dataesr/scientific-paragraphs-categorization huggingface dataset:
The worse percentage is 93. 3%. This model gives better result than the previous models. The best result is obtained from this model due to its nonlinear solver and estimation method mentioned above. Such multi-classes, a complex and overlapping dataset, can only be predicted with such a non-linear model. The Kernel Naïve Bayes model accuracy is obtained as 99. 0% in Figure  11. The blu line shows the true predictions percentages. According to these confusion matrices, fiv classes were detected as fully true-positive: Al, Cu, Fe, Ni, Sn. Total prediction percentage over total class gives us the accuracy. The worse percentage is 93. 3%. This model give better result than the previous models. The best result is obtained from this model due t its nonlinear solver and estimation method mentioned above.
Such multi-classes, a com plex and overlapping dataset, can only be predicted with such a non-linear model. One of the nonlinear algorithms of the support vector machine is the quadratic SVM This model uses the same technique with the SVM but the QSVM assumes the classifica tion line with hyper-planes, hyper-spheres, hyper-ellipsoids, hyper-paraboloids, and hy per-hyperboloids of various types. The quadratic SVM accuracy is obtained as 97. 3% in Figure  12. Four classes, Al, Cu Sn, and Zn are classified as true positive with 100%. It gives better results than the SVM so the nonlinear model of support vector machine is superior to the standard SVM for thi type of dataset. One of the nonlinear algorithms of the support vector machine is the quadratic SVM. This model uses the same technique with the SVM but the QSVM assumes the classification line with hyper-planes, hyper-spheres, hyper-ellipsoids, hyper-paraboloids, and hyperhyperboloids of various types.
The quadratic SVM accuracy is obtained as 97. 3% in Figure  12. Four classes, Al, Cu, Sn, and Zn are classified as true positive with 100%. It gives better results than the SVM, so the nonlinear model of support vector machine is superior to the standard SVM for this type of dataset. The distance of the point to be estimated from other points is calculated. The Minkowski distance calculation function is used for this calculation  [28]. Nonlinear fitting has been used for this algorithm, but sometimes linear KNN also can be used. In the direction of regression, accuracy is obtained as 97. 9% in Figure  13. Four classes are detected as completely true, and the lowest value belongs to Fe with 90%. Even though its total accuracy is high, class accuracy is low as in the QSVM. In the direction of these comparisons and interpretations, the Kernel Naïve Bayes gives the best result with an accuracy of 99.
0%; the Kernel Naïve Bayes was studied for LIBS in the literature as it is mentioned in the introduction part, so it is proved that this model gives high accuracy for the LIBS dataset  [7]. Secondly, the Gauss Naïve Bayes gives the best result with an accuracy of 98. 125%. Moreover, other nonlinear models are nearly the same, as in the linear models SVM and linear-regression models, however, linear regression gives a better result, which is quite similar to nonlinear models. The classification graph of eight substances with the Kernel Naïve Bayes model is shown below. The distance of the point to be estimated from other points is calculated. The Minkowski dista calculation function is used for this calculation  [28]. Nonlinear fitting has been used for this alg rithm, but sometimes linear KNN also can be used.
Even though its total accuracy is high, class accuracy is low as in the QSVM. In the direction of these comparisons and interpretations, the Kernel Naïve Bayes gives the best result with an accuracy of 99. 0%; the Kernel Naïve Bayes was studied for LIBS in the literature as it is mentioned in the introduction part, so it is proved that this model gives high accuracy for the LIBS dataset  [7]. Secondly, the Gauss Naïve Bayes gives the best result with an accuracy of 98. 125%. Moreover, other nonlinear models are nearly The prediction of the eight classes is demonstrated in Figure  14 ; classified data with the Kernel Naïve Bayes model have been shown. As it can be seen in Figure  14 , the orange points out of line mean the classified data have a deviation from other classes; however, it is known that the classification accuracy is 99.
