Signal Recovery Research Articles

A dual-core 3D DNA nanomachine based on DNAzyme positive feedback loop for highly sensitive MicroRNA imaging in living cells

A double 3D DNA walker nanomachine by DNAzyme self-driven positive feedback loop amplification for the detection of miRNA was constructed. This method uses two gold nanoparticles as the reaction core, and because of the spatial confinement effect the local concentration of the reactants increase the collision efficiency was greatly improved. Meanwhile, the introduction of positive feedback loop promotes the conversion efficiency. In presence of miRNA-21, a large amount of DNAzyme was released and hydrolyze the reporter probe, resulting the recovery of fluorescence signal. The linear range for miRNA-21 is 0.5–60 pmol/L, and the detection limit is 0.41 pmol/L (S/N = 3). This nanomachine has been successfully used for accurate detection of miRNA-21 expression levels in cell lysates. At the same time, it can enter cells for intracellular miRNA-21 fluorescence imaging, distinguishing tumor cells from normal cells. This combination of in vitro detection and imaging analysis of living cells can achieve the goal of jointly detecting cancer markers through multiple pathways, providing new ideas for early diagnosis and screening of diseases.

Analysis of T-Orthogonal greedy algorithm in noisy measurement

We define the Tikhonov Orthogonal Greedy algorithm (T-OGA), a variant of the orthogonal greedy algorithm, to study the recovery of sparse signals from noisy measurements. We establish sufficient conditions for T-OGA to recover sparse signals via restricted isometry property (RIP) inherited from frames. We introduce various concepts such as match matrix, match vector, and residue vector to execute T-OGA. Various technical lemmas have been established to prove our main theorem. In the execution of T-OGA, we employ Tikhonov regularization with regularization parameter λ to solve the minimization problem for the N-sparse solution. In our main result, we proved that if a frame satisfies the RIP of order N + 1 with isometry constant τ and (τ+λ)<13N , then T-OGA can recover every N-sparse signal in the atmost N iterations.

A novel predefined-time neurodynamic approach for mixed variational inequality problems and applications

In this paper, we propose a novel neurodynamic approach with predefined-time stability that offers a solution to address mixed variational inequality problems. Our approach introduces an adjustable time parameter, thereby enhancing flexibility and applicability compared to conventional fixed-time stability methods. By satisfying certain conditions, the proposed approach is capable of converging to a unique solution within a predefined-time, which sets it apart from fixed-time stability and finite-time stability approaches. Furthermore, our approach can be extended to address a wide range of mathematical optimization problems, including variational inequalities, nonlinear complementarity problems, sparse signal recovery problems, and nash equilibria seeking problems in noncooperative games. We provide numerical simulations to validate the theoretical derivation and showcase the effectiveness and feasibility of our proposed method.

Geomorphological changes and sediment carbon accumulation at the bare mudflat-saltmarsh interface: The role of typhoons

Blue carbon stored in coastal wetlands can be highly impacted by extreme events, such as typhoons. However, the sediment carbon accumulation capacity in this circumstance, remains unclear. In this study, a field site crossing the bare mudflat-saltmarsh interface was established in Southern Hangzhou Bay, to investigate the geomorphological and subsequent sediment carbon accumulation changes during the typhoon season (2 months) and the succeeding calm weather period (10 months). Surface Elevation Tables (SETs) were used, together with GPS-RTK and UAV-LiDAR surveys, to observe surface elevation changes at different spatial scales. The elevation and sediment organic carbon density data were used to estimate the gain/loss of sediment carbon pool under the impact of two successive typhoons, “In-Fa” and “Chanthu”. This revealed that during the typhoon season, 6.3 cm of surface erosion occurred on the bare mudflat whilst rapid depositions of 6.1 and 12.3 cm occurred in Scirpus mariqueter and Spartina alterniflora habitats, respectively. In the gap between the two typhoon events, the bare mudflat experienced deposition of 4.2 cm, signalling recovery from erosion, but deposition within the saltmarsh front slowed. During the calm weather period, sediment deposition increased landward, with magnitudes of 13.1 cm, 16.4 cm, and 18.2 cm. Sediment carbon budget estimates indicated that during the typhoon season, the bare mudflat lost ~3.4 Mg C ha−1 but saltmarshes gained ~4.9 Mg C ha−1 in their sediment organic carbon pools. These changes in sediment carbon accumulation rates over the typhoon season accounted for >30 % of annual change in the study area, and may be associated with tidal creek dynamics. This study suggests the important role of typhoons in mediating the sediment carbon pool across the mudflat-saltmarsh interface. With future climate change, increasingly frequent catastrophic events could cause considerable changes in coastal blue carbon sequestration capacity.

