dataesr/scientific-paragraphs-categorization huggingface dataset:
We first built the matrisome-specific 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 ). S3 ): cyan: core-matrisome proteins, orange: matrisome-associated proteins, pink: membrane proteins. (B) Matrisome-specific interactome of the LOX family in mesothelioma (MESO) and ovarian serous cystadenocarcinoma (OV). Nodes were color-coded using the fold change values calculated in cancer (MESO and OV) versus normal patients in Izzi et al.
Not detected: the expression level of ADAMTS4 was 0 in normal tissue, the fold-change was thus not calculated. No data: the gene is absent from Izzi et al. dataset  [53]. Differences were observed in the matrisome subnetwork of the LOX family between mesothelioma and ovarian cancer. "Epithelial-mesenchymal transition" (37% of proteins annotated, 12. 6-fold-enrichment) and "Integrin cell surface interactions" (22%, 19. 4-fold-enrichment) were the most significantly enriched pathways in the mesothelioma subnetwork (Supplementary Table  S11A ) but not in the ovarian cancer subnetwork (Supplementary Table  S11B ), which suggests that the changes in LOX and LOXL interactome associated with cancer might depend on the cancer type.
LOXL2, SRCR 1-2, and SRCR 1-4 domains diluted in 10 mM Hepes pH 7. 4 containing 150 mM NaCl and 0. 05% P20 as running buffer were injected over immobilized ligands. LOXL2 was covalently immobilized on a CM5 sensor chip via its primary amino groups, and the ectodomain of human collagen XIII was injected at different concentrations at 30 µL/min for 180 s over immobilized LOXL2 and the control flow cell. The association (ka) and dissociation rate (kd) constants and the equilibrium dissociation constant (K D ) were calculated using the BIAevaluation software (version 2. 0. 3).
Querying Interaction Databases and Large-Scale Datasets Interactions of LOX and LOXLs were retrieved by manual curation of the literature and by querying manually curated interaction databases including the database we created, MatrixDB (http://matrixdb. univ-lyon1. fr/, [37] [38] [39] [40] ), and IntAct  [42]. Both databases belong to the International Molecular Exchange (IMEx) consortium and follow its curation rules to capture interaction data  [41, 43]. Interaction partners of LOX and LOXLs were also identified in BioPlex 2. 0 and 3. 0 datasets  [35, 44]  generated by affinity chromatographymass spectrometry (AP-MS) profiling the entire human ORFeome collection in human embryonic kidney cells (HEK 293) and in a human colon cancer cell line (HCT 116). C-terminally FLAG-hemagglutinin-tagged baits from the human ORFeome v8.
Data Visualization The networks were visualized using Cytotoscape 3. 8. 0 [76] , and the Venn diagram was drawn with a freely available tool (http://bioinformatics. psb. ugent. be/webtools/Venn/). In order to color-code protein cellular location, their annotations from Gene Ontology (GO term "Cellular Component"  [77, 78] ), manually checked with UniProtKB keywords, were retrieved from UniProtKB  [79]. Matrisome and matrisome-associated annotations were retrieved from the Matrisome project (http://matrisome. org/) [80].
All the mutations that confer selectivity to greenFAST and redFAST occur in the N-terminal fragments (named greenNFAST and redNFAST respectively), meaning that the C-terminal fragment (named CFAST11) is the same for the two split systems. Split-greenFAST and split-redFAST were assessed directly in mammalian cells by confocal microscopy. We measured the association of the split constructs by inducing the formation of heterodimers of FRB and FKBP with rapamycin (Figure  4a, b, c ). Upon addition of 500 nM rapamycin, split-redFAST and split-greenFAST rapidly reassembled into their cognate fluorogen:protein complex in the presence of both fluorogens with an average 6-and 8-fold increase in fluorescence (Figure  4c ). Furthermore, the fluorescence time course revealed that split-greenFAST and split-redFAST show similarly rapid kinetics (seconds-minutes) as has previously been observed for splitFAST (Extended Data Fig.
