dataesr/scientific-paragraphs-categorization huggingface dataset:
For the interview analysis, we transcribed each interview and the transcripts were corrected and imported into New NVivo (QSR International Pty) for coding. The interviews were analyzed using thematic analysis adapting the steps suggested by  Braun and Clarke (2006). First, the transcripts were analyzed by the authors breaking down the data and re-organizing it through coding. The codebook derived both from the analytical framework  (Skrimizea et al. , 2020)  and from the recurring themes emerging from the data which were not evident in the existing framework (see Box S3 in Supplementary Material)  (Fereday and Muir-Cochrane, 2006). The codebook was used to sort concepts within the interview text according to one or more sub-codes. Text coded within each sub-code could then be quantified and crosstabulated in NVivo to identify common themes.
ACKNOWLEDGMENTS This research would not have been possible without the engagement of all interviewees and partners in our research from all three case studies. We are incredibly grateful for their time, knowledge, support and encouragement throughout our research. We are also very grateful for the help and expertise of  Estelle Balian , from the  FEA-L company , our professional facilitator for the workshops. Many thanks to  Pauline Geoffroy  for her brilliant film-making-despite a few bee stings, Pauline was undeterred, following us in all interviews and producing beautiful films.
Statistical analysis Within a single trial, statistical tests were done to compare the control group and groups with a change in incubation conditions. Hatchability, mortalities and sex ratio were compared by using a Chi-squared test. Weights and internal temperatures were compared by using one-way Anova or student and Wilcoxon tests depending on normality and homoscedasticity. For these tests, the individual was used as the experimental unit, and the variable response was studied according to the treatment. All statistical analyses were performed with R software version 3. 6. 2. Statistical testing was performed on datasets at a statistical significance of 5%.
Data Pre-processing and Transcriptome Assembly Once the raw data was obtained, a reading cleaning process was carried out to remove contaminants, low quality readings and adapters not previously removed using SeqTrimBB 1. A quality filter Q > 20 was applied as previously described  (Ortigosa et al. , 2021). Maritime pine reference transcriptome  (Canales et al. , 2014)  was implemented with sequencing data produced during this study (Supplementary Table  1 ). Reads were assembled using Trinity 2. 11  (Haas et al. , 2013)  as described in  Ortigosa et al. (2021). Redundancy was reduced using CD-HIT-EST  (Li and Godzik, 2006). This preliminary assembly was the input of the Mira assembler software  (Chevreux et al. , 2004). The maritime pine transcriptome  (Canales et al. , 2014; Cañas et al.
, 2017)  was used as the reference for the read mapping that was performed with BWA using the MEM option  (Li and Durbin, 2009). Expression level was obtained by reading counting using the phyton script sam2counts 2. Differentially expressed (DE) transcripts were identified using the edgeR package for R, and then normalized by cpm and filtered with 2 cpm in at least 6 samples  (Robinson et al. , 2010). Samples were grouped by SE or ZE stage. After differential expression analysis only transcripts with FDR < 0. 05 and a Fold Change >1 were considered as differentially expressed. These RNA-seq data have been deposited in the NCBI's Gene Expression Omnibus  (Edgar et al. , 2002)  and are accessible through GEO Series under the accession number GSE194039 3. Additionally, RNA seq and network results are accessible through a database in HTML format that can be installable with R packages and downloaded from GitHub 4.
Construction of Co-expression Networks and Mining of Hub Genes Using the normalized data from the RNA-seq reads, the coexpression networks were established. In this way we can obtain the grouped genes thanks to their joint expression profiles using a statistical correlation test. WGCNA R (R Core Team, 2021) package was necessary for the co-expression network construction  (Langfelder and Horvath, 2008). The function pickSoftThreshold was used to choose an appropriate softthresholding power based on a scale-free topology criterion. The weighted adjacency matrix was constructed using the softthresholding power. The relationships of the elements within these groups allow us to know those genes involved in the regulation of the candidate genes obtained in the previous analysis of RNA-seq and/or in the biological pathways of interest.
