dataesr/scientific-paragraphs-categorization huggingface dataset:
Assessment of PA Characteristics To assess the initial PA characteristics and the 6-month change in PA characteristics, we used data recorded during the first and sixth 30-day periods of activity tracker use (named M1 and M6 in the following text, respectively). We computed for each of these periods: (1) the mean daily steps and (2) the coefficient of variation (CV) of daily steps. The information provided by the activity tracker that we used in this study was the total daily steps, which is one PA parameter that reflects the user's PA behavior, and especially bipedal locomotion. This metric is considered a reliable proxy of the overall volume of the PA performed by a subject  [13]. Averaging it across a 30-day period gives us an indication of the individuals' PA levels. The CV of daily steps is the ratio of the SD of daily steps, estimated using data recorded during a 30-day period, to the corresponding mean multiplied by 100.
We first defined quartile-based cut-offs at M1 and used them to categorize PA level CV at M1 and M6, and the 6-month changes between these categories were then assessed. Statistical analyses were performed using SAS software (version 9. 4; SAS Institute), and two-sided P values <. 05 were considered statistically significant. Graphs were generated using SAS and Python (version 3. 5) software.
Machine learning approaches, including neural networks, can be explored to predict, for instance, the days when a user would be less active. Such information could be used by health-related apps to send motivational messages or propose services to the users to help them become more active. More generally, we strongly promote the creation of collaborations between academic researchers and wearable device manufacturers, which could give researchers access to rich and valuable databases. Future studies on these sources of data could help improve epidemiological and health knowledge, as well as enhance health-related wearable devices. From a public health perspective, our results suggest that weight-related health benefits can be observed below the controversial 10,000 steps per day threshold and emphasize the idea that "some PA is good, more PA is better" with regard to weight loss  [11, 32, 35].
Acknowledgments The authors would like to thank the anonymous reviewers for their thorough review and  Dr Joseph Rothwell  for careful proofreading of the manuscript. DE was supported by a doctoral grant from the  French National Cancer Institute  ( INCa_13563 ). This study was also supported by the  French Research Agency ANR (Agence Nationale de la Recherche)  via an  "Investissement d'Avenir" grant (investment for the future grant ,  ANR-10-COHO-0006 ), which supports the E4N study. GF is supported by the  Luxembourg Institute of Health. CG is supported by a doctoral grant from LIONS, a Luxembourgish association that supports cancer research. Study funders and sponsors had no role in the design of the study, analysis or interpretation of data, writing of the manuscript, or decision to submit the manuscript for publication.
Acknowledgment The authors gratefully acknowledge  N. Lepejova-Caudy  for the production and purification of Aβ peptides. This study has been carried out with financial support from the  French State , managed by the  French National Research Agency (ANR)  in the frame of "the Investments for the future"  Program IdEx Bordeaux - ANR-10-IDEX-03-02. S. Bonhommeau  thanks the  Nouvelle Aquitaine region  for its financial support through the  CPER 2015-2020 program. All the Raman, SERS and TERS measurements were performed at the platform SIV.
Participants 25 migraine patients (17 female, 8 male) suffering from migraine without aura were included in this study. Inclusion criteria were age between 18 and 60 years and a diagnosis of migraine with a reported migraine frequency between 2 to 5 days per month. Exclusion criteria for patients comprised migraine with aura, chronic migraine, and migraine preventive medication. Every patient was examined by a neurologist (GD, Hospices Civils de Lyon). As we were interested in studying attention during the interictal state, if the patient had a migraine attack during the 72 hours before the testing session, the session was postponed to an ulterior date. If the patient had a migraine attack during the 72 hours after the session, collected data were not used in the analyses, as it is common practice in neuroimaging studies of migraine  [21].
Data from 19 patients (13 female, 6 male) were analyzed: data from 5 patients were discarded because a migraine attack happened in the 72 hours following the recording session and data from 1 patient because the patient failed to perform the task correctly. Migraine participants filled the HIT-6 and the MIDAS scales to assess the severity of the disease  [50, 84]. 19 control participants free of migraine and matched to the patients for sex, age, laterality, education level, and musical practice 1 were included in this study. Exclusion criteria for all subjects 1 Pitch discrimination is required in the task described below, and is an ability increasing with musical practice. included a medical history of psychiatric or neurological disorder except migraine, ongoing background medical treatment other than contraceptive medication, pregnancy, and hearing disability.
