dataesr/scientific-paragraphs-categorization huggingface dataset:
Data Base Many UNFCCC Parties have revised their NDCs for COP-26 with a higher level of ambition. Where a country's NDC did not provide detailed information on adaptive projects, we used that country's National Adaptation Plan (NAP). The main objectives of a NAP are to identify medium-and long-term adaptation needs and develop and implement associated strategies and programs. The database consists of 3643 Excel rows (3643 projects) from 142 countries. However, not all the projects identified were considered "usable". A project is considered "usable" for the purposes of our analysis if: (1) When a hazard can be assigned, i. e. , when the NDCs and/or NAPs provide sufficient information to understand the climate issue of the project in question. They were classified as "unknown hazards" when they were not sufficiently well formulated to be classifiable in the taxonomy or as "multi-hazards" when their formulation was too general and did not allow for the identification of specific hazards.
From a statistical perspective, this sorting avoids the bias that would have resulted from a forced or arbitrary assignment of a key (project) to a lock (hazard). (  2 ) Where information on the intensity of the hazard in question is held in the database that was again chosen in this paper, namely DARA (see Sections 3. 2 and 3. 4) for the country initiating the project. Projects from the following countries were excluded from our analysis: Andorra, the State of Palestine, Monaco, and St. Kitts and Nevis. These countries are not included in the DARA database. By applying these two criteria, 2475 projects are considered usable, i. e. , 67. 9% of the total identified.
Categories of Physical Risks Adaptation cannot be detached from the impacts of the climate change-induced hazards it claims to reduce, which is why the authors have paid great attention to climate risk assessment  [30]. While vulnerability is defined for a specific system, sector, or region (as a degree of sensitivity and adaptive capacity to a risk), physical risk applies to all systems, sectors, and regions  [31]. We have therefore chosen, as was done by Boutang et al. in 2020  [2] , to associate adaptive projects with physical risks (hazards), which allow international comparisons. A database of climate prediction risks for each country is provided by DARA, independent of the assumptions made by the countries in drafting their NDCs. DARA is a non-profit organization that aims to improve the performance of humanitarian aid by conducting evaluations, research, and policy studies.
In total, a list of seven  (7)  physical hazards was compiled from the DARA database  [32]  and the study by Mirza et al. [12] : drought, flood, heat wave, sea level rise, storm, temperature increase, and rainfall. However, the names associated with the temperature increase and heat wave hazards are not obvious in DARA. For these two hazards, the DARA correspondences "heating and cooling" and "heat and cold illnesses" were used, respectively. Each of the 2475 selected projects was associated with one or more hazards. This project-hazard association makes it possible to construct a taxonomy.
The classification of these occurrences and the links from the most important to the weakest made it possible to create project categories. Selectively, we sorted the word pairs by category and relationship. Some word pairs that did not express a specific adaptation action were discarded, such as "climate change, adaptation action, climate resilience, [. ]". The taxonomy was then organized from the selected word pairs, starting from the most general association to the most specific pair. The number of categories and projects per category is available in the results section and in Appendix A. The taxonomy by hazard is visualized in tree form with the software "Xmind. 22. 09. 3168" (Appendix A).
, 2022 [18] ). Transformative adaptation in NDCs is analyzed to determine the extent to which adaptation actions align with transformative approaches, such as actions that aim to create systemic change through innovation, location shifts, or scale expansion in response to climate change. However, the analysis does not provide qualitative descriptions or a sense of priority, and it is not within its scope to assess the possible maladjustments or transformative power of these actions. The analysis is comparative within the same hazard class and internationally. It cannot be used to judge issue priorities directly, unlike the Adapt Now report from the Global Commission on Adaptation  [33]. However, the DARA country risk database can be used for this purpose  [32]. The taxonomy used in this study is "under construction," with meta-categories of projects (such as coastal management) being a grouping of projects with generic descriptions, rather than the sum of underlying projects.
Vulnerability and Hazard Future studies should use climate forecasts for regions affected by adaptive projects rather than entire nations. One limitation is that solutions are linked to hazards and not vulnerability in the strict sense. Vulnerability involves factors such as topography, geology, land use, and infrastructure. For example, water vulnerability from a precipitation hazard is assessed through complex modeling and in situ observations. A systematic comparison of 2475 projects between solutions and local vulnerability is currently not feasible. The study uses the DARA database, but the method can be replicated using any more reliable, complete, or up-to-date climate hazard databases. No statistics on the "rainfall" hazard were found in DARA.
