dataesr/scientific-paragraphs-categorization huggingface dataset:
and illustrated in Figure4b. Only summary results and measurements from selected amino acids are presented in this table. For the full set of results, please refer to Dataset S3 Element Age Age Code Sample Site analyzed category (years) δ 13 C collagen δ 15 N collagen δ 13 C Phe Δ 13 C GlyÀPhe δ 15 N Phe δ 15 N Glx TP #1 BLR04 BLR Radius/ Infant 2-3. 33 À21. 3 +11. 1 À28. 5 +13. 0 +3. 0 +15. 2 3. 7 ulna #2 BLR36 BLR Cranium Infant 0. 67-2. 83 À20. 9 +13. 2 À30. 9 +14. 9 +8. 0 +16. 9 3. 3 #3 BLR45 BLR Phalange Adult 30-49 À21. 5 +10. 9 À29. 7 +15. 1 +5. 5 +15. 3 3. 4 (hand) #4 BLR25 BLR Metatarsus Adult 25+ À21. 1 +11. 7 À28. 3 +13. 7 +10. 3 +16. 5 2. 9 #5 VPB56B VPB Cranium Infant 0 À20. 8 +13. 2 À28. 6 +10. 2 +11. 1 +16. 7 2. 8 #6 VPB129 VPB Rib Infant 0. 5-1. 75 À20. 8 +14. 4 À28. 6 +12.
9) microscope objective. The light transmitted back in the substrate is detected with the (25x, 0. 4) microscope objective coupled to the infrared camera. The polarization state of the outcoming light is analyzed with the rotating linear polarizer. In order to compare the transmission intensities regarding two opposite incident handedness, the raw optical images (Figs. 3(d ) and (f), left panels) are integrated over the circular area enclosed by the slit boundary. All the numerical simulations are carried out using the 3D Finite Difference Time Domain (FDTD) method available on a commercial software from Synopsis. The plasmonic core-shell nanowire forming the helical nanoantenna is modeled as a 105-nm diameter carbon cylinder covered with a 25-nm thick gold layer. Such a wire is wound up in the form of a 4-turn helix and is also used to terminate the structure on one end with a 100-nm long pedestal lying on a 100-nm thick gold layer covering a glass substrate.
Acknowledgement The authors are indebted to  Katja Höflich  for helpfull discussions. This work is funded by the  EIPHI Graduate School  (contract  ANR-17-EURE-0002 ), the  Region Bourgogne Franche-Comte , the  French Agency of Research  (contract  ANR-18-CE42-0016 ), the  I-SITE BFC  (contract  ANR-15-IDEX-0003 ). It is supported by the  French RENATECH network and its FEMTO-ST technological facility.
During this meeting, the TF decided to also include gout as one of the RMDs, due to the high prevalence and alleged relation to lifestyle. A systematic review was then conducted by JMG, MW, JRC and GC, aiming to identify systematic reviews and meta-analyses on these RMDs and exposures published between 1 January 2013 and 18 September 2018. A report of this review was written and presented to the TF at a teleconference in January 2019. At this teleconference, it was decided that alcohol should also be included as an exposure. 9] [20] [21]  Population, Intervention, Comparison, Outcome (PICO) strategies were written and search strategies were developed for each review. These strategies were implemented in the PubMed/Medline, EMBASE and CENTRAL databases. Inclusion criteria were: (1) patients with a confirmed RMD of interest, (2) the study assessed the association between an included exposure and disease progression, and (3) the study was longitudinal (either experimental or observational).
Each review was conducted by two members of the TF (diet: JMG and JRC; exercise: JMG and GC; weight loss: JMG and SMMV; Smoking: MW and Maxime Ransay (public health resident at the University of Lorraine); Alcohol: MW and JMG; Work: MW and PP), with oversight from the methodologist (FG). First, titles and abstracts were screened in pairs, with adjudication of any disagreements by a third member of the TF. Subsequently, full manuscripts of abstracts fulfilling the inclusion criteria were then assessed by the lead reviewer, with the second reviewer in each pair applying the inclusion criteria to the full texts of 10% of the included abstracts to assess agreement on the inclusion of full-texts, with good agreement demonstrated. Data were extracted from full manuscripts that met the inclusion and exclusion criteria by the lead reviewer.