Laser ultrasonic spatially resolved acoustic spectroscopy for grain size study based on Improved Variational Mode Decomposition (IVMD)

Laser Ultrasonic Spatially Resolved Acoustic Spectroscopy (SRAS) is a technique used for detecting the grain size of metal surfaces. It involves exciting Surface Acoustic Waves (SAW) using a grated light source and employs optical deflection techniques to detect ultrasound. The differences in sound velocity between different grains are utilized for grain size characterization. However, this technique is susceptible to the influence of sample surface roughness, resulting in issues like low signal-to-noise ratio and waveform distortion. To address these challenges, a study was conducted on grain size characterization using Laser Ultrasonic SRAS based on Improved Variational Mode Decomposition (IVMD). This involved optimizing Variational Mode Decomposition (VMD) by introducing Permutation Entropy (PE), Weighted Spectral Kurtosis (WSK), and Dynamic Time Warping (DTW). Adaptive decomposition and selection of simulated surface wave signals were employed to achieve signal recovery. A comparison with Empirical Mode Decomposition (EMD) demonstrated that the proposed algorithm had higher noise robustness. The Laser Ultrasonic SRAS detection system was used to characterize the grain size of Ti-6Al-4V casting alloys, and the data were processed using the proposed algorithm. The results were consistent with optical microscopy images, further confirming the reliability and effectiveness of the improved algorithm. The research findings indicate that Laser Ultrasonic Spatially Resolved Acoustic Spectroscopy based on IVMD can effectively reduce the impact of noise on detection signals, thereby enhancing the detectability of grain size measurement.

Real-time detection of 20 amino acids and discrimination of pathologically relevant peptides with functionalized nanopore

Precise identification and quantification of amino acids is crucial for many biological applications. Here we report a copper(II)-functionalized Mycobacterium smegmatis porin A (MspA) nanopore with the N91H substitution, which enables direct identification of all 20 proteinogenic amino acids when combined with a machine-learning algorithm. The validation accuracy reaches 99.1%, with 30.9% signal recovery. The feasibility of ultrasensitive quantification of amino acids was also demonstrated at the nanomolar range. Furthermore, the capability of this system for real-time analyses of two representative post-translational modifications (PTMs), one unnatural amino acid and ten synthetic peptides using exopeptidases, including clinically relevant peptides associated with Alzheimer’s disease and cancer neoantigens, was demonstrated. Notably, our strategy successfully distinguishes peptides with only one amino acid difference from the hydrolysate and provides the possibility to infer the peptide sequence.

Catalytic hairpin self-assembly amplification fluorescence detection of chloramphenicol based on cross-shaped DNA and UiO-66

A catalytic hairpin self-assembly (CHA) amplification method was developed for CAP detection based on cross-shaped DNA and UiO-66. MOF was used to quench the fluorescent signal of FAM labeled DNA. Cross-shaped DNA with four fluorophore group (FAM) was utilized to enhance the fluorescent intensity. CAP could open hairpin structure of H-apt and induce CHA reaction. The product of CHA hybridized with cross-shaped DNA, resulting its leaving from the surface of UiO-66 and recovery of fluorescent signal. The limit of detection (LOD) was low to 0.87 pM. This method had been successfully applied for the detection of CAP in actual samples. Importantly, the high sensitivity was attributed to the great amplification efficiency of CHA, strong fluorescent intensity of cross-shaped DNA structure and great fluorescent quenched efficiency of UiO-66.

