dataesr/scientific-paragraphs-categorization huggingface dataset:
Conclusion and future work We have presented RepligoSpaces, a replica-aware extension of goSpace, and the implementation of Klaim (pSpaces) in Go. We have also discussed how RepligoSpaces fits within a fully-mechanisable procedure for automated replication of programs over tuple spaces that relies on combining static analysis and program transformation. A lightweight static analysis pass on the initial program computes the sets of target spaces for replication so that the standard tuple manipulation routines can be replaced by equivalent replica-aware versions. The combined approach preserves strong consistency, thanks to a tracking mechanism embedded in the tuple manipulation routines and to the fact that the set of target spaces is safely over-approximated. To realistically model scenarios where memory is constrained, we have enriched the approach with memory-limited tuple spaces implementing several well-known replacement policies.
Dynamic techniques may also complement this work in interesting ways. For instance, one might extract a probabilistic model of a running program via a dynamic analysis, generate a goSpace program that captures this model, and finally use our workflow to analyse whether replication could guarantee any performance benefits. Dynamic analysis may also become necessary if we shift our focus towards more open systems, where processes may join and leave as they please. This would likely require extending the programming interface to register and de-register spaces and adjust the replication mechanism accordingly. To evaluate these extensions, we should consider further scenarios where replication has successfully been applied to other contexts, such as database systems  [31] , and cloud computing  [32] , as well as other consistency models  [21, 33].
Each optimization run included 60 iterations, and lasted approximately 8 minutes. 12 out of 24 subjects participated in the experiment remotely, using the remote access software TeamViewer (TeamViewer GmbH). We did not observe differences in the results between remote participants and participants who performed the tasks in the lab  (Supp Fig S2). Preference To measure subjects' preferences for different encoders after the optimization procedures, we simultaneously displayed the percepts predicted with two different encoders and asked the subject to indicate which one they preferred  (Fig 1B). To get an accurate measure of subjects' preferences we repeated the comparison for a set of 13 letters. The name of the letter was indicated above so that the subject knew what the reference stimulus was.
FigureS3: Illustration of the principle of hyperparameters transfer. The blue areas correspond to the GP posterior distribution. Red circles correspond to data points, and the red line corresponds to the true function. Two datasets of 400 comparisons between encoders are built using two different perceptual models. GP models are trained on the training dataset with three different kernels, and for each kernel, the hyperparameters are inferred using type-II maximum likelihood. After this model selection step, GP models are learned using the same hyperparameters on the test data set. The kernel for which the Brier score is the highest on this second data set is used in the experiment, with the same hyperparameters.
Code availability The Matlab code to perform the experiments and run the analyses is available at https://github. com/ TristanFauvel/Retinal_Prosthetic_Optimization_Code. git. The implementation of the QUEST+ adaptive psychophysics procedure is from  Jones (2018). To display stimuli and record responses, we used the Matlab toolbox Psychtoolbox  (Brainard, 1997; Pelli, 1997; Kleiner et al. , 2007).
Metabolic potential of MAGs An extensive genomic characterization of the 42 MAGs has been performed to explore the metabolic pathways and the possible adaptations of the microbial populations from which these MAGs originate. KEGG Decoder visualization highlighted various pathways associated with carbohydrate degradation, oxidative phosphorylation and sulfur, nitrogen, and amino-acid metabolisms, among others (Figure  3 ). To con rm these initial metabolic predictions, further annotation was performed by combining data generated by Prokka with the MetaCyc database. Efforts have been directed at studying catabolic pathways, particularly those involving inorganic electron donors and acceptors. These results are not representative of the metabolic diversity of all the hot spring ecosystems studied, but they do re ect some of the microbial catabolisms likely to be used in situ to produce energy and, by assumption, the most abundant ones.
