dataesr/scientific-paragraphs-categorization huggingface dataset:
2. 1. 1 Dataset 1: Self-paced button press task This dataset includes MEG recordings of 15 right-handed participants (9 male, 6 female, aged between 21 and 29). The participants were asked to use the index finger of their dominant hand once every 30 seconds. 275-channel CTF MEG system was used at a 600Hz sampling rate to acquire the data that were co-registered using subject-specific MRI which was parcellated using the AAL atlas with 78 regions  [10]  [24].
Dataset 2: HCP left hand movement task This dataset is extracted from the MEG data of 61 healthy individuals (28 male, 33 female, aged between 22 and 35) provided by the Human Connectome Project (HCP, MEG-1 release). Although the original data included the MEG recordings of left and right hand and foot recordings, only the left-hand movement trials were used in this study for simplicity. The MEG recordings were sampled at 508. 6275 Hz and co-registered using subject-specific MRI  [25].
Dataset 3: Sternberg working memory task This dataset includes MEG recordings of 19 healthy individuals (10 male, 9 female, aged 25±3years). The Sternberg task comprises two visual stimuli (geometric shapes showing on the screen for 0. 6 seconds each separated by 1 second) followed by 7 second maintenance period. Next, another visual stimulus is shown on the screen and the subjects were asked to use their right index finger to press the button if the current image matches with one of the previously visual stimuli. Following this, an immediate feedback was given to show the correct response. For each individual, a total of 30 trials were recorded separated by 30 second rest periods using 275-channel CTF MEG system at 600Hz sampling rate and co-registered using subjectspecific MRI  [10]. The datasets were approved by the Research Ethics Committee of the University of Nottingham Medical School  [10].
Processing Pipeline In this section, we illustrate the processing pipeline adopted to analyse the brain activity during cognitive tasks and extract task-related dominant brain networks. Figure  1  describes the overall workflow adopted to build dynamic FC matrices as a preliminary step. First, the MEG data was collected during a specific task along with the subjectspecific MRI data. After pre-processing the data to remove the unwanted noise and the artifacts, the MEG and the MRI data are used to estimate brain source activity using beamforming methods that project the external recorded MEG data inside the brain and estimate the time series (source signals) of   regions of interest (ROI). Next, the source data is processed to compute dynamic FC measures. In this study, we employ the AEC for the real datasets whose results are reported in the main document, and the PLV for the simulated data in the supplementary materials.
Data Pre-processing Datasets 1 and 3 were pre-processed and bad segments from muscle, eye, and head movement were visually examined and removed as described in  [10]. Dataset 2 was pre-processed according to the steps described by the HCP consortium. Bad channels, noisy segments, and unwanted independent components were removed. Then, each dataset was segmented and filtered according to the performed tasks. Segments were extracted in the interval of  [-12; 12sec]  relative to the button press onset for dataset 1, and in the interval of [-1. 2; 1. 2sec] relative to the electromyogram (EMG) onset for dataset 2. MEG data of both datasets 1 and 2 were filtered in the beta band  to keep the frequency components of interest relative to motor tasks. Regarding dataset 3, we segmented trials in the interval [-16; 28sec] relative to the stimulus presentation and filtered data in the  bandwidth to keep all the frequency components relative to the task.
The MEG data collected in datasets 1, 2, and 3 were processed to localize brain sources and estimate their activities using linearly constrained minimum variance beamforming (LCMV) on parcellated cortex using the AAL atlas with 78 ROI and by employing the subject-specific anatomical MRI registered to an MNI template. This procedure was followed by an inverse registration to project the data onto the anatomical subject space the details of which can be found in  [15]. Note that all trial data was used for the self-paced button press task with a total of 510 recordings for all the participants combined. Since the number of subjects for the HCP data was too large (61 subjects) each with a very large number of trials (total of 9150 trials), averaging across trials per subject was used to avoid memory issues associated with the PCA computation.
