High-Dimensional Physical Reservoir with Back-End-of-Line-Compatible Tin Monoxide Thin-Film Transistor.

This work demonstrates a physical reservoir using a back-end-of-line compatible thin-film transistor (TFT) with tin monoxide (SnO) as the channel material for neuromorphic computing. The electron trapping and time-dependent detrapping at the channel interface induce the SnO·TFT to exhibit fading memory and nonlinearity characteristics, the critical assets for physical reservoir computing. The three-terminal configuration of the TFT allows the generation of higher-dimensional reservoir states by simultaneously adjusting the bias conditions of the gate and drain terminals, surpassing the performances of typical two-terminal-based reservoirs such as memristors. The high-dimensional SnO TFT reservoir performs exceptionally in two benchmark tests, achieving a 94.1% accuracy in Modified National Institute of Standards and Technology handwritten number recognition and a normalized root-mean-square error of 0.089 in Mackey-Glass time-series prediction. Furthermore, it is suitable for vertical integration because its fabrication temperature is <250 °C, providing the benefit of achieving a high integration density.

Spike-Based Neuromorphic Hardware for Dynamic Tactile Perception with a Self-Powered Mechanoreceptor Array.

A self-powered mechanoreceptor array is demonstrated using four mechanoreceptor cells for recognition of dynamic touch gestures. Each cell consists of a triboelectric nanogenerator (TENG) for touch sensing and a bi-stable resistor (biristor) for spike encoding. It produces informative spike signals by sensing a force of an external touch and encoding the force into the number of spikes. An array of the mechanoreceptor cells is utilized to monitor various touch gestures and it successfully generated spike signals corresponding to all the gestures. To validate the practicality of the mechanoreceptor array, a spiking neural network (SNN), highly attractive for power consumption compared to the conventional von Neumann architecture, is used for the identification of touch gestures. The measured spiking signals are reflected as inputs for the SNN simulations. Consequently, touch gestures are classified with a high accuracy rate of 92.5%. The proposed mechanoreceptor array emerges as a promising candidate for a building block of tactile in-sensor computing in the era of the Internet of Things (IoT), due to the low cost and high manufacturability of the TENG. This eliminates the need for a power supply, coupled with the intrinsic high throughput of the Si-based biristor employing complementary metal-oxide-semiconductor (CMOS) technology.

Memristive Crossbar Array-Based Probabilistic Graph Modeling.

Modern graph datasets with structural complexity and uncertainties due to incomplete information or data variability require advanced modeling techniques beyond conventional graph models. This study introduces a memristive crossbar array (CBA)-based probabilistic graph model (C-PGM) utilizing Cu0.3Te0.7/HfO2/Pt memristors, which exhibit probabilistic switching, self-rectifying, and memory characteristics. C-PGM addresses the complexities and uncertainties inherent in structural graph data across various domains, leveraging the probabilistic nature of memristors. C-PGM relies on the device-to-device variation across multiple memristive CBAs, overcoming the limitations of previous approaches that rely on sequential operations, which are slower and have a reliability concern due to repeated switching. This new approach enables the fast processing and massive implementation of probabilistic units at the expense of chip area. In this study, the hardware-based C-PGM feasibly expresses small-scale probabilistic graphs and shows minimal error in aggregate probability calculations. The probability calculation capabilities of C-PGM are applied to steady-state estimation and the PageRank algorithm, which is implemented on a simulated large-scale C-PGM. The C-PGM-based steady-state estimation and PageRank algorithm demonstrate comparable accuracy to conventional methods while significantly reducing computational costs.

Exploring Spectrogram-Based Audio Classification for Parkinson's Disease: A Study on Speech Classification and Qualitative Reliability Verification.

Patients suffering from Parkinson's disease suffer from voice impairment. In this study, we introduce models to classify normal and Parkinson's patients using their speech. We used an AST (audio spectrogram transformer), a transformer-based speech classification model that has recently outperformed CNN-based models in many fields, and a CNN-based PSLA (pretraining, sampling, labeling, and aggregation), a high-performance model in the existing speech classification field, for the study. This study compares and analyzes the models from both quantitative and qualitative perspectives. First, qualitatively, PSLA outperformed AST by more than 4% in accuracy, and the AUC was also higher, with 94.16% for AST and 97.43% for PSLA. Furthermore, we qualitatively evaluated the ability of the models to capture the acoustic features of Parkinson's through various CAM (class activation map)-based XAI (eXplainable AI) models such as GradCAM and EigenCAM. Based on PSLA, we found that the model focuses well on the muffled frequency band of Parkinson's speech, and the heatmap analysis of false positives and false negatives shows that the speech features are also visually represented when the model actually makes incorrect predictions. The contribution of this paper is that we not only found a suitable model for diagnosing Parkinson's through speech using two different types of models but also validated the predictions of the model in practice.

