The MERGE dataset is a collection of audio, lyrics, and bimodal datasets for conducting research on Music Emotion Recognition. A complete version is provided for each modality. The audio datasets provide 30-second excerpts for each sample, while full lyrics are provided in the relevant datasets. The amount of available samples in each dataset is the following:

• MERGE Audio Complete: 3554
• MERGE Audio Balanced: 3232
• MERGE Lyrics Complete: 2568
• MERGE Lyrics Balanced: 2400
• MERGE Bimodal Complete: 2216
• MERGE Bimodal Balanced: 2000

Additional Contents

Each dataset contains the following additional files:

• av_values: File containing the arousal and valence values for each sample sorted by their identifier;
• tvt_dataframes: Train, validate, and test splits for each dataset. Both a 70-15-15 and a 40-30-30 split are provided.

Metadata

A metadata spreadsheet is provided for each dataset with the following information for each sample, if available:

• Song (Audio and Lyrics datasets) - Song identifiers. Identifiers starting with MT were extracted from the AllMusic platform, while those starting with A or L were collected from private collections;
• Quadrant - Label corresponding to one of the four quadrants from Russell's Circumplex Model;
• AllMusic Id - For samples starting with A or L, the matching AllMusic identifier is also provided. This was used to complement the available information for the samples originally obtained from the platform;
• Artist - First performing artist or band;
• Title - Song title;
• Relevance - AllMusic metric representing the relevance of the song in relation to the query used;
• Duration - Song length in seconds;
• Moods - User-generated mood tags extracted from the AllMusic platform and available in Warriner's affective dictionary;
• MoodsAll - User-generated mood tags extracted from the AllMusic platform;
• Genres - User-generated genre tags extracted from the AllMusic platform;
• Themes - User-generated theme tags extracted from the AllMusic platform;
• Styles - User-generated style tags extracted from the AllMusic platform;
• AppearancesTrackIDs - All AllMusic identifiers related with a sample;
• Sample - Availability of the sample in the AllMusic platform;
• SampleURL - URL to the 30-second excerpt in AllMusic;
• ActualYear - Year of song release

Acknowledgements

This work is funded by FCT - Foundation for Science and Technology, I.P., within the scope of the projects: MERGE - DOI: 10.54499/PTDC/CCI-COM/3171/2021 financed with national funds (PIDDAC) via the Portuguese State Budget; and project CISUC - UID/CEC/00326/2020 with funds from the European Social Fund, through the Regional Operational Program Centro 2020.

Renato Panda was supported by Ci2 - FCT UIDP/05567/2020.

This dataset contains Particle Size Distribution (PSD) and true density measurement of Indonesian middle-rank coal samples. For PSD measurement, the samples were separated into 3 categories, which named PSD Small, Large, and Bimodal combination of Small & Large particles. Particle size measurement was conducted using LS13320 laser difraction particle size analyzer. The size distribution was constructed with respect to equivalent volume of particles to the spheres. Obtained size distribution was characterized for its mean, median, and mean/median ratio.True density measurement was conducted using Pycnometer 50 mL Silberbrand. The measurement liquid consist of Teepol and Water with ratio 1:100. Measurement of PSD and true density was performed by Mineral and Coal Technology Research and Development Center in Bandung, West Java, Indonesia.Inside the xlsx file, spreadsheet containing PSD Small, PSD Large, Bimodal 70%L, and true density calculation could be found.

This dataset contains compaction data of briquette made from binary mixture of Indonesia middle-rank coal with different mass fraction of small and large particles. Density of briquette was measured using caliper while being pressed inside the dies during uniaxial briquetting. Based on each density, the compaction characteristics of each mixture was analyzed using Cooper-Eaton curve-fitting model. Curve-fitting was performed using Solver Add-Ins in Microsoft Excel 2016.Bimodal optimal composition, fractional packing density and coordination number of the bulk mixture at 0.002 MPa were calculated based on the formula found on R.M. German researches on Powder Metallurgy.Inside the xlsx file, spreadsheets of pressed density, bimodal mixture optimal composition, fractional density and average coordination number at 0.002 MPa, example of curve-fitting operations, compaction characteristics from Cooper-Eaton model, and the regression quality (R2) could be found.

