dataesr/scientific-paragraphs-categorization huggingface dataset:
g. personal communications prior to publication and convincing annotations from GenBank/Swiss-Prot). We began this work a year ago and as of September 2020, we now provide a literature reference to families for 96% of GH and PL families (76% of the functionally characterized enzymes in these classes). Entries with an activity not yet curated, and therefore possibly subject to changes, carry an EC number but do not carry a bibliographic link. We will complete the work on GHs and PLs before extending the same principle to other CAZyme categories. Moreover, CAZy homepage now displays a table that highlights the most recently integrated functions/publications at each update. The second main change applies to the family webpages (and similarly for subfamilies which are built on the same model).
Previously, browsing through family members was difficult as protein accessions were split into taxonomic tabs, itself divided into multiple table sheets. Therefore, we have simplified the display and a family page now only displays members that have been characterized either structurally or functionally. Another reason for this simplification is the multiplication of programs and users regularly scanning the whole CAZy website to copy its data, slowing down its usage. The complete lists of protein accessions are now available as downloadable text files. The links to these files are shown in Figure  1 , for each family (box 4) as well as for all families simultaneously (box 2). In a simplification process, we have changed the display of PDB codes, by merging chains with an identical underlying sequence in a single reference ID[comma-separated chains].
CONCLUDING REMARKS We hope that with the novel display of information and the recent added features presented here, the CAZy database will continue being a useful resource to the community in the years to come. The maintenance of this >20-year old database does not rely (and never did) on dedicated funding as data capture, curation and organization does not appear to be a funding priority for French scientific decision makers. Yet many external bots download the entirety of our site almost daily, and this frequently slows down its operation. While this systematic download of our resource is a sign of usefulness in an open science context, it may jeopardize our long term efforts by creating competing and funded resources based on our hard curation efforts. For-tunately, the continuation of the CAZy database has benefitted from multiple scientific collaborations for omics data analyses and from the amazing acceptance and support of the (glyco)biological and biotechnological communities interested in CAZymes, which we acknowledge here with gratitude.
Correct separation and the molecular weights of the proteins were estimated using a marker of molecular weight loaded on the same gel. For SOD2, an immunoblot assay was performed to assess the identity of the protein (see the Supplementary Materials, Figure  S2 ). After the electrophoretic run, the gel was transferred to a nitrocellulose membrane (Life Science) through a semi-dry Trans-Blot ® TurboTM Transfer System (BioRad, Hercules, CA, USA). The membrane was then blocked with 10% milk in Tris-buffered Saline (TBS) for 1 h at room temperature and subsequently incubated at 4 • C overnight with the primary antibody (alpha-SOD2-HPA001814, Merck KGaA, Darmstadt, Germany) diluted 1:1000 in Tris-buffered Saline with 0. 05% Tween20 (TBS-T). After incubation with the anti-rabbit IgG HRP-conjugate antibody (A0545, Merck KGaA, Darmstadt, Germany) diluted 1:20,000 at room temperature for 1 h, the protein was visualized using Immobilon ® Forte Western HRP Substrate (Millipore) with an Imager CHEMI Premium Detector (VWR).
We used the MultiComponentEPR827. vi program designed by Christian Altenbach to perform the simulations. The program is written in LabVIEW (National Instruments) and can be freely downloaded  [35]. Protein-Protein docking The molecular docking simulations were performed using four docking software packages that are freely available as webservers on the web. All of them allow the input of one or more residues to be considered for the interface: Clus-Pro  [36] , ZDOCK  [37] , GRAMM-X  [38] , and PatchDock  [39, 40]. The generation of the docking poses, the potential energy function that evaluates the energy of each docking pose, and the additional steps performed to rank the final docking poses all depend on the individual program (for details, we refer the reader to the relative references).
ZDOCK, GRAMM-X, and ClusPro are based on a FFT approach to probe a fine grid for the generation of the docking poses. PatchDock adopts a different approach to accelerate the generation of the possible docking poses by matching the surfaces of two proteins based on their geometric complementarity. All programs perform additional steps to score the docking poses, each characterized by a different function. Therefore, to compare the results, the top solutions of each program were pooled together and re-ranked using a common scoring function. Among the different possibilities, we chose to adopt CONSrank, a freely available webserver that allows the solutions from all programs to be easily scored at the same time without only minimal formatting on the output files  [41] [42] [43]. The re-ranking was based on the frequency of inter-residue contacts appearing in the solutions.
