Signal Analysis Research Articles

A highly sensitive approach for the analysis of tyrosine phosphoproteome in primary T cells

Tyrosine phosphorylation, a common post-translational modification process for proteins, is involved in a variety of biological processes. However, the abundance of tyrosine-phosphorylated proteins is very low, making their identification by mass spectrometry (MS) is difficult; thus, milligrams of the starting material are often required for their enrichment. For example, tyrosine phosphorylation plays an important role in T cell signal transduction. However, the number of primary T cells derived from biological tissue samples is very small, and these cells are difficult to culture and expand; thus, the study of T cell signal transduction is usually carried out on immortalized cell lines, which can be greatly expanded. However, the data from immortalized cell lines cannot fully mimic the signal transduction processes observed in the real physiological state, and they usually lead to conclusions that are quite different from those of primary T cells. Therefore, a highly sensitive proteomic method was developed for studying tyrosine phosphorylation modification signals in primary T cells. To address the issue of the limited T cells numbers, a comprehensive protocol was first optimized for the isolation, activation, and expansion of primary T cells from mouse spleen. CD3+ primary T cells were successfully sorted; more than 91% of the T cells collected were well activated on day 2, and the number of T cells expanded to over 7-fold on day 4. Next, to address the low abundance of tyrosine-phosphorylated proteins, we used SH2-superbinder affinity enrichment and immobilized Ti4+affinity chromatography (Ti4+-IMAC) to enrich the tyrosine-phosphorylated polypeptides of primary T cells that were co-stimulated with anti-CD3 and anti-CD28. These polypeptides were resolved using nanoscale liquid chromatography-tandem mass spectrometry (nanoLC-MS/MS). Finally, 282 tyrosine phosphorylation sites were successfully identified in 1 mg of protein, including many tyrosine phosphorylation sites on the immunoreceptor tyrosine-based activation motif (ITAM) in the intracellular region of the T cell receptor membrane protein CD3, as well as the phosphotyrosine sites of ZAP70, LAT, VAV1, and other proteins related to signal transduction under costimulatory conditions. In summary, to solve the technical problems of the limited number of primary cells, low abundance of tyrosine-phosphorylated proteins, and difficulty of detection by MS, we developed a comprehensive proteomic method for the in-depth analysis of tyrosine phosphorylation modification signals in primary T cells. This protocol may be applied to map signal transduction networks that are closely related to physiological states.

Behavioral events and functional analysis of bursicon signal during adult eclosion in the 28-spotted larger potato ladybird

To accommodate growth, insects must periodically shed their exoskeletons. In Manduca sexta, Drosophila melanogaster and Tribolium castaneum, Bursicon (Burs)/ Partner of bursicon (Pburs)-LGR2 signal is an indispensable component for the proper execution of ecdysis behavior during adult eclosion. Nevertheless, the behavioral events and the roles of bursicon signaling in other insects deserve further exploration. In the current paper, we found that the pupal-adult ecdysis in Henosepilachna vigintioctomaculata could be divided into three distinct stages, preecdysis, ecdysis and postecdysis. Preecdysis behavioral sequences included abdomen twitches, dorsal-ventral contractions and air filling that function to loosen the old cuticle. Ecdysis events began with anterior-posterior contractions that gradually split the old integument along the dorsal body midline, followed by freeing of legs and mouthparts, and culminated in detachment from pupal cuticle. Postecdysis behavioral processes contained three actions: perch selection and stretching of elytra and hindwings. RNA interference for HvBurs, HvPburs or Hvrk (encoding LGR2) strongly impaired wing expansion actions, and slightly influenced preecdysis and ecdysis behaviors. The RNAi beetles failed to extend their elytra and hindwings. In addition, injected with dsrk also caused kinked femurs and tibia. Our findings establish that bursicon pathway is involved in regulation of adult eclosion behavior, especially wing expansion motor programs. Given that wings facilitate food foraging, courtship, predator avoidance, dispersal and migration, our results provide a potential target for controlling H. vigintioctomaculata.

