dataesr/scientific-paragraphs-categorization huggingface dataset:
Purple: sharpening after application of the Gaussian filter allows recovery of some image details after smoothing edges. Green: The median filter is less sensitive to edges. Image processing was done with 3D Slicer for segmentation (C), MeshLab and Paraview, for editing and visualisation, respectively (D). The STL and Trimesh python packages were used to quantify volumes, surfaces and distances (E). From a watertight mesh, surface is obtained by summing the surface of each individual triangle present in the mesh. Volume is computed with a volume integral formula discretized over tetrahedrons as obtained from (Zhang and Chen, 2001). Distances between nearby point clouds of different objects within a cell were used to estimate organelle proximity. The whole process requires 10 to 15 days for a microalgal cell with a cell diameter comprised between 2 µm to 8 µm.
Acknowledgements: This project received funding from the  European Research Council :  ERC Chloro-mito  (grant no. 833184 ). Research was also supported by a  Défi X-Life grant  from  CNRS , funds from the  CEA DRF impulsion FIB-Bio program , the  LabEx GRAL  ( ANR-10-LABX-49-01 ), financed within the  University Grenoble Alpes graduate school (Ecoles Universitaires de Recherche) CBH-EUR-GS  ( ANR-17-EURE-0003 ) and the  ANR 'Momix' (Projet- ANR-17-CE05-0029 ). This project also received funds from the  European Union 's  Horizon 2020 research and innovation programme CORBEL  under the grant agreement No  654248. This work used the platforms of the Grenoble Instruct-ERIC centre (ISBG ;  UMS 3518 CNRS-CEA-UGA-EMBL ) within the  Grenoble Partnership for Structural Biology  (PSB), supported by  FRISBI  ( ANR-10-INBS-05-02 ) and GRAL.
The electron microscope facility is supported by the  Auvergne-Rhône-Alpes Region , the  Fondation Recherche Medicale (FRM) , the funds  FEDER  and the  GIS-Infrastructures en Biologie Santé et Agronomie (IBISA). J. D. was supported by  ATIP-Avenir program. C. U. is supported by a joint  UGA-ETH Zurich  PhD grant in the framework of the " Investissements d'avenir" programme  ( ANR-15-IDEX-02 ). The authors thank the  Roscoff Culture Collection  that provided phytoplankton strains and  Noan Le Bescot  ( Ternog Design ) for help in the conception and realisation of the figures of this article.
Results The ATPase PscN interacts in vivo with a wide range of T3SS proteins. The SctN ATPase is anchored to the T3SS apparatus through interactions with the central stalk SctO and the peripheral stalk SctL. To look for soluble partners of this protein in P. aeruginosa, full-length PscN was fused to a Strep-tag and expressed in the P. aeruginosa CHA strain harboring a chromosomal deletion of the pscN gene. After checking that the T3SS functionality was restored by this complementation (data not shown), Strep-PscN and its binding partners were enriched by affinity purification using streptactin beads and the corresponding fractions were analyzed by mass spectrometry (MS)-based proteomics. Among the identified proteins, several T3SS proteins were copurified with PscN, including the exoenzymes S, T, Y, the translocators PopB (SctE), PopD (SctB) and their cognate chaperone PcrH, the peripheral stalk PscL (SctL), the export gate protein PcrD (SctV), the accessory protein PscK (SctK) and the chaperone PscB of the gate-keeper (SctW) complex (Table  2 ).
9 μm, Dr. Maisch). MS and MS/MS data were acquired using Xcalibur (Thermo Scientific). Mascot Distiller (Matrix Science) was used to produce mgf files before identification of peptides and proteins using Mascot (version 2. 7) through concomitant searches against Uniprot (P. aeruginosa PAO1 taxonomy for Streptactin pull-down eluate and E. coli BL21 strain for His-tag pull-down eluate, April 2020 versions), specific sequences of tagged and coexpressed proteins (homemade), classical contaminants database (homemade) and the corresponding reversed databases. The Proline software  [80]  was used to filter the results (conservation of rank 1 peptides, peptide length ≥ 6 amino acids, peptide-spectrum match identification FDR < 1% as calculated on peptide scores by employing the reverse database strategy, minimum peptide-spectrum-match score of 25, and minimum of 1 specific peptide per identified protein group) and extract the quantification information.
1, 100 mM NaCl, 2. 5 mM DTT and 0. 05% Pluronic F127. The thermophoresis signal was then measured on a Monolith NT. 115 device (Nanotemper) with premium capillaries (MO-K025). Data from three independent replicates were used to estimate the KD using the MO. Affinity Analysis software provided by the manufacturer Nanotemper.
