dataesr/scientific-paragraphs-categorization huggingface dataset:
Data availability Taxonomic data for reptiles were from the Reptile Database (http:// www. reptile-database. org/index. html). All spatial and tabular data for the tetrapod analyses are permanently available at https://transfer. natureserve. org/download/Publications/Global_Reptiles/. Trees used for the phylogenetic diversity analyses are available at Zenodo (https:// Article zenodo. org/record/5974891). In addition, assessment data, including range maps, for all tetrapods are publicly available on the IUCN Red List of Threatened Species website (https://iucnredlist. org). Occasionally, where a species may be threatened because of over-collection, sensitive distribution information is not publicly available. Protected areas boundaries were from the World Database of Protected Areas (https:// www.
Statistical analysis Appropriate statistical analysis was performed with the use of IBM SPSS software. Descriptive statistics was performed to characterize study groups. Continuous data were tested for normality using the Kolmogorov-Smirnoff test and graphical methods (histogram, Q-Q plot). The Student's t-test was used to assess differences between groups for normally distributed variables, and the Mann-Whitney U test for skewed variables. For categorical data, the  2 test was used, or Fisher's exact test where data were not suitable for  2 testing. ANOVA or the Kruskal-Wallis test were used to test for differences in continuous variables among more than 2 groups, according to the variables' distribution. Correlations among continuous variables were analyzed using Spearman's or Pearson's test, according to the variables' distribution.
Furthermore, to be certified as training centers for PGHN by the German-speaking Society of PGHN, they must provide treatment and follow up for at least 25 pediatric patients with GI motility disorders per year and seek recertification every 2 years; the certified PGHN training centers (n=36) are shown on the Society's website  [24]. In addition, this focused on only one part of the assessment of children with neurogastroenterological disorders, while a wider multidisciplinary team, including psychologists/psychiatrists, nutritionists/dietitians, histopathologists and a pediatric/surgical team, is required for their proper management; the availability of such personnel was not studied. Notwithstanding the above limitations, this first collaborative work of the ESPGHAN National Societies Network provides the largest dataset on the infrastructure, staff, procedural volumes and training programs in PNGM across Europe.
Remote sensing Mapping of debris-covered glaciers received considerable attention in the late 2000s and early 2010s, with important improvements in monitoring capacity as satellite imagery improved in both spatial and temporal resolution and coverage. The release of the Randolph Glacier Inventory (RGI;  Pfeffer et al. 2014; RGI_Consortium 2017)  and subsequently the global supraglacial debris cover datasets constructed on the basis of the RGI  (Scherler et al. 2018; Herreid and Pellicciotti 2020 ) enabled a step-change in understanding the distribution of debris-covered glaciers at the large scale. However, while these global databases provide an initial and global perspective of glacier and supraglacial debris extent, they suffer from several limitations. The RGI's composition from distinct sources means that both datasets suffer from inconsistent methods between and often within regions, varying representative dates, user-subjective post-processing and manual delineation (e.
g. Paul et al. 2013) , geolocation or projection errors, and occasional inclusion of spatially descriptive but not explicit sources (e. g. World Glacier Inventory). These problems were partially mitigated in a manual revision limited to glaciers larger than 1 km 2  (Herreid and Pellicciotti 2020 ), but such an effort is laborious for repeat application at the global scale. Consequently, although debriscovered glaciers may be accurately represented in available databases for well-known sites that have been mapped carefully, their representation may be inconsistent and may occasionally commit and/or omit entire features for areas that are less well surveyed  (Racoviteanu et al. 2021). Thus, while acknowledging that the mapping of supraglacial debris within the bounds of the RGI (e. g. Scherler et al.
2014; Kraaijenbrink et al. 2018)  or energybalance inversion, often requiring model spin-up  (Mihalcea et al. 2008; Zhang et al. 2011; Foster et al. 2012; Rounce and McKinney 2014; Schauwecker et al. 2015; Rounce et al. 2021; Stewart et al. 2021 ). An intercomparison of these methods is needed and has been identified as a research target for the IACS Debris-covered Glaciers Working Group (https://cryosphericsciences. org/activities/ wgdebris/). o Elevation change/surface mass balance: since the thickness of debris moderates energy transfer to the ice, it also controls ice melt rates  (Østrem 1959; Nicholson and Benn 2012). Surface mass balance data can thus be used to invert an energy mass balance model for debris thickness  (Ragettli et al. 2015; Rounce et al. 2018) , although this often requires careful consideration of ice dynamics to estimate surface mass balance from elevation change, and the longduration melt modelling is computationally expensive  (Rounce et al.
