dataesr/scientific-paragraphs-categorization huggingface dataset:
Statistical Analysis Approximately 360 to 450 patients were planned to be screened, to randomize approximately 180 patients; however, recruitment to the study was closed prematurely on November 22, 2018. Further statistical methods are in eMethods in Supplement 2. Statistical calculations were performed with SAS statistical software (version 9. 4. SAS Institute, Inc). Analysis of the data occurred between September 13, 2019 and October 29, 2019. The statistical analysis plan is available in Supplement 3.
Study Population From July 2017 to November 2018, 254 patients were enrolled for screening; of these, a total of 60 (female n = 14, 23%) were randomized to either savolitinib (33 patients) or sunitinib (27 patients) treatment (Figure  1 ). For savolitinib and sunitinib, 4 (12%) and 10 (37%) patients were women, and the median (range) age was 60 (23-78) and 65 (31-77) years, respectively. The treatment groups were generally well-balanced (Table  1 ) (eTable 1 in Supplement 2). All patients randomized to savolitinib received 600 mg of savolitinib once daily. As of data cutoff, 23 (38%) patients were in ongoing treatment: 14 (42%) in the savolitinib group and 9 (33%) in the sunitinib group. Race and ethnicity were recorded by the investigator during screening to monitor the distribution of race and ethnicity across treatment groups, and to allow identification of race-or ethnicity-related treatment effects.
Clin Cancer Res. 2015;21(12):2811-2819. doi:10. 1158/1078-0432. CCR-14-2685 Abstract 4541: MET gene copy number gains evaluated by NGS is more predictive than other methods to enrich for papillary RCC patients sensitive to savolitinib, a selective MET inhibitor. Cancer Res. 2018;78(suppl 13):4541. doi:10. 1158/ 1538-7445. AM2018-4541 19. Gan HK, Millward M, Hua Y, et al. First-in-human phase I study of the selective MET inhibitor, savolitinib, in patients with advanced solid tumors: safety, pharmacokinetics, and antitumor activity. Clin Cancer Res. 2019;25(16):4924-4932. doi:10. 1158/1078-0432. CCR-18-1189 20. Choueiri TK, Plimack E, Arkenau HT, et al. Biomarker-based phase II trial of savolitinib in 27. Kroeger N, Xie W, Lee JL, et al. Metastatic non-clear cell renal cell carcinoma treated with targeted therapy agents: characterization of survival outcome and application of the International mRCC Database Consortium criteria.
Study design. Patients entered in the prospective Endurant Stent Graft Natural Selection Global Postmarket Registry (ENGAGE) were included. There were 79 participating centers worldwide, and ethical committee approval was obtained for the majority of the sites (>75%) and per local law. For the remaining centers, either the ethical committee was notified or approval was waived and standard of care treatment was followed for the patients. The full registry design, inclusion and exclusion criteria, and data collection and a detailed description Journal of Vascular Surgery of the Endurant (Medtronic, Santa Rosa, Calif) stent graft system have been previously published. 11 In short, the ENGAGE is a multicenter nonrandomized single-arm prospective registry. Participating centers were required to perform >20 EVAR procedures per annum.
RESULTS The ENGAGE registry included 1263 patients (89. 5% men; mean age, 73. 1 years [range, 43-93 years]). At completion angiography (t ¼ 0), 181 (14. 3%) type II endoleaks were identified, of which 105 (58. 0%) had spontaneously resolved at the first follow-up imaging. Follow-up data on endoleaks were available for 1090 (86. 4%) patients. During follow up (t ¼ 1 and onward), a total of 197 (15. 6%) patients with isolated type II endoleaks were identified and 893 (70. 8%) patients without any endoleak. Baseline characteristics are shown in Table  I  Isolated type II endoleak vs no endoleak. Indication for EVAR did not differ between the isolated type II and the no endoleak groups. Hypertension and hyperlipidemia incidences were significantly higher in the isolated type II endoleak group, whereas the incidence of smoking was higher in the no endoleak group.
DISCUSSION The overall incidence of isolated type II endoleaks in the ENGAGE registry is in line with earlier publications. 1, 2 The data confirm that the majority of isolated type II endoleaks are clinically not of concern. However, they also show that having a type II endoleak is associated with an increased risk of aneurysm growth and a higher incidence of secondary interventions. Moreover, a small group of patients with a type II endoleak presented with a late type I endoleak during follow-up, which resulteds in a 67. 5% secondary endovascular procedure rate through 5 years, increased aneurysm rupture risk, and aneurysm-related mortality. The exact pathophysiologic mechanism is unclear. In the introduction, two possible hypothesizes have been described. First, a type II endoleak may lead to aneurysmal disease progression and subsequent type I (A or B) endoleak.
