dataesr/scientific-paragraphs-categorization huggingface dataset:
In the resting state, decreasing FC was in both groups which also strongly related to aging in both groups. An overview of exploratory ICA, applied to deduce the functional connectivity from the fMRI data, was given by Calhoun et al. [148]. e authors discussed ICA in the spatial or temporal domain related to task and transiently taskrelated paradigms as well as physiology-related signals, the analysis of multisubject fMRI data, the incorporation of a priori information, and the analysis of complex-valued fMRI data. While most studies only take the magnitude of the fMRI signal into account, it was shown that the phase information of complex-valued fMRI has the potential to increase the sensitivity of ICA  [149]. More recently, it was demonstrated that spatial resting-state networks observed in fMRI could also be found in high-temporal-resolution EEG data using ICA  [150].
[130] , who applied probabilistic ICA and discussed the role of this exploratory technique can take in scientific investigations into the spatiotemporal structure of RSNs. e exploratory nature of ICA was also stressed by Rajapakse et al. [156]  in contrast to covariance-based methods such as principal component analysis (PCA) and structural equation modeling (SEM), where SEM is employed to automatically find the connectivity structure among elements in independent components. However, their hybrid ICA/SEM approach was restricted to task-related fMRI paradigms. Meanwhile, numerous studies have been published based on exploratory, data-driven fMRI analyses, which established ICA in its many flavors as a standard technique to analyze fMRI datasets with respect to FC, most notably of RSNs and the DMN.
Several attempts have been considered to resolve this issue  [159] [160] [161] [162] [163]. e most widely used approach is based on the gICA algorithm provided in the GIFT toolbox (http://mialab. mrn. org/software/gift/). A large-scale study  [164] , encompassing 603 healthy adolescents and adults, employed gICA to establish a multivariate analytic approach and applied it to the study of RSNs. e latter were identified and evaluated in terms of three primary outcome measures: time-course spectral power, spatial map intensity, and functional network connectivity. e study considered the impact of age and gender on resting-state connectivity patterns. e results revealed robust effects and suggested that the established analysis pipeline could form a useful baseline for investigations of human brain networks.
ey applied these methods to two large public data repositories (Multimodal MRI Reproducibility Resource and Human Connectome Project) and assessed two categories of dFC summary measures, namely, basic summary statistics, such as mean and variance of dFC across time, and summary measures derived from brain states, such as the dwell time. ough DCC methods outperformed SWCs with respect to summary statistics, the reliability of brain state-derived measures was low. Especially, DCC-derived dFC variances were significantly more reliable than those following from nonparametric estimation methods. ese findings show that dFC variance should form an important ingredient to any dFCderived summary measure. With a similar interest, ompson et al. [190]  studied time-varying connectivity (TVC) and developed a Python package, called tvc benchmarker (https://github.
Early work on connectivity analysis based on EEG and MEG techniques is mainly concerned with alternative ways to detect interdependencies between activity time series. Sato et al. [191]  proposed partial directed coherence (PDC)  [192]  as a frequency-domain alternative to GC-based connectivity analysis of multisubject fMRI data. e authors employed multisubject bootstrapping and decomposed EEG-deduced connectivity data in the frequency domain, thus separating out artifact signals such as scanner noise, breathing mode, and heartbeat. If GC is estimated via vector autoregressive models (VARs), numerous parameters need to be estimated, which encompasses low accuracies with small datasets. Siggiridou and Kugiumtzis  [193]  proposed a restricted VAR model, which combined a modified backward-in-time selection (BTS) of lagged variables with a conditional Granger causality index (CGCI).
