dataesr/scientific-paragraphs-categorization huggingface dataset:
E. Results The proposed methods demonstrate the efficiency of an automated rejection system using machine learning techniques, the resulting systems achieving perfect accuracy scores on both datasets, and being robust to noise added to the data. Furthermore, these methods are fast enough to be integrated into the manufacturing process of the devices. Table  3  summarizes some characteristics of the suggested approaches. The robustness study indicates that the measurement process is very sensitive to metallic elements which are not taken into account during calibration. It is also sensitive to misalignment of more than 1cm from the center point. It is, however, tolerant of an angle that could be introduced when placing these devices. This study gives the boundary conditions for the use of this method.
VI. PERSPECTIVES There are several prospects for the proposed work. A population discretisation method can focus on the transient parameters of communications (rise time, fall time, overshoot, etc. ). Design faults will inevitably have an impact on these parameters. As the proposed machine learning methods show excellent accuracy scores on the given data, and since inference times are low enough for the proposed methodology to work in real time (especially using the heatmap solution), the following points are proposed to improve the exposed work: -Increase the size of both the training and validation datasets, with the goal of reducing any uncertainty on the claimed accuracy scores. -Realize more thorough robustness tests, in order to determine which level of measurement noise can be acceptable for our systems to still work correctly.
Fragment counts. Foraminifera fragment counts were conducted on ODP Site 871 to monitor variations in carbonate preservation. Samples were sub-sampled using a splitter (in order to maintain homogeneity) and poured onto a picking tray. The fragmentation index (FI) was calculated following the approach of  Howard and Prell (1994)  and  Berger (1970)  where percentage fragment is defined as: 𝐹𝐼 = 100 *. /0123 )4 435*02. 67 89:;<= >? ?=@A:<8BCD89:;<= >? EF>G< B<CBC (4) Counts of whole intact grains and fragments of grains were conducted three times and averaged. The standard deviation (1s) of the fragmentation index is 1. 69. This approach followed that used in an early study at ODP Site 999  (Schmidt et al. 2006)  ensuring that the datasets between the two sites are comparable.
Uncertainty on the CO2 offset To constrain the offset between δ 11 B-derived CO2 and ice core CO2, each sediment age is compared to the ice core CO2 record by interpolation of the record of highest resolution (in this case the δ 11 B record onto the ice core compilation). To fully account for age uncertainty when interpolating the sediment age to the well-dated ice core record, a distribution of the ice core data was calculated within the 4s uncertainty of the d 11 B age and weighed by the respective likelihood based on the age difference between ice core and sediment core. The CO2 offset (or residual) is defined by: The linear relationships between the relative CO2 forcing DFCO2 and pH are determined with a York regression  (York et al. , 2004 ) that accounts for the uncertainty in both the independent and dependent variable (i.
Also, the baseline noise was < 750 𝑛𝑉 𝑅𝑀𝑆 and the Common Mode Rejection Ratio, CMRR, was >90dB that provided a superior signal quality. The EMG data was recorded and analyzed by using the "EMGworks® 3. 7 Software". The program is divided into two modules: the Real-Time Acquisition module (EMGworks acquisition) and the Data Analysis Module (EMGworks analysis). Bipolar electrodes positioned as suggested by currently available surface EMG recommendations  [14]  covered about 2. 5 𝑐𝑚 2 of the centro-proximal region. In our study, the two sensor units, each with two pairs of bipolar electrodes, were respectively positioned proximally and distally on the MG surface, as shown in Fig. 1. The distance between the bar electrode and the sensor's frame is 1 cm as shown in. The proximal IMU sensor was located ∼1 cm distal to the popliteal crease (total distance of the EMG bar electrode: 2 cm above the popliteal crease) with its most lateral part located ~2 cm medial to the junction between the MG heads.
E. Data Analysis All EMG data analysis was performed using EMGworks® software from Delsys Inc. Signals were high-pass filtered to remove movement and other common EMG artifacts, using a third-order Butterworth high-pass filter cutoff frequency at 30 Hz. We obtained the Root Mean Square (RMS) values from both proximal and distal medial gastrocnemius EMG samples. Through our comparative analysis in the past (100 ms vs 50 ms epoch), we found that the 50 ms epoch amplitude around the peak was greater and based on previous research  [4] , the RMS of the Medial Gastrocnemius (RMSMG) was calculated over the 50 ms epoch centered around the peak EMG during the agonist plantarflexion action of BHR (#1) (𝑅𝑀𝑆𝑀𝐺 𝐵𝐻𝑅 ) to obtain the Cocontraction Index (CCI) denominator, and during the antagonist action within IDF (#2) (𝑅𝑀𝑆𝑀𝐺 𝐼𝐷𝐹 ) to obtain the CCI numerator.