MEG recording and preprocessing Simultaneous EEG and MEG data were recorded with a sampling rate of 600Hz during task performance. A 275-channel whole-head axial gradiometer system (CTF-275 by VSM Medtech Inc. , Vancouver, Canada) was used to record electromagnetic brain activity (0. 016-150Hz filter bandwidth and third-order spatial gradient noise cancellation). Head movements were continuously monitored using 3 coils placed at the nasion and the two preauricular points. EEG was recorded continuously from 7 scalp electrodes and EOG with one bipolar derivation. Please note that EEG data are not presented in this article, as they were only collected for comparison with previous ERP studies, see  Masson et al. (2020)   [61]  for more details. For each participant, a 3D MRI was obtained using a 3T Siemens Magnetom whole-body scanner (Erlangen, Germany), locations of the nasion and the two preauricular points were marked using fiducials markers.
These images were used for reconstruction of individual head shapes to create forward models for the source reconstruction procedures. MEG data were processed offline using the software package for electrophysiological analysis (ELAN Pack) developed at the Lyon Neuroscience Research Center  [1]. Continuous MEG data were bandstop-filtered between 47 and 53Hz, 97 and 103Hz, and 147 and 153Hz (zero-phase shift Butterworth filter, order 3) to remove power-line artifacts. An independent component analysis (ICA) was performed on the 0. 1-40Hz band-pass filtered MEG signal to remove eye-movements and heartbeat artifacts. Component topographies and time courses were visually inspected to determine which ones were to be removed through an ICA inverse transformation. 2 to 5 components were removed from the "bandstop-filtered" MEG signal in each participant.
Time-frequency analyses All further analyses were conducted using the Fieldtrip MATLAB toolbox (MATLAB 2015A version, Fieldtrip release version 20151231  [72] ). This study is focusing on cue-induced alpha and distractor-induced gamma activities. Definition of time-frequency bands and time-windows of interest are based on previous works with a similar paradigm in healthy young adults by  Elshafei et al. (2018 Elshafei et al. ( , 2019) )   [26, 28]. Based on these previous studies, we expected the following pattern of modulations of oscillatory power: (1) decreased power in the low-alpha frequency band (7-11Hz), notably in the motor and auditory cortices, in anticipation of the target sound, reflecting top-down facilitatory processes in the task-relevant areas; (2) increased power in the high-alpha frequency band  in the occipital cortices in anticipation of the target sound, reflecting top-down inhibitory processes of task-irrelevant visual areas; (3) increased power in the gamma frequency band in the ventral attention network following the presentation of the distracting sound, reflecting the triggering of bottom-up attentional processes.
Analyses of behavioral data were already performed in a previous article using this dataset  [61]. Only trials with a correct response were retained and median RT for each subject and condition were computed. A repeated-measures ANOVA with CUE category (2 levels: uninformative, informative) and DISTRACTOR condition (3 levels: NoDIS, DIS1, DIS2) as withinsubject factors and GROUP category (2 levels: control, migraine) as a between-subject factor was conducted. To correct for possible violations of the sphericity assumption, Greenhouse-Geisser correction was applied to resulting p-values. Post-hoc comparisons were conducted using t-tests followed by a Bonferroni correction. Statistical analyses were conducted using the software JASP (version 0. 9).
This work was supported by the  French National Research Agency (ANR)  Grant  ANR-14-CE30-0001-01  (to  Aurélie Bidet-Caulet  and  Anne Caclin ). This work was performed within the framework of the  LABEX CORTEX  ( ANR-11-LABX-0042 ) and the  LABEX CeLyA  ( ANR-10-LABX-0060 ) of  Université de Lyon , within the program " Investissements d'Avenir"  ( ANR-16-IDEX-0005 ) operated by the  French ANR. The ethical approval of this work was obtained through the  Hospices Civils de Lyon. The authors declare that there is no conflict of interest regarding the subject matter of this article.