For Armatimonadota's members (RB10-MAG03, RB13-MAG04, RB32-MAG03), we predicted pathways for assimilatory sulfate reduction, carbon monoxide oxidation, selenate reduction and thiosulfate disproportionation; the members of the phylum are known as aerobic heterotrophs  [39]. In Zestosphaera's and Ignisphaera's MAGs (RB10-MAG02, RB13-MAG06) (RB10-MAG05, RB13-MAG08, RB13-MAG11, RB32-MAG05), we predicted sulfur reduction (sulfur and polysul des) pathways; those MAGs classi ed in the Desulfurococcaceae in the LPSN taxonomy, are known as heterotrophs respiring sulfur species  [40, 41]. MAGs belonging to the class Thermoproteia (RB10-MAG10, RB13-MAG07, RB32-MAG06), encode pathway for dissimilatory sulfate reduction; various catabolic pathways are described in this class  [42]. In MAGs related to the genus Caldipriscus (RB10-MAG09, RB32-MAG12), phylum Patescibacteria (RB32-MAG13), family Acidilobaceae (RB10-MAG01, RB13-MAG02, RB32-MAG01), family Thermo laceae (RB10-MAG06, RB13-MAG03, RB32-MAG09) and class Aenigmatarchaeia (RB13-MAG12), we did not predict any catabolic pathway of inorganic nutrients among those reported in the MetaCyc database.
This could be explained by the low completion of the MAGs and/or the fact that only well-known pathways are documented in this database. However, all these MAGs have pathways associated with carbohydrate and protein degradation. This may indicate that these taxa are chemoheterotrophs, which has already been reported in geothermal environments and already described for relatives of some of these taxa  [43, 44]. Sul de oxidation may be a possible energy production pathway for 28 MAGs based on KEGG Decoder (Figure  3 ), since they code for a sul de:quinone oxidoreductase (K17218) and a avoprotein chain of sul de dehydrogenase (K17229), but this hypothesis was not con rmed by MetaCyc except for RB13-MAG01. Due to high representations of sulfur metabolisms, genes encoded in MAGs were evaluated with DiSCo, which gave similar results to those obtained when analyzed with Pathway tools.
DiSCo con rmed complete dissimilatory sulfate reduction pathways for two MAGs, predicted for sulfate reduction process (RB13-MAG07) or sul de oxidation process by reverse sulfate reduction pathway (RB13-MAG01). The assimilatory sulfate reduction pathway is more represented in the overall dataset formed by all MAGs than the dissimilatory pathway, which is consistent with the low sulfate concentration measured in the four hot springs (Table  S1 ). The thiosulfate disproportionation pathway predicted by MetaCyc in many MAGs simply refers to the detection of an enzyme, the rhodanese-type thiosulfate sulfurtransferase. However, in the current state of knowledge on the disproportionation pathways of inorganic sulfur compounds  [45, 46] , this enzyme alone does not allow the implementation of this catabolic pathway.
Major elements analysis in water samples The major anions and cations were analyzed at LGL-TPE using ion-chromatography (Methrom ECO IC). A mixture of 3. 2 mM Na 2 CO 3 and 1 mM NaHCO 3 was used as an eluent for analysis of anions and a chemical suppression module (MSM) was used to suppress the conductivity. For cations, 1. 7 mM HNO 3 was used as an eluent. The anions and cations were separated using analytical columns, Metrosep A Supp5 Guard/4. 0 and Metrosep C4 250/4. 0, respectively. Data are presented in Table  S1.
Sequence processing, metagenomic assembly and binning Metagenome sequences' quality were controlled by FastQC (v0. 11. 9 -https://github. com/s-andrews/FastQC) and MultiQC (v1. 9https://github. com/ewels/MultiQC). Sequences were then processed with the snakemake of Anvi'o [48-50] (v7 -https://github. com/merenlab/anvio), ltered by integrated minoche script (v2. 8 -https://github. com/merenlab/illumina-utils). Next, we used MetaSpades  [51]  (v3. 14. 1 -https://github. com/ablab/spades) as genome assembler and Concoct  [52]  (v1. 1. 0 -https://github. com/BinPro/CONCOCT) as genome binner with anvi_cluster_contigs function with "all against all" mode. Furthermore, MAGs were manually re ned with anvi-re ne function. Genome mapping was performed with bowtie2  [53]  (v2. 4. 2https://sourceforge.
net/projects/bowtie-bio/ les/bowtie2/2. 4. 2/) and samtools  [54]  (v1. 7 -https://samtools. github. io/). MAGs' quality were estimated by Anvi'o and furthermore by CheckM  [55]  (v1. 1. 3 -https://ecogenomics. github. io/CheckM/), both with default parameters. Total length, number of contigs, N50, and GC% content were extracted with anvi-summarize function.