Contamination of a genetic sample with DNA from one or more non-target species is a continuing concern of molecular phylogenetic studies, both Sanger sequencing studies and Next-Generation Sequencing (NGS) studies. We developed an automated pipeline for identifying and excluding likely cross-contaminated loci based on detection of bimodal distributions of patristic distances across gene trees. When the contamination occurs between samples within a dataset, comparisons between a contaminated sample and its contaminant taxon will yield bimodal distributions with one peak close to zero patristic distance. Here we present an automated pipeline for identifying and excluding likely cross-contaminated loci based on detection of these bimodal distributions of patristic distances between taxa across gene trees. This new method does not rely on a priori knowledge of taxon relatedness nor does it determine the process(es) that caused the contamination. Exclusion of putatively contaminated loci fro...

This article reports our new interpretations of the depositional environment and provenance of the Dawashan Formation in the Longmuco–Shuanghu–Lancangjiang suture zone (LSLSZ), in the Southern Qiangtang terrane of northern Tibet, in order to gain a better understanding of the Ordovician tectonic evolution of the northern margin of Gondwana. The Dawashan Formation is dominated by greywacke and shale, with interlayered bimodal volcanic rocks that were deposited in a bathyal to abyssal marine basin. The detrital zircons in the greywacke of the Dawashan Formation have peak ages of 550, 988, 1640, and 2500 Ma, indicating a northern Gondwana margin provenance. The bimodal metavolcanic rocks from the Dawashan Formation are dominated by metarhyolite with subordinate metabasalt. The results of zircon LA-ICP-MS U–Pb dating indicate that the metarhyolite formed between 470 and 455 Ma. The metavolcanic samples are bimodal (SiO2 = 45.27–55.05 and 66.09–74.59 wt.%). In comparison, the metabasalt has a wide range of MgO concentrations and Mg# values, contains variable Cr and low Ni concentrations, is depleted in Rb, Ba, and Sr, and is enriched in TiO2, Th, U, Nb, and Ta. Geochemical diagrams show that the metabasalt erupted in an intra-plate environment. The metarhyolites have high SiO2, Th, and U concentrations, low concentrations of MgO, P2O5, Nb, Sr, and Ti, and negative Eu anomalies. The metarhyolites yield negative zircon εHf(t) values (–2.08 to – 4.50) and TCDM model ages of 1436–1567 Ma. The metarhyolites formed from magma derived from the partial melting of old continental crust. These data indicate that the Dawashan Formation records Middle–Upper Ordovician bathyal to abyssal turbidite deposition in a deep-water rift basin at the northern margin of Gondwana.

———————————————————————————————— ORIGINAL PAPERS ———————————————————————————————— Mano, Marsel, Anatole Lécuyer, Elise Bannier, Lorraine Perronnet, Saman Noorzadeh, and Christian Barillot. 2017. “How to Build a Hybrid Neurofeedback Platform Combining EEG and FMRI.” Frontiers in Neuroscience 11 (140). https://doi.org/10.3389/fnins.2017.00140 Perronnet, Lorraine, L Anatole, Marsel Mano, Elise Bannier, Maureen Clerc, Christian Barillot, Lorraine Perronnet, et al. 2017. “Unimodal Versus Bimodal EEG-FMRI Neurofeedback of a Motor Imagery Task.” Frontiers in Human Neuroscience 11 (193). https://doi.org/10.3389/fnhum.2017.00193.

This dataset named XP1 can be pull together with the dataset XP2, available here :

———————————————————————————————— EXPERIMENTAL PARADIGM ————————————————————————————————
Subjects were instructed to perform a kinaesthetic motor imagery of the right hand and to find their own strategy to control and bring the ball to the target. The experimental protocol consisted of 6 EEG-fMRI runs with a 20s block design alternating rest and task motor localizer run (task-motorloc) - 8 blocks X (20s rest+20 s task) motor imagery run without NF (task-MIpre) -5 blocks X (20s rest+20 s task) three NF runs with different NF conditions (task-eegNF, task-fmriNF, task-eegfmriNF) occurring in random order- 10 blocks X (20s rest+20 s task) motor imagery run without NF (task-MIpost) - 5 blocks X (20s rest+20 s task)

———————————————————————————————— EEG DATA ———————————————————————————————— EEG data was recorded using a 64-channel MR compatible solution from Brain Products (Brain Products GmbH, Gilching, Germany).

