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An evolutionarily conserved cation channel tunes the sensitivity of gustatory neurons to ephaptic inhibition in Drosophila
No full textIn ephaptic coupling, physically adjacent neurons influence one another’s activity via the electric fields they generate. To date, the molecular mechanisms that mediate and modulate ephaptic coupling’s effects remain poorly understood. Here, we show that the hyperpolarization-activated cyclic nucleotide–gated (HCN) channel lateralizes the potentially mutual ephaptic inhibition between Drosophila gustatory receptor neurons (GRNs). While sweet-sensing GRNs (sGRNs) engage in ephaptic suppression of the adjacent bitter-sensing GRNs (bGRNs), HCN expression in sGRNs enables them to resist ephaptic suppression from the bGRNs. This one-sided ephaptic inhibition confers sweetness dominance, facilitating ingestion of bitter-laced sweets. The role of fly HCN in this process can be replaced by human HCN2. Furthermore, unlike the mechanism in olfaction, gustatory ephaptic inhibition is independent of sensillum potential changes, suggesting that the compartmentalized arrangement of neighboring GRNs is dispensable for gustatory ephaptic inhibition. These findings indicate a role for the gating of ephaptic coding to ensure the intake of the essential nutrient despite bitter contaminants present in the feeding niche of Drosophila, and propose that studies in Drosophila gustation could reveal ephaptic principles conserved across diverse animals. Copyright © 2025 the Author(s).FALSEsciescopu
자율주행 차량의 경로 추종 정확도와 안정성 향상을 위한 모델 예측 제어에서 도로 지형 통합 및 경사각 추정
No full textRoad Topography, Model Predictive Control, Road Bank, Extended Kalman Filter with Unknown Inputs, Long Short-Term MemoryIn recent years, incorporating road topography into autonomous vehicle control strategies has gained importance due to its impact on vehicle stability and performance. This study verifies the performance of Model Predictive Control (MPC) by integrating road curvature and bank angle into the control strategy. A Nonlinear Model Predictive Control (NLMPC) framework is employed for path tracking, using two different methods for estimating road bank angle: the Extended Kalman Filter with Unknown Input (EKF-UI) and a Long Short-Term Memory (LSTM) neural network-based estimator. The LSTM estimator, expected to handle more complex nonlinearities, is compared with the EKF-UI in terms of estimation accuracy and stability in dynamic environments. To further enhance lateral stability, side slip constraints are incorporated using slack variables, and the Zero-Moment-Point (ZMP) is included as a standard constraint to improve stability by accounting for the bank angle. Simulations in MATLAB and CarSim validate the proposed control strategy, showing that the LSTM-based estimator significantly improves stability and accuracy in path tracking, especially in complex road conditions, outperforming the EKF-UI. In conclusion, the model incorporating road curvature and bank angle, particularly with the LSTM estimator, is proven to be more effective and reliable in dynamic scenarios. |최근 몇 년간 도로 지형을 자율주행 차량 제어 전략에 통합하는 것이 차량의 안정성과 성능에 미치는 영향 때문에 중요성이 커지고 있습니다. 본 연구는 도로 곡률과 뱅크 각을 제어 전략에 통합하여 모델 예측 제어(MPC)의 성능을 검증했습니다. 비선형 모델 예측 제어(NLMPC) 프레임워크가 경로 추종에 사용되었으며, 도로 뱅크 각 추정을 위해 두 가지 방법, 즉 미지 입력 확장 칼만 필터(EKF-UI)와 장단기 기억(LSTM) 신경망 기반 추정기를 사용했습니다. 더 복잡한 비선형성을 처리할 것으로 예상되는 LSTM 추정기는 EKF-UI와 비교하여 동적 환경에서의 추정 정확도와 안정성을 평가했습니다. 횡방향 안정성을 더욱 향상시키기 위해 슬랙 변수를 사용하여 사이드슬립 제약을 포함하였으며, 뱅크 각을 고려하여 안정성을 개선하기 위해 제로 모멘트 포인트(ZMP)를 표준 제약으로 포함시켰습니다. MATLAB과 CarSim에서의 시뮬레이션을 통해 제안된 제어 전략의 성능을 검증한 결과, LSTM 기반 추정기가 특히 복잡한 도로 조건에서 경로 추종의 안정성과 정확성을 크게 향상시켜 EKF-UI를 능가하는 성능을 보였습니다. 결론적으로, 도로 곡률과 뱅크 각을 통합하고 특히 LSTM 추정기를 활용한 모델이 동적 환경에서 더 효과적이고 신뢰할 수 있음을 입증했습니다.List of Contents
Abstract i
List of contents ii
List of tables iii
List of figures iv
I. Introduction 4
II. Related Works 7
III. System Design
3.1 Nonlinear Vehicle Dynamic Model 10
3.2 Extended Kalman Filter Based Vehicle State Estimator 14
