dataesr/scientific-paragraphs-categorization huggingface dataset:
Field sampling In this study, we analyzed faecal microbiota, which has been shown to be a good proxy for avian GM  (Videvall et al. , 2018; Berlow et al. , 2020) , with sample collection and storage as described in  Kropáčková et al. (2017). Faecal samples of temperate passerines (405 samples from 52 species), were obtained during the 2014 breeding season (April-July) at various sampling sites in the Czechia (Supplementary Table  1  and Supplementary Figure  1 ) and were previously presented in  Kropáčková et al. (2017)   1  and Supplementary Figure  1 ). Tropical samples were collected during both the rainy (September 2014 at 950 and 1 100 meters above mean sea level) and dry seasons (November/December 2014 at 650 and 2,280 meters above mean sea level), the latter corresponding to the breeding season for most tropical passerines included in our dataset.
Migration behavior, diet and host phylogeny Temperate species were categorized as trans-Saharan migrants with wintering grounds in tropical sub-Saharan Africa (n = 148 samples and 19 species; Supplementary Figure  1 ) or residents/shortdistance migrants (n = 257 samples and 33 species; Supplementary Figure  1 ) that do not fly as far as Sahara during their migration  (Cepák et al. , 2008). Most African species are sedentary or seasonal short-distance migrants and, while actual data on migration routes are mostly missing, it is unlikely that these species migrate over distances of more than 500 km  (Fry et al. , 1982). Data on diet for each species was extracted from the EltonTraits 1. 0 database  (Wilman et al. , 2014)  and classified on a continuous scale ranging from full herbivory/granivory to full insectivory/carnivory.
Bioinformatic processing of 16S rRNA data Sample demultiplexing and detection and trimming of genespecific primers were undertaken using Skewer  (Jiang et al. , 2014). Reads of low quality, i. e. , those with an expected error rate per paired-end read >2, were then eliminated. Dada2  (Callahan et al. , 2016)  was used for denoising of quality-filtered reads and subsequent quantification of 16S rRNA amplicon sequence variants (hereafter ASVs) in each sample. Chimeric ASVs were detected and eliminated using UCHIME  (Edgar et al. , 2011)  and gold. fna, a chimera-free reference database. 2 Using the Decontam package  (Davis et al. , 2018) , we identified and subsequently eliminated 69 putatively contaminating ASVs whose prevalence was increased in blank isolates compared to GM samples and/or showed greater representation in samples with a low concentration of metagenomic DNA (as assessed based on concentration of PCR products).
Furthermore, we excluded ASVs assigned as "Chloroplast" (18. 1% of reads after quality filtering), "Mitochondria" (7. 2% of reads after quality filtering), or those not assigned to any bacterial phylum (<0. 01% of reads after quality filtering) from all downstream analyses. Subsequently, we clustered all remaining ASVs at the 97% similarity threshold using vsearch  (Rognes et al. , 2016)  and assigned taxonomy of representative sequences for each of the 3,281 resulting operational taxonomic units (OTUs) using RDP classifier (>0. 5 posterior confidence;  Wang et al. , 2007)  and the Silva reference database  (v. 138; Quast et al. , 2013). Representative OTU sequences were further aligned using DECIPHER  (Wright, 2020) , the maximum likelihood tree being constructed using FastTree  (Price et al.
Specifically, for each pair of host categories (e. g. , dry vs. rainy season tropical hosts), we extracted two vectors of GM dissimilarity corresponding either to different host species from the same category or to different host species from different categories. Dissimilarities for same host species pairs were not considered. Next, we calculated the mean difference between these two vectors and corresponding 95% bootstrap-based confidence intervals using the two. boot() function in the R package simpleboot (n = 1,000 resampling steps;  Peng, 2019). In parallel, we analyzed the effect of climatic zone, migration and diet on GM alpha diversity and composition using generalized linear mixed models (GLMM), with phylogenetic correlations fitted using the R package phyloglmm  (Li and Bolker, 2019).
At the same time, tropical and temperate environments had no effect on GM diversity of the two species (GLM with Gaussian distribution: p > 0. 5 in both cases; Supplementary Figure  8 ). The negative binomial models implemented in the DESeq2 package  (Love et al. , 2014) , which account for variation between host species, showed that the relative abundances of 16 OTUs varied between the breeding and wintering grounds (Figure  5 ), with the most prominent changes detected in the case of Serratia and Staphylococcus OTUs.