RAW EEG DATA

EEG was sampled at 5kHz with FCz as the reference electrode and AFz as the ground electrode, and a resolution of 0.5 microV. Following the BIDs arborescence, raw eeg data for each task can be found for each subject in

XP1/sub-xp1*/eeg

in Brain Vision Recorder format (File Version 1.0). Each raw EEG recording includes three files: the data file (.eeg), the header file (.vhdr) and the marker file (*.vmrk). The header file contains information about acquisition parameters and amplifier setup. For each electrode, the impedance at the beginning of the recording is also specified. For all subjects, channel 32 is the ECG channel. The 63 other channels are EEG channels.

The marker file contains the list of markers assigned to the EEG recordings and their properties (marker type, marker ID and position in data points). Three type of markers are relevant for the EEG processing: R128 (Response): is the fMRI volume marker to correct for the gradient artifact S 99 (Stimulus): is the protocol marker indicating the start of the Rest block S 2 (Stimulus): is the protocol marker indicating the start of the Task (Motor Execution Motor Imagery or Neurofeedback)
Warning : in few EEG data, the first S99 marker might be missing, but can be easily “added” 20 s before the first S 2.

PREPROCESSED EEG DATA

Following the BIDs arborescence, processed eeg data for each task and subject in the pre-processed data folder :

XP1/derivatives/sub-xp1*/eeg_pp/*eeg_pp.*

and following the Brain Analyzer format. Each processed EEG recording includes three files: the data file (.dat), the header file (.vhdr) and the marker file (*.vmrk), containing information similar to those described for raw data. In the header file of preprocessed data channels location are also specified. In the marker file the location in data points of the identified heart pulse (R marker) are specified as well.

EEG data were pre-processed using BrainVision Analyzer II Software, with the following steps: Automatic gradient artifact correction using the artifact template subtraction method (Sliding average calculation with 21 intervals for sliding average and all channels enabled for correction. Downsampling with factor: 25 (200 Hz) Low Pass FIR Filter:Cut-off Frequency: 50 Hz. Ballistocardiogram (pulse) artifact correction using a semiautomatic procedure (Pulse Template searched between 40 s and 240 s in the ECG channel with the following parameters:Coherence Trigger = 0.5, Minimal Amplitude = 0.5, Maximal Amplitude = 1.3. The identified pulses were marked with R. Segmentation relative to the first block marker (S 99) for all the length of the training protocol (las S 2 + 20 s).

EEG NF SCORES

Neurofeedback scores can be found in the .mat structures in

XP1/derivatives/sub-xp1*/NF_eeg/d_sub*NFeeg_scores.mat

Structures names NF_eeg are composed by the following subfields: ID : Subject ID, for example sub-xp101 lapC3_ERD : a 1x1280 vector of neurofeedback scores. 4 scores per secondes, for the whole session. eeg : a 64x80200 matrix, with the pre-processed EEG signals with the step described above, filtered between 8 and 30 Hz. lapC3_bandpower_8Hz_30Hz : 1x1280 vector. Bandpower of the filtered signal with a laplacian centred on C3, used to estimate the lapC3_ERD. lapC3_filter : 1x64 vector. Laplacian filter centred on C3 channel.

———————————————————————————————— BOLD fMRI DATA ———————————————————————————————— All DICOM files were converted to Nifti-1 and then in BIDs format (version 2.1.4) using the software dcm2niix (version v1.0.20190720 GVV7.4.0)

fMRI acquisitions were performed using echo- planar imaging (EPI) and covering the entire brain with the following parameters

3T Siemens Verio EPI sequence TR=2 s TE=23 ms Resolution 2x2x4 mm3 FOV = 210×210mm2 N of slices: 32 No slice gap

As specified in the relative task event files in XP1\ *events.tsv files onset, the scanner began the EPI pulse sequence two seconds prior to the start of the protocol (first rest block), so the the first two TRs should be discarded. The useful TRs for the runs are therefore

task-motorloc: 320 s (2 to 322) task-MIpre and task-MIpost: 200 s (2 to 202) task-eegNF, task-fmriNF, task-eegfmriNF: 400 s (2 to 402)

In task events files for the different tasks, each column represents:

• 'onset': onset time (sec) of an event
• 'duration': duration (sec) of the event
• 'trial_type': trial (block) type: rest or task (Rest, Task-ME, Task-MI, Task-NF)
• ''stim_file’: image presented in a stimulus block: during Rest, Motor Imagery (Task-MI) or Motor execution (Task-ME) instructions were presented. On the other hand, during Neurofeedback blocks (Task-NF) the image presented was a ball moving in a square that the subject could control self-regulating his EEG and/or fMRI brain activity.

