dataesr/scientific-paragraphs-categorization huggingface dataset:
U. S. ) ANOVAs were conducted, and follow-up tests (Fisher's LSD) were performed for between-group comparisons if necessary. Third, to address potential influences from scanning parameter differences in the U. S. and France, we re-analyzed the volumetric data using a General Additive Model (GAM)  (Hastie and Tibshirani, 1986, 1990; Wood, 2006 Wood, , 2011) )  and analysis of variance (ANOVA) from the "mgcv" package in R Version 3. 1. 0 [http://www. r-project. org/] to test for group diagnostic differences and including covariates of scanner site, sex, age, and supratentorial volume (svol) in the model for each ROI: Model : brain i ∼ β 0 + β 1 site i + β 2 sex i + β 3 age i + β 4 svol i + β 5 diagnosis i + ε i Fourth, we examined whether alcohol history (i. e. , lifetime alcohol consumption, duration of alcoholism, and length of sobriety) and age could explain country-related differences in detected abnormalities.
, 1975)  or DSM-IV criteria for alcohol dependence (American Psychiatric Association, 1994) for the U. S. group, depending on inclusion year. Nevertheless, the Research Diagnostic Criteria for alcohol dependence (RDC), a forerunner of DSM criteria, demonstrated a high level of agreement with the DSM-III, with even stricter criteria for determining alcohol dependence than required by DSM  (Leonard al. , 1984). Likewise, the lifetime alcohol consumption was quantified through semi-structured interviews, but the questioning was not the same between countries. To mitigate such limitation, the current international multi-site prospective study conducted between France and the U. S. is using similar instruments to derive the alcohol history variables in order to standardize the collection of clinical data between sites and provide more reliable comparisons between countries.
Then, to avoid interpolation artifacts, all regional volumes were computed in the native space of each image, and therefore were based on the exact pixel size and slice thickness of that particular image. A final processing step used a common atlas-based parcellation approach for regional structural identification and volume determination. The second approach focused on data analysis. As a follow-up to the primary multivariate, covariate analysis (MANCOVA), we used a General Additive Model to account statistically for unwanted variance from scanner site protocol differences, sex, age, and supratentorial volume. These analyses yielded the same pattern of group differences identified with the primary analyses. These processing and analysis precautions provide reasonable assurance that the regional brain differences among the diagnostic groups are likely not attributable to image acquisition differences across sites.
+ - + + - s s G 21 ) ( 1 2 y u - 1 2853 11. 039 + s G 22 ) ( 1 = )) ( ) ( )( ( = i y i y i w J mod exp n i - ∑ (10) In the case of transfer functions ) ( 1 s G i , the weight ) (i w were equal to 1 when 15 ≤ i and equal to 10 when 16 ≥ i in order to give more weight to the final form of the shape of the response. For transfer functions ) ( 2 s G i , the weights ) (i w were all equal to 1. For transfer functions (6), knowing the approximate value of the final response, a constraint was imposed: i i i i K K a y 1 2 1 = ) ( - + ∞ δ (11) with i a read on the step response. For these transfer functions, three parameters were thus identified. For transfer functions  (7) , i K 2 ′ could have directly determined, but a simple identification with two parameters was carried out. Using the function "fminsearch" of Matlab, the transfer functions parameters have been obtained (Table  4 ).
Data The data consisted of 4614 individual milk samples of 1488 HF and 521 RE cows, collected between September 2011 and January 2012 from 28 single-breed herds (20 HF and 8 RE) of the Veneto region (northeast Italy). Farms were enrolled in official monthly test-day milk recording and their size ranged from 12 to 216 with a mean of 74 cows for HF and from 28 to 115 with a mean of 65 cows for RE breed. Animals were from parity 1 to 5, and from 5 to 600 days in milk (DIM). Milk samples were analysed in the laboratory of the Breeders Association of Veneto region (Padova, Italy) using Milko-Scan FT6000 (Foss, Hillerød Denmark). Traits recorded were fat, protein, casein and lactose contents,   (2012, 2013)  on Milko-Scan FT6000 and they were defined as rennet coagulation time (RCT, min) and curd firmness 30 min after rennet addition (a30, mm).
