Friendly Web Interface Research Articles

An Automated Approach for Classroom Attendance Using OpenCV

Abstract: The "AI Attendance: Automated Attendance Monitoring System" employs artificial intelligence and facial recognition to revolutionize attendance tracking, replacing inefficient methods like roll calls and ID cards. This system eliminates manual record-keeping, reducing administrative burdens while ensuring accurate real-time identification of individuals' attendance. Technologies such as Flask Framework, OpenCV, and Pandas Library are utilized to develop a user- friendly web interface for face registration and attendance marking.With Pickle for serialization, registered faces persist seamlessly, enhancing efficiency and accuracy in attendance monitoring across various sectors

Educational Resource Material Translation System

The proposed "Educational Resource Material Translation System" represents a significant leap in bridging language barriers, particularly for individuals who lack proficiency in English. By leveraging advanced technologies such as Python, Flask, Recurrent Neural Network (RNN) and Large Language Models (LLM), the system offers a robust solution for translating educational content from English to prominent Indian regional languages like Kannada, Telugu and Hindi. Its user- friendly web interface, developed with Flask, ensures a seamless and intuitive translation experience for users of varying technical backgrounds. Beyond mere language translation, this project facilitates cross- cultural communication and enhances accessibility to educational resources, thereby empowering individuals and businesses alike. The utilization of Machine Learning further underscores the system's adaptability and potential for delivering precise and efficient translations. In essence, the "Language Translator" not only serves as a valuable tool for linguistic inclusivity but also fosters knowledge dissemination and cultural exchange on a broader scale.

SurviveAI: Long Term Survival Prediction of Cancer Patients Based on Somatic RNA-Seq Expression.

Motivation:Prediction of cancer outcome is a major challenge in oncology and is essential for treatment planning. Repositories such as The Cancer Genome Atlas (TCGA) contain vast amounts of data for many types of cancers. Our goal was to create reliable prediction models using TCGA data and validate them using an external dataset.Results:For 16 TCGA cancer type cohorts we have optimized a Random Forest prediction model using parameter grid search followed by a backward feature elimination loop for dimensions reduction. For each feature that was removed, the model was retrained and the area under the curve of the receiver operating characteristic (AUC-ROC) was calculated using test data. Five prediction models gave AUC-ROC bigger than 80%. We used Clinical Proteomic Tumor Analysis Consortium v3 (CPTAC3) data for validation. The most enriched pathways for the top models were those involved in basic functions related to tumorigenesis and organ development. Enrichment for 2 prediction models of the TCGA-KIRP cohort was explored, one with 42 genes (AUC-ROC = 0.86) the other is composed of 300 genes (AUC-ROC = 0.85). The most enriched networks for both models share only 5 network nodes: DMBT1, IL11, HOXB6, TRIB3, PIM1. These genes play a significant role in renal cancer and might be used for prognosis prediction and as candidate therapeutic targets.Availability And Implementation:The prediction models were created and tested using Python SciKit-Learn package. They are freely accessible via a friendly web interface we called surviveAI at https://tinyurl.com/surviveai.

RiceENCODE: A comprehensive epigenomic database as a rice Encyclopedia of DNA Elements

RiceENCODE: A comprehensive epigenomic database as a rice Encyclopedia of DNA Elements

COVID-19 preVIEW: Semantic Search to Explore COVID-19 Research Preprints.

During the current COVID-19 pandemic, the rapid availability of profound information is crucial in order to derive information about diagnosis, disease trajectory, treatment or to adapt the rules of conduct in public. The increased importance of preprints for COVID-19 research initiated the design of the preprint search engine preVIEW. Conceptually, it is a lightweight semantic search engine focusing on easy inclusion of specialized COVID-19 textual collections and provides a user friendly web interface for semantic information retrieval. In order to support semantic search functionality, we integrated a text mining workflow for indexing with relevant terminologies. Currently, diseases, human genes and SARS-CoV-2 proteins are annotated, and more will be added in future. The system integrates collections from several different preprint servers that are used in the biomedical domain to publish non-peer-reviewed work, thereby enabling one central access point for the users. In addition, our service offers facet searching, export functionality and an API access. COVID-19 preVIEW is publicly available at https://preview.zbmed.de.

