dataesr/scientific-paragraphs-categorization huggingface dataset:
This may be done through methods specified in  [28]. In the case of the NWDAF, the so-called NF Service Authorization feature allows a consumer function to request a service, e. g. , receiving the requested data from the NWDAF service. The details of the authorization procedure based on NF profile are covered in  [11]  and  [28]. Moreover, due to roaming agreements and operator policies, a NF Service Consumer shall be authorized based on UE/subscriber/roaming information and NF type. While these procedures provide the fundamental means to securely provide services within and across domains, 3GPP largely restricts service consumption to CN CP NFs. The solution proposed here reuses and extends 3GPP mechanisms to allow inter-domain exposure and consumption, taking the use case of inter-domain data analytics services (DASs).
B. USE OF DATA ANALYTICS FOR RAN OPTIMIZATION FOR V2X SERVICES The expectation of SINR and the calculation of the expected KPI (e. g. link expected capacity) as provided by the database which may reside in a CU, taking into account the expected UE routes, may be beneficial for allowing RAN to optimize the resource allocation in a multi-cell environment. Here, two possible pro-active actions can be performed by RAN to ensure meeting the requirements, targeting vehicular scenarios which include safety related applications and due to high mobility the real-time resource optimization may be challenging: -Dynamic adjustment of 5G QoS indicator (5QI) attributes for UEs with good or bad channel conditions, to avoid service discontinuity due to poor coverage or cell-edge channel quality. -Pro-active adjustment of Resource Block (RB) selection using multi-cell joint scheduling to avoid interference, while not requiring real-time measurements from UEs at CU.
The sample was then centrifuged, and the resulting supernatant was precipitated with trichloroacetic acid (10% w/vol final). After centrifugation, the pellet was dissolved into 50 µL of LDS1X (Invitrogen), subjected to sonication in an ultrasonic bath for 5 min, and then heated for 10 min at 99°C. Hyphae and spore proteins were subjected to a short SDS-PAGE migration. The whole proteome from a single polyacrylamide band was treated and proteolyzed by trypsin, as previously described  [31]. Tryptic peptides were analysed with a Q-Exactive HF high resolution tandem mass spectrometer (ThermoFisher Scientific, Waltham, Ma) incorporating an ultra-high-field orbitrap analyser as previously described  [32]. MS/MS spectra were assigned with a protein sequence database derived from Frankia alni ACN14a strain genome with standard parameters.
Analyses of differentially expressed proteins: Homology-based functional analysis, Clusters of Orthologous Groups of proteins (COGs) prediction and genome mapping Proteins were quantified based on their spectral counts. Proteome comparison between hyphae and spores was done taking into account the six replicates with the TFold module from the PatternLab software, as previously described  [31]. Differentially detected proteins between spores and hyphae were categorized into 4 classes: i) Blue class proteins for which identifications satisfied both, the fold change (i. e. ratio of protein quantities) between spores and hyphae (≥ 1. 5) and Tfold statistical criteria (t-test, p-value ≤ 0. 05); ii) Orange class proteins for which identifications did not meet the fold criterion but have low p-values; iii) Green class proteins for which identifications satisfied the fold change criterion but not the statistical criterion; iv) and finally Red class for which identifications did not meet the fold and p-value criteria.
The sequences of the 1,385 proteins of F. alni belonging to the blue class (for which identifications satisfied both the fold >1. 5 and p-value < 0. 05) were uploaded in the STRING database with F. alni chosen as a query microorganism, in order to access sequence identity. These sequences were also assigned to COGs using MicroScope (Microbial Genome Annotation & Analysis) Platform from Genoscope (http://www. genoscope. cns. fr/agc/microscope/home/index. php). A circular genome map of the genome sequence F. alni was drawn using GenVision software from DNAStar (DNASTAR, Madison, WI).
Construction of actinobacterial predicted proteome databases and identification of candidate genes involved in Frankia sporulation by comparative proteome analysis Three databases were constructed from actinobacterial predicted proteomes downloaded from NCBI website (http://www. ncbi. nih. gov). A first database, named "14_Frankia. db", included the protein sequences of 14 Frankia strains described as sporulating in pure culture (Table  1 ), among which only four had complete genomes (Frankia alni ACN14a, F. casuarinae CcI3, Frankia EAN1pec and F. inefficax EuI1c). These four proteomes were used to construct a second database referred as "actino_sporange. db" together with six other sporulating actinobacteria out of the Frankia genus, chosen for their ability to sporulate by producing sporangia: four Actinoplanes strains, one Geodermatophilus strain and one Streptosporangium strain (Table  1 ).
