dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgments This project was supported by awards  U54HG007004 ,  U41HG007234 , and  R01MH101814  from the  National Human Genome Research Institute of the National Institutes of Health , as well as from the  Spanish Ministry of Economy and Competitiveness, Centro de Excelencia Severo Ochoa   2013-2017 ,  SEV-2012-0208 ,  Programa de Ayudas FPI del Ministerio de Economía y Competitividad   BES-2012-055848  to A. B. , and  Ministerio de Educación, Cultura y Deporte , under the  FPU programme (Formación de Profesorado Universitario ) with predoctoral fellowship  FPU15/ 03635  to  M. M. A. , as well as the support of the  CERCA programme/Generalitat de Catalunya. D. G. M. is supported by a  "la Caixa"-Severo Ochoa predoctoral fellowship   LCF/BQ/SO15/ 52260001. We also acknowledge support from the  European Research Council (ERC)  under the European Union's  Seventh Framework Programme  ( FP7/2007-2013) / ERC  grant agreement  294653.
When retrieving the video each day, we first checked whether parasitism had occurred, and then trimmed the video to include vision of the parasitic episode to analyze cases of egg laying by cuckoos. For each target nest, the device was installed for a maximum of 6 days. This was because oriental reed warblers usually have a clutch size of 4-6 eggs  (Liang et al. , 2014) , and the cuckoo usually parasitizes during the egg laying period of the host  (Li et al. , 2016; Yang et al. , 2016a Yang et al. , , 2016b Yang et al. , , 2017)). We stopped recordings when the hosts began to incubate. Well-recorded videos were used for analysis of the egg laying process of cuckoos and warblers. Supplementary materials, including the 53 videos (ESM Videos S1-S53) and data referred to this study, can be found at the Dryad Digital Repository (https://datadryad.
org/stash/sha re/9EhA4V4tI9emrU97VjREOhzmCYrE5ujC9owpPLL60VA). We used video-analysis software (Aijianji; accuracy: 0. 04 s per frame; Wangxu Technology Co. Ltd. , China) to analyze the egg-laying process and to record all egg-laying details of cuckoos -from the time the parasites entered the device's field of vision until they left, including the time at which parasitism occurred, presence of the host, time spent by cuckoos around the nest, time spent laying, number of eggs removed by cuckoos, and behaviors such as pecking/biting eggs or opening eyes during egg laying. In cases where nest surroundings were not visible in the video footage, we used sound to quantify the time cuckoos arrived at the nest as hosts would make a warning call or cuckoos would make noise when parasitism occurred.
Generalized linear mixed models (GLMMs) and log-link function were used to compare differences in egg-laying duration (gamma error structure) and number of eggs removed (Poisson error structure) by cuckoos between nests where hosts were or were not present. For all GLMMs, years were treated as a random term and all predicted variables were treated as fixed terms. All data were analyzed using R 3. 6. 1 software (http://www. r-project. org). Chi-square tests were used to test for the frequency of egg removal by cuckoos. Differences were considered significant at the 0. 05 level. In total, we observed 455 oriental reed warbler nests during the egg-laying period, 250 of which were parasitized by common cuckoos. We recorded 53 clear videos of parasitism by cuckoos (ESM Video S1-S53). The cuckoo parasitism process involved the following steps.
Foundation items: This work was supported by the  National Natural Science Foundation of China  ( 31660617  and  31960105  to L. W. W. , 31772453 and  31970427  to W. L. ). L. W. W. was funded by the  Initial Fund Key Laboratories of Guizhou Province  ( 2011-4005 ) and  Joint Fund of the National Natural Science Foundation of China and the Karst Science Research Center of Guizhou Province  ( U1812401 ) Foundation items: This work was supported by the  National Natural Science Foundation of China  ( 31660617  and  31960105  to L. W. W. , 31772453 and  31970427  to W. L. ). L. W. W. was funded by the  Initial Fund Key Laboratories of Guizhou Province  ( 2011-4005 ) and  Joint Fund of the National Natural Science Foundation of China and the Karst Science Research Center of Guizhou Province  ( U1812401 ).
