dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgements We gratefully acknowledge discussions, comments, and feedback from the user community, and ongoing collaborations with  A. Georges  and  S. Biermann. M. Aichhorn ,  M. Zingl , and  G. J. Kraberger  acknowledge financial support from the  Austrian Science Fund  ( Y746 ,  P26220 ,  F04103 ), and great hospitality at  Collège de France ,  École Polytechnique , and  CEA Saclay. L. Pourovskii acknowledges the financial support of the  Ministry of Education and Science of the Russian Federation in the framework of Increase Competitiveness Program of NUST MISiS  (No. K3-2015-038 ). L. Pourovskii  and  V. Vildosola  acknowledge financial support from  ECOS-A13E04. O. E. Peil  acknowledges support from the  Swiss National Science Foundation  (program  NCCR-MARVEL ). P. Seth acknowledges support from  ERC  Grant No.
After each step, the specimens were observed by means of SEM and the crack elongation ∆a was measured. With help of the new crack length, a new, lower K I,max was calculated, i. e. , a lower force F max was determined for the next step. This procedure was repeated until a load was reached at which no crack growth could be detected by SEM. During every step, the load cycles were counted to determine the medium velocity of the crack per cycle da/dN. To receive crack growth curves from the individual results of each step, all data points corresponding to the same material and heat treatment condition were fitted to the Erdogan-Ratwani equation  [17] da dN = C E (K I,C -K I,max ) m E (1 -R) K I,C -K I,max (2) with load ratio R = 0. 1, fracture toughness K I,C fixed at 23 MPa × m 1/2 , threshold value for long crack growth K th taken from crack growth results, empirical constants C E and m E fitted with the software Origin lab (Origin 2015G, Origin Lab Corporation, Northampton, MA, USA).
5 nm edge-length with the PACKMOL software  [39]. After several optimization and NVT equilibration (2. 5 ns, Nosé-Hoover  [40]  temperature coupling, T = 250 K, τ = 0. 1 ps, particle mesh Ewald electrostatics treatment  [41]  with 1 nm Coulomb cutoff) cycles with slightly increasing box size, the pressure dropped to an acceptable value (below 100 bar) so that the NPT equilibration (Parinello-Rahman pressure coupling  [42] , p = 1 bar, τ = 10 ps) converged quickly to a density of 943 kg m -3. The equilibration procedures have also been used with different temperatures for the ubiquitin-containing simulations as indicated in the main text. NPT production runs (setup as before), employ bond-constraints using the LINCS algorithm  [43]  and an increased integration time step of 2. 5 fs. The chromophores (rhodamine 575 C 5 -maleimide and QSY7 C 5 -maleimide functionalized cysteine residues) have been treated within a GAFF parametrization as described previously and the details of the FRETefficiency calculations used herein are also described in detail elsewhere  [15].
The circular dichroism as a function of the methanol concentration under identical solution conditions to those used in the electrospray are shown for bovine ubiquitin in Supplementary Figure  S4. There is a small change in the spectrum comparing H 2 O and the binary methanol water mixture, and a much larger change for the methanol only solution. As was noted above, these changes correlate to the degree of the unfolded A-state, which presents an increasingly helical character as the conditions are made more denaturing. It is possible to estimate the amount of α-helical and βstrand character present in each sample using the K2D3 prediction software  [56] , the results of which are shown in Supplementary Table  S1 , alongside the predicted values for the reference spectrum shown in Figure  2.
Acknowledgments The research leading to these results received funding from the  European Research Council  under the European Union's  Seventh Framework Programme  ( FP7/2007-2013  grant agreement no. 320659 ). Computer time granted by the  P2HPD (Pôle de Calcul Hautes Performances Dédiés, Université Lyon 1)  is gratefully acknowledged. A. K. acknowledges funding from the  Deutsche Forschungsgemeinschaft DFG  (Research Fellowship Ku  3251/1-1 ) and support by  COST Action   BM1403.
