dataesr/scientific-paragraphs-categorization huggingface dataset:
Our explorations focus mainly on the error reaching the desired position and the elapsed execution time needed to compute the trajectories in  [13]. All the raw data are available in  [12]. Time measurements are obtained by using the ''tic/toc'' MATLAB functions, thus they are machinedependent. 2  In the classic case the provided estimation time is straightforward, while in the parallel one the estimation is given by summing up the time needed to compute step 1 (t s1 ), plus the time needed to compute step 2 (t s2 ) weighted with respect to the available parallel computing units. Equation  13 provides an example of the formula used to estimate the elapsed execution time needed to compute N different parallel segments over k parallel computing units. time par = t s1 + N k * t s2 ( 13 ) Regarding the error, considering Z (x 1 , y 1 , z 1 ) as the final desired position of the end-effector, it is possible to define the error among the desired target and the actual reached position as computed by the chosen implementation.
To port the above-mentioned profiles in hardware over an MDC-compliant coprocessor the following steps have been performed. Starting from the MATLAB classic (see Section V) implementation of the DLS algorithm, the corresponding C code has been derived using the MATLAB Coder feature  [46]. Once obtained an imperative C version, the DLS execution has been partitioned into five operations, which constitute the dataflow actors: • J_Matrix calculates the Jacobian matrix J of the arm (see Equation  12 ); • J2_Matrix calculates J T J + λ 2 I ; • Min performs a first step for the calculation of J T J + λ 2 I -1 ; • J2Cof_Matrix finalizes the calculation of J T J + λ 2 I ; • Theta performs calculations of the new angles ing from the of the other actors and according to the target position of the end effector (see Equation  9 ).
VII. CONCLUSION In the context of CPSs design, it turned out to be particularly important for the embedded computing platforms to support complex execution workloads without 4 In FPGA this term is usually most important than the static one since this latter, being not dependent on the occupied resources, is always constant for a given operating condition. sacrificing flexibility, to be able to dynamically react to system-and environmental-driven triggers. Heterogeneous platforms, such as modern FPGAs, could easily serve the purpose and, lately, have been used in many new application fields, one of them is certainly the robotic one. In this article we presented the first attempt of implementing the DLS algorithm, for IK problems solution, over an FPGA substrate. The implementation has been realized leveraging on commercial (MATLAB, MATLAB Coder, Vivado HLS, Vivado) and open-source academic (MDC) tools, to mitigate the designer effort and make the implementation more straightforward also for software programmers.
In order to examine core features shared by all section Flavi species, clades, as well as features of individual species, we made an analysis of shared homologous genes within and across species  16  , and sorted these into homologous protein families (Fig. 2 ). This allowed the identification of (1) The core genome-protein families with at least one member in all compared species. This is expected to cover essential proteins. (2) Section-specific and clade-specific genes-genes that have homologs in all members of a clade/section, but not with any other species. (3) Species-specific genes-genes without homologs in any other species in the comparison. The core genome of all 31 species in this dataset is 2082 protein families. For the 29 Aspergillus species this number is 3853, and for section Flavi species alone constitutes 4903 protein families.
b A barplot showing the number of total (green), core (turquoise), and species-specific (orange) proteins for each species. The dark shading illustrates the number of proteins with a least one functional annotation based on InterPro  32. the number of species-specific genes are very high (166-2181), where we see 166 (A. sojae) to be an artificially low number, due to the gene calling in this genome being based on A. flavus and A. oryzae genomes. Species-specific genes often encode regulation and P450s. We wanted to see whether the species-specific genes could be linked to known Flavi functions such as food fermentation and plant and human pathogenicity. In order to do this, we examined predicted functions of the species-specific genes using InterPro, GO and KOG annotations  [32] [33] [34] [35].
