dataesr/scientific-paragraphs-categorization huggingface dataset:
ACKNOWLEDGEMENTS The work was supported by funding from the  German Federal Ministry for Education and Resarch (BMBF) , the  French Agence Nationale de la Recherche , and the  European Commission  ( ERA-NET NEURON, "SMART "), as well as the  Munich Cluster for Systems Neurology (SyNergy). A. A. N. was supported by a Bayhost Scholarship. We thank  Monika Zimmermann ,  Julia Vandrey , and  Irmgard Zaus  for technical assistance. We thank the  ENCODE consortium  for genomic data, particularly the  Bing Ren laboratory  for mouse embryonic ATAC-seq data and the  John Stamatoyannopoulos laboratory  for human DNase-seq data. The  Genotype-Tissue Expression (GTEx) Project  was supported by the  Common Fund  of the Office of the Director of the  National Institutes of Health , and by  NCI ,  NHGRI ,  NHLBI ,  NIDA ,  NIMH , and  NINDS.
Huanglongbing (HLB), known as citrus greening disease, is associated with phloem-restricted alpha-proteobacterium: 'Candidatus Liberibacter asiaticus,' 'Ca. L. africanus,' and 'Ca. L. americanus'  (Bov e 2006). Cacopsylla citrisuga (Yang & Li) (Hemiptera: Psyllidae), is an important pest of Citrus and Poncirus plants (Rutaceae), has been proved to be a new insect vector of 'Candidatus Liberibacter asiaticus'  (Cen et al. 2012). This pest belongs to the genus Cacopsylla Ossiannilsson, 1970, which is the largest psyllid genus comprising ca. 470 species (https://hemiptera-databases. org/). To date, 359 complete or near complete mitogenomes from species of the psyllids (family Psylloidea) have been sequenced, among which only 24 are from the genus Cacopsylla; in addition, the evolution relationship of Cacopsylla citrisuga with other psyllids is still less described.
In this study, we analyzed and reported the first mitogenomes of Cacopsylla citrisuga and built its phylogenetic history with other psyllids. The adult specimens used in this study were collected at Mengxiu, Yunnan, China (24. 075618 N, 97. 741441 E) in June 2018. The collected specimen was kept in 80% ethyl alcohol and deposited in Insect Collection of Zhejiang A&F University (ZAFU-INSECT-Y501). Whole genome DNA was extracted using the QIAamp DNA Micro Kit (Qiagen Hilden, Germany) and sequenced on a HiSeq 2000 platform (Illumina, USA) with a paired-end 150-bp sequencing strategy. Clean data screening and de novo assembly also follows the method of  Wang et al. (2018). We obtained 14,906 base-pair of assembled, circular mitochondrial genomic sequences for C. citrisuga (GenBank accession number: MT990978).
The assembled psyllid mitochondrial genome fit well within the conserved nature of the mitochondrial genomes of Hemiptera  (Wang et al. 2015) : contain 13 protein-encoding genes (PCGs; COI-III, ND1-6, ND4L, ATP6, ATP8, and CytB), 22 tRNA genes, two ribosomal RNAs and a non-coding A þ Trich region (Supplementary Table  S1 ; Figure  S1 ). The overall base composition of the mitochondrial genome is A (38. 2%), T (34. 5%), C (17. 7%), G (9. 6%). Phylogenetic trees were constructed based on 13 PCGs sequences from 40 species (Supplementary Table  S2 ) by Bayesian Inference (BI) and maximum likelihood (ML) analyses. The 40 mitochondrial sequences were aligned by the MAFFT version 7 software  (Katoh and Standley 2013). Bayesian Inference was conducted by MrBayes 3. 2. 1  (Ronquist and Huelsenbeck 2003)  and maximum likelihood (ML) analyses implemented in IQ-TREE 1.
