HGCLAMIR: Hypergraph contrastive learning with attention mechanism and integrated multi-view representation for predicting miRNA-disease associations.

Existing studies have shown that the abnormal expression of microRNAs (miRNAs) usually leads to the occurrence and development of human diseases. Identifying disease-related miRNAs contributes to studying the pathogenesis of diseases at the molecular level. As traditional biological experiments are time-consuming and expensive, computational methods have been used as an effective complement to infer the potential associations between miRNAs and diseases. However, most of the existing computational methods still face three main challenges: (i) learning of high-order relations; (ii) insufficient representation learning ability; (iii) importance learning and integration of multi-view embedding representation. To this end, we developed a HyperGraph Contrastive Learning with view-aware Attention Mechanism and Integrated multi-view Representation (HGCLAMIR) model to discover potential miRNA-disease associations. First, hypergraph convolutional network (HGCN) was utilized to capture high-order complex relations from hypergraphs related to miRNAs and diseases. Then, we combined HGCN with contrastive learning to improve and enhance the embedded representation learning ability of HGCN. Moreover, we introduced view-aware attention mechanism to adaptively weight the embedded representations of different views, thereby obtaining the importance of multi-view latent representations. Next, we innovatively proposed integrated representation learning to integrate the embedded representation information of multiple views for obtaining more reasonable embedding information. Finally, the integrated representation information was fed into a neural network-based matrix completion method to perform miRNA-disease association prediction. Experimental results on the cross-validation set and independent test set indicated that HGCLAMIR can achieve better prediction performance than other baseline models. Furthermore, the results of case studies and enrichment analysis further demonstrated the accuracy of HGCLAMIR and unconfirmed potential associations had biological significance.

Predicting multiple types of miRNA-disease associations using adaptive weighted nonnegative tensor factorization with self-paced learning and hypergraph regularization.

More and more evidence indicates that the dysregulations of microRNAs (miRNAs) lead to diseases through various kinds of underlying mechanisms. Identifying the multiple types of disease-related miRNAs plays an important role in studying the molecular mechanism of miRNAs in diseases. Moreover, compared with traditional biological experiments, computational models are time-saving and cost-minimized. However, most tensor-based computational models still face three main challenges: (i) easy to fall into bad local minima; (ii) preservation of high-order relations; (iii) false-negative samples. To this end, we propose a novel tensor completion framework integrating self-paced learning, hypergraph regularization and adaptive weight tensor into nonnegative tensor factorization, called SPLDHyperAWNTF, for the discovery of potential multiple types of miRNA-disease associations. We first combine self-paced learning with nonnegative tensor factorization to effectively alleviate the model from falling into bad local minima. Then, hypergraphs for miRNAs and diseases are constructed, and hypergraph regularization is used to preserve the high-order complex relations of these hypergraphs. Finally, we innovatively introduce adaptive weight tensor, which can effectively alleviate the impact of false-negative samples on the prediction performance. The average results of 5-fold and 10-fold cross-validation on four datasets show that SPLDHyperAWNTF can achieve better prediction performance than baseline models in terms of Top-1 precision, Top-1 recall and Top-1 F1. Furthermore, we implement case studies to further evaluate the accuracy of SPLDHyperAWNTF. As a result, 98 (MDAv2.0) and 98 (MDAv2.0-2) of top-100 are confirmed by HMDDv3.2 dataset. Moreover, the results of enrichment analysis illustrate that unconfirmed potential associations have biological significance.

Dual-convolutional neural network-enhanced strain estimation method for optical coherence elastography.

Strain estimation is vital in phase-sensitive optical coherence elastography (PhS-OCE). In this Letter, we introduce a novel, to the best of our knowledge, method to improve strain estimation by using a dual-convolutional neural network (Dual-CNN). This approach requires two sets of PhS-OCE systems: a high-resolution system for high-quality training data and a cost-effective standard-resolution system for practical measurements. During training, high-resolution strain results acquired from the former system and the pre-existing strain estimation CNN serve as label data, while the narrowed light source-acquired standard-resolution phase results act as input data. By training a new network with this data, high-quality strain results can be estimated from standard-resolution PhS-OCE phase results. Comparison experiments show that the proposed Dual-CNN can preserve the strain quality even when the light source bandwidth is reduced by over 80%.

Digital image correlation-assisted phase-sensitive optical coherence tomography.

