dataesr/scientific-paragraphs-categorization huggingface dataset:
Statistical analyses and interpretation of results For principal component analysis (PCA) missing values were imputed from a normal distribution with width 0. 3 and downshift 1. 8 using Perseus. Hierarchical clustering was used to visualize sample metadata in the context of a clustering of those proteins associated with disease (whether positively or negatively, with a 5% FDR), Figure  1  Quantitative proteomics analysis of epilepsy and non-epilepsy control cases. (A) Experimental workflow. Specific protein regions were excised by laser capture microdissection (LCM) from FFPE autopsy brain tissue from control and epilepsy cases (n ¼ 14 in each group) and proteins quantified by label-free quantitative mass spectrometry (MS). Statistical, pathway and network analyses were performed to identify proteins differing in these regions.
Three regions were analysed [hippocampus CA123 (n ¼ 14 control, n ¼ 13 epilepsy), frontal cortex (n ¼ 14 control, n ¼ 11 epilepsy) and dentate gyrus (n ¼ 14 control, n ¼ 13 epilepsy)]. Validation of protein differences was performed using immunohistochemistry (IHC). (B) LCÀMS/MS computational analysis. Proteins groups were filtered and quantified using MaxQuant. (C) Venn diagram of proteins identified in each region. A total of 3216 protein groups were detected across all brain regions by MS, with 2440 overlapping protein groups between the three regions and 2861 overlapping protein groups between the hippocampus and the frontal cortex. following a preliminary kmeans clustering using k ¼ 2 and the Hartigan and Wong algorithm using the ComplexHeatmap package in the R environment. 41 For the hierarchical clustering, Euclidean distance was used as the distance measure and complete-linkage was used to combine clusters.
Proteins with a positive log ratio have greater expression in epilepsy, and proteins with a negative log ratio have a lower expression in epilepsy. All color-coded proteins correspond to neuronal proteins (Neuron, blue; Inhibitory Neuron, dark green; Excitatory Neuron, dark green), oligodendrocyte proteins (yellow) and astrocyte proteins (orange). The cumulative cell type specific protein differences showed that there were significantly less neuronal proteins in the hippocampus in epilepsy (P ¼ 0. 001) but not in the frontal cortex (P ¼ 0. 371). No differences in the amount of oligodendroglial (hippocampus, P ¼ 0. 096; frontal cortex, P ¼ 1), astrocytic (hippocampus, P ¼ 0. 565; frontal cortex, P ¼ 0. 108), microglial (not shown) or endothelial proteins (data not shown) were observed. Significance was determined using a Fisher's exact test comparing cumulative cell type specific protein expression between epilepsy and control cases.
All color-coded proteins correspond to neuronal proteins (Neuron, blue; Inhibitory Neuron, dark green; Excitatory Neuron, dark green), oligodendrocyte proteins (yellow) and astrocyte proteins (orange). The cumulative cell type specific protein differences showed that there were significantly less neuronal proteins in the hippocampus in epilepsy (P ¼ 0. 001) but not in the frontal cortex (P ¼ 0. 371). No differences in the amount of oligodendroglial (hippocampus, P ¼ 0. 096; frontal cortex, P ¼ 1), astrocytic (hippocampus, P ¼ 0. 565; frontal cortex, P ¼ 0. 108), microglial (not shown) or endothelial proteins (data not shown) were observed. Significance was determined using a Fisher's exact test comparing cumulative cell type specific protein expression between epilepsy and control cases. Pathway analysis of the altered proteome in the hippocampus (D) and frontal cortex (E).
These perturbations can propagate to surface levels and modulate regional patterns of temperatures and pressures  (Gray et al. , 2010; Seppälä et al. , 2014). Investigation of atmospheric reanalysis datasets and coupled-climate model runs have shown that NO x and HO x have the potential to modify regional winter-time surface temperatures by as much as ±5 K, through re-distributing annular mode patterns at mid to high latitudes in both northern and southern hemispheres  (Seppälä et al. , 2009; Baumgaertner et al. , 2011). To understand these questions, it is necessary to make simultaneous observations of the EPP flux, energy spectral gradients, ion composition, and NO x , and measure EPP fluxes with good resolution in the energy and pitch angle. Further, the involved energy spectral gradients need to be described, along with the energy ranges that cover the deposition altitudes from the lower thermosphere to the mesosphere down to the stratopause.
