dataesr/scientific-paragraphs-categorization huggingface dataset:
To minimise potential bias, publicly available data sources were used when possible; academic partners were involved as co-authors; and all relevant antigens were included, not just Sanofi Pasteur products. In addition, the degree to which services were impacted was not quantified, except in part for vaccination coverage rates, where data were sparse. Further limitations relate to the limited quality, availability, and reliability of some data, and the timing of the survey relatively early in the pandemic, which has continued to evolve. Many solutions were still in the implementation phase, so impact of measures was not yet available. Since our survey, SEAR/WPR countries may have implemented measures to rectify vaccine disruptions, and there are anecdotal reports of improvements in vaccination services in some countries in the region in late 2020.
Acknowledgements We gratefully acknowledge feedback on study design from  Su-Peing Ng  and  Sandra Chaves. The authors thank all personnel participating in the study, including the following: Australia: Desiree Anthony; Australia & New Zealand:  Robert Menzies ;  Brunei: Romain Marcade ; China:  Steffi Liu ;  Hong Kong & Taiwan :  William Ting-Yu  YEH; India:  Ajit Nair ,  Irshikha Das ,  Badri Narayan Patnaik ,  Sumit Srivastva ; Indonesia:  Stephen Nawawi ,  Tya Listyati ; Japan:  Shoko Omuro ;  Malaysia: Aidi-Affendi Kussim ,  Liz Lim ,  Winnie Ng ;  Malaysia & Brunei :  Adelina Cheong ,  Jean Khor ,  Safwan Zharif bin Zaini ; Myanmar:  May Myint Oo ,  Onsiri Pitisuttithum ; Nepal: R. Srinivasan; New Zealand:  Claire H Wilson ; Pakistan:  Zeeshan Hassan ; Philippines:  Benjamin Sablan Jr ,  Maria Leila Penalosa ,  Teresa Ribano ; Singapore:  Charlene Lam ,  Shirley Ang ; South Korea: Hong-won OH; Thailand:  Onsiri Pitisuttithum ,  Tipruetai Belaud ;  Thailand & Myanmar :  Wasana Prasitsuebsai ;  Vietnam & Cambodia: Nguyen Trung Hien.
We therefore explore several questions: Does the mechanism change players' behaviors? Does it continue to affect them even after it is removed? Are players familiar with the mechanism, or have they learned to cooperate, and can they continue to cooperate even if the mechanism is removed? Unlike the existing literature, we test the robustness of the Nash threat in a two-player CPR game. First, we extend the dataset to confirm the effectiveness of the Nash threat observed in  [17]. We examine in detail the reason that the Nash threat as the disapproval benchmark reduces CPR over-extraction to understand how subjects behave under the threat of Nash extraction. Third, we test the order effect, i. e. , participants start with the CPR with the AM in sequence 1 and receive the AM in sequence 2. Fourth, we test the persistent effect of the Nash threat.
Data collection and sample design A survey was conducted in Morvan Park (MP), which is one of the 51 regional protected natural parks (PNR) in France. Created in 1970, MP is located 30 kilometres south of the regional capital (Dijon) in the region of Burgundy and spreads over four Departments (Yonne, Nièvre, Saône-et-Loire, and Côte-d'Or) with an area of 2,999 km 2 including 117 cities. MP is readily accessible by national and county roads from all directions and can be reached from Paris in two hours. Historically speaking, MP was the economic, political, and religious centre of Gaul. Two historical sites are well-known: Bibracte, where Vercingetorix was proclaimed head of the Gaulish coalition in 52 BC, and more recently, Château-Chinon, which was the electoral fief of former President François Mitterrand.
Analyses The descriptive statistics, the Exploratory Factorial Analyses (EFA), and the correlation table were compiled using SPSS 22. 0. The Exploratory Factor Analysis using the PCA (principal component analysis) method with Varimax rotation was conducted for the study. The number of factors was determined by using an eigenvalue greater than 1, and the percentage of variance was explained. In both cases, KMO is greater than 0. 7 and Bartlett's test is significant. The confirmatory factor analyses to validate the scale of protected natural park experience and the structural equations required to address the hypotheses were carried out using AMOS 22. 0  (Arbuckle, 2013) , taking into account the recommendations of  Hair, Black, Babin, and Anderson (2009). In order to validate the approach and the two hypothesis groups, a structural equation model was used, including relationships between the dimensions of experience and the two outcomes, arousal and memory.