However, the spatial resolution of these data (120-240 m) remains an issue, as the data have limited application for monitoring narrow debris-covered glacier tongues. Recent databases with improved spatial resolution (50 m)  (Millan et al. 2019 ) offer promise for monitoring of debris-covered surfaces, but this is limited by cloud cover. As for the debris thickness estimates, SAR can provide high-accuracy measurements of the direction and intensity of glacier flow in all weathers  (Kumar et al. 2011)  provided that corrections are applied to mitigate attitude effects and sensor distortions  (Scherler et al. 2008).
2017; King et al. 2020a ), but also important for understanding the diurnal and seasonal evolution of glacial discharge  (Fyffe et al. 2019a ). As supraglacial streams exist only where stream incision exceeds the background ablation rate  (Marston 1983) , they by definition directly contribute to melt; they also contribute indirectly to melt by promoting ice cliff development  (Mölg et al. 2020; Kneib et al. 2021). Streams can be mapped using hydrologic analysis tools on high-precision, high-resolution topographic datasets derived from satellite stereo or UAV images  (Benn et al. 2017; Miles et al. 2017b; Fyffe et al. 2020). Other efforts have mapped streams manually from satellite images or by walking their length in the field  (Miles et al. 2019). Mapping supraglacial streams with DEM drainage analysis and optical imagery is similarly challenged by apparent stream discontinuities due to ice arches and flow through debris.
• The development and testing of a standardized, integrated lake hazard ranking scheme remains a challenge. This requires better parameterization of key GLOF processes in the glacier lake system, and the ability to capture the multi-phase GLOF process cascade. In order to address these questions and to complete the picture of processes associated with the debris-cover glacier system, improved datasets are still needed, i. e. meteorological data from weather stations installed at high altitudes (e. g. Matthews et al. 2020) , monitoring of the rates and controls on rockwall debris supply, gauging of water and sediment amounts discharged within turbid glacier streams (e. g. Heckmann et al. 2016), spatially distributed measurements of ice thickness from new technologies such as airborne ice-sounding radar suited to debris-covered glaciers (e.
Acknowledgements We thank the  Geological Society  for their support in hosting the 'Debris-covered Glaciers and Related Lakes: Understanding the Challenges' workshop held in London in September 2019. We thank all the speakers in the workshop:  T. Bolch ,  J. Carrivick ,  E. Miles ,  D. Quincey ,  J. Reynolds ,  M. Westoby  and  S. Watson  for their insights during the workshop discussions, and all workshop participants for their efforts and enthusiasm. A. E. Racoviteanu et al. Downloaded from https://www. lyellcollection. orgby Aberystwyth University on Aug 23, 2022.
Purpose Patients receiving treatment for acromegaly often experience significant associated comorbidities for which they are prescribed additional medications. We aimed to determine the real-world prevalence of comorbidities and concomitant medications in patients with acromegaly, and to investigate the association between frequency of comorbidities and number of concomitantly prescribed medications. Methods Administrative claims data were obtained from the IBM® MarketScan® database for a cohort of patients with acromegaly, identified by relevant diagnosis codes and acromegaly treatments, and a matched control cohort of patients without acromegaly from January 2010 through April 2020. Comorbidities were identified based on relevant claims and assessed for both cohorts. Results Overall, 1175 patients with acromegaly and 5875 matched patients without acromegaly were included.
Furthermore, different routes of medication administration are associated with respective advantages and disadvantages related to ease of administration, medication absorption, and reactions with other medications  [28, 29]. Taking all these factors into account, it is important for physicians to consider the quantity and form of medications a patient is already receiving when prescribing additional treatments. Real-world evidence on the frequency of comorbidities and the associated prevalence of prescribed concomitant medications in patients with acromegaly is limited. Accordingly, we determined the prevalence of comorbidities and concomitant medications in a real-world population of patients with acromegaly, as well as in a control cohort of patients without acromegaly, using administrative claims data from the United States (US).
