dataesr/scientific-paragraphs-categorization huggingface dataset:
PDI = ̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅ %ofPlanktonicDiatoms (%ofLittoralPlanktonic + %ofLittoralDiatoms) √ Ecological interpretations are based on modern assemblage data from the East African Diatom Database within the European Diatom Database Index (EDDI)  (Battarbee et al. , 2001). Hydrochemical parameters (conductivity and pH) were determined using weighted averaging  (ter Braak and Looman, 1986; Gasse et al. , 1995). The modern African training set includes each taxon's optimum and tolerance for each environmental parameter. Paleoenvironmental parameters are estimated using the total sum of the relative percentage of each fossil taxon, weighted by its optimum, and standardized to the total percentage of the diatom assemblage used in the transfer function  (Chalié and Gasse, 2002).
Sample Lab ID Lithology Longitude (   Of the 82 taxa, 52 are reported within the EDDI (S4). The percentage of taxa missing in the transfer functions ranges from 0 to 36%, averaging to 9. 4%. The missing data can be attributed to an irregular Aulacoseira granulata morphotype (Plates 17/18/19) also recorded by  Gasse (1975) , Epithemia hirundiformis Müller, and Nitzschia radicula Hustedt. Stephanodiscus neoastraea Håkansson & Hickel, which was not recognized as a distinct species from Stephanodiscus astraea until 1986, is also not in the database, although modern studies show that the ecology is similar to Stephanodiscus astraea (Ehrenberg) Grunow  (Genkal, 2009). Consequently, these data, which likely include taxa of both S. neoastraea and S. astraea as one taxon, were used. Absence of data clearly limit the precision of the transfer functions, although comparison with other diatom proxies such as PDI and supplemental XRF data support their inferences.
Acknowledgements Thank you to the two anonymous reviewers who provided constructive feedback on this manuscript. Thank you to  Ines Bludau ,  Lisa Park Boush ,  Ran Feng , and  Christian Tryon  for their help in early edits and discussions of these data. Thank you to  Tatiana Miranda  for help on the SEM. Thank you to  Dan Olago  for his help in the acquisition of local geologic maps of Kenya used to create Fig. 1C. Thanks to the  Deutsche Forschungsgemeinschaft  for their funding of the  Suguta Valley  Project by M. T. and  M. Strecker  ( DFG TR 419/6 ,  June 1 ,  2007 -December 31 , 2014). E. R. acknowledges 2019 Charles A. & June R. P. Ross Research Funding through the  Geological Society of America  used for this project. This research is also part of " Wet Feet or Walking on Sunshine " awarded to A.
J. , funded by the  Ministry of Culture and Science (MWK) of Baden Württemberg , the  University of Tübingen , and  Senckenberg Centre for Human Evolution and Paleoenvironment, Germany. Funding was also awarded to R. B. O. by the  Research Grant Council Hong Kong  for the project " Temporal variations in the controls of lacustrine sedimentation during continental rift evolution: evidence from the northern Kenya Rift Valley. " S. K. also acknowledges funding from the  Deutsche Forschungsgemeinschaft  ( DFG KU 3512/2-1 , project-No. 408311491 ). C. R. acknowledges funding from the  Federal Ministry of Education and Research , the  Baden-Württemberg Ministry of Science, and University of Tübingen  as part of the  Excellence Strategy of the German Federal and State Governments  ( PRO-ROSCA-2021-11 ).
PB1-K578 resides in a surface exposed α-helix within the PB1 thumb domain that is part of a positively charged patch constituted of several positively charged residues in PB1 (R571, R572) and PB2 (R101). In the modeled structure, PB1-K578 is directly oriented towards the negatively charged residue PB2-E72, which resides at the tip of a mostly unstructured and flexible loop in the PB2-N1 domain (AA 61-82), List of the positions of all K-ε-GG bound lysines in the PB2, PA, and PB1 subunits detected in 5 independent replicates (Rep. ) at 5 h p. i. together with their location in functional domains. The number of replicates in which the site was detected is indicated (Rep. of 5). Surface exposure of the identified sites was assessed using 3D structural models of a WSN-adapted IAV polymerase monomer bound to either vRNA or cRNA as well as the 3D structures of the symmetric dimer of the H3N2 polymerase (PDB: 6QNW) and the ANP32A-stabilized asymmetric dimer of the influenza C virus polymerase.
