Tentonin 3 is a pore-forming subunit of a slow inactivation mechanosensitive channel.

Mechanically activating (MA) channels transduce numerous physiological functions. Tentonin 3/TMEM150C (TTN3) confers MA currents with slow inactivation kinetics in somato- and barosensory neurons. However, questions were raised about its role as a Piezo1 regulator and its potential as a channel pore. Here, we demonstrate that purified TTN3 proteins incorporated into the lipid bilayer displayed spontaneous and pressure-sensitive channel currents. These MA currents were conserved across vertebrates and differ from Piezo1 in activation threshold and pharmacological response. Deep neural network structure prediction programs coupled with mutagenetic analysis predicted a rectangular-shaped, tetrameric structure with six transmembrane helices and a pore at the inter-subunit center. The putative pore aligned with two helices of each subunit and had constriction sites whose mutations changed the MA currents. These findings suggest that TTN3 is a pore-forming subunit of a distinct slow inactivation MA channel, potentially possessing a tetrameric structure.

Control of artificial membrane fusion in physiological ionic solutions beyond the limits of electroformation.

Membrane fusion, merging two lipid bilayers, is crucial for fabricating artificial membrane structures. Over the past 40 years, in contrast to precise and controllable membrane fusion in-vivo through specific molecules such as SNAREs, controlling the fusion in-vitro while fabricating artificial membrane structures in physiological ionic solutions without fusion proteins has been a challenge, becoming a significant obstacle to practical applications. We present an approach consisting of an electric field and a few kPa hydraulic pressure as an additional variable to physically control the fusion, enabling tuning of the shape and size of the 3D freestanding lipid bilayers in physiological ionic solutions. Mechanical model analysis reveals that pressure-induced parallel/normal tensions enhance fusion among membranes in the microwell. In-vitro peptide-membrane assay, mimicking vesicular transport via pressure-assisted fusion, and stability of 38 days with in-chip pressure control via pore size-regulated hydrogel highlight the potential for diverse biological applications.

Neural Information Processing and Computations of Two-Input Synapses.

Information processing in artificial neural networks is largely dependent on the nature of neuron models. While commonly used models are designed for linear integration of synaptic inputs, accumulating experimental evidence suggests that biological neurons are capable of nonlinear computations for many converging synaptic inputs via homo- and heterosynaptic mechanisms. This nonlinear neuronal computation may play an important role in complex information processing at the neural circuit level. Here we characterize the dynamics and coding properties of neuron models on synaptic transmissions delivered from two hidden states. The neuronal information processing is influenced by the cooperative and competitive interactions among synapses and the coherence of the hidden states. Furthermore, we demonstrate that neuronal information processing under two-input synaptic transmission can be mapped to linearly nonseparable XOR as well as basic AND/OR operations. In particular, the mixtures of linear and nonlinear neuron models outperform the fashion-MNIST test compared to the neural networks consisting of only one type. This study provides a computational framework for assessing information processing of neuron and synapse models that may be beneficial for the design of brain-inspired artificial intelligence algorithms and neuromorphic systems.

Differentiating amnestic from non-amnestic mild cognitive impairment subtypes using graph theoretical measures of electroencephalography.

The purpose of this study was to explore different patterns of functional networks between amnestic mild cognitive impairment (aMCI) and non-aMCI (naMCI) using electroencephalography (EEG) graph theoretical analysis. The data of 197 drug-naïve individuals who complained cognitive impairment were reviewed. Resting-state EEG data was acquired. Graph analyses were performed and compared between aMCI and naMCI, as well as between early and late aMCI. Correlation analyses were conducted between the graph measures and neuropsychological test results. Machine learning algorithms were applied to determine whether the EEG graph measures could be used to distinguish aMCI from naMCI. Compared to naMCI, aMCI showed higher modularity in the beta band and lower radius in the gamma band. Modularity was negatively correlated with scores on the semantic fluency test, and the radius in the gamma band was positively correlated with visual memory, phonemic, and semantic fluency tests. The naïve Bayes algorithm classified aMCI and naMCI with 89% accuracy. Late aMCI showed inefficient and segregated network properties compared to early aMCI. Graph measures could differentiate aMCI from naMCI, suggesting that these measures might be considered as predictive markers for progression to Alzheimer's dementia in patients with MCI.

Lotus Seed Green Embryo Extract and a Purified Glycosyloxyflavone Constituent, Narcissoside, Activate TRPV1 Channels in Dorsal Root Ganglion Sensory Neurons.

Several studies have documented the broad-spectrum bioactivities of a lotus seed (Plumula nelumbinis [PN]) green embryo extract. However, the specific bioactive components and associated molecular mechanisms remain largely unknown. This study aimed to identify the ion channel-activating mechanisms of PN extracts. Using fluorometric imaging and patch-clamp recordings, PN extracts were screened for calcium channel activation in dorsal root ganglion (DRG) neurons. The TRPV1 channels in DRG neurons were strongly activated by the PN extract (mean amplitude of 131 ± 45 pA at 200 µg/mL) and its purified glycosyloxyflavone narcissoside (401 ± 271 pA at 100 µM). Serial treatment with a 200 µg/mL PN extract in TRPV1-overexpressing HEK293T cells induced robust desensitization to 10 ± 10% of the initial current amplitude. Thus, we propose that the PN extract and narcissoside function as TRPV1 agonists. This new finding may advance our knowledge regarding the traditional and scientific functions of PN in human health and disease.

