PyChelator: a Python-based Colab and web application for metal chelator calculations.

BACKGROUND: Metal ions play vital roles in regulating various biological systems, making it essential to control the concentration of free metal ions in solutions during experimental procedures. Several software applications exist for estimating the concentration of free metals in the presence of chelators, with MaxChelator being the easily accessible choice in this domain. This work aimed at developing a Python version of the software with arbitrary precision calculations, extensive new features, and a user-friendly interface to calculate the free metal ions. RESULTS: We introduce the open-source PyChelator web application and the Python-based Google Colaboratory notebook, PyChelator Colab. Key features aim to improve the user experience of metal chelator calculations including input in smaller units, selection among stability constants, input of user-defined constants, and convenient download of all results in Excel format. These features were implemented in Python language by employing Google Colab, facilitating the incorporation of the calculator into other Python-based pipelines and inviting the contributions from the community of Python-using scientists for further enhancements. Arbitrary-precision arithmetic was employed by using the built-in Decimal module to obtain the most accurate results and to avoid rounding errors. No notable differences were observed compared to the results obtained from the PyChelator web application. However, comparison of different sources of stability constants showed substantial differences among them. CONCLUSIONS: PyChelator is a user-friendly metal and chelator calculator that provides a platform for further development. It is provided as an interactive web application, freely available for use at https://amrutelab.github.io/PyChelator , and as a Python-based Google Colaboratory notebook at https://colab. RESEARCH: google.com/github/AmruteLab/PyChelator/blob/main/PyChelator_Colab.ipynb .

Dashing Growth Curves: a web application for rapid and interactive analysis of microbial growth curves.

BACKGROUND: Recording and analyzing microbial growth is a routine task in the life sciences. Microplate readers that record dozens to hundreds of growth curves simultaneously are increasingly used for this task raising the demand for their rapid and reliable analysis. RESULTS: Here, we present Dashing Growth Curves, an interactive web application ( http://dashing-growth-curves.ethz.ch/ ) that enables researchers to quickly visualize and analyze growth curves without the requirement for coding knowledge and independent of operating system. Growth curves can be fitted with parametric and non-parametric models or manually. The application extracts maximum growth rates as well as other features such as lag time, length of exponential growth phase and maximum population size among others. Furthermore, Dashing Growth Curves automatically groups replicate samples and generates downloadable summary plots for of all growth parameters. CONCLUSIONS: Dashing Growth Curves is an open-source web application that reduces the time required to analyze microbial growth curves from hours to minutes.

Development of interactive biological web applications with R/Shiny.

Development of interactive web applications to deposit, visualize and analyze biological datasets is a major subject of bioinformatics. R is a programming language for data science, which is also one of the most popular languages used in biological data analysis and bioinformatics. However, building interactive web applications was a great challenge for R users before the Shiny package was developed by the RStudio company in 2012. By compiling R code into HTML, CSS and JavaScript code, Shiny has made it incredibly easy to build web applications for the large R community in bioinformatics and for even non-programmers. Over 470 biological web applications have been developed with R/Shiny up to now. To further promote the utilization of R/Shiny, we reviewed the development of biological web applications with R/Shiny, including eminent biological web applications built with R/Shiny, basic steps to build an R/Shiny application, commonly used R packages to build the interface and server of R/Shiny applications, deployment of R/Shiny applications in the cloud and online resources for R/Shiny.

Latent disconnectome prediction of long-term cognitive-behavioural symptoms in stroke.

Stroke significantly impacts the quality of life. However, the long-term cognitive evolution in stroke is poorly predictable at the individual level. There is an urgent need to better predict long-term symptoms based on acute clinical neuroimaging data. Previous works have demonstrated a strong relationship between the location of white matter disconnections and clinical symptoms. However, rendering the entire space of possible disconnection-deficit associations optimally surveyable will allow for a systematic association between brain disconnections and cognitive-behavioural measures at the individual level. Here we present the most comprehensive framework, a composite morphospace of white matter disconnections (disconnectome) to predict neuropsychological scores 1 year after stroke. Linking the latent disconnectome morphospace to neuropsychological outcomes yields biological insights that are available as the first comprehensive atlas of disconnectome-deficit relations across 86 scores-a Neuropsychological White Matter Atlas. Our novel predictive framework, the Disconnectome Symptoms Discoverer, achieved better predictivity performances than six other models, including functional disconnection, lesion topology and volume modelling. Out-of-sample prediction derived from this atlas presented a mean absolute error below 20% and allowed personalize neuropsychological predictions. Prediction on an external cohort achieved an R2 = 0.201 for semantic fluency. In addition, training and testing were replicated on two external cohorts achieving an R2 = 0.18 for visuospatial performance. This framework is available as an interactive web application (http://disconnectomestudio.bcblab.com) to provide the foundations for a new and practical approach to modelling cognition in stroke. We hope our atlas and web application will help to reduce the burden of cognitive deficits on patients, their families and wider society while also helping to tailor future personalized treatment programmes and discover new targets for treatments. We expect our framework's range of assessments and predictive power to increase even further through future crowdsourcing.

