This reviewpresents a comprehensive technical analysis of deep learning(DL)methodologies in biomedical signal processing,focusing on architectural innovations,experimental validation,and evaluation frameworks.We syste...This reviewpresents a comprehensive technical analysis of deep learning(DL)methodologies in biomedical signal processing,focusing on architectural innovations,experimental validation,and evaluation frameworks.We systematically evaluate key deep learning architectures including convolutional neural networks(CNNs),recurrent neural networks(RNNs),transformer-based models,and hybrid systems across critical tasks such as arrhythmia classification,seizure detection,and anomaly segmentation.The study dissects preprocessing techniques(e.g.,wavelet denoising,spectral normalization)and feature extraction strategies(time-frequency analysis,attention mechanisms),demonstrating their impact on model accuracy,noise robustness,and computational efficiency.Experimental results underscore the superiority of deep learning over traditional methods,particularly in automated feature extraction,real-time processing,cross-modal generalization,and achieving up to a 15%increase in classification accuracy and enhanced noise resilience across electrocardiogram(ECG),electroencephalogram(EEG),and electromyogram(EMG)signals.Performance is rigorously benchmarked using precision,recall,F1-scores,area under the receiver operating characteristic curve(AUC-ROC),and computational complexitymetrics,providing a unified framework for comparing model efficacy.Thesurvey addresses persistent challenges:synthetic data generationmitigates limited training samples,interpretability tools(e.g.,Gradient-weighted Class Activation Mapping(Grad-CAM),Shapley values)resolve model opacity,and federated learning ensures privacy-compliant deployments.Distinguished from prior reviews,this work offers a structured taxonomy of deep learning architectures,integrates emerging paradigms like transformers and domain-specific attention mechanisms,and evaluates preprocessing pipelines for spectral-temporal trade-offs.It advances the field by bridging technical advancements with clinical needs,such as scalability in real-world settings(e.g.,wearable devices)and regulatory alignment with theHealth Insurance Portability and Accountability Act(HIPAA)and General Data Protection Regulation(GDPR).By synthesizing technical rigor,ethical considerations,and actionable guidelines for model selection,this survey establishes a holistic reference for developing robust,interpretable biomedical artificial intelligence(AI)systems,accelerating their translation into personalized and equitable healthcare solutions.展开更多
Tumor stroma,or tumor microenvironment(TME),has been in the spotlight during recent years for its role in tumor development,growth,and metastasis.It consists of a myriad of elements,including tumor-associated macropha...Tumor stroma,or tumor microenvironment(TME),has been in the spotlight during recent years for its role in tumor development,growth,and metastasis.It consists of a myriad of elements,including tumor-associated macrophages,cancer-associated fibroblasts,a deregulated extracellular matrix,endothelial cells,and vascular vessels.The release of proinflammatory molecules,due to the inflamed microenvironment,such as cytokines and chemokines is found to play a pivotal role in progression of cancer and response to therapy.This review discusses the major key players and important chemical inflammatory signals released in the TME.Furthermore,the latest breakthroughs in cytokine-mediated crosstalk between immune cells and cancer cells have been highlighted.In addition,recent updates on alterations in cytokine signaling between chronic inflammation and malignant TME have also been reviewed.展开更多
Water hammer diagnostics is an important fracturing diagnosis technique to evaluate fracture locations and other downhole events in fracturing. The evaluation results are obtained by analyzing shut-in water hammer pre...Water hammer diagnostics is an important fracturing diagnosis technique to evaluate fracture locations and other downhole events in fracturing. The evaluation results are obtained by analyzing shut-in water hammer pressure signal. The field-sampled water hammer signal is often disturbed by noise interference. Noise interference exists in various pumping stages during water hammer diagnostics, with significantly different frequency range and energy distribution. Clarifying the differences in frequency range and energy distribution between effective water hammer signals and noise is the basis of setting specific filtering parameters, including filtering frequency range and energy thresholds. Filtering specifically could separate the effective signal and noise, which is the key to ensuring the accuracy of water hammer diagnosis. As an emerging technique, there is a lack of research on the frequency range and energy distribution of effective signals in water hammer diagnostics. In this paper, the frequency range and energy distribution characteristics of field-sampled water hammer signals were clarified quantitatively and qualitatively for the first time by a newly proposed comprehensive water hammer segmentation-energy analysis method. The water hammer signals were preprocessed and divided into three segments, including pre-shut-in, water hammer oscillation, and leak-off segment. Then, the three segments were analyzed by energy analysis and correlation analysis. The results indicated that, one aspect, the frequency range of water hammer oscillation spans from 0 to 0.65 Hz, considered as effective water hammer signal. The pre-shut-in and leak-off segment ranges from 0 to 0.35 Hz and 0-0.2 Hz respectively. Meanwhile, odd harmonics were manifested in water hammer oscillation segment, with the harmonic frequencies ranging approximately from 0.07 to 0.75 Hz. Whereas integer harmonics were observed in pre-shut-in segment, ranging from 6 to 40 Hz. The other aspect, the energy distribution of water hammer signals was analyzed in different frequency ranges. In 0-1 Hz, an exponential decay was observed in all three segments. In 1-100 Hz, a periodical energy distribution was observed in pre-shut-in segment, an exponential decay was observed in water hammer oscillation, and an even energy distribution was observed in leak-off segment. In 100-500 Hz, an even energy distribution was observed in those three segments, yet the highest magnitude was noted in leak-off segment. In this study, the effective frequency range and energy distribution characteristics of the field-sampled water hammer signals in different segments were sufficiently elucidated quantitatively and qualitatively for the first time, laying the groundwork for optimizing the filtering parameters of the field filtering models and advancing the accuracy of identifying downhole event locations.展开更多
The increasing risk of ground pressure disasters resulting from deep well mining highlights the urgent need for advanced monitoring and early warning systems.Ground pressure monitoring,supported by microseismic techno...The increasing risk of ground pressure disasters resulting from deep well mining highlights the urgent need for advanced monitoring and early warning systems.Ground pressure monitoring,supported by microseismic technology,plays a pivotal role in ensuring mine safety by enabling real-time identifi cation and accurate classification of vibration signals such as microseismic signals,blasting signals,and noise.These classifications are critical for improving the efficacy of ground pressure monitoring systems,conducting stability analyses of deep rock masses,and implementing timely and precise roadway support measures.Such eff orts are essential for mitigating ground pressure disasters and ensuring safe mining operations.This study proposes an artificial intelligence-based automatic classification network model for mine vibration signals.Based on conventional convolutional neural networks,the proposed model further incorporates long short-term memory(LSTM)networks and attention mechanisms.The LSTM component eff ectively captures temporal correlations in time-series mining vibration data,while the attention mechanism enhances the models’ability to focus on critical features within the data.To validate the eff ectiveness of our proposed model,a dataset comprising 480,526 waveform records collected in 2022 by the microseismic monitoring system at Guangxi Shanhu Tungsten Mine was used for training,validation,and testing purposes.Results demonstrate that the proposed artifi cial intelligence-based classifi cation method achieves a higher recognition accuracy of 92.21%,significantly outperforming traditional manual classification methods.The proposed model represents a signifi cant advancement in ground pressure monitoring and disaster mitigation.展开更多
