dataesr/scientific-paragraphs-categorization huggingface dataset:
We further evaluated CALISTA's performance using four single-cell gene transcriptional datasets from cell differentiation systems with a variety of lineage topologies, including Bargaje et al. study on the differentiation of human induced pluripotent stem cells (iPSC) into cardiomyocytes  (Bargaje et al. , 2017) , Chu et al. study on the differentiation of human embryonic stem cells (hESC) into endodermal cells  (Chu et al. , 2016) , Moignard et al. study on hematopoietic stem cell (HSC) differentiation  (Moignard et al. , 2013) , and Treutlein et al. study on mouse embryonic fibroblast (mEF) differentiation into neurons  (Treutlein et al. , 2016). We again compared CALISTA with the same single-cell analyses as in the in silico case study above. Figures 3 summarizes the reconstructed lineage progression of the cell differentiation using MONOCLE 2, PAGA, and CALISTA.
The cell differentiation in these cell systems follows the lineage progression drawn in Figure  4A. As in the in silico case study above, CALISTA generated the most accurate lineage progressions, followed by MONOCLE 2 and lastly PAGA. Figures  4B-D  show the pseudotemporal ordering of cells produced by MONOCLE 2, DPT, and CALISTA, respectively. In assessing the accuracy of the pseudotimes, we relied on the known lineage progression and cell capture times, since the true cell differentiation times are not known in these datasets. All three methods performed equally well for the HSC differentiation dataset by  Moignard et al. (2013). For iPSC dataset  (Bargaje et al. , 2017) , CALISTA gave the most accurate pseudotimes, while MONOCLE2 and DPT had difficulties in assigning pseudotimes for one of the final cell type due to the close similarity of the gene expression with the progenitor iPSCs (see next section for more detail).
For mEF dataset  (Treutlein et al. , 2016) , CALISTA produced pseudotimes that are most consistent with the known lineage and capture times, followed by DPT and then MONOCLE 2. Finally, for hESC dataset  (Chu et al. , 2016) , CALISTA again outperformed DPT and MONOCLE2, but here MONOCLE 2 performed better than DPT. In summary, for both simulated and real life single-cell gene expression datasets, CALISTA is able to reconstruct lineage progression and singlecell pseudotimes much better than widely-used single-cell gene expression analyses.
Application to the Differentiation of Induced Pluripotent Stem Cells to Cardiomyocytes In the following, we demonstrated an end-to-end analysis of single-cell gene expression data using CALISTA. Here, we used the single-cell gene expression dataset from the differentiation of human iPSCs into cardiomyocytes in  Bargaje et al. study   (Bargaje et al. , 2017). The dataset includes single-cell expression of 96 genes measured by RT-qPCR for 1896 cells collected across 8 time points (day 0, 1, 1. 5, 2, 2. 5, 3, 4, 5) after induction to differentiate. First, we clustered the cells by using CALISTA. The optimal number of clusters was chosen to be five based on the eigengap plot (see Supplementary Figure  S1 ). The single-cell clustering of CALISTA, as shown in Figure  5B , recapitulates the previously identified developmental states.
Application of CALISTA to Massively Parallel Drop-Seq Datasets To demonstrate the scalability of CALISTA, we analyzed singlecell expression datasets from droplet-based assays. Singlecell Drop-seq is a massively parallel genome-wide expression profiling technology capable of analyzing thousands of cells in a single experiment. However, the bioinformatic analysis of large single-cell transcriptomics datasets poses a significant computational challenge  (Angerer et al. , 2017). To address this challenge, CALISTA offers a parallelization option for handling large datasets by splitting the greedy optimization runs among multiple computing cores (see Supplementary Note S2 for more details on CALISTA implementation). We first tested the single-cell clustering performance of CALISTA in analyzing Drop-seq datasets using the single-cell study of mouse spinal cord neurons by  Sathyamurthy et al.
