New approach for near-infrared wavelength selection using a combination of MIC and firefly evolution.

Full-length spectral data analysis has a big problem that the variables are highly in collinearity and correlation. Spectral wavelength selection is a continuing hot topic in quantitative or qualitative analysis. In this paper, we propose a new approach for near-infrared (NIR) wavelength selection. The novel strategy mainly refers to the modification of maximum information coefficient (MIC) method and an improvement of firefly evolutionary algorithm. We introduce the orthogonal decomposition to modify the MIC method, so as to search the informative signals conceived in projection vectors. We also raise the common firefly algorithm (FA) as in the discretized mode, and design a novel adaptive mapping function to improve its intelligent computing effect. In experiment, the modified MIC (MICm) method and the adaptive discrete FA algorithm (DFAadp) are joint together for combined optimization of the NIR calibration model. The proposed combined modeling strategy is applied for quantitative analysis of the fishmeal samples, in the concern to select their informative variables/wavelengths. Experimental results indicate that the combination of MICm and DFAadp perform better than traditional MIC method and common DFA. We conclude that the proposed combined optimization strategy is beneficial for wavelength selection in NIR spectral analysis. It is anticipated to be validated for further applications in a wide range.

Distribution and Location of BEVs in Different Genotypes of Bananas Reveal the Coevolution of BSVs and Bananas.

Members of the family Caulimoviridae contain abundant endogenous pararetroviral sequences (EPRVs) integrated into the host genome. Banana streak virus (BSV), a member of the genus Badnavirus in this family, has two distinct badnaviral integrated sequences, endogenous BSV (eBSV) and banana endogenous badnavirus sequences (BEVs). BEVs are distributed widely across the genomes of different genotypes of bananas. To clarify the distribution and location of BEVs in different genotypes of bananas and their coevolutionary relationship with bananas and BSVs, BEVs and BSVs were identified in 102 collected banana samples, and a total of 327 BEVs were obtained and categorized into 26 BEVs species with different detection rates. However, the majority of BEVs were found in Clade II, and a few were clustered in Clade I. Additionally, BEVs and BSVs shared five common conserved motifs. However, BEVs had two unique amino acids, methionine and lysine, which differed from BSVs. BEVs were distributed unequally on most of chromosomes and formed hotspots. Interestingly, a colinear relationship of BEVs was found between AA and BB, as well as AA and SS genotypes of bananas. Notably, the chromosome integration time of different BEVs varied. Based on our findings, we propose that the coevolution of bananas and BSVs is driven by BSV Driving Force (BDF), a complex interaction between BSVs, eBSVs, and BEVs. This study provides the first clarification of the relationship between BEVs and the coevolution of BSVs and bananas in China.

A Novel Approach of Surface Texture Mapping for Cone-beam Computed Tomography in Image-guided Surgical Navigation.

The demand for cone-beam computed tomography (CBCT) imaging in clinics, particularly in dentistry, is rapidly increasing. Preoperative surgical planning is crucial to achieving desired treatment outcomes for imaging-guided surgical navigation. However, the lack of surface texture hinders effective communication between clinicians and patients, and the accuracy of superimposing a textured surface onto CBCT volume is limited by dissimilarity and registration based on facial features. To address these issues, this study presents a CBCT imaging system integrated with a monocular camera for reconstructing the texture surface by mapping it onto a 3D surface model created from CBCT images. The proposed method utilizes a geometric calibration tool for accurate mapping of the camera-visible surface with the mosaic texture. Additionally, a novel approach using 3D-2D feature mapping and surface parameterization technology is proposed for texture surface reconstruction. Experimental results, obtained from both real and simulation data, validate the effectiveness of the proposed approach with an error reduction to 0.32 mm and automated generation of integrated images. These findings demonstrate the robustness and high accuracy of our approach, improving the performance of texture mapping in CBCT imaging.

A decision tree network with semi-supervised entropy learning strategy for spectroscopy aided detection of blood hemoglobin.

