Browser Application Research Articles

Insights on Source Lithology and Pressure‐Temperature Conditions of Basalt Generation Using Machine Learning

AbstractIdentifying the origin and conditions of basalt generation is a crucial yet formidable task. To tackle this challenge, we introduce an innovative approach leveraging machine learning. Our methodology relies on a comprehensive database of approximately one thousand major element concentrations derived from glass samples generated through experiments encompassing a wide range of source lithologies, pressure (from 0.28 to 20 GPa) and temperature (850–2100°C). We first applied the XGBoost classification models to assess the compositional characteristics of melts from three principal mantle source categories: peridotitic, transitional, and mafic sources. We obtained an accuracy of approximately 96% on the test data set. Furthermore, we also employ an XGBoost regression model to predict the pressure and temperature conditions of generation of basalts from diverse lithologic sources. Our predictions of temperature and pressure exhibit remarkable precisions, of about 49°C and 0.37 GPa, respectively. To enhance accessibility of our model, we have implemented a user‐friendly web browser application, available at (https://huggingface.co/spaces/lilucheng/sourcedetection). The web application allows users to swiftly recover the source lithology as well as pressure and temperature conditions governing basalt generation for a broad array of samples within a matter of seconds.

Spread.gl: visualising pathogen dispersal in a high-performance browser application.

Phylogeographic analyses are able to exploit the location data associated with sampled molecular sequences to reconstruct the spatio-temporal dispersal history of a pathogen. Visualisation software is commonly used to facilitate the interpretation of the accompanying estimation results, as these are not always easily interpretable. spread.gl is a powerful, open-source and feature-rich browser application that enables smooth, intuitive and user-friendly visualisation of both discrete and continuous phylogeographic inference results, enabling the animation of pathogen geographic dispersal through time. spread.gl can render and combine the visualisation of several data layers, including a geographic layer (e.g., a world map), multiple layers that contain information extracted from the input phylogeny, and different types of layers that represent environmental data. As such, users can explore which environmental data may have shaped pathogen dispersal patterns, that can subsequently be formally tested through more principled statistical analyses. We showcase the visualisation features of spread.gl on several representative pathogen dispersal examples, including the smooth animation of a phylogeny encompassing over 17,000 genomic sequences resulting from a large-scale SARS-CoV-2 analysis.

Towards determining perceived audience intent for multimodal social media posts using the theory of reasoned action

Increasing use of social media has resulted in many detrimental effects in youth. With very little control over multimodal content consumed on these platforms and the false narratives conveyed by these multimodal social media postings, such platforms often impact the mental well-being of the users. To reduce these negative effects of multimodal social media content, an important step is to understand creators’ intent behind sharing content and to educate their social network of this intent. Towards this goal, we propose Intent-o-meter, a perceived human intent prediction model for multimodal (image and text) social media posts. Intent-o-meter models ideas from psychology and cognitive modeling literature, in addition to using the visual and textual features for an improved perceived intent prediction model. Intent-o-meter leverages Theory of Reasoned Action (TRA) factoring in (i) the creator’s attitude towards sharing a post, and (ii) the social norm or perception towards the multimodal post in determining the creator’s intention. We also introduce Intentgram, a dataset of 55K social media posts scraped from public Instagram profiles. We compare Intent-o-meter with state-of-the-art intent prediction approaches on four perceived intent prediction datasets, Intentonomy, MDID, MET-Meme, and Intentgram. We observe that leveraging TRA in addition to visual and textual features—as opposed to using only the latter–results in improved prediction accuracy by up to 7.5%\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$7.5\\%$$\\end{document} in Top-1 accuracy and 8%\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$8\\%$$\\end{document} in AUC on Intentgram. In summary, we also develop a web browser application mimicking a popular social media platform and show users social media content overlaid with these intent labels. From our analysis, around 70%\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$70\\%$$\\end{document} users confirmed that tagging posts with intent labels helped them become more aware of the content consumed, and they would be open to experimenting with filtering content based on these labels. However, more extensive user evaluation is required to understand how adding such perceived intent labels mitigate the negative effects of social media.