A Hybrid Deep Learning Framework Using Scaling‐Basis Chirplet Transform for Motor Imagery EEG Recognition in Brain–Computer Interface Applications

ABSTRACTThe emerging field of brain–computer interface has significantly facilitated the analysis of electroencephalogram signals required for motor imagery classification tasks. However, the accuracy of EEG classification models has been restricted by the low signal‐to‐noise ratio, nonlinear nature of brain signals, and a lack of sufficient EEG data for training. To address these challenges, this study proposes a new approach that combines time‐frequency analysis with a hybrid parallel–series attention‐based deep learning network for EEG signal classification. The proposed framework comprises three main elements: first, a scaling‐basis chirplet transform designed to effectively capture the characteristics of nonstationary EEG signals; second, a hybrid parallel–series attention‐based deep learning network to extract features. The serial information flow continuously expands the receptive fields of output neurons, whereas parallel information flow extracts features based on different regions. Finally, machine learning classifiers are utilized to predict the corresponding motor imagery state. The developed EEG‐based motor imagery classification framework is assessed by two open‐source datasets, BCI competition III, dataset IIIa and BCI competition IV, dataset IIa and has achieved the average classification accuracy of 95.55% on BCI competition III, dataset IIIa and 90.18% on BCI competition IV, dataset IIa. The experimental findings illustrate that this study has attained promising motor imagery discrimination performance, surpassing existing techniques in terms of classification accuracy and kappa coefficient.

Identifying grain size in ASTM A36 steel using ultrasonic backscattered signals and machine learning

Ultrasonic nondestructive techniques can be useful tools in the microstructural classification of metallic alloys. Among the most common techniques for characterizing materials, backscattered signal analysis stands out because it does not require a back surface echo. This study classifies five ASTM A36 steel samples with varying grain sizes using ultrasonic waves. For each sample, ultrasound signals were obtained using two ultrasonic array probes with center frequencies of 5 and 10 MHz. An extensive feature engineering process was conducted using the tfresh package in Python, which extracts a wide range of features from time series data, transforming raw ultrasound signals into a structured format. Seven machine learning models were evaluated based on accuracy, precision, recall, and F1 score, with performances ranging from 70 to 100%. The XGBoost model exhibited the best performance with 10 MHz probe signals, achieving 100% accuracy. An additional validation test was conducted to evaluate the models’ generalization capability, considering inter-specimen variabilities. Despite a slight reduction in prediction metrics, the XGBoost model maintained good performance, with accuracy between 89% and 100% across all frequencies. Such findings demonstrate that backscattered grain noise can effectively identify grain sizes provided that an adequate machine learning model is used.

Strain clamp condition monitoring for transmission lines based on acoustic emission signals

Abstract To ensure the reliability and safety of the power supply in the electric system, it is significant to realize the strain clamp condition monitoring for transmission lines. A strain clamp vibration test rig was designed and built to collect acoustic emission (AE) signals generated by strain clamps with an excitation frequency of 30, 50, 70, 90, and 110 Hz, respectively. The characteristics of the collected AE signals are then analyzed in the time domain and time-frequency domain. The results of the signal analysis in the time domain show that, the increasing excitation frequency leads to a higher amplitude of the AE signal, and the signal period is consistent with that of the excitation. Meanwhile, both the root-mean-square (RMS) and the average signal level (ASL) of the AE signal increase continuously with the excitation frequency, of which the growth trends are different. Additionally, the results of the signal analysis in the time-frequency domain also show that the signal spectrum is mainly concentrated in the range of 30 kHz to 200 kHz at different excitation frequencies and the increasing excitation frequency significantly leads to more high-frequency components in the AE signal.