The third database ("actino_non_spor. db") grouped the predicted proteomes of seven Actinobacteria unable to differentiate spores, related to Bifidobacterium, Rhodococcus and Micrococcus genera (Table  1 ). All-against-all blasts of protein sequences were performed for each database "14_Frankia. db" and "actino_sporange. db" using the BLASTP software (version 2. 2. 26, with default parameters,  [33] ). Protein families gathering homologous sequences were assembled using the SiLiX software (version 1. 2. 9)  [34] , based on 45% identity over 80% of the length of the shortest sequence. Several identity thresholds ranging from 35 to 95% were tested and 45% was found to be the best compromise between false positives and false negatives within protein families (supplementary data 1). Based on the assumption that sporangia formation relied on similar (homologous) mechanisms in sporangia-forming Actinobacteria, we searched for protein families largely distributed in Frankia or sporangia-producing strains and absent in non-sporulating Actinobacteria.
To do so, we selected for each database homologous protein families represented in at least 80% of genomes included in the respective database. The longest protein sequence of each protein family was then used to query the "actino_non_spor. db" database using BLASTP (45% identity, e-value < 10 -4 ), in order to select groups without homologous protein sequences encoded in the genomes of nonsporulating Actinobacteria.
Search for secondary metabolite gene clusters in Frankia genome A total of 27 gene clusters encoding enzymes involved in production of secondary metabolites have been identified in Frankia ACN14a genome (accession number NC_008278) using AntiSMASH  [35]  running on the MicroScope Platform  [36] , including a total of 1,000 genes. These clusters were searched for among (i) protein sequences differentially detected between Frankia ACN14a spores and hyphae proteomes and (ii) families of interest selected from the comparative genomic analyses (using AntiSMASH).
Differentially expressed proteins between Frankia alni ACN14a spores and hyphae High-throughput shotgun proteomic analyses were performed to compare protein contents between Frankia alni ACN14a spores and hyphae. Proteins were quantified based on their spectral counts in 3 biological replicates of spores and 3 biological replicates of hyphae. A total of 2,193 and 2,070 proteins were detected in hyphae and spores, respectively, when compiling the replicates. When merging both spores and hyphae datasets, a total of 2,336 proteins was detected (1,927 proteins were detected in both conditions). Using a Fold Change (FC) threshold of 1. 5 and a p-value below 0. 05, 1,385 (59%) differentially detected proteins were identified (Blue class), including 432 proteins more abundant in spores compared to hyphae and 953 up-regulated in hyphae (supplementary data 2).
Selection of genes potentially involved in the Frankia sporulation by comparative proteome analysis This approach was based on the hypotheses that proteins involved in Frankia sporangia differentiation would be part of a core proteome shared between sporulating Frankia strains and other phylogenetically close Actinobacteria able to differentiate sporangia, and that they would be encoded by homologous genes (hypothesis 1), and would not have homologues in Actinobacteria unable to sporulate (hypothesis 2). Under these hypotheses, two different databases were constructed from the predicted proteomes of Frankia and other actinobacterial strains selected for their ability to differentiate sporangia (Table  1 ). The first database included only Frankia proteins (named "14_Frankia. db", 81,518 protein sequences).
This database could be queried to identify core protein families in Frankia. The second (named "actino_sporange. db", 72,406 protein sequences) could be used to identify proteins families shared between Frankia and sporangia-forming Actinobacteria. It gathered the four Frankia predicted proteomes annotated as complete and six other sporangia-forming actinobacterial predicted proteomes outside genus Frankia. For each database, homologous protein families were delineated using SILIX program: a total of 1,037 and 1,641 protein families were obtained for the "actino_sporange. db" and the "14_Frankia. db" databases, respectively. These 1,037 and 1,641 protein families reflected the core proteome shared between Frankia closely related sporangia-forming Actinobacteria proteomes, and between Frankia proteomes, respectively.
