dataesr/scientific-paragraphs-categorization huggingface dataset:
PacBio (Pacific Biosciences, Menlo Park, CA, USA) data reads aligned to Can-Fam3. 1 mtDNA genome were viewed in SMRT view v2. 3. 0 (Pacific Biosciences, Menlo Park, CA, USA) and in IGV  45, 46. The three or two most common haplotypes in 559T and 1315T, respectively, were phased by visual inspection, as shown in Fig. 2b. Additional haplotypes present at a low level (less than 5%) were also identified in both samples, and are not shown in Fig. 2b. Gene expression analysis. Two FASTQ files containing raw forward and reverse reads, respectively, were generated from each tumour BAM file using the bio-bambam2 49 v2. 0. 79 software, and gene expression was estimated via transcript abundance quantification using the Salmon 50 v0. 8. 2 software  13. Gene-specific differential expression analysis was performed using the DESeq2 51 v1.
Twenty-eight A1d1a CTVT histology samples were scored, together with 31 CTVT_HT1 and CTVT_HT2 CTVT controls. CTVT_HT1 and CTVT_HT2 controls were phylogenetically matched with A1d1a tumours. The 28 A1d1a CTVTs belonged to 8 horizontal transfer groups (HT3, 2 tumours; HT8, 6 tumours; HT9, 13 tumours; HT10, 1 tumour; HT13, 1 tumour; HT14, 1 tumour; HT16, 1 tumour; HT17, 3 tumours). Histology slides were scanned using a Hamamatsu Nanozoomer 2. 0-HT slide scanner (C9600) before scoring, and were viewed using the NDP. view2 software (Hamamatsu Photonics, Hamamatsu, Japan). Each field was defined to consist of at least half CTVT parenchyma, avoiding areas of artefactual distortion, ulceration or haemorrhage. The number of mitotic figures (including bizarre mitotic figures) per 10 high-power fields (40×, a mitotic box of area 0.
Acknowledgements This work was supported by a grant from  Wellcome  ( 102942/Z/ 13/A ) and by a  Philip Leverhulme Prize  from the  Leverhulme Trust. A. St. was supported by a  Postgraduate Student Award  from the  Kennel Club Charitable Trust , by an  EMBO   Short-Term Fellowship  ( 7761 ) and by the  Ruth Bowden Scholarship  from the  British Federation of Women Graduates. T. J. N. is the recipient of a  Sir Henry Dale Fellowship jointly  funded by the  Wellcome Trust  and the  Royal Society  ( 213464/Z/18/Z ) and a  Rosetrees and Stoneygate Trust Research Fellowship  ( M811 ). The work was also supported by grants from  Swedish Research Council  (M. Fal. and C. M. G. ),  Swedish Cancer Foundation  (M. Fal. and C. M. G. ),  European Research Council  (M. Fal. ) and the  Knut and Alice Wallenberg Foundation  (M.
Data availability Whole-exome sequence data have been deposited in the European Nucleotide Archive (ENA) under accession number ERP109580. PacBio long-read sequence data have been deposited in ENA under accession number ERP120021. Gene expression data have been deposited in ArrayExpress under accession number E-MTAB-9037. The remaining data supporting the findings of this study are available within the article and its supplementary information files.
None of the patients received adjuvant chemotherapy. All patients underwent an initial pre-treatment 18F-FDG PET/CT as part of the initial staging (PET1) and at the time of recurrence. Collected data included age and date of diagnosis, histology, FIGO stage, presence of positive lymph nodes on 18F-FDG PET/ CT, tumor size as measured on MRI, EBRT and BT dose, date and site of recurrence, as well as date and status at last follow-up. Recurrences were considered as local (vaginal and/or cervical), regional (pelvic/para-aortic), or distant (upper abdominal and/or extraabdominal)  [19]. All patients provided signed informed consent for the use of their clinical data for scientific purposes and for the anonymous publication of data. Our Institutional Review Board approved this study (29BRC18. 0015).
