dataesr/scientific-paragraphs-categorization huggingface dataset:
The tree is based on an alignment of 141 metazoan BUSCO genes of the 232 soil invertebrates sequenced in this work (coloured branches), and 118 NCBI RefSeq (grey branches), representing four phyla. A highresolution, annotated version of the tree is available as Supplementary Fig. 1. A more detailed tree and the alignment are available on FigShare  30. Boxplots reflect the genome size distribution of the taxa subsumed in the corresponding clades in gigabases (Gb). Numbers of sequenced genomes with genome size estimates are indicated for each group. Genome size estimation was not possible for some of the assemblies. Genome size estimates can be found in Supplementary Data 1. Center line: median; box limits: upper and lower quartiles; whiskers: 1. 5× interquartile range; points: outliers. Insights into genome size evolution.
2 ). Classic theory predicts that a few basic factors, in particular effective population size, should lead to causal relationships between genome properties and functional traits (Fig. 4a )  [32] [33] [34]. However, recent studies have shown that taxon-specific processes might be more important for genome size than demography  35, 36. We used our taxonomically broad data set to test the classical hypothesis of a few factors generally influencing genome size evolution vs. a more lineage-specific view with a series of structural equation models (SEMs, Fig. 4 ). We used genomes with at least 50% BUSCO completeness and 8× mode coverage. To parametrize the SEM and connect the 143 new genome assemblies with ecological traits, we first gathered trait data from original literature. Information about habitat preferences was added from the Edaphobase data warehouse for soil biodiversity (https://portal.
edaphobase. org/). We focussed on three traits: (a) body length as a proxy for body size (minimum female adult body length for nematodes, and mean adult body length for all other taxa), (b) reproduction mode, and c) the number of known habitat types where a species occurs, as a proxy of habitat generality (based on CORINE-Coordination of Information on the Environment 37 ). We annotated repetitive elements with species-specific repeat libraries. We estimated effective population size (theta) directly from the genome data by making use of the genome-wide heterozygosity in the reference individual. This proxy measure of effective population size was calculated individually for each genome assembly with at least 8X coverage. Genomic and ecological traits are accessible in Supplementary Data 1.
5 ; Supplementary Data 3, 4). Pyridine-containing molecules have a considerable spectrum of antimicrobial and antiviral activities  62  , and associated gene loss might be related to the gain of endogenous antibiotic synthesis ability by many springtail species  63. We also manually screened the UniProt database (accessed on 28. 6. 2023) for putative gene functions associated with genes missing from Collembola and Oribatida assemblies. We aimed to identify functional or other relevant commonalities among the genes which might be missed by an algorithmic GO enrichment analysis. We could not detect patterns in gene functions. It was noteworthy that all existing annotations originated from only two species: Drosophila melanogaster or Strigamia maritima. This highlights the general difficulties with transferring annotations gained from a few model taxa to the breadth of biodiversity, with targeted annotation of specific genes being a solution.
Methods Specimen sampling and species-level identification. Specimens were collected in the field or obtained from cultures, supplemented with existing soil invertebrate specimens from Senckenberg museum collections (Supplementary Data 1). Sampling occurred between 2011 and 2020, mostly in Germany, but in some cases also from countries in Europe. Soil macrofauna was mainly collected by hand, whereas meso-and microfauna were obtained from soil samples with MacFadyen  66  or Baermann extraction  67. DNA was extracted from over 500 single specimens, or occasionally from multiple individuals (single-species cultures of tardigrades and smaller-bodied nematodes, Supplementary Data 5). A non-destructive DNA extraction method  68  was preferred and used where possible. Otherwise, the MagAttract High Molecular Weight DNA Kit (Qiagen, Hilden, Germany) was used, mostly for cultured specimens.
Voucher specimens are deposited in the Senckenberg museum collection in Görlitz. For larger taxa such as Chilopoda, Diplopoda, Isopoda, Enchytraeidae and Lumbricidae, the species-level morphological identification was possible before DNA extraction, and only a single leg, a few body segments or musculature of mouthparts were used for DNA extraction, the rest of the body was kept as a voucher. For medium sized taxa like Acari and Collembola that normally would require clearing in lactic acid prior to species identification, the specimens were presorted on family or genus level, the whole specimens were used for non-destructive DNA extraction, and finally species-level identifications were carried out with recovered vouchers. In cases where non-destructive DNA extraction did not deliver sufficient amounts of DNA or the voucher was lost during extraction, identification was validated by aligning species markers (28 S, COI) from the whole-genome sequence data with existing species markers in GenBank or generated by us.
