dataesr/scientific-paragraphs-categorization huggingface dataset:
The measurements in the two channels are thus in agreement and are combined using the same procedure as for the differential cross sections described earlier in the section. Figure  4  shows the rapidity distribution dσ/d|y| measured in the combined dilepton channel with the prediction by FEWZ 3. 1 with the CT10 and NNPDF2. 1 NNLO PDF sets. The cross section is evaluated within the detector acceptance and is plotted for six different mass ranges. The DY differential cross section as measured in the combined dilepton channel and as predicted by NNLO FEWZ 3. 1 with CT10 PDF calculations, for the full phase space. The data point abscissas are computed according to Eq. (  6 ) in  [60]. The χ 2 probability characterizing the consistency of the predicted and measured cross sections is 91% with 41 degrees of freedom, calculated with total uncertainties while taking into account the correlated errors in the two channels.
The uncertainty bands in the theoretical expectations include the statistical and the PDF uncertainties from the FEWZ 3. 1 calculations summed in quadrature. The statistical uncertainty is significantly smaller than the PDF uncertainty, which is the dominant uncertainty in the FEWZ 3. 1 calculations. In general, the PDF uncertainty assignment is different for each PDF set. The CT10 PDF uncertainties correspond to 90% CL; to permit a consistent comparison with NNPDF2. 1 the uncertainties are scaled to 68% CL. In the low-mass region, the results of the measurement are in better agreement with the NNPDF2. 1 NNLO than with the CT10 NNLO estimate, which is systematically lower than NNPDF2. 1 NNLO in that region. The χ 2 probability calculated between data and the theoretical expectation with total uncertainties on the combined results in the low-mass region is 16% (76%) for the CT10 (NNPDF2.
Figure3: The DY differential cross section as measured in the combined dilepton channel and as predicted by NNLO FEWZ 3. 1 with CT10 PDF calculations, for the full phase space. The data point abscissas are computed according to Eq. (6) in [60]. The χ 2 probability characterizing the consistency of the predicted and measured cross sections is 91% with 41 degrees of freedom, calculated with total uncertainties while taking into account the correlated errors in the two channels.
Methods This prospective, randomised, double-blind single-centre study was conducted at the Department of Anaesthesiology and Surgical Intensive Therapy and at the Department of Neurosurgery between 2010 and 2014. It was carried out in close cooperation with the Institute of Clinical Chemistry and Biochemistry, University Medical Centre Ljubljana (trial registry: NCT02229201 at www. clinicaltrials. gov). The study was approved by the National Medical Ethics Committee of the Republic of Slovenia. Written informed consent was obtained from all participants. All the procedures were performed in accordance with the Helsinki declaration. The CONSORT recommendations for reporting randomized trials were followed. We enrolled 40 ASA (American Society of Anaesthesiologists) physical status I-III cooperative patients aged 18-80 years with a GSC (Glasgow coma score) of 15 who were scheduled for elective craniotomy for an intracerebral tumour.
Measurements The data recorded included demographics, duration of surgery and anaesthesia, the consumption of intraoperative drugs, fluid balance, emergence and haemodynamic parameters. Postoperative complications were monitored during 15 days after surgery. Postoperative complications were defined as any unintended changes in body function or well-being, such as hypertension (systolic blood pressure 30 % higher than the baseline level), postoperative nausea with vomiting, pain (visual analogue scale (VAS) > 3), infection, pulmonary, cardiovascular and neurological events, reoperation and death. The length of hospital stay was also recorded. Serum levels of IL-6, IL-8 and IL-10 were measured in the pre-, peri-and postoperative periods. Arterial blood samples for determinations of cytokines IL-6, IL-8 and IL-10 were drawn at the following time points: (1) before induction, (2) during tumour resection, (3) at the end of surgery, (4) 24 h after surgery, and (5) 48 h after surgery.
The exact Mann-Whitney U-test was used to compare the differences in IL-6 i /IL-6 1 , IL-8 i /IL-8 1 and IL-10 i /IL-10 1 median ratio values and the changes in mean arterial pressure (MAP) and cardiac output (CO) during surgery between the groups. The medians and the means of continuous variables are presented, and categorical data are summarized as counts. A p-value of less than 0. 05 was considered statistically significant. Data were analysed by SPSS 13. 0 software package.
