dataesr/scientific-paragraphs-categorization huggingface dataset:
ASPRING unveils evolutionary ancient patterns of repeat modulation The vast majority of the s-exons in the detected s-repeats are highly conserved, as measured by the species fraction that is the proportion of species where a s-exon is found (Fig. S5A ). Regarding the AS events, mutually exclusive and alternative events are enriched within ASRUs compared to the proportions computed over the entire proteome (Fig. S5A ). By contrast, insertions are under-represented, in agreement with a recent study  (Osmanli et al. , 2022). This observation indicates that the s-repeats are often considered as part of the canonical transcript by ThorAxe and their alternative inclusion or exclusion appear as deletions with respect to that transcript. It underlines the high evolutionary conservation of the s-repeats, since the canonical transcript is chosen as the most represented across species (see Supplementary Methods).
The s-repeats are highlighted in firebrick and lime. Type II SDS are indicated by spheres. The proportion of residues predicted as disordered and of residues in interaction are given for the two s-repeats, along with their lengths. A portion of the ESG showing the AS event linking the two s-repeats is also depicted. C. PDB codes 2RHK and 6URO for the complexes of CPSF4 bound to Flu NS1A and CPSF1-WDR33, respectively. D. PDB codes 3G9W and 4DX9 for the complexes of ITB1 bound to Talin-2 and ICAP1, respectively. leucine rich-repeat (LRR) containing G-protein coupled receptor 5 features ten single-s-exon s-repeats, conserved from human to frog (Fig. 5C ). They encompass up to 3 structural repeats annotated in RepeatsDB, most of them (8/10) matching well a single structural repeat. As noted in  Paladin et al.
Nevertheless, the current approach might not be adapted for cases where sequence similarity is extremely low. A future improvement could be to allow for performing the similar s-exon pair detection step relying on structure similarity, instead of sequence similarity, for instance using Foldseek  (van Kempen et al. , 2023) , or other methods. We applied ASPRING at large scale over hundreds of million years of evolution. We found that the detected repeats tend to be evolutionary conserved and we identified evolutionary ancient modulation patterns in their usage. We relied solely on gene annotations from the Ensembl database. We expect such annotations to grow rapidly in the coming years, thanks to the advent of long-read sequencing technologies. In this context, methods such as ASPRING will become instrumental to shed light on protein diversification.
Acknowledgments This work benefited from computing resources funded by a grant of the  French national research agency  ( MASSIV  project,  ANR-17-CE12-0009 ). The  ANR-20-CE45-0010-01 RoDAPoG  and the  Hasso Plattner Institute  provided a salary to A. S. Thanks to  F. Oteri  and  H. Ripoche  for their invaluable help with the computing infrastructure. Thanks to the  Inria TAU team of the University of Paris-Saclay.
A European project, the SIESTA database, specifically studied interrater reliability for overall sleep stages when using R&K rules compared to AASM rules. 25 The authors found a higher interrater reliability with the AASM versus R&K rules (Cohen's k of 0. 76 and 0. 68, respectively), interpreted as the result of additional EEG channels in AASM recommendations. However, interrater reliability is lower when considering separately each sleep stage. In their meta-analysis, Lee et al found a substantial agreement for stage W and R, moderate agreement for N2 and N3 and only fair agreement for N1 stage. 24 N1 stage is not a stable sleep stage. It is highly influenced by transitions between wakefulness and sleep onset and between sleep stages when arousals occur. In their interrater reliability program, Rosenberg and van Hout also found the poorest interrater reliability in stage N1.
There are, however, some limitations that should temper the enthusiasm for their use. First, most of them only provide a discrimination between wake and sleep states or between wake, NREM and REM sleep, and for some of them enable the diagnosis of sleep disorders such as obstructive sleep apnea, but do not allow a precise classification of sleep stages and even less an analysis of sleep microstructure. Second, most of these tools were tested in specific databases, and the performance may drop if they were used in other datasets. Third, one of the objectives of their development is the use in clinical practice, which means that they must be validated in various sleep disorders and populations including children, whereas most of them have been studied in healthy adult individuals or in obstructive sleep apnea only.
