dataesr/scientific-paragraphs-categorization huggingface dataset:
Protein Quantification Using ImageJ Software ImageJ is a freely available software (https://imagej. nih. gov/ij/ download. html), used to determine the protein concentration from SDS-PAGE gels. This software measures the relative density of each protein band from a selected lane of the gel and plot a graph according to their densities. In order to determine the protein concentration, peak area of the band of interest was calculated and compare protein band with the known concentration. To estimate the protein concentration, known amount of BSA (2-10 µg/µl) was run on 10% SDS-PAGE and the relative density of each band was calculated using ImageJ software. BSA standard curve was then prepared using calculated peak area from the software and plotted against the known concentration of BSA. The standard curve thus prepared was used for determining the concentration of solubilized protein from each buffer.
Bioinformatics Analysis Sequences of ICL2s from different strains of Mtb were aligned using EMBL-EBI tool-Clustal Omega (https://www. ebi. ac. uk/Tools/msa/clustalo/). All the sequences were retrieved from KEGG Database (https://www. genome. jp/kegg/). Expasy tools (https://www. expasy. org/proteomics/protein_structure) were used to predict the secondary structures (alpha, beta random coils and turns) of Rv1915/ICL2a. The disordered regions of Rv1915/ICL2a were further verified by different online servers namely-Prediction of Amyloid Structure Aggregation 2. 0 (PASTA 2. 0 -http://protein. bio. unipd. it/pasta2/) server, Predictor of Natural Disordered Regions (PONDER -http://www. pondr. com/) and Protein Disorder Prediction Server (PrDOS -http:// prdos. hgc. jp/cgi-bin/top. cgi). These servers are freely available and predict the disordered regions of a given protein using its amino acid sequence.
The software also predicts two additional segments (248-251 and 304-307) with tendency toward parallel aggregation. Two other servers PrDOS and PONDER endorsed the presence of disordered regions at the termini of Rv1915/ICL2a (Figure  S6 ). Specifically, ∼35 residues from the C-terminus and ∼15 residues from the N-terminus of the queried protein was predicted to be unstructured by all the three servers. In silico deletion of either of these in PASTA 2. 0 server did not reduce the number of amyloids, which could be achieved only after truncation of ∼90 residues (278-367) from the C-terminal end of Rv1915 (Table  3 ). This 90 residue long C-terminal region encompasses the second aggregation segment (residues 304-307), whose removal appears to reduce amyloid formation. As discernible from multiple sequence alignment (Figure  4A ), this section has low similarity with the larger ICL2, reflected in the structural differences as well.
Fortunately the crystal structure of ICL2 from Mtb strain CDC 1551/Oshkosh (PDB code 6EDW), has recently become available (Figure  4B )  (Bhusal et al. , 2019)  and helped in building homology model of Rv1915/ICL2a (Figure  4C ). Both  secondary structure and 3D model predicted disordered Cterminus Rv1915/ICL2a (residues 278-367) as opposed helical region of long ICL2. For reducing non-specific aggregation due to presence of floppy tails, recombinant DNA technology was employed for generating two variants of Rv1915 where 35 (Rv1915 35CT) and 90 (Rv1915 90CT) residues from the C-terminal were deleted. Only the later could be purified successfully that exhibited dual ICL and MICL activities, observed first time for Mtb H37Rv strain. Dual activity has been reported for the complete Mtb strain CDC 1551/Oshkosh ICL2  (Bhusal et al.
Materials and Methods Participants. Neuroimaging data were obtained from a communitybased sample of 1543 youth that were part of the Philadelphia Neurodevelopmental Cohort and related follow-up studies  (Jirsaraie et al. , 2019; Roalf et al. , 2019)  (ages 8. 17-26. 92 at first visit, M ϭ 15. 19, SD ϭ 3. 75; male/female ϭ 728/819). This neuroimaging dataset included up to five longitudinal visits per participant, resulting in 2321 total scans. From this original sample, 309 participants (419 scans) were excluded based on health criteria, including psychoactive medication use at the time of study, medical problems that could impact brain function, a history of psychiatric hospitalization, and gross structural brain abnormalities. A total of 641 scans were excluded for poor T2* image quality, including head motion, hyperintensities, and susceptibility artifacts, which was assessed with manual quality assurance procedures (see below).