Reliable measurements of T n have been difficult and less abundant compared to those of the neutral density itself where especially the analysis of drag on satellite orbits has boosted the available data in the recent decades. In diffusive equilibrium (for each gas component) the profiles of density and T n are not independent. Early models of the thermosphere were based on this assumption and an empirical formula, sometimes called the Bates profile: T n (z) = T ∞ -(T ∞ -T z0 ) exp - z -z 0 H (1) with T ∞ the exospheric temperature, T z0 the temperature at the base, z 0 the height of the base, and H a scale height  (Bates, 1959). Sources of T n measurements include mass spectrometers on sounding rockets, which naturally are relatively sparse, on satellites, which do not cover well the lower parts of the LTI, and by optical methods like UV occultations observed in space and ground-based Fabry-Perot interferometers.
855 Bunescu  et al. , 2015) , or to take advantage of multi-point in situ information, as provided, e. g. , by the Cluster and Swarm missions (e. g. ,  Dunlop et al. , 2002; Marchaudon et al. , 2009; Ritter et al. , 2013; Blȃgȃu and Vogt, 2019; Vogt et al. , 2020 ). An alternative option, which benefits from prior development and validation with ground-based data, is the spherical elementary 30 https://doi. org/10. 5194/angeo-2020-42 Preprint. Discussion started: 1 July 2020 c Author(s) 2020. CC BY 4. 0 License. current systems (SECS) technique  (Amm, 1997; Amm and Viljanen, 1999) , adapted also for Swarm data  (Amm et al. , 2015). A key advantage of this approach is that, when used with LEO satellite data, it provides both the FACs and the ionospheric currents in a consistent manner (such that the FAC density is equal to the divergence of the curl-free ionospheric current).
Acknowledgements. This work was funded under the  European Space Agency (ESA/ESTEC ) contract number  4000127346/19/NL/IA  with the  Democritus University of Thrace  for the Daedalus science and requirements consolidation study, in the framework of the Earth Explorer 10 Phase-0 feasibility studies (PI: TS). MP and MG are supported by  European Research Council   Consolidator grant   682068-PRESTISSIMO , and  Academy of Finland  grants  309937  and  312351. Work at the Democritus University of Thrace  is supported under project  KE82324. National Center for Atmospheric Research  is a major facility sponsored by the  National Science Foundation  under Cooperative Agreement No. 1852977. HLL's work is in part supported by  NSF  grants  OPP-1443726  and  AGS-1552153  and  NASA  grant  NNX16AB82G.
Code and data availability. The DMSP data used to make Figure  3a  are provided by NOAA and can be downloaded from the DMSP/SSJ archives at https://satdat. ngdc. noaa. gov/dmsp/data/. Figure  3b  was made using OvationPyme, the Python implementation of Ovation Prime 2010, which is available at https://github. com/lkilcommons/OvationPyme. The NRLMSISE-00 and IRI-2012 models used to produce Figure 4 are available online through NASA's Community Coordinated Modeling Center (CCMC) at https://ccmc. gsfc. nasa. gov/modelweb/ models/nrlmsise00. php and https://ccmc. gsfc. nasa. gov/modelweb/models/iri2012_vitmo. php, respectively. Data from the Swarm mission visualised in Figures 5 and 8(b) are provided by ESA via http://swarm-diss. eo. esa. int or ftp://swarm-diss. eo. esa. int. TIE-GCM model run data used to generate Figure 7 were produced at the Democritus University of Thrace, with AMIE inputs provided by Gang Lu, NCAR/HAO.
Need to build collaboration between institutes, combine databases, verify local/seasonal trends, the right tools to analyse climate data, to improve knowledge management capabilities. Need to have basic understanding climate data, be able to transform international national information. information into relevant Specialised education, training and guidance needed for using climate information, to build databases, to understand climate modelling, to conduct specialised studies. English language needed; collaboration with international experts. Remote assistance needed. Main obstacle is knowing Meteorologist (7) how to organise and save data appropriately, process climate data and to observe conclusive climate change trends at stations. A challenge to use the right tools. Also difficult to identify challenges, build shared awareness in Main driver is to better understand climate and future climate changes, conduct research, use science to drive policy.
