Easy Data Analysis Research Articles

Soundscapy: A python package for soundscape assessment and analysis

Soundscape questionnaires are widely used to gather subjective information about people's perceptions and attitudes towards their acoustic environment. Despite the widespread adoption of ISO/TS 12913-3 guidelines for analyzing soundscape survey data, there are still several interpretations and challenges in application. To enable the easy, accessible, and consistent analysis of soundscape data, an open-source python package called Soundscapy has been developed. This package implements a visualization approach for soundscape data analysis using a probabilistic method that depicts the collective perception of a soundscape as a distribution of responses within the circumplex. In addition, functions for psychoacoustic and acoustic analysis of binaural data are included, with a focus on consistent and optimized processing of multiple recordings. This conference paper outlines the important features of Soundscapy, explains its basic functioning, lists its current capabilities, and gives recommendations for its best use. Finally, the future development of Soundscapy is proposed, including the integration of predictive soundscape models for use in automated assessment and design.

Easy analysis of bacterial whole-genome sequencing data for clinical microbiologists using open-source Galaxy platform: Characterization of ESBL-producing Enterobacterales from bloodstream infections

ObjectivesClinical microbiologists require easy-to-use open access tools with graphical interfaces to perform bacterial whole-genome sequencing (WGS) in routine practice. This study aimed to build a bioinformatics pipeline on the open-source Galaxy platform, facilitating comprehensive and reproducible analysis of bacterial WGS data in a few steps. We then used it to characterize our local epidemiology of ESBL-producing Enterobacterales isolated from patients with bacteremia. MethodsWe built a bioinformatics pipeline consisting of the following sequential tools: Fastp (input data trimming); FastQC (read quality control); SPAdes (genome assembly); Quast (quality control of genome assembly); Prokka (gene annotation); Staramr (ResFinder database) and ABRicate (CARD database) for antimicrobial resistance (AMR) gene screening and molecular strain typing. Paired-end short read WGS data from all ESBL-producing Enterobacterales strains isolated from patients with bacteremia over one year were analysed. ResultsThe Galaxy platform does not require command line tools. The bioinformatics pipeline was constructed within one hour. It only required uploading fastq files and facilitated systematization of de novo assembly of genomes, MLST typing, and AMR gene screening in one step. Among the 66 ESBL-producing strains analysed, the two most frequent ESBL genes were blaCTX−M-15 (62.1%) and blaCTX−M-27 (13.6%). ConclusionsThe open-access Galaxy platform provides a graphical interface and easy-to-use tools suitable for routine use in clinical microbiology laboratories without bioinformatics specialists. We believe that this platform will facilitate fast and low-cost analysis of bacterial WGS data, especially in resource-limited settings.

BLACK BOX AND BLIND SPOT DETECTION SYSTEM IN AUTOMOBILES

This paper presents the development of an affordable Black Box and Blind Spot Detection System for automobiles, integrating various sensors and microcontrollers. The Black Box monitors crucial parameters such as speed, tilt, temperature, and alcohol presence, uploading data to a cloud-based Firebase database in real-time. The Blind Spot Detection System utilizes an ultrasonic sensor to alert drivers of objects in blind spots, aiming to prevent accidents. The system's user-friendly website allows easy data access and analysis, potentially enhancing road safety and facilitating crash investigations. Key words- Black box, Blind spot detection system, Firebase real time database.

디지털 트윈 기술로 협동 로봇의 고장 또는 결합 원인을 효율적으로 파악하는 시스템에 대한 연구

As the coexistence of humans and robots has increased since the start of the Fourth Industrial Revolution, this study proposes a platform for processing and analyzing data to efficiently address the causes and prevention of robot collisions at industrial sites. The platform facilitates easy and convenient data processing and analysis, allowing users to quickly identify the causes of collisions between collaborative robots as well as control equipment failures or defects. Utilizing data, the platform also enables the remote operation of equipment in virtual spaces, making it effective for communication and remote technical support in the manufacturing industry.

