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Abstract Non-radiative optical modes attracted enormous attention in optics due to strong light confinement and giant Q-factor at its spectral position. The destructive interference of multipoles leads to zero net-radiation and strong field trapping. Such radiationless states disappear in the far-field, localize enhanced near-field and can be excited in nano-structures. On the other hand, the optical modes turn out to be completely confined due to no losses at discrete point in the radiation continuum, such states result in infinite Q-factor and lifetime. The radiationless states provide a suitable platform for enhanced light matter interaction, lasing, and boost nonlinear processes at the state regime. These modes are widely investigated in different material configurations for various applications in both linear and nonlinear metasurfaces which are briefly discussed in this review.

The near-eye display (NED) systems, designed to project content into the human eye, are pivotal in the realms of augmented reality (AR) and virtual reality (VR), offering users immersive experiences. A small volume is the key for a fashionable, easy-to-wear, comfortable NED system for industrial and consumer use. Freeform surfaces can significantly reduce the system volume and weight while improving the system specifications. However, great challenges still exist in further reducing the volume of near-eye display systems as there is also a limit when using only freeform optics. This paper introduces a novel method for designing compact freeform NED systems through a powerful optical–digital joint design. The method integrates a geometrical freeform optical design with deep learning of an image compensation neural network, addressing off-axis nonsymmetric structures with complex freeform surfaces. A design example is presented to demonstrate the effectiveness of the proposed method. Specifically, the volume of a freeform NED system is reduced by approximately 63% compared to the system designed by the traditional method, while still maintaining high-quality display performance. The proposed method opens a new pathway for the design of a next-generation ultra-compact NED system.

Optical neural networks have significant advantages in terms of power consumption, parallelism, and high computing speed, which has intrigued extensive attention in both academic and engineering communities. It has been considered as one of the powerful tools in promoting the fields of imaging processing and object recognition. However, the existing optical system architecture cannot be reconstructed to the realization of multi-functional artificial intelligence systems simultaneously. To push the development of this issue, we propose the pluggable diffractive neural networks (P-DNN), a general paradigm resorting to the cascaded metasurfaces, which can be applied to recognize various tasks by switching internal plug-ins. As the proof-of-principle, the recognition functions of six types of handwritten digits and six types of fashions are numerical simulated and experimental demonstrated at near-infrared regimes. Encouragingly, the proposed paradigm not only improves the flexibility of the optical neural networks but paves the new route for achieving high-speed, low-power and versatile artificial intelligence systems.

Abstract Nitrogen has five valence electrons and can form a maximum of three shared electron-pair bonds to complete its octet, which suggests that its maximum bond order is three. With a joint anion photoelectron spectroscopy and quantum chemistry investigation, we report herein that nitrogen presents a quadruple bonding interaction with thorium in ThN. The quadruple Th≣N bond consists of two electron-sharing Th-N π bonds formed between the Th-6d xz/6d yz and N 2p x/2p y orbitals, one dative Th←N σ bond and one weak Th←N σ bonding interaction formed between Th-6d z 2 and N 2s/2p z orbitals. The ThC molecule has also been investigated and proven to have a similar bonding pattern as ThN. Nonetheless, due to one singly occupied σ-bond, ThC is assigned a bond order of 3.5. Moreover, ThC has a longer bond length as well as a lower vibrational frequency in comparison with ThN.

Abstract Since the invention of holography by Dennis Gabor, the fabrication of holograms has mainly relied on direct recording of wavefront by engraving the intensity fringes of interfering electric fields into the holographic material. The degree-of-freedom (DoF) is often limited, especially for its usage as a holographic optical element in imaging or display systems, as what is recorded is what to use. In this work, based on the emerging self-assembled photo-aligned liquid crystal, a polarization hologram with full DoF for local manipulation of optical structure is demonstrated. The ability to record an arbitrary wavefront (in-plane DoF) is achieved by freeform surface exposure, while the local adjustment of deposited liquid crystal (out-of-plane DoF) is realized by inkjet printing. The methodology for designing and fabricating such a hologram is exemplified by building a full-color retinal scanning display without color crosstalk. Here, the arbitrary wavefront modulation capability helps to eliminate the aberrations caused by mismatched exposure and display wavelengths. The local liquid crystal adjustment ability enables the suppression of crosstalk by variation of chiral pitch and film thickness to tune the peak and valley of Bragg diffraction band. The demonstrated method is expected to greatly impact the fields of advanced imaging and display, such as augmented reality and virtual reality, that require optics with an ultrathin form factor and high degrees of design freedom simultaneously.

Color metasurface holograms are powerful and versatile platforms for modulating the amplitude, phase, polarization, and other properties of light at multiple operating wavelengths. However, the current color metasurface holography can only realize static manipulation. In this study, we propose and demonstrate a multiplexing metasurface technique combined with multiwavelength code-division multiplexing (CDM) to realize dynamic manipulation. Multicolor code references are utilized to record information within a single metasurface and increase the information capacity and security for anti-cracks. A total of 48 monochrome images consisting of pure color characters and multilevel color video frames were reconstructed in dual polarization channels of the birefringent metasurface to exhibit high information density, and a video was displayed via sequential illumination of the corresponding code patterns to verify the ability of dynamic manipulation. Our approach demonstrates significant application potential in optical data storage, optical encryption, multiwavelength-versatile diffractive optical elements, and stimulated emission depletion microscopy.