Images were collected as Z-series using a Leica SP5 confocal microscope. Bassoon-stained active zones were quantified using 3D reconstructions (Imaris v7. 4. 2, Bitplane) of NMJs. Using this software, each NMJ was reconstructed and then evaluated empirically by rotating and examining the 3D image from presynaptic and postsynaptic perspectives to determine that the reconstruction matched the actual staining. Briefly, for each image, the postsynaptic receptor area was rendered by smoothing and thresholding using background subtraction based on local contrast. The result was then filtered based on a minimal voxel size to eliminate any artifactual staining not associated with the terminal area. The volume and surface area of the rendered volume were determined by the software. Puncta stained with bassoon were identified and counted in a three-step process.
Data collection and analysis. Custom software was used for collection and analysis of synaptic currents. At each NMJ, spontaneous miniature endplate currents (MEPCs) were collected for 1 min. The nerve was then stimulated (0. 5 Hz) and 15-20 individual evoked endplate currents (EPCs) were recorded. Quantal content was directly calculated by dividing the EPC amplitude by the average MEPC amplitude recorded for each synapse. We used the extent of EPC facilitation/depression during evoked trains  (Zucker and Regehr, 2002)  to indirectly evaluate probability of release following a protocol previously used at the mouse NMJ  (Kong et al. , 2009; Wang et al. , 2010). We recorded 15-20 responses to a 10 pulse, 50 Hz train delivered to the nerve and 20 responses were recorded. The extent of depression/potentiation was calculated by dividing the averaged amplitude of the 10th pulse by the first pulse.
The improved performance of CMT2D mice after administration of physostigmine, the lack of improved performance in the Figure  5. Electron microscopic analysis of NMJs. Electron micrographs of NMJs from LAL muscles of 2-month-old (A) wild-type and (B) Gars P278KY mice were captured (5 animals per genotype, 5-10 NMJs (median 8 per muscle). Mutant NMJ shown is representative of a "severely" affected NMJ with a low-vesicle count. Note junctional folds and other synaptic specializations are still evident at mutant synapses. Analysis showed vesicle size was similar for mutant and wild-type terminals (50 vs 54 nm, respectively; data not shown). C, Scatterplot shows that the area of terminal portions analyzed scale with vesicle counts for both genotypes. Note the relative absence of large, high vesicle count terminals (ellipse) and the preponderance of small, low vesicle count NMJs in the mutant (C, lower quadrant; expanded in inset).
Quantal analysis in the presence of 3,4 DAP To assess the capacity of mutant NMJs to increase quantal release, we performed an additional set of voltage-clamp experiments using muscles from Gars C201R to assess NMJ function in the presence 20 M 3,4 DAP. The data presented in Figure  7  show that mutant NMJs in 2-and 4-month-old LAL muscles are able to respond to the 3,4 DAP in a manner that is qualitatively similar to wild-type NMJs (note control values are replotted from Fig. 2 ). Lengthening the presynaptic depolarization increased EPC amplitude at mutant NMJs to values similar to control (Fig. 7C ). However, mean quantal content at mutant 2-month-old NMJs remained significantly lower than the respective wild-type mean (Fig. 7D ) and though it was still visibly reduced at 4-month-old mutant NMJs, it was not statistically different.
Activity monitoring We used Fitbit Ultra activity monitors (Fitbit, Inc. , San Francisco) and the self-report International Physical Activity Questionnaire (IPAQ) to collect physical activity data from participants. The Fitbit Ultra (herein referred to as Fitbit) is a pedometer with a built-in accelerometer and altimeter that calculates steps taken, steps climbed, and distance in miles. In a validation study, Fitbit was shown to underestimate energy expenditure by approximately 15%, but compliance was higher than with other devices. In order to facilitate compliance, the Fitbit was preferred due to the possibility of mildly impaired cognitive function in prodromal and early HD. In a treadmill validation study, the Fitbit was shown to be as reliable as ActiGraph for step counts, which was the primary metric used for analysis.
Participants were instructed to put the Fitbit on immediately after waking up on selected days, and to remove it only when engaging in physical activities involving water, when showering, and when going to sleep for the night. For physical activity that involved water and required removing the Fitbit, participants were instructed to record their activity on the record sheet. They were informed that they did not have to include showering as water activity. Participants were instructed to mail back the Fitbit trackers and record sheets in the provided addressed-and-stamped envelopes. Activity data were downloaded to the web interface provided by Fitbit, Inc. Fitbit activity scores were calculated by combining the mean number of steps, miles, and stairs climbed over the three days to generate a total activity score for each participant.
