dataesr/scientific-paragraphs-categorization huggingface dataset:
Ground Truth Estimation To train our model, ground truth segmentation needs to be computed on the PETRA volumes in the training dataset. In our case, ground truths are segmentation maps of the same size and characteristics as the PETRA, with segmented spheres having a different value, also called "label, " for the 65 EEG cap electrodes visible on the scalp and a value of 0 for the background. To ease the manual creation of these ground truths, a scalp segmentation mask was first estimated. As T1 images have a higher quality than PETRA on the scalp area, this mask is obtained by firstly registering the T1 image on the corresponding PETRA image and then by segmenting the registered T1 image using the FSL library  (25). These two inputs allow the use of a Matlab implementation, developed by  Butler (26) , of a method proposed by de Munck et al.
Training Framework The training dataset thus consists of 37 PETRA images, and their associated ground truth, described above. We use the nnU-Net framework  (27). This framework is a tool that can automate the choice of hyperparameters used to train a model from any dataset and for any segmentation task. This is very useful, especially since a large number of variations of neural network architectures have been proposed for segmentation, for example in the biomedical field, and the authors of  (27)  showed that slight design improvements hardly improve performance, while the choice of hyperparameters seems to be crucial. In fact, this framework with a basic U-Net architecture outperformed most of the specialized deep learning pipelines for 19 international competitions, and 49 segmentation tasks, demonstrating its efficiency but also its adaptability.
Among the different types of neural networks available, we chose the 3D U-Net (28) network whose operations such as convolutions and max pooling are replaced by their 3D counterparts. Once the neural network architecture is chosen, the framework automatically estimates the best training hyperparameters from the dataset provided as input. Here, our model is trained over 1,000 epochs (number of times each training data is considered) and 250 minibatches (number of samples considered before updating internal parameters), with a loss function which is the sum of cross-entropy and Dice loss and with a Stochastic Gradient Descent (SGD) optimizer. The patch used has a size of 128 × 128 × 128 and the default data augmentation scheme provided by nnU-net was used.
Deep Learning-Based Predictions and Template-Based Refinement Once the model is trained, PETRA images from the test dataset can be provided as input and the model can then perform predictions. The method for making predictions, available in the nnU-net framework, consists of a sliding window approach, using the same patch size that has been used during training, overlapping half of the patch at each step. In order to increase performance, to avoid artifacts, and overall to have a good quality of segmentation, several strategies have been selected: a Gaussian importance weighting is used to reduce edge problems and stitching artifacts, and a so-called "test-time augmentation, " which is data augmentation for test datasets, is used by generating slightly modified images from the tested image and averaging the detections made on them.
Validation on the Test Dataset To evaluate the proposed method, and for the test dataset, we compared the detected electrodes to the ground truth coordinates obtained manually. We computed the connected components for the two images and the position of their center. Finally, for each prediction point, its distance to the nearest point of ground truth is computed. This distance is therefore considered a position error. A prediction presenting an error greater than 10 mm, corresponding to the diameter of an electrode cup, is considered as a wrong detection (false positive). Since we systematically consider the nearest ground truth electrode, we do not consider the labeling when estimating the position error. The quality of the final labeling, as well as that of the intermediate labeling, will be evaluated separately.
Evaluation of the Robustness of the Method on a Different UTE Sequence In order to evaluate the robustness of the method, as well as to compare our results with those of  (21) , we also applied it to images acquired according to a different UTE sequence, the one described in the mentioned article. First, we directly used the model learned from the PETRA images, to study the generalizability of the learned model to another MR sequence. Then, we learned a new model from the different UTE database, containing fewer images, allowing us to investigate the importance of the number of data in the learning set, but also to compare our results to the previously introduced method.
RESULTS All the implementations were made on Nvidia Quadro M6000 24GB GPU (which was the most powerful graphics card in 2016 according to NVIDIA Corporation). The training then lasts between 1 and 2 weeks, depending on the number of processes launched on the GPU available. Classically in deep neural network methods, the prediction of one test data is much faster. The presented method predicts a segmentation map from a PETRA image in about 7 min on the abovementioned GPU.
