dataesr/scientific-paragraphs-categorization huggingface dataset:
MEG data acquisition MEG was acquired using a 306-sensor TRIUX MEGIN system, comprising 204 planar gradiometers and 102 magnetometers in a helmet-shaped array. The MEG gantry was positioned at 68 degrees for optimal coverage of frontal and posterior brain areas. Simultaneous EEG was recorded using an integrated EEG system and a 64-channel electrode cap (EEG data is not reported here, but is included in the shared dataset). During acquisition, MEG and EEG data were bandpass filtered (0. 01 and 330 Hz) and sampled at 1000 Hz. The location of the head fiducials, the shape of the head, the positions of the 64 EEG electrodes and the head position indicator (HPI) coil locations relative to anatomical landmarks were collected with a 3-D digitizer system (Polhemus Isotrack). ECG was recorded with a set of bipolar electrodes placed on the subject's chest.
MEG preprocessing The MEG data were converted to BIDS 17 using MNE-BIDS 18 , and preprocessed following the FLUX Pipeline 19 in MNE-Python v0. 24. 0 6. Preprocessing steps included MEG sensor reconstruction using a semi-automatic detection algorithm and Signal-Space Separation (SSS) 20 to reduce environmental artifacts. FastICA 21 was used to detect and remove cardiac and ocular components from the data for each subject (M=2. 90 components, SD=0. 92). Prior to ICA, data were segmented, and segments containing muscle artifacts were removed. After preprocessing, data were epoched into a 3. 5 s segment (1 s pre-stimulus to 2. 5 s post-stimulus onset). Trials where gradiometers values exceeded 5000 fT/cm, magnetometers exceeded 5000 fT, and/or contained muscle artifacts were rejected from the MEG dataset.
fMRI Preprocessing Source DICOM data were converted to BIDS using BIDScoin v3. 6. 3 26. This includes converting DICOM data to NIfTI using dcm2niix 27 and creating event files using custom Python codes. BIDS compliance of the resulting dataset was controlled using BIDS-Validator. Subsequently, MRI data quality control was performed using MRIQC 28 and custom scripts for data rejection. All (f)MRI data were preprocessed using fMRIPrep 20. 2. 3 29 , based on Nipype 1. 6. 1 30. For further details on the fMRIprep pipeline, see preregistration.
The reconstructed source-level MEG data within a subset of the predefined anatomical ROIs (GNWT: 'G_and_S_cingul-Ant','G_and_S_cingul-Mid-Ant', 'G_and_S_cingul-Mid-Post', 'G_front_middle','S_front_inf', 'S_front_sup' , IIT: 'G_cuneus', 'G_oc-temp_lat-fusifor', 'G_oc-temp_med-Lingual','Pole_occipital', 'S_calcarine','S_oc_sup_and_transversal', as they show high response to the stimulus on the optimization dataset) were extracted for further analysis (500 vertices and 800 vertices per hemisphere for each of the anatomical ROI defined by the theories). We applied temporal smoothing (0. 05 s window, 0. 01 sliding window), computed pseudotrials 42 , normalized the data, and selected the top 30 features within a given ROI as features for the different classifiers. A group-level one-sample t-test per time point was performed on the decoding accuracy results, corrected for multiple comparisons using a cluster-based correction 41.
Training and testing of the individual models followed all previously described cross-validation procedures and model comparison was performed using a variancecorrected paired t-test  46  and complemented with Bayesian analysis. Following Benavoli and colleagues 47 , the prior distribution of the mean difference in decoding scores between two classifier models was modeled as a Normal-gamma distribution conjugate to a normal likelihood, and the posterior distribution was obtained as a normal distribution. This posterior distribution was utilized to calculate the probability of one classification model being better than, worse than, or equivalent to the other model. As this estimation approach is applied using resampled datasets (e. g. , using 5-fold cross-validation), the performance of the model becomes dependent on the folds, and thus a variance corrected t-distribution was used 46.
