Realization of monolayer ZrTe5 topological insulators with wide band gaps.

Two-dimensional topological insulators hosting the quantum spin Hall effect have application potential in dissipationless electronics. To observe the quantum spin Hall effect at elevated temperatures, a wide band gap is indispensable to efficiently suppress bulk conduction. Yet, most candidate materials exhibit narrow or even negative band gaps. Here, via elegant control of van der Waals epitaxy, we have successfully grown monolayer ZrTe5 on a bilayer graphene/SiC substrate. The epitaxial ZrTe5 monolayer crystalizes in two allotrope isomers with different intralayer alignments of ZrTe3 prisms. Our scanning tunneling microscopy/spectroscopy characterization unveils an intrinsic full band gap as large as 254 meV and one-dimensional edge states localized along the periphery of the ZrTe5 monolayer. First-principles calculations further confirm that the large band gap originates from strong spin-orbit coupling, and the edge states are topologically nontrivial. These findings thus provide a highly desirable material platform for the exploration of the high-temperature quantum spin Hall effect.

Robust residual-guided iterative reconstruction for sparse-view CT in small animal imaging.

Objective. We introduce a robust image reconstruction algorithm named residual-guided Golub-Kahan iterative reconstruction technique (RGIRT) designed for sparse-view computed tomography (CT), which aims at high-fidelity image reconstruction from a limited number of projection views.Approach. RGIRT utilizes an inner-outer dual iteration framework, with a flexible least square QR (FLSQR) algorithm implemented in the inner iteration and a restarted iterative scheme applied in the outer iteration. The inner FLSQR employs a flexible Golub-Kahan bidiagonalization method to reduce the size of the inverse problem, and a weighted generalized cross-validation method to adaptively estimate the regularization hyper-parameter. The inner iteration efficiently yields the intermediate reconstruction result, while the outer iteration minimizes the residual and refines the solution by using the result obtained from the inner iteration.Main results. The reconstruction performance of RGIRT is evaluated and compared to other reference methods (FBPConvNet, SART-TV, and FLSQR) using projection data from both numerical phantoms and real experimental Micro-CT data. The experimental findings, from testing various numbers of projection views and different noise levels, underscore the robustness of RGIRT. Meanwhile, theoretical analysis confirms the convergence of residual for our approach.Significance. We propose a robust iterative reconstruction algorithm for x-ray CT scans with sparse views, thereby shortening scanning time and mitigating excessive ionizing radiation exposure to small animals.

A 19-Bit Small Absolute Matrix Encoder.

With the application of encoders in artificial intelligence and aerospace, the demand for the miniaturization and high measurement accuracy of encoders is increasing. To solve this problem, a new absolute matrix encoder is proposed in this paper, which can realize 19-bit matrix coding by engraving two circles of matrix code, and has the advantages of fewer circles of code disk engraving and higher measurement accuracy. This article mainly focuses on the design of a new matrix code disk, encoding and decoding methods, decoding circuit design, Matlab simulation analysis, and experimental error analysis. The experimental results show that the encoder designed in this paper achieves ultra-small volume Φ30 mm × 20 mm, and the angle measurement accuracy is 2.57".

Base editing of the HBG promoter induces potent fetal hemoglobin expression with no detectable off-target mutations in human HSCs.

Reactivating silenced γ-globin expression through the disruption of repressive regulatory domains offers a therapeutic strategy for treating ß-hemoglobinopathies. Here, we used transformer base editor (tBE), a recently developed cytosine base editor with no detectable off-target mutations, to disrupt transcription-factor-binding motifs in hematopoietic stem cells. By performing functional screening of six motifs with tBE, we found that directly disrupting the BCL11A-binding motif in HBG1/2 promoters triggered the highest γ-globin expression. Via a side-by-side comparison with other clinical and preclinical strategies using Cas9 nuclease or conventional BEs (ABE8e and hA3A-BE3), we found that tBE-mediated disruption of the BCL11A-binding motif at the HBG1/2 promoters triggered the highest fetal hemoglobin in healthy and ß-thalassemia patient hematopoietic stem/progenitor cells while exhibiting no detectable DNA or RNA off-target mutations. Durable therapeutic editing by tBE persisted in repopulating hematopoietic stem cells, demonstrating that tBE-mediated editing in HBG1/2 promoters is a safe and effective strategy for treating ß-hemoglobinopathies.