Following the BIDs arborescence, the functional data and relative metadata are found for each subject in the following directory

XP1/sub-xp1*/func

BOLD-NF SCORES

For each subject and NF session, a matlab structure with BOLD-NF features can be found in

XP1/derivatives/sub-xp1*/NF_bold/

In view of BOLD-NF scores computation, fMRI data were preprocessed using AutoMRI, a software based on spm8 and with the following steps: slice-time correction, spatial realignment and coregistration with the anatomical scan, spatial smoothing with a 6 mm Gaussian kernel and normalization to the Montreal Neurological Institute template For each session, a first level general linear model analysis modeling was then performed. The resulting activation maps (voxel-wise Family-Wise error corrected at p < 0.05) were used to define two ROIs (9x9x3 voxels) around the maximum of activation in the ipsilesional primary motor area (M1) and supplementary motor area (SMA) respectively.

The BOLD-NF scores were calculated as the difference between percentage signal change in the two ROIs (SMA and M1) and a large deep background region (slice 3 out of 16) whose activity is not correlated with the NF task. A smoothed version of the NF scores over the precedent three volumes was also computed.

The NF_boldi structure has the following structure

NF_bold → .m1 → .nf → .smoothnf
→ .roimean (averaged BOLD signal in the ROI) → .bgmean (averaged BOLD signal in the background slice) → .method
NFscores.fmri → .sma→ .nf → .smoothnf
→ .roimean (averaged BOLD signal in the ROI) → .bgmean (averaged BOLD signal in the background slice) → .method

Where the subfield method contains information about the ROI size (.roisize), the background mask (.bgmask) and ROI mask (.roimask).

More details about signal processing and NF calculation can be found in Perronnet et al. 2017 and Perronnet et al. 2018.

———————————————————————————————— ANATOMICAL MRI DATA ———————————————————————————————— As a structural reference for the fMRI analysis, a high resolution 3D T1 MPRAGE sequence was acquired with the following parameters

3T Siemens Verio 3D T1 MPRAGE TR=1.9 s TE=22.6

Data Set S1. Proportion of minerals determined by XRD in all whole rock and mineral separates. Data Set S2. Summary of electron probe microanalysis (EPMA) acquisition parameters. Data Set S3. Full EPMA dataset of major elements and Cl for halogen-bearing minerals including amphibole, iddingsite/serpentine, chlorite as well as thomsonite and prehnite. Data Set S4. Summary of irradiation parameters including neutron fluxes calculated from standards and monitors (Hb3Gr, scapolites, Shallowater), vertical variation in J, ɸtherm and ɸfast and associated linear regressions used to interpolate to sample positions. Data Set S5. Full neutron-irradiated dataset including Cl, Br, I, K, Ca, Ba and U abundances and natural noble gas abundances (Ar, Kr, Xe) and isotopes (40Ar/36Ar). Data Set S6. Full un-irradiated dataset including, He, Ne, Ar, Kr and Xe abundances and isotopes including 3He/4He. Data Set S7. LOI data measured on whole rock samples. Data Set S8. Calculated statistics and weighted average estimates of the halogen composition of altered oceanic crust Figure S1 - High resolution versions of transmitted light photomicrographs XRD spectra - XRD spectra from mineralogical analysis of whole rocks and minerals

CdSprites+ is a synthetic bimodal dataset, designed specifically for comparison of the joint- and cross-generative capabilities of multimodal VAEs. This dataset extends the dSprites dataset with natural language captions and additional features and offers 5 levels of difficulty (based on the number of attributes) to find the minimal functioning scenario for each model. Moreover, its rigid structure enables automatic qualitative evaluation of the generated samples.

