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Distance accuracy improvement of FMCW LiDAR using frequency control with a one-shot based frequency detector
No full textThis dissertation explores the improvement of distance measurement accuracy in Frequency-Modulated Continuous Wave (FMCW) Light Detection and Ranging (LiDAR) systems using innovative frequency measurement and control techniques. The study addresses the limitations of traditional frequency analysis methods, such as Fast Fourier Transform (FFT), in dynamic and noise-prone environments, and proposes the integration of a Frequency-to-Voltage Converter (FVC) and frequency control. The research concentrates on three primary contributions. First, the adoption of an FVC provides real-time frequency conversion, enabling higher resolution at high frequencies compared to FFT. Second, the implementation of frequency control techniques ensures consistent resolution over all frequency bands that FMCW LiDAR aims to measure, overcoming the ripple noise challenges inherent in FVCs. Third, the integration of advanced optical and signal processing methods is demonstrated to enhance system robustness in adverse environmental conditions, such as fog. Experimental results validate the proposed methods, showing a five times improvement in frequency resolution over FFT and a seven-fold improvement over FVC alone in low-frequency scenarios. Furthermore, performance assessments in controlled foggy environments highlight the advantages of longer wavelengths (1550 nm) in maintaining signal integrity compared to shorter wavelengths (650 nm), underscoring the system's adaptability to real-world challenges. This work contributes to the advancement of FMCW LiDAR technology, offering a framework for achieving high-precision distance measurements in diverse applications, including autonomous vehicles, robotics, and industrial automation. The proposed solutions provide a significant step forward in addressing the accuracy, resolution, and environmental resilience of FMCW LiDAR systems. Jubong Lee (이주봉). Distance accuracy improvement of FMCW LiDAR using frequency control with a one-shot based frequency detector (원샷 기반의 주파수 센서 및 주파수 제어를 이 용한 FMCW 라이다의 거리 정확도 향상). Department of Mechanical Engineering. 2025. 76p. Advisor Prof. Kyihwan Park (박기환)DoctorAbstract ․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․ i
List of contents ․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․ ii
List of figures ․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․ v
I. INTRODUCTION ․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․ 1
II. Optical Distance Measurement Methods ․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․ 2
2. 1. Time of Flight Method ․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․ 3
2. 2. Amplitude Modulated Continuous Wave Method ․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․ 4
2. 3. Frequency Modulated Continuous wave Method ․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․ 5
III. LiDAR Configuration based on the FMCW Method ․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․ 6
3. 1. Principle of FMCW LiDAR ․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․ 6
3. 2. Configuration of FMCW LiDAR ․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․ 8
IV. Transmitter ․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․ 11
4. 1. DFB laser source ․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․ 11
4. 2. Nonlinear Frequency Modulation ․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․ 13
4. 3. Linear Frequency Modulation Contro ․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․ 14
V. Optical System ․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․ 17
5. 1. Homodyne Mach-Zehnder Interferometer ․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․ 17
5. 2. Heterodyne Mach-Zehnder Interferometer ․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․ 19
5. 3. Fiber Optic System ․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․ 21
5. 4. Focusing Optic System ․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․ 22
VI. Reciever ․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․ 25
6. 1. Principle of Balanced Photo Detector ․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․ 26
6. 2. Optical Attenuation for BPD ․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․ 29
6. 3. Variable Optical Attenuator ․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․ 31
VII. Frequency Measurement Method ․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․ 33
7. 1. Fast Fourier Transform ․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․ 33
7. 2. Frequency Resolution of FFT ․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․ 36
7. 3. Phase Locked Loop ․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․ 36
7. 4. Frequency Resolution of Phase Locked Loop ․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․ 39
7. 5. One-shot based Frequency Detection Method ․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․ 39
7. 6. Frequency Resolution of FVC ․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․ 41
7. 7. Low Pass Filtering for Improving Frequency Resolution of FVC ․․․․․․․․․․․․․․․․․․․․․․ 43
7. 8. Research trends and Approaches for Improving Frequency Resolution of FVC
․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․ 44
7. 9. Comparison of Frequency Resolution of FFT and FVC ․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․ 46
VIII. Frequency Control of Interference Signal ․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․ 50
8. 1. Configuration of Frequency Control ․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․ 50
