dataesr/scientific-paragraphs-categorization huggingface dataset:
While the alignment obtained from CRAFT was essential for the early physics programme of CMS, its quality was still statistically limited by the available number of cosmic ray tracks, mainly in the forward region of the pixel detector, and systematically limited by the lack of kinematic diversity in the track sample. In order to achieve the ultimate accuracy, the large track sample accumulated in the proton-proton physics run must be included. This article describes the full alignment procedure for the modules of the CMS tracker as applied during the commissioning campaign of 2011, and its validation. The procedure uses tracks from cosmic ray muons and proton-proton collisions recorded in 2011. The final positions and shapes of the tracker modules have been used for the reprocessing of the 2011 data and the start of the 2012 data taking.
A similar procedure has later been applied to the 2012 data. The structure of the paper is the following: in section 2 the tracker layout and coordinate system are introduced. Section 3 describes the alignment of the tracker with respect to the magnetic field of CMS. In section 4 the algorithm used for the internal alignment is detailed with emphasis on the topic of systematic distortions of the tracker geometry and the possible ways for controlling them. The strategy pursued for the alignment with tracks together with the datasets and selections used are presented in section 5. Section 6 discusses the stability of the tracker geometry as a function of time. Section 7 presents the evaluation of the performance of the aligned tracker in terms of statistical accuracy. The measurement of the parameters describing the non-planarity of the sensors is presented in section 8.
The muon system is installed outside the solenoid and is embedded in the steel return yoke of the magnet. Figure  1 : Schematic view of one quarter of the silicon tracker in the r-z plane. The positions of the pixel modules are indicated within the hatched area. At larger radii within the lightly shaded areas, solid rectangles represent single strip modules, while hollow rectangles indicate pairs of strip modules mounted back-to-back with a relative stereo angle. The figure also illustrates the paths of the laser rays (R), the alignment tubes (A) and the beam splitters (B) of the laser alignment system. The CMS tracker is composed of 1440 silicon pixel and 15 148 silicon microstrip modules organised in six sub-assemblies, as shown in figure  1. Pixel modules of the CMS tracker are grouped into the barrel pixel (BPIX) and the forward pixel (FPIX) in the endcap regions.
The FPIX, TID, and TEC are all divided into two symmetrical parts in the forward (z > 0) and backward (z < 0) regions. Each of these halves is composed of a series of disks arranged at different z, with the FPIX, TID, and TEC having two, three, and nine such disks, respectively. Each FPIX disk is subdivided into two mechanically independent half-disks. The modules on the TID and TEC disks are further arranged in concentric rings numbered from 1 (innermost) to 3 (outermost) in TID and from 1 up to 7 in TEC. Pixel modules provide a two-dimensional measurement of the hit position. The pixels composing the modules in BPIX have a rectangular shape, with the narrower side in the direction of the "global" rϕ and the larger one along the "global" z-coordinate, where "global" refers to the overall CMS coordinate system introduced at the beginning of this section.
A local right-handed coordinate system is defined for each module with the origin at the geometric centre of the active area of the module. As illustrated in the left panel of figure  2 , the u-axis is defined along the more precisely measured coordinate of the module (typically along the azimuthal direction in the global system), the v-axis orthogonal to the u-axis and in the module plane, pointing away from the readout electronics, and the w-axis normal to the module plane. The origin of the w-axis is in the middle of the module thickness. For the pixel system, u is chosen orthogonal to the magnetic field, i. e. in the global rϕ direction in the BPIX and in the radial direction in the FPIX. The v-coordinate is perpendicular to u in the sensor plane, i. e. along global z in the BPIX and at a small angle to the global rϕ direction in the FPIX.
The tilt angles have been determined with the 2010 CMS data, and they have been used as the input for the internal alignment detailed in subsequent sections of this article. A repetition of the procedure with 2011 data led to compatible results. The measurement of the tilt angles is based on the study of overall track quality as a function of the θ x and θ y angles. Any non-optimal setting of the tilt angles will result, for example, in incorrect assumptions on the transverse field components relative to the tracker axis. This may degrade the observed track quality. The tilt angles θ x and θ y are scanned in appropriate intervals centred around zero. For each set of values, the standard CMS track fit is applied to the whole set of tracks, and an overall track quality estimator is determined.