These "hub genes" are those that maintain a greater number of significant interactions with the rest of the elements within the co-expression groups. To determine these "hub genes, " the topological adjacency matrix generated by WGCNA was used to determine those genes that have high connectivity, that is the 10% of the genes with more connections within each module and that have a high affiliation value per module  (Yang et al. , 2019). The identification of regulatory elements that take part in biological pathways of interest is intended. The relationship between one gene and all other ones in the analysis was incorporated, and the adjacency matrix was transformed into the topological matrix (TOM)  (Yip and Horvath, 2007). After hierarchical clustering, highly interconnected genes were assigned to the same module  (Ravasz et al.
Similarly, two modules showed positive correlation but only with zygotic stages: red (723 genes) and the false module gray (8). At last, two modules also correlated with zygotic stages but negatively: green (2,145 genes) and pink (541). These modules representing a specific correlation may include genes that are specifically involved in either somatic or zygotic developmental stages. To determine functions associated with the modules, enrichment analysis was performed using the AgriGO platform  (Tian et al. , 2017). Distribution of functional categories corresponding to each co-expression module is included in Supplementary File 2. It is worth highlighting the blue module made up of 6,462 genes in which there is a broad representation of genes involved in regulation of biological processes: transcriptional regulation (518), regulation of gene expression (628) and regulation of metabolic processes such as the metabolic regulation of nitrogenous compounds (574).
Search for Hub Genes During Maritime Pine Embryo Development Next, we searched for genes with a high level of connectivity in each module. As described in section "Materials and Methods" the 10% of the transcripts with more connections were considered hub genes. Using this criterion, a total of 1,657 hub genes were listed across modules and annotated in Supplementary Table  3 , and further characterized (Figure  4 ). Establishing a hierarchical cluster of hub genes based on the whole dataset, we obtained a heatmap showing the relationship of these highly connected genes with the corresponding embryo developmental stage. As shown in Figure  4A , a greater generalized expression of the hub genes was observed at early stages of the development in both zygotic and somatic embryos. To gain insights into the regulation of maritime pine embryos special attention was given to the identification of TFs that were at the same time hub genes (Supplementary Table  4 ).
Cytoscape version 3. 9. 1 platform was used to visualize the network  (Shannon et al. , 2003). Square symbols mean TF, triangles mean no-TF, blue edges indicate negative associations and red edges indicate positive associations. Color inside each node means the co-expression module where the gene is included. Gray symbols represent genes that in the B-ARR interaction network are only expressed in the cotyledonary stage (C) of the zygotic embryos. studied. Gene expression related to arginine metabolism is shown in Figure  7. The relative abundance of transcripts for glutamine synthetase (GS1b) in the four embryogenic lines was higher than that in zygotic embryos at either the EC-ES2 or C-ES3 stage. In contrast, the expression levels of Asparaginase (ASPG) were lower in the four lines at the EC-ES2 stage, although similar expression levels were found at the C-ES3 stage, with the exception of the PN519 line, which still exhibited lower transcript abundance than the zygotic line.
FUNDING Research work was supported by grants from the "Ministerio de Ciencia e Innovación" (BIO2015-69285-R and RTI2018-094041-B-I00),"Junta de Andalucía" (BIO-114), and "Conseil Régional de Nouvelle-Aquitaine" (17006494-0741, EMBRYOsoMATURE project). Handling of embryogenic lines at FCBA (initiation, cryopreservation, multiplication, and maturation) benefited from the technical support of the XYLOBIOTECH platform (ANR-10-EQPX-16).
The collective 3D-EM data argued that in the absence of H7, D13 forms a dense 3D network probably made by interconnected by short membrane elements, forming a regular pattern, intimately associated with ER-cisternae. The structure of the D13-spots was next analyzed with a hybrid cryoET method. Refrozen Tokuyasu sections show D13 trimers but a failure to form hexagons. Under wild-type infection conditions D13 forms hexamers of D13-trimers. The trimers measure 7-9 nm in diameter, while the hexamers measure roughly 20-22 nm from vertex to vertex, depending on the EM method used  4, 6. The average width of 22 nm of the D13-spots, measured in the STEM-tomograms, suggested that D13 might form hexamers but that lattice formation was impaired in the absence of H7. However, the conventional embedding and contrasting used, displayed D13 as uniform electron-dense dots and failed to resolve trimers or hexamers.
