Web Access Research Articles

Climate silence: a comparative study of governmental and non-governmental messaging on soil health in Wisconsin, USA

ABSTRACT Soils are the foundation of healthy ecosystems, and promoting soil health is a primary aim of agricultural strategies intended to sequester carbon and combat climate change. In this exploratory study, we conducted a qualitative content analysis to characterize soil health messaging from 10 governmental and 10 non-governmental sources targeting Wisconsin farmers. Data were collected through internet searches, focusing on accessible web content from governmental agencies, public universities, and local offices, as well as prominent industry, nonprofit, and advocacy groups within the agricultural sector. A coding framework, developed through an iterative and inductive approach, identified six primary categories and 37 sub-categories representing key themes in soil health messaging. The study compared messaging across source types by analyzing code term frequency, highlighting differences in content emphasis between governmental and non-governmental sources. Findings indicate that governmental messaging largely advocates for conservation agriculture and associated principles but does not reference climate change nor connections between soil health and broader environmental issues. In contrast, non-governmental messaging offers a wider array of information while drawing connections among soil health, organic agriculture, ecosystem health, and climate change. The omission of climate change by governmental sources may be a significant constraint on soil health discourse and climate action.

Special Section on Patient-Reported Outcomes and Informatics: Collection of Patient-Reported Outcome Measures in Rural and Underserved Populations.

The NIH Pragmatic Trials Collaboratory supports the design and conduct of 31 embedded pragmatic clinical trials, and many of these trials use patient-reported outcome measures (PROMs) to provide valuable information about their patients' health and wellness. Often these trials enroll medically underserved patients, including people with incomes below the federal poverty threshold, racial or ethnically minoritized groups, or rural or frontier communities. In this series of trial case reports, we provide lessons learned about collecting PROMs in these populations. The unbiased collection of PROM data is critical to increase the generalizability of trial outcomes and to address health inequities. Use of electronic health records (EHRs) and other digital modes of PROM administration have gained traction. However, engagement with these modes is often low among disparities prone populations due to lessened digital proficiency, device access, and uptake of EHR portals and web interfaces. To maximize the completeness and representativeness of their trial outcome data, study teams tested a range of strategies to improve PROM response rates with emphasis on disparities prone and underserved patient groups. This manuscript describes the approaches, their implementation, and the targeted populations. Optimized PROM collection required hybrid approaches with multiple outreach modes, high-touch methods, creativity in promoting digital uptake, multi-modal participant engagement, and text messaging.

A Novel Machine Learning Model and a Web Portal for Predicting the Human Skin Sensitization Effects of Chemical Agents

Skin sensitization is a significant concern for chemical safety assessments. Traditional animal assays often fail to predict human responses accurately, and ethical constraints limit the collection of human data, necessitating a need for reliable in silico models of skin sensitization prediction. This study introduces HuSSPred, an in silico tool based on the Human Predictive Patch Test (HPPT). HuSSPred aims to enhance the reliability of predicting human skin sensitization effects for chemical agents to support their regulatory assessment. We have curated an extensive HPPT database and performed chemical space analysis and grouping. Binary and multiclass QSAR models were developed with Bayesian hyperparameter optimization. Model performance was evaluated via five-fold cross-validation. We performed model validation with reference data from the Defined Approaches for Skin Sensitization (DASS) app. HuSSPred models demonstrated strong predictive performance with CCR ranging from 55 to 88%, sensitivity between 48 and 89%, and specificity between 37 and 92%. The positive predictive value (PPV) ranged from 84 to 97%, versus negative predictive value (NPV) from 22 to 65%, and coverage was between 75 and 93%. Our models exhibited comparable or improved performance compared to existing tools, and the external validation showed the high accuracy and sensitivity of the developed models. HuSSPred provides a reliable, open-access, and ethical alternative to traditional testing for skin sensitization. Its high accuracy and reasonable coverage make it a valuable resource for regulatory assessments, aligning with the 3Rs principles. The publicly accessible HuSSPred web tool offers a user-friendly interface for predicting skin sensitization based on chemical structure.

