High-speed submillimeter magnetic-driven rotational scanning side-imaging OCT probe.

A high-speed side-imaging magnetic-driven scanning (MDS) probe is proposed for endoscopic optical coherence tomography (OCT). In the distal end of the probe, a reflecting micromirror is attached to a tiny magnet, which is driven by an external fast-rotating magnetic field to achieve unobstructed 360-degree side-view scanning. A prototype probe was fabricated with an outer diameter of 0.89 mm. Using the prototype probe, OCT images of an ex vivo porcine artery with implanted stent were acquired in 100 frames per second. The OCT engine was a swept-source system, and the system sensitivity with the prototype probe was 95 dB with an output power of 6 mW. The axial and lateral resolutions of the system were 10.3 and 39.7 µm, respectively. The high-speed submillimeter MDS-OCT probe provides a promising alternative endoscopic OCT solution for intravascular imaging applications.

A phase II, single-center, randomized, double-blind, parallel control clinical study evaluating the immunogenicity and safety of a two-dose schedule of serogroups ACYW meningococcal polysaccharide conjugate vaccine.

BACKGROUND: This was a single-center, randomized, double-blind, parallel control study evaluating the immunogenicity and safety of a two-dose schedule of serogroups ACYW meningococcal polysaccharide conjugate vaccine with tetanus toxoid (TT) conjugate protein, in infants and toddlers of 3-35 months old. METHOD: 720 participants were stratified according to the age of 3-5 months old, 6-11 months old, and 12-35 months old and randomly assigned with an equal ratio to two different dose groups, i.e., 40- and 20-µg doses. Blood samples were taken from all participants before the first vaccination and 30 days after the full-course vaccination to detect the serogroups ACYW meningococcal antibodies. All adverse events occurred within 30 days after vaccination of each dose, and serious adverse events occurred within six months after full-course vaccination were collected for safety evaluation. This study was registered at the China drug trial registration with the identifier CTR 20182031. RESULTS: After 30 days of full-course vaccination, 92.78 % (95 % CI: 85.70 %-100.00 %) showed the immune response against all serogroups in both high-dose and low-dose groups by rabbit serum bactericidal antibody assay (rSBA) and the geometric mean titer (GMT) of all serogroups showed a high level (74.6-505.8, 95 % CI: 56.4-615.7). However, no significant difference between different dose groups was observed (P > 0.05). The common local and systemic adverse events in both groups were redness (3 %-7%), and fever (26 %-65 %), respectively. In addition, the grade 3 adverse event related to the vaccine was fever (1.67 %-12.50 %). No serious adverse event was reported to be associate with the vaccination. CONCLUSION: The serogroups ACYW meningococcal polysaccharide conjugate vaccine was safe and effective in the population aged 3-35 months. The vaccine efficacy and safety of the 20-µg dose group were not less than that of the 40-µg dose group.

Multiview Consensus Structure Discovery.

Multiview subspace learning has attracted much attention due to the efficacy of exploring the information on multiview features. Most existing methods perform data reconstruction on the original feature space and thus are vulnerable to noisy data. In this article, we propose a novel multiview subspace learning method, called multiview consensus structure discovery (MvCSD). Specifically, we learn the low-dimensional subspaces corresponding to different views and simultaneously pursue the structure consensus over subspace clustering for multiple views. In such a way, latent subspaces from different views regularize each other toward a common consensus that reveals the underlying cluster structure. Compared to existing methods, MvCSD leverages the consensus structure derived from the subspaces of diverse views to better exploit the intrinsic complementary information that well reflects the essence of data. Accordingly, the proposed MvCSD is capable of producing a more robust and accurate representation structure which is crucial for multiview subspace learning. The proposed method can be optimized effectively, with theoretical convergence guarantee, by alternatively iterating the argument Lagrangian multiplier algorithm and the eigendecomposition. Extensive experiments on diverse datasets demonstrate the advantages of our method over the state-of-the-art methods.

