dataesr/scientific-paragraphs-categorization huggingface dataset:
New TAPscan Version Even after improving the sensitivity of the profiles for the domains of particular interest, not all the expected bZIP E. siliculosus sequences were detected. To further investigate these missing sequences, a search was performed using InterPro 83. 0  [34]. For the TAP bZIP, 23 hits were expected based on the manual annotation, but only 21 were detected using the updated profiles. Since four of the detected sequences differed from the expected sequences, an InterPro search was carried out for the bZIP domains. This analysis detected a conserved domains database (CDD) entry "bZIP_AUREO-like" (cd14809) and an unintegrated bZIP, CDD entry (cd14686). Again, alignments of these domains were downloaded and profile HMMs were calculated (bZIP_AUREO and bZIP_CDD). These profile HMMs were added to better define the bZIP family.
Thus, the rules "bZIPAUREO; bZIP_AUREO; should", "bZIPAUREO; HLH; should not", and "bZIPAUREO; Homeobox; should not", and the same rules equivalent to bZIPCDD were added to the TAPscan v3 rule set. In addition, the appropriate changes to the TAPscan script and determined coverage values were added. Coverage values of 0. 75 and 0. 50 were used for bZIP_AUREO and bZIP_CDD, respectively. Using this approach, 26 bZIP hits were detected in E. siliculosus. Of the five newly detected sequences, three sequences were expected sequences and two were additional hits. Based on this result, we decided to use these two HMM profiles in addition to bZIP_1 and bZIP_2. After improving the HMG_box HMM profile, it was noticed that four of the expected sequences based on manual annotation were assigned to families PHD, CCAAT_HAP5, and two sequences to SWI/SNF_SNF2 instead to HMG_box.
Since C. merolae was the only species in the tree for the Cyanidiales, the branch lengths for Galdieria phlegrea and Galdieria sulphuraria were taken from Qiu et al. [42]  and were adjusted and set in relation to the branch length from the species tree reported by Leebens-Mack et al. [26]. The branch length for Cyanidium caldarium was set based on the species distribution in the phylogenetic tree reported by Miyagishima et al. [43]. Our dataset contains the TAPs for 30 species belonging to the Cryptophyta, Rhodophyta, Chlorophyta, Streptophyta, and Embryophyta. The Cryptophyta were used as an outgroup in the species tree.
Phylogenetic Inference Phylogenetic trees were inferred for the TAP families C2H2 and HSF (Supplementary Figures  S1-S4 ). First, to recover the corresponding (orthologous) sequences from the proteins of the 30 species from the Rhodophyta, Cryptophyta, Chlorophyta, streptophyte algae, and Embryophyta, the -A option of hmmsearch from the HMMER package  [33]  was used to save the protein sequences of all detected TAPs. The output was then filtered for C2H2 and HSF sequences. MAFFT version 7 with the -auto option was used to construct multiple sequence alignments for C2H2 and HSF. All gaps between the conserved regions were deleted in the HSF alignment using BioEdit  [32]. Jalview version 2. 11. 1. 3 was used to visualize the alignments  [44]. Quicktree version 2. 0 was used to calculate neighbor joining trees to provide initial estimates of the phylogenetic trees  [45].
Furthermore, the presence of just a single HSF in the unicellular Porphyridiophyceae P. purpureum is more similar to the number in the multicellular Bangiophyceae (all have either one or zero HSFs) than to the distributions in the Cyanidiales (one to six HSFs). and HSF TAP family members are shown for ten red algae species and were generated using TAPscan v3. On the x-axis, the species names are abbreviated in a five-letter code (cf. legend in Figure  3 ). HSFs are known to be involved in reactions to extreme environmental conditions and chemical and physiological stress  [54]. The numbers of HSFs in unicellular and multicellular red algae were not significantly different (p = 0. 06, Wilcoxon rank sum test). Between the Cyanidiales vs. multicellular members of the Bangiophyceae and Florideophyceae, however, it was significant (p = 0.
Second, we performed an integrated analysis of pericytes from published studies. We used publicly available and previously in-house generated scRNA-seq datasets from murine healthy lung and an untreated angiogenic Lewis lung cancer (LLC) tumor model  (Cohen et al. , 2018; Goveia et al. , 2020; He et al. , 2018b; Tabula Muris Consortium et al. , 2018; Vanlandewijck et al. , 2018)  and selected pericytes based on the expression of Pdgfrb and Mcam  (Armulik et al. , 2011; Yamazaki and Mukouyama, 2018). We pooled these pericytes with the pericytes from our non-enriched, as well as our EC-enriched, cohorts; applied batch correction  (Haghverdi et al. , 2018) ; performed graph-based clustering; and visualized a total of 842 pericytes via t-SNE (Figure  S3I ; Table  S1 ). This analysis showed that our quiescent, co-opted pericytes clustered together with pericytes from healthy lung, whereas angiogenic pericytes clustered together with pericytes from the angiogenic LLC tumor model (Figures  S3J  and S3K ).
