dataesr/scientific-paragraphs-categorization huggingface dataset:
Numerical resolution The model was implemented in the Matlab © software. The differ ential mass balance equations (Eqs. (  1 ) and (  2 )) were solved using a finite difference discretization method based on N grid nodes. The first and last nodes receive the conventional following boundary conditions: condition at node N, according to Eq. (  9 ): ∂ ∂ = - + + - - Q x z Q x Q x Q x z O z ( ) 3( ) 4( ) ( ) 2Δ (Δ ) L i k L i k L i k L i k 1 2 2 (9) A second order centred scheme is used for the second node (k = 2), according to Eq. (10). ∂ ∂ = - + + - Q x z Q x Q x z O z ( ) ( ) ( ) 2Δ (Δ ) L i k L i k L i k 1 1 2 (10) In this way, the set of 6 partial differential equations (3 components and 2 phases) is transformed into a set of 6N ordinary differential equations. The resulting system of ordinary differential equations and algebraic equations is integrated over time using a fourth order explicit Runge Kutta (RK4) method.
This assumption holds as long as the net mass flux through the interface is small as compared to the mass flow rate of each phase. This clearly constitutes a limitation of our modeling approach which is indeed only valid for dilute mixtures but actually corresponds to the recovery of dilute alcohols from fermentation broths. Solving this set of equations requires the calculation of the equili brium concentrations at the interface for each node at each time step. Implementing these thermodynamic equilibrium calculations using Simulis © proved to yield very lengthy computations because of the numerous calls to an external procedure (i. e. , the Simulis © software toolboxes). To improve the computational efficiency of this dynamic simulator, ternary equilibrium data were beforehand computed for a wide range of compositions, at the specified operation temperature and pressure, and stored as a set of cubic spline interpolation functions.
Simulation parameters and preliminary runs As mentioned, the model was validated by fitting experimental steady state fractionation data of dilute aqueous mixtures of IPA using scCO 2 , obtained by Lalam et al. using a bench scale counter current column, as reported in a previous work  [18]. The adjustable coefficients of Eqs. (  4 ) and  (5)  were obtained in order to reproduce the recovery ratio and mass fraction of IPA in the raffinate stream under different temperature, pressure and solvent to feed ratio conditions. Table  2  shows the column and packing characteristics used in the experiments, required by the model as input information. Two types of packing have been evaluated: stainless steel springs and a porous me tallic (FeCrAlY) foam. A first set of preliminary runs were performed in order to determine appropriate discretization and integration parameters.
Not only does this model simplify the programming of parallel programs, but it is also a prerequisite for some program analysis  [16, 4, 3]. Despite its importance, and to the best of our knowledge, textual alignment in spmd programs has not been the subject of an in-depth semantic study. Aiken and Gay introduced the concept of structural correctness for unrestricted programs. In structurally correct programs, parallel instruction streams produce the same sequence of collective operations. A type system is designed to determinate which branches can produce distinct behaviors and, in this case, compare the numbers of operations  [1, 11]. This work has been used for the design of the Titanium language  [23, 6]. A later proposal introduced textually aligned barriers in Titanium by revisiting structural correctness.
Integration of Soiling-Rate Measurements and Cleaning Strategies in Yield Analysis of Parabolic Trough Plants The issue of reflector soiling becomes more important as concentrating solar thermal power plants (CSP) are being implemented at sites subject to high dust loads. In an operational power plant, a trade-off between reducing cleaning costs and cleaning related collector availability on the one hand and keeping the solar field cleanliness (n field ) high to minimize soiling induced losses on the other hand must be found. The common yield analysis software packages system advisor model (SAM) and greenius only allow the input of a constant mean n field and constant cleaning costs. This oversimplifies real conditions because soiling is a highly time-dependent parameter and operators might adjust cleaning activities depending on factors such as soiling rate and irradiance.
In this study, time-dependent soiling and cleaning data are used for modeling the yield of two parabolic trough plant configurations at two sites in Spain and Morocco. We apply a one-year soiling rate dataset in daily resolution measured with the tracking cleanliness sensor (TraCS). We use this as a basis to model the daily evolution of the cleanliness of each collector of a solar field resulting from the application of various cleaning strategies (CS). The thus obtained daily average n field is used to modify the inputs to the yield analysis software greenius. The cleaning costs for each CS are subtracted from the project's financial output parameters to accurately predict the yield of a CSP project over its lifetime. The profits obtained with different CSs are compared in a parameter variation analysis for two sites and the economically best CS is identified.
