dataesr/scientific-paragraphs-categorization huggingface dataset:
as shown in Figure A10. (4) The climatology is updated and covers the period 1980-2022. Our methodology for AR detection is slightly different from the Wille et al. (2019)  algorithm, both in terms of input data, detection threshold, and implementation. Therefore a validation is carried out, comparing our results in terms of AR climatology and trends with those from the Wille et al. (2019)  catalog. The detection catalog from (Wille et al. , 2019)  covers the years 1980-2022 and is available through the ARTMIP database https://doi. org/10. 5065/D62R3QFS (Rutz et al. , 2019).
Thus, business model innovation is not just the creation, the adaptation or the adoption of a new business model  (Mihalache and Volberda, 2021) ; it also involves the modification or reconfiguration of a pre-existing one  (Massa and Tucci, 2013). Many established and new companies have taken advantage of digital technologies to reinvent their business models through innovation  (Rachinger et al. , 2018 ; Bhatti et al. , 2021). They have undertaken business model innovation based on digital platforms (e. g. , Ebay, Airbnb, TripAdvisor, Amazon), connected objects (e. g. , Apple Watch, bracelet Fitbit), artificial intelligence (e. g. , Alexa, Google Home) and even big data (e. g. , Uber, Google, Netflix). Regardless of the approach chosen -creation or redesign -the objectives are to obtain a competitive advantage, improve performance and grow or disrupt a market, environment or industry  (Massa and Tucci, 2013).
From its creation in 2008, Airbnb developed a strategy to replicate its accommodation business model geographically before innovating in tourism experiences. Airbnb' s originality is based on the openness of its digital content and the collaborative organization of value transactions which empower users to create a variety of offerings (accommodation, tourism experiences, etc. ) that are impossible for the company to provide through its internal resources alone. More importantly, as Airbnb' s digital platform provides a type of working template or operation principles, it makes the case particularly relevant to the current business model replication literature. From the perspective of a single case study, a qualitative approach using primary and secondary data is required to in-depth study the phenomenon  (Huberman and Miles, 2002).
Data collection Our data collection process focused on Airbnb' s business of tourism experiences. Launched in 2017, this innovative offer meets the company' s desire to provide more than just a simple accommodation rental service for travelers and guests. Indeed, Airbnb' s strategy is focused on the travel experience and this new business allows it to address the travel agency market. To develop this new value proposition, Airbnb has evolved its business model and has implemented a replication process to allow users (who do not need to be hosts or travelers) to offer tourism experiences to increase the activities that are available during a trip. Our primary data collection therefore focused on the company' s Paris, France site, where the research and development (R&D) team responsible for the tourism experience activity is located.
We chose this team because it manages the innovation process related to Airbnb' s new tourism experiences offer as well as the business model replication process. Between May 2018 and May 2019, we conducted five semi-directed interviews 1 and one focus group, all in French, following a three-step approach: 1. An initial collection of data through three semidirected interviews with the Global Experience Manager (which lasted between 60 and 90 minutes) to understand Airbnb' s strategy, the historical development of its business model, the role of the digital platform, its network of partners, the innovation process of tourism experiences and the business model replication process. This first step was crucial for understanding how Airbnb uses its digital platform to locally find replicable innovation opportunities, how it launches the process to develop the innovation (with strategic and commercial implications at the business model level) and then initiates the replication process on a larger scale.
We thus collected data on the specificities of local orchestration with several perspectives (process, tools and platform). At the global level, the interview with the European Business Manager allowed us to understand the role of the digital platform in the orchestration of the replication process and how Airbnb replicates new types of tourism experiences at the European level and engages large numbers of users in their development. The interviews were conducted in French, the mother tongue of both interviewers and interviewees, and transcribed manually by one author. These three data collection steps enabled us to understand the two processes of business model innovation and replication. These primary data were then validated, enriched and completed using a substantial amount of secondary data.
