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Abstract Understanding the function of proteins is of great significance for revealing disease pathogenesis and discovering new targets. Benefiting from the explosive growth of the protein universal, deep learning has been applied to accelerate the protein annotation cycle from different biological modalities. However, most existing deep learning-based methods not only fail to effectively fuse different biological modalities, resulting in low-quality protein representations, but also suffer from the convergence of suboptimal solution caused by sparse label representations. Aiming at the above issue, we propose a multiprocedural approach for fusing heterogeneous biological modalities and annotating protein functions, i.e., MIF2GO (Multimodal Information Fusion to infer Gene Ontology terms), which sequentially fuses up to six biological modalities ranging from different biological levels in three steps, thus leading to powerful protein representations. Evaluation results on seven benchmark datasets show that the proposed method not only considerably outperforms state-of-the-art performance, but also demonstrates great robustness and generalizability across species. Besides, we also present biological insights into the associations between those modalities and protein functions. This research provides a robust framework for integrating multimodal biological data, offering a scalable solution for protein function annotation, ultimately facilitating advancements in precision medicine and the discovery of novel therapeutic strategies.

Objective Long QT interval syndrome (LQTS) is a highly dangerous cardiac disease that can lead to sudden cardiac death; however, its underlying mechanism remains largely unknown. This study is conceived to investigate the impact of two general genotypes of LQTS type 2, and also the therapeutic effects of an emerging immunology-based treatment named KCNQ1 antibody. Methods A multiscale virtual heart is developed, which contains multiple biological levels ranging from ion channels to a three-dimensional cardiac structure with realistic geometry. Critical biomarkers at different biological levels are monitored to investigate the remodeling of cardiac electrophysiology induced by mutations. Results Simulations revealed multiple important mechanisms that are hard to capture via conventional clinical techniques, including the augmented dispersion of repolarization, the increased vulnerability to arrhythmias, the impaired adaptability in tissue to high heart rates, and so on. An emerging KCNQ1 antibody-based therapy could rescue the prolonged QT interval but did not reduce the vulnerable window. Conclusions Tiny molecular alterations can lead to cardiac electrophysiological remodeling at multiple biological levels, which in turn contributes to higher susceptibility to lethal arrhythmias in long QT syndrome type 2 patients. The KCNQ1 antibody-based therapy has proarrhythmic risks notwithstanding its QT-rescuing effects.

Abstract Bio-based coating materials have received increased attention because of their low-cost, environmentally friendly, and sustainable properties. In this paper, a novel coating material was developed to coat ureas using bio-based coating material derived from liquefied eggplant branches to form controlled-release ureas (CRUs). Also, the optimum proportion of liquefier was studied. Furthermore, dimethyl siloxane was used to modify liquified eggplant branches to make them hydrophobic, resulting in hydrophobic controlled-release ureas (SCRUs). This hydrophobic-enabled coating is environmentally friendly and highly efficient. The products were characterized by specific scanning electron microscopy, energy-dispersive X-ray spectroscopy, Fourier transform infrared spectroscopy, thermogravimetric analysis, and differential scanning calorimetry, and the water contact angles of CRUs and SCRUs were determined. The nutrient-release characteristics of the SCRUs in water were determined at 25 °C and compared with those of CRUs. The results showed that the modification with dimethyl siloxane reduced the N release rate and increased the longevity of the fertilizer coated with hydrophobic bio-based coating material. In addition, organosilicon atoms on the SCRU surface also block the micro-holes on the coating and thus reduce the entry of water onto the coating. The results suggest that the new coating technology can create a hydrophobic surface on bio-based coating material and thus improve their controlled-release characteristics.

Abstract Short QT syndrome (SQTS) is a rare but dangerous genetic disease. In this research, we conducted a comprehensive in silico investigation into the arrhythmogenesis in KCNH2 T618I-associated SQTS using a multi-scale human ventricle model. A Markov chain model of I Kr was developed firstly to reproduce the experimental observations. It was then incorporated into cell, tissue, and organ models to explore how the mutation provided substrates for ventricular arrhythmias. Using this T618I Markov model, we explicitly revealed the subcellular level functional alterations by T618I mutation, particularly the changes of ion channel states that are difficult to demonstrate in wet experiments. The following tissue and organ models also successfully reproduced the changed dynamics of reentrant spiral waves and impaired rate adaptions in hearts of T618I mutation. In terms of pharmacotherapy, we replicated the different effects of a drug under various conditions using identical mathematical descriptions for drugs. This study not only simulated the actions of an effective drug (quinidine) at various physiological levels, but also elucidated why the I Kr inhibitor sotalol failed in SQT1 patients through profoundly analyzing its mutation-dependent actions.