InclusiveWeb : A Smart Browser Approach to Enhancing Web Accessibility for Users with Diverse Disabilities

This article presents InclusiveWeb, an innovative accessibility-focused web browser designed to enhance digital inclusion for users with diverse disabilities. The browser addresses the persistent challenge of widespread non-compliance with web accessibility standards by implementing real-time content adaptation mechanisms, disability-specific tools, and a highly customizable user interface. InclusiveWeb's architecture incorporates advanced machine learning algorithms to dynamically modify web content, offering tailored solutions for users with visual impairments, dyslexia, language disorders, cognitive disabilities, and ADHD. The article details the browser's design principles, technical considerations, and user experience features, supported by a comprehensive evaluation methodology involving user studies with various disability groups. Results demonstrate significant improvements in task completion rates, user satisfaction, and overall accessibility compared to standard browsers. While acknowledging limitations and areas for future research, this article highlights InclusiveWeb's potential to revolutionize web accessibility, shifting the paradigm from reliance on content creators' compliance to browser-level adaptations. The implications of this approach for web accessibility standards, digital inclusion, and future technological developments are discussed, emphasizing the browser's role in creating a more equitable digital landscape.

Development and validation of a machine learning model for predicting drug-drug interactions with oral diabetes medications

Diabetes management is often complicated by comorbidities, requiring complex medication regimens that increase the risk of drug-drug interactions (DDIs), potentially compromising treatment outcomes or causing toxicity. Although machine learning (ML) models have made strides in DDI prediction, existing approaches lack specificity for oral diabetes medications and face challenges in interpretability. To address these limitations, we propose a novel ML-based framework utilizing the Simplified Molecular Input Line Entry System (SMILES) to encode structural information of oral diabetes drugs. Using this representation, we developed an XGBoost model, selecting molecular features through LASSO. Our dataset, sourced from DrugBank, included 42 oral diabetes drugs and 1,884 interacting drugs, divided into training, validation, and testing sets. The model identified 606 optimal features, achieving an F1-score of 0.8182. SHAP analysis was employed for feature interpretation, enhancing model transparency and clinical relevance. By predicting adverse DDIs, our model offers a valuable tool for clinical decision-making, aiding safer prescription practices. The 606 critical features provide insights into atomic-level interactions, linking computational predictions with biological experiments. We present a classification model specifically designed for predicting DDIs associated with oral diabetes medications, with an openly accessible web application to support diabetes management in multi-drug regimens and comorbidity settings.

An inclusive framework for automated web content accessibility evaluation

AbstractSince 1996, web accessibility evaluation has been an important aspect of web development to increase social inclusion for people with special needs. Several web accessibility evaluation and testing tools have been developed to automatically evaluate websites in order to identify barriers for people with disabilities. The developed tools are significant since their aim is to effectively represent accessibility issues. However, a few issues with website accessibility cannot be identified through the existing accessibility testing tools due to several limitations, including (i) inappropriate guideline selection, (ii) ambiguities in guideline understanding, (iii) avoiding user and expert suggestions as evaluation criteria, (iv) limited consideration of semantic perspectives, and (iv) unwillingness to incorporate the updated engineering methods. Therefore, reported results may be unclear and inappropriate for some users. Such limitations are critical factors that reduce the effectiveness of the developed tools. These issues cause unwillingness to use a particular tool or possible adoption of other tools. In this situation, we must identify which aspects are important to incorporate in the development in order to make the developed solution more effective as it allows users to make their website accessible to people with disabilities. In this paper, first, we present a literature review of different existing solutions for web accessibility testing to identify their challenges and limitations. Following the reported findings, we propose an automated web accessibility evaluation framework addressing several accessibility aspects to improve the evaluation results by mitigating the limitations of existing solutions. The proposed framework is validated by comparing it with existing automated solutions considering their functional properties. The proposed accessibility framework might be beneficial for web developers, accessibility engineers, and other practitioners to incorporate into their development and research.

A deep-learning-based histopathology classifier for focal cortical dysplasia (FCD) unravels a complex scenario of comorbid FCD subtypes.

