dataesr/scientific-paragraphs-categorization huggingface dataset:
Protein Sequence Analysis A sample of mixed stages of T. urticae was used to extract the total RNA and clone the four OBP sequences reported in the NCBI databases. We confirmed the published sequences, which are aligned in Figure  1. The four TurtOBPs are around 40% identical with each other and contain the conserved motif of six cysteines. S1. The tree was built using the neighbor-joining method and Kimura distances on the sequences aligned with ClustalW and default parameters. The tree was visualized with the software FigTree, version 1. 4. 2 (https://github. com/rambaut/figtree/releases (accessed on 12 March 2021)). On the other hand, proteomic analysis allowed the identification of a protein (XP_015792845) with some similarity to the four TurtOBPs, which presents seven cysteines, three of which can be aligned with those of TurtOBPs.
Blasting this sequence against the NCBI database yielded 11 additional members in T. urticae, all sharing seven conserved cysteines and about 30-60% identical at the amino acid level. These proteins are well distinct in sequence from the four TurtOBPs, with only 10-15% of identity between members of the two groups. Alignment of the 12 sequences and the four TurtOBPs is reported in Figure  S2. Interestingly, this second family of OBPs seems to be specific of   S1. The tree was built using the neighbor-joining method and Kimura distances on the sequences aligned with ClustalW and default parameters. The tree was visualized with the software FigTree, version 1. 4. 2 (https://github. com/rambaut/figtree/releases (accessed on 12 March 2021)). To put these sequences into a wider frame, we blasted them against the NCBI protein database, limiting the results to "mites&ticks".
Proteomic Analysis Next, we checked whether any of the four TurtOBPs encoded in the T. urticae genome was expressed at the protein level. Due to the tiny size of these mites, it was impossible to perform any organ dissection. Therefore, we prepared a crude extract of whole mites and separated the proteins by gel electrophoresis under denaturing conditions (SDS-PAGE), as shown in Figure  S1. Then, we excised gel bands corresponding to proteins migrating with apparent molecular mass lower than 30 kDa, as most of OBPs and other carrier proteins involved in chemical communication present molecular masses around 20 kDa, and performed corresponding proteomic analysis. Database searching of raw mass spectrometric data allowed identification of 123 proteins (Table  S2 ). Worth mentioning is the fact that in our analysis, we did not find any of the four OBPs encoded in the genome of T.
Blasting this sequence against the NCBI database yielded 11 additional members in T. urticae, all sharing seven conserved cysteines and about 30-60% identical at the amino acid level. These proteins are well distinct in sequence from the four TurtOBPs, with only 10-15% of identity between members of the two groups. Alignment of the 12 sequences and the four TurtOBPs is reported in Figure  S2. Interestingly, this second family of OBPs seems to be specific of Tetranychus; in fact, a BLAST search did not return similar entries in any other species of Chelicerata. Our proteomic analysis also produced four proteins classified as similar to apolipoprotein D (XP_015781506. 1, XP_015781509. 1, XP_015783215. 1, XP_015783283. 1) and two fatty acid binding proteins (XP_015782223. 1, XP_015783413. 1).
Three-Dimensional Folding To get some insight into potential binding characteristics of TurtOBP1, we built a model using the recently solved structure of V. destructor OBP1 (VdesOBP1, PDB ID 7NZA)  [42]. Sequence comparison between VdesOBP1 and TurtOBP1 revealed an amino acid identity of 49% over 147 residues without insertions nor deletions (Figure  S4A ). Furthermore, the disulfide pattern observed in VdesOBP1 is conserved in TurtOBP1. A divergent feature of TurtOBP1 as compared to VdesOBP1 is a N-terminus longer by 35 amino acids. This stretch, however, does not seem to be structured, as reported by the secondary structure prediction server, JPRED4  [43]  (Figure  S4B ). Hence, the structural features observed in VdesOBP1 should be conserved in TurtOBP1. To verify this hypothesis, we used the automated model building software Swiss-Model homology modeling  [44]  to generate a TurtOBP1 3D model using the VdesOBP1 X-ray structure P2 1 form (PDB ID 7NZA) as a matrix.