Background: Paediatric outpatient prescription (POP) monitoring is pivotal to identify inadequate prescriptions and optimize drug use. We aimed at describing recent trends in POPs in France. Methods: All reimbursed dispensations of outpatient prescribed drugs (excluding vaccines) were prospectively collected for the paediatric population (<18 years old) in the French national health database in 2010À2011 and 2018À2019 (mean 117,356,938/year). POP prevalence (proportion of children receiving 1 drug prescriptions/year) was calculated by age groups and compared by prevalence rate ratios (PRRs). Given the large sample size, 95% confidence intervals of POP prevalences and PRRs did not differ from estimates. Findings: Among the 14,510,023 children resident in France in 2018À2019, mean POP prevalence was 857% children.
General methodology We conducted a national administrative database analysis and followed the RECORD-PE guidelines to report the results  [27]. The French National Health Data System (Syst eme national de donn ees de sant e [SNDS]) covers 988% of the French population and includes reimbursement data for ambulatory care (the health insurance claims database, or Datamart de Consommation Inter-R egime  [DCIR] ) for all children covered by the Universal Public Health Insurance in France  [28] [29] [30]. Every user (or parent user for children) of the French Public Health Insurance is informed of his/her opposition right regarding the use of their data for research purposes. The EPI-PHARE scientific group has regulatory permanent access to the SNDS database (French Decree no. 2016À1871 from  December 26, 2016).
In this study, the term "prescriptions" refers to prescribed and dispensed reimbursed drugs in outpatient settings and excludes vaccines and not-reimbursed over-the-counter (OTC) drugs. Vaccines were excluded because free-of-charge (non-reimbursed) vaccinations are performed in dispensaries and account for as much as 5% of vaccinations in children <6 years old, thereby preventing a population-based exhaustive evaluation with the DCIR database. In France, some drugs available as OTCs are also reimbursed when prescribed because they are included on the list of reimbursable pharmaceutical specialities (e. g. , paracetamol, NSAIDs). They are then evaluable in the DCIR. However, drugs not included in this list (e. g. , homoeopathy, phytotherapy, carbocysteine) or purchased as OTCs without a prescription were not evaluable in the DCIR.
Added value of this study This comprehensive study of the French paediatric prescription databaseincluding a mean of 117,356,938 prescriptions/year showed up-to-date POP prevalences and the last decade trends by age groups. POPs remained highly prevalent in France throughout the 2010s and consistently by sex and different age groups including neonates, with only few improvements in selected therapeutic classes. This study especially highlighted a persistent high POP prevalence for children <6 years old (971%), for systemic corticosteroids (210%) and antibiotics (405%). Some regulatory decisions and safety warnings probably contributed to a decrease in some POP trends (eg. antibiotics, NSAIDs). Other decisions led to a substitution of drugs with additional safety concerns.
Statistical analyses To describe the general characteristics of the study population in 2010À2011 and 2018À2019, we calculated the total distribution of person-years by sex and age groups over these 2 periods. Then, we calculated the median (interquartile range  [IQR] ) number of drugs prescribed by year, for the overall paediatric population then by age groups adapted from those suggested by the International Council for Harmonisation of Technical Requirements for Pharmaceuticals  [1]  ( 6 weeks of life, 0À23 months, 2À5 years, 6À11 years, 12À17 years). Because the DCIR database is not exhaustively updated each year for children without any drug prescription, we approximated the total number of person-years spent in an age category by averaging the number of children residing in France on January 1 of the investigated year and the following year by using the census figures provided by the National Institute of Statistics and Economical studies  [32, 33].
Main results and interpretation In this first comprehensive analysis of the national paediatric prescription database in France, one of the largest in the world, the current overall POPs (prevalence of 857%) was the highest as compared with other countries or regions with similar economies, such as New Zealand (731%)  [14] , British Columbia (Canada) (550%)  [38] , Denmark (508%)  [39] , and Italy (491%)  [40]. The high POPs was observed consistently by sex and different age groups including neonates, the most vulnerable group, but also amongst most therapeutic classes. These high levels of POP are not explained by a different epidemiology of diseases in France versus neighbouring countries  [41]  but more probably by French specificities in prescribing and reimbursing drugs for the paediatric population  [21, 22, 42, 43].
