dataesr/scientific-paragraphs-categorization huggingface dataset:
These databases are independent for terrestrial mollusks-that is, they were compiled independently. MolluscaBase was not used in the curation of terrestrial Mollusca in the ANSP collection and the collection database had not before this exercise been queried for names of terrestrial mollusks missing from MolluscaBase. ANSP has long pursued having a collection as complete as possible for molluscan species and incorporates collections of many individuals that specialized in land snails, among them Robert Swift  (Clench, 1938) , Albert Dod Brown  (Foote, 1887) , George Washington Tryon  (Tryon, 1862; Leidy, 1867) , Paul Hesse  (Borrero & Rosenberg, 2015) , Jens and Christa Hemmen  (Kittel, Groh & Bank, 2012) , and Hideo Katori  (Rosenberg & Khoo, 2018). The collection holds representatives of more than half of the known species of land snails  (Rosenberg, 2014).
It is desirable that the first and second samples in a mark-recapture experiment be captured with different methods to avoid correlations  (Chao, Pan & Chiang, 2008). MolluscaBase and the ANSP malacology database were compiled with different methods, so they are independent methodologically. Their independence, however, does not mean that they are not correlated  (Chao, Pan & Chiang, 2008 ). If, for example, neither MolluscaBase nor ANSP had records from Madagascar, they would be positively correlated in that aspect with diversity underestimated since a geographical region was omitted. It is also possible that MolluscaBase and ANSP are negatively correlated, in which case diversity will be overestimated. Suppose MolluscaBase is complete for a region, but ANSP is weak in that area. The result would be that more tests will be done in areas where the ANSP collection is stronger, which will lead to overestimated diversity.
In practice, there will be both positive and negative correlations and we do not know if the effects will average out. We expect that as GSDs such as MolluscaBase and other large taxonomic databases become more complete, the size of the error bars on estimates of Box 1 About mollusks. All terrestrial mollusks are gastropods, but terrestrial gastropods are not monophyletic; we summarize here their classification as used in MolluscaBase. The Recent Gastropoda are divided into six subclasses: Patellogastropoda, Vetigastropoda, Neomphaliones, Neritimorpha, Caenogastropoda and Heterobranchia  (Bouchet et al. , 2017; MolluscaBase, 2021). The first three are marine only; the other three have marine, freshwater and terrestrial members. Subsets of terrestrial gastropods are often referred to as "pulmonates" (with a lung rather than a gill for gas exchange) and "operculates" (with a horny or calcareous operculum to close the shell aperture), which are grades, not clades.
their completeness and on known diversity of the clades and groups they cover will decrease. Therefore we can use successive estimates to understand the effect of the degree of correlation. If comparison of two estimates (in this case, one from August 2020 and one from July 2021) shows that the later estimate is significantly larger, then the databases were positively correlated; if it is significantly smaller, then they were negatively correlated, and if the estimate remains roughly constant, then the positive and negative correlations have averaged out. This will help us to judge whether the later estimate should be regarded as a minimum, maximum or accurate estimate of known diversity. Our method estimates the height of the species naming curve (see Box 2) as of a particular date, with the 95% CI shrinking to zero as the database evaluated approaches completion.
The CI is based on random sampling of the datasets that are compared. If errors or inconsistencies in the compilation of the datasets analyzed can be quantified, the error bar can be adjusted to take this additional uncertainty into account. Since our new estimate changes perception of the global diversity of land snails, we discuss the implications of this particular application of our method. The same methods can be applied to estimates of the total number of names of Recent mollusks (as opposed to names currently accepted), names of fossil mollusks, and accepted species richness in other phyla. If sufficient detail is captured, name usage curves (defined in Box 2 and shown in Fig. 1 ) can also be plotted from the data, which allows visualization of changing patterns of diversity in a group.
Bebber et al. (2007)  in trying to project total species numbers for birds found that omitting most of the earliest named species gave the tightest 95% CI. Their sample included 9,961 species and yielded estimates ranging from 10,023 to 11,997 and 9,994 to 19,998 (95% CIs), depending on whether all the data or various subsets were used. Yet none of their 95% CIs reached the level predicted by Barraclough et al. under a phylogenetic species concept, which shows the importance of species concepts in determining accepted levels of diversity. This criterion clearly is not met for names of species. Two ways in which this problem manifests are that recently named species are likely to be included in MolluscaBase, but unlikely to be included in the ANSP collection, and that some species are more abundant or widespread than others and their names are more likely to be represented in the literature and in collections.
