dataesr/scientific-paragraphs-categorization huggingface dataset:
Statistical analysis Data were analyzed using the SAS v. 9. 4 statistical software (SAS Institute Inc. , Cary, North Carolina, United States). The initial body weight was used as a covariate and analysis of variance (ANOVA) was applied to test the effects of treatment (control versus vitamin A) on the performance variables and carcass traits using the PROC GLM procedure according to the model: Yij = m + ti + εij, where Yij is the value observed for the experimental unit of treatment i in repetition j; m is the overall effect of the mean; t is the effect of treatment (control or vitamin A), and Ɛ is the experimental error. For the proteomics data, the mean and standard deviation of the selected spots were calculated. The images were compared between treatments (control and vitamin A). The spot data were tested for homogeneity of variances and normality using the Levene and Shapiro-Wilk tests, respectively.
PLOS PATHOGENS binding with Mxra8, originally identified as the receptor for CHIKV and several other alphaviruses, including MAYV  [44, 45]. Thus, we investigated whether the negative effect of E2-T179N on viral replication in human cells (Figs  2  and 3 ) is due to decreased viral binding prior to entry and whether binding and replication is altered via the Mxra8 receptor and or other putative MAYV receptors. To assess viral binding, we inoculated MRC-5 cells with either WT or MAYV E2-T179N at a MOI of 0. 1 and incubated the cells at 4˚C to allow for binding without internalization of the virus. We then measured the amount of bound virus via RT-qPCR, which showed significantly lower relative binding of MAYV E2-T179N compared to WT MAYV (~4. 3 fold less on average, p<0. 0001, Fig  4A ). To assess the potential impact of E2-T179N on MAYV E2 structure and binding to Mxra8, we used the cryo-EM structure of MAYV (PDB ID: 7KO8  [46] ) aligned with the CHIKV structure in complex with Mxra8 (PDB ID: 6JO8  [47] ).
TIM4 is a human T-cell immunoglobulin and mucin-domain-containing protein which binds phosphatidylserine and was shown to enhance infection of Eastern equine encephalitis pseudovirus particles  [51]. VLDLR and ApoER2 are receptors for many alphaviruses, including Semliki Forest virus, a close relative to MAYV  [52]. As such, we hypothesized these putative MAYV receptors could contribute to differences in replication of WT MAYV and MAYV E2-T179N in mammalian cells. We transfected 293A cells with the constructs, including ACE2 and Mxra8 as negative and positive controls, respectively, and infected cells 24 hours later with either WT MAYV or MAYV E2-T179N at a MOI of 0. 1. We measured replication by quantifying virus in the supernatant 1 d. p. i. After normalizing the data to replication in HEK-293A cells transfected with ACE2, we found that replication of MAYV E2-T179N was significantly decreased in cells expressing Mxra8 compared to WT MAYV (Fig 4G; p = 0.
DMS sequencing and analysis Viral RNA for sequencing was extracted using the Direct-zol RNA extraction kit (Zymo Research). RNA was converted to cDNA using the Maxima H Minus cDNA Synthesis Master Mix (Invitrogen). PCR amplicons containing the DMS region were amplified using SuperFi II master mix with MAYV specific primers: forward (5' AGTGGGTAAGCCTGGCGACA 3') and reverse (5' TAAATCGGTCCGCATCATGCAC 3'). The fragments were purified using a 1x ratio of AMPure XP (Beckman). Libraries were prepared using Nextera XT and sequenced on an Illumina NextSeq 500. Data was analyzed using the dms_tools2 software, which is available at https://jbloomlab. github. io/dms_tools2/. Differential selection was calculated by comparing the starting virus stock to the post-passage samples.
NGS data analysis Raw sequencing data was deposited under the Sequence Read Archive (SRA, NCBI) under bioproject number PRJNA796940. The raw NGS reads were first trimmed using BBDuk, a tool from the BBMap tool kit, to remove the sequencing adapters and reads with a quality score of less than 30  [67]. The sequences were then aligned to the MAYV genome using the Burrow Wheeler Aligner (BWA) tool with no added parameters  [68]. The resulting. bam files were then sorted using Sambamba  [69]. Indels and variants were called using LoFreq  [70]  and then filtered to remove variants present at less than 5% frequency. We then used snpdat to annotate the variants  [71]. A variant threshold was calculated by comparing the coverage depth to the allele frequency. Specifically, we took the reciprocal frequency of a given variant and multiplied by 10; if the sequencing coverage at this site was greater than that number, it was considered positive; otherwise, it was discarded  [72].
