dataesr/scientific-paragraphs-categorization huggingface dataset:
Smart Contract The healthcare IdM consists of two types of smart contracts (Health SC and Registry SC). The Health SC consists of five functions, including set_public_key, retrieve_public_key, set_hash, retrieve_hash and pause_SC, whereas the registry SC consists of functions, regis-ter_regulator, verify_regulator, register_attestation, verify_attestation, and revoke_attestation. The algorithm of each function is provided in Algorithms 1-10, respectively. The complete code of the smart contract is available in the github repository (https://github. com/ ibrahimtariqjaved/healthid (accessed on 1 May 2021)). set_public_key: This function allows the owner of the smart contract to upload and store its public key over the Ethereum network. Only the owner of the smart contract will be allowed to upload their public key.
Blockchain Deployment We choose a consortium blockchain to implement healthID. Consortium removes the centralized control as a group of healthcare regulators manages it in contrast to a single entity in a private blockchain. Furthermore, it is more privacy-ensuring and efficient due to the smaller node count compared to the public blockchain. We deployed the consortium blockchain using the Ethereum network on five nodes. Each node is set up on an EC2 virtual machine using an AWS cloud service. The virtual machine has 1 GB RAM and 20 GB storage with a Linux Ubuntu operating system. Each virtual machine is configured with the Go Ethereum client (Geth (https://github. com/ethereum/go-ethereum/wiki/geth (accessed on 1 May 2021))) that is implemented in Go language. The five nodes running the Geth client are connected using their eNode addresses.
After initializing the blockchain, we used Remix IDE (https://remix. ethereum. org/ (accessed on 1 May 2021)) to compile healthID smart contracts implemented in solidity language. We further used the Metamask (https: //metamask. io (accessed on 1 May 2021)) wallet to deploy the smart contract onto our consortium blockchain. After deploying our smart contacts, we submit dummy transactions over the blockchain. The commands used to set up and initialize the Ethereum blockchain are provided in the GitHub repository (https://github. com/ibrahimtariqjaved/healthid (accessed on 1 May 2021)). The PoA consensus agreement is used to deploy the consortium blockchain. The PoA blockchain in Ethereum is named the Clique network. The nodes that validate the block are called sealers. In PoA, the network consensus is achieved by a majority agreement among the sealer nodes.
The extraData is used to set the addresses of sealers when initializing the blockchain. Sealers are nodes that can validate and include a transaction on a block. In our case, healthcare regulators are set as sealers of the network. A consortium is deployed by initializing a set of sealers in the genesis block. The majority voting of existing sealers can include a new sealer. The genesis file is used to set the initial parameters necessary for the blockchain. We analyze the performance of blockchain by using three parameters (i) Block-Time, (ii) Gas-Limit, and (iii) Sealers Number. The consortium blockchain is tested by varying these three parameters. For each configuration, we run the blockchain for one hour to compute the performance.
This is reflected by the high completeness scores of arthropod core genes (C) as inferred via BUSCO, with few fragmented (F), missing (M), or duplicated (D) genes (C: 87. 5%, F: 6. 6%, M: 5. 9%, D: 8. 6% for O. nova; and C: 86. 2%, F: 6. 4%, M: 7. 4%, D: 7. 9% for O. subpectinata; SI Appendix, Table  S1 ). I. The divergence within asexual individuals is large and exceeds the divergence between populations. We analyzed within-individual and between-population divergence of individuals from three geographically distant locations in Germany (Hainich, H; Kranichstein Forest, KF; Schwäbische Alb, SA), using transcriptome data from three individuals per species and location (for sampling sites, see SI Appendix, Fig. S1  and Methods). Polymorphism data were generated by mapping transcriptome reads of each individual to the corresponding reference genome.
) in O. subpectinata, but by within-individual differences in O. nova (% σ 2 w-ind. ; % σ 2 ind. : % variation between individuals within location). Several previous studies suggested the Meselson effect on the basis of divergence between paralogs [as present, e. g. , in polyploid lineages  (22) ] rather than alleles  (22) (23) (24) (25). To exclude that our findings are explained by paralog divergence, we confirmed that the phased regions represent allele haplotypes and not paralogs using coverage comparisons. We verified that the genomic read coverage of phaseable regions was not exceeding the coverage of single-copy genes from the BUSCO arthropod database (paralogs should have an at least 2× higher coverage; Methods). This was indeed the case as single-copy BUSCO genes, phased regions, and the scaffolds from which phased regions derived showed a median coverage of 126×, 101×, and 87×, respectively.
