dataesr/scientific-paragraphs-categorization huggingface dataset:
The prepared SMRTbell template libraries were then sequenced on a Pacific Biosystem's Sequel sequencer using v3 sequencing primer, 1 M v3 SMRT cells, and Version 3. 0 sequencing chemistry with 1 × 1,440 sequencing movie run times. We performed a stringent quality control to avoid any spurious contaminating sequences in the genome assemblies. Any mitochondrial or non-target contaminant contigs were filtered from the final assembly before gene annotation. Contaminants were identified using the following postassembly methods: BLAST against NCBI databases (nt, refseq. fungi, ref_prok_rep, mitochondrial, NCBI gcontam, and Pacbio 2/4K-control sequences), principal component analysis of tetramer nucleotide frequency, contig length × GC × aligned read coverage, aligned the assembled rDNA IT sequences using BLAST against the UNITE database (https://unite.
MBE Comparative Genomic Features Functional annotation sets were integrated using the following databases: carbohydrate active enzyme (CAZy,  Lombard et al. 2014) , GO (The Gene Ontology Consortium 2015), Kyoto Encyclopedia of Genes and Genomes (KEGG,  Ogata et al. 1999), EuKaryotic Orthologous Groups (KOG; Tatusov et al. 2003) , Protein families (PFAM;  Finn et al. 2016). KOG, GO, KEGG, PFAM were obtained from MycoCosm, JGI. The latest CAZyme annotation was obtained from the CAZy team (http://www. cazy. org/). Statistics of JGI genome assemblies (i. e. , N50, number of genes and scaffolds, genome size) were obtained from JGI MycoCosm (https://mycocosm. jgi. doe. gov). Genome completeness with single-copy orthologs was calculated using BUSCO v3. 0. 2 with default parameters (basidiomycota_odb9 database;  Simão et al.
2015). The coverage of TEs in genomes was calculated and visualized using the visual omics pipeline Transposon Identification Nominative Genome Overview (TINGO;  Morin et al. 2019). Secreted CAZymes, lipases, proteases, and SSPs (< 300 amino acids) were identified  (Pellegrin et al. 2015). We calculated, visualized, and compared the count and ratio of total (present in the genomes) and predicted secreted CAZymes, lipases, proteases, and SSPs as a subcategory. We calculated the total count of the followings using both all and theoretically secreted plant cell wall degrading enzymes. Global trends of ecological groups were evaluated using Non-metric Multi-Dimensional Scaling (NMDS) with the count of total and predicted secreted CAZymes. The dissimilarities among the ecological groups were calculated and the relationship was converted into distances in 2D space with the function metaMDS in R package Vegan  (Oksanen et al.
The distance of genes to nearest repeat elements within 4. 5 kb was defined as "close proximity" because coding genes tend to be smaller than 4 kb (fig. 3a ). The process above was conducted with the visual pipeline Synteny Governed Overview (SynGO;  Looney et al. 2021). We performed ordination analyses and estimated Pearson correlation to examine associations between TE-gene distance of various genes coding for different protein types with R package PCAtools and Ggally  (Blighe and Lun 2020; Schloerke et al. 2021).
Acknowledgements: We would like to thank  F. Snijders  for technical support. We would like to thank  Dr. Y. Tamai  for providing the culture of P. orientalis OTSU through the  NITE Biological Resource Centre  for inclusion in this research. This work was supported by (proposal:  10. 46936/10. 25585/60001058 ) conducted by the  U. S. Department of Energy Joint Genome Institute  (https://ror. org/04xm1d337),a  DOE Office of Science User Facility , is supported by the  Office of Science of the U. S. Department of Energy  operated.
Pairwise adjusted means comparison was done using the multcomp() and cld() functions of the lsmeans and means package of R, with an alpha threshold of 5%. Millet and cowpea yield was averaged across the replicates and LER was computed with the average yields to study the impact of year, fertilization and cowpea variety. The following model was used (equation (  6 )): (Model 4) LER ijkl = aF i + bVar j + cA k + R ijkl (6) where LER ijkl is the variable to be explained (LER in grain and aboveground biomass), F i is the level of fertilization i, Var j is the level j of cowpea variety, A k is level k of the year, R ijkl is the residual, and aF i , bVar j , cA k are coefficients of fixed effects. Significant effects were analyzed using ANOVA. The comparison of means was done using the LSD (least significant difference) test at the 5% threshold in case of significance.
