Fission stories: Using PomBase to understand Schizosaccharomyces pombe biology

PomBase (www.pombase.org), the model organism database (MOD) for the fission yeast Schizosaccharomyces pombe, supports research within and beyond the S. pombe community by integrating and presenting genetic, molecular, and cell biological knowledge into intuitive displays and comprehensive data collections. With new content, novel query capabilities, and biologist-friendly data summaries and visualisation, PomBase also drives innovation in the MOD community.

Practical Techniques in Molecular Biotechnology

Practical Techniques in Molecular Biotechnology intends to familiarise students with the basics of the well-known experiments of molecular biotechnology and related courses like chemical biotechnology and cell biology. The content of the book will be useful in strengthening the basic skills and help students to apply the concepts to real-world problems. This book emphasises important concepts like bioanalytical techniques, biochemical analysis of proteins, recombinant DNA, and protein technology etc. The text will help students to understand the theoretical aspects of the techniques and provide experience with hands-on techniques to demonstrate practical troubleshooting and data analysis. The text is supported with diagrams, data, summaries for the quick recap and appendices with useful protocols and calculation methods.

Fission stories: Using PomBase to understand Schizosaccharomyces pombe biology

PomBase (www.pombase.org), the model organism database (MOD) for the fission yeast Schizosaccharomyces pombe, supports research within and beyond the S. pombe community by integrating and presenting genetic, molecular, and cell biological knowledge into intuitive displays and comprehensive data collections. With new content, novel query capabilities, and biologist-friendly data summaries and visualisation, PomBase also drives innovation in the MOD community.

Extracting an Optimal Set of Linguistic Summaries using Genetic Algorithm Combined with Greedy Strategy

The goal of extracting linguistic data summaries is to produce summary sentences expressed in natural language which represent knowledge hidden in numerical dataset. At the most general level, human users can get a very large number of linguistic summaries. In this paper, we propose a model of genetic algorithm combined with greedy strategy to extract an optimal set of linguistic summaries based on the evaluation measures of goodness and diversity of the set of linguistic summaries. The experimental results on creep dataset have demonstrated the outperformance of the proposed model of genetic algorithm combined with greedy strategy in comparison with the existing genetic algorithm models in extracting linguistic summaries from data.

Summary Visualizations of Gene Ontology Terms With GO-Figure!

The Gene Ontology (GO) is a cornerstone of functional genomics research that drives discoveries through knowledge-informed computational analysis of biological data from large-scale assays. Key to this success is how the GO can be used to support hypotheses or conclusions about the biology or evolution of a study system by identifying annotated functions that are overrepresented in subsets of genes of interest. Graphical visualizations of such GO term enrichment results are critical to aid interpretation and avoid biases by presenting researchers with intuitive visual data summaries. Amongst current visualization tools and resources there is a lack of standalone open-source software solutions that facilitate explorations of key features of multiple lists of GO terms. To address this we developed GO-Figure!, an open-source Python software for producing user-customisable semantic similarity scatterplots of redundancy-reduced GO term lists. The lists are simplified by grouping together terms with similar functions using their quantified information contents and semantic similarities, with user-control over grouping thresholds. Representatives are then selected for plotting in two-dimensional semantic space where similar terms are placed closer to each other on the scatterplot, with an array of user-customisable graphical attributes. GO-Figure! offers a simple solution for command-line plotting of informative summary visualizations of lists of GO terms, designed to support exploratory data analyses and dataset comparisons.

