dataesr/scientific-paragraphs-categorization huggingface dataset:
b. Estimation of vertical velocity Vertical velocities, w, were not directly measured in the SMILES Seasoar survey. However, the co-located drifter and ADCP datasets allow for the following mathematical framework which yields a solvable expression for w at a specific depth and a cross-frontal location in each Seasoar leg. Let x D (t), and u D (t) be the measured drifter position and velocity vectors at time t where dx D dt , dy D dt = u D (t), v D (t). (4) Let u E (x, y, z,t) be the Eulerian fluid velocity. Assume that the drifter moves with the vertically averaged Eulerian velocity at the horizontal location of the drogue from depth z 1 to z 2 du D dt = d dt ūE (x D (t), y D (t),t) (5) where ūE ≡ 1 z 1 -z 2 z 1 z 2 u E dz. (6) Here we set z 1 = 0 at the surface and z 2 = 50 m, the drifter drogue depth.
2001; Capet et al. 2008; Rosso et al. 2014). The relative contributions from submesoscale and mesoscale mechanisms cannot be quantified with the extant dataset; however, submesoscale dynamics along the periphery of the eddy might contribute to the large vertical velocities reported here. The Scotia Sea hosts an especially high abundance of mesoscale eddies  (Frenger et al. 2015)  in the eddy-rich Southern Ocean. Eddy kinetic energy (EKE) in this region, calculated from timemean removed, altimetry-derived geostrophic surface currents  (AVISO; 1993 -2015)  is O(0. 1 -1 m 2 s -2 ). Recent submesoscale-resolving modeling results indicate a strong correlation between mesoscale EKE and submesoscale vertical velocity in the Southern Ocean  (Rosso et al. 2015)  implicating a downscale energy transfer.
Acknowledgments. The  SMILES  project is funded through the  National Environmental Research Council , standard grant  NE/J009857/1. JR311 data collection and technical support were received from the  British Antarctic Survey , the crew of  RRS James Clark Ross , and the NEODAAS and PML remote sensing groups. Seasoar operations were led by  National Marine Facilities technicians Paul Provost ,  Dougal Mountifield ,  Julie Wood , and  Candice Cameron , from the  UK National Oceanographic Centre. Peter Ganderton  designed and built the drifter electronics package. The altimeter products were produced by  Ssalto/Duacs  and distributed by Aviso, with support from  Cnes  (http://www. aviso. altimetry. fr/duacs/). Microwave OI SST data are produced by  Remote Sensing Systems  and sponsored by  National Oceanographic Partnership Program (NOPP)  and the  NASA Earth Science Physical Oceanography Program.
EELS acquisition and data treatment EELS spectra were acquired on a FEI Tecnai F30 operating at 300 kV and equipped with a Gatan Tridiem 863 spectrometer. Convergence and collection angles were 6. 1 and 5. 9 mrad, respectively. To minimize carbon contamination and irradiation beam damage, experiments were performed at liquid nitrogen temperature. For the same reasons, the probe, of 1-2 nm, was constantly rastering a square area of 20 nm × 20 nm during the EELS acquisition. The rastering speed was 2 μs/pixel. The size of the rastered areas was much larger than sp 2 clusters size in amorphous carbons (typically a few nanometers  [27] ), which guarantees an EELS response independent of the orientation of the sp 2 clusters. The energy resolution, measured as the full width at half maximum of the zero loss peak (ZLP), was 1.
2). Once the R ratio was determined, the sp 2 fraction of the amorphous carbons was then calculated by using the following relation sp 2 % = R/R REF  [67] , where R REF is the R ratio obtained from a reference showing 100% sp 2 bonding. This work does not use magic angle condition because, as abovementioned, such condition at 300 kV is nearly impossible to attain, and the obtained results show that this condition is not required, see below. R REF was then chosen as the maximum R value that could be obtained from an HOPG sample in the same experimental condition. For this purpose, a specific routine in Matlab was written based on the analytical expression of Bocquet et al. [75] , which describes the variation of R as a function of all the experimental settings (convergence angle, collection angle, tilt angle and acceleration voltage) for uniaxial materials.