BEST: a Shiny/R web-based application to easily retrieve cross-related enzyme functional parameters and information from BRENDA.

BRENDA is the largest enzyme functional database, containing information of 84000 experimentally characterized enzyme entries. This database is an invaluable resource for researchers in the biological field, which classifies enzyme-related information in categories that are very useful to obtain specific functional and protein engineering information for enzyme families. However, the BRENDA web interface, the most used by researchers with a non-informatic background, does not allow the user to cross-reference data from different categories or sub-categories in the database. Obtaining information in an easy and fast way, in a friendly web interface, without the necessity to have a deep informatics knowledge, will facilitate and improve research in the enzymology and protein engineering field. We developed the Brenda Easy Search Tool (BEST), an interactive Shiny/R application that enables querying the BRENDA database for complex cross-tabulated characteristics, and retrieving enzyme-related parameters and information readily and efficiently, which can be used for the study of enzyme function or as an input for other bioinformatics tools. BEST and its tutorial are freely available from https://pesb2.cl/best/. Supplementary data are available at Bioinformatics online.

Service Life Modeling of Concrete with SCMs Using Effective Diffusion Coefficient and a New Binding Model

This paper presents a new algorithm that predicts the service life of concrete contains supplementary cementitious materials, SCMs, and determines time of corrosion initiation. The algorithm drives effective diffusivity from an apparent diffusion model, using experimental binding data performed in the lab, temperature, free ion concentration, and carbonation, and generates free chloride profiles for concrete with and without SCMs by using Fick’s law in a finite element model. Adjusting diffusion coefficient at each step of the solution, by addressing the impact of different parameters, simplifies the algorithm and reduces calculation time without jeopardizing the results’ quality. Results generated by the model compare well to the performance of concrete blocks constructed in an exposure site on the east coast of Saudi Arabia. The exposure site hosted five different mixes of Portland cement and SCMs, and the concrete blocks were exposed to harsh weather over the period of two years. Linear polarization and chloride profiling assessed the performance of the mixes against corrosion activities. Lab work identified the performance of the mixes through binding capacity and chloride profiling. Statistical analysis evidenced the accuracy of the model through correlation and regression analysis. Furthermore, a new proposed binding model, produced from binding data in different studies, alters the experimental binding data in the algorithm to decouple the solution from experimental values. The algorithm proves its accuracy when compared to the experimental free chloride profile. The proposed transport model proves that using effective diffusion and binding capacity are enough to generate reliable results, and the effective diffusion can be calibrated with environmental conditions such as temperature, age, and carbonation. Finally, the algorithm presents its features in an object-oriented programming using C# and user friendly web interface.

Interactive, semi-automatized and open source computational model applied to respiratory viruses surveillance

Acute respiratory infections (ARI) are an important cause of morbidity and mortality worldwide, affecting mainly children and the elderly. They are associated with a high economic burden, increased number of medical visits and hospitalizations. The surveillance of the circulation of respiratory viruses can reduce the health care associated costs, and to optimize the health response. A platform based on R and its package Shiny was designed, to create an interactive and friendly web interface for gathering, analysis and publication of the data. The data from the Chilean metropolitan respiratory viruses surveillance network, available since 2006, was uploaded into the platform. Using this platform, the researcher spends less than 1 minute to upload the data, and the analysis and publication is immediate, available to be seen by any user with a device connected to Internet, who can choose the variables to be displayed. With a very low cost, in a short time, and using the R programming language, it was possible to create a simple, and interactive platform, considerably decreasing the upload and analysis time, and increasing the impact and availability of this surveillance.

