dataesr/scientific-paragraphs-categorization huggingface dataset:
3%). However, surprisingly, 20. 2% VarI articles remain rated Stub for article quality. The Evolution & Comparative Genomics (EvolCompGen) and Regulatory and Systems Genomics (RegSys) COSIs also have 6 GA class articles, but more articles classified as relevant. The normalized quality scores of these COSIs (Q ¼ 0. 35 and Q ¼ 0. 34, respectively) are higher than that of the Computational Biology taskforce overall. The Bio-Ontologies COSI is a notable negative outlier in terms of mean article quality (1. 80). Despite having three relevant GA class articles, mean article quality is affected by this COSI having the highest proportion of Stub class articles of all COSIs (41. 4%). Further analysis reveals that many of these Stub class articles are a brief description of bioinformatics databases which are seldom expanded into more fully-formed articles.
The Biological Data Visualizations (BioVis) COSI scores highly in both mean article quality (2. 40) and importance (1. 72). However, unlike the above examples, this appears to be principally driven by a lack of low-quality articles: the BioVis COSI has the smallest proportion of Stub class articles of all COSIs (13. 9%), significantly lower than the Computational Biology taskforce overall (P < 0. 001). Further analysis shows these Stub class articles are all of low importance. Interestingly, the CAMDA COSI similarly scores highly in both measures (2. 36 and 1. 68), yet has the lowest normalized quality score of all COSIs (Q ¼ 0. 10), suggesting that the articles relevant to this COSI are less likely to be of very good or very poor quality. Our analysis of data from the COSI-Article matrix reveals a set of eight articles which are currently rated Top importance and B class for article quality (Fig.
We noted a strong similarity between the Structural Bioinformatics and Computational Biophysics (3D-SIG) and Computational Mass Spectrometry (COMP-MS) COSIs (J ¼ 0. 20), driven by articles relevant to protein structure, including the Structural Classification of Proteins database, UniProt, 3D-Jury and the Human Proteome Folding Project. We also uncovered notable similarities between COSIs from otherwise dissimilar clusters; examples include those between the Bio-Ontologies and Text Mining COSIs (J ¼ 0. 14), and the RegSys and Integrative RNA Biology (IRNA) COSIs (J ¼ 0. 17). Articles common to the Bio-Ontologies and Text Mining COSIs are mainly descriptions of bioinformatics databases and knowledge resources to which text mining methods may be applied. The small number of Note: Normalized article quality score (Q), number of articles (N total ) and number of quality articles (N quality ) are given for selected WikiProjects and taskforces.
At the community level, these synergies could be fostered by joint conference sessions, or by a shared online seminar series. In a pedagogical setting, it may be useful for educators in the area of one COSI to include examples from other clustered COSIs, to frame their teaching in a wider context. Lists of Wikipedia articles relevant to these clustered COSIs, as defined in this study, may also be used as an educational resource, providing background reading material to supplement the main focus of the class. Encouraging these wider connections would promote links between COSIs, but may also strengthen relationships of members within COSIs. While the COSI-Article matrix provides powerful insights into computational biology representation on Wikipedia, we acknowledge some limitations of the current dataset.
Increasing the number of postdoctoral and faculty-level participants to balance PhD student participants would also increase expertise to judge article relevancy in these processes. Second, introducing a relevancy scale, extending from binary data to integer data (e. g. on a 0-10 scale) would provide finer-grained information as to article relevance, which is almost certainly different with regard to each different COSI. Given the continually evolving nature of Wikipedia, we expect refining the dataset to be an iterative activity; we propose an annual hackathon event to address this and make other improvements to computational biology representation on Wikipedia. The iterative process for refining bioinformatics core competencies  (Mulder et al. , 2018; Welch et al. , 2014 Welch et al. , , 2016) )  within the Education COSI could be a model for dataset refinement, particularly in a group discussion session at in-person events.
