dataesr/scientific-paragraphs-categorization huggingface dataset:
Such accurate information will foster (1) initiatives for soil health assessment and monitoring as well as (2) modelling works of SOC dynamics and of the climate regulation function of soils. Code and data availability. The Rock-Eval ® data for the 105 reference topsoil samples in PARTY SOC v2. 0 are provided in Table  S3  as a CSV file. The R script used to extract Rock-Eval ® raw data and calculate Rock-Eval ® parameters, the Rock-Eval ® data and the R script used to build the PARTY SOC v2. 0 and PARTY SOC v2. 0 EU models and test their performance, and the PARTY SOC v2. 0 EU model (available as an R script and an R data file; please note that predictions of the centennially stable and active SOC contents -in g C kg -1 -are obtained by multiplying their respective proportions by the TOC RE6 Rock-Eval ® parameter) can be accessed on GitHub at https://github.
com/lauric-cecillon/PARTYsoc (last access: 17 June 2021) and on Zenodo at the permanent link https://doi. org/10. 5281/zenodo. 4446138  (Cécillon, 2021). Supplement. The supplement related to this article is available online at: https://doi. org/10. 5194/gmd-14-3879-2021-supplement. Author contributions. LC and PB designed the study with contributions from CC and FB. FB and FS performed the Rock-Eval ® measurements. LC wrote the R scripts used to calculate Rock-Eval ® parameters and built the second version of the PARTY SOC model with contributions from PB, LNS, and EK. BTC, UF, SH, TK, IM, FvO, CP, and JCQ provided the topsoil samples and the metadata for the reference sites. LC and PB wrote the paper with contributions from all authors. Competing interests. The authors declare that they have no conflict of interest.
Acknowledgements. We are indebted to the generations of technicians and scientists that started and managed the long-term experiments and archives of soil samples used in this work. We thank  Rothamsted Research  for access to samples and data from the  Rothamsted Sample Archive  and the electronic Rothamsted Archive (e-RA). We thank  David Montagne  and  Joël Michelin  ( AgroParis-Tech, France ), who provided information on the soil characteristics at Grignon. We thank our colleagues in the  ROMENS research group at Ecole normale supérieure (Paris, France) , especially  Samuel Abiven ,  Núria Catalán ,  Bertrand Guenet , and  Marcus Schiedung , who provided advice that improved this paper. Finally, we thank  Emanuele Lugato  ( JRC, Ispra, Italy ) and an anonymous reviewer for their constructive comments that further improved our paper.
Financial support. This research has been supported by the  French Agence nationale de la recherche  ( StoreSoilC  project, grant  ANR-17-CE32-0005 ), the  French Agence de la transition écologique (ADEME) , and  Ville de Paris  ( SOCUTE  project, emergence(s) programme). The Rothamsted Long-term Experiments are supported by the  UK Biotechnology and Biological Sciences Research Council  under the  National Capabilities programme grant  ( BBS/E/C/000J0300 ) and by the  Lawes Agricultural Trust.
Metabolites separation was achieved on a Waters Acquity BEH C8 (1 x 150 mm, 1. 7 µm) column with an injection volume of 5 µL and a flow rate of 0. 1 mLmin -1. The mobile phase was composed of octylammonium acetate 4 mM in H2O adjusted to pH = 4. 6 with acetic acid (phase A) and methanol (phase B) and the following gradient was used: 1% B for 5 min, 1 to 95 % B in 10 min, 95 to 99% B in 5 min, 99% B held for 3 min, then 1% B for 7 min for a total run time of 30 minutes. Data acquisition was realized under full scan switch (positive and negative) mode ionization from m/z 80 to 900 at 70,000 resolution followed by inclusion list dependent MS/MS. Samples were randomized and analyzed in triplicates. A quality control (QC) sample was added by pooling equal volumes of each sample to monitor suitability, repeatability and stability of the system and injected every 10 samples.
An extraction blank sample was also injected every 10 runs in order to monitor sample cross-contamination. Pure commercial standards (Fumaric acid, succinic acid, oxaloacetic acid, alpha-ketoglutaric acid, glutamic acid, phosphoenolpyruvic acid, dihydroxy acetone phosphate, citric acid, NADHP and pyruvic acid, Merk) were analyzed to monitor their retention time, m/z of the parent ion and fragment ions. Feature extraction, compound identification and retention time correction were performed using Compound Discoverer 2. 1 software (Thermo Fisher Scientific). Processing of the filtered feature table was realized using Metaboanalyst (www. metaboanalyst. ca) (Chong et al. , 2019). Samples were normalized using QCs, data were log transformed then Pareto-scaled (mean-centered and divided by the square root of the standard deviation of each variable).
