dataesr/scientific-paragraphs-categorization huggingface dataset:
99, SD = 0. 1) relative to the LST (M = 3. 1, SD = 0. 11, t(299) = 90. 48, p < 0. 00001, d = 10. 56). Also consistent with this idea, animals took significantly more trials on average to learn each new associations in the OPT (M = 16. 08, SD = 1. 34 trials) than for the LST (M = 10. 57, SD = 0. 55 trials, t(299) = 48. 41, p < 0. 00001, d = 5. 38). We analyzed 86 behavioral data sets from two monkeys in which they made at least 20 error trials within each session and learned at least one object-place association (same criteria as for LST). Using the same trial sorting strategy as we used for the LST, we calculated the mean performance of trials immediately after error trials and after the same number of correct trials from the beginning of the learning session during the memory acquisition stage.
Methods Subjects. All procedures and treatments were done in accordance with NIH guidelines and were approved by the NYU animal welfare committee. Six male and one female macaque monkeys were used in this study. The data sets acquired from the entorhinal cortex (EC) and hippocampus (HPC) of monkey A (rhesus, 11. 5 kg) and B (bonnet, 7. 8 kg) were previously described in Hargreaves et al. 22. The data acquired in the HPC of monkey A (the same monkey as EC recording but in different sessions) and C (rhesus, 13 kg) were from the same data set as described in Wirth et al. 32. The hippocampal neurons recorded in monkey EI (rhesus, 6. 0 kg) have not been previously published. The data set from Wirth et al. 32  was only used for calculating the stimulus-selectivity shift but not for calculating the outcome-selective signals because the neural activity during inter-trial intervals (ITI) were not recorded.
If the response was correct, an auditory feedback tone was played, and after a random delay (30-518 ms) 2-4 drops of juice were delivered as rewards. The animals were required to fixate from the beginning of the baseline period until the early or late bar release. The associations between the object-place combinations to the bar-release responses were counterbalanced as illustrated in Fig. 3b. Data analysis. All data analysis was done with custom-written Matlab programs (Mathworks, Natick, MA, USA). The outcome-related criteria and selectivity index analysis were the same as a previous publication  21  with several modifications to accommodate the present data set. The same as the previous study, the baseline period was defined as the 300 ms before the scene onset and the scene period was the 500 ms duration from the onset of the scene.
Acknowledgements We would like to thank  Prof. György Buzsáki  for his insightful comments on a previous version of the manuscript;  Dr. Hweeling Lee  for her helpful discussions in statistics;  Ellen Wang  and  Joyce Ho  for their assistance in animal experiments;  Shohan Hasan  for assistance in task programming and anonymous reviewers for suggestions to improve the manuscript. The work is supported by  NIH  Grants:  R01 MH084964-01  "The functional organization of the medial temporal lobe";  R01-NIMH "Neurophysiological  and fMRI studies of associative learning in the MTL and striatum".
Structural determination Crystallization of BsrV tagless was performed as previously described for the His-tagged proteins  [9]. Briefly, a highthroughput NanoDrop ExtY robot (Innovadyne Technologies Inc. ), the commercial Qiagen screens The JCSG + Suite and The PACT Suite and the Hampton Research screens Index, Crystal Screen and Crystal Screen 2 were used to get crystals by the sitting-drop vapor-diffusion method. Best crystals were obtained with 0. 1 M Bis-Tris propane pH 7. 5, 0. 2 M Sodium Iodide, and 24% (p/v) of PEG 3350. X-ray data collection was performed on the X06SA beamline at the SLS synchrotron-radiation facility in Villigen, Switzerland. Data sets were collected using a PILATUS 6 M detector, and were processed using XDS  [27]  and scaled using SCALA  [28]  from the CCP4 suite  [29].