The time-varying periodic variations in Global Navigation Satellite System(GNSS)stations affect the reliable time series analysis and appropriate geophysical interpretation.In this study,we apply the singular spectrum...The time-varying periodic variations in Global Navigation Satellite System(GNSS)stations affect the reliable time series analysis and appropriate geophysical interpretation.In this study,we apply the singular spectrum analysis(SSA)method to characterize and interpret the periodic patterns of GNSS deformations in China using multiple geodetic datasets.These include 23-year observations from the Crustal Movement Observation Network of China(CMONOC),displacements inferred from the Gravity Recovery and Climate Experiment(GRACE),and loadings derived from Geophysical models(GM).The results reveal that all CMONOC time series exhibit seasonal signals characterized by amplitude and phase modulations,and the SSA method outperforms the traditional least squares fitting(LSF)method in extracting and interpreting the time-varying seasonal signals from the original time series.The decrease in the root mean square(RMS)correlates well with the annual cycle variance estimated by the SSA method,and the average reduction in noise amplitudes is nearly twice as much for SSA filtered results compared with those from the LSF method.With SSA analysis,the time-varying seasonal signals for all the selected stations can be identified in the reconstructed components corresponding to the first ten eigenvalues.Moreover,both RMS reduction and correlation analysis imply the advantages of GRACE solutions in explaining the GNSS periodic variations,and the geophysical effects can account for 71%of the GNSS annual amplitudes,and the average RMS reduction is 15%.The SSA method has proved to be useful for investigating the GNSS timevarying seasonal signals.It could be applicable as an auxiliary tool in the improvement of nonlinear variations investigations.展开更多
The article discusses the use of pulse-width modulation signals to generate low-temperature atmospheric plasma in an inert gas environment.The results of studies of the energy consumption of a low-temperature plasma g...The article discusses the use of pulse-width modulation signals to generate low-temperature atmospheric plasma in an inert gas environment.The results of studies of the energy consumption of a low-temperature plasma generation system depending on the duty rate,as well as the pulse repetition rate,are presented.The operating modes of the system have been established,in which a minimum of energy consumption is achieved.The issues of evaluating the interaction of plasma with objects based on the analysis of changes in signal parameters in the high-voltage circuit of the generator are also considered.展开更多
Ca^(2+)signaling plays crucial roles in plant stress responses,including defense against insects.To counteract insect feeding,different parts of a plant deploy systemic signaling to communicate and coordinate defense ...Ca^(2+)signaling plays crucial roles in plant stress responses,including defense against insects.To counteract insect feeding,different parts of a plant deploy systemic signaling to communicate and coordinate defense responses,but little is known about the underlying mechanisms.In this study,micrografting,in vivo imaging of Ca^(2+)and reactive oxygen species(ROS),quantification of jasmonic acid(JA)and defensive metabolites,and bioassay were used to study how Arabidopsis seedlings regulate systemic responses in leaves after hypocotyls are wounded.We show that wounding hypocotyls rapidly activated both Ca^(2+)and ROS signals in leaves.RBOHD,which functions to produce ROS,along with two glutamate receptors GLR3.3 and GLR3.6,but not individually RBOHD or GLR3.3 and GLR3.6,in hypocotyls regulate the dynamics of systemic Ca^(2+)signals in leaves.In line with the systemic Ca^(2+)signals,after wounding hypocotyl,RBOHD,GLR3.3,and GLR3.6 in hypocotyl also cooperatively regulate the transcriptome,hormone jasmonic acid,and defensive secondary metabolites in leaves of Arabidopsis seedlings,thus controlling the systemic resistance to insects.Unlike leaf-to-leaf systemic signaling,this study reveals the unique regulation of wounding-induced hypocotyl-to-leaf systemic signaling and sheds new light on how different plant organs use complex signaling pathways to modulate defense responses.展开更多
Neutron time-of-flight(ToF)measurement is a highly accurate method for obtaining the kinetic energy of a neutron by measuring its velocity,but requires precise acquisition of the neutron signal arrival time.However,th...Neutron time-of-flight(ToF)measurement is a highly accurate method for obtaining the kinetic energy of a neutron by measuring its velocity,but requires precise acquisition of the neutron signal arrival time.However,the high hardware costs and data burden associated with the acquisition of neutron ToF signals pose significant challenges.Higher sampling rates increase the data volume,data processing,and storage hardware costs.Compressed sampling can address these challenges,but it faces issues regarding optimal sampling efficiency and high-quality reconstructed signals.This paper proposes a revolutionary deep learning-based compressed sampling(DL-CS)algorithm for reconstructing neutron ToF signals that outperform traditional compressed sampling methods.This approach comprises four modules:random projection,rising dimensions,initial reconstruction,and final reconstruction.Initially,the technique adaptively compresses neutron ToF signals sequentially using three convolutional layers,replacing random measurement matrices in traditional compressed sampling theory.Subsequently,the signals are reconstructed using a modified inception module,long short-term memory,and self-attention.The performance of this deep compressed sampling method was quantified using the percentage root-mean-square difference,correlation coefficient,and reconstruction time.Experimental results showed that our proposed DL-CS approach can significantly enhance signal quality compared with other compressed sampling methods.This is evidenced by a percentage root-mean-square difference,correlation coefficient,and reconstruction time results of 5%,0.9988,and 0.0108 s,respectively,obtained for sampling rates below 10%for the neutron ToF signal generated using an electron-beam-driven photoneutron source.The results showed that the proposed DL-CS approach significantly improves the signal quality compared with other compressed sampling methods,exhibiting excellent reconstruction accuracy and speed.展开更多
Sound contains mechanical signals that can promote physiological and biochemical changes in plants.Insects produce different sounds in the environment,which may be relevant to plant behavior.Thus,we evaluated whether ...Sound contains mechanical signals that can promote physiological and biochemical changes in plants.Insects produce different sounds in the environment,which may be relevant to plant behavior.Thus,we evaluated whether signaling cascades are regulated differently by ecological sounds and whether they trigger molecular responses following those produced by herbivorous insects.Soybean plants were treated with two different sounds:chewing herbivore and forest ambient.The responses were markedly distinct,indicating that sound signals may also trigger specific cascades.Enzymes involved in oxidative metabolism were responsive to both sounds,while salicylic acid(SA)was responsive only to the chewing sound.In contrast,lipoxygenase(LOX)activity and jasmonic acid(JA)did not change.Soybean Kunitz trypsin inhibitor gene(SKTI)and Bowman-Birk(BBI)genes,encoding for protease inhibitors,were induced by chewing sound.Chewing sound-induced high expression of the pathogenesis-related protein(PR1)gene,confirming the activation of SA-dependent cascades.In contrast,the sound treatments promoted modifications in different branches of the phenylpropanoid pathway,highlighting a tendency for increased flavonols for plants under chewing sounds.Accordingly,chewing sounds induced pathogenesis-related protein(PR10/Bet v-1)and gmFLS1 flavonol synthase(FLS1)genes involved in flavonoid biosynthesis and flavonols.Finally,our results propose that plants may recognize herbivores by their chewing sound and that different ecological sounds can trigger distinct signaling cascades.展开更多
