A Missing Key to Understand the Electrical Resonance and the Mechanical Property of Neurons: a Channel-Membrane Interaction Mechanism

The recent study of the interaction between the fatty acyl tails of lipids and the K+ channel establishes the connection between flexoelectricity and the ion channel's dynamics, named Channel-Membrane Interaction (CMI), that may solve the electrical resonance in neurons

Predicting Continuous Locomotion Modes via Multidimensional Feature Learning from sEMG

Walking-assistive devices require adaptive control methods to ensure smooth transitions between various modes of locomotion. For this purpose, detecting human locomotion modes (e.g., level walking or stair ascent) in advance is crucial for improving the intelligence and transparency of such robotic systems. This study proposes Deep-STF, a unified end-to-end deep learning model designed for integrated feature extraction in spatial, temporal, and frequency dimensions from surface electromyography (sEMG) signals. Our model enables accurate and robust continuous prediction of nine locomotion modes and 15 transitions at varying prediction time intervals, ranging from 100 to 500 ms. In addition, we introduced the concept of 'stable prediction time' as a distinct metric to quantify prediction efficiency. This term refers to the duration during which consistent and accurate predictions of mode transitions are made, measured from the time of the fifth correct prediction to the occurrence of the critical event leading to the task transition. This distinction between stable prediction time and prediction time is vital as it underscores our focus on the precision and reliability of mode transition predictions. Experimental results showcased Deep-STP's cutting-edge prediction performance across diverse locomotion modes and transitions, relying solely on sEMG data. When forecasting 100 ms ahead, Deep-STF surpassed CNN and other machine learning techniques, achieving an outstanding average prediction accuracy of 96.48%. Even with an extended 500 ms prediction horizon, accuracy only marginally decreased to 93.00%. The averaged stable prediction times for detecting next upcoming transitions spanned from 28.15 to 372.21 ms across the 100-500 ms time advances.Comment: 10 pages,7 figure

A Sandwiched/Cracked Flexible Film for Multithermal Monitoring and Switching Devices

Polydirnethylsiloxane (PDMS)-based flexible films have substantiated advantages in various sensing applications. Here, we demonstrate the highly sensitive and programmable thermal-sensing capability (thermal index, B, up to 126 x 10(3) K) of flexible films with tunable sandwiched microstructures (PDMS/cracked single-walled carbon nanotube (SWCNT) film/PDMS) when a thermal stimulus is applied. We found that this excellent performance results from the following features of the film's structural and material design: (1) the sandwiched structure allows the film to switch from a three-dimensional to a two-dimensional in-plane deformation and (2) the stiffness of the SWCNT film is decreased by introducing microcracks that make deformation easy and that promote the macroscopic piezoresistive behavior of SWCNT crack islands and the microscopic piezoresistive behavior of SWCNT bundles. The PDMS layer is characterized by a high coefficient of thermal expansion (alpha = 310 x 10(-6) K-1) and low stiffness (similar to 2 MPa) that allow for greater flexibility and higher temperature sensitivity. We determined the efficacy of our sandwiched, cracked, flexible films in monitoring and switching flexible devices when subjected to various stimuli, including thermal conduction, thermal radiation, and light radiation

Less Is More: Volatility Forecasting with Contrastive Representation Learning (Student Abstract)

Earnings conference calls are indicative information events for volatility forecasting, which is essential for financial risk management and asset pricing. Although recent volatility forecasting models have explored the textual content of conference calls for prediction, they suffer from modeling the long-text and representing the risk-relevant information. This work proposes to identify key sentences for robust and interpretable transcript representation learning based on the cognitive theory. Specifically, we introduce TextRank to find key sentences and leverage attention mechanism to screen out the candidates by modeling the semantic correlations. Upon on the structural information of earning conference calls, we propose a structure-based contrastive learning method to facilitate the effective transcript representation. Empirical results on the benchmark dataset demonstrate the superiority of our model over competitive baselines in volatility forecasting

Study on the sensitivities and damage mechanisms of ultra-low permeability sandstone reservoirs: taking Chang 6 reservoir in Jingbian oilfield as an example

