dataesr/scientific-paragraphs-categorization huggingface dataset:
Urinary excretion of metabolites of endogenous steroids in the recruited study groups Males had higher urinary concentrations of androgens, and data for endogenous steroids were therefore separated by sex (Table  2 ). Furthermore, there were few differences with respect to steroid concentrations between ex-smoking (n=65) and non-smoking (n=302) severe asthmatics and the current smokers were too few (24 female, 17 male) to enable sufficiently powered analyses of these subgroups (Table  S1 ). The data for SA are therefore merged irrespective of smoking history, providing one group of 253 females and another comprised of 155 males. The concentrations of cortisol and cortisone were the same in males and females (Table  2A ). In both sexes, the concentrations of all urinary steroid metabolites were substantially lower in SA than in MMA or HC (Figure  2A -D, Table  2A ).
One limitation of the U-BIOPRED study is that we have no information regarding the ratio between androgens and female sex hormones as the WADA doping panel is designed to measure EAAS. Likewise, data concerning female reproductive physiology (e. g. , menstrual cycle phase, use of contraceptives, or hormone replacement) was not collected. Concerning the potential influence of the menstrual cycle, the clinical and biomarker outcomes in the extreme group comparison were assessed in relation to DHEA-S, which is a long-lived metabolite not altered significantly during the menstrual cycle  [26]. It has also been established using the same analytical platform that the levels of EAAS metabolites change very little during a normal menstrual cycle  [27]. In our study, the greatest effects on androgens were observed in SA where only 27% were below the age of 45 (Table  S2 ).
This makes it unlikely that the use of contraceptives is a major confounder. On the other hand, use of hormone replacement therapy (HRT) might be an issue. We have not found data on the prevalence of HRT in severe asthma, but a large prospective study in a primary care database in the UK estimated that 16% of women aged 46-70 used HRT  [28]. As the U-BIOPRED study is pan-European, there may be variability between countries reflecting cultural and regional differences in the use of HRT. Assuming the UK prevalence, there is nevertheless no reason to believe that this subgroup should have been driving the differences in the levels of androgen metabolites between men and women with severe asthma. Given that the women with asthma in our cohort also demonstrate the same clear dose-response relations for suppression of adrenal androgens as the men in the study (low dose ICS < high dose ICS < high dose ICS+OCS; Tables  2A  and 2B ), we conclude that the possible bias due to HRT is likely to have been small and evenly distributed among women with different severities of asthma.
Acknowledgements: We thank the dedicated members of the  U-BIOPRED study group  in alphabetical order,  Ahmed H ,  Auffray C ,  Bansal A T ,  Bel E H ,  Bigler J ,  Billing B ,  Baribaud F ,  Bisgaard H ,  Boedigheimer M J ,  Bønnelykke K ,  Brandsma J ,  Brinkman P ,  Bucchioni E ,  Burg D ,  Bush A ,  Campagna D ,  Chalekis R ,  Chaiboonchoe A ,  Cheka T ,  Compton CH ,  Corfield J ,  Cunoosamy D ,  D'Amico A ,  De Meulder B ,  Emma R ,  Erpenbeck V J ,  Erzen D ,  Fichtner K ,  Fitch N ,  Fleming L J ,  Formaggio E ,  Frey U ,  Gahlemann M ,  Goss V ,  Guo Y ,  Hashimoto S ,  Haughney J ,  Hedlin G ,  Hekking P W ,  Higenbottam T ,  Hohlfeld J M ,  Knox A J ,  Konradson J ,  Lazarinis N ,  Lefaudeux D ,  Li C-X ,  Loza M J ,  Lutter R ,  Manta A ,  Masefield S ,  Matthews J G ,  Mazein A ,  Meiser A ,  Middelveld R J M ,  Miralpeix M ,  Mores N ,  Murray C S ,  Musial J ,  Myles D ,  Nordlund B ,  Olsson H ,  Pahus L ,  Pavlidis S ,  Postle A ,  Powel P ,  Praticò G ,  PuigValls M ,  Rao N ,  Reinke S ,  Roberts A ,  Roberts G ,  Rowe A ,  Sandström T ,  Schofield J P R ,  Seibold W ,  Selby A ,  Sigmund R ,  Singer F ,  Skipp P J ,  Smicker M ,  Ström M ,  Sun K ,  Thornton B ,  Uddin M ,  van Aalderen W M ,  van Geest M ,  Vestbo J ,  Vissing N H ,  Wagener A H ,  Wagers S S ,  Weiszhart Z ,  Wheelock Å ,  Wilson S J ,  Östling J.
