dataesr/scientific-paragraphs-categorization huggingface dataset:
Nevertheless over a large sample size, it does indicate that some lesions were more subtle. From our data FCD Type IIB was less likely to be MRI-negative, and therefore there may be an ascertainment bias in our dataset, with an overrepresentation of FCD Type IIB. As a subtle pathology on MRI, manual masking of FCD lesions is challenging. There is likely to be some heterogeneity in the masking process. At each site lesions were delineated by different individuals. Additionally, for some lesions that were difficult to detect on MRI delineation of the lesion boundaries was guided by post-surgical resection cavities. By contrast, other lesions might extend beyond what was visible. Future studies using automated lesion detection and masking 7 may yield more observer-independent and consistent lesion masks.
In Silico Docking Analysis Ten compounds were further studied theoretically using docking simulation. Our target was to find the binding affinity, inhibition constant and the intramolecular interactions of the detected compounds against ZIKV envelope protein and protease enzyme. The crystal structure of the envelope protein and protease enzyme is available online in RCSB PDB (www. rcsb. org, accessed on: 11 January 2021) with the pdb code 5H4I for the protease enzyme and 5JHM for the envelope protein. Three-dimensional structures of the potential inhibitors were downloaded from PubChem (www. pubchem. ncbi. nlm. nih. gov, accessed on: 11 January 2021) and ChemSpider (http://www. chemspider. com/, accessed on: 11 January 2021) databases. The structures were initially optimized using AM1 semiempirical method  [52]  and the atomic charges were fixed and saved in mol2 format.
In order to prepare the protein crystal structure for docking calculations, the co-crystalized molecules such as water molecules were removed. Autodock 4 (Molinspiration database: http://www. molinspiration. com, accessed on: 11 January 2021) was selected for the docking simulation. Kollman united atom charges were added to neutralize the protein and a grid box of 60 × 60 × 60 dimensions with 0. 375 Å distance was used. Lamarckian genetic algorithm was used to evaluate the 250 conformations of the inhibitors at the protein active site. Discovery studio 5. 0 visualizer was used to analyze the intramolecular interactions and prepare the figures.
Statistical Analysis For comparison among samples, one-way ANOVA test was performed. All values were given as mean ± SD of at least three independent tests. All statistical analyses were conducted using GraphPad prism software (version 8. 0; GraphPad software, La Jolla, CA, USA). Levels of significance are indicated on the figures as follows: * p < 0. 05; ** p < 0. 01; *** p < 0. 001, **** p < 0. 0001, n. s = not significant.
If performed on all the x coordinates in i, it can be used to assign a pixel value I i (x) to their corresponding x i→j in j and render a "reprojected" image I i→j. Alternatively, the exact same transformation can be used to assign to each x in i a unique pixel value I j (x i→j ) and render I j→i (see Figure  5c ) with Algorithm 1. Using this alternative, each and every point of the "reprojected" image is given a unique value, obviating the need for a depth buffer and the necessity to interpolate any pixels that would have remained blank after the transformation. Indeed, multiple x can reproject to the same x i→j , but all x reproject to some x i→j if a corresponding 3D point can be found in the mesh. Algorithm 1: Base pseudo-code of our image "reprojection" process CreateReprojectedImage (i, j) inputs : points of view i and j; 3D mesh; image I j output : "Reprojected" image I j→i I j→i ← ∅ ; foreach x from point of view i do back-project x to its corresponding X i ; project X i to x i→j in the point of view j ; I j→i (x) ← I j (x i→j ) ; return I j→i ; When every pixel of the reprojected image is computed, some pixels are associated with 3D points that were occluded in the original view and, therefore, have no RGB value (see in Figure  2 ).
Using Equations (  2 ) and (3), we can render S j→i with Algorithm 2. Algorithm 2: Pseudo-code of our "textured shadow" rendering process CreateTexturedShadows (i, j) inputs : points of view i and j; 3D mesh; image I j output : "Texture shadows" image S j→i S j→i ← ∅ ; foreach x from point of view i do back-project x to its corresponding X i ; project X i to x i→j in the point of view j ; if X i is occluded then S j→i (x) ← I j (x i→j ) ; return S j→i ; C j C i X i X i =X j =X j I j (x i→j ) I i (x) I i (x ) I j→i (x) S j→i (x ).
