dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgments: The authors wish to thank  Matteo Invidia ,  Agnese Bernabucci  and  Antonio Cataneo  for helping with data collection. Moreover, the authors wish to thank  Umberto Granziol  for his important suggestions in the computation of behavioral indexes. Finally, a special thank goes also to  D. Patanè  and his suggestion 'to open everything' as the best strategy to explore any possible relevant finding.
Single-cell RNA-seq analysis on NK cells isolated from human melanoma metastases showed that NK cells were present in melanoma metastases, even in patients who failed to respond to checkpoint blockade  (37). We have also analyzed the various RNA-seq datasets available from the TCGA database of more than 10,000 tumor samples for 33 different types of cancer to assess NK-cell infiltration (Fig. 1 ). Informative genes significantly overexpressed by NK cells were selected to establish a NK-cell signature and to infer the abundance of these cell types in the tumor samples analyzed. A 13-gene signature, CD160, CD244, CHST12, CST7, GNLY, IL18RAP, IL2RB, KLRC1, KLRC3, KLRD1, KLRF1, PRF1, and XCL2  (38) , to which NCR1 was added, was used as a metagene for NK-cell identification. For comparison, we also studied infiltration of the CD8 + cytotoxic T-cell subset, which is also involved in tumor elimination.
4h-n ). It appears that global climate shifted prominently across the MPB, while orbital forcing did not change markedly. e Al/Na from the Shilou red clay section (this study). f Compiled proxy record of atmospheric CO 2 concentrations from different methods, with error bars representing one-sigma uncertainties  [8] [9] [10]. g Gradient between planktic and benthic foraminiferal δ 13 C at ODP Site 1146, South China Sea 7. h-l Sea surface temperature (SST) reconstructions  6, 39  from DSDP Site 594 (South Pacific Ocean) and ODP Sites 1088 (South Atlantic Ocean), 907 (Norwegian Sea), 846 (tropical eastern Pacific Ocean), and 763 A (subtropical southeastern Indian Ocean). m ODP Site 849 benthic foraminiferal δ 18 O record, tropical eastern Pacific Ocean  41. The red smoothed curve is fitted using the locally weighted least squares error (LOWESS) method with Matlab.
n Modelled global eustatic sea level changes  5. The compiled data indicate increased moisture in Asian monsoon areas and aridification in arid Central Asia, as CO 2 and temperature increased across the MPB (shaded). To investigate Asian hydroclimate responses to CO 2 -induced global warming across the MPB, we used the Community Earth System Model (CESM 1. 0. 4)  58 to perform two simulations for late Miocene and Pliocene conditions, respectively (see "Methods" for details). The Pliocene simulation was conducted with Pliocene boundary conditions from the PRISM4 (Pliocene Research, Interpretation and Synoptic Mapping version 4) dataset  59  , including Pliocene orography, vegetation, ice sheets, lakes, and 400 ppm CO 2 concentration, along with modern orbital parameters, modern solar constant, and preindustrial CH 4 , N 2 O, and aerosol conditions.
Our simulations suggest that summer precipitation does not decrease to the same extent   20. c Zr/Rb from the Jiaxian red clay section  26. d Median grain size from the Xifeng section  20. e, f Fine (5-16 μm) and coarse (>20 μm) particle concentration from the Shilou section  56. Red smooth curves are fitted using the MatLab LOWESS function. g, h Cold-aridiphilous (CA) and thermo-humidiphilous (TH) terrestrial mollusk changes in the Dongwan red clay  57. everywhere across Central Asia from the late Miocene experiment to the Pliocene experiment (Fig. 6d-e ). Particularly, Central Asia has higher summer precipitation across the ~40-55°N latitudinal band (Fig. 6d ), but still becomes drier (Fig. 6e ) due to the large temperature increase and strong evaporation. In addition, warming-induced intensification of spiral air pumping tends to enhance anticyclonic deviation circulation gyres at high latitudes and facilitate high-latitude cold/dry air advection across Central Asia  60.
