dataesr/scientific-paragraphs-categorization huggingface dataset:
0. 2, ATACseq Genrich peak calling module (see "ChIP-seq, RELACS ChIP-seq, ATAC-seq and RNA-seq data processing" above). Peaks with a distance of up to 1 kb were merged and regions covered by merged peaks spanning more than 12 kb were discarded from downstream analysis. GRO-seq data processing and analysis. In order to match the custom GRO-seq protocol, an in-house protocol was developed. The workflow processes GRO-seq single-end library, and retains UMI-like randomers that are part of the custom GRO-seq protocol. First, adapters were trimmed using Cutadapt-v2. 5 85 (-a "NNNN"<TGGAATTCTCGGGTGCCAAGG > --overlap=3 --minimum-length=12 --max-n 0). UMI-like tetramers were trimmed and retained using UMI-tools-v1. 0 86. The SE library was mapped afterwards using Bowtie2-v2. 3. 3. 1  87  with default parameters and bowtie2 -local alignment.
First, genes were classified as zygotic (GRO-seq only, Stage 5 expressed only), maternally deposited (RNA-seq, unfertilized eggs only) or maternal-zygotic, if present in both. For this, TPMs of RNA-seq from Ctrl unfertilized eggs were computed (transcripts per million  95  ) (Supplementary Data 4) and genes with more than 5 TPMs in at least 3 replicates were retained. Genes were classified as Zeldazygotic, if they had TPM > 5 on Ctrl embryos, were Zelda dependent, zygotically expressed and active at ZGA by GRO-seq. H2A. Z RELACS quantification. The quantification H2A. Z was performed a) using the local scaling factors as defined in ref. 51  , with the focus on active promoters (±400 nt around TSS) and b) using the DEseq2 rlog transformation. For the local scaling factors, deeptools-v3. 4. 1' multiBamSummary 89 was use to count paired sequence reads in the defined promoters (multiBamSummary BED-file -extendReads) followed by the library-size corrected double ratios of IP and Input as described previously  51.
For the promoter quantification of H2Av RELACS IP and Input, reads per promoter were counted using featureCounts 94 (-t promoter -g transcript_id -f -Q 3 --primary -s 0 -p -B, subread-v1. 5. 3). Then, for the quantification, DEseq2-v1. 2. 6 was used to compute the rlog transformation of the read counts. Since RELACS is quantitative, the size factors for library size correction were computed from the input only. Finally, rlog values were averaged between replicates. Cut&Tag/Run processing and quantification. The quantitative Cut&Tag sequencing libraries contain DNA from D. melanogaster as well as a spike-in of lambda phage genome (Genbank: J02459. 1). Here, the spike-ins were used to rule out a global change in chromatin abundance. The sequencing libraries were mapped to a hybrid dm6 and Lambda phage genome using snakePipes-v2.
Z ChIP-seq data on wild type samples. In order to do that, alignments were filtered for their quality using 'samtools view -q 3' from samtools-v1. 10. 0 88. Fragment length of the filtered alignments were predicted using fraglen tool of NucHunter, these fragment lengths were used to run callnucs tool of NucHunter to predict the position of nucleosomes. Plus 1 nucleosomes (+1) were then predicted using the following criteria: 1) A nucleosome within 350 nucleotides from a TSS was predicted for at least one H2A. Z ChIP-seq replicate or 2) A nucleosome within 600 nucleotides from a TSS was predicted in both replicates, with less than 80 nucleotides difference from the center of the predicted nucleosomes between the two replicates. A promoter was considered H2A. Z positive, if there was a predicted +1 nucleosome overlapping or downstream the same promoter.
The -1 nucleosomes were identified in the same way, but scanning upstream of the TSS. Zld-dependent promoters identification and differential Pol II occupancy analysis. Differential Pol II occupancy was determined using the method described in ref. 10. In brief, reads were countered per transcript using featureCounts (-t transcript -g transcript_id -f -O -Q 3 --primary -s 0, subread-v1. 5. 3). Reads were counted multiple times if they overlap several isoforms. Then, per transcript read counts were processed with edgeR-v3. 28. 1  98  to compute differential Pol II occupancy. Like in the published method, transcripts spanning less than 125 nucleotides were discarded and only transcripts with read counts across replicates sum more that 10 reads were considered. This method was used to identify Zelda targets in dm6 (logFC < -1 and FDR < 0.