Non-invasive techniques for rapid blood testing are gaining traction in global healthcare as they optimize medical screening, diagnosis and clinical decisions. Fourier transform infrared (FT-IR) spectroscopy is one of the most common technologies that can be used for non-destructive aided medical detection. Typically, after acquiring the Fourier transform infrared spectrum, spectral data preprocessing and feature extraction and quantitative analysis of several indicators of blood samples can be accomplished, in combination with chemometric method studies. At present, blood hemoglobin (HGB) concentration is one of the most valuable information for the clinical diagnosis of patient's health status. FT-IR spectroscopy is employed as a green technique aided medical test of blood HGB. Then the acquired HGB concentration data is switched to the spectral feature data by the studies of advanced chemometric method, in help for hiding the sensitive medical information to protect the privacy of patients. The decision tree network architecture is proposed for feature extraction of FT-IR data in order to find the small set of wavenumbers that are able to quantify HGB. A semi-supervised learning strategy is designed for tuning the number of network neuron nodes, in the way of searching for the maximum entropy increment. Each neuron is optimized by the growing of a semi-supervised decision tree, to accurately identify the informative FT-IR wavenumbers. The features extracted by the semi-supervised learning decision tree network guarantees the FT-IR aided detection model has high efficiency and high prediction accuracy. A model of quantifying the HGB concentration shows that the proposed decision tree network with semi-supervised entropy learning strategy outperforms the usual methods of full spectrum partial least square model and the fully connected neural network model in prediction accuracy. The framework is expected to support the FT-IR spectral technology for aided detection of medical and clinical data.

Identification, characterization and prevalence in southern China of a new iflavirus in the leafhopper Recilia dorsalis (Hemiptera: Cicadellidae).

The leafhopper Recilia dorsalis (Hemiptera: Cicadellidae) is not only a significant pest in agriculture but also an important vector involved in transmitting numerous pathogens that are known to cause economic losses by affecting rice crops. Here, a new iflavirus was discovered in the leafhopper R. dorsalis by employing a transcriptomic approach. The complete viral genome was determined to be 10,711 nucleotides (nt) in length and contains a single open reading frame (ORF) encoding a putative polyprotein comprised of 3,161 amino acids (aa), which is flanked by 5' and 3' untranslated regions. The full viral genome nt and the deduced polyprotein aa sequence showed the highest similarity (71.6% and 77.8%, respectively) with Langfang leafhopper iflavirus. Phylogenetic analysis based on the RdRp domain indicated that the isolated virus, which we have tentatively named Recilia dorsalis iflavirus 2 (RdIV2), is clustered with the members of the family Iflaviridae. Moreover, the results of our surveys indicate that RdIV2 predominates in southwestern Guangdong and southeastern Guangxi, China, and was absent in the other three species of leafhoppers; Nephotettix cincticeps, N. virescens and N. nigropictus. Notably, R. dorsalis was found to be co-infected with RdIV2 and rice stripe mosaic virus (RSMV; a well-known rice-infecting virus vectored by R. dorsalis) in rice fields, although the co-infection rate is low.

Improvement of NIR prediction ability by dual model optimization in fusion of NSIA and SA methods.

Feature selection and sample partitioning are both important to establish a quantitative analytical model for near-infrared (NIR) spectroscopy. The classical interval partial least squares (iPLS) model for waveband selection can be improved in combination of the simulated annealing (SA) algorithm. The sample set partitioning based on a joint x-y distance (SPXY) method for sample partitioning is based on the distances of both the x- and y- dimensions; it is expected to be optimized using the non-dominant sorting strategies (NS) combined with the immune algorithm (IA). In this study, we investigated the dual model optimization mode for simultaneous selection of feature waveband and sample partitioning, and proposed a novel method defined as SA-iPLS & SPXY-NSIA. The method explores a population evolution process, and takes the candidate individual as the link for the fusion optimization of SA-iPLS and SPXY-NSIA. The method screens feature wavebands and observes a good partition of the modeling samples, to construct a combined optimization strategy for fusion optimization of the target waveband and suitable sets of sample partitioning. The performance of the SA-iPLS & SPXY-NSIA method was tested using a soil sample dataset. To prove model enhancement, the proposed method was compared to the two traditional methods of Kennard-Stone (KS) and SPXY in combination with SA-iPLS. Experimental results show that the fusion model established by SA-iPLS & SPXY-NSIA performed better than the KS-SA-iPLS and SPXY-SA-iPLS models. The best testing results of the fusion model is with RMSET, RPDT and RT observed as 0.0107, 1.7233 and 0.9097, respectively. The proposed method is prospectively able to effectively improve the predictive ability of the NIR analytical model.

