dataesr/scientific-paragraphs-categorization huggingface dataset:
Cancer Core Europe is a European legal alliance consisting of seven leading cancer centresmost of them Comprehensive Cancer Centres (CCCs) with a single portal system to engage in various research projects with partners. Cancer Core Europe was established to create a sustainable, high-level, shared research infrastructure platform hosting research collaborations and task forces (data sharing, clinical trials, genomics, immunotherapy, imaging, education and training, and legal and ethical issues), with a controlled expansion agenda. Translational cancer research covers the cancer research continuum from basic to preclinical to early clinical, late clinical, and outcomes research. Basic-preclinical research serves as the 'engine' for early clinical research by bridging the early translational research gap and is the primary and current focus of the consortium as exemplified by the launching of the Basket of Baskets trial, Europe's largest precision cancer medicine trial.
Expansion plan and initiation of associated members and networks The expansion plan by Core Centres will be performed at a controlled rate because centres need time to prepare for membership with respect to structural, legal and collaborative requirements. The second expansion level is per country, identifying associated members for participation in translational research networks with the core institutes. Examples could be the DKTK network in Germany (see a separate chapter in this volume), the SIRIC centres network in France, the personalized cancer medicine network in the Netherlands, and the Comprehensive Cancer Centres network in Catalonia. Ultimately a rapidly increasing superstructure will emerge with supracritical mass to address outcomes research programmes in Europe and to create databases facilitating patient care and treatment through artificial intelligence-driven discoveries  (Lawler et al.
All steps are under the expertise of a boards; imaging and radiomics; training and edu- task force with quality control. cation, legal and ethical issues, and coordination (d) Standardizing and developing radiomics: The added across borders) that have representatives from all (e) Patient participation: The role of the patients them- value of modern functional and molecular imaging participating centres. The consortium is in the pro-selves in the process is also of key importance since technologies and image analysis (radiomics) is cess of moving towards a virtual single 'e-hospital' they are the start as well as the end point. The another area of attention for the consortium; to with shared translational research activities and European Cancer Patient Coalition (ECPC) is analyse heterogeneity of metastatic disease and compiled databases.
Common clinical molecular working with the Cancer Core Europe Board of predict response to therapy, data must be shared profiling, initially developed in some centres, is Directors to develop the best synergistic relation- following image harmonization between centres. now shared by all institutes and linked to a data ship with patients, with the objectives of informa- Common clinical databases permit assessments of centre, which enables computational biology to tion, complete awareness and participation in clinical effectiveness and outcomes on a longitudi- provide validated diagnostic information; in addi-research projects, in treatment decision, follow-up nal term based on health-economics studies. tion, standard operating procedures are currently of care and outcomes, and therefore contributing Finally, educational activities are also considered under development for tissue and liquid biopsies to patient empowerment.
Ethical and regulatory approval This study, EudraCT number 2017-000246-21 and Clin-icalTrials. gov identifier NCT03177395, is funded by the Sponsor, PledPharma AB, Stockholm, Sweden, and was approved by the UK medicines regulator, Medicines and Healthcare products Regulatory Agency (MHRA; 25 April 2017), and West of Scotland Research Ethics Committee 1, Glasgow, UK (approval 11 April 2017). The trial was designed by collaborators at the Clinical Toxicology unit in the Royal Infirmary of Edinburgh and the Edinburgh Clinical Trials Unit. The protocol was designed in accordance with the Standard Protocol Items: Recommendations for Interventional Trials (SPIRIT) (Additional file 1). The trial overview is available as a SPIRIT figure (Fig. 1 ).
Secondary outcomes include: Clinical observations (pulse rate, blood pressure, respiratory rate, pulse oximetry, temperature) and haematological and clinical biochemistry parameters. Exploratory biomarkers in serum/plasma (miR-122, K18, GLDH) Incidence of hepatotoxicity Duration of hospital stay (days) Re-presentation to hospital (for any reason), representation with liver injury, repeat overdose, death, and transfer to liver transplantation unit, and AEs and SAES up to 90 days. Exploratory secondary outcomes will be collected to improve the design of future clinical studies: An independent SDMC will be appointed to review accumulating AE data and patient safety. The SDMC will evaluate the safety in relation to the PP100-01 dosing step increase. The committee will give recommendations on the continuation or termination of the study by detection of any safety signals as early as possible in accordance with the SDMC Charter.