Experimental Evaluation and Results The algorithm was implemented in C++11 using the source code from  [34]  as a basis. Meshes and camera poses were handled using GLOW (OpenGL Object Wrapper)  [41] , mathematical operations were computed with Eigen  [42] , and images were processed with OpenCV  [43]. The code will be available at https://github. com/InterDigitalInc/ (accessed on 22 January 2021). Our method was evaluated on several scenes that presented some changes: At least one object is removed, and in some cases, some are inserted. The sequences are made up of five pictures that display the location of the removals, which are introduced by adding 3D objects to an existing accurate mesh of the scene. Conversely, insertions are simulated by removing objects from that mesh. Meshes were taken from two sources: the dataset introduced in  [34]  and the ScanNet  [44]  dataset (all images and receiver operating characteristic (ROC) curves from the datasets are available in the appendix), which also provide estimated camera poses for the images.
The scenes in the dataset from  [34]  already showcase one or more insertions. Each mesh is already associated with five pictures that display the inserted objects. However, this dataset has some limitations: There are approximations in the meshes that can be detected as changes, and the camera distortion coefficients are not available for every camera used, which leads to inaccurate projection. In contrast, the ScanNet dataset images are noisier, but have been corrected distortionwise. The scenes do not contain any insertions, and all meshes are associated with a video with thousands of frames, from which we picked five with the least motion blur possible to ensure that the camera poses were accurate. Since we compare our method with the one in  [34] , the images were also chosen to showcase the location of the removed object we added to the mesh.
As explained in Section 3. 3. 2, our approach to the combination of delta maps is based on mask union rather than intersection. This makes the detection more robust for objects nearing the edge of the frame. Further details and results for each scene of both datasets are provided in the Supplementary Material. The ROC (receiver operating characteristic) curves in Figure  10  are used to compare the true positive rate and false positive rate of a binary operator for different discrimination thresholds  [32] , which, in our case, are the value thresholds in the delta maps. The automatic threshold is highlighted on the curves to evaluate its performance, and the other threshold values are all the integers between 0 and 255. A threshold of 255 means that only the most consistent pixels of a delta map are considered (near the origin of the graphs).
Change size estimation can be an issue, as the detection size is proportional to the occlusion size of the removed object (see "0000_02+statue"). If a removed object does not produce any occlusion, it will not be detected by this method. Such an object could be detected using the method from  [34]  if it is far enough from its background to greatly distort its textures. Not every image of the ScanNet dataset's sequences is perfectly aligned with its corresponding 3D mesh (see "0005_00+bucket'). While this has not severely impacted the results of the detection in our experiments, in theory, a misalignment of an image will generate 2D false positives and negatives for its point of view. On one hand, false positives are still dealt with by using the other images of the sequence. On the other hand, false negatives can negatively impact the detection by reducing the areas of detection and lead to 3D false negatives or inaccurate size estimation.
2 After neuropathological review (TW, AF), brain tissue was processed into formalin fixed paraffin embedded (FFPE) blocks and sections were stained with luxol fast blue counterstained with hematoxylin & eosin (LFB/H&E). Archival time for brain tissue storage in formalin was less than or equal to three years, thus patients were chosen from those that were enrolled in NASR between July 2014 to March 2017. Patients were age and sex matched from available NASR cases. There were no significant differences in age at death (p=0. 9190, unpaired t-test), disease duration (p=0. 7295), disease onset (p=0. 4797), or sex (p>0. 9999). Clinical and neuropathologic data on the 14 non-SUDEP epilepsy and 12 SUDEP patients are summarized in Table  1. Group sizes were determined based on the number of patients with significant findings as previously reported,  [14] [15] [16]  including our earlier studies in epilepsy patients with similar methods.
Label-free quantitative MS Proteomics: Label-free quantitative MS assessed differential protein expression, as described previously. 18, 2122  Peptides were gradient eluted from the column directly into the Orbitrap Fusion Lumos mass spectrometer using a 165 min ACN gradient (A=2% ACN 0. 5% AcOH / B=80% ACN 0. 5% AcOH). The flowrate was set at 200 nl/min. The mass spectrometer was operated in a data-dependent acquisition mode. High resolution full MS spectra were acquired with a resolution of 240,000, an AGC target of 1e6, with a maximum ion injection time of 50 ms, and scan range of 400 to 1500 m/z. Following each full MS scan data-dependent HCD MS/MS scans were acquired in the ion trap (scan rate rapid, AGC target of 2e4, NCE of 32). Precursor isolation window were set at 2 Da. Proteomics Computational Analysis: MS data were analyzed as previously described.
