dataesr/scientific-paragraphs-categorization huggingface dataset:
5a ). As shown previously  [18, 19] , TaASN2 showed a grain-specific expression profile, with transcript levels highest at Zadoks stage 85 (Z85), which corresponds to the soft dough stage  [27]  (Fig. 5a ). Furthermore, TaASN-A2 accounted for 83. 3% of all TaASN transcripts in grain tissues at Z85, while TaASN-D2 contributed just 3. 0%, consistent with previous results  [19]. To further explore the expression of TaASN genes in the grain, an expression dataset from six stages of grain development in the variety Azhurnaya was analysed  [25]. As expected  [18, 19] , high transcript levels of TaASN-A2 and TaASN-D2 were found in five developmental stages which, when combined, accounted for between 69 and 86% of all TaASN transcripts in these tissues (Fig. 5b ). The exception was the grain milk stage, where TaASN3.
1 transcript levels were higher than TaASN2 (Fig. 5b ), as shown previously  [18]. TaASN-B2 transcripts were detected at negligible levels in this dataset, suggesting its deletion in the Azhurnaya genome. Analysis of expression data from an embryo development timecourse in the common wheat variety AC Barrie  [28]  revealed that TaASN2 transcript levels were highest in the mature embryo stage (Fig. 5c ). Among TaASN2 homeologues, TaASN-A2 was again the most highly expressed gene, while TaASN-B2 and TaASN-D2 were expressed at similar levels (Fig. 5c ). However, TaASN2 transcripts were detected only at negligible levels at all other developmental timepoints, including earlier stages of embryo development and in endosperm and pericarp tissues, where TaASN3. 1 transcripts were more abundant (Fig.
001) interaction between TaASN-B2 presence/absence and sulphur treatment: there was no significant difference (p > 0. 05) in free asparagine concentrations between varieties with and without TaASN-B2 under sulphur deficiency, but varieties without TaASN-B2 had 32. 60% less free asparagine (p < 0. 01) than those with TaASN-B2 under sulphur sufficiency (Fig. 8c ). The REML analysis based on the complete combined datasets also identified a significant effect of TaASN-B2 as well as an interaction between TaASN-B2 and sulphur treatment (Additional file 1, Table  S3b ). This analysis also further demonstrated that the interaction between TaASN-B2 and sulphur treatment changed by year, as is suggested by Fig. 8b  and c , and that there is a significant interaction between year and sulphur treatment.
Genomic analyses Nucleotide sequence data for the wheat ASN genes from different wheat genotypes were obtained using the BLAST tools of the 10+ Wheat Genomes Project (https://webblast. ipk-gatersleben. de/wheat_ten_ genomes/), the Grassroots Genomics Project (https:// wheatis. tgac. ac. uk/grassroots-portal/blast)  [39] , and the Graingenes database (https://wheat. pw. usda. gov/cgi-bin/ seqserve/blast_wheat. cgi)  [40]. Some ASN genes lacked complete sequence information, and these exceptions are described in Additional file 1, Table  S4 ). Geneious Prime 2020. 1. 2 was used for alignments and sequence identity analyses between genes. The annotated genome from the pasta wheat (T. durum) variety Svevo (https:// www. interomics. eu/durum-wheat-genome) [41]  was used to compare the genomic region containing TaASN-B2 with the corresponding region in the RefSeq v1.
1 genome from the common wheat (T. aestivum L. ) landrace Chinese Spring (from Ensembl plants https://plants. ensembl. org/wheat) [21]. The softberry-FGENESH tool  [42]  was used to identify putative genes from the 12,770 bp deleted region encompassing TaASN-B2 from the variety Jagger. This analysis identified five putative ORFs. Each was analysed with HMMScan  [43]  using an e-value cutoff of 0. 05, which confirmed the presence of TaASN-B2 and a second gene encoding a protein containing an F-box PFam domain (PF00646). Transposon annotations were confirmed using the BLAST tool in TREP (TRansposable Elements Platform) (http://botserv2. uzh. ch/ kelldata/trep-db/index. html)  [23]. SIFT analysis  [44]  was performed by comparing each ASN protein from Chinese Spring with the protein encoded by the orthologous gene from other wheat varieties.