Evaluation and dissemination We will aim to evaluate these recommendations by conducting an online survey across several European countries, collecting data on the feasibility, acceptability and transparency of the recommendations. To disseminate these recommendations, we will develop information materials about healthy living and the impact of lifestyle on RMD progression in collaboration with patients and patient organisations. Summaries of evidence will also be written and distributed to health professionals and stakeholder organisations (WHO, EULAR, National Institute for Health and Care Excellence).
Acknowledgements The Taskforce would like to thank the library teams at the  University of Lorraine  and the  University of Manchester, especially Mary Ingram at the Centre for Epidemiology Versus Arthritis , for their help with the literature reviews. Thanks to  Maxime Ransay  for help with the systematic review on smoking. Thanks also to the administrative team of the  Division of Musculoskeletal and Dermatological Science at the University of Manchester  for their help with organising the face-to-face meetings.
Robot-assisted radical prostatectomy (RARP) is the preferred surgical approach for prostate cancer surgery in Western countries for the treatment of localized prostate cancer  [1]. It has been suggested that minimally invasive techniques including robotic assistance provide benefits in terms of blood loss, pain, hospital length of stay (LOS), and global postoperative recovery  [2]. This has led to wider acceptance of same-day-discharge (SDD) surgery  [3, 4]. However, to date, only a few large series, mainly from single centers, have been reported, and there are no countrywide data regarding the safety and utilization of SDD RARP in routine practice  [5]. Our study population consisted of all RARPs performed in France in 2020. Data were obtained from the nationwide French Programme de Médicalisation des Systèmes d'Informations registry  [6].
The third early Gaia data release (EDR3) has improved the accuracy of the astrometric parameters of numerous long-period variable (LPV) stars. Many of these stars are on the asymptotic giant branch (AGB), showing either a C-rich or O-rich envelope and are characterised by high luminosity, changing surface composition, and intense mass loss. This make them very useful for stellar studies. In a previous investigation, we used Gaia DR2 astrometry to derive the luminosity function, kinematic properties, and stellar population membership of a flux-limited sample of carbon stars in the solar neighbourhood of different spectral types. Here, we extend this initial study to more recent surveys with a greater number of Galactic carbon stars and related stars by adopting the more accurate EDR3 astrometry measurements.
The Gaia all-sky survey has changed this situation by providing astrometric data with an unprecedented accuracy of all Galactic stellar populations (Gaia Collaboration 2018), in particular of long period variables (LPV;  Mowlavi et al. 2018) , among which most of these carbon star types are found. The Gaia-EDR3 identification number of these stars together with the full version of Table  1  are only available at the CDS via anonymous ftp to cdsarc. u-strasbg. fr  (130. 79. 128. 5)  or via http: //cdsarc. u-strasbg. fr/viz-bin/cat/J/A+A/664/A45 In Paper I, we used Gaia DR2 astrometric measurements to derive the luminosities and kinematic properties of already wellknown red giant carbon stars of different spectral types located in the solar vicinity. We found that the luminosity function (LF) of N-and SC-type stars is quite similar, but is clearly different from that of J-type stars.
Interestingly, we also found extended tails to low and high luminosity in the LF of the N-type stars and discussed the implications that this may have for the theoretical lower and upper mass limits for the formation of intrinsic AGB carbon stars. On the other hand, the derived LF of R-hot stars revealed that these stars should be in the RGB phase rather than in the red clump of the HB, as previously suggested  (Bergeat et al. 2002). However, these findings were based on a small statistics, in particular for SC-type (10 objects), J-type (21 objects), and R-hot stars (35 objects). In the present study, we extend our previous analysis to more than 2000 objects that we extracted from different recent giant stars surveys. All the different spectral types of carbon stars mentioned above were considered, and we adopted the more accurate Gaia EDR3 astrometric data  (Lindegren et al.
2 µm that were not listed previously. In total, the sample includes 974 objects, most of them quoted as being carbon stars according to the SIMBAD 1 database. Nevertheless, a significant number of objects in the  Chen & Yang (2012)  sample have no spectral type identification and appear in SIMBAD just as possible carbon stars (see below). The LAMOST survey includes 894 carbon stars that were identified by several spectral line indices from low-resolution spectra. By combining several CN and C 2 bands,  Ji et al. (2016)  identified carbon stars of N-, CH-, and R-hot types, although several dozen objects were also quoted as of possible N-type or of unknown carbon type. Below, we show that most of these objects with dubious spectral classification in the LAMOST sample are probably not genuine AGB carbon stars.
