dataesr/scientific-paragraphs-categorization huggingface dataset:
Population and vaccination strategies The model considered US individuals aged 65 years and over from the years 2010 to 2019. Demographic data were extracted from the US Census Bureau and the CDC WONDER database. The 65 + years population steadily increased during the time period from 40. 1 million individuals in 2010 (57. 0% female) to an estimated 54. 6 million in 2019 (56. 0% female)  [18] [19] [20]. The population was divided between low and high risk groups as defined by the WHO  [5] , and the model considered a constant proportion of high-risk patients as reported in Molinari et al. [21]. The influenza vaccines included in the analysis were selected based on multiple criteria such as licensed indication in adults aged 65 + years old, ACIP recommendations and market shares. Because we aimed to assess the impact of HD TIV since its introduction in 2010, intervention mixes were defined for each influenza season as new vaccines recommended by ACIP entered the US market during this time horizon.
Clinical inputs 2. 5. 1. Influenza attack rate The CDC provided estimates of the influenza burden in the 65 years and over population extrapolated from the number of hospitalizations collected via the Influenza Hospitalization Surveillance Network (FluSurv-NET) (Appendix 2)  [25, 26]. Attack rates for each influenza season were calculated by dividing the estimated number of influenza symptomatic illnesses by the population aged 65 years and over. Data for the 65 + population specifically was not available for the 2018/19 season at the time of this study, and was derived by multiplying the 2018/19 burden estimates published by the CDC for the general population with the averaged proportion of the burden attributable to the 65 + calculated from the previous seasons  [27].
Hospitalization rates Hospitalization rates from the FluSurv-NET published by the CDC were used to inform the first hospitalization definition (influenza & pneumonia)  [29] , while rates for the respiratory and cardiorespiratory definitions were extracted from the HCUP database (Healthcare Cost and Utilization Project)  [30]. We consulted the database using the ICD codes from Mullooly et al. for the respiratory definition: ICD codes 460-519 from the ICD-9-CM were used for the years 2010 to 2015 and ICD codes J00-J99 from the ICD-10-CM were used from the year 2016. The same codes were used for the cardiorespiratory definition in addition to the codes 410/411/413/428 and I21. 09/I24. 1/I20. 8/I50. 9 for the years 2010-2015 and 2016 respectively  [31]. Since the rates reported in the HCUP database matched a calendar year and were not representative of an influenza season, it was assumed that influenza would contribute to 80% of the rate for each year.
The choice of an 80% adjustment factor was based on the research from van Aalst et al. who compared the effectiveness of HD TIV versus aTIV using a retrospective claims database analysis  [32]. The study compared respiratory and cardio-respiratory-related hospitalization rates in adults aged 65 years and older during the 2016/17 and 2017/18 influenza seasons. Two study periods were defined: a baseline period running from July 1 to the day of influenza vaccination, and an observation period starting two weeks after influenza vaccination and ending on June 30. Calculations based on Table  2  of the paper demonstrate that 78. 9% of all recorded cardio-respiratory-related hospitalizations occurred during the observation period (when combining hospitalizations of both seasons and vaccine groups).
Resource use and costs Prescription medicine costs were derived from the Medi-Span Price Rx website  [43]. Non-prescription medication costs, medical visit and ED costs were extracted from Putri et al. [2]. Hospitalization costs were collected from the HCUP database for each year and for each definition based on the previously mentioned ICD codes. Similar to hospitalization rates, the database only provided costs up to the year 2016, and costs for the following years were then inflated. All costs were inflated/deflated using the PCE health index for inflation  [23]. Vaccine costs were taken from the Centers for Medicare & Medicaid Services website using the appropriate Current Procedural Terminology (CPT) codes (Appendix 3). Costs for SD TIV and SD QIV were calculated for each year by averaging the costs of the different marketed TIVs and QIVs vaccines  [44].