As it can be seen from Fig. 2 , the maximum value of R is obtained for δ = π/2 rad i. e. when the basal planes of graphite are parallel to the electron beam (a configuration which is experimentally impossible to obtain) and this value was used as R REF to derive the sp 2 fraction. It should be noted that this method to determine the sp 2 fraction is valid only if the sp 2 clusters of the a:C-H material do not show any preferential orientation at the scale of the apparent electron probe during the EELS acquisition (20 nm × 20 nm in this work). This has been tested by checking that there is no variation of sp 2 fraction as a function of tilt angle for the amorphous carbon samples. Finally the most representative core-loss spectra were submitted to the open-access EELS Database as references  [81].
ACKOWLEDGMENTS The TEM and EELS studies were conducted at the Laboratorio de Microscopias Avanzadas, Instituto de Nanociencia de Aragon, Universidad de Zaragoza, Spain. Some of the research leading to these results has received funding from the European Union Seventh Framework Programme under Grant Agreements 312483-ESTEEM2 (Integrated Infrastructure Initiative -I3) and 604391 Graphene Flagship, as well as from EU H2020 Grant Agreement 696656 Graphene Flagship. R. A. acknowledges funding from the Spanish Ministerio de Economia y Competitividad (FIS2013-46159-C3-3-P) and from EU H2020 ETN project "Enabling.
This 4 m section was sampled in detail. From two drill cores and more than 20 other wells with rock chips, the total thickness of the shale unit has been determined as 17-27 m  (Wolter et al. , 2011). The complete Ordovician sedimentary succession in the Decorah region was obtained from local geological data and from investigations of wells, some of which penetrated the Cambrian. The Winneshiek Shale is disconformably overlain by the St. Peter Sandstone, which is widely distributed in the Upper Mississippi Valley of USA, and overlies a local un-named unit of conglomerate, sand and shale, including thick and massive impact breccia  (Liu et al. , 2009; McKay et al. , 2011).
Acknowledgments Acid treatment was performed by  Laurence Debeauvais  ( CNRS, Université Lille 1 ). This paper is a contribution to the  International Geoscience Programme (IGCP)  Project  653  -The onset of the  Great Ordovician Biodiversification Event. This work was supported by the  Agence Nationale de la Recherche, France  (grant number  ANR-11-BS56-0025 ), and  NSF of USA  (grants  EAR 0921245  and  EAR 0922054 ).
They found a strong and polarized bond between E121 and F (equilibrium distance r e =217 pm). The outermost frontier molecular orbitals of (E121)F were fairly similar to the ones of (Ac)F and there was no evidence of g-population. The radii of the 5g shell are small, even smaller than those of the radially equally nodeless, 4f shell  [1]. Therefore this shell is not expected to directly participate in the chemical bonding. The true placement of the 5g elements in the Periodic Table (PT) can only be settled by chemical studies or molecular calculations. We start the story with the diatomic E125 -F and continue with the possible existence of MF 6 (M=E125-E129). The intermediate oxidation states must be deferred to later study. We here choose the fully relativistic Dirac-Coulomb (DC) Hamiltonian as implemented  [11]  in the DIRAC program  [12].
lung cancer is the most common cancer globally. 5 tobacco smoking is well established as the main cause; for instance, in the United Kingdom, an estimated 85% of lung cancers in men and 47% of lung cancers in women are attributable to tobacco smoking. 6 asbestos is the most important occupational carcinogen, and lung cancer is the most common asbestos-related cancer. 7 sbestos was the first occupational exposure to be suggested to have a joint effect with smoking. 8 ] [11] [12] [13]  Here, we used a pooled dataset of lung cancer case-control studies conducted in europe and canada (the SYnergY project) to estimate lung cancer risk related to occupational asbestos exposure, and its interaction with smoking. the objectives of this work were to (1) estimate the lung cancer risk associated with quantitative indices of occupational asbestos exposure by sex, while adjusting for smoking; (2) assess the exposure-response relationship for asbestos and lung cancer by sex, major subtype, and smoking status; and (3) assess the joint effect of asbestos exposure and smoking on an additive and multiplicative scale.