However, a similar shortfall has not been reported for NSTX  55  , C-Mod 56 or AUG  57  , and GENE simulations of the original DIII-D shortfall discharge produce a substantially higher level of electron and ion heat transport than seen in GYRO and GEM simulations. The source of these code to code differences is under intense study 58 , but is not yet resolved. GYRO validation studies based on Alcator C-Mod experiments have compared heat fluxes and density fluctuations in ohmically heated plasmas  59, 60  , heat fluxes in RF-heated L-mode plasmas  56, 61, 62  , impurity transport in L-mode  17, 18, 63  , and multichannel L-mode studies  64, 65. The experimental conditions used for our turbulence simulations are described in Section II, and the basic simulation inputs, resolution choices, and initial simulations are discussed in Section III.
III. TURBULENCE SIMULATION METHODOLOGY The results described in Sections III -VI are taken from nonlinear turbulence calculations made by the continuum (or Eulerian) gyrokinetic code GYRO  27, 28  , which solves the gyroki- For the plasma conditions in these simulations the most unstable modes are driven mostly by the ion temperature gradient (ITG), but non-adiabatic electron effects provide very significant additional drive so the turbulence is best characterized as a hybrid of ITG and TEM, as first described in Refs. 72 and 73. Consequently, the simulations employ non-adiabatic treatments of all ions and the electrons. netic The simulations are based on measured profiles of electron temperature and density (the density is modified in some cases noted below), and modified ion temperature profiles shown in Fig.
VII. SUMMARY Density peaking predicted for low-collisionality plasmas 4 in ITER and other burning plasma tokamaks would raise fusion power, raise the bootstrap current, and transport edgefueled D and T to the plasma center. However, density peaking could also produce unwelcome changes such as lower β N due to the higher pressure peaking, reduced radiative cooling from the lower density near the plasma edge would tend to raise divertor heat loads, and impurity density peaking might lead to serious central radiative losses. Predictive capability is needed in order to guide hardware design and operational planning as needed. We have used the turbulence code GYRO to estimate the theoretically expected density peaking in a low-collisionality H-mode plasma in Alcator C-Mod. Modest electron density peaking is predicted by these simulations -and observed in the experiments -and higher collisionality is predicted to reduce peaking, as is observed in higher density EDA H-mode plasmas.
Background: The detection of synchronous metastases at primary diagnosis of breast cancer (BC) affects its initial management. A risk calculator that incorporates many factors to evaluate an individual's risk of harbouring synchronous metastases would be useful to adapt cancer management. Patients and Methods: Patients with primary diagnosis of BC were identified from three institutional databases sharing homogeneous work-up recommendations. A risk score for synchronous metastases was estimated and a nomogram was constructed using the first database. Its performance was assessed by receiver characteristic (ROC) analysis. The nomogram was externally validated in the two independent cohorts.
Hormone receptor positivity was defined based on positive staining for ER and/or PR in at least 10% of cancer cell nuclei in France, and X5% at MD Anderson Cancer Center (MDACC). HER2 status was defined according to the ASCO guidelines. The ER, PR and HER2 status were entered as positive or negative. The factors predictive for the presence of synchronous distant metastases at diagnosis were identified through univariate and multiple logistic regression analyses. Odds ratios were calculated to estimate the strength of the association between individual risk factors and synchronous metastases. Factors found to be significantly associated with synchronous metastases in the univariate analysis (Po0. 20) were included in the multivariate analysis. Using the IGR data set, a logistic regression-based nomogram was developed to predict synchronous metastases at diagnosis.
Backward elimination was performed to choose the covariates to be retained in the model. The discriminative power of the model was quantified in terms of discrimination and calibration. Discrimination was quantified by the area under the receiver-operating characteristic (ROC) curve (AUC) and 95% confidence intervals (CIs) were estimated. In all, 200 bootstrap resamples were used for internal validation of the accuracy estimates and to reduce overfit bias. Sensitivity, specificity, and positive (PPV) and negative predictive values (NPV) and their 95% CI were calculated for various cutoff points of the calculated risk score. External validation was performed by using the nomogram for patients from the two independent validation cohorts. All statistical analyses were two-sided and were performed using the R cran Design package (R Development Core Team, 2010).
