dataesr/scientific-paragraphs-categorization huggingface dataset:
The S-PLUS Data Release 3 For this work, the S-PLUS Data Release 3 (DR3; M. L. Buzzo et al. 2022, in preparation)  was used. The data structure in this data release, as well as the photometric extraction and calibration process, are the same as the S-PLUS Data Release 2 (DR2;  Almeida-Fernandes et al. 2022). The only difference is the addition of observations in the South Galactic Hemisphere for DR3. At the time the candidates for this work were selected, the catalogs were only available internally to the collaboration and are now publicly available through the S-PLUS Cloud 10  service. The first step in the data selection was to apply a series of restrictions in the DR3 database, which originally contained 13,416,120 sources, mostly related to the quality of the photometry, probability of being a stellar source, and a color range suitable to study low-metallicity stars.
Then, metal-poor star candidates were chosen for the medium-resolution spectroscopic campaign, based on their location on a metallicitydependent color-color diagram, as described below. The following restrictions were applied to the DR3 database: 1. CLASS_STAR 0. 95: sources having a high probability of being a star; 2. gSDSS 17. 5: brightness limit for spectroscopic follow-up within reasonable exposure times; 3. nDet_magPStotal = 12: only sources with all 12 magnitudes measured; 4. (gSDSS-iSDSS) ä [0. 2:1. 6]: color window to remove possible contamination from white dwarfs and A-type stars on the blue end and objects cooler than T eff ∼4000 K on the red end (see   2019 ) employ the (g-i) 0 color. Due to the dependency of the Ca II K line strength with temperature, there is a color range, roughly (g-i) 0  0.
3, where the metallicity-dependent color difference between a star with [Fe/H] = -2. 0 and -4. 0 becomes smaller than the typical uncertainties in the photometry. In an attempt to address this degeneracy, this work employs the (J0395-J0410)-(J0660-J0861) combination, that uses the filters centered on Hδ and Hα and provides better temperature sensitivity. This temperature-dependent metallicity color index should increase the success rate of finding metalpoor stars. The transmission curves for the six S-PLUS filters used in this selection are shown in the top right panel of Figure  1. The catalog generated from the S-PLUS DR3 selection above was crossmatched with the SDSS/SEGUE spectroscopic database. From that crossmatch, sources with CLASS==QSO, σ Teff > 200K, and S/N < 20 were excluded. The bottom right panel of Figure  1  shows a section of the color-color diagram, with each 0.
Medium-resolution Spectroscopy The spectroscopic follow-up campaign was conducted in semesters 2019A, 2019B, 2020A, 2021A, and 2022A. Data were collected for 522 metal-poor star candidates, selected from their S-PLUS photometry, described above. The stars were observed with two different telescope/instrument setups: Blanco/COSMOS and Gemini South/GMOS-S. Prior to the start of the observing campaigns in late 2018, the S-PLUS candidate list was crossmatched with the SIMBAD Astronomical Database 11  and stars with previously determined stellar parameters were excluded. After the follow-up observations were concluded, another crossmatch was conducted for the 522 observed targets, and four stars were found to have recent stellar parameter determinations: 2MASSJ03145801 -3236489 and 2MASSJ04441395-3356317  (Steinmetz et al.
05% of Tween 20 and 5% of milk for 30 min at room temperature. Membranes were incubated overnight at 4 °C with the appropriate primary antibody (anti-chemerin antibody). Then, membranes were incubated 90 min at room temperature with a Horse Radish Peroxidase-conjugated anti-mouse IgG. Chemerin protein was detected by enhanced chemiluminescence (ECL, Western Lightning Plus-ECL, Perkin Elmer, Villebon-sur-Yvette, France) with a G-box SynGene (Ozyme, St Quentin en Yvelines, France) and the GeneSnap software (Ozyme, St Quentin en Yvelines, France). Then, protein amounts were quantified with GeneTools software. The results were expressed as the intensity signal in arbitrary units after normalization of chemerin protein signals with the total protein amount as evaluated by Ponceau-red staining. Albumen of eggs from different avian species such as Galliforms (chicken Gallus gallus, turkey Meleagris gallopavo, pheasant Phasianus colchicus, quail Coturnix japonica, guinea fowl Numida meleagris and red-legged partridge Alectoris rufa), Anseriforms (ducks Cairina moschata and Anas platyrhynchos and goose Anser anser) and Colombiform (pigeon Columba livia) species were tested.
