dataesr/scientific-paragraphs-categorization huggingface dataset:
Similar analyses were performed for HGT events that simultaneously affected pairs of terminal branches, i. e. , for genes transferred to two terminal branches in two independent events. In both cases, we observed that lineages represented by genomes encoding cognate R-M systems coexchanged more genetic information (Fig. S6 A  and B ). Next, we restricted our analysis to clades having at least 10 comparisons between genomes encoding cognate R-M systems and 10 comparisons between genomes lacking cognate systems (but encoding R-M systems). This avoids the confounding effect of putting together in the same analysis clades with few R-M systems or with little diversity in these systems. This restricted our dataset to eight clades: Bacillus amyloliquefaciens, Bifidobacterium longum, Escherichia coli, Haemophilus influenzae, Listeria monocytogenes, N.
meningitidis, Salmonella enterica, and S. pneumoniae. Within this restricted dataset, the results were qualitatively identical: lineages associated with genomes encoding cognate R-M systems coexchanged more genetic information (Fig. 4 A-C ). We confirmed that these results were insensitive to uncertainties in phylogenetic reconstruction and to the effects of HR in phylogenetic inference (SI Methods and Fig. S7 ). The results on HR might be strongly affected by the ability of bacteria to engage in natural transformation. We restricted our analysis to the five naturally transformable species, following ref. 27 , and found similar results (P < 10 -3 ). We then tested whether the clade-associated traits covarying with HR and HGT-phylogenetic depth, average genome size, and number of genomes-were affecting our conclusions by making the comparisons on each clade separately.
Discussion Genome size is the result of the balance between accretion and deletion events moderated by natural selection. Larger bacterial genomes are expected to engage in more frequent HGT because this is the dominant mechanism of genetic accretion  (28). However, there are remarkably few studies demonstrating an association between HGT and genome size  (29). Here, we found that larger genomes exchange DNA at higher rates, both by HGT and by HR. This association is not just caused by sexually isolated endosymbiotic bacteria with very small genomes-e. g. , Chlamydiae, Buchnera, or Spirochaetes (Fig. 1  and Dataset S1)-because it remains significant for genomes larger than 2 Mb, which include few obligatory endosymbionts. Many reasons might explain the association between HGT, HR, and genome size: bacteria with larger genomes might have more diverse lifestyles, select for more diverse types of genes, inhabit more environments, or accommodate more MGEs.
XMM-Newton We obtained a 52. 6 ks observation of V404 Cyg with XMM-Newton (ObsID 0723310201) on 2013 October 13-14, covering part of the first NuSTAR observation (see Table  1 ). We used the Scientific Analysis System (SAS) v13. 0. 1 software package to reduce the data from the pn, MOS1, and MOS2 instruments, which were all operated in full frame mode with the medium blocking filter. The 10-12 keV light curve from the full pn detector shows that proton flares occurred at the beginning and end of the observation, and we filtered out the time when 10-12 keV pn count rate was greater than 1 c/s, leaving a net exposure time of 31. 5 ks for pn and 34. 9 ks for each of the MOS units. The part of the observation used to make energy spectra started at 2013 October 13, 20. 2 h UT and ended at 2013 October 14, 6.
NuSTAR We reduced the NuSTAR data using the NuSTAR Data Analysis Software (NuSTARDAS v1. 4. 1) available as part of HEASOFT v6. 16 and the latest CALDB files. The NuSTAR observations were taken during intervals of normal Solar activity, and we used standard filtering to remove periods of high background during South Atlantic Anomaly (SAA) passages and Earth occultation. We created cleaned, calibrated event files using the NUPIPELINE script with standard settings. The source spectra and light curves were extracted using a 50 ′′ radius circular region around the source. For background estimation on FPMA, we used nuskybgd tool that accurately generates the background by properly taking into account the non-uniformity of background across the focal plane (see  Wik et al. 2014 for details). FPMB, on the other hand, showed the presence of faint stray light leaking into the field-ofview from a nearby source during epochs 1 and 2.
