dataesr/scientific-paragraphs-categorization huggingface dataset:
, 2017; Löhrich et al. , 2018; Wu et al. , 2018). Finally, in Barbary macaques, urinary neopterin levels increase with age, which has been interpreted as a marker of immunosenescence  (Müller et al. , 2017). Wild non-human primates represent interesting models in eco-immunology because they are phylogenetically close to humans, and therefore represent a potential source of zoonoses  (Burgos-Rodriguez, 2011). Such proximity also facilitates the application of methods developed for humans to non-human primates. In addition, detailed field studies on wild non-human primates have gathered valuable datasets on the ecology, life-history and health of many individuals, and thus provide key opportunities to understand individual variation in immune phenotypes, as is the case of our field study of mandrills.
Analytical validation For each plate, we verified that the two internal quality controls yielded concentrations in acceptable ranges (control 1: 3. 3-7. 6 nmol/L; control 2: 13. 1-27. 1 nmol/L). We calculated inter-assay coefficients of variation using repeated concentrations of each of these two internal quality controls of low and high concentrations, run in each assay. CV were 29. 9% and 23. 3%, respectively. We then calculated parallelism using four serial dilutions of five mandrill blood samples (Supplementary Table  S1 ). The slope of the standard curve was compared to the five slopes of the antibody binding using an ANCOVA (SAS Studio). Slopes were found parallel for all five samples (Supplementary Table  S1 ). Finally, we measured spike recovery accuracy by adding standards of known concentration to each of the five blood samples (Supplementary Table  S2 ).
Statistical analyses We performed three sets of Linear Mixed Models (LMM) using the GLIMMIX procedure (SAS Studio) to study the relationship between plasma neopterin concentration (response variable) and different sets of predictors in three groups of individuals who may elicit different strategies of immune investment: (1) individuals of all sexes and ages, (2) sexually mature females, and (3) adult males. In all models, plasma neopterin concentrations were log-transformed to obtain normally distributed residuals and we standardized all continuous explanatory variables. Model fit was evaluated by checking the Gaussian distribution of the residuals and by performing a Shapiro-Wilk test (p > 0. 10 in all instances). All three sets of LMMs included individual identity as a random effect. In all three models, we checked for possible multicollinearities between continuous predictors by calculating variances of inflation (VIF; REG procedure, SAS Studio).
Finally, only two females were SIV positive, the corresponding effect was therefore impossible to test. We, however, kept these females into the final dataset because omitting them did not change the results as well (Supplementary Table  S3 ). In a last set of models, we restricted our analysis to adult males only (> 9 years old, N = 29 samples collected from 13 males; mean number of samples per males ± SD: 2. 2 ± 1. 2; range: 1-5) to test whether neopterin concentrations vary with male social rank and SIV status. Because of the limited sample size, we performed six univariate models. We studied the following fixed effects: (1) male age, (2) dominance rank (alpha vs. subordinate); (3) cumulated rainfall, (4) reproductive season; (5) two measures of Plasmodium parasitaemia, and (6) SIV infection status as a class variable with three modalities: negative (n = 2 samples from 2 males), primo-infection (n = 7 samples from 6 males), and chronic infection (n = 20 samples from 11 males).
For SIV, the variance was not equal across SIV classes (heteroscedasticity) because of a bimodal distribution of neopterin concentrations in males in primoinfection. We therefore studied this particular predictor using a nonparametric analysis of variance (the NPAR1WAY procedure; SAS Studio). Finally, we checked that the only significant effect found in these males (see Results) remained significant when considering a full model including all predictors mentioned above (Supplementary Table  S4 ). As above, we did not study storage time terms.
Acknowledgements We thank past and present field assistants of the Mandrillus Project for their help in data collection. We are also grateful to the  SODEPAL-COMILOG society (ERAMET group ) for their logistical contribution. This study was funded by a  Station d'Etudes en Ecologie Globale (INEE-CNRS ) and a  Laboratoire International Associé (CIRMF  and  INEE-CNRS ) to MC; a  PEPS program  ( MI-CNRS ) to EH; a  Conservation Action Research Network-Congo Basin Grant Program and Bourses SUD (ISEM)  to SD, EH, and MC. Data used in this manuscript are part of long-term data collected within the framework of the  Mandrillus  Project, funded by a grant of the  Deutsche Forschungsgemeinschaft (DFG ,  KA 1082-20-1 ;  2012-2015 ) to MC and two grants from the  Agence Nationale de la Recherche  ( ANR SLEEP 17-CE02-0002 ,  2017-2020  to MC;  ANR ERS-17-CE02-0008 ,  2018-2021  to EH).