RT-qPCR was performed using an Applied Biosys-tems™ 7500 Real-Time PCR System set to ddCt (relative quantitation) mode. Each reaction contained 10 μL SYBR Green Master Mix (Applied Biosystems), 5 μL primer mastermix (containing 0. 04 mM of each primer and ROX reference dye in nuclease-free water), and 5 μL cDNA (diluted to 6 ng/μL). The expression of each target gene was measured relative to three reference genes; GAPDH, PROSM, and SDH. Details of the primers used are found in Additional file 1, Table  S5. Relative expression values were calculated as described by Rieu and Powers  [48]. Applied Biosystems 7500 Real-Time PCR Software version 2. 0. 5 was used to calculate Ct values and exported Rn data were converted to PCR efficiency data using LinRegPCR  [49]. Statistical tests were performed using GenStat  [50]  to account for the blocking structure of the experiment and graphs were plotted in R  [51]  using the package ggpubr  [52].
RNA-seq data analysis Raw RNA-seq reads from a developmental timecourse in the landrace Chinese Spring  [24] , grain development samples from the variety Azhurnaya  [25] , an embryo development timecourse from the variety AC Barrie  [53]  and grain expression at 14 DPA and 30 DPA from a set of worldwide wheat varieties  [28] , were downloaded from the NCBI GEO database (https://www. ncbi. nlm. nih. gov/geo/) processed and mapped to the IWGSC RefSeq v1. 1 genome following the approach described previously  [54]. An additional contig corresponding to the TaASN-B2 coding sequence and including 1 kb of sequence upstream and downstream of the protein coding region was added to the reference genome to assay transcript levels of this gene. Raw counts were converted into TPM using a custom python script.
Heatmaps were generated in R (v1. 12. 5019) using the command 'heatmap' within the gplots package  [55]. The ternary plot was created using the ggtern package  [26]  within ggplot2  [56]. All expression data in TPM are presented in Additional file 2. Effect of TaASN-B2 on free asparagine in the grain Data from field trials performed in 2011-2012 and 2012-2013  [11]  were used to investigate the effect of the TaASN-B2 deletion on grain asparagine levels with sulphur either supplied (S+) or withheld (S-). Varieties from this field trial were screened as described above for the presence or absence of TaASN-B2. Data were log e transformed to account for heterogeneity of variance, as identified in the previous study  [11] , before performing the ANOVA and REML analyses. Analyses and plotting were performed in R  [51]  with the package ggpubr  [52].
Availability of data and material Expression data was generated from public RNA-seq datasets that were downloaded from https://www. ncbi. nlm. nih. gov/geo/ and processed as described in the results section. All expression values used to generate expression graphs are presented in Additional file 2. Amino acid data for protein sequences in the landrace Chinese Spring were described previously  [11]. DNA and protein sequences from other wheat varieties were derived from the 10+ Wheat Genomes Project (https://webblast. ipk-gatersleben. de/wheat_ ten_genomes/), the Grassroots Genomics Project (https://wheatis. tgac. ac. uk/ grassroots-portal/blast)  [39] , and the Graingenes database (https://wheat. pw. usda. gov/cgi-bin/seqserve/blast_wheat. cgi).
The general wideband modeling method of the power converter is presented on the example of DC-DC buck converter with GaN High Electron Mobility Transistors (HEMT). The models of all basic and parasitic components are briefly described. The two methods of Printed Circuit Board (PCB) layout parameter extraction are presented. The results of simulation in Saber@Sketch simulation software and measurements are compared. Next, the model of the converter is reduced to obtain one lumped inductance of the input filter PCB for the analytical prediction of transistor turn-off ringing frequency and overvoltage. The practical use of the model is presented for sizing optimal capacitance of snubber.