The GED was then performed on the two covariance matrices, resulting in N (= rank of the data) pairs of eigenvectors and eigenvalues. The eigenvector associated with the highest eigenvalue was selected as a GED spatial filter, which in turn was applied to the data to compute the singletrial GED component time series. A GED spatial filter was extracted also for the PFC ROI, on parcels from the Destrieux atlas 12 , to identify the distributed pattern of sources that are responsive to visually-presented stimuli. Specifically, a spatial filter was built by contrasting source-level frontal slow-frequency activity (30-Hz low-pass filter) after stimulus onset (0 to 0. 5 s) against baseline (-0. 5 to 0 s). V1/V2 areas were identified using the Wang Atlas  13  and a singular values-decomposition approach.
Acknowledgements Special thanks to  Dawid Potgieter  for spearheading the  ARC program ; to  Daniel Kahneman  for guidance to navigate adversarial collaborations; to  Heather Berlin ,  William Jaworski ,  Hakwan Lau  and  Cyriel Pennartz  for insightful discussions during the two-day meeting organized by the  Templeton World Charity Foundation  at the  Allen Institute, Seattle  in March 2018; to  Hakwan Lau  for helping conceptualize the proposed experiments; to  Orrin Devinsky ,  Werner Doyle ,  Patricia Dugan  and  Daniel Friedman  for supporting the recruitment and patient care at  NYU ; to  Essa Yacoub ,  Michael Kahana ,  Peter Zeidman ,  Karl Friston ,  Jean-Remi King ,  Michael Cohen ,  Fosca Al Roumi ,  Shlomit Yuval-Greenberg  and  Dejan Draschkow  for guidance on diverse data analysis; to  Caspar Schwiedrzik  for insightful discussions and feedback throughout the different phases of this study (conceptualization, data analysis and initial draft); to  Sarah Brendecke  and  Felix Bernoully  for help with figures and stimulus materials; to  Monique Smulders  and  Sarah Kusch  for help with fMRI data acquisition; to the patients and their families for generously supporting this study.
Participants Healthy volunteers and patients with pharmaco-resistant focal epilepsy participated in this study. The datasets reported here consist of: (1) Behaviour, eye tracking and invasive electroencephalogram (iEEG) data collected at the Comprehensive Epilepsy Center at New York University (NYU) Langone Health, Brigham and Women's Hospital, Boston Children's Hospital (Harvard), and University of Wisconsin School of Medicine and Public Health (WU). (2) Behaviour, eye tracking, magnetoencephalographic (MEG) data collected at the Centre for Human Brain Health (CHBH) of the University of Birmingham (UB), and at the Center for MRI Research of Peking University (PKU). (  3 ) Behaviour, eye tracking and functional magnetic resonance (fMRI) data collected at Yale Magnetic Resonance Research Center (MRRC) and at the Donders Centre for Cognitive Neuroimaging (DCCN), of Radboud University Nijmegen.
For both the MEG and fMRI datasets, a 1/3 of the data that passed quality tests (henceforth, Optimization dataset; see preregistration for details about quality test criteria) were used to optimize the analysis methods, which were subsequently added to the preregistration as an additional amendment. These preregistered analyses were then run on the remaining 2/3 of the data (henceforth, Replication dataset) and constitute the data reported in the main study. For comparison, results from the optimization phase are reported in the supplementary material. This procedure was not used for the iEEG data due to the serendipitous nature of the recording and electrode placement, the rarity of this type of data and the increased difficulty of data collection due to the COVID-19 pandemic. For the iEEG arm of the project, a total of 34 patients were recruited.
Two patients were excluded due to incomplete data. Demographic, medical and neuropsychological scores for each patient, when available, are reported in Supplementary Table  25. Three iEEG patients whose behavior fell slightly short of the predefined behavioral criteria (i. e. hits < 70%, FA > 30%) were nonetheless included given the difficulty to obtain additional iEEG data. A total of 97 healthy subjects were included in the MEG sample (mean age 22. 79 ± 3. 59 years, 54 females, all right-handed), 32 of those datasets were included in the optimization phase (mean age 22. 50 ± 3. 43 years, 19 females, all right-handed), and 65 in the replication sample (mean age = 22. 93 ± 3. 66, 35 females, all right-handed). Five additional subjects were excluded from.