As Airbnb is a highly mediatized international company, a wide variety of secondary data are available on the company' s history, values, strategic development over the years, business model, evolution of its digital platform and collaboration with the community, etc. As well as carefully studying the Airbnb website, we collected around 20 press articles, several YouTube videos (interview/s) and a book about Airbnb  (Gallagher, 2017). The summary of secondary data and their use is presented in the Table  1  below. The details of these data are presented in Appendix1.
Data analysis We analyzed the data in two stages. First, we analyzed the interviews and synthesized the information in a written summary structured around three points: 1) Airbnb' s strategy; 2) the development of the new innovative "experiences" offer and 3) the replication process from the local to the global level. By presenting this synthesis to and validating it with the Global Experience Manager, the head of the R&D team in Paris, we were able to identify and formalize the different steps in the business model innovation and replication processes. Second, we triangulated these primary data with our secondary data to reinforce the findings of our first analysis to better understand the global strategy of the company and the co-founders' vision about innovation, replication and scalability (cf.
As Figure  1  shows, during the innovation process, these three steps of replication are taken to test the value capture potential of the business model innovation. In the first step, which we call "community innovation and experimentation", the potential for creating and capturing value for the community is examined  During this step, the platform is also a space for dialogue, collaboration and regulation through the community management process, which is organized geographically. Our secondary data clearly shows that the Airbnb community is spread across multiple locations and the relationship with stakeholders takes place at different geographical levels, from the most local to the most global. This relationship is essential for Airbnb to stay close to the community, its needs and expectations, as well as for identifying opportunities for innovation and evolution.
" (European Business Manager, translated from French) In light of the secondary data that tracks the success of Airbnb and in particular the launch of the new tourism experience business model, we can see that Airbnb is thus able to standardize the value creation and value capture processes to promote large-scale replication. The digital platform is therefore the instrument of standardization. For example, the in-depth study of the process of creating a tourism experience on the Airbnb platform shows that the process is completely standardized and provides educational support for users to create an experience that meets Airbnb standards. Users must follow three steps in this process: 1) learn about Airbnb' s experience expectations; 2) create the experience; and 3) submit the experience for review.
Methods: We analysed PHC encounter counts due to respiratory complaints registered in the Brazilian database of the Universal Health System between January and July 2023. We applied EARS (variation C1-C2-C3) and EVI to estimate the weekly thresholds. An alarm was determined when the number of encounters exceeded the week-specific threshold. We used data on hospitalisation due to respiratory disease to classify weeks in which the number of cases surpassed predetermined thresholds as anomalies. We compared EARS and EVI's efficacy in anticipating anomalies. Findings: A total of 119 anomalies were identified across 116 immediate regions during the study period. The EARS-C2 presented the highest early alarm rate, with 81/119 (68%) early alarms, and C1 the lowest, with 71 (60%) early alarms. The lowest true positivity was the EARS-C1 118/1354 (8.
Data source Primary Health Care Data: Brazil's National Information System on Primary Health Care (SISAB) harbours data on all publicly funded PHC encounters in the country, coded by either the International Classification of Diseases (ICD-10) or the International Classification of Primary Care (ICPC-2). The PHC system covers at least 75% of the population in Brazil. 12  Data for PHC encounters were extracted from the SISAB database and were obtained under the permission of the Ministry of Health (MoH). We used weekly counts of every PHC encounter due to ILI from October 2022 to July 2023. We included 50 ICD-10 and ICPC-2 codes corresponding to conditions possibly related to ILI. (Supplementary Table  1 ). Hospital Information System: Brazil's National Information System on Hospitalizations (SIH) comprises information on all publicly funded hospitalisations in Brazil, coded by the International Classification of Diseases (ICD-10).
