dataesr/scientific-paragraphs-categorization huggingface dataset:
Investigation of territories reveals gene expression patterns linked to neighboring tissues and tissue morphology We also tested if gene expression patterns could be linked to the morphology of a territory. Each territory provided by Vesalius can be manipulated using morphological operators (See Methods) or even divided into layers. The use of morphological operators for example permits the inclusion of additional barcodes that are part of the neighboring tissue. A territory divided into layers can be used to compare gene expression between the center of the structure and the edge of the structure. Inflation and barcode clustering of the Dentate Gyrus (DG-GCL) revealed that this territory included a thin layer of barcodes belonging to the Dentate Gyrus -Sub-granular zone (DG-SGZ) and the Dentate Gyrus -Molecular layer (DG -ML) (Fig.
Discussion Spatial transcriptomics -as a technique -has demonstrated its ability to recover the transcriptome of cells within the context of a tissue 2. This technical advancement has highlighted the influence of spatial context and cellular micro-environment on the transcriptome of cells  29. Alongside experimental development, computational frameworks have been developed in order to analyze these new data sets  [11] [12] [13] [14] [15] [16] [17]. However, it still remains challenging to accurately recover spatial domains especially when these domains contain a multitude of cell types. Here, we present Vesalius -an R package to effectively perform in silico anatomization of high-resolution ST data. Vesalius provides a reproducible framework for the isolation and in-depth analysis of tissue In contrast, computational tools such as BayesSpace  12  and Giotto 13 assume that neighboring spots are likely to be part of the same cluster if they are transcriptionally similar.
Cell type deconvolution To infer the cell types of individual beads in the Slide-seq V2 data sets, RCTD (Robust Cell Type Decomposition) was used  27. RCTD is a R package that decomposes the cell type mixture of spatial beads with resolution lower than a single cell. It is trained with single cell RNA-sequencing (scRNA-seq) data sets with annotated cell types. Based this, RCTD predicts the cell types of each bead. As RCTD was specialized to spatial transcriptomics data with fine resolution such as Slideseq, we selected it as a deconvolution tool. We conducted simulation under doublet mode, which is recommended for Slide-seq data sets  27. It assumes that each bead contains up to two different cell types. We trained RCTD using scRNA-seq data set for hippocampus  33  that consists of 27,953 genes and 113,507 cells annotated with 17 cell types.
Vesalius -Transcriptome embedding into the RGB color space To embed the transcriptome into the RGB color space, Vesalius first computes Principal component analysis (PCA) followed by a 3-dimensional Uniform Manifold Approximation and Projection (UMAP). UMAP latent space is min-max normalized for each barcode Bi: 𝐵′ # = 𝐵 # -min (𝐵) max(𝐵) -min (𝐵) The normalized 3-dimesional space can simply be converted into an RGB color space (Fig. S1a ). Alternatively, Vesalius can directly use PCA loading values in which case Vesalius selects a PC slice composed of 3 principal components -one for each color channel (RGB). For each color channel c and for each barcode Bi, Vesalius takes the sum of the absolute value of all loading values associated to Bi in color channel c: 𝐵′ #. = /|𝐿 2 | 3 245 With k= 1…n and n being the number of non-zero loading values associated with barcode 𝐵′ #.
Vesalius -Building images arrays from Spatial transcriptomics Each ST data set contains a set of coordinates describing the x and y position of each barcode/spot. As the coordinates will not necessarily be uniformly distributed and contiguous, Vesalius expands punctual coordinate values into tiles using Voronoi tessellation and fills each tile using rasterization. First, we filter stray barcodes by comparing barcode density in a grid covering the entire ST assay and removing any barcodes that fall into a grid section with a low barcode density (Determined based on quantiles of grid density). Next, we produce a Voronoi diagram using the remaining barcodes. Each tile is then converted into a pixel set via rasterization. The color code obtained for each barcode is then assigned to its tile. RGB image arrays are three dimensional arrays and as such each color channel in the image will receive a distinct set of color value for each latent space dimension (UMAP or PCA).