Norm retrieval algorithms: A new frame theory approach

In this paper, we introduce a new approach based on the excess components of a frame for recognizing norm retrieval frames (NRFs) in finite‐dimensional Hilbert spaces. This method leads to a complete characterization of NRFs in and and then to 1‐excess NRFs in . This approach is not only more effective in higher dimensions for detecting NRF than the previous approaches but is also applicable to constructing NRF from a given Riesz basis. In addition, we establish a relationship between the norm retrievability of a frame and its duals. We also show that, unlike the phase retrieval property, the set of all norm retrievable dual frames is generally not dense in the set of all dual frames. Then, we present numerical results to illustrate the potentiality of the NRF approach in signal recovery with respect to the classical framework in frame theory, and finally, we raise some perspectives and problems concerning NRF.

WaveFormer: transformer-based denoising method for gravitational-wave data

With the advent of gravitational-wave astronomy and the discovery of more compact binary coalescences, data quality improvement techniques are desired to handle the complex and overwhelming noise in gravitational wave (GW) observational data. Though recent machine learning-based studies have shown promising results for data denoising, they are unable to precisely recover both the GW signal amplitude and phase. To address such an issue, we develop a deep neural network centered workflow, WaveFormer, for significant noise suppression and signal recovery on observational data from the Laser Interferometer Gravitational-Wave Observatory (LIGO). The WaveFormer has a science-driven architecture design with hierarchical feature extraction across a broad frequency spectrum. As a result, the overall noise and glitch are decreased by more than one order of magnitude and the signal recovery error is roughly 1% and 7% for the phase and amplitude, respectively. Moreover, on 75 reported binary black hole events of LIGO we obtain a significant improvement of inverse false alarm rate. Our work highlights the potential of large neural networks in GW data analysis and, while primarily demonstrated on LIGO data, its adaptable design indicates promise for broader application within the International Gravitational-Wave Observatories Network in future observational runs.

Interference Mitigation using Data-driven Sparse Component Analysis

To address the challenge of mitigating asynchronous and non-stationary interference under single-channel conditions, we proposes a sparse component analysis interference mitigation method based on the recurrent neural network. This method aims to recover the desired signal from the received time-frequency over-lapped co-channel signal. Unlike previous interference mitigation methods, our proposed method achieves ”end-to-end” signal recovery in the time domain without any priori requirements on the received signals, making it more universal than existing methods. Numerical results validate the effectiveness of our proposed method and demonstrate its significantly superior mitigation performance compared to existing schemes under different environmental noises, intensities of interfering signals, and generalization test conditions.

Binary Least Squares: An Algorithm for Binary Sparse Signal Recovery

Binary Least Squares: An Algorithm for Binary Sparse Signal Recovery

An accessible deep learning tool for voxel-wise classification of brain malignancies from perfusion MRI

Noninvasive differential diagnosis of brain tumors is currently based on the assessment of magnetic resonance imaging (MRI) coupled with dynamic susceptibility contrast (DSC). However, a definitive diagnosis often requires neurosurgical interventions that compromise patients' quality of life. We apply deep learning on DSC images from histology-confirmed patients with glioblastoma, metastasis, or lymphoma. The convolutional neural network trained on ∼50,000 voxels from 40 patients provides intratumor probability maps that yield clinical-grade diagnosis. Performance is tested in 400 additional cases and an external validation cohort of 128 patients. The tool reaches a three-way accuracy of 0.78, superior to the conventional MRI metrics cerebral blood volume (0.55) and percentage of signal recovery (0.59), showing high value as a support diagnostic tool. Our open-access software, Diagnosis In Susceptibility Contrast Enhancing Regions for Neuro-oncology (DISCERN), demonstrates its potential in aiding medical decisions for brain tumor diagnosis using standard-of-care MRI.

Efficient signal-to-noise ratio enhancement model for severely contaminated distributed acoustic sensing seismic data based on heterogeneous knowledge distillation

