dataesr/scientific-paragraphs-categorization huggingface dataset:
28 We included 14 967 participants who had valid information on BMI measured in 1996 and who were aged 50-57 years at the first wave. Data for Sweden came from five waves of the Swedish Longitudinal Occupational Survey of Health (SLOSH). 29 The first wave of SLOSH in 2006 was a postal questionnaire follow-up of all respondents to the 2003 Swedish Work Environment Survey (SWES), a cross-sectional, biennial survey of a random stratified sample of those gainfully employed people aged 16-64 years. At wave 2 in 2008, the sample was increased by adding the respondents from the 2005 SWES yielding an overall sample of n = 18 915 women and men originally representative of the working population in Sweden in 2003 and 2005. These people were then resurveyed in 2010, 2012 and 2014. In total, 77% responded at least once.
Statistical analyses Characteristics of the participating cohorts are presented at the first observation point, which refers to the date each participant was included in the dataset. We applied multistate models to longitudinal data to obtain transition probabilities between health states. Discrete-time multistate life table models were used to estimate partial LE and healthy LE and chronic disease-free LE between the ages of 50 and 75 years (in total 26 years). For both outcome measures, three health states were defined: healthy, unhealthy and dead. For healthy LE, there were four possible transitions between the health states, namely: healthy to sub-optimal health (onset), sub-optimal health to healthy (recovery), healthy to dead, sub-optimal health to dead. For chronic disease-free LE, there were only three possible transitions as, by definition, recovery was not possible.
5th and 97. 5th percentiles) for these multistate life table estimates was performed using a bootstrap method with 500 replicates for the whole analysis process (multinomial analysis and simulation steps). As BMI-related transitions to poor health and death may differ by sex, we repeated analyses including interactions between sex and BMI in the multinomial logistic models. Finally, since tobacco smoking is associated with increased risk of mortality and morbidity  33, 34  and it is also typically associated with lower weight,  35  we conducted sensitivity analyses among never smokers using a bootstrap method with 50 replicates. All analyses were conducted in SAS 9. 2 using the SPACE (Stochastic Population Analysis of Complex Events) program. 36, 37 This program uses the stochastic (that is, microsimulation) approach to estimate the healthy LE as opposed to another well-known program, IMaCh (Interpolation of Markov Chains) which uses a deterministic approach.
However, the fact that the main findings were similar across different source populations suggests that the associations observed are robust and not restricted to some population segments. Second, BMI was based on objectively measured body weight and height only in ELSA. All other cohorts relied on selfreported information, suggesting that the data could be subject to potential measurement errors which hampers comparisons between cohorts. Previous studies have shown that overweight and obese persons tend to underreport their weight which may lead to underestimation of BMI and potential misclassification of some individuals into incorrect BMI categories. 43, 44 Despite to this potential misclassification towards lower BMI categories in other cohorts than ELSA, the data from WHO's database suggests that adult obesity prevalence is highest in England (24% in men and 26% in women), followed by Finland (21 and 19%), Sweden (13 and 12%) and France (9 and 8%),  45  which corresponds with BMI distribution in our study cohorts.
ACKNOWLEDGEMENTS This study was financially supported by the  Academy of Finland  ( 264944 ), the  Swedish Research Council for Health for Working Life and Welfare  ( 2007-1762  and  2009-1758 ) and the  UK Economic and Social Research Council  [ ES/K01336X/1 ] under the  ERA-AGE2 initiative. SS is supported by the  Academy of Finland  ( 286294  and  294154 ) and  Finnish Ministry of Education and Culture. MK is supported by the  MRC  ( K013351 ),  NordForsk  and  ESRC. JH is partly supported by the  ESRC  ( ES//L002892 ).
9 kg m -2 ) Obese class II (⩾35 kg m -2 ) 0. 3 23. 5 52. 3 19. 2 4. 8 0. 9 32 38. 3 19. 3 9. 6 0. 2 35. 4 49 12. 4 3 0. 8 51. 1 33. 7 11 3. 4 0. 3 44. 1 48. 3 6. 5 0. 7 2. 9 69. 8 20. 4 5. 5 1. 6 0. 2 33. 6 52 12 2. 2 0. 9 52. 5 34. 2 9. 7 2. 7 Smoking Never smoking, % 27. 4 43. 5 50. 7 67. 7 35. 2 67. 9 52. 7 50. 4 Former smoking, % 53. 5 36 27. 7 17 35. 3 11. 8 32. 1 31. 3 Current smoking, % 19. 1 20. 5 21. 6 15. 3 29. 6 20. 3 15. 3 18. 3 aThe first observation point refers to the date each participant was included in the dataset. b Presence of chronic diseases includes illness reported at or before the first observation point.