Isometric spiracular scaling in scarab beetles-implications for diffusive and advective oxygen transport.

The scaling of respiratory structures has been hypothesized to be a major driving factor in the evolution of many aspects of animal physiology. Here, we provide the first assessment of the scaling of the spiracles in insects using 10 scarab beetle species differing 180× in mass, including some of the most massive extant insect species. Using X-ray microtomography, we measured the cross-sectional area and depth of all eight spiracles, enabling the calculation of their diffusive and advective capacities. Each of these metrics scaled with geometric isometry. Because diffusive capacities scale with lower slopes than metabolic rates, the largest beetles measured require 10-fold higher PO2 gradients across the spiracles to sustain metabolism by diffusion compared to the smallest species. Large beetles can exchange sufficient oxygen for resting metabolism by diffusion across the spiracles, but not during flight. In contrast, spiracular advective capacities scale similarly or more steeply than metabolic rates, so spiracular advective capacities should match or exceed respiratory demands in the largest beetles. These data illustrate a general principle of gas exchange: scaling of respiratory transport structures with geometric isometry diminishes the potential for diffusive gas exchange but enhances advective capacities; combining such structural scaling with muscle-driven ventilation allows larger animals to achieve high metabolic rates when active.

The Complete Mitochondrial Genome of Spirobolus bungii (Diplopoda, Spirobolidae): The First Sequence for the Genus Spirobolus.

Millipedes (Diplopoda) comprise one of the most important groups of large soil arthropods in terrestrial ecosystems; however, their phylogenetic relationships are poorly understood. Herein, the mitochondrial genome (mitogenome) of Spirobolus bungii was sequenced and annotated, which was 14,879 bp in size and included 37 typical mitochondrial genes (13 protein-coding genes (PCGs), two ribosomal RNA genes (rRNAs), and 22 transfer RNA genes (tRNAs)). Most of the 13 PCGs had ATN (AT/A/T/G) as the start codon except for COX1, which used CGA, and most PCGs ended with the T end codon. By comparing the gene arrangements of the mitogenomes among Diplopoda species, rearrangement occurred between and within orders. In contrast to Narceus annularus, the mitogenome genes of S. bungii had consistent orders but were transcribed in completely opposite directions, which was a novel finding in Spirobolidae. Moreover, the phylogenetic relationships within Diplopoda, which were based on the sequences of 13 PCGs, showed that S. bungii was clustered with N. annularus, followed by Abacion magmun. This indicated that there might be a close relationship between Callipodida and Spirobolida. These results could contribute to further studies on the genetics and evolutionary processes of S. bungii and other Diplopoda species.

Research on Particle Swarm Compensation Method for Subdivision Error Optimization of Photoelectric Encoder Based on Parallel Iteration.

Photoelectric encoders are widely used in high-precision measurement fields such as industry and aerospace because of their high precision and reliability. In order to improve the subdivision accuracy of moiré grating signals, a particle swarm optimization compensation model for grating the subdivision error of a photoelectric encoder based on parallel iteration is proposed. In the paper, an adaptive subdivision method of a particle swarm search domain based on the honeycomb structure is proposed, and a raster signal subdivision error compensation model based on the multi-swarm particle swarm optimization algorithm based on parallel iteration is established. The optimization algorithm can effectively improve the convergence speed and system accuracy of traditional particle swarm optimization. Finally, according to the subdivision error compensation algorithm, the subdivision error of the grating system caused by the sinusoidal error in the system is quickly corrected by taking advantage of the high-speed parallel processing of the FPGA pipeline architecture. The design experiment uses a 25-bit photoelectric encoder to verify the subdivision error algorithm. The experimental results show that the actual dynamic subdivision error can be reduced to ½ before compensation, and the static subdivision error can be reduced from 1.264″ to 0.487″ before detection.

New Progress in Artificial Intelligence Algorithm Research Based on Big Data Processing of IOT Systems on Intelligent Production Lines.