Colours in a, d represent feature architecture similarity among the identified orthologs and the reference gene, with a score between 1 (same architecture) and 0 (dissimilar architecture, or no features in the reference protein). The score is computed once by comparing the reference gene with the identified ortholog (FAS_F, dots on the graphic), and once by comparing the identified ortholog with the reference gene (FAS_B, background colour to dots). Data underlying the GO enrichment analysis are available as Supplementary Data 4. Illumina sequencing. Sequencing libraries for each specimen, or pool of specimens, were prepared in-house at Senckenberg, Frankfurt, Germany with the BEST protocol  70  or with the NEBnext ULTRA II DNA Library Prep Kit, according to the manufacturer's protocol. Short-read Illumina sequencing (300-bp paired-end) was carried out at Novogene Europe (Cambridge, UK) using the NovaSeq 6000 platform, with unique dual indexing as the library tagging strategy for multiplexing on the lanes.
Our central aim with the genome database was to improve species identifications. As this can be achieved with low sequencing coverage  65  , our initial sequencing efforts targeted 2 gigabase (Gb) per species. We increased efforts to 10 Gb per species as sequencing became more affordable. For most of the reported genomes we obtained ~10 Gb per species. Genome assembly pipeline. We established a pipeline to assemble reads into draft genomes (Fig. 1 ). First, the sequencing adapters were trimmed using Trimmomatic (v0. 39; paramaters: ILLUMINACLI-P:adapters. fasta:2:30:10:8:true SLIDINGWINDOW:4:20 MINLEN:50 TOPHRED33  71  ). The trimmed reads were queried against the human genome (GRCh38 assembly on NCBI) using Kraken2 (v2. 0. 9beta; --confidence set to 0. 2, other parameters default  72  ), and all 'human' positive reads, if any, were discarded.
The remaining reads were then assembled using SPAdes (v3. 14. 1; default settings  73  ). The resulting contigs were then queried against the NCBI non-redundant nucleotide database using blastn (megablast mode, -max_target_seqs 10, -max_hsps 1, -evalue 1e-25), and against the NCBI nonredundant protein database using Diamond (blastx mode, --sensitive --max-target-seqs 1, --evalue 1e-25  74  ). NCBI databases were downloaded on 27-Oct-2020. Blobtools2 (v2. 3. 3  7  ) was used to perform a taxonomic assignment based on the Blast and Diamond results, using the 'bestsumorder' rule. The contigs assigned to the phylum of the target organism as well as the unassigned contigs were kept (i. e. , contigs assigned to other phyla were considered obvious contaminants and removed). Redundans (v0. 14a  75  ) was used to reduce the amount of duplication in the retained contigs, as well as further scaffolding and gap closing (default parameters were used).
The resulting scaffolds were used as the final assembly draft for subsequent analyses. The Burrows-Wheeler Aligner (BWA) was then used to map the reads on the assembly and samtools 76 (v1. 11-2-g26d7c73) to compute and plot the mapping statistics (e. g. , GC content). Quality assessment of assemblies using BUSCO. Benchmarking Universal Single-Copy Orthologs (BUSCO) databases  27  are sets of genes for specific taxon groups, where every gene in the BUSCO set is expected to be present once in each member species. We searched for BUSCO genes in our final assemblies as a quality indicator of genome assembly completeness, we used the most specific BUSCO database that was available for each of the invertebrate groups (nematoda_odb10 BUSCO genes for nematode assemblies, arthropoda_odb10 for arthropods, meta-zoa_odb10 for tardigrades, enchytraeids and earthworms).
