dataesr/scientific-paragraphs-categorization huggingface dataset:
ACKNOWLEDGEMENTS We would like to thank  John Aach ,  Reza Kalhor ,  Erkin Kuru ,  Javier Fernandez ,  Gabriel Filsinger ,  Timothy Wannier  and  Benedikt Markus  for helpful discussions in the development of large-scale genome engineering; and  Susan Byrne ,  Bobby Dhadwar ,  Alex Ng  and  Alex Chavez  for plasmids, cell lines, reagents and scientific support throughout. In addition, we would like to acknowledge  Enzo Tramontano ,  Nicole Grandi ,  Angela Corona  and  Maria Paola Pisano  for their help providing background and bioinformatics materials and support on HERV-W sequences. We would like to thank  David Liu ,  Alexis Komor ,  Nicole Gaudelli  and  Johnny Hu  for sharing their knowledge and reagents for base editing. We would like to thank  Sergey Naumenko  of the  Harvard Chan Bioinformatics Core, Harvard T.
DATA AND AVAILABILITY Key plasmids developed during this study have been submitted to Addgene: pSB700 HL1 gR4 (# 124450), dABE (# 124447) and dCBE4-gam (# 124449). All NGS data used for the figures and supplementary figures have been made available at SRA BioProject Accession #PRJNA515875, #PRJNA518077 and #PRJNA561375 for 293T, PGP1 and WGS, respectively.
866 ± 0. 066). Based on the largest stroke collection (n = 1333)  30  , combined with the most comprehensive database of neuroimaging metaanalysis  26  and the best white matter mapping derived from the Human Connectome Project 7T  31, 32  we reveal the human disconnectome and demonstrate its relationship to the functional segregation of the human brain. Results indicate that functional territories are not only defined by white matter connections, but also by the highly stereotyped spatial distribution of brain disconnections. While the former has granted us the possibility to map 590 functions on the white matter of the whole brain, the latter compels a revision of the taxonomy of brain functions.
13, 23, 38 ). Importantly, disconnections in strokes (i. e. stroke disconnectome) demonstrated a higher level of clustering in comparison to the disconnection that was derived from paired synthetic lesions (i. e. synthetic disconnectome; Fig. 1d ). Redundancy in these datasets suggests that we should be able to summarise the pattern of brain areas disconnected by strokes into principal components. In order to do so, the pattern of brain areas that were disconnected in each stroke was first characterised by measuring the average level of disconnection in subcortical areas  16  as well as in areas derived from a multimodal atlas of the brain surface  25. Using a varimax-rotated principal component analysis (PCA) we modelled the main profiles of brain disconnections and revealed that 30 components, out of 46 in total, explained more than 90% of the variance of the regions disconnected by a stroke (Fig.
Main components of stroke disconnection and their functions. In order to assess the relationship between disconnection and brain function, the probability of disconnection of each component was compared to a manually curated version of the most extensive task-related functional magnetic resonance imaging (fMRI) meta-analytic dataset available  26, 32. Strikingly, 40 out of the 46 components disconnected a set of brain regions that significantly correlated with a set of specific task-related fMRI metaanalytic maps (Fig. 2b ) with a small to large effect size (all r > 0. 202, significant after Bonferroni correction for multiple comparisons-all p < 0. 00008; see Supplementary Information B. for a full description of main correlations for each component and Supplementary Data 2 for a complete report of all correlations).
Methods The following workflow was summarised in Supplementary Fig. 2. Stroke lesions. Lesion data were derived from 1333 patients admitted between 2001 and 2014 to University College London Hospitals (UCLH) with a clinical diagnosis of acute ischaemic stroke confirmed by DWI. Since DWI was routinely performed on the majority of attending patients, the sample was representative of the population, constrained mostly by contraindications and tolerability. The advantage of using a broad and unselected spectrum of strokes compared to a more narrowly defined cohort of stroke is that it is clinically relevant and generates characteristic patterns of disconnection that are the main variable of interest of our study. In contrast, narrowly defined cohorts of stroke still suffer from a biased distribution and are not representative of the stroke population and consequently cannot be used for data-driven clinical predictions.