We have synthesized a bimodal theranostic nanodelivery system (BIT) that is based on graphene oxide (GO) and composed of a natural chemotherapeutic agent, chlorogenic acid (CA) used as the anticancer agent, while gadolinium (Gd) and gold nanoparticles (AuNPs) were used as contrast agents for magnetic resonance imaging (MRI) modality. The CA and Gd guest agents were simultaneously loaded on the GO nanolayers using chemical interactions, such as hydrogen bonding and π–π non-covalent interactions to form GOGCA nanocomposite. Subsequently, the AuNPs were doped on the surface of the GOGCA by means of electrostatic interactions, which resulted in the BIT. The physico–chemical studies of the BIT affirmed its successful development. The X-ray diffractograms (XRD) collected of the various stages of BIT synthesis showed the successive development of the hybrid system, while 90% of the chlorogenic acid was released in phosphate buffer solution (PBS) at pH 4.8. This was further reaffirmed by the in vitro evaluations, which showed stunted HepG2 cancer cells growth against the above 90% cell growth in the control cells. A reverse case was recorded for the 3T3 normal cells. Further, the acquired T1-weighted image of the BIT doped samples obtained from the MRI indicated contrast enhancement in comparison with the plain Gd and water references. The abovementioned results portray our BIT as a promising future chemotherapeutic for anticancer treatment with diagnostic modalities.

The aggregation behavior of model molecules of asphaltene subfractions A1 and A2 dissolved in heptane, toluene, and tetrahydrofuran (THF) were studied using molecular dynamics simulations. The proposed asphaltene molecular models are based on previously studied structures with two new models, including a highly aromatic model with a prominent island-type molecule and another molecule with a prominent archipelago-type architecture. The aggregation mechanisms in toluene, THF, and heptane solvents were studied. The results in heptane and toluene were consistent with the solubility of asphaltenes and their subfractions in these solvents. The size of the aggregates is well-correlated with aromaticity. When considering THF, large aggregates are broken down into smaller aggregates. This could lead to the mixture of high, medium, and low molecular weight distribution bands usually observed when gel permeation chromatography (GPC) analyses are conducted on asphaltene samples in THF. The size distribution extracted from the simulations shows a bimodal distribution with profiles similar to the size distribution profiles usually found in GPC analysis for asphaltene samples in THF. The distributions of dipole moments of the aggregates against the number of molecules in aggregates were constructed in both THF and toluene and reveal that the dipole moment of the aggregates vanishes when the number of molecules increases as a result of a random structure of the aggregates. The contributions of different molecular interactions to the aggregation mechanism, such as π stacking, van der Waals, and hydrogen bonds, are described.

The body size data come from museum samples or from previous studies performed for other purposes (e.g., Bauwens & Verheyen 1987; Arribas 2009; Recknagel & Elmer 2019). Additional data were extracted from published histograms or individual values. Climate data were extracted from the Worldclim resource.

———————————————————————————————— ORIGINAL PAPERS ———————————————————————————————— Lioi, G., Cury, C., Perronnet, L., Mano, M., Bannier, E., Lécuyer, A., & Barillot, C. (2019). Simultaneous MRI-EEG during a motor imagery neurofeedback task: an open access brain imaging dataset for multi-modal data integration Authors. Accepted for publication in Scientific Data. https://doi.org/https://doi.org/10.1101/862375 Mano, Marsel, Anatole Lécuyer, Elise Bannier, Lorraine Perronnet, Saman Noorzadeh, and Christian Barillot. 2017. “How to Build a Hybrid Neurofeedback Platform Combining EEG and FMRI.” Frontiers in Neuroscience 11 (140). https://doi.org/10.3389/fnins.2017.00140 Lorraine Perronnet, Anatole Lecuyer, Marsel Mano, Mathis Fleury, Giulia Lioi, Claire Cury, Maureen Clerc, Fabien Lotte, and Christian Barillot. 2018. “Learning 2-in-1 : Towards Integrated EEG-FMRI-Neurofeedback.” BioRxiv, no. 397729. https://doi.org/10.1101/397729.

———————————————————————————————— OVERVIEW ———————————————————————————————— This dataset XP2 can be pull together with the dataset XP1, available here : https://openneuro.org/datasets/ds002336. Data acquisition methods have been described in Perronnet et al. (2017, Frontiers in Human Neuroscience). Simultaneous 64 channel EEG and fMRI during right-hand motor imagery and neurofeedback (NF) were acquired in this study (as well as in XP1). This study involved 16 subjects randomly assigned to two groups: in a first group they performed bimodal EEG-fMRI NF with a bi-dimensional feedback metaphor, in the second group the same task was executed with a mono-dimensional feedback.

