dataesr/scientific-paragraphs-categorization huggingface dataset:
The abbreviations used are: iLBP, intracellular lipid-binding protein; LCFA, long-chain fatty acid; FABP, fatty acid-binding protein; CRABP, cellular retinoic acid-binding protein; RMSD, root mean square deviation; PDB, Protein Data Bank; RA, retinoic acid; RAR, retinoic acid receptor; ANOVA, analysis of variance; PPAR, peroxisome proliferator-activated receptor; ITC, isothermal titration microcalorimetry; NLS, nuclear localization signal; OA, oleic acid; HSQC, heteronuclear single quantum coherence; CSP, chemical shift perturbation.
Conifers possess very large genomes sizes and abundant pseudogenes, which hinder genome assembly and analysis of large gene families, but transcriptome sequencing provides an effective alternative to rapidly acquire large datasets of expressed genes  [35] [36] [37]. The overall goal of this study was to investigate the makeup of NLRs in conifers with the aim of improving our understanding of the RNL subfamily in plants. We introduced 3816 expressed NLR sequences in seven conifers in the Pinaceae and the Cupressaceae families. Our specific objectives were to: (1) identify the repertoire of expressed NLR sequences through the analysis of seven conifer transcriptomes; (2) classify the poorly known conifer NLRs based on amino acid domains and motif analyses; (3) investigate the diversity and the drought responsiveness of conifer RNLs, testing whether or not conifers form a single group external to the Angiosperm groups as recently proposed  27.
Results nLRs are abundant and diverse in the Pinales. We developed a dataset of 3816 expressed NLR sequences (Supplementary Data S1) by analysing the transcriptomes of seven conifer species in five genera of the Pinaceae and Cupressaceae. The sequences were identified using the Pfam database (31. 0) in the predicted amino acid sequence datasets obtained from the assembled mRNA sequence. The expressed sequences contained at least one of the canonical NB-ARC, RPW8, or TIR domains while CC-only and LRR-only were excluded as they may be mispredicted or not specific to NLRs. We filtered the full dataset to eliminate potentially redundant NLR peptides of a same encoding-gene. The number of expressed NLR genes thus identified ranged from 338 in Abies balsamea to 725 in Picea mariana (Table  1 ), in line with the total of 679 NLR genes identified in the genome of P.
† The distribution is calculated using the total sequence number of each subfamilies in proportion of the total of assigned sequences (CNL, CNL2, RNL and TNL). We scanned the conifer sequences for conserved C-terminal signatures downstream of the LRR domain but none were clearly identified, in contrast to Dicot TNLs where post-LRR (PL) domains are ubiquitous. None of the known Dicot PL motifs were detected in the entire dataset of predicted peptides by using blastp or FIMO analysis. We also searched in datasets from ancient plants among Embryophytes (P. patens, Sphagnum fallax), Ginkgo biloba, another conifer (P. abies), and an early-diverged Angiosperm (Amborella trichopoda), and only found complete PL motifs in A. trichopoda and a few partial ones in G. biloba. These observations suggest a partial acquisition early in seed plant evolution, and a potential adaptive expansion in early flowering plants (Supplementary Fig.
S3 ). The tree topology obtained with the representative sequences by using an identity-based clustering (cut-off of 60%) did not differ from the groups delimited with the full set of sequences (Supplementary Fig. S4 ). We carried out a motif analysis of the RPW8 domain in the four groups separately and found increasing Next, we examined the conifer diversity of RNLs in view of the RNL land plant sequences. We retrieved the RPW8 domain from other plants (counted sequences in Fig. 2 ), together with Picea abies peptide sequences obtained from an independent dataset  39  , and applied an identity-based clustering (cut-off of 60%) on all of the sequences to examine the extent of RPW8 domain diversity. We thus obtained a final set of 197 RPW8 sequences to build an expanded unrooted tree. The RNL sequences generally clustered according to their taxonomic clades within each of the groups (Fig.
glauca by Stival Sena et al. 40. The study monitored plants of three unrelated genotypes that were well-watered (controls) or not watered at all (drought treatment) over a 22-day period, with sampling time points at 0, 7, 14, 18, 22 days. A principal component analysis of the entire dataset of foliage transcript levels showed that the drought treatment was the main factor that explained 53% of the observed variance (Supplementary Fig. S5 ) and supported samples groupings by treatment and genotype. The data showed major effects of the drought treatment after 18 and 22 days, independently of the clone used, in addition to smaller and more variable effects between clones at 14 days (Supplementary Fig. S5 ). We identified 119 differentially expressed (DE) NLR genes (adjusted p-value < 0. 05) in drought-treated plants compared to well-watered controls across the NLR repertoire, independently of the genotype.