8. 2. Voltage Controlled Oscillator ․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․ 51
8. 3. Control Performance of Frequency Control ․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․ 53
8. 4. Comparison of Frequency Detection Performance ․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․ 57
IX. Distance Accuracy Improvement of FMCW LiDAR ․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․ 60
9. 1. Experimental Setup ․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․ 60
9. 2. Experimental Results ․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․ 61
X. Feasibility Sstudy and Validation of FMCW LiDAR in Foggy Environments ․․․․․․․․․․․․․․․ 64
10. 1. Theoretical Analysis of Scattering, Absorption, and Wavelength relationship
․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․ 64
10. 2. Experimental Setup ․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․ 66
10. 3. Experimental Results ․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․ 69
XI. Conclusions ․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․ 73
References ․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․ 74
Acknowledgement ․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․ 77
Curriculum Vitae ․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․ 7
Alkali fusion-enhanced metal leaching of blast furnace slag for pretreatment of simultaneous carbon mineralization and rare earth elements recovery
No full textLandfilled waste containing alkaline earth metals is a promising feedstock for CO2 storage and carbon mineralization. It has recently gained attention as a resource for recovering rare earth elements (REEs) at concentrations of several hundred ppm. However, during the leaching of calcium (Ca) and REEs, silicate (Si) contained in landfilled wastes forms an inactive Si-passivation layer on the particle surface, thereby hindering the metal leaching efficiency. In this study, we propose an alkali fusion pretreatment to provide an alternative leaching pathway for blast furnace slag (BFS) and enhance the leaching efficiency of both Ca and REEs. The alkali fusion was conducted by roasting BFS with NaOH at 400, 600, and 800 °C, and structural changes, including crystalline and amorphous silicate phase, were investigated. A dramatic leaching improvement (98.5 and 88.2 % for Ca and REEs, respectively) was achieved even at the lowest fusion temperature of 400 °C, which was two-fold higher than the leaching of raw BFS. This significant improvement was mainly due to the destruction of silicate phases and the transformation of BFS into ‘ready-to-leach’ phases. Our study highlights mineralogical engineering as a pretreatment for achieving efficient leaching in the recovery of REEs and the carbon mineralization integrated process. © 2025 Elsevier B.V.FALSEsciescopu
Rapid Drying Principle for High-speed, Pinhole-Less, Uniform Wet Deposition Protocols of Water-Dispersed 2D Materials
No full textInexpensive, high-speed deposition techniques that ensure uniformity, scalability, wide applicability, and tunable thickness are crucial for the practical application of 2D materials. In this work, rapid drying is identified as a key mechanism for pioneering two high-speed wet deposition methods: hot dipping and air knife sweeping (AKS). Both techniques allow thickness control proportional to flake concentration, achieving tiled monolayers and pinhole-free coverage across the entire substrate, as long as evaporation outpaces flake diffusion. AKS prevents non-uniformity along substrate edges by eliminating contact line pinning. The achieved deposition speed of 0.21 m(2) min(-1) with AKS significantly surpasses traditional methods, enabling the equipment for large substrates > 1 m(2). Combined with the ultralow debonding force for mechanically susceptible flexible display production and short-circuit-proof nanometer-thin capacitors with capacitance comparable to commercial multilayer ceramic capacitors (MLCCs), these new protocols showcase simple and swift solutions for manufacturing 2D materials-based nanodevices.TRUEsciescopu
AI-guided discovery and optimization of antimicrobial peptides through species-aware language model
No full textThe rise of antibiotic-resistant bacteria drives an urgent need for novel antimicrobial agents. Antimicrobial peptides (AMPs) show promise solutions due to their multiple mechanisms of action and reduced propensity for resistance development. This study introduces LLAMP (Large Language model for AMP activity prediction), a target species-aware AI model that leverages pre-trained language models to predict minimum inhibitory concentration values of AMPs. Using LLAMP, we screened approximately 5.5 million peptide sequences, identifying peptides 13 and 16 as the most selective and most potent candidates, respectively. Analysis of attention values allowed us to pinpoint critical amino acid residues (e.g., Trp, Lys, and Phe). Using the critical amino acids, the sequence of the most selective peptide 13 was engineered to increase amphipathicity through targeted modifications, yielding peptide 13-5 with an overall enhancement in antimicrobial activity but a reduction in selectively. Notably, peptides 13-5 and 16 demonstrated antimicrobial potency and selectivity comparable to the clinically investigated AMP pexiganan. Our work demonstrates the potential of AI to expedite the discovery of peptide-based antibiotics to combat antibiotic resistance.TRUEsciescopu
Therapeutic Effects of Auditory Stimulation at 40 Hz on Sleep Disturbances in an Alzheimer`s Disease Mouse Model: Focusing on the Role of Astrocytes