Molecular dynamics simulations and calculation of binding free energy values All the structural models of MAYV in complex with the human Mxra8 receptor were built based on the coordinates position of CHIKV crystallographic structure in complex with Mxra8 (PDB ID: 6JO8  [47] ). To model the complexes between MAYV E2 subdomain and Mxra8 D2 domain, we removed from 7KO8 and 6JO8 structures all residues not included in those domains. The asymmetric unit of MAYV Cryo-EM structure (PDB ID: 7KO8) has 4 E1-E2 heterodimer copies whereas CHIKV crystallographic structure (PDB ID: 6JO8) has three copies. Thus, we cross-aligned, one by one, the E2 B subdomains of MAYV structure to each E2 B subdomains of CHIKV with the Chimera software  [75]. This cross-alignment returned 12 complexes between MAYV E2 B subdomains and Mxra8 D2 domain.
We estimated predicted free energy changes upon T179N mutation for all 12 MAYV complexes using FoldX 5 software  [76]. Before energy calculations, each structure was treated to optimize sidechain rotamers using the FoldX function RepairPDB. In FoldX, wild-type structures were mutated and the ΔΔG (in kcal/mol) was estimated by subtracting the resulting energy of the mutated complex by the wild-type complex. We reported the FoldX result as mean ± standard deviation of ΔΔG for the 12 MAYV complexes. These same WT and T179N complexes were used as initial structures for molecular dynamics simulations using Amber 20 suit of programs with the ff14SB force field. Using tleap, we added Na+ and Cl-counter ions to reach net-neutralization with salt excess to reach 150 mM NaCl  [77]. Each structure was solvated in a truncated octahedral box (15 A from the solute) filled with TIP3P water.
We estimated the relative binding free energy using 1-trajectory Molecular Mechanics Generalized Born Surface Area (MM/GBSA) calculations. For this, we used 20 frames from entire 2 ns simulations with MMPBSA. py script  [79]. The individual topology files for the calculations were generated using ante-MMPBSA. py script. Since our objective was only to compare wild-type and mutant interactions, we did not include the entropy term. As a result, the binding free energy estimate reported a relative value, not an absolute. In MMGBSA calculations, we used igb model 5 (all the other parameters were default). We reported the MMGBSA results as a ΔΔG of the difference between the relative ΔG estimated for T179N MAYV:Mxra8 and WT MAYV:Mxra8. To model the entire MAYV spike, formed by three E1-E2 heterodimers (not including transmembrane domains), in complex with Mxra8, we aligned individually the MAYV E1-E2 heterodimers (from PDB ID: 7KO8) with CHIKV E1-E2 heterodimers (from PDB ID: 6JO8) in Chimera.
Then, we created the MAYV T179N mutated structure with the YASARA software  [80]. Both wild-type and mutant MAYV trimeric complexes were submitted to energy minimization in solvent with YASARA to obtain an initial model. Then, we performed a 20 ns molecular dynamics simulation in quintuplicate for WT and T179N MAYV complexes, following the same simulation protocol described above without backbone restraints. The MMGBSA calculation was performed using 100 frames from the last 4 ns with the same parameters described above. Before any calculations or simulations, the protonation state at pH 7. 4 for each residue of the structural models was assigned using the pdb2pqr30 script  [81]  and propka as the titration state method  [82]. For all cases, we also modeled the missing loop of Mxra8 D2 domain using YASARA software  [80].
This work was funded by the  DARPA   PREEMPT program  administered through  DARPA Cooperative Agreement HR001118S0017 , this funding was awarded to M-C. S. , M. V, and J. W-L. This work also received funding from  Laboratoire d'Excellence Integrative Biology of Emerging Infectious Diseases  (grant  ANR-10-LABX-62-IBEID ) to M-C. S. and M. V. Further support was provided by startup funds awarded to J. W-L by the  Virginia-Maryland College of Veterinary Medicine  and a grant from the  One Health Research Funding Program  awarded to J. W-L and P. M. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
, 2013) , one of the ISMIP6-suggested climate models. While the NorESM1-M simulations do not provide a perfect representation of the past climate evolution, they were found to have the smallest biases in the Southern Ocean and atmosphere  (Barthel et al. , 2020). Present-day ocean conditions are taken from  Schmidtko et al. (2014)  and then adjusted using the basin-wide temperature corrections from the PICO parameter optimization presented in Sect. 2. 3. Atmospheric surface mass balance and surface temperatures are from  RACMOv2. 3 (1995 to 2014 averages, van Wessem et al. , 2018). Note that we apply the surface mass balance from the regional climate model directly and do not calculate melting in PISM internally with a positive degree day or equivalent model. These datasets are used for present-day climate, and anomalies are applied from the NorESM1-M output, following the ISMIP6 protocol.
