dataesr/scientific-paragraphs-categorization huggingface dataset:
Model Generation from Clinical MR Images As part of an MRI study approved by the ethics review board of the Medical University of Graz (EKNr: 24-126 ex 11/12), MRI data consisting of both a torso and iso-volumetric 3D whole heart were acquired for a healthy volunteer. The volunteer was male of 47 years of age. A 12 lead ECG was recorded using MRIcompatible electrodes at the time of the study at 2000 Hz using a clinical-based system. The 12 lead ECG was subsequently filtered using a 50 Hz notch filter, 0:5 Hz high-pass filter, and a low pass filter of 60 Hz in accordance with the clinical standards. Segmentation of the torso MRI was performed within publicly available software Seg3D. 6 The 3D whole heart cardiac MRI was segmented automatically using a convolutional neural network  22  followed by manual corrections in Seg3D when required.
Anatomical Reference Frame To automatically control spatially-varying entities of ventricular EP, such as locations of the EASs, pacing sites, or the SE domain, an anatomical reference frame X was introduced to provide anatomically meaningful descriptions of location and direction within the ventricles. As thoroughly detailed in Ref. 13, the reference frame utilizes universal ventricular coordinates (UVCs)  2  to define a bijective function B UVC : X biv ! D UVC , mapping between the Cartesian model X biv and the abstract UVC space D UVC. Each coordinate b ¼ ðz; q; u; mÞ 2 D UVC comprises an apicobasal (z), transmural (q), rotational (u) and ventricular (m) component and there exists a unique x 2 X biv , such that b ¼ B UVC ðxÞ as well as x ¼ B À1 UVC ðbÞ holds. A kd-tree based tool was implemented within the open source mesh manipulation software meshtool  20  to realize the mapping B UVC.
e. the reference soil suspension and its concentrates; the recovery was calculated as the ratio of the areas of the peaks (detected by the MALS detector presented below) of the fractionated and unfractionated sample expressed as a percentage); the mean value was found to be (98 ± 3%). A variable wavelength UltraViolet-Visible detector (Agilent Technologies 1100 series from Agilent, Tokyo, Japan) was used at 254 nm to monitor organic matter. Indeed, it was showed previously that this detector responded proportionally to the organic carbon (OC) contained in the suspensions of this soil  [16]. A MALS (DAWN DSP-F, Wyatt technology, Santa Barbara, USA) was used to determine the size of the colloidal particles. Data from UV-Vis and MALS detectors were collected and analyzed using Astra 5. 3. 4. 18 software (Wyatt technology).
In addition to Debye's formalism, several others derived from that of Debye are proposed by the Astra software in order to experimentally fit the MALS data (Zimm; Berry)  [24] [25] [26]. In the present study, Berry's first-order formalism was used because it fitted the data significantly, accurately, and without any bias. To complete, note that at a similar chemical composition, MALS is sensitive to both gyration size and concentration; thus, when differences in MALS signals are observed in different samples with similar gyration radius evolutions, these differences can be attributed to differences in concentration  [27]. Calculation of size distributions. Detector signals were numerically filtered (low-pass digital filtering). The size distribution expressed in gyration radius can be obtained from each fractogram by considering this fractogram as a set of n signal values S (ti) recorded at a time ti and with a regular interval dt; and using the following equation: f(Rgi) = [S(ti)/ΣSi] × [dt/dRg] (I) where f(Rgi) is the normalized signal of the i th recorded value corresponding to the gyration radius Rgi, S(ti) the concentration detector signal at the time ti, ΣSi the sum of the n signal values S(ti), and dt/dRg a factor taking account of the selectivity variation along the fractogram (with dRg the difference in gyration radius over the recorded interval dt)  [28].
Solid state NMR analysis Solid State NMR (ssNMR) measurements were performed with Bruker (Karlsruhe, Germany) Avance III spectrometers at 7. 04 T (hereafter called AV300, corresponding to a spin frequency of SF = 300. 13 and 75. 51 MHz for 1 H and 13 C, respectively) and 16. 42 T (designated as AV700, operating at SF = 700. 14 and 139. 10 MHz for 1 H and 29 Si, respectively). Samples were packed into ZrO 2 rotors. Magic Angle Spinning (MAS) was performed at ca. 12 kHz of rotation frequency (RO) in a 4 mm rotor on AV300 and at RO = 20 kHz in a 2. 5 mm probe on AV700 spectrometer. For 1 H experiments, OnePulse program was routinely used on AV700 with 4 scans (NS). T 1 longitudinal relaxation time was checked and a recycle delay (RD) of 5 s appeared to be suitable for quantitative experiments.