There is a lack of finance and capacity development opportunities. Need to make decisions, research, add value to project assessments, improve political decisions for adaptation to climate change. Need new approaches to problem solving, need to protect the environment and create resilience for the future. Driven to write better proposals for GCF. Need institutional collaborations, coordinated platforms and systems for data sharing and management (with quality assurance). A holistic approach in departmental structure. Need qualified personnel, skills in interpreting climate change data, training in sustainability. Have an expert on UNFCCC roster of experts (Cabo Verde). Education and long term guidance, specifically in modelling, database management, training in using the platform are needed.
decisions based on trusted ICT, Statistics, GIS, WRMA, Agriculture, basic level of understanding but need capacity for model choice, data inputs to generate useful outputs for proposals. Also need capacity in evaluating different datasets. Limited modelling experiences. Need understanding of climate change processes, links between global and local scales, and understand uncertainties. Education and guidance in modelling, examples of 'how to', data collections, using provided tools and methods. Both face-to-face and virtual interaction platforms needed. Develop regional capacity to learn and deliver, then extra-regional institutions provide support. Climate projection tools. Training in downscaling, bias adjustment and local climate assessments Education and specialised Other-medical dr, trainee, not specified (3) Missing data, use platform for further comparisons Want to improve the networks, cooperation between national and international institutions, know climate of the region Everything is needed guidance needed.
On 5 December 2020, rapidly increasing mortality was reported in a breeding mule duck farm in southwest France. In the hours that followed, the French national reference laboratory for avian influenza confirmed the presence of HPAI H5N8 virus subtype  (clade 2. 3. 4. 4. b). This was the first reported outbreak of HPAI in a French poultry farm since the devastating epidemic of HPAI (H5N8) in 2016-2017 that caused almost 500 outbreaks and resulted in the culling of 6. 8 million birds  (Guinat, Nicolas et al. , 2018). Following confirmation, all ducks from the infected farm were culled on 6 December 2020 and strict control measures were implemented, including movement restrictions and the establishment of 3 and 10 km radius protection and surveillance zones, respectively. By fitting a mechanistic model of withinflock transmission to the daily mortality data of this first infected flock in France, we inferred the date of the first infection and within-flock transmission parameters, which are key to provide policy support and anticipate further spread.
At the time of the outbreak, ducks were 9-weeks old. Data on daily duck mortality for the 30 days prior to the day of confirmation were obtained from the farm log book and confirmed by interviewing the farmer. Up to 3 December 2020, daily mortality was stable and low ranging between zero and eight dead ducks. Subsequently, it increased to 40 dead ducks on 4 December (the date when suspicion was reported to veterinary services) and 250 on 5 December. More than 300 ducks were found dead on the morning of 6 December 2020, the day when the flock was culled. To infer transmission parameters from mortality data, we adapted a modelling framework that was developed previously for African swine fever in the Russian Federation  (Guinat et al. , 2018). Briefly, the withinflock transmission of the virus was modelled using a stochastic SEIR epidemic model in which the duck population was divided into four classes: susceptible (S), exposed (i.
ACKNOWLEDGMENTS The authors would like to thank the  French Ministry of Agriculture  and the veterinary services of the  Landes department  for their support in implementing this study. The work was carried out within the framework of the  Chaire de Biosécurité aviaire at the École Nationale Vétérinaire de Toulouse , which is funded by the  French Ministry of Agriculture. CG has received funding from the  European Union 's  Horizon 2020 research and innovation programme  under the  Marie Sklodowska-Curie  grant agreement No. 842621. SG was funded by the  Biotechnology and Biological Sciences Research Council  (grant codes:  BB/E/I/00007036  and  BB/E/I/00007037 ).
II. SAREF-COMPLIANT SEMANTIC SENSOR ENERGY DICTIONARY We built a sensor dictionary for energy compliant with standards such as ETSI SAREF and IEC 61360 Common data Dictionary. Sensor Dictionary for Energy: We designed a pattern to classify sensors for the energy domain: for each sensor, we provide the produced measurements and the associated unit; we also deal with synonyms. Furthermore, we referenced for each sensor the source of knowledge using it (e. g. , past projects referenced within the ontology-based IoT project catalog (see Section III), and reasoning mechanisms to interpret energy sensor data (see the rule discovery project in Section III). Sensor Dictionary Compliant with IEC 61360 -Common Data Dictionary: The sensor dictionary is extended to be compliant with the IEC 61360 -Common Data Dictionary standard  6  (see Table  I , the last column).