Sales Analysis on Garment Industry with Datawarehouse and ETL Implementation on Star Schema

In the current industrial era, clothing companies face increasingly complex challenges in maintaining the quality and sales of their products. To overcome these obstacles, companies must have a good vision, mission, and decision-making capabilities supported by efficient data management. Data is becoming a valuable asset in Industry 4.0, pivotal in process management, increased productivity, and competitive advantage. This research explores the implementation of Data Warehouse (data warehouse) and ETL (Extraction, Transformation, and Load) processes on star schema for PT Golden Flower Tbk. A data warehouse is a container for storing company data, supporting decision-making, and providing business insights. Meanwhile, the ETL process ensures that the data entered into the warehouse is clean, structured, and ready for analysis. PTA garment manufacturing and exporter company, USPT Golden Flower Tbk, uses the Pentaho application to carry out the ETL process. Pentaho, a Business Intelligence tool, facilitates easy data processing and analysis. This research also explains the use of dimension tables and fact tables in star schemas, which form the basis for more in-depth data analysis. Through visualization and analysis using star schema, Mondrian, and schema workbench, companies can identify sales patterns in each branch and understand which products are most in demand by consumers. The results of the star schema, moMondrianand schema workbench provide a better understanding of the company's sales performance, enabling more informed decision-making and more effective strategies.

Targeted Quantification of Proteoforms in Complex Samples by Proteoform Reaction Monitoring.

Existing mass spectrometric assays used for sensitive and specific measurements of target proteins across multiple samples, such as selected/multiple reaction monitoring (SRM/MRM) or parallel reaction monitoring (PRM), are peptide-based methods for bottom-up proteomics. Here, we describe an approach based on the principle of PRM for the measurement of intact proteoforms by targeted top-down proteomics, termed proteoform reaction monitoring (PfRM). We explore the ability of our method to circumvent traditional limitations of top-down proteomics, such as sensitivity and reproducibility. We also introduce a new software program, Proteoform Finder (part of ProSight Native), specifically designed for the easy analysis of PfRM data. PfRM was initially benchmarked by quantifying three standard proteins. The linearity of the assay was shown over almost 3 orders of magnitude in the femtomole range, with limits of detection and quantification in the low femtomolar range. We later applied our multiplexed PfRM assay to complex samples to quantify biomarker candidates in peripheral blood mononuclear cells (PBMCs) from liver-transplanted patients, suggesting their possible translational applications. These results demonstrate that PfRM has the potential to contribute to the accurate quantification of protein biomarkers for diagnostic purposes and to improve our understanding of disease etiology at the proteoform level.

Design of intelligent monitoring of loneliness in the elderly using a serverless architecture with real-time communication API.

Loneliness and social isolation are recognized as critical public health issues. Older people are at greater risk of loneliness and social isolation as they deal with things like living alone, loss of family or friends, chronic illness, and hearing loss. Loneliness increases a person's risk of premature death from all causes, including dementia, heart disease, and stroke. To address these issues, the inclusion of technological platforms and the use of commercial monitoring devices are vastly increasing in healthcare and elderly care. The objective of this study is to design and develop a loneliness monitor serverless architecture to obtain real-time data from commercial activity wristbands through an Application Programming Interface. For the design and development of the architecture, the Amazon Web Services platform has been used. To monitor loneliness, the Fitbit Charge 5 bracelet was selected. Through the web Application Programming Interface offered by the AWS Lambda service, the data is obtained and stored in AWS services with an automated frequency thanks to the event bridge. In the pilot stage in which the system is, it is showing great possibilities in the ease of collecting data and programming the sampling frequency. Once the request is made, the data is automatically analyzed to monitor loneliness. The proposed architecture shows great potential for easy data collection, analysis, security, personalization, real-time inference, and scalability of sensors and actuators in the future. It has powerful benefits to apply in the health sector and reduces cases of depression and loneliness.

Enhancing On-Skin Analysis: A Microfluidic Device and Smartphone Imaging Module for Real-Time Quantitative Detection of Multianalytes in Sweat.

Wearable sweat sensors present exciting opportunities for advancing personal health monitoring and noninvasive biomarker measurements. However, existing sensors often fall short in accurate detection of low analyte volumes and concentrations and lack multimodal sensing capabilities. Herein, we present a highly portable four-channel microfluidic device capable of conducting simultaneous sweat sampling and fluorometric sensing of potential biomarkers, such as l-Tyr, l-Trp, Crt, and NH4+, specifically designed for kidney disease monitoring. Our microfluidic device seamlessly integrates with smartphones, facilitating easy data retrieval and analysis. The core of the sensing array is a novel fluorometric solid-state mechanism utilizing carbon polymer dots derived from dopamine, catechol, and o-phenylenediamine monomers embedded in gelatin hydrogels. The sensors exhibit exceptional performance, offering linear ranges of 5-275, 6-170, 4-220, and 5-170 μM, with impressively low detection limits of 1.5, 1.2, 1.3, and 1.4 μM for l-Tyr, l-Trp, Crt, and NH4+, respectively. Through meticulous optimization of operational variables, comprising the temperature, sample volume, and assay time, we achieved the best performance of the device. Furthermore, the sensors exhibited remarkable selectivity, effectively distinguishing between biologically similar species and other potential biological compounds found in sweat. Our evaluation also extended to monitoring kidney diseases in patients and healthy individuals, showcasing the device's utility in world scenarios. Promising results showcase the potential of low-cost, multidiagnostic microfluidic sensor arrays, especially with synthetic skin integration, for enhanced disease detection and healthcare outcomes.