, 1998; Cantelmo et al. , 2016; Carmeliet and Jain, 2011; Hellstro ¨m et al. , 1999 Hellstro ¨m et al. , , 2001;; Herbert and Stainier, 2011). Conversely, activated angiogenic ECs can also induce pericyte activation and migration  (Gaengel et al. , 2009; Potente et al. , 2011; Ribatti et al. , 2011). Pericytes were also quiescent in the 4T1 tumor model, in which vessel co-option occurs spontaneously. Only a few markers of angiogenic pericytes (Rgs5, Cd248, Cspg4, and Acta2)  (Armulik et al. , 2011; Bondjers et al. , 2003; Simonavicius et al. , 2008; Teicher, 2019)  have been reported to date. An integrated, unbiased analysis of pericytes from our, and other publicly available, datasets identified previously unrecognized markers (Col18a1, Actg2, and Serpina1b), which might facilitate future identification of angiogenic pericytes in other studies.
Data and code availability All raw sequencing data generated during this study are available at ArrayExpress. The accession number for the data reported in this paper is ArrayExpress: E-MTAB-9227. Publicly available single cell transcriptome data were derived from ArrayExpress (accession number E-MTAB-7458) and from Gene Expression Omnibus (accession numbers GSE109774, GSE119228 and GSE99235). The published article includes all data generated or analyzed during this study.
Bioluminescence imaging In vivo bioluminescence imaging was carried out on an IVIS Spectrum System (CaliperLS; Perkin-Elmer) and luminescence was quantified with Living Image software (version 4. 4. 17504). Mice were injected intraperitoneally with D-luciferin (126 mg/kg dissolved in PBS (15 mg/mL)) and anesthetized with isoflurane gas. Immediately after substrate administration, acquisition of consecutive frames was initiated until maximum signal intensity was reached, covering a total of at least 10 minutes. Image acquisition numbers and times varied between 10 and 15 frames of 60 s each, depending on the optimal acquisition settings in function of the signal intensity intrinsic to the grade of lung metastases. Photon flux per second (p/s) was measured through a region of interest (2. 9 cm 3 1.
Quality control and data normalization For the non-EC-enriched dataset, the following quality control steps were applied: (i) genes expressed by less than 20 cells and/or with a row mean of < 0. 01 were excluded from further analysis; (ii) cells that expressed fewer than 300 genes (low quality), and cells that expressed over 7,000 genes (potential doublets) were removed; (iii) cells in which more than 10% of unique molecular identifiers (UMIs) were derived from the mitochondrial genome were not considered. Quality control of the EC-enriched dataset was performed using a similar approach, see Table  S1  for dataset specific cut-off values and parameter settings. The data of the remaining cells were natural log-transformed (using log1p) and normalized using the Seurat package version 2. 3. 4  (Satija et al.
, 2015). EC-enriched dataset: in silico endothelial cell and pericyte selection All analyses were performed using the browser-based software BIOMEX  (Taverna et al. , 2020). For the EC-enriched dataset, data was auto-scaled, summarized by principal component analysis (PCA) using the flashPCA package  (Abraham et al. , 2017) , and the first 15 PCAs were visualized using t-Distributed Stochastic Neighbor Embedding (t-SNE, Rtsne package [van der Maaten and Hinton, 2008]) with a perplexity of 200 and learning rate of 100. Using the FindClusters function in Seurat, graph-based clustering was performed to cluster cells according to their gene expression profile (clustering resolution = 0. 5, k-nearest neighbors = 30). See Table  S1  for details. Clusters were annotated based on the expression of canonical marker genes, including Pecam1 and Cdh5 (ECs), Fxyd2 and Chchd10 (cancer cells), Ptprc (leukocytes), Mgp and Bgn (mesenchymal cells), Sftpa1, Sftpc and Ager (pneumocytes).