A bifunctional protein RANbody based on nanobody facilitates dual-mode immunoassay of Staphylococcal enterotoxin B in food samples

Staphylococcal enterotoxins B (SEB), the major virulence factor of Staphylococcus aureus (S. aureus), brings about grievous food poisoning by infiltrating the food supply chain at diverse phases. ELISA based on traditional antibody and single signal analysis is slightly less specific and stable in SEB detection. Here, we innovatively reported an immunoassay based on a bifunctional protein reconstructed by nanobody, greatly enhancing the specificity of ELISA. Meanwhile, this is complemented by the second signal response, quenching effect of oxidative chromogenic substances on fluorochrome, to heighten the accuracy with dual mode. Under optimal conditions, the dual-mode immunoassay showed a wide linear range from 0.31 to 2500ng/mL and a lower limit of detection (LOD) with 0.12ng/mL for colorimetric mode and 0.24ng/mL for fluorescence mode. There was no nonspecific binding occurred under the interference of S. aureus and other enterotoxins, demonstrating the splendid specificity. Moreover, the proposed assay was successfully employed in detecting SEB in food samples with satisfying practicability. This work integrated the nanobody modification technology with a dual signal sensing strategy, providing a reference for the development of a more specific and feasible platform for food safety.

A high-sensitivity balloon-type optical fiber sensor enables wide-range strain sensing with the assistance of CNN

A balloon-type optical fiber sensor (OFS) for strain measurement was proposed, and the measurement range was significantly increased with the assistance of convolutional neural networks (CNN). The presented OFS was prepared by bending a Mach-Zehnder structure based on polarization-maintaining fibers into a balloon-type, which exhibits a sensitivity of 26.3 pm/με under the strain range from 0 to 300 με. The CNN-based signal analysis method can effectively extract the data information in the spectral, and the strain range that can be measured is greatly enlarged to 0–2000 με, while the accuracy of the measurement can be guaranteed. Further, the temperature sensitivity of the OFS is −0.0252 nm/°C; the lower temperature sensitivity can effectively suppress the crosstalk problem caused by temperature changes during strain measurement. The proposed method remarkably increases the strain measurement range of OFS, which is conducive to the application and development of OFSs.

Development of a semi-supervised machine learning based noise filter for quantum cascade laser-coupled mid-infrared spectrometer

The rapid and accurate quantitative determination of molecular rovibrational spectral features is highly desirable for applications in fundamental high-resolution spectroscopy, trace chemical sensing, and environmental monitoring. But significant spectral noise can impede the accuracy and precision of such spectral measurements as well as the precise classification of fine and hyperfine spectral fingerprints. Nevertheless, the denoising of weak rovibrational spectral data has long been a computational and experimental challenge. Here, we develop a new semi-supervised machine learning (SSML) denoising method combining unsupervised spectral eigenvector space dimensionality reduction of the spectral data matrix exploiting principal component analysis with supervised Fourier domain residual analysis for enhancing high-resolution rovibrational spectral signal quality. We discussed the detailed implementation of the SSML algorithm on the real experimental mid-infrared rovibrational spectral features of nitrogen dioxide (NO2) in the gas-phase acquired from a quantum cascade laser-coupled cavity ring-down spectroscopic method. Our results confirm the robustness and feasibility of the SSML approach and could lead to a wide range of pertinent applications in infrared spectroscopy including precise spectral signal analysis, concentration retrieval and performance enhancement of laser-coupled spectroscopic sensors.

The distribution and evolutionary dynamics of dopaminergic neurons in molluscs.

Dopamine is one of the most versatile neurotransmitters in invertebrates. It's distribution and plethora of functions is likely coupled to feeding ecology, especially in Euthyneura (the largest clade of molluscs), which presents the broadest spectrum of environmental adaptations. Still, the analyses of dopamine-mediated signaling were dominated by studies of grazers. Here, we characterize the distribution of dopaminergic neurons in representatives of two distinct ecological groups: the sea angel - obligate predatory pelagic mollusc Clione limacina (Pteropoda, Gymnosomata) and its prey - the sea devil Limacina helicina (Pteropoda, Thecosomata) as well as the plankton eater Melibe leonina (Nudipleura, Nudibranchia). By using tyrosine hydroxylase-immunoreactivity (TH-ir) as a reporter, we showed that the dopaminergic system is moderately conservative among euthyneurans. Across all studied species, small numbers of dopaminergic neurons in the central ganglia contrast to significant diversification of TH-ir neurons in the peripheral nervous system, primarily representing sensory-like cells, which predominantly concentrated in the chemotactic areas and projecting afferent axons to the central nervous system. Combined with α-tubulin immunoreactivity, this study illuminates the unprecedented complexity of peripheral neural systems in gastropod molluscs, with lineage-specific diversification of sensory and modulatory functions.

