dataesr/scientific-paragraphs-categorization huggingface dataset:
The 145 testing sites are representative of the full data set in terms of spatial coverage and statistical distribution of site parameters. In the companion paper, the training sites were used to derive correction spectra for c-HVSR, whereas the testing data set was earmarked for evaluating and comparing the performance of different site-specific approaches, including c-HVSR, 1D-based GRA, and SRI methods. Likewise, in this article, we develop parametric and non-parametric site-response models on the training data and then examine and compare their effectiveness at the testing sites (Figure  3 ). Site metadata at the 1725 stations are collected from an open-source site database developed by  Zhu et al. (2021c, see ''Data and Resources''). Considered predictor variables include topographic roughness (Roug.
Thus, surficial proxies are considered to be the easiest-to-obtain proxies and are often used in regional seismic hazard/risk mappings. Surficial proxies are followed by site data from on-site measurements, including V S30 , f p , Z 2. 5 , and microtremors (Figure  4 ). V S30 has been included in many seismic codes (e. g. Eurocode 8, 2004), and thus tremendous efforts have been devoted to deriving this parameter at many sites (e. g. Cultrera et al. , 2021; Di Giulio et al. , 2021). f p can also be reliably obtained from mHVSR (e. g. Site EffectS assessment using AMbient Excitations (SESAME), 2004). Z 2. 5 can be derived by querying regional 3D velocity models, though such velocity models only exist for a few regions. 1D site profiles can be established by invasive or non-invasive techniques.
, B). Each bootstrap sample Figure  6. RF regression. X and Y are column matrices that contain predictor and site-response data, respectively. Each row in X and Y corresponds to one site. Each column in X represents one predictor variable, and each column in Y corresponds to the site response at a given oscillator frequency. n, p, and f are the total numbers of sites, predictor variables, and oscillator frequencies considered in this study. B is the total number of bootstrapped samples (or trees), and p* is the number of predictor variables selected at random at each decision split. OOB is the out-of-bag data. b is established by randomly selecting n out of n observations with replacement (one observation may appear multiple times in one bootstrap sample), and observations not selected for that bootstrapped data set (on average 37%) are placed in a separate Out-of-Bag (OOB) data set.
Acknowledgments This work is partially supported by the project '' DARE: Dense ARray for seismic site effect Estimation' ' funded by the  French National Research Agency  (Grant #  ANR19-CE31-0029 ) and the  Deutsche Forschungsgemeinschaft (DFG ,  German Research Foundation , project number  431362334 ). The authors thank the  National Research Institute for Earth Science and Disaster Prevention (NIED), Japan  for making the strong-motion and PS logging data available. They also thank  Dr Sean K. Ahdi  and two anonymous reviewers who provided very constructive feedback on this work.
Data and resources Site parameters used in this study are obtained from an open-source site database (https://doi. org/ 10. 5880/GFZ. 2. 1. 2020. 006). Seismograms and KiK-net borehole logging data (V0 profiles) were downloaded from http://www. kyoshin. bosai. go. jp (profiles were accessed on February 05, 2018). J-SHIS is an integrated online platform developed by the National Research Institute for Earth Science and Disaster Prevention (NIED) for the Probabilistic Seismic Hazard Maps for Japan and various underlying data (https://www. j-shis. bosai. go. jp/map/JSHIS2/download. html?lang = en). The software Strata  (Kottke et al. , 2013)  is used for GRA. MATLAB is used to carry out least-squares and RF regressions. Supplemental material contains a table (Supplemental Table  S1 ), a word document, and a zip file.
Data analysis Data were narratively summarized. We initially kept the inclusion criteria of our systematic review as broad as possible to map the available evidence around inhaler regimen switching. We subsequently applied a prioritization strategy to focus on the most robust and homogeneous dataset available. Priority studies were subsequently defined as those that reported a real-world nonclinical inhaler regimen switch in asthma and/or COPD patients. The following studies were consequently deprioritized: studies that switched patients for a clinical need, such as poor control on their current inhaler; clinical studies without a real-world reason for switching (ie, a trial question); crossover studies (which were considered to represent a clinical rather than a real-world study design); modelling studies or data based on a theoretical rather than real-world switch; conference abstracts without an associated full publication (to prioritize peer-reviewed evidence); studies not reported in English (owing to uncertainty over the quality of the translated data); and reviews.
