dataesr/scientific-paragraphs-categorization huggingface dataset:
Text visualization MiningVis includes a view to visualize news headlines over time in relation to mining pool rankings. The Text Visualization Browser  [34]  surveys the text visualization domain and provides a taxonomy based on multiple aspects (e. g. , analysis task, visualization task, data domain, visual encoding). Particularly relevant to our task, T1. 5 works on temporal text data. The stream graph, an extension of ThemeRiver  [23] , is one method to encode evolving topics in large-scale documents. CiteRivers  [24]  is an example of past work that applied a stream graph to show the citation popularity of multiple topics over time as a stream chart. Word clouds are overlayed on the top of the stream chart to show top keywords for each topic at each time. CloudLines  [32]  proposed different visual representation that shows the intensity of separated topics (x-axis) as a dots stream (y-axis).
V2: Mining distribution view The mining distribution view (Fig. 1  (V2)) allows analysts to detect dominating mining pool, the rise and decline of mining pools, and to find characteristics that possibly lead to changes in ranking. Visual Encoding: We chose a temporal ribbon chart that allows analysts to see both the changing rank and a quantitative metric related to each mining pool. The ribbon chart is filtered to the time period selected in V1 and users can choose among multiple statistical measures and how to group and color the pools. The chart's multiple stacked bars sorted by rank show data aggregated and displayed by months. By default mining pools are sorted from the highest value at the top of the stack to the lowest one at the bottom per month. The same mining pool is connected across months with a ribbon to highlight its rank changing.
V4: Bitcoin statistics view Apart from measures directly related to mining pools, broader Bitcoin statistics can influence people's behavior related to mining pools and can help to explain competition. We provide two views, V4 and V5, to help analysts find contextual information for observations made from V2 and V3: Bitcoin statistics (T1. 4) and Bitcoin-related news (T1. 5). Visual Encoding: The Bitcoin statistics view (Fig. 4 ) is located in the same position as V3 reachable via a toggle bar. We encode 17 different factors each as a gray temporal area chart including Bitcoininternal statistics such as the total number of blocks mined, the total hash rate, the median confirmation time for a block, total rewards paid out, or the total transaction fees. In addition, we calculated statistics with external data such as fees converted to USD according to the current market price or the trade volume in USD.
V5: Bitcoin news view News can be useful to give context for understanding historic mining behavior. Individual and groups of news articles might explain trends or patterns in the Bitcoin measures directly or might themselves have influenced miners' and mining pools' behavior. The Bitcoin news view (Fig. 1 (V5) ) allows analysts to browse news that are potentially relevant to Bitcoin mining activities. To structure and group the relevant news in our large corpus of documents (D4), we used topic modeling and designed a swarm plot with search functionality. The dataset we collected included >30,000 postings that cannot all be displayed in one chart. We, therefore, sort the news by its importance score and show only by default the top 300 news on the plot. To preserve news distribution over time, we choose the top news in proportion to the number of news for each month.
Previous work had used glyph charts  [69]  to show numbers over time but does not highlight the association between pools. Due to the fluctuations of miner counts in the data  [62] , we chose to show aggregated data to reduce outliers. A heatmap matrix is an alternative to display the percentage of cross-pooling miners ( [62, Fig. 6 ]) but the color coding turned out to be problematic due to the different largely varying scale of mining pools. In contrast, the chord diagram encodes the scale of mining pools as the arcs that allow analysts to see how large the mining pool is and detect significant miners' flow between pools.
After the session, they reported being slightly overwhelmed by the amount of information in the tool. Participants never used V6 to investigate miners' mobility even though it might have provided information for Task 2. We found out that pool hopping was not a familiar metric that related to participants' own work or Bitcoin usage. After completing the tasks, participants shared their first impressions on the tool. In general, they complimented our tool and mentioned that it was the most complete tool about mining data they were familiar with. They also pointed out interactions that mismatched their first intuition but that they quickly learned how to use the tool. After the two-week free exploration we asked participants to fill the System Usability Scale. The responses showed an average score of 73.
