dataesr/scientific-paragraphs-categorization huggingface dataset:
48 g/L and a urea-ammonium mixture 1. 6 g/L). The next two variables are in only two levels: two KH 2 PO 4 concentrations (1 g/L and 2 g/L) and two different volumetric oxygen transfer coefficient (K L a) values (86 h -1 and 135 h -1 ). These variables were investigated to determine the most influencing parameters for the production of fengycin, surfactin, biomass, specific fengycin production (Y Pf/X ), specific surfactin production (Y Ps/X ) and the best percentage of fengycin produced. The interactions between the variables were also investigated. The software Minitab ® 18 (Minitab LLC, State College, PA, USA) was used to design this factorial plan and to analyze the results. This plan makes it possible to obtain 16 different media with 2 volumetric oxygen transfer coefficient conditions each, i.
1% (v/v) formic acid/99. 9% (v/v) water) and solvent B (0. 1% (v/v) formic acid/99. 9% (v/v) acetonitrile): from 35% to 50% solvent B over 10 min, from 50% to 90% solvent B over 5 min followed by washing and equilibrating procedures with respectively 95% and 35% solvent B during 5 min. The eluate was directed into the electrospray ionization source of the ACQUITY QDa mass spectrometer (Waters Corporation). The cone voltage and the capillary voltage were set to 15 V and 0. 8 kV, respectively. MS data were collected for m/z values in the range of 30 and 1 250 Da with a sampling rate of 10 points/sec. The RP-UPLC-MS data were analyzed with Empower 3 software (Waters Corporation). Quantification was performed with the m/z 753. 44, 1072. 69 and 1107. 58. The ion of m/z 753. 44, corresponding to the most intense ion detected for fengycin in the samples, is associated to the diprotonated isoform of fengycin A C19 or B C17.
Statistical Analysis In the in vitro test on the efficiency of lipopeptides against V. inaequalis, six different wells of each modality were performed, the average of these six values is presented in Figures  2  and 3  as well as the confidence interval for each value. The statistical analysis of the experimental design detailed in part 2. 3 of the Materials and Methods section was carried out using Minitab ® 18 software (Minitab LLC, State College, PA, USA). A two-sided 95% confidence interval was defined. Analysis of variance (ANOVA) and a linear factorial regression analysis were performed to evaluate the significant of each factor in the different output parameters (i. e. , for the production of fengycin, surfactin, biomass, specific fengycin production (Y Pf/X ), specific surfactin production (Y Ps/X ) and the best percentage of fengycin produced).
The quality of the linear regression model was analyzed using the Fisher F test and the coefficient of determination (R 2 ). Pareto plots, main effects and interactions are obtained on the standardized effects through analysis in Minitab ® 18 software. During orchard efficiency testing, several observations were made in orchards during the cycle of primary contamination of the disease. The last one took place after the release of the last stains from the primary contaminations. The notations were carried out on the three central trees of each elementary plot in the four repetitions of the test. In each elementary plot, 210 leaves were observed to count the number of leaves attacked. A leaf was considered attacked as soon as a stain appeared (all-or-nothing notation). This choice is justified by the fact that at this stage of the evaluation of lipopeptides, the primary objective was to observe the effectiveness against the disease and not the development of a control strategy with the assessment of the attack's severity.
Statistical Analysis In the in vitro test on the efficiency of lipopeptides against V. inaequalis, six different wells of each modality were performed, the average of these six values is presented in Figures  2  and 3  as well as the confidence interval for each value. The statistical analysis of the experimental design detailed in part 2. 3 of the Materials and Methods section was carried out using Minitab ® 18 software (Minitab LLC, State College, PA, USA). A two-sided 95% confidence interval was defined. Analysis of variance (ANOVA) and a linear factorial regression analysis were performed to evaluate the significant of each factor in the different output parameters (i. e. , for the production of fengycin, surfactin, biomass, specific fengycin production (YPf/X), specific surfactin production (YPs/X) and the best percentage of fengycin produced).
Study of the Persistence of Molecules after Spraying on Fruit Trees QuEChERS method was first validated by adding powder of lipopeptides (surfactin, fengycin or mycosubtilin) to a crushed apple preparation before applying the QuEChERS method solid phase extraction in order to ensure that the three lipopeptides were well detected (data not shown). The calibration curves obtained for the three lipopeptides allowed to quantify them on skin and flesh of the different apples between 0. 004 to 0. 066 mg of lipopeptides by kg of apple. Quantification of the three families of lipopeptides was performed by RP-UPLC-MS to study the persistence of lipopeptides 45 days after the spraying on fruit trees. Control samples (untreated), samples treated with the MS mixture, samples treated with the FS mixture were analyzed.
