dataesr/scientific-paragraphs-categorization huggingface dataset:
On the other hand, the approach to solve the same problem implemented in this work was to split the task into two parts, namely: first a small subsample of N points is projected with SVA (e. g. N = 3000), then the encoder g takes the N projected points as parameters and is ready to project any amount of new incoming data. We believe that this choice is algorithmically simpler than modifications on the SVA to cope with large datasets. Moreover it allows for applications beyond data visualization, in projected dimensions higher than 3D, where SVA and g can be jointly used to yield auto-encoding structures, as a matter for the follow-up of this work.
Jugurta Montalvão  (This work supoorted in part by the  Coordenação de Aperfeiçoamento de Pessoal de Nivel Superior -Brasil (CAPES) -Finance Code 001. The work of  Jugurta Montalvão  was supported in part by the  Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)  under Grant  308319/2018-4.
Electroencephalography (EEG) alpha asymmetry is thought to reflect crucial brain processes underlying executive control, motivation, and affect. It has been widely used in psychopathology and, more recently, in novel neuromodulation studies. However, inconsistencies remain in the field due to the lack of consensus in methodological approaches employed and the recurrent use of small samples. Wearable technologies ease the collection of large and diversified EEG datasets that better reflect the general population, allow longitudinal monitoring of individuals, and facilitate realworld experience sampling. We tested the feasibility of using a low-cost wearable headset to collect a relatively large EEG database (N = 230, 22-80 years old, 64. 3% female), and an open-source automatic method to preprocess it.
, 1993; Carrier et al. , 2001; Miller et al. , 2002; Otero et al. , 2003; Morgan et al. , 2005; Gasbarri et al. , 2006 Gasbarri et al. , , 2007;; Stewart et al. , 2010; Kovacevic et al. , 2015; Müller et al. , 2015; Hashemi et al. , 2016). Similarly, the role age plays in EEG asymmetry is also not very well known. One solution to the lack of understanding of if and how demographic variables influence EEG asymmetry and well-being is to collect large and diversified datasets that better reflect the general population. A few studies with large samples found that age and gender mediate frontal asymmetry but that ethnicity or socioeconomic status did not  (Stewart et al. , 2010; Gable et al. , 2015; Arns et al. , 2016). However, these studies are rare and hard to replicate because of the time and cost involved in recording EEG data on a large number of subjects with conventional systems (equipment cost, EEG preparations time, participants compensation for their time, equipment cleaning, etc.
). Wearable EEG technologies make the collection of large datasets of diversified and under-represented populations more feasible and offer promising new applications for both clinicians and researchers in the long term  (Cannard et al. , 2020). These applications include brain monitoring in naturalistic settings and in real-time  (Hu et al. , 2015; Jebelli et al. , 2017) , brain-computer interfaces (BCI;  Park et al. , 2020) , neurofeedback interventions  (Angelakis et al. , 2007; Quaedflieg et al. , 2016; Brandmeyer and Delorme, 2020a) , neuromarketing  (Cartocci et al. , 2018; Ramsøy et al. , 2018) , or neuroaesthetics research (i. e. , the science studying the biological underpinnings of aesthetic experience;  Cheung et al. , 2019; Cartocci et al. , 2021). While these EEG systems can have inferior hardware capacities than conventional ones, recent technological and algorithmic advancements make the detection and measurement of mental states increasingly reliable  (Wu et al.
, 2017) , with as few as a single EEG channel (Umar  Saeed et al. , 2018; Arpaia et al. , 2020; Mahmoodi et al. , 2021). Additionally, these systems can easily combine other physiological measures such as electrocardiography (ECG) or galvanic skin response (GSR) to improve the efficacy of mental states detection (e. g. ,  Ahn et al. , 2019). Wearable EEG systems have been used extensively over the past few years to measure frontal asymmetry  (Peng et al. , 2011; Hu et al. , 2015; Hashemi et al. , 2016; Jebelli et al. , 2017 Jebelli et al. , , 2018;; Wu et al. , 2017; Zhao et al. , 2017; Hwang et al. , 2018; Umar Saeed et al. , 2018; Cao et al. , 2019; Arpaia et al. , 2020; Park et al. , 2020; Saeed et al. , 2020)  and were used in this present study to enable the collection of a large dataset.
