a 1 minute precision.
Code availability In order to ensure the replicability and reproducibility of our dataset, we share all codes that were developed for producing this dataset. The software is written in Python 2. 7 and can be found in the repository indicated as Data Citation 2. The input files needed are the air timetables provided to us by Innovata LLC, which are included in our dataset (Data Citation 3) and the 2010 snapshot the NPTDR data (Data Citation 1). In particular: • the folder identified in the code as "NPTDRTimetablesPath" is contained in all the unzipped version of the files present in the folder October-2010/Timetable Data/CIF/National; • the folder "NaPTANPath" contains the file present in the. zip file October-2010/NaPTANcsv. zip; • the folder "INNOVATAtimetablesPath" contains the file UKDOMESTICOCT10.
csv that accompanies our dataset (Data Citation 3). The workflow consists in the following steps: 0 correctRailTimetables. py Correct the Rail timetables from the '0000000' error. It produces a corrected copy of the timetable in the original. CIF format, used in step 1. 1 stops. py Recognizes active stops, performs the stops' coarse-graining, associates nodes with areacode, corrects inconsistencies in all timetables, computes intra-layer distances. It produces a set of intermediate files (nodes list, events list, intra-layer edges list) used for the steps 2 and 3.
Data Records This dataset is stored as a single zip file at the Dryad Digital Repository (Data Citation 3). It describes the Public Transport Network of Great Britain by using a multilayer node-list and edge-list, where each layer is associated to a mode of transport. Each node is geo-referenced, thus defining a spatial network, and associated to a wide list of administrative meta-data. For each edge, we indicate the minimal travel-time in minutes. In the case of inter-layer edges, their weight is defined as the walking time needed for the inter-modal connection. For each intra-layer edge, we also specify a list of temporal events representing the vehicles' rides along the edges, with origin time and travel duration expressed in minutes with a precision of one minute. In multilayer networks, each node may have several 'copies' in different layers.
For that reason, nodes are identified by two numbers, one specifying the node itself, and another the layer where it belongs. As a consequence, the edges are identified by four numbers, two for the origin and two for the destination (node and layer). The ordering of the fields in edge-list has been chosen to conform to the tensorial notation widely used for this type of networks. There is no established standard format for the temporal event list, and we therefore decided to use a format, derived from the idea of adjacency lists, which has been specifically adapted to this dataset. All files are plain ASCII text files. Each row represents an item and for all files (except the file events. txt) the number of fields and their order is the same. Conversely, a non-standard format has been used for encoding the events information (see below).
NaPTAN and NPTG metadata We include the original files from the National Public Transport Access Node (NaPTAN) and National Public Transport Gazeetteer (NPTG). We encourage the reader to refer to the comprehensive guide "naptanschemaguide-2. 5-v0. 67. pdf" available online [28] that can be found on the NaPTAN website (http://www. naptan. org. uk/schema/schemas. htm). Here the list of fields for each of these files:.
Technical Validation The reliability of the spatial and temporal information largely depends on the reliability of the source data provided by the transport agencies to the NPTDR. Further limits come from the multilayer aspects of the network we have defined. The absence of other data sources to compare our results with, limit our possibilities for validating the technical quality of our dataset. For this reason, we propose in the Supplementary Information File a statistical characterization of all quantities represented in the dataset. We use such analysis as 'sanity checks' to support the correctness of the information provided. In addition, we study the characteristics of time-respecting shortest paths through London, defined as the quickest journeys along a a sequence of connections with non-decreasing times  [3]  and studied by us in a recently published paper  [8].
Temporal aspects In our procedure, we correct some errors (manifestly wrong or impossible times), directly enhancing the quality of the data the original source. Nevertheless, from the statistical analysis we perform in the Supplementary Information, it emerges the most relevant limit of the dataset: the low reliability of the short traveltimes. Indeed, a fraction of 1% of the bus layer's edges have a max travel speed (edge length/minimal traveltime) higher than 90 km/h. The most extreme of those speeds are associated to extremely short traveltimes. This issue is inherited from the original NPTDR database and is a consequence of the low precision of bus timetables. This problem is reflected on the events data and magnified if one consider the max travel speed (thus our choice of a lower bound of 1 minute for the minimal traveltime, to avoid divisions by zero).
i) the Centres of an Area are identified by their latitude and longitude in the NaPTAN dataset. If not all Points are within a distance wd from the Centre, the centre of mass of all Stop Points is computed as the point with latitude and longitude equal to the mean value of all Points latitudes and longitudes respectively. If all Points are then within a distance wd from the centre of mass, it is used as the new Centre of the Area. ii) Points where Air, Ferry, Rail, Metro and Coach stops occur are always kept in the Area, and Bus stop Points further than wd from the Centre are removed; iii) Areas containing only Bus stops Points are corrected by iteratively removing the farthest stop from the centre and recalculating the centre of mass, until they become contained in a circular area of radius wd/2 (thus implying a maximal distance between two points in this area less than wd); iv) Airport Stop Points and Areas are joined together if they share the same IATA code; v) All Airport Stop Points are 'promoted' to Areas; vi) The Heathrow Airport Stop Area is reconstructed with a specific rule (lines 777-790 of 1 stops.