Robust speed estimation for a moving harmonic acoustic source with a single stationary sensor.

A noise-insensitive cost function was developed for estimating the speed of harmonic acoustic sources in uniform linear motion. This function weighs and integrates the energy distribution of received tones in the time-frequency plane to enhance the robustness of parameter estimation under low signal-to-noise ratio conditions, where weight values are intentionally combined with the law of observed instantaneous frequency. As the cost function is differentiable, the procedure of parameter estimations also has high computing efficiency. Processing data of SWellEx-96 experiments with real ocean noise confirmed the anti-noise capabilities of this cost function to conventional processing methods.

A dual-energy CT reconstruction method based on anchor network from dual quarter scans.

Compared with conventional single-energy computed tomography (CT), dual-energy CT (DECT) provides better material differentiation but most DECT imaging systems require dual full-angle projection data at different X-ray spectra. Relaxing the requirement of data acquisition is an attractive research to promote the applications of DECT in wide range areas and reduce the radiation dose as low as reasonably achievable. In this work, we design a novel DECT imaging scheme with dual quarter scans and propose an efficient method to reconstruct the desired DECT images from the dual limited-angle projection data. We first study the characteristics of limited-angle artifacts under dual quarter scans scheme, and find that the negative and positive artifacts of DECT images are complementarily distributed in image domain because the corresponding X-rays of high- and low-energy scans are symmetric. Inspired by this finding, a fusion CT image is generated by integrating the limited-angle DECT images of dual quarter scans. This strategy enhances the true image information and suppresses the limited-angle artifacts, thereby restoring the image edges and inner structures. Utilizing the capability of neural network in the modeling of nonlinear problem, a novel Anchor network with single-entry double-out architecture is designed in this work to yield the desired DECT images from the generated fusion CT image. Experimental results on the simulated and real data verify the effectiveness of the proposed method. This work enables DECT on imaging configurations with half-scan and largely reduces scanning angles and radiation doses.

Dual-color emissive OLED with orthogonal polarization modes.

Linearly polarized organic light-emitting diodes have become appealing functional expansions of polarization optics and optoelectronic applications. However, the current linearly polarized diodes exhibit low polarization performance, cost-prohibitive process, and monochromatic modulation limit. Herein, we develop a switchable dual-color orthogonal linear polarization mode in organic light-emitting diode, based on a dielectric/metal nanograting-waveguide hybrid-microcavity using cost-efficient laser interference lithography and vacuum thermal evaporation. This acquired diode presents a transverse-electric/transverse-magnetic polarization extinction ratio of 15.8 dB with a divergence angle of ±30°, an external quantum efficiency of 2.25%, and orthogonal polarized colors from green to sky-blue. This rasterization of dielectric/metal-cathode further satisfies momentum matching between waveguide and air mode, diffracting both the targeted sky-blue transverse-electric mode and the off-confined green transverse-magnetic mode. Therefore, a polarization-encrypted colorful optical image is proposed, representing a significant step toward the low-cost high-performance linearly polarized light-emitting diodes and electrically-inspired polarization encryption for color images.

A stable liver-specific urate oxidase gene knockout hyperuricemia mouse model finds activated hepatic de novo purine biosynthesis and urate nephropathy.

Urate oxidase (Uox)-deficient mice could be an optimal animal model to study hyperuricemia and associated disorders. We develop a liver-specific conditional knockout Uox-deficient (UoxCKO) mouse using the Cre/loxP gene targeting system. These UoxCKO mice spontaneously developed hyperuricemia with accumulated serum urate metabolites. Blocking urate degradation, the UoxCKO mice showed significant de novo purine biosynthesis (DNPB) in the liver along with amidophosphoribosyltransferase (Ppat). Pegloticase and allopurinol reversed the elevated serum urate (SU) levels in UoxCKO mice and suppressed the Ppat up-regulation. Although urate nephropathy occurred in 30-week-old UoxCKO mice, 90 % of Uox-deficient mice had a normal lifespan without pronounced urate transport abnormality. Thus, UoxCKO mice are a stable model of human hyperuricemia. Activated DNPB in the UoxCKO mice provides new insights into hyperuricemia, suggesting increased SU influences purine synthesis.