We investigate the suitability of statistical model checking techniques for analysing quantitative properties of software product line models with probabilistic aspects. For this purpose, we enrich the feature-oriented language FLAN with action rates, which specify the likelihood of exhibiting particular behaviour or of installing features at a specific moment or in a specific order. The enriched language (called PFLAN) allows us to specify models of software product lines with probabilistic configurations and behaviour, e. g. by considering a PFLAN semantics based on discrete-time Markov chains. The Maude implementation of PFLAN is combined with the distributed statistical model checker MultiVeStA to perform quantitative analyses of a simple product line case study. The presented analyses include the likelihood of certain behaviour of interest (e.
On the one hand, the execution of a process is constrained by the store (e. g. to avoid introducing inconsistencies), while on the other hand a process can query the store (e. g. to resolve configuration options) or update the store (e. g. to add new features, also at run time or by means of a staged configuration process). An implementation of FLAN in the executable modelling language Maude  [19]  allows one to exploit Maude's rich toolkit for a variety of formal analyses of FLAN models, ranging from consistency checking (by means of SAT solving) to model checking. In this paper, we introduce a probabilistic extension of FLAN: PFLAN allows to equip actions with rates to specify probabilistic SPL models (e. g. to model uncertainty, failure rates, randomisation). This paves the way for quantitative analyses (e.
g. to measure quality of service, reliability, performance). Here we present a proof-of-concept use of an implementation of PFLAN in Maude in combination with the distributed statistical model checker MultiVeStA  [36]  to estimate the likelihood of specific behaviour. Formally, our approach is to perform a sufficient number of probabilistic simulations of a PFLAN model to obtain statistical evidence (with a desired level of statistical confidence) of quantitative properties under scrutiny. The properties are formulated in MultiVeStA's property specification language Multi-QuaTEx, which allows to express and evaluate more than one property over the same simulated path (behaviour)  [36]. The advantage over exhaustive (probabilistic) model checking is that there is no need to generate entire state spaces.
PFLAN: Syntax and Semantics The feature-oriented language PFLAN is a probabilistic extension of FLAN  [6] , a process algebra that neatly separates declarative (pre-)configuration from procedural run-time aspects. PFLAN is inspired by the concurrent constraint programming paradigm of  [32] , its adoption in process calculi  [14]  and its stochastic extension  [12]. A constraint store allows one to specify all common constraints known from feature models in a declarative way, while a rich set of process-algebraic operators allow to specify the configuration and behaviour of product lines in a procedural way. The semantics smoothly unifies static (pre-configuration) and dynamic (run-time) feature selection. The core notions of PFLAN are features, constraints, processes (with action rates) and fragments, all visible in its syntax in Fig.
Note that the non-determinism introduced by choices and parallel composition is probabilistically resolved in the aforementioned DTMC semantics. Summarising, we note a variety of ways in which a feature f can be included in a configuration. First, an explicit and declarative way is to include the proposition has( f ) in the initial store; this is the way to include core features. Second, an implicit and declarative way is to derive f from other constraints (e. g. if a store contains g f and has(g), then f 's presence follows). Third, a procedural way is to dynamically install f at run time; this key aspect originating from FLAN enables staged configuration as known from dynamic software product lines  [13, 21]. Building on FLAN, PFLAN combines these three ways in an elegant and consistent manner.
Quantitative Analysis with MultiVeStA MultiVeStA  [36]  is a statistical analysis tool developed and maintained by S. Sebastio and A. Vandin. It extends the (distributed) statistical model-checking tools PVeStA  [2]  and VeStA  [37] , developed at the Department of Computer Science of the University of Illinois at Urbana-Champaign. Differently from its predecessors, MultiVeStA can easily be integrated with any formalism which allows for probabilistic simulations. It has so far been used to analyse transportation systems  [25] , volunteer clouds  [35] , crowdsteering  [31]  and swarm robotic  [10]  scenarios. In this paper, we use MultiVeStA to analyse PFLAN specifications in order to obtain statistical estimations of quantitative properties expressed in MultiVeStA's query language MultiQuaTEx (an extension of QuaTEx  [1] ).