Abstract Background Affinity prediction between molecule and protein is an important step of virtual screening, which is usually called drug-target affinity (DTA) prediction. Its accuracy directly influences the progress of drug development. Sequence-based drug-target affinity prediction can predict the affinity according to protein sequence, which is fast and can be applied to large datasets. However, due to the lack of protein structure information, the accuracy needs to be improved. Results The proposed model which is called WGNN-DTA can be competent in drug-target affinity (DTA) and compound-protein interaction (CPI) prediction tasks. Various experiments are designed to verify the performance of the proposed method in different scenarios, which proves that WGNN-DTA has the advantages of simplicity and high accuracy. Moreover, because it does not need complex steps such as multiple sequence alignment (MSA), it has fast execution speed, and can be suitable for the screening of large databases. Conclusion We construct protein and molecular graphs through sequence and SMILES that can effectively reflect their structures. To utilize the detail contact information of protein, graph neural network is used to extract features and predict the binding affinity based on the graphs, which is called weighted graph neural networks drug-target affinity predictor (WGNN-DTA). The proposed method has the advantages of simplicity and high accuracy.

ObjectiveThe thalamus is an integrative hub of motor circuits in Parkinson’s disease (PD). This study aimed to investigate the alterations of structure and functional connectivity (FC) of the thalamic subregions in the tremor-dominant (TD) subtype and the postural instability and gait difficulty (PIGD) subtype in PD.MethodsA total of 59 drug-naïve patients (24 TD and 35 PIGD) and 37 healthy controls were recruited. The volumes of the thalamus and the thalamic subregions were calculated using FreeSurfer. Functional connectivity (FC) analysis of the resting-state functional MRI (rsfMRI) was conducted on the thalamic subregions. Finally, the altered structure and FC were used for correlation analysis with clinical motor scores and for further motor subtypes differentiation.ResultsThe volumes of the left posterior parietal thalamus (PPtha) in TD patients were significantly lower than those of PIGD patients. Compared with PIGD patients, TD patients exhibited higher FC between the thalamic subregions, the left middle temporal gyrus (MTG), the right dorsolateral superior frontal gyrus (SFGdl), the left middle occipital gyrus (MOG), and the right superior temporal gyrus (STG). Compared with HCs, TD patients showed higher FC between the thalamic subregions and the right SFGdl, as well as the left MOG. Compared with HCs, PIGD patients showed lower FC between the thalamic subregions and the left MTG. In addition, the altered FC was closely related to clinical symptoms and performed high-discriminative power in differentiating the motor subtypes.ConclusionIncreased FC between the thalamic subregions and the sensory cortices in TD patients may indicate a better compensatory capacity for impairment of sensory information integration than that in PIGD patients. The altered FC between the thalamus and the MTG was a potential biomarker for the distinction of the PD motor subtypes.

The heart is a vital organ in the human body. Research and treatment for the heart have made remarkable progress, and the functional mechanisms of the heart have been simulated and rendered through the construction of relevant models. The current methods for rendering cardiac functional mechanisms only consider one type of modality, which means they cannot show how different types of modality, such as physical and physiological, work together. To realistically represent the three-dimensional synergetic biological modality of the heart, this paper proposes a WebGL-based cardiac synergetic modality rendering framework to visualize the cardiac physical volume data and present synergetic correspondence rendering of the cardiac electrophysiological modality. By constructing the biological detailed interactive histogram, users can implement local details rendering for the heart, which could reveal the cardiac biology details more clearly. We also present cardiac physical-physiological correlation visualization to explore cardiac biological association characteristics. Experimental results show that the proposed framework can provide favorable cardiac biological detailed synergetic modality rendering results in terms of both effectiveness and efficiency. Compared with existing methods, the framework can facilitate the study of the internal mechanism of the heart and subsequently deduce the process of initiation, development, and transformation from a healthy heart to an ill one, and thereby improve the diagnosis and treatment of cardiac disorders.