Large depth of field (DOF) and stereo photography are challenging yet rewarding areas of research in close-up photography. In this study, a compound-eye imaging system based on a discrete microlens array (MLA) was implemented for close-range thin imaging. A compact imaging system with a total length of 3.5 mm and a DOF of 7 mm was realized using two planar aspherical MLAs in a hexagonal arrangement. A new three-layer structure and discrete arrangement of sublenses were proposed to suppress stray light and enable the spatial refocusing method, which restores image information at different object depths. The system is successfully fabricated, and the system performance is carefully investigated. Our system offers a large depth of field, high resolution, and portability, making it ideal for close-up photography applications requiring a short conjugate distance and small device volume, while also addressing the issue of crosstalk between adjacent channels.

The authors present a single-shot 3D imaging approach utilizing carefully designed point clouds projection based on a metasurface device. They show submillimeter depth accuracy and demonstrate the potential for hand gesture detection.

Abstract Housing quality is essential to human well-being, security and health. Monitoring the housing quality is crucial for unveiling the socioeconomic development status and providing political proposals. However, depicting the nationwide housing quality in large-scale and fine detail is exceedingly rare in remote rural areas owing to the high cost of canonical survey methods. Taking rural China as an example, we collect massive rural house images for housing quality assessment by various volunteers and further build up a deep learning model based on the assessed images to realize an automatic prediction for huge raw house images. As a result, the model performance achieves a high R 2 of 0.76. Afterward, the housing qualities of 10,000 Chinese villages are estimated based on 50,000 unlabeled geo-images, and an apparent spatial heterogeneity is discovered. Specifically, divided by Qinling Mountains-Huaihe River Line, housing quality in southern China is much better than in northern China. Our method provides high-resolution predictions of housing quality across the extensive rural area, which could be a complementary tool for automatical monitoring of housing change and supporting house-related policymaking.

Abstract Holographic three-dimensional display is an important display technique because it can provide all depth information of a real or virtual scene without any special eyewear. In recent years, with the development of computer and optoelectronic technology, computer-generated holograms have attracted extensive attention and developed as the most promising method to realize holographic display. However, some bottlenecks still restrict the development of computer-generated holograms, such as heavy computation burden, low image quality, and the complicated system of color holographic display. To overcome these problems, numerous algorithms have been investigated with the aim of color dynamic holographic three-dimensional display. In this review, we will explain the essence of various computer-generated hologram algorithms and provide some insights for future research.

A thermosensitive optoelectronic upconversion device based on integrated microscale LED and photodiode structures, with temperature dependent of spectral shift and intensity change.

A brain-computer interface (BCI) facilitates bypassing the peripheral nervous system and directly communicating with surrounding devices. Navigation technology using BCI has developed—from exploring the prototype paradigm in the virtual environment (VE) to accurately completing the locomotion intention of the operator in the form of a powered wheelchair or mobile robot in a real environment. This paper summarizes BCI navigation applications that have been used in both real and VEs in the past 20 years. Horizontal comparisons were conducted between various paradigms applied to BCI and their unique signal-processing methods. Owing to the shift in the control mode from synchronous to asynchronous, the development trend of navigation applications in the VE was also reviewed. The contrast between highlevel commands and low-level commands is introduced as the main line to review the two major applications of BCI navigation in real environments: mobile robots and unmanned aerial vehicles (UAVs). Finally, applications of BCI navigation to scenarios outside the laboratory; research challenges, including human factors in navigation application interaction design; and the feasibility of hybrid BCI for BCI navigation are discussed in detail.

Abstract Background In biological systems, metabolomics can not only contribute to the discovery of metabolic signatures for disease diagnosis, but is very helpful to illustrate the underlying molecular disease-causing mechanism. Therefore, identification of disease-related metabolites is of great significance for comprehensively understanding the pathogenesis of diseases and improving clinical medicine. Results In the paper, we propose a disease and literature driven metabolism prediction model (DLMPM) to identify the potential associations between metabolites and diseases based on latent factor model. We build the disease glossary with disease terms from different databases and an association matrix based on the mapping between diseases and metabolites. The similarity of diseases and metabolites is used to complete the association matrix. Finally, we predict potential associations between metabolites and diseases based on the matrix decomposition method. In total, 1,406 direct associations between diseases and metabolites are found. There are 119,206 unknown associations between diseases and metabolites predicted with a coverage rate of 80.88%. Subsequently, we extract training sets and testing sets based on data increment from the database of disease-related metabolites and assess the performance of DLMPM on 19 diseases. As a result, DLMPM is proven to be successful in predicting potential metabolic signatures for human diseases with an average AUC value of 82.33%. Conclusion In this paper, a computational model is proposed for exploring metabolite-disease pairs and has good performance in predicting potential metabolites related to diseases through adequate validation. The results show that DLMPM has a better performance in prioritizing candidate diseases-related metabolites compared with the previous methods and would be helpful for researchers to reveal more information about human diseases.

The intermediate angular-spectrum method is an efficient way to calculate computer-generated holograms without zero-padding and cropping operation. In this paper, we present an optimized iteration algorithm to improve the reconstruction quality in the intermediate angular-spectrum method. The quadratic phase is multiplied with the target image as the initial complex amplitude distribution to suppresses the speckle noise and the freedom domain is introduced in the image plane to improve the reconstruction quality. Numerical simulations and optical experiments are performed to demonstrate that proposed method is an effective method for high-quality reconstruction. It is expected that proposed method could be widely applied in the holographic field in the future.