Densities recognized along the D13 trimeric particles by the application of the CNN trained algorithm that are located outside the D13 cluster are displayed in grey. In (c) the slice number 50 of the same volume is displayed. Here the green circles indicate the particles that are picked for the generation of the initial averaged model. In (d-f) a zoom of the respective boxed areas of (a-c). In (g,h) two perpendicular views (bottom and side view) of the initial D13 model in green superimposed to the crystal structure of the D13 trimer (dark green ribbons, PDB id 2YGB).
For STEM tomography, 800 nm sections of resin-embedded cells were cut and placed on bar grids without formvar coating. Gold particles were placed on both sides of the sections and overlayed with a thin layer of carbon. STEM tilt series were acquired with a Jeol JEM-2100F at 200 kV and the tilt angles -72° to + 72° with 1. 5° increment. The tilt series were recorded with the EM-Tools software (TVIPS, Tietz) and each image had 1024 × 1024 with the pixel size 2. 74 nm. All tilt series were reconstructed in IMOD using protein A coupled to 10 nm gold as fiducials  24. Movies and rendering were made in IMOD and Fiji. Quantification. Cryo-tomograms were binned 4 times using the binvol function of IMOD and the Mitchell antialiasing filter before further analysis. Smaller volumes were extracted, and their contrast was inverted using the mrcbyte function of IMOD with the -R flag on, prior to proceed with ChimeraX 25,26 iso-surface representations (Fig.
The rest of the remaining particles were used for the generation of the initial model using the stochastic gradient descent algorithm of eman2. 9  30  applying a threefold symmetry axis. The D13 trimer crystal structure was fitted to the low-resolution resulting model of D13 in ChimeraX (Fig. 5g, h ). Cloning, transfections and light microscopy. For transfection experiments, constructs of HA-tagged wild-type or mutants H7 were used. H7 gene was amplified by PCR using vaccinia virus genomic DNA as template. PCR product was then digested with NotI and SacI and subcloned into the modified pBluescript II vector  31. The same protocol was used for mutant's constructs using strings DNA fragments (Thermofisher) as template for PCR. All constructs were validated by sequencing. HeLa cells grown on glass slides were infected with VACV H7ind in the presence of 100 μM IPTG and fixed at 12 h post-infection with 3% PFA.
In (c) the slice number 50 of the same volume is displayed. Here the green circles indicate the particles that are picked for the generation of the initial averaged model. In (d-f) a zoom of the respective boxed areas of (a-c). In (g,h) two perpendicular views (bottom and side view) of the initial D13 model in green superimposed to the crystal structure of the D13 trimer (dark green ribbons, PDB id 2YGB).
Acknowledgements We thank the EM facilities of  Institut Pasteur (Paris)  and  Paul Ehrlich Institute (Langen, Germany)  for providing the resources to carry out the experiments,  Bernie Moss (NIH)  for providing us with the H7 inducible virus. Sylvia Panitz  ( PEI ) for excellent technical assistance and  Beata Turonova  ( Max Planck Institute, Frankfurt ) for advice on the image processing. Part of this work was funded by a joint  ANR/DFG  grant ( Agence national de recherche and Deutsch Forschungs Gemeinschaft- ANR-16-CE92-0008 ) to MFK and JKL. JKL and ST are funded by the excellence cluster ' Loewe-DRUID ' (Landes Offensive zur Entwicklung wissenschaftlich-oekonomischer Excellence-Drug targets against poverty-related and neglected diseases) of the university of  Giessen , and the state Hesse, Germany.
The Fruit-Bearing Cutting Technique as a Tool to Investigate Differences between Tempranillo Blanco and Tinto Different methods have been used to evaluate differences between Tempranillo Tinto and Tempranillo Blanco. This review includes genetic approaches, agronomic characterization and evaluation of the resulting wines under field conditions, response to climatic variables both in the field and under fully controlled conditions in greenhouses, and response to simulations of future, foreseen conditions related to climate change. Fruit-bearing cutting is an experimental approach that allows growing a grapevine plant in a pot, which facilitates its management and where abiotic stress factors can be applied either individually or in combination in a fully controlled way. In this review, data from Tempranillo Blanco and Tinto fruit-bearing cuttings were used.