Participant observation  (Kawulich, 2005)  was also employed to further explore the interaction between these stakeholders. The primary data was integrated with secondary sources: policy documents, trade and local press, reports from consultancies, regulatory and audit authorities. Both primary and secondary data were analysed with NVIVO, a software widely utilised to explore and organise the themes emerging from qualitative data  (Welsh, 2002). Recurring themes were identified for each case study and then compared across the countries to map similarities and differences across the EU broadband market  (Jonsen & Jehn, 2009).
The participants were informed about the objectives, potential risks and benefits of the survey, and their right to withdraw at any moment. At the end of the first page, the participants had to give their consent to take part in the survey by clicking on the button "I agree to participate in the survey. " The study was approved by the Ethical Committee of the University of Naples Federico II (project identification code: 6/2019; date of approval: 15 March 2019), as well as designed in accordance with the EU General Data Protection Regulation and the principles of the Declaration of Helsinki in relation to the Ethical Principles for Medical Research Involving Human Subjects. Privacy was guaranteed through the use of a secure gateway accessible only to the principal investigator. Furthermore, before sharing the dataset with other scholars, the principal investigator deleted the IP addresses of all the participants.
The participants were recruited between September 2019 and January 2020. The inclusion criteria to participate in the online survey were (1) being at least 50 years old, (2) having been resident in Italy for at least 30 years, and (3) having used the Internet for at least 5 years. Data from 114 participants (85 men and 29 women) aged from 52 to 79 years old (M = 62. 57 SD = 6. 19) were analyzed in this study. All the participants were cisgender (i. e. , their gender identity was aligned with their biological sex). As regards sexual orientation, 54 (47. 4%) self-identified as heterosexual, whereas 46 (52. 6%) as non-heterosexuals (i. e. , lesbian, gay, or bisexual). Regarding ethnicity, most of the sample self-identified as Caucasians (n = 110; 96. 5%), 1. 8% (n = 2) as Hispanics/Latinos, and 1.
Statistical Analyses All the statistical analyses were performed with SPSS 26, with the level of significance set at 0. 05. No missing data were present in the dataset. Bivariate correlations between the main variables of the study were calculated using Pearson's correlation coefficient. We performed three hierarchical linear regression analyses, based on the three dependent variables (i. e. , N-OSA, S-OSA, and P-OSA). In all these models, we entered the socio-demographic characteristics (i. e. , age, educational level, relationship status, and religion) in step 1 as covariates, gender and sexual orientation in step 2, and sexual permissiveness in step 3. As a final step of each hierarchical regression analysis, interaction terms between sexual permissiveness and gender, as well as between sexual permissiveness and sexual orientation, were added in order to test the moderating role of sexual permissiveness in the relationships between gender and sexual orientation and engagement in OSAs.
Building on this and other previous research  [29] [30] [31] [32]  , we focused our current study on archaeologically documented tool-making methods from the early and late Lower Paleolithic, a period that witnessed a nearly 3-fold increase in hominin brain size. This allows us to empirically address the over 100 years of theorizing linking increasingly complex tool-making with brain evolution and language origins  6, [33] [34] [35]. The early (Oldowan, ca. 2. 5 Mya) technology modeled here comprised the production of simple, sharp-edged stone flakes by striking one stone with another. The late (Late Acheulean, ca. 0. 5 Mya) technology comprised the production of refined, teardrop-shaped "handaxes" through intentional shaping. We defined a shared action alphabet, consisting of 7 event types encompassing the elementary body movements and object transformations present in every sequence of both technologies (Figure  2A ), and applied two established sequence learning algorithms to the coded event sequences: Hidden Markov Models (HMM) and k-Sequitur.