Background: Virtual reality (VR) has become a powerful and promising tool for education, and numerous studies have investigated the application and effectiveness of VR education. However, few studies have focused on the expectations and concerns of teenagers regarding head-mounted displays (HMDs), which are used for this purpose. Methods: In this paper, we aim to explore the current problems and necessary advancements required in VR education based on a survey of 163 senior high school students who experience VR educational content for 1h. The usability and comfort of the HMD system, the physical and psychological effects on the students, and their preferences and concerns are investigated. Results: The results show that HMDs increase students' interest, concentration, and enthusiasm for learning. However, isolated virtual environments make students feel nervous and afraid. The immersive environment also makes them worry about VR addiction and confusing the physical world with the virtual one. Conclusions: VR has great potential in the field of education, but the issue of safety needs to be considered in the future.

Problems associated with obtaining measurements of the concentration- and temperature-dependent liquid diffusion coefficient, D(C, T), and the concentration-dependent diffusion activation energy, Ea(C), include large experimental workload and time consumption. To account for these, this paper introduces an optical method for rapidly measuring D(C, T) and Ea(C), based on the imagery of a liquid-core cylindrical lens (LCL) and numerical calculation. This method requires only one diffusion image obtained from the diffusion experiment, and D(C) is measured at a particular temperature. First, we measured the D(C) coefficients of glycerin solution at 288.0, 293.0, 298.0, 303.0, 308.0, 313.0, and 318.0 K. Then, the ray tracing theory was used to study the ray propagation law in the LCL composed of an inhomogeneous solution, which simulated the diffusion images of the entire experimental process and provided a method to verify the measured values of D(C). Finally, the law of diffusion activation energy varying with concentration was discussed based on the Arrhenius theory and the acquired values of D(C, T). This study further improves the measurement technology for D(C, T) and Ea(C) and provides an efficient methodology to build extensive D(C, T) and Ea(C) databases in the biochemical, medical, semiconductor, and environmental protection industries.

Chirality reversal between enantiomers is of great importance in both fundamental science and practical applications in chiroptics, biomedicine, and analytical chemistry. Here, we demonstrate an abrupt sign reversal of circular dichroism (CD) between artificial plasmonic diastereoisomers, which are a kind of stereo twisted metamolecules with different strength of deformations. The sign of the CD response is reversed in the same wavelength region by simply engineering the deformation height of nanostructures. Electromagnetic multipolar analysis shows that the sign of CD is determined by the phase-controlled handedness-dependent excitations of electric quadrupole modes. The numerical simulations are further verified by experiments using a nano-kirigami fabrication method. This work reveals that under certain circumstances, the CD response of the plasmonic diastereoisomers can be very close to that of enantiomers, which is useful for the exploration of profound chiroptics, as well as for the applications in chirality switching, chiral biosensing, and chiral separation.

Ultrathin flat metalenses have emerged as promising alternatives to conventional diffractive lenses, offering new possibilities for myriads of miniaturization and interfacial applications. Graphene-based materials can achieve both phase and amplitude modulations simultaneously at a single position due to the modification of the complex refractive index and thickness by laser conversion from graphene oxide into graphene like materials. In this work, we develop graphene oxide metalenses to precisely control phase and amplitude modulations and to achieve a holistic and systematic lens design based on a graphene-based material system. We experimentally validate our strategies via demonstrations of two graphene oxide metalenses: one with an ultra-long (~16λ) optical needle, and the other with axial multifocal spots, at the wavelength of 632.8 nm with a 200 nm thin film. Our proposed graphene oxide metalenses unfold unprecedented opportunities for accurately designing graphene-based ultrathin integratable devices for broad applications.

The co-combustion of dry coal and sludge was carried out in a self-built laboratory scale fluidized bed furnace. We used FTIR spectroscopy, high-resolution gas chromatography, high-resolution mass spectrometry, and XRF to capture and dynamically observe the flue gas fly ash and bottom ash. The sludge sample was taken from a municipal sewage treatment plant and its mixing ratio was below 20%. The temperature distribution of the furnace was observed and the emissions of the main gaseous pollutants such as SO2, NOx, and dioxins were measured in the combustion process. The heavy metal contents of fly ash and bottom ash were later detected to analyze the migration characteristics. The results showed that all of the gaseous pollutants measured increased linearly with the sludge mixing ratio. However, it could also meet the 1 metal content of bottom ash and fly ash did not change much. Through the elaboration of the above points, this study can provide guidance for future research on the co-combustion of coal boiler sludge and suggest favorable conditions for the co-combustion of coal boiler sludge.

Abstract Holography has attracted tremendous interest due to its capability of storing both the amplitude and phase of light field and reproducing vivid three-dimensional scenes. However, the large pixel size, low resolution, small field-of-view (FOV) and limited space-bandwidth of traditional spatial light modulator (SLM) devices restrict the possibility of improving the quality of reconstructed images. With the development of nanofabrication technologies, metasurfaces have shown great potential in manipulating the amplitude, phase, polarization, frequency or simultaneously multiple parameters of output light in ultrashort distance with subwavelength resolution by tailoring the scattering behaviour of consisted nanostructures. Such flexibilities make metasurface a promising candidate for holographic related applications. Here, we review recent progresses in the field of metasurface holography. From the perspective of the fundamental properties of light, we classify the metasurface holography into several categories such as phase-only holography, amplitude-only holography, complex amplitude holography and so on. Then, we introduce the corresponding working principles and design strategies. Meanwhile, some emerging types of metasurface holography such as tunable holography, nonlinear holography, Janus (or directional related) and bilayer metasurfaces holography are also discussed. At last, we make our outlook on metasurface holography and discuss the challenges we may face in the future.

