Efficient carbon removal and excellent anti-clogging performance have been achieved in multilayer quartz sand horizontal subsurface flow constructed wetland for domestic sewage treatment.

The present study aimed to investigate the application of a multilayer quartz sand substrate horizontal subsurface flow constructed wetland (HSFCW) for campus sewage treatment. It aimed to assess the pollutant removal efficiency and anti-clogging performance under the suggested maximum organic loading rate (250 g/m2/d). The results of the multilayer HSFCW (CW6) were compared to the mololayer HSFCW (CW1) for the removal of the chemical oxygen demand (COD), solid accumulation, and microbial communities. During operation, the combination conditions of high hydraulic loading rate (HLR) with low COD concentration were better for COD removal under a high organic loading rate (OLR) of 200-300 g/m2/d. The maximum removal rate reached 80.4% in CW6 under high HLR, which was 13.8% higher than that in CW1, showing better adsorption and biodegradation ability of organic matter. Impressive clogging resistance capacity was found in CW6 due to the lower contents of the insoluble organic matter (IOM) that are prone to clogging, indicating full degradation of organic matters, particularly IOM, in CW6 under high HLR. Less abundance of unclassified Chitinophagaceae (under low HLR), Pedobacter and Saccharibacteria_genera_incertae_sedis (under high HLR) in CW6, which contributed to aerobic membrane fouling, helped to prevent clogging. Moreover, Brevundimonas, Cloacibacterium, Citrobacter, Luteimonas contributed to IOM degradation, thus further enhancing the anti-clogging performance. In view of the better clogging resistance performance, the application of CW6 operated under high HLR and low COD concentrations was recommended to achieve economical, efficient, and steady COD removal for domestic sewage treatment in long-term operation.

Compost-derived humic and fulvic acid coupling with Shewanella oneidensis MR-1 for the bioreduction of Cr(â ¥).

The compost-derived humic acids (HA) and fulvic acids (FA) contain abundant active functional groups with strong redox capacity, which can function as an electron shuttles for promoting the reduction of heavy metals, thus changing the form of the pollutants in the environment and reducing their toxicity. Therefore, in this study, UV-Vis, FTIR, 3D-EEM, electrochemical analysis were applied to study the spectral characteristics and electron transfer capacity (ETC) of HA and FA. Upon analysis, the results showed an increasing trend of ETC and humification degree (SUVA254) for both HA and FA during composting. However, the aromatic degree (SUVA280) of HA was higher than FA. After 7 days of culture, 37.95% of Cr (Ⅵ) was reduced by Shewanella oneidensis MR-1 (MR-1) alone. Whereas, only if HA or FA existed, the diminution of Cr (Ⅵ) reached 37.43% and 40.55%, respectively. However, the removal rate of Cr (Ⅵ) by HA/MR-1 and FA/MR-1 increased to 95.82% and 93.84% respectively. It indicated that HA and FA acted as electron shuttles, mediating the transfer of electrons between MR-1 and the final electron acceptor, effectively facilitating the bioreduction of Cr (Ⅵ) to Cr (Ⅲ) and also determined via correlation analysis. This study suggested compost-derived HA and FA coupling with MR-1 exhibited excellent performance for the bioreduction of Cr (Ⅵ) to Cr (Ⅲ).

Distribution, ecological risk assessment and source identification of heavy metals in surface sediments of Huixian karst wetland, China.

In this study, heavy metals including Cd, Pb, Zn, Mn, Cu, Ni, Cr, As, and Hg, in the surface sediment (0-10 cm) of the Huixian wetland in a karst region were investigated in terms of their spatial distribution, ecological risks, and possible sources. Samples were collected from 13 typical sites throughout the Huixian wetland and were analyzed via inductively coupled plasma-mass spectrometry. The results revealed that the mean concentrations of Cd, Pb, Mn, Cr, As, and Hg were higher than the background and Chinese safe standard values. Based on spatial distribution and ecological risk, the study area was differentiated into three groups of sites with the following order of risk: group 3 > group 2 > group 1. The observed concentrations fluctuated slightly with depth. However, an irregular decreasing trend in the concentration with soil depth was observed among the groups. Multivariate statistical analyses showed that the high accumulation of Cd, Pb, Zn and Cu in the sediments of group 3 sites is due to the natural ancient deposition of minerals rich in heavy metals, while the accumulation of Mn, Cr, As, and Hg is attributed to an anthropogenic origin. Agricultural activities, the use of fertilizers and, pesticides, and local automobile repair stations most probably enriched these heavy metals in the Huixian wetland sediments. Hg and Cd have the highest potential ecological risk, which follows the order Hg > Cd > Pb > As > Ni > Cu > Cr > Mn > Zn. The mean geoaccumulation index (Igeo) values of Pb (0.48) and Hg (1.12) suggested moderate contamination in the study area.

