dataesr/scientific-paragraphs-categorization huggingface dataset:
e. falsely high as compared to non-pregnant values). Interestingly, the platelet count often remains minimally modified but in some cases, significant thrombocytopenia may occur  [3]. To date, there is scarce data to accurately report on COVID-19associated coagulation changes in obstetric patients and identify possible mechanisms for the observed alterations. What we know has been extrapolated from studies in the non-pregnant population, from small series published to date assessing pregnant women, and from unpublished laboratory results on routine tests performed in French maternity units. In these cases, coagulation factor concentrations are often abnormally low (less than 100% and often in the range of 40-60%) and changes seem to occur in both ''intrinsic'' and ''extrinsic'' pathways. In the rare cases in which circulating anticoagulant antibodies were assessed, they were not found in the plasma of these pregnant women.
Multistress signals orchestrate plant transcriptomic responses In the past, most transcriptomic studies of abiotic and biotic stresses have examined them individually, a situation that rarely occurs under natural conditions. Analysis of data acquired in recent years, however, has led to the conclusion that abiotic and biotic stresses not only often occur simultaneously, but that the corresponding regulatory pathways can interact at several levels Downloaded from https://academic. oup. com/jxb/article/72/4/1020/5981710 by INRAE Institut National de Recherche pour l'Agriculture, l'Alimentation et l'Environnement user on 02 September 2021 Fig. 3. Meta-analysis of combined abiotic and biotic stress transcriptome data. Ten publicly available datasets were selected to study the modulation of defense gene expression in response to combined stresses.
Transcriptome data for nitrate limitation (LowN), Botrytis cinerea (Bc), and their combination were obtained from  Soulié et al. (2020) , whilst the data for cold and flagellin (FLG) and for heat and FLG were extracted from the Gene Expression Omnibus repository (accession GSE41935;  Rasmussen et al. , 2013). (a) Expression of 66 commonly up-regulated genes in all four multistress conditions Downloaded from https://academic. oup. com/jxb/article/72/4/1020/5981710 by INRAE Institut National de Recherche pour l'Agriculture, l'Alimentation et l'Environnement user on 02 September 2021 inside the plant. Recently, researchers have started to examine the transcriptomic responses of plants challenged with both biotic and abiotic stress (Table  1 ). Although the number of datasets remains limited, some lessons can be learned from their analysis.
To further decipher the impact of different stress combinations, we selected 10 transcriptomic datasets for analysis (Supplementary Table  S2 ). These comprise single cold, heat, and flagellin treatment and their combinations, together with single low-nitrate, E. amylovora, and B. cinerea stress and their combinations  (Rasmussen et al. , 2013; Farjad et al. , 2018; Soulié et al. , 2020). When comparing all 10 datasets, we identified only four genes that were significantly modulated among all single and combined stress conditions, which is consistent with previous observations made on other multistress combinations  (Prasch and Sonnewald, 2013). Interestingly, these stress-robust genes comprise a putative kinase, a membrane glycoprotein, and a putative TIR-domain NBS-LRR resistance protein, none of which has yet been functionally characterized (Supplementary Table  S2 ).
These multistressrobust genes can be considered to be generally stress-robust and are of great interest for future research. In order to better understand the modulation of expression of defense genes in response to stress combinations, we checked the expression of a manually curated list of genes (~1300) covering different aspects of plant defense in our multistress transcriptomic data (Supplementary Table  S3 ). This list contains genes related to defense as well as the family of WRKY TFs, which are known to play a key role in the response to both biotic and abiotic stresses. We first compared the modulation of these genes in the 10 datasets of our analysis and found three defense-related genes (Fig. 3c ) and four WRKY TFs (Fig. 3d ) to be modulated by all stress combinations, suggesting that they are robust stress-response genes.
