dataesr/scientific-paragraphs-categorization huggingface dataset:
2b ). Zero-padding masks were used in order to undersample the sinogram while retaining its size for a consistent usage of its size throughout the network iterative methodology. FBP was used to reconstruct images from (pre-processed) fully sampled datasets, to obtain ideal "reference" images. Then, undersampled datasets corresponding to acceleration factors of R = {4, 8, 12, 16, 20, 24, 28} were generated from the fully sampled test datasets and reconstructed using FBP, TwIST, U-NET and ToMoDL. The term acceleration factor indicates that R-times less data was used. It is defined as the ratio of the amount of data required for a fully sampled acquisition to the amount of data collected in an accelerated acquisition. The structural similarity index (SSIM) and peak signal-to-noise ratio (PSNR) were used to quantitatively assess the quality of the image reconstructed using FBP, TwIST, U-NET and ToMoDL in comparison with the reference images.
However, it was observed that poor performing reconstructions are found in volumes whose sections barely show any structure, such as the ones from the lower tail (see Supplementary Material, Fig. S1 ). Under these settings, both trainable methodologies present a consistently high structural similarity with the reference image. In order to assess the impact of learning from a scarce number of training samples, we performed an exploratory study to evaluate the performance of the trainable methods with a variable number of samples. In the next section we derive suggestive evidence that the U-Net could be overfitting the training dataset, despite our efforts to cross-validate with different volumes.
Discussion In this work, we proposed ToMoDL, a model-based deep learning reconstruction method, to greatly accelerate OPT acquisitions and reconstructions. Using the MoDL framework as our st arting point, the introduction of a fast Radon operator enabled the reconstruction of high-quality images with two orders of magnitude fewer learnable parameters than in DL methods for OPT. Furthermore, we observed that our ToMoDL implementation can rely on a small dataset ( ∼ 75 samples) to reconstruct images with a competitive performance. To our knowledge, this is the first time that an unrolled network has been applied to OPT, more specifically for reconstructing 3D images from undersampled OPT data in real-time. In the future, we plan to extend ToMoDL to other tomographic imaging modalities, such as Positron Emission Tomography (PET) and X-ray Computed Tomography (CT).
Acknowledgements This study received Portuguese national funds from  FCT -Foundation for Science and Technology  through projects  UIDB/04326/2020 ,  UIDP/04326/2020  and  LA/P/0101/2020 , " la Caixa" Foundation  and FCT, I. P. under the project code  LCF/PR/HR22/00533 ,  European Union 's  Horizon 2020 research and innovation program  under the  Marie Skłodowska-Curie  grant agreement no  867450  and  NVIDIA  GPU hardware grant. This project has also received funding from the  European Union 's  Horizon 2020 research and innovation program  under grant agreement no. 871124  and  Horizon Europe  under grant agreement no. 101094250. This work was also supported by the  Wellcome/EPSRC Centre for Medical Engineering  [ WT 203148/Z/16/Z ].
Results Whole-transcriptome sequencing as a first-tier method for concomitant mutation and viral detection. In this study, we used whole-transcriptome sequencing (RNA-Seq) as the initial sequencing method to study a cohort of 50 patients with typical and atypical EV. In the cases in which RNA-Seq results were unyielding, DNA samples extracted from blood were submitted for whole-exome sequencing (WES). RNA-Seq data aligned with homozygosity mapping (HM) allowed us to identify underlying disease-causing variants in TMC6, TMC8, and CIB1 associated with typical EV in 26 patients from 13 distinct consanguineous families. We found 9 distinct variants in TMC6, TMC8, or CIB1, 7 of which were previously unreported to our knowledge: 5 in TMC6, 1 in TMC8, and 1 in CIB1 (Figure  2, A-C ). The positions of the mutations found in the 26 patients with typical EV were mapped onto their 3D protein structures using PyMOL (Figure  2C ).
6  [10. 7]  years; 40% women) (Tables  1 2 3 ). Patient 1 exhibited extensive, flat, wart-like lesions with BCCs and SCCs at an early age. The patient's father developed a BCC at age 25 years, and his mother developed breast cancer at age 35 years (Figure  3 , A-D). We found a homozygous sequence variant, TMC6: c. 1273_1274delTG, p. Cys425Argfs*75 (Figure  2  and Figure  3, E-G ), which was absent in all publicly available control databases. The TMC6 mRNA expression level was significantly reduced in patient 1 compared with that of healthy control participants, whereas housekeeping genes were not dysregulated (Figure  3H ). VirPy detected β-HPV-5 in the proband's wart biopsy specimen (Figure  3I ). Additionally, given the high prevalence of cancer within this family, known cancer-associated genes were referenced, and a heterozygous pathogenic variant, MLH1: c.
