dataesr/scientific-paragraphs-categorization huggingface dataset:
Data analysis Quantitative variables are expressed as means (standard deviation, SD) in the case of normal distribution or medians (interquartile range Q1, Q3) otherwise. Categorical variables are expressed as numbers (percentage). The normality of the distributions was assessed using histograms and the Shapiro-Wilk test. After descriptive analysis, the measurement methods were compared using the Student t-test for Gaussian continuous variables and Mann-Whitney U test for non-Gaussian continuous and ordinal categorical variables, as appropriate. One-way ANOVA was not performed because the Levene test for homogeneity was signi cant (P < 0. 0001). Statistical testing was conducted at the two-tailed -level of 0. 05. Data were analyzed using SAS software version 9. 3 (SAS Institute, Cary, NC).
Statistical analysis Following the objective to determine the prescriptionfree consultation rate (expected around 22% according to the review), the inclusion of at least 500 patients was planned to allow a final precision of ± 5%. The main criterion (prescription-free consultation rate) was assessed in a weighted way in order to represent at best the Auvergne population (667,000 inhabitants on the 1/1/2019), given the stratified sampling scheme (3 stratas corresponding to 3 town size intervals). However, in the subsequent analysis, the weight was not taken into account, because the obtained sample was quite close to the expected balance of city sizes. The data of 31 practices was obtained: seven practices for the large towns, 12 practices for intermediary cities and small towns. One thousand two hundred and forty questionnaires were distributed with an expected 50% response rate.
Statistical analysis was performed with Stata software (version 15, StataCorp, College Station, USA). Given the exploratory nature of the analysis, no adjustment for the risk of type I error was made. A bilateral type I error risk of 5% was considered in all analyses. The samples were described by numbers and associated percentages for the categorical variables and by the mean and the standard deviation or the median and the interquartile range for the quantitative variables, with regard to their statistical distribution. Normality was checked through graphical representation. Comparisons are made through Chi 2 testing for categorical variables, and Mann Whitney Wilcoxon test for quantitative variables. The explaining factors of prescription-free consultation was searched through univariate logistic regressions, in order to choose the relevant variables (under 0.
Acknowledgments We thank all of the nursing staff members and doctors whose enthusiasm and work have made this clinical trial possible. We particularly thank  Cécile Jourdain ,  Leslie Lehaie  ( CH de Chartres ) for their major and invaluable role in providing technical and educational support to all the investigators under the supervision of the lead investigator; all the psychologists for conducting professional patient interviews and administering all the follow-up questionnaires with the greatest care and attention (alphabetically):.
Data We use rich administrative information to construct a comprehensive linked employer-employee dataset. The panel tracks the firms' productivity and profitability as well as high-frequency (monthly) employment-match characteristics such as monthly wages, occupations, job-to-job transitions, and firm dynamics. The data covers the period from January 2006 to October 2022. Specifically, the panel is built on social security data. For each worker, this data records, on a monthly basis, information on demographic characteristics (e. g. , date of birth, nationality, sex), labor earnings, and occupation at each job. We track employers by their unique corporate tax ID. Monthly labor earnings are not censored, and we use the universe of workers and firms in the formal sector. The occupation is recorded as a standardized four-digit ISCO-08 code.
Like most matched employer-employee datasets, the information does not contain the number of hours worked and does not track informal employment. 5  We add to the linked dataset firm-level balance sheet variables from the universe of corporate tax returns from 2005 to 2021 to define the industry, estimate labor productivity, and corroborate firm dynamics, job transitions, hirings, and separations. We follow standard data cleaning techniques (See  Sorkin (2018) ,  Gregory et al. (2021) ,  Crane et al. (2022)  and references within) oriented to precisely estimate the occurrence and timing of job transitions, construct monthly wages in the absence of hours worked, and rank workers, occupations, and firms.
6 We put special emphasis on the recommendations on how to select the control group  (Krolikowski, 2017)  and construct a harmonized dataset to identify job loss costs  (Bertheau et al. , 2022). 7 We do not observe if the worker enters unemployment or moves out of the labor force, which posits an important limitation. Nonetheless, we focus on displaced workers during a mass layoff, which likely suggests a worker's transition to unemployment. define their employment history. Since the chosen method is an unsupervised algorithm, as we do not observe the types, we follow Wang (2010) to define the optimal number of types. 8  More precisely, for each individual worker 𝑖, we compute their: • Worker fixed-effect 𝑎 𝑖 from an AKM model  (Abowd et al. , 1999) , interpreted as a combination of skills and other factors that are rewarded equally across employers.