Reads were trimmed of adapters with Trimmomatic 71. Reads were taxonomically assigned with kraken2 72 in a three-step process. First, we screened the metatranscriptomes against the human genome for eventual human contamination. Second, we assigned remaining reads with a custom database containing all bacterial, plant and fungal reference genomes from NCBI (accessed on 15. 1. 2023). Third, we then tested the impact of a dedicated genome database for soil invertebrate detection: unassigned reads from the second step were mapped against all springtail (57), oribatid mite (9) and myriapod genomes (8) available in NCBI RefSeq as of 20. 6. 2023, with and without including the 232 MetaInvert genomes (Supplementary Data 2). We visualised the richness of soil invertebrates along the elevation gradient at the genus level.
As nucleotide sequence counts are not normally distributed and they are frequently overdispersed  79  , we evaluated differences in community composition among the study years and habitats, and along the elevation with a model-based analysis of multivariate abundance data  80. Community analyses were performed in R v4. 2. 2  81. Building the phylogeny using metazoan BUSCO genes. We searched for BUSCO genes with the metazoan_obd10 database (v4. 1. 4) to generate a single phylogeny of the 232 soil invertebrate genomes and a selection of 118 publicly available invertebrate RefSeq genomes from NCBI (downloaded on 16. 09. 2021). The RefSeq genomes were included if they a) were from the same taxon group as our specimens, b) served to shorten the evolutionary distance between taxa in the tree. More specifically, we included any chromosome-level Protostomia genomes (excluding Insecta), genomes of any assembly quality for species within our 14 taxonomic groups of interest, and some additional specific outgroups (two Echinodermata, three Rotifera, a Priapulida, Machilis hrabei and Drosophila albomicans).
We found 141 metazoan BUSCO genes which were present in at least 75% of the genome assemblies (Supplementary Data 1, 6). The phylogenetic approach is based on the https://github. com/mag-wolf/BUSCO-to-Phylogeny pipeline. We aligned these with Mafft (v7. 481  82  ) with 1000 iterative refinements. These gene alignments were then concatenated into a supermatrix using FASCONCAT (v1. 04  83  ) and trimmed using clipkit (v1. 1. 5  84  ), keeping only parsimony-informative and completely conserved sites. We used IQ-TREE (v2. 0. 3  85  ) to build four separate maximum likelihood trees (each with 1000 bootstrap replicates), selecting the best one based on the -log Likelihood value closest to zero  86. We used R to visualise the phylogeny, using the packages ggtree (v3. 1. 5. 900  87  ), tidyverse, treeio (v1.
Otherwise, genome size was estimated by dividing the total mapped bases by the mode coverage. Estimating effective population size. Using mlRho (v2. 9) we estimated theta directly from the genome data by making use of the genome-wide heterozygosity in the reference individual. This proxy measure of effective population size was calculated individually for each genome assembly with at least 8X coverage, twice as high as recommended by Haubold et al. 93. Annotating repeat content. In addition to investigating several genome properties (i. e. , GC content, BUSCO gene content, genome size and effective population size), and because repeat content is particularly relevant for explaining genome size variation among species, we also annotated the repetitive elements. Speciesspecific repeat libraries were constructed using the automated RepeatModeler (v2.
0. 1) pipeline with LTR Structural discovery pipeline activated  94. For each genome, the resulting repeat libraries were merged with the RepBase (v26. 05) Arthropodaspecific section  95  and subsequently used for the annotation and estimation of proportion of repetitive elements with Repeat-Masker (v4. 1. 2-P1  96  ). Ecological trait data. To connect the 232 new genome assemblies with ecological traits of the respective species, we first gathered existing functional trait data from Edaphobase (https://portal. edaphobase. org/) and from literature. We focussed on a) body length (minimum female adult body length for nematodes, and mean body length for all other taxa) as a proxy for body size, b) reproduction mode, and c) known occurrences in different soil habitat types (based on level 2 hierarchies described by the Coordination of information on the environment (CORINE)  37  ).
We provide this collected information as an additional database resource in Supplementary Data 1. Structural equation models. We tested established or hypothesised causal relations among genomic, life-cycle and ecological variables through a series of structural equation models, with the aim of resolving multivariate relationships from the many interrelated variables. We selected only genomes at least 50% BUSCO and 8X mode coverage. Log transformations were applied to body size variables (due to non-normal distribution as determined by a two-sided Kolmogorov-Smirnov test (p < 0. 01). We fitted the SEMs with piecewiseSEM (v2. 1. 0  97  ). We performed the path analyses for all taxa together (linear mixed effect models, with soil invertebrate groups as random variable), and separately for each of the more densely sampled taxa (Collembola, Oribatida, combined Chilopoda and Diplopoda, and Nematoda, linear models).
