dataesr/scientific-paragraphs-categorization huggingface dataset:
Methods Participants and data acquisition. MEG recordings of 22 right handed participants, recruited as part of a protocol assessing time perception  75  , were used for this study. All participants had normal or correctedto-normal vision without any known neurological or psychiatric disorders. The experimental protocol was approved by the local Ethics Committee on Human Research 'Comité de Protection des Personnes Sud-Est VI' (protocol: CEA 100 049 / ID RCB: 2018-A02586-49), and all participants provided written informed consent in accordance with this protocol, and in in conformity with the Declaration of Helsinki (2018). Two participants had noisy MEG data and two participants did not comply with the task, yielding a total of 18 participants for the final analysis (10 males; age = 26 years, SD = 5).
The MEG data were collected using the whole-head Elekta Neuromag Vector View 306 MEG system (Neuromag Elekta LTD, Helsinki) in a magnetically shielded room at a sampling rate of 1000 Hz. The MEG system had 204 planar gradiometers and 102 magnetometers that measure the relative magnetic field strength (fT/cm) and absolute magnetic fields (fT), respectively. The direct current (DC) method was adopted during recording such that no high-pass filters were applied, to allow investigating low frequency components in the data. Horizontal and vertical electro-oculograms (EOG) and the electro-cardiogram (ECG) were recorded during the session. Participant's head position was measured before each run by means of four head position indicator coils placed over the frontal and mastoid areas. For behavioural responses, participants pressed one button on a Fiber Optic Response Pad (Elekta), using the index finger of their right hand.
From the original data set, we selected three runs per participant (runs 1, 5, and 7 in 14 participants, runs 2, 4, and 6 in three participants, and runs 2, 7, and 6 in one participant due to a technical problem in run 4). The differences in run numbers were due to a change in the protocol after the initial participants. Experimental conditions and tasks. The experiment (depicted in Fig. 1 ) was presented through Psychtoolbox  77, 78  for Matlab R2016a. From the 12 conditions recorded in the original study (total duration: 34 min), we chose three experimental conditions for this analysis: resting (eyes open), spontaneous finger tapping, and silent counting. The three conditions were chosen to vary the level of rhythmic behaviour, in order to examine the presence of delta oscillatory activity in the brain recordings.
Analyses of the behavioural data. Behavioural tapping rate. Participants' button presses were registered as time-stamped events with the MEG data, sampled at 1000 Hz. In accordance with the analyses of the MEG data (described below), the button-press time-series (coded as 0 when no button press occurred, and 1 when a press occurred) were subjected to a spectral analysis, by computing the Welch periodogram with a frequency resolution of 0. 1 Hz, as implemented in MNE Python  79, 80. Resulting power values were transformed to decibel (dB). A peak detection algorithm implemented in Scipy  81  was used to detect all peaks in the frequency range between 0. 1 and 4 Hz. Then, the most prominent peak (according to scipy's peak_prominences function) was retained as the behavioural tapping rate. Covert behavioural counting rate.
Assuming that individuals silently counted, the covert individual counting frequencies were estimated by dividing the final number reported by the participant by the run's duration (240 or 300 s). Since the counting was silent, we had no trace of the regularity of each individual's counting. This measure was not available for one participant, who did not report a final count. Relative subjective duration estimates. To obtain the relative individual subjective duration estimates for the tapping and counting runs, we divided the duration in seconds reported by the participant by the duration of the corresponding run. This way, relative estimates below one reflect underestimation, and relative estimates above one reflect overestimation of the run's duration. MEG pre-processing. MEG data were processed using MNE python  79, 80.
The pre-processing consisted of the following steps: applying spatial signal source separation (SSS), low pass filtering, down-sampling, epoching, manual inspection and rejection of noisy epochs, and removal of ocular and cardiac artifacts using independent component analysis (ICA). The raw data (DC, no high-pass filter was used) were corrected for environmental noise through spatial signal source separation, using the Maxfilter algorithm as implemented in MNE python, with the middle run (tapping) being used as a reference run to re-align the head coordinates. The algorithm also interpolates bad sensors, which are typically characterized by heavy distortions, flux jumps, baseline drifts, etc. , and were identified through visual inspection for each individual. Low-pass filtering was applied with a 100 Hz cut-off using a hamming windowed zero phase FIR filter.