F. Statistics We calculated mean and standard deviations (SD) for the resulting readings of the BHR, IDF testing sessions and swing phase of gait using the mean data for each individual child to obtain the group means and SDs. The mean agonist and antagonist gastrocnemius RMSEMG for each subject is presented in Fig. 3  and 4  which were obtained by averaging values across the three trials. The average Cocontraction Index (CCI) was obtained by averaging CCI values across eight gait cycles for the swing phase of gait and across the three trials for the BHR and IDF. The averaged values of CCIs between these measurements were used for the statistical analyses. Wilcoxon's paired sample non-parametric test was used for differences between CCIs conventional and experimental during IDF and swing phase of gait trials.
To assess the general relevance of the investigated positions in the context of alternative arGSTs, we introduced three selected substitutions (A/V12M, C13S and N108H) into the two well-studied arGSTs PhAN9 and VvGST4. In this experiment we observed similar trends on dehydratase and GST-transferase activity (Extended Data Fig. 7g-i ) as observed for PtGSTF8, suggesting that the proposed catalytic mechanism and active site architecture are conserved between arGSTs from different plant origins. To further probe the catalytic mechanism of arGSTs, we conducted quantum mechanics/molecular mechanics (QM/MM) calculations based on the ternary complex of GSH, (-)-catechin (12) and PtGSTF8. Extensive experimental and computational studies have shown that the binding of GSH to GSTs decreases the pK a of the GSH thiol group from around 9 to about 6 in the active site through hydrogen bonding and salt bridges and through the positive electrostatic potential of the G-site  41, 42.
Cell pellets were resuspended in 0. 3 ml small-scale lysis buffer (20 mM potassium phosphate, 200 mM NaCl, pH 7. 4, 1 mg ml -1 lysozyme, 0. 75 mg ml -1 polymyxin B, 0. 01 mg ml -1 DNase I) per well and incubated at 30 °C, 300 rpm, 5 cm shaking diameter for 30 min. Cell debris was removed by centrifugation at 4,400g at 4 °C for 30 min, and the clarified cell lysate was directly used for biocatalytic reactions. GST expression levels were analysed via SDS-PAGE. Gel pictures were captured using UV Lab (4. 1. 0) software.
In vitro GST assay with CDNB (5) Glutathione transferase assays with CDNB (5) were performed in 50 mM Tris, pH 7. 5 with 5% clarified cell lysate, 1 mM CDNB and 1 mM GSH. All components were mixed, and reactions were performed in 100 μl volume in 96-well microtitre plates (PS, F-Bottom, clear, Greiner). Plates were incubated for 15 min in a Spark 20M (Tecan) plate reader at 30 °C and A 340nm was measured every 26 s with a 5 s shaking interval between measurements at 180 rpm using a 3 mm shaking diameter. Data collection was carried out using the software Sparkcontrol (2. 3). Initial reaction velocities were calculated from the slope of the A 340nm data from 1 min to 3 min using ε = 9,600 cm -1 M -1 for the concentration calculation of the conjugation product.
For the experiment shown in Extended Data Fig. 3h , the concentration of PtGSTF8 was adapted as indicated on the x-axis label. For the experiment shown in Extended Data Fig. 3i , the concentration of GSH was adapted as shown on the x-axis label. For one-step reactions, all components were mixed in 96-well microtitre plates in 100 μl volume (PS, F-Bottom, clear, Greiner) and reactions were performed for 30 min in a Spark 20 M (Tecan) plate reader at 30 °C. A 580nm was measured every 26 s with a 5 s shaking interval between measurements at 180 rpm using a 3 mm shaking diameter. For two-step reactions, an initial reaction was performed in 90% of the final reaction volume with all components present except for GSH and GST. The reaction was carried out for 15 min in 2 ml microcentrifuge tubes at 30 °C, 1,200 rpm using a 3 mm shaking diameter.
