dataesr/scientific-paragraphs-categorization huggingface dataset:
Resting state fMRI preprocessing The first ten RS functional images were discarded to ensure signal equilibration, and the remaining images were preprocessed using SPM12 tools (http://fil. ion. ucl. ac. uk/spm/software/spm12). Functional images were first realigned, followed by co-registration of the mean functional image with the anatomical scan. Functional images were normalized to the MNI space with SPM12 "Segment", resampled to 3 mm isotropic voxels, and then spatially smoothed with a 6 mm full-width-at-half-maximum (FWHM) Gaussian filter. The average signal within a mask of white matter (WM) and cerebrospinal fluid (CSF) were extracted using the Data Processing Assistant for Resting-State fMRI toolbox  [51]. The effects of WM, CSF, and six motion parameters were regressed out from the time-course, and a bandpass filter (0.
BOLD signal variability Voxel-wise BOLD signal variability was obtained for each participant by computing the standard deviation of each preprocessed time-course. This approach is equivalent to the frequency-domain computation of the amplitude of low-frequency fluctuations (ALFF) between 0. 01 and 0. 1 Hz  [56, 57] , and is strongly correlated with mean-square successive differences (MSSD)  [28, 58]. BOLD signal variability maps were constrained to the binarized DARTEL template (excluding the cerebellum), and were z-scored across all voxels included in the template within each participant  [57]. Age, sex, scanner, and head motion (i. e. , mean FD) were linearly regressed out from the imaging data prior to the PLS analysis using a general linear model on MATLAB.
Note that percentages may add up to more than 100% because some individuals take more than one medication. depression, and mania (imaging and behavioral saliences, respectively). By linearly projecting the imaging and behavioral measures of each participant onto their respective saliences, we obtain individual-specific brain and behavior scores, which reflect the participants' imaging and behavioral contribution to each LC. Importantly, the PLS analysis was agnostic on the diagnostic group, so that transdiagnostic brainbehavior associations could be extracted. The statistical significance of the LCs was assessed by constructing a null distribution of the singular values using permutation testing (1000 permutations), whereby the behavioral data was permuted within each diagnostic group, so that latent components would not be driven by group differences.
ACKNOWLEDGEMENTS We wish to thank  Gwladys Rey  for her initial help in gathering a multi-site database,  Anne-Lise Küng  and  Eleonore Pham  for their help in the recruitment of participants, and  Alexandre Dayer  for his inspirational guidance and enthusiasm for this project. He will stay in our hearts. This work was supported by the  Swiss National Center of Competence in Research ; "Synapsy: the  Synaptic Basis of Mental Diseases ", financed by the  Swiss National Science Foundation  (grant number  51NF40-158776 ), a grant of the  Swiss National Science Foundation  to JMA (grant number  32003B-156914 ) and the  Fondamental Suisse Foundation. VK was partly supported by the  Singapore National Research Foundation (NRF)  Fellowship (Class of 2017) and the  Singapore Ministry of Defense  (Project  CURATE ).
Her work utilized resources provided by the  Center for Functional Neuroimaging Technologies ,  NIH P41EB015896  and instruments supported by  NIH   1S10RR023401 ,  NIH 1S10RR019307 , and  NIH   1S10RR023043  from the  Athinoula A. Martinos Center for Biomedical Imaging  at the  Massachusetts General Hospital. The computational work was partially performed on resources of the  National Supercomputing Centre, Singapore  (https://www. nscc. sg). This work was supported by the  French ANR  under the  "VIP" (MNP 2008 ) Project; the Investissements d'Avenir programs managed by the  ANR  under references  ANR-11-IDEX-004-02  ( Labex BioPsy ) and  ANR-10-COHO-10-01 ; and the  Fondation pour la Recherche Médicale  (" Bioinformatique pour la biologie 2014 "). This work was also supported by research grants from  Grenoble University Hospital  (http://www.
chu-grenoble. fr/). The Grenoble MRI facility IRMaGE was partly funded by the  French program  " Investissement d'avenir " run by the  "Agence Nationale pour la Recherche " (http://www. agence-nationalerecherche. fr/):Grant " Infrastructure d'Avenir en Biologie Santé " (grant number  ANR-11-INBS-0006 ).