Validation and development of population pharmacokinetic model of vancomycin using a real-world database from a nationwide free web application.

INTRODUCTION: Vancomycin requires a population pharmacokinetic (popPK) model to estimate the area under the concentration-time curve (AUC), and an AUC-guided dosing strategy is necessary. This study aimed to develop a popPK model for vancomycin using a real-world database pooled from a nationwide web application (PAT). METHODS: In this retrospective study, the PAT database between December 14, 2022 and April 6, 2023 was used to develop a popPK model. The model was validated and compared with six existing models based on the predictive performance of datasets from another PAT database and the Kumamoto University Hospital. The developed model determined the dosing strategy for achieving the target AUC. RESULTS: The modeling populations consisted of 7146 (13,372 concentrations from the PAT database), 3805 (7540 concentrations from the PAT database), and 783 (1775 concentrations from Kumamoto University Hospital) individuals. A two-compartment popPK model was developed that incorporated creatinine clearance as a covariate for clearance and body weight for central and peripheral volumes of distribution. The validation demonstrated that the popPK model exhibited the smallest mean absolute prediction error of 5.07, outperforming others (ranging from 5.10 to 5.83). The dosing strategies suggested a first dose of 30 mg/kg and maintenance doses adjusted for kidney function and age. CONCLUSIONS: This study demonstrated the updating of PAT through the validation and development of a popPK model using a vast amount of data collected from anonymous PAT users.

Machine learning models on a web application to predict short-term postoperative outcomes following anterior cervical discectomy and fusion.

BACKGROUND: The frequency of anterior cervical discectomy and fusion (ACDF) has increased up to 400% since 2011, underscoring the need to preoperatively anticipate adverse postoperative outcomes given the procedure's expanding use. Our study aims to accomplish two goals: firstly, to develop a suite of explainable machine learning (ML) models capable of predicting adverse postoperative outcomes following ACDF surgery, and secondly, to embed these models in a user-friendly web application, demonstrating their potential utility. METHODS: We utilized data from the National Surgical Quality Improvement Program database to identify patients who underwent ACDF surgery. The outcomes of interest were four short-term postoperative adverse outcomes: prolonged length of stay (LOS), non-home discharges, 30-day readmissions, and major complications. We utilized five ML algorithms - TabPFN, TabNET, XGBoost, LightGBM, and Random Forest - coupled with the Optuna optimization library for hyperparameter tuning. To bolster the interpretability of our models, we employed SHapley Additive exPlanations (SHAP) for evaluating predictor variables' relative importance and used partial dependence plots to illustrate the impact of individual variables on the predictions generated by our top-performing models. We visualized model performance using receiver operating characteristic (ROC) curves and precision-recall curves (PRC). Quantitative metrics calculated were the area under the ROC curve (AUROC), balanced accuracy, weighted area under the PRC (AUPRC), weighted precision, and weighted recall. Models with the highest AUROC values were selected for inclusion in a web application. RESULTS: The analysis included 57,760 patients for prolonged LOS [11.1% with prolonged LOS], 57,780 for non-home discharges [3.3% non-home discharges], 57,790 for 30-day readmissions [2.9% readmitted], and 57,800 for major complications [1.4% with major complications]. The top-performing models, which were the ones built with the Random Forest algorithm, yielded mean AUROCs of 0.776, 0.846, 0.775, and 0.747 for predicting prolonged LOS, non-home discharges, readmissions, and complications, respectively. CONCLUSIONS: Our study employs advanced ML methodologies to enhance the prediction of adverse postoperative outcomes following ACDF. We designed an accessible web application to integrate these models into clinical practice. Our findings affirm that ML tools serve as vital supplements in risk stratification, facilitating the prediction of diverse outcomes and enhancing patient counseling for ACDF.

