dataesr/scientific-paragraphs-categorization huggingface dataset:
The gene-based association statistics, coexpression matrix and gene Z scores per pathway or HPO term are then combined in a generalized least-squares regression model to obtain enrichment statistics  24. Enrichment analyses were performed for reactome, gene ontology and HPO terms using multi-tissue or brain-specific transcriptome datasets to calculate the coexpression matrix. The distribution of enrichment Z-score statistics was compared between analyses using multi-tissue or brain-specific coexpression matrices. Using the 'pyhpo' module in Python, all HPO terms were assigned to their parent term(s) in the 'phenotypic abnormality' (HP:0000118) branch, which includes phenotypic abnormalities grouped per organ system. Mendelian randomization. Causal inference through MR analysis was performed for 22 exposures for which large-scale GWASs are available and for which there is prior evidence for an association with ALS.
Acknowledgements and relevant funding details are provided in the Supplementary Note. author contributions Sample ascertainment: W. v. R. , R. A. A. v. d. S. , M. M. , A. M. D. ,  H. -J. Westeneng , G. H. P. T. , N. T. , J. C. -K. , B. N. S. ,  M. Gromicho ,  S. Chandran ,  S. Pal , K. E. M. , P. J. S. , J. H. , R. W. O. , M. S. , T. M. , N. B. , A. J. v. d. K. ,  A. Ratti ,  C. Gellera ,  G. Lauria , G. P. C. , C. C. , D. S. , S. D. ' A. ,  G. Sorarù ,  G. Siciliano , M. F. , A. P. ,  A. Chiò ,  A. Calvo ,  C. Moglia ,  M. Brunetti ,  A. Canosa ,  M. Grassano , E. B. , E. P. , G. Logroscino, B. N. , A. O. , A. N. , Y. L. ,  M. Zabari ,  M. Gotkine ,  R. H. Baloh , S. B. , P. V. ,  P. Corcia ,  P. Couratier ,  S. Millecamps , V. M. , F. S. , J. S. M. P. ,  A. Assialioui , R. R. -G. , P. A.
Code availability The following software packages were used for data analyses: R version 3. 6. 3 with additional packages tidyverse version 1. 3. 0, data. table version 1. 14. 0, ggplot2 version 3. 3. 3, MASS version 7. 3. 53, SNPRelate version 1. 26. 0, logistf version 1. 24, coloc version 5. 1. 0, twoSampleMR version 0. 5. 6, RadialMR version 1. 0, MVMR version 0. 3, survival version 3. 1. 8, coxme version 2. 2. 16 and survminer version 0. 4. 9 (https://www. r-project. org/), Python version 3. 7 with additional modules pandas version 1. 1. 3, numpy version 1. 18. 1, scipy version 1. 4. 1, CpGtools version 1. 0. 9, matplotlib version 3. 1. 3, pyliftover version 0. 4 and pyhpo version 2. 5. 0 (https://anaconda. org/), GenomeStudio version 2. 0 (https://emea. illumina. com/techniques/microarrays/array-data-analysis-experimental-design/ genomestudio.
html), GCTA version 1. 93. 2beta (https://cnsgenomics. com/ software/gcta/#Overview), EIGENSOFT version 6. 1. 4 (https://github. com/ DreichLab/EIG), SNPTEST version 2. 5. 4-beta3 (https://www. well. ox. ac. uk/~gav/ snptest/), PLINK version 1. 9 (http://www. cog-genomics. org/plink2), the Michigan Imputation Server (https://imputationserver. sph. umich. edu), EAGLE version 2. 3 through the Michigan Imputation Server (https://imputationserver. sph. umich. edu), SAIGE version 0. 29. 1 (https://github. com/weizhouUMICH/ SAIGE), METAL 2011-03-25 (https://genome. sph. umich. edu/wiki/METAL), SnpSift 4. 3p (https://pcingola. github. io/SnpEff), ANNOVAR version 2017-07-17 for LRT, Polyphen-2, MutationTaster2, Mutation Assessor, PROVEAN and SIFT (https://annovar. openbioinformatics. org/), Polyphen-2 (http://genetics.