The nuskybgd tool does not account for stray light; thus we did not use it for FPMB. Fortunately, for epoch 1, the source fell on a favorable position and we were able to remove stray light contamination by carefully selecting the background from a 100 ′′ radius circular region and subtracting it from the source. However, for epoch 2 the source was right on the edge of the stray light pattern, making it difficult to remove the contamination. Therefore, to safely avoid any contamination issue, we did not use epoch 2 FPMB data for any further analysis. FPMB epoch 3 data and all FPMA data are clean and free from any stray light issue and we use them for detailed scientific analysis.
Flagging, calibration and imaging of the data followed standard procedures and were carried out within the Common Astronomy Software Application (CASA, version 4. 1. 0) package  (McMullin et al. 2007). We set the primary calibrator 3C286 flux scales using the task setjy and the physical properties measured by  Perley & Butler (2013). The routines bandpass and gaincal were used to solve for the complex bandpass, the amplitude, and the phase gains. Stokes parameter values of the linearly-polarised calibrator 3C286 were assigned with setjy thanks to its fractional linear polarisation and its polarisation position angle listed in  Perley & Butler (2013). We derived calibration solutions for the leakage terms using J2355+4950. The flux densities of the secondary calibrators were bootstrapped using the task fluxscale, based on the absolute calibration of 3C286.
Further, we have also derived hardness ratios between the two NuSTAR energy bands, 3-8 keV (soft) and 8-30 keV (hard) to test for any X-ray spectral variations. We found no variation in the hardness ratio (hard/soft), indicating that there are no significant spectral changes in the Xray data. This is consistent with the previous Xray studies using Chandra  (Hynes et al. 2009 ) and Swif t  (Bernardini & Cackett 2014). In order to evaluate the broad-band X-ray characteristics of V404 Cyg during quiescence, we si-multaneously fitted XMM-Newton and NuSTAR data to achieve energy coverage from 0. 3-30 keV. As shown above, there is no significant spectral variation between the three epochs of NuSTAR observations, therefore we combined the data from the three NuSTAR epochs to improve the signalto-noise ratio.
We used the ftool addascaspec to combine the NuSTAR data, which properly combines the corresponding background and response files. In order to account for the cross-calibration between different instruments, we included a constant multiplicative factor in all the spectral modeling described below. This constant factor was held fixed to 1. 0 for XMM-Newton EPIC-pn and allowed to vary freely for other instruments. The EPIC-pn to NuSTAR normalization differences are below the expected 10% level  (Madsen et al. 2015). We employed several different spectral models to characterize the broad-band spectra of V404 Cyg during quiescence, including an absorbed power-law model, a cutoff power-law model, a thermal bremsstrahlung model and a thermally comptonized continuum model (see Figure  8 ). The best fit spectral parameters obtained through joint fitting of XMM-Newton and NuSTAR data are listed in Table  2.
The cut-off energy, being very close to the high end of the spectral data, is not tightly constrained. In order to assess the significance of the potential cutoff, we performed a series of spectral simulations. Using the same responses, background files and adopting the same exposure times as the data used here, we simulated 10,000 sets of XMM-Newton (pn, MOS1, MOS2) and NuSTAR (FPMA, FPMB) spectra with the FAKEIT command in XSPEC. These simulations were based on the best-fit power-law continuum, and each of the simulated datasets was rebinned in the same manner and analyzed over the same bandpass as adopted for the real data. We then fit each of the combined datasets with an absorbed powerlaw continuum with and without an exponential cutoff, again in the same manner as the real data, and noted the improvement in fit provided by the former over the latter.
Of the 10,000 datasets simulated, only 28 showed a chance improvement equivalent to or greater than that observed, implying that the detection significance of the cutoff in the real data is at the 3σ level. Next, we performed another set of simulations with the best fit cutoff power-law model as input, in order to assess the expected distribution of ∆χ 2 when compared to a simple power-law model given the available data. We found that it peaks near the observed value and this holds true even if we take into account that the power-law model already provides a good fit with reduced χ 2 <0. 9. All of these simulations favor the detection of a cutoff with 3σ significance in our broad-band XMM-Newton+NuSTAR data. Such a feature has been measured for the first time in V404 Cyg during quiescence.