3?" or "what is the probability that a property is satisfied?" over a given specification. Next to performing such kinds of analyses over products, MultiVeStA also allows to estimate the expected values of properties that can take on any value from R, like "what is the average cost of products generated from a software product line specification?". Estimations are computed as the mean value of n samples obtained from n simulations, with n large enough to grant that the size of the (1α) × 100% confidence interval (CI) is bounded by δ. In other words, if a MultiQuaTEx expression is estimated as x, then with probability (1α) its actual expected value belongs to the interval [xδ /2, x + δ /2]. A CI is thus specified in terms of two parameters: α and δ. In all experiments discussed in this section, we fixed α = 0.
1, and δ = 0. 1 and δ = 0. 5 for probabilities and costs of products, respectively. MultiVeStA's property specification language MultiQuaTEx is very flexible, based on the following ingredients: real-valued observations on the current 'state' (e. g. the total cost of installed features), arithmetic expressions and comparison operators, if-then-else statements, a one-step next operator (which triggers the execution of one step of a simulation) and recursion. Intuitively, we can use MultiQuaTEx to associate a value from R to each simulation and subsequently use MultiVeStA to estimate the expected value of such number (in case this number is 0 or 1 upon the occurrence of a certain event, we thus estimate the probability of such an event to happen).
When no more features can be installed, we speak of a final product. While not explicitly stated, all experiments discussed in this section refer to versions (defined below) of the PFLAN specification of Fig. 5  without the contextual information S 2. = in(Europe), so as to study properties of our example without restrictions to a specific context (thus implicitly allowing deadlocks). Property P 1 is useful for studying the correctness of the PFLAN specification of a product line, in this case by verifying the probability to successfully complete the pre-configuration phase of a product from the product line. Property P 2 is useful for studying how often (on average) a feature is actually installed in a product from the product line, which is important information for those designers or programmers responsible for the production or programming of a specific feature or software module.
5. All such analyses (41 × 9 different properties) were evaluated using the same simulations. The results are presented in two plots: one for costs (ProductCostAfterPreconf) in Fig. 7 , and one for probabilities (IsInstalledAfterPreconf) in Fig. 8. As expected, Fig. 7  shows that the average cost (on the y-axis) of the intermediate products generated from the software product line grows with respect to the number of performed simulation steps. In particular, it shows a fast growth during the first 6 steps, reaching an average cost of 13, and then it essentially stabilises, eventually reaching its maximum (14. 38) from step 26 onwards. This is consistent with our PFLAN specification, consisting of a pre-configuration phase during which the majority of the features are installed, followed by a run-time phase modelling the behaviour of the generated product (and possibly installing ringtone).
Conclusion In this paper, we have continued a line of research presented at earlier editions of FMSPLE  [6, 26]  by enriching FLAN, a high-level feature-based modelling language for software product lines, with quantitative information. The result, PFLAN, allows one to model and analyse the likelihood of installing features, the probabilistic behaviour of users of products of the product line, and the costs of products, next to probabilistic quantifications of ordinary temporal properties (e. g. "what is the probability that coffee is poured while no cup was available?"). In addition, we extended the qualitative analysis framework for software product lines implemented in Maude with statistical techniques for quantitative analysis. The modelling and analysis capabilities of PFLAN were illustrated on a simple product line of coffee machines.
Antibodies For immunoblotting analysis the following antibodies were used: rabbit LRRK2 MJFF2 (Cat# 3514-1, RRID:AB_10643781, 1 : 100; Epitomics), rabbit LRRK2 phospho-S935 (Cat# 5099-1, RRID:AB_ 11132319, 1 : 100; Epitomics, Cambridge, UK), rabbit LRRK2 phospho-T2483 (Cat#156577, 1 : 2000; Abcam, Cambridge, UK), rabbit LRRK2 phospho-T1491 (Cat#140106, 1 : 2000; Abcam, Cambridge, UK), mouse Flag M2 (Cat# F1804, 1 : 10000; Sigma), mouse c-Myc 9E10 (Cat# 11667149001, RRID:AB_ 390912, 1 : 5000; Roche Molecular Biochemicals, Indianapolis, IN, USA), rabbit PAK6 (Cat# HPA031124, RRID:AB_10601044, 1 : 2000, Prestige â ; Sigma), mouse b-tubulin (Cat# T8328, RRID: AB_1844090, 1 : 5000; Sigma), rabbit phospho-PAK4-5-6 (Cat# SAB4504052, 1 : 2000; Sigma), rabbit phospho-LIM domain kinase 1 (LIMK1) (Cat#3841, 1 : 1000; Cell Signaling Technology, Beverly, MA, USA), mouse LIMK1 (Cat#117623, 1 : 1000; Abcam).