Acknowledgements This work was supported by the associations " Vaincre la mucoviscidose " and " Gregory Lemarchal ", grants from the  AVIESAN T3SS  ( ANR PRP1. 4 ), the  Laboratory of Excellence GRAL  ( ANR-10-LABX-49-01 ) and the  Agence Nationale de Recherche  ( ANR-15-CE11-0018-01 ). Proteomic experiments were partly supported by the  Agence Nationale de la Recherche  ( ProFI  grant  ANR-10-INBS-08-01 ). Authors acknowledge the platforms supported by GRAL, financed within the  University Grenoble Alpes graduate school (Ecoles Universitaires de Recherche) CBH-EUR-GS  ( ANR-17-EURE-0003 ). We are grateful to  Caroline Mas  and  Cécile Morlot  for help with the Microscale Thermophoresis experiments and we sincerely acknowledge the help, support and critical reading of  Andréa Dessen.
Data Collection A systematic review framework was applied to the search, screening, and assessment of apps. We searched the 2 major commercially available app stores: Google Play and Apple iPhone Operating System (iOS) store, by using the application programming interfaces (APIs) on these platforms  [38, 39]. We first used manual keyword searches to create a set of seed apps, and then, we used a combination of API methodologies to identify and refine a set of relevant mood-monitoring apps on each platform. We then used API data scraping methods to collect all available user-generated reviews for the relevant apps identified. We used a combination of keyword searches to define a set of seed apps, followed by a snowball sampling technique to collect a series of similar apps. A full description of the API data collection methods can be found in Multimedia Appendix 1  [36, [40] [41] [42] [43] [44].
Following the guidelines on inductive analysis approaches in previous studies  [21, 35] , we developed a database of coded user reviews. Our approach to the analysis followed established coding techniques  [50]  across 3 phases: (1) data immersion, (2) data reduction, and (3) interpretation. A preliminary sample of 500 reviews was randomly selected to ensure adequate coverage of a range of apps. Three coders (EW, CG, and LC) each coded the reviews for positive, negative, or neutral sentiment and noted whether the review highlighted a specific feature of engagement. This allowed the coders to become familiar with the informational content and to generate first-stage concepts. After initial sentiment and feature identification, the coders developed a preliminary framework to organize codes (code name, description guidelines, and example quotes).
Limitations Our results should be considered in light of the following limitations. First, the data used in this study were publicly available reviews. Our sampling frame for contributors and the representativeness of the views expressed are unknown. User reviews on app stores do not provide demographic data; therefore, we are not aware of the age of users submitting reviews. Although we based this study on apps available to those aged less than 18 years, the user reviews analyzed may be from a wide variety of age ranges, including adults. This is a limitation of using publicly available app data; hence, the engagement features we reviewed cannot be generalized to youth populations specifically. Another limitation of looking at written user reviews is that they lack data surrounding user retention rates and periods of user engagement.
Acknowledgments EW, CG, and LC were supported by the  Medical Research Council (MRC) Mental Health   Data Pathfinder award  ( MC_PC_17214 ) at  Kings College London. JD received support from a  National Institute for Health Research (NIHR)   Clinician Scientist Fellowship  ( CS-2018-18-ST2-014 ) and  Psychiatry Research Trust Peggy Pollak Research Fellowship in Developmental Psychiatry. SV, AR, RS, ES, and JD are partially funded by the  NIHR Biomedical Research Centre  at the  South London and Maudsley NHS Foundation Trust and King's College London. The MRC was not involved in the study design, data collection, data analysis, report writing, and decision to submit the manuscript for publication.
To sum up, analytical data from plant tissues are difficult to apprehend and decipher unless a single plant species is harvested close to and around the point source emission being investigated in order to reduce as much as possible adverse inputs from environmental variabilities or from undesired sources. The deciduous species Cercis siliquastrum was selected because it is abundant on the earth berms and nearby, it presents a large surface leaf area, and it is not known to have capacity for trace elements translocation from soils to leaves. A total of 25 Cercis siliquastrum trees were sampled in a single day on 2 July 2019, which represents a good spatial coverage of the study area. Their heights vary from 0. 50 m on vegetated earth berms to a few meters for a tree close to the noise barrier side or the trees outside the motorway domain.
