dataesr/scientific-paragraphs-categorization huggingface dataset:
Then, the cells were immediately placed on ice and rapidly cooled by shaking and kept on ice for 15 min. The cells were centrifuged at 7,500 × g at 4°C for 3 minutes. The pellet was washed with 25 ml ice-cold 10% glycerol solution by resuspending and centrifuging at 7,500 × g at 4°C for 3 min thrice. Finally, the cells were resuspended at 100-fold lower volume of ice-cold 10% glycerol than the starting culture volume. Dialyzed mixtures of the gRNA-expressing plasmid and single donors with point mutations or library of mutations were electroporated at 1. 8 kV into 50 ll of the washed now electrocompetent cells. The cells were recovered in LB for 3 h at 30°C and subsequently plated on LB+agar and appropriate antibiotics. CREPE: Screening the target gRNA and spacer 1 Use the E. coli reference genome for MG1655 (accession number U00096) to get the sequence for the target of interest for mutagenesis.
Finally, resuspend the pellet in 50 ll of TE buffer (pH 8. 0). Boil the resuspended cells at 100°C for 10 min. 2 Amplify each library using primers with Illumina Nextera adapters (Appendix Table  S3 ) using Kapa polymerase following manufacturer guidelines using 1 ll of the cell extract. It is important to avoid overamplification for next-generation sequencing. Refer to the CREPE: genome library construction step for the protocol for optimizing cycles to prevent overamplification. 3 Amplify the PCR product for each experiment with a unique experimental Nextera barcode. Paired-end 2*300-bp read sequencing was performed using the Miseq platform following manufacturer guidelines for sequencing. CREPE: Data analysis 1 Next-generation sequencing data analysis: A custom analysis pipeline was built to quantify recombination trends and fitness calculation for our experiments (https://github.
com/Alaksh/ CREPE-Analysis-Code). Paired Illumina reads were first assembled using the Usearch Mergepairs algorithm  (Edgar, 2010). Then, the assembled reads were aligned to the wild-type sequence of the appropriate target gene using the usearch global alignment algorithm  (Edgar, 2010). The alignment generates a text file which summarizes details (id, number of mutations, Evalue, mismatches, indels, and mismatch/indel positions as a qrowdot alignment output). Variant counts were estimated as value counts for the qrowdot alignment output. Finally, we wrote a custom code to extract information about the nucleotide and amino acid changes corresponding to each qrowdot output. 2 Fitness calculations for resistance to rifampicin: Sample code for calculating the resistance to rifampicin has been provided (https://github.
Standard Error ¼ sqrt 1 C i;sel þ 1 C wt;sel þ 1 C i;input þ 1 C i;input After fitness estimates, we filtered the data to eliminate reads that may be erroneous. Since we targeted an essential gene, the occurrence of stop codons would be impossible. Therefore, we eliminated reads with the following filter: C i ! C maxÀstopcodon þ 2:56 Ã sqrtðC maxÀstopcodon Þ After read filtering, we combined the fitness estimates from each replicate using maximum-likelihood estimates for variant score and standard error using Fisher scoring iterations  (Rubin et al, 2017). The same protocol was also repeated with only synonymous mutations in our dataset. We classified a mutant as resistant to rifampicin if the fitness was 2. 96 standard deviations greater than the mean fitness of synonymous mutations.
Acknowledgements We would like to acknowledge the  University of Colorado Boulder 's nextgeneration sequencing core headed by  Dr. Amber S. Scott  for support with deep sequencing for our study. We would also like to thank  Dr. Carrie Eckert ,  Andrew Mogenthaler , and  Dr. Shelley Copley  for sharing plasmids. This work was supported by the  US Department of Energy   DE-SC0018368. AC was also funded by the 2017-2018  STEM Chateaubriand Fellowship  sponsored by the  French Institut National de la Santé et la Recherche Médicale (INSERM) European Research Council ,  FP7  grant  310944  to O. T.