Background: Carbon monoxide (CO) is gaining increased attention in air pollution-induced arrhythmias. The severe cardiotoxic consequences of CO urgently require effective pharmacotherapy to treat it. However, existing evidence demonstrates that CO can induce arrhythmias by directly affecting multiple ion channels, which is a pathway distinct from heart ischemia and has received less concern in clinical treatment.Objective: To evaluate the efficacy of some common clinical antiarrhythmic drugs for CO-induced arrhythmias, and to propose a potential pharmacotherapy for CO-induced arrhythmias through the virtual pathological cell and tissue models.Methods: Two pathological models describing CO effects on healthy and failing hearts were constructed as control baseline models. After this, we first assessed the efficacy of some common antiarrhythmic drugs like ranolazine, amiodarone, nifedipine, etc., by incorporating their ion channel-level effects into the cell model. Cellular biomarkers like action potential duration and tissue-level biomarkers such as the QT interval from pseudo-ECGs were obtained to assess the drug efficacy. In addition, we also evaluated multiple specific IKr activators in a similar way to multi-channel blocking drugs, as the IKr activator showed great potency in dealing with CO-induced pathological changes.Results: Simulation results showed that the tested seven antiarrhythmic drugs failed to rescue the heart from CO-induced arrhythmias in terms of the action potential and the ECG manifestation. Some of them even worsened the condition of arrhythmogenesis. In contrast, IKr activators like HW-0168 effectively alleviated the proarrhythmic effects of CO.Conclusion: Current antiarrhythmic drugs including the ranolazine suggested in previous studies did not achieve therapeutic effects for the cardiotoxicity of CO, and we showed that the specific IKr activator is a promising pharmacotherapy for the treatment of CO-induced arrhythmias.

Short QT syndrome (SQTS) is a genetic disease characterized by constantly short QT intervals and high risks of sudden death. SQTS6 is one of the identified SQTS genotype variants associated with the CACNA2D1 S755T mutation. However, the pathogenesis of SQTS induced arrhythmias remains unclear. To identify the underlying mechanisms of SQTS6 induced arrhythmias, a multi-scale human ventricle model comprising cell to organ levels was built. Cellular data was fitted at the cell level to reproduce the electrophysiological alterations reported in experiments. The influences were further explored at tissue and organ levels using idealized strand or tissue sheet models, and realistic ventricular slice and three-dimensional organ models. Simulation results suggested that, at the cellular level, the action potential duration (APD) and the effective refractory period (ERP) of myocytes were significantly abbreviated in the mutation condition. The unevenly changed APD and ERP led to transmural heterogeneity remodeling, and resulted in decreased temporal vulnerability. In addition, the S755T mutation shortened the critical length for initiating reentrant spiral waves, which enhanced the spatial vulnerability and provided substrates for reentry arrhythmias. Regarding the sustainability of arrhythmias, the evoked spiral waves or scroll waves persisted in the mutation condition but did not persist in the wild-type condition. The present study clearly suggested that the CACNA1DC S755T mutation can facilitate the initiation and maintenance of ventricular arrhythmias, and therefore contributes to higher risks of ventricular arrhythmias in SQTS6 patients.

Cardiovascular diseases are the primary cause of death of humans, and among these, ventricular arrhythmias are the most common cause of death. There is plausible evidence implicating inflammation in the etiology of ventricular fibrillation (VF). In the case of systemic inflammation caused by an overactive immune response, the induced inflammatory cytokines directly affect the function of ion channels in cardiomyocytes, leading to a prolonged action potential duration (APD). However, the mechanistic links between inflammatory cytokine-induced molecular and cellular influences and inflammation-associated ventricular arrhythmias need to be elucidated. The present study aimed to determine the potential impact of systemic inflammation on ventricular electrophysiology by means of multiscale virtual heart models. The experimental data on the ionic current of three major cytokines [i.e., tumor necrosis factor-α (TNF-α), interleukin-1 (IL-1β), and interleukin-6 (IL-6)] were incorporated into the cell model, and the effects of each cytokine and their combined effect on the cell action potential (AP) were evaluated. Moreover, the integral effect of these cytokines on the conduction of excitation waves was also investigated in a tissue model. The simulation results suggested that inflammatory cytokines significantly prolonged APD, enhanced the transmural and regional repolarization heterogeneities that predispose to arrhythmias, and reduced the adaptability of ventricular tissue to fast heart rates. In addition, simulated pseudo-ECGs showed a prolonged QT interval—a manifestation consistent with clinical observations. In summary, the present study provides new insights into ventricular arrhythmias associated with inflammation.

