MFS-DBF: A trustworthy multichannel feature sieve and decision boundary formulation system for Obstructive Sleep Apnea detection.

The fine identification of sleep apnea events is instrumental in Obstructive Sleep Apnea (OSA) diagnosis. The development of sleep apnea event detection algorithms based on polysomnography is becoming a research hotspot in medical signal processing. In this paper, we propose an Inverse-Projection based Visualization System (IPVS) for sleep apnea event detection algorithms. The IPVS consists of a feature dimensionality reduction module and a feature reconstruction module. First, features of blood oxygen saturation and nasal airflow are extracted and used as input data for event analysis. Then, visual analysis is conducted on the feature distribution for apnea events. Next, dimensionality reduction and reconstruction methods are combined to achieve the dynamic visualization of sleep apnea event feature sets and the visual analysis of classifier decision boundaries. Moreover, the decision-making consistency is explored for various sleep apnea event detection classifiers, which provides researchers and users with an intuitive understanding of the detection algorithm. We applied the IPVS to an OSA detection algorithm with an accuracy of 84% and a diagnostic accuracy of 92% on a publicly available dataset. The experimental results show that the consistency between our visualization results and prior medical knowledge provides strong evidence for the practicality of the proposed system. For clinical practice, the IPVS can guide users to focus on samples with higher uncertainty presented by the OSA detection algorithm, reducing the workload and improving the efficiency of clinical diagnosis, which in turn increases the value of trust.

Online lithium-ion battery intelligent perception for thermal fault detection and localization.

-Equipping lithium-ion batteries with a reasonable thermal fault diagnosis can avoid thermal runaway and ensure the safe and reliable operation of the batteries. This research built a lithium-ion battery thermal fault diagnosis model that optimized the original mask region-based convolutional neural network based on the battery dataset in both parameters and structure. The model processes the thermal images of the battery surface, identifies problematic batteries, and locates the problematic regions. A backbone network is used to process the battery thermal images and extract feature information. Through the RPN network, the thermal feature is classified and regressed, and the Mask branch is used to ultimately determine the faulty battery's location. Additionally, we have optimized the original mask region-based convolutional neural network based on the battery dataset in both parameters and structure. The improved LBIP-V2 performs better than LBIP-V1 in most cases. We tested the performance of LBIP on the single-cell battery dataset, the 1P3S battery pack dataset, and the flattened 1P3S battery pack dataset. The results show that the recognition accuracy of LBIP exceeded 95 %. At the same time, we simulated the failure of the 1P3S battery pack within 0-15 min and tested the effectiveness of LBIP in real-time battery fault diagnosis. The results indicate that LBIP can effectively respond to online faults with a confidence level of over 98 %.

Data-model alliance network for the online multi-step thermal warning of energy storage system based on surface temperature diffusion.

As an important type of energy storage, battery energy storage systems have been widely used. However, there are frequent cases of battery explosion due to high temperature. To address this issue, researches have been carried out either in the model-driven or the data-driven aspects to predict the temperature of the battery. In this paper, a two-node electrothermal model and a multi-scale long short-term memory network are established formulating a data-model alliance network (DMAN) for surface temperature diffusion. An improved adaptive boosting algorithm is then employed to enhance the bridge of the two models. Integrating a data-model alliance module (DMAM) and multi-step-ahead thermal warning network (MATWN), this DMAN provides an advanced online multi-step-ahead thermal warning structure to achieve early warning of temperature crossing. Experimental results verify the progressiveness of the proposed technique.

Multi-step ahead thermal warning network for energy storage system based on the core temperature detection.

The energy storage system is an important part of the energy system. Lithium-ion batteries have been widely used in energy storage systems because of their high energy density and long life. However, the temperature is still the key factor hindering the further development of lithium-ion battery energy storage systems. Both low temperature and high temperature will reduce the life and safety of lithium-ion batteries. In actual operation, the core temperature and the surface temperature of the lithium-ion battery energy storage system may have a large temperature difference. However, only the surface temperature of the lithium-ion battery energy storage system can be easily measured. The estimation method of the core temperature, which can better reflect the operation condition of the lithium-ion battery energy storage system, has not been commercialized. To secure the thermal safety of the energy storage system, a multi-step ahead thermal warning network for the energy storage system based on the core temperature detection is developed in this paper. The thermal warning network utilizes the measurement difference and an integrated long and short-term memory network to process the input time series. This thermal early warning network takes the core temperature of the energy storage system as the judgment criterion of early warning and can provide a warning signal in multi-step in advance. This detection network can use real-time measurement to predict whether the core temperature of the lithium-ion battery energy storage system will reach a critical value in the following time window. And the output of the established warning network model directly determines whether or not an early emergency signal should be sent out. In the end, the accuracy and effectiveness of the model are verified by numerous testing.

