dataesr/scientific-paragraphs-categorization huggingface dataset:
5. 3 This paper also provides a set of well-documented functions written for NetLogo  (Wilensky 1999 ) that facilitate the use of FL within this platform. Using these functions, it becomes simple to implement agent-based models where individual agents hold their own fuzzy sets (representing subjective imprecise concepts) and follow fuzzy rules which may affect the way agents behave and interact. Appendix C is a step-by-step tutorial that shows how to do this. As an example of a potentially useful application, our extension makes it particularly easy to analyse the robustness of a certain social theory expressed in natural language to different specifications of the imprecise concepts that the theory contains, such as e. g. "wealthy" (which may be represented with different fuzzy sets). Also, it facilitates the exploration of the effect that heterogeneity in concept interpretations may have in a society (i.
Study design This was a prospective, single-center, observational study. During the study period, all patients aged >18 years with a confirmed diagnosis of septic shock in the ICU were included. Exclusion criteria were patients with known AF (paroxysmal or sustained), and patients transferred from another ICU with prior septic shock. This information was obtained at admission from the patient's general practitioner or cardiologist, during verification of inclusion/exclusion criteria. We did not exclude patients with other known arrhythmias, or patients with implantable cardiac devices. Two groups of patients were created after Holter ECG monitoring analysis: NAF patients and patients who maintained sinus rhythm. Clinical data collection was exhaustive at admission and the following variables were recorded: sex, age, cardiovascular risk factors, cardiovascular, thyroid and pulmonary diseases, outpatient medications and ECG analysis (P wave duration, QRS duration, QT length with Bazett correction).
e. the interval between two atrial activations, is usually variable and <200 ms (>300 bpm)") and lasting at least 30s on a rhythm strip, should be considered as AF  [9]. Silent AF was defined as the occurrence of AF on the Holter ECG monitoring, regardless of the duration or number of episodes, and in the absence of any mention of AF in the medical file during the ICU stay (i. e. AF non diagnosed by the ICU physicians)  [7]. When AF was diagnosed on the Holter ECG recording, the following data were collected: date and time of the first episode, ventricular response to the first episode and mean ventricular response in AF, total burden of AF (total duration of all episodes of AF), number of episodes. The number of premature atrial and ventricular contractions and non-sustained supraventricular tachycardias (<30s) was also recorded from the automated count for all patients, and the daily mean ventricular frequency was calculated.
Acknowledgments The authors wish to thank  Henri Boucher  and the  Sorin Group (France)  who generously provided the Holter ECG devices and technical support to program the Holter monitors for the specific needs of this study. We also thank the clinical research department of the  University Hospital of Dijon, France , for their help with the study design and administrative procedures. The authors thank particularly  Thérèse Devaux ,  Marion Angué ,  Camélia Ez Zemmam ,  Karim Stamboul ,  Caroline Brulliard ,  Olivier Hachet ,  Vanessa Fernandez  and  Séverine Philibert  for their help in data collection. The authors also thank  Fiona Ecarnot  for translation and editorial assistance.
The second dimension was resolved in 10% SDS-PAGE gels. In a subset of samples, isoelectrofocusing (IEF) was performed in a pH zoom between 4. 5 and 6. 9 and 15% SDS-PAGE gels to specifi cally focus on the ApoA-I region. Gels were developed by fl uorescent staining (Flamingo; Bio-Rad). In two-dimensional gel electrophoresis (2-DE) analyses, the proteomic profi le of both groups was compared by using the PD-Quest 8. 0 software (Bio-Rad) that specifically analyzes the differences in protein patterns by using a single master that includes all the gels of each independent experiment (samples from control and DM patients). In this analysis, each spot in the gel is assigned a relative value that corresponds to the single spot volume compared with the volume of all spots in this gel. Afterwards, this value is subjected to background extraction and the fi nal intensity value is then normalized by the local regression model (LOESS) method of the software.
Blood collection and sample preparation Venous blood samples were collected to prepare serum that was aliquoted and stored at Ϫ 80°C. For proteomic studies, serum samples were prepared as previously described  ( 22, 23 ). Total HDL, HDL2, and HDL3 subfractions were prepared, as previously described, in KBr density gradients of 1. 063-1. 210, 1. 063-1. 125, and 1. 125-1. 210, respectively  ( 22, 23 ). VLDLs were isolated an Eclipse XDB-C18 C18 column and by MS/MS or high-resolution MS. The LIPIDVEIW database was used for identifi cation, and normalization was performed as previously described  ( 29 ).