Heterogeneous density-based clustering with a dual-functional memristive array.

In the big data era, the requirement for data clustering methods that can handle massive and heterogeneous datasets with varying distributions increases. This study proposes a clustering algorithm for data sets with heterogeneous density using a dual-mode memristor crossbar array for data clustering. The array consists of a Ta/HfO2/RuO2 memristor operating in analog or digital modes, controlled by the reset voltage. The digital mode shows low dispersion and a high resistance ratio, and the analog mode enables precise conductance tuning. The local outlier factor is introduced to handle a heterogeneous density, and the required Euclidean and K-distances within the given dataset are calculated in the analog mode in parallel. In the digital mode, clustering is performed based on the connectivity among data points after excluding the detected outliers. The proposed algorithm boasts linear time complexity for the entire process. Extensive evaluations of synthetic datasets demonstrate significant improvement over representative density-based algorithms, and the datasets with heterogeneous density are clustered feasibly. Finally, the proposed algorithm is used to cluster the single-molecule localization microscopy data, demonstrating the feasibility of the suggested method for real-world problems.

Thermo-chemical conversion kinetics of cannabinoid acids in hemp (Cannabis sativa L.) using pressurized liquid extraction.

Cannabinoid decarboxylation via thermo-chemical conversion has the potential to reduce the cannabinoid degradation and evaporation due to short reaction time and use of water as the solvent. When combined with pressurized liquid extraction (PLE), thermo-chemical conversion can be performed as the first stage in the extraction procedure. PLE utilizes a closed system at elevated temperatures and pressure to increase the solvation power, which contributes to decreased viscosity and increased diffusion rate. With this new in-extraction decarboxylation approach there remain variables that need full understanding before up scaling from bench top to pilot or commercial scale. Herein, the thermo-chemical decarboxylation kinetics was studied for industrial hemp via PLE at different temperatures (80-160 °C) and reaction times (1-90 min). The reaction was found to be pseudo-first order. Model verification on CBD and CBG resulted in acceptable results; however, an anomaly in the minor cannabinoids suggests that cannabinoid concentration may influence model kinetics.

Late-Onset Ataxia-Telangiectasia Presenting With Dystonia and Tremor: The Use of Nanopore Long-Read Sequencing Solving the Variant Phase.

Objectives: This study investigates atypical late-onset ataxia-telangiectasia (AT) cases in a Korean family, diagnosed via Nanopore long-read sequencing, diverging from the typical early childhood onset caused by biallelic pathogenic ATM variants. Methods: A 52-year-old Korean woman exhibiting dystonia and tremor, with a family history of similar symptoms in her older sister, underwent comprehensive tests including routine laboratory tests, neuropsychological assessments, and neuroimaging. Genetic analysis was conducted through targeted sequencing of 29 dystonia-associated genes and Nanopore long-read sequencing to assess the configuration of 2 ATM gene variants. Results: Routine blood tests and brain imaging studies returned normal results, except for elevated α-fetoprotein levels. Neurologic examination revealed dystonia in the face, hand, and trunk, along with cervical dystonia in the proband. Her sister exhibited similar symptoms without evident telangiectasia. Genetic testing revealed 2 heterozygous pathogenic ATM gene variants (p.Glu2014Ter and p.Glu2052Lys). Nanopore long-read sequencing confirmed these variants were in trans configuration, establishing a definite molecular diagnosis in the proband. Discussion: This report expands the known clinical spectrum of AT, highlighting a familial case of atypical AT. Moreover, it underscores the clinical utility of Nanopore long-read sequencing in phasing variant haplotypes, essential for diagnosing autosomal recessive disorders, especially beneficial for cases without parental samples.