The quality of the model as reported by Swiss-Model was very good, with a Q value of 1. 1 and as assessed by Molprobity (Figure  S5 )  [45]. As observed in VdesOBP1, the cavity is large and open (Figure  5A ). The r. m. s. d. between TurtOBP1 and VdesOBP1 is 0. 2 Å, which means that the structures are identical within experimental errors. The cavity volumes of TurtOBP1 and VdesOBP1 are identical, 2156 and 2153 Å 3 , respectively (CASTp calculation). Then, a molecule of 1-NPN was docked into the cavity, with the best position exhibiting a naphthalene stacking interaction on the side-chain of Trp109, and a hydrogen-bond of 1-NPN's N-H moiety with Glu124 (Figure  5A-C ). However, the cavity is much larger than the size of 1-NPN, as observed in the OBP1 of V. destructor or in the OBP of the blowfly Phormia regina (PDB ID: 5dic, by Ishida, Y.
Three-Dimensional Folding To get some insight into potential binding characteristics of TurtOBP1, we built a model using the recently solved structure of V. destructor OBP1 (VdesOBP1, PDB ID 7NZA)  [42]. Sequence comparison between VdesOBP1 and TurtOBP1 revealed an amino acid identity of 49% over 147 residues without insertions nor deletions (Figure  S4A ). Furthermore, the disulfide pattern observed in VdesOBP1 is conserved in TurtOBP1. A divergent feature of TurtOBP1 as compared to VdesOBP1 is a N-terminus longer by 35 amino acids. This stretch, however, does not seem to be structured, as reported by the secondary structure prediction server, JPRED4  [43]  (Figure  S4B ). Hence, the structural features observed in VdesOBP1 should be conserved in TurtOBP1. To verify this hypothesis, we used the automated model building software Swiss-Model homology modeling  [44]  to generate a TurtOBP1 3D model using the VdesOBP1 X-ray structure P21 form (PDB ID 7NZA) as a matrix.
The quality of the model as reported by Swiss-Model was very good, with a Q value of 1. 1 and as assessed by Molprobity (Figure  S5 )  [45]. As observed in VdesOBP1, the cavity is large and open (Figure  5A ). The r. m. s. d. between TurtOBP1 and VdesOBP1 is 0. 2 Å , which means that the structures are identical within experimental errors. The cavity volumes of TurtOBP1 and VdesOBP1 are identical, 2156 and 2153 Å 3 , respectively (CASTp calculation). Then, a molecule of 1-NPN was docked into the cavity, with the best position exhibiting a naphthalene stacking interaction on the side-chain of Trp109, and a hydrogen-bond of 1-NPN's N-H moiety with Glu124 (Figure  5A-C ). However, the cavity is much larger than the size of 1-NPN, as observed in the OBP1 of V. destructor or in the OBP of the blowfly Phormia regina (PDB ID: 5dic, by Ishida, Y.
, Leal, W. S. , Wilson, D. K. , unpublished), which exhibits a non-classical OBP fold similar in some way to that of VdesOBP1  [42]  and TurtOBP1. Coniferyl aldehyde was by far the best ligand of TurtOBP1, and we manually docked it within its cavity (Figure  5D ). As for NPN, we placed the phenyl ring in a stacking contact with Trp109. The aldehyde moiety is then in hydrogen bond position with Lys128. This position is the most favorable among the many ones that could occur in this large cavity. Superimposition by DALI  [46]  of TurtOBP1 structure on that of a classical OBP, in this case the OBP/PBP of Leucophaea maderae (PDB ID 1ow4  [47] ), results in a Z-value of 6. 1 and a r. m. s. d. of 3. 5 Å. It is worth noting that two disulfide bridges of TurtOBP1 are superimposed with those of LmadPBP: C2-C3 with C1-C3 and C4-C5 with C4-C6.
Discussion This study provides an original structural and functional characterization of odorantbinding proteins from an organism belonging to an order in the subphylum of Chelicerata. This is the first time that members of this protein family are subjected to detailed biochemical characterization. The reported results are in perfect agreement with those we recently observed in a parallel research performed on the mite V. destructor  [42] , suggesting the existence of common general features between these proteins in Chelicerata. The main structural differences between Chelicerata and insect OBPs reside in the cysteine pairing and the overall three-dimensional folding of the proteins. From an evolutionary point of view, we can suggest that OBPs of Chelicerata and those of insects may have been generated from a common precursor, although we have not found evidence so far of such ancestor in sequence databases.