Features of Bearing on Underwater Object Using Phase Information of a Differential Stereo Sensor

Introduction. Safety of navigation and development of underwater mineral deposits require the accurate detection of various underwater objects. The literature discusses the issues of tracking their motion and trajectory. Sonar methods are proposed to maintain high accuracy of underwater object positioning. High accuracy of the bearing of stereo sensors with an ultrashort base is noted. However, this equipment is sensitive to the sampling rate of the signals, which causes “sampling noise”. There are no publicly available publications dedicated to the solution to this problem. The presented study is designed to fill this gap. This work is aimed to study the possibility of obtaining data clarifying information about the bearing of underwater objects through using the phase information about echoed probing signals and an additional procedure for resampling the source data.Materials and Methods. The location of the object was determined using the experimental complex for studying hydroacoustic sensors created by V.A. Shirokov and V.N. Milich at the Udmurt Federal Research Center, the Ural Branch of the Russian Academy of Sciences. A stereo sensor with a small base (30 mm) was used compared to the distance to the object (≈800–900 mm). Digital filtering methods and mathematical apparatus of correlation analysis of return hydroacoustic signals obtained by the phase method were used for data processing.Results. The results of comparing two methods for determining the bearing on an object are presented: by the difference in the time of arrival of the pulse-leading edges and by the maximum of the cross-correlation function (CCF). The change in bearing as the object moves, is graphically shown. The use of the leading edge of the signal caused small outliers of values along the entire bearing curve (less than 0.12 rad). At the maximum CCF, emissions were recorded only in some areas, but they were quite significant (about 0.17 rad). It showed how to select points corresponding to a smoother and more valid object trajectory, and how to work with erroneous points. The presented method of error correction can be implemented programmatically. With a quasi-harmonious signal, rare measurements of the original signal are interpolated by frequent calculated values. Thanks to this virtual increase in the sampling rate (oversampling), intermediate indicators can be recorded in the digitized source data. Interpolation of the signal values by a cubic spline allowed us to obtain 20 points for 1 period of the signal instead of 5 points in the original version. In this case, the trajectory formed with the maximum CCF is more correct.Discussion and Conclusion. The direction-finding problem can be solved with the accuracy required for practical application. Taking into account the factor of smoothness and continuity of the object's trajectory makes it possible to qualitatively correct the selection of the maximum of the cross-correlation function of the stereo sensor signals. The proposed methods have great potential for the development of underwater vision systems.

Bionic fusion perspective: Audiovisual-motivated integration network for solar irradiance prediction

Accurate and reliable prediction of solar irradiance (SI) is an important requirement to develop solar energy while a challenging task due to stochastic and nonlinear data characteristics. Additionally, most deep networks show powerful prediction capabilities but lack the supports from biological science, reflecting that bionically-inspired networks in SI analysis are still not enough explored. To this end, this paper proposes an audiovisual-motivated Transformer-CNN integration network, called ATI-net, for predicting SI. The audiovisual cognition gives a superior design framework for ATI-net with signal capture, signal analysis, and prediction blocks. In the first block, through mimicking the function of both eye and ear in external signal conversion, multi-scale features are extracted by incorporating multi-branch convolutions with varying kernels, where the Mish function addresses the problem that traditional ReLU function stops learning when the input is negative. In the second block, through mimicking the function of left and right hemispheres in neuronal signal analysis, two structures triggered by Transformers and convolutions are designed to remember temporal evolutionary rules, where residual connections are beneficial to mine deep information and avoid forgetting. In the third block, through mimicking the function of a higher brain region in generating understanding, the above information is integrated to make the SI prediction. Besides, the nonlinear dependencies and linear relationships are independently extracted and integrated into the ATI-net, which not only reduces information interference but is consistent with the “divide and conquer” idea. Experimental results show that the ATI-net outperforms 18 benchmarks, and average improvements of root mean squared error (RMSE) are 26.28% and 26.01% for two datasets, respectively. In summary, the ATI-net is one of the reliable alternatives to SI prediction.