Human cohorts Male IPEX and healthy donor whole-blood samples were obtained under protocols reviewed and approved by the local Institutional Review Boards at each center (Boston Children's Hospital 04-09-113R, Necker/Imagine C 15-13_CODECOH_AR, HMS IRB15-0504). Anonymized clinical data included age at onset and at blood sampling, clinical symptoms (enteropathy, diabetes, eczema, other autoimmune diseases and allergy), ancillary testing, treatments and long-term outcomes (Supplementary Table  1 ). Two independent cohorts of samples were profiled (Supplementary Table  1 ). Each cohort contains samples from both centers. Cohort 1 samples were processed in two different scRNAseq runs, cohort 2 (replication cohort) in three scRNAseq runs. Samples from each cohort were sorted and sequenced as one batch for population RNA sequencing (Supplementary Table  4 ).
Flow cytometric profiling of IPEX and HD Cells were stained for flow cytometry in 100 μL of FACS buffer (phenol red-free DMEM, 2% FBS, 0. 1% azide and 10mM HEPES, pH 7. 9) for 10 min with 10uL FcBlock (supernatant of clone 2. 4G2, ATCC HB-197, hybridoma cultures) and the following antibodies: CD3 BV605 or A700 (2μL, OKT3, Biolegend), CD4 PerCP-Cy5. 5 (2 μL, OKT4, Biolegend), CD25 PE-Cy7 (3 μL, BC96, Biolegend), CD127 A488 (3 μL, A019D5, Biolegend), CD45RA PB (3μL, HI100, Biolegend). After permeabilization/fixation for 2 h on ice, FOXP3 (APC, clone PCH101; Biolegend; 2 μL) and anti-HELIOS PE (PE, clone 22F6, Biolegend, 2 μL) staining was performed overnight at 4°C in the dark in accordance with the manufacturer's instructions (eBioscience™ FOXP3 / Transcription Factor Staining Buffer Set). Data was recorded on an LSRII flow cytometer (BD Biosciences) and analyzed using FlowJo v10.
First strand cDNA was subjected to limited PCR amplification followed by Tn5 transposon-based fragmentation using the Nextera XT DNA Library Preparation Kit (Illumina). Samples were then PCR amplified for 12 cycles using barcoded primers such that each sample carries a specific combination of eight base Illumina P5 and P7 barcodes for subsequent pooling and sequencing. Paired-end sequencing was performed on an Illumina NextSeq 500 using 2 × 38bp reads with no further trimming. Reads were aligned to the human genome (GENCODE GRCh38 primary assembly and gene annotations v27) with STAR 2. 5. 4a (https://github. com/alexdobin/STAR/releases). The ribosomal RNA gene annotations were removed from GTF (General Transfer Format) file. The gene-level quantification was calculated by featureCounts (http:// subread.
sourceforge. net/). Raw read counts tables were normalized by median of ratios method with DESeq2 package from Bioconductor (https://bioconductor. org/packages/ release/bioc/html/DESeq2. html) and then converted to GCT and CLS format. Samples with less than 1 million uniquely mapped reads were excluded from normalization to mitigate the effect of poor quality samples on normalized counts. Genes with a minimum read count of 5 in all replicates of a population (31,448 human genes) were retained. A pseudo count of 1 was added and log2-transformed prior to quantile normalization. Quantile-normalized counts were converted back to a linear scale.
IPEX signature genes were identified by computing the ratio of expression in Tregs or Tconv of all IPEX patients vs all healthy donors (p. value as simple uncorrected t. test). IPEX-up indices for each individual were calculated by selecting the 100 transcripts most over-expressed in IPEX Tregs overall (FC>3), calculating their differential expression in Treg cells of each patient relative to the mean of all HD Tregs, and averaging the log2 of these foldchanges (and similarly for IPEX-down indices from transcripts with IPEX/HD <0. 37). The TregUp indices were similarly calculated (expression in Tregs of each individual over mean expression in all healthy donors, average the log2 of these foldchanges). Geneset enrichment analysis. -Gene signatures were curated from published datasets (references in the signature name)  6.
The Human and Mouse Treg signatures have been reported  4, 5. Data were downloaded from the Gene Expression Omnnibus (GEO) (https:// www. ncbi. nlm. nih. gov/geo/). Only datasets containing replicates were used. To reduce noise, genes with a coefficient of variation between biological replicates <0. 6 in either comparison groups were selected. Up-and downregulated transcripts were defined as having a fold change in gene expression >1. 5 or <0. 6 and a t-test p-value <0. 05. Other signatures were obtained from MSigDB C7 Immunologic signatures collection  53. Geneset enrichment analysis with the IPEX signature was performed using the hypergeometric distribution and type I error was controlled using FDR. Signatures with p-value <0. 001 (all with FDR <6%) are reported.