There are all kinds of unknown and known signals in the actual electromagnetic environment,which hinders the development of practical cognitive radio applications.However,most existing signal recognition models are di...There are all kinds of unknown and known signals in the actual electromagnetic environment,which hinders the development of practical cognitive radio applications.However,most existing signal recognition models are difficult to discover unknown signals while recognizing known ones.In this paper,a compact manifold mixup feature-based open-set recognition approach(OR-CMMF)is proposed to address the above problem.First,the proposed approach utilizes the center loss to constrain decision boundaries so that it obtains the compact latent signal feature representations and extends the low-confidence feature space.Second,the latent signal feature representations are used to construct synthetic representations as substitutes for unknown categories of signals.Then,these constructed representations can occupy the extended low-confidence space.Finally,the proposed approach applies the distillation loss to adjust the decision boundaries between the known categories signals and the constructed unknown categories substitutes so that it accurately discovers unknown signals.The OR-CMMF approach outperformed other state-of-the-art open-set recognition methods in comprehensive recognition performance and running time,as demonstrated by simulation experiments on two public datasets RML2016.10a and ORACLE.展开更多
Large dynamic range and ultra-wideband receiving abilities are significant for many receivers. With these abilities, receivers can obtain signals with different power in ultra-wideband frequency space without informat...Large dynamic range and ultra-wideband receiving abilities are significant for many receivers. With these abilities, receivers can obtain signals with different power in ultra-wideband frequency space without information loss. However, conventional receiving scheme is hard to have large dynamic range and ultra-wideband receiving simultaneously because of the analog-to-digital converter(ADC) dynamic range and sample rate limitations. In this paper, based on the modulated sampling and unlimited sampling, a novel receiving scheme is proposed to achieve large dynamic range and ultra-wideband receiving. Focusing on the single carrier signals, the proposed scheme only uses a single self-rest ADC(SR-ADC) with low sample rate, and it achieves large dynamic range and ultra-wideband receiving simultaneously. Two receiving scenarios are considered, and they are cooperative strong signal receiving and non-cooperative strong/weak signals receiving. In the cooperative receiving scenario, an improved fast recovery method is proposed to obtain the modulated sampling output. In the non-cooperative receiving scenario, the strong and weak signals with different carrier frequencies are considered, and the signal processing method can recover and estimate each signal. Simulation results show that the proposed scheme can realize large dynamic range and ultra-wideband receiving simultaneously when the input signal-to-noise(SNR) ratio is high.展开更多
The distributed fiber optic sensing system,known for its high sensitivity and wide-ranging measurement capabilities,has been widely used in monitoring underground gas pipelines.It primarily serves to perceive vibratio...The distributed fiber optic sensing system,known for its high sensitivity and wide-ranging measurement capabilities,has been widely used in monitoring underground gas pipelines.It primarily serves to perceive vibration signals induced by external events and to effectively provide early warnings of potential intrusion activities.Due to the complexity and diversity of external intrusion events,traditional deep learning methods can achieve event recognition with an average accuracy exceeding 90%.However,these methods rely on large-scale datasets,leading to significant time and labor costs during the data collection process.Additionally,traditional methods perform poorly when faced with the scarcity of low-frequency event samples,making it challenging to address these rare occurrences.To address this issue,this paper proposes a small-sample learning model based on triplet learning for intrusion event recognition.The model employs a 6-way 20-shot support set configuration and utilizes the KNN clustering algorithm to assess the model's performance.Experimental results indicate that the model achieves an average accuracy of 91.6%,further validating the superior performance of the triplet learning model in classifying external intrusion events.Compared to traditional methods,this approach not only effectively reduces the dependence on large-scale datasets but also better addresses the classification of low-frequency event samples,demonstrating significant application potential.展开更多
The monument thermal effect(MTE)displacements could result in periodical signals with several mil-limeters magnitudes in the vertical and horizontal GPS position time series.However,the interaction ofvarious origins o...The monument thermal effect(MTE)displacements could result in periodical signals with several mil-limeters magnitudes in the vertical and horizontal GPS position time series.However,the interaction ofvarious origins of periodic signals in GPS observations makes it difficult to isolate the millimeter-levelMTE displacement from other signals and noises.In this study,to assess the diurnal and semidiurnalsignals induced by MTE,we processed 12 very short GPS baselines(VSGB)with length<150 m.Themonument pairs for each baseline differ in their heights,horizontal structure,or base foundations.Meanwhile,two zero-baselines were also processed as the control group.Results showed that the sea-sonal signals observed in VSGB time series in the horizontal and vertical directions,were mainly inducedby seasonal MTE.Time-varying diurnal and semidiurnal signals with amplitude up to 4 mm wereobserved in the vertical direction for baselines with monument height difference(MHD)larger than10 m.Horizontal diurnal signal with an amplitude of about 2 mm was also detected for baselines withnon-axisymmetric monument structure.The orientation of the detected horizontal displacement wascoherent with the direction of daily temperature variation(DTV)driven by direct solar radiation,whichindicates that the diurnal and semidiurnal signals are likely induced by MTE.The observed high-frequency MTE displacements,if not well modeled and removed,may propagate into spurious long-term signals and bias the velocity estimation in the daily GPS time series.展开更多
Sparse array design has significant implications for improving the accuracy of direction of arrival(DOA)estimation of non-circular(NC)signals.We propose an extended nested array with a filled sensor(ENAFS)based on the...Sparse array design has significant implications for improving the accuracy of direction of arrival(DOA)estimation of non-circular(NC)signals.We propose an extended nested array with a filled sensor(ENAFS)based on the hole-filling strategy.Specifically,we first introduce the improved nested array(INA)and prove its properties.Subsequently,we extend the sum-difference coarray(SDCA)by adding an additional sensor to fill the holes.Thus the larger uniform degrees of freedom(uDOFs)and virtual array aperture(VAA)can be abtained,and the ENAFS is designed.Finally,the simulation results are given to verify the superiority of the proposed ENAFS in terms of DOF,mutual coupling and estimation performance.展开更多
We propose the Dantzig selector based on the l_(1-q)(1<q≤2)minimization model for the sparse signal recovery.First,we discuss some properties of l_(1-q)minimization model and give some useful inequalities.Then,we ...We propose the Dantzig selector based on the l_(1-q)(1<q≤2)minimization model for the sparse signal recovery.First,we discuss some properties of l_(1-q)minimization model and give some useful inequalities.Then,we give a sufficient condition based on the restricted isometry property for the stable recovery of signals.The l_(1-2)minimization model of Yin-Lou-He is extended to the l_(1-q)minimization model.展开更多
