dataesr/scientific-paragraphs-categorization huggingface dataset:
Mutation and structure analysis Conservation SARS-CoV-2 protein sequences were downloaded from Uniprot  (87)  and the orf1ab polyprotein split into sub-sequences based on the Uniprot annotation. A custom reference database was generated based on the NCBI virus coronavirus genomes dataset (NCBI Resource Coordinators, 2018), which includes sequences from a large range of coronaviruses. SARS-CoV-2, SARS and MERS sequences were filtered to only contain sequences from the Wuhan-Hu-1 strain, the Urbani strain and the HCoV-EMC/2012 strain respectively. Without this the dataset contains very large numbers of almost identical sequences from patient samples, which are not informative since SIFT4G looks to compare across species. The remaining sequences were clustered using MMseqs2  (88)  with an overlap threshold of 0.
Surface Accessibility Naccess (Hubbard and Thornton, 1993) was run on each structure using the default settings. Structures were filtered to only include the chain corresponding to the appropriate SARS-CoV-2 protein. This means some surface accessible positions are usually found in interfaces rather than facing the solvent. Since structures are not always complete, surface accessibility is an approximation and will not be accurate in all cases. The code managing the pipeline and analyses are available at https://github. com/allydunham/mutfunc_sars_cov_2. The web service source code is available at https://github. com/allydunham/mutfunc_sars_cov_2_frontend. The pipeline is managed through Snakemake  (91). For more information about the mutation and structure analysis, see prior work  (92).
Differential protein-protein interaction analysis and visualization We used the R package MSstats  (33)  to quantify changes in "prey" protein abundance between the corresponding mutant (VOC) and wave 1 "bait" forms. We first converted MaxQuant evidence files to MSstats format using MaxQtoMSstatsFormat with proteinID="Leading. razor. protein", useUniquePeptides=FALSE, summaryforMultipleRows=sum, removeFewMeasurements=FALSE, removeOxidationMpeptides=FALSE, and removeProtein_with1Peptide=FALSE. We then ran the dataProcess function with featureSubset="all", normalization="equalizeMedians", MBimpute=FALSE, and summaryMethod="TMP". In essence, we performed normalization by median equalization, did not impute missing values, and combined (i. e. , "summarized") intensities for multiple peptide ions or fragments into a single intensity for their protein group.
Lastly, statistical tests of differences in intensity between mutant and wave 1 were performed. We used defaults for MSstats for adjusted P values, even in cases of n = 2. By default, MSstats uses the Student's t-test for P value calculation and the Benjamini-Hochberg method of FDR estimation to adjust P values. This analysis resulted in log2 fold changes (log2FC) and p-values per interaction between corresponding mutant and wave 1 baits. We defined differential interactions based on two criteria: (1) The prey must be a high-confidence interaction in either the mutant or wave 1 bait (see scoring thresholds above) and (2) the prey must be changing in abundance between the mutant and wave 1 forms with an absolute value log2FC>0. 05 and p<0. 05. For all proteins that fulfilled these criteria, we extracted information about the stable protein complexes that they participated in from the CORUM database of known protein complexes  (28).
The ORF clone for Flag-MARK2 was obtained from Genscript (#OHu23943D). The antibodies used in the study include: rabbit polyclonal anti-beta-actin (Cell Signaling Technology #4967, RRID:AB_330288, used at 1:2000); mouse monoclonal anti-Strep tag (QIAGEN #34850, RRID:AB_2810987, used at 1:1000); rabbit polyclonal anti-flag antibody (Sigma Aldrich #F7425, RRID:AB_439687, used at 1:2000); rabbit polyclonal anti-LEO1 antibody (Atlas antibodies #HPA040741, RRID:AB_10794859, used at 1:1000); rabbit polyclonal anti-WDR61 antibody (Atlas antibodies # HPA040065, RRID: AB_10793926, used at 1:1000); rabbit polyclonal anti-PAF1 antibody (Proteintech # 15441-1-AP, RRID: AB_2174457, used at 1:1000); rabbit polyclonal anti-CTR9 antibody (Proteintech # 21264-1-AP, RRID: AB_10734585, used at 1:1000).
