Hydrated Electron Dynamics and Stimulated Raman Scattering in Water Induced by Ultrashort Laser Pulses.

For this study, we employed intense 400 nm, 100 fs pulses linearly propagated through a 50 cm water medium, initially self-stretching the excitation pulses to 2.50 ps. Subsequently, the self-stretched 2.50 ps pulses were focused into deionized water, and we conducted transient absorption experiments to measure and investigate the dynamics of hydrated electrons in water. The excess electrons generated were injected into the hydrogen bond network of the water cluster, leading to the observation of saturated hydrated electrons. Additionally, we observed the emergence of the forward stimulated Raman scattering (SRS) of water molecules. We report the experimental observation of a weak forward SRS emission at 463 nm (corresponding to 3400 cm-1), indicative of the ordinary OH stretching vibration in the liquid phase. Moreover, we observed an intense forward SRS emission at 460 nm in water, corresponding to two anomalous Raman shifts at 3260 cm-1 and 3355 cm-1. These anomalous Raman shifts resulted from changes in the hydrogen bond network structure. We determine that the formation of not fully hydrated and saturated hydrated electrons plays a crucial role in producing this phenomenon.

Sub-Diffraction Readout Method of High-Capacity Optical Data Storage Based on Polarization Modulation.

The big data era demands an efficient and permanent data storage technology with the capacity of PB to EB scale. Optical data storage (ODS) offers a good candidate for long-lifetime storage, as the developing far-field super-resolution nanoscale writing technology improves its capacity to the PB scale. However, methods to efficiently read out this intensive ODS data are still lacking. In this paper, we demonstrate a sub-diffraction readout method based on polarization modulation, which experimentally achieves the sub-diffraction readout on Disperse Red 13 thin film with a resolution of 500 nm, exceeding the diffraction limit by 1.2 times (NA = 0.5). Differing from conventional binary encoding, we propose a specific polarization encoding method that enhances the capacity of ODS by 1.5 times. In the simulation, our method provides an optical data storage readout resolution of 150 nm, potentially to 70 nm, equivalent to 1.1 PB in a DVD-sized disk. This sub-diffraction readout method has great potential as a powerful readout tool for next-generation optical data storage.

Graph kernel of brain networks considering functional similarity measures.

As a tool of brain network analysis, the graph kernel is often used to assist the diagnosis of neurodegenerative diseases. It is used to judge whether the subject is sick by measuring the similarity between brain networks. Most of the existing graph kernels calculate the similarity of brain networks based on structural similarity, which can better capture the topology of brain networks, but all ignore the functional information including the lobe, centers, left and right brain to which the brain region belongs and functions of brain regions in brain networks. The functional similarities can help more accurately locate the specific brain regions affected by diseases so that we can focus on measuring the similarity of brain networks. Therefore, a multi-attribute graph kernel for the brain network is proposed, which assigns multiple attributes to nodes in the brain network, and computes the graph kernel of the brain network according to Weisfeiler-Lehman color refinement algorithm. In addition, in order to capture the interaction between multiple brain regions, a multi-attribute hypergraph kernel is proposed, which takes into account the functional and structural similarities as well as the higher-order correlation between the nodes of the brain network. Finally, the experiments are conducted on real data sets and the experimental results show that the proposed methods can significantly improve the performance of neurodegenerative disease diagnosis. Besides, the statistical test shows that the proposed methods are significantly different from compared methods.

Prediction of Optical Properties in Particulate Media Using Double Optimization of Dependent Scattering and Particle Distribution.

The prediction of optical properties dominated by light scattering in particulate media composed of high-concentration and polydisperse particles is greatly important in various optical applications. However, the accuracy and efficiency of light propagation simulations are still limited by the huge computational burden and complex interactions between dense and polydisperse particles. Here, we proposed a new optimization strategy that can effectively and accurately predict optical properties based on Monte Carlo simulation with particle size and dependent scattering corrections. Both the scattering parameters of particles and the experimental reflectance spectrum are fully examined for verification. Furthermore, using the weighted solar reflectance of particulate media as a representative optical property, both numerical simulations and experiments confirm the superiority and universality of the proposed optimization approach in a variety of materials systems. Moreover, our work can guide the design of particulate media with specific optical features insightfully and will be applicable in many fields involving multiparticle scattering.

Unique phase retrieval with a bandlimited image and its Fourier transformed constraints.