The wideband model of GaN transistor applied for simulation study is presented in Figure  2. S L S R S R D L D D C DS C GD C DS R G L G G Figure 2. Equivalent circuit of GaN transistor. The internal structure of the GaN FET transistor used is cascode of high-voltage GaN HEMT and low-voltage MOSFET  [13, 14]. Without knowing the internal structure, it is impossible to measure the capacitance of each of these transistors separately. Therefore, the capacitances have been modeled as it is presented in manufacturer data-sheet. Moreover, the tools included in Saber@Sketch allow to model only one internal transistor. The parasitic capacitance of a transistor results from its internal structure, in which the areas with different potentials are very close to each other. Typical capacitance characteristics in transistor data sheets are input capacitance C ISS = C GS + C GD , output capacitance C OSS = C DS + C GD , and transfer capacitance C RSS = C GD.
PCB Layout Model The PCB layout, including the conducting tracks, wires, and cables, has primary importance in high-frequency behavior of the converter. The magnetic and electric couplings of conducting tracks can be represented by the coupled inductors and capacitors. These components are part of the resonant circuit responsible for overvoltages and oscillations at transistor turn off. Many software tools are offered to model PCB parasitics. In this paper, authors focus on the Q3D Extractor from Ansys and PEEC (Partial Element Equivalent Circuit) method [17] used by InCa3d from Altair. The geometry of layout is defined or exported to the applied software environment and next ready-to-use model is generated. The PEEC method allows calculating the value of resistance and self-and mutual inductances using geometrical and physical properties of the tracks.
The formulas can be directly implemented in the computing environment. In the presented example of buck converter, the PEEC method has been implemented in the Matlab software by authors. Based on formulas from in [17], the Matlab functions have been written, where the input variables were entered manually in accordance to the dimension of the conduction tracks. Using the Q3D Extractor, first sources and sinks should be defined and then the model including the following: • inductors equal to half of the inductance between two nodes, • inductive couplings between each inductor, • resistors representing AC and DC resistance of the tracks, • capacitors for capacitance between previously defined sources and between sources and artificial reference point. Using the artificial reference points allows to define the capacitance between each conducting track and also the ground if it is placed within the circuit.
The simplest and most effective method of checking the numerical stability is to run a series of simulations for various numerical conditions, e. g. , calculation step or truncation error. If the simulation results do not differ significantly (there are any unexpected variations or an increase in the current and voltage values), it can be concluded that the obtained results are not burdened with numerical errors. If the results are not stable, it is recommended to add low value resistors in series with the nonlinear elements. Another important aspect is adequate simulation time so that the circuit is in a quasisteady state (all capacitors should be charged). In the Saber@sketch software, it is possible to continue the second simulation with initial conditions from the previous one. The simulation of the presented circuit was performed as follows: 1.
Model Reduction Usually, models obtained with calculation software can not be used in analytical analyses, so the reduction to a few lumped components model is applied. In  [10] , the value of the leakage inductance was needed in order to calculate the value of overvoltage after commutation. The AC signal analysis has been performed to find the values of the lumped parasitic inductance of this part of the circuit. First, the model of tracks PCB of input filter was extracted and exported into circuit simulator Saber@Sketch (Figure  9 ) and fed by AC current source equal 1A (placed on the high-side transistor location) as it is shown in Figure  12a. The output of the PCB model was shortened. Using the results of the simulation, the impedance viewed from the current source was determined in the frequency domain.
The range and the changing step can be set in the software as "by step", linear, or logarithmic. In order to find the optimal value of the snubber capacitor parallel connected to transistors, the model from Saber@Sketch library was modified. The value of capacitance was set using an external control signal source for both capacitors as it is shown in Figure  13. Using the control source value as a parameter allows performing parametric analysis with changed capacitance value in both capacitors simultaneously. The simulation series were performed varying the snubber capacitors in the range from 10 pF to 10 nF. After extracting data from the result of the simulation series, the maximum value of voltage across the transistor T 1 can be presented in the function of the snubber capacitance as in Figure  14.