3e ), as previously described  (Farjad et al. , 2018). Interestingly, for several genes related to JA signaling  (JAZ 6, 7, 9) , the response to B. cinerea was prioritized over the response to nitrate limitation, while two genes related to SA signaling (EDS1, PR1) (fold change >1; see Supplementary Table  S2 ). (b) Gene Ontology analysis was performed using the Cytoscape and g:Profiler software according to  Reimand et al. (2019)  using the total of 197 up-and down-regulated genes shared in all multistress conditions (fold-change >1 or <-1; Supplementary Table  S2 ). (c. d) Venn diagrams showing (c) three defense genes and (d) four WRKYs commonly modulated by all the combined stress conditions. (e, f) Heat-maps showing expression of selected defense genes (e) and WRKY transcription factors (d), following single LowN stress, Bc infection, and their combinations.
Black shading indicates genes not significantly modulated (P>0. 05; Supplementary Tables  S2, S3 ). The same genes reported in the heatmaps were also screened in datasets related to the combination of heat, cold, and flagellin (see Supplementary Table  S3 ). Impact of abiotic stress on expression of defense and virulence genes | 1029 showed an induction specifically in the multistress combination (Fig. 3e ). Our analysis thus shows that the defense response to B. cinerea overtakes the response to an abiotic stress, in this case nitrate limitation. This is opposite to the effect of heat, which had a negative effect on resistance and for which several defense and WRKY genes were found to be cancelled (Supplementary Table  S3 ). These differences are consistent with a previous meta-analysis that showed that each multistress combination generated a specific response  (Zandalinas et al.
Furthermore, increasing the number of studies will allow a better comparison of datasets by meta-analysis. Our metadata analysis allowed us to identify a list of stress-robust genes that could be of great interest for future research, among which were several WRKY TFs and important defense-signaling genes (FRK1, JAZ1, and SIF4; Fig. 3 ). Our analysis also confirmed that the defense response can overtake the response to nutritional limitation. The third way by which abiotic stress impacts on biotic stress is by affecting pathogen fitness and virulence inside the host plant. Once inside the leaf tissue, the pathogen is completely dependent on plant metabolism to perform its life cycle. Most studies nowadays on pathogen development in planta are performed on plants grown in optimal conditions; however, several recent studies have shown that studying pathogen virulence in non-optimal conditions can unveil novel virulence genes that are not evident in optimal conditions  (Barcarolo et al.
Acknowledgements IJPB benefits from the support of  Saclay Plant Sciences-SPS  ( ANR-17-EUR-0007 ). This work was supported by  INRA-BAP grants Multipass and Nitropath  ( BAP2014_63 ). MZ was funded by  Fonds de la Recherche Scientifique-FNRS  ( LUX: F. 4502. 19 ).
Bernd Justin Juẗte is Assistant Professor, University of Nottingham; Senior Researcher, Faculty of Law, Vytautas Magnus University, Kaunas. Giulia Priora is Postdoctoral Researcher, Sant'Anna School of Advanced Studies, Pisa. 1 Charles Hodges et al, 'The Difference Between Emergency Remote Teaching and Online Learning' (Educause Review, 27 March 2020) <https://er. educause. edu/articles/2020/3/the-difference-between-emergency-remote-teaching-andonline-learning> accessed 1 June 2020. 2 Other significant concerns relate to data protection, see for a brief analysis Rossana Ducato et al, 'Emergency Remote Teaching: a study of copyright and data protection policies of popular online services (Part II)' (Kluwer Copyright online service? What were the risks teachers incurred when using third-parties' materials without permission? Could their students be exposed to similar risks? Those concerns are not radically new.
5 e aim of this article is to disentangle the most prominent copyright issues involved in remote teaching. To this end, we reviewed the terms of use, service agreements, community guidelines, etc. (together "terms") of a selection of services. We examined and compared these terms to identify and highlight problematic issues and best practices. The study encompasses standard terms of nine online services (referred to hereafter as "online services"), as last accessed on 27 April 2020. The selected services (Discord, Facebook, G-Suite for Education, 6 Jitsi, Microsoft Teams, MoodleCloud, Skype, Zoom and YouTube) include dedicated software for managing groups of users, content-sharing platforms, social networks, and video conferencing services. To varying degrees, they afford teachers to emulate in-person teaching through live discussions and asynchronous interactions with students, the provision of digitized learning materials and the submission of assignments.