Implementation of Bayesian networks and Bayesian inference using a Cu0.1Te0.9/HfO2/Pt threshold switching memristor.

Bayesian networks and Bayesian inference, which forecast uncertain causal relationships within a stochastic framework, are used in various artificial intelligence applications. However, implementing hardware circuits for the Bayesian inference has shortcomings regarding device performance and circuit complexity. This work proposed a Bayesian network and inference circuit using a Cu0.1Te0.9/HfO2/Pt volatile memristor, a probabilistic bit neuron that can control the probability of being 'true' or 'false.' Nodal probabilities within the network are feasibly sampled with low errors, even with the device's cycle-to-cycle variations. Furthermore, Bayesian inference of all conditional probabilities within the network is implemented with low power (<186 nW) and energy consumption (441.4 fJ), and a normalized mean squared error of ∼7.5 × 10-4 through division feedback logic with a variational learning rate to suppress the inherent variation of the memristor. The suggested memristor-based Bayesian network shows the potential to replace the conventional complementary metal oxide semiconductor-based Bayesian estimation method with power efficiency using a stochastic computing method.

Wireless sequential dual light delivery for programmed PDT in vivo.

Using photodynamic therapy (PDT) to treat deep-seated cancers is limited due to inefficient delivery of photosensitizers and low tissue penetration of light. Polymeric nanocarriers are widely used for photosensitizer delivery, while the self-quenching of the encapsulated photosensitizers would impair the PDT efficacy. Furthermore, the generated short-lived reactive oxygen spieces (ROS) can hardly diffuse out of nanocarriers, resulting in low PDT efficacy. Therefore, a smart nanocarrier system which can be degraded by light, followed by photosensitizer activation can potentially overcome these limitations and enhance the PDT efficacy. A light-sensitive polymer nanocarrier encapsulating photosensitizer (RB-M) was synthesized. An implantable wireless dual wavelength microLED device which delivers the two light wavelengths sequentially was developed to programmatically control the release and activation of the loaded photosensitizer. Two transmitter coils with matching resonant frequencies allow activation of the connected LEDs to emit different wavelengths independently. Optimal irradiation time, dose, and RB-M concentration were determined using an agent-based digital simulation method. In vitro and in vivo validation experiments in an orthotopic rat liver hepatocellular carcinoma disease model confirmed that the nanocarrier rupture and sequential low dose light irradiation strategy resulted in successful PDT at reduced photosensitizer and irradiation dose, which is a clinically significant event that enhances treatment safety.

Prevalence and Characterization of NOTCH2NLC GGC Repeat Expansions in Koreans: From a Hospital Cohort Analysis to a Population-Wide Study.

Background and Objectives: GGC repeat expansions in the NOTCH2NLC gene are associated with a broad spectrum of progressive neurologic disorders, notably, neuronal intranuclear inclusion disease (NIID). We aimed to investigate the population-wide prevalence and clinical manifestations of NOTCH2NLC-related disorders in Koreans. Methods: We conducted a study using 2 different cohorts from the Korean population. Patients with available brain MRI scans from Seoul National University Hospital (SNUH) were thoroughly reviewed, and NIID-suspected patients presenting the zigzag edging signs underwent genetic evaluation for NOTCH2NLC repeats by Cas9-mediated nanopore sequencing. In addition, we analyzed whole-genome sequencing data from 3,887 individuals in the Korea Biobank cohort to estimate the distribution of the repeat counts in Koreans and to identify putative patients with expanded alleles and neurologic phenotypes. Results: In the SNUH cohort, among 90 adult-onset leukoencephalopathy patients with unknown etiologies, we found 20 patients with zigzag edging signs. Except for 2 diagnosed with fragile X-associated tremor/ataxia syndrome and 2 with unavailable samples, all 16 patients (17.8%) were diagnosed with NIID (repeat range: 87-217). By analyzing the Korea Biobank cohort, we estimated the distribution of repeat counts and threshold (>64) for Koreans, identifying 6 potential patients with NIID. Furthermore, long-read sequencing enabled the elucidation of transmission and epigenetic patterns of NOTCH2NLC repeats within a family affected by pediatric-onset NIID. Discussion: This study presents the population-wide distribution of NOTCH2NLC repeats and the estimated prevalence of NIID in Koreans, providing valuable insights into the association between repeat counts and disease manifestations in diverse neurologic disorders.