EC-enriched dataset: batch effect correction The EC-enriched dataset was sequenced in three separate batches. For the selected ECs and mesenchymal cells, we first analyzed each batch separately, and removed clusters expressing ambiguous marker genes (i. e. , representing low quality cells, red blood cells, possible doublets, etc. ), as performed in other scRNA-seq studies  (Goveia et al. , 2020; Smillie et al. , 2019). To correct for batch effects, we used a recently developed algorithm, mnnCorrect available from the scran package  (Haghverdi et al. , 2018). The optimal neighborhood size, k, was empirically defined as 50 after optimizing over a range of 10-300. EC-enriched dataset: feature selection and dimensionality reduction After in silico cell selection and batch correction for joint analysis, we performed PCA on highly variable genes, detected using the FindVariableGenes function in Seurat.
This function calculates the mean expression and dispersion for each gene, groups genes into bins (size 20) by their mean expression and identifies any gene for which the z-score calculated from the dispersion exceeds a pre-defined cut-off. For most experiments, we used a cut-off of z = 0. 25 and mean expression in the range 0. 000125 to 8, all other parameters were default (see Table  S1  for parameter settings for each analysis). We then generated a t-SNE to construct a twodimensional representation of the data. This representation was only used to visualize the data. Non-EC enriched dataset: feature selection and dimensionality reduction For the non-EC-enriched dataset, we first identified highly variable genes using the Seurat FindVariableGenes function (mean lower threshold = 0. 125, mean higher threshold = 8, dispersion threshold = 0.
25). Data was then auto-scaled, summarized by principal component analysis (PCA) using the flashPCA package  (Abraham et al. , 2017) , and the first 30 PCAs were visualized using t-Distributed Stochastic Neighbor Embedding (t-SNE, Rtsne package) with a perplexity of 200 and learning rate of 100. Using the FindClusters function in Seurat, graph-based clustering was performed to cluster cells according to their gene expression profile (clustering resolution = 0. 8, k-nearest neighbors = 18). See Table  S1  for details. Clusters were annotated based on the expression of canonical marker genes, including Fxyd2 and Chchd10 (cancer cells), Pecam1 and Cdh5 (vascular ECs), Cd3g and Cd3e (T cells), Nkg7 and Gzma (NK cells), Cd79a and Cd79b (B cells), Ctss (macrophages and monocytes), Ccl17 (dendritic cells), S100a9 and S100a8 (neutrophils), Mgp and Bgn (mesenchymal cells), Sftpa1 and Sftpc (pneumocytes).
Cluster identification For each phenotype separately (ECs and mesenchymal cells from the EC-enriched dataset, and cancer cells, ECs, lymphocytes, myeloid cells, mesenchymal cells and pneumocytes from the non-EC-enriched dataset), we identified highly variable genes using the Seurat FindVariableGenes (for settings see Table  S1 ), performed PCA on highly variable genes, followed by t-SNE visualization. To estimate the number of distinct cell subtypes with discriminating gene expression patterns, we used an in-house developed tool to color-code t-SNE plots for all detected genes, to empirically define the clustering resolution for each phenotype. Next, we applied graph-based clustering as implemented in the FindClusters function in Seurat (for settings see Table  S1 ). Clusters were visualized using t-SNE to verify that all visually identified clusters were captured.
Marker gene analysis We identified top-ranking marker genes for each cell subtype by performing pairwise differential analysis of all clusters against all other clusters separately, using the limma package (version 3. 34. 9) (Ritchie et al. , 2015). The results of each differential analysis were ordered based on the log 2 fold change (the most upregulated genes received the lowest rank number (top-ranking marker genes) and the most downregulated genes received the highest rank number). We obtained a final ranked marker gene list for each cluster by calculating the rank product for all genes in all pairwise comparisons. To assess statistical significance, we used a recently developed algorithm to determine p values for each marker gene based on the rank product statistic  (Heskes et al. , 2014) , and obtained Benjamini-Hochberg adjusted pvalues using the R package q-value  (Storey et al.
ACKNOWLEDGMENTS The authors gratefully acknowledge the expert advice and help from  J. Goveia  (Unicle), as well as  A. Zecchin ,  L. Sokol ,  A. Pircher ,  B. Boeckx ,  T. Van Brussel ,  A. Bouche  ´,  A. Manderveld ,  A. Carton ,  K. Peeters , and  N. Dai  for assistance;  A. Reynolds  for providing the cell lines and scientific discussions; and the  CCB Single Cell  platform led by D. Lambrechts. L. -A. T. , L. D. , S. V. L. , and P. V. are supported by  Fonds Oncologie Augustinus-Koning Boudewijnstichting and GZA Ziekenhuizen ; A. C. , K. R. , N. V. C. , L. P. M. H. d. R. , and L. T. by the  Fonds Wetenschappelijk Onderzoek (FWO) ; S. J. D. by a  Marie Curie-IEF  fellowship; V. G. by  Strategisch Basisonderzoek FWO (SB-FWO ); Y. L. by  BGI-Research, Danish Research Council for Independent Research  ( DFF-1337-00128 ),  Sapere Aude Young Research Talent Prize  ( DFF-1335-00763A ), and  Aarhus University Strategic Grant  ( AU-iCRISPR ); and P.