Phase retrieval is a long-standing issue in imaging science; however, it always faces the problems of uniqueness and algorithm stagnation. Current methods to solve such problems rely heavily on support from prior information of the imaged object. In this paper, we propose an imaging method that leverages a bandlimited image and its Fourier transformed constraints for unique phase retrieval. This method can remove both trivial and nontrivial ambiguities by using the inherent constraints of the imaging system itself-without relying on prior information of the object. Furthermore, the proposed method has also been shown to succeed in special cases, including symmetric and pure phase objects. Improvement of convergence achieved by our approach is supported by numerical simulations.

Noise suppression in the reconstructed image of digital holography based on the BEMDV method using improved particle swarm optimization.

In digital holography, the speckle noise caused by the coherent nature of the light source and the light scattering generated by the light path system degrade the quality of the reconstructed image seriously. Therefore, in this paper, we propose what we believe to be is a novel noise reduction method combining bidimensional empirical mode decomposition (BEMD) with the variational method, termed BEMDV. The reconstructed image is first decomposed into a series of bidimensional intrinsic mode function (BIMF) components with different frequencies using the BEMD method, and then a certain number of BIMF components are selected for noise reduction by the variational method. An improved particle swarm optimization algorithm is adopted to optimize the key parameters of the proposed method, so as to further improve its noise reduction performance. A reflective off-axis digital holographic imaging system is used to collect the holograms of the coin and optical resolution plate, and the experimental research on noise reduction is carried out. The results with qualitative and quantitative analyses show that the proposed method achieves a better performance on noise reduction and detail preservation than other general methods, enormously enhancing the image quality of holographic reconstruction.

High-speed chaos-based secure optical communications over 130-km multi-core fiber.

Chaotic optical communication is of great significance for secure data transmission. Despite rapid development over the decades, high-speed (>100 Gbps) and long-distance (>100 km) chaotic optical communication in a single fiber is still full of challenges. Here, we propose and experimentally demonstrate high-speed and long-distance chaos-based secure optical communications using mutual injection of semiconductor lasers and space-division multiplexing (SDM) techniques. The encrypted signals are transmitted through all seven core channels of the multi-core fiber (MCF), which effectively expands the aggregate transmission capacity of a single fiber. A pair of source and synchronization devices based on mutual injection of semiconductor lasers are employed to effectively encrypt and decrypt signals. Chaos-based secure optical communications with 70-Gbps on-off keying (OOK) and 140-Gbps quadrature phase-shift keying (QPSK) signals over a 130-km MCF are successfully demonstrated in the experiment with favorable performance. The demonstration may pave the way for future ultrahigh capacity and ultra-long distance chaotic optical communications by fully exploiting multi-dimensional resources of light waves, including the spatial dimension.

Investigation of thermal control in phase-changing ABO3 perovskites via first-principles predictions: general mechanism of solar absorptivity.

The fundamental mechanism of solar absorbance during the phase-change process is investigated in ABO3 perovskites based on first-principles predictions. A Gaussian-like relationship between the solar absorbance and band gaps is established, which follows the Shockley-Queisser limiting efficiency. For ABO3 perovskites with bandgaps of Eg > 3.5 eV, a low solar absorbance is obtained, whereas a high solar absorbance is obtained for ABO3 perovskites, with band gaps ranging from 0.25 to 2.2 eV. The relationship between the orbital character of the density of states (DOS) and the absorption spectra reveals that ABO3 perovskites with magnetic (strongly interacting) and distorted crystal structures always exhibit a higher solar absorptivity. In contrast, non-magnetic and cubic ABO3 perovskites always exhibit a lower solar absorptivity. Moreover, the tunable solar absorptivity always undergoes a phase change from cubic to large distorted crystal structures in ABO3 perovskites with strong interactions. These results can be attributed to a rich structural, electronic, and magnetic phase diagram resulting from the strong interplay between the lattice, spin, and orbital degrees of freedom, which induce highly tunable optical characteristics in the phase-change process. The findings presented in this study are critical for the development of ABO3 perovskite-based smart thermal control materials in the spacecraft field.

TDFusion: When Tensor Decomposition Meets Medical Image Fusion in the Nonsubsampled Shearlet Transform Domain.

In this paper, a unified optimization model for medical image fusion based on tensor decomposition and the non-subsampled shearlet transform (NSST) is proposed. The model is based on the NSST method and the tensor decomposition method to fuse the high-frequency (HF) and low-frequency (LF) parts of two source images to obtain a mixed-frequency fused image. In general, we integrate low-frequency and high-frequency information from the perspective of tensor decomposition (TD) fusion. Due to the structural differences between the high-frequency and low-frequency representations, potential information loss may occur in the fused images. To address this issue, we introduce a joint static and dynamic guidance (JSDG) technique to complement the HF/LF information. To improve the result of the fused images, we combine the alternating direction method of multipliers (ADMM) algorithm with the gradient descent method for parameter optimization. Finally, the fused images are reconstructed by applying the inverse NSST to the fused high-frequency and low-frequency bands. Extensive experiments confirm the superiority of our proposed TDFusion over other comparison methods.

