dataesr/scientific-paragraphs-categorization huggingface dataset:
The rate constants extracted from these fits reveal that secondary nucleation dominates over primary nucleation by many orders of magnitude (Table  1 ). Given the relatively minor contribution of primary nucleation, it is difficult to establish the reaction order of this process. Both values of 1 and 2 yield similarly good fits to the datasets here presented (compare Figure  2B  with Figure  S2C ). Primary and secondary nucleation are generally considered to be the same molecular conversion, and we favor the solution with both reaction orders being 1 as further detailed below. IAPP Aggregation Kinetics Are Independent of the Peptide:Lipid Ratio. The reaction order indicates a lower boundary for the nucleus size, which could thus be as small as a monomer. If the critical nucleus size is indeed a monomer, the nucleation process should be independent of the local IAPP density on the membrane.
To test this hypothesis, we performed the aggregation assay at lower IAPP:lipid ratios of 1:10 and 1:50 at which the IAPP density on the vesicles is expected to be higher (Figure  3  and Figure  S3 ). The scaling exponents are very similar (Figure  3A ), and the datasets fit well to the same model of secondary nucleation with a reaction order of 1 (Figure  3B ,C and Figures  S4  and S5 ). Furthermore, the rate constants extracted from the fits to the 1:50 and 1:100 ratios are in close agreement (Table  1 ). Thus, the aggregation kinetics are essentially independent of the local peptide concentration on the vesicles. At a ratio of 1:10, we suspect that some IAPP that is not bound to the membrane aggregates in solution, and the scaling exponents and rate constants contain a minor contribution from this process.
When fitting datasets obtained at a fixed peptide:lipid ratio (Figures  2  and 3 ), the peptide density on the vesicles is expected to be identical for all IAPP monomer concentrations, and processes depending on the local crowding on the membrane surface are not taken into account. However, these processes do not appear to play a role. Indeed, aggregation data obtained at a constant concentration of lipids, leading to increased crowding of IAPP on the membrane with increasing monomer concentration, fit to the same mechanism with a reaction order for primary and secondary nucleation of 1 (Figure  4 ). Increasing the fraction of DOPS in the vesicles strongly accelerates the aggregation kinetics of IAPP (Figure  5 ), which is in agreement with stronger binding of IAPP at a higher negative charge of the membrane.
EEG data were continuously recorded at a sampling rate of 250 Hz and then filtered with an offline bandpass filter of 0. 5-50 Hz. For offline processing, all EEG data were imported into the Matlab toolbox EEGLAB v12 (http:// sccn. ucsd. edu/ eeglab/). We removed stereotypical eye movement artifacts, such as saccades or blinking, using Infomax ICA decomposition  (Jung et al. , 2000). This provides a good artifact separation performance for most artifact types whilst ensuring minimum information loss  (Inuso et al. , 2007). Recordings were further cleaned with an automated z-score based method, using the FASTER plugin  (Nolan et al. , 2010) , rejecting 1-second epochs that deviated from the mean by more than 1. 7 standard deviations.
Source-space measures of EEG activity Artifact-free EEG data were processed in Matlab with the Brainstorm Toolbox (http:// neuro image. usc. edu/ brain storm/). In line with previous approaches using a similar EEG setup in clinical populations  (Tokariev et al. , 2019) , we first computed a head model of the cortex surface for each EEG recording using overlapping spheres (OpenMEEG) and then estimated unconstrained cortical sources using the minimum-norm sLORETA algorithm implemented in Brainstorm  (Cosandier-Rimélé et al. , 2017). To normalize sources across participants, we projected (warped) the sources from each participant onto the MNI/Colin27 template brain  (Holmes et al. , 1998). The 15,000 voxel source-space was then divided into 68 cortical regions-of-interest (ROIs) according to the Desikan-Killiany neuroanatomical atlas  (Desikan et al.
Spectral power Spectral subdivision into the basic EEG frequency bands was estimated with a standard FFT approach using Welch's method (Matlab pwelch() function) and a Hanning windowing function (4 second epoch, 50% overlap). Based on the total number of collected and averaged epochs per subject, quantitative estimates of Absolute Power (amplitude, signal strength) was computed, and percent Relative Power (i. e. , % power, amount, abundance) was calculated as the ratio of the mean power in a specific EEG band and the broadband power (1-45 Hz).
