dataesr/scientific-paragraphs-categorization huggingface dataset:
) GeV. For the model with extra spatial dimensions, values of the effective Planck scale M D up to 2. 85-2. 90 TeV are excluded for between 3 and 6 extra dimensions. These limits on Λ and M D are the most sensitive monophoton limits to date. Hungarian Academy of Sciences, the New National Excellence Program ÚNKP, the NKFIA research grants 123842, 123959, 124845, 124850 and 125105 (Hungary); the Council of Science and Industrial Research, India; the HOMING PLUS program of the Foundation for Polish Science, cofinanced from European Union, Regional Development Fund, the Mobility Plus program of the Ministry of Science and Higher Education, the National Science Center (Poland), contracts Harmonia 2014/14/M/ST2/00428, Opus 2014/13/B/ST2/02543, 2014/15/B/ST2/03998, and 2015/19/B/ST2/02861, Sonata-bis 2012/07/E/ST2/01406; the National Priorities Research Program by Qatar National Research Fund; the Programa Estatal de Fomento de la Investigaci ón Científica y Técnica de Excelencia María de Maeztu, grant MDM-2015-0509 and the Programa Severo Ochoa del Principado de Asturias; the Thalis and Aristeia programs cofinanced by EU-ESF and the Greek NSRF; the Rachadapisek Sompot Fund for Postdoctoral Fellowship, Chulalongkorn University and the Chulalongkorn Academic into Its 2nd Century Project Advancement Project (Thailand); the Welch Foundation, contract C-1845; and the Weston Havens Foundation (USA).
During the period of the study, no changes in the care regime of hip-fracture patients were implemented. For the cohort of included patients, we assessed different clinical outcomes: the primary outcome was the incidence of hospital-acquired pressure ulcers and their predictive factors, the secondary outcomes were 30-day and one-year mortality and their predictive factors. Results on short-term outcomes  23, 24  have been previously published; we here present the one-year follow up results. Variables and data collection. The outcome of the study was the mortality occurrence at one year after hip fracture surgery, which was assessed by a telephone follow-up. Possible predictive factors for mortality were identified by a multi-professional team of experts, who selected the relevant variables based on clinical experience and data available in the literature  6, 8, [11] [12] [13] [14] [15] [16] [17] [18] [19] [20].
Moreover, the admission to an orthogeriatric ward, was assessed. The number of days of hospitalization was collected at patient's discharge. A telephone follow-up was also performed one-year from surgery to assess patient's survival and adherence to postoperative treatment after hospital discharge. Data collectors were trained Registered Nurses (RNs) and physiotherapists with experience in hip fracture care and in clinical research, not involved in patient's care, and independent from the researcher analysing the data.
Sample size. The method for determining the sample size was based on Soper, D. S. (2017). A-priori Sample Size Calculator for Multiple Regression [Software]. available from http://www. danielsoper. com/statcalc). Simulation studies examining predictor variables for inclusion in logistic regression models suggest 10 events are necessary for each predictor, to avoid overfitting  27. Considering the number of predictive parameters inserted into the multivariate analysis for the primary outcome [see  23  ], it was estimated that at least 800 patients had to be included.
Data analysis. The data collected were calculated using SPSS v. 19. 0 (IBM Corp. , Armonk, NY, USA). All continuous data were expressed in terms of the mean and the standard deviation of the mean or median and interquartile range when not normally distributed; the categorical data were expressed as frequency and percentages. For the categorical variables collected daily during hospitalisation, the percentage of days with the presence/ absence of the predictor in relation to number of days of hospitalisation was also calculated according to the following formula: percentage of days = (days/length of hospital stay) × 100. To assess the normal distribution and the homoscedasticity of the included variables, the Kolmogorov-Smirnov test and the Levene test were performed, respectively. Analysis of variance (ANOVA), Mann-Whitney test, Kruskal-Wallis test, Fisher exact test and Pearson Chi-square test were used for data analysis as appropriate.
Results Of the 1211 eligible patients, 128 did not fulfil the inclusion criteria. In the excluded patients. 51 (39. 8%) presented pre-existing pressure ulcers at hospital admission (exclusion criteria for the primary outcome), 30 (23. 4%) had a diagnosis of periprosthetic or pathological fractures, and 47 (36. 7%) did not agree to participate in the study (Fig. 1 ). The final population was 1083 patients. The planned one-year follow-up was reached in 728 patients. Among the excluded patients, 270 (76%) were admitted to one of the involved hospitals, which did not collect data at 12 months after surgery due to organizational problems, 69 (19. 4%) were no more contactable and 16 (4. 5%) died during hospitalization (Fig. 1 ). Comparing characteristics at hospital discharge of those patients included in the final cohort and those lost to one-year follow-up, no statistically significant differences emerged.