RESULTS The searches of the 4 electronic databases identified a total of 11,155 records. After the removal of 2,197 duplicates, 8,958 records were screened based on titles and abstracts. In total, 8,639 records were excluded, and 319 records were deemed potentially relevant; these were screened based on the full publication. A further 20 records were identified from conference proceedings, hand searching, reference checking, and the study commissioner. Out of these 339 records, 151 were excluded, and 167 were deprioritized to reflect the focus of the study on realworld evidence. Overall, a total of 21 priority studies were included in the review (Figure  1 ). The characteristics of the included studies are presented in Table  I. 5] [36] [37]  Two studies each were carried out in Canada,  38, 39  South Korea,  40, 41  or Sweden.
24, 29  Patients di  not consent to the inhaler regimen switch in 5 studies. 25, 31, 34, 36, 44  Three of these studies were surveys: 1 survey recruited participants who had experienced a substitution or alteration of a delivery device (and medication) without prior approval or knowledge  25  ; 1 survey recruited participants who had undergone a forced non-medical switch due to a formulary block on a Medicare plan  34  ; and 1 survey recruited participants who were switched by their HCP owing to a change in prescription practices at their attending institute. 44  In 1 chart review, patients who were judged to be clinically appropriate for a switch by a clinical pharmacist were subsequently discussed with their care provider, and a new prescription was written by that care provider. 36  In 1 database study, patients underwent a switch in inhaled corticosteroid device without an accompanying consultation on the same day, which was considered indicative of a non-consented switch.
31 our matched studies (with 5 datasets) (n ¼ 7,530) reported on risk domain control (a composite outcome that was generally defined as no hospital admission/attendance, no courses of OCS and no lower respiratory tract infections), and heterogeneous effects following switching were identified (Figure  2, B ). 29, 30, 32, 41 ll 4 studies had unclear consent for switching. 39, 41, 43  Reliever use was examined in 11 studies  [27] [28] [29] [30] [31] [32] 34, 37, [41] [42] [43]  (Table  E4 ; available in this article's Online Repository at www. jaci-inpractice. org). Six matched omparative studies (with 7 datasets) reported on SABA use. [29] [30] [31] [32] 41, 42  In 5 datasets (n ¼ 7,530), consent for switching was unclear, and a significant reduction in SABA use was reported following switching  29, 30, 32, 41  ; and in 2 datasets (n ¼ 2,574) with either non-consented or unclear consent for switching, a significant or numerical increase in SABA use was reported for switched versus non-switched patients.
This study reported a significant decrease in the proportion of asthma patients using SABA following switching versus a numerical increase in the proportion of COPD patients using SABA following switching. 43 xacerbations were reported in 10 studies 24,28-30,32,36,37,41-43 (Table  E5 ; available in this article's Online Repository at www. jaci-inpractice. org); and of these, 6 used a matched comparative design. 24, 29, 30, 32, 41, 42 Three of the 6 matched studies (with 5 datasets) (n ¼ 8,126) specifically used a composite measure of exacerbations that was based on a rate ratio (Figure  2, C ). 24, 30, 42 ne study (n ¼ 926) presented individual exacerbation rates per study arm and reported a significant increase in exacerbation rate for switched patients compared with non-switched patients.
24 All 3 studies had unclear consent for switching. 31] [32] 41  Overall, 2 datasets (n ¼ 3,564) reported NED in severe exacerbation rates  29, 32  and 2 datasets (n ¼ 2,008) reported a significant reduction in severe exacerbation rates. 32, 41 ther study types, including unmatched comparative or single-arm (pre-vs post-or post-only) cohort studies, reported heterogeneous directions of associations for exacerbations, including a significant increase, numerical increase (not statistically significant), NED, or a significant decrease. 28, 36, 37, 41, 43 ne single-arm (pre-vs post-switch) study reported differences in exacerbation rates trend for asthma versus COPD patients following switching. This study reported a significant decrease in the proportion of asthma patients experiencing exacerbations following switching versus NED for COPD patients.
