dataesr/scientific-paragraphs-categorization huggingface dataset:
75 and an angle of about 55 • , so very similar to SSMI/S. The biases are remarkably similar in most cases, indicating that the average of all SZA biases can be used to accurately represent the true viewing geometry biases, if needed. As many of the channels are the same, or similar, to channels in the previous instruments discussed, there are no big surprises in terms of behaviour. The water vapour channels 7-11 around GHz show only slightly bigger differences than the lower temperature sounding channels 1-7 between 50 -60 GHz. These follow the same pattern as the MSU temperature sounding channels, however, with lower standard deviations for the 25,000 profile dataset than the training profiles.
Spatial variation of bias from the independent dataset In forming the statistics shown in the previous section there are details lost in the averaging process that are revealed with a spatial view of the biases over the entire globe, so spatial variability for each profile in the independent dataset was also evaluated. For the infrared, 2 IRIS-D channels, one SIRS-B and one MRIR channels are shown. The three IRIS-D channels have a corresponding similar channel on another instrument (two in SIRS-B and one in MRIR) to test the robustness of the results. These comprise one surface channel (centred at 899 cm -1 ) one temperature (CO 2 ) channel (centred at 679 cm -1 ) and one water vapour channel (centred at 1510 cm -1 ). Figure  8  shows the spatial distribution of the difference between RTTOV and LBLRTM simulations for all independent profiles.
Conclusions The main objective of this study was to validate RTTOV coefficients using a large independent profile dataset for the historic infrared instruments: IRIS-D, SIRS-B, MRIR and HRIR and microwave instruments: MSU, SSM/I, SSM/T2, SMMR and SSMI(S). The top of atmosphere (TOA) radiances are computed at high spectral resolution for a large profile data set (NWPSAF 137 levels models profiles dataset interpolated to coefficient levels) using LBLRTM at nadir and AMSUTRAN at 6 angles view. The LBLRTM TOA radiances convolved with the instruments ISRF were compared against the RTTOV simulations for the infrared, whereas for the microwave a tophat/boxcar function is assumed for the passbands. The statistics of the comparison (mean, standard deviation and maximum) of the large profile dataset were then compared with the statistics of the profiles used to generate the RTTOV coefficients (83 training profiles dataset).
The results for the infrared sensors showed that the statistics for the independent profile dataset (25,000 profiles) are similar to those found when using the 83 training profiles. This confirms that it is acceptable to validate the RTTOV coefficients using the same profiles used to generate the coefficients. Differences between RTTOV and LBLRTM are higher in the water vapour channels, where the differences can reach 0. 4 K (up to 0. 2 K for the standard deviation) in the independent profiles. In almost all channels evaluated in this work the training profiles show differences smaller than the 25,000 profiles. The maximum differences were also observed in these channels and the values are higher in the independent profiles (up to 6 K). The latitudinal dependence of the bias is found in the channel centred at 679 cm -1 from SIRS-B instrument and the range of the bias is higher for multispectral instrument than for hyperspectral For the microwave sensors examined the biggest differences between RTTOV and AMSUTRAN are in water vapour channels, with means of up to 0.
02 K (up to 0. 1 K standard deviation) in the training profile set, however the validation with 25,000 profiles shows this increases to 0. 04 K (up to 0. 13 K standard deviation) which is still very low overall. Maximum differences in the training profile set reach -0. 3 K in these channels, whereas a value of nearly -2 K was seen in the larger profile set, however these very low values are extremely rare, and are associated with profiles that significantly deviate from the profile mean and located in regions with unique atmospheric conditions, such as deserts, tropics or the polar regions. Even though this study is restricted to historic sensors, the majority of which are no longer in operation, it confirms that the validation statistics for the 83 profile dataset are adequate to represent the overall biases for a range of different instruments.
Code and data availability. The RTTOV model can be downloaded from the NWP SAF website (https://nwp-saf. eumetsat. int/site/software/rttov/). The LBLRTM model can be downloaded from https://github. com/AER-RC/LBLRTM. The atmosphere profiles can be downloaded from https://nwp-saf. eumetsat. int/site/software/atmospheric-profile-data/.
https://doi. org/10. 5194/amt-2023-125 Preprint. Discussion started: 10 July 2023 c Author(s) 2023. CC BY 4. 0 License. wavelength. As for the IRIS-D channels, the maximum values for the SIRS-B are higher for the independent profiles, with values up to 3. 5 K against 0. 5 K for the training dataset.