The structure was solved by the molecular replacement method with MOLREP  [30]  from the CCP4 suite using the His-tagged version of BsrV (PDB code 4BEU) as initial model. Refinement was performed with PHENIX  [31]  and modeling with Coot  [32]. The stereochemistry of the models was verified using MolProbity  [33]. A summary of the data collection and refinement statistics is given in Table  1.
Statistical analysis The program GraphPad PRISM Ò Software (Inc. , San Diego CA, www. graphpad. com) has been used for all statistical analyses. To determine the significance of the data displayed in Fig. 3 , the ttest (unpaired) has been performed. P-values smaller than 0,05 were considered statistically significant, with the following ranking: p < 0,05(*); p < 0,001(***).
Acknowledgements We thank  F. Lopez-Gallego  for providing DAAO and  J. A. Ayala  for providing AmpC. Research in the Cava lab was supported by the  Swedish Research Council, Umeå University , the  Knut and Alice Wallenberg Foundation (KAW)  and the  Kempe Foundation. Additionally, this work was supported by the  BFU2017-90030-P  grant to J. A. H. from the  Spanish Ministry of Science and Innovation  and by the  S2010/BMD-2457  grant from CAM (J. A. H. and A. M. ).
(2020). These reconstructions resemble the 30 000 years around the KM5c interglacial in the mid-Pliocene that the Eoi 400 simulations are designed to represent. The  Foley and Dowsett (2019)  data, part of PRISM4, are a collection of alkenone palaeothermometry and U k 37 SST proxies. The  McClymont et al. (2020)  data are a combination of U k 37 reconstructions using a BAYSPLINE calibration and foraminifera Mg/Ca SST reconstructions (https://doi. org/10. 1594/PANGAEA. 911847). Together these proxies represent eight SST values from six different locations in the tropical Pacific.
Pre-industrial observations We have included the results from the HadISST 1920-2020 dataset as a reference for the pre-industrial simulation results. The time range of the observational data does not cover the pre-industrial period and includes anthropogenic forcing trends (although a linear trend is removed). An argument to use the most recent data is the observational uncertainty that is present in the instrumental record, especially before 1920, as shown by  Ilyas et al. (2017)  using HadCRUT4 data. It was decided to use a 100-year time series, as with the PlioMIP2 data, and use the most recent data because of a higher spatial resolution. Chelton and Risien (2016) mention zonal and meridional discontinuities in the HadISST dataset, specifically in the Pacific. They advise caution for high-resolution studies but deem the data adequate for largescale SST variability studies.
Haywood et al. (2020)  prefer to use the NOAA ERSST v5 dataset  (Huang et al. , 2017)  over the HadISST data because of consistency with other observational datasets on a global scale. The Niño 3. 4 moments of the E 280 simulations match reasonably well with those from the HadISST data (see Fig. 2 ). The ensemble mean shows a slightly higher Niño 3. 4 SD (0. 90) compared to the HadISST results (0. 77). The    (Brierley, 2015). Some models were a part of both ensembles, but note that a different protocol was used in the both MIPs. PlioMIP1 ensemble mean reported a slightly lower SD (0. 78,  Brierley, 2015)  and was compared to the Niño 3. 4 SD of the ERSST data (0. 69). The overestimation of the model Niño 3. 4 SD compared to observational data is also reported in the CMIP5 ensemble  (Kim et al.
Another difference is that the PlioMIP2 models show a clear but small reduction in zonal SST gradient where PlioMIP1 shows "no preferential warming in the eastern equatorial Pacific". An explanation for the difference between the ensemble results is that PlioMIP2 encompasses more and newer models (most CMIP6) that have a higher resolution and include the newest ocean-atmosphere models and parametrizations. Next to this, the boundary conditions of PlioMIP2 are more specifically tuned to a specific time slice in the mid-Pliocene  (Dowsett et al. , 2016). It should also be noted that  Brierley (2015)  used more than 100 years of data (up to 200 years) for analysis of some of the models, sometimes including more years of pre-industrial data than mid-Pliocene data for one model. This can impact the statistical confidence of spectral power density, particularly for the longer periods.