The prevalence of non-troublesome dyskinesia in Parkinson's disease.

INTRODUCTION: Levodopa-induced dyskinesia is a common complication of long-term treatment of Parkinson's disease (PD), but its impact on daily activities is somewhat controversial. This study investigated the prevalence and severity of dyskinesia, particularly non-troublesome dyskinesia, to provide insights into its significance for long-term PD management. METHODS: We reviewed electronic medical records of 2571 PD patients, who had been followed up at Seoul National University Hospital and were seen between January 2016 and June 2017. Dyskinesia severity had been assessed during follow-up and was recorded with the highest score by considering its impact on functioning (0 = no dyskinesia, 1 = minimal with patient unaware, 2 = mild disability, 3 = moderate disability, 4 = severe disability). RESULTS: The prevalence of dyskinesia increased progressively with longer PD duration; 8.2% in the group with disease duration of 0-5 years, 40.7% for 6-10 years, 66.0% for 11-15 years, 74.6% for 16-20 years, and 83.2% for 21 years or more. The prevalence of dyskinesia scores ≥2 also increased with disease duration, with rates of 6.3% for 0-5 years, 31.9% for 6-10 years, 54.8% for 11-15 years, 62.9% for 16-20 years and 73.7% for 21 or more years. CONCLUSION: Despite the increasing prevalence and severity of dyskinesia with longer PD duration, the study found that less than non-troublesome dyskinesia remained at approximately 26.3% even after more than 21 years of disease duration. These findings suggest that dyskinesia may not be troublesome for many PD patients even in long-term.

Symmetric and Profound Monoaminergic Degeneration in Parkinson's Disease with Premotor REM Sleep Behavior Disorder.

Background: Rapid eye movement sleep behavior disorder (RBD) may precede or follow motor symptoms in Parkinson's disease (PD). While over 70% of idiopathic RBD cases phenoconvert within a decade, a small subset develops PD after a more extended period or remains nonconverted. These heterogeneous manifestations of RBD in PD prompt subtype investigations. Premotor RBD may signify "body-first" PD with bottom-up, symmetric synucleinopathy propagation. Objective: Explore brainstem and nigrostriatal monoaminergic degeneration pattern differences based on premotor RBD presence and duration in de novo PD patients. Methods: In a cross-sectional analysis of de novo PD patients (n = 150) undergoing FP-CIT PET and RBD Single-Question Screen, the cohort was categorized into groups with and without premotor RBD (PDRBD +/-), with further classification of PDRBD + based on a 10-year duration of premotor RBD. Analysis of FP-CIT binding in the striatum and pons, striatal asymmetry, and striatum-to-pons ratios compared patterns of nigrostriatal and brainstem monoaminergic degeneration. Results: PDRBD + exhibited more severe and symmetrical striatal dopaminergic denervation compared to PDRBD-, with the difference in severity accentuated in the least-affected hemisphere. The PDRBD +<10Y subgroup displayed the most prominent striatal symmetry, supporting a more homogeneous "body-first" subtype. Pontine uptakes remained lower in PDRBD + even after adjusting for striatal uptake, suggesting early degeneration of pontine monoaminergic nuclei. Conclusions: Premotor RBD in PD is associated with severe, symmetrical nigrostriatal and brainstem monoaminergic degeneration, especially in cases with PD onset within 10 years of RBD. This supports the concept of a "widespread, bottom-up" pathophysiological mechanism associated with premotor RBD in PD.

Evaluating the validity and reliability of the Korean version of Scales for Outcomes in Parkinson's Disease-Cognition.

Objective: The Scales for Outcomes in Parkinson's Disease-Cognition (SCOPA-Cog) was developed to screen for cognition in PD. In this study, we aimed to evaluate the validity and reliability of the Korean version of the SCOPA-cog. Methods: We recruited 129 PD patients from 31 clinics with movement disorders in South Korea. The original version of the SCOPA-cognition was translated into Korean using the translation-retranslation method. The test-rest method with an intraclass correlation coefficient (ICC) and Cronbach's alpha coefficient were used to assess reliability. The Spearman's Rank correlation analysis with Montreal Cognitive Assessment-Korean version (MOCA-K) and Korean Mini-Mental State Examination (K-MMSE) were used to assess concurrent validity. Results: The Cronbach's alpha coefficient was 0.797, and the ICC was 0.887. Spearman's rank correlation analysis showed a significant correlation with the K-MMSE and MOCA-K scores (r = 0.546 and r = 0.683, respectively). Conclusions: Our results demonstrate that K-SCOPA-Cog exhibits good reliability and validity.