Reproduction mode was included only into the models of all taxa and of oribatids, as this data were limited in the other groups. Searching for core metazoan genes. As a first-look into the functional capacities of the soil invertebrates in our study, we searched the genomes for potential loss of protein-coding genes. To make this analysis robust, we decided to focus on evolutionarily old genes that were present already in the last common ancestor of the animals. Using 11 species from across the Metazoa tree of life (Supplementary Data 7) which were part of the Orthologous MAtrix database (OMA  98  ), we computed a list of 1482 core metazoan genes which were common to at least 9 of these 11 species using DCC (https://github. com/BIONF/dcc2) and precomputed ortholog groups from the OMA DB. Given the evolutionary age of these genes and their conserved presence throughout the animal evolution, it appears likely that their loss has a substantial functional impact.
We preferred to use a custom core gene set over the standard BUSCO Metazoa ODB10 data set mainly for two reasons First, the BUSCO set with only 954 core genes is considerably smaller than the set computed by us. This gives us more power to detect differences in the presence/absence pattern of genes in the analysed taxa. Second, OMA groups represent cliques of orthologous proteins, i. e. , all members within a group identify each other as pair-wise orthologs. As a consequence, OMA groups reconstruct orthologous relationships across proteins from many taxa with the highest precision among all available tools  98. We then searched for orthologs of these 1482 metazoan core genes among the more complete (>50% BUSCO completeness) soil invertebrate genomes (n = 177) with fDOG-Assembly (https://github.
com/ BIONF/fDOG/tree/fdog_goes_assembly). fDOG-Assembly performs targeted, feature-aware ortholog search without the need for annotated genomes as the starting point. Due to the taxonomic breadth of our dataset, six separate ortholog searches were performed, each using the three most closely-related reference species with protein annotations available (Supplementary Data 8). Genes without orthologs in all investigated species were excluded from the following analyses. The resulting phylogenetic ortholog profiles were visualised with PhyloProfile (v1. 8. 6  99  ) and clustered according to the euclidean distance of the presence and absence patterns of the ortholog groups. Hence, after visual inspection of the ortholog profiles, we were able to identify patches of core metazoan genes which were missing from certain groups.
We tested for gene ontology (GO) enrichment of the potentially missing genes using the InterProScan database 100 and the function runTest from the topGO package (v2. 42. 0 101 ). For this GO-enrichment analysis, 1482 core metazoan genes were assigned to their ontology group(s), where GO annotation data were available. Using this list as a comparison, the two gene lists of interest (50 genes missing from the 78 Collembola species; 97 genes missing from the 54 Oribatida species) were separately tested for any significant enrichment of genes belonging to any of the three gene ontology groups (biological process, cellular component, or molecular function). Significant enrichment of a gene ontology term in the missing genes was stated when the category was represented by more than five genes in the list of 1482 core metazoan genes and with a significant overrepresentation in a Fisher's exact test (p < 0.
05). Further, we manually screened putative functions associated with genes missing from Collembola and Oribatida assemblies in the UniProt database (accessed on 28. 6. 2023), aiming to identify functional or other relevant commonalities which might be missed by an algorithmic GO enrichment analysis. The mean empirical probability of not being able to detect a particular gene in a taxon was 0. 22 for all OMA genes, excluding those missing in springtails and oribatids. So this is also the probability of not finding a particular OMA gene in the genome of a new taxon. The probability that it is actually present in the majority of the taxa if it is also not found in the second sequenced species drops to 0. 05; already in the third species in which the gene is not found, the probability that the gene is actually present in the majority of the species of the taxon is below the significance level.