ICA was run in two steps: first, we ran one ICA on the epoched data of all conditions jointly, to identify and remove ocular artifacts, and second, we ran another ICA to identify cardiac artifacts. All ICAs were run on the epoched data, high-pass filtered at 1 Hz only for this purpose. For the detection of artefacts related to the electro-oculogram (EOG), we used an inbuilt routine in MNE python, which uses the signal from the external electrodes to estimate a participant's typical EOG activity, and returns the ICA components that correlate with these typical events (threshold: 3. 0, z-score). In order to best identify and remove the cardiac artifacts, we then used the EOG-cleaned data, and ran a set of ICAs, iterating over the number of ICA components, a parameter that determines the partition of variance explained by each component.
To determine the best decomposition for each participant, we iterated across all versions of the data after running ICA and rejecting the cardiac components, and computed an ECG score: the power ratio at the individual ECG peak in the MEG data, before and after the removal of the ECG components. The individual ECG peak frequency was first determined based on the power spectrum computed directly on the ECG recording (Welch power spectral density). We then computed a spatial filter based on the peak's topography in the MEG data (activity distribution across sensors at the peak frequency). The power ratio of this peak (dot product of all sensors multiplied with the spatial filter) was computed before and after component removal via ICA, and the ICA solution which yielded the greatest reduction in the cardiac peak topography was retained for the participant.
Spectral analyses. From the cleaned epochs we computed the power spectral density (PSD, see Fig. 2A  for an exemplary participant) using Welch's periodogram with a frequency resolution of 0. 1 Hz, resulting in power estimates at 1281 frequency values. The power spectrum for each magnetometer was obtained by averaging the power spectrums across all the epochs. Power spectra were cut at 45 Hz and transformed to decibel (dB). A major concern when analysing delta band activity in M/EEG data, is that the peaks assumed to reflect narrow-band periodic activity lie in the high-power region of the 1/f aperiodic spectral component. To separate aperiodic spectral components from periodic ones, we applied the irregular resampling technique IRASA  74. The IRASA technique consists in down-sampling the epoched data in the time domain at pairwise non-integer values (here: 0.
Peak sorting. The k-means clustering procedure produced several clusters per canonical frequency band, identified by their peak frequency, and ordered by cluster size (i. e. , the number of sensors showing a peak at or Per condition (rest, tap, count, depicted as rows and in blue, red, and orange), spectral peaks identified per sensor were clustered using the k-means algorithm. Histograms depict the sensor counts per cluster, the brightness codes for cluster strength (brighter = higher), with the largest clusters (number of sensors included in cluster) in saturated colours. The vertical dashed lines indicate the peak frequency per cluster (defined by the mode). The number of clusters were determined from the data by identifying an ' elbow' in the goodness of fit (SSD, the sum of squared distances from each point to its assigned cluster centre, see inset).
Analyses of oscillatory bursts. We performed a confirmatory analysis (depicted in Fig. 9 ) to further assess whether the spectral peaks we identified reflect endogenous periodicities in the time-domain data, which are likely not fully stationary. To this end, we quantified oscillatory bursts using the cycle-by-cycle toolbox  76. To our knowledge, this method has so far not been used for frequencies as low as delta. To test whether the sensors identified as having a spectral peak in a given canonical frequency band really show stronger periodic activity, we assessed the time-domain signal of those sensors for bursts. The data were band-pass filtered between 0. 5 and 4. 5 Hz prior to performing the analysis. The filter band was chosen somewhat larger than the band of interest to allow for transition bands.