Strengths and limitations. Two of the strengths of this study are its prospective design and sample size. A cohort of 728 patients with hip fractures who were consecutively admitted to two large acute hospitals during the one-year study period, were analysed. Furthermore, to our knowledge, a study analysing such a wide range of peri-operative variables has not been previously reported. Nevertheless, some limitations affect the study. First of all, almost 33% of the originally included patients were lost to one-year follow-up. Nevertheless, no statistically significant difference was noted between the included and lost patients with regard to clinical data at hospital discharge. Another important limitation of this study was related to the follow-up methodology; the assessment of mortality was based on calling patient's family by phone, thus resulting in some missed information, such as the cause of death.
Solid-State 13 C-NMR The 13 C-NMR spectrum of PFA powder was obtained with a Bruker Avance NEO 500 wide bore system (Bruker BioSpin, Rheinstetten, Germany) at the NMR centre of the Faculty of Chemistry at the University of Vienna. A 4 mm triple resonance magic angle spinning (MAS) probe was used with a resonance frequency for 13 C of 125. 78 MHz, and the MAS rotor spinning was set to 14 kHz. Cross polarization (CP) was achieved by a ramped contact pulse with a contact time of 2 ms. During acquisition, 1 H was high-power decoupled using SPINAL with 64 phase permutations. The chemical shifts for 13 C are reported in ppm and are externally referenced to adamantane by setting the low field signal to 38. 48 ppm. The data elaboration was done with the software Top-spin 4. 0. 6 (Bruker) and OriginPro (OriginLab, Northampton, MA, USA), while the calculations of the theoretical chemical shifts were done with the software NMR-Predict developed by the University of Lausanne (L.
Relative isotropic chemical shifts with respect to tetramethylsilane (TMS) were calculated (the computation of TMS and its properties was performed in a fully analogous way) for all carbon atoms for the selected (1), (  2 ), (  3 ) and (  7 ) molecules. Intensities of 0. 6 for (1), 0. 1 for (2), 0. 1 for (3), and 0. 2 for  (7) , respectively, were assigned to the signals (irrespective of the chemical connectivity of the carbon atoms) to obtain rough 13 C-NMR model spectra. All four model spectra were merged, and the merged spectrum was folded with a Lorentzian line shape of 5 ppm half-width. Finally, the chemical shielding values were scaled by a factor of 108/120 in order to obtain a match of the respective peak at 108 ppm in the experimental 13 C-NMR spectrum. For the calculation of the IR model spectrum, the lowest energy conformer search for all four structures from the DFT optimization has been performed with Grimme's xtb method and the "confscript" script using all standard settings  [29].
Acknowledgments: The authors would like to acknowledge the financial support provided by the  European Regional Development Fund and Interreg V-A Italy Austria   2014-2020  through the  Interreg Italy Austria  project  ITAT 1023 InCIMa "Smart Characterization of Intelligent Materials ". R. J. F. B. gratefully acknowledges. Dage Sundholm  and the  University of Helsinki  for the generous provision of computational resources and the  DFG SPP 1807  " Control of London dispersion interactions in molecular chemistry " ( BE 4632/2-1 ) for funding.
The Apollo 16 'Rusty Rock' impact melt breccia 66095 is a volatile-rich sample, with the volatiles inherited through vapor condensation from an internal lunar source formed during thermo-magmatic evolution of the Moon. We report Cu and Fe isotope data for 66095 and find that bulk-rocks, residues and acid leaches span a relatively limited range of compositions (3. 0 ±1. 3 wt. % FeO [range = 2. 0-4. 8 wt. %], 5. 4 ±3. 1 ppm Cu [range = 3-12 ppm], average δ 56 Fe of 0. 15 ± 0. 05‰ [weighted mean = 0. 16‰] and δ 65 Cu of 0. 72 ± 0. 14‰ [weighted mean = 0. 78‰]). In contrast to the extreme enrichment of light isotopes of Zn and heavy isotopes of Cl in 66095, δ 65 Cu and δ 56 Fe in the sample lie within the previously reported range for lunar mare basalts (0. 92 ± 0. 16‰ and 0. 12 ± 0. 02‰, respectively).
