dataesr/scientific-paragraphs-categorization huggingface dataset:
In this space, closer points represent more similar conformations, and denser regions of points represent the regions containing more likely or more stable (low-energy) conformations in the given dataset. The density of points in this space can be interpreted in terms of the free energy of the complex or by computing and analyzing the averages of the subtomograms and their associated atomic models in the regions of the space characterized by the lowest free energy of the complex. In this article, we showed that MDTOMO produces expected results using synthetic data (ABC exporter). Also, we showed that the results produced using experimental SARS-CoV-2 spike protein data are coherent with previously published results regarding the existence of conformations with all three RBDs closed and those with a single RBD open  22.
Additionally, they revealed the existence of gradual conformational transitions related to the opening of single RBDs, a lifting motion of a single NTB, and additional conformations with more than one fully or partially open RBD. In this article, we analyzed two datasets of two different proteins (ABC exporter and SARS-CoV-2 spike protein) of two different sizes (3000 particles and 20,000 particles, respectively). Although this study shows only two examples of application of MDTOMO, the method can be applied on macromolecular complexes of various sizes. Its computing time scales with the number of atoms included in the model and the size of the subtomograms. For instance, a method using a similar approach as MDTOMO (NMMD-based fitting) for applications in single particle image analysis, MDSPACE, successfully analyzed the variability of the large complex of 80S ribosome  10.
MDTOMO can also be applied on cryo-ET datasets with smaller voxel sizes and larger volumes (the subtomograms of SARS-CoV-2 spikes had a size of 128 3 voxels and a voxel size of 2. 65 Å 3 ). In the experiment with SARS-CoV2 spike data, we showed the results obtained using PCA and UMAP dimensionality reduction methods. PCA is a linear dimensionality reduction method that decomposes the data variability into a linear combination of principal component vectors. UMAP is a non-linear dimensionally reduction method that in some cases gives a better separation of different conformational populations than PCA, as was the case with the analysis of SARS-CoV2 spike data in this article. In particular, UMAP allowed revealing two clusters of the conformational states previously observed using the same dataset and the classical STA data analysis procedure  22  (all three closed RBDs and one-open RBD, Fig.
3b, c ) and two clusters of the states unobserved previously (Fig. 3d, e ). In the case of synthetic dataset of ABC exporter, we only showed the results obtained with PCA because PCA and UMAP produced similar conformational landscapes in the case of this dataset. However, as UMAP extracts non-linear features from the data, it produces a conformational landscape with non-linear parametrization in which the interpretation of energy densities must be done carefully. In this conformational embedding, equivalent distances are not necessarily equivalent conformational difference. For instance, in the UMAP embedding in Fig. 3 , the distance between the centers of the c and d regions and between the centers of the d and b regions is 2 and 4. 5, respectively, but these two distances correspond to approximately the same conformational difference (a rotation of the RBD of approximatively 30°).
1 and Eq. 4 in Vuillemot et al. 10  ). The weight given to the potential defined by the correlation coefficient, with respect to the classical MD potential, is a free parameter (the so-called force constant; k in Eq. 1 in Vuillemot et al. 10  ) whose value is set so as to drive the fitting toward the conformation in the subtomogram by preserving structural constraints, which reduces the risk of overfitting the noise and the missing-wedge-induced deformations in the subtomograms. Compared to the majority of other methods for analyzing conformational flexibility in single particle cryo-EM datasets (e. g. , 3DVA 11 in cryoSPARC  48  or cryoDRGN  12  ) or cryo-ET datasets (e. g. , Tomoflow  18  ), MDTOMO uses an available atomic structure, whose coordinates are displaced (by performing NMMD simulation) to match the conformations in the subtomograms.
The MDTOMO approach requires running one MD simulation per subtomogram, which can be computationally expensive. The MDTOMO software is parallelized so that multiple simulations can run on multiple CPU cores simultaneously, with one simulation running only on one CPU core. Furthermore, the use of coarse-grained simulation coupled with normal-mode-based acceleration allows performing MDTOMO in a reasonable amount of time. The total execution time on 8 nodes of 2 × 20 Intel 6248 2. 50 GHz for the two presented datasets is shown in Table  1. However, it is important to note that the computational cost scales with the molecular weight of the complex (the higher the number of atoms, the higher the cost of the MD simulation).
