dataesr/scientific-paragraphs-categorization huggingface dataset:
SNP and Indel calling The short reads of the 1082 silkworm strains were mapped to the silkworm reference genome by BWA 55 v0. 7. 17 mem with default parameters. The SAMtools 56 v1. 11 and Picard v2. 23. 5 (https://broadinstitute. github. io/picard/) programs were used to filter the unmapped and duplicated reads. A GVCF file of each sample was obtained using the GATK 57 v4. 1. 8. 1 HaplotypeCaller with the parameter -ERC = GVCF. The GVCF files of all samples were merged with GATK CombineGVCFs. The joint-call step of all 1082 samples was performed using GATK Genoty-peGVCFs to generate a combined VCF file. Finally, the variants were filtered by GATK VariantFiltration with the following parameters: -filter "QUAL < 50. 0"-filter-name LowQ -filter "DP < 200"-filtername LowD -filter "DP > 100000"-filter-name HigD-filter-expression "MQ<40.
Phylogenetic and PCA analysis VCF2Dis v1. 42 (https://github. com/BGI-shenzhen/VCF2Dis) was used to estimate p distances between every two samples based on a VCF file containing SNPs. PHYLIPNEW v3. 69 fneighbor (http://emboss. sourceforge. net/apps/cvs/embassy/phylipnew/) was used to build the Neighbor-Joining Tree. For PCA analysis, we used plink 58 v2. 0 (-make-bed) to convert a VCF file to a bed file. Subsequently, the GCTA 59 v1. 93. 0 beta program was used to estimate the genetic relationship matrix (GRM) and calculate eigenvectors.
Genome assembly Before assembly, Jellyfish 63 v2. 2. 6 was used to count k-mer frequencies (k = 17). Then, the genome characters including genome size, repetitiveness, and the rate of heterozygosity were predicted with genomeScope 64 v1. 0 using NGS data. The de novo genome assembly was performed with the following pipeline: (a) The ONT reads were corrected by Canu 65 v1. 8. (b) The corrected ONT reads were assembled into contigs by Smartdenovo 66 v1. 0 with the following parameters: wtpre -J 5000; wtzmo -k 16 -z 10 -Z 16 -U -1 -m 0. 6 -A 1000; wtclp -d 3 -k 300 -m 0. 6; wtlay -w 300 -s 200 -m 0. 6 -r 0. 95 -c 1; wtcns -m 0. 6. (c) The contigs were corrected three times with ONT reads by racon 67 v1. 3. 3, and one time by medaka v0. 7. 1 (https://github. com/nanoporetech/medaka). In this step, minimap2 68 v2.
17 was used to map ONT reads to the contigs, and racon was used to correct contigs and generated consensus sequences. The final correction was conducted using the medaka program. (d) pilon 69 v1. 23 software was used to polish the corrected contigs. In this step, NGS reads were mapped to corrected contigs using BWA mem, and pilon was used to polish with the following parameters:-fix bases-mindepth 20-verbose-diploid. We only used the reads with a mapping quality above 20 for this step. (e) If the assembled genome exceeded the estimated size of genome survey by >5%, we performed de-redundancy using Purge Haplotigs 70 v1. 0. 0. (f) To obtain chromosome-level genomes, we used RagTag 71 v1. 0. 0 to correct and anchor the contigs to chromosomes of the reference genome with the corrected ONT reads.
Repetitive element annotation For the annotation of repetitive elements, simple sequence repeats were identified by GMATA 73 v2. 2. Tandem repeats (TRs) were identified by Tandem Repeats Finder (TRF) 74 v4. 09. Then, we used ab initio, structure, and homology-based methods to annotate TEs. Briefly, MITE-Hunter 75 v2 (https://github. com/Adamtaranto/MITE_Hunter_2) was used to search miniature inverted-repeat transposable elements (MITEs). LTR_finder 76 v1. 07, GenomeTools v1. 5. 9 LTR_harverst 77 , and LTR_retriver 78 v2. 9. 0 were used to identify long terminal repeat (LTR) retrotransposons. We performed de novo searching to identify repeats by RepeatModeler v2. 0. 1 (http://www. repeatmasker. org/ RepeatModeler/) and we classified the repeats into TEs superfamilies through TEclass 79 v2. 1.
