Corrigendum to "Utilization of convolutional neural networks to analyze microscopic images for high-throughput screening of mesenchymal stem cells".

[This corrects the article DOI: 10.1515/biol-2022-0859.].

Utilization of convolutional neural networks to analyze microscopic images for high-throughput screening of mesenchymal stem cells.

This work investigated the high-throughput classification performance of microscopic images of mesenchymal stem cells (MSCs) using a hyperspectral imaging-based separable convolutional neural network (CNN) (H-SCNN) model. Human bone marrow mesenchymal stem cells (hBMSCs) were cultured, and microscopic images were acquired using a fully automated microscope. Flow cytometry (FCT) was employed for functional classification.

Downregulation of Sonic hedgehog signaling induces G2-arrest in genital warts.

Background: Human papillomavirus (HPV) infected keratinocyte dysfunction results in the formation of genital warts, and the specific role of Sonic hedgehog (SHh) signaling in genital warts remains elusive. Thus, this study aimed to identify the correlation between wart formation and SHh signaling.

Materials And Methods: In this study, nine male patients with genital warts were recruited, and the expression of SHh and its downstream signal molecules Patched-1 and GLI family zinc finger 1 (Ptch1 and Gli1) was detected.

Cell Recognition Using BP Neural Network Edge Computing.

This exploration is to solve the efficiency and accuracy of cell recognition in biological experiments. Neural network technology is applied to the research of cell image recognition. The cell image recognition problem is solved by constructing an image recognition algorithm.

Segmentation, Detection, and Tracking of Stem Cell Image by Digital Twins and Lightweight Deep Learning.

The current work aims to strengthen the research of segmentation, detection, and tracking methods of stem cell image in the fields of regenerative medicine and tissue damage restoration. Firstly, based on the relevant theories of stem cell image segmentation, digital twins (DTs), and lightweight deep learning, a new phase contrast microscope is introduced through the research of optical microscope. Secondly, the results of DTs method and phase contrast imaging principle are compared in stem cell image segmentation and detection.

Performance for rotor system of hybrid electromagnetic bearing and elastic foil gas bearing with dynamic characteristics analysis under deep learning.

The bearing-rotor system is prone to faults during operation, so it is necessary to analyze the dynamic characteristics of the bearing-rotor system to discuss the optimal structure of the convolutional neural network (CNN) in system fault detection and classification. The turbo expander is undertaken as the research object. Firstly, the hybrid magnetic bearing-rotor system is modeled into the form of four stiffness coefficients and four damping coefficients, so as to analyze and explain the dynamic characteristics of the system.

Control of hybrid electromagnetic bearing and elastic foil gas bearing under deep learning.

The hybrid electromagnetic and elastic foil gas bearing is explored based on the radial basis function (RBF) neural network in this study so as to improve its stabilization in work. The related principles and structure of hybrid electromagnetic and elastic foil gas bearings is introduced firstly. Then, the proportional, integral, and derivative (PID) bearing controller is introduced and improved into two controllers: IPD and CPID.

Brain-derived neurotrophic factor-TrkB signaling in the medial prefrontal cortex plays a role in the anhedonia-like phenotype after spared nerve injury.

Although depressive symptoms including anhedonia (i.e., loss of pleasure) frequently accompany pain, little is known about the risk factors contributing to individual differences in pain-induced anhedonia.