Sufficient Phosphorus Enhances Resistance and Changes Accumulation of Lead in Chlamydomonas reinhardtii.

Phosphorus (P) is critical for algal growth and resistance to environmental stress. However, little is known about the effects of P supply on the lead (Pb) toxicity and accumulation in microalgae. We set up two P concentrations, 315 (PL ) and 3150 µg L-1 (PH ), in algal culture, and the responses of Chlamydomonas reinhardtii to various Pb treatments (0, 200, 500, 1000, 2000, and 5000 µg L-1 ) were investigated. Compared with the PL condition, PH promoted cell growth but reduced cellular respiration by approximately 50%. Moreover, PH alleviated damage to the photosynthetic system in algal cells after Pb stress. After exposure to 200-2000 µg L-1 Pb, higher Pb2+ concentrations and Pb removal were observed in the PL medium. However, under exposure to 5000 µg L-1 Pb, less Pb2+ was present but more Pb was removed by the algal cells in the PH medium. More P supply enhanced the secretion of extracellular fluorescent substances by C. reinhardtii. Transcriptomic analysis showed that genes associated with synthesis of phospholipids, tyrosine-like proteins, ferredoxin, and RuBisCO were up-regulated after Pb exposure. Together the findings of our study demonstrated the critical roles of P in Pb accumulation and resistance in C. reinhardtii. Environ Toxicol Chem 2023;42:1960-1970. © 2023 SETAC.

Phase microscopy using band-limited image and its Fourier transform constraints.

In this Letter, we present a new, to the best of our knowledge, form of single-exposure quantitative phase microscopy based on the phase retrieval method by recording the band-limited image and its Fourier image simultaneously. Applying the intrinsic physical constraints of microscopy systems in the phase retrieval algorithm, we remove the inherent ambiguities of the reconstruction and achieve a rapid iterative convergence. In particular, this system does not require tight support of the object and the oversampling needed in coherent diffraction imaging. We have demonstrated that, in both simulations and experiments, the phase can be rapidly retrieved from a single-exposure measurement using our algorithm. The presented phase microscopy provides a promising technique for real-time quantitative biological imaging.

A Multidimensional Tensor Low Rank Method for Magnetic Resonance Image Denoising.

In this paper, we present the Magnetic Resonance Image (MRI) denoising method via nonlocal multidimensional low rank tensor transformation constraint (NLRT). We first design a nonlocal MRI denoising method by non-local low rank tensor recovery framework. Furthermore, a multidimensional low rank tensor constraint is used to obtain low-rank prior information combined with 3-dimensional structure feature of MRI image cubes. Our NLRT can achieve denoising by retaining more image detail information. The optimization and updating process of the model is solved via the alternating direction method of multipliers (ADMM) algorithm. Several state-of-the-art denoising methods are selected for comparative experiments. In order to reflect the performance of the denoising method, Rician noise with different levels is added to the experiment to analyze the results. The experimental results prove that our NLTR has more outstanding denoising ability and can obtain better MRI images.

3D LiDAR Point Cloud Registration Based on IMU Preintegration in Coal Mine Roadways.

Point cloud registration is the basis of real-time environment perception for robots using 3D LiDAR and is also the key to robust simultaneous localization and mapping (SLAM) for robots. Because LiDAR point clouds are characterized by local sparseness and motion distortion, the point cloud features of coal mine roadway environments show a weak texture and degradation. Therefore, for these environments, the traditional point cloud registration method to register directly will lead to problems, such as a decline in registration accuracy, z-axis drift, and map ghosting. To solve the above problems, we propose a point cloud registration method based on IMU preintegration with the sensor characteristics of LiDAR and IMU. The system framework of this method mainly consists of four modules: IMU preintegration, point cloud preprocessing, point cloud frame matching and point cloud registration. First, IMU sensor data are introduced, and IMU linear interpolation is used to correct the motion distortion in LiDAR scanning, and the IMU preintegration error function is constructed. Second, the point cloud segmentation is performed using the ground segmentation method of RANSAC to provide additional ground constraints for the z-axis displacement and to remove the unstable flawed points from the point cloud. On this basis, the LiDAR point cloud registration error function is constructed by extracting the feature corner points and feature plane points. Finally, the Gaussian Newton solution is used to optimize the constraint relationship between the LiDAR odometry frames to minimize the error function, complete the LiDAR point cloud registration and better estimate the position and pose of the mobile robot. The experimental results show that compared with the traditional point cloud registration method, the proposed method has a higher point cloud registration accuracy, success rate and computational efficiency. The LiDAR odometry constructed using this method can better reflect the authenticity of the robot trajectory and has higher trajectory accuracy and smaller absolute position and pose error.

