dataesr/scientific-paragraphs-categorization huggingface dataset:
Therefore, our present study is a primary attempt to develop a new comprehensive and high-resolution dataset over Vietnam from a large number of CMIP5 GCMs. The newly-built data, hereafter called CMIP5-VN, consisting of daily mean, maximum, and minimum temperature, and precipitation, are created using the BCSD downscaling method based on a total of 31 CMIP5 GCMs and four RCPs. The new CMIP5-VN dataset is expected to supplement the existing climate change data in Vietnam that were previously obtained by the dynamical downscaling method. In the Results and Discussion section, we also present an application of the CMIP5-VN in assessing future changes in temperature and rainfall over the different climatic sub-regions of Vietnam.
Daily observation data and associated processing method In this study, near-surface daily average (T2 m), daily maximum (T max ), and minimum (T min ) temperatures and daily rainfall data from 1980 to 2005 are acquired from the Vietnam Meteorological and Hydrological Administration (VMHA). There are 147 and 481 stations for temperature and rainfall, respectively (Figure  1 ). The station data are interpolated to a 0. 1°Â0. 1°g ridded dataset by using the Kriging interpolation technique  (Switzer 2014)  for temperature and the Sphere map interpolation technique  (Willmott et al. 1985)  for rainfall. Nguyen-Xuan et al. (2016)  showed that the Spheremap interpolation technique has advantages in creating a gridded rainfall dataset over Vietnam in comparison with several other interpolation methods such as  Cressman (1959) , Inverse Distance Weighted  (Shepard 1968 ), or Kriging  (Switzer 2014).
Model data Daily rainfall (R) and temperatures (T2 m, Tmax, Tmin) from 31 CMIP5 GCMs (Table  1 ) are obtained via the Earth System Grid Federation portal (ESGF, https://esgf-node. llnl. gov/projects/cmip5/). The present-day simulations for the period 1980-2005 are used as the basis to construct statistical relationships between the high-resolution OBS dataset and the coarse resolution GCMs. Those relationships are further used to statistically downscale the projected GCM variables for the period 2006-2100 under the RCPs 2. 6, 4. 5, 6. 0, and 8. 5.
The BCSD approach The BCSD approach consists of two stages: BC and SD  (Wood 2002; Wood et al. 2004 ) (Figure  2 ). Firstly, cumulative distribution functions (CDFs) for both observations and historical GCM simulations are separately constructed for each climate variable for each of the 12 months (January-December) within each grid cell. Then, transfer functions that map the distribution of simulations onto observations are created  (Gudmundsson et al. 2012). Next, the biases in the GCM monthly outputs, re-gridded to the intermediate resolution of 1°Â1°, are corrected by mapping the quantiles at the same probability between the GCM simulated data and the OBS dataset. Note that previous studies suggested preserving the climatic trends in the original GCM  (Wood et al. 2004; Maurer 2007; Sobie et al.
For training and testing the BCSD procedure, we use the split ratio of 60:40 for the reference period 1980-2005, i. e. , the first 16 years 1980-1995 (∼60% data), called the training period, are used for building the BC transfer functions, and the later 10 years 1996-2005 (∼40% data) are used for testing the results. The BCSD-downscaled data obtained for these training and testing periods are called BCSD-CMIP5. Finally, to maximize the construction period of the BCSD approach, following what has been done in previous studies  (Reiter et al. 2016; Trinh-Tuan et al. 2019) , the total 26 years period from 1980 to 2005 is used to bias correct and guide the SD for all GCM experiments listed in Table  1  for the future period 2006-2100, to obtain the final downscaled dataset for Vietnam, called CMIP5-VN.
The daily CMIP5-VN dataset, which is about 1. 08 Terabytes in size, can be downloaded free of charge at the following address: http://remosat. usth. edu. vn/∼thanhnd/Download/CMIP5-VN/. The performance of the BCSD-downscaled data for the CMIP5 GCMs, i. e. , the BCSD-CMIP5, in representing the observed temporal and spatial distributions is assessed in the next section. We also compare the performance of the BCSD approach with the traditional and effortless bilinear interpolation (BIP) method. Note that the BIP considers the closest 2Â2 pixels of each CMIP5 GCM surrounding the unknown pixel's location on the targetted 0. 1°Â0. 1°grid. Then it computes a weighted average of the values of these four pixels to produce the final interpolated data (hereafter called BIP-CMIP5). The performance comparison between the BCSD and the BIP uses the added value (AV) metric (Di Luca et al.
