Single-cell RNA sequencing data imputation using bi-level feature propagation

Single-cell RNA sequencing (scRNA-seq) enables the exploration of cellular heterogeneity by analyzing gene expression profiles in complex tissues. However, scRNA-seq data often suffer from technical noise, dropout events and sparsity, hindering downstream analyses. Although existing works attempt to mitigate these issues by utilizing graph structures for data denoising, they involve the risk of propagating noise and fall short of fully leveraging the inherent data relationships, relying mainly on one of cell-cell or gene-gene associations and graphs constructed by initial noisy data. To this end, this study presents single-cell bilevel feature propagation (scBFP), two-step graph-based feature propagation method. It initially imputes zero values using non-zero values, ensuring that the imputation process does not affect the non-zero values due to dropout. Subsequently, it denoises the entire dataset by leveraging gene-gene and cell-cell relationships in the respective steps. Extensive experimental results on scRNA-seq data demonstrate the effectiveness of scBFP in various downstream tasks, uncovering valuable biological insights.

Reinforcement of Positive Electrode-Electrolyte Interface without Using Electrolyte Additives Through Thermoelectrochemical Oxidation of LiPF6 for Lithium Secondary Batteries

Owing to the limited electrochemical stability window of carbonate electrolytes, the initial formation of a solid electrolyte interphase and surface film on the negative and positive electrode surfaces by the decomposition of the electrolyte component is inevitable for the operation of lithium secondary batteries. The deposited film on the surface of the active material is vital for reducing further electrochemical side reactions at the surface; hence, the manipulation of this formation process is necessary for the appropriate operation of the assembled battery system. In this study, the thermal decomposition of LiPF6 salt is used as a surface passivation agent, which is autocatalytically formed during high-temperature storage. The thermally formed difluorophosphoric acid is subsequently oxidized on the partially charged high-Ni positive electrode surface, which improves the cycleability of lithium metal cells via phosphorus- and fluorine-based surface film formation. Moreover, the improvement in the high-temperature cycleability is demonstrated by controlling the formation process in the lithium-ion pouch cell with a short period of high-temperature storage before battery usage.

Artificial Intelligence-Based Anomaly Detection Technology over Encrypted Traffic: A Systematic Literature Review

As cyber-attacks increase in unencrypted communication environments such as the traditional Internet, protected communication channels based on cryptographic protocols, such as transport layer security (TLS), have been introduced to the Internet. Accordingly, attackers have been carrying out cyber-attacks by hiding themselves in protected communication channels. However, the nature of channels protected by cryptographic protocols makes it difficult to distinguish between normal and malicious network traffic behaviors. This means that traditional anomaly detection models with features from packets extracted a deep packet inspection (DPI) have been neutralized. Recently, studies on anomaly detection using artificial intelligence (AI) and statistical characteristics of traffic have been proposed as an alternative. In this review, we provide a systematic review for AI-based anomaly detection techniques over encrypted traffic. We set several research questions on the review topic and collected research according to eligibility criteria. Through the screening process and quality assessment, 30 research articles were selected with high suitability to be included in the review from the collected literature. We reviewed the selected research in terms of dataset, feature extraction, feature selection, preprocessing, anomaly detection algorithm, and performance indicators. As a result of the literature review, it was confirmed that various techniques used for AI-based anomaly detection over encrypted traffic were used. Some techniques are similar to those used for AI-based anomaly detection over unencrypted traffic, but some technologies are different from those used for unencrypted traffic.

Photo-Assisted Ferroelectric Domain Control for α-In2 Se3 Artificial Synapses Inspired by Spontaneous Internal Electric Fields

α-In2 Se3 semiconductor crystals realize artificial synapses by tuning in-plane and out-of-plane ferroelectricity with diverse avenues of electrical and optical pulses. While the electrically induced ferroelectricity of α-In2 Se3 shows synaptic memory operation, the optically assisted synaptic plasticity in α-In2 Se3 has also been preferred for polarization flipping enhancement. Here, the synaptic memory behavior of α-In2 Se3 is demonstrated by applying electrical gate voltages under white light. As a result, the induced internal electric field is identified at a polarization flipped conductance channel in α-In2 Se3 /hexagonal boron nitride (hBN) heterostructure ferroelectric field effect transistors (FeFETs) under white light and discuss the contribution of this built-in electric field on synapse characterization. The biased dipoles in α-In2 Se3 toward potentiation polarization direction by an enhanced internal built-in electric field under illumination of white light lead to improvement of linearity for long-term depression curves with proper electric spikes. Consequently, upon applying appropriate electric spikes to α-In2 Se3 /hBN FeFETs with illuminating white light, the recognition accuracy values significantly through the artificial learning simulation is elevated for discriminating hand-written digit number images.