The 7-item IPAQ is a self-report measure of physical activity that prompts participants to report the number of days in the past week they engaged in sitting, walking, and moderate or vigorous physical activity, and to specify the number of hours and minutes per day they spent in each activity. The IPAQ has demonstrated adequate reliability and validity, including good test-retest reliability (Spearman's ρ = 0. 8 within 8 days), and fair to moderate correlation (ρ = 0. 30) between the IPAQ and accelerometer data. Although there are some reported concerns about the reliability of the short form , we decided to use the IPAQ because it is a short measure that creates minimal participant burden. While the Fitbit supplies data regarding the quantity of exercise a person participates in on a daily basis, the IPAQ provides the self-reported level of vigor of physical activity levels.
Participants completed the IPAQ at home and returned it with the Fitbit trackers. We calculated IPAQ scores using the METminutes (MET = median metabolic equivalent) method for continuous IPAQ scores. Essentially, each participant indicates (for each category: walking, moderate, and vigorous activities) the average number of days during which they engage in the specified activity, and how many hours each day they engage in said activity. These values are then multiplied together by a corresponding weight based on estimated MET. It should be noted that sitting time is not factored into the IPAQ variable analyzed below. The IPAQ and Fitbit participant scores were subsequently matched to the entire PREDICT-HD dataset for analysis. Outcomes of interest included variables consistently shown to be associated with disease progression in HD.
Analysis All analyses were performed using the statistical software program R (version 3. 1. 2). The overall goal of the analysis was to determine whether activity level is associated with progression in HD, and our exploratory aim was to examine whether being physically active may have a protective effect with respect to HD-associated changes. We calculated correlations between activity level (as measured by Fitbit and IPAQ) and cognitive, imaging, motor, and functional variables to assess whether activity level correlates with variables collected in PREDICT-HD. We also calculated correlations separately for gene-expanded and nongene-expanded individuals so as to assess whether the correlation structure changed by gene status. Next, we fitted linear mixed models (LMMs) using all available longitudinal data collected between 2002-2014 from PREDICT-HD to calculate progression indices for individuals in the subset with physical activity data.
Descriptive analyses For this sub study, 87 PREDICT-HD participants at the University of Iowa were invited to participate. Of these 87, one person declined to participate due to time concerns; one participant was ill the day of the study and withdrew; one participant did not return physical activity data; nine participants had incomplete data (four participants did not return IPAQ forms, four returned incomplete IPAQ forms, and six people returned Fitbits with no data on them); and one participant for which activity data were collected was removed from the analysis because that participant's data were not contained in the latest data cut. Therefore, 75 participants with complete Fitbit and IPAQ data were used for these analyses. Table  1  shows participant demographic data. Most of the gene-expanded participants were in the prodromal stage.
Activity monitoring Most participants returned the Fitbits along with the Fitbit activity record sheet and IPAQ questionnaires. Four participants did not return Fitbit activity records with the Fitbits; however, we were able to use the data for analyses by extrapolating their activity data and dates of activity from the Fitbit website. In these cases, the three days with the most complete data were used. Seven participants recorded removing the Fitbits to shower or for water activities (e. g. 5 hours at a water park, 1 hour water volleyball, 40 minutes water aerobics, 1 hour kayaking). Several participants made notes on the record sheets to indicate atypical activity. These aberrations were either more sedentary than usual (e. g. , long car rides, watching a football game, heavy rain inhibiting daily walk) or more active than usual (e.
Random slope correlations A correlation matrix for the random slopes calculated from a linear mixed-effects regression analysis and activity level data is presented in Table  4. WHODAS participant slopes were negatively correlated with Fitbit activity level (r = -0. 28, p = 0. 039), and WHODAS (companion) slopes were negatively correlated with Fitbit activity level (r = -0. 26, p = 0. 065) at the 0. 10 level. In this study, we examined associations between physical activity level and a variety of outcome variables with demonstrated sensitivity to changes in prodromal HD. We collected physical activity data at participants' most recent visit and compared this to their cognitive, behavioral, motor, functional, and imaging variables at the same visit, and to the rate of changes in the same variables from previous annual research visits.
