dataesr/scientific-paragraphs-categorization huggingface dataset:
2a ). These layers lie beneath a thin water layer at 20 m depth with P-and S-wave velocities of 1500 and 0 m s -1 , respectively, and density of 1000 kg m -3 (green in Fig. 2a ). The unstructured numerical mesh is generated using the Gmsh software  (Geuzaine and Remacle, 2009)  distributed under the terms of the GNU General Public License (GPL). It contains 2344 quadrilateral elements and, using an interpolation polynomial order of 5, allows simulations up to 12 Hz maximum frequency. A double-couple point source is located at X = 2500 m, Z = 500 m and is time-modulated by a Ricker wavelet with a central frequency of 4 Hz, corresponding to a M w = 1 earthquake. Receivers are regularly spaced at the bottom of the water layer from X = 400 m to X = 2600 m. Spatial and temporal sampling were set to be 5 m and 5 ms, respectively.
4). Noise records, sampled at 100 Hz and 10 m (spatial sampling is equal to the gauge length), were resampled to match the simulated data using a 2D interpolation function. Noise records were then differentiated to strain rate and rescaled to simulate challenging noise conditions, with an average signal-to-noise ratio (SNR) of 8. 2 (Fig. 2d ). Here, SNR was calculated as the root-mean-square (RMS) ratio of average signal and noise amplitude spectra between 2 and 12 Hz. In spite of the added noise, accelerations converted from strain rates are compared to simulated accelerations (derived from simulated velocities by a finite-difference time derivative). Noise was not added to the latter, constituting a stringent algorithm validation. A(t) = (V (t +dt)-V (t - dt))/(.
Data For a real data application of the conversion technique, eight local earthquakes are used. These were recorded by three dark ocean-bottom telecommunication cables deployed in the Mediterranean Sea. This dataset is identical to the one used by  Lior et al. (2021) , and the cables' locations, bathy-metric profiles and layout are detailed there. This information, as well as earthquake data, is briefly repeated here. Data were acquired by Géoazur on two cables deployed offshore from Methoni, southwestern Greece, and one cable deployed offshore from Toulon, southern France. In addition to DAS data, several on-land seismometers, installed near the cables, were available during the measurements. These will be used later (Sect. 6) to compare DAS, and seismometerderived source parameters. The earthquake data are detailed in Table  1 ; earthquakes, cables and seismometer locations are shown in Appendix A; and magnitude and hypocentral   1 ).
Hypocentral distances are measured to specific cable segments or broadband (BB) sensors. Solid and empty symbols correspond to seismometer and DAS data, respectively. Data for NESTOR, HCMR and MEUST are indicated by circles, triangles and diamonds, respectively, and the number of data points is indicated in parentheses in the legend. distance distribution are shown in Fig. 6. In the latter, variations of hypocentral distance correspond to the different analyzed fiber segments. The earthquake DAS records used in this study are dominated by scattered Scholte waves, with velocities between 100 and 400 m s -1  (Lior et al. , 2021). The two cables deployed offshore from Greece are used for the HCMR (Hellenic Centre for Marine Research) and NESTOR (Neutrino Extended Submarine Telescope with Oceanographic Research) projects.
The acquisitions were conducted on 18 and 19 and 19-25 April 2019 on the HCMR and NESTOR cables, respectively. A Febus A1 DAS interrogator was used, measuring strain-rate signals. Data were sampled at 6 and 5 ms for HCMR and NESTOR, respectively, and gauge length and spatial sampling were both set to 19. 2 m for both cables. These records amount to 688 and 1365 equally spaced channels for the 13. 2 and 26. 2 km long HCMR and NESTOR cables, respectively. In addition, two seismometers installed near the on-land end of the fibers were available during part of these campaigns, METN and METS. The cable deployed offshore from Toulon is used for the MEUST-NUMerEnv project (Mediterranean Eurocentre for Underwater Sciences and Technologies -Neutrino Mer Environnement)  (Lamare, 2016)  and was previously harnessed for DAS measurements by  Sladen et al.