In sum, following health exclusions and rigorous quality assurance we retained 922 participants (ages 8. 17-26. 92 at first visit, M ϭ 15. 1, SD ϭ 3. 72; male/female ϭ 436/486) with up to four longitudinal neuroimaging visits per participant, resulting in 1236 total scans (Fig. 1 ). From this neuroimaging sample, cognitive performance data (described below) was available for 818 individuals, with up to three time points per individual (ages 8. 17-26. 92 at first visit, M ϭ 14. 84, SD ϭ 3. 57; male/female ϭ 389/429), resulting in 1086 total cognitive assessments. Structural image processing. A high-resolution T1-weighted image was acquired for all visits for the purpose of nonlinear registration to a study specific template. The study-specific template was generated from a sam-   1 ). ple of 120 PNC subjects balanced across sex, race, and age bins using the "buildtemplateparallel" procedure in ANTs  (Avants et al.
, 2011a). Each participant's high-resolution structural image was processed using Advanced Normalization Tools (ANTs) software and the structural processing pipeline included bias field correction  (Tustison et al. , 2010) , brain extraction using a hybrid registration/segmentation approach  (Avants et al. , 2011b) , and diffeomorphic registration to the studyspecific template using SyN deformation provided by ANTs  (Avants et al. , 2008; Klein et al. , 2009). R2* acquisition and preprocessing. T2* data were estimated from multiecho gradient echo (mGRE) images acquired for each participant. Sequence parameters are summarized in Table  1. It should be noted that a subset of the data was acquired with minor variations in acquisition parameters. However, all scans were acquired on the same scanner, 99% used the same repetition time (TR, 1000 ms), and 86% used the same echo spacing (⌬TE, 2.
(see below). Acquired mGRE images were first smoothed with a 3 mm FWHM kernel. T2* was calculated from the pixel intensity of the two echo images according to the following: T2* ϭ Ϫ⌬TE In ͩ I TE2 I TE1 ͪ where I TE1 and I TE2 refer to the signal intensity at the first and second echo. T2* images were brain extracted using coregistration to the brainextracted structural image (above). T2* images were finally spatially normalized to the study-specific template by concatenating the affine registration between the T2* image and structural image and the nonlinear registration of the structural image to the study template. R2* was calculated as ͩ 1 T2* ͪ. R2* quality assurance. All R2* images were visually inspected by BL for data quality and the presence of artifacts, including motion artifacts, susceptibility artifacts, extreme values, and T2* hyperintensities, any of which could impact the R2* signal in the basal ganglia.
0, providing some support for use of a unidimensional model  (Reise et al. , 2015). Group-level statistical analysis. Before all statistical analyses, R2* data were harmonized to control for subtle differences in T2* acquisition parameters using ComBat, a batch-effect correction tool developed for genomics data  (Johnson et al. , 2007)  and previously applied to harmonize multisite neuroimaging data, including in developmental studies  (Fortin et al. , 2017 (Fortin et al. , , 2018)). ComBat removes unwanted interacquisition variability while preserving variability in biological variables of interest. ComBat was performed separately for observations included in developmental models (including covariates for age, sex, and visit number) and cognitive models (including covariates for age, sex, visit number, and cognitive performance).
To test for windows of significant change across the age range, we calculated the first derivative of the smooth function of age from the GAMM model using finite differences, and then generated a simultaneous 95% confidence interval of the derivative  (Simpson, 2018)  using the gratia package in R. Intervals of significant change were identified as areas where the simultaneous confidence interval of the derivative does not include zero. We next investigated the relationship between cognition and R2* and whether developmental trajectories of R2* interacted with cognitive ability. To test for a main effect of overall cognitive performance, we fit a GAMM with additive smooth terms for cognition and age and a main effect of sex. To assess whether developmental trajectories of R2* varied by cognitive ability, we allowed the smoothed age effect in the GAMM to interact with cognitive performance.