Multivariate vector autoregressive (MVAR) models are often used to estimate brain connectivity from EEG signal recordings. MVAR models have first been implemented by Antonacci et al. [195]  on ANNs. e authors showed that stochastic gradient descent with L1-regularization, if applied during learning directly on the estimated weights, can efficiently cope with small datasets as well as regressor collinearity and provide accurate brain connectivity estimates. Subsequently, Antonacci et al. [196]  extended their studies of network information processing with small available datasets acquired to investigate network physiology problems. ey proposed a state-space (SS) variant of a VAR model regularized by the LASSO constraint and applied it to the analysis of the physiological network of brain and peripheral interactions probed in humans under different conditions of rest and mental stress.
eir study corroborates the possibility to extract physiologically plausible patterns of interaction between the cardiovascular, respiratory, and brain wave amplitudes. Last but not least, Antonacci et al. [197]  focused on the estimation of Granger causality (GC) in adverse conditions of small datasets or a very high number of time series. e authors showed that it is still possible to estimate GC in linear interaction time series and to reconstruct the underlying network structure if VAR models are combined with statespace models and partial conditioning on a subset of most informative variables  [198]. e study of Nobukawa et al. [199]  dealt with related electroencephalogram (EEG) data to analyze the continuously captured time-varying instantaneous phase synchronization between resting-state EEG potentials from different brain regions.
e study focused on a rs-fMRI dataset which encompasses posttraumatic stress disorder patients and normal controls. e study revealed that the brain only switches between a set of few brain states. Furthermore, two HMMs, one for patients and one for healthy controls, were constructed, and classification could be performed by examining which of the two HMMs can better describe the observed connectome state sequence. Rather than relying on HMMs alone, Taghia et al. [204]  developed a Bayesian generative model within the framework of HMMs resulting in a dynamic variant of the static factor analysis model  [205, 206]. In their study, Bayesian switching factor analysis (BSFA) learns latent states, representing dFC networks, and their temporal evolution, as well as transition probabilities between these states.
As neuronal populations coordinate their activities on specific time scales through phase coherence, Vidaurre et al. [209]  employed HMMs to analyze magnetoencephalogram (MEG) data with respect to characterizing the dynamics in large-scale phase-coupled networks, which show coherent activity patterns. ey could show that RSNs can be represented on short time scales by transient brain states, which are characterized by short-lived spatial patterns of phase coherence and oscillator strengths that in part resemble DMNs. us, functional specialization in the brain may transiently occur at various intrinsic time scales through large-scale phase coupling mechanisms. In a subsequent study, Vidaurre et al. [210]  extended their HMM to the big data scenario. e amended HMM was able to infer robust and interpretable dFC across a set of data encompassing task-based and resting-state paradigms, recorded via either MEG or fMRI of thousands of subjects.
Such tools are essential to make progress while taking advantage of huge Such models are based on the idea that observable events x t are in a causal relation with some underlying hidden events h t. h t h t+1 h t+2 x t x t+1 x t+2 data repositories provided by the Human Connectome Project or the UK Biobank initiative. Although time-varying connectivity studies explored the nonstationary nature of the dynamical switching between discrete brain states, recent investigations in the field cast doubts on the nonstationarity assumption. Rather, these studies suggested that dFC might be attributed to sampling variability of static FC. is controversy led Liégeois et al. [211]  to reanalyze the stationarity and statistical testing of dFC. ey pointed out the relation of stationarity to ensemble statistics, while all FC measures in use are related to sample statistics.
Hence, nonlinear and/or non-Gaussian models may provide a better explanation of the dataset under study. e authors corroborate this in their study where 1st-order autoregressive (AR) models explained their data significantly better than static FC models. ey show that such models better replicate empirically observed dynamic connectivity patterns in rs-fMRI data, which, in their opinion, could indicate a lack of discrete brain states. On the contrary, the HMM can capture aspects of the data that the AR model cannot capture, for example, that the transitions between networks organize hierarchically  [212] , or how the visits to these networks relate to the sleep cycle  [213]. erefore, AR and HMM models can reveal complementary aspects of brain dynamics. Issues related to statistical testing of dFC models have also been discussed by  Khambhati et al.
