dataesr/scientific-paragraphs-categorization huggingface dataset:
Data Availability Statement: The data used in this study are from the long-term permanent grassland site of Theix (France), as part of the Système d'observation et d'expérimentation sur le long terme pour la recherche en environnement-Agro-écosystème, cycle biogéochimique et biodiversité, labelled as SOERE-ACBB (http://www. soere-acbb. com, accessed on 7 October 2022) and integrated in AnaEE-France (https://www. anaee-france. fr/service/experimentation-in-natura/agrosysteme/prairie-permanente, accessed on 7 October 2022) funded by ANR-11-INBS-001. The monitoring of weather data by the INRAE CLIMATIK platform (https://agroclim. inrae. fr/climatik, accessed on 7 October 2022), managed by the AgroClim laboratory (Avignon, France), is acknowledged. All other relevant data are available on request.
Generating a training data set. We first built a training data set made of noisy low-SNR images (x_train) paired with their corresponding high-SNR images (y_train). Because of the stochastic nature of the emissions in SMLM it is improbable to match low and high SNR acquisition sequences composed of exactly similar localizations in space and time at the scale of a field of view. To circumvent this, we worked on 64x16 pixels image patches -themselves a concatenation of a 16x16 pixels container centered around the PSF in the spatial channel and a 48x16 pixels container collecting the corresponding spectra in the spectral channel (Fig. 2  a. and b. ). At the localization scale, collecting the same single molecule several time with different SNR is possible. We imaged the ATTO 542 dye of a DNA-PAINT origami nanoruler (GATTA-PAINT 80RG with , GattaQuant GmbH) immobilized on a microscope glass slide.
Typical image stacks ranging from 10,000 to 15,000 frames with exposure time 40 ms were recorded. Image were processed by PeakFit (part of GDSC SMLM2 plugin) and/or ThunderSTORM an open source software packages for Fiji in order to retrieve molecules localization coordinates. The procedure described in Bongiovanni et al. (  6 ) was used for spectral calibration. All subsequent and additional image processing were performed using self-written scripts in Python (version 3. 8).
srUnet The srUnet was adapted from the princeps Unet paper  (27)  and built using Keras library package (2. 4. 0) running on Tensorflow backend (2. 5. 0). The model was composed of 6×6 convolutions with Exponential Linear Unit (ELU) activation function followed by batch normalization and a Leaky Rectified Linear Unit (Leaky ReLU) (Fig. 1, b ). The model had about 1. 3 • 10 6 trainable parameters. The root mean squared error loss function was minimized using RMSprop optimizer. The model was trained using batch size of 70 over about 200 epochs. The training time was fast and the algorithm converged smoothly.
Peak signal-to-noise ratio (PSNR) in dBP SN R = 20 • log 10 MAX I √ MSEwith MAX I = 216Structural similarity index measure (SSIM)SSIM(x, y) = (2µ x µ y + c 1 )(2σ xy + c 2 ) (µ 2 x + µ 2 y + c 1 )(σ 2 x + σ 2 y + c 2 )Software and Hardware Availability. The code used for the training set generation and training the network is available on https://github. com/hmanko/srSMLM_DeepL. Most figures of this article can be reproduced independently using the code found on this repository.
ACKNOWLEDGEMENTS We thank  Dr Isablle Schalk  for providing PAO1 bacteria strain. This research was supported by the  ITI Innovec  ( IdEx  ( ANR-10-IDEX-0002 ) and  SFRI  ( ANR-20-SFRI-0012 )). HM was funded by a 3-years contract (Contrat doctoral) from  Ministère de la Recherche, France. YM is grateful to the  Institut Universitaire de France (IUF)  for support and providing additional time to be dedicated to research.
2 Peak signal-to-noise ratio (PSNR) in dBP SN R = 20 • log 10 MAX I √ MSEwith MAX I = 216Structural similarity index measure (SSIM)SSIM(x, y) = (2µ x µ y + c 1 )(2σ xy + c 2 ) (µ 2 x + µ 2 y + c 1 )(σ 2 x + σ 2 y + c 2 )Software and Hardware Availability. The code used for the training set generation and training the network is available on https://github. com/hmanko/srSMLM_DeepL. Most figures of this article can be reproduced independently using the code found on this repository.