Examining the factors fostering metaverse experience browser acceptance under unified theory of acceptance and use of technology (UTAUT)

Metaverse technology has various uses in communication, education, entertainment, and other aspects of life. Consequently, it necessitates using some interactive mobile applications to enter the virtual world and gain real-time, face-to-face experiences, particularly among students. This research focused on the factors accelerating metaverse technology acceptance particularly, Metaverse Experience Browser application acceptance among the students under the factors proposed by the unified theory of acceptance and use of technology (UTAUT) model. Notably, lack of studies in metaverse browsers and their prevalence during the post pandemic era, indicates a strong literature gap. The researchers gathered data from n = 384 higher education students from the two cities in the United Arab Emirates and applied Structural Equation modelling (SEM) for data analysis. Results revealed that Performance Expectancy (p < 0.003) and Social Influence (p = 0.000) were significant factors affecting the Behavioral Intention of the students to consider Metaverse Experience Browser as an interactive mobile application. On the other hand, behavioural Intention significantly affects (p = 0.000) Effort Expectancy, which shows how fewer efforts and greater accessibility are associated with one’s behavioural Intention. Besides, the effect of Behavioral Intention (p = 0.000) on Metaverse Experience Browser acceptance also remained validated. Finally, Effort Expectancy (p = 0.000) also indicated its significant effect on the Metaverse Experience Browser. These results indicated that the factors proposed by UTAUT have greater applicability on the Metaverse Experience Browser as they showed their relevance to its acceptance. The present study concludes that the acceptance of Metaverse Experience Browser as an interactive mobile application is a level ahead in improving students’ experiences. Thus, the Metaverse Experience Browser is considered a modified way of creating, sharing, participating, and enjoying the virtual world, indicating its greater usage among students for different purposes, including education and learning.

The California Cognitive Assessment Battery (CCAB).

We are developing the California Cognitive Assessment Battery (CCAB) to provide neuropsychological assessments to patients who lack test access due to cost, capacity, mobility, and transportation barriers. The CCAB consists of 15 non-verbal and 17 verbal subtests normed for telemedical assessment. The CCAB runs on calibrated tablet computers over cellular or Wi-Fi connections either in a laboratory or in participants' homes. Spoken instructions and verbal stimuli are delivered through headphones using naturalistic text-to-speech voices. Verbal responses are scored in real time and recorded and transcribed offline using consensus automatic speech recognition which combines the transcripts from seven commercial ASR engines to produce timestamped transcripts more accurate than those of any single ASR engine. The CCAB is designed for supervised self-administration using a web-browser application, the Examiner. The Examiner permits examiners to record observations, view subtest performance in real time, initiate video chats, and correct potential error conditions (e.g., training and performance failures, etc.,) for multiple participants concurrently. Here we describe (1) CCAB usability with older (ages 50 to 89) participants; (2) CCAB psychometric properties based on normative data from 415 older participants; (3) Comparisons of the results of at-home vs. in-lab CCAB testing; (4) We also present preliminary analyses of the effects of COVID-19 infection on performance. Mean z-scores averaged over CCAB subtests showed impaired performance of COVID+ compared to COVID- participants after factoring out the contributions of Age, Education, and Gender (AEG). However, inter-cohort differences were no longer significant when performance was analyzed with a comprehensive model that factored out the influences of additional pre-existing demographic factors that distinguished COVID+ and COVID- cohorts (e.g., vocabulary, depression, race, etc.,). In contrast, unlike AEG scores, comprehensive scores correlated significantly with the severity of COVID infection. (5) Finally, we found that scoring models influenced the classification of individual participants with Mild Cognitive Impairment (MCI, z-scores < -1.50) where the comprehensive model accounted for more than twice as much variance as the AEG model and reduced racial bias in MCI classification. The CCAB holds the promise of providing scalable laboratory-quality neurodiagnostic assessments to underserved urban, exurban, and rural populations.

With Great Power Comes Great Responsibility: Security and Privacy Issues of Modern Browser Application Programming Interfaces

With Great Power Comes Great Responsibility: Security and Privacy Issues of Modern Browser Application Programming Interfaces

Development of an explainable artificial intelligence model for Asian vascular wound images.