Automated Learning of Subcellular Variation among Punctate Protein Patterns and a Generative Model of Their Relation to Microtubules.

Characterizing the spatial distribution of proteins directly from microscopy images is a difficult problem with numerous applications in cell biology (e.g. identifying motor-related proteins) and clinical research (e.g. identification of cancer biomarkers). Here we describe the design of a system that provides automated analysis of punctate protein patterns in microscope images, including quantification of their relationships to microtubules. We constructed the system using confocal immunofluorescence microscopy images from the Human Protein Atlas project for 11 punctate proteins in three cultured cell lines. These proteins have previously been characterized as being primarily located in punctate structures, but their images had all been annotated by visual examination as being simply "vesicular". We were able to show that these patterns could be distinguished from each other with high accuracy, and we were able to assign to one of these subclasses hundreds of proteins whose subcellular localization had not previously been well defined. In addition to providing these novel annotations, we built a generative approach to modeling of punctate distributions that captures the essential characteristics of the distinct patterns. Such models are expected to be valuable for representing and summarizing each pattern and for constructing systems biology simulations of cell behaviors.

Toward the virtual cell: automated approaches to building models of subcellular organization "learned" from microscopy images.

We review state-of-the-art computational methods for constructing, from image data, generative statistical models of cellular and nuclear shapes and the arrangement of subcellular structures and proteins within them. These automated approaches allow consistent analysis of images of cells for the purposes of learning the range of possible phenotypes, discriminating between them, and informing further investigation. Such models can also provide realistic geometry and initial protein locations to simulations in order to better understand cellular and subcellular processes. To determine the structures of cellular components and how proteins and other molecules are distributed among them, the generative modeling approach described here can be coupled with high throughput imaging technology to infer and represent subcellular organization from data with few a priori assumptions. We also discuss potential improvements to these methods and future directions for research.

Protein subcellular location pattern classification in cellular images using latent discriminative models.

MOTIVATION: Knowledge of the subcellular location of a protein is crucial for understanding its functions. The subcellular pattern of a protein is typically represented as the set of cellular components in which it is located, and an important task is to determine this set from microscope images. In this article, we address this classification problem using confocal immunofluorescence images from the Human Protein Atlas (HPA) project. The HPA contains images of cells stained for many proteins; each is also stained for three reference components, but there are many other components that are invisible. Given one such cell, the task is to classify the pattern type of the stained protein. We first randomly select local image regions within the cells, and then extract various carefully designed features from these regions. This region-based approach enables us to explicitly study the relationship between proteins and different cell components, as well as the interactions between these components. To achieve these two goals, we propose two discriminative models that extend logistic regression with structured latent variables. The first model allows the same protein pattern class to be expressed differently according to the underlying components in different regions. The second model further captures the spatial dependencies between the components within the same cell so that we can better infer these components. To learn these models, we propose a fast approximate algorithm for inference, and then use gradient-based methods to maximize the data likelihood. RESULTS: In the experiments, we show that the proposed models help improve the classification accuracies on synthetic data and real cellular images. The best overall accuracy we report in this article for classifying 942 proteins into 13 classes of patterns is about 84.6%, which to our knowledge is the best so far. In addition, the dependencies learned are consistent with prior knowledge of cell organization. AVAILABILITY: http://murphylab.web.cmu.edu/software/.

A comprehensive study of potentially toxic element contamination and source quantitative assessment by positive matrix factorization model: risk from the fine road dust of Chehe mining area, China.