(2019). The acquisition was conducted on 11-31 July 2019 using a chirped-pulse hDAS interrogator developed by Aragon Photonics, measuring strain signals  (Pastor-Graells et al. , 2016; Fernandez-Ruiz et al. , 2019; Williams et al. , 2019). Data were sampled at 10 and 2 ms for the first and last 10 d of the campaign, respectively, and gauge length and spatial sampling were both set to 10 m. These records amount to 4480 equally spaced channels for the 44. 8 km long cable. In addition, two seismometers were installed near the on-land end of the fiber, POSAN and POSAS.
Implications for source parameter inversion Seismic moment, source corner frequency and stress drop are determined by fitting converted earthquake DAS signals with an earthquake source model. The source model chosen is the commonly used omega-squared model (e. g. ,  Brune, 1970; Madariaga 1976; Sato and Hirasawa, 1973) , which describes the far-field body wave radiation for ground displacements, velocities and accelerations. The model is fit to the data via the single-step inversion of  Lior and Ziv (2018). This approach is advantageous as model fitting is done in the time domain, circumventing the time-to frequency-domain transformation and avoiding many spectral model fitting intricacies.
2 and 10 0. 2 , respectively. Strain (rate) signals are then filtered (panel b), converted to ground velocities (accelerations) (panel c), and differentiated and/or integrated to obtain ground displacements, velocities and accelerations. Following each differentiation/integration, the aforementioned filter is applied. The signals' RMSs are calculated in the time domain, and source parameters are determined as detailed in Appendix B. An example of the single-step inversion's results is shown in Fig. 9d  where the best-fitting model is plotted using Eq. (3c) and compared with observed stacked acceleration spectra, and Fig. 9e  shows the best-fitting parameter combination ( 0 , f 0 and κ), indicated by a red star, in log(f 0 ) -log(f κ ) space. The color code in panel e corresponds to the bestfitting 0 (for each log(f 0 ) -log(f κ ) combination), and the contours correspond to the objective function's value.
Figure6. Earthquake catalog magnitude (M L ) as a function of hypocentral distances for the earthquakes in this study (Table1). Hypocentral distances are measured to specific cable segments or broadband (BB) sensors. Solid and empty symbols correspond to seismometer and DAS data, respectively. Data for NESTOR, HCMR and MEUST are indicated by circles, triangles and diamonds, respectively, and the number of data points is indicated in parentheses in the legend.
Acknowledgements. We thank the team from the  Centre de Physique des Particules de Marseille  who facilitated the access to the MEUST infrastructure. We thank  Stavroula Tsagkli ,  Katerina Tzamarioudaki  and  Christos Markou  from  NCSR Demokritos , the  Greek Institute of Nuclear and Particle Physics , who maintain the NESTOR cable infrastructure and facilitated the acquisition campaign. We thank  Paris Pagonis  from the  Hellenic Centre for Marine Research  who aided in the access to the HCMR cable. This infrastructure is part of the  European Multidisciplinary Seafloor and water column Observatory (EMSO). Financial support. This work and  Itzhak Lior  were supported by the  SEAFOOD  project, funded in part by grant  ANR-17-CE04-0007  of the  French Agence Nationale de la Recherche. Part of the project was also supported by  Université   Côte d'Azur IDEX program UCA JEDI   ANR-15-IDEX-0001  and the  Doeblin Federation  ( FR2800 CNRS ).
I. Lior et al. : Strain to Ground Motion Conversion    Code and data availability. Simulated and observed DAS earthquakes are available on https://osf. io/98cnk/ (last access: 15 January 2021) and https://osf. io/4bjph/ (last access: 27 December 2020), respectively. Broadband seismometer data were acquired by Géoazur; data for the POSAN and POSAS stations were downloaded from RESIF (http://seismology. resif. fr/, (last access: 1 May 2020).