Statistical analyses Source-space (voxel-wise) of band-limited surface spectral power were export by using the Brainstorm Toolbox. Statistical comparisons between CUD patients and controls were performed using Statistical Parametric Mapping toolbox (SPM12) via independent two-tailed t-tests with a p < 0. 05 threshold across the four EEG bands. Individual attentional performance was measured by performance on the Go/NoGo task, using the Hit rate variable. Data from CUD group was subsequently used in a between-subject regression analysis (with a p < 0. 05 threshold) using the SPM toolbox. Between-group comparisons in inhibitory control (commission errors) and attention (hit rate) in the Go/NoGo task were performed by using independent two-tailed t-tests with a probability of type I error (α) = 0.
| Genotyping and quality control High-density genotypes were obtained at the INRAE genotyping Platform Gentyane (Clermont-Ferrand, France) for all the 176 French samples using the Affymetrix 665K SNP array recently developed for rainbow trout  (Bernard et al. , 2022). We only considered the genotypes for the 576,118 SNPs of the Rainbow Trout Axiom® 665K SNP array that were positioned on the Arlee genome (GCA_013265735. 3, Gao et al. , 2021; Bernard et al. , 2022). From the whole-genome sequence information of the 20 American samples  (Gao et al. , 2018) , we extracted the genotypes for the same 576,118 SNPs of the HD chip. Among the 177 French genotyped fish, 19 individuals with more than 30% identity-by-state (IBS) with other individuals were removed from the dataset. We thus kept for the analysis 76, 67, 20, and 14 fish sampled from LB, LC, HA, and SY populations, respectively.
Then, SNP quality control was performed using PLINK v1. 9 software  (Chang et al. , 2015). Note that, to avoid limitations due to the low number of individuals in SY, quality filters were made considering LC and SY together, as both populations were genotyped on the same SNP plate and are close genetically  (D'Ambrosio et al. , 2019). About 4,000 SNPs randomly distributed over all the genome were removed for all populations due to extreme deviation from Hardy-Weinberg equilibrium (p-value < 10-7). It allowed us to discared SNPs with high risk of wrong genotyping, in addition to the edit for SNP call rate lower than 97%. We retained 571,319 SNPs, 569,030 SNPs, and 573,793 SNPs on LB, 'LC-SY', and HA populations, respectively. Finally, crossing the three SNP lists, we kept the 546,903 common SNPs for the analysis.
| Genetic structure of the populations Genetic differentiation between populations was measured with a pairwise Fst estimate using the VCFtools v0. 1. 13 software  (Danecek et al. , 2011). In addition, a principal component analysis (PCA) was performed with the R package Adegenet (function glPca)  (Jombart & Ahmed, 2011)  to visualize the genetic structure of the populations.
| Integrated Haplotype Homozygosity Score (iHS) In the same way, we used the iHS test  (Voight et al. , 2006)  to evaluate the evidence of positive selection based on haplotype frequencies in a single population, using the ihh2ihs function of the package rehh. This statistic was based on the log-ratio of the integrated EHH (iHH) for haplotypes with the ancestral (A) versus the derived (D) alleles and was computed for each autosomal SNP as 𝑖𝐻𝑆 = ln( 𝑖𝐻𝐻 𝐴 𝑖𝐻𝐻 𝐷 ) -𝑀𝑒𝑎𝑛 𝑝 [ln( 𝑖𝐻𝐻 𝐴 𝑖𝐻𝐻 𝐷 )] 𝑆𝐷 𝑝 [ln( 𝑖𝐻𝐻 𝐴 𝑖𝐻𝐻 𝐷 )] The average (Meanp) and standard deviation (SDp) of ln(iHHA/iHHD) were computed over all the SNPs with a derived allele frequency p similar to that of the core SNP. In our study, the ancestral allele state is unknown. Therefore, we assumed that the most frequent allele represents the ancestral state as proposed by  Bahbahani et al.
| Detection of candidate regions To detect candidate regions for signatures of selection based on the iHS test, we used the calc_candidate_region function of the R package rehh  (Gautier & Vitalis, 2012). We considered windows of 500 kb across the genome containing at least 30 SNPs, with 10 kb of overlapping. A region was considered as under positive selection if at least one SNP had a log(p-value) > 4 and extreme iHS value i. e. |iHS| ≥ 2. 5.
| Biological functions of genes under positive or balancing selection Among the 282 genes in the 13 regions detected under either positive or balancing selection, most genes seem to play essential roles in fitness as expected with such a dataset comprising both European and US populations. They are related to all main biological functions (genome stability, cell organization, neuronal and embryonic development, energy metabolism, growth, reproduction, and immunity). All identified biological functions were already described in other studies of signatures of selection in farmed rainbow trout  (Cádiz et al. , 2021)  and other domesticated species  (López et al. , 2018 (López et al. , , 2018;; Naval-Sanchez et al. , 2020; Baesjou & Wellenreuther, 2021; Signer-Hasler et al. , 2022).