The  U-BIOPRED study  has received infrastructure and operational funding from the  Innovative Medicines Initiative  (IMI www. imi. europa. eu) joint undertaking under grant agreement  115010 , resources which are composed of financial contributions from the  European Union 's  Seventh Framework Programme  ( FP7/2007-2013 ) and the  European Federation of Pharmaceutical Industries and Associations (EFPIA). The partners have provided matching funding to enable studies utilising the data and bioresources collected in the study. For this report, funding to Karolinska Institutet from the following sources have been instrumental. the  Swedish Heart-Lung Foundation ,  Swedish Research Council , the  Stockholm Region County Council funds (ALF) ,  CIMED , the  Konsul Th C Berghs research foundation , the  ChAMP  ( Centre for Allergy Research Highlights Asthma Markers of Phenotype ) consortium, which is funded by the  Swedish Foundation for Strategic Research , the  Karolinska Institutet ,  AstraZeneca & Science for Life Laboratory Joint Research Collaboration , and the  Vårdal Foundation.
When significant effects were detected, polynomial contrasts were used to determine linear and/or quadratic responses in Exp. 1, 2 and 4, and means were compared by Fisher's protected LSD test using the StatGraphics Centurion 19 software (StatPoint Technologies, Inc. USA, 2020). Significant effects were declared at P < 0. 05 and tendencies at 0. 05 P < 0. 10.
This article reports data collected by survey from 100 dairy farmers located in a mountainous area (France), including 72 farmers engaged in the traditional Salers system and 28 farmers engaged in a specialised dairy system. The questionnaire covered all uses of all grass fields during the entire outdoor period, considering 'field' as an area that was used in the same way throughout the entire period. Cutting dates, grazing dates, animal categories and numbers were recorded using a grazing and harvesting schedule. We also recorded key geographical and physical characteristics of each field, i. e. main slope, altitude, area, and distance from the farmstead. Each field in the presented database is thus described by 47 quantitative and qualitative variables.
Objective Mountain farmers have to manage field patterns where slope, altitude and the harshness of the climate are added to other possible constraints (size, fragmentation, dispersion, and more). Mountain farming systems (especially dairy systems) are highly dependant on grass growth, which requires careful management of grazing and harvesting, mainly if non-local fodder supply is prohibited (e. g. PDO cheese area). The enlargement of the field patterns and the recurrent climatic hazards (particularly droughts) make grass management even more complex. In this context, it is useful to have information on how mountain farmers use their fields, as well as information on the characteristics (geographical and physical) of the fields. The database gathers a large amount of on-farm data collected from the farmers themselves.
Data Description The dataset is distributed between three files: -The first file (named Sibra_Brunschwig_Data_Dictionnary_20230324. tab) presents the definitions of all 76 variables produced from the farm surveys. -The second file (named Sibra_Brunschwig_Farm_Data_20230324. tab) presents the data on the 100 farms (29 variables), where each line corresponds to a separate farm. -The third file (named Sibra_Brunschwig_Field_Data_20230324. tab) presents the data on the 2341 grass fields, where each line corresponds to a single field within a farm. The variables concern first the geographical and physical characteristics of the field (9 variables, columns C to K), and then the uses of that field (33 variables, columns L to AR) and finally the on-field fertilisation practices (5 variables, columns AS to AW).
Experimental Design, Materials and Methods The data comes from two survey campaigns led in 2005 and in 2009. The farmers were surveyed via questionnaire by two interviewers in a single visit. All the farms were located in the mountainous Massif central area of south-central France (Auvergne-Rhône-Alpes region, Cantal and Puy-de-Dôme departments; see Fig. 1  ) and had a very large proportion of their area (94% on average) under permanent grassland. The first campaign surveyed 72 volunteer breeders engaged in the traditional Salers breed system, out of the total 90 breeders who were members of the 'Association Tradition Salers'  [2]. This traditional farming system is specific in that it allows for the simultaneous production of milk and weanling calves. The Salers' cows are milked throughout their lactation or they become suckler cows after a few months of milking, and they need to be beside their calf to start milking  [3].
The survey covered every grass field used in each farm. The second survey campaign involved 28 specialised dairy breeders recruited by the Puy-de-Dôme milk control authority  [7]. The questionnaire used was designed to collect the same data on farms and on grass fields ( n = 755) as in 2005, working to the same definition of 'field'. The database published in this data paper results from aggregation of the data collected in 2005 and 2009, thus providing data on a total of 100 mountain-area specialised dairy farms or mixed (dairy and suckler) system farms, and 2341 grass fields. Both survey questionnaires (the original French version and a translated English version) are available in Supplementary material, together with specific tables and schemes for collecting farm-level and field-level data.