Discussion Summary. Here, we assessed whether endogenous delta oscillations can be observed in non-invasively recorded human brain dynamics. As per the current state of the art, we define oscillations by a narrow peak in the power spectral density  71. We analysed an existing MEG dataset  75  , from which we selected three runs during which participants either rested, or engaged in rhythmic behaviour through spontaneous finger tapping (overt engagement of the motor system) and silent counting (covert engagement of the auditory-motor system), and concurrent prospective timing. Dedicated signal processing techniques were applied, involving a multi-step Figure  9. Analysis of oscillatory bursts, delta band, comparing the statistics resulting from the cycle-by-cycle analysis (amplitude, burst amplitude, frequency, burst count, burst duration) at the sensors that were identified to have a peak in the delta frequency band (dark violins) versus a randomly sampled selection of other sensors (light violins).
It might be the case that an activation of the auditory system by sensory inputs would have engaged internal oscillators in auditory regions more strongly, as endogenous delta oscillations have previously been reported in primary auditory cortices  8  , an assumption that can be tested in future work. We also examined the hypothesis that spontaneous rhythmic activity is orchestrated by a stable internal frequency, i. e. a propensity of the relevant neural populations to oscillate at this frequency, by correlating peaks in tapping and counting behaviour and delta peaks across conditions  [66] [67] [68] [69] 97. The behavioural frequencies measured in tapping (1. 17 Hz) and counting (0. 91 Hz) were clearly in the delta range, albeit somewhat lower than the frequencies of the spectral peaks in the MEG data (avg.
Limitations. Here, we present a first attempt to assess endogenous delta oscillations in non-invasive MEG recordings in humans. While our work shows that spectral peaks can be observed in the delta band, we would not want to assume that those peaks reflect endogenous oscillations in all experimental conditions. One particular limitation was the rather short recordings we used (2 min per condition and participant). Given the observation that oscillatory episodes might be transient  98, 101  short recordings might have resulted in a low signal to noise ratio. Longer datasets should be screened for endogenous delta oscillations with the same methods to observe their consistency over time, and their relationship with mental states and behavioural tasks in awake human participants. Longer datasets would also allow to address phase-amplitude coupling between delta oscillations and higher band power, as previously observed  8, 9.
Conclusions Here we examined whether endogenous delta oscillations can be observed in non-invasive MEG recordings in humans, by analysing human resting state recordings. To test whether spontaneous rhythmic behaviours incite an otherwise silent internal oscillator, we also selected conditions in which participants engaged in spontaneous finger tapping and silent counting. Narrow spectral peaks in the delta frequency range were found in all conditions, but additional analyses targeting non-stationary oscillations in the time domain showed that only the resting state data warranted an interpretation of endogenously periodic neural dynamics. We hope that the novel set of analyses steps and results presented here will foster a more detailed investigation of spontaneous oscillations in low frequency bands in humans, and thus contribute to better standards in characterizing the physiological signals underlying a hypothesized functional mechanism, and enable comparison across studies in healthy and clinical populations.
Funding This project has received funding from the  European Union 's  Horizon 2020 research and innovation programme  under grant agreement No. 101017727 ,  FET Experience , to VvW. HG is funded by the  European Union 's  Horizon 2020 research and innovation programme  under grant agreement No  800945 -NUMERICS -H2020-MSCA-COFUND-2017. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
The remainder of the data (9%) were collected from the WHO's Northern Sweden Multinational Monitoring of Trends and Determinants in Cardiovascular Disease (MONICA) study. Using a random selection of participants aged 25-74 years from the counties of Västerbotten and Norrbotten and repeated every 4-5 years since 1986, the MONICA study followed very similar protocols to the VIP  [10]. Both cohorts have been described in detail elsewhere  [11, 12]. Around 20% (374 participants) of the 1804 individuals in this study had a repeated measure, meaning that they participated in an NSHDS cohort on two occasions, for example, at 50 and 60 years of age.