Foods are often contaminated by multiple foodborne pathogens,which threatens human health.In this work,we developed a microfluidic biosensor for multiplex immunoassay of foodborne bacteria with agitation driven by pro...Foods are often contaminated by multiple foodborne pathogens,which threatens human health.In this work,we developed a microfluidic biosensor for multiplex immunoassay of foodborne bacteria with agitation driven by programmed audio signals.This agitation,powered by the vibration of a speaker cone during music playing,accelerated the mass transport in the incubation process to form bacterial complexes within 10 min.Immunoassay reagents of the two target bacteria(Escherichia coli O157:H7 and Salmonella typhimurium)were preloaded into the corresponding fore-vacuum storage chamber on the chip,and released to participate in the subsequent immune analysis process by piercing the chambers.All the detection processes were integrated into a single microfluidic chip and controlled by a smartphone through Bluetooth.Under selected conditions,wide linear ranges and low limits of detection(LODs<2CFU/m L)were obtained,and real food samples were successfully determined within 30 min.This biosensing method can be extended to wide-ranging applications by loading different recognizing reagents.展开更多
As modern communication technology advances apace,the digital communication signals identification plays an important role in cognitive radio networks,the communication monitoring and management systems.AI has become ...As modern communication technology advances apace,the digital communication signals identification plays an important role in cognitive radio networks,the communication monitoring and management systems.AI has become a promising solution to this problem due to its powerful modeling capability,which has become a consensus in academia and industry.However,because of the data-dependence and inexplicability of AI models and the openness of electromagnetic space,the physical layer digital communication signals identification model is threatened by adversarial attacks.Adversarial examples pose a common threat to AI models,where well-designed and slight perturbations added to input data can cause wrong results.Therefore,the security of AI models for the digital communication signals identification is the premise of its efficient and credible applications.In this paper,we first launch adversarial attacks on the end-to-end AI model for automatic modulation classifi-cation,and then we explain and present three defense mechanisms based on the adversarial principle.Next we present more detailed adversarial indicators to evaluate attack and defense behavior.Finally,a demonstration verification system is developed to show that the adversarial attack is a real threat to the digital communication signals identification model,which should be paid more attention in future research.展开更多
Temporal lobe epilepsy is a multifactorial neurological dysfunction syndrome that is refractory,resistant to antiepileptic drugs,and has a high recurrence rate.The pathogenesis of temporal lobe epilepsy is complex and...Temporal lobe epilepsy is a multifactorial neurological dysfunction syndrome that is refractory,resistant to antiepileptic drugs,and has a high recurrence rate.The pathogenesis of temporal lobe epilepsy is complex and is not fully understood.Intracellular calcium dynamics have been implicated in temporal lobe epilepsy.However,the effect of fluctuating calcium activity in CA1 pyramidal neurons on temporal lobe epilepsy is unknown,and no longitudinal studies have investigated calcium activity in pyramidal neurons in the hippocampal CA1 and primary motor cortex M1 of freely moving mice.In this study,we used a multichannel fiber photometry system to continuously record calcium signals in CA1 and M1 during the temporal lobe epilepsy process.We found that calcium signals varied according to the grade of temporal lobe epilepsy episodes.In particular,cortical spreading depression,which has recently been frequently used to represent the continuously and substantially increased calcium signals,was found to correspond to complex and severe behavioral characteristics of temporal lobe epilepsy ranging from gradeⅡto gradeⅤ.However,vigorous calcium oscillations and highly synchronized calcium signals in CA1 and M1 were strongly related to convulsive motor seizures.Chemogenetic inhibition of pyramidal neurons in CA1 significantly attenuated the amplitudes of the calcium signals corresponding to gradeⅠepisodes.In addition,the latency of cortical spreading depression was prolonged,and the above-mentioned abnormal calcium signals in CA1 and M1 were also significantly reduced.Intriguingly,it was possible to rescue the altered intracellular calcium dynamics.Via simultaneous analysis of calcium signals and epileptic behaviors,we found that the progression of temporal lobe epilepsy was alleviated when specific calcium signals were reduced,and that the end-point behaviors of temporal lobe epilepsy were improved.Our results indicate that the calcium dynamic between CA1 and M1 may reflect specific epileptic behaviors corresponding to different grades.Furthermore,the selective regulation of abnormal calcium signals in CA1 pyramidal neurons appears to effectively alleviate temporal lobe epilepsy,thereby providing a potential molecular mechanism for a new temporal lobe epilepsy diagnosis and treatment strategy.展开更多
Vascular plants have evolved intricate long-distance signaling mechanisms to cope with environmental stress,with reactive oxygen species(ROS)emerging as pivotal systemic signals in plant stress responses.However,the e...Vascular plants have evolved intricate long-distance signaling mechanisms to cope with environmental stress,with reactive oxygen species(ROS)emerging as pivotal systemic signals in plant stress responses.However,the exact role of ROS as root-to-shoot signals in the drought response has not been determined.In this study,we reveal that compared with wild-type plants,ferric reductase defective 3(frd3)mutants exhibit enhanced drought resistance concomitant with elevated NINE-CIS-EPOXYCAROTENOID DIOXYGENASE 3(NCED3)transcript levels and abscisic acid(ABA)contents in leaves as well as increased hydrogen peroxide(H_(2)O_(2))levels in roots and leaves.Grafting experiments distinctly illustrate that drought resistance can be conferred by the frd3 rootstock regardless of the scion genotype,indicating that long-distance signals originating from frd3 roots promote an increase in ABA levels in leaves.Intriguingly,the drought resistance conferred by the frd3 mutant rootstock is weakened by the CAT2-overexpressing scion,suggesting that H_(2)O_(2)may be involved in long-distance signaling.Moreover,the results of comparative transcriptome and proteome analyses support the drought resistance phenotype of the frd3 mutant.Taken together,our findings substantiate the notion that frd3 root-derived long-distance signals trigger ABA synthesis in leaves and enhance drought resistance,providing new evidence for root-to-shoot long-distance signaling in the drought response of plants.展开更多
The global incidence of infectious diseases has increased in recent years,posing a significant threat to human health.Hospitals typically serve as frontline institutions for detecting infectious diseases.However,accur...The global incidence of infectious diseases has increased in recent years,posing a significant threat to human health.Hospitals typically serve as frontline institutions for detecting infectious diseases.However,accurately identifying warning signals of infectious diseases in a timely manner,especially emerging infectious diseases,can be challenging.Consequently,there is a pressing need to integrate treatment and disease prevention data to conduct comprehensive analyses aimed at preventing and controlling infectious diseases within hospitals.This paper examines the role of medical data in the early identification of infectious diseases,explores early warning technologies for infectious disease recognition,and assesses monitoring and early warning mechanisms for infectious diseases.We propose that hospitals adopt novel multidimensional early warning technologies to mine and analyze medical data from various systems,in compliance with national strategies to integrate clinical treatment and disease prevention.Furthermore,hospitals should establish institution-specific,clinical-based early warning models for infectious diseases to actively monitor early signals and enhance preparedness for infectious disease prevention and control.展开更多
基金The Natural Sciences and Engineering Research Council of Canada(NSERC)funded this review study.