COVID-19 Vaccine Effectiveness and Digital Pandemic Surveillance in Germany (eCOV Study): Web Application-Based Prospective Observational Cohort Study.

BACKGROUND: The COVID-19 pandemic posed significant challenges to global health systems. Efficient public health responses required a rapid and secure collection of health data to improve the understanding of SARS-CoV-2 and examine the vaccine effectiveness (VE) and drug safety of the novel COVID-19 vaccines. OBJECTIVE: This study (COVID-19 study on vaccinated and unvaccinated subjects over 16 years; eCOV study) aims to (1) evaluate the real-world effectiveness of COVID-19 vaccines through a digital participatory surveillance tool and (2) assess the potential of self-reported data for monitoring key parameters of the COVID-19 pandemic in Germany. METHODS: Using a digital study web application, we collected self-reported data between May 1, 2021, and August 1, 2022, to assess VE, test positivity rates, COVID-19 incidence rates, and adverse events after COVID-19 vaccination. Our primary outcome measure was the VE of SARS-CoV-2 vaccines against laboratory-confirmed SARS-CoV-2 infection. The secondary outcome measures included VE against hospitalization and across different SARS-CoV-2 variants, adverse events after vaccination, and symptoms during infection. Logistic regression models adjusted for confounders were used to estimate VE 4 to 48 weeks after the primary vaccination series and after third-dose vaccination. Unvaccinated participants were compared with age- and gender-matched participants who had received 2 doses of BNT162b2 (Pfizer-BioNTech) and those who had received 3 doses of BNT162b2 and were not infected before the last vaccination. To assess the potential of self-reported digital data, the data were compared with official data from public health authorities. RESULTS: We enrolled 10,077 participants (aged ≥16 y) who contributed 44,786 tests and 5530 symptoms. In this young, primarily female, and digital-literate cohort, VE against infections of any severity waned from 91.2% (95% CI 70.4%-97.4%) at week 4 to 37.2% (95% CI 23.5%-48.5%) at week 48 after the second dose of BNT162b2. A third dose of BNT162b2 increased VE to 67.6% (95% CI 50.3%-78.8%) after 4 weeks. The low number of reported hospitalizations limited our ability to calculate VE against hospitalization. Adverse events after vaccination were consistent with previously published research. Seven-day incidences and test positivity rates reflected the course of the pandemic in Germany when compared with official numbers from the national infectious disease surveillance system. CONCLUSIONS: Our data indicate that COVID-19 vaccinations are safe and effective, and third-dose vaccinations partially restore protection against SARS-CoV-2 infection. The study showcased the successful use of a digital study web application for COVID-19 surveillance and continuous monitoring of VE in Germany, highlighting its potential to accelerate public health decision-making. Addressing biases in digital data collection is vital to ensure the accuracy and reliability of digital solutions as public health tools.

A Framework for Real-Time Gestural Recognition and Augmented Reality for Industrial Applications.

In recent decades, technological advancements have transformed the industry, highlighting the efficiency of automation and safety. The integration of augmented reality (AR) and gesture recognition has emerged as an innovative approach to create interactive environments for industrial equipment. Gesture recognition enhances AR applications by allowing intuitive interactions. This study presents a web-based architecture for the integration of AR and gesture recognition, designed to interact with industrial equipment. Emphasizing hardware-agnostic compatibility, the proposed structure offers an intuitive interaction with equipment control systems through natural gestures. Experimental validation, conducted using Google Glass, demonstrated the practical viability and potential of this approach in industrial operations. The development focused on optimizing the system's software and implementing techniques such as normalization, clamping, conversion, and filtering to achieve accurate and reliable gesture recognition under different usage conditions. The proposed approach promotes safer and more efficient industrial operations, contributing to research in AR and gesture recognition. Future work will include improving the gesture recognition accuracy, exploring alternative gestures, and expanding the platform integration to improve the user experience.

Integration of dynaMic water extents towards imProved lake wAter suRface Temperature (IMPART).