The dual-polarized ratio (DPR) algorithm is a new algorithm that enable calculation of Arctic sea ice concentration from the 36.5-GHz channel of the sensor Advanced Microwave Scanning Radiometer for EOS/Advanced Microwave Scanning Radiometer 2 (AMSR-E/AMSR2). In this paper, we demonstrate results that the sea ice concentration data using DPR algorithm (DPR-AMSR) are evaluated and compared with other eight Arctic sea ice concentration data products with respect to differences in sea ice concentration, sea ice area, and sea ice extent. On a pan-Arctic scale, the evaluation results are mostly very similar between DPR-AMSR and the bootstrap algorithm from AMSR-E/AMSR2 (BT-AMSR), the bootstrap algorithm from SSM/I or SSMIS (BT-SSMI), the ARTIST Sea Ice algorithm from AMSR-E/AMSR2 (ASI-AMSR), and the enhanced NASA Team algorithm from AMSR-E/AMSR2 (NT2-AMSR). Among of these products, the differences in sea ice concentration agree within ±5%. However, European Space Agency Climate Change Initiative algorithm from AMSR-E/AMSR2 (SICCI-AMSR), the European Organisation for the Exploitation of Meteorological Satellites Ocean and Sea Ice Satellite Application Facility from SSM/I or SSMIS (OSI-SSMI), the ARTIST Sea Ice algorithm from SSM/I or SSMIS (ASI-SSMI), and the NASA Team algorithm from SSM/I or SSMIS (NT1-SSMI) are all lower than DPR-AMSR at sea ice edge. And NT1-SSMI had the largest negative difference, which was lower than -15% or even 20%.The difference of sea ice area was consistently within ±0.5 million km2 between DPR-AMSR and BT-AMSR, BT-SSMI, ASI-AMSR, and NT2-AMSR in all years. The smallest difference was with BT-SSMI (less than 0.1 million km2), whereas the largest difference was with NT1-SSMI (up to 1.5 million km2). In comparisons of sea ice extent, BT-AMSR, NT1-SSMI, and NT2-AMSR estimates were consistent with that of DPR-AMSR and were within ±0.5 million km2. However, differences exceeded 0.5 million km2 between DPR-AMSR and the other data sets. When ship-based visual observation (OBS) values ranged from 85% to 100%, the difference between DPR-AMSR and OBS was less than 1%. There were relatively large differences between DPR-AMSR and OBS when OBS values were less than 85% or were recorded during the summer, although those differences were also within 10%.

Cardiovascular disease is the leading cause of death worldwide and kills over 17 million people per year. In the recent decade, growing epidemiological evidence links air pollution and cardiac arrhythmias, suggesting a detrimental influence of air pollution on cardiac electrophysiological functionality. However, the proarrhythmic mechanisms underlying the air pollution-induced cardiac arrhythmias are not fully understood. The purpose of this work is to provide recent advances in air pollution-induced arrhythmias with a comprehensive review of the literature on the common air pollutants and arrhythmias. Six common air pollutants of widespread concern are discussed, namely particulate matter, carbon monoxide, hydrogen sulfide, sulfur dioxide, nitrogen dioxide, and ozone. The epidemiological and clinical reports in recent years are reviewed by pollutant type, and the recently identified mechanisms including both the general pathways and the direct influences of air pollutants on the cellular electrophysiology are summarized. Particularly, this review focuses on the impaired ion channel functionality underlying the air pollution-induced arrhythmias. Alterations of ionic currents directly by the air pollutants, as well as the alterations mediated by intracellular signaling or other more general pathways are reviewed in this work. Finally, areas for future research are suggested to address several remaining scientific questions.

Molecular property prediction is important to drug design. With the development of artificial intelligence, deep learning methods are effective for extracting molecular features. In this paper, we propose a multichannel substructure-graph gated recurrent unit (GRU) architecture, which is a novel GRU-based neural network with attention mechanisms applied to molecular substructures to learn and predict properties. In the architecture, molecular features are extracted at the node level and molecule level for capturing fine-grained and coarse-grained information. In addition, three bidirectional GRUs are adopted to extract the features on three channels to generate the molecular representations. Different attention weights are assigned to the entities in the molecule to evaluate their contributions. Experiments are implemented to compare our model with benchmark models in molecular property prediction for both regression and classification tasks, and the results show that our model has strong robustness and generalizability.

