dataesr/scientific-paragraphs-categorization huggingface dataset:
Structure determination by molecular replacement The X-ray diffraction data were collected at the ID-14 beamline at the European Synchrotron Radiation Facility (ESRF, Grenoble). The data were then processed by XDS (Kabsch 2010). Initial attempts to solve the structure by molecular replacement using homologous Gemin2 structure (PDB ID 4V98) were unsuccessful. Molecular replacement using the already available SpYG-box structure (PDB ID 4RG5) was successful. Molecular replacement was done using PHASER  (McCoy et al, 2007). Structure refinement was performed by PHENIX  (Adams et al. , 2010). Displayed structural models were generated using PyMOL (Schrödinger, LLC).
SAXS data collection In a SAXS experiment, a sample of macromolecular solution in a quartz capillary is exposed to collimated monochromatic beam of X-rays (ESRF BM29 beamline, wavelength λ=1. 54 Å, beam geometry 0. 7 mm x 0. 7 mm) and the intensity of the scattered beam is recorded by a 2D X-ray detector (PILATUS 1M) as a function of the scattering angle 2θ. The scattering intensity I(s) is represented as a function of the scattering vector s=(4πsinθ)/λ. Data was collected from s=0. 032 nm -1 upto s=4. 994 nm -1 ). Since the macromolecules and the solvent particles in the sample are present in all possible orientations, the resulting scattering intensity is isotropic and is radially averaged yielding a 1D scattering curve. Scattering of the pure solvent is also recorded and subtracted from the sample scattering which yields the final 1D scattering curve of macromolecules inside the solution.
SAXS data interpretation 1D SAXS curve: lnI(s) vs s or lnI(s) vs ln(s) The 1D scattering curve of macromolecules (usually represented as semi-log plot lnI(s) vs s or as double logarithmic plot lnI(s) vs ln(s), is not informative in itself but rather must be transformed into several different forms to extract the shape and size information of the macromolecules. The most important transformations, their principles, and the information they provide are described below. The SAXS data was processed using the ATSAS 3. 0 package  (Franke et al. 2017). (Data quality assessment and data representations in the results section were followed closely as the prescribed guidelines  (Jacques et al. 2010; Trewhella et al. 2017; Chaudhuri et al. 2017 )).
SAXS based structural modeling As discussed in the previous section, important biophysical parameters such as MW, Rg, Dmax, Vp and P(r) were obtained from SAXS data. But in addition to this, SAXS data (angular range of up to 2-3 nm -1 ) was used to generate low-resolution structural models (bead models) of the macromolecule ab initio using the program DAMMIF (part of ATSAS 3. 0). Typically, starting with a search volume of diameter Dmax of the particle, dummy residues/beads are assigned until optimal values of parameters of the model are achieved. These values must give rise to a computationally generated scattering curve that fits the experimental data. The process is done iteratively until a minimum value for a discrepancy term called Χ 2 between the experimental scattering intensity Iexp(s) and computationally calculated scattering intensity Icalc(s) is achieved.
SEC-SAXS strategy SEC-SAXS is a small angle x-ray scattering data collection strategy where the scattering profile is collected as the protein sample elutes from a size exclusion chromatography (SEC) column (Superdex 200 10/300, 1 ml/min, 20 °C) and passes through a capillary exposed to x-ray beam. The data is collected as thousands of frames (1800 frames, 1 sec exposure per frame) over the elution profile where each frame in the data represents the scattering of the protein solution in the capillary at that instant. Buffer frames before or after the protein peak are used for background subtraction. For each frame within the peak, the radius of gyration (Rg) and molecular weight of the eluting particles is calculated. This reveals the distribution of particle sizes within the peak(s) (monodisperse or polydisperse).
Immunoprecipitation of endogenous SpSMN complex and identification by mass spectrometry Orthologs of SMN and Gemin2 in S. pombe (previously termed Yab8 and Yip1 respectively) have long been identified and characterized  (Hannus et al. 2000; Owen et al. 2000; Paushkin et al. 2000). So far, orthologs of the remaining Gemins have not been identified. Using computational methods and yeast two-hybrid assays, orthologs of Gemin8, Gemin7 and Gemin6 have been identified in the laboratory of Dr. Remy Bordonne and were shown to be essential for viability (unpublished data). For UniProt accession codes, sequences, and multiple sequence alignments of the proteins, refer to annexure (section 8). Using GFP-SpG6 as the sole source of SpG6, the endogenous SpSMN complex as well as the Sm proteins could be purified from S.
