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Abstract Background As genomes of many eukaryotic species, especially plants, are large and complex, their de novo sequencing and assembly is still a difficult task despite progress in sequencing technologies. An alternative to genome assembly is the assembly of transcriptome, the set of RNA products of the expressed genes. While a bunch of de novo transcriptome assemblers exists, the challenges of transcriptomes (the existence of isoforms, the uneven expression levels across genes) complicates the generation of high-quality assemblies suitable for downstream analyses. Results We developed Trans2express – a web-based tool and a pipeline of de novo hybrid transcriptome assembly and postprocessing based on rnaSPAdes with a set of subsequent filtrations. The pipeline was tested on Arabidopsis thaliana cDNA sequencing data obtained using Illumina and Oxford Nanopore Technologies platforms and three non-model plant species. The comparison of structural characteristics of the transcriptome assembly with reference Arabidopsis genome revealed the high quality of assembled transcriptome with 86.1% of Arabidopsis expressed genes assembled as a single contig. We tested the applicability of the transcriptome assembly for gene expression analysis. For both Arabidopsis and non-model species the results showed high congruence of gene expression levels and sets of differentially expressed genes between analyses based on genome and based on the transcriptome assembly. Conclusions We present Trans2express – a protocol for de novo hybrid transcriptome assembly aimed at recovering of a single transcript per gene. We expect this protocol to promote the characterization of transcriptomes and gene expression analysis in non-model plants and web-based tool to be of use to a wide range of plant biologists.

Abstract Background The burden of lumbar degenerative diseases (LDD) has increased substantially with the unprecedented aging population. Identifying elderly patients with high risk of postoperative adverse events (AEs) and establishing individualized perioperative management is critical to mitigate added costs and optimize cost-effectiveness to the healthcare system. We aimed to develop a predictive tool for AEs in elderly patients with transforaminal lumbar interbody fusion (TLIF), utilizing multivariate logistic regression, single classification and regression tree (hereafter, “classification tree”), and random forest machine learning algorithms. Methods This study was a retrospective review of a prospective Geriatric Lumbar Disease Database (age ≥ 65). Our outcome measure was postoperative AEs, including prolonged hospital stays, postoperative complications, readmission, and reoperation within 90 days. Patients were grouped as either having at least one adverse event (AEs group) or not (No-AEs group). Three models for predicting postoperative AEs were developed using training dataset and internal validation using testing dataset. Finally, online tool was developed to assess its validity in the clinical setting (external validation). Results The development set included 1025 patients (mean [SD] age, 72.8 [5.6] years; 632 [61.7%] female), and the external validation set included 175 patients (73.2 [5.9] years; 97 [55.4%] female). The predictive ability of our three models was comparable, with no significant differences in AUC (0.73 vs. 0.72 vs. 0.70, respectively). The logistic regression model had a higher net benefit for clinical intervention than the other models. A nomogram based on logistic regression was developed, and the C-index of external validation for AEs was 0.69 (95% CI 0.65–0.76). Conclusion The predictive ability of our three models was comparable. Logistic regression model had a higher net benefit for clinical intervention than the other models. Our nomogram and online tool ( https://xuanwumodel.shinyapps.io/Model_for_AEs/ ) could inform physicians about elderly patients with a high risk of AEs within the 90 days after TLIF surgery.

Abstract Background The alertness and practice of health care providers (HCPs) in the correct management of suspected malaria (CMSM) (vigilance) is a central component of malaria surveillance following elimination, and it must be established before malaria elimination certification can be granted. This study was designed to develop and validate a rapid tool, Simulated Malaria Online Tool (SMOT), to evaluate HCPs’ practice in relation to the CMSM. Methods The study was conducted in East Azerbaijan Province, Islamic Republic of Iran, where no malaria transmission has been reported since 2005. An online tool presenting a suspected malaria case for detection of HCPs’ failures in recognition, diagnosis, treatment and reporting was developed based on literature review and expert opinion. A total of 360 HCPs were allocated to two groups. In one group their performance was tested by simulated patient (SP) methodology as gold standard, and one month later by the online tool to allow assessment of its sensitivity. In the other group, they were tested only by the online tool to allow assessment of any possible bias incurred by the exposure to SPs before the tool. Results The sensitivity of the tool was (98.7%; CI 93.6–99.3). The overall agreement and kappa statistics were 96.6% and 85.6%, respectively. In the group tested by both methods, the failure proportion by SP was 86.1% (CI 80.1–90.8) and by tool 87.2% (CI 81.4–91.7). In the other group, the tool found 85.6% (CI 79.5–90.3) failures. There were no significant differences in detecting failures within or between the groups. Conclusion The SMOT tool not only showed high validity for detecting HCPs’ failures in relation to CMSM, but it had high rates of agreement with the real-world situation, where malaria transmission has been interrupted. The tool can be used by program managers to evaluate HCPs’ performance and identify sub-groups, whose malaria vigilance should be strengthened. It could also contribute to the evidence base for certification of malaria elimination, and to strengthening prevention of re-establishment of malaria transmission.