Because of its unique reductive properties, nanoscale zero-valent iron (NZVI) has been widely used in soil and groundwater remediation. It is crucial to understand the fate of NZVI in soils. In this work, a range of laboratory experiments was conducted to examine the adsorption, transformation, and transport of NZVI. The NZVI particles showed relatively strong sorption onto vertic shajiang-aquic cambosols, typic-hapli-udic argosols and typic ochri-aquic cambosols and isotherms can be well described by the Langmuir model. When cultivated in the soils, the NZVI was oxidized within seven days and the particle size also increased with average size reaching around 100 nm at 28 days. Columns were packed with quartz sand, sandy soil and diatomite as saturated porous media to evaluate the role of soil colloids in affecting the transport of NZVI. The results showed that NZVI transport in the columns was strongly affected by medium types, dispersion agent types, and soil colloids. Comparisons of breakthrough curves of different types of dispersed NZVI and corresponding colloid-NZVI revealed that natural soil colloids facilitated NZVI transport in porous media. Findings from this work can provide useful information to better evaluate the applications and impacts of NZVI as an environmental remediation agent.

Visible Infrared Imaging Radiometer Suite (VIIRS) data were systematically evaluated and used to detect harmful algal bloom (HAB) and classify algal bloom types in coasts of the East China Sea covered by optically complex and sediment-rich waters. First, the accuracy and spectral characteristics of VIIRS retrieved normalized water-leaving radiance or the equivalent remote sensing reflectance from September 2019 to October 2020 that were validated by the long-term observation data acquired from an offshore platform and underway measurements from a cruise in the Changjiang Estuary and adjacent East China Sea. These data were evaluated by comparing them with data from the Moderate-Resolution Imaging Spectroradiometer. The bands of 486, 551, and 671 nm provided much higher quality than those of 410 and 443 nm and were more suitable for HAB detection. Secondly, the performance of four HAB detection algorithms were compared. The Ratio of Algal Bloom (RAB) algorithm is probably more suitable for HAB detection in the study area. Importantly, although RAB was also verified to be applicable for the detection of different kinds of HAB (Prorocentrum donghaiense, diatoms, Ceratium furca, and Akashiwo sanguinea), the capability of VIIRS in the classification of those algal species was limited by the lack of the critical band near 531 nm.

The semi-enclosed bays impacted by heavy anthropogenic activities have weak water exchange and purification capacities. Most of the sea bays have suffered severe eutrophication, water quality deterioration, ecosystem degradation and other problems. Although many countries and local governments have carried out corresponding environmental protection actions, the evaluation of their effectiveness still requires monitoring technology and data support for long-term water environment change. In this study, we take Yueqing Bay, the fourth largest bay in China, as a case to study the satellite-based water quality monitoring and variation analysis. We established a nutrient retrieval model for Yueqing Bay to produce a long-term series of nutrient concentration products in Yueqing Bay from 2013 to 2020, based on Landsat remote sensing images and long-term observation data, combined with support vector machine learning and water temperature and satellite spectra as input parameters, and then we analyzed its spatiotemporal variations and driving factors. In general, nutrient concentrations in the western part of the bay were higher than those in the eastern part. Levels of dissolved inorganic nitrogen (DIN) were lower in summer than in spring and winter, and reactive phosphate (PO4-P) levels were lower in summer and higher in autumn. In terms of natural factors, physical effects (e.g., seasonal variations in flow field) and biological effects (e.g., seasonal differences in the intensity of plankton photosynthesis) were the main causes of seasonal differences in nutrient concentration in Yueqing Bay. Nutrient concentration generally increased from 2013 to 2015 but decreased slightly after 2015. Over the past decade, the economy and industry of Yueqing Bay basin have developed rapidly. Wastewater resulting from anthropogenic production and consumption was transported via streams into Yueqing Bay, leading to the continuous increase in nutrient concentrations (the variation rates: aDIN>0, aPO4−P>0), which directly or indirectly caused high nutrient concentrations in some areas of the bay (e.g., Southwest Shoal at the mouth of Yueqing Bay). After 2015, the various ecological remediation policies adopted by cities around Yueqing Bay have mitigated, to some extent, the increasing nutrient concentration trends (the variation rates: aDIN<0, aPO4−P<0), but not significantly (P > 0.1). The environmental restoration of Yueqing Bay also requires continuous and long-term ecological protection and restoration work to be effective. This research can provide a reference for ecological environment monitoring and remote sensing data application for similar semi-enclosed bays, and support the sustainable development of the bay.