Identifying microbe-disease association based on graph convolutional attention network: Case study of liver cirrhosis and epilepsy.

The interactions between the microbiota and the human host can affect the physiological functions of organs (such as the brain, liver, gut, etc.). Accumulating investigations indicate that the imbalance of microbial community is closely related to the occurrence and development of diseases. Thus, the identification of potential links between microbes and diseases can provide insight into the pathogenesis of diseases. In this study, we propose a deep learning framework (MDAGCAN) based on graph convolutional attention network to identify potential microbe-disease associations. In MDAGCAN, we first construct a heterogeneous network consisting of the known microbe-disease associations and multi-similarity fusion networks of microbes and diseases. Then, the node embeddings considering the neighbor information of the heterogeneous network are learned by applying graph convolutional layers and graph attention layers. Finally, a bilinear decoder using node embedding representations reconstructs the unknown microbe-disease association. Experiments show that our method achieves reliable performance with average AUCs of 0.9778 and 0.9454 ± 0.0038 in the frameworks of Leave-one-out cross validation (LOOCV) and 5-fold cross validation (5-fold CV), respectively. Furthermore, we apply MDAGCAN to predict latent microbes for two high-risk human diseases, i.e., liver cirrhosis and epilepsy, and results illustrate that 16 and 17 out of the top 20 predicted microbes are verified by published literatures, respectively. In conclusion, our method displays effective and reliable prediction performance and can be expected to predict unknown microbe-disease associations facilitating disease diagnosis and prevention.

A hybrid optimization method for sample partitioning in near-infrared analysis.

The division of calibration and validation is one of the essential procedures that affect the prediction result of the calibration model in quantitative analysis of near-infrared (NIR) spectroscopy. The conventional methods are Kennard-Stone (KS) and sample set partitioning based on joint x-y distances (SPXY). These algorithms use Euclidean distance to cover as many representative samples as possible. This paper proposes an Adaptive Hybrid Cuckoo-Tabu Search (AHCTS) algorithm for partitioning samples based on optimization. The algorithm combines the characteristics of cuckoo search (CS) and tabu search (TS) and fuses with an adaptive function. For comparison, using fishmeal samples as spectral analysis data, KS, SPXY, and AHCTS algorithms were used to divide the modeling samples to establish partial least squares regression (PLSR) models. The experimental results showed that the model established by the proposed algorithm performs better than KS and SPXY. It reveals that the AHCTS method may be an advantageous alternative for quantitative analysis of NIR spectroscopy.

A Novel Genetic Algorithm-Based Optimization Framework for the Improvement of Near-Infrared Quantitative Calibration Models.

The global fishmeal production is used for animal feed, and protein is the main component that provides nutrition to animals. In order to monitor and control the nutrition supply to animal husbandry, near-infrared (NIR) technology was utilized for rapid detection of protein contents in fishmeal samples. The aim of the NIR quantitative calibration is to enhance the model prediction ability, where the study of chemometric algorithms is inevitably on demand. In this work, a novel optimization framework of GSMW-LPC-GA was constructed for NIR calibration. In the framework, some informative NIR wavebands were selected by grid search moving window (GSMW) strategy, and then the variables/wavelengths in the waveband were transformed to latent principal components (LPCs) as the inputs for genetic algorithm (GA) optimization. GA operates in iterations as implementation for the secondary optimization of NIR wavebands. In steps of the variable's population evolution, the parametric scaling mode was investigated for the optimal determination of the crossover probability and the mutation operator. With the GSMW-LPC-GA framework, the NIR prediction effect on fishmeal protein was experimentally better than the effect by simply adopting the moving window calibration model. The results demonstrate that the proposed framework is suitable for NIR quantitative determination of fishmeal protein. GA was eventually regarded as an implementable method providing an efficient strategy for improving the performance of NIR calibration models. The framework is expected to provide an efficient strategy for analyzing some unknown changes and influence of various fertilizers.