Results Hidden Markov Modeling. HMM detects probabilistic regularities (hidden states) across sequences and can capture the structure of arbitrarily complex sequences given sufficient numbers of hidden states. The optimal number of hidden states provides a measure of structural complexity. We fitted HMMs to coded event sequences and computed the Bayesian Information Criterion (BIC) across different numbers of hidden states as a measure of model fit. BIC reached its minimum (less is better) at 4 hidden states for Oldowan and 6 for Acheulean data (Fig 2B ), indicating a 50% increase in complexity. These two models perfectly categorized the sequences (likelihood greater for correct model, Fig. 2C ). The fit was better for both models on the simpler Oldowan sequences. The close fit of the Acheulean model to Oldowan data (but not vice versa) indicates that the former captures most of the structure of the latter, and that Oldowan sequences may be considered a subset of Acheulean sequences.
We therefore used the Acheulean HMM to test for further structure. We obtained the most likely hidden state sequences for the Oldowan and Acheulean data and then fitted a second, 2-state HMM onto these higher-order sequences (Fig. 3 ). We found that Acheulean sequences oscillate between two superordinate states-of-states (SoS) whereas Oldowan sequences remain in one). Thus, Acheulean sequences display an additional level of structure not expressed by Oldowan sequences. Next, we fit the 6-state Acheulean HMM to Oldowan and Acheulean data and observed the probability of actions per hidden state as well as transitions between hidden states. Our Oldowan data are characterized by the repetition of one simple action "chunk" consisting a relatively invariant sequence of states (3->4->2: Fig. 3A ) that essentially corresponds to the removal of an individual flake and is entirely captured by SoS1 (Fig.
Formal Language Theory employs terminal symbols (in our case 7 event types) and non-terminal symbols (re-write rules expandable to terminal and/or non-terminal symbols) to generate strings. Whereas regular grammars and HMMs are driven by local relationships between symbols, CFGs capture nested dependencies of theoretically infinite length and depth. The standard algorithm to extract CFGs, Sequitur  41  , creates a new rule as soon as a symbol pair is observed twice in a sequence and repeats this pair-wise aggregation, adding new levels of superordinate rules until the complete sequence is described. This makes Sequitur powerful but liable to detect a high number of spurious (occurring <3 times) rules in the variable sequences generated by real human behavior. We therefore developed an algorithm, k-Sequitur, requiring a pair to occur k-times before generating a rule.
Increasing k makes the grammar discovery process less sensitive to infrequent pairs and less prone to creating rules from noise. In agreement with our HMM results, CFG extraction found Oldowan grammars to be a less complex sub-set of Acheulean grammars. Rule inference from combined Oldowan and Acheulean samples identified multiple rules that occur only in Acheulean sequences (Supplementary Fig. 1 ) and showed that the frequency of Acheulean-only rules increases at higher levels (0 at level 2, 1 at Level 3, 2 at Level 4, 5 at Level 5). No Oldowan-only rules were identified, even when rule inference was restricted to the Oldowan data set. CFG extraction achieved substantial compression of both Oldowan and Acheulean sequences (Fig. 3G ), however the rate (inverse slope) of Acheulean compression was more than twice as great (7.
Grammar: -Root -> r2 chases r3 r2 catches r3 r5 chases r2 r2 runs away from r5 -r2 -> the little cat (used 4 times) -r3 -> the mouse (used 2 times) -r5 -> the big cat (used 2 times) We ran Sequitur on each sequences in both the Acheulean and the Oldowan data sets and enumerated all the rules found across both data sets. After inferring rules from the combined Acheulean and Oldowan data set, we found that some rules only occurred in Acheulean sequences (Supplementary Fig. 1 ). The Sequitur algorithm reduces the length of the sequences by replacing terminal symbol strings with aggregating rule strings. This compresses the sequence by reducing its redundancy. Figure  3G  shows that sequences in our Oldowan and Achuelean samples share common compressible structure within samples but are distinct across samples.