Ceftaroline has demonstrated potent in vitro activity against bacterial isolates causing bloodstream infections in children; Table  1  shows the in vitro activity of several antibiotics against a range of isolates from pediatric patients aged 0-18 years worldwide from the Antimicrobial Testing Leadership And Surveillance (ATLAS) database  [43]. Of 8006 S. aureus isolates tested, 98. 1% were susceptible to ceftaroline (MIC 90, 1 mg/L), including 95. 9% of 3767 MRSA isolates (MIC 90, 1 mg/L), based on both European Committee on Antimicrobial Susceptibility Testing (EUCAST) and Clinical and Laboratory Standards Institute (CLSI) breakpoints. The high PBP-binding affinity of ceftaroline is thought to be responsible for the observed low MICs  [8, 41]. The clinical efficacy of ceftaroline has not been established against anaerobic microorganisms, although studies suggest ceftaroline has good in vitro activity against most Gram-positive anaerobes  [8, 44].
High-Dose Efficacy/Safety Extrapolation Ceftaroline fosamil high-dose recommendations for pediatric patients aged 2 months to less than 18 years were recently added to European labeling, for rare cases of cSSTI where S. aureus with ceftaroline MICs of 2-4 mg/L are suspected/ confirmed (Table  2 ). These dose recommendations were based on additional population PK modeling and exposure and PTA simulations, and exposure-matching to adults and other pediatric indications for extrapolation of efficacy and safety  [65]. A combined population PK model for ceftaroline fosamil and ceftaroline, which included data from 1248 participants (pediatric, n = 304; adult, n = 944), was used in the place of a previously planned clinical trial to assess the efficacy and safety of high-dose ceftaroline fosamil regimens in pediatric patients with cSSTI.
6  There is emerging evidence linking periodontitis with AF. 6  In a recent study, 5,958 patients had a periodontal assessment of severity and were monitored for AF. 7  Severe periodontitis was found to have higher associations with AF (multivariable adjusted HR 1. 31; 95% CI [1. 06-1. 62]). This study has significant strengths as it assessed periodontal disease using full-mouth clinical periodontal measurements with comprehensive measurements and followed up participants longitudinally for over 17 years. Incident AF was assessed using comprehensive assessments, such as 12-lead ECGs. A further retrospective cohort study in a large Taiwanese database has reported a positive association between periodontitis and AF, although this study has significant limitations as it did not assess for potential confounders.
9, 10  One of the studies investigated almost 30,000 individuals from a Taiwanese database and found that individuals who had at least one dental scaling procedure per year for at least 3 years had a lower incidence of new-onset AF (HR 0. 671; 95% CI [0. 524-0. 859]; p=0. 002). 9 However, the potential status of certain risk factors for AF, such as smoking, were not considered, and AF was only diagnosed using electronic records and not through validated methods like ECGs. Another observational study comprising 161,286 individuals from a Korean database found that improved oral hygiene and dental scaling also had a reduced risk for incidence of new-onset AF. 10  Although this study had more additional information included and adjusted for analysis like smoking status, exercise, and physical weight, there were still no validated methods used for diagnosis of AF and periodontal disease, with confirmation obtained only from electronic records.
The 640 × 640 pixels images were acquired with UPLSAPO 60X W NA:1. 20 objective lens. Numerical zooming to 0. 13 µm pixel size was conducted with respect to oversampling Nyquist criteria. A 3D optical sectioning of 0. 2 to 0. 4 µm was driven with a step Z-axis motor. The 3D images were analyzed with Imaris software (Bitplane, Belfast, UK). Time sequences of both transmission and fluorescence confocal images have been recorded in FM1-43 loaded cells. Hence, in each sequence, 300 fluorescence images with 5 s time resolution (time between two fluorescence acquisitions for each pixel position) were recorded on the same microscope field in cell preparations. Sequences of images were analyzed with ImageJ allowing us to follow fluorescence in each region of interest as a function of time. To monitor the formation of vacuole-like dilations (VLDs), mean fluorescence was counted in circle regions of interest surrounding a given VLD, normalized to basal fluorescence, and reported as a function of time.