43 Patients who were uncontrolled at the time of the switch were more likely to switch back to their previous inhaler than well-controlled patients. 42 Switched patients generally had increased or similar health care resource use compared with non-switched patients based on matched study datasets, including consultation visits, accident and emergency department attendance, and outpatient attendance. Changes in drug, general health care, or health care use costs showed heterogeneous directions of effect following switching. Most outcomes generally presented heterogeneous directions of associations, suggesting that the issue of inhaler regimen switching is a complex one. These differences likely arose from the heterogeneity in the definition of the switch, the population, and the consent status of the switch.
] 38, 41, 42  Only 3 of the 8 matched studies were rated at low risk of bias. 29, 31, 41 Some of these studies were unable to appropriately account for confounding factors. For example, 1 study reported that some clinical characteristics (such as the number of hospitalizations) remained significantly different between switched and non-switched patients at baseline following the matching process. 30 When the same study subsequently reported on incidence of hospitalizations as an outcome following switching, it was unclear whether this outcome changed as a result of the inhaler regimen switch or as a result of baseline differences. The main limitations of the included studies were difficulties in appropriately matching switched versus non-switched patient populations; the exclusion of non-adherent patients or patients who switched back to their previous inhaler from datasets; the restriction of inclusion criteria to either controlled or uncontrolled patients only; a lack of reported data on the motivation for the switch or whether the patient consented to the switch, particularly in database studies; short follow-up durations of 3 months or less; uncertainty regarding the duration of previous inhaled therapy prior to the switch; and a lack of baseline data, which precluded the assessment of the direction of change.
Embase (Ovid), MEDLINE (Ovid), EconLit, and EBM Reviews (including American College of Physicians [ACP] Journal Club, Cochrane Central Register of Controlled Trials [CENTRAL], Cochrane Clinical Answers, Cochrane Database of Systematic Reviews, Cochrane Methodology Register [to 2012], Database of Abstracts of Reviews of Effects [DARE] [to 2016], Health Technology Assessment [to 2016] and National Health Service [NHS] Economic Evaluation Database [NHS EED]) were searched to November 21, 2020. Search strategies are available in this article's Online Repository at www. jaci-inpractice. org. No search date, language, or study design limits were applied.
Acknowledgments The authors wish to thank  Stephen Mitchell, PhD ,  Catherine Mitchell, PhD ,  Shona H. Lang, PhD ,  Regina M. Leadley, BSc , and  Janine L. Ross ,  MSc, all from Mtech Access , for their help with this project. J. Bell  and  N. Stjepanovic  conceptualized the study and design, with important additional updates contributed by all authors. S. L. Swift, S. M. , C. M. , and J. L. R assessed eligibility of studies for inclusion. S. L. Swift, S. H. L. , and R. M. L. were involved in the acquisition of data for the article. All authors were involved in data interpretation. S. L. Swift wrote the first draft of the manuscript, and all authors critically revised the article for important intellectual content. All authors had full access to all the data in the study and had final responsibility for the decision to submit for publication.
The integration of genomic information has recently enhanced the capacity to point out extracts based on the potential of their phenotypic expression  (Caesar et al. , 2021). The introduction of Molecular Networking (MN) allowed visualization and interpretation of relatively large spectral/chemical spaces, easing the comparison of the extracts at the spectral level  (Wang et al. , 2016). MN can be combined with bioactivity test results and dereplication information to prioritize particular features [a peak with an m/z value at a given retention time (RT)] within an extract by novelty or biological activity potential  (Olivon et al. , 2017; Nothias et al. , 2018; Fox Ramos et al. , 2019; Wolfender et al. , 2019). Other published studies proposed mass-spectrometrybased workflows selecting extracts to accelerate the discovery of novel NPs, for example, utilizing liquid chromatography-mass spectrometry profiling and MS 1 level (exact mass and molecular formula match) annotation rates against databases of NPs.
, 2021). Finally, FERMO, a tool presently in development for the prioritization of relevant bioactive compounds (metabolites) within natural extracts based on chromatographic characteristics, bioactivity, and dereplication results. This tool aims to explore and suggest peaks of interest in a particular extract for isolation (Zdouc M. ,  Medema M. , van der Hooft J. , data not published). With the increasing capacities of the analytical profiling techniques, and the broad applications of bioinformatics tools in the field of NPs chemistry, the quantity of analytical information obtained increased proportionally. The clear and concise analysis of the resulting massive datasets is challenging and reduces the efficiency of data-driven prioritization  (Brejnrod et al. , 2019; Caesar et al. , 2021; Amara et al.