(2018)  (∼18 K nuclei) and the study of peripherical blood mononuclear cells by  Zheng et al. (2017)  (∼68 K cells). We noted that k-medoid clustering for large consensus matrices is computationally prohibitive. Thus, we bypassed the consensus clustering and took among the independent runs of the greedy algorithm, the cell clustering corresponding to the highest likelihood value. For Sathyamurthy et al. dataset, CALISTA clustering identified nine single-cell clusters based on the eigengap plot, which agrees well with the original study [by comparing Supplementary Figure  S7  with Figure  1D  in the original publication;  (Sathyamurthy et al. , 2018) ]. For the largest cluster (52% of total data) containing neurons, CALISTA further split the cells into two subpopulations-one comprising cells with high expressions of Snap25, Syp, and Rbiox3, and the other comprising cells with medium expression of these genes.
Notably, CALISTA was able to distinguish clearly Schwann and Meningeal cells, which in the original study [using SC3;  (Kiselev et al. , 2017) ], were placed into the same cluster  (Sathyamurthy et al. , 2018). Meanwhile, for Zheng et al. scDrop-seq dataset, CALISTA generated cell clusters that show a general agreement with the original study (see Supplementary Figure  S8 ). We then tested CALISTA's lineage progression reconstruction on scDrop-seq data of ∼38K cells, taken from 12 developmental stages of zebrafish embryogenesis  (Farrell et al. , 2018). The results of CALISTA are summarized in Figure  6. For this large dataset, we employed a modified clustering procedure (see Supplementary Note S2) and identified 82 clusters, 23 of which comprise cells at the final developmental stage. Figure  6A  depicts the reconstructed lineage progression by CALISTA (see also Supplementary Video 1), while Figure  6B  shows the cell type labels for the cell clusters at the final developmental stage based on the expression levels of 26 key gene markers (see Supplementary Figure  S9 ).
Despite the focus on stem cells in our work, each of the tools in CALISTA is agnostic to the source of the single-cell transcriptomic data analysis and thus can be used for other studies. CALISTA's single-cell clustering analysis is an adaptation of another method called SABEC  (Ezer et al. , 2016) , with a significant improvement in computational efficiency, thanks to the implementation of greedy algorithm. This improvement is critical and necessary for the analysis of extremely large singlecell datasets with tens to hundreds of thousands of cells, which will become more common in the near future. In addition to computational efficiency, CALISTA is able to account for random technical dropout events, a feature that is missing from SABEC. With the exception of the single-cell clustering analysis, CALISTA includes novel algorithms for transition gene identification, lineage progression inference and pseudotime calculations.
e. , columns and rows of the expression matrix, respectively) with a large fraction of zero expression values, exceeding a user-defined threshold (default threshold: 100% for genes and 100% for cells). For scRNA-seq datasets, CALISTA further selects a number of informative genes Y for the single-cell analysis following a previously described procedure  (Macosko et al. , 2015) , with Y is to the minimum among the following: 200, the number of genes G, and a user-defined percentage of the number of cells N. Next, CALISTA scales the single-cell expression values such that the maximum value of any gene is 200. The scaling is carried out as follows: m n,g = 2 log 2( mn,g +1 ) log 2( mmax,g ) log 2 (200) (1) where m n,g and m n,g represent the original and scaled expression value of gene g in the n-th cell respectively, and m max,g is the maximum expression value in gene g (i.
e. , the maximum of m n,g over all cells). The scaling above allows CALISTA to use a precomputed table for the maximum likelihood in the clustering analysis, thereby reducing the computational cost significantly. We tested using in silico single-cell gene expression datasets generated using the two-state gene transcriptional model, and confirmed that the scaling does not affect the clustering accuracy (see Supplementary Note S3). After scaling, for scDrop-Seq data, we re-rank the top Y genes in increasing value of the gene-wise likelihood v g computed following Equation (10) below, with the random dropout model in Equation (  4 ) incorporated in the calculation of likelihood. A lower gene-wise likelihood value indicates a broader distribution of single-cell expression. Genes with likelihood values exceeding a given threshold (by default set at the elbow of the curve of likelihood vs.
Super-resolution (SR) is able to improve the spatial resolution of remote sensing images, which is critical for many practical applications such as fine urban monitoring. In this paper, a new single-image SR method, deep gradient-aware network with image-specific enhancement (DGANet-ISE) was proposed to improve the spatial resolution of remote sensing images. First, DGANet was proposed to model the complex relationship between low-and high-resolution images. A new gradient-aware loss was designed in the training phase to preserve more gradient details in super-resolved remote sensing images. Then, the ISE approach was proposed in the testing phase to further improve the SR performance. By using the specific features of each test image, ISE can further boost the generalization capability and adaptability of our method on inexperienced datasets.
