dataesr/scientific-paragraphs-categorization huggingface dataset:
Finally mitochondrial sequences were screened out from the short and long read libraries of M. sphyraenae by mapping them against the mitochondrial genome using bwa-0. 7. 15  15  and minimap2  13  respectively. The assembled transcriptomes of M. sphyraenae and M. vickermani were decontaminated with the Blobtools2 pipeline  26. Briefly, this approach helps to identify contaminant sequences based on their biases in coverage and GC content, as well as on a taxonomic classification established by DIAMOND searches  41  against the 'nt' and Uniprot databases  56. In addition, a second cleaning step was done by performing DIAMOND searches against a database containing all the proteins of the prokaryotic Genome Taxonomy Database (GTDB)  57  and the eukaryotic-representative EukProt v3 database  39.
Completeness analysis. To assess the completeness of the decontaminated genome and transcriptome datasets, we employed the BUSCO v5. 3. 2 pipeline. We identified the percentage of near-universal single copy orthologs of the eukaryote_odb10 database  12  on the predicted proteomes of M. sphyraenae and M. vickermani, as well as those of other species belonging to the CRuMs supergroup available in the EukProt v3  39  database for comparison purposes (Fig. 2 ). Moreover, the comparison of the transcriptomic dataset and the genomic dataset of Mantamonas sphyraenae revealed that 96% of the proteins predicted in the transcriptome share similarity with the proteins derived from the genome (80% of these being identical) and 271 proteins were found to be present uniquely in the transcriptome. Adittionally, the mapping coverage from the clean transcriptomic reads to the genome sequence was of 97.
Gene sequence: The nuclear genome and transcriptomic read sequencing data from Mantamonas sphyraenae (strain SRT306) were deposited in GenBank under BioProject accession number PRJNA886733. Mantamonas vickermani sp. nov. Description: Cell size ∼3 μm (2. 5-4. 3 μm) long, ∼3. 5 μm (3. 0-4. 0 μm) wide; cells almost perfectly round, although in some cases possessing a small projection to the left side of the cell; without pseudopodia; anterior flagellum usually ≤2 μm long (1. 2-2. 7 μm), held forwards and to left ∼40-50° to longitudinal axis, does not beat except for slight terminal vibration; posterior flagellum ∼7 μm long (6-8. 9 μm), conspicuous and sometimes acronematic. Other characters as for genus. Type culture: CRO19MAN Type locality: Specimen isolated from the sediments of the marine lake Malo jezero in the island of Mljet, Croatia.
acknowledgements The authors thank Drs. J. Z. Xiang, D. Xu, and H. Shang at the Weill Cornell's Genome Resources Core Facility for their assistance with Illumina sequencing. We also thank Dr. S. Goodwin in the NGS sequencing core at Cold Spring Harbor Laboratory for her help with PacBio sequencing, Drs. J. A. Burns and A. A. Pittis for their initial data analysis efforts and K. Lukacs for her help with maintaining the M. sphyraenae cultures.
The high similarity between some WCE frames with different abnormalities makes complicate the identification process (see Figure  2 ). WCE images may also suffer from low lightening, noise, blurriness, and low resolution  [14]. There are two approaches to designing a computeraided diagnosis system. Traditional approaches are based on Hand-crafted feature extraction, and recent approaches are based on deep learning methods. There are many traditional approaches for WCE abnormality detection. In our recent works, we also focused on the traditional methods for abnormality detection  [15]  [13]  [16]. Recently, deep learning methods based on the Convolutional Neural Networks (CNNs) have surpassed traditional methods on the medical image processing and diagnosis system. However, lack of large and public dataset with annotation for WCE images is a great challenge in training CNNs for abnormality detection.
Indeed, training a deep structure needs a large and well-balanced dataset to optimize a tremendous number of parameters. Due to this challenge, transfer learning is an effective technique to take advantage of the deep learning methods. In transfer learning, a CNNs premodel, that is pre-trained with a dataset with sufficient data from other domains, is used to extract features from real data. In this paper, Transfer learning is used to extract deep features, and also hand-crafted features are extracted. We will show that we can surpass the current WCE classification systems, which focus on one traditional or deep approach. In the proposed method, to extract deep features, we used ResNet50 pre-trained structure as the feature extractors, and the features are extracted from the AveragePooling2D layer.
