dataesr/scientific-paragraphs-categorization huggingface dataset:
, 2019). HybPiper was run using the BLASTx option  (Camacho et al. , 2009)  since it has been found to produce longer contig sequences  (Murphy et al. , 2020). Reads were mapped to each of these target sequences (with the abovementioned BLASTx algorithm), were then assembled de novo using SPAdes  (Bankevich et al. , 2012) , and coding sequences (hereafter called exons) were extracted using exonerate  (Slater and Birney, 2005). Noncoding sequences (i. e. , introns and untranslated regions [UTRs]) flanking the exons were recovered and combined to the exon sequences into so-called supercontigs using the script intronerate. py available with HybPiper. Sequences from all taxa were combined for each exon and supercontig, and the resulting single locus matrices were each aligned separately using  MAFFT v7 (mafft-7.
419-gcc_fc6. x86; Katoh and Standley, 2013) , with accuracy-oriented methods (--localpair; --maxiterate 1000) and the option to generate reverse complement sequences to align them with the remaining sequences based on 6-mer counting (--adjustdirectionaccurately). Singlelocus alignments were subsequently trimmed using phyutility (https://github. com/black rim/phyut ility) to remove nucleotide sites missing in at least 80% of the taxa (-clean 0. 8). Plastome recovery-To evaluate the success of Hyb-Seq  (Weitemier et al. , 2014)  in our analyses, we performed a plastome recovery on the entire sample using the HybPiper pipeline with a target file of coding sequences  (genes, rRNA, tRNA, etc. )   ). This step allowed evaluation of the overall recovery of the plastome across the study samples, and between families, type of material used (herbarium versus silica dried), sample DNA concentration, library length (in bp), sequencing success (read number), and enrichment success (number of reads on target nuclear exons + flanking introns).
The comparison was made by fitting linear models including additive effects of some or all these predictors on plastome length recovered (in bp), trying all possible combinations of predictors. The function regsubsets of the R package leaps (scripts from Thomas Lumley based on Fortran code by  Alan Miller [2020] ) was used to select the best model among models with a given number of predictors, and the model with the smallest corrected AIC (estimated using the function AICc of the R package AICcmodavg; Mazerolle, 2019) was then chosen among these best models. Analyses were performed in R Studio (R Core Team, 2020; RStudio Team, 2020). Recovery statistics-Recovery statistics and heatmaps were generated using scripts from HybPiper v1. 3  (Johnson et al. , 2016). Additional statistics were generated using HybPiper_stats_general.
sh (scripts available at Zenodo, https://doi. org/10. 5281/zenodo. 4268317), which allowed evaluation of exon and (flanking partial) intron coverage. For this, we combined all reads found by HybPiper to map the reference target files, then mapped them against the recovered sample gene sequences using the BWA algorithm  (Li and Durbin, 2010). Resulting SAM files were parsed using a custom python script to keep only reads mapping with less than three mismatches and a score >30 to produce conservative coverage estimates. The filtered SAM files were analyzed with SAMtools  (Li et al. , 2009)  mpileup function to produce coverage information per base pair, and outputs were parsed with custom Python scripts to calculate intron and exon average coverage for each gene of each sample. Intron-exon boundaries were obtained from the genomic feature format (GFF) annotation files produced by HybPiper.
Model violation and tree landscape assessment- To explore the influence of potential incorrect nucleotide substitution models that might impact the accuracy of phylogenetic reconstructions, model assumption violations were tested. The test was implemented in IQ-TREE v2. 0  (Naser-Khdour et al. , 2019). Tests of two lessconstraining conditions than the defaults parameters rejected 38 and 52 exons from the data set with p-value of 0. 0001 and 0. 001, respectively. The default parameters (p-value: 0. 05) excluded 106 loci. The three resulting data sets with the remaining loci (Analyses V4, V5, and V6, respectively) were used to assess the impact of model violation on the topology of Myrtales. The phylogenetic landscape was investigated using the R package treespace  (Jombart et al. , 2017). The statistical distribution of the tree topologies was calculated using principal component analysis with the Kendall-Colijn (KC) distance for rooted trees  (Kendall and Colijn, 2016).