Contreras et al. [227]  combined rs-fMRI with brain connectomics to characterize changes in whole-brain FC. Individual FC matrices were concatenated into a group FC matrix. e latter was decomposed with FastICA into ICs. Each of the resulting independent FC patterns was considered a response in a multilinear regression model including extraneous variables. Various ICs resulting from this connectivity independent component analysis (connICA), including RSN and DMN, were then further analyzed with respect to Alzheimer's disease. Recently, Amico and Goñi  [73]  proposed an extension to the connICA by decomposing hybridized structural and functional connectivity patterns. e independent, joint structural-functional patterns, extracted across a cohort of 100 datasets from the Human Connectome Project, represented two task-sensitive features, each encompassing connections within and between well-defined cortical areas.
e integrated patterns can be considered connectivity fingerprints of a subject, deduced in a purely data-driven way. Hence, despite considerable efforts to characterize dFC configurations deduced from rs-fMRI, the dynamics governing state transitions and their relationship to sFC still remains an open problem. Furthermore, the hypothesized latent brain states are yet to be related to the underlying SC. Surampudi et al. [76]  recently proposed a graph-theoretic model that parameterized the low-dimensional manifold, which represents the temporal structure of functional connectivity patterns, by a set of local density distributions and learned the parameters from data via a temporal multiple-kernel learning (tMKL) strategy. e latter directly links dynamics to the underlying structure via a statetransition Markov model (see Figure  8 ).
Finally, the model predicts the grand average FC across a group of subjects but retains sensitivity towards subject-specific anatomy. e model was tested using the rs-fMRI data of 46 healthy participants, and its generalizability was proven through a test on a cohort of 100 subjects from the Human Connectome Project. e authors claimed that their tMKL model performs considerably better than a whole-brain dynamic mean-field (DMF) model  [84] , a single diffusion kernel (SDK) model  [48] , or a multiple-kernel learning (MKL) model  [228].
In future, additional information from faster modalities such as EEG and MEG will be crucial in order to overcome these limitations of MRI and help to further close the gap between the brain structure and its function  [16, 57]. For example, simultaneous EEG-fMRI techniques gain already more and more attention in multimodal studies and clinical assessments  [232, 233]. Such data fusion approaches could also contribute valuable insights into the structure-function relation because they would allow us to observe the propagation of neural signals onto the anatomical substrate at considerably higher temporal resolution than in fMRI alone. erefore, incorporating multimodal imaging techniques will likely be a key aspect in our efforts to obtain a more comprehensive picture of neural connectivity in the human brain.
B. 2. Independent Component Decomposition. Given a centered data matrix X ∈ R N×M , N ≥ M, with zero mean, its independent component decomposition reads X ≈ AS, (B. 4) whereby rows 􏽥 s l of component matrix S shall be as statistically independent as possible. Note that a perfect factorization might not be possible always. With fMRI, ICA comes in two flavors, spatial (sICA) and temporal (tICA) independent component analysis. With sICA, s S represents independent spatial maps, while with tICA, t S represents independent voxel time series. A spatiotemporal blind signal decomposition tries to decompose the data matrix into a product of two independent factor matrices  [139].
Here we present the first database dedicated to type I TA systems, named T1TAdb. It is an open-access web database (https://d-lab. arna. cnrs. fr/ t1tadb) with a collection of ∼1900 loci in ∼500 bacterial strains in which a toxin-coding sequence has been previously identified. RNA molecules were annotated with a bioinformatic procedure based on key determinants of the mRNA structure and the genetic organization of the T1TA loci. Besides RNA and protein secondary structure predictions, T1TAdb also identifies promoter, ribosome-binding, and mRNA-asRNA interaction sites. It also includes tools for comparative analysis, such as sequence similarity search and computation of structural multiple alignments, which are annotated with covariation information. To our knowledge, T1TAdb represents the largest collection of features, sequences, and structural annotations on this class of genetic modules.
