dataesr/scientific-paragraphs-categorization huggingface dataset:
Network generations Each dataset was pre-processed independently before the network generation. The gene expression data were filtered to remove lowly expressed genes (TPM < 1 or FPKM < 1) in each tissue or cancer type to avoid bias. For the multiomics data, we corrected them by removing the effects of age (in all networks) and sex (in the non-gender-specific networks) using trimmed mean robust regression  (13). We combined the data from each omics into a matrix to generate a network and analysed it using the Spearman correlation function from the SciPy package. Each data source was processed independently. After filtering for striking correlations between the pairs, we filtered the pairs with FDR <0. 05. Furthermore, we performed community detection analysis using the Leiden algorithm  (15)  in the iGraph package to identify sub-network clusters for downstream analysis.
Moreover, in the longitudinal wellness profiling studies, we calculated both cross-sectional and delta networks. These networks are generated based on the methodology presented in the P100 wellness study  (13). Cross-sectional networks were calculated by correlating data from all visits to represent correlations in the context of individualized variation. Meanwhile, delta networks were calculated by correlating the analyte changes between visits to allow users to investigate features that co-vary within the same time intervals. The number of the nodes and edges in the generated networks varying between 5673 (liver cancer)--12 581 (testis) nodes and 1034 (Delta IS)--566 390 (SCAPIS--SciLifeLab Longitudinal Male) edges in MOBNs (Supplementary Table  S1 ).
Besides, wherever possible, analytes are linked with external databases such as KEGG (  16 ), Human Protein Atlas  (17) (18) (19) , Uniprot  (20)  and HMDB  (21)  to facilitate further biological interpretation and investigation. All of this information can be downloaded directly and is compatible with other network analysis tools and software, such as Cytoscape or iGraph package in Python and R. By using these tools, users can merge multiple networks and perform additional downstream analysis. Moreover, in the iNetModels 2. 0, we implemented programmatic access to the database using an in-house-built Python package that can be found under the 'API' section. With the API, users can retrieve more extensive networks (>50 neighbours per-queried nodes) programmatically. We currently limit the query to one query per second.
Case study related to administration of CMA in NAFLD One of our platform's unique features and superior strengths is that iNetModels 2. 0 is the first and only platform supporting the exploration of MOBNs based on personalized data. This is a great advantage to avoid bias since all data were analysed in a paired manner. In our recent study  (22) , we tested a potential therapeutic strategy for NAFLD patients through CMA administration. We provided CMA to 10 subjects involved in the trial and collected plasma samples during the day to generated proteomics and metabolomics data. The data generated in the clinical trial were analysed using metabolic modelling. The results of the analysis were validated by performing animal experiments, where L-serine was supplemented to mouse and a reduction in the liver triglycerides (TG) and markers of liver tissue functions, e.
We also envisage that this platform will be a key resource for computational biologists working in omics data integration, network science, systems biology and systems medicine. In this context, we expect iNetModels to be an essential resource for more in-depth multi-omics analysis that may reveal novel molecular mechanisms underlying human wellness and disease. In the future, in addition to the inclusion of more studies, we are planning to expand the functionalities of the platform. First, with the increasing number of personalized wellness profiling study, we plan to develop a method to build a consensus MOBN based on various studies to increase the robustness of the findings generated from this platform. Second, we plan to add a functional analysis feature to show the enriched pathways or biological processes by integrating the selected network information to other databases, such as KEGG  (16)  or Metabolic Atlas  (28).
ACKNOWLEDGEMENTS We appreciate the data sharing from dbGaP. This work was supported in part by the  Robert Wood Johnson Foundation , the  M. J. Murdock Charitable Trust ,  NIH  grants  2P50GM076547 ,  ES017885 ,  RC2HG005805 , and  Arivale. The  Genotype-Tissue Expression (GTEx) Project  was supported by the  Common Fund  of the Office of the Director of the  National Institutes of Health , and by  NCI ,  NHGRI ,  NHLBI ,  NIDA ,  NIMH , and  NINDS. The data used for the.