Recently, we developed a first artificial intelligence (AI)-based digital pathology classifier for focal cortical dysplasia (FCD) as defined by the ILAE classification. Herein, we tested the usefulness of the classifier in a retrospective histopathology workup scenario. Eighty-six new cases with histopathologically confirmed FCD ILAE type Ia (FCDIa), FCDIIa, FCDIIb, mild malformation of cortical development with oligodendroglial hyperplasia in epilepsy (MOGHE), or mild malformations of cortical development were selected, 20 of which had confirmed gene mosaicism. The classifier always recognized the correct histopathology diagnosis in four or more 1000 × 1000-μm digital tiles in all cases. Furthermore, the final diagnosis overlapped with the largest batch of tiles assigned by the algorithm to one diagnostic entity in 80.2% of all cases. However, 86.2% of all cases revealed more than one diagnostic category. As an example, FCDIIb was identified in all of the 23 patients with histopathologically assigned FCDIIb, whereas the classifier correctly recognized FCDIIa tiles in 19 of these cases (83%), that is, dysmorphic neurons but no balloon cells. In contrast, the classifier misdiagnosed FCDIIb tiles in seven of 23 cases histopathologically assigned to FCDIIa (33%). This mandates a second look by the signing histopathologist to either confirm balloon cells or differentiate from reactive astrocytes. The algorithm also recognized coexisting architectural dysplasia, for example, vertically oriented microcolumns as in FCDIa, in 22% of cases classified as FCDII and in 62% of cases with MOGHE. Microscopic review confirmed microcolumns in the majority of tiles, suggesting that vertically oriented architectural abnormalities are more common than previously anticipated. An AI-based diagnostic classifier will become a helpful tool in our future histopathology laboratory, in particular when large anatomical resections from epilepsy surgery require extensive resources. We also provide an open access web application allowing the histopathologist to virtually review digital tiles obtained from epilepsy surgery to corroborate their final diagnosis.

Designing Inclusive Web Applications with Accessibility in Mind

This article explores the critical importance of designing inclusive web applications with accessibility in mind. It delves into the ethical, legal, and business imperatives driving the need for accessible web design, highlighting that over one billion people worldwide live with some form of disability. The article discusses key accessibility standards, particularly the Web Content Accessibility Guidelines (WCAG), and outlines essential techniques and tools for implementing and testing web accessibility. These include semantic HTML, ARIA roles and attributes, color contrast considerations, keyboard navigation, and alternative text for images. The article also examines various testing tools such as Lighthouse, WAVE, axe DevTools, and screen readers. Furthermore, it presents a compelling business case for accessibility, emphasizing benefits such as an expanded user base, improved SEO, enhanced brand reputation, and legal compliance. Through comprehensive exploration of these topics, the article underscores that accessibility is not just a regulatory requirement but a fundamental responsibility that leads to better digital experiences for all users

Design and Development Model of a Web Accessibility Ecosystem

The article examines issues of web accessibility ecosystems for people with special needs. Methods, models, accessibility standards, and technologies related to the structure, design, and functionality of the web accessibility ecosystem are studied. The stages of developing an accessibility ecosystem are explored. The accessibility of the design, functionalities, structure, and content of a particular ecosystem are presented. Several themes for the design of the system with an emphasis on its accessibility for blind users are explored and analyzed. UX/UI design and the ontological model of accessibility, used in the implementation of the model of the ecosystem and its elements, are studied. A web accessibility ecosystem model has been developed, compliant with the Web Content Accessibility Guidelines and based on semantic technologies. Other qualities of this model are easy access to information resources on the topic of accessibility, convenience for users with different needs, and the possibility of expansion and enrichment in the future.

Why effect sizes are systematically larger for progression-free survival than overall survival in cancer drug trials: prognostic scores as a way forward

Cancer drugs have accumulated the most approvals over the past years. Overall survival (OS) is considered the gold standard for cancer trial outcomes. However, its use has declined over the past years, in favor of surrogate endpoints, such as progression-free survival (PFS). PFS allows to assess outcomes earlier and, thus, accelerates approval of cancer drugs. Previous studies have demonstrated a poor correlation between PFS and OS. Using simulation models, we examined why PFS usually overestimates survival benefit. We created a publicly accessible web application that allows users to run the simulations with different parameter settings. Based on the findings, we propose that assessment of preliminary evidence should be based on a combination of OS result and prognostic scores that reflect the health status of surviving patients.

