dataesr/scientific-paragraphs-categorization huggingface dataset:
Strain AdcA-like AdcAII-like Pht-like Internalin A-like PhtD (t-PhtD) in which Zn 2+ is bound to the first histidine triad motif of PhtD. We provide new structural data on this challenging protein family that are exploited in comparison with the previous structure of a PhtA fragment. We propose a new Prosite pattern for the identification of histidine triad motifs in protein sequences from the existing databases. NMR titration experiments demonstrated that Zn 2+ is preferentially transferred from t-PhtD to AdcAII but no stable complex between the two proteins in any state could be detected. On the other hand, direct contact between the full-length PhtD protein and AdcAII was evidenced by immunoprecipitation experiments on native membranes in the presence and absence of a crosslinking agent (DTSSP).
Acknowledgments We acknowledge the platforms of the Grenoble Instruct centre (ISBG;  UMS 3518 CNRS-CEA-UJF-EMBL ) with support from  FRISBI  ( ANR-10-INSB-05-02 ) and  GRAL  ( ANR-10-LABX-49-01 ) within the  Grenoble Partnership for Structural Biology  (PSB) for access to the NMR facilities and the support provided.
Extracting stellar fundamental parameters from SPectro-Interferometric (SPI) data requires reliable estimates of observables and with robust uncertainties (visibility, triple product, phase closure). A number of fine calibration procedures is necessary throughout the reduction process. Testing departures from centro-symmetry of brightness distributions is a useful complement. Developing a set of automatic routines, called SPIDAST (made available to the community) to reduce, calibrate and interpret raw data sets of instantaneous spectro-interferograms at the spectral channel level, we complement (and in some respects improve) the ones contained in the amdlib Data Reduction Software. Our new software SPIDAST is designed to work in an automatic mode, free from subjective choices, while being versatile enough to suit various processing strategies.
DEFINING THE BASIC INTERFEROMETRIC OBSERVABLES The coherent flux c i j is provided by the measurement of the instantaneous observables delivered by the AMBER Data Reduction Software amdlib 1. This quantity traces the sine-like molulated component superimposed on the continuum component, in the observed intensity distribution. It is computed using a χ 2  linear fit of the individual interferograms in the detector plane, for each spectral channel (see  Tatulli et al. 2007, for details). At this point, we define the other interferometric observables: (i) the squared flux, which is the product of the photometric fluxes p i and p j , associated with the baseline B i j f 2 i j = p i p j ; (1) (ii) the squared visibility, which is the ratio of the squared modulus of the coherent flux to the squared flux v 2 i j = c i j 2 f 2 i j ; (2) (iii) the complex bispectrum  (Weigelt 1977) , which is the product of the 3 coherent fluxes contributing to the baseline triplet B 12 , B 23 , B 31 b 123 = c 12 c 23 c 31 ; (3) (iv) the triple product, which is the ratio of the bispectrum to the product of the 3 fluxes contributing to the baseline triplet t 123 = b 123 f 12 f 23 f 31.
REDUCING THE RAW DATA To process the temporal series of frames (composing the "exposures") produced by the AMBER instrument, we use the data reduction software amdlib, which consists of a core library of C functions plus a high-level interface in the form of a Yorick 2 plugin (a high-level language environment, similar to IDL or Matlab, which is in the public domain). The standard procedure to extract the basic interferometric observables from the AMBER raw data includes a frame-selection step, which provides a set of "good" frames according to a given criterion, or a sequence of several selection criteria  (Malbet et al. 2011). The criterion used the most is based on the Signal to Noise Ratio (SNR) of the fringe-contrast in each frame. Two selection methods are provided in standard by amdlib: (i) retaining a given percentage of frames sorted according to their SNR values (called the "percentage" method), (ii) retaining the frames with SNR values higher than a given threshold (called the "threshold" method).