No full textThe sensory stimulation at 40 Hz showed therapeutic impacts on Alzheimer`s disease (AD). Previous studies reported that amyloid-beta (Aβ) accumulation and sleep disturbances in AD have positive feedback loop and impaired Ca2+ influx and volume transient in astrocytes. However, the role of astrocytes to sleep disturbances remains unknown. We hypothesized that 40-Hz auditory stimulation could ameliorate sleep disturbances by enhancing the astrocytic Ca2+ influx. We used 6-month-old 5xFAD as Alzheimer`s disease model. Two-hour daily 40-Hz auditory stimulation were applied for two-weeks. We recorded the 24-hour electroencephalographic (EEG) for sleep-wake analysis at pre- and post-stimulation day. Reactive astrocytes and GABA were measured using immunohistochemistry. Patch clamping recordings measured the tonic GABA currents of nNOS neurons. Using GCaMP6f and intrinsic optical singals were measured astrocytic Ca2+ and volume swelling. qRT-PCR measured the transcriptional levels. Our studiy demonstrated that repeated auditory steady state responses (rASSR) at 40 Hz mitigated Aβ pathology and sleep disturbances. We observed that astrocytic γ-aminobutyric acid (GABA) reduced inhibition on neuronal nitric oxide synthase (nNOS)-containing neurons, sleep-promoting cortical neurons, by rASSR. Additionally, rASSR enhanced neuronal activity-induced transient volume and Ca2+ influx in astrocytes, which subsequently decreased monoamine oxidase B (MAO-B) and increased nuclear factor erythroid 2-related factor 2 (Nrf2). Finally, optogenetic boosting of astrocytic Ca2+ influx using MonSTIM1 mimicked the therapeutic effects of the rASSR at 40 Hz. Our findings suggest that 40-Hz rASSR ameliorates Aβ pathology and sleep disturbances by neuron-astrocyte interaction.DoctorAbstract ․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․ 5
List of Contents ․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․ 7
List of Figures ․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․ 11
List of Tables ․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․ 15
1. Introduction ․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․ 16
1. 1. Alzheimer`s disease ․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․ 16
1. 1. 1. Status of Alzheimer`s disease in the world ․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․ 16
1. 1. 2. Tauopathy․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․ 20
1. 1. 3. Amyloidopathy․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․ 22
1. 1. 4. Production of Amyloid-beta․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․ 24
1. 1. 5. Toxicity of Amyloid-beta․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․. 27
1. 1. 6. Degradation of Amyloid-beta․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․. 30
1. 1. 7. Clearance of Amyloid-beta․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․ 33
1. 1. 8. Alzheimer`s disease mouse model ․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․. 35
1. 2. Sleep ․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․ 38
1. 2. 1. Sleep circuit․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․ 38
1. 2. 2. Sleep disturbances in Alzheimer`s disease․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․ 40
1. 2. 3. Amyloid-beta deposition in sleep-promoting areas ․․․․․․․․․․․․․․․․․․․․․․ 42
1. 3. Aberrant electroencephalogram in Alzheimer`s disease․․․․․․․․․․․. 45
1. 3. 1. Aberrant gamma band power in Alzheimer`s disease․․․․․․․․․․․ 45
1. 3. 2. Aberrant sleep spindle density in Alzheimer`s disease․․․․․․․․․․․ 48
1. 3. 3. Therapeutic effects of 40-Hz sensory gamma stimulation to
Alzheimer`s disease ․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․ 53
1. 4. Reactive gliosis in Alzheimer`s disease․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․ 53
1. 4. 1. Amyloid-beta degradation by activated microglia ․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․ 53
1. 4. 2. Amyloid-beta degradation by reactive astrocytes․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․ 56
1. 4. 3. GABA production by reactive astrocytes ․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․ 59
1. 4. 4 Impairment of transient volume changes and Calcium dynamics in reactive
astrocytes․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․ 62
1. 5. Optogenetically enhancing the astrocytic calcium influx․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․ 65
1. 6. Hypothesis ․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․ 66
2. Methods and Materials ․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․ 67
2. 1. Animals ․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․ 67
2. 2. Animal Surgical Procedures ․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․ 69
2. 3. 40-Hz rASSR generation ․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․ 72
2. 4. Blue light illumination ․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․ 73
2. 5. Sleep recording and analysis ․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․ 75
2. 6. Sleep spindle analysis ․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․ 77
2. 7. Mouse brain homogenization ․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․ 79
2. 8. Sandwich Enzyme linked immunosorbent assay (ELISA) ․․․․․․․․․․․․․․․․․․․․․․․․ 80
2. 9. Immunohistochemistry ․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․ 81
2. 10. Tonic GABA recording ․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․ 84
2. 11. Intrinsic Optical Signal imaging and Ca2+ imaging ․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․ 86
2. 12. qRT-PCR ․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․ 88