There are several other important and related tasks which could be carried out as future hackathons, either as part of the ISCB Student Council Symposium  (Hassan et al. , 2018)  or the main ISMB conference  (Gibson, 2012). These tasks may include creating a supervised learning system to refine the COSI-Article classifications, based on keywords supplied by each COSI. Refining the classifications in this way may reduce the subjectivity inherent in the current classification. COSI-defined keywords could also be used to systematically identify existing articles that are relevant to computational biology but not currently tagged as such and are therefore not part of the dataset used in this study. Such a system could be implemented using the Pywikibot library, one of the most commonly used frameworks for creating Wikipedia bots to perform maintenance of MediaWiki sites  (Pfundner et al.
Acknowledgements We thank  ISCB CEO Diane E. Kovats and ISCB Student Council members Nazeefa Fatima ,  Aishwarya Alex  and  Bart Cuypers  for their support in organizing the 2021  ISCB SCS Wikipedia Hackathon. We thank the  ISCB  and the  Harvard Medical School Master of Biomedical Informatics (MBI) Program  for sponsoring the hackathon. We thank  Aparna Chauhan  and  Nusrat Jahan Lily  for hackathon participation,  Russell Schwartz  for useful discussion and  Alex Bateman  and ISMB reviewers for manuscript comments. Financial Support: none declared.
Patients Our earlier prospective study was undertaken to describe and characterise the impact of the clindamycin-administration route on its PK interaction with rifampicin (ClinicalTrials. gov, accessed on 9 September 2022, Identifier: NCT 02629770)  [5]. Only the data obtained from the patients not prescribed rifampicin were used for the present population-PK analysis. This multicenter study was conducted in the Referral Center for Bone and Joint Infections  [18]  of Diaconesses-Croix Saint-Simon Hospital (Paris, France) and Cochin Hospital (Paris, France) from December 2015 to November 2019. The inclusion criteria were patients at least 18 years old hospitalised for a chronic PJI, evolving for >4 weeks and caused by clindamycin-and rifampicin-susceptible staphylococci, streptococci or anaerobic bacteria.
PK Models Concentrations-time data were analyzed using the non-linear mixed-effects modelling program NONMEM 7. 5, combined with the Pirana graphical user interface  [19]. One-and two-compartment models, with zero-and first-order absorption processes were investigated. Interindividual variability was described by assuming that individual parameters arise from a multivariate log-normal distribution with mean vector and variance-covariance matrix to be estimated. Finally, additive, exponential and mixed residual error models were tested.
OBJECTIVE To describe current CKRT practices across European PICUs. DESIGN, SETTING, AND PARTICIPANTS This cross-sectional survey of PICUs in 20 European countries was conducted by the Critical Care Nephrology Section of the European Society of Pediatric and Neonatal Intensive Care from April 1, 2020, to May 31, 2022. Participants included intensivists and nurses working in European PICUs. The survey was developed in English and distributed using SurveyMonkey. One response from each PICU that provided CKRT was included in the analysis. Data were analyzed from June 1 to June 30, 2022. MAIN OUTCOME AND MEASURES Demographic characteristics of European PICUs along with organizational and delivery aspects of CKRT (including prescription, liberation from CKRT, and training and education) were assessed.
Introduction 2] [13] [14] [15] [16]  Most protocols for CKRT are therefore based on institutional, personal, or historical experience. Although a limited number of pediatric studies  2, 3, [5] [6] [7] [8] [9]  have described the demographic characteristics and outcome data of children with critical illness requiring CKRT, detailed information on current practices for the management of pediatric CKRT in Europe is lacking. To address this gap, the Critical Care Nephrology Section of the European Society of Paediatric and Neonatal Intensive Care (ESPNIC) conducted a survey with the aim of describing the current CKRT practices across European PICUs. The results of this study may serve as a catalyst for providing training and education in this field to safely deliver CKRT to children with critical illness.
Study Design and Ethics Approval The Critical Care Nephrology Section of ESPNIC was launched in 2013 and consists of nursing, medical, and allied health professional members. From April 1, 2020, to May 31, 2022, we conducted a cross-sectional anonymous online survey focused on different aspects of CKRT practices in European PICUs. We followed the American Association for Public Opinion Research (AAPOR) reporting guideline. 17 Since no demographic, observational, or interventional data were collected on patients in this survey study, ethical approval was not deemed necessary in accordance with the Common Rule; however, the project was registered as a quality improvement project with King's College Hospital in London, UK. Informed consent from participants was implied from their completion of the survey.