Introduction The Western Pacific region experiences high mortality rates by suicide, but there has been little or no research in certain island communities while important efforts are being made in the larger countries of the Asia-Pacific region to improve the epidemiological monitoring of suicidal behaviour and promote prevention. 1  Official data give only yearly suicide mortality rates by sex and sometimes by age. These data do not make it possible to identify risk and protective factors for suicide in order to implement prevention strategies adapted to the local socio-cultural and geographic contexts. This is particularly the case for French Polynesia (FP), one of the eight French overseas territories, the others being New Caledonia, Martinique, Guadeloupe, French Guiana, R eunion, Mayotte, Saint Pierre and Miquelon.
They all have a strong cultural identity, and are all islands, except for French Guiana. FP includes more than 113 islands, spread over an area larger than Europe. For the 275 918 inhabitants of the 2017 census, the standardized suicide rates have fluctuated significantly over the years but have remained relatively stable over the period 1992-2016  (Source: Public Health Direction of French Polynesia). In FP and in the WHO Western Pacific Region, a programme called World Health Organization (WHO)/Suicide Trends in At-Risk Territories (START) was launched in 2007 to improve the quality of suicide data and initiate suicide prevention programmes. 2  The WHO/START study had four components: (i) a monitoring system to collect systematic information on fatal and non-fatal suicidal behaviours; (ii) a randomized study of brief intervention (psychoeducation on suicide) and contact (through 9 phone calls) for non-fatal suicidal behaviours and controls over an 18month period; (iii) a study of suicide cases by psychological autopsy; and (iv) the follow-up of medically serious suicide attempts.
The results of the first two components were described elsewhere. 3, 4 his current article presents the results of the third component of the WHO/START study, which are suicide data in FP collected during the preparation of psychological autopsies. This component is very important because it offers the best method to identify risk factors among people who died by suicide in FP and verify if they are similar to those identified in other countries. This current study collected clinical data related to suicides that occurred in FP between 1992 and 2016.
Data sources on suicide mortality After agreement from the Ethics Committee of the Public Health Department of French Polynesia (opinion no. 29 of 11. 01. 2007), as part of the WHO/START study between 2008 and 2016, retrospective clinical data were collected at the FP Court of Justice of Papeete and Gendarmerie Nationale with the consent of the Prosecutor of the Republic. Different sources of data were included to provide information on risk factors associated with the outcome of suicide cases. First, the archived records of all suicide cases followed by the Hospital Centre of French Polynesia (CHPF, the only hospital structure with a psychiatric department) between 2008 and 2016 were retrieved as soon as the research group were informed of a case of suicide. The information was retrieved from the medical records and interviews with the relatives made qualitative analyses possible.
Then, research was undertaken on the Centre for Epidemiology on Medical Causes of Death (C epiDc) website, which is the national database of causes of mortality, in order to have crude suicide rates in Mainland France and in the French overseas territories. These territories have notable differences but also similarities: demographics (multiethnicity and multiculturalism) and geographic (insularity). However, according to previous research, suicide rates were significantly higher in FP than in one overseas territory (Martinique) for the period 2005-2010. 5, 6.
Quantitative data on suicide mortality The Direction of Public Health in FP was contacted to collect the official suicide death figures between 1992 and 2016. The information provided was usually limited to the numbers and the standardized rates of suicide by sex, without information on the method of suicide or the age of the subject (with the exception of ad hoc studies for limited periods). The causes of death in the medical death certificates were coded according to the International Classification of Diseases (ICD) of the WHO in ICD-9 (1984-1998)  and ICD-10 (1999-2016). They were coded according to WHO rules by the Direction of Public Health in FP from 1992 to 2004, then by C epiDc-Inserm, France. The database on the cause of death according to the C epiDc for mainland France and the French overseas departments was used as a means of comparison.
This database also shows the numbers and crude suicide rates by sex. These data have been available since 2000, with the exception of figures for French Guiana, available since 2001 only, and from two territories (Mayotte, and St Pierre and Miquelon). The Overseas Pacific Territories are not included in this database, so suicide data were gathered for New Caledonia thanks to the team that conducted the psychological autopsy of the WHO/START study. 7 alitative data on suicide Information was collected from Judicial Police Investigations (JPI) records, which are systematically ordered in FP Prosecutor in the event of suicide and violent death. In order to carry out this investigation, researchers attended the FP Court of Justice and consulted the reports drawn up by the gendarmes between 2008 and 2011 and between 2015 and 2016, in order to consult the JPI records of all individuals that died of suicide in FP during that period.