The RNA-seq data for these time points was mined from a publicly available dataset as explained in the "Bulk RNA-seq and data analysis" section below. Total RNA extraction iPSC microglia. Approximately 2x10 5 iPSC derived microglial cells were harvested using TRIzol reagent (Ambion Life Technologies 15596018) for total RNA isolation. Total RNA was isolated using Direct-Zol TM RNA microprep columns (Zymo research R2062) according to the manufacturer's protocol. Sciatic nerves. Total RNA was extracted from sciatic nerves using 1 mL of TRIzol reagent (Ambion Life Technologies 15596018) on ice using the manufacturer's instructions with slight modifications. Briefly, 100% ethanol was substituted for isopropanol to reduce the precipitation of salts. In addition, RNA precipitation was carried out overnight at -20˚C in the presence of glycogen (0.
Bulk RNA-seq and data analysis For the iPSC samples, total RNA was extracted and sent to Genewiz, Germany for paired-end bulk RNA-seq. The raw data, final processed data and the metadata are available in the GEO database (GSE178924). For the sciatic nerves, bulk RNA-Seq data was obtained from a previously published study  [29]. The raw data can be accessed at the Zenodo repository (https:// zenodo. org/record/1154250). Only the WT datasets at P3 and P21 were mined and re-analyzed. Fastq files were aligned using the STAR algorithm (version 2. 7. 6a). Reads were then counted using RSEM (v1. 3. 1) and the statistical analyses on the read counts were performed with the DESeq2 package (DESeq2_1. 28. 1) to determine the proportion of differentially expressed genes between the two experimental conditions  [30].
The standard DESeq2 normalization method (DESeq2's median of ratios with the DESeq function) was used, with a pre-filter of reads and genes (reads uniquely mapped on the genome, or up to 10 different loci with a count adjustment, and genes with at least 10 reads in at least 3 different samples). The biomaRt package (v2. 44. 4) was used to substitute gene names for the Ensembl IDs in the count matrix  [31]. The DESeq2 pipeline was used to fit a generalized linear model (GLM) for the expression of each gene relative to the experimental groups. To determine the variation in gene expression, the dispersion value computed by the GLM was tabulated into the results data frame. Square root of dispersion was calculated using sqrt function from base functions in R (v4. 0. 2). The coefficient of dispersion (CVfromDisp) was calculated by multiplying square root of dispersion by 100.
Reference gene selection from RNA-Seq The data frame was subjected to 4 filtration methods to select stable genes in both the iPSC and Sciatic nerve datasets. First filtration was done by retaining all the genes with padj value above 0. 05. The resulting data frame was ordered based on the log2FoldChange (log2FC) column and the second filtration was performed by retaining all the genes with log2FC between -0. 1 to 0. 1 implying negligible intergroup variation. Third filtration was done by retaining all the genes that lie in the CVfromDisp range between 10 & 20. The fourth and last filtration was performed by retaining all the genes that exhibited a Basemean value greater than or equal to 500 implying sufficient expression for qPCR detection. Once these criteria were applied, 683 genes were obtained (basemean values ranging from 500 to 123000) in the iPSC dataset and 42 genes were obtained (basemean values ranging from 500 to 48000) in the sciatic nerve dataset.
Conventional reference gene selection All conventional reference genes chosen do not have any proven stability for the experimental setting in question. For the iPSC dataset, the conventional human reference genes chosen were ACTB, GAPDH, GUSB, HPRT, PGK1, PPIA, RPL13A, TBP, and UBC. For the sciatic nerve dataset, the conventional mouse reference genes chosen were Actb, Gapdh, Tbp, Sdha, Pgk1, Ppia, Rpl13a, Hsp60, Mrpl10, Rps26. These mouse reference genes have been previously used to establish the qPCR data analysis workflow  [12].
Target gene selection Differentially expressed genes that exhibited Padj<0. 05 were first retained from the results dataframe. Next, genes that exhibited log2FC values above +0. 6 and below -0. 6 were retained and partitioned into 2 separate lists. 3 target genes were randomly chosen from each of these lists for differential expression analysis in the iPSC dataset. In the Sciatic nerve dataset, 3 upregulated genes and 3 downregulated genes were chosen based on the recently published Sciatic Nerve Atlas https://www. snat. ethz. ch [32].