After XRF analysis, samples were used for colour reflectance measurements with a Minolta CM-508i Spectrophotometer. Results are expressed as the spherical L*a*b* colour space and reflectance intensity between 400 and 700 nm. L* describes the lightness between black (0) and white (100), while a* and b* denote red-green and yellow-blue chromaticity, respectively. All proxy measurements were carried out at the Institute of Earth Environment, Chinese Academy of Sciences, Xi'an, China. We used an automatic orbital tuning approach  88  and the Acycle software  89  to generate an astronomical time scale and to evaluate orbital signatures, respectively. Based on the most recent magnetostratigraphic data for the Shilou red clay section  4, 19, 30  , we first established an untuned magnetochronology through linear interpolation using geomagnetic polarity reversals for age control, assuming constant long-term sedimentation rates between reversals (Supplementary Fig.
3 ). We then conducted spectral analyses of the Rb/Sr record in the untuned magnetochronology, which suggest a continuous 100-kyr eccentricity band throughout the late Miocene-Middle Pliocene, with weaker and less continuous obliquity and precession cycles (Supplementary Fig. 4a ). Accordingly, we tuned large-amplitude 100-kyr Rb/Sr variations to the astronomical solution  31  to achieve cycle-to-cycle correlation within magnetochronological constraints. We filtered the 100-kyr component from the Rb/Sr record using a 90-125 kyr Gaussian bandpass filter with the Acycle software  89  , with 0. 009 cycle/kyr centre and 0. 001 cycle/kyr bandwidth. Ages for palaeomagnetic reversals were not kept fixed to optimise tuning results given uncertainties in palaeomagnetic boundaries (10-60 cm; Supplementary Table  1 ), post-depositional natural remanent magnetisation (NRM) lock-in depth in aeolian sediments  90, 91  , and GPTS ages  92.
10 ). This indicates that CESM 1. 0. 4 can reproduce present-day climate features  58  and is useful for simulating late Neogene Asian palaeoclimate  94, 95. Based on recent modelling of the global monsoon responses to Pliocene boundary conditions  95  , two other numerical experiments were performed in detail to evaluate climatic responses of monsoonal Southeastern Asia and arid Central Asia to CO 2 -induced global warming across the MPB. One scenario represents Pliocene conditions and was conducted with reconstructed Pliocene orography, ice sheets, vegetation, lakes, and a 400 ppm atmospheric CO 2 level from the PRISM4 (Pliocene Research, Interpretation and Synoptic Mapping version 4) dataset  59  , with modern orbital parameters (year 1950), modern solar constant (1365 W/m 2 ), and preindustrial CH 4 , N 2 O, and aerosol conditions.
Acknowledgements This study was supported financially by the  Chinese Academy of Sciences  ( CAS) Strategic Priority Research Program  ( XDB 40000000 ), the  Second Tibetan Plateau Scientific Expedition and Research (STEP) program  ( 2019QZKK0707 ), the  Chinese Academy of Sciences Key Research Program of Frontier Sciences  ( QYZDB-SSW-DQC021 ), the  National Natural Science Foundation of China , the  Ministry of Science and Technology of China, Australian Research Council (ARC) Australian Laureate Fellowship  grant  FL120100050  to  E. J. R. ,  ARC  grant  DP120103952  to A. P. R. , ERC consolidator grant  MAGIC 649081  to G. D. -N. , and  Heising-Simons Foundation  Grant # 2016-05  to C. J. P.
Data availability All of our measured proxy data presented here are attached in the Supplementary Dataset 1 and are also available in the East Asian Paleoenvironmental Science Database (http://paleodata. ieecas. cn/index. aspx, Data https://doi. org/10. 12262/ IEECAS. EAPSD2021004). The model simulation data used to support our dynamic interpretations are available at: https://zenodo. org/record/4964420.
Acknowledgments, Samples, and Data There are no real or perceived financial conflicts of interests for any author. All data supporting the conclusions can be obtained within the article and in the referenced published work, or in the Supplemental Material (Smith R. , 2021) found here: http://dx. doi. org/10. 17632/srm3v7sr76. 3. This work was funded through a  NASA Participating Scientist award  to SMM ( JPL Subcontract 1457128 ). A portion of this research was carried out at the  Jet Propulsion Laboratory, California Institute of Technology , under a contract with the  National Aeronautics and Space Administration  ( 80NM0018D0004 ). We thank  Shaunna Morrison  for providing assistance with CheMin interpretations,  Troy Rasbury  for assistance with statistics, and  Linda Kah ,  Steven Chemtob , and one anonymous reviewer for providing helpful comments that greatly improved a previous version of this manuscript.