Strengths and limitations This is the first analysis of POPs in France including in-depth analyses by therapeutic classes and ATC level 5 drugs as well as 10-year trends. This time interval allowed for observing the potential impact of some clinical guidelines or regulatory decisions that occurred during this period. The SNDS database is optimal for drug prescription monitoring owing to its exhaustive population coverage including for example 14,510,023 children in the 2018À2019 period. A: alimentary tract and metabolism, B: blood and blood forming organs, D: dermatologicals, G: genito-urinary system and sex hormones, H: systemic hormonal preparations, J: anti-infective agents for systemic use, M: musculoskeletal products, N: nervous system, R: respiratory system, S oph: sensory organs, ophthalmological, S oto: sensory organs, otological; NSAID: non-steroidal anti-inflammatory drugs; FGD: functional gastrointestinal disorders; GORD: gastroesophageal reflux disease.
A: alimentary tract and metabolism, B: blood and blood forming organs, D: dermatologicals, G: genito-urinary system and sex hormones, H: systemic hormonal preparations, J: anti-infective agents for systemic use, M: musculoskeletal products, N: nervous system, R: respiratory system, S oph: sensory organs, ophthalmological, S oto: sensory organs, otological; NSAID: non-steroidal anti-inflammatory drugs; FGD: functional gastrointestinal disorders; GORD: gastroesophageal reflux disease. Our study has limitations. First, the national pharmaceutical claims database does not collect information on the indications for drug prescriptions, which precludes any analysis of the appropriateness of drug prescriptions. Second, as in many other studies, dispensed prescriptions were used as a proxy for drug prescription  [14, 15].
Data statement The procedures carried out with the French data privacy authority (CNIL, Commission nationale de l'informatique et des libert es) do not provide for the transmission of the database. Consultation by the editorial board or interested researchers may nevertheless be considered, subject to prior determination of the terms and conditions of such consultation and in respect for compliance with the applicable regulations. All requests for access must be submitted to the Health data hub. Further information to do this request is available on these websites: À https://www. snds. gouv. fr/SNDS/Processus-D-acces-aux-donnees À https://documentation-snds. health-data-hub. fr/introduction/03acces-snds. html#les-acces-sur-projet.
Pathologies related to the cardiovascular system are the leading causes of death worldwide. One of the main treatments is conventional surgery with autologous transplants. Although donor grafts are often unavailable, tissue-engineered vascular grafts (TEVGs) show promise for clinical treatments. A systematic review of the recent scientific literature was performed using PubMed (Medline) and Web of Science databases to provide an overview of the state-of-the-art in TEVG development. The use of TEVG in human patients remains quite restricted owing to the presence of vascular stenosis, existence of thrombi, and poor graft patency. A total of 92 original articles involving human patients and animal models were analyzed. A meta-analysis of the influence of the vascular graft diameter on the occurrence of thrombosis and graft patency was performed for the different models analyzed.
Statistical Analysis A meta-analysis of the influence of the vascular graft diameter on thrombosis occurrence and patency was performed for the different models analyzed. For this purpose, the normality of the data distribution was analyzed using the Kolmogorov-Smirnov test. As the distribution of the data was not normal, we used the nonparametric Mann-Whitney U-test for independent samples to compare the data. Additionally, the relationship between qualitative variables of heparin use during surgery and graft patency and the onset of thrombosis was analyzed. The Fisher's exact test was used for this purpose. All statistical analyses were conducted using R (version 4. 0. 0, R Foundation for Statistical Computing, Vienna, Austria). For all tests, p < 0. 05 was considered to be statistically significant.
Selection of Articles A total of 680 and 838 records were identified in PubMed and Web of Science databases, respectively. Based on the analysis of the title and abstract of each article, 907 records were excluded because: 1) the subject did not comply with the goal of this review (431); 2) they were types of studies that did not meet the inclusion criteria (454); or 3) they were in a language other than English (22). After completely reading these articles, 92 studies were selected, 89 of which used animal models and three were conducted with human patients (Figure  1 ).
Never before such a vast amount of data, including genome sequencing, has been collected for any viral pandemic than for the current case of COVID-19. This offers the possibility to trace the virus evolution and to assess the role mutations play in its spread within the population, in real time. To this end, we focused on the Spike protein for its central role in mediating viral outbreak and replication in host cells. Employing the Levenshtein distance on the Spike protein sequences, we designed a machine learning algorithm yielding a temporal clustering of the available dataset. From this, we were able to identify and define emerging persistent variants that are in agreement with known evidences. Our novel algorithm allowed us to define persistent variants as chains that remain stable over time and to highlight emerging variants of epidemiological interest as branching events that occur over time.