Visualization, analysis, and plotting of the docking models were performed using UCSF Chimera software, developed by the Resource for Biocomputing, Visualization, and Informatics at the University of California, San Francisco with support from NIH P41-GM103311  [44]. Molecular Dynamics simulations Molecular Dynamics (MD) simulations and the analysis of production runs were carried out using the YASARA Structure  [45]  on the following hardware: Processor Intel CORE i7 10,700 10th generation; SOCKET 1200 2. 9 GHZ (Max 4. 8 GHZ) 16 M cores/threads 8/16, 2; Memory Kingston HX426C16FB3/8G HyperX 2666 Mhz; Disc SSD Kingston A400 240GB SATA 7 mm; Linux Ubuntu 20. 04 LTS 64 bit. The coordinates corresponding to wild-type FXN (PDB ID: 1EKG) were solvated, and standard minimization protocols were applied to remove steric clashes.
After 18 days of incubation for SK-MEL3, 15 days for NRAS 1007, and 8 days for B16F10 cells, medium was removed and colonies were rinsed once with PBS. Colonies were then fixed by methanol absolute during 3 min then revealed with 0. 5% crystal violet aqueous solution. The counting of colonies was realized using the ImageJ software. Plating efficiency (PE) and survival fraction (SF) were determined as described  [29].
Western Blotting Western blot analysis was carried out as previously described  [30]. The following primary antibodies (Abs) were used: anti-phospho-H2A. X (S139) (1/2000), anti-H2A. X (1/1000), anti-phospho-ERK1/2 (Thr202/Tyr204) (1/2000), anti-ERK1/2 (1/2000), anti-phospho-MEK1/2 (Ser217/221) (1/2000), and anti-MEK1/2 (1/2000) from Cell Signaling Technology (Danvers, MA, USA), and anti-PARP-1 (1/200; Santa Cruz Biotechnology, (Dallas, TX, USA)) and anti-Actin (1/10,000; Sigma-Aldrich). The following secondary Abs were used: anti-rabbit-HRP (1/5000) and anti-mouse-HRP (1/5000 and 1/10,000 for actin) from SouthernBiotech (Nanterre, France). The Western blots were scanned using ChemiDoc imaging system (Bio-Rad, Marnes-la-Coquette, France). The densitometric analyses were performed using an ImageJ software (National Institutes of Health, Bethesda, MD, USA) (accessed on 11 November 2020).
Biodistribution Study Thirty-six days following tumor implantation, 15 mice received an intra-venous (i. v. ) injection of 0. 37 MBq [ 131 I]ICF01012. Three mice were sacrificed per timepoint (1, 3, 6, 24, and 72 h) prior to removing and weighing the organs and tumors. Their radioactivity was analyzed using a γ-counter (Wizard 1480, Perkin Elmer) and the gamma counting data were corrected for physical decay and background. The percentage of injected activity in tumors was determined by the ratio of counted activity per organ divided by the organ weight (% IA/g).
Dosimetry The dose was assessed from organ activity measured at each timepoint (1, 3, 6, 24, and 72 h, n = 3 per timepoint). The percentage of injected activity curves %I A(t) = Activity(t) weight. Activity injected were calculated for each organ. Curves were adjusted using MatLab software (TheMathWorks, Natick, MA, USA): tumor: y = a 1exp -bt exp -ct ; eyes: y = a 1exp -bt exp -λ physique t with λ physique = ln 2 T physique(h) ; other organs (bi-exponential): y = a. exp -bt + c. exp -dt. The MatLab adjustment parameters were: Method: NonlinearLeastSquare; Robust: Bisquare; Algorithm: Trust-Region. Cumulative activity confidence intervals were assessed by bootstrapping (adjustment with a 153-fit simulation and the leave-one-and-two-out method) and 95% CI, with the 2. 5 and 97. 5 percentiles.
SPECT-CT Imaging Multimodal SPECT-CT imaging was performed using a NanoScan SPECT/CT camera (Mediso Ltd. , Budapest, Hungary) equipped with four detectors and multi pinhole collimation (APT62). Mice were placed in a Multicell Mouse L bed (Mediso Ltd. ) with temperature control (37 • C). SPECT-CT imaging was performed on representative mice (n = 2 at 1 h and 6 h and n = 5 at 24 h, 72 h, and 168 h) of each group at different time points (i. e. , 1 h, 6 h, 24 h, 72 h and 168 h) after the injections of [ 131 I]ICF01012. Nucline software (Mediso Ltd. ) was used for image acquisitions and reconstructions (Nucline 3. 00. 018). CT parameters were as follows: helical scan with 480 projections (300 ms per projection), 50 kV, 590 uA, pitch 1. 0, binning 1:4, and field of view: max. SPECT images were acquired within the CT scan range, with a standard resolution.