Robustness of ENSO amplitude and significant-peak analysis Previous studies from  Wittenberg (2009)  and  Tindall et al. (2016)  show that ENSO variability can exhibit significant changes on the interdecadal and centennial scale. While a time-series length of 100 years as used here is clearly sufficient to study properties such as amplitude and spectra of ENSO on its typical interannual timescales, the presence of centennial-scale modulations on ENSO properties could imply that the choice of the 100-year segment affects our conclusions on interannual variability differences between preindustrial and mid-Pliocene. In order to address this issue, we repeated the calculation of the ENSO amplitude with 500 years of data. For this we used data of two PlioMIP2 models, MIROC4m and CESM2. The results are included in the Supplement.
There are clear variations in the ENSO amplitude on centennial timescales. The change in Niño 3. 4 SD varies around ±9 % and ±4 % of the value when using 500 years of data for CESM2 and MIROC4m, respectively. However, these variations are not large enough to change the conclusion regarding the clear reduction of the ENSO amplitude in the PlioMIP2 ensemble. These results are in accordance with the findings by  Tindall et al. (2016) , who use 2500 model years of HadCM3 simulations. When interpreting the ±9 % variation as an error margin on the change in Niño 3. 4 SD, only three out of the seven models that contributed to both PlioMIP1 and PlioMIP2 show a similar result in the both MIPs, namely IPSLCM5A, MIROC4m and MRI2. 3. The other models (CCSM4, COSMOS, HadCM3 and NorESM-L) show no overlap in their PlioMIP1 and PlioMIP2 values.
1G). PlioMIP2 data from CESM2, EC-Earth3. 3, NorESM1-F, IPSLCM6A and GISS2. 1G can be obtained through the Earth System Grid Federation (ESGF) (https://esgf-node. llnl. gov/search/ cmip6/, last access: 25 February 2021, ESGF, 2020). A selection of the data from the scatter plots presented in the paper is available in the Supplement. The Python scripts (Jupyter Notebooks) used for data analysis are freely accessible through Zenodo (https://doi. org/10. 5281/zenodo. 5718483, Oldeman, 2021). Supplement. The supplement related to this article is available online at: https://doi. org/10. 5194/cp-17-2427-2021-supplement. Author contributions. AMO, MLJB, ASvdH and HAD conceived the presented ideas in this study. AMO performed the analysis and wrote the draft of the paper. JCT contributed data and advice in data analysis.
Oceanographic biophysical measurements Hydrographic data were obtained from lowered conductivity, temperature, and pressure (CTD) measurements using a SeaBird SBE 9-plus CTD system equipped with two sets of pumped sensors. Water samples for oxygen, nutrients, and salinity were taken using 24 Niskin bottles (10 L) mounted on a General Oceanics rosette. Salinity samples were analyzed on board with a Guildline Autosal 8 model 8400B salinometer to calibrate conductivity measurements to practical salinity (PSS-78) with an uncertainty of 0. 003 g kg -1. Practical salinity was converted to absolute salinity (TEOS-10) using routines of the Gibbs Seawater toolbox (https: //github. com/TEOS-10/GSW-Python, last access: 7 September 2020). The CTD was also equipped with an oxygen sensor and a WET Labs (USA) fluorometer.
SUNA nitrate values correlated highly with the autoanalyzer values (r squared: 0. 9972, with the 10 % most deviating samples removed). SUNA values were also compared to the combined autoanalyzer concentrations of nitrate and nitrite, but the resulting correlation was somewhat weaker (r squared: 0. 9964, with the 10 % most deviating samples removed). The SUNA measurements were thus corrected to match the nitrate (NO 3 ) concentration by applying the following correction term, where NO 3, old is the original measurement, and NO 3, new is the final corrected value: NO 3, new = 1. 2813 + 1. 0576 × NO 3, old. We applied this calibration to all SUNA nitrate concentrations. All observational datasets mentioned above  (Krahmann, 2018; Dengler et al. , 2019a, b; Tanhua and Visbeck, 2018)  are published at the world data center PANGAEA following the SFB 754 data policy (https://www.
