Parvalbumin and Somatostatin: Biomarkers for Two Parallel Tectothalamic Pathways in the Auditory Midbrain.

The inferior colliculus (IC) represents a crucial relay station in the auditory pathway, located in the midbrain's tectum and primarily projecting to the thalamus. Despite the identification of distinct cell classes based on various biomarkers in the IC, their specific contributions to the organization of auditory tectothalamic pathways have remained poorly understood. In this study, we demonstrate that IC neurons expressing parvalbumin (ICPV+) or somatostatin (ICSOM+) represent two minimally overlapping cell classes throughout the three IC subdivisions in mice of both sexes. Strikingly, regardless of their location within the IC, these neurons predominantly project to the primary and secondary auditory thalamic nuclei, respectively. Cell class-specific input tracing suggested that ICPV+ neurons primarily receive auditory inputs, whereas ICSOM+ neurons receive significantly more inputs from the periaqueductal gray and the superior colliculus (SC), which are sensorimotor regions critically involved in innate behaviors. Furthermore, ICPV+ neurons exhibit significant heterogeneity in both intrinsic electrophysiological properties and presynaptic terminal size compared with ICSOM+ neurons. Notably, approximately one-quarter of ICPV+ neurons are inhibitory neurons, whereas all ICSOM+ neurons are excitatory neurons. Collectively, our findings suggest that parvalbumin and somatostatin expression in the IC can serve as biomarkers for two functionally distinct, parallel tectothalamic pathways. This discovery suggests an alternative way to define tectothalamic pathways and highlights the potential usefulness of Cre mice in understanding the multifaceted roles of the IC at the circuit level.

Oxygen-Binding Sites of Enriched Gold Nanoclusters for Capturing Mitochondrial Reverse Electrons.

Reverse electron transfer (RET), an abnormal backward flow of electrons from complexes III/IV to II/I of mitochondria, causes the overproduction of a reduced-type CoQ to boost downstream production of mitochondrial superoxide anions that leads to ischemia-reperfusion injury (IRI) to organs. Herein, we studied low-coordinated gold nanoclusters (AuNCs) with abundant oxygen-binding sites to form an electron-demanding trapper that allowed rapid capture of electrons to compensate for the CoQ/CoQH2 imbalance during RET. The AuNCs were composed of only eight gold atoms that formed a Cs-symmetrical configuration with all gold atoms exposed on the edge site. The geometry and atomic configuration enhance oxygen intercalation to attain a d-band electron deficiency in frontier orbitals, forming an unusually high oxidation state for rapid mitochondrial reverse electron capture under a transient imbalance of CoQ/CoQH2 redox cycles. Using hepatic IRI cells/animals, we corroborated that the CoQ-like AuNCs prevent inflammation and liver damage from IRI via recovery of the mitochondrial function.

Adaptive node feature extraction in graph-based neural networks for brain diseases diagnosis using self-supervised learning.

Electroencephalography (EEG) has demonstrated significant value in diagnosing brain diseases. In particular, brain networks have gained prominence as they offer additional valuable insights by establishing connections between EEG signal channels. While brain connections are typically delineated by channel signal similarity, there lacks a consistent and reliable strategy for ascertaining node characteristics. Conventional node features such as temporal and frequency domain properties of EEG signals prove inadequate for capturing the extensive EEG information. In our investigation, we introduce a novel adaptive method for extracting node features from EEG signals utilizing a distinctive task-induced self-supervised learning technique. By amalgamating these extracted node features with fundamental edge features constructed using Pearson correlation coefficients, we showed that the proposed approach can function as a plug-in module that can be integrated to many common GNN networks (e.g., GCN, GraphSAGE, GAT) as a replacement of node feature selections module. Comprehensive experiments are then conducted to demonstrate the consistently superior performance and high generality of the proposed method over other feature selection methods in various of brain disorder prediction tasks, such as depression, schizophrenia, and Parkinson's disease. Furthermore, compared to other node features, our approach unveils profound spatial patterns through graph pooling and structural learning, shedding light on pivotal brain regions influencing various brain disorder prediction based on derived features.

Comparative analysis of brain age prediction using structural and diffusion MRIs in neonates.