Designing and implementing a collaborative health knowledge system

The huge diversity and quantity of data and information, and the requirements for knowledge extraction out of them put new challenges for knowledge management, synthesis, conflict detection and reasoning This paper elaborates on the design and development of COSMOS, an intelligent system which supports a) collaboration of experts for developing common knowledge bases and b) diagnosis derivation which takes into account time and uncertainty. The health domain is used for illustration and discussion of the features of our approach. Initially, we present the syntax and the semantics of the rules which incorporate time (temporal rules) and the data items upon which reasoning is performed. Then we introduce its inference engine, able to perform reasoning on top of rules and data, handling also the embedded time and uncertainty. We proceed further to define a conflict detection policy for supporting the difficult and error prone task of rule generation. The complexities of the aforementioned tasks are hidden from users via a well-designed and user friendly web interface that possesses strong collaboration features enabling multiple experts to work on defining a rule and on developing a common knowledge base. Evaluation of COSMOS has been performed using a) students studying expert systems and b) health experts in order to demonstrate the usability of the approach and the considerable advantages gained. To the best of our knowledge, COSMOS is one of the very few systems combining temporal rules, a powerful inference engine handling uncertainty and conflict detection and progresses beyond state of the art by adopting strong collaboration features and paradigms.

CirGRDB: a database for the genome-wide deciphering circadian genes and regulators.

Circadian rhythms govern various kinds of physiological and behavioral functions of the living organisms, and disruptions of the rhythms are highly detrimental to health. Although several databases have been built for circadian genes, a resource for comprehensive post-transcriptional regulatory information of circadian RNAs and expression patterns of disease-related circadian RNAs is still lacking. Here, we developed CirGRDB (http://cirgrdb.biols.ac.cn) by integrating more than 4936 genome-wide assays, with the aim of fulfilling the growing need to understand the rhythms of life. CirGRDB presents a friendly web interface that allows users to search and browse temporal expression patterns of interested genes in 37 human/mouse tissues or cell lines, and three clinical disorders including sleep disorder, aging and tumor. More importantly, eight kinds of potential transcriptional and post-transcriptional regulators involved in the rhythmic expression of the specific genes, including transcription factors, histone modifications, chromatin accessibility, enhancer RNAs, miRNAs, RNA-binding proteins, RNA editing and RNA methylation, can also be retrieved. Furthermore, a regulatory network could be generated based on the regulatory information. In summary, CirGRDB offers a useful repository for exploring disease-related circadian RNAs, and deciphering the transcriptional and post-transcriptional regulation of circadian rhythms.

Smart monitoring of the criminal justice system

To gain insight into the success and possible bottlenecks in the criminal justice domain, good and trustworthy management information is required. This paper presents a case study of a data space system that facilitates the monitoring of data flows within as well as between organizations involved in the Dutch criminal justice system. To build such a system several challenges are faced. Data from heterogeneous sources are extracted and related by means of domain knowledge. A proper system architecture is developed based on the concept of data spaces. Our data space system consists of three layers: a data space, a space manager, and an interface layer. The data space layer consists of a set of heterogeneous databases. The space manager layer – implemented as a variable database and a relationship module – consists of domain knowledge. The relationship module decides which databases in the data space layer are required to answer a query. The interface layer consists of a user friendly web interface, which facilitates policy makers to interpret data flows within the justice domain. The evaluation of our system shows that it has an added value in improving policy monitoring and accountability by clarifying goals and providing insightful indicators.

PCTFPeval: a web tool for benchmarking newly developed algorithms for predicting cooperative transcription factor pairs in yeast.

BackgroundComputational identification of cooperative transcription factor (TF) pairs helps understand the combinatorial regulation of gene expression in eukaryotic cells. Many advanced algorithms have been proposed to predict cooperative TF pairs in yeast. However, it is still difficult to conduct a comprehensive and objective performance comparison of different algorithms because of lacking sufficient performance indices and adequate overall performance scores. To solve this problem, in our previous study (published in BMC Systems Biology 2014), we adopted/proposed eight performance indices and designed two overall performance scores to compare the performance of 14 existing algorithms for predicting cooperative TF pairs in yeast. Most importantly, our performance comparison framework can be applied to comprehensively and objectively evaluate the performance of a newly developed algorithm. However, to use our framework, researchers have to put a lot of effort to construct it first. To save researchers time and effort, here we develop a web tool to implement our performance comparison framework, featuring fast data processing, a comprehensive performance comparison and an easy-to-use web interface.ResultsThe developed tool is called PCTFPeval (Predicted Cooperative TF Pair evaluator), written in PHP and Python programming languages. The friendly web interface allows users to input a list of predicted cooperative TF pairs from their algorithm and select (i) the compared algorithms among the 15 existing algorithms, (ii) the performance indices among the eight existing indices, and (iii) the overall performance scores from two possible choices. The comprehensive performance comparison results are then generated in tens of seconds and shown as both bar charts and tables. The original comparison results of each compared algorithm and each selected performance index can be downloaded as text files for further analyses.ConclusionsAllowing users to select eight existing performance indices and 15 existing algorithms for comparison, our web tool benefits researchers who are eager to comprehensively and objectively evaluate the performance of their newly developed algorithm. Thus, our tool greatly expedites the progress in the research of computational identification of cooperative TF pairs.