It is a "current" name that is not acceptable, but it is treated as accepted in MolluscaBase. Such names are included in our counts herein. MolluscaBase is a subset of the World Register of Marine Species (WoRMS Editorial Board, 2021) but expanded from the usual focus of WoRMS to include freshwater, terrestrial and fossil species. It is intended to be an authoritative, continuously updated GSD for Mollusca and thus provides a cornerstone of the taxonomic infrastructure for this phylum  (Bieler, Mikkelsen & Giribet, 2013; Bank et al. , 2014). Compilation of data in MolluscaBase is based on the standards of WoRMS as stated by  Horton et al. (2017). The number of species of Recent terrestrial Mollusca was determined by using the advanced search function in MolluscaBase for Status "accepted", Rank "Species", Environment "Yes" for terrestrial and "Any" for other environments, and Flag "Extant, not fossil only".
Various error checks were run to find species erroneously marked as terrestrial or missing the terrestrial flag, including checking all species listed as terrestrial with at least one other environment indicated; those lacking any indication of environment; and searching for species in entirely terrestrial clades such as Stylommatophora, Cyclophoroidea, Helicinoidea or Pomatiidae that were not indicated as terrestrial. Names used as valid in the ANSP malacology database were extracted in July 2020. Duplicates on the list were removed (e. g. , different generic combinations of the same epithet) so that each name appeared on the list once. Each name was assigned a random number using the rand() function in Microsoft Excel. The list of 13,157 names is provided in Table  S1. Our first test (completed 11 August 2020) used the first 1,100 names when sorted lowest to highest by the assigned random number.
Comparison of estimates from 2020 and 2021 is shown in Fig. 3 , with 230 species added to the total for 2020 to account for species named since the earlier estimate was completed. Estimates from 2020 are slightly higher than those for 2021, but all the estimates have overlapping 95% CIs, which indicates that positive and negative correlations between MolluscaBase and the ANSP collection averaged out. As expected, estimates from 2021 have smaller 95% CIs than those from 2020, approximately ± 400 vs. > ± 700. The effects of potential errors and inconsistencies in compilation of the data beyond those reflected in the sampling error are estimated at 475 ± 430 species (Table  5 ). Adding this additional uncertainty to the estimate from method 4 in 2021 of 28,420 ± 369, which also accounts for biases in the data, yields our final estimate of terrestrial gastropod diversity of 28,895 ± 630 species known by the end of 2021 (Fig.
Continental regions had lower percentages of missing species, with some areas being essentially complete (Australia/NZ, Europe, and North America). Overall, 80% of missing species were on islands in the first sample, and 75% in the second sample. Among continental regions, only Africa was higher than the average across all regions for missing species, with 17. 6% in 2020 and 6. 5% in 2021 vs. overall of 16. 7% in 2020 and 5. 3% in 2021. The taxonomic breakdown of missing species is shown in Table  S5  with raw data in Tables  S2  and S3. The eupulmonates have a similar proportion missing to the overall dataset, partly because they are numerically dominant, constituting about 80% of all terrestrial gastropods. They had 16. 4% missing in 2020, while operculates had 18. 4%, but both groups improved in 2021, to 5.
Since the ANSP collection and MolluscaBase are negatively correlated in this respect, diversity should be overestimated. When recently named species are set aside (method 2), estimates from 2020 decreased by 433 species (1. 5%) and from 2021 by 152 species (0. 5%) (Fig. 3 ; Tables  3 4 ). The magnitude of the effect is inversely correlated to completeness of the enumerations: in 2021, only 5. 3% of sampled species led to missing accepted species; in 2020, the corresponding figure was 16. 7% (Table  2 ). Since the estimates from 2021 are within the error bars for the estimates of 2020, the effect is not significant, but it could be significant in cases where the database is less complete. A different situation is seen when taking abundance into account. When samples from ANSP are divided into three abundance classes (<5, 5-15 and >15 samples corresponding to a name), there is a negligible increase: nine species in 2020 and 12 species in 2021 (Tables  3 4 ).