We investigated for HGTs using BLASTp to examine the best hits in the NCBI nonredundant database. Recent HGTs are expected to have high sequence identity with another group of species where it would have been transferred from, and not be found in the closely related species  39. None of these islands showed signs of recent HGTs. Furthermore, only 23 of the 80 genes in the non-syntenic blocks were A. oryzae-specific. It thus seems likely that these non-syntenic islands are caused by a mix of significant rearrangements, duplication events, and the emergence of A. oryzae-specific genes. Taken together, the fact that we observe some very conserved chromosomes and some highly rearranged non-syntenic blocks could indicate an evolutionary pressure for stability in some regions while other regions are frequently subject to gene shuffling and rearrangements, i.
e. , rearrangement hot spots. Section Flavi is a rich source of carbohydrate-active enzymes. Carbohydrate-Active enZymes (CAZymes) are essential for what carbon sources a species can degrade and utilize. Within section Flavi the CAZymes/carbon utilization is mainly described for A. oryzae 1,2,40 and to a lesser extent for A. flavus  [41] [42] [43] [44] [45]  and A. sojae  46, 47  , while only incidental studies have been performed with other species of this group  [48] [49] [50] [51] [52] [53] [54]  , often describing production or characterization of a certain CAZyme activity or protein, respectively. We used the CAZy database to predict the CAZyme content in the genomes of the section (Fig. 4 ). A total of 13,759 CAZymes were predicted for the 23 Flavi species (average 598/species). This is quite rich compared with included reference Aspergilli (508/ species).
This is more than 15 extra per species compared with the very prolific Penicillium genus  65. We wanted to examine how unique the SMGCs are, and thus constructed families of SMGCs (Supplementary Data 3). For the entire dataset, we could collapse it into 477 SMGC families, and for section Flavi 308 SMGC families. Out of these, 150 SMGC clusters are only found in one section Flavi species (Fig. 6a ), showing a large number of unique clusters in each species (6. 8 unique SMGCs/species). Compared with Aspergillus section Nigri, the number of clusters per species in this study is slightly lower, but the number of members in each SMGC family is also lower, demonstrating greater diversity in secondary metabolism in section Flavi compared with section Nigri.
Dereplicating secondary metabolism predicts toxin producers. To assess the potential for SM production qualitatively, we used a pipeline of "genetic dereplication" where predicted clusters are associated with verified characterized clusters (from the MIBiG database  66  ) in a guilt-by-association method  67. Based on this, 20 cluster families were coupled to a compound family (Fig. 6b ). Some cluster families were found in all or nearly all Flavi genomes, e. g. , those similar to the naphthopyrone  68  , nidulanin A 69 , azanigerone  70  , 4,4′-piperazine-2,5-diyldimethyl-bis-phenol, and aflavarin  71  /endocrocin  72, 73  clusters. Most families generally follow the phylogenetic groups, suggesting a loss-based distribution pattern, but some, like the SMGC families similar to the asperfuranone  74  , pseurotin A  75  , or fumagillin  76  clusters did not follow the phylogeny.
Moreover, potential producers of known toxins such as aflatoxin and aspirochlorine were identified (Fig. 6b ). Combination of data and analysis links a compound to a cluster. Extending from the known SMGC clusters, we were interested in linking compounds and clusters based on the presence/absence pattern of produced compounds and predicted clusters. We therefore created a heatmap of all the cluster families found in at least five species, added the predicted compound families from the MIBiG dereplication, in addition to manually curated compound families from a literature survey (Supplementary Fig. 9 ). In addition to this, we measured the SM production of the Flavi species (Supplementary Data 4). Of particular interest was miyakamides. They are originally isolated from an A. flavus isolate and shown to have antibiotic  properties  77  , but the biosynthetic gene cluster is not known.
Sequencing of the flow cells was performed on the Illumina HiSeq2000 sequencer using a TruSeq SBS sequencing kit, v3, following a 2 × 150 indexed run recipe. After sequencing, the genomic FASTQ files were quality control-filtered to remove artifacts/process contamination and assembled using Velvet54. The resulting assemblies were used to create in silico long mate-pair libraries with inserts of 3000 ± 90 bp, which were then assembled with the target FASTQ using AllPathsLG release version R4771055. Illumina transcriptome reads were assembled into consensus sequences using Rnnotator v3. 3. 256. Genome annotation. All genomes were annotated using the JGI annotation pipeline  81, 82  as previously described  16, 80. Genome assembly and annotations are available at the JGI fungal genome portal MycoCosm 81 (see URLs) and have been deposited in the DNA Data Bank of Japan (DDBJ)/European Molecular Biology Laboratory (EMBL)/GenBank under the accession numbers provided in the Data Availability Statement.