Acknowledgements This work was supported by funds from the  AFD  project " Ecological Intensification and Inequality " and the  USAID SIMCO  project (agreement number  201403286-10 ) funded by the  Feed The Future Sustainable Innovation Lab (SIIL)  through the  USAID   AID-OOA-L-14-00006. Y. Senghor  was also supported by the  UE LEAP-Agri RAMSES II project  (Grant No. 840727715 ) and the  UE H2020 Sus-tainSAHEL  project (Grant No. 861974 ). We thank the  AIDA Research Unit of the Coopération Internationale en Recherche Agronomique pour le Développement (CIRAD) , the  Université Gaston Berger de Saint-Louis/Senegal (UGB)  and the  Institut sénégalais de recherche agricole (ISRA) , in particular the  Centre de recherche agricole de Bambey , for their technical support.
, 2020; Kornhuber et al. , 2020; Mehrabi and Ramankutty, 2019; Tigchelaar et al. , 2018)  as noted in a recent review on multiple breadbasket failures  (Hasegawa et al. , 2022). Modes of climate variability such as ENSO, however, are complex phenomenon  (Timmerman et al. , 2018)  that vary on interdecadal timescales in terms of amplitude and return-period  (Wittenberg, 2009). The length of records used by previous analyses, therefore, is insufficient to quantify the observed effects of these modes of variability on multiple breadbasket failures or to remove their potentially confounding effects from an analysis of observed historical trends  (Hasegawa et al. , 2022). Our analysis addresses this constraint by collating a century-long maize and wheat yield dataset to provide the most complete picture to date of multiple breadbasket yield shocks, including observed trends and how climate modes have affected them.
We compile a dataset of subnational crop yield anomalies for maize and wheat consisting of over 34,000 observations from 132 subnational units in 25 countries dating back to 1900 (Fig. 1 ), which we use to robustly estimate (1) the probability of multiple breadbasket yield shocks, (2) changes to that probability over the last century, and (3) to characterize how interannual modes of climate variability have affected the probability of such yield shocks in the historical record. The results presented here represent the most comprehensive assessment to date of the historical risk posed by climate variability to multiple maize and wheat breadbasket yield shocks.
Data Crop yield data are taken from the Twentieth Century Crop Statistics Data Set version 1, from which we use 34,000 observations from 132 subnational units in 25 countries dating back to 1900  (Anderson et al. , 2022). The data used in this analysis are crop statistics at the subnational and national level, largely collected by national statistics agencies or departments and ministries of agriculture. Some of the data used has been previously analyzed, such that of Austria, Croatia, Czech Republic, and Belgium  (Trnka et al. , 2016) , subnational data in France  (Schauberger et al. , 2018 (Schauberger et al. , , 2022;; Ceglar et al. , 2020) , Subnational data in southern Brazil  (Cunha et al. , 1999) , and data in Argentina  (Podestá et al. , 1999) , subnational data in Spain and Portugal  (Páscoa et al.
To calculate potentially damaging temperatures, we calculate both damaging maximum temperature and damaging minimum temperature during the crop growing season for maize and wheat in each location using the Berkeley Earth daily temperature dataset  (Rohde 2013). Because crop yields respond nonlinearly to extreme heat  (Schlenker and Roberts, 2009) , we calculate extreme degree days (EDDs) as the sum of maximum temperatures exceeding an optimum threshold (29 • C for maize or 26 • C for wheat) over the growing season for each crop  (Sanchez et al. , 2014). Likewise, to calculate damaging minimum temperatures we calculate chilling degree days (CDDs) as the sum of daily minimum temperatures falling below an optimum threshold (8 • C for maize or 0 • C for wheat) over the growing season for each crop.
Because crops may respond differently to these extreme temperatures in either very hot or very cold climates, however, we next convert both EDDs and CDDs to a z-score at the subnational level. In this way, we incorporate information about both baseline absolute temperature threshold exceedance and information on the frequency of such exceedance at the local scale. Interannual modes of climate variability are assessed using the Niño 3. 4 sea surface temperature (SST) index (5S-5 N and 170-120 W) and the Dipole Mode Index, which is the difference between western (50E-70E, 10S-10 N) and eastern (90E -110E, 10S-0 N) Indian Ocean SSTs, both calculated by NOAA Physical Sciences Laboratory based on the HadISST1. 1 dataset  (Rayner et al. , 2003) , available for download at htt ps://psl. noaa. gov/gcos_wgsp/Timeseries.
