dataesr/scientific-paragraphs-categorization huggingface dataset:
The secondary antibody anti-mouse-IgG-HRP (Sigma-Aldrich, 1 : 2000) was added in 3 % milk in TBS-T at room temperature for 1 h. Afterward the membrane was washed again. The detection was performed via enhanced chemiluminescence (ECL Prime) after incubation for 5 min in the dark with a FUSION-SL (PEQLAB) and the FUSION-CAPT software. After detection of acetylated tubulin the whole procedure was repeated with the primary antibody antiacetyl-histone H3 (Millipore 06. 599, 1 : 2000) and the secondary antibody anti-rabbit IgG-HRP (Sigma-Aldrich, 1 : 5000) to detect the acetylation of histone H3 and again with the primary antibody antiGAPDH (Sigma-Aldrich 69545-200UL, 1 : 5000) and the secondary antibody antirabbit IgG-HRP (Sigma-Aldrich, 1 : 10 000) to control the loading amount.
Cellular testing (fluorescence microscopic and flow cytometric analysis of LysoTracker® red staining) 54a] Ligand preparation: MOE (version 2014. 09, Chemical Computing Group, Montreal, Canada) was used to generate the molecular structures of all compounds. The ligands were prepared for docking using the LigPrep tool as implemented in Schrödinger's software (version 2018-1), where all possible tautomeric forms, as well as stereoisomers, were generated. They were subsequently energy minimized using the integrated Optimized Potentials for Liquid Simulations (OPLS_2005) force field. A maximum of 64 conformers of prepared ligands were calculated with ConfGen using the default settings (Schrödinger's software version 2018-1).
Protein preparation: The crystal structures of HDAC10 (drHDAC10; PDB ID: 6UHU), HDAC1 (hsHDAC1; PDB ID: 5ICN), HDAC6 (hsHDAC6; PDB ID: 5EDU) and HDAC8 (hsHDAC8; PDB ID: 2V5X) were downloaded from the Protein Databank (PDB; www. rcsb. org)  [64] with the exception of water molecules occupying the catalytic pockets that were used for the docking procedures. Further preparations of the protein structures were done using the Protein Preparation Wizard of Schrödinger's software (version 2018-1). Bond orders were assigned and hydrogen atoms added, and the hydrogen bond network was subsequently optimized. The protonation states at pH 7. 0 were predicted using the Epik-tool. The structures were finally subjected to a restrained energy minimization step (rmsd of the atom displacement for terminating the minimization was 0.
3 Å) using the OPLS2005 force field. Docking in HDAC6 with monodentate zinc coordination: The cocrystallized ligand and three water molecules (HOH921, HOH999 and HOH1011) from PDB ID 5EF7 were retrieved and inserted into the herein used crystal structure of HDAC6 (PDB ID 5EDU) prior the protein preparation and minimization steps mentioned above. Docking in HDAC6 with bidentate zinc chelation: three water molecules (HOH1015, HOH1006 and HOH1083) were retrieved from PDB ID 6CSQ were retrieved and inserted into the herein used crystal structure of HDAC6 (PDB ID 5EDU) prior the protein preparation and minimization steps mentioned above.
Acknowledgment We thank the  Deutsche Forschungsgesellschaft (DFG,, W. S. ( Si868/22-1  project number  469954457 ), I. O. ( Oe542/2-1 ) and  INST   39/931-1  for co-financing of the NMR console) for funding. The work of I. O. and J. R. was supported by the H. W. &J. Hector foundation (Grant reference number:  M91 ). Thanks to the  National Institute of Health (NIH)  for funding D. W. C. ( GM49758 ) and S. A. S. ( F32 GM125141 ). C. R. was supported by institutional funds from the  Centre National de la Recherche Scientifique (CNRS) , the  Institut National de la Santé et de la Recherche Médicale (INSERM)  and the  Université de Strasbourg. This work is based on research conducted at the  Northeastern Collaborative Access Team beamlines , which are funded by the  National Institute of General Medical Sciences  from the  National Institutes of Health  ( P30 GM124165 ).
12  predictive modelling is a discipline where algorithms are deployed to generate estimates for a specific target output. these models learn to identify the relationships between a set of features, also called predictors (patient age, Bmi, level of fitness, etc. ) and the selected target (e. g. occurrence of a myocardial infarction). statistical models and machinelearning techniques identify complex target-predictors relationships in a specific dataset. 13  predictive models are usually deployed in contexts where the prediction of the output is difficult or time-demanding. When the likelihood of an adverse event in the future is high, a proactive intervention can be deployed to modify the course of action and avoid such an adverse event. the increasing availability of large digital healthcare datasets has facilitated the application of predictive models in different healthcare settings such as cognitive impairment detection,  14  prediction of heart failure patient readmission,  15  or medical image segmentation.