JAMA Network Open | Pediatrics Continuous Kidney Replacement Therapy Practices in European PICUs Care Nephrology Section members, the questionnaire was spread across 20 countries in Europe. Demographic characteristics of European PICUs along with organizational and delivery aspects of CKRT (including prescription, liberation from CKRT, and training and education) were assessed. No identifiable data regarding staff or patients were collected. We targeted CKRT lead professionals from all units to avoid misdiagnosis of variation in practice based on the level of experience of health care professionals. Responses to the survey were excluded if any of the following conditions were met: the PICU did not perform CKRT, the unit was a neonatal intensive care unit only, and the PICU was outside Europe.
This number of IoT devices is expected to continue increasing and reach 43 billion by 2023  (Fredrik, Mark, Alexander, & Jonathan, 2019). Healthcare can take advantage of the potential benefits of IoT devices. According to a recent study conducted by Allied Market Research, 4 the global internet of things in the healthcare market was valued at $113. 751 billion in 2019 and is expected to reach $332. 672 billion by 2027, registering a CAGR of 13. 20% from 2020 to 2027. Smart devices based on IoT technology offer several possibilities of applications such as online surveillance, monitoring of fitness and diseases programs, taking medication, and caring for the elderly. For  Almotiri, Khan, and Alghamdi (2016) , the IoT for health is revolutionizing clinical research and disease therapies. The data is recorded and transferred online which helps doctors to make a quick and accurate diagnosis.
Given this gap, the purpose of this study is to examine the impact of trust on the engagement in the use of IoT for healthcare. This objective leads to the following question: To what extent does trust reinforce the engagement in IoT usage for healthcare settings? Our theoretical model can help to answer this question by analyzing the trust-engagement relationship in the use of IoT devices based on experience and influenced by utility and concerns related to the security and privacy. A non-probability convenience sample was collected of 109 French patients, and the data were then processed using the PLS-SEM approach. This method is chosen for three reasons: it requires neither (1) a large sample size (2) nor data normality and is perfectly adapted (3) to analyze complex structural models with numerous mediating and moderating constructs  (Richter, Cepeda Carrión, Roldán, & Ringle, 2016).
Research methodology The present study aims to determine factors affecting engagement in the use of IoT for healthcare. To test our hypotheses, we are using an exploratory approach within the framework of a questionnaire survey. The recommendations of this research will be validated through a deductive approach that consists in confronting the hypotheses with reality. The perspective adopted follows the "positivist" epistemology. The data for the study were collected from French customers. They were asked to complete an online questionnaire consisting of the model variable measurement scales used in previous studies.
Target population and questionnaire administration In our research context, and according to available resources, we chose the questionnaire data collection method with the online administration mode. This method has become the most widely used by researchers because it allows getting a graphically clear and interactive presentation and guarantees the anonymity of respondents  (Gavard-Perret et al. , 2012). We limited our study to French people who use this technology with filter questions to obtain answers based on experience and interaction with devices. Our data were collected in groups of Facebook that focus on wellness and illness. For better understanding, we started the questionnaire with an introduction that explains IoT for health. Generally, the questionnaire collection method does not allow interaction, but, in our study, we were able to cover this gap depending on Facebook that facilitated interaction with respondents via comments and private messages.
Statistical tool for empirical analysis To empirically examine our theoretical model, we chose the PLS-SEM approach and used SmartPLS software to test the empirical model. This method is based on variance analysis and optimization of the explanatory power of manifest variables  (Fernandes, 2012). It is the most suitable for our research context because it requires neither a large sample size nor data normality. It is more adapted to handle variables with single and multi-item measures  (Hair, Hult, Ringle, & Sarstedt, 2021)  and to solve complex models that contain mediating and moderating variables  (Richter et al. , 2016). It offers an original methodology for dealing with incomplete and missing data and is adapted to new situations where theory is less developed. The PLS-SEM approach proceeds in two steps.