To overcome these sources of errors, temporally variable SR and cleaning frequency are used combined with more detailed plant modeling software. The technical and financial output parameters of a CSP project over its lifetime can be calculated using yield analysis software such as greenius  [16] [17] [18]  or the system advisor model (SAM)  [19]. Their calculations are based on a timeresolved meteorological dataset, the technical plant layout, and financial input parameters. The above-mentioned software products only offer the possibility to enter a constant yearly cleanliness value for the solar field to quantify soiling-induced losses. CCs are assumed as constant values per unit mirror area. In the framework of this work, greenius was enhanced with an external software module that allows the treatment of timeresolved SR and cleaning including the corresponding collector outages and cleaning costs.
We use this enhanced software to analyze cleaning decisions and its financial consequences in more depth for two parabolic trough plant configurations at two different sites. The following steps outline our approach: and cleaning unit availability on plant yield output. In the second section of this paper, we describe the input parameters for the modeling with a focus on the TraCS cleanliness measurements. Also, the financial and technical parameters used as inputs to greenius and CC calculation are presented. Section 3 describes the CSs and methodology to calculate CC and n field. [22]. The meteorological parameters applied in this study are DNI (direct normal irradiance) measured with pyrheliometers, global and diffuse horizontal irradiance measured with pyranometers, ambient air temperature, relative humidity, atmospheric pressure, wind speed and wind direction.
These parameters are saved in 1-min resolution and averaged to hourly resolution. The same time interval from first of May 2013 until the Apr. 30, 2014 was evaluated for both sites. The data were quality checked on a daily basis following Refs. [23]  and  [24]. The DNI yearly sums for the sites used in the simulations are 2388 kWh/m 2 /a in ES and 2307 kWh/m 2 /a in MOR. 2. 2 Soiling Rate Dataset. Reflectance and SR measurements with handheld devices are time-consuming and the time difference Dt in Eq. (  2 ) between two subsequent measurements is usually in the order of days or weeks. This means that the resulting SR represents only a mean value in the measurement interval and is therefore not suitable for a daily modeling of soiling and cleaning in a solar field. In this study, we employ the TraCS.
It is an automatic device that measures cleanliness in 10-min resolution by comparing the directly measured DNI to the DNI reflected by a sample mirror mounted on a solar tracker. The effective acceptance half-angle is 13. 6 mrad and similar to a CSP collector  [20, 21]. The measurement area on the sample mirror is approximately 30 cm 2 in size. The SR is derived in daily resolution. The one-year SR dataset has been measured with TraCS at PSA. The sample mirror has been cleaned approximately every 2 weeks. In the measurement period, the cleanliness of the sample mirror never dropped below 0. 82. The SR dataset is shown in Fig. 1. The average SR is À0. 0052/d with a standard deviation of 6 0:0095=d and the highest SR is À0:089=d. Positive SR values are due to rain cleaning of the sample mirror and are also considered in the cleaning simulations.
The yearly dividend varies during the project lifetime: It is negative during construction phase and increases steadily until all loans have been paid off. Variation caused by different DNI datasets is excluded in this study since the same meteorological year is used for all simulated years. Here, we calculate the mean annual net dividends (D y ) as D y ¼ ðD wocl =T lt Þ À CC ( 12 ) where D wocl is the total dividend without considering cleaning costs, T lt is the lifetime of the project, and CC is the yearly cleaning cost as defined in Eq. (  5 ). Normalizing D y to the net dividends achieved by application of the reference cleaning strategy (D y,ref ) leads to the relative profit increase (RPI) defined as RPI ¼ D y D y;ref À 1 Á 100% ( 13 ) The RPI is used in the following as the main comparative parameter.
Soiling Scenario With Five Heavy Soiling Events. In order to investigate the effects of different CSs at a site subjected to more severe soiling conditions, the SR dataset measured at PSA is altered by the introduction of five heavy soiling events as could be caused by sand storms or aerosol wet deposition events. The SR values and days are created randomly to À0. 21, À0. 10, À0. 15, À0. 11, and À0. 22 per day for the days of the year 31, 49, 158, 166, and 302. The results of the simulation runs for the CS Threshold and Assisted for this altered soiling rate time series are shown for the power plant AS in Fig. 6. The maximum RPIs roughly double for both CS compared to the results for the original soiling rate data set. The maximum RPIs for CS Threshold are achieved with one more CU compared to the original SR dataset.