Acknowledgements This work is a result of the  LOEWE Centre   for Translational Biodiversity Genomics  funded by the  Hessen State Ministry of Higher Education, Research and the Arts (HMWK). This project contributes to the  Soil Invertebrate Genome Initiative  (https:// tbg. senckenberg. de/sigi/), affiliated with the Earth BioGenome Project. Special thanks to  Damian Baranski ,  Jürgen Otte  and  Jörg Müller  for DNA extractions, to  Astrid König  for extraction and preparation of nematodes and tardigrades from Senckenberg cultures and soils, to  Lena Bonassin  and  Jade Tessien  for help with organising the datasets and barcodes, to  Prof. Dr. Florian Grundler  ( INRES Molekulare Phytomedizin, Bonn University, Germany ) for thousands of J2 juveniles of Heterodera schachtii and Meloidogyne incognita in ethanol from their cultures, and to  Magnus Wolf  for the BUSCO-tophylogeny pipeline.
Data availability Vouchers are deposited in the collections of the Senckenberg Museum of Natural History Görlitz (SMNG), Germany. Raw sequence files and draft assemblies accessible through the ENA/NCBI project PRJNA758215. 28 S and COI barcodes are publicly available at dx. doi. org/10. 5883/DS-TBGMI. Genome metadata can be accessed at the Genomes on a Tree (https://goat. genomehubs. org/projects/METAINVERT). Repeat elements can be accessed in the Dfam database (https://www. dfam. org/). Alignment of BUSCO genes and the resulting phylogenetic tree are available in FigShare (https://doi. org/10. 6084/m9. figshare. 24435052)  29. Source data for Fig. 2  are part of Supplementary Data 1. Source data for Fig. 3  are provided in Supplementary Data 2. Source data for Fig. 5c, d  are provided as Supplementary Data 4.
Analysis of gene copy number of transformed strains Total DNA from all the transformed and wild-type L. lactis subsp. lactis strains were extracted using a "Wizard Genomic DNA Purification" kit (Promega, Madison, USA). Real-time PCR was done using a Rotor-Gene Q machine (Qiagen, Hilden, Germany) with the QuantiTect SYBR Green PCR Kit (Qiagen, Germany). For absolute quantification of the nisA and nisRK genes in transformed and wild-type L. lactis subsp. lactis strains, two standard curves at different dilutions (1:10; 1:100; 1:1000, and 1:10000) were established using the genes previously cloned in pGEM-T Easy Vector (Promega, Madison, USA). The data were determined in terms of copies of the nisA and nisRK genes / µL of qPCR reaction. The primers used in the experiments are listed in Table 1  [29].
Real-time PCR conditions were as follows: an initial 95°C denaturation step for 15 min followed by denaturation for 15 sec at 95°C, annealing for 30 sec at 55°C, and extension for 30 sec at 72°C for 40 cycles. Data were analyzed by one-way analysis of variance using GraphPad Prism 5. 0 (GraphPad Software, Inc, California). Significant differences among means were determined by Tukey's Multiple Comparison Test least significant difference mean separation at P < 0. 05.
Realtime PCR conditions were as follows: an initial 95°C denaturation step for 15 min followed by denaturation for 15 sec at 95°C, annealing for 30 sec at 55°C, and extension for 30 sec at 72°C for 40 cycles. The relative levels of genes were expressed as 'mean normalized expression' data using Q-Gene software  [36]. The Q-Gene software application is a tool for dealing with complex quantitative real-time PCR experiments on a large scale, significantly accelerating and rationalizing experimental setup, data analysis, and data management while ensuring maximum reproducibility. Data were analyzed by one-way analysis of variance using GraphPad Prism 5. 0 (GraphPad Software, Inc, California). Significant differences among means were determined by Tukey's Multiple Comparison Test least significant difference mean separation at P < 0.
Highly concentrated electrolytes (HCEs), created simply by increasing the lithium salt concentration from the conventional 1 M to 3-5 M, have been suggested as a path towards safer and more stable lithium batteries. Their higher thermal and electrochemical stabilities and lower volatilities are usually attributed to the unique solvation structure of HCEs with not enough solvent available to fully solvate the Li + ions-but much remains to be understood. Here the structural features that characterize the behavior of electrolytes in general and HCEs in particular, and especially the transition from conventional to highly concentrated behavior, are reported for lithium bis(trifluoromethanesulfonyl)imide (LiTFSI) in acetonitrile (ACN), a common HCE system. We analyze four different salt concentrations using ab initio molecular dynamics (AIMD) and the CHAMPION software, to obtain trends in global and local structure, as well as configurational entropy, to elucidate what truly sets apart the highly concentrated regime.