———————————————————————————————— EXPERIMENTAL PARADIGM ————————————————————————————————

The experimental protocol consisted of 5 EEG-fMRI runs with a 20s block design alternating rest and task. 1 block = 20s rest + 20s task. Task description : _task-MIpre : motor imagery run without NF. 8 blocks. _task-1dNF or _task-2dNF : bimodal neurofeedback, with either a mono-dimensional neurofeedback display (mean of EEG NF and fMRI NF scores), either a bi-dimensional display (one modality per dimension). The list of subjects with 1d or 2d is given above. Each subjects had 3 runs. 8 blocks per run. _task-MIpost : motor imagery run without NF. 8 blocks. Subjects with mono-dimensional feedback display : xp201 : 1D xp202 : 1D xp203 : 1D xp206 : 1D xp211 : 1D xp218 : 1D xp219 : 1D xp220 : 1D xp222 : 1D

Subjects with bi-dimensional feedback display : xp204 : 2D xp205 : 2D xp207 : 2D xp210: 2D xp213 : 2D xp216 : 2D xp217 : 2D xp221 : 2D

———————————————————————————————— EEG DATA ———————————————————————————————— EEG data was recorded using a 64-channel MR compatible solution from Brain Products (Brain Products GmbH, Gilching, Germany).

RAW EEG DATA

EEG was sampled at 5kHz with FCz as the reference electrode and AFz as the ground electrode, and a resolution of 0.5 microV. Following the BIDs arborescence, raw eeg data for each task can be found for each subject in

XP2/sub-xp2*/eeg

in Brain Vision Recorder format (File Version 1.0). Each raw EEG recording includes three files: the data file (.eeg), the header file (.vhdr) and the marker file (*.vmrk). The header file contains information about acquisition parameters and amplifier setup. For each electrode, the impedance at the beginning of the recording is also specified. For all subjects, channel 32 is the ECG channel. The 63 other channels are EEG channels.

The marker file contains the list of markers assigned to the EEG recordings and their properties (marker type, marker ID and position in data points). Three type of markers are relevant for the EEG processing: R128 (Response): is the fMRI volume marker to correct for the gradient artifact S 99 (Stimulus): is the protocol marker indicating the start of the Rest block S 2 (Stimulus): is the protocol marker indicating the start of the Task (Motor Execution Motor Imagery or Neurofeedback)
Warning : in few EEG data, the first S99 marker might be missing, but can be easily “added” 20 s before the first S 2.

PREPROCESSED EEG DATA

Following the BIDs arborescence, processed eeg data for each task can be found for each subject in

XP2/derivatives/sub-xp2*/eeg_pp/*eeg_pp.*

and following the Brain Analyzer format. Each processed EEG recording includes three files: the data file (.dat), the header file (.vhdr) and the marker file (*.vmrk), containing information similar to those described for raw data. In the header file of preprocessed data channels location are also specified. In the marker file the location in data points of the identified heart pulse (R marker) are specified as well.

EEG data were pre-processed using BrainVision Analyzer II Software, with the following steps: Automatic gradient artifact correction using the artifact template subtraction method (Sliding average calculation with 21 intervals for sliding average and all channels enabled for correction. Downsampling with factor: 25 (200 Hz) Low Pass FIR Filter:Cut-off Frequency: 50 Hz. Ballistocardiogram (pulse) artifact correction using a semiautomatic procedure (Pulse Template searched between 40 s and 240 s in the ECG channel with the following parameters:Coherence Trigger = 0.5, Minimal Amplitude = 0.5, Maximal Amplitude = 1.3). A Pulse Artifact marker R was associated to each identified pulse. Segmentation relative to the first block marker (S 99) for all the length of the training protocol (las S 2 + 20 s).

EEG-NF SCORES

Neurofeedback scores can be found in the .mat structures in

XP2/derivatives/sub-xp2*/NF_eeg/d_sub*NFeeg_scores.mat

Structures names NF_eeg are composed by the following subfields: ID : Subject ID, for example sub-xp201 lapC3_ERD : a 1x1280 vector of neurofeedback scores. 4 scores per secondes, for the whole session. eeg : a 64x80200 matrix, with the pre-processed EEG signals with the step described above, filtered between 8 and 30 Hz. lapC3_bandpower_8Hz_30Hz : 1x1280 vector. Bandpower of the filtered signal with a laplacian centred on C3, used to estimate the lapC3_ERD. lapC3_filter : 1x64 vector. Laplacian filter centred above C3 channel. ———————————————————————————————— BOLD fMRI DATA ———————————————————————————————— All DICOM files were converted to Nifti-1 and then in BIDs format (version 2.1.4) using the software dcm2niix (version v1.0.20190720 GVV7.4.0)

fMRI acquisitions were performed using echo- planar imaging (EPI) and covered the superior half of the brain with the following parameters 3T Siemens Verio EPI sequence TR=1 s TE=23 ms Resolution 2x2x4 mm N of slices: 16 No slice gap

As specified in the relative task event files in XP2\ *events.tsv files onset, the scanner began the EPI pulse sequence two seconds prior to the start of the protocol (first rest block), so the the first two TRs should be discarded.