Names in m 2 in estimates of diversity, however, should not be thought of as recaptured, because there are names where we cannot tell whether they were recaptured. These correspond to item e in Table  2 : "Epithet missing, accepted name uncertain or unknown". Names in item b in Table  2 , "Epithet present, accepted name uncertain or unknown", appear similar but are not problematic-their status is given in MolluscaBase, because the epithet is present. The method presented herein provides a means of estimating the number of species currently accepted in a group that does not yet have a complete taxonomic database, but the 95% CIs provided are based only on sampling error. Our best estimate for accepted diversity of terrestrial gastropods in 2021, accounting for potential correlations between the databases, is 28,420 ± 369 species.
Additional uncertainty comes from clerical and observational errors. We have controlled for these errors to the degree possible (see Table  5  and Supplemental Data), but they grade into taxonomic uncertainty. Adding in the additional uncertainty from clerical and observational errors of 475 ± 430 (Table  5 ) yields a rounded estimate of terrestrial gastropod diversity of 28,895 ± 630 species known by the end of 2021 (Fig. 3 ). The added uncertainty means that the error term represents a range rather than a statistical confidence interval. Taxonomic databases contain "eclectic species"  (Blackburn & Tyler, 1987; Barraclough et al. , 2016) , discovered with various methods and proposed under a variety of taxonomic traditions and species concepts. Technological advances such as scanning electron microscopy, micro-CT scanning and DNA sequencing mean that the methods used by taxonomists are changing-more data sources are available and methods of analysis grow more powerful.
Integrative methods lead to better understanding of taxa  (Pante, Schoelinck & Puillandre, 2015; Goulding & Dayrat, 2016) , as have debates about species concepts  (Padial & De la Riva, 2006 ). Since it is not possible to revise all the species in large groups simultaneously, current methods will not yet have been applied to some of the component taxa  (Gaston, 2008). Also, some species concepts may not be appropriate for various groups-Mollusca for example contains some groups that are parthenogenetic, so their members cannot be treated under the Biological Species Concept. Large taxonomic databases thus will always contain eclectic species. (See Box 2 for an example of the importance of species concepts). Our method estimates the height of the species naming curve (Box 2), with the error bar shrinking to zero as the database evaluated approaches completion.
A species naming curve may show known diversity approaching an asymptote, as with unionoids (Fig. 1 ), or rapidly increasing as with marine mollusks  (Rosenberg, 2014, fig. 1) , terrestrial gastropods (Fig. 2 ), and many other groups of organisms  (Bebber et al. , 2007; Edie, Smits & Jablonski, 2017). As taxonomic research progresses, the number of species recognized in a group as of a given date may increase or decrease, depending on the flux of names into and out of synonymy. This flux can be shown in a name usage curve (Fig. 1 ). Alroy (2002) , based on North American fossil mammals, proposed a method to take this flux into account in correcting estimates of accepted diversity, but MolluscaBase does not have the detail necessary to implement his method. It might be implemented for freshwater mussels (Unionida) since  Graf & Cummings (2021a; 2021b)  have captured all taxonomic actions relevant to the group in the MusselP database.
(See Supplemental Data under "species concepts" for examples with Cerion. ) Although many previously named molluscan species-group taxa have not been evaluated with modern methods, it is nonetheless possible to predict on a qualitative basis some of the effects of taxonomic revision on taxa that have already been named. As of 16 Aug 2021, MolluscaBase had 2,300 taxa inquirenda, 6,400 subspecies and more than 10,000 synonyms for terrestrial gastropods. MolluscaBase is not as complete in its treatment of synonyms and subspecies as it is for accepted species, but these numbers show that there is considerable potential to add accepted species by clarifying taxa inquirenda and reevaluating synonyms and subspecies. That means the species naming curve (Fig. 2 ) will increase in height, even without new discovery, since resurrected taxa enter the curve in the year they were named.