204_205del, p. Thr69Lysfs*2, was found. The proband of family 2, with 2 healthy children, has had warts since age 5 years, and contents of fine-needle aspiration of a thyroid nodule at age 42 years showed papillary thyroid carcinoma (Figure  4 , A and B). Histopathology confirmed the presence of koilocytes in a biopsied wart sample (Figure  4C ). A homozygous missense variant with potentially damaging splicing effects, TMC6: c. 889G>C, p. Ala297Pro (rs750652637), was found in the sequence encoding the final amino acid of exon 8 (Figure  2  and Figure  4 , D-G). To our knowledge, this is the first time this missense/splicing mutation has been reported in TMC6. This variant has not been reported as homozygous in healthy individuals in publicly available databases. On the basis of various available algorithms for missense pathogenicity prediction (including SIFT, Polyphen 2, VARITY, and Functional Analysis through Hidden Markov Models [FATHMM]), the rs750652637 sequence variant was determined to be pathogenic, with a CADD score of 32, well above the mutation significance cutoff (MSC) of 22.
R E S E A R C H A R T I C L E A low level of β-HPV9 was also detected in their normal-appearing skin (Figure  5H  and Table  1 ). The proband of family 4 was a 40-year-old man with flat warts, and examination of a skin biopsy specimen confirmed the presence of koilocytes. Multiple SCCs were surgically removed from his scalp when he was 36 years old (Figure  6, A-C ). We documented a homozygous missense variant, TMC6: c. 1651C>T, p. Arg551Trp, with a CADD score of 33 and a VARITY_ER_LOO score of 0. 943 (Figure  2  and Figure  6, E-G )  (14). The arginine residue at this position is evolutionarily conserved. This VUS was present in population databases (rs779481795; gnomAD 0. 001%) as heterozygous (n = 3) but does not appear to have been reported in the homozygous state. The HSF predicted a "significant alteration of exonic splicing enhancer/silencer motifs ratio" of -4 for this missense variant.
This suggested that warts have a more distinct biological basis than the normal-looking skin of patients with EV (Figure  13B ). The volcano plot of DEGs between 11 wart and 7 cancer samples of patients with EV (Figure  13C ) shows differential expression of cancer-associated genes, such as FOS, JUND, PCNA, CHEK1, and CDK-N2A. The 1,808 dysregulated genes in wart versus cancer samples with P < 0. 05 and log fold change greater than 2 or less than -2, and common to edgeR and DESeq2, were selected for gene set enrichment analysis (GSEA) by WebGestalt 2019 (http://www. webgestalt. org/option. php). The Reactome database, which provides known biological processes and pathways, showed the 5 cell cycle-related pathways (an average of 5 pathways: normalized enrichment score [NES], 3. 2; FDR, <2. 2 × 10 -16 ; P = 0) being positively dysregulated (Figure  13D ).
The Kyoto Encyclopedia of Genes and Genomes database, which clusters genes on the basis of participation within the same biological process, showed transcriptional dysregulation in cancer (NES, 2. 0017; FDR, 0. 0651; P = 0) and cell cycle (NES, 2. 4256; FDR, <2. 2 × 10 -16 ; P = 0. 0023) as the most positively enriched pathways in warts compared with normal-appearing skin. Panther, which also captures the collective knowledge represented in biological pathways, showed angiogenesis (NES, 1. 41; FDR, 0. 253; P = 0. 039) and p53 signaling (NES, 1. 79; FDR, 0. 0772; P = 0. 019) pathways were the most positively enriched (Figure  13D ). Collectively, these data confirm the precancerous nature of warts and implicate them in the transformation of normal-appearing skin to the NMSCs observed in all of our patients with TMC6, TMC8, and CIB1 mutations.
Excisional biopsy from scalp lesion showed atypical squamoproliferative tumor with invasive buds (upper right panel). (C and I) Stepwise bioinformatic filtering on the data generated from RNA-Seq reduced the number of candidate variants leading to the identification of CIB1 as the candidate gene. (D and J) The mutation, CIB1: NM_006384. 3, Exon 3, c. 124C>T, p. Gln42*, was confirmed as homozygous in the probands through Sanger sequencing. (E) Heatmap expression profiling of RNA-Seq data displays significantly reduced CIB1 mRNA levels in the proband of family 9, compared with randomly selected housekeeping genes and other EV-associated genes, suggesting nonsense-mediated mRNA decay. (F and L) Sashimi plot  (patient 17)  showing the identification and quantification of full-length β-HPV-14, via VirPy, with expression in the wart and no expression in normal-appearing skin biopsy specimens of the proband.