Insights from Data with R

Knowledge of how to get useful information from data is essential in the life and environmental sciences. This book provides learners with knowledge, experience, and confidence about how to efficiently and reliably discover useful information from data. The content is developed from first- and second-year undergraduate-level courses taught by the authors. It charts the journey from question, to raw data, to clean and tidy data, to visualizations that provide insights. This journey is presented as a repeatable workflow fit for use with many types of question, study, and data. Readers discover how to use R and RStudio, and learn key concepts for drawing appropriate conclusions from patterns in data. The book focuses on providing learners with a solid foundation of skills for working with data, and for getting useful information from data summaries and visualizations. It focuses on the strength of patterns (i.e. effect sizes) and their meaning (e.g. correlation or causation). It purposefully stays away from statistical tests and p-values. Concepts covered include distribution, sample, population, mean, median, mode, variance, standard deviation, correlation, interactions, and non-independence. The journey from data to insight is illustrated by one workflow demonstration in the book, and three online. Each involves data collected in a real study. Readers can follow along by downloading the data, and learning from the descriptions of each step in the journey from the raw data to visualizations that show the answers to the questions posed in the original studies.

Michigan system for opioid overdose surveillance

Community rapid response may reduce opioid overdose harms, but is hindered by the lack of timely data. To address this need, we created and evaluated the Michigan system for opioid overdose surveillance (SOS). SOS integrates suspected fatal overdose data from Medical Examiners (MEs), and suspected non-fatal overdoses (proxied by naloxone administration) from the Michigan Emergency Medical Services (EMS) into a web-based dashboard that was developed with stakeholder feedback. Authorised stakeholders can view approximate incident locations and automated spatiotemporal data summaries, while the general public can view county-level summaries. Following Centers for Disease Control and Prevention (CDC) surveillance system evaluation guidelines, we assessed simplicity, flexibility, data quality, acceptability, sensitivity, positive value positive (PVP), representativeness, timeliness and stability of SOS. Data are usually integrated into SOS 1-day postincident, and the interface is updated weekly for debugging and new feature addition, suggesting high timeliness, stability and flexibility. Regarding representativeness, SOS data cover 100% of EMS-based naloxone adminstrations in Michigan, and receives suspected fatal overdoses from MEs covering 79.1% of Michigan’s population, but misses those receiving naloxone from non-EMS. PVP of the suspected fatal overdose indicator is nearly 80% across MEs. Because SOS uses pre-existing data, added burden on MEs/EMS is minimal, leading to high acceptability; there are over 300 authorised SOS stakeholders (~6 new registrations/week) as of this writing, suggesting high user acceptability. Using a collaborative, cross-sector approach we created a timely opioid overdose surveillance system that is flexible, acceptable, and is reasonably accurate and complete. Lessons learnt can aid other jurisdictions in creating analogous systems.

Supplemental Material: Minimal stratigraphic evidence for coseismic coastal subsidence during 2000 yr of megathrust earthquakes at the central Cascadia subduction zone

Includes tables and imagery showing core and sampling locations; figures showing stratigraphy at additional sites and results of transfer function reconstructions of elevation using diatom floras from core S; tables of foraminiferal and diatom data; summaries of previous investigations; the tidal marsh setting of our study site; methods of measuring sampling elevations; explanation of variance added to radiocarbon age errors; and listing of code for OxCal radiocarbon age models.

Supplemental Material: Minimal stratigraphic evidence for coseismic coastal subsidence during 2000 yr of megathrust earthquakes at the central Cascadia subduction zone

Includes tables and imagery showing core and sampling locations; figures showing stratigraphy at additional sites and results of transfer function reconstructions of elevation using diatom floras from core S; tables of foraminiferal and diatom data; summaries of previous investigations; the tidal marsh setting of our study site; methods of measuring sampling elevations; explanation of variance added to radiocarbon age errors; and listing of code for OxCal radiocarbon age models.

Spatio-temporal summarized visualization of smartX multi-view visibility in cloud-native edge boxes

The existing data summarization (and archival) techniques are generic and are not designed to leverage the unique characteristics of the spatio-temporal visualization at multi-resource level. In this paper, we propose and explore a family of data summaries that take advantage of the multiple layers i.e. physical/virtual resources with temporal and spatial correlation among distributed edge boxes. Significant challenges in measuring spatio-temporal data, however, contribute to both a tendency towards identifying efficient metrics with summarizing function alities and effective verification methods. In this paper, we present our idea of maintaining summarized spatio-temporal data and verify through visualization of gathered operational data.