Machine learning-based evaluation of spontaneous pain and analgesics from cellular calcium signals in the mouse primary somatosensory cortex using explainable features

IntroductionPain that arises spontaneously is considered more clinically relevant than pain evoked by external stimuli. However, measuring spontaneous pain in animal models in preclinical studies is challenging due to methodological limitations. To address this issue, recently we developed a deep learning (DL) model to assess spontaneous pain using cellular calcium signals of the primary somatosensory cortex (S1) in awake head-fixed mice. However, DL operate like a “black box”, where their decision-making process is not transparent and is difficult to understand, which is especially evident when our DL model classifies different states of pain based on cellular calcium signals. In this study, we introduce a novel machine learning (ML) model that utilizes features that were manually extracted from S1 calcium signals, including the dynamic changes in calcium levels and the cell-to-cell activity correlations.MethodWe focused on observing neural activity patterns in the primary somatosensory cortex (S1) of mice using two-photon calcium imaging after injecting a calcium indicator (GCaMP6s) into the S1 cortex neurons. We extracted features related to the ratio of up and down-regulated cells in calcium activity and the correlation level of activity between cells as input data for the ML model. The ML model was validated using a Leave-One-Subject-Out Cross-Validation approach to distinguish between non-pain, pain, and drug-induced analgesic states.Results and discussionThe ML model was designed to classify data into three distinct categories: non-pain, pain, and drug-induced analgesic states. Its versatility was demonstrated by successfully classifying different states across various pain models, including inflammatory and neuropathic pain, as well as confirming its utility in identifying the analgesic effects of drugs like ketoprofen, morphine, and the efficacy of magnolin, a candidate analgesic compound. In conclusion, our ML model surpasses the limitations of previous DL approaches by leveraging manually extracted features. This not only clarifies the decision-making process of the ML model but also yields insights into neuronal activity patterns associated with pain, facilitating preclinical studies of analgesics with higher potential for clinical translation

Wideband signal analysis and synthesis applied to electromagnetic transient waveforms

This thesis presents the bandpass inverse fast Fourier transform (IFFT) filter bank and the multirate digital filter bank techniques to synthesize test point waveforms from constituent waveforms recorded by two instruments as part of an aircraft electromagnetic hardness evaluation test. The component waveforms are recorded by two separate measurement systems (High Powered Pulse Waveform (HPW) in the time domain and Continuous Sweep Waveform (CSW) in the frequency domain) under two different aircraft orientations (parallel and perpendicular). Data from two orientations are combined using the sinusoidal modeling algorithm (SMA). The tree structured filter bank with power symmetric overlap method and the bandpass IFFT with spectral concatenation method are developed to further combine these waveforms with an overlapping frequency spectrum to produce the corresponding synthesized test point waveformhttp://archive.org/details/widebandsignalna1094532168NARepublic of Korea Army authorApproved for public release; distribution is unlimited

Effect of conducting additives on the properties of composite cathodes for lithium-ion batteries

In an attempt to achieve lithium-ion batteries with high rate capability, the effect of conducting additives with various shapes and contents on the physical and electrochemical performances was evaluated. Although the density of the cathode decreased upon the addition of the additives, the electrical conductivity and electrochemical performance were greatly improved. The composite cathodes with well-dispersed multi-walled carbon nanotubes (MWCNTs) exhibited excellent high rate capabilities and cyclabilities. In the case of cathode with 8 wt.% of MWCNTs (low density-LD), the highest discharge capacity of 136 mAh/g was obtained at 5 C-rate and capacity retention of 97% for 50 cycles was observed at 1 C-rate of discharge. The cathode with a mixture of 2 wt.% of Super P and 4 wt.% of MWCNTs (LD) also exhibits improved cycle performances. The volume changes in the charge and discharge processes were successfully controlled by the bundles distributed between the host particles.close8