Intelligent production line is the abbreviation of intelligent production line. Intelligent production line refers to a form of production organization that uses intelligent manufacturing technology to realize the production process of products. The actual manufacturing process includes different levels and links, and each step cooperates to create a high-efficiency production line. The intelligent production line includes 3 levels covering automation equipment, digital workshops, and intelligent factories and runs through 6 major links of intelligent manufacturing (intelligent management, intelligent monitoring, intelligent processing, intelligent assembly, intelligent inspection, and intelligent logistics). The emergence of the Internet of Things system has changed the way of information dissemination. The system combines radio frequency automatic identification and global positioning system technologies to achieve functions such as information exchange and processing, enabling information processing to be intelligent and improving resource utilization. Big data processing includes multiple data processing procedures, but data quality is the most important link in the entire process, and each data processing link will have an impact on the quality of big data. The big data processing process mainly includes data collection, data preprocessing, data storage, data processing and analysis, data display, data visualization, data application, and other links. This article aims to study the new progress of artificial intelligence algorithms for big data processing of IOT systems on intelligent production lines. It is hoped that through the development of intelligent production lines and big data processing technologies, ways to optimize artificial intelligence algorithms can be found. This study proposes a metadata replication method based on a separate replication strategy, which separates the replication process of the data operation log, each is independent, and shortens the data replication time. Combining the existing intelligent production line network platform in the laboratory and carrying out the research of the intelligent production line network state prediction system based on the neural network to design a network prediction system can prejudge the operation status of the intelligent production line network. The experimental results in this article show that when the Namenode mode is used to read data and when the number of clients reaches 8, the data processing basically remains unchanged. When the NCluster system reads data and when the number of clients is 6, the data is processed 1256. When the number of clients is 20, the data is processed 2100, the NCluster system will remain stable when the number of clients reaches 12, and compared with the Namenode system, it has obvious advantages.

Itaconic acid induces ferroptosis by activating ferritinophagy.

Itaconic acid is an unsaturated dicarbonic acid. It has a wide range of applications in the industrial production of resins and is also a mediator of immunometabolism in macrophages. Here, we show a previously unrecognized role of itaconic acid in triggering ferroptosis, a form of iron-dependent cell death driven by lipid peroxidation. We found that supraphysiological itaconic acid dose-dependently induces ferroptosis, rather than apoptosis, in human cancer cell lines. Mechanistically, we determined that itaconic acid activates NOCA4-mediated ferritinophagy, which leads to ferroptosis through ferritin degradation and subsequent iron overload and oxidative damage. In contrast, itaconic acid-induced expression and activation of NFE2L2 serves as a defense mechanism to limit ferroptosis by producing antioxidant genes. Consequently, impaired NCOA4 expression prevented, whereas a disrupted NFE2L2 pathway enhanced, sensitivity to itaconic acid-induced ferroptosis in vitro and in xenograft models. These findings establish a dynamic model of metabolite-induced ferroptotic cancer cell death, which may contribute to the development of new targeted therapies.

Financial constraints, government subsidies, and corporate innovation.

To investigate the relationships between financial constraints, government subsidies, and corporate innovation, a semi-logarithmic fixed-effect panel model and mediation effect test were applied, based on the data of Chinese listed companies from 2007 to 2017. We find that (1) financial constraints suppress corporate innovation. (2) Government subsidies are targeted at bailing out firms facing financial constraints. (3) Government subsidies promote corporate innovation (4) Government subsidies partially offset the suppression of financial constraints on innovation. We contribute to the fields of public finance, corporate finance, and corporate innovation by: (1) justifying the government subsidies target strategy as a bailout of corporate financial constraints, (2) verifying the corporate-innovation promotion of government subsidies, thus justifying the efficiency of government subsidies, and (3) showing that different types of innovation benefit differently from subsidies, thus justifying subsidies as a structural innovation engine.

Self-supervised CT super-resolution with hybrid model.