文摘This reviewpresents a comprehensive technical analysis of deep learning(DL)methodologies in biomedical signal processing,focusing on architectural innovations,experimental validation,and evaluation frameworks.We systematically evaluate key deep learning architectures including convolutional neural networks(CNNs),recurrent neural networks(RNNs),transformer-based models,and hybrid systems across critical tasks such as arrhythmia classification,seizure detection,and anomaly segmentation.The study dissects preprocessing techniques(e.g.,wavelet denoising,spectral normalization)and feature extraction strategies(time-frequency analysis,attention mechanisms),demonstrating their impact on model accuracy,noise robustness,and computational efficiency.Experimental results underscore the superiority of deep learning over traditional methods,particularly in automated feature extraction,real-time processing,cross-modal generalization,and achieving up to a 15%increase in classification accuracy and enhanced noise resilience across electrocardiogram(ECG),electroencephalogram(EEG),and electromyogram(EMG)signals.Performance is rigorously benchmarked using precision,recall,F1-scores,area under the receiver operating characteristic curve(AUC-ROC),and computational complexitymetrics,providing a unified framework for comparing model efficacy.Thesurvey addresses persistent challenges:synthetic data generationmitigates limited training samples,interpretability tools(e.g.,Gradient-weighted Class Activation Mapping(Grad-CAM),Shapley values)resolve model opacity,and federated learning ensures privacy-compliant deployments.Distinguished from prior reviews,this work offers a structured taxonomy of deep learning architectures,integrates emerging paradigms like transformers and domain-specific attention mechanisms,and evaluates preprocessing pipelines for spectral-temporal trade-offs.It advances the field by bridging technical advancements with clinical needs,such as scalability in real-world settings(e.g.,wearable devices)and regulatory alignment with theHealth Insurance Portability and Accountability Act(HIPAA)and General Data Protection Regulation(GDPR).By synthesizing technical rigor,ethical considerations,and actionable guidelines for model selection,this survey establishes a holistic reference for developing robust,interpretable biomedical artificial intelligence(AI)systems,accelerating their translation into personalized and equitable healthcare solutions.
文摘Tumor stroma,or tumor microenvironment(TME),has been in the spotlight during recent years for its role in tumor development,growth,and metastasis.It consists of a myriad of elements,including tumor-associated macrophages,cancer-associated fibroblasts,a deregulated extracellular matrix,endothelial cells,and vascular vessels.The release of proinflammatory molecules,due to the inflamed microenvironment,such as cytokines and chemokines is found to play a pivotal role in progression of cancer and response to therapy.This review discusses the major key players and important chemical inflammatory signals released in the TME.Furthermore,the latest breakthroughs in cytokine-mediated crosstalk between immune cells and cancer cells have been highlighted.In addition,recent updates on alterations in cytokine signaling between chronic inflammation and malignant TME have also been reviewed.
基金support from the National Natural Science Foundation of China(No.52374019).
文摘Water hammer diagnostics is an important fracturing diagnosis technique to evaluate fracture locations and other downhole events in fracturing. The evaluation results are obtained by analyzing shut-in water hammer pressure signal. The field-sampled water hammer signal is often disturbed by noise interference. Noise interference exists in various pumping stages during water hammer diagnostics, with significantly different frequency range and energy distribution. Clarifying the differences in frequency range and energy distribution between effective water hammer signals and noise is the basis of setting specific filtering parameters, including filtering frequency range and energy thresholds. Filtering specifically could separate the effective signal and noise, which is the key to ensuring the accuracy of water hammer diagnosis. As an emerging technique, there is a lack of research on the frequency range and energy distribution of effective signals in water hammer diagnostics. In this paper, the frequency range and energy distribution characteristics of field-sampled water hammer signals were clarified quantitatively and qualitatively for the first time by a newly proposed comprehensive water hammer segmentation-energy analysis method. The water hammer signals were preprocessed and divided into three segments, including pre-shut-in, water hammer oscillation, and leak-off segment. Then, the three segments were analyzed by energy analysis and correlation analysis. The results indicated that, one aspect, the frequency range of water hammer oscillation spans from 0 to 0.65 Hz, considered as effective water hammer signal. The pre-shut-in and leak-off segment ranges from 0 to 0.35 Hz and 0-0.2 Hz respectively. Meanwhile, odd harmonics were manifested in water hammer oscillation segment, with the harmonic frequencies ranging approximately from 0.07 to 0.75 Hz. Whereas integer harmonics were observed in pre-shut-in segment, ranging from 6 to 40 Hz. The other aspect, the energy distribution of water hammer signals was analyzed in different frequency ranges. In 0-1 Hz, an exponential decay was observed in all three segments. In 1-100 Hz, a periodical energy distribution was observed in pre-shut-in segment, an exponential decay was observed in water hammer oscillation, and an even energy distribution was observed in leak-off segment. In 100-500 Hz, an even energy distribution was observed in those three segments, yet the highest magnitude was noted in leak-off segment. In this study, the effective frequency range and energy distribution characteristics of the field-sampled water hammer signals in different segments were sufficiently elucidated quantitatively and qualitatively for the first time, laying the groundwork for optimizing the filtering parameters of the field filtering models and advancing the accuracy of identifying downhole event locations.
基金supported in part by the National Science Fund for Distinguished Young Scholars under Grant (42025403)the National Key Research and Development Plan of China (2021YFA0716800)the National Key Research and Development Plan of China (2022YFC2903804)。
文摘The increasing risk of ground pressure disasters resulting from deep well mining highlights the urgent need for advanced monitoring and early warning systems.Ground pressure monitoring,supported by microseismic technology,plays a pivotal role in ensuring mine safety by enabling real-time identifi cation and accurate classification of vibration signals such as microseismic signals,blasting signals,and noise.These classifications are critical for improving the efficacy of ground pressure monitoring systems,conducting stability analyses of deep rock masses,and implementing timely and precise roadway support measures.Such eff orts are essential for mitigating ground pressure disasters and ensuring safe mining operations.This study proposes an artificial intelligence-based automatic classification network model for mine vibration signals.Based on conventional convolutional neural networks,the proposed model further incorporates long short-term memory(LSTM)networks and attention mechanisms.The LSTM component eff ectively captures temporal correlations in time-series mining vibration data,while the attention mechanism enhances the models’ability to focus on critical features within the data.To validate the eff ectiveness of our proposed model,a dataset comprising 480,526 waveform records collected in 2022 by the microseismic monitoring system at Guangxi Shanhu Tungsten Mine was used for training,validation,and testing purposes.Results demonstrate that the proposed artifi cial intelligence-based classifi cation method achieves a higher recognition accuracy of 92.21%,significantly outperforming traditional manual classification methods.The proposed model represents a signifi cant advancement in ground pressure monitoring and disaster mitigation.