Chronic wounds contribute to significant healthcare and economic burden worldwide. Wound assessment remains challenging given its complex and dynamic nature. The use of artificial intelligence (AI) and machine learning methods in wound analysis is promising. Explainable modelling can help its integration and acceptance in healthcare systems. We aim to develop an explainable AI model for analysing vascular wound images among an Asian population. Two thousand nine hundred and fifty-seven wound images from a vascular wound image registry from a tertiary institution in Singapore were utilized. The dataset was split into training, validation and test sets. Wound images were classified into four types (neuroischaemic ulcer [NIU], surgical site infections [SSI], venous leg ulcers [VLU], pressure ulcer [PU]), measured with automatic estimation of width, length and depth and segmented into 18 wound and peri-wound features. Data pre-processing was performed using oversampling and augmentation techniques. Convolutional and deep learning models were utilized for model development. The model was evaluated with accuracy, F1 score and receiver operating characteristic (ROC) curves. Explainability methods were used to interpret AI decision reasoning. A web browser application was developed to demonstrate results of the wound AI model with explainability. After development, the model was tested on additional 15 476 unlabelled images to evaluate effectiveness. After the development on the training and validation dataset, the model performance on unseen labelled images in the test set achieved an AUROC of 0.99 for wound classification with mean accuracy of 95.9%. For wound measurements, the model achieved AUROC of 0.97 with mean accuracy of 85.0% for depth classification, and AUROC of 0.92 with mean accuracy of 87.1% for width and length determination. For wound segmentation, an AUROC of 0.95 and mean accuracy of 87.8% was achieved. Testing on unlabelled images, the model confidence score for wound classification was 82.8% with an explainability score of 60.6%. Confidence score was 87.6% for depth classification with 68.0% explainability score, while width and length measurement obtained 93.0% accuracy score with 76.6% explainability. Confidence score for wound segmentation was 83.9%, while explainability was 72.1%. Using explainable AI models, we have developed an algorithm and application for analysis of vascular wound images from an Asian population with accuracy and explainability. With further development, it can be utilized as a clinical decision support system and integrated into existing healthcare electronic systems.

The prediction of acute toxicity (LD50) for organophosphorus-based chemical warfare agents (V-series) using toxicology in silico methods.

Nerve agents are organophosphate chemical warfare agents that exert their toxic effects by irreversibly inhibiting acetylcholinesterase, affecting the breakdown of the neurotransmitter acetylcholine in the synaptic cleft. Due to the risk of exposure to dangerous nerve agents and for animal welfare reasons, in silico methods have been used to assess acute toxicity safely. The next-generation risk assessment (NGRA) is a new approach for predicting toxicological parameters that can meet modern requirements for toxicological research. The present study explains the acute toxicity of the examined V-series nerve agents (n = 9) using QSAR models. Toxicity Estimation Software Tool (ver. 4.2.1 and ver. 5.1.2), QSAR Toolbox (ver. 4.6), and ProTox-II browser application were used to predict the median lethal dose. The Simplified Molecular Input Line Entry Specification (SMILES) was the input data source. The results indicate that the most deadly V-agents were VX and VM, followed by structural VX analogues: RVX and CVX. The least toxic turned out to be V-sub x and Substance 100A. In silico methods for predicting various parameters are crucial for filling data gaps ahead of experimental research and preparing for the upcoming use of nerve agents.

Spatial transcriptomics reveal basal sex differences in supraoptic nucleus gene expression of adult rats related to cell signaling and ribosomal pathways