Overlapped read libraries were generated by merging the paired forward and reverse reads of the 180-bp read libraries and additionally merging unpaired reads, followed by normalization using BBnorm v37. 82  (61). These normalized overlap read libraries were assembled into contigs using SPAdes v3. 10. 1, a multi k-mer assembler  (62) , with options -m 400-careful -k 21, 33, 55, 77, 99, 111, 127. The resulting contigs were ordered into scaffolds using the 350-, 500-, and 3,000-bp read libraries using SSPACE v3. 0  (63)  with default parameters. To close gaps emerging during scaffolding, GapCloser v1. 12 (64) with option -l 125 was run. For details, see https://github. com/AsexGenomeEvol/HD_Oppiella: assembly and mites. Scaffolds that were likely from contaminants (e. g. , bacteria, fungi) were removed by first annotating and visualizing contaminations using BlobTools v1.
0 (65), followed by custom filtering. For this, coverage of each scaffold was estimated by mapping reads back to the scaffolds using bwa mem v0. 7. 15  (66)  and coverage calculated with BBTools v73. 82  (61). Additionally, for annotation, scaffolds were blasted using ncbi-blast v2. 7. 1+ blastn with options -outfmt '6 qseqid staxids bitscore evalue std sscinames sskingdoms stitle' -max_target_seqs 10 -max_hsps 1 -evalue 1e-25 against the nt database v 2016-06. Scaffolds without hits to metazoans were filtered out from the assemblies using a custom script (see https://github. com/AsexGenomeEvol/HD_Oppiella: contamination_filtration. py). Next, scaffolds were sorted by decreasing length, scaffold headers renamed and scaffolds shorter than 500 bp removed, resulting in the final assemblies (v03).
0. 2 results (arthropoda_odb9 lineage with the-long option). A combination of UniProtKB/Swiss-Prot (release 2018_01) and the BUSCO arthropoda_odb9 proteome were used as protein evidence. The Trinity assembled mRNA-seq sequences (described below) were used as transcript evidence. The resulting gene models from iteration 1 were then used to retrain Augustus as well as SNAP v2013. 11. 29  (73)  and a second iteration was performed. Subsequently, predicted protein coding genes were functionally annotated using Blast2GO v5. 5. 1  (74)  with default parameters against the NCBI nonredundant arthropods protein database and the Drosophila melanogaster (drosoph) database v 2018-10. The MAKER configuration files are available at https://github. com/AsexGenomeEvol/HD_Oppiella. For the MAKER annotation, RNA-seq reads were quality trimmed with Trimmomatic v0.
36 with options adapters. fa:2:30:12:1:true LEADING:3 TRAIL-ING:3 MAXINFO:40:0. 4 MINLEN:80. For generating genome-guided transcriptome assemblies, trimmed reads were first mapped against the genomes using STAR v2. 5. 3a (75) under the "2-pass mapping" mode and default parameters. Following, the outputs were used with Trinity v2. 5. 1 (76) set to "genome guided" mode (parameters:-genome_guided_max_intron 100000-SS_lib_type RF-jaccard_clip). For quality filtering of the resulting transcriptomes, the trimmed RNA-seq reads were mapped back against the transcriptomes using Kallisto v0. 43. 1  (77)  with options-bias and-rf-stranded, then transcripts with at least 1 TPM in any samples were retained. All computation for genome assembly and annotation were run on the Vital-IT cluster of the Swiss Institute of Bioinformatics.
Pairwise Divergence between Sister Species. Transcript sequences were reconstructed from annotated reference genomes using GffRead (option -w)  (78). Single-copy orthologs were identified using Orthofinder [standard parameters; option -d (  79 )], aligned using muscle [standard parameters  (80) ], positions including any gaps removed [trimal -nogaps (81)], and pairwise divergence calculated using Geneious [p-distance  (82) ]. Haplotype Divergence: RNA-seq, Quality Control, and Mapping. RNA extracts were fragmented to 175 nt for strand-specific library preparation using the NEBNext Ultra II Directional RNA Library Prep Kit. Paired-end sequencing with a read length of 100 bp was performed on a HiSeq 2000 platform at the Genomics Technology Facility (Lausanne, Switzerland). Data processing for haplotype divergence inference was done using the high-performance computing cluster of the Gesellschaft für Wissenschaftliche Datenverarbeitung (Göttingen, Germany).