Scenario analysis Scenario analyses were conducted by changing input parameter values and/or assumptions using alternative data sources or hypothesis. The following parameters were tested in the analysis considering cardiorespiratory hospitalization definition: rVEs of aTIV/ccQIV against influenza and hospitalizations (Scenario 1), percentage of hospitalizations attributable to influenza (Scenario 2) and CDC estimates for overall VE (alternative back-calculation method of the attack rate of an hypothetical unvaccinated cohort) (Scenario 3). Alternative parameter values used in all the scenario analyses are displayed in Table  3. A worst case (scenario 4) and best case scenarios (scenario 5) were also developed to estimate the full range of HD TIV benefits. The parameters included in these scenarios are the same as those used in the DSA and are presented in Appendix 4.
However this outcome is coherent considering the parameters included in the best/worst case scenarios are those included in the DSA and that HD TIV rVE was the only parameter with a significant impact. The strengths of our study are that we considered a large time horizon with 9 influenza seasons which allowed to us to estimate the overall benefits of HD TIV while alleviating seasonal intervariabilities. Also, most of the inputs relied on national observational data (e. g. coverage rates, attack rate, vaccine costs) and were then representative of the US setting. The study presents several limitations. The first limitation lies in the choice of a 80% adjustment factor for the respiratory and cardiorespiratory hospitalization rates. Rates for these definitions were extracted from the HCUP database which reports rates for a calendar year and are not perfectly representative of an influenza season.
Although our choice was justified by the publication of van Aalst et al. , this parameter had a significant impact on the results, as illustrated in scenario analysis 2  [32]. Additional studies confirming the results of van Aalst et al. would be valuable to further strengthen this assumption. However, our model showed that HD TIV still had a positive economic and public health impact when considering the influenza & pneumonia definition for hospitalization, which was the most conservative among our three definitions as it didn't require any adjustment factor since it considered official rates reported by the CDC. Second, the rates provided by the HCUP database for the year 2015 are incomplete, and data are reported over the first nine months instead of twelve due to the change from the ICD-CM 9th version to the ICD-CM 10th ver-sion.
Consequently, there was a gap in the number of respiratory and cardiorespiratory hospitalizations for the season 2015/2016, leading to an underestimation of the influenza economic burden. Third, the HCUP database only provided data up to the year 2016 at the time this study was conducted and hospitalization rates for the subsequent years had to be extrapolated. The simple extrapolation technique that was used did not consider the variations across seasons and may lack accuracy, especially for the season 2017/18 which is considered one of the most severe seasons of the past decade, while our model reported the season 2016/17 to be the most severe when considering the cardiorespiratory definition  [27]. Thus, it would be interesting to update the model analyses once the HCUP data for the year 2017 are available.
Finally, vaccine coverage rates in both strategies were calculated based on internal market share information and estimations from Sanofi Pasteur, thus representing a potential bias in the study. Market share estimations could be further explored through a claim database analysis to confirm their accuracy. Our study leveraged official observed data over multiple years and suggests TIV HD added value ultimately offsets its higher cost. Future research could assess TIV HD from a societal perspective and/or consider using a dynamic model instead of a decision tree to fully evaluate the benefits of HD TIV on a broader scale.
Related Work There is a huge number of sources addressing the role of geometric figures in general, so we focus here only on a closely related work about the visualization of proofs in geometry which have either been generated automatically (by automated theorem proving) or machine checked (by interactive theorem proving). Bertot et al. [4]  proposed to embed a dynamic geometry software into PCoq  [5, 1]  (PCoq is a user interface for the Coq proof assistant  [9] ). The second author implemented a prototype dynamic geometry software GeoProof which can export statements in the syntax of Coq  [20]. Gao's MMP/geometer software, an automatic geometry theorem prover based on algebraic methods, offered a way to draw figures corresponding to the statements  [12]. Wang proposed generating figures from a set of geometrical constraints by decomposing the system of polynomials into irreducible representative triangular sets, and finding an adequate numerical solution from each triangular set  [27].