Tailoring Subthreshold Swing in A-IGZO Thin-Film Transistors for Amoled Displays: Impact of Conversion Mechanism on Peald Deposition Sequences

Amorphous IGZO (a-IGZO) thin-film transistors (TFTs) are standard backplane electronics to power active-matrix organic light-emitting diode (AMOLED) televisions due to their high carrier mobility and negligible low leakage characteristics. Despite their advantages, limitations in color depth arise from a steep subthreshold swing (SS) (≤ 0.1 V/decade), necessitating costly external compensation for IGZO transistors. For mid-size mobile applications such as OLED tablets and notebooks, it is important to ensure controllable SS value (≥ 0.3 V/decade). In this study, a conversion mechanism during plasma-enhanced atomic layer deposition (PEALD) is proposed as a feasible route to control the SS. When a pulse of a diethylzinc (DEZn) precursor is exposed to the M2 O3 (M = In or Ga) surface layer, partial conversion of the underlying M2 O3 to ZnO is predicted on the basis of density function theory calculations. Notably, significant distinctions between In-Ga-Zn (Case I) and In-Zn-Ga (Case II) films are observed: Case II exhibits a lower growth rate and larger Ga/In ratio. Case II TFTs with a-IGZO (subcycle ratio of In:Ga:Zn = 3:1:1) show reasonable SS values (313 mV decade-1 ) and high mobility (µFE ) of 29.3 cm2 Vs-1 (Case I: 84 mV decade-1 and 33.4 cm2 Vs-1 ). The rationale for Case II's reasonable SS values is discussed, attributing it to the plausible formation of In-Zn defects, supported by technology computer-aided design (TCAD) simulations.

3D Neuromorphic Hardware with Single Thin-Film Transistor Synapses Over Single Thin-Body Transistor Neurons by Monolithic Vertical Integration

Neuromorphic hardware with a spiking neural network (SNN) can significantly enhance the energy efficiency for artificial intelligence (AI) functions owing to its event-driven and spatiotemporally sparse operations. However, an artificial neuron and synapse based on complex complementary metal-oxide-semiconductor (CMOS) circuits limit the scalability and energy efficiency of neuromorphic hardware. In this work, a neuromorphic module is demonstrated composed of synapses over neurons realized by monolithic vertical integration. The synapse at top is a single thin-film transistor (1TFT-synapse) made of poly-crystalline silicon film and the neuron at bottom is another single transistor (1T-neuron) made of single-crystalline silicon. Excimer laser annealing (ELA) is applied to activate dopants for the 1TFT-synapse at the top and rapid thermal annealing (RTA) is applied to do so for the 1T-neuron at the bottom. Internal electro-thermal annealing (ETA) via the generation of Joule heat is also used to enhance the endurance of the 1TFT-synapse without transferring heat to the 1T-neuron at the bottom. As neuromorphic vision sensing, classification of American Sign Language (ASL) is conducted with the fabricated neuromorphic module. Its classification accuracy on ASL is ≈92.3% even after 204 800 update pulses.

Wearable Liquid Metal Composite with Skin-Adhesive Chitosan-Alginate-Chitosan Hydrogel for Stable Electromyogram Signal Monitoring

In wearable bioelectronics, various studies have focused on enhancing prosthetic control accuracy by improving the quality of physiological signals. The fabrication of conductive composites through the addition of metal fillers is one way to achieve stretchability, conductivity, and biocompatibility. However, it is difficult to measure stable biological signals using these soft electronics during physical activities because of the slipping issues of the devices, which results in the inaccurate placement of the device at the target part of the body. To address these limitations, it is necessary to reduce the stiffness of the conductive materials and enhance the adhesion between the device and the skin. In this study, we measured the electromyography (EMG) signals by applying a three-layered hydrogel structure composed of chitosan-alginate-chitosan (CAC) to a stretchable electrode fabricated using a composite of styrene-ethylene-butylene-styrene and eutectic gallium-indium. We observed stable adhesion of the CAC hydrogel to the skin, which aided in keeping the electrode attached to the skin during the subject movement. Finally, we fabricated a multichannel array of CAC-coated composite electrodes (CACCE) to demonstrate the accurate classification of the EMG signals based on hand movements and channel placement, which was followed by the movement of the robot arm.