Portable Smart Emergency System Using Internet of Things (IOT)

The portable smart emergency system is a pre-test tool, implemented using a set of modern devices and technologies to monitor the patient's health. This tool has capability to send reports of the patient to the doctor treating the patient as well as to the relatives, close friends of the patient in real time. Health parameters of the patient viz. heart rate, blood oxygen and temperature are monitored using electronic devices viz. WEMOS D1, MAX30100, DS18B20, SIM808 on the LCD screen and stored using the MySQL database. PHP script is used to connect MySQ database for easy tracking and analysis of medical data. Doctors are facilitated to monitor the health update in real time, at the same time, communicate the same to the patient and their relatives, close friends through a dynamic web site constituted of HTML, CSS and JavaScript for the purpose of easy tracking and analysis of the medical data. To aid further, as a part of value addition, an Android based mobile app is also developed by using App Inventor to further facilitate patients, family members & close friends to monitor sensor data, receive messages and access medical history details, all in real time. Terminal cases, where the health update received from the sensor shows alarmingly high or low readings, then web enabled computing system, also sends a high alert message by playing a warning sound to the doctor, at the same time, also communicates patient’s location to him via text message to enable immediate help. By using Wi-Fi technology and the SIM808 module, the patient's location can be monitored in emergency situations and a text message containing the patient's geographical location can be sent to the treating doctor. This application also includes an option to enter the patient's medical history information using a PHP script into the database.

Versatile open-source fluorescence documentation system

Fluorescence has long been the best method for detecting bio-molecules at high sensitivity and with the possibility of easy data analysis and is routine for gel electrophoresis and much more. However, the systems for detecting the fluorophores remain expensive and thus inaccessible to many. Most commercial systems are often only optimized for one specific application, making reuse difficult. This paper demonstrates, characterizes and evaluates an inexpensive and versatile system for the detection of fluorophores with two wavelengths using high power LEDs. The LEDs are arranged in two banks angled downward at a tray in a way that allows for bright and uniform illumination while preventing direct reflections into the camera. The emitted light is filtered through an exchangeable filter frame and can be detected using the camera of a smartphone or similar device. By using filters both in front of the LEDs and the camera, very low background and using sufficient exposure times, very high sensitivity can be achieved. The two wavelengths of excitation light and the exchangeable filters allow for optimization for the specific fluorophore used and thus highest brightness.

Application of a Technology-Based Data Collection System for the Writing Class of the IAIN Cirebon Student Da'wah Institute

Effective data management plays a crucial role in optimizing the performance of educational institutions. This study aims to explore the implementation of a technology-based data management system for writing classes in the Student Preaching Institution of the State Islamic Institute (IAIN) Cirebon. The objective is to improve the efficiency and effectiveness of data collection, storage, and analysis, ultimately enhancing the overall quality of the writing classes. This research utilizes a mixed-methods approach, incorporating qualitative and quantitative data collection techniques. Firstly, a comprehensive review of the existing data management practices is conducted to identify the limitations and challenges faced by the institution. Secondly, a technology-based data management system is developed, integrating modern tools and techniques to streamline the data collection process. The system is designed to facilitate easy data entry, retrieval, and analysis, ensuring accurate and reliable information for decision-making purposes. The implementation of the technology-based data management system is evaluated through a series of case studies and interviews with relevant stakeholders. The findings reveal several significant benefits, including increased efficiency in data management, improved accuracy in reporting, and enhanced collaboration among the stakeholders involved in the writing classes. Moreover, the system enables the institution to track the progress of individual students, identify their strengths and weaknesses, and provide targeted interventions to support their learning and development. The results of this study contribute to the growing body of literature on the application of technology in educational institutions. The findings highlight the importance of leveraging technology to optimize data management processes, particularly in the context of writing classes in the Student Preaching Institution of IAIN Cirebon. The insights gained from this research can serve as a valuable resource for other educational institutions seeking to enhance their data management practices and improve the overall quality of their programs.