g. humans, software agents) as well as nonautonomous entities (e. g. physical devices, digital services, knowledge repositories) are represented in terms of Web resources. Agents navigate the hypermedia environment to discover and use resources to achieve their goals -for instance, to cooperate with other agents or to enrich their action repertoire by exploiting non-autonomous entities and their affordances. In Hypemedia MAS, the agents' environment is designed as a distributed hypermedia application where agents -human and software -access and use devices and other tools modeled as artifacts to achieve their goals. Artifacts are designed based on the Artifacts & Agents (A&A) meta-model  [12] , where artifacts provide agents with access to physical resources (e. g. industrial robots), computational resources (e.
We take a special interest in deriving behaviors for agents through operating instructions of artifacts, as this method could facilitate the flexible and efficient interaction with heterogeneous resources that are discovered dynamically in the environment. Similar to how humans use instructions found in manuals and tutorials to learn about the gainful use of new or complex tools, we design 5 https://www. w3. org/TR/wot-architecture/#sec-web-thing 6 We design and program the autonomous agents based on the Belief-Desire-Intention model, i. e. in terms of beliefs about the world, goals desired to be achieved and plans for achieving intended goals  [4]. 7 Agents in MAS acquire information through three types of sources: a) internal mental notes, i. e. beliefs about known behaviors that are pre-programmed or generated by the agent itself, b) perceptual information derived from the environment, and c) communication with other agents  [4].
Artifact Manuals MAS researchers have already examined the specification of operating instructions for artifacts in an attempt to design maintainable and extensible goal-directed autonomous consumers of resources (e. g. [11] ). Following the methodology in  [11] , we enable autonomous agents to dynamically read and interpret high-level operating instructions that encapsulate low-level operations of artifacts. Such information is explicitly described in artifact manuals and is meant to provide the agent with the operating instructions that are necessary to adequately exploit artifact functionalities. Artifact manuals can be discovered, managed within manually curated repositories, or consulted by agents that look for behaviors. In this demonstrator, we define artifact manuals in terms of usage protocols (see Listing 1 for an example) that are defined in the language introduced in  [15]  and are characterized by 8  : • A function which defines the goal achieved by the described behavior (e.
System Overview Our demonstrator consists of several components: The React-based user interface  13  (Fig. 2 , left) is served by a lightweight Express. js 14 back-end which instantiates a WebSocket server. We use the Ja-CaMo 15  MAS platform and Yggdrasil 16  , a platform for hypermedia agent environments, to program and deploy the Hypermedia MAS in our demonstrator. Both the user interface and the MAS connect to the WebSocket server to exchange messages at run-time (i. e. , user inputs and agent messages to the GUI). All resources in our system (agents, workspaces, manuals, artifacts, devices etc. ) and relations among them are described in RDF  17. Browser artifacts for devices and other artifacts are described along with their interaction affordances using the W3C WoT TD. For describing the agent environment, we use the EVE ontology  [5]  which we extended to address artifact manuals (eve:Manual) and their relations (eve:explains, eve:hasManual) to artifacts (eve:Artifact).
In this paper, we propose a method that removes raindrops with light field image using image inpainting. We first use the depth map generated from light field image to detect raindrop regions which are then expressed as a binary mask. The original image with raindrops is improved by refocusing on the far regions and filtering by a high-pass filter. With the binary mask and the enhanced image, image inpainting is then utilized to eliminate raindrops from the original image. We compare pre-trained models of several deep learning based image inpainting methods. A light field raindrop dataset is released to verify our method. Image quality analysis is performed to evaluate the proposed image restoration method. The recovered images are further applied to object detection and visual localization tasks.
collected image The associate editor coordinating the review of this manuscript and approving it for publication was Feng Shao. pairs with and without raindrops and trained a Generative Adversarial Network (GAN)  [5]  in supervised learning paradigm to remove raindrops, that can recover test images effectively. However, due to the relatively small dataset for neural network training, we found that there is still room for improvement of model generalization ability, especially when inputting non-homologous images. Although a direct way in this matter is to increase the model capacity by feeding more training examples, it is very challenging to collect image pairs with and without raindrops in different lighting conditions, scenes, and environments. As an emerging device, light field cameras not only retain light intensity information, but also direction information of the ray.