The useful TRs for the runs are therefore

-task-MIpre and task-MIpost: 320 s (2 to 302) -task-1dNF and task-2dNF: 320 s (2 to 302)

In task events files for the different tasks, each column represents:

• 'onset': onset time (sec) of an event
• 'duration': duration (sec) of the event
• 'trial_type': trial (block) type: rest or task (Rest, Task-MI, Task-NF)
• 'stim_file': image presented in a stimulus block. During Rest or Motor Imagery (Task-MI) instructions were presented to the subject. On the other hand, during Neurofeedback blocks (Task-NF) the image presented was a ball moving in a square for the bidimensional NF (task-2dNF) or a ball moving along a gauge for the unidimensional NF (task-1dNF) that the subject could control self-regulating his EEG and fMRI brain activity.

Following the BIDs arborescence, the functional data and relative metadata are found for each subject in the following directory

XP2/sub-xp2*/func

BOLD-NF SCORES

For each subject and NF session, a matlab structure with BOLD-NF features can be found in

XP2/derivatives/sub-xp2*/NF_bold/

In view of BOLD-NF scores computation, fMRI data were preprocessed using AutoMRI, a software based on spm8 and with the following steps: slice-time correction, spatial realignment and coregistration with the anatomical scan, spatial smoothing with a 8 mm Gaussian kernel and normalization to the Montreal Neurological Institute template For each session, a first level general linear model analysis modeling was then performed. The resulting activation maps (voxel-wise Family-Wise error corrected at p < 0.05) were used to define two ROIs (9x9x3 voxels) around the maximum of activation in the ipsilesional primary motor area (M1) and supplementary motor area (SMA) respectively.

The BOLD-NF scores were calculated as the difference between percentage signal change in the two ROIs (SMA and M1) and a large deep background region (slice 3 out of 16) whose activity is not correlated with the NF task. A smoothed version of the NF scores over the precedent three volumes was also computed.

The NF_boldi structure has the following structure

NF_bold → .m1→ .nf → .smoothnf
→ .roimean (averaged BOLD signal in the ROI) → .bgmean (averaged BOLD signal in the background slice) → .method
NFscores.fmri → .sma→ .nf

Additional file 2: Method S1. Quantification of ammonia concentration in crab gills. Male individuals of Cranuca inversa and Thalamita crenata were collected from the Ibn Sina Field Research Station mangrove at KAUST (KSA) and kept in dedicated aquaria with fresh sediment for C. inversa and filtered fresh seawater flushed with air to maintain an oxygen saturation of 98%, at 21°C, 1 atm (assessed through a Fibox4 logger, Presence, Regensburg, Germany). After 12 h of acclimation, 10 individuals of each species were sacrificed, and the left gills were extracted. Gills were weighed and soaked with 300 µL of sterile ultrapure water (Invitrogen, Waltham, USA). Samples were then manually homogenised with plastic pestles for 1.5 µL tubes and centrifuged for 5 min, 13000g. After centrifugation, the supernatants were collected to be centrifuged again for another 10 min at 13000g. The final supernatant was used to quantify ammonia concentration in the crab gills using the ammonia assay kit MAK310 (Merck, Darmstadt, Germany) following the manufacturer's instructions. Fluorescence readings were performed with a TECAN infinite 200 pro spectrophotometer (TECAN, Grödig, Austria) in 96-well clear bottom black polystyrene microplates (Corning, NY, USA). Results were calculated following manufacturers’ indications and normalised on the fresh weight of initial gill tissue. Table S1. Pairwise comparison of the bacterial beta-diversity among Sites and Species (including sediments). Table S2. List of FISH probes used in this study. Table S3. General statistics for metagenomes and assemblies of fiddler gill and burrow sediments microbiomes. Table S4. Summary of 16S rRNA gene sequences retrieved from individual metagenomes under study. Table S5. List of KEGG orthology (KO) further investigated in this study related to carbon, sulfur, and nitrogen metabolism as well as the detoxification of sulfur compounds and xenobiotics. Table S6. General information and statistics for metatranscriptomes and assemblies from the gill tissues of the fiddler crab C. inversa collected in the Red Sea, KAUST coastline mangroves. Figure S1. Rarefaction (A) and Goods’ coverage index (B) of the bacterial 16S rRNA gene amplicon sequencing dataset. Figure S2. Eulero-Venn Diagram that shows the shared OTUs (numbers represent the percentage weighted for the OTUs relative abundance) among sediment, seawater and crab gills, and Taxonomy of the overall samples (A) and considering only the samples from Red Sea (B). Figure S3. (A) Taxonomical composition of bacterial communities in T. crenata and C. inversa highlights the large presence of Ilumatobacter sp. in the semiterrestrial fiddler crab gills and its paucity in the aquatic crabs. Notably, the “other Actinobacteria” detected in T. crenata mainly belonged to Propionibacteriaceae and Microtrichaceae. (B) Quantification of ammonia concentration in the crab gills. Significantly different ammonia concentrations on the gill of the swimming crabs Thalamita crenata and the fiddler crabs Cranuca inversa (Mann-Whitney test, U=14, p

