dataesr/scientific-paragraphs-categorization huggingface dataset:
DISCUSSION This study brings to light several important findings. First, psoriasis was frequent at the onset of axSpA and its prevalence increased regularly over time. Second, axSpA patients with psoriasis were more likely to develop synovitis and to be treated with methotrexate. Other outcomes including disease activity, functional capacity and patient-reported outcomes were similar in patients with and without psoriasis. This study has strengths and limitations. In the DESIR cohort, patients were included if they had inflammatory back pain suggestive of axSpA, rather than a formal classification of axSpA. However, 92% patients fulfilled at least one set of classification criteria for SpA. 17 Some patients were lost to follow-up and some had missing data over time which is frequent in longitudinal cohorts.
Purpose: In the phase 3 SINUS-24 (NCT02912468) and SINUS-52 (NCT02898454) studies in adults with severe chronic rhinosinusitis with nasal polyps (CRSwNP), dupilumab significantly improved the co-primary endpoints of change from baseline to Week 24 in nasal polyp score (NPS) and nasal congestion/obstruction (NC) vs placebo on background intranasal corticosteroids (standard of care [SOC]). This post hoc analysis of SINUS-24/-52 investigated the direction and magnitude of within-patient change in these endpoints over time. Methods: NPS (scale 0-8) was assessed at Weeks 4, 8, 16, 24, 40, and 52 in SINUS-52 and Weeks 8, 16, and 24 in SINUS-24. Daily patient-reported NC scores (0 [no symptoms]-3 [severe symptoms]) were averaged over 28 days. Within-patient changes from baseline were assessed through Week 24 in pooled SINUS-24/-52 (n = 438/286 dupilumab/SOC) and through Week 52 in SINUS-52 (n = 150/153).
Materials and Methods This was a post hoc analysis of the SINUS-24 (NCT02912468) and SINUS-52 (NCT02898454) trials, details of which have been published previously. 6 In brief, eligible patients had severe CRSwNP on a background of mometasone furoate nasal spray (MFNS; 2 × 50 µg actuations in each nostril twice daily [400 µg daily dose] or once daily [200 µg daily dose] if twice daily was not tolerated) and prior history of treatment with systemic corticosteroids and/or surgery for nasal polyposis. Patients were randomized 1:1 to dupilumab 300 mg subcutaneously (SC) or matching placebo every 2 weeks (q2w) for 24 weeks in SINUS-24 and 1:1:1 to dupilumab 300 mg SC q2w for 52 weeks, placebo q2w for 52 weeks, or dupilumab 300 mg SC q2w for 24 weeks, followed by dupilumab 300 mg SC every 4 weeks for 28 weeks in SINUS-52.
Acknowledgments Research sponsored by  Sanofi  and  Regeneron Pharmaceuticals, Inc. ClinicalTrials. govIdentifiers:  NCT02912468  ( SINUS-24 ) and  NCT02898454  ( SINUS-52 ). The authors thank  Nadia Daizadeh, PhD  ( formerly of Sanofi ), for statistical analyses and  Neil Anderson, PhD , of  Adelphi Group, Macclesfield, UK , for medical writing/editorial assistance funded by  Sanofi and Regeneron Pharmaceuticals, Inc. in accordance with Good Publications Practice.
vivax from P. falciparum were a subset of P. vivax sporozoites expressing genes associated with translational regulation and repression. Finally, our comparison of single-cell transcriptomic data from P. vivax sporozoite and erythrocytic forms reveals gene usage patterns unique to sporozoites. In defining the transcriptomic signatures of individual P. vivax sporozoites, our work provides new insights into the factors driving their developmental trajectory and lays the groundwork for a more comprehensive P. vivax cell atlas.
berghei and P. falciparum sporozoites  (Bogale et al. , 2021; Howick et al. , 2019; Real et al. , 2021; Ruberto et al. , 2021). However, the extent to which P. vivax sporozoites vary at the single-cell level has not been studied. Therefore, the application of scRNA-seq technology provides an opportunity to explore heterogeneity amongst P. vivax sporozoites and examine the existence of distinct transcriptional signatures that may help better understand the sporozoite's developmental fate. In this study, we analyse the transcriptomes of 9,947 P. vivax sporozoites captured using droplet-based scRNA-seq technology. We first cross-reference the data with sporozoite bulk microarray and RNA-seq data to show consistent transcription of known genes upregulated in sporozoites. Next, we represent the data in low dimensional space and identify sporozoites in various transcriptomic states using both clustering and pseudotime trajectory methods.