Gene annotation Three approaches including ab initio prediction, homology-based prediction, and transcriptome-based assembly were used to predict gene structure in 100 genomes, including 14 wild, 41 local, 15 improved, and 30 genetic stocks. For homology-based prediction, GeMoMa 80 v1. 6. 10 was used to align the protein sequences of the reference silkworm, Drosophila melanogaster, Apis mellifera, and Danaus plexippus to the newly assembled silkworm genomes. For the transcriptome-based assembly, STAR 81 v2. 7. 3a was used to map the mRNA sequencing reads from 56 RNA samples, 15 strains, and 7 tissues (CNGB BioProject ID: CNP0001815, NCBI project ID: PRJNA262539, PRJNA264587, PRJNA407019) to the newly assembled genomes, stringtie 82 v2. 1. 4 was used to perform RNA assembly, and PASA v2. 3.
For each genome, information about gene function, motifs, and domains was obtained through homology searches against public databases including Swiss-Prot, NCBI non-redundant (NR) proteins, Kyoto Encyclopedia of Genes and Genomes (KEGG), Eukaryotic Orthologous Groups (KOG), and Gene Ontology (GO). The GO terms of genes were identified using the InterProScan 86 v5. 41-82. 0 program with default parameters. For the other four annotations, the proteins of each genome were used as queries in BLAST+ 87 v2. 9. 0 BLASTP (e value <1e-5) searches against Swiss-Prot, NR, KEGG, and KOG databases.
ncRNA annotation For the annotation of non-coding RNA (ncRNA), tRNAscan-SE 88 v2. 0 was used to identify transfer RNAs (tRNAs), and Infernal 89 v1. 1. 3 cmscan was used to identify microRNAs and small nuclear RNAs (snRNAs) by searching against the Rfam database (http://rfam. xfam. org/). Then, RNAmmer 90 v1. 2 was used to identify rRNAs.
Acknowledgements We thank  Chao Su  at  Northwest A&F University, Bing Li at Soochow University, Keping Chen at Jiangsu University, Yuyin Chen at Zhejiang University, Weizheng Cui at Shandong Agriculture University ,  Chaobin Luo  at  Sericulture Institute of Guizhou ,  Yongqiang Wang  at  Zhejiang Academy of Agriculture Science ,  Wenfu Xiao ,  Gang Liu , and  Yian Chen  at  Sericulture Research Institute of Sichuan, Zhanpeng Dong at Sericulture and Apicultural Research institute of Yunnan, Anjie Wang at Shandong Institute of Sericulture, Tao Fan at Sericulture Institute of Anhui, Lihui Bi at Sericulture Institute of Guangxi Zhuang Autonomous Region (GZAR ),  Fan Wu  at  Economy Crops Institute of Hubei ,  Yuxia Wang  at  Shanxi Research Institute of Sericulture , and  Junwen Ai  at the  Sericultural Research Institute of Hunan  for helping in the collection of silkworm strains.
Data availability Raw data of the long-read sequencing and short-read sequencing (including RNA-seq and whole-genome sequencing) used in this study have been deposited in the CNGB Nucleotide Sequence Archive (CNSA) of China National GeneBank DataBase (CNGBdb, https://db. cngb. org) with BioProject ID CNP0001815 and also in the Genome Sequence Archive (https://ngdc. cncb. ac. cn/gsa/) with accession number CRA007878. All 545 genome assemblies, 100 genome annotations (gff files), pan-genome, and VCF files (SNP, SV) have also been deposited in the CNGBdb with BioProject ID CNP0002456. This study also analyzed data for four released wild silkworm genomes that are available in the Sequence Read Archive (SRA) database according to accession numbers DRX054041, DRX054040, ERS402904, ERS402902. All phenotypic data have been listed in the eighth column of Supplementary Data 1.