, 2014). In yonlong grouper, ctnna2 seems implicated in vertebral development, because significantly differentially expressed between normal and fish with lordosis  (Li et al. , 2022). In mice, a homozygous for a mutation of ctnna2 reduced body weight, male fertility, and induced brain abnormalities (hypoplastic cerebellum, abnormal foliation pattern, ectopic Purkinje cells, and abnormal pyramidal cells in the hippocampus). While the protein associated to this gene is highly conserved among vertebrates  (Uvarov et al. , 2014 ; Supplementary information S17), the gene exhibits a strong polymorphism in all the four studied rainbow trout populations. However, a large part of its polymorphism is located in one intronic region (intron 6-7) of ctnna2. In the zfin database, five transcripts of this gene were identified (three mRNA and two non-coding RNA).
Near-IR photometry with ExTrA The ExTrA facility  (Bonfils et al. 2015) , located at La Silla observatory, consists of a near-IR (0. 88-1. 55 µm) multi-object spectrograph fibre-fed by three 60-cm telescopes. Five fibre positioners intercept the light from one target and four comparison stars at the focal plane of each telescope. We observed five transits of TOI-3884 b on nights UTC 2022 February 22, 2022 March 3, 2022 April 4, 2022 April 13, and 2022 May 15. We observed with one, two, or three telescopes simultaneously. We used the 8 aperture fibres, the low-resolution mode of the spectrograph (R ∼ 20), and 60-s exposures. We chose comparison stars with effective temperatures (Gaia Collaboration 2018) similar to that of TOI-3884. The ExTrA data were analysed using custom data reduction software.
Seeing-limited optical photometry We obtained one additional ground-based photometric followup observation of TOI-3884 with the Las Cumbres Observatory Global Telescope (LCOGT;  Brown et al. 2013 ) and as part of the TESS Follow-up Observing Program 2  (TFOP; Collins 2019), in an attempt to rule out or identify nearby eclipsing binaries (NEBs) as potential sources of the TESS detection, to measure the transit-like event on target to confirm its depth, and thus the TESS photometric deblending factor, and to refine the TESS ephemeris. We scheduled our transit observations using the TESS Transit Finder, which is a customised version of the Tapir software package  (Jensen 2013). The LCOGT observation was performed with a 1-m telescope at Teide observatory in the g filter, with an exposure time of 300 s.
PHARO has a pixel scale of 0. 025 per pixel for a total field of view of ∼25. The AO data were processed and analysed with a custom set of Interactive Data Language tools. The flat fields were generated from a median of dark subtracted exposures of the sky. Those flats were then normalised to a median value of one. The sky frames were generated as the median of the 15 dithered science frames; each science image was then sky-subtracted and flat-fielded. The reduced science frames were combined into a single combined image using an intra-pixel interpolation that conserves flux, shifts the individual dithered frames by the appropriate fractional pixels, and median-co-adds the frames. The resolution of the combined image was determined from the full-width half-maximum (FWHM) of the point spread functions: 0.
The spot contrast 6  is a free parameter for each of the g , TESS, and ExTrA bands, which have a respective λ pivot  (Koornneef et al. 1986 ) of 476, 770, and 1165 nm. We oversampled the model in time and accounted for the integration time of the observations through binning  (Kipping 2010). We used the pymc3 package  (Salvatier et al. 2016; Hoffman & Gelman 2011)  to sample from the posterior. The list of parameters, priors, and posteriors are shown in Table  A. 4, and the data and model posterior are shown in Fig. 1. Figure A. 9 shows the spot contrast for the three bands. From these three spot contrasts, we estimate a photosphere-spot difference temperature of 187 ± 21 K (Sect. B), which is in the range measured for M dwarfs  (Berdyugina 2005; Barnes et al. 2015). Follow-up transit observations combined with the highresolution imaging rule out most nearby eclipsing binary scenarios.
