dataesr/scientific-paragraphs-categorization huggingface dataset:
Kinetics assay MAB_4324c was concentrated to 7. 5 mg. mL -1 in buffer E supplemented with 50% glycerol for use in kinetic assays. The reaction was performed at 25ºC in a quartz cuvette and consisted of monitoring the production of 2-thio-5-nitrobenzoic acid (TNB; extinction coefficient of 14150 M -1 cm -1 at 412 nm) resulting in the reduction of DTNB by the free CoA. Data points were recorded with a time interval of 5 sec for 50 to 60 sec using a Nanodrop2000c spectrophotometer (ThermoFisher Scientific). All compounds and the enzyme were dissolved and diluted in buffer E. MAB_4324c (0. 25 μM) was added to the reaction mixture consisting of DTNB (0. 5 mM), ACO (0. 5 mM) as well as substrates when requested in a final volume of 100 μL. A reaction mixture lacking the substrate was used to determine the reaction background and to normalize the values.
Standard errors were calculated from triplicates and a nonlinear least-squares regression algorithm was used to determine the Michaelis-Menten equation (GraphPad Prism software). colonies were then grown in 10 mL of Middlebrook 7H9 OADC supplemented with 0. 025% tyloxapol and 250 µg. mL -1 KAN at 37ºC for 4 days, harvested and frozen at -80ºC.
mL -. Bacterial extracellular growth was prevented by refreshing the culture medium containing AMK 50 µg. mL -  every two days. Before plating the bacteria, THP-1 macrophages were washed thrice with PBS 1X. Cell lysis was achieved with 100 µL of 1% Triton X100 in PBS 1X. Then, 900 µl of 1x PBS was added to each well and ten-fold dilutions were plated onto LB agar. This procedure was repeated at 0, 1, 3 and 5 days post-infection. After 5 days of incubation at 37ºC, the number of CFU was assessed. The CFUs counts were plotted on a logarithmic scale and the standard error bars were calculated using the GraphPad Prism software.
Growth curves of M. abscessus strains Cultures of 20 mL were inoculated with single-cell preparations in 7H9 OADC supplemented with 0. 025% tyloxapol (starting OD 600nm of 0. 05) and incubated at 37ºC under agitation (100 rpm) for 5 days. The OD was monitored daily. The OD values were plotted and error bars were calculated using the GraphPad Prism software.
MAB_4324c is a tandem repeat N-acetyltransferase To support the biochemical data, we engaged in structural studies of MAB_4324c. We succeeded in obtaining exploitable crystals of the protein when it was co-crystallized with acetyl-CoA. Structure phasing was achieved through the sulfur-single-wavelength anomalous dispersion method (Table  II ). The partial model built thanks to the anomalous map was further used to solve the structure obtained from a 2Å resolution native dataset. Two monomers occupy the asymmetric unit (Fig. 2A ). Most of the residues could be rebuilt for the two monomers except for the first 11 and 12 residues of our expressed construct (that contains one extra Gly residue coming from the TEV cleavage site on top of the MAB_4324c sequence) in monomers A and B respectively.
4 ): the mycothiol synthase from Mycobacterium tuberculosis (PDB id: 2C27)  48  bound to des-acetylmycothiol and CoA that possesses a r. m. s. d. of 1. 9Å and sequence identity of 19%, and the structure of glucosamine N-acetyltransferase bound to chitosan, GlmA, from Clostridium acetobutylicum (PDB id : 5kgp) sharing a r. m. s. d. of 2. 6 Å and sequence identity of 12%  49. These two enzymes are also tandem repeat N-acetyltransferases but are involved in different pathways. MshD is an essential enzyme for mycobacteria as it catalyzes the last step of mycothiol synthesis, a key component in protection against oxidative stress. Mycothiol is made of an acetylated Cys residue, a glucosamine residue, and an inositol group. GlmA ensures the transfer of an acetyl group from ACO to the amino group of glucosamine 50 and seems to be involved in cell wall recycling.
5D ). The sequences of the MAB_4324c orthologues were retrieved with the HMMER server (http://hmmer. org/). Some of the sequences sharing more than 40% protein sequence identity were selected and realigned using the ClustalX software. WT comp. ), Δ4324c and Δ4324c complemented strains (Δ4324c comp. ). The lysine acetylome was detected with a pan anti-acetyl lysine primary antibody (abcam 21623) and revealed with an antirabbit secondary antibody produced in goats and coupled to the horseradish peroxidase. On the left is shown the membrane after transfer and colored by ponceau red while on the right is the immunoblot revealed by chemiluminescence. 100 μg of total protein were loaded on the 10% SDS-PAGE for high molecular weight (HMW) proteins and 12% SDS-PAGE for proteins of medium molecular weight (MMW) proteins.
