dataesr/scientific-paragraphs-categorization huggingface dataset:
Univariable and multivariable statistical analyses A univariable analysis was conducted on each baseline characteristic in order to assess its independent association with long-term survival. Continuous variables are summarised by descriptive statistics, which included number of patients, mean and standard deviation. Categorical data are summarised by the number and percentage of patients in each category. For continuous variables, the P value was obtained from the t-test statistic. For categorical variables, the chi-square test statistic was used to perform the assessment; Fisher's exact test was used for categorical variables where the number of patients was < 5 in any comparison group. The percentage of patients achieving an OS of ≥ 2 years at the time of data cutoff were analysed using a multivariable logistic regression analysis including variables identified in the univariable analysis with P ≤ 0.
05 as covariates. For each independent variable (predictor) included in the analysis, the odds ratio, two-sided 95% confidence interval (CI) and P value were calculated. Similarly, the percentage of patients achieving PFS of ≥ 6 months at the time of data cutoff were analysed using a multivariable logistic regression analysis. All analyses were post hoc and exploratory, and thus no adjustment for multiple testing was performed. Median OS and median PFS were calculated using the Kaplan-Meier method. The difference in ORRs between the OS ≥ 2 years and OS < 2 years groups was compared using the chi-square test. All statistical analyses were conducted using SAS v 9. 4 software (SAS Institute, Cary, NC, USA).
Discussion Among patients with mUC who had progressed on or after platinum-based chemotherapy and had received durvalumab or durvalumab plus tremelimumab, 24% survived 2 years or longer, with some patients surviving up to 5 years at the time of data cutoff. The results of our multivariable analysis showed that ECOG PS, PD-L1 expression, haemoglobin level and absolute neutrophil count were significantly associated with OS ≥ 2 years. Sex, prior surgery, time from initial diagnosis to study entry, lymph node-only involvement, visceral disease, absolute monocyte count, NLR, ratio of neutrophils to lymphocytes and LDH level showed significant independent associations with long-term survival in the univariable analysis but were not significantly associated with long-term survival when included in the multivariable logistic regression model.
Acknowledgements The authors would like to thank the patients, their families and caregivers, and all investigators involved in Study 1108 and Study 10. We thank  Shaum M. Kabadi  (formerly of  AstraZeneca ) for contributions to study conception and data interpretation and  Natasha Angra  (formerly of  AstraZeneca ) for contributions to acquisition and interpretation of data. Medical writing and editorial assistance, in accordance with Good Publication Practice (GPP3) guidelines, were provided by  Ward A. Pedersen, PhD , and  Anne-Marie Manwaring  of  Parexel  and were funded by  AstraZeneca.
Availability of data and materials The datasets generated and/or analysed during the current study are available via the Vivli platform. Data underlying the findings described in this manuscript may be obtained in accordance with AstraZeneca's data sharing policy described at https:// astra zenec agrou ptria ls. pharm acm. com/ ST/ Submi ssion/ Discl osure. Data for studies directly listed on Vivli can be requested through Vivli at www. vivli. org. Data for studies not listed on Vivli could be requested through Vivli at.
The central objective is to provide a simple and accurate tool to capture the full FSI from a single measurement and to improve the PIV results. The proposed physics-informed method uses apriori knowledge of the flexible structure in particular its bending characteristics, in conjunction with morphological image processing. As an application case, an actively pitched and highly deformable hydrofoil experiment in the water tunnel at LEGI Labs Grenoble is used. The results of the experiment's reference case with known shape serve as an evaluation case of the accuracy of the method itself deploying the PIV raw footage of the known geometry. A test case from synthetic data similar to this example has been created and allows for the quantitative determination of the accuracy of the developed procedure.
40 GHz on a single thread. Generating and applying the respective mask takes about 5. 5 h on the same system. This indicates that the bottleneck for the computation occurs in I/O operations, primarily from loading and saving the masked images to disk through a network drive. While the code is written for parallel execution, the parallelization is not implemented in practice, since performance gains are not to be expected as a consequence of the I/O limitation in the study presented here. The framework with which the PIV computation was achieved, FluidImage  (Augier et al. , 2016) , natively supports image pre-processing. This would allow for masking of the images without writing them to storage. After computing all mean lines, these lines could be saved and later accessed by FluidImage to create the instantaneous mask.
7  for an illustration). This specific shape was chosen as it was manually determined from high-speed recordings of the load-deformed hydrofoils in the study at hand. The inclination angle of this foil is quantified by the angle from the leading to the trailing edge relative to the horizontal plane (as shown in Fig. 6  (Right)), which we will subsequently call the angle of attack. Its real value in the considered problem is 24. 2°from the pivot point or 0°from the leading edge to the trailing edge. Additionally, reflections and other false positives are added with the same method as the generated seed, but with an adjusted threshold. The area of a reflection is 1 px 2 with a value that is sufficient to trigger the algorithm; their number was chosen to visually resemble the real data at hand.
