dataesr/scientific-paragraphs-categorization huggingface dataset:
In the several 16S surveys considered here, asymptotic genus-wise and sample-wide richness estimates heavily tracked their respective observed richness values (97-100% Pearson correlations, Figs. S13-S17 ). We therefore propose the same regression models above for standard inverse variance weighted regression analyses of asymptotic richness estimates. As expected, results from such a procedure were similar to those obtained with observed richness as the response variable. Also see reference  26  for a heterogeneity test of potential interest. We implement these procedures in an R package Prokounter. Supplementary Note 2 presents further discussions on the regression models above. Dilution experiment: A monoisolate was prepared overnight from a Luria-Bertani (LB) agar plate into a 5 mL LB liquid, which grew to 10 9 cells.
16S datasets and taxa reconstruction pipelines: The mouse microbiome 16S data was obtained from the R/Bioconductor package metagenomeSeq  71. The moderate to severe diarrheal 16S survey was obtained from the R/Bioconductor package msd16S  72. The long-term time series 16S survey 2 was obtained from the supplementary data of the corresponding paper. The wastewater 16S survey 51 was obtained on request from the authors of the original study. MBQC handling laboratory B's (HL-B) sequencing reads was obtained from the Microbiome Quality Control (MBQC) project  27. We generated three varieties of taxa count data from each of the Pseudomonas, PIH100 16S and MBQC HL-B (handling lab B) sequencing data. These include sequence similarity threshold based taxa clustering methods for 99% and 97% sequence similarities (Qiime1), and a probabilistic taxa clustering method (Dada2) as follows.
Quality filtering of sequencing reads: Paired-end reads were processed with Trimmomatic 73 (v0. 38) to remove universal adapters and low-quality reads. Reads with ambiguous bases were removed or truncated using Dada2's filterAndTrim  74  function. The 16S V1-V2 regions in both our Pseudomonas and PIH100 data were sequenced using 2x300bp paired-end reads. Based on sequencing read quality score profiles, we retained the first 240bp and 210bp in the forward and reverse reads for the Pseudomonas dataset. These numbers were respectively 200bp and 190bp for PIH100. For HL-B, we removed the first 2bp following the primers in the forward and reverse reads. This allowed us to neglect the trailing low quality bases adversely affecting the taxa reconstructions, while still allowing for sufficient overlap to merge paired-end reads.
Reads with either the designed primers or their reverse complements were filtered using cutadapt  75. The quality filtered reads were then clustered with Qiime1 76 and Dada2 74 as below. Qiime 1 : Quality filtered forward and reverse reads were merged using Pear 77 , and then clustered using pick_open_reference_otus. py (Qiime1 version 1. 9. 1), which implements the Qiime1 open reference OTU clustering algorithm. Briefly, closed reference clustering of merged reads were performed against the Silva132 database at 97% and 99% sequence similarity thresholds, using Uclust 78 v. 1. 2. 22q. Reads that did not map to the database were subsampled and used as new centroids for a de novo OTU clustering step at the respective sequence similarity thresholds. Remaining unmapped reads were subsequently close clustered against the de novo OTUs.
Finally, another step of de novo clustering was performed on the remaining unmapped reads. Taxonomy was assigned to taxa representative sequences with Uclust based on the Silva132 79 database. These sequences were filtered with Pynast 80 , and OTU tables generated. Dada2: Dada2 allows denoising forward and the reverse reads independently. Error rates were estimated separately for the quality filtered forward and reverse reads for each sample. This estimation step is based on a sample of reads for computational tractability. Reads were deduplicated and sequence clusters inferred based on the estimated error rates. Taxa from forward and reverse reads were merged at the end of the workflow. Chimeric taxa were removed with the function removeChimeraDenovo. The resulting taxa were assigned taxonomic labels based on the Silva132 database, using their naïve Bayes classifier.
