Particle Labels Research Articles

Viral capsid-mediated efficient cell labeling of micron-sized magnetic particles for highly sensitive long-term in vivo tumor cells MRI tracking

As a contrast agent, micron-sized iron oxide particles (MPIO) offer major advantages for monitoring tumor cells in vivo—applications crucial for studies of tumorigenesis and metastasis. However, labeling tumor cells with MPIO is often dependent on phagocytosis, which limits the labeling efficiency of the cells and thus affects in vivo tumor cell tracking. To address this issue, we prepared a virus-like particle (VLP) consisting of the SV40 VP1 protein modified on the surface of a MPIO (vMPIO). The vMPIO can enter cells via a pathway similar to that of viral infection (caveolae-dependent endocytosis pathway). This approach increased the number of vMPIO in individual labeled cells, which in turn enhances magnetic resonance imaging (MRI) signal intensity and the number of vMPIO in daughter cells. These vMPIO-labeled cells can be detected by MRI in vitro and in vivo even in very small numbers (≤10 cells). Furthermore, vMPIO-labeled cells were successfully applied to long-term tracing of tumor tissue and detection of early metastatic lesions. This VLP-mediated MPIO labeling strategy (vML) not only enables highly sensitive in vivo MRI but also introduces a useful technical tool for investigating tumor invasion and early metastasis.

Formulation and In-vivo Characterization of 177Lu-tin-colloid as a Radiosynovectomy Agent.

Arthritis is an inflammatory disorder that affects one or more joints of the body for various reasons, including autoimmune disorders, trauma, or infection. In many cases, traditional long-term treatment with various drug combinations (NSAIDs, diseasemodifying antirheumatic drugs, systemic corticosteroids, etc.) can provide relief, but many joints require additional local treatment. Radiosynovectomy (RSV) is an alternative method to current treatment options. Both the global supply shortage of 90Y in recent years and the increasing use of 177Lu-labeled radiopharmaceuticals in the field of nuclear medicine have made it possible to develop 177Lu-labeled microparticles and test them in small groups as RSV agents. This study aimed to develop the 177Lu labeled tin colloid formulation and demonstrate its invivo characterization. Particle size, shape, and labelling efficiency of the four formulations developed were determined. The formula with the highest labelling efficiency was selected for further studies. The quality of the formulation was evaluated based on radionuclidic, radiochemical, and microbial purity. In-vitro stability was evaluated by determining the labelling efficiency. In-vitro stability was tested in PBS and synovial fluid. The biological characterization was assessed using SPECT/CT after injecting the formulation into the normal knee joints of the rabbits. Aggregated colloidal particles were spherical with a particle size of <5 μm. Labelling efficiency and radiochemical purity were >95 and 97.65% (Rf=0.2), respectively. The formulation was stable in vitro for up to 72 hours, both in PBS and synovial fluid. The formulation was homogeneously distributed in the joint at 0 and 1 hour after injection, and radioactivity- related involvement and inguinal lymph node involvement due to possible leakage were not detected in the late period. No pyrogenic/allergic side effects were observed during this period. 177Lu-tin-colloid was successfully prepared under optimized reaction conditions with high binding efficiency and radiochemical purity. The radiolabeled colloid was found to be stable in-vitro both in PBS and synovial fluid at room temperature. Serial PCET/CT images revealed that the activity was completely retained within the synovial cavity, with no activity leakage out of the joint until 48 hours after the injection. With the support of the results from further clinical studies, it may be possible for the formulation to enter clinical use.

DaedalusData: Exploration, Knowledge Externalization and Labeling of Particles in Medical Manufacturing - A Design Study.

In medical diagnostics of both early disease detection and routine patient care, particle-based contamination of in-vitro diagnostics consumables poses a significant threat to patients. Objective data-driven decision-making on the severity of contamination is key for reducing patient risk, while saving time and cost in quality assessment. Our collaborators introduced us to their quality control process, including particle data acquisition through image recognition, feature extraction, and attributes reflecting the production context of particles. Shortcomings in the current process are limitations in exploring thousands of images, data-driven decision making, and ineffective knowledge externalization. Following the design study methodology, our contributions are a characterization of the problem space and requirements, the development and validation of DaedalusData, a comprehensive discussion of our study's learnings, and a generalizable framework for knowledge externalization. DaedalusData is a visual analytics system that enables domain experts to explore particle contamination patterns, label particles in label alphabets, and externalize knowledge through semi-supervised label-informed data projections. The results of our case study and user study show high usability of DaedalusData and its efficient support of experts in generating comprehensive overviews of thousands of particles, labeling of large quantities of particles, and externalizing knowledge to augment the dataset further. Reflecting on our approach, we discuss insights on dataset augmentation via human knowledge externalization, and on the scalability and trade-offs that come with the adoption of this approach in practice.