Acknowledgements This work was funded by the  French National Research Agency (ANR)  BrightSwitch  19-CE29-0005-01  ( MC ),  ANR-19-CE16-0012  (LD), and by  FLAG-ERA  (grant  Senseï  by  ANR-19-HBPR-0003 ) (LD). We acknowledge the  NeurImag imaging facility , member of the national infrastructure  France-BioImaging  supported by the  French National Research Agency  ( ANR-10-INBS-04 ). We thank  Sésame Région Ile-de-France  for funding the Zeiss 880 Confocal/ Airyscan system at  NeurImag facility (IPNP). Fluorescent Probes • Lipid Droplets • Photoconversion • Photooxidation • Photoswitching.
The proton irradiation was performed with a 3 MV Tandetron™ (IFIN-HH, Magurele, Romania) accelerator  [17]. The incident beam was masked to a 5 × 5 mm square to provide similar irradiated area between samples. The energy was 3 MeV, and the doses were 2. 5 × 10 7 , 5 × 10 7, and 10 × 10 7 Gy. The penetration depth of the protons was simulated with TRIM software (SRIM-2013, by J. F. Ziegler) (http://www. srim. org, accessed on 28 March 2022). A linear accelerator with 2 MW peak power, tuned by an EEV M5125 (Teledyne e2v Ltd. , Chelmsford, UK) type magnetron operating in S-band (2992 MHz-3001 MHz) (at Accelerators Laboratory, INFLPR, Magurele, Romania), was used for the radiation treatment with electrons. The energy of the beam was 6 MeV, and the doses were 0. 5 × 10 4 , 1. 0 × 10 4, and 5. 0 × 10 4 Gy.
Acknowledgments: This work made use of Tampere Microscopy Center facilities at  Tampere University. Ion beam irradiations have been performed at 3 MV Tandetron™ accelerator from  "Horia Hulubei" National Institute for Physics and Nuclear Engineering (IFIN-HH ) and were supported by the  Romanian Governmental Programme  through the  National Programme "Instalat , ii s , i Obiective de Interes Nat  , ional".
Funding:  Academy of Finland Flagship Programme of Photonics Research and Innovation and Academy Project  [grant numbers  PREIN-320165 , 326418];  French Region Nouvelle-Aquitaine  [grant number  2019-1R1MO1 ];  LIGHT S&T Graduate Program in PIA3 Investment for the Future Program  [grant number  ANR-17-EURE-0027 ];  Romanian Ministry of Research, Innovation and Digitization under Romanian National Nucleus Program  [grant number LAPLAS VI-contract n. 16N/2019]. Institutional Review Board Statement: Not applicable. Informed Consent Statement: Not applicable.
3°, 43°, and 67° (Figure  4A, B ) due to the presence of the γ-alumina phase in the Si0A samples prepared via HSG routes (JCPDS reference no. 00-010-0425 in the Internation Centre for Diffraction Data database, Table  S1 ). Independently of the synthesis medium, all samples showed broad peaks at 2θ = 2 attributed to amorphous silica  [25]. Decreasing SiO2 loading in the composition of t sample led to a decrease in the intensity of this peak. No broad reflections typical of bu γ-alumina were detected for the NHSG samples. It is well known that the coordination of aluminum in amorphous silica-aluminas has an impact on their acidic properties and catalytic performance. The signal attributed to Al IV sites shifted up-field when the SiO 2 loading increased from 0 to 75%; this effect has been previously ascribed to the decreased concentration of aluminum atoms around Al sites  [18, 25, 26].
Diffraction patterns showed similarities in profile; peak diffraction broadened at around 2θ = 22-24 • as occurs in the diffraction pattern of the silica amorphous phase, but reflections were observed at 2θ = 37. 3 • , 43 • , and 67 • (Figure  4A, B ) due to the presence of the γ-alumina phase in the Si 0 Al 100 samples prepared via HSG routes (JCPDS reference no. 00-010-0425 in the International Centre for Diffraction Data database, Table  S1 ). Independently of the synthesis medium, all samples showed broad peaks at 2θ = 23 • attributed to amorphous silica  [25]. Decreasing SiO 2 loading in the composition of the sample led to a decrease in the intensity of this peak. No broad reflections typical of bulk γ-alumina were detected for the NHSG samples. Temperature programmed desorption of ammonia (NH3-TPD) was used to examine the acidity of the SixAly samples.