The former requires only one image of the target scene for generating high-resolution output  [5] , while the latter requires multiple images that differ in terms of their satellite, angles, and sensors. For example, the authors of  [6]  improved the spatial resolution of Landsat images by fusion with SPOT5 images; the authors of  [7]  fused the information of multi-angle images for super-resolution; HR image. Then, the HR image is further improved via the ISE method. No additional information is needed in this module, which focused on the specific characteristics of the single image and on realizing adaptive enhancement. In summary, our contributions are fourfold: (1) A new SISR method, DGANet-ISE, which includes a deep gradient-aware network and an image-specific enhancement approach, is proposed to improve the spatial resolution of RS images.
Results In this section, the data sets and the implementation details are introduced firstly. Subsequently, the performance of DGANet-ISE is verified by comparison with 14 different SR methods, and crossdatabase tests are performed to evaluate the generalization ability of our method. Landsat-test is widely used, and the super-resolution of these images is of great value for many applications, such as finer land cover monitoring. The test images used in this paper were Landsat 5 TM data of band 7 (2. 08-2. 35 µm), band 4 (0. 76-0. 90 µm), and band 2 (0. 52-0. 60 µm). The data set was downloaded from the Google Earth Engine (https://developers. google. com/earthengine/datasets/catalog/LANDSAT_LT05_C01_T1). The spatial resolution is 30 m. During the experiment, we cut the image into approximately 400 small images of 256 × 256 pixels.
Implementation Details As there are not enough corresponding LR-HR image pairs in reality, we downsampled the images using the Bicubic interpolation (BCI) algorithm with a factor of  ∈ {2, 4, 8} to obtain LR images of different scales. Many studies, such as  [29, 35] , have used BCI to generate LR-HR image pairs. The RSI-CB256 data set was used to compare our model with commonly used SISR methods, and all images were randomly divided into training samples (80%) and test samples (20%). Furthermore, in order to evaluate the generalization ability of our model, the Landsat-test data set was utilized for a cross-database test. That is, the models trained on RSI-CB256 were directly applied to the SR experiments of Landsat-test without any tuning. This is difficult because the data were collected from different sensors and have different spatial resolutions.
In addition, the UCMerced data set was randomly split into two balanced halves for training and testing, according to  [35] , which is consistent with other RS-SR methods, including CNN-7  [25] , LGCNet  [25] , DCM  [29] , etc. Therefore, we compared our method with these RS-SR methods using the UCMerced data set. The interpolation-based and deep learning-based methods were implemented in Python, and the machine learning-based methods were implemented in MATLAB. In addition, the models were used with the default settings suggested by the authors. The generation of LR images and the calculation of the evaluation criteria were implemented in the same Python environment to ensure the consistency and accuracy of the results.
Comparison with Baselines In this section, we experimented on the RSI-CB256 and UCMerced data sets, respectively. UCMerced is widely used in RS-SR; therefore, we compared our methods with the following RS-SR methods available in the literature  [29] , which experimented on the UCMerced data set as well. The upscale factors were 2 and 4, and the PSNR and SSIM results are shown in Table  3. In addition, Figure  5  provides a more vivid presentation of the comparison of the PSNR and SSIM results. Additionally, another experiment was conducted on the RSI-CB256 data set, and DGANet-ISE was compared with three kinds of SISR methods: interpolation-based methods (nearest-neighbor interpolation (NNI), bilinear interpolation (BLI), and BCI), machine learning-based methods (simple functions (SF), neighbor embedding (NE), and classical sparsity-based super-resolution (SCSR)), and deep learning-based methods (super-resolution convolutional neural network (SRCNN), efficient sub-pixel convolutional network (ESPCN), and enhanced deep super-resolution network (EDSR)).
Cross-Database Test To further evaluate the robustness and generalization capability of DGANet-ISE, the Landsattest data set was applied for a cross-database test, i. e. , the models trained on the RSI-CB256 data set were directly used to super-resolve the Landsat-test data set without any fine-tuning or modification. The results of the comparison between the different approaches under the three upscale factors are presented in Table  5 , and Figure  9  shows the visual results of the different methods on an example image. The Landsat-test data set differs substantially from the training samples, since the spatial resolution of training samples is 0. 3-3m, while the spatial resolution of Landsat-test images is 30 m, and the scene contents in the same image size are very different. Therefore, it poses a great challenge to apply these supervised learning models directly to such test images.