The nomogram was then validated externally using the CRH and MDACC data sets. The CRH cohort characteristics were comparable to those of the training IGR cohort, while those of the MDACC data set were significantly different, particularly cT, ER, PR and HER2 status and histological grade (Table  2 ). The rates of synchronous metastases were different between the IGR and CRH cohorts (4. 4% and 3. 2%; P ¼ 0. 02), but not between the IGR and MDACC series (4. 4% and 5. 1%; P ¼ 0. 35) (Table  2 ). In the CRH cohort, only 591 (13. 2%) patients received neoadjuvant treatment, while 2399 (94%) of the MDACC patients received primary treatment. Figure  4  shows the distribution of the predicted probability of having synchronous metastases for each patient calculated with the nomogram. Most patients (Figure  4 ) with BC had a predicted metastatic risk lower than 10%.
The observed AUC of the constructed nomogram was 86. 1% (95% CI, 83. 2-89. 1%) for the CRH validation cohort and 63. 8% (95%CI, 58. 8-68. 8%) for the MDACC validation set (Figure  2 ). Therefore, the prediction model based on the IGR data overestimated the actual probability compared with the MDACC data. The respective sensitivity, specificity, NPV and PPV are shown in Table  3. Sensitivity and specificity were good for the IGR and CRH data sets and lower for the MDACC cohort. In all three cohorts, the NPV was higher than 96%. Finally, a computer program was developed to help physicians determine the risk of synchronous metastases at primary BC diagnosis. The program called 'synchronous metastases in BC' is in Java. An internet browser with Java capability is required to run the applets. An example of a screen is shown in Figure  5.
Methods Nucleotide sequences of HBV genomes were searched from GenBank using an updated database (last accessed on 31 January 2014) at the National Center for Biotechnology Information (http://www. ncbi. nlm. nih. gov/nucleotide), using "Hepatitis B virus", "complete genome" and "genotype X" (X represents A, B, C, D, E, F or G) as the terms for search query. Full-length HBV genome sequences of genotypes A through G from European countries were retrieved. The background information consisted of an assessment of genome length, regions, genotype, resource, open reading frame (ORF) location and gene function. HBV genome sequences were manually excluded from the database when they were shorter than 3050 base pairs (bp), or had incomplete background information  [17]. Multiple alignments of the 383 HBV sequences, containing complete functional genes or defined gene mutations, were conducted using the CLUSTALX 1.
83 program (UCD Conway Institute, Dublin, Ireland). The genotype of each analyzed sequence was compared with the original report to confirm the background genotyping information. If a discrepancy existed between the observed data and the original report, updated information was collected for further analysis. CpG islands were analyzed using MethPrimer (http:// www. urogene. org/methprimer/index1. html) by examining the GC content and the observed-to-expected ratio in a window size. The location and size of CpG islands within each analyzed sequence were identified according to the current knowledge of CpG island distributions in the HBV genomes  [18]. Because each genotype contains multiple sequences and the lengths of the sequences are different, we calculated the length of CpG island of each genotype according to their location in the sequence: the most proximal site of the CpG island in the sequence was designated the start and the most distal site was designated the end.
Geographical distribution of HBV genotypes in Europe A total of 383 HBV isolates were identified as 7 datasets: genotype A (n = 150, 39. 16%), B (n = 6, 1. 57%), C (n = 1, 0. 26%), D (n = 199, 51. 96%), E (n = 14, 3. 66%), F (n = 2, 0. 52%) and G (n = 11, 2. 87%). Genotype A isolates were observed in 7 countries including Belgium (n = 72, 48%), Poland (n = 45, 30%), Germany (n = 13, 8. 6%), Russia (n = 8, 5. 3%), France (n = 4, 2. 7%), Latvia (n = 4, 2. 7%) and Estonia (n = 4, 2. 7%). Genotype D isolates were from 12 countries including Turkey (n = 93, 46. 7%), Belgium (n = 28, 14. 1%), Russia (n = 20, 10. 2%), Estonia (n = 13, 6. 5%), Italy (n = 12, 6. 0%), Greenland (n = 10, 5. 0%), Poland (n = 8, 4. 0%), Sweden (n = 4, 2. 0%), Germany (n = 3, 1. 5%), France (n = 3, 1. 5%), Serbia (n = 3, 1.
Novel CpG islands in HBV genotypes A-G The three novel CpG islands in the HBV genome were first described by Zhang et al. , and were thought to be potential targets for DNA methylation  [6]. We also found novel islands at the same locations in the HBV genome. In our study, novel island IV was present in genotypes A, D and E, island V was present in genotype F; and island VI was not present in A-G genotypes. In the report by Zhang et al. , island IV was detected in genotypes B, C and D, island V in genotypes B, F and H, and island VI in genotype C. The reason for differences between both studies might be that data was obtained from different sources. Our data were from 383 isolates of European patients, and Zhang's data was from 176 representative isolates of patients residing in 48 different countries worldwide.