Characterization of the Catalysts The atomic percentages of Si and Al were obtained via energy dispersive X-ray spectroscopy (EDX). Measurements were carried out using an X-Max Silicon Drift detector mounted on an FEI Quanta FEG 200 scanning electron microscope. To obtain reliable results in the analyses, the data for each sample are the averages of three separate measurements. The use of a piece of Scotch ® to fix the powder and the partial vacuum (0. 3 Torr) in the measuring chamber did not allow for a correct analysis of the concentrations of C and O. X-ray powder diffractograms (XRD) were obtained on a Philips X-pert Pro II diffractometer using Kα copper Cu radiation (λ = 1. 5418 Å) as a radiation source. The diffractograms were obtained in the range of 2θ from 20 to 80 • in steps of 0.
The metadata are available in the Supplementary Data. Spectroscopic and electron microscopy techniques were used to identify, locate, and characterize AuNPs. UV-vis spectra scans of metallic solutions carried on the light wave spectrophotometer (Biochrom WPA1100nm II, CAM, UK) identified characteristic adsorption peaks that would indicate any nano-to-microscale precipitate  [43]. Transmission electron microscopy (TEM) imagery was obtained by a Zeiss TEM 900 (Carl Zeiss, Oberkochen, Germany). The protocol is detailed in the Supplementary Data. The dimensions (width and length) of the bacteria cells and the average of 10 identified nanoscale precipitates were determined using ImageJ software (Windows softwater, IE 6. 0, Microsoft Java 1. 1. 4) [44]. For SEM determination in a Quanta-450F field emission gun (FEG) (Thermo Scientific, USA), 200 µL of filtered samples (0.
Acknowledgments: S. M. gratefully thanks  VLIR-UOS  and  PWVR-ESPOL  for supporting her education in Ghent. J. A. was supported by  Grenoble-Alpes University , the  French Minister of National Education , and  KIC InnoEnergy Ph. D. School. Qingdao Beibao Marine Science & Technology Co. Ltd. , Qingdao West-coast economic new area, China, supported CGT. K. R. and J. V. are thankful for the support from the  Flemish SIM MaRes program , under grant agreement no. 150626  ( Get-A-Met  project). K. R. acknowledge UGent support from  BOF-GOA  grant number:  01G02619. Special thanks to  Lisa Claeys ,  Gijs Du Laing ,  Joachim Neri ,  Andre Skirtach , and  Ran Rumes  for granting access to their facilities.
Cataplasma Tests Physical changes in the composite specimens were measured during the exposure time. As specimens absorbed water, physical changes in weight, width and thickness were monitored (Figures  7 8 9 ). Hemp composite 1300 g/m 2 ; B: Hemp composite 600 g/m 2 ; C: Linseed composite 1300 g/m 2 ; D: Linseed composite 600 g/m 2 ). Raw data can be found in Table  A1. As shown in Figures  7 8 9 , the dimensions of the hemp-based composites increased up to 35% in weight and up to 15% in thickness during the first 24 h of exposure, while their width increased ~1% compared to the initial value. In contrast, the weight increases during the first week for the linseed were around 20%, whereas, for the hemp, the increment goes up to 40% of its initial value. At the same time, the width and thickness remain equal.