, 2001)  and on a synthesis of several evaluations (clinical, scalp/depth-EEG, MRI/PETscan). Patients were candidates for a curative surgery and the fMRI evaluations were performed as a part of their presurgical assessment. Patients as well as healthy participants provided written informed consent for the study that was approved by the local ethic committee (CPP: 09-CHUG-14, 04/ 06/2009 and 2017-A00384-49). ) and TLE patients (N = 16), performed resting-state and sentence recall task. Subpanel 2. We focused on the language-and-memory ROIs defined in previous work  (Roger et al. , 2019). Subpanel 3. ROIs were separated based on the resting-state network (RSN) they belonged to. Subpanel 4. We performed community detection in two groups and two tasks to explore LMN configurations, state reconfiguration and their reorganization.
Subpanel 5. We determined the role (connector hub, provincial hub, satellite or peripheral node) of each LMN region based on its connectivity within module (intra-modular connectivity) and with other modules (inter-modular connectivity). In order to explore LMN integration and its state reconfiguration we focused on the connector hubs. The roles are schematically presented on Subpanel 4 with numbers corresponding the role in Subpanel 5. Panel B: Schematic representation of main terms and analyses. Both segregation and integration of LMN were explored for each state, extrinsic and intrinsic (state LMN configuration) as well as its reconfiguration between the sates in healthy participants (green). LMN configurations and state reconfiguration in terms of segregation and integration were tested using the same approach in TLE patients (pink), as well as the differences in LMN configurations and state reconfiguration between healthy controls and TLE patients (dashed lines).
Neuropsychological data in patients All patients underwent complete neuropsychological assessment including language and memory functions carried out by a neuropsychologist and a speech therapist. This general cognitive assessment was used in further analyses of the efficiency of LMN configurations and state reconfiguration in TLE patients. Specifically, the following cognitive scores were used in the analyses: (a) language scores: verbal comprehension index (VCI; WAIS IV,  Wechsler, 2008) , naming (DO80;  Deloche & Hannequin, 1997) , semantic fluency (SFL) and phonological fluency (PFL;  Godefroy, 2008) ; and (b) memory scores: auditory memory index (AMI), immediate memory index (IMI) and delayed memory index (DMI; WMS IV,  Wechsler, 2009). Test scores were standardized by gender, age and sociocultural level.
MR acquisition Functional MRI experiments were performed at the MR facility. MR images were acquired with a whole-body 3 T MR Philips imager (Achieva 3. 0 T TX Philips, Philips Medical Systems, Best, NL) with a 32-channel head coil for all of the participants. During the recall task, the manufacturer-provided gradient-echo/T2* weighted EPI method was used. Forty-two adjacent axial slices parallel to the bicommissural plane were acquired in sequential mode (3 mm thickness, TR = 2. 5 s, TE = ms, flip angle = 82 • , in-plane voxel size = 3 × 3 mm; field of view = × 240 × 126 mm; data matrix = 80 × 80 pixels; reconstruction matrix = 80 × 80 pixels). During resting-state, four hundred cerebral rs-fMRI volumes were acquired using a gradient echo planar imaging sequence (FEEPI, 36 axial slices, 3.
Prior data analysis and data preprocessing 2. 5. 1. Statistical analyses of demographic, clinical and neuropsychological characteristics We included TLE patients with left (LTLE) and right (RTLE) origin of seizures. Since previous studies showed that these patients can differ regarding cognitive functioning and neural organization  (Besson et al. , 2014; de Campos et al. , 2016; Phuong et al. , 2021; Roger et al. , 2020b) , before conducting the main analyses planned in this study, we tested whether the LTLE and RTLE patients in our sample significantly differed regarding their clinical characteristics (age, epilepsy duration, number of AEDs, hippocampal atrophy and gender), hippocampal volume, neuropsychological performance. We did not flip the images of patients in the L-R direction in line with recommendations  (Lee et al.
Functional MRI preprocessing. The preprocessing was performed using SPM12 (Welcome Department of Imaging Neuroscience, London, UK, http://www. fil. ion. ucl. ac. uk/spm/) running under Matlab R2015b (Mathworks Inc. , Sherborn, MA, USA) using the standard routines. All images were realigned to correct the head motion, timecorrected with the mean image as the reference slice, spatially normalized to MNI (Montreal Neurological Institute) space and then spatially smoothed with an 8 mm FWHM (Full Width at Half Maximum) Gaussian kernel. The T1-weighted anatomical volume was co-registered to the mean image created by the realignment procedure and was normalized within the MNI (Montreal Neurological Institute) space. The anatomical normalization parameters were subsequently used for the normalization of functional volumes.