Distributed acoustic sensing (DAS) is an emerging seismic acquisition technique with great practical potential. However, various types of noise seriously corrupt DAS signals, making it difficult to recover signals, particularly in low signal-to-noise ratio (S/N) regions. Existing deep-learning methods address this challenge by augmenting data sets or strengthening the complex architecture, which can cause overdenoising and a computational power burden. Hence, the heterogeneous knowledge distillation (HKD) method is developed to more efficiently address the signal reconstruction under low S/N. HKD uses ResNet 20 as the teacher and student (T-S) model. It uses residual learning and skip connections to facilitate feature representation at deeper levels. The main contribution is the training of the T-S framework with different noise levels. The teacher model that is trained using slightly noisy data serves as a powerful feature extractor to capture more accurate signal features because high-quality data are easy to recover. By minimizing the difference between the outputs of T-S models, the student model that is trained using severely noisy data can distill the absent signal features from the teacher to improve its own signal recovery, which enables heterogeneous feature distillation. Furthermore, simultaneous positive and negative learning (P&amp;NL) is developed to extract more useful features from the teacher, enabling the T-S framework to learn from the predicted signal and noise during training. Consequently, a new loss function that combines student denoising loss and HKD loss weighted by P&amp;NL is developed to alleviate signal leakage. The experimental results demonstrate that the HKD achieves distinct and consistent signal recovery without increasing computational costs.

Channel Estimation of Urban 5G Communication System based on Improved Particle Swarm Optimization Algorithm

In order to solve the problem that the channel estimation accuracy of the traditional urban communication system is not high, the author proposes the channel estimation of the urban 5G communication system based on the improved particle optimization algorithm. This method converts the channel estimate into a regression fit and adjusts the fit. Focusing on regression fitting problems, big data models are used to display offline data, study channel nonlinearities, and obtain initial channel prediction models. To solve the adaptive problem, the author collects real-time teaching data in a real online learning mode and integrates blended learning to update the model, to avoid the problem of overspending on offline training. Offline tests show that the performance of the channel estimation model is the best for different channels. As the signal-to-noise ratio increases, the MSE value is stable at around 1200. Conclusion: The channel estimation method can produce different characteristics of channel estimation in different situations and improve the signal recovery function of the communication system.

Compressed sensing for rapid tabletop X-ray absorption spectroscopy

X-ray Absorption Fine Structure (XAFS) spectroscopy is an analytical technique of chemical insight. Improvements to this technique are continuously carried out for reduction of the acquisition time and increasing the resolution, especially for studying operando processes. The accurate reconstruction of XAFS signals requires that the collected data are noise-free to extract elemental structure information in post-processing. A novel approach to rapid sampling (encoded measurement) is validated conceptually. The technique relies on sparse signal recovery, i.e. using compressed sensing to reconstruct under-sampled signals. The proof of concept is demonstrated by using a python code to reconstruct under-sampled XAFS spectra. A case study of transition metal samples and their oxides is considered for this purpose. The results demonstrate a robust recovery of chemical information from a 30% sampled signal for the near-edge region analysis and from a 45% sampled signal for extended-edge region analysis. The application of this procedure is important for demonstrating the feasibility of tabletop operation using laboratory X-ray sources which are not continuously tunable.

Distribution-Agnostic Deep Learning Enables Accurate Single-Cell Data Recovery and Transcriptional Regulation Interpretation.

Single-cell RNA sequencing (scRNA-seq) is a robust method for studying gene expression at the single-cell level, but accurately quantifying genetic material is often hindered by limited mRNA capture, resulting in many missing expression values. Existing imputation methods rely on strict data assumptions, limiting their broader application, and lack reliable supervision, leading to biased signal recovery. To address these challenges, authors developed Bis, a distribution-agnostic deep learning model for accurately recovering missing sing-cell gene expression from multiple platforms. Bis is an optimal transport-based autoencoder model that can capture the intricate distribution of scRNA-seq data while addressing the characteristic sparsity by regularizing the cellular embedding space. Additionally, they propose a module using bulk RNA-seq data to guide reconstruction and ensure expression consistency. Experimental results show Bis outperforms other models across simulated and real datasets, showcasing superiority in various downstream analyses including batch effect removal, clustering, differential expression analysis, and trajectory inference. Moreover, Bis successfully restores gene expression levels in rare cell subsets in a tumor-matched peripheral blood dataset, revealing developmental characteristics of cytokine-induced natural killer cells within a head and neck squamous cell carcinoma microenvironment.

Measurement and Data Correction of Channel Sampling Timing Walk-Off of Photonic Analog-to-Digital Converter in Signal Recovery.