Web App MeMo is a Web app that can be used on any Web browser (computer or tablet). On a tablet, the app can be installed on the home screen to look like a native app and can be used offline. Users first create an account, so their progress can be logged in the database and visualized in the form of charts. Clinicians can create professional accounts and follow the game progression of all patients who agree to be added to their professional lists. The app was developed in HTML5/CSS3 with PHP5/MySQL on backend. gnosis. The platform opened in April 2015. In November 2019, 9654 accounts had been created and 327,838 exercises had been performed. MeMo is accessible at the following URL: https://www. memory-motivation. org/home-2/. It is available in French and English, and an Italian version is in development.
Statistical Analysis Data are presented as mean (SD) for quantitative variables and as frequency and percentage for qualitative variables (sex, education level, diagnosis, and concomitant treatment). Comparisons between the different groups were performed using the Student t-test or Wilcoxon-Mann-Whitney test for quantitative variables and the χ 2 or Fisher exact test for qualitative variables. Paired Wilcoxon tests were used to assess the evolution of scores between different times 2 to 2 (baseline, week 12, and week 24) for each diagnosis of cognitive impairment. Mixed models taking into account data from the same patient were used to assess the interaction (time × group). These results were adjusted for age and sex. All scores were used as the dependent variable. In the active MeMo group, an additional analysis was performed according to the instruction that the app had to be used regularly.
In order to better explore the MeMo group, we checked whether participants adhered to the proposed study protocol. Data for 22 participants were retrieved directly from the app. For one participant, the data could not be recovered at week 12 (no update on the tablet), and another participant had an adverse event at week 12. The number of days of use varied from 1 day to 78 days over a maximum period of 80 days. The same variability was observed in the number of games played (mean 527 [SD 661]). Therefore, to better assess the impact of high and low game uses, the population was divided into participants who used MeMo according to the instructions (about once every 2 days; active MeMo group) and those who used it less (nonactive MeMo group). A 40-day threshold was selected because it is close to the number of active days described by the protocol, and moreover, it appropriately splits the participants into two groups (<40 active days for the nonactive MeMo group and 40-78 active days for the active MeMo group).
Transient hypoparathyroidism and hypocalcemia can also occur with magnesium (Mg) depletion or excess. Many cases remain idiopathic. 3) (4)  This systematic review was prepared in consultation with an experienced medical librarian who executed a comprehensive search in PubMed and Embase. The complete search strings are supplied in Fig. 1. All articles from the database search were added to a reference manager, and duplicates were removed. Inclusion and exclusion criteria were developed to screen articles by abstract using two independent reviewers (KW and CY). Inclusion criteria included articles with publication dates from 1980 to 2020, in the English language, and with primary data and a focus on etiology and pathophysiology of hypoparathyroidism. Exclusion criteria included any article not primarily focusing on the etiology or pathophysiology of hypoparathyroidism, and those with a primary focus on genetic etiologies of hypoparathyroidism were excluded because that topic is covered elsewhere.
Conference abstracts, opinion pieces, commentaries, letters, and case reports were excluded. The full text of articles selected by title and abstract screening were reviewed using the same criteria and methods. Articles retained for analysis underwent cited reference searching. Articles from cited reference searching and those recommended by experts were included if they met the above inclusion and exclusion criteria. Results are presented in a Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) flow diagram and articles were further categorized according to subtopic (Fig. 1 ). A metaanalysis was not performed given the noninterventional nature of the data collected. Risk-of-bias (RoB) and quality assessment were not performed given the nature of the question and data collected.
Acknowledgments We acknowledge unrestricted financial support from:  Amolyt, Ascendis, Calcilytix , and  Takeda. They  had no input into the planning or design of the project, the conduct of the reviews, evaluation of the data, writing or review of the manuscript, its content, conclusions, or recommendations contained herein. Authors' roles: Conceptualization, Visualization: DMS, JLP, KW, CTY. Data Curation, Formal Analysis, Investigation, Methodology, Validation, Software: DMS, JLP, KW, CTY. Project Administration, Funding Acquisition, Resources: DMS, JLP. Supervision: DMS, JLP. Writing-original draft: DMS, JLP, SC, DD, SF, RG, PH, MAL, KW, CTY, JP, AS. Writing-review & editing: DMS, JLP, SC, DD, SF, RG, PH, MAL, KW, JP, CTY, AS, NDP.