Physico-chemical Measurements Steady state UV-Vis absorption spectra were recorded using a Cary 300 UV-Vis spectrometer (Agilent Technologies), equipped with a Versa20 Peltier-based temperature-controlled cuvette chamber (Quantum Northwest) and fluorescence data were recorded using a LPS 220 spectrofluorometer (PTI, Monmouth Junction, NJ), equipped with a TLC50TM Legacy/PTI Peltier-based temperature-controlled cuvette chamber (Quantum Northwest) operated with Felix GX software. Thermodynamic dissociation constants and quantum yield measurements for HMBR or HBR-3,5DOM were determined as previously described using either FAST:HMBR or FAST:HBR-3,5DOM as a reference  [7] [8] [9]. Thermodynamic dissociation constants were.
ACKNOWLEDGEMENT We thank  K. D. Wittrup , for providing us with the pCTCON2 vector and the EBY100 yeast strain for the yeast display selection. We also thank the flow cytometry facility  CISA  ( Cytométrie Imagerie Saint-Antoine ) of  UMS LUMIC  at the  Faculty of Medicine of Sorbonne University , and, more particularly,  Annie Munier  for her assistance. This work has been supported by the  European Research Council  ( ERC-2016-CoG-724705 FLUOSWITCH  to A. G. ,  ERC  StG  714688  NanoCellActivity to P. D. , and ERC-2019-CoG-863869 FUSION to S. P-P. ), the  Wellcome Trust   Core Award  ( 203141  to S. P-P), and the  Research Foundation-Flanders  ( G0B8817N  to P. D. and  1514319N  to B. M. ) The authors declare the following competing financial interest: A. G. is co-founder and hold equity in Twinkle Bioscience / The Twinkle Factory, a company commercializing the FAST technology.
One of its main advantages is a higher throughput in sample preparation, which will prove important given the synchrotron upgrades taking place around the world. Data for the scales was collected on the beamline ID16B 8 at the ESRF. The phase contrast image technique used is holotomography which means that 4 tomographies are acquired, each one having a propagation distance slightly different than the first one. The difference in the sample position was 1 mm between the first and second scan, 5 mm between the second and the third and 15 mm between the third and the fourth. For each tomography scan, 3000 projection images with a field of view of 64 µm horizontally and 54 µm vertically were acquired on a PCO Edge 5. 5 CMOS camera which has a pixel size of 25 nm. The X-ray incident beam had an energy of 17.
The raw projection images were processed to the final 3D data set on the ESRF cluster, using standard ESRF instrument routines which included the GNU Octave programming environment (http://www. octave. org), the public domain image analysis program ImageJ (http://rsbweb. nih. gov/ij/) and the Python programming language (https://www. python. org/). These routines included different steps: (i) the alignment of the projection images at each propagation distance, (ii) phase retrieval calculation, (iii) axis alignment, and (iv) final reconstruction of the 3D volume. The alignment of the projection images consists of the resizing and alignment of the individual radiographs recorded at the four distances. A specialised padding scheme, which takes into account the geometry of the sample cross-section as determined from the fourth distance with the largest field of view, is also employed at this stage to reduce the artefacts arising from the edges of the scale which were sometimes temporarily outside the field of view  9.
Those images are used to obtain the final 3D volume (2560 × 2560 × 2160 pixels) where each pixel represented a single greyscale value representative of the refractive index of the material, using a filtered back projection algorithm with the ESRF High Speed Tomography in Python (PyHST2) software  11. The greyscale level of a particular 3D pixel, is directly proportional to the electron density and therefore the mass density in that location  12. The greyscale values for the reconstruction were stored as 32-bit single precision floats which resulted in a matrix size of ~56 GB per completed tomography. assembly and alignment. Once all the final tomography scans had been reconstructed, the next step was to assemble the individual overlapping scans into a single matrix containing an entire beetle scale.
Filtering and thresholding. The target dataset for the single scales was a matrix thresholded into pixels of either chitin (dark pixels) or air (light pixels). This involved several image processing steps  13  as follows. First, the matrices were saturated such that the pixel values of the darkest and the lightest 0. 5% of the greyscale values were set to the maximum greyscale value for the 99. 5 percentile and the minimum greyscale value for the 0. 5 percentile as shown in Fig. 2a, b. This was carried out because these extreme greyscale values can most likely be attributed to experimental noise. Second, the matrices were filtered using a non-local means denoising filter (Fig. 2c ) which effectively preserves sharp edges in the images while successfully reducing the ring artefacts from reconstruction  14.