Web reference decay: Passing the HTTP errors instead of quality information

Web resource links routinely fail, and the content of links might change suddenly and without notice. When referrals to online sites offer proof or supporting data, these web dynamics are harmful. Any hyperlink’s final content is susceptible to two phenomena: the link breaks (link rot) and the content changes from its original state (content drift). The combination of both effects is referred to as reference rot. This paper aims to determine the application of online citations, the number of lost web citations, and the process of recovering missing web citations through the application of the Internet Archive’s Wayback Machine. According to the analysis, the Webology journal published 428 research articles between 2004 and 2020 that had 2,273 online citations. There are 1,260 (55.43%) accessible web citations out of the mentioned ones, whereas 1,013 (44.57%) are missing. There are 1,856 (81.65%) active web citations, up from 1,260 (55.43%) after an effort was made to retrieve 596 lost web citations using the Wayback Machine tool. That meant that the overall increase of the active URLs was 26.22%. The article presents a method for creating specific collections for online archiving in the future, even though web-archiving tools now take snapshots of websites in real time. The paper recommends that future curation standards for real-time web archiving shall include platform dynamics and cultural variations in link-sharing habits.

A Security Model for Web-Based Communication

Web access involves various protocols to resolve domain names to IP addresses, establish data exchange channels with Web servers, and to authenticate communication partners. Each protocol has its own set of requirements and security measures. In addition to technical features, operating the Web also introduces organizational and political aspects which are important to consider when deploying a secure basis for Web-based communication. In this paper, we propose an algorithmic security model based on the widely deployed technologies DNS(SEC) and Web PKI to cover the three dimensions identification , resolution , and transaction . Our model enables quantification and qualification of the security assurance provided by an online service provider. To verify the applicability of our model, we investigate the online presence of Alerting Authorities in the U.S., selected German Emergency Service providers, and UN member states . We observe partially enhanced security relative to global Internet trends, yet find cause for concern as only about 6% of unique hosts cater to secure resolution. About 46% of investigated organizations use shared certificates with 1% of all organizations having no or invalid certificates. Two thirds of organizations are not uniquely identifiable and as such lack the basic requirement of trustworthy communication.

Impact of COVID-19 in the web accessibility of higher education institutions: a pending challenge

AbstractThe COVID-19 pandemic forced educational institutions to close and led to a radical transformation of the global education system. This process of change had to take place in a short period of time and, as a result, methodologies and learning tools were adapted. The closure of educational institutions and the disruption to learning impacted all students. However, vulnerable students, including those with disabilities, were particularly affected due to the lack of preparedness for inclusive learning during the rapid transition to emergency online learning. One of the many challenges faced by educational institutions during this transition was ensuring web accessibility for students with disabilities. However, it appears that some of the positive changes that took place in the education system during the pandemic, such as the enhancement of web accessibility, have been declining recently. The aim of this article is to review how a group of universities responded to the closure caused by the pandemic from the point of view of web accessibility, whether they improved the accessibility of their websites and whether this improvement has been maintained over time. To achieve this, the web accessibility of the home pages of selected universities around the world was evaluated against Web Content Accessibility Guidelines 2.1, for the period from 2018 to 2024. The results show that there was an improvement in web accessibility immediately after the COVID-19 outbreak, but this trend has not been maintained.

Distinct negative-sense RNA viruses induce a common set of transcripts encoding proteins forming an extensive network.

