dataesr/scientific-paragraphs-categorization huggingface dataset:
Nanoparticles tracking analysis CM-EVs and MBVs were isolated from 1. 5x10 5 pCAFs or pNFs, which were seeded in serum-free medium in 6 cm cell culture dishes and isolated as described above. After isolation, the EVs were resuspended in 1 ml of PBS. EV size and concentration were determined using a NanoSight LM10 (Malvern Panalytical, Malvern, UK) and the NTA 3. 1 software. Each measurement is the result of three acquisitions of 60s. The camera level was set to 14 and the detection threshold to 4. The PBS used for EV isolation and collection was filtered with the 0. 02 μm filter.
The following primary antibodies were used: THY1 (1/2,000, [D3V8A] 13801 Cell Signaling Technology, Danvers, MA) and GAPDH (1/1,000, sc-48167 Santa Cruz Biotechnology, Inc. , Dallas, TX). The membrane was incubated with HRP-conjugated (1/2,500 New England Biolabs, Ipswich, MA) or IRDye ® (1/10,000 LI-COR Biosciences, Lincoln, NE) antibody for 45min at room temperature. Western blot images were acquired using a myECL Imager (Thermo Fisher Scientific) or a LI-COR Odyssey CLx scanner (Image Studio software, version 5. 0. 21).
After 24h, cells were fixed in 4% PFA. DAPI and Alexa ® 647 Phalloidin (1/100, Life Technologies) were used for nuclear and F-actin staining, respectively. Images were acquired with a Zeiss LSM 880 confocal microscope in Airyscan mode (Carl Zeiss, Plan-Apochromat 63x/1. 4 Oil DIC M27 objective, zoom 1. 8, z-stacks of 5-9 μm, 28-48 slices). Images were Airyscan processed with Zen software using default settings. The 3D reconstruction and analysis were performed using Imaris software (version 9. 5, Bitplane, Oxford Instruments, Abingdon, UK). For α-SMA staining, 5x10 3 pCAFs were seeded in each well of a 96-well plate. The following day, cells were fixed in 50% acetone/50% ethanol for 20min and permeabilized with 0. 05% saponin (Sigma-Aldrich) in 1% BSA in PBS for 30min. Cells were incubated with anti-α-SMA antibody (1/200, [1A4] ab7817 Abcam) in 1% BSA in PBS for 1.
5h and then with Alexa Fluor ® 647 secondary antibody (1/250, Life Technologies) for 1h. HCS CellMask TM Green Stain (1/10,000, Life Technologies) and DAPI were used for cytoplasm and nuclear staining, respectively. For each well, 45 images were acquired on an Opera Phenix high-content imaging system (20x objective, z-stacks of 0. 8 μm, PerkinElmer, Waltham, MA). Image analysis was performed using Harmony imaging analysis software (PerkinElmer, version 4. 9).
Eluting peptides were electrosprayed into the mass spectrometer using a nanoelectrospray ion source (Thermo Fisher Scientific). An Active Background Ion Reduction Device (ABIRD, ESI source solutions) was used to decrease air contaminants signal level. The Xcalibur software (Thermo Fisher Scientific) was used for data acquisition. Full scans over mass range of 375-1500 m/z were acquired at 60,000 resolution at 200 m/z. Multiply charged ions from two to five were selected through a 1. 4 m/z window and fragmented. Higher energy collisional dissociation fragmentation was performed on the 15 most intense ions, using normalized collision energy of 27, and resulting fragments were analyzed in the Orbitrap at 15,000 resolution, using a maximum injection time of 25ms or a target value of 10 5 ions.
MS proteomic data analysis The. RAW files were processed with MaxQuant software (version 1. 5. 5. 1 for the proteome analysis of pCAF/pNF proteome, version 1. 6. 3. 3 for all the other experiments) (77) and searched with the Andromeda search engine. The following setting was used: minimal peptide length 7 amino acids, trypsin specific digestion mode with maximum 2 missed cleavages, carbamidomethyl (C) as fixed modification, and oxidation (M) and acetylation (Protein N-term) as variable modifications. For the analysis of EV proteome, TMTzero TM was added as fixed modification and maximum 4 missed cleavages were allowed. Minimum peptide ratio count was set to 2, except that for the trans-SILAC experiment and the analysis of pCAF1 proteome where this parameter was set to 1. "Unique + razor" peptides were used for quantification in the analysis of THP-1 and pCAF/pNF proteome; "unique" peptides were used for quantification in all the other experiments.