the data were collected in 1985-2010, and almost all interviews with study participants were conducted face-toface. lUcaS and MOrgen collected data using self-administered questionnaires, and women in MOntreal and some participants in tOrOntO were interviewed by phone. nextof-kin were interviewed for most cases and some controls in lUcaS and some participants in icare and MOntreal (9% of cases, 6% of controls). controls were individually or frequency-matched to cases by sex and age, and mainly recruited from the general population (79%). lung cancer subtypes were classified according to WHO guidelines after histological or cytological confirmation. reference pathology was performed for the german cases. 14 ethical approvals for the original studies were obtained in accordance with legislation in each country, and in addition from the iarc ethics committee.
Assessment of Occupational Asbestos Exposure Quantitative measurements of fibers (71,816) from 14 countries (mainly germany, the UK, canada, italy, France, and norway) were entered into the project-specific exposure database expoSYn according to a standardized protocol. 15 ost data points were determined by phase-contrast microscopy (>95%). it can be assumed that most data represented chrysotile (67%). regarding measurement strategies, 53% of the measurements were considered "representative," 9% "worst case," and 38% "unknown. "  15 all measurements were linked to a standardized (iScO-68) job title. Statistical models were applied to the personal measurements (27,958) collected in 1971-2009 to develop a project-specific quantitative job-exposure-matrix (SYn-JeM) for occupational asbestos exposure.
lagging of cumulative exposure was applied, in which exposure in the 5, 10, 15, or 20 years before diagnosis/interview was disregarded. as results did not differ by lag-times, we used unlagged models in the main analyses. the slope of the exposure-response relationship reflects the average excess relative risk per fiber-year. it was obtained from a linear Or model adjusted for study, "list a" occupations, time-since-quitting smoking cigarettes, and smoking pack-year categories using maximum likelihood estimation and was expressed as K L * 100, that is, 100 times the excess relative risk per fiber-year. We performed additional spline analyses using nonparametric smoothing as implemented in the r package mgcv to assess in more detail the shape of the exposure-response relationship. the optimal smoothing parameter was selected based on generalized cross-validation and under the assumption that the total degrees of freedom required for a biologically plausible model would not exceed 3.
instead, exposures in SYnergY represent a wider exposure range, with very few workers exposed to high levels and most being downstream users or indirectly exposed workers occasionally exposed or exposed to lower concentrations of fibers only. Our large dataset may be particularly informative to explore the shape of the exposure-response function in the low-dose range. an additional advantage was stratification or detailed adjustment for smoking. in our analysis of pooled general population studies, the exposure-response slope estimated as excess risk per 100 fiber-years (K L * 100) was 6. 1 (95% ci, 4. 1, 8. 1) in men overall and 3. 3 (95% ci, 1. 5, 5. 0) among male blue-collar workers. the K L * 100 slope was flat in a recent meta-analysis of 18 occupational cohort studies (K L * 100, 0. 13; 95% ci, 0.
25, 36 Our dataset is less informative regarding the highdose range; only 90 of 29,997 male participants were assessed as exposed to ≥15 fiber-years. the exposure-response results presented here are based on exposure-response modeling agreed upon a priori. this model assumes ln(Or) is proportional to ln(exposure). Other exposure-response models will result in different risk estimates. We observed a stronger association between asbestos exposure and small-cell lung carcinoma among neversmokers in both men and women. this is noteworthy because small-and squamous-cell lung carcinomas occur almost exclusively in cigarette smokers; in SYnergY, only 4% of small-and squamous-cell cases were never-smokers, whereas 14% of lung adenocarcinoma cases were neversmokers. However, we cannot rule out biased recall of smoking habits.
Methods: The study used a cross-sectional survey design. Ethical approval for this study was obtained from the establishment of the lead investigator (School of Nursing and Midwifery, Trinity College Dublin Research Ethics Committee) and return of the questionnaire was taken as consent. The investigation conforms to the principles outlined in the Declaration of Helsinki (Br Med J 1964; ii: 177). Data collection, sample and population: Data were collected by an anonymous questionnaire during the period April 2015-September 2015. The inclusion criteria for the study were nurses or allied health professions working with cardiac patients. The sampling frame included CCNAP members, non-member visitors to CCNAP and national society's websites and attendees at the EuroHeartCare Conference 2015. Members of CCNAP received an e-mail inviting them to participate in the study.