They were compared to a third proteomic database (named "actino_non_spor. db") gathering the protein sequences of seven Actinobacteria unable to sporulate (Table  1 ). Sequence similarity-based comparisons allowed identifying 39 and 221 protein families from "actino_sporange. db" and "14_Frankia. db" databases, respectively, without homologues in the "actino_non_spor. db" database and were considered as strong candidates potentially involved in Frankia sporulation (supplementary data 3). Among them, 25 and 139 (from "actino_sporange. db" and "14_Frankia. db" databases, respectively) presented functional annotations in ACN14a or CcI3 Frankia genomes (not "hypothetical proteins"). Fourteen protein families with functional annotations were found in common from both "actino_sporange. db" and "14_Frankia.
db" databases (Table  3 ). Two of them included sequences with functional annotations undoubtedly related to bacterial sporulation. The protein family #7 contained sequences annotated "Sporulation and cell division protein SsgA" (e. g. FRAAL2127, FRAAL4594), with SsgA-like proteins (SALPs) previously described to control spore differentiation in S. coelicolor  [15]. The protein family #8 included "Stage 0 sporulation protein YaaT" annotated proteins, YaaT protein being involved in the phosphorelay cascade allowing the activation of the DNA binding protein regulator Spo0A in B. subtilis  [37]. It is worth noting that these sequences were annotated "hypothetical protein" in all Frankia proteomes used in this study and could only be identified as YaaT protein sequences based on Actinoplanes and Streptosporangium protein annotations.
Conclusions The aim of the present study was to set up a comprehensive and consistent database of sporulation-associated determinants. It is important to keep in mind that the link between candidate genes and proteins discussed here and their effective role in Frankia sporulation has to be confirmed using functional studies, for instance through gene complementation and tracking their expression during sporulation through RT-qPCR, as functional shifts may have occurred during Actinobacteria diversification. Indeed, in Streptomyces, the spore production process is initiated by the production of aerial hyphae with a hardened tip by the action of a cellulose-like synthase  [44]  and the synthesis of a lipid-rich envelope comprising an aminated hopanoid to render the spores mildly hydrophobic and permit them to float  [62].
Electron Microscopy and EELS Study TEM samples were prepared in the cross-section geometry; they were mechanically thinned down using a tripod polisher and then ion-milled in a GATAN-PIPS apparatus. The EELS experiments and data analysis for a:C-H materials have been detailed elsewhere  [31]. Briefly, EELS spectra were acquired on a FEI Tecnai F30 operating at 300 kV equipped with a Gatan Tridiem 863 spectrometer and liquid nitrogen temperature. Convergence and collection angles were 6. 1 and 5. 9 mrad, respectively. Background subtraction for the C-K edge was performed by modeling the usual inverse power law function, and the multiple scattering was then removed by Fourier-ratio deconvolution. The π * peak was modeled by using a sum of Gaussian functions of equal standard deviation and separated by 0.
Multiwavelength Analysis of the Exposed HOPG Samples: Double Resonance Mechanism Evidenced Figure  4. PDOS spectral region (300-900 cm -1 ) for diamond, a-C:H post heated at 500 °C, a-C (taken from [24] and  [42] ) and deuterium implanted HOPG. (a) PDOS references (see text for details). Experimental data for (b) 514 nm and (c) 633 nm wavelength excitation. The sharp band at 520 cm -1 is due to the underlying silicon wafer. (d), (e) and (f) zoom of the low intensity modes for exposed HOPG probed with 633, 514 and 325 nm, respectively. Note that the 2D band (near 2700 cm -1 ) coming from the underlying unmodified HOPG can be seen, as the ion implantation depth is lower than the laser photon penetration depth. The spectral data in (d), (e), and (f) corresponding to the HOPG implanted with 400 eV deuterium ions have been rescaled for better visualization.
This laser excitation dependency was not expected as for all the other samples, the spectral shapes were the same for 514 and 633 nm. A wavelength dependency could suggest in-depth inhomogeneity, but this explanation has to be ruled out as we know that we probe the same (i. e. , entire) volume of exposed HOPG with all the wavelengths, as discussed in Section 3. 1. As we know the double resonance mechanism effect is at play with graphenic materials, the hypothesis of a double resonance mechanism for ion-exposed HOPG should be investigated as well. PDOS spectral region (300-900 cm -1 ) for diamond, a-C:H post heated at 500 • C, a-C (taken from [24] and  [42] ) and deuterium implanted HOPG. (a) PDOS references (see text for details). Experimental data for (b) 514 nm and (c) 633 nm wavelength excitation.