In the instance that multiple values of k are used, Vesalius will smooth and segment the image as describe above and repeat the process for all values of k. A decreasing k value at each round will iteratively decrease the color space. To decrease computational time, the clustering is only applied to the center pixel value. The center pixel is defined by the pixel corresponding to the original barcode location before tessellation and rasterization. Vesalius also provides the option to smooth and segment images using all pixels instead. Using all pixels for segmentation produces sharper segments between homogenous territories. However, this sharpness in homogenous territories may come at the cost of increased noise in heterogenous territories.
Vesalius -Isolating Territories Once barcodes have been assigned to a color cluster, each color cluster is further subdivided into territories. For every barcode present in a color cluster, Vesalius checks for all barcodes that are within a certain capture radius of each other and assigns them to a territory (Fig. S1b ). This process is repeated for all barcodes in a color cluster until they have all been assigned to a territory. Vesalius repeats this process for each color cluster. The capture radius is defined as the distance value that corresponds to the proportion of maximum distances between all barcodes in the ST assay. First, Vesalius computes a distance matrix between all barcodes in 2D space and extracts the maximum possible distance between barcodes. A user-selected parameter defines the proportion of this maximum distance that should be used as a capture radius.
The uniform regime contains n cell types in equal proportion. Each territory contains different sets of randomly sampled cell types. Finally, the exponential regime contains n cell types in unequal proportion: 𝑃 7 = 𝑒 7 ∑ 𝑒 7 3 745 Where n is the number of different cell types and Pl is the proportion of cell type l. Each territory contains the same cell types in different proportions. To reduce any potential cell type bias, we ran the simulation 10 times each time randomly selecting different cell types and different barcodes. The simulated data set produced at each sampling round was the same between each tool and a summary of the cell types sampled at each round can be found in Supplementary Table  The code used to run simulations and method comparison (see below) can be found here: https://github.
org/), the lifeMaps/geneCard database  36  (https://discovery. lifemapsc. com/in-vivo-development) or panglaodb database  37  (https://panglaodb. se/) to assign cell type to clusters and territories. Manual annotation of cell clusters was preferred over automated methods to ensure correct tissue annotations, rare cell type annotation and finally to maintain subtle spatially driven patterning. Cell type markers used for annotation are available in Supplementary Table  2. in each territory. The exponential regime contains n cell types in varying proportions between territories. The cell types are the same between territories. (d) Territory layering uses images representation of territory to iteratively select the edge of a territory and assign a layer value to that edge. Once all barcodes have been assigned to a layer, the number of layers can be reduced by merging neighboring layers.
Code availability The Modified version of BayesSpace to accommodate Slide-seqV2 data is available here: https://github. com/patrickCNMartin/BayesSpace Vesalius is available here: https://patrickcnmartin. github. io/Vesalius/index. html The analysis presented in this manuscript is both described and discussed here: https://patrickcnmartin. github. io/Vesalius/articles/Vesalius_Analysis/Vesalius_analysis. html The code related to the comparison and the simulation of high-resolution data is available here: https://github. com/patrickCNMartin/Vesalius/tree/main/methodComp.
Hancheng et al. 35  developed a meta-learning 36 method to estimate the quality score of images with new distortion, which addressed the generalization problem of IQA. Although many methods have been provided for IQA and achieved excellent results, most of them focus on natural images and require a huge number of annotated labels, which are not practical for X-ray tomography images. For example, the FR-IQA methods need a reference image for each estimation of a distorted image, which implies a high demand for annotations. The already developed NR-IQA methods require less data than the FR-IQA method but are still relatively large (hundreds of annotations) to avoid overfitting. Besides, the existing opensource datasets  [37] [38] [39]  of battery electrodes tomographic images are not for IQA task, i.
• The correlation between image quality score and segmentation results is studied to guide the pre-processing step. The remainder of the paper is organized as follows: In section "Results", we show the results of our data generation method and TIQA method. Moreover, the segmentation result and the link between quality score and segmentation performance are demonstrated in this section as well. In section "Discussion", we summarize the results and emphasize the features of our method. We also propose several potential applications and future directions of our method. In section "Methods", we introduce our dataset and the experiment details.