28 to 27. 95%) and testing (À35. 89 to 34. 24%) datasets. Note that the rainfall biases of the BCSD products are still large over some regions. These large values indicate that they are not entirely systematic biases and rainfall highly varies with time and space; therefore, the TFs learned in the training period could hardly be truly applicable in the testing period. From Figure  4 , it is worth mentioning that relatively small absolute biases may appear as significant percentage biases in regions and seasons with dry (i. e. little rainfall) conditions, e. g. , in December-January-February (DJF). The ability of the BCSD-ENS in reproducing the seasonal cycle is illustrated in Figure  5 , where temporal correlations between the BC outputs and OBS for the testing period are displayed. The average correlation values are very high, i.
CONCLUSIONS In this study, the high-resolution (10 km) daily temperature (including near-surface daily average, daily maximum, and minimum temperatures) and precipitation dataset for Vietnam, called CMIP5-VN, has been successfully developed by applying the BCSD method for 31 CMIP5 GCMs for the baseline period 1980-2005 and the future period 2006-2100 under four GHG concentration scenarios RCP2. 6, RCP4. 5, RCP6. 0, and RCP8. 5. With the newly-built CMIP5-VN dataset, future scenarios for Vietnam are not only limited to RCP4. 5 and RCP8. 5 as shown in previous studies (e. g. MONRE 2016 MONRE , 2021;; Trinh-Tuan et al. 2019 ) but also available for RCP2. 6 and RCP6. 0. The CMIP5-VN is recommended for studies on climate change assessment and climate change impacts in Vietnam. As outputs of CMIP6 GCMs have recently become available on the ESGF, future studies will therefore need to downscale, both statistically and dynamically, the CMIP6 outputs for the region.
Furthermore, it should also be noted that the CMIP5 and CMIP6 results may not cover all probabilities that could occur in the future. Thus, a study that applies the probabilistic method suggested by  Rasmussen et al. (2016)  and  Hsiang et al. (2017)  could be considered to build a next climate change dataset for Vietnam. This new approach is expected to better depict the tails of the probability distribution and, therefore, could better project the extreme risks that may occur in the future.
1, HEPES 10, NaCl 2, MgATP 5, Na 2 GTP 0. 4, Na 2 -phosphocreatine 10 and biocytin (4 mg/mL). Series resistance was <20 MΩ and was monitored throughout the recordings. Data were discarded if the series resistance changed more than 10% during the experiment. For synaptic response measurements in voltage clamp mode, no compensation was applied. The liquid junction potential was not corrected. Data were obtained using a Multiclamp 700B amplifier and digitized using a Digidata 1550. Data were sampled at 10 kHz. pClamp10 software was used for data acquisition. Pen 5 ]Enkephalin, Tocris), an agonist for DORs. We have previously shown that this concentration effectively induces DOR-mediated iLTD in area CA2 and has no effect on inhibitory transmission in slices prepared from DOR knock-out mice  (Piskorowski & Chevaleyre, 2013).
We also performed an examination of the paired-pulse ratio (PPR) before and after manipulations that results in decreased GABAergic release. Thus, we measured the ratio of the first and second IPSC before and after altering GABA transmission, pooling all the data with electrical stimulation, direct activation of DORs by DPDPE and direct blockade of ErbB4 by AG1478. We consistently observed an increase in the PPR for recordings from SE conditions (Figure  3d ; from 0. 53 ± 0. 01 to 0. 58 ± 0. 01, n = 20, p = 2. 5 Â 10 À5 ). In contrast, we did not see a significant change in the PPR from slices prepared from EEhoused mice (Figure  3d ; from 0. 58 ± 0. 02 to 0. 59 ± 0. 01, n = 29, p =. 54). Interestingly, when we compare the overall mean PPR for each condition that precedes synaptic plasticity induction, we saw that the basal PPR was significantly lower for SE than for EE conditions (Figure  3d ; p =.
ACKNOWLEDGMENTS This work was funded by a  National Alliance for Research on Schizophrenia and Depression  independent investigator grant from the  Brain and Behavior Research Foundation , funding from the  Ville de Paris Program Emergences, Fondation pour la Recherche Medicale   EQU202003010457  and  Agence National de la Recherche  projects  ANR-13-JSV4-0002-01 ,  ANR-18-CE370020-01 , and  ANR-18-CE16-0006.
