Figure 7 - uploaded by Jan Woyzichovski
Content may be subject to copyright.
Ranges of spore size for manual and automated measurements. (A) Box plots of spore size in Physarum albescens determined by manual and automated measurements (box: 25th to 75th empirical quartiles, black whiskers: 1.5 times the interquartile range, black thick horizontal line: median, dots: single outliers, white horizontal line: mean, white whiskers: 95% CI). Varying numbers of spores were measured on single images (all up to 520 spores) and 144 images (last box, 35,176 spores). Different lower-case letters in the legend indicate significant differences in spore size in pairwise post hoc tests between zoom levels. Lower case letters on the upper facet level represent a grouping results of pairwise post hoc test according to the numbers of spores measured. Letters on the lower facet level refer to post hoc grouping results for zoom levels grouped by amount levels. (B-D) Examples of individual spores representing (B) upper outliers (oversized spores), (C) average-sized, and (D) lower outliers (unusually small spores), with the arrows pointing to the respective diameter in (A). Bars = 10 µm.
Source publication
Measuring spore size is a standard method for the description of fungal taxa, but in manual microscopic analyses the number of spores that can be measured and information on their morphological traits are typically limited. To overcome this weakness we present a method to analyze the size and shape of large numbers of spherical bodies, such as spor...
Contexts in source publication
Context 1
... oversized spores (15.9 ± 0.5 µm diameter), the I-value was not significant (−0.10, p = 0.99), indicating a random distribution of spores for this size class. Accuracy of the method Figure 7 shows the results of different manual and automatic measurements of spore size from the same sample. For automated measurements including all images, 95% confidence interval of the means were very small (10.62-10.63 ...
Context 2
... (−0.10, p = 0.99), indicating a random distribution of spores for this size class. Accuracy of the method Figure 7 shows the results of different manual and automatic measurements of spore size from the same sample. For automated measurements including all images, 95% confidence interval of the means were very small (10.62-10.63 µm, in Fig. 7, last bar), which was consistent with the spore size measurements of various samples (Supplement 1B). This low variation is a direct effect of the large number of objects measured, which also increases the number of extreme outliers (Fig. 7, compare results from 520 spores with those from a total of 35,176 spores sized on 144 images). A linear ...
Context 3
... measurements including all images, 95% confidence interval of the means were very small (10.62-10.63 µm, in Fig. 7, last bar), which was consistent with the spore size measurements of various samples (Supplement 1B). This low variation is a direct effect of the large number of objects measured, which also increases the number of extreme outliers (Fig. 7, compare results from 520 spores with those from a total of 35,176 spores sized on 144 images). A linear mixed model (Table 3) revealed a significant three-way interaction among zoom level, the number of analyzed spores, and repeated measurements (F(16, 2,481) = 3.63, p < 0.0001) for spore diameter, indicating that all factors ...
Context 4
... levels and number of analyzed spores showed that manual measurement without the digital zoom and low spores numbers led to significantly smaller average spore sizes (p < 0.001). Regardless of the number of measured spores, all automated measurements grouped with 100 manually measured spores (without zoom) and resulted in intermediate spore sizes (Fig. ...
Context 5
... of different sizes respond slightly differently to the vibration device we used. The resulting sorting effect leads to a better random distribution of the oversized (Moran's I = −0.1, p = 0.99) compared to the smaller spores. Larger spores appear to be more susceptible to the applied vibration and move further than the smaller spores (compare Figs. 7B-7D). The high and highly significant I-value (0.69, p < 0.001) for normal-sized spores can be explained by the interaction between the highly concentrated spore suspension and Hoyer's medium when placing the cover slip on the suspension. It is thus recommended to analyze numerous images (144 in our case) at different positions of the spore ...
Context 6
... objects revealed a number of distinguishing features such as area, circularity, aspect ratio and maximum diameter (see Table 2) for every object and ensured large sample sizes and extremely small confidence intervals (see above). As a result, the estimated median spore sizes in large samples approached the true mean of the entire spore population (Fig. ...
Citations
... Measurements errors for spore size are within the low ranges explained in Woyzichovski et al. (2021) and can be neglected for the purpose of this study. ...
... First, mitochondria, containing DNA as well, and other cell structures might cause background fluorescence. Second, even if the spores in our mounts are precisely arranged in a plane (see Woyzichovski et al. 2021), their nuclei may overlap each other. Due to the sheer amount of possible observations, such overlaid nuclei constitute a minor fraction, but the number of binucleated spores may be rather under-than overestimated. ...