Lake water surface temperature (LWST) is a critical component in understanding the response of freshwater ecosystems to climate change. Traditional estimation of LWST estimation considers water surface bodies to be static. Our work proposes a novel open-source web application, IMPART, designed for estimating dynamic LWST using Landsat reflectance and MODIS temperature datasets from 2004 to 2022. Results presented globally for over 342 lakes reveal a root mean square deviation of 0.86 °C between static and dynamic LWST. Additionally, our results demonstrate that 57% of the lakes exhibit a statistically significant difference between the static and dynamic LWST values. Improved LWST will ultimately enhance our ability to comprehensively monitor and respond to the impacts of climate change on freshwater ecosystems worldwide. Furthermore, based on the Koppen-Geiger climate classification, our zonal analysis demonstrates the deviation between static and dynamic LWST. It identifies specific zones where considering waterbodies as dynamic entities is essential.

Nurses' satisfaction with an educational web application to prevent transmission of multidrug-resistant organisms.

Multidrug-resistant organism infections are a serious health problem globally, and can result in patient mortality and morbidity. In this descriptive study, we produced the first web application for transmission prevention specific to the situation based on nursing experience, knowledge, and practice guidelines and to evaluate web application satisfaction among Thai nurses. The sample comprised 282 Thai registered nurses experienced in caring for patients with multidrug-resistant organisms in a tertiary hospital. A demographic form and knowledge test were completed anonymously online. Data were analyzed using descriptive statistics. The application emphasized crucial topics for which participants had low preliminary knowledge and included tutorial sessions, pictures, video clips, drills, and a post-test. The application was piloted with a random sample of 30 nurses, and an instrument tested their satisfaction with this. Results revealed that preliminary knowledge scores for preventing transmission were moderate, and participants were highly satisfied with the application. Findings suggest the application is suitable for Thai nurses and could be applied to nursing practice elsewhere. However, further testing is recommended before implementing it into nursing practice.

KidneyGPS: a user-friendly web application to help prioritize kidney function genes and variants based on evidence from genome-wide association studies.

BACKGROUND: Genome-wide association studies (GWAS) have identified hundreds of genetic loci associated with kidney function. By combining these findings with post-GWAS information (e.g., statistical fine-mapping to identify independent association signals and to narrow down signals to causal variants; or different sources of annotation data), new hypotheses regarding physiology and disease aetiology can be obtained. These hypotheses need to be tested in laboratory experiments, for example, to identify new therapeutic targets. For this purpose, the evidence obtained from GWAS and post-GWAS analyses must be processed and presented in a way that they are easily accessible to kidney researchers without specific GWAS expertise. MAIN: Here we present KidneyGPS, a user-friendly web-application that combines genetic variant association for estimated glomerular filtration rate (eGFR) from the Chronic Kidney Disease Genetics consortium with annotation of (i) genetic variants with functional or regulatory effects ("SNP-to-gene" mapping), (ii) genes with kidney phenotypes in mice or human ("gene-to-phenotype"), and (iii) drugability of genes (to support re-purposing). KidneyGPS adopts a comprehensive approach summarizing evidence for all 5906 genes in the 424 GWAS loci for eGFR identified previously and the 35,885 variants in the 99% credible sets of 594 independent signals. KidneyGPS enables user-friendly access to the abundance of information by search functions for genes, variants, and regions. KidneyGPS also provides a function ("GPS tab") to generate lists of genes with specific characteristics thus enabling customizable Gene Prioritisation (GPS). These specific characteristics can be as broad as any gene in the 424 loci with a known kidney phenotype in mice or human; or they can be highly focussed on genes mapping to genetic variants or signals with particularly with high statistical support. KidneyGPS is implemented with RShiny in a modularized fashion to facilitate update of input data ( https://kidneygps.ur.de/gps/ ). CONCLUSION: With the focus on kidney function related evidence, KidneyGPS fills a gap between large general platforms for accessing GWAS and post-GWAS results and the specific needs of the kidney research community. This makes KidneyGPS an important platform for kidney researchers to help translate in silico research results into in vitro or in vivo research.

CaspSites: A Database and Web Application for Experimentally Observed Human Caspase Substrates Using N-Terminomics.

CaspSites is a free-to-use database and web application for experimentally observed human caspase substrates using N-terminomics. It can be accessed and used by all users at the web URL www.caspsites.org. CaspSites stores cleavage site information identified for human caspases 1-9 in lysates and apoptotic cells, collected from their corresponding published studies. The database can be queried, viewed, and exported using the search page of the web application. The main parameters offered are protein substrate, cleavage site (P4-P4') residues, and individual caspase data sets, which can be connected using OR, AND, or NOT logical operators for custom user-built queries. CaspSites will be regularly updated with new experimental findings for understudied caspases, providing researchers insight into the distinctive roles human caspases play in cellular processes by identifying their target proteins in relation to each other.