Using machine learning techniques to predict brain age from multimodal data has become a crucial biomarker for assessing brain development. Among various types of brain imaging data, structural magnetic resonance imaging (sMRI) and diffusion magnetic resonance imaging (dMRI) are the most commonly used modalities. sMRI focuses on depicting macrostructural features of the brain, while dMRI reveals the orientation of major white matter fibers and changes in tissue microstructure. However, their differential capabilities in reflecting newborn age and clinical implications have not been systematically studied. This study aims to explore the impact of sMRI and dMRI on brain age prediction. Comparing predictions based on T2-weighted(T2w) and fractional anisotropy (FA) images, we found their mean absolute errors (MAE) in predicting infant age to be similar. Exploratory analysis revealed for T2w images, areas such as the cerebral cortex and ventricles contribute most significantly to age prediction, whereas FA images highlight the cerebral cortex and regions of the main white matter tracts. Despite both modalities focusing on the cerebral cortex, they exhibit significant region-wise differences, reflecting developmental disparities in macro- and microstructural aspects of the cortex. Additionally, we examined the effects of prematurity, gender, and hemispherical asymmetry of the brain on age prediction for both modalities. Results showed significant differences (p<0.05) in age prediction biases based on FA images across gender and hemispherical asymmetry, whereas no significant differences were observed with T2w images. This study underscores the differences between T2w and FA images in predicting infant brain age, offering new perspectives for studying infant brain development and aiding more effective assessment and tracking of infant development.

Comparative evaluation of interpretation methods in surface-based age prediction for neonates.

Significant changes in brain morphology occur during the third trimester of gestation. The capability of deep learning in leveraging these morphological features has enhanced the accuracy of brain age predictions for this critical period. Yet, the opaque nature of deep learning techniques, often described as "black box" approaches, limits their interpretability, posing challenges in clinical applications. Traditional interpretable methods developed for computer vision and natural language processing may not directly translate to the distinct demands of neuroimaging. In response, our research evaluates the effectiveness and adaptability of two interpretative methods-regional age prediction and the perturbation-based saliency map approach-for predicting the brain age of neonates. Analyzing 664 T1 MRI scans with the NEOCIVET pipeline to extract brain surface and cortical features, we assess how these methods illuminate key brain regions for age prediction, focusing on technical analysis with clinical insight. Through a comparative analysis of the saliency index (SI) with relative brain age (RBA) and the examination of structural covariance networks, we uncover the saliency index's enhanced ability to pinpoint regions vital for accurate indication of clinical factors. Our results highlight the advantages of perturbation techniques in addressing the complexities of medical data, steering clinical interventions for premature neonates towards more personalized and interpretable approaches. This study not only reveals the promise of these methods in complex medical scenarios but also offers a blueprint for implementing more interpretable and clinically relevant deep learning models in healthcare settings.

Harmonizing three-dimensional MRI using pseudo-warping field guided GAN.

In pursuit of cultivating automated models for magnetic resonance imaging (MRI) to aid in diagnostics, an escalating demand for extensive, multisite, and heterogeneous brain imaging datasets has emerged. This potentially introduces biased outcomes when directly applied for subsequent analysis. Researchers have endeavored to address this issue by pursuing the harmonization of MRIs. However, most existing image-based harmonization methods for MRI are tailored for 2D slices, which may introduce inter-slice variations when they are combined into a 3D volume. In this study, we aim to resolve inconsistencies between slices by introducing a pseudo-warping field. This field is created randomly and utilized to transform a slice into an artificially warped subsequent slice. The objective of this pseudo-warping field is to ensure that generators can consistently harmonize adjacent slices to another domain, without being affected by the varying content present in different slices. Furthermore, we construct unsupervised spatial and recycle loss to enhance the spatial accuracy and slice-wise consistency across the 3D images. The results demonstrate that our model effectively mitigates inter-slice variations and successfully preserves the anatomical details of the images during the harmonization process. Compared to generative harmonization models that employ 3D operators, our model exhibits greater computational efficiency and flexibility.

The effect of preterm birth on thalamic development based on shape and structural covariance analysis.