Alignment of nucleic and proteic RARRES 2 sequence of various avian species The gene and protein sequences of RARRES2 of different avian species were downloaded from the National Centre for Biotechnology Information (NCBI http://www. ncbi. nlm. nih. gov) under GenBank accession number. Multiple RARRES2 sequences of different avian species were aligned using Clustal Omega to check their percent homology and identity matrix for RARRES2 gene and protein of avian species compared to RARRES2 chicken.
Statistical analysis The GraphPad Prism ® software (version 6) was used for all analyses. All data are represented as means ± standard error of mean (SEM) with a level of significance less than 0. 05 (p < 0. 05). One-way ANOVA and multiple comparisons were used to compare reproductive parameters, chemerin concentration in albumen and blood among the different breeds. A Pearson test was used to analyse correlations. The correlation was noted "r" and the p value was considered significant if p value <0. 05. Different letters indicate significant differences (p < 0. 05).
Chemerin in hen plasma and egg white As shown in Figure  2A , hen plasma chemerin concentrations were similar (p= 0. 261), whereas they were very different in egg whites from the seven breeds. In addition, as shown in Figure  2B , chemerin concentrations were up to 5 to 10 fold higher in egg white as compared to their mother's plasma. Chemerin concentration in egg white was about two-fold higher in R-, R+ and Fayoumi breeds than in OD, Gavora, Cheptel1 and DwNa breeds (p < 0. 0001) (Figure  2B ). The intra-hen variability coefficient was determined from chemerin concentrations in albumen of each egg laid during five successive days, whereas the inter-hen variability coefficient was calculated from chemerin concentrations in egg white for all hens of each breed. Data showed a lower intra-hen variability of chemerin concentration compared with the inter-hen variability except for R+ breed (Table  4 ).
Comparison of chemerin concentration in hen egg white measured by ELISA and western-blot analysis Samples of the hens's albumen were analyzed by two methods: Western-blot and ELISA. As shown in Figure  3A , the amount of egg white chemerin as determined by westernblot analysis in 4 or 5 successive eggs showed a three-fold difference between two hens (C254 vs. C261 hen). This difference was similar between egg white chemerin concentrations measured by ELISA (Figure  3B ). These data were confirmed for six hens. We also determined correlation between these two methods (ELISA vs. Western-blot) by using egg white samples from hens of seven breeds (n = 61 with only one value for each hen). In this case, the correlation analysis confirmed the comparability of the results from both methods (p < 0.
Discussion In the present study, we investigated for the first time plasma and egg white chemerin concentrations in seven hen breeds by using a home-made chicken chemerin monoclonal antibody and a new ELISA assay. We showed that chemerin egg white concentration was not correlated with chemerin plasma concentration and laying rate, whereas it was negatively correlated with total, egg white, and egg yolk weights, and fecundity and fertility. We observed that chemerin concentrations in egg white were significantly higher than in blood plasma and there was no correlation between these two parameters. These data are in good agreement with our previous study  (Estienne et al. , 2022)  showing that chemerin was 10 times more concentrated in egg white than in plasma. In the present study, we developed a specific chicken chemerin ELISA by using specific monoclonal chicken chemerin antibodies and recombinant chicken chemerin.
e. , of dimensions 16 × 11). As a result, this produces, for each slot, a set of I = {1,. , 176} mini-slots and a corresponding set of B = {1,. , 549} candidate blocks with respect to the numerology, where every candidate block consists of 4 elements of I. The resource block details are given in Table  2. Blocks of shape 1 (corresponding to a shape of 4 × 1 minislots), Moreover, the chosen latency tolerance and bit rate demands for the URRLC users are τ = {0. 5, 1, 1. 5, 2} msec and q = {16, 32, 64, 128, 256, 512} kbits/sec (kbps), respectively. The latency tolerance for the eMBB users is fixed and equal to τ = 2 msec. The SNR range is generated by numbers uniformly distributed in the interval  [5, 30]  dB. In the following, we refer to the ''optimal shceduling'' to denote the solutions provided by the Gurobi optimization solver, used as a benchmark for the evaluation of the optimality gap of the proposed heuristics.