Optical Coherence Tomography-Derived Changes in Plaque Structural Stress Over the Cardiac Cycle: A New Method for Plaque Biomechanical Assessment.

Introduction: Cyclic plaque structural stress has been hypothesized as a mechanism for plaque fatigue and eventually plaque rupture. A novel approach to derive cyclic plaque stress in vivo from optical coherence tomography (OCT) is hereby developed. Materials and Methods: All intermediate lesions from a previous OCT study were enrolled. OCT cross-sections at representative positions within each lesion were selected for plaque stress analysis. Detailed plaque morphology, including plaque composition, lumen and internal elastic lamina contours, were automatically delineated. OCT-derived vessel and plaque morphology were included in a 2-dimensional finite element analysis, loaded with patient-specific intracoronary pressure tracing data, to calculate the changes in plaque structural stress (ΔPSS) on vessel wall over the cardiac cycle. Results: A total of 50 lesions from 41 vessels were analyzed. A significant ΔPSS gradient was observed across the plaque, being maximal at the proximal shoulder (45.7 [32.3, 78.6] kPa), intermediate at minimal lumen area (MLA) (39.0 [30.8, 69.1] kPa) and minimal at the distal shoulder (35.1 [28.2, 72.3] kPa; p = 0.046). The presence of lipidic plaques were observed in 82% of the diseased segments. Larger relative lumen deformation and ΔPSS were observed in diseased segments, compared with normal segments (percent diameter change: 8.2 ± 4.2% vs. 6.3 ± 2.3%, p = 0.04; ΔPSS: 59.3 ± 48.2 kPa vs. 27.5 ± 8.2 kPa, p < 0.001). ΔPSS was positively correlated with plaque burden (r = 0.37, p < 0.001) and negatively correlated with fibrous cap thickness (r = -0.25, p = 0.004). Conclusions: ΔPSS provides a feasible method for assessing plaque biomechanics in vivo from OCT images, consistent with previous biomechanical and clinical studies based on different methodologies. Larger ΔPSS at proximal shoulder and MLA indicates the critical sites for future biomechanical assessment.

Extended-aperture Hartmann wavefront sensor with raster scanning.

In this paper, we propose an extended-aperture Hartmann wavefront sensor (HWFS) based on raster scanning. Unlike traditional HWFS, where there is a trade-off between the dynamic range and spatial resolution of wavefront measurement, our extended-aperture HWFS breaks the trade-off and thus could achieve a large dynamic range and high spatial resolution simultaneously. By applying a narrow-beam raster-scanning scheme, the detection aperture of our HWFS is extended to 40 × 40 mm2 without using the enlarging 4f relay system. The spatial resolution of our setup depends on the scanning step, the pinhole size, and the wavelength. The sensitivity and dynamic range can be adjusted flexibly by varying the axial distance between the pinhole plane and the imaging sensor plane, because our decoupled large dynamic range could be reasonable traded-off to achieve better sensitivity. Furthermore, compared with tradition HWFS, our method does not need to compute the positions of a two-dimensional spots array where complicated spots tracking algorithms are necessary to achieve high dynamic range, thus remarkably reduces the spots aliasing issue and the computational cost. It should be noted that this scheme is not only applicable for HWFS but also for Shack-Hartmann wavefront sensor (SHWFS) with microlens array to achieve higher accuracy and better power efficiency. Experiments were performed to demonstrate the capability of our method.

Extended aperture line-scanning Hartmann wavefront sensor.

We first propose a line-scanning Hartmann wavefront sensor (LS-HWS) with extended aperture. In the LS-HWS, a line-scanning imaging sensor was driven by a motor and scanning behind a large-area Hartman mask. Compared to the traditional Hartman wavefront sensor with two-dimensional imaging sensors, our method can significantly enlarge the aperture because of the larger imaging area with line-scanning imaging sensors. Cross correlation registration was adopted to reduce the scanning error. Experiments on two single spherical lenses and a free-form lens were performed to demonstrate the capability of the LS-HWS method. The results show that our method can achieve an aperture of 17.5×37.5mm2 with the prototype system, which could be further extended easily and is limited only by the size of the line-scanning imaging sensor and the scanning range of the motor. We believe that the LS-HWS method is promising for many wavefront sensing applications where a large aperture is preferred.