Hydrogen sulfide (H2S), an ambient air pollutant, has been reported to increase cardiac events in patients with cardiovascular diseases, but the underlying mechanisms remain not elucidated. This study investigated the pro-arrhythmic effects of H2S in healthy and ischemic conditions. Experimental data of H2S effects on ionic channels (including the L-type Ca2+ channel and ATP-sensitive K+ channel) were incorporated into a virtual heart model to evaluate their integral action on cardiac arrhythmogenesis. It was shown that H2S depressed cellular excitability, abbreviated action potential duration, and augmented tissue’s transmural dispersion of repolarization, resulting in an increase in tissue susceptibility to initiation and maintenance of reentry. The observed effects of H2S on cardiac excitation are more remarkable in the ischemic condition than in the healthy condition. This study provides mechanistic insights into the pro-arrhythmic effects of air pollution (H2S), especially in the case with extant ischemic conditions.

In the process of drug discovery, identifying the interaction between the protein and the novel compound plays an important role. With the development of technology, deep learning methods have shown excellent performance in various situations. However, the compound–protein interaction is complicated and the features extracted by most deep models are not comprehensive, which limits the performance to a certain extent. In this paper, we proposed a multiscale convolutional network that extracted the local and global features of the protein and the topological feature of the compound using different types of convolutional networks. The results showed that our model obtained the best performance compared with the existing deep learning methods.

In the above article [1], there are two errors in Figures 8 and 19. The error of Figure 8 was caused by incorrectly referencing the same figure as Figure 9.

Molecular toxicity prediction is one of the key studies in drug design. In this paper, a deep learning network based on a two-dimension grid of molecules is proposed to predict toxicity. At first, the van der Waals force and hydrogen bond were calculated according to different descriptors of molecules, and multi-channel grids were generated, which could discover more detail and helpful molecular information for toxicity prediction. The generated grids were fed into a convolutional neural network to obtain the result. A Tox21 dataset was used for the evaluation. This dataset contains more than 12,000 molecules. It can be seen from the experiment that the proposed method performs better compared to other traditional deep learning and machine learning methods.

Purpose: The development level and quality of education depend on the merits and efficiency in the use of teaching resources, especially in the case of obvious contradiction between the demand and supply of teaching resources. So to integrate teaching resources, improve the efficiency in the use of high quality teaching resources, and take the road of content development to enhance the competitiveness of education has become very important and urgent.Design/methodology/approach: On the basis of analysis on the teaching resources of universities and the problems they faced, this paper introduced the basic concepts of cloud storage, and built the integration architecture of high quality teaching resources in universities based on the cloud storage.Findings and Originality/value: The HDFS-based cloud storage proposed in this paper is a dynamically adjustable and Internet-based storage solution, and the users can access storage targets using the network through a common and easy-to-use protocol and application programming interfaces. This new technology is useful for end users benefits. With the continuous development and improvement of cloud storage, it will necessarily result in more and more applications in the institutions of higher learning and education network.Originality/value: This paper introduced the cloud storage into the integration of high quality teaching resources in universities first and as a new form of service, it can be a good solution.

Human activity recognition is important for healthcare and lifestyle evaluation. In this paper, a novel method for activity recognition by jointly considering motion sensor data recorded by wearable smart watches and image data captured by RGB-Depth (RGB-D) cameras is presented. A normalized cross correlation based mapping method is implemented to establish association between motion sensor data with corresponding image data from the same person in multi-person situations. Further, to improve the performance and accuracy of recognition, a hierarchical structure embedded with an automatic group selection method is proposed. Through this method, if the number of activities to be classified is changed, the structure will be changed correspondingly without interaction. Our comparative experiments against the single data source and single layer methods have shown that our method is more accurate and robust.