Statistical analyses Patients will be included in the full analysis population, the primary population for analysis of efficacy, if they have received any PP100-01 or NAC. Data will be analysed according to the randomised treatment group. The stringent per-protocol population includes patients from the full analysis population for whom the study protocol has been followed without any major violations. The population for safety analysis will be patients who have received any PP100-01 or NAC. Data will be analysed according to the treatment received (NAC plus PP100-01, or NAC alone). Any patient who withdraws during the treatment phase of the study will be included in the safety population (AEs and laboratory parameters). Data for all patients will be listed, and a list of withdrawn patients, with all reasons for withdrawal, will be given.
Data will also be listed for those patients who, after having consented to participate, underwent baseline examinations required for inclusion into the study but who, because a criterion for exclusion was met or for other reasons, were not included in the study. With the clinical safety data that are available we do not expect any SAEs, but we note that PP100-01 has not been administered in POD patients treated with NAC. We deem that six patients per group in this initial dose escalation study will allow initial exploration of effects on biomarkers and potential dose-limiting toxicity. A CONSORT diagram  [14]  depicting the flow of participants and representing the number of patients split by treatment group through the study will be reported. Descriptive statistics will be used to report baseline characteristics by treatment group and overall.
Continuous variables will be summarised by the mean, standard deviation, median, and minimum and maximum; categorical variables will be summarised using the number and percentage in each category. Log transformation will be used where appropriate. Baseline characteristics will include participant details, overdose details, concomitant medications during the preceding 30 days, physical examination including cardiovascular, respiratory, and gastrointestinal systems, vital signs, ECG, and clinical bloods. We will keep missing data to an absolute minimum, but where there are missing data those records will be removed from the analysis; if missing data rates are substantial the effect of this will be investigated using sensitivity analyses. Binary outcomes (including the primary outcome) will be reported by treatment group and overall using the proportion and exact 95% confidence interval.
The objective of this study is to develop a therapeutic regimen for POD where a NAC 12-h regime is combined with a SOD mimetic, PP100-01, to evaluate if reduction of the oxidative stress in the liver will be safe and well tolerated. It would also allow preliminary data to be collected on a new approach of giving NAC together with PP100-01 using the 12-h regimen, which included a slower initial intravenous infusion over 2 h instead of 1 h in the 21-h NAC regime. The primary study outcome is therefore chosen to inform about the safety and tolerability of PP100-01 co-treatment with the NAC regime in patients with POD. In addition, data on the incidence of adverse effects caused by the NAC plus PP100-01 regimen, and changes in liver toxicity markers induced by APAP-induced liver injury within this modified NAC treatment will be obtained.
Acknowledgements The POP trial Investigators are: Chief Investigator (CI):  Dr. James W Dear Edinburgh  Royal Infirmary:  Mr. Ruaridh Buchan ,  Dr. Thomas Caparrotta ,  Prof Michael Eddleston ,  Dr. Emma E Morrison ,  Dr. Matthew Reed Edinburgh Clinical Trials Unit (ECTU ):  Mr. Robert J Lee ,  Mr. Garry Milne ,  Ms. Lynsey Milne ,  Ms. Katherine Oatey ,  Mr. Phillip Rayson ,  Ms. Michelle Steven ,  Prof Christopher  J Weir The Emergency Medicine Research Group Edinburgh (EMERGE):  Ms. Polly Black ,  Ms. Caroline Blackstock ,  Ms. Rachel O'Brien ,  Ms. Bernadette Gallagher ,  Ms. Julia Grahamslaw ,  Mr. Adam Lloyd ,  Mr. Allan Macraild ,  Ms. Megan McGrath ,  Ms. Mary Morrissey ,  Ms. Emma Ward ,  Ms. Liz Hasseld ,  Ms. Mia Paderanga ,  Ms. Miranda Odam ,  PledPharma AB  (sponsor):  Dr. Dennis Henriksen ,  Ms.