Acting as a central hub in regulating brain functions, the thalamus plays a pivotal role in controlling high-order brain functions. Considering the impact of preterm birth on infant brain development, traditional studies focused on the overall development of thalamus other than its subregions. In this study, we compared the volumetric growth and shape development of the thalamic hemispheres between the infants born preterm and full-term (Left volume: P = 0.027, Left normalized volume: P < 0.0001; Right volume: P = 0.070, Right normalized volume: P < 0.0001). The ventral nucleus region, dorsomedial nucleus region, and posterior nucleus region of the thalamus exhibit higher vulnerability to alterations induced by preterm birth. The structural covariance (SC) between the thickness of thalamus and insula in preterm infants (Left: corrected P = 0.0091, Right: corrected P = 0.0119) showed significant increase as compared to full-term controls. Current findings suggest that preterm birth affects the development of the thalamus and has differential effects on its subregions. The ventral nucleus region, dorsomedial nucleus region, and posterior nucleus region of the thalamus are more susceptible to the impacts of preterm birth.

Discovery and Characterization of Pyridoxal 5'-Phosphate-Dependent Cycloleucine Synthases.

Pyridoxal 5'-phosphate (PLP)-dependent enzymes are the most versatile biocatalysts for synthesizing nonproteinogenic amino acids. α,α-Disubstituted quaternary amino acids, such as 1-aminocyclopentane-1-carboxylic acid (cycloleucine), are useful building blocks for pharmaceuticals. In this study, starting with the biosynthesis of fusarilin A, we discovered a family of PLP-dependent enzymes that can facilitate tandem carbon-carbon forming steps to catalyze an overall [3 + 2]-annulation. In the first step, the cycloleucine synthases use SAM as the latent electrophile and an in situ-generated enamine as the nucleophile for γ-substitution. Whereas previously characterized γ-replacement enzymes protonate the resulting α-carbon and release the acyclic amino acid, cycloleucine synthases can catalyze an additional, intramolecular aldol or Mannich reaction with the nucleophilic α-carbon to form the substituted cyclopentane. Overall, the net [3 + 2]-annulation reaction can lead to 2-hydroxy or 2-aminocycloleucine products. These studies further expand the biocatalytic scope of PLP-dependent enzymes.

Lithium Orbital Hybridization Chemistry to Stimulate Oxygen Redox with Reversible Phase Evolution in Sodium-Layered Oxide Cathodes.

Searching for high energy-density electrode materials for sodium ion batteries has revealed Na-deficient intercalation compounds with lattice oxygen redox as promising high-capacity cathodes. However, anionic redox reactions commonly encountered poor electrochemical reversibility and unfavorable structural transformations during dynamic (de)sodiation processes. To address this issue, we employed lithium orbital hybridization chemistry to create Na-O-Li configuration in a prototype P2-layered Na43/60Li1/20Mg7/60Cu1/6Mn2/3O2 (P2-NaLMCM') cathode material. That Li+ ions, having low electronegativity, reside in the transition metal slabs serves to stimulate unhybridized O 2p orbitals to facilitate the stable capacity contribution of oxygen redox at high state of charge. The prismatic-type structure evolving to an intergrowth structure of the Z phase at high charging state could be simultaneously alleviated by reducing the electrostatic repulsion of O-O layers. As a consequence, P2-NaLMCM' delivers a high specific capacity of 183.8 mAh g-1 at 0.05 C and good cycling stability with a capacity retention of 80.2% over 200 cycles within the voltage range of 2.0-4.5 V. Our findings provide new insights into both tailoring oxygen redox chemistry and stabilizing dynamic structural evolution for high-energy battery cathode materials.

Reactions of Benzyl Phosphine Oxide/Sulfide with (COCl)2: Synthesis of Novel Acyl Chloride-Substituted Chlorophosphonium Ylides.

New reactions of benzyl phosphine oxide/sulfide with oxalyl chloride are presented. The resulting reactive intermediates, acyl chloride-substituted chlorophosphonium ylides, are capable of undergoing esterification and Friedel-Crafts acylation reactions, ultimately yielding either methyl 2-(2-bromophenyl)-2-(diphenylphosphoryl)acetate or ß-carbonyl-diarylphosphine oxide derivatives. Additionally, when an alkynyl group is contained in the acyl chloride-substituted chlorophosphonium ylide, intramolecular cyclization occurs, leading to the formation of a pair of trans- and cis-dichlorophosphonyl benzofulvene isomers. The generation process of acyl chloride-substituted chlorophosphonium ylide was carefully monitored by using 31P{1H} NMR spectroscopy, and a plausible reaction mechanism was proposed.