PET/CT and CT planning registrations For each patient, a rigid registration of the CT component of the pretherapeutic PET/CT with the radiation planning CT was performed using the 3D Slicer TM Expert Automated Registration module  [21]  optimized with the Mattes mutual information metric  [22]. The transform was initialized with a registration of the two centers of mass of the images with a box centered on the cervix. The obtained transform was then applied to the corresponding PET. In cases of obvious misalignments (in case of tumor response and/or deformation by the applicator at the time of BT), manual adjustments by translation were allowed.
Coverage analysis First, we converted the CTV-HR into a volume and then evaluated the inclusion of the segmented high-uptake sub-volumes (V1) in the CTV-HR for each brachytherapy session. This was done using the ''Dose Volume Histogram" module in 3D Slicer. The average of the 3-4 BT sessions was reported. We also evaluated the coverage of the V1 by different isodose lines (from the 85 Gy isodose line to the isodose that allowed 100% coverage of the CTV-HR). To evaluate the spatial distribution of the hotspots, all PET sets with delineated hotpots were registered with a reference planning scan from a standard patient after automatic deformable registration using the ATLAS option of the MIM Vista Ò 6. 5. 2 Software.
Indeed, radiation oncologists deal now with functional and molecular imaging to increase the definition of target volumes in daily practice, and are increasingly familiar with registration between various imaging modalities, especially with the emergence of stereotactic radiotherapy  [39, 40]. To accommodate this, radiation oncology planning softwares now provide registration functionalities. The registration parameters are then easily and rapidly applied to the PET. Finally, hotspot outlining is exported to the brachytherapy software and its inclusion in the CTV HR is evaluated (one minute step). In total, we calculated a workload of less than 10 min is required for the entire procedure, which is certainly achievable in daily clinical practice. Our previous results suggested that 2 radiomics features in 18F-FDG PET and in ADC maps from Diffusion-weighted MRI (DWI MRI) are powerful predictors of the efficacy of CRT in the treatment of CC  [35].
Choice of appropriate libraries to control the driver The PiCam is connected to the GPU (Graphics Processor Unit) of the RaspberryPi, called VideoCore. So, all the software solutions need to deal with this hardware level. Amongst "off-theshelf" solutions, the most used is Raspicam that provides an API (Application Programming Interface) to use the PiCam. This library raises a major problem by its behavior: it is not, in reality, photographic captures but a recovery of frames from a video stream. The function which requests the acquisition of an image (grab) only signal to the acquisition thread the necessity to save the next available frame. Either, the frame has just been taken and it is ready for saving, or it is still being acquired and it will be saved after a short delay (see figure  3 ).
Launching the camera flows at the same time does not guarantee a synchronization of acquisitions. This way is not adapted to our constraints. If we go a step deeper in the software architecture, there are two main APIs that provide hardware abstractions to access and control the data of the camera in a standardized way via the GPU (see figure  4 ). The first one, used by RaspiCam, is MMAL (Multi-Media Abstraction Layer) which is a proprietary API created and implemented by Broadcom. The second one is OpenMax which is a semi-open API (BSD license) created by Khronos group (Industrial Consortium, which also produced the OpenGL and Vulkan APIs for 3D). Although older and less maintained, OpenMax is better documented than MMAL by its open-source nature and therefore easier to use in a manner suited to our needs.
A real-time OS to control the jitter As seen before, precise control of the launch date and execution time (jitter) of the acquisition tasks is essential. The control of these parameters is only done by the OS, because it is the only one that has the overview of all the active processes and their priority. To facilitate maintenance and security, we choose to stay on open-source solutions of the Linux type which are supported a lot by the community. OS can be classified into two main families: classic OS and real-time OS. The latter are distinguished from the former by the importance accorded to managing precisely the jitter and the tasks order. To respect our contrainsts, the choice of a real-time OS is therefore essential, because in classic OS this jitter can easily go up to several milliseconds per task within a program.