Acknowledgements The authors thank  Professor Tianhua Huang  from  Shantou University Medical College  for his assistance in revising the final draft of manuscript. YZ is supported by an  Applied Basic Research Program of Sichuan Province, China  (No. 2014JY0110 ). 1  Faculty of Science and Engineering, University Paul Sabatier , Toulouse 31062, France. 2  Key Laboratory of Birth Defects and Reproductive Health of National Health and Family Planning Commission, Chongqing Research Institute for Population and Family Planning Science and Technology, Chongqing 400020 ,  China. 3Jinxin Research Institute for Reproductive Medicine and Genetics, Chengdu Jinjiang Hospital for Maternal and Child Health Care, Chengdu 610066, China. 4Research Center for Reproductive Medicine, Shantou University Medical College, Shantou 515041, China.
Electronics The electronics driving the AFM are a complete Nanonis SPECS consisting of the modules RCS, SC4, OC4 and the power supply to drive the piezoelectric elements of the scanner. The AFM images and force curves are acquired using the software included with the electronics. Two additional custom-made LabVIEW programs drive the step motors that control the long-range approach between the AFM head and its base and the picomotors which displace the scanner.
S1 ). Each point in the reflectivity curve was measured limiting the exposure to 1 s. Each reflectivity curve was acquired in 20 min. We acquired 209 points that corre-spond to a total exposure time of 3 min and 29 s and a dose of approximately 23 Â 10 3 Gy, calculated from  Elleaume et al. (1999). Data have been treated modeling the interface with a seven-slabs model  (Novakova et al. , 2006; Daillant et al. , 2005)  including a silicon oxide layer of 1 nm, a layer of water molecules in between the substrate and the lipid heads, two hydrocarbon tails regions including a CH 3 slab in between and lastly the second lipid heads slab. Data have been fitted employing the GenX software.
Acknowledgements This work was performed at the  Surface Science Laboratory  and at the ID03 endstation of the  European Synchrotron Radiation Facility. The samples were prepared in the Science Building of the European Photon and Neutron campus. The authors acknowledge  Fabio Comin  and  Roberto Felici  for their support and for fruitful discussions. The authors acknowledge  Michael Caminale  for the preparation of the drawing in Fig. 1(a).
The data and discussions can be found in the supplementary Fig. S2  and Fig. S3. The monolayer modified samples were then coated with a 20-nm thick layer of silicon dioxide by atomic layer deposition (ALD) and subsequently annealed at 1050 °C for 2 min (Fig. 1 ). To evaluate the nitrogen doping process, Van der Pauw technique was employed to measure the sheet resistance of the silicon samples at room temperature. Table  1  lists the experimental results of the silicon samples with and without nitrogen doping, as well as the sample with both nitrogen and phosphorus doping (for the I-V curves, refer to the supplementary Fig. S4 ). The sheet resistance of the original undoped silicon substrate was measured to be 143 ± 21 kΩ. A piece of silicon wafer labeled as "blank sample" was selected to undergo all the capping and annealing processes except the SAM formation.
All energies are reported as binding energies in eV and referenced to the C 1s signal (corrected to 285. 0 eV) for aliphatic carbon on the analysed sample surface. Survey scans were carried out selecting 250 ms dwell time and analyser pass energy of 160 eV. High-resolution scans were run with 0. 1 eV step size, dwell time of 100 ms and the analyser pass energy set to 40 eV. After background subtraction using the Shirley routine, XPS spectra were fitted with a convolution of Lorentzian and Gaussian profiles by using software Casa XPS. Secondary-ion Mass Spectroscopy (SIMS) was conducted to obtain dopant profile at the top 300 nm of substrate at Evans Analytical Group, NJ, USA. EFM experiments were performed on a Nanonavi E-Sweep system (Seiko, Japan) operated in tapping mode. Electrical Characterization.