Investigation of thermal control in phase-changing ABO3 perovskites via first-principles predictions: general mechanism of emittance.

Phase-change thermal control has recently seen increased interest due to its significant potential for use in smart windows, building insulation, and optoelectronic devices in spacecraft. Tunable variation in infrared emittance can be achieved by thermally controlling the phase transitions of materials at different temperatures. A high emittance in the mid-infrared region is usually caused by resonant phonon vibrational modes. However, the fundamental mechanism of emittance variation during the phase-change process remains elusive. In this work, the electronic bandgaps, phononic structures, optical-spectrum properties, and formation energies of 76 kinds of phase-changing ABO3 perovskites were predicted based on first-principles calculations in the mid-infrared region. The variation in emittance between two phases of a single material was found to have an exponential correlation with the bandgap difference (R2 ∼ 0.92). Furthermore, a strong linear correlation (R2 ∼ 0.92) was found between the emittance variation and the formation-energy difference, and the emittance variation was also strongly correlated with the volume-distortion rate (R2 ∼ 0.90). Finally, it was concluded that a large lattice vibrational energy, a high formation energy, and a small cell volume are conducive to high emittance. This work provides a strong dataset for training machine-learning models, and it paves the way for further use of this novel methodology to seek efficient phase-change materials for thermal control.

Ion Transport in 2D Nanostructured π-Conjugated Thieno[3,2-b]thiophene-Based Liquid Crystal.

Leveraging the self-assembling behavior of liquid crystals designed for controlling ion transport is of both fundamental and technological significance. Here, we have designed and prepared a liquid crystal that contains 2,5-bis(thien-2-yl)thieno[3,2-b]thiophene (BTTT) as mesogenic core and conjugated segment and symmetric tetra(ethylene oxide) (EO4) as polar side chains for ion-conducting regions. Driven by the crystallization of the BTTT cores, BTTT/dEO4 exhibits well-ordered smectic phases below 71.5 °C as confirmed by differential scanning calorimetry, polarized optical microscopy, temperature-dependent wide-angle X-ray scattering, and grazing incidence wide-angle X-ray scattering (GIWAXS). We adopted a combination of experimental GIWAXS and molecular dynamics (MD) simulations to better understand the molecular packing of BTTT/dEO4 films, particularly when loaded with the ion-conducting salt lithium bis(trifluoromethanesulfonyl)imide (LiTFSI). Ionic conduction of BTTT/dEO4 is realized by the addition of LiTFSI, with the material able to maintain smectic phases up to r = [Li+]/[EO] = 0.1. The highest ionic conductivity of 8 × 10-3 S/cm was attained at an intermedium salt concentration of r = 0.05. It was also found that ion conduction in BTTT/dEO4 is enhanced by forming a smectic layered structure with irregular interfaces between the BTTT and EO4 layers and by the lateral film expansion upon salt addition. This can be explained by the enhancement of the misalignment and configurational entropy of the side chains, which increase their local mobility and that of the solvated ions. Our molecular design thus illustrates how, beyond the favorable energetic interactions that drive the assembly of ion solvating domains, modulation of entropic effects can also be favorably harnessed to improve ion conduction.

The Analysis of Relevant Gene Networks Based on Driver Genes in Breast Cancer.

BACKGROUND: The occurrence and development of breast cancer has a strong correlation with a person's genetics. Therefore, it is important to analyze the genetic factors of breast cancer for future development of potential targeted therapies from the genetic level. METHODS: In this study, we complete an analysis of the relevant protein-protein interaction network relating to breast cancer. This includes three steps, which are breast cancer-relevant genes selection using mutual information method, protein-protein interaction network reconstruction based on the STRING database, and vital genes calculating by nodes centrality analysis. RESULTS: The 230 breast cancer-relevant genes were chosen in gene selection to reconstruct the protein-protein interaction network and some vital genes were calculated by node centrality analyses. Node centrality analyses conducted with the top 10 and top 20 values of each metric found 19 and 39 statistically vital genes, respectively. In order to prove the biological significance of these vital genes, we carried out the survival analysis and DNA methylation analysis, inquired about the prognosis in other cancer tissues and the RNA expression level in breast cancer. The results all proved the validity of the selected genes. CONCLUSIONS: These genes could provide a valuable reference in clinical treatment among breast cancer patients.