Cleaning Strategy Threshold at a Constant Soiling Rate. In the following, the CS Threshold is applied to a scenario of a constant SR equal to the mean value of the PSA data set of À0. 0052 per day. The reference CS yield was also calculated for this constant SR. Figure  7  shows that the maximum RPI is 0. 21% smaller for this scenario compared to the original SR dataset (see Fig. 5 ). The CS Threshold cannot increase profits in comparison to SR Constant for this scenario. If the SR of a site is approximated by one constant value for all days of the year, the best number of CUs and cleaning mode can be determined sufficiently for the CS Constant in this case. A potentially higher RPI for fluctuating SR and advanced CSs cannot be detected. For the best planning of cleaning action and the best estimation of financial project outputs, time-resolved SR measurements are therefore recommended.
4. 4. 2 Assumption of a Constant Solar Field Cleanliness. In many existing studies, a constant n field of 0. 95 is assumed  [43] [44] [45] [46]. The average value of n field resulting from the application of the reference CS to the original SR dataset is 0. 965 with a standard deviation of 0. 013. The reference CS (calculated with the original SR dataset) achieves a profit that is 2. 2% higher than the one achieved with n field ¼ 0. 95 even if CC are set to 0 for the constant cleanliness scenario. If the CC for the case of a constant n field is set to the same value as resulting from the reference CS, the difference in profits is 9. 4%. The assumption of a constant n field can thus cause significant errors. If the average cleanliness for the reference CS and the soiling time series (0. 965) is set as the constant n field and the same CC as in the reference CS are used, the constant scenario results in a profit that is 0.
Sensitivity Analysis The influence of the input parameters on the RPI is investigated in the following. The parameters from Table  4  as well as the lifetime of the CUs, the feed-in tariff, and the mean SR are varied while keeping the rest of the inputs at their original values. The effects on the RPI obtained with the SR Threshold with N CU ¼ 3 and n lim ¼ 0. 977 in AS in ES are shown in Fig. 8. The average of the SR dataset is the most influential of the parameters if varied from 50% to 150% of their initial values. The SR was varied by simple multiplication of each daily value with a constant factor. The feed-in tariff has the greatest influence on the RPI among the financial input parameters followed by the prices applied in the calculation of CC. The influence of the latter roughly corresponds   to their share in the CC.
The CS Assisted does not result in higher maximum RPIs compared to the CS Threshold for the soiling rates measured at PSA, but it is advantageous in cases with occasional high SRs for which not enough CUs are present. The latter can be shown in a scenario where five artificial intense soiling events are introduced to the SR dataset resulting in higher RPIs for CS Assisted compared to CS Threshold. Power plants with storage tend to tolerate wrong cleaning decisions better than those without storage. In Morocco, where labor costs are low, a higher cleaning frequency pays off better than in Spain despite the higher Spanish feed-in tariff. Constant, daily cleaning strategies result in higher profit in Morocco as compared to Spain. If a constant SR or constant n field is assumed, significant errors were found by comparison to the time-resolved simulations of this work.
If the constant SR is set to the mean of the reference SRdataset, the potential increase of 0. 22% on profits for the CS Threshold is not detected. If n field is set to the mean n field obtained with the reference CS, the profits differ by 0. 58% from that with a time-resolved n field. This represents the accuracy increase achieved with the time-resolved simulation of cleaning and soiling for the investigated case. Therefore, the here presented method increases the accuracy of yield analysis studies. The CSs, cost parameters, and power plant layouts investigated in this study are to be understood as examples. The presented method allows for the simulation of more elaborate or custom CSs that can be investigated in future publications and that can serve as a basis for optimizing the cleaning strategies in commercial power plants.
Apply a one-year SR dataset in daily resolution measured with the tracking cleanliness sensor (TraCS) [9,20,21] at Plataforma Solar de Almer ıa (PSA) in the Tabernas desert in Spain. (2) Model the cleanliness of each collector and thus n field (t) using the cleaning action of each cleaning unit (CU). (3) n field (t) is used to modify the DNI dataset used as an input to greenius. (4) Use realistic cleaning related cost parameters and calculate CC for each service hour and CU. (5) Analyze the effects of different cleaning strategies (CS).