Photoredox/Copper-Cocatalyzed Domino Annulation of Oxime Esters and NH4SCN: Access to Fully Substituted 2-Aminothiazoles.

Domino cyclization of oxime esters and NH4SCN facilitated by photoredox and copper cocatalysis has been established. Various structurally diverse fully substituted 2-aminothiazoles have been obtained in good yields at room temperature. It is featured by mild conditions, favorable functional group tolerance, and wide substrate scope. The present reaction is amenable to gram-scale synthesis, which is expected to find potential applications in organic synthesis and drug discovery. A plausible reaction mechanism is proposed.

Theoretical insights into the mechanism underlying aflatoxin B1 transformation by the BsCotA-methyl syringate system.

Global concern exists regarding the contamination of food and animal feed with aflatoxin B1 (AFB1), which poses a threat to the health of both humans and animals. Previously, we found that a laccase from Bacillus subtilis (BsCotA) effectively detoxified AFB1 in a reaction mediated by methyl syringate (MS), although the underlying mechanism has not been determined. Therefore, our primary objective of this study was to explore the detoxification mechanism employed by BsCotA. First, the enzyme and mediator dependence of AFB1 transformation were studied using the BsCotA-MS system, which revealed the importance of MS radical formation during the oxidation process. Aflatoxin Q1 (AFQ1) resulting from the direct oxidation of AFB1 by BsCotA, was identified using ultra-high-performance liquid chromatography-tandem mass spectrometry (UPLC-MS/MS). The results of UPLC-MS/MS and density functional theory calculations indicated that the products included AFQ1, AFB1-, and AFD1-MS-coupled products in the BsCotA-MS system. The toxicity evaluations revealed that the substances derived from the transformation of AFB1 through the BsCotA-MS mechanism exhibited markedly reduced toxicity compared to AFB1. Finally, we proposed a set of different AFB1-transformation pathways generated by the BsCotA-MS system based on the identified products. These findings greatly enhance the understanding of the AFB1-transformation mechanism of the laccase-mediator system.

Correction: Dong et al. Identification of the Trace Components in BopuzongJian and Macleaya cordata Extract Using LC-MS Combined with a Screening Method. Molecules 2021, 26, 3851.

The article in question [...].

Effects of long-term different-scale rice-duck farming on the growth and yield of paddy rice.

BACKGROUND: To maintain rice production and increase revenue, rice-duck (RD) farming is a contemporary ecological cycle technology that has been widely used in Asia. However, due to the clustering activity of duck flocks, the consequences of long-term RD farming on rice growth at different scales are still unknown. Here, we studied RD farming using several different treatments (CK: conventional rice farming; RD1: 667 m2 ; RD2: 2000 m2 ; and RD3: 3333 m2 ). RESULTS: The results demonstrated that the maximum tillers, effective spikes, dry matter accumulation, and lodging index of rice under RD farming were significantly decreased by 17.9%, 9.8%, 14.8%, and 17.8%, respectively, which ultimately caused a significant decrease in yield of 10.6%. However, RD farming significantly increased root oxidation activity and the ear-bearing tiller rate of rice by 25.5% and 11.1%, respectively, and improved yield stability. For different scales of RD farming, the lodging resistance index of RD1 was significantly lower than that of RD2 and RD3 by 10.0% and 15.2%, respectively, whereas the root oxidation activity and dry matter accumulation of RD2 were significantly higher than those of RD1 and RD3 by 11.1%, 4.7%, 8.6%, and 5.1%, respectively. For rice yield, there was no significant difference among the different scales. CONCLUSION: This long-term experiment helped elucidate the complicated effects of RD farming at different scales on the growth and yield of rice. It is also critical to consider the economic advantages of different scales of RD farming to assess the impact of this system more thoroughly. © 2023 Society of Chemical Industry.

Dual-Anionic Coordination Manipulation Induces Phosphorus and Boron-Rich Gradient Interphase Towards Stable and Safe Sodium Metal Batteries.

