dataesr/scientific-paragraphs-categorization huggingface dataset:
Alternatively, the algorithm could be directly trained on a subset of experimental data obtained by manual or semi-automatic analyses to be then automatically applied to the rest of the dataset. One must keep in mind that the output of the machine learning strongly depends upon the quality of the dataset used for its training and hence must be analyzed carefully. FIGURE 6 | Comparison between the direct trait and the Random Forest approach, for the different root system types and the different levels of noise. For each metric, we computed both the r-squared value from the linear regression between the estimation and the ground-truth (left panels), as well as the Mean Relative Error (right panel). The gray points represent the values obtained with the direct estimation (best descriptor, no noise).
Data Availability All data used in this paper (including the image and RSML libraries) are available at the address http://doi. org/10. 5281/ zenodo. 208214 An archived version of the codes used in this paper is available at the address http://doi. org/10. 5281/zenodo. 208499 An archived version of the machine learning framework is available at the address https://github. com/FaustFrankenstein/ RandomForestFramework/releases/tag/v1. 0.
We analyzed differences in diversification rates between Holarctic and equatorial regions for turtles, squamates and crocodiles using the Binary State change Speciation and Extinction model (BiSSE  65, 66  , see Methods). We did not use the Geographic State change Speciation and Extinction model  67  , which is appropriate when dealing with widespread species, because most species in our datasets are endemic to a region and to consider a character state in SSE models, it must represent at least 10% of the total diversity  68. We did not apply the BiSSE model on crocodiles because simulation studies have shown that trees containing fewer than 300 species may lack sufficient phylogenetic signal to produce enough statistical power  68. First, we used the time-constant BiSSE model as is generally applied to studies of the LDG  33, 34, 35, 45, 48.
Fossil-based diversification analyses: evidence for ancient tropical extinctions? The turtle fossil dataset comprised 4084 occurrences for 420 genera (65 extant and 355 extinct) (Supplementary Table  9 ). The squamate fossil dataset comprised 4798 occurrences for 638 genera (120 extant and 518 extinct) (Supplementary Table  10 ). The crocodile fossil dataset comprised 1596 occurrences for 121 genera (9 extant and 112 extinct) (Supplementary Table  11 ). To infer the global diversification dynamics, we first analysed the fossil datasets as a whole using a Bayesian approach that infers the temporal dynamics of origination and extinction rates based on fossil occurrences  71  (see Methods). The results of the turtles show that origination rates peaked during the Jurassic and decreased since then until the present.
16, 17). We performed additional analyses with different subsets of the three fossil datasets to tease apart the signal of speciation and extinction between geographic regions (equator or Holarctic) and ecological conditions (temperate or tropical, see Methods). These analyses evidenced that Paleogene diversity losses in turtles and crocodiles are mostly attributed to species living in the Holarctic and under tropical conditions (Figs. 6, 7 , Table  2 ). The global diversity loss inferred for crocodiles during the Neogene is attributed to both taxa living in Holarctic and equatorial regions, and adapted to tropical and temperate conditions, reinforcing the hypothesis that the group as a whole is in decline. Diversification rates of temperate taxa are estimated high in all groups during the Oligocene but decrease during the Neogene.
During Cenozoic cooling, squamates in the higher latitudes did not go extinct more than in the equator but the opposite, with diversity losses inferred in the equator (Fig. 6 ). Diversity dynamics for species distributed in the equator, however, are difficult to trust because the equatorial dataset is the poorest of our study due to the small number of fossil lineages and the small number of records per lineage (Supplementary Table  12 ). As a result, there are uncertainties on the estimated rates with wide credibility intervals probably due to geographic biases in the fossil record  82. In the Holarctic, turnover was very elevated, indicating that species did disappear from high latitudes but were replaced by a new squamate community. It is possible the number of Squamata species was always imbalanced between regions.