The ASTRAL topologies derived from exons (coding regions) vs. supercontig (exons+flanking introns) datasets are also compared (Fig. 4 ). References to numbered clades throughout refer to those shown in Figs. 2  and 3A-C. All alignments, and Newick tree files can be found at Zenodo (https://doi. org/10. 5281/zenodo. 4268317).
77% (125,384 bp), respectively. Complete statistics for combined exons and supercontigs alignments as well as for each single locus alignment are provided in Appendix S10. For the molecular model violation testing, 38, 53, and 106 loci (with p-values of 0. 0001, 0. 001, and 0. 05, respectively), were excluded from the V3 gene data set. Axis 1 of the PCA using the KC distance (Appendix S11) explained 95% of the variation in the tree topology, while axis 2 explained 4. 1%. The topology without modification (V1) clustered with the topologies obtained after excluding the genes that did not pass the model violation tests (V5 and V6). This result indicates that model violation has little impact on topology and supports the argument that the V3 data set is appropriate for downstream analysis and taxonomic discussion.
The addition of flanking intron data has an unpredictable effect at the level of section in Eugenia, increasing support for the position of Eugenia section Jossinia as sister to section Umbellatae, but not for relationships between sections Racemosae, Speciosae, and Phyllocalyx (contrasted with  Van der Merwe et al. [2005] ;  Mazine et al. [2014 Mazine et al. [ , 2018]] ). Within Eucalypteae and Syzygieae, somewhat elevated support levels in the supercontig analysis suggest this extra data should be included in future analyses of species-rich groups enriched with Angiosperms353. However, incongruence remains, as evidenced by the strongly supported relationship of Syzygium unipunctatum as sister to the rest of Syzygium s. s. in both exon-only and supercontig analyses, that differs considerably from previous studies  (Biffin et al.
CONCLUSIONS The Angiosperms353 probe set has proved efficient in evaluating relationships in Myrtales at various taxonomic levels. Analytical methods building species trees under the multispecies coalescent process require new ways to evaluate support, since gene tree congruence/ incongruence needs to be accounted for. Such methodologies require support to be evaluated beyond traditional bootstrapping, with quartet support allowing to interpret gene tree congruence/ incongruence. Recovery of low-copy nuclear loci was satisfactory, with an average of 288 loci retrieved across the data set. McLay et al. , 2021)  demonstrate that recovery might be further improved by adapting the target file to the taxonomic group. However, recovery of the plastome via genome skimming was less efficient as on average, less than a quarter of the plastome was recovered, with no clear pattern emerging.
The taxonomic results described are the most remarkable, but exploration of every relationship is not possible in a single work. Additional studies, using the Angiosperms353 probe set and phylogenomic methods relying on the multispecies coalescent process, with increased taxonomic sampling within the individual families of Myrtales, will surely follow. These studies will continue to clarify relationships and increase statistical support, particularly at some of the weaker nodes along the backbone. There are some cases of very short branches that may receive high LPP support but low QS values and for which the ASTRAL polytomy test of  Sayyari and Mirarab (2018)  cannot reject the null hypothesis that the node is a polytomy. In these cases, such as the nodes closest to the Myrtales root, it is possible that additional genetic data will help resolve these nodes with strong support and reject the null hypothesis of the polytomy test.
ACKNOWLEDGMENTS We gratefully acknowledge assistance from colleagues at RBGKew:  Dion Devey  (supporting laboratory work);  Imalka Kahandawala  and  Rosie Woods  ( DNA Bank );  Paul Bailey ,  Kevin Leempoel , and  Eduardo Toledo , (bioinformatics). We are grateful to the editors and anonymous reviewers who have patiently helped improve the content and format of this manuscript. This work was funded by grants from the  Calleva Foundation  and the  Sackler Trust  to the Plant and Fungal Trees of Life Project (PAFTOL) at the  Royal Botanic Gardens, Kew , with additional funding from the  Garfield Weston Foundation. A. A. received support from the 2018  CAS-TWAS President 's Fellowship for International Doctoral Students. E. L. and A. G. received support from the  Bentham-Moxon Trust  providing funds for sequencing Eugenia species and  Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq -proc.