Primer design All primers used in the study were designed using the Primer 3 plus software (https:// primer3plus. com/cgi-bin/dev/primer3plus. cgi). Splice variants and the protein-coding sequence of the genes were identified using the Ensembl database (www. ensembl. org). Constitutively expressed exons among all splice variants were then identified using the ExonMine database  [33]. Primers that spanned two subsequent constitutively expressed exons were then designed using the Primer 3 plus software. The amplicon size of all primers was between 88bp-200bp. For detailed information on Primer sequences refer to the metadata -https:// doi. org/10. 6084/m9. figshare. 15169104. v2.
qPCR statistical analysis and data visualization qPCR readouts were analyzed in Precision Melt Analysis Software v1. 2 (Sciatic nerve samples) and QuantStudio Real-Time PCR Software v1. 7. 1 (iPSC samples). The amplicons were subjected to Melt Curve analysis and were verified for a single dissociation peak at a Melting Temperature (Tm) > 75˚C as expected from the primer constructs. The Cq data was exported to Microsoft Excel for further calculations. Each biological sample had three technical replicates thereby generating three individual Cq values. The arithmetic mean of the triplicates was taken to be the Cq representing the biological sample. The standard deviation (SD) of the triplicates was also calculated and samples that exhibited SD > 0. 20 were considered inconsistent. In such cases, one outlier Cq was removed to have at least duplicate Cq values for each biological sample and an SD < 0.
However, all genes satisfied the CV<50% criterion for NormFinder screening. NormFinder results for these genes are also shown in Table  2. Interestingly and in contrast to the reference genes derived from the RNA--Seq data (Table  1 ), the Stability S score of the conventional genes are largely lower than the genes derived from RNA-Seq, thus indicating better stability. The first 6 ranks from RPL13A to GAPDH have S scores less than 0. 1 whereas in the previous dataset (Table  1 ), only the first 2 ranks (CNBP & CCNT2) exhibit scores less than 0. 1. Finally, among all the conventional reference genes screened, NormFinder suggested the use of RPLP0 and GUSB as the best pair for data normalization with a grouped stability of 0. 03 (Table  2 ); which in comparison, is lesser than the Stability Score of 0.
Reference gene selection from RNA-Seq of mouse sciatic nerves (P3 vs P21) To rule out any circumstantial evidence that may support our observations in the iPSC dataset, we sought to validate our results using a completely different experimental setting. We wanted to rule out the possibility that the conventional reference genes that we chose in the iPSC dataset were indeed stable reference genes by mere happenstance and thus they rendered the same profiles of differential expression as the reference genes filtered from RNA-Seq. Therefore, in a confirmatory experimental approach we mined and re-analyzed RNA-Seq data that was previously generated from mouse sciatic nerves at Postnatal day 3 (P3) and P21  [29]. This dataset permits us to study RNA expression changes in the sciatic nerves during postnatal myelination.
Reference gene validation of RNA-Seq derived reference genes from mouse sciatic nerves (P3 vs P21) We evaluated the intrinsic intergroup variation of these reference genes by visual representation of their non-normalized profiles (2 -ΔCq ) using P3 samples as the calibrator (Fig  4 ). As previously described, statistical inference was then assessed using the non-parametric Mann Whitney U test owing to reduced sample sizes. Interestingly, and in contrast to the iPSC dataset, 6 out of 10 genes (Fkbp4, Lama2, Coq9, Chmp2a, Leprotl1 & Supt4a) exhibit significant intergroup variation across the two experimental groups (Fig 4A  , 4B, 4C, 4D, 4E  and 4F ). We next performed the CV analysis to assess overall variation (Table  3 ). The genes tested exhibited CV values between 12. 32% and 36. 24% and they were ranked as shown in Table  3.
Comparison of differential expression between qPCRs and RNA-Seq (P3 vs P21) Similar to the analysis performed in the iPSC dataset, we next investigated in detail if the Fold Change distributions in the P21 experimental group computed across the 3 differed significantly from one another. We used non-parametric ANOVA Wallis Test) of ordinal distributions followed by Dunn's multiple comparison post-test (S2 Fig). As shown in the iPSC dataset, we performed multiple comparisons between qPCRs (Comparison 1) and also between qPCR and RNA-Seq (Comparison 2). Non-parametric ANOVA revealed no significant difference between the FC distributions of Mki67 and Mbp (S2A and S2E Fig) suggesting that the qPCR fold changes concur with RNA-Seq fold changes in these genes. Sox2, p75NTR and Mpz FC distributions varied significantly between the groups (Kruskal Wallis P < 0.