Post-mortem human tissues analysis Post-mortem human tissue samples were obtained from Queen Square Brain Bank (London, UK). Sample demographics are listed in Table  1. The 5% sodium dodecyl sulfate fractions from the basal ganglia from three G2019S LRRK2 mutation cases, four matched iPD cases and four control cases were prepared according to the methods described in Mamais et al. (Mamais et al. 2013). 40 lg of proteins was loaded onto 12% Tris-Glycine gels (Bio-Rad) and transferred onto PVDF membrane. The membranes were probed with primary antibodies at 1 : 1000 dilution (phospho-S602/560 antibody Sigma Cat no SAB4504722; PAK-6 Sigma Cat no HPA031124). Immunohistochemistry with PAK phospho-S602/ 560 antibody was performed on formalin fixed wax-embedded slides. Briefly, sections 8-lm-thick were dewaxed in xylene, blocked for endogenous peroxidase with H 2 O 2 (0.
(d) Quantification of LRRK2 binding to PAK6 and 14-3-3. Data are representative of three independent experiments and bars represent the mean AE SEM relative to 14-3-3. (e) PAK6 interacts with LRRK2 in vivo. Endogenous LRRK2 was immunoprecipitated from wild-type and LRRK2 knock-out brain lysates as control using anti-LRRK2 antibody. Samples were analyzed by immunoblotting using anti-PAK6 and anti-LRRK2. (f) PAK6 and LRRK2 co-localize in neurons. Primary cortical neurons were transfected with Flag-LRRK2 and Myc-PAK6 and then subjected to immunocytochemistry techniques using anti-Flag (green) and anti-PAK6 (red) antibodies. To explore the impact of PAK6 kinase activity in regulating neuronal branching, we then generated PAK6 kinase dead (K436M) and hyper-active (S531N) mutants (Fig. 4a )  (Schrantz et al.
PAK6 is aberrantly activated in post-mortem tissues from PD brains To investigate if PAK6 is aberrantly activated in pathological conditions, we next measured PAK6 S560 phosphorylation in G2019S and iPD brains. Western blot analysis shows that phospho-PAK6 is increased in basal ganglia from iPD (n = 4 cases) as well as mutant G2019S LRRK2 (n = 3 cases) PD patients of ~2-fold compared to age-matched healthy controls (n = 4 cases) (Fig. 7a-b ). These results were further supported by immunohistochemistry of basal ganglia sections from iPD and G2019S LRRK2 patients versus controls (Fig. 7c-d ). Taken together, these data support a functional interplay between LRRK2 and PAK6 in the pathophysiology of human PD and suggest that LRRK2 may exert its toxicity through an aberrant regulation of PAK6 in PD.
Acknowledgments and conflict of interest disclosure We are grateful to the  Michael J Fox Foundation  for supporting this study. We also thank the financial support of  Telethon -Italy  (Grant no. GGP12237 ) and the  CARIPLO Foundation  (grant  2011-0540 ). LC was supported by a short-term  EMBO  fellowship (MBO ASTF  526-2012/Award ). We thank the  FWO-Vlaanderen  ( FWO projects G. 0666. 09 ,  KAN2012 1. 5. 216. 12  and fellowship to JMT), the  IWT   SBO/80020  and  SBO/100042  projects and the Fund Druw e-Eerdekens managed by the  King Baudouin Foundation  for their support. This research was supported in part by the  Intramural Research Program  of the  NIH, National Institute on Aging. This project is supported by an  MRC  grant to PAL ( MR/ L010933/1 ). PAL is a Parkinson's  UK research  fellow (grant  F1002 ).