, 2019; Wolf et al. , 2017)  or for some specific years  (Gerber, Steinfeld, et al. , 2013; Herrero et al. , 2013). These data sets cover either all livestock types  (Crippa et al. , 2020; EPA, 2012; FAOSTAT, 2020; Janssens-Maenhout et al. , 2019; Wolf et al. , 2017)  or major categories  (Chang et al. , 2019; Dangal et al. , 2017; Gerber, Steinfeld, et al. , 2013; Herrero et al. , 2013). Livestock methane emission estimates from inventories differ substantially depending on the choice of the methodological tier, emission factors, and livestock activity data (e. g. , from globally available FAOSTAT statistics or from national/regional information). For example, estimates of emissions from enteric fermentation of ruminants in 2000, obtained from different inventories  (Chang et al. , 2019) , range from 60.
4, Chapter 10 (Table  S1 ). Further, we derived new estimates of emission intensities, expressed as emissions per kg of protein in products including milk and meat from cattle, buffaloes, goats, and sheep, meat from swine, and meat and eggs from poultry, by combining our emission estimates with the FAOSTAT production statistics  (FAOSTAT, 2020). Finally, we investigated how our update to emission calculations using the latest IPCC Guidelines affects future projections from this sector by the year 2050 for three global socio-economic scenarios  (FAO, 2018) , and contrasted pathways of livestock production efficiency changes. To facilitate the usage of these new methods for assessing livestock methane emissions, we have provided a full package of the R code on Zenodo for producing these two new estimates and associated projections.
10. 1029/2021AV000391 volatile solids excreted by livestock (VS), maximum methane production capacity for manure produced by livestock (B 0 ), methane conversion factors (MCF) for each manure management system and each climate region, and the fraction of livestock manure handled using each animal waste management system (AWMS) in each region. The estimation was made at the grid cell level with the following parameters: (a) distributing country level VS into grid cells following the livestock distributions in the GLW3 data set (see Section 2. 4) and (b) using MCF depending on manure management system and the IPCC climate zones. Text S2 presents a detailed description of the methods used for estimating manure management emissions. Another set of livestock CH 4 emissions was estimated using the IPCC Tier 1 method updated by the 2019 Refinement to the 2006 IPCC Guidelines for National Greenhouse Gas Inventories (IPCC, 2019) Vol.
Estimated Livestock Methane Emissions and Recent Changes The magnitude of global livestock methane emissions estimated for 2010 using our 2019 MT method (122 ± 13 Tg CH 4 yr -1 ; Figure  1a ) is consistent with that estimated by EDGAR v4. 3. 2 (Janssens-Maenhout et al. , 2019) (115 Tg CH 4 yr -1 ), EDGAR v5. 0  (Crippa et al. , 2020)  (113 Tg CH 4 yr -1 ), and  (Wolf et al. , 2017)  (118 ± 18 Tg CH 4 yr -1 ). All these data sets consider trends in liveweight and/or productivity of livestock (Table  S1 ). They are all higher than those of the most recent FAOSTAT (FAOSTAT, 2020) data (104 Tg CH 4 yr -1 ) and the US EPA data set (EPA, 2012) (103 Tg CH 4 yr -1 ). The FAOSTAT used the default 2006 T1 emission factors, while the US EPA data set (EPA, 2012) used the 2006 T1 supplemented by country-reported inventory data.
The higher emission intensities per kg of protein from either the 2019 MT or the 2019 T1 method, compared to the 2006 T1 method, led to projections of larger livestock methane emissions in the future, for a given scenario of livestock numbers and production from the FAO (2018). The projections using the new 2019 MT and 2019 T1 methods are 18%-21% and 24%-28% higher, respectively, than that given by the 2006 T1 method (Figures  4a-4c ). Moving to the methodology of the 2019 IPCC Refinement(IPCC, 2019) is important, as the differences can be substantial, particularly in regions such as Sub-Saharan Africa, Near East and North Africa, and South Asia, where large positive trends on livestock production (Figure  S12 ) and emissions (Figure  S13 ) are projected in the future scenarios. In the SSP database  (Riahi et al.