Conclusions This paper represents the classification of different polymer composite materials a performed based on their hardness. The hardness-determination process of polymers w made first, then the wavelength-intensity graph of polymers was obtained from LIBS eight classes, which are aluminium, copper, bismuth, iron, magnesium, nickel, tin, a zinc. These substances (reinforcements) were classified with seven different models a their comparisons have been shown. Firstly, the data adjustment has been made to crease the accuracy, and no adjusted data effect has been conceived. In light of the traini of seven different model and their data adjusting eight classes were trained with sev different model with adjusted dataset. Nearly the same prediction of the response (Figu 6) has been obtained in the classification of graph of Kernel Naïve Bayes model.
Conclusions This paper represents the classification of different polymer composite materials and performed based on their hardness. The hardness-determination process of polymers was made first, then the wavelength-intensity graph of polymers was obtained from LIBS for eight classes, which are aluminium, copper, bismuth, iron, magnesium, nickel, tin, and zinc. These substances (reinforcements) were classified with seven different models and their comparisons have been shown. Firstly, the data adjustment has been made to increase the accuracy, and no adjusted data effect has been conceived. In light of the training of seven different model and their data adjusting eight classes were trained with seven different model with adjusted dataset. Nearly the same prediction of the response (Figure  6 ) has been obtained in the classification of graph of Kernel Naïve Bayes model.
3, prodigal version 2. 6. 3 for syntaxic annotation, mmseqs version 2. 11. e1a1c for gene clustering, mafft version 7. 407 for persistent genome alignment defined as 90. 0% of all genomes in each family and IQ-TREE  (Nguyen, Schmidt et al. 2015) version 2. 0. 3 based on GTR model and 1000 bootstrap for tree construction. Comparative genomics. The average nucleotide identity (ANI) value of genomes was determined using fastANI version 1. 32 (fragment length 500)  (Jain, Rodriguez et al. 2018). V. crassostreae and V. chagasii core, variable, accessory and specific genomes were established using mmseqs2 reciprocal best hit version 13-45111 with 80% identities on 80% coverage thresholds  (Steinegger and Soding 2018). Synteny plots were generated using dedicated python scripts based on the "DNA Features Viewer" library (https://github.
Genome sequencing, assembly. Phages were sequenced by the Biomics platform at the Pasteur Institute (Paris, France). The sequencing library (2 × 75 bp paired end) was prepared using a TruSeq Illumina kit and sequenced on a NextSeq550 Illumina sequencer. After read trimming conducted with Trimmomatic v0. 39 (LEADING:3, TRAILING:3, SLIDINGWINDOW:4:15, MINLEN:36)  (Bolger, Lohse et al. 2014 ), de novo assembly was perfomed using SPAdes v3. 15. 2 (--careful --cov-cutoff auto -k 21,33,55,77 -m 10). Vector contamination contigs were excluded using the UniVec Database and the one-contig phage genome was manually decircularized. Syntaxic annotation was performed with PHANOTATE v1. 5. 0 (McNair, Zhou et al. 2019). Large terminase subunit was identified using DIAMOND v2. 0. 8. 146 (blastp, default parameters)  (Buchfink, Reuter et al.
2021 ) on previously annotated vibrio large terminase and using HMMSCAN (HMMER v3. 3. 2, default parameters with evalue≤10 -3 ) with PFAM profiles (PF03354. 17, PF04466. 15, PF05876. 14, PF06056. 14) (Mistry, Chuguransky et al. 2021 ). The phage genome was manually ordered starting from the gene for the large terminase subunit. Genome annotation. The tRNA were identified with tRNAscan-SE v2. 0. 9  (Chan and Lowe 2019). Functional annotation used multiple approaches. First, we used HMMSCAN (evalue≤10 -3 ) with PVOGS profiles  (Grazziotin, Koonin et al. 2017 ). We also used DIAMOND blastP against first the Nahant collection genomes and then against other bacterial RefSeq (version 212) and phage GenBank genomes (2022-24-06, with 30% identities and 50% coverage)  (Kauffman, Brown et al. 2018 , Buchfink, Reuter et al.
