dataesr/scientific-paragraphs-categorization huggingface dataset:
Movement behaviour classification in the training dataset Our machine-learning method accurately classified movement behaviours in accelerometer data: when evaluated using leaveone-participant-out cross-validation in 2501 hours of free-living data from the CAPTURE-24 study (online supplemental table 4), mean per-participant accuracy was 88% (95% CI 87% to 89%) and mean per-participant Cohen's kappa was 0. 80 (95% CI 0. 79 to 0. 82). This was consistent across age groups: in the 72 participants aged 38 years or older, mean per-participant accuracy was 86% (95% CI 85% to 88%) and mean per-participant Cohen's kappa was 0. 79 (95% CI 0. 76 to 0. 82). Mean per-participant precision and recall for each behaviour show most examples of all behaviours were correctly classified, with highest performance for sleep (online supplemental figure  1 ).
Misclassifications were most common between similar behaviours (table  1 ). As expected, classification performance was worse on individuals with very few true examples of a behaviour (online supplemental figure  1 ). While the different behaviours classified preclude an overall comparison, our model identified moderate-to-vigorous physical activity behaviours with substantially higher precision (overall precision 0. 75 vs 0. 37) and similar recall (overall recall both 0. 66) compared with using the standard 100 mg cut-point, and had higher face validity in UK Biobank, with median 25 min/ day in MVPA according to our model compared with 1. 5 hours/ day with the standard cutpoint. Overall, the behaviour classification showed high face validity when applied to UK Biobank participants' data (online supplemental figure  2 ).
Our epidemiological findings extend previously reported results by showing how reallocating time between behaviours is associated with cardiovascular risk (after adjustment for other behaviours). The results of this study are consistent with our previous results, which showed a dose-response association across quartiles of device-measured moderate physical activity for cardiovascular events in the UK Biobank (with 42% lower risk in the highest quartile for moderate physical activity compared with the lowest). 38 Notably, the current study extends those previous results by measuring and adjusting for behaviours throughout the 24-hour day using a compositional data analysis approach. These results are also consistent with results from a study of community-dwelling older women in the USA, which found 69% higher risk of incident CVD in the highest quartile for device-measured sedentary behaviour compared with the lowest,  39  and 22% lower risk of incident CVD in the highest quartile for light physical activity compared with the lowest.
However, in light of remaining challenges in validating sleep measurement and in studying sleep epidemiologically (eg, a fuller treatment may consider factors beyond duration, including sleep quality), these results are best considered as hypothesis generating. The performance of our behaviour classification model represents an improvement on previously reported machinelearning approaches in free-living data (Cohen's kappa 0. 80 vs 0. 68), likely due to careful curation of the behaviour classes in labelled data by two reviewers. 11 Our approach also performed better than traditional 'cut-point' approaches. While some characteristics of the CAPTURE-24 sample differ from UK Biobank, it is a large, varied dataset, and consistent performance of our methods across age groups suggests our methods are relatively robust.
Strengths This study has several strengths, notably including the use of device-based measurements to characterise movement behaviours in a large, comprehensive prospective study. Compared with self-reported measurements of behaviour, device-based measurements are at reduced risk of recall and reporting bias,  6  and they can capture behaviours such as light physical activity well. 7 The use of a wrist-worn device with a full 24-hour wear protocol (with high compliance) allowed the full day of behaviours to be measured. 24 The use of free-living data with 'ground truth' behaviour labels to develop and validate behaviour classification methods ensures they perform well in real-world settings. All methods used in this study are open-source and available for use in other wrist-worn accelerometer datasets.
Funding This research has been conducted using the UK Biobank Resource under Application Number  59070. CAPTURE-24 data collection and annotation was supported by the  UK Economic and Social Research Council  (grant number  ES/ L011662/1 ). RW is supported by a  Medical Research Council Industrial Strategy Studentship  (grant number  MR/S502509/1 ). AD, DB, and KR are supported by the  National Institute for Health Research (NIHR) Oxford Biomedical Research Centre (BRC). AD and SC are supported by  Health Data Research UK (HDR UK) , an initiative funded by  UK Research and Innovation (UKRI) ,  Department of Health  and Social.