Acknowledgments We thank the staff at  SLS  beamlines for support during data collection. Funding for this work was supported by the  Fondation pour la Recherche Médicale (FRM)  [grant number  DEQ20150331719  to LK] and, the  Lundbeck Foundation ,  Denmark  ( R303-2018-2964  to HA). KLU Ph. D. fellowship was supported by the  National Research Agency  [ ANR-17-CE11-0008-01 -MyTraM  to MB]. M. D. J. received a postdoctoral fellowship granted by  Labex EpiGenMed , an " Investissements d'Avenir" program  ( ANR-10-LABX-12-01 ). We would like to thank  Dr. W. Daher  for his help with macrophage preparations. We thank  Dr J. Wong  for the critical reading of the manuscript.
This method is widely used for creep stress analysis  [27]. Three key parameters were optimised using a fitting tool in MATLAB so that the modelled relaxation curves and the experimental relaxation curves show good agreement with errors of less than 1%, as shown in Figure  3 (a). The confidence intervals for the three parameters are ±0. 2 × 10 -20 J for ∆F , ±1b 3 for ∆V and ±5MPa for σ c. This numerically fitted parameters are broadly similar to those used by other studies of titanium alloys  [3, 9, 21]. Figure  3 (b)-(d) show the three key parameters vs. temperature for the six different alloys. Both ∆F and ∆V increased with temperature, where the relationship between ∆F and temperature was almost linear, which is consistent with the observations in CP-Ti and BCC steel  [28, 24]. The effect of temperature on ∆F was seen to be markedly stronger than the alloy-to-alloy variation.
Acknowledgements The authors acknowledge funding from the  EPSRC  through the  HexMat programme grant  ( EP/K034332/1 ) and the  Diamond Light Source  for beam time under experiment  EE17222. We are grateful for use of characterisation facilities within the  David Cockayne Centre for Electron Microscopy, Department of Materials, University of Oxford , which has benefitted from financial support provided by the  Henry Royce Institute  (Grant ref  EP/R010145/1 ). YX expresses gratitude to the financial support of  China Scholarship Council (CSC)  and ET acknowledges  EPSRC for support through Fellowship grant  ( EP/N007239/1 ).
[29]. (c) Superposition of the X-ray structures of enH DNA (pdb code 1HDD, pink) and free (pdb code 1ENH, blue), both obtained at very h centration. (d) Electrostatic potential of enHD in two orientations. The highly positiv face that is involved in the interaction with DNA is displayed at the bottom. To characterize the interplay between cognate DNA binding and confor enHD we employ NMR and circular dichroism. NMR is well suited for the struc ysis of the relatively small DBDs, including homeodomains. NMR has hence b used to structurally characterize DBDs and their interactions with DNA, sta work in the early 90 s on the free DBDs (e. g. ,  [32, 47, 48] ), as well as subsequen their complexes with DNA: see  [49] [50] [51]  as some examples. Such works have s DNA recognition often involves electrostatic interactions of the protein with phosphate backbone in which arginine and lysine side chains play a major r NMR has also been used to demonstrate that the lac repressor exhibits differ mational dynamics when bound specifically or non-specifically to DNA [5 To characterize the interplay between cognate DNA binding and conformation on enHD we employ NMR and circular dichroism.
Far UV Circular Dichroism (CD) Spectroscopy Samples for the circular dichroism analysis of the ionic strength dependence were prepared at 40 µM EnHD in 20 mM Tris buffer at pH 7. 5. The ionic strength was changed by adding different amounts of potassium fluoride (KF), which is the most UV-transparent of the monovalent salts, from a 700 mM stock solution prepared in the same buffer. Samples for the CD analysis of the effects of cognate DNA binding were prepared with only EnHD at 15 µM, as a negative control, cognate DNA at 15 µM (see below), for the subtraction of the inherent CD spectrum of DNA, and with both enHD and cognate DNA at equimolar concentrations. CD spectra were acquired on a Chirascan Plus spectrophotometer (Applied Photophysics, Leatherhead, UK). Data were collected with 1 nm resolution in the 190-250 nm range for the ionic strength dependence (2 s acquisition per nm), and in the 190-300 nm (10 s acquisition per nm) for DNA binding.