Methods able to adjust for seasonality, such as the improved Farrington method, tend to perform better in the true positive metric. 16  The sustained high fluctuations of PHC encounters due to COVID-19 cases from 2020 to 2022, with misleading low numbers during lockdown periods, hinder the use of methods that require longer historical data as a baseline. 17, 18  Bédubourg and Le Stratt (2017)  16  compared 21 early warning methods using simulated datasets and found that the probability of detection (POD) of an outbreak ranged from 43. 3 to 84. 4% and the false positive rate ranged from 0. 7 to 59. 9%. The EARS variations showed a POD ranging from 54. 2 to 68. 0% and a FPR of 6. 9 to 8. 5%. Similar to our findings, the C2 EARS variation presented the best performance metrics. The authors concluded that no single method presented outbreak detection performances sufficient enough to provide reliable monitoring for a large surveillance system.
Related challenges and benchmarks In recent years, there are many challenges of computational modeling, segmentation and computer-aided diagnosis for cardiovascular problems. Thanks to those challenges, researchers can develop, test and compare computational algorithms on the same dataset. Table  1  presents the recent challenges and public datasets for cardiac segmentation. One can see that only  Karim et al. (2016)  and  Karim et al. (2013)  focused on the LV/ left atrial (LA) scar segmentation from LGE CMR. None of them was aimed to combine multi-source images. Though there was a challenge with multi-sequence CMR images, it was aimed to segment myocardium from LGE CMR by referring to the training images from other sequences  (Zhuang et al. , 2022). In contrast, MyoPS challenge was aimed to segment and classify the pathology of myocardium combining the complementary information related to the pathology and morphology from the three-sequence CMR, i.
The next benchmark study  (Bakas et al. , 2018)  of the challenge aimed to assess for each case with a diagnosis of ischemic stroke. Their benchmark study found that no algorithmic characteristic of any methods was proved better than others, and emphasized the importance of the characteristics of stroke lesion appearances, their evolution and the observed challenges 140  (Maier et al. , 2017). Other related challenges for other organs include the I2CVB  (Vall and Lemaitre, 2016)  and CHAOS  (Kavur et al. , 2021). I2CVB provided a multi-parametric MR image dataset, including T2 MR, dynamic contrast enhanced 145 (DCE) MR, DWI MR and MR spectroscopic imaging data, and was aimed for prostate cancer segmentation  (Vall and Lemaitre, 2016). CHAOS combined CT and MR images from the abdomen for organ segmentation, including liver, kidneys and spleen  (Kavur et al.
Data MyoPS challenge provides 45 paired three-sequence CMR images (bSSFP, LGE and T2 CMR) acquired from the same patient. The three CMR sequences were all breathhold, multi-slice, acquired from the cardiac short-axis views using Philips Achieva 1. 5T. All patients are male with acute MI, and the average age and weight are 56. 2 ± 7. 92 years and 74. 4±5. 65 kg, respectively. Table  3  provides the acquisition parameters of the three CMR sequences. The data acquisition had been anonymized, and approved by the institutional ethics board. All the data have been preprocessed using the MvMM method  (Zhuang, 2019) , to align the three-sequence images of the same patient into a common space and to resample them into the same spatial resolution. Each sequence typically contains 2-6 slices with an in-plane resolution of 0.
T2 was used to guide the scar segmentation of LGE, and LGE was also employed to guide the edema annotations of T2. (3) The annotation of scars is contained in that of edema. The manual labeling was performed slice-by-slice 230 using a brush tool in the software ITK-SNAP  (Yushkevich et al. , 2006). All the manual segmentation results were validated by three experts in cardiac anatomy before used in the construction of gold standard segmentation. The final segmentation was obtained by averaging the multiple man-235 ual delineations using a shape-based approach  (Rohlfing and Maurer, 2006). The inter-observer variations of manual scar and edema segmentation in terms of Dice overlap were 0. 569 ± 0. 198 and 0. 701 ± 0. 168, respectively. The manual segmentation of edema was evidently more con-240 sistent between the raters than that of scar segmentation, probably due to the fact that the regions of edema are generally larger (in terms of size) and less patchy (in terms of shape) from T2 images.