Asymmetric Supervised Consistent and Specific Hashing for Cross-Modal Retrieval.

Hashing-based techniques have provided attractive solutions to cross-modal similarity search when addressing vast quantities of multimedia data. However, existing cross-modal hashing (CMH) methods face two critical limitations: 1) there is no previous work that simultaneously exploits the consistent or modality-specific information of multi-modal data; 2) the discriminative capabilities of pairwise similarity is usually neglected due to the computational cost and storage overhead. Moreover, to tackle the discrete constraints, relaxation-based strategy is typically adopted to relax the discrete problem to the continuous one, which severely suffers from large quantization errors and leads to sub-optimal solutions. To overcome the above limitations, in this article, we present a novel supervised CMH method, namely Asymmetric Supervised Consistent and Specific Hashing (ASCSH). Specifically, we explicitly decompose the mapping matrices into the consistent and modality-specific ones to sufficiently exploit the intrinsic correlation between different modalities. Meanwhile, a novel discrete asymmetric framework is proposed to fully explore the supervised information, in which the pairwise similarity and semantic labels are jointly formulated to guide the hash code learning process. Unlike existing asymmetric methods, the discrete asymmetric structure developed is capable of solving the binary constraint problem discretely and efficiently without any relaxation. To validate the effectiveness of the proposed approach, extensive experiments on three widely used datasets are conducted and encouraging results demonstrate the superiority of ASCSH over other state-of-the-art CMH methods.

Projective Double Reconstructions Based Dictionary Learning Algorithm for Cross-Domain Recognition.

Dictionary learning plays a significant role in the field of machine learning. Existing works mainly focus on learning dictionary from a single domain. In this paper, we propose a novel projective double reconstructions (PDR) based dictionary learning algorithm for cross-domain recognition. Owing the distribution discrepancy between different domains, the label information is hard utilized for improving discriminability of dictionary fully. Thus, we propose a more flexible label consistent term and associate it with each dictionary item, which makes the reconstruction coefficients have more discriminability as much as possible. Due to the intrinsic correlation between cross-domain data, the data should be reconstructed with each other. Based on this consideration, we further propose a projective double reconstructions scheme to guarantee that the learned dictionary has the abilities of data itself reconstruction and data crossreconstruction. This also guarantees that the data from different domains can be boosted mutually for obtaining a good data alignment, making the learned dictionary have more transferability. We integrate the double reconstructions, label consistency constraint and classifier learning into a unified objective and its solution can be obtained by proposed optimization algorithm that is more efficient than the conventional l1 optimization based dictionary learning methods. The experiments show that the proposed PDR not only greatly reduces the time complexity for both training and testing, but also outperforms over the stateof- the-art methods.

Transferable Linear Discriminant Analysis.

Linear discriminant analysis (LDA) has been widely used as the technique of feature exaction. However, LDA may be invalid to address the data from different domains. The reasons are as follows: 1) the distribution discrepancy of data may disturb the linear transformation matrix so that it cannot extract the most discriminative feature and 2) the original design of LDA does not consider the unlabeled data so that the unlabeled data cannot take part in the training process for further improving the performance of LDA. To address these problems, in this brief, we propose a novel transferable LDA (TLDA) method to extend LDA into the scenario in which the data have different probability distributions. The whole learning process of TLDA is driven by the philosophy that the data from the same subspace have a low-rank structure. The matrix rank in TLDA is the key learning criterion to conduct local and global linear transformations for restoring the low-rank structure of data from different distributions and enlarging the distances among different subspaces. In doing so, the variations of distribution discrepancy within the same subspace can be reduced, i.e., data can be aligned well and the maximally separated structure can be achieved for the data from different subspaces. A simple projected subgradient-based method is proposed to optimize the objective of TLDA, and a strict theory proof is provided to guarantee a quick convergence. The experimental evaluation on public data sets demonstrates that our TLDA can achieve better classification performance and outperform the state-of-the-art methods.