With Test-Time Augmentation The results are assessed by measuring the position error as described in the validation section, for all volumes in the test dataset. Table  1  presents the average results for all subjects in the PETRA test dataset. This test set consists of 23 volumes, from  8 different subjects not included in the learning data set, with sampling resolutions of either 30k or 60k spokes, corresponding to a more or less long acquisition time. The average position error is equal to 2. 24 mm, to be compared with the diameter of one electrode cup, 10 mm. The number of good (true positive) and wrong (false positive) detections was also assessed, taking that distance of 10 mm as the threshold. As can be seen in the table, after the deep learning step, the number of detections was too high on average, and it was corrected after the registration step, resulting in a better Positive Predictive Value (PPV) defined as the percentage of detections that are true positive relative to the total number of detections.
Tests on a Different UTE Sequence In order to evaluate the robustness of our method, we challenged it by testing it on a data set from another MRI sequence, the original UTE one  (21). Eleven subjects were included in this new study. A 60k-spokes acquisition was done for all subjects and a 30k-spokes image was acquired for seven of them. First, the previous model, learned using the PETRA images, was used to detect the electrode positions on these 18 new images, acquired with a different UTE sequence. Results are shown in Table  5. As expected, the detections estimated by the neural network were not as good as in the previous case. Indeed, the average number of electrodes provided was lower than 57. However, and very interestingly, these electrodes were mostly true detections. For this reason, and as can be seen in the table, the ICP-based registration step was able to retrieve almost all missing electrodes, leading once again to excellent performance results.
Our registration-based refinement step thus brings robustness to the method, allowing to limits the risk of overfitting, and improving its generalizability. Finally, in order to compare our results to  (21) , we learned a new neural network, using only this different UTE sequence, applied the refinement step, and evaluated the resulting performance. From the previously described UTE dataset, we built two groups: 9 MRI volumes in the training set and 9   (21). For example, the mean PPV is now 99. 3% and was between 88 and 94% for 30k and 60k spokes images, respectively. For both of these cases, all the detected electrodes were once again well-labeled: there was no mislabeling among the true positives.
CONCLUSION We presented a new method for the detection and labeling of EEG electrodes in an MR volume acquired using PETRA sequence. The first step is to train a model from a set of training data and associated manual ground truths, then use this model to obtain a segmentation map, and finally to apply a step using the ICP registration algorithm to improve the detections and their labeling. This fully automatic method is easy to implement, requires very few steps, and gives excellent results. For all these reasons, we strongly believe that it can be very useful for all protocols with simultaneous EEG-fMRI acquisitions. In particular, when an EEG source localization is planned later, as is often the case when studying epilepsy, accurate information on the position of the electrodes is a definite advantage.
Electrodes detection on the UTE dataset, using a new model, learned using images acquired with the same UTE sequence. inthe test set, again ensuring that no subjects were present in both sets. Table6shows the corresponding results. Training the model using the same type of images as in the tests slightly improves the quality of the detections, compared to when training the model on PETRA images. Moreover, and despite this smaller group size (compared to the PETRA study), our results are now better than those reported in.
Cell nuclei were stained with DAPI (Molecular Probes). Slides were washed thoroughly, mounted in PBS/glycerol (1:1), and observed by an epifluorescence microscope (Nikon Optiphot 2). The number of phagosomes was quantified, as described previously by us. 19 ransverse sections were obtained from the central retina, covering the whole width of the retina from one periphery to the other. Taking the POS/RPE interface as a baseline, any immunopositive inclusion exceeding 1 μm lying within the RPE subcellular space was scored as a phagosome. Phagosomes were counted by aligning a 150 μm × 150 μm grid parallel with the RPE layer and displacing it dorsally and ventrally (a total of 20 grids were counted per section) with respect to the optic nerve, along the POS/RPE interface. Phagosomes were counted on 12 sections per eye (1 eye per animal) and averages of these values represent 1 data point in our quantification.
3. 1-Applied Biosystems). Each PCR reaction was performed in duplicate. A dilution curve of the pool of all cDNA samples from one series was used to determine working dilution and to calculate the amplification efficiency for each assay (values were between 1. 8 and 2 for all assays). No-template control reactions were performed as negative controls for each assay. One 96-well plate corresponded to the analysis of one gene and one genotype. Data analysis was performed with qBase software (free v1. 3. 5)  32 and transcript levels were normalized using Hprt and Tbp that showed constant expression (Cosinor analysis of their relative expression levels as expressed by eff -ΔCt ; P =. 676 for Hprt in WT; P =. 992 for Tbp in WT; P =. 219 for Hprt in KO; P =. 347 for Tbp in KO) in their mRNA during the 24-hours cycle (data not shown).
