Background Artifacts Research Articles

Structure-dependent amplification for denoising and background correction in Fourier ptychographic microscopy.

Fourier Ptychographic Microscopy (FPM) allows high resolution imaging using iterative phase retrieval to recover an estimate of the complex object from a series of images captured under oblique illumination. FPM is particularly sensitive to noise and uncorrected background signals as it relies on combining information from brightfield and noisy darkfield (DF) images. In this article we consider the impact of different noise sources in FPM and show that inadequate removal of the DF background signal and associated noise are the predominant cause of artefacts in reconstructed images. We propose a simple solution to FPM background correction and denoising that outperforms existing methods in terms of image quality, speed and simplicity, whilst maintaining high spatial resolution and sharpness of the reconstructed image. Our method takes advantage of the data redundancy in real space within the acquired dataset to boost the signal-to-background ratio in the captured DF images, before optimally suppressing background signal. By incorporating differentially denoised images within the classic FPM iterative phase retrieval algorithm, we show that it is possible to achieve efficient removal of background artefacts without suppression of high frequency information. The method is tested using simulated data and experimental images of thin blood films, bone marrow and liver tissue sections. Our approach is non-parametric, requires no prior knowledge of the noise distribution and can be directly applied to other hardware platforms and reconstruction algorithms making it widely applicable in FPM.

Assessing the Hemodynamics in Residual Cavities of Intracranial Aneurysm after Coil Embolization with Combined Computational Flow Dynamics and Silent Magnetic Resonance Angiography

Background and PurposeMetal artifacts limit computational fluid dynamics analysis after coil embolization. Silent magnetic resonance angiography reduces metal artifacts and improves visualization of the residual cavity of coil-embolized aneurysms. This study investigated the flow dynamics of the residual cavity after coil embolization using silent magnetic resonance angiography and computational fluid dynamics to elucidate the hemodynamic characteristics of recanalization. MethodsTwenty internal carotid-posterior communicating aneurysm cases treated with coil embolization and without stent assistance were followed up (mean±standard deviation, 13.0±6.1 months) and assessed using silent magnetic resonance angiography. The hemodynamic characteristics of the residual cavities in both types of aneurysms were compared between neck remnants, which persisted for >12 months (NR group), and those treated with coil compaction-induced body filling (BF group). Computational fluid dynamics analysis of each aneurysm was performed using morphological data obtained from silent magnetic resonance angiography. Pressure, pressure difference, normalized wall shear stress, and flow velocity were measured. ResultsThe residual cavity was well-visualized using silent magnetic resonance angiography and compared with those imaged using conventional time-of-flight magnetic resonance angiography, and eight internal carotid-posterior communicating aneurysms with neck remnants and body filling were investigated. The maximum pressure area was localized to the aneurysm wall in the NR group (n=4) and to sides of the coil surface in the BF group (n=4). No significant differences were observed for each hemodynamic parameter. ConclusionsCombination of silent magnetic resonance angiography and computational fluid dynamics helps to understand the hemodynamic characteristics of residual cavity in coil- embolized aneurysms. The flow-impingement zone at the coil surface (maximum pressure area) may influence the risk for future coil compaction.

A high-throughput imaging and quantification pipeline for the EVOS imaging platform.

Self-contained imaging systems are versatile instruments that are becoming a staple in cell culture laboratories. Many of these machines possess motorized stages and on-stage incubators that permit programmable imaging of live cells that make them a sensible tool for high-throughput applications. The EVOS imaging system is such a device and is capable of scanning multi-well dishes and stitching together multiple adjacent fields to produce coherent individual images of each well. Automated batch analysis and quantification of these tiled images does however require off-loading files to other software platforms. Our initial attempts to quantify tiled images captured on an EVOS device was plagued by some expected—and other unforeseeable—issues that arose at nearly every stage of analysis. These included: high background, illumination and stitching artifacts, low contrast, noise, focus inconsistencies, and image distortion—all of which negatively impacted processing efficiency. We have since overcome these obstacles and have created a rigorous cell counting pipeline for analyzing images captured by the EVOS scan function. We present development and optimization of this automated pipeline and submit it as an effective and facile tool for accurately counting cells from tiled images.