Measuring the material, geometry, and ambient lighting of surfaces is a key technology in the object's appearance reconstruction. In this article, we propose a novel deep learning-based method to extract such information to reconstruct the object's appearance from an RGB image. Firstly, we design new deep convolutional neural network architectures to improve the performance by fusing complementary features from hierarchical layers and different tasks. Then we generate a synthetic dataset to train the proposed model to tackle the problem of the absence of the ground-truth. To transfer the domain from the synthetic data to the specific real image, we introduce a self-supervised test-time training strategy to finetune the trained model. The proposed architecture only requires one image as input when inferring the material, geometry, and ambient lighting. The experiments are conducted to evaluate the proposed method on both the synthetic data and real data. The results show that our trained model outperforms the existing baselines in each task and presents obvious improvement in final appearance reconstruction, which verifies the effectiveness of the proposed methods.

Designing artificial sensory neural system for neuroprosthetics and smart robotics applications remains a challenge. Here, the authors demonstrate an all-in-one bionic sensory transmission nerve capable integrating the functions of perception, recognition and transmission of mechano-sensitive signal.

Abstract Background Over the past decades, a large number of long non-coding RNAs (lncRNAs) have been identified. Growing evidence has indicated that the mutation and dysregulation of lncRNAs play a critical role in the development of many complex human diseases. Consequently, identifying potential disease-related lncRNAs is an effective means to improve the quality of disease diagnostics and treatment, which is the motivation of this work. Here, we propose a computational model (LncDisAP) for potential disease-related lncRNA identification based on multiple biological datasets. First, the associations between lncRNA and different data sources are collected from different databases. With these data sources as dimensions, we calculate the functional associations between lncRNAs by the recommendation strategy of collaborative filtering. Subsequently, a disease-associated lncRNA functional network is built with functional similarities between lncRNAs as the weight. Ultimately, potential disease-related lncRNAs can be identified based on ranked scores derived by random walking with restart (RWR). Then, training sets and testing sets are extracted from two different versions of a disease-lncRNA dataset to assess the performance of LncDisAP on 54 diseases. Results A lncRNA functional network is built based on the proposed computational model, and it contains 66,060 associations among 364 lncRNAs associated with 182 diseases in total. We extract 218 known disease-lncRNA pairs associated with 54 diseases to assess the network. As a result, the average AUC (area under the receiver operating characteristic curve) of LncDisAP is 78.08%. Conclusion In this article, a computational model integrating multiple lncRNA-related biological datasets is proposed for identifying potential disease-related lncRNAs. The result shows that LncDisAP is successful in predicting novel disease-related lncRNA signatures. In addition, with several common cancers taken as case studies, we found some unknown lncRNAs that could be associated with these diseases through our network. These results suggest that this method can be helpful in improving the quality for disease diagnostics and treatment.

Abstract Background Automatic vascular segmentation in X-ray angiographic image sequence is of crucial interest, for instance, for better quantifying coronary arteries in diagnostic and interventional procedures. Methods A novel inter/intra-frame constrained vascular segmentation method is proposed to automatically segment vessels in coronary X-ray angiographic image sequence. First, a morphological filter operator is applied to remove structures undergoing the respiratory motion from the original image sequence. Second, an inter-frame constrained robust principal component analysis (RPCA) is utilized to remove the quasi-static structures from the image sequence. Third, an intra-frame constrained RPCA is employed to smooth the final extracted vascular sequence. Fourth, a multi-feature fusion is designed to improve the vascular contrast and the final vascular segmentation is realized by thresholding-based method. Results Experiments are conducted on 22 clinical X-ray angiographic image sequences. The global and local contrast-to-noise ratio of the proposed method are 6.6344 and 4.2882, respectively. And the precision, sensitivity and F1 value are 0.7378, 0.7960 and 0.7658, respectively. It demonstrates that our method is effective and robust for vascular segmentation from image sequence. Conclusions The proposed method is effective to remove non-vascular structures, reduce motion artefacts and other non-uniform illumination caused noises. Also, the proposed method is online which can just process one image per time without re-optimizing the model.

Abstract Background As the terminal products of cellular regulatory process, functional related metabolites have a close relationship with complex diseases, and are often associated with the same or similar diseases. Therefore, identification of disease related metabolites play a critical role in understanding comprehensively pathogenesis of disease, aiming at improving the clinical medicine. Considering that a large number of metabolic markers of diseases need to be explored, we propose a computational model to identify potential disease-related metabolites based on functional relationships and scores of referred literatures between metabolites. First, obtaining associations between metabolites and diseases from the Human Metabolome database, we calculate the similarities of metabolites based on modified recommendation strategy of collaborative filtering utilizing the similarities between diseases. Next, a disease-associated metabolite network (DMN) is built with similarities between metabolites as weight. To improve the ability of identifying disease-related metabolites, we introduce scores of text mining from the existing database of chemicals and proteins into DMN and build a new disease-associated metabolite network (FLDMN) by fusing functional associations and scores of literatures. Finally, we utilize random walking with restart (RWR) in this network to predict candidate metabolites related to diseases. Results We construct the disease-associated metabolite network and its improved network (FLDMN) with 245 diseases, 587 metabolites and 28,715 disease-metabolite associations. Subsequently, we extract training sets and testing sets from two different versions of the Human Metabolome database and assess the performance of DMN and FLDMN on 19 diseases, respectively. As a result, the average AUC (area under the receiver operating characteristic curve) of DMN is 64.35%. As a further improved network, FLDMN is proven to be successful in predicting potential metabolic signatures for 19 diseases with an average AUC value of 76.03%. Conclusion In this paper, a computational model is proposed for exploring metabolite-disease pairs and has good performance in predicting potential metabolites related to diseases through adequate validation. This result suggests that integrating literature and functional associations can be an effective way to construct disease associated metabolite network for prioritizing candidate diseases-related metabolites.