01) using published Pol II ChIP-seq data in Zld mutants  10. Pol II occupancy per transcript was quantified using edgeR's FPKM calculations. This same method was also used to quantify Pol II in DomKD and Ctrl ZGA embryos, with the following modifications: First, edgeR norm. factors were computed on our Zelda zygotic targets (see "Maternal/Zygotic classification and Zelda zygotic target definition" above). Differential transcripts were identified using the likelihood ratio test (edgeR::glmLRT). Then, unique promoters were identified and selected based on the most significant change among all transcripts for a given promoter (min FDR, edgeR results). Motif enrichment analysis. Motif enrichment analysis was performed using MEME-suite-v5. 0. 2 AME 99 with default parameters. Unique promoters were used.
The core promoter analysis was performed on core promoters defined and constructed as -300 nt to 100 nt around the TSS. The core promoter motif database was constructed from Haberle & Stark, 2019  52  complemented with Ramalingam et al. , 2021  100  as well as a known Zelda motif (Motif ID: MA1462. 1)  101. The TF motif analysis was performed using regions -600nt to 400nt around the TSS using Jaspar 2020 motifs  101. For each of the three groups: H2A. Z positive, H2A. Z negative and Zelda-dependent, the corresponding background set was constructed from the other two groups. Gene ontology analysis. Gene Ontology analysis was performed using Metascape 56. The software of Metascape is freely available at: https://metascape. org and was used with default parameters. For Zelda-dependent and H2A.
Z negative analysis, a single list containing all genes in the group was used for each. For H2A. Z positive genes, 8 random lists with 1000 genes each were generated and analyzed in the "Multiple gene list" mode. Prior to visualization, the observed gene count was translated into a gene ratio (number of observed / number of genes in a particular term). For visualization, the top 5 or the top 20 significant GO terms were selected per group and ordered using hierarchical clustering on the distance of -log10 p-value. Mass spectrometry data analysis. Raw data was analyzed using MaxQuant v1. 6. 14. 0 102. Files were searched against a Drosophila melanogaster UniProt database containing Swiss-Prot and TrEMBL sequences (23,370 entries) plus an extended version of the MaxQuant contaminant database.
Acknowledgements We would like to thank our colleagues  Peter Becker ,  Alessandro Scacchetti  and  Kenneth Börner  from  LMU, Munich  for kindly sharing the DomA-, DomB-, and deltaDom-GFP-Flag flies, for useful feedback and for critical reading of the manuscript. We also acknowledge  Carla Margulies  her generous gift of Rpb3 antibody,  Melissa Harrison  her generous gift of Zelda antibody,  Vanja Haberle  and  Alexander Stark  for providing the core promoter motif database. We are particularly grateful to  Eva Loeser ,  Melanie Schaechtle ,  Nazerke Antinbayeva  and  Filippo Ciabrelli  from the  Iovino lab, the Bioinformatics and Sequencing  facilities at the  Max Planck Institute of Immunobiology and Epigenetics (MPI-IE) ; as well as the  Imaging facility, Proteomics facility  ( Gerhard Mittler ,  Witold Szymanski  and  Yaarub Musa ), and Fly facility at  MPI-IE.
Data availability The data that support this study are available from the corresponding author upon reasonable request. The sequencing data generated in this study have been deposited in the NCBI's Gene Expression Omnibus and are accessible through GEO Series accession number GSE173240. The mass-spectrometry proteomics data generated in this study have been deposited to the ProteomeXchange Consortium via the PRIDE partner repository with the dataset identifier PXD029061. MNase-seq data presented in Fig. 1a , was obtained from ref. 22  , and is accessible through GEO accession numbers: GSM3736319, GSM3736320, GSM3736321, GSM3736322, GSM3736323, GSM3736324, GSM3736325. RNA-seq data presented in Supplementary Fig. 3m-o  was obtained from ref. 57  and is accessible through SRA: SRP001696. Specific samples used for this study have the accession numbers SRR1197327 to SRR1197338 and SRR1197363 to SRR1197370.
