Open-source Web-based Software Research Articles

Modeling hydrogen storage capacities, injection and withdrawal cycles in salt caverns: Introducing the GeoH2 salt storage and cycling app

Salt caverns have been used as hydrogen (H2) storage solutions in four locations worldwide with refineries and the petrochemical industry relying on these supplies as strategic back-up. The viability of storing H2 within salt caverns is advantageous given their large volumetric capacities, their flexible operation with large injection and withdrawal rates, and for being a proven technology for the underground storage of a wide variety of gases and liquids. However, to our knowledge, there are no open-source web-based software tools to assess the technical potential of salt caverns for H2 storage. This work aims to fill that gap by introducing the GeoH2 Salt Storage and Cycling App, a computer program that models H2 storage capacities, and injection/withdrawal cycles in salt caverns.The GeoH2 Salt Storage and Cycling App is a web-based thermodynamic simulator, which consists of the following modules: (a) H2 physical properties, (b) volumetric, (c) production, (d) injection, and (e) cycling. The physical properties module provides the user with the main thermodynamic, transport, and thermal properties of H2. The volumetric module allows the user to estimate H2 storage capacities in salt caverns. The production and the injection modules simulate the withdrawal and the injection of H2, respectively. Finally, the cycling module models sequential withdrawal and injection processes.This study validates the results of the physical properties and the volumetric modules with real data. We validate the results of the production and the injection modules for synthetic cases using an open-source thermodynamic simulator.This work presents a novel tool suitable to assess the technical potential of H2 storage, injection, withdrawal, and cycling operations in salt caverns. This application can also be used, along with subsurface geological information, as a first order screening tool to assess H2 storage capacity at a regional or hub scale.

Amica: an interactive and user-friendly web-platform for the analysis of proteomics data.

Quantitative proteomics has become an increasingly prominent tool in the study of life sciences. A substantial hurdle for many biologists are, however, the intricacies involved in the associated high throughput data analysis. In order to facilitate this task for users with limited background knowledge, we have developed amica, a freely available open-source web-based software that accepts proteomic input files from different sources. amica provides quality control, differential expression, biological network and over-representation analysis on the basis of minimal user input. Scientists can use amica's query interface interactively to compare multiple conditions and rapidly identify enriched or depleted proteins. They can visualize their results using customized output graphics, and ultimately export the results in a tab-separated format that can be shared with collaborators. The code for the application, input data and documentation can be accessed online at https://github.com/tbaccata/amica and is also incorporated in the web application. The strong emphasis on dynamic user interactions, the integration of various databases and the option to download processed data, facilitate the analysis of complex proteomic data for both first-time users and experienced bioinformaticians. A freely available version of amica is available at https://bioapps.maxperutzlabs.ac.at/app/amica .

Prevalence of hyperglycemia in masters athletes.

BackgroundAgeing is associated with decreased physical activity, obesity and subsequently an increased risk of developing type 2 diabetes mellitus (T2dm). Master athletes (MA) have initiated exercise or sport later in life or pursued a physically active lifestyle for an extended period. Subsequently, MAs have been proposed as a model of successful ageing as this active lifestyle is associated with health benefits including decreased health risk of chronic diseases and a reduction in premature mortality. Given long-term physical activity/exercise has previously been shown to be protective against hyperglycemia, a risk factor for T2dm, it is plausible that MA may have protective benefit against developing hyperglycemia. Therefore, the aim of this study was to investigate the prevalence of hyperglycemia via fasting plasma glucose (FPG) in MAs competing at the World Masters Games (WMG).MethodsThis cross-sectional, observational survey utilized an online survey using open-source web-based software was used to investigate MAs physiological and medical-related parameters. Over 28,000 MAs competed in the WMG, of which 8,072 MAs completed the survey. Of these MAs, a total of 486 (males 277, females 209; range 27 to 91 years, mean age 55.1 ± 10.2 years) attained recent pathology results which included FPG which was subsequently analyzed for this study. FPG and other outcome variables were compared between genders and to the Australian and United States general population.ResultsMean FPG for MAs was 5.03 mmol (±1.2, 95% CI [4.9–5.1] mmol) with majority (75.5%) of MAs reporting a normal (<5.5 mmol) FPG, followed by pre-diabetes (20.2%, >5.51 to <5.99 mmol) and abnormal (4.3%, >7.0 mmol). There was no significant difference (P = 0.333) in FPG between genders however, males had a slightly higher (+2.1%) FPG as compared to females (5.08 ± 1.2 mmol (95% CI [4.9–5.22] mmol) versus 4.98 ± 1.1 mmol (95% CI 4.8-5.1 mmol)). The majority of males (71.8%) and females (80.3%) were classified with a normal FPG. With regard to an abnormal FPG level, only 4.0% of males and 4.9% of females were classified abnormal which was suggestive of undiagnosed T2dm. With regard to age by decade, there was no significant difference (P = 0.06–1.00) between age groups and no relationship between the MAs’ age and FPG (r = .054, P = 0.24). As a group, MAs had a significantly lower FPG as compared to the Australian (−3.2%, P = 0.005) and United States general populations (−13.9%, P < 0.001).ConclusionsMost, however not all, MAs were found to have normal glycaemia, with only a small percentage indicating a risk of developing T2dm (i.e., impaired fasting glucose) and a smaller percentage identified with an abnormal FPG, suggestive of T2dm. These findings suggest MAs appear to be at low metabolic risk for developing T2dm based upon FPG and the physical activity/exercise they complete as MAs may indeed be protective against hyperglycemia whilst maintaining an active lifestyle.

