Small Field Of View Research Articles

Seeking the growth of the first black hole seeds with JWST

ABSTRACT In this paper, we provide predictions for the black hole (BH) population that would be observable with planned JWST surveys at 5 ≤ z ≤ 15. We base our study on the recently developed Cosmic Archaeology Tool, which allows us to model BH seeds formation and growth while being consistent with the general population of active galactic nuclei (AGNs) and galaxies observed at 4 ≤ z ≤ 7. We find that JWST planned surveys will provide a complementary view on active BHs at z > 5, with JADES-Medium/-Deep being capable of detecting the numerous BHs that populate the faint-end of the distribution, COSMOS-Web sampling a large enough area to detect the rarest brightest systems, and CEERS/PRIMER bridging the gap between these two regimes. The relatively small field of view of the above surveys preferentially selects BHs with masses 6 ≤ Log(MBH/M⊙) < 8 at 7 ≤ z < 10, residing in relatively metal poor (Log(Z/Z⊙) ≥ −2) and massive (8 ≤ Log(M*/M⊙) < 10) galaxies. At z ≥ 10, only JADES-Deep will have the sensitivity to detect growing BHs with masses 4 ≤ Log(MBH/M⊙) < 6, hosted by more metal poor (−3 ≤ Log(Z/Z⊙) < −2) and less massive (6 ≤ Log(M*/M⊙) < 8) galaxies. In our model, the latter population corresponds to heavy BH seeds formed by the direct collapse of supermassive stars in their earliest phases of mass growth. Detecting these systems would provide invaluable insights on the nature and early growth of the first BH seeds.

Fast stitching method for multi-view images of cupping spots

Aiming at the problems of cupping machine camera with a small field of view, complete and high-definition cupping spot characteristics need to be monitored in real time, so as to effectively ensure that the cupping machine can accurately control the cupping time to avoid harm to the human body and extracting the complete back contour for automatic acupuncture point positioning, a fast stitching method for multi-view image of cupping spots is proposed. The present study uses linear transformation and Gaussian smoothing to preprocess the images to enhance image details, improve contrast, and reduce the influence of tank occlusion; Then, we combine the BRISK algorithm and the match factor to match the image and estimate the overlapping area through establishing a binary tree model so as to improve the efficiency of feature point detection and matching. Experimental results show that compared with the AutoStitch, the image stitched which is high definition has no obvious seams, ghosting, and distortion. What’s more, the algorithm in this paper is more efficient and the back contour is more complete. The algorithm in this paper has good real-time performance and the clarity of the cupping spots is high, which is conducive to the subsequent real-time detection of the cupping spots. At the same time, the back contour of the image stitched by the algorithm in this paper is complete, which is conducive to the subsequent automatic acupuncture point positioning.

The chemical compositions of multiple stellar populations in the globular cluster NGC 2808

ABSTRACT Pseudo two-colour diagrams or Chromosome maps (ChM) indicate that NGC 2808 host five different stellar populations. The existing ChMs have been derived by the Hubble Space Telescope photometry, and comprise of stars in a small field of view around the cluster centre. To overcome these limitations, we built a ChM with U, B, I photometry from ground-based facilities that disentangle the multiple stellar populations of NGC 2808 over a wider field of view. We used spectra collected by GIRAFFE@VLT in a sample of 70 red giant branch and seven asymptotic giant branch (AGB) stars to infer the abundances of C, N, O, Al, Fe, and Ni, which combined with literature data for other elements (Li, Na, Mg, Si, Ca, Sc, Ti, Cr, and Mn), and together with both the classical and the new ground-based ChMs, provide the most complete chemical characterization of the stellar populations in NGC 2808 available to date. As typical of the multiple population phenomenon in globular clusters, the light elements vary from one stellar population to another; whereas the iron peak elements show negligible variation between the different populations (at a level of ≲0.10 dex). Our AGB stars are also characterized by the chemical variations associated with the presence of multiple populations, confirming that this phase of stellar evolution is affected by the phenomenon as well. Intriguingly, we detected one extreme O-poor AGB star (consistent with a high He abundance), challenging stellar evolution models that suggest that highly He-enriched stars should avoid the AGB phase and evolve as AGB-manqué star.

