dataesr/scientific-paragraphs-categorization huggingface dataset:
Transcriptomic analyses RNA was extracted from three biological replicates using the GTC/Trizol method  (Waddell and Butcher, 2010). Mtb RNA (2 μg) was enzymatically labelled with Cy3 fluorophore and hybridised to a Mtb complex microarray (ArrayExpress accession number A-BUGS-41) as previously described  (Salina et al. , 2014). For quantitative RT-PCR, total RNA was isolated from triplicate Mtb cultures; cDNA was generated using Superscript Reverse Transcriptase II and mycobacterial genome-directed primers  (Rachman et al. , 2006). qPCR was performed in a Corbett Rotor Gene 6000 real-time thermocycler using Absolute qPCR SYBR Green mix and gene expression values were normalised to 16S rRNA expression. For qPCR three biological and two replicates were assessed. ChIP-Seq analysis DNA-Lsr2 interactions in Δlsr2 T112A, Δlsr2 T112D, Δlsr2 WT were assayed using ChIP-seq methods as previously described  (Minch et al.
1  (Güntert, 2004). Backbone ϕ and φ torsion angle constraints were obtained from a database search procedure on the basis of backbone ( 15 N, HN, Hα) chemical shifts using TALOS+  (Shen et al. , 2009). For each protein, a total of 200 three-dimensional structures were generated using the torsion angle dynamics protocol of CYANA 2. 1. The 20 best structures of each protein (based on the final target penalty function values) were minimised with CNS 1. 2. All statistical parameters are summarised in (Table  S6 ). Relaxation rate constant measurements were performed on a 0. 5mM protein sample, at 18. 8 T (800 MHz). The pulse sequences used to determine 15 N R N (N z ) (R 1 ), R N (N xy ) (R 2 ), and 15 N{ 1 H} NOE values were similar to © 2019 The Authors. Molecular Microbiology published by John Wiley & Sons Ltd, Molecular Microbiology, 112, 1847-1862 those described  (Barthe et al.
Acknowledgement The following reagent was obtained through the  NIH Biodefense and Emerging Infections Research Resources Repository, NIAID, NIH :  Monoclonal Anti-Mycobacterium tuberculosis GroEL2 (Gene Rv0440 ),  Clone IT70  ( DCA4 ) (produced in vitro), NR-13657; Genomic DNA from Mtb,  Strain H37Rv ,  NR-48669. We acknowledge the  Centre for Core Biotechnology Services at the University of Leicester  for support with containment level 3 experiments and analysis of mycobacterial proteins. The project was supported by the  UK Biotechnology and Biological Sciences Research Council  grants  BB/K000330/1  and  BB/P001513/1  (GVM); the  French Infrastructure for Integrated Structural Biology (FRISBI)   ANR-10-INBS-05  grant (MCG), the  Ministry of Higher Education and Scientific Research Iraq (KA) , and the  Wellcome Trust   204538/Z/16/Z  (SJW).
Data availability statement Database: Mycobrowser release 3 2018-06-05, 8 https :// mycob rowser. epfl. ch (Kapopoulou et al. , 2011). The accession numbers for the mass spectrometry proteomics data reported in this paper are ProteomeXchange Consortium via the PRIDE partner repository PXD009239 and 10. 6019/ PXD009239 (http://www. prote omexc hange. org). The accession numbers for microarray data -ArrayExpress, E-MTAB-7627, https ://www. ebi. ac. uk/array express; ChIP-Seq datasets -the European Nucleotide Archive (ENA), PRJEB31102, http://www. ebi. ac. uk/ena/data/view/PRJEB 3110; for Lsr2 protein structures -the Protein Data Bank https ://www. wwpdb. org, PDB6QKP and PDB6QKQ; the chemical shifts -the BMRB, BMRB ID 34358 and BMRB ID 34358.
95 In this work, we have used AOD data provided by the AERONET permanent Cimel CE318 reference instrument to compare the AOD derived with the EKO MS-711 spectroradiometer. The different Cimel reference have been shown to have a good traceability with the world AOD reference  (Cuevas et al. , 2019). The world AOD reference is maintained by the World Optical Depth Research and Calibration Center (WORCC)  (Kazadzis et al. , 2018b). The Cimel CE318 photometer is an automatic sun-sky scanning filter radiometer that measures AOD at 340, 380, 440, 500, 675, 870 and 1020 nm (nominal wavelength; extended wavelength versions additionally have 1640 nm) with a full opening angle of 1. 2 •. The uncertainty in AOD measurements from Cimel field instruments, was estimated to be ± 0. 01 in the VIS range and near-IR, increasing to ± 0.