The NOMA sum bit rate gains with respect to the OMA are depicted with the lighter color in each bar. The latency tolerance and bit rate demands considered are τ = 1 msec and q = {16, 32, 64, 128, 256, 512} kbps, respectively, for five K ( ) and five K (c) users. In all cases, as expected, flexible numerology significantly outperforms the fixed and multiple-fixed numerology. Moreover, multiple-fixed overpasses the per-FIGURE 2. Sum bit rate of K (c) services when employing OMA, 2-user NOMA (2u-NOMA) and multiple-user NOMA (mNOMA), considering fixed, multiple flexible numerology, under several q k data demands and delay tolerance of k ∈ K ( ) τ k = 1 msec. The lighter colors depict the NOMA sum bit rate gains in comparison to the OMA. Fixed and multiple-fixed numerologies result in infeasible outputs for q k = 512 kbps.
show that the performance of the heuristic algorithms compares well with the global optimum (obtained through Gurobi solvers), while keeping the complexity very low. Note that the proposed algorithms, exceed the performance of the baseline heuristic, especially for τ > 0. 5 msec, verifying the results of the previous experiments Figs. 5(a) , 7. This showcases that indeed, the reformulation of the optimal scheduling as a conflict minimization problem is highly pertinent and allows shedding light on how to jointly address the constraints (  5 ) and (6) of P0. It is also noteworthy that more elaborate heuristics could be proposed in the same context, by looking at algorithms with lower optimality gaps to the optimal bin packing solution. The same conclusions can be reached in Figs. 11  and 12  for URLLC demands of 128 and 256 kbps, respectively.
This dataset is composed by spatial (e. g. location) and thematic (e. g. diseases, symptoms, virus) entities concerning avian influenza in social media (textual) data in English. It was created from three corpora: the first one includes 10 transcriptions of YouTube videos and 70 tweets manually annotated. The second corpus is composed by the same textual data but automatically annotated with Named Entity Recognition (NER) tools. These two corpora have been built to evaluate NER tools and apply them to a bigger corpus. The third corpus is composed of 100 YouTube transcriptions automatically annotated with NER tools. The aim of the annotation task is to recognize spatial information such as the names of the cities and epidemiological information such as the names of the diseases. An annotation guideline is provided in order to ensure a unified annotation and to help the annotators.
Subject Data Science: Data Mining and Statistical Analysis Specific subject area Identify epidemiological events on social networks Type of data Table How data were acquired Data are the result of a manual and automatic annotation of spatial and thematic entities. The corpora 1 and 2, including 80 transcriptions and tweets, were automatically extracted from the platforms YouTube (December 2020-January 2021) and Twitter (March-April 2021). The corpus 3, including 100 transcriptions, was automatically extracted from the YouTube platform (February 2019 to October 2021). These three corpora were preprocessed after the extraction. Preprocessing programs are included in the dataset. Manual annotations were performed in accordance to the annotation guideline available within this dataset  1. For right privacy, the content of tweets is not available in the dataset, instead there is the id of the tweet.
Data format Raw and Standardized Parameters for data collection The collection of texts respects two conditions: the texts have to be written in English and the term 'avian influenza' must be present in the text body for the tweets and in their title for the YouTube videos (i. e. primary data sources). The textual transcriptions collected from YouTube have been normalized by adding punctuation and capitalization, and by correcting transcription and lexical errors for specific terms (e. g. disease names). For Twitter data, non-Unicode characters (emoticons, etc. ) have been removed, as well as correcting lexical errors of specific terms (i. e. secondary data sources).
Value of the Data • This dataset contributes to the available resources for Natural Language Processing (NLP) on specialized domains and more precisely in the field of epidemiology. • This dataset is useful for computer scientists for NLP and data mining tasks. • This dataset can be used for evaluation or training purposes for the entity recognition task. • The annotators have identified a variety of entities (e. g. diseases, symptoms, virus, hosts). These entities are relevant to recognize epidemiological information.
Data Description After an extraction process from YouTube and Twitter (i. e. primary data sources), the dataset is constituted of five table data files (. tab) normalized (i. e. secondary data sources) and annotated (i. e. final data sources). One annotation guide (. pdf) details the instruction to the annotators as well as the choices that were made while designing the study. The python codes to reproduce the transformation of the documents for the annotation step is available on github. 3 he five data files are distributed as follows:.