Defocus Corrected Large Area Cryo-EM (DeCo-LACE) for label-free detection of molecules across entire cell sections.

A major goal of biological imaging is localization of biomolecules inside a cell. Fluorescence microscopy can localize biomolecules inside whole cells and tissues, but its ability to count biomolecules and accuracy of the spatial coordinates is limited by the wavelength of visible light. Cryo-electron microscopy (cryo-EM) provides highly accurate position and orientation information of biomolecules but is often confined to small fields of view inside a cell, limiting biological context. In this study, we use a new data-acquisition scheme called Defocus-Corrected Large-Area cryo-EM (DeCo-LACE) to collect high-resolution images of entire sections (100- to 250-nm-thick lamellae) of neutrophil-like mouse cells, representing 1-2% of the total cellular volume. We use 2D template matching (2DTM) to determine localization and orientation of the large ribosomal subunit in these sections. These data provide maps of ribosomes across entire sections of mammalian cells. This high-throughput cryo-EM data collection approach together with 2DTM will advance visual proteomics and provide biological insight that cannot be obtained by other methods.

Deep-Learning Image Stabilization for Adaptive Optics Ophthalmoscopy

An adaptive optics scanning laser ophthalmoscope (AOSLO) has the characteristics of a high resolution and a small field of view (FOV), which are greatly affected by eye motion. Continual eye motion will cause distortions both within the frame (intra-frame) and between frames (inter-frame). Overcoming eye motion and achieving image stabilization is the first step and is of great importance in image analysis. Although cross-correlation-based methods enable image registration to be achieved, the manual identification and distinguishing of images with saccades is required; manual registration has a high accuracy, but it is time-consuming and complicated. Some imaging systems are able to compensate for eye motion during the imaging process, but special hardware devices need to be integrated into the system. In this paper, we proposed a deep-learning-based algorithm for automatic image stabilization. The algorithm used the VGG-16 network to extract convolution features and a correlation filter to detect the position of reference in the next frame, and finally, it compensated for displacement to achieve registration. According to the results, the mean difference in the vertical and horizontal displacement between the algorithm and manual registration was 0.07 pixels and 0.16 pixels, respectively, with a 95% confidence interval of (−3.26 px, 3.40 px) and (−4.99 px, 5.30 px). The Pearson correlation coefficients for the vertical and horizontal displacements between these two methods were 0.99 and 0.99, respectively. Compared with cross-correlation-based methods, the algorithm had a higher accuracy, automatically removed images with blinks, and corrected images with saccades. Compared with manual registration, the algorithm enabled manual registration accuracy to be achieved without manual intervention.

Calculation of aberration fields for freeform imaging systems using field-dependent footprints on local tangent planes.

Aberration theory is a fundamental understanding of the optical aberrations and remains the best way to guide optical system design. The nodal aberration theory, which can be used to describe the aberration fields of freeform imaging systems, is limited by the small field of view (FOV) of the imaging system. In this paper, we propose a method to predict the induced aberration of Fringe Zernike terms with field-dependent footprints. The footprint of each field point is calculated in its corresponding local tangent plane of the optical surface; therefore, a more accurate prediction of the induced aberrations of Fringe Zernike terms can be achieved. Both the FOV and highly tilted architecture of freeform imaging systems are considered when calculating the footprints. Two examples are presented to verify the effectiveness of the proposed method, which we believe can provide good guidance for the design of freeform imaging systems with a relatively large FOV.

Identification of bird taxa species in ancient Egyptian mummies: Part 2, a qualitative evaluation of the utility of CT scanning and 3D printing