The free and open-source software Jitsi is the only service among those analyzed that strictly limits the use of its users' contents to what is necessary for operating and enabling the service. 15 All other service providers ask for their users' permission to use their content for additional purposes. Some providers include in their terms a license to use uploaded or shared content to improve their service. 16 The vagueness of such a purpose, especially when the service provided is broadly defined to encompass commercial features 17 , makes it difficult to foresee how users' contents will effectively be used. Potentially problematic are also clauses whereby users are expected to license their content not only to the service provider, but to third parties as well. Here a distinction must be drawn between third parties that are other users of the service, and third parties that are its business contractors.
If those two fictional educators were to share infringing materials -through Teams' screen sharing or chat features or by embedding the display of protected works in the recorded lectures -the one in the former scenario has a better chance of escaping rightsholders' scrutiny. In addition, some service providers offer rights management tools to copyright owners. This is notably the case for YouTube, which provides its Content ID system to eligible rightsholders. 50 ontent ID scans all videos uploaded on YouTube against a database of video and audio reference files, provided by rightsholders and identifying their protected works. If Content ID finds a "match", it gives copyright owners three options. They can either block access to the allegedly infringing video, monetize it (by running ads against it), or receive detailed viewership statistics.
If the possession of writing was the criterion of civilization, and above all the appearance of Sanskrit language in Southeast Asia was evidence of Indianization, then one would have to conclude that civilization began at Sri Ksetra in ca. 5th-6th century. But the in situ material evidence presented above (including dated stratified data from the Yahanda Mound), and supported by Chinese records, reveal significant pre-existing hydraulic, agricultural, and urban development at Sri Ksetra ca. 3rd century BCE-4/5th century CE. What the inscribed stone urns do record is the emergence of kingship at Sri Ksetra and the desire of its kings to associate themselves with the fame of the Guptas, notably with Chandragupta II, who adopted the style vikramāditya. This form of Indianization took place in a context quite different from that envisaged by Coedès  (1964) and captures synergies between two cultures, not colonization.
Acknowledgements: I am very grateful to the  Leverhulme Trust and Cardiff University  for enabling me to complete this research and conduct fieldwork. Special thanks to  Dr. Pushpa Tiwari  for kindly helping me to arrange fieldwork, and to  Dr. M. C. Gupta  and  Arpit Tiwari , who accompanied me to Gaṛhwa,  Bhita, Kausámbi , and  DevīPrayāga. Many  thanks, too, to  Dr. Simon Brodbeck ,  Prof. James Hegarty ,  Prof. Adam Hardy , and  Dr. Dániel Balogh  for their valuable suggestions. Lastly, thanks to the editors of this volume,  Prof. Peter Bisschop  and  Dr. Elizabeth Cecil , for organizing such an inspiring conference in the form of Asia Beyond Boundaries:  Transdisciplinary Perspectives on Primary Sources from the Premodern World , held in Leiden in August 2018. Acknowledgments: Field research summarized in this paper was supported by the  National Science Foundation  (NSF # 0242327 ), the  NASA   Space Archaeology Program  (Grant  NNX08A26G ), and a  Center for Khmer Studies grant  from the  Rockefeller Foundation.
The ATLAS experiment at the Large Hadron Collider employs a two-level trigger system to record data at an average rate of 1 kHz from physics collisions, starting from an initial bunch crossing rate of 40 MHz. During the LHC Run 2 (2015-2018), the ATLAS trigger system operated successfully with excellent performance and flexibility by adapting to the various run conditions encountered and has been vital for the ATLAS Run-2 physics programme. For proton-proton running, approximately 1500 individual event selections were included in a trigger menu which specified the physics signatures and selection algorithms used for the data-taking, and the allocated event rate and bandwidth. The trigger menu must reflect the physics goals for a given data collection period, taking into account the instantaneous luminosity of the LHC and limitations from the ATLAS detector readout, online processing farm, and offline storage.