Identification of job displacement effects Our main goal is to estimate the treatment effect of job displacement on workers under an event study framework to (i) evaluate its impact in terms of earnings inequality, (ii) assess its persistence, and (iii) identify heterogeneous effects across worker types. Consequently, we need to clearly define the treatment, i. e. , a job displacement, and both the treatment and control groups. Job displacement. We focus on mass layoffs to estimate the effects of job displacement, both prior to and due to COVID-19. Since we do not observe the reason for the employment separation, we must identify those events that are likely to be involuntary in order to avoid endogenous separations. 14  As shown by  Flaaen et al. (2019) , the best alternative is to rely on administrative data to identify establishments that suffered an episode of large employment contraction.
𝑦 𝑚 𝑖𝑡 = Firm and occupation-level characteristics. We estimate ex-ante firm and occupation-level characteristics relative to the pandemic period (2020-2021) as we seek to identify reallocation or sorting patterns, i. e. , if displaced workers move to more productive and higher-paying establishments and jobs. To alleviate identification concerns that job losses and worker allocation contribute directly to firm performance and within-occupation wages, we restrict information to 2010 and 2019, as available. 19 Therefore, we do not speak about productivity and wage dynamics after COVID-19. Similar to the tenure restriction imposed on workers, we consider firms operating in January 2020 with at least three years of prior information. 20 To rank firms and occupations, we compute the ex-ante average of the specific firm and occupation-level characteristics using our administrative microdata covering the universe of workers and firms in the labor market.
Conclusions This paper provides valuable insights into the economic consequences of job displacement for workers and firms. By using a rich dataset that links employer-employee information, we identify three distinct worker types based on unobserved heterogeneity and examine the impact of job displacement on their earnings. Our results reveal persistent earning losses for displaced workers, especially during economic recessions, such as the recent pandemic. Additionally, we find that job finding, or the extensive margin, plays a crucial role in explaining these earning losses. We further document that displaced workers during the pandemic are moving to more productive and higher-paying firms. The findings emphasize the importance of new job characteristics in explaining earning losses and suggest that future research should focus on the productivity-enhancing nature of labor reallocation and the occupational dynamics in the aftermath of downsizing events.
Ethical statement This survey was registered in the Health Data Hub (project database No. F20230110115509). According to article R1121-1 of the French Public Health Code, our survey was considered outside the scope of the field of biomedical research. Indeed, objectives relating to the sociological experience of PwD do not contribute to the expansion of biological or medical knowledge. For this reason, IRB or ethical approval for our study were not required. However, in order to protect patients' data, this study was conducted in accordance with Reference Methodology MR-004 as set out by the French national commission on information technology and liberties, i. e. 'Research not involving human subjects (studies and evaluations in the health field)'.
Characteristics of the population Characteristics of the population are presented in Table  1. There were 3968 PwD enrolled in the database. Responses from 2513 PwD were analyzed (499 did not meet the inclusion criteria and 599 did not complete the questionnaire). Among the analyzed sample, 37. 5% had T1D and 62. 5% T2D. The mean age of the population was 60. 8 § 13. 4 years. PwT1D were significantly younger than PwT2D (54. 0 years § 14. 8 vs 64. 9 years § 10. 5; p < 0. 001). Among female respondents (50. 9%), we observed a significantly higher proportion of PwT1D than PwT2D (59. 7% vs 45. 6%; p < 0. 001). The level of education of PwT1D was more often higher than or equivalent to high school diploma that of PwT2D (76. 2% vs 55. 6%; p < 0. 0001). The mean time since diabetes diagnosis was 31.
Study participants GPs were identified from the RHI database and were considered eligible if they worked full time in primary care and had overprescribed antibiotics during the second quarter 2017. Overprescribers were defined as those whom prescribed more than 25 systemic antibiotic courses per 100 patients (aged 16-65 years) without a chronic illness, and if the proportion of patients treated with broad-spectrum antibiotics (i. e. , amoxicillin-clavulanate, cephalosporins and fluoroquinolones) exceeded 27% of all patients treated with antibiotics, i. e. , GPs who did not reach the objectives set by the NHI as part of the French outpatient pay-for-performance system 'Remuneration on Public Health Objectives' (ROSP). Results of these two indicators by GP were available in the RHI database.