Study design This analysis presents results from a real-world, retrospective cohort study of administrative claims data obtained from the IBM® MarketScan® claims database in the US from January 2010 through April 2020. The database is compliant with the US Health Insurance Portability and Accountability Act of 1996 (HIPAA), with all patient-level data deidentified. MarketScan covers resource use and cost data for inpatient and outpatient services, with claims from patients with multiple insurance types throughout the US. Information is derived from medical claims linked to inpatient treatment, outpatient prescription drug claims, and person-level enrollment data. Claims associated with medical diagnoses and treatment prescriptions were extracted for eligible patients. Comorbidities included in the analysis were selected by referring to published consensus statements and in consultation with the co-authors to reflect acromegaly-related comorbidities seen in clinical practice  [15].
Patients A cohort of patients with acromegaly and a control cohort of patients without acromegaly were extracted from the Mar-ketScan database. Patients were eligible for inclusion in the acromegaly cohort if they had: at least two claims associated with any ICD, 9th Revision, Clinical Modification (ICD-9-CM) or ICD, 10th Revision, Clinical Modification (ICD-10-CM) diagnosis codes for acromegaly (E22. 0 or 253. 0), with more than 30 days between the first and second claim; received at least one treatment for acromegaly with specific treatments including lanreotide depot, octreotide long-acting release (LAR), pasireotide, cabergoline, bromocriptine, and pegvisomant; and had at least 3 months of claims data before the earlier date of either the claim for first diagnosis or the claim for first treatment.
Inclusion criteria were selected to ensure that patients had a definite diagnosis of acromegaly, and that the data captured the correct treatment start date, to avoid data bias. As one of the main focuses of this analysis was the use of concomitant medications in patients who were actively receiving medical therapy to treat acromegaly, patients who were not receiving medical therapy (such as those in remission due to surgery and/or radiation) were not included. All patients who met the eligibility criteria for acromegaly using data from MarketScan were included in the study. Patients were eligible for inclusion in the control cohort if they had no medical claims related to acromegaly, although it was possible for patients in the control cohort to have records for other underlying conditions.
A random sample (10%) of patients in the MarketScan database with no medical claims for any ICD-9-CM or ICD-10-CM diagnosis codes for acromegaly (E22. 0 or 253. 0) within the study period were selected as the initial pool of patients in the control cohort. Direct matching 1:5 (patients with acromegaly to patients without acromegaly) was performed based on age and sex. The observation period (first record date to last record date) of each patient in the control cohort had to include the study period (from the first treatment date to the last record date) of the matched patient with acromegaly. The index date (the date on which the patient fulfilled the eligibility criteria) of each patient in the control cohort was the same as the index date of their matched patient with acromegaly, and in both cohorts, patients had at least 3 months of data prior to the index date.
Statistical analysis Demographic characteristics were analyzed using descriptive statistics, with continuous variables presented as the mean and standard deviation (SD), and categorical or discrete variables presented as percentages. Python language was used for data preparation and statistical analyses  [31]. Chi-squared tests were used for inter-cohort proportional comparisons and mean CCI score was compared between the acromegaly and control cohorts using an unpaired t-test. An unpaired t-test was also used to compare the average count of different active medication ingredients between the acromegaly and control cohorts. Medications containing multiple active ingredients were analyzed by individual ingredient. Spearman correlation coefficients and p-values were calculated between the number of different concomitant medications and the number of different comorbidities.
0%)  [32]. Similarly, a large study using the Liege Acromegaly Survey Database reported high rates of metabolic disorders (such as diabetes), cardiovascular disorders, and thyroid disorders in patients with acromegaly  [33]. As expected, each acromegaly-related comorbidity observed in our study was experienced by significantly more patients with acromegaly compared with the control cohort. For instance, the proportion of patients with cerebrovascular disorders (within the cardiovascular disorders category) in the acromegaly cohort was nearly twice that of the matched control cohort. Additionally, the significantly higher CCI score for patients with acromegaly calculated in our analysis demonstrates a broad comorbidity presence. While the CCI has not been validated for acromegaly specifically, it provides a valuable, quantified measurement for comparison between the acromegaly and control cohorts.