The HadISST dataset uses a two-stage reduced-space optimal interpolation procedure, followed by a superposition of quality-improved gridded data. It improves on previous long reconstruction data in terms of SST mode variance and persistence through time. The North Atlantic Oscillation was identified using a station-based index, which, while noisier than an index derived from a full atmospheric pressure field, has the advantage of being defined by constant stations through time back to the mid-ninteenth century, thus eliminating the potential complication of changing observation systems  (Hurrell et al. , 1995).
Breadbasket yield shocks To remove crop yield trends, we use the low-frequency Gaussian filter with a kernel density of five years, which is similar to the fifteen year running mean. We next convert yield anomalies at the subnational scale to percent anomalies by dividing the absolute yield anomalies by the expected yield. We define breadbaskets by identifying those countries that collectively contribute to 90% of maize or wheat production in 2010 and include all countries for which data is available. Based on these criteria, a total of 75% of maize production and 67% wheat production was available in our yield dataset from which we select breadbaskets that account for 66% and 58% of maize and wheat production, respectively. To decide how to define breadbaskets we consider average temperature and moisture regimes as an indication of agroclimate growing conditions.
As such, we aggregate absolute crop yield anomalies up to a time series of production anomalies for each breadbasket using static harvested area measurements averaged over 2005-2010 to represent present-day production systems. This results in a single time series for each breadbasket, which is converted to a percent yield anomaly as described earlier for the subnational and national-scale time series. For results using the historical dataset rather than static breadbaskets, which may be of interest with respect to the observed frequency of breadbasket yield shocks, see SI Figure  1. Breadbasket failures have been defined either as the lower quartile of observed yield anomalies  (Gaupp et al. , 2020; Raymond et al. , 2022)  or as a 10% deviation from expected yields  (Caparas et al. , 2021; Tigchelaar et al.
, 2018). The lower quartile of yield anomalies in our breadbaskets generally corresponds to yield deficits of between ~5% and 10%. Accordingly, we define a yield shock at two thresholds: 5%, representing moderate yield shocks, and 10%, representing major yield shocks. Note that because we are using time-invariant harvested areas, our 10% yield threshold is equivalent to a 10% production threshold, which is the threshold used by  Tigchelaar et al. , (2018). Because a 10% threshold yields relatively few events, even in a century-long dataset, we additionally use the 5% threshold to be consistent with past literature and as an indication of moderate regional-scale yield deficits. Taken together, these two thresholds demonstrate the robustness of results. To test whether the frequency of multiple breadbasket shocks have increased in the most recent period compared to earlier reporting periods we plot the probability density functions for the frequency of yield shocks in each period and use a two-tailed Kolmogorov-Smirnov test to evaluate whether the two distributions are statistically significantly different from one another.
Climate-drivers of multiple breadbasket shocks The general lack of widespread breadbasket yield shocks in Figs. 3 4 5 lends credibility to the notion that producing crops in breadbaskets with relatively uncorrelated risks buffers against the probability of multiple simultaneous crop yield shocks. However, as pointed out by  Anderson et al. (2019) , global modes of climate, such as the El Niño Southern Oscillation (ENSO), may affect the climate in such a way that increases the likelihood of these simultaneous crop yield shocks relative to other years. Using our century-long yield dataset, we find that modes of climate variability have had a large effect on the probability of joint yield shocks in pairs of breadbaskets. The probability of joint yield shocks differs by up to 40% in opposite phases of major climate modes (Figs.
Discussion and conclusions Using our century-long dataset of national and subnational crop yields, we find that simultaneous yield shocks in wheat and maize breadbaskets have been less frequent in recent decades as compared to mid-century. While damaging climate stress during maize and wheat growing seasons has increased in breadbasket areas after the turn of the century, -particularly the incidence of joint moisture-temperature stress for maize -the frequency of multiple simultaneous wheat and maize breadbasket yield shocks has decreased (see Figs. 3 4 5 ). These results are consistent with  Gaupp et al. (2020)  and  Sarhadi et al. (2018) , who found increases in the frequency of temperature-and precipitation-related risks across breadbaskets and agricultural areas, respectively, in recent decades.