Motif usage in conifers and implications in NLR evolution. Extensive studies on NB-ARC domains have highlighted their motif flexibility involving the reorganisation of pre-existing building blocks that can be observed at a domain or motif level  16. In our dataset, we observed conserved motifs (P-loop, Kinase 2/3, and GLPL) involved in crucial ADP/ATP binding and shifting  42. These motifs are conserved in all NLR subfamilies and in NB-ARC of prokaryotes, which do not display upstream N-terminal domains  16. Thus, these motifs are not influenced by the N-terminal domain identity. The over-representation of RNL sequences in several conifers allowed us to define reliable NB-ARC RNL signatures. We showed that RNL proteins strictly required a glutamine, first residue of the MHD motif in Fig. 1  (Q1) in place of the methionine (M1) (i.
The transcriptome assemblies were performed with Gydle software NUCLEAR version 3. 2. 16, RESOLVE version 2. 6. 12 and VISION version 2. 6. 12, and guided using the white spruce gene catalogue obtained by the sequencing of full-length insert cDNAs (FLICs) from white spruce cDNA libraries as described in  59. The detailed protocol of assembly is available in Supplementary Methods. The accessions numbers for all of the transcriptome assemblies and the sequence reads are mentioned in the additional data and a detailed list is available in Table  2  of the Supplementary Methods.
Functional annotation and motif discovery. For each of the seven conifer species, we applied a first filter by analysing the entire predicted protein dataset using the Pfam database with a cut-off value of 1. 0. Sequences displaying the Pfam entries NB-ARC (PF00931), TIR (PF01582), RPW8 (PF05659) and LRR 1 to 6, 8, 9, and LRRNT (PF00560, PF07723, PF07725, PF12799, PF13306, PF13516, PF13855, PF14580 and PF01462, respectively) were extracted. Then, we characterised the domain organisation using HMMER web server version 2. 19. 0 with the selection of Pfam, TIGRFAM and superfamily HMM databases with significance e-values set to 0. 01 (model) and 0. 03 (Hit)  60. Atypical NLR sequences were also verified with InterProScan by using the entire database available. Motifs analysis and scanning were carried out using the MEME suite  61  with maximum number of motifs set to 10, minimum-maximum motif width set to 5-30 and 5-50 and minimum sites per motif equal to 2.
Additional data sampling and extraction data. The full list of additional plant protein datasets and the versions that were screened is available in Supplementary Methods. RNL sequences were extracted using BlastP searches with (i) RPW8 domains of ADR1 (BAF00531. 1), NRG1 (AAY54606. 1), RPW8. 1 (ACJ05907. 1), RPW8. 2 (ACJ72031. 1) and a representative of each of the four conifer RNL clades detected in this study and (ii) NB-ARC RNL laying between the RNBS-D and the QHD motifs as queries with broad e-value < 1. When TNL numbers were not available in the literature, we produced our own estimates by using BlastP (e-values < 10 -8 ) searches with N (Q40392. 1), RPS4 (CAB50708. 1) and Ma (CAR94514. 1) sequences deprived of LRR, as queries. BlastP (e-values < 30) with PL motifs  22  were used to retrieve PL domains.
Sequence alignment, clustering, and phylogenetic reconstruction. Sequence alignments were performed with Muscle 62 using default parameters and trimmed using MEGA7  63. Clustering analyses were conducted with CD-HIT 64 using default parameters and variable sequence identity cut-off values: 0. 97 for elimination of redundant sequences (allelic sequences or alternative transcripts) leading the total number of sequences to be reduced to 3734, and 0. 50-0. 90 for the conifer RPW8 domains. To identify RNL groups in conifers, 185 full-length RPW8 sequences (>100 amino acids) were extracted and trimmed from our dataset. These RPW8 domains together with RPW8 domains of ADR1, RPW8. 1, RPW8. 2 and NRG1 were aligned to undertake the first phylogenetic analyses of conifer sequences. Then, the conifer sequences were clustered using a cut-off value of 0.