In this Letter, we introduce a digital image correlation-assisted (DIC-assisted) method to tackle the challenges of phase decorrelation and the inability to measure lateral displacement in phase-sensitive optical coherence tomography (PhS-OCT). This DIC-assisted PhS-OCT (DIC-PhS-OCT) first employs DIC to track displacements from the measured amplitude spectra and subsequently uses these tracked displacements to correct supra-pixel displacement offsets in the measured phase spectra. As a result, it effectively mitigates phase decorrelation resulting from both axial and lateral displacements while enabling the acquisition of sub-pixel-level lateral displacements during the DIC computation. Our experiments demonstrate the effectiveness of DIC-PhS-OCT in addressing these challenges while retaining the ultrahigh sensitivity of conventional PhS-OCT.

An Efficient Convolutional Denoising Autoencoder-Based BDS NLOS Detection Method in Urban Forest Environments.

The BeiDou Navigation Satellite System (BDS) provides real-time absolute location services to users around the world and plays a key role in the rapidly evolving field of autonomous driving. In complex urban environments, the positioning accuracy of BDS often suffers from large deviations due to non-line-of-sight (NLOS) signals. Deep learning (DL) methods have shown strong capabilities in detecting complex and variable NLOS signals. However, these methods still suffer from the following limitations. On the one hand, supervised learning methods require labeled samples for learning, which inevitably encounters the bottleneck of difficulty in constructing databases with a large number of labels. On the other hand, the collected data tend to have varying degrees of noise, leading to low accuracy and poor generalization performance of the detection model, especially when the environment around the receiver changes. In this article, we propose a novel deep neural architecture named convolutional denoising autoencoder network (CDAENet) to detect NLOS in urban forest environments. Specifically, we first design a denoising autoencoder based on unsupervised DL to reduce the long time series signal dimension and extract the deep features of the data. Meanwhile, denoising autoencoders improve the model's robustness in identifying noisy data by introducing a certain amount of noise into the input data. Then, an MLP algorithm is used to identify the non-linearity of the BDS signal. Finally, the performance of the proposed CDAENet model is validated on a real urban forest dataset. The experimental results show that the satellite detection accuracy of our proposed algorithm is more than 95%, which is about an 8% improvement over existing machine-learning-based methods and about 3% improvement over deep-learning-based approaches.

GDCL-NcDA: identifying non-coding RNA-disease associations via contrastive learning between deep graph learning and deep matrix factorization.

Non-coding RNAs (ncRNAs) draw much attention from studies widely in recent years because they play vital roles in life activities. As a good complement to wet experiment methods, computational prediction methods can greatly save experimental costs. However, high false-negative data and insufficient use of multi-source information can affect the performance of computational prediction methods. Furthermore, many computational methods do not have good robustness and generalization on different datasets. In this work, we propose an effective end-to-end computing framework, called GDCL-NcDA, of deep graph learning and deep matrix factorization (DMF) with contrastive learning, which identifies the latent ncRNA-disease association on diverse multi-source heterogeneous networks (MHNs). The diverse MHNs include different similarity networks and proven associations among ncRNAs (miRNAs, circRNAs, and lncRNAs), genes, and diseases. Firstly, GDCL-NcDA employs deep graph convolutional network and multiple attention mechanisms to adaptively integrate multi-source of MHNs and reconstruct the ncRNA-disease association graph. Then, GDCL-NcDA utilizes DMF to predict the latent disease-associated ncRNAs based on the reconstructed graphs to reduce the impact of the false-negatives from the original associations. Finally, GDCL-NcDA uses contrastive learning (CL) to generate a contrastive loss on the reconstructed graphs and the predicted graphs to improve the generalization and robustness of our GDCL-NcDA framework. The experimental results show that GDCL-NcDA outperforms highly related computational methods. Moreover, case studies demonstrate the effectiveness of GDCL-NcDA in identifying the associations among diversiform ncRNAs and diseases.

High-resolution optical coherence tomography using gapped spectrum and real-valued iterative adaptive approach.

Optical coherence tomography (OCT) is a powerful imaging technique that is capable of imaging cross-sectional structures with micrometer resolution. After combining with phase-sensitive detection, it can sense small changes in the physical quantities inside an object. In OCT, axial resolution is generally improved by expanding the bandwidth of the light source. However, when the bandwidth is expanded discontinuously, the wavelength gap induces abnormal sidelobes when estimating OCT signals in the depth domain. This problem can lead to poor axial resolution. Herein, we present a method based on a real-valued iterative adaptive approach (RIAA) to achieve a high axial resolution under a discontinuous bandwidth condition. The method uses a weighted matrix to suppress the abnormal sidelobes caused by the wavelength gap and, therefore, can realize high-resolution measurements. A single-reflector OCT spectrum was first measured for validation, and its amplitude in the depth domain was estimated using different methods. The results indicate that the RIAA had the best capability of suppressing abnormal sidelobes, thereby achieving a high axial resolution. In addition, cross-sectional images and phase-difference maps of three different samples were measured. A comparison of the results validated the practical value of this method.