Age ranged from 18 to 97 years (mean 63. 89, standard deviation 15. 91), and the proportion of males was 0. 561. The study was performed under ethical approval by the West London & GTAC Research Ethics Committee for consentless use of fully anonymised data. The majority of the data have been previously published  30. The lesions used in this research were non-linearly registered to the Montreal neurological institute space (2 × 2 × 2 mm, MNI; http://www. bic. mni. mcgill. ca) so as to allow direct comparisons across individuals. Data were routinely acquired in the clinic and were anonymised prior to analysis and their collection was approved and monitored by the UK Health Research Authority. See ref. 30  for further details. Synthetic lesions. To assess the impact of the spatial distribution of stroke lesion we computed synthetic lesions paired in size and hemispheric lateralisation with the stroke lesion dataset.
To do so, we repeatedly divided each hemisphere of the brain mask of all the stroke lesions using k-mean clusterings 50 applied to the coordinates of each voxel varying the number of clusters from 2 to 50,000. This produced more than 1 million synthetic lesion masks. In order to minimise the bulkiness of the synthetic lesion, each mask was subsequently smoothed with a fullwidth half-maximum of 10 mm, thresholded at 0. 3 and binarized. Finally, for each lesion of the stroke dataset, we sampled a lesion of the synthetic pool with the same size and localised in the same hemisphere. This produced a dataset of 1333 synthetic lesions exactly paired with the stroke dataset but pseudo-randomly distributed in the brain. The code used for the production of synthetic lesion paired with real lesions is available as supplementary code (see 'Code availability' section) Disconnectome.
Similarly to previous work  19, 27, 35  the probability of disconnection induced by each lesion was computed with the 'disconnectome map' tool of the BCBtoolkit software  27. Firstly, we tracked white matter fibres that pass through each lesion in 163 healthy controls tractographies (0. 448 males) dataset acquired at 7 T by the Human Connectome Project Team  31  and computed according to  32  (see Supplementary Information A. for full details on the processing of the DWI data). For each lesion, tractography maps of the 163 healthy controls were subsequently binarised and averaged together so that each voxel represented a probability of disconnection from 0 to 1. This produced a stroke lesion disconnectome dataset (n = 1333) and a synthetic lesion disconnectome dataset (n = 1333). Spatial embedding.
Stroke lesions and synthetic lesions, as well as their respective disconnectome maps' spatial distributions, were visually compared using a machine learning visualisation approach, t-SNE  33. t-SNE provided a twodimensional visualisation of the similarity and difference between the maps so that a smaller distance between two points represented a higher similarity between the maps. t-SNE was computed with the help of NiBabel 3. 1. 1, scikit learn 0. 23 in Python 3 with a perplexity of 30, an exaggeration of 12 (to increase the space between clusters) a learning rate of 200 and 1000 iterations. This step provided us with the visualisation of the stroke and synthetic lesions and disconnectome map distribution that is displayed in Fig. 1a, b. Data compression. In order to properly deal with redundancy, lesions and disconnectome of the stroke and the synthetic datasets were first characterised using the multimodal parcellation of human cerebral cortex (MMP, ref.
1 ). Plot of the components according to their cumulative explained variance can be seen in Fig. 2a. Component scores were systematically extracted for all components identified by the PCA by means of multiple regression. This provided us with the contribution of each parcel of the MMP and subcortical areas to each component. Relationship to task-related fMRI metanalysis. We used a meta-analytic database  26  (http://Neurosynth. org) that summarises the details of 11,406 fMRI literature sources and manually curated it as described in ref. 32. The manual curation consisted in the previously published selection of 590 maps related to specific cognitive processes out of the whole Neurosynth database. The curated database represented 590 cognitive term maps that were converted into a matrix using the average z value for each MMP parcels and the manually defined subcortical areas mentioned above.
fmrib. ox. ac. uk/fsl)  51, 52. To address the replicability of our maps, the 1333 disconnectome maps dataset was split in two 666 datasets and the multiple regression was run twice. Quality of the results duplication was assessed by means of Pearson correlations between the two set of maps derived from the analyses. This section resulted in two white matter maps per component. Atlas of the white matter function. Next, we explored the contribution of each component map voxels to the task-related fMRI meta-analytic maps. A permuted (n = 1000) linear regression was computed between the correlation value of each task-related fMRI meta-analytic map as an independent variable and each component maps voxels as a dependant variable. This analysis was run twice, once for each set of the component maps and its replication quality was assessed as previously mentioned.
subjacent to each component allowing to derive a statistical map of the function of white matter connections [as disrupted by stroke]. Accordingly, component maps were calculated using permuted (n = 1000) linear regressions. In order to assess the reproducibility of the component maps, we split the dataset in two (n = 666). Pearson correlation indicated a good reproducibility across the two sets of component maps (average Pearson R = 0. 813 ± 0. 079).