Lipid Class of Rapeseed Lecithin The lipid class of rapeseed lecithin was determined by the Iatroscan device (Iatron Laboratories Inc. , Tokyo, Japan). First, the samples were spotted on Chormarod (quartz wires with silica coating). Then, Chromarod was exposed to a mixture of solvents, including hexane, diethyl ether, and formic acid for 20 min, and then dried at 100 • C for 1 min. Eventually, the neutral lipids were detected by Iatroscan. In the next stage, the mixture of the polar solvent, including chloroform, methanol, and ammonia, was used for the identification of polar lipids. Then, internal software ChromStar was exerted for the interpretation of the results  [28].
Under constant stirring at 25 • C (environmental condition), the samples were excited and emitted at 360 nm and 430 nm, respectively. After data analysis by Magellan 7 software (Tecan Group Ltd. , Mannedorf, Switzerland), the intensity of polarization of each sample (P) was calculated using the following Equation (3): P = I -GL ⊥ I + 2GI ⊥ (3) In this relation, I and I ⊥ represent the extent of parallel and perpendicular fluorescent intensity to the excitation plane. G denotes the transmission factor, which is associated with the features of the device. The inverse of polarization, (1/P), was considered membrane fluidity.
The Flow Behavior Tests To measure the shear-steady viscosity and shear stress, cone and plane geometry (1 • , 50 mm) was used. To determine the steady-state shear viscosity, the shear rate was increased within the range of 10 -3 -10 3 S -1. Then, data of the shear stress against shear rate were fitted with the power-law model (Equation (  4 )). σ = kγ n (4) In this equation, σ is the shear stress (Pa), k is the consistency index, and n shows the flow behavior index. Furthermore, to estimate the plastic behavior of nanoliposome samples, the square root of shear stress was plotted against the square root of shear rate, whose data were fitted with a structure-based model, the Casson model (Equation (  5 )). (σ ) 0. 5 = k 0C + k c (γ) 0. 5 (5) In Equation (  5 ), Casson yield stress (σ 0C ) and Casson plastic viscosity (η casson ) were calculated through σ 0C = (k 0C ) 2 and η casson = (k c ) 2 , respectively.
Rheological Characteristics 3. 9. 1. Flow Properties In steady-state rheological tests, the flow behavior of nanoliposome samples with different formulations was evaluated. Also, the obtained data were fitted to power law and Casson models (Figure  2 ), and the parameters related to these models are presented in Table  3. As shown (Figure  2a ), the shear stress had virtually a linear dependence on the shear rate for LP-FR-UH and LP-AC-UH samples. However, the addition of chitosan to the colloidal systems led to changes in the flow behavior. As presented in Table  3 , based on the k value, the samples with chitosan coating had higher apparent viscosity compared to samples without it. Furthermore, concerning n values, the samples without and with chitosan revealed Newtonian and shear thinning (pseudoplastic) behavior, respectively.
Currently, only one bioinformatic tool specifically address the question of the methylation of TE sequences. This tool, EpiTEome  (Daron and Slotkin, 2017) , aims at detecting both new TE insertion sites and their corresponding DNA methylation status. To achieve this goal, the first step is to use BS-seq data to identify new TE insertions when compared to a reference genome. Then the methylation status of these insertions is assessed as well as at the region surrounding them. The tool takes as input BS-seq reads that do not map on the reference genome using specific mappers designed to handle such data like Bismark  (Krueger and Andrews, 2011). These reads are then split and each split-read is mapped on the reference genome to identify breakpoint locations on the read so that part of the read corresponds to a TE and the other part to a genomic sequence into which the new insertion occurred.
, 2021). Chromosome interactions may also be interesting to study on the TE point of view. Since it has been shown that some TEs may be associated to particular 3D conformation  (Cournac et al. , 2016; Lu et al. , 2021) , they may be implicated in long distance gene regulation. Very recently, the pipeline HiC-TE  (Lexa et al. , 2021;  https://gitlab. fi. muni. cz/lexa/hic-te) was proposed to study interactions in nuclei of different repeat sequences, and in particular TEs, in long distance or interchromosomal interactions. The tool identifies and quantifies the interactions of repeats in the 3D organization of the genome using as an input HiC-seq data. It can work according two modes, reference-based and reference-free, to enhance the robustness of the results. Using HiC-seq data can also allow the identification of new TE insertions as proposed by the tool HiTea  (Jain et al.