It greatly reduces the tedious work of selecting good images and facilitates the automation of analyzing X-ray tomography images. In addition, it provides a more reliable assessment of pre-processing image results, which avoids the conflicts of different human observers, and promises an outperformed segmentation analysis. However, some limitations remain to be solved. The undistorted images are not well evaluated in our method due to the lack of images with excellent quality. Although our approach does not need hundreds of images for training, the estimation results of image quality can still be improved with the larger dataset. These limitations can be solved with the contribution of the community by sharing open-source X-ray tomographic data, such as Tomobank  [37] [38] [39]. Interestingly, thanks to the demand for automatically analyzing the tomography images, our TIQA method can be extended to improve the image quality by using different image processing methods.
Dataset creation We collected 40 8-bit images from 11 different types of batteries with different resolutions. All the images were rescaled to the same resolution 224 × 224. To avoid deformation, we resized the original image to the width or height equaling 255 while preserving the aspect ratio, then randomly cropped the region with a size of 224 × 224. We also maintained six original images for the analysis of the impact of the downsampling operation on the image quality score. To expand the dataset, we applied different algorithms with different parameters to generate images with different types of distortion. For example, we generated several rings with different radius and intensities based on original images and add them together to imitate the ring artifact distortion. For blur and noise, we used the methods implemented in scikit-image  47.
Similar to Hanne's method  48  , we manually set the parameter values that control the distortion amount such that the visual quality of the distorted images varies, from an expected rating from 1 (terrible) to 5 (excellent). The distortion parameter values were chosen based on a small set of images and applied the same for the remaining images in our database. We performed two surveys for subjective image quality scores and conveyed them to different people who included beginners and experts in this field for annotation among five levels: terrible, bad, average, good, and excellent. For each image, we collected annotations from 15 people and set the average number as its quality score.
Training and testing parameters In the score prediction module, we used 32 original images, which were expanded to 512 images after data generation but without labels for training the rank. The initial learning rate was set at 3e-5 and decayed after several iterations. The network was trained for 30 epochs, and on each epoch, it iterated on the whole dataset. The rate of the dropout was set at 0. 5 to avoid overfitting. The Adam 53 optimizer was applied for optimizing the rank loss. After training the rank, the model was fine-tuned in the score regression step. The training dataset contains 29 images with the size of 224 × 224 × 3 and their corresponding labels, which are in the range from 1 to 5. The data generation method was also implemented to expand the training dataset to 464 images with generated annotations.
Then, they were inputted into the network for regression with the MSE loss. The network iterated 20 epochs with the initial learning rate at 5e-5, which decayed every 4 epochs. The dropout rate was 0. 5 in training. For the testing procedure, a total of 56 images were tested and evaluated with corresponding human annotations. All the experiments were conducted in python with the TensorFlow 54 library. The computing hardware was Tesla K80.
Evaluation metrics The PLCC (Pearson's linear correlation coefficient) is the linear correlation coefficient between the predicted score and human-labeled score. It measures the prediction accuracy of an IQA metric, i. e. , the capability of the metric to predict the subjective scores with low error. The PLCC is calculated as follows: PLCC ¼ P Md i¼1 ŷi À ŷaug À Á y i À y aug À Á P Md i¼1 ŷi À ŷaug À Á 2 1 2 P Md i¼1 y i À y aug À Á 2 1 2 (2) where ŷi and y i are the predicted score and the human-labeled score of the ith image in a dataset of size M d respectively, ŷavg and y avg are the average of the predicted scores and human-labeled scores, respectively. The SROCC (Spearman's rank-ordered correlation coefficient) is the rank correlation coefficient between the predicted score and labeled score, and it compares the monotonicity of the prediction performance, i.