After fragmentation, cDNA synthesis was performed and resulting fragments were used for dA-tailing followed by ligation of TruSeq indexed adapters. PCR amplification was finally achieved to generate the final barecoded cDNA libraries (12 amplification cycles). The 9 libraries were equimolarly pooled and subjected to qPCR quantification using the KAPA library quantification kit (Roche). Sequencing was carried out on the NovaSeq 6000 instrument from Illumina based on a 2 × 100 cycles mode (pairedend reads, 100 bases) using an S1 flow cell in order to obtain around 35 million clusters (70 million raw paired-end reads) per sample. Reads were mapped to the mouse reference genome (gencode m13 version-GRCm38. p5) using STAR  [34]. STAR was also used to determine the expression matrix. Expression matrix was first filtered by expression level to keep only sufficiently expressed genes using the edgeR package  [35].
Principal component analysis (PCA) was performed using FactomineR  [36]. 3D PCA animated gif was made with the help of the pca3d package (https://cran. r-project. org/web/packages/pca3d/index. html) (accessed on June 2020). The significance of the clustering was evaluated using PERMAN-NOVA. The deconvolution was conducted using the R-package mcp-counter  [37]. To the gene set distributed with the package have been added two gene sets containing genes respectively specific of melanocytes (Mitf, Pax3, Mc1r, Rxrg, and Tspan10) and of keratinocytes (Flg, Krt1, Dsc1, Sprr1b, and Krt14) in the skin. Differential gene expression was performed with R following the limma-voom pipeline using the limma package  [38]. edgeR and limma packages are both available from Bioconductor (http://www. bioconductor.
org) (accessed on June 2020). The threshold for significantly differentially expressed genes was set as an absolute fold-change > two times the standard deviation of the fold-change and an adjusted p-value < 0. 05. The volcano plot depicting the results was generated using the R package ggplot2  [39]. The interactive volcano plot was made with the plotly R package  [40]. Gene-ontology and enriched pathway analysis were performed using Enrichr  [41]  on genes found overexpressed by the differential analysis either in the TRT-treated tumors or in the control tumors (also designed as CTRL-treated tumors. Gene-set enrichment analysis (GSEA) was performed using three collections from the Molecular Signatures Database (MSigDB v7. 2): H (Hallmark), C2 (curated gene sets), and C5 (ontology gene sets), and a gene set describing the different sub-population in melanoma  [42].
Statistical Analyses Statistical analyses were performed with GraphPad (Addinsoft, New York, NY, USA) and StatView (Abacus concepts, Southfield, MI, USA) software using Log-rank for survival analyses, Student's t tests for animal experiments, and two-way analysis of variance (ANOVA) associated with post-hoc Tukey's multiple comparison test for in vitro experiments. A p-value < 0. 05 was considered to represent statistically significant differences.
We performed differential analysis to compare TRT-treated and control tumors. In total, 316 genes were upregulated and 331 downregulated in TRT-treated vs. control tumors with a fold-change > 2SD and an adjusted p-value < 0. 05 (Figure  5F ). )  showing a clear demarcation between TRT-treated tumors and mock-treated tumors. (F) Volcano-plot depicting the differential analysis results between TRT-treated tumors and control tumors. Differentially expressed genes were defined as genes with an adjusted p-value < 0. 05 and a fold-change > 2 SD of the fold change (https://www. ncbi. nlm. nih. gov/geo/query/acc. cgi?acc= GSE162536) (accessed on June 2020). In total, 316 genes were enriched in TRT-treated tumors vs. 331 in mock-treated tumors. An interactive volcano plot is presented in Supplementary Figure  S6.