Sequencing and bioinformatics analyses Amplicons were sequenced by the Get-PlaGe sequencing service (INRA, Toulouse, France) using Illumina MiSeq 250 bp paired-end technology. Bioinformatics processing of the data was performed using the method described by  Escudié et al. , 2018  on the Galaxy FROGS pipeline  (Afgan et al. , 2018). After a pre-processing step (merging, denoising, and dereplications of the reads), the sequences were clustered into Operational Taxonomic Units with an aggregation distance of three bases. OTUs containing less than 0. 0005% of the total sequences were deleted, as were chimeric OTUs. Taxonomic assignments were performed using the Silva database v. 128  (Pruesse et al. , 2007). The number of sequences per sample was normalized based on the minimum number of sequences per sample found (7744 sequences).
The sequence data have been deposited in GenBank under accession number PRJNA648017. The statistical analyses were performed using R Studio version 3. 2. 3 software (http://www. r-project. org). Diversity indices (Richness, Chao, Exponential of Shannon, and Inverse of Simpson) were calculated using Vegan and Bat packages. The differences in the diversity index between the different samples and conditions were tested using the Kruskal-Wallis test. Non-metric distance scaling (NMDS) plots of the community data were generated using the Bray-Curtis distance measures with the Phyloseq and ggplots2 packages. Heatmaps and Venn diagrams were produced using gplots package.
Sequence data analysis, community diversity indices, and beta diversity The microbial community composition was evaluated by amplicon sequencing of the 16 rRNA transcripts and the 16S rRNA gene in order to compare the active and the total microbial community. Samples collected in triplicate at the different sampling times (T0, T1, and T2) and under both the mesophilic and the thermophilic conditions were analyzed by amplicon sequencing of the 16S rRNA transcripts. The digestates sampled at the initial and the final sampling time (T0 and T2) were analyzed by amplicon sequencing of the 16S rRNA gene only. A total of 650,496 sequences were collected after normalization (7744 sequences per sample). Almost 96. 3% of the total sequences were assigned to the bacterial domain, while the remaining 3.
Acknowledgements The authors are grateful for the financial support from the  ANRT  through the thesis of  Guillaume Cazaudehore  (Grant Number  2018/ 1080 ). The authors also thank the European financial support ( Interreg V-A Spain-France-Andorra -Poctefa 2014-2020) attributed to the  BIOPLAST  project (Grant number  AF A253/16 ) as part of the  Interreg Poctefa program. The authors are also grateful to the  Nouvelle Aquitaine region (FRANCE)  for the financial support through the  FEDER program , which allowed us to perform the microbial analysis on the various assays performed in this study.
The fluorescence values were normalized by the controls (vehicle treated cells) and expressed as percent of proliferation or viability. The IC50 values of mycotoxins on cell proliferation or viability (i. e. , the concentration of derivative causing a reduction of 50% of the cell division or survival) were calculated using GraphPad ® Prism 7 software (San Diego, CA, USA). t-Test and two ways ANOVA analyses were used to address the significant differences between mean values with significance set at p < 0. 05.
Intensive chayote cultivation in Réunion almost disappeared in the 2000s due to significant yield losses from fruit fly attacks on this historically important crop (Dacus ciliatus, Zeugodacus cuurbitae and Dacus demmerezi). Since the late 2000s, the adoption of agroecological crop protection practices have led to the effective management of fruit fly populations, a significant reduction in pesticide use, an increase in chayote production and plantations, and the development of organic production. To assist in fruit fly management, a qualitative model which simulates fruit fly damage to chayote crops, known as IPSIM-chayote, was developed, providing satisfactory prediction results. It has a user-friendly interface and is now available free of charge online, in three languages (French, English and Spanish): https://pvbmt-apps.
g. Zadoks, 1981). However, these models generally do not accurately take into account cropping practices  (Aubertot et al. , 2005)  or often ignore crop losses  (Savary et al. , 2006). On the other hand, specific damage mechanisms have been incorporated into some crop growth models to simulate yield losses  (Savary et al. , 2018). In addition, these models often take into account only one pest and a limited number of processes. In order to overcome these limitations, the IPSIM modeling platform was proposed  (Aubertot and Robin, 2013). This innovative model allows users to combine four sources of knowledge: (1) expert knowledge (from researchers, agricultural engineers, crop advisers, farmers); (2) scientific and technical literature; (3) datasets from field experiments and commercial field regional diagnoses (i.