Introduction The Trigger and Data Acquisition (TDAQ) system  [1]  is an essential component of the ATLAS experiment  [2]  at the Large Hadron Collider (LHC)  [3]  as it is responsible for deciding in real time whether to record data from a given collision. Its successful operation has a crucial impact on the dataset used in physics analyses. The TDAQ system operated efficiently during the LHC  Run 1 (2009 Run 1 ( -2013) )   [4]  at instantaneous luminosities up to 8 × 10 33 cm -2 s -1 and primarily at centre-of-mass energies of 7 TeV and 8 TeV, collecting more than three billion proton-proton ( ) collision events. In preparation for Run 2 (2015-2018), the TDAQ system underwent substantial upgrades and modifications to cope with the challenging conditions expected for data-taking during those years  [4] [5] [6].
In Run 2, the higher instantaneous luminosities, which surpassed the original design luminosity of 10 34 cm -2 s -1 , as well as the larger number of interactions per bunch crossing, and an increased centre-of-mass energy of  13  TeV led to a significant increase in the event rate from interesting physics processes. In addition to   collisions, the LHC provided heavy-ion collisions at the end of each data-taking year. In 2015 and 2018, Pb+Pb collisions were recorded at a centre-of-mass energy per nucleon of 5. 02 TeV, in 2016 two datasets with +Pb collisions were recorded at 5. 02 TeV and 8. 16  TeV, and in 2017 Xe+Xe collisions were recorded at 5. 44  TeV. The operation of the trigger system under these challenging   data-taking conditions is described in this document. This paper is organised as follows.
The ATLAS detector and TDAQ system is described in Sections 2 and 3. This is followed by a brief description of the LHC fill cycle, the fill patterns used in Run 2 and a description of a typical ATLAS run in Section 4. Section 5 introduces the 'shifter and expert' model of the ATLAS trigger operations team. The following two sections, Section 6 and Section 7, discuss the trigger selections for the nominal   data-taking together with their evolution during Run 2 and provide a few examples of special runs, respectively. Section 8 presents changes of configuration and conditions that can be updated in real time during data-taking, while Section 9 discusses the trigger configuration, database design and prescale mechanism. The validation and sign-off cycle of the trigger software used during data-taking is described in Section 10.
ATLAS trigger and data acquisition system The ATLAS TDAQ system is responsible for online processing, selecting and storing events of interest for offline analysis, and is shown diagrammatically in Figure  1. Events are selected using a two-stage trigger system  [5]. The Level-1 (L1) trigger is a hardware-based system that uses custom electronics to trigger on reducedgranularity information from the calorimeter and muon detectors  [9]. The L1 calorimeter (L1Calo) trigger takes signals from the calorimeter detectors as input  [10]. The analogue detector signals are digitised and calibrated by the preprocessor, and sent in parallel to the Cluster Processor (CP) and Jet/Energy-sum Processor (JEP). The CP system identifies electron, photon, and -lepton candidates above a programmable threshold, and the JEP system identifies jet candidates and produces global sums of total and missing transverse energy.
g. the total energy in the calorimeter), the multiplicity of objects above thresholds (e. g. the transverse momentum of a muon, etc. ), or by considering topological requirements (such as invariant masses or angular distances). The topological requirements are applied in the L1Topo trigger to geometric or kinematic combinations between trigger objects received from the L1Calo or L1Muon systems. The L1 trigger accepts events at a rate up to the maximum detector read-out rate of 100 kHz, down from the bunch crossing rate of about 40 MHz, within a latency of 2. 5 s. For each L1-accepted event, the Front-End (FE) detector electronics read out the event data for all detectors. The data are sent first to ReadOut Drivers (RODs), performing the initial processing and formatting, and then to the ReadOut System (ROS) to buffer the data.