The metal contacts on silicon for electrical measurements were realized by evaporating 200-nm aluminum or chromium/gold films in a thermal evaporation system (Angstrom Engineering, Canada). Van der Pauw measurements were performed on square-shaped samples on which the metal contacts are exactly located at the four corners. The custom-made probe station is equipped with four solid tungsten probe tips (the tip size < 1 μ m). Keithley 2400 sourcemeter units and a custom-written Labview script were employed to generate and collect current/voltage data. Hall effect measurements were performed on samples in Hall bar geometry in a Physical Property Measurement System (PPMS, Quantum Design, USA).
Acknowledgements This research was financially supported by the national " 1000 Young Scholars" program  of the Chinese central government, the  National Science Foundation of China  ( 61376001 ), the " Pujiang Talent Program " of the  Shanghai municipal government , and the 54 th general fund of  China Postdoctoral Science Foundation. We thank  Dr. Limin Sun  ( Instrumental Analysis Center, Shanghai Jiao Tong University ) for her help with XPS,  Mr. Ang Wang  and  Mr. Xiaofei Zhou  ( University of Michigan -Shanghai Jiao Tong University Joint Institute ) for wire bonding.
Electrophoresis was performed in a field of 0. 7 V/ cm and 300 mA current for 30 min. Slides were then neutralized with 0. 4 M Tris pH 7. 5 and stained with 50 μL of 20 μg/ml ethidium bromide. At least 50 comets per slide were analyzed under a fluorescence microscope (Zeiss) equipped with a 350-390 nm excitation and a 456 nm emission filter at ×20 magnification. Percentage of tail DNA was measured by using Comet IV software (Perceptive Instruments, Suffolk, UK).
Ten percent gels (160x200x1. 5 mm) were used for the second dimension. The Tris taurine buffer system  [43]  was used and operated at a ionic strength of 0. 1 and a pH of 7. 9. Detection was carried out by fast silver staining  [44]. Image analysis. The gel images were analyzed using the Delta 2D software (v 3. 6). Three gels coming from three independent cultures were used for each experimental group. Spots that were never expressed above 100 ppm of the total spots were first filtered out. Then, significantly-varying spots were selected on the basis of their Student T-test p-value between the treated and the control groups. Spots showing a p-value lower than 0. 05 were selected. Mass spectrometry. The spots selected for identification were analyzed by NanoLC-MS/ MS analysis, performed using a nanoLC-QTOF-MS system and a nanoLC-IT-MS system operated as described previously  [32]  (details can also be found in the S1 Methods).
The MS/MS data were interpreted using MASCOT 2. 4. 0 (Matrix Science, London, UK) against UniProtKB/SwissProt (version 2012_08, 537,505 sequences). The search was carried out in all species. A maximum of one trypsin missed cleavage was allowed. Spectra from Qtof were searched with a mass tolerance of 15 ppm for MS and 0. 05 Da for MS/MS data and spectra from Ion Trap were searched with a mass tolerance of 0. 3 Da in MS and MS/MS modes. Carbamidomethylation of cysteine residues and oxidation of methionine residues were specified as variable modifications. Protein identifications were validated with at least two peptides with Mascot ion score above 20.
Support was provided by:  Commissariat à l'Energie Atomique, Programme transversal Toxicologie , NanoBioMet and Nanostress grants, to TR;  Université Grenoble Alpes, Ecole Doctorale Chimie et Sciences du Vivant , PhD Fellowship program, to ST; Labex Serenade, to TR. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
hMSCs were seeded on the implant 2 hours before implantation. The skin was sealed with sutures and the animals' behavior observed after waking up. The mice were sacrificed with an intraperitoneal injection of a lethal dose of ketamine and the samples were extracted after 30 days of implantation. histology For histology, samples (the implants) were fixed for 24 hours in Bouin-Hollande and embedded in paraffin. Serial sections (7 μm) were stained with Mallory's stain. After long-term implantations, the samples were demineralized in 15% EDTA for 48 hours. After the removal of paraffin wax, sections of the calvaria implants were stained using Mallory coloration over 2 days. The percentage of mineralized area on histological sections was measured with ImageJ software by selecting the mineralized area and total implant area.
seM/energy dispersive X-ray spectroscopy The different implants were morphologically and compositionally analyzed using a field-emission scanning electron microscope (Inspect™ F50; FEI, Hillsboro, Oregon, USA) equipped with an energy-dispersive X-ray spectrometer to map the elemental composition of the samples during the inspection. The surfaces were Pt-sputtered before observation and examined at an accelerating voltage of 10 kV and a working distance of 10 mm. The fiber diameter was measured with ImageJ software. 47.