A new energy density calculation method for reservoir delineation using seismic low‐frequency signal

ABSTRACTIn comparison to high‐frequency signals, low‐frequency seismic signals suffer less from scattering and intrinsic attenuation during wave propagation, penetrate deeper strata and thus can provide more energy information related to the hydrocarbon reservoirs. Based on the asymptotic representation for the frequency‐dependent reflections in the fluid‐saturated pore‐elastic media, we first derive a novel equation of the reservoir energy density and present an efficient workflow to calculate the reservoir energy density using low‐frequency seismic data. Then, within a low‐frequency range (from 1 to 30 Hz), we construct an objective function to determine the optimal frequency, using the energy densities calculated from the post‐stack seismic traces close to the wells. Next, we can calculate the reservoir energy density using the instantaneous spectra of optimal frequency at the low‐frequency end of the seismic spectrum. Tests on examples for synthetic and field data demonstrate that the proposed reservoir energy density can produce high‐quality images for the fluid‐saturated reservoirs, and it produces less background artefacts caused by elastic layers. This method provides a new way to detect the location of hydrocarbon reservoirs and characterize their spatial distribution.

Effects of high relative humidity and dry purging on VOCs obtained during breath sampling on common sorbent tubes

Offline breath analysis by thermal desorption gas chromatography mass spectrometry (TD-GC-MS) requires the use of sorbent traps to concentrate and store volatile compounds. The selection of which sorbent to use and best practices for managing high relative humidity are important considerations to allow for reproducible, untargeted, biomarker discovery in water saturated breath samples. This work aims to assess three commonly used sorbent materials for their use in breath volatile sampling and determine how the high relative humidity inherent in such samples effects the capture of volatile compounds of interest. TenaxGR, TenaxTA/Carbograph1TD and TenaxTA/Carbograph5TD tubes were selected as they are the most commonly used sorbents in the breath sampling literature. The recovery of 29 compounds in a standard mix loaded using high humidity gas was tested for each sorbent and compared to loading in dry gas. Water retention and dry purge rates were determined for each sorbent for 500 ml and 1000 ml breath collections. Finally, breath samples were collected simultaneously on to each sorbent type using the ReCIVA and analysed by TD-GC-MS. All three sorbents exhibited acceptable reproducibility when loaded with the standard mix in dry gas (RSD < 10%). Loading the standard mix in humid gas led to reduced recovery of compounds based on their chemical properties. Dry purging performance for each sorbent material was assessed and was shown to be 1.14, 1.13 and 0.89 mg H2O min−1 for TenaxGR, TenaxTA/Carbograph1TD and TenaxTA/Carbograph5TD respectively when flushed with 50 ml min−1 of N2. A comparison of breath profiles on different sorbents showed differences in background artefacts (sulfur dioxide, cyclopenten-1-one and 3-nonene) and endogenous breath compounds (2-methyl-furan and furfural). This work demonstrates that high relative humidity during sampling reduces the ability of sorbent tubes to capture volatile compounds and could impact method detection limits during breath sampling. Sufficient water to impair accurate analysis was retained on all tubes. Minimal differences were observed between sorbent materials when used to sample breath, however, suggestions are provided for sorbent selection for future studies.

Second-order optimized regularized structured illumination microscopy (sorSIM) for high-quality and rapid super resolution image reconstruction with low signal level

Structured illumination microscopy (SIM) is a widely used super resolution imaging technique that can down-modulate a sample's high-frequency information into objective recordable frequencies to enhance the resolution below the diffraction limit. However, classical SIM image reconstruction methods often generate poor results under low illumination conditions, which are required for reducing photobleaching and phototoxicity in cell imaging experiments. Although denoising methods or auxiliary items improved SIM image reconstruction in low signal level situations, they still suffer from decreased reconstruction quality and significant background artifacts, inevitably limiting their practical applications. In order to improve the reconstruction quality, second-order optimized regularized SIM (sorSIM) is designed specifically for image reconstruction in low signal level situations. In sorSIM, a second-order regularization term is introduced to suppress noise effect, and the penalty factor in this term is selected to optimize the resolution enhancement and noise resistance. Compared to classical SIM image reconstruction algorithms as well as to those previously used in low illumination cases, the proposed sorSIM provides images with enhanced resolution and fewer background artifacts. Therefore, sorSIM can be a potential tool for high-quality and rapid super resolution imaging, especially for low signal images.