Acknowledgments: The authors would like to thank students from Polytech-lille (Sarah Berrada, Alexis Delrue, Nadine Halabi, Sarah Klai, Julia Morieux, Raphaël Leroux, Margot Thiblet et Mélissa Legrand) for their participation in the optimization Landy medium for the production of lipopeptides by B. subtilis ATCC 21332 and for the validation of QuEChERS method, as well as Emeline Bertrand from Institut Charles Viollette and Thomas Delhez from University of Liège for their help in lipopeptide production. DoE and mass spectrometry analysis were and performed on the REALCAT platform, funded by a French governmental subsidy managed by the French National Research Agency (ANR) within the framework of the "Future Investments' program (ANR-11-EQPX-0037). The Hauts-de-France region, FEDER, Ecole Centrale de Lille, and Centrale Initiatives Foundation are also warmly acknowledged for their financial contributions to the acquisition of the REALCAT platform equipment.
Material Data were captured in an already published cross-sectional case-control study including 38 women (median age 50. 5 years, IQR 9 years)  [23]. Participants were recruited at a university pain clinic. There, the study took place in a laboratory. Twenty women with chronic neck pain >6 months and a score >10/50 on the Neck Disability Index (neck pain group), and 18 age-matched asymptomatic women without a history of recurrent lower back or neck pain (control group) performed multiple tasks, during which the shear modulus of the neck extensor muscles was measured using ultrasound SWE (Acuson S3000; Siemens, Erlangen, Germany) (Figures  1  and 2 ). Exclusion criteria for both groups included systemic pathologies and medications that may influence muscle tone or performance, pregnancy, and BMI > 30 kg/m 2 (image quality).
Muscle stiffness measured over all scanned neck muscles, including the trapezius, splenius capitis, semispinalis capitis, semispinalis cervicis, and multifidus muscles (Figure  1A ) was recorded in longitudinal transducer orientation approximately 1 cm lateral to the spinous processes, centered at C4 (Figure  1B ), at 9 MHz with a 9L4 linear transducer with 4 cm footprint. The thyroid preset and the maximal range of shear wave speed (10 m/s) was set. Gain (14-20 dB), dynamic range (45-65 dB), and image depth of 3. 5-4. 5 cm were adjusted for good visualization. The shear modulus was computer-extracted over the complete elastogram (all visible neck extensor muscles) and also in muscle-specific regions using a custom MATLAB program  [23]. Stiffness measurements were averaged over the three task repetitions.
The visual check of data quality demonstrated artifacts  [26, 27]  in 28% of the elastograms of MVIC, and shear wave velocities exceeding 9. 98 m/s, suggesting measurement saturation  [26] , in 30% of the MVIC trials (Figure  2 ). MVIC data were excluded. Measurements of the resting state demonstrated insufficient reliability, ICC 3,3 = 0. 138, and were excluded  [23]. Data from nine tasks with active muscle performance remained in the analysis (Table  A1  in Appendix A). The insufficient data quality of MVIC and resting state prevented their use as references for normalization. (all visible neck extensor muscles) and also in muscle-specific regions using a custom MATLAB program  [23]. Stiffness measurements were averaged over the three task repetitions. The visual check of data quality demonstrated artifacts  [26, 27]  in 28% of the elastograms of MVIC, and shear wave velocities exceeding 9.
Statistical Analysis The mean of the raw and the normalized stiffness measures of each participant was computed, i. e. , nine measurements of different activities were averaged per participant for comparing groups. The Shapiro-Wilk test indicated skewed data distribution in the raw measures of the neck pain group (p = 0. 001) and with normalization to head lift in both groups (both p < 0. 001). To enable the comparison of all measures, within-group variation was described using the ratio of the interquartile range (IQR) divided by the median as a non-parametric coefficient. The change of variation with each normalization approach was expressed as a percentage of the variation of the raw measures. Between-group difference was examined using the non-parametric Mann-Whitney test, and the parametric independent t-test for the measures with normal distribution.