For more information, please see Supplementary Materials and Methods, and for technical details, see the report by Youssefian et al. (18). unrelated, healthy control participants. Processing of RNA-Seq data was done as previously described  (18, 19). Also, GSEA was performed in WebGestalt (20) using 1,087 downregulated and 721 upregulated genes in normal-appearing skin versus warts with at least a 2-fold change. The count data were transformed using EdgeR: log2(counts per million+c), with pseudocount c = 5. For differential expression analysis, limma-voom was used within the iDEP workspace. For more information, please see Supplementary Materials and Methods. Variant calling and HM from RNA-Seq data. RNA-Seq was used as the first-tier method for mutation detection in skin biopsy specimens of probands.
(C and I) Stepwise bioinformatic filtering on the data generated from RNA-Seq reduced the number of candidate variants leading to the identification of CIB1 as the candidate gene. (D and J) The mutation, CIB1: NM_006384. 3, Exon 3, c. 124C>T, p. Gln42*, was confirmed as homozygous in the probands through Sanger sequencing. (E) Heatmap expression profiling of RNA-Seq data displays significantly reduced CIB1 mRNA levels in the proband of family 9, compared with randomly selected housekeeping genes and other EV-associated genes, suggesting nonsense-mediated mRNA decay. (F and L) Sashimi plot(patient 17)  showing the identification and quantification of full-length β-HPV-14, via VirPy, with expression in the wart and no expression in normal-appearing skin biopsy specimens of the proband. β-HPV-14, -22, and -21 were detected in the wart of patient 18.
Acknowledgments The authors thank the patients and their families for their participation in this study. The authors' original studies were supported by the  US NIH  (grant  R01AI143810 ) and the  LEO Foundation  (grant  LF-OC-22-000965 ). Carol Kelly assisted in manuscript preparation. This study is in partial fulfillment of  AHS 's PhD thesis. Address correspondence to:  Hassan Vahidnezhad ,  Department of Dermatology and Cutaneous Biology, Sidney Kimmel Medical College at Thomas Jefferson University, Jefferson Institute of Molecular Medicine , 233 S. 10th Street, Suite 408 BLSB, Philadelphia, Pennsylvania 19107,  USA. Email: hassan. vahidnezhad@jefferson. edu.
Data availability. Data sets related to this article can be found at https://submit. ncbi. nlm. nih. gov/subs/sra/ SUB11910228/files in the Sequence Read Archive with submission identification document SUB11910228. Statistics. P < 0. 05 was considered significant. Study approval. This study was approved by the IRB of the Pasteur Institute of Iran, Tehran, Iran. Written informed consent was obtained from all adult patients and the parents or guardians of children to participate in research and publish their images.
(e. g. , sorting intolerant from tolerant [SIFT]; Polymorphism Phenotyping, version 2 [PolyPhen2]; combined annotation-dependent depletion [CADD]; MetaLR [https://useast. ensembl. org/info/genome/variation/prediction/protein_function. html]; Mendelian Clinically Applicable Pathogenicity [M-CAP]; Primate Al; rare exome variant ensemble learner [REVEL]; VARITY [http://varity. varianteffect. org/]). Allele frequencies were verified using publicly available databases for healthy control participants (13) (e. g. , Genome Aggregation Database [gnomAD] Aggregated, Trans-Omics for Precision Medicine [TOPMed], BRAVO [https://bravo. sph. umich. edu/freeze8/hg38/], Iranome, Greater Middle East [GME] Variome). JCI Insight 2023;8(3):e162558 https://doi. org/10. 1172/jci. insight. 162558.
In the asynchronous setting, interestingly, the link between gather and symmetry breaking is even more clear: gathering is in fact equivalent to the election problem  [5]. Few papers have examined gathering in the asynchronous setting (see  [6] [7] [8] ) usually assuming two agents with distinct IDs. Recall that, in our setting, both nodes and agents are anonymous, and the system is fully asynchronous. Black Hole Search. Among the severe security threats faced in systems supporting mobile agents, a particularly troublesome one is a harmful host; that is, the presence at a network site of harmful stationary processes. The problem posed by the presence of a harmful host has been intensively studied from a programming point of view (e. g. , see  [9] [10] [11] ), and also from an algorithmic prospective  [12].
