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Spatio-temporal feature learning with reservoir computing for T-cell segmentation in live-cell [Formula: see text] fluorescence microscopy.

ABSTRACT: Advances in high-resolution live-cell [Formula: see text] imaging enabled subcellular localization of early [Formula: see text] signaling events in T-cells and paved the way to investigate the interplay between receptors and potential target channels in [Formula: see text] release events. The huge amount of acquired data requires efficient, ideally automated image processing pipelines, with cell localization/segmentation as central tasks. Automated segmentation in live-cell cytosolic [Formula: see text] imaging data is, however, challenging due to temporal image intensity fluctuations, low signal-to-noise ratio, and photo-bleaching. Here, we propose a reservoir computing (RC) framework for efficient and temporally consistent segmentation. Experiments were conducted with Jurkat T-cells and anti-CD3 coated beads used for T-cell activation. We compared the RC performance with a standard U-Net and a convolutional long short-term memory (LSTM) model. The RC-based models (1) perform on par in terms of segmentation accuracy with the deep learning models for cell-only segmentation, but show improved temporal segmentation consistency compared to the U-Net; (2) outperform the U-Net for two-emission wavelengths image segmentation and differentiation of T-cells and beads; and (3) perform on par with the convolutional LSTM for single-emission wavelength T-cell/bead segmentation and differentiation. In turn, RC models contain only a fraction of the parameters of the baseline models and reduce the training time considerably.

SUBMITTER: Hadaeghi F

PROVIDER: S-EPMC8050068 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:The Josephson effect in point contacts between an "ordinary" superconductor [Formula: see text]In[Formula: see text] ([Formula: see text]) and single crystals of the Fe-based superconductor Ba[Formula: see text]K[Formula: see text](FeAs)[Formula: see text] ([Formula: see text]), was investigated. In order to shed light on the order parameter symmetry of Ba[Formula: see text]K[Formula: see text](FeAs)[Formula: see text], the dependence of the Josephson supercurrent [Formula: see text] on the temperature and on [Formula: see text] with [Formula: see text] was studied. The dependencies of the critical current on temperature [Formula: see text] and of the amplitudes of the first current steps of the current-voltage characteristic [Formula: see text] [Formula: see text] on the power of microwave radiation with frequency [Formula: see text] were measured. It is shown that the dependencies [Formula: see text] are close to the well-known Ambegaokar-Baratoff (AB) dependence for tunnel contacts between "ordinary" superconductors and to the dependence calculated by Burmistrova et al. (Phys Rev B 91, 214501 (2015)) for microshorts between an "ordinary" superconductor and a two-band superconductor with [Formula: see text] order parameter symmetry at certain values of the transparency of boundaries and thickness of the transition layer. It is found that the dependencies [Formula: see text] cannot be approximated within the resistively shunted model using the normalized microwave frequencies [Formula: see text] with characteristic voltages [Formula: see text], [Formula: see text]-normal resistance of the contact) found from the low-voltage parts of the current-voltage characteristics. The reasons for this failure are discussed and a method is proposed for accurately determining the value of [Formula: see text], which takes into account all the features of the point contact affecting the period of the dependence [Formula: see text]. An analysis of the [Formula: see text] and [Formula: see text] dependencies shows that the superconducting current of the Josephson contacts under investigation is proportional to the [Formula: see text] of the phase difference [Formula: see text], [Formula: see text]. The implications of these results on the symmetry of the order parameter are also discussed.

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Spatio-temporal feature learning with reservoir computing for T-cell segmentation in live-cell [Formula: see text] fluorescence microscopy.

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