The first author implemented the GCLC 1 software in which geometry configurations are described using a custom language, conjectures about configurations can be expressed and then proved automatically by several available automated theorem provers  [14, 15]. Pham and Bertot implemented a prototype connecting GeoGebra to PCoq  [23]. Botana et al. implemented automated theorem provers based on algebraic methods within GeoGebra  [7]. All the above works are focused on the visualization of statements. Regarding visualization of proofs, Wilson and Fleuriot designed and implemented a tool for the visualization of proofs as direct acyclic graph with nodes depicted using geometric figures  [28]  for the full-angle method  [8]. The most significant contribution related to visualization of geometric proofs is the work of Ye et al.
implemented in JGEX  [32]. Ye et al. use the algebraic characterization and triangulation to generate figures automatically for the statements. JGEX proposes several modes for illustration of proofs: illustration by animated diagrams, or animated diagrams + text, and they apply this method to two kinds of proofs: manually crafted proofs  [30] , or proofs generated automatically by the full-angle method or the deductive database method  [31].
Method Implementation We implemented the described method within our automated theorem prover for coherent logic, Larus  [17]. Larus' flexible architecture already had proof export to L A T E X and Coq supported. So, we have implemented just one class more -the class that exports generated CL proofs (in Larus' internal representation) to visual representation. The new code has less than 200 lines of C++. For the target language, i. e. , the language of visual representation we chose the GCL language  [16] , a rich, special purpose language for mathematical, especially geometry illustrations. For each theorem τ (given in the TPTP format, the standard format for theorem provers  [25] ), once it has been proved, the prover generates a file that contains only a function that corresponds to that theorem, and a ,,main" file that includes files with all used axioms/theorems, along with the file for τ.
, 2008; Collins et al. , 2013). Here we illustrate the use of attribution based observational constraints. This method assumes that the true observed climate response, y obs , to historical forcing is a linear combination of one or more (n) individual forcing fingerprints, X j , scaled by adjustable scaling factors, β j , to observations. We use the gridded observations E-OBS v19. 0e dataset  (Haylock et al. , 2008) , with monthly values computed from the daily data. Scaling factors are determined that optimize the fit to observations. Hence this method uses the response in observations to estimate the amplitude of a model-estimated space time pattern of response, with the rationale that uncertain feedbacks may lead to a larger or smaller response than anticipated in climate models (e. g. ,  Hegerl and Zwiers, 2011).
FUNDING All authors were supported by the EUCP project funded by the European Commission's Horizon 2020 programme, Grant Agreement number 776613. JM was also supported by the french ANR MOPGA project ARCHANGE and by the EU-H2020 Blue Action (GA 727852) and 4C projects (GA 821003). MGD also received funding by the Spanish Ministry for the Economy, Industry and Competitiveness grant reference RYC-2017-22964.
Digital Contact Tracing According to the Centers for Disease Control and Prevention (CDC), 2 main types of digital contact-tracing tools are used for case management and proximity-tracing or exposure notification apps  [31]. Case management tools involve apps and devices that health workers involved in contact-tracing activities can use to capture data and manage contact databases of people tested for the virus. When a person tests positive, contact tracers interview them to recall where, when, and with whom they have been. The contacts are then triaged for assignment to case managers who call and notify contacts, providing options for testing, self-isolation, and referral to a health care provider if necessary. This activity can be done manually and on paper-and-pencil forms, so the technology allows for the streamlining of the process of data entry and management.
Conversely, a recent simulation study showed that centralized models could be effective only when 80% of the population uses these technologies  [51]. However, centralized models might discourage uptake among users who do not trust the organizations managing the centralized database. In their seminal paper, Ferretti et al  [25]  argued that app designers and governments supporting contact-tracing apps should be guided by ethical principles (eg, beneficence, reducing misery, equity, and social justice) and follow transparent practices to generate trust in citizens and promote app uptake. Transparency could be achieved, for example, by creating independent oversight advisory boards, publishing the code of the app and the algorithms used, integrating evaluation and research by third parties, and clearly communicating privacy and data protection principles.