3476358 or reading a document. Clearly, supporting such dialogs requires more than exposing and interpreting commands in a fixed syntax or grammar, pushing systems to process and interpret user requests in natural language. This represents a challenge as systems now need to deal with the richness of human language, e. g. , another user may ask "How is the coronavirus transmitted?" in allusion the previous COVID-19 question, thus illustrating the different ways the same request can be expressed. Failing to correctly identify and process such nuances of human language can have a negative impact on the effectiveness, user experience and ultimately the adoption of Conversational AI systems  [18, 28]. Training Conversational AI systems to deal with the expressiveness of natural language often requires collecting a large and linguistically diverse dataset of utterances  [12, 30].
Pivot Translation  [2]. The idea behind pivot translation is that two sentences with the same (foreign) translation can be assumed to have the same meaning. Thus, paraphrases can be generated by translating a sentence in source language S into a foreign language F and then back-translating it into S. Informed by previous literature  [7, 31] , we support the configuration of various parameters of pivot translation that are known to affect the generation of paraphrases. The paraphrase system, refers number of pivot languages that can be used to generate paraphrases, a single-pivot system when one language is used as pivot, and multi-pivot when it is made up of a set of single-pivot systems. Having multiple machine translation engines can maximise the chances of getting more diverse paraphrase options  [31] , and so we support a selection of online neural machine translators (NMTs), such as Google Translate 1  , Deepl 2  and MyMemory 3  ; as well as pre-trained NMTs like the Huggingface Marian Machine Translator  [11, 26] , and Open-NMT  [13].
In the pipeline we include a paraphrasing technique based on T5  [21] , a transformer implemented by Google to perform sequence transduction. By default T5 does not perform paraphrasing, so we trained it on the Quora Question Pairs dataset  [23]  and Para-NMT datsets  [25]  to generate paraphrases, following the work of Goutham  [9]. For each given input sentence the T5 model will generate a list of candidate paraphrases. 2. 1. 2 Candidate selection. While the techniques in the over-generation step aim at exploring different variations of the input sentence, the candidate selection aims at pruning the candidate list to remove paraphrases that do not contribute to the desired paraphrase dataset quality attributes. We mentioned earlier that existing techniques can lead to semantically unrelated, duplicates, or grammatically incorrect paraphrases, so by adding a quality control step in the pipeline, we enable application, ation of quality control techniques.
More details on these models can be found in the appendix. On the basis of the design of its storage system in terms of rated voltage and capacitance, the above hybrid source was tested with NEDC driving cycles at the FClab platform in Belfort. Contrary to former works quoted, the power of the hybrid source and the weight of the fictitious vehicle have been varied for more complete investigation of the behavior of the hybrid source-vehicle system. Two dynamics of the source have therefore been considered in the present study by varying the number of SC modules directly connected to two 3 kW Ballard fuel cells in series. The vehicle with a fictitious weight varying from 400 to 520 kg, has been simulated using suitable data and laws for mechanical friction of electric vehicles. Energy sharing between the fuel cell and the SC has been followed, in repeated driving cycles.
2. The braking chopper, whose general purpose is to manage the consumption of the energy produced by the hybrid source (corresponding current regime: IHS > 0), was simply driven by a hysteresis comparator, as to maintain the DC link voltage below a given level (160 V here), which must be higher than the maximum voltage attainable by the hybrid source. When required for recovery braking emulation (corresponding current regime: IHS < 0), the power supply was connected to the DC link through a diode, and its output voltage was set to 155 V, i. e. , between the highest HS voltage and the limit for the DC link voltage at 160 V. The test bench supervision (DC-DC converter control, braking chopper control, computation of power and current references of the hybrid source, safety shutdown), was managed by two dSPACE ® real time cards (computation sample time: 100 μs), in combination with Controldesk ® software and Matlab-Simulink ® environment.
Outputs are overvoltages at membrane/electrode interfaces, and voltage drop across the membrane. The second one (on right) is a standard electrical scheme, from which the fuel cell voltage can be deduced as a function of fuel cell current, equilibrium potentials and overvoltages, and feeding back the transport sub-model with fuel cell current and faradaic currents at electrodes. Model assumptions are as follows: 1) constant and homogeneous temperature, 2) single phase gas mixtures, 3) 1D mass and charge transports, 4) gas-tight membrane, 5) catalyst layers treated as interfaces. All details of the model are given in  [25] : basic equations, parametric laws, analog formulation, implementation in Saber ® software and experimental validation. As previously mentioned, this model was employed in  [23]  and  [24]  for investigating direct association of a supercapacitor device with small-scale fuel cells: a 100 cm 2 PEM single cell (including a seven layered MEA composed of a 50 m thick membrane, two Pt catalyst layers, and two 285 m thick GDL), and a 3-cell stack (made with the kind of cell described above).