16 he combination of digital healthcare datasets and 3d images can generate new factors that may contribute to improved prediction of operating time for complex tKa. 3d imaging has shown improvements in surgical accuracy and preoperative planning,  [17] [18] [19]  which may translate to more accurate operating time predictions. thus, the purposes of our study were to design and validate a predictive model to estimate operating time for tKa based on demographic data, and evaluate the added predictive power of Ct scan-based predictors and their impact on the accuracy of the predictive model. Our two hypotheses were: demographic data can be used to generate operating time prediction in tKa; and adding Ct-based 3d image patient-specific features will improve operating time prediction accuracy.
Methods Data sources and population. a retrospective study was conducted on 1,061 robotic-assisted primary tKas from January 2016 to december 2019. data were obtained from two arthroplasty fellowship-trained orthopaedic surgeons at two different hospital centres within the Usa (south County health, Rhode island, and the Orthopaedic Center, Oklahoma). the stryker performance solutions (sps) hospital Reported Outcomes database (stryker, Usa) was used, which consists of de-identified real-world data from electronical health records including patient demographic information and patient-specific 3d data from Ct scans. the robotic-assisted maKO (stryker) system is an image-based system, using a preoperative Ct scan to create a 3d model of the patient's bony anatomy used for the surgical planning.
this robotic platform provides real-time 3d feedback to the surgeon on the implant position and limb alignment, as well as virtual flexion and extension gaps. this robotic-assisted system is semiautonomous, with a robotic arm extended by a saw, controlled and manipulated by the surgeon. demographic data included age, sex, height, weight, and laterality. preoperative Ct scans were obtained in this same patient cohort, and femoral and tibial axis angles (medial proximal tibial angles (mpta) and lateral distal femoral angles (ldfa))  20  and osteophyte volume were automatically extracted from the 3d images as described below. Operating time was defined as incision to closure, which were obtained from each hospital's electronic health record system. the time prediction models were trained using two datasets: demographic data only, and demographic and 3d patient anatomy (e.
the partition used orthogonal planes derived from the epicondylar axis and a femur shaft centre point for the femur, along with an axis connecting the deepest point per tibial plateau (resection landmarks) and a tibia shaft centre point for the tibia (figure  2 ). Model development. figure 3 outlines the architecture used to develop each model. prior to modelling, the input data were analyzed using cross-correlation tabulation, and any inputs with > 0. 8 correlation were removed to ensure the model was parsimonious. for example, Bmi correlated 0. 84 with weight (figure  4 ), and as Bmi is a function of height and weight, removing this feature would simplify the dataset. the initial dataset underwent an 85:15 split between train and test. as this dataset was skewed towards one surgeon (885 to 176), the split was stratified to ensure a representative proportion of cases from both surgeons.
three models were trained on these datasets: linear regression, random forest regression, and CatBoost regression. We use the training dataset to train and tune the model's hyperparameters. the training dataset underwent a five-fold cross-validation wherein the training dataset was split into five subsets (one subset for validation and four for training the algorithm). this was repeated five times until each subset has been used once for validation (figure  3 ). each five-fold cross-validation was performed with a different set of hyperparameters, and the optimum combination of parameters was then retained for the next phase. after hyperparameter tuning per model and dataset, we trained the final model on the original test split with the defined hyper-parameter values. to compare the R² values of the model, we used the bootstrap resampling technique.
Model results. the R² values were significantly improved across all three machine-learning models (linear Regression, Random forest Regression, and CatBoost Regression) once 3d patient-specific information was implemented into the dataset (p < 0. 001 across all three models). Between all three models, the CatBoost regression model achieved superior performance with an R² value of 0. 764 in dataset 2 and 0. 722 in dataset 1 (table iii). the random forest model achieved an R² value of 0. 732 in dataset 2 and 0. 705 in dataset 1; the linear regression model achieved an R² value of 0. 708 in dataset 2 and 0. 686 in dataset 1 (table iii). We derived the optimum model (CatBoost Regression on dataset 2) using the entire training dataset (original 85% split without bootstrapping) with the optimal tuned hyperparameters from the 'training, tuning and evaluation segment' of the process (figure  3 ).