Sequential vessel segmentation via deep channel attention network

Accurately segmenting contrast-filled vessels from X-ray coronary angiography (XCA) image sequence is an essential step for the diagnosis and therapy of coronary artery disease. However, developing automatic vessel segmentation is particularly challenging due to the overlapping structures, low contrast and the presence of complex and dynamic background artifacts in XCA images. This paper develops a novel encoder–decoder deep network architecture which exploits the several contextual frames of 2D+t sequential images in a sliding window centered at current frame to segment 2D vessel masks from the current frame. The architecture is equipped with temporal–spatial feature extraction in encoder stage, feature fusion in skip connection layers and channel attention mechanism in decoder stage. In the encoder stage, a series of 3D convolutional layers are employed to hierarchically extract temporal–spatial features. Skip connection layers subsequently fuse the temporal–spatial feature maps and deliver them to the corresponding decoder stages. To efficiently discriminate vessel features from the complex and noisy backgrounds in the XCA images, the decoder stage effectively utilizes channel attention blocks to refine the intermediate feature maps from skip connection layers for subsequently decoding the refined features in 2D ways to produce the segmented vessel masks. Furthermore, Dice loss function is implemented to train the proposed deep network in order to tackle the class imbalance problem in the XCA data due to the wide distribution of complex background artifacts. Extensive experiments by comparing our method with other state-of-the-art algorithms demonstrate the proposed method’s superior performance over other methods in terms of the quantitative metrics and visual validation. To facilitate the reproductive research in XCA community, we publicly release our dataset and source codes at https://github.com/Binjie-Qin/SVS-net.

A novel virtual barcode strategy for accurate panel-wide variant calling in circulating tumor DNA

BackgroundHybrid capture-based next-generation sequencing of DNA has been widely applied in the detection of circulating tumor DNA (ctDNA). Various methods have been proposed for ctDNA detection, but low-allelic-fraction (AF) variants are still a great challenge. In addition, no panel-wide calling algorithm is available, which hiders the full usage of ctDNA based ‘liquid biopsy’. Thus, we developed the VBCALAVD (Virtual Barcode-based Calling Algorithm for Low Allelic Variant Detection) in silico to overcome these limitations.ResultsBased on the understanding of the nature of ctDNA fragmentation, a novel platform-independent virtual barcode strategy was established to eliminate random sequencing errors by clustering sequencing reads into virtual families. Stereotypical mutant-family-level background artifacts were polished by constructing AF distributions. Three additional robust fine-tuning filters were obtained to eliminate stochastic mutant-family-level noises. The performance of our algorithm was validated using cell-free DNA reference standard samples (cfDNA RSDs) and normal healthy cfDNA samples (cfDNA controls). For the RSDs with AFs of 0.1, 0.2, 0.5, 1 and 5%, the mean F1 scores were 0.43 (0.25~0.56), 0.77, 0.92, 0.926 (0.86~1.0) and 0.89 (0.75~1.0), respectively, which indicates that the proposed approach significantly outperforms the published algorithms. Among controls, no false positives were detected. Meanwhile, characteristics of mutant-family-level noise and quantitative determinants of divergence between mutant-family-level noises from controls and RSDs were clearly depicted.ConclusionsDue to its good performance in the detection of low-AF variants, our algorithm will greatly facilitate the noninvasive panel-wide detection of ctDNA in research and clinical settings. The whole pipeline is available at https://github.com/zhaodalv/VBCALAVD.

Machine-learning-assisted insight into spin ice Dy2Ti2O7

Complex behavior poses challenges in extracting models from experiment. An example is spin liquid formation in frustrated magnets like Dy2Ti2O7. Understanding has been hindered by issues including disorder, glass formation, and interpretation of scattering data. Here, we use an automated capability to extract model Hamiltonians from data, and to identify different magnetic regimes. This involves training an autoencoder to learn a compressed representation of three-dimensional diffuse scattering, over a wide range of spin Hamiltonians. The autoencoder finds optimal matches according to scattering and heat capacity data and provides confidence intervals. Validation tests indicate that our optimal Hamiltonian accurately predicts temperature and field dependence of both magnetic structure and magnetization, as well as glass formation and irreversibility in Dy2Ti2O7. The autoencoder can also categorize different magnetic behaviors and eliminate background noise and artifacts in raw data. Our methodology is readily applicable to other materials and types of scattering problems.