The contact numbers or addresses of the relatives of the people that had died of suicide and other relevant information were retrieved. The police records are a rich source of information, with an average of eight people (family, friends, witnesses, neighbours) interviewed by the gendarmes for each case. The information collected was recorded on an information sheet by the interviewer and included sociodemographic data (identity; age; sex; dates of birth and death; profession; marital and professional status; identity of people interviewed during the forensic investigation) and investigation data (facts preceding the death reported by the interviewed people; details of suicide method; history of suicidal behaviour; communication of suicidal intent [verbal or non-verbal, such as previous suicide attempts]; medical history, including possible contact with health services; history of psychic disorders; use of addictive substances; critical life experiences; and changes in the behaviour of the deceased).
Statistical analyses The data were entered using the Epi-info software and analysed with the open source R software version 3. 5. 1. 14 Data are presented as n of subjects (%) or mean. Categorical data were analysed using v 2 -test and the Fisher's exact test in case of numbers < 5. Student's t-test was used for continuous variables. Two sample t-test was used to compare the baseline data between two groups (general population versus suicide population, males versus females). To compare groups by sociodemographic categories (i. e. sex, age, ethnicity), odds ratios (OR) with their respective 95% confidence intervals (95% CI) were calculated. A P-value < 0. 05 was considered statistically significant.
0/100 000 for those aged 46-65 years old; and 17. 0/100 000 for those > 65 years). In 2016, rates became the highest for the age group 26-35 years (17. 0/ 100 000), remained stable in those aged 46-65 years old and decreased in those > 65 years (becoming close to nil). For comparisons between French overseas territories and mainland France, crude suicide rates were used because standardized rates for Overseas were not available. This comparison (based on data from C epiDc) shows, depending on the year, intersecting and reversing curves, with sometimes synchronous peaks and trends that can be interpreted according to the economic, cultural and social contexts of these territories. Figure  3  shows the crude suicide rates from 2000 to 2016 in FP, French overseas territories and mainland France.
When mainland France was compared with FP, there were differences in the trends in the suicide rates. In mainland France, there has been a continuous decrease in suicide rates over the period 2000-2016. This decrease was also observed with the standardized suicide rates for both sexes in the WHO database: rates go from 16. 9/100 000 in 2000 to 14. 8/100 000 in 2010, to 12. 5/100 000 in 2015 and to 12. 1/ 100 000 in 2016. 16 In France, the sex ratio is  the same as that in FP, close to 3; that is to say that, overall, three-times more men than women die by suicide. Mainland France had much higher crude suicide rates but they approximated with those of the French overseas territories over time. Comparisons of crude suicide rates show lower mean rates in FP (10. 6/100 000) than in mainland France (15.
He finally jumped off the crane and crashed to his death by the side of the road. The decrease in suicide cases observed between 2008 and 2010 in FP coincides with the implementation of the WHO/START programme described above and the WHO/ SUPRE programme,  21  which included raising awareness and sentinel training in the main islands of FP, funded by a prevention institute EPAP in French. The closure of this institute and the interruption of suicide prevention programmes in 2011 possibly coincided with a subsequent rise in suicide rates. 22 nother aspect to consider is the balance between suicide and homicide rates. The homicide rates observed in the C epiDc database were high in some overseas regions where suicide rates were low (such as in Martinique versus FP). 23 In some cultures, the underlying aggressiveness that is often observed in the suicidal state might be expressed more in hetero-aggressive rather than in the self-aggressive behaviours.