Functional Connectivity Measures Next, the dynamic functional connectivity (dFC) matrices were computed. In this work, we employed two FC measures as follows. AEC using the Hilbert transform between all ROIs was applied for datasets 1 and 3 followed by leakage correction (multivariate symmetric orthogonalization  [27] ) to avoid erroneous estimates of FC matrices. To account for the dynamic behavior of FC estimates, we used the sliding window approach for both datasets 1 and 3 where 6-second intervals with 0. 5-second overlap were adopted. Since the duration of the recordings of dataset 2 was relatively short, the instantaneous amplitude correlation (IAC) measure that extracts high temporal resolution amplitude envelope correlation, was computed for each trial of dataset 2 followed by pair-wise orthogonalization since the interval is relatively short  [28] [29]  [30]  for leakage correction.
Results To provide a comparison between PCA and DMD, we provide in the supplementary material a comparative analysis on synthetic toy data to pinpoint the effect of inter-trial and inter-subject variability in terms of temporal phase lags. The assumption is that different trials (subjects) exhibit different responses during the recording process. Thus, we present the analysis of the toy data example to study how DMD and PCA respond to temporal latency jitters in the supplementary material. Briefly, the results show that DMD is robust to the temporal latency jitters and can capture the spatial and the temporal dynamics more robustly when compared to the PCA. In addition, the supplementary material contains results on simulated EEG data  [22]  as a proof of concept. In this section, we report the results of the methodology described in section 2 on three real MEG datasets to extract dominant spatial brain configurations and their respective temporal dynamics.
To determine the number of components to consider, we employ the DIFFIT technique. For the self-paced button press and the left-hand movement datasets, the DIFFIT function found that 2 components were enough to describe the spatial configurations. Finally, for the working memory task dataset, we computed the DIFFIT values across all the windows and in each window, the optimal number of components was found to be 2 as well. Note that, since we are localized in time in our computations and we are stacking the trials and subject data preserving the temporal localization, the results of the DIFFIT function (which is 2 in all cases) is valid since brain networks can ideally have a few configurations at a time. Thus, we employ 2 components in all our studies. In the case of the DMD-based analysis, since 2 modes correspond to one, we employ 2 modes only but use the real and the imaginary networks as separate networks.
We will be discussing this phenomenon in the experiments below. For the first 2 real data sets, the results show the mean spatial networks, the temporal dynamics, the statistical networks, and the spatial network similarities across all subjects and their trials across time using backfiting. Since the first two datasets were processed to extract dFC for 49 and 36 time stamps respectively, we employ the workflows depicted in figures 1 and 2. However, for the case of the memory task we employ additionally process the data with the workflow stage depicted in figure  3  where stages 1 and 2 of the pipeline are applied in a sliding window fashion and then a global PCA is applied to find the common spatial configurations and their corresponding temporal evolution time series. The motor task results show the dominant networks and the corresponding Note that for illustration purposes, we manually tuned the thresholds for the mean networks (figures A and B in figures 4 and 6 and figures A, B, and C for figures 5 and 7) in the following manner: 75 % of the maximum for the self-paced button press task and the HCP task, and 65 % for the working memory task.
Note that, the dataset which comprises data from 15 subjects has a variable number of trials per subject which adds up to 506 trials. This allows us to compare the different trials' and different participant' brain configuration evolution during this simple button press task. As shown in the figure  4G , principal network 1 (the random microstate color-coded in blue) occupies the intervals of time before and after the button press in the majority of trials across all subjects while the principal network 2 (the motor microstate color-coded in green) occupies the interval during the button press in all the trials. Figure  4H  is the averaged value of the state numbers across all trials of all subjects which clearly shows the dominance of the motor network during the button press task since its value is close to 2.
Discussion and Future Work In this study, we introduced the DMD methodology and compared it with the PCA method to automatically extract spatial components and the temporal evolution in three different tasks. Our methodology employs all the trials in the data and imposes temporal constraints by processing the stacked trial data of all patients all at once taking inter-trial and inter-subject variability into consideration. In summary, our work employs the PCA and the DMD method to extract dominant brain network configurations and the corresponding time dynamics. The resulting networks per trial are then averaged and statistically analysed to find significant spatial networks. Since the length of the datasets are different and brain networks often change states rapidly, we analysed dynamic networks using a sliding window approach.