Purine Metabolic Pathway Alterations and Serum Urate Changes after Oral Inosine Loading in Male Chinese Volunteers.

BACKGROUND: Oral inosine loading is a new method to evaluate the effects of purine on urate metabolism. However, individuals respond differently to acute purine intake, and the effects on the metabolism of other purines remain to be explored. METHODS: 35 male participants are recruited. Participants received 500 mg of inosine orally after an overnight fast, and blood and urine samples are collected before and at various time points over 180 min after inosine administration. RESULTS: The serum urate concentration is significantly different between the hyperuricemia (n = 14) and non-hyperuricemia (n = 16) groups before inosine intake, but there is no in urate change after inosine intake. When grouped according to the baseline estimated glomerular filtration rate (eGFR), the increase in urate level in the high-eGFR group is significantly higher than that in the low-eGFR group (p  =  0.047). The high-eGFR group showed higher levels of serum xanthine and xanthine oxidase (XOD), the key enzyme in urate synthesis, after inosine loading (p < 0.01). CONCLUSIONS: The increase in urate level is positively related to eGFR after oral acute inosine administration, which may have been due to a higher level of XOD.

ALDOB/KAT2A interactions epigenetically modulate TGF-ß expression and T cell functions in hepatocellular carcinogenesis.

BACKGROUND AND AIMS: Cross talk between tumor cells and immune cells enables tumor cells to escape immune surveillance and dictate responses to immunotherapy. Previous studies have identified that downregulation of the glycolytic enzyme fructose-1,6-bisphosphate aldolase B (ALDOB) in tumor cells orchestrated metabolic programming to favor HCC. However, it remains elusive whether and how ALDOB expression in tumor cells affects the tumor microenvironment in HCC. APPROACH AND RESULTS: We found that ALDOB downregulation was negatively correlated with CD8 + T cell infiltration in human HCC tumor tissues but in a state of exhaustion. Similar observations were made in mice with liver-specific ALDOB knockout or in subcutaneous tumor models with ALDOB knockdown. Moreover, ALDOB deficiency in tumor cells upregulates TGF-ß expression, thereby increasing the number of Treg cells and impairing the activity of CD8 + T cells. Consistently, a combination of low ALDOB and high TGF-ß expression exhibited the worst overall survival for patients with HCC. More importantly, the simultaneous blocking of TGF-ß and programmed cell death (PD) 1 with antibodies additively inhibited tumorigenesis induced by ALDOB deficiency in mice. Further mechanistic experiments demonstrated that ALDOB enters the nucleus and interacts with lysine acetyltransferase 2A, leading to inhibition of H3K9 acetylation and thereby suppressing TGFB1 transcription. Consistently, inhibition of lysine acetyltransferase 2A activity by small molecule inhibitors suppressed TGF-ß and HCC. CONCLUSIONS: Our study has revealed a novel mechanism by which a metabolic enzyme in tumor cells epigenetically modulates TGF-ß signaling, thereby enabling cancer cells to evade immune surveillance and affect their response to immunotherapy.

Microstructure refinement and enhanced mechanical properties in rapid-quenched MnCrFeCoNi high-entropy alloy.

High-entropy alloys (HEAs) have gained significant attentions in recent years, due to their unique properties derived from the combination of multiple elements in equimolar or near-equimolar ratios. The mechanical properties of HEAs are influenced by microstructural characteristics. In this study, MnCrFeCoNi HEA ribbons were produced using a technique called melt spinning, for which the wheel speed was adjusted to control the undercooling levels. The rapid solidification process under undercooling condition resulted in refined grain sizes to micrometers in the ribbons. One notable feature was the appearance of twin boundaries, which especially accounted for approximately 7.36 % of the microstructure for the ribbons produced at a wheel speed of 10 m/s. For the ribbons with thickness of micrometer scale, the mechanical properties (ultimate tensile strength up to 2.5 GPa and hardness up to 300 MPa) were analyzed by microstructure (grain boundaries and homogeneity) and exterior factors (e.g. thickness). Overall, this study provides a new approach for tailoring the microstructures and mechanical properties of HEAs via melt spinning technique. The HEA ribbons present a novel form that could potentially broaden the scope of applications for these materials.