Time dependence is achieved by replacing an alignment parameter ∆p t in the linearised form of equation (  1 ) by several parameters, each to be used for one period of time only. This method allows the use of the full statistical power of the whole dataset for the determination of parameters that are stable with time, without neglecting the time dependence of others. This is especially useful in conjunction with a hierarchical alignment: the parameters of larger structures can vary with time, but the sensors therein are kept stable relative to their large structure.
Computing optimisation The MILLEPEDE II program proceeds in a two-step approach. First, the standard CMS software environment  [5]  is used to produce binary files containing the residuals m ijf ij , their dependence on the parameters ∆p and ∆q of the linearised track model, the uncertainties σ ij , and labels identifying the fit parameters. Second, these binary files are read by an experimentindependent program that sets up equation  (2) , extends it to incorporate the Lagrangian multipliers to implement constraints, and solves it, e. g. by the iterative MINRES algorithm  [36]. In contrast to other fast algorithms for solving large matrix equations, MINRES does not require a positive definite matrix, and because of the Lagrangian multipliers C is indefinite. Since the convergence speed of MINRES depends on the eigenvalue spectrum of the matrix C , preconditioning is used by multiplying equation (  2 ) by the inverse of the diagonal of the matrix.
The elements of the symmetric matrix C in general require storage in double precision while they are summed. For the 200 000 alignment parameters used in this study, this would require 160 GB of RAM. Although the matrix is rather sparse and only non-zero elements are stored, the reduction is not sufficient. High alignment precision also requires the use of many millions of tracks of different topologies that are fitted several times within MILLEPEDE II, leading to a significant contribution to the CPU time. To cope with the needs of the CMS tracker alignment described in this article, the MILLEPEDE II program has been further developed, in particular to reduce the computer memory needs, to enlarge the number of alignment parameters beyond what was used in  [13] , and to reduce the processing time.
Details are described in the following. Since the non-zero matrix elements are usually close to each other in the matrix, further reduction of memory needs is reached by bit-packed addressing of non-zero blocks in a row. In addition, some matrix elements sum contributions of only a few tracks, e. g. cosmic ray tracks from rare directions. For these elements, single precision storage is sufficient. Processing time is highly reduced in MILLEPEDE II by shared-memory parallelisation by means of the Open Multi-Processing (OPENMP R ) package  [37]  for the most computing intensive parts like the product of the huge matrix C with a vector for MINRES, the track fits for the calculation of ∆q j and C -1 j , and the construction of C from those. Furthermore, bordered band matrices C j are automatically detected and root-free Cholesky decomposition is applied subsequently (see section 4.
This step is based on the full exploitation of different track topologies, making use of resonance mass and vertex information. The details are described in the rest of this section. The second step treats the four relevant movements of the pixel detector after the end of June, identified with the methods of section 6. 2. Six alignment parameters for each BPIX layer and FPIX halfdisk are redetermined by a stand-alone alignment procedure, keeping their internal structures unchanged and the positions of the strip modules constant. Tracks from several data sets are used simultaneously in the alignment procedure. The following selection criteria are applied: • Isolated muons: Global muons  [16]  are reconstructed in both the tracker and the muon system. They are selected if their number of hits N hit in the tracker exceeds nine (at least one of which is in the pixel detector, N hit (pixel) ≥ 1), their momenta p are above 8 GeV/c (in order to minimise the effects of Multiple Coulomb scattering), and their transverse momenta p T are above 5 GeV/c.
Data in peak mode were recorded in a dedicated cosmic data taking period before the restart of the LHC operations in 2011 and during the beam-free times between successive LHC fills. In addition, cosmic ray data were taken in deconvolution mode both during and between LHC fills, making use of a dedicated trigger selecting cosmic ray tracks passing through the tracker barrel. In total 3. 6 million cosmic ray tracks with p > 4 GeV/c and N hit > 7 are used, where about half of the sample has been collected with the strip tracker operating in peak mode, while the other half during operations in deconvolution mode. For all the data sets, basic quality criteria are applied on the hits used in the track fit and on the tracks themselves: • the signal-over-noise ratio of the strip hits must be higher than 12  (18)  when the strip tracker records data in deconvolution (peak) mode; • for pixel hits, the probability of the hit to match the expected shape of the charge cluster for the given track parameters  [38]  must be higher than 0.