BackgroundThe supraoptic nucleus (SON) of the hypothalamus contains magnocellular neurosecretory cells that secrete the hormones vasopressin and oxytocin. Sex differences in SON gene expression have been relatively unexplored. Our study used spatially resolved transcriptomics to visualize gene expression profiles in the SON of adult male (n = 4) and female (n = 4) Sprague-Dawley rats using Visium Spatial Gene Expression (10x Genomics).MethodsBriefly, 10-μm coronal sections (~ 4 × 4 mm) containing the SON were collected from each rat and processed using Visium slides and recommended protocols. Data were analyzed using 10x Genomics’ Space Ranger and Loupe Browser applications and other bioinformatic tools. Two unique differential expression (DE) analysis methods, Loupe Browser and DESeq2, were used.ResultsLoupe Browser DE analysis of the SON identified 116 significant differentially expressed genes (DEGs) common to both sexes (e.g., Avp and Oxt), 31 significant DEGs unique to the males, and 73 significant DEGs unique to the females. DESeq2 analysis revealed 183 significant DEGs between the two groups. Gene Ontology (GO) enrichment and pathway analyses using significant genes identified via Loupe Browser revealed GO terms and pathways related to (1) neurohypophyseal hormone activity, regulation of peptide hormone secretion, and regulation of ion transport for the significant genes common to both males and females, (2) Gi signaling/G-protein mediated events for the significant genes unique to males, and (3) potassium ion transport/voltage-gated potassium channels for the significant genes unique to females, as some examples. GO/pathway analyses using significant genes identified via DESeq2 comparing female vs. male groups revealed GO terms/pathways related to ribosomal structure/function. Ingenuity Pathway Analysis (IPA) identified additional sex differences in canonical pathways (e.g., ‘Mitochondrial Dysfunction’, ‘Oxidative Phosphorylation’) and upstream regulators (e.g., CSF3, NFKB complex, TNF, GRIN3A).ConclusionThere was little overlap in the IPA results for the two different DE methods. These results suggest sex differences in SON gene expression that are associated with cell signaling and ribosomal pathways.

Human-Browser Interaction: Investigating Whether the Current Browser Application’s Design Actually Make Sense for Its Users?

As a society, we can get so engrossed in using the World Wide Web (WWW) also known as the web. We look for, read about and interact with different people, text, images, videos etc. that we often take for granted how we are doing this. We all use a web browser, we must, to get around the web. Yet, do we ever stop to give much thought to this gateway to the web? The web browser can be fittingly described as a transparent technology. This is in the sense that we don’t really stop to think about the browser and its features, we just unconsciously absorb and use it. This paper reports on a study that explores just how effectively we understand and use our web browser applications. The study was focused on people’s perceived awareness of desktop web browser functionality, what features they are using/aware of and most importantly what understanding they take from these? The findings show that the majority (69%) of the five hundred and twenty-eight participants studied do not fully understand what the padlock feature on their web browsers represents. In fact, the findings highlight that many of the participants feel that the padlock represents a safe website which it clearly does not. This paper succinctly draws attention to the fact that the current desktop web browser application design is not fit for purpose. In summary, the research pushes for more effective web browser application designs; it provides design recommendations aimed at achieving web browser consistency and creating designs that promote safety, trust, and confidence.

Hypermedia-based software architecture enables Test-Driven Development.

Using agile software development practices, develop and evaluate an architecture and implementation for reliable and user-friendly self-service management of bioinformatic data stored in the cloud. Comprehensive Oncology Research Environment (CORE) Browser is a new open-source web application for cancer researchers to manage sequencing data organized in a flexible format in Amazon Simple Storage Service (S3) buckets. It has a microservices- and hypermedia-based architecture, which we integrated with Test-Driven Development (TDD), the iterative writing of computable specifications for how software should work prior to development. Relying on repeating patterns found in hypermedia-based architectures, we hypothesized that hypermedia would permit developing test "templates" that can be parameterized and executed for each microservice, maximizing code coverage while minimizing effort. After one-and-a-half years of development, the CORE Browser backend had 121 test templates and 875 custom tests that were parameterized and executed 3031 times, providing 78% code coverage. Architecting to permit test reuse through a hypermedia approach was a key success factor for our testing efforts. CORE Browser's application of hypermedia and TDD illustrates one way to integrate software engineering methods into data-intensive networked applications. Separating bioinformatic data management from analysis distinguishes this platform from others in bioinformatics and may provide stable data management while permitting analysis methods to advance more rapidly. Software engineering practices are underutilized in informatics. Similar informatics projects will more likely succeed through application of good architecture and automated testing. Our approach is broadly applicable to data management tools involving cloud data storage.