DecodeSTORM: A user-friendly ImageJ plug-in for quantitative data analysis in single-molecule localization microscopy

Quantitative data analysis in single-molecule localization microscopy (SMLM) is crucial for studying cellular functions at the biomolecular level. In the past decade, several quantitative methods were developed for analyzing SMLM data; however, imaging artifacts in SMLM experiments reduce the accuracy of these methods, and these methods were seldom designed as user-friendly tools. Researchers are now trying to overcome these difficulties by developing easy-to-use SMLM data analysis software for certain image analysis tasks. But, this kind of software did not pay sufficient attention to the impact of imaging artifacts on the analysis accuracy, and usually contained only one type of analysis task. Therefore, users are still facing difficulties when they want to have the combined use of different types of analysis methods according to the characteristics of their data and their own needs. In this paper, we report an ImageJ plug-in called DecodeSTORM, which not only has a simple GUI for human–computer interaction, but also combines artifact correction with several quantitative analysis methods. DecodeSTORM includes format conversion, channel registration, artifact correction (drift correction and localization filtering), quantitative analysis (segmentation and clustering, spatial distribution statistics and colocalization) and visualization. Importantly, these data analysis methods can be combined freely, thus improving the accuracy of quantitative analysis and allowing users to have an optimal combination of methods. We believe DecodeSTORM is a user-friendly and powerful ImageJ plug-in, which provides an easy and accurate data analysis tool for adventurous biologists who are looking for new imaging tools for studying important questions in cell biology.

A Single Cell Atlas and Interactive Web-Resource of Pediatric Cancers and Healthy Bone Marrow

Single-cell profiling allows for in-depth characterization of the tumor microenvironment and revealing the heterogeneity of pediatric cancers. Ease of access and analysis of the primary tumor single-cell data from studies on solid and blood pediatric tumors is essential for improving diagnostics and therapies. We have developed the Pediatric Single Cell Cancer Atlas (PedSCAtlas), with the goal of highlighting the heterogeneity of malignant and microenvironment cells across pediatric cancers. The atlas allows for quick exploration, comparative analysis, visualization, and prognostic assessment of genes/pathways across pediatric cancers without requiring bioinformatics analysis or computational support. The PedSCAtlas currently contains expression data of >1.2 million single cell (sc) and single nuclei (sn) from 134 pediatric hematological and solid-organ cancer samples and healthy bone marrow (BM). The hematological cancer samples are dominated by acute leukemias containing scRNA-seq data of acute myeloid leukemia (AML), B-cell acute lymphoblastic leukemia (B-ALL), T-cell ALL (T-ALL), and mixed phenotype acute leukemia (MPAL). The scRNA-seq data are generated from the BM samples collected during the clinical diagnosis of the disease. The data for the atlas was either generated at the Bhasin lab or collected from public resources including the GEO database, ScPCA (https://scpca.alexslemonade.org/), and HCA (https://data.humancellatlas.org/). Additionally, the atlas also contains bulk RNA-sequencing data and clinical information retrieved from the TARGET Initiative (https://ocg.cancer.gov/programs/target). The single cell/nuclei datasets are processed using a uniform workflow that includes quality control and filtering, normalization, integration with batch correction (if needed), dimensionality reduction, clustering, and cellular annotations. The data normalization and integration are performed using sctransform (Hao and Hao et al, Cell 2021) and harmony (Korsunsky et al, Nature Methods 2019) algorithms respectively. The visualization of integrated pediatric cancers single cell was achieved using Uniform Manifold Approximations and Projection (UMAP) method (Becht et al, Nature Biotech 2018). The clusters were annotated based on the expression of cell-specific markers. Differential expression analysis was used to identify potential biomarker sets for malignant cells of disease subtypes. To further ascertain that these biomarkers were malignant cell (blast) specific in the acute leukemia subtypes, the genes were filtered to exclude those with consistent expression in healthy BM cell types. To compare the expression profile of genes between single cells and bulk data, the raw counts of bulk RNA-seq datasets were filtered and normalized using the Voom algorithm (Ritchie et al, Nucleic Acids Research 2015). The resource has modules for biomarker analysis, bulk RNA deconvolution, and survival analysis to facilitate easy analysis of single-cell data. Survival analysis is available for the bulkRNA-seq dataset; the survival is calculated using the overall survival values and gene expression/enrichment input by the user. The SingleCell module allows users to visualize the pediatric cancer dataset on a UMAP by selecting the aspect (i.e., cell type) to group the dataset by; it also allows users to enter a gene name or select a pathway of their interest and view its expression or enrichment in a feature or violin plot. The ImmuneCell module allows users to interact with the adult healthy BM dataset by inputting a gene and viewing its expression on a feature plot. The Biomarkers module contains pre-defined biomarker sets for each disease subtype in the selected cancer. The final module, BulkExpression, contains the bulk RNA-seq data for the selected cancer type and allows users to view gene expression or analyze the survival associations of an input gene or gene set. The web resource source code is written in R programming language and the interactive web server has been implemented using the R Shiny and ShinyDashboard packages. The PedScAtlas resource provides a unique and straightforward tool for biomarker identification, analysis of pediatric cancer subtype heterogeneity, and transcriptome profile of the tumor microenvironment. The resource is available online at https://bhasinlab.bmi.emory.edu/PediatricSC/. Figure 1View largeDownload PPTFigure 1View largeDownload PPT Close modal