Acknowledgements The authors thank the breeders who agreed to be surveyed, the  VetAgro Sup  students on the "Livestock and production systems" course in  20 05  and  20 09  who carried out the surveys,  Claire Agabriel  and  Fabienne Blanc  for their expertise and supervision of the students,  Anne-Lise Jacquot ,  Hervé Molénat , and  Sylvain Bouscayrol  for their involvement in processing and mobilizing the data, and the not-for-profit ' Association Tradition Salers ' and the  Puy-de-Dôme milk control authority  for their cooperative collaboration. The authors also thank  METAFORM LANGUES  for proofreading the English text.
In addition, this study is the first to determine the PGP activity of Brevibacterium anseongense. Finally, Sun et al. conducted a bibliometric review of scientific literature concerning rhizosphere microorganisms uncovering data from the last decade (2012-2022). The results have shown that the interest in this area is increasing which is evident from the rapidly increasing number of publications in many countries. Furthermore, cooperation networks between them are revealed, indicating high collaboration at the global level in this Research Topic. In conclusion, we would like to emphasize that research on PGPR is very advanced at the academic level, and yet, the acceptance and actual field application of biofertilizers are still far from complete realization. Furthermore, differences can be observed in the application of PGPR amongst countries, both for crop optimization (agricultural) and environmental management.
Methodology The following online databases were employed to collect the literature data for this paper: Science Direct, Google Scholar, Web of Science, Pub-Med, and Scopus, using the keywords: "bee pollen"; "bee-collected pollen"; "chemical composition of bee pollen"; "therapeutic effect of bee pollen"; "functional effect of bee pollen", "protective effect of bee pollen", and "nutritional value of bee pollen". After collecting and reviewing all selected articles, their general ideas were summarized and used in this review.
This enabled the activity of individuals to be monitored on a near continuous basis. We quantified activity by comparing the change in signal strength between subsequent received recordings using a 4 dB threshold (see  Zúñiga et al. , 2016  for details of calibration and general procedures of the ARU System). Raw data were visually inspected for artefacts and for evidence of malfunctioning transmitters. The processed data provided a binary estimate of whether a bird was active approximately every 4 min throughout the study. Individual radio transmitters were deployed approximately a month before expected Zugunruhe in early September  (Partecke & Gwinner, 2007; Zúñiga et al. , 2016)  and remained on individuals until early-December after the autumn migration period. We characterized the timing of Zugunruhe using complementary methods.
Chemicals and Mg Salt Properties The compounds supplied as foliar sprays were magnesium chloride (MgCl 2 •6H 2 O), magnesium nitrate (Mg(NO 3 ) 2 •6H 2 O) and magnesium sulfate (MgSO 4 •7H 2 O) sources (all of them were ACS reagents, 99%; Sigma-Aldrich, Taufkirchen, Germany). All treatments contained 0. 1% Genapol X-80 (Sigma-Aldrich) which reduced solution surface tension to approximately 27. 21 ± 0. 66 mN m -1  [32]. The solubility in molarity (M) of these salts was determined by PHREEQC code  [33]  using the Pitzer database. The values of Mg(NO 3 ) 2 •6H 2 O are not included in this database; therefore, the saturation molality (m) of this salt was taken from  [34]  for calculating its solubility (molarity, M).
We identified the genes encoding potential homologs of DAF-36, DHS-16, HSD-1 and DAF-9 in the B. malayi genome (S1 Table ), which share high homology with enzymes in C. elegans. Importantly, we found in the D. immitis genome a gene with 92% identity with the potential homolog of daf-9 in B. malayi. This suggests that filarial nematode parasites could also produce their own DAs. However, the presence of a gene does not necessarily reflect its expression status, which is a prerequisite for any enzymatic activity. In order to assess the DA synthesis pathways of filarial nematodes, we have exploited a publicly available transcriptomic dataset from B. malayi, across its entire in vivo life cycle  [27]. We retrieved RNA sequencing read counts of DA synthesis enzymes and DAF-12 target genes (Fig 7A ), from the iL3 to 8 days post-infection (dpi).
