dataesr/scientific-paragraphs-categorization huggingface dataset:
To address this problem,  Xie (2019)  proposes Bitcoin forecasting tools based on least squares model averaging. Nevertheless, central bank presidents continue to declare that cryptocurrencies are 'worthless' and that only a digital currency with the central bank as a guarantor would have a fundamental role in money creation. 1  This paper seeks to exploit a multiplicative decomposition of the CAPM beta into hourly, daily, and weekly frequencies as a direct extension of previous works by  Cenesizoglu and Reeves (2018)  and  González et al. (2018)  with an original application to the two most liquid and largest cryptos by market capitalization (i. e. , Bitcoin and Ethereum). The sample includes intraday data from 15 May 2018 until 17 January 2023. This period allows us to explore for the first time the effect of the FTX exchange crash.
Robustness checks include jump tests in high-frequency BTC/ETH data (up to 25%), as well as the subsequent estimation of jump-robust estimators of realized volatility for BTC and ETH. For jumps, as visible from the graphs, we showed similarities in all the jump-robust estimators. In particular, it is worth noticing that the lowest tracking errors are recorded for realized quadpower volatility (QPRV,  Andersen et al. 2012). Regarding the potential interest in detecting co-jumps, it would have been ideal to have the intraday CRIX to compare with Bitcoin's and Ethereum's high-frequency data. However, as the S&P Royalton CRIX does not distribute intraday data for the market index, this task is impossible to implement. Therefore, the results only concern BTC and ETH, which are analyzed in parallel in the paper.
Data and Methods The database covers a period from 15 May 2018 to 17 January 2023. For Bitcoin and Ethereum, the data source is Crypto Data Download, specifically for the Bitfinex exchange, which is by far the most used by traders for its liquidity in the spot and derivatives markets (see, e. g. ,  Alexander and Heck (2020)  for a documented study of Bitcoin exchanges' liquidity). The cryptocurrency market index is obtained directly from S&P Global Indices.
Results Standard CAPM theory predicts that cryptocurrency returns (e. g. , Bitcoin or Ethereum) must be related to the return on the market portfolio (e. g. , the CRIX). Preliminary computational steps typically involve the correlation between the return on the market portfolio and the cryptocurrency returns, on the one hand, and the standard deviations of returns on Bitcoin, Ethereum, and the CRIX, on the other hand. For the asset under consideration, the beta is simply the measure of the dependence of the cryptocurrency returns on the market's overall return. It is referred to as systematic risk. In this section, we inspect whether the availability of high-frequency data improves our understanding of the systematic risk for Bitcoin and Ethereum, as postulated by the theory. Notice that the non-systematic risk can be eliminated for investors through diversification.
3D Graph of Realized vs. Daily vs. Weekly Betas Having documented new features of the rolling betas for Bitcoin and Ethereum (e. g. , their ability to dampen the CRIX fluctuations), we focus in this section on the impact of the frequency at which the data is observed when we compute them. For this purpose, we visualize three-dimensional plots of hourly vs. daily vs. weekly portfolio betas in Figures  5  and 6  for, respectively, BTC and ETH. Vertically speaking, we witness a variance escalation between the dark red coloring when β = 1 (rarely) and the dark blue coloring where the lowest rolling betas are recorded. This phenomenon was documented in previous CAPM studies dedicated to classic financial assets (see, for instance,  Armitage and Brzeszczynski 2011). Horizontally speaking, we observe a 'plateau' in the area of green coloring when the rolling betas enter their median values on each plot (respectively, 0.
The algorithm can be set to choose the tolerance of the perception of 'close to zero', and a higher tolerance will lead to a higher optimal value. 16 By following this procedure, the tuning parameter k n is set to 4 for both BTC and ETH. The interested reader can find in  Li and Linton (2022)  the derivation of the asymptotic covariance for the ReMeDI estimator. For both BTC and ETH, we collect 21,599 unsampled ticks during the day of 27 June 2023. This amounts to two ticks per second (or roughly 3 h of "raw" high-frequency data). In comparison, Li and Linton (2022) collect five ticks per second for the Coca-Cola ticker from the TAQ database. As shown in Figure  11 , the microstructure noise decreases exponentially and disappears after 446 ticks for BTC (431 ticks for ETH), that is to say under 4 min.