Our comparative dataset uncovered substantial changes in passerine GM composition and alpha diversity between rainy and dry seasons within the same geographic location, with the changes being of a comparable, or even more pronounced effect size as GM variation between temperate and tropical host populations separated by >5,000 km. This suggests that actual environmental conditions at a given locality have a decisive effect on passerine GM. At the proximate level, the observed changes could be linked with a range of different mechanisms, such as environmental sources of GM bacteria being affected by seasonal variation in abiotic conditions. The diet consumed is also likely to vary between the dry and rainy seasons, and thus could contribute to the observed difference in GM. Last but not least, physiological and behavioral aspects associated with reproduction could also play some role in seasonal GM changes as reproduction of most tropical passerines in our study area mainly takes place during the dry season  (Serle, 1981; Tye, 1992; Fotso, 1996).
Moreover, a Staphylococcus OTU, which contributed most to GM compositional change between wintering and breeding grounds, was also more abundant in tropical species during the dry season than during the rainy season. Altogether, our data suggest that environment is the main driver of GM variation between wintering and breeding grounds in trans-Saharan migrant species. Consistent with our results, several previous studies have reported variation in GM composition between breeding and wintering grounds, or during spring and autumn migrations in other migratory birds  (Lewis et al. , 2016a,b; Wu et al. , 2018; Zhang et al. , 2020). However, none of these previous studies took the opportunity to contrast corresponding GM profiles with an extensive comparative dataset that included other species residing at the breeding and wintering grounds.
Method of analysis The FG corpus was transcribed manually by the first author. To process the data, we relied on the method of thematic analysis  [21]  which consisted of capturing all of the themes of the data collected during the FG to identify the central themes that answered the research question. This method was composed of the following steps: i) coding/thematization; ii) categorization; iii) linking of the data; iv) presentation of the data; and v) verification of the data. This method made it possible to understand and interpret the data while remaining faithful to what the participants said. The reading of the FG transcript and its analysis were carried out by an advanced practice nurse and a psychiatric physician in a blinded fashion. Neither of these two individuals had been involved in the facilitation of this program under review, but they had previous experience in facilitating it.
Ethical considerations Each participant was given an oral presentation of the study design, and signed informed consent was obtained. The protocol was validated by the ethics committee (Groupe Nantais d'Ethique dans le Domaine de la Santé, GNEDS) on 23 July 2020. To avoid the identification of any participant, the data collected in this study were processed in an entirely anonymous manner. Anonymity was ensured by a numerical code accessible only by the researchers.
Another disadvantage of manual classification of USVs is its dependency on the operator, so that the same person needs to analyse the entire dataset, to avoid biases linked to variations in the subjective interpretation of USVs' characteristics. However, the manual classification of USVs still allows for the most highly detailed characterization. Hence, it is not surprising that several automatic classification systems have been created, including the MUPET system for mice  (Van Segbroeck et al. , 2017) , the WAAVES system for rats  (Reno et al. , 2013) , and DeepSqueak for various animal calls  (Coffey et al. , 2019). All these tools can automatically separate audio inputs into calls and background noise. They then use a classification algorithm to label the obtained vocalizations. WAAVES, for instance, employs a binary classification with two categories: frequency modulated calls and flat calls.
MUPET and DeepSqueak, on the other hand, execute unsupervised clustering with a greater number of categories. MUPET can only cluster into a known a priori number of classes that must be determined by the user, whereas DeepSqueak can automatically discover the best-fitting number of clusters using statistical approaches. Furthermore, MUPET cannot apply its models to datasets other than those for which they were originally developed for, thus requiring separate models for each dataset. On the basis of a well-known pre-established set of call types, the USV classification pattern published by  Scattoni, Gandhy, et al. (2008) , we have developed an ad hoc method for automatic USVs classification  (Premoli, Baggi, et al. , 2021). We began our classification by using an audio recording that had already been segmented into vocalizations and noise, with a dataset of 48,699 labelled segments.
Our method is based on a top-down approach whereas the other methods are based on a bottom-up one  (Ivanenko et al. , 2020; Leonardi & Migliorati, 2002). We designed and employed two deep learning methods, a Convolutional Neural Network (CNN) and a Multilayer Perceptron (MP), as well as two machine learning techniques, that is, Support Vector Machines (SVM) and Random Forests (RF), in order to achieve an automatic classification task. The dataset was manually created using Avisoft software, which includes capabilities for visualizing and manually segmenting audio tracks in the form of spectrograms. The obtained USVs were then processed to extract some informative features required for training the learning algorithms. The results demonstrated that utilizing the entire time/frequency information of the spectrogram results in a better performance than taking into account only a subset of numerical features.
