dataesr/scientific-paragraphs-categorization huggingface dataset:
We have now clarified our line of reasoning in the Statistical analyses section. The section now reads: "Hypothesis 1a will be supplemented with equivalence testing using the TOSTER package 91 implemented in R 66. Here, the aim is to assess, in the event that the OXT group, relative to placebo group, shows a significant increase in monetary investments, whether OXT's effect on interpersonal trust is large enough to be considered a meaningful psychological finding  40, 47. We will set the upper bound (ΔU) t to d=. 33, and the lower bound (ΔL) to d=-. 33 (testing one-sided). Although at first glance this may seem like a rather wide range, it is not our aim to demonstrate equivalence for the smallest possible effect sizes. Rather, we aim to demonstrate equivalence for a range of effect sizes that are smaller than effect sizes commonly encountered in psychological research  92.
diagnostics. To date (November 2023), DAMAE Medical has installed over 50 devices worldwide, mainly in Europe (France, Germany, and Italy), as well as in the USA. A button and a scroll wheel on the deepLive™ probe handle allows the user to select the imaging mode (vertical, horizontal, or 3D) and to adjust the lateral/depth positions in the vertical/horizontal real-time imaging modes. Clicking on the button starts image recording. DAMAE Medical has developed user-friendly software connected to the imaging device. A patient file can be created by registering their demographic information and indicating the lesion location on a designed body mapper. The recorded LC-OCT images with their associated dermoscopy images can be reviewed. Image stacks can be displayed as texture-based volume renderings or orthoslices.
Clicking on the button starts image recording. DAMAE Medical has developed user-friendly software connected to the imaging device. A patient file can be created by registering their demographic information and indicating the lesion location on a designed body mapper. The recorded LC-OCT images with their associated dermoscopy images can be reviewed. Image stacks can be displayed as texture-based volume renderings or orthoslices. For specific needs, various custom image processing algorithms are available to facilitate image review and analysis. Additionally, deepLive™ software V1. 6. 1 allows for the registration of clinical suspicions, LC-OCT diagnosis, patient management, and histological diagnosis to facilitate patient follow-up. The complete typical patient examination procedure with deepLive™ is described in detail in  [25].
The LC-OCT Imaging Modes 2. 2. 1. Vertical Imaging A vertical section LC-OCT image is acquired by activating the PZT stage to scan into the depth of skin tissues. The PZT stage oscillates at a frequency of 8 Hz according to asymetrical sawthooths with a duty cycle of 80%. The effective amplitude of the scanned depth is Z = 400 µm. The camera frame rate is set to 70 kHz so that the step between two consecutive lines acquired by the camera during the displacement of the PZT stage is δ = 70 nm, corresponding to a phase-shift of π/2. A stack of Z/δ = 5700 lines is acquired during each positive slope of the depth scan. A vertical section image is generated by applying a five-frame fringe envelope detection algorithm  [26]  to the acquired stack of lines. Acquisition and data processing is repeated continuously during the oscillation cycles of the PZT stage.
In this work, we show how the mouse gut microbiome and its links with nutritional patterns can be investigated accurately and comprehensively via shotgun metagenomics by leveraging a computational taxonomic profiling approach called MetaPhlAn 4,  30  which integrates massive assemblies in its database. MetaPhlAn 4 considers over 22,718 metagenome-assembled genomes (MAGs) retrieved from 1,906 mouse-derived fecal, cecal, and ileal metagenomes with the species-level genome bin (SGB) strategy. 31  By applying the approach on a large and heterogeneous catalog of mouse microbiomes, we show that accounting for metagenomically defined species is necessary in the context of nutritional studies in mouse models. We also found that the microbial species not detectable by mapping against genomes from isolates account for the greatest proportion of the diet-associated microbiome changes.
RESULTS A multi-cohort dataset for studying the impact of highfat diets on the mouse gut microbiome To study the influence of a high-fat diet on the mouse gut microbiome, we collected publicly available metagenomic datasets that assessed the mouse microbiome with respect to the content and the variable amount of fat in the diet (Table  S1 ). For the collected samples, we manually curated and validated relevant mouse covariates including age, antibiotic usage, multiple time points when available, genetic background, sampling bodysite, and dietary information (Table  S2 ). On diet, we specifically focused on the percentage of calories derived from fat in the diet (from here on referred to as ''fat percentage'') and on the time elapsed since the start of the diet intervention (Table  S2 ). We initially retrieved 623 samples from 15 public datasets, which were reduced to 525 samples from eight datasets by retaining only datasets with at least two dietary treatments (either a high-fat and a low-fat group or multiple dietary fat-intake groups) and a minimum of 20 total samples.
