Computer-based Tools Research Articles

Advances in Genomics and Bioinformatics Methods for Analyzing Gene Expression in Staphylococcus Aureus Associated with Atopic Dermatitis

This comprehensive review explores the latest advancements in genomics and bioinformatics methods for studying how Staphylococcus aureus behaves in Atopic Dermatitis (AD). It focuses on crucial aspects to understand how this bacterium acts in AD. Firstly, it examines how genes in S. aureus are controlled and how they affect AD. It also looks at the less explored area of how S. aureus' genes are influenced by factors beyond their DNA sequence. The review also investigates how S. aureus settles on the skin in AD and how it manages to stay there. This sheds light on possible ways to intervene and stop its presence. Additionally, it tackles the problem of S. aureus becoming resistant to drugs used to treat AD related infections. It mines into the reasons behind this resistance and how it impacts potential treatments. Moreover, the review emphasizes the crucial role of computer-based tools in studying S. aureus' genes. It evaluates different methods used to understand the complex genetic information linked to AD related S. aureus. By combining different areas like genetics, how the bacterium lives on the skin, drug resistance, and the tools scientists use, this review offers a complete understanding of S. aureus in AD.

How Do Different Image Modules Impact the Accuracy of Working Length Measurements in Digital Periapical Radiography? An In Vitro Study

Background/Objectives: This study aims to evaluate the accuracy of digital dental radiography in determining working length in root canal treatment via various image modules. Methods: A total of 40 intact single-rooted, single-canal human premolar teeth were examined. Following meticulous cleaning, the teeth were placed in a maxillary premolar socket within a dry human skull. X-ray images were systematically captured via a Carestream RVG digital sensor under standardized conditions. The digital images are processed under five distinct image modules: 1. original module, 2. autoenhancement module, 3. autoenhancement+negative module, 4. negative module, 5. colored module. Three calibrated observers determined the working length of each digital radiograph after the specified image modules were applied. The agreement between the actual working length and the lengths determined by the observers was evaluated via Pearson correlation analysis. A significance level of 0.05 was set for the statistical tests. Results: A high level of intra- and interobserver agreement, with a strong correlation between the actual measurements and all image module groups, was obtained (p < 0.001). The original image module group demonstrated the highest compatibility (ICC = 0.940, r = 0.912), whereas the colored image module group exhibited the lowest compatibility (ICC = 0.924, r = 0.879) with the actual measurement. Conclusions: This study demonstrates the accuracy of digital radiography in determining working length through the application of various image modules, with the original image module exhibiting the closest alignment to actual working lengths. These findings support the continued use and further development of computer-based image processing tools to optimize clinical outcomes in root canal therapy.

Endoscopic size measurement of colorectal polyps: a systematic review of techniques.

Accurate size measurement of colorectal polyps is critical for clinical decision making and patient management. This systematic review aimed to evaluate the current techniques used for colonic polyp measurement to improve the reliability of size estimations in routine practice.A comprehensive literature search was conducted across PubMed, EMBASE, and MEDLINE to identify studies relevant to size measurement techniques published between 1980 and March 2024. The primary outcome was the accuracy of polyp sizing techniques used during colonoscopy.61 studies were included with 34 focusing on unassisted and assisted endoscopic visual estimation and 27 on computer-based tools. There was significant variability in visual size estimation among endoscopists. The most accurate techniques identified were computer-based systems, such as virtual scale endoscopes (VSE) and artificial intelligence (AI)-based systems. The least accurate techniques were visual or snare-based polyp size estimation. VSE assists endoscopists by providing an adaptive scale for real-time, direct, in vivo polyp measurements, while AI systems offer size measurements independent of the endoscopist's subjective judgment.This review highlights the need for standardized, accurate, and accessible techniques to optimize sizing accuracy during endoscopic procedures. There is no consensus on a gold standard for measuring polyps during colonoscopy. While biopsy forceps, snare, and graduated devices can improve the accuracy of visual size estimation, their clinical implementation is limited by practical, time, and cost challenges. Computer-based techniques will likely offer improved accuracy of polyp sizing in the near future.

An introduction to machine learning tools for the analysis of microplastics in complex matrices.