In Scotland, MDMW is linked to SCI-Diabetes-a national diabetes electronic record containing data on all >300,000 PWD in the country. 16 MDMW takes a subset of data from primary and secondary care, including key diabetes indicators (HbA 1c , blood pressure [BP] and body mass index [BMI]), eye and foot screening results, medications and clinical correspondence. The platform provides users access to these records, as well as to advice and resources (information, structured education and videos, curated web links) tailored to each user through data characteristics and the implementation of rules and algorithms. 17 sers can view their retrospective data through history graphs (Figure  1 ). A bullseye-style 'target chart' (Figure  2 ) displays key diabetes indicators, encouraging the user to bring their HbA 1c , BP, BMI and cholesterol into the central green region.
MDMW also contains a checklist of care targets achieved for each user, based on the Diabetes UK '15 Healthcare Essentials',  18  highlighting any overdue assessments. Users can enter home-recorded data (e. g. weight, BP) into MDMW, and can also use the site to set health and lifestyle goals for themselves. In response to previous user feedback from 2015,  15  additional functionality was introduced in 2016 allowing users to upload home blood glucose readings (either manually or through integration with a third-party product-DIASEND  19  ). Additionally, since July 2019, MDMW has been able to link to users' Fitbit activity data. User feedback from 2015 surfaced some site navigation issues. 15 The front end of the MDMW platform was therefore updated in 2018 in an effort to provide an improved user experience (Figures  1  and 2 ).
The questions in the current questionnaire focussed on gaining insights that would inform further improvements to MDMW. Questions were agreed through 'Face validation'  20  by several health informatics experts. The questionnaire was deployed in Survey Monkey and sent via email to MDMW users in Scotland. 'MDMW Users' were defined as the cohort of individuals who had successfully logged on to access the service at least once prior to the launch of the questionnaire in September 2019. The questionnaire remained open for 12 weeks. Question items are shown in Appendix. My Diabetes My Way-held demographic data on respondents were retrospectively (pseudonymously) linked to questionnaire responses. These included age, ethnicity, sex, diabetes type, diabetes duration and socio-economic status (SES).
Utility score was used as the outcome variable in univariable regression analyses. All demographic (predictor) variables that exhibited nonsignificance (p > 0. 1) in univariable analyses were excluded from the multivariable analysis. Demographic groups were compared using t-tests where data were normally distributed (e. g. for age) and Mann-Whitney U tests where not normally distributed (e. g. for diabetes duration). Free-text responses were analysed by the lead author through deductive thematic analysis, with major themes and subthemes generated from the data. This was a service improvement project, and medical research ethics review was therefore not required. All users invited to complete the questionnaire had previously (upon MDMW enrolment) consented to receiving unsolicited emails from the MDMW team for service improvement purposes.
In response to this finding, user guides will be 'pushed out' to users more frequently within existing monthly newsletter emails. Many users desired MDMW to encompass all their healthcare records, not just those directly related to diabetes, apparently viewing the platform as a potential 'gateway' to accessing all their health information. From the user point-of-view, this may seem a simple request. However, the digital landscape for diabetes in Scotland is relatively mature compared with other medical specialties, making wider data integration problematic. The Scottish Government's long-term plan is for care in Scotland to be 'enhanced and transformed' through the use of digital technology, with an aspiration to improve interoperability across different systems and provide patients with access to their health records.
28 As more specialties 'catch up' to diabetes in this respect, it is envisaged that data will be able to be shared between different electronic clinical records. Some users also expressed a desire for their healthcare team to be more aware of, and engaged with, MDMW, and to refer to it during consultations. Using ePHRs such as MDMW as a basis for conversations about the patient's care would fit well with the 'house of care' model, which recognises the importance of clinicians and patients working together collaboratively in shared decision-making. 29 he house of care model is being actively promoted in Scotland. A new study is currently underway to assess (for the first time) healthcare professional perceptions of MDMW, which should shed more light on this area. There were some calls from users for more 'personalisation' within the site.