PMUs data based detection of oscillatory events and identification of their associated variable: Estimation of information measures approach

Information theory can be a useful tool for quantifying the perturbations in the associated state variables at the time of disturbance occurrence. The study introduces a framework for the spectral decomposition of multivariate information measures to detect initiation of low frequency oscillations (LFOs), caused due to physical events in the power grid. A frequency-specific quantification of the information is shared between a target variable and two source variables from their time series data. Initially, the approach is applied on different synthetic test signals having different oscillatory frequency modes and decay time constant. Then, approach is extended on PMUs signals. The combination of cross-spectral and information-theoretic approaches is applied for the multi-variable analysis of PMUs signals from the same bus. The interdependence among the frequency, voltage angle and voltage magnitude, corresponding to specific oscillations, manifested due to cause-effect relationships obtained in terms of statistics is estimated. The dynamics in terms of unique (interaction), redundant and synergetic information is determined with the contribution from two of these three signals as source variables to target variable (frequency/voltage angle). This provides a direct coupling to identify driver-response relationships between source variables and target variable to indicate the onset of LFOs, following physical events in power network. The extension of approach among the variables from different buses aids to identify the responsible area of event occurrence.

Fast inhibition slows and desynchronizes mouse auditory efferent neuron activity.

The encoding of acoustic stimuli requires precise neuron timing. Auditory neurons in the cochlear nucleus (CN) and brainstem are well-suited for accurate analysis of fast acoustic signals, given their physiological specializations of fast membrane time constants, fast axonal conduction, and reliable synaptic transmission. The medial olivocochlear (MOC) neurons that provide efferent inhibition of the cochlea reside in the ventral brainstem and participate in these fast neural circuits. However, their modulation of cochlear function occurs over time scales of a slower nature. This suggests the presence of mechanisms that reduce MOC inhibition of cochlear function. To determine how monaural excitatory and inhibitory synaptic inputs integrate to affect the timing of MOC neuron activity, we developed a novel in vitro slice preparation ('wedge-slice'). The wedge-slice maintains the ascending auditory nerve root, the entire CN and projecting axons, while preserving the ability to perform visually guided patch-clamp electrophysiology recordings from genetically identified MOC neurons. The 'in vivo-like' timing of the wedge-slice demonstrates that the inhibitory pathway accelerates relative to the excitatory pathway when the ascending circuit is intact, and the CN portion of the inhibitory circuit is precise enough to compensate for reduced precision in later synapses. When combined with machine learning PSC analysis and computational modeling, we demonstrate a larger suppression of MOC neuron activity when the inhibition occurs with in vivo-like timing. This delay of MOC activity may ensure that the MOC system is only engaged by sustained background sounds, preventing a maladaptive hyper-suppression of cochlear activity.Significance Statement Auditory brainstem neurons are specialized for speed and fidelity to encode rapid features of sound. Extremely fast inhibition contributes to precise brainstem sound encoding. This circuit also projects to medial olivocochlear (MOC) efferent neurons that suppress cochlear function to enhance detection of signals in background sound. Using a novel brain slice preparation with intact ascending circuitry, we show that inhibition of MOC neurons can also be extremely fast, with the speed of the circuit localized to the cochlear nucleus. In contrast with the enhancement of precision afforded by fast inhibition in other brainstem auditory circuits, inhibition to MOC neurons instead has a variable onset that delays and desynchronizes activity, thus reducing precision for a slow, sustained response to background sounds.