As microplastic (MP) particles continue to spread globally, their pervasive presence is increasingly problematic. Analyzing MPs in matrices as varied as soil, river water, and biosolid fertilizers is critical, as these matrices directly impact the food sources of plants, animals, and humans. Current analytical methods for quantifying and identifying MPs are limited due to labor-intensive extraction processes and the time and effort required for counting and analysis. Recently, Machine Learning (ML) has been introduced to the analysis of MPs in complex matrices, significantly reducing the need for extensive extraction and increasing analysis speeds. This work aims to illuminate various ML techniques for new researchers entering this field. It highlights numerous examples in the application of these models, with a particular focus on spectroscopic techniques such as infrared and Raman spectroscopy; tools which are used to quantify and identify MPs in complex matrices. By demonstrating the effectiveness of these computer-based tools alongside the hands-on techniques currently used in the field, we are confident that these ML methodologies will soon become integral to all aspects of microplastic analysis in the environmental sciences.

Teaching Statistical Concepts Using Computing Tools: A Review of the Literature

As technological advances continue to shape the field of statistics, approaches to teaching statistics have evolved to incorporate computer-based tools. To determine the types of computing tools that have been developed to help undergraduate and graduate students develop their conceptual understanding of specific statistical concepts, we conducted a literature review. Using a systematic search strategy, we identified 70 articles and summarized them by educational setting, computing tool, and statistical concept. We found that almost half of the articles used the R programming language. In addition, the majority of the articles were for teaching introductory-level statistical concepts, did not require students to write or run code, and did not run studies to evaluate the impact of the tool on student understanding. We concluded by providing recommendations for future research and discussing the important role that publishing can play in the sharing and evaluating of teaching approaches.

Computer-Based Gamified Tools for Facial Rehabilitation: A Scoping Review.

Motor rehabilitation in patients with facial disorders, such as facial paralysis (FP), has faced challenges in maintaining the patients' engagement and motivation in the intensive and repetitive execution of the exercises. To assist in the facial rehabilitation process, gamified tools (GTs) have been proposed to promote stimulation and engagement in affected patients. This study provides a comprehensive review on the use of GTs to aid facial rehabilitation, with the aim of investigating the potential of such computer-based tools to motivate facial mime exercises. A systematic search conducted on May 22, 2024, across five databases (Scopus, Web of Science, IEEE Xplore, ACM Digital Library, and MEDLINE/PubMed) returned 1043 articles. The selection of studies resulted in 15 articles included for analysis, most of which were intended for patients recovering from FP. Bell's palsy, paralysis resulting from stroke, orofacial apraxia, and cerebral palsy were the patient disorders addressed in the articles. Facial mimic movements used for rehabilitation varied, the main actions considered in the studies were opening and closing the eyes, smiling, and raising the eyebrows. Studies focused on developing tools and conducting experiments with participants. Despite advances made by the selected studies, proposed computer-based tools for facial rehabilitation have not yet reached the necessary maturity for their use in clinical settings. Consequently, additional efforts are needed to perform new studies to mitigate the challenges presented in this research area. Therefore, this is a constantly evolving area of research and offers a huge window of opportunity for further research.

A preliminary conceptual structure for Computer-based Process Maturity Models, using a Cone-Based Conceptual Network and NIAM diagrams

Process maturity models support audit and assessment missions (AAMs) focusing on organizational routines. Under Traditional Process Maturity Models (TP2Ms), auditors use document-based surveys whereas organizations produce data in their daily activities employing Information Technologies (IT). Therefore, how to bridge the gap between IT capabilities and AAMs? One answer could be to design a Trace-Based System (TBS) capturing raw data of the daily activity and transmuting it into transformed traces from which it can be possible to automatically reconstruct processes and evaluate their maturity. Despite its obvious practical value, this processing is not so easy. A first phase must be realized, which consists of modeling the conceptual structure of current TP2Ms and of future possible Computer-based Process Maturity Models (CP2Ms) based on TBSs. To achieve this goal, this paper proposes a Cone-Based Conceptual Network (CBCN) to give the big picture of TP2Ms' and CP2Ms' scopes, then it proposes to model this CBCN with the use of Nijssen Information Analysis Method (NIAM) and to verify the semantic consistency of this preliminary conceptual structure. The result is a first step (at an early stage) of the development of computer-based (or trace-based) process maturity assessment tools. It allows auditors and IT specialists to have a big picture of the domain of interest and to map the different knowledge areas they need to acquire and combine to perform AAMs.