With both methods, the choice of the optimal value (assumed not biasing the final results) of the selection criterion (in percentage of threshold) is done a posteriori, from a sequence of processings using different values of the selection criterion (see  Millour et al. 2007, for details). Two drawbacks of this are: the need for a severalsteps procedure (to be performed manually), and the risk for biasing the final results if the selection criterion is ill-determined. In our method, that we have implemented in a specific Yorick script added to amdlib, we apply the automatic procedure: (i) remove only the aberrant measurements (in squared visibility and triple product), for each spectral channel independently. Using this specific script results in amounts of useful data larger than the amount obtained with the standard procedure, without increasing biasing effect (only the lower and upper tails of the input data histograms are rejected); (ii) assign a weight to each individual measurement.
Note that our specific script computes the real and imaginary parts of the triple product, from which we extract the calibrated closure phase (see Sect. 4. 2). Figure  4  shows an example of squared visibility and triple product produced by the reduction script (before calibration) from one single exposure of 300 input frames, obtained with the calibrator target γ Lib, for the three baselines A0-D0 (32 m), D0-H0 (64 m), and A0-H0 (96 m). In complement to these interferometric observables, we compute two other quantities used further in the calibration procedure (see Sect. 4. 2. 3): (i) the weight of each exposure, defined as the product of two ratios: one is the average of the SNR to its variance; the other is the average of the inverse of the piston excursion (absolute value) to its variance; (ii) the final number of non-aberrant data, for each spectral channel.
Computing the spectral shifts To reach a high precision in the angular diameter estimation using model fitting, spectral calibration is a critical point (see e. g. ,  Wittkowski et al. 2008; Domiciano de Souza et al. 2008; Stefl et al. 2011). Given the lack of any internal instrumental module for wavelength calibration in the optical setup of AMBER  (Robbe-Dubois et al. 2007; Petrov et al. 2007) , amdlib provides calibrated wavelength tables, computed from a theoretical polynomial dispersion law  (Mérand et al. 2010 ), but with coefficients still badly known, leading to wavelength shifts up to ∼10 pixels in the detector plane, at medium spectral resolution  (Malbet et al. 2011). To correct for this drawback, amdlib shifts the wavelength table using the correlation of the measured spectrum with a template table containing the telluric lines.
126-2. 474 µm spectral range  (Martí-Vidal et al. 2011). Figure  5  shows the synthetic spectrum produced by the MARCS + Turbospectrum code, for the calibrator γ Lib. Some reference spectral lines are shown for identification of the corresponding stellar atmospheric elements. For each exposure, obtained with each calibrator, and with the working instrumental setting, our automatic spectral calibration process contains the following steps: (i) apply the heliocentric and systemic radial velocity corrections, and multiply the synthetic spectrum by the atmospheric and instrumental transmittance profiles. Atmospheric transmission data for the southern sites (CTIO, Chile) are given by the USAF atmospheric code PLEXUS  (Cohen, Wheaton & Megeath 2003) ; (ii) remove the continuum parts of the raw and synthetic spectra, and normalize the resulting spectra.
7  the corrections from the initial polynomial dispersion law of AMBER, given by the two softwares. While amdlib computes only a unique correction value applied over the whole spectrum, our method computes a polynomial correction (here parabolic from three subwindows), with sub-pixel deviations with respect to the polynomial law, caused by the final ensemble averaging of the corrected wavelength tables. The choice of the degree of the polynomial correction provided by SPIDAST allows the accuracy of the wavelength correction to be adapted to the scientific programme, reaching a sub-pixel precision if necessary.
Determining calibrator angular diameters To derive the IRF for each spectro-interferometric (SPI) observable, we firstly need to get a reliable estimate of the angular diameter of each calibrator, and its associated error. Fitting the stellar models of  Kurucz (1979)  on the absolutely calibrated spectrophotometric templates of  Cohen et al. (1999) , the CCSL contains 374 calibrators, with limb-darkened angular diameters ranging from 1 to 3 mas. Since our selected calibrators might have no angular-diameter estimate (one third of the calibrators of our dataset have no value found in the catalogues), or poorly estimated, we have added to SPIDAST various routines for determination of the angular diameter from indirect methods, presented in detail in  Cruzalèbes et al. (2010). These routines compute the angular diameter: (i) by combining the linear diameter, deduced from absolute luminosity and effective temperature from the Morgan-Keenan (MK) spectral type, with the parallax; (ii) by using experimental laws based on the interstellarcorrected colour index (surface brightness method); 3 www.