ChIP-sequencing Preparation of libraries and sequencing was performed at GENEWIZ (Leipzig, Germany). Here, libraries were prepared using NEBNext Ultra DNA Library Preparation Kit for Illumina (New England Biosystems) following the manufacturer's recommendations. Briefly, the ChIP DNA was end repaired and adapters were ligated after adenylation of the 3'ends. Adapter-ligated DNA was size selected, followed by clean up, PCR enrichment. ChIP libraries were validated using an Agilent TapeStation, and quantified using Qubit 2. 0 Fluorometer as well as qRT-PCR (Applied Biosystems, Carlsbad, CA, USA). Normalized libraries were multiplexed, clustered, and loaded on one lane of a flow cell (Illumina). Libraries were sequenced on a HiSeq 4000 platform (Illumina) using a 2x150 PE configuration. Image analysis and base calling were conducted by the HiSeq Control Software (HCS).
Network analysis A Network integrating RNA-seq data from the HGF condition of the in vitro sugar challenge experiment and ChIP-seq data was generated using FastHeinz from the R BioNet package  (Beisser et al. , 2010)  with the HumanCyc metabolic pathways (http://humancyc. org/) downloaded from http://www. ndexbio. org (Pillich et al. , 2017). Next, a weight matrix was constructed with all expressed genes in the Klf10 flox/flox and Klf10 Δhep primary hepatocytes taking the lowest p value from the ChIP-seq RNA-seq datasets. Network visualization was done using Cytoscape and AutoAnnotate app  (Kucera et al. , 2016; Shannon et al. , 2003). Clustering was performed using the MCL partitioning algorithm from clusterMaker2  (Morris et al. , 2011)  and gene ontology analysis was performed using BiNGO  (Maere et al.
Quantification and statistical analyses For the circadian time course experiment, sequences were preprocessed using FASTQ groomer and checked using Fastqc (https://www. bioinformatics. babraham. ac. uk/projects/fastqc/). Reads were mapped against the Mus musculus genome (mm10) downloaded from Ensembl. org using STAR_2. 4. 0a  (Dobin et al. , 2013)  with the Encode RNA-seq best practices options indicated ("-outFilterIntronMotifsRemoveNoncanonicalUnannotated -alignMatesGapMax 1000000 -outReadsUnmapped Fastx -alignIntronMin 20 -alignIntronMax 1000000 -alignSJoverhangMin 8 -alignSJDBoverhangMin 1 -outFilterMultimapNmax 20"). After alignment to the genome reads were counted with featureCounts  (Liao et al. , 2014)  with "-primary -g gene_name -p -s 1 -M -C" options indicated. Gene stable IDs (GRCm38.
p6) were downloaded from Ensembl Genes 94 (Ensembl) and used to filter for mouse protein coding genes. Matrix of raw counts from both genotypes across time was generated in R (R Core Team, 2018). Transcripts with less than 8 counts across all samples were excluded from downstream analyses. All transcript counts were normalized using edgeR  (Robinson et al. , 2010)  and expressed in counts per million (cpm). For the in vitro sugar challenge experiment, sequences were preprocessed using FASTQ groomer and checked using Fastqc (https://www. bioinformatics. babraham. ac. uk/projects/fastqc/). Reads were mapped against the Mus musculus genome (mm10) downloaded from Ensembl. org using HISAT2with default parameters  (Kim et al. , 2015). After alignment to the genome reads were counted with featureCounts  (Liao et al.
, 2014)  with with default parameters. Matrices containing the counts were loaded into R. For downstream analyses, we analyzed protein coding genes with a sum >20 counts across all samples. Circadian expression of mRNA was determined using MetaCycle  (Wu et al. , 2016)  with the following options: minper = 24, maxper = 24, cycMethod = c ("ARS," "JTK," "LS"), analysisStrategy = "auto," outputFile = TRUE, outIntegration = "both," adjustPhase = "predictedPer," combinePvalue = "fisher," weightedPerPha = TRUE, ARSmle = "auto," and ARSdefaultPer = 24. Transcripts with an integrated p-value (meta2d_pvalue) < 0. 05 were considered rhythmically expressed. Circadian pathways were determined by Phase Set Enrichment Analysis (PSEA)  (Zhang et al. , 2016)  based on the sets of circadian transcripts with a relative amplitude value (rAMP; the ratio between amplitude and baseline expression) of ≥ 0.