The Pearson's and the Spearman's correlation coefficients are reduced to 0. 69 (with a p-value of 5. 06 × 10 -4 ) and to 0. 74 (with a p-value of 1. 97 × 10 -4 ), respectively. Next, we tried to verify the traffic origin of the PM deposition by identifying the vehicle-related tailpipe, non-tailpipe, and natural emissions from trace elements associations. To this end, a principal component analysis (PCA) was performed with the FactoMine R package tools  [53]  on selected chemical elements characteristic of traffic emissions such as Te, Cs, Ba, Sb, Ni, Sn, Zr, Sc, Sr, Zn, Pb, Cr, U, V, Mo, Hg, As, Mn, Se, Fe, and Cu. Unfortunately, we have been able to perform such analysis solely on soil samples because many targeted chemical elements are present in soil but there are not enough concentrated on plant leaves.
Principal component analysis on trace elements derived from XRF data reveals three clusters of relatedness (Figure  4 ). The first two components account for 42. 5% of the total variance in the dataset. Cluster 1 is mainly composed of Sc, Sr, Zn, and Pb and is negatively correlated with cluster 3, which represents associations between Cr, V, As, Mn, Se, Fe and Cu elements. On the other hand, cluster 2 demonstrates associations between Te, Cs, Ba, Sb, and Zr chemical elements. The traffic origin seems clear. Elements in cluster 1 are often associated with fuel and additive residues  [54] [55] [56] [57] , elements in cluster 2 are mostly linked to vehicle wear on the roadway  [55, 57, 58] , and finally, elements in cluster 3 may be linked to the vehicle and highway equipment wear  [56, 59, 60].
PM Dispersion Modeling Results The openFoam test cases were uploaded to the Zenodo open-access repository  [62]  and are available under the Common Creative license to reproduce the results obtained in the present study. Equation (  1 ) was resolved with the scalarTranportFoam solver included in the open source openFoam platform tools after having made some slight modifications to the source code in order to consider the turbulent diffusivity. This modification is required, since for PM in general  [33]  and for PM2. 5 in particular, the effective diffusivity (. ) is mainly controlled by the turbulent diffusivity ( 8 ) rather than the laminar ( 7 ) diffusivity. In the present case,  8 computed from the model outcome with Equation (  4 ) is within the range 10 -3 to 10 1 m 2 s -1 , which compares with 1.
These volumetric sources are centered on each 3-lane carriageway. We applied PM2. 5 emission values equal to 0. 35 and 0. 42 µg. m -3 s -1 for A9 and A709 motorways, respectively, which were calculated from the traffic data (Table  1 ) and an overall PM2. 5 emission rate of 0. 025 g veh -1. km -  found in the bibliography for a current mixed light/heavy-duty traffic  [37]. Table  1. Mean daily traffic density used to calculate fleet momentum and turbulence. LD and HD are daily numbers of light duty and heavy duty vehicles with their respective proportions used in the model (LD:HD). Distance corresponds to the distance between two consecutives vehicles. Note that only the numbers in bold characters are derived from traffic data (see text for explanation) in the north-south direction. We assumed that these values are the same for traffic in the opposite direction.
The stationary field u in Equation (  1 ) was calculated in two steps. By means of a precursor zerowind model, the mean fleet-induced turbulence and momentum were calculated from an average traffic configuration constructed from 4-month-averaged daily data provided by the motorway company. In the absence of precise information on traffic volume by lane, we have arbitrarily distributed the proportion of vehicle on each lane with the only constraint preventing heavy goods vehicles from using the leftmost lane reserved to the light-duty vehicles. Furthermore, care has been taken to keep the ratio between light-and heavy-duty vehicles for the 3-lane carriageways close to the data. From this vehicle density, we calculated a distance between two consecutive vehicles by considering that all vehicles travel at the maximum authorized speed of 90 km/h.
Acknowledgments: We thank  Mathilde Bardon ,  Cinderella Boyrie  and  Julie Faure  for helping us during the soil sampling, as well as  Julien Camps  for his contribution in the development of the numerical topography and in the traffic simulations. We would particularly like to thank  Georges Fandos  from the MDPMA9 citizen association and the municipality of Saint-Aunès through its first magistrate,  Alain Hugues , who have greatly contributed to the development of the experimental site along the motorway. We finally acknowledge the three anonymous reviewers for their constructive comments and discussion, which helped to improve the manuscript.