Dynamic exclusion was enabled on both MS systems. Raw MS data were processed using MaxQuant (v. 2. 0. 1. 0) with the built-in Andromeda search engine. Tandem mass spectra were searched against the Influenza A virus (strain A/Wilson-Smith/1933 H1N1; UP000000834. fasta; version from 10/2016) as well as against the human UniprotKB database (UP000005640_9606. fasta; version from 04/2019) concatenated with reversed sequence versions of all entries and also containing common lab contaminants. Carbamidomethylation on cysteine residues was set as fixed modification for the search in the database, while oxidation at methionine and acetylation of the protein N-termini were set as variable modifications. For the identification of ubiquitination sites, modification of lysine residues with the remnant diglycyl motif was allowed as additional variable modification.
Trypsin was defined as the digesting enzyme, allowing a maximum of two missed cleavages and requiring a minimum length of 7 amino acids. The maximum allowed mass deviation was 20 ppm for MS and 0. 5 Da for MS/MS scans. Common lab contaminants and proteins containing reverse sequences that were derived from the decoy database were filtered out from the dataset prior to any further analysis. Protein groups were regarded as being unequivocally identified with a false discovery rate (FDR) of 1% for both the peptide and protein identifications. Ubiquitination sites were accepted when they were identified with a localization probability of >0. 75 (class I sites). PX partial: All mass spectrometry data have been deposited to the ProteomeXchange Consortium (http://proteomecentral. proteomexchange.
Viral genome sequencing Following isolation of the viral RNA from virus supernatant using QIAamp Viral RNA Mini Kit (Qiagen), each vRNA segment was reverse transcribed using the SuperScript TM III One-Step RT PCR System with Platinum TM Taq DNA polymerase (Invitrogen). In the following, approximately 1 ng of the pooled PCR products was used as template and introduced into library preparation with the Nextera XT DNA Sample Preparation Kit (Illumina) and paired-end sequenced with the 2 × 250 bp MiSeq Reagent Kit v2 (Illumina) with an average insert size of 300 bp on a MiSeq instrument (Illumina). The protocols for library preparation and sequencing were conducted as recommended by the manufacturer (Illumina). After automatic demultiplexing on the MiSeq instrument, the resulting fastq files were mapped onto the Influenza A virus strain A/WSN/1933(H1N1) reference sequence (GenBank accession numbers CY034132-CY034139) using the BWA mapping algorithm implemented in the Ridom SeqSphere + software (v7) with default parameters.
Reverse transcription was terminated by incubation at 85 °C for 5 min. CT values were measured in duplicates using a LightCycler® 480 II (Roche) and the LightCycler® 480 Software (v1. 5. 1. 62), normalized to the expression data of the housekeeping gene glyceraldehyde 3-phosphate dehydrogenase (GADPH) and analyzed using the 2-ΔΔCT method  73. Quantification of viral RNA by strand-specific RT-PCR HEK293-T cells were transfected with 250 ng of pCAGGs plasmids expressing WT or mutant PB2, PB1, and PA, 1000 ng of NP and 100 ng of a pol-I driven minigenome firefly reporter plasmid expressing vRNA, using Lipofectamin™ 2000. 16 h p. t. RNA was isolated using TRIzol (Thermo Fisher) and dissolved in RNase-free water. Residual DNA was digested using recombinant DNase I (Roche). For reverse transcription a mixture of tagged primers directed to either m-, c-, or vRNA of the firefly minigenome (designed according to Kawakami et al.
