dataesr/scientific-paragraphs-categorization huggingface dataset:
This does not alter our adherence to PLOS ONE policies on sharing data and materials. Participants. Patients were diagnosed with OSA based on the apnea-hypopnea Index (AHI � 5  [13] ). Insomnia was diagnosed with the Insomnia Severity Index (ISI > 15). The MDD patients met DSM-4 or -5 criteria (using MINI 6. 0 or 7. 0) and had a score �30 on the Inventory of Depressive Symptomatology (IDS-C30) or on the Montgomery-Åsberg Depression Rating Scale (MADRS) and a score � 4 (markedly ill or worse) on the Clinical Global Impressions Severity Scale (CGI-S). 454 recording nights from 244 subjects were included in the database: 79 nights from 26 healthy participants (Age (Mean±SD): 25±5. 8y; F/M ratio: 13/13), 30 nights from 28 patients with OSA (Age (Mean±SD): 54±13y; F/M ratio: 12/16, AHI (Mean±SD): 22±18), 135 nights from 66 patients with insomnia (Age (Mean±SD): 44±14y; F/M ratio: 44/22) and 210 nights from 124 patients with MDD (Age (Mean±SD): 46±13y; F/M ratio: 83/41).
To take into consideration the multiple nights from the same subject, the mean sleep parameters of each subject were calculated and only one data point per subject was used for the analysis. Because this paper comprises recording nights from different study protocols, time in bed (TIB) duration varied between studies. To allow comparison between the different studies, all recording nights with a TIB above 388. 5 min, corresponding to the shortest TIB of the sample, were cut 388. 5 min after Lights-off to fit this minimum duration.
Study design All the recording nights included standard PSG with ECG and actimetry recordings. Polysomnography. Multiple PSG recording systems were used in the various studies (Compumedics ProFusion PSG 3; Compumedics Siesta 802a [Compumedics, Abbotsford, Australia]) but all had at least 6 EEG derivations (C3-A2, C4-A1, F3-A2, F4-A1, O1-A2, O2-A1), 2 electro-oculogram electrodes, 2 chin electro-myogram and 2 ECG electrodes. All PSG recorded data were converted into European Data Format (EDF) in order to be processed on a computer screen for visual analysis and scoring  [14]. Sleep staging was performed according to the American Academy of sleep medicine rules and the resulting reference classes were obtained by combining N1 and N2 into a single "N1+N2" class while the remaining classes (wake, N3, and REM) were unchanged.
The nights from the healthy and the OSA subjects were scored by one experienced scorer per study. The insomnia and the depression studies were scored by an independent expert scorer of the Siesta Group (Vienna, Austria) using the computer-assisted Somnolyzer software  [15]. Cardiac activity from ECG. Successive inter-beat intervals (R-R intervals) were obtained from the ECG leads from the PSG. Heart rate data were calculated from R-R intervals as heart rate = 60 / RR (in sec). Wrist movement from actimetry. Non-dominant wrist movement activity was recorded using ActiGraph (Actigraph LLC, Pensacola, FL, USA) activity monitor. Raw data were filtered and accumulated every second. The wrist actimetry was measured through the vector magnitude of accelerations obtained every second in the 3 dimensions of the space and its value is given in counts per second.
Statistical analysis Overall differences in sleep parameters between the healthy versus the pathology groups and between PSG and Somno-Art Software were evaluated. Mixed model analyses of variance (PROC MIXED) were carried out for each sleep parameter separately (TST, sleep efficiency (SE), WASO, sleep onset latency (SL), REM sleep latency (REML), N1+N2, N3, NREM and REM sleep) and included the fixed factors "group" (healthy and pathology [OSA, insomniac, MDD]), "technology" (PSG, Somno-Art Software), and pathology by technology interaction, and random effect for subject nested within technology. An unstructured covariance matrix was used to model the covariance of within-subject scores. P-values were based on Kenward-Roger's corrected degrees of freedom (Kenward & Roger, 1997). To further examine the differences observed between the healthy subject and each pathology within each technology, the Mann-Whitney U-tests was performed for each sleep parameter.
