dataesr/scientific-paragraphs-categorization huggingface dataset:
led to similar values (Fig S9). To perform differential analysis, two options are implemented (Fig. S10 ). The first option is to apply a t-test for each k-mer on the log transformed counts, normalized with the previously computed NF. Transformation of raw counts in conjunction with linear model analysis have been successfully used for differential analysis of counts  [29]. We perform the t-test independently on each k-mer and avoid complex variance modeling strategies to reduce execution time of the analysis. The t-test option has been implemented in C in the dekupl-TtestFilter binary. Note that this t-test option is not appropriate for small samples  [30]. To increase the power of the analysis, in particular for small samples (typically less than 6 vs 6 libraries), we strongly advise to use the second option based on generalized linear model, implemented in the R package DESeq2  [19].
DE-kupl run on EMT data DE-kupl was run using RNA-seq libraries from  [20]  retrieved on the GEO web site under accession GSE75492. For stage "E" we used libraries GSM1956974, GSM1956975, GSM1956976, GSM1956977, GSM1956978, GSM1956979, and for stage "M" GSM1956992, GSM1956993, GSM1956994, GSM1956995, GSM1956996, GSM1956997. DE-kupl parameters were kmer_length 31, min_recurrence 6, min_recurrence_abundance 5, pvalue_threshold 0. 05, lib_type stranded, diff_method Ttest. Output files are provided as supplementary material. The DE-kupl contig summary table was analyzed interactively using R commands to extract lists of contigs based on the filtering rules described in Table  3. Visualization of selected contigs was performed with IGV  [35] , using the bed file produced by DE-kupl and read mapping files produced by STAR  [36].
Cross-validation DEkupl was applied to 8 skin and 8 colon libraries from GTEx  [24] : skin library IDs: SRR1308800, SRR1309051, SRR1309767, SRR1310075, SRR1311040, SRR1351501, SRR1400467, SRR1479595; colon library IDs: SRR1316343, SRR1396146, SRR1397292, SRR1477732, SRR1488307, SRR807751, SRR812697, SRR819486. DE-kupl parameters were kmer_length 31, min_recurrence 6, min_recurrence_abundance 5, pvalue_threshold 0. 05, lib_type unstranded, diff_method Ttest. DE-kupl contigs were interactively classified using R commands, applying the same rules as in Table  3. Classes asRNA and SNV-DU were not included since asRNA identification is not possible using the unstranded GTEx and HPA libraries, and we had no reason to expect common SNVs with differential usage in this dataset. DE contigs were sorted by fold-change and k-mer labels of the top 100 DE contigs in each class were extracted (50 for class 'unmapped' due to lower event number).
DEkupl output files and selected k-mers are provided in supplementary material. For cross-validation, we used the same 6 skin and 6 colon RNA-Seq data as in Figure  1  (10. 1126/science. 1260419, E-MTAB-2836). K-mers were counted in each library using Jellyfish with options k = 31 and -C (canonical k-mers) as GTEx data were unstranded. All k-mers selected from the GTEx analysis were queried against the jellyfish databases using jellyfish query command. Finally the extracted k-mers counts were processed with DESeq2  [19]  and the resulting adjusted p-values were plotted for each event class (Figure  8 ). Intersection of k-mers present in Gencode transcripts and RNA-Seq data from three tissues: bone marrow, skin and colon. The set of k-mers for each tissue was defined as the common k-mers shared by all six individuals.
B. Intersection of k-mers present in Gencode transcripts, the reference human genome (GRCh38) and RNA-Seq data (same as in A). B1. Repartition of k-mer abundances for each tissue represented in A and B. K-mers shared with Gencode are labelled as "GENCODE", among other k-mers, those shared with the human genome are labelled as "GRCh38", the remaining k-mers are labelled as "tissue-specific". The same procedure was applied in B2 and B3. B2. Repartition of k-mer diversity for each tissue. B3. Mapping statistics of k-mers labeled as "tissue-specific" in B2. These k-mers were first mapped to Gencode transcripts, and unmapped k-mers were then mapped to the GRCh38 reference using Bowtie1, allowing up to 2 mismatches in a 31-mer. 2  Principal Component Analysis on non-reference k-mers discriminates tissues.