Residual Learning Diagnosis Detection: An Advanced Residual Learning Diagnosis Detection System for COVID-19 in Industrial Internet of Things.

Due to the fast transmission speed and severe health damage, COVID-19 has attracted global attention. Early diagnosis and isolation are effective and imperative strategies for epidemic prevention and control. Most diagnostic methods for the COVID-19 is based on nucleic acid testing (NAT), which is expensive and time-consuming. To build an efficient and valid alternative of NAT, this article investigates the feasibility of employing computed tomography images of lungs as the diagnostic signals. Unlike normal lungs, parts of the lungs infected with the COVID-19 developed lesions, ground-glass opacity, and bronchiectasis became apparent. Through a public dataset, in this article, we propose an advanced residual learning diagnosis detection (RLDD) scheme for the COVID-19 technique, which is designed to distinguish positive COVID-19 cases from heterogeneous lung images. Besides the advantage of high diagnosis effectiveness, the designed residual-based COVID-19 detection network can efficiently extract the lung features through small COVID-19 samples, which removes the pretraining requirement on other medical datasets. In the test set, we achieve an accuracy of 91.33%, a precision of 91.30%, and a recall of 90%. For the batch of 150 samples, the assessment time is only 4.7 s. Therefore, RLDD can be integrated into the application programming interface and embedded into the medical instrument to improve the detection efficiency of COVID-19.

Neuropeptide S Attenuates the Alarm Pheromone-Evoked Defensive and Risk Assessment Behaviors Through Activation of Cognate Receptor-Expressing Neurons in the Posterior Medial Amygdala.

Neuropeptide S (NPS) acts by activating its cognate receptor (NPSR). High level expression of NPSR in the posterior medial amygdala suggests that NPS-NPSR system should be involved in regulation of social behaviors induced by social pheromones. The present study was undertaken to investigate the effects of central administration of NPS or with NPSR antagonist on the alarm pheromone (AP)-evoked defensive and risk assessment behaviors in mice. Furthermore, H129-H8, a novel high-brightness anterograde multiple trans-synaptic virus, c-Fos and NPSR immunostaining were employed to reveal the involved neurocircuits and targets of NPS action. The mice exposed to AP displayed an enhancement in defensive and risk assessment behaviors. NPS (0.1-1 nmol) intracerebroventricular (i.c.v.) injection significantly attenuated the AP-evoked defensive and risk assessment behaviors. NPSR antagonist [D-Val5]NPS at the dose of 40 nmol completely blocked the effect of 0.5 nmol of NPS which showed the best effective among dose range. The H129-H8-labeled neurons were observed in the bilateral posterodorsal medial amygdala (MePD) and posteroventral medial amygdala (MePV) 72 h after the virus injection into the unilateral olfactory bulb (OB), suggesting that the MePD and MePV receive olfactory information inputs from the OB. The percentage of H129-H8-labeled neurons that also express NPSR were 90.27 ± 3.56% and 91.67 ± 2.46% in the MePD and MePV, respectively. NPS (0.5 nmol, i.c.v.) remarkably increased the number of Fos immunoreactive (-ir) neurons in the MePD and MePV, and the majority of NPS-induced Fos-ir neurons also expressed NPSR. The behavior characteristic of NPS or with [D-Val5]NPS can be better replicated in MePD/MePV local injection within lower dose. The present findings demonstrated that NPS, via selective activation of the neurons bearing NPSR in the posterior medial amygdala, attenuates the AP-evoked defensive and risk assessment behaviors in mice.

Neuropeptide S Displays as a Key Neuromodulator in Olfactory Spatial Memory.