Parallel to this project, we searched app databases to identify benchmark apps and used them as a reference for privacy-preserving contact-tracing apps. This was one of the main drivers urging us to undertake a comprehensive systematic review of contact-tracing apps and focus on data protection and privacy aspects. This study aimed to identify, map, and evaluate all available COVID-19 contact-tracing apps developed worldwide in a systematic way. The specific objectives of this study were to (1) identify and map existing contact-tracing apps; (2) evaluate the type of data collected to define the risks to users' privacy based on the permissions required; and (3) evaluate the readability and content of privacy policy documents to establish whether these documents transparently communicate details about privacy, data protection, management, and retention.
Searches and Sources of Information We used two main strategies to identify contact-tracing apps: (1) searching for keywords in the Google Play and on the Apple App Store using Google and (2) scanning the list of apps included in 5 websites identified via Google search. For the first search strategy, we applied the following two search queries: (1) "allintext:COVID-19|covid|covid19|coronavirus AND tracing|exposure site:play. google. com" and (  2  The second search strategy consisted of scanning 5 webpages containing lists of COVID-19 apps, such as the Wikipedia page on COVID-19 apps (first published on April 1, 2020, and last edited on October 20, 2021)  [36] ; the MIT Covid Tracing Tracker project (first published on May 7, 2020, and last updated on January 25, 2021)  [37] ; the database of contact-tracing apps of the Council of Europe (last updated on June 10, 2020, and then discontinued)  [65] ; an article on COVID tracing app roundup on Android Police (published on September 1, 2020, and last updated on November 21, 2020)  [66] , with 26 US states using Google Exposure Notification System (ENS), 37 international apps using the same ENS system, and 30 apps not using the ENS framework; and the List of countries using Google and Apple's COVID-19 Contact Tracing API on the XDA Developers website (published on June 24, 2020, and updated on February 25, 2021)  [67].
App Selection Process We followed a multistage selection process. MB exported the Google search results by looking at the Apple App Store and Google Play in Microsoft Excel. MB then screened the links for relevance, and MAD confirmed the selection. Next, we resolved all disagreements through discussions. Finally, we entered the Google Play links in the Exodus Privacy database  [68] , which is the auditing platform for Android apps. The Exodus platform looks for embedded trackers (a software meant to collect user data) and permissions requested by each app. An app was excluded if the link to the Exodus database was not working.
App Characteristics We extracted the following information from Google Play pages: number of installs, a link to the privacy policy document, 5-star reviews, number of reviews, version of the app, version of the operating system, sponsor, and permission designations. From the Apple App Store page (if available), we extracted the following information: 5-star ratings and number of ratings, app version, seller, operating system version, and language. MB extracted the information, and MAD double-checked it. Any discrepancies were flagged and resolved through discussion.
Privacy Policy Data Extraction For apps with available privacy policy information, if the document was in a language other than English, it was translated using Google Translate and saved in PDF format with a timestamp. Similar to the procedure for extracting permission information, 4 authors (MB, NM, MAD, and RAS) independently completed a privacy policy assessment using a standardized web-based checklist. The checklist was adapted from a similar study focusing on data security and privacy in mobile apps addressing depression  [71]. The inventory contained a total of 13 specific items (Table  1 ), which we grouped into 3 main categories: 4 items were in the privacy category; 6 items were in the data management category; and 3 items were in the legal framework category for data protection (eg, the GDPR for European countries or any other framework) explicitly mentioned the right to delete or edit the data, which should be clarified in the legislative framework.