Sandwich Immunosensor Based on Particle Motion: How Do Reactant Concentrations and Reaction Pathways Determine the Time-Dependent Response of the Sensor?

To control and optimize the speed of a molecular biosensor, it is crucial to quantify and understand the mechanisms that underlie the time-dependent response of the sensor. Here, we study how the kinetic properties of a particle-based sandwich immunosensor depend on underlying parameters, such as reactant concentrations and the size of the reaction chamber. The data of the measured sensor responses could be fitted with single-exponential curves, with characteristic response times that depend on the analyte concentration and the binder concentrations on the particle and substrate. By comparing characteristic response times at different incubation configurations, the data clarifies how two distinct reaction pathways play a role in the sandwich immunosensor, namely, analyte binding first to particles and thereafter to the substrate, and analyte binding first to the substrate and thereafter to a particle. For a concrete biosensor design, we found that the biosensor is dominated by the reaction pathway where analyte molecules bind first to the substrate and thereafter to a particle. Within this pathway, the binding of a particle to the substrate-bound analyte dominates the sensor response time. Thus, the probability of a particle interacting with the substrate was identified as the main direction to improve the speed of the biosensor while maintaining good sensitivity. We expect that the developed immunosensor and research methodology can be generally applied to understand the reaction mechanisms and optimize the kinetic properties of sandwich immunosensors with particle labels.

Sub-Visible Particle Classification and Label Consistency Analysis for Flow-Imaging Microscopy Via Machine Learning Methods

Sub-visible particles can be a quality concern in pharmaceutical products, especially parenteral preparations. To quantify and characterize these particles, liquid samples may be passed through a flow-imaging microscopy instrument that also generates images of each detected particle. Machine learning techniques have increasingly been applied to this kind of data to detect changes in experimental conditions or classify specific types of particles, primarily focusing on silicone oil. That technique generally requires manual labeling of particle images by subject matter experts, a time-consuming and complex task.In this study, we created artificial datasets of silicone oil, protein particles, and glass particles that mimicked complex datasets of particles found in biopharmaceutical products. We used unsupervised learning techniques to effectively describe particle composition by sample. We then trained independent one-class classifiers to detect specific particle populations: silicone oil and glass particles. We also studied the consistency of the particle labels used to evaluate these models. Our results show that one-class classifiers are a reasonable choice for handling heterogeneous flow-imaging microscopy data and that unsupervised learning can aid in the labeling process. However, we found agreement among experts to be rather low, especially for smaller particles (< 8 µm for our Micro-Flow Imaging data). Given the fact that particle label confidence is not usually reported in the literature, we recommend more careful assessment of this topic in the future.

Label-free imaging of nuclear membrane for analysis of nuclear import of viral complexes

HIV-1 enters the nucleus of non-dividing cells through the nuclear pore complex where it integrates into the host genome. The mechanism of HIV-1 nuclear import remains poorly understood. A powerful means to investigate the docking of HIV-1 at the nuclear pore and nuclear import of viral complexes is through single virus tracking in live cells. This approach necessitates fluorescence labeling of HIV-1 particles and the nuclear envelope, which may be challenging, especially in the context of primary cells. Here, we leveraged a deep neural network model for label-free visualization of the nuclear envelope using transmitted light microscopy. A training image set of cells with fluorescently labeled nuclear Lamin B1 (ground truth), along with the corresponding transmitted light images, was acquired and used to train our model to predict the morphology of the nuclear envelope in fixed cells. This protocol yielded accurate predictions of the nuclear membrane and was used in conjunction with virus infection to examine the nuclear entry of fluorescently labeled HIV-1 complexes. Analyses of HIV-1 nuclear import as a function of virus input yielded identical numbers of fluorescent viral complexes per nucleus using the ground truth and predicted nuclear membrane images. We also demonstrate the utility of predicting the nuclear envelope based on transmitted light images for multicolor fluorescence microscopy of infected cells. Importantly, we show that our model can be adapted to predict the nuclear membrane of live cells imaged at 37 °C, making this approach compatible with single virus tracking. Collectively, these findings demonstrate the utility of deep learning approaches for label-free imaging of cellular structures during early stages of virus infection.