(1998). The aerosol optical properties stored in the datasets used are: the extinction coefficient, scattering coefficient, absorption coefficient, volume phase function, single scattering albedo and asymmetry parameter. Due to the location of the IZO station we have selected the desert mixtures for the cases of low and high aerosol load respectively. At this point we should note that the use of 1D simulations with the DISORT  (Stamnes et al. , 1988)  solver implies that the Sun is supposed to be a Dirac function, while, the Sun has an angular radius of 960. 12 ± 0. 09"  (Emilio et al. , 2012). However,  Stamnes et al. (1988)  demonstrated that the error in DN I S U N simulations, when the Sun is assumed to be a point source, is negligible with respect to the finite Sun assumption  (Stamnes et al.
In Figure  4b  the penumbra function, i. e. P (ξ, ϕ) is shown, and in Figure  4c , the result of multiply P (ξ, ϕ) L(ξ, ϕ). Note that the angular grid has been selected in steps of 0. 1 •. The expected CSR will be obtained by integrating the radiation field P (ξ, ϕ) L(ξ, ϕ) as indicated in Eq 12. The integration is done by using the angres tool  (Mayer and Kylling, 2005)  provided in the LibRadtran package which uses a Monte Carlo integration in 2D to obtain the diffuse radiation in the considered radiation field.
AOD retrievals In this section, we present the results obtained when comparing Cimel AOD and EKO AOD with no CSR corrections (CSR uncorrected AOD) and applying a CSR correction (CSR corrected AOD). The comparisons were done considering measurements   of both instruments that match within 2 minutes for all wavelengths. This approach produced a Cimel and EKO AOD dataset with a total of 14706 quasi-coincident measurements. The results show (Table  4 ) that there is a good agreement between EKO AOD and Cimel AOD for all channels, even for no CSR correction, except for the lowest 340 nm UV channel. The uncorrected EKO AOD shows slopes ∼1. 06 and correlation coefficients over 0. 97 for all wavelengths. The RMS ranges from 0. 017 (28. 9%) at 340 nm to 0. 004 (18. 8%) at 870 nm. These results improve significantly when taking into account the.
295 The linear AOD-correction equations were determined by using data measured from April 1 st to July 31 th 2019 at Izaña Observatory (Table  5 ). The validation of these linear AOD-correction equations was performed using an independent period of data (between August 1st and September 30 th 2019). Note that MB ≥-1. 6% for all wavelengths except for 340 nm for which a significantly larger MB (-4. 0%) is registered. In any case it should be noted that the CSR correction applied in this study has been made under the presence of mineral dust. It would be necessary to verify that these CRS corrections have similar validity 300 under moderate-high influence of other types of aerosols, such as marine or biomass burning aerosols. In order to check the quality of EKO AOD, we have applied the WMO traceability criteria (WMO, 2005) defined for finite FOV instruments as:  where m a is the optical air mass.
The percentage of data meeting the WMO traceability requirements (95% of the AOD differences of an instrument compared to the WMO standards lie within specific limits) is > 95% at 500, 675 and 870 nm, taking the AERONET-Cimel as the reference (Figure  10 ). U 95 = ±(0. 005 + 0. 010m a ) ( The percentage of EKO AOD data meeting the WMO criteria increases considerably when we take into account the CSR corrections, increasing from 67% to more than 86% at 340 nm, and above 95% for the rest of the channels. The poorest results shown by the 340 nm channel (86%), might be partially explained by the. EKO's 7nm FWHM influence on the smaller 2 and 4nm band pass UV channels. The instrument uncertainty is larger in the UV range, which is mostly associated with stray-light in the instrument inner optics  (Zong et al.
Acknowledgements. This work has been developed within the framework of the activities of the  World Meteorological Organization (WMO) Commission for Instruments and Methods of Observation (CIMO) Izaña  test bed for aerosols and water vapor remote sensing instruments. The authors are grateful to  EKO Instruments  for its availability that the EKO-MS711 spectroradiometer has been tested and evaluated independently by the  WMO CIMO Izaña  testbed. The LibRadtran Radiative Transfer Model has been used to estimate the circumsolar radiation. AERONET Sun photometers at Izaña have been calibrated within the  AERONET Europe TNA , supported by the  European Union   Horizon 2020 research and innovation program  under grant agreement no. 654109  ( ACTRIS-2 ). This research benefited from the results of the project funding by  MINECO   RTI2018-097864-B-I00.