DSD evaluates inter-gene network distance based on differences of diffusion by random walks from the two genes, and was a major portion of the best overall method for network module detection in Choobdar et al. (100). The DSD distance matrix was used as input to the R function hclust with agglomeration method set to "average". The resultant hierarchical clustering was divided into modules by the function cutreeHybrid in R package dynamicTreeCut (version 1. 63-1) with deepSplit set to 3. Modularization was visualized by coloring genes by module on a t-SNE plot of the regulated genes built using the R package Rtsne (version 0. 16) with the DSD distance matrix as input. Modules were named by performing over representation analysis on sets of genes annotated by all gene ontology terms using the R package clusterProfiler (version 4.
Network propagation analysis We downloaded the human physical protein-protein interaction network from String, version 11. 5  (101). We focused on genes with full RNA data across the 11 conditions whose encoded proteins are present in the network. For each gene and each condition, we set its log2 fold-change (24hpi relative to mock) in absolute value as a prior value for that condition. We subjected these values to a network propagation process with symmetric normalization and a parameter of 0. 5, which smooths the values across the network while accounting for their prior values (102). The resulting values were averaged for every module and condition and compared to these averages across all modules (AHR, average host response). We further computed nominal p-values per module by comparing a module's AHR correlation value to those obtained by randomizing module assignment while preserving module sizes.
(A) Distribution of FoldX ΔΔG predictions (left) and evolutionary conservation SIFT4G scores (right) for mutations in SARS-CoV-2 variants of concern (VOCs) compared to the background distribution of all possible mutations. Interpretation is that VOC mutations are generally less structurally destabilizing and more evolutionarily tolerated than background. FoldX was run based on the highest quality structure available in the SARS-CoV-2 SWISSmodel repository (73) containing 19 of the 28 viral proteins and SIFT4G was run against a clustered database of coronaviridae sequences. (B) Three scoring thresholds were generated, all included the requirement of average spectral counts (AvgSpec) to be greater than two: (i) SAINT only requiring SAINT BFDR<0. 05, (ii) SAINT and MiST requiring BFDR<0. 05 & MiST>0.
(32,95) high-confidence protein-protein interactionsIdentified proteins were subjected to protein-protein interaction scoring with both SAINTexpress (v. 3. 6. 3)(31)andMiST(https://github. com/kroganlab/mist)(32,95)scoring algorithms. For SAINT scoring, each APMS experimental batch was scored separately relative to independent negative controls: empty vector (EV) and enhanced green fluorescence protein overexpression (eGFP).
ACKNOWLEDGEMENTS We thank  Wendy Barclay  ( Imperial College London, UK ), and  Alex Sigal  and  Khadija Khan  ( Africa Health Research Institute, Durban, South Africa ) for the generous gifts of VOC isolates. SARS-CoV-2 cross-reactive antibody was a gift from  Laura McCoy  ( University College London, UK ), and Caco-2 cells were a gift from  D. Bailey. We thank  Dr. Randy Albrecht  for support with the BSL3 facility and procedures at the ISMMS as well as  Richard Cadagan  for excellent technical assistance. We thank  Tom Peacock  at  Imperial College London  for helpful discussions.
(32,95) high-confidence protein-protein interactionsIdentified proteins were subjected to protein-protein interaction scoring with both SAINTexpress (v. 3. 6. 3)(31)andMiST(https://github. com/kroganlab/mist)(32,95)scoring algorithms. For SAINT scoring, each APMS experimental batch was scored separately relative to independent negative controls: empty vector (EV) and enhanced green fluorescence protein overexpression (eGFP). For MiST scoring, all samples across all batches were scored together. Three scoring thresholds were generated, all included the requirement of average spectral counts (AvgSpec) to be greater than two: (i) SAINT only requiring SAINT BFDR<0. 05, (ii) SAINT and MiST requiring BFDR<0. 05 & MiST>0. 5, and our final selected threshold (iii) SAINT, MiST, and Gordon et al. (2020) requiring [BFDR<0.