Thin-film microscale light-emitting diodes (LEDs) are efficient light sources and their integrated applications offer robust capabilities and potential strategies in biomedical science. By leveraging innovations in the design of optoelectronic semiconductor structures, advanced fabrication techniques, biocompatible encapsulation, remote control circuits, wireless power supply strategies, etc., these emerging applications provide implantable probes that differ from conventional tethering techniques such as optical fibers. This review introduces the recent advancements of thin-film microscale LEDs for biomedical applications, covering the device lift-off and transfer printing fabrication processes and the representative biomedical applications for light stimulation, therapy, and photometric biosensing. Wireless power delivery systems have been outlined and discussed to facilitate the operation of implantable probes. With such wireless, battery-free, and minimally invasive implantable light-source probes, these biomedical applications offer excellent opportunities and instruments for both biomedical sciences research and clinical diagnosis and therapy.

The quantitation of morphology information of X-ray angiography image has an important value in the diagnosis of coronary artery disease. This paper proposes an automatic morphology estimation method by using directed graph construction for X-ray angiography images. The quantitation of vascular tree morphology is achieved in three steps, including undirected graph construction, edge direction estimation, and directed graph construction. First, an undirected graph is constructed to represent the primary topology of the vessel tree. Vessel centerline is extracted and is then split and reassembled as edges of the undirected graph. Second, each edge is assigned with a direction by an iterative algorithm, where graph structure and geometrical parameters of vessels are used as constraints. Third, each edge is assigned to its parent based on a matching function, and the directed graph is finally constructed according to the parent-child relation between edges. As the output of the proposed method, the directed graph can provide the morphology information of the vessel tree, including the radius of each vessel segment, the blood flow direction, and the connectivity between vessel segments. The proposed method is validated with the simulated data and the real clinical coronary angiography data. An average edge parent accuracy of 97.44% and an average edge direction accuracy of 99.44% can be archived.

The liver segmentation in CT scan images is a significant step toward the development of a quantitative biomarker for computer-aided diagnosis. In this paper, we propose an automatic feature learning algorithm based on the deep belief network (DBN) for liver segmentation. The proposed method was based on training by a DBN for unsupervised pretraining and supervised fine tuning. The whole method of pretraining and fine tuning is known as DBN-DNN. In traditional machine learning algorithms, the pixel-by-pixel learning is a time-consuming task; therefore, we use blocks as a basic unit for feature learning to identify the liver, which saves memory and computational time. An automatic active contour method is applied to refine the liver in post-processing. The experiments on test images show that the proposed algorithm obtained satisfactory results on healthy and pathological liver CT images. Our algorithm achieved 94.80% Dice similarity coefficient on mixed (healthy and pathological) images while 91.83% on pathological liver images, which is better than those of the state-of-the-art methods.

Three-dimensional (3D) semimetals with fourfold degenerate Dirac points are of prominent importance in topological photonics as the parent states to Weyl nodes, line nodes, & etc. The dispersions on all the momentums’ directions are linear, which represents that the Dirac point and topologically protected helicoid surface states may exist. Here, we have demonstrated an acoustic metamaterial with Dirac points by designing the sign of coupling terms, specifically incorporating negative couplings. Tuning the coupling parameter along longitudinal direction, the transition from 3D Dirac point to Weyl points can be obtained. In realistic topological metamaterial designing, the negative coupling is realized by inserting additional off-resonant sites. The simulated band dispersion clearly shows four-band crossing point. The helicoid surface states are also proved. Our study provides a new approach of constructing 3D topological phase and shows the transition between nodal ring and Dirac point. Our results can be the theoretical basement of topological protected devices.

Although genome sequencing has become increasingly popular, the simulation of individual genomes is still important. This is because sequencing a large number of individual genomes is costly and genome data with extreme and boundary conditions, such as fatal genetic defects, are difficult to obtain. Privacy and legal barriers also prevent many applications of real data. Large sequencing projects in recent years have provided a deeper understanding of the human genome. However, there is a lack of tools to leverage known data to simulate personal genomes as real as possible. Here, we designed and developed PGsim, a comprehensive and highly customizable individual genome simulator, that fully uses existing knowledge, such as variant allele frequencies in global or world main populations, mutation probability differences between protein-coding regions and non-coding regions, transition/transversion (Ti/Tv) ratios, Indel incidence, Indel length distribution, structural variation sites, and pathogenic mutation sites. Users can flexibly control the proportion and quantity of known variants, common variants, novel variants in both coding and non-coding regions, and special variants through detailed parameter settings. To ensure that the simulated personal genome has sufficient randomness, PGsim makes the generated variants more real and reliable in terms of variant distribution, proportion, and population characteristics. PGsim is able to employ a huge volume database as background data to simulate personal genomes and does not require SQL database support. Users can easily change the variant databases used as needed. As a Perl script, there is no obstacle to running PGsim on any version of the MAC OS or Linux systems, and no libraries, packages, interpreters, compilers, or other dependencies need to be installed in advance. The PGsim tool is publicly available at https://github.com/lrjuan/PGsim.