Ancient Egyptians believed that objects created in the image of a god, in either animal or hybrid form, from artificial or organic materials could act as a communication device, effectively enabling a dialogue between the devotee and the deity. The mummified remains of animals concealed within linen wrappings were popular votive offerings. These mummies are now investigated using non-destructive techniques such as three-dimensional Computed Tomography (CT). Using techniques developed in medicine it is possible to generate three-dimensional computer models of the bones within animal mummies from CT data. These models can be used to produce visualisations of the bones but can also be physically reproduced by 3D Printing. There are a number of parameters and decisions that can be made at both the scanning and 3D printing stages that can affect the quality of the final physical artefacts and how closely they resemble the real bones. This study aimed to explore the effects of different CT scanning Field of View, 3D printing processes and materials and qualitatively evaluate the extent to which the resulting 3D Printed replica bones accurately reproduced the identifiable features of real bones. Two different CT scanning sessions were conducted and 30 3D printed replica bones produced using three different processes, material extrusion, stereolithography and PolyJet modelling. Different materials were used to investigate the effects of colour and transparency. The replicas were provided to five experts in bird anatomy who were asked to evaluate the replicas and comment on their utility for species identification by the bone-in-hand method. The results showed that transparent materials were less useful than those with a uniform neutral colour. Replicas made with thinner layers were preferred due to their smoother surface. Overall, the PolyJet modelling replicas were ranked highest. The replicas made from higher resolution CT data (a smaller Field of View) produced superior replicas. This study provides a rigorous evaluation of the practice of producing 3D Printed replicas of animal anatomy from archaeological remains and has shown the utility of the practice for species identification.

Chromosome maps of globular clusters from wide-field ground-based photometry

ABSTRACT Hubble Space Telescope (HST) photometry is providing an extensive analysis of globular clusters (GCs). In particular, the pseudo-two-colour diagram dubbed 'chromosome map (ChM)’ allowed to detect and characterize their multiple populations with unprecedented detail. The main limitation of these studies is the small field of view of HST, which makes it challenging to investigate some important aspects of the multiple populations, such as their spatial distributions and the internal kinematics in the outermost cluster regions. To overcome this limitation, we analyse state-of-art wide-field photometry of 43 GCs obtained from ground-based facilities. We derived high-resolution reddening maps and corrected the photometry for differential reddening when needed. We use photometry in the U, B, and I bands to introduce the ΔcU, B, I versus ΔB, I ChM of red-giant branch (RGB) and asymptotic-giant branch stars. We demonstrate that this ChM, which is built with wide-band ground-based photometry, is an efficient tool to identify first- and second-generation stars (1G and 2G) over a wide field of view. To illustrate its potential, we derive the radial distribution of multiple populations in NGC 288 and infer their chemical composition. We present the ChMs of RGB stars in 29 GCs and detect a significant degree of variety. The fraction of 1G and 2G stars, the number of subpopulations, and the extension of the ChMs significantly change from one cluster to another. Moreover, the metal-poor and metal-rich stars of Type II GCs define distinct sequences in the ChM. We confirm the presence of extended 1G sequences.

The impact of cone beam computer tomography field of view on the precision of digital intra-oral scan registration for static computer-assisted implant surgery: A CBCT analysis.

Registration of intra-oral surface scans to cone beam computer tomography (CBCT) is critical in the digital workflow for static computer-aided implant surgery (sCAIS). This study aimed to assess the impact of CBCT field of view (FoV) on the precision of digital intra-oral scan registration. Cone beam computer tomography data and intra-oral scans from 20 patients were included. Small FoV CBCT's were created by digitally segmenting a large FoV into three sextants. Virtual implant planning was performed. Digital intra-oral scans were repeatedly registered onto their corresponding large and small FoV CBCT datasets. The distances and angulations between the matching implant positions of each repeated registration were used to determine the precision of the registration process. Wilcoxon Signed Rank Paired Tests were used to compare the differences between large FoV and small FoV. The threshold for statistical significance was set at p= .05. Differences in 3D implant position based on the registration precision between small FoV and large FoV present at both the implant entry point (0.37 ± 0.25 mm vs 0.35 ± 0.23 mm, p=.482) and implant tip (0.49 ± 0.34 mm vs 0.37 ± 0.24 mm, p < .001). Differences in overall angular precision were observed between small FOV and large FoV (1.43 ± 1.36° vs 0.51 ± 0.38°, p < .001). CBCT with a small FoV is accompanied by greater precision errors in intra-oral scan registration. However, when sufficient well-distributed teeth are visible in small FoV CBCT, the precision of digital intra-oral scan registration appears to be within clinically acceptable limits for sCAIS.