Acknowledgments Weston Anderson would like to acknowledge the mentorship and leadership of  Dr. Lisa Goddard  during her time as his postdoctoral advisor. Lisa passed away prior to the publication of this work, but was a tireless advocate for building scientific institutions that benefited society and reflected the values of its employees. This work was funded by  ACToday , the first of  Columbia University 's World Projects. All climate data used in this analysis, including both model output and observational estimates, are publicly available from the sources indicated in the methods section. The crop yield data has been deposited with the Socioeconomic Data and Applications Center (SEDAC) and is available from https://sedac. ciesin. columbia. edu/data/set/food-twentiethcentury-crop-statistics-1900-2017.
Data collection technique and tool A semi-structured questionnaire was used to collect informations. The questionnaire was administered to THPs face-to-face in their native language by trained and qualified interviewers. The questionnaire was divided in three parts: • Part 1 -sociodemographic and professional data of the participants: age, gender, marital status, educational level, religion, area of residence, professional category, source of acquisition of traditional medical knowledge, diseases treated by THPs, initial and continuing education;.
Ethical considerations. The study protocol was approved by the National Ethics Committee for health research of Burkina Faso, n°2020-9-201 on 2 September 2020. An information sheet was made available to study participants. It included information about the title, objectives, study setting, research team, voluntary participation in the study, anonymity of the questionnaire by assigning an anonymous numerical code, confidentiality of data, the course of the survey and telephone contacts of the principal investigator and the National Ethics Committee. THPs were interviewed after obtaining their informed signatures on a pre-determined form. A witness signature was requested when the THPs could not read or write. The confidentiality of personal data and the anonymity of the participants were guaranteed during the data collection and processing.
Data capture and analysis The data were entered using Sphinx version 5 software and then analyzed using Statistical Package for Social Science (SPSS) version 25 software. Descriptive statistics-numbers, frequencies, means, standard deviations, medians-were used to process the results. The names of the medicinal plants cited by the THPs were collected in their common names in French or in the main local languages of the country, notably Mooré and Dioula. The Latin binomials and families of plant species were then obtained using the national guide to medicinal plants in Burkina Faso, and then verified on www. thepl antli st. org and www. world flora online. org  [27, 28].
Discussion The objective of this study was to describe the traditional phytopharmaceutical practices of traditional practitioners in Burkina Faso. The relatively low participation rate of the study (67 out of 190 listed) reveals certain difficulties in conducting studies among TM practitioners in Burkina Faso. The first concerns the absence of an updated national directory of THPs, making it difficult to sample this population during epidemiological studies. The second concerns the absence of a map of the places of practice of the THPs and the updating of their directories at community level. However, the current public health code provides in Article 143 that the organization of THPs shall be determined by regulation  [20]. This will provide an up-to-date national database of THPs that will facilitate ethnomedical surveys.
RNA seq data analysis After demultiplexing and removal of adapter sequences from Fastq files with Cutadapt  [28] , reads were mapped on S. cerevisiae S288C genome using RNA STAR  [29]. Default parameters were used except for maximum intron size (1500), maximum gap between two mates (1500), minimum overhang for spliced alignments  (25)  and the annotated GTF file Saccharomyces cerevisiae (R64-1-1. 104) from ENSEMBL was used for mapping. Indexed BAM files were generated using Samtools_sort  [30]  and then read counts were obtained using featureCounts  [31]  and a GTF file from  [32] , was used as the gene annotation file. Default parameters were used except that both multi-mapping and multi-overlapping features were included. Differential analysis of RNA-Seq data from three independent biological replicates was performed by SAR-Tools, using DESeq2 software  [33].
Protein database search All RAW files were processed together in a single run by MaxQuant  [34]  version 2. 0. 3. 0 with default parameters unless otherwise specified (http://www. maxquant. org). Database searches were performed with the built-in Andromedasearch engine against the reference yeast proteome (downloaded on 2021. 10. 09 from Uniprot, 6050 entries). Precursor mass tolerance was set to 6 ppm in the main search, and fragment mass tolerance was set to 20 ppm. Digestion enzyme specificity was set to trypsin with a maximum of two missed cleavages. A minimum peptide length of 7 residues was required for identification. Relative label-free quantification of proteins based on intensities was done using the MaxLFQ algorithm integrated into MaxQuant  [35]. Proteins that shared same identified peptides were combined into a single protein group.