A novel process ‘coalescence-airflotation-carrier preferential adsorption’ utilising an oil-water separation flotation column with a unique structure was used in the oil-water separation field. The oil-water separation flotation column contains the cyclonic separation and airflotation separation which has advantages in oily sewage treatment, especially in polymer-flooding-drive oily sewage treatment. In this paper, different dimensions of flotation column with 1 m3 d−1, 30 m3 d−1 and 2,000 m3 d−1 oil-water separation systems were investigated. In addition, several operating parameters which impact separation, such as feeding speed, aeration rate, circulating pressure, adsorbents consumption and frother consumption were also investigated. The optimum operating parameters determined for 1 m3 d−1 the oil-water separation flotation column were a feeding speed of 0.042 m3 h−1, an aeration rate of 0.10 m3 h−1, a coal consumption of 4 (g coal)·(g oil)−1, a frother consumption of 10 mg L−1, and a circulating pressure of 0.12 MPa. The novel process cost reduced 55.8% than conventional two-stage airflotation process. In the 2,000 m3 d−1 oil-water separation experiment, the oil concentration and the oil removal efficiency of outlet are 23.39 mgL−1, 97.70%, respectively. Sediment is not produced during the oily sewage treatment using the novel process and flotation column.

Subcutaneous veins should be accurately segmented to satisfy the emerging demands in biometrics-based identity recognition, automated intravenous injection, and vascular surgical navigation. This paper proposes a novel segmentation method for extracting vascular vein structures from near-infrared images based on multiple feature classification. First, the isotropic undecimated wavelet transform in combination with the Hessian function is used to enhance the visibility of vascular structures. An approximated vascular structure is extracted by multiple-scale features based on the enhanced image. The establishment of the mapping relationship between the sketch image and source image spaces is assisted by mending and fitting procedures. Vascular features are also extracted from the enhanced contour image by a matched filter. Furthermore, the image can be classified into vascular, skin, and blurred regions by a K-means algorithm. The final vascular segmentation outcome is a refined structure. Segmentation results demonstrated that the proposed method is robust for subcutaneous vein images with different blur scales, where the completeness and smoothness of the extracted vein structure are retained. The proposed method is accurate and completely automatic, with potential applications in biometric feature recognition and intravenous injection.

Accurate segmentation of coronary arteries in X-ray angiograms is an important step for the quantitative study of coronary artery disease. However, accurate segmentation is a challenging task because coronary arteries are thin tubular structures with relatively low contrast and the presence of artifacts. In this paper, a novel deep-learning-based method is proposed to automatically segment the coronary artery from angiograms by using multichannel fully convolutional networks. Since the artifacts appear in both live images (after the injection of contrast material) and mask images (before the injection of contrast material) and the blood vessels appear only in live images, we take the mask images into consideration to distinguish real blood vessel structures from artifacts. Therefore, both live images and mask images are used as multichannel inputs to provide enhanced vascular structure information. The hierarchical features are then automatically learned to characterize the spatial associations between vessel and background and are further used to achieve the final segmentation. In addition, a dense matching between the live image and mask image is processed for a precise initial alignment. The experimental results demonstrate that our method is effective and robust for coronary artery segmentation, compared with several state-of-the-art methods.

In magnetic resonance (MR) images, detection of focal cortical dysplasia (FCD) lesion as a main pathological cue of epilepsy is challenging because of the variability in the presentation of FCD lesions. Existing algorithms appear to have sufficient sensitivity in detecting lesions but also generate large numbers of false-positive (FP) results. In this paper, we propose a multiple classifier fusion and optimization schemes to automatically detect FCD lesions in MR images with reduced FPs through constructing an objective function based on the F-score. Thus, the proposed scheme obtains an improved tradeoff between minimizing FPs and maximizing true positives. The optimization is achieved by incorporating the genetic algorithm into the work scheme. Hence, the contribution of weighting coefficients to different classifications can be effectively determined. The resultant optimized weightings are applied to fuse the classification results. A set of six typical FCD features and six corresponding Z-score maps are evaluated through the mean F-score from multiple classifiers for each feature. From the experimental results, the proposed scheme can automatically detect FCD lesions in 9 out of 10 patients while correctly classifying 31 healthy controls. The proposed scheme acquires a lower FP rate and a higher F-score in comparison with two state-of-the-art methods.