RNA Extraction, cDNA Synthesis, Cloning, and Sequencing Total RNA was extracted from whole adult mites using the Trizma reagent (Merck, Austria) along with the enclosed protocol. Single strand cDNA was synthesized using the qScript cDNA SuperMix kit (Quanta bio) following the provided method. PCR was per-formed using specific primers (custom made at Eurofins Genomics, Ebersberg, Germany), which were designed based on the sequences encoding the four OBPs of T. urticae that are available in the NCBI database. Crude amplification products were then ligated into pGEM vector (Promega, Walldorf, Neckar, Germany); positive colonies were selected and custom sequenced at Eurofins Genomics (Ebersberg, Germany).
Chromatographic eluents were 99. 9/0. 1 (v/v) water/formic acid (solvent A), and 19. 95/79. 95/0. 1 (v/v/v) water/acetonitrile/formic acid (solvent B). The elution program was set as follows: 0 min, 2% solvent B; 5 min, 2% solvent B; 195 min, 40% solvent B; 205 min, 90% solvent B; 207 min, 90% solvent B; 210 min, 90% solvent B; flow rate was set at 300 nL/min. Mass spectra were acquired in positive ion mode, setting the spray voltage at 1. 7 kV, the capillary voltage and temperature respectively at 42 V and 175 • C, and the tube lens at 120 V. Data were acquired in data-dependent mode with dynamic exclusion enabled (repeat count 2, repeat duration 15 s, and exclusion duration 40 s). Survey MS scans were recorded in the Orbitrap analyzer in the mass range of m/z 350-2000 at a 60,000 nominal resolution at m/z = 400; then, up to seven of the most intense ions in each full MS scan were fragmented (isolation width m/z 1, normalized collision energy 35).
Mono-charged ions did not trigger MS/MS experiments. The raw file was analyzed using the MaxQuant software (version 1. 6. 10. 43) [50]  searching data against the protein database of T. urticae (NCBI) merged with a set of commonly observed contaminants, namely human keratins, bovine serum proteins, and trypsin. In the parameter section, we set trypsin as the proteolytic enzyme, allowing up to two missed cleavages. The minimum required peptide length was seven amino acids. Carbamidomethylation of cysteine and oxidation of methionine were set as variable modifications. As no labeling was performed, multiplicity was set to 1. Mass tolerance was set to ±4. 5 ppm for precursors and to ±0. 5 Da for MS/MS fragments. Peptide spectrum match (PSM) and protein identifications were filtered using a target-decoy approach at a false discovery rate (FDR) of 1%.
MS/MS spectra were acquired in a dynamic m/z range, with a nominal resolution of 17,500, a normalized collision energy of 28%, an automatic gain control target of 50,000, a maximum ion injection time of 110 ms, and an isolation window of 1. 2 m/z. Dynamic exclusion was set to 20 s. Raw data files were initially analyzed with Proteome Discoverer v. 2. 4 package (Thermo Fisher Scientific), including by Mascot v. 2. 6. 1 (Matrix Science, UK) and ByonicTM v. 2. 6 (Protein Metrics, Cupertino, CA, USA) software. Database searching was performed against a customized database containing T. urticae OBPs plus common protein contaminants, trypsin and chymotrypsin. Parameters for database searching were those reported in the previous section plus deamidation at Asn/Gln and pyroglutamate formation at Gln.
Disulfide bridge assignment was obtained by searching raw data files with BioPharma Finder v. 4. 0 (Thermo Fisher Scientific) and pLink v. 2. 3. 9  [41]  software. Both programs were used enabling the specific function of disulfide bridge attribution and applying the parameters described above for Proteome Discoverer and Byonic analyses. Confident disulfide-bridged peptide identification was considered when BioPharma Finder results showed a confidence score of >95 and/or pLink assignments had an E-value of <10 -10. Candidate fragmentation spectra were always subjected to manual interpretation and verification.
This protocol was used for OBP1 and OBP4, while with TurtOBP2 and TurtOBP3, the addition of 1-NPN did not produce any blue shift of the emission peak, thus preventing any accurate measurement of the bound probe. The affinities of other ligands to OBP1 and OBP4 were measured in competitive binding assays by titrating a solution of the protein and 1-NPN, both at the concentration of 2 µM with aliquots of 1 mM solutions in methanol of each ligand to final concentration values of 2 to 16 µM. The affinity to 1-NPN was calculated using Prism software. Dissociation constants of competing ligands were evaluated from the corresponding [IC] 50 values (the concentration of each ligand halving the initial value of fluorescence), using the equation: K d = [IC] 50 /1 + [1-NPN]/K 1-NPN where  is the concentration of free 1-NPN and K 1-NPN the dissociation constant of the complex OBP/1-NPN.