Deep unsupervised adversarial domain adaptation for underwater source range estimation.

In this study, an underwater source range estimation method based on unsupervised domain adaptation (UDA) is proposed. In contrast to traditional deep-learning frameworks using real-world data, UDA does not require labeling of the measured data, making it more practical. First, a classifier based on a deep neural network is trained with labeled simulated data generated using acoustic propagation models and, then, the adaptive procedure is applied, wherein unlabeled measured data are employed to adjust an adaptation module using the adversarial learning algorithm. Adversarial learning is employed to alleviate the marginal distribution divergence, which reflects the difference between the measured and theoretically computed sound field, in the latent space. This divergence, caused by environmental parameter mismatch or other unknown corruption, can be detrimental to accurate source localization. After the completion of the adaptive procedure, the measured and simulated data are projected to the same space, eliminating distribution discrepancy, which is beneficial for source localization tasks. Experimental results show that range estimation based on UDA outperforms the match-field-processing method under four scenarios of few snapshots, few array elements, low signal-to-noise ratio, and environmental parameter mismatch, verifying the robustness of the method.

Relevance of PUFA-derived metabolites in seminal plasma to male infertility.

Aim: This study aims to investigate the biological effects of polyunsaturated fatty acid (PUFA)-derived metabolites in seminal plasma on male fertility and to evaluate the potential of PUFA as a biomarker for normozoospermic male infertility. Methods: From September 2011 to April 2012, We collected semen samples from 564 men aged 18 to 50 years old (mean=32.28 years old)ch., residing in the Sandu County, Guizhou Province, China. The donors included 376 men with normozoospermia (fertile: n=267; infertile: n=109) and 188 men with oligoasthenozoospermia (fertile: n=121; infertile: n=67). The samples thus obtained were then analyzed by liquid chromatography-mass spectrometry (LC-MS) to detect the levels of PUFA-derived metabolites in April 2013. Data were analyzed from December 1, 2020, to May 15, 2022. Results: Our analysis of propensity score-matched cohorts revealed that the concentrations of 9/26 and 7/26 metabolites differed significantly between fertile and infertile men with normozoospermia and oligoasthenozoospermia, respectively (FDR < 0.05). In men with normozoospermia, higher levels of 7(R)-MaR1 (HR: 0.4 (95% CI [0.24, 0.64]) and 11,12-DHET (0.36 (95% CI [0.21, 0.58]) were significantly associated with a decreased risk of infertility, while higher levels of 17(S)-HDHA (HR: 2.32 (95% CI [1.44, 3.79]), LXA5 (HR: 8.38 (95% CI [4.81, 15.24]), 15d-PGJ2 (HR: 1.71 (95% CI [1.06, 2.76]), and PGJ2 (HR: 2.28 (95% CI [1.42, 3.7]) correlated with an increased risk of infertility. Our ROC model using the differentially expressed metabolites showed the value of the area under the curve to be 0.744. Conclusion: The PUFA-derived metabolites 7(R)-MaR1, 11,12-DHET, 17(S)-HDHA, LXA5, and PGJ2 might be considered as potential diagnostic biomarkers of infertility in normozoospermic men.

Metabolomics and Machine Learning Identify Metabolic Differences and Potential Biomarkers for Frequent Versus Infrequent Gout Flares.

OBJECTIVE: The objective of this study was to discover differential metabolites and pathways underlying infrequent gout flares (InGF) and frequent gout flares (FrGF) using metabolomics and to establish a predictive model by machine learning (ML) algorithms. METHODS: Serum samples from a discovery cohort of 163 patients with InGF and 239 patients with FrGF were analyzed by mass spectrometry-based untargeted metabolomics to profile differential metabolites and explore dysregulated metabolic pathways using pathway enrichment analysis and network propagation-based algorithms. ML algorithms were performed to establish a predictive model based on selected metabolites, which was further optimized by a quantitative targeted metabolomics method and validated in an independent validation cohort with 97 participants with InGF and 139 participants with FrGF. RESULTS: A total of 439 differential metabolites between InGF and FrGF groups were identified. Top dysregulated pathways included carbohydrates, amino acids, bile acids, and nucleotide metabolism. Subnetworks with maximum disturbances in the global metabolic networks featured cross-talk between purine metabolism and caffeine metabolism, as well as interactions among pathways involving primary bile acid biosynthesis, taurine and hypotaurine metabolism, alanine, aspartate, and glutamate metabolism, suggesting epigenetic modifications and gut microbiome in metabolic alterations underlying InGF and FrGF. Potential metabolite biomarkers were identified using ML-based multivariable selection and further validated by targeted metabolomics. Area under receiver operating characteristics curve for differentiating InGF and FrGF achieved 0.88 and 0.67 for the discovery and validation cohorts, respectively. CONCLUSION: Systematic metabolic alterations underlie InGF and FrGF, and distinct profiles are associated with differences in gout flare frequencies. Predictive modeling based on selected metabolites from metabolomics can differentiate InGF and FrGF.

