dataesr/scientific-paragraphs-categorization huggingface dataset:
LFP dataset For each mouse we have sets of brain state episodes, determined with the sleep scoring, corresponding to specific brain states with the following data: EEG, EMG and a set of 5 to 6 LFP (all sampled at 1kHz). LFPs implanted in each mouse corresponded to the configuration presented in the previous table. In this study, we only use the first configuration, with 7 mouse recordings in: S1, S2, M1, mPFC, dCA1. This allows the study of interactions between S1 and other areas of the somatomotor cortex. Quantitatively this database includes 341 episodes of W (total duration of 14866 s, mean episode duration: 43. 6 +-36. 05), 318 episodes of NREM (total duration 12274. 08s; mean: 38. 6 +-25. 1s) and 120 episodes of REM (total duration 7418. 68s (mean: 61. 82 +-40. 1s).
Spectral analysis To compute the spectrogram of LFP, from the dataset or from the model, we used the Welch method. The power spectral density is estimated using a Hamming window of 4 seconds with 2 seconds overlapping, with the exception of 1000ms windows with 300ms overlap for the phasic theta analysis. The power spectrum is expressed in V 2. One spectrogram is computed for each brain state episode recorded, and they are used to compute band power and to find frequency peaks in the delta band. The maximum frequency peak is the highest peak in the frequency range. The absolute delta power is the integral of the area, between the frequency bands and below the periodogram line. To integrate this area, we approximate it using the composite Simpson's rule. The idea behind it is that the area is decomposed into several parabola and then summed up their area.
Delta cycle segmentation of the signal To detect and analyze Delta cycles in our data, we used a method inspired and based on the Bycycle algorithm (for cycle-by-cycle analysis) and python library (  [7] ). In this method, the LFP signal is segmented in cycle periods in a few steps illustrated in supplementary figure  6. The signal is bandpass filtered, and the zero-crossings are indexed, then back on the raw data peaks and troughs were found as the minima and maxima between two zero-crossing time points. Finally, flank midpoints are the point halfway between the peak and trough voltage. Instantaneous phase is estimated by interpolating between extrema (the peaks and troughs) and flank midpoints. Here, to find delta cycles, we used the algorithm to compute cycles from trough to trough, centered on the peak with a narrow bandpass filter between [2 and 4.
In this article we used a previously established experimental dataset  [11]  to question recent findings about REM cortical state. Natural follow-up to this study would be experiments designed to detail some of the observations and interactions we described. A first study is to better determine the neocortical-hippocampal relations in REM sleep, and in particular with respect to SWA. Our results suggest that,in REM, the hippocampus activity is coordinated with the neocortical activity. Moreover, thalamic activity is clearly an important missing link in determining which structure -hippocampus or thalamus-plays the essential role in the temporo-spatial distribution of cortical activity during REM. During REM the sensory activation threshold is higher, especially for the sense of touch. One may therefore wonder whether SWA in S1 and S2 do not play a role as a sensory filter.
In our model we will use the TVB mouse counterpart, The Virtual Mouse Brain (TVMB,  [27] ). TVMB can construct individual mouse brains from dMRI experiments or from tracer experiments realized by the Allen Institute. Thanks to that we could use a detailed connectome of the mouse cortex that fit well the level of description we have in our biological dataset. To model neuronal populations, we used an AdEx mean-field model, as described in  [13]. This biologically realistic mesoscale model was built to correctly predict the level of spontaneous activity observed in AdEx spiking neural networks with Asynchronous Irregular (AI) or Up and Down regimes. Two network regimes that are somehow the computational analogs of biological Wakefulness and SWS sleep respectively  [11]. In those models, the spike-triggered adaptation parameter is sufficient to switch from one regime to the other.
We use a connectome composed of 104 cortical nodes, such as proposed in [ref ??]. We selected this connectome over more detailed ones that includes thalamus and other subcortical nuclei for two principal reasons. First we don't have simultaneous recordings including subcortical areas in our dataset. Second, the population model we decided to use was built to match the dynamics of cortical spiking neuronal network models. Therefore more complex connectomic dataset are not more relevant regarding our biological dataset while it could induce some errors in the network dynamics.