High Dynamic Range (HDR) and Wide Color Gamut (WCG) screens are able to render brighter and darker pixels with more vivid colors than ever. To assess the quality of images and videos displayed on these screens, new quality assessment metrics adapted to this new content are required. Because most SDR metrics assume that the representation of images is perceptually uniform, we study the impact of three uniform color spaces developed specifically for HDR and WCG images, namely, ICtCp , J z a z b z and HDR-Lab on 12 SDR quality assessment metrics. Moreover, as the existing databases of images annotated with subjective scores are using a standard gamut, two new HDR databases using WCG are proposed. Results show that MS-SSIM and FSIM are among the most reliable metrics. This study also highlights the fact that the diffuse white of HDR images plays an important role when adapting SDR metrics for HDR content.
Second, the databases used to evaluate the different metrics were most of the time defined with an HDR display only capable of displaying a BT. 709  [1]  gamut. The WCG gamut BT. 2020  [5]  is currently addressed neither by current metrics nor by current databases. To overcome these limitations, in this paper, we adapt existing SDR metrics to HDR/WCG images using uniform color spaces adapted to HDR. Indeed, most SDR metrics assume that the representation of images is perceptually linear. To be able to evaluate HDR metrics that include both luminance and chromatic information, we propose two new image databases, that include chrominance artifacts within the BT. 2020 wide color gamut. This paper is organized as follows. First, we describe the adaptation of SDR metrics to HDR/WCG images using perceptually uniform color spaces.
Databases Presentation Five image databases are used for carrying out the comparison. Three were already available online and two were created to handle WCG image quality assessment. To describe objectively the images of each database, we use four indicators: • the image dynamic range (DR): DR = log(Y max ) -log(Y min ) (24) where Y min and Y max are the minimum and the maximum luminance (in the XYZ linear domain), respectively. They are computed after excluding 1% of the brightest and darkest pixels to be more robust to outliers.
Existing Databases In this section, three databases available online are presented. They all include compression artifacts. Some of them use a backward compatible compression. This method allows the images to be displayable with SDR equipment while preserving the dynamic range for the display on HDR screens. A Tone Mapping Operator (TMO) is used to tone map the HDR images into SDR range. These tone-mapped images are then compressed using different codecs. After the decoding process, they are tone expanded to recover their HDR characteristics. These three databases use the same HDR SIM2 display (HDR47ES4MB) which has a measured dynamic range going from 0. 03 to 4250 cd/m 2.
• Narwaria et al. [34] 's database (Available at http://ivc. univ-nantes. fr/en/databases/JPEG_HDR_ Images/) is composed of 10 source images, which have been distorted by a backward compatible compression with the JPEG codec and the iCam  TMO [36]. This database was used along with others to tune HDR-VDP2. The angular resolution used during the subjective test was 60 pix/degree and the surround luminance was 130 cd/m 2. For this database due to its surround luminance above 100 cd/m 2 , we consider a surround luminance of 20 cd/m 2 to obtain the color space HDR-Lab 100. The characteristics of each reference image are reported on Figure  3. • Korshunov et al. [35] 's database (Available at http://mmspg. epfl. ch/jpegxt-hdr) consists in images distorted with a backward-compatible compression scheme using the JPEG-XT standard and either the Mantiuk et al.
[37]  or the Reinhard et al. [38]  TMO. The angular resolution used during the subjective test was 60 pix/degree and the surround luminance was 20 cd/m 2. The characteristics of each reference images are reported on Figure  4. The main limitation of these databases is the limited BT. 709 gamut used during their creation. The wider BT. 2020 color gamut is more and more associated with HDR images and videos. In addition, Standards Developing Organizations such as DVB, recommend the use of the BT. 2020 gamut with HDR content  [41].
Proposed Databases To deal with the limitations of existing databases, we propose two new databases (Available at www-percept. irisa. fr/software/). The first one is a database with BT. 709 contents encapsulated in a BT. 2020 gamut and the second one is composed with native BT. 2020 content. We used the same display for both of them: the SONY BVM-X300. This is a professional HDR video monitor able to faithfully display the brightness of signals  [42]. It has a peak brightness at 1000 cd/m 2 and a luminance of a black pixel that was too low to be measured by our equipment (<0. 2 cd/m 2 ). In this paper, we assume a luminance for the black pixel at 0. 001 cd/m 2. This monitor also allows us to force the use of a chosen EOTF without having to consider the image metadata. To display the images on the screen, we used the b<>com *Ultra Player* which allows to distribute uncompressed YUV content with a 10 bits quantization and 4:2:0 chroma sub-sampling.