Review of the Current State of Freely Accessible Web Tools for the Analysis of 16S rRNA Sequencing of the Gut Microbiome.

Owing to the emergence and improvement of high-throughput technology and the associated reduction in costs, next-generation sequencing (NGS) technology has made large-scale sampling and sequencing possible. With the large volume of data produced, the processing and downstream analysis of data are important for ensuring meaningful results and interpretation. Problems in data analysis may be encountered if researchers have little experience in using programming languages, especially if they are clinicians and beginners in the field. A strategy for solving this problem involves ensuring easy access to commercial software and tools. Here, we observed the current status of free web-based tools for microbiome analysis that can help users analyze and handle microbiome data effortlessly. We limited our search to freely available web-based tools and identified MicrobiomeAnalyst, Mian, gcMeta, VAMPS, and Microbiome Toolbox. We also highlighted the various analyses that each web tool offers, how users can analyze their data using each web tool, and noted some of their limitations. From the abovementioned list, gcMeta, VAMPS, and Microbiome Toolbox had several issues that made the analysis more difficult. Over time, as more data are generated and accessed, more users will analyze microbiome data. Thus, the availability of free and easily accessible web tools can enable the easy use and analysis of microbiome data, especially for those users with less experience in using command-line interfaces.

Evaluation of EPISEQ SARS-CoV-2 and a Fully Integrated Application to Identify SARS-CoV-2 Variants from Several Next-Generation Sequencing Approaches.

Whole-genome sequencing has become an essential tool for real-time genomic surveillance of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) worldwide. The handling of raw next-generation sequencing (NGS) data is a major challenge for sequencing laboratories. We developed an easy-to-use web-based application (EPISEQ SARS-CoV-2) to analyse SARS-CoV-2 NGS data generated on common sequencing platforms using a variety of commercially available reagents. This application performs in one click a quality check, a reference-based genome assembly, and the analysis of the generated consensus sequence as to coverage of the reference genome, mutation screening and variant identification according to the up-to-date Nextstrain clade and Pango lineage. In this study, we validated the EPISEQ SARS-CoV-2 pipeline against a reference pipeline and compared the performance of NGS data generated by different sequencing protocols using EPISEQ SARS-CoV-2. We showed a strong agreement in SARS-CoV-2 clade and lineage identification (>99%) and in spike mutation detection (>99%) between EPISEQ SARS-CoV-2 and the reference pipeline. The comparison of several sequencing approaches using EPISEQ SARS-CoV-2 revealed 100% concordance in clade and lineage classification. It also uncovered reagent-related sequencing issues with a potential impact on SARS-CoV-2 mutation reporting. Altogether, EPISEQ SARS-CoV-2 allows an easy, rapid and reliable analysis of raw NGS data to support the sequencing efforts of laboratories with limited bioinformatics capacity and those willing to accelerate genomic surveillance of SARS-CoV-2.

SSA-CAE-Based Abnormal Data Classification Method in Edge Intelligence Device of CNC Machine

Smart factories and big data are important factors in the Fourth Industrial Revolution. Smart factories aim for automation and integration; however, the most important part is the application of data. Despite extensive research on the maintenance and quality management of big data-based production equipment, industrial data gathered for analysis contain more normal data than abnormal data. In addition, a significant amount of energy is expended in the data pre-processing process to analyze the acquired data. Therefore, to maintain production equipment and quality management, data classification technology that allows easy data analysis by classifying abnormal data into normal data is required. In this paper, we propose an abnormal data classification architecture for cycle data sets gathered from production facilities through SSA-CAE along with data storage methods for each product unit. SSA-CAE is a hybrid technique that combines singular spectrum analysis (SSA) techniques that are effective in reducing noise in time series data with convolutional auto encoder (CAE) that have performed well in time series.