Secured delivery of basic fibroblast growth factor using human serum albumin-based protein nanoparticles for enhanced wound healing and regeneration

BACKGROUND

Basic fibroblast growth factor (bFGF) is one of the critical components accelerating angiogenesis and tissue regeneration by promoting the migration of dermal fibroblasts and endothelial cells associated with matrix formation and remodeling in wound healing process. However, clinical applications of bFGF are substantially limited by its unstable nature due to rapid decomposition under physiological microenvironment.

RESULTS

In this study, we present the bFGF-loaded human serum albumin nanoparticles (HSA-bFGF NPs) as a means of enhanced stability and sustained release platform during tissue regeneration. Spherical shape of the HSA-bFGF NPs with uniform size distribution (polydispersity index < 0.2) is obtained via a simple desolvation and crosslinking process. The HSA-bFGF NPs securely load and release the intact soluble bFGF proteins, thereby significantly enhancing the proliferation and migration activity of human dermal fibroblasts. Myofibroblast-related genes and proteins were also significantly down-regulated, indicating decrease in risk of scar formation. Furthermore, wound healing is accelerated while achieving a highly organized extracellular matrix and enhanced angiogenesis in vivo.

CONCLUSION

Consequently, the HSA-bFGF NPs are suggested not only as a delivery vehicle but also as a protein stabilizer for effective wound healing and tissue regeneration.

Designing Effective Visual Feedback for Facial Rehabilitation Exercises: Investigating the Role of Shape, Transparency, and Age on User Experience

Facial expression recognition technology has been utilized both for entertainment purposes and as a valuable aid in rehabilitation and facial exercise assistance. This technology leverages artificial intelligence models to predict facial landmark points and provide visual feedback, thereby facilitating users' facial movements. However, feedback designs that disregard user preferences may cause discomfort and diminish the benefits of exercise. This study aimed to develop a feedback design guide for facial rehabilitation exercises by investigating user responses to various feedback design methods. We created a facial recognition mobile application and designed six feedback variations based on shape and transparency. To evaluate user experience, we conducted a usability test involving 48 participants (24 subjects in their 20s and 24 over 60 years of age), assessing factors such as feedback, assistance, disturbance, aesthetics, cognitive ease, and appropriateness. The experimental results revealed significant differences in transparency, age, and the interaction between transparency and age. Consequently, it is essential to consider both transparency and user age when designing facial recognition feedback. The findings of this study could potentially inform the design of more effective and personalized visual feedback for facial motion, ultimately benefiting users in rehabilitation and exercise contexts.

APSec1.0: Innovative Security Protocol Design with Formal Security Analysis for the Artificial Pancreas System

The Medical Internet-of-Things (MIoT) has developed revolutionary ways of delivering medical care to patients. An example system, showing increasing demand, is the artificial pancreas system that offers convenience and reliable support care to patients with Type 1 Diabetes. Despite the apparent benefits, the system cannot escape potential cyber threats that may worsen a patient's condition. The security risks need immediate attention to ensure the privacy of the patient and preserve safe functionality. Motivated by this, we proposed a security protocol for the APS environment wherein support to essential security requirements is guaranteed, the security context negotiation is resource-friendly, and the protocol is resilient to emergencies. Accordingly, the security requirements and correctness of the design protocol were formally verified using BAN logic and AVISPA, and proved its feasibility through the emulation of APS in a controlled environment using commercial off-the-shelf devices. Moreover, the results of our performance analysis indicate that the proposed protocol is more efficient than the other existing works and standards.

Enhanced Operational Characteristics Attained by Applying HfO2 as Passivation in AlGaN/GaN High-Electron-Mobility Transistors: A Simulation Study

This study investigates the operating characteristics of AlGaN/GaN high-electron-mobility transistors (HEMTs) by applying HfO₂ as the passivation layer. Before analyzing HEMTs with various passivation structures, modeling parameters were derived from the measured data of fabricated HEMT with Si₃N₄ passivation to ensure the reliability of the simulation. Subsequently, we proposed new structures by dividing the single Si₃N₄ passivation into a bilayer (first and second) and applying HfO₂ to the bilayer and first passivation layer only. Ultimately, we analyzed and compared the operational characteristics of the HEMTs considering the basic Si₃N₄, only HfO₂, and HfO₂/Si₃N₄ (hybrid) as passivation layers. The breakdown voltage of the AlGaN/GaN HEMT having only HfO₂ passivation was improved by up to 19%, compared to the basic Si₃N₄ passivation structure, but the frequency characteristics deteriorated. In order to compensate for the degraded RF characteristics, we modified the second Si₃N₄ passivation thickness of the hybrid passivation structure from 150 nm to 450 nm. We confirmed that the hybrid passivation structure with 350-nm-thick second Si₃N₄ passivation not only improves the breakdown voltage by 15% but also secures RF performance. Consequently, Johnson's figure-of-merit, which is commonly used to judge RF performance, was improved by up to 5% compared to the basic Si₃N₄ passivation structure.