The large group of negative-strand RNA viruses (NSVs) comprises many important pathogens. To identify conserved patterns in host responses, we systematically compared changes in the cellular RNA levels after infection of human hepatoma cells with nine different NSVs of different virulence degrees. RNA sequencing experiments indicated that the amount of viral RNA in host cells correlates with the number of differentially expressed host cell transcripts. Time-resolved differential gene expression analysis revealed a common set of 178 RNAs that are regulated by all NSVs analyzed. A newly developed open access web application allows downloads and visualizations of all gene expression comparisons for individual viruses over time or between several viruses. Most of the genes included in the core set of commonly differentially expressed genes (DEGs) encode proteins that serve as membrane receptors, signaling proteins and regulators of transcription. They mainly function in signal transduction and control immunity, metabolism, and cell survival. One hundred sixty-five of the DEGs encode host proteins from which 47 have already been linked to the regulation of viral infections in previous studies and 89 proteins form a complex interaction network that may function as a core hub to control NSV infections.IMPORTANCEThe infection of cells with negative-strand RNA viruses leads to the differential expression of many host cell RNAs. The differential spectrum of virus-regulated RNAs reflects a large variety of events including anti-viral responses, cell remodeling, and cell damage. Here, these virus-specific differences and similarities in the regulated RNAs were measured in a highly standardized model. A newly developed app allows interested scientists a wide range of comparisons and visualizations.

The Pitfalls of the Certificate-Based User Authentication Scheme on Korean Public Websites

The Korean government has employed a certificate-based user authentication scheme powered by Internet Explorer and ActiveX plug-ins for the past two decades. Users must obtain accredited digital certificates, install all required plug-ins on their machines, and undergo all user authentication procedures. Most clients mistakenly take a series of authentication procedures for granted and unwittingly make copies of security code cards, store accredited digital certificates on hard disks, and mechanically click “Yes” or “OK”. Public websites lack cross-platform and cross-browser compatibility and discriminate against those who do not use Windows. The government should require public organizations to provide at least a password-based n-factor authentication scheme over SSL/TLS and should reform institutional arrangements so that service providers take primary responsibility for online transactions. This “Galapagos e-government” case illustrates the importance of global technology standards and web accessibility.

E-waste Management Using Blockchain Technology

Introduction; Bridging the digital divide requires the provision of affordable, fair and quality ICT. With nearly two-thirds of the world’s population still offline, there is a need to provide affordable web access for everyone. For developing countries, increasing the popularity of information and communication technology has become the most important factor in reducing poverty. The danger of electrical and electronic waste disposal contains hazardous substances, but most of the electrical and electronic equipment is still disposed of in an unhealthy environment in the field development area, affecting the level of contamination in Water, Air and Soil ultimately affecting people’s health. Eliminating E-waste responsibility and protecting the environment is a challenge for countries. Smart cities can solve environmental problems through proper waste management for improving human health, protecting water resources, and reducing pollution. Objective; In this paper, we explore how blockchain technology can help smart cities to manage E-waste by providing consistency, immutability, transparency, and accountability control in a distributed, reliable, and secure manner. We discussed the advantages of blockchain technology in various aspects of E-waste management, such as instant tracking and monitoring, E-waste disposal and E-waste management regulation compliance, proper disposal management, E-waste management, and material handling, etc. All examples of disposal services, but in our study we have found that there is no fool proof system to check the disposal of E-waste whether it has been disposed off Fully or Partially. We mainly focused on the tracking of E-waste management system for 100% safe and eco-friendly disposal from the originating point of E-waste to end disposal point of total disposal. Methods; For this, we have used machine-learning model to find the existing percentage of disposal of E-waste at the end-point which reveals that it is never 100%. And partial disposal of E-waste means we have still partial E-waste around us in different forms, which will be a threat for the society to be indulged in hazardous after effects of randomly dumping E-waste.Results; After this we have modelled a Disposal Tracking System(DTS) using blockchain technology to create an E-waste data storage as Decentralized Shareable Ledger (DSL) which records the quantity and state of E-waste data from its originating point to a different level of disposal unit and finally reflect the balance of E-waste data as NIL at the end of last disposal point.Conclusion; This system will helpful for safe and ecofriendly E-waste management and it provides complete transparency and traceability of E-waste during the life cycle of complete disposal. After implementation of this system any district or Block or Village authority can ensure to its citizens for E-waste hazards free environment and safety of natural resources

Usage of Artificial Intelligence Tools to Improve Digital Web Accessibility

Accessibility of websites considers challenges related to creation of web pages adapted to user with different types of impairments. The goal of web accessibility is to remove barriers that may prevent people with disabilities from accessing or interacting with web content effectively. The paper presents some web accessibility standards and challenges related to their complying. A special attention is paid to some popular artificial intelligence tools and how they could be used to solve accessibility issues on web sites development.