bwh. harvard. edu/pph2/), MutationTaster2 (http://www. mutationtaster. org/), Mutation Assessor release 3 (http://mutationassessor. org/r3/), PROVEAN version 1. 1 (http://provean. jcvi. org/index. php), SIFT version 6. 2. 1 (https://sift. bii. a-star. edu. sg/), SnpEff 4. 3p (https://pcingola. github. io/SnpEff), LDSC version 1. 0. 1 (https://github. com/bulik/ldsc), ExpansionHunter version 4 (https://github. com/ Illumina/ExpansionHunter), ExpansionHunter Denovo (https://github. com/ Illumina/ExpansionHunterDenovo), SMR (https://cnsgenomics. com/software/ smr/), MAGMA version 1. 6 (https://ctg. cncr. nl/software/magma), FUMA (https://fuma. ctglab. nl/), FUMA Cell-type (https://fuma. ctglab. nl/celltype), summary-BayesR (https://cnsgenomics. com/software/gctb/#SummaryBayesianAlp habet), S-PrediXcan (https://github.
Second, our sample set was small. Future studies with a larger number and variety of tumoral tissues would allow us to generate more digitalized samples that in turn would improve the automatic segmentation process. A larger dataset would also allow us to better evaluate whether blood capillaries and bile canaliculus-like structures, which are common features in HB and more generally in epithelial liver tumours, influence the polarity and spatial organization of tumour cells and their organelles. These techniques coupled with current omic approaches would provide much insight into the relationship between the structural organization of tumours, and their cellular functions and metabolic activities. Future possible directions of onconanotomy-based exploratory studies are the following: a) analysing virtually all types of solid tumours to generate a virtual 3D biorepository whose data would be particularly valuable in the case of rare tumours or precious samples such as biopsies; b) comparing matched tumour samples such as changes occurring before and after treatment or primary versus metastatic features, and c) identifying structural subtypes and verifying their overlap with different histological groups such as embryonal versus foetal versus small undifferentiated cell subtypes described in HB  6, 14.
Since it is adapted to lab tumour models like cell xenografts, tumour-like spheroids and organoids, our combined approach could help investigators to clarify the role of genes and molecular pathways involved in the architecture of tumoral tissue and to investigate the response of tumour cells to drugs (e. g. checkpoint inhibitors, monoclonal antibodies) and treatments (e. g. radiotherapy). In conclusion, our study aims to foster the development of what we call onconanotomy, a novel research field in oncology, that will complement other innovative imaging technologies such as two-photon excitation microscopy (also known as 2PEF) and correlative light-electron microscopy (also known as CLEM). This field could be backed up by the development of opensource databanks gathering all 3D imaging data at a worldwide level in order to boost research and promote cooperation in cancer, as was previously the case with Gene databanks and Cell and Tissue atlases.
By accessing 3D image databanks, investigators could upload valuable information and perform comparative investigations to unravel the ultrastructure of solid tumours. The potential focuses include cell-cell and cell-extracellular matrix interactions, intratumoral heterogeneity, pro-metastatic mechanisms, angiogenesis, drug response, therapeutic efficacy and regulatory factor gradation. We believe that, in few decades from now and thanks to digital approaches and artificial intelligence, the emerging field of onconanotomy could profoundly change the face of medical oncology with the support of other technologies such as single-cell RNA-seq, microfluidics and organoids. It might especially provide groundbreaking insights into the predisposition of cells to disseminate and into the permeability of tissues to drugs.
Infographics Blender version 2. 90. 1 (https://www. blender. org) was used to create the illustrations (images and Supplementary videos). The Cycles render engine was used for all the final renderings. To render polygonal meshes, the data were imported from VTK files using the BVTK addon (https://github. com/tkeskita/BVtkNodes). Depending on the data complexity, the meshes were decimated to lower the polygon count, but still preserving the overall shape and most of the details. For volumetric rendering, the original stack data was converted from a multi-page Tiff image file to a downscaled 2D tile map, which was then loaded and translated to a 3D volume into Blender using a custom shader setup. ImageJ (https://imagej. nih. gov/ij/), XNview (https://www. xnview. com) and the "montage" command line tool of ImageMagick (https://imagemagick.
Acknowledgements This work was supported by the charity  Eva pour la Vie, La Fondation ARC pour la Recherche sur le Cancer  (contract  N° PJA 20191209631 ),  La Région Nouvelle-Aquitaine ,  La Fondation Groupama pour la Santé and Groupama Centre-Atlantique. Microscopy Imaging was performed at the  Bordeaux Imaging Centre , which is a member of the  FranceBioImaging national infrastructure  ( ANR-10-INBS-04 ). Computational analysis was carried out using the PlaFRIM experimental testbed, supported by  Inria ,  CNRS  (LABRI and IMB),  Université de Bordeaux, Bordeaux INP  and  Conseil Régional d'Aquitaine  (see https://www. plafrim. fr/). Computer time for this study was provided by the computing facilities at  MCIA (Mésocentre de Calcul Intensif Aquitain) of the Université de Bordeaux  and of the  Université de Pau et des Pays de l'Adour.