Acknowledgements We acknowledge the support of the  French National Research Agency-ANR  ( ANR-19-CE11-0008  to SJ and  IR  and  ANR-20-CE11-0020-03  to SJ); the cooperation between the  CNRS  and the  University of Melbourne (The Melbourne-CNRS Network ,  PRC 2889  to SJ and IR, CNRS 80 Prime to SJ, joint PhD scholarship to RV); The University of Melbourne start-up fund (to IR); and access to HPC resources of CINES and IDRIS granted by  GENCI  ( A0100710998R ,  A0100710998 ,  A0070710998 ,  AP010712190 ,  AD011012188  to SJ). We thank  Beata Turoňová  ( Max Planck Institute of Biophysics, Frankfurt am Main, Germany ) for providing the subtomograms and the initial rigid-body alignment parameters that were used in the SARS-CoV-2 S protein experiment described in this article (from the dataset available in the EMPIAR database under the code  EMPIAR-10453 ) as well as for critical reading of our manuscript and discussing the results that are presented here.
5b, c ). In the supplementary methods of the present paper (Supplementary Figs. S1-S5 ), we systematically apply the set of analyses underlying the demonstration proposed in ref. 32 and compare its results to those obtained in the present dataset (learning + transfer). This exercise confirmed that the combination of RELASYM and Qc-REG models faithfully capture our participants' behavior (choice and confidence judgments) throughout our experimental framework (learning and transfer phase), and that learning biases are fundamentally linked with confidence biases (Supplementary Fig. S5 ).
Afterward, they experienced a 16trial practice with the same learning task (but using different symbols) as well as a lottery incentivize procedure outside of the MRI scanner. The final payout was computed as follows: show-up fee (20€), accumulated outcome from the learning task and bonus from the confidence incentivization procedure. The mean and standard deviation of the payout was 32. 18 ± 3. 46€. All the tasks were implemented using MatlabR2015a® (MathWorks) and the COGENT toolbox.
The direction of changes was analyzed by follow-up t-tests. In particular, one-sample t-tests were used when comparing data to a reference value (e. g. , guessing level: 50%), and paired t-tests were used to compare responses across different conditions (e. g. , gain vs. loss) and different measures (e. g. , averaged learning performance vs. averaged confidence). All statistical analyses were performed using MatlabR2021a® (MathWorks) and its built-in functions (i. e. , one-sample t-test: t-test; paired t-test: ttest2; repeated ANOVA: anovan; Pearson's correlation: corr), with a statistical significance level of alpha 0. 05. Unless otherwise specified, significance level for t-tests correspond to two-tailed hypothesis test.
e. , sequence of choices) given the current model M and its parameters θ M. P θ M jM À Á refers to the prior probability of the parameters. We used broad priors based on the literature  93  : The prior distributions of learning rates (α) and imaginary outcome weight (w) were defined as beta distributions (Beta(1. 1, 1. 1) in MATLAB), and the prior distribution of the inverse temperature parameter β was defined as a gamma distribution (Gamma(1. 2, 5) in MATLAB). Parameter search was initialized from random starting points selected from certain ranges (i. e. , 0 < α < 1; 0 < w < 1 ; 0 < β < ∞) and used an L-BFGS-B algorithm implemented via Matlab's fmincon function  94. For model comparison, we calculated, for each individual, the Laplace approximation to the model evidence (LAME), which penalizes model complexity (i.
e. , number of parameters) as follows: LAME = ÀnLPP + df 2 log 2π ð ÞÀ 1 2 log H j j ð13Þ where n is the number of trials, df is the number of free parameters and H is the Hessian. Quantitative model comparison was performed via a formal BMS random-effect procedure  95  and implemented in the mbb-vb-toolbox (http://mbb-team. github. io/VBA-toolbox/). This toolbox performs the Bayesian model selection procedure and estimates two indicators: the expected frequencies (EF) and the exceedance probability (EP) for each model. Specifically, the expected frequency EF of a model quantifies the probability that the model generated the data for any randomly selected subject. Note that the EF should be higher than chance level given number of models in the model space. EP, on the other hand, quantified the belief that the model is more likely than all the other models of the model-space.
φ(x) is the logistic link function φ(x) = 1/(1 + e -x ). Confidence modelsmodel optimization and comparison. Confidence model parameters were estimated by fitting robust linear regression, via the procedure of maximizing log-likelihood (LL), as implemented in MATLAB robustfit functions. Considering that no principled priors for the confidence models are available, we used LL to approximate model evidence for each subject and each model as the BIC (Bayesian information criterion), defined as BIC = nlogðmÞ À 2LL ð16Þ where n is the number of parameters and m is the number of data points (trials). Similarly to the learning models, we fed the BIC (from each subject in each model) to the random-effect BMS routine implemented in the mbb-vb-toolbox (http://mbb-team. github. io/ VBA-toolbox  95  ).