Taxonomic and phylogenetic inference of metagenomic assemblies and MAGs According to the standards proposed elsewhere  [25] , bins were de ned as high-quality MAGs (> 90% completion, < 5% contamination, presence of the 23S, 16S and 5S rRNA genes and at least 18 tRNAs), nearly high-quality MAGs (> 90% completion, < 5% contamination, other criteria partially covered), medium-quality MAGs (≥ 50% completion, < 10% contamination) and poor-quality MAGs (< 50% completion, < 10% contamination). The taxonomic a liation of the MAGs was rst investigated by placing the MAGs in a phylogenomic context. The phylogenetic reconstructions were based on 122 archaeal or 120 bacterial single copy conservative marker genes according to the Genome Taxonomy Data Base (R95 release) and were constructed using de novo work ow implemented in GTDB-Tk (v1.
4. 1 -https://github. com/Ecogenomics/GTDBTk) [56, 57]. Visualization and analysis of trees were done using ARB software  [58]. As the taxonomy proposed by GTDB is new and does not correspond exactly to the one recognized by the International Code of Nomenclature of prokaryotes (ICNP), we also analyzed data according to the rules of the Code and its nomenclature. For this purpose, we implemented a combination of genomic indices classically used for the delineation of the different taxonomic ranks, namely: 16S rRNA gene sequence similarity, average nucleotide identity score (ANI) and average amino-acid identity value (ANI). The approach followed and the results are given in supplementary material (Text S1, Table  S2 , Figure  S2 ).
Acknowledgements We thank  Guillaume Lannuzel  for providing assistance with molecular biology analyses. We are grateful to the  Territoire des Terres Australes et Antarctiques Françaises (TAAF) , the  IPEV (Institut Polaire Paul-Emile Victor)  and its logistical staff for logistic support for transit and eldwork in the Kerguelen archipelago. We thank  M. Jebbar  and  Z. Shao , co-supervisors of M. A. , for letting him do this study which was not in the scope of his thesis. We acknowledge the  LABGeM (CEA/Genoscope & CNRS   UMR8030 ), the  France Génomique and French Bioinformatics Institute  national infrastructures (funded as part of  Investissements d'Avenir program  managed by  Agence Nationale pour la Recherche , contracts  ANR-10-INBS-09  and  ANR-11-INBS-0013 ) are acknowledged for support within the MicroScope annotation platform.
We are grateful for the nancial support of  IPEV  provided by the  TALISKER program   N°1077  to D. G. and HOTVIR program  N°408  to M. L. R. Metagenomic analyses were funded by the  Sino-French IRP   1211 MicrobSea  to K. A. and by the  MERLIN "Abyss" program  funded by Ifremer to K. A. The study was supported by two grants from the  French Ministry of Higher Education and Research , and from the  Région Bretagne , to M. A and S. Y.
Human activities cause major ecological changes by reshuffling the spatial distribution of species. The extent to which this process affects belowground biota is a critical issue because soil organisms play a key role in ecosystem functioning and maintenance. However, the magnitude of the reshuffling of soil species remains unknown so far because of the lack of a historic baseline. Here, we begin to fill this gap with the largest spatiotemporal database of native and alien earthworms in North America. Our results reveal that the entire continent is being invaded by non-native earthworms through a variety of pathways. We show that these aliens bring novel ecological functions in most regions and thus represent a major threat to native ecosystems. Our findings demonstrate that earthworms, and most likely other soil organisms, represent a major but overlooked pool of invasive species with strong ecological impact.