DIGITAL STORYTELLING IN THE MIDDLE SCHOOL: STUDENTS’ PERCEPTION AND ACADEMIC PERSPECTIVES

Digital storytelling (J. Lambert) is an interdisciplinary tool that helps teachers to personalize educational process and students to acquire learning material better, improve st century skills and develop agency. It implies narratives and animated presentations of ideas, created by students with the use of computer-based tools. The work on a digital story improves students’ computational thinking skills and multimodal literacy, associated with visual perception, understanding and responding to digital and multimedia texts. In order to study learning potential and students’ perception of such educational technology the research was conducted by The Center for Interdisciplinary Research on Contemporary Childhood in March, . It was completed on the basis of School № in Kashira within the government assignment «Development of technology for overcoming adolescent risks: school theater based on role-playing experimentation».  adolescents took part in  lessons in Fine Arts. After reading “Letters about the good and beautiful” by Dmitriy Likhachev, they fi gured out the appropriate statements to develop them into digital stories of moral choice. Having sketched the plots of their stories in pencil, the schoolers worked on animated fi lms in stop-motion technique. That work included drawing scenery and characters, taking and editing photos, processing photos into short animated fi lms, adding sound and subtitles. Adolescents completed  digital stories. Due to their  refl ective journals, fi lled in the form of open-ended questions, they mostly experienced interest, pleasure and fun at the lessons. Adolescents wrote that they learned to work in teams, be attentive to others and overcome interaction challenges. They found diffi cult to come up with visual metaphors and story plots, but all their new skills might be practical for other school projects. To conclude, digital storytelling may be incorporated into the curriculum to update and enhance studying as well as bridge Literature, IT and Fine Arts together.

A study of the Orr–Sommerfeld and induction equations by Galerkin and Petrov–Galerkin spectral methods utilizing Chebyshev polynomials

The article discusses two spectral methods, namely the Galerkin and the Petrov–Galerkin methods, for linear stability analysis of magneto-hydrodynamic (MHD) equations describing the flow of an electrically conducting fluid in the presence of a tangential magnetic field. The stability and spectral accuracy of both methods have been compared by examining the most unstable eigensolution of the Orr–Sommerfeld (OS) and induction equations. The Petrov–Galerkin spectral method (PGSM) used in this work has been developed by choosing function spaces and basis functions that always lead to banded coefficient matrices. The Galerkin spectral method (GSM), on the contrary, leads to dense matrices when Chebyshev polynomials are utilized in a weighted inner product space. We have found that both the GSM and the PGSM can produce results with minimal round-off errors, as confirmed by computing the most unstable eigenvalue of the OS equations (Re =104) to 14 decimal places of accuracy in double precision. We show that with properly scaled basis functions the GSM leads to coefficient matrices with bounded condition numbers, both for the OS equation and for the coupled OS and induction equations. This allows to achieve accurate results with double precision for any number of N for both the GSM and the PGSM. The analysis of the different behavior of the condition numbers suggests that the proposed two methods, based on the Chebyshev polynomials, can become a useful computer-based tool that is capable of finding a numerical solution to both the hydrodynamic and the MHD equations at very high Reynolds numbers.

Strengths and weaknesses of computerized clinical decision support systems: insights from a digital control center (C3 COVID-19) for early and personalized treatment for COVID-19.

Clinical Decision Support Systems (CDSS) are computer-based tools that leverage the analysis of large volumes of health data to assist healthcare professionals in making clinical decisions, whether preventive, diagnostic, or therapeutic. This review examines the impact of CDSS on clinical practice, highlighting both their potential benefits and their limitations and challenges. We detail the experience of clinical medical professionals in the development of a virtual control center for COVID-19 patients (C3 COVID-19) in Spain during the SARS-CoV-2 pandemic. This tool enabled real-time monitoring of clinical data for hospitalized COVID-19 patients, optimizing personalized and informed medical decision-making. CDSS can offer significant advantages, such as improving the quality of inpatient care, promoting evidence-based clinical and therapeutic decision-making, facilitating treatment personalization, and enhancing healthcare system efficiency and productivity. However, the implementation of CDSS presents challenges, including the need for physicians to become familiar with the systems and software, and the necessity for ongoing updates and technical support of the systems.