The "match between runs" option was enabled for the analysis of pCAF/pNF proteome. For SILAC experiments, multiplicity was set to 2: light labels were Arg0 and Lys0; heavy labels were Arg10 and Lys8. Label free quantification (LFQ) setting was enabled for all the other experiments. The false discovery rates (FDRs) at protein and peptide levels were set to 1%. Perseus software (version 1. 5. 5. 3 for the analysis of pCAF/pNF proteome and version 1. 6. 2. 2 for all the other experiments) (78) was used for data analysis. Potential contaminants, reverse peptides and proteins only identified by a modification site were filtered out. Only proteins identified with at least one unique peptide were kept for the analysis. To define the transferred proteins in the trans-SILAC experiment, we selected proteins that have a "Ratio H/L count" value higher in HUVECs co-cultured with CAFs compared with monoculture.
For the analysis of pCAF1, CM-EVs and MBVs proteome, the intensity value of each protein was divided by the MW and transformed by log2. For the analysis of pCAF/pNF proteome, the SILAC ratio was inverted, transformed by log2 and normalized by subtracting the median from each column. For the analysis of the α-SMA high and α-SMA low pCAF proteome, LFQ intensity was transformed by log2, three valid values were required for at least one pCAF1 or pCAF3 subpopulation and one valid value was required for at least one pCAF4 subpopulation. Missing values were replaced from the normal distribution using the recommended setting in Perseus software and proteins with a fold change ≥ 1. 5 and P ≤ 0. 05 (two-sided Student's T-test) in at least two out of the three pCAF lines were selected. The Z-score was calculated by row.
Proteomic datasets The proteomic data of EVs were downloaded from three publicly available datasets  (51) (52) (53). For each dataset, we considered the proteins unique to each EV subpopulation and those with abundance significantly different between the two as statistically analyzed by the authors, except that for  (53) , for this paper we considered proteins with at least a two-fold change and P < 0. 05. For immunofluorescence analysis, 2. 5x10 4 4T1 cells were resuspended in 100 μl of PBS and injected in the tail vein of 4-5-month-old female mice expressing or not (control mice) the RFP. Mice were culled three weeks after the injection. A small incision was made in the trachea and 1 ml of 2% low-melting point agarose was introduced slowly into the lungs through a 22G needle. Lungs were excised and fixed in 4% PFA for 2h at 4°C.
Tile stitching, maximum Z projection and linear unmixing was performed using Zen Black software (version 2. 3 SP1), and images were visualized in Zen Blue software (version 2. 3). More detailed imaging of three tissue samples was performed using a Plan-Apochromat 40x/1. 3 Oil DIC M27 objective and Z-stack mode. Maximum Z projection and linear unmixing was performed as above. Image processing was performed in Fiji (ImageJ, version 1. 53f51).
Data and materials availability The raw files and the MaxQuant search results files have been deposited to the ProteomeXchange Consortium via the PRIDE partner repository  (80)  with the dataset identifier PXD034450 (proteins transferred from cancer-associated fibroblasts to endothelial cells: trans-SILAC), PXD034460 (THP-1 proteome), PXD034467 (proteomic analysis of CM-EVs and MBVs secreted by pCAFs), PXD034519 (proteomic analysis of pCAFs expressing high and low α-SMA protein levels), PXD034573 (pCAF1 proteome). pCAF/pNF proteomic dataset is available upon request.
Floral visitor behaviour To characterise the behaviour of floral visitors in response to the activity of V. velutina, one video sample of 15 min per recording was randomly chosen and analysed. Within this sample, all individual insects appearing in frame were identified to at least the family level, and their activity over time was recorded using BORIS software  (v 7. 9. 22-2020 -11-17, Friard & Gamba, 2016). For each insect, we recorded the time spent in frame, and the number and duration of visits to hermaphrodite and female flowers. To account for variations in flower density across sampling areas, visitation rates were then normalised via the following formula: (Number of flower visits / number of flowers) × 100 These observations yielded the subsequent measures: number of insect visits to the observed patch in 15 min (patch visitation rate); number of visits to hermaphrodite and female flowers in 15 min (flower visitation rate); and duration of visits to hermaphrodite and female flowers by individual insects (flower visitation duration).
The fit of all GLMs was validated using omnibus tests, assessment of the deviance to degrees of freedom ratios, and analyses of the Pearson residuals. Full model structures are detailed in (Appendix B), and the raw data for fruit and seed set are available at (DOI:10. 6084/m9. figshare. 2011 0463). All statistical analyses were performed in SPSS (release v. 28. 0. 1. 0).