• Connectivity analyses The FC within the LMN was calculated based on the resting-state and the recall activity using a Graph theory (GT) analysis  (Fornito, 2016; Rubinov & Sporns, 2010). The CONN toolbox (Functional Connectivity Toolbox, Gabrieli Lab, Massachusetts Institute of Technology) was used to obtain FC matrices and the Brain Connectivity Toolbox (BCT, Rubinov & Sporns, 2010, available at https://sites. google. com/site/bctnet/) and GraphVar toolbox  (Kruschwitz et al. , 2015 , available at https://www. nitrc. org/projects/graphvar/) were used for graph theory analyses. The CONN Toolbox (Functional Connectivity Toolbox, Gabrieli Lab, Massachusetts Institute of Technology) provides ROI-to-ROI correlation analysis according to the temporal fluctuations of BOLD signals. The first step consisted of denoising the pre-processed unsmoothed data by regressing out the BOLD signal from the white matter, the CSF as well as outliers and movements obtained by ART and SPM.
For the recall task, we also entered in the regression a separate regressor for the experimental condition, according to other previous studies validating this approach  (Cao et al. , 2014; Cole et al. , 2014; Mohr et al. , 2016). The resulting residual time series were temporally filtered to remove lowfrequency scanner drifts and/or high-frequency physiological noise using band-pass filtered (0. 008-0. 09 Hz) for the resting-state and highpass filter (0. 008 Hz) for the recall task. Resting-state data processed in this way reflected intrinsic network, while that resulting from the recall task, reflected extrinsic network. Then the Z score of r-Pearson correlation coefficient was calculated for each participant by CONN toolbox for every possible pair of residual time series (2701 pairs of 74 ROIs of the LMN).
Data analysis 2. 6. 1. LMN segregation • LMN configurations and state reconfiguration Modularity or community detection enables a data-driven partition of a network into modules showing its segregation properties  (Fornito et al. , 2012). Community detection algorithm was performed in healthy participants to analyze LMN state configurations. Data-driven community structure was assessed by applying modularity maximization algorithm (Louvain greedy algorithm,  Blondel et al. , 2008; Rubinov & Sporns, 2010)  to individual correlation matrices with positive, weighted edges, for each state separately. Given that the community partition can vary with each run of the algorithm  (Rubinov & Sporns, 2010) , we applied a consensus approach  (Lancichinetti & Fortunato, 2012; Sporns & Betzel, 2016)  similarly to prior studies  (Dwyer et al.
2  and module affiliation of each region is provided in Table  S4. The proportion of reconfiguration (pr) used to quantify the extent to which the resting-state modules changed compared to task are presented in the Panel A, Subpanel 1 of Fig. 2  and Table  S4  provides details on movement of each region. The most reconfigured modules in healthy participants were the second DMN-DAN fronto-temporo-parietal (pr = 1) and the temporo-limbic module (pr = 0. 526).
Reorganization of LMN configurations and state reconfiguration In TLE patients, the transition between the states was accompanied by a change of the number of modules, as indicated by a significant difference between the modularity indexes Q (ΔQ = 0. 05, p <. 05) and the module partitions were significantly different between two states (VIn = 0. 22, p <. 001). The extrinsic configuration of LMN in TLE patients was found to be more modular and to be comprised of a higher number of modules in comparison to healthy participants (ΔQ = 0. 07, p < 0. 01), while the difference was not significant between intrinsic Fig. 2. Segregation of LMN in terms of community structures found in healthy participants (Panel A) and TLE patients (Panel B) during the two states. The alluvial diagrams present the dynamics of state reconfiguration of modules from resting-state (Subpanel 1) to sentence recall (Subpanel 2) in healthy participants (Panel A) and its reorganization it TLE patients (Panel B).
Contrary to our expectations, the segregation did not vary across intrinsic and extrinsic states in terms of number of modules. However, rest and task differed in modular compositions of LMN configurations in line with the idea that task-related reconfigurations are necessary to adapt to task-demands  (Cohen & D'Esposito, 2016). More specifically, LMN-intrinsic configuration included the following five modules (see Fig. 2 , Panel A, Subpanel 1): fronto-parietal, frontal, temporo-limbic and two fronto-temporo-parietal. The second fronto-temporo-parietal module includes language key regions (left triangularis and orbitalis, left superior and middle temporal, supramarginal gyri) which is in agreement with previous results revealing correlated activity of language systems during rest  (Alavash et al.
