Neural Components Research Articles

The fMRI global signal and its association with the signal from cranial bone

The nature of the global signal, i.e. the average signal from sequential functional imaging scans of the brain or the cortex, is not well understood, but is thought to include vascular and neural components. Using resting state data, we report on the strong association between the global signal and the average signal from the part of the volume that includes the cranial bone and subdural vessels and venous collectors, separated from each other and the subdural space by multispectral segmentation procedures. While subdural vessels carried a signal with a phase delay relative to the cortex, the association with the cortical signal was strongest in the parts of the scan corresponding to the laminae of the cranial bone, reaching 80% shared variance in some individuals. These findings suggest that in resting state data vascular components may play a prominent role in the genesis of fluctuations of the global signal. Evidence from other studies on the existence of neural sources of the global signal suggests that it may reflect the action of multiple mechanisms (including cerebrovascular reactivity and autonomic control) concurrently acting to regulate global cerebral perfusion.

Proton Conducting Neuromorphic Materials and Devices.

Neuromorphic computing and artificial intelligence hardware generally aims to emulate features found in biological neural circuit components and to enable the development of energy-efficient machines. In the biological brain, ionic currents and temporal concentration gradients control information flow and storage. It is therefore of interest to examine materials and devices for neuromorphic computing wherein ionic and electronic currents can propagate. Protons being mobile under an external electric field offers a compelling avenue for facilitating biological functionalities in artificial synapses and neurons. In this review, we first highlight the interesting biological analog of protons as neurotransmitters in various animals. We then discuss the experimental approaches and mechanisms of proton doping in various classes of inorganic and organic proton-conducting materials for the advancement of neuromorphic architectures. Since hydrogen is among the lightest of elements, characterization in a solid matrix requires advanced techniques. We review powerful synchrotron-based spectroscopic techniques for characterizing hydrogen doping in various materials as well as complementary scattering techniques to detect hydrogen. First-principles calculations are then discussed as they help provide an understanding of proton migration and electronic structure modification. Outstanding scientific challenges to further our understanding of proton doping and its use in emerging neuromorphic electronics are pointed out.

Skeletal muscle dysfunction with advancing age.

As a result of advances in medical treatments and associated policy over the last century, life expectancy has risen substantially and continues to increase globally. However, the disconnect between lifespan and 'health span' (the length of time spent in a healthy, disease-free state) has also increased, with skeletal muscle being a substantial contributor to this. Biological ageing is accompanied by declines in both skeletal muscle mass and function, termed sarcopenia. The mechanisms underpinning sarcopenia are multifactorial and are known to include marked alterations in muscle protein turnover and adaptations to the neural input to muscle. However, to date, the relative contribution of each factor remains largely unexplored. Specifically, muscle protein synthetic responses to key anabolic stimuli are blunted with advancing age, whilst alterations to neural components, spanning from the motor cortex and motoneuron excitability to the neuromuscular junction, may explain the greater magnitude of function losses when compared with mass. The consequences of these losses can be devastating for individuals, their support networks, and healthcare services; with clear detrimental impacts on both clinical (e.g., mortality, frailty, and post-treatment complications) and societal (e.g., independence maintenance) outcomes. Whether declines in muscle quantity and quality are an inevitable component of ageing remains to be completely understood. Nevertheless, strategies to mitigate these declines are of vital importance to improve the health span of older adults. This review aims to provide an overview of the declines in skeletal muscle mass and function with advancing age, describes the wide-ranging implications of these declines, and finally suggests strategies to mitigate them, including the merits of emerging pharmaceutical agents.

Tlx3 mediates neuronal differentiation and proper condensation of the developing trigeminal ganglion

The trigeminal ganglion, the largest of the vertebrate cranial ganglia, is comprised of sensory neurons that relay sensations of pain, touch, and temperature to the brain. These neurons are derived from two embryonic cell types, the neural crest and ectodermal placodes, whose interactions are critical for proper ganglion formation. While the T-cell leukemia homeobox 3 (Tlx3) gene is known to be expressed in placodally-derived sensory neurons and necessary for their differentiation, little was known about Tlx3 expression and/or function in the neural crest-derived component of the developing trigeminal ganglion. By combining lineage labeling with in situ hybridization in the chick embryo, we show that neural crest-derived cells that contribute to the cranial trigeminal ganglion express Tlx3 at a time point that coincides with the onset of ganglion condensation. Importantly, loss of Tlx3 function in vivo diminishes the overall size and abundance of neurons within the trigeminal ganglion. Conversely, ectopic expression of Tlx3 in migrating cranial neural crest results in their premature neuronal differentiation. Taken together, our results demonstrate a critical role for Tlx3 in neural crest-derived cells during chick trigeminal gangliogenesis.