Statistical Analysis The mean of the raw and the normalized stiffness measures of each participant was computed, i. e. , nine measurements of different activities were averaged per participant for comparing groups. The Shapiro-Wilk test indicated skewed data distribution in the raw measures of the neck pain group (p = 0. 001) and with normalization to head lift in both groups (both p < 0. 001). To enable the comparison of all measures, within-group variation was described using the ratio of the interquartile range (IQR) divided by the median as a non-parametric coefficient. The change of variation with each normalization approach was expressed as a percentage of the variation of the raw measures. Betweengroup difference was examined using the non-parametric Mann-Whitney test, and the parametric independent t-test for the measures with normal distribution.
The elution was performed with a gradient mode using water (solvent A) and ACN (solvent B), all acidified with 0. 1% formic acid. The linear gradient elution program was as follows: 10% of B for 2 min; linear increase from 10% to 90% of solvent B for 20 min; 1 min at 90% of B; and 2 min of re-equilibration with 10% of B for a total run of 25 min. The LC flow rate was 400 µL/min and the injection volume was 10 µL. The column oven temperature was 30 • C and the sample tray temperature 4 • C. Eluted compounds were detected in positive mode by a full mass scan (m/z 200 to 500) using a Orbitrap Mass Spectrometer Exactive (Thermo Fisher Scientific Inc. Waltham, MA, USA) equipped with a heated electrospray ionization source (HESI-II). Instrument parameters were as follows: sheath gas 35, auxiliary gas 10 (both arbitrary units), spray voltage 2.
4 kV, capillary temperature 275 • C, capillary voltage 67. 5 V, tube lens voltage 120 V, skimmer voltage 24 V and source temperature 350 • C. Mass spectra were recorded at an ultra-high resolution (100,000). Instrument control, data acquisition and processing were performed using the associated Thermo Scientific software package (Xcalibur v. 2. 2 and Exactive v1. 1).
Concentrations of compound 7i in the unknown plasma and brain samples were determined using the respective calibration curve. The reportable linear range of the assay was determined, along with the lower limit of quantitation (LLQ). The exposure levels of compound 7i in the plasma and brain samples as determined by LC-MS/MS were reported (ng/mL). The [brain]/[plasma] ratios were calculated. Pharmacokinetic modeling was performed using the non-compartmental method based on statistical moments. In this method, exchanges between kinetic spaces are expressed in time. The curves were obtained using R software (Package ggplot2, v3. 3. 6).
Pro-Arrhythmic Effects Electrocardiograms were recorded on C57BL/6 male mice (n = 2-3 animal/group) anesthetized with isoflurane (5% induction, 1. 5% maintenance). Four electrodes were inserted subcutaneously in the mouse limbs. Electrical signals were recorded with a Powerlab (Ad instruments) and analyzed with LabChart Pro software (v8. 1. 19). Heart rate and PR, QRS and QT intervals were measured as described in Supplementary Figure  S3. We compared basal QT duration and its modification 3, 6, 10, 20 and 30 min after i. p. administration of compound 7i, at concentrations of 0. 5 mg/kg, 1 mg/kg, 5 mg/kg and 10 mg/kg, or its vehicle. Quinidine (Quinimax, Sanofi Aventis, Gentilly, France) is an anti-arrhythmic drug known to prolong QT duration and was used as a positive control (10 mg/kg, 100 mg/kg).
Acknowledgments: We thank  M. Bellard  and  PE Larchanché  for technical assistance,  D. Lefranc  for MS and EAE knowledge and experience,  N. Sergeant  for scientific discussion, LM. Goveas for English correction and the  Lille Pasteur Institute (France)  and the  Hospitalo-Universitary Department of Experimental Research of Lille (DHURE, France)  for their advice in animal manipulations. The 300 MHz NMR facilities were funded by the  Région Nord Pas-de-Calais (France) , the  Ministère de la Jeunesse, de l'Education Nationale et de la Recherche (MJENR)  and the  Fonds Européens de Développement Régional (FEDER).
Genome Extraction, Sequencing and Assembly DNA was extracted using a NucleoSpin Microbial DNA extraction kit (Macherey-Nagel, Hoerdt, France), according to the manufacturer's instructions. Library preparation was performed using Nextera XT technology and sequencing was performed on a NovaSeq 6000 instrument (Illumina, San Diego, CA, USA). After sequencing, reads were quality trimmed and de novo assembled using Shovill v1. 0. 4 and the quality of assemblies was assessed using QUAST v5. 0. 2. Quality control statistics of all sequenced isolates are provided in Supplemental Table  S2. All genomic sequences were deposited in DDBJ/EMBL/GenBank under the BioProject accession number PRJNA841857. Sequence types (STs) and resistance genes were determined using the CGE online tools (http://www. genomicepidemiology.