Yonss S. Jose received funding from the BMBF (Humboldt Tipping Points, 01LC1823B). We are grateful to the Peruvian authorities for the permission to carry out scientific work in their national waters. Special thanks go to the captain and the crew of the R/V Meteor for their support during the M136 cruise and to the chief scientist Marcus Dengler. For processing the nutrient samples used for the NO 3 sensor calibration, we would like to thank the group of Stefan Sommer and their technicians. We would also like to thank Clarissa Karthäuser and Gabriele Klockgether for help with sample analysis for the primary-production measurements. Financial support. This research has been supported by the DFG (SFB; grant no. 754), the European Commission Horizon 2020 Framework Programme (WACO; grant no. 749699), and the BMBF (Humboldt Tipping Points; grant no.
Acknowledgements. This work is a contribution of the  Sonderforschungsbereich 754 Climate-Biogeochemistry Interactions in the Tropical Ocean  (https://www. sfb754. de/),which is funded by the  Deutsche Forschungsgemeinschaft (DFG). Soeren Thomsen  received funding from the  European Commission (Horizon 2020 ,  MSCA-IF-2016 ,  WACO 749699 :  Fine-scale Physics, Biogeochemhttps://doi. org/10. 5194/bg-18-3605-2021 Biogeosciences, 18,  3605-3629 , 2021.
Code and data availability. All code and data are available upon request. The PYTHON toolbox used for visualization of the simulations can be downloaded at https://doi. org/10. 5281/zenodo. 4899097 (Hauschildt, 2021) or https://github. com/jaard/xcroco. The lowered CTD (https://doi. org/10. 1594/PANGAEA. 892564, Krahmann, 2018), underway CTD (https://doi. org/10. 1594/PANGAEA. 904288, Dengler et al. , 2019b), and VMADCP (https://doi. org/10. 1594/PANGAEA. 901425, Dengler et al. ,  2019a)  data collected on R/V Meteor during cruise M136 are available on the PANGAEA database (http://www. pangaea. de, last access: 1 December 2020). Model output cannot be efficiently hosted online due to storage space constraints and energy considerations but will instead be made available upon request. Supplement.
The richness of rodent species, along with certain traits such as their fast-paced lives, population dynamics, opportunism, and synanthropism makes them efficient amplifiers, spreaders, and transmitters of viruses  [23, [26] [27] [28]. This is illustrated by the 31 mammalian viral families reported from the DRodVir online database, as of 21 July 2021  [29, 30]. Rodents also play a key role in viral emergence phenomena, with many species being reservoirs and carriers of viral pathogens, for example, hantaviruses or mammarenaviruses, some of which are associated with human diseases  [31] [32] [33] [34] [35]. Amazonia is known for its high biodiversity of mammals, plants, invertebrates, and microbes  [36] [37] [38] , comprising a large set of potential hosts for viruses. This implies that high viral diversity is expected from the region and thus a high number of potential zoonotic viruses  [39, 40].
Then cleaned reads were mapped back to the contigs using the BWA-MEM mapper  [57]  and Samtools  [58]  in order to obtain the number of reads for each contig (Step 2, Figure  1 ). We used BLAST  [59, 60]  for the taxonomic assignment of contigs (Step 3, Figure  1 ). All contigs were submitted to DISCONTIGOUS MEGABLAST BLASTn (e-value ≤ 10 -1 ). Those without results in BLASTn were submitted to BLASTx. The BLASTx process comprised two steps: (1) using an in-house viral protein database, which was created by clustering (CD-HIT, 100% homology) the NCBI-nr database viral protein sequences (August 2018); (2) the set of positive contigs was subsequently submitted to BLASTx (e-value ≤ 10 -1 ) against the whole NCBI-nr database. Both BLASTn and BLASTx results were filtered using in-house python scripts, which selected the best scoring match for each contig (max e-value of 10 -1 and coverage of, respectively, >50 nucleotides and 17 amino acids in length for BLASTn and BLASTx) (Filter 1).