This is indicated by the fact that their pre and post-compression lengths are linear and have distinct slopes. Linear regression fit for Acheulean data is R 2 = 0. 9852 with slope = 0. 13; for Oldowan data R 2 =0. 9982 and slope = 0. 34. The inverse slope on this plot corresponds to the data compression rate through rule extraction. Sequitur as a compression algorithm is loss-less, in that reverse applying the rules recovers the original sequence error free, and thus the same information is communicated by fewer symbols. This contrasts with the hidden states of the HMM that only capture probabilistically a higher order structure. A Sequitur compressed sequence must have more information per character and this gain in information density can be quantified using Shannon's entropy measure. Shannon's entropy is computed directly as the log probability of each symbol averaged over all symbols.
Acknowledgements This work was supported by the Commission of the European Communities Research Directorate-General Specific Targeted Project number  029065 ,  "Hand  to Mouth: A framework for understanding the archaeological and fossil records of human cognitive evolution" and  National Science Foundation (USA)  grant  SMA-1328567. The individual depicted in Figure 1A is the first author (DS). We thank  Andreas Thomik  for his work developing and applying pattern recognition methods,  Francois Belletti  for software development support, and  James Steele  and  Daniel Wolpert  for support and encouragement that made this project possible at the start.
Although less popular than female reproductive health, and acne treatment, information on oral hygiene is present on the platform. Also, it is generally accepted that nutrition and diet should be included for a holistic health program since they are common risk factors for oral diseases  [31]. The question is whether applications with oral hygiene educational content are consistent with evidence-based dentistry and provide a high-quality context for oral self-management in children and adolescents. The aim of this study is to assess the quality of the content related to oral health education on TikTok platform, as well as to evaluate its possible impact on the young population.
Sample Videos A total of 203 TikTok videos were extracted with the hashtag #oralhealtheducation, which was associated with 6. 8 million views on 28 September 2021 (Figure  1 ). Next, two university researchers (LF, FC) selected the videos. Videos were included if they (i) focused on human health, (ii) contained an educational or preventive message, advertising content, or commercial advice related to oral health, (iii) were in the French or English language. TikTok videos related to animal health, surgery, therapeutic or orthodontic treatment, piercing or tooth carving were excluded. After watching all the videos, 69 videos met the inclusion criteria and were selected for the evaluation study, representing 34%.
Sample Videos A total of 203 TikTok videos were extracted with the hashtag #oralhealtheducation which was associated with 6. 8 million views on 28 September 2021 (Figure  1 ). Next, tw university researchers (LF, FC) selected the videos. Videos were included if they (i) fo cused on human health, (ii) contained an educational or preventive message, advertisin content, or commercial advice related to oral health, (iii) were in the French or Englis language. TikTok videos related to animal health, surgery, therapeutic or orthodonti treatment, piercing or tooth carving were excluded. After watching all the videos, 69 vid eos met the inclusion criteria and were selected for the evaluation study, representin 34%.
Statistical Analysis The online questionnaire was constructed to be sure that all the raters provided an answer to each question; no missing data were allowed. For the categorical item, we provided the number of agreements (i. e. , when all the raters quoted the same response) and the number of "yes" per agreement for categorical variables (yes/no). For the ordinal item, we provided the number of agreements on the five-item responses (1 totally disagree to 5 totally agree) and the number of raters who agree (score: 4) or strongly agree (score: 5). To measure the inter-and intra-rater reliability between OHP and HEP, the Kappa of Fleiss (Kf) between categorical variables was used. A poor agreement corresponds to Kf < 0, a slight agreement to 0 <Kf < 0. 2, a weak agreement to 0. 2 < Kf < 0. 4, an average agreement to 0.
4 < Kf < 0. 6, a good agreement to 0. 6 < Kf < 0. 8 and an excellent agreement to Kf > 0. 8. The p-value indicates whether the calculated kappa is significantly different from zero and thus different from a random agreement; if the p-value is less than or equal to 5%, we conclude that the agreement between raters is significantly different from the value that would be obtained by chance. For the ordinal variables, the intra-class coefficient (ICC) and its 95% confidence interval were calculated. An ICC close to 1 indicates high similarity between responses in the same group, while an ICC close to zero means that values in the same group are not similar. Statistical analysis was performed using the package "irr" of the R Project for Statistical Computing (version 4. 1. 1. 2021-08-10).