Funding: This research received no external funding. Acknowledgments: CP wants to acknowledge his son and wife for giving him energy to write this manuscript during heatwave time. The TEM and EELS measurements were performed at the  Laboratorio de Microscopias Avanzadas (LMA) ,  Instituto de Nanociencia de Aragon (INA) ,  Universidad de Zaragoza (Spain). R. A. gratefully acknowledges the support from the  Spanish Ministerio de Economia y Competitividad  ( MAT2016-79776-P ) and from the  European Union   H2020 program  " ESTEEM3" (823717 ). ".
4 Figure 4. 44 Figure 4. PDOS spectral region (300-900 cm -1 ) for diamond, a-C:H post heated at 500 • C, a-C (taken from [24] and [42] ) and deuterium implanted HOPG. (a) PDOS references (see text for details). Experimental data for (b) 514 nm and (c) 633 nm wavelength excitation. The sharp band at 520 cm -1 is due to the underlying silicon wafer. (d-f) zoom of the low intensity modes for exposed HOPG probed with 633, 514 and 325 nm, respectively. Note that the 2D band (near 2700 cm -1 ) coming from the underlying unmodified HOPG can be seen, as the ion implantation depth is lower than the laser photon penetration depth. The spectral data in (d-f) corresponding to the HOPG implanted with 400 eV deuterium ions have been rescaled for better visualization.
Slides were washed three times with PBS and 0. 1% Tween, rinsed carefully with water, dried, and measured by an Axon GenePix 4300A fluorescent scanner (Molecular Devices, Sunnyvale, CA). The values used for analysis correspond to the average of the mean fluorescent intensities after background subtraction of triplicates. Statistical Analysis. Statistical analysis was performed using GraphPad Prism (GraphPad Software Inc. , La Jolla, CA). Unpaired Student's t test was used to compare different sets of data, whereas two-way analysis of variance was used to compare anti-glycan antibody levels over time between immunized and non-immunized groups. The log-rank test was employed for analysis of survival between different groups. Statistical significance within figures is shown as follows: *p < 0.
, 2007). The accuracy of the IMS product compared to station data over the TP has been evaluated by  Yang et al. (2015)  and  Li et al. (2018) , who found the pixel matching accuracy (snow or no snow) to be over 90 %. Another widely used satellite optical product is the Moderate Resolution Imaging Spectroradiometer (MODIS) 8 d snow cover  (Basang et al. , 2017). Joint analysis of MODIS and station data by  Basang et al. (2017)  indicated that, despite large day-to-day snow cover fluctuations and the non-synchronicity of these two datasets, there is a high temporal correlation (0. 77) if the station data are preprocessed in 8 d bins in a similar fashion as the MODIS data, i. e. if the station data are considered snow covered in the 8 d period when snow covered for at least 1 d. Nevertheless,  Basang et al.
Station data We obtained 5 years (2009 to 2013) of in situ daily meteorological observations from 33 stations over the TP from CMA. The observations comprise minimum and maximum temperature, snow depth and total precipitation. The measuring time is 00:00 UTC (08:00 Beijing time). The operational procedures dictate that when more than 50 % of the ground surrounding the weather station is covered by snow, the snow depth (SD) is measured. When converting in situ  snow depth to a snow cover fraction (SCF), a 100 % SCF is assumed when snow depth exceeds 1 cm, according to the in situ observing rules of CMA  (CMA, 2003). The in situ observations also provide the snow cover days (SCDs), i. e. the number of days when the snow covers more than 50 % of the ground in the sight from the station. The geographical locations of the stations are shown on Fig.
These two estimates provide a range of values, reflecting the uncertainty inherent to aggregating the 4 km binary data, e. g. a value of 1 in a pixel means 50 % to 100 % snow coverage. The choice of IMS snow cover is motivated by its use in the ECMWF analysis system. A key point is that it is used differently in ERA-I  (Drusch et al. , 2004)  than in ERA5 (de  Rosnay et al. , 2014 Rosnay et al. , , 2015;; Hersbach et al. , 2018). First, the high-resolution 4 km product is used in ERA5, while the coarser-resolution 24 km product is used in ERA-I. More importantly, IMS data are not used above 1500 m in ERA5, i. e. in high-altitude regions such as the TP, while it was still in use in ERA-I. When comparing the snow cover in IMS with the one in ERA-I or ERA5, one has to recall that these are not independent datasets.