Figure1The diversity of 31-mers in RNA-Seq libraries exceeds that of reference sequences. A. Intersection of k-mers present in Gencode transcripts and RNA-Seq data from three tissues: bone marrow, skin and colon. The set of k-mers for each tissue was defined as the common k-mers shared by all six individuals. B. Intersection of k-mers present in Gencode transcripts, the reference human genome (GRCh38) and RNA-Seq data (same as in A). B1. Repartition of k-mer abundances for each tissue represented in A and B. K-mers shared with Gencode are labelled as "GENCODE", among other k-mers, those shared with the human genome are labelled as "GRCh38", the remaining k-mers are labelled as "tissue-specific". The same procedure was applied in B2 and B3. B2. Repartition of k-mer diversity for each tissue.
Acknowledgements We thank  Damien Drubay  for useful statistical discussions and  Emilie Drouineau  for setting up the DE-kupl Docker container. This project was supported in part by grants " Plan Cancer -Systems Biology " # bio2014-04  and  ANR-10-INBS-0009  ( France Génomique ) from  Agence Nationale pour la Recherche  to DG. GTEx data was downloaded from the dbGaP web site under phs000178/GRU.
We first adjusted the parameters for saccades (variant S1) and then tested two extreme hypotheses of how manual decision and non-decision times could compare to saccadic ones (Section 4): -M1 assumes manual responses share the exact same decision process as saccades, while the extra delay compared to saccades entirely reflects the longer and more variable non-decision times. -M2 assumes manual response non-decision time is the minimum delay suggested by electrophysiology, and the remainder of the extra delay compared to saccades reflects longer decision times. To anticipate, we find that variant M1 provided a much better match to our data, and can be further improved by finetuning the balance and timings of inputs (i. e. the decision process). To help constrain these changes, we acquired a second independent dataset (Experiment 2), using the same participants for the saccadic and manual versions of the same task, at SOAs likely to show interference -0, 20 and 40 ms (Section 5).
Stimuli and procedure We used published saccadic data from  Bompas and Sumner (2011) , which we directly compared with new data from new participants using the same protocol but with button press responses instead of saccades. Targets could appear randomly on the left or right of fixation, and small distractors appeared in the alternative target location (i. e. a task-relevant location) randomly on 5 out of 6 trials (83%). Stimuli were displayed binocularly with 72 cm viewing distance on a Sony Trinitron 19 in. GDM-F400T9 monitor, driven by a Cambridge Research Systems (CRS) ViSaGe graphics board at 100 Hz, calibrated with a CRS ColorCal and associated software. Participants were instructed to fixate and fixation was monitored using the CRS high-speed (250 Hz) video eye-tracker mounted on a combined chin and headrest.
We repeated this procedure 10 times and compared the Kolmogorov-Smirnov (KS) distance between the raw manual RTs and the raw saccadic RTs to which noise randomly drawn from each distribution had been added. Manual latency distributions were obtained with a bin size of 4 ms (for consistency with the saccadic analysis using an eye-tracker at 250 Hz). To get robust estimates of dip timings, all distributions were smoothed using the same Gaussian kernel with 7 ms window and 3 ms standard deviation and interpolated to obtain 1 ms precision (previous work using twice more trials per condition used 5 and 1). This smoothing is necessary to get meaningful estimates from noisy data but it tends to underestimate estimates of dip onset (T 0 ) and minimum RT by about 7 ms. Because smoothing is consistently applied through all conditions, we did not correct for this.
The other parameters were inherited from the previously published version of the 200N DINASAUR model, and constant between modalities. Critically none of these parameters were allowed to vary between SOAs (SOA affects only the distractor arrival time). The numerous sources of non-linearity introduced in DINASAUR (the model has no analytical solutions) and the time taken by simulations make it currently impossible for us to perform formal fitting (see Section 8. 5. Limitations). Instead, we adopted an iterative, simulation-driven approach to constrain our free parameters in a sequential hypothesis-driven manner, in order to simply find whether the model is sufficient to capture the patterns of data (even if the parameters we use are not the exact optimal settings, nor necessarily the unique ones).