This study has limitations. First, the inherent limitations of registries apply, and not all patients had follow-up data on the occurrence of type II endoleaks available. The follow-up protocols were not dictated by the ENGAGE registry and varied between participating centers, which could have influenced outcomes. Data available for analysis are limited to the data as reported on the case report forms. The ENGAGE case report form has broad categories for additional devices used during the implantation procedure. Because the "other" category could include anything from aortouni-iliac devices to cuffs, coils, and limb extensions, a direct comparison of specific additional devices was not possible. In this study, patients who showed signs of an isolated type II endoleak on completion angiography that had disappeared on the first postoperative follow-up imaging were considered benign and excluded from further analysis.
SIMBIO-SYS Scientific Objectives -Topography and Morphology The SIMBIO-SYS instrument will devote many measurements to the global topography of the planet. In particular, the STC channel will deliver a global DTM with a grid resolution better than 120 m/pixel, while HRIC will provide updates of the reference system and prime meridian by selecting and observing specific topographic targets during the mission. Both channels will concur in creating a global context for the BELA range measurements between the spacecraft and the Hermean surface. All these observations are fundamental to the comprehension of Mercury interior and surface. Surface morphology will be analyzed in two dimensions at different scales and resolution using STC and HRIC images; in addition, 3D patterns will be delivered by using the STC stereo datasets.
Indeed, if Mercury core is still to be well constrained, much more can be done to assess crustal-mantle boundary. Based on the MESSENGER data, the thickness of the crust has been estimated to be on average between 20 km and 50 km. It shows substantial variation, with the crust being thicker at low northern latitudes and thinner (15-30 km) at the high northern smooth plain and at major impact basins, such as Caloris, Odin Budh, Borealis, and Rachmaninoff (within the inner peak ring)  (Smith et al. 2012; Padovan et al. 2015; Phillips et al. 2018; Beuthe et al. 2020). Based on MESSENGER geochemical data and gravity and topography maps,  Beuthe et al. (2020)  show that the crustal thickness variations correlate with lateral variations in mantle melt production. However, very limited data have been provided by MESSENGER about the southern hemisphere and direct information about potential mantle inhomogeneities and the relationship between geochemical terrains or tectonic features (isolated lobate scarps, fold and thrust belts, long-wavelength undulation) and Mercury interior is still lacking.
Observing Strategy HRIC will operate during the BepiColombo nominal mission to perform targeted highresolution observations of specific areas of interest, which will be selected according to the scientific analysis and questions mentioned in Sect. 2 and on the data acquired from STC and/or VIHI in the first phase (global coverage during the first six months of the mission). We expect to observe up to 20% of the Mercury surface through selected observations. In principle, HRIC will operate in a wide range of true anomalies of Mercury, i. e. , in a wide range of solar incidence angles and latitudes, that will be taken into account according to the specific scientific objective of the observations (e. g. , albedo features study, tectonic structure detection and morphology, radiance vs. phase angle).
7 km providing a vertical accuracy of 80 m, assuming a conservative 1-pixel accuracy in the image matching. For each MPO orbit (2. 3 h), Mercury rotates by 0. 595 • , which corresponds to 25. 3 km at the equator. It means that a FoV of 3. 6 • is required to obtain two successive camera  swaths with no gaps at the equator. Since STC has a FoV of 5. 38 • , there will be always overlap between adjacent orbits, that is important to improve the image matching in the mosaicking. The cross track FoV decreases as the latitude increases, in order to maintain the same percentage of overlap. The Color Mapping will be performed in the second six months of the nominal mission and will provide target-oriented acquisitions of multiband images with the four broad-band filters. To limit the loss of data volume, STC will acquire images with a channel at a time, maintaining the overlap between two consecutive acquisitions between 10% and 15%.
The measurements completed are listed in Table  8. Due to time constraints, the strategy was to automate, as much as possible, the calibration measurements. All the sequences were thus prepared, validated and simulated before the campaign. Nevertheless, a preparation phase of a few days was still necessary to tune instrumental parameters (such as integration time and number of frame acquisition) as well as device parameters (for instance, the position of the hexapods corresponding to different points on the STC and HRIC detectors and VIHI slit). Radiometric measurements have been performed at 3 thermal configurations (nominal, hot, and cold), while the other sequences have been acquired at nominal temperatures. A quick-look data analysis has been performed. We evaluated the following parameters for both STC and HRIC: map of spurious pixels, background evaluation, size of the illuminated spot, integrated flux for each filter of the cameras, and a first estimate of the transfer functions.
We tried to work on the data with more complex fitting law to extend the dynamical range. We obtained good results using spline fitting. The same approach has been used for the 4 broad-band filters as the analysis of the calibration measurements showed that the radiometric response was not well reproduced by a simple linear model. It has been necessary to apply a spline fitting to derive a satisfactory radiometric response curve. The non-linear fit allows extending the dynamical range of the detector. In the case of the PAN filter, the cubic fit has been used extending the dynamic range of about 10%, taking into account also the relatively high level for the FPN, allowing to optimize the instrument observations. For the BB filters, the best results have been achieved by using the spline. The radiometric performances will be checked during the mission with dedicated calibration acquisitions.