Acknowledgements S. V. R. -N, S. M and J. G were supported by the  Interreg Atlantic Area Program  ( EAPA_800/2018-Atlantic-Positive ). T. A. O. -W, J. P and J. O were funded by  BBSRC  (Project no. BB/S015523/1 ). Funding for open access charge:  Universidade de Vigo/CISUG. We wish to thank the  Pazo da Touza and the residents of Gondomar, Nigrán, Oia ,  O Rosal , and  Tomiño  for providing us with field sites. D. Boisits ,  P. Kennedy , and  P. Álvarez  contributed to the development of the work, and  A. Lagoa , R. Curtis, and  L. Castro  assisted with the identification of insects.
| Obtaining genomic and transcriptomic data and habitat assignment We analysed the aquaporin gene repertoire of 485 species from seven animal phyla where there has been at least one ecological transition to land: Arthropoda, Onychophora, Tardigrada, Nematoda, Mollusca, Annelida and Vertebrata (Figure  1a , Figure  S1 ). The full list of species included and the sources from which they were obtained is available as Data S1. Aquaporins from vertebrates, M. tardigradum, and C. elegans were directly obtained from the literature  (Finn et al. , 2014; Grohme et al. , 2013; Huang et al. , 2007, respectively). The only exceptions were the aquaporins from the amphibian Leptobrachium leishanense, for which we downloaded the genome annotation  (Cunningham et al. , 2021). In the case of annelids,  Mucciolo et al.
(2021)  provided a fairly good representation of aquaporins in Pleistoannelida, the most abundant clade within annelids. However, as noted by the authors, the methodology followed could have underestimated the identification of aquaporins due to the incompleteness or low quality of data within the source database. For instance, an example of this is Eisenia andrei, for which only one Aqp was identified. To verify this information, we downloaded the genome annotation of E. andrei  (Shao et al. , 2020)  to annotate its Aqp-coding genes ourselves as described below. We identified a total of 27 Aqp-coding genes, in contrast with the one reported by  Mucciolo et al. (2021). Data from the remaining 415 invertebrate species were obtained from MATEdb  (Fernández et al. , 2022) , which provides standard comparable proteomes for phylogenomic studies.
| Annotating genes encoding for aquaporins in downloaded proteomes From the whole set of proteomes, genes encoding for aquaporins were annotated using BITACORA version 1. 3  (Vizueta et al. , 2020)  in "protein mode". Briefly, BITACORA combines BLAST and HMMER  (Eddy, 2011)  searches against a custom curated database of genes belonging to the gene family of interest. Due to the conservation of aquaporins across all animals, our aquaporin curated database contained a list of 123 metazoan aquaporin sequences that have been experimentally confirmed in previous studies (Data S2). To benchmark this methodology, we used Drosophila melanogaster to compare the number of aquaporins identified using BITACORA with the number of aquaporins annotated in its genome, obtaining the same result. In addition, it should be noted that the annotation of aquaporins is dependent on the data used and their absence in a species does not mean that it may not be actually present in its genome.
| Phylogenetic tree inference of aquaporincoding genes To identify which aquaporins belonged to which subtype (i. e. , Aqp1like, Aqp3-like, Aqp8-like or Aqp11-like) for posterior analyses, we first independently classified aquaporins of all animal species included in this study, except for the ones that had already been classified in previous publications  (Finn et al. , 2014; Mucciolo et al. , 2021). We based this classification on the description of the aquaporin phylogenetic clusters and their relative position in the phylogenetic tree with respect to the already classified sequences, which were considered as a backbone. For arthropods, onychophorans and molluscs (i. e. , lineages whose aquaporins we identified de novo in this study) we manually curated the classification by checking for unclustered aquaporin sequences and long branches that could represent outliers using BLASTP against the nonredundant NCBI database.
| Phylogenetic comparative methods to explore aquaporin evolution In order to test whether there were differences in the mean total number of aquaporins between terrestrial and aquatic species, we implemented a phylogenetic generalized linear model using the phyloglm R package  (Ho & Ané, 2014). Briefly, we fitted a phylogenetic Poisson regression model to our data describing the relationship between the number of aquaporins and habitat, accounting for the phylogenetic history of the species using the combined ultrametric species tree. For habitat, we classified species in two different ways: marine versus nonmarine (i. e. , freshwater, brackish and terrestrial), and terrestrial versus nonterrestrial (i. e. , freshwater, brackish and marine). This classification allowed us to identify shifts in aquaporin number during marine to freshwater and terrestrial transitions, and marine and freshwater to terrestrial transitions.