Spectra were acquired with fl exControl on refl ector mode (mass range, m/z 850-4,000; refl ector 1, 21. 06 kV; refl ector 2, 9. 77 kV; ion source 1 voltage, 19 kV; ion source 2 voltage, 16. 5 kV; detection gain, 2. 37×) with an average of 3,500 added shots at a frequency of 200 Hz. Each sample was processed with fl exAnalysis (v3. 0; Bruker Daltonics) considering a signal-to-noise ratio over 3, applying statistical calibration, and eliminating background peaks. For identifi cation, peaks between 850 and 1,000 were not considered because, in general, only matrix peaks are visible in this mass range. After processing, spectra were sent to the interface, BioTools (v3. 2; Bruker Daltonics), and MASCOT search on Swiss-Prot 57. 15 database was done [taxonomy: Homo sapiens , mass tolerance 50-100, up to 2 miss cleavage; global modifi cation: carbamidomethyl (C); variable modifi cation: oxidation (M)].
Identifi cation was accepted with a score higher than 56. Analysis of the truncated form was investigated using the Sequence Editor tool of BioTools. For theoretical digestion of ApoA-I, the sequence reported as P02647 in Swiss-Prot was used and the parameters for peptide generation were the same as for the MASCOT search. The peptides generated in Sequence Editor were then sent back to BioTools for comparison with the experimental data and MASCOT result.
Western blot analysis Protein extracts were resolved by, one-dimensional gel electrophoresis (1-DE) (LDL and VLDL samples) and 2-DE (LDL, VLDL, rat serum, and liver samples) under reducing conditions and electrotransferred to polyvinylidene difl uoride membranes in semi-dry conditions (semi-dry transfer system; Bio-Rad). Protein detection was performed using rabbit polyclonal antibody against total ApoA-I (178422, 1:1,000 dilution; Calbiochem) combined with the Dye Double Western blot kit (Invitrogen). Band fl uorescence was determined with Typhoon FLA 9500 (GE Healthcare) and band quantifi cation was performed using Im-ageQuant TL v7. 01 software (GE Healthcare). Protein load was normalized using total protein fl uorescent signal.
Cathepsin D cleaves ApoA-I and shows increased liver activity in diabetic rats Data mining analysis opened tentative pathways to explain the posttranscriptional truncation of ApoA-I. An experimental model of diabetes was used to proof the concept. First, the proteomic profi le of rat serum revealed the presence of two ApoA-I forms of 28 and 26 kDa, similar to the forms (molecular mass) observed in human serum (supplementary Fig. 6A ). Rat ApoA-I behaves differently in the proteomic profi le due to the already described differences in the pI of the pro-ApoA-I (full-length) and the mature ApoA-I forms. As stated in the protein database UniProt, the mature form of human ApoA-I (aas 25-267; SwissProt accession number, P02647) has a more acid pI (5. 27) than pro-ApoA-I (full-length; aas 19-267; pI 5.
45). In contrast, in rat, the mature form of ApoA-I (aas 25-259; accession number, P04639) has a more basic pI (5. 51) than pro-ApoA-I (fulllength; aas 19-259; pI 5. 41), giving an inverted proteomic profi le in the 2-DE analysis as a result. Therefore, our results showed that the truncated low molecular mass ApoA-I form of 26 kDa is detected in both cases underneath the full-length signifi cant increase in the intensity of ApoA-I ⌬ (1-38) spot in DM patients when compared with nonDM individuals (DM: 1. 74 [1. 54-2. 04] AU vs. nonDM: 1. 01 [0. 87-1. 14] AU; P < 0. 05; Fig. 4C  ). ApoB-100 (molecular mass of 515 kDa) does not appear because it is out of the range of the 2-DE analysis used here, that was selected to obtain the ApoA-I profi le.
Both are related to hyperglycemia in type 1 and type 2 diabetes, where AGE adducts are associated with ApoA-I, impairing its ability to activate LCAT, ApoA-I form (pro-ApoA-I). As in humans, diabetic rats showed signifi cantly higher levels of the ApoA-I form of 26 kDa in serum than nondiabetic rats ( P < 0. 05; supplementary Fig. 6B ). This observation led us to investigate the potential enzymes involved in the cleavage of both human and rat ApoA-I molecules. In silico analysis using MEROPS database  ( 31 )  revealed cathepsin D as the only enzyme able to cleave ApoA-I sequence from both species (supplementary Fig. 6C ). In vitro treatment of ApoA-I with cathepsin D abolished the 28 kDa ApoA-I form and led to the detection of a lower molecular mass band in agreement with the 2-DE profi le of both ApoA-I forms (supplementary Fig.