In other words, developing a program on a soft real-time OS is much simpler, faster and maintainable than on a hard real-time OS. Compared to our needs, the performance offered by the soft real-time OS is sufficient and its ease of use, especially when working with images, fully justifies this choice. Among the OS of this soft real-time family, two solutions emerge: Linux PreemptRT (standard linux kernel to which the PREEMPT-RT patch has been applied) or Xenomai. To run a program in real time, the developers of Linux PreemptRT directly modified the linux kernel in depth: as a result, all programs run in real time. The developers of Xenomai have made another strategic choice: to co-exist two Linux kernels, one real time and the other a classic one. Real time programs only run on the latter.
Compared to our problem, Xenomai is not a good choice because to make our capture program be able to be executed in real time, it would be necessary to recode the driver of the camera so that it can be integrated in the real-time kernel. This is particularly complicated because the drivers are often proprietary and very little documented. This coding step is useless with Linux PreemptRT since everything runs natively in real time. Its maximum jitter is also compatible with our use  (Arthur et al. , 2007 , Dias et al. , 2014). So we chose this OS for the final solution.
RESULTS To assess the obtained synchronization, measurement campaigns were carried out with 3 synchronized PiCams. The first one uses a digital timer to the thousandth of a second displayed on a screen. It shows that the use of RaspiCam (see section 3. 1) on a non-real-time OS (Linux) gave a synchronization delay of 50ms (reference time), which is already better than for GoPro cameras by a factor of 10. By switching to the Omxcam library, the shots of the timer do not differ: this means that the synchronization difference has fallen below 20ms. Indeed, a standard screen refreshes at 60 Hz, that is to say, every 16. 7ms. In order to be able to measure the synchronization delay more precisely, we completed the experimental bench with a chaser composed of 10 LEDs alternated in red, green and blue and framed by 2 white LEDs (see figure  6 ).
Using the soft real time OS PreemptRT made it possible to descend strictly below the millisecond, without drift over time. The objective of the next test campaign is to verify that the quality of the image synchronization is sufficient to deal with dynamic objects likely to be found in the study areas. If the synchronization is successful and the movement has been frozen, then the stereoscopic pair can be treated as a classic pair by the 3D reconstruction chain. Namely, all the elements of the scene can be matched correctly. On the other hand, if there remains local movements, these matched points will be contrary to the global geometry and will then be eliminated. We will therefore rely on this matching criterion to assess the adequacy of synchronization in our tests. The multi-domain database used is composed of stereoscopic pairs containing various dynamic objects (birds, fish, anemones, coral reefs, caustics, etc.
The matching algorithm used  (Avanthey et al. , 2016)  relies on a self-adapting Harris point detector and on local statistical filtering on the vector flow formed by the matched points to discard outliers (bad matches). The results of our tests show that we obtain an average rate of 45% of good matches (inliers) on all of our synchronized pairs. This results is close to the rate of 53% of inliers obtained by  (Avanthey et al. , 2016)  on a database of 200 images of relatively static scenes taken under various conditions. Figures  7  and 8  show examples of our results. We will note that anemones, accompanied by clownfish (genus Amphiprion), are a complex case for matching algorithms because the textures are relatively plain and the patterns very similar. On this type of image, the results obtained are around 30% of good pairings (see figure  9 ).
The Structure-from-Motion/Close-Range Photogrammetry Close-range photogrammetry is another measuring technique for digital 3D reconstruction of the objects' shape. Unlike terrestrial laser scanning, which is an active technique, photogrammetry is an image-based passive method, which provides high resolution and accurate datasets, being at the same time a low-cost solution characterized by high level of automation. These features made close-range photogrammetry very flexible and thus, widely used measuring method successfully applied in a variety of fields including civil engineering  [64, 65] , forestry  [66, 67] , geology  [68, 69] , archaeology  [70, 71] , and cultural heritage documentation  [72, 73]. Modern close-range photogrammetry applies methods and algorithms are derived from Computer Vision (CV) such as Structure-from-Motion (SfM) and Multi-View Stereo (MVS).