All GAMMs were fit in R using the 'gamm' function from the mgcv package  (Wood, 2004 (Wood, , 2017)). To model this continuous interaction, we compared two interaction models within the GAMM framework: a fully nonlinear interaction (bivariate smooth) and a nonlinear-by-linear interaction in which the smooth effect of age interacts with a linear effect of cognitive performance (varying coefficient model). Model selection was performed using Bayesian information criterion (BIC), and the significance of the interaction term was evaluated for the selected model. Finally, we evaluated three-way age-bycognition-by-sex interactions, which were not significant and not considered further. All models included visit number as a covariate factor of no interest and participant ID as a random effect. For all GAMMs, significance of the fixed effect smooth terms was assessed in a fixed degree-of-freedom context to ensure stable and accurate estimation, and all p-values of interest were confirmed using a parametric bootstrap likelihood ratio test ( pbkrtest package) for significance estimation in the mixed model context  (Halekoh and Højsgaard, 2014).
Additionally, as this work is focused on a normative developmental sample, and future work should investigate how developmental abnormalities of iron are associated with neuropsychiatric illness. Second, a small subset of T2* scans were acquired with minor variations in acquisition parameters. Though we do not expect these variations to significantly impact T2* estimation, we rigorously addressed this possibility using ComBat harmonization  (Johnson et al. , 2007) , which has been effective in harmonizing neuroimaging datasets  (Fortin et al. , 2017 (Fortin et al. , , 2018)). Third, the data acquired to calculate R2* was generated with a two-echo mGRE sequence with somewhat lower in-plane resolution (3. 75 ϫ 3. 75 mm) than has been used in other studies (e. g. ,  Langkammer et al. , 2010; Sedlacik et al.
In vitro binding assays, combined to queries of interaction databases and large interaction datasets, were used to retrieve interaction data. We identified 31 new interactions and 14 new partners of LOXL2 and its N-terminal domains, including the α5β1 integrin, involved in both angiogenesis and cancer. This is, to the best of our knowledge, the first report of a direct interaction between a member of the LOX family and an integrin. We built the first draft of the interactome of the LOX family, which comprises 320 proteins, 5 glycosaminoglycans, and 399 interactions. Computational analyses showed that the global LOX family interactome contributes to ECM organization, cell-matrix interactions, protein folding and chaperone activity (with a focus on cytoskeleton proteins), blood vessel development, cellular response to stress, and signal transduction.
The Interaction Dataset of the Lysyl Oxidase Family Several sources of interaction data were combined to build a comprehensive interactome of the lysyl oxidase family. Experimental data were collected by querying manually curated databases MatrixDB  [37] [38] [39] [40]  and IMEx databases, including IntAct  [41] [42] [43] , manually curating additional interactions, querying the large BioPlex 2. 0  [44]  and 3. 0  [35]  datasets generated by large-scale affinity purification-mass spectrometry, and performing in vitro binding assays. Surface plasmon resonance imaging (SPRi) binding assays were first performed using the protein and glycosaminoglycan (GAG) arrays we developed  [33, 45, 46]  comprised of 44 different biomolecules to identify new extracellular and membrane partners of LOXL2, which plays a prominent role in cancer, and of its N-terminal scavenger receptor cysteine-rich domains (SRCR).
LOXL1, LOXL3, and LOXL4 established 32, 50, and 91 interactions, respectively (Supplementary Table  S1 ). The high-throughput affinity purification-mass spectrometry (AP-MS) technique used to generate the BioPlex datasets from normal HEK 293 cells and from the tumor cell line HCT 116 allowed the identification of additional interactions of the LOX family. They represented 9. 3% of LOX interactions, 25% of LOXL1 interactions, 53. 3% of LOXL2 interactions, 36% of LOXL3 interactions, and 82. 4% of LOXL4 interactions. However, it should be noted that this approach is based on the purification of protein complexes, which means that the partners of the LOX family identified by this approach did not necessarily bind directly to the LOX family members. These interactions might not be direct, but might represent indirect interactions reflecting proteins identified within a complex containing at least one member of the LOX family.
Enrichment Analysis of the Interactome of LOX Family Using BiNGO The complete analysis is available in Supplementary Table  S4. The most significantly over-represented annotations for the Gene Ontology (GO) term "Cellular Component" were related to the extracellular matrix and basement membranes, "chaperonin-containing T complex", "anchored to membrane", "microtubule cytoskeleton", and "membraneenclosed lumen" (Figure  4 ). Binding, including "Receptor binding" and "Unfolded pro- The LOX and LOXL interaction dataset will be freely available via MatrixDB web site (http://matrixdb. univ-lyon1. fr/), the ECM interaction database we have created and we maintain  [37] [38] [39] [40] , and via the dataset of the International Molecular Exchange consortium (https://www. imexconsortium. org/) to which MatrixDB belongs  [41, 43].