P. vivax sporozoites have distinct transcriptomic signatures relative to P. falciparum sporozoites Whereas P. vivax sporozoites can form hypnozoites in hepatocytes, P. falciparum sporozoites cannot. Therefore, we compared sporozoite transcriptomes from these two species to assess the extent to which their gene expression profiles differ and search for signatures of potential importance for hypnozoite formation. We first identified orthologous gene groupings to allow for the cross-species comparison  (Emms and Kelly, 2019). After subsampling the P. vivax data to balance the proportions of P. falciparum  (Real et al. , 2021)  and P. vivax cells, we then combined the two species' datasets to produce an integrated UMAP (Figure  5A  top). The projection revealed that sporozoites in the P. vivax and P.
falciparum and P. vivax sporozoites. In total, we identified 98 differentially transcribed genes (adjusted p-value < 0. 05, Table  S11 ), several (38/98; 38%) of which have no known function. Interestingly, genes that displayed significantly greater transcription in ORTHO_C4 (Figure  5E ) contained Pfam domains associated with translational machinery (PF01200 and PF01253) and RNA-binding proteins (PF00076, PF00806), including mRNA-binding protein PUF2 (PVP01_0526500). PUF2 is an important eukaryotic cell-fate regulator  (Zhang et al. , 1997) , and plays a key-role in translationally repressing transcripts, including uis3 and uis4, required during liver-stage development  (Gomes-Santos et al. , 2011; Lindner et al. , 2013). Interestingly, in the absence of PUF2 (Pfam: PF00806::PUF RNA binding repeat), salivary gland sporozoites may initiate exoerythrocytic development independently of the transmission-associated environmental cues  (Gomes-Santos et al.
, 1977)  proposed to be destined for hypnozoite formation. This population is potentially of a sufficient size to account for the proportion of liver forms found to be hypnozoites in vitro (47-61%). However, it is not possible to conclude any link between these cells and liver-stage development at this stage. Clearly, further experimentation will be required to resolve this issue. Future analyses comparing P. vivax to the other sporozoites derived from parasites that cause relapsing malaria, such as P. cynomolgi and P. ovale, may help shed light on the relapsing-specific factors associated with the parasite's developmental fate. However, the differences in methodology used to generate sporozoite scRNA-seq datasets should be considered for these future studies. Of note, scRNA-seq of P. falciparum sporozoites was performed after dissecting salivary glands into Schneider's insect medium and then sequencing individual sporozoites following Accudenz purification, immunofluorescent staining and fluorescence-activated cell sorting  (Real et al.
, 2021). In this study we used only a density gradient to purify sporozoites from salivary gland debris before loading them onto a 10x Genomics' controller to generate single-cell RNA libraries. Both collection, purification, and capture workflows involve 1-3 hr of time from dissection to RNA capture, during which time the buffers and temperature used could impact sporozoite activation state  (Hegge et al. , 2010; Roth et al. , 2018a). Additionally, for both the P. vivax and P. falciparum datasets, pseudotime analysis was found useful for understanding the developmental state of sporozoites, meaning the day, and possibly hour, of dissection post blood-meal could impact comparisons. Despite these differences, we note the substantial overlap in P. vivax and P. falciparum developmental states in low dimensional space.
Read alignment, cellular barcode assignment, and quantification The P. vivax P01 genome (version 3, October 2020) and its corresponding general feature format (gff) file (which contained UTR coordinates) were downloaded from ftp://ftp. sanger. ac. uk/pub/genedb/releases/latest/PvivaxP01 and used to create a genome index with STAR (v2. 7. 3a)  (Dobin et al. , 2013; Kaminow et al. , 2021)  with options: --runMode genomeGenerate -genomeSAindexNbases 11 -sjdbOverhang 90 (for v3 libraries), or 74 (for libraries derived from  (Sà et al. , 2020) ). Mapping, demultiplexing and gene quantification was performed with STARsolo and the following options specified: --soloType CB_UMI_Simple --soloCBlen 16 --soloUMIlen 12 (for v3 libraries) or 10 (for v2 libraries) --soloCBwhitelist /path/to/10x/version/specific/whitelist --alignIntronMin 1 --alignIntronMax 2756 --soloUMIfiltering MultiGeneUMI --soloFeatures Gene.