Following the first reaction step, the AtLDOX protein was removed by filtering the reaction through an Amicon Ultra-4 (10 kDa cut-off) (Merck Millipore) and distributing 90 μl aliquots in 96-well microtitre plates (PS, F-Bottom, clear, Greiner). The second reaction was initiated through the addition of the remaining 10% (10 μl) reaction volume containing GSH and GST and the same plate reader method as used for one-step reactions was applied to follow the reaction progress. Initial reaction velocities were calculated from the slope of the A 580nm data of the first minute using a calibration curve with a commercial cyanidin (8) reference compound. For the Michaelis-Menten kinetics, the first step was adapted to 700 μM 3,4-cis-leucocyanidin (6) and 2 μM AtLDOX. The second step after filtering was performed with 0.
23 μM PtGSTF8 and varying dilutions of the first step reaction in buffer to cover substrate concentrations from 389 μM to 6. 5 μM. The same plate reader method as used for one-step reactions was applied to follow the reaction progress by measuring A 580nm every 12 s. Exact flavan-3,3,4-triol (4) concentration was evaluated by a full conversion control reaction containing 5 μM PtGSTF8 in the second step and quantification of the product cyanidin (8) by HPLC-MS. Initial reaction velocities were calculated from the slope of the A 580nm data of the first minute using a calibration curve with a commercial cyanidin (8) reference compound. For analysis of flavonoids by HPLC-MS, reaction volumes were split after the plate reader incubations, and a 40 μl aliquot was extracted with 80 μl of 75% methanol in H 2 O, 1 mM EDTA, 0.
HPLC-MS analysis of flavonoids HPLC-MS analyses of samples from in vitro reactions or S. cerevisiae cultures were analysed with either an Agilent Technologies 1260 Infinity LC System coupled to a 1260 DAD (G4212B) and a LC/MSD XT (G6135B) for quantification of flavonoids or an Agilent Technologies 1290 Infinity coupled to a 1290 DAD (G1316C) and a 6540 UHD Accurate-Mass Q-TOF LC/MS (G6540B) for recording high-resolution mass spectra. For HPLC-MS analyses, data collection was carried out with OpenLAB CDS ChemStation Edition (C. 01. 07 SR4), while for data analysis the OpenLAB CDS (2. 4 Update_06) was used. For high-resolution HPLC-MS runs, data collection was done via MassHunter Workstation Software (B. 08. 00) and data analysis relied on MassHunter Workstation Software Qualitative Analysis (10.
Multiple sequence alignments and sequence logos A multiple protein sequence alignment of known arGSTs of the phi class (PhAN9 28 (O24261), AtTT19 27 (Q9FE46), VvGST4 30 (Q56AY1), PfGST1 54 (B1B5E6), CsGST3 55 (I2FHU9), DcGSTF2 56 (I4DUE3), PpRiant 21 (M5WTI5), LcGST4 57 (A0A0U4AVK2), FvRAP 20 (UPI0002C2E8B9), MdGST1 25 (G3LX80), AcGST1 26 (A0A2R6R622), IbGSTF4 58 (A0A8E4DFU3), CsGSTa 59 (A0A4P8P9K5), PcGSTF12 60 (A0A6G5X592), GtGST1 24 (NCBI accession number: BCD52748. 1), LhGST 23 (A0A5B9G8S8), EpBract1 61 (NCBI accession number: QTX16320. 1), GhGSTF12 22 (A0A5D2YJA6), RsGST1 62 (A0A6J0K8F1), PtGSTF8 39 (NCBI accession number, 5F07_A)-Uniprot accession numbers unless stated otherwise) was constructed using Clustal Omega 1. 2. 2 63 with standard settings. Sequence logos were generated from the multiple sequence alignment using the R package ggseqlogo  64.
1 mm litholoops and flash-freezing it in liquid nitrogen. Diffraction data were collected at the Swiss Light Source (Paul Scherrer Institut, Villigen AG) on beamline X06DA-PXIII at a temperature of 100 K and 0. 999995 Å wavelength using the serial synchroton crystallography software suite. The data were integrated by XDS  65  run through autoproc  66. A dataset with 1. 09 Å resolution was obtained. PDB 5F07 was used in molecular replacement to determine the starting phases for refinement with anisotropic B-factors and hydrogen atoms using MOLREP  67. Refinement and manual model building were performed with REFMAC  68  and coot  69  , respectively, using the ccp4i2  70  interface. The final model contains all protein residues except Met1, as well as GSH and (-)-catechin. The MolProbity 71 validation of this model gave 97.