, 2016; Vergnaud et al. , 2018). Robust phylogenetic relationships can be obtained from nucleotide sequencing data owing to the strictly clonal evolution of classical Brucella spp. (Whatmore and Foster, 2021). The first MLST scheme included nine loci, seven housekeeping genes, the outer membrane protein gene omp25 and int-hyp. Six B. abortus MLST9 sequence types (STs) were initially described  (Whatmore et al. , 2007). Twenty-six B. abortus MLST9 STs are recorded in the current version of the Brucella spp. MLST database. 1 Including 12 housekeeping genes in the 1 https://pubmlst. org/organisms/brucella-spp/ (Accessed January 24, 2023). genotyping resulted in the more discriminatory MLST21. Thirty MLST21 STs were initially described in 172 B. abortus strains defining three clades (A, B, C including C1 and C2;  Whatmore et al.
, 2016)  and 43 STs are recorded in the current version of the Brucella spp. MLST database (see text footnote 1). The rare clades A and B include strains originating almost exclusively from Africa and allow defining the most ancestral nodes within the B. abortus phylogeny. Clades C1 and C2 are found on five continents and their presence in Africa seem to result from livestock importation  (Whatmore et al. , 2016; Vergnaud et al. , 2018). Several MLVA genotyping schemes have been proposed for Brucella, one most commonly used scheme is MLVA16  (Scholz and Vergnaud, 2013). MLVA16 combines two panels of markers: one VNTR panel with a low discriminatory ability allows determining the species and main genetic lineages, and the other VNTR panel with a high discriminatory ability but low phylogenetic value allows differentiating strains in local outbreaks  (Scholz and Vergnaud, 2013; Whatmore and Foster, 2021).
The current version (Brucella v4_6_5) of the Brucella MLVA data hosted by MLVAbank 2  contains in vitro data derived from more than 1,400 B. abortus strains. The eight VNTR loci with the low discriminatory power (MLVA8) cluster B. abortus into 28 genotypes represented by at least two entries. MLVA alone is not suitable for phylogenetic reconstructions because of the high homoplasy at VNTR loci  (Keim et al. , 2004) , but interestingly, MLVA using the low discriminatory markers (MLVA8, MLVA10 or MLVA11) and MLST clustering are highly congruent allowing to indirectly deduce phylogenetic signal from MLVA data  (Vergnaud et al. , 2018). Consequently, MLVA may constitute a first-line assay with low phylogenetic resolution until whole-genome sequencing (WGS) becomes sufficiently low-cost to be directly used as first line assay.
13  (Bankevich et al. , 2012). The assemblies had an average of 25 contigs (range 18 to 60), an average N50 value of 417 kb (range 251 to 884 kb) and an average total assembly length of 3. 27 Mb (range 3. 260-3. 327 Mb). Whole-genome MLST was run on genome assemblies using the BioNumerics "MLST for WGS" and "WGS tools" plugin and associated Brucella spp. scheme (Applied-Maths, Sint-Martens-Latem, Belgium). The plugins were also used to recover the MLST9 and MLST21 assignments  (Whatmore et al. , 2007 (Whatmore et al. , , 2016) )  defined in the Brucella spp. database hosted by PubMLST  (Jolley et al. , 2018 ; Supplementary Table  S1 ). Raw reads were deposited in the European Nucleotide Archive BioProject PRJNA892249 (Kazakhstan collection) and PRJNA901374 (BCCN collection, France). Individual sequence reads archives (SRA), bioproject and biosample accessions are indicated in Supplementary Table  S1.
Whole-genome single-nucleotide polymorphism analysis All publicly available Brucella assemblies, sequence reads archive, and associated metadata, were downloaded from EBI ENA (read archives) or NCBI (assemblies) (last updated 2022-09-10). Sequence read archives were de novo assembled with SKESA. All assemblies were imported into BioNumerics version 8. 1 (Applied-Maths, Sint-Martens-Latem, Belgium). The assemblies were used to produce artificial reads data sets (50 bp long, 10x coverage) for SNP calling by read mapping using genome assembly GCA_000740155 (B. abortus strain Tulya) as reference genome  (Blouin et al. , 2012; Vergnaud et al. , 2018). The BioNumerics parameters for reads mapping were 95% minimum sequence identity, minimum inter-SNP distance 12 base-pairs. Maximum parsimony trees were drawn within BioNumerics.