Software-based methods can improve CT spatial resolution without changing the hardware of the scanner or increasing the radiation dose to the object. In this work, we aim to develop a deep learning (DL) based CT super-resolution (SR) method that can reconstruct low-resolution (LR) sinograms into high-resolution (HR) CT images. We mathematically analyzed imaging processes in the CT SR imaging problem and synergistically integrated the SR model in the sinogram domain and the deblur model in the image domain into a hybrid model (SADIR). SADIR incorporates the CT domain knowledge and is unrolled into a DL network (SADIR-Net). The SADIR-Net is a self-supervised network, which can be trained and tested with a single sinogram. SADIR-Net was evaluated through SR CT imaging of a Catphan700 physical phantom and a real porcine phantom, and its performance was compared to the other state-of-the-art (SotA) DL-based CT SR methods. On both phantoms, SADIR-Net obtains the highest information fidelity criterion (IFC), structure similarity index (SSIM), and lowest root-mean-square-error (RMSE). As to the modulation transfer function (MTF), SADIR-Net also obtains the best result and improves the MTF50% by 69.2% and MTF10% by 69.5% compared with FBP. Alternatively, the spatial resolutions at MTF50% and MTF10% from SADIR-Net can reach 91.3% and 89.3% of the counterparts reconstructed from the HR sinogram with FBP. The results show that SADIR-Net can provide performance comparable to the other SotA methods for CT SR reconstruction, especially in the case of extremely limited training data or even no data at all. Thus, the SADIR method could find use in improving CT resolution without changing the hardware of the scanner or increasing the radiation dose to the object.

Review and Prospect: Artificial Intelligence in Advanced Medical Imaging.

Artificial intelligence (AI) as an emerging technology is gaining momentum in medical imaging. Recently, deep learning-based AI techniques have been actively investigated in medical imaging, and its potential applications range from data acquisition and image reconstruction to image analysis and understanding. In this review, we focus on the use of deep learning in image reconstruction for advanced medical imaging modalities including magnetic resonance imaging (MRI), computed tomography (CT), and positron emission tomography (PET). Particularly, recent deep learning-based methods for image reconstruction will be emphasized, in accordance with their methodology designs and performances in handling volumetric imaging data. It is expected that this review can help relevant researchers understand how to adapt AI for medical imaging and which advantages can be achieved with the assistance of AI.

Screening potential topological insulators in half-Heusler compounds via compressed-sensing.

Ternary half-Heusler compounds with widely tunable electronic structures, present a new platform to discover topological insulators (TIs). Due to time-consuming computations and synthesis procedures, the identification of new TIs is however a rough task. Here, we adopt a compressed-sensing approach to rapidly screen potential TIs in half-Heusler family, which is realized via a two-dimensional descriptor that only depends on the fundamental properties of the constituent atoms. Beyond the finite training data, the proposed descriptor is employed to screen many new half-Heusler compounds, including those with integer and fractional stoichiometry, and a larger number of possible TIs are predicted.

Detecting the normal-direction in automated aircraft manufacturing based on adaptive alignment.

In aircraft manufacturing, the vertical accuracy of connection holes is important indicator of the quality of holes making. Aircraft products have high requirements for the vertical accuracy of holes positions. When drilling and riveting are performed by an automatic robotic system, assembly errors, bumps, offsets and other adverse conditions, can affects the accuracy of manufacturing and detection, and in turn the fatigue performance of the entire structure. To solve this problem, we proposed a technology for detecting the normal-direction based on the adaptive alignment method, built a mathematical model for posture alignment, and studied the calibration method and mechanism of the detection device. Additionally, we investigated techniques for error compensation using an electronic theodolite and other devices when the adaptive method is used for detection. In verification experiments of the method, multiple sets of results demonstrated that the key technical indicators are as follows: normal accuracy <0.5°, average deviation after correction =0.0667°. This method can effectively compensate the errors affecting hole making work in automated manufacturing, and further improve the positioning accuracy and normal-direction detection accuracy of the robot.

A problematic animal fossil from the early Cambrian Hetang Formation, South China.