Tabby2: a user-friendly web tool for forecasting state-level TB outcomes in the United States.

BACKGROUND: In the United States, the tuberculosis (TB) disease burden and associated factors vary substantially across states. While public health agencies must choose how to deploy resources to combat TB and latent tuberculosis infection (LTBI), state-level modeling analyses to inform policy decisions have not been widely available. METHODS: We developed a mathematical model of TB epidemiology linked to a web-based user interface - Tabby2. The model is calibrated to epidemiological and demographic data for the United States, each U.S. state, and the District of Columbia. Users can simulate pre-defined scenarios describing approaches to TB prevention and treatment or create their own intervention scenarios. Location-specific results for epidemiological outcomes, service utilization, costs, and cost-effectiveness are reported as downloadable tables and customizable visualizations. To demonstrate the tool's functionality, we projected trends in TB outcomes without additional intervention for all 50 states and the District of Columbia. We further undertook a case study of expanded treatment of LTBI among non-U.S.-born individuals in Massachusetts, covering 10% of the target population annually over 2025-2029. RESULTS: Between 2022 and 2050, TB incidence rates were projected to decline in all states and the District of Columbia. Incidence projections for the year 2050 ranged from 0.03 to 3.8 cases (median 0.95) per 100,000 persons. By 2050, we project that majority (> 50%) of TB will be diagnosed among non-U.S.-born persons in 46 states and the District of Columbia; per state percentages range from 17.4% to 96.7% (median 83.0%). In Massachusetts, expanded testing and treatment for LTBI in this population was projected to reduce cumulative TB cases between 2025 and 2050 by 6.3% and TB-related deaths by 8.4%, relative to base case projections. This intervention had an incremental cost-effectiveness ratio of $180,951 (2020 USD) per quality-adjusted life year gained from the societal perspective. CONCLUSIONS: Tabby2 allows users to estimate the costs, impact, and cost-effectiveness of different TB prevention approaches for multiple geographic areas in the United States. Expanded testing and treatment for LTBI could accelerate declines in TB incidence in the United States, as demonstrated in the Massachusetts case study.

Molecular characterization, biological function, tumor microenvironment association and clinical significance of m6A regulators in lung adenocarcinoma.

N6-methyladenosine (m6A) modification can regulate a variety of biological processes. However, the implications of m6A modification in lung adenocarcinoma (LUAD) remain largely unknown. Here, we systematically evaluated the m6A modification features in more than 2400 LUAD samples by analyzing the multi-omics features of 23 m6A regulators. We depicted the genetic variation features of m6A regulators, and found mutations of FTO and YTHDF3 were linked to worse overall survival. Many m6A regulators were aberrantly expressed in tumors, among which FTO, IGF2BP3, YTHDF1 and RBM15 showed consistent alteration features across 11 independent cohorts. Besides, the regulator-pathway interaction network demonstrated that m6A modification was associated with various biological pathways, including immune-related pathways. The correlation between m6A regulators and tumor microenvironment was also assessed. We found that LRPPRC was negatively correlated with most tumor-infiltrating immune cells. On the other hand, we established a scoring tool named m6Sig, which was positively correlated with PD-L1 expression and could reflect both the tumor microenvironment characterization and prognosis of LUAD patients. Comparison of CNV between high and low m6Sig groups revealed differences on chromosome 7. Application of m6Sig on an anti-PD-L1 immunotherapy cohort confirmed that the high m6Sig group demonstrated therapeutic advantages and clinical benefits. Our study indicated that m6A modification is involved in many aspects of LUAD and contributes to tumor microenvironment formation. A better understanding of m6A modification will provide more insights into the molecular mechanisms of LUAD and facilitate developing more effective personalized treatment strategies. A web application was built along with this study (http://www.bioinfo-zs.com/luadexpress/).

Advancing personalized prognosis in atypical and anaplastic meningiomas through interpretable machine learning models.