Note that parameter recovery and model recovery for the confidence models are also detailed in ref. 32. fMRI fMRI acquisition. The fMRI data were acquired using a 3. 0-Tesla Philip Achieva scanner with 32 channels head array coil. We recorded both structural images and functional brain images. T1 weighted structural scans were recorded with the following parameters: FOV (Field of View): 240 × 180 × 220 mm 3 , Voxel size = 1 × 1 × 1 mm 3 , TR = 8. 2 ms and TE = 3. 7 ms. Each T2*-weighted functional scan consisted of 36 axial echo-planar images (EPI) acquired in ascending sequence with voxel size of 3 × 3 × 3 mm 3 , slice gap = 0. 3 mm, TR= 2000 ms, TE = 28 ms and the flip angle of 76°. Each subject completed three runs in a scanning session. Given the task was self-paced and the fMRI scanner was manually terminated (i.
Acknowledgements The authors thank  Tiffany M. Hrkalović  for assistance with the fMRI data collection. This study was funded by startup funds from the  Amsterdam School of Economics  awarded to J. B. E. C. C. T. is supported by  GSSA ,  MOE Taiwan Scholarship  ( 1081007012 ). M. L. is supported by an  ERC Starting Grant  ( INFORL-948671 ). S. P. is supported by an  ERC Consolidator Grant  ( RaReMem-101043804 ) and by the  Agence National de la Recherche  ( CogFinAgent :  ANR-21-CE23-0002-02 ;  RELATIVE :  ANR-21-CE37-0008-01 ;  RANGE :  ANR-21-CE28-0024-01 ).
Genetic variation is instrumental for adaptation to new or changing environments but it is poorly understood how it is structured and contributes to adaptation in pelagic species without clear barriers to gene flow. Here we use extensive transcriptome datasets from 20 krill species collected across the Atlantic, Indian, Pacific and Southern Oceans and compare genetic variation both within and between species across thousands of genes. We resolve phylogenetic interrelationships and uncover genomic evidence in support of elevating the cryptic Euphausia similis var. armata into species. We estimate levels of genetic variation and rates of adaptive protein evolution among species and find that these are comparably low in large Southern Ocean species endemic to cold environments, including the Antarctic krill Euphausia superba, suggesting their adaptive potential to rapid climate change may also be low.
While the constant advances of sequencing technologies and protocols promise to ease whole genome sequencing of challenging organisms in the future, transcriptomics offers many possibilities to advance insight into zooplankton ecology  (Lenz et al. 2021 ). Here we produce, for the first time, a comprehensive comparative dataset at the genome-scale for krill, spanning 20 species from the Atlantic, Indian and Southern Oceans (fig. 1 ). Euphausiids are an attractive model for zooplankton because of their importance for ocean ecosystems and distribution across all latitudes. Moreover, they belong to a comparatively small and ecologically wellcharacterized order of zooplankton and are not extremely divergent, facilitating association between genes and phenotypes through comparative analyses. We sidestep the issue of prohibitively large genomes of these non-model organisms by using RNA-seq data to analyze genetic variation in expressed protein-coding genes  (De Wit et al.
Genome-scale datasets to study variation and adaptation We compiled RNA-seq data spanning 124 transcriptomes from 20 species (102 transcriptomes from 17 species are new for this study). Morphological and molecular species identifications agreed for all samples with barcodes in the MetaZooGene database (Table  S1 ). After removing redundant and noncoding isoforms, the reference transcriptomes contained 20-53 K coding transcripts per species (Table  S2 ). BUSCO analyses indicated high completeness (median=92. 5%) and low duplication levels (median=3. 3%), amenable for tracing evolutionary patterns using base-level substitutions and polymorphisms. We measured intraspecific genetic variation in nine species, using up to 20 specimens per species and detected between 260-2,200 K high-quality SNPs across 12-22 K genes (Table  S3 ).