Alpha Frequencies and Bounds The third main limitation in EEG asymmetry research is the handling of alpha-band frequencies and bounds. The alpha band is dominantly considered as a single phenomenon in EEG asymmetry studies. However, previous evidence suggested that it should not. For instance, measuring alpha power spectral density (PSD) on the traditionally a priori-defined bandwidth 8-13 Hz does not account well for interindividual differences because parts of the alpha power distribution fall outside this range for some individuals  (Klimesch et al. , 1990; Klimesch, 1997). Furthermore, differential changes in opposing directions within the same dataset have been observed between lower (8-10. 5 Hz) and upper (11-13 Hz) alpha oscillations, as well as between local and global properties  (Klimesch, 1999; Nunez et al.
(2017)  have implemented these algorithms into a fast, reliable, opensource toolbox operating in MATLAB and Python  (Corcoran et al. , 2017). This method seems suitable for large datasets with a relatively low signal-to-noise ratio (SNR) acquired with a wearable dry EEG system. Hence, calculating alpha asymmetry scores on PSD estimated on the predefined alpha band (8-13 Hz), the lower (8-10. 5 Hz) and upper (11-13 Hz) alpha sub-bands, and the CoG may help us understand more about the underlying mechanisms of alpha asymmetry. The present study incorporates these metrics to evaluate differences in these measures and their relationship to well-being. We expect well-being to be positively correlated with CoG values, differently correlated with lower and upper alpha (no specific direction is hypothesized), and positively correlated with CoG-asymmetry (and we expect this association to be stronger than that with the traditional whole alpha band asymmetry, by better accounting for interindividual differences).
EEG Data Collection Once participants completed the survey, continuous EEG was recorded using InteraXon's Muse wearable EEG headband (version 2016). Electroencephalography data were recorded while participants were instructed to focus their attention on their breath and count inhalation/exhalation cycles. They were instructed to bring their attention back to their breath and start counting again if they lost track of their count or noticed their minds wandered. This task reduces EEG artifacts occurring naturally with eye movements. Most importantly, this task can later be implemented into practical translational and therapeutical applications aimed at increasing well-being levels through the modulation of alpha asymmetry and the underlying brain processes  (Angelakis et al. , 2007; Sessa, 2007; Moynihan et al.
, 2013; Doll et al. , 2016; Schmalzl et al. , 2018; Prpa et al. , 2020). Electroencephalography data were with a sampling rate of 256 Hz and 12-bits of data resolution. This system has five active dry electrodes: two frontal silver (AF7 and AF8), two temporoparietal (TP) silicone electrodes (TP9 and TP10), and a reference electrode (FPz). Before positioning the headband on the subjects' heads, their skin was cleaned with alcohol swipes at electrode sites, and a thin layer of water was applied with a sponge to the electrodes to improve signal quality. EEG data were acquired on Chromebooks using the Muse Monitor App and were uploaded onto Dropbox at the end of the recording. Random unique identifiers were used to link survey and EEG data. Impedance check was provided by the App (horseshoe symbol) and visually confirmed by the raw signal displayed on the screen in real-time.
Data Preprocessing Data preprocessing was done in EEGLAB version 2020. 0  (Delorme and Makeig, 2004)  in MATLAB v2020a. EEG data were imported with the muse_monitor plugin v3. 2, low-pass filtered at 30 Hz (transition bandwidth 12. 5 Hz; passband edge 50 Hz; cutoff frequency -6 dB 56. 25 Hz; linear non-causal filter) to remove high-frequency artifacts, and high-passed filtered at 1 Hz (transition bandwidth 1 Hz; passband edge 1 Hz; cutoff frequency -6 dB 0. 5 Hz; linear non-causal filter) to remove lowfrequency drifts. 10-20 channel template locations from BESA spherical coordinates were used in EEGLAB. Artifactual channels (with ∼50% of data being noisy or artifactual) were manually tagged and removed with a custom-made single-page figure displaying each channel's overall raw data, standard deviation, and power spectra.
Files with at least one bad channel were removed for analyses. An existing automatic method to clean EEG artifacts over this large sample was cross-validated: 150 files were randomly selected from the database to be cleaned manually and automatically with EEGLAB's clean_rawdata plugin v2. 2 (Euclidean method). Performance was calculated on each channel by comparing each sample as either true positive (TP, bad sample correctly rejected), true negative (TN, good sample correctly kept), false positive (FP, good sample incorrectly rejected), or false negative (FN, bad sample incorrectly kept). "Positive" and "negative" refer to presence or absence. Then, the true positive rate (TPR, i. e. , sensitivity) and the true negative rate (TNR, i. e. , specificity or selectivity) were calculated for each channel with: TPR = TP/(TP + FN) and TNR = TN/(TN + FP).
