Investigating the vertical distribution of dissolved organic matter in 5-m soil profiles in farmland and typical woodland on the southern loess plateau.

With the implementation of the 'Grain-for-Green' program on the Chinese Loess Plateau (CLP), drought-tolerant deep-rooted plants have been increasingly introduced to the northwest in China. However, the vertical features of dissolved organic matter (DOM) in deep soil profiles on CLP during the 'Grain-for-Green' program is still not well understood. In the study, ultraviolet-visible (UV-Vis) spectroscopy and three-dimensional fluorescence excitation-emission matrices (3D-EEMs) with parallel factor analysis (PARAFAC) were used to characterize DOM in 5-m profile of farmland and forestland (Pinus tabulaeformis and Robinia pseudoacacia) in the southern CLP. The results demonstrated that the average dissolved organic carbon (DOC) content of the surface layer of farmland (119.3 mg kg-1 soil) was lower than that of forestland (Pinus tabulaeformis 175.5 mg kg-1 soil; Robinia pseudoacacacia 166.4 mg kg-1 soil). The DOC content gradually decreased with increasing soil depth and reached stability after 2 m depth. Three substances, including tryptophan-like substances (C1) and two humic acid-like substances (C2, C3), were detected from all samples. Tryptophan-like substances (C1) significantly increased with soil depth while humic acid-like substances (C2, C3) significantly decreased particularly in farmland. The humic acid-like content of surface soils (Robinia pseudoacacia) was relatively higher, but the difference between the two vegetation soils was not significant. The freshness index (ß/α) values of DOM as well as biological index (BIX) values were significantly higher in farmland than that in forestland, and the humification index (HIX) values were lower than in forestland soils, indicating that the change of soil DOM in farmland was more active than that in forestland and more dependent on local terrestrial sources. These results could contribute to a better understanding of the vertical distribution and features of soil DOM during the 'Grain-for-Green' program of CLP.

[Clinical and genetic analysis of a rare fetus with Protein C deficiency due to compound heterozygous variants of PROC gene].

OBJECTIVE: To explore the genetic etiology for a fetus with hydrocephalus and intraventricular hemorrhage. METHODS: Trio whole exome sequencing was carried out. Candidate variants were verified by Sanger sequencing of the fetus and its parents. RESULTS: The fetus was found to harbor c.818G>A (p.W273X) and c.833T>C (p.L278P) compound heterozygous variants of the PROC gene, which were respectively inherited from its mother and father. Based on the guidelines of the American College of Medical Genetics and Genomics (ACMG), both variants were predicted to be likely pathogenic (PVS1_Strong+PM2_Supporting+PP4; PM2_Supporting+PM3+PP1+PP3+PP4). CONCLUSION: The fetus was diagnosed with Protein C deficiency due to the c.818G>A (p.W273X) and c.833T>C (p.L278P) compound heterozygous variants of the PROC gene. Above finding has enriched the spectrum of PROC gene variants and enabled genetic counseling and prenatal diagnosis for the family.

(20S) Ginsenoside Rh2-Activated, Distinct Apoptosis Pathways in Highly and Poorly Differentiated Human Esophageal Cancer Cells.

Ginsenoside Rh2 (G-Rh2), a rare ginsenoside isolated from red ginseng, has considerable anti-cancer activity and induces apoptosis in a variety of cancer cells, but its activity in esophageal cancer cells is unclear. In this study, we examined the cytotoxic activity of (20S) G-Rh2 in highly differentiated esophageal squamous ECA109 cells and poorly differentiated esophageal squamous TE-13 cells. (20S) G-Rh2 exerted intense cytotoxicity in ECA109 and TE-13 cells with an IC50 of 2.9 and 3.7 µg/mL, respectively. After treatment with G-Rh2, Bcl-2, and Bcl-xL, the two main anti-apoptosis Bcl-2 family proteins upregulated, and Bax and Bak, the two key pro-apoptosis proteins translocated to mitochondria in both cell lines. At the same time, cytochrome c and Smac released from mitochondria, followed by caspase-9 activation, indicating that a mitochondria-mediated intrinsic apoptosis pathway was activated in both cell lines upon treatment with (20S) G-Rh2. It is noteworthy that (20S) G-Rh2 upregulated the transcription and protein expression of two death receptors, Fas and DR5, and subsequently activated Caspase-8 in the TE-13 cells but not in the ECA109 cells. Taken together, we demonstrated the potent anti-esophageal cancer cell activity of (20S) G-Rh2 and showed its working mechanism in two differentiated esophageal cancer cells, which can provide important evidence for developing an effective strategy for anti-esophageal cancer treatment.

