One-step inverse generation network for sparse-view dual-energy CT reconstruction and material imaging.

Objective.Sparse-view dual-energy spectral computed tomography (DECT) imaging is a challenging inverse problem. Due to the incompleteness of the collected data, the presence of streak artifacts can result in the degradation of reconstructed spectral images. The subsequent material decomposition task in DECT can further lead to the amplification of artifacts and noise.Approach.To address this problem, we propose a novel one-step inverse generation network (OIGN) for sparse-view dual-energy CT imaging, which can achieve simultaneous imaging of spectral images and materials. The entire OIGN consists of five sub-networks that form four modules, including the pre-reconstruction module, the pre-decomposition module, and the following residual filtering module and residual decomposition module. The residual feedback mechanism is introduced to synchronize the optimization of spectral CT images and materials.Main results.Numerical simulation experiments show that the OIGN has better performance on both reconstruction and material decomposition than other state-of-the-art spectral CT imaging algorithms. OIGN also demonstrates high imaging efficiency by completing two high-quality imaging tasks in just 50 seconds. Additionally, anti-noise testing is conducted to evaluate the robustness of OIGN.Significance.These findings have great potential in high-quality multi-task spectral CT imaging in clinical diagnosis.

Prior image-based generative adversarial learning for multi-material decomposition in photon counting computed tomography.

BACKGROUND: Photon counting detector computed tomography (PCD-CT) is a novel promising technique providing higher spatial resolution, lower radiation dose and greater energy spectrum differentiation, which create more possibilities to improve image quality. Multi-material decomposition is an attractive application for PCD-CT to identify complicated materials and provide accurate quantitative analysis. However, limited by the finite photon counting rate in each energy window of photon counting detector, the noise problem hinders the decomposition of high-quality basis material images. METHODS: To address this issue, an end-to-end multi-material decomposition network based on prior images is proposed in this paper. First, the reconstructed images corresponding to the full spectrum with less noise are introduced as prior information to improve the overall signal-to-noise ratio of the data. Then, a generative adversarial network is designed to mine the relationship between reconstructed images and basis material images based on the information interaction of material decomposition. Furthermore, a weighted edge loss is introduced to adapt to the structural differences of different basis material images. RESULTS: To verify the performance of the proposed method, simulation and real studies are carried out. In simulation study of structured fibro-glandular tissue model, the results show that the proposed method decreased the root mean square error by 67 % and 26 % on adipose, 66 % and 28 % on fibroglandular, 52 % and 8 % on calcification, compared to butterfly network and dual interactive Wasserstein generative adversarial network. CONCLUSION: Experimentally, the proposed method shows certain advantages over other methods on noise suppression effect, detail retention ability and decomposition accuracy.

Semantic contrast with uncertainty-aware pseudo label for lumbar semi-supervised classification.

BACKGROUND: Lumbar disc herniation (LDH) is a prevalent spinal disease that can result in severe pain, with Magnetic resonance imaging (MRI) serving as a commonly diagnostic tool. However, annotating numerous MRI images, necessary for deep learning based LDH diagnosis, can be challenging and labor-intensive. Semi-supervised learning, mainly utilizing pseudo labeling and consistency regularization, can leverage limited labeled images and abundant unlabeled images. However, consistency regularization solely focuses on maintaining the semantic consistency of transformed unlabeled data but fails to utilize the semantic information from labeled data to guide the unlabeled data, and additionally, pseudo labeling is prone to confirmation bias. METHOD: We propose SeCoFixMatch, an innovative approach that seamlessly integrates semantic contrast and uncertainty-aware pseudo labeling into semi-supervised learning. Semantic contrast constraints the semantic consistency between labeled and unlabeled images. Pseudo labels are generated by combining predictive confidence and uncertainty, with uncertainty computing by optimizing the Kullback-Leibler (KL) loss between predictive and target Dirichlet distribution. RESULTS: Comparison with other semi-supervised models and ablation experiment with varying labeled data demonstrate the effectiveness and generalization of proposed model. Notably, SeCoFixMatch, trained with just 40 labels, outperforms the baseline model trained with 200 labels, reducing the annotation effort by a remarkable 80%. CONCLUSIONS: Proposed pseudo labeling algorithm generates more precise pseudo labels for semantic contrastive learning and semantic contrastive learning facilitates better feature representation, thereby further improving the prediction accuracy of pseudo label. The mutual reinforcement of pseudo labeling and semantic contrast constraints boosts the performance of semi-supervised algorithm.