The stars and arrows on the brain (left) depict the different predictions about the expected synchrony patterns (green: GNWT; blue: IIT). b. The scientific framework under which the Cogitate Consortium (Collaboration On GNW and IIT: Testing Alternative Theories of Experience), tested IIT and GNWT included: adversarial collaboration, (theory-neutral) team science, invasive and non-invasive multimodal data (iEEG, fMRI, MEG), large samples (>250 subjects), standardized protocols across multiple laboratories, built-in preregistered replication, and open methods, data and code. c. Conscious experience is multifaceted in content. Looking at the image of Mona Lisa by Leonardo da Vinci underscores the fact that conscious experiences are rich:The painting is experienced as occupying a location in space, pertaining to a given category (i.
Physical interactions between proteins are central to all biological processes. Yet, the current knowledge of who interacts with whom in the cell and in what manner relies on partial, noisy, and highly heterogeneous data. Thus, there is a need for methods comprehensively describing and organizing such data. LEVELNET is a versatile and interactive tool for visualizing, exploring, and comparing protein-protein interaction (PPI) networks inferred from different types of evidence. LEVELNET helps to break down the complexity of PPI networks by representing them as multi-layered graphs and by facilitating the direct comparison of their subnetworks toward biological interpretation. It focuses primarily on the protein chains whose 3D structures are available in the Protein Data Bank. We showcase some potential applications, such as investigating the structural evidence supporting PPIs associated to specific biological processes, assessing the co-localization of interaction partners, comparing the PPI networks obtained through computational experiments versus homology transfer, and creating PPI benchmarks with desired properties.
© 2023 The Authors. Proteomics published by Wiley-VCH GmbH. of protein sequential, structural, and functional information  [4, 15, 20, 50, 52, 53]  and of experimental evidence for PPIs  [21, [33] [34] [35] 38]  has stimulated the development of databases and web-interfaces for curating, inferring, and browsing PPI networks  [7, 10, 14, 17, 22, 23, 26, 30, 31, 47]. These resources compile experimental and computational data and provide user-interactive visualization. A handful of them build upon the 3D structural evidence contained in the Protein Data Bank (PDB)  [5] , for example, Interactome3D  [30] , PPI3D  [10] , and 3did  [31]. Moreover, to cope with the biases, noise and uncertainty inherent to PPI-related data  [16, 22] , a lot of effort has been devoted to improve the reliability and interpretability of the inferred PPI networks, by integrating other types of information (e.
Significance Statement Almost all biological processes depend on physical interactions between proteins. Yet, the current partial, noisy, and highly heterogeneous data associated with these interactions lead to incomplete and fragmented PPI networks. Computational approaches for investigating PPIs are fundamental to obtaining accurate assessments of the protein interaction map in the cell. LEVELNET is a versatile and interactive tool that helps experimental and computational biologists to achieve this goal. It provides a user-friendly web interface designed to visualize, explore and compare PPI networks deduced from different data sources. Using a user-controlled graph grid allows for analysing the transfer of interactions from one species to another and visualizing topology changes in the network.
Overview of LEVELNET LEVELNET offers three types of analysis. The first two types operate in an interactive mode, allowing the user to interactively explore and compare networks of interactions defined either based on a certain neighbouring depth around a single input protein chain (Figure  S1 ) or among a set of input protein chains. The users can designate the input chain(s) with their PDB code(s) or their Uniprot identifier(s). When several chains are given as input, the users may optionally specify some relationships between them with a real-valued pairwise matrix (Figure  1A ). In the third type of analysis, LEVELNET discovers the interacting patches on the surface of the input protein and returns the results for offline investigation.
Source databases PDB (July 2022 release) entries were downloaded from the FTP archive https://rsync. wwpdb. org/ftp/data/biounit. Entries with more than 100 chains or with a resolution lower than 5 Å were discarded. Protein chains smaller than 20 residues or with more than 20% of unknown residues were also discarded. To identify sets of homologous protein chains, we directly exploited the clusters computed by MMSeqs2  [44]  and available from the PDB https://www. rcsb. org/docs/ programmatic-access/file-download-services#sequence-clustersdata. The HIPPIE database (v2. 2) was downloaded from http://cbdm-01. zdv. uni-mainz. de/ ∼ mschaefer/hippie/download. php and Negatome database (v2. 0) from http://mips. helmholtz-muenchen. de/proj/ppi/ negatome/.