In this particular case, the unit vectors i 0 , j 0 , k 0 and i 1 , j 1 , k 1 are the parameters of the normal vector of the wall plane and the sensor plane, respectively. In order to determine the plane equation parameters minimum three points are needed. The vector product r for two vectors r 1 and r 2 formed according to the Equation (  13 ) r = r 1 × r 2 , (13) As the number of control points is four or more, the least square adjustment  [83]  method is used to compute the parameters [a, b, c] from the Equation  (14). a(x -x 0 ) + b(y -y 0 ) + c(z -z 0 ) = 0, (14) Since the examined object is represented by a very large number of points (e. g. , a cloud of points from laser scanning or the SfM method), the pcfitplane [84] function in the Matlab software was used for the calculation. The plane is described by a planeModel object while maxDistance is a maximum allowed distance from an inlier point to the plane.
The proposed electromagnetic (EM) sensor system consists of two wideband horn antennas that operate in the frequency range of 2-18 GHz. The antennas are attached to the designed aluminum frame at a certain angle to each other in order to control the penetration depth of the sensor signal. Both antennas play different role in the setup, namely one acts as a transmitter and the second one acts as a receiver of the reflected signal. The measurements are provided using S-parameters, namely S 21 (transmitting the signal from port one and receiving to port 2) via Vector Network Analyzer (VNA) Rohde and Schwarz ZVL13. The captured data is in a complex data format (real and imaginary data), which is recorded using a bespoke LabVIEW program on a laptop that is connected to the VNA via TCP/IP connection.
Overview of the Approach The proposed method of geolocalisation of the sensor system is a multi-stage process based on the combination of commonly used algorithms implemented in Agisoft Metashape and the original software. The proposed method is a novel approach allowing both orientation of the electromagnetic device and its integration with other measuring devices. The whole experimentation involved the acquisition of data on three test sites differing in features, containing materials and surface types commonly present in case of cultural heritage objects. Detailed characteristics of the test sites were presented in the next section. Figure  5    Calculation of these parameters (performed in two different ways) makes it possible to determine the sensor position, but on the other hand they can also be used in the EM data processing as a basis for the calculation of corrections applied to the raw data acquired by the sensor.
1. 1. Three different surveying methods were selected for this purpose: classical surveying with Leica TCPR 1202 Total Station (TS), terrestrial laser scanning (TLS) with a Z + F 5006 h terrestrial laser scanner, and close-range photogrammetry (SfM/MVS method) with a Canon EOS 5D Mark II SLR camera. 1. 2. Data processing at this stage involved the computation of coordinates of control points and check points as well as the determination of parameters of base planes at each test site. For these two operations, two sources of data were used: total station observations and TLS measurements. Both geodetic measurements and TLS data were processed separately. As a result, two sets of point coordinates and two base planes (TS base plane and TLS base plane) were calculated for each test site. 1. 3.
The coordinates of control points and check points were calculated separately in two ways: based of TS observations through geodetic adjustment and based on TLS measurements, from, which the XYZ values for each point were derived. 1. 4. The parameters of base planes were computed using the coordinates of control points. 1. 5. For photogrammetric elaboration of close-range images, the SfM approach was used. The proposed methodology involved the pre-processing step of verification whether the image sets were acquired correctly. For this purpose, relative orientation of images was performed separately for each dataset containing the images covering the wall at each test site (denoted as SfM-the wall on the diagram) as well as for sets of images taken at each sensor's position (denoted as SfM-EM positions).
This scenario involved the use of a featured-based orientation method implemented in the Agisoft Methashape software. Initially the images covering only the wall were aligned with the use of control and check points measured with a total station. This set of images served as a reference in the next step of data processing. The next step involved the relative orientation of images taken at each sensor station. Sets of images covering single stations were aligned, so that their relative orientation was computed. Next, tie points were automatically detected both on the images of each wall and on the images of the sensor at each position. Based on these tie points, the exterior orientation of image sets of all sensor stations was calculated. 2. 4. Scenario IV: the fourth scenario consisted of the same steps as Scenario III with the difference that in this case the coordinates of signalised points were derived from TLS data.