Statistical analysis Data were presented as mean ± standard deviations or when indicated as percentage. Chi-square was performed to determine the significance of differences in the prevalence of opportunistic infections in both groups. Student's t-test version 3. 3. 2 was used to compare the CD4 lymphocytes counts and viral load of both groups. The statistical SPSS program (version 16. 0) was used for all the analysis. Results were considered significant at p-value less than 0. 05.
Acknowledgements We want to acknowledge the  laboratory of Biochemistry, Faculty of Medicine and Biomedical Sciences, University of Yaounde 1 , for their limitless support during data collection; The  Chantal Biya International Research Centre ,  Yaounde-Cameroon , for materials and reagents support, and the  HIV/AIDS  patients for their voluntarily participation in the study. This work was supported by the supplements from the  Spirulina producer's Federation of French and Ecole de formation Agricole de Hyères-France.
Orthorectification of images The orthorectification of Pléiades images was conducted using the Orthoengine module of the software PCI Geomatica 2013. The original orientation of each image was obtained from the rational polynomial coefficients available in the ancillary data. Without ground control points (GCPs), the horizontal location accuracy of the images was estimated at 8. 5 m (CE90, circular error at a confidence level of 90%;  Lebègue and others, 2013). The initial orientation was then refined using five GCPs and between 20 and 30 tie points collected in the overlapping region of the five images. The GCPs were measured in the field with a Trimble GeoExplorer 6000 Series GNSS receiver in static mode. Each GCP was measured for at least 45 min, and differential correction was performed using the Osorno permanent GPS station (84 m a.
Cross-correlation of orthoimages Horizontal ground displacements were retrieved from the sub-pixel correlation of successive Pléiades orthoimages using the COSI-Corr package, a plug-in for the Environment for Visualizing Images (ENVI) software  (Leprince and others, 2007). Once orthorectified and co-registered, the correlation between two images results in two offset fields, representing the east-west and north-south horizontal ground displacements, and a signal-to-noise ratio (SNR) field reflecting the quality of the correlation for each pixel in the images. To allow the measurement of large displacements without losing resolution on the displacement fields, COSI-Corr multiscale correlation analysis was performed using an initial search window size of 256 down to 8 pixels with steps of 16 pixels.
This sequential application of filters allowed for efficient identification and removal of most anomalous values due to poor correlation, shadows, avalanches or serac falls, the upward tilt of seracs, and changes in the snow cover (Fig. 2a-d ). Finally, glacier surface velocity maps for each period (March, April, May and June; Table  2 ) were obtained after applying a spline interpolation to fill data gaps generated by the data exclusion in previous steps. A spline interpolation results in a smooth surface without modifying the original values of pixels adjacent to the voids when they are small, but can results in spurious undulations over large voids. Thus, we only interpolated data gaps smaller than 0. 15 km 2 and left larger gaps (only on the June surface velocity map) unfilled. Finally, we estimated the mean annual surface velocity for glaciers of Monte Tronador for 2012, from the mean displacement measured between March, April and May.
ACKNOWLEDGEMENTS We acknowledge the support from  Secretaría de Ambiente y Desarrollo Sustentable de Argentina, Agencia de Promoción Científica  (projects PICT  2010-1438 ) and  Consejo Nacional de Investigaciones Científicas y Técnicas, Argentina (CON-ICET) , for funding. Etienne Berthier  acknowledges support from the  French Space Agency (CNES)  through the  TOSCA program. Pléiades images were provided at no cost by  Airbus Defense and Space  through the  Pléiades User Group. Administración de Parques Nacionales  kindly provided permission and logistical assistance to work at  Monte Tronador inside Parque Nacional Nahuel Huapi. The mass-balance and stake position measurements used in this study could not have been conducted without the valuable field knowledge and collaboration provided by  Mauricio Cadillo ,  Ernesto Corvalán ,  Jorge Luis Lambert  and  Juan Pablo Scarpa.