Model Building Considering the high sequence identity (49%) and the conserved disulfide pattern observed between VdesOBP1 and TurtOBP1, we used the automated model building software Swiss-Model homology modeling  [44]. The VdesOBP1 X-ray structure P2 1 form (PDB ID 7NZA)  [42]  was used as a matrix. The 1-NPN ligand was obtained from the PDB (PDB Id: 3S0B)  [56]  and docked in the cavity manually with Coot [57], taking into account hydrophobic contacts and hydrogen bonds. The coniferyl aldehyde structure was taken from the PDB entry 6klj. Figures were made with PyMOL software (https://pymol. org/ (accessed on 15-16 March 2021)).
a b s t r a c t Brewers' Spent Grain (BSG) is the primary waste of the beer brewing process, which comprises a plethora of nutritionally appealing ingredients such as proteins, dietary fibres, essential lipids and micronutrients. In our previous study  [1]  , the acid-induced gelation capacity of BSG protein isolate as influenced by the thermal pre-treatment severity was systematically investigated. In the present work, we aimed at providing a dataset outlining the gastrointestinal fate of the acid gels under simulating pre-absorptive digestion conditions adopting the INFOGEST static in-vitro digestion protocol. Protein hydrogel digestibility was assessed by quantification of the total soluble nitrogen content in the initial acid gels as well as the obtained gastric and small intestine chymes.
Value of the Data • Experimental evidence of the performance of acid gels made from thermally treated brewers' spent grain protein isolate under in-vitro digestion conditions is provided for the first time. • The dataset article can be used by R&D scientists working in the design of alternative proteinbased food products and/or food supplements adopting the "waste-to-fork" concept. • The dataset provides a technical roadmap to determine the gastrointestinal fate of alternative protein ingredients and their derivatives thereof. • Notwithstanding the heat treatment conditions, all acid gels exerted a quite restricted protein digestibility (less than 10 -15%) under in-vitro gastric conditions. • BSG-PI acid gels achieved very high digestibility rates (up to 93%) under simulated duodenal conditions.
The aminoacid composition of the native BSG-PI powder are provided as raw data in the file "AA composition. xlsx" available online, including the composition of the 3 amino acid standard mixes used for creation of the standard curve and the resulting quantification of BSG-PI amino acids. The analyzed data expressed on a dry basis of the BSG-PI total solids and protein matter, respectively are presented in Table  1. It was not possible to achieve a satisfactory quantification of methionine, cysteine, and tryptophan due to their partial decomposition during the acid hydrolysis step. Nevertheless, the sum of the three essential aminoacid calculated by difference was estimated at 11. 9 g. 100 g -1 of protein matter. In addition, asparagine and glutamine were deamined and converted into aspartic and glutamic acid during the acid hydrolysis, respectively and thus, only one concentration was reported for both aminoacids.
SDS-PAGE analysis of the digesta Protein molecular weight distribution was characterized by reducing SDS-PAGE using a Bio-Rad Criterion TM XT Bis-Tris 12% gel and Precision Plus unstained Protein standards. Samples (8. 4 μL) were dissolved in 3 μL of XT Sample Buffer, mixed with 0. 6 μL of XT reducing agent, heated at 95 °C for 5 min, loaded (12 μg protein/lane) onto the precast gel and subjected to electrophoresis at a constant voltage of 200 V until the migration front reached the bottom of the gel (45-50 min). After staining with Serva Purple, following manufacturer's instructions, the gel was imaged using a Typhoon FLA 9500 Imager and analyzed using the Image J software. The raw images of the three replicates (SDS-Page Gel 1-3) and the gastric fluids (SDS-PAGE Gel 4) were uploaded to the linked repository.
In-vitro digestibility determination The in-vitro digestibility of the protein acid gels was measured in duplicate via the total nitrogen quantification method as described in  Hu et al. (2019)   [8]. Briefly, 5 mL of the intestinal chymes were centrifuged at 80 0 0 g at 4 °C for 20 min. The soluble nitrogen content of digesta supernatant was determined by the Kjeldahl method and the in-vitro digestibility was expressed as follows: In -vitro digestibility = Protein content in the digesta supernatant × dilution factor Protein content of the initial acid gel Raw data of the results obtained for two replicates of the gels for the five condition are presented in the excel file "digestibility" sheet "in-vitro digestibility".