Microwave satellite data A long-term (1978-2010) MW daily snow depth product has been developed to account for the special conditions of the TP by  Che et al. (2008)  and  Dai et al. (2017)  at the Cold and Arid Regions Environmental and Engineering Research Institute (CAREERI) of the Chinese Academy of Sciences. We used the 2 years (2009 and 2010) that overlap with our comparison period  (2009) (2010) (2011) (2012) (2013) , the latter dictated by our station data record. Over these 2 years, the MW product is based on measurements from the NASA Advanced Microwave Scanning Radiometer for the Earth Observing System (AMSR-E). The gridded dataset has a horizontal resolution of 0. 25 •.
Snow depth Figure  2  presents a comparison of daily SD over the 5-year period  (2009) (2010) (2011) (2012) (2013)  between the observations, comprising both in situ station and MW data, and the four reanalyses collocated at the station coordinates. The comparison is an average over the 33 stations. The in situ observations, even in the mean over all available stations, reveal a very thin and rapidly fluctuating snowpack, with episodes of fast build-up and decay, followed by periods void of snow, even in mid-winter. This concurs with previous studies showing that large parts of the TP can remain snow-free even in winter  (Basang et al. , 2017; Li et al. , 2018). A quantitative estimate of the discrepancy between in situ daily observations and the other datasets as a root mean square error (RMSE) is presented in Fig.
4  (left panel). The RMSE is calculated for the 2 years 2009 and 2010 when satellite MW data are available. We also cal-culated the temporal correlation matrix between the different SD datasets (Table  2 ), using daily data year-round over the 5-year period, except for the satellite MW data where only the 2 years 2009 and 2010 are used. It is clearly apparent that, with the exception of MERRA-2, the reanalyses show a regular seasonal cycle, with a snowpack that grows nearly steadily during the cold season and culminates in February or March. This is unlike the in situ observations, which show rapidly fluctuating snow increases with little snow accumulation throughout winter. In comparison with in situ observations, MERRA-2 has the best performance among reanalyses for both the RMSE and the temporal correlation, closely followed by JRA-55.
It is not surprising that JRA-55 performs well, since it assimilates SDs from a dozen CMA stations over the TP area and snow cover from satellite MW data (see Appendix). Note that the assimilated MW data are not from the same instrument as the one used in the CAREERI MW data. A key factor in the relatively good performance of ERA-I is the fact that it assimilates SCF from IMS in high-altitude regions, hence also over the TP. On the contrary, as mentioned earlier, assimilation of IMS SCF above 1500 m was discontinued in the production of ERA5. Among the differences in the snow treatment between ERA-I and ERA5, the tendency to reduce or remove the snowpack imposed by the IMS observational constraint during assimilation appears to be the most likely key factor in bringing ERA-I SDs significantly closer to the in situ observations.
While the in situ station data (e. g. Fig. 3 ) also display thin layers of a few centimetres, they are very transient. Further validation is provided in terms of the annual-mean SCDs. Table  3  compares SCDs among the datasets, both for the 5-year average and for individual years, based on monthly-mean SCD values. The values of SCDs compare well between in situ observation and IMS. On the other hand, ERA5 and JRA-55 reanalyses largely overestimate SCDs (ERA5 by nearly 3 times), while ERA-I is closer to the observed values. The temporal correlation coefficients for monthly SCDs tend to be higher than that for the daily SCF or SD. Note the values of SCDs in MERRA-2 are small due to the SCF being below the 0. 5 threshold used in the SCD definition (see Fig. 3 ). Moving to the geographical distribution over the TP region, Fig.
Values of the annual means of monthly snow cover days averaged during the whole 5-year period (the second column) and during the years 2009 to 2013 (the third to seventh columns) for each dataset (a). Temporal correlation matrix between in situ observations and reanalyses for monthly SCD during the whole 5-year period (b). The SCDs are defined as the days when the SCF is greater than 0. 5 and are averaged over the 33 stations.