Machine learning framework to segment sarcomeric structures in SMLM data

Varga, Dániel; Szikora, Szilárd; Novák, Tibor; Pap, Gergely; Lékó, Gábor; Mihály, József; Erdélyi, Miklós

doi:10.1038/s41598-023-28539-7

Cited by 4 publications

(2 citation statements)

References 32 publications

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“…S2(B)), which is the structure of several sarcomeric proteins with known dimensions from previous measurements [ 46 ]. Simulation parameters were used as described earlier [ 47 ]. Briefly, we generated disk patterns with a radius of 750 nm, forming double-line patterns with a distance of 110 nm.…”

Section: Theoretical Resultsmentioning

confidence: 99%

Triggered cagedSTORM microscopy

Bíró,

Novák,

Czvik

et al. 2024

Biomed. Opt. Express

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In standard SMLM methods, the photoswitching of single fluorescent molecules and the data acquisition processes are independent, which leads to the detection of single molecule blinking events on several consecutive frames. This mismatch results in several data points with reduced localization precision, and it also increases the possibilities of overlapping. Here we discuss how the synchronization of the fluorophores’ ON state to the camera exposure time increases the average intensity of the captured point spread functions and hence improves the localization precision. Simulations and theoretical results show that such synchronization leads to fewer localizations with 15% higher sum signal on average, while reducing the probability of overlaps by 10%.

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Section: Theoretical Resultsmentioning

confidence: 99%

Triggered cagedSTORM microscopy

Bíró,

Novák,

Czvik

et al. 2024

Biomed. Opt. Express

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“…Spatial coordinates of the localized events were stored and the final super-resolved image was visualized with a pixel size of 10 nm. ROI was segmented as described in Varga et al, 2023 [ 62 ]. Structure averaging and line distance measurements were performed in IFM Analyzer v2.1 [ 39 , 56 ].…”

Section: Methodsmentioning

confidence: 99%

Peripheral thickening of the sarcomeres and pointed end elongation of the thin filaments are both promoted by SALS and its formin interaction partners

Farkas,

Szikora,

Jijumon

et al. 2024

PLoS Genet

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During striated muscle development the first periodically repeated units appear in the premyofibrils, consisting of immature sarcomeres that must undergo a substantial growth both in length and width, to reach their final size. Here we report that, beyond its well established role in sarcomere elongation, the Sarcomere length short (SALS) protein is involved in Z-disc formation and peripheral growth of the sarcomeres. Our protein localization data and loss-of-function studies in the Drosophila indirect flight muscle strongly suggest that radial growth of the sarcomeres is initiated at the Z-disc. As to thin filament elongation, we used a powerful nanoscopy approach to reveal that SALS is subject to a major conformational change during sarcomere development, which might be critical to stop pointed end elongation in the adult muscles. In addition, we demonstrate that the roles of SALS in sarcomere elongation and radial growth are both dependent on formin type of actin assembly factors. Unexpectedly, when SALS is present in excess amounts, it promotes the formation of actin aggregates highly resembling the ones described in nemaline myopathy patients. Collectively, these findings helped to shed light on the complex mechanisms of SALS during the coordinated elongation and thickening of the sarcomeres, and resulted in the discovery of a potential nemaline myopathy model, suitable for the identification of genetic and small molecule inhibitors.

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Principal component analysis–artificial neural network-based model for predicting the static strength of seasonally frozen soils

Sun,

Zhou,

Meng

et al. 2023

Sci Rep

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Seasonally frozen soils are exposed to freeze‒thaw cycles every year, leading to mechanical property deterioration. To reasonably describe the deterioration of soil under different conditions, machine learning (ML) technology is used to establish a prediction model for soil static strength. Six key influencing factors (moisture content, compaction degree, confining pressure, freezing temperature, number of freeze‒thaw cycles and thawing duration) are included in the modelling database. The accuracy of three typical ML algorithms (support vector machine (SVM), random forest (RF) and artificial neural network (ANN)) is compared. The results show that the ANN outperforms the SVM and RF. Principal component analysis (PCA) is combined with the ANN, and the PCA–ANN algorithm is proposed, which further improves the prediction accuracy. The deterioration of soil static strength is systematically researched using the PCA–ANN algorithm. The results show that the soil static strength decreased considerably after the first several freeze‒thaw cycles before the strength plateau occurred, and the strength reduction increased significantly with increasing moisture content and compaction degree. The PCA–ANN model can generate a reasonable prediction for the static strength or other soil properties of seasonally frozen soil, which will provide a scientific reference for practical engineering.

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Machine learning framework to segment sarcomeric structures in SMLM data

Cited by 4 publications

References 32 publications

Triggered cagedSTORM microscopy

Triggered cagedSTORM microscopy

Peripheral thickening of the sarcomeres and pointed end elongation of the thin filaments are both promoted by SALS and its formin interaction partners

Principal component analysis–artificial neural network-based model for predicting the static strength of seasonally frozen soils

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