Computed tomography (CT) is an essential tool in modern medicine and is frequently used to diagnose a wide range of conditions, particularly in industrial countries, such as Japan and Germany. However, markedly higher doses of ionizing radiation are delivered during CT imaging than during conventional X-ray examinations. To assess pediatric CT practice patterns, data from three university hospital databases (two in Japan and one in Germany) were analyzed. Anonymized data for patients aged 0 to 14 years who had undergone CT examinations between 2008 and 2010 were extracted. To assess CT practice, an interdisciplinary classification scheme for CT indications, which incorporated the most common examination types and radiosensitive tissues, was developed. The frequency of CT examinations was determined according to sex, age at examination, and indications.
However, the X-ray examination attributable cancer risk is estimated to be higher in Japan (the highest globally) than in Germany  [19]. In addition, unlike other countries such as Germany, the USA and the UK, Japan does not have commonly agreed clinical guidelines for the use of ionizing radiation in diagnostic radiology, especially in the pediatric field  [20]. We assessed pediatric CT practice patterns, including the associated indications, in Japan. In addition, German data were used to identify potential differences compared with Japanese CT practices, which could aid the development of best-practice guidelines for CT use in Japan.
Study population and data collection We extracted anonymized examination data from the Radiological Information Systems (RISs) at three university hospitals: two in Japan (Nagasaki University Hospital and Fukushima Medical University Hospital) and one in Germany (Mainz University Medical Center). All pediatric patients who had undergone at least one CT examination during the study period (1 July 2008 to 30 June 2010) and were between 0 and 14 years of age at the time of the first CT examination were included in this study. Only residents of the respective countries were eligible. Data regarding sex, date of birth, the date of the examination, and the indications under which the examinations were conducted were extracted for all eligible patients.
Data management and analysis We calculated the mean number of CT examinations for the whole study population and according to sex, age at the time of the examination (hereafter referred to as 'age at examination'), indication, and the hospital at which the scan was carried out. The Chi-squared test for independent samples was used to assess the differences in the mean of the examination number, number of each sex, or age at examination between the two countries. An interdisciplinary classification scheme combining the most common examination types and radiosensitive tissues was jointly developed by a small team led by senior physicians from each country. The classification scheme was piloted on a randomly drawn subset of the original data. The categories included tumor-related examinations ('tumor diagnoses and therapy' or 'tumor exclusion'), trauma, congenital/other typical childhood diseases, and general examination regions ('cranium', 'spine', 'thorax', and 'abdomen'), as well as 'other' examinations (Fig.
1 ). We separated out the cases involving tumor-or tumor-related or trauma indications because CT examinations are usually essential in such cases. As our aim was to develop best practice guidelines based on an international comparison, it was considered that separating cases in which CT examinations were essential from other cases might provide some insight into the number of 'non-essential' (i. e. potentially unnecessary) examinations. Statistical analysis was performed using SPSS Statistics 22. 0 (IBM Japan, Tokyo, Japan). Medical Chamber of Mainz, and from the Data Protection Officer of Mainz University Medical Center before the start of the study. The study was conducted in accordance with the guidelines outlined in the Declaration of Helsinki. All of the data used in this study were analyzed anonymously and securely protected under the guidance of the Data Protection Officer of each hospital.
qRT-PCR Total RNA was extracted from tissues using TRIzol reagent (TransGen Biotech) according to the manufacturer's protocol. Four micrograms of total RNA were reverse-transcribed in a reaction of 20 l by using DNase I and M-MLV Reverse transcriptase (Invitrogen) to obtain cDNA. The qRT-PCR was performed in an optical 96-well plate that included SYBR Premix EX Taq and 0. 5 l of Rox Reference Dye II (Takara), 1 l of the reverse transcription reaction, and 0. 25 M of each gene-specific primer in a final volume of 25 l on the ABI PRISM 7500 PCR instrument (Applied Biosystems). Data were collected using the ABI PRISM 7500 sequence detection system following the instruction manual. The relative expression levels were analyzed using the 2 -CT method  (46). The rice Actin1 gene was used as the internal control.