The structural aspects of neural dynamics and information flow.

BACKGROUND: Neurons have specialized structures that facilitate information transfer using electrical and chemical signals. Within the perspective of neural computation, the neuronal structure is an important prerequisite for the versatile computational capabilities of neurons resulting from the integration of diverse synaptic input patterns, complex interactions among the passive and active dendritic local currents, and the interplay between dendrite and soma to generate action potential output. For this, characterization of the relationship between the structure and neuronal spike dynamics could provide essential information about the cellular-level mechanism supporting neural computations. RESULTS: This work describes simulations and an information-theoretic analysis to investigate how specific neuronal structure affects neural dynamics and information processing. Correlation analysis on the Allen Cell Types Database reveals biologically relevant structural features that determine neural dynamics-eight highly correlated structural features are selected as the primary set for characterizing neuronal structures. These features are used to characterize biophysically realistic multi-compartment mathematical models for primary neurons in the direct and indirect hippocampal pathways consisting of the pyramidal cells of Cornu Ammonis 1 (CA1) and CA3 and the granule cell in the dentate gyrus (DG). Simulations reveal that the dynamics of these neurons vary depending on their specialized structures and are highly sensitive to structural modifications. Information-theoretic analysis confirms that structural factors are critical for versatile neural information processing at a single-cell and a neural circuit level; not only basic AND/OR but also linearly non-separable XOR functions can be explained within the information-theoretic framework. CONCLUSIONS: Providing quantitative information on the relationship between the structure and the dynamics/information flow of neurons, this work would help us understand the design and coding principles of biological neurons and may be beneficial for designing biologically plausible neuron models for artificial intelligence (AI) systems.

Characterization of multiscale logic operations in the neural circuits.

Background: Ever since the seminal work by McCulloch and Pitts, the theory of neural computation and its philosophical foundation known as 'computationalism' have been central to brain-inspired artificial intelligence (AI) technologies. The present study describes neural dynamics and neural coding approaches to understand the mechanisms of neural computation. The primary focus is to characterize the multiscale nature of logic computations in the brain, which might occur at a single neuron level, between neighboring neurons via synaptic transmission, and at the neural circuit level. Results: For this, we begin the analysis with simple neuron models to account for basic Boolean logic operations at a single neuron level and then move on to the phenomenological neuron models to explain the neural computation from the viewpoints of neural dynamics and neural coding. The roles of synaptic transmission in neural computation are investigated using biologically realistic multi-compartment neuron models: two representative computational entities, CA1 pyramidal neuron in the hippocampus and Purkinje fiber in the cerebellum, are analyzed in the information-theoretic framework. We then construct two-dimensional mutual information maps, which demonstrate that the synaptic transmission can process not only basic AND/OR Boolean logic operations but also the linearly non-separable XOR function. Finally, we provide an overview of the evolutionary algorithm and discuss its benefits in automated neural circuit design for logic operations. Conclusions: This study provides a comprehensive perspective on the multiscale logic operations in the brain from both neural dynamics and neural coding viewpoints. It should thus be beneficial for understanding computational principles of the brain and may help design biologically plausible neuron models for AI devices.

Spondylolytic Spondylolisthesis of Cervical Spine.

Cervical spondylolytic spondylolisthesis is a rare congenital anomaly. It is often misunderstood as a result of trauma. However, most of them are congenital deformities. The vast majority of patients with radiographically proven cervical spondylolysis can be treated confidently with conservative measures. Cervical spondylolytic spondylolisthesis that cause symptoms requiring surgery is very rare. Surgical intervention should be reserved for those who fail non-operative management or exhibit neurologic compromise referable to an unstable spondylolytic defect. We report a case of cervical radiculopathy in a 45-year-old female patient who had been diagnosed with spondylolytic spondylolisthesis at the sixth verterba and treated with surgery.

Power Failure of Mitochondria and Oxidative Stress in Neurodegeneration and Its Computational Models.

The brain needs more energy than other organs in the body. Mitochondria are the generator of vital power in the living organism. Not only do mitochondria sense signals from the outside of a cell, but they also orchestrate the cascade of subcellular events by supplying adenosine-5'-triphosphate (ATP), the biochemical energy. It is known that impaired mitochondrial function and oxidative stress contribute or lead to neuronal damage and degeneration of the brain. This mini-review focuses on addressing how mitochondrial dysfunction and oxidative stress are associated with the pathogenesis of neurodegenerative disorders including Alzheimer's disease, amyotrophic lateral sclerosis, Huntington's disease, and Parkinson's disease. In addition, we discuss state-of-the-art computational models of mitochondrial functions in relation to oxidative stress and neurodegeneration. Together, a better understanding of brain disease-specific mitochondrial dysfunction and oxidative stress can pave the way to developing antioxidant therapeutic strategies to ameliorate neuronal activity and prevent neurodegeneration.