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•A deformable RPP-based logical stochastic resonance (LSR) system is proposed.•In both noise-free and noisy condition, the parameter-induced LSR can be induced.•A wide potential well can improve the noise robustness of RPP-based LSR.•The noise margin of the RPP-based LSR is greater than traditional LSR.•The RPP-based LSR has a stronger robustness to noise pulses than the traditional LSR.

Detecting QRS waves with high sensitivity and precision in noisy electrocardiogram (ECG) recordings is crucial for cardiovascular disease monitoring. A main challenge is the in-band noise contamination that overlaps with the ECG signal spectrum, which is difficult to completely eliminate using traditional filtering methods. Inspired by solid-state physics, we propose a 1-D lattice potential (OLP)-based algorithm to enhance robustness against in-band noise. The algorithm first bandpass filtered the raw ECG recordings to mitigate out-of-band noise. Subsequently, the ECG signal undergoes nonlinear processing through the OLP with a variable damping coefficient. For the QRS complex, it experiences weak damping and is enhanced by bistable stochastic resonance (SR) effect, where noise energy transfers to the effective signal during transitions between two potential wells. In contrast, non-QRS segments are suppressed by applying strong damping. The enhanced ECG signal is then processed through a high-pass filter (HPF) and thresholding stage to locate QRS complexes. Testing on four different ECG databases shows that the ${F}1$ -score of our proposed OLP-based algorithm ranges from 98.87% to 99.99%, outperforming other state-of-the-art algorithms. Moreover, compared to traditional monostable SR-based algorithm, the OLP-based QRS detector induces a stronger SR effect, resulting in a greater signal-to-noise ratio (SNR) gain. Consequently, in the environment with large real-world artifact and noise level, the proposed OLP-based QRS detector can improve the noise margin for maintaining optimal performance by 133%–269% compared to traditional monostable SR-based QRS detector.

Epilepsy is a chronic disorder that leads to transient neurological dysfunction and is clinically diagnosed primarily by electroencephalography. Several intelligent systems have been proposed to automatically detect seizures, among which deep convolutional neural networks (CNNs) have shown better performance than traditional machine-learning algorithms. Owing to artifacts and noise, the raw electroencephalogram (EEG) must be preprocessed to improve the signal-to-noise ratio prior to being fed into the CNN classifier. However, because of the spectrum overlapping of uncontrollable noise with EEG, traditional filters cause information loss in EEG; thus, the potential of classifiers cannot be fully exploited. In this study, we propose a stochastic resonance-effect-based EEG preprocessing module composed of three asymmetrical overdamped bistable systems in parallel. By setting different asymmetries for the three parallel units, the inherent noise can be transferred to the different spectral components of the EEG through the asymmetric stochastic resonance effect. In this process, the proposed preprocessing module not only avoids the loss of information of EEG but also provides a CNN with high-quality EEG of diversified frequency information to enhance its performance. By combining the proposed preprocessing module with a residual neural network, we developed an intelligent diagnostic system for predicting seizure onset. The developed system achieved an average sensitivity of 98.96% on the CHB-MIT dataset and 95.45% on the Siena dataset, with a false prediction rate of 0.048/h and 0.033/h, respectively. In addition, a comparative analysis demonstrated the superiority of the developed diagnostic system with the proposed preprocessing module over other existing methods.

Colorectal cancer (CRC) is a prevalent malignancy, ranking third in incidence among all cancers. The inherent heterogeneity and the immunosuppressive tumor microenvironment (ITME) of CRC poses significant challenges in developing effective treatment strategies. In this study, we found that etoposide (ETO) could induce immunogenic cell death (ICD) in CRC cells, highlighting its potential to activate anti-tumor immunity. To overcome the limitations of ETO's poor solubility and low bioavailability, we developed ETO-loaded poly (lactic-co-glycolic acid) (PLGA) nanoparticles. Additionally, we employed biomimetic modification using CT26 cell-derived membranes to enhance tumor accumulation through homologous targeting and to evade macrophage phagocytosis. In vitro experiments confirmed the enhanced cytotoxicity and ICD characteristics in CT26 cells. In vivo studies further validated the improved tumor targeting, resulting in significant tumor inhibition. Notably, there was a marked increase in the proportion of CD8+T cells in tumors and lymph nodes (LNs), indicating an improved immunosuppressive microenvironment. The construction of ETO-PLGA@CM presents a promising approach for CRC treatment by synergistically activating specific anti-tumor immune responses.

Adipose-derived stem cells (ADSCs) are employed as a promising alternative in treating cartilage injury. Regulating the inflammatory “fingerprint” of ADSCs to improve their anti-inflammatory properties could enhance therapy efficiency. Herein, a novel injectable decorin/gellan gum hydrogel combined with ADSCs encapsulation for arthritis cartilage treatment is proposed. Decorin/gellan gum hydrogel was prepared according to the previous manufacturing protocol. The liquid–solid form transition of gellan gum hydrogel is perfectly suitable for intra-articular injection. Decorin-enriched matrix showing an immunomodulatory ability to enhance ADSCs anti-inflammatory phenotype under inflammation microenvironment by regulating autophagy signaling. This decorin/gellan gum/ADSCs hydrogel efficiently reverses interleukin-1β-induced cellular injury in chondrocytes. Through a mono-iodoacetate-induced arthritis mice model, the synergistic therapeutic effect of this ADSCs-loaded hydrogel, including inflammation attenuation and cartilage protection, is demonstrated. These results make the decorin/gellan gum hydrogel laden with ADSCs an ideal candidate for treating inflammatory joint disorders.