1821, 22 RAW MS data were processed using the MaxQuant 23 software (v. 1. 6. 3. 4) and the SwissProt human protein database (www. uniprot. org) containing 20,421 entries. Database including a common list of common laboratory contaminants (248 entries) were also used in the search. All peptide-spectrum matches, peptide and protein identifications were filtered to get a desired FDR level below 1% (calculated using decoy database approach). For the MS/MS search enzyme specificity was set to trypsin (up to 2 miscleavages), precursor mass tolerance was set to 20 ppm with subsequent non-linear mass recalibration. Carbamidomethylation of cysteine was set as a fixed modification, protein N-term acetylation and methionine oxidation were set as a variable modifications. Match between runs (MBR) algorithm was enabled to transfer peptide feature identifications between MS runs based on LC retention time (0.
An unpaired two-tailed t test was performed for PCA1 in each brain region to determine significance of separation in the SUDEP and non-SUDEP epilepsy patients. All other analyses were done using nonimputed data. A Student's two sample t-test was used to access statistical significance of the changes in protein abundance between conditions. Obtained p-values were adjusted for multiple hypothesis testing using permulation-based FDR to a cutoff of 5%. Cell type specific annotations were included in the data available on Dryad (Table  e -3) and on volcano plots in Fig. 1F-H , derived from previous data. 26 notations were included when a protein had only one associated cell type after removing cerebellar annotations and when the annotation included more than one associated cell type (both excitatory and inhibitory neuron annotations) and were thus assigned a general neuron annotation, for a total of 1066 possible annotations.
Proteomics Correlation: For the correlation in protein abundance between conditions and brain regions we used averaged over replicates LFQ values. A Pearson's correlation was calculated for proteins detected in both SUDEP and non-SUDEP epilepsy patients for each brain region, with 2715 proteins for hippocampal CA1-3, 2464 proteins for dentate gyrus, and 2695 proteins for the frontal cortex. Immunohistochemistry: Immunohistochemistry was performed to validate the identified protein of interest, ermin (ERMN) as previously described. 18, 27 Briefly, FFPE sections (8 µm) were deparaffinized and rehydrated through a series of xylenes and ethanol dilutions. Heat-induced antigen retrieval was performed with 10 mM sodium citrate, 0. 05% triton-x 100; pH6. Blocking with 10% normal donkey serum was followed by ERMN primary antibody (1:200, Sigma HPA038295) overnight at 4°C.
Confocal imaging was used to collect representative images of ERMN immunohistochemistry, using a Zeiss LSM800 confocal microscope with the same settings on each slide with a Plan-Apochromat 20X/0. 8 M27 objective and a pinhole of 38 µm. RNA-sequencing datasets: Small RNA-sequencing (small RNAseq) and RNAsequencing (RNAseq) data sets were retrieved form the European Genome-phenome Archive (accession number: EGAS00001003922) from MTLE patients undergoing surgical resection and with available PGES duration greater than 1 second. 17 The patients were age and sex matched, with no significant differences in age at surgery (p=0. 6622, unpaired t-test), disease duration (p=0. 4391), disease onset (p=0. 4612), or sex (p>0. 9999). Small RNAseq and RNAseq data was retrieved for 6 patients with PGES < 50 sec, indicating a potential low-risk for SUDEP, and 8 patients with PGES ≥ 50 sec, indicating a potential high-risk for SUDEP as previously described.
4 Table  2  summarizes the clinical characteristics of these patients. PGES occurrence and duration was assessed by two epileptologists (CS, RT). Bioinformatic analysis of RNAseq data: Bioinformatic analysis was performed as described previously. 17 Briefly, library normalization and differential expression testing was carried out using the R package DESeq2. The Wald test identified differentially expressed genes using a Benjamini-Hochberg adjusted p-value <0. 05 for significance. Cell type specific annotations were included (Dryad table e-4, table e-5), and on volcano plots in Fig. 2C, 2E , derived from previous data. 26 Annotations were included when a protein had only one associated cell type after removing cerebellar annotations and when the annotation included more than one associated cell type (both excitatory and inhibitory neuron annotations) and were thus assigned a general neuron annotation, for a total of 1066 possible annotations.