17 these data have shown a demonstrable benefit for tKa with patientspecific instrumentation (psi). 18 With the development of image-based robotic-assisted tKa, the benefits of preoperative 3d imaging have been also realized. several studies suggest that image-based robotic-assisted tKa improves surgical accuracy. 19, 26 this study continues to show additional advantages of 3d patient-specific data with a specific focus on operating time prediction. two types of data were derived from 3d patient scans in our operating time prediction model: patient morphology (knee joint angles) and osteophyte volume. the former can be measured on preoperative radiographs. the patient morphology was defined in our study by the angles of frontal deformity, as measured usually on standard radiographs. therefore, these measurements on Ct scans were likely not very helpful in improving the operating time.
27, 28 no study has assessed the impact of osteophytes on operating time. Our predictive model was based on accurate quantification of osteophyte volume and their localization, which is only attainable if derived from 3d images. these data represent a proxy for exposure difficulties, potentially limited ROm, and time for osteophyte removal. 21 ur findings should be considered in light of the key limitations of this study. the dataset is composed of a limited number of cases which were performed by only two orthopaedic surgeons. a dataset containing more tKa cases, and encompassing several surgeons and centres, is warranted to extend its use to other geographies and healthcare systems. furthermore, this was not a comparative study using demographic data and preoperative radiographs to assess the benefit of 3d Ct over conventional 2d radiographs.
were identified in the 774. 53 to 774. 78 m interval using a Tescan Vega II XMU scanning electron microscope (SEM). Geochemical analyses were performed with a 40 mm 2 EDAX Apollo SSD 40 energy dispersive spectrometer (EDS). Concentrations of major and minor oxides were measured via secondary electron beam X-ray microanalysis with an accelerating voltage of 15 kV, focused beam (1 to 2 m) operated at a constant distance from the specimen with a count time of 100 live seconds. The net peak intensities were converted to oxide weight percent using the semiquantitative EDAX GenesisTM software. To improve analytical precision, mounts were polished and cleaned and eleven particles were analyzed using SEM-EDS and a Cameca SX-100 electron microprobe equipped with 5 wavelength dispersive spectrometers (WDS).
The instrument was calibrated with a suite of appropriate mineral standards; peak count times were 30 s for all elements except Mg (50), Mn (50 s), Na (12 s), Cl (50 s), and P (50 s). Reference glasses from the Max Plank institute (MPI-DING suite) bracketing the possible chemistries were also analyzed alongside the NGRIP tephra. Full details on accuracy and error, plus datasets and secondary standards for the tephra analysis are presented in supplementary information and in Data S6 to S13.
Acknowledgments We thank the  NGRIP, GRIP , and GISP2 communities;  M. Twickler , and the  NSF-ICF  for providing access to archived GISP2 samples;  J. P. Steffensen ,  A. Svensson , and staff from the  Niels Bohr Institute , for providing access to archived GRIP and NGRIP samples. Gwydion Jones ,  Gareth James ,  Laura Robinson , and  Victoria Smith  for assistance with the ice-core sampling, tephra preparation, and geochemical work. This work benefited from participation by some authors in the  Past Global Changes Volcanic Impacts on Climate  and Society working group. M. Si. also thanks  Peter Abbott  and  Eric Wolff. C. P. thanks  Peter Brewer ,  Matthew Salzer ,  Peter Kuniholm , and  David Frank.
Methods and materials This section details the collection of the "meta"-meta-data and the strategy used to convert it into pairwise similarity matrices along a number of dimensions. It also presents the model used to regress differences in the reported distribution of Zipf estimates by meta-properties, controlling for technical and territorial specifications. The material of the present study consists in a corpus of studies which have all published estimations of Zipf's power law exponent on empirical city size distributions. It makes use of the openly available database MetaZipf 4 , which contains 1962 such empirical estimations from 86 studies, along with their specifications. The 86 studies have been selected to fulfill three criteria: "they contain at least one estimate of the rank-size exponent based on population ; the regression is made on empirical urban data; the regression model is bivariate (i.
Collecting full-texts For an article from the MetaZipf database to be included in "the corpus", it has to available in openaccess or accessible with an extensive institutional subscription, in a machine-readable format. Additionally, only published journal articles written in English were selected, in order to run a coherent textual analysis. This excludes texts in other languages and formats, such as books and dissertations. This choice is detrimental to the recognition that science is plural in forms, languages and origins. However, it did not affect the original sample too much, since most references in MetaZipf were already predominantly in English and in an article format. The corpus is thus composed of 66 full-texts of English-written articles. Out of the original document, only the body of the text was retained.