Acknowledgements We thank  Tianbo Xu , who generated all the stroke lesion masks, and  Lauren Sakuma , for useful discussion and edits to the manuscript. We thank  Maurizio Corbetta ,  Stephanie Forkel ,  Emmanuelle Volle ,  Patrick Friedrich  and  Sandrine Cremona  for discussion of the results. We also thank  Laurent Petit  and his team ( GIN ) for providing us with fMRI maps of left and right hands finger tapping as well as  University of Bordeaux  and  CNRS  for the infrastructural support. This project has received funding from the  European Research Council (ERC)  under the European Union's  Horizon 2020 research and innovation programme  (grant agreement no. 818521  to M. T. d. S. ). P. N. is funded by the  Wellcome Trust  and the  UCLH NIHR Biomedical Research Centre.
Data availability The two sets of component maps and the atlas of white matter function (original and replication) are available at https://identifiers. org/neurovault. collection:7735. The atlas of white matter function is also available at A-C terms: https://identifiers. org/neurovault. collection:7756. D-H terms: https://identifiers. org/neurovault. collection:7757. I-N terms: https://identifiers. org/neurovault. collection:7758. O-R terms: https://identifiers. org/ neurovault. collection:7759. S-U terms: https://identifiers. org/neurovault. collection:7760. V-Z terms: https://identifiers. org/neurovault. collection:7761. The raw dataset analysed in the current study is available at https://www. humanconnectome. org (7 T diffusion data) and http://www. neurosynth. org (metanalytic functional MRI maps).
Code availability The code used in the analyses is available as part of the BCBtoolkit package http://toolkit. bcblab. com. The code used for the production of paired synthetic lesions as well as the effect size maps is available at https://github. com/chrisfoulon/BCBlib/blob/devel/bcblib/ scripts/generate_synth_lesions. py, https://github. com/chrisfoulon/BCBlib/blob/devel/ bcblib/scripts/pick_up_matched_synth_lesions. py, and https://github. com/chrisfoulon/ BCBlib/blob/devel/bcblib/scripts/effectsize_T2R. py. Any additional information is available on request to M. T. d. S.
coli population dynamics, such as vitamin D, for which the absence of the vitamin D receptor in the intestinal epithelium of mice has been associated with increased E. coli loads  [35]. Our findings are consistent with those of another study in which the fecal microbiota of Simmental calves was followed during their first 3 months of life, although only six calves were included in the study and the sampling was sparse  [17]. The relative abundance of the genus Escherichia was found to be maximal during the milk-feeding period and decreased before weaning. Lactose, which is one of the main components of milk powder (approximately 45% of the dry weight), is hydrolyzed to the monosaccharides glucose and galactose by bacteria that synthesize the β-galactosidase enzyme. We therefore looked for the presence of this enzyme in available annotated genomes from the NCBI genome database of the Megasphaera, Enterococcus, Dialister, and Mitsuokella genera.
The sequence of E. coli LacZ βgalactosidase was also compared to the protein sequences found in members of these genera using the blastp program  [36, 37]. A β-galactosidase gene sequence was found in the genome of Mitsuokella multacida, which was isolated from human feces  [38] , and several species of the Enterococcus genus, which have been isolated from cattle (E. faecalis, E. faecium, E. hirae, E. thailandicus, E. malodoratus, E. devriesei, E. casseliflavus, E. italicus)  [39] [40] [41] [42]  (data not shown). The concomitant fluctuations of this lactose-rich source and the relative abundance of the genera Enterococcus and Mitsuokella strongly suggest a direct role of host diet on members of the fecal microbiota. We did not find a βgalactosidase annotated gene in the 24 Megasphaera annotated genomes nor in the 18 Dialister annotated genomes available on NCBI  [37].