Automatic annotation of spatial and thematic entities -Corpus 3 (big) • A table data file containing the transcriptions YouTube data annotated automatically from corpus 3, named corpus 3Y (datafile_3Y_automatic_annotation. tab). The files from the three corpora include data organized in tables. An example of the YouTube transcription data row is given in Table  1  and an example of the Twitter data row is reported in Table  2. The data are described through a set of features from YouTube transcription data of the three corpora: -id: the id of the data on the social media; -publication_date: the date of publication of the data on the social media; -raw_text: the raw text (no transformation) of the data;   -normalized_text: the normalized text (after applying transformations) of the data; -spatial_entity: A list of spatial entities annotated, with their labels (GPE, LOC, FAC); -thematic_entity: A list of annotated thematic entities, with their labels (Disease or Syndrome, Virus, Sign or Symptom).
The data are described through a set of features from Twitter data of both corpora: -id: the id of the data on the social media; -publication_date: the date of publication of the data on the social media; -spatial_entity: A list of annotated spatial entities, with its label (GPE, LOC, FAC); -thematic_entity: A list of annotated thematic entities, with its label (Disease or Syndrome, Virus, Sign or Symptom). The annotation guideline ( annotation_guide_spatial_thematic_entities. pdf ) presents the instructions to the annotators. These instructions and the choices made are summarized in the next section. The annotation framework defines several tags to annotate the texts. Spatial concepts are annotated with three tags: • GPE (Geopolitical entity) is used to annotate entities representing countries, cities, states etc; • LOC (Non-GPE locations) is used to annotate entities representing mountain ranges, bodies of water, etc; • FAC (Faculty) is used to annotate entities representing buildings, airports, highways, bridges, etc.
Acquisition and pre-processing The corpora were obtained automatically from the platforms YouTube and Twitter, thanks to their dedicated APIs. The texts from the web were stored in. txt files, with the aim to obtain distinct files to annotate. The pre-processing consists of four main steps applied on the raw corpora, detailed below. First, in order to optimize the recognition of entities, the raw text of these files has been normalized with the automatic addition of punctuation by using the python library punctuator  4  and with the automatic addition of capital letters, by using the POS tagging provided by the python library SpaCy 5. Then, the text has been cleaned by deleting non Unicode symbols and eventual noise originated by the transcription process. As a final normalization step of the raw text, a correction of specific terms (e.
Automatic data labeling In parallel, the spatial and thematic entities were automatically annotated on the same raw text data (70 tweets and 10 transcriptions) with NER tools. We applied SpaCy  [2]  on the data to recognize the spatial entities and SciSpaCy  [3]  combined with UMLS  [4]  in order to recognize the thematic entities. The result of this process is the corpus 2 (2T and 2Y). Once these two annotations (manual and automatic) were performed, we evaluated the automatic recognition of the spatial and thematic entities on corpus 2, by comparing the annotation results of the corpus 1 and the corpus 2, and measured the evaluation with three metrics: Precision, Recall and F-Measure. The evaluation is presented in Table  6. The Precision value of spatial and thematic entity recognition is quite good, with a result of 0.
7 and 0. 6 respectively. The Recall for automatic recognition of thematic entities is low (0. 2), the tool does not recognize many thematic entities that are not included in the thesaurus associated with SciSpaCy. We then apply the processes of Acquisition and pre-processing and Automatic data labeling on a larger corpus (corpus 3) called 3Y (100 transcriptions). This enables to check the scalability of the proposed process. All text related information such as copyright, texts, authors list or references were discarded. The dataset is composed of the extracted annotations. The extraction program, available in the dataset, preserves the text and adds the textual entities and their annotated categories at the end of the text.
Acknowledgments We thank  Augustin Borderon  for his help in annotating the thematic entities of tweets and transcripts, and to  Bahdja Boudoua  and  Elena Arsevska  for assistance and adjustment of the definitions and choice for thematic entity annotation. This study was partially funded by  EU  grant  874850 MOOD  and is catalogued as MOOD033. The contents of this publication are the sole responsibility of the authors and do not necessarily reflect the views of the  European Commission.
CRediT Author Statement Camille Schaeffer: Methodology, Software, Investigation, Resources, Data curation, Writing -original draft; Roberto Interdonato: Methodology, Writing -review & editing; Renaud Lancelot: Methodology, Writing -review & editing; Mathieu Roche: Methodology, Writingreview & editing; Maguelonne Teisseire: Methodology, Writing -review & editing.