This approach captures transient changes in the atmosphere or ocean while linearly correcting biases. Ocean and atmosphere climate conditions for the initial state in 1850 were obtained as follows: we first generate a time series of atmosphere and ocean anomalies from the modelled historic evolution that pass through zero anomalies between 1995 and 2014. We then add these anomalies to the present-day climatologies for the atmosphere and ocean, which make sure that our forcing time series passes through the present-day dataset in the period between 1995 and 2014. Finally we take the average over the first 30 years of the respective time series to arrive at historic conditions. Note that the atmospheric forcing as provided by ISMIP6 starts in 1950, and we keep it constant at the 1950 to 1980 average between 1850 and 1950.
3c ), slightly lower than observations ranging between 1173. 1 ± 148. 5 Gt a -1 in 1994, 1570 ± 140 Gt a -1 in 2009, and 1160 ± 150 Gt a -1 in 2018  (Adusumilli et al. , 2020). Differences between ensemble members occur due to different ice shelf extents in the initial states. Our historic simulations do not replicate the particularly high melt fluxes in 2009, which may be the reason why overall mass losses are at the lower end of observations. Surface mass balance varies only from 1950 onward, since no data were available beforehand, and is held constant during 1850 to 1950 (see Fig. 3d ). It is similar for all ensemble members since the maximum ice extent is pre-defined based on BedMachine Antarctica, and the integrated surface mass balance is hence similar for all members. 3. 4 Results: long-term equilibrium grounding-line positions under present-day climate conditions We investigate the long-term evolution of present-day Antarctic grounding lines by keeping the present-day climatology constant following the historic simulation and letting the ice sheet state evolve towards a new equilibrium for 10 000 years.
Acknowledgements. This work is part of the  TiPACCs  project, which receives funding from the  European Union 's  Horizon 2020 research and innovation programme  under grant agreement no. 820575. Development of PISM is supported by  NASA  grants  20-CRYO2020-0052  and  80NSSC22K0274  and  NSF  grant  OAC-2118285. The authors gratefully acknowledge the  European Regional Development Fund (ERDF) , the  German Federal Ministry of Education and Research , and  Land Brandenburg  for supporting this project by providing resources on the high-performance computer system at the  Potsdam Institute for Climate Impact Research. We would like to thank  Christian Schoof  for providing helpful input on stability analysis and  Chris Bull  and  Adrian Jenkins  for helpful discussions on the PICO parameter selection.
We are grateful to the editor,  Florence Colleoni , for handling our article and to  Michele Petrini  and an anonymous reviewer for proving helpful comments on our article. Financial support. This research has been supported by the  Horizon 2020 (TiPACCs  (grant no. 820575 )). Review statement. This paper was edited by  Florence Colleoni  and reviewed by  Michele Petrini  and one anonymous referee.
Code and data availability. PISM code is publicly available at https://github. com/pism/pism (last access: 19 June 2023). The PISM code, the PISM data, and the scripts to analyse the simulations and create the figures are available at https://doi. org/10. 5281/zenodo. 8101891  (Reese et al. , 2023). Supplement. The supplement related to this article is available online at: https://doi. org/10. 5194/tc-17-3761-2023-supplement.
Sensitivity To study the neural representation of confidence, we made the critical assumption that confidence is represented in some neural activity profile, rather than in synaptic weights  (Aitchison et al. , 2021). We made this assumption for different reasons. First, measuring synaptic weights requires invasive recording, which is not possible in humans. Second, confidence changes rapidly in the task and can be reported; a neural representation coded in activity levels rather than in synaptic weights could be changed more quickly and read out more easily  (Hunt & Hayden, 2017). More specifically, as in most fMRI studies, we assumed a code in terms of activity levels monotonically increasing or decreasing with the encoded variable. This choice formally corresponds to a linear rate code  (Dayan et al.