Deep learning-based weak micro-defect detection on an optical lens surface with micro vision.

To solve limited efficiency and reliability issues caused by current manual quality control processes in optical lens (OL) production environments, we propose an automatic micro vision-based inspection system named MVIS used to capture the surface defect images and make the OL dataset and predictive inference. Because of low resolution and recognition, OL defects are weak, due to their ambiguous morphology and micro size, making a poor detection effect for the existing method. A deep-learning algorithm for a weak micro-defect detector named ISE-YOLO is proposed, making the best for deep layers, utilizing the ISE attention mechanism module in the neck, and introducing a novel class loss function to extract richer semantics from convolution layers and learning more information. Experimental results on the OL dataset show that ISE-YOLO demonstrates a better performance, with the mean average precision, recall, and F1 score increasing by 3.62%, 6.12% and 3.07% respectively, compared to the YOLOv5. In addition, compared with YOLOv7, which is the latest version of YOLO serials, the mean average precision of ISE-YOLO is improved by 2.58%, the weight size is decreased by more than 30% and the speed is increased by 16%.

Distortion spot correction and center location base on deep neural network and MBAS in measuring large curvature aspheric optical element.

Large curvature aspheric optical elements are widely used in visual system. But its morphological detection is very difficult because its accuracy requirement is very high. When we use the self-developed multi-beam angle sensor (MBAS) to detect large curvature aspheric optical elements, the accuracy will be reduced due to spot distortion. Therefore, we propose a scheme combining distorted spot correction neural network (DSCNet) and gaussian fitting method to improve the detection accuracy of distorted spot center. We develop a spot discrimination method to determine spot region in multi-spot images. The spot discrimination threshold is obtained by the quantitative distribution of pixels in the connected domain. We design a DSCNet, which corrects the distorted spot to Gaussian spot, to extract the central information of distorted spot images by multiple pooling. The experimental results demonstrate that the DSCNet can effectively correct the distorted spot, and the spot center can be extracted to sub-pixel level, which improves the measurement accuracy of the MBAS. The standard deviations of plano-convex lenses with curvature radii of 500 mm, 700 mm and 1000 mm measured with the proposed method are respectively 0.0112 um, 0.0086 um and 0.0074 um.

Deep learning-based method for non-uniform motion-induced error reduction in dynamic microscopic 3D shape measurement.

The non-uniform motion-induced error reduction in dynamic fringe projection profilometry is complex and challenging. Recently, deep learning (DL) has been successfully applied to many complex optical problems with strong nonlinearity and exhibits excellent performance. Inspired by this, a deep learning-based method is developed for non-uniform motion-induced error reduction by taking advantage of the powerful ability of nonlinear fitting. First, a specially designed dataset of motion-induced error reduction is generated for network training by incorporating complex nonlinearity. Then, the corresponding DL-based architecture is proposed and it contains two parts: in the first part, a fringe compensation module is developed as network pre-processing to reduce the phase error caused by fringe discontinuity; in the second part, a deep neural network is employed to extract the high-level features of error distribution and establish a pixel-wise hidden nonlinear mapping between the phase with motion-induced error and the ideal one. Both simulations and real experiments demonstrate the feasibility of the proposed method in dynamic macroscopic measurement.

Generic saturation-induced phase error correction for structured light 3D shape measurement.

Intensity saturation is a challenging problem in structured light 3D shape measurement. Most of the existing methods achieve high dynamic range (HDR) measurement by sacrificing measurement speed, making them limited in high-speed dynamic applications. This Letter proposes a generic efficient saturation-induced phase error correction method for HDR measurement without increasing any fringe patterns. We first theoretically analyze the saturated signal model and deduce the periodic characteristic of saturation-induced phase error. Based on this, we specially design a saturation-induced phase error correction method by joint Fourier analysis and Hilbert transform. Furthermore, the relationship among phase error, saturation degree, and number of phase-shifting steps is established by numerical simulation. Since the proposed method requires no extra captured images or complicated intensity calibration, it is extremely convenient in implementation and is applicable to performing high-speed 3D shape measurements. Simulations and experiments verify the feasibility of the proposed method.