Outlook Our adaptation of mark-recapture methods to estimating accepted species diversity is applicable to taxonomic databases for other phyla and will enable more sophisticated tests of the completeness of global species databases (GSDs). If our method is applied to paleontological databases, a stratified sampling approach should be used to ensure adequate coverage across geological epochs. If appropriate versions or backups of a database exist, it may be possible to implement our method comparing present to past versions instead of needing to wait for database growth. The result will be better understanding of the completeness of existing GSDs. As taxonomic coverage in GSDs increases, it will no longer be necessary for researchers to compile their own "graveyards" of names  (Gittenberger, 1993)  to work on a taxon.
CONCLUSIONS Mark-recapture methods can be successfully adapted to estimating accepted species level diversity from comparisons of independent taxonomic databases. The estimate allows a confidence interval to be placed on the height of a species naming curve. In the cases of terrestrial gastropods all estimates from two samples a year apart had overlapping 95% CIs, indicating that correlations between the databases did not cause significant problems. Taking abundance into account did not have a significant effect on the estimates. Taking into account other sources of uncertainty, we estimate accepted species-level diversity of terrestrial gastropods as 29,000 species (28,895 ± 630) at the end of 2021. This is a minimum estimate of true diversity. Attempts to project future levels of species richness that might be recognized must take into account both revision of known taxa and discovery of new species.
ACKNOWLEDGEMENTS Many people helped make this work possible, in particular, members of the data management team at the  Flanders Marine Institute  where WoRMS is based, and members of the  WoRMS steering committee. Bram Breure ,  Bruce Marshall  and  Maxim Vinarksi  all made significant contributions to editing terrestrial mollusks in MolluscaBase. At  ANSP ,  Ria Capone  helped with tracking down reference material. At Drexel University ,  Joshua McGinnis  answered algebraic questions. Yana Kane-Esrig  helped explain properties of the binomial distribution. The authors are all editors for Mollusca in WoRMS/MolluscaBase. The order of authorship is alphabetic except for the first author, who developed the method of estimation. The development and continuation of MolluscaBase is-along with the voluntary contributions of its editors-made possible through support of the  LifeWatch Species Information Backbone.
Funding This work was supported by  NSF  grants  DBI 1902328  (lead PI:  N. Yeung , through a subaward to  Gary Rosenberg ) for Pacific Island land snails,  EF-02667  (PIs:  P. Sierwald  and  Rüdiger Bieler ) for terrestrial and aquatic North American mollusks, and DBI 2001570 (PI:  Gary Rosenberg ) and DBI 2001510 (PI:  Rüdiger Bieler ) for mollusks of the  Eastern Seaboard of the United States. The work of the WoRMS Data Management Team is funded by  Research Foundation-Flanders (FWO)  as part of the Belgian contribution to LifeWatch. In addition, the involved authors have previously received financial support through the Belgian contribution to LifeWatch, to expand the content and enhance the quality of MolluscaBase. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Tuberculosis (TB) disease causes up to 1. 5 million deaths every year and represents an important problem of public health at worldwide level. Here, we quantified the gene expression signatures of granuloma biopsies across human TB pulmonary lesions, and validated the best gene candidates using NanoString technology, profiling 157 samples from 40 TB patients who underwent surgery. We characterised the transcriptional profile of the TB granuloma in comparison to healthy tissue, described an 11-gene signature and measured 7 proteins in plasma associated with it. We demonstrated a gradient of immune-related transcript abundance across the granuloma substructure and evidenced metabolically-active Mycobacterium tuberculosis in the lesions. Patients who converted to sputum negative after two months of starting treatment, showed enriched inflammatory pathways in the lesion several months after, supporting use of sputum culture conversion (SCC) as a prognostic biomarker during clinical management and as a factor to prioritise patients when considering lung surgery.