Participants Twenty-nine (fifteen females) healthy adults with normal or corrected vision, aged between 19 and 37 years old (mean 25. 41, SEM 1. 02), participated in the experiment. The study was approved by the local ethics committee (references: CPP-100032 and CPP-100055 Ile-de-France) and all participants gave written informed consent prior to participating. Data from three subjects were discarded because of acquisition problems, resulting in a final sample size of 26 participants.
1, 0. 9], and when a change point occurred it was resampled in the same interval with the constraint that the change in odd ratios should be at least fourfold, i. e. that. Before the experiment, subjects were fully informed in a |𝑙𝑜𝑔( θ 𝑡-1 1-θ 𝑡-1 ) -𝑙𝑜𝑔( θ 𝑡 1-θ 𝑡 )| > 𝑙𝑜𝑔(4) non-technical way about the generative process of sequences, except for the numeric values of the change point probability (which was just said to be "rare"), and the constraints on the sampling of item probabilities. The task was programmed and run using Python (version 3. 7) and Expyriment (0. 10. 0).
MRI data collection and preprocessing Subsequently, a B1 field map was acquired, on the basis of which the system voltage was chosen according to the reference voltage computed from the B1 map values in the intraparietal sulcus region. Before each session, two single volumes were acquired with the parameters listed above but with opposite phase encode directions in order to be used later for distortion correction (see "MRI data analysis"). T1-weighted anatomical images were acquired with 0. 75 mm isotropic resolution using an MP2RAGE sequence, with the following parameters: GRAPPA acceleration Research Systems; resolution=1920x1080; refresh rate=120 Hz) at the back-end part of the scanner (viewing distance=200 cm) and viewed through a mirror fixed onto the head coil. Participants reported responses using their right hand and a fiber optic response pad (fORP, Current Designs, Philadelphia, PA, USA) featuring five buttons.
Statistical analysis Individual surface-based, normalized maps of regression coefficients entered a group level analysis: a t-test against 0. The resulting statistical maps were thresholded at a cluster-forming threshold of p < 0. 001 and corrected for multiple comparisons across vertices at the cluster level at p FWE < 0. 05 (Monte Carlo-based cluster correction from FreeSurfer). FIR estimates in each cluster entered a paired t-test testing for the difference between low vs. high confidence reports. Peri-question time points exhibiting a difference (two-sided test) significant at p<0. 05 formed clusters, and cluster-level p FWE (correcting for multiple comparisons across time points) were estimated using 10,000 permutations (permutation_cluster_1samp_test from MNE Python  (Gramfort, 2013) ).
Acknowledgements We thank all recruited volunteers who participated in the study, the fMRI staff of the  NeuroSpin center  ( Véronique Joly-Testault ,  Laurence Laurier ,  Gaëlle Médiouni ,  Valérie Berland ,  Chantal Ginisty ,  Yann Lecomte ),  Isabelle Denghien  and  Christophe Pallier  for advice in the fMRI data preprocessing. This work was funded by the  French National Research Agency  (grant  ANR  # 18-CE37-0010-01 CONFI-LEARN  to F. Meyniel), the  European Research Council  (grant  ERC  # 947105 StG NEURAL-PROB  to F. Meyniel)  INSERM , the  Leducq Foundation  ( ERPT Equipment Program  for the  7T MRI clinical device ), the  Agence de l'Innovation pour la Défense  (grant awarded to  T. Bounmy ) and the  Ecole Doctorale FIRE -Programme Bettencourt.
MRI data were acquired on a Siemens MAGNETOM 7 Tesla MRI scanner (Siemens Healthcare, Erlangen, Germany) with whole-body gradient and 32-channel head coil (Nova Medical, Wilmington, MA, USA). Functional MRI data were T2*-weighted fat-saturation echo-planar image (EPI) volumes with 1. 5 mm isotropic voxels, recorded using a multi-band sequence (Moeller et al. , 2010) (https://www. cmrr. umn. edu/multiband/) with the following parameters: multi-band factor [MB] = 2, GRAPPA acceleration factor [IPAT] = 2, partial Fourier [PF] = 7/8, matrix =130 x 130, number of slices = 68, slice thickness = 1. 5 mm, repetition time [TR] = 2 s, echo time [TE] = 22 ms, echo spacing [ES] = 0. 71 ms, flip angle [FA] = 68°, bandwidth [BW] = 1832 Hz/px, phase-encoding direction from anterior to posterior. The calibration preparation was done using.