2021). Finally, we added InterProScan v5. 52-86. 0 (evalue≤10 -3 ) and eggNOG v5. 0. 2 annotation results  (Jones, Binns et al. 2014 , Huerta-Cepas, Szklarczyk et al. 2019). Clustering, lifestyle and comparative genomics. We clustered phages using VIRIDIC v1. 0r3. 6 (default parameters)  (Moraru, Varsani et al. 2020). Intergenomic similarities were identified using BLASTN pairwise comparisons. Virus assignment into family (≥50% similarities), genera (≥70% similarities) and species (≥95% similarities) ranks followed the International Committee on Taxonomy of Viruses (ICTV) genome identity thresholds. Phage lifestyle was predicted using BACPHILIP version 0. 9. 3-alpha and lysogeny associated genes were searched using InterProScan (v. 5. 52-86. 0) based on integrase (IPR002104), resolvase (IPR006119), replicative transposases (IPR004189), and transcriptional repressor domains (IPR010982, IPR010744, IPR001387, IPR032499).
ACKNOWLEDGEMENTS We warmly thank  Sylvain Gandon ,  François Blanquart  and  Martin Polz  for fruitful discussions on the manuscript. We are grateful to  Ian Probert  for proofreading the manuscript. We thank  Sabine Chenivesse ,  Sophie Le Panse , the staff of the  Ifremer Argenton and Bouin stations and of the ABIMS (Roscoff) and LABGeM (Evry)  platforms for technical assistance. We thank all members of the "GV team" for support with field sampling. We thank  Zachary Allouche ,  Biomics Platform ,  C2RT ,  Institut Pasteur (Paris, France ) supported by  France Génomique  ( ANR-10-INBS-09 ) and IBISA. This work was supported by funding from the  European Research Council (ERC)  under the European Union's  Horizon 2020 research and innovation program  (grant agreement No  884988 ,  Advanced ERC Dynamic)  and the  Agence Nationale de la Recherche  ( ANR-20-CE35-0014 « RESISTE » ) to FLR.
Method used to estimate of the global number of vapers in 2020 The global state of tobacco harm reduction (GSTHR) project (https://gsthr. org/) collects information on the prevalence of NVP use and use of other SNP. The project provides global, regional and national reporting on the availability and use of SNPs and related regulations. The project collects information from research publications and government, and NGO reports and information is also gathered through a network of national and regional correspondents in 201 countries (262 including territories and significant regions within a country). The GSTHR database compiles country-based information over time and product categories (GSTHR, 2022b). The GSTHR project published the first global estimate of the number of NVP users, which stood at 68 million in 2020  (Jerzy nski et al.
, 2021). The 2020 figure was derived in two stages, firstly, an estimation of the global population of e-cigarette users using 2018 data and then a projection to 2020 using adjustment for market value data. Data on e-cigarette use were available from 49 countries up to 2018. The way in which e-cigarette use is assessed varied across studies. For example, Eurobarometer asks respondents to choose from one of a range of responses ("you currently use it", "you used to use it but you have stopped", "you have tried only once or twice" or "you have never used it") and for those who currently use it asks a supplementary question on frequency of use ("every day", "every week", "every month", "less than monthly" or "you have tried only once or twice"). Another wording, but also based on the term "current use", was used in Global Adult Tobacco Surveys.
It asked "do you currently use electronic cigarettes or any other vaping device on a daily basis, less than daily, or not at all?" and respondents could check "daily", "less than daily" or "not at all". In most cases, the statistics reported in this field concerned current use. Hence, in our study, we also extracted data referring to current use. To estimate global numbers, we used the method of assumed similarity which is commonly used in global health estimates where there is an absence of data at country level  (Fuller, 1974; Mandell, 1974; Little and Smith, 1987; Rubin, 1987; AbouZahr et al. , 2017). This assumes that countries that share characteristics will have similar health metrics. This allows imputation of unknown trait values from the aggregate values in countries where they are known.
This method has been used to estimate the prevalence of drug injecting  (Aceijas et al. , 2004) , mortality associated with pandemic influenza in 2009  (Verity et al. , 2020)  and the severity of coronavirus disease  (Verity et al. , 2020). The method is used by WHO and other UN agencies and donors to generate global and national estimates from a variety of sources on a wide range of characteristics, in particular the level and dynamics of demographic and social indicators such as population, fertility and mortality  (Boerma and Mathers, 2015). For our analysis, we used the WHO region, the WB income classification and the legal status of NVP. Regarding legal status, there are numerous ways in which e-cigarettes are controlled or regulated (and more often not). The top-level categorisation of legal status used in our GSTHR database is: "Allowed" which means that there is some specific law or regulation that permits the marketing, sale, purchase and use of the product (and which might include for example product registration and quality control, product specification, age of purchase, place of purchase and use in public places)globally 75 countries; "Banned" which means that there is some specific or legacy law which prohibits marketing or use of the productglobally 36 countries; and "Not regulated" which means that there is no specific law or regulation regarding the productglobally 85 countries  (Shapiro, 2020, p.