Extreme mining activities can risk human life and the environment via potentially toxic elements (PTEs) in road dust, thus making their quantification and assessment unavoidable. For this purpose, we collected 50 fine road dust samples from the Chehe mining area, China, to quantify the level of contamination and ecological and health risks of PTEs comprising As, Cd, Co, Cr, Cu, Mn, Ni, Pb, Sb, and Zn, and their quantitative source apportionment using the positive matrix factorization model (PMF). Results indicated that the average values of Cd, Sb, As, Zn, Pb, and Cu in road dust were 1555.21, 586.78, 429.68, 429.43, 72.88, and 26.61 times higher than their background values. Pollution indices of PTEs revealed a strong level of contamination by Cd, Sb, As, Zn, and Pb, which were extremely polluted in the study area. The average values of the Nemerow integrated risk index (NIRI) and potential ecological risk index (RI) were 104.09 and 86.49 times the highest risk limit, respectively, which are extremely high ecological risks. Based on PMF for quantitative source identification, mining activities and fuel combustion were the main sources of PTEs in road dust contributing 57.25% and 35.95%, respectively. Furthermore, the health risk assessment indicated that Sb, As, Cr, Cd, and Pb in the Chehe road dust could lead to significantly serious carcinogenic and non-carcinogenic risks to both children and adults. The results of this study could be used to opt for strategies to mitigate the ecological and human health risk in the mining area of Hechi, China.

Morphological characterization, hazardous metal contamination, source identification, and health risk assessment of the fine road dust from Dachang mining area, China.

Dachang mining area in China is known as "paradise for mineralogists" due to its most reserves of Sn, Sb, Pb, and Zn non-ferrous metal resources; thus, its evaluation for heavy metal assessment and consequent health risk is unavoidable. Sixty road dust samples were collected from study area to explore pollution level, ecological, and health risks from heavy metals and were analyzed by an inductively coupled plasma optical emission spectrometer and atomic fluorescence spectrometer. The results showed that average concentration of all the heavy metals in road dust in both mining and residential areas were remarkable higher than its corresponding background values, the former being more severe, except for Cr and Co. The morphological investigation showed that most of the particles were much less than 100 µm illustrating fine part of the road dust samples. Based on integrated pollution indices, Cd, Sb, As, Zn, and Pb were extremely contaminated and exceeded hundred times of the maximum risk value. The health risk assessment revealed substantially higher carcinogenic and non-carcinogenic risks to children and adult. Highest non-carcinogenic risk resulted from arsenic in mining and residential area with HQing of 644.56 and 267.94 respectively (standard HQ ≥ 1) while carcinogenic risk to children (1.94E + 00) which greatly exceeded from the threshold value of (1.0E-4). Sb, Cd, and Pb also posed carcinogenic and non-carcinogenic risk in road dust which is caused by excessive mining activities and heavy vehicle movement in the study area.

Source-specific ecological risk assessment and quantitative source apportionment of heavy metals in surface sediments of Pearl River Estuary, China.

In this study, surface sediments of the Pearl River Estuary were collected from 29 stations and investigated the spatial distribution, pollution level, quantitative source apportionment, and source-specific ecological risk of 10 heavy metals. The mean concentrations followed the order of Mn > Zn > Cr > Cu > Ni > Pb > As > Co > Cd > Hg. In terms of spatial distribution, it showed that the heavy metals were enriched in the inner Pearl River Estuary with 'extremely high' level of Hg, whereas, Cd and Zn posed 'moderate to high' contamination potential. We apportioned four main sources using positive matrix factorization model, in which natural geogenic and industrial manufacturing sources accounted for 36.84% and 27.11% of the total, respectively. However, the source-specific risk assessment suggested that mixed anthropogenic sources were the main contributors, and ecological risks were strongly affected by anthropogenic imports from the surrounding cities.

Spatial and temporal variation of dissolved heavy metals in the Lijiang River, China: implication of rainstorm on drinking water quality.

Lijiang River is an essential drinking water source and natural scenery in the Guilin City. For the first time, implications of rainstorm were taken into consideration by investigating spatial and temporal variation of dissolved heavy metals (HMs) in the Lijiang River water. A total of 68 water samples were collected during low flow (normal) season and high flow (rainstorm) season from 34 sampling sites. Dissolved HMs including Cr, Mn, Co, Cu, Zn, As, Cd, Sb, and Pb were found to meet the respective drinking water standards, while comparatively higher concentration was observed after the rainstorm season, except for Cr. Multivariate statistical analysis showed that Co, Cu, Cr, Zn, Sb, and Pb in normal season were mainly controlled by anthropogenic sources. Furthermore, higher concentrations of Mn, Cu, Cd, Pb, Co, and Zn during the high flow season were attributed to rainstorm. The water quality index (WQI) showed good grades and comparatively lower in rainstorm season. The results of health risk assessment revealed that HMs in Lijiang River posed limited health risk; however, As posed potential health risk specially in rainstorm season. It is suggested to adopt preventive measures for mining activities and industrial waste-water discharge at the river's upstream and downstream.