, 2021). This approach uses reads that are discarded in classical analyses of HiC data and map them on TE consensus to identify non reference TE insertions. In their application, the authors used split HiC read information and coverage to detect insertions of three major classes of TEs active in human. They were able to detect 1,085 new insertions using Hi-C data from the GM12878 cell line, which represent 62% of the new insertions identified by whole genome re-sequencing using long reads.
(Babaian et al. , 2019)9)iption and small RNA analysis. (Kongetal. , 2019)or ExplorATE (https://github. com/FemeniasM/ExplorATE_shell_script). The first tool takes advantage of the weight-mapping method Salmon(Patro et al. , 2017)using RepeatMasker outputs and performs expression analysis down to the TE sub-family level. The quantification at individual locus level is accomplished with regard to the host gene positions i. e. whether the sub-family copies are intergenic, intronic or exonic. The second tool is an R package aims at identifying active TEs among RNA-seq data. It deals with both co-transcription and multi-mapping issues, and performs differential expression analyses. It may also be interesting to directly assess the effect of TEs on gene transcription. This is what is proposed using the R package TEffectR(Karakülah et al.
(2019) intron sequences ExplorATE Copy-specific TE expression Genome sequence, gene Salmon; target transcript assignation based on https://github. com/ annotation, TE annotation RNA-seq the percentage of identity for a class/family FemeniasM/ExplorATE_ of TEs shell_script TEffectR Influence of TEs on neighboring Gene annotation, TE annotation, Reads with 100% overlap with given TE regions Karakülah et al. (2019) gene expression aligned RNA-seq reads on genome are considered LIONS Identification and analysis of RNA-seq, References genome, Tophat2 (random assignment of best match) Babaian et al. (2019) chimeric TE-gene transcripts gene and TE annotation PiPipes Analyze piRNAs and TE-derived SmallRNA-seq, RNA-seq, TE STAR (mRNA) and Bowtie1 (smallRNA) (EM- Han et al. (2015) RNAs copies algorithm) PingPongPro Detection of ping-pong cycle SmallRNA-seq Weighted read count Uhrig and Klein, (2019) activity in piRNA-Seq data.
Specific gut bacteria species activate an SREBP-dependent lipogenesis in adult adipocytes Our previous data showed that Drosophila enteric infection by the phytopathogen E. cc affects lipogenesis in adult adipocytes  [20]. To further assess the effects of these bacteria of adult lipogenesis we monitored SREBP (Sterol Regulatory Element Binding Protein) activation in gnotobiotic flies orally infected with specific bacterial species (see Material and methods). SREBP is a conserved transcription factor that control lipid synthesis. Produced as a pro-peptide inserted into the endoplasmic reticulum (ER) membrane, it is post-translationally matured upon physiological modifications. In response to cellular lipids needs, SREBP exits the ER and travels to the Golgi apparatus where its active domain is freed by two successive proteolytic cleavages  [22].
Quantitative real-time PCR RNA from whole dissected organs (n = 12) was extracted with RNeasy Mini Kit. Three hundred ng of total-RNA was then reverse transcribed in 10 μl reaction volume using the Superscript III enzyme (Invitrogen) and random hexamer primers. Quantitative real-time PCR was performed on a CFX96 Real-Time PCR Detection System (BIO-RAD) in 96-well plates using the FastStart Universal SYBR Green Master (Sigma-Aldrich). The amount of mRNA detected was normalized to control rp49 mRNA values. Normalized data was used to quantify the relative levels of a given mRNA according to cycling threshold analysis (ΔCt). All datasets were organized and analyzed in Microsoft Excel 2016. Plasmid pP{LexA:: dSREBPg} An 8. 7 kb fragment (containing the entire dSREBP gene, 2. 9 kb upstream and 0.
This study was supported by the following grants:  Investissements d'Avenir-Labex INFORM , grant nr. ANR-11-LABX-0054 , (https:// anr. fr/) to JR;  ANR PEPTIMET , grant nr. ANR-18-CE15-0018-02 , (https://anr. fr/)to JR;  Equipe Fondation pour la Recherche Medicale , grant nr. EQU201903007783 , (https://www. frm. org/) to JR;  Institut Universitaire de France  (https://www. iufrance. fr/)to JR. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.