1. Gene sets were downloaded from the Molecular Signatures database (MSigDB) H (HallmarkGene Sets)  (Subramanian et al. , 2005). Sets containing fewer than 5 circadian transcripts were excluded from the analysis. The Kuiper test was used to identify circadian gene sets by comparing the acrophases of all circadian transcripts belonging to each gene set to a uniform background distribution and by testing for non-uniformity (q < 0. 01). For the in vitro sugar challenge experiment, differential expression analysis between conditions was performed using the TestDE function in the R package "edgeR"  (Robinson et al. , 2010). Genes with an FDR value < 0. 05 when comparing LG vs HGF conditions in each respective genotype were considered differentially expressed and used to assess the pathways enriched under the LG and HGF conditions.
Acknowledgements This study was supported by  French Agence Nationale pour la Recherche (ANR) : # ANR-15-CE14-0016-01 , # ANR-18-CE14-0019-02  and by the  "Investments for the Future" LABEX   SIGNALIFE  (# ANR-11-LABX-0028-01 ), the  UCA JEDI Investments  in the  Future project  (# ANR-15-IDEX-01 ) and ATER grant from  Université Côte d'Azur (JSR). We thank the  Canceropôle Provence-Alpes-Côte d'Azur , and the  Provence-Alpes-Côte d'Azur Region  for the financial support provided to the  MetaboCell  project. We thank  Pierre Chambon  for the gift of SA-CreER T2 mice. We thank  Aurélie Biancardini ,  Angelo Barnabeo ,  Pierre Chérel ,  Antoine Landouar  and the staff of the mouse facility for assistance with the animal care. We thank  Sama Rekami  for the help with the histopathology and the  PFTC facility  for technical support.
The JEG-3 cell line was transfected with pSpCas9 vector cloned with each sgRNA and analyzed as follows. First, HLA-G expression was measured by FC on JEG-3 edited cells. The results revealed that using any of the sgRNAs (1A, 1B, 2A or 2B), the cell membrane HLA-G expression was downregulated. Compared with the wild type JEG-3, the proportion of HLA-G reduction with each sgRNA was 72%, 35%, 80% and 62% for 1A, 1B, 2A and 2B-sgRNAs, respectively (Fig. 3A, B,  and D ). Moreover, transcriptomic analysis by RT-qPCR with primers located 3' of the ATG start site (257F/526R) also confirmed these results, with a reduction of HLA-G mRNA of 60%, 40%, 65% and 55% for 1A, 1B, 2A and 2B-sgRNAs, respectively (Fig. 3A  and B ; data were normalized to the unedited cells, wt JEG-3). Finally, the genomic HLA-G sequence was analyzed in JEG-3 edited cells with each sgRNA to determine which editions occurred.
Design and preparation of sgRNA vectors Four sgRNAs were designed: two sgRNAs upstream exon 1 ATG (named 1A-and 1B-sgRNAs), and two sgRNAs upstream of the ATG situated in exon 2 (named 2A-and 2B-sgRNAs). All the sgRNAs were designed using Benchling Life Sciences R&D Cloud Software (https://benchling. com/) and cloned into the pSpCas9(BB)puroV2. 0 vector (Addgene #62988), which expresses both Cas9 and puromycin resistance genes  23, 24. The sgRNA sequences are listed in Table  1  and the schematic representation of sgRNAs design is shown in Figure  1.
RNA Extraction, cDNA synthesis and real-time RT-qPCR RNA extraction from puromycin resistant cells (RCC7/HLA-G1 and JEG-3) was performed with TRIzol Reagent (Invitrogen). For cDNA synthesis, 500-1000 ng of the total RNA was retro-transcribed with MMLV reverse transcriptase (Promega), according to manufacturer's instructions. For RT-qPCR, cDNA samples were diluted 5-fold and it was performed with StepOne Plus Real Time PCR System (Applied Biosystems). The FastStart Universal SYBR Green Master Mix (Roche) was used for all reactions. Primers efficiency and initial molecule (N0) values were determined by LinReg software 3. 0, and gene expression was normalized to RPL7 housekeeping gene, for each condition. All the oligonucleotide sequences are listed in Table  1.