Pathogenic variants of the aconitase 2 gene (ACO2) are responsible for a broad clinical spectrum involving optic nerve degeneration, ranging from isolated optic neuropathy with recessive or dominant inheritance, to complex neurodegenerative syndromes with recessive transmission. We created the first public locus-specific database (LSDB) dedicated to ACO2 within the "Global Variome shared LOVD" using exclusively the Human Phenotype Ontology (HPO), a standard vocabulary for describing phenotypic abnormalities. all the variants and clinical cases listed in the literature were incorporated into the database, from which we produced a dataset. We followed a rational and comprehensive approach based on the HPO thesaurus, demonstrating that ACO2 patients should not be classified separately between isolated and syndromic cases.
A recent review of the literature has listed a total of 26 individuals reported from 15 families with the syndromic form since 2012, while only seven individuals from four families have been reported with the recessive form of isolated optic neuropathy  6. More specific clinical presentations, possibly without optic neuropathy, have also extended the clinical spectrum, such as a form of hereditary spastic paraplegia reported in two siblings  7. The recent description by our team of 116 additional cases belonging to 94 families brings the total to over a hundred cases listed in the literature to date  5. The high frequency of dominant ACO2 mutations in our molecular diagnosis experience of optic neuropathies and the diversity of the neurological involvement led us to develop a reliable database dedicated to ACO2 as part of the Global Variome shared Leiden Open-source Variation Database (LOVD) installation  8, 9.
Indeed, we recently developed such a database to list the genetic variants and clinical presentations of OPA1-related disorders, the major cause of dominant optic neuropathies with either isolated (80%; MIM# 165500) or syndromic (20%; MIM# 125250) presentations, and with some rare biallelic cases affected by early severe syndromes (MIM# 605390)  10. The interoperability and the use of an international clinical thesaurus 11 should, therefore, make it possible to progressively improve understanding of how this increasing number of genes responsible for optic neuropathies contributes to the diversity of the pathophysiological mechanisms responsible for their common clinical phenotype. In this article, we describe the construction of this ACO2 dataset, listing all the patients referenced in the literature, and drawing statistical information on gene variants and clinical diversity.
Methods Nomenclature. All names, symbols, and OMIM numbers were checked for correspondence with current official names indicated by the Human Genome Organization (HUGO) Gene Nomenclature Committee  12  and the Online Mendelian Inheritance in Man database (OMIM)  13. The phenotype descriptions are based on the standardised Human Phenotype Ontology (HPO)  11  , indicating the HPO term name and identifier. ACO2 variants are described according to both the NCBI genomic reference sequence NG_032143. 1 and transcript reference sequence NM_001098. 2, including 18 exons encoding a protein of 780 amino acids reference sequence NP_001089. 1  14. The numbering of the nucleotides reflects that of the cDNA, with "+1" corresponding to the "A" of the ATG translation initiation codon in the reference sequence, according to which the initiation codon is codon 1, as recommended by the version 2.
Data collection. Since no locus-specific database (LSDB) dedicated to the ACO2 gene previously existed (http://www. hgvs. org/locus-specific-mutation-databases, accessed on January 12, 2021), this work was done from scratch. The causative variants were collected from the literature published to date (January 2021)  [3] [4] [5] [6] [7] [18] [19] [20] [21] [22] [23] [24]  using the NCBI PubMed search tool  25  with the keyword "ACO2, " and from the classifications of diagnostic laboratories in the Netherlands who recently decided to share them publicly (so-called the VKGL initiative)  26. The p sitions of variants in the reference transcript were determined according to the HGVS nomenclature version 2. 0  15. Correct naming at the nucleotide and amino acid levels were verified, and reestablished when necessary, using the Mutalyzer 2.
0. 32 Syntax Checker  27. Al  clinical descriptions of the dataset have been strictly and exhaustively translated using exclusively the HPO vocabulary, i. e. each phenotype mentioned having successfully matched an HPO term (or one of the synonyms associated with). integrity of the dataset. The work was coordinated by a single ophthalmologist to ensure consistency, with the help of our clinical and research team specializing in hereditary optic neuropathies, which performs monitoring of literature on the subject for several years. No data was rejected, the corresponding molecular biologist or ophthalmologist were contacted when clarification was required. Finally, the consistency and integrity of the entire dataset was validated by the curator of the database, who is a referent specialist, before the technical validation that followed.