The PDA wavelength range was from 210 nm to 400 nm with a resolution of 1. 2 nm. Control of the instruments was done using Thermo Scientific Xcalibur 3. 1 software. For the centroid data-dependent MS 2 (dd-MS 2 ) experiments in positive ionization mode, full scans were acquired at a resolution of 35,000 FWHM (at m/z 200) and MS 2 scans at 17,500 FWHM in the range 100-1500 m/z. The dd-MS 2 scan acquisition events were performed in discovery mode with an isolation window of 1. 5 Da and stepped normalized collision energy (NCE) of 15, 30, and 45 units. Additional parameters were set as follows: default mass charge: 1; Automatic gain control (AGC) target 2E 5 ; Maximum IT: 119 ms; Loop count: 3; Min AGC target: 2. 6E 4 ; Intensity threshold: 1. Up to three dd-MS 2 scans (Top 3) were acquired for the most abundant ions per scan in MS 1 , using the Apex trigger mode (2-7 s), dynamic exclusion (9.
Data preprocessing The data were converted from. RAW (Thermo) standard data format to an open. mzXML format employing the MS Convert software, part of the ProteoWizard package  (Chambers et al. , 2012). The converted files were processed with the MZmine3 software  (Pluskal et al. , 2010). For mass detection at the MS 1 level, the noise level was set to 1. 0E 6 for positive mode and 1. 0E 5 for negative mode. For MS 2 detection, the noise level was set to 0. 00 for both ionization modes. The ADAP chromatogram builder parameters were set as follows: minimum group size in number of scans, 4; Group intensity threshold, 1. 0E 6 (1. 0E 5 negative); Minimum highest intensity, 1. 0E 6 (1. 0E 5 negative) and Scan to scan accuracy (m/z) of 0. 0020 or 10. 0 ppm. The ADAP feature resolver algorithm was used for chromatogram deconvolution with the following parameters: S/N threshold, 30; minimum feature height, 1.
MS/MS spectral organization A molecular network was constructed from the. mgf file exported from MZmine, using the feature-based molecular networking workflow (https://ccms-ucsd. github. io/ GNPSDocumentation/) on the GNPS website  (Nothias et al. , 2020). The precursor ion mass tolerance was set to 0. 02 Da with an MS/MS fragment ion tolerance of 0. 02 Da. A network was created where edges were filtered to have a cosine score above 0. 7 and more than six matched peaks. The spectra in the network were then searched against GNPS' spectral libraries. All matches between network and library spectra were required to have a score above 0. 6, and at least three matched peaks. Jobs links: https://gnps. ucsd. edu/ ProteoSAFe/status. jsp?task=df71854c6e644b979228d96b521a490b (positive), https://gnps.
Taxonomically informed metabolite annotation The. mgf file exported from MZmine was also annotated by spectral matching against an in-silico database to obtain putative annotations  (Allard et al. , 2016). The resulting annotations were subjected to taxonomically informed metabolite scoring  (Rutz et al. , 2019)  (https://taxonomicallyinformedannotation. github. io/tima-r/, v 2. 4. 0) and re-ranking from the chemotaxonomical information available on LOTUS  (Rutz et al. , 2022). The in-silico database used for this process includes the combined records of the Dictionary of Natural Products (DNP, v 30. 2) and the LOTUS Initiative outputs  (Rutz et al. , 2022).
SIRIUS metabolite annotation The SIRIUS. mgf file exported from MZmine (using the SIRIUS export module) that contains MS 1 and MS 2 information was processed with SIRIUS (v 5. 5. 5) command-line tools on a Linux server  (Dührkop et al. , 2019). The molecular formula and metabolite database used for SIRIUS includes NPs from LOTUS  (Rutz et al. , 2022)  and the Dictionary of Natural Products (DNP). The parameters were set as follows: Possible ionizations: [M + H] + , [M + NH 4 ] + , [M-H 2 O + H] + , [M + K] + , [M + Na] + ,[M-4H2O + H] + ; Instrument profile: Orbitrap; mass accuracy: 5 ppm for MS 1 and 7 ppm for MS 2 , database for molecular formulas and structures:BIO and custom databases (LOTUS, DNP), maximum m/z to compute: 1000. ZODIAC was used to improve molecular formula prediction using a threshold filter of 0.