Chemical shifts (δ) were referenced to tetramethylsilane (TMS; δ = 0 ppm) for all experiments. Experimental quantitative 29 Si HPDec-MAS spectra were fitted using DmFit modeling software  [54]. The deconvolution was made on a spectrum recorded after NS = 4. 000 scans for the PG and 6. 000 scans for the AL (as there is only a small amount of powder in the case of the AL, only enough to fill the 2. 5 mm diameter rotor to approximately two thirds). The ranges of 29 Si chemical shifts in solid silicates are classified according to a Q n notation  [55]  where Q represents the SiO 4 4-units and n the degree of connectivity of these units (Table  1 ). The silica condensation degree, so called D, that represents the ratio of Si-O-Si bonds over all Si-O-X bonds (X = H or Si in the present case with only Q species), was calculated using the following formula D = [56], was also used in order to better elucidate the 3D silica network.
Minor peaks (traces) at ca. δ = 1. 3 ppm and 3. 8 ppm are also visible, that can probably be assigned to protons coming from -CH 3 and -CH 2 -groups, respectively, as can be found in aliphatic chains in organic matter. The PG contains approximately twice the amount of protons from water than the AL. An advanced deconvolution of the spectra providing   4. 000 scans for PG spectrum, and using a line broadening LB = 100 Hz for the post-treatment. The spectral deconvolution using Dmfit software is shown for the AL underneath the spectra allowing calculation of Q 2 , Q 3 and Q 4 contributions. Spectra were normalized based on the Q 4 component as the rotor could not be completely filled (approximately at two thirds), by lack of material. c 1 H OnePulse MAS ssNMR spectra of AL (in blue) and PG (in green) recorded on a Bruker AV700 spectrometer using a 2.
5 mm MAS Bruker probe at RO = 20 kHz, SF = 700. 14 MHz, RD = 5 s with NS = 4 scans for both samples. For the purpose of a quantitative comparison of the spectral profiles, PG spectrum was drawn with a diminishing scaling factor (0. 67), as the rotor of sample AL was filled only to 2/3. *: 2. 5 BL probehead signal (systematically present, whatever the sample). d 1 H MAS ssNMR spectra of glassy silica: Spin echo experiment (in blue) and OnePulse experiment (in green). Experiments were recorded on a Bruker AV700 spectrometer using a 2. 5 mm MAS Bruker probe at RO = 20 kHz (MAS), SF = 700. 14 MHz, RD = 5 s with NS = 16 scans for both. *2. 5 BL probehead signal (systematically present, whatever the sample). e XRD powder diffractograms of AL (in black) and PG (in red). f FT-IR spectrum of the PG absolute data would not be realistic, but the comparison of the balance between the two components of the spectra could be considered.
Both experiments were undertaken in the same conditions, analysing precisely the same amount of powder (11. 0 mg), that makes a quantitative comparison possible between both samples. Using data processing software (Fullprof ), and after a baseline correction, the integrated intensity values show that the AL contains ca. 26% amorphous silica more than the PG. A separate and complementary experiment was conducted in the same conditions, using a pure amorphous silica specimen as a reference, to quantify the relative amount of silica in each sample. The relative integrated intensity of each sample, and the related relative uncertainty over the pure amorphous silica reference, were calculated, showing that AL and PG contain 69% (± 2%) and 50% (± 2%) of amorphous silica, respectively. FTIR analysis (Fig.
A central composite design is an approach of the response surface methodology for exploring the model, and it is an effective design for fitting the second order polynomial model. The extraction condition variables investigated in this study were the extraction time (4-6 h) and stirring speed (200-600 rpm). The appropriate range of these variables was determined on the basis of previous studies  (Kartika et al. 2016 (Kartika et al. , 2018 (Kartika et al. , 2019)). The central composite design gave a total of eleven experiments, and the central point experiment was conducted with three replications. Design-Expert software (trial version 12. 0) was utilised for the data analysis and the accuracy assessment of the second order polynomial model was conducted by the F-test with P = 0. 05 as a significance threshold.