Amplification and cloning Total RNA from B. malayi L3 larvae was isolated using the Trizol method (Invitrogen). cDNA was obtained by reverse transcription using Superscript III first strand (Invitrogen). The full length BmaDAF-12 (Bm8452. 1) was then amplified by High Fidelity Platinum Taq DNA polymerase (Invitrogen) using BmaDAF-12Fw/BmaDAF-12Rv primers (S2 Table ) encompassing the predicted initiation and stop codon of the respective ORF based on the B. malayi genome reference Bmal-4. 0. The product was subcloned into a pCR4 vector (Invitrogen) and the exact sequence was then confirmed by sequencing (McGill University/ Genome Quebec Innovation Centre). We found only one BmaDAF-12 cDNA sequence, which has been deposited in the GenBank database and is available under the accession number ON714136.
Acknowledgments We thank the  NIH/NIAID   Filariasis Research Reagent Resource Center  ( FR3 ) (www. filariasiscenter. org)for distribution by  BEI Resources , for providing the following reagent: Stage iL3 Dirofilaria immitis,  Strain Missouri ,  Infective Larvae  (Live), NR-48909. We thank  Marie Garcia  for her technical supports and Melanie Alberich and Francois Andrefor their fruitful discussions. We thank  Beatrice Roques ,  Philippe Pinton ,  Joelle Laffitte  and  Alix Pierron  for providing animal sera.
Tethered flight procedure The flight mill assays were conducted in a temperature-controlled room at 25 °C (see Supplementary Information for details). We used flight mills connected to a multi-channel flight mill data acquisition (DAQ) system controlled from a laptop computer. A maximum of nine flies were tested per session (i. e. , group of flies tested at the same time). The body mass of each fly was determined using analytical balances before being placed on the flight mill (see Supplementary Information for details). Each session included at least two flies from each temperature group. Each session started when the last fly was attached to the flight mill and was recorded for exactly 2 h. We ran the assays until having between 14 and 24 flying individuals of each sex and temperature group.
ACKNOWLEDGEMENTS The European Union's  Horizon 2020 research and innovation program  under agreement No  818184  ( FF-IPM ) funded this research. We thank  Aruna Manrakhan  for providing the pupae to establish cultures of B. dorsalis. We also thank  Pertunia Moropa  for her assistance with the flight mill procedure. The authors acknowledge the  Plant Protection Platform  ( 3P , IBISA), where some experiments were conducted. Valuable reviews from an anonymous reviewer and  Nicholas Manoukis  greatly improved the manuscript.
Climate data and analysis approach Based on our transdisciplinary partnership with key actors, the organisation that manages the network of generators (ARELFA) in Burgundy, composed of three sub-networks (Fig. 2 -centre pane), provided the dataset linked to the generators (location, stack temperature and dates of hail alerts). The hail alerts come from the Keraunos consultancy firm (https:// www. kerau nos. org/) which transmits the information to the volunteers who then activate the generators. This firm's "Observatoire français des tornades et des orages violents" (French observatory of tornadoes and violent storms") publishes national maps. We mobilised hybrid meteorological data (observations/models) produced by the SAFRAN-ISBA-MODCOU (SIM) chain and validated by Météo France  (Habets et al.
2008; Quintana-Segui et al. 2008; Soubeyroux et al. 2012). These daily data, with a spatial resolution of 8 km, cover the whole of metropolitan France. We delimited the study area (Centre-France) and analysed data over the period 1959-2020. This allowed us to look back at the evolution and geography of the climate beyond the period in which the generators were set up and the area in which they were located. We used liquid and solid precipitation data which are summed to obtain actual precipitation (PR), mean surface temperature (T), potential evapotranspiration (PET) and the soil moisture index (SWI). These data were analysed according to two temporalities: -Only on days with precipitation, defined as having a value greater than 1 mm from the spatial average. For these days, the relationship between PR and the uses of generators was tested.
The interviews complied with the General Data Protection Regulation, including signed consents from respondents. To maintain anonymity, direct quotations from study participants are included in this paper using the following codes: breeder (BR1-BR8); wine producer (VI1-VI4); hybrid farmers (HY1-HY4). Interviews were recorded, transcribed verbatim and transcripts coded manually. We coded the transcripts according to a coding protocol using seven main themes: 1. Perception of climate change; 2. Narrative of the conflict and of the Matour meeting; 3. Knowledge and opinions related to generators; 4. Role of actors and organisations in the conflict process; 5. Links and gaps between wine producers and cattle breeders; 6. Sources of knowledge and expertise regarding climate change; 7. Capacity to plan for the future.