Results Mixed model analyses were carried out for each sleep parameter investigated. As shown in Table  1 , significant differences were observed between the healthy and the pathology group for all sleep parameters except N1+N2 sleep. No significant differences in the overall characterization of sleep parameters were observed between PSG and Somno-Art Software, except for REM sleep. The interaction of "group" by"technology" was non-significant for all sleep parameters. The differences in sleep parameter between the healthy subjects and each pathology were investigated with Mann-Whitney U-tests within each technology.
Sleep continuity Both technologies showed lower TST and SE for OSA (difference of the means: PSG: -60 min and -15%; Somno-Art Software: -49 min and -13%), insomniac (PSG: -69 min and -18%; Somno-Art Software: -55 min and -14%) and MDD patients (PSG: -59 min and -15%; Somno-Art Software: -42 min and -11%) compared to healthy subjects. The significant longer WASO observed with PSG for OSA (+43 min), insomniac (+33 min) and MDD (+29 min) patients was also detected with Somno-Art Software (respectively, +31 min, +25 min and +14 min).
Sleep latencies Both technologies characterized a longer SL for OSA (PSG: +17 min; Somno-Art Software: +18 min), insomniac (PSG: +35 min; Somno-Art Software: +31 min) and MDD (PSG: +28 min; Somno-Art Software: +26 min) patients compared to healthy subjects. REM sleep latency showed similar pattern between healthy and pathologies with Somno-Art Software and PSG. REM sleep latency was significantly longer in OSA and MDD patients with PSG (+30 min for both pathology). With Somno-Art Software, the difference between healthy and OSA or MDD was significant only for MDD patients (+25 min and +26 min respectively). Both technologies showed no significant differences in REM sleep latency duration between insomniac patients and healthy subjects (PSG: +12 min; Somno-Art Software: +15 min).
Sleep architecture Similarly to PSG, Somno-Art Software detected a significant difference in NREM sleep duration between healthy subjects and OSA (PSG: -39 min; Somno-Art Software: -28 min), insomniac (PSG: -57 min; Somno-Art Software: -34 min) and MDD patients (PSG: -46 min; Somno-Art Software: -23 min). With PSG, N1+N2 sleep was not significantly different between healthy subjects and OSA (+5 min), or MDD (-13 min) patients while insomniac patients showed a significant lower N1 +N2 sleep duration (-27 min) compared to healthy subjects. With Somno-Art Software, OSA (-1 min), insomniac (-10 min) and MDD (0 min) patients had no significant different N1+N2 sleep duration compared to healthy subjects. Both technologies characterized lower N3 sleep in the three pathologies compared to the healthy subjects (OSA: PSG: -44 min and Somno-Art Software: -27 min; insomniac: PSG: -29 min and Somno-Art Software: -24 min; MDD: PSG: -33 min and Somno-Art Software: -23 min).
Discussion After two validation studies of the Somno-Art Software on healthy subjects  [10]  and various pathologies  [9] , the present research is evaluating the performances of the algorithm in identifying differences in sleep parameters observed in various pathologies compared to healthy subjects. Indeed, the Somno-Art Software is intended to be used as an aid for diagnosis of sleep disorders and this paper aims at determining if similar clinical conclusions, such as longer sleep latency in insomniac patients, can equally be drawn when using gold standard PSG or the Somno-Art Software. A first indication of a correct identification of pathology-induced sleep modification was given by the mixed model analysis. This analysis showed a significant effect of the group factor on each sleep parameter investigated, except for N1+N2 sleep.
The type of technology used (PSG, Somno-Art Software) did not affect the sleep parameters. An exception was observed for REM sleep with a 3. 6 ± 2. 2 min overestimation by Somno-Art Software compared to PSG. However, the differences in REM sleep observed with PSG between the healthy and pathological sleep profiles (e. g. Mann Whitney U-test) was detected by Somno-Art Software. No significant interaction between the type of group (healthy or pathology) and the technology was detected, suggesting that the observed differences in pathologies are independent of the technology used. The differences within each technology revealed that PSG detected significantly lower TST and SE and significantly longer WASO in OSA patients as compared to healthy subjects. The same differences in sleep continuity parameters were discriminated with the Somno-Art Software.