The RSV upside estimator is pictured in Figure  17  for both BTC and ETH. As visible in Table  5 , the lowest tracking errors are achieved for Andersen et al. (  2012 )'s realized quadpower volatility (QPRV). Throughout the array of jump-robust estimators deployed in Section 5. 3, the reader will appreciate the robustness of our results for BTC and ETH high-frequency data, with respect to the naive realized volatility estimator implemented initially. There are some jumps, but not enough to alter the paper's results. Regarding the possible investigation of co-jumps, we are limited by the availability of high-frequency data for BTC and ETH only. Indeed, S&P Royalton CRIX does not distribute intraday data for the CRIX, which limits the practical implementation of such a task in our study. Instead, for interested readers, we shall suggest below potential frameworks to investigate co-jumps present in asset prices for future studies.
Conclusions The primary driver for very short-term volatility in financial markets is the systematic (downside) risk, which we aim to track in this paper in its 'pure' form based on highfrequency data. Recent developments in financial econometrics feature a taste for intraday high-frequency data, as providers' availability and quality have been significantly modernizing in the industry  (Engle 2000). In this paper, we empirically consider whether the 60 min return frequency for calculating the CAPM's realized betas for Bitcoin and Ethereum brings additional benefits to traders and investors compared to the traditional daily and weekly intervals. The sample includes intraday data from 15 May 2018 to 17 January 2023. To separate the microstructure noise from the underlying semimartingale efficient price, we establish that the optimal sampling frequency is equal to four minutes for BTC/ETH.
The investigation of the jump component in high-frequency BTC/ETH data (up to 25%) has been tackled as well, based on the implementation of statistical tests and the estimators of jump-robust estimators (where the realized quadpower volatility records the lowest tracking errors). Avenues for future research include the implications of the latest demise in the U. S. banking sector with Silvergate Capital, First Republic Bank, and Silicon Valley Bank filing for bankruptcy, which threatens major stablecoins such as USDC and BUSD to de-peg and endangers the financing of the whole crypto-ecosystem (not limited to Bitcoin and Ethereum). Last but not least, the financial econometrics literature has moved to the issue of co-jumps. The issue for the econometrician is to detect two (or more) successive jumps that could trigger bubble-like variations in intraday asset prices.
Data Availability Statement: The 13-week U. S. Treasury bill was last accessed on 15 January 2023 from FRED at https://fred. stlouisfed. org/categories/116. Bitcoin and Ethereum daily and hourly prices can be downloaded directly from https://www. cryptodatadownload. com/ by selecting the Bitfinex exchange. CRIX daily data can be accessed either directly from S&P Global Indices (per subscription) or from https://www. royalton-crix. com/.
Study design The approach used for this study is inspired by Sharan Merriam's  [7]  view of a process description and analysis; which relies on qualitative data to understand the inner workings of a process, with a document review as an essential phase contributing to the research design. Given that the Tunisian process was a long-term one which involved several phases culminating in health policies development, a longitudinal approach  [8]  was adopted in terms of examining the process over time. This method is most suitable for transitional periods'  [9]. The unit of analysis is linked to "events" by explaining the dynamics of the process  [10]  to identify the causes of the change  [11]. Semi-structured interviews took place from May to September 2018. A document review was undertaken of all relevant national documents and reports produced by the SD process.