Fluorescence kinetics were analyzed with a computer program developed in our laboratory under IDL 5. 3 structured language on a PC computer. Several parameters were extracted from the analyzed curves: amplitude (Plateau) and kinetic parameters of VLD formation (t0: time to fluorescence appearance, V max : maximal rate of fluorescence increase, and t Vmax : time to V max ). Surface variations were determined by manually measuring the surface of individual cells in a field as a function of time using transmission images (non-treated (NT) VLD, n = 47; NT cracked, n = 58; AA, n = 96; DHA, n = 88; four independent experiments). Since myoblasts can be considered as hemispheres, variations in the area of a given cell, provided that the distance between this given section and the base of the hemisphere remains constant (which is guaranteed by the fact that cells are adhered to the support), are directly proportional to surface variations of the overall hemisphere.
Molecular Dynamics Simulations All molecular dynamics (MD) simulations the MARTINI coarse-grain (CG) model  [19, 20]. The systems were built using the insane script  [21]  with the defined lipid parameters (with four beads for the oleoyl tail). The systems contained only PC species since they are the main components of human cellular membranes. The composition of the systems was optimized to fit at best the lipidomic results from MS. To do so, we mixed four different types of fatty acid tails (palmitoyl, 16:0; oleoyl, 18:1; AA, 20:4; and DHA, 22:6) in the following combinations: 16:0-16:0, 16:0-18:1, 18:1-18:1, 16:0-20:4, and 16:0-22:6. Note that the coarse-grained simplification does not distinguish C16:0 from C18:0, C20:4 from C20:5, and C22:6 from C22:5. The two leaflets were equivalent, and no flip-flop events were observed.
Every system was composed of around 3000 lipids at full hydration with 10 standard MARTINI water molecules  [19]  per lipid. The membrane composition was obtained by mixing the selective PC species indicated in the relevant Figures at a 50%/50% ratio. All simulations were performed using the GROMACS simulation package version 4. 5. 3 [22] , using an integration time step of 30 fs. The temperature was maintained at 310 K (37 • C) using the v-rescale algorithm  [23]  with a relaxation time of 1. 0 ps. Semi-isotropic pressure coupling was applied with a reference pressure of 1 bar using the Parrinello-Rahman algorithm  [24, 25]  with a relaxation time of 12. 0 ps; van der Waals interactions were turned off between 0. 9 and 1. 2 nm using the shift function in GROMACS. This shift function was also used to turn off the electrostatic interactions between 0 and 1.
Statistical Analyses p values were calculated either by two-tailed t-tests or ANOVA, completed by adequate post-tests, as indicated in the corresponding figure legends. These analyses were performed using the GraphPad Prism 5 software. ns: non-significant; **** p < 0. 0001, *** p < 0. 001, ** p < 0. 01 and * p < 0. 05. The multivariate analysis of the dataset, represented by principal component analysis (PCA), was performed using the Past software version 3. 25 (http://folk. uio. no/ohammer/past, accessed on 6 February 2020). For the PCA analysis, the double bond (DB) index of a given lipid was used to compute the PCA. When considering the dataset corresponding to phosphatidylcholine species, PCA-1 explained 62. 8% of the variation in the data, and PCA-2 explained 32. 7% of the residual variation.
Fatty Acid Distribution within Phospholipids Varies Depending on the Organ In a first step, we determined the fatty acid distribution within the different PLs species, namely PC, PE, PS, and PI, in various organs from mice under a standard diet. The present study was performed on representative digestive organs (liver and pancreas), skeletal muscles (tibialis anterior muscle), the heart (right ventricle and atrium), the brain, and the lung. With this aim, total PLs were extracted from the various organs and analyzed by mass spectrometry in both the positive and negative ion modes, as described in the previous section. The results obtained for PC are displayed in Figure  1  as radar graphs and Figure  S1  in Supplementary Materials as histograms, and the data corresponding to PI, PE, and PS are shown in Figures  S2-S4.