LA acknowledges funding from a Juan de la Cierva-Formación (grant agreement no. FJC2019by an FPI grant (grant agreement no. BES-2017-081050) financed by MCIN/AEI/10. 13039/501100011033 and by ESF "Investing in your Future". RF acknowledges support from the following sources of funding: Ramón y Cajal fellowship (grant agreement no. RYC2017-22492 funded by MCIN/ AEI/10. 13039/501100011033 and ESF "Investing in your future"), Agencia Estatal de Investigación (project PID2019-108824GA-I00.
Image acquisition and deposition analysis Image acquisitions were performed using a planar single detector gamma camera (Inter Medical Galaxy R, Inter Medical GmbH, Lübeck, Germany) to assess the pulmonary and extrapulmonary deposition (Fig. 1 , detailed in Additional files 1, 2)  [23, 24]. A penetration index was calculated as the outer-to-inner lung region ratio (O/I) from the 99m Tc-DTPA acquisition normalized to the O/I ratio from the 99m Tc flood-field acquisition  [23]. A complementary analysis was performed to correct lung deposition data for drug trickling from the ETT and the tracheal area after the end of the nebulization if an increase of activity superior to 10% was measured in lung regions of interest. Pulmonary and extrapulmonary deposition of radiolabeled particles were expressed in counts or as a percentage of the nominal dose, i.
Statistical analysis Statistical analysis was performed using SPSS software (version 29. 0. 0, IBM software). The estimated highest and lowest mean aerosol deposition values were 20% and 8%, respectively, based on previous data  [10, 19]. Sample size calculation was based on a 5% standard deviation and a 7% expected difference in a mean deposition for a statistical power of 80%. No attrition rate was considered in the research design. Data are expressed as mean ± standard deviation (SD) or median [25-75% interquartile range (IQR)] depending on the data distribution normality test. The four-group A conventional circuit (IMMED, Brussels, Belgium) with the nebulizer directly connected between the endotracheal tube and an HME filter (ETT group). ETT endotracheal tube, HH heated humidifier, SCAT specific ventilator circuit for aerosol therapy comparison was conducted with a one-way analysis of variance (ANOVA).
A higher deposition into the ETT and the tracheal area was measured in the SCAT and the ETT group when compared to both HH groups (p < 0. 001). When compared to the left lung, a higher right lung deposition was observed for the SCAT group only (p < 0. 05). Intersubject variability of aerosol distribution between both lungs in each group is illustrated in Fig. 3A  and B. The penetration index was comparable between both lungs in the four groups. Interestingly, the dynamic acquisition revealed a further increase of the activity in lung regions after the nebulization in three patients from the SCAT group and three patients from the ETT group (Additional file 4): this phenomenon likely represents drug trickling from the ETT to the lungs as depicted in Fig. 4. Deposition data were corrected for drug trickling from the ETT to the lungs during the 2-min acquisition for lung deposition after the nebulization (Additional file 5).
Data point 4 of shorter acquisition duration was normalized to the 120-s acquisitions of other data points. Patient A Drug trickling was observed during the nebulization as indicated by the exponential increase of counts in the right inner region and after the nebulization in the left lung as indicated by the increase of activity inside the inner region while the activity inside the outer lung region plateaued. Patient B Drug trickling was observed in both lungs, especially in the inner to the outer right lung region. ETT endotracheal tube, SCAT specific ventilator circuit for aerosol therapy trap more than a spacer, especially when the nebulizer is placed at the inlet of the HH with a bias flow of 2 L/min. Imposing a higher bias flow with another brand of ventilator would have probably increased lung delivery with the HH configuration, as suggested in vitro  [9, 12].
4 µm). While larger particles were filtered in the circuit in the HH groups, they deposited in the proximal pieces, the ETT, the trachea and the first bronchi in the SCAT and the ETT group (see Fig. 3 ). Drug trickling from the ETT to the lungs only impacted the latter groups. We quantified drug trickling only after the nebulization, but it also occurred during the nebulization (patient A in Fig. 4 ). It tended to increase lung doses and drug penetration from central to peripheral airways with a preferential Fig. 5  Correlation between inhaled doses of radiolabel measured in vitro in a filter positioned at the distal tip of the endotracheal tube and lung deposition of radiolabel measured in vivo using planar scintigraphy. Data are expressed as a percentage of the nominal dose. A correlation was found for the whole dataset (R = 0.