INTRODUCTION Toxin-antitoxin (TA) systems are encoded within small genetic loci found in most bacterial genomes including those of pathogens. They are usually composed of two adjacent genes: a stable toxin and a labile antitoxin that inhibits the toxin's action or expression and whose depletion rapidly leads to cell death or growth arrest  (Harms et al. 2018). Six types of TA systems have been described so far depending on the nature and mode of action of the antitoxin (for review, see  Page and Peti 2016) , and a seventh type has been recently proposed  (Wang et al. 2021). While the toxin is always a protein, the antitoxin can be either a protein (types II, IV, V, VI, and VII) or an RNA (types I and III). Type II TA systems are by far the most well studied class of TA systems. Extensive data for these systems are available in two databases, TADB 2.
0  (Xie et al. 2018)  and TASmania  (Akarsu et al. 2019) , which also include limited data for other types of TA systems. These databases also provide a tool (TAfinder and TASer, respectively) to scan and predict TA systems  (Xie et al. 2018; Akarsu et al. 2019). Other TA-specific resources include BtoxDB  (Barbosa et al. 2015) , a database of TA protein structural data, and RASTA-Bacteria  (Sevin and Barloy-Hubler 2007) , a tool to scan for toxins and antitoxins in bacterial genomes (unfortunately, both are no longer maintained). Overall, none of these existing databases contains expanded data about type I TA (T1TA) systems. The RNA families database Rfam  (Kalvari et al. 2021)  currently contains more than 600 antitoxin RNA entries that belong to 13 known T1TA families. Rfam is a useful resource that provides, for all families, sequences, alignments, structures, covariance models (that can be used to search genomes), phylogenetic trees, and links to Wikipedia articles.
RESULTS AND DISCUSSION We have developed T1TAdb, the first database dedicated to T1TA systems. It can be accessed by users through a graphical web interface. A preliminary, development version was put online in January 2019 and has been regularly updated, corrected, and improved (https://d-lab. arna. cnrs. fr/t1tadb). The database provides sequence, secondary structure, and genomic information on T1TA loci. In its current, initial version, it is limited to bacterial genomes that were reported in the literature to carry T1TA systems. It contains 1894 loci belonging to 24 families from 218 bacterial species and 493 strains. Data on toxin mRNA, antitoxin asRNA, and toxin peptides were taken from the current literature. Only a small number of loci have been experimentally characterized and the bulk of the data in T1TAdb are mainly based on the results of genome-wide bioinformatic studies.
This includes the AapA/IsoA family that has been extensively curated in about 100 genomes of Helicobacter and Campylobacter  (Arnion et al. 2017)  and the large number of loci found in hundreds of genomes in the study of  Fozo et al. (2010). The analyses by  Fozo et al. (2010)  did not investigate the mRNA genes and recovered only partially the asRNA genes. Therefore, in our work, a major effort to build the T1TAdb database has been devoted to the annotation of the genomic locations of the toxin mRNA and antitoxin asRNA corresponding to each toxin ORF of known family reported by  Fozo et al. (2010) , in order to reconstruct the complete loci.
Overview of T1TAdb features and tools Information in T1TAdb can be searched by entering one or several keywords via the "Keyword Search" tab. A given keyword may be matched against any field in the database or against a specific field such as toxin or antitoxin name, species, strain, taxonomy, locus ID, or genome accession number. Submitting the search form will return a table listing all loci that match the search criteria. The "Select loci" button in the upper left corner of the table allows to further refine the selection of loci by replicon (chromosome or plasmid), TA family or taxonomy, and to select/deselect all loci. The table indicates the locus identifier (TAnnnnn), TA family, host strain and taxonomy, and reference publication of each locus. For loci that were predicted using structural descriptors of a TA family different from that reported in the literature (i.