Taxonomies were deduced from the BLAST results and fragments were assigned to selected viruses matching Taxids against the full name lineages file from NCBI (f)  [61]. These two data sets were filtered again (Filter 2) according to the e-values and coverage according to both BLASTn and BLASTx (e-value = 10 -5 ; coverage ≥ 250 nt or 83 amino acids in length) so as to consolidate the results. The remaining contigs were used for counting in the results. To provide a more accurate definition of the virus's taxonomic status, each viral taxonomic identification was associated with host types, such as bacteria, vertebrates, invertebrates, amoeba, and plants, by consulting the ICTV  [62] , ViralZone, and virus host database websites  [63, 64]. As the presence of bacteriophages is unexpected in compartments such as the kidney, spleen, and blood, and it is difficult to attribute such bacteriophages to bacterial infections, contigs assigned to bacteriophages were discarded from the data set.
The heatmap represents the number of contigs associated with each viral family by species-habitats. The viral genomes' completeness of assigned contigs was tested using CHECKV (version v0. 7. 0) and its associated database  [65]. Lastly, a quantitative data set associating each contig with its number of reads was constructed with the number of reads associated with each taxonomic category at the subfamily or genus level. The main steps are shown in solid-line boxes in green font characters, with the number in brackets representing the corresponding step in order of execution (de novo assembly, read mapping to contigs, and taxonomic assignment). BLASTn and BLASTx, as sub-steps of taxonomic assignment, are shown in solid-line boxes with purple font characters. For each output, (+) and (-) stand for positive/kept and negative/discarded results, respectively, where they appear; taxonomic information acquisition and the filter-1 step are represented by red stars.
Phylogenetic Analyses Contigs assigned to four viral families (Polyomaviridae, Flaviviridae, Togaviridae, and Phenuiviridae) were selected from assembly files. Contigs were checked manually using the NCBI BLASTx web tool for the presence of stop codons. Then sequences presenting stop codons were deleted from further analysis. In order to infer phylogenetic relationships, we selected the closest sequences provided by BLAST during the taxonomic assignment process and some representatives of the family to be analyzed. In addition, for the Polyomaviridae and Flaviviridae analyses, sequences detected in rodents worldwide were also included. For the Alphavirus and Phlebovirus analysis, the data set including the closest sequences provided by BLAST was completed by sequences representative of the known antigenic complexes.
All reference sequences were downloaded from the NCBI-nt database. Accession numbers of viral sequences used to infer the phylogenetic trees are given in the respective phylogenetic reconstructions. After selecting the best-suited region for phylogenetic analyses, the Muscle algorithm  [72]  was used for multiple sequence alignments with default parameters. Pairwise sequence identity (at the nucleotide and amino acid levels) for each selected region were calculated using uncorrected p-distances. The best-fitted model of nucleotide or amino acid substitution for each analysis was selected using jModelTest 2  [73]  and ProtTest 3  [74] , respectively, under corrected Akaike information criteria (AICc). Bayesian phylogenetic analyses were performed using MrBayes 3. 2  [75, 76]. The Markov chain Monte Carlo (MCMC) algorithm was run with four chains with 2 million generations each, with trees sampled every 500 generations and a 25%burnin.