8. 31 (default settings) and then trimmed using gblocks version 0. 91b (-t = p -b4 = 5 -b5 = h). Following 200 of these monocore sequences (with length between 150 and 1000 AA) were selected randomly and concatenated and used to construct a phylogentic tree using RaxML version 8. 2. 8 using the PROT-GAMMAWAG substitution model and 1000 bootstraps. Prediction of CAZymes. CAZymes were predicted using the CAZymes database (CAZy, www. cazy. org  83 ) and the method described in our previous work  16. Each Aspergillus protein model was compared using BLASTp with proteins listed in the CAZYmes database (CAZy)  83, 84. Models with over 50% identity over the entire length of an entry in CAZy were directly assigned to the same family (or subfamily when relevant). Proteins with less than 50% identity to a protein in CAZy were all manually inspected, and conserved features, such as the catalytic residues, were searched whenever known.
Because 30% sequence identity results in widely different e-values (from nonsignificant to highly significant), for CAZy family assignments, we examined sequence conservation (percentage identity over CAZy domain length). Sequence alignments with isolated functional domains were performed in the case of multimodular CAZymes. The same methods were used for Penicillium digitatum and Neurospora crassa. Prediction of secondary metabolite gene clusters. Secondary metabolite gene clusters (SMGCs) and SMGC families were predicted based on the SMURF algorithm  64  and the method described in our previous work  16. For the prediction of SMGCs, we developed a command-line Python script roughly following the SMURF algorithm: According to SMURF the following genes were predicted as "backbone" genes: Genes that have at least three PFAM domains-ketoacyl-synt (PF00109), Ketoacyl-synt_C (PF02801), and Acyl_transf_1 (PF00698)-were predicted as 'PKS' genes.
Genes that have ketoacyl-synt (PF00109) and Ketoacyl-synt_C (PF02801) but not Acyl_transf_1 (PF00698) were predicted as "PKS-like" genes. Genes that have at least the three domains AMP-binding (PF00501), PPbinding (PF00550), and Condensation (PF00668) were predicted as "NRPS" genes. Genes that have an AMP-binding (PF00501) domain and at least one of the domains PP-binding (PF00550), Condensation (PF00668), NAD_binding_4 (PF07993), and Epimerase (PF01370) were predicted as "NRPS-like" genes. Genes that have both "PKS" and "NRPS" domains were predicted as "Hybrid" genes. Genes that have a Trp_DMAT domain were predicted as "DMAT" genes. Genes that have Terpene_synth (PF01397) or Terpene_synth_C (PF03936) domains were predicted as "Terpene cyclase/synthase" genes. Secondary metabolite-specific PFAM domains were taken from Supplementary Data 1 of the SMURF paper  64.
The sum of the BLASTp percent identity (ptailoring/ pbackbone) of all hits for tailoring enzymes between two clusters was divided by the maximum amount of tailoring enzyme (ttailoring/tbackbone) and multiplied by 0. 35. Then the score for the backbone enzymes was calculated in the same manner but multiplied by 0. 65 to give more weight to the backbone enzymes. The scores were added to create an overall cluster similarity score: avg pident tailoring 0:35 þ avgðpident backbones Þ 0:65 ð2Þ Annotation of SMGC families using MIBiG (genetic dereplication). SMGC families were annotated based on the MIBiG database  67. Known gene clusters were coupled to SMGC families, making it possible to predict the compounds or derivatives thereof a species can potentially produce. Cluster families containing one cluster highly similar to a known compound cluster are labeled after the known compound.
Genome synteny analysis. Orthologs were defined as a pair of genes found between two genomes from different species by bidirectional best hits using BLASTP with e-value < 10 10. When two genes within 10 kbp on the first genome have corresponding orthologs within 10 kbp on the second genome, the region between the two genes was defined as a syntenic block. The distance between the two genes was calculated by the formula, |PC1 -PC2| -1/2 (LN1 + LN2), where PCn and LNn are nucleotide position of the center and nucleotide length of gene "n" (n = 1 or 2), respectively. Analysis of chromosomal localization. For visualization of chromosome, chromosomal location, and gene density the R package karyoploteR was used  85. Secondary metabolite gene cluster analysis and visualization. For visualization of cluster synteny and similarity EasyFig was used  86.