SAREF-Compliant Semantic Annotation: The sensor dictionary is compliant with the terms employed by SAREF when possible, as illustrated in Table  I. The limitations of SAREF are summarized in Section IV. Sensor energy datasets (e. g. , JSON, XML) follow the SenML format  7  to represent sensor measurement, its value, its unit, and its timestamp. Available demonstrators are providing code examples (see Section III). A simple rule repository for the semantic annotation is already available such as if "t" or "temp" or "temperature" and located in the room probably it is a room temperature and will be annotated following the dictionary mentioned above which is implemented as an ontology.
III. SAREF-COMPLIANT ENERGY SCENARIOS We designed scenario demonstrators (collected in Table  II ) to answer the following competency questions by executing the reasoning and query engines: • Retrieving the sensors relevant for the energy domain. • Discovering knowledge (e. g. , rules) for each sensor (e. g. , power meter sensor). • Executing the reasoning engine to perform the full scenario (e. g. , interpreting power meter data). • The dataset must be compliant with at least one of those ontologies (e. g. , SAREF and its extensions, M3 8  , W3C SSN/SOSA 9  , iot. schema. org 10 , etc. ). The sequence diagram (illustrated in Figure  1 ) explains the interactions of the sensor dataset and its annotation, storage and execution of reasoning and query engines: 1) Semantically annotating the energy dataset to be compliant with ontologies, 2) Storing triples (semantic sensor data) to the triplestore, 3) Loading semantic datasets, 4) Loading ontologies, 5) Loading rules, 6) Executing the reasoning engine with the set of rules compliant with the ontologies which will update the triplestore, and 7) Loading and executing the SPARQL query to retrieve the semantic datasets and ontologies by selecting a subset of the triplestore content.
Knowledge Discovery and Reasoning for Energy (S-LOR Energy): The sensor energy dictionary depicted in Table  I  described in Section II is employed within the energy rule discovery  [34]  (see Table  II  for URL). Each sensor for the energy domain can be automatically retrieved using SPARQL queries; the sensor dictionary is displayed in Figure  2 , the content shown on the GUI is from Table  I  and Table  III. End-to-End Energy Scenarios: The knowledge acquired to infer meaningful information is implemented as rules. We simulated sensor data (e. g. , power consumption data using the SenML format). We annotated the data to be compliant TABLE I: Subset of the SAREF-compliant sensor dictionary, applied to energy (similar sensor tables for building, weather, and air quality can be provided). We focused on sensors employed within the Interconnect project.
with ontologies (the file can be access though the demos referenced in Table  II ). The semantic reasoner (e. g. , Jena inference engine) is executed on the semantic sensor dataset and the set of rules all compliant with each other. For instance, after executing the reasoning engine, a rule has been executed that deduces that a mobile phone uses 5 Watt, as illustrated in Figure  3. We have more and more scenarios (Table  II ) including various sensors (introduced in Table I), as an example: • Energy Data: power, frequency, luminous efficiency, electric current, electrical potential, resistance, thermal con-Fig. 2 : Demonstrator: the sensor dictionary queried and used via web services and displayed on the GUI ductivity, etc. • Weather Data: solar radiation data with 5 rules for various thresholds (see Table  IV  or the S-LOR Energy online demo for a more exhaustive sensor dictionary (URL in Table  II ).
demos with different kind of sensor data (as referenced within Table  I ) and more and more rules to deduce high level information. Technologies employed in the implementation: Our demonstrators (Table  II ) are implemented with the Jena Semantic Web framework that can deal with RDF, RDFS, OWL languages to implement the sensor dictionary. This dictionary is implemented within the M3 ontology which is refined for the energy domain; Java to develop REST web services with the JAX-RS library to hide the complexity of using semantic web technologies, and the Graphical User Interface with Ajax, JQuery, JavaScript, HTML, and CSS. IV: Subset of the SAREF-compliant sensor dictionary applied to the weather domain.