In addition, 75/83 (90%) of the participants we enrolled completed the activity portion of the study, in line with another study involving an exercise intervention for people with diagnosed HD that had a retention rate of 81%. This indicates that physical activity intervention programs are feasible, even in patients with early diagnosed HD. This study had some limitations. We did not measure physical activity longitudinally. Instead, we made the assumption that one-time physical activity data for each participant would reflect their recent exercise history. However, collecting longitudinal physical activity data would be preferable. We have a small sample size for this study. More data are needed with larger samples to determine the effects of physical activity on cognitive function. We also encountered some technical issues with the Fitbit, as noted above.
More recent models of the Fitbit are now available and could provide more accurate data. It is also possible that people did not accurately report compliance with wearing the Fitbits. However, the number of people recording deviations in following the protocol (temporary removal) provides some indication that at least those participants were conscientious about using the Fitbit as instructed. There were other instances where a Fitbit was returned without self-reported date sheets. In these situations we extrapolated accurate data by using dates with physical activity recorded in the Fitbit database. There is also a limitation of the reliability of the accelerometer accuracy in a population that could have chorea movements. It is not known how this Fitbit device would calculate these disease-associated movements.
Also, we recruited participants year round. It is possible that participants recruited in the winter months had lower activity levels than others given that it is more difficult to participate in outdoor activities in cold weather. We also did not account for water activities that were not recorded by the Fitbit. In future studies, water activity should be accounted for, and all activity scores should be converted to one metric, such as METs. Despite the limitations in using the Fitbit and the IPAQ, our study demonstrates it is feasible to collect physical activity data from participants with prodromal and early stage HD. Future studies might also benefit from ensuring that a companion is involved, which might help ensure accurate completion of forms. The prodromal participants in our study may be older than other prodromal individuals in the population.
Chromatographic separations were carried out on a Waters BEH C18 column (100 ˆ2. 1 mm, 1. 7 µm, Milford, MA, USA). The mobile phase A was 0. 1% (v/v) formic acid, and the mobile phase B was acetonitrile containing 0. 1% (v/v) of formic acid. A linear gradient from 5% of phase B to 60% of phase B over 22 min was used to separate analytes. The flow rate was 400 µL¨min ´1 and the column was kept at 35 ˝C. Between the injections, the column was equilibrated with 10 volumes of 5% phase B. All data were acquired using electrospray ionization in the negative ion mode. Linear (centroid) spectra were acquired over a mass range from m/z 100 to m/z 2000 with the following parameters of the mass spectrometer: capillary voltage 3 kV; dry gas flow 4 L¨min ´1; dry gas temperature 180 ˝C; nebulizer pressure 0.
Chemical shift referencing was carried out using the internal solvent resonances at δ H 3. 31 and δ C 49. 0 (CD 3 OD), or at δ H 2. 50 and δ C 39. 51 (DMSO-d 6 ), calibrated to TMS at 0. 00 ppm. Chemical shifts are in ppm and the J values in Hz. Data processing was performed with the Topspin software (version 3. 5pl2, Bruker BioSpin). Analytical Thin Layer Chromatography (TLC) was carried out on precoated silica gel 60 F 254 TLC plates (art 105554, Merck, Darmstadt, Germany) using 60:15:20:5 MeCN/H 2 O/CHCl 3 /HCOOH solvent system. The plates were visualized under ultraviolet light (λ 254 nm) and by charring the compounds with 5% sulphuric acid in MeOH. A vacuum liquid chromatography was carried out on LiChroprep RP-18 (40-63 µm) (Merck). A low-pressure column chromatography was done using Sephadex LH-20 (25-100 µm, Sigma-Aldrich, St.
7 mg), 7 (4. 8 mg), 8 (4. 6 mg), and 12  (25. 8 mg). The structure elucidation of the isolated compounds was performed using optical rotation, ECD, IR, UV, ESI-MS and NMR spectral data analyses and comparison with appropriate literature values. ´; 13 C-and 1 H-NMR data are shown in Tables  1  and 3  respectively.