A Reactome pathway enrichment analysis was performed using the R package ReactomePA. The differentially expressed genes from the RNAseq differential expression analysis were put into R and tested for over-representation of enriched Reactome pathways using hypergeometric testing. Pathways with a Benjamini-Hochberg corrected p-value <0. 05 were considered significantly enriched. Bioinformatic analysis of small RNAseq data: Bioinformatic analysis of the small RNAseq data was performed as described previously. 17 Briefly, library normalization and differential expression testing was carried out using the R package DESeq2. The Wald test identified differentially expressed genes with a Benjamini-Hochberg adjusted p-value <0. 05 considered significant. RNAseq validation by qPCR: The gene expression of GDNF Family Receptor Alpha 1 (GFRA1) was assessed in the same cohort of samples used in the RNAseq analysis for which sufficient RNA remained (PGES < 50s, n=4, PGES ≥ 50s, n=7).
Of note, microdissected regions did not necessarily contain observed FCD as it may have been present in other brain regions. Similarly, neuropathology was unrelated to SUDEP status (FCD in 50% of SUDEP patients versus 28. 6% of non-SUDEP epilepsy patients, Fisher's exact test, p = 0. 4216). There were no significant differences in protein expression between SUDEP and non-SUDEP epilepsy patients in any brain region (Fig. 3A-C , Dryad fig. e-1A-C , table e-3). Further, a correlation of LFQ values for all proteins showed the similarity in protein expression when comparing SUDEP and non-SUDEP epilepsy patients in each brain region by a Pearson's correlation (p < 0. 0001) with the corresponding R 2 values being ≥0. 98 (Dryad, fig. e-1 ). Brain cell type specific annotation was evaluated in the 2847 identified proteins, derived from previous methods,  26  with 19.
8% (564/2847) proteins having an annotation while the remaining 80. 2% did not and were more ubiquitously expressed or cell type is unknown. Most (78. 2%; 502/564) annotated proteins were generally neuronal, with excitatory neuron proteins predominating (48. 1%; 271/564) (Fig. 3A-C , Dryad table e-3). Some proteins showed a trend for altered expression in SUDEP patients (p<0. 01; Dryad table e-1-2), but these were not statistically significant at a 5% FDR. Several of these protein changes have been reported in epilepsy animal models and non-epilepsy patients or include proteins encoded by genes in which mutations have been previously linked to epilepsy. Yet, none of the proteins trending for altered expression in this study (Dryad table e-1-2) have been previously linked to.
Analysis of RNAseq and small RNAseq in low and high-risk SUDEP patients To determine whether there is a pathological difference in epilepsy patients of low (PGES <50 seconds, n=6) and high (PGES ≥50 seconds, n=8) risk of SUDEP, RNAseq and small RNAseq analyses were performed on resected surgical frozen hippocampal and temporal cortex tissue. Patient histories are summarized in Table  2  and Fig. 4A. A t-SNE (t-distributed stochastic neighbor embedding) plot revealed that anatomical region rather than PGES segregated patients (Fig. 4B ). A differential expression analysis comparing the hippocampus of low and high-risk SUDEP patients identified 55 differentially expressed genes: 11 were decreased and 44 were increased in high-risk SUDEP patients (Fig. 4C , Dryad table e-4). Brain cell type specific annotation was evaluated in the 55 differentially expressed genes in the hippocampus, derived from previous methods,  26  with 14.
5% (8/55) of genes having a cell type specific annotation: 4 generally neuronal, 3 excitatory neuron, and 1 inhibitory neuron. The dominant transcripts for the differentially expressed genes in hippocampus were: 37 protein-coding, 15 long non-coding RNAs (lncRNAs), and three awaiting confirmation (Fig. 4D ). A Reactome pathway analysis on the 55 significant genes in the hippocampus did not reveal a significant association with any signaling pathways. Several of these genes have been associated with epilepsy human disease and have been studied in animal models, however none of the genes in Table  3  have been linked to SUDEP pathogenesis. The most significantly decreased protein-coding gene in the high-risk SUDEP patients, GFRA1, was validated by real time quantitative PCR (RT-qPCR, Table  3 , Dryad fig.
S3 ). In accordance with the RNAseq analysis, GFRA1 was decreased 1. 7-fold in the high-risk SUDEP patients (Mann-Whitney U test, p-value = 0. 0121). In the temporal cortex, one protein-coding gene (SLC6A5) with an "undefined" cell type annotation was significantly decreased in the high-risk SUDEP patients, within this small group of patients (Fig. 4E , Dryad table e-5). No genes were differentially expressed in the small RNAseq analyses in the hippocampus and temporal cortex (Dryad table e-6-7).