ACKNOWLEDGEMENTS SP received funding from  Bordeaux University, CNRS ,  Association Autour de Williams and Fondation pour l'Audition  ( FPA-RD-2020-8 ). MW is supported by grants from the  Deutsche Forschungsgemeinschaft (DFG; German Research Council ; e. g. , MW  1732/4-2 ), the  Fonds Wetenschappelijk Onderzoek -Vlaanderen (FWO; Research Foundation -Flanders ; e. g. ,  G0C0522N  and  G0E6722N ) and a start-up grant at  KU Leuven  ( PXF-E0120-STG/20/062 ). NS is supported by intramural funds from the  University of Cagliari. SAB is supported by grants from the  University of Brescia. Open Access Funding provided by  Universita degli Studi di Brescia  within the  CRUI-CARE Agreement.
Additionally, these levels could also be explained by the chronic use of filgrastim causing an increased hematopoietic activity of the bone marrow and an extramedullary hematopoiesis  [12, 14]. For Melis and collaborators, patients showed a high frequency of autoimmunity (five out of eight were affected): autoimmune thyroiditis, myasthenia gravis, inflammatory bowel disease, and rheumatoid arthritis  [12]. The new oral antidiabetic drug Empagliflozin, an SGLT2 inhibitor or Gliflozins, shows great promise in improving outcomes for patients with GSD1b but is still considered experimental. Investigating inhibitors of 1,5-anhydroglucitol(1,5-AG) reuptake, reducing dietary 1,5-AG content, or exploring its intestinal production represent potential avenues for further research  [15]. According to the Catalogue for Transmission Genetics in Arabs (CTGA) database (https:// cags.
As a tool for the implementation of AIRWAYS-ICPs, MASK-air ® (Mobile Airways Sentinel NetworK for airway diseases) is an mHealth app. It provides direct patient data and offers new insights into AR phenotypes and management in a patient-centred approach in order to facilitate shared decision making. 22 MASK-air ® is a Good Practice of DG Santé for digitally-enabled, patient-centred care pathways. 23 It helps to address certain unmet needs. MASK-air has been reported in the JRC Scientific and Policy Reports on Strategic Intelligence Monitor on Personal Health Systems Phase 3 (SIMPHS3). 24 This publication is a Science and Policy Report by the Joint Research Centre, the European Commission's in-house science service. MASKair ® is one example of the WHO-ITU (International Telecommunication Union) 'Be He@lthy, Be Mobile' handbook on how to implement mBreatheFreely for asthma and COPD.
| mHEALTH APPS IN RHINITIS Few apps addressing AR patients have been evaluated. This has made their selection difficult. We have introduced a new approach to market research for AR apps based on the automatic screening of the Apple App and Google Play stores. 26 A JavaScript programme has been devised for automatic app screening and applied in a market assessment of allergic rhinitis self-management apps. We searched the Google Play and Apple App stores of three countries (USA, UK, Australia) with the following search terms: hay fever, hayfever, asthma, rhinitis, allergic rhinitis. Apps were eligible if symptoms were evaluated. Three apps could be used in 2021, according to criteria required for the study, and two for the purposes of the Combined Symptom-Medication Score (CSMS) (AllergyMonitor  27, 28  and MASK-air ® ).
| Characteristics and geographical distribution MASK, the Phase 3 ARIA initiative, was developed from the MASKair ® app to a flexible e-platform for allergic diseases and asthma. It is operational in 29 countries and 19 languages (Figure  2 ). Over 58,000 users have been registered. MASK includes: (i) a freely available app (MASK-air ® , formerly the Allergy Diary, free on Android and iOS),  29  (ii) tools to support healthcare professionals in shared decision making through an interoperable electronic decision support system (e-CDSS),  30  (iii) a web-based interoperable questionnaire for physicians,  31  (iv) a questionnaire on asthma and rhinitis (CARAT: Control of Allergic Rhinitis and Asthma Test) for screening allergic diseases and assessing their control  32, 33  and (v) a sentinel network for air quality (air pollution) and pollen seasons.