This could be studied more by analysing the delta cycles of an extended dataset of REM model simulations with an appropriate detection. Our model was used to predict the response to sensory stimulation in the different states. While there are some issues with the responses we observed in the model (addressed later in discussion), the general dynamics of the response across the cortex is consistent with experimental data  [32, 28]. In the REM-model, the responsiveness was modulated with the level of adaptation, evolving from similar to W-model for areas with low adaptation to somehow closer to SWS-model responses for the low to high adaptation levels respectively. In those areas, such as in S1, the peak response was smaller than in the SWS-model which might indicate that a smaller portion of the neuronal population is recruited for the response.
11 First, one could perform a similar approach based on the human brain. One of the problems to overcome is that local slow-waves may not appear in the EEG, and would ideally require intracranial recordings. However, such recordings are usually rare in primary sensory areas, so one could use the model to simulate local slow-wave activity, use models to calculate the EEG and determine how such local slow-waves affect the EEG. The aim could be to find identifiable EEG signatures of local slow-waves in REM sleep. Another possible outcome would be to use intracranial recordings in monkey to confirm the presence of slow-waves in some sensory areas, and test the response to stimuli. A TVB model of the macaque monkey brain is currently available based on the Cocomac database  [35]. One could use this model constrained by intracranial recordings in monkey to further investigate the role of local slow-waves with respect to sensory or other stimuli in REM sleep.
Result summary In this thesis we revisit the complex cortical activity of REM sleep using data analysis and modelling approaches. We first performed a close examination of a dataset of multi-site recordings of LFP in the mouse cortex by focusing on the newly discovered REM slow wave activity (SWA). summary We show that this SWA differs from that of SWS. In REM, SWA is composed of bouts of a few seconds characterised by large delta waves but no slow oscillation (<2Hz). This activity is mostly present in S1 and S2 areas of the somatosensory cortex. The occurrence of delta bouts is loosely coordinated between these two cortical areas. SWA is more rarely observed in the motor cortex and is absent in the dorsal hippocampus (dCA1) and the medial prefrontal cortex (prelimbic area). We also showed that the occurrence of cortical Delta bouts in REM could be related to the hippocampal activity, and particularly to the phasic theta period of dCA1 (and also in the EEG) Then we proposed a computational model to simulate the cortical activity we observed.
In general, volume conduction is still much debated among authors. However, the heterogeneous activity in the cortical signals recorded in neighboring cortical areas that we have analysed suggest that these risks are limited. Electrophysiology work, notably in mice, has shown that LFPs recorded in referential mode may be unlikely to be contaminated by volume conduction over a large distance (inferior to a cortical column,  [53]. Moreover, as indicated in the Methods of the 1st article, the analysis of oscillations by the cycle-by-cycle method  [23]  relies on the arbitrary choice of values for certain parameters. Rather than developing a statistical approach, we preferred to do direct blind tests of delta oscillations detection by visual exploration of a sample of dataset aiming at confirming or not the detection capacities of these oscillations by the Bycycle algorithm.
Specifically, the aim was to identify potential strengths and deficiencies among countries in the implementation of measures overall and within six domains derived from the WHO FCTC and MPOWER: tobacco control capacity, taxation and price policies, national key regulations, public awareness raising and communication, tobacco use cessation, and monitoring. MPOWER assessments. WHO MPOWER data for the year 2020 were available from the WHO Global Health Observatory  20. The officially submitted implementation reports of the Parties to the WHO FCTC are publicly available in the WHO FCTC Implementation Database  28. In the present study, through participation of the WHO FCTC Knowledge Hub on Surveillance, full datasets for the 2020 reporting cycle deriving from the reporting platform of the WHO FCTC, including updated information provided by the Parties, were also utilized.