Acknowledgments This work has been carried out by the financial support of grant projects Agencia  PICT-2013-1291  and  CONICET PIP 2011-2013 0048  awarded to  M. C. Ghiglione. The authors are grateful to  Parques Nacionales of Argentina  for permitting access to  Perito Moreno National Park. Special thanks to the park rangers at  Lago Belgrano ,  Comisi on de Fomento de Hip olito Yrigoyen , and the people of  Lago Posadas  and  Familia Fortuny  for allowing accesses to outcrops, kind logistical support and assistance. We want to acknowledge the detailed and insightful reviews from  Dr. Robert Pankhurst , Editor  Dr. Eduardo Koutsoukos  and two anonymous reviewers. This is contribution R-152 of the  Instituto de Estudios Andinos Don Pablo Groeber (UBA-CONICET ).
Background: The implementation of collaborative TB-HIV services is challenging. We, therefore, assessed TB treatment outcomes in relation to HIV infection and antiretroviral therapy (ART) among TB patients attending a primary care service with co-located ART and TB clinics in Cape Town, South Africa. Methods: In this retrospective cohort study, all new TB patients aged ≥ 15 years who registered and initiated TB treatment between 1 October 2009 and 30 June 2011 were identified from an electronic database. The effects of HIV-infection and ART on TB treatment outcomes were analysed using a multinomial logistic regression model, in which treatment success was the reference outcome. Results: The 797 new TB patients included in the analysis were categorized as follows: HIV-negative, in 325 patients (40.
Data collection Data from the Cape Town City Health electronic TB register (ETR. net) were compared with that from the Western Cape Department of Health's electronic eKapa TB and ART database and any differences were resolved by reviewing patient folders and paper-based TB registers. The data are not freely available but permission for access was obtained from Cape Town City Health and Western Cape Department of Health. Variables for all patients meeting the inclusion criteria were obtained from the synchronized electronic TB register database. Variables included TB registration number, registration date, age, gender, HIV status, CD4 count at TB diagnosis, ART status and TB treatment outcome. Ethical approval for anonymized data collection was obtained from the Research Ethics Committees of the University of Cape Town and the International Union against Tuberculosis and Lung Disease.
Acknowledgements The authors would like to thank all the  TB  staff at the  Nyanga Community Health Centre and City Health  for access to electronic data. The authors would also like to thank the helpful comments and statistical input provided by  Dr. Debbie Bradshaw  and  Ms. Ria Laubscher  from the  South African Medical Research Council. MDN was supported as an operational research fellow by The  International Union Against Tuberculosis and Lung Disease.
The direct stiffness value (8. 105Nm -1 ) was chosen in order to be as close as possible of the characteristics of the magnetic bearings. The designer is free to determine the AMB stiffness. Typically, this direct stiffness value is chosen where the slope of the three first modes varies as a function of the critical speed map. This value enables significant action on the system strain energy and damping. This constant stiffness is then removed and the AMB is considered as a restoring force. ROTORINSA® software is used to compute the modal matrixes. Table  1  presents the natural frequencies of the rotor computed for the frequency bandwidth studied. The general modeling approach is presented in Fig. 3. The dynamic matrix expresses only the linear contribution of the system studied. All the other contributions, i.
Fuzzy controller This consisted of the sum of a fuzzy PI with a fuzzy PD controller. The displacement was measured and the velocity calculated by numerical derivation. These two data (displacement and velocity) were used as inputs for the fuzzy controller. Four generalized bell shape membership functions were utilized (Matlab). These membership functions were associated with four fuzzy sets: positive/negative displacements and positive/negative velocities The design of a membership function takes into account the measurement noise. The displacements and velocities are measured, and their respective amplitudes make it possible to obtain the measurement uncertainty directly. These amplitudes of displacements and velocities should be insufficient to change the prevalent membership function. On the other hand, in order to be efficient small amplitudes of displacement and velocity should belong wholly to one of the two fuzzy sets (positive or negative).
First, data can be accessed using multiple devices (such as computers, smartphones and tablets), which allows users to work more flexibly. Second, as the host manages the technical administration, teachers don't need to focus on this issue. Finally, students may master SNS easily as in many cases they already use it in their private lives  [15, 16]. The use of SNS for teaching and learning has already been studied. The objectives of previous researches were mainly to discuss about privacy and professionalism  [17] [18] [19] , to evaluate the users' satisfaction or SNS benefits to enhance empathy, reflection, clinical skills or knowledge  [20] [21] [22]. However, the way medical students use SNS for learning and their experience as users have been poorly studied, especially in postgraduate general medicine training.