001 (0. 01) in the u (v) direction; • for all hits, the angle between the track and the module surface must be larger than 10 • (20 • ) for tracks from proton-proton collisions (cosmic rays) to avoid a region where the estimates of the hit position and uncertainty are less reliable; • to ensure a reliable determination of the polar track angle, θ, tracks have to have at least two hits in pixel or stereo strip modules; • tracks from proton-proton collisions have to satisfy the "high-purity" criteria  [14]  of the CMS track reconstruction code; • in the final track fit within MILLEPEDE II, tracks are rejected if their χ 2 value is larger than the 99. 87% quantile (corresponding to three standard deviations) of the χ 2 distribution for the number of degrees of freedom N dof of the track. The tracker geometry, as determined by the alignment with the 2010 data  [31] , is the starting point of the 2011 alignment procedure.
In general, for each sensor all nine parameters are included in the alignment procedure. Exceptions are the v coordinate for strip sensors since it is orthogonal to the measurement direction, and the surface parameterisation parameters w 10 , w 01 , w 20 , w 11 , w 02 for the FPIX modules. The latter exception is due to their small size and smaller sensitivity compared to the other subdetectors, caused by the smaller spread of track angles with respect to the module surface. The hierarchical alignment approach discussed in section 4. 3 is utilised by introducing parameters for shifts and rotations of half-barrels and end-caps of the strip tracker and of the BPIX layers and FPIX half-disks. For the parameters of the BPIX layers and FPIX half-disks the differential alignment is used as well.
The fit of the vertex must have at least 4 degrees of freedom. For each of these vertices, the impact parameters are measured for tracks with: • more than six hits in the tracker, of which at least two are in the pixel detector, • at least one hit in the first layer of the BPIX or the first disk of the FPIX, • p T > 1 GeV/c, • χ 2 /N dof of the track smaller than 5. The vertex position is recalculated excluding the track under scrutiny from the track collection. A deterministic annealing clustering algorithm  [40]  is used in order to make the method robust against pileup, as in the default reconstruction sequence. The distributions of the unbiased track-vertex residuals in the transverse plane, dxy , and in the longitudinal direction, dz , are studied in bins of η and ϕ of the track. Random misalignments of the modules affect only the resolution of the unbiased track-vertex residual, increasing the width of the distributions, but without biasing their mean.
Statistical accuracy of the alignment A method for assessing the achieved statistical precision of the aligned positions in the sensitive direction of the modules has been successfully explored and adopted in the alignment of the CMS tracker during commissioning with cosmic rays, described in  [13]. The results from the validation are based on isolated muon tracks with a transverse momentum of p T > 40 GeV/c and at least ten hits in the tracker. The tracks are refitted with the alignment constants under study. Hit residuals are determined with respect to the track prediction, which is obtained without using the hit in question to avoid any correlation between hit and track. From the distribution of the unbiased hit residuals in each module, the median is taken and histogrammed for all modules in a detector subsystem.
The average sizes of the sagittae of the sensor curvatures along the u-(w 20 ) and v-direction (w 02 ), as determined sensor-by-sensor, are shown in figure  13  for the different layers and rings, further differentiating for stereo and rϕ modules, i. e. grouping modules with similar sensors and mounting. While the average sagitta w 20 for BPIX sensors is almost zero, it is usually around -30 µm in the strip subdetectors. This matches well the sagittae of the average module shapes along u seen for the "flat module" distributions on the left pane of figure  10. Stronger curvatures with average sagittae up to w 20 < -80 µm are observed for specific sensor types and mounting positions, e. g. the rϕ modules in TEC ring 2. The average sagitta w 02 shows variations from +30 µm for BPIX modules (matching with the left pane of figure  11 ) to almost -60 µm for some sensor types in the TEC.