A two-channel, time-wavelength interleaved photonic analog-to-digital converter (PADC) system with a sampling rate of 10.4 GSa/s was established, and a concise method for measuring and data correcting the channel sampling timing walk-off of PADCs for signal recovery was proposed. The measurements show that for the two RF signals of f1 = 100 MHz and f2 = 200 MHz, the channel sampling timing walk-off was 12 sampling periods, which results in an ENOB = -0.1051 bits for the 100 MHz directly synthesized signal, while the ENOB improved up to 4.0136 bits using shift synthesis. In addition, the peak limit method (PLM) and normalization processing were introduced to reduce the impacts of signal peak jitter and power inconsistency between two channels, which further improve the ENOB of the 100 MHz signal up to 4.5668 bits. All signals were analyzed and discussed in both time and frequency domains. The 21.1 GHz signal was also collected and converted using the established two-channel PADC system with the data correction method, combining the PLM, normalization, and shift synthesis, showing that the ENOB increased from the initial -0.9181 to 4.1913 bits, which demonstrates that our method can be effectively used for signal recovery in channel-interleaved PADCs.

A Modified Form of Inertial Viscosity Projection Methods for Variational Inequality and Fixed Point Problems

This paper aims to introduce an iterative algorithm based on an inertial technique that uses the minimum number of projections onto a nonempty, closed, and convex set. We show that the algorithm generates a sequence that converges strongly to the common solution of a variational inequality involving inverse strongly monotone mapping and fixed point problems for a countable family of nonexpansive mappings in the setting of real Hilbert space. Numerical experiments are also presented to discuss the advantages of using our algorithm over earlier established algorithms. Moreover, we solve a real-life signal recovery problem via a minimization problem to demonstrate our algorithm’s practicality.

Exogenous Co-Reactant-Free Electrochemiluminescent Biosensor for Ratiometric Measurement of α-Glucosidase Based on a ZIF-67-Regulated Hydrogen-Bonded Organic Framework.

The development of highly sensitive and selective analytical approaches for monitoring enzymatic activity is critical for disease diagnosis and biomedical research. Herein, we develop an exogenous co-reactant-free electrochemiluminescence (ECL) biosensor for the ratiometric measurement of α-glucosidase (α-Glu) based on a zeolitic imidazolate framework (ZIF-67)-regulated pyrene-based hydrogen-bonded organic framework (HOF-101). Target α-Glu can hydrolyze maltose to α-d-glucose, which can subsequently react with GOx to produce gluconic acid. The resultant gluconic acid can dissolve ZIF-67, leading to the recovery of the HOF-101 cathodic ECL signal and the decrease of the luminol anodic ECL signal. The long-range ordered structure of HOF-101 can speed up charge transfer, resulting in a stable and strong cathodic ECL signal. Moreover, ZIF-67 can not only efficiently quench the ECL signal of HOF-101 due to ECL resonance energy transfer between HOF-101 and ZIF-67 as well as the steric hindrance effect of ZIF-67 but also enhance the anodic ECL emission of luminol in dissolved O2 system because of its ordered and porous crystalline structure and the atomically dispersed Co2+. Notably, HOF-101 possesses a higher ECL efficiency (32.22%) compared with the Ru(bpy)32+ standard. Importantly, this ratiometric ECL biosensor shows high sensitivity (a detection limit of 0.19 U L-1) and a broad linear range (0.2-50 U L-1). This biosensor can efficiently eliminate systematic errors and enhance detection reliability without the involvement of exogenous co-reactants, and it displays good assay performance in human serum samples, holding great promise in biomedical research studies.

Communication Prototype for Post-Stroke Patients Using Electrooculography (EOG)

The background of this prototype is meant for post-stroke patients who have difficulties owing to trouble completing daily activities, particularly speaking with others. They have trouble communicating, which reduces their quality of life after a stroke. The goal of developing novel communication aids for post-stroke patients is to make it easier for post-stroke patients to communicate with caretakers. Electrooculography (EOG) signals generated by the movement of the eye muscles during eye glances collected by the MaM Sense sensor are employed in post-stroke communication aids. The microcontroller processes the method's command signal, which is subsequently displayed on a 20 x 4 Character LCD. For the four communications utilized, the MaM Sense sensor reading was successful 80.5% of the time. Thus, communication tools for post-stroke patients can help them communicate while also assisting caretakers in understanding the patient's wants. Improvements will be made in the design and wireless technologies for EOG signal recovery in the future.