These differences are consistent with the literature that reports fragmented sleep due to frequent sleep apnea leading to lower SE and longer wake duration  [16]. Comparable differences in sleep latencies between OSA and healthy subjects were detected with PSG and Somno-Art Software: OSA patients had longer sleep onset latencies and REM sleep latencies than healthy subjects but for REM sleep latency, the difference was significant only with PSG. Somno-Art Software detected similar sleep architecture differences between OSA and healthy subjects than PSG. In accordance with the literature, OSA patients showed lower N3 and REM sleep duration than healthy subjects  [17]. In insomniac patients, all 3 sleep continuity parameters investigated, TST, SE and WASO, as well as SL showed comparable differences to healthy subjects when using PSG or Somno-Art Software.
Indeed, in line with the literature, insomniac patients showed lower TST and SE and longer WASO and SL than healthy subjects  [1]. PSG and Somno-Art Software detected no significant difference in REM sleep latency in insomniac patients compared to healthy subjects. Both PSG and Somno-Art Software detected less NREM, N3 and REM sleep in insomniac patients compared to healthy subjects. PSG detected significantly lower N1+N2 sleep in insomniac patients which was not the case of Somno-Art Software. However, to diagnose insomnia, clinicians focus on the duration of TST, WASO and SL, which were all correctly discriminated with Somno-Art Software. Both PSG and Somno-Art Software detected lower TST and SE and longer WASO and SL in MDD patients compared to healthy subjects. Both PSG and Somno-Art Software detected significant longer SL and REM sleep latency in MDD patients compared to healthy subjects.
Sleep architecture parameters N3 sleep, NREM and REM sleep were significantly lower in MDD patients compared to healthy subjects with PSG and Somno-Art Software. In the literature, MDD patients often present changes in REM pressure with shorter REM sleep latency and more REM sleep which are the key sleep parameters investigated in this pathology  [5, 6]. This discrepancy between the differences in REM sleep observed in this dataset and the literature may come from the type of dataset used. Indeed, the overall dataset is not stratified by age or gender, demographic factors that are known to impact sleep structure and thus the differences investigated in this paper. This heterogeneity may explain this inconsistency to the literature. Moreover, to allow comparison between study groups, the recording nights were cut at the shortest TIB (388.
5 min), removing the last hours of sleep which are characterized by the predominance of REM sleep through the night. However, as the goal of this paper was to evaluate the performance of Somno-Art Software in detecting sleep parameters differences observed with PSG, and not to characterize the sleep profile of the pathologies per se, this point should not impact the results presented. Finally, it is worth noting that this study describes an achievement in the development of wearable sleep stage analysis based on actimetry and cardiac activity. Indeed, this research confirms the ability of Somno-Art Software, a cardiac-based sleep scoring algorithm, to discriminate differences in sleep parameters in patients with disrupted sympatho-vagal balance such as OSA, insomniac or MDD patients  [18] [19] [20].
Conclusion In the present study, both Somno-Art Software and the gold standard PSG showed significant differences in sleep parameters between OSA, insomniac or MDD patients and healthy subjects. Based on the results obtained in this research, the use of the standard PSG or Somno-Art Software leads the clinician to the same diagnostic conclusions about a patient's sleep. Somno-Art opens a new way to measure sleep at home, consistent with PSG, in a less invasive and more time-saving way and easy to use technology which allow for replication.
des personnes est IV, RCB N˚: 2014-A00795-42; OSA study: Comitede protection des personnes est IV, RCB N˚: 2014-A00382-45; Insomniac study: local IRB (USA (Florida), Germany (Berlin, Hamburg, Schwerin), Netherlands (Leiden)), EudraCT N˚: 2015-001672-22; Depressive study 1: Central IRB (USA_WCG) and local IRB (Belgium (Aalst, Brussel, Duffel), Germany (Berlin, Hamburg, Schwerin), Netherlands (Leiden)), EudraCT N˚: 2014-005182-75; Depressive study 2: Local IRB (Moldova (Chisinau), Poland (Gdansk, Torun, Bialystok, Warszawa), Finland (Helsinki), Latvia (Riga)), EudraCT N˚: 2015-000306-18)) in accordance with the Declaration of Helsinki and the guidelines on Good Clinical Practice. Written consent was obtained from all participants according to local requirements.