58  ), oligo-dT primers, and 250 ng RNA were heated at 65 °C for 10 min, immediately chilled on ice for 5 min and then heated at 60 °C for 5 min. Then, a mixture of First Strand Buffer (5x, Invitrogen), 0. 1 M dithiothreitol, dNTPs, and Maxima H Minus Reverse transcriptase (Thermo Scientific) was added and incubated at 60 °C for 60 min. Reverse transcription was terminated by incubation at 85 °C for 5 min. RT-PCR was carried out using Brilliant III SYBR Green (Agilent) and primers directed to the respective tags. CT values were measured in duplicates using a Light-Cycler® 480 II (Roche) and the LightCycler® 480 Software (v1. 5. 1. 62), normalized to the expression data of the housekeeping GADPH and analyzed using the 2-ΔΔCT method. Primer sequences are included within Supplementary Data 1.
0), 500 mM NaCl, 5 mM EDTA, 1% (v/v) Igepal CA-630). Precipitated proteins were eluted in Laemmli buffer (1X) and subjected to SDS-PAGE and western blot. Coprecipitated proteins were detected using the primary antibodies rabbit anti-NP (GTX125989, Genetex; 1:1000 in blocking buffer), rat anti-HA-Tag (clone: 3F10, Roche; 1:5000 in blocking buffer), rabbit anti-PB1 (GTX125923, Genetex; 1:1000 in blocking buffer), rabbit anti-PB2 (GTX125926, Genetex; 1:1000 in blocking buffer) and mouse anti-Tubulin (clone DM1A, Sigma-Aldrich; 1:1000 in blocking buffer) and the secondary antibodies anti-Rabbit IgG-800CW (LI-COR; 1:10,000 in blocking buffer), anti-Rat IgG-HRP (Cell signaling Technologies; 1:3000 in blocking buffer) and anti-Mouse IgG-680RD (LI-COR; 1:10,000 in blocking buffer). Uncropped blots are provided in a source data file.
Precipitated proteins were detected by western blot using the primary antibodies rat anti-HA-Tag (clone: 3F10, Roche; 1:500 in blocking buffer), mouse anti-Strep-Tag (clone GT661, Sigma-Aldrich), rabbit anti-PB1 (GTX125923, Genetex; 1:1000 in blocking buffer), rabbit anti-PB2 (GTX125926, Genetex; 1:1000 in blocking buffer) and mouse anti-Tubulin (clone DM1A, Sigma-Aldrich; 1:1000 in blocking buffer) and the secondary antibodies, anti-Rat IgG-800CW (LI-COR; 1:10,000 in blocking buffer), anti-Mouse IgG-HRP (Cell signaling Technologies; 1:3000 in blocking buffer), and anti-Rabbit IgG-680RD (LI-COR; 1:10,000 in blocking buffer). Uncropped blots are provided in a source data file.
Homology modeling of WSN polymerase structures For both models of IAV WSN polymerase bound to vRNA as well as the IAV WSN polymerase model bound to cRNA comparative homology modeling was performed using MODELLER (v9. 19). For the vRNAbound polymerase model the crystal structure of the heterotrimeric Bat Influenza A polymerase bound to the vRNA promoter (PDB: 4WSB) served as a template, for the cRNA-bound polymerase model the crystal structure of Influenza B virus in complex with 5′ cRNA (PDB: 5EPI) served as a template. Crystal structure sequences were aligned with the polymerase subunit sequences of WSN ((i) A/WSN/1933(H1N1) PA (GenBank: CY034137); (ii) A/WSN/1933(H1N1) PB1 (GenBank: CY034138 and (iii) A/WSN/1933(H1N1) PB2 (GenBank: CY034139) and 100 homology models (including loop models) were created of both conformation states based on these WSN sequences.