8 plus 50 mM NaCl. All the NMR samples were prepared using a 5% D 2 O/95% H 2 O mixture. EnHD/DNA samples for each step in the binding titration were prepared by mixing solutions of enHD and hybridized cognate DNA at each suitable molar ratio but at concentrations 10-fold diluted relative to the target final concentration of 200 µM enHD. A volume of 3 mL of the 10× diluted sample was then concentrated down to 300 µL final sample volume using 3K MWCO Amicon Ultra Centrifugal Filters (Sigma, St. Louis, MO, USA) spun at 3000× g for 60 min at 4 • C. Sample preparation at 10-fold dilution avoids the precipitation of enHD during the mixing with DNA. [1H-15N]-HSQC spectra were acquired at 298K in a Bruker Avance III 600 MHz spectrometer. NMR data were processed using NMRPipe  [70]  and Sparky  [71]  was used to visualize the processed data.
22 Figure 5. 225 Figure 5. (a) Difference in the 1 H chemical shifts of enHD in the presence of DNA (red, from panel (a)) or at 700 mM NaCl (cyan) relative to the spectrum of free enHD at 25 mM NaCl. The tryptophan sidechain chemical shift is also shown and indicated with a star. (b) As in (a) but for 15 N chemical shifts. (c) Changes in amide chemical shifts upon binding to cognate DNA projected onto the X ray structure of free enHD (pdb code 1ENH). The thickness and color gradient (yellow to red) of the backbone reflects the combined 1 H and 15 N chemical shift deviations observed in the HSQC spectrum on a log scale.
Objectives: The aim of this analysis was to describe nirmatrelvir/ritonavir real-world effectiveness in preventing hospitalization among high-risk US COVID-19 patients during SARS-CoV-2 Omicron predominance. Design: An ongoing population-based cohort study with retrospective and prospective collection of electronic healthcare data in the United States. Methods: Data for this analysis were collected from the US Optum® de-identified COVID-19 Electronic Health Record (EHR) dataset during December 22, 2021-June 8, 2022. Key eligibility criteria for inclusion in the database analysis were ≥12-years-old; positive SARS-CoV-2 test, COVID-19 diagnosis, or nirmatrelvir/ritonavir prescription; and high risk of severe COVID-19 based on demographic/clinical characteristics. Potential confounders between groups were balanced using propensity score matching (PSM).
Data Source This study involved data analysis from ongoing data collection from electronic health records (EHRs) of US patients. Data were derived from the Optum® de-identified COVID-19 Electronic Health Record dataset (Supplementary Appendix), which includes patients who had documented clinical care from Patients also had to have ≥1 characteristic or underlying medical condition associated with increased risk of developing severe COVID-19 in the year before cohort entry (Supplementary Appendix),  [14] [15] [16]  which aligned closely with EPIC-HR. 4  The nirmatrelvir/ritonavir group included all patients meeting inclusion criteria who were prescribed nirmatrelvir/ritonavir; the non-nirmatrelvir/ritonavir group included those not prescribed nirmatrelvir/ritonavir or molnupiravir. The index date in the nirmatrelvir/ritonavir group was the date of first prescription.
Analyses Continuous variables were described by means with standard deviations or medians with interquartile ranges. Dichotomous or categorical variables are presented using numbers and percentages. Conditions were captured using ICD-10 codes; treatments using National Drug Code and Healthcare Common Procedure Coding System codes; and procedures using ICD-10 procedure codes and CPT codes. Followup started at index date and extended to 30 days (primary analysis) or 15 days (sensitivity analysis), the first hospitalization occurrence, loss to follow-up, death, or data cutoff (June 8, 2022), whichever occurred first. A death date algorithm and lost to follow-up definition are presented in the Supplementary Appendix. All analyses were executed using SAS version 9. 4.
Patients Among patients in the Optum COVID-19 database between December 22, 2021 and June 8, 2022, 12,025 patients were prescribed nirmatrelvir/ritonavir and 600,690 patients with positive SARS-CoV-2 tests or COVID-19 diagnoses were not prescribed nirmatrelvir/ritonavir (Figure  1B ). After applying inclusion and exclusion criteria, 2811 and 194,542 patients were included in the nirmatrelvir/ritonavir and non-nirmatrelvir/ritonavir groups, respectively. Before PSM, differences in characteristics between patients in the nirmatrelvir/ritonavir versus nonnirmatrelvir/ritonavir groups emerged, with patients in the nirmatrelvir/ritonavir group being less racially diverse; older; using more Medicare and less Medicaid; having a greater number of comorbidities; and having higher COVID-19 vaccination rates (Table  1 ).