Antiferromagnetic artificial neuron modeling of the withdrawal reflex.

Replicating neural responses observed in biological systems using artificial neural networks holds significant promise in the fields of medicine and engineering. In this study, we employ ultra-fast artificial neurons based on antiferromagnetic (AFM) spin Hall oscillators to emulate the biological withdrawal reflex responsible for self-preservation against noxious stimuli, such as pain or temperature. As a result of utilizing the dynamics of AFM neurons, we are able to construct an artificial neural network that can mimic the functionality and organization of the biological neural network responsible for this reflex. The unique features of AFM neurons, such as inhibition that stems from an effective AFM inertia, allow for the creation of biologically realistic neural network components, like the interneurons in the spinal cord and antagonist motor neurons. To showcase the effectiveness of AFM neuron modeling, we conduct simulations of various scenarios that define the withdrawal reflex, including responses to both weak and strong sensory stimuli, as well as voluntary suppression of the reflex.

Neuroprosthetic contact lens enabled sensorimotor system for point-of-care monitoring and feedback of intraocular pressure

The wearable contact lens that continuously monitors intraocular pressure (IOP) facilitates prompt and early-state medical treatments of oculopathies such as glaucoma, postoperative myopia, etc. However, either taking drugs for pre-treatment or delaying the treatment process in the absence of a neural feedback component cannot realize accurate diagnosis or effective treatment. Herein, a neuroprosthetic contact lens enabled sensorimotor system is reported, which consists of a smart contact lens with Ti3C2Tx Wheatstone bridge structured IOP strain sensor, a Ti3C2Tx temperature sensor and an IOP point-of-care monitoring/display system. The point-of-care IOP monitoring and warning can be realized due to the high sensitivity of 12.52 mV mmHg−1 of the neuroprosthetic contact lens. In vivo experiments on rabbit eyes demonstrate the excellent wearability and biocompatibility of the neuroprosthetic contact lens. Further experiments on a living rate in vitro successfully mimic the biological sensorimotor loop. The leg twitching (larger or smaller angles) of the living rat was demonstrated under the command of motor cortex controlled by somatosensory cortex when the IOP is away from the normal range (higher or lower).

Leverage Effective Deep Learning Searching Method for Forensic Age Estimation.

Dental age estimation is extensively employed in forensic medicine practice. However, the accuracy of conventional methods fails to satisfy the need for precision, particularly when estimating the age of adults. Herein, we propose an approach for age estimation utilizing orthopantomograms (OPGs). We propose a new dental dataset comprising OPGs of 27,957 individuals (16,383 females and 11,574 males), covering an age range from newborn to 93 years. The age annotations were meticulously verified using ID card details. Considering the distinct nature of dental data, we analyzed various neural network components to accurately estimate age, such as optimal network depth, convolution kernel size, multi-branch architecture, and early layer feature reuse. Building upon the exploration of distinctive characteristics, we further employed the widely recognized method to identify models for dental age prediction. Consequently, we discovered two sets of models: one exhibiting superior performance, and the other being lightweight. The proposed approaches, namely AGENet and AGE-SPOS, demonstrated remarkable superiority and effectiveness in our experimental results. The proposed models, AGENet and AGE-SPOS, showed exceptional effectiveness in our experiments. AGENet outperformed other CNN models significantly by achieving outstanding results. Compared to Inception-v4, with the mean absolute error (MAE) of 1.70 and 20.46 B FLOPs, our AGENet reduced the FLOPs by 2.7×. The lightweight model, AGE-SPOS, achieved an MAE of 1.80 years with only 0.95 B FLOPs, surpassing MobileNetV2 by 0.18 years while utilizing fewer computational operations. In summary, we employed an effective DNN searching method for forensic age estimation, and our methodology and findings hold significant implications for age estimation with oral imaging.