Dependency on the Type of Loss Functions For evaluating the effectiveness of gradient-aware loss, we trained the same network with the 1, 2, and gradient-aware losses on training samples of the RSI-CB256 data set. The detailed results on the test samples of the RSI-CB256 data set are presented in Table 6Error! Reference source not found. As we can see, the PSNR results of 1 loss are slightly better than those of the 2 loss, and our proposed loss function yields the best results. When the upscale factor is 2, the PSNR obtained with the gradient-aware loss (37. 92 dB) is 0. 2 dB better compared to the L1 loss (37. 72 dB) and the L2 loss (37. 66 dB). To assess the detailed information preservation capability of the gradient-aware loss, an image that belongs to the "Residence" class was selected for further analysis.
Impact of ISE Since ISE aims to improve the SR results of inexperienced data, in this part, cross-data set test performances of the method with and without ISE were compared. The DGANet and EDSR trained on the RSI-CB256 data set were directly applied to the Landsat-test data. The results are presented in Table  7  and Figure  11  gives a visual comparison. The results in Table  7  illustrate that the ISE approach can further improve the SR performance. For instance, when the upscale factor is 2, the PSNR of EDSR and DGANet is improved by 0. 46 dB and 0. 23 dB, respectively. By using the specific information of the test image, ISE can further enhance the HR results of DGANet. As ISE is an unsupervised approach, it can greatly improve the generalization and adaptivity of the models, and reduce the dependence on the training set.
The spatial resolution is 30 m. During the experiment, we cut the image into approximately 400 small images of 256 × 256 pixels. This data set is different from the RSI-CB256 and UCMerced data sets in two aspects: (1) the resolution of the Landsat data set is much smaller than the other two data sets, resulting in very different geographic information and scene content in the same image size; (2) the first two data sets are artificially selected into categories, while Landsat-test data set is a real unselected scene.
Second, neurons can remain undetectable over large periods of time because calcium sensors can be significantly brighter than background only when neurons are firing. Third, tracking methods in freely behaving animals have to handle animal motion  [5]  and also potential body deformations  [8]. For these reasons, standard single particle tracking methods are not sufficiently robust, and tracking is usually performed manually  [4]  or semi-manually  [9]. This limits the analysis to a few hundreds of frames, introduces operator bias and ultimately, hinders our understanding of the functional organization of nervous systems. To track particles with intermittent detectability (neurons) in a cluttered and deforming environment, we report the development of a method and software named Elastic Motion Correction and Concatenation (EMC 2 ).
In contrast to traditional tracking methods, EMC 2 does not set expected priors for particle motion (diffusion and/or linear motion typically). Instead, it uses information about local motion and deformation contained in detectable and tracked particles in the neighborhood of undetectable particles. EMC 2 is therefore more versatile, and does not require motion priors or heuristics to close potentially long tracking gaps. For the local tracking of detectable particles, EMC 2 uses a probabilistic method and is therefore robust to very cluttered conditions. We validate the robustness and accuracy of EMC 2 with manual tracking of neurons in calcium imaging of the Hydra nervous system while the animal is freely behaving and deforming. Hydra imaging datasets represent perhaps the worst possible scenario for tracking purposes, since animals can have major changes in body size with non-isometric deformations.
6-Finally, complete single neuron tracks over the whole time-lapse sequence are obtained by applying the elastic transformation of the field-of-view to concatenated short tracks. Contrary to gap-closing GDM approaches  [17] , EMC 2 contains only one free parameter: the maximal distance between propagated end-and starting-points of short tracks for concatenation. Moreover, it handles complex natural motions and deformations, contrary to GDM methods that only account for confined or linear motion. EMC 2 is therefore more robust and versatile. For the sake of reproducibility and dissemination of our method, we implemented the EMC 2 multi-step procedure in the bio-image analysis software suite Icy  [10]  (http://icy. bioimageanalysis. org/). Icy is an open-source platform that is particularly well-suited for multi-step analysis thanks to graphical programming (plugin protocols) where each step of the analysis can be implemented as a block with inputs and outputs that can be linked to the other blocks (Figure  2  and Materials and Methods).