Our results and model did not require an increased threshold for manual responses, but such a situation can be accommodated (see the end of Section 6 where model variant M5 is discussed). It may be considered a weakness of DINASAUR that one parameter can be traded against another in this way, so that the data do not constrain a unique solution (see discussion of limitations below). Alternatively, such interplay between parameters helps us develop our understanding of models and what to look out for in data. In simpler models it is possible that differences in data that do not actually correspond to our conceptual understanding of threshold and caution are captured by differences in threshold because other parameters are absent. Thus we believe it is useful to understand how both simple and more complex models would capture the data, and which parameters can be traded against which.
Metabolic pathways are similar in M. persicae and A. pisum A global analysis of the metabolism enzymes of M. persicae was generated based on the annotated gene models (Additional file 3) and is available in the ArthropodaCyc metabolic database collection (http:// arthropodacyc. cycadsys. org/) [26]. Metabolic reconstruction in A. pisum has highlighted the metabolic complementarity between the aphid and its obligate bacterial symbiont, Buchnera aphidicola, with the symbiont generating essential amino acids for the aphid  [26]. We compared the amino acid metabolism pathways identified in the two clones of M. persicae with those previously identified in A. pisum  [27, 28]. A. pisum and the two M. persicae gene sets share 170 enzymes belonging to known amino acid metabolism pathways. A. pisum has 22 enzymes that were not found in either of the two M.
With the exception of the placement of Pediculus humanus, all phylogenetic relationships received maximum support and are in agreement with a recently published large-scale phylogenomic study of insects  [33]. Annotation of the M. persicae genome reveals a gene count approximately half that of the specialist aphid A. pisum and similar to that of other insect species (Fig. 1 ), implying that the massive increase in gene content observed in A. pisum  [28]  may not be a general feature of aphid species. Using our comparative dataset, we find that the larger gene count of A. pisum compared to M. persicae is explained by two features, an increase in lineage-specific genes and widespread duplication of genes from conserved families  -267 ). In addition to the differences observed between the two aphid species, there also appears to have been considerable change in gene content during aphid evolution relative to other insect orders.
Genome assembly and annotation Full details of genome assembly, annotation and quality control are given in Additional file 2. Briefly, the genomes of M. persicae clones G006 and clone O were independently assembled using a combination of short insert paired-end and mate-pair libraries (Additional file 1: Table  S1 ). Clone G006 was assembled with ALLPATHS-LG  [68]  and Clone O with ABySS  [69]  followed by scaffolding with SPPACE  [70]  and gapclosing with SOAP GapCloser  [71]. Repetitive elements were annotated in both genomes with the REPET package (v2. 0). We then predicted protein-coding genes for each genome using the AUGUSTUS  [72]  and Maker  [73]  gene annotation pipelines using protein, cDNA and RNA-seq alignments as evidence. A set of integrated gene models was derived from the AUGUSTUS and Maker gene predictions, along with the transcriptome and protein alignments, using EVidenceModeler  [74].
When multiple transcripts of a gene were present in the proteome the sequence with the longest CDS was used. Paralogous gene pairs were retained if they aligned over at least 150 amino acids with a minimum of 30% identity  [80]. For each protein, only the nearest paralog was retained (highest scoring BLASTP hit, excluding self-hits) and reciprocal hits were removed to create a non-redundant set of paralog pairs. For each paralog pair, a protein alignment was generated with muscle v. 3. 8. 31 [76]. These alignments were then used to guide codon alignments of the CDS of each paralog pair using PAL2NAL  [81]. From these codon alignments, pairwise d N and d S values were calculated with paml v4. 4 using YN00  [82]. Paralog pairs with d S > 2 were excluded from our analysis as they likely suffer from saturation.
A p value less than 0. 05 was considered to imply a significant gene family expansion. In total 6148 MCL families found in both aphid species and at least one other taxon were tested. MCL gene families are ordered by prevalence in taxa other than aphids with the most widespread families listed first. MCL families with at least one M. persicae member differentially expressed in the host swap RNA-seq experiment are highlighted in yellow. Each expanded MCL family is annotated with expression information for M. persicae family members in the host swap RNA-seq experiment (averaged across all six RNA-seq libraries and expressed in fragments per kilobase of transcript per million mapped reads (FPKM)), number and proportion of M. persicae members differentially expressed in the host swap RNA-seq experiment, InterProScan domains and descriptions, InterProScan GO terms and Blast2GO GO terms   S5.