Time-calibrated phylogeny for each group was obtained from published studies. For turtles, we used the time-calibrated phylogeny of Jaffe et al. 112  including 233 species. We preferred this phylogeny over other more recent and slightly better sampled trees  113  because the divergence times estimates in Jaffe et al. 112  are more congruent with recent estimates based on genomic datasets  72, 114. For squamates, we retrieved the most comprehensive dated tree to date including 4161 species  34  , and for crocodiles we obtained a complete phylogeny  115. Fossil occurrences were downloaded from the Paleobiology Database (https://paleobiodb. org/#/, last accessed October 25 th 2017). To reduce potential biases in the taxonomic assignation of turtle, crocodiles and squamates fossils, we compiled occurrence data at the genus level.
22 We also assessed the effect of species distribution on diversification, while allowing for rates to change at specific points of time, which reduces the bias of using constant rates, and allow testing the role of extinction and climate change on the generation of the LDG. We used the time-dependent BiSSE (BiSSE. td) model, in which speciation, extinction, and dispersal rates are allowed to vary between regions and to change after the shift times  98. We Analyses were performed using the R-package diversitree 0. 9-7 66 using the functions make. bisse to construct the likelihood functions for each model based on the data, and the functions constrain and find. mle to apply different diversification scenarios. Finally, we used a Markov Chain Monte Carlo (MCMC) approach to examine the credibility intervals of the parameter estimates.
Following the recommendations from FitzJohn 66 , we used an exponential prior 1/(2r) and started the chain with the parameters obtained by maximum likelihood. We ran 10,000 steps of MCMC and applied a burn-in of 10%. Inferring ancestral geographic distribution with and without fossils. We performed biogeographic analyses with the parametric likelihood method DEC  69  using the fast C++ version  117. Turtle, squamate, and crocodile species distributions were obtained from online databases (www. iucnredlist. org and www. reptile-database. org). We chose 23. 4°N and 23. 4°S as the threshold latitudes defining the tropics and categorized each species as living in the Holarctic, in the southern temperate regions, or in the equatorial tropics and subtropical regions  48. We considered that all ranges comprised of three areas could be an ancestral state (maxareas =3).
In total, we implemented 23 fossil constraints for turtles (Supplementary Table  4 ), 30 fossil constraints for squamates (Supplementary Table  5 ), and 8 for crocodiles (Supplementary Table  6 ). We included the fossil distribution following two different approaches: (i) a hard (HFC), and (ii) a soft fossil constraints (SFC). With HFC, estimation of ancestral areas was fixed to the fossil occurrence location. This was done using existing functions in the C++ version of Lagrange 117 (https://github. com/rhr/lagrange-cpp), with the command "fixnode". By fixing nodes to the area of distribution of fossils, we assume fossil occurrences reflect the distribution of the ancestors, i. e. the fossil record is complete. For SFC approach, fossil data are incorporated into the anagenetic component of the likelihood framework.
We overcame this issue by assuming that the rates of origination and extinction are part of two families of parameters following a common prior distribution, with parameters estimated from the data using hyper-priors  123. We ran PyRate for 10 million of MCMC generations on each of the 10 randomly replicated datasets. We monitored chain mixing and effective sample sizes by examining the log files in Tracer 1. 6  124. After excluding the first 20% of the samples as burn-in period, we combined the posterior estimates of the origination and extinction rates across all replicates to generate rates-through-time plots. Rates of two adjacent intervals were considered significantly different when the mean of one lay outside of the 95% credibility interval of the other, and conversely. We looked at the marginal posterior distributions of origination and extinction rates through the evolutionary history of the three groups and looked at the effect of the major environmental changes like the greenhouse world (Cretaceous), the climatic transition (Paleogene), and the icehouse world (Neogene until the present).
In the context of the LDG and the hypothesis of tropical extinction, we performed additional analyses with different subsets of fossils to tease apart the signal of speciation and extinction between geographic regions (equator or Holarctic) and ecological conditions (temperate or tropical). For example, for turtles, we split the global fossil dataset into four subsets: one for the fossil genera occurring in the equator (429 occurrences), one for the fossils occurring in the Holarctic (3558 occurrences), one for the fossil genera considered as being adapted to temperate conditions (993 occurrences), and one for the fossils considered being tropically adapted (2996 occurrences). We excluded the few fossil occurrences for the southern regions of the South Hemisphere (about 180) for the different subset analyses, as they are poorly represented in our dataset.