This algorithm has two main input parameters, the patch size and the patch distance. The patch size, in pixels, tells the algorithm the patch size used for denoising while the patch distance tells the algorithm the maximum distance from the pixel of interest to search for similar pixels  15. This algorithm is freely available as a part of the scikit-image processing module for Python  16. The non-local means denoising filter is computationally intensive; as the matrices to be filtered were ~25 and 40 GB respectively for the Cyphochilus and L. stigma scales, it was not possible to filter the matrices on a single central processing unit (CPU). Instead, each matrix was divided into overlapping segments which could be run independently. The patch size used for filtering the tomography data was 5 pixels and the patch distance was 7 pixels.
Data Records The data sets referenced in this paper have been deposited at Figshare  18. The following matrices are provided for both the Cyphochilus (CY) and L. stigma (LS) scales. The original consecutive overlapping tomography scans, post reconstruction on the ESRF cluster, are listed in numerical order (CY_1-6 and LS_1-8). The complete scale matrices with the original greyscale values after being assembled and aligned (CY_greyscale and LS_greyscale). The complete thresholded scale matricies post saturation, filtering and thresholding (CY_thresholded and LS_thresholded). Two unique slabs of the internal scale cut from the complete matrix (CY_slab_1-2 and LS_slab-1-2) and a representative 5 µm cube of the internal structure (CY_cube and LS_cube) which had the least mean squared error compared to the average directional correlation functions of the 275 cubes sampled.
Usage Notes The data sets which are <1. 5 GB, which include the slabs and cubes cut from the full scale matrices, have been simply left as. npy files and can be loaded into Python using numpy. load(). For the larger data sets, all of the greyscale data has been zipped before uploading into a Python. npz format. To load the greyscale data simply load the. npz file using numpy. load() which will load the data into a dictionary like object with a single element, the greyscale matrix, which can be called with the default key of 'arr_0'. An example script to load greyscale matrices has been provided in the README. txt. The thresholded data sets have been packed into uint8 arrays prior to zipping. Therefore, once loaded from the. npz file, the thresholded matrix must be unpacked using numpy. unpackbits() and then reshaped into a 3D matrix according to the appropriate values in Tables  1  and 2.
acknowledgements This project was co-funded by  Innovate UK  in partnership with  AkzoNobel  and the  University of Sheffield  (Grant Number  33692-239251 ). We acknowledge the  Science and Technologies Facilities Council  for access to facilities at the  ESRF  via experiment SC 4363. We would also like to acknowledge the artist Paul Evans who was part of the experimental team that measured the beetle scale structures,  Nicholas Mullin  who helped design the procedure for mounting the beetle scales and  Mike Croucher  who provided the initial access to the high memory nodes used to process the data.
Code availability The ESRF High Speed Tomography in Python (PyHST2) software which was used to reconstruct the phase images is open source and can be found at: https://software. pan-data. eu/software/74/pyhst2. The current pipeline for processing the raw data prior to its use in the PyHST2 algorithm is a large collection of scripts in MATLAB, Python and GNU Octave which makes it difficult to bundle into a single tomography pipeline. However, the ESRF is currently working to convert all of the scripts to Python to create a completely open source pipeline, though additional computing power, such a high performance computing cluster will likely be necessary. All additional image processing was done using open source Python libraries; these have been noted at the appropriate stages in the text.
His work concerns selection problems with the goal of minimizing the number of queries that are necessary to find the optimal solution. After the initiation by Kahan  [32]  on selection problems. further problems have been studied in this uncertainty model including finding the k-th smallest value in a set of uncertainty intervals  [19, 31, 32]  (also with non-uniform query cost  [19] ), caching problems in distributed databases  [50] , computing a function value  [35] , and classical combinatorial optimization problems, such as shortest path  [18] , finding the median  [19] , the knapsack problem  [26] , and the MST problem  [17, 23, 43]. While most work aims for minimal query sets to guarantee exact optimal solutions, Olsten and Widom  [50]  initiate the study of tradeoffs between the number of queries and the precision of the found solution.