Search Results The selection process is illustrated in Figure  1. We applied the search queries to Google on  May 7, 2020   [36] , 81 from the MIT Covid Tracing Tracker project  [37] , 52 from the Council of Europe database of contact-tracing apps  [65] , 93 from the Android Police page  [66] , and 65 from the XDA Developers page  [67]. Finally, we excluded apps that were not designed for contact tracing (52/146, 35. 6% from Google Play; 42/129, 32. 6% from the App Store; and 59/152, 38. 8% from Wikipedia). The final list included 180 unique COVID-19 contact-tracing apps that were potentially eligible for review. Of these, 85. 6% (154/180) had a Google Play link to generate an Exodus platform permission report  [68] , and 76. 1% (137/180) had an associated privacy policy document. A total of 14.
Another limitation was the use of data generated from Google Play as some apps were developed only for iOS and were not included in the study. Unfortunately, the App Store for iOS does not include information about the permissions that the apps require; this is due to the different software architecture between iOS and Android. Another limitation is related to the extreme volatility of the mobile app market and its characteristics. We provided a global snapshot of all available contact-tracing apps as of October 31, 2021, after having monitored the market for approximately a year. Considering that the pandemic is still ongoing, existing contact-tracing apps might disappear, new ones could be developed, or different technological solutions could be adopted to provide exposure notifications (eg, merging databases or aligning data exchange protocols between European or US states).
The BaGaTel database, guided by an ontology of dairy gels, has been developed to organise and integrate data on dairy products (process, composition, structure, nutritional, sensory and environmental quality), using a common vocabulary and metadata organisation. BaGaTel was queried to explore relationships between composition, rheological properties and sensory perception in 68 model cheeses from six different projects. Principal component analyses were performed on the total set of 68 samples and on sample subsets. Sensory hardness was well explained by the low moisture-in-non-fat-substances ratio. As expected, salty taste was correlated with salt content but, interestingly, in cheese with a low amount of salt, salty taste was less intense at low water content and was perceived better with increased chewing activity.
To tackle this challenge, a database, called BaGaTel, has been built, guided by a process and observation ontology in food science, PO 2 ontology  (Ibanescu, Dibie, Dervaux, Guichard, & Raad, 2016)  and hosted by the PLASTIC platform INRAE (https://www6. inrae. fr/ pfl-cepia/). This database gathers data in the field of reformulation of dairy products taking into account their nutritional, sensory and environmental properties, using a consensual model and a shared structured vocabulary (available online at http://agroportal. lirmm. fr/ontologies/PO2_DG/?p ¼ summary). Data from a total of 65 different projects have been imported in BaGaTel (list available at http://plasticnet. grignon. inra. fr/portailbagatel/); 693 samples, 450 methods, 297 materials and 1045 measured characteristics are included, up to November 2020.
The data are either non-published but used with the permission of the authors or are extracted from the literature. The database is continuously enriched with data from new articles in the field. This database has already been successively used to (i) find correlations between one rheological parameter, the Young's modulus and one sensory descriptor, i. e. , firmness in hard cheeses, (ii) estimate missing experimental data due to this correlation and (iii) estimate the environmental impact for specific foods, for which a process was described in BaGaTel  (P enicaud et al. , 2019). In the present paper, we aim to demonstrate that the BaGaTel database can be successfully used (i) to gather data obtained in different projects that used similar methodologies and (ii) to reach a critical number of samples from similar food products differing in composition, to better understand the relationships between physico-chemical data and sensory perception.
Data preparation Specific queries were done to export data from the BaGaTel database. We searched for samples, which had data on composition, rheological properties and sensory profile analyses. In BaG-aTel, 271 samples are characterised by rheological data, 365 samples by sensory data, while 228 samples by both sets of data. However, the composition and structure of the dairy products and the measured variables were often very different. We thus restricted our query to hard cheeses and related model cheeses. For such types of cheeses, the materials and methods used for rheological analyses were comparable (data available in BaGaTel) and the bulk of sensory description can be considered as quite similar. We finally selected 68 cheese samples (hard-type model cheeses) from six different projects.