IV. Controller Design
4.1 Driving Safety Constraints for Vehicle Stability Control 16
4.2 NLMPC controller design 19
4.3 PI Control for Longitudinal Velocity Command Generation 22
V. Road Bank Estimation
5.1 EKF-UI Algorithm-based Road Bank Estimation 24
5.2 LSTM-based Road Bank Estimation 26
VI. Simulation
6.1 Simulation Scheme 28
6.2 Simulation Results
6.2.1 Results at 60km/h 31
6.2.2 Results at 80km/h 37
VII. Conclusion 44MasterdCollectio
Resting state of human brain measured by fMRI experiment is governed more dominantly by essential mode rather than default mode network
No full textresting-state fMRI, task fMRI, singular value decomposition, essential mode, default mode network인간의 뇌는 휴식 상태인 Resting-state에서도 특유의 활동 패턴을 보이는 것이 알려져 있다. Resting-state는 뇌의 reference 상태로 간주되기에 Resting-state에 대한 이해는 큰 중요성을 갖는다. 연구자들은 Task-state에서는 활동이 적은 반면, Resting-state에서는 활발한 활동을 보이는 뇌 영역들의 모임인 default mode network (DMN)을 발견하였고, DMN은 Resting-state를 특정하는 공간적 뇌 활동 패턴으로 알려져 Resting-state fMRI 분야에서 집중적인 연구의 대상이 되어 왔다. 본 연구에서는 과연 DMN이 Resting-state를 특정하는 유일한 공간적 패턴인지 탐구하기 위해, 166명의 Resting-state fMRI 데이터를 특이값 분해를 통해 독립적인 공간적 기저들과, 그 공간적 기저들의 변화를 설명하는 시간적 기저들로 분해한 뒤, 공간적 기저들의 clustering을 통해 166명에게서 공통적으로 발견되는 공간적 뇌 활동 패턴들을 탐지하였다. 그 결과, Resting-state BOLD signal에 가장 큰 영향력을 갖는 공간적 패턴은 우리가 essential mode (EM)라고 명명한, 뇌 전반을 어우르는 공간적 기저였고, 종래에 알려진 DMN은 두 번째로 영향력이 큰 공간적 기저로 밝혀졌다. 따라서 앞으로 Resting-state는 DMN 뿐만 아니라, EM 패턴의 시각에서도 분석되어야 함을 제안한다.|Even in the Resting-state, it is known that the human brain exhibits characteristic activation pattern. For the Resting-state is regarded as the reference state of the human brain, an understanding of the Resting-state human brain has significance in brain and cognitive science research. Researchers have identified a network of regions in the human brain which deactivates in Task-states, but activates in the Resting-state, named it default mode network (DMN). Subsequently, DMN has been known as the brain activation pattern that characterizes the Resting-state of the human brain, and studied extensively by researchers in fMRI field. In this study I wanted to see if there are any additional brain activation patterns which are also crucial for understanding the Resting- state. I have decomposed the Resting-state fMRI BOLD signal data of 166 people into spatial patterns and their corresponding temporal patterns through singular value decomposition, then conducted clustering on spatial patterns to identify common spatial activation pattern shared among 166 people. As a result, I have identified essential mode (EM) which has the most dominant influence in the Resting-state. DMN was identified as the second most dominant spatial activation pattern, following EM. It is therefore recommended that when studying the Resting-state of human brain, researchers should investigate not only DMN, but also EM.Ⅰ. Introduction 1
1.1) Origin of magnetic resonance imaging 1
1.1.1) Nuclear magnetic resonance 1
1.1.2) Magnetic resonance imaging 2
1.2) functional MRI 3
1.2.1) Blood oxygen level dependent signal 3
1.2.2) Task fMRI 4
1.3) Methods to analyze fMRI data 6
1.3.1) fMRI data and preprocessing 6
1.3.2) Functional connectivity analysis 9
1.3.3) Decomposition based analysis 10
1.4) Resting-state fMRI 11
1.4.1) Task-negative region 11
1.4.2) Default mode network 11
1.5) Motivation and overview of study 12
1.5.1) Is DMN the most representative component characterizing the resting-state?12
1.5.2) Necessity of holistic approach 12
1.5.3) Overview of study 13
ⅠI. Theoretical Background 14
2.1) Eigenvalue and eigenvector 14
2.1.1) Eigen-decomposition 14
2.1.2) Eigen-basis representation of a matrix 15
2.2) Singular value decomposition 16
2.2.1) Singular value decomposition 16
2.2.2) Applications of singular value decomposition 17
2.3) Spectral graph theory 18
2.3.1) Matrix representation of a graph 18
2.3.2) Laplacian clustering 19
ⅠII. Methods 21
3.1) Materials 21
3.1.1) HCP dataset 21
3.1.2) HCP fMRI image acquisition protocol 21
3.2) Methods 23