3B-C ), which was also supported by high FST (0. 68) (fig. 3D ). We next assembled 500-800 bp mtCOI fragments and assessed divergence. Sequences segregated consistently with the nuclear data and were ~6. 2% divergent, clustering with different MetaZooGene similis barcodes derived from southern Atlantic Ocean samples (fig. 3E ; supplementary fig. S3 ). Because of the consistent molecular and phenotypic differences between similis and armata, we treated them as separate evolutionary lineages in subsequent analyses. We detected comparably weak north-south structuring in our limited N. megalops sample (FST=0. 06), but did not observe any structure in the subtropical E. spinifera, nor in the subantarctic E. vallentini and E. triacantha collected from the Antarctic Polar Front (supplementary fig.
Comparative analyses establish comprehensive gene orthologies and a robust species tree To enable direct comparative analyses of molecular evolution, we inferred gene orthology among the 20 krill species and seven outgroups (supplementary materials) and reconstructed a species tree. We detected 103,292 orthogroups (OGs) with genes from two or more krill species, but only 880 OGs contained single-copy orthologs spanning all 20 species. We produced two subsets of OGs, balancing species missingness against the number of OGs. The first dataset spanned 2,280 OGs, >1 million amino acid positions and 18+ krill species (Table  S4 ), and was used to reconstruct a fully supported phylogeny (fig. 5A ). The topology is mostly consistent with inferences of krill interrelationships by Vereshchaka and co-workers based on morphology and four molecular markers: Meganyctiphanes + Nematoscelis + Thysanoessa form the monophyletic clade Nematoscelinae, which is the sister taxon of Euphausiinae  (Vereshchaka et al.
2019). Groupings within Euphausia confirm a monophyletic Southern Ocean "Euphausia superba group" including frigida, vallentini, crystallorophias and superba, while our clade with spinifera, longirostris, similis, similis var. armata and triacantha also include recurva and mucronata, mixing other species groups. Resolving phylogenetic interrelationships is necessary for understanding the evolution of euphausiid genes, traits and ecologies and numerous attempts have been made using morphology or sparse molecular datasets such as mitochondrial markers (see  Bucklin et al. 2007; Vereshchaka et al. 2019 ; Schram and Koenemann 2022 for reviews). Our reconstruction is the first phylogenomic species tree for krill and shows that E. triacantha is only distantly related to other Euphausia of the Southern Ocean.
Scans for positive selection reveal candidate genes for environmental adaptation We aimed to detect genes that may underlie episodic adaptation to cold or warm climates in krill. To this end, we applied branch-site tests to the OG dataset spanning >13,000 OGs (>5 million codon positions; Table  2 ; Table  S4 ) to detect genes with locally elevated ω between species associated with different environments  (Yang and Nielsen 2002). In the first contrast, we compared five Southern Ocean cold-adapted Euphausia against the rest of the phylogeny (E. superba, E. crystallorophias, E. frigida, E. vallentini and E. triacantha; fig. 5A ; Table  S4 ). We identified 832 gene candidates (LRT p<0. 01; n=629 genes with Drosophila homologs; Table  S5 ), containing 1,800 selected codons. A gene ontology (GO) enrichment test found enrichment for genes involved in detecting temperature among candidates (fig.
2010)  and Photoreceptor dehydrogenase (Pdh), which also processes retinal pigment. We identify an opsin (OG98713; supplementary fig. S9B ), automatically annotated as Rh5. However, queries against KrillDB 2  (Urso et al. 2022)  and NCBI Genbank suggest it is a peropsin, a light-sensitive yet non-visual receptor with poorly understood function in crustaceans but with expression patterns that oscillate daily in Antarctic krill  (Henze and Oakley 2015; Biscontin et al. 2016; Palecanda et al. 2022). In both contrasts, we detect selection signals across multiple genes encoding cuticular proteins (ncold=10; nwarm=5) and chitinases (ncold=1; nwarm=1). Cuticular protein genes are among the largest gene families in arthropod genomes  (Magkrioti et al. 2004) , and help build the exoskeleton. In krill, cuticular development and gene expression are closely linked to physiological and seasonal cycles of ecdysis (molting), growth and reproduction  (Buchholz and Buchholz 2010; Urso et al.