, 2017). To examine the distribution of volatile elements in and on the Moon, we present new Cu and Fe isotope data for the 'rusty rock', 66095. Sample 66095 is notable for several reasons. First, it is a type-example of a volatile-enriched lunar sample, with elevated contents of Bi, Br, Cd, Cl, Ge, I, Pb, Sb, Tl and Zn compared to other lunar samples  (Krahenbuhl et al. , 1973; Wanke et al. , 1981; Jovanovic & Reed, 1981; Ebihara et al. , 1992; Day et al. , 2017). It is a fine to medium-grained impact melt breccia with elevated highly siderophile element abundances (0. 001-0. 1 × CI Chondrite;  Day et al. , 2017)  that contains clasts of anorthosite, troctolite and basalt and an incompatibleelement rich ['KREEPy'] geochemical signature. Fragments of the impact melt breccia define an 87 Rb-87 Sr formation age of 4.
Under typical running conditions, a 300 ppb Cu solution generated a 0. 06-0. 08 nA ion beam (6-8 V total signal using 10 11 Ω resistors). To ensure sufficient counting statistics on the 62 Ni/ 60 Ni ratio, the 62 Ni signal was kept above 0. 5 V. Instrument background signal (typically <10 mV 63 Cu) was measured at the beginning of each analytical session. The total procedural blank contained ~1 ng Cu, which equates to <1 % of the Cu sample analyte. Isotope ratios were measured in static mode, with each measurement consisting of 30 cycles of 4. 194 second integrations, with a 1 second idle time. Ratios were calculated in the Thermo Neptune Data Evaluation software, which discarded any outliers at the 2-sigma confidence level. To correct for instrumental mass bias, isotope measurements were calculated using the standard sample bracketing protocol relative to the NIST SRM976 standard and corrected for by using the 62 Ni/ 60 Ni ratio using the exponential law.
Results Copper and Fe isotope and abundance results are presented in Tables  1  and 2  and  Cu data is shown in Figure  1. Bulk samples of 66095 have concentrations of Cu of 4. 7 and 6. 3 µg g -1 , in good agreement with ICP-MS data (3. 6-5. 4 µg g -1 ), with δ 65 Cu of 0. 70 and 0. 79‰. Residue samples generally have a lower range of Cu abundances (2-4. 3 µg g - 1 ; except residue J14 at 18. 5 µg g -1 ) and δ 65 Cu of between 0. 59 and 0. 77‰. Leaches and etches of HCl and HF-HNO3 have a range in Cu abundances (2. 5-11. 6 µg g -1 ) and a wide range in δ 65 Cu of between 0. 63 and 1. 38‰. None of the water leaches analyzed for Fe and Zn were analyzed for Cu isotopes due to low total Cu contents. Iron isotope and abundance results are presented in Figures  2  and 3. All data are mass-dependent in δ 56 Fe-δ 57 Fe space conforming to δ 57 Fe (and its uncertainty) ≈1.
5 × δ 56 Fe. Iron abundance results obtained on purified Fe fractions for MC-ICP-MS agree well with ICP-MS solution data (Table  1 ), except for a single 1M HCl leach for 66095, 430. Bulk samples of 66095 have between 2. 2 and 4. 8 wt. % FeO and a range of δ 56 Fe between 0. 14 to 0. 26‰. Residue samples have lower FeO contents (1. 6-2. 6 wt. %) and a more restricted range of δ 56 Fe (0. 10 to 0. 15‰). Leaches and etches of HCl and HF/HNO3 give the widest range of FeO contents (2. 3-24. 6 wt. %) and heavier δ 56 Fe (0. 15 to 0. 19‰) than associated residues. Water leaches have a range in FeO contents (4. 0-8. 6 wt. %) and the lightest δ 56 Fe for the dataset (-0. 05 to 0. 04‰). Copper and iron isotopes in 66095 show a much larger relative range in isotopic compositions than for Zn in the same samples.
The heavy Fe isotopic value in 66095, 430 relates to the highest Fe content of the dataset and is consistent with sampling of impact-generated Fe-metal grains formed by partial impact-vaporization and preferential loss of light Fe isotopes, as proposed previously byWang et al. (2012). Prior work on terrestrial analog systems have demonstrated that large (2-3‰) Fe isotope fractionations occur due to redox reactions between ferrous and ferric species (e. g. ,Hill & Schauble, 2008;Polyakov & Soultanov, 2011;Syverson et al. , 2017). However, in the case of the Moon, where the majority of Fe speciation is considered to be ferrous (Fe 2+ ) or native (Fe 0 ), such fractionations are likely to be of lower relevance. Th oretical studies indicate that changes in bond partners (i. e. , different ligands or coordination numbers) should result in significant isotopic fractionation, but conditions similar to those attained on airless bodies have not been well-constrained.
