AI-aided holographic flow cytometry for label-free identification of ovarian cancer cells in the presence of unbalanced datasets

Liquid biopsy is a valuable emerging alternative to tissue biopsy with great potential in the noninvasive early diagnostics of cancer. Liquid biopsy based on single cell analysis can be a powerful approach to identify circulating tumor cells (CTCs) in the bloodstream and could provide new opportunities to be implemented in routine screening programs. Since CTCs are very rare, the accurate classification based on high-throughput and highly informative microscopy methods should minimize the false negative rates. Here, we show that holographic flow cytometry is a valuable instrument to obtain quantitative phase-contrast maps as input data for artificial intelligence (AI)-based classifiers. We tackle the problem of discriminating between A2780 ovarian cancer cells and THP1 monocyte cells based on the phase-contrast images obtained in flow cytometry mode. We compare conventional machine learning analysis and deep learning architectures in the non-ideal case of having a dataset with unbalanced populations for the AI training step. The results show the capacity of AI-aided holographic flow cytometry to discriminate between the two cell lines and highlight the important role played by the phase-contrast signature of the cells to guarantee accurate classification

The IFT-A complex regulates Shh signaling through cilia structure and membrane protein trafficking

Two intraflagellar transport (IFT) complexes, IFT-A and IFT-B, build and maintain primary cilia and are required for activity of the Sonic hedgehog (Shh) pathway. A weak allele of the IFT-A gene, Ift144, caused subtle defects in cilia structure and ectopic activation of the Shh pathway. In contrast, strong loss of IFT-A, caused by either absence of Ift144 or mutations in two IFT-A genes, blocked normal ciliogenesis and decreased Shh signaling. In strong IFT-A mutants, the Shh pathway proteins Gli2, Sufu, and Kif7 localized correctly to cilia tips, suggesting that these pathway components were trafficked by IFT-B. In contrast, the membrane proteins Arl13b, ACIII, and Smo failed to localize to primary cilia in the absence of IFT-A. We propose that the increased Shh activity seen in partial loss-of-function IFT-A mutants may be a result of decreased ciliary ACIII and that the loss of Shh activity in the absence of IFT-A is a result of severe disruptions of cilia structure and membrane protein trafficking

Modeling, Control, And Experimental Results For A Single Phase One Quadrant Unity Power Factor Rectifier

In this paper, we propose an improved average dynamic model for a single phase one quadrant unity power factor rectifier (UPFR). The new model is motivated by unsatisfactory experimental results using control strategies that were developed using a previously available model. Based on the new model, an adaptive control strategy is designed. Experimental results are obtained and compared with a digital redesign of the standard cascaded linear controller. Comparison shows a better steady state performance for the nonlinear controller while the linear strategy is seen to have a faster dynamic response. © 2006 IEEE

Setpoint Regulation Of Continuum Robots Using A Fixed Camera

In this paper, we investigate the problem of measuring the shape of a continuum robot manipulator using visual information from a fixed camera. Specifically, we capture the motion of a set of fictitious planes, each formed by four or more feature points, defined at various strategic locations along the body of the robot. Then, utilizing expressions for the robot forward kinematics as well as the decomposition of a homography relating a reference image of the robot to the actual robot image, we obtain the three-dimensional shape information continuously. We then use this information to demonstrate the development of a kinematic controller to regulate the manipulator end-effector to a constant desired position and orientation. © 2007 Cambridge University Press

Nanoscale Nonlinear Dynamic Characterization Of The Neuron-Electrode Junction

Extracellular recordings from neurons using microelectrode and field effect transistor arrays suffer from many problems including low signal to noise ratio, signal attenuation due to counter-ion diffusion from the bulk extracellular medium and a modification of the shape of the cell-generated potentials due to the presence of a highly dispersive dielectric medium in the cell-electrode cleft. Attempts to date to study the neuron-electrode interface have focused on point or area contact linear-equivalent-circuit models. We present here the results obtained from a \u27data-true\u27 nonlinear dynamic characterization of the neuron-electrode junction using Volterra-Wiener modeling. For the characterization, NG108-15 cells were cultured on microelectrode arrays and stimulated with broadband Gaussian white noise under voltage clamp mode. A Volterra-Wiener model was then estimated using the input signal and the extracellular signal recorded on the microelectrode. The existence of the second order Wiener kernel confirmed that the recorded extracellular signal had a nonlinear component. The verification of the estimated model was carried out by employing the intracellular action potential as an input to the Volterra-Wiener model and comparing the predicted extracellular response with the corresponding extracellular signal recorded on the microelectrode. We believe that a \u27data-true\u27 Volterra-Wiener model of the neuron-electrode junction shall not only facilitate a direct insight into the physicochemical processes taking place at the interface during signal transduction but will also allow one to evolve strategies for engineering the neuron-electrode interface using surface chemical modification of the microelectrodes. Copyright © 2008 American Scientific Publishers All rights reserved