3.2.1) Preprocessing 23
3.2.2) Singular value decomposition of fMRI BOLD signal matrix 25
3.2.3) Laplacian clustering with distillation 26
3.2.4) Finding the optimal threshold for Laplacian clustering 28
3.2.5) Fractional distillation of U vector clusters 30
3.2.6) Visualization of U vector clusters 31
3.2.7) Frequency analysis of V-vectors and its quadratic differential 32
ⅠV. Results 34
4.1) Singular value decomposition 34
4.1.1) Singular value decomposition result 36
4.2) Laplacian clustering and fractional distillation for resting-state fMRI 37
4.2.1) Laplacian clustering result overview 37
4.2.2) EM: Cluster of the most dominant U modes 39
4.2.3) DM: Cluster of the second most dominant U modes 41
4.2.4) Leftover groups 42
4.2.5) Time-robustness of our analysis 44
4.3) Laplacian clustering and fractional distillation for motor-task fMRI 46
4.3.1) Applying Laplacian clustering on motor-task fMRI 46
4.3.2) Findings from motor-task fMRI analysis 48
4.4) Frequency analysis 50
4.4.1) Quadratic differential of V-modes 51
4.4.2) Similarities between two leftover groups, shown in the power spectrum of
quadratic differentials of V-modes 51
4.4.3) Time-evolution of EM and DM exhibit higher power under 0.2 Hz frequency
region 52
4.4.4) Other characteristics of quadratic differential of V-modes 53
V. Discussion 56
5.1) Preprocessing 56
5.1.1) Global signal regression 56
5.2) EM is the most dominant mode of resting-state, and DM is the second most dominant
mode 59
5.2.1) Discovery of common and dominant modes: EM and DM 59
5.2.2) EM: Characteristics 59
5.2.3) Why EM was more dominant than DM 60
5.2.4) Shortcomings of the motor-task result 60
5.3) The low-frequency oscillation in both EM and DM are distinct from 1/f noise · 61
5.3.1) Shadowing problem of 1/f noise in the low-frequency band 61
5.3.2) Quadratic differential reveals the genuine signal from the shadowed frequency
band 61
5.3.3) EM and DM shows genuine oscillatory behavior in low frequency band 61
5.4) Summary and conclusion 63
5.4.1) The resting-state brain is a composition of EM and DM, modulating in time
according to their genuine oscillation 63
5.4.2) EM shows the brain regions known to be responsible for task execution in the
motor-task fMRI 63
5.4.3) EM and DM show the genuine time oscillatory behavior in the low-frequency band
· 63
5.4.4) Advantages of Laplacian clustering with distillation method 64
5.4.5) Future work 64DoctordCollectio
Instant gait classification for hip osteoarthritis patients: a non-wearable sensor approach utilizing Pearson correlation, SMAPE, and GMM
Get PDFThis study aims to establish a methodology for classifying gait patterns in patients with hip osteoarthritis without the use of wearable sensors. Although patients with the same pathological condition may exhibit significantly different gait patterns, an accurate and efficient classification system is needed: one that reduces the effort and preparation time for both patients and clinicians, allowing gait analysis and classification without the need for cumbersome sensors like EMG or camera-based systems. The proposed methodology follows three key steps. First, ground reaction forces are measured in three directions-anterior–posterior, medial–lateral, and vertical-using a force plate during gait analysis. These force data are then evaluated through two approaches: trend similarity is assessed using the Pearson correlation coefficient, while scale similarity is measured with the Symmetric Mean Absolute Percentage Error (SMAPE), comparing results with healthy controls. Finally, Gaussian Mixture Models (GMM) are applied to cluster both healthy controls and patients, grouping the patients into distinct categories based on six quantified metrics derived from the correlation and SMAPE. Using the proposed methodology, 16 patients with hip osteoarthritis were successfully categorized into two distinct gait groups (Group 1 and Group 2). The gait patterns of these groups were further analyzed by comparing joint moments and angles in