60 to obtain 45 representative RPW8 sequences. Simultaneously, a second dataset was prepared using 351 Angiosperm, 13 G. biloba and 17 P. abies RNL sequences. These sequences were used to similarly extract full RPW8 domains (>100 amino acids) and clustered (cut-off values: 0. 60) to obtain 152 representative RPW8 sequences for this second dataset. The 197 representative sequences were included into the phylogenetic analysis conducted using the web service http://www. phylogeny. fr  65. The phylogenetic trees were reconstructed using the maximum likelihood (ML) method implemented in the PhyML program (v3. 1/3. 0 aLRT). The WAG substitution model was selected with an estimated proportion of invariant sites and 4 gamma-distributed rate categories to account for rate heterogeneity across sites.
The gamma shape parameter was estimated directly from the data (gamma = 3. 981 and gamma = 4. 611). Reliability for internal branch was assessed using the aLRT test (SH-Like). The phylogenetic trees were then drawn using Dendroscope 3  66. Similarly, the JTT substitution model was used (with Gamma shape parameter: 3. 648) resulting in the same tree topology (Supplementary Fig. S8 ). transcriptomic analysis of drought stress in three P. glauca clones. Foliage samples from the drought experiment in P. glauca described in Stival Sena et al. 40  were analysed by RNA-sequencing. The samples were collected at 0, 7, 14, 18 and 22 days from the beginning of the watering treatments. We used three unrelated and clonally-propagated genotypes (clones 8, 11 and 95) each with two replicates, in two conditions (well-watered control and drought with no watering) with five different sampling time points, and obtained 59 RNA samples to analyse (lacking one control replicate for the clone 95 at the time 7 days).
20. 0 package and the LRT approach. A lists of differentially expressed NLR genes with a FDR adjusted p-value < 0. 05 are shown (Supplementary Table  S4 ). Cluster v2. 0. 7. , factoextra v1. 0. 5, and pheatmap v1. 0. 10 packages were used to generate heatmap with gene clustering analyses using manhattan distances and the ward. D2 clustering method  [67] [68] [69] [70] [71] [72] [73] [74] [75] [76]. Accession numbers. The raw data from the seven conifer transcriptomes are deposited in the European Nucleotide Archive (ENA) as part of the study SRP134160, bioproject PRJNA437248 (SRA accession SRR6816977 to SRR6816983). A list of accession numbers is available in supplementary methods.
Acknowledgements We thank  F. Larochelle  and  M. -A Paré  for assistance with plant procurement and growth,  S. Laviolette  and  S. Senneville  for project management, and  K. Guay ,  K. Beaupré-Boivin ,  D. Miclette  and  A. Azaiez  for sample collections and processing. This study was developed in part as a collaboration with the  ProCoGen  project (https://bfw. ac. at/rz/bfwcms2. web?dok=9020) (funded by the  European Union 's  Seventh Framework Programme ). This work was supported by a research grant from the  Québec Ministry for the Economy, Science and Innovation, program  for the support of international initiatives to AD, JB and PR. CVG was supported by the Plant -Nematode Interaction team, at  ISA-INRA in Sophia Antipolis, France. JM is supported by M. and  A. Wood  and by the  P.
The surface morphology of silica nanocapsules was observed with a scanning electron microscope (SEM, JSM-7610F, JEOL). Samples dispersed at an appropriate concentration were cast onto a silicon wafer at room temperature. The morphology of silica nanocapsules was observed by transmission electron microscopy (TEM, JEM-ARM 200F, JEOL) at an acceleration voltage of 200 kV. Samples dispersed at an appropriate concentration were cast onto a carbon-coated copper grid. The average size of silica nanocapsules was determined by counting 100 particles with the SemAfore software 5. 21 (JEOL). For UV spectroscopy investigations, stock solutions of Fe 3+ were prepared using FeCl 3 in acetonitrile:water (1:1, v:v) or water. Solutions of rhodamine derivative and silica nanocapsules with encapsulated dye for spectroscopic studies were also prepared in acetonitrile:water (1:1, v:v) or water.