As a next step, the method should be validated directly and quantitatively for flexible structures, either using laser interferometry along the trailing edge following  (Ducoin et al. , 2012) , or by laser triangulation as implemented by  Kalmbach and Breuer (2013). Those steps will allow broader use of the method presented here, helping with the analysis of PIV data in fluidstructure experiments and soft actuation or flexible underwater robotics. The proposed method may be uniquely suitable to assess the kinematics of damaged flexible blades. For example, to assess blade motion before, during, and after impact damage with debris or after fatigue. This would allow for a more rapid laboratory assessment method for testing and validating the implementation of self-healing materials and condition monitoring for flexible blades in the future.
Acknowledgments The authors thank  Christian-Toralf Weber  and  Carsten Laddey  ( University of Applied Sciences Magdeburg ) for providing the foundation for image material from structural mechanics analysis of the hydrofoil. The authors are grateful to  Cyrille Bonamy  and  Pierre Augier  ( LEGI ) for their work and their assistance regarding the incorporation of the PIV framework ''FluidImage'' into the workflow. Olivier Cleynen  is thanked for language editing and commenting on the first manuscript of this article. The authors are grateful to the open science team of the  University Magdeburg  for their data repository services. This project is part of the  EU Marie Curie International Training Network on Soft, Self-responsive, Smart Materials for Robots (SMART-ITN ) and has received funding from the  European Union 's  Horizon 2020 research and innovation program  under the  Marie Sklodowska-Curie  grant agreement No  860108.
Data availability A recent snapshot of the Python code along with a small segment of a high-speed video for testing is provided online in the open research data repository hosted by Otto-von-Guericke-University Magdeburg: 10. 24352/UB. OVGU-2022-085. The code is under continuous development. The most recent version can be found on the author's git repository: https://github. com/ikoesters/FSI_tracking.
(2011)  as part of a leak detection/localization technique. This approach simulates the minimum and maximum values of dependent parameters (flows and pressures) for an extended period, using this information to fuzzify independent parameters (pipe coefficients, nodal demands, water levels, etc. ). They are used to calibrate a hydraulic model and estimate an array of dependent parameters, finally achieving three sets of solutions. Then, monitoring data is evaluated with respect to the fuzzy numbers from these solutions, raising a detection alarm if the pressure is lower than the most likely value. The location process is based on the concept of index of leakage propensity (ILP): if the monitored values are beyond the derived extreme values, then the ILP will be higher than 1, and the highest ILP would indicate the leaky node/pipe.
Then, the Loess decomposition (STL)  (Cleveland, Cleveland, McRae, & Terpenning, 1990 ) method and -means clustering are applied to the pressure residuals from the comparison between nominal (simulated) and leaky (measured) data to identify the existence of leaks. By updating the model at each detection of non-repaired leaks, the localization stage can be performed, selecting the pipe with the highest probability based on the simulated and actual leaks. The application of the method to the BattLeDIM2020 challenge was successful, obtaining the first place in the competition. Another BattLeDIM2020 approach was proposed by Wang, Li, Liu, Yu, and Ma (2022), which exploits both statistical methods and hydraulic modelling to detect and localize leaks. First, the expected flows and pressures in leak-free conditions are retrieved by means of empirical model decomposition (EMD) and vector autoregressive models.
To this end, a two-level architecture is considered: a main ANN identifies the branch that contains the leak, whereas the second level is formed by several ANNs, in cascade to the main one, that estimate the leak size and location in the selected branch. An example is considered first to compare the two-level approach with a single ANN scheme, obtaining results that illustrate the advantages of the former. Then, a realistic case study is used to test the performance of the two-level strategy. The trained ANN was able to identify the correct branch in all test cases, and satisfactory performance was reached for the second-level ANNs. Years later, Rojek and Studziński (2014) followed a similar scheme to test different kinds of neural network structures, trained with datasets of leak-and leak-free scenarios for different operating conditions from a hydraulic model.
In this case, apart from the MLP architecture, Kohonen networks  (Kohonen, 2013)  were also tested. The latter are self-organizing nets with the capability of adapting to input data that was not previously known. A water network was considered to test the performance of both ANN schemes. Their comparison shown that the MLP outperformed the Kohonen net, although no sources of noise or uncertainty were considered. Another work dealing with neural networks and classification is presented in  Sun, Parellada, Puig, and Cembrano (2019). Pressure data from sensors is interpolated using the Kriging spatial interpolation approach  (Oliver & Webster, 1990) , that allows to obtain approximate values at not measured nodes from the measured ones, additionally considering the network topology. Then, two different types of classifiers are considered: ANNs and linear discriminant analysis or LDA.