Acknowledgements. Funding for the TESS mission is provided by  NASA's Science Mission Directorate. We acknowledge the use of public TESS data from pipelines at the  TESS Science Office and at the TESS Science Processing Operations Center. This research has made use of the Exoplanet Follow-up Observation Program website, which is operated by the  California Institute of Technology , under contract with the  National Aeronautics and Space Administration  under the  Exoplanet Exploration Program. Resources supporting this work were provided by the  NASA   High-End Computing (HEC) Program  through the  NASA Advanced Supercomputing (NAS) Division at Ames Research Center  for the production of the SPOC data products. This paper includes data collected by the TESS mission that are publicly available from the  Mikulski Archive for Space Telescopes (MAST).
This work makes use of observations from the LCOGT network. Part of the LCOGT telescope time was granted by  NOIRLab through the Mid-Scale Innovations Program (MSIP). MSIP is funded by  NSF. This research has made use of the  Spanish Virtual Observatory  (https://svo. cab. inta-csic. es)project funded by  MCIN/AEI/10. 13039/501100011033 /through grant  PID2020-112949GB-I00. The Pan-STARRS1 Surveys ( PS1 ) and the PS1 public science archive have been made possible through contributions by the  Institute for Astronomy , the  University of Hawaii , the Pan- STARRS  Project Office, the  Max-Planck Society  and its participating institutes, the  Max Planck Institute for Astronomy, Heidelberg  and the  Max Planck Institute for Extraterrestrial Physics, Garching , The  Johns Hopkins University, Durham University , the  University of Edinburgh , the  Queen's University Belfast , the  Harvard-Smithsonian Center for Astrophysics , the  Las Cumbres Observatory Global Telescope Network Incorporated , the  National Central University of Taiwan , the  Space Telescope Science Institute , the  National Aeronautics and Space Administration  under Grant No.
NNX08AR22G  issued through the  Planetary Science Division of the NASA Science Mission Directorate , the  National Science Foundation  Grant No. AST-1238877 , the  University of Maryland, Eotvos Lorand University (ELTE) , the  Los Alamos National Laboratory , and the  Gordon and Betty Moore Foundation. Based on observations collected at the  European Southern Observatory  under  ESO  programme  109. 24EP. This work has been supported by a grant from  Labex  OSUG at 2020 (Investissements d'avenir - ANR10 LABX56 ). Based on data collected under the  ExTrA  project at the  ESO La Silla Paranal Observatory. ExTrA is a project of  Institut de Planétologie et d'Astrophysique de Grenoble (IPAG/CNRS/UGA ), funded by the  European Research Council  under the  ERC Grant  Agreement n. 337591-ExTrA.
, 2017; Gasda et al. , 2021). If ((observation point oxide value) -RMSEP point ) ≥ ((bedrock median value) + RMSEP bedrock )), then this observation point is enriched; if not, then this point is not enriched. The median bedrock value used is based on the type/location of the target and its corresponding chemical endmember (coherent; rubbly; Groken/Mary Anning; Hutton; and Glasgow). We expect large differences in the mineralogy between that of diagenetic materials and the bedrock; thus, a stricter criterion based on RMSEP, rather than instrument precision, is necessary for this study. Within Glen Torridon, we observed the largest variations in composition for the oxides: Na 2 O, MgO, CaO, MnO, and FeO T , and these oxides were used in the database  (Gasda et al. (2022) ; described in Text S1 in Supporting Information S1) to determine enrichments.
Identifying Features We used data from a combination of Curiosity instruments: ChemCam spectra, ChemCam RMIs, Mastcam, and MAHLI images, to identify diagenetic features in Gale crater. Initially, we looked at each RMI image colorized with Mastcam M100 images and determined if a diagenetic feature was present in the image. If MAHLI images were also acquired for a target, these images were also used to determine the category of a particular observation point. Almost every ChemCam LIBS target has an associated Mastcam M100 image, but the cross-targets with MAHLI is a limited data set. All Mount Sharp group targets in Glen Torridon are classified in the supplemental database (described in Text S1 in Supporting Information S1), and provided with metadata  (Gasda et al. , 2022). ChemCam targets were sorted by feature type, including nodular bedrock, nodules, veins, dark strata, and cemented  (Gasda et al.
, 2022). Each of these categories are further broken down by their chemistry and placed in separate subsheet. along with their chemistry  (Gasda et al. , 2022). Only Mount Sharp group targets are included in the database; dark-toned float rocks, pediment rocks, and regolith targets have been excluded. Out-of-focus points and points in shadow are also excluded. First targets were sorted by morphology, then individual ChemCam observation points were sorted if they directly hit a type of feature, and then these features were subdivided by their chemistry. If a vein or nodule was visible in the RMI, but was not hit directly by the laser, we categorized that target as having a "diagenetic feature. " If nodules were visible in the RMI, but the ChemCam laser did not directly hit nodules, these targets were categorized as "nodule-rich bedrock.