Алгоритм аппроксимации сигналов для классификации электроэнцефалограмм человека

The article examines the problem of classification of electroencephalograms (EEG), where noise in the signals, caused by various factors, prevents effective analysis and interpretation of the data. The main goal of the study is to analyze the effectiveness of a signal approximation algorithm using a wavelet technique with the next piece-wise approximation in order to effectively remove noise and subsequently solve the problem of signal classification using a convolutional neural network. The classification accuracy of the proposed algorithm with a low-pass filter is compared at different cutoff frequencies. One of the key findings of the study is a two-step approach to signal processing. In the first stage, the model is trained on the raw data, and then the trained convolution kernel with the highest variance is applied to the original signals. This solves the problem of choosing the mother function. This approach aims to enhance the informative components in the signal. The second processing step involves applying the proposed approximation algorithm after the convolution, which complements the first step to create a comprehensive method. This method not only effectively reduces noise in the data, but also has high potential to improve the overall accuracy in solving EEG signal classification problems. Thus, the results of the study provide important practical and theoretical implications, highlighting the prospects for applying the proposed method in the field of signal analysis.

Redesigned Electrodes for Improved Intraoperative Nerve Conduction Studies during the Treatment of Peripheral Nerve Injuries.

Traumatic peripheral nerve injuries (PNI), present with symptoms ranging from pain to loss of motor and sensory function. Difficulties in intraoperative visual assessment of nerve functional status necessitate intraoperative nerve conduction studies (INCSs) by neurosurgeons and neurologists to determine the presence of functioning axons in the zone of a PNI. This process, also referred to as nerve "inching", uses a set of stimulating and recording electrode hooks to lift the injured nerve from the surrounding surgical field and to determine whether an electrical stimulus can travel through the zone of injury. However, confounding electrical signal artifacts can arise from the current workflow and electrode design, particularly from the mandatory lifting of the nerve, complicating the definitive assessment of nerve function and neurosurgical treatment decision-making. The objective of this study is to describe the design process and verification testing of our group's newly designed stimulating and recording electrodes that do not require the lifting or displacement of the injured nerve during INCSs. Ergonomic in vivo analysis of the device within a porcine model demonstrated successful intraoperative manipulation of the device, while quantitative nerve action potential (NAP) signal analysis with an ex vivo simulated "inching" procedure on healthy non-human primate nerve tissue demonstrated excellent reproducible recorded NAP fidelity and the absence of NAP signal artifacts at all points of recording. Lastly, electrode pullout force testing determined maximum forces of 0.43 N, 1.57 N, and 3.61 N required to remove the device from 2 mm, 5 mm, and 1 cm nerve models, respectively, which are well within established thresholds for nerve safety. These results suggest that these new electrodes can safely and successfully perform accurate PNI assessment without the presence of artifacts, with the potential to improve the INCS standard of care while remaining compatible with currently used neurosurgical technology, infrastructure, and clinical workflows.

Improving the performance of resonator-based optical sensors using time-lagged correlation and Fourier phase shift analysis

The performance of time-lagged correlation and Fourier phase shift analysis in resonator-based optical sensors was evaluated and compared to centroiding algorithms. 100 emission spectra of an Er3+ oxyfluoride microsphere were recorded and the average of all data was used as a noiseless spectrum. The Whispering Gallery Mode (WGM) signal, superimposed on the Er3+ emission band, was subtracted and shifted to simulate sensor measurements. Additive random Gaussian noise with different values of the variance was added to the original and WGM-shifted spectra, and the WGM shift was estimated using time-lagged correlation, Fourier phase shift analysis and centroiding algorithms. The procedure was repeated 10000 times to evaluate the accuracy and precision of all numerical methods, and different values of the noise level and WGM shift were considered. These pseudo-experimental studies show that the use of time-lagged correlation and Fourier phase shift analysis can improve the accuracy and precision of the sensor when compared to traditional centroiding algorithms. Additionally, signal analysis can be automated without the need for peak identification and matching.