A Residual Deep Learning Method for Accurate and Efficient Recognition of Gym Exercise Activities Using Electromyography and IMU Sensors

The accurate and efficient recognition of gym workout activities using wearable sensors holds significant implications for assessing fitness levels, tailoring personalized training regimens, and overseeing rehabilitation progress. This study introduces CNN-ResBiGRU, a novel deep learning architecture that amalgamates residual and hybrid methodologies, aiming to precisely categorize gym exercises based on multimodal sensor data. The primary goal of this model is to effectively identify various gym workouts by integrating convolutional neural networks, residual connections, and bidirectional gated recurrent units. Raw electromyography and inertial measurement unit data collected from wearable sensors worn by individuals during strength training and gym sessions serve as inputs for the CNN-ResBiGRU model. Initially, convolutional neural network layers are employed to extract unique features in both temporal and spatial dimensions, capturing localized patterns within the sensor outputs. Subsequently, the extracted features are fed into the ResBiGRU component, leveraging residual connections and bidirectional processing to capture the exercise activities’ long-term temporal dependencies and contextual information. The performance of the proposed model is evaluated using the Myogym dataset, comprising data from 10 participants engaged in 30 distinct gym activities. The model achieves a classification accuracy of 97.29% and an F1-score of 92.68%. Ablation studies confirm the effectiveness of the convolutional neural network and ResBiGRU components. The proposed hybrid model uses wearable multimodal sensor data to accurately and efficiently recognize gym exercise activity.

Proposal for a Mechanistic Disease Conceptualization in Clinical Neurosciences: The Neural Network Components (NNC) Model.

Clinical neurosciences, and psychiatry specifically, have been challenged by the lack of a comprehensive and practical framework that explains the core mechanistic processes of variable psychiatric presentations. Current conceptualization and classification of psychiatric presentations are primarily centered on a non-biologically based clinical descriptive approach. Despite various attempts, advances in neuroscience research have not led to an improved conceptualization or mechanistic classification of psychiatric disorders. This perspective article proposes a new-work-in-progress-framework for conceptualizing psychiatric presentations based on neural network components (NNC). This framework could guide the development of mechanistic disease classification, improve understanding of underpinning pathology, and provide specific intervention targets. This model also has the potential to dissolve artificial barriers between the fields of psychiatry and neurology.

Unsupervised learning-enabled pulsed infrared thermographic microscopy of subsurface defects in stainless steel

Metallic structures produced with laser powder bed fusion (LPBF) additive manufacturing method (AM) frequently contain microscopic porosity defects, with typical approximate size distribution from one to 100 microns. Presence of such defects could lead to premature failure of the structure. In principle, structural integrity assessment of LPBF metals can be accomplished with nondestructive evaluation (NDE). Pulsed infrared thermography (PIT) is a non-contact, one-sided NDE method that allows for imaging of internal defects in arbitrary size and shape metallic structures using heat transfer. PIT imaging is performed using compact instrumentation consisting of a flash lamp for deposition of a heat pulse, and a fast frame infrared (IR) camera for measuring surface temperature transients. However, limitations of imaging resolution with PIT include blurring due to heat diffusion, sensitivity limit of the IR camera. We demonstrate enhancement of PIT imaging capability with unsupervised learning (UL), which enables PIT microscopy of subsurface defects in high strength corrosion resistant stainless steel 316 alloy. PIT images were processed with UL spatial–temporal separation-based clustering segmentation (STSCS) algorithm, refined by morphology image processing methods to enhance visibility of defects. The STSCS algorithm starts with wavelet decomposition to spatially de-noise thermograms, followed by UL principal component analysis (PCA), fine-tuning optimization, and neural learning-based independent component analysis (ICA) algorithms to temporally compress de-noised thermograms. The compressed thermograms were further processed with UL-based graph thresholding K-means clustering algorithm for defects segmentation. The STSCS algorithm also includes online learning feature for efficient re-training of the model with new data. For this study, metallic specimens with calibrated microscopic flat bottom hole defects, with diameters in the range from 203 to 76 µm, were produced using electro discharge machining (EDM) drilling. While the raw thermograms do not show any material defects, using STSCS algorithm to process PIT images reveals defects as small as 101 µm in diameter. To the best of our knowledge, this is the smallest reported size of a sub-surface defect in a metal imaged with PIT, which demonstrates the PIT capability of detecting defects in the size range relevant to quality control requirements of LPBF-printed high-strength metals.