67%. The 13,069,588 pairedend reads were filtered according to the read qualities using FastQC version 0. 11. 8 (https://www. bioinformatics. babraham. ac. uk/projects/fastqc/) with default parameters. Finally, the obtained forward and reverse reads were assembled using SPAdes version 3. 13. 1, as previously described  (3, 4). The resulting draft genome sequence has a length of 3,784,443 bp with a GϩC content of 36%. It is composed of 38 contigs (N 50 , 236,370 bp; L 50 , 6) and 36 scaffolds (N 50 , 300,736 bp; L 50 , 6), with 20ϫ coverage. Genome annotation was conducted using Prokka version 1. 14. 5  (5)  with the following options: coding sequences were predicted using Prodigal version 2. 6  (6) , and then the predicted bacterial protein sequences were searched against the GenBank (https://www.
ncbi. nlm. nih. gov/genbank/) database using BLASTp. The search for tRNAs and transfermessenger RNAs (tmRNAs) was done with ARAGORN version 1. 2  (7) , whereas rRNAs were predicted with Barrnap version 0. 4. This annotation predicted a total number of 3,914 genes, out of which 3,830 were protein-coding genes and 84 were RNA-coding genes (10 rRNAs and 74 tRNAs). We performed a BLAST search for the predicted 16S rRNA against the GenBank database. S. euzebyi strain Q1438 showed a 16S rRNA similarity of 99. 93% with S. euzebyi strain CVS-14 T (DSM 19612 T ; GenBank accession number NR_042538. 1). Sixty four percent of genes were assigned to clusters of orthologous groups. Using the ARG-ANNOT (8) and VFDB (9) databases, neither resistance nor virulence genes were found in the genome of S. euzebyi strain Q1438.
Exclusion criteria were: a valid patient identifier was not available in the dataset, if age/sex information was not available, or if they had undergone a TT procedure (Ontario Health Insurance Plan feecode Z914) in the 2 years prior to January 1, 1994. To further characterize the cohorts, the diagnosis of cleft palate/lip was sought (Table  1 ), using a window of 2 years prior to the index date and 5 years following the index date.
Data sources and data linkage The data for this study was obtained from administrative datasets housed at ICES. ICES is an independent, nonprofit research organization funded by the Ontario Ministry of Health and Long Term Care whose comprehensive data holdings include all health care related events for the population of Ontario. The Registered Persons Database (RPDB) contains demographic information for all Ontario residents who are eligible for the Ontario Health Insurance Plan (OHIP). RPDB data is maintained by the Ontario Ministry of Health and Long Term Care. RPDB data includes: health card number, date of birth, gender, address, and deceased date (if applicable). At ICES, all personal identifying information in RPDB is removed. An anonymous unique identifier, the ICES Key Number (IKN) is generated from each health number.
Statistical analysis Descriptive statistics of patient characteristics were obtained. Logistic regression analyses were then utilized to compare ear surgical procedures performed (i. e. , boneconducting hearing aids) and obtainment of an assistive hearing device (i. e. , unilateral/bilateral hearing aids and FM system) claimed in patients between the TT cohort and the No-TT cohort, and between the TT cohort and the Control cohort. Statistical analyses were performed at ICES using SAS Enterprise Guide software, version 7. 1 (SAS Institute, Cary, NC). Two-sided statistical significance was a p-value< 0. 05.
1 ). This analysis was performed only for patients who had an index date of April 1, 1999 or later, as earlier information was not available from the ADP database. Average time from index date to first implanted hearing device (bone-conducting hearing aid) was above 5 years in all three cohorts (Fig. 2 ). By cohort, risks of diagnosis of cleft palate/lip were 1. 621% (TT cohort), 0. 221% (No-TT cohort), and 0. 072% (Control cohort). Evaluating the risks of cleft palate/lip in the subgroup of each cohort who obtained "Any Hearing Rehabilitative Device" revealed that only a minority of patients in each cohort who had a hearing device were patients with cleft palate/lip: 6. 780% (TT cohort), 2. 088% (No-TT cohort), and 1. 292% (Control cohort).