Digitalization of Colorimetric Sensor Technologies for Food Safety.

Colorimetric sensors play a crucial role in promoting on-site testing, enabling the detection and/or quantification of various analytes based on changes in color. These sensors offer several advantages, such as simplicity, cost-effectiveness, and visual readouts, making them suitable for a wide range of applications, including food safety and monitoring. A critical component in portable colorimetric sensors involves their integration with color models for effective analysis and interpretation of output signals. The most commonly used models include CIELAB (Commission Internationale de l'Eclairage), RGB (Red, Green, Blue), and HSV (Hue, Saturation, Value). This review outlines the use of color models via digitalization in sensing applications within the food safety and monitoring field. Additionally, challenges, future directions, and considerations are discussed, highlighting a significant gap in integrating a comparative analysis toward determining the color model that results in the highest sensor performance. The aim of this review is to underline the potential of this integration in mitigating the global impact of food spoilage and contamination on health and the economy, proposing a multidisciplinary approach to harness the full capabilities of colorimetric sensors in ensuring food safety.

Electrochemical signal quantification in saliva: investigation of signal analysis methods

Electrochemical signal quantification in saliva: investigation of signal analysis methods

Label-free electrochemical biosensor based on dual amplification of gold nanoparticles and polycaprolactones for CEA detection

Carcinoembryonic Antigen (CEA), an acidic glycoprotein with human embryonic antigen properties, is found on the surface of cancer cells that have differentiated from endodermal cells. This paper presents a label-free electrochemical immunoassay for the dual amplification detection of CEA using gold nanoparticles loaded with polypyrrole polydopamine (Au/PPy-PDA) and polymerized polycaprolactone (Ng-PCL) prepared by ring-opening polymerization (ROP). First, the composite Au/PPy-PDA was adhered to the electrode surface. Then, gold nanoparticles form a Au–S bond with the sulfhydryl group in Apt1 to secure it on the electrode surface. Subsequently, the non-specific binding sites on the electrodes surface are closed by bovine serum albumin (BSA). Next, CEA is dropped onto the electrode surface, which is immobilized by antigen-antibody specific recognition, and the carboxyl-functionalized Apt2 forms a "sandwich structure" of antibody-antigen-antibody by specific recognition. Polymeric Ng-PCL is adhered to the electrode surface, leading to an increase in the electrochemical impedance signal, resulting in a complete chain of signal analysis. Finally, the response signal is detected by electrochemical impedance spectroscopy (EIS). Under optimal experimental conditions, the method has the advantages of high sensitivity and wide linear range (1 pg mL−1∼100 ng mL−1), and the lower limit of detection (LOD) is 0.234 pg mL−1. And it has the same high sensitivity, selectivity and interference resistance for the real samples detection. Thus, it provides a new way of thinking about biomedical and clinical diagnosis.

Sliding window functional connectivity inference with nonstationary autocorrelations and cross-correlations.

Functional connectivity (FC) is the degree of synchrony of time series between distinct, spatially separated brain regions. While traditional FC analysis assumes the temporal stationarity throughout a brain scan, there is growing recognition that connectivity can change over time and is not stationary, leading to the concept of dynamic FC (dFC). Resting-state functional magnetic resonance imaging (fMRI) can assess dFC using the sliding window method with the correlation analysis of fMRI signals. Accurate statistical inference of sliding window correlation must consider the autocorrelated nature of the time series. Currently, the dynamic consideration is mainly confined to the point estimation of sliding window correlations. Using in vivo resting-state fMRI data, we first demonstrate the non-stationarity in both the cross-correlation function (XCF) and the autocorrelation function (ACF). Then, we propose the variance estimation of the sliding window correlation considering the nonstationary of XCF and ACF. This approach provides a means to dynamically estimate confidence intervals in assessing dynamic connectivity. Using simulations, we compare the performance of the proposed method with other methods, showing the impact of dynamic ACF and XCF on connectivity inference. Accurate variance estimation can help in addressing the critical issue of false positivity and negativity.