The average sensitivity and specificity were then calculated over all channels to obtain the overall performance of the automatic method compared to manual rejection. After testing different parameters, the best performance obtained showed 81% sensitivity and 83% specificity [settings: "burst_criteria" = 6, "window_criteria" = 0. 3, "window_tolerance" = "(-Inf 7)"]. 50 additional datasets were randomly selected for crossvalidation, showing 84% sensitivity and 89% specificity. Since further increasing the sensitivity scores (i. e. , removing more subtle artifacts) corresponded to a decrease in specificity (i. e. , removing more non-artifactual data), these thresholds were considered most suited for this analysis. On average, this method removed an additional 11. 4 s of data (± 23. 0). Thus, bad channels were manually tagged and data were cleaned using this automated method and parameters.
Power Spectral Density and Asymmetry Estimates Power Spectral Density (PSD) was calculated using MATLAB's pwelch function on 1-s hamming tapered windows (42. 5 dB sidelobe attenuation) with 50% overlap [per guidelines  (Allen et al. , 2004; Smith et al. , 2017) ], since the pwelch method smooths over non-systematic noise and is more robust compared to the more popular fft method that is more sensitive to noise and nonstationarities. Power spectra were then converted to 10 * log10 deciBels (dB) as untransformed power values tend to be positively skewed due to individual differences in skull thickness that influence the signal amplitude  (Allen et al. , 2004). The CoG was estimated for each channel using the automated, open-source method developed by  Corcoran et al. (2017)  which uses curve-fitting algorithms and a smoothing Savitzky-Golay Filter (SGF).
Statistical Analyses Robust linear regression models were generated in MATLAB 2021a using MATLAB's fitlm package. Because of small portions of artifacts remaining in some EEG data after automatic preprocessing, robust least-squares regressions (Tukey's bisquare function; default tuning constant = 4. 685) were used for statistical analysis to down-weight the residuals' influence on the model, using iterative reweighted least-squares (IRLS;  Huber and Ronchetti, 2009). All models were tested for lack of fit first using a degenerate model consisting of only a constant term. Reported Fstatistics with a p-value, therefore, indicate a valid fit for the model but do not inform on the relationship between the dependent and independent variables. The Beta (β) coefficient estimates and their standard error (SE) are reported in the first column and indicate a significant linear relationship between the predictor and the outcome variables when p-values are present.
Thus, future investigators could consider using the more recent Muse S that was developed for sleep studies. The Muse S is made of a flexible fabric that can stretch and keep stronger pressure on the electrodes, preventing them from disconnecting as much. Furthermore, we recorded the data when participants already started the task with their eyes closed to reduce data cleaning over the large sample. Automatic cleaning performance would have likely been increased by adding a period before the task that includes obvious artifacts (e. g. , asking participants to produce eye blinks and jaw clenching) to help the automatic method algorithms create a more robust baseline and therefore reject artifacts more efficiently. Thus, higher-grade and -density wearable EEG systems and longer recordings (at least 4 min of continuous data to ensure having at least 2 min of artifact-free data on a larger portion of the sample) are recommended for future studies to keep the advantages of wearable technologies to acquire large datasets without compromising data quantity and quality.
Intestinal organoids are an excellent model to study epithelial biology. Yet, the selection of analytical tools to accurately quantify heterogeneous organoid cultures remains limited. Here, we developed a semi-automated organoid screening method, which we applied to a library of highly specific chemical probes to identify epigenetic regulators of intestinal epithelial biology. The role of epigenetic modifiers in adult stem cell systems, such as the intestinal epithelium, is still undefined. Based on this resource dataset, we identified several targets that affected epithelial cell differentiation, including HDACs, EP300/CREBBP, LSD1, and type I PRMTs, which were verified by complementary methods. For example, we show that inhibiting type I PRMTs, which leads enhanced epithelial differentiation, blocks the growth of adenoma but not normal organoid cultures.
, 2019). From this screen dataset, we identified several mediators of IEC biology that we verified with complementary methods. We envision that this resource will be useful for the research community and will lay basis for further mechanistic investigation. Specifically, we find new regulators of organoid size related to ISC frequency, as well as new regulators of IEC differentiation. Finally, we explore the potential of some of these probes for treatment of intestinal cancer by application on intestinal tumor organoids.