* This research was carried out in the framework of Matheon supported by  Einstein Foundation Berlin , the  German Science Foundation (DFG)  under contract  ME 3825/1  and  Bayerisch-Französisches Hochschulzentrum (BFHZ). Further support was provided by  EPSRC  grant  EP/S033483/1  and the  ANR  grant  ANR-18-CE25-0008. The second author was supported by a study leave granted by  University of Leicester  during the early stages of the research. A preliminary version of this paper appeared in The 9th  Innovations in Theoretical Computer Science Conference (ITCS) ,.
We quantified 5265 protein from HeLa cell lysates and 6068 from HeLa cells. After applying filtration criteria based upon relative peptide abundance changes and fixed statistical significance thresholds, we identified 52 putative drug targets with at least one differentially abundant peptide in lysates and 37 candidate proteins from the treatment of live cells, including the known target FKBP1A in both cases (Fig. 1a ) (Supplementary Data 1, 2). Since rapamycin is known to interact with only one protein, FKBP1A, but we identified targets in addition to FKBP1A in both experiments, we realized that this workflow with one drug dose may not discriminate unequivocally true protein interactors and false positive identifications. This is likely due to the high complexity of the human proteome and we therefore sought an expanded approach that emphasizes target prioritization amongst potential targets identified.
1B, 2A ). Each of these four LiP-Quant score components contributes to the predictive power of the approach, outperforming either experiments with only one drug dose (LiP-SMap) or dose-response correlation alone (LiP-Quant score component I) (Supplementary Fig. 2B ). We observed that LiP-Quant scores show a bimodal distribution across the positive control datasets with target protein peptide scores clearly enriched in the high-scoring peak of the distribution. Therefore, we defined putative target peptides as those with a LiP-Quant score above 1. 5, which is the median score of non-target peptides plus three standard deviations (Fig. 1b , Supplementary Fig. 3 , Methods section: guidelines for interpreting LiP-Quant results). This threshold equates to an average positive predictive value (PPV) of 30% across positive control experiments, although higher PPVs can be achieved by applying more stringent thresholds (e.
g. the top 10 peptides yields an average PPV of 70%) (Supplementary Fig. 2C ). After validation, we tested the LiP-Quant pipeline with rapamycin in HeLa lysates using a project specific spectral library of 8213 proteins. Of the 110,668 peptides mapping to 5295 proteins quantified by DIA-MS, multiple LiP-Quant peptides, including the 5 top-scoring peptides, mapped to FKBP1A (Fig. 1d ) (Supplementary Data 3). This made FKBP1A the highest-ranking candidate protein target and showed the ability of LiP-Quant to identify correct drug targets. To further test the LiP-Quant pipeline we selected a different compound, FK506, which is also known to target FKBP1A. Here, we obtained very consistent results to those of rapamycin as again the top 5 LiP-Quant peptides mapped to FKBP1A (Supplementary Fig. 4A ).
Within the LiP-Quant staurosporine dataset a higher median protein sequence coverage was observed among successfully detected kinase targets (Fig. 2c ) when compared to the entire proteome but in particular relative to undetected kinases. This suggests that high sequence coverage is crucial for the identification of drug targets by LiP-Quant. From this we hypothesized that peptide under sampling (i. e. lower protein coverage) could lead to reduced kinase target identification, and thus attempted to increase proteome sequence coverage via longer LC-MS gradients (Deep LiP-Quant) on the same samples. Here we found that the performance of LiP-Quant for target identification improved, as the Area Under the Curve (AUC) of receiver operating characteristic curves increased from 0. 76 to 0. 81 (Fig.