The Camden and Islington Viral Hepatitis Identification Tool (CIVHIT): Use of a Clinical Database Case-Finding Tool for Hepatitis B, Hepatitis C and HIV in Primary Care.

Despite the availability of effective treatment and vaccines for hepatitis B virus (HBV) and C virus (HCV), many people are still infected and remain unaware of their infection. The Camden and Islington Viral Hepatitis Identification Tool (CIVHIT), a computer-based search tool, was introduced in 60 general practices (GPs) in April 2014 to support identification, testing and treatment of individuals at high risk for blood-borne viruses (BBVs). CIVHIT searched electronic medical records (EMRs), flagging all those with codes linked to risk factors or medical conditions associated with BBVs. CIVHIT was associated with a 78.5% increase in BBV tests in primary care in both boroughs. This translated to a 55.8% rise in new diagnoses. HBV testing saw the largest increase resulting in twice as many people diagnosed. Only 23.2% of HBV and 14.9% of HCV-positive tests were referred to secondary care. In an index practice, the most common flag was a history of STIs (477/719, 66.3%). Individuals with previous or current drug use and those with a known hepatitis contact were more likely to be offered a test compared to those flagged due to a history of STI. HIV and HBV testing was lower in males following a test offer. There was an increased likelihood of testing for HBV and HCV with increasing age. Additionally, individuals with previous or current drug use and individuals with a known hepatitis contact were more likely to test for HCV compared to individuals flagged due to STI history. CIVHIT shows promise to assist with the elimination of BBVs.

Development of an Expert-Based Scoring System for Early Identification of Patients with Inborn Errors of Immunity in Primary Care Settings - the PIDCAP Project.

Early diagnosis of inborn errors of immunity (IEIs) has been shown to reduce mortality, morbidity, and healthcare costs. The need for early diagnosis has led to the development of computational tools that trigger earlier clinical suspicion by physicians. Primary care professionals serve as the first line for improving early diagnosis. To this end, a computer-based tool (based on extended Jeffrey Modell Foundation (JMF) Warning Signs) was developed to assist physicians with diagnosis decisions for IEIs in the primary care setting. Two expert-guided scoring systems (one pediatric, one adult) were developed. IEI warning signs were identified and a panel of 36 experts reached a consensus on which signs to include and how they should be weighted. The resulting scoring system was tested against a retrospective registry of patients with confirmed IEI using primary care EHRs. A pilot study to assess the feasibility of implementation in primary care was conducted. The scoring system includes 27 warning signs for pediatric patients and 24 for adults, adding additional clinically relevant criteria established by expert consensus to the JMF Warning Signs. Cytopenias, ≥ 2 systemic infections, recurrent fever and bronchiectasis were the leading warning signs in children, as bronchiectasis, autoimmune diseases, cytopenias, and > 3 pneumonias were in adults. The PIDCAP (Primary Immune Deficiency "Centre d'Atenció Primària" that stands for Primary Care Center in Catalan) tool was implemented in the primary care workstation in a pilot area. The expert-based approach has the potential to lessen under-reporting and minimize diagnostic delays of IEIs. It can be seamlessly integrated into clinical primary care workstations.

What causes Emergency Medical Services (EMS) delay? Unravel high-risk buildings using citywide ambulance trajectory data

Emergency Medical Services (EMS) delay poses a challenge to public health, especially in ultra-dense cities and aging societies. The potential relationship between the built environment and the ambulance response time (ART) has not been thoroughly investigated at the building-level. This impedes the unraveling of high-risk buildings for EMS delay, and thus the precise optimization of urban EMS systems. Our study develops a quantitative-qualitative approach to construct a building-level ART prediction model and then validated it with ambulance drivers' experiences. A comprehensive theoretical framework is tailored for ART prediction incorporating neglected built environment factors of land use and development intensity. Based on 73,129 ambulance trajectories, a machine learning model is constructed and then employed to predict the ART for each of the 253,475 buildings in central Shanghai under multiple combinations of traffic periods and weather conditions. The results show that the accuracy of high-risk buildings for EMS delay achieves 91%. Moreover, three of the neglected built environment factors, medical POIs density, business POIs density and floor area ratio, rank high in the factor importance. The ambulance drivers’ experiences validate the importance of these built environment factors and emphasize the value of bus priority lanes for ambulance to borrow for swift access. In addition, to illustrate potential applications, we simulated the effects of built environment interventions in case areas predicted to concentrate high-risk buildings. This work provides a new computer-based tool to pinpoint the service blind spots of such urban emergency systems and to develop built environment interventions.