Acknowledgments: The study group would like to thank the following for their support: CCNAP National associations committee members for distributing the link to their national committees;  Prof Christi Deaton  and  Prof Tiny Jaarsma  for their work in the development of the Be Guideline Smart campaign and  Novartis  for their continued support of the Be Guideline Smart campaign; the  EURObservational Research Programme (EORP)  for developing the online version of the questionnaire and the initial analysis. This research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors.
Overall, broadleaf species are much more abundant in the study region than conifer species, accounting for approximately three-fourths of the forest cover  (Dafis, 1997). The study sites are distributed along the whole latitudinal range of the Italian Peninsula and tree-ring proxies include both RW and MXD series collected within the NEXT-DATA project, from Italian universities, and from the ITRDB (www. ncdc. noaa. gov site consulted on September 2015; see Table  1  for full bibliographic references). The data set is based on 27 forest sites composed of several species (conifers at 16 sites, and broadleaves at 11 sites), from which tree-ring series of conifers (RW and MXD) and of broadleaves (RW) were prepared (Fig. 1 , Table  1 ).
Chronology construction, climate sensitivity and climate reconstructions Raw data. We examined all individual series of RW and MXD for correct dating using visual and statistical crossdating. In particular, we used statistical techniques to remove potential dating errors by comparing each individual series from one site against the mean site chronology, which was constructed excluding the analyzed individual series. Using the COFECHA software (www. ldeo. columbia. edu), the individual series were moved forward and backward 10 years from their initial positions, and similarity indices were calculated over a 50-year time window, thus highlighting the potential dating errors. Site chronologies. We used the regional curve standardization approach (RCS;  Briffa et al. , 1992; Briffa and Melvin, 2011; Esper et al.
, 2003)  both with the RW and MXD series to preserve the low-frequency variability in the site chronologies. We used the ARSTAN software (ver. 44 h3, www. ldeo. columbia. edu) and did not consider the pith offset estimates between the first measured ring and the actual first year of growth  (Esper et al. , 2009; Leonelli et al. , 2016 ). The regional curve (RC) for the mean chronology, which was obtained after the series alignment to the first measured ring, was smoothed using a cubic spline with a width of 10 % of the chronology length  (Büntgen et al. , 2006). We computed ratios of raw measurements vs. the values of growth predicted by the RC for all years of the individual series, and the resulting indexed series were averaged by a biweight robust mean to obtain the site chronologies of RW and of MXD.
We computed correlations using the DEN-DROCLIM software  (Biondi and Waikul, 2004) , applying a bootstrap with 1000 iterations, and the obtained results were analyzed by grouping together conifer and broadleaf species. Testing for climate-growth relationships at the site level. To assess the influence of environmental settings on climategrowth relationships, for the conifer MXD site chronolo-Table  1. References for all the dendrochronological data used in this research, information on site locations, types of parameter used at each site and the tree species. Sites are ordered along a decreasing latitudinal gradient, after differentiating between conifers and broadleaves (horizontal line). gies (i. e. , the chronologies holding the strongest climatic signal; see Results), we performed a redundancy analysis (RDA) selecting the bootstrapped correlation coefficients of climate-growth relationships (Fig.
Database information and site location We used the results of the climate sensitivity analysis to detect the driving climate variables (DCVs; of temperature, precipitation and SPI) for each of the three groups of chronologies: MXD conifer, RW conifer and RW broadleaf. Specifically, for each group of chronologies and for each climate variable, we first identified the months with significant correlations at most sites (> 50 %) and with mean correlation values of |r| > 0. 25 (black-filled squares in Fig. 3 ). Starting from the monthly climatic averages of the sites presenting significant correlations with these selected months, we constructed regional climate series by z scoring the monthly series of each site and calculating regional mean departures; the series of each site were then completed over the maximum period covered by data and reconverted in original units (based on regional mean departures and their specific means and standard deviations), and finally averaged between sites.