Electroencephalographic spectro-spatial covariance patterns related to phenoconversion in isolated rapid eye movement sleep behavior disorder and their longitudinal trajectories in α-synucleinopathies.

STUDY OBJECTIVES: This study aimed to identify electroencephalographic (EEG) spectro-spatial covariance patterns associated with phenoconversion in isolated rapid eye movement sleep behavior disorder (iRBD) patients and explore their longitudinal trajectories within α-synucleinopathies. METHODS: We assessed 47 participants, including 35 patients with iRBD and 12 healthy controls (HC), through baseline eye-closed resting EEGs. Patients with iRBD underwent follow-up EEG assessments and 18 patients with iRBD converted (12 to Parkinson's disease (PD), 6 to dementia with Lewy bodies [DLB]) during follow-up. We derived EEG spectro-spatial covariance patterns for PD-RBD and DLB-RBD from converters and HC. Correlations with motor and cognitive function, baseline distinctions among iRBD converters and nonconverters, and longitudinal trajectories were examined. RESULTS: At baseline, converters exhibited higher PD-RBD and DLB-RBD beta2 pattern scores compared to nonconverters (each area under curve [AUC] = 0.7751). The delta and alpha spatial patterns effectively distinguished both PD and DLB converters from HC, with the alpha pattern showing high discriminative power (AUC = 0.9097 for PD-RBD, 0.9306 for DLB-RBD). Movement Disorder Society-Sponsored Revision of the Unified Parkinson's Disease Rating Scale part III scores correlated positively with PD-RBD and DLB-RBD delta patterns (Spearman's rho = 0.688, p = 0.00014; rho = 0.539, p = 0.0055, respectively), with age and sex as cofactors. Distinct trajectories emerged during follow-up among PD converters, DLB converters, and iRBD nonconverters. CONCLUSIONS: Unique EEG spectro-spatial patterns specific to PD-RBD and DLB-RBD offer potential as predictive markers for phenoconversion to α-synucleinopathies in iRBD.

Vertically Stackable Ovonic Threshold Switch Oscillator Using Atomic Layer Deposited Ge0.6Se0.4 Film for High-Density Artificial Neural Networks.

Nanodevice oscillators (nano-oscillators) have received considerable attention to implement in neuromorphic computing as hardware because they can significantly improve the device integration density and energy efficiency compared to complementary metal oxide semiconductor circuit-based oscillators. This work demonstrates vertically stackable nano-oscillators using an ovonic threshold switch (OTS) for high-density neuromorphic hardware. A vertically stackable Ge0.6Se0.4 OTS-oscillator (VOTS-OSC) is fabricated with a vertical crossbar array structure by growing Ge0.6Se0.4 film conformally on a contact hole structure using atomic layer deposition. The VOTS-OSC can be vertically integrated onto peripheral circuits without causing thermal damage because the fabrication temperature is <400 °C. The fabricated device exhibits oscillation characteristics, which can serve as leaky integrate-and-fire neurons in spiking neural networks (SNNs) and coupled oscillators in oscillatory neural networks (ONNs). For practical applications, pattern recognition and vertex coloring are demonstrated with SNNs and ONNs, respectively, using semiempirical simulations. This structure increases the oscillator integration density significantly, enabling complex tasks with a large number of oscillators. Moreover, it can enhance the computational speed of neural networks due to its rapid switching speed.

Optimized treatment parameter by computer simulation for high-intensity focused ultrasound treatment of uterine adenomyosis: Short-term and long-term results.