[Analysis of TUBB2B gene variant in a fetus with complex cortical dysplasia with other brain malformations-7].

OBJECTIVE: To explore the genetic basis for a fetus with dysgenesis of corpus callosum and other brain malformations. METHODS: Whole exome sequencing was carried out for the fetus and its parents. Suspected pathogenic variants were verified by Sanger sequencing. RESULTS: A novel de novo missense variant c.758T>A (p.L253Q) of the TUBB2B gene was identified, which was unreported previously. Based on the guidelines from the American College of Medical Genetics, the c.758T>A variant was predicted to be likely pathogenic. Bioinformatics analysis predicted that the leucine at position 253 was highly conserved among various species, and the c.758T>A variant may impact the formation of hydrogen bonds between Leu253 and Asp249 and Met257 residues, which in turn may affect the combination of GTP/GDP and function of the TUBB2B protein. CONCLUSION: The c.758T>A variant of the TUBB2B gene probably underlay the fetal malformations in this Chinese family. Above discovery has enriched the spectrum of TUBB2B gene variants and provided a basis for genetic counseling and prenatal diagnosis.

[Variation analysis of EPG5 gene in a Vici syndrome family].

OBJECTIVE: To explore the genetic etiology of Vici syndrome in a Chinese family. METHODS: Whole exome sequencing (WES) technology was used to detect gene variants in a fetus of abnormal ultrasonic structure without abnormalities in routine chromosome karyotype analysis and SNP-array. Sanger sequencing and bioinformatics prediction were performed for the suspected variants of the fetus and parents. RESULTS: The fetus and the elder sister have carried c. 2427delC (p.T809fs) and c.1886A>T (p.E629V) compound heterozygous variants of the EPG5 gene, which were respectively inherited from their mother and father. Neither variant was reported previously. According to ACMG guidelines, the c.2427delC variant was predicted as pathogenic, while the c.1886A>T variant was of uncertain significance. PolyPhen-2 and PROVEAN software indicated that c.1886A>T variant was probably damaging. CONCLUSION: The c.2427delC and c.1886A>T variants of the EPG5 gene probably underlie the pathogenesis of the Vici syndrome in this family. Above finding has enriched the variational spectrum of EPG5 gene and provided a basis for genetic counseling and prenatal diagnosis for the family.

Epileptic Seizure Detection Using Geometric Features Extracted from SODP Shape of EEG Signals and AsyLnCPSO-GA.

Epilepsy is a neurological disorder that is characterized by transient and unexpected electrical disturbance of the brain. Seizure detection by electroencephalogram (EEG) is associated with the primary interest of the evaluation and auxiliary diagnosis of epileptic patients. The aim of this study is to establish a hybrid model with improved particle swarm optimization (PSO) and a genetic algorithm (GA) to determine the optimal combination of features for epileptic seizure detection. First, the second-order difference plot (SODP) method was applied, and ten geometric features of epileptic EEG signals were derived in each frequency band (δ, θ, α and ß), forming a high-dimensional feature vector. Secondly, an optimization algorithm, AsyLnCPSO-GA, combining a modified PSO with asynchronous learning factor (AsyLnCPSO) and the genetic algorithm (GA) was proposed for feature selection. Finally, the feature combinations were fed to a naïve Bayesian classifier for epileptic seizure and seizure-free identification. The method proposed in this paper achieved 95.35% classification accuracy with a tenfold cross-validation strategy when the interfrequency bands were crossed, serving as an effective method for epilepsy detection, which could help clinicians to expeditiously diagnose epilepsy based on SODP analysis and an optimization algorithm for feature selection.

Directed Brain Network Analysis for Fatigue Driving Based on EEG Source Signals.

Fatigue driving is one of the major factors that leads to traffic accidents. Long-term monotonous driving can easily cause a decrease in the driver's attention and vigilance, manifesting a fatigue effect. This paper proposes a means of revealing the effects of driving fatigue on the brain's information processing abilities, from the aspect of a directed brain network based on electroencephalogram (EEG) source signals. Based on current source density (CSD) data derived from EEG signals using source analysis, a directed brain network for fatigue driving was constructed by using a directed transfer function. As driving time increased, the average clustering coefficient as well as the average path length gradually increased; meanwhile, global efficiency gradually decreased for most rhythms, suggesting that deep driving fatigue enhances the brain's local information integration abilities while weakening its global abilities. Furthermore, causal flow analysis showed electrodes with significant differences between the awake state and the driving fatigue state, which were mainly distributed in several areas of the anterior and posterior regions, especially under the theta rhythm. It was also found that the ability of the anterior regions to receive information from the posterior regions became significantly worse in the driving fatigue state. These findings may provide a theoretical basis for revealing the underlying neural mechanisms of driving fatigue.