We used the Gamma-Zero model to compare all species, which fits a gamma distribution for neutral and deleterious mutations to the data. It produced estimates similar to the Displaced-Gamma model, both of which behaved consistently across all datasets, producing estimates correlated with but higher than basic non-parametric methods and were considered accurate in recent tests (Al-Saffar and Hahn 2022). We detected the highest rates of adaptive protein evolution in the subtropical E. similis var. armata lineage (α=0. 59; ωA=0. 086) (fig. 7A-C ; Table  S8 ), whereas adaptive evolution appears more limited in the Antarctic krill E. superba (α=0. 20; ωA=0. 035) and subantarctic E. triacantha (α=0. 30; ωA= 0. 059). We then analyzed how effective population size (Ne), here approximated by πS, life history traits and habitat conditions correlate with adaptive rates to identify factors that may influence adaptive potential.
long lifespan, small vs. large bodies or propagules) have previously been linked to high vs. low genetic diversity across metazoans  (Romiguier et al. 2014; Ellegren and Galtier 2016). However, associated traits like fecundity are not known in either species, and across our dataset we otherwise find no statistically significant correlations between such life history traits and levels of variation. We are therefore unable to identify general determinants of diversity in krill, which may need sampling of more species and ecological information. Compared to the neutral levels of genetic diversity of other arthropods, we find that krill has intermediary levels of variation (πS: mean±SD): 1. 5±0. 5% in krill vs. 1. 4±0. 9% in 43 species  (Leffler et al. 2012)  or 1. 4%±1. 2% in 26 species  (Romiguier et al.
Atlantic Ocean samples We collected M. norvegica (Northern krill) specimens (n=7) from the Swedish Gullmarsfjord in 2018 using a small Isaacs-Kidd midwater trawl (IKMT) and preserved them in RNAlater™ (Invitrogen™). We used material from three North Atlantic-Arctic Thysanoessa species: T. longicaudata and T. raschii specimens were collected in August 2012 in the Kongsfjorden (Spitzberg) and frozen in liquid nitrogen. We re-used a published RNA-seq library (ENA: SRR2174568) for T. inermis from  (Huenerlage et al. 2016).
CT50 experiments Experiments were performed using live E. triacantha and E. vallentini samples from the Indian Ocean and M. norvegica from the Arctic Ocean. The experimental protocol was identical to that used for Euphausia crystallorophias and Euphausia superba  (Cascella et al. 2015)  or the Arcto-boreal Thysanoessa inermis  (Huenerlage et al. 2016). After acclimation of 24 h at ambient sea water temperatures, actively swimming animals were transferred to an experimental tank. Temperature was increased by 1°C per 10 min. The animals were maintained in the tank until they no longer responded to tactile stimuli of a probing rod. At this point, it was considered that the critical temperature had been reached. The CT50 was determined through the non-linear curve fitting option in Prism9 (GraphPad Software, LLC).
Molecular species validation For each library, we queried 1 million forward-oriented reads against all krill COI reference barcodes in the MetaZooGene database (MZGDB v3; n=3,003 sequences from 64 fully identified species)  (Bucklin et al. 2021)  using BLASTN  (Tan et al. 2006; Camacho et al. 2009 ) and recorded best hits. For barcodes with 20+ hits, we computed the average identity scores. We took the barcode with the highest average score as an indication of species.
RNA trimming, assembly and annotation We used Trim Galore! v0. 6. 1 https://github. com/FelixKrueger/TrimGalore/ and Cutadapt v2. 3  (Martin 2011)  to trim low quality positions from reads (phred<20), only keeping trimmed reads of at least 50 bp. Trinity v2. 11. 0  (Grabherr et al. 2011 ) was then used with default settings to assemble 19 transcriptomes, making one "reference" transcriptome per species. When possible, we pooled reads from up to five specimens to maximize gene completeness. Preliminary tests indicated that pooling data produced on average 1. 1-1. 3× as many complete BUSCO genes compared to a large (44-133 M read pairs) or single small library (~22 M read pairs). Due to unexpected population structure between E. similis and E. similis var. armata, a separate reference transcriptome was assembled for armata.
The T. raschii transcriptome was assembled similarly to T. inermis in  (Huenerlage et al. 2016). We used the Trinity script "get_longest_isoform_seq_per_trinity_gene. pl" to reduce redundancy by keeping only the longest splice isoform per gene. We used TransDecoder v5. 5. 0 https://github. com/TransDecoder/TransDecoder to identify protein-coding transcripts and TransDecoder. LongOrfs to detect open reading frames (ORFs) >300 bp. ORFs were queried for domain homology against Swissprot with BLASTP v2. 9. 0+  (Camacho et al. 2009 ) (e-value cut-off 1e -5 ) and Pfam (release 34. 0) with HMMER3 hmmscan v3. 3 http://hmmer. org/. We used TransDecoder. Predict to identify and carry forward only protein-coding transcripts. Coordinates for functional regions (UTRs or coding sequence) were kept in GFF files.