Experimental 2. 1. General Information. Analytical grade reagents were used. Te solvents were dried over 4 Å molecular sieves. Te IR and UV-vis spectra were measured on a JASCO 480 spectrophotometer. Elemental analysis was performed by Atlantic Microlab in Norcross, Georgia, and [H(DMSO) 2 ] [trans-RuCl 4 (DMSO) 2 ] and mer-[RuCl 3 (DMSO-S) 2 (DMSO-O)] complexes were prepared according to the literature procedures  [19]. X-ray difraction data were collected at low temperature on a Nonius Kappa CCD difractometer equipped with Mo Kα radiation source (λ = 0. 71073 Å), a graphite monochromator, and an Oxford cryostream low-temperature device. Absorption collections were made by the multiscan method. Data collection parameters and selected bond angles and lengths are listed in Tables  1  and 2.
Thus, to be sure to pair homogeneous endmembers with similar water contents (maximum difference of 0. 5 wt%), we performed line scans by micro-Raman spectroscopy across pieces of the starting glasses before the preparation of the diffusion-couple samples using a LabRAM HR 800 Raman spectrometer. Raman spectra were collected using a 532 nm laser and a grating with 300 lines/mm in the range 100-4000 cm -1 with a three-time data acquisition for 30 s. The water content was calculated following the procedure described in  Behrens et al. (2006)  and  Schiavi et al. (2018)  for rhyolitic glasses using the intensity of the OH-bands between 3000 and 3800 cm -1. Subsequently, we compared the results with the difference to 100% of the major element analyses by the electron probe of the glasses. The Raman values for a given samples scattered within ± 0.
Laser ablation craters could potentially give problems to the SIMS data acquisition, therefore, two procedures have been adopted: for samples from DIFF1 to DIFF5 the analyses were done on the two different halves of the sample, while for the remaining samples, the mounts have been re-polished after LA-ICP-MS analyses, prior to SIMS sessions. Lithium and boron data collection by LA-ICP-MS was done at the GeoForschung-sZentrum Potsdam (Germany), which verified the presence of diffusion profiles after the experiments. Measurements were conducted using the Analyte Excite 193 nm ArF* excimer-based laser ablation (LA) system (Teledyne Photon Machines, Bozeman, MT, USA), coupled to a quadrupole ICP-MS iCAP by Thermo Scientific. The LA-system was equipped with a HelEx II two-volume ablation cell (gas flow in I/min; chamber 0.
(1999)  which reports B diffusion in hydrated conditions (H 2 O 3. 7 wt%). Activation energies reported for each dataset highlight that in hydrated silicate melts, the diffusion of Li and B is enhanced and proceeds faster than in the case of a dry matrix evaluation on the differences between the two dataset is not possible. Through the comparison with the studies of  (Baker 1992 and Chakraborty et al. 1993) , it is clear that B activation energy decreases strongly with increasing water content in a system, which is similar to the case for Li (Fig. 4b ). Boron is a network former and coexists in silicate melts in either trigonal or tetrahedral coordination. The trigonal coordination is likely the dominant species in highly silicic melts, likely accommodating an extremely high percentage of the total B.
The Eyring equation was successfully used to describe the diffusion of slow-moving ions such as Si and O (e. g. ,  Chakraborty 1995; Mungall 2002) , using a characteristic jump length of 0. 3 nm. This value coincides with the diameter of the SiO 4 -tetrahedron  (Behrens and Haack 2007). Therefore, this relation provides a lower limit of diffusion for a given silicate melt composition and temperature, which is controlled by viscous flow. Figure  6a  shows the Arrhenius relation of the three datasets, showing that overall our   (1996). The Arrhenius relation calculated using Giordano's viscosity is in better agreement with the experimental data for higher temperatures, but the slope of the trending line is steeper than the slope determined with our dataset, resulting in a significantly higher E a (220 kJ/mol).
The calculation made with Schulze's viscosity has a better agreement in the slope and determination of the E a , (164 kJ/ mol) with a shift in the entire dataset, due to the model parameters. b The overall agreement of the two models with the experimental data indicates that B diffusion is a process of viscous flow  (Mungall 2001 ) and allows to compare E a (B) with E a from  Schulze et al. (1996). The data from this study and from  Mungall et al. (1999)  show exceptional agreement with the plot from  Schulze et al. (1996) , therefore, we can numerically describe the effect of water on the mobility of B in highly silicic melts experimental results are in good agreement with the two sets calculated from the viscosity models. The diffusivity obtained from the viscosity calculated according to  Schulze et al.