Gene counts were obtained by aligning reads to the hg38 transcriptome using CellRanger software (v3. 0. 2) (10X Genomics) (default parameters). Hashtag libraries. -Hashtag libraries were made separately as described in Stoeckius et al. 36  (https://citeseq. files. wordpress. com/2019/02/cell_hashing_protocol_190213. pdf). In brief, at the cDNA amplification step in the Single Cell 3′ Reagent Kit protocol, the yield of HTO products was increased using an "additive" primer to cDNA PCR. Hashtag-derived cDNAs (<180bp) and mRNA-derived cDNAs (>300bp) were then separated using 0. 6x SPRI bead selection. The supernatant contains the Hashtag-derived cDNA that is purified with two rounds of 2x SPRI beads. The sequencing oligos are added by PCR which also amplifies the Hashtag library. Libraries were sequenced on the NextSeq 500 platform (28/8/0/91, Read1/i7/i5/Read2).
Hashtag count matrices were obtained from CITE-Seq-Count package (https://zenodo. org/ record/2590196) (default parameters). Each droplet from the Gene Count matrix was matched to a Hashtag using the HTODemux function from the Seurat v3. 1. 2 package. Doublets (droplets with 2 hashtags) were excluded and cells were assigned to the max hashtag signal. The Hashtag count data was also analyzed by tSNE for a visual check (clear separated clusters for each hashtag). All single cells from the gene count matrix were matched unambiguously to a single hashtag (and therefore their original donor). Quality control. -Cells with less than 500 (V2) or 1000 (V3) counts (V2) were excluded. Dead cells with more than 10% of counts mapping to mitochondrial genes were excluded. Doublets were excluded using scrublet (doublet score > 0.
3)  54. Finally, cells were annotated using singleR  55  with the BluePrintEncode reference data. Cells that were not annotated as CD4+ cells were excluded. Batch correction. -Batch correction was performed using the Integration method in Seurat V3 as described in Stuart et al. 37  and only used to visualize the whole dataset with UMAP. Cohort 1 and cohort 2 samples were first normalized independently using the SCTransform function in Seurat V3 with parameters to regress out the following variables: experiment date, percent of mitochondrial gene mapping and the number of counts of each single cell (SCTransform(vars. to. regress = c("experiment_date","percent_mito", "nCount_RNA"), variable. features. n = 500). Integration of both cohorts together was then performed using the top 500 variable genes in both cohorts.
Each cohort was projected into a common CCA (Canonical Correlation Analysis) space. Anchors (robust pairwise cell correspondence between datasets) were found by knn and snn graphs (FindIntegrationAnchors function) and they were used to transform the data in an integrated space. We used this integrated space, reduced to the top 40 PCs by PCA to visualize with UMAP  56. The number of significant principal components (PCs) was determined by comparison to PCA over a randomized matrix, as described by Klein et al. 57. Of note, another batch correction method, scAlign 58 produced similar results. Clustering. -We found shared clusters across HD and IPEX samples ("regressing out the IPEX effect") using the Integration method in Seurat V3 as described in Stuart et al. 37. This time, each sample was first normalized independently using the SCTransform function in Seurat V3 with parameters to regress out the following variables: percent of mitochondrial gene mapping and the number of counts of each single cell (SCTransform(vars.
to. regress = c("percent_mito", "nCount_RNA"), variable. features. n = 500). Integration of samples together was then performed using the top 500 variable genes. Each cohort was projected into a common reciprocal PCA space. Anchors (robust pairwise cell correspondence between samples) were found by knn and snn graphs (FindIntegrationAnchors) and they were used to transform the data in a shared space. We used this shared space and reduced it to the top 32 most significant PCs by PCA for clustering  56. In this space, a shared nearest neighbor graph was constructed from a k-nearest-neighbor graph (k = 20) by pruning cellcell edges with less than 1/15 neighbor overlap. Community detection using the Louvain algorithm at a resolution of 0. 5 found 11 clusters. Automated annotation using singleR with the BluePrintEncode reference data and manual annotation using canonical markers (see Figs.
bam fastq) and aligned the reads using STAR v020201 ($STAR_PATH/STAR --genomeDir GRCh38_fasta --readFilesIn $READ2 --runThreadN 32 --outSAMtype BAM SortedByCoordinate) to the GRCh38 human genome in order to be able to run bcftools. Variants on the X chromosome were called using bcftools v1. 10. 2 (bcftools mpileup -Q 30 -A -x -Ou --threads 32 -r X -f $FASTA_REF Aligned. sortedByCoord. out. bam | bcftools call -mv -Ov -o calls. vcf). We then used scAlleleCount (https://github. com/barkasn/scAlleleCount) to obtain for each single cell the reference and alternate allele count for the X chromosome variants (cell x variant matrices). We then constructed the IPEX (ΔFoxp3) X chromosome haplotype (present in sons and carrying the FOXP3 mutation) and the complementary haplotype (present in the mother and carrying the "WT" FOXP3).