We used the geometric means of the  relative expression levels of Hprt and Tbp genes to normalize the data of all target genes. Within one experiment (one gene expression profile, one genotype), relative expression levels were calculated relative to the average expression of the dataset throughout the 6 time points, which was rescaled to one. In this configuration, the amplitudes determined by cosinor analysis represent the maximal deviation from the 100% mean and can potentially be compared between genotypes, as was previously performed by Hiragaki and colleagues. 33.
| Bioinformatics The photoreceptor and RPE RNA-seq data were analyzed separately, but with the same software versions and parameter settings unless indicated otherwise. Raw sequencing data were subjected to quality control using FastQC (v. 0. 11. 15), Picard Tools, and dupRadar. 34 All samples were of sufficient quality. Reads were trimmed for adapter sequences using Trimmomatic (v0. 32). 35 Trimmed reads were aligned to the mouse genome (Ensembl GRCm38. p6) using HISAT2 (v2. 1. 0). 36 Gene level counts were obtained using HTSeq (v0. 11)  37  with default parameters except -stranded = reverse and the mouse GTF from Ensembl (release 93). Statistical analyses were performed using the edgeR  38  and limma R (v3. 5. 0)/Bioconductor (v3. 7) packages. 39 Genes with more than two counts in four or more samples (photoreceptors) or in three or more samples (RPE) were retained.
Count data were transformed to log2-counts per million (logCPM), normalized by applying the trimmed mean of M-values method and precision weighted using voom. 40 Pairwise differential expression between the conditions of interest was assessed using an empirical Bayes moderated t test within limma's linear model framework, including the precision weights estimated by voom. Both for WT and Per1 -/-Per2 Brdm1 , a moderated F test was used to determine which genes are differentially expressed between time points. The resulting p-values were corrected for multiple testing using the Benjamini-Hochberg false discovery rate (FDR). An adjusted P-value <. 05 was considered significant for photoreceptors. For the RPE, an adjusted P-value of <. 1 was considered significant. Additional gene annotation was retrieved from Ensembl (photoreceptors: release 94, RPE: release 98) using the biomaRt R/Bioconductor package.
We deposited the RNA-seq data in NCBI's Gene Expression Omnibus. These data are accessible through GEO Series accession number GSE172440 (https://www. ncbi. nlm. nih. gov/ geo/query/ acc. cgi?acc=GSE17 2440). The SubSeries accession numbers of the RPE and photoreceptor RNA-seq data are: GSE172438 (https://www. ncbi. nlm. nih. gov/geo/query/ acc. cgi?acc=GSE17 2438) and GSE172437 (https://www. ncbi. nlm. nih. gov/geo/query/ acc. cgi?acc=GSE17 2437). Gene ontology and pathway enrichment analyses were performed using g:Profiler. 41 We set all identified transcripts in our RNA-seq dataset as a reference background. We set an adjusted P <. 05 as a threshold for significantly enriched pathways using the g:SCS method to correct for multiple testing. 41 We investigated interactions between protein products of the list of potential POS phagocytosis candidate genes by STRING analysis.
| Statistics Data are represented as means ± SEM. Plots were generated using GraphPad Prism (La Jolla, CA, USA), SigmaPlot (Systat Software, San Jose, CA, USA) or R (Bell Labs, Murray Hill, NJ, USA). Normality of distribution was tested using the Shapiro-Wilk test. In case of non-normal distribution, the analysis was performed using ANOVA on ranks. Circadian expression profiles were determined using nonlinear regression fitting to the equation y = y0 + c • cos [2π (t-ϕ)/24], where y0 represents mesor, c amplitude, and ϕ acrophase. 43, 44 The function featured the following constraints: ϕ < 24, ϕ > 0 and c > 0. Gene expression profiles were considered to be rhythmic when significant fitting (P <. 05) was observed to the equation y = y0 + c • cos [2π (t-ϕ)/24]. Further analyses, where indicated, were performed using 1way or 2-way ANOVA analysis followed by Holm-Sidak's post hoc tests.