... Preparation of spore mounts: Using the lightmicroscopic method described in Woyzichovski et al. (2021), between 10,000 and 20,000 spores per sporocarp were analyzed. For 39 accessions from 6 mountain ranges, (1-)3-5 sporocarps were analyzed (Supplementary Material Table S2). ...
Spore size enables dispersal in plasmodial slime molds (Myxomycetes) and is an important taxonomic character. We recorded size and the number of nuclei per spore for 39 specimens (colonies of 50–1000 sporocarps) of the nivicolous myxomycete Physarum albescens, a morphologically defined taxon with several biological species. For each colony, three sporocarps were analyzed from the same spore mount under brightfield and DAPI-fluorescence, recording ca. 14,000 spores per item. Diagrams for spore size distribution showed narrow peaks of mostly uninucleate spores. Size was highly variable within morphospecies (10.6–13.5 µm, 11–13%), biospecies (3–13%), even within spatially separated colonies of one clone (ca. 8%); but fairly constant for a colony (mean variation 0.4 µm, ca. 1.5%). ANOVA explains most of this variation by the factor locality (within all colonies: 32.7%; within a region: 21.4%), less by biospecies (13.5%), whereas the contribution of intra-colony variation was negligible (<0.1%). Two rare aberrations occur: 1) multinucleate spores and 2) oversized spores with a double or triple volume of normal spores. Both are not related to each other or limited to certain biospecies. Spore size shows high phenotypic plasticity, but the low variation within a colony points to a strong genetic background.
... The size of the field of view of the image that can be presented is determined by the number of fields of view of the eyepiece. The size of plant-fungal spores is generally a few micrometers to tens of micrometers [3,28,33]. When observing fungal spores with traditional optical microscopes, a larger magnification is required which will lead to a decrease in the number of actual fungal spores observed. ...
... The preprocessing flow of the diffraction fingerprint image of fungal spores is shown in Figure 3. First, in order to solve the problems of underexposure and uneven illumination that may exist in the fungal spore diffraction fingerprint collection system a new twodimensional Gamma function was constructed to correct the brightness of the original image of the collected fungal spore diffraction fingerprints. The expression of the twodimensional Gamma function is as follows [33]: ...
... Measuring and identifying spores is a standard method for describing fungi classification and with manual microscopic analyses, the number of spores that can be measured and their morphological traits can be observed. However, due to the small size and the large number of fungal spores, and the small field of view of the microscope, large counting errors can occur easily [33]. Lens-less CMOS image sensors are favored by related researchers due to their large imaging field of view and low cost [39]. ...
The detection and control of fungal spores in greenhouse crops are important for stabilizing and increasing crop yield. At present, the detection of fungal spores mainly adopts the method of combining portable volumetric spore traps and microscope image processing. This method is problematic as it is limited by the small field of view of the microscope and has low efficiency. This study proposes a rapid detection method for fungal spores from greenhouse crops based on CMOS image sensors and diffraction fingerprint feature processing. We built a diffraction fingerprint image acquisition system for fungal spores of greenhouse crops and collected diffraction fingerprint images of three kinds of fungal spores. A total of 13 diffraction fingerprint features were selected for the classification of fungal spores. These 13 characteristic values were divided into 3 categories, main bright fringe, main dark fringe, and center fringe. Then, these three features were calculated to obtain the Peak to Center ratio (PCR), Valley to Center ratio, and Peak to Valley ratio (PVR). Based on these features, logistics regression (LR), K nearest neighbor (KNN), random forest (RF), and support vector machine (SVM) classification models were built. The test results show that the SVM model has a better overall classification performance than the LR, KNN, and RF models. The average accuracy rate of the recognition of three kinds of fungal spores from greenhouse crops under the SVM model was 92.72%, while the accuracy rates of the LR, KNN, and RF models were 84.97%, 87.44%, and 88.72%, respectively. The F1-Score value of the SVM model was higher, and the overall average value reached 89.41%, which was 11.12%, 7.18%, and 5.57% higher than the LR, KNN, and RF models, respectively. Therefore, the method proposed in this study can be used for the remote identification of three fungal spores which can provide a reference for the identification of fungal spores in greenhouse crops and has the advantages of low cost and portability.