05% in two different tubes (5,000 to 10,000 sorted cells for each sample) for sequencing in two 10X lanes, and libraries were constructed with a Single Cell 3′ Reagent Kit (V3 chemistry) (see also Supplementary Table  4 ). Libraries were sequenced on the NextSeq 500 platform (28/8/0/91, Read1/i7/i5/Read2). Gene counts were obtained by aligning reads to the mm10 transcriptome using CellRanger software (v3. 0. 2) (10X Genomics) (default parameters). The mm10 transcriptome was complemented with the transgene sequences of the Ires-Gfp, Yfp, Cre in order to map reads to the Foxp3 locus in Foxp3 ires-gfp /B6 and Foxp3 ΔGFPiCre xR26 -YFP /B6 mice. In Foxp3 ires-gfp /B6 mice, Foxp3 locus expression was calculated as the sum of reads mapping to Foxp3 and Ires-Gfp. In Foxp3 ΔGFPiCre xR26 -YFP /B6 mice, Foxp3 locus expression was calculated as the sum of reads of mapping to Foxp3 and Cre (not GFP because the Read 2 length is not long enough to reach the GFP sequence).
Hashtag libraries. -See similar section in the human analysis (scRNAseq analysis of human PBMC samples) Quality control. -See similar section in the human analysis (scRNAseq analysis of human PBMC samples). SingleR was used with the Immgen reference data. Batch correction. -Experiment 1 and 2 were profiled with 10X V2 and V3, respectively. Batch correction was performed using the Integration method in Seurat V3 as described in Stuart et al. 37  and only used to visualize the whole mouse dataset with UMAP. Experiment 1 (10x V2) and experiment 2 (10X V3) samples were first normalized independently using the SCTransform function in Seurat V3 with parameters to regress out the following variables: percent of mitochondrial gene mapping and the number of counts of each single cell (SCTransform(vars.
Statistics Analysis was done using R-3. 6. 2. Heatmaps were made with Morpheus (https:// software. broadinstitute. org/morpheus) or the pheatmap package in R (https://cran. rproject. org/web/packages/pheatmap/index. html). All other plots are made with ggplot2 61. Statistical tests are described in their respective method section. c. Index and coefficient of variation (CV) of Treg Up signature transcripts (each dot is a sample). *** two-sided t. test p < 0. 001. d. FoldChange vs p value (volcano) plot comparing normalized expression in all IPEX to all HD samples. Genes with differential expression (two-sided t. test p<0. 01, FC >2) are highlighted (452 up genes and 1032 down genes), and numbers shown. e. Heatmap of the expression ratio, for IPEX signature genes defined in d, in Tregs from each donor, computed against the mean expression in HD Tregs (each cohort computed against its own HD Treg set).
ACKNOWLEDGEMENTS We thank:  Drs. M. Levings  and  A. Rudensky  for insightful discussions;  K. Hattori ,  C. Araneo ,  K. Seddu , and the  Klarman Cell Observatory team , for help with mice, cell sorting and single-cell profiling. This work was funded by grants from the  NIH  to CB&DM ( AI116834 ,  AI125603 ,  AI150686 ),  TAC  ( AI085090 ), LMC ( AI153174 ), the  Institut National de la Santé et Recherche Médicale , the  European Union Seventh Framework  (# 269037  and # 261387 ) and  Horizon 2020  (# 693762 ), the  Agence Nationale pour la Recherche  ( Investissement d'Avenir   ANR-10-IAHU-01 ) to IA, ES, MD, JL, MC, BN, FRL, FR and NCB. JL was supported by an  INSERM Poste d'Accueil  and an  Arthur Sachs scholarship.