Participants and sampling The semi-structured interviews were carried out for two groups of key actors involved at different levels of the process (citizen jury (CJ) members and experts). Purposive sampling was adopted according to preselected criteria: the active involvement in the SD (phase one and two) through participation in dialogue spaces for examples; the sound understanding of/familiarization with key deliverables produced by the SD process and reflecting the diversity of profiles as mentioned in the Tables  1  and 2. Sample size was not fixed prior to data collection and was adjusted according to the saturation of the data  [12]. People who best met the criteria were first contacted. All the contacted people agreed to take part in the study. Out of the 15 key informants interviewed, eight were experts belonging to the technical committee or the working groups of the SD and seven were citizen juries (four lay citizens and three health professionals).
Data collection tool and procedures An interview guide was used to elicit the views and experiences of interviewees on the SD process overall (phase one and two) regarding its results and methodology in particular public participation methods used, communication and future perspectives (Cf. Appendix 2). The language used for the interviews was French. The participants responded in French and in the local language which is a mix of French and Arabic. The transcriptions were then translated to French by an independent bilingual expert and the translation verification was done by the Tunisian researchers. The interview guide was piloted with 2 sample interviewees before finalization. Each interview lasted between 30 and 60 min. All the interviews were audio-recorded after obtaining verbal consent and were conducted, according to the convenience of the interviewee, either at the Ministry of Health (office of the administrative management unit for SD) or in the WHO Tunisia office or by phone.
3 Against this framework, we should question about how international organizations, national governments, and their public agencies aim to exploit the whole set of affordances and opportunities brought forth by AI for health, while protecting human rights and interests of all parties. In addition to declarations of principles and ethical guidelines, the focus of scholars has mostly been on acts and statutes against misuses and overuses of technology. For example, in EU law, the forthcoming Artificial Intelligence Act, the general data protection regulation, or GDPR, the machinery safety and cybersecurity regulations, the medical device regulation, etc. These normative acts represent a necessary, but insufficient element of the analysis. Such investigation should be complemented with the initiatives that have been taken by public agencies, governments, and organizations to tackle possible underuses of AI in the health sector with the soft tools of the law.
The constraints and affordances of the digital dimension of data-driven technologies, such as the use of AI systems, affect all stakeholders, from patients and clinicians to software engineers and computer scientists, manufacturers and distributors, health technology assessments groups and the public at large. Such impact of technology on the health sector also affects the way in which lawmakers and public agencies have conceived the governance of this sector. As declared repeatedly by the FDA in its 2020s reports, the impact of AI and further emerging technologies makes it necessary modernizing the organization of such agencies, as the FDA, fragmented in watertight scientific review processes  [16]. Initiatives of engagement and coordination have similarly had to be reengineered. As shown by the focus groups set up by both ITU and WHO in their 2020-2022 AI for Health (AI4H) project, [102] traditional research on bacteria and dental issues, diabetes and endoscopies, radiology and malaria, has been complemented with the new data-driven scenarios of today's biomedicine and AI that span across all traditional fields of medicine and health: analysis on software life cycle and data requirements, best practices and evaluation considerations, scale up and adoption of AI technologies, down to the assessment of AI applications and platforms.
In fact, many authors have underscored its crucial role regardless of its limitations and have called for the rebuilding of EBM on more solid bases  [6, 48]. Indeed, it allows a rational use of care for example by limiting over-treatment  [49]  and allowing a justified halt to unvalidated common practices when RCTs do not prove the benefit of a drug  [50]. But there can be no truly informed decision without deciding whether or not the evidence is conclusive for a specific drug in a specific context  [51, 52]. For the moment, to our knowledge, there is no tool or database that allows general practitioners and patients to know what they can really expect of a drug in a specific clinical situation expressed in terms of effect-size on a PIO. In keeping with the call to rebuild the evidence-base  [2, 6, 48] , and because doctors cannot evaluate the soundness of multiple guidelines  [53] [54] [55]  or of multiple meta-analyses on the same topic  [56, 57] , we aim to propose a solid and objective method for reading and interpreting data (from RCTs or meta-analyses of RCTs) about the treatment effect of drugs.