[Genetic analysis of a rare fetus with mandibulofacial dysostosis Guion-Almeida type].

OBJECTIVE: To delineate the clinical and genetic features of a fetus with micrognathia, low-set ears, microtia, polyhydramnios and anechoic stomach by ultrasonography. METHODS: Whole exome sequencing (WES) was carried out to detect genetic variant in the fetus, for which routine chromosomal karyotyping and chromosomal microarray analysis (CMA) yielded no positive finding. Candidate variants were verified by Sanger sequencing and bioinformatic analysis. RESULTS: WES revealed that the fetus has carried a de novo nonsense c.2302C>T (p.Q768X) variant in exon 23 of the EFTUD2 gene, which was detected in neither parent. The variant was unreported previously and may lead to premature termination of the translation of EFTUD2 protein at the 768th amino acid. Bioinformatic analysis predicted the amino acid to be highly conserved and may alter the structure and function of the EFTUD2 protein. CONCLUSION: The c.2302C>T variant of the EFTUD2 gene probably underlay the mandibulofacial dysostosis Guion-Almeida type in the fetus. Discovery of the novel variant has enriched variant spectrum of the EFTUD2 gene and provided a basis for genetic counseling and prenatal diagnosis for the family.

Brain Complex Network Characteristic Analysis of Fatigue during Simulated Driving Based on Electroencephalogram Signals.

Fatigued driving is one of the major causes of traffic accidents. Frequent repetition of driving behavior for a long time may lead to driver fatigue, which is closely related to the central nervous system. In the present work, we designed a fatigue driving simulation experiment and collected the electroencephalogram (EEG) signals. Complex network theory was introduced to study the evolution of brain dynamics under different rhythms of EEG signals during several periods of the simulated driving. The results show that as the fatigue degree deepened, the functional connectivity and the clustering coefficients increased while the average shortest path length decreased for the delta rhythm. In addition, there was a significant increase of the degree centrality in partial channels on the right side of the brain for the delta rhythm. Therefore, it can be concluded that driving fatigue can cause brain complex network characteristics to change significantly for certain brain regions and certain rhythms. This exploration may provide a theoretical basis for further finding objective and effective indicators to evaluate the degree of driving fatigue and to help avoid fatigue driving.

Spike-frequency adaptation of a two-compartment neuron modulated by extracellular electric fields.

Spike-frequency adaptation has been shown to play an important role in neural coding. Based on a reduced two-compartment model, here we investigate how two common adaptation currents, i.e., voltage-sensitive potassium current (I(M)) and calcium-sensitive potassium current (I(AHP)), modulate neuronal responses to extracellular electric fields. It is shown that two adaptation mechanisms lead to distinct effects on the dynamical behavior of the neuron to electric fields. These effects depend on a neuronal morphological parameter that characterizes the ratio of soma area to total membrane area and internal coupling conductance. In the case of I(AHP) current, changing the morphological parameter switches spike initiation dynamics between saddle-node on invariant cycle bifurcation and supercritical Hopf bifurcation, whereas it only switches between subcritical and supercritical Hopf bifurcations for I(M) current. Unlike the morphological parameter, internal coupling conductance is unable to alter the bifurcation scenario for both adaptation currents. We also find that the electric field threshold for triggering neuronal steady-state firing is determined by two parameters, especially by the morphological parameter. Furthermore, the neuron with I(AHP) current generates mixed-mode oscillations through the canard phenomenon for some small values of the morphological parameter. All these results suggest that morphological properties play a critical role in field-induced effects on neuronal dynamics, which could qualitatively alter the outcome of adaptation by modulating internal current between soma and dendrite. The findings are readily testable in experiments, which could help to reveal the mechanisms underlying how the neuron responds to electric field stimulus.

Exploring how extracellular electric field modulates neuron activity through dynamical analysis of a two-compartment neuron model.