Discussion In a comprehensive dataset, this study reports the need for assistive hearing devices in ear tube patients, recurrent ear disease patients managed non-operatively, and healthy control patients. Furthermore, there is an association between TT and needing an assistive hearing device. This study is not able to comment on causation, and the authors believe causation should not be inferred. Controversy exists regarding the role of TT in pediatric patients with recurrent/persistent middle ear disease. While studies support short term resolution of fluid and improvement of hearing in children with otitis media with effusion  [9] [10] [11] , and possibly reduction in episodes of AOM in children with recurrent acute otitis media  [12] [13] [14] [15] [16] , there is also a possibility that the tubes themselves contribute to the development of chronic ear disease and abnormalities of the eardrum  [3, [17] [18] [19].
Acknowledgements 1. This study was supported by  ICES , which is funded by an annual grant from the  Ontario Ministry of Health and Long-Term Care (MOHLTC). The opinions, results and conclusions reported in this paper are those of the authors and are independent from the funding sources. No endorsement by  ICES  or the Ontario MOHLTC is intended or should be inferred. 2. The dataset was cut at  ICES Queen's (Kingston, Ontario). The dataset creation and data analysis was performed at  ICES Queen 's by  Dr. Paul Nguyen.
(ii) A C. equi isolate probably mistakenly deposited (sometime in the 1950's) as C. hoagii ATCC 7005, which then became the type strain-and only isolate in circulation-of the species  [20]. Given the notorious difficulties in differentiating coryneforms in the 1950/60's and even later, a possible mix-up between two similar-looking cultures is indeed not an implausible scenario. Such mistakes are not uncommon, as illustrated e. g. by the whole genome sequence of the type strain of Rhodococcus rhodnii NRRL B-16535 T (GenBank accession number GCA_000720375. 1), which phylogenetically does not belong to the rhodococci but to an unknown, distant actinobacterium  [13, 14]. Taking together all the above, we understand there are sufficient grounds to consider that the name R. hoagii under which both C.
These virulence plasmids do not appear to Fig. 1. Whole genome sequences of strains DSM 20307 T (=ATCC 6939 T , type strain of Rhodococcus equi; GenBank assembly accession number GCA_002094305. 1) and DSM 20295 T (=ATCC 7005 T , type strain of C. /R. hoagii; GenBank assembly accession number GCA_001646645. 1) compared with the reference (complete) genome sequence of R. equi (hoagii) isolate 103S (NCBI RefSeq NC_014659. 1) [14]. Alignment reconstructed with Mauve software (http://asap. ahabs. wisc. edu/mauve/). Similarity plot is in pink, height indicates level of similarity; plot pointing downwards indicates similarity to the reverse strand of the genome. Blank regions represent fragments that were not aligned or contain genome-specific sequences. Red vertical divisions indicate contigs. Note the dominant solid pink sequence segments which denote near complete conservation between the genomes, consistent with the high degree of genomic relatedness between R.
a b s t r a c t IS1548, a 1316-bp element of the ISAs1 family affects the expression of several genes of the opportunistic pathogen Streptococcus agalactiae. Furthermore, certain lineages of S. agalactiae are more frequently associated to particular diseases than other  [1, 2]. We took advantage of the release of the genome sequences of a huge number of epidemiologically unrelated S. agalactiae strains of various origin to analyze the prevalence of IS1548 among S. agalactiae strains. To this end, S. agalactiae genome available at the National Center for Biotechnology Information (NCBI) database were blasted with IS1548 DNA sequences. A sequence type (ST), based on the allelic profile of seven housekeeping genes, was assigned to each strain possessing IS1548. These strains were then grouped into clonal complexes (CCs).
Data description One hundred and twenty-one S. agalactiae strains with IS1548 positive blastN similarity finding were identified among the 911 strains whose genomes where sequenced and available at the NCBI database on January 19, 2018  [2]. The dataset comprises the analysis of the Multi Locus Sequence type of these strains with the MLST program (Table  1 ) and their classification in clonal complex with the goeburst software (Figs. 1e9), respectively.
Value of the Data As IS1548 plays a critical role in the evolution, the virulence and the host adaption ability of Streptococus agalactiae  [1, 2] , it is important to know which sequenced strains in the huge collection of the NCBI harbor (or not) this IS. It is also useful to know to which sequence type and clonal complex belong these strains as certain lineages of S. agalactiae are more frequently associated to particular diseases than other. Researchers involved in the study of: virulence and host adaptation mechanisms of S. agalactiae, of mobile genetic elements, of bacterial population structure and dynamics, and of S. agalactiae typing, can benefit from these data. The data gives the possibility to compare the sequenced genomes of Streptococcus agalactiae strains, available at the NCBI database, based on their lineage and/or possession of IS1548.