In the present case, this diode is already included in the FC system model. The hybrid source model was implemented in Saber ® software, together with the NEDC power profile described in section 2. 2. Figure  A3  presents FC, SC and HS power profiles under NEDC conditions for M = 520 kg and SC capacitance at 165 F, without recovery. This figure is to be related to Figure  5b , obtained experimentally under the same conditions. Features on HS power are very similar, so are experimental and predicted FC and SC power features as shown below: As mentioned in section 3. 1. 2, weight M could be taken at 1000 kg with the above hybrid source, if WLTC-2 speed profile was considered instead of NEDC one. This profile is 1477 s long, it covers 14. 66 km, with an average velocity near 36 km/h and a maximum velocity at 85 km/h.
1 ). In a few cases, a plant species in our data set was not found in the BIOFLOR database. In most cases, morphotypes are constant at the genus level, and missing morphotypes were added based on the morphotypes found for congeners. In the case of the genus Phyteuma, we followed  Neumayer and Paulus (1999)  and classified it as a group 7 flower head. Morphotypes 9 and 10 were extremely rare in our study system and morphotype 8 was completely absent, so these morphotypes were omitted from all analyses. Plants belonging to morphotype 0 were typically not recorded during floral surveys due to their lack of conspicuous floral displays, and they were rarely visited by bumble bees, so this morphotype was also omitted from all analyses. Kugler morphotype classifications of the plants in our study system were accessed via the BIOFLOR database  (Klotz et al.
e. site-dates), thus preserving the within-sample correspondence between visitation and abundance. Simulated interactions are then summarized for each consumer species by taking the mean number of interactions with each resource across all individuals of the consumer species and all samples of the dataset. As this process is iterated, the result is a distribution of visitation rates of each consumer species to each resource under the null assumption that resource selection is determined only by resource abundance. The visitation distributions of the null model can then be compared to observed visitation rates to infer positive or negative preference of each consumer toward each resource. Since the mean of the null distribution can be interpreted as the expected value under null assumptions, the difference between the mean null value and the observed value can be interpreted as a magnitude of preference  (Vaughan et al.
2018) , and statistical significance can be estimated as the proportion of simulated null values more extreme than the observed value. We followed the procedure described above, simulating our entire dataset in one modeling process to infer aggregate preferences of bumble bee species for each floral morphotype. Using the results of this analysis, we modeled preferences for each floral morphotype as a function of bumble bee tongue length with hierarchical generalized additive modeling (HGAM), following the type I effect structure described by  Pedersen et al. (2019). To verify that our inference of preference was not sensitive to our decision to pool queen and worker visitation, we recalculated the null model and tongue length HGAM using only data from workers. Finally, we compared morphotype preferences across bumble bee species by performing a principal components analysis on the bumble bee species × floral morphotype preference matrix, and we mapped bumble bee tongue length onto the ordination space by fitting a 2-dimensional GAM surface to the first two principal components.
With this limitation in mind, we fit HGAMs of the same form to the relative abundance of each floral morphotype, and we present the results together to be interpreted jointly. All data processing and analyses were conducted in R (<www. r-project. org>). Data handling and visualization were performed with the tidyverse suite  (Wickham et al. 2019). Null models were constructed using econullnetr  (Vaughan et al. 2018). GAM analyses were performed with mgcv  (Wood 2017 ) and visualized with mgcViz  (Fasiolo et al. 2020 ) and tidymv  (Coretta 2021). PCA was performed with the vegan  (Oksanen et al. 2019 ) and visualized using ggplot2  (Wickham 2016 ) and ggvegan  (Simpson 2019). Annotated R code and complete output of our analytical workflow is available in the Supporting information.