ASSESSMENT OF WEB ACCESSIBILITY REGULATION ADHERENCE BY INSTITUTIONS WITH COMPLIANCE OBLIGATIONS

Web accessibility is crucial for ensuring universal access to information, a value highly regarded by the European Union and the Spanish government, which have enacted legislation mandating compliance, particularly within the public sector and to a lesser extent in the private sector. This study contrasts the commitment of private entities with that of public ones in adhering to web accessibility standards. Initially, a database of URLs, categorized for reference, was compiled. Subsequently, automated web analysis techniques were employed to produce comparative statistics by sector. The findings indicate that while the public sector has made progress in meeting the regulations, the private sector lags considerably behind. An examination of common errors on websites of private companies obligated to these standards suggests that ignorance of legal responsibilities is the primary cause of accessibility shortcomings. Keywords: Web Accessibility, Accessibility Evaluation, Regulatory Compliance, Web Content Accessibility Guidelines (WCAG), Digital Accessibility

Integration of Google Trends Data in Predicting New HIV Cases in Western Visayas, the Philippines

Recent studies have demonstrated the capacity of Internet search data to be used for monitoring and tracking infectious diseases. Given that the Philippines has one of the most rapidly expanding human immunodeficiency virus (HIV) epidemics in the globe, it is imperative to enhance the local surveillance and prediction of HIV by utilizing easily accessible web data such as Google Trends. This study aims to [1] evaluate the advantages of utilizing Google Trends data (GTD) in predicting new monthly HIV diagnoses in the Western Visayas and [2] determine the suitable modeling approach that effectively utilizes this tool in forecasting HIV cases in the region. Data on new monthly HIV cases from January 2012–December 2022 in Western Visayas was requested from the Department of Health Region VI Office and HIV-related internet search data was obtained from Google Trends. Data imputations were applied to mitigate the effects of COVID-19 on HIV data. The results of the traditional seasonal autoregressive integrated moving average (SARIMA) model were compared with the results of the neural network autoregression (NNAR) model using cross-validation via rolling forecast origin. GTD have a moderately weak but significant association with HIV case numbers in Western Visayas. NNAR models outperformed the traditional SARIMA models in terms of mean absolute percentage error, mean absolute error, and root mean squared error. The use of Google search data in NNAR models improved the predictive accuracy of the forecasts when using moving average imputation as a replacement for outliers. However, this does not accurately represent the overall NNAR process, as the linear interpolation replacements did not yield comparable outcomes. This study showed that, if suitable models are adopted, utilizing HIV-related web data has the potential to produce real-time surveillance systems for HIV monitoring and forecasting. This presents an opportunity to enhance local surveillance in the country not just for HIV but also for other infectious diseases such as tuberculosis and dengue. Internet search data is free and readily available, and public health agencies can adopt this type of modeling, even with limited funding; however, further study is advised.

Statistical design for health monitoring in laboratory animal facilities using sentinel animals.

Regular health monitoring is crucial in laboratory animal facilities to determine the presence or absence of specific pathogens. One common approach to monitoring involves the use of sentinel animals, which are periodically exposed to biological material from the cages being monitored. At a certain point, some of these sentinel animals are tested for pathogens. This article discusses designing an effective sampling scheme to meet desired quality standards. It addresses questions such as the number of sentinel animals required, the frequency of sampling biological material, the selection of cages based on facility set-up, and the optimal frequency and quantity of sentinel animal tests. While existing design formulas are available for simple random sampling, no quantitative recommendation exists for using sentinel animals to the best of our knowledge. We propose a Monte Carlo simulation-based approach in this article to address this. Our algorithm has been implemented in a publicly accessible web page at http://nolan.cnb.csic.es/sentinelcagesmanager.