Constrained Discriminative Projection Learning for Image Classification.

Projection learning is widely used in extracting discriminative features for classification. Although numerous methods have already been proposed for this goal, they barely explore the label information during projection learning and fail to obtain satisfactory performance. Besides, many existing methods can learn only a limited number of projections for feature extraction which may degrade the performance in recognition. To address these problems, we propose a novel constrained discriminative projection learning (CDPL) method for image classification. Specifically, CDPL can be formulated as a joint optimization problem over subspace learning and classification. The proposed method incorporates the low-rank constraint to learn a robust subspace which can be used as a bridge to seamlessly connect the original visual features and objective outputs. A regression function is adopted to explicitly exploit the class label information so as to enhance the discriminability of subspace. Unlike existing methods, we use two matrices to perform feature learning and regression, respectively, such that the proposed approach can obtain more projections and achieve superior performance in classification tasks. The experiments on several datasets show clearly the advantages of our method against other state-of-the-art methods.

Latent Elastic-Net Transfer Learning.

Subspace learning based transfer learning methods commonly find a common subspace where the discrepancy of the source and target domains is reduced. The final classification is also performed in such subspace. However, the minimum discrepancy does not guarantee the best classification performance and thus the common subspace may be not the best discriminative. In this paper, we propose a latent elastic-net transfer learning (LET) method by simultaneously learning a latent subspace and a discriminative subspace. Specifically, the data from different domains can be well interlaced in the latent subspace by minimizing Maximum Mean Discrepancy (MMD). Since the latent subspace decouples inputs and outputs and, thus a more compact data representation is obtained for discriminative subspace learning. Based on the latent subspace, we further propose a low-rank constraint based matrix elastic-net regression to learn another subspace in which the intrinsic intra-class structure correlations of data from different domains is well captured. In doing so, a better discriminative alignment is guaranteed and thus LET finally learns another discriminative subspace for classification. Experiments on visual domains adaptation tasks show the superiority of the proposed LET method.

Flexible Affinity Matrix Learning for Unsupervised and Semisupervised Classification.

In this paper, we propose a unified model called flexible affinity matrix learning (FAML) for unsupervised and semisupervised classification by exploiting both the relationship among data and the clustering structure simultaneously. To capture the relationship among data, we exploit the self-expressiveness property of data to learn a structured matrix in which the structures are induced by different norms. A rank constraint is imposed on the Laplacian matrix of the desired affinity matrix, so that the connected components of data are exactly equal to the cluster number. Thus, the clustering structure is explicit in the learned affinity matrix. By making the estimated affinity matrix approximate the structured matrix during the learning procedure, FAML allows the affinity matrix itself to be adaptively adjusted such that the learned affinity matrix can well capture both the relationship among data and the clustering structure. Thus, FAML has the potential to perform better than other related methods. We derive optimization algorithms to solve the corresponding problems. Extensive unsupervised and semisupervised classification experiments on both synthetic data and real-world benchmark data sets show that the proposed FAML consistently outperforms the state-of-the-art methods.

Unsupervised feature extraction by low-rank and sparsity preserving embedding.

Manifold based feature extraction has been proved to be an effective technique in dealing with the unsupervised classification tasks. However, most of the existing works cannot guarantee the global optimum of the learned projection, and they are sensitive to different noises. In addition, many methods cannot catch the discriminative information as much as possible since they only exploit the local structure of data while ignoring the global structure. To address the above problems, this paper proposes a novel graph based feature extraction method named low-rank and sparsity preserving embedding (LRSPE) for unsupervised learning. LRSPE attempts to simultaneously learn the graph and projection in a framework so that the global optimal projection can be obtained. Moreover, LRSPE exploits both global and local information of data for projection learning by imposing the low-rank and sparse constraints on the graph, which promotes the method to obtain a better performance. Importantly, LRSPE is more robust to noise by imposing the l2,1 sparsity norm on the reconstruction errors. Experimental results on both clean and noisy datasets prove that the proposed method can significantly improve classification accuracy and it is robust to different noises in comparison with the state-of-the-art methods.