| Visual analogue scales Even though there is an independence of data in MASK-air, all PROMs are highly correlated (Figure  3 ). 40, 58, 59, 61, 62, 67 It is unlikely that the results can be explained by a low quality of data arising from repeated VAS measures. 48 PROMs (VAS global, nose, eye, work, sleep and asthma  41  ) can be used in clinical trials, observational studies and clinical practice. PROMs for asthma have also been evaluated. The same questionnaire will be used 9  Allergy Specific [WPAI-AS]), (ii) quality-of-life (EQ-5D VAS) and (iii) control of allergic diseases (CARAT). 67 e following scores were defined and tested (Table  5 ) and then validated in different countries. There was a very high reproducibility of CSMSs in the 9 countries where there were enough data to provide statistical analyses (Figure  4 ).
| Strengths � The MASK-air ® app is available in 29 countries (20 languages) and is inter-operable with a web-based physician's questionnaire  82  and an e-CDSS for AR. 30  � It currently (September 2022) includes over 58,000 users and around 600,000 days (Figure  5 ). � An analysis of the intervention against a range of criteria, including effectiveness and efficiency. � A range of policy options to help policy-makers enhance the performance of the intervention. � An assessment of how transferable the intervention is to other Member States. � The rhinitis assessment is nearing completion in over 20,000 users (current paper) and the asthma assessment has been initiated in over 8000 users. � In asthma, all categories of patients are included and the database can be used to compare asthmatics of different severity grade.
| Discovery of the 'extreme allergy-asthma phenotype' Multimorbidity in allergic airway diseases was well known, but no data existed on the daily dynamics of symptoms and their impact on work. A cross-sectional observational study was performed on 4210 users, 32,585 days and 19 countries. 39 Five VASs assessed the daily burden of the disease (global, nose, eyes, asthma and work). VAS levels <20/100 were categorised as 'Low' burden and VAS levels ≥50/100 as 'High' burden. Eight hypothesis-driven patterns were defined. A novel Rhinitis High-Asthma High-Conjunctivitis High pattern was identified in 2. 9% of days. They had the greatest impact on uncontrolled VAS global measured and impaired work productivity (Figure  6 ). Limitations of the study: Overall limitations.
| SLIT is more effective than SCIT The reported control of AR symptoms (VAS global allergy symptoms), work (VAS work) and CSMS was studied in users receiving sublingual AIT (SLIT) or subcutaneous AIT (SCIT), and in those with no AIT. The MASK-air ® data of European users with self-reported AR and grass pollen allergy were studied. Bayesian mixed-effects models-with clustering by patient, country and pollen season-were analysed (Figure  13 ). 94.
| AIT impact on educational activities in young people differs from pharmacotherapy Several studies have suggested an impact of AR on academic productivity. However, large studies with direct patient data are not available. We assessed variables measuring the impact of allergies on academic performance (VAS school, WPAI + CIQ:AS impact of allergy symptoms on academic performance and percentage of hours of school lost due to allergies). Additionally, factors associated with the impact of allergic symptoms on academic productivity were assessed using multivariable mixed models. AIT showed a strong negative association with VAS school (Table  7 ). On the other hand, a worse rhinitis control (CSMS) was associated with worse VAS school, higher impact on academic productivity and the percentage of hours of school missed due to allergy.
� Evidence-based analysis including meta-analyses. � Integration of direct patient data including MASK-air ®. � GRADE Evidence to Decision (EtD) frameworks. 121   11. 9 | Change management and political agenda for digitally-enabled, patient-centred care pathways ARIA has evolved from a guideline using the best approach  124  to integrated care pathways using mobile technology in patients with allergic rhinitis (AR) and asthma multimorbidity. 125 The proposed next phase of ARIA is change management. 126 The aim of this phase is to provide an active and healthy life to patients with rhinitis and to those with asthma multimorbidity across the lifecycle, irrespective of their sex or socioeconomic status. The aim is to reduce health and social inequities incurred by the disease. ARIA will follow the 8-step model of Kotter (i) to assess and implement the effect of rhinitis on asthma multimorbidity and (ii) to propose NextGen guidelines.