05 & MiST>0. 5] | [BFDR<0. 05 & present in Gordon et al. (2020) using default settings and searching against a database of Uniprot Homo sapiens sequences (downloaded 22 March 2022), a SARS- CoV-2 proteome of 149 protein sequences spanning five variants of concern (Alpha 20I V1, Beta 20H V2, Gamma 20J V3, Delta 21J, and Omicron 21K) and 32 viral proteins, including N-star (N*), and the enhanced green fluorescence protein (eGFP) sequence. Detected peptides and proteins were filtered to 1% false-discovery rate in MaxQuant.
Estimation on Empirical Sequence Data The two alternative models of inference just considered, namely the Muse and Gaut (MG) and the MF codon models, were then applied to empirical protein-coding sequence alignments. Several examples were analyzed: the nucleoprotein in Influenza Virus (as human host) assembled in  Bloom (2017) , the b-lactamase in bacteria gathered in  Bloom (2014) , as well as orthologous AT-rich genes (such as to prevent the confounding effect of gBGC) in primates extracted from the OrthoMam database  (Scornavacca et al. 2019)  as shown in table  1. For alignments globally biased toward AT (nucleoprotein and AT-rich concatenate in primates), similarly to what was observed in the simulation experiments presented above, the mutational bias estimates under the two codon models are greater than the observed nucleotide bias (i.
Acknowledgments We gratefully acknowledge the help of  Laurent Gueguen ,  Laurent Duret ,  Christophe Douady , and  Benoit Nahbolz  for their input on this work and their comments on the manuscript. This work was performed using the computing facilities of the  CC LBBE/PRABI. This article was funded by  French National Research Agency  (Grant No. ANR-15-CE12-0010-01/DASIRE ).
Data Availability The data underlying this article are available in Github, at https://github. com/ThibaultLatrille/NucleotideBias (last accessed January 31, 2021), as well as scripts and instructions necessary to reproduce the simulated and empirical experiments. The simulators written in Cþþ are available at https://github. com/ThibaultLatrille/SimuEvol (last accessed January 31, 2021).
Limited data regarding the resources and methods used by organic fruit growers to learn about production practices are available, even though this information is crucial to improving the efficacy of knowledge transfer. Therefore, a survey to gain information from knowledge networks dealing with organic fruit production about their structural organisation, tasks and methods of communication was carried out in twenty-one countries from Europe and the Mediterranean basin. A total of 56 networks representing about 42,500 professionals were identified as a result of the survey. The vast majority of them were only active at the regional or national level and were composed of farmers, advisors and researchers. About 3/4 of the networks were developing improved strategies for agronomic practices and about half of them were also involved in different knowledge-transfer activities between their members.
According to the IN-SIGHT project study  [19] , innovation starts with actors and evolves through hybrid networks, as it has been demonstrated for innovation in rural tourism  [20]  or in novel forms of direct relations between producers and consumers typical of organic farming, such as internet selling and solidarity purchasing groups  [21]. Informal farmers' knowledge exchange and learning have been analysed under different contexts and agricultural systems  [22, 23] , including organic farming systems  [24, 25] , always showing that this method has a considerable potential to promote the adoption of sustainable practices typical of organic production. There are limited data regarding the resources and methods used by organic fruit growers to learn about production practices. This information, however, is crucial in order to improve the efficacy of knowledge transfer to serve the organic fruit sector.
Each selected network was requested to provide information following a questionnaire during semi-structured interviews carried out in local language, with data about the organisation of the network, its activities and other kinds of information related to technical knowledge (in total fourteen topics, see questionnaire form in Supplementary Material). The majority of the questions proposed multiple-choice answers, to favour a standardised approach by respondents, while few allowed customised answers. The focus of the survey was not only related to the way informal knowledge is exploited or organised, but also about how the networks are developed, organised, structured and financed. This was assumed to give a broader understanding of their functioning and how policies could further foster network development.