Experimental design, materials, and methods The list of the S. agalactiae strains analyzed is available in Table  S1  of reference  [2]. Sequences of the genomes of these strains were available as whole genome contigs or as complete genome sequences at the NCBI database on January 19, 2018 (https://www. ncbi. nlm. nih. gov/genome/genomes/ 186?). Strains with an IS1548 genomic insertion were identified by blasting these genomes with the IS1548 DNA sequences described in Ref. [2]. To this end, the nucleotide collection (nr/nt) and the whole-genome shotgun contigs (wgs) databases of S. agalactiae (taxid:1311) were screened with the     blastN program optimized for highly similar sequences (megablast). The algorithm parameters were defined as followed: maximum number of aligned sequences to display: 1000; parameters for short input sequences automatically adjusted; expect threshold: 10; word size: 28; maximum matches in a query range: 0; match/mismatch scores: 1,-2; gap costs: linear; filter for low complexity region activated; mask for lookup table only activated.
S. agalactiae strains with an IS1548 positive blastN similarity finding were typed by Multi Locus Sequence Typing (MLST). Seven housekeeping genes were analyzed: alcohol dehydrogenase gene (adhP), phenylalanine tRNA synthetase gene (pheS), amino acid transporter gene (atr), glutamine synthetase gene (glnA), serine dehydratase gene (sdhA), glucose kinase gene (glcK) and transketolase gene (tkt). A sequence type, based on the allelic profile of these housekeeping genes, was assigned to each strain by submitting the complete genome sequence or all of the contigs sequences of each strain to the Streptococcus agalactiae MLST database (http://pubmlst. org/sagalactiae/, [3] , Table  1 ). Identified MLST types were then assigned to clonal complexes defined by the goeBURST algorithm (http://www. phyloviz.
net/goeburst/, [4] , Figs. 1e9) with the ST1 to ST1193 MLST profiles available at the MLST database (https://pubmlst. org/bigsdb? db¼pubmlst_sagalactiae_seqdef&page¼downloadProfiles&scheme_id¼1). A goeBURST clonal complex was defined as all allelic profiles sharing six identical alleles with at least one other member of the group.
Objectives Better knowledge of epidemiology of depressive disorders in people living with hypertension can help to implement pertinent strategies to address its burden. The objective was to estimate the prevalence of depressive disorders and symptoms in people living with hypertension in Africa. Design Systematic review and meta-analysis. Data sources PubMed, EMBASE, African Index Medicus, African Journals OnLine were searched up to 31 January 2020, regardless of the language of publication. Eligibility criteria We included studies conducted among adult patients with hypertension (≥18 years) living in Africa and reporting the prevalence of depressive disorders and symptoms. Data extraction and synthesis Two independent investigators selected studies, extracted data and assessed the methodological quality of included studies by using the tools developed by Joanna Briggs Institute.
Eligibility criteria Condition We considered studies reporting the prevalence (or enough data to compute this estimate) of depressive disorders and symptoms. We considered depressive disorders (and major depressive disorders) diagnosed according to the Diagnostic and Statistical Manual of Mental Health Disorders IV or V,  23 24  or International Statistical Classification of Diseases and Related Health Problems-10. 25 In the studies where depressive disorders were not defined using the aforementioned criteria, we considered the definition used by authors, especially including diagnostic scores such as the Patient Health Questionnaire-9. Major depressive symptoms notably included depressive mood and anhedonia.
Data collection and management A preconceived and standardised data extraction form was used to collect information on the first author's name, study country, year of publication, period of participants' recruitment, study design, setting, sampling method, timing of data collection, response rate, mean or median age of the population, age range, proportion of men, number of participants with hypertension, and number of participants with depressive disorders. In case of multinational studies, data were separated to show the estimate within individual countries. Two investigators (FTE and JJB) independently extracted the data from individual studies, with disagreements being resolved through consensus. Two investigators (FTE and JJB) independently assessed the methodological quality of the included studies with tools developed by the Joanna Briggs Institute,  21  with disagreements being resolved through discussion and consensus.