The PUs are designed to make decisions within a few hundred milliseconds. A step in such a sequence of algorithms will typically execute one or multiple feature-extraction algorithms requesting event-data fragments from within an RoI and terminate on a hypothesis algorithm which uses the reconstructed features to decide whether the trigger condition is satisfied or not. In some cases, information from the full detector is requested in order to reconstruct physics objects (e. g. for the reconstruction of the missing transverse momentum  [18] ). The HLT software is largely based on the offline software Athena  [19] , which itself is based on Gaudi  [20] , a framework for data processing for HEP experiments. Gaudi/Athena is a component-based framework where each component (e. g. algorithm, service, tool) is configured by a set of properties that can be defined during the configuration stage of the application.
While the configuration of some bunch groups is given by the LHC (e. g. the colliding BCIDs) through the fill pattern, others can be defined to contain any desired list of BCIDs for specific data-taking requests (e. g. in van der Meer scans  [33]  or single-beam background studies). The 16 bunch group configurations are together called a bunch group set, which is different for each different LHC filling scheme. The bunch group sets are generated for each filling scheme in advance of running, using information about the positions of bunches with protons in each beam provided by the LHC. The generated bunch group set is then checked against the measured beam positions to ensure that it matches. The CTP pairs each L1 trigger item with a specific bunch group defined in the set. Those L1 trigger items Figure  4 : The software and hardware components of the ATLAS detector follow the transitions of a finite-state machine used to synchronise the configuration of all applications and detectors within ATLAS.
The ATLAS run structure An ATLAS run is a period of data acquisition with stable detector configuration and in the case of physics data-taking usually coincides with an LHC fill, which can last many hours. Another example is a cosmic-ray data-taking run, which takes place when there is no beam in the LHC and the ATLAS detector is used to detect cosmic rays to study detector performance  [36]. The DAQ system assigns a unique number to every run at its beginning. An identifier is assigned to each event and is unique within the run (starting at 0 for each run). A run is divided into Luminosity Blocks (LB), with a length of the order of one minute and identified by an integer unique within a given run. A LB defines an interval of constant luminosity and stable detector conditions (including the trigger system and its configuration).
To define a data sample for physics, quality criteria are applied to select LBs where conditions are acceptable. The instantaneous luminosity in a given LB is multiplied by the LB duration to obtain the integrated luminosity delivered in that LB. The length of the LB can be changed during the run and a new LB can be started at any time (following a 10 second minimum delay). From a data quality point of view, the LB represents the smallest quantity of data that can be declared good or bad for physics analysis. To start a run, the software and hardware components of the ATLAS detector have to follow the transitions of a finite-state machine  [37]. The transitions performed by the applications are shown in Figure  4. During 'boot', all applications are being started. In the 'configure' and 'connect' transitions, the hardware and applications are configured and connections between different applications are established where necessary.
• Online: responsible for the proper operation of the ATLAS trigger and primary support for the ACR trigger shifter. • Trigger menu: responsible for the preparation of the trigger configuration of active triggers and their prescale factors (see Section 6). • Online release: collection and review of software changes and monitoring the state of the software release for online usage via validation tests that run every night; deployment of the online software release on the machines used during data-taking. • Reprocessing: in charge of validating the online software release (see Section 10) by running the simulation of the L1 hardware and the HLT software on a dedicated dataset to spot errors by running on large samples. • Data quality and debug stream: responsible for the data quality assessment of recorded data; investigate and recover the events in the debug stream (see  Section 11).
• Signature-specific: monitor the performance of triggers for signatures, assist in data quality assessment and reprocessing sign-off; several trigger signatures are grouped together (muon and -physics and Light States; jet, missing transverse momentum and calorimeter energy clusters; -lepton, electron and photon; -jet signature and tracks). • Level-1: each L1 trigger system (L1Calo, L1Muon barrel, L1Muon endcap, and CTP) has an on-call expert who helps to ensure smooth operation of the L1 trigger and monitors the data quality for their respective system. In additon to data-taking, the trigger operation group participates in special runs of a technical nature together with the ATLAS DAQ team to develop and test the online software and tools to be used for data-taking. It also provides support for other ATLAS systems during detector commissioning runs and for special tests during LHC downtime periods.