The lower-middle Hetang Formation (Cambrian Stage 2-3) deposited in slope-basinal facies in South China is well-known for its preservation of the earliest articulated sponge fossils, providing an important taphonomic window into the Cambrian explosion. However, the Hetang Formation also hosts a number of problematic animal fossils that have not been systematically described. This omission results in an incomplete picture of the Hetang biota and limits its contribution to the understanding of the early evolution of animals. Here we describe a new animal taxon, Cambrowania ovata Tang and Xiao, new genus new species, from the middle Hetang Formation in the Lantian area of southern Anhui Province, South China. Specimens are preserved as carbonaceous compressions, although some are secondarily mineralized. A comprehensive analysis using reflected light microscopy, scanning electron microscopy, energy dispersive X-ray spectroscopy, and micro-CT reveals that the new species is characterized by a spheroidal to fusoidal truss-like structure consisting of rafter-like crossbars, some of which are secondarily baritized and may have been internally hollow. Some specimens have aperture-like structures that are broadly similar to oscula of sponges, whereas others show evidence of a medial split reminiscent of gaping carapaces. While the phylogenetic affinity of Cambrowania ovata Tang and Xiao, new genus new species remains problematic, we propose that it may represent carapaces of bivalved arthropods or more likely sponges in early life stages. Along with other problematic metazoan fossils such as hyolithids and sphenothallids, Cambrowania ovata Tang and Xiao, new genus new species adds to the diversity of the sponge-dominated Hetang biota in an early Cambrian deep-water slope-basinal environment.

High Thermoelectric Performance Originating from the Grooved Bands in the ZrSe3 Monolayer.

Low-dimensional layered materials have attracted tremendous attentions because of their wide range of physical and chemical properties and potential applications in electronic devices. Using first-principles method taking into account the quasi-particle self-energy correction and Boltzmann transport theory, the electronic transport properties of the ZrSe3 monolayer are investigated, where the carrier relaxation time is accurately calculated within the framework of electron-phonon coupling. It is demonstrated that the high power factor of the monolayer can be attributed to the grooved bands near the conduction band minimum. Combined with the low lattice thermal conductivity obtained by solving the phonon Boltzmann transport equation, a considerable n-type ZT value of ∼2.4 can be achieved at 800 K in the ZrSe3 monolayer.

A Sparse-View CT Reconstruction Method Based on Combination of DenseNet and Deconvolution.

Sparse-view computed tomography (CT) holds great promise for speeding up data acquisition and reducing radiation dose in CT scans. Recent advances in reconstruction algorithms for sparse-view CT, such as iterative reconstruction algorithms, obtained high-quality image while requiring advanced computing power. Lately, deep learning (DL) has been widely used in various applications and has obtained many remarkable outcomes. In this paper, we propose a new method for sparse-view CT reconstruction based on the DL approach. The method can be divided into two steps. First, filter backprojection (FBP) was used to reconstruct the CT image from sparsely sampled sinogram. Then, the FBP results were fed to a DL neural network, which is a DenseNet and deconvolution-based network (DD-Net). The DD-Net combines the advantages of DenseNet and deconvolution and applies shortcut connections to concatenate DenseNet and deconvolution to accelerate the training speed of the network; all of those operations can greatly increase the depth of network while enhancing the expression ability of the network. After the training, the proposed DD-Net achieved a competitive performance relative to the state-of-the-art methods in terms of streaking artifacts removal and structure preservation. Compared with the other state-of-the-art reconstruction methods, the DD-Net method can increase the structure similarity by up to 18% and reduce the root mean square error by up to 42%. These results indicate that DD-Net has great potential for sparse-view CT image reconstruction.

Physics Model-Based Scatter Correction in Multi-Source Interior Computed Tomography.

Multi-source interior computed tomography (CT) has a great potential to provide ultra-fast and organ-oriented imaging at low radiation dose. However, X-ray cross scattering from multiple simultaneously activated X-ray imaging chains compromises imaging quality. Previously, we published two hardware-based scatter correction methods for multi-source interior CT. Here, we propose a software-based scatter correction method, with the benefit of no need for hardware modifications. The new method is based on a physics model and an iterative framework. The physics model was derived analytically, and was used to calculate X-ray scattering signals in both forward direction and cross directions in multi-source interior CT. The physics model was integrated to an iterative scatter correction framework to reduce scatter artifacts. The method was applied to phantom data from both Monte Carlo simulations and physical experimentation that were designed to emulate the image acquisition in a multi-source interior CT architecture recently proposed by our team. The proposed scatter correction method reduced scatter artifacts significantly, even with only one iteration. Within a few iterations, the reconstructed images fast converged toward the "scatter-free" reference images. After applying the scatter correction method, the maximum CT number error at the region-of-interests (ROIs) was reduced to 46 HU in numerical phantom dataset and 48 HU in physical phantom dataset respectively, and the contrast-noise-ratio at those ROIs increased by up to 44.3% and up to 19.7%, respectively. The proposed physics model-based iterative scatter correction method could be useful for scatter correction in dual-source or multi-source CT.

