Automated rapid & intelligent microplastics mapping by FTIR microscopy: A Python-based workflow.
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ABSTRACT:
SUBMITTER: Renner G
PROVIDER: S-EPMC7063176 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:BACKGROUND:Oils and bioproducts extracted from cultivated algae can be used as sustainable feedstock for fuels, nutritional supplements, and other bio-based products. Discovery and isolation of new algal species and their subsequent optimization are needed to achieve economical feasibility for industrial applications. Here we describe and validate a workflow for in situ analysis of algal lipids through confocal Raman microscopy. We demonstrate its effectiveness to characterize lipid content of algal strains isolated from the environment as well as algal cells screened for increased lipid accumulation through UV mutagenesis combined with Fluorescence Activated Cell Sorting (FACS). RESULTS:To establish and validate our workflow, we refined an existing Raman platform to obtain better discrimination in chain length and saturation of lipids through ratiometric analyses of mixed fatty acid lipid standards. Raman experiments were performed using two different excitation lasers (? = 532 and 785 nm), with close agreement observed between values obtained using each laser. Liquid chromatography coupled with mass spectrometry (LC-MS) experiments validated the obtained Raman spectroscopic results. To demonstrate the utility and effectiveness of the improved Raman platform, we carried out bioprospecting for algal species from soil and marine environments in both temperate and subtropical geographies to obtain algal isolates from varied environments. Further, we carried out two rounds of mutagenesis screens on the green algal model species, Chlamydomonas reinhardtii, to obtain cells with increased lipid content. Analyses on both environmental isolates and screened cells were conducted which determined their respective lipids. Different saturation states among the isolates as well as the screened C. reinhardtii strains were observed. The latter indicated the presence of cell-to cell variations among cells grown under identical condition. In contrast, non-mutagenized C. reinhardtii cells showed no significant heterogeneity in lipid content. CONCLUSIONS:We demonstrate the utility of confocal Raman microscopy for lipid analysis on novel aquatic and soil microalgal isolates and for characterization of lipid-expressing cells obtained in a mutagenesis screen. Raman microscopy enables quantitative determination of the unsaturation level and chain lengths of microalgal lipids, which are key parameters in selection and engineering of microalgae for optimal production of biofuels.
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Project description:One of the biggest issues in microplastic (MP, plastic items <5 mm) research is the lack of standardisation and harmonisation in all fields, reaching from sampling methodology to sample purification, analytical methods and data analysis. This hampers comparability as well as reproducibility among studies. Concerning chemical analysis of MPs, Fourier-transform infrared (FTIR) spectroscocopy is one of the most powerful tools. Here, focal plane array (FPA) based micro-FTIR (µFTIR) imaging allows for rapid measurement and identification without manual preselection of putative MP and therefore enables large sample throughputs with high spatial resolution. The resulting huge datasets necessitate automated algorithms for data analysis in a reasonable time frame. Although solutions are available, little is known about the comparability or the level of reliability of their output. For the first time, within our study, we compare two well-established and frequently applied data analysis algorithms in regard to results in abundance, polymer composition and size distributions of MP (11-500 µm) derived from selected environmental water samples: (a) the siMPle analysis tool (systematic identification of MicroPlastics in the environment) in combination with MPAPP (MicroPlastic Automated Particle/fibre analysis Pipeline) and (b) the BPF (Bayreuth Particle Finder). The results of our comparison show an overall good accordance but also indicate discrepancies concerning certain polymer types/clusters as well as the smallest MP size classes. Our study further demonstrates that a detailed comparison of MP algorithms is an essential prerequisite for a better comparability of MP data.
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Project description:BACKGROUND:Accurate measurement of the liver iron concentration (LIC) is needed to guide iron-chelating therapy for patients with transfusional iron overload. In this work, we investigate the feasibility of automated quantitative susceptibility mapping (QSM) to measure the LIC. PURPOSE:To develop a rapid, robust, and automated liver QSM for clinical practice. STUDY TYPE:Prospective. POPULATION:13 healthy subjects and 22 patients. FIELD STRENGTH/SEQUENCES:1.5 T and 3 T/3D multiecho gradient-recalled echo (GRE) sequence. ASSESSMENT:Data were acquired using a 3D GRE sequence with an out-of-phase echo spacing with respect to each other. All odd echoes that were in-phase (IP) were used to initialize the fat-water separation and field estimation (T2 *-IDEAL) before performing QSM. Liver QSM was generated through an automated pipeline without manual intervention. This IP echo-based initialization method was compared with an existing graph cuts initialization method (simultaneous phase unwrapping and removal of chemical shift, SPURS) in healthy subjects (n = 5). Reproducibility was assessed over four scanners at two field strengths from two manufacturers using healthy subjects (n = 8). Clinical feasibility was evaluated in patients (n = 22). STATISTICAL TESTS:IP and SPURS initialization methods in both healthy subjects and patients were compared using paired t-test and linear regression analysis to assess processing time and region of interest (ROI) measurements. Reproducibility of QSM, R2 *, and proton density fat fraction (PDFF) among the four different scanners was assessed using linear regression, Bland-Altman analysis, and the intraclass correlation coefficient (ICC). RESULTS:Liver QSM using the IP method was found to be ~5.5 times faster than SPURS (P < 0.05) in initializing T2 *-IDEAL with similar outputs. Liver QSM using the IP method were reproducibly generated in all four scanners (average coefficient of determination 0.95, average slope 0.90, average bias 0.002 ppm, 95% limits of agreement between -0.06 to 0.07 ppm, ICC 0.97). DATA CONCLUSION:Use of IP echo-based initialization enables robust water/fat separation and field estimation for automated, rapid, and reproducible liver QSM for clinical applications. LEVEL OF EVIDENCE:1 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2019;50:725-732.
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Project description:IntroductionTechnologies abstract intelligence and provide predictor and precision insight in workflows that manage disorders, similar to cardiology and hematological disease. Positive perceptions of Artificial Intelligence (AI) that support Machine Learning (ML) and Deep Learning (DL) manage transformations with a safe system that improves wellbeing. In sections, workflow introduces an eXamination (X = AI) as an end-to-end structure to culture workstreams in a step-by-step design to manage populace health in a governed system.MethodTo better healthcare outcomes, communities and personnel benefit from an explanation and an interpretive that elucidates workflow for citizens or practitioners to comprehend personalized platforms. Therefore, the author undertook structure and practice reviews and appraised perspectives that impact the management of AI in public health and medicine.ResultsFigures for the management of AI workflow illustrate and inform on the model, structure, culture, assurance, process steps, values, and governance required for abstract insights in public health and medicine. The papers' end-to-end structure with explanans in a work culture interprets the step-by-step designs that manage the success of AI. Personalized care graphics offer an explanandum in the management of biological analytic value.DiscussionHealthcare leadership collaboratives plan population health with an upstream, workplace and workstream format. Secure workflow and safety wellbeing system requirements prove that genomics and AI improve medicine. Therefore, the paper discusses group understanding of current practice, ethics, policy, and legality.Conclusion"Culture, intelligent workflow, structure, and steps" improve wellbeing with personalized care and align a percept for national opportunities, regional control, and local needs. Personalized practice cultures support analytic systems to describe, predict, precision, and prescript medicine in population health management eXaminations.
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2024-11-12 | PXD052369 | Pride