For the countries for which the WHO FCTC implementation report was not available due to recent acceptance of the treaty (i. e. Andorra) or not being part of the Convention (i. e. Monaco and Switzerland), only data from MPOWER were extracted. Hence, WHO FCTC data were utilized for 50 countries while MPOWER data were utilized for 53 countries in the region. This study was carried out under the project Joint Action on Tobacco Control 2 (JATC2)  29  Work Package 9 (WP9), which is focused on the best practices to develop an effective and comprehensive tobacco endgame strategy. The WP9 partners representing 15 countries also reviewed their country data and had the possibility to provide recent updates. The updates were minor and did not substantially change the general information gathered from the existing databases.
e. publication of industry data, campaigns, trainings); tobacco use cessation, (i. e. resources directed to cessation of tobacco), and monitoring (i. e. availability of different key data and promoting research). The maximum score for each of the domains was 23 for capacity, 15 for taxation and price policies, 54 for other national key regulations, 13 for public awareness raising and communications, 34 for tobacco use cessation, and 7 for monitoring. Regarding the MPOWER measures, 10 indicators were used: smoking bans and compliance with smoking bans; health warnings and anti-tobacco mass campaigns; bans in advertising and compliance; offer help to quit smoking; share of total taxes in the retail price, affordability trend (since 2010), % of gross.
Considering each of the domains, the highest level of implementation of measures of WHO FCTC and associated guidelines is in the monitoring domain, for which many parties achieve full implementation. This means that monitoring systems are well established in a great part of the countries, including data availability on smoking prevalence and other relevant indicators for argumentation of new measures or evaluation of implemented ones. Secondly, an estimated 54% of parties have implemented at least 80% of measures in the other national key regulations domain. However, the WHO FCTC implementation database does not include indicators describing the level of compliance with the measures, which would show the actual implementation of those measures in practice. Thirdly, and surprisingly, 60% of the parties have implemented at least 80% of the measures within the taxation and prices domain, which also included measures to control illicit trade in the current assessment.
Based on the WHO data on prices of cigarettes in international dollars at purchasing power parity  30  , we would assume that a lower percentage of countries would show high implementation. In this case, we believe that some other indicators would benefit the analysis to show the actual differences in the taxation and pricing of tobacco products. In Europe, the EU-wide tracking and tracing regime may also explain some of the higher scores in the current analysis. Regarding the public awareness raising and communications domain, 38% of the parties achieved at least 80% implementation. Here we assume that the most extensive differences among WHO FCTC parties reporting are present fundamentally on awareness and training programs in different settings, due to no validation of reported data in the WHO FCTC Implementation Database.
Moreover, qualitative data from the WHO FCTC Implementation Database, which may have added further context to the implementation status, were not considered in our analysis. Also, while the majority of indicators retained from MPOWER had a validated score, which we obtained from the WHO Global Health Observatory 20 , scores for 'affordability trend since 2010 to 2020' and '% GDP per capita to purchase 2000 cigarettes of the most sold brand' were estimated ad hoc and may not correctly estimate the implementation of these measures. Besides, implementation of the indicators was assessed using a semi-quantitative scoring (i. e. scores were assigned to the categories of implementation), which lack the discrimination capacity of continuous scales. The update of the status of implementation of the WHO FCTC indicators at the national level performed by JATC2 WP9 partners, resulted in only a few updates which requested only minor corrections of the data gathered from WHO FCTC reports.
FUNDING This work was supported by the European Union's Health Program (2014-2020) under grant agreement N°101035968. The content of this document represents the views of the authors only and is their sole responsibility; it cannot be considered to reflect the views of the European Commission and/or the European Health and Digital Executive Agency (HaDEA) or any other body of the European Union. The European Commission and the Agency do not accept any responsibility for use that may be made of the information it contains.