Mean estimation empirical mode decomposition method for terahertz time-domain spectroscopy de-noising.

The wavelet-domain de-noising technique has many applications in terahertz time-domain spectroscopy (THz-TDS). However, it requires a complex procedure for the selection of the optimal wavelet basis and threshold, which varies for different materials. Inappropriate selections can lead to de-noising failure. Here, we propose the Mean Estimation Empirical Mode Decomposition (ME-EMD) de-noising method for THz-TDS. First, the THz-TDS signal and the collected reference noise are decomposed into the intrinsic mode functions (IMFs); second, the maximum and mean absolute values of the noise IMF amplitudes are calculated and defined as the adaptive threshold and adaptive estimated noise value, respectively; finally, these thresholds and estimated noise values are utilized to filter the noise from the signal IMFs and reconstruct the THz-TDS signal. We also calculate the signal-to-noise ratio (SNR) and mean square error (MSE) for the ME-EMD method, the "db7" wavelet basis, and the "sym8" wavelet basis after de-noising in both the simulation and the real sample experiments. Both theoretical analysis and experimental results demonstrated that the new ME-EMD method is a simple, effective, and high-stability de-noising tool for THz-TDS pulses. The measured refractive index curves are compared before and after de-noising and demonstrated that the de-noising process is necessary and useful for measuring the optical constants of a sample.

X-ray scatter correction for multi-source interior computed tomography.

PURPOSE: The schemes of multi-source interior computed tomography (CT) have shown promise for ultra-fast, organ-oriented, and low-dose dynamic imaging. Besides forward scattering, x-ray cross scattering from multiple x-ray sources activated simultaneously can further degrade image quality. Here, we investigate the overall x-ray scattering artifact in a recently proposed multi-source interior CT architecture, and present two methods for scatter correction. METHODS: Compared to single-source global CT, scattering in multi-source interior CT architecture is affected by two new factors: cross scattering from simultaneously activated multiple x-ray sources and region-of-interest (ROI) oriented interior CT mode. The scatter artifact in the multi-source interior CT architecture was evaluated through both numerical simulation and physical experimentation, and compared to that from conventional single-source global CT. Monte Carlo simulation was conducted with a modified numerical CATphan® 600 phantom. Physical experiments were performed in an in-house developed CT imaging platform with a custom-built phantom. The simulation and experiments were carried out on the single-source CT architecture and the multi-source CT architecture, respectively in the global CT mode and the interior CT mode for comparison. To correct the scattering artifact, two new methods were presented. The first is a beam-stopper-array (BSA)-based method, which enables an online correction of forward scattering and cross scattering simultaneously. The second is a source-trigger-sequence (STS)-based method dedicated to cross-scatter correction. It enables on-the-fly measurements of the cross scattering signals at a few pre-selected views. The CT image quality was quantitatively evaluated in terms of contrast-to-noise ratio (CNR) and CT number deviation before and after the scatter correction. RESULTS: X-ray cross scattering degraded image quality in both the simulation and experiments. Before the scatter correction, the multi-source interior CT mode yielded a reduction of CNR at the ROIs by up to 68.5% and 50.7% in the simulation and experiments, respectively. The stationary BSA-based method significantly improved CNR and CT number accuracy in the images from multi-source interior CT mode, by reducing the negative effects from both forward scattering and cross scattering. The STS-based method enabled multi-source interior CT mode to provide comparable image quality to that with the single-source interior CT mode, by correcting the artifact from cross scattering. The remaining forward scattering artifact can be corrected with the fast adaptive scatter kernel superposition (FASKS) technique. With the proposed scatter correction methods, the CT number error at the ROIs was reduced to less than 37 HU in both simulation and experiments, respectively. CONCLUSIONS: Cross scattering, in addition to forward scattering, can cause significant image quality degradation in the multi-source interior CT architecture. However, image quality can be significantly improved with the proposed scatter correction methods.