■ ACKNOWLEDGMENTS We are grateful for discussions with  Elisa Campazzi ,  Professor Sebastien Touzain , and  Professor Juan Creus. This work was partially supported by the  Thailand Research Fund  (Grant  RSA6180063 ), the  Office of Higher Education Commission of Thailand (OHEC) , the  DFG priority program SPP1568  " Design and Generic Principles of Self-Healing Materials ", and the  Vidyasirimedhi Institute of Science and Technology (VISTEC). R. J. appreciates the Postdoctoral Fellowship of the  Vidyasirimedhi Institute of Science and Technology. Instrumental support from the  Frontier Research Center (FRC)  at  VISTEC  is also acknowledged.
Method A team composed of social psychologists and historians (as part of a large trans-European COST networkiii) jointly designed a survey in order to address our main Hypothesis. The survey (an online questionnaire) was conducted among university students in social sciences (mainly psychology). The characteristics of the sample are similar to the ones used in most studies on the topic (see e. g. ,  Bobowik et al. , 2014; Liu et al. , 2009)  Participants were presented the questionnaire in their language of education. Items were translated from English by the researcher(s) in charge of data collection in each specific country. The translated versions were then checked by the first author and native speakers. Completing the questionnaire took on average 30 minutes. Only those parts of the survey that are relevant to the current analysis are presented here (the full questionnaire is available here: https://osf.
Limitations A first limitation relates to the small size of some samples and to the overrepresentation of women among most of them. This issue is the result of the pragmatic nature of cross-countries studies where a large amount of data must be collected in a short period of time (see e. g. ,  Liu et al. , 2009). Our goal was to collect data from as many major actors from the conflict as possible in order to provide a panorama of the representations in most European countries. Our desire to report data from many countries prevailed over the diversity and size of some samples. It is thus important for the reader to be aware of this limitation when interpreting the results. A second limitation is based on the type of measurement used to appraise the SR of the war. The presentation of a limited number of items to be assessed regarding perceived importance as reasons for war does not allow the emergence of representations of the conflict to which the authors had not paid attention.
This paper explicitly describes the requirements for deploying data analytics in a 5G system and subsequently presents the current status of standardization activities. The main contribution of this paper is the investigation and design of an integrated data analytics framework as a key enabling technology for the service-based architectures (SBAs). This framework introduces new functional entities for application-level, data network, and access-related analytics to be integrated into the already existing analytics functionalities and examines their interactions in a service-oriented manner. Finally, to demonstrate predictive radio resource management, we showcase a particular implementation for application and radio access network analytics, based on a novel database for collecting and analyzing radio measurements.
This article aims to investigate how data analytics can be used to enhance the 5GS performance and its impact on the architecture. In this context, we initially provide an overview of the 5G architecture with particular focus on data analytics, and explicitly describe the requirements for data analytics in 5G, assuming different optimization objectives set by the network operator or vertical customers. Given the requirements for network and service optimization, which can have multiple flavors subject to the service/slice requirements and the network capabilities, we further propose an integrated data analytics framework. This framework uses as basis the current 5G architecture that supports network data analytics functionality (NWDAF) and management data analytics functionality (MDAF). On top of that, to employ analytics for real-time operations and allow vertical customers to have a tight control on some network operations, we propose additional functionalities for RAN-centric, data network-centric, and application-level analytics.
For these functionalities, their integration in current architecture is examined, and subsequently the required extensions in standardized functions and interfaces are investigated. Finally, a case study that requires data analytics at RAN to perform real-time resource management is evaluated, using application-level analytics on the user mobility, based on real measurements collected in a database residing at RAN. This trend of per User Equipment (UE) radio conditions given the expected routes are analyzed and output to the scheduler at the base station, in order to can dynamically adjust the Quality of Service (QoS) attributes or pro-actively adapt the resource allocation for a given time window. The use of analytics for the resource optimization can have significant impact in V2X safety-related and low-latency services, where the complexity and signaling costs strongly affect the performance in dense RAN deployments.