Abstract. Cyclohexene (C 6 H 10 ) is commonly used as a proxy for biogenic monoterpenes, when studying their oxidation mechanisms and secondary organic aerosol (SOA) formation. The ozonolysis of cyclohexene has been shown to be effective at producing highly oxygenated organic molecules (HOMs), a group of molecules known to be important in the formation of SOA. Here, we provide an in-depth look at how the formation and fate of the broad range of observed HOMs changed with perturbations from NO x and seed particles. HOMs were produced in a chamber from cyclohexene ozonolysis and measured with a chemical ionisation mass spectrometer (CIMS) using nitrate (NO - 3 ) as reagent ion. As high-resolution CIMS instruments provide mass spectra with numerous ion signals and a wealth of information that can be hard to manage, we employed a primarily statistical approach for the data analysis.
(2015). The CI-APi-TOF mass spectrometry data were analysed in MATLAB (R2016a) with the toolbox tofTools (R607)  (Junninen et al. , 2010). In further analysis the signals at different times were made comparable by normalising all time series by dividing them with the sum of the three reagent ion time series (NO - 3 , HNO 3 NO - 3 , (HNO 3 ) 2 NO - 3 ;  Jokinen et al. , 2012). As quantification of HOMs remains challenging  (Riva et al. , 2019b) , we opted for analysing only the relative changes to HOM concentrations in this work.
Acknowledgements. We thank  Liine Heikkinen  for assistance during the experiments,  Pontus Roldin  for helpful discussion and the tofTools team for providing tools for mass spectrometry data analysis. Financial support. This research has been supported by the  King Abdullah University of Science and Technology  (grant no. OSR-2016-CRG5-3022 ), the  European Research Council  (grant no. 638703-COALA ) and the  Academy of Finland  (grant nos. 307331 ,  317380 ,  320094  and  326948 ). Open-access funding was provided by the  Helsinki University Library. Review statement. This paper was edited by  Manabu Shiraiwa  and reviewed by two anonymous referees.
Small-angle X-ray scattering (SAXS) experiments are important in structural biology because they are solution methods, and do not require crystallization of protein complexes. Structure determination from SAXS data, however, poses some difficulties. Computation of a SAXS profile from a protein model is expensive in CPU time. Hence, rather than directly refining against the data, most computational methods generate a large number of conformers and then filter the structures based on how well they satisfy the SAXS data. To address this issue in an efficient manner, we propose here a Bayesian model for SAXS data and use it to directly drive a Monte Carlo simulation. We show that the automatic weighting of SAXS data is the key to finding optimal structures efficiently. Another key problem with obtaining structures from SAXS data is that proteins are often flexible and the data represents an average over a structural ensemble.
To address this issue, we first characterize the stability of the best model with extensive molecular dynamics simulations. We analyse the resulting trajectories further to characterize a dynamic structural ensemble satisfying the SAXS data. The combination of methods is applied to a tandem of domains from the protein PTPN4, which are connected by an unstructured linker. We show that the SAXS data contain information that supports and extends other experimental findings. We also show that the conformation obtained by the Bayesian analysis is stable, but that a minor conformation is present. We propose a mechanism in which the linker may maintain PTPN4 in an inhibited enzymatic state.
Yet, the optimal weight one should put on the data is never known beforehand. These parameter choices have important consequences, and even more so if SAXS data are to be used together with other data, for which similar problems exist. When modeling structures from experimental data, appropriate relative weighting is of particular importance. In crystallography, for example, the free R-value  Brünger (1992)  is often used to find suitable values for unknown parameters such as the weight on the experimental data. This becomes rapidly cumbersome if more than one value needs to be optimized, and it is hardly an option for data with low information content such as SAXS or SANS. A more powerful and statistically more accurate solution to this problem can be obtained in the context of a Bayesian treatment of the structure determination problem.
This model allows us to automatically weight the SAXS data based on its agreement with other structural modeling terms. The modeling is performed in several stages, adding additional detail at each stage, starting with rigid body motions of protein domains, and subsequently adding and sampling conformations of the linker and the termini. This is followed by extensive unbiased molecular dynamics (MD) simulation starting from the optimal structure. We apply the new formalism and modelling strategy to the determination of the structure of the tandem domain of the protein PTPN4. This is a good test case since, due to its flexible linker, several conformations may be simultaneously present and influence the measured SAXS data, which hampered previous attempts to obtain useful insights with more standard approaches to interpret SAXS data obtained for this protein.