(G-L) Gene-set enrichment results from gene-set enrichment analysis (GSEA). The gene sets enriched in TRT-treated tumors were related to antioxidant activity (G), an inflammatory response (H), and activation of the P53 pathway (I). The gene sets enriched in control tumors were related to proliferation (J), mitosis (K), and melanoma metastasis (L). An interactive volcano plot is presented in Supplementary Figure  S6. The differentially expressed genes are also listed in Supplementary Table  S5. Pathway and ontology enrichment analysis by over-representation were conducted on the BioPlanet (BioPlanet 2019) and Gene Ontology (GO-Biological Pathways 2018) databases, respectively, using the 316 and 331 differentially expressed genes. The 331 genes enriched in the control tumors mapped to pathways and ontologies involved in the cell cycle, cell proliferation, and cell replication (Supplementary Figure  S7A, B ).
Acknowledgments: We thank  A Briat  from the  Ivia Platform  for help with the SPECT-CT imaging. We are grateful to  A Cayre  and  M Roche  from the anatomopathological department of the  Jean Perrin Center  and  C Vacchias  from the confocal imaging facilities (CLIC platform). High-throughput sequencing was performed by the  ICGex NGS platform of the Institut Curie , supported by the grants  ANR-10-EQPX-03  ( Equipex ) and  ANR-10-INBS-09-08  ( France Génomique Consortium ) from the  Agence Nationale de la Recherche  (" Investissements d'Avenir" program ), the  Canceropole Ile-de-France , and the  SiRIC-Curie program-SiRIC Grant INCa-DGOS-4654.
Data Availability Statement: The data generated by the transcriptomic study (Table  S3 ) have been deposited in the Gene Expression Omnibus of the NCBI and are accessible through GEO Series accession number GSE GSE162536 (https://www. ncbi. nlm. nih. gov/geo/query/acc. cgi?acc=GSE162 536). This series contains the raw reads (fastq) and aligned-reads (BAM) of nine tumors (3 treated and.
Conclusions In conclusion, this study reinforces the potential of [ 131 I]ICF01012-TRT in the management of pigmented metastatic melanoma, especially its capacity to reduce metastatic dissemination. Future experiments that aim to clarify the mechanisms underpinning such high radiosensitivity to [ 131 I]ICF01012-TRT will likely lead to a specific clinical trial including patients harboring NRAS metastatic melanoma, following the ongoing dose-escalation phase 1 study MELRIV1 (NCT03784625).
Inclusion criteria were a clinical diagnosis of blindness for blind subjects and normal or corrected normal vision for SC. Exclusion criteria were significant neuro-psychiatric comorbidity and alcohol or drug addiction. All participants had previously been enrolled in other functional or structural brain imaging studies conducted by our group, in which a T1weighted structural MRI brain scan had been collected. The total sample comes from a Danish (BRAINlab, Copenhagen) and a Canadian (Institut Nazareth et Louis Braille, Montreal) study cohort; they are further referred to as the "Copenhagen" and "Montreal" data-sets. The Copenhagen data set was acquired at the Danish Research Center for Magnetic Resonance (DRCMR). The study protocols had been approved by the local research ethics committees and all participants had given their written informed consent.
Image processing Scans were converted from DICOM into mgz or nifti format, depending on the brain imaging center where they had been acquired, and then into MINC format (Montreal Neurological Institute, McGill University) that we used for further image processing. In order to standardize the images and correct for interindividual differences in gross brain size, MRI volumes were transformed into MNI space by using "ICBM152" model atlas "bestlinreg. pl" algorithm in MINC (https://github. com/BIC-MNI/EZminc/blob/master/scripts/bestlinreg. pl). For the manual labelling of the optic nerve, optic chiasm and optic tract, the experimenter was unaware to which group the images belonged.
Retiofugal projections segmentation We used DISPLAY (http://www. bic. mni. mcgill. ca/software/Display/Display. html), an interactive program developed by the brain imaging center of the Montreal Neurological Institute, to display the optic nerve, optic chiasm, and optic tract. This program permits labelling of voxel regions on each slice of the MRI volume and allows for the simultaneous visualization of the movement of the cursor on the screen within the sagittal, horizontal and coronal planes of the MRI. A selection of any area can be made by using the DISPLAY 'mousebrush' that marks the voxels by colouring. This colouring procedure accompanied by the simultaneous 3-D view of the MRI planes allows a better identification and selection of the regions of interest. The optic nerve and optic tract were mapped as the structures in continuation of the body of the optic chiasm for four mm to the frontal border and to four mm to the posterior to the occipital border, respectively.