The list of all public datasets evaluated is presented in Supplementary Table  S2. Supplementary Table  S2  includes comments facilitating the selection of strains, including "duplicates" (more than one dataset available for the same strain), "redundant" (coincident wgSNP genotype). Some datasets provided poor coverage or induced topological issues, due to sample mix or inappropriate assembly procedure. For input in BEAST version 1. 10. 4 (Suchard et al. , 2018) , a table of SNP positions from selected strains was exported from BioNumerics and SNPs were concatenated as fasta files. BEAST was run under a general time-reversible model of nucleotide substitution with a gamma distribution between sites, a relaxed molecular clock, Bayesian skyline plot (BSP) demographic model, lognormal distribution for population sizes as previously described  (Kamath et al.
MLST assignment of the 49 Brucella abortus strains from Kazakhstan Four MLST9 STs were present, ST1 (two strains) ST2 (45 strains), ST5 (one strain), and ST119 (new MLST9 genotype, represented by one strain). ST2 and the closely related ST119 belong to subclade C1 whereas ST1 and ST5 belong to subclade C2  (Whatmore et al. , 2007). MLST21 resolves one additional genotype, as subclade C1 strain Kaz041 defined a new allele at MLST21 locus csdb due to a singlenucleotide variation (Supplementary Table  S2 ). Using the wgMLST analysis assay, alleles could be called at 3,312 up to 3,345 loci in the 46 clade C1 strains. Seven hundred and three loci were called in all 46 strains and were polymorphic. Supplementary Figure  S1  shows the clustering derived from the corresponding wgMLST character dataset of the 46 clade C1 strains.
Comparison of the 46 Brucella abortus subclade C1 strains from Kazakhstan with public datasets For comparison, we recovered 148 public WGS datasets, and eight B. abortus subclade C1 WGS datasets from the BCCN collection (Supplementary Table  S2 ). Fifty-three duplicate datasets were removed (WGS data available as assembly and sequence reads archives, or identical strains sequenced by different institutions). Datasets contributing terminal branches shorter than five SNPs were also removed. Figure  3  shows the result of wgSNP analysis of the 46 B. abortus subclade C1 strains from Kazakhstan together with 77 selected WGS datasets of worldwide origins. Starting from the root, four main lineages were defined. Branch length from root to tip vary from 280 up to 309 SNPs. All 46 Kazakhstan strains clustered in one of these four clade C1 sub-lineages.
Comparison of the three Brucella abortus clade C2 strains from Kazakhstan with public datasets Public databases contain 880 B. abortus subclade C2 WGS datasets including assemblies and sequence read archives (Supplementary Table  S2 ). After removal of datasets achieving a poor coverage of the reference genome, 870 datasets were available for comparison. Strain Kaz031 from Kazakhstan clustered in a tight group of approximately 100 strains, including representatives of strains 2308, RB51, S19, and B. abortus reference strain 544. RB51 is used as a vaccine, and was derived from 2308  (Schurig et al. , 1991). Of note, Kaz031 differs by two SNPs from Brucella vaccine strain A19 (assembly accession GCA_003290345). The two other subclade C2 strains from Kazakhstan clustered together with vaccine strain 104 M.
This hypothesis is also supported by alanine mutagenesis that shows that RSV mutants were less efficient in replication initiation, elongation, and cap addition on viral RNA  [118]. The priming loop in the down position is associated with an initiation compatible mode of the L protein for RNA synthesis. In this state, the priming loop is in the central cavity formed between the RdRp and the PRNTase domains, and it is close to the active site of the polymerase. The down conformation was modeled by alignment of the priming loop from the VSV polymerase complex structure (PDB: 5A22) to the AlphaFold RSV L model. The priming loop in the up position, as obtained from the AlphaFold RSV L model, is associated with an elongation compatible mode of the L protein and is accommodated close to the PRNTase active site, as is depicted in Figure  5.