The advantage of our system is that it provides a single category for each source, whereas Scimago has a varying number of entries for different journals, and no entry for French journals like "Région et Développement" which is externally cited 6 times in our corpus, or for dissertations and World bank databases. After applying this ad-hoc translation to all external references, we can see a stark difference between the distribution of corpus articles by disciplinary field and that of their bibliography (table  1 ). Indeed, while most corpus are published in regional science and economics journals, but their framework of reference comes primarily from economics and geography, or at least from articles published in economics and geography journals. Secondarily, corpus articles draw from the statistics (for estimation methods and tools) and regional science literatures.
Control with PML profile from total extract in Supplementary Fig. 1j. g, h Representative of n = 6 mice per condition over three independent experiments. Source data are provided as a Source Data file. To further support the relevance of our APL model for the identification of PML NB-regulated proteins, we performed the same SUMO proteomic analyses using mice engrafted with Pml-deficient APL cells (Supplementary Data 4). We compared Pml -/-APL cells expressing or not His 10 -SUMO2, from leukemic mice treated or not with arsenic for 3 h. Among the 554 His 10 -SUMO2 targets in Pml -/-APL mice, 86 were differentially conjugated in arsenic-treated mice. Remarkably, only a few arsenic-modulated His 10 -SUMO2 targets identified in the previous Pml +/+ APL cohorts also varied upon arsenic treatment in Pml-deficient APL mice, PML/RARA being the top enhanced one (Supplementary Data 4).
01 (right). j Fold increase of Etn-ERV2 in Pml -/-expressing KAP1-YFP and treated or not with ML792, normalized to GAPDH and are relative to untreated Pml +/+. Mean +/-SD, *p < 0. 05, two-tailed unpaired Welch's t-test. g-j Representative of three independent SD of n = 4 biological replicates experiments. Source data are provided as a Source Data file. signals were sorted by flow cytometry. HeLa and mES cells were transfected with pBOS-KAP1-YFP plasmid using lipofectamine 2000 (Invitrogen, #11668019) and Lipofectamine 3000 (Invitrogen, #L3000001), respectively. KAP1-YFP positive mESCs were selected using 6 μg/ml basticidin (Thermo Fischer #R21001); and YFP + living cells were analyzed and sorted by flow cytometry (BD FACSAria2, v9. 0. 1), after FCS/SSC gating. Cells were treated with 1 μM As 2 O 3 (Sigma, #01969), 1 μM retinoic acid (Sigma, # R2625), or 1 μM ML792 (Medchemtronica # HY-108702) when requested.
Pml E167R/E167R mice were obtained by CRISPR/Cas9 genome edition, performed on BALB/cByJ zygotes, using TAKE methods  81. Briefly, three to four-week-old BALB/cByJ females were super ovulated using CARD HyperOva (Cosmo bio, #KYD-010-EX) and human Chorionic Gonadotropin, Sigma; #CG-10) and then mated with males (8-20 weeks) to get zygotes. crRNA, TracrRNA, ssDNA, and Cas9 nuclease were purchased from IDT and electroporated (NEPA21; Sonidal) to introduce Pml point mutation encoding E167R substitution (Supplementary Data 7). Genotyping performed as described above and PCR products were sequenced (Supplementary Data 7). BALB/c Pml E167R/E167R mice were then crossed with C57BL/6 Sumo1 His6-HA/His6-HA mice to obtain Pml E167R/-;Sumo1 His6-HA/wt heterozygote mice. For APL mouse model, leukemic blasts of derived from h-MRP8-PML/RARA transgenic mice  82  were transduced with MSCV-IRES-EGFP-His 10 -SUMO2 or -His 10 -SUMO2K/R and transplanted by intravenous (i.
Secondary antibodies: anti-Rabbit Alexa Fluor 488 (1/200; Cat#A-11034, Sigma), anti-Mouse Alexa Fluor 594 (1/ 200;Cat#A-115-585-003, Jackson ImmunoResearch), anti-Rabbit Alexa Fluor 647 (1/200; Cat#A-111-605-003; Jackson ImmunoResearch). Proximity ligation assay was performed as previously described  9  with anti-E4BP4/NFIL3 (1/1000; Cat# 14312, Cell signaling), anti-PML(1/ 2000; Cat#MAB3738, Millipore) and anti-MORC3 (1/1000; Cat#NBP1-83036, Biotechne) antibodies. Image acquisitions were done by confocal microscopy (Spinning disk (CSU-WI, Yokogawa) and LSM 880 (Zeiss)) or by using an Axiovert-200 inverted-fluorescence microscope (Zeiss). Images were analyzed with FIJI software. MEFs stably expressing GRX1-roGFP2 or GRX1-roGFP2-SP100 redox sensors were seeded in glass-bottom µ-dish (Biovalley).