The connection to the screen was done using 3G-SDI cables. To create the distortions, we used HDRTools (v0. 16) (Available at https://gitlab. com/standards/ HDRTools/) to apply format conversion, chrominance sub-sampling or gamut conversion. For the compression and decompression of the images, we used the reference software of HEVC, the HEVC test Model (v16. 17) (Available at https://hevc. hhi. fraunhofer. de/). For both subjective tests, we used the same methodology: the Double-Stimulus Impairment Scale (DSIS) variant I methodology  [43]  with a side-by-side comparison. Pairs of images were presented to the viewers, one side being always the reference. 50% of the participant had the reference always on the right-hand side, 50% always on the left-hand side. To avoid a bias with the order of presentation, the pairs of images were randomized for each participant with the condition that the same content was never shown twice consecutively.
This is even more significant for BT. 709 content encapsulated in a BT. 2020 gamut. This behaviour creates a shift in the bitrate allocation from the chrominance to the luminance. This can potentially create visible chrominance artifacts. The chroma Qp adaptation proposed in the recommendation is a method to counter this effect. Because we wanted to create realistic color artifacts, we distort the images using the compression with and without the chroma Qp adaptation to study the sensibility of the color metrics. The first database we propose was already presented in  [45]. Eight images were selected from 3 collections: two are from the MPEG HDR sequences (FireEater and Market)  [46] , one is from the Stuttgart HDR Video Database  [47]  and the remaining five images are from the HDR photographic survey  [48].
Note that these images also belong to Zerman et al. 's database  [19]. The characteristics of the images are not exactly the same as in Zerman et al. [19]  because we used only half of the images (944 × 1080) to be able to display simultaneously both the reference image and the distorted image on the same screen. The characteristics of the images can be found on Figure  6. Fifteen naive subjects participated in this test (11 males, 4 females) with an average age of 25. 8. All declared normal or corrected-to-normal vision. One participant was removed from the analysis using the methodology described in  [43]. Because we used the display in HD mode, we choose to call this database HDdtb in this paper. Four kinds of distortions have been chosen:.
• Gamut mismatch: two kinds of distortion were created: on one hand, the BT. 709 images were considered as if they had been already encapsulated in a BT. 2020 gamut leading to more saturated images. On the other hand, we took images already encapsulated in a BT. 2020 gamut and considered them as BT. 709 images and re-encapsulated them in a BT. 2020 gamut. This creates less saturated images. For the second database, we used eight 4K images produced by Harmonic Inc. The characteristics of the images are given in Figure  7. We used only part of these images so the reference and the distorted image could fit on our display (1890 × 2160) with a band of 60 black pixels. We called this new database 4Kdtb. We used the same visualization distance as in the previous database. Thirteen experts or sensitized subjects participated in this test (11 males, 2 females) with an average age of 40.
Performance Indexes We present the performances of the different quality metrics for each database with four different indicators. Before computing these indicators, a non linear regression is applied to the quality scores thanks to a logistic function: Q i = a + b 1 + e -(Q i -c) d ( 31 ) where Q i is the score of the quality metrics on the image i and Q i the mapped quality score. Parameters a, b, c and d are determined by the regression conducted by the lsqcurvefit function of Matlab. The four performance indicators are given below: • the Pearson correlation coefficient (PCC) (cf. Tables  A1  and A2 ) measures the linearity between two variables: PCC S,Q = cov(S, Q) σ S σ Q (32) where, S corresponds to the subjective scores (MOS), Q to the predicted quality score, cov(S, Q) is the covariance of S and Q and σ S (resp.