Acknowledgements We thank  Ted Dawson ,  Edward Yeh , and  Gerrit Praefcke  for providing plasmids. We owe our thanks to  Max Trauernicht ,  Lars Schmitz ,  Julius Kindsgrab ,  Josephine Friedrich ,  Hannah Junginger ,  Nadja Rotte ,  Ramona Wördemann , and  Josua Janowski  for support and  Sebastian Giese  and  Kevin Ciminski  for helpful comments and discussions. We are grateful for the support by  Geoffrey Chase  for editing of the manuscript. This work was funded by the  German Research Foundation (DFG)  (Grant  BR5189/1-1  to L. B. ;  LU 477/23-2  to S. L. , SCHW 632/21-1 to M. S. ) and the  Clinical Research Unit   CRU342  (Project  P6  to L. B. and S. L. ; project  P4  to A. M. ), Further support was received by the  Interdisciplinary Centre for Clinical Research (IZKF)  (Grant  Bru2/015/19  to L.
Data analysis and statistics Quantitative data were analyzed and plotted using GraphPad PRISM™ (v7. 04) and Origin 2022 (v9. 9). Immune fluorescence images were analyzed using ImageJ (v1. 53c). Intensities of immuno-stained western blots were quantified using Image Studio 5. 2. 5 (Li-COR Biosciences). Biochemistry experiments were performed at least in triplicate unless otherwise stated, and representative gels are shown. Details of statistical tests and replicates are provided in the figure legends.
Data availability The mass spectrometry proteomics data generated in this study have been deposited to the ProteomeXchange Consortium via the PRIDE partner repository with the dataset identifier PXD030816, https://doi. org/10. 6019/PXD030816 (http://proteomecentral. proteomexchange. org/cgi/GetDataset?ID=PXD030816). MD simulations were conducted using the commercially available software YASARA dynamics (v21. 6. 2 and v21. 8. 27) and the implemented macros "md_run. mcr" and "md_analyze. mcr". Source code of YASARA macros can be downloaded from www. yasara. org. Raw simulation data encompassing around 40 gigabytes are available from the corresponding author. The study made use of the publicly available datasets: NCBI: PB1, PB2, and PA sequences from isolates of human, swine, and avian IAV origin were downloaded on 09/15/2017 (PB2, PB1) and 10/01/2017 (PA).
PDB entries: 4WSB, 5EPI, 6QNW, 7NHA, 4WSA, 5D9A, 6T0N, 6TW1, 6T0S, 6T0V, 6SZU, 6T2C, 6T0U, and 2VQZ. GenBank: accession numbers CY034132, CY034133, CY034134, CY034135, CY034136, CY034137, CY034138, and CY034139. UniprotKB database (UP000000834. fasta version from 10/2016; UP000005640_9606. fasta version from 04/.
To validate the microwave approach, drug quality is examined with the laser-induced breakdown spectroscopy (LIBS) technique, an optical emission laser used for both qualitative and quantitative investigations of elements contained in a sample. The existing elements are classified using the National Institute of Standards and Technology (NIST) database and categorized according to their spectral line wavelengths. The experiments show the presence of optimal wavelength values for carbon (C), hydrogen (H), nitrogen (N), and oxygen (O) at 247. 92 nm, 656. 49 nm, 244. 23 nm, and 777. 48 nm, respectively. The microwave experimental results show a shift frequency of approximately 1 MHz on average when the tablet is heated at 80 • C for 15 min. Meanwhile, the LIBS measurement shows a remarkable shift in terms of intensity of approximately 8884 and 812 for carbon and hydrogen, respectively.
supply chain (production, storage, transport, etc. ) in general but especially in tropical areas where humidity and temperature are high  [7] [8] [9]. During processing, drugs are generally sampled at specific periods to measure their quality in relation to their moisture content. This method is time-consuming and only supplies an average result for the analyzed tablets  10. Pharmaceutical quality analysis protocols are essentially based on offline and cumbersome laboratory methods. Although these methods are relatively effective and offer high-precision qualitative analysis, these so-called destructive methods remain unsuitable for online and portable analysis. In fact, Big Pharma is looking for online analysis solutions capable of providing drug quality data throughout the supply chain. In the context of drugs and equivalent substances, many interesting technologies can be found in the literature for qualitative online analysis, such as optical and infrared spectroscopy  11  , chromatographic analysis  12  , and microwave sensing spectroscopy  13.