Different computer-aided methods investigated AD lesions. Deeba et al. [25]  introduced a saliency map-based method to detect AD lesions. The saliency map combine a color distinctness map and a pattern distinctness map. The color map was the logarithmic ratio of the red component with respect to the green component in RGB color mode. The pattern map also was acquired by computing the distance of all overlapping patches with the average patches in the image. The accuracy of this method was considerable on a dataset with 3602 images, but this method just could be used for lesions like AD that has red color. While our method is capable to detect lesions with different colors. In Fonseca et al. [26]  method also transfer learning method was used to classify WCE into normal or abnormal classes containing three different abnormalities (Angiectasia, Blood-Fresh, and Polyp).
Feature Extraction In this step, we extract three types of descriptors, including color, texture, and shape to detect different lesions. The extract color descriptors, at first the more worthiness color channels in different color space for representing each abnormality is identified by the method that we introduced in  [15]. In this method, for each abnormality and for each channel of RGB, LAB, HSV and YCbCr color spaces, we calculate one worthiness measure. To calculate this measure, for one abnormality in a specific channel, 30 percent of existing images with this abnormality in dataset is selected randomly. For each image the Normalized Cumulative Histogram (NCH) is calculated, and then the lesion area is removed from this image, and the NCH is calculated for this image again. In the next step, the mean of absolute difference (MAD) of the two NCHs related to this image and corresponding abnormal region deleted image is calculated.
Transfer Learning In this section, we used pre-trained ResNet50 structure as the feature extractors for abnormality detection in WCE images. The basic idea of Residual Networks (ResNets)  [36]  is using shortcut connections to form blocks (residual blocks) skip blocks of convolutional layers. This structure mitigates the degradation problem existing in deep networks and improves training efficiency. The basic blocks in ResNet 50 follow two design rules. 1) The number of filters is same in the layers for the same size of output feature map 2) The number of filters is doubled if the feature map size is halved. ResNet50 is pre-trained on ImageNet dataset and can be used to extract discriminative features from WCE frames based on the transfer learning theory. Due to the small number of WCE datasets and the limited number of images in them, fine-tuning step is not considered to avoid over fitting  [22]  and images are directly fed to the architectures and a feature vector of 1 × 2048 is extracted from AveragePooling2D layer of ResNet50.
Datasets In this research, four publicly available datasets was used to evaluate and compare the proposed method with other existing methods. The Kvasir dataset  [40] , The Gastrointestinal Image Analysis Challenge 2017 (GIANA 2017)  [41] , The Red-lesion endoscopy dataset  [42] , and the KID dataset  [43]. Kvasir-Capsule is a publicly available dataset with different labeled frames that was created in 2020 and updated in 2021  [6]. In the Kvasir-capsule dataset, 47238 images with different classes are exist. Among the existing class, 10 classes are related to pathological findings (Angiectasia, Blood, Erythematous, Hematin, Erosion, Foreign Bodies, Ulcer,Polyp) or normal classes. Images in this dataset are annotated by medical doctors (experienced endoscopists) and the lesion area is specified within a box.
The size of images in this dataset is 336 × 336 pixels. In Table  3 , the number of different lesions existing in the dataset is specified. Some image samples from this dataset can be seen In Figure  11. The GIANA 2017 contains 1200 WCE frames, including 600 normal and 600 frames with AD lesions. The frames are fully annotated with experts, and the size of them are 704 × 704 pixel. The Redlesion endoscopy dataset consists of 3895 images including 2325 normal and 1570 bleeding WCE frames. The binary ground truth mask of each frames is also available in the dataset. The size of frames in the dataset is 320 × 320 pixels. In the proposed method, we resize all images into 320 × 320 pixels. Some frames examples in this dataset are shown in Figure  12. The public KID dataset contains two albums with different lesions (kid dataset 1 and kid dataset 2).