An Evolving Hypergraph Convolutional Network for the Diagnosis of Alzheimer's Disease.

In the diagnosis of Alzheimer's Disease (AD), the brain network analysis method is often used. The traditional network can only reflect the pairwise association between two brain regions, but ignore the higher-order relationship between them. Therefore, a brain network construction method based on hypergraph, called hyperbrain network, is adopted. The brain network constructed by the conventional static hyperbrain network cannot reflect the dynamic changes in brain activity. Based on this, the construction of a dynamic hyperbrain network is proposed. In addition, graph convolutional networks also play a huge role in AD diagnosis. Therefore, an evolving hypergraph convolutional network for the dynamic hyperbrain network is proposed, and the attention mechanism is added to further enhance the ability of representation learning, and then it is used for the aided diagnosis of AD. The experimental results show that the proposed method can effectively improve the accuracy of AD diagnosis up to 99.09%, which is a 0.3 percent improvement over the best existing methods.

Custom 3D fMRI Registration Template Construction Method Based on Time-Series Fusion.

As the brain standard template for medical image registration has only been constructed with an MRI template, there is no three-dimensional fMRI standard template for use, and when the subject's brain structure is quite different from the standard brain structure, the registration to the standard space will lead to large errors. Registration to an individual space can avoid this problem. However, in the current fMRI registration algorithm based on individual space, the reference image is often selected by researchers or randomly selected fMRI images at a certain time point. This makes the quality of the reference image very dependent on the experience and ability of the researchers and has great contingency. Whether the reference image is appropriate and reasonable affects the rationality and accuracy of the registration results to a great extent. Therefore, a method for constructing a 3D custom fMRI template is proposed. First, the data are preprocessed; second, by taking a group of two-dimensional slices corresponding to the same layer of the brain in three-dimensional fMRI images at multiple time points as image sequences, each group of slice sequences are registered and fused; and finally, a group of fused slices corresponding to different layers of the brain are obtained. In the process of registration, in order to make full use of the correlation information between the sequence data, the feature points of each two slices of adjacent time points in the sequence are matched, and then according to the transformation relationship between the adjacent images, they are recursively forwarded and mapped to the same space. Then, the fused slices are stacked in order to form a three-dimensional customized fMRI template with individual pertinence. Finally, in the classic registration algorithm, the difference in the registration accuracy between using a custom fMRI template and different standard spaces is compared, which proves that using a custom template can improve the registration effect to a certain extent.

Physiological and proteomic responses of Chlamydomonas reinhardtii to arsenate and lead mixtures.

Arsenic (As) and lead (Pb) are frequently emitted from various sources into environment, but microbial responses to their combined toxicity have not been systematically investigated. In this study, Chlamydomonas reinhardtii was exposed to two levels of arsenate (As (V), 50, 500 µg/L), Pb (II) (500, 5000 µg/L) and their mixture (50 µg/L As (V) + 500 µg/L Pb (II); 500 µg/L As (V) + 5000 µg/L Pb (II)). The growth of C. reinhardtii was inhibited more remarkably by As (V) than by Pb (II). The As stress was alleviated by Pb in the 50 µg/L As (V) + 500 µg/L Pb (II) treatment, but was enhanced upon the 500 µg/L As (V) + 5000 µg/L Pb (II) exposure, with more pronounced changes in a number of physiological parameters of the algal cells. Proteomic results showed that 71 differently expressed proteins (DEPs) in the treatment of 50 µg/L As (V) + 500 µg/L Pb (II), and 167 DEPs were identified in that of 500 µg/L As (V) + 5000 µg/L Pb (II). These proteins were involved in energy metabolism, photosynthetic carbon fixation, reactive oxygen scavenging and defense, and amino acid synthesis. Taken together, these physiological and proteomic data demonstrated that C. reinhardtii could resist the As (V) and Pb (II) combined treatments through extracellular complexation and intracellular pathways.

NDCN-Brain: An Extensible Dynamic Functional Brain Network Model.

As an extension of the static network, the dynamic functional brain network can show continuous changes in the brain's connections. Then, limited by the length of the fMRI signal, it is difficult to show every instantaneous moment in the construction of a dynamic network and there is a lack of effective prediction of the dynamic changes of the network after the signal ends. In this paper, an extensible dynamic brain function network model is proposed. The model utilizes the ability of extracting and predicting the instantaneous state of the dynamic network of neural dynamics on complex networks (NDCN) and constructs a dynamic network model structure that can provide more than the original signal range. Experimental results show that every snapshot in the network obtained by the proposed method has a usable network structure and that it also has a good classification result in the diagnosis of cognitive impairment diseases.