RAP: RNA-Seq Analysis Pipeline, a new cloud-based NGS web application.

BackgroundThe study of RNA has been dramatically improved by the introduction of Next Generation Sequencing platforms allowing massive and cheap sequencing of selected RNA fractions, also providing information on strand orientation (RNA-Seq). The complexity of transcriptomes and of their regulative pathways make RNA-Seq one of most complex field of NGS applications, addressing several aspects of the expression process (e.g. identification and quantification of expressed genes and transcripts, alternative splicing and polyadenylation, fusion genes and trans-splicing, post-transcriptional events, etc.).Moreover, the huge volume of data generated by NGS platforms introduces unprecedented computational and technological challenges to efficiently analyze and store sequence data and results.MethodsIn order to provide researchers with an effective and friendly resource for analyzing RNA-Seq data, we present here RAP (RNA-Seq Analysis Pipeline), a cloud computing web application implementing a complete but modular analysis workflow. This pipeline integrates both state-of-the-art bioinformatics tools for RNA-Seq analysis and in-house developed scripts to offer to the user a comprehensive strategy for data analysis. RAP is able to perform quality checks (adopting FastQC and NGS QC Toolkit), identify and quantify expressed genes and transcripts (with Tophat, Cufflinks and HTSeq), detect alternative splicing events (using SpliceTrap) and chimeric transcripts (with ChimeraScan). This pipeline is also able to identify splicing junctions and constitutive or alternative polyadenylation sites (implementing custom analysis modules) and call for statistically significant differences in genes and transcripts expression, splicing pattern and polyadenylation site usage (using Cuffdiff2 and DESeq).ResultsThrough a user friendly web interface, the RAP workflow can be suitably customized by the user and it is automatically executed on our cloud computing environment. This strategy allows to access to bioinformatics tools and computational resources without specific bioinformatics and IT skills. RAP provides a set of tabular and graphical results that can be helpful to browse, filter and export analyzed data, according to the user needs.

Evaluating faecal egg count reduction using a specifically designed package “eggCounts” in R and a user friendly web interface

The seemingly straightforward task of analysing faecal egg counts resulting from laboratory procedures such as the McMaster technique has, in reality, a number of complexities. These include Poisson errors in the counting technique which result from eggs being randomly distributed in well mixed faecal samples. In addition, counts between animals in a single experimental or observational group are nearly always over-dispersed. We describe the R package “eggCounts” that we have developed that incorporates both sampling error and over-dispersion between animals to calculate the true egg counts in samples of faeces, the probability distribution of the true counts and summary statistics such as the 95% uncertainty intervals. Based on a hierarchical Bayesian framework, the software will also rigorously estimate the percentage reduction of faecal egg counts and the 95% uncertainty intervals of data generated by a faecal egg count reduction test. We have also developed a user friendly web interface that can be used by those with limited knowledge of the R statistical computing environment. We illustrate the package with three simulated data sets of faecal egg count reduction experiments.