Protocol The protocols will be registered, for each drug of interest, in a secure database such as PROSPERO (https://www. crd. york. ac. uk/PROSPERO/) or other (eg. https://osf. io/registries) and will follow the P-Prisma guidelines  [59]. The procedure for these reviews will follow established best methods and Cochrane standards  [60]. Drugs chosen initially will be specific molecules used in every day practice in general practice, notably drugs prescribed for comfort, such as for pain control or benign symptoms. But the method could be used for any type of drug, no matter the indication.
Literature search We will search at least in MEDLINE, Cochrane Central Register of Controlled Trials (CENTRAL) and Embase without any language restriction, but others database (Scopus, Web of Science, etc ) may be used. We will combine both MeSH and free text terms to identify relevant articles (example international non-proprietary name). Once the MEDLINE strategy is determined, it will be adapted to the syntax and subject headings of the other 6 databases. We will also search the publication of study protocols : at least in ClinicalTrials. gov, but others also such as the World Health Organization (WHO) International Clinical Trials Registry Platform (ICTRP) Search Portal (https://trialsearch. who. int/) to detect ongoing or unpublished trials in order to assess publication bias. And, when possible, Cochrane's systematic review on the same topic will be consulted so as to identify additional eligible etudies.
Data collection We will collect the following information from each eligible RCT: -General study characteristics: author name, year of publication, total number of patients randomised, number of treatment groups, length of follow-up, study phase, funding source, trial registry number, countries involved and number of study sites; -Patient characteristics: gender, age and pertinent characteristics according to the drug of interest and the target population; -Intervention: baseline treatment, details of intervention and control groups (eg, International Non-proprietary Name of drug, dosage and duration of treatment) and concomitant treatments throughout the study; -Comparator: placebo or active drug (standard or not); -Outcomes: how primary and secondary outcomes are defined and measured in each study or protocol, number of events and patients included for analyses in each group, as well as adjusted data if available.
statistical analysis For all outcomes, the number of events in each group will be extracted. As much as possible, we will pool the risk ratios reported in the studies as these are often hazard ratios or adjusted risk ratios. For dichotomous (binary or qualitative) outcomes, we will calculate Risk Ratios (RR) with 95% CIs from the number of events and participants in each treatment group. For continuous (quantitative) outcomes, we will calculate the difference in means (or standardised mean differences if necessary) with 95% confidence intervals (CIs). If events are very rare (0 or 1 in each group of the RCT), we will pool data using Peto's methods (because corrections for zero cell counts are not necessary) with reported pooled Peto odds ratios and associated 95% CIs. We will use a random-effect model meta-analysis to assess the treatment-effect of a drug.
Deficits included in the Frailty Index covered a range of systems, including cardiovascular, gastrointestinal, sensory, motor and neural systems. If participants were missing data for ≥ 20% of the deficits, the Frailty Index could not be determined. The Frailty Index results in a score ranging from 0 (least frail) to 1 (most frail), although scores above 0. 67 have been reported to be too high to be compatible with life. Two additional methods for defining frailty were based on self-reported questionnaires, comprising the EQ-5D-3L and the SF-36. The EQ-5D-3L is a questionnaire on five dimensions: mobility, self-care, usual activities, pain/discomfort and anxiety/depression  [16]. For each dimension, the participants scored their health on three levels. Using the population norms of the Netherlands, a composite score ranging from -1 (most frail) to 1 (least frail) was set.
The SF-36, a set of generic, coherent, and easily administered quality-of-life measures, comprises 36 multiple-choice questions  [15]. The complete SF-36 questionnaire results in scores in eight domains, ranging from 0 (most frail) to 100 (least frail). In this study, we only used data from two domains, PF and HG, as they displayed the most variation in scores. Additionally, we compared the four measures of frailty with the Health Rating score and the number of prescription medications. The Health Rating score, ranging from 0 (most frail) to 10 (least frail) was a simple rating scored by the participant based on the question "I rate my health with a score of …". The number of prescription medications included every type of medication that was prescribed by a physician, also including ointments, eye/ear drops and specific vitamins.