Integration of P. vivax sporozoite scRNA-seq data Filtered, normalized matrices from the three replicates were merged in a manner described in the Seurat (version 4) vignette, Introduction to scRNA-seq integration, available on the Satija Lab's website (https://satijalab. org/seurat/articles/integration_introduction. html). Briefly, highly variable features were identified in each replicate using the 'FindVariableFeatures' function with the following parameters selected: selection method = "vst", features = (genes # of detected in dataset) * 0. 3. Next, integration anchors were identified using the 'FindIntegrationAnchors' function with its default settings. Lastly, using these anchors, the three datasets were integrated using the 'IntegrateData' function with its default settings.
Trajectory analysis To infer the developmental trajectory of sporozoites, we used the Slingshot package  (Street et al. , 2018)  to uncover the global structure of clusters of cells and convert this structure into a smoothed lineage representing "pseudotime". Lineages were first generated using the getLineages function on the UMAP embeddings generated previously (described in Dimension reduction, clustering and cluster marker identification). Cluster C1 was selected as the starting cluster because it contained putative immature salivary gland sporozoite markers. Next, smoothed lineage curves were constructed using the getCurves function with the default parameters. We then used the tradeSeq package (van den  Berge et al. , 2020)  to analyze transcription along the trajectory. To this end, we ran the fitGAM function on the SlingshotDataset to fit a negative binomial general additive model on the data.
falciparum sporozoites  (Real et al. , 2021). Orthogroup IDs for merge were only retained if they had only one entry per species. In a P. vivax-centric approach, we retained orthogroup IDs if either P. falciparum, P. berghei ANKA, P. cynomolgi M or P. cynomolgi B had at least one corresponding gene orthologue. P. vivax data were processed as described earlier (section Filtering and normalization of scRNA-seq count matrices) and P. falciparum data was obtained from https://github. com/vhowick/pf_moz_stage_atlas/tree/master/counts_and_metadata. Following the replacement of gene IDs with new orthogroup IDs, we additionally performed another round of cell filtering to account for changes in gene count per cell information with the removal of some genes without equivalent orthogroup IDs. Data integration were also performed as described earlier (section Integration of P.
vivax sporozoite scRNA-seq datasets). The P. vivax data were randomly subset (500 sporozoites per replicate) for subsequent analyses to match the proportion of cells in the P. falciparum dataset prior to integration and account for the disproportionate number of sporozoites between the two species. Clusters of sporozoites were identified using 'FindNeighbours' function using the 'pca' reduction, dims = 1:15, k. param = 20, and 'FindClusters' function with the Leiden clustering algorithm (algorithm = 4) at a resolution of 0. 2. Marker genes for the defined clusters were identified with either the 'FinderConservedMarkers' or 'FindMarkers' functions (Seurat) using a Wilcoxon Rank Sum test. The number of cells, percent difference, and fold-change parameters for each of the analyses are indicated in the R markdown document provided.
Integration of P. vivax sporozoite and blood stage scRNA-seq data Filtering and normalization for the P. vivax blood-stage scRNA-seq data was performed in the same manner used for the sporozoite scRNA-seq data described in Filtering and normalization of scRNA-seq count matrices. Of the 10 P. vivax blood-stage replicates, we used the 7 generated from samples without chloroquine treatment. Before integration, variable features for the combined sporozoite (3) and combined blood-stage (7) datasets were identified using the FindVariableFeatures function with the following parameters: selection method = "vst", features = (genes # of detected in dataset) * 0. 3. Next, we used the FindIntegrationAnchors function to identify anchors between the sporozoite and blood-stage datasets. Guided by the recommendations provided by the Satija lab's vignette Fast integration using reciprocal PCA (RPCA) (https://satijalab.
org/seurat/articles/integration_rpca. html), namely when cells in one dataset have no matching type in the other, we selected the RPCA parameter in FindIntegrationAnchors (reduction = "rpca") to identify anchors between the sporozoite and blood-stage data. Last, using these anchors, the datasets were integrated using the IntegrateData function with its default settings. Assessment of differentially transcribed genes in sporozoite and blood-stage parasites were performed in the same manner described in Differential genes transcription analysis with additional parameters indicated in the provided R markdown files.