the lower limbs among healthy individuals and the classified patient groups. This study demonstrates that gait pattern classification can be reliably achieved using only force-plate data, offering a practical tool for personalized rehabilitation in hip osteoarthritis patients. By incorporating quantitative variables that capture both gait trends and scale, the methodology efficiently classifies patients with just 2–3 ms of natural walking. This minimizes the burden on patients while delivering a more accurate and realistic assessment. The proposed approach maintains a level of accuracy comparable to more complex methods, while being easier to implement and more accessible in clinical settings. © The Author(s) 2025.TRUEsciescopuskc
PRPF4 Knockdown Suppresses Glioblastoma Progression via the p38 MAPK and ERK Signaling Pathways
No full textBackground/Aim: Pre-mRNA processing factor 4 (PRPF4), a core protein of U4/U6 small nuclear ribonucleoproteins (snRNPs), is crucial for maintaining their structure by interacting with PRPF3 and Cyclophilin H. Beyond its role in splicing, PRPF4 has been implicated in cell survival, apoptosis, and oncogenesis. Although PRPF4 mutations have been associated with retinitis pigmentosa, its role in glioblastoma (GBM) remains unclear. This study aimed to investigate the function of PRPF4 in GBM progression and its potential as a therapeutic target. Materials and Methods: Gene expression profiling was conducted to compare PRPF4 levels between GBM tumors and normal tissues. PRPF4 expression was also evaluated in various cancer and GBM cell lines. Stable PRPF4 knockdown cell lines were established using A172 and T98G GBM cell lines. Cellular proliferation, apoptosis, migration, and invasion were assessed through gene expression and functional assays. Additionally, molecular pathways affected by PRPF4 knockdown were examined, focusing on the p38 MAPK signaling pathway. Finally, metabolic processes in PRPF4 knockdown cells were estimated through proteomic analysis. Results: PRPF4 expression was elevated in GBM. Knockdown of PRPF4 reduced cell proliferation, induced apoptosis, and suppressed migration and invasion in GBM cells. PRPF4 knockdown also suppressed MKK3/6-p38-ATF2 and RAS-MEK-ERK1/2 signaling pathways. Proteome analysis revealed disruptions in metabolic pathways, including glutathione and carbon metabolisms, which are associated with GBM progression. Conclusion: PRPF4 knockdown inhibits GBM progression by reducing p38 MAPK and ERK signaling cascade with metabolic alterations. Targeting PRPF4 may offer novel therapeutic strategies for GBM treatment. © 2025 International Institute of Anticancer Research. All rights reserved.FALSEsciescopu
Functional separator for safe, long-life Lithium metal batteries
No full textLithium metal batteries, sulfone electrolyte, solid electrolyte, separator, coatingⅠ. Introduction 1
1.1 Sulfone-based gel polymer electrolytes (SGPE) 2
1.2 Ceramic-coated separator 3
1.3 References 4
Ⅱ. Ceramic-coated separator for Sulfone-based gel polymer electrolytes 5
2.1 Experimental methods 5
2.1.1 Materials and chemicals 5
2.1.2 Preparation of PE-LF separator 6
2.1.3 Characterization of separator 6
2.1.4 Electrochemical measurements 6
2.1.5 Battery performance 7
2.2 Results and discussion 7
2.2.1 Coating design 7
2.2.2 Thermal stability 13
2.2.3 Cycle performance and Li deposition of Li symmetric cells 14
2.2.4 Cycle performance and Li deposition of Li/Cu cells 16
2.2.5 Application to NCM811/Li cells 17
2.3 Conclusions 20
2.4 References 21MasterdCollectio
Toward Connected Sky: Modeling and Performance Analysis for Cellular-connected UAV Communications
No full textUnmanned aerial vehicle (UAV) communications, non-terrestrial network (NTN), integrated access and backhaul (IAB), rate-splitting multiple access (RSMA).I. Introduction 1
1.1 Backgrounds 1
1.2 Outline and Contributions 2
1.2.1 Chapter 2 2
1.2.2 Chapter 3 3
1.2.3 Chapter 4 3
1.2.4 Chapter 5 3
II. Non-terrestrial networks for UAVs: Base station Service Provisioning Schemes With Antenna Tilt 4