Note that a given fossil can be present both in the 'Holarctic' and in the 'tropical' datasets. To encode tropical/temperate preferences, we considered that conditions in the Holarctic were paratropical until the end of the Eocene following  [57] [58] [59]  (and references therein), and assumed that taxa inhabiting the warm Holarctic were adapted to tropical-like conditions (i. e. a high global temperature would indicate probable adaptations to tropical climates). This is, of course, a simplifying assumption that may cause bias in the analysis, but general patterns might still emerge from it 125. Further, this assumption could be justified by the presence of early Cenozoic vegetation assemblages in the Holarctic that represent tropical vegetation, as evidenced by paleofloristic composition  53, 56.
For turtles, this assumption is supported by a recent study that modelled their climatic niche during the Late Cretaceous and found that Holarctic ancestors of turtles were adapted to tropical conditions  81. After the late Eocene, we categorized each species as living in the temperate biome or the tropical biome following the threshold latitudes defining the tropics (23. 4°N and 23. 4°S) following ref. 48. This delineation is also overall congruent with the Köppen climate classification. With these datasets, we reproduced the exact same PyRate analyses than for the whole datasets (see above). In general, the fossil datasets include mostly Holarctic fossils, with a more limited number of occurrences for the equator. Therefore we remain cautious with the conclusions derived from the equatorial datasets.
Acknowledgements We are grateful to  Gary Mittlebach ,  Emmanuelle Jousselin  and  Jonathan Rolland  for their comments on early versions of the manuscript. Financial support was provided by a  Marie-Curie   FP7-COFUND (AgreenSkills fellowship-26719 ) to A. S. M. and  Marie Curie FP7-IOF  (project  627684   BIOMME ) to F. L. C. This work has benefited from an " Investissements d'Avenir " grant managed by  Agence Nationale de la Recherche  (CEBA, ref. ANR-10-LABX-25-01 ). This is contribution ISEM 2017-xxx of the  Institut des Sciences de l'Evolution de Montpellier  ( UMR 5554 -CNRS ). This is Paleobiology Database contribution no. xxx.
, 2014) , de novo copy-number variants (CNVs)  (Kirov et al. , 2012) , non-synonymous de novo single-nucleotide variants (SNVs), and small insertions or deletions (indels)  (Fromer et al. , 2014)  in schizophrenia cases. ARC protein has been described to accumulate at synapses in Angelman syndrome  (Greer et al. , 2010)  and increased and/or decreased in several animal models of Alzheimer's disease and patient-derived cells (for a review, see  Kerrigan and Randall, 2013). These data suggest that Arc is a component of protein complexes that are involved with human cognitive disorders. In this paper, we have conducted an extensive proteomic and genetic dissection of Arc protein complexes, which is a generic strategy suitable for the characterization of potentially any synaptic protein. We have focused on the following four challenges: isolation of native multiprotein complexes from brain tissue; visualization of the endogenous protein using genetic tagging; genetic dissection of protein complex organization and localization using mouse genetic models; and genetic dissection of complexes using human genetic data, including human disease and cognitive phenotypes.
Arc Complexes in Disease Proteins within the postsynaptic proteome are assembled into complexes and supercomplexes  (Frank et al. , 2016) , and this supramolecular organization is of crucial importance in human genetic disorders because it is a mechanism by which the many different gene products functionally converge. We therefore combined our proteomic datasets with human genetic (A) Arc complexes were isolated from Arc TAP/+ and Arc TAP/TAP mice crossed with PSD95 knockout mice (Arc TAP/TAP xPSD95 À/À ) by FLAG capture and Tev protease release (single-step purification). Total lysate (IN, input) and the same volume of lysate upon purification (SN) and Tev elution from both genotypes were blotted against Arc and quantified. Eluted Arc levels following the FLAG capture from Arc TAP/+ and Arc TAP/TAP xPSD95 À/À lysates were not statistically different (Mann-Whitney U test, p = 0.