Acknowledgments We thank the P. vivax patients of  Mondulkiri Province, Cambodia , for participating in this study. We thank the  Institut Pasteur insectary  staff ( Makara Pring ,  Koeun Kaing ,  Nora Sambath ) for An. dirus mosquito colony maintenance, the laboratory staff ( Eakpor Piv ,  Chansophea Chhin ,  Sreyvouch Phen ,  Chansovandan Chhun ,  Sivcheng Phal, Baura Tat ) for assistance with the mosquito dissections and the in vitro assays, and the field site manager ( Saorin Kim ) for logistical assistance. This work was supported by the  Agence Nationale de la Recherche  ( ANR-17-CE13-0025  to A. A. R, G. S. , I. M. ), the  National Health and Medical Research Council of Australia (NHMRC ;  1092789  and  1134989  to I. M. ), and a  NHMRC   Principal Research Fellowship  ( 1155075  to I.
Data Availability: All raw sequencing data generated from P. vivax sporozoites in this study will be deposited and accessible in the European Nucleotide Archive (www. ebi. ac. uk/ena/) upon acceptance of the manuscript. P. vivax blood-stage scRNA-seq data were downloaded from NCBI's Short Read Archive (Bioproject ID: PRJNA603327). P. falciparum sporozoite scRNA-seq data were obtained from: https://github. com/vhowick/pf_moz_stage_atlas Scripts and supporting files will be available on GitHub upon acceptance of the manuscript. Archived scripts and output files as at time of publication will be available on Zenodo. com upon acceptance of the manuscript.
Glaucoma is an eye condition that, if not diagnosed in time, leads to loss of vision and blindness. While diagnosing campaigns are regularly launched, these require human experts to estimate in a limited time subtle changes in the shape of the optic disc from retinal fundus images. Automatic glaucoma detection methods are desirable to help with the screening of patients, reducing the diagnosis time by analyzing the increasing amount of images. Deep learning methods have been satisfactory to classify and segment diseases in retinal fundus images. To guarantee a good generalization, these methods require vast amounts of annotations, which can be highly problematic given that existing annotated glaucoma datasets contain at most few hundreds of samples. Furthermore, deep learning techniques are computationally greedy, which can be a problem in scenarios with limited resources.
The objective of this work is to make optimal use of the few annotated data at disposal and obtain better generalization performance of glaucoma diagnosis deep learning models. We achieve this by designing and training a novel multi-task deep learning model that leverages the similarities of related eye-fundus tasks and measurements used in glaucoma diagnosis. The proposed multi-task deep learning model with independent optimizers (MTL-IO) simultaneously learns different segmentation and classification tasks, thus benefiting from their similarity. The framework uses a U-Net encoder-decoder convolutional network as backbone architecture. Our MTL-IO approach was trained in a retinal fundus glaucoma challenge dataset, including 1200 retinal fundus images (400 for training, 400 for validation, 400 for testing) from different cameras and medical centers.
Over the last years, glaucoma detection using deep learning models reached a remarkable performance at the pair with residents in ophthalmology  3, [8] [9] [10] [11]  , thus representing a viable alternative to support current visual assessment. However, automating glaucoma diagnosis suffers from lack of data. Existing annotated datasets therefore contain a few hundred samples, while deep learning models require extensive databases to guarantee a good generalization. Moreover, they include millions of trainable parameters, requiring significant computational resources for training and deployment  12, 13. Therefore, it is essential to develop methods that can make the most from the limited resources: computational requirements and the available annotated images, thus operating in a low data size regime while guaranteeing a good generalization.
However, automating glaucoma diagnosis with deep learning models suffers from a lack of data. Existing annotated datasets contain a few hundred samples, while deep learning models require extensive databases to guarantee a good generalization Multi-task learning (MTL)  14  is a learning paradigm that aims to improve generalization by using the domain information contained in the training signals of related tasks as an inductive bias. In practice, this is done by training a shared model for all tasks. In deep MTL, the shared model consists in the parameters of a deep network  15  , hence, the resulting model is smaller than having separate networks for each task. Thanks to these features, MTL is a well-suited approach to automated glaucoma detection, where multiple tasks as OC and OD segmentation, and fovea localization are pre-requisite tasks for computer aided diagnosis (CAD) of retinal diseases  16.