Molecular Characterization of Plasmids The replicon content and plasmid formula were identified from the WGS data using PlasmidFinder 2. 0. 1 and pMLST 2. 0 (http://www. genomicepidemiology. org/, last accessed on 21 June 2022). When in silico data showed no co-occurrence of resistance and replicon markers on the same contig, plasmids carrying the ESBL and carbapenemase genes were detected using Southern blot (using adequate probes tagging the genes and plasmids of interest) on PFGE-S1 gels (6 V/cm for 20 h with an angle of 120 • at 14 • C with pulse times ranging from 1 to 30 s)  [17]. When plasmidic location could not be evidenced, the chromosomal location was looked for by PFGE on I-Ceu1-digested DNA, followed by Southern blot hybridization using a 16S rDNA probe and probes corresponding to the ESBL and carbapenemase genes.
• Behavioral assessment At the end of treatment, locomotor asymmetry was measured with the cylinder test  [37]. Animals were placed for 5 min in a cylinder, 20 cm high and 20 cm in diameter, and their forelimb use during rearing was videotaped (OBS studio open-source software). The test was carried out blind to the experimental conditions. Slow motion recorded videos were viewed to count the number of forelimbs uses, defined by the placement of the whole palm on the wall of the arena. Unilateral lesion with 6-OHDA resulted in preferential use of the ipsilateral forelimb paw  [37].
The residual water signal was pre-saturated for 2 seconds. Assignment of peaks was performed using 2-dimensional experiments and databases  [40]. Then, as previously described, metabolite relative concentrations were extracted from CMPG spectra  [19]  and mean chain length and unsaturation were calculated from diffusion-edited spectra  [41]. The total acquisition time lasted 20 minutes. The free induction decays were Fourier transformed and manually phased with the Bruker software Topspin version 3. 6. 2. Then, further pre-processing steps (baseline correction, alignment, bucketing) were performed using NMRProcFlow v1. 4 online (http://nmrprocflow. org). The spectra were segmented in 0. 001 ppm buckets between 0 and 8. 5 ppm with exclusion of residual water peaks, macromolecule signals (for CPMG) and other regions corresponding to pollution.
• Statistical analysis All univariate analyses were performed using Graphpad Prism 8 software (San Diego-USA). All results were expressed as mean values ± standard error of mean (SEM). Parametric analyses were performed after verification of the assumptions of normality (Shapiro-Wilk and Kolmogorov-Smirnov tests) and sphericity (Bartlett's test). For all results, two-way ANOVA followed by a post-hoc Tukey test with correction for multiple comparisons were performed. Significance for p values was set at α = 0. 05.
Funding This work was supported by  ANR ,  DOPALCOMP ,  Fondation de France  (grant# 00086205 ), the  Institut National de la Santé et de la Recherche Médicale , and  Grenoble Alpes University. IRMaGe is partly funded by the  French program Investissement d'Avenir run  by the  French National Research Agency ,  grant Infrastructure d'avenir en Biologie Sante  [ ANR-11-INBS-0006 ]. This project was partly funded by  NeuroCoG IDEX UGA  in the framework of the  Investissements d'avenir program  [ ANR-15-IDEX-02 ].
This study is supported by the  Imperial  College PhD Scholarship for supporting  AGC ,  EU Horizon 2020 Research and Innovation Program GAN0682938 (JV) ,  Agence Nationale de la Recherche  grants  ANR-SNF 310030E-164245  ( JV ),  ANR-10-LABX-0030-INRT (JV) , and  ANR-10-IDEX-0002-02  (JV), the  Foundation Lefoullon Delalande   2019  for supporting RC, and  Imperial College startup funding (CHY). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Participants We tested thirty-six participants (M age = 22. 1 years, SD = 2. 8 years, 27 women) in the study approved by the research ethics committee at the Institute of Applied Psychology at the Jagiellonian University (Kraków, Poland). The sample size was based on previous work  (Korolczuk et al. , 2018 (Korolczuk et al. , , 2022)). All participants had normal or corrected-to-normal vision and no history of neurological or psychiatric disorders. All participants gave written informed consent. Data from seven participants were discarded from the analysis because of excessive artifacts (±2 SDs of the group average) in EEG recordings (two individuals) or noisy or "flat" EMG recordings (five individuals). The final sample consisted of 29 participants.