In addition, climate change is facilitating their spread from South America to Mexico and into the northern parts of the continent, where the permafrost is melting, potentially introducing a positive feedback loop with climate change  [28] [29] [30]. Here, we reconstruct the spatiotemporal spread and putative introduction pathways of exotic earthworm species at the scale of a continent over a century. To achieve this, we built the database EWINA, which is the most comprehensive database of native and exotic earthworm species occurrence ever built. This database collates more than 68k records dating from 1850 to 2021, across 2510 geographical units in North America (Extended Data Fig. 1 ). We completed this database using EWINA_IPATHS, a second database which focuses on the introduction pathways of alien earthworm species into the USA between 1945 and 1975.
Although these data are not standardized and only represent a fraction of the total introductions, they show that introductions were happening consistently between 1945 and 1970, with a peak in 1949 (Fig. 3B ). This means that apart from 1950, the propagule pressure was fairly constant over this period of time. Third, we analyzed the nature of the introduction pathways and their geographical features. Alien earthworm species principally originate from Europe and Asia (Fig. 4A ). Most species were introduced by airplane (Fig. 4B ). Islands such as Hawaii and Caribbean islands seem to have played an important role as both sinks and sources of alien species. Alien earthworms were mostly intercepted in coastal regions of the US, in airports and harbors, which is consistent with the observed spatiotemporal colonization pattern (Fig.
For example the trade of alien earthworm species is allowed without restrictions between most states of the US. It is difficult to assess whether the trend we found is specific to North America because of the lack of other similar databases on alien earthworms in other parts of the world. The few other available data on earthworm alien richness suggest that they may also be substantial in other regions. For example, the Northern Russian Plain is believed to be inhabited mainly by European alien species  32. A few countries such as China, Korea, and Myanmar also seem to host a high level of alien species, between 20 and 30%  33. At the other end of the gradient, Africa is believed to have very few alien earthworm species  34  , though no quantitative data are available to confirm these statements.
EWINA_RICH: Native and alien Species richness To estimate the magnitude of introductions of Alien earthworm across North America, we calculated the Relative Alien Species Richness (RASR) of each of the North American geographical units, as defined below. RASR was computed as the ratio of the number of alien species versus the total number of earthworm species (natives and aliens). This metric has numerous advantages: it is independent of region size and scale of analysis, it is comparable across regions and ecosystems and between groups of taxa  65. We used only data from 2000 to 2021 for the modeling and mapping of RASR. To build the database, we compiled data of both native and alien earthworm species occurrence across 2510 geographic units in North America (Mexico, United States, and Canada, Greenland), covering 73% of the land surface (Extended Data Fig.
1 ). The data gathers the species name, the locality and date of observation, and when available, the abundance, the geographical coordinates and habitat features. The database contains individual records of species, but also community data, where all species were sampled together at the same place. We used the finest possible geographical resolution to define the geographic units, which in the US and Canada is the level 3 of the GADM (https://www. gadm. papers from this journal with earthworm data and extracted the data from the text and from the maps. After this step, we compiled all references cited in these papers, get all possible original sources, and entered their data. To complete this process, we compiled the list of species present in North America and did a specific search for each native species.
Once this step was achieved, we established the list of authors who published data on native earthworm in North America and did a systematic search of all the papers published by these authors. Finally, we did classical research on web of science and Google Scholar, with two approaches. We combined the key word "earthworm" or "oligochaete" and a key word for location. We used several location key words such as country names and each state or province names of North America. After this step, we used the list of all native species to do a search on web of science and in GBIF. In order to minimize identification issues in GBIF, we removed all GBIF data originating from human observations and kept only the trusted ones from museums and molecular biology portals. Once all data were compiled, we homogenized the taxonomy with the Csuzdi's database  66  and the GBIF Backbone Taxonomy.
We aggregated subspecies at the species level. We obtained 68 938 records, from which we eliminated the duplicates based on their locality and date of observation (month and year). At the end, the database holds 66 512 unique species occurrence. With this list of species occurrences, we computed the list of species by geographical units at two resolutions: at county level and at TDWG-level 4 units. TDWG4 units are standardized geographical entities used to compare biodiversity data across taxa  31. Counties boundaries were obtained from GADM level 2 (parishes) in Canada, from the tiger 2014 database of the US Bureau of Census for the US, and from GDAM level 1 for Mexico. The TDWG-4 boundaries were obtained from a paper  4  on invasive species from which we extracted data (see below). The different spatial layers were then merged with snapping activated and cleaned by geo-scripting to fix geometry issues such as duplicated points, auto intersections or sliver polygons.