3. The analysis of results obtained with each scenario. Each of the above-mentioned geolocalization scenarios was examined with regard for their accuracy and efficiency. Efficiency in this case shall be understood as the time which is necessary to measure reference points and then calculate their coordinates applying different scenarios. Complete analysis required the verification of parameters listed below: 3. 1. The verification of base plane approximation using different source data: as the orientation of the EM sensor is computed in relation with the base plane, firstly it is required to verify the accuracy of approximation of the base plane. Two datasets were tested as source data to approximate the base plane: TLS data and classical surveying data. The quality assessment contained: (1) the analysis of RMSE values obtained on marked control and check points measured with a total station, (2) the analysis of RMSE values obtained on marked control and check points measured by TLS, and (3) the analysis of deviations between the TLS base plane and measured TLS points.
The analysis of exterior image orientation using RMSE values obtained on control and check points performed both for "the wall" and "the wall + the sensor" datasets on each test site: in order to verify the quality of exterior orientation of the images, the quality assessment and accuracy analysis concerning marked control and check points were performed for each test site. 3. 4. The analysis of exterior image orientation using scalebars: additional verification involved the accuracy assessment on scalebars located both on the wall and on the sensor frame. 3. 5. Dense point cloud analysis involving base planes: it consisted of the calculation of deviations of dense point clouds generated for all sensor stations, obtained in various scenarios from the base planes determined at earlier stages of data processing.
TLS measurements were done with a Z + F Imager 5006 h terrestrial laser scanner. In the case of total station measurements, the point coordinates were calculated by means of geodetic adjustment while in the case of TLS data a semi-automatic calculation method implemented in Z + F LaserControl software was applied. Table  1  presents the accuracy of coordinates of the reference points expressed through RMSE values. The accuracy of point coordinates is presented in a different way depending on the applied measurement technique because of different methods used to calculate them. Surveying involves a direct measurement of a distance from the instrument to the desired point as well as and two angles: horizontal and vertical. The final XYZ coordinates are computed in the process of geodetic adjustment.
On the contrary, TLS technique does not allow direct measurements to selected points, but the surrounding areas are scanned. For this reason, it was necessary to use the algorithm implemented in Z + F LaserControl software, which identifies the center of the target automatically based on the initial part of the point cloud target area. Therefore, the accuracy of coordinates is represented by linear RMSE without splitting it into XYZ components. The RMSE values presented in the Table  1  indicate similar accuracy of results obtained by TLS measurements on all test sites. However, this trend cannot be observed for total station measurements-it is clearly seen that RMSE values observed for the test site I differ significantly from the others. It is a consequence of applied measurement methodology as well as resulting accuracy of the adjustment of observations.
As far as the mean value of the RMSE of the reprojection error is concerned, it can be seen that its value is approximately 0. 3 pixels while the maximum is about 1. 2 pixels both for the SfM and TLS methods. These results suggest that unlike in the Test Site I, TLS reference points did not influence the quality of relative orientation. It is noteworthy that the values of minimum, maximum and mean reprojection error are all the same for all methods in the case of the sandstone block. Summarizing the results obtained for all datasets, it can be observed that among all methods the highest accuracy was acquired by the TLS method, while geodetic measurements proved to slightly decrease the accuracy of relative orientation. When the additional control and check points are used, it is necessary to consider whether the quality of these points (meaning location accuracy and the accuracy of measurement performed independently with another measuring technique) do not have a negative impact on the final accuracy of the bundle adjustment.
Relative Orientation of the Images Covering Fragments of Walls and the Sensor The second dataset consisted of images covering both reference walls and the sensor at each station. The Tables 3-5 contain the main characteristics of relative orientation accuracy obtained on three test sites. In this case, five different orientation methods were applied. Being aware of the principles of SfM algorithms, their method of operation and the way the object's features influence the final result, it was supposed that RMSE values obtained for this dataset would be slightly lower than in the case of reference walls. This assumption was supported by the fact that the texture of pictures covering both the walls and the sensor is less uniform, and therefore, it allows to achieve better results at the stage of image matching.