Automated standardization of images of Drosophila embryos

Modeling expression patterns of Drosophila, in space and time, plays a critical role to understand the development of multicellular organisms. In confocal microscopy, to produce precise quantitative data it is frequently necessary to process and analyze large amounts of digital images. Automatic preprocessing is a crucial step in this scenario, essential to standardize significant features such as orientation, size, position, direction, lighting condition and texture of embryo images. Even though a lot of efforts have been made, a robust embryo standardization strategy is still needed. In this paper, we propose the method Embrystandar. It is designed to remove background artifacts and standardize the direction and orientation of a Drosophila embryo through a sequence of automatic operations. To test its potential for large-scale image processing, Embrystandar was applied in different databases. It showed to be robust and precise, reaching more than 90% success rate.

Reconstruction of X-Ray Fluorescence Computed Tomography From Sparse-View Projections via L1-Norm Regularized EM Algorithm

X-ray fluorescence computed tomography (XFCT) as a molecular imaging modality can simultaneously identify the localization and quantify the concentration of high-atomic-number contrast agents such as gold nanoparticles (GNPs). Commonly used benchtop pencil-beam XFCT, consisting of a polychromatic x-ray source and a single-pixel spectrometer, suffers from long scanning time and high imaging dose. Sparse-view strategy benefits XFCT to reduce both scanning time and imaging dose. Nevertheless, its reconstruction undergoes ill-posedness induced by the compressive sampling. To preserve consistent imaging quality for sparse-view XFCT, we proposed an iterative Bayesian algorithm based on L1-norm constraint, wherein the L1-norm regularization is included in the one-step-late expectation maximization (OSL-EM) algorithm with regularization parameter determined based on L-curve criteria. The proposed algorithm was verified by imaging a 3-cm-diameter water phantom with 4 inserts containing GNP solutions with concentrations of 0.02, 0.04, 0.08, and 0.16 wt.%, on an in-house-developed dual-modality transmission CT and XFCT system. Different numbers (i.e. 36, 18, 9, and 6) of projection views were used for XFCT reconstruction, to evaluate the performance of various reconstruction algorithms. L1-regularized EM algorithm demonstrated the consistent robustness to suppress background artifacts and localize low-concentration GNPs (0.02 wt.%) with submillimeter accuracy, when the number of projection views reduces from 36 to 9. Moreover, our method’s potential for small tumor spare-view XFCT imaging was validated on a mouse surgically implanted with a 6-mm GNP target.

RETRACTED: Knockdown PEG10 deteriorates H2O2-injury of PC-12 cells by targeting miR-34a-5p/TLX

This article has been retracted: please see Elsevier Policy on Article Withdrawal (https://www.elsevier.com/about/our-business/policies/article-withdrawal). This article has been retracted at the request of the Editor-in-Chief. As originally reported in https://pubpeer.com/publications/9D9EBE8EAD0041A196D37E10E1BF18, Fig. 6I shows evidence of data manipulation, visible as patterns of repeated background artifacts in independent western blots that are unlikely to be the result of serendipity. The authors were contacted to investigate further the validity of these allegations and to provide the raw data but the journal received no response and therefore the Editor-in-Chief decided to retract the article.

Comprehensive longitudinal study of epigenetic mutations in aging

BackgroundThe role of DNA methylation in aging has been widely studied. However, epigenetic mutations, here defined as aberrant methylation levels compared to the distribution in a population, are less understood. Hence, we investigated longitudinal accumulation of epigenetic mutations, using 994 blood samples collected at up to five time points from 375 individuals in old ages.ResultsWe verified earlier cross-sectional evidence on the increase of epigenetic mutations with age, and identified important contributing factors including sex, CD19+ B cells, genetic background, cancer diagnosis, and technical artifacts. We further classified epigenetic mutations into High/Low Methylation Outliers (HMO/LMO) according to their changes in methylation, and specifically studied methylation sites (CpGs) that were prone to mutate (frequently mutated CpGs). We validated four epigenetically mutated CpGs using pyrosequencing in 93 samples. Furthermore, by using twins, we concluded that the age-related accumulation of epigenetic mutations was not related to genetic factors, hence driven by stochastic or environmental effects.ConclusionsHere we conducted a comprehensive study of epigenetic mutation and highlighted its important role in aging process and cancer development.