The combination of color images, RMI, and chemistry is important for categorizing features. In many cases, there were targets with a nodular texture, but these targets have no corresponding enrichments or depletions of elements, and/or did not have changes in color. In these cases, we excluded these targets in our database of diagenetic targets or placed them into the nodule-rich bedrock target list, depending on their morphology. These targets may merely be erosional features or chemical differences that are not large enough to be resolved by ChemCam (see Texts S1 and S5 in Supporting Information S1). We are not using a truly random sample of diagenetic features in Glen Torridon because we used RMI and MAHLI images for this analysis. Rover stops, where observations were made, are chosen based on rover traversability between strategic waypoints chosen by scientists on the Curiosity rover team.
If we consider only the elements detected in the spectra, using the ChemCam prediction model  (Clegg et al. , 2017) , these targets have oxide totals of ∼75 wt%, and using the  David et al. (2020)  model, the oxide total is ∼45 wt%. These values suggest that there is likely at least one phase present that is not well represented in any current ChemCam model and/or a significant amount of an unidentified or unquantified element in these targets (e. g. , Cl is present in some observation points, and H, Ni have relatively intense peaks compared to other Gale crater targets). Note that strong Fe lines can obscure weak S lines in the LIBS spectrum, so potentially S is present in these targets as well, but undetected in our spectra. Therefore, the chemical composition in the database and Table 3  (Gasda et al.
Conclusions A wealth of observational data suggest that diagenesis has been a major process shaping the sediments in Glen Torridon. Some of these observations and the hypotheses put forth in this paper lack constraints on their formation conditions. For example, improvements to the major element oxide composition models and detection limits for transition metals in ChemCam spectra are needed to better understand the composition, and thus the formation conditions, of the veins. Additionally, modeling of Gale crater and post-impact hydrothermal activity in light of these new observations are needed to better constrain the source and formation conditions of the Fe, Mn, F, Mg, and Li in the dark veins and linear features. The role and timing of siderite mineralization is a unique aspect of the Glen Torridon sediments and suggests an early carbonate-rich secondary mineral assemblage derived from atmospheric CO 2 -charged groundwaters.
Conflict of Interest The authors declare no conflicts of interest relevant to this study. Alexander Bryk, William Dietrich, and Valerie Fox,  for organizing the special issue. P. J. Gasda thanks Sean Czarnecki, Woodward Fischer, and Olivier Forni for their helpful discussions of the work and Scott VanBommel for helping prepare the supplemental database for archiving on the Planetary Data System. P. J. Gasda thanks Kristen Bennett, Travis Gabriel, and two anonymous reviewers for their helpful comments on the manuscript. J.
0 software for viability and scavenger assays. Data were expressed as mean ± SD, and values of p < 0. 05, p < 0. 01, and p < 0. 001 were considered as statistically significant. 2. 4. Theoretical Calculations. All calculations were performed with Orca5. 0. 2,  24  with additional single points done with Gaussian 16  25  to generate data files compatible with the Mesra software  26  for analysis of the nature of electronic transitions. Geometry optimizations were systematically followed by frequency calculations, either analytical or numerical, to characterize the corresponding stationary points, except for the benchmark. Excited states were calculated with time-dependent density-functional theory (TD-DFT). The Tamm-Dancoff approximation was used only for geometry optimization of excited states and switched off for energy and properties calculations.
As discussed in the results section, B3LYP yielded results in fair agreement with experiments, especially for the difference in energy between excited states. We thus decided to select B3LYP/cc-pVDZ/ cc-pVTZ-DK(iodine)/CPCM(DMSO) for geometry optimizations and B3LYP/aug-cc-pVDZ/cc-pVTZ-DK(iodine)/CPCM(DMSO) for excited states and properties calculations. 9] [50]  A locally modified version of the Mesra software was used in combination with Gaussian 16 outputs to calculate detachment/attachment densities between states of interest and related descriptors. Natural transition orbitals (NTOs) were computed with Theodore,  51  and JMol was used to generate plots shown in the Supporting Information. Spin orbit coupling matrix elements were calculated with Orca5. 0. 2, and the magnitude of the spin orbit coupling was computed as the square root of the square modulus over real and imaginary parts of x, y, and z components.