(2020)  do further distinguish their low-surface brightness objects into spiral and dwarf galaxies, employing machine learning. To do so, we first manually classify 5567 objects, then we find the best set of hyperparameters to define our neural network, and then finally train this network. Before jumping into the topic of finding a suitable architecture, we will give a brief introduction to neural networks, focusing on the techniques we are going to apply. The code can be found on GitLab 1.
Since this internal encoding was learnt during the initial training using natural images, other types of images may not be represented well using this encoding. Therefore, the success of transfer learning from stateof-the art natural image classification networks for types of imaging such as histology, medical imaging, or astronomical images depends on the choice of network, data set size and task  (Zhang et al. 2016; Cha et al. 2017; Martinazzo et al. 2020). 3. DATA  Tanoglidis et al. (2020)  selected a set of 20'000 low-surface brightness objects detected in DES  (Tanoglidis et al. 2021)  and predicted them to be real low-surface brightness objects (in contrast to artefacts) by the use of machine learning techniques. We downloaded the centered images of these objects from the DES archive using a modified script provided in the  We will split the sample into three different sets, a training, a validation, and a test set, which are randomly drawn from the total sample of galaxies.
This gives also a base-line for the variability of the classification coming from the same person. In total, we have labeled 2925 objects to be spiral galaxies, 2037 to be dwarf ellipticals, and 605 to be dwarf irregulars. For those, 2431 spirals (83. 1%), 1658 dwarf ellipticals (81. 4%), and 376 dwarf irregulars (62. 1%) have received three times the same vote, meaning that we were confident 3 https://github. com/wbwvos/pidgey. about the label. Uncertainties in labelling will have two main sources: one is coming from assigning them a wrong label (i. e. clicking on the wrong button), the other is coming from the true uncertainty about the morphology of the object. The latter is difficult to assess, because we do not know the ground truth of these objects and therefore can't directly quantify it.
One may wonder what the use of the neural network is if all the galaxies have already been classified by hand. On the one hand, our findings regarding suitable architectures and hyperparameters can serve as a starting point and benchmark for training similar tasks on different data sets. On the other hand, there is the potential of transfer learning. Our neural network trained on the data from the Dark Energy Survey may be used on a different set of data, like for example the Kilo Degree Survey (KiDS). By using a pre-trained network, the training data needed to achieve similar (or better) results can go down by magnitudes, reducing the workload of manually classifying only a subset of the total data and then use the newly trained neural network for the remaining objects. Along these lines, it will be interesting to study what kind of data set we would need to use transfer learning on upcoming large-scale surveys like Euclid or LSST.
Ainsworth and Jones' ParaMedic  [8] ,  [10] , which has generated industrial IP  [9] , is an architecture that uses the fact that duplicate fault-tolerance runs of an application are more amenable to parallelization than the original computation. This allows the execution of fault-checking code to be split up onto many small, highly efficient cores, reducing overheads by an order of magnitude compared to dual-core lockstep. When full fault tolerance can be achieved at lower overheads than are already paid by overvolting and underclocking commodity systems for resilience  [54] , this raises the question of what happens if we eliminate these margins, and instead achieve correct execution by utilizing redundant hardware to check and repeat incorrect computation. This perspective gives the potential for both better error coverage and lower power consumption even for commodity processors.
EVAL  [61]  uses a DIVA-like setup to allow aggressive undervolting, like ParaDox. Still, unlike DIVA, ParaDox does not require ECC on architectural state, and thus avoids affecting the critical paths of modern superscalars. Razor  [18] ,  [24]  is an architecture that augments critical-path flip-flops with shadow latches that are guaranteed against delay errors: this can protect against some forms of delay-based soft error, though with vulnerabilities to metastability  [15]  and with a high inner-complexity cost. Tan et al. [65]  experimentally measure the dependency of soft error rates with undervolting, with a software approach to provide fault tolerance for linear algebra and matrix applications. Other fault models come from designs that aim at preventing errors altogether. Jiao et al.