RNAseq analysis Sequencer output raw data files (binary base call format) were converted to Fastq format using the CASAVA (Consensus Assessment of Sequence And VAriation) Illumina software. The global quality of sequences was assessed using FastQ Illumina filter and FastQC software (Babraham Institute, Cambridge, UK). Sequencing adapters were removed using Cutadapt version 1. 14  (Martin, 2011). Then, the reads were splicealigned on the peach reference genome version 2. 0. a1 (Verde et al. , 2017)  using STAR version 2. 5. 1b software  (Dobin et al. , 2013). Finally, transcript expression was quantified using RSEM version 1. 3. 0 (Li and Dewey, 2011). The raw sequencing data were filtered by excluding transcripts with zero or less than 8 counts (corresponding to at least one count for half of the samples), thus reducing the number of transcripts detected to 20606, out of the 26873 protein-coding genes predicted in version 2.
0. a1 of the peach reference genome. To evaluate the statistically significant changes in gene expression, an "Independent hypothesis weighting" test was applied according to the DESeq2 R package protocol, with a max fold change of 2, a p. value threshold set to 0. 05 and an adjusted false discovery rate (FDR). To overcome the poor annotation of the peach transcriptome, we performed a blastp of the full peach proteome version 2. 0. a1 against Arabidopsis thaliana Araport11 protein sequences. The set of best Arabidopsis homologs obtained for each peach protein was used to conduct overrepresentation tests and enrichment of expression data via panther. org (Thomas et al. , 2003)  and gProfiler  (Raudvere et al. , 2019)  through the Gene Ontology (GO) and KEGG (Kyoto Encyclopaedia of Genes and Genomes) databases.
A P. persica protein was considered as the "true" ortholog of an Arabidopsis protein when it was also the best match of a reverse blastp (from Arabidopsis to P. persica proteomes); a total of 13097 Arabidopsis orthologs were found. Detailed gene function was also retrieved from The Universal Protein Resource (UniProt) and The Arabidopsis Information Resource (TAIR) online databases  (Berardini et al. , 2015; The UniProt Consortium, 2021). Enrichment analysis was conducted using fold enrichment cut-off of 1. 5 and p-value cut-off of 0. 05. Fold enrichment is the ratio between the frequency of term genes in a given list (here the lists of induced or repressed genes) and the frequency of term genes expected in this list, based on the frequency in the reference list (complete list of annotated genes).
One microliter of sample was injected in split mode (1:50) at 230°C. Helium was used as carrier gas at 0. 6 mL/min. The initial oven temperature was kept at 70°C for 1 min and then increased to 320°C (9°C/min) and maintained for 10 min. The m/z scan range was 70-600 with a cycle time of 20 scans/s. Source temperature and transfer line were set at 250°C. The MultiPurpose Sampler was controlled by Maestro Version 1. 4. 40. 1. Gerstel and gas chromatography system with mass spectrometer were controlled by ChromaTOF Version 5. 20. 38. 0. 54864 (LECO, Saint Joseph, MI, USA). GC-EI-TOFMS data were deconvoluted with the LECO NTD software (LECO, St. Joseph, MI, USA), then the peak list was curated manually to remove incorrectly deconvoluted peaks and contaminants. Peak annotation was based on spectral and retention index (RI) similarity using mass spectral libraries (Golm database, NIST 2014, Leco-fiehn rtx5).
Identification level of each metabolites was determined according to criteria inspired by  Schymanski et al. (2014) : level 1, confirmed structure by comparison with authentic standard, reverse match > 850, difference between retention index (RI) < 1%; level 2, probable structure, reverse match > 800 and difference between RI < 1%; level 3, tentative candidate, 600 < reverse match < 1000 and difference between RI > 1%. A specific extracted ion chromatogram (XIC) was chosen for each molecule for integration; then peak areas were normalized against the internal standard so the final dataset consisted of semiquantitative information. The list of metabolites detected and their analytical features can be found in Supplementary Table  S2 ; raw dataset (accession number MSV000084377) can be downloaded from the publicly available MassIVE repository at the UCSD Center for Computational Mass Spectrometry website.