In particular, we formally define the property, referred to as "polyendochrony", that allows one to consider a given desynchronized network of synchronous Signal processes (a GALS architecture) as the implementation of a corresponding KPN model (an asynchronous network of Khandeterministic functions). For this class of networks, we formalize the Signal program analysis and transformations that define synchronous clusters of Signal processes of guaranteed deterministic behavior in an asynchronous network, that is, without synchronizing communications in the entire network. This definition yields a new strategy of multithreaded code generation that is available in the open-source Polychrony toolset of the Signal language and blurs the limits between the asynchronous and polychronous models of computation.
Goal In this paper, we revisit the above line of works by an analysis of the polychronous model of computation of Signal under the perspective of its semantic relations with the original model of Kahn process networks, knowing that both models have given rise to efficient implementations in different domains of application such as time-critical systems and stream processing. We will make the choice to present and expand these formal results by considering them under a close relationship to the present implementation of the Signal language in the open-source Polychrony toolset,  2  which does not integrate advanced analysis to synthesize weakly-endochornous networks.
The rules that define these restrictions provide a static analysis which is called "clock calculus"  [15, 32]. Clock calculus is a verification of clock relations (timing relations between streams) in Lustre, while it is a synthesis of such relations in Signal (cf. Section 3. 8). In  [33] , the authors relate synchronous constraints to "listlessness"  [34]  and extend the class of static synchronous dataflow to higher order and some class of recursive networks. They call their programs "synchronous Kahn networks". Another restricted dataflow model is that of SDF (for Synchronous Data Flow, again)  [35] , which is one of the basic models of computation of the Ptolemy framework  [36, 37]. As it is the case for synchronous languages, SDF models have a "consistency checking" that makes possible static scheduling at compile time.
Self-expressiveness is a mathematical property that aims at characterizing the relationship between instances in a dataset. This property has been applied widely and successfully in computer-vision tasks, time-series analysis, and to infer underlying network structures in domains including protein signaling interactions and social-networks activity. Nevertheless, despite its potential, self-expressiveness has not been explicitly used to infer gene networks. In this article, we present Generalizable Gene Self-Expressive Networks, a new, interpretable, and generalization-aware formalism to model gene networks, and we propose two methods: GXN•EN and GXN•OMP, based respectively on ElasticNet and OMP (Orthogonal Matching Pursuit), to infer and assess Generalizable Gene Self-Expressive Networks.
We evaluate these methods on four Microarray datasets from the DREAM5 benchmark, using both internal and external metrics. The results obtained by both methods are comparable to those obtained by state-of-the-art tools, but are fast to train and exhibit high levels of sparsity, which make them easier to interpret. Moreover we applied these methods to three complex datasets containing RNA-seq informations from different mammalian tissues/cell-types. Lastly, we applied our methodology to compare a normal vs. a disease condition (Alzheimer), which allowed us to detect differential expression of genes' sub-networks between these two biological conditions. Globally, the gene networks obtained exhibit a sparse and modular structure, with inner communities of genes presenting statistically significant over/under-expression on specific cell types, as well as significant enrichment for some anatomical GO terms, suggesting that such communities may also drive important functional roles.
Data-Driven Methods These techniques aim at analyzing high-throughput datasets, to score the level of dependency between each regulator and each gene, and then select highly scored links to reconstruct a GRN  [2]. These methods are popular due to their speed, their simplicity, and their accuracy  [2]. In practice, different measures have been used to score regulatory links, giving birth to two major groups: (i) The first group of methods is based on the assumption that a gene and its regulators should exhibit correlated gene expressions, and uses correlation (e. g. ,  [7] ) or more sophisticated information theory statistics such as Mutual Information, to score regulatory links (e. g. ,  [8] ). (ii) The second group of methods relies on the assumption that it should be possible to predict the expression of a target gene from the expressions of its regulators, and aim at training algorithms to model the expressions of each gene from those of the regulators.
According to  [2] , this paradigm is among the most popular due to its scalability, and its ability to capture gene expression's high-order conditional dependencies, that could not be captured by correlation or mutual information-based methods. In this second family of methods, a feature importance score is assigned to each regulator depending on its importance in the prediction task. Finally a subset of regulatory links is often chosen to define a putative GRN, by selecting for instance the k links with the highest scores. In practice, mainly regression algorithms have been used to this aim (e. g. ,  [9, 10] ) but recently some works have also used classification algorithms (e. g. ,  [11] ). Unlike traditional machine learning applications, most GRN inference methods based on classifiers and regressors do not split the data into training and test subsets, and tend to train the models and compute the feature importance scores relatively to the same dataset, and thus such scores may not reflect the generalization capabilities of the predictive features, inducing potential misleading interpretations.