), and lightning discharges, as well as meteors and, possibly, samples of strange quark matter (nuclearites)  [3]. Ground-based light sources like anthropogenic lights and bioluminescence can also be observed. To prove the UHECRs detection principle and study the aforementioned phenomena and their possible impact on the detection of UHECRs from space, the Mini-EUSO mission has been developed as a joint project between the Italian (ASI) and Russian (Roscosmos) space agencies. Mini-EUSO has been installed inside the ISS in August 2019, at an altitude of ∼400 km on the UV transparent window in the Russian Zvezda module. It is active since October 2019, with some data acquisition sessions being performed each month. Mini-EUSO and its main science objectives are depicted in Fig. 1. Among the other objectives already described for a space-based telescope, Mini-EUSO will provide the mapping of the Earth in the near-UV band at night, giving important information on the emissions from the Earth's surface, which will also represent the background for the detection of other phenomena occurring in the atmosphere.
The Mini-EUSO EM The Mini-EUSO EM is a detector similar to the Mini-EUSO flight model but with a plano-convex lens of 2. 5 cm diameter and 30 cm focal length as optical system, and a focal surface corresponding to the central EC of the Mini-EUSO focal surface. The decision of using a different lens system with respect to Mini-EUSO was made to avoid the risk of damaging the original Fresnel lenses. Since the main goal of the data acquisitions was to test the performance of the electronics, the loss in terms of light collection caused negligible consequences. Also the Mini-EUSO EM has a square field of view but it is ∼ 10 • × 10 • , due to the reduced focal surface and the different optics. In Fig. 2  the exterior of the Mini-EUSO EM is shown in the panel (c), including the lens at the center of the front side, while its focal surface is shown in the panel (d), composed only of the central EC.
The Mini-EUSO EM at TurLab In February and March 2018, the Mini-EUSO EM was tested at TurLab. After preliminary operation tests at its arrival, it was hung on the ceiling above the TurLab tank to be tested for its general performance as well as for the data acquisition and control software  [12]  and the trigger system  [13]. The setup for the TurLab measurements is shown in Fig. 3. In the central panel the TurLab tank is shown, and on top of it, the detector to be tested is installed on the ceiling as shown in the area surrounded by a red frame in the left panel. The pictures in Fig. 4  show the light sources and materials placed on the floor of the tank to reproduce various phenomena that Mini-EUSO is able to observe from space. In Table  2  materials and phenomena reproduced in the laboratory are listed with the corresponding means of emulation.
Tests on the L2 trigger and D3 continuous data acquisition At TurLab, also the L2 trigger was tested. Bright events lasting relatively long such as Lissajous figures produced by an analogue oscilloscope, which can give similar time profiles to those of transient luminous events, were detected using the L2 trigger. It also triggered on the mimicked city light, as the light increased rapidly when it entered the field of view of the detector from inside the tank, but any other material or light source such as glass dust or the Arduino-driven LED strip did not generate any trigger, i. e. , no fake triggers occurred during this test. Also, as shown in Fig. 5  for the D3 data, a continuous data acquisition was stored during the measurements, and each illuminated material and light source can be clearly recognized in the light curve.
As a preliminary estimation, assuming that the reflec- tivity of polished aluminum is about 0. 92, the reflectance of the different materials varying from 0. 17 (electronics board) to 0. 92 (fixed reference value for the polished aluminum foil) was measured. Simulation studies applying a dedicated trigger algorithm indicated the capability of a Mini-EUSO-like detector to observe and track space debris. Such detectors need a shade to prevent direct sunlight reaching the lens without hindering the field of view, otherwise stray light brings protection-mode to not damage the device. By using ESAF (EUSO Simulation and Analysis Framework)  [18]  an estimation of the minimum sizes and maximum distances of detectable space debris was made  [17]. Furthermore, the number of expected space debris detections was estimated by using an ESA software called MASTER (Meteoroid and Space debris Terrestrial Environment Reference)  [19] , which provides the density of space debris with parameters such as altitude and inclination.
Such a detector was evaluated to have a potential detection rate of hundreds of space debris per year  [17]. An important contribution was provided by the detection of a real space debris with the Mini-EUSO EM during a data acquisition session at the Astrophysical Observatory, discussed in Section 4. 3. 2. Size and distance of this rocket body have been rescaled in order to reproduce the detection of space debris with Mini-EUSOlike detectors. These studies are important to verify the working principle of a space-based system under development for the detection and remediation of space debris  [5].