Data Availability Statement The data used in this study are available in the Supporting Information. The raw data are from the FAO: http://www. fao. org/faostat/en/#data and http://www. fao. org/global-perspectives-studies/food-agriculture-projections-to-2050/en/. The results of this study, and the R code and the parameter files used to produce them are available at following website: https://doi. org/10. 5281/zenodo. 4663448.
Cognitive brain imaging is accumulating datasets about the neural substrate of many different mental processes. Yet, most studies are based on few subjects and have low statistical power. Analyzing data across studies could bring more statistical power; yet the current brain-imaging analytic framework cannot be used at scale as it requires casting all cognitive tasks in a unified theoretical framework. We introduce a new methodology to analyze brain responses across tasks without a joint model of the psychological processes. The method boosts statistical power in small studies with specific cognitive focus by analyzing them jointly with large studies that probe less focal mental processes. Our approach improves decoding performance for 80% of 35 widely-different functional-imaging studies.
In particular, we use dropout regularization  [56]  in the layered model and stochastic optimization  [13]  to obtain good out-of-sample performance. Study-specific decoding is thus performed on a shared low-dimensional brain representation. This representation is supported on 128 different combinations of the first 465 functional units identified with resting-state data. These combinations form diffuse networks of brain regions, that we call multi-study task-optimized networks (MSTONs). MSTONs differ from the notion of brain networks in the neuroscience literature-the later are typically obtained using a low-rank factorization of resting-state data, with a much lower number of components (k ≈ 20) than what we use to extract the functional units of the first layer. As we will show, projecting data onto MSTONs improves across-subject predictive accuracy, removing confounds while preserving the cognitive signal.
Mathematical definitions of the metrics in use are reported in Section C. 2. Exploring MSTONs. Our model optimizes its second and third layers to project brain images on representations that help decoding. These representations boil down to MSTONs combinations: MSTONs form a valuable output of the model, as they can easily be reused to project data for new decoding tasks. We provide 2D and 3D views of the MSTONs, showing how they cover the brain. We evaluate the importance of each network for decoding a certain contrast by computing the cosine similarity between the MSTON and the classification map associated with this contrast. We represent these contrasts' names as specified in their original studies with word-clouds, with a size increasing with their similarity with a given MSTON. Classification maps.
Reusable tools and resources Our approach can be used to improve statistical power of decoding in new fMRI studies. To facilitate its use, we have released resources and the cogspaces library (http://cogspaces. github. io). We include software to train the models. Pre-trained MSTONs networks (with associated word-clouds) can be downloaded and inspected on a dedicated page (https://cogspaces. github. io/assets/MSTON/components. html). The statistical maps used in the present study may be downloaded using our library, or on.
Discussion The methodology presented in this work harnesses the power of deep representations to build multi-study decoding models for brain functional images. It brings an immediate benefit to functional brain imaging by providing a universal way to improve the accuracy of decoding in a newly acquired dataset. Decoding is a central tool to draw inferences on which brain structures implement the neural support of the observed behavior. It is most often applied to task-fMRI studies with 30 or less subjects, which tend to lack statistical power  [72]. In this regime, aggregating existing studies to a new one using a multi-study model as the one we propose is likely to improve decoding performance. This is further evidenced in diversity of cognitive paradigms. Our transfer-learning model has such flexibility, as it does not require explicit correspondence across experiments.