While some users complained about the 'overly secure' login processes, others expressed concerns about the security and privacy of their MDMW-held data. These views can be seen as two sides of the same coin, and they highlight the challenge faced by a digital health intervention such as MDMW in striking an acceptable balance between security/data privacy and usability. 30 It should be noted that the majority of users did not express concerns over either of these issues. One of the outcomes of the previous 2015 questionnaire was the development of the MDMW mobile App. The App is now the preferred means of accessing MDMW for 24% of users. In the current questionnaire, a minority of users reported that the App did not perform as well as the website (e. g. that their records were not up-to-date within the App).
There was a known issue with data synchronisation at the time of the survey, and this issue has now been addressed. The addition of functionality that allows Fitbit data to be uploaded to MDMW in response to the 2015 questionnaire has been popular with users, some of whom asked for data from other Apps and wearable devices to be integrated. Integration with Apple Health data is planned within the next 12 months, and it is planned that more devices will follow shortly, once funding has been secured.
| CONCLUSION This study uncovered largely very positive user views of MDMW. For the majority of users, MDMW helped them to understand their results better, prepare better for face-to-face consultations, acted as a reminder of consultations and allowed users more time to digest complex health information. This could be expected to have beneficial downstream impacts on treatment adherence and therefore diabetes management. The questionnaire used in this study was designed to highlight areas where further improvements can be made to MDMW. Some users' limited digital literacy is a barrier to benefitting fully from using MDMW, and more will be done to signpost users to help in this area. There is a growing expectation from users that MDMW should link data from more wearable devices and health apps (following the success of Fitbit data being linked up with the system).
Hf Analytical Procedures We refer the reader to  Mueller et al. (2019 Mueller et al. ( , 2022) )  for U-Pb methods and data. All zircon U-Pb samples were analyzed for Hf isotopes at The University of Arizona LaserChron Center. Cathodoluminescence (CL) images ensured that 40 μm diameter Hf spots were placed above U-Pb pits that only ablated a zircon core or rim. Measurement precision for individual analyses are expressed using 2 s average uncertainty (±∼3 εHf units). We collected Hf data for isotopic fingerprinting from 21 late Cretaceous-Eocene sedimentary and extrusive volcaniclastic rock zircon samples  (Mueller et al. , 2022; Ocakoğlu et al. , 2018). Hafnium (Hf) isotopes were analyzed from 508 individual zircon with ages between 120 and 40 Ma. Generally, ∼10 unknowns were analyzed for each age peak according to  Gehrels and Pecha (2014).
Broadly speaking, geodynamic models describing the evolution of same-dip double subduction zone systems based on changes in the length and buckling of a middle plate explain late Cretaceous supra-subduction zone extension within the Neotethys and the transition from rifting to drifting in the Western Black Sea backarc basin. MATLAB data analysis tool can be found in the supplemental materials (https://github. com/ClayFC/binnedmean. git).
Acknowledgments This research is supported by the  U. S. National Science Foundation  grant  NSF   EAR 1543684  to K. C. B. and M. H. T. We extend a warm thanks to  B. Kurtoğlu ,  S. Mattingly ,  C. Ocakoğlu , and  M. Wood  for field assistance during various stages of this project. We also recognize  J. Oalmann , and  T. Tenpenny  at  The University of Kansas Isotope Geochemistry Laboratory  and  M. Pecha ,  K. Sundell , and  N. Giesler  at the  Arizona LaserChron Center  for analytical assistance. K. Sundell  and  N. McLean  provided assistance regarding the statistical treatment of Hf datasets. We appreciate the careful handling of this manuscript by  P. van der Beek  and thorough reviews from  N. Kaymakçı ,  E. Gülyüz , and an anonymous reviewer. D. J. J. van Hinsbergen ,  G. Topuz , and  E.
Data Availability Statement All Hf data collected in this paper can be accessed through "earthchem. org; Campbell et al. (2023)  (https:// ecl. earthchem. org/view. php?id=2742). " Plotter software can be found in Vermeesch et al. (2016) (https://doi. org/10. 1016/j. sedgeo. 2016. 01. 009) and Sundell et al. (2019) (https://doi. org/10. 1016/B978-0-12-816009-1. 00014-9). Maximum depositional age software from Keller et al. (2018) (https://doi. org/10. 7185/geochemlet. 1826).