Mass spectrometry-based extract vectorization (MEMO) The MS 2 spectra were processed with the memo_ms package (0. 1. 3). The parameters were set as follows: min_rel_intensity: 0. 01, max_relative_intensity: 1, min_peaks_required: 10, losses_from: 10, losses_to: 00, n_decimal: 2. All the Peak/loss present in the blanks were removed before the computation of the distance matrix  (Gaudry et al. , 2022).
Implementation of Inventa All the previously described information was fed into a set of scripts called Inventa (https://luigiquiros. github. io/inventa/v1. 0. 0). These scripts are made available as a Jupyter notebook that can be deployed directly on the cloud using a Binder link  (Jupyter et al. , 2018). All the components were calculated, and the same weight (w = 1) was given to each. For the cleaning-up of the GNPS annotations the following parameters were used, max_ ppm_error: 5, shared_peaks: 10, min_cosine: 0. 6, ionisation_ mode: 'pos', max_spec_charge: 2. For calculation of the feature component the following parameters were used, min_specificity: 0. 9, min_score_final: 0. 3, min_ZODIACScore: 0. 9, min_ ConfidenceScore: 0. 25, annotation_preference: 0. For the literature component calculations the max_comp_reported_sp, max_comp_reported_g, max_comp_reported_f were set to 20, 100, 500 respectively.
Electronic circular dichroism calculations The absolute configuration of all compounds was assigned according to the comparison of the calculated and experimental ECD. Based on their relative configuration proposed by NMR 2D ROESY experiments, the structures were employed for the random conformational search using MMFF94s force field by Spartan Student v7 (Wavefunction, Irvine, CA, United States). From the results, the 20 isomers with lower energy were subjected to further successive PM3 and B3LYP/6-31G(d,p) optimizations in Gaussian 16 software ( © 2015-2022, Gaussian Inc. , Wallingford, CT, United States) using the CPCM model in acetonitrile  (Nugroho and Morita, 2014; Mándi and Kurtán, 2019). All optimized conformers in each step were checked to avoid imaginary frequencies. After a cut-off of 4 kcal/mol in energy, conformers were submitted to Gaussian16 software for ECD calculations, using TD-DFT B3LYP/def2svp as a basis set with the CPCM model in acetonitrile.
The conception of the priority score Inventa focuses on the discovery of novel NPs in a series of extracts by giving a rank of prioritization for the extracts before being subject to phytochemical studies. Additional information on potentially putative new compounds within such extract is available for precise localization of the features of interest for targeted isolation. Inventa takes a Feature Based Molecular Network (FBMN) job as minimum input. This workflow is preferred over the classical MN since it incorporates mass spectrometry (MS 1 and MS 2 ) and semi quantitative chromatographic information (retention time, intensity/ area) specific for each feature  (Nothias et al. , 2020). FBMN was considered since it became a widely used workflow for data comparison, spectral space visualization, and automated annotation against experimental databases.
From these results, Inventa will use as input the feature table, the annotation results, and the taxonomic information of the extracts. The specificity for each feature will be assigned according to the aligned feature table (generated initially by MZmine). Other software can be used, if compatible with GNPS, the user can recover the table from the MN results. Their annotation status is based on the GNPS annotation results. To guarantee a minimum quality of the putative identities, the GNPS annotations are automatically cleaned and filtered (cosine, error in ppm, number of shared peaks, polarity, etc. ) before the calculations (https://github. com/lfnothias/gnps_postprocessing). Additional feature dereplications results using in-silico databases and reponderation strategies to improve the putative annotation  (Allard et al.
Regarding this calculation, it is essential to recall that the reported chemistry is not specified to a plant-organ level in the databases. Thus, no partspecific relation can be constructed relative to the tissue involved. For example, a specific plant part extract could have a high FC due to a specific profile with no annotation and a bad LC score because the taxon presents a high number of the reported compounds, not necessarily in the same organ. Reports in the genus and family are considered for prioritizing a particular lack of annotation but belonging to an extensive phytochemically studied genus or family. The Class Component (CC, Figure  1C ) indicates if an unreported chemical class is detected in each extract compared to those reported in the species and the genus. A CC value of 1 implies that the chemical class is new to both the species (CCs 0.