4Kdtb Database With our proposed 4Kdtb (cf. Figure  8 ), for each color-blind metrics, the best color spaces are always the ICtCp, HDR-Lab 100 and the PU-encoding. J z a z b z and HDR-Lab 1000 provide the lowest performances. The best performing color-blind metrics is FSIM used with the PU-encoding, closely followed by FSIM ICtCp and FSIM HDR-Lab 100. MS-SSIM used with the PU encoding, ICtCp and, HDR-Lab 100 are almost on par with the second best performing metrics HDR-VDP2 (cf. Appendix B). The only color space that provides good performances on all color metrics is the ICtCp color space.
Zerman et al. Database With the Zerman et al. database, as previously, the color spaces, ICtCp, HDR-Lab 100 and the PU-encoding provide the best performances for almost all color-blind metrics (cf. Figure  9 ). However, there is one exception with FSIM. Used with the following color spaces, J z a z b z , HDR-Lab 100 and HDR-Lab 1000 , it provides slightly better performances than ICtCp and the PU-encoding. The best performing color-blind metrics are, with almost the same performances, HDR-VDP2, HDR-VQM and MS-SSIM ICtCp.
HDdtb Database With our proposed HDdtb (cf. Figure  10 ), for color-blind metrics, the color space J z a z b z provides slightly lower performances for all metrics, except with FSIM. For this metric, the performances with J z a z b z are higher. The best performing color-blind metrics for this database are FSIM J z a z b z , FSIM HDR-Lab 1000 and MS-SSIM HDR-Lab 100. For the color metrics, the metrics based on color difference metrics (∆E, ∆E S and CSSIM) have very low performances. This is partially due to the presence of the gamut mismatch artifact. As noticeable on Table  4 , discarding this artifact increases the performances of these metrics. For the participants of our subjective test, the distortions on the images are clearly visible but are not directly associated with a loss in quality perception.
11 ). Most of the metrics have high correlation coefficients and the choice of color space has close to no impact on the performances especially on color-blind metrics. Even using non-perceptually linear color space like the Y Cr Cb color space impacts only moderately the performances of MS-SSIM, FSIM, PSNR-HVS-M and PSNR-HMA. For this database, the best performing color-blind metrics are FSIM J z a z b z , FSIM HDR-Lab 1000 and MS-SSIM J z a z b z.
Narwaria et al. Database With the Narwaria et al. database (cf. Figure  12 ), J z a z b z is the best color space for SSIM and MS-SSIM while the PU-encoding and the HDR-Lab 100 are the best color spaces for FSIM. The best metrics for this database are MS-SSIM J z a z b z , HDR-VDP2 and HDR-VQM. The good performances of HDR-VDP2 were expected for this database because it was part of the training set of this metric. For this database, the performances of the PSNR and the PSNR-HVS-M are relatively low compared to the other databases. The fact that PSNR-HMA with the adequate color space significantly increases the performances of PSNR-HVS-M suggests that the backward compatible compression used by Narwaria et al. (Section 3. 1. 1) creates distortions that impact the mean luminance and the contrast of the images.
Results Summary For all studied databases, HDR-VDP2 has generally high performances although it is not always on the top three metrics (cf. Appendix B). FSIM and MS-SSIM with appropriate perceptually uniform color space are often on par if not better than HDR-VDP2. Among all metrics, FSIM is the less sensitive metrics to the choice of color space assuming that this color space is perceptually uniform. The color extension of FSIM, namely FSIMc, does not improve the performances of FSIM even for our proposed database 4Kdtb which focuses on chromatic distortions. Worst, the metrics becomes much more sensitive to the color space choice. We observe the same behavior for the color extension of PSNR-HMA, PSNR-HMAc, which decreases the performances of the metrics for any color spaces. When using the two non-uniform color space XYZ and Y Cr Cb , the performances of all metrics drop significantly compared to the other color spaces for all the databases and especially for our proposed database 4Kdtb, the Zerman et al.
General Discussion We separate our general discussion in two parts. First, we study the impact of the color space on the metrics performances. Moreover we emphasize the influence of the diffuse white luminance. As a reminder, the luminance of the diffuse white correponds to the luminance of a 100% reflectance white card without any specular highlight. In HDR imaging, it is different from the peak brightness. In the second part of our analysis, we discuss the sensitivity of chrominance artifacts on color metrics using our proposed database 4Kdtb.