Flow cytometry To determine the HLA-G silencing in JEG-3 cell line by CRISPR/Cas9, the protein expression was analyzed by FC. JEG-3 cells transfected with each sgRNA individually or all sgRNAs together, were dissociated and stained with a primary antibody anti-HLA-G conjugated with FITC (Invitrogen), in a 1:50 dilution for 30 min at room temperature. FC analyzes were performed in a BD Accuri cytometer. Data was analyzed with FlowJo Software.
Update (16 June 2021) on Studies with FIASMAs in the SARS-CoV-2 Infection To review the extensive evidence about FIASMAs as a therapeutic modality for COVID-19, authors attempted to answer the following key questions. First, would FIASMAs provide benefits in relation to COVID-19? Second, should FIASMAs be used as early intervention in COVID-19 disease? We manually searched two electronic databases, PubMed and Google Scholar, for English-language titles and abstracts using the terms "Alverine OR. OR. Trimipramine" (64 drugs, see  [2] ) and "COVID-19 OR SARS-CoV-2" Among the 231 articles (PubMed and Google Scholar), 91 were retained  [3, 12,. Twenty-seven have been reviewed in our previous article  [4]  (23 in silico, in vitro or in vivo studies, 5 human studies (one common with in vitro studies)) and 64 were new studies highlighting the interest of the scientific community for FIASMAs.
Accepted Article This article is protected by copyright. All rights reserved. (1998) to find the inflection points and convexities of the curves. This was done with the RockMagAnalyzer 1. 1 software  (Leonhardt, 2006). Hysteresis loops, isothermal remanent magnetization (IRM) acquisition curves and further direct current IRM demagnetization or back field saturation IRM (SIRM) curves were performed in 53 samples using a J-Meter Coercivity-Spectrometer, originally designed at Kazan federal University's paleomagnetism laboratory and improved by  Jasonov et al. (1998). This instrument consists of a non-magnetic wheel, 50 cm in diameter, which rotates 18 times per second through an electromagnet. The in-field magnetic remanence of the sample, which is secured in a holder on the outer rim of the wheel, is measured while the electromagnetic field increases up to 500 mT and then decreases to the opposite polarity (-500 mT).
Discussion 5. 1 A new paleointensity secular variation curve for Uzbekistan between 600 BCE and 600 CE. The new mean intensities are compared in Fig. 5  with previous paleointensity results from archeological sites located within a radius of 1000 km around Termez and corresponding to the period 600 BCE -600 CE. As with the new data, the previous dataset was also relocated to the Termez coordinates. The number of the previous studies is significant and 72 paleointensities derived from the study of bricks, potteries or baked materials  (Burlatskaya et al. , 1995; Nachasova and Burakov, 1994 , 1996 , 1997). These older data were obtained with the Thellier and Thellier intensity method (1959) but, in case of  Burlatskaya et al. , (1995) , without considering the TRM anisotropy effect upon paleointensity estimations.
Accepted Article This article is protected by copyright. All rights reserved. supporting information). Moreover, the cooling rate effect upon intensity estimates was also not considered in these studies. In a recent work,  Hervé et al. (2019a)  have studied the influence of cooling rate in a set of 35 baked bricks and concluded that, on average, the non-corrected results overestimated the expected value by 5-6%. These values are of the same order of magnitude as the mean correction proposed by  Genevey et al. (2008)  from an analysis of the global database. In our studied collection cooling rate factors up to 12% have been obtained with a mean value for the overall collection of 5% in agreement with the results obtained by  Hervé et al. (2019a) and Genevey et al. (2008). Therefore, another potential source of error can be identified in the ancient data available for the targeted region.
In order to consider this effect and in agreement with the studies cited before, a 5% reduction in the paleointensity estimates have been applied for the previous intensities available. [Insert Figure 5]  As it can be seen in Figure  5  new and previous data are quite consistent, although some discrepancies can also be observed. As discussed before, the reliability of the data cannot be considered as equal, and we decided to classify the data in three different categories using an appropriate weighting scheme designed for this purpose. Data obtained with 3 or more specimens and including the TRM and cooling rate corrections and pTRM checks were considered as the most reliable group and a weight of 10 was used. Data with TRM anisotropy and cooling rate corrections and pTRM checks but obtained with less than 3 specimens were assigned to an intermediate weight of 5 and, finally, the other data were considered as the less reliable group with an associated weight of 1.