Acknowledgements We dedicate this manuscript to the memory of Lawrence A. Taylor who held a fascination with the Rusty Rock, 66095, throughout his career studying the Moon. We thank the  NASA curation  staff and CAPTEM for samples. Comments by  C. Renglli ,  A. Ruzicka  and two anonymous reviewers are gratefully acknowledged. This work was supported by the  NASA   Emerging Worlds program  ( NNX15AL74G ) and an IPGP Visiting Professor position to JD. FM acknowledges funding from the  European Research Council  under the  H2020 framework program / ERC  grant agreement # 637503  (Pristine), as well as financial support of the  UnivEarthS Labex program at Sorbonne Paris Cité  ( ANR-10-LABX-0023  and  ANR-11-IDEX-0005-02 ), and the  ANR  through a chaire d'excellence  Sorbonne Paris Cité.
The study sponsor is Paris Saint-Joseph Hospital. The study was registered in the French National Agency for Medicines and Health Products Safety (No N°E UDRACT_ID RCB: 2016-A00657-44) and was approved by the ethics Committee for Persons Protection (Comité de Protection des Personnes Ile de France II, IRB number 1072 and n°initial agreement 2016-08-02). The total duration of the trial is 6 years (inclusion period = 36 months; follow-up = 3 years; ClinicalTrials. gov identifier NCT02849041). Prolonged follow-up of trial participants will be performed each year after the end of the scheduled trial period. The first study participant has been enrolled on January 2017; last patient's enrollment is expected on December 2019.
Collection of data The usual demographic data will be recorded, including gender, age, weight, height, history of dyslipidemia, diabetes, hypertension, smoking habits, previous diseases and use of medications. History of coronary heart disease and cerebro-vascular disease will be recorded. For PAD, clinical and hemodynamic evaluations (ankle-brachial or toe-brachial index) will provide insights on the ischemia severity. All clinical, biological and imaging data will be anonymized and stored in electronic format.
Statistical analysis and sample size Data will be analyzed using SAS and R software. Alpha level for statistical significance (p) will be set at less than 5%. Characteristics of the study population will be described as mean plus or minus standard deviation for continuous variables with a normal distribution and as median [interquartile range] for skewness distribution. Categorical variables will be described as numbers and proportions. Student's t-test or nonparametric Wilcoxon test will be used to compare quantitative parameters between different groups of patients and Chi2 test or Fischer test for qualitative parameters. The primary outcome will focus on the sensibility and specificity of the screening strategy associating detection of CTCs and LDCT. Statistical analysis is in accordance with the STARD (Standards for Reporting of Diagnostic Accuracy Studies) guidelines  [24].
Psychological study Volunteer patients will attend 3 individual interviews with the psychologist. The interviews will take place at key-moments of the procedure that are likely to trigger a significant emotional response: (1) within a month following the inclusion, (2) after the results of the first screening round, and (3) one year after the first screening round. The psychological study relies on the principles of the grounded theory approach  [25]. It is inductive and iterative. The patient-physician communication will be quantitatively and qualitatively analyzed. Individual interviews will be conducted with each of the volunteers. The psychologist has an active-listening role, asking clarification questions while enabling the patient to address other topics of his choice. Data collection will continue until theoretical saturation is achieved  [26].
A main challenge of the study is the detection method of CTCs, considering their low abundance in peripheral blood, fragility, heterogeneity and the lack of organ-specific markers  [37]. If positive this study could pave the way for other cardiovascular population with a history of smoking, such as patients with coronary artery disease. approved by the ethics Committee for Persons Protection (Comité de Protection des Personnes Ile de France II, IRB number 1072 and n°initial agreement 2016-08-02, ClinicalTrials. gov identifier NCT02849041).
Acknowledgements The members of the  DETECTOR Project Study Group include all authors  and  V Gautier ,  D Sfeir ,  U Michon-Pasturel ,  S Bonhomme ,  R Attal ,  C Ostrowka ,  C Caucanas ,  T Klejtman ,  G Petkova ,  M Jayat ,  Y Frigout. We especially thank  Juliette Courtiade Mahler  and  Marianne Maillet , for their generous contribution to the realization of this study.