Standard constructs: 1. SpG2  where the complexes were exposed to X-ray beam as they eluted from a Superdex 200 10/300 gelfiltration column. Data analysis was performed by ATSAS 3. 0 software package. For all data interpretation and SAXS terminologies, please refer to methods section 3. 2. 3. 3. For data collection parameters refer to annexure Table  8. 3.
SpG2 ∆arm •SpSMN-FL throughout SEC-SAXS chromatogram Existence of the oligomeric YG-box structure of SpSMN and available structure of Gemin2 subunit from Drosophila melanogaster (PDB ID: 4V98) made it possible to generate theoretical conformers of oligomeric SpG2 ∆arm •SpSMN-FL (a pool of 10,000 conformers each for tetramers, hexamers and octamers) with random conformations of SpSMN's unstructured region (residues 36-119). Each theoretical pool was individually subjected to EOM analysis using SAXS data from the peak, front and tail regions of SEC-SAXS chromatogram (Figure  4. 18 A). Fit of the computed scattering curves of the resulting final ensemble of conformers to the experimental data was analysed (Figure  4. 18 B-D). Optimized ensembles of hexameric SpG2 ∆arm •SpSMN-FL (orange) yielded excellent fits (peak:Chi 2 1.
The newly discovered SpGemin8, SpGemin7 and SpGemin6 subunits have been shown to be essential for viability (data not published). Although these subunits share low sequence identity with their human counterparts, their secondary structure predictions show high structural homology (see Figure  1. 4 A and B). The only known structure of the SpSMN complex till date is a dimeric YG-box domain of SpSMN fused to an MBP-tag (Figure  1. 5 D). In this work, SpSMN sub-complexes were recombinantly co-expressed and purified from E. coli. The sub-complexes as well as the reconstituted pentamer were characterized by gelfiltration for their oligomeric states and hydrodynamic properties. Using X-ray crystallography, the structural basis of higher order oligomers of the YGbox was elucidated. Using small angle X-ray scattering coupled with gelfiltration (SEC-SAXS), various biophysical properties such as, oligomeric states, intrinsic disorder (Guinier analysis), domain architecture (p(r) function), flexibility (Kratky plot), and conformational distributions (EOM) were determined.
Statistical Analysis Data were analyzed based on an experimental design that contained five independent replicates, each consisting of a pool of two individual plants, except at D 0 where four plants were needed to ensure enough material was available for analysis. The quantity (Q) (µg or mg) and elemental concentration (ppm) are given as the mean ± standard error (S. E) for n = 5. The NU corresponding to the difference in the whole plant quantity of each element between D 0 and D 10 was calculated by considering all random combinations between two sets of 5 replicates and thus is given as the mean ± S. E. for n = 25. Statistical analyses were performed using R software (version 3. 5. 1) (R Core Team, 2019) and RStudio (version 1. 1. 456) (RStudio Team, 2020). Significant difference in the means between control and deprived plants were determined using Student's T-test and differences among all treatments were established using one-way ANOVA and Tukey post-hoc tests.
Smooth spatiotemporal variation is possible for large datasets with a Gaussian Markov random field (GMRF) solved via integrated nested Laplace approximations (INLA)  (Lindgren et al. , 2011; Rue et al. , 2009). Recently,  Sarafian et al. (2019)  showed that GMRFs with spatially smooth daily random effects predicted daily PM2. 5 more accurately than mixed models both near to and far from monitors in the northeastern United States. GMRFs have also performed well predicting daily PM10 in northwest Italy  (Cameletti et al. , 2013)  and annual mean PM across Europe  (Beloconi et al. , 2018). Other studies have used various statistical approaches, including geographically weighted regression  (Hu et al. , 2013; Ma et al. , 2014; Song et al. , 2014; Van Donkelaar et al. , 2016) , geographically and temporally weighted regression  (Guo et al.
Air quality monitoring data We obtained hourly PM2. 5 and PM10 (µg/m 3 ) measurements from 12 regional air quality monitoring networks (federated by ATMO France) through the French Central Air Quality Monitoring Laboratory. Monitors were mostly clustered in urban areas; the number of PM10 monitors increased from 222 to 330 and the number of PM2. 5 monitors increased from 9 to 142 over the course of the study period (Figure  1 ). To limit the impact of instrument malfunctions and rare events, we excluded hourly PM2. 5 concentrations > 200 µg/m 3 and hourly PM10 concentrations > 300 µg/m 3 (0. 003% of all observations). We indexed each monitor to the containing 1 km grid cell and calculated daily mean PM for days with at least 18 hourly observations.