ACKNOWLEDGEMENTS The authors thank  Ms Véronique Pretschner  for submitting the paper. None for the study. MASK-air ® has been supported by  Charité Universitätsmedizin Berlin ,  EU  grants ( EU Structural and Development Funds Languedoc Roussillon and Region PACA ; POLLAR: EIT Health; Twinning: EIP on AHA; Twinning DHE:  H2020 ; Catalyse:  Horizon Europe)  and educational grants from  Mylan-Viatris, ALK ,  GSK ,  Novartis ,  Stallergènes-Greer and Uriach. Open Access  funding enabled and organized by Projekt DEAL.
G U R E 2 Geographical distribution of MASK-air ®. 8 of 30 - 22 ) tegic overview (adapted from22 ) BOUSQUET ET AL. Acronym Name Dates WHO-associated projects ARIA Allergic Rhinitis and its Impact on Asthma 1999- WHO collaborating Centre on rhinitis and asthma 2004-2014 GARD Global Alliance against Chronic Respiratory Diseases 2003- WHO-ITU 'Be He@lthy, Be Mobile' handbook on asthma and COPD 2017 EU grants and projects GA 2 LEN Global Allergy and Asthma European Network (FP6) 2004- MeDALL Mechanisms of the Development of Allergy (FP7) 2009-2014 EIP on AHA European Innovation Partnership on Active and Healthy 2012-2020 Ageing (DG Santé & CONNECT) JCR Science and Policy Report JRC Scientific and Policy Reports on Strategic Intelligence Monitor 2017 on Personal Health Systems Phase 3 (SIMPHS3) Twinning Transfer of Innovation (DG Santé & CONNECT) 2017-2019 DHE twinning Transfer of innovation in severe asthma (H2020) 2019-2020 POLLAR Impact of Pollution on Asthma and Rhinitis (EIT Health) 2018-2019 Catalyse Climate change (Horizon Europe) 2022- Good Practice DG Santé on digital health (DG Santé) 2018 Candidate Best Practice OECD-DG Santé 2023 from 0.
� The MASK-air database does not have any missing values due to the structure of the app. � It is an MDR Class IIa. � It is fully validated. � It includes pollen data (daily and predictive) based on the patients' geolocation (and pollution, 2022). 34,73 � It is a Good Practice of DG Santé for digitally-enabled, patientcentred care and a candidate Good Practice of OECD. In 2019, the European Commission embarked on a project with the OECD (Organisation for Economic Co-operation and Development) entitled 'The Best Practice Project'. This project was designed to produce evidence to assist policy-makers of Member States in the selection, implementation and evaluation of best practice interventions, targeting non-communicable diseases and digitallyenabled integrated care. As part of this project, the OECD develops a range of analyses based on the case studies of candidate best practices.
Conclusion and outlook Nowadays techniques for RS of volcanic gases enable DOAS measurements of SO 2 emissions, which, when operated from autonomous platforms, provide global and continuous data (e. g. ,  Carn et al. , 2017; Theys et al. , 2019; Arellano et al. , 2021). Further species (e. g. , H 2 O, CO 2 , SO 2 , HF, HCl, CO) can be measured by more intricate techniques that almost exclusively provide data during dedicated field campaigns. We show here, that the use of high-resolution FPIs in spectrograph implementations could overcome the limitations of present-day volcanic gas RS techniques in many respects. In the first part of this article, we describe volcanic field measurements with a FPI spectrograph prototype and demonstrate the feasibility of the quantification of volcanic OH radicals.
Acknowledgments We are grateful to the  Goma Volcano Observatory (OVG)  for the invitation and logistic help. Further we thank the  United Nations Organization Stabilization Mission in the Democratic Republic of the Congo (MONUSCO)  and the  Institut Congolais pour la Conservation de la Natur (ICCN)  for providing the logistics inside the  Virunga National Park. We thank  SVG Geneve  for providing the logistics to descent into Nyiragongo's crater and particularly  Pierre-Yves Burgi  and  Patrick Marcel  for their support during the measurement. We thank  SLS Optics Ltd. , for their excellent services and kind support.
Acknowledgments: The Agence Nationale de la Recherche (ANR-19-CE07-0013) and the Ministère de l'Enseignement Supérieur et de la Recherche are gratefully acknowledged for Ph. D. grant to U. A. and post-doctoral fellowship for V. S. V. S. N. S. The authors would like to thank the CNRS and the Université de Toulouse, UPS for financial support. NMR, mass and x-ray diffraction services from the Institut de Chimie de Toulouse (ICT -FR2599) are acknowledged for support.