1] [102] [103]  Finally, at a time when work on Big Data is exploding and public health needs are exponential, it is likely that this system would rely mainly on artificial intelligence, provided that a critical human eye remains capable of detecting possible errors or inconsistencies. The number and heterogeneity of these requirements suggest that the development of such a system for describing and quantifying vigilance states might be utopic and that hierarchical choices will have to be made. A consensual reflection between researchers, doctors, engineers and developers appears in any case crucial.
Thus, one cannot rule out the possibility that some recorded influenza diagnoses were not caused by an influenza virus. Second, influenza is a disease that may quite easily be self-treated, and therefore, not all patients with the illness come to the attention of healthcare providers. Other methodological issues include the relatively short follow-up of studies to date. At the same time, it has been confirmed that past exposure to vaccines against diphtheria or tetanus, poliomyelitis and influenza may protect against subsequent development of AD  [135]. Other authors have also shown that influenza vaccination reduces dementia risk in chronic kidney disease patients in a cohort of 11,943 patients from the National Health Insurance Research Database of Taiwan  [136]. In this study, there was not only an overall significant protection of influenza vaccination against dementia, but the effect was more evident in those aged > 70 years, and vaccination dose dependently reduced the risk of dementia in all subgroups  [136].
The reaction mixture contained 1X buffer, 0. 2 mm dNTP, 0. 3 μm per primer, 2. 5 mm MgCl 2 , 0. 05 U μL -1 Taq polymerase (Eurogentec, Seraing, Belgium), and DNA solution. PCR was performed with Bio-Rad S1000 Thermal Cycler (Bio-Rad Laboratories, Marne-la-Coquette, France). PCR products were then separated by electrophoresis (30 min at 100 V) on 0. 8% agarose gel in 0. 5X TAE. DNA was stained with ethidium bromide and detected using a UV-illuminator system. PCR product purification was performed using QIAquick PCR Purification Kit (QIAgen, Hilden, Germany). Sequencing was achieved by Eurofins Genomics (Ebersberg, Germany) and the resulting sequences were compared with NCBI Nucleotide database with the BLASTN program. To characterize the genotype of bacterial strains a Rep-PCR method based on (GTG) 5 primer (5′-GTGGTGGTGGTGGTG-3′) (Eurogentec, Seraing, Belgium) was performed according to Fessard and Remize (2019).
3D designing and printing We designed all parts with Tinkercad (Autodesk, CA, USA), which were 3D printed with a Form 2 printer (Formlabs) with a resolution of 50 µm and Grey Pro Resin (Formlabs, MA, USA). After printing, we cured them with UV light at 80°C for 15 minutes (Form cure, Formlabs). All design files are available on our GitHub repository (https://github. com/INS-PhysioNet/npx-ghestem).
Spike sorting The 12 h blocks of data recorded on a given day were concatenated using the dedicated Neurosuite  (Hazan et al. , 2006)  function. We used Kilosort v3. 0  (Pachitariu et al. , 2023)  for data preprocessing and spike sorting with default parameters. To reject noise and to merge erroneously split units, we visually inspected and manually curated the resulting clusters with Phy (https://github. com/cortex-lab/phy). We rejected units as noise either if their waveform clearly did not correspond to an action potential, or if the waveform was identical on all the channels. Units were rejected as excessively noisy if they presented excessive refractory period violations in their autocorrelograms. We inspected in phy all the pairs of units labelled as good by Kilosort and merged those that showed a similarity index > 0.