The images with size 360 × 360 pixels in this dataset are also fully annotated with experts. The lesions in kid dataset 1 are AD, Apthae, Bleeding, Chylous Cysts, and Lymphangiectasis Nodular. The kid dataset 2 contains Inflammatory, Normal, polypoid, and vascular frames. Figure  13  and Figure  14  show the image samples from the Red-lesion endoscopy dataset and KID dataset, respectively. We combined these four datasets to evaluate our proposed method. In Table  4 , the number of participating frames from each dataset in the final fusion dataset is illustrated. to  (20). The metrics are based on four well-known basic metrics, including true positive (TP), false positive (FP), false negative (FN) and true negative (TN).
Results In this section, the proposed method is evaluated and compared with other existing methods in terms of segmentation and classification. Our investigations show that no method exists that detect these six classes together. Hence, the comparisons are with the researches that consider some of these lesions in their methods. In the first experiment, the ROI extraction method is evaluated. Since on the existing datasets, the AD lesions were annotated accurately in the GIANA 2017 dataset and KID dataset and other methods also reported their result on this lesion. Therefore, we compareed our method with other existing AD lesion segmentation method. Vieira et al. introduced two methods (method 1  [20]  and method 2  [44] ) for AD lesions segmentation and detection based EM algorithm and hidden Markov model.
In Figure  15  our method with and without considering initialization were compared with these two methods on the KID dataset. In the boxplot of the proposed method the value of first, second and third quartile, minimum and maximum were higher than other methods. Therefore, it can be concluded that the proposed method with initialization could segment AD lesions more accurately than other methods. In addition, the boxplot was more compact compared with other methods, so the proposed method was more reliable. Also, the use of initialization had a positive impact on increasing the accuracy of the proposed method. The ROI extraction method was also compared on the AD lesions in the GIANA 2017 dataset with Deeba et al. [25]  method. The Authors in  [25]  shared their implementation code in GitHub 2.
From the results in Figure  16 , it can be concluded, that our proposed method was more accurate in extracting ROI. We also compared our method with two other methods that their results were reported on the GIANA 2017 dataset in the term of DS and IoU in Table  5. In Shvets's method  [45]  and Gobpradit's method  [46]  the AD lesions were segmented using the deep learning methods. The table showed the superiority of the present method over these two methods. One experiment was considered to select the appropriate size of features subset (𝜉) in features selection step. In this experiment, at first the best value for 𝜉 was acquired by trial and error. Then, in the smoothed slop graph of scores, the corresponding value of 𝜉 in graph was selected as Threshold for all other experiments. In trial and error step, the accuracy of method versus the different values of 𝜉 was calculated.
Therefore, each step had a positive impact on the proposed method. We also tested the effectiveness of different steps of Hand-crafted feature extraction similar to the previous experiment. For this purpose, we considered the proposed method without deep features, which contained Hand-crafted features. In this new structure, the impact of removing Image Enhancement and ROI extraction were tested. From the results were reported In Table  9 , it can be concluded that these steps also had a positive effect on the performance of the proposed method. In the next experiment, The ResNet50 pre-trained structure is compared with other pre-trained structures. In order to perform this evaluation, the images in the hybrid database are given to the input and the deep features are extracted from the fully connected layer of each considered structure.
The results of the classification evaluation of different structures are reported in Table  10. As can be seen. The Resnet50 deep network performed best in all metrics. We also evaluate the method on all lesions existing in the Kvasir dataset. The results of classifying 10 existing classes in this dataset were shown in Table  11. The reported results showed that although the method was designed to identify specific lesions, it was not limited to the considered lesions and it was able to identify more lesions. We also compared our results on this dataset with our recent work  [13]  in Table  12. Our method in  [13]  was also evaluated on the Kvasir dataset. As can be seen, the proposed method had improved significantly compared to our previous method. In continue the proposed method were compared with other methods on different datasets.
Firstly, our proposed method was compared with four other method including Deeba's method, Yuan's method and our two recent works on the GIANA2017 dataset. The reported results in Table  13  showed that the Yuan's method and Deeba's method had poor performance but our recent works and the proposed method had high performance on this dataset. Of course, the best performance in all evaluation criteria belonged to the proposed method. In the next experiment, the proposed method was compared with Caroppo et al. [22]  method on the Redlesion endoscopy dataset. As mentioned in the related work section, they proposed a deep transfer learning method for bleeding detection. They reported the result on the Red-lesion endoscopy dataset. Therefore, we also tested our proposed method on this dataset. From the results reported in Table  14  the superiority of our proposed method in all evaluation criteria was inferred.