(Please also refer to the section Author contributions. ) implementation of the dataset. The ACO2 dataset belongs to the Global Variome shared Leiden Open-source Variation Database (LOVD) currently running under LOVD v. 3. 0 Build 23  9  , following the guidelines for LSDBs  28  and hosted under the responsibility of the Global Variome/Human Variome Project  8. The dataset reviews clinical and molecular data from patients carrying ACO2 variants published in peer-reviewed literature, as well as unpublished contributions that are directly submitted.
Data classification. The criteria of pathogenicity, which depend upon the clinical context and molecular findings, are stated under the headings: "ClassClinical" for the classification of the variant based on standardised criteria, and "Affects function (as reported)" and "Affects function (by curator)" respectively for the pathogenicity reported by the submitter and concluded by the curator (Fig. 1d ). As several patients can be registered with different reported pathogenicity or new patients with existing variants added to the database, the status of the variants is reassessed on the basis of the data submitted and stated in the "SUMMARY record. ".
Dataset and analysis. Starting with version ACO2:200608 of the ACO2 LSDB (last updated on June 8, 2020), we produced a dataset. To carry out the statistical analysis, the HPO terms have been checked and prepared using the suite of R packages ontologyX  29  within R version 4. 0. 0  30  to read in the OBO file version hp/ releases/2020-06-08  11. Hierarchical clustering is performed using the hclust function from the R-Core package  30.
Data Records The ACO2 dataset is available on the Code Ocean cloud-based computational reproducibility platform as a Code Ocean "compute capsule  31  ": snapshot of the ACO2 dataset as of June 8, 2020, /data/LOVD_full_down-load_ACO2_2020-06-21_18. 03. 52. txt (LOVD flat file format); the Human Phenotype Ontology data-version hp/ releases/2020-06-08, /data/hp. obo. 2020-06-08. txt (OBO flat file format), which is also available in figshare  32. The updated dataset is accessible on the Global Variome shared LOVD server (https://www. lovd. nl/ACO2; or through the Mitochondrial Dynamics variation portal: https://mitodyn. org). The data can also be retrieved via an application programming interface (API), i. e. a web service allowing simple queries and retrieval of basic gene and variant information (documentation available on the web page of the database)  9  ; as well as serving as a public beacon in The Global Alliance for Genomics and Health Beacon Project  33.
General information is available on the database home page. The process for submitting data begins by clicking the "Submit" tab. The ACO2-LOVD dataset contains four main interconnected tables. These tables are visible on a typical web page entry as shown in Fig. 1. The "Individual" table contains details of the patient examined, including gender, geographic origin, and patient identification, if applicable (Fig. 1a ). The "Phenotype" table indicates the clinical phenotypic features, described according to the root of the Phenotypic abnormality subontology (HPO# HP:0000118; Fig. 1b ). The "Screening" table gives details of the methods and techniques used for investigating the structural variants and the tissue analysed (Fig. 1c ). The "Variants" table includes information about the sequence variations at the genomic (DNA) and the transcript variant (cDNA) levels, as well as the reported and concluded status for each variant (Fig.
1d ). To date, the dataset records 123 patient records and 126 unique variants (Online-only Table  1 ). Since the ACO2 dataset is built on the same central platform (to allow interoperability) and on the same model (for ease of handling) as our previous OPA1 gene database, the description of these data records extends our related work  10  , but the data recorded relates to a new gene and is entirely different. technical Validation Molecular relevance. The dataset contains 126 unique variants, of which 73% (92) are considered pathogenic sequence variants with almost two thirds in a dominant condition and the last third recessive, 7% (9) of unknown significance and 20% (  25 ) benign (Online-only Table  1 ). The variants considered pathogenic, which affect the coding sequence and exon-intron boundaries of the gene (Fig.