LC-MS/MS and data processing Total proteins were extracted from tobacco leaves that express OsGAPDH1 tagged with GFP, then the protein were immunoprecipitated with anti-GFP antibody-conjugated beads magnetic beads. Next, 0. 5 g of the eluate was performed deacetylation assay with 0. 5 g of the eluate as control. Then the two mixtures were resolved by SDS/PAGE. The bands were excised from SDS/PAGE gel, fully trypsinized, and analyzed by reverse-phase LC-MS/MS. MS was carried out at Thermo Scientific™ Q Ex-active™ HF and data were processed using the comparative proteomics analysis software Proteome Discoverer2. 1.
OsGAPDH Family gene expression profile analysis Expression profile of OsGAPPDH family genes in 27 tissues was extracted from the Rice Oligonucleotide Array Database (http://www. ricearrary. org/index. shtml). Expression values were obtained by searching the data using Affymetrix probe set ID of each gene for gene cluster analysis by the Cluster 3. 0 program and the results were visualized by the Treeview program.
The analysis revealed, for the same amounts of OsGAPDH1-GFP fusion protein, a higher acetylation level of the protein was detected in the RNAi, and a lower acetylation level was detected in the over-expression backgrounds compared to the wild type (Figure  5D ). Finally, to test whether OsSRT1 could deacetylate OsGAPDH1 in vitro, we produced and isolated OsGAPDH1, 2 or 5-GFP from tobacco cells and incubated with E coli-produced OsSRT1-GST or GST alone. Lysine acetylation levels of OsGAPDH-GFP fusions were tested by immunoblots with anti-LysAc and anti-GFP (for loading controls). The analysis confirmed that OsSRT1 could deacetylate OsGAPDH1, 2 and 5 (Figure  5E ; Supplementary Figure  S4D ). To further confirm the deacetylation of OsGAPDH1 by OsSRT1, we performed mass spectroscopy analysis of tobacco cell-produced OsGAPDH1 and found that 16 lysine residues of OsGAPDH1 were acetylated (Figure  5E , Supplementary Table  S2 ; Supplementary Dataset 1).
Immunoblots with anti-GAPDH were performed to analyze the total and nuclear protein fractions extracted from seedling leaves of wild type and the OsSRT1 transgenic lines treated with or without 0. 1% H 2 O 2. Anti-histone H3 antibody was used as loading controls. The analysis revealed that there were relatively higher nuclear accumulations of GAPDH in the RNAi and lower accumulations in the over-expression lines compared to their respective wild type (Figure  8C ). The H 2 O 2 treatment increased the nuclear accumulation in the wild type, while the increase was further enhanced in the RNAi but attenuated in the over-expression lines (Figure  8C ). The data suggested that OsSRT1 may have a function to prevent stress-induced nuclear accumulation of GAPDH in rice, which is presumably achieved through deacetylation of the protein.
Method Data collection The FATSEIN study  [12]  included breast cancer patients recruited in the cancer care centers of Nancy, Dijon, and Strasbourg in France and followed up for 24 months after surgery. Informed consent was obtained from all individuals participants included in the study. Eligibility criteria included women newly diagnosed with invasive breast cancer, undergoing breast surgery as primary treatment, age ≥18 years, without history of other cancer, no other major disabling medical or psychiatric conditions, no previous chemotherapy or radiotherapy, no metastases, and no inflammatory breast cancer. The study has been approved by the institutional review board and is registered at www. clinicaltrials. gov (NTC01064427). Patients were recruited for a 2 year-follow-up including 10 visits (Appendix 1).
Socio-demographic and medical data were collected before surgery. Cancer-related fatigue was measured with the MFI-20  [13]  over the 10 visits. This questionnaire explores four domains of fatigue (physical or mental fatigue, reduction in activities or motivation) and provides a global fatigue score. Higher scores indicate more reported fatigue. Patients also completed the QLQ-C30 quality of life (QoL) questionnaire  [14]  and the State-Trait Anxiety Inventory (STAI-State) to measure transient anxiety  [15]  over time. The STAI-Trait  [15]  and the LOT (LOT)  [16]  were completed at inclusion to measure enduring levels of trait anxiety and optimism. Higher scores for the State-Trait Anxiety Inventory and for the LOT indicate more reported anxiety and optimism, respectively.