9% versus placebo. 4   s is encouraging given the SARS-CoV-2 epidemiologic landscape differed between the 2 studies in that EPIC-HR was conducted during the Delta wave whereas the current study was conducted during Omicron predominance. In contrast to EPIC-HR, this real-world study did not use a composite effectiveness endpoint that included death because the exact day of death was not captured in the EHR database (only month of death was available 13 ). Therefore, accurate characterization of nirmatrelvir/ritonavir effectiveness against mortality was not possible. This lack of precision led to the concern of an increased outcome misclassification and precluded an analysis of mortality taking a person-time approach. Mortality was therefore not included within the effectiveness endpoint, but was summarized as described to show potential censoring of hospitalizations.
32 By contrast, a Hong Kong study identified 4. 4% and 6. 2% hospitalization rates among nirmatrelvir/ritonavir and non-nirmatrelvir/ritonavir recipients, respectively, within 28 days of diagnosis. 33 Our study found higher hospitalization rates than in the Massachusetts/New Hampshire and Israeli studies (nirmatrelvir/ritonavir group, 1. 2%; nonnirmatrelvir/ritonavir group, 6. 9%) and slightly lower rates than online-published results from a US database study (nirmatrelvir/ritonavir group, 1. 9%; non-nirmatrelvir/ritonavir group, 9. 7%  34  ). Differences in hospitalization rates and effectiveness may be partially explained by differing percentages of patients who were vaccinated or had prior SARS-CoV-2 infection as well as differences in study design. 29, 31, 33, 35  Ad itionally, 2 studies excluded patients whose initial diagnosis coincided with hospitalization admission, which may have biased hospitalization rates downward.
29, 35 SARS-CoV-2 circulation and community immunity may also vary during different study periods. 35  Study strengths include the large population-based design, which extracted data from a national longitudinal US EHR database and captured medical records linked by outpatients and inpatients within integrated delivery networks. We also employed various strategies to reduce effects of confounding variables or other biases that were not used, or were less comprehensive, in other nirmatrelvir/ritonavir real-world studies. 29, 31, 33, 35  Th  PSM strategy used in our analysis to control for residual confounding was extensive and included covariates such as healthcare resource utilization, previous COVID-19 infection, and 50 most common medications, diagnoses, and procedures not often used in other realworld studies.
Relatedly, patients in the nirmatrelvir/ritonavir and non-nirmatrelvir ritonavir groups were matched by cohort entry data to help reduce prescriber bias and variation in COVID-19-related hospitalization rates over time. We also used PTDM to control for immortal time bias, which arises in epidemiology studies using existing data, whereby there is a span of cohort follow-up when the studied outcome could not have occurred because of the exposure definition. 17 r study has several limitations. Optum data are encounter-based and do not cover all COVID-19 treatment and prescribing sites (eg, pharmacy, telehealth, public health departments). Thus, study patients in both groups can be sicker than the general population and a slower uptake of nirmatrelvir/ritonavir was observed. However, we expect that our cohort likely reflects the target population who are at higher risk for severe COVID-19 outcomes.
Lack of medical chart review prevented effectively addressing exposure and outcome misclassifications. The database may not completely collect vaccination data, which precluded assessing effectiveness in the unvaccinated patient subgroup. We used hospitalization as a proxy of COVID-19-attributed hospitalization; however, because the data do not capture causes of hospitalization, inclusion of admission and encounters for conditions other than COVID-19 could result in outcome misclassification. The sensitivity analysis used a narrower risk window (ie, 15 days), as adopted in other real-world studies,  29, 32  to help reduce potential effects of misclassification due to a longer risk window. However, this would not affect incidental hospitalization on the COVID-19 diagnosis date. There is a need to understand the frequency of non-COVID-19 hospitalizations carrying a COVID-19 diagnosis (specifically whether those are incident or past diagnoses).