Although several initiatives to share imaging and other types of data already exist, such as Figshare, Zenodo, or IDR  9, 10  , these are generic in purpose and are not optimally suited to gather and exploit SMLM data in a manner consistent with the FAIR principles (findable, accessible, interoperable, reusable)  11. An important specificity of SMLM data is that each super-resolution image is built by extracting molecular coordinates from many thousands of low-resolution images (Fig. 1a ), which often 9/8/21 1:09 PM Shareloc draft V15 3 reach cumulated sizes of many GB-this is further exacerbated by a trend towards faster and longer imaging, or extensions to high-throughput or volumetric imaging  1,12- 15. Extracted localization files, though much smaller than the raw data, are usually too large to be sent by e-mail, and come in various formats, complicating reanalysis, and metadata are often unstructured or simply absent.
Another restriction of generic data repositories is that they lack specific tools to visualize SMLM data. As a consequence of these limitations, only a minuscule fraction of the acquired SMLM data is made easily and publicly available, preventing their reanalysis and slowing the progress of machine learning methods dedicated to SMLM. Here, we present an open platform called ShareLoc designed to facilitate the sharing, visualization, and community-based reutilization of SMLM data. The ShareLoc platform (Fig. 1b ) consists of two parts: (i) a storage service backed by Zenodo, a widely-used general-purpose open-access repository, and (ii) an extendable system of web plugins built on top of ImJoy  16  , a platform for building and deploying interactive data science tools, that now comprises dedicated plugins for SMLM visualization.
Using a Zenodo login, users can easily upload and store SMLM data sets up to 50 GB each (including localization data and/or raw images) through the online ShareLoc platform (https://shareloc. xyz), and a digital object identifier (DOI) will be automatically generated for future reference. Upon approval by the ShareLoc team, the new data will be shown on the ShareLoc repository and available for download, export, and reanalysis (Fig. 1e ). Each data set can be linked to other data sets or analysis tools, and by default SMLM data will be linked to the dedicated SMLM visualization plugin (see below). To facilitate rapid transmission and sharing of SMLM data, we developed a new dedicated compressed binary file format (extension *. smlm). The *. smlm data format allows significant reductions to the size of localization files, e.
The ShareLoc platform allows exporting SMLM data as CSV files, if needed, and we release an ImageJ plugin to import and export *. smlm files in conjunction with ThunderSTORM, a widely used localization software  17  (Supplementary Software 1), as well as command line tools in Python allowing batch downloading and conversion of single or multiple data sets  18. Taking advantage of this simple and efficient file format, we developed a WebGLbased viewer plugin in ImJoy  16  , for quick and fluid visualization of 2D, multicolour or 3D  19  SMLM images. This viewer includes zooming, panning and rotation features (Fig. 1e  and Supplementary Movie 1). Unlike previous SMLM visualization 9/8/21 1:09 PM Shareloc draft V15 5 software  17, 20, 21  , the viewer plugin is entirely browser-based, requires no installations or updates and operates across all operating systems, including mobile devices.
The SMLM viewer is integrated (along with other plugins) in the online ShareLoc platform and makes it straightforward to visualize SMLM data uploaded onto Zenodo using various formats (including *. smlm, *. csv and *. xls). Data on ShareLoc can be easily shared, for example as a simple link by e-mail or using social networking platforms like Twitter (Fig. 1e ). ShareLoc is already used by multiple labs and contains a public repository of over a hundred data sets, including images of microtubules, actin filaments, nuclear pores, clathrin coated pits and mitochondria, which can be easily browsed from a "gallery" view (Fig. 1e , Supplementary Figure  2 ). A simple and flexible tagging system is used to annotate individual images and search for particular tags, such as "microtubules" or "dna-paint " or "3d" (Supplementary Note 2).
Acknowledgements We thank  Felix Woitzel  for development and sharing of the Fairy Dust viewer. We thank  M. Lelek  for advice on metadata. CL acknowledges funding from  CNRS   ATIP AO2016. SB and PK acknowledge funding support from  Mechanobiology Institute  seed funding,  Singapore Ministry of Education  ( MOE2019-T2-2-014 ) and  National Research Foundation  ( QEP-P7 ). MS, MC and CZ acknowledge funding by  Agence Nationale de la Recherche  (grant  ANR   17 CE13 0026 02 ), and MC by  Labex DYNAMO  ( ANR-11-LABX-0011-DYNAMO ) and  MITOFUSION  ( ANR-19-CE11-0018 ). JB, BL and CZ acknowledge funding by  Institut Pasteur  and the  Region Ile-de-France  in the framework of DIM ELICIT.