Detailed lists of differentially expressed PR genes are shown in Table  S3. Red numbers represent the number of upregulated differentially expressed genes, whereas blue ones are downregulated. D, A substantial number of identified transcripts showed a time effect in WT PR and RPE. There is considerable overlap (n = 119) between timeaffected genes in these two tissues, a number of which overlap with the RetNet list of eye disease-related genes. 45 Detailed lists of these genes are shown in Table  S12. E, Functional annotation (performed using g:Profiler) revealed that overlapping time-affected genes in RPE and PR are enriched in glucose metabolism and neurotransmission-related pathways. The orange line represents the significance level cut-off (adjusted P <. 05). WikiPathways (WP), Reactome, and KEGG are databases of biological pathways repair-related pathways (Table  S8 ).
POS phagocytosis occurs as a peak in WT mice on a molecular and functional level. 46 ,47 Thus, we selected genes that are both upregulated at CT1 vs CT22 and downregulated at CT10 vs CT1 in WT RPE. We removed possible photoreceptor "contaminants" from this list by mouse signature cone and rod genes  48  and the Gene Ontology database (POS cellular component, GO:0001750). Using this strategy, we obtained a list of 57 candidate genes (Figure  4B , Table  2 ). These genes are functionally enriched in neurotransmission-related pathways (Figure  4C , Table  S13 ). To reveal the interactions that protein products of these genes are involved in, we constructed a proteinprotein interaction network using STRING  42  (Figure  4D ). Our list revealed a number of functional associations in which the protein products of candidate genes are involved in, most of which are associated with the term cell junction (highlighted in red in Figure  4D ).
C, Functional enrichment analysis using g:Profiler showed that these genes are enriched in neurotransmission-related pathways from the WP, Reactome, KEGG databases. The orange line represents the significant cut-off (adjusted P <. 05). D, STRING network analysis of protein functional associations of products of RPE genes implicated in initiating phagocytosis. displayed in both human patients  16  and animal models. 15, 30, 61 ome literature stresses the importance of precise timing of POS phagocytosis in maintaining retinal health. 30, 61 This view is corroborated by our finding that a number of eye disease-related genes vary across time points in the RPE and photoreceptors. However, it was recently reported that dopamine D2 receptor knockout mice had no peak in POS phagocytosis and displayed no apparent retinal pathologies.
ACKNOWLEDGMENTS We thank  Anneloor LMA ten Asbroek  and  Nguyen-Vy Vo  for technical assistance. We thank  Dr D. Sage ,  Dr S. Reibel  and  N. Lethenet  from the  Chronobiotron  ( UMS 3415 ) for animal care and  Dr U. Albrecht  ( University of Freiburg ) for the  Per1 -/-Per2 Brdm1  mice. This project has been funded with support from the  NeuroTime Erasmus+  grant ( European Union ),  Rotterdamse Stichting Blindenbelangen (Netherlands) ,  Nelly Reef fund (Netherlands) ,  Stichting voor Ooglijders (Netherlands) ,  Stichting tot Verbetering van het Lot der Blinden (Netherlands) , and  Retina France (France).
Resuspended cells were processed using a cell sorter (model XDP, MoFlo), and viable single cells expressing mRFP (CAG 1 ) or mRFP and GFP (Hes5 1 ) were individually collected in 96-well plates with lysis buffer. Individual lysed collected cells were processed for scRNA-seq using a modified Smart-seq2 protocol  (Chevée et al. , 2018). Data were aligned to mouse reference genome (mm10) using Hisat2  (Kim et al. , 2015)  and were quantified against a modified reference transcriptome  (GENCODE version M15; Frankish et al. , 2019)  with cuffquant  (Trapnell et al. , 2014). Default parameters were set for both programs except for parallelization value (-p 6) to improve processing time. scRNA-seq analysis Data were analyzed using Seurat R package version 2  (Butler et al. , 2018). To import data into a Seurat object and eliminate low-quality cells/genes (quality check), CreateSeuratObject function selected cells with at least 300 genes, and genes that were expressed in at least three cells.