Our semi-automated method is based on performing telomere-specific FISH combined with multiplex immunofluorescence to detect a basal cell-specific cytokeratin, prostate epithelial-cell specific nuclear markers (NKX3. 1 and FOXA1), and lymphocyte-specific markers (CD3 and CD20) using FFPE tissue samples. This staining process is then followed by semi-automated slide scanning and multi-channel acquisition of fluorescent microscopy images. Cell-type specific telomere and nuclear DNA content data are then obtained from these collected images via semi-automated image analysis allowing us to measure telomere length in all cells of the specified type in focal plane without selection by the operator. While our original findings pointed to the potential prognostic utility of the telomere biomarker, we next sought to confirm, in a larger study of five cohorts, that the telomere biomarker indeed is independently associated with risk of poor outcome in men surgically treated for clinically-localized prostate cancer using our optimized, semi-automated method.
16 TMAs were constructed with matched case-control pairs spotted on the same TMA. Evaluable spots were available for 376 matched recurrence pairs (Table  1 ). Supplement Table  3  shows case and control characteristics for men included in the telomere biomarker analysis. Intermediate-High Risk Case-Cohort Study -I. The Brady Urological Institute at Johns Hopkins developed an intermediate and high-risk case-cohort study and associated 9 TMAs for use in identifying tissue markers of metastasis in men at higher risk for poor outcome  (19) (20) (21). Men were selected from the Johns Hopkins radical prostatectomy (RP) clinical research database (>20,000 patients, of which >13,000 have long term follow-up) of men who underwent RP at Johns Hopkins between 1992 and 2010 and who had intermediate or high-risk disease by the Cancer of the Prostate Risk Assessment (CAPRA)-S score ≥3  (22).
Included in the telomere analysis were 253 men from the subcohort (94. 8%) and 115 of the metastatic cases (96. 6%) (Table  1 ). Supplement Table  4  shows characteristics of all of the men, men in the subcohort, and cases outside of the subcohort who were included in the telomere biomarker analysis. Intermediate-High Risk Case-Cohort Study -II. The Brady Urological Institute subsequently developed a second case-cohort study of men with intermediate and high-risk prostate cancer and associated 9 TMAs also for use in identifying tissue markers of progression to disease with a lethal phenotype in men at higher-risk. Men with intermediate and high-risk disease who underwent RP between 2007 and 2015, had not had neoadjuvant therapy, and had data available on all required clinical and outcome variables in the Johns Hopkins radical prostatectomy database were selected.
Supplemental Methods). Microscopy and Image Analysis. The TissueFAXS Plus (Tissue Gnostics, Vienna, Austria) automated microscopy workstation, which contains an 8-slide ultra-precise motorized stage and utilizes a Zeiss Z2 Axioimager microscope, was used for automated image acquisition. First, a DAPI preview image is captured with a 10X objective to allow for appropriate orientation. Next, the TMA spots are identified and images are captured with a 40X oil objective using the DAPI, GFP, Cy3, and Cy5 filters. An autofocus algorithm in the DAPI filter and the extended focus parameter by capturing 3 steps above and below (step size=0. 8 µm) was utilized. An entire TMA with 400 spots can be imaged in ~14 hours, which is faster than other current imaging modalities  (26). For image analysis, a separate high-performance workstation with the TissueQuest software module to analyze the fluorescent images with precise nuclear segmentation was used  (25, 27).
A region of interest is set (e. g. , stroma or cancer) and processed for nuclear segmentation. If required, exclusion regions were set to exclude benign prostate glands. Categorizing the telomere biomarker. After exporting the data to Excel spreadsheets, we converted them to a SAS dataset and merged the data with the TMA spot individual identifiers. For each man and for each cell, we calculated the ratio of Cy3 dot sum intensity and DAPI dot sum intensity and multiplied by 1000; this ratio is the telomere ratio. The telomere ratio for each nucleus compensates for differences in nuclear cutting planes and ploidy. Separately, for each cohort we performed the following: For each man, we calculated median telomere ratio for cancer-associated stromal cells; this is the first of the two components of the telomere biomarker.