To investigate how extracellular electric field modulates neuron activity, a reduced two-compartment neuron model in the presence of electric field is introduced in this study. Depending on neuronal geometric and internal coupling parameters, the behaviors of the model have been studied extensively. The neuron model can exist in quiescent state or repetitive spiking state in response to electric field stimulus. Negative electric field mainly acts as inhibitory stimulus to the neuron, positive weak electric field could modulate spiking frequency and spike timing when the neuron is already active, and positive electric fields with sufficient intensity could directly trigger neuronal spiking in the absence of other stimulations. By bifurcation analysis, it is observed that there is saddle-node on invariant circle bifurcation, supercritical Hopf bifurcation and subcritical Hopf bifurcation appearing in the obtained two parameter bifurcation diagrams. The bifurcation structures and electric field thresholds for triggering neuron firing are determined by neuronal geometric and coupling parameters. The model predicts that the neurons with a nonsymmetric morphology between soma and dendrite, are more sensitive to electric field stimulus than those with the spherical structure. These findings suggest that neuronal geometric features play a crucial role in electric field effects on the polarization of neuronal compartments. Moreover, by determining the electric field threshold of our biophysical model, we could accurately distinguish between suprathreshold and subthreshold electric fields. Our study highlights the effects of extracellular electric field on neuronal activity from the biophysical modeling point of view. These insights into the dynamical mechanism of electric field may contribute to the investigation and development of electromagnetic therapies, and the model in our study could be further extended to a neuronal network in which the effects of electric fields on network activity may be investigated.

Multiplexed target detection using DNA-binding dye chemistry in droplet digital PCR.

Two years ago, we described the first droplet digital PCR (ddPCR) system aimed at empowering all researchers with a tool that removes the substantial uncertainties associated with using the analogue standard, quantitative real-time PCR (qPCR). This system enabled TaqMan hydrolysis probe-based assays for the absolute quantification of nucleic acids. Due to significant advancements in droplet chemistry and buoyed by the multiple benefits associated with dye-based target detection, we have created a "second generation" ddPCR system compatible with both TaqMan-probe and DNA-binding dye detection chemistries. Herein, we describe the operating characteristics of DNA-binding dye based ddPCR and offer a side-by-side comparison to TaqMan probe detection. By partitioning each sample prior to thermal cycling, we demonstrate that it is now possible to use a DNA-binding dye for the quantification of multiple target species from a single reaction. The increased resolution associated with partitioning also made it possible to visualize and account for signals arising from nonspecific amplification products. We expect that the ability to combine the precision of ddPCR with both DNA-binding dye and TaqMan probe detection chemistries will further enable the research community to answer complex and diverse genetic questions.

Topology identification of uncertain nonlinearly coupled complex networks with delays based on anticipatory synchronization.

This paper presents an adaptive anticipatory synchronization based method for simultaneous identification of topology and parameters of uncertain nonlinearly coupled complex dynamical networks with time delays. An adaptive controller is proposed, based on Lyapunov stability theorem and Barbǎlat's Lemma, to guarantee the stability of the anticipatory synchronization manifold between drive and response networks. Meanwhile, not only the identification criteria of network topology and system parameters are obtained but also the anticipatory time is identified. Numerical simulation results illustrate the effectiveness of the proposed method.

circRNF13, a novel N6-methyladenosine-modified circular RNA, enhances radioresistance in cervical cancer by increasing CXCL1 mRNA stability.

BACKGROUND: Circular RNAs (circRNAs) and N6-methyladenosine (m6A) have been shown to play an increasingly critical role in the development of different cancers. However, there is limited evidence on how circRNAs and m6A interact to affect the radiosensitivity of cervical cancer (CC). This study provides a mechanistic understanding of the novel m6A-regulated circRNF13 in enhancing radioresistance in CC. METHODS: Differentially expressed circRNAs were identified from radiosensitive and radioresistant CC tissues. Meanwhile, these circRNAs were subjected to methylated RNA immunoprecipitation (Me-RIP). Finally, the effects of these circRNAs on radiosensitivity were characterized. RESULTS: CircRNF13 was poorly expressed in CC patients that were sensitive to concurrent radiochemotherapy. Experiments conducted both in vitro and in vivo confirmed that the knockdown of circRNF13 potentiated the radiosensitivity of CC cells. Further mechanistic studies revealed that METTL3/YTHDF2 promoted the degradation of circRNF13 and subsequently affected the stability of CXC motif chemokine ligand 1 (CXCL1), ultimately enhancing the radiosensitivity of CC cells. CONCLUSION: This study identified circRNF13 as a novel m6A-modified circRNA and validated the METTL3/YTHDF2/circRNF13/CXCL1 axis as a potential target for CC radiotherapy.