There are 1688 objects in total. The stars we selected are listed in Table  1. We preferentially adopted their variable star designation (from  Samus & Durlevich 2004)  or, when this was not available, their IRAS name  (Neugebauer et al. 1984) , their name in the LAMOST catalogue  (Zhao et al. 2012) , or the name that is used most frequently in the literature according to the SIMBAD database. Table  1  also reports their Gaia-EDR3 identification (Col. 2), the distance estimation (Col. 8), and the infrared 2MASS photometry (J, K s ) (Cols. 6 and 7) corrected for Galactic extinction (Cols. 3-5; see below). According to the 2MASS survey, the average uncertainty in the J and K s magnitudes in our stellar sample is 0. 05 ± 0. 12 and 0. 07 ± 0. 20 mag, respectively. Many of the objects studied here lack an accurate or individual determination of the radial velocity.
Luminosities We retrieved the 2MASS J and K photometry  (Cutri et al. 2003)  from the SIMBAD database and corrected them for interstellar extinction using the Bailer-Jones et al. (  2021 ) distances and the Gaia Galactic coordinates. We followed the same method as in Paper I to derive the luminosities of the stars. We adopted the empirical BC K versus (J -K) relation for carbon stars obtained by  Kerschbaum et al. (2010) , which is based on a critical revision of previous studies of bolometric corrections for cool giants. For (J -K) between 1. 0 to 4. 4 mag, the maximum standard deviation of this relation is 0. 11 mag, although for values (J -K) ≥ 2. 2 mag the scatter increases up to 0. 15 mag. For very red objects, in particular for many IRAS sources in the Chen & Yang (2012) sample, we therefore instead used the empirical BC K versus K -[12.
25 mag as a typical error for M K s and M bol in the full sample of stars. The actual uncertainty is probably slightly higher because we did not consider the uncertainty in A V in this estimate (see above). The corresponding bolometric luminosity distributions and luminosity functions (LF) obtained for all the giant carbon star types in our sample are shown in Fig. 7. We note that to construct the final LF for normal N-type stars (blue histogram, top panel in Fig. 7 ), we excluded the stars that were classified as of unknown type (36 objects) by  Chen & Yang (2012) , even though they are quoted as possible carbon stars in the SIMBAD database. Indeed, ∼30% of these objects are classified as RCrB type stars, and another ∼10% as binary or multiple stellar systems in SIMBAD. The R Coronae Borealis (RCrB) stars are rare hydrogen-deficient carbon-rich supergiants that are best known for their spectacular declines in brightness at irregular intervals.
, in prep. ). We point out, however, that as described by  Mowlavi et al. (2019)   2  , the carbon-rich nature of these stars might also be confirmed through the Gaia spectro-photometers, which provide low-resolution spectra that allow us to identify possible carbon molecules (C 2 and CN) in their atmosphere. For these future studies, the complete list of these new carbon stars is available through the CDS database.
Acknowledgements. This study has been partially supported by project  PGC2018-095317-B-C21  financed by the  MCIN/AEI FEDER  "Una manera  de hacer Europa ". M. R. G. and F. F. acknowledge the funding by  Spanish MICIN /AEI/10. 13039/501100011033  and " ERDF A way of making Europe " by the " European Union " through grant  RTI2018-095076-B-C21 , and the  Institute of Cosmos Sciences University of Barcelona (ICCUB, Unidad de Excelencia 'María de Maeztu')  through grant  CEX2019-000918-M. This work has made use of data from the  European Space Agency (ESA)  mission Gaia (https://www. cosmos. esa. int/gaia), processed by the Gaia Data Processing and Analysis Consortium (DPAC, https://www. cosmos. esa. int/web/gaia/dpac/ consortium). Funding for the DPAC has been provided by national institutions, in particular the institutions participating in the Gaia Multilateral Agreement.
Statistical analysis To assess the pH sensitivity of uorescence peaks and indices, the percent change in F int for the peaks was calculated between each triplicate of the pH adjusted samples and the mean of triplicate samples at a reference pH of 6. 5. The results were plotted to identify the pH range where uorescence intensity remained within 5% of the reference value. A reference pH value was required to make a statistical comparison of deviations and a pH of 6. 5 was determined from assessing the full distribution of pH values across Swedish surface waters, whereby the mean pH is 6. 5 (median 6. 8), with the lower 10th (pH 5. 5) and upper 90th (pH 7. 5) percentiles lying within AE1. 0 pH units of the median (ESI Fig. 2 † ). The pH distribution included 48 358 data points including 7691 Swedish lakes and rivers and was compiled from the long-term monitoring program of the Swedish Agricultural University.