Acknowledgements The authors acknowledge all members of the  JEM-EUSO Collaboration , especially the Mini-EUSO team. This work was supported by  State Space Corporation ROSCOSMOS , by the  Italian Space Agency  through the  ASI INFN  agreement n. 2017-8-H. 0and contract n. 2016-1-U. 0,by  Basic Science Interdisciplinary Research Projects of RIKEN  and  JSPS   KAKENHI grant  ( JP17H02905 ,  JP16H02426  and  JP16H16737 ), by the  Italian Ministry of Foreign Affairs and International Cooperation , by the  French space agency CNES , by the  National Science Centre in Poland , grant  2017/27/B/ST9/02162  and grant  2020/37/B/ST9/01821. PRISMA was funded by  2016/0476  and  2019/0672  Research and Education grants from  Fondazione Cassa di Risparmio di Torino  and by a 2016 grant from  Fondazione Agostino De Mari (Savona).
RESULTS Sampling and antibiotic usage. A total of 280 individual fecal samples were collected from healthy broiler flocks (5 birds/flock; birds from 12 to 45 days old) during summer and fall 2018 in 56 selected farms representing the main regions of poultry production in Lebanon. Farm rearing practices were collected through a questionnaire, and answers are summarized in Table  S1  in the supplemental material. Antimicrobial treatment data revealed that all farms except 4 located in South (n = 3) and Mount Lebanon (n = 1) are systemically using antibiotics for prophylactic purposes. Depending on farms, antimicrobial treatments were applied at the chick's arrival but differed in duration from 3 to 30 days and in the number of antibiotics administered, from doxycycline alone to up to 8 different antibiotics (Table  1 ).
coli genomic data regarding farm information did not return FIG 3 Legend (Continued) Values of 1, indicating maximum support, are not displayed. The tree was rooted according to the known relationships between E. coli phylogroups. For each strain, the sequence type, serotype, identifier, region, and farm of origin are indicated in columns 1 to 5 on the right side of the tree. Presence of bla CMY-2 , bla ESBL genes, mcr-1, and qnr resistance genes detected using ResFinder 2. 1 are indicated with a filled box. The strain identifier code includes the region initial followed by a unique sampled animal number per region, the abbreviation of the selection antibiotic, and an isolate number if needed (e. g. , S10CTX: isolate selected on cefotaxim from animal number 10 in the region South). any relevant explicative factor for the selection or dissemination of these resistances relative to breeding practices.
Moreover, all ESC-resistant E. coli isolates were considered multidrug resistant ($3 antimicrobial classes) but remained susceptible to carbapenems. These isolates showed mainly ESBL (71%), AmpC (11%), or both phenotypes. During the sample collection, we collected only generic data of antibiotic administration (used antibiotics, treatment duration, and causes of administration) as it was impossible for most farms to obtain precise information on the amount of antibiotics, the frequency of administration, and effective reasons of usage (treatment or prevention of infections and growth enhancement). Depending on the farm, sampled flocks were treated with a variety of antibiotic regimens, ranging from one antibiotic to a large cocktail of antibiotics (Table  1 ). Doxycycline, colistin, gentamicin, and ciprofloxacin were the most used antibiotics in the sampled flocks, indicating that they are frequently used in poultry farms in Lebanon (Fig.
25/23. 75 mg). Whole-genome sequence analysis. A set of 102 E. coli isolates representative of the diversity of antimicrobial resistance profiles and origins was subjected to whole-genome sequencing. Briefly, DNA extraction was performed using a NucleoSpin 8 tissue kit (Macherey-Nagel, Düren, Germany), and DNA samples were sent to Novogene Co. , Ltd. , for library preparation using the New England BioLabs Next Ultra DNA library prep kit and whole-genome sequencing on NovaSeq (Illumina) using 2 Â 150-bp paired-end sequencing (Novogene, Beijing, China). The paired-end, 150-bp-long raw reads were quality filtered, and Illumina adapters were removed by Novogene before data delivery, resulting in read quality above 30 at all positions. After a second quality checking using fastQC v0. 11. 7 (https://www.