5/100 (sd: 13. 3, median: 72. 5) which is an above average (68) SUS score  [53]  but still shows room for improvement. No individual question scores were below average (on a negative to positive response scale); with the highest scores given for confidence of use and learnability. The lowest (but still positive) scores were given for ease of use. From feedback outside the survey itself, we found that in particular the unavailability of real-time mining data kept people from considering extended future use of the tool. One participant from the first interview even declined to participate in the follow-up due to a lack of real-time data. Usability Improvements: Based on feedback and observations, we improved the tool's usability: 1) Several participants expected the highlight brush in V2 to zoom in on a specific time interval instead of only highlighting pools and time intervals.
Value of the Visualization We evaluated the value of the implemented visualizations with an adaptation of the ICE-T questionnaire  [65]. We used a scale of five to score the heuristics (from 1-Strongly Disagree to 5-Strongly Agree) and calculated the average score for each value component. The tool was rated high on its ability to provide insights (average: 4. 30, sd: 0. 36) on Bitcoin mining pools and essence (4. 05, sd: 0. 43) to see overall mining pool evolution. In the interview participants mentioned in particular the good analytic structure of the tool and the diversity of available Bitcoin mining indicators. Participants also gave a high score for confidence (3. 73, sd: 0. 54), except for "the visualization helps to understand data quality" (2. 8, sd: 0. 75). This is fair as we did not specifically reveal quality-related information in the tool.
While the overall feedback on usability and visualization design was positive, and we could confirm our design choices, participants in particular wanted real-time data in order to make small-scale personal decisions. In the follow-up interview, four participants suggested that the tool has a potential benefit to researchers and mining companies to analyze historical trends of Bitcoin mining. We concluded that our current tool is most useful for modeling and long-term decision making from a historical perspective. Integration with real-time data: Real-time updates of Bitcoin transaction data requires attributing each new block to a mining pool. The calculation of cross-pooling poses a bottleneck as we need to track transaction flows to identify individual miners. For future work, we plan to update the Bitcoin transaction data in real-time and integrate a script to crawl external data.
CONCLUSION We present the MiningVis tool to analyze the Bitcoin mining pool emergence and evolution. Mining is a critical activity in cryptocurrencies that involves multiple longitudinal factors, both internal and external to the Bitcoin ecosystem. MiningVis allows analysts to relate multiple measures from complementary datasets in a multi-coordinated view. The tool helped our economist collaborator discover new findings and is currently used to develop an economic model. We conducted a user study with Bitcoin miners to study both the usability and insights obtained from the tool. By observing their usage patterns and through interviews we confirmed the design of our main views but also learned that pool hopping was less interesting to general miners than our collaborator. Participants rated the tool highly for usability and visualization value and we were able to make improvements based on participants' detailed feedback.
Yet, no systematic analysis of drug-induced myocarditis has been performed, with available studies only focusing on specific drugs. Moreover, temporal analysis of drug-induced myocarditis reporting for each class of drugs has not been done. Here, we analyzed data extracted from the World Health Organization (WHO) global database of individual case safety reports (ICSR), aiming to identify drug substances and drug classes significantly associated with myocarditis, to describe their relative prevalence over time, and finally report the clinical features of all reported drug-induced myocarditis according to drug classes. In this work, we highlight the existence of at least five categories of drugs: antipsychotics, cytotoxic drugs, immunotherapies, vaccines, and salicylates. These classes present distinct clinical presentation, time to onset, and subsequent mortality, suggesting class effect.
Result Overall analysis. From VigiBase ® inception (1967) to January 2020, 6823 ICSR of suspected drug-induced myocarditis were reported from a total of 21,185,309 ICSR in the full database, and from 47 countries. We included 5108 ICSR, corresponding to 62 drugs that were significantly associated with myocarditis (with a disproportionality analysis information component (IC) estimate credibility interval lower bound IC 025 above 0, Fig. 1 ). The median age of affected patients was 35 [interquartile range: 25; 50] years, with men representing 73. 8% (3592/4866) of cases. Median time-to-onset (TTO) was 15 [10; 23] days. Overall myocarditis-ICSR resulted in death in 10. 3% (524/5108) of cases. Reporting identified a single suspected drug in 72. 5% (3704/5108) of cases. Reports originated from the standard of care in 4857/ 5108, 95.
