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".

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.

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.

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.

Polymer microfiber meshes facilitate cardiac differentiation of c-kit(+) human cardiac stem cells.

Electrospun microfiber meshes have been shown to support the proliferation and differentiation of many types of stem cells, but the phenotypic fate of c-kit(+) human cardiac stem cells (hCSCs) have not been explored. To this end, we utilized thin (~5µm) elastomeric meshes consisting of aligned 1.7µm diameter poly (ester-urethane urea) microfibers as substrates to examine their effect on hCSC viability, morphology, proliferation, and differentiation relative to cells cultured on tissue culture polystyrene (TCPS). The results showed that cells on microfiber meshes displayed an elongated morphology aligned in the direction of fiber orientation, lower proliferation rates, but increased expressions of genes and proteins majorly associated with cardiomyocyte phenotype. The early (NK2 homeobox 5, Nkx2.5) and late (cardiac troponin I, cTnI) cardiomyocyte genes were significantly increased on meshes (Nkx=2.5 56.2±13.0, cTnl=2.9±0.56,) over TCPS (Nkx2.5=4.2±0.9, cTnl=1.6±0.5, n=9, p<0.05 for both groups) after differentiation. In contrast, expressions of smooth muscle markers, Gata6 and myosin heavy chain (SM-MHC), were decreased on meshes. Immunocytochemical analysis with cardiac antibody exhibited the similar pattern of above cardiac differentiation. We conclude that aligned microfiber meshes are suitable for guiding cardiac differentiation of hCSCs and may facilitate stem cell-based therapies for treatment of cardiac diseases.

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.

Study on intelligent recognition detection technology of debond defects for ceramic matrix composites based on terahertz time domain spectroscopy.

With the wide use of high-temperature-resistant ceramic matrix composites (CMCs) in aviation and space flight, it is important to detect the quality of the bonding. This paper used terahertz (THz) time-domain spectroscopy nondestructive testing technology to inspect the bonding defects of the CMC. This paper puts forward a method-extraction method, which is applied to make samples to simulate the bonding defect of CMC by embedding polytetrafluoroethylene (PTFE) sheets with 0.12 mm thickness into the adhesive layer and extracting it after curing and presetting the bonding defects. On the basis of the classical and analytical algorithms, such as the maximum in time-domain and power spectrum integration, through further study in the THz spectral characteristics of bonding samples for CMC, we specifically introduce the upper debond coefficient, lower debond coefficient, average absorption coefficient for the frequency domain, centroid coefficient for the frequency domain, and other characteristics. By optimizing the THz detection characteristics set, as a sample, we adopt the neural network intelligent recognition algorithm to detect the upper and lower debond defects in samples and realize the intelligent identification for CMC debond defects.

Interior tomographic imaging of mouse heart in a carbon nanotube micro-CT.

BACKGROUND: The relatively high radiation dose from micro-CT is a cause for concern in preclinical research involving animal subjects. Interior region-of-interest (ROI) imaging was proposed for dose reduction, but has not been experimentally applied in micro-CT. OBJECTIVE: Our aim is to implement interior ROI imaging in a carbon nanotube (CNT) x-ray source based micro-CT, and present the ROI image quality and radiation dose reduction for interior cardiac micro-CT imaging of a mouse heart in situ. METHODS: An aperture collimator was mounted at the source-side to induce a small-sized cone beam (10 mm width) at the isocenter. Interior in situ micro-CT scans were conducted on a mouse carcass and several micro-CT phantoms. A GPU-accelerated hybrid iterative reconstruction algorithm was employed for volumetric image reconstruction. Radiation dose was measured for the same system operated at the interior and global micro-CT modes. RESULTS: Visual inspection demonstrated comparable image quality between two scan modes. Quantitative evaluation demonstrated high structural similarity index (up to 0.9614) with improved contrast-noise-ratio (CNR) on interior micro-CT mode. Interior micro-CT mode yielded significant reduction (up to 83.9%) for dose length product (DLP). CONCLUSIONS: This work demonstrates the applicability of using CNT x-ray source based interior micro-CT for preclinical imaging with significantly reduced radiation dose.

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.

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.

X-ray fluorescence tomographic system design and image reconstruction.

In this paper, we presented a new design of x-ray fluorescence CT imaging system. For detecting fuorescence signals of gold nanoparticles in-vivo, multiple spectroscopic detectors are arranged and rotated orthogonal to an excited region of interest so that a localized scan can be acquired with a maximized efficiency. Excitation filtration was employed to minimize the effects of low-energy x-rays and background scattering for lowering radiation dose to the object. Numerical simulations showed that the radiation dose is less than 300 mGy/second for a complete 30 views tomographic scan; and the sensitivity of 3D fluorescence signal detection is up to 0.2% contrast concentrations of nanoparticles. The x-ray fluorescence computed tomography is an important molecular imaging tool. It can be used directly in samall animal research. It has great translational potential for future clinical applications.

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.

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.

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.

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.

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.

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.

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.

Multibeam field emission x-ray system with half-scan reconstruction algorithm.

PURPOSE: In this article, the authors propose a multibeam field emission x-ray (MBFEX) system along with a half-scan fan-beam reconstruction algorithm. METHODS: The proposed system consists of a linear CNT-based MBFEX source array, a single large area detector that is divided into same number of segments as the number of x-ray beams, a multihole collimator that aligns each beam with a corresponding detector segment, and a sample rotation stage. The collimator is placed between the source and the object to restrict the x-ray radiations through the target object only. In this design, all the x-ray beams are activated simultaneously to provide multiple projection views of the object. The detector is virtually segmented and synchronized with the x-ray exposure and the physiological signals when gating is involved. The transmitted x-ray intensity from each beam is collected by the corresponding segment on the detector. After each exposure, the object is rotated by a step angle until sufficient data set is collected. The half-scan reconstruction formula for MBFEX system is derived from the conventional filtered backprojection algorithm. To demonstrate the advantages of the system and method in reducing motion artifacts, the authors performed simulations with both standard and dynamic Shepp-Logan phantoms. RESULTS: The numerical results indicate that the proposed multibeam system and the associated half-scan algorithm can effectively reduce the scanning time and improve the image quality for a time-varying object. CONCLUSIONS: The MBFEX technique offers an opportunity for the innovation of multisource imaging system.