Heating temperature In As a result, the change in water amount brought on by temperature vaporization shifts the resonance frequency of the loaded sensor, thus changing the dielectric characteristics of the sample. As a consequence, the rate of paracetamol variation (concentration) increased as the temperature increased. Moreover, for each of the three-sample situations, the suggested microwave sensor readings are successful in discriminating between different temperature levels. To confirm the results obtained by the developed sensor, the LIBS technique was employed to examine the presence of C, H, N and O under different temperature conditions. The ingredients were extensively analyzed to ensure that they could be identified with high accuracy. To examine the spectral lines associated with different elements according to their wavelength and energy, the data were gathered using a laser source and spectrometer.
Fig. 13  shows a typical spectrum for each element present in the samples, displayed individually for the sake of exposition. Peaks belonging to nitrogen, oxygen, hydrogen, and carbon could be seen in every spectrum. These lines can be linked to the main constituents (active medicinal substances) of the drug, which are organic compounds. We believe that the additional metals might be impurities or sodium-containing excipients. The findings of the required constituents of the paracetamol samples are properly matched to the results from the NIST database in Table  4 , compared to the observed peaks with LIBS in Fig. 11a. The chemical formula of paracetamol is C8H9NO2, which means that the molecule is composed of 8 carbon atoms associated with 9 hydrogen atoms, 1 nitrogen atom, and 2 oxygen atoms.
Data availability The datasets generated and/or analyzed during the current study are available in the OneDrive Cloud repository, you can access all the data through this link https:// um6p-my. share point. com/: f:/g/ perso nal/ youne ss_ zaaro ur_ um6p_ ma/ EshQO EITZh 9JoT8 e9Dhs PuMB8 mlh8s oj82S RejNN C7C4mQ? e= rfa61P.
Results To investigate how brain stimulation modulated hierarchy learning, we conducted panel logistic or linear regressions, depending on the form of dependent variable (accuracy, reaction time, or confidence rating for each trial), on the population-average (generalized estimating equation, GEE) [see Methods for details of statistics]. These analyses allowed us to observe the effect of stimulation at the level of the population, by considering the effect of time. The independent variables were the tDCS stimulation (Anode/Sham/Cathode), hierarchy condition (Social/Nonsocial), and block number (1-12). The percentage change effect was estimated via the marginal effect (reported as β value below, see Methods). The marginal estimation calculates the average effect across all individuals and time periods in the panel dataset, which can easily be interpreted as the discreet change of the dependent variable given a unitary change of an independent variable.
Thus, the data from 128 participants (males=63, mean age=19. 90±0. 145) were analyzed (Sham=44: Male=23, Female=21; Cathode=42: Male=21, Female=21; Anode=42: Male=21, Female=21). The study was approved by the ethics committee of South China Normal University and all participants received 45 CNY after the task. Stimuli. Images of faces in the Social condition were selected from the CAS-PEAL Large-Scale Chinese Face Database  53. Silhouettes (2 faces, 1 female, 1 male), were used to represent "You" which refers to the participant in the experiment. Frontal images (12 neutral faces, 6 females, 6 males) identified fictive hierarchy members for the subsequent experiments. The hair and neck were preserved for the facial pictures. For female participants, the hierarchy was composed of pictures of females and vice versa for male participants.