Introduction Since the early 2000s, multivariate analyses of brain data have become a powerful tool for cognitive neuroscience  (Norman et al. , 2006; Naselaris et al. , 2011; Haxby et al. , 2014; Stokes et al. , 2015; Cichy and Teng, 2017). Before the introduction of these techniques, the conventional approach in neuroimaging research (also known as the activation-based framework;  Kriegeskorte et al. , 2006; Mur et al. , 2009)  employed spatially aggregated activations means to describe the neural correlates of major cognitive processes. Hence, psychological functions could be identified with increases in functional Magnetic Resonance Imaging (fMRI) hemodynamic signal across macro-scale regions or increases in mean voltage amplitude registered with magneto/electroencephalography (M/EEG).
Decoding Generally, decoding entails using brain data to predict experimental conditions, in opposition to encoding, which entails using experimental conditions to predict brain data (as in univariate General Linear Model estimation). In this work, we use the term decoding to refer more specifically to its most popular implementation, in which machine-learning classifiers are trained and tested on brain activity patterns. It informs about how discriminable two (or more) experimental conditions' activity patterns are. Cross-decoding Implementation of decoding analysis in which the machine-learning classifiers are trained and tested on datasets obtained in different experimental conditions (i. e. , training the classifier with data from task A and testing it with data from task B). It informs about how generalizable the encoding space is among conditions.
Functional localizer Tasks designed to isolate brain region(s) engaged by specific cognitive processes. This methodology, used in the univariate analysis tradition, has enabled restricting the analyses of independent datasets (i. e. , from a main experimental paradigm) to a series of functional regions of interest, defined individually for each participant. testing them against the main task data, to assess the presence or absence of encoded information (e. g. ,  Senoussi et al. , 2016; Kok et al. , 2017; Liu et al. , 2019). Moreover, similarity measures derived from RSA  (Walther et al. , 2016)  can also be computed between the localizers' and the main paradigm's activity patterns to inform about the activation strength of the individual templates' representations  (Wimber et al. , 2015; González-García et al.
The stimuli sensory templates (Localizer ) are estimated with a task where upside-down images need to be detected, while semantic templates (  Localizer ) are estimated with a task in which the stimuli are categorized as mammals or non-mammals. Those templates are then compared against the activity patterns associated with the items retrieved during the main paradigm. Critically, the activation strength from the retrieved image template (in the example, the cat, shown in red) can be compared against the non-retrieved ones (e. g. , the dog templates), which can act as baseline condition. Cat. , Category; Subcat. , Subcategory. Stimulus images were retrieved from the stimulus database employed in González-García et al. ( ), CCBY. illustrated in the above-mentioned study by  Wimber et al. (2015) , the employment of localizer tasks and similarity measurements can facilitate access to individual representations' activation strength.
However, despite approaches akin to CTT have been present in the literature since the early beginnings of the informationbased approach  (Polyn et al. , 2005) , no work to date attempted to provide a comprehensive methodological description of this technique. We believe that providing clear guidelines on how to implement this analysis is still required to contribute to better practices with this method (as has happened with other facets of MVPA; for instance, see  Gorgolewski et al. , 2016). Aiming to fill that gap in the literature, here we provide a step-by-step tutorial on CTT. We will first focus on important design issues regarding the nature of the localizer tasks, to later expose the relevant analytical decision that should be made during the process. In doing so, we cover issues that affect spatially resolved (fMRI) and time-resolved (M/EEG) datasets.
Step-by-step tutorial on CTT implementation All CTT experiments provide at least two independent datasets per participant: one for the main experimental paradigm and another for the localizer. Before starting the CTT protocol, raw data will be preprocessed following the general recommendations for multivariate analyses. In the case of fMRI data, and according to the standards in the field, we recommend not normalizing nor smoothing the data before the analyses to avoid distortion in the activity patterns  (Kriegeskorte et al. , 2006; Mur et al. , 2009 , but see Op de Beeck, 2010; Gardumi et al. , 2016). For time-series data (EEG, MEG), we refer to previous work assessing the impact of different preprocessing steps on distributed activity patterns  (Grootswagers et al. , 2017; van Driel et al.