The molecular functions enriched in the interactome of the LOX family were "Extracellular matrix structural constituent", "Chaperone activity", "Metallopeptidase activity", and "Heat shock protein activity", but these terms annotated a limited number of proteins (≤12%). The over-represented biological processes were "Cell growth and maintenance" (2. 7-fold enrichment) and "Protein metabolism" (2. 5-fold enrichment) (Supplementary Table  S5 ). The biological pathways over-represented were related to "Integrin cell surface interactions" (7. 4-fold enrichment) and chaperonin activity or protein folding, but, although there was a 35-fold enrichment in the pathway, "Formation of tubulin folding intermediates by CCT/TriC", only 4. 5% of the dataset were associated with this pathway (Supplementary Table  S5 ).
The molecular functions enriched in the interactome of the LOX family were "Extracellular matrix structural constituent", "Chaperone activity", "Metallopeptidase activity", and "Heat shock protein activity", but these terms annotated a limited number of proteins (≤12%). The over-represented biological processes were "Cell growth and maintenance" (2. 7fold enrichment) and "Protein metabolism" (2. 5-fold enrichment) (Supplementary Table  S5 ). The biological pathways over-represented were related to "Integrin cell surface interactions" (7. 4-fold enrichment) and chaperonin activity or protein folding, but, although there was a 35fold enrichment in the pathway, "Formation of tubulin folding intermediates by CCT/TriC", only 4. 5% of the dataset were associated with this pathway (Supplementary Table  S5 ).
Last, the zinc finger transcription factor SP1 was over-represented among those regulating the expression of the network proteins. Enrichment analyses were also performed on the partners of each member of the LOX family (Supplementary Table  S6A-E ). The extracellular location was enriched in the partners of LOX, LOXL2, and LOXL3 containing 59. 1, 58. 8, and 37% of extracellular proteins, respectively. Forty percent of LOXL1 partners were extracellular but without a significant enrichment. In contrast, LOXL4 partners were significantly enriched in proteins located in the cytosol (24. 7%), the centrosome (18. 5%), and the microtubules (11%). The presence of a signal peptide, reflecting secretion outside the cell, was found in ∼70% of LOX (71. 1%) and LOXL2 (73. 4%) partners, and was significantly enriched in both datasets whereas it was present in 40% of LOXL1 and LOXL3 partners without significant enrichment.
Pathway Analysis of the Interactome of LOX Family Using Reactome Reactome is a curated database of pathways and reactions in human biology. Of all the pathways, 973 were hit by a least one protein belonging to the interactome of the LOX family. A genome-wide overview of the pathway analysis of LOX family interactome is displayed in Figure  6. The top-level pathways over-represented in the LOX family interactome compared to the human proteome are "Extracellular matrix organization", "Metabolism of proteins", "Cellular Response to external stimuli", and "Signal transduction". The 100 most relevant pathways identified by Reactome and sorted by p-value are listed in Supplementary Table  S7. The over-represented sub-pathways in the most overrepresented pathway "Extracellular matrix organization" are collagen and elastic fiber formations, assemblies of collagen fibrils and other multimeric structures, and cross-linking of collagen fibrils, as expected, but also extracellular matrix and collagen degradation, "Non-integrin membrane-ECM interactions", and "Integrin cell surface interactions".
0 dataset) to compare the interactome of the LOX family in normal and tumor cells. The interaction repertoire of the LOX family differed between these two cell types (Figure  7 ). The LOX family had twice as many interactions in tumor cells than in normal cells. 63 interactions were established by the LOX family in HEK 293 cells, and 124 in HCT 116 cells. It should be noted that no interaction involving LOX was identified in the tumor cell line HCT 116 in the experimental conditions used (Figure  7 ). 12 interactions were found in both cell types, LOXL2 being involved in 9 of them, LOXL3 in 2, and LOXL4 in 1. These interactions involved 6 secreted proteins, 4 intracellular proteins, and 2 membrane proteins (Figure  7 , Supplementary Table  S1 ). It should be noted that one interaction involved two different isoforms of the platelet-derived growth factor receptor alpha (PDGFRA) in the normal and tumor cells, a membrane form in HEK 293 cells and a secreted form in HCT 116 cells.