The proposed method also was compared with hajabdollahi et al. method  [23]. The results of this method were reported on the fusion of two publicly available datasets. 728 normal, five bleeding from KID dataset 1 and 50 bleeding frames from deeba's dataset  [47]. We also evaluated our methods on this dataset and the results were shown in Table  15. The results showed that our method had better performance compared to this method in all evaluation criteria. We also compared our method with Fonseca et al. [26]  method. In this method the author used Kvasir capsule dataset for classifying images into normal and abnormal classes. The abnormal class was containing all existing images in polyp, AD and bleeding classes. We also used this dataset to validate and compare our method with Fonseca et al.
method. The results that was shown in Table  16  shows superiority of the proposed method. Finally, the proposed method was compared with De Maissin et al. [48]  method on the CrohnIPI dataset  [49]  contains several types of abnormality caused by Crohn's disease. This dataset includes seven types of abnormalities and three of which are related to ulcer abnormalities. We evaluate our method on all existing abnormalities in this dataset. Smoothed slope graph of scores resulting from their method. We have calculated the evaluation criteria from this matrix for their method. The results of these evaluations are reported in Table  17. As can be seen, the proposed method has performed better in all evaluation criteria except FPR which In abnormality detection in the medical images the most important metric is FNR.
Conclusions In this paper, a new computer aided method was proposed to detect different lesions in WCE images. The proposed method was based on the combination of deep and traditional learning methods. For this purpose, transfer learning was used to extract deep features. Due to the lack of a public and large database of endoscopic capsule images, most existing methods have used the traditional learning method to abnormality detection. The Hand-crafted features also extracted in three main step containing image enhancement, ROI extraction and feature extraction from ROI and the ROI extraction is the most important step. But the most of existing methods are limited to extract abnormalities with a specific color or texture, so they could identify few abnormalities. But the results in this research showed that the proposed method has the ability to extract different types of abnormalities with no restrictions on color and texture.
The results also showed that using the combination of traditional and deep features increased the performance of the proposed method. In fact, we cloud surpass the current WCE classification systems which focuses on one traditional or deep approaches. In the Hand-crafted feature extraction steps, we proposed an EM based method to extract ROIs. The EM method was initialized with a new novel fast segmentation method. We also proposed a novel method to extracting sudden changes due to existing lesions with different color in the background. The proposed method was evaluated on our different datasets. The results show the ability of the proposed method on abnormalities detection in WCE images. Also, the proposed method is compared with several existing methods for lesion detection, and in all comparisons, the superiority of the proposed method is concluded   13 -Comparing the proposed method with our two recent works  [13] [15] , the Yuan's method  [21]  and the Deeba method  [25].
The bars represent the mean fold induction of firefly luciferase compared with unstimulated cells (not transfected with pEF6FLAGDRIG-I). EV, empty vector. Data points represent 3 independent experiments. Error bars indicate SD. (B) Amino acid differences between P CVS and P SAD in region 176-186. (C) IFN induction inhibition by SAD and SAD-S179P assessed using the luciferase assay. HEK293T cell lysates were analyzed using WB with anti-P and antitubulin antibodies. The bars represent the mean fold induction of firefly luciferase compared with that in mock-infected cells. Data points represent 3 independent experiments. Error bars indicate SD. (D) RABV P does not block IFN induction in HEK293T cells expressing IRF3-5D. IFN induction was assessed using the luciferase assay. Cell lysates were analyzed using WB with anti-P and anti-tubulin antibodies.
Limitations of the study Although our data are consistent with TBK1 condensate formation being required for IFN-b induction, we cannot rule out that this rather regulate the cytosolic concentration of TBK1. Furthermore, there might be several kinds of TBK1 condensates. 38, 55, 77 ere, in addition, we show that they can assemble into ring-like structures. Those distinct condensates, having distinct composition, may have distinct functions. Additional investigations will be required for a better understanding of their role as well as those of NAP1 and SINTBAD condensates.