The investigators generated a random-number table on a computer (function RAND in Excel software), used the table to prepare envelopes for random patient allocation. General anesthesia was induced using propofol 2. 5 mg kg 21 , fentanyl 2 mg kg 21 and was maintained with propofol 0. 05 mg kg. 21 min 21 and fentanyl 0. 02 mg kg 21 min 21 with nitrous oxide in oxygen, with a fraction of inspired oxygen of 0. 4. LMA (size 4, females; size 5, males) were inserted according to the manufacturer's instructions (Laryngeal MaskCo. , Henley-on-Thames, UK). The volume of air in the cuff was adjusted at an airway sealing pressure greater than 15 cmH 2 O. Mechanical ventilation was initiated (Servo 900C ventilator Siemens-Elema, Solna, Sweden). In SB-group, patients underwent SB throughout the ventilator circuit, in CMV-group, patients underwent CMV (expiratory tidal volume: VTe of 8 mL/kg, a respiratory rate (RR) of 10 cycles/min and an inspiratory/total duty cycle (Ti/Ttot) ratio of 1:3 with a time plateau pressure at 15% of the inspiratory time) and in PSV-group patients underwent PSV (PSV level was set to obtain a VTe between 7-8 mL/kg and RR between 10-16 cycles/ min, inspiratory trigger was fixed at 22 cmH 2 O and the termination of inspiratory support was fixed at the drop of flow at 25% peak inspiratory flow value).
The study design is summarized in Fig. 1. All data were recorded at baseline (T0: before general anesthesia induction), 10 min after general anesthesia induction (T1), at the surgical incision (T2), at the end of anaesthetic drugs infusion (T3) and when the patient was totally awake (T4). Drugs consumption during general anesthesia and duration of general anesthesia were recorded at the end of the surgical procedure. We also calculated drugs consumption during general anesthesia (except induction dose) normalized to weight and duration of general anesthesia expressed in mg kg 21 min 21 for propofol and mg kg 21 min 21 for fentanyl. Hemodynamic (Heart rate, MAP) and ventilatory variables (RR, VTi, VTe, Ti/ Ttot, maximal and plateau pressures, Sp O2 , Pet CO2 ) were recorded during a 2-min stable state for each period.
Peak pressure, plateau pressure and flow were measured with Capnomac Ultima at the tube opening. Airway leaks were determined by noting the difference between inspired and expired tidal volume (VTi-VTe). Airway occlusion pressure (P0. 1) was measured at each point except in T1, T2 and T3 for the CMV group  [22]. For T0 and T4, ventilatory variables were obtained in awake patients spontaneously breathing through the circuit via a mouth piece with a nose clip. Although the staff members who collected data during surgery were aware of the group assignments, end points assessors were unaware of these assignments throughout the study. At the end of surgery, airway fiberoscopy was performed throughout the LMA to detect the presence or absence of methylene blue regurgitation  [23]. The removal time (open mouth to command) of the LMA was determined by questioning every minute the patient and the emergence time was defined as the time to obtain a 10 point score on a five questions test.
Data Analysis Metabolic data were analysed using SPM8 software (Wellcome Department of Cognitive Neurology, Institute of Neurology, London, UK) implemented in MATLAB (Mathworks, Sherborn, MA). Images were spatially normalized into the ICBM standard space using 2 nd degree B-spline interpolations, then smoothed using a 16-mm full-width half-maximum isotropic Gaussian kernel. Areas of significant relative (instead of absolute) change between conditions of interest were estimated according to the general linear model using linear contrasts. Global metabolism adjustment was performed using proportional scaling. Age was entered in the analysis as a confounding variable. Main contrasts estimated the main effect of pathology [controls vs. KLS at baseline, controls vs. KLS in acute episode] to identify the brain regions where glucose metabolism was decreased (or increased) in the KLS as compared to controls.
Systematic review methodology A systematic review was conducted, using a structured search (Additional file 1) via the OVID platform of the following electronic databases: CENTRAL (March 2013), Medline and Medline in Process (1946 to 9th April 2013), and Embase (1980 to 2013 week 14)  [44]. The search strategy employed was set up to be sensitive, to ensure that all relevant publications would be retrieved by the search, although this strategy would also retrieve a high proportion of irrelevant abstracts which would subsequently need to be excluded during the screening process. In addition, the 2012 and 2013 (where available) conference proceedings from the following organisations were searched: American College of Cardiology (ACC), American Diabetes Association (ADA), American Heart Association (AHA), European Association for the Study of Diabetes (EASD), The Obesity Society and The.