Abstract Background 3D ultrasound volume reconstruction from B-model ultrasound slices can provide more clearly and intuitive structure of tissue and lesion for the clinician. Methods This paper proposes a novel Global Path Matching method for the 3D reconstruction of freehand ultrasound images. The proposed method composes of two main steps: bin-filling scheme and hole-filling strategy. For the bin-filling scheme, this study introduces two operators, including the median absolute deviation and the inter-quartile range absolute deviation, to calculate the invariant features of each voxel in the 3D ultrasound volume. And the best contribution range for each voxel is obtained by calculating the Euclidian distance between current voxel and the voxel with the minimum invariant features. Hence, the intensity of the filling vacant voxel can be obtained by weighted combination of the intensity distribution of pixels in the best contribution range. For the hole-filling strategy, three conditions, including the confidence term, the data term and the gradient term, are designed to calculate the weighting coefficient of the matching patch of the vacant voxel. While the matching patch is obtained by finding patches with the best similarity measure that defined by the three conditions in the whole 3D volume data. Results Compared with VNN, PNN, DW, FMM, BI and KR methods, the proposed Global Path Matching method can restore the 3D ultrasound volume with minimum difference. Conclusions Experimental results on phantom and clinical data sets demonstrate the effectiveness and robustness of the proposed method for the reconstruction of ultrasound volume.

Holograms generally need to be as thick as a wavelength of light to introduce the necessary optical phase shifts that create true three-dimensional images. Here, Yueet al. use a high-index topological insulator material to create a resonant optical cavity and thin holograms to the nanometre scale.

Circular RNAs (circRNAs) are a novel class of endogenous noncoding RNAs that have well-conserved sequences. Emerging evidence has shown that circRNAs can be novel biomarkers or therapeutic targets for many diseases and play an important role in the development of various pathological conditions. Therefore, identifying potential disease-related circRNAs is helpful in improving the efficiency of finding therapeutic targets for diseases. Here, we propose a computational model (PreCDA) to predict potential circRNA–disease associations. First, we calculated the circRNA expression similarity based on circRNA expression profiles. The circRNA functional similarity is calculated based on cosine similarity, and the disease similarity is used as the dimension of each circRNA vector. The associations between circRNAs and diseases are defined based on the circRNA functional similarity and expression similarity. We constructed a disease-related circRNA association network and used a graph-based recommendation algorithm (PersonalRank) to sort candidate disease-related circRNAs. As a result, PreCDA has an average area under the receiver operating characteristic curve value of 78.15% in predicting candidate disease-related circRNAs. In addition, we discuss the factors that affect the performance of this method and find some unknown circRNAs related to diseases, with several common diseases used as case studies. These results show that PreCDA has good performance in predicting potential circRNA–disease associations and is helpful for the diagnosis and treatment of human diseases.

Orientation selectivity is a fundamental property of visual cortical neurons and plays a crucial role in pattern perception. Although many studies have dedicated to explain how the orientation selectivity emerged, the mechanism underlying orientation selectivity is still not clear. In this work, we investigated the synchronization between spikes and local field potentials (LFP) in gamma band, with the aim of providing a new avenue to analyze the orientation selectivity. The experimental data were recorded by utilizing two chronically implanted multi-electrode arrays, where each array consisted of 48 electrodes and was placed over V1 and V4, respectively, in two macaques performing a selective visual attention task. An unbiased and robust measure for quantifying the synchronization between spikes and LFP was employed in the analysis process, which is termed as spike-triggered correlation matrix synchronization (SCMS) and performs reliably for limited samples of data. We observed the spike-LFP synchronization in three cases, i.e., spikes and LFP in V1, spikes and LFP in V4, spikes in V4 and LFP in V1. From the orientation tuning curves based on the spike-LFP synchronization, it is found that there is a strong correlation between the synchronization and grating orientation. The neurons in both V1 and V4 exhibit orientation selectivity, but V1 is stronger. In addition, the spike-LFP synchronization strength between V1 and V4 also shows orientation selectivity to drifting gratings. It means that the synchronization not only reflects the basic features of visual stimulation, but also describes the orientation tuning characteristics of neurons in different regions. Our results suggest that the spike-LFP synchronization can be used as an alternative and effective method to study the mechanism for generating orientation selectivity of visual neurons.

Metasurfaces based on geometric phase acquired from the conversion of the optical spin states provide a robust control over the wavefront of light, and have been widely employed for construction of various types of functional metasurface devices. However, this powerful approach cannot be readily transferred to the manipulation of acoustic waves because acoustic waves do not possess the spin degree of freedom. Here, we propose the concept of acoustic geometric-phase meta-array by leveraging the conversion of orbital angular momentum of acoustic waves, where well-defined geometric-phases can be attained through versatile topological charge conversion processes. This work extends the concept of geometric-phase metasurface from optics to acoustics, and provides a new route for acoustic wave control.

In the fields of neuroscience and biomedical signal processing, spike sorting is a crucial step to extract the information of single neurons from extracellular recordings. In this paper, we propose a novel deep learning approach based on one-dimensional convolutional neural networks (1D-CNNs) to implement accurate and robust spike sorting. The results of the simulated data demonstrated that the clustering accuracy in most datasets was greater than 99%, despite the multiple levels of noise and various degrees of overlapped spikes. Moreover, the proposed method performed significantly better than the state-of-the-art method named “WMsorting” and a deep-learning-based multilayer perceptron (MLP) model. In addition, the experimental data recorded from the primary visual cortex of a macaque monkey were used to evaluate the proposed method in a practical application. It was shown that the method could successfully isolate most spikes of different neurons (ranging from two to five) by training the 1D-CNN model with a small number of manually labeled spikes. Considering the above, the deep learning method proposed in this paper is of great advantage for spike sorting with high accuracy and strong robustness. It lays the foundation for application in more challenging works, such as distinguishing overlapped spikes and the simultaneous sorting of multichannel recordings.