APSLAP: An Adaptive Boosting Technique for Predicting Subcellular Localization of Apoptosis Protein

Apoptotic proteins play key roles in understanding the mechanism of programmed cell death. Knowledge about the subcellular localization of apoptotic protein is constructive in understanding the mechanism of programmed cell death, determining the functional characterization of the protein, screening candidates in drug design, and selecting protein for relevant studies. It is also proclaimed that the information required for determining the subcellular localization of protein resides in their corresponding amino acid sequence. In this work, a new biological feature, class pattern frequency of physiochemical descriptor, was effectively used in accordance with the amino acid composition, protein similarity measure, CTD (composition, translation, and distribution) of physiochemical descriptors, and sequence similarity to predict the subcellular localization of apoptosis protein. AdaBoost with the weak learner as Random-Forest was designed for the five modules and prediction is made based on the weighted voting system. Bench mark dataset of 317 apoptosis proteins were subjected to prediction by our system and the accuracy was found to be 100.0 and 92.4%, and 90.1% for self-consistency test, jack-knife test, and tenfold cross validation test respectively, which is 0.9% higher than that of other existing methods. Beside this, the independent data (N151 and ZW98) set prediction resulted in the accuracy of 90.7 and 87.7%, respectively. These results show that the protein feature represented by a combined feature vector along with AdaBoost algorithm holds well in effective prediction of subcellular localization of apoptosis proteins. The user friendly web interface "APSLAP" has been constructed, which is freely available at http://apslap.bicpu.edu.in and it is anticipated that this tool will play a significant role in determining the specific role of apoptosis proteins with reliability.

AmylPepPred: Amyloidogenic Peptide Prediction tool.

We present an efficient computational architecture designed using supervised machine learning model to predict amyloid fibril forming protein segments, named AmylPepPred. The proposed prediction model is based on bio-physio-chemical properties of primary sequences and auto-correlation function of their amino acid indices. AmylPepPred provides a user friendly web interface for the researchers to easily observe the fibril forming and non-fibril forming hexmers in a given protein sequence. We expect that this stratagem will be highly encouraging in discovering fibril forming regions in proteins thereby benefit in finding therapeutic agents that specifically aim these sequences for the inhibition and cure of amyloid illnesses.AvailabilityAmylPepPred is available freely for academic use at www.zoommicro.in/amylpeppred

A repository based on a dynamically extensible data model supporting multidisciplinary research in neuroscience

BackgroundRobust, extensible and distributed databases integrating clinical, imaging and molecular data represent a substantial challenge for modern neuroscience. It is even more difficult to provide extensible software environments able to effectively target the rapidly changing data requirements and structures of research experiments. There is an increasing request from the neuroscience community for software tools addressing technical challenges about: (i) supporting researchers in the medical field to carry out data analysis using integrated bioinformatics services and tools; (ii) handling multimodal/multiscale data and metadata, enabling the injection of several different data types according to structured schemas; (iii) providing high extensibility, in order to address different requirements deriving from a large variety of applications simply through a user runtime configuration.MethodsA dynamically extensible data structure supporting collaborative multidisciplinary research projects in neuroscience has been defined and implemented. We have considered extensibility issues from two different points of view. First, the improvement of data flexibility has been taken into account. This has been done through the development of a methodology for the dynamic creation and use of data types and related metadata, based on the definition of “meta” data model. This way, users are not constrainted to a set of predefined data and the model can be easily extensible and applicable to different contexts. Second, users have been enabled to easily customize and extend the experimental procedures in order to track each step of acquisition or analysis. This has been achieved through a process-event data structure, a multipurpose taxonomic schema composed by two generic main objects: events and processes. Then, a repository has been built based on such data model and structure, and deployed on distributed resources thanks to a Grid-based approach. Finally, data integration aspects have been addressed by providing the repository application with an efficient dynamic interface designed to enable the user to both easily query the data depending on defined datatypes and view all the data of every patient in an integrated and simple way.ResultsThe results of our work have been twofold. First, a dynamically extensible data model has been implemented and tested based on a “meta” data-model enabling users to define their own data types independently from the application context. This data model has allowed users to dynamically include additional data types without the need of rebuilding the underlying database. Then a complex process-event data structure has been built, based on this data model, describing patient-centered diagnostic processes and merging information from data and metadata. Second, a repository implementing such a data structure has been deployed on a distributed Data Grid in order to provide scalability both in terms of data input and data storage and to exploit distributed data and computational approaches in order to share resources more efficiently. Moreover, data managing has been made possible through a friendly web interface. The driving principle of not being forced to preconfigured data types has been satisfied. It is up to users to dynamically configure the data model for the given experiment or data acquisition program, thus making it potentially suitable for customized applications.ConclusionsBased on such repository, data managing has been made possible through a friendly web interface. The driving principle of not being forced to preconfigured data types has been satisfied. It is up to users to dynamically configure the data model for the given experiment or data acquisition program, thus making it potentially suitable for customized applications.