A two-level analysis was then implemented in SPM8 software (http://www. fil. ion. ucl. ac. uk /spm/software/spm8/). For each participant, data were high-pass filtered and modelled by regressors obtained by convolution of the five experimental conditions plus the button presses with the canonical SPM hemodynamic response function. Individual contrast images for the five types of stimuli minus rest were obtained at the first level and used to derive individual activation maps and peaks (see the next paragraph). To further take into account between-subject differences in functional anatomy, these individual contrast maps were smoothed with an additional isotropic Gaussian filter (2 mm FWHM) and entered into a second-level wholebrain ANOVA with subjects as random factor, stimulus category as within-subject factor, and musical status as between-subject factor.
These models were implemented in R software using the 'lme4' package  (Bates et al. , 2012). The effect of interaction of group x peak was obtained by likelihood ratio tests of the full model against the model without the interaction term. Main effects of group and peak were each assessed by comparing models with both main effects against models removing only the effect in question. Moreover, to assess the spatial similarity between music and word activations, we computed the cross-correlation between the functional contrast maps of words and of music, compared to the mean of faces, tools and houses, within a probabilistic atlas of grey matter mask (atlas provided in FSL thresholded at 50% likelihood). Only positive contrast values were retained, so as to compare the similarity between activations more specific to words and music.
We also assessed the spatial similarity between the projections of music and word tracts. To do so, the crosscorrelation between the endpoints of both tracts in each subject was computed within the same grey matter mask. Cross-correlation values both of functional networks and tracts were Fisher-transformed, and then compared between groups. Because the z-transformed tract crosscorrelation values were largely skewed, permutation-based multiple linear regressions were applied using the 'lmPerm' package in R  (Wheeler, 2010) , with distance, age and gender as covariates.
There was no region more connected to music than to word peaks in either the control or in the musician group, likely due to a decrease in power when splitting the data into two groups, and to the close proximity of music and word seeds in VOT. There was no significant group Â peak interaction. Analyses with uncorrected threshold. This first analysis thus showed no significant correlate of musical expertise. When lowering the statistical threshold to p < 0. 01 uncorrected for exploratory purposes, both musicians and controls showed a higher connectivity for music than for word peaks in the posterior lateral temporal lobe, as in the pooled analysis. At this uncorrected threshold, it is interesting to observe that musicians showed stronger connectivity to music than to word peaks far beyond the ventral temporal lobe, including ventral and lateral occipital cortex, posterior superior temporal cortex (notably Heschl's gyrus), the inferior parietal lobule and intraparietal sulcus, the insula, the inferior and middle frontal gyri and the premotor cortex (Fig.
(2017) , we found a highly significant increase in the similarity of functional activations for word and for music reading, in musicians as compared to non-musicians. This increase in similarity with musical expertise likely reflects the richer development of music representations in musicians than in non-musicians, similarly to the mechanisms at play in the case of second language acquisition  (Abutalebi and Green, 2007; Berken et al. , 2015; Cao et al. , 2013; Kim et al. , 1997; Perani et al. , 1998)  or of motor learning and action representations  (Beilock et al. , 2008; Calvo-Merino et al. , 2006 , 2004). The same logic predicts that the similarity of connectivity patterns for music and words should also increase with musical expertise, which is precisely what we found. It is noteworthy that the effects of expertise were quite parallel between functional and anatomical imaging, although the two datasets and methods were fully independent.
Acknowledgments This work was supported by  ICM ,  INSERM ,  CNRS , the program " Investissements d'avenir " (grant number  ANR-10-IAIHU-06 ). FB was supported by a doctoral fellowship of the  R egion Ile-de-France  and the " Ecole Doctorale Fronti eres du Vivant (FdV) -Programme Bettencourt ", and by  NIH   R01HD115089.