113). Using the information available from the surveys, we calculated the average prevalence of vaping for each WHO region, the WB income classification group and the legal status of e-cigarettes from our own database (Table  1 ). The intersection of these factors allowed us to separate 72 subgroups (four income groups, six regions and three types of legal regulation). The average prevalence of vaping was calculated for each group. The calculated values were used as proxies for prevalence data in countries where this information was unknown. Each of the 72 calculated values was assigned to a country using known values of the grouping factors. We calculated the number of vapers by taking the total adult population (over 15 years of age) obtained from the UN database for 2018 (World Population Prospects, 2019).
Method used to estimate the global number of vapers in 2021 As in our previous estimate, we used information on the prevalence of vaping in known countries to impute the prevalence of vaping in countries where this information was missing. Some new data has been released following our previous estimate. The largest was the Special Eurobarometer 506 (27 EU Member States), published in February 2021  (European Commission, 2021). Additional new datapoints also include the UK, New Zealand, Taiwan and the USA. This created a new source data set of 51 countries with general population surveys reporting prevalence of daily or current use of e-cigarettes (references to the data sources are in the supplementary document titled "Surveys on vaping. docx"). There were three questionable data points in the new data set: Tonga, Uganda and Jordan.
In the data sets, we considered, the adult data were derived from ages 15 and 16 and less frequently 18 and over. Therefore, we used UN population figures for people 15 years and older. We then adjusted by survey date and regional market growth rate. We obtained data for US dollar market value of e-cigarette sales by region for  2015 -2021 (Euromonitor International, 2021)  and developed projections based on the actual year of data adjusted by regional market growth rate. The largest market was North America, mainly accounted for by the USA, whose market size in 2021 was estimated at $10. 3bn, whereas Canada's market size was $1. 4bn. This was followed by Western Europe at $6. 6bn, Asia Pacific at $4. 4bn and Eastern Europe at $1. 6bn. The smallest market was the Middle East and Africa at $490mn, Latin America at $122mn and Australasia at $118mn.
For the Asia Pacific region, the market grew 1. 27 times (127%) between 2015 and 2021. By applying this regional growth rate for the period 2015-2021 to the 2015 prevalence data, we estimated the increase in the number of e cigarette users in China between 2015 and 2021. In Taiwan, the latest prevalence survey was conducted in 2020. Taiwan is also in the Asia Pacific region, but for the one-year period between 2020 and 2021, the market grew only 1. 03 times (103%). Application of this growth rate data to the 2020 prevalence data enabled us to estimate the increase in the number of e cigarette users in Taiwan between 2020 and 2021. This market rate modification was performed for datasets older than 2021. The study year was capped from the bottom to 2015. We treated all older studies as if their implementation took place in 2015.
Summary and conclusions Global, regional and national estimates of the numbers of vapers are important indicators of trends in nicotine use. Since the introduction of NVD into many global markets from 2007 onwards, there has been a steady upward rise in the estimated numbers of vapers. Our new estimate of 82 million in 2021 shows a continued increase over our previous estimate of 58 million in 2018 and projection of 68 million in 2020. The increase is mainly accounted for by growth in North American and Western European markets. All global measures of health indicators are estimates  (Boerma and Mathers, 2015). There is uncertainty in all estimates, and the fewer the data points the greater the uncertainty of the estimate. Our estimates are more robust for high-and upper middle-income countries, for which there are more datasets and measures over time.