Overall, our results show that both PCA and DMD approaches successfully extracted dominant spatial configurations and their respective time dynamics on the simulated and the real MEG data when compared with the results of previous works that used the same dataset  [15]   [23]. Simulated data results that were reported in the supplementary material showed how network configurations shifted across time and were comparative with the ground truth. Although the two methods showed different network configurations due to the relative mathematical assumptions, the results were overall consistent. For the self-paced button press task, the dominant network identified with both PCA and DMD approaches was the motor network that was dominantly active during the button press across all trials and subjects.
The null hypothesis statistics were derived from all the windows which may include other non-essential information. Other approaches might be needed to extract more accurate spatial networks such as ANOVA  [36]. It is also worth noting that we didn't use trial averaging except for the case of HCP data (that contained a very large number of subjects with many trials) and argued the need to average them to reduce computational cost and resources needed. For the other datasets, all the trials were used to allow the PCA and the DMD to statistically derive meaningful brain dynamics. One limitation of the method is that when the duration of the brain network activity is short and windowbased approaches miss capturing them. In the future, we intend to solve these problems using an incremental version of the methodology using incremental DMD and instantaneous dFCs and constrained dictionary learning approaches such as LANDO  [37]  to enhance resolution and extract all the dynamics in the data.
Conclusion Extracting dominant brain dynamical networks has been extensively studied lately to link tasks to specific brain configurations and network transitions. In this paper, we described a framework for extracting spatial brain networks and their temporal evolution during tasks through multi-trial multi-subject analysis. Experiments performed on synthetic and real MEG data during three motor tasks (simple button press, left hand movement, working memory) showed consistency between the PCA-based and the DMD-based approaches as well as with other studies that employed the same datasets. The main objective of this paper is to design a framework for automatic identification of brain states. In particular, the DMD based approach showed better temporal resolution than the PCA-based approach.
LAM-HTGTS Data Analyses LAM-HTGTS data analysis was conducted as previously reported  (Hu et al. , 2016). In brief, illumina reads were de-multiplexed and adapter sequence trimmed using the fastq-multx tool from ea-utils (http://code. google. com/p/eautils/) and the SeqPrep utility (https://github. com/jstjohn/SeqPrep) respectively. Reads were normalized by Seqtk (https://github. com/lh3/seqtk) and mapped to the hg19 reference genome using TranslocWrapper to map translocations. Reads were also aligned to the bait region using HTGTS Rep to identify rejoining events and then combined to translocations using Rejoin. We note duplicates of translocations were removed to eliminate the artificial duplication of PCR, with features of exact bait sequence and exact prey sequence, which may not be perfect since some of them may be true biological duplications.
Acknowledgements We thank  Professor Marc Nadal  for discussion, and  Shuang Wang  for a generous gift of RNAP. We thank  Marie Le Bouteiller  and  Paul George Barghouth  for advice on data analysis. We thank the  Ligue Nationale Contre le Cancer and Agence Nationale pour la Recherche  ( ANR-17-CE11-0042 ) for providing financial support. R. L. F is a V Scholar for the  V Foundation for Cancer Research.
On the other hand, recent studies show that environmental exposures may modify gene expression via epigenetic modifications, especially DNA methylation, and thus influence the health of individuals. For example, epigenetic signatures associated with environmental determinants of asthma, such as living on a farm, smoking, and air pollution have been identified  [63] [64] [65]. To our knowledge, there are no studies specifically addressing the impact of exposure to DCP on DNA methylation. The aim of the CRESPI study is to examine the impact of environmental exposures to DCP on the respiratory health of workers and children (<4 years) in daycares. Three specific aims were defined: (1) evaluate environmental exposure to DCP by complementary and innovative tools, i. e. indoor air and settled dust measurements in daycares, a specific standardized questionnaire, and identification of substances in the DCP via a database and a smartphone application to scan barcodes and record information related to the product use; (2) evaluate the impact of occupational exposure in daycares on the respiratory health of workers; (3) evaluate the impact of early exposure in daycares on the respiratory health of young children.