M. persicae genes that are differentially expressed in aphids reared on different host plants. Genes that show differential expression (>1. 5-fold with 10% FDR) on Nicotiana benthamiana vs. Brassica rapa (Nb/Br) are listed and grouped by KEGG functional classification. Top: genes more highly expressed on B. rapa; bottom: more highly expressed on N. benthamiana. Fold-change is the average over three biological replicates on each host plant, p value and FDR-adjusted p value (padj) based on DE-seq analysis. Annotations were conducted by NCBI blastX using cDNA sequences. FC is fold-change of Nb expression vs. Br. The presence of a predicted secretory signal peptide is indicated with '*' in the SP column. Tissue-specific expression is indicated with '+' in the 'SG' (salivary gland), 'Gut' and 'Head' columns, based on detection of the sequence in tissue-specific EST data  [23].
Grey circles on branches indicate bootstrap support greater than 80%. Transcripts that show elevated expression on B. rapa are indicated with red arrowhead and on N. benthamiana with a green arrowhead. Bottom: scaffold 338 containing four differentially expressed cytochrome-P450 genes (red), together with three non-regulated p450s (white) is shown. (*locus 000111270 is one of eight M. persicae P450 genes that were excluded from phylogenetic analysis after manual curation as they were either fragmented or had incorrect annotations. (PDF 885 kb) Additional file 16: Figure  S8. ML phylogeny of M. persicae, A. pisum and D. melanogaster lipase-like genes (MCL family 16). Protein sequences were aligned with Muscle  [76]  and the phylogeny estimated using RAxML  [30]  with automatic model selection and gamma distributed rate variation.
Acknowledgements We thank  Brian Fenton  ( James Hutton Institute, Dundee, UK ) for his help with genotyping M. persicae clone O and  Linda M. Field  ( Rothamsted Research, Harpenden, UK ) for being a co-investigator on the Capacity and  Capability Challenge  ( CCC-15 ) project that funded the first round of genome sequencing of M. persicae clone O. We are grateful to  Danielle Goff-Leggett ,  Ian Bedford  and  Gavin Hatt  ( JIC Insectary ) for rearing and care of aphids and the  John Innes Horticultural Services  for growing the plants used in this study. Next-generation sequencing and library construction was delivered via the  BBSRC National Capability in Genomics  ( BB/J010375/1 ) at the  Earlham Institute (formerly The Genome Analysis Centre ), Norwich, by members of the  Platforms and Pipelines Group.
Availability of data and materials All assemblies and annotation features are available for download at AphidBase (http://bipaa. genouest. org/is/aphidbase/) [90]. Genome assemblies, annotations, gene family clustering results and host transfer RNAseq gene expression data are also available through the Earlham Institute open data resource at http://opendata. earlham. ac. uk/ Myzus_persicae/. Sequence data have been deposited in the sequence read archive at the European Nucleotide Archive (ENA) and are available under BioProject accessions PRJEB11304 (clone O), PRJNA319804 and PRJNA296778 (G006).
We also conducted an exploratory qualitative study in the four regions to collect data on the pre-existing scheme and activities related to KT and the potential local barriers. The questions were the following: what kind of KT activities are possible (types, timeline, duration, management)? Who may be involved? What structural/organisational mechanisms would be affected? What contextual factors, outside the control of those involved, would need to be addressed? Data will be collected by means of semistructured interviews with IREPS directors and ARS public health directors (8 people). Based on the behaviour change wheel theory  [23]  and an exploration of the behavioural theories used in KT strategies  [24] , we hypothesize that the change of which will occur in knowledge use may be notably due to the motivation to use knowledge, the perception of its usefulness and practicality and the ability to adapt it in to fit different settings.
This work will be conducted in a preliminary 2-day workshop, gathering ARS and IREPS professionals. These theories will be applied in the 4 regions, for 12 months, and data will be collected in order to characterize the contexts, mechanisms and outcomes and to determine the effective CMO configurations. Finally, a cross-sectional analysis of the case studies will be conducted allowing us to identify potential regular CMO patterns, which would constitute an adjusted middle-range theory. The different stages are presented in Fig. 1  (Cf Fig. 1 : The realist sequences).