RETRACTED: Curcumin inhibits proliferation and soluble collagen synthesis of NIH/3T3 cell line by modulation of miR-29a and via ERK1/2 and β-catenin pathways

This article has been retracted: please see Elsevier Policy on Article Withdrawal (https://www.elsevier.com/about/our-business/policies/article-withdrawal). This article has been retracted at the request of the Editor-in-Chief. As originally reported in https://pubpeer.com/publications/F4F79D78FA1249D847FBB6EA4B3381, Fig. 4E shows evidence of data manipulation, visible as patterns of repeated background artifacts in independent western blots that are unlikely to be the result of serendipity. The authors were contacted to investigate further the validity of these allegations and to provide the raw data but the journal received no response and therefore the Editor-in-Chief decided to retract the article.

Self-Motion-Assisted Tensor Completion Method for Background Initialization in Complex Video Sequences.

The background Initialization (BI) problem has attracted the attention of researchers in different image/video processing fields. Recently, a tensor-based technique called spatiotemporal slice-based singular value decomposition (SS-SVD) has been proposed for background initialization. SS-SVD applies the SVD on the tensor slices and estimates the background from low-rank information. Despite its efficiency in background initialization, the performance of SS-SVD requires further improvement in the case of complex sequences with challenges such as stationary foreground objects (SFOs), illumination changes, low frame-rate, and clutter. In this paper, a self-motion-assisted tensor completion method is proposed to overcome the limitations of SS-SVD in complex video sequences and enhance the visual appearance of the initialized background. With the proposed method, the motion information, extracted from the sparse portion of the tensor slices, is incorporated with the low-rank information of SS-SVD to eliminate existing artifacts in the initiated background. Efficient blending schemes between the low-rank (background) and sparse (foreground) information of the tensor slices is developed for scenarios such as SFO removal, lighting variation processing, low frame-rate processing, crowdedness estimation, and best frame selection. The performance of the proposed method on video sequences with complex scenarios is compared with the top-ranked state-of-the-art techniques in the field of background initialization. The results not only validate the improved performance over the majority of the tested challenges but also demonstrate the capability of the proposed method to initialize the background in less computational time.

Vessel Segmentation of X-Ray Coronary Angiographic Image Sequence.

To facilitate the analysis and diagnosis of X-ray coronary angiography in interventional surgery, it is necessary to extract vessel from X-ray coronary angiography. However, vessel images of angiography suffer from low quality with large artefacts, which challenges the existing vascular technology. In this paper, we propose a ávessel framework to detect vessels and segment vessels in angiographic vessel data. In this framework, we develop a new matrix decomposition model with gradient sparse in the tensor representation. Then, the energy function with the input of the hierarchical vessel is used in vessel detection and vessel segmentation. Through experiments conducted on angiographic data, we have demonstrated the good performance of the proposed method in removing background structure. We evaluated our method for vessel detection and segmentation in different clinical settings, including LAO/RAO with cranial and caudal angulation, and showed its competitive results compared with eight state-of-the-art methods in terms of extensive qualitative and quantitative evaluation. Our method can remove a large number of background artefacts and obtain a better vascular structure, which has contributed to the clinical diagnosis of coronary artery diseases.

Identification of single nucleotide variants using position-specific error estimation in deep sequencing data

BackgroundTargeted deep sequencing is a highly effective technology to identify known and novel single nucleotide variants (SNVs) with many applications in translational medicine, disease monitoring and cancer profiling. However, identification of SNVs using deep sequencing data is a challenging computational problem as different sequencing artifacts limit the analytical sensitivity of SNV detection, especially at low variant allele frequencies (VAFs).MethodsTo address the problem of relatively high noise levels in amplicon-based deep sequencing data (e.g. with the Ion AmpliSeq technology) in the context of SNV calling, we have developed a new bioinformatics tool called AmpliSolve. AmpliSolve uses a set of normal samples to model position-specific, strand-specific and nucleotide-specific background artifacts (noise), and deploys a Poisson model-based statistical framework for SNV detection.ResultsOur tests on both synthetic and real data indicate that AmpliSolve achieves a good trade-off between precision and sensitivity, even at VAF below 5% and as low as 1%. We further validate AmpliSolve by applying it to the detection of SNVs in 96 circulating tumor DNA samples at three clinically relevant genomic positions and compare the results to digital droplet PCR experiments.ConclusionsAmpliSolve is a new tool for in-silico estimation of background noise and for detection of low frequency SNVs in targeted deep sequencing data. Although AmpliSolve has been specifically designed for and tested on amplicon-based libraries sequenced with the Ion Torrent platform it can, in principle, be applied to other sequencing platforms as well. AmpliSolve is freely available at https://github.com/dkleftogi/AmpliSolve.