A SLAM METHOD FOR HANDHELD HEMISPHERICAL VIEW LASER SCANNING SYSTEM

Abstract. In view of the problem that some multi-line light detection and ranging (LiDAR) scans a small field of view in the vertical direction, a framework that uses an integrated handheld hemispherical view LiDAR and inertial measurement unit (IMU) scanning system for simultaneous localization and mapping (SLAM) is proposed. For the structural characteristics of the hemispherical view LiDAR scan lines, a ground segmentation pre-processing module based on seed points is designed. The segmented ground points are downsampled to eliminate redundant vertical constraints. The IMU data and the pre-processed point cloud are performed state estimation via a tightly coupled iterative Extended Kalman Filter (iEKF) to obtain the real-time poses. The detected loop closures provide global constraints for the point cloud map. The factor graph is used to process the back-end optimization incrementally to eliminate the accumulation error of the system. Data from diverse scenes are collected via a prototype system. Both qualitative and quantitative experiments are performed to prove the accuracy and performance of the framework. Experiments show that our framework outperforms the state-of-the-art SLAM methods for the hemispherical view LiDAR-IMU integrated scanning system.

Large field-of-view microlens array with low crosstalk and uniform angular resolution for tabletop integral imaging display

Tabletop autostereoscopic displays have the advantage of displaying realistic three-dimensional (3D) content. In particular, the Integral imaging (InIm) method is superior in tabletop displays, as it is possible to make the system small using microlens arrays (MLA). Off-the-shelf MLAs have a small field of view (FOV) and are unsuitable for tabletop displays where the half viewing angle is typically around 45 degrees. The MLA for tabletop display also needs to have uniform angular resolution and low crosstalk to display 3D content. In this paper, we designed a new MLA with a multilayer structure and a reversed lens shape. This lens array has a wide viewing angle of −58 degrees to +58 degrees, with uniform angular resolution and low crosstalk of less than 0.2. We implemented a tabletop display using our MLA and verified its properties experimentally. The misallocation error of the elemental image is experimentally measured and compensated for as well.

The Effect of Different Field of View Sizes on Contrast-to-Noise Ratio of Cone-Beam Computed Tomography Units: An In-Vitro Study.

Objectives: 'Field of view (FOV) size' affects the quality of radiographic images and the radiation dose received by patients. In cone-beam computed tomography (CBCT) FOV should be selected according to therapeutic purposes. While aiming for the highest diagnostic image quality, the radiation dose should be kept to a minimum to reduce the risk for patients. The purpose of this study was to assess the effect of different sizes of FOV on contrast-to-noise ratio (CNR) in five different CBCT units. Materials and Methods: In this experimental study, CBCT scans were taken from a dried human mandible containing a resin block fixed to the lingual cortex and a resin ring was used to simulate soft tissue during scans. Five CBCT units including, NewTom VGi, NewTom GiANO, Soredex SCANORA 3D, Planmeca ProMax, and Asahi Alphard 3030 were evaluated. Each unit had 3 to 5 different FOVs. Images were obtained and analyzed with ImageJ software and CNR was calculated in each image. ANOVA and T-test were used for statistical analysis (P<0.05). Results: Comparison among different FOVs of each unit showed significant CNR reductions in small FOVs (P<0.05). Similar FOV sizes of different CBCT devices were also compared and demonstrated significant differences (P<0.05). Conclusion: A direct relationship between FOV size and CNR was observed in all five CBCT units, but differences in exposure parameters of these units led to variable CNR in FOVs with similar sizes.

Uniformity and Sensitivity Measurements for Small Field of View Pixelated SPECT System with Coded Aperture

Uniformity and Sensitivity Measurements for Small Field of View Pixelated SPECT System with Coded Aperture

Label-free single-particle imaging approach for ultra-rapid detection of pathogenic bacteria in clinical samples

Rapid detection of pathogenic bacteria within a few minutes is the key to control infectious disease. However, rapid detection of pathogenic bacteria in clinical samples is quite a challenging task due to the complex matrix, as well as the low abundance of bacteria in real samples. Herein, we employ a label-free single-particle imaging approach to address this challenge. By tracking the scattering intensity variation of single particles in free solution, the morphological heterogeneity can be well identified with particle size smaller than the diffraction limit, facilitating the morphological identification of single bacteria from a complex matrix in a label-free manner. Furthermore, the manipulation of convection in free solution enables the rapid screening of low-abundance bacteria in a small field of view, which significantly improves the sensitivity of single-particle detection. As a proof of concept demonstration, we are able to differentiate the group B streptococci (GBS)-positive samples within 10 min from vaginal swabs without using any biological reagents. This is the most rapid and low-cost method to the best of our knowledge. We believe that such a single-particle imaging approach will find wider applications in clinical diagnosis and disease control due to its high sensitivity, rapidity, simplicity, and low cost.