It makes deep learning based image inpainting methods  [10] ,  [11]  have million-level training datasets and work well even on the non-homologous images with pre-trained models. We therefore believe that the combination of light field image and image inpainting is helpful for raindrop removal task in general scenarios. In this paper, we propose a method that can effectively remove raindrops from the images captured by a handheld light field camera using image inpainting.
The contributions of this paper are threefold: • We propose a novel raindrop removal method with light field image using image inpainting. • We release a new dataset which contains light field images with raindrops collected in different scenarios, publicly available at https://github. com/cavayangtao/ light-field-raindrop-dataset. • We comparatively evaluate our method in different scenarios using image quality analysis and apply the recovered images to object detection and self-localization tasks. The experimental results show that the use of recovered images can help improve system performance. This paper is organized as follows: in Section II, we give the related work of light field imaging, image inpainting, and raindrop removal methods; Section III describes in detail our proposed method; Section IV first introduces the dataset of light field images with raindrops and then shows the experimental results and analysis; Section V summarizes the paper and prospects future work.
However, multiple exposures require longer time and larger amount of data storage space. Ng et al. [7]  therefore simplified the design of a plenoptic camera and made a handheld light field camera based on a conventional camera. The device we used for dataset collection was the first generation Lytro camera. It is actually a plenoptic camera consisting of a microlens array inserted in front of the sensor FIGURE 2. The ray going from the position (u, v ) of the lens to the position (x, y ) of the sensor, where F is the distance between the lens and sensor, can be represented by a four-dimensional light field function L F (x, y , u, v )  [7]. The light field is all the rays in space. (see Fig. 3 ). This light field camera has a refocusing function because it is capable of capturing multi-viewpoint images  [7].
Eigen et al. [38]  first used CNN to restore the raindrop image by end-to-end training. Qian et al. [4]  improved the raindrop removal and image recovery quality by GAN and attention mechanism. However, limited to the size of the training set, the generalization ability of neural network methods needs to be further improved. In summary, to the best of our knowledge, removing raindrops in light field image by deep learning based image inpainting and the light field raindrop dataset are proposed for the first time in this paper.
[39]  built a dictionary with atoms of fixed disparity and used sparse coding representation to find which elements in the dictionary can best describe the patch. Jeon et al. [40]  estimated correspondences from the light field based on EPI with sub-pixel accuracy using the cost volume. Tao et al. [41]  developed a method for local shape estimation based on defocus and correspondence cues, and then used shading to further refine the depth. However, the Lytro software (associated with the camera) we used can directly provide competitive depth output, which can effectively lightweight our system on the one hand, and also conducive to system reuse on the other. As shown on the left side of Fig. 6 , the raindrops are well distinguished in the depth map generated by the software. Furthermore, we convert the depth map D to a binary image M for subsequent image  The refocusing of the light field image is achieved by assuming a virtual imaging plane.
A. LIGHT FIELD RAINDROP DATASET We evaluated the proposed method on a new dataset collected with the first generation Lytro camera. The dataset contains 90 light field raindrop images that cover different camera angles and scenes. For each image, we provide the light field source file, reference image, and reference depth map given by the Lytro software. The light field source file can be easily decoded by the light field toolbox. 1  We collected raindrop images with glass plate, window of train, and window of car. The camera is placed 10 to 15 cm in front of the glass plate and windows. The first 40 images in our dataset cover different situations evenly, which are used for image quality analysis and object detection task. Using the EU long-term dataset  [43]  as reference, the last 50 images are applied to self-localization task.