However, we were not able to increase performance by departing from linear models: introducing non linearities in our models provides no improvement on left-out accuracy. On the other hand, we have shown that pooling many fMRI data sources enables to learn deeper models, although these remain linear. Techniques developed in deep learning prove useful to fit models that generalize well across subjects: using dropout regularization  [14]  and advanced stochastic gradient techniques  [13]  is crucial for successful transfer and good generalization performance. Sticking to linear models brings the benefit of easy interpretation of decoding models. The use of sparsity and non-negativity in the training and consensus phase allow to obtain interpretable networks. Using sparsity only in each phase (as originally advocated by  [74] ) yields "contrast" networks with both positive and negative regions, that are harder to interpret (see also  [60] ).
g. [81]  for a comparison of task and rest brain networks). We use many different fMRI studies to distill MSTONs across various tasks. This data aggregation approach requires little supervison. The flip side is that it leads to coarse results by nature: our approach is obviously not sufficient to recover the detailed brain-to-mind mapping, collective knowledge of psychologists and neuroscientists, that has emerged from decades of research on multimodal datasets and careful behavioral experiments. Specific brain-to-mind associations are best resolved with dedicated experiments using experimental-pyschology paradigms tailored to the question at hand. Other data than fMRI, for instance more invasive, may also provide stronger evidence. For instance a double dissociation in brain-lesion patients give unambiguous evidence of distinct cognitive processes via distant neural supports, as with Broca and Wernicke's separation of language understanding and generation  [82] , or the more recent teasing out of emotional and cognitive empathy  [83].
S1 Appendix The appendix is structured as follow: in the first section, we formalize the learning setting and method, after describing decoding baselines. In the second section, we perform supportive experiments to explain the observed results, and discuss various alternatives for the model, to further support modelling choices. Finally, we provide reproduction details, along with data and software notes. A visualization of all MSTONs components (that reproduces https://cogspaces. github. io/assets/MSTON/components. html) is provided for completeness in S1 Components. Notations. We denote scalars, vectors and matrices using lower-case, bold lower-case and bold upper-case letters, e. g. , x, x and X. We denote the elements of X by x i,j and its rows by x i. We write x j a value that is specific to study number j.
B. 4. 1 Effect of selecting grey-matter components We project data onto a subset of 465 out of 512 functional networks learned on HCP resting-state data, selecting the networks that intersect with an anatomical grey-matter mask. This avoids finding MSTONs that are distributed or formed with non grey-matter regions. In  as expected, performing classification from grey-matter components only brings a non-significant performance loss (0. 03% median accuracy gain).
Our reweighting amounts to giving every study j an "effective sample size" n j eff = N i=1 n i n j β N i=1 n i β , that is larger than the true sample size for smaller studies and smaller for larger studies. We observe on Fig MA that the negative transfer learning endured by large-study decoders such as HCP and LA5C reduces as these studies are given more weight (β → 1). On the other hand, the performance on small datasets slightly reduces for β > 0. 6. It also reduces for low β, hinting at the importance of using large studies for improving small studies decoding. We thus have provided justifications for all the technical design choices made in training our decoding model: regularization, joint training, training refinements, choice of study weights.
C. 1 Software and parameters We used nilearn  [98]  and scikit-learn  [99]  in our experiment pipelines, the stochastic solver from  [59]  to learn resting state dictionaries and pytorch  [100]  for model design and training. The cogspaces package that we have published provides the multi-scale resting-state dictionaries extracted from HCP, as those are costly to learn. It also provides the reduced representations of the data from the 35 studies we consider. General cross-validation scheme. For every validation experiment and comparison, we perform 20 half-split of all data. Namely, we consider half of the subjects of every study for training, and test the decoder on the other half. As two studies  [38]  share subjects, we also ensure that no single subject appears in both the training and the test sets across studies.
We tried λ ∈ {10 -5 , 10 -4 , 10 -3 , 10 -2 }. Higher sparsity leads to a slight decrease in performance, lower sparsity is softer on symmetry breaking, which may reduce interpretability. This parameter has little influence as long as the sparsity remains higher than 50%. Word-clouds. In Fig  4 , we form word-clouds associated with the k-th MSTON network Dl k as follows. We compute the correlations between each classification map w c , associated with a condition c, and the network Dl k as d k,c = Dl k , w c Dl k 2 w c 2. We then show the 20 contrast names with highest correlation values-this corresponds to the contrasts whose likelihood increases the most when the input data is pushed in the direction of Dl k. The height of the contrast name c in the word-cloud reflects the rank of the contrast in the sorted values (d k,c ) c and the value d k,c , using heuristics from the Python word cloud package (https://github.