NanoString sequencing and analysis RNA from human tissue was extracted using the RNAstorm FFPE RNA extraction kit (Cell Data Sciences, Fremont, CA, USA) on two 10 µm curls per sample cut from BA4. RNA was eluted in 50 uL nuclease-free water, after which sample concentrations were measured using a NanoDrop 1000 (ThermoFisher Scientific, Waltham, MA, USA). Samples that did not meet the minimum 60 ng/uL were concentrated using an Eppendorf Concentrator Plus (Eppendorf, Hamburg, Germany) for 10 minutes at 45°C, measured again, and concentrated for an additional 5 minutes at 45°C if necessary. Samples were diluted in nuclease-free water to a final concentration of 600 ng RNA in 10 uL water for NanoString sequencing. Sequencing was performed by Host and Tumour Profiling Unit (HTPU) Microarray Services with the nCounter neuroinflammation panel (for more information see https://nanostring.
com/products/ncounter-assays-panels/neuroscience/neuroinflammation/), which includes 770 genes related to immunity and inflammation, neurobiology and neuropathology, and metabolism and stress  29  (Supplementary Material). All samples passed routine quality control checks. Differential gene expression analyses between control and ALS cohorts were performed in RStudio (R version 4. 1. 1) 30 using "DESeq2"  31  (version 1. 32. 0) with "RUVSeq" 32 (version 1. 26. 0) to estimate and regress out unwanted variation (k = 3 factors of unwanted variance  33  ). P-values were adjusted using Benjamini-Hochberg false discovery rate thresholds (p < 0. 05). Plots were made using the "ggplot2" package 34 (version 3. 3. 5) in R. Gene ontology (GO) enrichment analysis  35  was performed with the "topGO" package (version 2.
44. 0)  36  in R, and gene set analysis correlation adjusted mean rank (CAMERA)  37  from the "limma" package 38 (version 3. 48. 3) in R was performed using the Molecular Signatures Database (MSigDb)  39, 40  GO category terms, with the nCounter neuroinflammation gene panel as the background gene set and gene annotations taken from Ensembl (version 96)  41. GO and CAMERA results were considered significant if -log10(p-value) > 1. 3; significant CAMERA results were filtered for gene sets with n = 10+ genes. Clustering analyses were performed using the "pheatmap" package 42 (version 1. 0. 12) in R. Correlations of IHC and ECAS data with housekeeping-normalised counts were calculated with the "corrplot" package  43  (version 0. 91) in R, using Spearman's test. Gene set testing was also performed with cell-type-specific gene sets derived from published Brain RNA-seq data  20  to determine cell-type-specific dysregulation of transcripts.
For this analysis, ratios were calculated for the expression of each gene in each cell type compared to its maximum expression in any other cell type. 'Human_(cell type)_5_times' indicates all genes for which the calculated ratio is >5, 'human_(cell type)_10_times' indicates all genes for which the calculated ratio is >10, and 'human_(cell type)_top100' indicates the 100 genes with the highest ratio for that cell type, that is, the most specific genes for each cell type. Differential expression analysis between NPS1 and NPS2 cases in the C9-ALS cohort was conducted as above, and all genes with an unadjusted p-value < 0. 05 and adjusted p-value ≠ NA were taken through clustering analyses with the "pheatmap" package 42 (version 1. 0. 12) in R.
Public RNA sequencing data analysis A raw count matrix of publicly available frontal cortex and cerebellum RNA sequencing data  26  were accessed via the NCBI Gene Expression Omnibus (accession number GSE67196). Data were divided by brain region and counts for C9-ALS and sALS cases were extracted. Data were variance stabilised and scaled (i. e. , z-transformed) across samples using "DESeq2" 31 (version 1. 32. 0) in RStudio (R version 4. 1. 1)  30. Clustering analyses were performed for each region using the "pheatmap" package 42 (version 1. 0. 12) in R. Heatmaps included equivalent demographic, clinical or pathological information available with the public data analysed: sex, region of onset, and disease duration.
Immunohistochemistry (IHC) Post-mortem brain tissue was obtained from Brodmann areas (BA) BA4, BA39, BA44, BA46 and fixed in 10% formalin for a minimum of 72 h. These regions were selected for their associations with clinical phenotypic correlates as we have shown previously 28 ; BA4 -motor, BA39 -language, BA44 -fluency and language, BA46 -executive function. For this validation dataset, an additional case and alternate control (Case 11 and Control 11 in Supplementary Materials) were included due to differences in tissue availability at the time of request. Tissue was dehydrated in a 70-100% ascending alcohol series and subsequently washed three times for 4 hours in xylene. Three 5-hour paraffin wax embedding stages were performed, after which formalin-fixed, paraffin-embedded (FFPE) tissue was cooled and sectioned on a microtome (ThermoFisher Scientific) into 4 μm serial sections.