Mike 21 Model Based Numerical Simulation of the Operation Optimization Scheme of Sedimentation Basin

The sedimentation basin is an important part of hydraulic engineering. Because the prototype size is too large and not easy to analyze, Mike 21 has the advantages of easy input, conversion, analysis, and demonstration of data results, which are flexible and convenient; however, this method has not been used in hydraulic engineering by previous researchers. This research used the sedimentation basin of the Yellow River Diversion Project as the research object, and a simulation model of the sedimentation basin channel #1 was developed using Mike 21 software to analyze its water and sediment characteristics and conditions in different periods. Using numerical simulation methods, three types of sedimentation basin operation schemes were targeted, the sedimentation changes and sedimentation law of channel #1 were evaluated in each scheme, and the sedimentation effects were compared. The results show that in conditions of different incoming water and sediment and diversion timings during the flood and non-flood seasons, the sedimentation basin adopts an operation scheme to lower the outlet water level, and the siltation volumes are 180,000 and 150,000 m3 lower than the high-outlet water levels, respectively. Moreover, its siltation status is significantly improved, which can effectively extend the service life of the sedimentation basin, prolong the period of silting removal by about 3.5 months, and provide a scientific basis and reference for the optimization of the operation mode of the sedimentation basin in other types of hydraulic engineering.

ScreenGarden: a shinyR application for fast and easy analysis of plate-based high-throughput screens

BackgroundColony growth on solid media is a simple and effective measure for high-throughput genomic experiments such as yeast two-hybrid, synthetic dosage lethality and Synthetic Physical Interaction screens. The development of robotic pinning tools has facilitated the experimental design of these assays, and different imaging software can be used to automatically measure colony sizes on plates. However, comparison to control plates and statistical data analysis is often laborious and pinning issues or plate specific growth effects can lead to the detection of false-positive growth defects.ResultsWe have developed ScreenGarden, a shinyR application, to enable easy, quick and robust data analysis of plate-based high throughput assays. The code allows comparisons of different formats of data and different sized arrays of colonies. A comparison of ScreenGarden with previous analysis tools shows that it performs, at least, equivalently. The software can be run either via a website or offline via the RStudio program; the code is available and can be modified by expert uses to customise the analysis.ConclusionsScreenGarden provides a simple, fast and effective tool to analyse colony growth data from genomic experiments.

FUNKI: interactive functional footprint-based analysis of omics data.

MotivationOmics data are broadly used to get a snapshot of the molecular status of cells. In particular, changes in omics can be used to estimate the activity of pathways, transcription factors and kinases based on known regulated targets, that we call footprints. Then the molecular paths driving these activities can be estimated using causal reasoning on large signalling networks.ResultsWe have developed FUNKI, a FUNctional toolKIt for footprint analysis. It provides a user-friendly interface for an easy and fast analysis of transcriptomics, phosphoproteomics and metabolomics data, either from bulk or single-cell experiments. FUNKI also features different options to visualize the results and run post-analyses, and is mirrored as a scripted version in R.Availability and implementationFUNKI is a free and open-source application built on R and Shiny, available at https://github.com/saezlab/ShinyFUNKI and https://saezlab.shinyapps.io/funki/.Supplementary information Supplementary data are available at Bioinformatics online.

Measurement of contaminant adsorption on soils using cycling modified column tests

An innovative cycled column test with supporting batch equilibrium and kinetic analysis for adsorption or desorption were developed for evaluation of adsorption behavior of soils. Non-equilibrium adsorption was observed in the cycled column tests as the traditional testing methods. The isotherm of local equilibrium of the soil was conducted based on the testing results within a relatively short duration with simple analysis. The concentration curves of influent and effluent of the cycled column tests were simulated by dual-porosity (DP) model with a modified inlet boundary. Based on the modeling results, the isotherm of local equilibrium is close to that of the mobile phase adsorption capacity, whereas the immobile phase of the soil is nearly inactive in the retardation of the contaminants. The testing results from cycled column tests are hardly interfered by desorption or the sorption rate according to the modeling for corresponding scenarios. The cycled column test can be used as an alternative or supplementary method to the traditional column test for the determination of local equilibrium isotherm, with advantages of shorter testing duration and easier data analysis.