Statistical analysis Several steps were performed (Fig. 1 ). SAS version 9. 4 and Mplus version 7. 2. were used. Two-sided P values<0. 05 were considered statistically significant. Firstly, the change in global fatigue score of the MFI-20 was assessed using a mixed model from which the residuals were retained. The final model was selected using an ascending strategy where time effect and sociodemographic covariates were added according to Wald tests (P < 0. 05). Since it was expected that chemotherapy treatment could modify fatigue change, it was added as a fixed effect, as well as its interaction with time. The addition of random effects on the intercept or the slope was assessed using a likelihood ratio test (LRT). The studentized residuals were obtained by dividing the conditional residuals of the mixed model by the estimate of their standard deviation; they were subsequently centered by removing their mean for each subject.
There were 154 (33%) women in LC1, 61 (13%) in LC2, 118 (25%) in LC3, and 112 (24%) in LC4. Twenty-one patients could not be assigned to a LC because of missing data on marital status (n = 9), treatment group (n = 9) or both (n = 3). At inclusion (Table  1 ), the mean ages were significantly different (P < 0. 05) between LC1 (58. 3 years) and LC4 (54. 4 years), and there were more patients without chemotherapy in LC1 (n = 83, 54%) as compared to other LC especially LC4 (n = 42, 38%). The mean enduring levels of anxiety measured by the STAI-Trait were not significantly different between the LC (P = 0. 33) but the optimism scores of the LOT were lower in LC3 as compared to LC2 (P < 0. 05). During follow-up, change in the observed transient anxiety levels measured by the STAI-State was not significantly different between the LC (P = 0.
Modeling Description. To model the force acting on the tip over the sample, we used the AC/DC module (Electrostatics) of Comsol Multiphysics software. This software uses finite-element method to solve Poisson's equation for the tip/sample system, which results in a map of the electrostatic potential distribution. The Maxwell stress tensor is then calculated and its integration around the tip surface gives the resultant electrostatic force at each position of the scan line  [33]. The probe was modeled as presented in Figure  3. The probed region of the nanodielectric model consists of a nanoparticle surrounded by an interphase placed in a matrix. The polymer matrix is represented as a disk of 20 m diameter, thickness, , and dielectric constant,  m. The nanoparticle is modeled as a solid sphere of radius  p and dielectric constant  p.
Comsol software only calculates the interaction force. In order to obtain the force gradient, we calculate the force  at different tip-sample distances. Then, we calculate the first derivative of the resulting equation of () to get the gradient (). Our material geometry and permittivities have been modeled as presented in Figure  2. We fixed all parameters and changed the thickness of alumina shell  i. We present in Figure  15  EFM experimental and simulation results for PS + Al 2 O 3 shells at  = 26 nm and for Al 2 O 3 films alone at  = 18. 5 nm for different alumina thicknesses  i. We can notice the same trend between simulations and experiments and a good agreement of  values between EFM measurements and our model. However, the fitting is more robust for Al 2 O 3 films alone. Over PS + Al 2 O 3 assembly, the fitting is less accurate especially at high shell thicknesses.
X-ray diffraction experiments were carried out at 100 K on protein crystallography beamlines X06SA-PXI or X10SA-PXII at the Swiss Light Source, Villigen, Switzerland. Measurements were made in steps of 0. 1-0. 2°at speeds of 1-2°s À 1 with either the EIGER 16M or the PILATUS 6M-F detector operated in a continuous/shutterless data collection mode at a sample-todetector distance of 40-50 cm. For Se-Met SAD phasing, diffraction data were collected on Se-Met-derivative E. coli Lnt (LntEco-Se) crystals at the weavelength and flux values of 0. 9792 Å and 1. 2 Â 10 11 photons per s, respectively. Native data from LntEco crystals were measured at wavelengths and flux values of 1. 0 Å and 2. 2 Â 10 11 photons per s, respectively. Native data from LntEcoC387A were measured at wavelengths and flux values of 1.