The noise normalization techniques proposed for the fMRI field had been also extended to temporally-resolve datasets  (Guggenmos et al. , 2018). Due to the lower spatial resolution of EEG and MEG, the most common practice is to generate the activity patterns using the totality of channels registered. Nonetheless, as the number of channels increases (especially in MEG recording), so does the dimensionality of the estimated representational space, which can have a detrimental impact on further analyses. This motivated the employment of dimensionality reduction techniques, mostly Principal Components Analysis (PCA), to generate activity patterns from less and more informative features, without imposing further anatomical assumptions  (Hebart et al. , 2018). Extracting the activity patterns from the components accounting for 99% of the data variability has been evidenced to significantly improve the signal-to-noise ratio  (Grootswagers et al.
Estimation of the canonical templates The second step in CTT is to estimate the canonical neural templates from the localizer task(s). This procedure is similar to pattern extraction performed on the main experimental paradigm data. Nonetheless, the former is more flexible, allowing the researcher to choose whether multiple (trial-wise) or single (run-wise) conditions patterns were computed. In the case of canonical neural template estimation, the preferred option is to obtain more robust and stable single activity patterns. Thus, we recommend employing single beta estimates for each localizer condition in the case of fMRI data (potentially improved with univariate or multivariate noise correction) and condition-wise averaged multi-channel activity patterns in the case of M/EEG recordings (either in time, frequency or timefrequency domain).
Nonetheless, if the tracking is performed using a decoding-based approach (see Section 3. 3), this stage should generate localizer data suitable to train the machinelearning classifiers. Hence, instead of a single, robust canonical template, several exemplars of each template condition should be estimated from the localizer data (more similar to the main paradigm activity pattern extraction). A decision of particular importance in this step is to select either where in the brain (in the case of fMRI data) or when in time (in the case of time series data) are the canonical templates estimated. In the spatial domain, the region(s)-of-interest (ROIs) strategy accommodates well in the CTT framework since it can constrain the canonical templates generation to functionally relevant regions. In this case, for each ROI, the templates are estimated using all the voxels assigned to that region (see Figure  2 , left upper inset).
, 2017; Karimi-Rouzbahani et al. , 2022) , the CTT method proposed here is so far restricted to within-region comparisons. Further investigation is still needed to validate its application using data from different brain regions. Regarding the temporal domain, the most frequent approach in multivariate testing has been to explore the full epoch of interest, extracting independent activity patterns at each time bin sampled. The same logic could be followed with CTT, dynamically estimating the canonical template at each individual time point (see Figure  2 , left lower inset). In this regard, the CTT framework could be also extended with the temporal generalization logic. This approach, developed for decoding, implies training a classifier at a time point t, and testing its performance at every time points t' , including t = t'  (King and Dehaene, 2014).
Similarly, the template tracking procedure could be performed by iterating across the localizer and main paradigm's time bins (see Figure  2 , right upper inset). This allows adopting an agnostic position regarding the temporal profile of the effect of interest (e. g. ,  Desender et al. , 2019; Senoussi et al. , 2020). Nonetheless, template estimation can be also carried out in a more constrained fashion. In this sense, it is possible to derive single templates, collapsing data from theoretically informed time windows, or focusing on time bins identified with data-driven approaches which seek to maximize pattern discriminability.
Template tracking After activity pattern estimation, the canonical templates are tracked on the main paradigm data to assess whether and to what extent they are activated. To do so, the most promising approach incorporates the same similarity measurements within the RSA framework. Those measures have two major advantages: they have been already tested and validated in neural and simulated data  (Nili et al. , 2014; Walther et al. , 2016) , and they are remarkably flexible regarding the number of canonical templates that can be considered simultaneously. The simplest option is to compute the Pearson correlation coefficient between the main task's activity patterns and the canonical template(s)  (Wimber et al. , 2015). Significantly higher correlations would be interpreted as increased activation of the targeted representation during the paradigm.