e. data obtained from field observations performed by agricultural advisers and researchers), and (4) simulation models. The primary characteristic of this approach is that it considers nominal or ordinal input variables to predict classes of injury. In IPSIM approaches, accuracy is preferred to precision. This allows users to benefit from a broader source of information (notably datasets), and helps to promote adoption of the model. It generates transparent, flexible, and easy-to-understand hierarchical aggregative models that can be designed collectively by practitioners. These properties make models designed with the IPSIM approach great qualitative predictive tools to aid in decision making, adapted for communication and teaching for a range of stakeholders. Based on a literature review of fruit fly models on horticultural (fruit and vegetable) crops with emphasis on cucurbits, thermal time (TT), regression, mechanistic and artificial intelligence (AI) models have been proposed  (Inayatullah et al.
Model building process After reviewing the literature and acquiring expertise on the subject (Supplementary data, Section 1), the IPSIM-chayote model was built using the method described on the IPSIM website (www6. inra. fr/ipsim, Robin et al. , 2018)  and in previous studies (e. g. Robin et al. , 2013). It is based on the DEX method, implemented with the DEXi software  (Bohanec, 2020). DEXi software can support hierarchical qualitative models, such as DEXiPM  (Pelzer et al. , 2012)  which was initially used for the assessment of agroecosystems described by expert knowledge  (Angevin et al. , 2017). IPSIM-chayote was designed in four phases which are presented in the following sections  (Bohanec, 2003) : (i) identification and organization of attributes (main variables that may affect fruit fly damage intensity and their interdependent relationships), (ii) definition of qualitative attribute scales for each attribute, (iii) definition of aggregative tables that synthesize the impact of combinations of attributes to determine the final intensity and (iv) calculation of the attribute weights in order to summarize the influence of each attribute to the final output.
Aggregative tables The aggregative tables are based on scientific and technical knowledge. They summarize the influence of attribute interactions at all levels. The aggregation is performed via sets of qualitative "if-then" decision rules. In DEXi, the rules corresponding to each aggregated attribute are gathered in tabular form that corresponds to aggregative tables, initially called "utility functions". The software then performs a bottom-up aggregation from input attributes toward the root of the model, i. e. the intensity of fruit fly damages on chayote.
Description of data collection used for model evaluation To take into account an observed situation to assess the predictive quality of the model, it is necessary to have information on the intensity of fruit fly attacks, cropping practices, the environment around the plot and the climate. Depending on the nature of the information available, quantitative data were converted into qualitative data to obtain variables compatible with the IPSIM framework. For example, we translated quantitative data of the type "percentage of fruit punctured by fruit flies" into qualitative data, ranging from "zero damage" to "very high damage". Intensity of injuries is here considered as similar as intensity of damages. The data collection and the dataset used to evaluate the model came from three sources: surveys carried out with chayote producers during field visits; bioecological studies on chayote flies by CIRAD (Centre for International Cooperation in Agronomic Research for Development) and tests conducted by FDGDON (Departmental Federation of Defence Groups against Harmful Organisms).
Assessment of predictive quality The evaluation stage compared observed and simulated classes of fruit fly injuries using independent datasets including a large number of various production situations and crop management options  (Aubertot and Robin, 2013). In order to assess its predictive quality, IPSIM-chayote was used with the dataset described in the preceding section. It is important to state that this dataset was not used to fine-tune the model and constitutes an independent dataset for its evaluation. A confusion matrix was built to compare predictions to observations. We used several metrics (Table  2 ) to measure the predictive quality of IPSIM-chayote. Accuracy is defined as the rate of correct predictions for the entire dataset  (Nguwi and Cho, 2010). The Cohen's quadratic weighted Kappa  (Agresti, 2010)  can be interpreted as the proportion of variability explained by the model  (Fleiss and Cohen, 1973).