Acknowledgments: We are very grateful to  Anne Cantereau  ( Confocal and Electron Microscopy facilities, University of Poitiers, Poitiers, France ) for her precious advice regarding confocal microscopy imaging and to  Pauline Hardouin  for her precious help with organ preparations. Tom Ferreira  is acknowledged for helping in the mathematical processing of the surface variations displayed in this study. This work was supported by the  Ministère de l'Education Nationale, de l'Enseignement Supérieur et de la Recherche (French MENRT)  and the  Fonds Européen de Développement Régional (FEDER) , with grants to L. K. and A. B.
Background The mosquito microbiome is critical for mosquito development, and it can have significant effects on vector competence, host immune system signaling, and longevity  [1] [2] [3] [4] [5]. It has gained attention over the past decade for its influence on vector-borne pathogen transmission and as a potential avenue for vector-borne disease control. With this increasing interest, > 300 scientific publications on mosquito microbiome research per year can now be retrieved from scientific literature databases (Fig. 1 ). This increase in independent mosquito microbiome studies has led to large amounts of data on different mosquito species, with various underlying physiological characteristics, and from diverse geographical locations. It would be ideal for the data generated from these studies to be curated in a centralized location, as well as collected, analyzed, and stored in a way that would facilitate quick and easy access for various purposes including new hypothesis testing, development of novel research questions, and/or meta analyses.
Metadata Regardless of whether samples are collected from the field or laboratory, a comprehensive record of sample characteristics, collection, handling methods, and experimental conditions should be maintained. This would not only benefit downstream data analysis and result interpretation, but would facilitate study repeatability and/or reproducibility, and allow for subsequent data reuse and reanalysis. Based on the FAIR (Findable, Accessible, Interoperable, and Reusable) guiding principles for scientific data management and stewardship  [27] , a minimum information standard for reporting arthropod abundance data, MIReAD  [28] , has been developed. MIReAD's list and description of specific data fields that should be included in data collection sheets are relevant sample collection characteristics for mosquito microbiome studies, particularly those involving field collections.
Irrespective of chosen methods, each study should ideally use a single storage method to prevent the introduction of batch effects. Where this is not possible, for example when working with historical samples, batch effect should be accounted for during data analysis and its effect subsequently discussed. Sample processing, sequencing, and controls Each sample processing step, the type of sequencing approach employed, along with the type of controls included in each step should be recorded. The Genomic Standard Consortium's minimum specifications  [29, 30]  can be used as a baseline.
Metadata recommendations that are specific to field studies Location It is essential to record the origin and/or collection site of mosquito samples, as the mosquito microbiome is known to vary spatially  [53, 66, 67]. These records would facilitate study replication, reproducibility, and/or spatiotemporal analysis. In addition to the location name, the GPS coordinates of mosquito collection sites should be recorded with the most precise geodetic system. The best choice is often the local system which can subsequently be converted into international standards for example using the World Geodetic System. It should be noted that while the collection or breeding sites of immature mosquitoes represent sample origin, this might not be the case for adult mosquitoes, as some species can fly up to 50 km from their breeding sites  [68].
Thus, for field-collected adults, sampling location, rather than sample origin, should be indicated in the metadata records. Climatic and environmental data As with location, the mosquito microbiome shows seasonal  [69, 70]  and environmental  [71]  variation. Thus, where possible and applicable, climatic and environmental data such as temperature, humidity, atmospheric pressure, land cover, and proximity to human dwellings should be recorded. The environmental type (e. g. , breeding water or Type of semi-field structure Glass house, mesh house, mesh cage, etc. Dimensions of semi-field structure 00. 00 × 00. 00 m a May not apply to field-derived mosquitoes b Not applicable to field-derived mosquitoes atmospheric) and geographic scale (e. g. , microscale data collected with a data logger or macroscale data collected from a meteorological station) to which the data applies should also be indicated, along with the time scale over which the data apply.
Thus, in addition to metadata collected for field or laboratory studies (depending on the mosquito source), semi-field characteristics should be recorded. These should include the description and dimensions of the semi-field structure or the name, along with a reference of the facility if it is widely known and used (e. g. , the semi-field system at Ifakara Health Institute, Kilombero, Tanzania  [81] , or MalariaSphere at the International Centre of Insect Physiology and Ecology, Mbita, Kenya  [32] ).