Transcriptional regulatory networks We used the Reconstruction of Transcriptional Networks package  (Castro et al. , 2016)  to generate a gene regulatory network (GRN) based on scRNA-seq data, as previously described  (Jackson et al. , 2020). Specifically, we used scRNA-seq data generated from the mouse dorsal telencephalon at E13  (Yuzwa et al. , 2017). This GRN defines regulons (possible target genes) for a set of curated transcription factors. The regulon for each TF is composed of all the genes whose expression data display significant mutual information with those of a given transcription factor and are therefore likely to be regulated by that transcription factor  (Castro et al. , 2016; Jackson et al. , 2020). To select mouse TFs, a transcription factor database (TcoF-DB, version 2;  Schmeier et al.
5 (Fig. 7A ). Sox9 Arrowheads point mRFP 1 /GFP 1 cells, and arrows point to mRFP 1 /GFP -cells. F, Schematics of scRNA-seq strategy. The electroporated area was isolated and cells were individually separated by FACS 16 h after electroporation. Isolated cells were sequenced and analyzed using bioinformatics tools. G, t-SNE visualizations of the sorted cells displaying gene expression (gray to dark blue color gradient) of cell type-specific markers for RGCs (Hes5, Pax6, Nes), IPs (Eomes and Hes6), and early neurons (Neurod6, Tubb3, Dcx, and Snap25). G, t-SNE visualizations of the sorted cells displaying gene expression (gray to dark blue color gradient) of cell type-specific markers for RGCs (Hes5, Pax6, Nes), IPs (Eomes and Hes6), and early neurons (Neurod6, Tubb3, Dcx, and Snap25). H, t-SNE visualizations of the scRNA-seq dataset according to expression of the cell type-specific markers (shown in G) and particular cell populations identified (blue, RGCs; green, IPs; red, early neurons).
I, t-SNE visualizations of the scRNA-seq dataset exhibiting CAG 1 cell group (mRFP 1 ; red) versus Hes5 1 cell group (mRFP 1 , GFP 1 ; green). J, Proportion analysis of CAG 1 and Hes5 1 cells in the three groups of cell types. Hes5 1 cells accounted for the majority of cells with an RGC transcriptional signature, whereas CAG 1 cells constituted the vast majority of ENs. Scale bars: A, C, D, 50 mm; E, 25 mm. overexpression affected the distribution of electroporated cells with reduced invasion of the CP and a greater accumulation of labeled cells in the VZ (PCIG vs PCIG-FL S9: VZ, p = 0. 00049; IZ, p = 0. 011; CP, p = 1. 80 Â 10 À6 ; unpaired t test; n = 4; Fig. 7B-D ). Overexpression of Sox9 did not noticeably affect the RGCs scaffold, as revealed by the visualization of their end feet (Fig.
H, t-SNE visualizations of the scRNA-seq dataset according to expression of the cell type-specific markers (shown in G) and particular cell populations identified (blue, RGCs; green, IPs; red, early neurons). I, t-SNE visualizations of the scRNA-seq dataset exhibiting CAG 1 cell group (mRFP 1 ; red) versus Hes5 1 cell group (mRFP 1 , GFP 1 ; green). J, Proportion analysis of CAG 1 and Hes5 1 cells in the three groups of cell types. Hes5 1 cells accounted for the majority of cells with an RGC transcriptional signature, whereas CAG 1 cells constituted the vast majority of ENs. Scale bars: A, C, D, 50 mm; E, 25 mm.
001). PCA increased along the AD continuum although being generally low (P < 0. 001). Finally, increasing cerebrovascular burden, as measured with Fazekas scale rating white matter hyperintensities and Potter scale rating PVS, was shown progressing along the AD continuum (P < 0. 001). Comparison of demographics, clinical, neuropsychological, and radiological data among the A-T-N-subjects in the AD continuum and SNAP is reported in Table  S1  in supporting information. Compared to A-T-N-, SNAP were older, had a higher male prevalence, lower CDR and RBANS total scores, and higher scores on the AIADL question-  A-T-N-(P-value < 0. 01 for attention, delayed, and immediate memory). Their vascular burden was similar to A-T-N-, while their atrophy profiles were similar to subjects in the AD continuum, showing increased neurodegeneration compared to A-T-N-(P-value < 0.