USING DIGITAL SOFTWARE TOOLS FOR ASSESSING STUDENT KNOWLEDGE (The Example of MyTextX Software)

This article analyzes the advantages and possibilities of using computer-based test software tools to assess students' knowledge in the education system. The MyTextX software is recommended for creating non-standard, interactive electronic tests. Additionally, the installation of the MyTextX software and the stages of preparing tests of various types are described in detail.

Novel color vision assessment tool: AIM Color Detection and Discrimination.

Color vision assessment is essential in clinical practice, yet different tests exhibit distinct strengths and limitations. Here we apply a psychophysical paradigm, Angular Indication Measurement (AIM) for color detection and discrimination. AIM is designed to address some of the shortcomings of existing tests, such as prolonged testing time, limited accuracy and sensitivity, and the necessity for clinician oversight. AIM presents adaptively generated charts, each a N×M (here 4×4) grid of stimuli, and participants are instructed to indicate either the orientation of the gap in a cone-isolating Landolt C optotype or the orientation of the edge between two colors in an equiluminant color space. The contrasts or color differences of the stimuli are adaptively selected for each chart based on performance of prior AIM charts. In a group of 23 color-normal and 15 people with color vision deficiency (CVD), we validate AIM color against Hardy-Rand-Rittler (HRR), Farnsworth-Munsell 100 hue test (FM100), and anomaloscope color matching diagnosis and use machine learning techniques to classify the type and severity of CVD. The results show that AIM has classification accuracies comparable to that of the anomaloscope, and while HRR and FM100 are less accurate than AIM and an anomaloscope, HRR is very rapid. We conclude that AIM is a computer-based, self-administered, response-adaptive and rapid tool with high test-retest repeatability that has the potential to be suitable for both clinical and research applications.

Assessing benefits of computer-based video training and tools on learning outcomes and motivation in mechanical engineering education: digitalized intervention and approach

Traditionally, laboratory work has been a common approach to facilitate the acquisition of practical skills and familiarize the undergraduate students, particularly engineering students, with specialized tools and equipment. However, the conventional in-person labs often experience challenges such as limited resources and space, instructor availability, and inflexible schedules. The emergence of digital tools and the recent COVID-19 pandemic have prompted educators to reconsider their teaching methods. To this effect, this paper introduces an innovative approach and teaching methodology to address the challenges in traditional engineering education within the laboratory. It presents a methodology that combines cost-effective instructional videos and portable kits to promote autonomous development of practical skills in undergraduate engineering students. The proposed teaching methodology was grounded on the Descriptive Decision theory and Learning-Oriented Assessment (LOA), which are theoretical framework and type of students’ learning outcome (SLO) model that studies the rationality behind the decisions that the users are disposed to make, as well as level of outcome of the students’ learning process or performance. Motivation among the students was assessed using the Model of Academic Motivation Inventory (MUSIC Inventory) to evaluate the impact of the proposed teaching method and learning intervention on the interest of the students. The method was implemented in four independent courses at two different campuses of Tecnologico de Monterrey. The results show that the proposed learning approach was effective in helping students develop hard skills with reduced instructor intervention. Moreover, high levels of motivation was reported through the MUSIC methodology and test administered to the participating students at the end of the courses. The outcome of this study can be used to inform and support the curriculum design by the educators, promote effective policy and decision making by the university leaders, and encourage wide adoption of the digitalized-education.

MolPlay: Democratizing Interactive Molecular Simulations and Analyses with a Portable, Turnkey Platform.