[Pollution Characteristics and Health Risk Assessment of Heavy Metals in the Water of Lijiang River Basin].

In order to clarify the pollution levels of heavy metals in the drinking water sources of the Lijiang River Basin, surface water samples were collected from 62 sites throughout the Lijiang River during May 2019. Heavy metals, including As, Cd, Cr, Mn, Cu, Zn, Hg, Co, and Sb, in the water samples were analysed. Health risk assessments associated with these nine heavy metals were conducted using the health risk assessment model from the US EPA. The results indicated that the order of the average concentrations of heavy metals in the water samples were Mn > Zn > As > Cr > Cu > Sb > Co > Cd > Hg. No heavy metals exceeded the limit values of the drinking water health standards in China (GB 5749-2006), and the concentrations were lower than the limitations of Grade Ⅰ level in the environmental quality standards for surface water (GB 3838-2002). According to the spatial distribution, the high contents areas of As, Cr, Zn, and Sb were predominantly distributed downstream of the Lijiang River, while the high contents areas of Cd, Cu, Hg, Co, and Mn were mostly distributed in the upper reaches. Multivariate analysis indicated that Cd, Mn, Cu, and Co were primarily from agricultural production; Cr, Zn, and Sb were mainly from tourism transportation; As was predominantly from the weathering of rock parent material and soil erosion; Hg was mainly from the improper disposal of domestic garbage and atmospheric deposition. The results of the health risk assessment indicated that children were more susceptible to the threat of heavy metal pollution than adults, and the average annual risk of carcinogenic heavy metals to human health through drinking water ingestion were higher than those of non-carcinogenic metals. The maximum personal average annual health risk of Cr was higher than the maximum allowance levels recommended by the International Commission on Radiological Protection (5×10-5 a-1). The average annual risk of non-carcinogenic heavy metals (10-14-10-9 a-1) decreased in the order of Co > Cu > Hg > Zn > Sb > Mn, which were far below the maximum allowance levels recommended by the ICRP.

Heavy metal pollution, ecological risk, spatial distribution, and source identification in sediments of the Lijiang River, China.

The Lijiang River is of great ecological and environmental importance for Guilin City, which is located in the karst area of southeast China. Given its importance, a detailed evaluation of the heavy metals (HMs) in the river sediment is required. For the first time, 61 sediment samples were collected along the entire Lijiang River to determine pollution level and ecological risk posed by 10 HMs (Co, Cr, Cu, Mn, Ni, Pb, Zn, As, Hg, and Cd). These were assessed using the geo-accumulation index, potential ecological risk index, and modified degree of contamination. The results showed that the mean concentrations of the majority of HMs exceeded their corresponding background values and followed the trend: midstream > downstream > upstream. Based on the spatial distributions and pollution indices of the 10 HMs, the Lijiang River was found to have a high accumulation of Cd, Hg, Zn, and Pb in the sediments. The midstream area was the most polluted with respect to Cd and Hg, and also posed a relatively higher potential ecological risk than the downstream and upstream areas. The sources of the assessed HMs were inferred based on a correlation analysis and principal component analysis, which identified both natural and anthropogenic sources. A higher pollution potential was associated with Cd, Hg, Pb, and Zn in the midstream and downstream areas due to higher organic and carbonate content, urbanization, agricultural activities, and leisure activities (e.g., boating and cruises). In contrast, natural erosion and weathering processes were responsible for the HM concentrations in the upstream area. The findings of this study will help the local authorities to protect the important water resource of the Lijiang River.

Pollution characteristics and toxicity of potentially toxic elements in road dust of a tourist city, Guilin, China: Ecological and health risk assessment☆.