35%) solvent B (80% -resp. 99. 9% -acetonitrile, 0. 1% formic acid) over a period of 120 min (resp. 98 min), followed by a column regeneration of 20 min, giving a total run time of 140 min (resp. 118 min). Peptides were analyzed in the Orbitrap cell, in full ion scan mode, at a resolution of 120,000 (resp. 70,000) with a mass range of m/z 350-1550 (resp. 375-1500) and an AGC target of 4. 10 5 (Resp. 3. 10 6 ). For P SAD, fragments were obtained by Higher-energy C-trap Dissociation (HCD) activation with a collisional energy of 27%, and a quadrupole isolation window of 1. 6 Da. MS/MS data were acquired in the Ion trap in a Top-Speed mode with 3 s cycles, with an AGC target of 1. 10 4 and with a dynamic exclusion of 60 s. The maximum ion accumulation times were set to 100 ms for MS acquisition and 35 ms for MS/MS acquisition.
For P CVS , fragments were obtained by HCD activation with a collisional energy of 27%, and a quadrupole isolation window of 1. 4 m/z. MS/MS data were acquired in the Orbitrap with a resolution of 17,500, a TopN of 20, with an AGC target of 2 3 105 and with a dynamic exclusion of 30 s. The maximum ion accumulation times were set to 50 ms for MS acquisition and 45 ms for MS/MS acquisition.
LC-MS/MS data processing Data were processed with the PEAKS Online Xpro software (Bioinformatics Solutions Inc. , Waterloo, ON, Canada). Raw MS data were searched against the Homo Sapiens UniProtKB 2021_03 protein database (reviewed, 20,371 entries) with addition of P SAD and P CVS bait sequences. The precursor and product ion tolerances of peptides were set at 20 ppm and 0. 05 (for P CVS ) or 0. 5 Da (for P SAD ) respectively. The digest mode of trypsin was set as specific with 2 missed cleavages and the max variable PTM per peptide was set at 3. Oxidation (+15. 99), deamidation (+0. 98) and phosphorylation (+79. 97) were specified as variable modifications and half of a disulfide bridge (À1. 01) as a fixed modification. Protein/peptide quality identification was ensured using built-in filters, especially a false-discovery rate (FDR) of 1% was applied at both peptide and protein levels.
In addition, the PEAKS Q module was used for label-free quantification (LFQ) of the digested peptides generated under various experimental conditions. Qlucore Omics Explorer software version 3. 7 (Qlucore AB, Lund, Sweden) was used for statistical analysis and processing of LFQ data. The mass spectrometry proteomics data have been deposited to the ProteomeXchange Consortium via the PRIDE partner repository  82  with the dataset identifier PXD028740. Co-immunoprecipitation of TBK1 with P protein N2A cells (6. 5 3 10 5 cells) were infected with either SAD or SAD-S179P. 16 h post-infection, cells were incubated with 300 mM DSP (Thermo Fisher Scientific) in phosphate buffer saline (PBS) for 30 min at room temperature. The reaction was then stopped with 20 mM glycine pH 7. 4 in PBS for 15 min at room temperature.
FRAP experiments were performed using iLas 2 module (GATACA Systems) and the whole system was driven by MetaMorph software version 7. 7 (Molecular Devices). FRAP experiments on ring-like structures were performed at the frame rate of 1 image every second using the following sequence: 3 s of pre-bleach, 10-15 ms or 40-60 ms to bleach GFP-mTBK1 or mCherry-NAP1 respectively, 90 s post-bleach. Bleaching was performed in a circular region of 1. 4 mm diameter. FRAP experiments for NBs were performed with the following sequence: 6 s pre-bleach, 40 ms to bleach GFP-mTBK1, 120 s post-bleach at a frame rate of 1 image every 500 ms for the first 10 s then every 2 s for 110 s. Bleaching was performed in a circular region (diameters of 2. 8 and 1. 4 mm for whole NB or subvolumes, respectively). For the FRAP data analysis we used a home-made macro on FIJI  85  performing the following steps.