Subjects with missing covariate data (described in Table  1 ) were excluded on a model-specific basis. Covariate-specific estimands are provided in Supplementary Table  S1. We also evaluated the joint association of obesity (BMI ≥30 kg/m 2 ) or abdominal obesity (waist circumference ≥88 cm) and history of "any obesityrelated metabolic condition," defined as one or more of the following conditions: dyslipidemia, hypertension, diabetes type-2, or PCOS, using as the reference category the absence of both obesity (or clinical abdominal obesity) and any of the above-mentioned conditions. Metabolic syndrome was not included in "any obesityrelated metabolic condition" as it is defined as a cluster of risk factors including abdominal obesity. Multiplicative interactions between excess adiposity and presence of any medical condition were tested by adding a cross-product term to the model.
2 ). The duration of true hurricane anticyclonic activity extended from the early evening of September 10 to the morning of September 11 (samples 4, 5, and 6), with a minimum atmospheric pressure of 980 hPa and a maximum sustained wind speed of 109 km h -1 detected at 00:30 on September 11 (during sample 6) at the Cape Race weather station, marking the passing of the eye of the hurricane. Heavy rainfall occurred from 22:00 September 10 to 01:00 September 11, for a recorded total of 9. 4 mm at Cape Race (Fig. 2 ). Wind direction at Cape Race shifted from the southwest during the height of the storm to gentler westerly winds after the hurricane passed (samples 7-11) with speeds between 20 and 50 km h -1. No local measurements of wind speed, direction, or amount of precipitation were possible at the sample location due to a lack of equipment; however, weather data collected at Cape Race (Supplementary Fig.
Unfortunately, we did not have the equipment to measure rainfall at the sample site, so we had to rely on data collected at Cape Race weather station, located approximately 60 km from the sample site. In future studies, we would ideally be able to measure precipitation, wind speed, and wind direction at the sample site. Furthermore, to fully quantify the full potential extent of the impact of future hurricanes on the deposition of MPs, we recommend the continued monitoring of multiple hurricane events, forming at different latitudes with different trajectories and strengths. The HYSPLIT model-and particularly the meteorological model outputs that are used to drive the HYSPLIT model-used in this study have limitations in reconstructing the complex atmospheric dynamics during a hurricane. This includes the use of air parcel (trajectory) modelling instead of particle modelling due to the overall small size of the particles and since MPs are thought to behave differently to the non-plastic particles for which these models were originally designed.
All filters were stored individually in 50 mm diameter sterilised aluminium containers to minimise sample contamination. All samples were analysed using µRaman spectroscopy followed by Nile Red fluorescence microscopy. MP particle counts and polymer composition results are derived directly from µRaman analysis (Supplementary Data 1), with fluorescence microscopy providing the particle size distribution results (Supplementary Data 2). µRaman analysis was undertaken using a Renshaw InVia spectrometer with a 780 nm wavelength laser, 1200 gr mm-1 grating, and using up to 25% power (filter). Spectra were collected over 200-3000 cm -1 using a minimum of 5 acquisitions of 10 s. Filters were analysed using the sub-sampling method presented in Huppertsberg and Knepper  70  to ensure a representative 25% of each filter was analysed (n = 2873 particles).
Filters were dyed using 0. 1 µg mL -1 Nile Red solution (powder dissolved in 99% pure ethanol (Sigma Aldrich), prefiltered through 1. 2 µm GF/C filter), air dried overnight in a 30 °C oven (covered in sterilised foil to minimise sample contamination), and then placed in a dark, 4 °C fridge prior to analysis. Fluorescence microscopy was completed using an OMAX microscope and imaged using the AmScope MU500 camera and software, with 5% of the sample assessed and the images analysed using FIJI (ImageJ)  74. Particles were classified as fragments or fibres, with fibres defined as particles with at least a 3:1 length-to-width ratio  44. Back-trajectory modelling. Back-trajectory modelling was carried out with the HYbrid Single-Particle Lagrangian Integrated Trajectory (HYSPLIT) model version 5. 2 using Global Data Assimilation System GDAS 1-degree archived global meteorology data (https://www.
Acknowledgements We wish to thank  Guang Yang , Facility Manager at the  Dalhousie Cosmogenic Isotope Lab , for her support in preparing the rapid-response field campaign. This work was enabled by the funding provided by the  Natural Sciences and Engineering Research Council of Canada (NSERC)  Grant  RGPIN-2020-04461  to V. M. , Grant  RGPIN-2018-04119  to T. R. W. ,  Ocean Frontier Institute  iPDF to V. M. and T. R. W. ,  Leverhulme Trust  Grant  ECF-2019-306  and  Carnegie Trust  grant  RIG009318  to D. A.