MISD-IR: material-image subspace decomposition-based iterative reconstruction with spectrum estimation for dual-energy computed tomography.

Background: Dual-energy computed tomography (DECT) is a promising technique, which can provide unique capability for material quantification. The iterative reconstruction of material maps requires spectral information and its accuracy is affected by spectral mismatch. Simultaneously estimating the spectra and reconstructing material maps avoids extra workload on spectrum estimation and the negative impact of spectral mismatch. However, existing methods are not satisfactory in image detail preservation, edge retention, and convergence rate. The purpose of this paper was to mine the similarity between the reconstructed images and the material images to improve the imaging quality, and to design an effective iteration strategy to improve the convergence efficiency. Methods: The material-image subspace decomposition-based iterative reconstruction (MISD-IR) with spectrum estimation was proposed for DECT. MISD-IR is an optimized model combining spectral estimation and material reconstruction with fast convergence speed and promising noise suppression capability. We proposed to reconstruct the material maps based on extended simultaneous algebraic reconstruction techniques and estimation of the spectrum with model spectral. To stabilize the iteration and alleviate the influence of errors, we introduced a weighted proximal operator based on the block coordinate descending algorithm (WP-BCD). Furthermore, the reconstructed computed tomography (CT) images were introduced to suppress the noise based on subspace decomposition, which relies on non-local regularization to prevent noise accumulation. Results: In numerical experiments, the results of MISD-IR were closer to the ground truth compared with other methods. In real scanning data experiments, the results of MISD-IR showed sharper edges and details. Compared with other one-step iterative methods in the experiment, the running time of MISD-IR was reduced by 75%. Conclusions: The proposed MISD-IR can achieve accurate material decomposition (MD) without known energy spectrum in advance, and has good retention of image edges and details. Compared with other one-step iterative methods, it has high convergence efficiency.

Broad-band-enhanced plasmonic random laser in silver nanostar arrays.

As a novel optical device, the plasmonic random laser has unique working principle and emission characteristics. However, the simultaneous enhancement of absorption and emission by plasmons is still a problem. In this paper, we propose a broad-band-enhanced plasmonic random laser. Two-dimensional silver (Ag) nanostar arrays were prepared using a bottom-up method with the assistance of self-assembled nanosphere templates. The plasmon resonance of Ag nanostars contributes to the pump light absorption and photoluminescence (PL) of RhB. Coherent random lasing was achieved in RhB@PVA film based on localized surface plasmon resonance (SPR) dual enhancement and scattering feedback of Ag nanostars. Ag nanostars prepared with different nanosphere diameters affect the laser emission wavelength. In addition, the random laser device achieves wavelength tunability on a flexible substrate under mechanical external force.

Ultrastrong and ductile medium-entropy alloys via hierarchical ordering.

Long-range ordered phases in most high-entropy and medium-entropy alloys (HEAs/MEAs) exhibit poor ductility, stemming from their brittle nature of complex crystal structure with specific bonding state. Here, we propose a design strategy to severalfold strengthen a single-phase face-centered cubic (fcc) Ni2CoFeV MEA by introducing trigonal κ and cubic L12 intermetallic phases via hierarchical ordering. The tri-phase MEA has an ultrahigh tensile strength exceeding 1.6 GPa and an outstanding ductility of 30% at room temperature, which surpasses the strength-ductility synergy of most reported HEAs/MEAs. The simultaneous activation of unusual dislocation multiple slip and stacking faults (SFs) in the κ phase, along with nano-SF networks, Lomer-Cottrell locks, and high-density dislocations in the coupled L12 and fcc phases, contributes to enhanced strain hardening and excellent ductility. This work offers a promising prototype to design super-strong and ductile structural materials by harnessing the hierarchical ordered phases.

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.

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 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.

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.

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.

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.

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.

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.

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.

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.