1% and 4. 9% from investigational drug studies. The summary of ICSR characteristics is detailed in Table  1. Sensitivity analysis by age subgroups and after exclusion from the full database of ICSR including drugs already identified as at known risk of myocarditis in Food and Drug Administration's (FDA) labels among these 62 drugs is available in Supplementary Dataset 1, and showed similar results. Drugs associated with myocarditis. The most represented drugs were clozapine (3035/5108, 59. 4%), immune checkpoint inhibitors (522/5108; 10. 2%), mesalazine (311/5108, 6. 1%), and smallpox-vaccine (383/5108, 7. 5%) (Table  2 ). Among 62 drugs, 41 belonged to 5 drug classes (as defined by the Anatomical Therapeutic Chemical (ATC)), which included antipsychotic (3108/5108, 60. 8%), cytotoxic drugs (190/5108, 3.
7%), immunotherapies (538/5108, 10. 5%), vaccines (790/5108, 15. 5%), and salicylates (340/5108, 6. 7%) (Table  1 ). Twenty-one (21/62, 33. 9%) uncategorized drugs were regrouped in a miscellaneous category (243/5108, 4. 8% of ICSR); including among the most represented drugs, valproic acid (45/5108, 0. 9%), and minocycline (25/5108, 0. 5%). The first drug-induced myocarditis to be reported was associated with chlorpromazine in June 1979. The year when IC 025 became significant, for each liable drug substance is reported in Supplementary Dataset 2. Overlaps between drug classes within these ICSR are displayed in Fig. 2  and Supplementary Fig. 1. The number of drugs for which myocarditis signal was not previously flagged in the FDA's label was 38/62, 61. 3% (including balsalazide, influenza, and hepatitis vaccines).
Among these 38 drugs, 20 (52. 6%) shared similar pharmacological properties as others included in drug classes known as purveyors of myocarditis, which we described in the Discussion part. We performed a separate pharmacovigilance analysis regarding the 18 incriminated uncategorized drug substances, to assess the plausibility of myocarditis due to these drugs in Supplementary Dataset 3  13. Cumulative cases reported. Cumulative cases reported over time are presented in Fig. 3A. Myocarditis reports per year increased for antipsychotics from 10 in 1995 to 463 in 2019, at which point it was overwhelmingly represented by clozapine in 98. 7% (457/ 463) of cases. More recently, immunotherapy-associated myocarditis reports increased from 6 in 2015 to 250 in 2019. Anti-PD1 drugs (cemiplimab, nivolumab, and pembrolizumab) were felt to be the culprit in 84.
5%) and salicylates the lowest (9/340, 2. 6%, p < 0. 0001) (see Fig. 4A ). Cytotoxics had a mortality of 46/190, 24. 2%, antipsychotics, 181/3108, 5. 8%, and vaccines, 83/790, 10. 5% (see Supplementary Dataset 2 for details per drug substances). In immunotherapies, mortality was higher when two or more immunotherapy drugs (in 98/99, 99. 0% of combinations were an association of anti-CTLA4 with anti-PD1) were associated than one (46/99, 46. 5% vs 129/439, 29. 4%, p = 0. 001). Mortality increase in combination therapy was also observed in the antipsychotic drug class (14/93, 15. 1% vs 167/3015, 5. 5%, p < 0. 001) (clozapine was involved in most combinations with other antipsychotics, in 87/93, 93. 5%). In the other drug classes, combination within each drug class was not associated with higher mortality (salicylates, p = 1.