MR-imaging protocol. MRI data were collected on a 1. 5 T clinical MRI (Avanto, Siemens Healthineers). First, scout images were acquired on the subject to approximately locate the laser probe. Then, a 3D MPRAGE sequence was run with the following acquisition parameters (TI = 1000 ms, TE = 3 ms, TR = 2000 ms, FA = 15°, Field Of View (FOV) = 192 mm × 162 mm × 240 mm, 1 mm isotropic voxel size). The 3D volume was analyzed on the MRI console and images were reformatted to visualize the laser tip trajectory in two perpendicular projections. MR temperature images were obtained using a multi-slice single-shot echo planar imaging (EPI) sequence  18, 42  positioned perpendicular to the laser probe from the two selected orthogonal slices from the 3D MPRAGE sequence. A stack of 8 slices was acquired every second using the following parameters: TE = 21 ms, TR = 1000 ms, FA = 70°, 1.
4 mm × 1. 4 mm × 3 mm voxel resolution, GRAPPA acceleration = 2, partial Fourier = 6/8, bandwidth/pixel = 1445 Hz. The FOV was 180 mm × 180 mm for phantom studies and 160 mm × 160 mm for in vivo experiments. Two saturation slabs of 50 mm each were positioned on each side of the FOV in the phase encoding direction to avoid folding artifacts. MR-signal was acquired using a 4-channel array coil and two elements of the spine coil surrounding the subject. In total, 12 channels were used for image reconstruction. Raw data were streamed to a real-time reconstruction and thermometry pipeline  41. The reference phase image used for the PRF-based (using a PRF constant of -0. 0094 ppm/°C) thermometry reconstruction was created by averaging the 10 first dynamic acquisitions for each slice. A phase drift correction based on a spatio-temporal first-order polynomial function was applied on images with a high SNR (threshold set to 5% of the maximum of the average magnitude over 20 consecutive frames) and temperature comprised between two predefined thresholds ( T min < T < T max ) initialized to -5 °C and + 5 °C, respectively.
During the cooling phase, only the diffusion term remains in Eq. (  3 ). It has been demonstrated  34  that by applying a Fourier Transform on the spatial dimensions of Eq. (  3 ) one can obtain a first-order differential equation whose inverse Fourier transform of its solution is a Gaussian function given in Eq. (  6 ): were σ 2 (t) = 2Dt varies linearly in time with a slope equal to 2D. Thus, a fit of the temperature maps was done during the cooling period with a 2D Gaussian function (with σ x = σ y = σ assuming a constant value of D over the heated area) on the hottest slice for each dynamic during the cooling period, and the resulting standard deviation values were plotted as a function of time. The curve was then fitted to a linear function to estimate D. Data were processed in homemade code written in Python language.
Analysis of the efficiency of temperature regulation. To evaluate the efficiency of the regulation algorithm, the difference between the target and experimental temperature curves was computed as a function of time during the heating period (i. e. , target temperature = 0). Mean difference value and RMSE were calculated for the resulting curve and each temperature plateau, to investigate whether the efficiency varies depending on the value of the target temperature. The standard deviation of baseline temperature measurements was also calculated to compare the RMSE of the controller and the measured temperature uncertainty. Data were processed using dedicated Python scripts. (5) T(t) =    0 if t ≤ t 0 α • P • τ • ln t-t 0 +τ τ if t 0 < t ≤ t 1 α • P • τ • ln t-t 0 +τ t-t 1 +τ if t ≥ t 1.
Acknowledgements Stéphane Bloquet ,  Emilie Escurier , and  Virgine Loyer  are gratefully acknowledged for their assistance during the animal experiment. This study was conducted in the framework of the  University of Bordeaux 's  IdEx "Investments for the Future" program RRI "IMPACT " that received financial support from the  French government. This work was partly funded by research grants from  Agence Nationale de la Recherche  (projects  CARCOI  ( ANR-19-CE19-0008-02 ) and  IHU-LIRYC  ( ANR-10-IAHU04-LIRYC) ).