High-attenuation artifact reduction in breast tomosynthesis using a novel reconstruction algorithm

PurposeTo assess the effect on reducing the out-of-plane artifacts from metal objects in breast tomosynthesis (BT) using a novel artifact-reducing reconstruction algorithm in specimen radiography. Methods and MaterialsThe study was approved by the Regional Ethical Review Board. BT images of 18 partial- and whole mastectomy specimens from women with breast cancer were acquired before and after a needle was inserted close to the lesion. The images were reconstructed using both a standard reconstruction algorithm, and a novel algorithm; the latter uses pre-segmentation to remove highly attenuating artifact-inducing objects from projection images before reconstruction. Images were separately reconstructed with and without segmentation, and combined into an artifact-reduced reconstruction. Standard and artifact-reduced BT-algorithms were compared visually and quantitatively using clinical images of mastectomy specimens and a physical anthropomorphic phantom. Six readers independently assessed the visibility of the lesion with and without artifact-reduction in a side-by-side comparison. A quantitative analysis was performed, comparing the signal-difference to background ratio (SDBR) and artifact spread function (ASF) between the two reconstruction methods. ResultsThe magnitude of out-of-plane artifacts was clearly reduced with the novel reconstruction compared to BT-images without artifact reduction. Lesion masking by artifacts was largely averted; tumour visibility was comparable to standard BT images without a needle. In 76 ± 8% (standard deviation) of cases overall, readers could confidently state needle location. The same figure was 94 ± 6% for whole mastectomy cases, compared to 62 ± 17% for partial mastectomies. With metal artifact reduction, SDBR increased by 97% in the phantom, and by 69% in the mastectomies. The artifact spread function was substantially narrower. ConclusionArtifact reduction in BT using a novel reconstruction method enables qualitatively and quantitatively improved clinical use of BT when metal artifacts can be a limiting factor such as in tomosynthesis-guided biopsy.

Chapter 4 - Filtering of neurophysiologic signals

Clinical neurophysiologic signals cover a broad range of frequencies. Filters help to emphasize waveforms that are of clinical or research interest and to mold their frequency characteristics to suit the purpose of the investigation. Some frequency content is obvious and well known, such as the alpha rhythm (8-11Hz) or spindles (12-14Hz) in the EEG. Other frequencies are not initially discriminable from background activity and require filtering in order to examine them, such as high-frequency oscillations (80-500Hz) in EEG and brainstem auditory evoked potentials (100-3000Hz). Often used to mitigate the effects of background noise or artifact, filters can be used specifically to attenuate unwanted frequencies, such as mains interference (50 or 60Hz) and electrode offset potential (<0.1Hz). For digital instrumentation, an antialiasing filter (below Nyquist) is always needed prior to sampling by the analog-to-digital converter. Once the signals are in the digital realm, sophisticated filtering operations can be carried out post hoc; but in order not to be misled, the neurophysiologist must always bear in mind the effect of filtering on the physiological waveform.

Optimizing the trainable B-COSFIRE filter for retinal blood vessel segmentation.

Segmentation of the retinal blood vessels using filtering techniques is a widely used step in the development of an automated system for diagnostic retinal image analysis. This paper optimized the blood vessel segmentation, by extending the trainable B-COSFIRE filter via identification of more optimal parameters. The filter parameters are introduced using an optimization procedure to three public datasets (STARE, DRIVE, and CHASE-DB1). The suggested approach considers analyzing thresholding parameters selection followed by application of background artifacts removal techniques. The approach results are better than the other state of the art methods used for vessel segmentation. ANOVA analysis technique is also used to identify the most significant parameters that are impacting the performance results (p-value ¡ 0.05). The proposed enhancement has improved the vessel segmentation accuracy in DRIVE, STARE and CHASE-DB1 to 95.47, 95.30 and 95.30, respectively.