TY - JOUR AU - Bíró, Péter AU - Novák, Tibor AU - Czvik, Elvira AU - Mihály, József AU - Szikora, Szilárd AU - van de Linde, Sebastian AU - Erdélyi, Miklós TI - Triggered cagedSTORM microscopy JF - BIOMEDICAL OPTICS EXPRESS J2 - BIOMED OPT EXPRESS VL - 15 PY - 2024 IS - 6 SP - 3715 EP - 3726 PG - 12 SN - 2156-7085 DO - 10.1364/BOE.517480 UR - https://m2.mtmt.hu/api/publication/34856915 ID - 34856915 N1 - Funding Agency and Grant Number: Kulturalis Technologiai Miniszterium [TKP2021-NVA-19]; Orszagos Tudomanyos Kutatasi Alapprogramok [FK138894, K132782]; Magyar Tudomanyos Akademia; Nemzeti Kutatasi, Fejlesztesi es Innovacios Alap [UNKP-22-5]; Szegedi Tudomanyegyetem Funding text: Kulturalis Technologiai Miniszterium (TKP2021-NVA-19); Orszagos Tudomanyos Kutatasi Alapprogramok (FK138894, K132782); Magyar Tudomanyos Akademia (Janos Bolyai Research Scholarship); Nemzeti Kutatasi, Fejlesztesi es Innovacios Alap (UNKP-22-5); Szegedi Tudomanyegyetem (Open Access Fund). AB - 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%. LA - English DB - MTMT ER - TY - JOUR AU - Farkas, Dávid AU - Szikora, Szilárd AU - Jijumon, A S AU - Polgár, Tamás Ferenc AU - Patai, Roland AU - Szütsné Tóth, Mónika Ágnes AU - Bugyi, Beáta AU - Gajdos, Tamás AU - Bíró, Péter AU - Novák, Tibor AU - Erdélyi, Miklós AU - Mihály, József TI - Peripheral thickening of the sarcomeres and pointed end elongation of the thin filaments are both promoted by SALS and its formin interaction partners JF - PLOS GENETICS J2 - PLOS GENET VL - 20 PY - 2024 IS - 1 PG - 31 SN - 1553-7390 DO - 10.1371/journal.pgen.1011117 UR - https://m2.mtmt.hu/api/publication/34506131 ID - 34506131 AB - 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. LA - English DB - MTMT ER - TY - JOUR AU - Curd, Alistair AU - Cleasby, Alexa AU - Baird, Michelle AU - Peckham, Michelle TI - Modelling 3D supramolecular structure from sparse single-molecule localisation microscopy data JF - JOURNAL OF MICROSCOPY-OXFORD J2 - J MICROSC-OXFORD PY - 2023 PG - 6 SN - 0022-2720 DO - 10.1111/jmi.13236 UR - https://m2.mtmt.hu/api/publication/34374736 ID - 34374736 N1 - Faculty of Biological Sciences, Astbury Centre for Structural Molecular Biology, School of Molecular and Cellular Biology, University of Leeds, Leeds, United Kingdom Cell and Developmental Biology Centre, National Heart, Lung and Blood Institute, National Institutes of Health, Bethesda, MD, United States Export Date: 29 January 2024 CODEN: JMICA Correspondence Address: Curd, A.; Faculty of Biological Sciences, United Kingdom; email: a.curd@leeds.ac.uk AB - Single-molecule localisation microscopy (SMLM) has the potential to reveal the underlying organisation of specific molecules within supramolecular complexes and their conformations, which is not possible with conventional microscope resolution. However, the detection efficiency for fluorescent molecules in cells can be limited in SMLM, even to below 1% in thick and dense samples. Segmentation of individual complexes can also be challenging. To overcome these problems, we have developed a software package termed PERPL: Pattern Extraction from Relative Positions of Localisations. This software assesses the relative likelihoods of models for underlying patterns behind incomplete SMLM data, based on the relative positions of pairs of localisations. We review its principles and demonstrate its use on the 3D lattice of Z-disk proteins in mammalian cardiomyocytes. We find known and novel features at similar to 20 nm with localisations of less than 1% of the target proteins, using mEos fluorescent protein constructs. LA - English DB - MTMT ER - TY - JOUR AU - Sun, Yiqiang AU - Zhou, Shijie AU - Meng, Shangjiu AU - Wang, Miao AU - Mu, Hailong TI - Principal component analysis-artificial neural network-based model for predicting the static strength of seasonally frozen soils JF - SCIENTIFIC REPORTS J2 - SCI REP VL - 13 PY - 2023 IS - 1 PG - 12 SN - 2045-2322 DO - 10.1038/s41598-023-43462-7 UR - https://m2.mtmt.hu/api/publication/34374737 ID - 34374737 N1 - College of Civil Engineering and Architecture, Harbin University of Science and Technology, Harbin, 150080, China Key Laboratory of Earthquake Engineering and Engineering Vibration, Institute of Engineering Mechanics, China Earthquake Administration, Harbin, 150080, China School of Architecture and Civil Engineering, Heilongjiang University of Science and Technology, Harbin, 150022, China College of Architecture and Civil Engineering, Heilongjiang Province Hydraulic Research Institute, Harbin, 100050, China Export Date: 29 January 2024 Correspondence Address: Sun, Y.; College of Civil Engineering and Architecture, China; email: syq_iem@163.com AB - 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. LA - English DB - MTMT ER -