Exclusions • Results from uncontrolled open label extensions of RCTs. • Studies of SU used as part of a triple therapy regimen. • Study populations with moderate to severe renal impairment. • Study arms using treatment dosing regimens that are not licensed in the EU. EU, European Union; Hba1c, glycated haemoglobin; RCT, randomisedcontrolled trial; SU, sulfonylurea; T2DM, type 2 diabetes mellitus. International Diabetes Federation. The clinical trials registry (ClinicalTrials. gov) was hand-searched for unpublished trials. Citations were included in the systematic review if they met the pre-defined inclusion criteria (Table  1 ). The initial screening for relevant studies was based on the citation abstract and title. For the second stage, the full-text publication was retrieved and the citations were re-screened to ensure their eligibility for inclusion in the systematic review.
Direct meta-analysis and Bucher indirect comparisons Fixed and random-effects direct meta-analyses were conducted in Stata IC version 12. 1 using the metan package SJ9_2: sbe24_3  [49, 50]. The random-effects model uses the method of DerSimonian & Laird, with the estimate of heterogeneity being taken from the fixed-effect Mantel-Haenszel or inverse variance model. The inverse variance approach calculates study weight based on the assumption that variance is indirectly proportional to the study importance, whereas the Mantel-Haenszel approach uses a similar methodology whilst assuming a fixed treatment effect. In most instances, the betweenstudy heterogeneity based on the I 2 statistic from the direct random-effects meta-analysis could not be assessed due to the limited scope of the analysis.
The WinBUGS models were run for a minimum of 100,000 iterations to ensure model convergence. An estimate of how well the values predicted by the model fitted the observed dataset was provided by the mean residual deviances (total residual deviance divided by number of datapoints) as well as the deviance information criteria (DIC) output by WinBUGs  [34]. The model with the lowest DIC output by WinBUGs was deemed to best predict a replicate dataset of the same structure of that observed, provided differences were more than 5  [53, 54]. Both random-effects and fixed-effect models were tested. Random-effects NMA allows the true treatment effect (eg. OR between two treatments) to vary between studies due to heterogeneity. In these random-effects models, a uniform (uninformative) prior was used for the between-studies SD (as per Hasselblad  [55]  and Gelman  [56] ).
Systematic review search results The OVID database search retrieved 2,923 citations of which 1,848 were unique. In addition, 52 abstracts were retrieved from the conference proceedings search and 1 unpublished study was identified from the clinical trial registry. After reviewing the title/abstract, 1,870 citations were excluded, with a further 24 excluded following fulltext review (Additional file 2). Seven citations (representing 7 different studies) were eligible for inclusion in the systematic review, with 5 studies fulfilling the eligibility criteria for inclusion in the meta-analysis (Figure  1 , Table  2 ).
Three outcomes were evaluated based on clinical priorities and data availability, including the risk of hypoglycaemia. Specific safety outcomes other than hypoglycaemia were not included in the current analysis. The relative rates of key adverse events observed in placebo-controlled trials of dapagliflozin have previously been meta-analysed using data from across the dapagliflozin clinical trial program  [65, 66]. Safety profiles of DPP-4 inhibitors and GLP-1 analogues have also been extensively compared and reviewed  [67] [68] [69] [70] [71]. Results highlighted that all 3 classes of anti-diabetes treatments used as add-on therapy to SU monotherapy provided better short-term glycaemic control than an SU used alone, and may suggest that no single class of treatment was able to significantly reduce HbA1c to a greater extent than any other.
Acknowledgements The authors thank  Dr. V. Taly ,  Dr. K. Hantke ,  Dr. M. Brinkmann ,  Prof. S. Herminghaus ,  Prof J. Enderlein  and  Prof. N. T. Nguyen  for insightful discussions. We acknowledge the financial support of the  Max-Planck Society. S. H. T. also acknowledges the support from the  GGNB doctoral school. B. S. acknowledges the financial support from  BP. A patent application with priority date March 31st 2012 has been filed for the control of droplet production in electric fields.