Abstract Background Dizziness and vertigo affect around 15% of adults annually and represent common reasons for contacting health services, accounting for around 3% of all emergency department visits worldwide. Vertigo is also associated with excessive use of diagnostic imaging and emergency care and decreased productivity, primarily because of work absenteeism. Vestibular rehabilitation is an evidence-based treatment for chronic dizziness and supervised group exercise therapy has recently been shown to be effective after vestibular neuritis, a common cause of acute onset vertigo. However, such interventions are not readily available and there is a need for more easily accessible tools. The purpose of this study is to investigate the effects on vestibular symptoms of a 6-week online vestibular rehabilitation tool after acute onset vertigo, with the aim of aiding vestibular rehabilitation by presenting a more accessible tool that can help to reduce recovery time. Methods Three hundred twenty individuals diagnosed with acute vestibular syndrome (AVS) will be recruited from multiple hospitals in Sweden and the effects of an online vestibular rehabilitation tool, YrselTräning, on vestibular symptoms after acute onset vertigo will be compared to standard care (written instructions leaflet) in a two-armed, evaluator-blinded, multicenter randomized controlled trial. The primary outcome will be the Vertigo Symptom Scale Short Form (VSS-SF) score at 6 weeks after symptom onset. Secondary outcomes include effects of the intervention on activities of daily living, mood and anxiety, vestibular function recovery, mobility measures, health economic effects, and the reliability of the Swedish VSS-SF translation. Discussion Participants using the online vestibular rehabilitation tool are expected to recover earlier and to a greater extent from their symptoms as compared to standard care. Since up to 50% of people with AVS without treatment develop persistent symptoms, effective treatment of AVS will likely lead to a higher quality of life and help reduce the societal costs associated with dizziness and vertigo. Trial registration Clinicaltrials.gov NCT05056324 . Registered on September 24, 2021.

Abstract Genetic map is a linear arrangement of the relative positions of sites in the chromosome or genome based on the recombination frequency between genetic markers. It is the important basis for genetic analysis. Several kinds of software have been designed for genetic mapping, but all these tools require users to write or edit code, making it time-costing and difficult for researchers without programming skills to handle with. Here, MG2C, a new online tool was designed, based on PERL and SVG languages. Users can get a standard genetic map, only by providing the location of genes (or quantitative trait loci) and the length of the chromosome, without writing additional code. The operation interface of MG2C contains three sections: data input, data output and parameters. There are 33 attribute parameters in MG2C, which are further divided into 8 modules. Values of the parameters can be changed according to the users’ requirements. The information submitted by users will be transformed into the genetic map in SVG file, which can be further modified by other image processing tools. MG2C is a user-friendly and time-saving online tool for drawing genetic maps, especially for those without programming skills. The tool has been running smoothly since 2015, and updated to version 2.1. It significantly lowers the technical barriers for the users, and provides great convenience for the researchers.

Abstract Background Most cost-effectiveness analyses in the context of cervical cancer prevention involve the use of mathematical models to simulate HPV infection, cervical disease and prevention strategies. However, it is common for professionals who would need to perform these analyses to not be familiar with the models. This work introduces the Online Cost-Effectiveness ANalysis tool, featuring an easy-to-use web interface providing health professionals, researchers and decision makers involved in cervical cancer prevention programmes with a useful instrument to conduct complex cost-effectiveness analyses, which are becoming an essential tool as an approach for supporting decision-making that involves important trade-offs. Results The users can run cost-effectiveness evaluations of cervical cancer prevention strategies without deep knowledge of the underlying mathematical model or any programming language, obtaining the most relevant costs and health outcomes in a user-friendly format. The results provided by the tool are consistent with the existing literature. Conclusions Having such a tool will be an asset to the cervical cancer prevention community, providing researchers with an easy-to-use instrument to conduct cost-effectiveness analyses.

Abstract Background High-throughput technologies have brought tremendous changes to biological domains, and the resulting high-dimensional data has also posed enormous challenges to computational science. A Bayesian network is a probabilistic graphical model represented by a directed acyclic graph, which provides concise semantics to describe the relationship between entities and has an independence assumption that is suitable for sparse omics data. Bayesian networks have been broadly used in biomedical research fields, including disease risk assessment and prognostic prediction. However, the inference and visualization of Bayesian networks are unfriendly to the users lacking programming skills. Results We developed an R/Shiny application, shinyBN, which is an online graphical user interface to facilitate the inference and visualization of Bayesian networks. shinyBN supports multiple types of input and provides flexible settings for network rendering and inference. For output, users can download network plots, prediction results and external validation results in publication-ready high-resolution figures. Conclusion Our user-friendly application (shinyBN) provides users with an easy method for Bayesian network modeling, inference and visualization via mouse clicks. shinyBN can be used in the R environment or online and is compatible with three major operating systems, including Windows, Linux and Mac OS. shinyBN is deployed at https://jiajin.shinyapps.io/shinyBN/. Source codes and the manual are freely available at https://github.com/JiajinChen/shinyBN.

Abstract Background Microarray experiments comprise more than half of all series in the Gene Expression Omnibus (GEO). However, downloading and analyzing raw or semi-processed microarray data from GEO is not intuitive and requires manual error-prone analysis and a bioinformatics background. This is due to a lack of standardization in array platform fabrication as well as the lack of a simple interactive tool for clustering, plotting, differential expression testing, and testing for functional enrichment. Results We introduce the Bioinformatics Array Research Tool (BART), an R Shiny web application that automates the microarray download and analysis process across diverse microarray platforms. It provides an intuitive interface, automatically downloads and parses data from GEO, suggests groupings of samples for differential expression testing, performs batch effect correction, outputs quality control plots, converts probe IDs, generates full lists of differentially expressed genes, and performs functional enrichment analysis. We show that BART enables a more comprehensive analysis of a wider range of microarray datasets on GEO by comparing it to four leading online microarray analysis tools. Conclusions BART allows a scientist with no bioinformatics background to extract knowledge from their own microarray data or microarray experiments available from GEO. BART is functional on more microarray experiments and provides more comprehensive analyses than extant microarray analysis tools. BART is hosted on bart.salk.edu, includes a user tutorial, and is available for download from https://bitbucket.org/Luisa_amaral/bart.