, 2019; Muller & Meyer, 2014). The segregation of LMN extrinsic configuration, illustrated in in Fig. 2  (Panel A, Subpanel 2), shows five modules: frontoparietal, frontal, temporo-limbic, fronto-temporo-parietal and temporomesial. There were two main segregation changes in task compared to rest in HC: (a) language key-regions from second fronto-temporoparietal module migrated to other modules; (b) the hippocampus and amygdalae of temporo-limbic module migrated into a separate temporomesial module (Fig. 2 , Panel A). The temporo-mesial module in sentence recall task could be engaged in the episodic retrieval and simultaneous encoding processes, as well as in the binding of retrieved episodes and lexico-semantic information into a coherent experience  (Cooper & Ritchey, 2020; Ranganath & Ritchey, 2012).
, 2020). Globally, the segregation and integration results suggest that LMN state reconfigurations described in HC are mainly based on modular flexibility of connector nodes when facing task requirement. The further objective of this study was to understand LMN configurations and state reconfigurations in patients with TLE. The state reconfiguration of LMN showed increase in segregation and significant changes in modular composition. LMN was composed of four modules in its intrinsic configuration during rest: fronto-parietal, frontal, temporolimbic and temporal lateral (Fig. 2 , Panel B, Subpanel 1) and six in its Fig. 4. Reorganization of LMN state configurations in terms of integration (inter-modular connectivity) expressed via HDI (hub disruption index). Subpanel A shows group differences between healthy participants and TLE patients in terms of HDI for resting-state and sentence recall.
Fronto-temporo-parietal module found during rest includes language key-regions that separate during the task into modules that are engaged in lexico-semantic access, syntactic processing and sentence generation. Panel B shows cognitive processes recruited by the composite language and declarative memory function and the underlying LMN during the task. Within each subpanel we present language and declarative memory subprocesses and the corresponding modules presented in color. Fronto-temporo-parietal and temporo-limbic modules are engaged in lexicosemantic access while temporo-limbic module is also engaged in episodic retrieval (Subpanel 1). Hippocampo-amygdalar module is engaged in episodic retrieval and fronto-parietal module in concept access (Subpanel 2). Through the connection of hippocampo-amygdalar and fronto-parietal modules, the sentence is recalled and the information is transferred to working memory.
Fronto-parietal and frontal modules are engaged in syntactic processing and sentence production (Subpanel 3). Hippocampo-amygdalar module can also access the information from all modules, combine it into a complete experience and perform encoding in parallel (Subpanel 4). to dysfunctions within DMN which generally assures this interface  (Bettus et al. , 2009; Duff & Brown-Schmidt, 2012; Waites et al. , 2006). TLE patients indeed have difficulties in language and memory tasks performance  (Alessio et al. , 2006; Bartha-Doering & Trinka, 2014; Zhao et al. , 2014; Allone et al. , 2017; Bell et al. , 2011; Tramoni-Negre et al. , 2017) , which is probably the reason they need additional resources that are provided by DAN. Additional HDI analyses revealed that TLE patients show disruption of inter-modular integration (Fig.
Funding sources This work has been funded by the  French program  " AAP GEN-ERIQUE 2017 " run by the  "Agence Nationale pour la Recherche " grant ' REORG ' [grant number  ANR-17-CE28-0015-01 ]; and by  NeuroCoG IDEX UGA  in the framework of the " Investissements d'avenir" program  [grant number  ANR-15-IDEX-02 ]. IRMaGe MRI/Neurophysiology facility was partly funded by the  French program  " Investissement d'Avenir " run by the  "Agence Nationale pour la Recherche" ; grant " Infrastructure d'avenir en Biologie Santé " [grant number  ANR-11-INBS-0006 ].
, 2015) , to minimise the likelihood of pre-depositional exposure. However, while these criteria have good qualitative reasoning, many have not been tested quantitatively. In turn, further work is required to test existing criteria for sample selection and to develop quantitative methods which minimise the effects of geologic processes  (Dortch et al. , 2013 (Dortch et al. , , 2021)). These developments have the potential to significantly improve the robustness of TCN datasets and the chronological utility of the moraine record  (Applegate et al. , 2012). Within this context, this paper focuses on a fundamental component of TCN sample selection; the effect of moraine crest sampling on boulder exposure age. Of the above criteria, boulder location is critical, as boulders on moraine crests are prioritised, while those on moraine slopes are typically rejected, irrespective of their individual characteristics.