Smartphone Applications During a previous collaboration with Epiconcept (http://www. epiconcept. fr/; accessed on 16 May 2023), a barcode smartphone application was developed for the assessment of use of DCP in hospitals  [46]  and at home  [47]. For the CRESPI study, two smartphone applications have been developed with Epiconcept to evaluate (1) exposures-(a) Inserm fieldworkers have scanned barcodes of products used by workers in daycares, and workers have responded to a short face-to-face questionnaire about their use during the visit day, and (b) parents have scanned barcodes of products used at home and responded to a short questionnaire about their use-and (2) respiratory health of children, with monthly follow-up (by the parents).
Database of Cleaning Products A first database of products scanned in 107 out of the 108 daycares is available and includes 9985 records of products scanned in the 107 daycares for all workers present in the daycare during the visit, corresponding to 891 different commercial products (extract online, Table  S1 ). In addition, the complete composition (ingredient list) of these 891 products, listed in descending order of ingredient concentration, was recorded from internet searches or after emailing the manufacturer. Currently, the complete composition of 551 products and uncompleted composition for 308 products have been found and are available in a second database. Some products are waiting for a response from the manufacturers (n = 5; uncompleted composition mostly found), and 4 manufacturers have refused to provide the exhaustive list of substances in their products (uncompleted composition mostly found).
Recruitment and Inclusion of Participants-Sample Size As of 1 November 2022, out of 108 daycares visited, 2100 workers and 5790 families (parents) were invited to participate in the CRESPI study (Figure  2 ) through the daycare manager, with three reminders sent to the families by e-mail. Despite difficulties and delay in the recruitment of daycares and participants (Online supplement, Figure  S1 ) due to the COVID-19 pandemic, 1698 parents and workers accepted to participate (reply coupon) and we mailed them questionnaires and consent forms. As of 1 November 2022, 51% (863/1698) returned completed questionnaires and completed/signed consent. Data were collected on a secure platform (Voozanoo) set up by Epiconcept. Automatic email reminders were set up monthly from January 2021 to parents/workers who agreed to participate but did not return their questionnaires/consent.
Recruitment and Inclusion of Participants-Sample Size As of November 1st, 2022, out of 108 daycares visited, 2100 workers and 5790 families (parents) were invited to participate in the CRESPI study (Figure  2 ) through the daycare manager, with three reminders sent to the families by e-mail. Despite difficulties and delay in the recruitment of daycares and participants (Online supplement, Figure  S1 ) due to the COVID-19 pandemic, 1698 parents and workers accepted to participate (reply coupon) and we mailed them questionnaires and consent forms. As of November 1st, 2022, 51% (863/1698) returned completed questionnaires and completed/signed consent. Data were collected on a secure platform (Voozanoo) set up by Epiconcept. Automatic email reminders were set up monthly from January 2021 to parents/workers who agreed to participate but did not return their questionnaires/consent.
Statistical Analyses Statistical analyses will be performed to evaluate the associations between environmental exposures in daycares, evaluated through complementary and precise methods (e. g. , measurements of VOCs and SCOVs, DCP emissions, smartphone application), and respiratory health of workers and children, after adjustment for potential confounders. The main outcomes will be current asthma in workers, and wheezing outcomes in children. Among children, longitudinal wheezing phenotypes will be defined by longitudinal latent class analysis (statistical clustering approach), making full use of data collected by questionnaire during follow-up (first monthly and then every six months), from infancy to age 4-years. The impact of DCP exposures on asthma and other respiratory symptoms such as nocturnal cough will be specifically evaluated among adults and children.