Data collection Data will be collected to document the support scheme's mechanisms and contexts parameters involved in effectiveness. They will be collected before the implementation of the KT scheme at the end and throughout the implementation. They will be collected on the 3 categories of people described before; 20 people per each region (80 at all). Collected data will characterise the context, the mechanisms relating to the organisation and to the individuals involved, the PBs and the set-up for KT. A description of data collected and how and they will be collected are presented in Tables  1  and 2 , but these variables will be adjusted according to the four theories of intervention and the action plans (Cf. Table  1  : Expected outcomes and Table  2  : Contexts and mechanisms expected).
Data will be collected by means of: Semi-structured interviews conducted with the above-mentioned population (20 people per region) The observation of health promotion meetings and collective events resulting from the scheme's implementation: project selection committees, selection processes, trainings, seminars, presentations etc. The aim of these observations is to study the types of interactions between the professionals who deal with KT strategies (for instance, leadership, uptake, bottom up or top down approaches). A documentary analysis (calls for project, action plans, projects applications, reports of meeting, etc. ) The observation and documentary analysis grids and the interview guideline will be designed based on the four intervention theories and tested on a sample of five stakeholders not involved in the process, but belonging to the IREPS network.
Data analysis Data will be processed through a content analysis  [26]  defined as "A set of systematic and objective procedures for analysing communication processes in order to obtain indicators (quantitative or not) inferring knowledge related to the conditions (inferred variables) under which meaningful information is both sent and received". This analysis will code, classify and grade content in order to identify patterns, trends and specific features. We will use a software program called Nvivo to assist us in conducting and integrating a thematic analysis of the interviews and an analysis of the observation reports. The qualitative analysis will lead to: Document the uptake of evidence and the practice changes triggered by the intervention. This will be carried out on a case-per-case basis in monographic format, in order to identify the mechanisms at play, the degree of intervention, the contextual contingencies and the changes arising in the three types of knowledge use (instrumental, conceptual, persuasive).
Identify the most regular CMO configurations by a cross-analysis of the different cases and a combination of the different data collected according to their linkage with the "context" meanings, "mechanism" meanings and "outcomes" meanings (cf Tables  1  and 2 ). Based on the observed elements, we will classify the outcomes in three categories of use as recorded in the literature  [27, 28]. Instrumental use: knowledge users draw on the IBs to make decisions or to change their practice; Conceptual use which implies changes in understanding and thinking inspired by the IBs; Persuasive use (also called strategic or symbolic use) which refers to a use of knowledge as a means to justify decisions or actions. We will process these data to characterise the mechanisms identified in the intervention theory and induced by the intervention.
System design The system (Figure  1 ), composed of hardware and software, was designed for 3D angles measurement which is a prerequisite for spinal movement estimation. The hardware prototype is composed as follows: • three inertial sensors (Bosh Sensortec BNO055) mounted with a safety pin on a stretch tee-shirt at the level of the seventh cervical vertebrae (C7), the twelfth thoracic vertebrae (T12) and the fifth lumbar vertebrae (L5). Each sensor includes gyroscope, magnetometer and accelerometer; • a WIFI-compatible microcontroller (SparkFun ESP32 Thing) to firstly gather data from sensors and then transmit them to laptop using wireless connection. The software was performed to process the sensors data and to provide to users a visual feedback through a graphic interface. This feedback consists on a 3D model of spine developed with Unity 3D, called the Avatar (Figure  2(C) ).
Jakowski and Morokuma have implemented Ehrenfest dynamics based on Liouville-von Neumann Our surface hopping based on linear-response TD-DFTB implementation relies on a general interface between the newest versions of the DFTB+ and Newton-X codes, which are both publicly available free of charge. It enables diverse new features to improve the description of ground and excited state energies, such as the DFTB third-order expansion (DFTB3 model)  17  and onsite corrections for TD-DFTB;  18  to accelerate the dynamics, such as fast nonadiabatic coupling calculations based on the recently proposed orbital derivative method;  19  and to enhance the data analysis, via a direct module for advanced transition density analysis based on the program TheoDORE. 20 Moreover, this interface is ready to profit from analytical energy gradients for TD-DFTB based on long-range corrected functionals,  12  which are currently under development.