As antibiotic-resistant bacteria can make it difficult to treat patients with complex infections, rapid and effective detection of resistance mechanisms can be vital in choosing the best antimicrobial  [19]. For this reason, the proteomics approach becomes an extremely important tool for the identification of expressed proteins in a differentiated way and for the discovery of new biomarkers in order to understand which proteins are more or less abundant in a diseased state compared to the healthy state  [23, 24]. As biomarkers are discovered, inclusive information on the nature of proteins and their expression in relation to antimicrobial agents and resistance mechanisms should be stored in databases  [25]. Thus, we aimed to identify VRE from processed meat (hamburgers, meatballs, and minced meat), investigate antimicrobial resistance, and find biomarkers of resistance in the Enterococcus genus, which will also be important for VRE from other reservoirs, as bacteria that can be studied in a One Health context.
Whole-Genome Sequencing, Genome Assembly, and Gene Detection Bacterial DNA was extracted from overnight cultures with a DNeasy Ultraclean Microbial Kit ™ (Qiagen, Hilden, Germany) as recommended by the manufacturer. The whole genome sequence (WGS) of strains was determined by de novo assembly of 2 x 150-bp paired-end reads generated with Illumina sequencing technology (San Diego, CA, USA) using assembler SPAdes [28], Burrows-Wheeler Aligner  [29] , and Pilon  [30]  for final polishing. The antibiotic resistance genes were characterized with the ARIBA  [31]  package by mapping short reads against a manually curated and updated database of resistance-associated genes derived from  CARD and Resfinder [32, 33].
Statistics and Bioinformatics Analysis of Spectra The sample mass spectra were analyzed using ClinProToolsTM software (version 3. 0, Bruker Daltonics, Bremen, Germany), which includes three types of machine learning algorithms to generate classification models: quick classifier (QC), genetic algorithm (GA), and supervised neural network (SNN). The selected spectra were submitted to the three algorithms, QC, GA, and SNN, where the cross validation is based on the precision of the algorithm in the correct assignment of a random sample to the correct group. For each peak, the analysis of the operational characteristic of the receiver (ROC) was performed based on the area under the ROC curve (AUC).
Furthermore, the peaks at m/z 2153. 18, m/z 4227. 42, and m/z 4652. 66 were not detected simultaneously by the three classification algorithms; however, they can be used as biomarkers of resistance since they were only identified in isolates resistant to this antibiotic. Nevertheless, the values of the AUC curve were not as significant (0. 63, 0. 71, and 0. 54, respectively), which means these peaks can be false positives. Figure  4  focuses on the part of the spectrum highlighting the peak at m/z 4652. 66. Biology 2020, 9, x 8 of 17 Figure 3. Peaks obtained for tetracycline. From the analysis of the ClinProTools software, the control spectrum without antibiotics (red) and the spectrum with the action of tetracycline (green) were obtained. The results show statistically significant peaks, with a p-value of 0.
Funding: This work was supported by the " Espectrometria de massa e sequenciação aplicadas à microbiologia clínica moderna: pesquisa e identificação de biomarcadores em espécies bacterianas multiresistentes " ( Programa de Acções Universitárias Integradas Luso-Francesas , Ação n. º:  TC-14 / 2017 ) and by the  Associate Laboratory for Green Chemistry, LAQV , which is financed by national funds from  FCT/MCTES  ( UID/QUI/50006/2020 ). The authors are grateful for the support of  UIDB/00211/2020  funded by  FCT/MCTES  through national funds. We also thank the  Ministere de l'Education Nationale, de l'Enseignement Superieur et de la Recherche ,  INSERM  ( UMR1071 ),  INRA  ( USC-2018 ),  Santé Publique France , and the  Centre Hospitalier Regional Universitaire de Clermont-Ferrand, France.
00 ± 3. 75 %ID/g), showed no uptake of the tracer. Analysis of the dissected aortas and gamma counting confirmed the specific lesion targeting with [ 18 F]AlF(RESCA)-cAbVCAM1-5 Nb as seen on the PET/CT images. Uptake in the aortas of ApoE -/- When comparing the imaging data obtained with two distinct preclinical PET devices in a crossover study, better image quality was achieved with the β-CUBE than with the LabPET8 (Figure  3A ). In vivo image contrast was evaluated by calculating target-to-brain (T/B) and target-to-heart (T/H) ratios. In both cases, significantly higher values were obtained with the β-CUBE than with the LabPET8 (T/B: 3. 88 ± 0. 88 vs. 2. 57 ± 0. 54, p < 0. 05; T/H: 1. 75 ± 0. 30 vs. 1. 40 ± 0. 24, p < 0. 05; respectively).