IV. KEY CONTRIBUTIONS AND LESSONS LEARNT We designed a sensor dictionary and reasoner compliant with those standards: SAREF (SAREF-Core, SAREF4ENER, and SAREF4BLDG), IEC 61360 Common Data Dictionary, and W3C SOSA/SSN, employed within our semantic datasets, SPARQL queries, and rules. We have analyzed the following limitations of SAREF specifications which highlights the need of our unified dictionary explained in Section II. to SAREF, is to link and unify existing ontologies to achieve semantic interoperability. We added links such as rdfs:subclassOf, owl:equivalentClass, and rdfs:seeAlso. SAREF or W3C SOSA do not consider the IEC 61360 -Common Data Dictionary standard  13  that we address in Table  I  and Table  IV. • IoT Alliance: Our work is taken as a baseline in AIOTI  14 (Alliance for the Internet of Things Innovation) WG03 Standardization Semantic Interoperability white papers  [3] [5].
Section III and TableIII)). Similarly, the S-LOR project (see Section III) suggests reasoning mechanisms (e. g. , rules) for specific sensor type. It is not provided by SAREF. • Interlinking ontologies: our proposed solution, compared is implemented within the M3 ontology designed and maintained since 2012, (see also the M3-lite 12 refined for the FIESTA- IoT H2020 European-funded project running from 2015 to 2018) [35]. SAREF is supported by ETSI M2M since 2015. • Provenance metadata: SAREF does not keep track the • Measurement types: saref:Property, provenance of the information. For each sensor, we ref- m3:MeasurementType and erence sources such as scientific publications or project sosa:ObservableProperty are similarly designed. deliverables referenced on the LOV4IoT ontology catalog • Inconsistency, lack of unification, or project ( duplication are found for naming such as saref-core:TemperatureSensor and saref4agri:Thermometer, it demonstrates the complexity to search for the right terms and handle synonyms.
We also have to deal with missing concepts and handle cross-domains (e. g. , weather forecasting is dependent to energy; there is no SAREF for weather forecasting). • Sensor data values: saref:hasValue, m3:hasValue, and sosa:hasSimpleResult are similarly designed; all can be used within our semantic datasets, SPARQL queries, and rules. • Units: saref:isMeasuredIn and m3:hasUnit are similarly designed; all can be used within our semantic datasets, SPARQL queries, and rules. Ontologies' main goal is to explicitly describe the data, descriptions such as saref:TemperatureUnit does not exactly remove ambiguities regarding the unit used such as Celsius or Fahrenheit which might lead to mistakes with automatic reasoning. • Unifying sensor metadata: We structure sensor data in TableIand Table IV: 1) sensor name, 2) the produced measurement, 3) the associated unit (e.
The latent components each evolve according to a set of equations that can be deterministic or stochastic. The raw signal is difficult to interpret as such. However, once decomposed using an ad hoc model, it is possible to detect a long-term trend, repeating seasonal patterns (day, week, year), calendar phenomena (bank holidays,. ), or the influence of exogenous phenomena. However, the implementation requires databases collected over relatively long time periods and the incorporation of a priori knowledge in the model calibration to increase the interpretive power. These models have been widely used in several application fields such as economics  (Koopman and Ooms 2011) , tourism  (Chen et al. 2019) , meteorology  (Murthy, Saravana, and Rajendra 2019)  or energy consumption  (Mousavi and Ghavidel 2019)  and are of great interest in analyzing mobility data.
This paper focuses on models for the decomposition of time-series of transit station ridership. For this purpose, we used datasets on passenger flows collected over nine years in the railway station 'La Défense Grande Arche' which is located on a multimodal transport hub in the Paris region known as 'La Défense'. Ticketing logs collected by Automated Fare Collection (AFC) systems were used to capture the volume of incoming flows to two transport lines at this station: an express rail line (RER A) that crosses the Paris region in the East-West direction and a metro line (line 1) that serves downtown Paris. These two lines have the advantage of sharing several stations and thus highlight possible modal shift phenomena. Considering station ridership data as time series and decomposing it into multiple underlying components allows us to study its structure and answer several questions: • How do the variations in the original series translate into each component? • What is the impact of exogenous events on passengers' decisions to use one transport line or the other? The contributions of this article are the following: • A time series of public transport ridership is very often noisy, and it can be challenging to identify the effect of a factor of interest on this series.
The article is organized as follows. Section 2 provides a state of the art on the work carried out on ticketing data and time series decomposition models. Section 3 details the mobility dataset considered in this paper and presents the expected challenges. Section 4 presents the formalism of structural time series models for mobility data. Section 5 details the calibration steps of the model and provides the main results obtained on the ridership data of the two transit stations. Section 6 concludes this paper and outlines prospects for future work.