RNAseq data shows separation by brain region rather than SUDEP risk status. C) A volcano plot shows the results of differential expression analysis of the hippocampus from low-risk (n=4) and high-risk (n=8) SUDEP patients. Eleven genes were decreased and 44 genes were increased in hippocampus of high-risk SUDEP patients. The Wald test identified differentially expressed genes using a Benjamini-Hochberg adjusted p-value <0. 05 for significance. Cell type specific gene annotation is included, with the most predominant listed in decreasing order in the legend. Genes annotated "General -Neuron" have both excitatory and inhibitory neuron annotations. D) Biotypes of differentially expressed genes are depicted in the hippocampus for high-risk SUDEP compared to low-risk SUDEP patients. Of the 55 differentially expressed genes 67.
SUDEP pathogenesis. Ermin (ERMN) had the strongest trend for difference in SUDEP with a 2. 8-fold decrease in the hippocampal CA1-3 region when comparing SUDEP and non-SUDEP epilepsy patients by MS (Dryad fig. e-2A). Further, ERMN was detected in more non-SUDEP epilepsy than SUDEP patients by MS, indicating lower abundance of this protein in SUDEP. Validation of the quantitative MS findings with semiquantification of immunohistochemistry (Dryad fig. e-2B) also showed a decrease of ERMN in SUDEP patients with a 1. 3-fold change but was not significant (student's unpaired t test, p-value = 0. 4871). Because ERMN may play a role in myelinogenesis and myelin maintenance, we reviewed the mature oligodendrocyte marker myelin basic protein (MBP) but found no difference between SUDEP and non-SUDEP epilepsy patients in the hippocampal CA1-3 region by MS (Dryad fig.
NINDS  UO1 NS090415 05  Center for SUDEP Research: The  Neuropathology of SUDEP, Finding A Cure for Epilepsy and Seizures (FACES) ,  National Institute of Aging   P30AG066512 ,  European Union 's  Seventh Framework Program  ( FP7/2007-2013)  under grant agreement  602102  (EPITARGET; EAvV, EA), and the  Top Sector Life Sciences & Health  via a PPP Allowance made available to the  Dutch Epilepsy Foundation  to stimulate public-private partnerships (EAvV, EA). This work was supported by funding from the  Bluesand Foundation  to ED,  Philippe Chatrier Foundation  to GP. The proteomics work was in part supported by the  NYU School of Medicine  and a shared instrumentation grant from the  NIH ,  1S10OD010582-01A1 , for the purchase of an  Orbitrap Fusion Lumos.
Microfluidics and microfabrication Microfabrication. Microfluidic chips were generated using custom-made microfluidic master molds. The master molds were made using standard soft-photolithography processes using SU-8 2025 photoresist (Microchem, USA). The designs were made on AutoCAD (github. com/TAspert/Continuous_filtration) to produce chrome photomasks (jd-photodata, UK). The observation device was taken from a previous study  (Goulev et al. 2017). The mold of the dust filter chip (see below for details) was made by spin-coating a 25µm layer of SU-8 2025 photo-resist on a 3" wafer (Neyco, FRANCE) at 2700rpm for 30s. Then, we used a soft bake of 7min at 95°C on heating plates (VWR) followed by exposure to 365nm UVs at 160mJ/cm² with a mask aligner (UV-KUB3 Kloé®, FRANCE). Finally, a post-exposure bake identical to the soft bake was performed before development using SU-8 developer (Microchem, USA).
Image processing and data quantification Calibration of the cytosolic pH probe. To calibrate the probe, 2mL of exponentially growing culture (0. 5 OD600) were centrifuged and resuspended in 200μL calibration buffer (50 mM MES, 50 mM HEPES, 50mM KCl, 50mM NaCl, 200 mM NH4CH3CO2) at various pH from 5 to 8, and supplemented with 75μM monensin, 10μM nigericin, 10mM 2-deoxyglucose, and 10mM NaN3, as described previously  (Mouton et al. 2020 ). The cells were incubated in this buffer for 30min and then imaged in the microfluidic device to perform a ratiometric fluorescence measurement (Figure  1  -figure  supplement 1I ). Image processing. The raw images were processed using Matlab-based software (PhyloCell) and custom additional routines  (Fehrmann et al. 2013; Paoletti et al. 2016). This software has a complete graphical interface for cell segmentation (based on a watershed algorithm), tracking (assignment cost minimization), and fluorescence signal quantification.