2 and the VirulenceFinder tool v. 2 using default parameters for serotype and virulence gene detection, respectively  (27) (28) (29) (30). The phylogroup assignation was determined with Clermontyping v1. 4. 1  (31). This tool uses two different algorithms to infer the phylogroup: an in silico PCR method based on the presence/absence of 5 loci and a method based on the whole-genome similarity to reference strains. In case of discordant assignation, the genome-based output was preferred, except for five genomes (A14FEP [CAJPVB000000000], ND35FEP [CAJPXD000000000], S24CTX [CAJPYG000000000], S31CTX [CAJPYK000000000], and S32CTX [CAJPYQ000000000]). These strains were all assigned to phylogroup G by the in silico PCR method and to phylogroup F with the whole-genome method due to a reference genome incorrectly assigned in this tool at the time of the study (E.
coli 327_20; O. Clermont, personal communication). A phylogenetic tree based on genome-wide single nucleotide polymorphisms (SNPs) was produced using the parSNP tool, which is based on FastTree 2 maximum likelihood phylogenetic reconstruction  (32, 33). The software was run with the Escherichia coli K-12 MG1655 genome (GenBank accession U00096) as a reference for SNP calling and other parameters as default. The phylogenetic tree was visualized and annotated with the iTOL web interface tool v4  (34). Statistical analysis. The analysis of the phenotypic and genomic data sets was conducted in Python using the Pandas 1. 1. 0 library for data analysis (https://pypi. org/project/pandas/) and the Prince 0. 7 library for factor analysis (https://pypi. org/project/prince/): principal-component analysis (PCA) for numeric variables, multiple component analysis (MCA) for categorical variables, and factor analysis of mixed data (FAMD) for combinations of numeric and categorical variables.
The "n_iter" parameter of Prince analyses was set to 3 as recommended as it converges quickly. At the phenotypic level, the 341 isolates were studied for the association between their antibiotic resistance profiles and the explanatory variables of the questionnaire, including antibiotic administration, purpose of antibiotic usage, diet, nutrition supplements, region, and farm. Antibiotic resistance profiles were created as Pandas categorical data frames (resistant or susceptible) and fitted to MCA. At the genomic level, the 102 E. coli isolates were studied for the association between the resistance genes present in the genomes and the explanatory variables of the questionnaire as listed above plus their sequence type (ST). Data availability. Genome assemblies can be accessed through the European Nucleotide Archive (ENA) database under project accession number PRJEB43674.
ACKNOWLEDGMENTS We thank  Karine Praud  and  Isabelle Foubert  for their technical assistance and  Pierre Germon  for discussions on E. coli virulence factors. We are grateful to the  Ministry of Agriculture of Lebanon  for the help in contact with the different broiler farms. Finally, we warmly thank all involved veterinarians and broiler farm owners who participated in this study. This work was supported by  Saint Joseph University (USJ) of Beirut in Lebanon  and the  National Research Institute for Agriculture, Food and Environment (INRAE)  in France. M. M. was funded by a  USJ PhD studentship  (grant number  FP72/73 ), by the  National Center for Scientific Research in Lebanon (CNRS-L) , and by the  Francophone University Agency (AUF)  in Lebanon during her stay in France. The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.
16s rRNA analysis Raw 16S rRNA amplicon reads were processed with FastX-Toolkit to remove reads with quality score below 20. Paired reads were then merged and filtered using VSEARCH  29  remove reads with over 1% error rate, yielding an average of 65835 (11054-106171) (min-max) clean reads per sample (66813 (11054-106171) for human samples and 63910 (-45479-80168) for mice samples). Remaining reads were converted to FASTA format and subjected to chimera filtering using VSEARCH de novo mode and the rdp_gold. fa database. Remaining reads were classified using the UTAX algorithm with RDP database and reads classified as either chloroplast or not classified at domain level were removed. Operational taxonomic units (OTU) tables were generated using the UPARSE algorithm  30  implemented in VSEARCH software and all reads across samples.
The remaining reads were then clustered at 97% similarity and annotated by the rdp_16s_v18. fa database. All reads per sample were then mapped to representative sequences using VSEARCH to generate OTU abundance tables. Human samples were rarefied to 12,126 reads One sample from AIEC-positive group with less than the said numbers were removed. Mice samples were rarefied according to the sample with lowest number of reads (45,479 reads per sample). The resulting OTU table and representative sequences were then used to predict the metabolic functional profiles with PICRUSt2 (Phylogenetic Investigation of Communities by Reconstruction of Unobserved States). 31 Metabolic pathways were annotated using MetaCyc. 32.