Implementation of The Information Service Application Portal for Registration of Prospective Santri and Recording of School Activities Using The Extreme Programming Method

For Islamic boarding schools, the website functions as a medium of information and school introduction to the public. In order for prospective students to register easily, complete information is needed so that the boarding school can easily manage the data of prospective students. Apart from teaching and learning, teachers and students also have other activities at school. The categories of activities carried out by teachers are training, teaching, RPP (Learning Implementation Plans), and clubs or CCA (Co-Curricular Activities), while the categories of activities carried out by students are extracurricular activities, organizations, and competitions. All information on teacher and student activities requires data to be stored properly for a certain period of time. This study aims to build an information service application for registration of prospective students and recording of school activities using the extreme programming method. This application is made using the PHP programming language with the CodeIgniter framework, a MySQL database, and extreme programming as an application development method. Based on the tests carried out, this application can run on various browser applications, such as Google Chrome, Mozilla Firefox, and Microsoft Edge.

A Network Function Virtualization Architecture for Automatic and Efficient Detection and Mitigation against Web Application Malware

This paper proposes and implements a Network Function Virtualization (NFV) security architecture to provide automatic and efficient detection and mitigation against Web application malware. The mitigation is given by dynamically chaining a Virtual Security Function (VSF) to the data stream to block malicious exploitation traffic without affecting the benign traffic. We implement an NFV Security Controller (NFV-SC) that interacts with an Intrusion Detection System and a Web Application Firewall (WAF), both implemented as VSFs. We also implement a vulnerability scanner and a mechanism to automatically create rules in advance in the WAF-VSF when a security vulnerability is found in an application, even if no malicious traffic has attempted to exploit the flaw. In addition, it dynamically identifies and removes no longer used security rules to improve performance. We implement and evaluate our security proposal in the Open Platform for NFV (OPNFV). The evaluation results in our experimental scenarios show that the NFV security architecture automatically blocks 99.12% of the HTTP malicious traffic without affecting 93.6% of the benign HTTP requests. Finally, we show that the number of rules in the WAF-VSF severely affects the latency to load HTTP response headers and that the number of redirection OpenFlow rules within Open vSwitches is not enough to significantly impact the end-user experience in modern web browser applications.

GMWI-webtool: a user-friendly browser application for assessing health through metagenomic gut microbiome profiling.

We recently introduced the Gut Microbiome Wellness Index (GMWI), a stool metagenome-based indicator for assessing health by determining the likelihood of disease given the state of one's gut microbiome. The calculation of our wellness index depends on the relative abundances of health-prevalent and health-scarce species. Encouragingly, GMWI has already been utilized in various studies focusing on differences in the gut microbiome between cases and controls. Herein, we introduce the GMWI-webtool, a user-friendly browser application that computes GMWI, health-prevalent/-scarce species' relative abundances, and α-diversities from stool shotgun metagenome taxonomic profiles. Users of our interactive online tool can visualize their results and compare them side-by-side with those from our pooled reference dataset of metagenomes, as well as export data in.csv format and high-resolution figures. GMWI-webtool is freely available here: https://gmwi-webtool.github.io/. Supplementary data are available at Bioinformatics online.

Web-accessible application for identifying pathogenic transcripts with RNA-seq: Increased sensitivity in diagnosis of neurodevelopmental disorders

For neurodevelopmental disorders (NDDs), a molecular diagnosis is key for management, predicting outcome, and counseling. Often, routine DNA-based tests fail to establish a genetic diagnosis in NDDs. Transcriptome analysis (RNA sequencing [RNA-seq]) promises to improve the diagnostic yield but has not been applied to NDDs in routine diagnostics. Here, we explored the diagnostic potential of RNA-seq in 96 individuals including 67 undiagnosed subjects with NDDs. We performed RNA-seq on single individuals' cultured skin fibroblasts, with and without cycloheximide treatment, and used modified OUTRIDER Z scores to detect gene expression outliers and mis-splicing by exonic and intronic outliers. Analysis was performed by a user-friendly web application, and candidate pathogenic transcriptional events were confirmed by secondary assays. We identified intragenic deletions, monoallelic expression, and pseudoexonic insertions but also synonymous and non-synonymous variants with deleterious effects on transcription, increasing the diagnostic yield for NDDs by 13%. We found that cycloheximide treatment and exonic/intronic Z score analysis increased detection and resolution of aberrant splicing. Importantly, in one individual mis-splicing was found in a candidate gene nearly matching the individual's specific phenotype. However, pathogenic splicing occurred in another neuronal-expressed gene and provided a molecular diagnosis, stressing the need to customize RNA-seq. Lastly, our web browser application allowed custom analysis settings that facilitate diagnostic application and ranked pathogenic transcripts as top candidates. Our results demonstrate that RNA-seq is a complementary method in the genomic diagnosis of NDDs and, by providing accessible analysis with improved sensitivity, our transcriptome analysis approach facilitates wider implementation of RNA-seq in routine genome diagnostics.