Here-in order to evaluate a long-term impact-the positive forest expectation value (FEV) (capital value of bare land plus timber at year y) was considered as index of economic profitability. FEV corresponds to the present value of cashflows arising from both the land and the tree, in perpetuity  40. Input data to run r. green. biomassfor in Italy were derived from  19  and are provided in Tables  S1  and S2. The complete procedure was developed on raster basis with a resolution of 1 ha (squared pixel of 100 × 100 m). Wood losses due to O 3 were computed taking into account the reduction of forest increment. In the present work, two scenarios were developed to quantify FEV and-as (6)  f SWC = min 1, max f min , SWC -WP FC -WP Table  5. Forest-type parameterization of DO3SE model according to  6.
Genomic Prediction Models We start the simulation with Nref lines (757 in our example) that have been genotyped and virtually "phenotyped" for one quantitative trait, twenty times to avoid any bias in QTL positions. For GPS, the first vector of marker effects  ̂0 is calculated using a ridge regression on those phenotypes. The database of phenotypes is incremented at each phenotyping step and the vector of marker effects  ̂ is updated at each cycle k. ̂= (X ′ X + λI) -1 ′, with y the vector of phenotypes, X the matrix of the 12,019 markers after removing the 100 markers sampled to simulate QTLs, λ chosen to make X'X non-singular, using the R package rrBLUP. We evaluated the accuracy of genomic predictions using the correlation between TBV and GEBV of lines at the third step. At the first step of each cycle, NC crosses are produced between the NP best individuals according to phenotyping results at step 4 of previous cycle (or Nref individuals for the first cycle).
Selection is based on phenotypic data with a rate of selection α3. In GPS scheme, there is no trial, the selection is based on GEBVs calculated as the cross product between the vector of marker effects and the matrix of genotypes of progenies, excluding markers to which QTLs were assigned. Only N4 (N3α3) progeny are multiplied in nursery. At step 4, N4 progeny are evaluated in five trials (different environments, e. g. , different locations). 𝑁𝑁 4 = 𝑁𝑁 3 𝛼𝛼 3 (5) The last step is a phenotypic selection of NP parents for the next cycle in both PS and GPS schemes with a selection rate α4. 𝑁𝑁 𝑝𝑝 = 𝑁𝑁 4 𝛼𝛼 4 (6) Instead of defining α3 and α4 independently, we fixed a relative selection rate (called λ) between steps 4 and 3 as follow: α3=α34 λ (7) and α4=α34 1-λ , (8) with: 𝛼𝛼 34 = 𝛼𝛼 3 𝛼𝛼 4 = 𝑁𝑁 𝑃𝑃 𝑁𝑁 2 𝛼𝛼 2 , ( 9 ) If λ = 0.
type bread wheat lines developed by the Institut National de la Recherche en Agriculture, Alimentation et Environnement (INRAE, formerly INRA) and its subsidiary company Agri-Obtentions. These lines were selected between 2000 and 2013. Each line was genotyped using a 280K SNP array [51]. The datasets with the genotyping data are available in the INRAE Dataverse repository (https://data. inrae. fr/dataset. xhtml?persistentId=doi:10. 15454/M8SAYH).
FUNDING Doctoral work of SBS was funded by a grant from the  Auvergne Rhônes-Alpes region  and from the  European Regional Development Fund (FEDER). This work was supported by the  Breed  wheat project thanks to the funding from the  French Government  managed by the  National Research Agency (ANR)  in the framework of Investments for the  Future  ( ANR-10-BTBR-03 ) France AgriMer and the  French Fund  to support Plant Breeding (FSOV).