Based on edge detection in each stack layer, this workflow can be used to robustly quantify organoid size (object area) and classify, e. g. , by determining intensity, organoid phenotypes over time (Figure  1C , Supplementary Figures  1b, c ). The workflow is robust to changes in morphology, stitching artifacts, and can be adjusted to image data from different automated microscopes. As the object classification by Ilastik is not dependent on deep learning and extensive training data, it can easily be adapted to new phenotypes and changes in experimental conditions. In addition to determining organoid size, the cellular composition is of critical interest. We therefore selected transcripts that are specific for individual IEC lineages  (Haber et al. , 2017) , and performed qRT-PCR on these within a 24-96 h time course (Figure  1D ).
However, incubation with SGC-CBP30 or I-CBP112 could not enhance organoid growth under low EGF concentrations, replace R-Spondin in the culture medium, or overcome treatment with the WNT inhibitor IWP2 (Supplementary Figures  3f, g ). To determine which genes are under the control of EP300/CREBBP in the intestinal epithelium, we performed mRNA sequencing on untreated vs. I-CBP112 treated organoid cultures. In accordance with a transcriptional co-activator role for EP300/CREBBP, we found 53 genes upregulated and 110 genes downregulated using a log2 fold change cutoff of 0. 5 and padjust ≤ 0. 01 (Figure  3D ). Furthermore, signatures of transcription factors known to interact with either EP300 or CREBBP were negatively enriched (Supplementary Figure  3h ). Remarkably, Lgr5 was the most significantly upregulated gene in our dataset, substantiating our previous results (Figure  3D ).
BET Inhibition Reduces Relative Abundance of Tuft Cells Secretory cell lineage differentiation, such as goblet and Paneth cells, is well-studied and is generally thought to involve NOTCHmediated lateral inhibition. Tuft cell differentiation, however, is less defined. Therefore, we next focused on the BRD/BET inhibitors (+)-JQ1 and bromosporine in our marker gene expression dataset (Figure  4A ) as treatment with these led to a strong reduction of tuft cell marker genes Dclk1, Trpm5, and Gfi1b (Figure  5A ). Organoids treated with these probes also had altered expression in some of the other IEC lineage marker genes, but the downregulation of tuft cell marker genes was consistent and prominent (Supplementary Figure  5a ). Although probe A-366, an inhibitor of Euchromatic histone-lysine Nmethyltransferase 1 and 2 (EHMT1/2, GLP/G9A) also reduced tuft cell marker genes, two other EHMT1/2 inhibitors, UNC0638 and UNC0642, did not (Figure  4A , Supplementary Figure  4k ).
DNA repair, which is a well established function of type I PRMTs  (Guccione and Richard, 2019) , was among the negatively correlated GO:BP terms (Supplementary Figures  6e, g ). We found that PRMT1 was the type I PRMT with the highest gene expression level in SI crypts and organoids (Supplementary Figure  6h ). Enhanced PRMT levels are found in various malignancies and high PRMT1 expression is negatively correlated with survival in colon cancer  (Mathioudaki et al. , 2008; Jarrold and Davies, 2019). Furthermore, Prmt1 gene expression was highest in ISC, transitamplifying cells, and early enterocyte progenitors compared to fully differentiated lineages in a published IEC scRNA-seq dataset  (Haber et al. , 2017)  (Supplementary Figure  6i ). We therefore hypothesize that inhibition of type I PRMTs leads to maturation of IECs, which aligns with the observation that differentiated cells have lower Prmt1 levels.
DISCUSSION Working with heterogeneous organoid cultures is challenging with respect to reproducibility and quantification. Our toolkit, which we present in this article enables reproducible results across biological replicates using standard equipment and is suitable for screening setups. We established a quantification workflow that is based on the open source tools ImageJ and Ilastik, which is a simple yet robust alternative to recent standalone software options  (Borten et al. , 2018; Kassis et al. , 2019)  and could easily be adapted to different tissue organoids. In addition, qRT-PCR and flow cytometry of IEC lineages is sufficiently sensitive for initial screening and was subsequently confirmed by additional methods such as reporter organoids. We used this screening setup to test a set of 39 chemical probes targeting epigenetic modifiers and identified probes that strongly affected organoid size or IEC lineage composition.