Designing Automated, High-throughput, Continuous Cell Growth Experiments Using eVOLVER.

Continuous culture methods enable cells to be grown under quantitatively controlled environmental conditions, and are thus broadly useful for measuring fitness phenotypes and improving our understanding of how genotypes are shaped by selection. Extensive recent efforts to develop and apply niche continuous culture devices have revealed the benefits of conducting new forms of cell culture control. This includes defining custom selection pressures and increasing throughput for studies ranging from long-term experimental evolution to genome-wide library selections and synthetic gene circuit characterization. The eVOLVER platform was recently developed to meet this growing demand: a continuous culture platform with a high degree of scalability, flexibility, and automation. eVOLVER provides a single standardizing platform that can be (re)-configured and scaled with minimal effort to perform many different types of high-throughput or multi-dimensional growth selection experiments. Here, a protocol is presented to provide users of the eVOLVER framework a description for configuring the system to conduct a custom, large-scale continuous growth experiment. Specifically, the protocol guides users on how to program the system to multiplex two selection pressures - temperature and osmolarity - across many eVOLVER vials in order to quantify fitness landscapes of Saccharomyces cerevisiae mutants at fine resolution. We show how the device can be configured both programmatically, through its open-source web-based software, and physically, by arranging fluidic and hardware layouts. The process of physically setting up the device, programming the culture routine, monitoring and interacting with the experiment in real-time over the internet, sampling vials for subsequent offline analysis, and post experiment data analysis are detailed. This should serve as a starting point for researchers across diverse disciplines to apply eVOLVER in the design of their own complex and high-throughput cell growth experiments to study and manipulate biological systems.

Open-source Software for Demand Forecasting of Clinical Laboratory Test Volumes Using Time-series Analysis

Background: Demand forecasting is the area of predictive analytics devoted to predicting future volumes of services or consumables. Fair understanding and estimation of how demand will vary facilitates the optimal utilization of resources. In a medical laboratory, accurate forecasting of future demand, that is, test volumes, can increase efficiency and facilitate long-term laboratory planning. Importantly, in an era of utilization management initiatives, accurately predicted volumes compared to the realized test volumes can form a precise way to evaluate utilization management initiatives. Laboratory test volumes are often highly amenable to forecasting by time-series models; however, the statistical software needed to do this is generally either expensive or highly technical. Method: In this paper, we describe an open-source web-based software tool for time-series forecasting and explain how to use it as a demand forecasting tool in clinical laboratories to estimate test volumes. Results: This tool has three different models, that is, Holt-Winters multiplicative, Holt-Winters additive, and simple linear regression. Moreover, these models are ranked and the best one is highlighted. Conclusion: This tool will allow anyone with historic test volume data to model future demand.

WholeCellKB: model organism databases for comprehensive whole-cell models.

Whole-cell models promise to greatly facilitate the analysis of complex biological behaviors. Whole-cell model development requires comprehensive model organism databases. WholeCellKB (http://wholecellkb.stanford.edu) is an open-source web-based software program for constructing model organism databases. WholeCellKB provides an extensive and fully customizable data model that fully describes individual species including the structure and function of each gene, protein, reaction and pathway. We used WholeCellKB to create WholeCellKB-MG, a comprehensive database of the Gram-positive bacterium Mycoplasma genitalium using over 900 sources. WholeCellKB-MG is extensively cross-referenced to existing resources including BioCyc, KEGG and UniProt. WholeCellKB-MG is freely accessible through a web-based user interface as well as through a RESTful web service.

Plant-Based Microarray Data at the European Bioinformatics Institute. Introducing AtMIAMExpress, a Submission Tool for Arabidopsis Gene Expression Data to ArrayExpress

ArrayExpress is a public microarray repository founded on the Minimum Information About a Microarray Experiment (MIAME) principles that stores MIAME-compliant gene expression data. Plant-based data sets represent approximately one-quarter of the experiments in ArrayExpress. The majority are based on Arabidopsis (Arabidopsis thaliana); however, there are other data sets based on Triticum aestivum, Hordeum vulgare, and Populus subsp. AtMIAMExpress is an open-source Web-based software application for the submission of Arabidopsis-based microarray data to ArrayExpress. AtMIAMExpress exports data in MAGE-ML format for upload to any MAGE-ML-compliant application, such as J-Express and ArrayExpress. It was designed as a tool for users with minimal bioinformatics expertise, has comprehensive help and user support, and represents a simple solution to meeting the MIAME guidelines for the Arabidopsis community. Plant data are queryable both in ArrayExpress and in the Data Warehouse databases, which support queries based on gene-centric and sample-centric annotation. The AtMIAMExpress submission tool is available at http://www.ebi.ac.uk/at-miamexpress/. The software is open source and is available from http://sourceforge.net/projects/miamexpress/. For information, contact miamexpress@ebi.ac.uk.