MST Proteins were fluorescently labeled with Alexa 647 according to the manufacture's protocol (L001, Nanotemper technologies, Germany). Labeled proteins were kept at 20 nM. The unlabeled protein was titrated starting at 50,000 nM and serially diluted in 1:1 ratio in reaction buffer (20 mM sodium phosphate pH 8, 150 mM NaCl, 2 mM EDTA, 5 mM DTT). The measurements were performed at 40% LED and 20, 40, and 60% MST power on NanoTemper Monolith NT. 115. Data were analyzed using NT Analysis Software (Nanotemper technologies, Germany).
Sedimentation velocity The experiments were conducted on a Beckman Coulter XL-A analytical ultracentrifuge at 20˚C using absorption optics. Samples were loaded at 17, 9, 5, 3, and 0. 7 μM in a standard cell (400 μl). Data were acquired (120 scans) in intensity mode at 280 nm at 6-min intervals with a rotor speed of 48 krpm for p85α PR1-BH, 45 krpm for p85α SH3-PR1-BH-PR1, and 42 krpm for WT p85α. Data were analyzed in SEDFIT 14. 3e  (Schuck, 2000)  using the continuous c(s) distribution model. Solution densities ρ and viscosities η were calculated using the program SEDNTERP 1. 09  (Cole et al. , 2008). Analyses of sedimentation coefficients were carried out using s range of 0. 05-10 with linear resolution of 100 and confidence levels of 0. 95. Fits were achieved with root mean square deviations ranging from 0.
Sedimentation equilibrium The experiments were performed on a Beckman Optima XL-A analytical ultracentrifuge at 20˚C. Samples were loaded at 1. 0, 0. 5, and 0. 25 mg/ml into a 2-channel, 12-mm path-length cells (100 μl). Data were acquired at 11. 8, 12. 8, 13. 8 krpm, as an average of 5 absorbance measurements at 280 nm using a radial spacing of 0. 001 cm. Experiments were started from zero to the lowest rotor speed by taking scans at 2-hr intervals and testing for equilibrium by determining the differences between successive scans in SEDFIT. The rotor speed was then increased and the process repeated at each rotor speed studied and sedimentation equilibrium was achieved within 48 hr. Data for the individual proteins collected at different speeds and loading concentrations at 280 nm were sorted and assembled in SEDFIT and analyzed globally in terms of monomer-dimer-tetramer (monomer-mmer-n-mer ) or monomer-dimer model in SEDPHAT 10.
Acknowledgements We thank the  KAUST Bioscience core lab  for instrument access, support for recombinant protein production, and experiments with the MicroCal ITC200 calorimeter. We are grateful to  Dr Jonathan M Backer  ( Albert Einstein College of Medicine, NY ) for the plasmids and proteins;  Drs Subbareddy Maddika  ( Centre for DNA Fingerprinting and Diagnostics, Nampally, India ) and  Junjie Chen  ( MD Anderson Cancer Center ) for the WWP2 and PTEN constructs. Our appreciation also goes  Dr Osman Bakr  ( Functional Nanomaterials Laboratory, KAUST ) for granting us access to the analytical ultracentrifugation facility. We thank  Virginia A Unkefer  for editorial help. This work was supported by  Uterine SPORE  ( NCI 2P50 CA098258-06 ),  Cancer Target Discovery  and  Development grant  ( NCI U01 CA168394 ),  Stand Up to Cancer/American Association for Cancer Research Dream Team Translational Cancer Research Grant  ( SU2C-AACR-DT0209 ),  Program Project Grant  ( 5 P01 CA099031 ) to GBM; and CCSG functional proteomics core (NCI# CA16672 ) in MDACC; Uterine  SPORE career development grant  ( NCI P50CA098258 ) to LWT.
A coupled hydrogeophysical forward and inverse modeling approach is developed to illustrate the ability of frequency-domain airborne electromagnetic (AEM) data to characterize subsurface physical properties associated with sublacustrine permafrost thaw during lake-talik formation. Numerical modeling scenarios are evaluated that consider non-isothermal hydrologic responses to variable forcing from different lake depths and for different hydrologic gradients. A novel physical property relationship connects the dynamic distribution of electrical resistivity to ice saturation and temperature outputs from the SUTRA groundwater simulator with freeze-thaw physics. The influence of lithology on electrical resistivity is controlled by a surface conduction term in the physical property relationship.