EWINA_IPATHS: Introductions pathways' database The second database, "EWINA_IPATHS" (Earthworm Introduction Pathways) documents the introduction pathways of earthworm in the United States. This database centralizes data of earthworm interception events at the US borders between 1945 and 1975. These data came from the U. S. Bureau of Plant Quarantine, U. S. Department of Agriculture. They were principally reported by G. E. Gates in a list of articles published between 1956 and 1982. Each record in the EWINA_IPATHS database relates an interception event of introduced earthworms. Interception events are described by the name of the intercepted species, its abundance, the date and locality of interception, the geographical point of origin, the transportation mode (boat, plane, car), and the substrate in which the earthworms were found (e.
Environmental drivers of RASR We tested 12 environmental features as potential determinants of RASR level (see Extended Data Table  1  for a full description of all examined variables). First, we selected variables that were identified as global drivers of invasions in several other aboveground groups. These general macroecological variables included climate  68  : Mean Annual Temperature (bio1) and Annual Precipitation (bio12), human activity (Global Human Influence Index  69  ), the proportion of grassland and cropland in landscape  70  , and two habitat features: elevation and elevation heterogeneity (GTOPO30  71  ). Second, we added environmental features that are -according to previous studies -specific to earthworm invasions  50, 72. These variables were related to soil properties: the soil carbon content on surface and at 30 cm depth, the soil pH in the ten first centimeters  73  , as well as the (log) density of roads  50  (length of roads by units of surface).
Model of RASR We modeled and predicted RASR as a function of the 12 environmental covariates with a random forest model. We used only observations posterior to 2000 and removed geographical units with less than five species, to remove under sampled units. The model was bagged in order to assess the test error without the need to perform additional cross validation to estimate the predictive accuracy  74. Bagging (Bootstrap aggregation) consists in drawing many random samples from the training dataset (bootstraps), fit a separate model on each bootstrapped dataset, and average the predictions. On average each model use around two-third of the observations. The remaining one third observations, not used to fit the model, are referred to as the out of bag (OOB) observations. We can predict RASR and estimate the prediction accuracy of each observation using all the models in which the observation was OOB.
We computed the graph of neighborhood based on topology and used it to test autocorrelation with global and local Moran tests  75. Both tests were not significant, indicating the absence of spatial patterns in the residuals of the model (Extended Data Fig. 9 ). Finally we mapped the prediction uncertainty (Extended Data Fig. 10 ) and found that it was generally below 20%. We used the jackknife-after-bootstrap  76  for bagging to estimate the standard errors based on out-of-bag (OOB) predictions with the package ranger  77. predictors' space. In these plots, the expected values are centered to their value at the lowest value of the covariate X_i in order to facilitate the comparison among individual conditions. In a second step, we explored potential interactions between predictors by a double approach.
We started by drawing all pairwise 2-D Partial dependent plots, restricting the co-variables to lie within with convex hull of their possible values, and searched for potential interactions. Then we used the joint-Variable Importance Measure (JVIMP) test  79  to assess the significant interactions. In this approach, two co-variates are paired and their paired VIMP is calculated. The VIMP for each separate variable is also calculated. The sum of these two values is referred to as 'Additive' importance. A large positive or negative difference between 'Paired' and 'Additive' indicates an association worth pursuing if the univariate VIMP for each of the paired-variables is reasonably large. The find. interaction () function of the R package randomForestSRC  80  was used with the option vimp. We found no evidence of significant interaction.
Comparison with the invasion patterns in other groups We compared our results on earthworm to data of invasion patterns on several aboveground organisms. These data came from a global database of invasive species  4. We restricted our analysis on taxa with the best geographical coverage across North America: mammals, birds, plants, and spiders. This database reports the number of invasive species in the TGWD-4  4  geographical units previously mentioned. We computed the number of alien earthworm species in the TDWG-level 4 units from the complete EWINA database, excluding Greenland, (Extended Data Fig. 6 ) and compared it to the above ground organisms with a Spearman correlation.