5 kV; source gases were set (in arbitrary units min -1 ) for sheath gas, auxiliary gas and sweep gas at 30, 10 and 5, respectively; capillary temperature was set at 250 °C; capillary voltage at 5 V; tube lens, split lens and front lens voltages at 30 V, -62 V and -8. 75 V, respectively. Ion optics parameters were optimized by automatic tuning using a standard solution of arginine at 0. 1 g. L -1 infused in mobile phase (A/B: 50/50) at a flow rate of 5 μL. min -1. Full scan MS spectra (70 to 210 m/z) were performed on a LTQ analyzer (Linear Trap Quadripole). Raw data were processed using the XCALIBUR software program (version 2. 1, http://www. thermoscientific. com ). The result obtained presents the average of at least the four replicates of the acid hydrolysis. When two concentrations (i.
AMH was supported by  CNRS ,  Inserm ,  Collège de France ,  FRM  (Equipe FRM  DEQ20160334884 ),  ANR  ( ANR-13-BSV2-0007-02   PlasTiSiPi ;  ANR-15-CE13-0001-01 , AbsCyStem), and  Bettencourt-Schueller Foundations. EB was supported by the  Foundation pour la Recherche medicale and foundation ARC pour la recherche sur le cancer  ( PJA20171206129 ), IDEX-ISITE initiative  16-IDEX-0001  ( CAP20-25 ),  ANR  ( ANR-17-CE12-0030-03 EpiTET  and  ANR-20-CE12-0005-01  CHApiTRE projects). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
The availability of crop type reference datasets for satellite image classification is very limited for complex agricultural systems as observed in developing and emerging countries. Indeed, agricultural land use is very dynamic, agricultural census are often poorly georeferenced, and crop types are difficult to photo-interpret directly from satellite imagery. In this paper, we present nine datasets collected in a standardized manner between 2013 and 2020 in seven tropical and subtropical countries within the framework of the international JECAM (Joint Experiment for Crop Assessment and Monitoring) initiative. These quality-controlled datasets are distinguished by in situ data collected at field scale by local experts, with precise geographic coordinates, and following a common protocol. Altogether, the datasets completed 27 074 polygons (20 257 crop and 6 817 non-crop) documented by detailed keywords.
, 2015)  because the fields are often small to medium size  (Fritz et al. , 2015) , the crops are easily confused with natural vegetation and fallows, and cropping systems are typically highly variable in time and space. Each farming system has its own specificities in terms of crop type and composition, field size, cropping calendar, irrigated/rainfed mode and other practices  (Bégué et al. , 2018). It is thus necessary to adapt the classification approaches (satellite data and algorithms as well as training and validation in situ data) to the large variability of the farming systems in the world  (Dixon et al. , 2001) , and thus to have access to appropriate training data. The arrival of Sentinel-1 and 2 satellite image time series, the emergence of new classification algorithms in the domain of machine learning and artificial intelligence, and an easy access to pre-processed images and image processing tools on webplatforms, have democratized image processing, and opened-up new avenues for LULC mapping over large areas.
Following this trend, large benchmark datasets acquired using annotation tools of satellite images all over the world have multiplied to train algorithms and validate remote sensing-derived products  (Long et al. , 2020). However, these datasets have a broad LULC nomenclature, and agricultural land use is often reduced to a single class due to difficulties in discriminating cropping practices from satellite images. The main data sources currently available for agricultural land use mapping in the Southern countries are listed below. At a global and continental scale, initiatives that freely distribute land cover reference datasets exist (see review by  Tsendbazar et al. (2015) ) such as GOFC-GOLD (Global Observation for Forest and Land Cover Dynamics; http://www. gofcgold. wur. nl/sites/gofcgold_refdataportal.
php) that regroups existing global datasets prior 2015, or the LULC reference dataset (150,000 samples ; 300 m-1 km resolution)  (Fritz et al. , 2017)  and the cropland dataset (36 000 points ; 300 m resolution)  (Laso Bayas et al. , 2017b)  both collected through crowdsourcing campaigns using the Geo-Wiki tool (photointerpretation of very high spatial resolution satellite images). Lately, the LandCoverNet dataset has been released for the African continent  (Alemohammad et al. , 2020)  with 130 million of labelled 20 m pixels of 1 980 image chips (256 x 256 pixels), spanning 66 tiles of Sentinel-2 acquired in 2018. These data are used to validate global  (Hoskins et al. , 2016)  or national cropland maps  (Laso Bayas et al. , 2017a)  as the nomenclature used for labelling the classes does not specify the crop type.