Radiolabeling of Micro-/Nanoplastics via In-Diffusion.

Micro- and nanoplastics are emerging pollutants with a concerning persistence in the environment. Research into their environmental impact requires addressing challenges related to sensitively and selectively detecting them in complex ecological media. One solution with great potential for alleviating these issues is using radiolabeling strategies. Here, we report the successful introduction of a 64Cu radiotracer into common microplastics, namely polyethylene, polyethylene terephthalate, polystyrene, polyamide, and polyvinylidene dichloride, which allows the sensitive detection of mere nanograms of substance. Utilizing a Hansen Solubility Parameter screening, we developed a swelling and in-diffusion process for tetraphenylporphyrin-complexed 64Cu, which permits one-pot labeling of polymer particles.

Modeling of heating and cooling behaviors of laminated glass facades exposed to fire with three-dimensional flexibilities

To develop a precise and efficient computer model for predicting the heating and cooling behaviors of laminated glass facades exposed to fire, there is an urgent need to reduce the huge computational requirements associated with simulating heat transfer in layered structures that feature a down-flowing water film. We overcome this challenge by proposing an efficient three-dimensional finite difference method (3DFDM) which has high numerical stability when solving heat transfer equations with water film and air convection. To capture the moving particles of the water film, we developed a unique computational algorithm for particle labeling, which has two significant advantages: (1) it eliminates the time-consuming process of searching for neighboring particles in conventional meshfree methods, and (2) it ensures that every main particle interacts only with limited neighboring particles without utilizing any weights, thus significantly reducing the computational effort. We validated our 3DFDM through experiments in heating and cooling scenarios and compared its thermal results with those obtained from commercial packages to demonstrate its high efficiency and accuracy. Furthermore, we examined the feasibility of our model in evaluating the effects of thickness of the interlayer and water film release time on the cooling behavior of laminated glass during a fire.

The role of Lagrangian drift in the geometry, kinematics and dynamics of surface waves

The role of the Lagrangian mean flow, or drift, in modulating the geometry, kinematics and dynamics of rotational and irrotational deep-water surface gravity waves is examined. A general theory for permanent progressive waves on an arbitrary vertically sheared steady Lagrangian mean flow is derived in the Lagrangian reference frame and mapped to the Eulerian frame. A Lagrangian viewpoint offers tremendous flexibility due to the particle labelling freedom and allows us to reveal how key physical wave behaviour arises from a kinematic constraint on the vorticity of the fluid, inter alia the nonlinear correction to the phase speed of irrotational finite amplitude waves, the free surface geometry and velocity in the Eulerian frame, and the connection between the Lagrangian drift and the Benjamin–Feir instability. To complement and illustrate our theory, a small laboratory experiment demonstrates how a specially tailored sheared mean flow can almost completely attenuate the Benjamin–Feir instability, in qualitative agreement with the theory. The application of these results to problems in remote sensing and ocean wave modelling is discussed. We provide an answer to a long-standing question: remote sensing techniques based on observing current-induced shifts in the wave dispersion will measure the Lagrangian, not the Eulerian, mean current.

A Primary-Auxiliary Coupled Neural Network for Three-Dimensional Holographic Particle Field Characterization

Particle field measurement is an important topic in many industrial branches. However, there are always complex imaging scenes in the engineering experiments, resulting in severe imaging artifact, noise, and blur, such as the optical holography. In this article, we propose a primary-auxiliary coupled neural network (PANet) for 3-D holographic particle field characterization, which can obtain a comprehensive particle measurement, including the identification, focus determination, segmentation, and size estimation. PANet is constituted by two subnets that are arranged in a coupled architecture, i.e., a Primary-Net (PNet) and an AuxiliaryNet (ANet). As the main frame, PNet is designed to accomplish the detection of most particles, while ANet aims to detect the tiny particles that PNet cannot identify. We exploit an alternative training method to realize their functional differentiation and complementation. PANet is evaluated on two kinds of holographic particle field data, i.e., high-energy laser shock aluminum target and droplet breakup in high Mach shock wave. By means of semisupervised learning and a specific loss function, the effect of deficient particle labeling can be alleviated. Experimental results demonstrate that PANet can achieve excellent performance in particle field characterization, especially for those with a wide size span and complex image background.