Additionally, we sequenced the cecal and ileal gut microbiomes of a mouse microbiome study in which mice were subjected to multiple types of gut microbiome perturbations, including several dietary regimens and antibiotic treatments, for a total of 97 metagenomes (see STAR Methods). Overall, we analyzed nine datasets and 622 samples (269 samples following a high-fat-intake regimen, average 57% fat content, 95% confidence interval (CI) of [0. 51, 0. 63]; and 353 following a low-fat-intake regimen, average 13%, 95% CI [0. 09, 0. 16]) (Tables  S1  and S2 ).
Indeed, after incorporating 22,718 MAGs reconstructed from 1,906 mouse gut metagenomes from multiple sources (Table  S3 ), MetaPhlAn 4 was able to identify and quantify 336 mouse-associated uSGBs that were not captured by the MAG-reconstructing procedure performed on the same samples, highlighting the strength of this approach. In the 622 samples considered in this study, we identified a total of 740 microbial SGBs (425 uSGBs and 316 kSGBs; average number of SGBs per sample 142, 95% CI [28. 32, 255. 68]) from 703 distinct species, greatly overcoming the previous detection of only 197 species by MetaPhlAn 3. We then evaluated single SGB prevalence, estimating the overall prevalence as the average of the prevalence across the nine datasets. We found the number of uSGBs with >50% prevalence in the considered cohort to be greater than the number of highly prevalent kSGBs (51 uSGBs vs.
Accounting for uSGBs improves the discrimination between high-and low-fat-diet interventions We then evaluated the impact of a high-fat diet on the microbiome of laboratory mice through changes in alpha-and beta-diversity. Shannon diversity increased in the high-fat groups of six datasets (average standardized mean difference [aSMD] = 1. 16, 95% CI [1. 06, 1. 26], four significant at p < 0. 05 [Wilcoxon signed rank]) compared with low-fat groups, but these alpha-diversity increases were not significant when confounders were taken into account (see STAR Methods and Table  S2 ). Beta-diversity was analyzed in each dataset using the Permanova analysis of Bray-Curtis dissimilarity matrices and found significance in eight datasets out of nine (univariate Permanova p < 0. 05; Figure  S1A ). Measuring the group mean differences between pairwise distances in low-and high-fat mice showed that six out of nine of the low-fat groups were more variable (overall aSMD = 0.
To further assess the association between the mouse gut microbiome and high-vs. low-fat diet regimen, we used a random forest  32  classifier approach to link the dietary conditions with the presence and abundance of the SGBs present in the samples. [33] [34] [35]  We built two cross-prediction matrices  36, 37  by (1)  training and evaluating the classifier in cross-validation (CV) on each dataset separately, (2) training a classifier on one dataset and applying it on a different dataset, and (3) training a classifier on all but one dataset and applying it on the left-out dataset (leave-one-dataset-out [LODO]). [36] [37] [38] [39]  When considering only samples without antibiotic interventions (Table  S2 ), the microbiome-based models achieved high discrimination between mice fed with high-fat vs.
low-fat diets in all the prediction settings (Figure  1D ), and substantial improvements were obtained when using the MetaPhlAn 4 profiles that incorporate all the SGBs compared with those obtained when only taxa present in RefSeq  40  are included (RefSeq-based SGBs, see STAR Methods). While the performance in CV did not substantially change between the two CV settings, it markedly increased in the LODO setting when incorporating the MetaPhlAn 4 database, resulting in an average area under the curve (AUC) of 0. 80 (compared with AUC 0. 74 with RefSeq-based SGBs only) (Figure  1D ). Similar improvements were achieved when considering samples from mice which undertook antibiotic treatments, reaching 0. 92 average AUC in CV and 0. 79 in LODO (compared with 0. 91 and 0. 72 average using only RefSeq-based SGB AUC, respectively; Figure  S2 ).