An update of the Cochrane review of fluoride mouthwashes for preventing dental caries in children and adolescents found that supervised regular use of fluoride mouthwash by children and adolescents is associated with a large reduction in caries increment in permanent teeth (the quality of evidence is moderate)  [70]. The TikTok videos do not specify the targeted individuals or prescriptions (i. e. , dose, frequency, time in mouth). In terms of oral health education, nutrition and diet should be included in a global health program as they constitute common risk factors for non-communicable diseases  [71, 72]. Reviews of the literature have concluded that the use of nutrition-related mobile applications increases knowledge and improves behavior  [73, 74]. In our study, nutrition is mentioned in 0.
The framework is to empower, promote oral health and knowledge in the detection and self-management of the most common oral symptoms or diseases with a universal language, both of which are currently lacking. The content will have to evolve towards more rigor and scientific quality and evidence-based information, with full transparency regarding conflicts of interest. There are several strengths to our analysis. This study is the first to analyze the TikTok videos corresponding to the hashtag #oralhealtheducation. Moreover, the point of view of three different groups of viewers (OHP, HEP, and young) was compared. However, this study also has limitations. First, the videos were only identified using #oralhealtheducation, and additional hashtags such as #toothbrushing, #mouthwash, #teethcleaning, among other related hashtags, often co-occurring with #oralhealtheducation, might yield subsets of videos with differing content, sentiment, and levels of incorrect content and misinformation.
The unavailability of food items triggered panic buying of the missing items throughout retail stores in impacted and non-impacted areas. It remains unclear how these reported events impacted the consumption behavior of people. In addition, the dynamics of individual purchases during a time of crisis remain poorly studied, which gives us further motivations to study purchase behavior from a time perspective during the transient period of the El Niño event of 2017. In this study, we aimed to determine the resilience of retail structures by measuring the collective response of consumers living in Lima's greater area. In particular, we developed our analysis to better understand the consumer habit changes during a period of climatic stress. To achieve this goal, we performed a multi-scale analysis of the consumption patterns based on a credit and debit card transaction dataset of roughly 6 million Peruvian citizens gathered over a 2-year period from 2016 to 2017.
Kullback-Leibler divergence (KLD) analysis of the bank transaction distribution To investigate how the ENSO events in February and March 2017 impacted consumption patterns in Lima, we examined how the relative frequency of merchant categories evolved in time using the KLD. In addition, we demonstrate in Fig. 3 (a) that customers' indeed slowed significantly both in number and volume during the first event of February 2017. Furthermore, a smaller number of transactions was observed during the second event in March, but the magnitude was less than that of the February event. The same behavior was observed for cash withdrawal (see Fig. 3(b) ). As a reference, Fig. 3c  illustrates the distribution of the share of spending in each category of purchases (VISA Merchant Category Classification MCC) in our dataset.
The figure displays only the 50 most consumed categories throughout the country averaged throughout our dataset. It can be observed that the distribution of the frequency of each category of purchases follows a Zipf-like distribution with dominant purchases in for food-related stores (i. e. , grocery stores and supermarkets), as expected. To further explore the evolution of the consumption pattern in response to the events, we used two different divergence measure D (1) and D (2) to quantify the deviation of the purchase behavior at a given time from normal purchase behavior. To compute the purchase distribution we classified each purchase record with its MCC information into 15 different categories based on the Classification of Individual Consumption According to Purpose (CIOCOP), as displayed in Table  3.
9 , two additional phenomena can be observed: first, an increase in insurance purchases after the first event in February (cluster 3), and second, an increase in purchases of technology related items during first event (cluster 2). We observe a surge in the purchased of new insurance policies after the first event. It should be noted that Peru is an underinsured country (only approximately three in hundred houses are insured), and in the aftermath of the event it appears that people decided to purchase new insurance policies. With respect to technology-related items, a query to the database at our disposal revealed that no transaction were made in that category from February 15 to 19, 2017. We observed a shift in purchases after February 19. This behavior appear to be due to the purchases of prepaid cell phone plans.