Segmenting and Predicting Musical Phrase Structure Exploits Neural Gain Modulation and Phase Precession.

Music, like spoken language, is often characterized by hierarchically organized structure. Previous experiments have shown neural tracking of notes and beats, but little work touches on the more abstract question: how does the brain establish high-level musical structures in real time? We presented Bach chorales to participants (20 females and 9 males) undergoing electroencephalogram (EEG) recording to investigate how the brain tracks musical phrases. We removed the main temporal cues to phrasal structures, so that listeners could only rely on harmonic information to parse a continuous musical stream. Phrasal structures were disrupted by locally or globally reversing the harmonic progression, so that our observations on the original music could be controlled and compared. We first replicated the findings on neural tracking of musical notes and beats, substantiating the positive correlation between musical training and neural tracking. Critically, we discovered a neural signature in the frequency range ∼0.1 Hz (modulations of EEG power) that reliably tracks musical phrasal structure. Next, we developed an approach to quantify the phrasal phase precession of the EEG power, revealing that phrase tracking is indeed an operation of active segmentation involving predictive processes. We demonstrate that the brain establishes complex musical structures online over long timescales (>5 s) and actively segments continuous music streams in a manner comparable to language processing. These two neural signatures, phrase tracking and phrasal phase precession, provide new conceptual and technical tools to study the processes underpinning high-level structure building using noninvasive recording techniques.

Neural Filtering of Physiological Tremor Oscillations to Spinal Motor Neurons Mediates Short-Term Acquisition of a Skill Learning Task.

The acquisition of a motor skill involves adaptations of spinal and supraspinal pathways to alpha motoneurons. In this study, we estimated the shared synaptic contributions of these pathways to understand the neural mechanisms underlying the short-term acquisition of a new force-matching task. High-density surface electromyography (HDsEMG) was acquired from the first dorsal interosseous (FDI; 7 males and 6 females) and tibialis anterior (TA; 7 males and 4 females) during 15 trials of an isometric force-matching task. For two selected trials (pre- and post-skill acquisition), we decomposed the HDsEMG into motor unit spike trains, tracked motor units between trials, and calculated the mean discharge rate and the coefficient of variation of interspike interval (COVISI). We also quantified the post/pre ratio of motor units' coherence within delta, alpha, and beta bands. Force-matching improvements were accompanied by increased mean discharge rate and decreased COVISI for both muscles. Moreover, the area under the curve within alpha band decreased by ∼22% (TA) and ∼13% (FDI), with no delta or beta bands changes. These reductions correlated significantly with increased coupling between force/neural drive and target oscillations. These results suggest that short-term force-matching skill acquisition is mediated by attenuation of physiological tremor oscillations in the shared synaptic inputs. Supported by simulations, a plausible mechanism for alpha band reductions may involve spinal interneuron phase-cancelling descending oscillations. Therefore, during skill learning, the central nervous system acts as a matched filter, adjusting synaptic weights of shared inputs to suppress neural components unrelated to the specific task.

Identification of multicomponent LOFAR sources with multimodal deep learning

ABSTRACT Modern high-sensitivity radio telescopes are discovering an increased number of resolved sources with intricate radio structures and fainter radio emissions. These sources often present a challenge because source detectors might identify them as separate radio sources rather than components belonging to the same physically connected radio source. Currently, there are no reliable automatic methods to determine which radio components are single radio sources or part of multicomponent sources. We propose a deep-learning classifier to identify those sources that are part of a multicomponent system and require component association on data from the LOFAR Two-Metre Sky Survey. We combine different types of input data using multimodal deep learning to extract spatial and local information about the radio source components: a convolutional neural network component that processes radio images is combined with a neural network component that uses parameters measured from the radio sources and their nearest neighbours. Our model retrieves 94 per cent of the sources with multiple components on a balanced test set with 2683 sources and achieves almost 97 per cent accuracy in the real imbalanced data (323 103 sources). The approach holds potential for integration into pipelines for automatic radio component association and cross-identification. Our work demonstrates how deep learning can be used to integrate different types of data and create an effective solution for managing modern radio surveys.