2.1 Introduction 4
2.2 System Model 7
2.2.1 Network Model 7
2.2.2 Channel Model 8
2.2.3 Vertical Antenna Gain 9
2.2.4 BS Association Rule 11
2.3 Outage Probability Analysis 13
2.3.1 Interference Model 14
2.3.2 Outage Probability 14
2.4 Numerical Results 18
2.4.1 Outage Probability of Ground and Air Users 19
2.4.2 Results of IS-BS Scheme 21
2.4.3 Results of ES-BS Scheme 23
2.4.4 Comparison Between IS-BS Scheme and ES-BS Scheme 24
2.5 Conclusion 27
III.Lightening Backhaul Load for Aerial Multi-hop Integrated Access and Backhaul Networks 28
3.1 Introduction 28
3.2 System Model 30
3.2.1 Network Model 30
3.2.2 Signal-to-Interference-plus-Noise Ratio (SINR) 30
3.2.3 Lightening Backhaul Load Scheme 31
3.3 Successful Transmission Probability Analysis 32
3.3.1 Association Probability 32
3.3.2 Successful Transmission Probability 34
3.4 Numerical Results 36
3.5 Conclusion 38
IV.Precoding Design for Integrated Access and Backhaul Networks: Rate-Splitting Approach 39
4.1 Introduction 39
4.2 System Model 42
4.2.1 Network Model 42
4.2.2 Signal Model of IAB Donor 43
4.2.3 Signal Model of IAB Node 45
4.3 Precoding and Backhaul Rate Design for IAB Networks With Rate-Splitting Approach 47
4.3.1 Problem Formulation 48
4.3.2 Algorithmic Design for Precoding Vector 49
4.3.3 Optimal Solution for Backhaul Rate 53
4.3.4 Algorithmic Design for Precoding Vector and Backhaul Rate 55
4.4 Numerical Results 56
4.5 Conclusion 57
V. Conclusions 59
References 60DoctordCollectio
Interface Chemistry and Engineering with Nano-Colloidal Electrolytes in Lithium-Metal Batteries
No full textLi-metal batteries, nano-colloidal electrolyte, solid-electrolyte interphase, Li+ transportList of Contents
Abstract i
List of Contents ⅱ
List of Tables ⅳ
List of Figures ⅴ
Chapter 1. Introduction
1.1. Recent Perspectives and Backgrounds on Lithium Secondary Battery 2
1.2. Lithium-Ion Batteries 6
1.2.1. Historical Milestones in Lithium-Ion Batteries Development 6
1.2.2. Principles of Lithium-Ion Batteries: Needs for Next-Generation Batteries 8
1.3. Lithium Metal Batteries 9
1.3.1. Promises and Limitations of Lithium Metal Anode 9
1.3.2. Lithium Dendrite Triggering Factors Ⅰ: Li+ Transport 13
1.3.3. Lithium Dendrite Triggering Factors Ⅱ: SEI Chemistry· 14
1.4. Historical Evolutions of Liquid Electrolyte for Lithium Metal Batteries 16
1.5. Beyond the Liquid Electrolyte: Nano-Colloidal Electrolytes (NCEs) 17
1.5.1. Concepts and Structures 18
1.5.2. Modulation of Li+ Microenvironment 21
1.5.3. Modulating Interface Chemistry 23
1.6. Research Goals 24
1.7. Reference 27
Chapter 2. Modulating Surface Functionality of Nanoparticles
2.1. Introduction 32
2.2. Experimental Methods 34
2.3. Results and Discussion 36
2.4. Conclusion 50
2.5. Reference 52
Chapter 3. Shaping Geometry and Internal Structure of Nanoparticles
3.1. Introduction 58
3.2. Experimental Methods 60
3.3. Results and Discussion 62
3.4. Conclusion 73
3.5. Reference 75
Chapter 4. Controlling Nanoparticle Motion with External Force Engineering
4.1. Introduction 78
4.2. Experimental Methods 80
4.3. Results and Discussion 82
4.4. Conclusion 103
4.5. Reference 105
Chapter 5. Concluding Remark
5.1. Concluding Remark 109
Summary in Korean 112DoctordCollectio
Exploring the effect of arginine methylation on RNA-regulated FUS LLPS
No full textFUS, LLPS, RNA, arginine methylationⅠ. Introduction 1
Ⅱ. Materials and Methods 5
Ⅲ. Results 10
3.1 Validation of wild-type FUS (FUSWT) fused with fluorescent protein constructs 10
3.2 RNA depletion within FUS granules impairs their ability to assemble in cellular environment 14
3.3 Investigation of the arginine methylation in the RGG1 and RGG2 regions of FUS on its LLPS 17
Ⅳ. Discussion 23
4.1 Cellular stressors and FUS mislocalization 23
4.2 The role of RNA within FUS LLPS in cellular environments 24
4.3 Arginine methylation and its hydrophobicity at the RGG1 and RGG2 regions in FUS-RNA interactions 24
Supplementary information 26
References 33
요약문 (Abstract in Korean) 38MasterdCollectio