Previous studies have shown facial gender can influence perceptions, judgments, and behavior related to social hierarchy and social dominance  [54] [55] [56]. By matching the gender between participants and stimuli we sought to ensure that participants perceived and evaluated the facial stimuli consistent with their own gender-related expectations, to reduce the number of potential factors that might influence the effect of social hierarchy learning and tDCS stimulation. Images of galaxies were selected from a public astronomy website (http://hubblesite. org/gallery/album/nbula). All pictures were processed by Adobe Photoshop software to ensure grayscale and resolution were consistent. The hierarchical ranks of galaxies and face stimuli were randomized across the groups. The experiment was programmed in Eprime 2.
Statistical analyses. Behavioral analyses were conducted in STATA 14. For the panel regressions using the population-average effect (generalized estimating equation approach, GEE), this allowed us to estimate the effect of brain stimulation at the level of population and take into account the effect of time (Learning). The Panel data of t=480 trials clustered on each of i=128 participants were used. We reported significant marginal effect of estimated β values. The model equation of GEE for panel logistic regressions: logit (P (Yit = 1)) = Xit * β Yit represents the binary dependent variable for individual i at time t. Xit represents the vector of explanatory variables for individual i at time t. 𝑃 represents the probability of a given event. β is the vector of coefficients to be estimated, representing the population-average marginal effects.
The marginal effect of the variable 𝑋it is estimated as: The marginal effect estimation equation calculates the average marginal effect across all individuals and time periods in the panel dataset. Thus, the estimated marginal effect can easily be interpreted as the discreet change of the dependent variable given a unitary change of an independent variable. It quantifies the change in the log-odds of the dependent variable associated with a one-unit change in the corresponding independent variable, while holding all other variables constant. In other words, it quantifies the impact of a unit change in the independent variable on the dependent variable, taking into account the effects of other variables in the model. For the panel linear regressions, reported β value represents Xit, i. e. the regression coefficient.
Acknowledgments This research has benefited from the financial support of  IDEXLYON from Université de Lyon  (project  INDEPTH ) within the  Programme Investissements d'Avenir  ( ANR-16-IDEX-0005 ) and of the  LABEX CORTEX  ( ANR-11-LABX-0042 ) of  Université de Lyon , within the program  Investissements d'Avenir  ( ANR-11-IDEX-007 ) operated by the  French National Research Agency. This work was also supported by a grant ( ADOSTRESS ,  ANR-21-CE37-0032-01 ) from the  Agence Nationale pour la Recherche  and a grant from the  Fondation de France  ( FDF 00089590 ) to JCD, and a grant from  National Natural Science Foundation of China  to QC ( 31970982 ;  32171019 ). We thank  Siying Li , and  Yaner Su  for helpful assistance with data collection.
β = ∑[P( Yit = 1) * (1 -P(Yit = 1)) * Xit * βj] / N β represents the estimated marginal effect. P(Yit = 1) represents the predicted probability of the dependent variable being equal to 1 for individual i at time t. Xit represents the vector of explanatory variables for individual i at time t. βj represents the estimated coefficients from the GEE model. N represents the total number of observations in the panel dataset.
CLC Main workbench version 8. 0 (CLC Bio, https://www. qiagenbioinformatics. com/products/clc-main-workbench/) was used to create consensus sequences using the forward and reverse sequences of the ITS region for each isolate. All consensus sequences were compared in a BLAST analysis against the GenBank database (NCBI; http://www. ncbi. nlm. nih. gov/genbank/) to confirm the identity of each isolate. To determine the ITS haplotype for each confirmed isolate of D. pini, sequences were compared to those reported in  Barnes et al. (2016) ;  Mullett et al. (2018)  using MEGA 7. 0. 14 (Kumar et al. , 2016).
Individual isolates were considered clones if they had the same combination of alleles for each marker analyzed. The R package poppr  (Kamvar et al. , 2014)  was used to determine the number of MLHs in the dataset. Two datasets were generated for further analyses; the dataset that had not been clone-corrected included all individuals and the clone-corrected dataset contained single representatives of each unique MLH per population. Individuals from each particular country were grouped as populations.