Accepted Article This article is protected by copyright. All rights reserved. dataset together with the new results (shown in Fig. 5 ) and using the mentioned weights. To fit the paleointensity data to a time-continuous curve we applied the same approach than  Rivero-Montero et al. (2021a). In summary, this method provides an ensemble of 5,000 PSV curves by bootstrapping the paleointensity data in both age and intensity domains (including the error estimations). In addition, to obtain a more robust PSV curve, we eliminate possible outliers by detecting the data which residuals (the difference between data and curve) are 3 times higher than the root mean square error of the curve fitting. Only one data point has been identified as an outlier, data around 485 BCE  (Burlatskaya et al. , 1995) , (see Table  S3  from the supporting information for more details).
The mean and the standard deviation (1 or 2) of the ensemble of 5,000 PSV curves provide the final PSV curve for South Uzbekistan. The new local PSV curve obtained (Fig. 5 ) shows an intensity maximum around 400 BCE followed by a rapid decrease of about 15 µT/century until 100 BCE. However, we must notice that this maximum is constrained by some archeointensity values higher than 80 µT that are included in the less reliable group, suggesting that this intensity bump might not reflect a real feature of the past geomagnetic field. New quality data covering this time interval are needed to confirm this hypothesis. The curve also shows an intensity minimum value around 100 BCE followed by a slowly growing trend until 500 CE. Finally, the compiled dataset has been compared with global paleomagnetic reconstructions: SHA.
Comparison with neighboring regions and global implications To analyze if intensity changes in Uzbekistan are reflecting global or regional patterns of the past geomagnetic field, we have compared the virtual axial dipole moment (VADM) directly calculated from the data and the dipole moment (DM) and Axial Dipole Moment (ADM) derived from the global reconstructions previously mentioned. As shown in Fig. 6  the VADMs exhibit much stronger fluctuations than the global ADM-DM, showing higher values between 600 and 300 BCE and lower estimations from 200 BCE to 300 CE. This suggests an important regional geomagnetic field behavior due to the non-dipolar contributions. [Insert Figure  6 ] In order to investigate if similar geomagnetic field intensity trends are observed in other regions, we used the complete global database from 600 BCE -600 CE (GEOMAGIA50V3.
4 database, Brown et al. , 2015 updated with recent publications:  García-Redondo et al. , 2020; Rivero-Montero et al. , 2021a)  to compile the VADM values available for different regions (1000 km of radius, see Fig. 7 ). The results are plotted from west to east in Fig. 7. In Mexico, a VADM maximum of 16•10 22 Am 2 can be seen around 200 BCE but this feature is only based on two high-quality values  (Hervé et al. , 2019b). Nevertheless, this maximum is not observed in Peru, where high-quality data seem to present a minimum of VADM around this age. Moving to Europe, a maximum of 17•10 22 Am 2 is observed around 500-450 BCE followed by a decreasing trend until 100 BCE in.
In Eastern Asia and Japan  (Cai et al. , 2017 (Cai et al. , , 2020;; Hong et al. 2013) , there is no evidence of a maximum around 500-400 BCE if the high-quality dataset is considered, but some high VADM values of low quality are present in China. It is, however, hard to define a clear tendency in these areas due to the important dispersion of the corresponding regional databases. [Insert Figure  7 ] We also calculated the PSV curves obtained from the different regional datasets (see supporting information) following the methodology described before, using the mentioned categories of high-and low-quality data and different weights for each category, giving weights from 1 to 10 depending on the quality of the data. From this, the intensity secular variation rates (µT/century) have been calculated in 100-year time windows.
Conclusions Fifty-one new paleointensity data, at the sample level, have been obtained for South Uzbekistan with ages ranging between 310 BCE and 300 CE, a period for which no highquality paleointensity data were previously available. Our new results provide evidence of a rapid drop in intensity during the second half of the 1 st millennium BCE showing a minimum of about 30 µT around 200 BCE -1 CE. Together with previous archeointensities our results allow us to present a new local PSV curve for Central Asia. The results suggest a V-shaped intensity behaviour in Central Asia during the 600 BCE up to 600 CE period. High-intensity field values around 400 BCE were achieved in Uzbekistan followed by a sharp decrease until 100 BCE -1 CE associated with rates of changes of about 15 µT/century. A progressive recovery of geomagnetic field strength is then observed during the first centuries of the CE.
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