Neuropeptide S (NPS) is an endogenous peptide recently recognized to be presented in the brainstem and believed to play an important role in maintaining memory. The deletion of NPS or NPS receptor (NPSR) in mice shows a deficit in memory formation. Our recent studies have demonstrated that central administration of NPS facilitates olfactory function and ameliorates olfactory spatial memory impairment induced by muscarinic cholinergic receptor antagonist and N-methyl-D-aspartate receptor antagonist. However, it remains to be determined if endogenous NPS is an indispensable neuromodulator in the control of the olfactory spatial memory. In this study, we examined the effects of NPSR peptidergic antagonist [D-Val5]NPS (10 and 20 nmol, intracerebroventricular) and nonpeptidergic antagonist SHA 68 (10 and 50 mg/kg, intraperitoneal) on the olfactory spatial memory using computer-assisted 4-hole-board olfactory spatial memory test in mice. Furthermore, immunofluorescence was employed to identify the distributions of c-Fos and NPSR immunoreactive (-ir) neurons in olfactory system and hippocampal formation known to closely relate to the olfactory spatial memory. [D-Val5]NPS dosing at 20 nmol and SHA 68 dosing at 50 mg/kg significantly decreased the number of visits to the 2 odorants interchanged spatially, switched odorants, in recall trial, and simultaneously reduced the percentage of Fos-ir in NPSR-ir neurons, which were densely distributed in the anterior olfactory nucleus, piriform cortex, subiculum, presubiculum, and parasubiculum. These findings suggest that endogenous NPS is a key neuromodulator in olfactory spatial memory.

Neuropeptide S ameliorates olfactory spatial memory impairment induced by scopolamine and MK801 through activation of cognate receptor-expressing neurons in the subiculum complex.

Our previous studies have demonstrated that neuropeptide S (NPS), via selective activation of the neurons bearing NPS receptor (NPSR) in the olfactory cortex, facilitates olfactory function. High level expression of NPSR mRNA in the subiculum complex of hippocampal formation suggests that NPS-NPSR system might be involved in the regulation of olfactory spatial memory. The present study was undertaken to investigate effects of NPS on the scopolamine- or MK801-induced impairment of olfactory spatial memory using computer-assisted 4-hole-board spatial memory test, and by monitoring Fos expression in the subiculum complex in mice. In addition, dual-immunofluorescence microscopy was employed to identify NPS-induced Fos-immunereactive (-ir) neurons that also bear NPSR. Intracerebroventricular administration of NPS (0.5 nmol) significantly increased the number of visits to switched odorants in recall trial in mice suffering from odor-discriminating inability induced by scopolamine, a selective muscarinic cholinergic receptor antagonist, or MK801, a N-methyl-D-aspartate receptor antagonist, after training trials. The improvement of olfactory spatial memory by NPS was abolished by the NPSR antagonist [D-Val(5)]NPS (40 nmol). Ex vivo c-Fos and NPSR immunohistochemistry revealed that, as compared with vehicle-treated mice, NPS markedly enhanced Fos expression in the subiculum complex encompassing the subiculum (S), presubiculum (PrS) and parasubiculum (PaS). The percentages of Fos-ir neurons that also express NPSR were 91.3, 86.5 and 90.0 % in the S, PrS and PaS, respectively. The present findings demonstrate that NPS, via selective activation of the neurons bearing NPSR in the subiculum complex, ameliorates olfactory spatial memory impairment induced by scopolamine and MK801 in mice.

Neuropeptide S facilitates mice olfactory function through activation of cognate receptor-expressing neurons in the olfactory cortex.

Neuropeptide S (NPS) is a newly identified neuromodulator located in the brainstem and regulates various biological functions by selectively activating the NPS receptors (NPSR). High level expression of NPSR mRNA in the olfactory cortex suggests that NPS-NPSR system might be involved in the regulation of olfactory function. The present study was undertaken to investigate the effects of intracerebroventricular (i.c.v.) injection of NPS or co-injection of NPSR antagonist on the olfactory behaviors, food intake, and c-Fos expression in olfactory cortex in mice. In addition, dual-immunofluorescence was employed to identify NPS-induced Fos immunereactive (-ir) neurons that also bear NPSR. NPS (0.1-1 nmol) i.c.v. injection significantly reduced the latency to find the buried food, and increased olfactory differentiation of different odors and the total sniffing time spent in olfactory habituation/dishabituation tasks. NPS facilitated olfactory ability most at the dose of 0.5 nmol, which could be blocked by co-injection of 40 nmol NPSR antagonist [D-Val(5)]NPS. NPS administration dose-dependently inhibited food intake in fasted mice. Ex-vivo c-Fos and NPSR immunohistochemistry in the olfactory cortex revealed that, as compared with vehicle-treated mice, NPS markedly enhanced c-Fos expression in the anterior olfactory nucleus (AON), piriform cortex (Pir), ventral tenia tecta (VTT), the anterior cortical amygdaloid nucleus (ACo) and lateral entorhinal cortex (LEnt). The percentage of Fos-ir neurons that also express NPSR were 88.5% and 98.1% in the AON and Pir, respectively. The present findings demonstrated that NPS, via selective activation of the neurons bearing NPSR in the olfactory cortex, facilitates olfactory function in mice.