Proteomic data analysis To identify interactors, replicates of affinity-enriched bait samples were compared to a set of negative control samples (n�3). Proteomics data analysis was performed in the Perseus environment (version 1. 6. 15) (https://maxquant. org/perseus/)  [36]. "Proteingroups. txt" file from MaxQuant was loaded. Protein groups identified by a single "razor and unique peptide" were filtered out from the data set. Protein group LFQ intensities were log2 transformed. A minimum of valid values (60%) was required in at least one group. Missing values were assumed to be biased toward low abundance proteins that were below the MS detection limit. Imputation of these missing values was performed separately for each sample from a distribution with a width of 0. 3 and downshift of 1. 8.
eIF2A binds mRNAs encoding proteins required for the cell wall organization While, the function of eIF2A is not yet fully understood, some data from the literature indicate that eIF2A could be a non-canonical translation initiation factor [24 and references therein]. To identify mRNAs associated with eIF2A, we performed an RNA-binding protein immunoprecipitation followed by a sequencing (RIP-Seq) experiment. For this purpose, we affinity purified an eIF2A-TAP protein, which is functional, as it allowed the growth of the cells under stress conditions that affected the eif2aΔ strain, as shown in S1A Fig. Total  and eIF2A -associated mRNAs from exponential-phase culture were extracted, reverse transcribed into cDNA and sequenced. Enrichment of each mRNA in the immunoprecipitated fraction relative to total RNA was calculated from three independent biological replicates and normalized using DESeq2  [33]  from three independent biological replicates.
We found 146 mRNAs significantly enriched with eIF2A, according to the selection criteria that we applied (see Materials and Methods) (Fig  1 , S5 Table and S1 Dataset). Gene ontology (GO) term enrichment analysis was done on the Saccharomyces Genome Database (SGD) site according to  [37]. It revealed that 24% of the selected mRNAs encode proteins required for cell wall biogenesis with a p-value of 6. 56 e-22. This high percentage revealed a strong enrichment for mRNAs involved in the cell wall biogenesis, while this class of mRNAs represented less than 3% of the total mRNA  [38]. Among the most highly enriched mRNAs, we found TOS1, CCW14, which encode covalently bound cell wall proteins; SUN4, which encodes a glucanase localized in bud scars; CTS1 and SRL1, which respectively encode an endochitinase and a mannoprotein exhibiting a tight association with the cell wall (  The specific association of cell wall biogenesis related mRNAs with eIF2A suggests that this protein could play a role in cell wall homeostasis.
Together, our results are consistent with eIF2A association with cell wall-related mRNAs and strengthen the hypothesis that eIF2A could be involved in the expression of transcripts required for cell wall biogenesis. To validate RIP-Seq results, we investigated four identified eIF2A mRNA targets (TOS1, CCW14, SUN4, CLN1) by Western Blot analysis, to further explore the behavior of the corresponding TAP-tagged proteins upon eIF2A overexpression. Interestingly, eIF2A overexpression reproducibly decreased the amount of Tos1, Ccw14 and Sun4 proteins by approximatively 2-fold and by 5-fold in the case of Cln1, while as a control, G6PDH protein level did not change (Fig 3A  and 3B ). In agreement with the RIP-Seq data (Fig To further explore whether the protein level changes observed upon eIF2A overexpression conditions occurred at the transcriptional or translational level (Fig  3 ), we performed a genome-wide RNA sequencing (RNA-Seq) analysis.
For that purpose, we compared the transcriptome of strains harboring either pCM190: eIF2A or the pCM190 empty vector and showed that no major changes in the general mRNAs levels were detected after 5 hours of eIF2A overexpression  (Fig 4A and S2 Dataset). Similarly, compared to the wild-type strain, the absence of eIF2A did not substantially disturb the transcriptome (Fig 4B and S2 Dataset), suggesting that eIF2A does not control the expression of its mRNA targets at a transcriptional level. Together, our results combined with the fact that eIF2A associates specifically with 40S and 80S ribosomal subunits  [21] , strongly suggest that eIF2A acts as a negative translational regulator of a class of mRNAs involved in the cell wall organization and biogenesis.