Resistivity models, which reflect changes in subsurface conditions, are used as inputs to simulate AEM data in order to explore the sensitivity of geophysical observations to permafrost thaw. Simulations of sublacustrine talik formation over a 1000-year period are modeled after conditions found in the Yukon Flats, Alaska. Synthetic AEM data are analyzed with a Bayesian Markov chain Monte Carlo algorithm that quantifies geophysical parameter uncertainty and resolution. Major lithological and permafrost features are well resolved by AEM data in the examples considered. The subtle geometry of partial ice saturation beneath lakes during talik formation cannot be resolved using AEM data, but the gross characteristics of sub-lake resistivity models reflect bulk changes in ice content and can identify the presence of a talik.
Modeling experiments used the SUTRA groundwater modeling code  (Voss and Provost, 2002)  enhanced with capabilities to simulate freeze-thaw processes  (McKenzie and Voss, 2013). The phase change between ice and liquid water occurs over a specified temperature range and accounts for latent heat of fusion as well as changes in thermal conductivity and heat capacity for ice-water mixtures. Ice content also changes the effective permeability, thereby altering subsurface flowpaths and enforcing a strong coupling between hydraulic and thermal processes. The modeling domain, which is adapted for this study, is axis-symmetric with a central lake and upwards-sloping ground surface that rises from an elevation of 500 m at r = 0 to 520 m at the outer extent of the model, r = 1800 m (Fig. 1 ). The model uses a layered geology consistent with the Yukon Flats  (Minsley et al.
Parameter estimation and uncertainty quantification The inverse problem involves estimating subsurface resistivity values given the simulated forward responses and realistic assumptions about data errors. Geophysical inversion is inherently uncertain; there are many plausible resistivity models that are consistent with the measured data. In addition, the ability to resolve true resistivity values is limited both by the physics of the AEM method and the level of noise in the data. Here, we use a Bayesian Markov chain Monte Carlo (McMC) algorithm developed for frequency-domain electromagnetic data  (Minsley, 2011)  to explore the ability of simulated AEM data to recover the true distribution of subsurface resistivity values at 20-year intervals within the 1000-year lake-talik simulations. This McMC approach is an alternative to traditional inversion methods that find a single "optimal" model that minimizes a combined measure of data fit and model regularization  (Aster et al.
, 2005). Although computationally more demanding, McMC methods allow for comprehensive model appraisal and uncertainty quantification. AEM-derived resistivity estimates for the simulations considered here will help guide interpretations of future field data sets, identifying the characteristics of relatively young versus established thaw under different hydrologic conditions. The McMC algorithm provides comprehensive model assessment and uncertainty analysis and is useful in diagnosing the ability to resolve various features of interest. At every data location along the survey profile, an ensemble of 100 000 resistivity models is generated according to the Metropolis-Hastings algorithm  (Hastings, 1970; Metropolis et al. , 1953). According to Bayes' theorem, each model is assigned a posterior probability that is a measure of (1) its prior probability which, in this case, is used to penalize models with unrealistically large contrasts in resistivity over thin layers; and (2) its data likelihood, which is a measure of how well the predicted data for a given resistivity model match the observed data within data errors.
The McMC average model result for the simulated shallow electromagnetic data is shown in Fig. 10c. An error model with 4 % relative data errors and an absolute error floor of 75 ppm was used for the GEM-2 data. Channel thicknesses and resistivity values are better resolved compared with the AEM result, though the 1 m deep channel near r = 800 m appears both too thick and too resistive. In addition, the shallow electromagnetic data show some sensitivity to the interface at 2 m depth between frozen silty sands and frozen gravels, though the depth of this interface is overestimated due to the limited sensitivity to these very resistive features.
Although the focus here is on AEM data, other types of electrical or electromagnetic measurements could be readily simulated using the same resistivity model. Future efforts will focus on the simulation of other types of geophysical data (e. g. , nuclear magnetic resonance or ground penetrating radar) using the same basic modeling approach. In the specific examples of lake-talik evolution presented here, which are modeled after the physical setting of the Yukon Flats, Alaska  (Minsley et al. , 2012) , AEM data are shown to be generally capable of resolving large-scale permafrost and geological features (Fig. 5 ), as well as thermally and hydrologically induced changes in permafrost (Figs. 8  and 9 ). The Bayesian McMC analysis provides useful details about model resolution and uncertainty that cannot be assessed using traditional inversion methods that produce a single "best" model.