This study aimed to investigate the efficacy and safety of using optimized parameters obtained by computer simulation for ultrasound-guided high-intensity focused ultrasound (HIFU) treatment of uterine adenomyosis in comparison with conventional parameters. We retrospectively assessed a single-institution, prospective study that was registered at Clinical Research Information Service (CRiS) of Republic of Korea (KCT0003586). Sixty-six female participants (median age: 44 years) with focal uterine adenomyosis were prospectively enrolled. All participants were treated with a HIFU system by using treatment parameters either for treating uterine fibroids (Group A, first 20 participants) or obtained via computer simulation (Group B, later 46 participants). To assess the treatment efficacy of HIFU, qualitative indices, including the clinically effective dysmenorrhea improvement index (DII), were evaluated up to 3 years after treatment, whereas quantitative indices, such as the nonperfused volume ratio and adenomyosis volume shrinkage ratio (AVSR), on MRI were evaluated up to 3 months after treatment. Quantitative/qualitative indices were compared between Groups A and B by using generalized linear mixed effect model. A safety assessment was also performed. Results showed that clinically effective DII was more frequently observed in Group B than in Group A (odds ratio, 3.69; P = 0.025), and AVSR were higher in Group B than in Group A (least-squares means, 21.61; P = 0.001). However, two participants in Group B developed skin burns at the buttock and sciatic nerve pain and required treatment. In conclusion, parameters obtained by computer simulation were more effective than the conventional parameters for treating uterine adenomyosis by using HIFU in terms of clinically effective DII and AVSR. However, care should be taken because of the risk of adverse events.

Bio-Inspired Organic Synaptor with In Situ Ion-Doped Ultrathin Polyelectrolyte Containing Acetylcholine-Like Cation.

An ion-based synaptic transistor (synaptor) is designed to emulate a biological synapse using controlled ion movements. However, developing a solid-state electrolyte that can facilitate ion movement while achieving large-scale integration remains challenging. Here, a bio-inspired organic synaptor (BioSyn) with an in situ ion-doped polyelectrolyte (i-IDOPE) is demonstrated. At the molecular scale, a polyelectrolyte containing the tert-amine cation, inspired by the neurotransmitter acetylcholine is synthesized using initiated chemical vapor deposition (iCVD) with in situ doping, a one-step vapor-phase deposition used to fabricate solid-state electrolytes. This method results in an ultrathin, but highly uniform and conformal solid-state electrolyte layer compatible with large-scale integration, a form that is not previously attainable. At a synapse scale, synapse functionality is replicated, including short-term and long-term synaptic plasticity (STSP and LTSP), along with a transformation from STSP to LTSP regulated by pre-synaptic voltage spikes. On a system scale, a reflex in a peripheral nervous system is mimicked by mounting the BioSyns on various substrates such as rigid glass, flexible polyethylene naphthalate, and stretchable poly(styrene-ethylene-butylene-styrene) for a decentralized processing unit. Finally, a classification accuracy of 90.6% is achieved through semi-empirical simulations of MNIST pattern recognition, incorporating the measured LTSP characteristics from the BioSyns.

EEG-based machine learning models for the prediction of phenoconversion time and subtype in isolated rapid eye movement sleep behavior disorder.

STUDY OBJECTIVES: Isolated rapid eye movement sleep behavior disorder (iRBD) is a prodromal stage of α-synucleinopathies and eventually phenoconverts to overt neurodegenerative diseases including Parkinson's disease (PD), dementia with Lewy bodies (DLB), and multiple system atrophy (MSA). Associations of baseline resting-state electroencephalography (EEG) with phenoconversion have been reported. In this study, we aimed to develop machine learning models to predict phenoconversion time and subtype using baseline EEG features in patients with iRBD. METHODS: At baseline, resting-state EEG and neurological assessments were performed on patients with iRBD. Calculated EEG features included spectral power, weighted phase lag index, and Shannon entropy. Three models were used for survival prediction, and four models were used for α-synucleinopathy subtype prediction. The models were externally validated using data from a different institution. RESULTS: A total of 236 iRBD patients were followed up for up to 8 years (mean 3.5 years), and 31 patients converted to α-synucleinopathies (16 PD, 9 DLB, 6 MSA). The best model for survival prediction was the random survival forest model with an integrated Brier score of 0.114 and a concordance index of 0.775. The K-nearest neighbor model was the best model for subtype prediction with an area under the receiver operating characteristic curve of 0.901. Slowing of the EEG was an important feature for both models. CONCLUSIONS: Machine learning models using baseline EEG features can be used to predict phenoconversion time and its subtype in patients with iRBD. Further research including large sample data from many countries is needed to make a more robust model.