Immunofluorescence staining of Vangl1 and ZO1 - Samples were first fixed in 100% methanol, 10 minutes at -20°C. Then, washed 3x3min in PBS at 4°C and blocked with BSA 3% in PBS 30min at RT. Primary antibodies against Z01 (mouse monoclonal IgG1 Thermo Scientifique 33-9100) and Vangl1 (rabbit Sigma Aldrich HPA025235) were diluted at 1/200 and 1/500 respectively in blocking medium and incubated 45min at RT. After 2x5min washes with PBS-Triton 0. 1%, secondary antibodies: Alexa mouse IgG1 568 (ThermoFisher A21124) diluted at 1/800 and Alexa Fluor anti-rabbit 488 (Life Technologies A21206) diluted at 1/500 were incubated in blocking medium 30min at RT. Finally, samples are washed 2x5min. The membrane of the Transwell was cut and mounted in the coverslip.
Quantification of Vangl1 orientation -We measured manually the orientation of Vangl1 protein on each individual cell in the regions of interest using imageJ software. Then we plotted the distributions of beat directions according to the measured Vangl1 orientations with beat direction = Vangl1 orientation + 90°. Image processing and quantification -Cilia density ߮, beating directions of cilia and localisation of topological defects were quantified using in-house image processing programs implemented in python and applied on videomicroscopy acquisitions (40 fps). We first performed a standard deviation projection over 40 frames to reveal the trajectory of the tip of the cilia. We binarised this image by applying a threshold to compute ߮. We implemented the algorithm described in Püspöki et al.
Established sleep cycling is one of the main hallmarks of early brain development in preterm infants, therefore, automated classification of the sleep stages in preterm infants can be used to assess the neonate's cerebral maturation. Tensor algebra is a powerful tool to analyze multidimensional data and has proven successful in many applications. In this paper, a novel unsupervised algorithm to identify neonatal sleep stages based on the decomposition of a multiscale entropy tensor is presented. The method relies on the difference in electroencephalography(EEG) complexity between the neonatal sleep stages and is evaluated on a dataset of 97 EEG recordings. An average sensitivity, specificity, accuracy and area under the receiver operating characteristic curve of 0. 80, 0. 79, 0. 79 and 0. 87 was obtained if the rank of the tensor decomposition is selected based on the age of the infant.
Database The proposed method is evaluated on a dataset consisting of EEG signals recorded at the neonatal intensive care unit of the University Hospitals of Leuven, Belgium. All neonates included in the study were born between 2012 and 2014 at a gestational age below 32 weeks (range: 24 weeks 4 days-32 weeks). In total 97 multichannel EEG signals were measured from 26 neonates at a postmenstrual age (PMA) between 27 and 42 weeks. So, each subject had at least two serial EEG recordings during their stay in the NICU. The study was approved by the Ethics committee of the University Hospitals of Leuven and parental consent was obtained for all recruited patients. Criteria for selecting the subjects were as follows: a normal neurodevelopmental outcome at 9 and 24 months corrected age (Bayley Scales of Infant Development-II, mental and motor score > 85), no severe brain lesions assessed by ultrasound and not taking any sedative or antiepileptic drugs during the EEG registration.
EEG was recorded using nine electrodes: Fp1, Fp2, C3, C4, T3, T4, O1, O2 and Cz using the modified 10-20 system  [17]. Electrode Cz served as a reference and was not taken into account during the analysis. The EEG data were acquired at a sampling frequency of 250 Hz using BrainRT equipment (OSG bvba, Rumst, Belgium). The analysis of the data was carried out in Matlab 2017b (The MathWorks, Inc. , Natick, MA, USA), and the tensor decompositions were performed using Tensorlab  [18]. Quiet sleep periods were annotated by two independent expert clinicians upon agreement. All other sleep states are merged and will be referred to as non-quiet sleep. The goal of the proposed algorithm is to automatically label EEG segments as either quiet sleep (QS) or non-quiet sleep (NQS). The most important feature in discriminating quiet sleep from non-quiet sleep is the continuity of the EEG trace.
Multiscale Entropy Computation After filtering the data, the EEG signal is segmented into nonoverlapping windows of 100 s  [11]. To assess the complexity of each of these multichannel EEG segments, the multiscale entropy is computed. Multiscale entropy evaluates the complexity of a signal by measuring the regularity of the signal at multiple time scales  [19, 20]. So, the first step in its computation is to obtain signals at different scales. The coarse-grained signal y τ j at scale τ is obtained by taking the average of all data points within consecutive nonoverlapping windows of length τ. So the coarse-grained time series of the signal {x 1 , x 2 ,. , x N } at scale factor τ can then be written as: y τ j = 1 τ jτ ∑ i=(j-1)τ+1 x i , 1 j N τ. ( 1 ) This actually corresponds to a moving average operation with a window of length τ followed by a downsampling with factor τ.