Database of PPI networks Here, we describe how we pre-computed the PDB-based grid of PPI networks. We assume  and  are two clusters of protein chains sharing a certain percentage of sequence identity, containing chains P, P ′ and Q, Q ′ respectively (Figure  1H ). We considered that chains P from cluster  and Q from cluster  were in physical contact (observed interaction, type 1, blue edge) if we could find more than M = 5 pairs of residues (r, s), where r belongs to P and s belongs to Q, located less than D = 5Å away from one another. More formally, ∃P ∈ ∃Q ∈ . P ≠ Q ∧ |{(r, s)|r ∈ P, s ∈ Q, d(r, s) < D}| > M where d(r, s) is the minimum distance between any atom of residue r to any atom belonging to residue s. D is the distance threshold set to 5 Å by default, M is the minimum number of interface residues set to 5 for both proteins by default.
Implementation details To generate the PDB interaction database, we developed a pipeline in Python that processes PDB protein complexes, their interfaces, and chain clusters by sequence identity. We created an interactive environment based on recent advances in web development including HTML5 and related technologies such as D3, JavaScript, Vue, and SVG. HTML5, CSS3 and Vue are used for the front-end and provide a stylish and userfriendly interactive interface. Data visualization is performed using D3  [6]  and SVG. Codes developed in JavaScript and Python process databases and user queries. The whole pipeline is optimized to respond to queries rapidly. LEVELNET does not require any plugins; and is compatible with all web browsers. It is accessible on smartphones and tablets to facilitate the collaboration between researchers.
Benchmark databases used in the applications We used three benchmark databases to showcase the applications of LEVELNET. These databases are the Docking Benchmark ZDock version 5. 5 (ZDockv5)  [54]  and version 2. 0 (ZDockv2)  [29]  and Dock-Ground (DG4)  [24]. We used all the single-chain proteins of these databases. For ZDockv2, we chose a subset of 88 single-chain proteins (out of 168 proteins) for our evaluation purposes (see Results) and called it ZDockv2_s88.
Localizing PPIs in the Wnt signalling pathway Next, we focused on the highly evolutionarily conserved canonical Wnt signalling pathway that regulates gene transcription by passing signals from the cell surface receptors to the nucleus  [1]. We used LEV-ELNET to visualize the structurally determined PPIs involved in this pathway in the context of their subcellular localisation. As input, we considered the list of 85 PDB entries reported in ref. [1]. We defined a custom adjacency matrix where we put a value of 1 for each pair of protein chains sharing the same cellular location, and gave it to LEVELNET as input, along with the chains. LEVELNET built a network comprising 235 nodes linked by 2734 edges by exploiting information coming from the PDB and considering homology transfer at >30% sequence identity.
Comparing predicted versus homology-transferred PPIs We used LEVELNET to compare the PPIs predicted by a complete cross-docking experiment  [27]  with the structurally characterized PPIs available in the PDB (Figure  4  and S2 ). We considered an ensemble of about 4000 putative protein pairs coming from the ZDockv2_s88 F I G U R 3 Network inferred for the canonical Wnt signalling pathway. The input PDB codes were taken from  [1] , and we show the layer corresponding to the interactions inferred from the PDB at 30% sequence identity. LEVELNET determined the spatial arrangement of the nodes based on a user-defined input adjacency matrix where any pair of chains sharing the same cellular localization according to ref. [1]  are adjacent. The groups corresponding to the different protein cellular locations and roles are labelled.
Interacting patches on the glucocorticoid ligand-binding domain We applied the offline analysis of all interacting surfaces exhibited by a set of protein chains to the human glucocorticoid receptor ligandbinding domain (Figure  5 ). Starting from the input query chain 1NHZ:A, LEVELNET retrieved 61 interacting patches at > 90% sequence identity. One patch was observed for the input chain (from the 1NZH PDB entry). It comprises 33 residues and corresponds to a cyclic C2 homodimer (Figure  5A ). 60 patches were inferred by homology transfer, and 57 of them correspond to chains that are in fact identical to the query (same Uniprot Id P04150). These patches, totalling 130 residues, unveil a wide diversity of alternative self-assemblies (Figure  5B ). This result is in line with our previous work emphasizing the importance of accounting for the multiple usage of a protein's surface residues by several partners and for the variability of protein interfaces coming from molecular flexibility when assessing protein interface predictors  [12].