Digital transformation in rock physics: Deep learning and data fusion

Rock physics plays an essential role in geophysical reservoir characterization. It aims to build a bridge between geophysical measurements and in-situ rock and fluid properties. With the advancement of microscopic imaging and computer science, rock physics is transitioning to the digital age. This is referred to as digital rock physics (DRP). DRP provides a nondestructive and efficient way to determine physical rock properties directly from digital images. Over the last decades, it has become a routine tool in reservoir characterization by complementing or replacing expensive and time-consuming laboratory measurements. With the emergence of deep learning, DRP has advanced significantly from image processing to physical simulation. This paper presents an application of deep learning in multiscale fusion of digital rock images. It aims to overcome the trade-off between image resolution and field of view (FoV) by integrating imaging data from multiple sources including (1) 3D microcomputed tomography images at micronscale with a large FoV and (2) 2D scanning electron microscopy images at nanoscale with a small FoV. The reconstructed image integrates information of microstructures at different scales and helps characterize heterogeneous porous rocks more accurately. It is helpful to improve the prediction accuracy of effective rock properties and to have deeper insight into physical processes at pore scale. Data fusion based on deep learning would unlock new pathways for geophysical characterization of porous rocks, with broad implications for various subsurface applications such as groundwater transport, enhanced oil recovery, and geologic carbon sequestration.

In Situ Analysis of Membrane-Protein Binding Kinetics and Cell-Surface Adhesion Using Plasmonic Scattering Microscopy.

Surface plasmon resonance microscopy (SPRM) is an excellent platform for in situ studying cell-substrate interactions. However, SPRM suffers from poor spatial resolution and small field of view. Herein, we demonstrate plasmonic scattering microscopy (PSM) by adding a dry objective on a popular prism-coupled surface plasmon resonance (SPR) system. PSM not only retains SPRM's high sensitivity and real-time analysis capability, but also provides ≈7 times higher spatial resolution and ≈70 times larger field of view than the typical SPRM, thus providing more details about membrane protein response to ligand binding on over 100 cells simultaneously. In addition, PSM allows quantifying the target movements in the axial direction with a high spatial resolution, thus allowing mapping adhesion spring constants for quantitatively describing the mechanical properties of the cell-substrate contacts. This work may offer a powerful and cost-effective strategy for upgrading current SPR products.

In Situ Analysis of Membrane‐Protein Binding Kinetics and Cell–Surface Adhesion Using Plasmonic Scattering Microscopy

Surface plasmon resonance microscopy (SPRM) is an excellent platform for in situ studying cell-substrate interactions. However, SPRM suffers from poor spatial resolution and small field of view. Herein, we demonstrate plasmonic scattering microscopy (PSM) by adding a dry objective on a popular prism-coupled surface plasmon resonance (SPR) system. PSM not only retains SPRM’s high sensitivity and real-time analysis capability, but also provides ~7 times higher spatial resolution and ~70 times larger field of view than the typical SPRM, thus providing more details about membrane protein response to ligand binding on over 100 cells simultaneously. In addition, PSM allows quantifying the target movements in the axial direction with a high spatial resolution, thus allowing mapping adhesion spring constants for quantitatively describing the mechanical properties of the cell-substrate contacts. This work may offer a powerful and cost-effective strategy for upgrading current SPR products.

ContransGAN: Convolutional Neural Network Coupling Global Swin-Transformer Network for High-Resolution Quantitative Phase Imaging with Unpaired Data.