For illustrative purposes, we then selected 1 meta-analysis per domain based on the following criteria: number of citations, journal impact, and comprehensiveness of the meta-analysis. https://doi. org/10. 1371/journal. pcbi. 1009477. t002 methods have been recently applied to high-dimensional data sets. Given the complexity of processes and mechanisms underpinning most psychiatric conditions, it is likely that they cannot be captured by 1 biomarker. Moreover, independent single features may have small effect size, yet a group of such features considered in a multivariate fashion might effectively have a "high effect size"-the so-called Lewontin's fallacy  [41]. Hence, multiple scores may be combined through predictive pattern-learning algorithms to identify subtypes  [42] [43] [44]  (for review, see  [45] ).
g. , interquartile ranges, SD from the median) in order to facilitate the comparison of results across different data distributions. • Always provide confidence intervals irrespective of sample size so that readers can judge whether the estimate is sufficiently precise for their purposes. If the data set is small, be aware of limited "precision" of effect size estimates. • Preanalytic validation: We recommend carrying out test-retest reliability for any new measures or measures for which such information is not available. While no universally acceptable values are available, several textbooks recommend r > 0. 70. Test variability (and potentially known moderating factors, e. g. , fatigue, time of day) should be included in the interpretation of scores. Standardisation of instructions, standard operating procedures (SOPs), acquisition, and preprocessing parameters are essential to ensure comparability between researchers, clinicians, and laboratories.
Measures should be optimised and key dependent variables specified. • Clinical validation and/or validation against another level of explanation: Relate single measure to clinical outcome, define cutoff for clinical relevance accordingly. • Increase scientific knowledge by facilitating replication, script, and data sharing: Seek to replicate findings of percentages and relationship to external variables in an independent data set. • Actively deposit your data set in open data repositories (e. g. , Zenodo, Open Science Framework) and share your task paradigm and scripts with other colleagues to enable data pooling and replication.
Translating means and SDs into nonparametric equivalents to assess the fre- quency and severity of atypicalities in nonnormal distributions. packages were used to generate all numerical simulations. Scripts that reproduce the results of the present study are readily accessible and open for reuse (https://gist. github. com/deep-introspection/4280aeee34a0f1ab4491a386adcd5dad/). We generated 2 populations with varying sample sizes per group (20 and 100). (DOCX) S2 Text.
No costs within the database for Singapore -or for other Southeast Asian countries -were exclusively associated with proactive management, highlighting that a shortage of reporting on the costs of invasions is mirrored by a lack of investment in management. Moreover, invasion cost entries in Singapore were under-reported relative to import levels, but total costs exceeded expectations, based on land area and population size, and to a greater extent than in other Southeast Asian countries. Therefore, the evaluation and reporting of economic costs of invasions need to be improved in this region to provide efficient databased support for mitigation and management of their impacts. Penaklukan spesis di Singapura dan Asia Tenggara: jurang data gagal untuk menutup kos ekonomi yang berpotensi besar).
Aucun coût dans la base de données pour Singapour -ou pour d'autres pays d'Asie du Sud-Est -n'était exclusivement associé à une gestion proactive, ce qui souligne qu'un manque de rapports sur les coûts des invasions se traduit par un manque d'investissement dans la gestion. De plus, les entrées de coûts d'invasion à Singapour ont été sous-déclarées par rapport aux niveaux d'importation, mais les coûts totaux ont dépassé les attentes fondées sur la superficie des terres et la taille de la population, et dans une plus grande mesure que dans d'autres pays d'Asie du Sud-Est. Par conséquent, l'évaluation et la communication des coûts économiques des invasions doivent être améliorées dans cette région pour fournir un soutien efficace basé sur des données pour l'atténuation et la gestion de leurs impacts.
Singapore is a highly urbanised and densely populated, but biodiverse, tropical island city state, centrally located within Southeast Asia  (Tan et al. 2010; Ng et al. 2011; World Bank 2019). The few publications reporting costs of invasive species in Singapore have suggested they might be important  (Nghiem et al. 2013 ), yet costs have lacked synthesis. At least 142 non-native animal species have been reported in Singapore  (Yeo and Chia 2010) , including species listed on several 'worst invasive alien species' lists (e. g. IUCN). Recently, the available literature on economic costs of invasive species globally was compiled in the InvaCost database  (Diagne et al. 2020b)  with the aim of generating the means to fill knowledge gaps on invasion costs worldwide. Using data available from this database, we synthesised and described the available information on economic costs of invasions in Southeast Asia, focusing on Singapore in particular.