Direct Multi-Material Reconstruction via Iterative Proximal Adaptive Descent for Spectral CT Imaging.

Spectral computed tomography (spectral CT) is a promising medical imaging technology because of its ability to provide information on material characterization and quantification. However, with an increasing number of basis materials, the nonlinearity of measurements causes difficulty in decomposition. In addition, noise amplification and beam hardening further reduce image quality. Thus, improving the accuracy of material decomposition while suppressing noise is pivotal for spectral CT imaging. This paper proposes a one-step multi-material reconstruction model as well as an iterative proximal adaptive decent method. In this approach, a proximal step and a descent step with adaptive step size are designed under the forward-backward splitting framework. The convergence analysis of the algorithm is further discussed according to the convexity of the optimization objective function. For simulation experiments with different noise levels, the peak signal-to-noise ratio (PSNR) obtained by the proposed method increases approximately 23 dB, 14 dB, and 4 dB compared to those of other algorithms. Magnified areas of thorax data further demonstrated that the proposed method has a better ability to preserve details in tissues, bones, and lungs. Numerical experiments verify that the proposed method efficiently reconstructed the material maps, and reduced noise and beam hardening artifacts compared with the state-of-the-art methods.

Trans-Reflective Structural Color Filters Assisting Multifunctional-Integrated Semitransparent Photovoltaic Window.

Multifunction-integrated semitransparent organic photovoltaic cells (STOPVs), with high power generation, colorful transmittance/reflectance, excellent ultraviolet (UV) protection, and thermal insulation, are fully in line with the concept of architectural aesthetics and photoprotection characteristics for building-integrated photovoltaic-window. For the indelible rainbow color photovoltaic window, one crucial issue is to realize the integration of these photons- and photoelectric-related multifunction. Herein, dynamic transmissive and reflective structural color controllable filters, with asymmetrical metal-insulator-metal (MIM) configurations (20 nm-Ag-HATCN-30 nm-Ag) through machine learning, are deliberately designed for colorful STOPV devices. This endows the resultant integrated devices with ≈5% enhanced power conversion efficiency (PCE) than the bare-STOPVs, gifted UV (300-400 nm) blocking rates as high as 93.5, 94.1, 90.2, and 94.5%, as well as a superior infrared radiation (IR) (700-1400 nm) rejection approaching 100% for transparent purple-, blue-, green- and red-STOPV cells, respectively. Most importantly, benefiting from the photonic recycling effect beyond microcavity resonance wavelength, a reported quantum utilization efficiency (QUE) as high as 80%, is first presented for the transparent-green-STOPVs with an ultra-narrow bandgap of 1.2 eV. These asymmetrical Febry-Pérot transmissive and reflective structural color filters can also be extended to silicon- and perovskite-based optoelectric devices and make it possible to integrate additional target optical functions for multi-purpose optoelectric devices.

Super-resolution image reconstruction from sparsity regularization and deep residual-learned priors.