In this work, a mutation is a single change in the amino acid sequence of the Spike protein (substitution, addition, deletion), while a Spike variant, or simply variant, is a unique sequence of amino acids in the Spike protein that appears in clusters. Like other coronaviruses, the SARS-CoV-2 has relatively low mutation rates  2  , nevertheless the current COVID-19 pandemic has seen the emergence of several epidemiologically relevant variants. Efficient nucleotide sequencing has allowed to track 2/ 16  sequence mutations along the genome of SARS-CoV-2, and to identify dangerous variants  3, 4  that appeared to increase the infectivity compared to the initial form that was sequenced from the outbreak in Wuhan, China  5  (GenBank: MN908947. 3). Since the second half of 2020, variants of concern (VoCs) and of interest (VoIs) have been identified in various regions of the world.
Results Spike protein sequences have been extracted from the GISAID repository on a country-specific basis and the date-stamp associated to each sequence has been used to obtain a temporal dimension of viral variants appearance. Note that each genome sequence in the GISAID data collection corresponds to the most frequent Spike variant occurring in a single infected individual. The pruned dataset (see supplementary material) has been clustered per month to obtain groups of distinct variants. Firstly, we computed the LM between each pair of sequences, thus counting by an unweighted approach the minimal number of amino acid substitutions, deletions and insertions needed to transform one sequence into the other, and vice-versa. Secondly, the algorithm constructed a tree of proximity by pairing sequences that are the closest to each other into a branch.
To combine branches that contain more than one sequence, we used Ward's method, after having checked that other choices do not significantly affect the results (more details in the supplementary material). The tree is completed when all sequences are grouped into a single branch. To define the clusters, we considered a cutoff in the distance so that branches whose Ward distance is larger than the cutoff are considered as separate clusters. We applied the same cutoff to all branches. As England has the largest available sequencing sample, with 646. 697 sequences as of the end of August 2021, we mainly focused on this dataset. This minimises statistical and sampling bias errors. After pruning, 461. 122 sequences were retained,      out of which we identified 13. 887 distinct ones.
Emerging variants as time-ordered cluster chains The time evolution and emergence of SARS-CoV-2 variants can be studied by applying our ML algorithm to the Spike sequence data binned in time, by calendar month. Hence, we have divided the sequence dataset for England following the date tag in the GISAID repository. For each month, we run the ML algorithm on the pruned data to define clusters, retaining only the ones comprising at least 1% of the monthly dataset. The cutoff on the Ward distance r W between branches, as well as the 1% threshold above, were chosen to optimise the coverage of the dataset (i. e. we required that the defined clusters cover at least 90% of the data) while keeping the number of clusters below 10. The optimisation analysis, presented in the supplementary material, showed that the optimal range for the branch cutoff is r W ∈ [50, 200].
In this respect, the results for r W = 200 can be directly compared to the VoCs identified by the WHO: Comparing the frequencies of occurrence, we see that v2 can be associated to the Alpha VoC, while v3 matches the epidemiological data for the Delta VoC. We also checked that the dominant Spike variant for the two chains presents the mutations characteristic of the two VoCs: N501Y, D614Y and P681H for the Alpha VoC; L452R, T478K, D614G and P681R for the Delta VoC. These results have been corroborated by a cluster analysis of the global dataset, without time binning, and by a similar analysis for the data of Wales and Scotland, as shown in the supplementary material. The chain analysis, however, allowed us to better probe the time evolution and emergence of the persisting variants. To do so, for the clusters at the beginning of each chain, we defined a branching link with the cluster in the previous month.
In the middle plots, the data are shown by dots, where blue corresponds to the total and the colours show the number of infected associated to each variant. The solid lines show the result of the fits to the MeRG model (note that only for the UK we fit the "standard variant" -in greenwith two logistic functions). In the left plots, the error derives from the expected statistical variation on the number of daily sequences (after smoothening). For all the plots, the classification in variants derived from the GISAID data.