On the other hand, real-world data coming from the administrative database used for reimbursement of care, such as the French "Système national d'information inter-régimes de l'Assurance maladie" (SNIIRAM), may have larger volumes of data. However, in the latter, quality of data is driven by economic and administrative focus with lack of basic information (duration, effective start-and end-date of drug intake) and lack of information of drugs that are not reimbursed. Furthermore, as for any other measures of disproportionality, the need for caution to interpret quantitative results is paramount and IC values primarily serve to triage which drugs or drug categories require scrutiny while building case reviews  15. Hence, the primary aim of such methods is to look at plausible drug-ADR associations, before delving deeper using combined in vitro and in vivo translational methods to assess causality  14, 16.
We acknowledge several and important bias due to the nature of the pharmacovigilance database. The first being underreporting, associated with halo bias and lack of information on the exposed population for calculation of incidence, which would require sales data from the industry. Indeed, myocarditis being mostly caused by viral infection (among other causes), the differential etiologic diagnosis may have interfered in some results  1, 22. Notably, the lack of antibiotic drugs as a full-fledged drug class may be explained by underreporting due to septic myocarditis alternative diagnosis. Drug-induced myocarditis remains a rare entity, and true incidence remains elusive, due to numerous factors. The lack of consensual definition, with multiple criteria possible: clinical symptoms, EKG modification, cardiac enzyme elevation, decrease in cardiac function assessed by either echocardiography or cMRI and exclusion of differential diagnoses; all for which details were not mentioned in VigiBase ®22.
Methods Study design. This is a worldwide observational case-non-case cross-sectional study focusing on drug-induced myocarditis using the international pharmacovigilance database, VigiBase ® (NCT03855982)  32. VigiBase ® is the WHO global ICSR deduplicated database, managed by the Uppsala Monitoring Centre (Uppsala, Sweden) (accessible at www. vigiaccess. org). It contains over 21 million ICSR received from over 130 countries since 1967 with over 25,000 drugs and vaccines. ICSR originate from different sources, such as healthcare professionals, patients, and pharmaceutical companies, and are generally notified post-marketing. ICSR includes administrative information (country, type of report, and reporter), patient data (age, sex), date of onset of reaction(s), and nature of the outcome using the latest version of Medical Dictionary for Regulatory Activities (MedDRA) terms (currently v22.
1)  33. Drug(s) involved (name, drug start and stop dates, indication, dose) are also indicated. Drugs are coded using the WHO drug dictionary and Fig. 2  Overlap between drugs associated with myocarditis. Overlap between drug classes in the dataset (A), between drug substances in the antipsychotic group (B) and salicylate group (C), between subclasses in immunotherapy (D) and cytotoxic (E), and types of vaccines in the vaccine group (F). DTPP diphteria, tetanus, pertussis, and/or polio vaccine, HepA/HepB hepatitis A and/or hepatitis B vaccine. Subclasses in immunotherapy regroup anti-PD1 (cemiplimab, nivolumab, and pembrolizumab), anti-PDL1 (atezolizumab, avelumab, and durvalumab), anti-CTLA4 (ipilimumab), and interleukin-2 (aldesleukin). Subclasses in cytotoxic regroup alkylating agents (busulfan and cyclophosphamide), anthracyclines (daunorubicin, doxorubicin, epirubicin, and idarubicin), and antimetabolites (cytarabine and fluorouracil).
The use of VigiBase ® to perform pharmacovigilance analyses is not dependent on an institutional review board approval, yet, it is conditioned on institutional access provided and approved by the Uppsala-Monitoring-Centre. Because spontaneous reporting systems are based on anonymity and the process only requires a lack of opposition from the patient, no informed consent was sought to use VigiBase ® in this study. Of note, 100 cases of drug-induced myocarditis reports, randomly extracted from the French pharmacovigilance database (part of VigiBase ® with narratives accessible to our group), were retrospectively analyzed to compute the positive predictive value (true positive/(true positive+false positive)) of clinically suspected myocarditis, as defined by the ESC guidelines  35  , and assess the proportion of biopsy-proven or cardiac magnetic resonance imaging-proven myocarditis.