Findings In this section, we present all the results and analyze the factor loads that confirm the hypotheses put forward in our literature review. The data analysis consists of three phases. In the first stage, we examined the demographic profile of respondents. The second step inspected measurement model analysis and the third step consisted of structural model analysis using Smart PLS and Structural Equation Modeling (SEM). The final step was an analysis of the mediator effect using the macro process for SPSS recommended by  Hayes (2017). The Table  below  explains the characteristics of our study sample. First, it shows that the number of respondents is almost equally divided between men and women. They are mainly Executives, or they have Intermediaries professions. Finally, half of the respondents are between 25 and 39 years old (Table  2 ).
The mediating effect of desire for self-development The results of our study examined the mediating role of desire for selfdevelopment and perceived autonomy in the relationship between user trust and engagement. To analyze these effects, we used the macro process for SPSS recommended by  Hayes (2017). After running the Bootstrap technique, we verified the indirect relationship between user trust and engagement. As the results in the Table below (Table  7 ). This relationship is significant  (Hayes, 2017). Next, we calculated the confidence interval for the relationship between user trust and engagement with the desire for self-development as a mediator. As shown in the Table below (Table  8 ), the two limits of this confidence interval are greater than zero, which allows us to confirm the mediating role of the desire for self-development  (Hayes, 2017).
The libraries with unique barcodes were pooled together and sequenced on the Illumina NovaSeq 6000 system using the NovaSeq 6000 S4 300 Cycle Reagent Kits (Illumina, San Diego, CA, USA). In addition to the above, we also capitalized the publicly available whole-genome sequence data for this species (Table  S2 , Supplementary Materials), by retrieving 22 sequence sets representative of nine countries.
Characterization of Variants in the Myostatin Locus The genotype file was loaded into the RStudio software v. 1. 4. 1103 (R version 4. 0. 3) and the variants in the range of ±5 kb upstream and downstream of the myostatin gene were retained by using the Tidyverse package v. 1. 3. 2. Overall, our target region was 16,757 bps long, including 6757 bps of the MSTN gene and 5000 bps upstream and downstream (chr5: 58,454,553-58,471,309). The variants were classified based on their nature (SNPs or indels), their localization (5 -UTR, exonic, intronic, 3 -UTR, intergenic) and their occurrence in the previously published paper by Favia et al. [21]. In addition, the popular [28] Tajima's D neutrality test  [29] , as implemented in VCFtools v0. 1. 13 [30] , was carried out on the chromosome (5) harboring the MSTN locus, performing the analysis on 2000 bps windows.
If the D values are too large or too small, the neutral null hypothesis is rejected. As a rule of thumb, we considered here that values greater than +2 (balancing selection) or less than -2 (directional selection) are likely to be significant  [28]. For the SNPs detected in the exonic regions, the effect of the polymorphisms on the MSTN amino-acid sequence and the codon usage frequency patterns were evaluated. The latter were assessed via the ATGme software  [31] , using the C. dromedarius codon usage table available at http://www. kazusa. or. jp/codon/cgi-bin/showcodon. cgi?species=9838 (accessed on 7 August 2022).
Inter-Specific Comparative Sequence Variant Analysis A sequence comparative analysis of the target myostatin region described above was carried out by BLASTing, the publicly available Camelus dromedarius myostatin reverse-complement sequence (GenBank accession: GCA_000803125. 3) against Camelus bactrianus (GenBank accession: GCA_000767855. 1) and Camelus ferus (GenBank accession: GCA_009834535. 1) correspond-ing sequences. We opted here for using the reverse-complement sequence of the C. dromedarius CamDro3 reference genome (Genebank accession: GCA_000803125. 3) in order to allow for an easier comparison with the previously published paper by Favia et al. [21]. Hence, all of the subsequent analyses were also performed on the CamDro3 reverse-complement sequence.
In Silico Functional Prediction The web-based software TFBIND  [32] , available at https://tfbind. hgc. jp/ (accessed on 7 August 2022), was used to identify the transcription factor binding sites (TFBS) and their possible disruption due to the presence of Single Nucleotide Polymorphism or indels. The TFBIND analysis was carried out, considering the 2 kb upstream the C. dromedarius myostatin gene. In addition, the online bioinformatic tool Human Splicing Finder (HSF)  [33] , available at https://www. genomnis. com/ (accessed on 7 August 2022), was used to perform a Sequence Analysis (Version 1. 5. 1. ). The HSF tool employs several matrices to predict the variants' putative effect on the splicing motifs, including the acceptor and donor splice sites, the branch point and auxiliary sequences known to either enhance or repress the splicing.