Metasurfaces offer an approach for computer generated holograms with good efficiency and ease of fabrication. Here, Ye et al.report on spin and wavelength multiplexed nonlinear metasurface holography, showing construction of holographic images using fundamental and harmonic generation waves of different spins.

Wide-area registration in outdoor environments on mobile phones is a challenging task in mobile augmented reality fields. We present a sensor-aware large-scale outdoor augmented reality system for recognition and tracking on mobile phones. GPS and gravity information is used to improve the VLAD performance for recognition. A kind of sensor-aware VLAD algorithm, which is self-adaptive to different scale scenes, is utilized to recognize complex scenes. Considering vision-based registration algorithms are too fragile and tend to drift, data coming from inertial sensors and vision are fused together by an extended Kalman filter (EKF) to achieve considerable improvements in tracking stability and robustness. Experimental results show that our method greatly enhances the recognition rate and eliminates the tracking jitters.

The blurred gray/white matter junction is an important feature of focal cortical dysplasia (FCD) lesions. FCD is the main cause of epilepsy and can be detected through magnetic resonance (MR) imaging. Several earlier studies have focused on computing the gradient magnitude of the MR image and used the resulting map to model the blurred gray/white matter junction. However, gradient magnitude cannot quantify the blurred gray/white matter junction. Therefore, we proposed a novel algorithm called local directional probability optimization (LDPO) for detecting and quantifying the width of the gray/white matter boundary (GWB) within the lesional areas. The proposed LDPO method mainly consists of the following three stages: (1) introduction of a hidden Markov random field-expectation-maximization algorithm to compute the probability images of brain tissues in order to obtain the GWB region; (2) generation of local directions from gray matter (GM) to white matter (WM) passing through the GWB, considering the GWB to be an electric potential field; (3) determination of the optimal local directions for any given voxel of GWB, based on iterative searching of the neighborhood. This was then used to measure the width of the GWB. The proposed LDPO method was tested on real MR images of patients with FCD lesions. The results indicated that the LDPO method could quantify the GWB width. On the GWB width map, the width of the blurred GWB in the lesional region was observed to be greater than that in the non-lesional regions. The proposed GWB width map produced higher F-scores in terms of detecting the blurred GWB within the FCD lesional region as compared to that of FCD feature maps, indicating better trade-off between precision and recall.

Precise automatic vertebra segmentation in computed tomography (CT) images is important for the quantitative analysis of vertebrae-related diseases but remains a challenging task due to high variation in spinal anatomy among patients. In this paper, we propose a deep learning approach for automatic CT vertebra segmentation named patch-based deep belief networks (PaDBNs). Our proposed PaDBN model automatically selects the features from image patches and then measures the differences between classes and investigates performance. The region of interest (ROI) is obtained from CT images. Unsupervised feature reduction contrastive divergence algorithm is applied for weight initialization, and the weights are optimized by layers in a supervised fine-tuning procedure. The discriminative learning features obtained from the steps above are used as input of a classifier to obtain the likelihood of the vertebrae. Experimental results demonstrate that the proposed PaDBN model can considerably reduce computational cost and produce an excellent performance in vertebra segmentation in terms of accuracy compared with state-of-the-art methods.

In this paper, we propose an application of a compressive imaging system to the problem of wide-area video surveillance systems. A parallel coded aperture compressive imaging system is proposed to reduce the needed high resolution coded mask requirements and facilitate the storage of the projection matrix. Random Gaussian, Toeplitz and binary phase coded masks are utilized to obtain the compressive sensing images. The corresponding motion targets detection and tracking algorithms directly using the compressive sampling images are developed. A mixture of Gaussian distribution is applied in the compressive image space to model the background image and for foreground detection. For each motion target in the compressive sampling domain, a compressive feature dictionary spanned by target templates and noises templates is sparsely represented. An l1 optimization algorithm is used to solve the sparse coefficient of templates. Experimental results demonstrate that low dimensional compressed imaging representation is sufficient to determine spatial motion targets. Compared with the random Gaussian and Toeplitz phase mask, motion detection algorithms using a random binary phase mask can yield better detection results. However using random Gaussian and Toeplitz phase mask can achieve high resolution reconstructed image. Our tracking algorithm can achieve a real time speed that is up to 10 times faster than that of the l1 tracker without any optimization.

Real-time intraoperative optical coherence tomography (OCT) imaging of blood vessels after anastomosis operation can provide important information the vessel, such as patency, flow speed, and thrombosis morphology. Due to the strong scattering and absorption effect of blood, normal OCT imaging suffers from the problem of incomplete cross-sectional view of the vessel under investigation when the diameter is large. In this work, we present a novel cooperative three-view imaging spectral domain optical coherence tomography system for intraoperative exposed vascular imaging. Two more side views (left view and right view) were realized through a customized sample arm optical design and corresponding mechanical design and fabrication, which could generate cross-sectional images from three circumferential view directions to achieve a larger synthetic field of view (FOV). For each view, the imaging depth was 6.7 mm (in air) and the lateral scanning range was designed to be 3 mm. Therefore, a shared synthetic rectangle FOV of 3 mm × 3 mm was achieved through cooperative three view scanning. This multi-view imaging method can meet the circumferential imaging demands of vessels with an outer diameter less than 3 mm. Both phantom tube and rat vessel imaging confirmed the increased system FOV performance. We believe the intraoperative application of this cooperative three-imaging optical coherence tomography for objective vascular anastomosis evaluation can benefit patient outcomes in the future.