Low-rank and sparse embedding for dimensionality reduction.

In this paper, we propose a robust subspace learning (SL) framework for dimensionality reduction which further extends the existing SL methods to a low-rank and sparse embedding (LRSE) framework from three aspects: overall optimum, robustness and generalization. Owing to the uses of low-rank and sparse constraints, both the global subspaces and local geometric structures of data are captured by the reconstruction coefficient matrix and at the same time the low-dimensional embedding of data are enforced to respect the low-rankness and sparsity. In this way, the reconstruction coefficient matrix learning and SL are jointly performed, which can guarantee an overall optimum. Moreover, we adopt a sparse matrix to model the noise which makes LRSE robust to the different types of noise. The combination of global subspaces and local geometric structures brings better generalization for LRSE than related methods, i.e., LRSE performs better than conventional SL methods in unsupervised and supervised scenarios, particularly in unsupervised scenario the improvement of classification accuracy is considerable. Seven specific SL methods including unsupervised and supervised methods can be derived from the proposed framework and the experiments on different data sets (including corrupted data) demonstrate the superiority of these methods over the existing, well-established SL methods. Further, we exploit experiments to provide some new insights for SL.

Approximate Low-Rank Projection Learning for Feature Extraction.

Feature extraction plays a significant role in pattern recognition. Recently, many representation-based feature extraction methods have been proposed and achieved successes in many applications. As an excellent unsupervised feature extraction method, latent low-rank representation (LatLRR) has shown its power in extracting salient features. However, LatLRR has the following three disadvantages: 1) the dimension of features obtained using LatLRR cannot be reduced, which is not preferred in feature extraction; 2) two low-rank matrices are separately learned so that the overall optimality may not be guaranteed; and 3) LatLRR is an unsupervised method, which by far has not been extended to the supervised scenario. To this end, in this paper, we first propose to use two different matrices to approximate the low-rank projection in LatLRR so that the dimension of obtained features can be reduced, which is more flexible than original LatLRR. Then, we treat the two low-rank matrices in LatLRR as a whole in the process of learning. In this way, they can be boosted mutually so that the obtained projection can extract more discriminative features. Finally, we extend LatLRR to the supervised scenario by integrating feature extraction with the ridge regression. Thus, the process of feature extraction is closely related to the classification so that the extracted features are discriminative. Extensive experiments are conducted on different databases for unsupervised and supervised feature extraction, and very encouraging results are achieved in comparison with many state-of-the-arts methods.

Analysis and Control of Output Synchronization in Directed and Undirected Complex Dynamical Networks.

This research focuses on the problem of output synchronization in undirected and directed complex dynamical networks, respectively, by applying Barbalat's lemma. First, to ensure the output synchronization, several sufficient criteria are established for these network models based on some mathematical techniques, such as the Lyapunov functional method and matrix theory. Furthermore, some adaptive schemes to adjust the coupling weights among network nodes are developed to achieve the output synchronization. By applying the designed adaptive laws, several criteria for output synchronization are deduced for the network models. In addition, a design procedure of the adaptive law is shown. Finally, two simulation examples are used to show the effectiveness of the previous results.

Passivity and Output Synchronization of Complex Dynamical Networks With Fixed and Adaptive Coupling Strength.