B. IMAGE QUALITY ANALYSIS Image quality assessment measures image degradations such as noise, blur, etc. It can be categorized into full-reference and no-reference approaches. The former needs an ideal reference image to calculate image quality metrics, while the latter predicts image quality using statistical model. We use the no-reference CNN based image quality predictor that relies on the state-of-the-art deep object recognition networks, which is end-to-end trained on the dataset of image quality assessment  [44]. We measured the Image Quality Scores (IQS) of the original sub-aperture image, raindrop-removed image using Edge Connect, and raindrop-removed image using DFNet, respectively. In addition, we also give the results when disabling different components in our method. The average IQS of the first 40 images of each type are calculated as shown in Table  2 , where ''I'' is the original image; ''R1'' is the recovered image with Edge Connect; ''R2'' is the recovered image with DFNet; ''R1 -A'' is the output without image inpainting, which is equivalent to the enhanced image I f ; ''R1 -B'' is the recovered image with Edge Connect but disabling the refocusing module; ''R1 -C'' is the recovered image with Edge Connect but disabling the high-pass filter.
D. SELF-LOCALIZATION Finally, we perform self-localization tasks using light field images from number 41 to 90. The reference images are extracted from the data ''2018-07-20'' of the EU long-term dataset  [43]  by one-tenth of the original sampling rate of the front central camera. The localization is implemented by image retrieval, where we calculate the similarities between a processed light field image and all the reference images, then use the reference image with the maximal similarity as our localization result. The similarity score is calculated by DEep Local Feature (DEFL)  [3] , which is based on CNN trained on a large landmark image dataset to identify semantic local features for image retrieval. Instead of using extracted features directly, an attention mechanism is designed in DELF for keypoint selection.
V. CONCLUSION In this paper, we proposed a method that removes raindrops with light field image using image inpainting. The depth map generated from the light field image was used to detect raindrop regions, which were then expressed as a binary mask. In parallel, the original image was improved by refocusing on the far regions and filtering by a high-pass filter. Image inpainting was finally utilized to eliminate raindrops with the binary mask and enhanced image. A new publicly available dataset of light field raindrop images covering different camera angles and scenes was built to evaluate our method. Image quality analysis, object detection, and vision-based self-localization were performed to prove the raindrop removal enhancement with light field images. It should be noted that our method still has many steps which makes it difficult to run in real-time.
This work was supported in part by the  Natural Science Foundation of Shaanxi Province, China , under Grant  2019ZY-CXPT-03 , in part by the  PHC Barrande Programme  under Grant  40682ZH  ( 3L4AV ) and the  CZ MSMT Projects  under Grant  FR-8J18FR018 , and in part by the  CSF  under Grant  17-27006Y  and Grant  20-27034J.
This omics technique provides a functional validation of variants and completes the outliers detected in transcriptomics. A significantly reduced protein level in a sample compared to other samples or controls is a strong evidence of the presence of a variant that is responsible for this decrease  (Stenton et al. , 2019). This work has paved the way for multi-omics approaches in the study of MD (Figure  1 ). Instead of using the results of the omics analyses separately, better results are obtained if the results of several different omics analyses are cross-referenced. The use of existing databases, bioinformatics and literature, in addition to multi-omics improves the understanding of mitochondrial diseases in order to improve the health of patients through personalized treatment. 1 https://undiagnosed.
Mitochondrial Databases Since their first employ, omics techniques have generated a significant amount of complex and voluminous data. These data are available on online databases. There are many mitochondrion-specific ones such as MitoCarta  (Calvo et al. , 2016) , MITOMAP  (Kogelnik et al. , 1996) , MitoMiner  (Smith and Robinson, 2018) , or HmtDB  (Clima et al. , 2017)  that contain data on the mitochondrial genome and its variants. An exhaustive list of these databases is provided in Table  2. However, the volume of data creates considerable challenges to enable meaningful conclusions to be drawn. To date, MITOMAP contains 14,431 mtDNA variants, MitoMiner and MitoCarta contain approximately 1,157 human and mouse genes encoding mitochondrial proteins. There are also databases associating clinical data or pathologies with genetic variants such as OMIM  (Amberger et al.