Tables  Table 1:  Relative to study variables, within-genus taxa accumulation trends capture bulk of the systematic variation in 16S surveys' genus-specific taxa accumulations. For each 16S survey dataset mentioned in column 1, the year of publication is listed in column 2, the partial 16S segment targeted, machine technology and sequence clustering approach used are specified in column 3. FST. x refers to sequence clustering at an a priori fixed sequence similarity threshold of x%. McFadden's pseudo- ( for explaining genus-specific taxa accumulations with two negative binomial regressions (NB) are listed in columns 4 and 5. The fourth column is obtained when the NB regression includes within-genus taxa accumulation trend ( + & (⋅), Methods) alone as predictor. The fifth column additionally includes the genus identifier, total sample depth, and experimental design matrix for each dataset as predictors (methods).
Corresponding Akaike Information Criteria (AIC) are listed in columns 6 and 7. Table  2 : Relative to study variables, within-genus taxa accumulation trends capture bulk of the systematic variation in 16S surveys' sample-wide taxa accumulations. For each 16S survey dataset mentioned in column 1, the year of publication is listed in column 2, the partial 16S segment targeted, machine technology and sequence clustering approach used are specified in column 3. FST. x refers to sequence clustering at an a priori fixed sequence similarity threshold of x%. McFadden's pseudo- ( for explaining sample-wide taxa accumulations with two negative binomial regressions (NB) are listed in columns 4 and 5. The fourth column is obtained when the NB regression includes within-genus taxa accumulation trend ( + & (⋅), Methods) alone as predictor.
Figure2. Concordant taxa accumulations across genera, confounded differential richness inference and the Prokounter strategy. (A) Sample-wide taxa accumulations are visualized with respect to sample depth (left). Within-genus taxa accumulations are visualized with respect to the total recovered genus abundances for two genera, i. e. , the sum of the abundances of all taxa within the genus (center). Dataset-wide taxa accumulations for any taxonomic level is strongly predicted by recovered abundance alone (right). Red line illustrates a linear fit. (B) Differential richness log-fold changes (LFC, y-axis) track differential relative abundance fold changes (LFC, x-axis) in the waste-water treatment survey. (C) Prokounter exploits within-genus accumulation data to model false taxa accumulation rates.
, 2007) , we wanted to gain a better understanding of the relative importance of different ecologically relevant aspects of dispersal on shaping the genetic diversity of populations. In particular, we expected allelic richness, observed, and expected heterozygosity to be higher in more central sections (i. e. , downstream or confluences) of the riverine network  (Paz-Vinas & Blanchet, 2015; Ritland, 1989). This increase in genetic diversity in central sections of the network might be caused by a strong signal of dispersal rate, upstream movement probability, and habitat carrying capacity, leading to those sections receiving more migrants and sustain larger populations (centrality aspect;  Altermatt, 2013). We addressed this with microsatellite data from 3319 amphipod individuals collected from 95 sites across a riverine network covering more than 28,000 km 2 and compared it to the output of stochastic simulation models examining alternative parameter combinations influencing dispersal, but conducted on the identical riverine network structure.
| Genetic data collection We conducted the study in the river Rhine drainage within Switzerland, which encompasses about 28,000 km 2 of its headwater area. We sampled Gammarus fossarum amphipods from 281 sites evenly and representatively spaced across the river Rhine headwaters between 2007 and 2015 by a kicknet approach. We morphologically identified all individuals to the species-complex level  (Altermatt et al. , 2019). We further genotyped a subset of individuals of the G. fossarum complex using microsatellites  (Westram et al. , 2013) , conventional 16S sequencing, or SNP pyrosequencing  (Westram et al. , 2011). We used the 16S mitochondrial gene sequence, or three SNPs therein, to reconstruct the realized distribution of both G. fossarum type A and type B (see  Westram et al. , 2011  for detailed methods).
We relied on published and unpublished sequences and SNPs  (Altermatt et al. , 2014; Westram et al. , 2013; Westram et al. unpublished data; Alther et al. unpublished data). We used the microsatellite data for subsequent population genetic analyses. We extracted DNA for microsatellite analyses from complete individuals or their heads using a hotshot approach  (Montero-Pau et al. , 2008). We amplified fragments using multiplex amplifications with the qiagen Multiplex PCR Kit chemicals. We used nine different microsatellite markers (gf08, gf10, gf13, gf18, gf19, gf22, gf24, gf27, gf28 sensu  Westram et al. (2010) ), specifically designed for G. fossarum and previously established in several studies  (Altermatt et al. , 2014; Westram et al. , 2013)  (note that microsatellite marker gf21 by  Westram et al.