Indeed, previous studies testing the same nebulizer in similar configurations measured higher inhaled doses  [9, 11]  and similar penetration index  [19]  with more usual ventilator settings. This study has limitations. Pulmonary deposition of a radiotracer cannot be directly extrapolated to the deposition of a specific drug in patients with lung diseases. We included patients with healthy lungs to assess the impact of the inhalation technique without any influences of lung diseases. Second, ventilator settings were fixed for all patients using sedatives to theoretically optimize lung deposition. However, lung deposition data obtained in this study would have been very likely different with ventilator settings adapted to the patient's need (e. g. lower tidal volume, lower duty cycle or spontaneously breathing activity in pressure support ventilation).
Transcriptomic analysis RNAseq datasets were processed in our previous publication  (Ferrarini et al. , 2023) , and raw reads are available at BioProject PRJNA918957. Briefly, quality check was performed with FastQC v0. 11. 8 (Andrews, 2010), and raw reads were quality trimmed using trim_galore from Cutadapt v0. 6. 7  (Martin, 2011) , then mapped to the S. oryzae's genome (GCA_002938485. 2 Soryzae_2. 0) using STAR v2. 7. 3a  (Dobin et al. , 2013). Then, uniquely mapping reads were counted with featureCounts v2. 0. 1  (Liao et al. , 2014) , and mapping quality was assessed by multiqc v1. 13  (Ewels et al. , 2016). Differential expression analysis was conducted with SarTOOLS, taking advantage of a global linear model from Deseq2  (Love et al. , 2014, 2) , and log2 fold changes and adjusted p-values were obtained for sequential pairwise comparisons, i.
Choice of candidate reference genes based on transcriptome data To find S. oryzae reference genes to be used across gut and bacteriomes development (from larval stage 4 to 9-day-old adults), we scanned a comprehensive transcriptomic dataset spanning S. oryzae life cycle  (Ferrarini et al. , 2023). Candidate reference genes should be stable between conditions, highly expressed, and present transcript isoforms suitable for primer design. There are 17 957 genes in S. oryzae  (Parisot et al. , 2021)  and our previous analysis showed that 11 908 were differentially expressed between sequential conditions  (Ferrarini et al. , 2023). In order to find stable genes across development, we removed genes presenting an adjusted p-value < 0. 05 between time points (∼1 500 remaining genes), along with a significant log2 fold change lower than -1 or higher than 1 between two consecutive stages, yielding 100 candidate genes.
6 and 78. 4, respectively; average Ct values of 38. 01 and 32. 58, respectively); and were therefore excluded from the list of potential candidates (Table  2 ). Moreover, although successful, YTH3 expression levels appear lower than expected based on the transcriptomic analysis. Discrepancies between amplification in qRT-PCR and normalized read counts could be due to low primer efficiency, a tendency to primer dimerization, or suboptimal amplification of transcripts in our standardized qRT-PCR protocol, as well as differences in normalization strategies between the two techniques. One should also note that for qRT-PCR, larval samples consisted of bacteriomes and whole guts, while in the previous transcriptomic dataset on which the candidate reference gene selection was based, only larval bacteriomes were dissected  (Ferrarini et al.
Evaluation of expression stability and ranking of candidate reference genes To identify the most suitable reference gene to compare transcript expression across S. oryzae development, we relied on the comparison of statistical methods recently conducted by  The rankings were obtained for adult stages only. SV, stability value; GM, geomean of ranking values. Optimal number of reference genes for normalization in S. oryzae at various developmental stages. The pairwise variation values obtained from GeNorm analyses were used to determine the minimum number of reference genes for normalization. The value of Vn/Vn + 1 < 0. 15 indicates that the minimum number of reference genes to be used for qPCR data normalization is n. Sundaram et al. (2019). After describing the merits and limitations of various comparison algorithms, the authors suggested the following as the best pipeline for the identification of reliable reference genes: (i) visual estimation of expression variation across stages, (ii) analysis of CV% values [  (Boda et al.
We took advantage of existing transcriptomic datasets produced from adult symbiotic and aposymbiotic weevils at different adult stages, to compare the expression of all candidate genes between symbiotic stages (Figure  5 ;  Ferrarini et al. , 2023). Differential expression analysis of all nine candidate genes between symbiotic and aposymbiotic weevils shows only three genes are equally expressed between symbiotic conditions: PAN3, TAO, and YTH3. Two of these genes were chosen as suitable candidates for symbiotic analysis, TAO and YTH3 and should constitute a good set for symbiotic/aposymbiotic comparisons.