Randomisation and recruitment Once each eligible GP had been identified, they were randomised to either the Intervention or Control group. Then, those allocated into the Intervention group were contacted by a delegate from the RHI who explained the study and the AntibioCharte intervention, and the GPs who accepted to participate and provided their written informed consent were enrolled. GPs randomised to the Control group did not receive the AntibioCharte intervention and were informed neither about the study nor that their prescribing data would be anonymously collected, which is allowed by French regulation. Randomisation was performed in variable block sizes using SAS® software in a ratio 1. 5 (Intervention group):1 (Control group) and stratified by rural or urban geographical location.
Data source and collection We used data retrospectively and routinely collected from the NHI database (SNDS: Système National des Données de Santé). The SNDS contains individual, anonymised and linkable health-related and reimbursement data for almost the entire population (67 million inhabitants) in France. As all antibiotics are reimbursed in France, the SNDS reimbursement data is a reliable proxy measure of antibiotic prescription. The SNDS does not record the indication for which the drugs were prescribed. It is one of the largest databases in the world and it has been extensively used to guide public health policies in France  [27, 28]. Therefore, the term prescription in this manuscript refers to prescribed antibiotics, dispensed by community pharmacies, reimbursed by the NHI and thus recorded in the SNDS.
Using a secure SNDS database access and a unique identification number that anonymised the GP, for each antibiotic prescribed in the Intervention and the Control group, we collected the molecule(s), the number of tablets and date dispensed. Also, the allocation group (Intervention or Control), the type of practice (group or solo practice), the practice location (rural, urban) and the number of active patients (i. e. , the number of patients the GP saw at least once a year) and the number of consultations per month in 2017, 2018 and 2019 were obtained.
Strengths and limitations This study has a pragmatic design that allowed the intervention to be evaluated in real-life conditions. Furthermore, the data sources used to measure the outcomes were robust, reliable, health administrative databases. Considerable thought and care went into designing this study in line with recent recommendations on antimicrobial stewardship intervention evaluation  [41] , considering evaluation methods for non-pharmacological interventions are heterogeneous in primary care research today  [42]. However, several methodological issues were faced in this study. Firstly, the SNDS database gives information on prescribed antibiotics that have been dispensed by community pharmacies and might thus underestimate the overall number of antibiotic prescriptions. However, this underestimation is probably low as patients put high pressure to get antibiotics and there is no reason to think it differed significantly between the two study groups.
Acknowledgments AntibioCharte Scientific Committee:  Jacques Birgé ,  Jean-Marc Boivin ,  Virginie Chopard ,  Diane Heranney ,  Joëlle Kivits ,  Christian Rabaud ,  Maïa Simon. The authors would like to thank  Dr Odile Blanchard , director of the  DRSM Grand Est (RHI ), for her support. The authors also acknowledge  Amanda Whereat  ( Speak the Speech Consulting ) for her support writing the manuscript.
The results were compared with the spectral particle collection efficiency of NaCl expressed in aerodynamic diameter. For discharged mask, the expremental data for NaCl filtration efficiency  was determined from optical counting. The filtration efficiencies obtained using the Welas counter were expressed as aerodynamic diameters by comparing PNSDs. A shift coefficient was applied to the different Welas diameter channels in order to correlate the median diameters of the two PNSDs i. e. , the original aerodynamic distribution by APS and the aerodynamic distribution calculated by Welas. This procedure assumes that for a given aerosol, the density and dynamic shape factors are the same whatever the particle diameter. The results demonstrated a clear concordance between MS2 and NaCl particle filtration efficiency for a same aerodynamic diameter for charged and discharged mask material.
Acknowledgement We are grateful to have received generous funding from the  European Union   H2020 research and innovation program  under the  Marie S. Curie Grant  Agreement (PhotoReAct, No  956324 , S. B. , M. L. , A. L. , G. M. , E. Z. C. , T. N. ; CHAIR, No  860762 , A. P. , M. J. , T. N. ). We also would like to thank  Ed Zuidinga  for help with the HRMS measurements.