, 2010  was also included initially, but then discarded because of signs of null alleles, as also reported by  Westram et al. , 2013). We used primers in different concentrations (see Table  1  in  Westram et al. , 2010)  in reaction volumes of 12. 5 μl, with 6. 25 μl of PCR Master Mix, 1. 25 μl Q solution and 1 μl DNA template. The PCR consisted of an initial denaturation step at 95°C (15 min), 35 cycles at 94°C (30 s), 60°C (90 s), 72°C (60 s), and a final elongation step at 60°C (30 min). We diluted the resulting amplicons (1:20) and combined them with size standard (GeneScan 500 LIZ, Applied Biosystems). We sequenced fragments on an Applied Biosystems 3730xl DNA Analyser at the Genomic Diversity Centre of ETH Zurich, Switzerland. We analysed and manually edited the electropherograms using softgenetics genemarker software (v.
The detailed methods of how we prepared the extensive graph object are described in  Alther and Altermatt (2018). We then restricted the analysis to the part of the riverine network that is actually inhabitable by either one or both of the studied amphipod species, based on an empirically validated cropping of the network. We used a database on amphipod occurrences in Switzerland with >2000 sites covered  (Altermatt et al. , 2019)  to distinguish between nodes containing G. fossarum from unoccupied nodes. After preparation of the initial complete Rhine riverine network, we selected nodes (subcatchments) that contain one or both species of the G. fossarum complex and all their spatially interconnecting nodes (Figure  S1 ). This resulted in the truncated riverine network that is empirically validated to be accessible and inhabitable to G.
fossarum, containing 2401 nodes (referred to as G. fossarum network). The corresponding graph object is available on GitHub (see data accessibility section). We subsequently mapped our microsatellite data from G. fossarum complex sites of Switzerland to the nodes of the prepared graph using arcgis 10. 5. 1 (ESRI Inc. ). We refer to sites as nodes hereafter. Removing nodes that had <15 individuals successfully genotyped resulted in 95 nodes for subsequent analysis, harbouring 3319 individuals. The corresponding microsatellite data are available on GitHub (see data accessibility section). Data were available for 67 nodes for G. fossarum type A (2257 individuals) and 33 nodes for G. fossarum type B (1062 individuals), with five nodes having data on both (Figure  S1 ). This preparation step resulted in a vector containing the corresponding node IDs where microsatellite data of either one or both species of the G.
| Statistical analyses For the spatial data (explanatory variables), we calculated a series of network metrics in order to identify the influence of network topology based on the G. fossarum network containing 2401 nodes and the subset of 95 nodes with microsatellite data available. The calculations were done in r 3. 6. 1 (R Core Team, 2019) with the package igraph 1. 2. 4. 2  (Csárdi & Nepusz, 2006). The network metrics for single nodes were upstream distance from the outlet node, total upstream catchment area, directed and undirected betweenness centrality, directed and undirected closeness centrality, and degree centrality. node and all other nodes in the riverine network. We standardized the closeness centrality (c) for analysis using the following approach: c i -min(c) max(c) -min(c).
Additionally, we log-transformed the total upstream catchment area and the directed betweenness centrality to reduce skewedness (Figure  S3 ). We modelled the genetic response variables separately using linear models (LM) using the lm() function since the residuals followed a Gaussian distribution. We included all network metrics (upstream distance, total catchment area, directed betweenness centrality, undirected closeness centrality) and species as factors with all higher-level interaction terms. We applied backward stepwise selection (function step()) using AIC scores to reduce interaction terms. Additionally, we ran models without interaction terms and selected the most parsimonious one with a dredge approach using function dredge() from mumin package  (Bartoń, 2020)  based on AICc scores.