Climate-driven impacts on marine trophic pathways worldwide are compounded by sea-ice loss at northern latitudes. For the Arctic, current information describing food-web linkages is fragmented, and there is a need for tools that can describe overarching trophic structure despite limited species-specific data. Here, we tested the ability of a mass-balanced ecosystem model (Ecopath with Ecosim, EwE) to reconstruct the trophic hierarchy of 31 groups, from primary producers to polar bears, in the Canadian Beaufort Sea continental shelf. Trophic level (TL) estimates from EwE were compared with those derived from two nitrogen stable isotope (SI) modelling approaches (SI linear and scaled) to assess EwE accuracy, using a data set of 642 δ 15 N observations across 282 taxa. TLs from EwE were strongly, positively related to those from both SI models (R 2 > 0.
2017). Effective tools that can integrate available ecological information and account for data-poor species are, therefore, necessary to characterize present food-web structure and to predict implications of climate change. To that end, ecosystem modelling software, such as Ecopath with Ecosim (EwE)  (Christensen et al. 2005; Buszowski et al. 2007) , can approximate food-web structure using both locally relevant and inferred data, and can facilitate comparisons within and among ecosystems  (Walters et al. 1997; Plaganyi 2007; Colléter et al. 2015; Steenbeek et al. 2015). EwE uses mass-balance approaches  (Plaganyi 2007)  to estimate key food-web parameters that can describe structure and function of an ecosystem, and can include food-web members ranging from primary producers to top predators  (Walters et al.
(2021) , and a summary of dietary sources and types of data used in calculating diets are shown in Supplementary Table  S2  2. Of the 73 dietary data sources noted for the EwE model (see Supplementary Table  S2  2 ), 12 references (16. 4%) summarize SI data, with 7 references (9. 6%) based on SI data from within the greater Beaufort Sea (including areas outside of model area and into Alaska). Of the remaining references used in creating the diet structure in the EwE model, 31 (42. 5%) summarize stomach contents, 9 (12. 3%) identify clearance rates and prey types of filter feeders, 3 (4. 1%) summarize fatty acid analyses, and 6 (8. 2%) are observational studies. When possible, dietary data from the model area was used. Of the dietary data sources, 29 (39. 7%) were from the Beaufort Sea in and around the model area, 32 (43.
Nitrogen SI and TL calculations Nitrogen SI data were gathered from a collection of published and unpublished sources generated by field programs led by Fisheries and Oceans Canada and in collaboration with the University of Waterloo (Waterloo, Ontario, Canada) and University of Manitoba (Winnipeg, Manitoba, Canada). Data for offshore fishes, benthic invertebrates, and zooplankton were provided by the Beaufort Regional Environmental Assessment Marine Fishes Project  (2012 -2014; Stasko et al. 2017; C. Michel, unpublished data). Data for beluga whales, coastal marine fishes, and anadromous fishes were provided by the Arctic Coastal Ecosystem Study  (Brewster et al. 2016a (Brewster et al. , 2016b (Brewster et al. , 2017) )  and various other riverine and coastal sampling programs  (Tran 2014; McNicholl et al.
2018; M. Power and J. Reist, unpublished data). Detailed methodologies for SI analyses are outlined in the respective publications reported above, or else follow  Stasko et al. (2017)  for unpublished sources. In addition, a standardized literature search was conducted to compile δ 15 N data for species groups not represented in the field programs and to supplement the remaining groups. The Web of Science electronic database and Google Scholar were searched using the phrases "Beaufort Sea" OR "Western Arctic" AND "δ 15 N" OR "stable nitrogen". Publications were discarded if they did not include raw or averaged δ 15 N data, did not report data for organisms captured on the Beaufort Sea Shelf and (or) adjacent regions, did not cover the period from 2013 and earlier (to match with EwE model), or contained data already captured by another publication.
The only exception was the model group Birds, for which no δ 15 N data were identified from the Beaufort Sea. Instead, data from Lancaster Sound were used as a representative for model cross-validation. All elemental SI ratios ( 15 N: 14 N) were expressed in standard δ notation as parts per thousand (‰) relative to the international standard atmospheric N 2 for nitrogen  (Mariotti 1983). The resulting database comprised 1039 entries of mean δ 15 N by taxon, year, and (or) sample site from 28 sources spanning the sampling period of 1983 to 2013 and is provided with references, relevant metadata, and detailed methodology for data compilation by  Ehrman et al. (2021). For the present study, only data reported from the Mackenzie or Alaska Beaufort Sea continental shelves (<200 m depth) were included in analyses, to remain consistent with the existing EwE model and to limit variability from changes in taxonomic composition and trophic structure that exist for demersal communities beyond the shelf-break (e.