For instance, in cases where patients are already receiving a complex regimen of oral medications that requires fasting, it may be preferable to prescribe additional medications in injectable form wherever possible, while an oral or SC route of administration for additional medications may be preferred for patients treated with anticoagulants. Clinical trials in acromegaly reported similar findings to this real-world analysis. In the PRIMARYS (NCT00690898) and LEAD (NCT00701363) open-label trials investigating lanreotide depot, patients with acromegaly generally experienced a high prevalence of comorbidities, and most patients were prescribed one or more concomitant medications  [49]. While results from clinical trials are generally robust and well-controlled, data derived from claims databases cover more diverse patient populations and may provide a more accurate representation of patients in the real-world and their clinical management.
Strengths To our knowledge, this study is the first to clearly define comorbidities associated with acromegaly compared with a control population without acromegaly, and to investigate a wide range of associated concomitant medications in a real-world setting. Previous studies have assessed concomitant medication use in patients with acromegaly, generally focusing on specific classes of medications  [50]. The claims database used in this study is large, containing thousands of patients with acromegaly and up-to-date results spanning approximately the last decade. The availability of data for patients without acromegaly allowed for a matched control cohort for comparison with the acromegaly cohort. Finally, 1:5 matching reduced the standard error of the outcome variables in the control group, therefore strengthening the power of the between-group comparisons.
Limitations This study analyzed data solely from the US; as such, generalization to other populations is limited. Since the results were obtained from an administrative claims database, missing data or coding errors were encountered, and it was not possible to verify actual rates of medication adherence. In addition, laboratory data to determine biochemical control of acromegaly, such as GH and IGF-1 levels, were not available. It should be noted that concomitant medication prescriptions were analyzed throughout the course of each patient's study period. While this method ensured that all prescriptions were counted, patients may have received different prescriptions at different times, and therefore may not have been taking all recorded medications at the same time. Furthermore, as this analysis sought to explore concomitant medication use in patients receiving medical therapy for acromegaly, patients in remission due to surgery or radiotherapy would not have been included.
Acknowledgements The authors acknowledge  Aude Houchard, MPH , from  Ipsen , for statistical advice,  Kathryn Munoz, PhD , for contributions to study design, analysis, and interpretation of the data, and  Isabel Haber , BS, and  Oliver Palmer, BSc  (Hons), from  Costello Medical , for medical writing and editorial assistance based on the authors' input and direction on the detailed outline and manuscript. This study was funded by  Ipsen.
Human Ago4 is a functionally relevant genetic target of LegK1 in infected macrophages We next analysed whether LegK1 genetically targets human Ago functions to promote L. pneumophila virulence. Using available RNA-seq datasets  86  , we first noticed that human Ago2 and Ago4 mRNAs were the most abundant Ago transcripts in THP-1 cells (Figure  5e ). This analysis supports recent findings showing that Ago4 is highly expressed in immune-related cells  56. Consistently, we detected a higher Ago4 protein accumulation in THP-1 cells compared to HEK293T and A549 epithelial cells, which was also detected for Ago2, albeit to a lesser extent (Figure  5f ). Based on these data, we decided to knock-out human Ago2 or Ago4 in THP-1 cells for further functional assays. A single guide RNA approach was used to generate CRISPR/Cas9-based deletions in the second exon of the Ago2 and in the third exon of the Ago4 loci.
PRM data analyses The PRM data were analysed using Skyline 4. 1 (MacCoss Lab Software, Seattle, Washington [https://skyline. ms/project/home/software/Skyline/begin. view]). Fragment ions for each targeted mass (Table  S3 ) were extracted and peak areas were integrated. The peptide areas were log2 transformed and the mean log2-area was normalised by the mean area of five Ago2 peptides (ELLIQFYK, SGNIPAGTTVDTK, SIEEQQKPLTDSQR, VELEVTLPGEGK and VLQPPSILYGGR) using software R 3. 1. 0. On each phospho-peptide, a linear model was used to estimate the mean fold change between the conditions, its 95% confidence interval and the p-value of the twosided associated t-test. The p-values were adjusted with the Benjamini-Hochberg procedure  108. Data are available via ProteomeXchange with identifier PXD037279 (Username: reviewer_pxd037279@ebi.