For example, the SPO for mean allelic richness with d = 0. 001, W = 0, K = 0 was subtracted from the SPO for mean allelic richness with d = 0. 01; W = 0; K = 0. If the simulation fit was higher for d = 0. 001, this subtraction would result in a negative sign. Repeating this across all simulation combinations and all response variables (mean allelic richness, mean observed heterozygosity, expected heterozygosity) resulted in a fraction of comparisons with a negative sign. If this fraction was higher than 0. 5, the former parameter value was considered superior, since models with this parameter value combination performed better in more than half of all comparisons. We calculated the SPO, the MPO, and the DMPO using our own functions, included in the analysis script available on GitHub (see data accessibility section).
| Population genetics of the Gammarus fossarum complex Empirically assessed mean allelic richness ranged from 1. 5 to 5. 0 (mean: 3. 3; median: 3. 4; SD: 0. 7) for G. fossarum type A, and from 2. 4 to 4. 2 (mean: 3. 2; median: 3. 2; SD: 0. 4) for G. fossarum type B. Both species showed spatial gradients of within-population genetic diversity when using mean allelic richness as a diversity metric, with higher values in more central nodes, visually apparent when plotted on a map (Figure  1a  Linear models explained allelic richness and observed heterozygosity by network topology. Models with higher-level interactions performed worse than models without interactions based on AIC and VIF (data not shown) and we used the latter. Mean allelic richness was best explained by upstream distance in combination with directed betweenness centrality and undirected closeness centrality (Figure  2a ; F 3,96 = 8.
5; p <. 001; R 2 adj = 0. 19). It significantly decreased with upstream distance from the outlet node within the riverine network in both species of the Gammarus fossarum complex. This translates to higher allelic richness in more central and betterconnected nodes of the network. In addition, higher carrying capacity generally increased allelic richness, both in the simulations as well as in the empirical data (data not shown). Mean observed heterozygosity was best explained by undirected closeness centrality and directed betweenness centrality (Figure  2d ; F 2,97 = 7. 94; p <. 001; R 2 adj = 0. 12). Observed heterozygosity was higher in more central and better-connected nodes of the network, hence it increased with increasing closeness centrality and higher betweenness centrality for both species of the G.
Here we introduce a method and software: the Functionnectome, that unlocks the function of the white matter. Functional white matter analysis is derived from the combination of taskrelated functional MRI and new anatomical priors for brain circuits. In doing so, we provide the first functional white matter maps revealing the joint contribution of connected areas to motor, working memory, and language functions. The Functionnectome is available as a companion open-source software (http://www. bcblab. com and https://github. com/NotaCS/Functio nnectome).
The tractography used is openly available at https://osf. io/5zqwg/. In total, 228,453 mapsone per brain voxel-were generated and are part of the Functionnectome software's priors. The Functionnectome uses the probability indicated in these priors to project the signal from a given voxel to the brain. A second "simplified" set of priors consisted of 438 cortical 30 and subcortical regions  31  and their probability of structural connectivity with all other brain voxels. We used the second set of "simplified" priors for validation (i. e. comparison with restingstate networks) and possible replication of the analyses in more modest configurations such as laptops. Validation of the priors. To verify the anatomical priors' validity, we used the method developed by O'Muircheartaigh and Jbabdi  32.
Briefly, it compares statistically independent components  33  of resting-state fMRI and structural connectivity. In our analysis, the independent component analysis applied to resting-state fMRI data of the HCP test-retest dataset produced 17 resting-state networks (out of 20 components). Similarly, the independent component analysis applied to the 438 "region-wise" priors produced 50 components. Cross-correlations indicated that resting-state networks were significantly associated with pairs of structural connectivity components (Fig. 1a ). For instance, structural connectivity components 2 and 4 corresponded to the left and right visual resting-state network (RSN2, Fig. 1b ) and connected the two hemispheres through the posterior part of the corpus callosum. Likewise, structural connectivity components 37 and 39 corresponded to the language resting-state network (RSN4, Fig.