RNA-seq reads were adapter-and quality-trimmed using TrimGalore v0. 6. 5 with default options (Phred quality threshold 20; adapter auto-detection)  (83, 84). Contaminating sequences were removed using kraken2 (-paired;-db minikraken2_v2) (85) followed by mapping pairedend reads of each individual simultaneously against their respective reference genome, scaffolds flagged as contaminating sequences assembled together with the respective mite reference genomes (identified as described above), the human reference genome GRCh38. p12 (GenBank assembly accession: GCA_000001405. 27), and the human microbiome (downloaded from https:// www. hmpdacc. org/hmp/HMREFG/all/index. php) using bbmap v37. 66 (bbsplit; maxindel = 100k; ambiguous = best)  (61). Portions of reads were found to be derived from contaminating RNA of human and microbial origin with fractions ranging from 40.
36 to 90. 31% in O. subpectinata and 53. 33 to 93. 04% in O. nova (SI Appendix, Table  S7 ). Only oribatid-mite-exclusive reads, i. e. , read pairs that mapped best against the mite reference genomes, were kept for further processing and mapped to the reference genomes using STAR v2. 7. 3a with standard parameters. All commands are available under https://github. com/ AsexGenomeEvol/HD_Oppiella: mapping. U. Haplotype Divergence: Variant Calling. Read-group information was added and PCR and optical duplicates were removed from mapped reads using Picard v2. 20. 2  (86). Reads without a mapping mate were deleted using samtools view  (87)  and reads sorted by coordinate using GATK v4. 0. 3. 0 Sort-Sam  (88). Next, the nine thus-filtered alignments per species were merged with samtools merge for subsequent SNP calling.
Sequences spanning intronic regions were removed using GATK SplitNCigarReads. GATK HaplotypeCaller was run per individual with -ERC set to BP_RESOLUTION to enable calling of nonvariant sites and -dont-use-soft-clipped-bases to exclude soft-clipped overhangs from SplitNCigarReads. Individual gvcf-files were combined into one species-gvcf-file using GATK CombineGVCFs. Genotypes were called using GATK GenotypeGVCFs and the option -allSites. All commands used are available under https://github. com/AsexGenomeEvol/HD_Oppiella: calling. U Multidimensional Scaling Plots and AMOVA. For calculating multidimensional scaling (MDS) plots and AMOVA, first genotypes with a coverage <10 were removed from gvcf-files using vcftools v0. 1. 15 (89). Next, sites including at least one missing genotype, monomorphic or triallelic variants, and indels were removed.
subpectinata using vcfsubset (vcftools). For SFS calculation, subsetting was done for seven individuals of O. nova potentially showing the Meselson effect (Results) with F is < 0. Afterward, sites including at least one missing genotype, monomorphic or triallelic variants, and indels were removed from the different subsets. F is was calculated based on the filtered sets of SNPs using vcftools (option-het). The SFS was calculated using Pop-Con and standard parameters  (95). The fold excesses of shared heterozygous SNPs over HWE were estimated by running Pop-Con with the option -fold for a range of parameters and comparing the specific genotype profiles for an indication of excess (shown as part of expected SFS). All commands used are available under https://github. com/ AsexGenomeEvol/HD_Oppiella: heterozygosity.
U, Fis. U and SFS. U. Haplotype Phasing. For phasing, variants were filtered as described above (except only sites completely missing any genotype information, i. e. , all individuals missing a genotype, were excluded). Phasing of haplotypes was done per individual using phASER v1. 1. 1 with minimum mapping quality of reads set to 30, minimum base quality set to 20, and bottom cutoff to quantile for alignment score set to 0 in paired-end mode for each individual, separately, utilizing heterozygous variants with minimum coverage of 10 for each individual  (96). Haplotypes with <10 unique reads mapping were removed from the output data. Output data from phASER were converted into haplotype sequences for the corresponding positions in the reference genome using a custom script (available under https://github.
Haplotypes were labeled according to their divergence from the reference genome (haplotype A being closer to the reference genome, haplotype B being more diverged). For this, positions including degenerate bases were deleted using trimAl (81) and the pairwise distance of each haplotype to the reference genome was calculated using snp-dist  (97). Only thus modified phaseable regions ≥100 bp were included for downstream analyses. All commands and two scripts used are available under https://github. com/AsexGenomeEvol/HD_Oppiella: phasing. U, refgenomedist. U, extract. pl and convert_fasta. py. To identify putative paralogs in the phased regions, these regions were tested for double coverage compared to the genomic baseline. Reads used to assemble genomes were mapped back to single-copy genes and duplicated genes identified by BUSCO (see above), and additionally to the phased regions and to scaffolds from which the phased regions were derived (but that were masked in the phased regions), using bowtie2 v2.