Data Analysis The effects of status and deservingness (as a quasi-experimental variable) on envy were analyzed using the Generalized Linear Mixed-effect Model (GLMM, IBM SPSS 22). In the model the fixed effects included status (OSS vs. SSS), deservingness (undeserved vs. deserved), and type of envy (benign vs. malicious), and each participant's ID was included as a random factor. All possible two-way and three-way interactions of the fixed factors were tested. Statistical tests were two-tailed and the α value was set at 0. 05. Sequential Sidak correction was applied in all post hoc pairwise comparisons. All statistics were performed using IBM SPSS Statistics 21.
Results Task structure. We analyzed behavioral data from four macaque monkeys in three experiments using a binary-choice probabilistic bandit task (overall 65 sessions of 200 trials each). In addition, we also analyzed neural data from one of the three experiments (25 sessions)  19, 20. Choice options were allocated pseudorandomly to the right and left side of the screen and monkeys responded towards congruently located right or left sensors. Choice outcomes were either a drop of juice or nothing (Fig. 1a ). The rewards were delivered probabilistically and the probabilities of two of the options reversed towards the middle of a session and monkeys' choice frequencies followed this reversal (Fig. 1b ). Importantly, each session contained three new choice stimuli, but only two of them were choosable on each trial (Fig.
In turn, they switch away from a choice -even if that specific choice has been rewarded latelymore often if it is encountered when in a low GRS. Note that this analysis also controls for the history of the upcoming alternative option (Supplementary Fig. 1b ), is stable when varying the length of the reward history considered (Supplementary Fig. 1c ) and even holds when analyizing monkeys individually (Supplementary Fig. 2 ). These results suggest that the GRS alters the animals' response to rewards received for a current choice. To investigate this directly, we regressed all effects of the previous GLM (Fig. 1f ), except those of CxR-history(t) and R-history, out of the choice data. We then examined the residual choice probabilities (as provided by Matlab's glmfit function). We binned them, first, by the current outcome, and second, by the binarized GRS (median split of R-history; Fig.
Methods Subjects. Four male rhesus monkeys (Macaca mulatta) were involved in the experiment. They weighed 10. 4-11. 9 kg and were 7 years of age. They were group housed and kept on a 12-h light dark cycle, with access to water 12-16 h on testing days and with free water access on non-testing days. All procedures were conducted under licenses from the United Kingdom (UK) Home Office in accordance with the UK The Animals (Scientific Procedures) Act 1986 and with the European Union guidelines (EU Directive 2010/63/EU). Behavioral training. Prior to the data acquisition, all animals were trained to work in an MRI compatible chair in a sphinx position that was placed inside a custom mock scanner simulating the MRI scanning environment. They were trained to use custom-made infrared touch sensors to respond to abstract symbols presented on a screen and learned the probabilistic nature of the task until reaching a learning criterion.
The experimental task was the same in all three experiments as were the four monkeys, but data collection for the experiments was separated by several months and acquired over the course of a 2-year period. For the MRI experiment, each animal performed five to seven sessions (25 sessions overall). For the two behavioral experiments, each animal performed five sessions (20 sessions per behavioral experiment). Behavioral data analysis. We ran logistic general linear model (GLM) analyses, implemented in Matlab 2018a version 9. 4. 0, on the behavioral choice data. To be able to estimate complex GLMs with a large regressor set, we concatenated experimental sessions per animal per experiment and applied the GLM to the concatenated sessions (Fig. 1f, g ; Supplementary Fig. 1b, c ; Fig. 4  uses the same procedure with simulated choice data).
Note that Supplementary Fig. 1  describes all 22 regressors used in our main behavioral GLM. As all four animals participated in all three experiments, this procedure resulted in 12 beta weights (per regressor) overall, three per animal. We tested these resulting beta weights for significance using analyses of variance (ANOVA) and one-sample t-tests against zero, implemented in Matlab and Jasp version 0. 9. 0. 1. For GLMs with smaller regressor sets effect sizes were estimable on the basis of single sessions. In such a case we applied the GLMs separately for each of the 65 sessions collapsed over experiments, for instance for the reaction time GLMs. Reaction time analyses used a linear link function. Beta weights were tested for significance using and one-sample t-tests against zero. Basic RL architecture.