2c ). Although only a few mutations are recurrent, two have been significantly more frequently reported, both located in exon 3 (Fig. 2a ) with a recessive mode of inheritance: the c. 220C>G variant, which induces a missense mutation p. (Leu74Val), has been reported 19 times affecting one allele (of which 14 belongs to compound heterozygous genotypes); the c. 336C>G variant, which induces a missense mutation p. (Ser112Arg), has been reported 10 times affecting both alleles (homozygous). The Global Variome shared LOVD server has integrated the data from The Genome Aggregation Database (gnomAD), which is the aggregation of the high-quality exome (protein-coding region) DNA sequence data for about 150,000 individuals  35. It was decided to indicate for each variant its frequency in gnomAD rather than adding it as a new record, in order not to swamp the locus-specific databases (LSDBs) with data not associated to a phenotype.
This information is mainly used to assist the curator in estimating the relevance of the classification of variants. In total, only two of the unique variants in our dataset (less than 2%) have assigned a "likely pathogenic" status with a frequency slightly below 1% in gnomAD. Interestingly, one of these, the previously mentioned c. 220C>G variant, has a frequency from 0. 2 to 0. 4% (depending on versions of gnomAD) which does not allow its definitive classification as rare or frequent; it is registered in dbSNP (Build 153; dbSNP# rs141772938)  36  referring to ClinVar (record last updated May 9, 2020; ClinVar# VCV000189310. 8)  37 for an unclear interpretation ("Conflicting interpretations of pathogenicity"). This last-mentioned heterozygous variant has been reported 19 times by at least five independent sources in our dataset, of which 14 are compound heterozygous disease cases.
Clinical relevance. The dataset includes 123 patient records (71 males, 49 females, and three records of unspecified gender). Among these, 99 patients had isolated optic neuropathy  [4] [5] [6] 18  , 8 had infantile cerebellar-retinal degeneration (ICRD; MIM# 614559)  3  , 8 had various forms of encephalopathy (including epileptic encephalopathy   Rows are clustered using hclust by separating the terms descending from Abnormal eye physiology (HPO# HP:0012373) and Abnormal eye morphology (HPO# HP:0012372); human readable shortened ontological term names were used (where possible). In columns, the identifiers of the patients (8 digits) are prefixed by code of the disease reported (3 letters): ICD: degeneration, cerebellar-retinal, infantile; RDA; dystrophy, retinal, with or without extraocular anomalies; ENM: encephalomyopathy, mitochondrial; ENC: encephalopathy; ENE: encephalopathy, epileptic; ENS: encephalopathy, neonatal, severe; NDG: neurodegeneration; OPN: neuropathy, optic; SPG: paraplegia, spastic; SZR: seizures.
Usage Notes The databases recording pathogenic variations, i. e. the so-called LSDBs, have proven to be the most complete because they rely on a curator who is a referent specialist for the gene or disease considered  43. However, they are often based on isolated initiatives which use various interfaces, to the detriment of intuitiveness, and which are hosted on local servers, preventing their interoperability, unlike other types of databases which are central, i. e. encompassing all the genes of an organism, as in sequence databases  44, 45  or in databases oriented towards non-pathogenic variations  36, 46. With the aim to overcome this, the Human Variome Project currently favors the centralisation of LSDBs  8, 47. Our current objective with the work reported here is to achieve a cluster of LOVD databases integrating the main genes responsible for optic neuropathies, whether isolated or syndromic, and with recessive or dominant transmission.
Interoperability between these databases will be useful for molecular biologists analysing such panels of genes with, in particular, the possibility of detecting digenism. The ACO2-dataset, which is, to our knowledge, the only clinicobiological dataset dedicated to ACO2, interfaces molecular biology with medicine thanks to a common vocabulary, making it possible to link the phenotypic profiles of ACO2 patients with those involving mutations in other genes or clinical presentations. It will also allow a better understanding of the complex and overlapping relationships between isolated optic neuropathies and neurological syndromes involving optic neuropathy. Thus, this open-access dataset should prove useful for molecular biologists, researchers and clinicians.
Code availability The source codes written in R programming language are available on the Code Ocean cloud-based computational reproducibility platform as a Code Ocean "compute capsule, " together with the dataset analysed in this article  31  : snapshot of the ACO2 dataset as of June 8, 2020, /data/LOVD_full_download_ACO2_2020-06-21_18. 03. 52. txt (LOVD flat file format); the Human Phenotype Ontology data-version hp/releases/2020-06-08, / data/hp. obo. 2020-06-08. txt (OBO flat file format). Thus, readers can reproduce and verify the results of this article without having to download or install anything. All the content is available under MIT License.