IDecisionSupport – a Web-based Framework for Decision Support Systems

This paper describes iDecisionSupport, a collaborative decision-making support system that is characterized by and provides safety, usefulness, efficiency and usability. Its development is based on the principles of Decision Support Systems and is designed as a framework that can integrate third party applications as decision support tools. Routine procedures are facilitated by software agents and an internal workflow engine, while there is the advantage that the system can be accessed from anywhere at any time through a friendly web-interface.

The Seal suite of distributed software for high-throughput sequencing

http://www.bioinfo.no/http://www.uni.no/computing/units/cbuModern DNA sequencing machines have opened the flood gates of whole genome data; and the current processing techniques are being washed away. Medium- sized sequencing laboratories can produce Terabytes of data per week that need processing. Unfortunately, most programs available for sequence processing are not designed to scale easily to such high data rates, nor are the typical bioinformatics workflow designs. As a consequence, many sequencing operations are left struggling to cope with the high data loads, often hoping that acquiring additional hardware will solve their problems. In contrast, we believe that a change in paradigm is required to solve this problem: a shift to highly parallelized software is required the handle the parallelization that has taken place in sequencing.In response to the growing processing requirements of the CRS4 Sequencing and Genotyping Platform (CSGP), which now houses 4 Illumina HiSeq 2000 sequencers for a total capacity of about 7000 Gbases/month, we began the development of Seal [3], a new suite of sequence processing tools based on the MapReduce [1] programming model that run on the Hadoop framework. Seal aims to replace many of the tools that are customarily used in sequencing workflows with Hadoop-based, scalable alternatives. Currently, Seal provides distributed MapReduce tools for: demultiplexing tagged reads, mapping reads to a reference (it includes a distributed version of the BWA aligner [2]), and sorting reads by alignment position. In the near future we will also be adding tools for read quality recalibration.Seal tools have been shown to scale well in the amount of input data and the amount of computational nodes available [4]; therefore, with Seal one can increase processing throughput by simply adding more computing nodes. Moreover, thanks to the robust platform provided by Hadoop, the effort required by operators to run the analyses on a large cluster is generally reduced, since Hadoop transparently handles most hardware and transient network problems, and provides a friendly web interface to monitor job progress and logs. Finally, the Hadoop Distributed File System (HDFS) provides a scalable storage system that scales its total throughput hand in hand with the number of processing nodes. Thus, it avoids creating a bottleneck at the shared storage volume and avoids the need for an expensive high-performance parallel storage device.Seal is currently in production use at the CRS4 Sequencing and Genotyping Platform and is being evaluated at other various sequencing centers.

BiodEnz:A database of biodegrading enzymes.

Azo dyes, which re characterized by azo bonds, are a predominant class of colorants used in tattooing, cosmetics, foods, textile and consumer products. Laccases (EC 1.10.3.2), lignin peroxidases (EC 1.11.1.14) , Azo reductases (EC 1.7.1.6) of different micro organisms are mainly useful for the development of biodegradation systems as they catalyse reductive cleavage of azo groups (-N=N-) . Laccases have very broad substrate specificity with respect to the electron donor and is capable of oxidizing phenols and aromatic amines. Azoreductase belongs to the family of oxidoreductases, acting on other nitrogenous compounds as donors with NAD+ or NADP+ as acceptor. Lignin peroxidase enzymes are highly non-specific and are well reported to decolourize various dyes We have developed BiodEnz database by collecting information like strains that produce particular enzymes, azo dyes that are degraded , substrate specificity, molecular weight, the optimum temperature and pH, sequence data of the above enzymes ,as the most effective inoculants used for bioremediation are able to degrade dyes over a broad concentration range, tolerate a range of environmental conditions of temperature, pH, and activity of the enzymes. The database can be searched by using a user friendly web interface. The database is available for free at http://www.biodenzdatabase.in.