Moreover, potential selection bias may have occurred in the inclusion phase for both workers and children. To evaluate selection bias among workers, we will compare the use of DCP among participants and non-participants, using data from the smartphone application and the ingredient list DCP database available for all workers present during the one-day daycare visits. To evaluate selection bias among parents, we will compare information from the reply-coupon of participants who sent their consent and questionnaires to those who did not. The originality of the CRESPI study lies in the fact that it is the first epidemiological survey focusing on the effect of early environmental exposure to DCP in daycares on respiratory health in the first years of life, as well as the first one which will allow to evaluate associations between occupational daycares exposures and health among workers.
Moreover, this cohort was set up through multidisciplinary collaborations between epidemiologists, chemistry researchers, and computer scientists at the cutting edge of technology. The visits of the daycares have been successful; the daycare acceptance rate was higher than initially expected, all workers present the one-day daycare visit accepted to scan DCP used, and all the measurements were conducted as planned. CRESPI benefits from complementary and innovative technologies, via smartphone applications, to improve assessment of daycare exposure to DCP. Indoor pollution (aldehydes and VOCs in indoor air, SVOCs in settled dust) was measured in each daycare. This provides a uniquely precise database characterizing the daycare environment, a common early-life environment for many children. Another strength of the CRESPI study is to collect data on the respiratory health of children and workers through standardized methods (questionnaires) and innovative tools.
Conclusions The goal of visiting 100 randomly selected daycares was reached; 9985 products (861 unique ones with barcodes scanned) have been recorded in daycares. A large set of data is available related to these products, with their composition, their VOC, and SVOC emissions, and the indoor concentrations of some of these VOCs and SVOCs, which is unique to date. Inclusion was closed in December 2022 and the follow-up of children is funded until June 2023. Furthermore, we aim to pursue the follow-up of children included in the CRESPI cohort beyond 2023 (conditional to funding) to explore the incidence and evolution of respiratory health of the children to at least the age of 6. Such a cohort, with a rich evaluation of exposure to specific DCP substances and of respiratory health, is essential to evaluate the impact of DCP used in daycares on asthma and to thereafter take adequate preventive measures for both workers and children.
Various steps of the set-up of the CRESPI study; Figure S2: Databases available in the CRESPI cohort; Table S1: Extract of the cleaning products' composition database. Author Contributions: N. L. M. , O. D. , P. B. , B. L. B. , E. S. , I. P. , V. S. and C. M. contributed to write the initial study protocol, funding proposals, conceptualize the CRESPI study, the data and biological collections. A. E. N. , P. B. , C. M. and N. L. M. were responsible for data curation and of the logistical set-up of the CRESPI study in daycares. E. S. , A. E. N. , O. D. , V. S. and N. L. M. were in charge of the development/improvement of the smartphone application. N. L. M. , O. D. , V. S. and C. M. received funding for various parts of the study. N. L. M. drafted the first version of the manuscript. All authors have read and agreed to the published version of the manuscript.
The CRESPI study (PI: N Le Moual) is supported by Anses (Grant Anses-PNR-EST-2017-1-101), Ademe (Grant 1762C0021), Fondation de France (Grant 00081167), and ARS Ile de France (Grant 2018-2019). Additional financial support was obtained from ANR (ANR-18-CE36-0002; CRESMINA; PI: O Dumas); Anses (CHIMAIR-METHYL; Grant 'Programme Environnement-Santé-Travail de l'Anses avec le soutien des ministères chargés de l'écologie et du travail' 2019/1/097; PI: V Siroux); and Fondation Sante Environnement from the Mutuelle Familiale, Paris, France (Grant AP-FSE-19-001; CRESPI-EMIS; PI: C Mandin) for the three complementary projects. Institutional Review BoardStatement: The CRESPI study (Inserm C18-05; ID RCB n • 2018-A02657-48) has been approved by the French ethic commitee 'Comité de protection des personnes' (CPP Sud-Est I n • 2019-38; May 2019) and the French Data Protection Authority 'Commission Nationale de l'Informatique et des Libertés" (CNIL n • 919185; October 2019).