10 with 1,000 bootstrap replicates and model-testing included  (100). For O. nova, we restricted the analysis to the seven individuals representing the two divergent lineages (Results and Fig. 2A ). The O. nova asex-trees were constrained to separate the haplotypes A and B at its base, lineages I and II per haplotype, and finally the populations per lineage and haplotype (for the constraining tree, see https://github. com/AsexGenomeEvol/HD_Oppiella: Onasex. tre). The sex-trees were constrained to separate the lineages I and II at its base and the populations per lineage (no haplotype separation; for the constraining tree, see https://github. com/AsexGenomeEvol/HD_Oppiella: Onsex. tre). For O. subpectinata, the asex-trees were constrained to separate the haplotypes A and B at its base and the populations per haplotype (to provide an unrooted tree including a trichotomy the haplotypes B of the most divergent population SA were separated from haplotypes B of the other two populations; for the constraining tree, see https://github.
com/AsexGenomeEvol/HD_Oppiella: Osasex. tre). The sex-trees were constrained to separate exclusively the populations (no haplotype separation; for the constraining tree, see https://github. com/ AsexGenomeEvol/HD_Oppiella: Ossex. tre). Next, the distance between the two resulting trees and an unrestricted best-fitting ML tree was estimated according to  Kuhner and Felsenstein (42)  with the dist. topo function implemented in the R package ape  (101). To enable the comparison between distances of different phaseable regions, the topological distances of the bestfitting ML tree to the haplotype-separating tree and to the populationseparating tree were combined as Δ dist. asex-tree -dist. sex-tree value for each phaseable region. To test whether the haplotype-separating tree was a significantly better fit to the alignment than the population-separating tree, trees were compared using RELL approximation with 10,000 RELL replicates and an approximately unbiased (AU) test with iqtree  (102, 103).
Using the AU test, we also compared both constrained trees to the unconstrained tree. For detailed information, see https://github. com/AsexGenomeEvol/HD_Oppiella: treecalcandtopotest. U and topodist. UR. Parallel Divergence Testing. Phasing and haplotype reconstruction were done as described above but coverage was reduced to a minimum of five to increase the number of informative sites, and thereby the number of nonpolytomous trees. Calculation of best-fitting ML trees was done as described above. Resulting topologies were screened by eye for being nonpolytomous, for showing haplotype separation and for parallel divergence. To calculate the probability of observing parallel divergence in at least eight trees out of 31 haplotype separating eight taxa trees by chance (lineage I), we used the binomial formula: ∑ Data Availability.
Reference genomes and annotations of the two species have been deposited in the European Nucleotide Archive (ENA) (accession number PRJEB39968). Transcriptomes and RNA-seq reads of the two species used for inferring allelic sequence divergence and for annotations have been deposited in the Sequence Read Archive (SRA) (accession number PRJNA662767). All scripts and commands as referred to in the article have been deposited in the GitHub repository (https://github. com/AsexGenomeEvol/HD_Oppiella). ACKNOWLEDGMENTS. We thank Juergen Brandt for adjusting figures to the needs of people with color vision deficiency and Christoph Bleidorn for advice regarding topology testing. This study was supported by core funding (to S. S. ), by Swiss National Science Foundation (NSF) Grant PP00P3_170627 (to T.
Brandt et al. PNAS | 7 of 10 Haplotype divergence supports long-term asexuality in the oribatid mite Oppiella nova https://doi. org/10. 1073/pnas. 2101485118 of molecular variance (AMOVA) and a randomness test with the packages vcfR and poppr in R (91-94). All commands used are available under https:// github. com/AsexGenomeEvol/HD_Oppiella: MDS. U, AMOVA. R.