BACKGROUND: Computed tomography (CT) plays an important role in the field of non-destructive testing. However, conventional CT images often have blurred edge and unclear texture, which is not conducive to the follow-up medical diagnosis and industrial testing work. OBJECTIVE: This study aims to generate high-resolution CT images using a new CT super-resolution reconstruction method combining with the sparsity regularization and deep learning prior. METHODS: The new method reconstructs CT images through a reconstruction model incorporating image gradient L0-norm minimization and deep image priors using a plug-and-play super-resolution framework. The deep learning priors are learned from a deep residual network and then plugged into the proposed new framework, and alternating direction method of multipliers is utilized to optimize the iterative solution of the model. RESULTS: The simulation data analysis results show that the new method improves the signal-to-noise ratio (PSNR) by 7% and the modulation transfer function (MTF) curves show that the value of MTF50 increases by 0.02 factors compared with the result of deep plug-and-play super-resolution. Additionally, the real CT image data analysis results show that the new method improves the PSNR by 5.1% and MTF50 by 0.11 factors. CONCLUSION: Both simulation and real data experiments prove that the proposed new CT super-resolution method using deep learning priors can reconstruct CT images with lower noise and better detail recovery. This method is flexible, effective and extensive for low-resolution CT image super-resolution.

Projection domain processing for low-dose CT reconstruction based on subspace identification.

PURPOSE: Low-dose computed tomography (LDCT) has promising potential for dose reduction in medical applications, while suffering from low image quality caused by noise. Therefore, it is in urgent need for developing new algorithms to obtain high-quality images for LDCT. METHODS: This study tries to exploit the sparse and low-rank properties of images and proposes a new algorithm based on subspace identification. The collection of transmission data is sparsely represented by singular value decomposition and the eigen-images are then denoised by block-matching frames. Then, the projection is regularized by the correlation information under the frame of prior image compressed sensing (PICCS). With the application of a typical analytical algorithm on the processed projection, the target images are obtained. Both numerical simulations and real data verifications are carried out to test the proposed algorithm. The numerical simulations data is obtained based on real clinical scanning three-dimensional data and the real data is obtained by scanning experimental head phantom. RESULTS: In simulation experiment, using new algorithm boots the means of PSNR and SSIM by 1 dB and 0.05, respectively, compared with BM3D under the Gaussian noise with variance 0.04. Meanwhile, on the real data, the proposed algorithm exhibits superiority over compared algorithms in terms of noise suppression, detail preservation and computational overhead. The means of PSNR and SSIM are improved by 1.84 dB and 0.1, respectively, compared with BM3D under the Gaussian noise with variance 0.04. CONCLUSION: This study demonstrates the feasibility and advantages of a new algorithm based on subspace identification for LDCT. It exploits the similarity among three-dimensional data to improve the image quality in a concise way and shows a promising potential on future clinical diagnosis.

The Effect of Rotary-Die Equal-Channel Angular Pressing Process on the Microstructure, the Mechanical and Friction Properties of GW103 Alloy.

In this study, the effect of rotary-die equal-channel angular pressing (RD-ECAP) on the microstructure and texture evolution of GW103 alloy is studied. RD-ECAP processes were carried out for 1, 4 and 12 passes at 450 °C. The mechanical properties and friction behavior of RD-ECAP-processed Mg-10Gd-3Y (wt%) alloy (GW103) are discussed. The results reveal that the size of dynamic recrystallized grains and second-phase particles are significantly refined to about 1.3 µm and 1 µm, respectively. The texture evolution of the processed samples is studied by X-ray diffraction and electron backscattered diffraction techniques. The multiple texture components formed are not observed after the conventional ECAP process. Moreover, different dynamic recrystallization (DRX) mechanisms are systemically analyzed and discussed in view of the texture evolution of ECAP processed samples. The final textures obtained after 12 passes are identified as two types: The C-texture type induced by continuous and discontinuous DRX, and the random texture components induced by reorientation of the initial <101¯0> fiber. Based on the grain refinement, precipitate strengthening and texture weakening mechanisms, a high-performance ternary alloy of Mg-Gd-Y was firstly obtained through 12 passes RD-ECAP processing, with a combination of high yield strength of 312 MPa and a high ductility of 22%. In addition, the friction behaviors are also studied. The multi-pass-processed samples exhibit a relatively lower friction coefficient under a load of 10 N at room temperature.

Fatty Acid Profiling in Facial Sebum and Erythrocytes From Adult Patients With Moderate Acne.