Methods We used a four-stage process to predict PM2. 5 and PM10 for the 4. 58×10 9 1 km grid cell-days in the study domain (Figure  2 ). Briefly, we: 1) alleviated the sparsity of PM2. 5 monitors by training a random forest (RF) to impute daily PM2. 5 at monitors that only measured PM10; 2) filled gaps in MAIAC AOD data by training monthly RFs to impute missing MAIAC AOD based on co-located EAC4 or MERRA2 AOD; 3) trained three base learners for each year (linear mixed models [LMM], Gaussian Markov random fields [GMRF], and random forests [RF]) to predict daily 1 km PM based on gap-filled AOD, meteorology, NDVI, and spatial predictors; 4) increased accuracy by ensembling the base learner predictions with annual generalized additive models (GAM) that weight the base learners according to spatiotemporal variations in their performance.
Stage 2: filling gaps in MAIAC AOD Clouds and snow cover often prevented MAIAC AOD retrieval over part of the study area. To fill these gaps, we trained RFs to predict MAIAC AOD based on co-located modelled AOD from atmospheric reanalysis. For computational reasons, we used 96 trees per forest, tuned using one spatiotemporally blocked 50% subsample of the data (section 3. 5), and trained one RF for each month in the study period (mean observations per month ≈ 4. 36×10 6 ): and εst is the error at cell s on day t. We estimated accuracy using 5-fold CV with spatiotemporal blocking (see section 3. 5) and used the RFs to predict AOD for the 3. 54×10 9 1 km grid cell-days without MAIAC AOD. 𝐴𝑂𝐷 𝑠𝑡 𝑀 = 𝑓(𝑅.
This ensured that no sameday observations from test regions were in the training set. In stages 3 and 4 (predicting daily 1 km PM with three base learners and ensembling the predictions), we estimated accuracy at unmonitored locations. We also trained the stage 4 ensembles on base learner predictions for held-out monitors (to emulate performance at unmonitored locations) and ensured that data held out to test the ensemble had not been used to train the base learners. We used multi-stage CV blocked by monitor (Figure  S2 ). In stage 3, we randomly assigned each monitor to one of 5 folds, and for each combination of three of the folds (10 combinations total) we trained the base learners and predicted for the two held-out test folds. In stage 4, we used the test predictions from all base learners that held out the same one of the five folds to train an ensemble and predict for the held-out fold.
We increased accuracy by supplementing sparse PM2. 5 observations with imputed data and by ensembling the predictions of three base learners. We confirmed that Gaussian Markov random fields predict daily PM concentration better than linear mixed models and provide the first evidence that that may also outperform random forests. Our dataset of daily 1 km PM2. 5 and PM10 is available to health and ecosystems researchers in France and may inform policy makers on air quality issues.
Acknowledgements Funding: This work was supported by the  French National Centre for Scientific Research  ( ANR PRC 2018-2020 ),  Fondation de France  (no. 00081169 ), and  Israeli Ministry of Science and Technology  ( PRC 2018(PRC -2020 )). I. H. is supported by  Univ. Grenoble Alpes  ( ANR-15-IDEX-02 ) and  Ben Gurion University of the Negev. Most data processing and analyses were performed using the GRICAD infrastructure (https://gricad. univ-grenoble-alpes. fr), which is supported by  Equip@Meso  ( ANR-10-EQPX-29-01 ). We thank the  LCSQA  for providing validated PM monitor data, INERIS for providing a spatial emissions inventory, and  NASA ,  ECMWF , Copernicus, IGN, and OpenTransportMap for their open datasets.
Conclusions This paper presented a three-phase flying capacitor topology used as shunt active power filter with PNs-based control. The simulation results using MATLAB/SIMSCAPE ELECTRICAL have proved the effectiveness of this proposed study. In fact, instantaneous power theory determines with great precision the reference currents to be injected to the grid. The control based on Petri Nets representation using two cascaded representations permits the SAPF to pursue the required reference currents. Therefore, the first PNs representation determines the needed voltage level for the output current regulation, and the second one defines the switching state to ensure the required voltage level and the capacitor voltage regulation. These strategies enhance the performance and reliability of the system.
Statistical methods The data were analyzed using Statistical Package for Social Sciences (SPSS TM, USA Version 25). Non-parametric descriptive statistics were used due to the small sample size and heterogeneity of the constructs. Kruskal-Wallis and Mann-Whitney U tests were conducted to explore relationships between variables of independence. Wilcoxon-Signed ranks test were used to explore related groups at different data points. Two attempts of follow-up were made to collect missing data. Data was analyzed based on sample size available for each measurement. Scatter plots were used as part of data analysis to analyze sensitivity of the data findings.