This procedure yields a reliable estimate of the diffusion of each variant. For this purpose, we used the full dataset from GISAID for the whole UK, using the VoC classification embedded in the GISAID data. As shown before, this classification is equivalent to the result of our ML approach. The result is shown in the top row of Fig. 5 , where we show the number of sequences (left plot), the new number of infections per variant and the result of the MeRG fit (middle) and the frequency of the VoCs (right). Note that the total numbers are plotted in blue, while the VoCs in colours. We considered the epidemiological data from the most recent waves, which developed between September 2020 and February 2021. The green curve in the middle plot shows that, after the first peak at the beginning of November, a second smaller peak developed.
We saw empirically from Fig. 2  that persisting chains contain at least three clusters, hence an emerging variant could be defined only two months later (January 2021 for the Alpha VoC). To improve the performance of the ML algorithm in terms of prediction, we reanalysed the same data using a weeklyrather than monthly-based binning (Table  2 ). The cutoff on the Ward distance needs to be adjusted for the weekly analysis, thus leading to a choice that differs slightly from that of Fig. 2. Using a cutoff r W = 95 (instead of 100) as well as the 1% threshold,   2. Early warning for the Alpha VoC. Results of the ML analysis applied to weekly binned data after the first detection of the Alpha VoC in England. The columns contain the 2020 calendar week number, with the initial date (Monday), the total number of sequences in the dataset for each week and the percentage of Alpha VoC sequences identified a posteriori in the data.
I. ) for the early warning, and it is plotted in Fig. 6B  as a function of the Alpha VoC percentage in each week dataset. The C. I. can be interpreted as the probability of identification of the correct emerging variant via the first cluster. The result shows that probabilities above 50% require the presence of the new variant in at least 1% of the sequences. In Fig. 6C , we show the Alpha VoC percentage as function of time in weeks. An early warning can be issued as soon as the new variant surpasses 1% of the data, leading to an early warning for the Alpha VoC in week 44, i. e. 6 weeks after the first detection. If the chain persists for 3 more weeks, an emerging variant is identified in week 47, hence 6 weeks before the official classification as a VoC by WHO. It is possible to reduce the time-scale below a week by increasing the sequencing.
We validated the method with both monthly and weekly time binning. We applied the algorithm to the sequencing data from England, which offers the largest dataset on the GISAID open-source genome repository. Among the emerging variants, the officially recognised VoCs (Alpha and Delta) were clearly identified and isolated. Similar results for Wales and Scotland (despite a more limited number of available sequences) confirmed the 12/16 effectiveness of the algorithm, while comparison of our approach (that uses data of the Spike protein sequences only) to other informed methods based on the complete genome validated the algorithm. Furthermore, the temporal organisation of clusters into chains served as a tool not only to monitor the genetic evolution of the Spike protein but also to help shed light on its mechanisms.
On the one hand, from the temporal chain analysis branching relations arouse from which we can reconstruct the evolutionary diversification that leads to the establishing of emergent variants. On the other hand, within a single chain, the analysis of mutations of subdominant Spike variants permitted to distinguish regions of the sequence with a high frequency of mutations from those in which no amino acid substitutions take place over time. Using the relative percentage of each variant in the sequencing dataset to estimate the number of individuals infected by each variant, we correlated our temporal chain analysis with epidemiological data. We discovered that each new wave of the COVID-19 pandemic in England (and similarly in Scotland and Wales) was driven and dominated by a new emerging variant.
This observation corroborates the hypothesis that there exists a strong and direct causal relation between the emergence of a new variant and the onset of a new epidemic wave. We modelled the cumulative number of infected individuals by use of the MeRG framework that we proposed in a companion manuscript  24. We also used epidemiological data from the whole UK, California, India and South Africa to confirm the validity of the model. Finally, in view of potential future waves of COVID-19, we tested the viability and performance of our ML algorithm as an early warning tool to detect the emergence of a new, epidemiologically dangerous, variant. We demonstrated that the Alpha VoC could be established as the dominant variant of an emerging persistent chain 9 weeks after its first detection in the England data set.