Comparing the final calibrated observables with amdlib Although the calibration routine provided with the amdlib package is not yet officially validated  (Malbet et al. 2011) , we compute the differences in squared visibility and closure phase between SP-IDAST and amdlib, and compare them with the associated uncertainties. Figure  12  shows the results obtained on the science target δ Oph, with the baseline triplet A0-D0-H0, under good and poor seeing conditions. The histograms of the uncertainties show that the uncertainties in squared visibility estimated with amdlib are smaller than with SPIDAST (whatever the seeing), while it is the contrary for the uncertainties in closure phase. To explain the disagreement between the uncertainties in squared visibility, we remind that SPIDAST takes into account instrumental and environmental conditions in the calculation of the uncertainties, while amdlib uses only unweighted variance.
Thus, we consider as underestimated the uncertainties in squared visibility produced by amdlib. Regarding the closure phase, it is more difficult to explain why the uncertainties from amdlib are larger than the ones from SPIDAST. Indeed, no details are found in  Malbet et al. (2011) , expected to describe the amdlib calculation. SPIDAST computes the uncertainties on the real and imaginary parts of the calibrated triple product, and derives the uncertainties on the final closure phase from them. A deep analysis based the source code of the calibration routine provided by amdlib reveals that amdlib computes the uncertainties in the final closure phase, adding the uncertainties in closure phase of the science target and of the estimated transfer function. Since the closure phase is less stable than the triple product, the final uncertainties in closure phase handled with amdlib are larger than with SPIDAST.
Global closure phase The integration of the real and imaginary parts of the triple product T true , over the observation spectral band [λ min ; λ max ], leads to the global closure phase, defined by  Ragland et al. (2006)  and  Tatebe et al. (2006)  as A value of the global closure phase close to zero (less than 1 • , for our sample) suggests a high degree of centro-symmetry of the brightness distribution, related to the observation spectral band. We compute the uncertainty on the global closure phase thanks to the direct-bootstrap method, using random sampling with replacement of the spectral dataset of the calibrated triple product. The values of the global closure phases in K, and their associated uncertainties, are given in the third column of Table  3 , for each science target. ψ band = atan λmax λ min ℑT true (λ) dλ λmax λ min ℜT true (λ) dλ.
4. 2. 1). Note that the LITpro software 5 , developed by the JMMC working group "Model fitting", uses a set of elementary geometrical and center-to-limb darkening functions, as well  (Tallon-Bosc et al. 2008 ). However, it does not offer, contrary to SPIDAST, the possibility to fit stellar-disk models with synthetic tabulated radiance data (or exitance, for fits on spectro-photometric 5 www. jmmc. fr/litpro_page. htm measurements), such those produced by the MARCS + Turbospectrum code. Since the uncertainties of the final calibrated data are not normally distributed, the covariance matrix, that comes out of the chisquared fit, cannot be used to infer the parameter uncertainties  (Press et al. 2007 ). To compute the uncertainties, SPIDAST uses the residual-bootstrap method, described in detail in  Cruzalèbes et al.
(2010) : (i) "synthetic" datasets are produced from random sampling with replacement of the Pearson residuals (difference between calibrated and fitted values divided by the uncertainty of the observed value), added to the initial fitted values; (ii) fitting the model on these new datasets produces a set of chisquared minima, following a probability distribution, from which we extract the boundaries of the confidence interval, with a given confidence level; (iii) the parameter values associated with these chi-squared boundaries give the upper and lower limits of the parameter estimates, leading to asymmetric uncertainties. The whole procedure allows the computing of reliable estimates of the parameter uncertainties.