The capillary voltage was set to 0. 8 kV and the cone voltage to 40 V. Nitrogen was used as the drying and nebulizing gas, with 50 L/h gas flow and 800 L/h desolvation gas flow. Analysis was performed twice, in negative and positive ionization modes, with a resolution of 40 000. Data independent acquisitions were performed simultaneously in MS and MS/MS modes (MS e , continuum), with a collision energy ramp from 20 V to 70 V. Mass spectra were recorded at 0. 2 second per scan from 50 to 1200 m/z. Chromatograms from negative mode acquisition were scanned to extract the major compounds. Metabolites were identified at the level 3  (Schymanski et al. , 2014)  by a manual examination of MS and MS/MS spectra in positive and negative modes and spectral comparison with in-house database and literature (Supplementary Table  S3 ).
Targeted UPLC-DAD-ESI-TQMS profiling Analyses were performed with an Acquity I-Class UPLC system (Waters, Mildorf, MA) equipped with a diode array detector (DAD) and hyphenated to a Xevo TQ-XS (Waters, Mildorf, MA) equipped with an ESI source. Pure standards (Merck KGaA, Darmstadt, Germany) were injected and MRM (multiple reactions monitoring) methods were optimized by testing ESI polarity, cone voltage and collision energy. Data acquisition and analysis were performed in using MassLynx software (Waters, Mildorf, USA). Identification level of each metabolites was determined according to  Schymanski et al. (2014)  as follows: level 1, confirmed structure by comparison with authentic standard; level 2, probable structure by comparison with data bases and/or literature; level 3, MS 2 spectrum interpretation and light absorbance spectrum matched a tentative candidate.
Data analysis Boxplots were generated with the R packages "PMCMRplus" and "ggoplot" and statistically significant differences were found with a Kruskal-Wallis test and Conover post-hoc test with Bonferroni adjustment. Principal component analysis (PCA) and Heatmap were generated with the R package "FactoMineR" and "Pheatmap" respectively, after performing log transformation and Pareto scaling. Metabolites were mapped to metabolic pathways using the Plant Metabolic Network database  (Hawkins et al. , 2021).
Seven dpi, infested Rubira plants had a 39% lower increase in stem height compared to control plants, while stem height increase of GF305 plants was not affected by infestation (Figures  1E, F ). Overall trends in the transcriptomic and metabolomic responses to GPA 48 hpi A PCA performed on the transcriptomic raw dataset (Figure  2 ) revealed that 88% of the total variance associated to PC1 was driven by aphid-induced differentially expressed genes (DEGs) in the resistant genotype Rubira. The second PC only expressed 9% of the total variance and was driven by the genotype differential gene expression. Among the 20 606 expressed genes, we found a total of 5743 DEGs: 284 genes were found differentially expressed between the 2 genotypes under control condition (175 DEGs upregulated in Rubira and 109 downregulated, Supplementary Tables  S1, S5 ), only 35 DEGs were found between control and infested apices of the susceptible GF305 (34 upregulated and 1 downregulated, Supplementary Table  S6 ) and 5424 DEGs between control and infested apices of the resistant Rubira (2990 upregulated and 2434 downregulated, Supplementary Tables  S1, S7  and Table 1 ).
Homologs of typical PRR genes involved in PTI were activated (Table  1 ), like Prupe_4G029800, an homolog of CLRK1/CLRK2 in Arabidopsis and homolog of Csa5M642150 Significant variations (p. adj) were determined using the DeSEQ2 package (R) at the threshold a = 0. 05. Repressed genes with negative Log fold changes (LFC) are indicated in blue and up-regulated genes are indicated in red. (*) https://www. uniprot. org in cucumber, identified as a candidate for resistance to Aphis gossypii  (Liang et al. , 2016). Prupe_1G542300 is the ortholog of RLK7, a cell surface receptor which triggers immune response upon detection of the PAMP-induced secreted peptide 1 (PIP1) and controls the accumulation of ROS detoxifying enzymes such as the superoxide dismutase  (Pitorre et al. , 2010; Hou et al. , 2014).
Data availability statement The full dataset of transcripts is available in the NCBI Sequence Read Archive. (BioProject ID PRJNA877419): https://www. ncbi. nlm. nih. gov/bioproject/?term=PRJNA877419 Raw metabolomics dataset (accession number MSV000084377) can be downloaded from the publicly available MassIVE repository at the UCSD Center for Computational Mass Spectrometry website: https://massive. ucsd. edu/ProteoSAFe/ dataset. jsp?task=56e8ba4bcb814e3d92863c88702f235c.