( 1 ) 1 Apply a one-year SR dataset in daily resolution measured with the tracking cleanliness sensor (TraCS) [9,20,21] at Plataforma Solar de Almer ıa (PSA) in the Tabernas desert in Spain. (2) Model the cleanliness of each collector and thus n field (t) using the cleaning action of each cleaning unit (CU). (3) n field (t) is used to modify the DNI dataset used as an input to greenius. (4) Use realistic cleaning related cost parameters and calculate CC for each service hour and CU. (5) Analyze the effects of different cleaning strategies (CS).
The detailed study of abundance profiles and abundance ratios within R 500 also highlights the power of the X-IFU in providing constraints on the various enrichment models. From synthetic observations out to z = 2, we also quantify its ability to track the chemical elements across cosmic time with excellent accuracy, and thereby to investigate the evolution of metal production mechanisms as well as the link to the stellar initial mass-function. Our study demonstrates the unprecedented capabilities of the X-IFU in unveiling the properties of the ICM but also stresses the data analysis challenges faced by future high-resolution X-ray missions such as Athena.
24, Ω b = 0. 04, H 0 = 72 km s -1 Mpc -1 (i. e. , H 0 = h × H 100 , where h = 0. 72 and H 100 = 100 km s -1 Mpc -1 ), σ 8 = 0. 8 and n s = 0. 96, consistently with WMAP-7 constraints given in  Komatsu et al. (2011). The zoom-in simulations were performed with a version of the Tree-PM Smoothed-Particle-Hydrodynamics (SPH) code GADGET-3  (Springel 2005) , including an improved hydrodynamical scheme  (Beck et al. 2016 ) and a variety of physical processes describing the evolution of the baryonic component (see  Rasia et al. 2015 , for more details). Briefly, these comprise metallicitydependent radiative cooling  (Wiersma et al. 2009) , star formation and stellar feedback (thermal supernova feedback and galactic winds, see  Springel & Hernquist 2003) , cold and hot gas accretion onto super-massive black holes powering AGN thermal feedback  (Steinborn et al.
Photon list generation Each simulated cluster comes as a list of SPH particles, which may emit X-ray photons. To generate the photon list used in the E2E simulation, the particle emission is modelled by a collisional diffuse thermal plasma using the APEC code  (Smith et al. 2001). More specifically, the vvapec model on XSPEC  (Arnaud 1996 ) is adopted, as it can be parametrised according to the particles physical properties listed above, notably the individual abundances of each element. The corresponding atomic database used for the emission model is derived from ATOMDB v3. 0. 9. For the galactic absorption, the wabs model  (Morrison & Mc-Cammon 1983)  is preferred for computational speed, although more accurate absorption models do exist (e. g. TBabs,  Wilms et al. 2000). For all four clusters we fixed the column density to n H = 0.
Observational setup For each simulation, we consider an exposure time ∆t = 100 ks over the entire X-IFU field-of-view. The "complete" photon list is used as input for the xifupipeline function of SIXTE, which samples the photon list accounting for the energy-dependence of the effective area to create the event list seen by the X-IFU detector over ∆t. The pipeline accounts for the most up-to-date responses of the current baseline of the telescope (i. e. , 15-row mirror modules corresponding to a mirror effective area of 1. 4 m 2 at 1 keV 3 ) and for a hexagonal detector array of 3832 microcalorimeter pixels, more specifically Large Pixel Array 2 (LPA2) pixel configuration, developed for the X-IFU and described in  Smith et al. (2016). Pile-up, telescope point spread function, vignetting and detector geometry effects are also included as function of the pixels corresponding off-axis angles.
Temperature and metal maps The resulting unbinned spectra are fitted within XSPEC, using a log-normal likelihood minimisation (i. e. , C-statistics adapted from Cash 1979), which is well adapted for Poissonian data sets (i. e. , channels with low number of counts). An absorbed singletemperature thermal plasma model (i. e. , wabs*vvapec) was fitted to the spectrum accumulated in each region. The column density n H is fixed to its input value, while temperature, abundances of metals traced in the input numerical simulations, (see Sec. 2), redshift and normalisation are left free. As a first approach, all parameters (metallicity for each of the 12 chemical elements Z X , temperature, redshift and normalisation) are fit simultaneously over the 0. 2 -12 keV energy band (broadband fit). The background components are accounted as an additional model, as described in Sec 3.