Other examples include Network Slice Selection Function (NSSF) and Policy Control Function (PCF). The PCF may use per slice data analytics in its policy decisions. NSSF may use the load level analytic information for slice selection. Therefore, a new data analytics module is required to be deployed in 5G Core, which should be capable of providing per slice data analytics to multiple domains (e. g. , other NFs, AFs, and operation, administration, maintenance -OAM) based on the ondemand slice-tailored data analytics requirements. Another key function envisioned within SBA is the NWDAF, enabling NFs to access to the operator-driven analytics for different purposes, including intelligent slice selection and control. More information on the evolution of network analytics in 3GPP will be covered as part of Section IV.
In this context, given the deployment and the functional requirements (real-time, non-real time, nearreal-time) as well as the slice isolation policies, the above functional blocks can be either centralized at the CU (or above CU to a dedicated RAN controller) or distributed in DUs. Data analytics could greatly benefit from such virtualized deployments, since the virtualization of control functions may impose signaling and complexity constraints especially for dynamic radio resource management mechanisms. The deployment of databases in central RAN nodes which run algorithms for predicting radio related context may support RRM functions so as to avoid relying on real-time measurements, while exploiting the benefits of centralized radio resource control and management.
B. 5G CN ARCHITECTURE The key technological components of the 5G CN Architecture rely upon principles of architecture modularisation, CP and UP separation, and SBI. These are reflected in the SBA as mentioned earlier (specified since 3GPP Release 15) where the CP NFs are interconnected via a common SBI as shown in Figure  5. Compared to the traditional functional based network architecture design, SBA is expected to have the advantage of short role out time for new network features, extensibility, modularity, reusability, and openness. As outlined earlier, NWDAF is one key function within SBA, facilitating access to network data analytics. In Release 16, 3GPP SA2 started a new study Item, FS_eNA  [22] , to study enablers for Network Automation for 5G to further clarify the usage of data analytics capability in the network layer.
FS_eNA envisions improving NWDAF scope via introducing use cases and solutions for supporting network automation deployment and the related framework (as shown in  [11] ) to collect/provide data analytics in relation to different NFs, AFs, and Management Functions (MFs), i. e. , OAM. NWDAF reuses similar service exposure mechanisms as other 5G NFs (as described in 3GPP Release 15) for data collection and data analytics exposure from / to other NFs. There are also ongoing proposals to define a new service for unified data collection/analytics exposure from/to NFs/AFs. The studied use cases in eNA study item include: NWDAassisted QoS provision/traffic handling/customized mobility management/policy determination/QoS adjustment/5G edge computing/load (re-)balancing of NFs/determination of areas with oscillation of network conditions/slice SLA assurance/predictable network performance.
NWDAF is also used for information retrieving from the application function, performance improvement and supervision of massive IoT terminals, prevention of various security attacks and UE driven analytics sharing. In addition, a new use case on ''UE-driven analytics sharing'' has been proposed in this study item for enhancement of NWDAF and/or other NFs. In this use case, UEs are natural data collection points to gather more localized analytics within the network. Examples of data that a UE can provide are positioning information (e. g. , collected from inertial or other sensors of a UE) or user profiling info (e. g. , when a UE changes environment from outdoor to indoor or from vehicular to pedestrian mode). Such information may help the NWDAF to make more intelligent decisions on slice selection (e.
g. , to switch from a slice with more flexible resources to a resilient one or vice versa). As UEs can simultaneously connect to or switch across different slices (e. g. in case of mobility), they can have more prominent role for data preparation for the network to provide relevant localized contextual information and to identify earlier any changes in the network compared to the past intra-slice and/or inter-slice. Some key issues proposed and currently investigated for this use case include ''How the NWDAF collects the UE's information'' or ''How the NWDAF uses the data provided by the UE to do analytics and provides the analytics information to other NFs''. One key consideration for allowing the inter-domain interaction of data analytics in 5GS is discussed in 3GPP SA5. For the interaction of NWDAF with OAM and RAN, the data collection from OAM may reuse the existing SA5 services.