Proteins were detected using Dura ECL or Super Signal West Femto (ThermoFisher), using Vilber Fusion-Fx (BIO-1D v15. 07). Quantifications of proteins of interest (as indicated in the figures) were relative to loading control. All uncropped blots are provided within the Source Data file. For His 6 -HA-SUMO1 purification, frozen livers were homogenized as above and lysed with 6 M guanidine-HCl, 100 mM Na 2 HPO 4 / NaH 2 PO 4 pH 8. 0, 10 mM imidazole pH 8. 0 and boiled at 95 °C for 5 min. After sonication, the protein concentration was determined using TM BCA Protein Assay kit (Thermo Scientific, #23225). Lysates were equalized and His 6 -HA-SUMO1-conjugates were enriched on nickelnitrilotriacetic acid (NiNTA) agarose beads (Qiagen, #L30210) as described in  15. For dual purifications, the samples were then diluted with RIPA buffer, 0.
Mass spectrometry data acquisition Both His 10 -SUMO conjugates were processed by mass spectrometry analyses in Alfred Vertegaal laboratory (LUMC, Netherland). All the experiments were performed on an EASY-nLC 1000 system (Proxeon, Odense, Denmark) connected to a Q-Exactive Orbitrap (Thermo Fisher Scientific, Germany) through a nano-electrospray ion source as previously described  18. The Q-Exactive was coupled to a 15 cm analytical column with an inner diameter of 75 μm, in-house packed with 1. 9 μm C18-AQ beads (Reprospher-DE, Pur, Dr. Manish, Ammerbuch-Entringen, Germany). The gradient length was 120 min from 2 to 95% acetonitrile in 0. 1% formic acid at a flow rate of 200 nL/minute. For the samples from cohort 2 and 3, two technical repeats were performed and the mass spectrometer was operated in data-dependent acquisition mode with a top 5 method.
Mass spectrometry data analysis All RAW data were analyzed using MaxQuant (version 1. 5. 3. 30) according to ref. 84. We performed the search against an in silicodigested UniProt reference proteome for Mus musculus (24 March 2016) and the human PML/RARA protein. Database searches were performed with Trypsin/P, allowing four missed cleavages. Oxidation (M) and Acetyl (Protein N-term) were allowed as variable modifications with a maximum number of 5. Match between runs was performed with 0. 7 min match time window and 20 min alignment time window. The maximum peptide mass was set to 5000. Label-Free Quantification was performed using the MaxLFQ approach, not allowing Fast LFQ  85. Instrument type was set to Orbitrap. Protein lists generated by MaxQuant were further analyzed by Perseus (version 1.
Then, we excluded proteins which total amount might increase upon arsenic, based on their increase upon arsenic in the Ctrl APL background, by comparing LFQ FC in Ctrl APL mice treated or not with arsenic. We used a similar strategy for His 10 -S2 mESCs and Ctrl mESCs, with Pml +/+ or Pml -/-genotypes, treated with 2 h of As 2 O 3 1 μM. We selected proteins as being SUMO2 targets when the difference between His 10 -SUMO2 mESCs samples and their respective Ctrl mESC samples (with the same genotype) was statistically significant for p < 0. 05 (FDR) and larger than +0. 7 (log2). Comparison with PML interactant was made using the Biogrid database [https://thebiogrid. org/111384/summary/homo-sapiens/ pml. html].
Chromatin immunoprecipitation ChIP was performed with the iDeal ChIP-qPCR kit (Diagenode, #C01010180) according to the recommendation of the manufacturer. Briefly, mESC cells were crosslinked in 1% formaldehyde (Euromedex, #EM-15686) for 15 min at room temperature and quenched with 0,12 M glycine provided in the kit. The extracted chromatin was sonicated with a Bioruptor Pico (Diagenode) and immunoprecipitation was performed using 1ug of an anti-H3K9me3 (Abcam, #ab8898) and 1ug of a rabbit IgG-isotype (Diagenod, #C15410206) control antibody. Eluted DNA was quantified by real-time PCR on a Roche LightCycler by using FastStart Universal SYBR Green Master (Roche, #4913850001) with the set of primers listed in Supplementary Data 7. Data were normalized with respect to percentage of input and correspond to the mean +/-SD from at least three replicates.