Mass spectrometry (MS) detection was performed on an Agilent 6545 QTOF MS equipped with an Agilent dual-jet stream electro spray ion (ESI) source with a drying gas temperature of 160 °C, gas flow of 13 L min -1 , sheath gas temperature of 300 °C, and flow of 16 L min -1. Capillary voltage was set to 4000 V, and nozzle voltage, to 500 V in positive mode. MS spectra were recorded as centroid data, at an m/z of 100-1700, and auto MS/HRMS fragmentation was performed at three collision energies (10, 20, and 40 eV), on the three most intense precursor peaks per cycle. The acquisition was 10 spectra s -1. Data were handled in the software Agilent MassHunter Qualitative Analysis (Agilent Technologies, Santa Clara, CA). Reporting summary. Further information on research design is available in the Nature Research Reporting Summary linked to this article.
Acknowledgements Genome sequencing was kindly supported by  Joint BioEnergy Institute and Joint Genome Institute. M. R. A. , J. L. N. , S. T. and T. C. V. gratefully acknowledge funding from the  Villum Foundation , Grant  VKR023437. M. R. A. and T. C. V. further acknowledge funding from the  Danish National Research Foundation , grant number  DNRF137. M. R. A. further acknowledges support from the  Jorck Foundation. The work conducted by the  US Department of Energy Joint Genome Institute , a  US Department of Energy Office of Science User Facility , is supported by the  Office of Science of the US Department of Energy  under Contract No. DE-AC02-05CH11231. The  US Department of Energy Joint BioEnergy Institute  (www. jbei. org) is supported by the  US Department of Energy, Office of Science, and Office of Biological and Environmental Research , through Contract  DE-AC02-05CH11231  between  Lawrence Berkeley National Laboratory  and the  US Department of Energy.
15 (CHTESSEL) and the Météo-France Interaction between Soil Biosphere and Atmosphere model (ISBA) within the SURface EXternalisée modelling platform (SURFEX-ISBA) for the 2004-2015 period. The results show that the daily maximum LST simulated by CHTESSEL over Iberia is affected by a large cold bias during summer months when compared against the Satellite Application Facility on Land Surface Analysis (LSA-SAF), reaching magnitudes larger than 10ºC over wide portions of central and southwestern Iberia. This error is shown to be tightly linked to a 20 misrepresentation of the vegetation cover. In contrast, SURFEX simulations did not display such a cold bias. We show that this was due to the better representation of vegetation cover in SURFEX, which uses an updated land cover dataset (ECOCLIMAP-II) and an interactive vegetation evolution, representing seasonality.
The representation of vegetation over Iberia in CHTESSEL was improved by combining information from the European Space Agency Climate Change Initiative (ESA-CCI) land cover dataset with the Copernicus Global Land Service (CGLS) Leaf Area 25 Index (LAI) and fraction of vegetation coverage (FCOVER). The proposed improvement in vegetation also includes a clumping approach that introduces seasonality to the vegetation cover. The results show significant added value, removing the daily maximum LST summer cold bias completely, without reducing the accuracy of the simulated LST, regardless of season or time of the day. The striking performance differences between SURFEX and CHTESSEL were fundamental to guide the developments in CHTESSEL highlighting the importance of using different models. This 30 work has important implications: first, it takes advantage of LST, a key variable in surface-atmosphere energy and water exchanges, which is closely related to satellite top-of-atmosphere observations, to improve model's representation of land surface processes.
105 Operation Environmental Satellites (GOES) together with in-situ and reanalysis datasets to constrain several surface hydrology parameters in a land surface mode. Orth et al. (2017)  used several observational datasets (including satellitebased, in-situ, and combined model-observation products) of LST, evapotranspiration, soil moisture and terrestrial, water storage to constrain several parameters in the European Centre for Medium Range Weather Forecasts (ECMWF) Carbon-Hydrology Tiled ECMWF Scheme for Surface Exchanges over Land  (CHTESSEL, van den Hurk et al. , 2000;  110  Balsamo et al. , 2009; Dutra et al. 2010; Boussetta et al. , 2013). They found that model simulations of LST are highly sensitive to the skin conductivity, the minimum stomatal resistance and the soil moisture stress function.