This paper takes the ultra-low permeability sandstone reservoir of Jingbian oilfield in Ordos Basin as the research object, analyzes the petrological characteristics, diagenesis, physical characteristics and pore structure characteristics of the reservoir, and carries out reservoir sensitivity evaluation by using rock casting thin sections, X-ray diffraction, and sensitive flow experiments. The research results show that the ultra-low permeability Chang 6 sandstone reservoir has weak velocity sensitivity, medium-weak water sensitivity, weak salt sensitivity, weak alkali sensitivity and strong acid sensitivity; the damage mechanism of reservoir sensitivity mainly depends on the composition of clay minerals and pore structure after diagenesis. The clay mineral content from high to low is chlorite, illite, a small amount of illite / smectite layer, and kaolinite, of which the chlorite content is as high as 75 %; the reservoir has poor physical properties, the types of small hole-thin throat and small hole-fine throat. The reservoir is prone to blockage such as bridge plugging. Therefore, ultra-low permeability sandstone reservoirs are prone to different degrees of sensitivity. The reservoir characteristics are consistent with the reservoir sensitivity evaluation results

Core/Shell Microstructure Induced Synergistic Effect for Efficient Water-Droplet Formation and Cloud-Seeding Application

Cloud-seeding materials as a promising water-augmentation technology have drawn more attention recently. We designed and synthesized a type of core/shell NaCl/TiO₂ (CSNT) particle with controlled particle size, which successfully adsorbed more water vapor (∼295 times at low relative humidity, 20% RH) than that of pure NaCl, deliquesced at a lower environmental RH of 62–66% than the hygroscopic point (<i>h</i>_g.p., 75% RH) of NaCl, and formed larger water droplets ∼6–10 times its original measured size area, whereas the pure NaCl still remained as a crystal at the same conditions. The enhanced performance was attributed to the synergistic effect of the hydrophilic TiO₂ shell and hygroscopic NaCl core microstructure, which attracted a large amount of water vapor and turned it into a liquid faster. Moreover, the critical particle size of the CSNT particles (0.4–10 μm) as cloud-seeding materials was predicted <i>via</i> the classical Kelvin equation based on their surface hydrophilicity. Finally, the benefits of CSNT particles for cloud-seeding applications were determined visually through <i>in situ</i> observation under an environmental scanning electron microscope on the microscale and cloud chamber experiments on the macroscale, respectively. These excellent and consistent performances positively confirmed that CSNT particles could be promising cloud-seeding materials

The effect of the subthreshold oscillation induced by the neurons' resonance upon the electrical stimulation-dependent instability

Repetitive electrical nerve stimulation can induce a long-lasting perturbation of the axon's membrane potential, resulting in unstable stimulus-response relationships. Despite being observed in electrophysiology, the precise mechanism underlying electrical stimulation-dependent (ES-dependent) instability is still an open question. This study proposes a model to reveal a facet of this problem: how threshold fluctuation affects electrical nerve stimulations. This study proposes a new method based on a Circuit-Probability theory (C-P theory) to reveal the interlinkages between the subthreshold oscillation induced by neurons' resonance and ES-dependent instability of neural response. Supported by in-vivo studies, this new model predicts several key characteristics of ES-dependent instability and proposes a stimulation method to minimize the instability. This model provides a powerful tool to improve our understanding of the interaction between the external electric field and the complexity of the biophysical characteristics of axons

Systems-level analyses of protein-protein interaction network dysfunctions via epichaperomics identify cancer-specific mechanisms of stress adaptation

Abstract Systems-level assessments of protein-protein interaction (PPI) network dysfunctions are currently out-of-reach because approaches enabling proteome-wide identification, analysis, and modulation of context-specific PPI changes in native (unengineered) cells and tissues are lacking. Herein, we take advantage of chemical binders of maladaptive scaffolding structures termed epichaperomes and develop an epichaperome-based ‘omics platform, epichaperomics, to identify PPI alterations in disease. We provide multiple lines of evidence, at both biochemical and functional levels, demonstrating the importance of these probes to identify and study PPI network dysfunctions and provide mechanistically and therapeutically relevant proteome-wide insights. As proof-of-principle, we derive systems-level insight into PPI dysfunctions of cancer cells which enabled the discovery of a context-dependent mechanism by which cancer cells enhance the fitness of mitotic protein networks. Importantly, our systems levels analyses support the use of epichaperome chemical binders as therapeutic strategies aimed at normalizing PPI networks