Other international data systems are similarly deficient. The Global Adult Tobacco Survey (WHO, 2017) last conducted in 2018 (in China, Romania and Tanzania) has been conducted in 32 countries but only has data on e-cigarette use for six countries. The WHO STEPS (WHO, 2020) data collection system which is used for NCD risk factor surveillance now includes optional questions for e-cigarettes implemented in six countries (WHO, 2021a). The US Centers for Disease Control Global Tobacco Surveillance System (CDC's Office on Smoking and Health, 2016) aim to enhance country capacity to design, implement and evaluate tobacco control interventions, but none of the surveys it promotes measures adult NVP use. There are clearly data deficiencies. Despite international and national policy interest in the uptake of NVP, global availability of good data remains scarce.
Literature search and data extraction We performed a literature search in PubMed and Embase to identify all surgical publications reported by Endo-ERN MTG6 pituitary reference centers between January 2010 and September 2019. Search terms were the names of the reference centers' representatives as known on October 2019. These names were combined with a previously used search strategy for pituitary surgery  (8). Articles were eligible if they reported surgical outcomes of noncongenital pituitary patients with acromegaly, Cushing's disease, prolactinomas, and non-hormone-producing adenomas, operated in MTG6 pituitary reference centers. Although Endo-ERN is especially of value for patients with ultra-rare conditions (e. g. TSH-and gonadotropinproducing adenoma), parasellar masses, and exceptional presentations (e.
Main analysis For the main analyses, studies reporting data on adult patients treated within MTG6 centers were used. Pooled results are reported with 95% CI for the percentage of patients achieving biochemical remission, gross total adenoma resection, visual improvement in those with preoperative impairment, and surgical complications. Biochemical remission was analyzed separately for patients with GH-, ACTH-, and prolactin-producing adenomas. Gross total resection was analyzed for all patients together and for non-functioning and hormone-producing adenomas separately. Complications were analyzed without distinction between adenoma types.
Statistical methods employed Random-effects meta-analysis was used, following the Dersimonian and Laird method  (10). A Freeman-Tucky double arcsine transformation was performed to include studies with 0 or 100% outcomes  (11). In-between study heterogeneity was described with I 2. Analyses with less than three studies were omitted. All statistics were performed using IBM SPSS Statistics for Windows version 23. 0 and Stata version 14. 1 (StataCorp).
Our results also exemplify the need for further collaborative research. The number of published studies on pituitary adenoma surgery is still relatively small, especially for patients with prolactinomas and non-functioning adenomas, and even more for specific conditions, such as aggressive tumors, pediatric tumors, and patients with ultra-rare subtypes of adenoma, such as TSH-producing adenomas and gonadotropins-producing adenomas. However, in recent years, the number of publications is increasing, although large patient numbers cannot be achieved by single centers. Through harmonized data collection within the Endo-ERN registries, surgical results of these rare conditions will be collected and highlighted in future Endo-ERN publications. Furthermore, the use of patient-reported outcome measures (PROM) was very limited in the published studies (n = 3 studies).
Future directions While the results of this study could be used as a first benchmark for the outcomes of pituitary adenoma surgery within Endo-ERN, the great heterogeneity between studies in the definitions, measurement and reporting of outcomes hampers comparability. Currently, EuRREca is collecting data for pituitary conditions through an e-reporting program (e-REC) and a core registry. Uniform data collection is a prerequisite, requiring clear and uniform definitions of collected data elements to ensure e220349 12:1 comparability of data  (16). Therefore, the board of MTG6 pituitary is planning to develop a consensus statement on the reporting of surgical results. Information on the definitions of outcomes as provided by our study will provide a starting point for this consensus statement.
We further analyze observations of this AR by AIA  (Lemen et al. 2012 ) on board the Solar Dynamics Observatory (SDO;  Pesnell et al. 2012)  with 12 s cadence and an exposure time of 2. 9 s. The AIA observations contain seven broad passbands; however, we focused on the AIA 304 Å channel dominated by the He II line (forming at ≈10 5 K) for the rain detection and its analysis. The pixel size of AIA is 0 6 pixel -1 ; however, for the purpose of co-alignment, we have rebinned the AIA data to match the SJI plate scale. In order to distinguish between the different data sets used in this study, we will use specific names for each. The data set we mentioned above hereafter will be referred to as the "rain data set," while the data set below will be referred to as the "pulsation data set. " We also study the long-term intensity pulsations of this region using 3-day (starting from the 1st of June and ending on the 4th of June) level 1.