基金supported by the National Natural Science Foundation of China(NO.42104028,42174030 and 42004017)the Open Fund of Hubei Luojia Laboratory(No.220100048 and 230100021)the Scientific Research Project of Hubei Provincial Department of Education,and Research Foundation of the Department of Natural Resources of Hunan Province(No.20230104CH)。
文摘The time-varying periodic variations in Global Navigation Satellite System(GNSS)stations affect the reliable time series analysis and appropriate geophysical interpretation.In this study,we apply the singular spectrum analysis(SSA)method to characterize and interpret the periodic patterns of GNSS deformations in China using multiple geodetic datasets.These include 23-year observations from the Crustal Movement Observation Network of China(CMONOC),displacements inferred from the Gravity Recovery and Climate Experiment(GRACE),and loadings derived from Geophysical models(GM).The results reveal that all CMONOC time series exhibit seasonal signals characterized by amplitude and phase modulations,and the SSA method outperforms the traditional least squares fitting(LSF)method in extracting and interpreting the time-varying seasonal signals from the original time series.The decrease in the root mean square(RMS)correlates well with the annual cycle variance estimated by the SSA method,and the average reduction in noise amplitudes is nearly twice as much for SSA filtered results compared with those from the LSF method.With SSA analysis,the time-varying seasonal signals for all the selected stations can be identified in the reconstructed components corresponding to the first ten eigenvalues.Moreover,both RMS reduction and correlation analysis imply the advantages of GRACE solutions in explaining the GNSS periodic variations,and the geophysical effects can account for 71%of the GNSS annual amplitudes,and the average RMS reduction is 15%.The SSA method has proved to be useful for investigating the GNSS timevarying seasonal signals.It could be applicable as an auxiliary tool in the improvement of nonlinear variations investigations.
文摘The article discusses the use of pulse-width modulation signals to generate low-temperature atmospheric plasma in an inert gas environment.The results of studies of the energy consumption of a low-temperature plasma generation system depending on the duty rate,as well as the pulse repetition rate,are presented.The operating modes of the system have been established,in which a minimum of energy consumption is achieved.The issues of evaluating the interaction of plasma with objects based on the analysis of changes in signal parameters in the high-voltage circuit of the generator are also considered.
基金National Natural Science Foundation of China(U23A20199)Yunnan Revitalization Talent Support Program“Yunling Scholar”and Yunnan Fundamental Research Projects(202201AS070056)。
文摘Ca^(2+)signaling plays crucial roles in plant stress responses,including defense against insects.To counteract insect feeding,different parts of a plant deploy systemic signaling to communicate and coordinate defense responses,but little is known about the underlying mechanisms.In this study,micrografting,in vivo imaging of Ca^(2+)and reactive oxygen species(ROS),quantification of jasmonic acid(JA)and defensive metabolites,and bioassay were used to study how Arabidopsis seedlings regulate systemic responses in leaves after hypocotyls are wounded.We show that wounding hypocotyls rapidly activated both Ca^(2+)and ROS signals in leaves.RBOHD,which functions to produce ROS,along with two glutamate receptors GLR3.3 and GLR3.6,but not individually RBOHD or GLR3.3 and GLR3.6,in hypocotyls regulate the dynamics of systemic Ca^(2+)signals in leaves.In line with the systemic Ca^(2+)signals,after wounding hypocotyl,RBOHD,GLR3.3,and GLR3.6 in hypocotyl also cooperatively regulate the transcriptome,hormone jasmonic acid,and defensive secondary metabolites in leaves of Arabidopsis seedlings,thus controlling the systemic resistance to insects.Unlike leaf-to-leaf systemic signaling,this study reveals the unique regulation of wounding-induced hypocotyl-to-leaf systemic signaling and sheds new light on how different plant organs use complex signaling pathways to modulate defense responses.
基金supported by the National Defense Technology Foundation Program of China(No.JSJT2022209A001-3)Sichuan Science and Technology Program(No.2021JDRC0011)+1 种基金Nuclear Energy Development Research Program of China(Research on High Energy X-ray Imaging of Nuclear Fuel)Scientific Research and Innovation Team Program of Sichuan University of Science and Engineering(No.SUSE652A001).
文摘Neutron time-of-flight(ToF)measurement is a highly accurate method for obtaining the kinetic energy of a neutron by measuring its velocity,but requires precise acquisition of the neutron signal arrival time.However,the high hardware costs and data burden associated with the acquisition of neutron ToF signals pose significant challenges.Higher sampling rates increase the data volume,data processing,and storage hardware costs.Compressed sampling can address these challenges,but it faces issues regarding optimal sampling efficiency and high-quality reconstructed signals.This paper proposes a revolutionary deep learning-based compressed sampling(DL-CS)algorithm for reconstructing neutron ToF signals that outperform traditional compressed sampling methods.This approach comprises four modules:random projection,rising dimensions,initial reconstruction,and final reconstruction.Initially,the technique adaptively compresses neutron ToF signals sequentially using three convolutional layers,replacing random measurement matrices in traditional compressed sampling theory.Subsequently,the signals are reconstructed using a modified inception module,long short-term memory,and self-attention.The performance of this deep compressed sampling method was quantified using the percentage root-mean-square difference,correlation coefficient,and reconstruction time.Experimental results showed that our proposed DL-CS approach can significantly enhance signal quality compared with other compressed sampling methods.This is evidenced by a percentage root-mean-square difference,correlation coefficient,and reconstruction time results of 5%,0.9988,and 0.0108 s,respectively,obtained for sampling rates below 10%for the neutron ToF signal generated using an electron-beam-driven photoneutron source.The results showed that the proposed DL-CS approach significantly improves the signal quality compared with other compressed sampling methods,exhibiting excellent reconstruction accuracy and speed.
文摘Sound contains mechanical signals that can promote physiological and biochemical changes in plants.Insects produce different sounds in the environment,which may be relevant to plant behavior.Thus,we evaluated whether signaling cascades are regulated differently by ecological sounds and whether they trigger molecular responses following those produced by herbivorous insects.Soybean plants were treated with two different sounds:chewing herbivore and forest ambient.The responses were markedly distinct,indicating that sound signals may also trigger specific cascades.Enzymes involved in oxidative metabolism were responsive to both sounds,while salicylic acid(SA)was responsive only to the chewing sound.In contrast,lipoxygenase(LOX)activity and jasmonic acid(JA)did not change.Soybean Kunitz trypsin inhibitor gene(SKTI)and Bowman-Birk(BBI)genes,encoding for protease inhibitors,were induced by chewing sound.Chewing sound-induced high expression of the pathogenesis-related protein(PR1)gene,confirming the activation of SA-dependent cascades.In contrast,the sound treatments promoted modifications in different branches of the phenylpropanoid pathway,highlighting a tendency for increased flavonols for plants under chewing sounds.Accordingly,chewing sounds induced pathogenesis-related protein(PR10/Bet v-1)and gmFLS1 flavonol synthase(FLS1)genes involved in flavonoid biosynthesis and flavonols.Finally,our results propose that plants may recognize herbivores by their chewing sound and that different ecological sounds can trigger distinct signaling cascades.