• RSV L protein modeling Structural predictions were conducted on the L protein of RSV (UNIPROT: G8EJ12_HRSV). We used a local installation of AlphaFold2 to perform the full-length prediction (2165 residues)  [63]. Given that RSV L is a multidomain protein of which only the polymerase and PRNTase core have been experimentally solved by cryo-EM in complex with its co-factor P (PDB: 6PZK), and it is known to undergo conformational changes, we decided to use a naïve prediction protocol (without PDB input) and use the experimental structure as external validation. Since the different domains are separated by a long flexible region, we used the core structure as a filter to select the most likely compatible positions of the CD, MTase, and CTD domains (i. e. , no steric clash between domains). The reliability analysis of the model prediction was done using AlphaPickle, and the pLDDT values were outputted in the B-factor field of the PDB file for each prediction  [64].
Structure representations were performed with ChimeraX  [65, 66]. • Modeling of the PRNTase priming loop conformational change: The priming loop in the up position was obtained from the AlphaFold RSV L model. The homologous structure of the VSV L (PDB: 5A22) has its priming loop in a down conformation that blocks the exit path to RNA. The polymerase-PRNTase domains of VSV present an overall root mean square deviation (RMSD ~1,3 Å) with the ones of RSV. The major distortion was observed around the priming loop, which makes the VSV structure a good template for modeling the loop. Using MODELLER and a templated structural alignment between VSV and RSV, a model of RSV with the priming loop in a down conformation with the best discrete optimized protein energy (DOPE) score was generated and selected  [182, 183].
The down conformation was modeled by alignment of the priming loop from the VSV polymerase complex structure (PDB: 5A22) to the AlphaFold RSV L model. The priming loop in the up position, as obtained from the AlphaFold RSV L model, is associated with an elongation compatible mode of the L protein and is accommodated close to the PRNTase active site, as is depicted in Figure5. A.
The plurality of existing indicators and the lack of consensus on how to compare food security situations has led to the development of composite indicators  [42]  whose construction is not neutral for a policy point of view  [43]. It is nowadays recognized than the use of one single indicator gives an incomplete picture of food security  [30]  and that multiple indicators should be used to capture the complexity of food insecurity  [32, 35]. In this paper, three food security indicators are used, who had been obtained at the household level, through surveys. The diversity in the types of food security indicators implies diversity in data collection scales, ranging from national and regional scales (country population, country cereal balance sheets, vegetation indexes, rainfall estimations, food prices) to household and individual scales (consumption proxies, caloric intakes and anthropometric measures) (Table  1 ).
The Permanent Agricultural Survey data in Burkina Faso In Burkina Faso, the Permanent Agricultural Survey of rural households has been conducted annually by the Ministry of Agriculture since 1982. This survey is a policy tool used in the field of agriculture and food security that provides decision-makers and food organizations with provincial crop forecasts during the growing period (around August) and with agricultural production estimates during the harvest period (around October). Since 2009, information on household food consumption has also been collected to calculate food security indicators after harvest (around December). Collected data are nationally representative and agricultural production estimates and food security indicators derived from  Household Dietary Diversity Score  [24]  Household Coping Strategies Index  [31]  Household Food expenditures  [45]  Household Household Hunger Scale  [1]  Household Anthropometric measures  [28]  Individual Caloric intake  [7]  Individual this official survey are key elements used in national and regional food security warning systems  [39].
The FCS and HDDS food security indicators have been calculated from 2009 to 2017 and the rCSI indicator has been calculated from 2014 to 2017 (information about coping strategies is available as of 2014). As in any survey, biases can affect the reliability of the data obtained from household surveys. Some authors have shown that since the early 2000s, the quality of survey data has declined, as people are increasingly solicited to participate in surveys and are increasingly concerned about protecting their privacy  [33]. The non-observance biases related to the failure to collect information (coverage and sampling bias, non-consent bias, non-response bias)  [26]  and the measurement bias due to measurement error during collection (related to the investigator, the respondent or the questionnaire)  [22]  are the two main types of biases.
CFD Modelling A 3D model of the experimental setup, consisting of 4 crates filled with 15 carrageenan spheres each, was developed using the commercial CFD code ANSYS 21. 0. The spheres were separated by a free distance of 3 mm. The model had 3 subdomains, composed of the free airflow fluid zone, the solid spheres zone, and the solid PMMA crates zone. As explained in Section 2. 1. 1, the main reason for simulating only a single column per pallet layer was to reduce the computational time.