Self-Expressiveness Applications Self-expressiveness is a mathematical property that has been used to characterize the relationship between instances in a dataset: it states that a data point laying in a union of subspaces can be expressed as a linear combination of other data points from the same subspace  [25]. This property was introduced and applied in the domain of Subspace Clustering  [25] , an important unsupervised learning technique that has been extensively applied to computer vision tasks, such as image compression  [26] , image segmentation  [27] , video segmentation  [28]  or motion segmentation  [29] , and has also been extended to time series analysis  [30]. The first, and most important step in subspace clustering aims at characterizing the relationship between pairs of data points, by applying the self-expressiveness property to learn a so-called self-expressive affinity matrix.
Contribution of the Paper In this work we introduce Generalizable Gene Self-Expressive Networks, a new, simple, interpretable, and predictive formalism to model gene networks. We describe the relationship between this new formalism and previous regression-based GRN inference methods. We present two methods, termed GXN•EN and GXN•OMP, based respectively on ElasticNet  [36]  and OMP  [37] , that aim at inferring, assessing and tuning Generalizable Gene Self-Expressive Networks. Moreover, we evaluate the generalization capabilities of new approaches and state-of-the-art methods, introducing a new internal measure to assess such algorithms, showing the importance of developing generalization-aware methods. The evaluation of these new methods was performed using (i) Four Microarray datasets from the well-known DREAM5 benchmark  [24] , including three datasets from real organisms, namely Escherichia coli, Streptococcus aureus and Saccharomyces cerevisiae, and an in silico simulated dataset, (ii) Three RNA-seq datasets from multiple tissues of complex eukaryotes, namely Canis familiaris, Rattus norvegicus and Homo sapiens.
Finally, in order to illustrate the potential of this methodology to perform differential regulatory communities analysis, we applied the GXN•OMP method on a control/disease RNA-seq case study, that reports the levels of expression of H. sapiens genes in different parts of the brain of Alzheimer disease patient and control donors. For the sake of reproducibility, the experiments and the methods implementations are available online in a dedicated gitlab repository https://gitlab. com/bf2i/gxn (last updated on 20 February 2023) and the software can be installed as a Python library, from the Python Package Index (PyPi), the official third-party software repository for Python https://pypi. org/project/GXN/ (last updated on 20 February 2023).
Self-Expressiveness Property According to the self-expressiveness property, a data point that lays in a union of subspaces, can be expressed as a linear combination of other datapoints from the same subspace  [25]. More formally, let X ∈ R D×N be a dataset with N instances X ,j ∈ R D , ∀j ∈ {1,. , N}. Data points are described in n unknown subspaces {S 1 ,. , S n }, such that each subspace S k ⊂ R D has an unknown number d k of dimensions, and N k=1 S k ⊆ R D. Moreover, let C ∈ R N×N be a coefficients matrix, such that C i,j denotes the element at row i and column j. The self-expressiveness property can be stated as follows: X ,j = X • C ,j s. t. C j,j = 0 ∀ j ∈ {1,. , N} ⇔ X = X • C s. t. C j,j = 0 ∀ j ∈ {1,. , N} (1) Among the possible solutions that Equation (1) may have, the literature on self-expressiveness focuses on so-called subspace preserving solutions, that expresses data points as linear combinations of other points laying in the same subspace, i.
In practice, Ψ can represent the set of Transcription Factors, but if such a subset is not available, all genes could be considered as potential regulators, i. e. , Ψ = Γ. The GXN inference task can be formulated as an objective function analogous to Equation  (2) , that aims at finding a regularized subspace preserving matrix C for a given gene expression dataset X. Whenever a subset of regulators Ψ is available, a supplementary constrain should be incorporated, in order to ensure that only regulators are used as predictive features, i. e. , ∀(j, g) ∈ Γ 2 , such that ∀j / ∈ Ψ, then C j,g = 0. In this context, the constrain stated in Equation (  2 ), ensuring that C g,g = 0 ∀g ∈ Γ, implies that the expression level of each regulator should be expressed as a linear function of other regulators (distinct from itself).