Spin-Momentum-Locked Edge Mode for Topological Vortex Lasing.

Spin-momentum locking is a direct consequence of bulk topological order and provides a basic concept to control a carrier's spin and charge flow for new exotic phenomena in condensed matter physics. However, up to date the research on spin-momentum locking solely focuses on its in-plane transport properties. Here, we report an emerging out-of-plane radiation feature of spin-momentum locking in a non-Hermitian topological photonic system and demonstrate a high performance topological vortex laser based on it. We find that the gain saturation effect lifts the degeneracy of the paired counterpropagating spin-momentum-locked edge modes enabling lasing from a single topological edge mode. The near-field spin and orbital angular momentum of the topological edge mode lasing has a one-to-one far-field radiation correspondence. The methodology of probing the near-field topology feature by far-field lasing emission can be used to study other exotic phenomena. The device can lead to applications in superresolution imaging, optical tweezers, free-space optical sensing, and communication.

Stable, high-performance sodium-based plasmonic devices in the near infrared.

Plasmonics enables the manipulation of light beyond the optical diffraction limit1-4 and may therefore confer advantages in applications such as photonic devices5-7, optical cloaking8,9, biochemical sensing10,11 and super-resolution imaging12,13. However, the essential field-confinement capability of plasmonic devices is always accompanied by a parasitic Ohmic loss, which severely reduces their performance. Therefore, plasmonic materials (those with collective oscillations of electrons) with a lower loss than noble metals have long been sought14-16. Here we present stable sodium-based plasmonic devices with state-of-the-art performance at near-infrared wavelengths. We fabricated high-quality sodium films with electron relaxation times as long as 0.42 picoseconds using a thermo-assisted spin-coating process. A direct-waveguide experiment shows that the propagation length of surface plasmon polaritons supported at the sodium-quartz interface can reach 200 micrometres at near-infrared wavelengths. We further demonstrate a room-temperature sodium-based plasmonic nanolaser with a lasing threshold of 140 kilowatts per square centimetre, lower than values previously reported for plasmonic nanolasers at near-infrared wavelengths. These sodium-based plasmonic devices show stable performance under ambient conditions over a period of several months after packaging with epoxy. These results indicate that the performance of plasmonic devices can be greatly improved beyond that of devices using noble metals, with implications for applications in plasmonics, nanophotonics and metamaterials.

Optimization Model for the Distribution of Fiducial Markers in Liver Intervention.

The distribution of fiducial markers is one of the main factors affected the accuracy of optical navigation system. However, many studies have been focused on improving the fiducial registration accuracy or the target registration accuracy, but few solutions involve optimization model for the distribution of fiducial markers. In this paper, we propose an optimization model for the distribution of fiducial markers to improve the optical navigation accuracy. The strategy of optimization model is reducing the distribution from three dimensional to two dimensional to obtain the 2D optimal distribution by using optimization algorithm in terms of the marker number and the expectation equation of target registration error (TRE), and then extend the 2D optimal distribution in two dimensional to three dimensional to calculate the optimal distribution according to the distance parameter and the expectation equation of TRE. The results of the experiments show that the averaged TRE for the human phantom is approximately 1.00 mm by applying the proposed optimization model, and the averaged TRE for the abdominal phantom is 0.59 mm. The experimental results of liver simulator model and ex-vivo porcine liver model show that the proposed optimization model can be effectively applied in liver intervention.

Kernel functions embedded in support vector machine learning models for rapid water pollution assessment via near-infrared spectroscopy.