A deep learning framework for identifying Alzheimer's disease using fMRI-based brain network.

Background: The convolutional neural network (CNN) is a mainstream deep learning (DL) algorithm, and it has gained great fame in solving problems from clinical examination and diagnosis, such as Alzheimer's disease (AD). AD is a degenerative disease difficult to clinical diagnosis due to its unclear underlying pathological mechanism. Previous studies have primarily focused on investigating structural abnormalities in the brain's functional networks related to the AD or proposing different deep learning approaches for AD classification. Objective: The aim of this study is to leverage the advantages of combining brain topological features extracted from functional network exploration and deep features extracted by the CNN. We establish a novel fMRI-based classification framework that utilizes Resting-state functional magnetic resonance imaging (rs-fMRI) with the phase synchronization index (PSI) and 2D-CNN to detect abnormal brain functional connectivity in AD. Methods: First, PSI was applied to construct the brain network by region of interest (ROI) signals obtained from data preprocessing stage, and eight topological features were extracted. Subsequently, the 2D-CNN was applied to the PSI matrix to explore the local and global patterns of the network connectivity by extracting eight deep features from the 2D-CNN convolutional layer. Results: Finally, classification analysis was carried out on the combined PSI and 2D-CNN methods to recognize AD by using support vector machine (SVM) with 5-fold cross-validation strategy. It was found that the classification accuracy of combined method achieved 98.869%. Conclusion: These findings show that our framework can adaptively combine the best brain network features to explore network synchronization, functional connections, and characterize brain functional abnormalities, which could effectively detect AD anomalies by the extracted features that may provide new insights into exploring the underlying pathogenesis of AD.

A new view into three-dimensional excitation-emission matrix fluorescence spectroscopy for dissolved organic matter.

Three-dimensional excitation-emission matrix fluorescence spectroscopy (3D EEMs) has been extensively used for dissolved organic matter (DOM) characterization. However, the application of 3D EEMs is constantly limited by issues such as contradictory component identification, confusing interpretation of spectral indicators, and inability to establish biodegradability. In this study, some improvements were proposed by investigating the 3D EEMs, spectral indicators, and degradability of the standard and representative DOM. To overcome the unclear identification of DOM components, it was recommended to partition 3D EEMs into three subareas: aromatic protein (New-I), humic-like (New-II), and soluble microbial by-product-like (New-III). Significant strong positive correlations (ρ = 0.727, P < 0.001) were observed between fluorescence index (FI) and biological index (BIX), and (R = 0.809, P < 0.001) humification index (HIX) and specific ultraviolet absorbance of 254 nm (SUVA254). Except for FI (R = -0.483, P = 0.023), no other spectral indicators (P > 0.05) were found to be significantly correlated with molecular weight. As thence results, the FI and HIX were the most suitable indicators for evaluating DOM. The half-life (20 < 21 < 26 < 29 < 46 days) revealed that the degradability of individual DOM components was in the order of tyrosine > tryptophan > fulvic acid > protein > humic acid. The degradation dynamics were governed by first-order decay kinetics (R2 = 0.91-0.99). This study clarified the fluorescence properties and degradability of DOM, as well as the reliability of spectral indicators. The degradation performance of individual DOM components engaged in the carbon cycling process was revealed, paving the path for further applications of 3D EEMs in DOM research.

A novel framework of MOPSO-GDM in recognition of Alzheimer's EEG-based functional network.

Background: Most patients with Alzheimer's disease (AD) have an insidious onset and frequently atypical clinical symptoms, which are considered a normal consequence of aging, making it difficult to diagnose AD medically. But then again, accurate diagnosis is critical to prevent degeneration and provide early treatment for AD patients. Objective: This study aims to establish a novel EEG-based classification framework with deep learning methods for AD recognition. Methods: First, considering the network interactions in different frequency bands (δ, θ, α, ß, and γ), multiplex networks are reconstructed by the phase synchronization index (PSI) method, and fourteen topology features are extracted subsequently, forming a high-dimensional feature vector. However, in feature combination, not all features can provide effective information for recognition. Moreover, combining features by manual selection is time-consuming and laborious. Thus, a feature selection optimization algorithm called MOPSO-GDM was proposed by combining multi-objective particle swarm optimization (MOPSO) algorithm with Gaussian differential mutation (GDM) algorithm. In addition to considering the classification error rates of support vector machine, naive bayes, and discriminant analysis classifiers, our algorithm also considers distance measure as an optimization objective. Results: Finally, this method proposed achieves an excellent classification error rate of 0.0531 (5.31%) with the feature vector size of 8, by a ten-fold cross-validation strategy. Conclusion: These findings show that our framework can adaptively combine the best brain network features to explore network synchronization, functional interactions, and characterize brain functional abnormalities, which can improve the recognition efficiency of diseases. While improving the classification accuracy of application algorithms, we aim to expand our understanding of the brain function of patients with neurological disorders through the analysis of brain networks.