4d ). We calculated the center of mass of the #6 and #7 ranking LiP-Quant peptides to estimate the position of the BAYE-004 binding site within Bcin06g02870. Here we considered two LiP-Quant peptides for the center of mass calculation, as there were only two high scoring LiP-Quant peptides that could be used. The putative binding site of the drug maps in the ATP-binding site of Bcin06g02870, which is also a common binding site for kinase inhibitors (Fig. 4e , Supplementary Data 3). Finally, we performed a LANCE Ultra kinase assay (Perkin Elmer) to measure the phosphorylation activity on a peptide substrate of a purified MBPtagged version of Bcin06g02870. BAYE-004 was found to inhibit Bcin06g02870 activity with a low nanomolar IC50 of 12. 5 nM, confirming the relevance of the target identified (Fig.
Samples were block randomized before acquisition. PicoFrit PicoTip Emitters (SELF/P Tip 10 µm) were packed with ReproSil-Pur C18-AQ 1. 9 µm phase (Dr. Maisch, Ammerbuch-Entringen Germany) and connected to an Easy-nLC 1200. All experiments were run on a Q-Exactive HF mass spectrometer (Thermo Scientific) with the exception of the calyculin A dataset, which was acquired on a Q-Exactive HF-X. Peptides were separated by a 2 h segmented gradient at a flow rate of 250 nl/min with increasing solvent B (0. 1% formic acid, 85% ACN) mixed into solvent A (0. 1% formic acid, 1% ACN). Solvent B concentration was increased from 1% after 3 min according to the following gradient: 4% over 3 min, 5% for 3 min, 7% for 4 min, 9% for 5 min, 11% for 8 min, 16% for 19 min, 26% for 41 min, 29% for 9 min, 31% for 6 min, 33% for 5 min, 35% for 4 min, 38% for 4 min, 40% for 3 min, 44% for 3 min, 55% for 3 min and 90% in 10 s.
This final concentration was held for 10 min followed by a rapid decrease to 1% over 10 s, which was then held for 5 min to finish the gradient. A full scan was acquired between 350 and 1650 m/z at a resolution of 120,000 (ACG target of 3e6 or 7 ms maximal injection time). A total of 37 DIA segments on HF were acquired at a resolution of 30,000 (ACG target of 3e6 or 47 ms maximal injection time) and 42 on the HF-X (ACG target of 3e6 or 55 ms maximal injection time). The normalized collision energy was stepped at 25. 5, 27, 30. First mass was fixed at 200 m/z. For DDA (Data Dependent Acquisition) runs from the same samples, peptides were separated by the same 2 h segmented gradient as utilized above for DIA runs with the exception that the final 1% solvent B flow was held for 4 min and 40 s (rather than 5 min).
All experiments were run on a Q-Exactive HF mass spectrometer (Thermo Scientific) with the exception of the rapamycin (Q-Exactive HF-X) and FK506 datasets (Q-Exactive). A top 15 method was used across a scan range of 350 to 1650 m/z with a full MS resolution of 60,000 (ACG target of 3e6 or 20 ms injection time). Dependent MS2 scans were performed with a resolution of 15,000 (ACG target of 2e6 or 25 ms injection time) with an isolation window of 1. 6 m/z and a fixed first mass of 120 m/z. Peptide samples generated from Saccharomyces cerevisiae were analyzed on an Orbitrap Q Exactive Plus mass spectrometer (Thermo Fisher Scientific) equipped with a nano-electrospray ion source and a nano-flow LC system (Easy-nLC 1000, Thermo Fisher Scientific). MS data acquisition in DDA and DIA modes was essentially carried out as in Piazza et.
al. 2018. Mass spectrometric data analysis. DIA spectra were analyzed with Spectronaut X (Biognosys AG)  40  using the default settings. In brief, retention time prediction type was set to dynamic iRT (adapted variable iRT extraction width for varying iRT precision during the gradient) and correction factor for window 1. Mass calibration was set to local mass calibration. The false discovery rate (FDR) was estimated with the mProphet approach  41  and set to 1% at both the peptide precursor and protein level. Statistical comparisons were performed on the modified peptide level using fragment ions as quantitative input. The DDA spectra were analyzed with the SpectroMine (Biognosys AG) software using the default settings with the following alterations. Digestion enzyme specificity was set to Trypsin/P and semi-specific.