PURPOSE: The primary purpose of this study was to utilize machine learning (ML) models to create a web application that can predict survival outcomes for patients diagnosed with atypical and anaplastic meningiomas. METHODS: In this retrospective cohort study, patients diagnosed with WHO grade II and III meningiomas were selected from the National Cancer Database (NCDB) to analyze survival outcomes at 12, 36, and 60 months. Five machine learning algorithms - TabPFN, TabNet, XGBoost, LightGBM, and Random Forest were employed and optimized using the Optuna library for hyperparameter tuning. The top-performing models were then deployed into our web-based application. RESULTS: From the NCDB, 12,197 adult patients diagnosed with histologically confirmed WHO grade II and III meningiomas were retrieved. The mean age was 61 (± 20), and 6,847 (56.1%) of these were females. Performance evaluation indicated that the top-performing models for each outcome were the models built with the TabPFN algorithm. The TabPFN models yielded area under the receiver operating characteristic (AUROC) values of 0.805, 0.781, and 0.815 in predicting 12-, 36-, and 60-month mortality, respectively. CONCLUSION: With the continuous growth of neuro-oncology data, ML algorithms act as key tools in predicting survival outcomes for WHO grade II and III meningioma patients. By incorporating these interpretable models into a web application, we can practically utilize them to improve risk evaluation and prognosis for meningioma patients.

Development of personalized machine learning-based prediction models for short-term postoperative outcomes in patients undergoing cervical laminoplasty.

PURPOSE: By predicting short-term postoperative outcomes before surgery, patients undergoing cervical laminoplasty (CLP) surgery could benefit from more accurate patient care strategies that could reduce the likelihood of adverse outcomes. With this study, we developed a series of machine learning (ML) models for predicting short-term postoperative outcomes and integrated them into an open-source online application. METHODS: National surgical quality improvement program database was utilized to identify individuals who have undergone CLP surgery. The investigated outcomes were prolonged length of stay (LOS), non-home discharges, 30-day readmissions, unplanned reoperations, and major complications. ML models were developed and implemented on a website to predict these three outcomes. RESULTS: A total of 1740 patients that underwent CLP were included in the analysis. Performance evaluation indicated that the top-performing models for each outcome were the models built with TabPFN and LightGBM algorithms. The TabPFN models yielded AUROCs of 0.830, 0.847, and 0.858 in predicting non-home discharges, unplanned reoperations, and major complications, respectively. The LightGBM models yielded AUROCs of 0.812 and 0.817 in predicting prolonged LOS, and 30-day readmissions, respectively. CONCLUSION: The potential of ML approaches to predict postoperative outcomes following spine surgery is significant. As the volume of data in spine surgery continues to increase, the development of predictive models as clinically relevant decision-making tools could significantly improve risk assessment and prognosis. Here, we present an accessible predictive model for predicting short-term postoperative outcomes following CLP intended to achieve the stated objectives.

Digital Action Plan (Web App) for Managing Asthma Exacerbations: Randomized Controlled Trial.

BACKGROUND: A written action plan (WAP) for managing asthma exacerbations is recommended. OBJECTIVE: We aimed to compare the effect on unscheduled medical contacts (UMCs) of a digital action plan (DAP) accessed via a smartphone web app combined with a WAP on paper versus that of the same WAP alone. METHODS: This randomized, unblinded, multicenter (offline recruitment in private offices and public hospitals), and parallel-group trial included children (aged 6-12 years) or adults (aged 18-60 years) with asthma who had experienced at least 1 severe exacerbation in the previous year. They were randomized to a WAP or DAP+WAP group in a 1:1 ratio. The DAP (fully automated) provided treatment advice according to the severity and previous pharmacotherapy of the exacerbation. The DAP was an algorithm that recorded 3 to 9 clinical descriptors. In the app, the participant first assessed the severity of their current symptoms on a 10-point scale and then entered the symptom descriptors. Before the trial, the wordings and ordering of these descriptors were validated by 50 parents of children with asthma and 50 adults with asthma; the app was not modified during the trial. Participants were interviewed at 3, 6, 9, and 12 months to record exacerbations, UMCs, and WAP and DAP use, including the subjective evaluation (availability and usefulness) of the action plans, by a research nurse. RESULTS: Overall, 280 participants were randomized, of whom 33 (11.8%) were excluded because of the absence of follow-up data after randomization, leaving 247 (88.2%) participants (children: n=93, 37.7%; adults: n=154, 62.3%). The WAP group had 49.8% (123/247) of participants (children: n=45, 36.6%; mean age 8.3, SD 2.0 years; adults: n=78, 63.4%; mean age 36.3, SD 12.7 years), and the DAP+WAP group had 50.2% (124/247) of participants (children: n=48, 38.7%; mean age 9.0, SD 1.9 years; adults: n=76, 61.3%; mean age 34.5, SD 11.3 years). Overall, the annual severe exacerbation rate was 0.53 and not different between the 2 groups of participants. The mean number of UMCs per year was 0.31 (SD 0.62) in the WAP group and 0.37 (SD 0.82) in the DAP+WAP group (mean difference 0.06, 95% CI -0.12 to 0.24; P=.82). Use per patient with at least 1 moderate or severe exacerbation was higher for the WAP (33/65, 51% vs 15/63, 24% for the DAP; P=.002). Thus, participants were more likely to use the WAP than the DAP despite the nonsignificant difference between the action plans in the subjective evaluation. Median symptom severity of the self-evaluated exacerbation was 4 out of 10 and not significantly different from the symptom severity assessed by the app. CONCLUSIONS: The DAP was used less often than the WAP and did not decrease the number of UMCs compared with the WAP alone. TRIAL REGISTRATION: ClinicalTrials.gov NCT02869958; https://clinicaltrials.gov/ct2/show/NCT02869958.