DATASET DESCRIPTION The messAIh dataset is a fork of CMU MOSEI. Unlike its parent, MESSAIH is indended for unimodal model development and focusses exclusively on audio classification, more specifically, Speech Emotion Recognition (SER). Of course, it can be used for bimodal classification by transcribing each audio track. MESSAIH currently contains 13,234 speech samples annotated according to the CMU MOSEI scheme:

Each sentence is annotated for sentiment on a [-3,3] Likert scale of: [−3:… See the full description on the dataset page: https://huggingface.co/datasets/mirix/messaih.

This data set contains values on the cheilanthane (tricyclic terpenoids) ratios of C22/C21 and C24/23 from Mesoproterozoic to Silurian rocks of global distribution. Lipid extractions were performed following standard protocols. The yield was contamination-controlled via exterior vs. interior comparison of individual peak concentrations. Values were obtained via integration of multiple-reaction-monitoring (MRM) measurements. In comparing our data of cheilanthane ratios to that reported for younger rocks and oils, we noticed consistently lower C24/C23 values in our samples, suggesting a bimodal character of cheilanthane distribution in time. We tentatively attribute this to the rise of a source of oxidatively decarboxylated cheilanthatriol derived from ferns. Fossil cheilanthanes likely represent a composite mixture of various biological sources, whose secular patterns may record more than the Paleozoic rise of terrestrial plants presented here.

The concept of “enterotypes” in microbiome research has attracted substantial interest, particularly focusing on the abundance of Prevotella spp. in the human gut. In this study, the intricate dynamics of Prevotella spp. in the human gut microbiota was investigated, based on the metagenomic method. First, 239 fecal samples from individuals across four regions of China revealed a bimodal distribution, highlighting the abundance and variability in Prevotella spp. within the Chinese population. Second, the longitudinal cohort study included 184 fecal samples from 52 time points collected from seven individuals who demonstrated either the outbreaks or disappearances of Prevotella spp., emphasizing the transient nature of Prevotella abundance levels and suggesting shifts in Prevotella “enterotypes.” Furthermore, a turnover of the dominant Prevotella spp. was observed, indicating the potential presence of diverse subtypes of Prevotella enterotype. Notably, the genomic analysis demonstrated the persistence of specific Prevotella strains within individuals over extended periods, highlighting the enduring presence of Prevotella in the human gut. In conclusion, by integrating the temporal and geographical scales in our research, we gained deeper insights into the dynamics of Prevotella, emphasizing the importance of considering the dynamics at the time and species level in gut microbiota studies and their implications on human health.

We leverage 80% of samples from PBMC160k dataset to build a bimodal reference and use remaining 20% with only mRNA count as query. We expect a powerful mapping method can accurately infer missing modality value of query cells. The measured protein expression will be used as ground truth for validation. Concerto follows similar smoothing protocol as in Symphony and achieves consistent prediction result (Top 20 prediction, Pearson r: 0.966-0.998). scatter_plot_gt.h5ad: the ground truth of protein expression for each cell.scatter_plot_pred.h5ad: the prediction of protein expression for each cell.

Characteristics of 450 enrolled T. cruzi seroreactive blood donors, Chaco region, 2018-2019.