Spectral Calibration The spectral calibration consists of the determination of the transmittance curve that characterizes the STC filter performance. The theoretical transmittance curve has been obtained by measuring the filters before mounting them in the optical system (dashed lines in Fig. 20 ). The wings of the transmittance function are the ascending and descending parts of the curve. The data have been acquired by dividing each filter into three sub-windows, which illuminate the left-side, central, and right-side part of each filter, respectively. For each subwindow, a series of images has been taken. The wavelength of the light-beam spans the band width of each filter with a step that differs for the PAN and BB filters. The images have been acquired with two different integration times: a long IT for the wings of the transmittance function and a short IT for the plateau region.
Spectral Calibration The VIHI spectral calibration, including the full spectral range, sampling, and spectral response function, is determined by acquiring calibrated light emitted by a monochromator and by comparison with the spectral sources of the internal calibration unit. The monochromator scan is performed continuously across the entire spectral range at steps of 1 nm by illuminating the entire spectrometer's field of view with a collimated beam. The measurements and related data processing are described in detail in  Altieri et al. (2017). The resulting spectral parameters are summarized in Table  14. The VIHI spectral sampling is not constant: it follows a quadratic dispersion along the band axis. As a result of this effect, the minimum sampling interval is 6. 248 nm (band = 0) and the maximum one is 6.
Geometric Calibration The instrument geometric performances are determined by means of controlled, subpixel spatial scans of point sources with the aim to measure the FOV and the Instantaneous Field of View (IFoV) along and across the slit direction. A detailed description of the measurements setups and data processing is reported in  Filacchione et al. (2017). In the slit (pixel) function, an illuminated test slit is placed parallel (perpendicular) to the VIHI slit during the scan. The collimated optical beam emitted by the test slit is moved at subpixel steps for each acquisition with the aim to oversample the angular resolution. This method has been already used to characterize geometrical performance of similar VIS-IR imaging spectrometers, like  VIRTIS on Rosetta  (Ammannito et al. 2006 ) and JIRAM on Juno  (Adriani et al.
Electrical Ground Support Equipment The SIMBIO-SYS Ground Segment software has two main objectives: the conversion of the telemetry packets into the PDS4 products (TM2RAW pipeline) and to check the consistency of the telecommand timeline and the data produced. The software will also generate the event list and a report of the errors. Figure  30  shows the block diagram. The spacecraft sends the packets to the EDDS server (ESA Ground Operation System's Data Dissemination System) that orders them, following the generation time, and provides them to the instrument team in XML format. The XML file, produced by the EDDS, is one of the pipeline inputs. The pipeline is designed by following a modular programming approach. Each module has a specific functionality and the communication between the modules is based on files (Fig.
If the kernel files are not in the input folder, the pipeline stops the process with an error message. The simGen script runs two main modules: simEvent and simAir: -simEvent is a python module that filters the packets and selects only the events and the acknowledgments message. It produces a log file containing a brief description of the event and a correlation between the acknowledgment and the corresponding TC. To simplify the block diagram in Fig. 30 , the module is not reported but is included in the generic "check" block. -simAir is a C++ module. It reads all the telemetry files and splits the packet by the Application Process IDentifier (APID). Every packet is converted, the images are assembled and decompressed, the house-keeping are converted ito physical units using a LiteSQL database.
Using the information in the header of the packets, an integrity and completeness test is performed. If the data is incomplete or damaged (e. g. , corrupted telemetry file or handling of data gap or incomplete product), the software will set the missing pixels to a special value (-999) in order to complete the data. The pipeline will produce the output data in PDS4 format. The SIMBIO-SYS team, with the collaboration of the ESA SGS team, projects the structure of the PDS label and the parameters to characterize and contextualize the data as much as possible. The data coming from simAir are formatted in PDS4 format by a specific module. This module provides also a statistics file containing, e. g. , the number of sessions and the number of acquisitions for each session. The PDS4 files are moved to the output folder.
Max Stress Test This test was performed by putting each channel in continuous science acquisition for 10 minutes with the following configurations: -HRIC, acquisition of an area of 512×512 pixels with 8×8 binning, ibr = 63 and RT 0. 5 s; -STC, acquisitions in CM with 3. 9375 bit/px and RT 0. 2 s; -VIHI, full-frame acquisition with dark subtraction and RT 0. 04 s. The test was executed with no evidence of errors indicating that the system (i. e. , ME) is capable of managing the operability of all the channels even in such a stressing configuration. The only drawback came from the fact that some of the STC data were lost due to errors in the Solid State Mass Memory management.