Statistical Analysis For all analyses, we used SAS v. 9. 4 (Cary, NC). For the HPFS and PHS studies, we used Cox proportional hazards regression to estimate hazard ratios (HRs) and 95% confidence intervals (CI) and adjusted for age at diagnosis, year of surgery, prostatectomy Gleason sum, pathologic stage, and pre-operative PSA. For the Johns Hopkins Intermediate-High Risk Study I and II, we used Cox proportional hazards regression with robust variance correction to estimate HRs and 95% CIs and adjusted for age, race, pathologic stage, prostatectomy Gleason sum, year of surgery, pre-operative PSA (if missing, a separate indicator variable was used), and surgical margins. For the Johns Hopkins Recurrence Nested Case-Control Study, we used conditional logistic regression to estimate odds ratios (ORs, as unbiased estimates of the HR) and 95% CIs and adjusted for year of surgery, primary and secondary Gleason pattern, pre-operative PSA, and surgical margins (cases and controls were matched on age, race, categories of prostatectomy Gleason sum, and categories pathologic stage).
For each cohort, we estimated the association of more (vs. less) variability in telomere length among cancer cells and shorter (vs. longer) telomere length in cancer-associated stromal cells with recurrence, progression to metastasis, and prostate cancer death. Next, for each study we estimated the association between the telomere biomarker and these same outcomes using the less variable/longer combination as the reference group. We repeated this analysis stratified by Gleason sum (<7, 7, >7) and by PTEN status (intact, null). Because of the differences in study design, disease severity at diagnosis, timing relative to the PSA era, the telomere length determination method used (original 5 HPFS TMAs nonautomatic, all others optimized semi-automated), the fact that the TMAs were run in different batches with slight modifications to the scanning parameters, we could not pool the data from the five cohorts.
For the additional 2 HPFS TMAs as well as in the 4 other cohorts, we used a multiplexed set of markers for the image analysis software to identify the relevant cell types for inclusion and exclusion, and investigated all relevant cancer and stromal (excluding lymphocytes) cells that were in the image's plane of focus. For the stromal cells, the new method identified hundreds to thousands of cells per cell type, likely capturing a different distribution of cells (compositionally and spatially) compared to the user-selected method. While the RR is smaller, with these additional HPFS and other cohort data provided greater precision (substantially narrower 95% CI). Our study has a number of strengths. To confirm the telomere biomarker as prognostic for progression to metastatic disease and prostate cancer death, we used 5 studies of men who underwent prostatectomy developed with different criteria and different source populations (HPFS, PHS, Johns Hopkins).
Men were classified as having died from their prostate cancer (underlying cause on the death certificate) if they also had documented extensive metastatic disease. Physicians' Health Study. The parent study, Physicians' Health Study (PHS) was a large, randomized chemoprevention trials for cancer and cardiovascular disease  (1). The PHS included 29,067 male physicians who were 40 to 84 years old and who did not have a history of cancer at the time of randomization in 1983. Prostate cancer diagnoses were identified by self-report on a mailed questionnaire and were confirmed by medical record and pathology report review. Data abstracted from these records included Gleason sum, TNM staging, and PSA concentration at the time of diagnosis. The men were followed from the date of their surgery to 2014.
Characteristics and trends of surface ocean dimethylsulfide (DMS) concentrations and fluxes into the atmosphere of four Earth system models (ESMs: CNRM-ESM2-1, MIROC-ES2L, NorESM2-LM, and UKESM1-0-LL) are analysed over the recent past  and into the future, using Coupled Model Intercomparison Project 6 (CMIP6) simulations. The DMS concentrations in historical simulations systematically underestimate the most widely used observed climatology but compare more favourably against two recent observation-based datasets. The models better reproduce observations in mid to high latitudes, as well as in polar and westerlies marine biomes. The resulting multi-model estimate of contemporary global ocean DMS emissions is 16-24 Tg S yr -1 , which is narrower than the observational-derived range of 16 to 28 Tg S yr -1.
And, second, by combining these lines of evidence, we provide an assessment of both the direction and the magnitude of the change in marine DMS emissions. In Sect. 2, we provide details of the key characteristics of the ESMs and the observational datasets used in this work. In Sect. 3, we focus on the analysis of the modern mean state. Building upon this analysis, we investigate the recent and future evolution of marine DMS concentration and emission in Sect. 4. We assess the reliability of the model predictions in the light of key biogeochemical and physical drivers in Sect. 4. 2. We further focus on the Arctic domain where model long-term behaviours are scrutinised against longterm measurements of DMS concentrations and emissions in Sect. 4. 3. 2 Models and observational datasets.