A network division/clustering algorithm is used to iteratively split the network, to then identify where the leak is located using information from pressure loggers and ANNs. A hydraulic model is required to obtain the pressure data for the ANN training. The Balerma benchmark  (Reca & Martínez, 2006)  is used as a case study to test the method. Several scenarios are considered regarding uncertainty sources and number of simultaneous leaks. The results show a perfect functioning for the cases without uncertainty, and a degradation of performance in the presence of uncertainty. In the same year, Cantos, Juran, and Tinelli (2020) proposed a machine-learning-based methodology for leak detection and localization based on risk assessment. The approach makes profit of a hydraulic database generated through hydraulic simulations, to then proceed with a risk assessment strategy that establishes a spatial time series of leak likelihood indicators through a statistical data analysis of the measured flows.
The fuzzy recognition process then calculates the closeness of an incoming sample and the ones of the library to determine to which class the sample belongs. A water network from China is used to assess the performance of the methodology. The presented leak localization result is correct, showing the feasibility of the method, despite it may be insufficient to demonstrate its full performance. Years later, another fuzzy-based method was proposed in  Wachla, Przystalka, and Moczulski (2015) , which is composed of two stages. First, residue signals are generated by means of water flow models, and then neuro-fuzzy classifiers are used to locate the leak at predefined network areas. SVM was selected to implement these models, which were trained by means of flow data collected at the real network that was used as case study.
Deep learning approaches Since its appearance, deep learning (DL) has been successfully applied to numerous pattern recognition problems in a wide range of fields. In the specific case of leak management, several recent works have considered this technique. In  Zhou et al. (2019) , a hydraulic simulator is necessary to emulate the behaviour of the network in the presence of leaks to obtain a training dataset for the DL scheme, which is based on a Fully-linear DenseNet (FL-DenseNet). Two case studies are used to show the reliability of the approach. First, the method is tested over the Anytown network  (Walski et al. , 1987) , yielding excellent performance for both node-level and 5-top-candidates accuracy for low uncertainty values on the roughness and demands, while a decrease of performance is observed for higher values, although the results are still satisfactory.
The process is repeated until the desired level of division, and the trained DL networks are hierarchically organized, yielding a classification tree. A case study of a real network is used to assess the method performance, feeding actual data to a trained classification tree, leading to a localization result that qualitatively and quantitatively outperforms a compared state-of-the-art model-based approach. Furthermore, Li, Zheng and Lu (2022) proposed a ResNet-based  (He, Zhang, Ren, & Sun, 2016)  leak localization scheme based on four stages: dataset generation, training, classification and regression. A hydraulic model generates hydraulic information for every possible leak scenario, providing data of the selected sensors as input to the ResNet network. Once trained, the classification process provides as output the probability of each pipe to be the origin of the leak, using the regression process to determine the exact location of the leak over the pipe with maximum probability.
Other classifiers for mixed model-based/data-driven methods During the years, other techniques and algorithms have been exploited to solve leak management problems. In  Tao, Huang, Li, and Xin (2014) , an artificial immune system -AIS  (Galeano, Veloza-Suan, & González, 2005)  network is used to identify the position of bursts in WDNs. This method, inspired by the immune system, is a stochastic optimization algorithm, similar to evolutionary algorithms. Data for the considered leak scenarios is obtained through EPANET to train and calibrate the AIS for pattern recognition, finally locating the leak by means of a nearest-neighbour approach. Three case studies of growing complexity are used to test the methodology. Excellent accuracy was achieved for an example network considering no uncertainty, as well as for a bigger network where the leak localization result worked at area-level.
Pressure data is obtained from simulations for the different leak scenarios, obtaining pressure residuals and then the sensitivity matrices. From these matrices, the within-class-scatter and the between-class-scatter matrices are computed. This works uses a Fisher Discriminant Analysis -FDA  (Mai, 2013)  classifier, whose aim is to maximize the scatter between classes and minimize the scatter within classes. To this end, perpendicular FDA vectors achieving these goals are iteratively computed to derive the eigenvectors of a discriminant matrix, which allows to generate a discriminant function for each class. This function can be evaluated to locate the leak of incoming data. The FDA method is compared to the angle strategy in  Casillas et al. (2014)  over the Hanoi example, leading to a smaller localization error for two and three sensors (no uncertainty is considered).