Trigo et al. (2015)  used the satellite-based LST estimates from the Satellite Application Facility Land Surface Analysis (LSA-SAF) dataset to constrain Leaf Area Index (LAI) and the surface roughness lengths for heat and momentum over most of Africa and Europe in the ECMWF Integrated Forecast System (IFS), which uses CHTESSEL as LSM. The changes 115 to LAI were found to have a positive but limited impact on simulated LST, while the revised surface roughness lengths lead to overall positive impacts on LST. The improvements were particularly pronounced over arid and semi-arid regions (including wide portions of Iberia) where daytime skin temperature was affected by a significant cold bias in the original formulation. Recently, Johanssen et al. (  2019 ) (henceforth JO19) used the LSA-SAF dataset as reference to compare the estimates 120 of summer daily maximum LST over Iberia between the ECMWF 4 th and 5 th generation reanalysis, respectively ERA-Interim and ERA5.
ERA5 presented an overall improvement compared to ERA-Interim. However, both reanalysis displayed a large cold bias in the daily maximum LST over most of Iberia. Additionally, this cold bias was shown to be tightly related to the differences in the fraction of vegetation coverage (FCOVER) in the CHTESSEL land-surface model (which is the land surface component ERA5) and the satellite-based FCOVER estimates from the Copernicus 125 Global Land Service (CGLS). By focusing on four grid-points in southern Portugal, JO19 found that a significant reduction of the LST cold bias could be obtained by correcting the model high and low vegetation fractions using the satellite-based European Space Agency Climate Change Initiative (ESA-CCI) land cover dataset. Finally, a sensitivity test showed that the model vegetation density parameter (which controls the percentage of bare soil in CHTESSEL) played a critical role for the summer daily maximum LST in these four grid-points.
Similarly, Guo et al. (2019)  used 130 Leaf Area Index (LAI) estimates from MODIS to revise several vegetation coverage parameters over China in a regional climate model. The revised parameters were shown to have a positive impact on near-surface air temperature (a variable highly correlated with LST) and precipitation when compared to a gridded dataset interpolated from (station) in-situ measurements. Zheng et al. (2012)   Building on these previous works, here we use the LSA-SAF LST estimates, together with the CGLS FCOVER and LAI products and with the ESA-CCI vegetation cover dataset, to make an extensive revision of the vegetation cover 140 in CHTESSEL over Iberia. This revision includes the model vegetation types and fractions, LAI and parameterization of vegetation density, which in turn impacts several other parameters such as, the surface roughness lengths for momentum and heat, skin conductivity.
Additionally, we also evaluate the most recent version (v8. 1) of Météo-France modelling platform SURFEX (SURface EXternalisée,  Masson et al. , 2013)  which provides an additional constraint, given its use of the recent ECOCLIMAP-II land cover database. The present manuscript is organized as follows: the 145 datasets and models are described in Section 2; the vegetation coverage revision and the offline LSM simulations performed in the study are described in Section 3; the analysis of the ERA5 and control offline CHTESSEL simulation (with the original vegetation coverage) are described in Section 4; the results of the simulations with revised vegetation coverage are presented in Section 5; the results are discussed in Section 6, and the main conclusions of this study are presented in Section 7.
g. Urraca, 2018; Beck et al. , 2019; JO19; Rivas and Stoffelen, 2019; Nogueira, 2020). Additionally, an increased number and more recent versions of a wide variety of observational datasets are assimilated in ERA5. In this study, we use ERA5 reanalysis LST, total 165 cloud cover (TCC) and surface evaporation fields, all retrieved at hourly frequency over a 0. 25º × 0. 25º regular grid for the 2004 to 2015 period from the Copernicus Climate Change Service Information website (https://climate. copernicus. eu/climate-reanalysis, last access: November 2019). The LSA-SAF LST is derived from measurements performed by the Spinning Enhanced Visible and InfraRed Imager (SEVIRI) onboard the Meteosat Second Generation (MSG) series of satellites by employing the generalized "split-170 window" technique described in  Freitas et al.
(2014). The ESA-CCI land cover dataset was also considered in the present investigation. This dataset is derived by combining remotely sensed surface reflectance and ground-truth observations  (Defourny et al. , 2014). It provides consistent maps at 300 m spatial resolution on an annual basis from 1992 to 2015. It includes a total of 22 level-1 land cover classes and level-2 sub-classes, based on the land cover classification system developed by the United Nations Food and Agriculture Organization. In this study the 2010 data was chosen, considered to be representative of the full 1992-185 2015 period, since land cover changes over Iberia were very minor during that time interval.