Sequence Analysis Our sequence variant analysis performed in the ±5 kb upstream and downstream region of the MSTN gene highlighted the presence of a total of 99 variants (Table  S3 , Supplementary Materials). Out of them, 77 were Single Nucleotide Polymorphisms (SNPs) and 22 were indels. Based on the localization, 72 variants were intergenic (namely, 38 variants in the 5 kb region upstream of the MSTN gene and 34 variants in the 5 kb region downstream of the MSTN gene), 1 SNP in the 5 UTR region, 1 SNP in exon 1, 11 variants in intron 1, 1 SNP in exon 2, 9 variants in intron 2, and 4 SNPs in exon 3 (Table  S3 , Supplementary Materials). The 11 SNPs, previously detected by Favia et al. [21] , in the sequence overlapping with our target region using a dataset of nine dromedaries, were also found in our 183 samples' dataset.
Out of the six SNPs that we detected in the exonic regions of the myostatin gene, none of them was responsible for the amino acid substitution at the protein level (Table  1 ). The frequency of codon usage was tested, for the six SNPs in Table  1. Two rare codons (CCG and GTA, Allele 2 codons in Table  S4 , Supplementary Materials) were detected in the reference MSTN sequence in exon 1 (frequency 8. 1‰) and exon 2 (frequency 5. 5‰). These were converted into common codons (CCA and GTC, Allele 1 codons in Table  S4 , Supplementary Materials) when the alternative alleles detected in our study were fed into the ATGme software. On the other hand, when considering the alternative alleles, a codon in exon 3 (CAT) resulted in being rare (6. 4‰), while being common when considering the reference sequence (codon CAC).
Given the potential relevance of such an observation, we checked whether the TAATAA allele was present in a homozygous state in at least one animal in our cohort, but it appeared that the seven copies of the TAATAA allele were all in a heterozygous state in the seven animals harboring the insertion. These animals were sampled in different countries (Tunisia, Libya, Mauritania, Sudan), with the exception of two samples coming from Morocco but belonging to two different sub-populations (Guerzni and Marmouri). Although all of the animals bearing the TAATAA allele were of African (mainly North-African) origin, still, the geographically scattered distribution of this allele may suggest the presence of homozygous animals as rather uncommon. Similarly to our results, Ceccobelli et al. , also in Marchigiana cattle,  [54]  did not found homozygote-mutated animals in the investigated dataset of 78 samples, and observed only a light superiority in the live weight reached by heterozygous bulls compared to wild-type bulls, although it was not significant.
In site-specific site-response assessments, observation-based site-specific approaches requiring a target-reference recording pair or a regional recording network cannot be implemented at many sites of interest. Thus, various estimation techniques have to be used. How effective are these techniques in predicting site-specific site responses (average over many earthquakes)? To address this question, we conduct a systematic comparison using a large data set which consists of detailed site metadata and Fourier outcrop linear site responses based on observations at 1725 K-NET and KiK-net sites. We first develop machine learning (i. e. random forest (RF)) amplification models on a training data set (1580 sites). Then we test and compare their predictive powers at 145 independent testing sites with that of the one-dimensional (1D) ground response analysis (GRA).
Thus, in this study, geospatial methods are not compared with the approaches based on more site-specific information. A systemic comparison of different approaches (i. e. regression and ML models, GRA, SRI, and c-HVSR) on a common homogeneous data set is instigated by the establishment of a Fourier site-response data set from recordings via GIT  (Nakano et al. , 2015)  and an open-source site database  (Zhu et al. , 2021c)  for thousands of K-NET and KiK-net strongmotion recording sites in Japan (National Research Institute for Earth Science and Disaster Resilience, 2019). Based on these data,  Zhu et al. (2021a, referred  to as the companion paper hereafter) tested and compared the effectiveness of GRA, SRI, and c-HVSR in reproducing the observed site responses at 145 testing sites chosen arbitrarily.