European members of the species-rich sect. Vaginatae of the genus Amanita are studied focusing on Central (Austria, Hungary, N Italy, Romania), Western (France) and North European (Norway) collections. The combination of a 4-locus (nrDNA ITS, nrDNA LSU, rpb1 and tef1-α) molecular phylogenetic analysis and a morphological approach resulted in the discovery of three new species: Amanita de exa and A. griseofulva from stirps Fulva and A. opaca from stirps Coryli. Furthermore, the albinotic white forms of A. griseofulva (f. albida) and A. opaca (f. cettoi) are formally named. As a result of the comprehensive taxonomic revision of Amanita sect. Vaginatae, 370 newly generated sequences are deposited to GenBank and 17 species new to Hungary are reported. This study provides another step towards understanding the taxonomy and diversity of Amanita section Vaginatae in Europe.
Habitat and distribution: in mixed deciduous forests under Quercus, Carpinus and Fagus on all types of soils, from calcareous to oligotrophic soils. Presumably not unfrequent in Western and Central Europe, but veri ed data are scattered to date with known occurrences from France, Germany, UK (soil sample -GenBank: KM374392) and Hungary where it seems widespread from the westernmost to the easternmost areas. Fruiting in summer.
Photographs were taken by Canon EOS 700D and Nikon Coolpix P5100 cameras. Spore length, width and Qvalue (length/width ratio of a basidiospore in side-view) were calculated using Piximètre (www. piximetre. fr) or manually determined for 30 spores per specimen. The data were analysed by ANOVA and other tests and post hoc Tukey's pairwise tests were conducted to study differences of spore data of different species using the software PAST4. 09  (Hammer et al. 2001 ). Sections of volva were made on their upper part, where remains of partial veil on their internal face are usually absent.
The reaction included 5 min initial denaturation step at 98°C, 41 cycles of denaturation at 98°C, for 5 sec. , annealing at 55°C for 5 sec. and extension at 72°C for 7 sec, and nal extension at 72°C for 1 min. PCR products were checked in 1. 5% agarose gel stained with EcoSafe, and illuminated by UV-transilluminator (NuGenius, Syngene). Sanger sequencing were performed with primers used in PCR at LGC Genomics (Berlin, Germany) or at Euro ns Genomics (Ebersberg, Germany). PreGap4 and Gap4 of Staden Package  (Staden et al. 2000)  as well as the CodonCode Aligner package (CodonCode Corp. , Centerville, Massachusetts, USA) were used for sequence assembly and editing. The puri ed sequences were compared to those downloaded in GenBank (https://www. ncbi. nlm. nih. gov/genbank/) and UNITE (https://unite.
ut. ee/) databases using BLASTn  (Altschul et al. 1990 ). ITS, LSU, rpb1 and tef1-α sequences obtained were aligned for each gene separately with MAFFT online v7 (http://mafft. cbrc. jp/alignment/server) using E-INS-i strategy for ITS, G-INS-i for LSU, FFT-INS-i for rpb1 and tef1-α (Katoh and Standley 2013). In the case of ITS, the phylogenetically informative indels were coded using the "simple indel coding" algorithm  (Simmons et al. 2001 ) in FastGap 1. 2  (Borchsenius 2009). Nucelotide alignments and the binary character matrix were edited in MEGA 7 software  (Kumar et al. 2016 ) and concatenated in SeaView 5  (Gouy et al. 2021). For maximum likelihood phylogenetic analysis, raxmlGUI 2. 0  (Edler et al. 2021 ) was employed. The GTRGAMMA substitution model was applied for the six nucleotide partitions (ITS1, 5.
Phylogeny The nal alignment is composed of 390 nrDNA ITS, 201 LSU, 75 rpb1 and 93 tef1-α sequences and 5453 characters (ITS: 892, LSU: 1441, rpb1: 1367, tef1-α: 1187, binary characters: 566). The concatenated 4-locus alignment is included in the Supplementary Material. Phylogenetic trees from maximum likelihood and Bayesian inference analyses showed congruent topologies. The best-scoring ML tree was chosen to depict phylogenetic relationships in Amanita sect. Vaginatae shown in Figs. 1  and 2. The 370 newly generated sequences (157 ITS, 84 LSU, 74 rpb1, 55 tef1-α) were deposited in GenBank (Table  1)  A compressed tree is shown in Fig. 1  presenting six clades and 22 stirps as the main phylogenetic structure within sect. Vaginatae. Half of the lineages (stirps Arctica, Arenicola, Basiana, Crocea, Friabilis, Lignitincta, Nivalis, Spadicea, Verrucosivolva, Vladimirii, Zonata) are newly recognized.