For example, if option A was chosen on trial t, the corresponding PE would be calculated as: PE m;t A ð Þ ¼ r t þ w R R-trace t À Q t A ð Þ ð14Þ Model fitting. All RL modeling was conducted in Matlab (version 2018a). We used an iterative expectation-maximization (EM) algorithm to fit the models  25. We estimated models both over all experiments and for each experiment and monkey separately. This ensured that our results were valid over all data sets but also within each experiment and monkey. It also enabled us to use the experiment-appropriate parameter estimates for the analysis of the MRI data. In all cases the fitting comprised two levels: the lower level of the individual sessions and the higher-level reflecting either all of the sessions together, all sessions from the same experiment, or all sessions from the same monkey.
1 (plus some added noise) and variance of 100. During the maximization step, we recomputed μ and σ based on the estimated set of h i and their Hessian matrix H i (as calculated with Matlab's fminunc) over all N sessions. μ ¼ 1 N X i h i ð17Þ σ 2 ¼ 1 N X i ½h 2 i þ diagðpinv H i ð ÞÞ À μ 2 ð18Þ where the diagonal terms of the inverted Hessian matrix (computed in Matlab with diag(pinv(H i ))) give the second moment around h i , approximating the variance, and thus the inverse of the uncertainty with which the parameter can be estimated  25. We repeated expectation and maximization steps iteratively until convergence of the posterior likelihood PP i summed over the group or a maximum of 800 steps. Convergence was defined as a change in PP i < 0. 001 from one iteration to the next. Note that bounded free parameters (for example the learning rates) were transformed from the Gaussian space into the native model space via appropriate link functions (e.
We used 25 regressors in the GLM; one for each choice-outcome conjunction. The conjunctions were coded as 1, -1 or 0 depending on whether the choice of interest was made and rewarded, another choice was made and rewarded or no reward occurred, respectively. As in previous reports, regressors relating to the last time points of choices or rewards (e. g. Choice(t-5) and reward(t-1)) were confound regressors and are therefore not shown in Fig. 4. Imaging data acquisition. Awake-animals were head-fixed in a sphinx position in an MRI-compatible chair. We collected fMRI using a 3T MRI scanner and a fourchannel phased array receive coil in conjunction with a radial transmission coil (Windmiller Kolster Scientific Fresno, CA). FMRI data were acquired using a gradient-echo T2* echo planar imaging (EPI) sequence with 1.
5 × 1. 5 × 1. 5 mm 3 resolution, repetition time (TR) = 2. 28 s, Echo Time (TE) = 30 ms, flip angle = 90, and reference images for artifact corrections were also collected. Proton-densityweighted images using a gradient-refocused echo (GRE) sequence (TR = 10 ms, TE = 2. 52 ms, flip angle = 25) were acquired as reference for body motion artifact correction. T1-weighted MP-RAGE images (0. 5 × 0. 5 × 0. 5 mm 3 resolution, TR = 2,5 ms, TE = 4. 01 ms) were acquired in separate anesthetized scanning sessions. fMRI data preprocessing. FMRI data were corrected for body motion artifacts by an offline-SENSE reconstruction method  63  (Offline_SENSE GUI, Windmiller Kolster Scientific, Fresno, CA). The images were aligned to an EPI reference image slice-by-slice to account for body motion and then aligned to each animal's structural volume to account for static field distortion  64  (Align_EPI GUI and Align_Anatomy GUI, Windmiller Kolster Scientific, Fresno, CA).
The aligned data were processed with high-pass temporal filtering (3-dB cutoff of 100 s) and Gaussian spatial smoothing (full-width half maximum of 3 mm). The data that were already registered to each subject's structural space were then registered to the CARET macaque F99 template 65 using affine transformation. fMRI whole-brain analysis. We employed a univariate approach within the general linear model (GLM) framework to perform whole-brain statistical analyses of functional data as implemented in the FMRIB Software Library version 5. 0. 11  66 here each of the psychological regressors was convolved with a hemodynamic response function (HRF) specific for monkey brains  67, 68. Using this framework we initially performed a first-level fixed effects analysis to process each individual experimental run.