Image analysis For analysis of NF-κB and FKBP5 immunohistochemical staining, whole tissue sections were scanned with Brightfield at 40x magnification using a Hamamtsu NanoZoomer XR (Hamamatsu Photonics (UK) Ltd, Welwyn Garden City, UK). Using NDP. view2 viewing software (Hamamatsu), regions of interest (ROIs) were taken from key regions for quantification. Three ROIs were taken from grey matter regions including layer V neurons, and three ROIs were taken from white matter regions. ROIs were analysed with QuPath software  46  cell segmentation; cells were segmented using a watershed method based on haematoxylin counterstaining, with different parameters for grey and white matter and for neurons and glia to best distinguish between cell types. Full scripts used for the automated cell segmentation and quantification of NF-κB and FKBP5 are included in Supplementary Information.
Cells were classified as nuclear-and/or cytoplasmic-positive for each stain based on the DAB mean intensity of each compartment. Measurements were exported at the image (number of nuclear-and/or cytoplasmic-positive cells) and cell level (intensity and morphological features). Data were visualised in RStudio with the "ggplot2" package 34 (version 3. 3. 5). Data were found to be non-normal via Shapiro-Wilk's test and subjected to nonparametric tests (i. e. , Mann-Whitney U for two-group comparisons, pairwise Wilcoxon for three-group comparisons and Spearman's for correlations). Comparisons were only conducted between groups with n ≥ 3. Results were presented as ungrouped or grouped by brain region, grey or white matter, and vascular or nonvascular adjacent. Analysis methods for CD68, Iba1, pTDP43 and GFAP staining can be found in our previous study  24.
Importantly, as the number of significantly dysregulated genes in this analysis is relatively low, the GO findings must be interpreted with caution. Thus, we also employed a competitive gene set analysis, CAMERA, which considers whole shifts in expression of groups of genes based on fold changes. Neuron development, projection, and differentiation, gene sets were downregulated in C9-ALS cases relative to controls, as well as gene sets for synaptic structure, plasticity and transmission, cell projection organisation, and cytochrome C release; blood microparticle, platelet degranulation, endopeptidase inhibitor activity, and inflammatory response gene sets were upregulated (Figure  1c ). Finally, microglia-specific genes were found to be significantly upregulated in our dataset, in line with our previous findings  24  , further supporting an increase in microglial activation  24  (Table  2 ).
While previous studies have used RNA sequencing to investigate and identify important gene expression signatures across the transcriptome  26, 60  , our targeted approach to investigating neuroinflammatory signatures without amplification bias ensured a focused evaluation of neuroinflammation specifically. We identified two distinct molecular profiles, NPS1 and NPS2, with immune response terms enriched in Gene Cluster 1 and axon transport and synaptic signalling terms enriched in Gene Cluster 2. These signatures do not segregate clearly with known demographic, clinical, or pathological data in our cohorts suggesting that these signatures are not readily identifiable through visible features. These signatures are present in multiple ALS cohorts, within our study and in an independent publicly available dataset, underscoring their generalisability and, crucially, highlighting the importance of molecular stratification in clinical trials.
Promising candidates are based on the 20 genes from our NPS-defining gene list that strongly delineate clusters in an independent, publicly available dataset (i. e. , GRIN2B, RALB, BCL2L2, PINK1, MAP2K1, TBR1, PAK1, ATP6V1A, NEFL, GRIA1, CAMK4, MEF2C, CD47, MAPK10, RAB6B, PRKACB, RB1CC1, HOF1A, GCLC) and two additional NPS-defining genes (CD44 and TYROBP) that also appear in a recently identified gene list defining a molecular subgroup relating to glial activation  61. Molecular stratification, in the form of tissue derived and circulating biomarkers, is the mainstay of patient stratification for clinical trials in oncology  76  ; given the convergence of these studies with our data, molecular subtyping should be considered for future trials implementing targeted therapies in people with ALS.
OD, JMG; Supervision: SA, CRS, JMG. Acknowledgments This research was funded in part by the  Wellcome Trust  ( 108890/Z/15/Z ) to OMR, a  Pathological Society and Jean Shanks Foundation  grant ( JSPS CLSG 202002 ) to JMG and JOS, an  NIH  grant ( 5-R01-NS127186-02 ) to JMG, FMW, and JOS, a  Motor Neuron Disease (MND) Scotland  grant to JMG and CRS ( 2021/MNDS/RP/8440GREG ), and a  Sir Henry Dale Fellowship  jointly funded by the  Wellcome Trust  and the  Royal Society  ( 215454/Z/19/Z ) to CRS. For the purpose of open access, the author has applied a CC BY public.