Prediction of W-motifs W-motifs were predicted using the Wsearch algorithm  70  , which scores the W-motifs according to a Position-Specific Scoring Matrice (PSSM) derived from experimentally validated Ago-binding motifs from animals. Briefly, the protein sequences of all bacterial organisms tested were retrieved from Uniprot (UniProt Consortium, 2018) and subjected to Wsearch either on the website or using the python script. The score of the different positive motifs were then added together, corresponding to the final score. For the first pre-selection, we applied an arbitrary cut-off greater than six on the average W-scores of all motifs present in the candidate protein sequence. Among the bacterial candidate proteins exhibiting the highest W-score, we further selected known secreted virulence factors or putative virulence factors predicted to be secreted using PSORTb 3.
Alignment of orthologous protein sequences and phylogeny The protein sequence of LegK1 (from the lpp1439 gene) from L. pneumophila strain Paris was used as a reference to retrieve homologous LegK1 protein sequences from Legionella species (taxid: 445). To this end, a BLASTP (Basic Local Alignment Search Tool) was performed on the NCBI (National Center for Biotechnology Information) website. An identity cut-off of 40%, an Expect (E)-value cut-off of 10 -5 and a minimum percentage match length of subject and query of 65% were used. The set of homologous protein sequences were then aligned using ClustalW2 on Geneious 10. 2. 6 software. Tree was conducted using Neighbor-joining.
D84G10, Cell Signaling Technology, 5053), anti-Ago2 (C34C6, Cell Signaling Technology, 2897), anti-Ago3 (4B1-F6, Active Motif, 39787), anti-Ago4 (D10F10, Cell Signaling Technology, 6913), anti-DDX6 (Bethyl, A300-460A), anti-Dicer (Cell Signaling Technology, 3363), anti-Flag (M2, Sigma-Aldrich, F1804), anti-GAPDH (14C10, Cell Signaling Technology, 2118), anti-GFP (D5. 1, Cell Signaling Technology, 2956), anti-PABPC1 (Atlas Antibodies, HPA045423), anti-phospho-IκBα (Ser32) (14D4, Cell Signaling Technology, 2859), anti-TNRC6A (Bethyl, A302-329A) and antia-Tubulin (DM1A, Cell Signaling Technology, 3873). Both antibodies were diluted 1000×. As secondary antibodies, anti-mouse (Cell Signaling Technology, 7076S) or anti-rabbit (Cell Signaling Technology, 7074S) were used and proteins were detected with Agrisera ECL SuperBright (Agrisera, AS16 ECL-S-100) using a LAS 4000 mini (GE Healthcare).
Acknowledgments We thank  Dr. Alice Lebreton ,  Dr. Julie Guignot ,  Dr. Benoit Garin ,  Dr. Lauriane Quenee ,  Dr. Clément Carré ,  Pr. Gunter Meister ,  Dr. Hervé Le Hir  for providing the Staphylococcus aureus SH1000 genomic DNA, Pseudomonas aeruginosa PAO1 strain and Escherichia coli O157:H7 gDNA, Bordetella pertussis Tohama gDNA, Yersinia pestis gDNA, AutomiG reporter, a vector expressing T6B, pET22-6His-SUMO and pET28-CBP-6His, respectively. We thank  Dr. Sophie Goudey  for the generation of KO HeLa cell lines. We also thank all members of the  Navarro lab  for their inputs, discussions and critical reading of the manuscript, as well as  Dr. Alice Lebreton ,  Dr. Michael Mourez , and  Miss Isabelle Barbosa  for helpful advices and discussions. This work was supported by the  European Research council  ( 281749 ,  Silencing & Immunity , to L.
Data availability All materials generated in this study are available upon request. Mass spectrometry data are available via ProteomeXchange with identifier PXD037279. Sequencing data corresponding to the mutant DlegK1 have been submitted to Zenodo (https://zenodo. org/record/7190772). All data reported in this paper will be shared by the lead contact upon request. Correspondence and requests for materials should be addressed to Lionel Navarro (lionel. navarro@ens. psl. eu).