2011] ; https://github. com/igvteam/igv. js/wiki) for viewing the genomic context around the locus. The "mRNA" and "sRNA" panels provide the sequence and secondary structure diagram of the toxin mRNA and antitoxin asRNA, respectively. The location of relevant motifs (promoter -10 box, start/stop codon, SD sequence, leader ORF, mRNA-asRNA interaction region) are annotated on the sequences and/or diagrams. The "Peptide" panel shows the sequence, secondary structure model and hydrophobicity plot of the toxin peptide. The "mRNA," "sRNA," and "Peptide" panels include a "BLAST Sequence" link for launching a sequence homology search (using BLAST+  [Camacho et al. 2009 ]) of the selected query against every mRNA, asRNA, ORF, peptide, or genome sequence in T1TAdb. The database also allows the user to input or upload one or several query sequences to perform a BLAST search via the "Sequence Search" tab.
The T1TAdb home page contains links to a full list of organisms ("view organism list"; "T1TAdb Organisms" page) and TA families ("view family list"; "T1TAdb Families" page) included in the database. Selecting a particular organism name or family name will return a table listing all loci that belong to that organism or family. The family table on the "T1TAdb Families" page provides links to the corresponding Rfam entries for antitoxin RNA families that are included in Rfam. In addition, the table is organized to indicate groups of T1TA families that share some degree of homology (in sequence, structure, and/or genetic organization). A notable implementation of T1TAdb is that, in addition to homology searches by BLAST, users can perform multiple sequence alignments (using MAFFT  [Katoh and Standley 2013] ) to compare mRNA, asRNA, or peptide sequences.
Example of insights gained from the use of T1TAdb We present below an example illustrating the usefulness and added value provided by T1TAdb. In particular, we would like to highlight the type of sequence and structure analysis that can be done with the database. Examination of the structural diagrams of individual Sok antitoxin asRNAs from the Hok/Sok family suggested that there may be different structural types. This hypothesis could be tested in T1TAdb by performing structural alignments of different subsets of Sok RNAs which revealed two subgroups: one subgroup had a 2D structure conforming to that reported by  Franch et al. (1997)  (see also Fig. 3A ), while the other carries an additional stem-loop element at the 5 ′ end (Fig. 3B ). The covariation analysis that is run on the alignments in T1TAdb confirmed that this extra element is well supported by covarying positions that reveal compensatory substitutions in a number of sequences.
Conclusions and perspectives In this work, we have built the first web database totally dedicated to T1TA systems, named T1TAdb. T1TA systems are widespread in bacteria, including pathogenic species, and are studied for numerous aspects including RNA and protein structure and function, regulation of gene expression and cell physiology. In addition to giving access to the collection of loci that have been reported in the literature, the database brings an added value by the annotation of toxin mRNAs and antitoxin asRNAs of loci that were predicted solely by analysis of the toxin peptide sequence, and by providing tools to compare their sequences and structures. This information, together with a wealth of sequence, structure, and genomic data provided on T1TA systems, along with the other databases and tools dedicated to TA systems (such as TADB and TASmania), will certainly serve the scientific community to gain deeper insights in the distribution, evolution, structure, and function of TA systems.
This could reveal to be particularly important for the TA systems located in bacterial chromosomes, whose function remains largely unknown. We also believe that the knowledge contained in T1TAdb, and more specifically the RNA structures and loci organization, is a strong asset to help design new tools for identifying new T1TA systems. In future developments of T1TAdb, we plan to expand the collection of T1TA systems by predicting TA loci in all bacterial genomes available. In addition, the procedure described in this work for the annotation of mRNAs and asRNAs of T1TA systems, which is based on structural features, can be turned into an automatic de novo prediction tool, most likely with additional steps to control the false-positive rate. The RNA data in T1TAdb may also be used to generate alignments and covariance models in order to search genome databases for conserved RNAs, as exemplified in the Rfam database  (Kalvari et al.
2021). We also anticipate to incorporate transcriptome (RNA-seq) data into T1TAdb. As the number of sequenced bacterial transcriptomes is increasing, such information would be valuable to check whether TA loci are expressed and to verify the genomic coordinates of the predicted RNAs. Further improvements to T1TAdb will also include tools for simultaneously visualizing and comparing the genomic context (gene neighborhoods) across multiple strains or for the reconstruction of phylogenetic trees, and will allow users to submit data for inclusion in T1TAdb.