05, P< 0. 01 and P<0. 01 respectively); In summation, all the genes tested in the sciatic nerve dataset show no significant differences between Conventional NF and RNA-Seq-derived NF results. In summation, these results show once again that stable reference genes filtered from RNA-Seq data do not give any added advantage to qPCR data normalization and that not all genes exhibit comparable Fold Changes when comparing qPCR results and RNA-Seq.
Discussion The central premise of this article was to demonstrate that stable reference genes for qPCR data normalisation can be obtained from any conventional set of candidates provided the statistical approach of reference gene validation is sound and consistent. To this end, using our qPCR workflow we have shown using two separate and unique datasets that conventionally chosen reference genes can render the same results of differential expression when compared to stable references selected from RNA-Seq data. The advantage of this approach in filtering the best candidates from any conventional set lies in combining 3 methods that are complementary to each other. Although multiple statistical methods have been proposed before, we have previously highlighted the potential pitfalls and assumptions of these computational methods  [12].
Previous datasets that we published have indicated that the Normalization Factor (NF) computed by NormFinder when including genes that exhibit CV>50% exhibit significant intergroup variation in the non-normalized profiles  [12]. Below this cut off, NF expression levels are stable across experimental groups. Indeed, the user can be more stringent by using a lower CV cut off but our data from this study and the previous one suggests that this is not necessary. However, this criterion gives rise to a theoretical limitation of our approach. In a given experimental setup, if all the candidate reference genes tested exhibit CV values of above 50%, then our approach will fail and NormFinder results would no longer be reliable. In such cases, a new list of candidates is required. However, our experience suggests that as little as 6 genes after eliminating genes exhibiting CV>50% can still provide a sound NF for qPCR data normalization  [12].
Although not the primary objective of the study, the comparison of qPCR results with RNA-Seq merits discussion. In the iPSC dataset, comparisons made between qPCR and RNA--Seq results revealed that 4 out of 6 genes (ACSS3, SYDE1, TREM2 and TBX10) showed no significant difference between the fold changes computed by both the methods (S1D, S1E, S1F and S1C Fig). However, changes in the expression of CD52 and P2RY12 significantly differed between the two methods (Figs 3, S1A and S1B). In the sciatic nerve dataset, 5 out of 6 genes tested show no difference between qPCR and RNA-Seq data in multiple comparisons (S2A, S2B, S2C, S2E and S2F Fig). However, this analysis is not optimal as the number of samples used in qPCR is much higher (N = 7) than the number of samples in RNA-Seq data that we mined (N = 3).
The consensus from these studies is that the extent of correlation between RTqPCR and RNAseq data is dependent on how the sequencing data was aligned, mapped and counted based on existing protocols. Additionally, though not discussed in these articles, we believe that this would equally depend on how reference genes were selected for qPCR assays that validate the RNAseq data. Overlooking these technicalities, these studies also show that around 85% of all genes tested show concordant differential expression (comparable magnitudes with the same tendency) between the two methodologies with correlation coefficients typically above 0. 8. Although these numbers are relatively reassuring at the outset, from an absolute perspective, the implications raise important concerns. For a hypothetical bulk RNA-Seq dataset of around 18000 protein-coding transcripts, 85% concordance leaves 2700 genes whose differential expression is discordant between RNA-Seq and qPCR.
Of note, the study conducted by Everaert and colleagues  [23]  conclude that discordant genes typically are smaller, have fewer exons and are expressed at lower levels. Indeed, and as mentioned in the introduction, these observations can be possibly substantiated by the bias and the bias against genes with lower expression in RNA-Seq. In our study, the discordant genes CD52 and P2RY12 in the microglia dataset have only 2 exons. The other 4 concordant genes have between 4-13 exons and therefore are relatively larger. The basemean values (and therefore the expression levels) of the 2 discordant genes are largely higher than the 4 concordant genes. Therefore, number of exons appears to be more important than expression levels for concordance between qPCR and RNA-Seq for these target genes. On the contrary, we did not observe any correlation between discordance and small gene size or lower expression in the sciatic nerve samples.