Analysis of PtGSTF8 crystal structures F o - F c composite omit maps were calculated with the CCP4 7. 1. 018: comit  72  (v. 0. 1. 0) function using the fast method. Molecular docking was performed with AutoDock Vina 1. 2. 0 73 using default settings, except for increasing the exhaustiveness to 32. Figure  2b  with colourcoded RMSD between crystal structures was generated using the ColorByRMSD Pymol script (https://pymolwiki. org/index. php/ ColorByRMSD, accessed on 16 June 2022). The interactions of ligands to the enzyme were analysed using PLIP  74  with standard settings. The RMSD of matched atoms between ligands was calculated using LigRMSD 75 with standard settings.
QM/MM calculations Enzyme-substrate complexes were generated through molecular docking using the GOLD software  76  (ChemScore fitness function) on the crystal structure of PtGSTF8 preserving crystallographic Na + ions and flav-3-on-4-ol coordinates optimized at the B3LYP/6-31 G(d) quantum mechanical level (see below for details). The docking cavity was centred on the sulfur atom of the GSH's cysteine and allowed to extend in a spherical surrounding region with a 15 Å radius. The number of GA (genetic algorithm) runs was set to 30.
All stationary points were characterized by a frequency analysis performed at the same level used in the geometry optimizations from which thermal corrections were obtained at 298. 15 K. Single-point energies were calculated on the optimized geometries using the ωB97X-D hybrid functional  87  and the 6-311 + G(2d,p) basis set with ultrafine integration grids for the QM part and the ff14SB, gaff2 and TIP3P force fields for the MM part (protein, substrate and water, respectively) with electrostatic embedding. The lowest-energy conformer for each calculated stationary (available in PDB format as Supplementary Figs. 1  and 2 ) was considered in the discussion; computed geometries and energies can be accessed through the Zenodo repository (https://doi. org/10. 5281/ zenodo. 8069429). ONIOM energies, entropies, enthalpies, Gibbs free energies and lowest frequencies of the calculated structures are summarized in Supplementary Table  8.
Acknowledgements This work was created as part of  NCCR Catalysis , a  National Centre of Competence  in Research funded by the  Swiss National Science Foundation  (grant number  180544  to R. M. B) and by  AEI (Spain)  (grant number  RTI2018-099592-B-C22  to G. J. -O). F. P. thanks  MINECO  for a  Juan de la Cierva Incorporacion  fellowship ( IJC2020-045506-I ). We thank  I. Kroslakova  for acquiring the MALDI data,  S. Kern  for support in interpreting MS data,  M. Niklaus  for assistance with sequence logos and  D. Patsch  for assistance with Python.
Data availability Nucleotide sequences of codon-optimized genes can be found in Supplementary Table  6. The diffraction images were deposited to Integrated Resource for Reproducibility in Macromolecular Crystallography  88  (http://proteindiffraction. org/) and can be accessed via PDB 8AGQ. Crystallographic coordinates of the ternary complex of PtGSTF8 have been deposited in the PDB as 8AGQ. The binary PtGSTF8 crystal structure used in molecular replacement experiments can be accessed via PDB 5F07. Computed geometries and energies can be accessed through the Zenodo repository (https://doi. org/10. 5281/ zenodo. 8069429). Source data are provided with this paper.
Information sources and search strategy Literature searches were performed using PubMed (including Medline), EMBASE, Web of Science, and Scopus databases. Electronic database searches were conducted from their starting coverage date through February 1, 2022. More information on the search strategy is provided in Appendix 1 (which can be found online). All references were managed and the duplicated hits were removed by using reference manager software (EndNote X7 ® Basic-Thomson Reuters, New York, USA).
Selection process Article selection followed a two-phase process. In phase 1, two reviewers (A. E. C. & P. -E. C. ) independently screened all published papers meeting the inclusion criteria in the electronic databases, using titles and abstracts. In phase 2, the same two authors independently evaluated the full text of each paper, applied the eligibility criteria, collected key information from the selected studies, and crosschecked the information. The final selection was based solely on full-text assessment of the studies. Discrepancy in paper selection led to discussion until mutual agreement. When disagreement occurred, a third author (A. L. P) was involved to make a final decision about whether to include or exclude a study.
Study risk of bias assessment The selected studies were evaluated using the Joanna Briggs Institute's critical appraisal tools to assess risk of bias. The answer could be 'yes', 'unclear', 'no', or 'not applicable'. Two reviewers (A. E. C. & P. -E. C. ) independently classified the quality of each included study. In case of discrepancy, the two reviewers conciliated. Following these ratings, the risk of bias was categorized as high, if one or more criteria were not met; low, if all criteria were met; or unclear, if one or more criteria were not rigorously described  (23). Figures of the quality assessment of all studies were generated using Review Manager software (RevMan v. 5. 3, The Nordic Cochrane Center, Copenhagen, Denmark).