A Local Electricity and Carbon Trading Method for Multi-Energy Microgrids Considering Cross-Chain Interaction.

The objective of this paper is to propose a local electricity and carbon trading method for interconnected multi-energy microgrids. A local electricity market and a local carbon market are established, allowing microgrids to trade electricity and carbon allowance within the microgrid network. Specifically, excessive electricity and carbon allowance of a microgrid can be shared with other microgrids that require them. A local electricity trading problem and a local carbon trading problem are formulated for multi-energy microgrids using the Nash bargaining theory. Each Nash bargaining problem can be decomposed into two subproblems, including an energy/carbon scheduling problem and a payment bargaining problem. By solving the subproblems of the Nash bargaining problems, the traded amounts of electricity/carbon allowance between microgrids and the corresponding payments will be determined. In addition, to enable secure information interactions and trading payments, we introduce an electricity blockchain and a carbon blockchain to record the trading data for microgrids. The novelty of the usage of the blockchain technology lies in using a notary mechanism-based cross-chain interaction method to achieve value transfer between blockchains. The simulation results show that the proposed local electricity and carbon trading method has great performance in lowering total payments and carbon emissions for microgrids.

Adaptive incremental method for strain estimation in phase-sensitive optical coherence elastography.

We proposed an adaptive incremental method for the cumulative strain estimation in phase-sensitive optical coherence elastography. The method firstly counts the amount of phase noise points by mapping a binary noise map. After the noise threshold value is preset, the interframe interval is adaptively adjusted in terms of the phase noise ratio. Finally, the efficient estimation of cumulative strain is implemented by reducing the cumulative number. Since the level of phase noise is related to the different strain rates in accordance with the speckle decorrelation, the proposed method can estimate the large strains with high computation efficiency as well as signal-to-noise ratio (SNR) enhancement in nonlinear change of sample deformations. Real experiments of visualizing polymerization shrinkage with nonlinear change of deformations were performed to prove the superiority of adaptive incremental method in estimating the large strains. The proposed method expands the practicability of the incremental method in more complex scenes.

Deep-learning-based approach for strain estimation in phase-sensitive optical coherence elastography.

In this Letter, a deep-learning-based approach is proposed for estimating the strain field distributions in phase-sensitive optical coherence elastography. The method first uses the simulated wrapped phase maps and corresponding phase-gradient maps to train the strain estimation convolution neural network (CNN) and then employs the trained CNN to calculate the strain fields from measured phase-difference maps. Two specimens with different deformations, one with homogeneous and the other with heterogeneous, were measured for validation. The strain field distributions of the specimens estimated by different approaches were compared. The results indicate that the proposed deep-learning-based approach features much better performance than the popular vector method, enhancing the SNR of the strain results by 21.6 dB.

UMLF-COVID: an unsupervised meta-learning model specifically designed to identify X-ray images of COVID-19 patients.

BACKGROUND: With the rapid spread of COVID-19 worldwide, quick screening for possible COVID-19 patients has become the focus of international researchers. Recently, many deep learning-based Computed Tomography (CT) image/X-ray image fast screening models for potential COVID-19 patients have been proposed. However, the existing models still have two main problems. First, most of the existing supervised models are based on pre-trained model parameters. The pre-training model needs to be constructed on a dataset with features similar to those in COVID-19 X-ray images, which limits the construction and use of the model. Second, the number of categories based on the X-ray dataset of COVID-19 and other pneumonia patients is usually imbalanced. In addition, the quality is difficult to distinguish, leading to non-ideal results with the existing model in the multi-class classification COVID-19 recognition task. Moreover, no researchers have proposed a COVID-19 X-ray image learning model based on unsupervised meta-learning. METHODS: This paper first constructed an unsupervised meta-learning model for fast screening of COVID-19 patients (UMLF-COVID). This model does not require a pre-trained model, which solves the limitation problem of model construction, and the proposed unsupervised meta-learning framework solves the problem of sample imbalance and sample quality. RESULTS: The UMLF-COVID model is tested on two real datasets, each of which builds a three-category and four-category model. And the experimental results show that the accuracy of the UMLF-COVID model is 3-10% higher than that of the existing models. CONCLUSION: In summary, we believe that the UMLF-COVID model is a good complement to COVID-19 X-ray fast screening models.

Spectroscopic interferometer with a large length range by rotating diffraction grating.