Selection of study participants Cohort participants who later developed CRC were identified through linkage with the Swedish Cancer Registry. Individuals that participated in MONICA or VIP before 19th January 2016 were subject for inclusion, and the cutoff date for case diagnosis was on 31st May 2016. Tumor stage and anatomical tumor location data were retrieved through the Swedish Colorectal Cancer Registry and, when necessary, through individual patient records. Tumor site was defined using the International Classification of Disease 10th edition (ICD-10) codes: C. 18. 0 and C 18. 2-18. 4 for proximal colon, C18. 5-18. 7 for distal colon, and C19. 9 and C20. 9 for rectum. Exclusions were made for participants with previous cancer diagnoses other than non-melanoma skin cancer. Individual matching of cases and controls was based on the following: sex, age at baseline (± 1 year), cohort, year of blood sampling and data collection (± 1 year), number of freeze-thaw cycles of the plasma samples (92% with exact match), and fasting status at blood collection.
Metabolomics data preprocessing Vendor raw data files were converted into mzML format (Proteo Wizard, version 3. 0) for data preprocessing, which was mainly performed using the R package "XCMS"  [15]. Data from reversed-phase positive (RP) and negative (RN) modes were processed separately. The key parameters of XCMS were optimized with the aid of the R package "IPO"  [16]. In total, 8236 metabolite features were obtained for RP and 6599 features for RN. Imputation for missing values in the metabolomics data was conducted using an in-house random forest-based algorithm (the mvImpWrap function from https:// gitlab. com/ CarlB runius/ StatT ools). Within-and betweenbatch normalization were performed using the R package "BatchCorr"  [14]. After normalization, 4804 features for RP and 4461 features for RN with coefficient of variation (CV) ≤ 30% among sQCs were retained.
Finally, features presumably derived from the same metabolite were grouped with the R package "RAMClustR"  [17]  using manually optimized parameters. The feature with the highest intensity in each group was selected to represent the corresponding metabolite. Features not grouped were retained as singletons. The final data set comprised 2644 features for RP and 2391 features for RN. Parameters used for XCMS and RAMClustR are presented in additional notes.
Odds ratios (OR) were calculated per 1 standard deviation increase in the metabolite feature and expressed with a 95% confidence interval (CI). Significant features were also studied in the earlier samples from the 374 individuals with repeated sample occasions. Classification of participants according to disease status and clinical and molecular tumor subtypes was performed using a random forest model with incorporated recursive variable selection in a repeated double cross-validation procedure (R package MUVR)  [24]. Parameters in the models were set to the following: var-Ratio = 0. 85, nOuter = 6, and nrep = 30. Separate models were constructed based on time intervals from sampling to diagnosis according to < 5 years prior to diagnosis, 5-9 years prior to diagnosis, 10-15 years prior to diagnosis, and > 15 years prior to diagnosis.
Sensitivity analysis was performed by stratifying by sex as well as limiting to participants who had fasted > 8 h. Subgroup analysis was conducted by considering CRC cases defined by the following: tumor location (proximal colon, distal colon, rectum), tumor stage (stages I-II, stages III-IV), KRAS mutation (mutation, wild type), BRAF mutation (mutation, wild type), combined KRAS/BRAF mutation status (KRAS mutated, BRAF mutated, both wild type), and MSI status (MSI, MSS). A set of a priori defined potential confounders with adequate data available was included together with the metabolomics data for selection in the random forest models. The aim in a random forest model is to reduce classification error by splitting the data so that the variance is minimized. The model does not consider the confounding effect of covariates in the split of the data, so the potential confounders were added to the model to see if they were retained and could improve predictions.
Replication of previously reported CRC biomarker candidates Metabolites previously reported to be associated with incident and/or prevalent CRC were identified through a literature search in PubMed in March 2021 for studies on metabolite biomarkers for CRC (Additional Table  1 ). Identification of candidate masses in our metabolomics data was performed using exact mass when reported in the original publications. In addition, monoisotopic masses for all candidates were extracted from HMDB, and theoretical m/z-values were obtained for positive and negative ionization-specific adducts, respectively: [M + H] + , [M + Na] + , [M + K] + , [M + NH 4 ] + , [M + 2H] + , [M + ACN + H] + , and [M -H 2 O + H] + for positive ionization and [M -H] -, [M -H 2 O -H] -, [M -2H] -, [M + Na -2H] -, [M + K -2H] -, [M + Cl] -, and [M + FA -H] -for negative ionization.