Most cross-dataset microbial biomarkers of high-vs. low-fat diet are uSGBs For each SGB and on each dataset separately, we then performed differential abundance analysis in high-vs. low-fat-diet regimes using linear models adjusted for sex, age, antibiotic treatment, mouse strain, and sampling body-site. The results from the linear models for each SGB were then pooled together in meta-analyses by a random-effects models  41, 42  (see STAR Methods). We identified 37 SGBs significantly associated with a high-fat diet and 10 SGBs with a low-fat diet (false discovery rate [FDR] < 0. 2, considering SGBs at >20% prevalence and pre-sent in at least five datasets; Table  S5 ). Importantly, 32 of these 47 biomarkers are uSGBs (chi-squared p on the expected even frequency: 0. 01). Considering the 30 SGBs with the highest differences between the groups (Figure  2A ), only 11 are kSGBs, 9 of which still belong to poorly characterized microbial species (i.
44  A. caecimuris (SGB14802) could in addition be potentially linked to the leptin increase in high-fat regimen, as well as correlated with serum primary bile acids. [45] [46] [47]  Only ten SGBs were increased in the low-fat group in at least five datasets, and the one showing the strongest association was Clostridia SGB41001 (a uSGB, aSMD = À0. 61, 95% CI [À1. 14, À0. 08], Q = 0. 13, fold change = 1. 6, 95% CI [0. 95, 2. 7]). The sole SGB for which the genus had been previously identified among the low-fat-associated entries was Turicibacter sp. TS3 (SGB39153) (aSMD = À0. 57, 95% CI [À1. 08, À0. 06], Q = 0. 15, fold change = 4. 5, 95% CI [1. 0, 19. 4]). This finding is concordant with previous studies that demonstrated Turicibacter declining in abundance in obesity,  49  a negative correlation with the nuclear factor-kB-protein-complex  50  that has a central role in inflammation signaling, and a lower abundance in patients with inflammatory bowel disease.
Independent longitudinal datasets show consistent diet-related uSGB dynamics To further validate our first set of observations, we analyzed two additional longitudinal datasets  7, 48  in which groups of mice were subjected to multiple dietary interventions. From the dataset by Nobel et al. 7  we retrieved 18 metagenomes sampled from three mice (six time points each; Figure  2D ), while the dataset by Kim and Bae  48  is internally subdivided into two different groups of three mice each (Figures  2E  and 2F ) undergoing different multiple dietary treatments. Out of the 30 SGBs having the highest discrimination coefficients in the cross-sectional meta-analysis (Figure  2A ), we selected the eight uSGBs most highly ranked by the random forest algorithm (Table  S7 ). Assessing the temporal trajectory of these uSGBs, we observed a clear, definite increase of their relative abundances in response to a higher fat intake in all three datasets (Figures  2D-2F ; Wilcoxon signed-rank test p = 5.
DISCUSSION Here, we taxonomically profiled the gut microbiome of laboratory mice at an enhanced resolution by the integration of a massive number of MAGs (22,718 mice-derived MAGs) in the reference database of the MetaPhlAn 4 marker-based approach. To investigate whether this increased resolution can lead to the discovery of relevant associations between the mouse microbiome and host conditions, we focused on the analysis of the microbiome links with diet. We collected, manually curated, and profiled a set of nine mouse microbiome datasets (one of which was made available by this work), all characterized by the presence of multiple diet regimes differing in the percentage of fat. Machine learning and meta-analyses on the cohorts profiled with the MAG-enhanced database revealed cross-cohort associations with diet that are stronger than what was previously available and that were mostly driven by uncharacterized microbial species (uSGBs).
Standardised mean differences meta-analysis For each SGB, we evaluate the per-dataset dependency of the single feature to the contrast high-fat/low-fat using linear models. For each dataset and each SGB, a linear model was evaluated of the type: ''feature $ age (days) + sampling-body-site + mouse-background + antibiotic-usage + diet-type''. Sampling body-site, genetic background, antibiotic-usage and diet-type (high-fat/low-fat) were encoded as categorical variables. From each of these models, a standardised effect-size and its standard errors were computed as a measure equivalent to the Cohen's d as described in Nakagawa and Cuthill,  62  starting from the t-value of the relative covariate. Significance of the diet-feature relationship was assessed with the Wald-test. Linear models, t-tests and their significance were computed using ordinary least squares (OLS) in Statsmodels python library (Seabold and Perktold  63  , ver.