Other Public Omics Databases Multi-omics data broadly cover the domains of "-omes" and can provide useful biological information at several levels and thus help to understand the mechanisms of pathologies, contribute to diagnosis, prognosis and potential therapeutic interventions  (Urbanski et al. , 2019). There are many examples of massive data production in specific applications for human diseases. Most of these projects are publicly funded and are collected in open access online databases (listed in Table  3 ). However, much of this data is either not used or not fully analyzed, creating a great disparity between the generation and use of data. Moreover, this huge amount of data does not translate into knowledge and it is not currently applied in clinical practice. Finding an appropriate method for data integration and interpretation is often complicated because the data are heterogeneous, large and composed of several variables.
THE ERA OF MULTI-OMICS New Classification of Multi-Omics Integration Methods Multi-omics methods are emerging as valuable tools for understanding the functioning of the mitochondria. Data integration is defined as a process by which data from different sources are combined statistically to make large-scale conclusions about a disease and to obtain a comprehensive view of biological processes. Omics integrative approaches increase the reliability of a biological discovery if it can be validated by concordant omics signatures (genomics, transcriptomics, and proteomics)  (Maldonado et al. , 2019). The classification of methods for multi-omics integration is currently quite complex because each article proposes its FIGURE 1 | The contribution of multi-omics for MD. Blood or tissue samples (skin or muscle biopsy) are obtained from patients affected by MD.
Fibroblasts or myotubes are prepared and cultured from tissue biopsies. Different omics analyses are carried out separately in order to identify the gene(s) responsible for the pathology. However, these analyses are not always conclusive. Despite the validity of the single-omics approach, the complexity of MD limits the success of the diagnosis. This is where multi-omics comes in. Thanks to bioinformatics and the development of algorithms, the integration of data from several different omics analyses is possible and leads to better results. Multi-omics combined with databases and literature provide with a better understanding of mitochondrial diseases. Their diagnosis is therefore improved and a personalized treatment can be proposed for each patient. All these classifications are mainly based on the type of algorithm, making difficult to choose which method to use depending on the characteristics of the dataset.
Feature Selection Methods Feature selection methods take the results of simple omics data, concatenate them, and perform variable selection using different techniques (Figure  2B ). The most cited methods are mixOmics  (Rohart et al. , 2017) , JIVE  (Lock et al. , 2013) , CONEXIC  (Akavia et al. , 2010) , jActive Modules  (Ideker et al. , 2002) , and IntergrOmics  (Lê Cao et al. , 2009). For example, MixOmics  (Rohart et al. , 2017)  allows the integration of multiomics datasets using different methods such as PCA and partial least squares regression. MixOmics can address both disease subtype and biomarker prediction. It is very often used in the context of cancers. Another example, multi-omics factor analysis (MOFA)  (Argelaguet et al. , 2018)  is an unsupervised method for finding the main sources of variation in multi-omics data sets.
Clustering Methods Clustering methods use clustering techniques to identify clusters on single omics data (Figure  2C ). In classification problems, data can first be transformed through similarity or covariance before joining them. This category preserves the specific properties of the data types and allows the addition of external classification (biological, metabolic pathways) to improve the performances. Methods such as Similarity network fusion (SNF)  (Wang et al. , 2014) , iClusterPlus  (Mo et al. , 2013) , or Pathway Recognition Algorithm using Data Integration on Genomic Models (PARADIGM)  (Vaske et al. , 2010)  are widely used and are applicable to all types of omics. SNF method  (Wang et al. , 2014)  Table listing the databases containing information on mitochondria and mitochondrial diseases, links to web site databases and the year of their last update.