Outcomes Clinical outcomes included 90-day mortality after ICU admission and the use of invasive mechanical ventilation in ICU. Data concerning the Sequential Organ Failure Assessment (SOFA) score  [38]  (a validated score for organ failure), the Acute Physiology and Chronic Health Evaluation (APACHE) II score  [39]  (a validated score for severity of critical illness scored after 24 h in ICU), partial pressure of oxygen to fraction of inspired oxygen (P/F ratio) (a marker for lung injury), ventilation-free days (VFDs) and 90-day mortality were extracted from the electronic patient database management system. VFDs were defined as: 28 minus "x" if successfully weaned from mechanical ventilation at "x" days after ICU admission; in case of death within 28 days of mechanical ventilation or continued mechanical ventilation for more than 28 days, the number of VFDs is zero  [40].
Unsupervised clustering of critical COVID-19 patients COVID-19 patients were separated into latent subgroups/clusters based on profiles of all biomarkers by Gaussian Mixture modeling. Moreover, unsupervised clustering of COVID-19 patients was performed based on trajectories/kinetics of individual biomarkers over the three consecutive measurements using longitudinal k-means clustering. Details of the procedures can be found in Supplementary Information. All R coding for the unsupervised clustering can be found at: https://github. com/cedricpeleman.
ACKNOWLEDGEMENTS We thank the clinicians at the  Ghent University Hospital  ( Joris Vermassen ,  Jan Fierens ,  Patrick Druwé ,  Sandra Oeyen ,  Kirsten Colpaert ,  Liesbet De Bus ,  Carl Roossens ,  Johan Decruyenaere ,  Lander Vanhulle ,  Klaas Vanderbiest ,  Harlinde Peperstraete ,  Ingrid Herck ,  Lien Van Laethem ,  Wim Vandenberghe ,  Sofie Gevaert ), staff of study nurses ( Daisy Vermeiren ,  Anouska Desmeytere ,  Jolien Van Hecke ,  Stephanie Bracke ,  Veerle Brams ) and ICU nursing staff for collecting the samples and data. We thank  Walter.
e. subsampling at 33 885 reads per sample for bacteria, 33 834 reads per sample for archaea, and 15 645 reads per sample for respective abundances, to correct for variability in sequencing depths among samples), and (6) taxonomic affiliation against the 16S SILVA database release 138  (Quast et al. , 2013)  based on NCBI blastn+  (Altschul et al. , 1990)  and allowing for multiple affiliations. These different steps were performed with FROGS (Find, Rapidly, OTUs with Galaxy Solution;  Escudié et al. , 2018)  on the Galaxy web platform  (Afgan et al. , 2018)  of the Pôle Rhône-Alpes de Bioinformatique. The OTU abundance tables and taxonomic assignments produced at this stage were then analysed using the vegan R package  (Oksanen et al. , 2020)  to calculate alpha diversity indexes (OTU richness and Shannon diversity index).
1  in the Supplement). The influence of the sediment layer and treatment on sedimentary bacterial (or archaeal) community structure was visualized using a non-metric multidimensional scaling (NMDS) performed with data of OTU abundances obtained from the different cores. Differences in bacterial (or archaeal) community structures between sediment layers and treatments were tested using permutational multivariate analyses of variance (PERMANOVA;  Anderson 2001 ). Statistical tests were based on 999 permutations of the Bray-Curtis matrix. To determine whether the experiment affected strictly anaerobic micro-organisms, supplementary analyses were performed on bacterial taxa involved in sulfate reduction and archaeal taxa involved in methane production. Three sulfate-reducing bacterial orders (Desulfobacterales, Desulfovibrionales, and Syntrophobacterales) were selected based on the literature  (Wasmund et al.
Acknowledgements. We thank  Edouard Metzger  and  Florian Cesbron  for their helpful comments on oxygen profile interpretation and  Jean Charles Pavard  for his assistance with foraminiferal extraction in preparation for the experiment. The authors thank the  Région Hauts-de-France , the  Ministère de l'Enseignement Supérieur et de la Recherche (CPER Climibio) , and the  European Fund for Regional Economic Development  for their financial support. Dewi Langlet  was supported by the  Région Hauts-de-France STaRS fellowship COFFEE  and the  JSPS KAKENHI  (grant no. 23K05942 ). The authors thank the two anonymous reviewers and the associate editor,  Jack J. Middelburg , for their insightful comments and suggestions. Financial support. This research has been supported by the  Région Hauts-de-France (CPER Climibio  and  STaRS fellowship COF-FEE) , the  Ministère de l'Enseignement Supérieur et de la Recherche (CPER Climibio) , and the  Japan Society for the Promotion of Science  (grant no.