Computer-based tools for visualizing and manipulating molecular structures in real-time hold immense potential for accelerating research and improving education, but are only used to a limited extent. This paper explores the possibilities of these powerful techniques and presents a classification of common interactive modeling tasks, such as assembly, deformation, sampling of rare events, along with relevant use cases, especially for the study of membranes and membrane proteins. I introduce MolPlay─a platform that provides a ready-to-use software environment with a curated set of hands-on examples to democratize access to interactive molecular simulations and analyses (IMSA). These research tools allow researchers to visualize, manipulate, and study digital models of molecules in real-time and can be started directly from a small, portable storage device. The portable, self-contained nature of MolPlay enables versatile deployment scenarios ranging from outreach to teaching in computer laboratories to facilitating the distribution of research software. Initial tests at an international workshop have shown that MolPlay appeals to different target groups and lowers the access barriers to IMSA. By consolidating more than a decade of expertise into one accessible platform, MolPlay represents a significant step toward broader adoption of these underutilized but powerful computational techniques in education and research.

Development of a computer-based tool to obtain a family health history in Vietnam.

Background: Family health history (FHH) is central to human genomic profiling construction; however, there is no protocol for documenting FHH in a pedigree format in Vietnam.Aim: A "Gia Su Suc Khoe" (GSSK) tool was developed to create a user-friendly interface for collecting FHH and offering diseases' risk assessment.Results: A tool was described (https://giasusuckhoe.vn/) with good feedback from genetic counselors and family-medicine doctors. Among 20 surveys, 100% of respondents noted that the report accurately reflected their FHH and were satisfied with the tool's display. About 74% of familial conditions were covered. Overall, all constructive feedback has been adapted into the updated version.Conclusion: Gia Su Suc Khoe has the potential to significantly improve healthcare delivery and outcomes in Vietnam.

Visual tests, touch responses: Computer-based neuropsychological tools

Objective: Investigate three new computer tests of visual abilities relative to traditional paper-and-pencil (P&P) tests on groups with and without cerebral neurological impairment (CNI, Non-CNI) based on MRI and EEG criteria. The new tests employ an intuitive interface with audio instructions, touch responses, non-response prompts, and videography of test behavior. The Presidents Test was designed as an achievement-related measure of visual knowledge; the Railroad Test – visual perception and delayed recognition; and the Swamp Test – visual attention. Attitudes toward testing were assessed with an original Testing Experience Questionnaire (TEQ). Method: Of the 129 participants, 84 were women, 73 identified as non-White, average age 45.5 and education 14.3 years. Procedures included the new computer tests and a selection of standard neuropsychological measures including performance validity tests (PVT). Participants who failed two or three PVTs or had missing PVT data were excluded from main analyses, resulting in N = 115. Results: The new computer tests demonstrated adequate reliability. Correlations and factor analyses confirmed the computer tests as functioning in accordance with design. The Presidents Test was associated with academic achievement. The Railroad and Swamp Tests were linked to visual perception and visual attention. Correlations between computer total test duration time and traditional speed of processing tasks were modest. Computer and traditional tests demonstrated similar discriminability between CNI and Non-CNI groups. TEQ indicated positive attitudes toward testing in general, and computer testing in particular. Conclusions: The new computer tests evaluated in this study were found to be reliable, functioned to assess the designed cognitive domains, and discriminated between CNI and Non-CNI participants similarly to the traditional neuropsychological measures. Attitudes toward computer testing were favorable.

Canonical Correlations on Positive and Negative Attitudes of Computer-Based Statistics among Business Students

This study aims to investigate the canonical relationship between positive and negative attitudes of economics and business students when using computer-based analytical tools. The subjects of this study were students of the Faculty of Economics and Business in Medan. Samples were taken from two universities, including state universities and private universities. Using a random sampling technique, this study collected 247 responses, consisting of 47 male and 200 female respondents. The data in this study were collected using a questionnaire with a 5-Likert scale and analyzed by canonical correlation. The results of the canonical correlation analysis in this study revealed that Perceived Usefulness (PU) and Perceive Enjoyment (PE) were critical predictors of the construct set of positive attitudes. At the same time, Strain (ST) and Negative Affectivity (NA) were essential predictors of the formation of negative attitudes among users when using computer-based statistical tools. This study produces a new insight related to attitude toward Information Technology (IT) in a specific task, namely statistical analysis. This study also has practical recommendations for universities and lecturers to invest in and use statistical software that is easy and convenient for teaching statistics.