Road dust samples from industrial, urban, and tourist areas of the international tourist city of Guilin, China, were collected to study the concentration, spatial distribution, pollution level, and health risk of potentially toxic elements (PTEs) using an array-based risk assessment model from the United States Environmental Protection Agency. The geoaccumulation index (Igeo), ecological risk index, and spatial interpolation were used to investigate the road dust pollution level. The results indicated that apart from Ni and Al, the concentration of all the heavy metals (Pb, Zn, Ni, Cu, Cr, Cd, Fe, Mn, and As) were markedly higher than the corresponding background values in the three functional areas. Based on the Igeo, the study area had an uncontaminated to moderate pollution level, and the industrial area was slightly more polluted and posed a greater ecological risk than the urban and tourist areas. Comparatively, Pb, Zn, and Cu exhibited higher pollution levels in the three functional sites. Hotspots of PTEs were more concentrated in urban and industrial areas than in tourist areas. Furthermore, the health risk model revealed significant non-carcinogenic risks to children from As in urban, industrial, and tourist areas as the hazard quotients (1.64, 2.04, and 1.42, respectively) exceeded the threshold standard of 1.00. The carcinogenic risk via ingestion (RIing) illustrated significant risks to children from Cr, As, and Ni because the RIing values were considerably higher than the threshold standard (1.00E-6 to 1.00E-4) in the three functional areas. However, no cancer risk was observed from the dermal and inhalation pathways.

[Three-dimensional motion analysis for GLUT4 vesicles in TIRF microscopy].

In this paper, GLUT4 vesicles are observed in real-time under TIRF microscopy and a new three-dimensional single particle tracking algorithm according to the unique features of TIRF is put forward. Firstly a fluorescence correction procedure was processed to solve the problem of fluorescence bleaching over time and mobile vesicles were segmented by an adaptive background subtraction method. Kalman filtering was then introduced to track the granules so as to reduce the searching range and to avoid the disturbance of background noise and false targets. In the experiments the algorithm was applied in analyzing the long-distance movement of GLUT4 vesicles. The experimental results indicate that the algorithm has achieved robust tracking of the vesicles in the imaging plane and has effectively calculated the position in the direction orthogonal to the imaging plane.

Estimating microtubule distributions from 2D immunofluorescence microscopy images reveals differences among human cultured cell lines.

Microtubules are filamentous structures that are involved in several important cellular processes, including cell division, cellular structure and mechanics, and intracellular transportation. Little is known about potential differences in microtubule distributions within and across cell lines. Here we describe a method to estimate information pertaining to 3D microtubule distributions from 2D fluorescence images. Our method allows for quantitative comparisons of microtubule distribution parameters (number of microtubules, mean length) between different cell lines. Among eleven cell lines compared, some showed differences that could be accounted for by differences in the total amount of tubulin per cell while others showed statistically significant differences in the balance between number and length of microtubules. We also observed that some cell lines that visually appear different in their microtubule distributions are quite similar when the model parameters are considered. The method is expected to be generally useful for comparing microtubule distributions between cell lines and for a given cell line after various perturbations. The results are also expected to enable analysis of the differences in gene expression underlying the observed differences in microtubule distributions among cell types.

Automated analysis and reannotation of subcellular locations in confocal images from the Human Protein Atlas.

The Human Protein Atlas contains immunofluorescence images showing subcellular locations for thousands of proteins. These are currently annotated by visual inspection. In this paper, we describe automated approaches to analyze the images and their use to improve annotation. We began by training classifiers to recognize the annotated patterns. By ranking proteins according to the confidence of the classifier, we generated a list of proteins that were strong candidates for reexamination. In parallel, we applied hierarchical clustering to group proteins and identified proteins whose annotations were inconsistent with the remainder of the proteins in their cluster. These proteins were reexamined by the original annotators, and a significant fraction had their annotations changed. The results demonstrate that automated approaches can provide an important complement to visual annotation.

Automated Analysis of Human Protein Atlas Immunofluorescence Images.

The Human Protein Atlas is a rich source of location proteomics data. In this work, we present an automated approach for processing and classifying major subcellular patterns in the Atlas images. We demonstrate that two different classification frameworks (support vector machine and random forest) are effective at determining subcellular locations; we can analyze over 3500 Atlas images with a high degree of accuracy, up to 87.5% for all of the samples and 98.5% when only considering samples in whose classification assignments we are most confident. Moreover, the features obtained in both of these frameworks are observed to be highly consistent and generalizable. Additionally, we observe that the features relating the proteins to cell markers are especially important in automated learning approaches.