A novel 268 kb deletion combined with a splicing variant in IL7R causes of severe combined immunodeficiency in a Chinese family: a case report.

BACKGROUND: Severe combined immunodeficiency (SCID) is a group of fatal primary immunodeficiencies characterized by the severe impairment of T-cell differentiation. IL7R deficiency is a rare form of SCID that usually presents in the first months of life with severe and opportunistic infections, failure to thrive, and a high risk of mortality unless treated. Although recent improvements in early diagnosis have been achieved through newborn screening, few IL7R-related SCID patients had been reported in the Chinese population. CASE PRESENTATION: Here, we retrospectively analyzed a case of SCID in a 5-month-old girl with symptoms, including severe T-cell depletion, recurrent fever, oral ulcers, pneumonia, hepatosplenomegaly, bone marrow hemophagocytosis, and bacterial and viral infections. Whole-exome sequencing (WES), quantitative PCR (qPCR), and chromosome microarray analysis (CMA) were performed to identify the patient's genetic etiology. We identified a 268 kb deletion and a splicing variant, c.221 + 1G > A, in the proband. These two variants of IL7R were inherited from the father and mother. CONCLUSIONS: To our knowledge, this is the first report of whole IL7R gene deletion in combination with a pathogenic splicing variant in a patient with SCID. This deletion also expands the pathogenic variation spectrum of SCID caused by IL7R. The incorporation of exome-based copy number variant analysis makes WES a powerful molecular diagnostic technique for the clinical diagnosis of pediatric patients.

Parameter estimation of the FitzHugh-Nagumo model using noisy measurements for membrane potential.

This paper proposes an identification method to estimate the parameters of the FitzHugh-Nagumo (FHN) model for a neuron using noisy measurements available from a voltage-clamp experiment. By eliminating an unmeasurable recovery variable from the FHN model, a parametric second order ordinary differential equation for the only measurable membrane potential variable can be obtained. In the presence of the measurement noise, a simple least squares method is employed to estimate the associated parameters involved in the FHN model. Although the available measurements for the membrane potential are contaminated with noises, the proposed identification method aided by wavelet denoising can also give the FHN model parameters with satisfactory accuracy. Finally, two simulation examples demonstrate the effectiveness of the proposed method.

[Nonlinear dynamic analysis of electrical signals of wide dynamic range neurons in the spinal dorsal horn evoked by acupuncture manipulation at different frequencies].

OBJECTIVE: To study the encoding information of electrical signals of wide dynamic range (WDR) neurons in the spinal dorsal horn evoked by acupuncture manipulation at different frequencies using nonlinear dynamics analysis. METHODS: Microelectrode extracellular recordings were used to observe the WDR neuron discharge evoked by acupuncture manipulation at Zusanli point (ST36) with different frequencies (0.5, 1, 2, and 3 Hz) in SD rats. The nonlinear dynamics analysis method was used to extract the nonlinear characteristic parameters, such as interspike interval, the Lyapunov exponent, Lempel-Ziv complexity, and the neural coding of the electrical signal evoked by acupuncture manipulations at different frequencies. RESULTS: Different characteristics were manifested with acupuncture manipulations at 4 different frequencies. More than a simple linear correlation was shown between the firing rate of the WDR neurons and the frequency of the acupuncture manipulation. The electrical signals evoked by acupuncture manipulation at Zusanli point (ST36) showed distinguished chaotic features. CONCLUSIONS: It is applicable and feasible to describe and summarize the rhythm of the acupuncture electrical signal using the concepts and terminology of the nonlinear dynamics. Different acupuncture manipulation methods could interfere the transmission, coding, and processing of electrical signals in the spinal dorsal horn.