Water pollution is a challenging problem encountered in total environmental development. Near-infrared (NIR) spectroscopy is a well-refined technology for rapid water pollution detection. Calibration models are established and optimized to search for chemometric algorithms with considerably improved prediction effects. Machine learning improves the prediction capability of NIR spectroscopy for the accurate assessment of water pollution. Least squares support vector machine (LSSVM) algorithm fits parameters to target problems in a data-driven manner. The modeling capability of this algorithm mainly depends on its kernel functions. In this study, the LSSVM method was used to establish NIR calibration models for the quantitative determination of chemical oxygen demand, which is a critical indicator of water pollution level. The effects of different kernels embedded in LSSVM were investigated. A novel kernel was proposed by using a logistic-based neural network. In contrast to common kernels, this novel kernel can utilize a deep learning approach for parameter optimization. The proposed kernel also strengthens model resistance to over-fitting such that cross-validation can be reasonably utilized. The proposed novel kernel is applicable for the quantitative determination of water pollution and is a prospective solution to other problems in the field of water resource management.

Rapid Detection of Pomelo Fruit Quality Using Near-Infrared Hyperspectral Imaging Combined With Chemometric Methods.

Pomelo is an important agricultural product in southern China. Near-infrared hyperspectral imaging (NIRHI) technology is applied to the rapid detection of pomelo fruit quality. Advanced chemometric methods have been investigated for the optimization of the NIRHI spectral calibration model. The partial least squares (PLS) method is improved for non-linear regression by combining it with the kernel Gaussian radial basis function (RBF). In this study, the core parameters of the PLS latent variables and the RBF kernel width were designed for grid search selection to observe the minimum prediction error and a relatively high correlation coefficient. A deep learning architecture was proposed for the parametric scaling optimization of the RBF-PLS modeling process for NIRHI data in the spectral dimension. The RBF-PLS models were established for the quantitative prediction of the sugar (SU), vitamin C (VC), and organic acid (OA) contents in pomelo samples. Experimental results showed that the proposed RBF-PLS method performed well in the parameter deep search progress for the prediction of the target contents. The predictive errors for model training were 1.076% for SU, 41.381 mg/kg for VC, and 1.136 g/kg for OA, which were under 15% of their reference chemical measurements. The corresponding model testing results were acceptably good. Therefore, the NIRHI technology combined with the study of chemometric methods is applicable for the rapid quantitative detection of pomelo fruit quality, and the proposed algorithmic framework may be promoted for the detection of other agricultural products.

Parametric-scaling optimization of pretreatment methods for the determination of trace/quasi-trace elements based on near infrared spectroscopy.

This work proposes a parametric-scaling strategy to optimize the pretreatments of near infrared (NIR) spectroscopic data, so as to cope with the difficulty of NIR technology in detecting trace or quasi-trace elements. This novel strategy helps enhancing the signal to noise ratio and contributes to extracting features from the raw spectrum, so that the information corresponding to the trace elements could be detected much easier. However, due to the complexity of NIR data, it is difficult to comprehensively evaluate and compare the performance of different pretreatment methods, especially when multiple target components are determined simultaneously. For this reason, we create some comprehensive model indicators to define the goodness of pretreatments in simultaneous multiple detection of trace elements. In this paper two near infrared data sets have been investigated, one is used to determinate the key indices in the primary screening of thalassemia and the other one is used to detect the heavy metal pollutants in farmland soil. Results show that the proposed parametric-scaling optimization strategy can improve the effect of pretreatments in the determination of trace/quasi-trace elements, and the model performance with the optimized pretreated data is significantly superior to that with the raw data. The optimized Savitzky-Golay smoother (SGS) keeps its merits in the real data examples. Especially, the newly emerged methods optical path length estimation and correction (OPLEC) and Whittaker smoother (WTK), as well as their parametric-scaling modified methods, show their advantages in the comparison with other pretreatments. According to the results of our experiments, they have shown promising potential in the NIR rapid analysis of trace/quasi-trace elements in the field of biomedical science and agricultural science. This is expected to be tested for other analytes with larger variation.

A high-performance topological bulk laser based on band-inversion-induced reflection.