Amino Surface Modification and Fluorescent Labelling of Porous Hollow Organosilica Particles: Optimization and Characterization

Surface modification of silica nanoparticles with organic functional groups while maintaining colloidal stability remains a synthetic challenge. This work aimed to prepare highly dispersed porous hollow organosilica particles (pHOPs) with amino surface modification. The amino-surface modification of pHOPs was carried out with 3-aminopropyl(diethoxy)methylsilane (APDEMS) under various reaction parameters, and the optimal pHOP-NH2 sample was selected and labelled with fluorescein isothiocyanate (FITC) to achieve fluorescent pHOPs (F-HOPs). The prepared pHOPs were thoroughly characterized by transmission electron microscopy, dynamic light scattering, FT-IR, UV-Vis and fluorescence spectroscopies, and microfluidic resistive pulse sensing. The optimal amino surface modification of pHOPs with APDEMS was at pH 10.2, at 60 °C temperature with 10 min reaction time. The positive Zeta potential of pHOP-NH2 in an acidic environment and the appearance of vibrations characteristic to the surface amino groups on the FT-IR spectra prove the successful surface modification. A red-shift in the absorbance spectrum and the appearance of bands characteristic to secondary amines in the FTIR spectrum of F-HOP confirmed the covalent attachment of FITC to pHOP-NH2. This study provides a step-by-step synthetic optimization and characterization of fluorescently labelled organosilica particles to enhance their optical properties and extend their applications.

Electron microscopic visualization of single molecules by tag-mediated metal particle labeling.

Genetically encoded tags have introduced extensive lines of application from purification of tagged proteins to their visualization at the single molecular, cellular, histological and whole-body levels. Combined with other rapidly developing technologies such as clustered regularly interspaced palindromic repeats (CRISPR)/CRISPR-associated protein 9 (Cas9) system, proteomics, super-resolution microscopy and proximity labeling, a large variety of genetically encoded tags have been developed in the last two decades. In this review, I focus on the current status of tag development for electron microscopic (EM) visualization of proteins with metal particle labeling. Compared with conventional immunoelectron microscopy using gold particles, tag-mediated metal particle labeling has several advantages that could potentially improve the sensitivity, spatial and temporal resolution, and applicability to a wide range of proteins of interest (POIs). It may enable researchers to detect single molecules in situ, allowing the quantitative measurement of absolute numbers and exact localization patterns of POI in the ultrastructural context. Thus, genetically encoded tags for EM could revolutionize the field as green fluorescence protein did for light microscopy, although we still have many challenges to overcome before reaching this goal.

Robust Chemical Strategy for Stably Labeling Polyester-Based Nanoparticles with BODIPY Fluorophores.

Aliphatic polyesters are among materials most extensively used for producing biodegradable polymeric nanoparticles currently in development as delivery carriers and imaging agents for a range of biomedical applications. Their clinical translation requires robust particle labeling methodologies that allow reliably monitoring the fate of these formulations in complex biological environments. In the present study, a practical and versatile synthetic strategy providing conjugates of poly(D,L-lactide) representative of this class of polymers with BODIPY fluorophores varying in functional groups and excitation/emission maxima was investigated as a tool for making traceable nanoparticles. Polymer-probe conjugation was accomplished by carbodiimide-induced and 4-(dimethylamino)pyridinium 4-toluenesulfonate-catalyzed esterification of the polymer's terminal hydroxyl group, either directly with a carboxy-functionalized fluorophore or with amine-protected amino acids (Boc-glycine or Boc-6-aminohexanoic acid). In the latter case, the amino acid-derivatized polymeric precursors were reacted with amine-reactive BODIPY dyes after the removal of the protective group. Unlike nanoparticles encapsulating a strongly hydrophobic BODIPY505/515 (logPo/w = 4.3), nanoparticles labeled covalently with its carboxy-functionalized analogue (BODIPY FL) demonstrated stable particle-tracer association under perfect sink conditions. Furthermore, in contrast to the encapsulated dye rapidly partitioning from particles onto cell membranes but not stably retained by cultured cells, the internalization of the covalently attached probe was an irreversible process requiring the presence of serum, consistent with active nanoparticle uptake by endocytosis. In conclusion, the conjugation of particle-forming polymers with BODIPY fluorophores offers an effective and accessible labeling strategy for making traceable polyester-based biodegradable nanoparticles and is expected to facilitate their development and optimization as therapeutic carriers and diagnostic agents.