For prototyping purpose, we usually embed our control laws in a dSPACE R 1104 control board which acquires in real-time sensor signals and generates the servo-valve control signals. The code is generated from Simulink R models. To close the control loop of servo-pneumatic systems, position sensors and pressure sensors are used. As it is very complex to integrate the pressure sensors inside each chamber, we generally put them as close as possible of the cylinder air connectors. Position sensors are not specific to this kind of application. Linear potentiometers or Linear Variable Differential Transformers (LVDT) usually fit. When necessary, non-specific force sensors can be embedded. The article  [30]  lists common sensors used in pneumatic actuation applications. A schematic of the whole architecture with two servovalves is shown in Figure  2.
C. metallidurans CH34 T grew in the presence of 10 mM NiSO 4 , while AE104 was unable to grow at 3 mM NiSO 4. Previous studies had established that other symbiotic C. taiwanensis strains LMG 19424 T from Taiwan  [13]  and C. taiwanensis STM 6018 from French Guiana  [6]  were also unable to grow at 3 mM NiSO 4 (data not shown). In light of the observed Ni 2+ tolerance of STM 6070, we examined the tolerance of the Cupriavidus symbionts to other metal ions. In the presence of Cu 2+ , STM 6070, 6018 and LMG 19424 T were able to grow in media containing 1. 0 mM Cu 2+ , however, growth of STM 6070 was inhibited from 0. 6 mM Cu 2+ (Figure  S3 ). In addition, STM 6070 was able to grow in media containing 15 mM Zn 2+ , whereas STM 6018 and LMG 19424 T were far more sensitive and could not grow at this concentration (data not shown).
Comparisons of Cupriavidus neocaledonicus STM 6070 with other sequenced genomes of symbiotic Cupriavidus The comparison of gene orthologues of STM 6070 with those of the symbiotic Cupriavidus strains LMG 19424 T , STM 6018, UYPR2. 512 and AMP6, performed using the "Gene Phyloprofile" tool in the Microscope MaGe platform  [48]  (Fig. 2 ), showed that these strains have a large core set of 4673 genes, representing from 55. 5 to 78. 1% of the total number of genes in these organisms (70. 2% for STM 6070). Each species harbours a set of unique genes, which range from 226 for LMG 19424 T to 1993 for UYPR2. 512; larger genomes had a greater number of unique genes (Fig. 2 ). STM 6070 harbours 483 unique genes, which represent 7. 2% of the total number of genes in the genome. The majority of these unique genes (376) encode hypothetical proteins.
Metal resistance determinants in the STM 6070 genome To understand the genetic basis of STM 6070 metal tolerance, we then searched for the presence of common and specific heavy metal resistance (HMR) markers within the genomes of STM 6070 and the other symbiotic Cupriavidus species, using the TransAAP tool on the TransportDB website (http://www. membranetransport. org/) [50]  to find genes encoding predicted transporter proteins. Given that STM 6070 is nickel-and zinc-tolerant, we were particularly interested in identifying HMR proteins within known transporter superfamilies (Transporter Classification Database: http://www. tcdb. org/) [45, 51, 52]. TransAAP analysis revealed a total of 834 putative transporters within STM 6070, of which 156 were classified within the MFS, CDF, RND, CHR, ACR3 and P-ATPase protein families (Table  S5 ).
Major facilitator superfamily (MFS) proteins The MFS is one of the two largest families of membrane transporters found in living organisms. Within the MFS permeases, 29 distinct families have been described, each transporting a single class of compounds  [53]. Of the 106 STM 6070 TransAAP-identified genes encoding putative MFS proteins, two genes (nreB and arsP) were associated with HME functions. The nreB gene located in the nreAB operon (cluster I), and the arsP gene located in the arsRIC1C2BC3H1P operon (cluster K), encode putative nickel and arsenic efflux systems, respectively (Fig. 3 )  [45].
) and Xanthomonas (X. ) and other named reference strains. The HME class of the protein is designated according to the current classification scheme  [45, 59, 60]. HME1 to 5 represent five classes of HME systems, HAE represents here an RND protein group involved in in export of hydrophobic and amphiphilic compounds. The evolutionary history was inferred by the Neighbor-Joining method with a bootstrap consensus tree inferred from 500 replicates. The evolutionary distances were computed using the Poisson correction method and are presented as the number of amino acid substitutions per site. The rate variation among sites was modelled with a gamma distribution (shape parameter = 1). Evolutionary analyses were conducted in MEGA6. STM 6070 HME locus tags are displayed in Fig. 2  and Table  S6. GenBank accession numbers or locus tags are given in brackets.