9 ). When the thickness reaches 1100 m, the PmP reflection is not observed at zero offset (last panel of Fig. 9 ). These results of diffused Moho reflections are consistent with the results obtained by Ohira et al. [2017], where the zero-offset data show weak amplitude or absence of reflection from the Moho when the thickness of the MTZ is ~1 km. When the thickness of the MTZ is greater than 1100 m, the offset at which PmP is observed increases with the increasing MTZ thickness (Fig. S2 ), and the maximum amplitude of the PmP arrival also increases. The critical distance linearly increases with the thickness of MTZ, i. e. by ~350 m for every 100 m increase in the MTZ thickness (Fig. 10 ). To calculate the critical distance, here we have used the locally maximum amplitude of the PmP arrival where the Pn arrival is tangent to the PmP.
Synthetic seismogram for lithology from the oman drilling project We have used the velocity data of the ophiolite samples obtained from the recently drilled borehole in Oman for computing synthetic seismograms. There are 5 borehole sequences; GT1, GT2, GT3, CM1 and CM2, each with ~400 m thickness (Section S. 2 and Fig. S3  in supplementary material;  [Okazaki et al. , 2018; Kelemen et al. , 2020] ). The physical properties like P-wave velocity, bulk-density and porosity were measured for each sample from the lithology of the borehole sequence. For the synthetic seismogram modelling, we assigned GT1, GT2 and GT3 sequences for the lower crust and CM1, CM2 sequences for the crust to mantle transition zone. The P-wave velocities in the X, Y and Z directions are averaged to get single P-wave velocity for each sample from the borehole.
The presence of a broad high amplitude PmP arrival in the real data shown in Fig. 1  indicates that the Moho is likely to be a sharp boundary  [Vaddineni et al. , 2021]. The above analyses indicate that some insight could be obtained about the MTZ from the AVO analyses from high-quality zero offset to 40-45 km offset data. However, in real world, we either have Moho reflections at near offsets from MCS data or wide-angle PmP arrivals from ocean bottom seismometer data (OBS) over 15-40 km offset range with the interference of the Pg and Pn arrivals around the critical distance (Fig. 1 ). Since the offset range in the MCS is short and the signal-tonoise ratio for OBS data is generally low, especially when the seafloor morphology is rough, the AVO analysis for MTZ has never been performed.
Limits of the present study and suggestions for the future We have mainly focused on 1D structures in the lower crust and MTZ for certain crustal velocity structures and upper mantles, with one case of 2D model, based on the Bay of Islands ophiolites. Our findings will change depending upon the change in these parameters. Furthermore, the Earth is 3D visco-elastic anisotropic, and one has to take all these effects into account in quantifying the nature of the MTZ. The presence of 3D structures would create more complex waveforms and amplitude versus offset variations, whereas the attenuation due to visco-elasticity will cause a decrease in amplitude with offset, and may even change the waveforms. Secondly, most of near offset data are acquired using 3-6 km long streamers and wide-angle data using OBSs sparsely deployed on the seafloor where the signal-to-noise ratio is generally very poor, and hence these data are not suitable for the type of analyses suggested here.
In order to provide a quantitative information about the lower crust and MTZ, one would require high-quality multi-channel type data recorded from 0 to 40-50 km, with a shot interval of 100-200 m. To obtain such an offset, we suggest to acquire two-vessel data, with each vessel carrying 15-km long streamers and identical sources, spaced at 30 km, moving in the same direction and shooting alternatively (Fig. 15 ). When Vessel 1 shoots, these shots will be recorded on Streamer 1, providing offsets of 0-15 km (black arrows), which will also be recorded by Streamer 2, with offsets from 30 to 45 km (orange arrows). When Vessel 2 shoots, the data will be recorded by Streamer 1 recording an offset Fig. 14. Amplitude versus offset (AVO) for MTZ models: a) Normalised reflection coefficient (RC) as a function of angle (offset) for a simple two layer model containing lower crust and upper mantle with the Moho as an interface.