Statistics Statistical analysis and data visualization were done in R (v4. 2. 1) statistical programming language. Associations between two continuous variables (frailty vs age and frailty vs cytokine measurements) were tested with the permutational framework for Spearman correlation test test implemented in the coin (v1. 4. 2) R package. The analysis considered sex as a 'block' for stratification and the null distribution of the test statistic was approximated via Monte-Carlo resampling (n resample = 100,000). The associations of the different frailty measures with cytokines in older adults included two different approaches. First comparison tested associations without correcting for sex and age and second comparison included stratification combined for age and sex to avoid cofounding in this study.
To stratify based on BMI, we categorized participants into lean (less than 25), overweight (between 25. 0 and 30) and obese (above 30). Additionally, multiple regression analyses were performed in IBM SPSS Statistics 28 with the Frailty Index scores as dependable outcome and age and the biomarkers as predictable variables. Due to the non-normal distribution of the data, the Frailty Index scores and biomarker levels were log transformed. Participants with missing data were also excluded from the analysis. This analysis was performed for all participants, and separately for female and male participants. Multicollinearity was checked with VIF statistics < 10 and thereafter the collinearity diagnostics. The PCA was performed using log-transformed concentrations of CRP, sCD14, Calprotectin, IL-8, Elastase, PR3, Angiopoietin-2, C5a, CCL2, sCD25, sCD163, CXCL10, sGP130, sIL-6R, IL1RA, PTX-3, YKL-40, Neopterin, iFABP2, GM-CSF, TNFα, IL-6 and IL-10.
Only participants above 60 years of age and with complete data on these markers were included. We used the cut-off of 60 years of age and older, rather than 65, to have sufficient power to cluster the participants based on biomarker levels. The PCA was performed using the prcomp function (factoextra package) and visualized with the ggplot2 package in RStudio 2022. 07. 2. Individuals were grouped and colored based on the Frailty Index score. The eclipses cover 80% of the data point per frailty group. The amount of variance explained by Principle Components (PC) 1 and 2 are mentioned on the axis.
Acknowledgements The  VITAL  project has received funding from the  Innovative Medicines Initiative 2 Joint Undertaking (JU)  under grant agreement No. 806776  and the  Dutch Ministry of Health, Welfare and Sport. The JU receives support from the  European Union 's  Horizon 2020 research and innovation programme. The authors thank  Wivine Burny ,  Carole Desion ,  Roberto Zoffoli  and  Daniel Laroque , partners in the  VITAL consortium , for their feedback on this study. We also thank the volunteers for participating in this study.
Participants A convenient sample of 1015 including 510 (50. 24%) females and 505 (49. 76%) males participated in this study. Most of the respondents (69%) were willing to participate in the research. As for the rest, they are located near the researchers' areas geographically (17%) and work colleagues (14%). Their mean age was 35. 9 ± 12. 3 years. Of whom, 64. 53% (510) were urban residents and 35. 47% (360) were rural residents. Their education level ranged from primary education (7. 40%), high school (27. 57%), and Bachelor's degree (49. 75%) to postgraduate (15. 28%). As for their employment status, respondents were distributed between three main categories, namely, health care professionals (27. 09%), employed outside health care sector (40. 39%) and unemployed (32. 51%). Participants' informed consent was obtained, after they all were told that the data collected would only be used for scienti c purposes.
Study design and population This report is a descriptive and analytical cross-sectional study (see S1 Checklist). To our knowledge, this is the first study on the subject in Senegal and West Africa. Thus, we consider this study exploratory, seeking to answer our objective without formulating any hypotheses at this stage. The results of the study may be useful for subsequent hypothetico-deductive studies. The data were collected from April 26 to July 22, 2022. The study population comprised people aged 18 and over in the general population with a mobile phone number and not already a member of a CBHI.