Nonetheless, it is important to stress that implementing a cross-validation procedure is less straightforward in the context of CTT than in regular singleparadigm designs. In the latter, different task runs or trials can be flexibly included in k-fold or leave-one-out cross-validation methods. In the CTT case, the main paradigm and localizer data should be treated separately. One option would consist of keeping the localizer templates fixed in the cross-validation while iterating across different and independent main task data chunks. However, future research is needed to properly characterize cross-validated distance measurements from the CTT perspective. The above-mentioned measurements (correlation and Euclidean distance) are especially useful when the goal is to address the presence or absence of particular templates.
In this case, a machine learning classifier is trained on the localizer data and tested against the task activity patterns. However, the standard implementation of this approach could restrict the flexibility of the CTT framework. Specifically, if only the classifier accuracy is considered, it will generally indicate to what extent the templates generalize to the main paradigm data instead of obtaining evidence for the individual templates' reactivation (as it happens with the abovementioned methods). Additional output measurements can be computed to enrich the conclusions drawn with this approach. For instance, we can also extract trial-wise distances from the hyperplane, as the d values obtained with LDA classifiers (e. g. ,  Linde-Domingo et al. , 2019). These measurements are assigned to each activity pattern from the main paradigm during the classifier testing phase, and not only indicate the canonical template label assigned to the trial, but also the confidence of the classification (with higher d values indicating that the trial was further away from the boundary decision, and hence, more easily classified as the corresponding template label).
Either correlation distance or classification accuracy are suitable methods to address the presence of specific templates. However, when the research question requires the comparison among multiple templates, similarity measurements based on multiple regressions or semi-partial correlations can help disentangle each activity pattern's contribution. Having said that, further methodological work would be key to providing more robust recommendations in this regard. In the scripts provided with this work, we demonstrate two implementations of template tracking on simulated MRI data (also available in the repository, simulated with The RSA Toolbox;  Nili et al. , 2014). First, we compute correlation-based similarity metrics, comparing two conditions' canonical templates (using one as baseline as described in Section 2.
Discussion Despite the increased interest in multivariate brain data analyses, no work to date had provided methodological guidelines on CTT. With this work, we aimed to illustrate the advantages of this technique and to discuss the crucial design and analysis decisions for its correct implementation. On one hand, we stressed the importance of carefully considering the task demands and experimental conditions in the localizer tasks(s). On the other hand, although CTT partially overlaps with (cross-) decoding and RSA, we highlighted the specific steps and decisions regarding template estimation and tracking. Moreover, we provided a comprehensive tutorial covering spatially and temporally resolved neuroimaging recordings, together with a set of scripts to implement the analysis using a popular toolbox for multivariate pattern analysis  (Hebart et al.
We consider that a key future direction could entail the elaboration of a taxonomy of localizer tasks that can be employed to evoke different representational formats and contents. Such a strategy would facilitate the experimental design of CTT experiments, maximize the reproducibility of the results, and facilitate collaborative data sharing. Similarly, the analysis procedure per se could be further optimized. In this work, we aimed to synthesize the different approaches previously used in studies akin to CTT and to extend the methodology from decoding and RSA which could be compatible with this technique. In doing so, we emphasized two steps central to CTT: the estimation of the canonical templates and the tracking procedure. The former capitalizes on the robustness and stability of the extracted neural representations.
Conclusion The introduction of multivariate analyses in cognitive neuroscience has opened a window to a more mechanistic, representational understanding of brain function. Aiming to extend the available methods, here we described a third MVPA implementation, CTT, which empirically estimates specific neural representations from localizer tasks, and assesses its (re)activation during independent cognitive paradigms. While CTT is tightly related to decoding and RSA, we highlighted how it can complement these techniques thanks to its specificity and its ability to tap into the representational format. To facilitate its implementation in future studies, we provided a detailed tutorial regarding experimental design and data analyses and identified the main limitations and pitfalls linked to this technique.