Some years later, Lučin, Lučin, Čarija, and Sikirica (2021) presented another localization scheme based on classifiers. As usual, a hydraulic simulator was used to generate leak scenarios, randomly selecting the locations, magnitude and demands. The classifier considered in this work is the random forest algorithm  (Breiman, 2001). It consists of multiple decision trees where each tree is trained independently on a random subset of the dataset. During the application stage, the classification with more occurrences is chosen by the random forest and considered as the class prediction. The Hanoi and Net3 benchmarks are used to evaluate the methodology, analysing several factors that may affect the performance. The results showed that the demand variation was the responsible of the larger errors, greatly reducing the localization accuracy.
Statistical analysis approaches During the years, many leak detection techniques have made the most of statistical-based techniques to analyse the network behaviour in abnormal scenarios. In  Buchberger and Nadimpalli (2004) , the historic data of the flow measurements at the DMA inlet is analysed; the largest value of the hourly mean and standard deviation is searched. Then, it is checked if the new measurements exceed those two indicators to detect a leak. The leak size is estimated when the leak is detected by computing the difference in both means. Several examples based on simulated and measured flows were used to test the method, considering constant and variable leaks. Years later, a detection method based on Kernel PCA (KPCA) was presented in  Nowicki and Grochowski (2011) , which extends the linear PCA method and extends it to the non-linear case.
The model training is iterative, as outliers are extracted from the input data when the matrix updates the PCA model. Data from a real Spanish WDN was used to evaluate the suitability of the methodology. The results showed that DMOD is the best out of the two metrics, although its results were satisfactory at most. An abnormal-event detection technique was presented in Romano, Kapelan, and Savić (  2014 ). An ANN is created using Normal Operating Patterns -NOP data for training and testing. From the discrepancies between the training and testing data, the mean and standard deviation are computed to evaluate statistical tests. The outputs of three test indicators feed two inference systems based on Bayesian Networks (BNs). One assesses the probability of an event occurrence by means of these outputs and the daily average difference between the NOP values and the current ones (which can also be used to estimate the leak size).
The residual obtained using the real measurement is compared with a threshold calculated using the standard deviation of the leak-free case. Once the leak is detected, the difference between prediction and measurement is computed to estimate the leak size. The method was evaluated using data from several actual water networks, leading to a high rate of true positives and a reduced ratio of false positives, although the uncertainty in the demand dynamics may hinder the operation of the method. In  Jung and Lansey (2015) , a Non-linear Kalman Filter (NKF) was used in combination with pressure and flow measurements inside the network to estimate the nodal demands, which are compared with historic data. The difference is analysed using Statistical Process Control (SPC) techniques like CUSUM and Hotelling  2  (Aparisi, 1996)  technique and a threshold for leak detection purposes.
The method was able to detect the simulated leaks, but the pressure estimation by the PF was oscillating and hence not satisfactory, as well as the case study size was quite small and the flow sensors density was high. In  Romano, Woodward, and Kapelan (2017) , three SPC control charts were used to compare, on a daily basis, descriptive statistics gathered from pressure meters for the analysed night with the corresponding values computed for the previous nights, in order to find unexpected variations that can be caused by a leak. The outputs of the three tests are then unified in one indicator used to rank the sensors from the most affected to the least affected. The area surrounding the most affected sensor is the leak area candidate. The method was tested using data from a set of real DMAs, selecting both pressuremanaged and gravity-fed ones, through historical datasets of real-life burst events.
Learning techniques Most of learning-based methods fit in the mixed model-based/datadriven category due to the necessity of labelled data from a hydraulic model, covering the complete set of possible scenarios. However, additional application methods or learning philosophies helped to derive purely data-driven detection approaches, e. g. , online learning (the samples are gathered from historical datasets or the method learns while an additional leak detection approach operates), unsupervised learning. Burst detection using pressure and flow measurements data from DMAs with artificial neural networks was proposed in  Mounce and Machell (2006). Results were presented using data from an experimental setup in a real WDN from UK, where leaks were forced by means of hydrant flushing. Despite the satisfactory performance, the study revealed the difficulty of detecting leaks when other abnormal events occur at the network, e.
g. , unusual demands or system changes. The difficulty of obtaining labelled samples for training is also discussed. This method was improved in  Mounce, Boxall, and Machell (2008)  by combining the ANN scheme with a Fuzzy Inference System (FIS), as well as reducing the required data to only flow measurements. The ANN model is trained by means of a continuously updated historical datasets that construct a probability density model, which the FIS compares with observed flows to link confidence intervals to alerts, helping to even supply a precise estimation of the leak size. The method was tested in a real WDN from UK, running together with an existing leak detection method. Despite the performance was satisfactory, improving the overall detection solution, several false alarms were raised at events that were not correlated to bursts repairs or reports.