F I G U R E 5 Interacting surfaces of the glucocorticoid receptor ligand-binding domain. The query protein chain (PDB id: 1NHZ:A) is displayed as a grey cartoon. (A) Homodimer corresponding to the entry 1NHZ (second biological assembly). The interacting surface is highlighted in surface and the second copy of the protein is in marine cartoons. (B) Different binding modes for the protein self-assembly. The highlighted surface covers the residues engaged in at least one interaction involving the query chain or a homolog at >90% sequence identity in the PDB. The blue, magenta and green cartoons represent interacting copies of the protein in the PDB complexes 1NHZ, 3E7C, and 4LSJ, respectively.
CONCLUSION LEVELNET is a valuable asset for the community to explore protein interactions. It is useful for the biologists interested in the physical contacts of a particular protein or a set of proteins as well as for those who develop and assess computational predictive approaches for interface, partner and complex predictions. It provides a convenient mean to account for different types of relationships between proteins, for example, functional annotations, cell co-localization, spatiotemporal proteomics, or co-occurrences in publications, and investigate how the latter are correlated to physical interactions. We performed all the analyses reported here based on the June 2020 release of the PDB. Nevertheless, we have been updating LEVELNET based on more recent releases, which include 3D models predicted by AlphaFold  [20] , and will continue to maintain the pre-computed databases up-to-date.
Future developments will concern the integration of other databases, be they computational or experimental. In particular, we will upgrade LEVELNET with information coming from reliable predictions of protein complexes using AlphaFold, RosettaFold  [3]  or methods inspired by the latter. We expect the body of these predictions to massively increase in the coming years. Another direction will be to allow for merging and selecting nodes based on annotations from Uniprot  [51] , SCOP  [8, 13]  or CATH  [42]  -LEVELNET already displays node-specific information of this type. Future versions of LEVELNET will investigate different ways of defining protein entities, for instance based on Uniprot segments and annotations coming from PDBe-KB  [37]. Directions of improvement include a more refined homology-transfer procedure accounting for the differences in the plausibility of the inferred interactions depending on the protein functional classes.
offers the possibility to compare this information with user-defined annotations that can represent predicted interactions, co-localization or any other type of relationship. It permits to dynamically visualize the evolution of a PPI network upon changing the edge confidence level. Compared to Interactome3D, LEVELNET has the advantage of providing PPI confidence scores and to handle homology transferred interactions. Compared to PPI3D, it has the advantage of featuring an interactive visualization of PPI networks. Moreover, it processes user queries very rapidly by pre-building the interaction and physical contact databases. PPI3D has a longer response time due to PSI-BLAST queries performed for inferring interactions by homology.
Method for the numerical continuation of periodic solutions Contrary to previously derived formulations  [16, 22] , the Galerkin approach presented here allows us to discretize the continuous 1-D problem into a set of nonlinear time-dependant equations with constant spatial operators. This makes it compatible for use in algorithms for the continuation of periodic solutions. As opposed to time-domain simulations, bifurcation analysis can provide a much broader view of the emerging limit-cycle oscillations (LCO), and how they vary as a function of specific control parameters. In this work we have used the open-source software Manlab 4. 0  [35]  which combines the Harmonic Balance Method (HBM) for the time-discretization of the dynamical system with the Asymptotic Numerical Method (ANM) for the numerical continuation of the solution path.
Survey Instrument The data collection process of the same using a questionnaire-based survey, employing a 5-point likert scale was carried out on purchase managers  (Choi & Hartley, 1996) , of different food manufacturers in India as they receive the raw materials from one or more suppliers which are subsequently processed into finished goods. The data was collected across a span of 8 months from February 2022 to September 2022 across all 28 states and 8 union territories of India through online and offline approaches. The responses were recorded in both online and offline modes through soft and hard copies of the same questionnaire by physically approaching the respondents for offline and approaching the respondents through e-mail and their respective social media handles for online modes.