This paper considers a complex dynamical network model, in which the input and output vectors have different dimensions. We, respectively, investigate the passivity and the relationship between output strict passivity and output synchronization of the complex dynamical network with fixed and adaptive coupling strength. First, two new passivity definitions are proposed, which generalize some existing concepts of passivity. By constructing appropriate Lyapunov functional, some sufficient conditions ensuring the passivity, input strict passivity and output strict passivity are derived for the complex dynamical network with fixed coupling strength. In addition, we also reveal the relationship between output strict passivity and output synchronization of the complex dynamical network with fixed coupling strength. By employing the relationship between output strict passivity and output synchronization, a sufficient condition for output synchronization of the complex dynamical network with fixed coupling strength is established. Then, we extend these results to the case when the coupling strength is adaptively adjusted. Finally, two examples with numerical simulations are provided to demonstrate the effectiveness of the proposed criteria.

Three-dimensional endoscopic OCT using sparse sampling with a miniature magnetic-driven scanning probe.

We propose to apply sparse sampling and compressive sensing (CS) reconstruction in three-dimensional (3D) endoscopic optical coherence tomography (OCT) to reduce the amount of data required in the imaging process. We used a homemade miniature side-imaging magnetic-driven scanning probe with an outer diameter of 1.4 mm in a 1310 nm swept-source OCT system to acquire two-dimensional (2D) circumferential cross-sectional images of an ex vivo pigeon trachea sample. 3D imaging is then achieved by reconstruction from the multiple 2D images acquired while pulling the sample with a translation stage. Given a total translation distance, we achieved sparse sampling by randomizing the step sizes of the translation stage such that the total number of the acquired 2D frames was reduced compared with conventional 3D imaging with equally spaced step positions. We tested the CS reconstruction with reduced 2D frame numbers of 40%, 60%, and 80% compared with the case of equally spaced step positions. The results show that it is possible to recover reasonable OCT images using sparse sampling with CS reconstruction. Compared with the conventional equally spaced sampling method, our method provides a novel way for image acquisition and reconstruction that could significantly reduce the amount of 3D OCT imaging data, and thus the acquisition time.

Multilayer pixel super-resolution lensless in-line holographic microscope with random sample movement.

We report a multilayer lensless in-line holographic microscope (LIHM) with improved imaging resolution by using the pixel super-resolution technique and random sample movement. In our imaging system, a laser beam illuminated the sample and a CMOS imaging sensor located behind the sample recorded the in-line hologram for image reconstruction. During the imaging process, the sample was moved by hand randomly and the in-line holograms were acquired sequentially. Then the sample image was reconstructed from an enhanced-resolution hologram obtained from multiple low-resolution in-line holograms by applying the pixel super-resolution (PSR) technique. We studied the resolution enhancement effects by using the U.S. Air Force (USAF) target as the sample in numerical simulation and experiment. We also showed that multilayer pixel super-resolution images can be obtained by imaging a triple-layer sample made with the filamentous algae on the middle layer and microspheres with diameter of 2 µm on the top and bottom layers. Our pixel super-resolution LIHM provides a compact and low-cost solution for microscopic imaging and is promising for many biomedical applications.

Resolution enhancement method for lensless in-line holographic microscope with spatially-extended light source.

We propose a resolution enhancement method for lensless in-line holographic microscope (LIHM) with spatially-extended light source, where the resolution is normally deteriorated by the insufficient spatial coherence of the illumination. In our LIHM setup, a light-emitting diode (LED), which was a spatially-extended light source, directly illuminated the sample, and the in-line hologram were recorded by a CMOS imaging sensor located behind the sample. In our holographic reconstruction process, the in-line hologram was first deconvoled with a properly resized image of the LED illumination area, and then back-propagated with scalar diffraction formula to reconstruct the sample image. We studied the hologram forming process and showed that the additional deconvolution process besides normal scalar diffraction reconstruction in LIHM can effectively enhance the imaging resolution. The resolution enhancements capability was calibrated by numerical simulations and imaging experiments with the U.S. air force target as the sample. We also used our LIHM to image the wing of a green lacewing to further demonstrate the capability of our methods for practical imaging applications. Our methods provide a way for LIHM to achieve satisfactory resolution with less stringent requirement for spatial coherence of the source and could reduce the cost for compact imaging system.