EMG and EEG Recordings We recorded electrophysiological data from 64 Ag/AgCl active pre-amplified electrodes (Biosemi Inc. ) at a rate of 1024 Hz (analogue bandwidth limit: from direct current to 268 Hz, -3 dB at one fifth of the sampling rate). The electrodes were positioned in accordance with the extended 10-20 convention. Two electrodes lateral to the external canthi were used to record the EOG and measure horizontal eye movements. To measure vertical eye movements and blinks, we recorded activity from an electrode beneath the left eye and subtracted this activity from the FP1 electrode. In addition, we recorded the bipolar electromyographic activity of the flexor pollicis brevis from each hand also using Ag/AgCl active electrodes positioned 2 cm apart on the thenar eminence.
EMG and EEG Preprocessing All the preprocessing steps and analysis of EMG and EEG data were conducted using BrainVision Analyzer 2. 0 (Brain Products GmbH), MNE Python toolbox  (Gramfort et al. , 2013) , and customized Python scripts (www. python. org). To detect the onset and offset of EMG activity, we used a customized Python program (Spieser & Burle, 2022), 1 which is based on a combination of two algorithms: "integrated profile"  (Liu & Liu, 2016; Santello & McDonagh, 1998)  and a variance comparison  (Hodges & Bui, 1996). Then, a naive observer, unaware of the trial type, manually corrected (if needed) the EMG onsets detected by the script. Based on this procedure, we distinguished and analyzed four types of trials: (1) pure correct go trials (i. e. , go trials with a single suprathreshold EMG activation for the correct hand), (2) failed stop trials (i.
01 and 100 Hz using a second-order infinite impulse response Butterworth digital filter (slope: 12 dB/Oct). The MNE Python toolbox  (Gramfort et al. , 2013; Uusitalo & Ilmoniemi, 1997)  was used to correct ocular artifacts. Data were then visually inspected for any remaining noise and artifacts. All electrodes were rejected even if only a small local artifact was present to allow for subsequent use of the CSD computation, which is particularly sensitive to local artifacts.
Behavioral Data Analysis The mean RT from correct go trials was calculated separately for each cue (temporal/neutral) and FP (short/long) condition. Similarly, the error rate in go trials (3%) was calculated for cue and FP conditions. The omission rate (3. 8%) was not further analyzed. The SSD was quantified for each cue and FP condition as the average delay between the target onset and the auditory stop signal onset for both successful and failed stop trials. The SSRT (the mean time to inhibit a response) was estimated using the integration method  (Logan, 1994). First, the RTs from correct-only trials were rank ordered for each participant and for each of the four conditions (temporal/neutral cue; short/long FP). Then, the number of all responses in a given condition was multiplied by the probability of responding to a stop signal at a given delay [p(respond|signal)] to produce the critical RT.
EMG Data Analysis To investigate the covert mechanisms for inhibiting actions to temporally predictable targets, we measured the partial response rate and the partial response correction ratio (the equivalent of the correction ratio in choice RT tasks) in stop trials. The partial response was computed as the proportion of stop trials containing a partial response (i. e. , a subthreshold activation of the correct hand) to all successfully stopped trials. The partial response correction ratio was computed by dividing the number of trials with partial responses by the overall number of incorrect activation trials (both failed stop trials and partial response trials). It indexes the ability to inhibit a response after it has been initiated. Our paradigm included only two FPs and no catch trials.
EEG Data Analysis The analysis of EEG data was conducted on short FP trials only. In go trials, we first analyzed the effects of temporal predictability on the frontocentral negativity known as the N-40 component, which has been shown to vary with the difficulty in response selection  (Carbonnell et al. , 2013; Vidal et al. , 2003 Vidal et al. , , 2011)). Activity over the FCz electrode was segmented from -500 msec to 500 msec time-locked to EMG onset, and baseline correction (from -500 msec to -300 msec time-locked to EMG onset) was performed. Next, data for individual participants were averaged for each cue condition (temporal/neutral) for short FP trials only. We then performed the CSD computation using BrainVision Analyzer 2. 0. The signal was interpolated using the spherical spline interpolation procedure  (Perrin, Pernier, & Bertrand, 1989) , setting the degree of spline to three.