PROCESS MONITORING IN HYBRID ELECTRIC VEHICLES BASED ON DYNAMIC NONLINEAR METHOD

Highway third-level faults can significantly deteriorate the reliability and performance of hybrid electric vehicle (HEV) powertrains. This study presents a novel process monitoring method aimed at addressing this issue. We propose a multivariate statistical method based on dynamic nonlinear improvement, namely dynamic neural component analysis (DNCA). This method does not require the establishment of precise analytical models; instead, it only necessitates acquiring data from HEV powertrains. Through numerical simulation and real HEV experiments, we demonstrate the effectiveness of this approach in monitoring highway third-level faults. The testing outcomes demonstrate that DNCA outperforms traditional dynamic methods like dynamic principal component analysis (DPCA), conventional nonlinear methods such as kernel PCA (KPCA) and NCA, as well as traditional dynamic nonlinear methods like DKPCA.

Dissociable neuronal mechanism for different crossmodal correspondence effects in humans.

Crossmodal correspondences (CMCs) refer to associations between seemingly arbitrary stimulus features in different sensory modalities. Pitch‑size correspondences refer to the strong association of e.g., small objects with high pitches. Pitch‑elevation correspondences refer to the strong association of e.g., visuospatial elevated objects with high pitches. We used functional magnetic resonance imaging (fMRI) to study the neural components, which underlie the CMCs in pitch‑size and spatial pitch‑elevation. This study focuses on answering the question of whether or not different CMCs are driven by similar neural mechanisms. The comparison of congruent against incongruent trials allows the estimation of CMC effects across different CMCs. The analysis of the measured neural activity in different CMCs strongly pointed toward different mechanisms which are involved in the processing of pitch‑size and pitch‑elevation correspondences. Differential, whole brain effects were observed within the superior parietal lobule (SPL), cerebellum and Heschls' gyrus (HG). Further, the angular gyrus (AnG), the intraparietal sulcus (IPS) and anterior cingulate cortex (ACC) were engaged in processing the CMCs but showed different effects for processing congruent compared to incongruent stimulus presentations. Within pitch‑size significant effects in the AnG and ACC were found for congruent stimulus presentations whereas for pitch‑elevation, significant effects in the ACC and IPS were found for incongruent stimulus presentations. In summary, the present results indicated differential neural processing in different simple audio‑visual CMCs.

Exploring Contrastive Learning Neural-Congruency on EEG Recording of Children with Dyslexia

Electroencephalogram (EEG) recordings of children are often used to study the underlying neural basis of causal factors of reading disorders and dyslexia. However, the inter-subject variability in EEG and the unconstrained nature of reading experiments used to elicit these factors made it challenging for traditional EEG analysis methods to extract neural components of these factors. In this work, we aim to explore the use of novel deep neural network architectures and contrastive learning methods to overcome the methodological limitations of traditional techniques and enhance the extraction process of neural components during reading tasks. Notably, we formulate a neural network architecture to extract EEG embedding using contrastive loss that maximizes the neural congruency in non-dyslexic children compared to children with dyslexia. We plan to evaluate our approach on three EEG datasets involving children with dyslexia performing Rapid Automatized Naming (RAN) and Phonological Processing (PA) tasks. The proposed contrastive learning framework will provide an enhanced tool to facilitate studying the neural underpinnings of naming speed and their association with reading performance and related difficulties.

A survey of neurosymbolic visual reasoning with scene graphs and common sense knowledge

A survey of neurosymbolic visual reasoning with scene graphs and common sense knowledge

Modality-specific impacts of distractors on visual and auditory categorical decision-making: an evidence accumulation perspective.

Our brain constantly processes multisensory inputs to make decisions and guide behaviors, but how goal-relevant processes are influenced by irrelevant information is unclear. Here, we investigated the effects of intermodal and intramodal task-irrelevant information on visual and auditory categorical decision-making. In both visual and auditory tasks, we manipulated the modality of irrelevant inputs (visual vs. auditory vs. none) and used linear discrimination analysis of EEG and hierarchical drift-diffusion modeling (HDDM) to identify when and how task-irrelevant information affected decision-relevant processing. The results revealed modality-specific impacts of irrelevant inputs on visual and auditory categorical decision-making. The distinct effects on the visual task were shown on the neural components, with auditory distractors amplifying the sensory processing whereas visual distractors amplifying the post-sensory process. Conversely, the distinct effects on the auditory task were shown in behavioral performance and underlying cognitive processes. Visual distractors facilitate behavioral performance and affect both stages, but auditory distractors interfere with behavioral performance and impact on the sensory processing rather than the post-sensory decision stage. Overall, these findings suggested that auditory distractors affect the sensory processing stage of both tasks while visual distractors affect the post-sensory decision stage of visual categorical decision-making and both stages of auditory categorical decision-making. This study provides insights into how humans process information from multiple sensory modalities during decision-making by leveraging modality-specific impacts.