CONCLUSION In the present paper, we introduce the new SPIDAST software, developed since 2006 with the aim to reduce, calibrate, and interpret the visibility and triple product measurements obtained with the VLTI/AMBER facility. SPIDAST contains a whole set of modules, which can be launched separately or in an automatic batch file, and summarised herafter: (i) The raw data reduction used by SPIDAST computes the weighted average of non-aberrant data, at spectral-channel level, providing estimates of the SPI observables using an automated procedure, while the method presently used with amdlib selects the "best" frames according to a quality threshold determined a posteriori, after several trials. (ii) The wavelength calibration procedure performed by SPI-DAST provides spectral shifts following a polynomial law, tracing them at the channel level.
This is done by computing the crosscorrelation of the measured spectra of the calibrators with their synthetic spectra produced by the MARCS model, while amdlib only provides a constant spectral shift over the spectrum, from the correlation with the telluric lines. (iii) For selected calibrators not included in the calibrator catalogues, SPIDAST provides several routines for estimation of the angular diameter with indirect methods, the most accurate being the fit of stellar-atmosphere model spectra given by MARCS on spectro-photometric data. The calibrator synthetic observables are derived using CLV functions produced by MARCS. (iv) To obtain an accurate interferometric calibration (via an automated procedure), SPIDAST: (1) divides the calibrator raw data with the associated synthetic observables in each spectral channel, which gives the instrumental response function in squared visibility and triple product; (2) interpolates or fits the response at the time of each exposure on the science target; (3) divides the science raw data with the interpolated/fitted response, which gives the science calibrated observables for each exposure; (4) computes their weighted average over each OB.
ACKNOWLEDGMENTS The authors thank the  ESO-Paranal VLTI team  for supporting their AMBER observations, especially the night astronomers  A. Mérand ,  G. Montagnier ,  F. Patru ,  J. -B. Le Bouquin ,  S. Rengaswamy , and  W. J. de Wit , the VLTI group coordinator S. Brillant, and the telescope and instrument operators A. and  J. Cortes ,  A. Pino ,  C. Herrera ,  D. Castex ,  S. Cerda , and  C. Cid. They also thank the  Programme National de Physique Stellaire (PNPS)  for supporting part of this collaborative research. A. J. is grateful to  B. Plez ,  K. Eriksson , and  T. Masseron  for their ongoing support on the use of the 6 https://forge. oca. eu/trac/spidastMARCS code. S. S. was (partly) supported by the  Austrian Science Fund  through  FWF  project  P19503-N16 ; A. C. is post-doctoral fellow from  F.
R. S. -FNRS (Belgium ; grant  2. 4513. 11 );E. P. is supported by  PRODEX. The data were partially reduced using the publicly available data reduction software package amdlib, kindly provided by the  Jean-Marie Mariotti Center. This study used the  SIM-BAD  and VIZIER databases at the  CDS, Strasbourg (France) , and NASAs ADS bibliographic services.
Chaste -Cancer, Heart And Soft Tissue Environment -is an open source C++ library for the computational simulation of mathematical models developed for physiology and biology. Code development has been driven by two initial applications: cardiac electrophysiology and cancer development. A large number of cardiac electrophysiology studies have been enabled and performed, including high-performance computational investigations of defibrillation on realistic human cardiac geometries. New models for the initiation and growth of tumours have been developed. In particular, cellbased simulations have provided novel insight into the role of stem cells in the colorectal crypt. Chaste is constantly evolving and is now being applied to a far wider range of problems. The code provides modules for handling common scientific computing components, such as meshes and solvers for ordinary and partial differential equations (ODEs/PDEs).
Re-use of these components avoids the need for researchers to 're-invent the wheel' with each new project, accelerating the rate of progress in new applications. Chaste is developed using industrially-derived techniques, in particular testdriven development, to ensure code quality, re-use and reliability. In this article we provide examples that illustrate the types of problems Chaste can be used to solve, which can be run on a desktop computer. We highlight some scientific studies that have used or are using Chaste, and the insights they have provided. The source code, both for specific releases and the development version, is available to download under an open source Berkeley Software Distribution (BSD) licence at http://www. cs. ox. ac. uk/chaste, together with details of a mailing list and links to documentation and tutorials.