Pedogenic carbonates -results All results are listed in Table  3  and shown in Figure  5. Stable carbon isotope ratios of pedogenic carbonates of GPL-1 vary between -9. 3 and -5. 9 ‰ with an average value of -7. 3 ± 1. 0 ‰, while oxygen isotopes ratios fluctuate from 25. 4 to 26. 5 ‰ with an average of 25. 9 ± 0. 3 ‰. There is very low correlation between δ 13 C and δ 18 O present (R 2 = 0. 1). Overall stratigraphic trends cannot be detected in either of the two datasets. Carbonate content of the nodules is generally >50 % with only one sample having a significantly lower carbonate content (16 %), but comparable isotopic values. The average carbonate content is 80 ± 20 %. Carbon isotope values average -7. 3 ± 1. 0 ‰ and never exceed -5. 9 ‰. Such low values are typical for C3 dominated ecosystems characterized by woodland, bushland, or wooded grassland environment with a mix of C3/C4 vegetation.
In overall characteristics, the shark teeth have the cockscomb appearance typical of the genus Galeocerdo, tiger sharks. To assess the taxonomic affinities of the Gorongosa shark specimens, we carried out a series of 2D morphometric analyses of the two complete specimens. We compiled a set of fossil shark photographs from the existing literature to obtain a suitable comparative sample of 600 specimens (Supplementary Table  S5 ). From this comparative sample we used three datasets including: 1) all 600 specimens from four different genera (Galeocerdo, Physogaleus, Carcharhinus, and Hemipristis), 2) a subset of 547 specimens from species of Galeocerdo and Physogaleus, 3) a subset including 436 specimens from different species of the genus Galeocerdo. We carried out Principal Component Analyses (PCA) of these datasets followed by multi-group Linear Discriminant Analyses (LDA) to classify the Gorongosa specimens into taxonomic categories (see Supporting Information: Morphometric Analysis: Chondrichthyes for methodological details).
In another analysis using only the m3 from mandible PPG2018-P-2, we used four curves with 10 landmarks each (Figure  13A ). This dataset was then compared with the 3D models of 25 hyracoids. Thirteen of these specimens are also housed at the NMK and that were digitized using photogrammetry using the same protocol that was described above, whilst the rest of the sample was obtained from Morphosource https://www. morphosource. org/ (Supplementary Table  S8 ). The comparative sample derives from five families of Hyracoidea: Geniohyidae (Bunohyrax), Saghatheriidae (Saghatherium, Thyrohyrax, Megalohyrax), Titanohyracidae (Afrohyrax, Mereohyrax), Pliohyracidae (Parapliohyrax), and Procaviidae (Dendrohyrax and Procavia). This dataset was also subjected to a General Procrustes Analysis (GPA) to obtain shape variables.
Our analyses of the Gorongosa specimen place it close to the primates Cercopithecoides williamsi, Simiolus enjessi, and some chimpanzees. Thus, conservatively, we assign this specimen to an indeterminate species of catarrhine. Paleogene and early Neogene African primates are associated with wooded or forested environments. The fossils documented here represent the first descriptions of a substantial fossil record that is just emerging. The Gorongosa paleontological record opens up the possibility of testing important hypotheses about the role of the eastern coastal forests in shaping the evolution of African mammals  (Joordens et al. , 2019; Kingdon, 2003). As the fossil record from Gorongosa is further described and analyzed, it will yield a potent database spanning different intervals of the Miocene, which will then be compared to other sites on the continent.
Acknowledgements We are deeply thankful to  Greg Carr  for his visionary approach to preserving the Gorongosa ecosystem, and for vital support to the  Paleo-Primate Project Gorongosa. Research permits were granted by the  Direcção Nacional do Património Cultural ,  Mozambique , with the support of professors  Hilário Madiquida  and  Solange Macamo  of  Eduardo Mondlane University. We are very grateful to  Gorongosa National Park , the  Park Warden Pedro Muagura , the fiscais who provide support, and the  Park  staff, including  Vasco Galante and Patricia Álvares da Guerra. We appreciate the help with logistics provided by  Jason Denlinger  and  Tongai Castigo. We thank  Katarina Almeida-Warren ,  Robert Anemone ,.
, 2014; Galve et al. , 2015). Such challenges can be tackled by regular image-processingoriented landslide area monitoring, in the aftermath of earthquakes, using SAR interferometric methods and optical images, for instance. Actually, since SAR (synthetic aperture radar) is an active sensor system that uses microwave signals to collect data backscattered from the earth's surface, the use of satellite imaging systems like interferometric SAR methods appears to be a cost-effective way for measuring Published by Copernicus Publications on behalf of the European Geosciences Union. millimeter-level displacements of the earth surface  (Herrera et al. , 2009 ) at a regional scale and can be used as an early warning system for the safety of structures and their surroundings  (Galve et al. , 2015; Roque et al.