The bacteria of these two genera did not carry a protein similar to the E. coli LacZ β-galactosidase (data not shown). The discrepancy between the associations found for the genera Megasphaera and Dialister and the absence of the β-galactosidase enzyme sequence in the genome of known members of these genera could be explained by the utilization of another nutrient present in the milk powder by the members of these taxa. Another explanation for this discrepancy could be the limited redundancy of carbon source use among members of these genera coupled with a small number of genomes from these genera in the NCBI database. Our study had several limitations. First, we followed the fecal microbiota of calves reared in commercial veal farms. Thus, the calves were not randomly assigned to the different farms (nor antibiotic treatments) and neither the environmental nor dietary variables could be controlled as they would be in a randomized trial.
Processing of 16S rRNA gene sequences The quality of the reads was verified using FASTQC  [55]  and they were processed using mothur (version 1. 35. 1) [56, 57]. Contigs were generated by assembling forward and reverse reads. Low-quality contigs were discarded if the total length was outside 289 to 292 bases, if there were more than five ambiguous bases ("N"), or if homopolymer runs exceeded five bases. After a clean-up step, sequences were aligned to those of the SILVA reference database (February 2017, release 128)  [58]. OTU assignment was made after clustering the sequences with a similarity cutoff of 97%. Singletons, duplicates, and triplicates were discarded. The taxonomy of each detected OTU was obtained using the RDP quality-controlled, aligned, and annotated Bacterial and Archaeal 16S rRNA gene sequence database  [59].
Chimeric sequences were removed after de novo chimera detection using the VSEARCH tool, version 2. 3. 4  [60]. Sequences flagged as chloroplasts, mitochondria, or eukaryotes were discarded from the dataset. We assessed the taxonomic composition of the microbiota, focusing on the phyla and genera, and focused on the five most abundant genera in each sample. For each calf, the OTUs detected in a sample were classified according to their detection in the previous sample to determine the monthly degree of change of the calf microbiota at the OTU level. For each time point, we determined the proportion of OTUs that were not previously detected and the proportion of OTUs that were not detected in the previous sample. This analysis was performed to identify concurrent patterns of acquisition and persistence of OTUs among calves.
Unweighted and raw-weighted Unifrac distances were computed as β-diversity metrics  [61]. For each calf, distances were computed between consecutive samples, and the distances between all calves were computed at the first, second, and last sampling. In summary, the available data for each sample were (i) the relative abundance of taxa at the phylum and genus level, (ii) the α-diversity metrics Shannon index and the number of observed OTUs, (iii) the weighted and unweighted Unifrac distances to the previous and next sample for the same calf, and (iv) the absolute number of E. coli per gram of feces. In addition, Unifrac distances were computed at the first, second, and last sampling between calves.
Exploring associations between the abundance of genera and the dose of milk powder A link between the relative abundance of genera and the dose of milk powder was explored for farms B and C. The Spearman correlation test was used to look for positive associations between the dose of milk powder and the relative abundance of genera. As multiple tests were performed, the P values were adjusted using the Bonferroni correction. We searched for the presence of the βgalactosidase enzyme, which hydrolyzes lactose to the monosaccharides glucose and galactose, in the available annotated genomes in the NCBI genome and protein databases for genera that were found to have a significant moderate to strong positive association with the dose of milk powder (r s > 0. 4)  [37]. We also blasted the protein sequence of the LacZ β-galactosidase enzyme in the NCBI protein database using the program blastp  [36, 37].
As E. coli is a lactose-fermenting species, we searched for an association between the estimated daily doses of milk powder and the predicted number of E. coli/g for farms B and C using Spearman's correlation test. The numbers of E. coli/g were obtained adding the estimates of the farm parameters the intercept in our final model. Means were presented with standard deviations. All statistical analyses were performed using R software (R version 3. 1. 0) [66]. Mixed effect models were built using nlme  [67]  and PERMANOVA tests were performed using the "adonis" function in the vegan package  [68].
Availability of data and materials Sequencing reads were deposited as entire raw data in the European Nucleotide Archive repository (ENA) under the BioProject ID PRJEB33072, separately for each sample. The code used for processing the 16S rRNA gene sequences was originally developed by Kozich et al. [57]  and is available at the website https://www. mothur. org/wiki/MiSeq_SOP (access on January 2017). The unrarefied OTU table and the corresponding taxonomic classification analyzed during the current study are available from the corresponding author upon reasonable request. The Escherichia-specific qPCR dataset has been included as Additional file 11: Table  S6.