16 +1. 15  -0. 89 keV using Suzaku XIS data, in agreement with our measurement. Corbel et al. (2008)  revisited the radio-X-ray flux correlation for V404 Cyg by re-analyzing some of the 1989 outburst data and adding two points from their 2003 Chandra/VLA data and 2000 Ginga/VLA observations during quiescence. They found a strong correlation between the two energy bands and conclude that the correlation observed during the decay of the 1989 outburst holds down to the quiescent state. Using our new simultaneous NuSTAR/VLA observations, we were able to add one more point to that correlation, shown in Figure  11. Earlier data were taken from  Corbel et al. (2008)  (see their Figure  4 ) and we did not attempt any fitting. Our new data point lies right on their best fit curve, nicely following the long-term correlation from previous data.
Summary V404 Cyg shows strong variability on timescales of hours to a couple of hours, at both X-ray and radio energies. Several small flares were detected at X-ray energies by both XMM-Newton and NuSTAR, during which the count rate changed by a factor of 4-5 in a few hours. On the other hand, the radio flares are comparatively short lived and asymmetric, with fast rises changing flux density by a factor of ∼7 in 5 min followed by a slower decay. Our broad-band X-ray spectral analysis using absorbed power-law models provides consistent results with previous studies using just X-ray data below 10 keV. However, with NuSTAR extending the spectral coverage above 10 keV, we were able to measure a cut-off at 20 +20 -7 keV, which can be explained using thermal bremsstrahlung emission from the accretion flow or synchrotron emission from the base of a jet outflow.
We thank  Michael A. Nowak  for useful discussion on synchrotron model. This work was supported under  NASA  contract No. NNG08FD60C , and made use of data from the NuSTAR mission, a project led by the  California Institute of Technology , managed by the  Jet Propulsion Laboratory , and funded by  NASA. We thank the  NuSTAR Operation, Software and Calibration teams  for support with the execution and analysis of these observations. This research has made use of the NuSTAR Data Analysis Software (NuSTARDAS) jointly developed by the  ASI Science Data Center (ASDC, Italy)  and the  California Institute of Technology (USA). SC would like to thank  James Miller-Jones  for excellent help during the preparation of the VLA observations. SC and AL acknowledge the financial support from the  Uni-vEarthS Labex programme of Sorbonne Paris Cité  ( ANR-10-LABX-0023  and  ANR-11-IDEX-0005-02 ), and from the  CHAOS  project  ANR-12-BS05-0009  supported by the  French Research National Agency.
Commercial Systems The commercial systems (with remote or local access) to monitor the biomedical variables in bedridden patients are expensive and unaffordable for most families. Currently, the reference price is about €8600  [8]. The clinical bioPLUX is an innovative medical device for physiotherapy and rehabilitation developed by Plux  [8]  (Figure  1 ). The system has a software GUI bioPLUX for muscle rehabilitation, which makes the intervention of a therapist more efficient. This software is a web-based application, being developed with html, css, and JavaScript, available also for Android. The device is based on a model of service delivery in two formats: @Clinics, to support the work carried out between the user and the physical therapist in the clinic, and @Home, to complement the work at home  [8].
Medical Care Terminal (MCT) In this section, the operation principle of the developed system, including the software and hardware development, is presented. In particular, the hardware system, the sensors used in the MCT, the software acquisition, and data processing as well as the remote communication system are detailed. The user interface was developed in LabVIEW software and the reading of biomedical and environmental sensors was carried out at the Arduino platform. In the first case, for each sensor there is a guide and a dedicated interface, where the real and the expected values are registered as well as the alarms. The values obtained by the sensors can be saved in an Excel file to allow further analysis.