We present results from the FAOSTAT agri-food systems emissions database, relative to 236 countries and territories and over the period 1990-2019. We find that in 2019, world-total food systems emissions were 16. 5 billion metric tonnes (Gt CO2eq yr -1 ), corresponding to 31% of total anthropogenic emissions. Of the agri-food systems total, global emissions within the farm gate -from crop and livestock production processes including onfarm energy use-were 7. 2 Gt CO2eq yr -1 ; emissions from land use change, due to deforestation and peatland degradation, were 3. 5 Gt CO2eq yr -1 ; and emissions from pre-and post-production processes -manufacturing of fertilizers, food processing, packaging, transport, retail, household consumption and food waste disposal-were 5. 8 Gt CO2eq yr -1. Over the study period 1990-2019, agri-food systems emissions increased in total by 17%, largely driven by a doubling of emissions from pre-and post-production processes.
, 2020; Tubiello et al. , 2021a). This larger food systems perspective expands the potential for designing GHG mitigation strategies that can address options in food and agriculture across the entire food system, i. e. , over and above the more traditional focus on agricultural production and land use management within countries' Nationally Determined Contributions  (FAO, 2019). Significant progress has recently resulted in the development of novel databases with global coverage of countrylevel data on agri-food systems emissions  (Crippa et al. , 2021a,b; Tubiello et al. , 2021a). Tubiello et al. (2021a)  in particular provided a mapping of emission categories of the Intergovernmental Panel on Climate Change (IPCC), used for climate reporting by countries of national GHG inventories (NGHGI), to more relevant food and agriculture concepts that, developed by FAO and used to disseminate food and agriculture statistics in FAOSTAT, are more easily understood by farmers and planners in Ministries of Agriculture.
, 2020). We present and discuss results from the first emissions database in FAOSTAT of food and agriculture emissions. The new database covers, as in previous versions  (Tubiello et al. , 2013)  agriculture production activities within the farm gate and associated land use and land use change emissions on agricultural land. Importantly, it also includes estimates of emissions from pre-and post-production processes along food supply chains, including: energy use within the farm gate, food processing, domestic and international food transport, retail, packaging, household consumption and food waste disposal. The new FAOSTAT database provides information of emissions of the four main GHG gases CO2, CH4, N2O and F-gases, as well as their combined CO2eq levels, by country, over the period 1990-2019.
Emissions Estimates We provide here the basic estimation methods used for this work, while referring the interested reader to a series of technical papers that document the underlying methodologies in full, detailing all coefficients and data sources used to estimate emissions from energy use in fertilizers manufacturing, food processing, transport, retail, household consumption, waste disposal  (Tubiello et al. , 2021b; Karl and Tubiello, 2021a, b) ; as well as energy use on the farm  (Flammini et al. , 2021). More generally, a step-wise approach was followed for the estimation of agrifood systems emissions: Step 1 identified, for each food systems component, the relevant international statistics needed to characterize country-level activity data (AD). Step 2 determined the food-related shares of the activity data (ADfood) and assigns relevant GHG emission factors (EF) to each activity.
Conversely, the latter was the dominant emissions component in Brazil, Indonesia and the DRC. Additionally, the new database allowed for an in-depth analysis by country of pre-and post-production emissions along the agri-food chain, highlighting a significant variety in most relevant sub-process contribution (Tab. 3). For the year 2019, pre-and post-production emissions were dominated in China by food household consumption processes (463 Mt CO2eq yr -1 ), whereas food waste disposal was the dominant pathway in Brazil, Indonesia (77 Mt CO2eq yr -1 ), DRC (8 Mt CO2eq yr -1 ), Pakistan (33 Mt CO2eq yr -1 ) and Mexico, (56 Mt CO2eq yr -1 ). Emissions from food retail dominated the pre-and post-production component in the USA (292 Mt CO2eq yr -1 ), Russian Federation (177 Mt CO2eq yr -1 ) and Canada (20 Mt CO2eq yr -1 ).
Discussion The most important trend over the 30-year period since 1990 to present that emerges from our analysis is the increasingly important role of food-related emissions generated outside of agricultural land, in pre-and postproduction processes along food supply chains, at all scales from global, regional and national, from 1990 to 2019. Importantly, the FAOSTAT database presented here allows for an estimation of the percentage share contribution of food systems emissions in total anthropogenic emissions, by country as well as at regional and global levels, over the period 1990-2019. The FAOSTAT-PRIMAP database covering all sectors which underlies this study estimates total anthropogenic emissions at about 52 Gt CO2eq yr -1 without land use, land use change and forestry emissions (LULCUF), and about 54 Gt CO2eq yr -1 with LULUCF-consistently with recent estimates (IPCC, 2019).