Computational methods The structures of the synthesized compounds (4a-4i) were drawn using the Chem-Draw 12. 0 tool. The molecular geometries were optimized, and the energy of each molecule was minimized under gasphase conditions in their neutral states. This process was accomplished using the Density Functional Theory (DFT) method, specifically employing Becke's three-parameter hybrid exchange functional known as B3LYP  [34] , in combination with the Lee-Yang-Parr correlation functional  [35]. The Triple Zeta polarized (TZP) basis set implemented in the AMS program  [36]  was used. Vibrational frequencies were calculated at the same level as the optimization, except for the 4i compound, several computational functionals were considered, including two Generalized Gradient Approximation methods (PBE gradient-corrected exchange-correlation functional of Perdew-Burke-Ernzerhof  [37]  and the BLYP exchange-correlation functional by Becke  [38]  and Lee, Yang, and Parr  [39] ), two well-established hybrid functionals (B3LYP  [40] , PBE0  [41] ), and two range-separated functionals (CAMY-B3LYP, CAM-B3LYP  [42] ), with the TZP basis set  [36].
Quantum Bader Atom In Molecule (QTAIM)  [43, 44] , Molecular Electrostatic Potential (MEP)  [45] [46] [47] [48] , and global reactivity descriptors, such as ionization potential, electrophilicity, and net electrophilicity, are also exhibited through HOMO and LUMO energies. The Total Density of States (TDOS) for all compounds was investigated using the AMS program  [36]  and the corresponding plots were generated with OriginPro software  [49].
Molecular Docking Study To assess the interaction between synthesized molecules and the target protein, we conducted a molecular docking study using Auto-DockVina 4. 2 (MGLTools 1. 5. 7)  [50]. The study involved optimized structures of nitroaldol quinoline derivatives and the standard molecule Ciprofloxacin, formatted as a pdb file through the Open Babel GUI tool  [51]. The crystal structure of DNA-gyrase with PDB ID 1KZN was chosen as the protein model, obtained from the RSC Protein Data Bank (http://www. rcsb. org). Initial validation of the molecular docking protocol included re-docking the co-crystallized ligand (Clorobiocin) into the active site of DNA-gyrase. The PyMol software  [52]  as then used to calculate the RMSD (Root-Mean-Square Deviation) value for validation. Protein preparation involves eliminating water molecules, adding polar hydrogen atoms, assigning Kollman charges, and consolidating non-polar hydrogen atoms.
As a type II topoisomerase, DNA gyrase differs from other topoisomerases by introducing negative supercoils into DNA, making it a unique and essential target for certain antibiotics like fluoroquinolones, for instance, Ciprofloxacin. Inhibition of DNA gyrase by these antibiotics disrupts bacterial DNA replication, ultimately leading to bacterial cell death. Understanding the structure and function of DNA gyrase is critical for developing antibacterial agents and studying bacterial physiology. It remains a focus of research in the field of microbiology and drug discovery due to its vital role in bacterial DNA metabolism. Table  4  provides a detailed overview of molecular docking results for nine synthesized ligands (4a-4i) within the active cavity of DNA-gyrase (PDB Code: 1ZNK). Affinity values, types of interactions, residual amino acids, and interaction lengths are presented for each ligand.
Characterization SAXS experiments were performed on a SAXSess mc 2 instrument (Anton Paar), using a line collimation system. This instrument is attached to a ID 3003 laboratory X-Ray generator (General Electric) equipped with a sealed X-Ray tube (PANalytical, λ Cu, Kα = 0. 1542 nm) operating at 40 kV and 50 mA. A multilayer mirror and a block collimator provide a monochromatic primary beam. A translucent beam stop allows the measurement of an attenuated primary beam at q = 0. Materials were put between two sheets of Kapton® placed in a powder cell before being introduced inside the evacuated chamber. All data were corrected for the background scattering from the Kapton® and for slitsmearing effects by a desmearing procedure from SAXSQuant software using the Lake method. Powder X-ray diffraction patterns were recorded using a PANalytical X'Pert PRO diffractometer equipped with a Cu X-ray tube (λ Cu(Kα) = 0.