Data Rate Stress Test This test was performed putting each channel in continuous science acquisition for 1 min with the following configurations: -HRIC, PAN acquisition with 3. 9375 bit/px and RT 1. 115 s; -STC, acquisitions in CM with 3. 9375 bit/px and RT 0. 2 s; -VIHI, full-frame acquisition with 7 bit/px and RT 0. 04 s. The test was executed with no evidence of errors indicating that the system (i. e. , ME) is capable of managing the operability of all the channels when producing their maximum data rate.
A lot of people nowadays use online education platforms. Most of them run on the free «Open edX» open-source software platform. Using the logs that the platform provides us, we can get psychometrics of students, which can be used to improve the presentation of material or other things, which can increase the quality of online courses. We provide a ready-to-use tool that will help figure out how and for what purpose you can analyze the log files of platforms based on «Open edX».
Introduction Online electronic educational learning platforms are very popular nowadays. One of the biggest and widely used platforms is «Open edX»  [1] [2] [3]. This is an open-source software platform that provides off-the-shelf tools for educational services. One of the important features of the platform is that it generates student and teacher activity log files. But the disadvantage of the platform is that it does not provide any data analysis tools for monitoring educational progress and success. One of the most popular educational platform based on «Open edX» in our region is «Open Education»  [4]. The problem of the Open edX platform and in particular of one of its implementations «Open Education» is that the teachers, conducting courses on this platform, are missing the tools for analyzing the educational process, which leads to missing control on the educational process and decreasing its efficiency.
The result of that activity is that there is a list of 18 analytics tasks that help students and teacher to monitor the progress;  implement the software solution, that demonstrates the idea and all the possibilities that Open edX logs provide. As an outcome, there is a tool for extracting, transforming, and preserving logs from a specific course / courses from this platform and the number of analytics tasks implemented in that tool. The result of the analysis is presented in the form of files with metrics, and graphs based on the data obtained. The paper has the following structure. Section 2 shows the related work. Section 3 describes the system design. Section 4 discusses the implementation process and result of the pilot project. Section 5 presents the conclusion.
Related work There are some articles facing the similar problem of online courses activity analysis. In [6] (below, Article 1), authors take Moodle as a target platform of further analysis. Authors also use log files as a data source for further analysis. The files are stored in a database, processed and visualized to provide data implementation for teachers who are the final users of that tool. The metrics which authors take for analysis are "the grades of online assignments, reading time, the total number of login times, the total number of online discussions" and others. The choice of these metrics is based on log files content -such values may be easily extracted from raw data. Speaking about analytics, authors of [7] (Article 2) use advanced machine learning methods such as Random Forest to provide analytics of the online learning process.
Most authors base their results on log files data from educational platforms. Talking about platforms, Moodle is the most popular one within paper authors. Some papers also contain descriptions of prediction methods to make forecasts of student academic performance. However, only one paper describes a ready tool which contains all analytics methods described there and which can be utilized by other people. Also, any of these articles doesn't describe work with the Open edX platform. Within our work we are going to implement a tool for analysis of online courses data based on log files of the Open edX platform. Ultimately, our tool differs from others in that we directly process the platform logs, which allows flexibility in the approach to the analysis of what happened on the course. Thus, the teacher can get an answer to the question on a very specific task, in contrast to other tools.
System design In our solution, we are using microservice architecture presented in fig. 1. This allows us to conveniently implement and modify the logic of the service rebuild and redeploy a small part of the tool instead of full application rebuilding. We are using Docker and other DevOps practices. Docker effectively helps us in leveraging microservices architecture  [11]. We see three microservices here. • UI Service -allows the end-user to interact with the application. • ETL Service -is responsible for receiving, transforming unstructured logs, and loading them into the Database; This service can receive logs from the local machine or directly from the platform based on «opened». In our case, this is the platform -«Open education». • The analytical service contains a set of analytical scripts that work with the database and a module for building the output file -result or report, which will be sent to the appropriate user interface.
In addition, we are developing a new graphical user interface that is not yet included in the stable version. It consists of several pages on which the user can see the instructions, select the logs, start the analysis, and see the results. The graphical user interface is more user-friendly. We have our log analysis algorithm. Using a query in the database, we get the information necessary for analysis from the log, then we analyze it using various mathematical techniques. In one of the tasks, we use some innovation.
Innovative aspects of the design Since the tasks of analytics depend on the requirements of the customer, it is necessary to create such a backend architecture in which we can easily integrate new tasks for analytics. OOP architecture and Reflections API is well suited for solving this problem, so we can add new tasks inherited from an abstract task by adding only the database queries without changing the architecture of the project. At each startup, the system overloads the log, which makes it resistant to user crashes and software error implementation.