The qualifying AF event was recorded by a 12-lead ECG, 24 h ECG Holter or other electrocardiographic documentation and should have been occurred within the 12 months before the enrolment. Follow-up data were recorded 1 year after the enrolment date according to the procedures previously described. 13 From February 2012 to March 2013, a total of 3119 AF patients were enrolled. All patients with available data about PAD status were included in the present analysis. Peripheral arterial disease diagnosis was established by investigators at site level. The presence of PAD was defined by a positive history of any of the following: intermittent claudication, previous surgery, percutaneous intervention or thrombosis on abdominal or thoracic aorta, and lower extremity vessels. This assessment was performed by any physician during the clinical assessment and/or by searching through medical records, if available.
Additional stepwise models were then performed inserting in any model a specific class of drugs with a known role in CV prevention (i. e. influencing atherosclerosis progression and/or reducing CV events) such as antiplatelets, statins, angiotensin-converting enzyme (ACE) inhibitors, and calcium-channel blockers. A Hosmer and Lemeshow goodness-of-fit test was used to verify that the models were optimal. A two-sided P-value of ,0. 05 was considered statistically significant. All analyses were performed using SAS statistical software version 9. 3 (SAS Institute, Inc. , Cary, NC, USA).
30 Nonetheless, definitive data on CV risk reduction for AF patients with concomitant symptomatic PAD are lacking. The role of statin therapy in lowering the incidence of CV events and death in PAD patients among the general population, even if never specifically tested in a properly designed study in this setting, has largely been confirmed. 31 For all-cause death, data from the Reduction of Atherothrombosis for Continued Health (REACH) registry showed that statin therapy in patients with PAD conferred an almost 20% relative risk reduction. 32 Conversely, a large Cochrane systematic review on pharmacological therapy for PAD documented inconclusive results of statins and all-cause death, with a non-statistical significant inverse relationship with statins. 33 ngiotensin-converting enzyme inhibitors in general PAD patients may reduce CV events,  34  but definitive data for ACE inhibitors in modulating major adverse events in AF patients with PAD are lacking.
In STXM monochromatic X-rays of different energies are focused onto the sample by a Fresnel zone plate  55  and the transmitted X-rays detected. The spectra were obtained by (x, y) raster-scanning the sample at the focus of the X-rays over a range of photon energies so that a sequence of images at different energies was acquired, known as a "stack"  56. The nitrogen 1s spectra were recorded on a different region of the same sample with the STXM on beamline 5. 3. 2. 2 at the Advanced Light Source in Berkeley, USA. Data processing was similar in both cases. The oxygen 1s spectra were measured with the full field transmission X-ray microscope (TXM) at the U41-FSGM beamline, BESSY II electron storage ring, Berlin, Germany  57. The spectra were recorded at room temperature in transmission mode by taking a sequence of images over a range of photon energies covering the investigated adsorption edges with a spectral resolution larger than 5000.
The zone plate used provided a spatial resolution of 25 nm. Stack alignment, conversion to optical density, and extraction of NEXAFS spectra from small, free-standing regions were performed using the aXis2000 software  58. Since the photon flux at the sample varies as a function of energy and time, the aligned image sequences were converted to optical densities by dividing the transmitted signal recorded on the sample region by the incident photon flux recorded in a sample-free region. Thus, the ultimately acquired image sequences at different energies provide spatially resolved NEXAFS spectra. X-ray photoelectron spectroscopy (XPS) measurements were performed in a VG Escalab 220i XL with background pressure of 1× 10 -10 Torr, equipped with a source of monochromatic Al Kα X-rays (hν = 1486 eV).
Simulated X-ray adsorption spectra were calculated using the WIEN2k program package  64  with the LDA functional and using the GGA-PBE optimised atomic structures. The GGA functional is better suited for the geometry optimisation, whilst our test calculations demonstrated that LDA gave slightly better C 1s peak seperations for C 60 than GGA for NEXAFS simulations (see Supporting Materials). In order to describe properly the impact of the two-particle electron-hole interactions on the XAS spectra, one should go beyond DFT by using the Bethe-Salpeter approach  65  or time-dependent DFT  66. Here, due to the size of the systems, we have chosen to mimic these many-body effects by considering a static screened electron-hole interaction using the Slater transition state, i. e. a half-reduced occupation in the C 1s and N 1s core levels when calculating the the C and N 1s NEXAFS of C 60 and (C 59 N) 2.