The role of iNOS-derived NO in the antihypertrophic actions of B-type natriuretic peptide in neonatal rat cardiomyocytes.

In the infarcted rat heart, the increase of NO occurs in the hypertrophied myocardium of non-infarcted areas and its antihypertrophic efficacy has been well established. As another endogenous regulator and the reliable index of heart pathology, B-type natriuretic peptide also exhibits the antihypertrophic properties in many tissues by elevating intracellular cGMP. Several studies indicate that natriuretic peptides family may exert some actions in part via a nitric oxide pathway following receptor-mediated stimulation of iNOS. Therefore, it raises our great interest to ask what role NO plays in the antihypertrophic actions of B-type natriuretic peptide in cardiomyocytes. Incubation of cardiomyocytes under mild hypoxia for 12 h caused a significant increase in cellular protein content, protein synthesis and cell surface sizes. This growth stimulation was suppressed by exogenous B-type natriuretic peptide in a concentration dependent manner. Furthermore, the generation of intracellular cGMP, the upregulation of iNOS mRNA expression, the increase of iNOS activity and subsequent nitrite generation in hypertrophic cardiomyocytes was also increased by B-type natriuretic peptide. AG, a selective iNOS inhibitor, inhibited the upregulation of iNOS expression and the increase of iNOS activity by the combination of B-type natriuretic peptide/mild hypoxia or by the combination of 8-bromo-cGMP/mild hypoxia. Rp-8-br-cGMP, cGMP dependent protein kinase inhibitor, attenuated the actions of B-type natriuretic peptide and 8-bromo-cGMP which increases intracellular cGMP independent of B-type natriuretic peptide. In conclusion, our present data suggest that B-type natriuretic peptide exerted the antihypertrophic effects in cardiomyocytes, which was partially attributed to induction of iNOS-derived NO by cGMP pathway.

B-type natriuretic peptide enhances mild hypoxia-induced apoptotic cell death in cardiomyocytes.

In the case of left ventricle remodeling after myocardial infarction, cardiomyocyte apoptosis is attributed to increased cardiac workload by the stimulus such as chronic hypoxia. B-Type natriuretic peptide, being known as a reliable prognostic of cardiovascular pathology, plays an important role in the myocardial infarction. However, the action of B-type natriuretic peptide on cardiomyocytes undergoing apoptosis is unclear. In the present study, B-type natriuretic peptide have exhibited the enhancive effects on the mild hypoxia-induced cardiomyocyte apoptosis with the manifestation of facilitating phosphatidylserine evagination and increasing typical fragmented nuclei. In addition, B-type natriuretic peptide aggravated the dissipation of delta psi(m), the depletion of intracellular ATP and the increase of caspase-3 activity. 8-Bromo-cGMP, which increased cGMP independent of B-type natriuretic peptide, could mimic B-type natriuretic peptide's effects; whereas cGMP-dependent protein kinase inhibitor, Rp-8-br-cGMP inhibited that. Further study revealed the enhancive effect of BNP on down-regulation of Bcl-2 mRNA expression in the presence of mild hypoxia. In conclusion, the present study demonstrated that B-type natriuretic peptide aggravated the cardiomyocyte apoptosis by influencing hypoxia-induced mitochondrial death pathway, which is true at least in this oxygen deprivation model; and this effect was partially realized through intracellular cGMP.

Bi-directional transport of GLUT4 vesicles near the plasma membrane of primary rat adipocytes.

Insulin stimulates glucose uptake into adipocytes by mobilizing intracellular membrane vesicles containing GLUT4 proteins to the plasma membrane. Here we applied time-lapse total internal reflection fluorescence microscopy to study moving parameters and characters of exogenously expressed GLUT4 vesicles in basal, insulin and nocodazole treated primary rat adipocytes. Our results showed that microtubules were essential for long-range transport of GLUT4 vesicles but not obligatory for GLUT4 distribution in rat adipocytes. Insulin reduced the mobility of the vesicles, made them tethered/docked to the PM and finally had constitutive exocytosis. Moreover, long-range bi-directional movements of GLUT4 vesicles were visualized for the first time by TIRFM. It is likely that there are interactions between insulin signaling and microtubules, to regulating GLUT4 translocation in rat adipocytes.