A telemedical interface for at-home cognitive testing.

The COVID-19 pandemic has limited in-lab cognitive testing. While at-home alternatives exist (testing over the phone), differences in test design and delivery complicate direct comparison of most in-lab and at-home tests. Here we describe the design, infrastructure, and implementation of the California Cognitive Assessment Battery (CCAB), a cognitive test battery and administration system. Using automated remote administration over cellular networks, identical computerized cognitive tests can be administered at-home or in the lab. The CCAB comprises 30+ computerized cognitive tests. Test delivery is automated through text, text-to-speech, and graphical instructions; test scoring is also automated, using automatic speech recognition for verbal responses. The test delivery infrastructure consists of two components: the test kit (MS Surface Pro tablet computer with mouse, headset, and cellular dongle) , and a web-browser application "CCAB Examiner," used by the test administrator to control test delivery, view real-time progress and scores, and monitor/chat with the participant via AV feeds. The system is coordinated by a cloud-based server which also delivers the CCAB Examiner application to any browser. Once connected, the "CCAB Test Station" application and CCAB Examiner communicate over a commercial Communication as a Service (CAAS) provider, which transmits AV streams and a text messaging protocol. CCAB Test Station is designed to isolate control, communications, and testing functions for maximal fault tolerance. For example, test administration continues automatically even if Examiner and Test Station lose connection. Test data is uploaded to the server during sessions and re-synced when the test kit returns. 442 participants underwent three 90-minute CCAB test sessions on successive days (72 total tests per participant). More than 98% of participants finished all three sessions, and >99% of tests were completed. Cellular connection failures occurred occasionally, but >98% resulted only in the loss of A/V feeds for the examiner, while tests continued automatically and examiners were able to monitor real-time performance data. The CCAB permits identical tests to be administered at home or in the laboratory, while cellular-network based remote administration allows for nationwide access for all socioeconomic groups, even for people in remote areas without Wi-Fi, or those with limited technical expertise.

DEVELOPMENT OF A WEBSITE-BASED ONLINE LEARNING MODE AS A MEANS OF INCREASING STUDENT INDEPENDENCE IN LEARNING

Now with advances in technology students and teachers alike can learn in the hands of technology. Currently, all countries in the world are facing the Covid-19 pandemic. All activities are restricted to prevent the spread of the Corona virus, including educational activities. In Indonesia, it has been more or less three months, all educational activities, from primary and secondary to tertiary levels, are conducted online. In this research concept, what researchers will do is how to make a website as a learning medium easily and can be understood by students and readers from all walks of life. Features can also be used properly so that students are interested and enthusiastic in independent learning. At the stage of making the website the researcher uses wordpress as a medium for editing the appearance of the theme or other features. So that it really can help researchers in simplifying and accelerating the creation of a website as a learning medium that helps students to learn independently online. At this stage the research model used is the waterfall model. Starting from the level and progress of the system, using this model has a systematic sequential software development method. In developing a website-based online learning mode as a means of increasing student independence in learning, teachers can update subject data, input new subject data, and delete unnecessary data. Subject data whose access time limit has been determined will be deleted by itself at a predetermined time. The teacher only needs to register as a teacher and can immediately become a teacher on the Website-Based Online Learning Mode Development Website as a Means to Increase Student Independence in Learning after passing certain predetermined phases and conditions. After completing teaching and learning activities, users can close the account or Logout from an existing account on the Website. After that, the website can be closed by closing the browser application which is used to open a website-based online learning mode development website as a means of increasing student independence in learning.