c-Extracting single neuron activity We extracted the fluorescence trace of each individual neuron using the Track Processor Intensity profile within the TrackManager plugin in Icy  [10]. For each detection within the track, the extracted intensity corresponded to the mean intensity over a disk centered at the detection's position, with a 2 pixel diameter. When detections are missing (tracking gap), the intensity was set to 0. Then, for each individual neuron, we denoised its non-zero fluorescence trace using wavelet denoising (wdenoise) in Matlab. We then automatically extracted spikes with a custom procedure where we first computed the discrete derivative of the smoothed fluorescence signal, then set to 0 all negative variations and finally, we detected significant positive variations of the signal (discrete positive derivative > quantile at 98% of all empirical positive variations) that putatively corresponded to spikes.
Acknowledgements: This work was supported by the  NSF  ( CRCNS 1822550 ) and the  NINDS  ( R01NS110422 ). This material is also based upon work supported by, or in part by, the  U. S. Army Research Laboratory  and the  U. S. Army Research Office  under contract number  W911NF-12-1-0594  (MURI). T. L. was partly supported by the  Fondation pour la Recherche Médicale  and the  Philippe Foundation. MBL research was supported by competitive fellowship funds from the H. Keffer Hartline ,  Edward F. MacNichol ,  Jr. Fellowship Fund, The  E. E. Just Endowed Research Fellowship Fund, Lucy B. Lemann Fellowship Fund ,  Frank R. Lillie Fellowship Fund Fellowship Fun, Fries Trust Research Award, Hartline MacNichol Research Award, L. & A. Colvin Summer Research Fellowship, and John M. Arnold Fellowship Research Award  of the  Marine Biological Laboratory in Woods Hole, MA.
Since then, the RNA structure modeling approaches, including secondary and tertiary structure modeling, have been in intense development. A variety of useful programs were produced in this period [for recent reviews see  (Miao and Westhof, 2017; Ponce-Salvatierra et al. , 2019) ]. With the development of whole-genome sequencing techniques and the availability of various metagenome sequences, hundreds of RNA families have been discovered  (Weinberg et al. , 2007 (Weinberg et al. , , 2009 (Weinberg et al. , , 2010) )  which greatly expanded our knowledge of the RNA sequence space. Almost all of these RNA sequence families have been described in the database of Rfam  (Kalvari et al. , 2018). However, up to January 2020, only 99 RNA families in the Rfam database have experimentally determined structures available in the Protein Data Bank (PDB)  (Berman et al.
, 2000)  or the Nucleic Acids Database (NDB)  (Berman et al. , 1992; Coimbatore Narayanan et al. , 2014). The experimental methods of RNA structure determination can be classified as biophysical and biochemical methods. Biophysical experiments such as x-ray crystallography  (Suddala and Zhang, 2019) , small-angle scattering (SAS)  (Hura et al. , 2009) , and cryogenic electron microscopy (cryo-EM)  (Zhang et al. , 2019)  are uncovering the structural basis of RNA functions at nanometer-or angstrom-level resolutions. Alternatively, biochemical approaches [e. g. , chemical probing  (Peattie and Herr, 1981) ], have been used systematically to validate RNA structures. The recent coupling of RNA structure probing with high-throughput sequencing  (Strobel et al. , 2018)  has changed the estimation from the electrophoresis on denaturing gels  (Lucks et al.
RNA STRUCTURE CHARACTERIZATION EXPERIMENTS Both biophysical and biochemical approaches (Figure  1A ) have been conceived to determine RNA structures, dynamics, and interactions with other biomolecules. Biophysical experiments are normally used to generate 3D structural information in the form of shapes to describe the molecular structure, ranging from the angstrom-level methods of x-ray crystallography  (Shi, 2014)  and cryo-EM  (Zhang et al. , 2019)  to the nanometerlevel methods like SAS  (Hura et al. , 2009)  and AFM  (Pallesen et al. , 2009). Biochemical approaches probe the RNA structures by generating the local structural features or restraints  (Kubota et al. , 2015)  in computational modeling from early on  (Westhof et al. , 1989). Normally, biophysical experiments determine the structure of one RNA each time at the angstrom-level resolution, while biochemical can probe RNA structures in a high-throughput way by sacrificing the resolution.