基金fully supported by National Natural Science Foundation of China(61871422)Natural Science Foundation of Sichuan Province(2023NSFSC1422)Central Universities of South west Minzu University(ZYN2022032)。
文摘There are all kinds of unknown and known signals in the actual electromagnetic environment,which hinders the development of practical cognitive radio applications.However,most existing signal recognition models are difficult to discover unknown signals while recognizing known ones.In this paper,a compact manifold mixup feature-based open-set recognition approach(OR-CMMF)is proposed to address the above problem.First,the proposed approach utilizes the center loss to constrain decision boundaries so that it obtains the compact latent signal feature representations and extends the low-confidence feature space.Second,the latent signal feature representations are used to construct synthetic representations as substitutes for unknown categories of signals.Then,these constructed representations can occupy the extended low-confidence space.Finally,the proposed approach applies the distillation loss to adjust the decision boundaries between the known categories signals and the constructed unknown categories substitutes so that it accurately discovers unknown signals.The OR-CMMF approach outperformed other state-of-the-art open-set recognition methods in comprehensive recognition performance and running time,as demonstrated by simulation experiments on two public datasets RML2016.10a and ORACLE.
文摘Large dynamic range and ultra-wideband receiving abilities are significant for many receivers. With these abilities, receivers can obtain signals with different power in ultra-wideband frequency space without information loss. However, conventional receiving scheme is hard to have large dynamic range and ultra-wideband receiving simultaneously because of the analog-to-digital converter(ADC) dynamic range and sample rate limitations. In this paper, based on the modulated sampling and unlimited sampling, a novel receiving scheme is proposed to achieve large dynamic range and ultra-wideband receiving. Focusing on the single carrier signals, the proposed scheme only uses a single self-rest ADC(SR-ADC) with low sample rate, and it achieves large dynamic range and ultra-wideband receiving simultaneously. Two receiving scenarios are considered, and they are cooperative strong signal receiving and non-cooperative strong/weak signals receiving. In the cooperative receiving scenario, an improved fast recovery method is proposed to obtain the modulated sampling output. In the non-cooperative receiving scenario, the strong and weak signals with different carrier frequencies are considered, and the signal processing method can recover and estimate each signal. Simulation results show that the proposed scheme can realize large dynamic range and ultra-wideband receiving simultaneously when the input signal-to-noise(SNR) ratio is high.
基金Supported by the Scientific Research and Technology Development Project of Petrochina Southwest Oil and Gas Field Company(20230307-02)。
文摘The distributed fiber optic sensing system,known for its high sensitivity and wide-ranging measurement capabilities,has been widely used in monitoring underground gas pipelines.It primarily serves to perceive vibration signals induced by external events and to effectively provide early warnings of potential intrusion activities.Due to the complexity and diversity of external intrusion events,traditional deep learning methods can achieve event recognition with an average accuracy exceeding 90%.However,these methods rely on large-scale datasets,leading to significant time and labor costs during the data collection process.Additionally,traditional methods perform poorly when faced with the scarcity of low-frequency event samples,making it challenging to address these rare occurrences.To address this issue,this paper proposes a small-sample learning model based on triplet learning for intrusion event recognition.The model employs a 6-way 20-shot support set configuration and utilizes the KNN clustering algorithm to assess the model's performance.Experimental results indicate that the model achieves an average accuracy of 91.6%,further validating the superior performance of the triplet learning model in classifying external intrusion events.Compared to traditional methods,this approach not only effectively reduces the dependence on large-scale datasets but also better addresses the classification of low-frequency event samples,demonstrating significant application potential.
基金funded by the Independent Innovation Project of Changjiang Institute of Survey,Planning,Design and Research Corporation (CX2020Z32)supported by the National Natural Science Foundation of China (Grant Numbers42204006 and 42104028)the Open Fund of Hubei Luojia Laboratory (Grant Numbers 230100020 and 230100019)
文摘The monument thermal effect(MTE)displacements could result in periodical signals with several mil-limeters magnitudes in the vertical and horizontal GPS position time series.However,the interaction ofvarious origins of periodic signals in GPS observations makes it difficult to isolate the millimeter-levelMTE displacement from other signals and noises.In this study,to assess the diurnal and semidiurnalsignals induced by MTE,we processed 12 very short GPS baselines(VSGB)with length<150 m.Themonument pairs for each baseline differ in their heights,horizontal structure,or base foundations.Meanwhile,two zero-baselines were also processed as the control group.Results showed that the sea-sonal signals observed in VSGB time series in the horizontal and vertical directions,were mainly inducedby seasonal MTE.Time-varying diurnal and semidiurnal signals with amplitude up to 4 mm wereobserved in the vertical direction for baselines with monument height difference(MHD)larger than10 m.Horizontal diurnal signal with an amplitude of about 2 mm was also detected for baselines withnon-axisymmetric monument structure.The orientation of the detected horizontal displacement wascoherent with the direction of daily temperature variation(DTV)driven by direct solar radiation,whichindicates that the diurnal and semidiurnal signals are likely induced by MTE.The observed high-frequency MTE displacements,if not well modeled and removed,may propagate into spurious long-term signals and bias the velocity estimation in the daily GPS time series.
基金supported by China National Science Foundations(Nos.62371225,62371227)。
文摘Sparse array design has significant implications for improving the accuracy of direction of arrival(DOA)estimation of non-circular(NC)signals.We propose an extended nested array with a filled sensor(ENAFS)based on the hole-filling strategy.Specifically,we first introduce the improved nested array(INA)and prove its properties.Subsequently,we extend the sum-difference coarray(SDCA)by adding an additional sensor to fill the holes.Thus the larger uniform degrees of freedom(uDOFs)and virtual array aperture(VAA)can be abtained,and the ENAFS is designed.Finally,the simulation results are given to verify the superiority of the proposed ENAFS in terms of DOF,mutual coupling and estimation performance.
基金supported by the National Natural Science Foundation of China“Variable exponential function spaces on variable anisotropic Euclidean spaces and their applications”(12261083),“Harmonic analysis on affine symmetric spaces”(12161083).
文摘We propose the Dantzig selector based on the l_(1-q)(1<q≤2)minimization model for the sparse signal recovery.First,we discuss some properties of l_(1-q)minimization model and give some useful inequalities.Then,we give a sufficient condition based on the restricted isometry property for the stable recovery of signals.The l_(1-2)minimization model of Yin-Lou-He is extended to the l_(1-q)minimization model.
