Recent Approaches to Design and Analysis of Electrical Impedance Systems for Single Cells Using Machine Learning.

Individual cells have many unique properties that can be quantified to develop a holistic understanding of a population. This can include understanding population characteristics, identifying subpopulations, or elucidating outlier characteristics that may be indicators of disease. Electrical impedance measurements are rapid and label-free for the monitoring of single cells and generate large datasets of many cells at single or multiple frequencies. To increase the accuracy and sensitivity of measurements and define the relationships between impedance and biological features, many electrical measurement systems have incorporated machine learning (ML) paradigms for control and analysis. Considering the difficulty capturing complex relationships using traditional modelling and statistical methods due to population heterogeneity, ML offers an exciting approach to the systemic collection and analysis of electrical properties in a data-driven way. In this work, we discuss incorporation of ML to improve the field of electrical single cell analysis by addressing the design challenges to manipulate single cells and sophisticated analysis of electrical properties that distinguish cellular changes. Looking forward, we emphasize the opportunity to build on integrated systems to address common challenges in data quality and generalizability to save time and resources at every step in electrical measurement of single cells.

Challenges in Developing a Real-Time Bee-Counting Radar.

Detailed within is an attempt to implement a real-time radar signal classification system to monitor and count bee activity at the hive entry. There is interest in keeping records of the productivity of honeybees. Activity at the entrance can be a good measure of overall health and capacity, and a radar-based approach could be cheap, low power, and versatile, beyond other techniques. Fully automated systems would enable simultaneous, large-scale capturing of bee activity patterns from multiple hives, providing vital data for ecological research and business practice improvement. Data from a Doppler radar were gathered from managed beehives on a farm. Recordings were split into 0.4 s windows, and Log Area Ratios (LARs) were computed from the data. Support vector machine models were trained to recognize flight behavior from the LARs, using visual confirmation recorded by a camera. Spectrogram deep learning was also investigated using the same data. Once complete, this process would allow for removing the camera and accurately counting the events by radar-based machine learning alone. Challenging signals from more complex bee flights hindered progress. System accuracy of 70% was achieved, but clutter impacted the overall results requiring intelligent filtering to remove environmental effects from the data.

High-voltage 10 ns delayed paired or bipolar pulses for in vitro bioelectric experiments.

Recent studies proved that classical bio-effects induced by nanosecond pulsed electric field (nsPEF) can be reduced by the delivery of a negative polarity pulse generated immediately after a positive polarity pulse. This phenomenon is known as "bipolar cancellation" and it was reported for a wide range of bipolar pulses with pulse duration from 2 ns to 900 ns. On the contrary, paired pulses, i.e., two identical pulses with the same polarity, increased traditional nsPEF outcomes. Herein, we propose a novel robust and flexible generator, based on the frozen-wave concept, able to produce a broad range of pulses with the duration of 10 ns and delay between 17 and 360 ns. Numerical simulations and experimental measurements were performed to fully characterize the proposed generator. YO-PROTM-1 uptake was investigated in the U87-MG human glioblastoma cell line as a marker of membrane permeabilization in response to 10 ns, 11.5MV/m nsPEF. Our results showed that bipolar cancellation occurred for delays of 0-30 ns and decreased as a function of the interphase interval. In addition, we observed that cellular response following the application of paired nsPEF was more than two-fold compared to the unipolar pulse response and was independent from the interphase interval.

Assessment of cytoplasm conductivity by nanosecond pulsed electric fields.

The aim of this paper is to propose a new method for the better assessment of cytoplasm conductivity, which is critical to the development of electroporation protocols as well as insight into fundamental mechanisms underlying electroporation. For this goal, we propose to use nanosecond electrical pulses to bypass the complication of membrane polarization and a single cell to avoid the complication of the application of the "mixing formulas." Further, by suspending the cell in a low-conductivity medium, it is possible to force most of the sensing current through the cytoplasm for a more direct assessment of its conductivity. For proof of principle, the proposed technique was successfully demonstrated on a Jurkat cell by comparing the measured and modeled currents. The cytoplasm conductivity was best assessed at 0.32 S/m and it is in line with the literature. The cytoplasm conductivity plays a key role in the understanding of the basis mechanism of the electroporation phenomenon, and in particular, a large error in the cytoplasm conductivity determination could result in a correspondingly large error in predicting electroporation. Methods for a good estimation of such parameter become fundamental.