Development of the user interface A new user interface was created which is more user-friendly than the DEXi software, which is a generic software, not aimed at end-users such as farmers. This new interface is specific to users and for a specific purpose. It allows the authors to transfer and share the IPSIMchayote damage prediction model. Fig. 2  presents the conceptual model of the user interface. This interface will allow researchers, technicians and agricultural advisers to interact with the model more easily, and facilitates the exchange of current knowledge on the effect of agroecological practices on chayote fruit damage, to allow farmers to understand and predict fruit fly damage on their own plots. It aims to.
Season The specifications for the user interface design were established after evaluation of the IPSIM-chayote model and are presented below. The user interface had to use the initial model based on the DEX method, and had to be free, open-source and accessible online from any device (smartphone, tablet or computer). It also had to be intuitive to use, and be available in several languages. 1  For statistical and information technology purposes, the application must be compatible with the R programming language (R Core Team, 2019), which is free and widely used by the scientific community today. To meet these specifications, Shiny was chosen  (Chang et al. , 2019). Shiny allows variables (inputs) to be modified and the results (outputs) of the model, coded in R, to be interactively and dynamically obtained through a user-friendly interface.
Prediction quality analysis The confusion matrix obtained is presented in Fig. 5. The associated statistical criteria are presented in Table  4. The results of the confusion matrix were satisfying as most situations were correctly predicted and errors did not exceed two classes of difference. This was confirmed by good accuracy (58% of situations were perfectly predicted), and the Fig. 3. Organization of IPSIM-chayote (structure, attributes and value scales). On the left-hand side, the hierarchical structure of IPSIM-chayote (screenshot of the DEXi software). Bold and non-bold terms represent aggregated and basic attributes, respectively. On the right-hand side, the value scale used for each attribute of IPSIM-chayote. Red represents the value beneficial to chayote fruit flies and detrimonial to chayote.
Black represents a neutral value. Green represents the value beneficial to chayote and detrimental to fruit flies. (For interpretation of the references to colour in this figure legend, the reader is referred to the Web version of this article. ) Fig. 4. "Fruit fly management" attribute table of IPSIM-chayote (screenshot of the DEXi software), decision rules and values of this attribute for the eight possible combinations (two levels of sanitation, two levels of sexual trapping, and two levels of adulticide baits).
79;  McHugh, 2012)  and makes it possible to accurately predict the intensity of fruit fly damage to chayote crops. This is of key interest for producers, agricultural technicians and teachers, researchers, students, public authorities. This type of model has multiple uses: a prediction and decision-making tool, an educational resource for agroecological crop protection, a resource for scientific investigation and exchange and a planning and forecasting tool to assist in agricultural development. The use and transfer of IPSIM-chayote has been facilitated by the simple, ergonomic user interface which is available in three languages and can be used on any computer, tablet or smartphone, free of charge.
In order to address the portability issue of DEXi, we developed a user-friendly graphical application of IPSIM-chayote through Shiny. This R package allows the user to create interactive web applications, by converting R code to HTML, which are hosted from the cloud or on a physical server  (Beeley, 2016). Explanations on how to use the user interface can be found in the Supplementary data, Section 4. In addition, examples of use for specific situations are also given in the Supplementary data, Section 5. Figs. 7 and 8 represent screenshots corresponding to a situation of agroecological management of chayote flies. A contrasting situation (agrochemical management of chayote flies) is also presented in the Supplementary data, Section 5.
Transfer to users The primary aim during the development of the model was to make it accessible to a wide range of users. All groups of users were consulted in the design phase of the IPSIM-chayote model, as well as during the transfer phase. The transfer phase took place from July 2019 to February 2020. In Réunion, training sessions were organized in Saint-Pierre with agricultural technicians, DAAF officials and researchers, in Salazie (with chayote producers) and in Saint-Paul (with agricultural instructors). In addition, the IPSIM-chayote model is now taught in university modules at Bachelor and Master level at the University of Réunion. More information on the IPSIM-chayote model is available here: https://pvbmt -apps. cirad. fr/apps/ipsim-chayote/?lang=en. The knowledge gained developing the IPSIM-chayote model can be used for other qualitative IPSIM-type models.