Simulations In this paper, we use monthly outputs of the CMIP6 historical (1850-2014) and ssp585 scenario (2015-2100) experiments. All datasets were downloaded from Earth System Grid Federation (ESGF) nodes. The number of realisations of each model for both experiments is reported in Table  2. NorESM2-LM has been run with two different grid resolutions of the atmospheric model: the version labelled LM has a low atmosphere (250 km) and a medium ocean (100 km) resolution, while MM has a medium atmosphere (100 km) and a medium ocean resolution. Both versions have the same number of realisations (three for historical and one for ssp585). We evaluated and compared both versions, and they appeared to be very similar regarding the various metrics used hereafter. Thus, only NorESM2-LM is presented in the study.
Surface ocean DMS concentration In this study, we compare model outputs to three climatologies of surface ocean DMS concentration. First, the widely used climatology of  Lana et al. (2011)  (hereafter referred to as L11) is compared. It is based on a large database of in situ measurements (over 47 000 data) from the Global Surface Seawater (GSS) DMS database (https://saga. pmel. noaa. gov/dms/, last access: 20 April 2021). Most measurements were performed between 1980 and 2009. The interannual variations are not accounted for; thus, the resulting monthly climatology is considered to be representative of an average over this period. The dataset is processed as follows: no quality control is applied on the data, but the largest values above the 99. 9 percentile are removed (i. e. values above 148 nM).
Monthly data are first binned into a 1 • × 1 • grid, and a monthly mean is then calculated in each of the 54 static biogeographical provinces defined by  Longhurst (2007, see Appendix A2 for details). In each of these provinces, a minimum of three data points is required to get a valid (monthly) value. For regions with at least four valid values over the year, a temporal interpolation is applied to fill the gaps. Conversely, in provinces with 3 or less months of data, neighbouring or biogeographically equivalent provinces are used to construct an annual cycle, which is scaled with the few available data, if any. This results in a "first-guess field" global dataset  (Lana et al. , 2011, Sect. 2. 2). Last, a distance-weighted interpolation process (radius of influence of 555 km) is applied twice to smooth the resulting global monthly climatology: a first time to smooth the transitions across the province borders and a second time after re-introducing individual in situ data  (Lana et al.
, 2011, Sect. 2. 3 and Fig. S2  in the Supplement). On top of the monthly climatology, L11 provides an assessment of the uncertainty range through two other monthly datasets representing the estimated minimum and maximum DMS concentrations. The raw data, binned into the 1 • × 1 • grid, are also provided along with the climatology. Despite the large number of measurements included in this DMS climatology, the spatial and temporal data coverage is limited  (Figs. 1 and S1 of Lana et al. , 2011) , and many regions remain poorly documented. Thus, as pointed out by  Tesdal et al. (2016) , small-scale features are transformed into large-scale ones by the interpolation procedure, and anomalous values observed at local scale could induce bias when extrapolated across data-sparse regions. This is illustrated by  Hayashida et al.
(2020) , who show that the entire Arctic region in L11 is based on extremely limited data (0 %-4 % areal coverage north of 60 • N). The resulting extrapolation of open water DMS concentration to sea-ice-covered areas, where primary production is presumably lower, may lead to a positive bias in L11. Another potential positive bias in L11 stems from the overrepresentation of biologically productive conditions in the in situ DMS database from which L11 is built upon. This is supported by the study of  Galí et al. (2018, Fig. 7 and Sect. 4. 1) , who pointed out that the distribution of DMS concentration in L11 is right-skewed as compared to DMS concentration derived from satellite chlorophyll measurements. Conversely, recent studies report on high DMS concentrations measured in the North Atlantic  (Bell et al.
25 nM; see Table  3 ). Another methodology using an artificial neural network (ANN) has very recently been developed by  Wang et al. (2020a)  (here referred to as W20) in order to improve the interpolation method used by  Lana et al. (2011). This study relies on the same database of in situ DMS measurements, which now contains twice as many measurements (over 93 000 after removing concentrations below 0. 1 nM and above 100 nM) as in the study of  Lana et al. (2011). Wang et al. (2020a)  used a machine learning algorithm, trained with existing DMS data along with a set of eight ancillary variables (MLD, SST, sea surface salinity (SSS), photosynthetically active radiation (PAR), Chl, nitrate, phos-phate, and silicate) plus the time and location. While DMS data measurements are scarce in vast areas of the oceans, and especially at high latitudes during the winter, these other variables are better constrained through observations or climatologies.