MCT User Interface in LabVIEW. The user interface was developed in LabVIEW (Figure  12 )  [12, [26] [27] [28] [29]. The interface allows a friendly interaction with the user. The interface has four sections: the first one corresponds to the system welcome page, the second one is dedicated to the monitoring of the environmental variables profiles (room temperature, brightness, and air quality), the third section shows the patient data that can be accessed through the biomedical sensors placed on the patient, and, finally, the fourth section allows the remote communication with a medical specialist. From the interface, the measured data can be recorded in MS Excel, allowing further analysis. In Figure  13 , an environmental sensor interface, namely, the light sensor, is shown. The interface includes a light emitting diode warning when there is a lack of light in the room.
Experimental Results In this section, some results obtained with the MCT regarding physiological and environmental data are presented. The first test was carried out to obtain the value of body temperature. Figure  16  refers to the sensor placement and Figure  17  shows the user interface where the temperature is acquired through wireless communication and registered in a chart. The second test was carried out to obtain the value of the galvanic skin response of the patient. Figures  18  and 19  show the sensor positioning in the patient and the corresponding user interface, respectively. Figures  20  and 21  show the obtained values of oxygen saturation for the two patients, respectively. Blood oxygenation level is associated with cardiovascular fitness, and raising oxygenated blood coloration in human faces increases perceived health  [30] [31] [32] [33] [34].
Acknowledgments This research was funded by  Portuguese Foundation for Science and Technology (FCT)  through the  R&D  Project  FCOMP-01-0124-FEDER-022601  (reference  FCT PTDC/ CPE-PEC/122329/2010 ). This work has also been supported by  FCT  in the scope of the project:  PEst-UID/ CEC/00319/2013.
It is common belief that high impact research in software reuse requires assessment in realistic, non-trivial, comparable, and reproducible settings. However, real software artefacts and common representations are usually unavailable. Also, establishing a representative ground truth is a challenging and debatable subject. Feature location in the context of software families is a research field that is becoming more mature with a high proliferation of techniques. We present EFLBench, a benchmark and a framework to provide a common ground for this field. EFLBench leverages the efforts made by the Eclipse Community which provides real feature-based family artefacts and their implementations. Eclipse is an active and non-trivial project and thus, it establishes an unbiased ground truth. EFLBench is publicly available and supports all tasks for feature location techniques integration, benchmark construction and benchmark usage.
The Eclipse Project The As the project evolves over time, new packages appear and some other ones disappear depending on the interest and needs of the community. For instance, in 2011 there were 12 packages while the next year 13 packages were available with the addition of one targeted to Automotive Software developers. Continuing with Eclipse terminology, a simultaneous release (release hereafter) is a set of packages which are public under the supervision of the Eclipse Foundation. Every year, there is one main release, in June, which is followed by two service releases for maintenance purposes: SR1 and SR2 usually around September and February. For each release, the platform version changes and traditionally celestial bodies are used to name the releases, for example Luna for version 4.
4 and Mars for version 4. 5. The packages present variation depending on the included and not-included features. For example, Eclipse package for Testers is the only one that includes the Jubula Functional Testing features. On the contrary, other features like the Java Development tools are shared by most of the packages. There are also common features for all the packages, like the Equinox features that implement the core functionality of the Eclipse architecture. The online documentation of each release provides a high-level information of the features that each package provides  1. It is important to mention that in this work we are not interested in the variation among the releases (version 4. 4, 4. 5 and so on), known as variation in time, because this is related to software maintenance and evolution.
We focus on the variation of the different packages of a given release, known as variation in space, which is expressed in terms of included and not-included features. Each package is different in order to support the needs of the targeted developer profile by including only the appropriate features. Eclipse is feature oriented and based on plugins. Each feature consists of a set of plugins that are the actual implementation of the feature. Table  1  shows an example of feature with four plugins as implementation elements that, if included in an Eclipse package, adds support for a versioning system based on CVS. At technical level, the actual features of a package can be found within a folder called features. This folder contains meta-information regarding the installed features including the list of plugins associated to each of the features.