Digital resources can become a key tool to overcome the language barrier [49]. Online multilingual platforms, such as the Organic Farm Knowledge platform (https://organic-farmknowledge. org/, accessed on 28 December 2021), can provide access to high-quality and trusted digital learning resources on organic agriculture to farmers from different countries  [50]. Exploiting internet-based translation services (e. g. , Google Translator or DeepL) can also help, though the technical vocabulary can be more difficult to be correctly translated automatically. However, the general opinion is that digital media could not replace but only complement face-to-face knowledge exchange, which still remains fundamental  [50].
Nevertheless, considering that organic farmers are generally younger than conventional farmers, with the share of organic farm managers under 40 years of age (21%) being twice that of non-organic farms (10. 5%) [60], it can be assumed that they are more willing to use internet resources  [61]. This fact points out the need of promoting organised platforms where researchers and practitioners could exchange information, also benefiting from the activity of innovation brokers  [15]. The platform GECO (https://geco. ecophytopic. fr, accessed on 28 December 2021) is an example of such a collaborative platform set up in France, including a database of knowledge and a forum for discussion. In line with the preferred methods of communication, it also emerged that the most used way of elaborating agronomical strategies related to the major practices of orchard management is through personal relations and participation in meetings and workshops where the results of the experiences gathered (also through trials) are discussed, divulgated and communicated (Figure  6 ).
1. Tracer addition experiments, particularly using isotopic tracers, are becoming increasingly important in a variety of studies aiming at characterizing the flows of molecules or nutrients at different levels of biological organization, from the cellular and tissue levels, to the organismal and ecosystem levels. However, performing rigorous statistical analyses to gain reliable quantitative insights from these experiments often remains challenging. 2. We present an approach based on Hidden Markov Models (HMMs) to estimate nutrient flow parameters across a network, and its implementation in the R package isotracer. The isotracer package is capable of handling a variety of tracer study designs, including continuous tracer drips, pulse experiments and pulse-chase experiments. It can also take into account tracer decay when radioactive isotopes are used.
3. To illustrate its use, we present three case studies based on published data and spanning different levels of biological organization: a molecular-level study of protein synthesis and degradation in Arabidopsis thaliana, an organismallevel study of phosphorus incorporation in the eelgrass Zostera marina and an ecosystem-level study of nitrogen dynamics in Trinidadian montane streams. With these case studies, we illustrate how isotracer can be used to estimate uptake rates, turnover rates and total flows, as well as their uncertainty. We also show how to perform model selection to compare alternative hypotheses. 4. The isotracer package allows researchers from a broad range of disciplines to fully take advantage of their datasets through rigorous statistical analyses. We conclude by discussing isotracer's further applications, limitations and possible future improvements and expansions.
, 2000; Mulholland et al. , 2000). Such an approach does not take into account the uncertainty in assumed trophic relationships  (Ainsworth et al. , 2010; Dodds et al. , 2014) , and does not allow either the uncertainty in flow estimates at one trophic level to be taken into account when estimating flows in the rest of the trophic network. Statistically reliable estimates of parameter uncertainty are particularly important when performing comparative or experimental studies (e. g. Barneche et al. , 2021; Collins et al. , 2016; Norman et al. , 2017; Tank et al. , 2018; Whiles et al. , 2013). We created the isotracer R package in an effort to develop a statistical framework for such analyses more easily accessible for researchers. Our package implements the mathematical framework recently described in  López-Sepulcre et al.
We choose a Bayesian approach to leverage its ability to easily perform marginalization and propagate errors from estimated parameters to derived parameters of interest such as network-wide properties (e. g. total nutrient flow in an ecosystem). Model comparison is possible when several plausible models exist. Additionally, the package also allows researchers to perform power analyses when designing their tracer addition experiments in order to make the most out of such cost-and effort-intensive studies. We first describe the mathematical framework used in our modelling approach and an overview of its implementation in the package. We then illustrate the use of the package through three case studies based on published datasets: protein turnover in Arabidopsis thaliana leaves  (Li et al. , 2017) , phosphate uptake in Zostera marina individuals  (McRoy & Barsdate, 1970)  and nitrogen flows in a Trinidadian mountain stream  (Collins et al.