Social relationships and residence Examining both pedigrees, we found that generations are linked almost exclusively through the male line, that is, all descendants but one are connected to the family tree through their father's line. The Y-chromosome haplogroup G2a2b2a1a2 (terminal SNP Z38302) is carried by 51 out of 57 male individuals and is the main male lineage of the group (Supplementary Table  7  and Extended Data Fig. 2 ). Pedigree A yields the sole exception, where a new lineage (C in Fig. 1a ) is linked through a lineage woman (GLN325). Her reproductive partner and his brother, her sons and two other unrelated male individuals carry haplogroup H2m, the only other Y-chromosome lineage observed in the dataset (Fig. 1a  and Extended Data Fig. 2 ). The main genealogical lineage of pedigree A is also visible in the funerary features, as one son, GLN237A, and one grandson, GLN221B, of the main ancestor GLN270B are buried in the largest pits of the necropolis (Fig.
1a , Supplementary Note 1 and Extended Data Fig. 6 ). GLN270B, the main ancestor of 52 individuals in pedigree A, represents the only secondary burial at the site, consisting of long bones that were buried together with the female individual GLN270A, for whom no genomic data could be obtained (Fig. 1d ). The position at the apex of the pedigree suggests that his remains were transferred and buried during the early occupation of the site, representing, together with his brother GLN231A, the main male posthumous ancestors of pedigree A. The association between GLN270B and female individual GLN270A, and not with his brother, for example, suggests that this female individual was important-perhaps she was his partner, or someone genetically related representing the lineage. When we investigated the spatial organization of the graves by measuring the physical distance between individuals, we observed significantly closer spatial proximity between each father-son pair than any other related pair (Fig.
2 ), which is surprising given the expected mortality patterns in archaic populations  26, 27. Notably, this trend is reversed across the last three generations, with 20 out of 25 individuals being subadults. These observations are consistent with a scenario in which an entire group of several generations moved to this new burial site, leaving behind their deceased children at a previous funerary site, but transferring the 'ancestor/founder' GLN270B. Moreover, the fact that many parents are missing in the last generations suggests that the group moved on to settle elsewhere, leaving behind children who had passed away. The Sr isotope data provide a further line of evidence for these interpretations, as 87 Sr/ 86 Sr values were low (around 0. 709) for the earlier generations, overall similar to exogenous female individuals (Fig.
Moreover, we must keep in mind that our dataset represents a selection of individuals gathered in a funerary site, which might not be the reflection of the world of the living, if specific rules govern the access to this necropolis. The contemporaneous monumental sites in the regional context were clearly built for selected individuals  10. This has been demonstrated at Fleury-sur-Orne (Normandy), the only monumental site from the Cerny area genetically investigated to date, which shows a strong social selection of individuals of different patrilineal lineages, each buried in a separate monument  33. By contrast, the absence of potential selection of individuals on the basis of sex, age, economic or social hierarchies given the available archaeological context, or consequences of specific funerary practices in Gurgy, leads us to speculate that the site represented the burial practices of the non-elite, which gives an unprecedented view of the microdemography of a non-elite community represented over several generations (Supplementary Note 14 and Supplementary Table  19 ).
Read processing After demultiplexing, raw sequencing data were processed using EAGER. This included clipping adaptors with AdapterRemoval  49  , mapping with BWA (Burrows-Wheeler Aligner, mapping quality ≥30; v. 0. 7. 12)  50  against the human reference genome hs37d5, and removing duplicate reads with the same orientation and start (and end positions for paired-end sequencing reads). After using mapDamage (v. 2. 0. 6)  51  to observe characteristic aDNA damage patterns, we used BamUtil (https://genome. sph. umich. edu/wiki/BamUtil) to clip two bases at the ends of each read for each sample to remove residual deamination. Different libraries of the same individual were processed separately until after quality control, after which the BAM files were treated accordingly and merged per individual.
Authentication criteria Samples that were covered at less than 20,000 SNPs on the 1,240,000 SNP set were excluded from further analyses. We evaluated the authenticity of the samples by observing typical patterns of deamination towards read ends. We used the ANGSD (Analysis of Next Generation Sequencing Data) package to test for heterozygosity of polymorphic sites on the X chromosome in male individuals, applying a contamination threshold of 5% that none of our samples have reached  53  (maximum = 2. 41%; Supplementary Table  1 ). For mito-captured samples, we estimated contamination levels using ContamMix (v. 1. 0. 10)  54  by comparing the consensus mitogenome of the ancient sample to a panel of 311 worldwide mitogenomes as a potential contamination source (Supplementary Tables  1  and 5 ).