Conclusions and recommendations -Fluorescence intensity of the ve main peaks was stable within the pH range of 5. 5 to 7. 5, which is common for natural water samples from inland waters, but may vary outside of this range. We recommend that pH is always reported, with particular emphasis placed on the maximum pH range across the dataset (i. e. all samples ranged from _. _ to _. _ pH units). If samples within a dataset are in this range and within 1. 0 pH unit of each other, this point can be noted to alleviate concerns about pH interferences. -Humic-like peaks (peaks A, C and M) were robust to pH changes in the 5. 5 to 7. 5 range. One sample was robust to pH effects across the full pH range tested (3. 5 to 9. 0; Strandsjön). In when the pH of one sample ( Ängbybadet) was adjusted to <4. 0, a decrease of up to 55% in uorescence was observed.
Since the dataset used here was limited to three natural water samples, we encourage follow up studies including a wider range of samples from across ecosystems. -Protein-like peaks (peaks T and B) were robust to pH changes in the 5. 5 to 7. 5 range, with peak T having a wider range of stability than peak B. For instance, two natural samples had a range of stability spanning from 4. 0 to 9. 0 for peak T. -The commercially available Nordic Reservoir Humic Substance was highly sensitive to pH changes. The protein-like peaks decreased in uorescence even within 0. 5 pH units of the reference 6. 5. The most extreme loss in uorescence was observed at pH 4. 0 for peak T, when a loss of 60% uorescence was observed. -The most commonly used indices seem to be robust across a pH range from 5. 5 to 7.
Acknowledgements This work was supported by funding from the  Swedish National Science Foundation  (D. N. K. ) Vetenskapsrådet starting grant ( VR 2016-04108 ), open call (VR  2020-03249 ), the  Swedish Research Council for Environment, Agricultural Sciences and Spatial Planning  ( FORMAS 201800778 ), an early career research award from  King Carl XVI Gustaf of Sweden , a  Marie Sklodowska Curie Action  of the  European Commission  to N. C. ( CHROME -839709 ) and the  Knut and Alice Wallenberg Foundation  ( Wallenberg Scholar   KAW 2018-0191 ). We would like to thank  Christoffer Bergvall  for lab support and  Karla Münzner  for discussions regarding algae.
NOWAC Cohort Between 1991 and 2007, questionnaires were sent to over 320,000 women aged 31-76 years, randomly drawn from the Norwegian National Population Register. 23 More than 172,000 participants answered (response 54%). A first and a second follow-up questionnaire was sent after five to seven years (response >79%). All women in NOWAC provided informed consent, and data were handled in accordance with the relevant ethical guidelines and regulations. The study has been approved by the Regional Committees for Medical Health Research Ethics of North Norway (2021/252094/ REK Nord) and the Norwegian Centre for Research Data (2021/147992).
Statistical Analyses Trajectories LCMM (also known as growth mixture modeling) was used to estimate life-course PA trajectories, ie, classes (groups) of participants following a similar PA pattern over time,  32  separately in each subcohort (31-39 years, 40-49 years and ≥50 years at baseline). Age (14, 30 and age at baseline) was set as time indicator and included as fixed terms (linear and quadratic) and class-specific terms (linear and quadratic). For each subcohort, trajectory models including 2, 3, 4 and 5 classes were evaluated. We chose the number of classes (k) that best fit with our data based on Akaike Information Criterion (AIC), Bayesian Information Criterion (BIC), entropy, classes' distribution and posterior probability. A grid of 100 initial values was used to prevent any convergence toward a local maximum.
Three-Step Approach To assess the association between PA trajectories and melanoma risk, we used a three-step approach  33, 34  combined with Cox proportional hazards regression. The trajectory analysis, described above, is the first step. The second step consists in assigning class membership to each participant. Posterior probabilities of belonging to each of the k classes were computed for each participant. We then used the proportional assignment method,  34, 35  ie, each participant was replicated k times in the dataset and assigned to each of the k classes together with its related posterior probabilities. In the third step, we investigated the association between class membership of PA trajectories from step 2 and melanoma risk in a Cox proportional hazard model estimating hazard ratios (HRs) and 95% confidence intervals (CIs).