Evaluation metrics Though the labels of LV, RV and Myo were provided for the training data, the evaluation of test data only focused on the myocardial pathology segmentation, i. e. , scars and edema. To evaluate the segmentation accuracy, we cal- Dice(V seg , V GD ) = 2 |V seg ∩ V GD | |V seg | + |V GD | , (1) where V GD and V seg denote the gold standard and automatic segmentation, respectively. Note that without specific indication, these metrics are generally calculated in a 3D volumes, by considering all slices of the target image at the same time, namely subject-wise evaluation. We compute them for 2D slices only when comparing the performances with regard to different slice positions, namely slice-wise evaluation. Moreover, Dice score of scars is annotated as Dice when the ground truth contains no scar.
Organization We submitted a proposal to the MICCAI challenge sub-290 mission system to apply for our MyoPS challenge. One can access our challenge proposal in the zenodo website. At the same time, we applied for a CMT platform to run this challenge, mainly managing the paper submission. After preparing all the dataset, we scheduled a timetable for 295 the challenge, including the date of data release (1st April, 2020), result/ paper submission (22nd/ 29th July, 2020), associated workshop and result release (4th Oct, 2020). Besides, we designed the task, the distribution of dataset and evaluation metrics. Note that the organizers were not 300 allowed to participate the challenge.
Registration and submission To access the challenge dataset, researchers were required to sign a data agreement file and return it to the organizers. Before the conference, participants can train 305 their model with the training data, and submit their results to the organizers for evaluation. Each team was allowed to submit their testing results 2 times at most. After the conference, we have released the encrypted ground truth of test data and corresponding evaluation tool to 310 simplify the evaluation step for subsequent participants. Therefore, in principle they can evaluate their models unlimited times. The participants were encouraged to summarize their methods and results by submitting a paper to the CMT-.
Specification of the learning process As Table  5  shows, five teams implemented their works in a two-stage manner (coarse-to-fine), by extracting ROIs on the myocardium prior to a fine process of pathology segmentation. The others conducted their models in an end-to-end fashion. In addition, the way of utilizing the extracted ROIs were different, which might explain the discrepancies of their results. For example, after obtaining the mask including the RV, LV and Myo, UESTC cropped the ROI from all the three sequences and concatenated them using this mask. They took the concatenation as an input for the final prediction of pathologies. This strategy can help the segmentation model to take advantage of the extracted knowledge. Their experiments on the validation dataset have shown advantages of this setting, particularly on the edema with more than 2% Dice improvement.
Ensemble learning strategy 475 Given the limited training data in this study, it is difficult to know the best model in advance. Also, models trained with different samples or images from different views could learn diverse knowledge. Therefore, several ensemble learning strategies have been employed to 480 reduce uncertainty in models and increase generalization capabilities in this challenge. For example, UESTC performed a weighted ensemble strategy on the predictions of 2D and 2. 5D networks. Their experimental results showed that this ensemble strategy delivered better results. UBA 485 adopted a different strategy by generating a number of datasets with synthesized images, and they trained fifteen models using different training data. As their ablation study demonstrated, this ensemble could capture a greater number of non-trivial unconnected components.
From the distribution maps, one can see that scars mainly occur in the inferolateral regions and anterior segments of middle slices in this test dataset; and edema extends to more regions of basal slices and almost all segments of apical and middle slices. From the SEN (true positive rate) and SPE (true negative rate) maps of pathologies, one can observe that the regional values of SPE were generally higher than that of SEN, which could be due to the definition of classification, namely the pixels not segmented as pathologies by an algorithm were regarded as negatives by default. For scar segmentation, we found that the SEN values were higher in middle septum segments. It could be attributed to the good contrast from C0 and T2 for myocardium segmentation of septum, leading to an easier scar segmentation task from LGE myocardium.
The best performances on scar and edema were respectively 0. 708±0. 191 and 0. 731±0. 109 in terms of Dice score. The results have already presented promising potential, 870 particularly when comparing with the inter-observer Dice scores of scars and edema, which are 0. 569 ± 0. 198 and 0. 701 ± 0. 168, respectively. We also calculated the clinical indices for the evaluated algorithms, most of which were comparable to the ground truth results. As no extra 875 dataset from other center/ vendor was provided for evaluation, we can not analyze the generalizability of submitted algorithms. The processing in test stage consists of data preprocessing, prediction, and post-processing, which all contribute to the spent time. In this challenge, for data 880 preprocessing, we have registered the three sequences in advance, which makes the subsequent segmentation task easier.