0. 11. 1). Effect-sizes were then combined in random-effect meta-analysis using a python script implementing the procedure described in Borenstein et al. 64  and the Paule-Mandel heterogeneity from Statsmodels (ver. 0. 11. 1). Meta-analysis coefficients (average Standardised Mean Differences) confidence intervals at 95% confidence were computed as described in Borenstein et al. 64  Wald-Ps and Spearman's Ps from the single datasets in the first and in the second meta-analyses, as well as random-effect raw p-values were then corrected for false discovery rate (FDR) using Benjamini-Yakuteli procedure (Scikit-Bio, ver. 0. 5. 6) and a significance value of 0. 2.
Log fold changes meta-analysis An epsilon was added to the zero values (0. 0001) of the SGB relative abundances. Those were then log-2 transformed and averaged, and the difference was taken (log fold change). The first element of the log ratio was chosen as the high-fat group when the standardised mean difference effect-size was positive and as the low-fat group when it was true the viceversa. Variances of the log fold changes were computed as the sum of the variances of the two log variates being independent in all cases. Log fold changes were summarised in a meta-analysis by fixed effect-model using these variances and these effects. The same script as before was used in this step. The standard error of the log fold changes meta-analysis coefficient was taken as the square root of the variance of the averaged effect divided by the square root of the number of datasets analyzed, and 95% confidence intervals were computed as the average effect ± this standard error multiplied by the 97.
Correlation meta-analyses Two similar procedures were carried out meta-analysing Spearman's partial correlation coefficients which had been computed, together with their significances, using the Pingouin python library  65  (ver. 0. 3. 7). Correlation coefficients were Fisher-Z transformed, summarised and reverted back using a custom python script reproducing the result from Balduzzi et al. 66  We meta-analysed both the duration of the diet-administration correcting by the whole set of covariates and the percentage of fat, and the fat-percentage controlling by the whole set of covariates plus the duration of the administration. Significance of the random-effect coefficients were computed as described in Borenstein et al. 64  Wald-Ps and Spearman's Ps from the single datasets in the first and in the second meta-analyses, as well as random-effect raw p-values were then corrected for False Discovery Rate (FDR) using Benjamini-Yakuteli procedure (Scikit-Bio, ver.
ACKNOWLEDGMENTS The work was supported by the  European Research Council  ( ERC-STG  project  MetaPG-716575  and  ERC-CoG microTOUCH-101045015 ) to N. S. and  ERC-CoG  (project grant agreement no. 101001355 ) to O. K. , by the  European H2020 program  ( ONCOBIOME-825410  project and  MASTER-818368  project) to N. S. , by the  National Cancer Institute of the National Institutes of Health  ( 1U01CA230551 ) to N. S. , by the  Premio Internazionale Lombardia e Ricerca 2019  to N. S. , by the  European Research Council  under the European Union's  Horizon 2020 Research and Innovation Programme  ( ERC-2016-StG-71577 ) to H. S. , by  VolkswagenStiftung's initiative  '' Niedersa ¨chsisches Vorab''  (grant no. 76251-99 ) to T. S, and by the  European Research Council  ( CoG 865466 ) to T. S.
( A) Number (barplot and left-side y axis) and ratio (line plot and right-side y axis) of k-and uSGBs for each 2% prevalence interval when considering all the 622 samples in the nine cohorts used in the paper. SGB prevalence is computed as the average prevalence across the datasets. The 2% bin size for the histogram has been chosen for visualization purposes. (B) Distribution of k-and uSGBs detected per sample. The box marks the distribution between the 25th and 75th percentiles, and whiskers are extended for 1. 53 interquartile range (IQR). p value assessed via two-tailed Wilcoxon signed-rank test. (C) Distribution of relative abundance of k-and uSGBs per sample. p value assessed via two-tailed Wilcoxon signed-rank test. The box marks the distribution between the 25th and 75th percentiles, and whiskers are extended for 1.