Identification of a gene signature of the human TB lesion We analysed total RNA from three different sections of the human TB granuloma from surgical biopsies (Central Lesion (CL), Internal Wall (IW), External Wall (EW)) and Pulmonary Nodules (PN). Additionally, healthy parenchyma (HT) from the same involved lung was collected as tissue control (Fig. 1a ). Patients were divided into a Discovery Set (14 patients) and a Validation Set (26 patients) (Supplementary Figure  1 ). The Discovery Set comprised 61 samples from the 14 patients and was employed to characterise genome-wide gene expression across TB lesions. (Supplementary Table  1 ). The patients were matched according to sex and drug sensitivity of the Mtb isolate (Supplementary Table  2 ). The associations of the demographic, clinical and TB-related data of each participant in the Discovery Set at the time of surgery, and sex were assessed (Supplementary Table  3 ).
s e e m a n u s c r i p t D O I f o r d e t a i l s W I T H D R A W N Plasma samples from the same 40 patients were collected prior to surgery (SH-TBL Cohort) and kept frozen at -80ºC at the IGTP until further analysis. Samples used as controls in the biomarker analysis in plasma came from 13 patients from the ongoing STAGE-TB project (registered at the ClinicalTrials. gov database under the code NCT03691883) who had completed 6 months of ATT, and that were stored frozen at -80ºC in the IGTP facilities.
Preprocessing of raw data included quality control through FastQC (v. 011. 7) and MultiQC (v. 1. 9)  43. Before further steps in read preprocessing, Illumina adapters were trimmed off with Trimmomatic (v. 0. 39)  44. The human genome sequence GRCh38. 89 and human gene annotations were downloaded from the ENSEMBL web repository. Files from each Discovery Set sample were aligned to the human reference genome through the Spliced Transcripts Alignment to a Reference (STAR) package (v. 2. 7. 5b)  45  , with the built-in gene counts quantification mode for stranded RNA-Seq data. BAM files were generated, and the SAMtools package applied to calculate the percentage of successful read alignment against the reference human genome (v. 1. 10)  45.
Discovery Set statistical analysis The overall pipeline for the data handling, plotting and statistical analysis was conducted in R (v. 3. 6. 2)  46. After STAR mapping, a gene count data table was obtained including CL, IW, EW, PN, and HT samples belonging to the Discovery Set. Genes with mean raw counts that were below or equal to five were discarded as they were too lowly expressed s e e m a n u s c r i p t D O I f o r d e t a i l s.
W I T H D R A W N to be reliably quantified and could potentially interfere with the differential gene expression analysis. The differential expression analysis from tissue count tables was conducted using the DESeq2 Bioconductor package (v. 1. 28. 1)  47. Genes were considered as significant DEGs when the Benjamini-Hochberg adjusted p-value was equal to or less than 0. 05 (P ≤ 0. 05). The differential expression examination for two-factor levels under an experimental design was employed through the contrast argument. The contrast argument is the linear combination of the estimated log2FC, testing if differences between the groups were equal to zero. Patient's sex and drug-sensitivity type were accounted for in DESeq2 modelling. The spatial transcriptional model analysis within the human TB granuloma was evaluated in a linear additive manner, obtaining a gradient of gene expression throughout the TB lesion architecture.
Validation Set using NanoString technology In order to validate the RNA-Seq findings, we used a second patient set and an alternative measure of transcript abundance. The Validation Set of patients consisted of 95 tissue samples (70 human TB granuloma biopsies and 25 HT) from 26 SH-TBL Cohort subjects (Supplementary Table  1 ). Six CL and one HT were discarded as the RINe values were below four. The Genes of Interest panel consisted of 18 genes (MMP3, LTB, LTF, MAP4K1, FCMR, CCL19, P2RX7, LHFPL2, C1QB, SLC6A9, GNPTAB, FAM71A, HPSE, IL2RG, CD14, CXCL10, CD5L, and MMP9), and five housekeeping (HK) genes (ACTB, ACTG1, ADCY3, PCBP1, and PLXNB2) (Supplementary Table  5 ), assayed using the NanoString platform. A 27-probe set custom panel was designed and the Validation Set samples assayed using the NanoString nCounter XT assay, following manufacturer's instructions.
We used the standard gene expression protocol for 12x RNA hybridization with 100 ng total-RNA as input, a hybridization time of 18h for all samples and mixing controls and TB samples in order to avoid any technical bias. Samples were analysed in a SPRINT nCounter system (NanoString). Raw counts were obtained and analysed through NanoString's nSolver (v. 3. 0) digital analyser software. Technical normalization was carried out using the geometric mean of the positive controls, whereas the background was established using s e e m a n u s c r i p t D O I f o r d e t a i l s.