CopD is a membrane protein involved in transfer of Cu 1+ from the periplasm to the cytoplasm for CopA binding  [80, 81] ), and CopC is thought to regulate copper uptake by CopD. The STM 6070 CopA protein shows 75. 8% identity to both CH34 T CopA1 (pMOL30) and CopA2 (chromid) proteins. Interestingly, the alignment of corresponding proteins reveals the presence of a histidine-rich sequence (GHG GHS GDS GHS GDS (GHS) 5 GDS GHG AHA GHG) located in the middle of the methionine-rich CopA motif in the STM 6070 protein, which is absent from other CopA sequences deposited in the NCBI database. The Escherichia coli HRA-1 and 2, Enterococcus hirae CopB  [83]  and Rhizobium leguminosarum ActP  [84]  Cu-exporting P-type ATPase proteins also contain histidine-rich leaders, which we postulate bind to copper ions.
Genome sequencing and assembly The genome of C. taiwanensis STM 6070 was sequenced at the Joint Genome Institute (JGI) using Illumina technology  [88]. An Illumina standard shotgun library was constructed and sequenced using the Illumina HiSeq 2000 platform which generated 26,402,396 reads totaling 3960. 4 Mbp. All raw Illumina sequence data was passed through DUK, a filtering program developed at JGI, which removes known Illumina sequencing and library preparation artifacts (Mingkun, L. , Copeland, A. and Han, J. , unpublished). The following steps were then performed for assembly: (1) filtered Illumina reads were assembled using Velvet  [89]  (version 1. 1. 04), (2) 1-3 kb simulated paired end reads were created from Velvet contigs using wgsim (https://github. com/lh3/wgsim), (3) Illumina reads were assembled with simulated read pairs using Allpaths-LG  [90]  (version r39750).
Genome annotation For the general genome content description genes were identified using Prodigal  [91]  as part of the DOE-JGI annotation pipeline  [92, 93]. The predicted CDSs were translated and used to search the National Center for Biotechnology Information (NCBI) nonredundant database, UniProt, TIGRFam, Pfam, PRIAM, KEGG, COG, and InterPro databases. The tRNAScanSE tool  [94]  was used to find tRNA genes, whereas ribosomal RNA genes were found by searches against models of the ribosomal RNA genes built from SILVA  [95]. Other non-coding RNAs such as the RNA components of the protein secretion complex and the RNase P were identified by searching the genome for the corresponding Rfam profiles using INFERNAL (http://infernal. janelia. org). Additional gene prediction analysis and manual functional annotation was performed within the Integrated Microbial Genomes (IMG-ER) platform (http://img.
Phylogenetic analyses Gene fragments sequences were corrected with Chromas Pro v1. 33 software (Technelysium) and aligned using either ClustalX  [96]  or MUSCLE as implemented in MEGA, version 6  [97]. Alignments were manually edited using GeneDoc software  [98]. Phylogenetic analyses were performed in MEGA6  [97]  using the Neighbor-Joining method  [99]. Bootstrap analysis  [100]  with 1000 replicates was performed to assess the support of the clusters.
In order to perform the alignments using progressive Mauve software  [51] , the scaffolds of each draft genome (STM 6070, STM 6018, UYPR2. 512 and AMP6) were firstly reordered using Mauve software on the basis of the C. taiwanensis LMG 19424 T concatenated genome. Then, reordered genomes were used to perform the alignment with progressiveMauve. Circular views by BlastAtlas were performed using the CGview server hosted at Stothard Research Group (http://stothard. afns. ualberta. ca/cgview_server/), for alignment of the STM 6070 sequence, aligned firstly on the LMG 19424 T concatenated sequence (CHR1, pSym, CHR2). The essential amino acid residues that form the proximal and distal heavy-metal-binding sites have been identified for the CH34 T zinc-specific RND HME3a transmembrane transporter ZneA  [64].
Using the ZneA protein as a backbone, we aligned the eight STM 6070 RND transmembrane proteins with those used for phylogenetic analysis (Fig. 4 ), to compare and identify the corresponding essential amino acid residues that form the putative proximal and distal heavy-metal-binding sites in these transporters (Table  S7 ). In addition, we used the MaGe Microscope annotation platform  [47]  to analyse the syntenic arrangements of the eight HME-RND efflux systems present in STM 6070 and compare them with the HME-RND efflux systems found within six other Cupriavidus strains, as outlined below. BETASYM ANR-09-JCJ-0046), Curtin University Sustainability Policy Institute, and the funding received from Murdoch University Small Research Grants Scheme.