Development of based learning media with App Inventor

The research aims to develop an Android-based learning media. The stages of the research are analysis, implementation, and evaluation. The participants of the research were students at West Kalimantan Vocational High School. The research method used is Research and Development with the ADDIE approach. Data analysis used descriptive qualitative. The subjects in this study were students of class X AK 1 and X AK 2 at Vocational High School West Kalimantan, totaling 56 students, for a small-scale trial taken 5 students from X AK 1 and 5 students from X AK 2. Research instrument was using a questionnaire with a scale. Research results based on research statements and the development of Android-based learning media with App Inventor on word processing software, it can be concluded that to make applications with App Inventor software it is not enough to just use a browser application. the development of learning media using the App Inventor media after validation with two media experts is very feasible to use and by using this application the teaching and learning process run very independently and make it easier for students to understand the learning material

AIRRscape: An interactive tool for exploring B-cell receptor repertoires and antibody responses.

The sequencing of antibody repertoires of B-cells at increasing coverage and depth has led to the identification of vast numbers of immunoglobulin heavy and light chains. However, the size and complexity of these Adaptive Immune Receptor Repertoire sequencing (AIRR-seq) datasets makes it difficult to perform exploratory analyses. To aid in data exploration, we have developed AIRRscape, an R Shiny-based interactive web browser application that enables B-cell receptor (BCR) and antibody feature discovery through comparisons among multiple repertoires. Using AIRR-seq data as input, AIRRscape starts by aggregating and sorting repertoires into interactive and explorable bins of germline V-gene, germline J-gene, and CDR3 length, providing a high-level view of the entire repertoire. Interesting subsets of repertoires can be quickly identified and selected, and then network topologies of CDR3 motifs can be generated for further exploration. Here we demonstrate AIRRscape using patient BCR repertoires and sequences of published monoclonal antibodies to investigate patterns of humoral immunity to three viral pathogens: SARS-CoV-2, HIV-1, and DENV (dengue virus). AIRRscape reveals convergent antibody sequences among datasets for all three pathogens, although HIV-1 antibody datasets display limited convergence and idiosyncratic responses. We have made AIRRscape available as a web-based Shiny application, along with code on GitHub to encourage its open development and use by immuno-informaticians, virologists, immunologists, vaccine developers, and other scientists that are interested in exploring and comparing multiple immune receptor repertoires.

Метод обробки неструктурованої інформації на веб-ресурсах

З часом стає складно знайти інформацію в мережі Інтернет, яка зацікавила раніше, навіть якщо відомо, на якому ресурсі вона була розміщена. Пошук відбувається за заданими параметрами, якщо інформація, що знаходиться на ресурсі, є структурованою та систематизованою. Якщо ж це не так, пошук значно ускладнюється, часто й уповільнюється, через що користувач перериває сесію пошуку та оновлює параметри вибору. Тому багато інфор­мації, що знаходиться в мережі, не потрапляє до результатів запитів користувачів. Задачею цієї статті є представлення методу обробки обсягу неструктурованої неперервної, дискретизованої, регулярної та випадковоїінформації на веб-ресурсах у вигляді чітко структурованого набору розподілених даних. У роботі аналізується можливість формування масиву інформації як кількості точок — джерел інформації. У цьому випадку для реалізації процесу структуризації найбільш універсальним є адаптивний алгоритм, який додаватиме нові точки — джерела інформації для її обробки веб-сервісом або для пошуку в масивах неструктурованої та слабо систематизованої інформації в залежності від розподілу вхідних даних. Зокрема, пропонується Z-перетворення, оскільки методи Z-апроксимації початково базуються на адаптивних алгоритмах, здатних змінювати свої функціональні особливості та при цьому надавати змінну точність обчислень. Особливістю вирішення поставленої задачі є приведення неперервної, дискретизованої, регулярної та випадкової інформації з її обробкою в цьому процесі до необхідного формату, що математично можна описати окремими функціями, які й використовуватимуться в алгоритмах обробки. Зазначене може бути використане для розробки веб-сервісів обробки інфор­мації для довідкових, пошукових, рекомендаційних систем та платформ дистанційного навчання, а також для вдосконалення алгоритмів обробки та відображення інформації для прикладного програмного забезпечення роботи браузерів.