53 IQR. (D) Cross-prediction matrices for the prediction of a high-fat vs. a low-fat diet using a random forest classifier trained on arcsine-square-rooted relative abundances of SGBs spanned by RefSeq reference genomes (left) or all the SGBs available in MetaPhlAn 4 (right). Cells in the matrices represent AUCs obtained by the classifier trained on the corresponding row-dataset and tested on the corresponding column-dataset. Diagonal values are 10-fold cross-validations of AUC values. The ''LODO'' row reports the leave-one-dataset-out AUC values obtained by training the algorithm on each cohort but one and testing it on the left-out cohort, iteratively on all the cohorts. In the machine-learning experiments, we considered only samples not undertaking antibiotic treatments, and datasets with at least 20 samples from both types of diets.
SGB abundances have been arcsin-square-rooted before the linear modeling. Species available in the RefSeq database are labeled with a white asterisk. L. denotes Lachnospiraceae. Horizontal colored (blue and dark-yellow) lines mark the 95% confidence interval for the pooled effect size. (B) Random-effect coefficients of a partial-correlation meta-analysis on diet-fat percentage, controlling also by duration of the diet, for the 30 SGBs most associated in the previous analysis. Correlations have been Fisher-Z transformed before the meta-analysis and then reverted back. SGB abundances have been arcsin-square-rooted before the linear modeling.
(C) Per-dataset average prevalences (%) stratified by high-fat and low-fat mice. (D-F) Temporal fat-related trajectories for three mice from the Nobel et al. 7  cohort and six mice from the Kim and Bae 48 cohort. The lines are relative to the six top SGBs of the RF feature ranking chosen among the 30 strongest effect sizes in the meta-analysis, and describe the relative abundance (in square-root scale) of the single SGB in multiple dietary shifts. N, nursing; LFD, $10%(low)-fat diet; HFD, $40%(high)-fat diet; LLFD, 6. 2%(low)-fat diet. (D) Trajectories in the Nobel et al. 7  cohort, (E) trajectories in the Kim and Bae 48  cohort, group #1, and (F) trajectories in the Kim and Bae 48 cohort, group #2. Cell Reports 42, 112464, May 30, 2023 5 Report (Figures S3 and S5; Table.
Dataset, acquisition parameters, and preprocessing The dataset used in this study comes from the young-adult cohort of Human Connectome Project (HCP)  (Van Essen et al. 2013). It consists of 3T MRI scans with anatomical, diffusion (dMRI), and functional (fMRI) MRI acquisitions. The anatomical and diffusion scans are from a subset of 100 randomly selected participants and were used to generate normative white matter priors (containing structural connectivity information) used in the Functionnectome analyses. The functional scans are from a subset of 46 participants, the same participants as the one used in the original Functionnectome paper  (Nozais et al. 2021 ) and independent from the 100 participants used for the priors. The HCP data was acquired by the WU-Minn Consortium with IRB approval, all participants gave their informed consent, and the WU-Minn HCP Consortium Open Access Data Use Terms were respected in the study.
The code for this extension method is the "scil_tractogram_GM_extension. py" script available as part of a branch of scilpy: https://github. com/NotaCS/scilpy. Such a procedure is similar to tractography toolboxes when building connectomes, like the tck2connectome function from MRtrix3  (Smith et al. 2015; Tournier et al. 2019 ). However, the current method differs from these approaches as they usually only allow the association of a streamline to a whole grey matter region, while our method was specifically tailored to our needs by associating the streamlines to the grey matter voxels they reach. It was necessary for testing the effect of such extension on Functionnectome analyses.
Functionnectome, white matter priors, and activation maps To evaluate the effect of splitting the tractograms by fiber class on a structural-functional analysis, we used each set of tractograms in Functionnectome analyses  (Nozais et al. 2021). The whole procedure is described in the original paper, but we summarized it below. First, the white matter priors are computed using the tractograms (n = 100). These priors correspond to maps, one per brain voxel, giving the probability of structural connectivity from this voxel to the other brain voxels. These priors can then be used by the Functionnectome program (available at http://www. bcblab. com) to project grey matter BOLD signals onto the related white matter. Essentially, for a given white matter voxel, the Functionnectome uses the structural connectivity information from the white matter priors to do a weighted average of the BOLD time-series from the grey matter voxels connected to the said white matter voxel.