Modeling and Analysis of Central and Peripheral Autonomic Contributions to Decreased Cardiac Vagal Tone Following Myocardial Infarction

Loss of cardiac function following myocardial infarction is accompanied by neural adaptation in baroreflex systems that are compensatory in the short term but then become associated with long-term disease progression. A hallmark of these baroreflex changes is decreased vagal tone. The closed-loop feedback structure of the baroreflexes confounds the source of decreased vagal tone following myocardial infarction (MI). We recognize that in a feedback network causality is distributed in the network interactions, and so explore the effects of MI on the potential contribution to vagal withdrawal of each neural component of our previously published closed-loop baroreflex computational model. The model is composed of differential equations representing the blood flow through various body compartments and sigmoidal functions to represent the saturating firing behavior of neurons. Neuronal adaptations leading to decreased vagal activity could occur in various neuronal groups located centrally in the brainstem. Adaptations could also occur peripherally at the baroreceptors or the heart’s “little brain”, the intrinsic cardiac nervous system. We developed five alternative models to reflect these hypotheses. Of the alternative models, only the model representing adaptation in the baroreceptors predicted both physiological baroreflex function and a decrease in vagus nerve activity. These findings suggest that decreased vagal activity after myocardial ischemia may be due, at least in part, to a decrease in baroreceptor sensitivity. We also link these model predictions with experimental evidence from the literature. NIH U01-HL133360, NIH OT2-OD030534, NSF 1940700, and NSF OAC-1919839. This is the full abstract presented at the American Physiology Summit 2024 meeting and is only available in HTML format. There are no additional versions or additional content available for this abstract. Physiology was not involved in the peer review process.

Disparities in Indications and Outcomes Reporting for Spinal Column Shortening for Tethered Cord Syndrome: The Need for a Standardized Approach.

Systematic review. To identify commonly reported indications and outcomes in spinal column shortening (SCS) procedures. SCS is a surgical procedure used in patients with tethered cord syndrome-characterized by abnormal attachment of neural components to surrounding tissues-to shorten the vertebral column, release tension on the spinal cord/neural elements, and alleviate associated symptoms. PubMed and EMBASE searches captured SCS literature published between 1950 and 2023. Prospective/retrospective cohort studies and case series were included without age limit or required follow-up period. Review articles without new patient presentations, meta-analyses, systematic reviews, conference abstracts, and letters were excluded. Studies included adult and pediatric patients. The 29 identified studies represented 278 patients (aged 5-76yr). In 24.1% of studies, patients underwent primary tethered cord syndrome intervention through SCS. In 41.4% of studies, patients underwent SCS after failed previous primary detethering (24.1% of studies were mixed and 10.3% were unspecified). The most commonly reported nongenitourinary/bowel surgical indications were back pain (55.2%), lower-extremity pain (48.3%), lower-extremity weakness (48.3%), lower-extremity numbness (34.5%), and lower-extremity motor dysfunction (34.5%). Genitourinary/bowel symptoms were most often described as nonspecific bladder dysfunction (58.6%), bladder incontinence (34.5%), and bowel dysfunction (31.0%). After SCS, nongenitourinary/bowel outcomes included lower-extremity pain (44.8%), back pain (31.0%), and lower-extremity sensory and motor function (both 31.0%). Bladder dysfunction (79.3%), bowel dysfunction (34.5%), and bladder incontinence (13.8%) were commonly reported genitourinary/bowel outcomes. In total, 40 presenting surgical indication categories and 33 unique outcome measures were reported across studies. Seventeen of the 278 patients (6.1%) experienced a complication. The SCS surgical literature displays variability in operative indications and postoperative outcomes. The lack of common reporting mechanisms impedes higher-level analysis. A standardized outcomes measurement tool, encompassing both patient-reported outcome measures and objective metrics, is necessary. Level IV.