DovePress Clinical Epidemiology 2022:14 1574 naïve trajectories and melanoma risk was then investigated using Cox proportional hazard models with the same adjustment sets as in the three-step approach in each subcohort. Only participants with complete information for PA at ages 14, 30 and baseline were included in the naïve trajectories. For graphical representations of predicted life-course trajectories and Sankey diagrams of PA trajectory classes obtained with latent class mixed model (LCMM) and naïve life-course trajectory analysis, participants needed to belong to a unique trajectory class. Thus, the modal assignment method was used (ie, each participant was assigned to the class with the highest posterior probability computed in step 2 above). Statistical analyses were carried out using the R software (https://www.
2, 4, 6, 8, 41 We used proportional assignment for PA trajectory classes to take the classification uncertainty into account in the Cox regression. Our study included population-based data from a large prospective cohort and a combination of occupational and recreational PA, reported at baseline for up to three different time points (ages 14, 30 and study baseline) for more than 150,000 Norwegian women. The linkage to the CRN is also an important strength. Reporting of incident cancers to the CRN is compulsory in Norway, and 99. 9% of the melanomas are morphologically verified. 42, 43 However, some misclassification of PA may exist. Questionnaires were used to record PA by a 10-point scale validated to rank current PA levels in the Norwegian female population,  24  thus considered a suitable measure to differentiate PA in our study.
However, no validation study has been conducted for the retrospective assessment of PA at ages 14 and 30. 5] [46]  Depending on the subcohort, PA trajectories were estimated based on up to three PA measures over an average period of 13 to 33 years. A strength of the LCMM method applied in the current study is that it includes participants with incomplete data (ie, who did not report PA for all time points). 37 As PA might change from age 30 to age at baseline, more PA measures would have improved the estimation of PA trajectories. Moreover, there may be more misclassification of PA at ages 14 and 30 years in the oldest subcohort, since the time period from age 30 to baseline was much longer (20-46 years) than for the youngest subcohort (1-9 years). Importantly, misclassification is likely nondifferential since this is a cohort study with exposure recorded prior to cancer diagnosis.
material-bone impingement in half of the patients, and allowed to properly plan reorientation of the acetabular component, revision of the femoral head to increase hip offset, reorientation of the femoral stem, increasing femoral neck length, and removing impinging bone (i. e. , anterior inferior iliac spine and anterior aspect of the proximal femur) to increase hip range of motion. In the other half of their patients, no impingement or limited range of motion were found, suggesting a soft tissue cause  [83]. Those data seem to encourage the realization of EOS images or radiographs in different positions and 3D CTbased templating but additional studies are required before recommending their systematic use, in our opinion. Dislocation is thought to be related to impingement between bone, implants, or soft tissues, as a dynamic process driven by multiple factors, including hip offset, implant design, component position, and bony geometry  [83].
Trace elements via LA-ICPMS Concentrations of trace elements in glass, feldspars, pyroxenes, and biotites were determined by LA-ICPMS using a 193 nm Resonetics ArF excimer laser coupled to a Thermo Element XR ICPMS within the Institute of Geochemistry and Petrology, ETH Zürich. Analytical spot sizes varied between 29 and 43 μm with laser output energy maintained around 3. 5 J/ cm 2. For all trace element determinations, a set of two NIST612 glasses and some combination of reference glasses GSD-1G and ATHO-G were measured after every 25 unknowns to monitor for drift. Raw data were reduced using the MATLAB-based program SILLS  (Guillong et al. , 2008) , with the internal standard for the phase and sample based on microprobe measurements. All trace elemental determinations are considered to have precision better than 5% relative based on homogeneous standard reproducibility where abundances are significantly (typically 3×) the limits of detection.
Pyroxene in these rocks typically hosts abundant inclusions of Fe -Ti oxide phases and apatite, minerals which typically have δ 18 O values 1 to 3 per mil lower than pyroxene at magmatic temperatures (e. g. Bindeman, 2008 ). An additional exception comes from the feldspar of the P5 tephra that had a low yield during analysis and gave a δ 18 O value much lower than the dataset as a whole, and from co-existing mineral phases in that sample. The P5 feldspar δ 18 O value is thus not considered further. To calculate melt δ 18 O values shown in Fig. 8  we preferentially use biotite as opposed to alkali feldspars because the latter is known to be affected by pre-fluorination prior to analysis. We note, however, that the overall agreement between the mafic phases and the feldspar from a given sample suggests that the feldspar values are robust and the Pepom magmas had a δ 18 O composition of 4.