However, 128 protein partners of LOX and LOXLs were not functionally linked to any member of the network or any drug, and were not displayed, although 11 of them were associated with cancer. This is due to the lack in Reactome FIViz of functional links for these proteins. EGFL7, for instance, is connected to a single member of the LOX family, although we report here that this protein binds to the five members of the family. The interactome generated in this study will thus be useful to update applications using interaction datasets. Neither LOX, nor LOXLs, were directly associated with an anti-cancer drug. Only eight members of the functional network were connected to anti-cancer drugs, including five tyrosine kinase receptors (MAST1 or Microtubule-associated serine/threonine-protein kinase 1, PDGFRA or Platelet-derived growth factor receptor alpha, BTK or Tyrosine-protein kinase, RAF1 or RAF proto-oncogene serine/threonine-protein kinase, and MAP4K3 or Mitogen-activated protein kinase kinase kinase kinase 3), the tubulin beta chain (TUBB), the histone deacetylase 3 (HDAC3), and carbonic anhydrase 6 (CA6) (Figure  9 ).
FDA-approved cancer drugs were visualized in the context of the functional interaction network of the LOX family built by Reactome FIViz, to investigate the potential functional impacts of displayed cancer drugs. However, 128 protein partners of LOX and LOXLs were not functionally linked to any member of the network or any drug, and were not displayed, although 11 of them were associated with cancer. This is due to the lack in Reactome FIViz of functional links for these proteins. EGFL7, for instance, is connected to a single member of the LOX family, although we report here that this protein binds to the five members of the family. The interactome generated in this study will thus be useful to update applications using interaction datasets. Neither LOX, nor LOXLs, were directly associated with an anti-cancer drug.
The Matrisome Part of LOX Family Interactome in Cancer We investigated the expression data of matrisome proteins collected by Izzi et al. in 32 tumor types  [53]  (Supplementary Table  S10 ), to identify those where the members of the LOX family were present. No dataset contained the five members of the LOX family. However, four (LOX, LOXL1, LOXL2, LOXL4) and three (LOX, LOXL1, LOXL4) members were present in the mesothelioma (MESO; 87 patients, 239 genes) and ovarian serous cystadenocarcinoma (OV; 427 patients, 209 genes) datasets. Moreover, the highest fold change in the expression of LOX family members compared to normal tissues were found in MESO and OV patients for LOX (MESO: 7. 32, OV: 5. 89), LOXL2 (MESO: 7. 41), and LOXL4 (MESO: 4. 17, OV: 4. 17). We, thus, focused on mesothelioma and ovarian datasets to investigate the possible role of the LOX family in cancer.
We first built the matrisomespecific interactome of the LOX family, based on the annotations of Supplementary Table  S3 , and comprised of 86 proteins and 113 interactions (Figure  10A ). The matrisome interactome of the LOX family specific to mesothelioma and ovarian cancer was then built by selecting the partners expressed in mesothelioma and in ovarian cancer, and ranking them according to their fold-change in cancer relative to normal tissue (Figure  10B ). Of the global LOX network, 42 and 27 proteins were found in the MESO and OV datasets, respectively, with 24 proteins found in both cancer datasets (Supplementary Table  S10 ). Among the common partners, bone marrow proteoglycan or proteoglycan 2 (PRG2) and the matricellular protein connective tissue growth factor (CTGF) exhibited the highest fold changes in the MESO dataset (102.
42 and 90. 35, respectively), whereas CTGF was also highly overexpressed in the OV dataset (fold change: 87. 46). CTGF, a transcriptional target of TGF-β signaling, is a therapeutic target in high grade serous ovarian cancer  [54]. All the proteins of the matrisome-specific interactome of the LOX family expressed in ovarian cancer were also expressed in mesothelioma, except cystatin-like 1 (CSTL1), THSD4 (ADAMTSL-6), and chorionic somatomammotropin hormone 1 (CSH1), which were expressed only in ovarian cancer (Figure  10B ). Figure  9. Drug-target interactions identified in the functional interaction network generated using Reactome FIViz. Green: proteins, Red: proteins annotated with cancer-related condition, Diamonds: drugs. Blue edges: interactions between drugs and targets, arrowheads: activating and catalyzing or expression regulation, dotted lines: predicted functional interactions, ⊥ for inhibition, solid line for complexes or reactions.