Voxel intensity measurements The voxel intensity (VI) value is an indirect measure of the myelin content of the axons in that, the higher the myelin content, the higher is the VI signal and vice-versa  (Bottomley et al. , 1984). The intensity measurements were averaged for every voxel segmented. The data were extracted using the "intensity_statistics" algorithm in MINC (http://www. bic. mni. mcgill. ca/~stever/software_map. html). To take into account the fact that averaged intensity values differed between scans, data were analyzed as a ratio taking the genu of the corpus Fig. 1. Anatomical delineation of the optic chiasm. A 3D rendered image of the optic nerve (in yellow), optic chiasm (in green) and optic tract (in cyan) from a sighted participant. We delineated the optic nerve on coronal slices from 4 mm anterior to the frontal border of the optic chiasm body; the optic tract was segmented from 4 mm posterior to the occipital border of the optic chiasm body.
We set a BF 10 < 1 as strong evidence in support of the null hypothesis, while a BF 10 > 10 was used as strong evidence for the alternative hypothesis  (Lee and Wagenmakers, 2014; Kelter, 2020; Keysers et al. , 2020). First, intergroup comparisons were conducted to test for differences between the Copenhagen data and Montreal data-sets. Then, Mann-Whitney t-tests were performed comparing outcome variables for each categorical demographic variable grouping (gender and handedness). The effect of age on outcome variables was then evaluated both with simple linear regressions and with multiple linear regressions including all disease-related independent variables. Finally, the age-corrected effects of all disease-related characteristics (vision yes/no, blindness group, blindness onset age, retinopathy of prematurity versus other causes, years blind, blindness duration index) on outcome variables were analysed separately with multiple linear regressions.
Acknowledgements The study was supported by grants from the  Lundbeck foundation (Denmark)  and the  Harland Sanders Foundation (Montreal). The authors would like to thank  Nina Reislev  for collecting the MRI data of the Copenhagen cohort,  Frederik Fiederspiel  and  Sune Darkner  ( University of Copenhagen ) for their contribution to an early version of this work, and also the volunteers who have participated in the study.
Differential gene expression analyses Raw data (median of pixel intensity) were analyzed using R (www. r-project. org, R v. 3. 1. 2) Bioconductor packages (www. bioconductor. org, v 3. 0  17  ) as described in GEO entry GSE99725. Briefly, raw data were filtered, log2 transformed, corrected for bath effects (washing and labeling serials), and normalized using the quantile method  18. A first exploratory and statistical analysis showed a possible correlational structure among gene expression, which could negatively impact the multiple testing procedures. We applied the FAMT method  19  to reduce the dependence structure. A model was fitted using the limma lmFit function  20  considering "patients" as a blocking factor for the time pairs using a duplicate correlation function. A correction for multiple testing was then applied using the Benjamini-Hochberg procedure  21  for false discovery rate (FDR).
Transcriptional pathways identification, comparison analysis, and composite z-scores analyses The biological pathways involved in MDD were predicted from lists of differentially expressed genes using the "Upstream Regulator Analysis" from Ingenuity Pathway Analysis (IPA, Qiagen, USA). To calculate the composite z-scores representing a given signaling pathway, we first extracted the list of target genes for a given transcription factor from ChIPseq experiments available in ChEA 2016, an integrative database of public ChIP-seq data (available in http://amp. pharm. mssm. edu/Enrichr/). Then, from the list of target genes for a given transcription factor, we selected the ones that were expressed in our data set. For each of the patients, the expression data for each gene was first standardized by calculating a z-score, z-score = xÀμ σ , where x is the gene expression data, μ the mean of the gene expression for the population (all patients of the study) and σ the standard deviation for the population.
1B , with a Venn diagram proportional to the number of DEG. For the later analysis (MDD versus non-MDD after surgery), no significant DEGs were detected as expected since patients were no longer depressed in either group. We found no significant changes in immune cell subtypes' proportion in the different analyses with significant DEG using CellCODE (Supplementary Table  1 ). For the MDD versus non-MDD comparison at baseline, the transcript origin analysis revealed that upregulated transcripts derived preferentially from NK cells, CD4 + T cells, and CD8 + T cells. For the MDD after versus before surgery comparison, the upregulated transcript derived from monocytes and the downregulated genes from monocytes, dendritic cells, and NK cells. For the non-MDD after versus before surgery comparison, the upregulated transcripts originated preferentially from CD4 + T cells and CD8 + T cells and the downregulated genes from NK cells.