2d ). These two regressors were the only ones added to the basic set of regressors and they were coded in terms of chosen minus unchosen, similar to the decision variable in GLM 1. Specifically, the two quantities were defined as: DV choice chosen À unchosen ð Þ ¼ DVðchosen À unchosenÞ ÀDV value ðchosen À unchosenÞ ð36Þ DV value (chosen-unchosen) and DV choice (chosen-unchosen) were both timelocked to DEC. For brevity, DV value (chosen-unchosen) and DV choice (chosenunchosen) are just referred to as DV value and DV choice in the main text. Region of interest analyses. We analyzed ROI data using Matlab 2018a version 9. 4. 0, Jasp version 0. 9. 0. 1, and SPM version 12. For all ROI analyses, we used spherical ROIs with 2 mm radius centered on peak coordinates reported in Supplementary Table  1.
We repeated this procedure for all sessions and applied statistical tests to the resulting independent ROIs. We used the ROI timecourse analyses to examine the temporal evolution of neural signals in our ROIs. To this end, we extracted the preprocessed BOLD time series and up-sampled it by a factor of 10 (using spline intrapolation) and aligned the time series trial-wise. On every time point, we applied a set of z-scored regressors to the data. The time course GLM (tGLM1) performed the following calculations to each time point of the upsampled data in a session by session manner. First, we used a regressor set comprising DV value (chosen-unchosen), DV choice (chosen-unchosen), Q-unp, CS-unp, CL-trace, and Cloc. After applying this regressor set to the data time-locked to the feedback phase onset with a linear GLM, we calculated the residuals of the BOLD timecourse.
We used Matlab's fitglm function together with the convolved design matrix and the BOLD timecourse for this regresssion and extracted the maximum log likelihood estimates from the resulting model. These log likelihood estimates from the two GLMs were then fed into a Bayesian model selection random-effects analysis (using the spm_BMS routine from SPM12  29  ), which computed the exceedance probability of each GLM. This analysis indicated which GLM best explained the neural data.
Acknowledgements This study was funded by the  Wellcome Trust  ( WT100973AI ;  203139/Z/16/Z ;  105238/Z/ 14/Z ) and  MRC  ( MR/P024955/1 ;  CQR01330 ) and a  UKRI FLF  to E. F. ( MR/T023007/1 ). M. K. was funded by the  Centre National de la Recherche Scientifique (Mission pour l'Interdisciplinarité). We thank  Tim Behrens ,  Nils Kolling ,  Alizée Lopez-Persem , and  Jacqueline Scholl  for helpful discussions.
Code availability The above repository also comprises the full reinforcement modeling pipeline including model comparisons implemented in Matlab. All variables used for the MRI analyses are derived from this pipeline. In addition, code for the primary behavioral GLM is included (Fig. 1f, g ). Accession code to the repository is the following and a README inside the repository explains the details of its use: https://osf. io/358cg/?view_only=0e6fda7925364 d86930374cd4ae4a59f. Any remaining code that support the findings of this study are available from the corresponding author upon reasonable request.
ACKNOWLEDGMENTS This protocol has been developed by applying the standards of veterinary practice to laboratory mouse surgery and refined with time and interaction with esteemed colleagues. We are especially grateful to  Randal Hand ,  Santos Franco ,  Tommy Lewis ,  Olivier Raineteau ,  Franck Polleux , and members of the  Polleux laboratory  for technical advice and discussions. The use of F1 hybrid mice was a recommendation of  Santos Franco  for which we are extremely grateful. We wish to thank  Virginie Courchet , DVM, for technical and veterinary advice. We thank the scientists and laboratories who published protocols on similar procedures and apologies for not citing them all in the references. Finally, we thank the personnel at the Service Commun.
fxb file provided as supporting file, see the section Data accessibility) to create nine distorted versions of these chords. Lastly, we generated nine guitar-shaped noise samples by filtering white noise with the spectral envelope estimated frameby-frame (frame size = 25 ms) on the distorted guitar chords (see Figure  1 -context column). Both vocalizations and backgrounds (noise, clean guitar, distorted guitar) were normalized in loudness using the maximum value of the long-term loudness given by the Glasberg and Moore's model for time-varying sounds  (Glasberg & Moore, 2002) , implemented in Matlab in the Psysound3 toolbox. The vocalizations were normalized to 16 sones for the first level of anger, 16. 8 (=16*1. 05) sones for the second level, and to 17. 6 (=16*1. 05*1. 05) for the third and highest level.