Acknowledgements We thank  CTI-CSIC  for computational facilities. We thank ILL for neutron beam-time allocation (experiment 7-05-470). Financial support by the  Spanish Ministry of Science and Innovation  ( CEX2021-001230-S  grant funded by  MCIN/AEI/10. 13039/501100011033  funded by " ERDF A way of making Europe " and  TED2021-130191B-C41  grant funded by the  European Union NextGenerationEU/PRTR)  are gratefully acknowledged. Authors thank also the financial support by  Generalitat Valenciana  ( Prometeo 2021/077 ). This study forms part of the  Advanced Materials programme  and was supported by  MCIN  with partial funding from  European Union Next Generation EU  ( PRTR-C17. I1 ) and by  Generalitat Valenciana  ( MFA/2022/047 ).
7Cl0. 3(s). The calculation was conducted at a temperature of 25 ⁰C, with the total concentration of Fe species held at 0. 1 M, and a NaCl concentration of 5 mM. All equilibrium constants for the given species were used as reported in the software database,  38, 47  except for Fe2O3 (hematite) and Fe(OH)2. 7Cl0. 3(s). For hematite, we adjusted the equilibrium constant (K) to account for a deviation in the reported values of its Gibbs free energy of formation,  39, 47, 48  thereby ensuring a better correlation with the results reported in our study. We set the lgK of the reaction 2Fe 3+ + 3H2O ↔ 6H + + Fe2O3(s) (2) to -1. 5, in accordance with the notation used in the Hydro Meduza Database entries. 38  We also adjusted the K value for Fe(OH)2. 7Cl0. 3(s) formation from 3. 0 (as reported in the Hydro Meduza database) to 1.
Period 1: Initial Formation of Surface Films on Fe Interface Upon examining the images of the entire Fe interface at 0. 8 s, 1. 0 s, and 1. 5 s in Fig. 3a , it is apparent that the darkening of the metal interface starts preferentially at the edge of the Fe electrode and then propagates towards the center of the electrode. It is important to note that the initial image at 0 s (not displayed here but available in the Zenodo repository) exhibits a completely white appearance. This https://doi. org/10. 26434/chemrxiv-2023-stbm1-v2 ORCID: https://orcid. org/0000-0003-4289-9678 Content not peer-reviewed by ChemRxiv. License: CC BY-NC-ND 4. 0 corresponds to 100% of 1+∆𝑅𝑅 10 𝑅𝑅 ⁄ , indicating that there were no observable changes prior to the application of chronoamperometry, which commenced at the 0-second mark.
Oxylipins, small polar molecules derived from the peroxidation of polyunsaturated fatty acids (PUFAs), serve as biomarkers for many diseases and play crucial roles in human physiology and inflammation. Despite their significance, many non-enzymatic oxygenated metabolites of PUFAs (NEO-PUFAs) remain poorly reported, resulting in a lack of public datasets of experimental data and limiting their dereplication in further studies. To overcome this limitation, we constructed a high-resolution tandem mass spectrometry (MS/MS) database comprising pure NEO-PUFAs (both commercial and self-synthesized) and in vitro free radicalinduced oxidation of diverse PUFAs. By employing molecular networking techniques with this database and the existent ones in public repositories, we successfully mapped a wide range of NEO-PUFAs, expanding the strategies for annotating oxylipins, and NEO-PUFAs and offering a novel workflow for profiling these molecules in biological samples.
As part of our continuing interest in NEO-PUFA chemistry, we developed a streamlined molecular networking dereplication pipeline based on the implementation of a MS/MS library that we named non-enzymatic oxylipins MS/MS library (NEO-MSMS). It contents an in-house collections of synthesized and unique NEO-PUFA standards, commercially available enzymatic oxylipins, and a curated MS/MS data collection of commercially available oxylipins from Watrous et al. study  6. Moreover, in vitro non-enzymatic oxidation of the four main PUFA (AA, eicosapentaenoic acid [EPA], docosahexaenoic acid [DHA] and α-Linolenic acid [ALA]) lead to the creation of a vast MS/MS database of NEO-PUFAs which was sought to extend the coverage signature of oxylipins. This data descriptor presents the deposition of all this data in Zenodo and MASSIVE repositories along with its subsequent technical validation.