SIMUNEO: Control and Monitoring System for Lung Ultrasound Examination and Treatment of Neonatal Pneumothorax and Thoracic Effusion.

Training with real patients is a critical aspect of the learning and growth of doctors in training. However, this essential step in the educational process for clinicians can potentially compromise patient safety, as they may not be adequately prepared to handle real-life situations independently. Clinical simulators help to solve this problem by providing real-world scenarios in which the physicians can train and gain confidence by safely and repeatedly practicing different techniques. In addition, obtaining objective feedback allows subsequent debriefing by analysing the situation experienced and learning from other people's mistakes. This article presents SIMUNEO, a neonatal simulator in which professionals are able to learn by practicing the management of lung ultrasound and the resolution of pneumothorax and thoracic effusions. The article also discusses in detail the hardware and software, the main components that compose the system, and the communication and implementation of these. The system was validated through both usability questionnaires filled out by neonatology residents as well as through follow-up sessions, improvement, and control of the system with specialists of the department. Results suggest that the environment is easy to use and could be used in clinical practice to improve the learning and training of students as well as the safety of patients.

Automated Street Light Adjustment System on Campus with AI-Assisted Data Analytics.

The smart city concept has been popularized in the urbanization of major metropolitan areas through the implementation of intelligent systems and technology to serve the increasing human population. This work developed an automatic light adjustment system at Thammasat University, Rangsit Campus, Thailand, with a primary objective of optimizing energy efficiency, while providing sufficient illumination for the campus. The development consists of two sections: the device control and the prediction model. The device control functionalities were developed with the user interface to enable control of the smart street light devices and the application programming interface (API) to send the light-adjusting command. The prediction model was created using an AI-assisted data analytic platform to obtain the predicted illuminance values so as to, subsequently, suggest light-dimming values according to the current environment. Four machine-learning models were performed on a nine-month environmental dataset to acquire predictions. The result demonstrated that the three-day window size setting with the XGBoost model yielded the best performance, attaining the correlation coefficient value of 0.922, showing a linear relationship between actual and predicted illuminance values using the test dataset. The prediction retrieval API was established and connected to the device control API, which later created an automated system that operated at a 20-min interval. This allowed real-time feedback to automatically adjust the smart street lighting devices through the purpose-designed data analytics features.

Generative Adversarial Network (GAN)-Based Autonomous Penetration Testing for Web Applications.

The web application market has shown rapid growth in recent years. The expansion of Wireless Sensor Networks (WSNs) and the Internet of Things (IoT) has created new web-based communication and sensing frameworks. Current security research utilizes source code analysis and manual exploitation of web applications, to identify security vulnerabilities, such as Cross-Site Scripting (XSS) and SQL Injection, in these emerging fields. The attack samples generated as part of web application penetration testing on sensor networks can be easily blocked, using Web Application Firewalls (WAFs). In this research work, we propose an autonomous penetration testing framework that utilizes Generative Adversarial Networks (GANs). We overcome the limitations of vanilla GANs by using conditional sequence generation. This technique helps in identifying key features for XSS attacks. We trained a generative model based on attack labels and attack features. The attack features were identified using semantic tokenization, and the attack payloads were generated using conditional sequence GAN. The generated attack samples can be used to target web applications protected by WAFs in an automated manner. This model scales well on a large-scale web application platform, and it saves the significant effort invested in manual penetration testing.