High-voltage sodium metal batteries (SMBs) present a viable pathway towards high-energy-density sodium-based batteries due to the competitive cost advantage and abundant supply of sodium resources. However, they still suffer from severe capacity decay induced by the notorious decomposition of the electrolyte under high voltage and unstable cathode/electrolyte interphase (CEI). In addition, the high reactivity of Na metal and flammable electrolytes push SMBs to their safety limits. Herein, a special dual-anion aggregated Na+ solvation structure is designed in a nonflammable trimethyl phosphate-based localized high-concentration electrolyte, and a gradient CEI enriched with phosphorus and boron compounds is formed on the cathode. This thin and stable interphase effectively suppresses the parasitic reaction, improves the interfacial stability of the cathode, and facilitates Na+ transport through the interface by the synergistic effect of multi-components, thus optimizing the cycling stability and safety of SMBs. The Na0.95Ni0.4Fe0.15Mn0.3Ti0.15O2//Na batteries employing such electrolyte provide a discharge capacity of 167.5 mA h g-1 and high retention in the capacity of 85.2% after 800 cycles at 1 C. This approach offers a general strategy for the design of flame-retardant high-voltage electrolytes and the practical application of SMBs.

Monolithic Interphase Enables Fast Kinetics for High-Performance Sodium-Ion Batteries at Subzero Temperature.

In spite of the competitive performance at room temperature, the development of sodium-ion batteries (SIBs) is still hindered by sluggish electrochemical reaction kinetics and unstable electrode/electrolyte interphase under subzero environments. Herein, a low-concentration electrolyte, consisting of 0.5M NaPF6 dissolving in diethylene glycol dimethyl ether solvent, is proposed for SIBs working at low temperature. Such an electrolyte generates a thin, amorphous, and homogeneous cathode/electrolyte interphase at low temperature. The interphase is monolithic and rich in organic components, reducing the limitation of Na+ migration through inorganic crystals, thereby facilitating the interfacial Na+ dynamics at low temperature. Furthermore, it effectively blocks the unfavorable side reactions between active materials and electrolytes, improving the structural stability. Consequently, Na0.7Li0.03Mg0.03Ni0.27Mn0.6Ti0.07O2//Na and hard carbon//Na cells deliver a high capacity retention of 90.8 % after 900 cycles at 1C, a capacity over 310 mAh g-1 under -30 °C, respectively, showing long-term cycling stability and great rate capability at low temperature.

Style transfer generative adversarial networks to harmonize multisite MRI to a single reference image to avoid overcorrection.

Recent work within neuroimaging consortia have aimed to identify reproducible, and often subtle, brain signatures of psychiatric or neurological conditions. To allow for high-powered brain imaging analyses, it is often necessary to pool MR images that were acquired with different protocols across multiple scanners. Current retrospective harmonization techniques have shown promise in removing site-related image variation. However, most statistical approaches may over-correct for technical, scanning-related, variation as they cannot distinguish between confounded image-acquisition based variability and site-related population variability. Such statistical methods often require that datasets contain subjects or patient groups with similar clinical or demographic information to isolate the acquisition-based variability. To overcome this limitation, we consider site-related magnetic resonance (MR) imaging harmonization as a style transfer problem rather than a domain transfer problem. Using a fully unsupervised deep-learning framework based on a generative adversarial network (GAN), we show that MR images can be harmonized by inserting the style information encoded from a single reference image, without knowing their site/scanner labels a priori. We trained our model using data from five large-scale multisite datasets with varied demographics. Results demonstrated that our style-encoding model can harmonize MR images, and match intensity profiles, without relying on traveling subjects. This model also avoids the need to control for clinical, diagnostic, or demographic information. We highlight the effectiveness of our method for clinical research by comparing extracted cortical and subcortical features, brain-age estimates, and case-control effect sizes before and after the harmonization. We showed that our harmonization removed the site-related variances, while preserving the anatomical information and clinical meaningful patterns. We further demonstrated that with a diverse training set, our method successfully harmonized MR images collected from unseen scanners and protocols, suggesting a promising tool for ongoing collaborative studies. Source code is released in USC-IGC/style_transfer_harmonization (github.com).

Mendelian randomization analysis suggests no associations of herpes simplex virus infections with systemic lupus erythematosus.