Funding This work was supported in part by grants from  La Fondation pour la Recherche Médicale  to M. M. (grant number  FDM20150633309 ) and E. D. (équipe FRM 2016, grant number  DEQ20161136698 ), and from the  European Union 's  Horizon 2020 Research and Innovation Programme  under Grant Agreement No. 773830  (Project  ARDIG ,  EJP One Health)  and  INTERBEV (Protocol   N°SECU-15-31 ). The funding organizations were not involved in the study design, or collection, analysis, or interpretation of the data, or writing of the manuscript.
Behavioural observations All fish were monitored for air breathing within groups, not in isolation. One week after respirometry, groups were selected for behavioural study using a random number generator. Each group consisted of the seven individuals from a tank, with six groups in total, providing data on individual uptake and behaviour for 42 fish overall. Group observations were performed in a behavioural arena consisting of a circular tank (3117 cm 2 ) placed within an outer water reservoir (4760 cm 2 ). Water was mixed and aerated in the reservoir, to minimise disturbance to the fish and improve video quality in the arena. The walls of the arena were pierced throughout to allow free exchange of water with the outer reservoir. Oxygen levels were monitored using a galvanic oxygen electrode (OxyGuard Mini Probe, OxyGuard International, Farum, Denmark) placed within the arena.
Statistical analysis All data are available from Mendeley Data (https://data. mendeley. com/datasets/g68pknt2n9/1). All analyses were conducted in R version 3. 5. 3 (http://www. R-project. org/) using the lme4 (http:// CRAN. R-project. org/package=lme4) and MuMIn (http://cran. rproject. org/package=MuMIn) packages. The effect of acclimation temperature on individual SMR was assessed using a linear mixedeffects model (LME) with log SMR as the response variable, log mass and temperature (categorical) as fixed effects. Individual ID was nested within group and treated as a random factor to account for repeated measures of individuals between temperatures. The effect of environment on group air-breathing behaviour was assessed using a generalised linear mixed-effects model (GLMM) with a negative binomial distribution to account for over-dispersion.
Molecular Docking The Dyrk1A 3D structure (PDB ID: 5AIK) was downloaded from the Protein Data Bank (PDB)  [48]. The protonation states of all ionizable residues were computed using the PROPKA3 program  [49]. The compounds, EGCG, ECG and EC, were built using Marvin software (Marvin 17. 3. 13. 0, 2017, ChemAxon: http://www. chemaxon. com). The Smina program  [50] , a fork of AutoDock Vina  [51] , with Vinardo scoring function  [52]  was used to perform the computational molecular docking. The compounds rotatable bonds were flexible during the docking procedure, and all the protein residues kept rigid. The Gasteiger atomic partial charges were assigned to the compounds and the protein. The compounds and the protein were converted in PDBQT format using the AutoDockTools package  [53]. The entire protein surface was subjected to the molecular docking search.
Data Analysis Statistical analysis was done with the Student's t-test using Statview software (Statview 3, Abacus Corporation, Baltimore, MD, USA). For multiple pairwise comparisons between genotypes and treatments, statistical analysis was done with two-way ANOVA followed by the Bonferroni/Dunnet post hoc test using Statview software. The results are expressed as mean ± SEM (standard error of the mean). Data were considered significant when p < 0. 05. A p value of 0. 06-0. 10 was considered to indicate a statistical tendency given the small sample size. Correlations were determined by using Spearman's rank correlation, as data were not normally distributed according to the Shapiro-Wilk test.
Acknowledgments: We acknowledge the platform accommodation and animal testing of the animal house at the  Institute Jacques-Monod (University Paris Diderot ), and the  FlexStation3 Facility of the Functional and Adaptive Biology (BFA) laboratory. We thank  Daniel Quintas  for technical assistance. We also acknowledge the Mass spectrometry platform of UMR 8601 laboratory. We also acknowledge the support of the  Spanish Ministry of Science and Innovation  to the EMBL partnership, the  Centro de Excelencia Severo Ochoa  and the  CERCA Programme/Generalitat de Catalunya.