Mounted Single Particle Characterization for 3D Mineralogical Analysis—MSPaCMAn

This paper demonstrates a new method to classify mineral phases in 3D images of particulate materials obtained by X-ray computed micro-tomography (CT), here named mounted single particle characterization for 3D mineralogical analysis (MSPaCMAn). The method allows minimizing the impact of imaging artefacts that make the classification of voxels inaccurate and thus hinder the use of CT to characterize natural particulate materials. MSPaCMAn consists of (1) sample preparation as particle dispersions; (2) image processing optimized towards the labelling of individual particles in the sample; (3) phase identification performed at the particle level using an interpretation of the grey-values of all voxels in a particle rather than of all voxels in the sample. Additionally, the particle’s geometry and microstructure can be used as classification criteria besides the grey-values. The result is an improved accuracy of phase classification, a higher number of detected phases, a smaller grain size that can be detected, and individual particle statistics can be measured instead of just bulk statistics. Consequently, the method broadens the applicability of 3D imaging techniques for particle analysis at low particle size to voxel size ratio, which is typically limited due to unreliable phase classification and quantification. MSPaCMAn could be the foundation of 3D semi-automated mineralogy similar to the commonly used 2D image-based semi-automated mineralogy methods.

Sortase-Mediated Phage Decoration for Analytical Applications.

Phage-borne peptides and antibody fragments isolated from phage display libraries have proven to be versatile and valuable reagents for immunoassay development. Due to the lack of convenient and mild-condition methods for the labeling of the phage particles, isolated peptide/protein affinity ligands are commonly removed from the viral particles and conjugated to protein tracers or nanoparticles for analytical use. This abolishes the advantage of isolating ready-to-use affinity binders and creates the risk of affecting the polypeptide activity. To circumvent this problem, we optimized the phage display system to produce phage particles that express the affinity binder on pIII and a polyglycine short peptide fused to pVIII that allows the covalent attachment of tracer molecules employing sortase A. Using a llama heavy chain only variable domain (VHH) against the herbicide 2,4-D on pIII as the model, we showed that the phage can be extensively decorated with a rhodamine-LPETGG peptide conjugate or the protein nanoluciferase (Nluc) equipped with a C-terminal LPETGG peptide. The maximum labeling amounts of rhodamine-LPETGG and Nluc-LPETGG were 1238 ± 63 and 102 ± 16 per phage, respectively. The Nluc-labeled dual display phage was employed to develop a phage bioluminescent immunoassay (P-BLEIA) for the detection of 2,4-D. The limit of detection and 50% inhibition concentration of P-BLEIA were 0.491 and 2.15 ng mL-1, respectively, which represent 16-fold and 8-fold improvement compared to the phage enzyme-linked immunosorbent assay. In addition, the P-BLEIA showed good accuracy for the detection of 2,4-D in spiked samples.

Low CFB expression is independently associated with poor overall and disease-free survival in patients with lung adenocarcinoma.