, 2018; Bredesen and Rehmsmeier, 2019; Khabiri and Freddolino, 2019)  and experimental datasets  (Wessels et al. , 2019)  (Fig. 1 ). First, we have analyzed whether each one of the miRBase v22 mature miRNAs described (Methods) showed PcG and trxG binding enrichment. Second, we have investigated the potential of each miRNA gene to be regulated by PcG/trxG through the presence of PREs in its promoter region. The results of these analyses have identified a novel and comprehensive set of miRNAs that may act as regulators of PcG/trxG gene expression as well as a set of miRNA genes potentially regulated by the PcG/trxG proteins. Interestingly, the combination of the results has uncovered cross-regulatory interactions between miRNAs and PcG/trxG that may define direct regulatory circuits. Our results provide a computational resource ready to be applied to the discovery of the molecular mechanisms involved in the biological processes mediated by the miRNAs and PcG/trxG networks in Drosophila.
Moreover, the results presented here can be used as a foundation for future work to deepen our understanding on the intricate regulatory networks formed between these two large groups of transcriptional regulators. Fig. 1. Conceptual map of the computational framework A) Search for miRNA binding of PcG and trxG. TargetScanFly miRNA target predictions  (Agarwal et al. , 2018)  and experimentally defined mature miRNA  (Wessels et al. , 2019)  were paired and filtered to generate the Refined miRNA Target predictions set (RmiRTps). Next, an overrepresentation analysis (ORA) was applied to RmiRTps using the selected PcG/trxG gene sets, resulting in a list of miRNAs enriched in PcG or trxG transcripts predictive binding. B) Search for pre-miRNAs with neighboring PREs. Intersection of SVM-MOCCA  (Bredesen and Rehmsmeier, 2019)  and KF19  (Khabiri and Freddolino, 2019)  predictions generated the Refined PRE prediction set (RPREps) to which the pre-miRNAs genomic coordinates were compared and filtered according to the selection criteria of 5 kb.
Data sources miRBase v22  (Kozomara et al. , 2019)  was accessed to obtain pre-miRNA genomic coordinates ("dme_miRBase. gff3") and to annotate the correspondence between pre-miRNAs and mature miRNAs ("miR-NA. dat"). FlyBase (V2. 0  (Thurmond et al. , 2019) , was consulted for the generation of the PcG/trxG gene sets. TargetscanFlyV7 resource database was used for miRNA target predictions (www. targetscan. org/fly_72/,  (Agarwal et al. , 2018). Three precomputed prediction files were downloaded: 1) the Conserved site context scores prediction file, 2) The Nonconserved site context scores prediction file, and 3) the Summary Counts, all predictions file. In Files 1 and 2 every site with homology to a mature miRNA seed region is classified depending on the MicroRNA Response Element (MRE) site's conservation and scored by the contextþþ score (a measure defined by 14 features described in  (Agarwal et al.
Third, the resulting contextþþ score distribution was analyzed and compared to the score distribution of the MRE predictions. Finally, a cut-off was empirically established in a contextþþ score of À0. 04 to filter the TargetScanFlyV7 MRE predictions and a list of the "miRNA:transcript pairs' below the À0. 04 contextþþ score was extracted. Next, the condensed miRNA families from the Summary Counts, all predictions file (file 3 from  (Agarwal et al. , 2018) , see above and Fig. S1 ) were expanded to cover all individual miRNAs and the predictions were filtered with the list of miRNA:transcript pairs below the established cut-off. Only the summarized predictions included in the miRNA:transcript pairs list were selected, and thus excluded all the summarized predictions that were exclusively formed by MRE predictions not fulfilling the À0.
fdr" function nested inside "prot2D 00 Bioconductor package  (Artigaud et al. , 2013). Only rFDRs of 22% for trxG, and 10% for PcG (corresponding with p-values 0. 01) were considered, as the main goal of this study is to provide a bonafide list of candidate miRNAs for their in vivo experimental validation.
1C ). dme-miR-10404 and dme-miR-11182 were excluded due to differences between the miRBase version utilized in this analysis and TargetScan-FlyV7 predictions  (Agarwal et al. , 2018). A network was produced to show the relation between miRNAs, PcG and trxG by exploring the individual PcG/trxG targets of each miRNA using Cytoscape  (Shannon et al. , 2003)  (Fig. 5 ). For this, miRNAs were considered as source nodes and PcG/trxG transcripts as target nodes. Edges between source and target nodes are drawn based on RmiRTPs, considering the expected repression (i. e. , CWCS) of the predictions as the edge weight (the thicker the edge the stronger the expected repression). For representation of Fig. 5C , source nodes were filtered to keep only miRNAs involved in regulatory circuits (See Data availability).