1). Data are presented as mean ± SEM. (B) Isolated complexes from (A) were resolved by SDS-PAGE and stained with colloidal Coomassie. Three independent purifications are shown. The lanes were cut for LC-MS/MS analysis, and the identified proteins are listed in Table  S7. TAP-tagged Arc, PSD95, and the Tev enzyme are indicated. (C) Dimethyl labeling-based quantitative MS of TAP-purified proteins from Arc TAP/+ and Arc TAP/TAP crossed with PSD95 knockout mouse forebrain (Arc TAP/TAP 3PSD95 À/À ). The plot displays enrichment ratios of Arc TAP/TAP 3PSD95 À/À versus Arc TAP/+ (x axis) and iBAQ enrichment values of the step purification (y axis). Proteins meeting criteria for enrichment (>1. 5 fold) are highlighted in green and for depletion (< 0. datasets to understand the importance of Arc-interacting proteins in human disease.
Preliminary proteomic data from Arc TAP mice was previously used to implicate the disruption of ARC complexes in human psychiatric disorders  (Fromer et al. , 2014; Kirov et al. , 2012; Purcell et al. , 2014). The first such study revealed that components of Arc complexes were enriched in de novo CNVs from individuals with schizophrenia  (Kirov et al. , 2012) , with subsequent studies finding enrichment for rare point mutations in individuals with schizophrenia, autism, and intellectual disability (ID). Here we extend preliminary proteomic data from Arc TAP mice using complete sets of Arc-interacting proteins and additional genetic datasets, including epilepsy and healthy control de novo datasets. We first sought to replicate the initial finding of  Kirov et al. (2012)  with the comprehensive Arc interactome.
Utilizing the same genetic dataset as  Kirov et al. (2012) , we found that de novo CNVs from schizophrenia probands were enriched for Arc complex genes compared with de novo CNVs from unaffected individuals (p = 0. 0047). Arc interactors whose association with Arc is depleted in PSD95 knockout mice largely drove this enrichment (p = 0. 0165), indicating the importance of the postsynaptic 1. 5-MDa complexes. We next investigated enrichment of the Arc interactome for rare point mutations and indels contributing to brain disorders, using exome sequencing data from a case/ control schizophrenia study  (Purcell et al. , 2014)  and de novo studies performed in cohorts of schizophrenia, autism, ID, and epilepsy (Supplemental Experimental Procedures). Combining evidence from each of these independent datasets, we found strong support for the enrichment of both nonsynonymous (NS) and loss-of-function (LoF) disease-related mutations among Arc interactors (p = 9.
01 3 10 À12 and 2. 051 3 10 À7 , respectively; Table  1 ). All five datasets contributed to this enrichment (Table  1 ; Tables  S8  and S9 ), indicating that disruption of Arc complexes may contribute to a wide range of brain disorders. Consistent with the analysis of de novo CNVs, much of the enrichment in LoF and NS mutations was attributable to Arc interactors whose expression is altered in the PSD95 À/À mouse. This suggests that it is postsynaptic Arc-PSD95 complexes and not cytoplasmic Arc complexes that are relevant to these disorders.
There are over 130 brain diseases linked to mutations in the postsynaptic proteome  (Bayes et al. , 2011)  and a large number of uncharacterized multiprotein complexes  (Frank et al. , 2016) , many of which contain at least one protein encoded by a disease gene. The integrated workflow shown here, which is centered on genetically tagged mice and proteomic approaches, offers a general and scalable approach toward understanding how the polygenic basis of brain disease is linked to the supramolecular organization of proteins in the postsynaptic terminal of central synapses. All datasets are freely available through the Genes to Cognition website (http:// www. genes2cognition. org).
Enrichment Analysis of CNVs and Rare Coding Mutations in Arc Interactors in Human Neuropsychiatric Disease The protein IDs from Table  S1  were converted into both mouse genome informatics (MGI) and mouse NCBI/Entrez gene IDs using the online ID mapping tool provided by Uniprot and then converted to human Entrez IDs using the mapping file ''HOM_MouseHumanSequence. rpt,'' available from MGI (http:// www. informatics. jax. org/). Any genes with a non-unique (e. g. , 1-many) mapping between species, or where MGI and mouse Entrez IDs mapped to different human genes, were excluded. De novo CNV enrichment analysis and de novo mutation exome sequencing datasets are detailed in the Supplemental Experimental Procedures.