(1996)  results in similar linear trend and similar activation energy (164 kJ/mol). We observe a small shift between the two datasets (difference ln D = 0. 5), which might be attributed to the differences in the composition of the two glasses: haplogranitic in the case of  Schulze et al. (1996)  and rhyolitic for our study (Table  1 ). Using the viscosity calculated according to  Giordano et al. (2008) , the diffusivity data are in good agreement at high temperature (1200-1250 °C) but the model deviates with a steeper slope and consequently a higher activation energy at lower temperatures (Fig. 6a ). The equation of  Schulze et al. (1996)  was calibrated using a set of hydrous haplogranitic samples, which were very close to our rhyolitic composition, while the model of  Giordano et al. (2008)  was calibrated using a wide spectrum of compositions.
Nonetheless, the good agreement of our results with the Eyring equation allows us to consider B diffusion as primarily associated to the viscous flow dynamics  (Mungall 2002) , and thus we may use the activation energy determined for the viscosity and its relation to the water content of the silicate melt  (Schulze et al. 1996)  to extrapolate to B diffusivity. In Fig. 6b , we plotted the activation energy of B diffusivity determined by both our study and existing literature as a function of water content, and compared our diagram with the corresponding data of  Schulze et al. (1996). The two datasets are in an exceptionally good agreement. The two points of our study and  Mungall et al. (1999)  related to measured diffusivities lie perfectly on the trend line defined by  Schulze et al. (1996) , confirming that B diffusion is limited by viscous flow and that the presence of water strongly affects the mobility of B, especially for water concents below 1 wt%, while for increasing water concentrations, the effects on B mobility is still significant, but less pronounced.
Acknowledgements We thank  Christine Fischer  and  Jens Bölke  for their support in all the practical steps of sample preparation, and for the support in mechanical and technical issues encountered during this project. We would like to thank also the entire mechanical workshop of the GFZ, particularly  Reik Sünkel ,  Michael Voigt  and  Michael Kreplin  for their precious support for the management of the experimental setup. We are also grateful to  Christina Günther  for the essential help for EPMA analyses,  Uwe Dittman  for SIMS sample preparation and  Fredéric Couffignal  for the analytical contribution during all SIMS sessions. We also acknowledge the  Deutsche Forschungsgemeinschaft  for financial support through the project  WI 2000/14-1.
22 or 0. 40% at pH 7 and pH 14). It is clear that these results make sense only when free solar energy may be exploitable. The economic value of the products will be analyzed deeply in section 3. 2. 4. 3. 2. 2. Effect of Pressure. Reviews on the effect of operating parameters on this reaction have highlighted the beneficial effect of temperature increase up to 80 °C and pressure higher than ambient, although very limited reports refer to significantly higher pressure. 33, 34 The data reported in Figure  6  were recorded at three different pressures (i. e. , 8, 13, and 18   Industrial & Engineering Chemistry bar; notice that, due to sampling mode, testing should be done with a pressurized reactor, P > 4 bar), with a catalyst concentration of 0. 6 g/L and 0. 85 g/L of HS. We found that the pressure of CO 2 mainly impacts the overall productivity when working at basic pH (14).
Synthesis by dissolution of a Te salt in HF and recrystallisation was also unsuccessful. It is likely that these single crystals result from a chemical transport process with InF 3 acting as a fluorinating agent on initial TeO 2. That explains the non-reproducibility of the reaction and the formation of only few crystals inside the Pt-sealed tubes. This new phase is likely metastable and exists in a very narrow thermal domain. The diffraction pattern is recorded on a 4-circle P4-Siemens diffractometer equipped with a graphite monochromator and a scintillation counter using the XSCANS program  [4]. Absorption correction by a psi-scan method uses the program XEMP  [5]. The structure is solved by direct methods using SHELXS-86 and refined with SHELXL-2018/3  [6] , using the WINGX integrator V2018-3  [7].