Risk of bias across studies and reporting bias assessment The risk of bias across studies was assessed as an overall risk which could influence a meta-analysis. Methodological and statistical heterogeneity were evaluated by comparing the variability in study design and the risk of bias. When the required data were not complete, the reviewers (A. E. C. & P. -E. C. ) attempted to contact the study authors to obtain specific unpublished information. Three attempts were made in a 30-day period, by email to the first, second and last authors.
Study selection The initial database search identified 621 articles. After eliminating duplicate hits, 297 articles remained of which 272 were excluded after title and abstract review, leaving 25 articles for phase 2. During phase 2, nine more articles were excluded (reasons for exclusion can be found in Appendix 2), leaving 14 articles for qualitative synthesis. A flowchart of the process of identification and article inclusion and exclusion are shown in Figure  1.
This treatment, supported by evidence of NFATc1 pathway involvement in cherubism and with measurable outcomes, paves the path to stronger studies targeting both osteoclasts and immune cells, as tacrolimus does  (28). Clinical outcomes were satisfying with imatinib, a tyrosine kinase inhibitor, as the volume of the lesions decreased  (39). Interestingly, this off-study and off-label treatment efficient to decrease the volume of the patients' lesions did not have any effect on the cherubism mouse  (44). However, Yoshimoto and colleagues demonstrated that treating with a very new secondgeneration SYK inhibitor (Entospletinib) rescued the cherubism phenotype  (45). However, mainly adult patients suffering for malignant hemopathies (such as acute myeloid or lymphoblastic leukemia), were so far included in clinical trials  (46) (see clinicaltrials.
Study Population This study included a total of 399,067 participants from the UK Biobank who did not have missing data for ASCVD risk calculation and had no previous cardiovascular (CV) events. "CV diseases, including heart attack, angina, and stroke, were identified based on doctor diagnoses reported in the questionnaires"  [30]. Moreover, this study also excluded 112,022 participants due to missing data for air pollutants (N = 33,774) and for other covariates (N = 78,248), resulting in a final analysis cohort of 287,045 participants (Figure  1 ).
Air Pollution The annual average concentrations of PM 2. 5 , PM 10 , PM 2. 5-10 , NO 2 , and NO x were estimated using the Land Use Regression (LUR) model, which was developed as part of the European Study of Cohorts for Air Pollution Effects project  [37, 38]. The LUR model incorporated geospatial predictor variables derived from a Geographic Information System, including factors such as traffic, land use, and topography. These variables were used to calculate the spatial variations in annual average air pollutant concentrations. For this study, the residential addresses provided by participants during the baseline visit in the UK Biobank were utilized to link the air pollution exposures of each participant to their records. The exposure data for PM 2. 5 , PM 10 , PM 2. 5-10 , NO 2 , and NO x were collected in 2010.
Limitations The study's primary strength is its large sample size, which is drawn from the UK Biobank cohort. However, the cross-sectional design of the study limits the establishment of causal relationships and precludes determining reverse causation. Although the study had a response rate of only 5. 5%, the robust sample size and high internal validity minimize the likelihood of participant bias influencing the observed associations  [105, 106]. The generalizability of the study's results to other age groups or ethnic populations may be limited since it focused exclusively on middle-aged participants from the UK. Nevertheless, the UK Biobank employed standardized protocols for data collection, ensuring replicability across all participants. Several limitations should be acknowledged. Firstly, socioeconomic data, medical history, and comorbidities were collected through self-reported questionnaires or physician assertion, which introduces the potential for bias.
A prompt photon region and three control regions are then defined using this method. The number of Z +jets events in SR, CR and Extended SR is obtained from the number of events in the three control regions by assuming that the ratio of non-prompt isolated and non-isolated photon candidates is the same for tightly identified photons and for photons failing to satisfy the tight identification criteria. The small residual correlation between the two variables and the leakage of prompt photon candidates into the non-prompt photon region are estimated from simulation. The correlation is also estimated in data using a control region where the photon fails track isolation and the difference between the MC and data results are included in the systematic uncertainty (Section 6). The shape of this background is obtained from both control regions where the photon candidate fails to satisfy the tight identification criteria.