Summary of previous challenges related to the cardiac segmentation from MICCAI/ ISBI society. LV: left ventricle; Myo: myocardium; RV: right ventricle; LA: left atrium; WHBP: whole heart blood pool; WH: whole heart; SM: single modality; MM: multi-modality; MI: myocardial infarction; MH: myocardial hypertrophy; ConHD: congenital heart disease; DCM: dilated cardiomyopathy; CorHD: coronary heart disease; AF: atrial fibrillation; HCM: Hypertrophic cardiomyopathy; HHD: Hypertensive Heart Disease; ARV: abnormal right ventricle; AHS: athlete's heart syndrome; IHD: ischemic heart disease; LVNC: left ventricle non-compaction; ‡ : multi-center datasets.
Study Population COBLAnCE is a multicentre cohort of bladder cancer patients (eleven public and three private hospitals). It includes newly diagnosed patients with bladder cancer who were within 1 year of their initial diagnosis and aged 18 years or older. Recruitment was performed from December 2012 to June 2018. A total of 1800 patients were enrolled in the COBLAnCE cohort. Demographic information and detailed smoking data were collected from questionnaires completed by trained nurses who asked questions of patients at baseline.
Smoking Phenotypes A complete history of tobacco smoking was ascertained separately for cigarettes, cigars, and pipes. Depending on the availability of data in the COBLAnCE cohort and based on previous genetic associations in the literature, three phenotypes were selected: Smoking status: whether the patient reported as never smoker (less than 100 cigarettes/ lifetime) or ever smoker. This phenotype was considered a binary phenotype coded by 0 (never smoker) or 1 (ever smoker). Age at smoking initiation: the age at which the patient started smoking (cigarette/pipe or cigar). This phenotype was considered a continuous phenotype and was log-transformed since it is highly skewed. Average tobacco consumption in grams for ever-smokers: using a complete history of tobacco smoking collected separately for cigarettes, cigars, and pipes.
Statistical Analysis GWAS was performed on COBLAnCE cohort participants of European ancestry with available genotyping data after QC and imputation. We employed a case-only study design to estimate gene-environment interactions to test the association between SNPs and smoking phenotypes in bladder cancer patients. The analyses were conducted using logistic regression (for smoking status phenotype) and linear regression models (for age at Quality control (QC) on genotyped data was performed using PLINK version 1. 9  [29, 30]. We excluded samples with a call rate <95% (N = 2) and with a sex-discordant call (N = 1). Additionally, 2 pairs of related subjects were identified, and for each pair, the patient with the greater number of missing genotype data was excluded. Of the remaining 1757 patients, 1732 were of European ancestry.
Of the 687,572 SNPs that were successfully obtained, we excluded duplicated SNPs (n = 1168) with a call rate <95% (n = 9572), SNPs not in Hardy-Weinberg Equilibrium (HWE) (with the GWAS threshold p < 5 × 10 -8 ) (n = 1214), monomorphic SNPs (n = 104,352) and SNPs with a minor allele frequency (MAF) <1% (n = 63,173). The final set of SNPs retained comprised 508,093 SNPs. Imputation of missing SNPs was performed only on the European ancestry population (N = 1732 patients and n = 508,093 SNPs), and 1000 Genome Phase 3 was used as the reference panel  [31]. Additional filtering was applied to the genotyped data to increase the accuracy of the imputation. We, therefore, excluded the following SNPs from imputation: SNPs with a call rate < 98% (n = 7449) and MAF < 1% (n = 1571), ambiguous SNPs (n = 141,456), SNPs with different frequencies compared to the reference panel (n = 1108), non-autosomic SNPs (n = 8365), and SNPs that could not be linked to the reference panel (n = 348).