By controlling the electrical current in the developed device, the electrical conductivity was studied in the environment and by exposing the nanosensor to a test gas. The nanosensors were tested at operating temperatures ranging from room temperature (RT) to 175 °C to observe the effect of this parameter on UV and gas detection performances. All nanosensors were tested at a relative humidity of 10% in the test chamber, and the effect of humidity was investigated by raising the humidity to 50% RH. Nanosensors ZnO:Eu3 were tested in dual-mode to see clearly detection of H2 gas during continuous UV light pulse. We have performed DFT simulations, employing the generalized gradient approximation (GGA) within the PBE-Perdue-Burke-Ernzerhof 29 exchange-correlation functional and the projected augmented wave (PAW) approach  30, 31  , as implemented in the Vienna ab initio Simulation Package (VASP)  32.
ACKNOWLEDGMENTS  C. Lupan  gratefully acknowledges  Kiel University, Functional Nanomaterials, Germany and PSL Université, Chimie-ParisTech IRCP, Paris, France  for internship positions in 2018-2019 and  TUM, Chisinau, Republic of Moldova , for constant support. C. Lupan  would like to express special appreciation and thanks to  Professor Trofim Viorel  (TUM) as  MSc Thesis supervisor , for the encouragement, fruitful discussions on this work, patient guidance, and advice he has provided throughout time. Funded by the  Deutsche Forschungsgemeinschaft (DFG, German Research Foundation ) -Project-ID  434434223 -SFB 1461  and  SFB 1261. This work was partially supported by the  Technical University of Moldova  and through the  ANCD-NARD  Grant No. 20. 80009. 5007. 09at   TUM. This investigation was partially supported by the  German Research Foundation (DFG -Deutsche Forschungsgemeinschaft)  under the schemes FOR 2093 and AD 183/18-1.
MATERIAL AND METHODS Antibodies: anti-p42/44 MAPKs, anti-p42/44 MAPKs pT202/Y204, anti-p38 MAPK, anti-p38 MAPK pT180/Y182, anti-AKT, anti-AKT pS473, anti-EPHA2 pY594, anti-Src pY418, anti-Stat3 pY705, anti-Cyclin D3 (Cell Signaling Technology), anti-EPHA2, anti-Stat3, anti-Myc (Santa Cruz Biotechnology), anti-Src specific (2. 17 Phylogenetic analyses: Nucleic and protein SFK sequences were retrieved from NCBI annotated nr database (http://www. ncbi. nlm. nih. gov). Accessions are listed Table  S1. Protein sequences were aligned using MAAFT v7. 450  (39). Nucleic sequence alignments were based on protein alignments. Phylogenetic trees were estimated by PhyML (40), using the General Time Reversible (GTR) model with invariant and gamma decorations. Nonsynonymous versus synonymous substitution ratios (ω= dN/dS) were calculated using PAML 4.
ACKNOWLEDGEMENTS We thank the Montpellier RIO Imaging and RHEM platforms for imaging and immunohistochemistry analyses, and the  Proteomic Core Platform  of  EPFL  for proteomic analyses. This work was supported by fundacion  Marato TV3 ,  La Ligue Nationale Contre le Cancer (LNCC) ,  Montpellier SIRIC  Grant  «INCa-DGOS-Inserm 6045 »,  CNRS , and the  University of Montpellier. RHEM facility supported by  SIRIC Montpellier Cancer Grant INCa_Inserm_DGOS_12553 , the european regional development foundation and the occitanian region ( FEDER-FSE 2014-2020 Languedoc Roussillon ),  IBiSA  and Ligue contre le cancer for processing our animal tissues and histology technics. ML is supported by the  Fondation pour la Recherche Médicale (FRM)  and the  Fondation de France. SR is an INSERM investigator.
88 89 In addition to Pb isotope measurements of silver Alexander coinage (alexanders), the present 90 work also presents data on two other types of silver coinage that were unlikely to have been 91 minted from the central Persian treasury. First, silver sigloi (shekel) are the only significant 92 coinage produced by the Persian empire in its western satrapies  (Alram 1993; Tuplin 2014). 93 They are known, together with archaic Greek coins and Indian punch-mark coinage from 94 Gandhara, from the Kabul hoard deposited ca. 350 BCE  (Schlumberger 1953 ), but so far have 95 not benefited from isotopic work (e. g. ,  Kraay and Emeleus 1962). Second, although no 96 known evidence substantiates a significant input of Indian silver to Persian treasury 97 (Herodotus, III. 98-105), the present work provides Pb isotope data on early Indian punch-98 mark coinage to corroborate this conjecture by objective, concrete means  (Reddy 2014).