Complement factor B (CFB) serves a pivotal role in the alternative signaling pathway of the complement system and exerts a key role in the labelling of target particles, resulting from effective clearance of the target. The present study aimed to investigate the association between low expression levels of CFB and the clinical features and survival status of patients with lung adenocarcinoma (LUAD). Patient data were based on RNA-sequencing and clinical data from The Cancer Genome Atlas database. All patients were divided into two groups based on the median expression of CFB. Kaplan-Meier curve and univariate Cox regression analyses were used to investigate the association between CFB and survival status. Gene set enrichment analysis was used to examine the effects of CFB expression on signaling pathway impairment. Furthermore, reverse transcription-quantitative PCR (RT-qPCR) and western blotting were used to verify the relative expression levels of CFB in LUAD tissues. The data revealed that residual tumor classification, Karnofsky performance score and cancer stage were associated with overall survival, and that Karnofsky performance score and stage were associated with disease-free survival. The results demonstrated that high expression levels of CFB were associated with increased patient overall and disease-free survival according to both continuous and categorical models. The results of multivariate analysis identified that high expression levels of CFB were associated with increased overall and disease-free survival according to both the continuous model [hazard ratio (HR), 0.48; 95% confidence interval (95% CI), 0.25–0.93; P=0.029 for overall survival; HR, 0.29; 95% CI, 0.15–0.59; P=0.001 for disease-free survival] and the categorical model (HR, 0.46; 95% CI, 0.22–0.93; P=0.031 for overall survival; HR, 0.25; 95% CI, 0.12–0.55; P=0.001 for disease-free survival) after adjusting for corresponding covariates (residual tumour classification, Karnofsky performance score and stage). Furthermore, the results of both RT-qPCR and western blotting indicated that the relative mRNA and protein expression levels of CFB in lung tumor tissues were downregulated compared with those in adjacent non-tumor tissues. Collectively, the present results suggested that CFB expression was an independent predictor of overall and disease-free survival in patients with LUAD.

A deep learning approach to identifying immunogold particles in electron microscopy images

Electron microscopy (EM) enables high-resolution visualization of protein distributions in biological tissues. For detection, gold nanoparticles are typically used as an electron-dense marker for immunohistochemically labeled proteins. Manual annotation of gold particle labels is laborious and time consuming, as gold particle counts can exceed 100,000 across hundreds of image segments to obtain conclusive data sets. To automate this process, we developed Gold Digger, a software tool that uses a modified pix2pix deep learning network capable of detecting and annotating colloidal gold particles in biological EM images obtained from both freeze-fracture replicas and plastic sections prepared with the post-embedding method. Gold Digger performs at near-human-level accuracy, can handle large images, and includes a user-friendly tool with a graphical interface for proof reading outputs by users. Manual error correction also helps for continued re-training of the network to improve annotation accuracy over time. Gold Digger thus enables rapid high-throughput analysis of immunogold-labeled EM data and is freely available to the research community.

Synthesis of Rough Colloidal SU-8 Rods and Bananas via Nanoprecipitation

Surface roughness plays an important role in determining the mechanical properties, wettability, and self-assembly in colloidal systems. In this work, we develop a simple and fast method to produce rough colloidal SU-8 rods, bananas, and spheres, via the nanoprecipitation of SU-8 in water. During this process, SU-8 nanospheres are absorbed onto the surface of the colloidal SU-8 particles and then cross-linked using UV-light. The size of the spherical asperities and the asperity density are controlled by the concentration of SU-8 used during the nanoprecipitation reaction. Fluorescent labeling of the rough SU-8 colloidal particles allows for their confocal imaging, which demonstrates their stability at high packing fractions. With these newly developed rough particles, we provide a colloidal model system that allows for studies addressing the impact of surface roughness on materials composed of anisotropic particles.

Contact evolution in granular materials with inherently anisotropic fabric

This paper presents the results of two triaxial compression tests performed on approximately 9×103 oblate spheroids (lentils) with different initial orientations with respect to the loading axis (approximately 30° and 60°). Typical stress-strain information is complemented with x-ray tomography scans every 1 % strain. Starting from an initial labelling of particles, a new technique is used to obtain unprecedented detail of tracking of all the particles through time. This rich dataset is analysed from the perspective of inter-particle contacts (building on previous metrological work on this subject) revealing that the mean contact orientation in both samples rotates towards the direction of σ1 at a rate depending on the initial orientation. Different trends are observed for the alignment of contact orientation, with a significant evolution observed in the sample prepared at 30° which is not as pronounced as in the other sample.

Characterization of nanoparticles using coupled gel immobilization and label-free optical imaging.

Accurate quantification of number concentration of nanoparticles (NPs) is critical for their biomedical and catalytic applications. We developed a novel NP analysis platform based on coupled gel immobilization and a three-dimensional (3D) scattered light imaging (SLI) platform. This imaging-based technique enables high-throughput analysis of silver nanoparticles (AgNPs) at single-particle level without the need for particle labeling or modification. This is a well-established quantitative characterization technique that can simultaneously measure the number concentration and size distribution of AgNPs. It also demonstrates the visualization and quantification of the size and 3D morphology of AgNP agglomerates in solution.