The use of confidential electronically processed anonymized patient data was approved by the French National Commission for Data Protection and Liberties (Commission Nationale de l'Informatique et des Libertés; reference: 1922081). The computed positive predictive value was 94/100, 94%. At the time of reporting, the proportion of biopsy-proven myocarditis was 6/100, 6%, coronary involvement was excluded in 50/100, 50% and cardiac MRI was available in 37/ 100, 37%. In addition, we performed a pharmacovigilance causality assessment analysis following the French method, on all drugs that were not previously described as associated with myocarditis, nor shared similar pharmacological properties as drugs that were known to be associated with myocarditis. This analysis was based on three criteria: chronological, semiological, and extrinsic accountability.
To do so, we used the VigiLyze software with the English version 22. 1 of MedDRA (Uppsala Monitoring Centre, Sweden). Disproportionality analysis compares the proportion of a selected specific ADR reported for a single drug with the proportion of the same ADR for a control group of drugs (i. e. , full database with all drugs). The denominator in these analyses is the total number of ADR reported for each group of drugs. If the proportion of cases associated with a specific drug is greater than in patients without this ADR (noncases), there is a disproportionality association (signal identification) between the ADR and the drug. In the present work, the calculated Bayesian disproportionality estimate was the IC  8. Herein, we also performed for selected previously unknown liable drugs a sensitivity analysis excluding from full database the ICSRs in which drugs already known to be associated with myocarditis were reported.
Calculation of the IC using a Bayesian confidence propagation neural network was developed and validated by the Uppsala Monitoring Centre as a flexible, automated indicator value for disproportionate reporting that compares observed and expected drug-ADR associations to find new drug-ADR signals with identification of probability difference from the background data (full database)  36. Probabilistic reasoning in intelligent systems (information theory) has proved to be effective for the management of large datasets, is robust in handling incomplete data, and can be used with complex variables. The information theory tool is ideal for finding drug-ADR combinations with other variables that are highly associated compared with the generality of the stored data  36. Several examples of validation with the IC exist, showing the power of the technique to find signals sooner after drug approval than by a regulatory agency, and to avoid false positives, whereby an association between a common drug and a common ADR occurs in the database only because the drug is widely used and the ADR is frequently reported (e.
This number is computed from N drug , N effect , and N total. N observed is the actual number of case reports for the drug-ADR combination. N drug is the number of case reports for the drug, regardless of ADR. For example, if overall, a drug substance X was reported in 10 cases of rash, 5 cases of fever, and 3 cases of myocarditis (all independently), N drug for drug substance X would be 10 + 5 + 3 = 18. N effect is the number of case reports for the ADR, regardless of drug. N total is the total number of case reports in the database. Drugs categorized as immunosuppressant (ATC label L04XX) were excluded to avoid indication bias. Sensitivity analysis studying the association between selected liable drugs and myocarditis by age subgroups has been performed in VigiBase using IC 0005 > 0 as a significant threshold to account for multiple testings.
Acknowledgements The supplied data from VigiBase ® come from various sources. The likelihood of a causal relationship is not the same in all reports. The information does not represent the opinion of WHO. L. S. N. and J. -E. S. were involved in the planning, conduct, and reporting of the work described in the article. B. B. M. was involved in the conduct of the study and data management. All other authors were involved in the conduct of the study, and the editing of the manuscript. J. -E. S. is responsible for the overall content as guarantor. All authors agreed on the submitted version.
Chemical Risk Assessment and Microbial Risk Assessment Although differences between Chemical Risk Assessment (CRA) and Microbial Risk Assessment (MRA) exist, the same four key steps are followed. Chemical Risk Assessments are made to evaluate risks associated with the consumption of foods containing food additives, chemical residues or contaminants and exposure to these  (FAO & WHO, 2009). Several guides and toolkits are available to be used in performing RAs of chemicals such as those published by the IPCS  (FAO & WHO, 2009) , Redbook by the USFDA, the WHO RA toolkit (WHO, 2010) and EFSA (Table  1 ), as well as databases on the maximum residue of veterinary residues, veterinary drugs and food additives in foods. On the other hand, information on the structure and activity of chemicals can also be used during hazard characterization, especially when there is relatively little information or a lack of existing studies  (Camel, Rivière, & Le Bizec, 2018; WHO, 2010).