Participants who later developed colorectal cancers were identified through cancer registries, and control participants were selected with matching for sex, age at baseline, cohort, year of blood sampling and data collection, number of freeze-thaw cycles of the plasma samples, and fasting status at blood collection. After exclusion of samples due to technical losses, unavailable confounder data, and incomplete case-control pairs, 2178 samples (1089 case-control pairs) remained for statistical analysis. In total, 1804 samples were included in the main analysis after filtering out 374 earliest samples from participants with repeated sampling occasions The directions of all associations were consistent with the main analyses, with lower concentrations in cases than controls. After inclusion of possible confounders in the models for the additional data set, two features had nominal p-values < 0.
Metabolomic profiles and subtypes of CRC In addition to studying metabolites associated with overall CRC risk, we used the data to classify subjects according to clinical and molecular tumor subtypes. None of the potential confounders was selected in the variable selection step of the random forest models, with the exception of BMI, in the tumor location analysis restricted to samples taken < 5 years prior to diagnosis. In general, all models showed low predictive performance with balanced error rates (BER) close to what would be expected by chance, and no clear trend in performance was observed for CRC subtypes related to time to diagnosis (Table  4 , Additional Table  3 ).
Acknowledgements The authors are grateful to the participants in  Västerbotten Intervention Programme  and the  Northern Sweden MONICA Study  and to  Region Västerbotten , for providing data and samples. We would like to thank the staff at  Biobank Research Unit at Umeå University , with a special thanks to  Robert Johansson  and  Åsa Ågren  for helpful support. The authors also thank  Carl Zingmark ,  Anna Löfgren-Burström ,  Åsa Stenberg , and  Roger Stenling  at the  Department of Medical Biosciences, Pathology, Umeå University , for invaluable assistance with the tumor tissue retrieval and analyses, as well as  Björn Gylling  and  Robin Myte  for their contribution to the definition and characterization of the nested case-control study. Thanks also to  Henrik Antti ,  Department of Chemistry, Umeå University , for valuable input in planning the study and to the staff at the  Chalmers Mass Spectrometry Infrastructure (CMSI)  for the instrumental LC-MS metabolomics analysis.
, 2021)  and carbon dioxyde emission by power plants  (Cusworth et al. , 2021). The aim of the pre-processing is to estimate the surface reflectance below the aerosol plume by a combination of multispectral flaring, sinter plant and coal-fired plant study cases, respectively. S2/MSI has 13 spectral bands between 0. 4 µm and 2. 2 µm. The pixel resolution is between 10 and 60 m depending on the channel. Both PRISMA and S2/MSI images are first corrected from background atmospheric effects using COCHISE software  (Poutier et al. , 2002) , a front-end of MODTRAN software. The atmospheric parameters used for the atmospheric correction are the ones provided by ESA along with the S2/MSI images  (Main-Knorn et al. , 2017). The co-registration between both images is operated by using an optical flow algorithm named GEFOLKI  (Brigot et al.
In the presence of a plume, all the radiative terms except the surface reflectance ρ are affected. The direct model associated with pixel i for a spectro-imager is based on the COMANCHE software(under plane and parallel atmospheric hypothesis) for the calculation of the different terms L atm , E d , E s , T d , T s and S. A first calculation is done corresponding to the atmospheric conditions of the acquisition, then a second calculation is necessary to introduce the aerosol plume radiative impact. To avoid a complex 3D atmospheric model while taking into account the spatial heterogeneity of the plume concentration, we introduce a dual AOT model. The dual AOT model for a Nadir viewing angle is illustrated in  L tot = L δ atm + ρ (E δ * d + E δ s )(T δ d + T δ s ) π(1 -ρS) (3) Where, X δ means a calculation of X including a plume homogeneous plane parallele layer associated to the AOT value δ.