Physical reservoir computing (RC), which can be implemented by various physical systems, is a low-cost neuromorphic framework with a fast learning capability. In the previous studies, an overdamped bistable system-based RC (OBRC) inspired by the FitzHugh-Nagumo neuron model has been proposed to construct an outstanding physical RC system. Benefitting from the stochastic resonance effect, the OBRC requires less power and has stronger noise robustness than many conventional physical RC systems. However, compared with conventional physical RC systems, its learning ability is not superior. To boost the performance of the OBRC, we propose an OBRC with time-delayed feedback (TOBRC). In this work, the TOBRC is implemented in a physical setting with time-multiplexing nodes design and simulated on a conventional computer. Moreover, we adopt a powerful optimization algorithm to automatically determine the optimal hyperparameters for both the OBRC and TOBRC; thus, a more precise quantitative discussion on the upper limit of the system can be made. To compare the TOBRC and OBRC, we conducted short-term memory and parity check tasks to assess the short-term memory ability and nonlinearity, which are the two core abilities of physical RC for learning. The results prove that the short-term memory ability and nonlinearity of the proposed TOBRC are 6.46 and 2.15 times higher than those of the OBRC, respectively. Moreover, the TOBRC outperforms the OBRC under different noise conditions. On the MNIST handwritten digit recognition benchmark, the TOBRC exhibited a lower error rate than the OBRC; it was comparable with that of advanced physical RC systems. Our study confirms that the TOBRC can exhibit excellent learning ability in practical problems. • Traditional overdamped bistable physical reservoir computing model (OBRC) is improved by adding time-delayed feedback. • Hyperparameters are optimized for discussing the upper limit of system performance more accurately. • The short-term memory ability and nonlinearity of the proposed model are 6.46 and 2.15 times higher than those of the OBRC, respectively. • In MNIST handwritten digit recognition task, the error rate of the proposed model is only 59 % of that of the OBRC. [ABSTRACT FROM AUTHOR]

Alteration of the hydrogen-bond (H-bond) network by trehalose is acknowledged as a bioprotective agent. However, most studies exploring the hydration superiority of the trehalose structure are limited structure are limited by the computational cost or a narrow-range spectrum. In the present study, the structural and dynamical behaviors of the H-bond network of trehalose and maltose solutions were observed and compared with a broadband dielectric spectrum (100 MHz–18 THz) to investigate the influence of the trehalose structure on the bioprotective function. From the relaxation time, the reorientation cooperativity, resonant frequency, and damping constant of water–water vibration, the symmetric structure of trehalose allowed a more significant H-bond strengthening effect and homogeneous aqueous environment. In contrast, the difference in the hydration number between trehalose and maltose was negligible. Thus, the enhanced H-bond strengthening effect and homogeneous aqueous environment owing to the symmetric structure are the essential factors that contribute to the remarkable bioprotective effect of trehalose.

Stochastic resonance (SR) is a phenomenon wherein an information-carrying signal is enhanced via noise in a nonlinear system. This phenomenon enables living beings to adapt to noisy environments and use environmental noise to obtain useful information. A novel activation function of the echo state network (ESN) based on bistable SR is proposed in this study. Instead of using the tanh activation function—which is representative of the traditional threshold activation function—the bistable SR activation function is used to improve the noise adaptability of the ESN. Further, the proposed activation function provides a short-term memory (STM) ability that is not provided by the widely used threshold activation function, and thus, a physical reservoir can be designed using the proposed function. An STM task and a parity check task are used to verify the short-term memory and nonlinear ability of the bistable SR activation function. Further, two different prediction benchmarks prove that the proposed activation function can improve the noise adaptability of ESN. Finally, a visual recognition task is performed to demonstrate the potential of the SR activation function for physical reservoir computing. [ABSTRACT FROM AUTHOR]

• The phase locking behavior of superparamagnetic tunnel junctions (STJs) as stochastic magnetic bits induced by colored noise is studied. • White, blue and violet noise can promote the subthreshold STJ synchronization. • The phase locking of STJs is suppressed by pink noise and red noise. • Blue noise can make STJs get the lowest phase locking power consumption with order of 10 − 13 J. Superparamagnetic tunnel junctions (STJs) are nanostructures with very low turnover barriers. The barrier height of an STJ is generally equal to the heat energy at room temperature; thus, it can oscillate automatically without external driving. Previous studies have shown that the randomness of an STJ can be driven by a subthreshold voltage. This synchronization can be adjusted using electrical noise, which is often considered as zero-field Gaussian white noise. However, the actual circuit and environment are inevitably associated with colored noise, which has not been considered previously. In this work, numerical simulations were performed to study the phase-locking characteristics of a single STJ with the aid of several typical types of colored noise. The results show that the phase-locked behavior of an STJ can be effectively enhanced by colored noise whose power spectral density per unit of bandwidth is proportional to its frequency. Meanwhile, colored noise whose power spectral density per unit of bandwidth and frequency are inversely proportional can suppress the synchronization of STJs by suppressing the increase in junction frequency. [ABSTRACT FROM AUTHOR]

A method of magnetocardiography (MCG) measurement using an overdamped bistable model based stochastic resonance (SR) technique for advancing biomagnetic sensors is proposed. To determine the system parameters for SR, an evolutionary algorithm combined with the coherent detection (CD) method is applied. We acquire and process an animal MCG at room temperature, detected by a commercially available optically pumped magnetometer. A comparison with a lowpass filter and the traditional CD method verified the ability of the proposed method to obtain a higher signal-to-noise ratio and peak value of the MCG, whilst retaining sufficient dynamic information.