Geographic data The widths of vehicle lane in the streets, street lengths and average building heights were obtained from the BD TOPO database ("Base de Données TOPOgraphiques", https://geoservices. ign. fr/bdtopo). The information for the sidewalk width and the highway shoulder width (the A86 highway passes through the modelling domain) is not available in the BD TOPO database. A width of 3 m is used for sidewalk of the streets, and 20 m (including two urban train lanes) for the shoulder of the A86 highway )  (Kim et al. , 2018).
Acknowledgements. This work was partly funded by the  Departement of Green Spaces and Environment (Mairie de Paris)  and the  École des Ponts ParisTech  (grant CIFRE no. 2017/064 ), and by the sTREEt  ANR  project ( ANR-19-CE22-0012 ). The authors acknowledge Airparif for providing the measured concentration data. YZ acknowledges funding from the  NOAA Office of Climate   AC4  Program ( NA20OAR4310293 ).
Hence, alternative approaches are required to validate or identify the actual interactions between each protein pair. PPI network reconstruction remains a challenge, although some databases are devoted to help such a task. The STRING-db  7  database, well appreciated by the community, combines genetic interactions, text mining, text association, predicted and experimental PPI databases to provide an overview of the corresponding networks. However, it usually lacks of structural information on the macromolecular interactions involved in the corresponding (sub-)networks although it indicates the knowledge (or not) of the structure for each partner. Similarly PPI databases, such as the Biological General Repository for Interaction Datasets (BioGRID 8 ) gather the results of multiple interactomics studies, but are also lacking structural information.
Combining proteomics data with 3D search and comparative modeling has now emerged as a valuable tool to compensate limitations of each of the above approaches. Servers such as Interactome3D  9  and Proteo3Dnet  10, 11  allow one to dig efficiently into available 3D structures, gathered in the Protein Data Bank (PDB 12 ) to unravel likely physical interactions within a set of proteins. The Complex Portal (www. ebi. ac. uk/complexportal/) is also often used to match the proteins involved in 297 already known mammalian complexes at the structural level. However, they mainly focus on domain-domain interactions, while a vast number of PPIs involve so-called short linear motifs (SLiMs) in one partner and a recognition domain in another partner. 2  They usually correspond to short segments (4-12 residues) and are frequently encountered in disordered regions or (long) loops although exception exist, while dedicated folded domains are involved in their specific recognition.
13  The Eukaryotic Linear Motif resource (ELM  14  ), is a reference database for SLiM annotation and prediction. From 3559 publications this database gathers 289 manually curated SLiM classes. Classes are defined by the signature -or patterns -of the binding motifs which are also associated with interacting and structured domains as annotated from the Protein Familly (Pfam) database. 15  Each pattern is associated with an E-value estimating its frequency in protein sequences. The ELM database is now largely used by the community to identify potential SLiMs-mediated PPI. The main difficulty is the huge number of predicted interaction motifs for a given protein (over 200 per human protein on average), among which many are false positive matches. More frequently found in intrinsically disordered regions of proteins or in external accessible loops, SLiMs can be considered to act as peptides that bind onto a target structured domain.
16  For this reason, the current state-of-the-art for SLiM predictions also includes additional features, such as prediction of the disorder state of the SLiM. The software IUpred2 17 is often used to predict the probability for a given amino-acid to be in a disordered part of a protein. Despite this efficient filtering, the analysis of a given data sets for an interactomic analysis is leading to a large number of matches. Furthermore, systematically filtering out SLiMs with high E-value (e. g. : E-value >0. 005) or predicted as too ordered (IUpred <0. 3) may exclude various SLiMs (e. g. : PDZ motifs which are often too close to a folded domain). Currently, performed on one protein at a time, this constitutes a huge burden when one is analyzing an interactomic output made of hundred(s) of proteins.