Biology subjects

Stratford, J.

Publications and source records attributed to Stratford, J..

4 recordsLinked to original sources

bioRxiv2026

Parallelised detection of bacteria viability using an electrode array and the Exeter Multiscope

Antimicrobial resistance remains a global existential threat. Given that antimicrobial therapy commonly starts before pathogen identification, rapid and scalable methods capable of determining effective antimicrobial compounds are needed. In this paper, we demonstrate a 2 x 2 array of parallelised microscopes that uses low numerical aperture (NA=0.25) detection optics and LED excitation to determine bacterial viability based on their fluorescence response to an electrical stimulus. Following a 2-hour incubation, the fluorescent viability readout requires less than one minute. We use K-means clustering to classify pixels in a time lapse sequence of widefield fluorescence images and extract changes seen within bacterial clusters. We demonstrate sufficient sensitivity to measure fluorescence changes after electrical stimulation in a bacterial monolayer. To capture these subtle fluorescence changes at high signal-to-background ratios, we place a limit on the minimum optical density of the bacterial sample. This novel approach is scalable to 96-well formats using a suitable consumable electrode array.

microbiology↗

bioRxiv2024

CureSCi Metadata Catalog - finding and harmonizing studies for secondary analysis of hydroxyurea use for sickle cell disease

ObjectivesSickle cell disease (SCD) is a rare group of inherited red blood cell disorders that affect hemoglobin, resulting in serious multi-system complications. The limited number of patients available to participate in research studies can inhibit investigating sophisticated relationships. Secondary analysis is a research method that involves using existing data to answer new research questions. Data harmonization enables secondary analysis by combining data across studies, especially helpful for rare disease research where individual studies may be small. The National Heart, Lung, and Blood Institute Cure Sickle Cell Initiative (CureSCi) Metadata Catalog is a web-based tool to identify SCD study datasets for conducting data harmonization and secondary analysis. We present a proof-of-concept secondary analysis to explore factors associated with discontinuation of hydroxyurea, a safe and effective first line SCD therapy, to illustrate the utility of the CureSCi Metadata Catalog to expedite and enable more robust SCD research. MethodsWe performed secondary analysis of SCD studies using a multi-step workflow: develop research questions, identify study datasets, identify variables of interest, harmonize variables, and establish an analysis method. A harmonized dataset consisting of eight predictor variables across five studies was created. Secondary analysis involved a generalized linear model was employed to identify factors that significantly impact hydroxyurea discontinuation. ResultsThe CureSCi Metadata Catalog provided a platform to efficiently find relevant studies and design a harmonization strategy to prepare data for secondary analysis. Multivariate analysis of the harmonized identified that patients who are older, are female, had a history of blood transfusion therapy, had episodes of acute chest syndrome, and had the SC sickle cell genotype are more likely to stop hydroxyurea treatment. ConclusionThis secondary analysis provides a template for how the CureSCi Metadata Catalog expedites dataset discovery of sickle cell studies for identifying relationships between variables or validating existing findings.

bioinformatics↗

bioRxiv2021

CureSCi Metadata Catalog - Making sickle cell studies findable

ObjectivesTo adopt the FAIR principles (Findable, Accessible, Interoperable, Reusable) to enhance data sharing, the Cure Sickle Cell Initiative (CureSCi) MetaData Catalog (MDC) was developed to make Sickle Cell Disease (SCD) study datasets more Findable by curating study metadata and making them available through an open-access web portal. MethodsStudy metadata, including study protocol, data collection forms, and data dictionaries, describe information about study patient-level data. We curated key metadata of 16 SCD studies in a three-tiered conceptual framework of category, subcategory, and data element using ontologies and controlled vocabularies to organize the study variables. We developed the CureSCi MDC by indexing study metadata to enable effective browse and search capabilities at three levels: study, Patient-Reported Outcome (PRO) Measures, and data element levels. ResultsThe CureSCi MDC offers several browse and search tools to discover studies by study level, PRO Measures, and data elements. The "Browse Studies," "Browse Studies by PRO Measures," and "Browse Studies by Data Elements" tools allow users to identify studies through pre-defined conceptual categories. "Search by Keyword" and "Search Data Element by Concept Category" can be used separately or in combination to provide more granularity to refine the search results. This resource helps investigators find information about specific data elements across studies using public browsing/search tools, before going through data request procedures to access controlled datasets. The MDC makes SCD studies more Findable through browsing/searching study information, PRO Measures, and data elements, aiding in the reuse of existing SCD data.

bioinformatics↗

bioRxiv2018

Disturbance During Biofilm Community Succession Promotes Cooperation and Diversity

Diversity-disturbance relationships have found widespread application in ecology, conservation and biodiversity management. In spite of their explanatory power, these conceptual frameworks have yet to be systematically applied to understanding succession in diverse microbial biofilms. Here we investigate community assembly in biofilms formed in replicate microbial bioelectrochemical systems using time-course sequencing of community 16S rRNA genes, corresponding to hundreds of operational taxonomic units (OTUs). For the first time we present a statistical model showing that a simple diversity-disturbance relationship can be used to explain dynamic changes in high diversity biofilm communities. This simple model reveals that succession in these systems is guided towards either a low diversity, generalist-dominated biofilm or a high diversity, cooperative-specialist biofilm, depending on the level of endogenous disturbance measured within the community. The pattern observed shows remarkable symmetry with findings from macro-scale communities such as grasslands, forests and coral reefs.

ecology↗