Data Analysis and Statistics Data are expressed as mean ± standard deviation. Comparisons between groups were performed using unpaired Student's t-test; for comparisons between scanners a paired Student's t-test was used. A p-value ≤ 0. 05 was considered significant. Statistical analysis was performed using Prism 5 (Graph Pad Software) or SPSS Statistics software (version 24. 0. 0, IBM Company, Brussels, Belgium).
Funding: This project has received funding from  FWO  project  N • G005815N  and  G0D8817N. This work was funded by a grant from the  Scientific Fund W. Gepts UZ Brussel. J. Bridoux  is funded by the  EU   H2020 MSCA ITN PET3D. Frederik Cleeren  is a Postdoctoral Fellow of  FWO  ( 12R3119N ).
Electrical simulations Electrical simulations were conducted with a TCAD software, Sentaurus Synopsys. This software solves the Poisson equation for the studied geometry. TCAD simulations give a detailed description of the electrical field of the microdosimeter as well as the transient of a heavy ion through any angle of the device, giving the charge collection. The simulated device is a 20 µm diameter n-type (with a doping concentration of 8. 61 × 10 11 cm -3 ) microdosimeter. The silicon wafers which the silicon detectors are fabricated from have a thickness of 5. 5 ± 0. 5 µm, with the thickness of this detector sample determined to be 5. 3 µm. The silicon dioxide charge surface density used is 10 11 cm -2. The electrical simulations were carried out with 0 V, 5 V and 10 V bias voltage. Experimental measurements with 5 MeV alpha particles indicate a full depletion of the microdosimeter sensitive volume at a 5 V bias voltage  (Fleta et al.
Monte Carlo simulations The experimental work was benchmarked against a Monte Carlo simulation of the beam energy deposition in an individual cell of the detector. Monte Carlo simulations were performed using the FLUKA Monte Carlo code (Ferrari, Sala, Fasso There is a predefined configuration in the code for its use in hadrontherapy ("HADROTHErapy") that was selected for the simulations, changing the default thresholds for the transport of all particles, multiple scattering and delta ray production and corrections to form factor for Compton Scattering. A stricter additional limit to the kinetic energy threshold was selected, changing it to 1 keV. This option was selected to reproduce the experimental results more accurately without adding too much additional computation time, as detailed in  (Böhlen, Cerutti, Dosanjh, Ferrari, Gudowska, Mairani & Quesada 2010).
The beam had a FWHM varying from 3. 46 µm (X) up to 4. 85 µm (Y). In those tests, the beam position was scanned following a two-dimensional grid while the detector collected charge in each energy deposition event was recorded. Normalized data is shown in figure  5. The CCE was calculated by normalizing the energy deposition values to the most probable energy deposition of the proton beam in the 5. 5 µm silicon layer of the sensor. It can be seen that most of the events apparently deposit less energy into the volume than expected, with a strong dependence on the position. Events with low charge collection efficiency occur in the periphery of the cylinder, whereas events with high CCE are more aggregated into the microsensor central region. These numerical results from the microbeam measurements and the CCE model are compared in next section.
Pipettes used to record LFP had 1-2 MW resistance while those used for juxtacellular patch-clamp recordings typically had 5-7 MW resistance. LFP and patch electrodes were pulled from borosilicate glass capillaries. Signals were amplified with a Multiclamp 700B patch-clamp amplifier (Molecular Devices), sampled at 20 KHz and filtered online at 10 KHz. Signals were digitized with a Digidata 1440A and acquired, using the pClamp 10 software package (Molecular Devices).
1 CaCl2, 2 MgCl2, 4. 6 Mg-ATP, 0. 4 Na-GTP, 5 QX314-Cl; pH adjusted to 7. 3 with CsOH; 290-300 mOsm. Under these recording conditions, activation of GABA A receptors resulted in inward currents at a holding potential (Vh) of À70 mV. Voltage values were not corrected for liquid junction potential. Patch electrodes were pulled from borosilicate glass capillaries and had a typical tip resistance of 2-3 MW. Signals were amplified with a Multiclamp 700B patch-clamp amplifier (Molecular Devices), sampled at 20-50 KHz and filtered at 4 KHz (for voltage-clamp experiments) and 10 KHz (for current-clamp experiments). Signals were digitized with a Digidata 1440A and acquired, using the pClamp 10 software package (Molecular Devices). For paired recordings, unitary synaptic responses were elicited in voltage-clamp mode by brief somatic depolarizing steps (À70 to 0 mV, 1-2 ms) evoking action currents in presynaptic cells.