Optical quantitative phase imaging (QPI) is a frequently used technique to recover biological cells with high contrast in biology and life science for cell detection and analysis. However, the quantitative phase information is difficult to directly obtain with traditional optical microscopy. In addition, there are trade-offs between the parameters of traditional optical microscopes. Generally, a higher resolution results in a smaller field of view (FOV) and narrower depth of field (DOF). To overcome these drawbacks, we report a novel semi-supervised deep learning-based hybrid network framework, termed ContransGAN, which can be used in traditional optical microscopes with different magnifications to obtain high-quality quantitative phase images. This network framework uses a combination of convolutional operation and multiheaded self-attention mechanism to improve feature extraction, and only needs a few unpaired microscopic images to train. The ContransGAN retains the ability of the convolutional neural network (CNN) to extract local features and borrows the ability of the Swin-Transformer network to extract global features. The trained network can output the quantitative phase images, which are similar to those restored by the transport of intensity equation (TIE) under high-power microscopes, according to the amplitude images obtained by low-power microscopes. Biological and abiotic specimens were tested. The experiments show that the proposed deep learning algorithm is suitable for microscopic images with different resolutions and FOVs. Accurate and quick reconstruction of the corresponding high-resolution (HR) phase images from low-resolution (LR) bright-field microscopic intensity images was realized, which were obtained under traditional optical microscopes with different magnifications.

FAST-LIO2: Fast Direct LiDAR-Inertial Odometry

This article presents FAST-LIO2: a fast, robust, and versatile LiDAR-inertial odometry framework. Building on a highly efficient tightly coupled iterated Kalman filter, FAST-LIO2 has two key novelties that allow fast, robust, and accurate LiDAR navigation (and mapping). The first one is directly registering raw points to the map (and subsequently update the map, i.e., mapping) without extracting features. This enables the exploitation of subtle features in the environment and, hence, increases the accuracy. The elimination of a hand-engineered feature extraction module also makes it naturally adaptable to emerging LiDARs of different scanning patterns; the second main novelty is maintaining a map by an incremental k-dimensional (k-d) tree data structure, incremental k-d tree ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">ikd-Tree</i> ), that enables incremental updates (i.e., point insertion and delete) and dynamic rebalancing. Compared with existing dynamic data structures (octree, R <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$^\ast$</tex-math></inline-formula> -tree, and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">nanoflann</i> k-d tree), <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">ikd-Tree</i> achieves superior overall performance while naturally supports downsampling on the tree. We conduct an exhaustive benchmark comparison in 19 sequences from a variety of open LiDAR datasets. FAST-LIO2 achieves consistently higher accuracy at a much lower computation load than other state-of-the-art LiDAR-inertial navigation systems. Various real-world experiments on solid-state LiDARs with small field of view are also conducted. Overall, FAST-LIO2 is computationally efficient (e.g., up to 100 Hz odometry and mapping in large outdoor environments), robust (e.g., reliable pose estimation in cluttered indoor environments with rotation up to 1000 deg/s), versatile (i.e., applicable to both multiline spinning and solid-state LiDARs, unmanned aerial vehicle (UAV) and handheld platforms, and Intel- and ARM-based processors), while still achieving a higher accuracy than existing methods. Our implementation of the system FAST-LIO2 and the data structure <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">ikd-Tree</i> are both open-sourced on Github.

Flat-field superoscillation metalens

Superoscillation metalenses have demonstrated promising prospects in breaking the theoretical diffraction limitations on the resolution of optical devices and systems. However, most reported superoscillation metalenses have a very small field of view of several tenths of a degree, which greatly limits their applications in imaging and microscopy. Therefore, it is of critical importance to achieve absolute high resolution by increasing the numerical apertures (NAs) of optical devices and systems. Unfortunately, similar to the case in traditional optics, it is challenging to realize a large field of view at high NA, especially in the superoscillation regime. To date, no attempt has been made to achieve flat-field focusing in the superoscillation regime, to our knowledge. Here, we demonstrate a high-NA superoscillation metalens with an entrance aperture stop, which is optimized for superoscillation performance with a comparatively large field of view. The proposed flat-field superoscillation metalens has an effective NA as large as 0.89 and achieves superoscillation focusing within a field of view of 9°. Such a superoscillation metalens may offer a promising way toward superoscillation imaging and fast-scanning label-free far-field superoscillation microscopy.