Fatty acid (FA) metabolism has been involved in acne vulgaris, a common inflammatory skin disease frequently observed in adolescents and adults, but it remains poorly defined whether the distributions or location of FA in facial sebum and those in the circulation differentially correlate with the disease. In a cohort of 47 moderate acne patients and 40 controls, sebum samples from forehead and chin areas were collected using Sebutape adhesive patches, and erythrocytes were separated from the fasting blood. Total FAs were analyzed by the gas chromatograph-mass spectrometry method. Compared to control female subjects, female patients showed increased levels of saturated fatty acids (SFAs) and monounsaturated fatty acids (MUFAs) from both facial areas, whereas decreased levels of polyunsaturated fatty acids (PUFAs) from chin areas were observed. Interestingly, the levels of docosahexaenoic acid (DHA) in the circulating erythrocytes were significantly decreased in male patients compared with control. In addition, DHA levels in erythrocytes were positively correlated with PUFAs from sebum only in male subjects. Furthermore, female patients with moderate acne had more severe sebum abnormity and chin-specific FA profiles, consistent with higher acne incidences than males in adulthood, especially in the chin areas. Importantly, serum insulin-like growth factor 1 (IGF-1) levels were positively correlated with SFAs and MUFAs from sebum only in male subjects. In summary, differential spatial FA distributions in facial sebum and correlation with those in erythrocytes and IGF1 levels in serum may shed some light on the pathology of acne in male and female adults.

Dual chaos encryption for color images enabled in a WGM-random hybrid microcavity.

Chaotic cryptography as an important means for digital image encryption has become a great cryptographic project in the current information age. As a novel microcavity laser, a random laser (RL) has a natural advantage for a chaotic system, relying on its spectral randomness. Nevertheless, this encrypted image generally suffers from outline exposition when an unsuitable key from a single RL spectrum is employed. Herein, to realize reliable dual chaotic encryption, an internally integrated hybrid microcavity in random and whispering-gallery-mode (WGM) is reported. Within this coupled microcavity, the rhodamine-6G-doped inner-wall of the fiber serves as the gain medium and the optical cavities for WGM lasing; an RL mode is enabled by scattered particles and the gain medium (Rh6G). Interestingly, the smooth inner wall of the fiber with a high-quality (Q) factor and tight optical confinement make WGM lasing occur earlier than RL. What is more, a fast energy transfer process from the WGM laser to Ag nanoparticles and the resultant localized surface plasmon resonance effects from Ag NPs to RL jointly promote the output of the random laser. As a result, a free transformation from the WGM laser to RL is successfully modulated by varying the pump power alone, thus providing two initial values for dual chaos image encryption. This work provides an in-depth understanding of a WGM-random inner-coupled cavity and promotes the application of a hybrid microcavity in the field of information security.

Aldehyde dehydrogenase 2 and PARP1 interaction modulates hepatic HDL biogenesis by LXRα-mediated ABCA1 expression.

HDL cholesterol (HDL-C) predicts risk of cardiovascular disease (CVD), but the factors regulating HDL are incompletely understood. Emerging data link CVD risk to decreased HDL-C in 8% of the world population and 40% of East Asians who carry an SNP of aldehyde dehydrogenase 2 (ALDH2) rs671, responsible for alcohol flushing syndrome; however, the underlying mechanisms remain unknown. We found significantly decreased HDL-C with increased hepatosteatosis in ALDH2-KO (AKO), ALDH2/LDLR-double KO (ALKO), and ALDH2 rs671-knock-in (KI) mice after consumption of a Western diet. Metabolomics identified ADP-ribose as the most significantly increased metabolites in the ALKO mouse liver. Moreover, ALDH2 interacted with poly(ADP-ribose) polymerase 1 (PARP1) and attenuated PARP1 nuclear translocation to downregulate poly(ADP-ribosyl)ation of liver X receptor α (LXRα), leading to an upregulation of ATP-binding cassette transporter A1 (ABCA1) and HDL biogenesis. Conversely, AKO or ALKO mice exhibited lower HDL-C with ABCA1 downregulation due to increased nuclear PARP1 and upregulation of LXRα poly(ADP-ribosyl)ation. Consistently, PARP1 inhibition rescued ALDH2 deficiency-induced fatty liver and elevated HDL-C in AKO mice. Interestingly, KI mouse or human liver tissues showed ABCA1 downregulation with increased nuclear PARP1 and LXRα poly(ADP-ribosyl)ation. Our study uncovered a key role of ALDH2 in HDL biogenesis through the LXRα/PARP1/ABCA1 axis, highlighting a potential therapeutic strategy in CVD.