K E Y W O R D S behavioural ecology, digital video recording, DIY camera trap, e-ecology, low-cost technology, plant-insect interaction, pollination biology, Raspberry Pi To protect the components from natural elements, PICT is sealed in a food storage case of about 1 L in volume. Each component inside the case is fixed in place by adhesive Velcro ® strips. A mount with a standard ¼ in screw is glued onto the case to allow PICT to be fixed to a standard camera mount. At the time of writing, the full cost of building one operational unit is less than 170 USD. The components needed for a PICT with functionality comparable to a retail CT, that is, without a mount, battery or memory card, would cost less than 100 USD (Table  1 ). The camera is operated through the picamera Python package (https://picam era.
readt hedocs. io/) installed on a Raspberry Pi Zero, which is a credit card-sized, low-cost, high-performance single-board computer. All the Raspberry Pi models with an integrated Wi-Fi controller can provide the functionality required, but we recommend on which the operating system, programs and data are stored. It is powered through a 5V mini-USB port that can be supplied by a standard lithium-ion power bank (Figure  1 ). F I G U R E 1 Overview of PICT. (a) We used the 5-megapixel Raspberry Pi Camera Module v1 (OmniVision © OV5647 sensor), based on a 2,592 × 1,944 photosites, ¼ in format sensor. It comes in customized versions with (a) an embedded 3. 3V power output that can be connected to a nearinfrared LED without need for soldering, (b) a 3. 6-mm lens with a diagonal field of view of 75 degrees and adjustable focus distance, (c) no embedded infrared filter, improving lens speed and allowing illumination of the night scene with IR light.
, 2014). The image resolution of PICT can be freely determined by the user. Because of the lens characteristics of Raspberry Pi Camera Module v1, the smallest resolvable point is larger than the actual pixel size on the sensor (1. 4 × 1. 4 µm). For this reason, we recommend setting the resolution to 1,296 by 972 pixels where a 2 × 2 binning is applied by the camera to downsample the image. This camera output resolution has the added benefit of doubling sensitivity and improving the signal to noise ratio. At this resolution, the camera can capture up to 42 frames per seconds (FPS) and up to 90 FPS at 640 by 480 pixels  (Barnes, 2020).
| P OWER CON SUMP TI ON AND DATA S TO R AG E Low power consumption is essential to avoid the need for heavy or bulky batteries and to provide autonomous operation times that exceed the duration of the targeted phenomenon (the duration of anthesis for instance). To reduce the power drawn by the PICT by about 0. 13W, we deactivated the components that are not needed for our application: the HDMI port, Bluetooth and activity LEDs. We used an electronic multimeter (RuiDeng UM25C) to measure the power drawn by a PICT under various operating conditions. The observed power load of each of the components and for different camera settings is given in Table  2. We found that both frame rate and resolution settings have a substantial effect on power use (Table  2 ; Figure  S1 ). We used a resolution of 1,296 by 972 pixels and 15 frames per second (FPS) to achieve the lowest possible power consumption and storage needs while not affecting the ability to identify insects.
| Deployment and data processing To assess the performance of PICT in the field, we studied two plant species with contrasting habits, pollination ecologies and floral char- Cyrtorchis letouzeyi, a sub-endemic orchid primarily found in Cameroon, is remarkable for its ivory white flowers with a nectar spur measuring up to 10 cm in length. The flowers emit a strong lilac/jasmine scent at night. This epiphytic species is easily observed growing at a height of 1-3 m on shrubs bordering the grasslands on inselbergs of the Dja Faunal Reserve (East Cameroon). Pollination syndromes suggest that this species could be visited by long-tongued hawkmoths  (Cribb, 1989).
This could be implemented using open-source libraries available for Raspberry Pi (e. g. https://github. com/Motio n-Proje ct/motion and https://opencv. org/). This technique is relevant when post-processing time or data storage space needs to be reduced. However, in the absence of external power source, either autonomy or portability would be sacrificed to power the motion detection algorithm. Second, the durability of the Raspberry Pi in harsh environments can be affected by electrostatic damage, flaws in the sealing or mishandling of the plastic container. Occasional malfunction of electronic components has not however posed a substantial challenge ) F I G U R E 3 in our experiments due to the ease and low-cost of acquiring spare parts to repair possible damages. Finally, PICT is designed to be controlled remotely via Wi-Fi.
57 W are needed by the motion detection algorithms implemented in RPi Cam Web Interface (https://github. com/silva nmelc hior/RPi_Cam_Web_ Inter face) and MotionEyeOS (https://github. com/ccris an/motio neye) respectively (Table 2) which would increase power draw over 24 hr (with 12 hr of daylight) by c. 67% and 43% respectively. Once the operating system is installed on a 64 GB micro SD card, 57 GB will remain free for data. The videos are saved on the micro SD card as MP4 files encoded in H. 264 compression standard. A video recording of 1 hour at default compression, and above settings in outdoor conditions would take up around 700 MB. Therefore, storage space is not a limiting factor, as the battery would run out TA B L E 2 a Total power drawn by the RPi Cam Web Interface and MotionEyeOS performing motion detection with LED switched off.