Availability of data and materials The datasets generated and/or analysed during the current study are available in the NCBI repository under bioproject 165313, [https://www. ncbi. nlm. nih. gov/bioproject/?term=165313&utm_source=gquery&utm_medium= search]; the NCBI Sequence Read Archive [https://www. ncbi. nlm. nih. gov/ sra?linkname=bioproject_sra_all&from_uid=165313]; the NCBI sequence data [https://www. ncbi. nlm. nih. gov/nuccore?term=165313%5BBioProject%5D]; and the IMG repository [https://jgi. doe. gov/data-and-tools/img/] under the accession number 2513237165. All protein sequences are available in the above databases and can also be accessed through Uniprot [https://www. uniprot. org/].
Quasigeoid determination methodology The RCR technique is a well-known method for gravimetric quasigeoid determination. It is realized by summation of three terms according to the formula: where ζ GGM is computed using a global geopotential model, ζ RTM expresses the Residual Terrain Model (RTM) effect, and ζ res is computed from residual gravity anomalies employing the Stokes' integral or LSC. The residual gravity anomalies used to determine ζ res are computed as follows: where g FA is the free-air gravity anomaly, g GGM is the gravity anomaly computed with a GGM, and g RTM is the RTM effect on the gravity anomaly. In this study, we used the GRAVSOFT software (Forsberg and Tscherning 2008) developed at DTU (Technical University of Denmark) to perform the quasigeoid computations. The gravity anomaly and height anomaly were computed using the GRAVSOFT GEOCOL program.
Moreover, as it includes a large variety of surface measurements, uncertainties also arise from datum inconsistency in the compiled gravity database and from variability of the measurement density and accuracy. In  Gilardoni et al. (2013) , a GOCE model was successfully used to improve the geoid model accuracy by combining spherical harmonic coefficients of the EGM2008 model with a GOCE gravity model. Following previous studies that successfully used mixed GOCE and EGM2008 models for the removal of the long and medium wavelengths to compute geoids of Malaysia  (Jamil et al. 2017) , Nepal  (Forsberg et al. 2014a) , and the Philippines  (Forsberg et al. 2014b) , we constructed a similarly combined model to remove the long to medium wavelength components of the gravity field up to d/o 719. We used the fifth release of GOCE global potential model obtained from the direct approach, named EGM-DIR-R5  (Bruinsma et al.
Fortunately, most of the countries have been re-surveyed from 2003 to 2011, through the project "Measurement and Improvement of Vietnam National Gravity Data", carried out in collaboration with the Moscow Institute of Geodesy, Cartography and Aerial Images, Moscow State University of Geodesy and Cartography (MIIGAiK), Russia. This new dataset comprises 10,940 points, including an absolute gravity network of 11 base reference stations with an accuracy of better than ± 5 μGal and their tie points, 29 first-order gravity stations with an accuracy of better than ± 15 μGal and their tie points, and 92 third-order gravity points and more than 10,000 detailed points measured from 2005 to 2009 (red points in Fig. 3 ). The base reference stations and first-order gravity network were determined from absolute measurements using GBL instrument (Final Report of VIGAC 2012).
For the VIGAC gravity surveys, GNSS has been used to determine coordinate and height, Fig. 2  Standard deviation of the differences between the GOCE GGMs in combination with EGM2008 and the GNSS/levelling data so this is less prone to errors in positioning. The IGP data are less accurate than the VIGAC data. However, the combination of the IGP and VIGAC data enhances the coverage considerably, especially in the South of Vietnam. Finally, our land gravity data set was also complemented by a set of gravity data provided by BGI for Vietnam and surrounding areas (including 895 points in Vietnam, 229 points in Cambodia, and 351 points in Thailand). We have used two procedures to detect localized gross errors in the data. The first uses SRTM3arc data to verify the gravity observation elevations in the IGP data.
The results are listed in Table  2. The differences at GNSS/ levelling points show a STD of 6. 86 m and an average bias of -3. 13 m, which are in line with results of  Denker (2005)  in Germany. The VIGAC data show a STD of 23. 46 m and average bias of -4. 23 m, while SRTM3arc has a reported vertical accuracy of better than 16 m  (Farr et al. 2007 ). This proves that SRTM provides very good height information in the computation of the RTM effect. The differences of the gravity observation elevations in the IGP data with SRTM3arc are 73. 50 m in STD and -14. 95 m in mean bias. This indicates that the elevations in the IGP data, which were determined from barometric levelling, have gross errors. A horizontal error in gravity data will also result in a discrepancy between the gravity observation elevations in the IGP data and those derived from DTM, especially in areas of steep elevation changes; consequently, we cannot use elevation derived from the DTM and we need a different procedure to detect gross errors in IGP data.