1b ) and included the arcuate fasciculus (the primary language pathway). The good correspondence between classical functional networks and our structural connectivity priors supports their anatomical validity in assessing the probability of connection between functionally activated regions. Functionnectome. Based on these priors, we developed a method, the Functionnectome, which combines the functional signal from distant voxels of the classical fMRI 4D volume (Fig. 2a ) using their probabilistic structural relationship given by anatomical priors of the involved brain circuits (Fig. 2b ). These priors are probability maps derived from high-resolution tractography (Fig. 2d ), indicating the probability of structural connectivity between each grey matter voxel and the rest of the brain. By projecting the fMRI signal from grey matter voxels to the white matter and weighing the signal by the probability of connection, a "functionnectome" (Fig.
2c ) is generated. This new 4D volume is compatible with the usual statistical tools to reveal the activation patterns emerging from the signal (Fig. 2e ). Here, we illustrated this process by applying the Functionnectome to motor (n = 46), working memory (n = 45), and language (n = 44) functional volumes of the test-retest Human Connectome Project (HCP) dataset. Results from the functionnectome maps are presented side by side with classical task activation analysis. The white matter "activations" displayed on the functionnectome z-maps correspond to the associated pathways' significant involvement during a task. Importantly, these white matter activations are arising from the projected grey matter signal, and are not direct white matter BOLD signal analysis  22, 23. The white matter activations displayed on the functionnectome z-maps correspond to the associated pathway's significant involvement during a task.
In addition to cortico-cortical pathways, the cortico-pontocerebellar tract was also activated and connects the classical fMRI (Fig. 5b ), in which disruption has been reported to impair working memory  39. Language (semantics). The Functionnectome analysis for a language task (semantic) is displayed in Fig. 6a  and revealed the language "ventral stream" involvement. The ventral stream is composed of the uncinate and the inferior fronto-occipital fasciculi connecting the inferior frontal gyrus with the temporal lobe and the occipital lobe  [40] [41] [42]. The analysis also showed the middle longitudinal fasciculus, which links the temporal pole to the inferior parietal lobe  43  and the posterior segment of the arcuate fasciculus, which links posterior temporal regions to the parietal lobe  18.
Both hemispheres could interact during the task thanks to the involvement of callosal connections. We also observed significant participation of the fornix during the task. As expected, the classic fMRI maps displayed activation in the posterior inferior frontal gyrus, the superior temporal gyrus, and the inferior parietal lobe, forming the semantic network  18, 44. The anterior part of the temporal lobe was activated (Fig. 6b ), which is in line with the literature highlighting it as a central hub for semantic processing  45. The orbitofrontal cortex has previously been linked to language and semantic cognition  45. Lastly, activation of the amygdala, mammillary bodies, and hippocampus reflect a limbic network's involvement, integrating emotions with semantic memory  46. For each analysis reported above, we repeated the analysis in a replication dataset of the same participants (same session, opposite phase of acquisition).
Discussion We propose a method, the Functionnectome, to investigate brain circuits' functional involvement during task-related cerebral processes based on openly available anatomical connectivity priors. Applying the Functionnectome to a high-quality functional neuroimaging dataset revealed-for the first time in healthy human brains-the white matter circuits supporting motor, working memory, and language activations. Results also indicated a higher reproducibility of the Functionnectome maps compared to classical task-related activation methods. To support this method's broad uptake and facilitate its application to a wide range of datasets, including in the clinic, we provide a GUI and a terminal-based companion software, as well as simplified priors for faster processing for more modest configurations.
This toolbox allows the application of the Functionnectome to any previously acquired fMRI dataset. The toolbox is flexible and users can integrate their own priors and the current release opens up novel avenues for research on the integrative function of white matter. The Functionnectome incorporates structural connectivity information into the functional analysis, allowing for assessing brain regions' interaction rather than their independent contribution during brain processes. Compared to previous work seeding tractography directly from blobs of functional activations  47  , the Functionnectome allows for a statistical assessment of the white matter circuits involved. Additionally, standard fMRI analyses employ spatial filtering before the   statistical analysis that is agnostic to the structural connection between the voxels and may mix the signals from functionally unrelated voxels.