6h ). These data supported a similar LPC-induced ASIC3-dependent persistent pain state upon two intra-articular injections of LPC16:0 in either knee or ankle joints, which suggested a central sensitization process. The activity of spinal dorsal horn neurons was therefore recorded in vivo in painful WT mice following LPC16:0 (20 nmol) knee injections (day≥30). Recordings were obtained from spinal dorsal horn neurons receiving either non-noxious sensory inputs (low threshold neurons, LTNs) or noxious nociceptive inputs (high threshold neurons, HTNs), with receptive fields located in mice hindpaws. Brushing-evoked activity of ipsilateral LTNs, i. e. , the mean number of emitted action potentials (APs), was not modified in LPC16:0-treated mice compared to vehicle or to contralateral LTNs (fig.
Cohort of patients with different joint diseases (second cohort) Fifty patients suffering from different painful joint diseases (32 women and 18 men, average age 68. 6 years, range 26-94 years), recruited in the Rheumatology Department of Nice University Hospital, France, participated and were distributed as follow: rheumatoid arthritis (RA, n=6), chondrocalcinosis (CCA, n=12), spondyloarthritis (SPA, n=5), psoriatic arthritis (PA, n=4), gout (n=5) and OA (n=18). All subjects provided informed consent before inclusion and the study was approved by the Nice University Institutional Review Board for Research on Human Subjects. The study has been conducted in accordance with the French national regulations regarding patient consent and ethical review. The study was registered in the ClinicalTrials.
The resulting organic phases were complemented with 1 ml of a 3:48:47 (v/v/v) CHCl3/CH3OH/H2O solution, shaken for 10 min and centrifuged for 5 min. The aqueous phases were eliminated and the organic phases containing whole lipids were transferred into new glass tubes. Lipid extracts were then evaporated until dryness at 60°C under a stream of nitrogen, re-dissolved in 500 µl CHCl3/CH3OH (1:2, v/v) and stored at -20°C until further analysis. LPC species were quantified by normalizing the intensity of the protonated ion [M+H] + corresponding to each LPC individual species in the full scan spectrum to the intensity of the internal standard LPC 13:0 [M+H] + , and multiplying by the amount of the internal LPC standard. In addition, corrections were applied to the data for isotopic overlap. Total LPC amount was calculated by summing the quantities of all individual LPC species.
Monocytes, macrophages and osteoclasts lineage was labelled with a rat antibody against myeloid protein CD68 (1:1,000, AbD Serotec, #MCA1957) and peptide-rich sensory nerve fibres with a rabbit anti-CGRP (1:2,000, Calbiochem, #PC205L). After washes, sections were incubated with the corresponding secondary antibody (1:1,000, AlexaFluor ® 488 and AlexaFluor ® 546 for CD68 and CGRP, respectively, Molecular Probes, USA). After PBS washes, sections were then cover-slipped with fluorescent mounting medium (Dako) and observed with Nikon Eclipse Ni-E Quantitative analyses were performed with NIS-Elements software and at least 3-6 sections per joint per animal (n=4-5 per group) were quantified using regions of interest (ROIs) on tibiotarsal joint. Results are presented as the mean intensity of signal per joint area in one ROI (2500x2500µm) for CD68 and in two ROIs (570x570µm) for CGRP.
In vivo imaging Inflammation and bone remodeling were assessed in vivo using an IVIS Spectrum small animal imaging system (Perkin Elmer). Each mouse received 2 nmol of MMPSense 680 (150 µl; Perkin Elmer, France) or Cat K 680 FAST TM (100 µl; Perkin Elmer, France) by intravenous route at day 7 and day 14, after LPC16:0 (10 nmol) or vehicle, in order to assess inflammation and bone remodeling, respectively (n=5 in each group). Six hours or 24h later for Cat K 680 or MMP680, respectively, acquisition was performed under isoflurane anesthesia (1. 5%). Imaging was performed using the ex/em = 675/720 nm bandpass filters. Quantification analysis was performed with Living Image Software (Perkin Elmer) using fixed-sized rectangular ROI focused on ipsilateral or contralateral tibio-tarsal joint of the hindpaws.
pH of extracellular solutions were adjusted to different values with NMDG-Cl, including the control pH7. 4 solution and the test pH6. 6 solution. During patch-clamp recordings, the cell under investigation was continuously superfused with extracellular solutions using a homemade eight-outlet system digitally controlled by solenoid valves (Warner Instruments), allowing rapid changes of the immediate cell environment from control to test solutions. Electrophysiological signals generated by patch-clamped cells were amplified and low-pass filtered at 2kHz using a Axopatch 200B amplifier (Molecular Devices, UK), digitized with a 1550 A-D/D-A (Molecular Devices, UK), sampled at 20kHz and stored on a computer using Clampex software (V10. 7, Molecular Devices). Analyses of these electrophysiological signals were then made off-line using Clampfit software (V10.