Topological insulators are materials that behave as insulators in the bulk and as conductors at the edge or surface due to the particular configuration of their bulk band dispersion. However, up to date possible practical applications of this band topology on materials' bulk properties have remained abstract. Here, we propose and experimentally demonstrate a topological bulk laser. We pattern semiconductor nanodisk arrays to form a photonic crystal cavity showing topological band inversion between its interior and cladding area. In-plane light waves are reflected at topological edges forming an effective cavity feedback for lasing. This band-inversion-induced reflection mechanism induces single-mode lasing with directional vertical emission. Our topological bulk laser works at room temperature and reaches the practical requirements in terms of cavity size, threshold, linewidth, side-mode suppression ratio and directionality for most practical applications according to Institute of Electrical and Electronics Engineers and other industry standards. We believe this bulk topological effect will have applications in near-field spectroscopy, solid-state lighting, free-space optical sensing and communication.

Room-temperature high-performance CsPbBr3 perovskite tetrahedral microlasers.

With the goals of faster coherent light sources with lower power consumption, laser miniaturization has been intensively pursued in the past four decades. Novel microscale cavities with efficient feedback and distinct emission profiles are essential for excellent performance of laser devices and for exploring their new functionalities. Here, for the first time, well-defined high-quality tetrahedron-shaped CsPbBr3 perovskite microcavities with smooth surfaces were synthesized via the vapor growth method. A room-temperature high-performance tetrahedral microlaser was realized based on CsPbBr3 perovskite single crystals. The three-dimensional total internal reflection mode inside the tetrahedral cavity led to a high-performance microlaser with a linewidth of only ∼0.3 nm at 538 nm emission wavelength and a unique profile with three emission beams into free space with triangular symmetry. In addition, the perovskite tetrahedral microlasers could be pumped by two-photon absorption with threshold only about 2.5 times higher than that of a one-photon laser. The high-performance tetrahedral microlaser with a distinct emission profile enriches the microscale laser family and may find exceptional applications in optical manipulation, communication and on-chip beam steering.

High Performance Plasmonic Nanolasers with External Quantum Efficiency Exceeding 10.

Plasmonic nanolasers break the diffraction limit for an optical oscillator, which brings new capabilities for various applications ranging from on-chip optical interconnector to biomedical sensing and imaging. However, the inevitably accompanied metallic absorption loss could convert the input power to heat rather than radiations, leading to undesired low external quantum efficiency and device degradation. To date, direct characterization of quantum efficiency of plasmonic nanolasers is still a forbidden task due to its near-field surface plasmon emissions, divergent emission profile, and the limited emission power. Here, we develop a method to characterize the external quantum efficiency of plasmonic nanolasers by synergizing experimental measurement and theoretical calculation. With systematical device optimization, we demonstrate high performance plasmonic nanolasers with external quantum efficiency exceeding 10% at room temperature. This work fills in a missing yet essential piece of key metrics of plasmonic nanolasers. The demonstrated high external quantum efficiency of plasmonic nanolasers not only clarifies the long-standing debate, but also endorses the exploration of them in various practical applications such as near-field spectroscopy and sensing, integrated optical interconnects, solid-state lighting, and free-space optical communication.

High-Performance Single-Crystalline Perovskite Thin-Film Photodetector.

The best performing modern optoelectronic devices rely on single-crystalline thin-film (SC-TF) semiconductors grown epitaxially. The emerging halide perovskites, which can be synthesized via low-cost solution-based methods, have achieved substantial success in various optoelectronic devices including solar cells, lasers, light-emitting diodes, and photodetectors. However, to date, the performance of these perovskite devices based on polycrystalline thin-film active layers lags behind the epitaxially grown semiconductor devices. Here, a photodetector based on SC-TF perovskite active layer is reported with a record performance of a 50 million gain, 70 GHz gain-bandwidth product, and a 100-photon level detection limit at 180 Hz modulation bandwidth, which as far as we know are the highest values among all the reported perovskite photodetectors. The superior performance of the device originates from replacing polycrystalline thin film by a thickness-optimized SC-TF with much higher mobility and longer recombination time. The results indicate that high-performance perovskite devices based on SC-TF may become competitive in modern optoelectronics.