| Overview The model exposed above is implemented in isotracer using a Bayesian approach. The Stan program  (Carpenter et al. , 2017)   Note that, while the matrix exponential approach is numerically accurate and is the default solver used by isotracer, runtime can become prohibitively long for large models (i. e. with a large number of compartments and/or a large number of unique observation times). A fallback solver is available to the user: it uses a forward Euler scheme with constant step size, but offers no extensive check of numerical accuracy during the numerical solving. This approach allows for greater speed of model evaluation and is expected to perform robustly when an appropriate dt time step and reasonable parameter priors are chosen, but numerical accuracy is not guaranteed. As such, it should be used only for prototyping purposes when exploring the modelling of large datasets, and the matrix exponential solver should always be used for final runs.
| Interface overview The isotracer package separates the process of modelling tracer additions into two steps: (a) model definition, and (b) model fitting. This is due to the amount of information required to define a network model, namely: network topology, initial conditions, observations and priors. Figure  2  shows an overview of the typical workflow when using isotracer and Table  1  presents the main functions that are used in the model building step. The package functions have been designed for convenient use with the pipe operator %>% provided by the magrittr package and familiar to tidyverse users, but can also be used without the pipe. A new, empty network model is initialized with the new_network-Model() function. This function returns a tibble data frame with zero rows and the supplementary class networkModel, ready to be populated as the user defines the model in subsequent steps.
F I G U R E 2 Workflow overview for network modelling with isotracer Importantly, while many packages implementing Bayesian methods provide default priors, isotracer does not: the user has to explicitly set priors for all parameters. This is a conscious design choice made to encourage users to make reasoned, explicit choices about their model priors, rather than trusting some default priors that might be woefully inappropriate for the system being modelled. The tutorials presented in appendix and the package documentation provide some guidance for choosing slightly informative priors.
list object). Table  2  provides an overview of the main functions available for post-run processing of mcmc. list objects. The isotracer package provides additional methods for the  b Flows can be averaged over the predicted trajectories or calculated for steady states (if the network admits steady states). is provided for tidy data and posterior prediction calculation: its output contains the observed data as a y list element and the corresponding posterior predictions as a y_rep list element, which can be fed directly into the posterior predictive check functions provided by bayesplot, such as ppc_dens_overlay() or ppc_ intervals(). The output of tidy_dpp() also contains a vars list element which can be used to provide the time points to the bayesplot plotting functions, and facetting variables when grouping factors were used in the model.
| C A S E S TUD IE S The following case studies demonstrate the use of the isotracer package. In the first case study on Arabidopsis protein turnover, we introduce basic concepts such as the use of covariates and the calculation of derived parameters. In the second case study on Zostera phosphate uptake, we demonstrate how to model radioactive isotopes, and how to compare derived parameters to a reference value. Finally, the study of the nitrogen cycle in a Trinidadian stream illustrates how to model drip experiments, non-homogeneous compartments, and how to perform model comparison. These case studies also illustrate the diversity of addition regimes that can be used in tracer additions (Figure  3 ). All datasets used in the case studies are available from the isotracer package.
Those leaves were used in chromatography and mass spectrometry analysis to estimate relative abundance changes for individual proteins. The data from  Li et al. (2017)  are available as a Dryad repository  (Li et al. , 2018)  and the isotracer package ships several tables derived from this dataset. Here, we only focus on a handful of proteins and estimate their turnover in Arabidopsis leaves. The proteins we selected are RBCL (ribulose-bisphosphate carboxylase or 'rubisco', the most abundant protein in leaves in ppm), CPN60A (chaperone involved in rubisco folding), PSBA (protein of the photosystem II), THI1 (thiazole biosynthetic enzyme) and PGK1 (phosphoglycerate kinase 1). Several ways exist in which the biological question at hand can be answered with isotracer. For illustrative purposes, we choose to use a network model where the topology is extremely simple and each of the five protein compartments receives nitrogen 'directly' from the growth medium (Figure  4a ).