Calculating SH-calibrated exposure ages As granitic lithologies have proved effective for calibratedrelative age dating, SH R-values are used here as a proxy for exposure age based on a 10 Be-SH calibration dataset developed by  Tomkins et al. (2018b). This dataset comprises 52 10 Be ages, distributed between 4. 2 ± 0. 3 ka and 51. 8 ± 4. 5 ka (Fig. 4 ), obtained from granite and granodiorite glacial boulders and glaciallysculpted bedrock from across the central and eastern Pyrenees and their corresponding SH R-values  (Tomkins et al. , 2018b). This dataset has been updated to include two additional 10 Be dated surfaces from the Val de Molières (MUL01 and MUL03;  Pallàs et al. , 2006 ; see Supplementary Table  1 ). To utilise these data, 10 Be ages were recalculated using the CRONUS Earth Web Calculator (Version 2.
0;  Marrero et al. , 2016 , available at: http://cronus. cosmogenicnuclides. rocks /2. 0/, accessed: 01/09/2020), relative to the production rate dataset in  Borchers et al. (2016)  and the time-dependent Lm scaling scheme  (Lal, 1991; Stone, 2000) , and assuming 0 mm ka -1 erosion. Recalculated 10 Be ages are minimum estimates, as no corrections were made for shielding by snow, sediment or vegetation, surface erosion, or isostatic adjustment. To ensure consistency, all 10 Be and 36 Cl TCN ages discussed in this paper have been recalculated using these input parameters. This includes published ages from  Pallàs et al. (2006) ,  Rodés (2008), and Palacios et al. (2015) , in addition to the 10 new 10 Be dated samples from the Arànser catchment. Full sample details used for exposure age calculation are provided in the Supplementary Information.
, 2018a). To assess the accuracy of the 10 Be-SH calibration curve, 15 10 Be and 36 Cl ages from the studied moraines were located and re-sampled with the SH. Based on this calibration curve, mean R-values from the 635 sampled boulders were converted into "SH-calibrated exposure ages" through interpolation. While uncertainty estimates for individual SH-calibrated exposure ages are larger than typical uncertainties associated with individual TCN exposure ages, landform age estimates can be of comparable precision to established techniques when derived from large SH datasets (e. g. n boulders ≥ 30;  Tomkins et al. , 2018b)  and when appropriate statistical approaches for outlier identification and error propagation are employed  (Applegate et al. , 2012; Dortch et al. , 2013 Dortch et al. , , 2021)).
Calculating landform ages To determine the timing of moraine deposition at each site, we analysed the distribution of SH-calibrated exposure ages using the Probabilistic Cosmogenic Age Analysis Tool (P-CAAT Version 1. 0;  Dortch et al. , 2021). This method builds on the earlier work of  Dortch et al. (2013)  and utilises non-linear curve fitting and a Monte Carlo style approach to isolate component Gaussian distributions to account for positive (prior exposure) and negative skew (incomplete exposure) of age datasets. The results of this analysis are presented in Fig. 5  and Table  2. To assess the validity of these landform ages, we compared these data to the distribution of 10 Be and 36 Cl ages from the studied landforms (Table  1 ;  Pallàs et al. , 2006; Rodés, 2008; Palacios et al. , 2015).
Spatial analysis The spatial distribution of "good" and "bad" boulders was analysed using global and local Moran's I spatial autocorrelation and based on a row-standardised distance-band weights matrix, where the distance band threshold is the minimum distance required to ensure that each boulder has at least two neighbours (Table  3 ). The Python implementation is available on GitHub: https://github. com / matt -tomkins /moraine -crest -or-slope. At the global level, Moran's I was used to assess whether the overall clustering of the data The summed probability density estimate (PDE) and lower probability component Gaussians are denoted by black and grey distributions, respectively. For each moraine, we include the bandwidth estimator used and its associated numeric bandwidth, the P-CAAT model fit (R 2 ), the total number of SH-calibrated exposure ages (n) and in brackets, the number of ages which are enclosed by the selected component Gaussian distribution at 2σ.