Introduction Cancer, Heart And Soft Tissue Environment (Chaste) has been developed to enable the study of novel problems in computational physiology and biology. The following quotation from a recent article by Wilson highlights two problems that Chaste has been designed to overcome: required for simulations. In computational physiology and biology, many problems share a need for the same underlying components and numerical schemes. It is still common for each new PhD student or post-doctoral researcher to 're-invent the wheel' and develop, for example, their own mesh structures, ordinary/partial differential equation (ODE/PDE) solvers and input/output (IO) interfaces. This not only slows progress, but a lack of formal software training in structuring and documenting code can lead to code that is difficult to follow and untangle (known as 'spaghetti code')  [3].
Mark-up languages such as SBML  [7] , CellML  [8] , FieldML  [9]  and SED-ML  [10]  help to satisfy these requirements in a machine-readable format. Given the complexity of modern mathematical models and numerical algorithms, we believe the use of such standards in open source software is a pre-requisite for rapid progress, reliability and reproducibility. To date, our primary applications have been in computational physiology and biophysics. In these fields, a wide array of models are represented as continuum ODE/PDE problems, individual or agent-based discrete models, or a hybrid of these two. Examples of problems falling into these categories include cardiac electrophysiology and electromechanics, tumour growth, and developmental biology. In 2005, we began to build Chaste as a software environment that could be used for simulation of these types of problem, which would overcome many of the pitfalls discussed above.
Most commercial software is closed source and difficult to extend to study novel models, as these may include a different class of equation or completely different modelling paradigm. In the limited cases where free open source software was available for our applications, we frequently found it difficult to test and extend. We wanted to create a software library that could rapidly evolve, to keep pace with our scientific investigations. To achieve this, Chaste comprises a library of fully-tested modules for the common elements of our application areas , which can be easily utilised and readily extended to the simulation of novel models, and to the use of novel numerical algorithms. We believe Chaste is a good example of software that follows the ten simple rules for open development of scientific software  [11].
Other notable open source codes have been developed, including OpenCMISS and Continuity for continuum modelling  [12, 13] , CompuCell3D for cellular Potts modelling  [14]  and MultiCellXML for agent-based modelling and simulation data  [15]. However, Chaste is the only open source software available for many of its application areas, and is exceptional in that industrial software engineering standards have been used for its development. Chaste is also unique in being well suited for both continuum and discrete modelling approaches, and is wellpositioned for the investigation of hybrid approaches  [16]. Release 1. 0 of Chaste occurred in 2009 and has been described previously  [17, 18]. Through the use of examples, we will describe the capabilities of the newly-released version 3. 1, novel scientific applications, and future directions.
Design and Implementation In this section, we discuss the Chaste development strategy, as this is fundamental to its properties, capabilities and extensibility for novel problems. We then introduce the code layout and the available model types and algorithms. Chaste is written in C++, a compiled language that allows object-oriented class definitions. This makes the code suitable for applications where efficient memory management and performance are key, but also allows simple extension and inheritance of existing functionality. At present Chaste can only be used with Linux, although it works well via a Linux virtual machine (using software such as VirtualBox) on a host running Microsoft Windows or Mac OS X. The code needs to be compiled after downloading and setting up dependencies. For users (rather than developers modifying Chaste itself) this is a one-off event.
Development Strategy As far as we are aware, Chaste is the only code of its kind that has been developed using agile and test-driven development. We believe that our experience of this development style has valuable insights for other research groups, highlighted in this section. We have found the following practices invaluable in terms of rapid development, delivering high performance, and ensuring reliable results. An independent analysis of Chaste development activity and code composition can be found at Ohloh (http://www. ohloh. net/p/Chaste). Test-driven development. Test-driven development is fundamental to our efforts. In this style of development, 'test code' is written before the 'source code' which will actually perform the function we require. Once a test is in place, the source code is then written to make the test pass.