No reuse allowed without permission. The copyright holder for this preprint this version posted April 15, 2021. ; https://doi. org/10. 1101/2021. 03. 29. 437487 doi: bioRxiv preprint spinal neurons that were distinguished as follow: (i) low threshold (LT) neurons only responding to non-noxious sensory stimulations and (ii) high threshold (HT) neurons that dynamically respond to noxious stimulations. The depth of spinal neurons recorded in this study was 80+/-16µm (range from 10µm to 228 µm) and 202+/-21µm (range from 10µm to 571µm) for LT and HT neurons, respectively. Activities of neurons were sampled at 20 kHz, filtered (0. 3-30 kHz band and amplified using a DAM80 amplifier (WPI, Europe), digitized with a A/D-D/A converter (1401 data acquisition system, Cambridge Electronic Design, Cambridge, UK), and recorded on a computer using Spike 2 software (Cambridge Electronic Design, Cambridge, UK).
Statistical analysis Data are presented as mean  SEM. Significant differences between the datasets are evaluated using p-values, were calculated using either parametric or non-parametric tests followed by adequate post-hoc tests (see figure legends). Statistical analyses were performed using GraphPad Prism software. Association between VAS knee, VAS global, KOOS and LPC16:0 content was assessed using analysis of variance (ANOVA) using age, gender, BMI and IL-6 as covariates. The analysis was performed using R 3. 6. 3. Differences between sets of data and correlations were considered significant when p-values were less or equal to 0. 05. (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission. LPC16:0 injections in male and female mice.
Acknowledgements We thank  Drs A. Baron ,  S. Diochot ,  J. Noel , and  M. Salinas  for helpful discussions,  V. Friend  and  J. Salvi-Leyral  for technical support, and  V. Berthieux  for secretarial assistance. We also thank  Tycho Tullberg, MD. , PhD ,  CEO of Stockholm Spine Center  during the data collection period for generous support and for providing research facilities at  Stockholm Spine Center. The authors thank surgeons  Ingemar Gladh  and  Per Gerdin  for patient recruitment and taking tissue samples during surgery at  Ortho Center, Stockholm. Furthermore , we thank  Carola Skärvinge , research nurse at  Stockholm Spine Center  for excellent logistic assistance and  Azar Baharpoor ,  Department of Physiology and Pharmacology, Karolinska Insitutet  for support and laboratory assistance.
This work was supported by the  Centre National de la Recherche Scientifique (CNRS) , the  Institut National de la Santé et de la Recherche Médicale (INSERM) , the  Association Française contre les Myopathies  (AFM grant # 19618 ), the  Agence Nationale de la Recherche  ( ANR-11-LABX-0015-01  and  ANR-17-CE16-0018 ) and the  NeuroMod Institute of University Côte d'Azur (UCA). This work was also supported by the  Conseil Regional Auvergne-Rhone-Alpes  (project  Ressourcement S3, Arth-Innov ) and Feder well as the  French government IDEX-ISITE  initiative  16-IDEX-0001  ( CAP 20-25 ). We thank the multimodal imaging platform IVIA and  CICS, Clermont-Ferrand, France , for in vivo imaging and electronic microscopy, respectively. The study has also received funding from  Stockholm County Council, Swedish research Council  ( K2013-52X-22 , 199-01-3 ), and  Eli Lilly, USA.
TACC1-A*, -J* and -S* were predicted but have not yet been identified  [15]. In addition, TACC1 has been classified into long and short TACC1 isoforms according to whether two serine-and proline-rich AZU-1(SPAZ) motifs are contained in exons 2 and 3, respectively  [16, 17]. As of December 2019, in GenBank, a total of 44 mRNA transcript variants have been described in human TACC1 (Gene ID:6867), including 31 encoding transcript variants, seven noncoding transcript variants and six predicted transcript variants. TACC1 and its variants are suggested to exhibit temporal and spatial expression patterns in human organs  [15]. Changes in TACC1 mRNA splicing patterns in cancer cells may interfere with TACC1 function  [2]. In mouse breast tumours, as an important potential activating factor in tumour formation, full-length TACC1 expression results in increased phosphorylation of ERK and PKB.