Nevertheless, hospitalizations occurring outside of integrated delivery networks would not be captured in the database, leading to potential underestimation of hospitalization in both groups. The EHR data source lacked the specific date of COVID-19 symptom onset, although this likely occurred ≤14 days from diagnosis for most cases. 36 Nirmatrelvir/ritonavir effectiveness may also be underestimated because of incomplete treatment initiation, non-adherence, and undocumented nirmatrelvir/ritonavir receipt in the non-prescribed group. As previously mentioned, while death was included as a censoring event to define follow-up end date via an algorithm, mortality was not included as an endpoint because of concern of increased outcome misclassification. The study started when nirmatrelvir/ritonavir was granted EUA; its use has since expanded rapidly  34  and earlier results may not reflect current use.
≥65-year-old patients: The covariates of age (continuous), hypertension, and chronic kidney disease were adjusted in the model. <65-year-old patients: The covariates of cohort month/year and medical history of autoimmune arthritis/rheumatoid arthritis were adjusted in the model. The database may not completely collect vaccination data, which precluded assessing effectiveness in the unvaccinated patient subgroup. Figure Legends Error bars depict 95% CIs. PSM=propensity score matching.
January 2007 through the most current monthly data release, a documented exposure to or testing for SARS-CoV-2 (positive or negative result) and a COVID-19 or acute respiratory illness diagnosis after February 2020. Patient demographics, mortality, and clinical interventions (eg, medications prescribed) collected from EHRs derived from both acute inpatient stays and outpatient visits are included. As of June 8, 2022, which includes the Omicron-predominant period, the COVID-19 database included ~12 million individuals, derived from Optum's EHR repository with >700 hospitals and 7000 clinics from all US states. 13 Study Design, Population, Definitions Study population inclusion and exclusion criteria for this retrospective population-based cohort study are outlined in Figure 1A. Briefly, ≥12-year-old patients irrespective of COVID-19 vaccination status were included if they had a positive SARS-CoV-2 PCR/antigen test, COVID-19 diagnosis (ICD-10 U07.
Acknowledgments The authors thank  Sengwee Toh, ScD  ( Harvard Medical School and Harvard Pilgrim Health Care Institute ) for invaluable discussions in the study design and development of the manuscript and  Haitao Chu, MD, PhD  ( Pfizer Inc ) for guidance regarding the study design and methods. Editorial/medical writing support was provided by  Judith Kandel, PhD ,  Sheena Hunt, PhD , and  Tricia Newell, PhD , at  ICON (Blue Bell, PA, USA ), and was funded by  Pfizer Inc.
The electro-thermomechanical modeling study of the High Electron Mobility Transistor (HEMT) has been presented, all the necessary equations are detailed and coupled. This proposed modeling by the finite element method using the Comsol multiphysics software, allowed to study the multiphysics behaviour of the transistor and to observe the different degradations in the structure of the component. Then, an optimization study is necessary to avoid failures in the transistor. In this work, we have used the Covariance Matrix Adaptation-Evolution Strategy (CMA-ES) method to solve the optimization problem, but it requires a very important computing time. Therefore, we proposed the kriging assisted CMA-ES method (KA-CMA-ES), it is an integration of the kriging metamodel in the CMA-ES method, it allows us to solve the problem of optimization and overcome the constraint of calculation time.
All these methods are well detailed in this paper. The coupling of the finite element model developed on Comsol Multiphysics and the KA-CMA-ES method on Matlab software, allowed to optimize the multiphysics behaviour of the transistors. We made a comparison between the results of the numerical simulations of the initial state and the optimal state of the component. It was found that the proposed KA-CMA-ES method is efficient in solving optimization problems.
Finite element model The HEMT electro-thermomechanical modeling is based on the resolution of the above coupling equations. The chosen method of solving is the finite element method (FEM)  [33] , it is the most used method for solving such problems, the analytical solution of the equations is very difficult. Therefore, it is necessary to introduce numerical methods. Comsol multiphysics software is based on the finite element method, it allows to reconstruct the system geometrically, to apply loads and boundary conditions and par to solve all equations related to the developed modeling (Figure  2 ). The recommended mesh in the case of HEMT modeling is the tetrahedral mesh, thanks to the good results obtained by this type. The mesh is very fine in the critical areas, and becomes normal in the rest of the component to optimize the calculation time.
To solve the optimization problem, the coupling between two models has been developed: the finite element model developed under Comsol multiphysics software, and the statistical model of KA-CMA-ES coded under Matlab software. The statistical model is used to determine the optimal values that minimize the objective function and to build the metamodel necessary for the calculation. The finite element model makes it possible to compute the values of the von Mises stresses for each variation of the geometrical parameters.