The SAD method was employed for experimental phasing using a data set from LntEco-Se crystals. Substructure determination was performed with 5,000 SHELXD trials  41  using the HKL2MAP interface  42. CRANK2 (ref. 43) was used to obtain an initial model, which was completed using the LntEco high resolution data with PHENIX. AutoBuild  44  and manual building in Coot  45. The structures of LntEcoC387A and LntPae were solved by MR by means of the program MOLREP  46  with LntEco as search model. BUSTER  47  and PHENIX. refine  48 were used during the refinement of all structures. Structure quality scores were obtained using MolProbity implemented from the PHENIX suite  48. Refinement statistics are reported in Table  1. Figures were generated using PyMOL (http://www. pymol. org)  49. Docking and molecular modelling and simulations.
Simulations were performed with an integration time step of 2 fs. The MDS were analysed using Gromacs tools, MDAnalysis  63  and locally written python and perl scripts. Homologous sequences were identified in the UniProt database, using a single iteration of Jackhmmer  64  , with the default search parameters. This identified 1,116 non-identical Lnt sequences. Percentage conservation for each residue in the LntEco sequence were mapped to the B-factor column of the LntEco wild-type structure and shown in Supplementary Fig. 6. A Weblogo representation of the sequence alignment was created and shown in Supplementary Figs  6  and 12. PROPKA  65  was used to estimate the pK a values of the titratable residues within the Lnt structures (Supplementary Table  1 ).
Acknowledgements We thank past ( T. El Arnaout ,  G. Heinecke ,  E. Hurley ,  T. Kwan ) and present members of the MS&FB group for their assorted contributions to this study. The assistance and support of beamline scientists at the  Swiss Light Source  ( X06SA  and  X10SA ),  Diamond Light Source  ( I24 ) and the  Advanced Photon Source  ( 23-ID ) are acknowledged. The work was funded by  Science Foundation Ireland  grant  12/IA/1255. M. W. was supported, in part, by  Swiss National Science Foundation Early Postdoc. Mobility fellowship  grant  P2BSP3_15254. D. W. was supported, in part, by the  Deutsche Forschungsgemeinschaft (German Research Foundation ). P. J. S. was supported by  BBSRC  grant  BB/I019855/1. MDS were performed using the  Irish Centre for High-End Computing (ICHEC)  facilities.
48 97. 68 95. 26 Allowed (%) 3. 52 2. 32 4. 74 Outliers (%) 0. 00 0. 00 0. 00 MolProbity Clash score 6. 26 9. 94 8. 52 PDB, protein data bank. *Data processing statistics is reported with Friedel pairs separated. Values in parentheses are for the highest resolution shell.
Data RCM imaging was performed using an apparatus Vivascope 1500. The in-vivo images were acquired from the stratum corneum, the epidermis layer, the dermis-epidermis junction (DEJ) and the upper papillary dermis. Each RCM image shows a 500 × 500µm field of view (1000 × 1000 pixels). Forty-five women aged 60 years and over were recruited. The study was conducted according to the principles of the declaration of Helsinki, and was approved by the Comité de Protection des Personnes Sud-Ouest et Outre-mer III in Bordeaux, France, No. CPP 2011/36. All the volunteers gave their informed consent for examination of skin by RCM. According to the clinical evaluation performed by a physician, volunteers were divided into two groups. Twenty-seven (m = 27) women with at least 3 lentigines on the back of the hand formed the first group and eighteen (n = 18) women with no lentigo formed the control group.
For each volunteer, the acquisition on the same location was repeated twice, giving two stacks for each patient. Images were taken on lentigo lesions for volunteers of the first group and on healthy skin on the back of the hand for the control group. An examination of each acquisition was performed in order to locate the stratum corneum and the DEJ precisely on each image. Consequently, our database contained M = 45 patients. For each patient, we retained two stacks of 25 RCM images giving a total of 2250 images. Images were registered in order to correspond to the same depths.
Performance of the SVM classification For the classification, we processed separately three images (C = 3) corresponding to characteristic depths (49. 5µm, 54µm, 58. 5µm) for each patient. As described in section 2. 4, a GGD distribution was fitted at each scale (#1, #2, #3, #4) to wavelet coefficients form separate bands (H, V, D). The 2 parameters (α, β) were estimated, along with the variance and the entropy. For each of these parameters, SVM classifiers were trained and tested to classify patients into 2 classes referred to as "lentigo" and "healthy". The leave-one-out cross-validation method was used to compute the different probabilities of errors. This method uses M -1 images for training (where M = 45 is the number of patients in the database) and the remaining image for testing. This operation was run N = 1000 times.