For an improved segmentation that is robust to stitching artifacts, allows to filter out debris and organoid clusters and to distinguish different organoid phenotypes, the ImageJ workflow was combined with the interactive machine learning software Ilastik v1. 3. 2  (Berg et al. , 2019) ("combined workflow"). Training data was taken from the analyzed experiment and excluded from further analysis. In a first step, pixel classification on an intensity summary projection of the ImageJ edge stack was used to separate between background and object outlines. The generated pixel prediction maps were then used as input in a second step of object classification together with minimum projections of the ImageJ bright-field stack. Routinely, the following label classes were used: Organoid, big sphere, small sphere, cluster, debris, background mislabeled as organoid, air bubble, edges of well plate.
Primer-probe combinations were selected based on the Universal Probe Library System (Roche) and are listed in Supplementary Table 2, primers were purchased from Sigma-Aldrich. Hprt was used as housekeeping gene. CT values were calculated as CT = CT (housekeeping gene)-CT (gene of interest) (such that higher values indicate higher relative expression); CT values referred to the calibrator as indicated, and fold change was calculated as 2 CT. Target gene "perturbation" was calculated as Euclidean distance of the log2 median fold change using GNU R package pheatmap. Defa22 gene expression was below the detection limit for some samples and was therefore omitted from the Euclidean distance ranking but is provided in the Supplementary Material.
For intracellular staining, samples were subsequently fixed with 2% paraformaldehyde (PFA) for 15min, and incubated with or without rabbit anti-DCLK1 (Abcam #ab31704, 1:500 in PBS/2% FCS/0. 05% Saponin) for 1 h at 4 • C, followed by incubation with Goat anti-Rabbit IgG-AF405 (Invitrogen, 1:1,000 in PBS + 2% FCS + 0. 05% Saponin). Samples were analyzed on a BD LSRII instrument (Becton Dickinson) equipped with 405, 488, 561, 647 nm laser lines. Single fluorochrome stainings of cells and compensation particles (BD CompBead, Becton Dickinson) were included in each experiment. For analysis, FlowJo software v10. 6. 2 and GNU R/Bioconductor v3. 6. 3/v3. 10 packages flowCore, CytoML/flowWorkspace, ggcyto, flowViz were used  (Van et al. , 2018). If not indicated otherwise, only samples with more than 10,000 viable cells in the parent gate were included.
5, CD44-AF647, CD117-PE-Cy7 (all Biolegend, see Supplementary Table  3  for detailed list), and UEA1-FITC (Invitrogen) was carried out as described above. Samples were run on a MACSQuant X instrument (Miltenyi Biotec) equipped with 405, 488, 647 nm laser lines and analyzed as described above. Euclidean distance clustering tree of normalized median population frequencies was generated with GNU R package ggtree  (Yu et al. , 2018).
mRNA Sequencing Analysis Read quality was assessed using FastQC v0. 11. 8, reads were aligned with STAR v2. 7. 3a to the Mus musculus genome build mm10, and MultiQC v1. 7 was used to summarize logs from STAR and FastQC  (Dobin et al. , 2013; Leggett et al. , 2013; Ewels et al. , 2016). The number of reads that uniquely aligned to the exon region of each gene in GENCODE annotation M18 of the mouse genome was then counted using featureCounts v1. 6. 4  (Liao et al. , 2014; Frankish et al. , 2019). Genes that had a total count <10 were filtered out. Differential expression was then determined with GNU R/Bioconductor v3. 6. 1/v3. 10 package DESeq2 v1. 26. 0 using default settings and shrunken log2foldchange was calculated with the apeglm method  (Love et al. , 2014; Zhu et al. , 2019). GSEA enrichment was performed using GNU R/Bioconductor v3.
6. 3/v3. 10 package ClusterProfiler v3. 14. 3 by shrunken log2 fold change and with the shrunken log2 fold change as weights using 10,000 permutations  (Yu et al. , 2012). Gensets for celltype signatures were assembled based on single-cell and bulk RNA-Sequencing data from sorted samples based on datasets by  Haber et al. (2017) (GSE92332)  and  Muñoz et al. (2012) (GSE33949). Transcription factors interacting with murine or human EP300 or CREBBP were retrieved from protein-protein interactions with an minimum medium experimental confidence level (≥0. 4) from STRING-DB v11  (Szklarczyk et al. , 2019). Genesets regulated for the mouse and human version of these transcription factors were retrieved from TRRUST v2  (Han et al. , 2018). For human genesets, murine orthologue genes retrieved from Ensembl GRCh38.