Illumina Sequencing and Bioinformatics Analyses Overall, 453,865,988 paired-end raw reads (907,731,976 individual reads) were obtained that were 250-300 bp in length. After trimming by FaQcs, 96. 62-99. 56% of the reads were kept, totaling 894,996,464 reads. These cleaned reads were assembled de novo for a total of 5,268,112 contigs using MEGAHIT (Figure  1  and Supplemental data, Table  S3  for assembly statistics). The number of contigs ranged from 25,215 to 347,957. The mean number was 146,336 contigs/sample (Table  2 ). All these contigs were submitted to taxonomic assignment. After megaBLASTn, 4,411,189 (83. 73%) contigs were assigned to an organism, among which 50,431 were attributed to viruses. The remaining 856,923 (16. 27%) unassigned contigs were first submitted to a BLASTx search against the in-house viral protein database (BLASTx1) (Figure  1 ).
Only 274,991 contigs matched viral proteins, and they were all put through a second BLASTx step against the entire nr protein database (BLASTx2) (Figure  1 ). Overall, after this second BLASTx step (BLASTx2), 257,152 contigs were re-assigned to viruses, 3776 were assigned to other types of organisms, and 17,839 were not assigned at all (Figure  1 ). To further avoid artifacts and false-positive results, the virus-assigned contigs were filtered at a coverage of ≥250 bp for BLASTn or ≥83 amino acids for BLASTx results and an e-value of ≤e-5 (Filter 2), resulting in 101,867 viral contigs, accounting for 1. 93% of the total initial number of contigs and 15% of the assigned contigs (Figure  1 ). Among the 101,867 viral contigs, 945 assigned to bacteriophages were discarded from further analyses.
bicolor (disturbed forest), two in Z. brevicauda (savannah and disturbed forest), and one in H. megacephalus (disturbed forest). After alignment, the longest common sequences identified from the four species covered a fragment of 498 nucleotides of the Large T antigen (LTAg). Sequence fragments were named O. bicolor polyomavirus 1 (ObicPyV-1, GenBank accession. number. MT732103), Z. brevicauda polyomavirus 1 (ZbrePyV-1, GenBank accession number MT732104), P. guyannensis polyomavirus 1 (PguyPyV-1, GenBank accession number MT732102), and H. megacephalus polyomavirus 1 (HmegPyV-1, GenBank accession number MT732105).
bicolor (disturbed forest), two in Z. brevicauda (savannah and disturbed forest), and one in H. megacephalus (disturbed forest). After alignment, the longest common sequences identified from the four species covered a fragment of 498 nucleotides of the Large T antigen (LTAg). Sequence fragments were named O. bicolor polyomavirus 1 (ObicPyV-1, GenBank accession. number. MT732103), Z. brevicauda polyomavirus 1 (ZbrePyV-1, GenBank accession number MT732104), P. guyannensis polyomavirus 1 (PguyPyV-1, GenBank accession number MT732102), and H. megacephalus polyomavirus 1 (HmegPyV-1, GenBank accession number MT732105). The ObicPyV-1 and ZbrePyV-1 sequences shared 100% nucleotide and amino acid identity. HmegPyV-1 displayed 99. 22% nucleotide identity and 98. 83% amino acid identity with the two previous sequences (Supplemental data, Table  S5 ).
PguyPyV-1 displayed substantial divergence from those two sequences-50. 80% nucleotide identity and 39. 16% amino acid identity with the ObicPyV-1 and ZbrePyV-1 sequences-and showed 50% and 37. 95% nucleotide identity and amino acid identity, respectively, with HmegPyV-1. Compared with other polyomavirus sequences, ObicPyV-1 and ZbrePyV-1 showed the highest percentages of identity (61. 45% and 57. 23% in nucleotide identity and amino acid identity, respectively) with Sciurus carolinensis polyomavirus 1 (GenBank accession number MK671096); and HmegPyV-1 showed 60. 04% nucleotide identity and 54. 02% amino acid identity with it (Supplemental data, Table  S5 ). PguyPyV-1 showed the highest level of nucleotide identity (67. 78%) with Pan troglodytes PyV3 (GenBank accession number YP_009094197) and 63.