WM-STGCN: A Novel Spatiotemporal Modeling Method for Parkinsonian Gait Recognition

Parkinson's disease (PD) is a neurodegenerative disorder that causes gait abnormalities. Early and accurate recognition of PD gait is crucial for effective treatment. Recently, deep learning techniques have shown promising results in PD gait analysis. However, most existing methods focus on severity estimation and frozen gait detection, while the recognition of Parkinsonian gait and normal gait from the forward video has not been reported. In this paper, we propose a novel spatiotemporal modeling method for PD gait recognition, named WM-STGCN, which utilizes a Weighted adjacency matrix with virtual connection and Multi-scale temporal convolution in a Spatiotemporal Graph Convolution Network. The weighted matrix enables different intensities to be assigned to different spatial features, including virtual connections, while the multi-scale temporal convolution helps to effectively capture the temporal features at different scales. Moreover, we employ various approaches to augment skeleton data. Experimental results show that our proposed method achieved the best accuracy of 87.1% and an F1 score of 92.85%, outperforming Long short-term memory (LSTM), K-nearest neighbors (KNN), Decision tree, AdaBoost, and ST-GCN models. Our proposed WM-STGCN provides an effective spatiotemporal modeling method for PD gait recognition that outperforms existing methods. It has the potential for clinical application in PD diagnosis and treatment.

Feasibility of artificial intelligence-based decision supporting system in tolvaptan prescription for autosomal dominant polycystic kidney disease

PURPOSE

Total kidney volume (TKV) measurement is crucial for selecting treatment candidates in autosomal dominant polycystic kidney disease (ADPKD). We developed and investigated the performance of fully-automated 3D-volumetry model and applied it to software as a service (SaaS) for clinical support on tolvaptan prescription in ADPKD patients.

MATERIALS AND METHODS

Computed tomography scans of ADPKD patients taken between January 2000 and June 2022 were acquired from seven institutions. The quality of the images was manually reviewed in advance. The acquired dataset was split into training, validation, and test datasets at a ratio of 8.5:1:0.5. Convolutional, neural network-based automatic segmentation model was trained to obtain 3D segment mask for TKV measurement. The algorithm consisted of three steps: data preprocessing, ADPKD area extraction, and post-processing. After performance validation with the Dice score, 3D-volumetry model was applied to SaaS which is based on Mayo imaging classification for ADPKD.

RESULTS

A total of 753 cases with 95,117 slices were included. The differences between the ground-truth ADPKD kidney mask and the predicted ADPKD kidney mask were negligible, with intersection over union >0.95. The post-process filter successfully removed false alarms. The test-set performance was homogeneously equal and the Dice score of the model was 0.971; after post-processing, it improved to 0.979. The SaaS calculated TKV from uploaded Digital Imaging and Communications in Medicine images and classified patients according to height-adjusted TKV for age.

CONCLUSIONS

Our artificial intelligence-3D volumetry model exhibited effective, feasible, and non-inferior performance compared with that of human experts and successfully predicted the rapid ADPKD progressor.

Characterization of radiation-resistance mechanism in Spirosoma montaniterrae DY10T in terms of transcriptional regulatory system

To respond to the external environmental changes for survival, bacteria regulates expression of a number of genes including transcription factors (TFs). To characterize complex biological phenomena, a biological system-level approach is necessary. Here we utilized six computational biology methods to infer regulatory network and to characterize underlying biologically mechanisms relevant to radiation-resistance. In particular, we inferred gene regulatory network (GRN) and operons of radiation-resistance bacterium Spirosoma montaniterrae DY10[Formula: see text] and identified the major regulators for radiation-resistance. Our results showed that DNA repair and reactive oxygen species (ROS) scavenging mechanisms are key processes and Crp/Fnr family transcriptional regulator works as a master regulatory TF in early response to radiation.