667 fold) are highlighted in red. The names of depleted and enriched PSD95 interactors are indicated. See the Supplemental Experimental Procedures for enrichment criteria. (D) Mouse interactome network constructed from the publicly available databases BioGrid, Database of Interacting Proteins (DIP), IntAct, Molecular INTeraction Database (MINT), STRING database, UniProt, Biomolecular Interaction Network Database (BIND) and mentha using the Psicquic software package. The network is visualized using Visone. Proteins highlighted in green/red meet the enrichment/depletion criteria discussed in the Supplemental Experimental Procedures.
For de novo studies, O/E indicates the observed versus expected (under a null model) number of mutations in that class. For the schizophrenia (SCZ) case/control study, O/E indicates the number of case versus control mutations. P indicates Bonferroni multiple-test correction: four tests for CNVs, 12 tests for Lof/NS combined analyses, and 52 tests for LoF/NS in individual studies. Fisher's method was used to combine p values from the five independent genetic datasets (''combined'' p value for LoF and NS mutations). PSD95 À/À , PSD95/Dlg4 knockout mice.
ACKNOWLEDGMENTS Funding was provided by the  European Union Seventh Framework Programme  under grant agreement  HEALTH-F2-2009-242167  ('' SynSys-project '') and  HEALTH-F2-2009-241498  ('' EUROSPIN '' project), the  Wellcome Trust , the  Biotechnology and Biological Sciences Research Council (BBSRC) , and the  Medical Research Council (MRC). E. F. was supported by a  Federation of European Biochemistry Societies postdoctoral fellowship ,  Marie Curie   FP7-PEOPLE-2010-IEF , and the  Department of Defense   W81XWH-15-1-0361. C. B. was supported by  VIB , the  Department of Defense   W81XWH-15-1-0361 , and the  Associazione Italiana Sindrome X Fragile. A. J. P. was funded by the  Medical Research Council (MRC) Centre  ( MR/L010305/1 ) and  Program Grants  ( G0801418 ). W. D. H. was supported by  Age UK  ( Disconnected Mind ) and  BBSRC/MRC  ( MR/K026992/1 ).
It could also be possible, although not currently done by any available software, to add prescribed kinetic properties (fast folding and absence of kinetic traps) to the objectives of design, as explored in earlier studies  [35]. As will be noticed in the quantitative comparison performed in the continuation, these different computational frameworks will also make it impossible to draw a conclusion as to which program is better for use based on a benchmark. Instead, when facing an application that requires inverse RNA folding, the goal is to try and identify which is the most suitable program for each case. Some inverse RNA folding programs are geared toward more specific biological problems, compared with the ones mentioned up until now that are general in their application scope. For example, nanostructure design including pseudoknots was performed with Nanofolder for the case of multistranded RNA secondary structure  [12].
In another example, for designing the most stable and unstable messenger RNA sequences, which code for a target protein, an algorithm was developed in  [53]. On a similar topic, in  [54] , an algorithm for designing a proteincoding sequence with the most stable secondary structure called CDSfold is provided. For designing multiple target artificial miRNAs, a Tabu search was used in conjunction with biochemical considerations in  [55]. For designing RNA sequences that fold into multiple target structures, which makes it possible to efficiently design multi-stable RNA sequences, a program called RNAdesign was introduced in  [56]. Another specialized application, given here for completeness, is to allow game players to propose a set of rules for RNA design, as part of the Eterna project. Based on experimental results, Eterna players came up with a set of design rules, and EternaBot was developed to design a sequence based on those rules  [57].
Finally, for the problem of fixed backbone three-dimensional design of RNA, the Web server RNA-redesign was put forth  [58]. To the best of our knowledge, this is the first implementation of an RNA design program that considers tertiary structure, although it is a local design. In this respect, in secondary structure, sequences that are generated by point mutations performed on an input sequence to optimize a certain objective function may also be considered a design procedure in the weaker (local) sense. Example of such programs are RNAmutants  [59]  that uses an efficient sampling procedure based on the Boltzmann-weighted ensembles of mutants, and RNAmute  [60]  that uses suboptimal solutions of the RNA free energy minimization prediction  [61, 62]  for simulating only selective mutations from all possible ones.