Systemic lupus erythematosus (SLE) characterized by immune dysfunction is possibly more vulnerable to herpes simplex virus (HSV) infection. The infection has been intensively considered a common onset and exacerbation of SLE. This study is aimed at elucidating the causal association between SLE and HSV. A bidirectional two-sample Mendelian Randomization (TSMR) analysis was systematically conducted to explore the causal effect of SLE and HSV on each other. The causality was estimated by inverse variance weighted (IVW), MR-Egger and weighted median methods based on the summary-level genome-wide association studies (GWAS) data from a publicly available database. Genetically proxied HSV infection exhibited no causal association with SLE in the forward MR analysis using IVW method (odds ratio [OR] = 0.987; 95% confidence interval [CI]: 0.891-1.093; p = 0.798), nor did HSV-1 IgG (OR = 1.241; 95% CI: 0.874-1.762; p = 0.227) and HSV-2 IgG (OR = 0.934; 95% CI: 0.821-1.062; p = 0.297). Similar null results with HSV infection (OR = 1.021; 95% CI: 0.986-1.057; p = 0.245), HSV-1 IgG (OR = 1.003; 95% CI: 0.982-1.024; p = 0.788) and HSV-2 IgG (OR = 1.034; 95% CI: 0.991-1.080; p = 0.121) were observed in the reverse MR where SLE served as the exposure. Our study demonstrated no causal association between the genetically predicted HSV and SLE.

Brain age predicted using graph convolutional neural network explains neurodevelopmental trajectory in preterm neonates.

OBJECTIVES: Dramatic brain morphological changes occur throughout the third trimester of gestation. In this study, we investigated whether the predicted brain age (PBA) derived from graph convolutional network (GCN) that accounts for cortical morphometrics in third trimester is associated with postnatal abnormalities and neurodevelopmental outcome. METHODS: In total, 577 T1 MRI scans of preterm neonates from two different datasets were analyzed; the NEOCIVET pipeline generated cortical surfaces and morphological features, which were then fed to the GCN to predict brain age. The brain age index (BAI; PBA minus chronological age) was used to determine the relationships among preterm birth (i.e., birthweight and birth age), perinatal brain injuries, postnatal events/clinical conditions, BAI at postnatal scan, and neurodevelopmental scores at 30 months. RESULTS: Brain morphology and GCN-based age prediction of preterm neonates without brain lesions (mean absolute error [MAE]: 0.96 weeks) outperformed conventional machine learning methods using no topological information. Structural equation models (SEM) showed that BAI mediated the influence of preterm birth and postnatal clinical factors, but not perinatal brain injuries, on neurodevelopmental outcome at 30 months of age. CONCLUSIONS: Brain morphology may be clinically meaningful in measuring brain age, as it relates to postnatal factors, and predicting neurodevelopmental outcome. CLINICAL RELEVANCE STATEMENT: Understanding the neurodevelopmental trajectory of preterm neonates through the prediction of brain age using a graph convolutional neural network may allow for earlier detection of potential developmental abnormalities and improved interventions, consequently enhancing the prognosis and quality of life in this vulnerable population. KEY POINTS: •Brain age in preterm neonates predicted using a graph convolutional network with brain morphological changes mediates the pre-scan risk factors and post-scan neurodevelopmental outcomes. •Predicted brain age oriented from conventional deep learning approaches, which indicates the neurodevelopmental status in neonates, shows a lack of sensitivity to perinatal risk factors and predicting neurodevelopmental outcomes. •The new brain age index based on brain morphology and graph convolutional network enhances the accuracy and clinical interpretation of predicted brain age for neonates.

Bacterial DNA metabolism analysis by metagenomic next-generation sequencing (mNGS) after treatment of bloodstream infection.

BACKGROUND: With the advent of metagenomic next-generation sequencing (mNGS), the time of DNA metabolism can be explored after bacteria be killed. In this study, we applied mNGS in investigation of the clearance profile of circulating bacteria DNA. METHODS: All of the rabbits were injected with the inactivated Escherichia coli. Using mNGS, we analyzed serial samples of plasma collected from rabbits to detect clearance profile of circulating E. coli DNA. RESULTS: In this study, we found that the of E. coli DNA could still be detected 6 h after injecting killed bacteria. The clearance half-lives associated with the 2 phases are 0.37 and 1.81 h. We also explored there is no correlation between the disease severity with the E. coli DNA reads in circulation. CONCLUSIONS: After the bacteria were completely killed, their DNA could still be detected in the blood circulation. The metabolism of bacterial DNA in the circulation had two phases: fast and slow phases, and there were no correlations between the level of bacteria reads with the severity of patients' disease after the bacteria have been completely killed.