基金supported financially by“Kunlun Talents High-end Innovation and Entrepreneurship Talents”of Qinghai Province in 2022National Natural Science Foundation of China(Nos.22322401 and 82073816)Beijing Nova Program(No.20220484055)。
文摘Foods are often contaminated by multiple foodborne pathogens,which threatens human health.In this work,we developed a microfluidic biosensor for multiplex immunoassay of foodborne bacteria with agitation driven by programmed audio signals.This agitation,powered by the vibration of a speaker cone during music playing,accelerated the mass transport in the incubation process to form bacterial complexes within 10 min.Immunoassay reagents of the two target bacteria(Escherichia coli O157:H7 and Salmonella typhimurium)were preloaded into the corresponding fore-vacuum storage chamber on the chip,and released to participate in the subsequent immune analysis process by piercing the chambers.All the detection processes were integrated into a single microfluidic chip and controlled by a smartphone through Bluetooth.Under selected conditions,wide linear ranges and low limits of detection(LODs<2CFU/m L)were obtained,and real food samples were successfully determined within 30 min.This biosensing method can be extended to wide-ranging applications by loading different recognizing reagents.
基金supported by the National Natural Science Foundation of China(61771154)the Fundamental Research Funds for the Central Universities(3072022CF0601)supported by Key Laboratory of Advanced Marine Communication and Information Technology,Ministry of Industry and Information Technology,Harbin Engineering University,Harbin,China.
文摘As modern communication technology advances apace,the digital communication signals identification plays an important role in cognitive radio networks,the communication monitoring and management systems.AI has become a promising solution to this problem due to its powerful modeling capability,which has become a consensus in academia and industry.However,because of the data-dependence and inexplicability of AI models and the openness of electromagnetic space,the physical layer digital communication signals identification model is threatened by adversarial attacks.Adversarial examples pose a common threat to AI models,where well-designed and slight perturbations added to input data can cause wrong results.Therefore,the security of AI models for the digital communication signals identification is the premise of its efficient and credible applications.In this paper,we first launch adversarial attacks on the end-to-end AI model for automatic modulation classifi-cation,and then we explain and present three defense mechanisms based on the adversarial principle.Next we present more detailed adversarial indicators to evaluate attack and defense behavior.Finally,a demonstration verification system is developed to show that the adversarial attack is a real threat to the digital communication signals identification model,which should be paid more attention in future research.
基金supported by the National Natural Science Foundation of China,Nos.62027812(to HS),81771470(to HS),and 82101608(to YL)Tianjin Postgraduate Research and Innovation Project,No.2020YJSS122(to XD)。
文摘Temporal lobe epilepsy is a multifactorial neurological dysfunction syndrome that is refractory,resistant to antiepileptic drugs,and has a high recurrence rate.The pathogenesis of temporal lobe epilepsy is complex and is not fully understood.Intracellular calcium dynamics have been implicated in temporal lobe epilepsy.However,the effect of fluctuating calcium activity in CA1 pyramidal neurons on temporal lobe epilepsy is unknown,and no longitudinal studies have investigated calcium activity in pyramidal neurons in the hippocampal CA1 and primary motor cortex M1 of freely moving mice.In this study,we used a multichannel fiber photometry system to continuously record calcium signals in CA1 and M1 during the temporal lobe epilepsy process.We found that calcium signals varied according to the grade of temporal lobe epilepsy episodes.In particular,cortical spreading depression,which has recently been frequently used to represent the continuously and substantially increased calcium signals,was found to correspond to complex and severe behavioral characteristics of temporal lobe epilepsy ranging from gradeⅡto gradeⅤ.However,vigorous calcium oscillations and highly synchronized calcium signals in CA1 and M1 were strongly related to convulsive motor seizures.Chemogenetic inhibition of pyramidal neurons in CA1 significantly attenuated the amplitudes of the calcium signals corresponding to gradeⅠepisodes.In addition,the latency of cortical spreading depression was prolonged,and the above-mentioned abnormal calcium signals in CA1 and M1 were also significantly reduced.Intriguingly,it was possible to rescue the altered intracellular calcium dynamics.Via simultaneous analysis of calcium signals and epileptic behaviors,we found that the progression of temporal lobe epilepsy was alleviated when specific calcium signals were reduced,and that the end-point behaviors of temporal lobe epilepsy were improved.Our results indicate that the calcium dynamic between CA1 and M1 may reflect specific epileptic behaviors corresponding to different grades.Furthermore,the selective regulation of abnormal calcium signals in CA1 pyramidal neurons appears to effectively alleviate temporal lobe epilepsy,thereby providing a potential molecular mechanism for a new temporal lobe epilepsy diagnosis and treatment strategy.
基金supported by grants from the National Natural Science Foundation of China(31900230 to P.X.Z.)the China Postdoctoral Science Foundation(2020T130634 and 2019M652200 to P.X.Z.).
文摘Vascular plants have evolved intricate long-distance signaling mechanisms to cope with environmental stress,with reactive oxygen species(ROS)emerging as pivotal systemic signals in plant stress responses.However,the exact role of ROS as root-to-shoot signals in the drought response has not been determined.In this study,we reveal that compared with wild-type plants,ferric reductase defective 3(frd3)mutants exhibit enhanced drought resistance concomitant with elevated NINE-CIS-EPOXYCAROTENOID DIOXYGENASE 3(NCED3)transcript levels and abscisic acid(ABA)contents in leaves as well as increased hydrogen peroxide(H_(2)O_(2))levels in roots and leaves.Grafting experiments distinctly illustrate that drought resistance can be conferred by the frd3 rootstock regardless of the scion genotype,indicating that long-distance signals originating from frd3 roots promote an increase in ABA levels in leaves.Intriguingly,the drought resistance conferred by the frd3 mutant rootstock is weakened by the CAT2-overexpressing scion,suggesting that H_(2)O_(2)may be involved in long-distance signaling.Moreover,the results of comparative transcriptome and proteome analyses support the drought resistance phenotype of the frd3 mutant.Taken together,our findings substantiate the notion that frd3 root-derived long-distance signals trigger ABA synthesis in leaves and enhance drought resistance,providing new evidence for root-to-shoot long-distance signaling in the drought response of plants.
文摘The global incidence of infectious diseases has increased in recent years,posing a significant threat to human health.Hospitals typically serve as frontline institutions for detecting infectious diseases.However,accurately identifying warning signals of infectious diseases in a timely manner,especially emerging infectious diseases,can be challenging.Consequently,there is a pressing need to integrate treatment and disease prevention data to conduct comprehensive analyses aimed at preventing and controlling infectious diseases within hospitals.This paper examines the role of medical data in the early identification of infectious diseases,explores early warning technologies for infectious disease recognition,and assesses monitoring and early warning mechanisms for infectious diseases.We propose that hospitals adopt novel multidimensional early warning technologies to mine and analyze medical data from various systems,in compliance with national strategies to integrate clinical treatment and disease prevention.Furthermore,hospitals should establish institution-specific,clinical-based early warning models for infectious diseases to actively monitor early signals and enhance preparedness for infectious disease prevention and control.
