Dataset Information

Applicability of Automated Cell Counter with a Chlorophyll Detector in Routine Management of Microalgae.

ABSTRACT: Microalgae have attracted attention for several industrial applications, but all such applications demand culture quality because of their sensitivity to environmental changes. Although simplicity, speed, and accuracy are important to assess algal cultures, researchers have expended vast amounts of labor to monitor algal health using hemocytometry. Along with its user bias, quantifying the cell status aside from the cell density is not easy. This paper describes the easy and rapid evaluation of algal number and status using an image-based cell counter (Countess II FL; Thermo Fisher Scientific Inc.) with a fluorescent filter for chlorophyll. Unlike mammalian cultured cells larger than microalgae, it is not easy for a low-resolution camera alone to distinguish microalgae from grimy spots and microbubbles on counting plates. To assess this method's performance, freshwater/marine microalgae and environmental samples were evaluated using the instrument. Results reveal that an instrument with a fluorescence filter can distinguish microalgae from other particles more precisely than a device with no filter. Values obtained using the instrument were not significantly different from those obtained using hemocytometry. Moreover, the cell counter, but not hemocytometry, can qualify the algal status. Results demonstrate that this system, which has no user bias, can contribute to algal assessment.

SUBMITTER: Takahashi T

PROVIDER: S-EPMC5862891 | biostudies-other | 2018 Mar

REPOSITORIES: biostudies-other

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Applicability of Automated Cell Counter with a Chlorophyll Detector in Routine Management of Microalgae.

Takahashi Toshiyuki T

Scientific reports 20180321 1

Microalgae have attracted attention for several industrial applications, but all such applications demand culture quality because of their sensitivity to environmental changes. Although simplicity, speed, and accuracy are important to assess algal cultures, researchers have expended vast amounts of labor to monitor algal health using hemocytometry. Along with its user bias, quantifying the cell status aside from the cell density is not easy. This paper describes the easy and rapid evaluation of ...[more]

PMID: 29563559

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Project description:BackgroundTo investigate reproducibility of texture features and volumetric bone mineral density (vBMD) extracted from trabecular bone in the thoracolumbar spine in routine clinical multi-detector computed tomography (MDCT) data in a single scanner environment.MethodsPatients who underwent two routine clinical thoraco-abdominal MDCT exams at a single scanner with a time interval of 6 to 26 months (n=203, 131 males; time interval mean, 13 months; median, 12 months) were included in this observational study. Exclusion criteria were metabolic and hematological disorders, bone metastases, use of bone-active medications, and history of osteoporotic vertebral fractures (VFs) or prior diagnosis of osteoporosis. A convolutional neural network (CNN)-based framework was used for automated spine labeling and segmentation (T5-L5), asynchronous Hounsfield unit (HU)-to-BMD calibration, and correction for the intravenous contrast medium phase. Vertebral vBMD and six texture features [varianceglobal, entropy, short-run emphasis (SRE), long-run emphasis (LRE), run-length non-uniformity (RLN), and run percentage (RP)] were extracted for mid- (T5-T8) and lower thoracic (T9-T12), and lumbar vertebrae (L1-L5), respectively. Relative annual changes were calculated in texture features and vBMD for each vertebral level and sorted by sex, and changes were checked for statistical significance (P<0.05) using paired t-tests. Root mean square coefficient of variation (RMSCV) and root mean square error (RMSE) were calculated as measures of variability.ResultsSRE, LRE, RLN, and RP exhibited substantial reproducibility with RMSCV-values below 2%, for both sexes and at all spine levels, while vBMD was less reproducible (RMSCV =11.9-16.2%). Entropy showed highest variability (RMSCV =4.34-7.69%) due to statistically significant increases [range, mean ± standard deviation: (4.40±5.78)% to (8.36±8.66)%, P<0.001]. RMSCV of varianceglobal ranged from 1.60% to 3.03%.ConclusionsOpportunistic assessment of texture features in a single scanner environment using the presented CNN-based framework yields substantial reproducibility, outperforming vBMD reproducibility. Lowest scan-rescan variability was found for higher-order texture features. Further studies are warranted to determine, whether microarchitectural changes to the trabecular bone may be assessed through texture features.

Dataset Information

Applicability of Automated Cell Counter with a Chlorophyll Detector in Routine Management of Microalgae.

Publications

Applicability of Automated Cell Counter with a Chlorophyll Detector in Routine Management of Microalgae.

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