Each feature has an id, a name and a description as written by the feature providers of the Eclipse community. A plugin has an id and a name written by the plugin providers but it does not have a description. Table  2  presents data regarding the evolution of the Eclipse releases over the years. In particular, it presents the total number of packages, features and plugins per release. To illustrate the distribution of packages and features in the project, Figure  1  depicts a matrix of the different Eclipse Kepler SR2 packages. In this figure, a black box denotes the presence of a feature (horizontal axis) in a package (vertical axis). We observe that some features are present in all the packages while others are specific to only few, one or two, packages. The Eclipse CVS Client org. eclipse.
team. cvs. core CVS Team Provider Core org. eclipse. team. cvs. ssh2 CVS SSH2 org. eclipse. team. cvs. ui CVS Team Provider UI  437 features are alphabetically ordered by their id and, for instance, the feature Eclipse CVS Client, tagged in Figure  1 , is present in all of the packages except in the Automotive Software package. Features, as in most of the feature oriented systems, have dependencies among them. Includes is the Eclipse terminology to define subfeatures and Requires means that there is a functional dependency between the features. Figure  2  shows the dependencies between all the features of Eclipse Kepler SR2. We tagged in Figure  2  some features and subfeatures of the Eclipse Modeling Framework. Functional dependencies are mainly motivated by the existence of dependencies between the plugins of one feature with the plugins of other features.
As discussed before, given the few amount of Eclipse packages under consideration, FCA is able to distinguish 3 https://github. com/but4reuse/but4reuse/wiki/Benchmarks blocks which may actually correspond to a high number of features. For example, all the plugins that correspond specifically to the Eclipse Modeling package, will be grouped in one block while many features are involved. The first location technique (FCA + SFS) does not assume meaningful names given that no IR technique is used. The features are located in the elements of a whole block obtaining a high recall. Eclipse feature names and descriptions are probably written by the same community of developers that create the plugins and decide their names. In the approaches using IR techniques, the authors expected a higher increment of precision without a loss of recall but the results suggest that certain divergence exists between the vocabulary used at feature level and at implementation level.
The same authors proposed a benchmark for combinatorial interaction testing techniques for SPLs  [18]. Betty  [28]  is a benchmark for evaluating automated feature model analysis techniques, which has long history in software engineering research  [6]. Feature location on software families is also becoming more mature with a relevant proliferation of techniques. Therefore, benchmarking frameworks to support the evolution of this field are in need. Many different case studies have been used for evaluating feature location in software families  [5]. For instance, ArgoUML variants  [8]  have been extensively used. However, none of the presented case studies have been proposed as a benchmark except the variants of the Linux kernel by Xing et al. [32]. This benchmark considers 12 variants of the Linux kernel from which a ground truth is extracted with the traceability of 2400 features to code parts.
However, even if the Linux kernel can be considered as an existing benchmark, EFLBench is complementary to foster feature location research because a) it maps to a project that is plugin-based, while Linux considers C code, and b) the characteristics of the Eclipse natural language corpora is different from the Linux kernel corpora. This last point is important because it has a major influence on the IR-based feature location techniques. Finally, using the Linux kernel benchmark, the ground truth may be also constructed but there is no framework to support the experiment. EFLBench is associated with BUT4Reuse which integrates feature location techniques making easier to control and reproduce the settings of the studied techniques.
Conclusion We have presented EFLBench, a framework and a benchmark for supporting research on feature location. The benchmark is based on the Eclipse releases and is designed to support research on software reuse in the context of software product lines. Existing and future techniques dealing with this problem can find a challenging playground that is: a) real, b) contains a valid ground-truth and c) is directly reproducible. We also demonstrated example results of four approaches using the EFLBench. As further work we aim to create a parametrizable generator for Eclipse packages. This generator will combine different features in order to use the benchmark in special and predefined characteristics. We also aim to generalize the usage of feature location benchmarks inside BUT4Reuse providing extensibility points for other case studies.