One half-scan dual-energy CT imaging using the Dual-domain Dual-way Estimated Network (DoDa-Net) model.

BACKGROUND: Compared with single-energy computed tomography (CT), dual-energy CT (DECT) can distinguish materials better. However, most DECT reconstruction theories require two full-scan projection datasets of different energies, and this requirement is hard to meet, especially for cases where a physical blockage disables a full circular rotation. Thus, it is critical to relax the requirements of data acquisition to promote the application of DECT. METHODS: A flexible one half-scan DECT scheme is proposed, which acquires two projection datasets on two-quarter arcs (one for each energy). The limited-angle problem of the one half-scan DECT scheme can be solved by a reconstruction method. Thus, a dual-domain dual-way estimation network called DoDa-Net is proposed by utilizing the ability of deep learning in non-linear mapping. Specifically, the dual-way mapping Generative Adversarial Network (DM-GAN) was first designed to mine the relationship between two different energy projection data. Two half-scan projection datasets were obtained, the data of which was twice that of the original projection dataset. Furthermore, the data transformation from the projection domain to the image domain was realized by the total variation (TV)-based method. In addition, the image processing network (Im-Net) was employed to optimize the image domain data. RESULTS: The proposed method was applied to a digital phantom and real anthropomorphic head phantom data to verify its effectiveness. The reconstruction results of the real data are encouraging and prove the proposed method's ability to suppress noise while preserving image details. Also, the experiments conducted on simulated data show that the proposed method obtains the closest results to the ground truth among the comparison methods. For low- and high-energy reconstruction, the peak signal-to-noise ratio (PSNR) of the proposed method is as high as 40.3899 and 40.5573 dB, while the PSNR of other methods is lower than 36.5200 dB. Compared with FBP, TV, and other GAN-based methods, the proposed method reduces root mean square error (RMSE) by, respectively, 0.0124, 0.0037, and 0.0016 for low-energy reconstruction, and 0.0102, 0.0028, and 0.0015 for high-energy reconstruction. CONCLUSIONS: The developed DoDa-Net model for the proposed one half-scan DECT scheme consists of two stages. In stage one, DM-GAN is used to realize the dual map of projection data. In stage two, the TV-based method is employed to transform the data from the projection domain to the image domain. Furthermore, the reconstructed image is processed by the Im-Net. According to the experimental results of qualitative and quantitative evaluation, the proposed method has advantages in detail preservation, indicating the potential of the proposed method in one half-scan DECT reconstruction.

Heavy-Atom-Free Room-Temperature Phosphorescent Rylene Imide for High-Performing Organic Photovoltaics.

Organic phosphorescence, originating from triplet excitons, has potential for the development of new generation of organic optoelectronic materials. Herein, two heavy-atom-free room-temperature phosphorescent (RTP) electron acceptors with inherent long lifetime triplet exctions are first reported. These two 3D-fully conjugated rigid perylene imide (PDI) multimers, as the best nonfullerene wide-bandgap electron acceptors, exhibit a significantly elevated T1 of ≈2.1 eV with a room-temperature phosphorescent emission (τ = 66 µs) and a minimized singlet-triplet splitting as low as ≈0.13 eV. The huge spatial congestion between adjacent PDI skeleton endows them with significantly modified electronic characteristics of S1 and T1 . This feature, plus with the fully-conjugated rigid molecular configuration, balances the intersystem crossing rate and fluorescence/phosphorescence rates, and therefore, elevating ET1 to ≈2.1 from 1.2 eV for PDI monomer. Meanwhile, the highly delocalized feature enables the triplet charge-transfer excitons at donor-acceptor interface effectively dissociate into free charges, endowing the RTP electron acceptor based organic solar cells (OSCs) with a high internal quantum efficiency of 84% and excellent charge collection capability of 94%. This study introduces an alternative strategy for designing PDI derivatives with high-triplet state-energy and provides revelatory insights into the fundamental electronic characteristics, photophysical mechanism, and photo-to-current generation pathway.