Data acquisition Information on the economic cost of invasions in all the Southeast Asian countries (Brunei, Cambodia, East Timor, Indonesia, Laos, Malaysia, Myanmar, Philippine, Singapore, Thailand and Vietnam) was extracted from the InvaCost database  (Diagne et al. 2020b; Angulo et al. 2021)  concerning the global costs of invasive species, based on published literature, enabling comprehensive quantification of costs associated with invasive species at various spatio-temporal scales. The latest version of the database, as well as a summary of the whole procedure used to build and update it, can be directly accessed at https://doi. org/10. 6084/m9. figshare. 12668570. Briefly, the data in Inva-Cost were collected following (i) a series of literature searches using the Web of Science platform (https://webofknowledge.
com/), Google Scholar database (https://scholar. google. com/) and the Google search engine (https://www. google. com/) and (ii) targeted searches through contacting experts and stakeholders to request potentially unpublished and/or publicly unavailable documents containing cost information. All the retrieved costs were standardised in an up-to-date currency (2017 USD), while also taking into account an inflation factor  (Diagne et al. 2020b ). We performed descriptive analyses of a subset of this database, by filtering data ('Official_country' column) to exclusively ascertain invasion costs in each country.
Cost calculation and description We considered the total costs of invasions by amalgamating the recorded raw costs (column 'Cost_estimate_per_year_2017_USD_exchange_rate') per year from our subset. Due to the variability of temporal scales of cost estimates in InvaCost, we annualised the data, based on the difference between the "Probable_starting_year_adjusted" and "Probable_starting_year_adjusted" columns using the "summarizeCosts" function of the 'invacost' package (v. 0. 3-4) in R (v. 4. 0. 2)  (Leroy et al. 2020). Each expanded entry thus corresponded to a single year for which costs were available following this expansion process (i. e. costs spanning multiple years were divided amongst those same years). The resulting costs attributed to recorded species were examined according to different descriptive fields of the database (an updated description of these descriptive fields is openly available at https://doi.
Impacted_sector: the activity, societal or market sector that was impacted by the cost (Suppl. material 2); note that individual cost entries not allocated to a single sector were classified under "Mixed" in the "Impacted_sector" column. A detailed summary of all descriptors can be found in Suppl. material 1 (see also  Diagne et al. 2020b ) and the final dataset in Suppl. material 2.
Temporal dynamics and cost extrapolations To investigate the temporal dynamics of invasion costs, we used the "summarizeCosts" function implemented in the R package 'invacost'  (Leroy et al. 2020). With this method, we calculated the observed cumulative and average annual costs covering the period for which costs were recorded, displaying the changes in invasion costs over time. As cost information for invasive alien species in Singapore, which we used as an example, was limited (three species; see Results for more details), we also extrapolated potential costs for a few additional known invasive species present in Singapore, but which had recorded costs outside Singapore. For this, we used the most recent comprehensive list of alien animal species in Singapore (n = 142;  Yeo and Chia 2010).
With this information, we first estimated the mean annual cost of the species listed in  Yeo and Chia (2010)  outside Singapore (at the "country" scale) that was available in InvaCost, assuming the InvaCost database contained recorded cost information for Singapore over the same period. We then applied a correction factor that considers the cost difference between the average costs of all invasive alien species in Singapore and the average costs of all invasive alien species outside Singapore (excluding extreme val-ues, i. e. the upper and lower 12. 5% when implementing the correction factor to cost data). The corrected mean cost of each of these species was then summed to obtain an additional cost of biological invasions not directly available from records in Singapore.
We focused on the 50 countries ranking highest in import value, but with recorded data in InvaCost. Further, we collected the data of species that have been introduced in all countries in Southeast Asia (see Results for more details) from the Global Alien Species First Records Database  (Seebens et al. 2018; accessed in June 2020). Finally, we examined the relationships between invasion costs (observed and high reliability costs only) and (i) land area and (ii) human population size using linear regressions (log-transformed) and examined how Singapore compared to other countries globally and in Southeast Asia particularly. Land area and population size per country were obtained using 2020 data from worldometer (https://www. worldometers. info/ world-population/population-by-country/).
5%), costs and expenditure in Singapore were around three times lower than those in the rest of the world. From the 142 species recorded in  Yeo and Chia (2010) , only an additional 11 were recorded in the InvaCost database (Suppl. material 4). Applying the average annual monetary cost discrepancy as a correcting factor to the average annual costs of the 11 invasive species, using the InvaCost data from outside Singapore, resulted in an additional projected annual average cost of US$ 22. 33 million per year and a total of US$ 893. 13 million additional costs considering the period 1975-2015.