Data availability. The raw reads generated for this study have been deposited in the NCBI database under the BioProject accession number PRJNA924244. The raw oxygen profile interpretation data and code are available on the following Zenodo repository: https: //doi. org/10. 5281/zenodo. 8274938. Supplement. The supplement related to this article is available online at: https://doi. org/10. 5194/bg-20-4875-2023-supplement.
Open access Costs of procedures will be estimated by applying standard unit costs to resource use data captured within the trial plus other procedures that would be offered to patients in either pathway. Estimates of the resources used (procedures, tests, radiotherapy, chemotherapy, other therapies, surveillance visits and other care events) on the two treatment pathways will be obtained for the theoretical bpMRI cohort using decisions made initially by the MDT with information from the bpMRI scan and any biopsies as a result of that scan, and estimates of the treatment pathway resources used in the theoretical mpMRI cohort will be made subsequently by the MDT on viewing additional information from the mpMRI scan and any further biopsies performed as a result of that scan. This thought experiment is required due to the ethical requirement to use all available information, that is, not just bpMRI and biopsies or just mpMRI and biopsies, when making the actual treatment decision with the patient.
The analysis perspective will be that of the NHS and personal social services. Standard unit costs (eg, NHS reference costs) will be supplemented by unit cost data from the participating trial sites. A microcosting study to provide this information will be undertaken in a small number of sites as part of the trial to investigate the resources employed to deliver bpMRI and mpMRI scans. This information will allow us to understand the MRI booking system, consumption of consumables and staff time as related to delivering bpMRI and mpMRI scans. Depending on the within-trial cost-effectiveness findings, consideration will be given to extending this analysis using decision analytical modelling to estimate qualityadjusted life-years gained over a lifetime horizon. Quality of life information will be estimated from anonymised patient-level data by the same group from an earlier study in this instance.
Statistical methods A statistical analysis plan will be finalised before our database lock and before any statistical analysis occurs. A consort diagram will be presented. All continuous variables will be described using the mean and SD, or median and IQR, as appropriate. Categorical variables will be described using frequencies and percentages. Baseline characteristics will be examined and presented for those with and those without clinically significant PCa. The assumptions underpinning the statistical methods used will be assessed. The use of transformations will be considered to satisfy statistical assumptions.
PRIME operations group The PRIME operations group consists of the CI, the Clinical Operations Group, National Cancer Imaging Translational Accelerator, the UCL Surgical and Interventional Trials Unit and the electronic case report form database managers. This group is responsible for ► Study planning. ► Preparation of protocol and revisions. ► Assistance with international review board/independent ethics committee applications. ► Preparation of investigators brochure and CRFs. ► Organisation of steering committee meetings. ► Provide annual progress reports to the ethics committee. ► Reporting serious adverse events to the sponsor and ethics committee when necessary. ► Responsible for trial master file. ► Budget administration and contractual issues with individual centres. ► Advice for PIs.
Roles and Responsibilities Please see table  6  for roles and responsibilities of the trial sponsor and involved committees. placed on this supplemental material which has been supplied by the author(s) 2. I understand that my participation is voluntary and that I am free to withdraw at any time without giving any reason, without my medical care or legal rights being affected. BMJ Open doi 3. I understand that relevant sections of my medical notes and data collected during the study, may be looked at by individuals from the sponsor of the trial (University College London), responsible persons authorised by the sponsor, from regulatory authorities, from the NHS Trust and from PRIME study researchers who may be outside of my local centre, where it is relevant to my taking part in this research.