Among the authors that have explored MTL techniques, Mojab et al. proposed a multi-task model for glaucoma detection composed of two modules for OD and OC segmentations and glaucoma prediction 20 , obtaining 90. 1 of F-score; the authors did not account for the dissimilarity between the distributions of the segmentation and prediction tasks. Chelaramani reported a novel MTL-based teacher ensemble method for knowledge distillation  21. The proposed method requires a dataset with a variety of different eye pathologies, which may be difficult to obtain in practice. This work aims to determine if the relation between tasks associated to glaucoma CAD, i. e. OD and OC segmentation, fovea location and glaucoma detection, can be exploited within an MTL framework to improve model generalization and accuracy for glaucoma detection in a low sample size, low computational resources regime.
To this end, a deep MTL model is trained to leverage the similarities of the segmentation of the OD and OC tasks, together with localization of the fovea to detect the presence of glaucoma in retinal fundus images. The proposed MTL approach uses a U-Net encoder-decoder convolutional network as a backbone architecture and adapts it to handle the four tasks using independent optimizers (IO) that can simultaneously learn the segmentation and classification tasks. We denote it MTL-IO. We evaluate our method using the Retinal Fundus Glaucoma Challenge (REFUGE) dataset, including 1200 retinal fundus images (400 for training, 400 for validation, 400 for testing) from different cameras and medical centers, achieving better AUC performance than the same network trained for the single task of detecting glaucoma (92.
Materials: REFUGE challenge dataset In 2018 the Retinal Fundus Glaucoma Challenge (REFUGE) was launched as a satellite event at the 2018 MICCAI conference. For this event, 1200 retinal fundus images (400 for training, 400 for validation, 400 for testing) from different cameras and medical centers have been collected and annotated by human experts. Annotations were provided for four different tasks: glaucoma diagnosis, optic disc segmentation, optic cup segmentation, and fovea localization. For the diagnosis task, the ground truth is provided as binary labels, attesting to the presence of glaucoma. In the segmentation tasks, the regions defined by the OD (optic nerve head) and the OC (the white elliptic region located inside the optic disc) are provided as binary segmentations. In the fovea localization case, the ground truth is given as the fovea's (x, y) pixel location.
Acknowledgements Sebastian Otálora thanks the  Colombian science ministry Minciencias  for partially funding his Ph. D. studies through the call "756 -Doctorados en el exterior". The French government supports  Maria A. Zuluaga  through the  3IA Côte d'Azur Investments in the Future  project managed by the  National Research Agency (ANR)  ( ANR-19-P3IA-0002 ).
Data and MaterialsAvailability Statement The data used to run train our models and run experiments is available, upon registration from the REFUGE Challenge (https: //refuge. grand-challenge. org/Home2020/). All code to reproduce the results of this article is available in a GitHub repository (https://github. com/robustml-eurecom/glaucoma_mtl). The code can be anonymously downloaded from the following link: https://github. com/robustml-eurecom/glaucoma_mtl/archive/ refs/heads/main. zip.
In Pakistan, sparrows were shown to play a very important role in the transmission of the virus between farms  [20]. In general, the sensitivity and receptivity of H9N2 is strongly dependent on the avian species (chicken and turkey). However, other species raised for consumption and/or hunting, such as guinea fowl, quail, pheasant, partridge, duck, goose, and ostrich are also considered sensitive. LPAI H9N2 virus has also been reported to be transmissible to mammals including dogs and cats  [21]  as well as humans  [22] [23] [24] [25]. The LPAIV H9N2 strain (SF1, GenBank accession number SCA48100) introduced in Morocco in January 2016, belongs to the G1 lineage, and is closely related to viruses circulating in the Middle East. As a response, the competent authorities authorized vaccination of any type of chicken as the best choice to limit the rapid spread of this disease  [26].
Full Genome Amplification and Sequencing of H9N2 Moroccan Isolates Viral RNA was extracted from allantoic fluids harvested from the 7 SPF embryonated eggs with the highest viral load, using the Macherey Nagel kit (Duren, Germany, No. 740956. 250). Whole genome sequencing of 2018-2019 Moroccan isolates was performed with an Illumina MiSeq System (Illumina, San Diego, CA, USA) as previously described  [28]. The preparation of libraries was performed using an Illumina Nextera XT library prep kit (FC-131-1096) (Illumina, San Diego, CA, USA) following the manufacturer's instructions. A tape station was used to verify the library quantity and quality. CLC genomic workbench was used for genomes assembly. The nucleotides sequences of all characterized strains in this study are submitted in the GenBank database under accession numbers summarized in Table  1.