, 1988)  statistics to assess the quality of the reconstructions. We then used the reconstructed series of late summer temperatures over the period 1901-1980 to build a spatial correlation map with the KNMI Climate Explorer (https://climexp. knmi. nl/; Trouet and van Oldenborgh, 2013), using the 0. 5 • grid of August-September average temperature and of AS average precipitation (CRU TS 4. 0, Climatic Research Unit, University of East Anglia  Harris et al. , 2014). We used this independent data set instead of the Italian one, as our primary goal was to analyze how far from the Italian Peninsula the reconstructed climatology is still representative. Table  2. Main characteristics of the chronologies used in this research, separating RW (comprised of both broadleaf and conifer species) and MXD (only conifer species).
Weight loss could be a confounding factor in this association. Further studies describing weight trajectories across the AD continuum are needed to better define the precise temporal association between preclinical AD and body weight changes. Several limitations should be considered when interpreting the data reported here. Firstly, intentional weight loss cannot be differentiated from unintentional weight loss in the ADNI cohort. However, since previous studies have shown that intentional weight loss in healthy obese subjects and in obese MCI patients has beneficial cognitive effects  [43] [44] [45] , it is unlikely that the observed weight loss in our study was intentional. A further limitation is that we cannot specify the type of body composition changes or quantify physical activity levels  [46, 47]  since this information is not available in ADNI database.
Participants Data used in the preparation of this article were obtained from the ADNI database (adni. loni. usc. edu). The ADNI was launched in 2003 by the National Institute on Aging (NIA), the National Institute of Biomedical Imaging and Bioengineering (NIBIB), the Food and Drug Administration (FDA), private pharmaceutical companies and non-profit organizations, as a $60 million, 5-year publicprivate partnership. The primary goal of ADNI has been to test whether serial magnetic resonance imaging (MRI), positron emission tomography (PET), other biological markers, and clinical and neuropsychological assessment can be combined to measure the progression of mild cognitive impairment (MCI) and early AD. Determination of sensitive and specific markers of very early AD progression is intended to aid researchers and clinicians to develop new treatments and monitor their effectiveness, as well as lessen the time and costs of clinical trials.
For up-to-date information, see http://adni-info. org/. We selected all cognitively healthy controls (HC) with available baseline and longitudinal anthropometric and cognitive data for a minimum follow-up period of 12 months. After revision of the ADNI subjects table, and in order to exclude other causes associated with weight loss, we excluded subjects that had: cancer, heart failure, renal disease, liver disease, stroke, gastroesophageal reflux disease, Lyme disease, pulmonary disease or Barretts disease. There were no differences between ADNI-GO and ADNI2 with respect to the healthy controls inclusion criteria, the protocols of clinical follow-up, CSF measure, FBP PET or structural MRI acquisitions.
Clinical and cognitive data Baseline demographic data (age, gender, education and Type 2 Diabetes (T2D) medication), neuropsychological performance, anthropometric data (height, weight, systolic and diastolic blood pressure) and laboratory data (fasting plasmatic glucose -FPG-, cholesterol, and triglycerides) were downloaded from the ADNI database. Diabetes status was assigned according to FPG ≥126 mg/dL (n = 20) and/or the use of glucose lowering agents (n = 22)  [16]. Follow-up visits were conducted yearly. Data on body weight and ADAS-Cog score were downloaded from each subject at each followup visit. We selected ADAS-Cog score 13 as measure of cognitive performance because it is the most widely used general cognitive measure in clinical trials of AD  [17].
Genetic and CSF data APOE genotype was determined at the ADNI Biomarker Core Laboratory. Participants were considered APOE ε4 positive (APOE4) if they carried at least one APOE ε4 allele. CSF biomarker values were obtained from the ADNI database. Two hundred and forty of 363 participants (66. 1%) included in the study had CSF available at baseline. Methods for CSF acquisition and analysis have been previously reported  [20]. We used the ratio between t-tau and Aß 42 (tau/Aß 42 ratio) to evidence the AD pathophysiological process  [15]. T-tau was used instead of p-tau because in ADNI, t-tau has a higher specificity than p-tau (92,3% vs 73,1%)  [20]. www. impactjournals. com/oncotarget.