2017e, for line ratios) and to disentangle fine spectroscopic effects (such as resonant scattering, Hitomi  Collaboration et al. 2017b ). This emphasises the need for dedicated tools able to process and analyse future X-IFU high-resolution spectroscopy data-cube. On this regard, the Athena mission will certainly benefit from the advances expected in processing tools, fitting methods and atomic databases, from the future XRISM mission  (Ishisaki et al. 2018). Not only do these E2E simulations allow to explore the capabilities of the future X-IFU instrument, but they also give crucial information on the effect of instrumental parameters in science observations. In this study for instance, the spectral shape of all the foreground/background components were assumed to be perfectly known. For the more local and massive clusters however, the field-of-view of the X-IFU will easily be encompassed within the angular extension of R 500.
Acknowledgements  V. Biffi ,  S. Borgani  and  E. Rasia  acknowledge financial contribution from the contract  ASI-INAF n. 2017-14-H. 0. E. Rasia  acknowledges the ExaNeSt and  Euro Exa  projects, funded by the  European Union 's  Horizon 2020 research and innovation programme , under grant agreement No  754337. S. Borgani  and  L. Tornatore  acknowledge support from the  EU H2020 Research and Innovation Programme  under the  ExaNeSt  project (Grant Agreement No. 671553 ). S. Borgani  also acknowledge support from the  INFN IN-DARK  grant. S. Ettori  acknowledges financial contribution from the contracts  NARO15 ASI-INAF I/037/12/0 ,  ASI 2015-046-R. 0  and  ASI-INAF n. 2017-14-H. 0. M. Gaspari  is supported by  NASA  through  Einstein Postdoctoral Fellowship Award Number   PF5-160137  issued by the Chandra X-ray Observatory Center, which is operated by the  SAO  for and on behalf of  NASA  under contract  NAS8-03060.
Hereditary breast and ovarian cancers are mainly due to BRCA1 (MIM #113705) and BRCA2 (MIM #600185) pathogenic variants. The BRCA genes embody the problem of variant interpretation due to their wide mutational spectrum, which is mostly devoid of specific hot spots. To exemplify this issue, over 30% of the variants in the Breast Cancer Information Core (BIC), ClinVar and BRCA Share databases are VUS  (3) (4) (5). Spliceogenic variants are probably the most challenging for the geneticists as each nucleotide variation, regardless of its location, can potentially affect pre-mRNA splicing and be pathogenic via disruption of 5 or 3 splice sites (5 /3 ss), creation of new 5 /3 ss or alteration of splicing regulatory elements. It is estimated that ∼15% of all point mutations causing human inherited disorders disrupt splice-site consensus sequences  (6).
Datasets Among this initiative, 395 variants occurring in the consensus 5 /3 ss regions of 11 genes were collected, along with their respective RNA studies, and distributed between a training set and a validation set (Figure  1 ). The training set (Supplementary Table  S1 ) comprises 142 BRCA1 and BRCA2 variants from the UGG network. We performed transcript analyses as previously described  (7). Briefly, protocols for transcript analyses included (i) minigene-based splicing assays, (ii) RNA extracted from lymphoblastoid cell lines treated/untreated with puromycin. (iii) RNA extracted from blood collected into PAXgene tubes (Qiagen), (iv) RNA extracted from stimulated T lymphocytes. Controls (samples without variant) were always included in these experiments. No discordance was observed between in vitro studies for the same variants.
We used SSF-like, a version of SSF, allowing calculation score of donor splice site with GT and GC canonical motifs, embedded in Alamut ® and in SPiCE. At last, GS is based on a decision tree method. It captures potential strong dependencies between signal positions by dividing the dataset into subsets based on pairwise dependency between positions and modeling each subset separately  (20). The outcomes of each of these tools were simultaneously obtained by using the commercial software (Alamut ® Visual software version 2. 8 rev. 1 and Alamut ® Batch version 1. 5. 2. , Interactive Biosoftware).