Acknowledgments The authors thank the  Animal House Core Facility  and the  Cyto2BM Biology Platform of BioMedTech Facilities  ( INSERM US36/CNRS UMS2009 ) for the animals and research services pertaining to the generation of qPCR data. We also thank  Benjamin SAINTPIERRE  and  Dr. Franck LETOURNEUR  from the  Genomic Platform at Institut Cochin, Paris  for the quality control of RNA samples and for their assistance in the analysis of RNAseq data. NKS would like to thank  Prof. Christopher E SHAW  and  Tanisha LEWIS  from the  UK Dementia Research Institute, King's College London , for their support towards this work.
This Research Topic presents a collection of recent (or recently updated) web-based tools and databases in the field of computational modeling and analysis of biomolecular interactions. Over the past few decades, web-based tools and databases have flourished thanks to their wide availability and ease of use. They offer several advantages over stand-alone tools, such as not requiring special skills for installing them, and ubiquity (as only a web browser is needed to access and use them). Importantly, they also represent an honest attempt to democratize science. In addition, when well identified and referenced, these tools and databases have the potential to be interconnected and enriched through their composition as building blocks.
Another server, QSalignWeb, is dedicated to the prediction and analysis of quaternary structure states of protein homo-oligomers, allowing users to submit their own structures to the QSalign pipeline, which makes use of evolutionary conservation to predict physiologically relevant protein assemblies. Finally, a database dedicated to the analysis of protein interactions between G-protein coupled receptors (GPCRs) of the opioid family and their partners involves the integration of multiple modeling strategies to uncover key structural determinants involved in GPCR interaction specificity. Three papers then address the topic of protein-ligand complexes. Fuzzle 2. 0 is a recent update of an evolution-related database to identify and analyze conserved parts of a protein. In the version 2. 0 of the Fuzzle database, protein fragments binding to specific ligands can be also identified and analyzed.
PreBINDS is a web tool to build datasets for compound-protein interaction (CPI) prediction with supervised machine learning. Via the web server interface, users can customize the CPI dataset derived from ChEMBL by setting positive and negative thresholds to be adjusted according to the user definitions. SeamDock aims at providing a free and accessible protein-ligand (small molecule) docking tool, in particular for teaching purposes. SeamDock's ease of use combined with a complete 3D visualization in a collaborative mode makes it a perfect tool for nonspecialists outside of the molecular modeling community. Two papers describe web tools for visualization and analysis of 3D structures. 3dRS is an online tool with the goal of sharing biomolecular structure representations, including molecular dynamics trajectories.
3dRS provides an unique URL to share and discuss structural data in an interactive fashion, including the possibility to use it as a live figure for scientific papers. VTR is a novel approach with a visual web interface that can be used to analyze, compare, and scrutinize analogous contacts in protein pairs. VTR can be used for understanding differences and similarities between homologous proteins with similar 3D structures but differences in sequences. Two more papers complement this collection of web tools related to 3D structures of biomolecules. SHAPER is a fast and accurate web server for predicting SHAPE (selective 2′-hydroxyl acylation analyzed by primer extension) probing reaction profile for any given RNA structure, providing information about the local flexibility of RNA. An overview paper presents the WeNMR-EOSC collection of web services for structural biology workflows, including experimental NMR, cryoEM, crosslinking-mass spectrometry data analysis, docking and several more; this computational infrastructure has been successfully running for over 10 years and it is part of the European Open Science Cloud portal, overall reaching a wide community of users.
Last but not least, three non-structural papers complete this Research Topic. The SGnn web server can be used to predict the recruitment of prion-like domain sequences in proteins to condensates, specifically stress granules, on the basis of biophysical properties used as features in a neural network model. GeneTrail, a suite of web tools, allows the user to analyze and visualize various types of "omics" profiles in an integrative approach, taking data from bulk, time-series, or single-cell experiments. The online GeneTrail analysis aims to identify deregulated biological processes by comparing disease with control data and to propose candidates that might drive this deregulation. Finally, the WebMaBoss web interface enables easy manipulation of Boolean models and further simulation of Boolean networks, such as biological networks involved in gene regulation or signaling, followed by online visualization and analysis.