A new method for spectroscopic interferometry using rotating diffraction grating was developed for industrial measurements. Two diffraction gratings increase the spectroscopic resolution, and the effective measuring range can be extended considerably. Instead of calibrating the wavelength, we used the Fabry-Perot Etalon (standard) to calibrate the system and determine the absolute position. The rotation diffraction gratings may also be used as a spectroscopic element over extensive ranges for low-cost and high-speed measurement. Our experiments indicate a length range of approximately 4.00 mm with repeatability of 0.17µm (0.0167%) for the narrow range and 3.84 µm (0.0955%) for the wide range.

Super-resolution reconstruction of speckle phase in depth-resolved wavelength scanning interference using the total least-squares analysis.

Depth-resolved wavelength scanning interferometry (DRWSI) is a tomographic imaging tool that employs phase measurement to visualize micro-displacement inside a sample. It is well known that the depth resolution of DRWSI is restricted by a wavelength scanning range. Recently, a nonlinear least-squares analysis (NLS) algorithm was proposed to overcome the limitation of the wavelength scanning range to achieve super-resolution; however, the NLS failed to measure speckle surfaces owing to the sensibility of initial values. To the best of our knowledge, the improvement of depth resolution on measuring a speckle surface remains an open issue for DRWSI. For this study, we redesigned the signal processing algorithm for DRWSI to refine the depth resolution when considering the case of speckle phase measurement. It is mathematically shown that the DRWSI's signal is derived as a model of total least-squares analysis (TLSA). Subsequently, a super-resolution of the speckle phase map was obtained using a singular value decomposition. Further, a numerical simulation to measure the micro-displacements for speckle surfaces was performed to validate the TLSA, and the results show that it can precisely reconstruct the displacements of layers whose depth distance is 5 µm. This study thus provides an opportunity to improve the DRWSI's depth resolution.

Ectopic Expression of Poplar ABC Transporter PtoABCG36 Confers Cd Tolerance in Arabidopsis thaliana.

Cadmium (Cd) is one of the most toxic heavy metals for plant growth in soil. ATP-binding cassette (ABC) transporters play important roles in biotic and abiotic stresses. However, few ABC transporters have been characterized in poplar. In this study, we isolated an ABC transporter gene PtoABCG36 from Populus tomentosa. The PtoABCG36 transcript can be detected in leaves, stems and roots, and the expression in the root was 3.8 and 2 times that in stems and leaves, respectively. The PtoABCG36 expression was induced and peaked at 12 h after exposure to Cd stress. Transient expression of PtoABCG36 in tobacco showed that PtoABCG36 is localized at the plasma membrane. When overexpressed in yeast and Arabidopsis, PtoABCG36 could decrease Cd accumulation and confer higher Cd tolerance in transgenic lines than in wild-type (WT) lines. Net Cd2+ efflux measurements showed a decreasing Cd uptake in transgenic Arabidopsis roots than WT. These results demonstrated that PtoABCG36 functions as a cadmium extrusion pump participating in enhancing tolerance to Cd through decreasing Cd content in plants, which provides a promising way for making heavy metal tolerant poplar by manipulating ABC transporters in cadmium polluted areas.

Wavenumber synthesis approach to high-resolution wavenumber scanning interference using a mode-hoped laser.

A new method for the synthesis of wavenumber series before and after mode hopping is proposed for depth-resolved wavenumber scanning interferometry. The classical Fourier transform is not suitable for mode hopping; consequently, the wavenumber scanning range of diode lasers is rather narrow, reducing the depth resolution and measurement accuracy. We show that the discontinuity in wavenumber domain interferograms caused by mode hopping can be removed by introducing the phase compensation of the interference spectrum. Thus, the wavenumber series before and after mode hopping can be synthesized. Experiments and numerical simulations validate the proposed method, and the measurement error is within 5nm.

Highly sensitive, wide dynamic range displacement sensor combining chromatic confocal system and phase-sensitive spectral optical coherence tomography.

A displacement sensor with nanometer-sensitivity and a submillimeter dynamic range is proposed. It integrates a chromatic confocal system and phase-sensitive spectral optical coherence tomography (PhS-SOCT) into the fiber-based Michelson interferometer and codes interference and confocal signals with spectral multiplexing. A displacement is evaluated using depth-resolved phase information decoded from the interference signal, which is unwrapped based on the position information decoded from the confocal signal. A sensor system with a 0.102mm dynamic range was built to validate the method. The temperature induced sample surface displacement was measured with a root mean square error of 3.9nm.