2. 13. Illumina Hiseq library preparation and RNA sequencing ․․․․․․․․․․․․․․․․․․․․․․ 90
2. 14. Next Generation Sequencing Data Analysis ․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․ 91
2. 15. Statistical Analysis ․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․ 92
3. Results ․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․ 93
3. 1. 40-Hz rASSR ameliorated the Aβ pathology and activated microglia ․․․ 93
3. 2. Improved sleep disturbances by rASSR ․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․ 98
3. 3. rASSR restored aberrant EEG in AD ․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․ 102
3. 4. rASSR disinhibited cortical nNOS neuronal activity ․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․ 107
3. 5. rASSR reduced GABA-postive reactive astrocytes ․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․ 112
3. 6. rASSR modulated gene expressions ․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․ 116
3. 7. rASSR restored astrocytic transient volume changes and calcium influx
.․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․ 118
3. 8. Astrocytic calcium influx reduced Aβ and activated microglia ․․․․․․․․․․․․ 121
3. 9. Astrocytic Calcium influx improved sleep and EEG ․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․ 124
3. 10. Astrocytic calcium influx reduced GABA-positive reactive astrocytes ․ 127
3. 11. Astrocytic calcium influx modulated gene expressions ․․․․․․․․․․․․․․․․․․․․․․․․ 129
4. Discussion ․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․ 149
4. 1. Summary ․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․ 149
4. 2. rASSR reduced Aβ pathology and activated microglia ․․․․․․․․․․․․․․․․․․․․․․․․․․․ 150
4. 3. rASSR improved sleep disturbances ․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․ 151
4. 4. rASSR improved the aberrant EEG and sleep spindle ․․․․․․․․․․․․․․․․․․․․․․․․․․․․ 152
4. 5. rASSR disinhibits nNOS neuron`s activity ․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․ 154
4. 6. rASSR converts neurotoxic reactive astrocytes to neurosupportive
astrocytes ․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․ 155
4. 7. rASSR improved astrocytic transient volume changes and Calcium influx
and modulating Gene expressions ․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․ 156
4. 8. Astrocytic calcium influx mimicked the therapeutic effects of rASSR
․ 157
4. 9. Conclusion ․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․ 159
Summary ․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․ 160
References ․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․ 162
Curriculum Vitae ․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․ 181
Acknowledgement ․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․ 18
Interpretable dose-dependent cancer-drug response prediction for precision medicine
No full textPersonalized medicine represents a transformative approach in healthcare by tailoring treatment strategies to an individual’s unique genetic profile, unlike traditional one-size-fits-all approaches. This strategy aims to optimize therapeutic outcomes by aligning medical treatments with the specific characteristics of each patient. However, the extensive anti-cancer drug space poses significant challenges, as the number and diversity of compounds make it difficult to identify the most proper therapy for an individual patient. Recent advances in deep learning offer a promising solution by predicting which anti-cancer drugs are likely to be most beneficial, potentially overcoming the limitations imposed by the large number of therapeutic options. However, although deep learning models can suggest anti-cancer drugs that may be effective for individual patients, there are unsolved limitations. One significant challenge is the lack of interpretability inherent in many deep learning approaches, which can obscure the rationale behind their predictions. Furthermore, while these models can indicate promising drug candidates, they fall short of providing practical treatment specifics, such as optimal drug dosing regimens. In this dissertation, I addressed these limitations by proposing advanced drug response prediction models with biological knowledge. First, I proposed a deep learning model, HiDRA, to predict the response of drug on given cancer cell using gene expressions of the cancer cell, structural fingerprint of the compound, and biological pathway. Compared to state-of-the-art (SOTA) models, the proposed model showed the best performance in the Half maximal inhibitory concentration (IC50) prediction task. Through attention analysis, the proposed model demonstrated its interpretability by identifying drug target genes and target pathways. The predictive power of HiDRA was also proved by the in vitro experiments. In the second study, I focused on developing a model named DD-PRiSM, that predicts the efficacy of combination therapy at arbitrary concentrations. To predict the efficacy of combination therapy, I enhanced HiDRA to develop a model that predicts the efficacy of monotherapy at arbitrary concentrations based on Hill equation. Using the predicted efficacies and mechanisms of action (MoA) of the monotherapies, the proposed model successfully predicted the efficacy of combination therapy with high performance. DD-PRiSM demonstrated its interpretability through the additional analyses that confirm the characteristics of drug combinations prone to synergy effects and the differences in synergy effects across various cancer types. In conclusion, the model proposed in this dissertation for predicting the efficacy of anticancer treatment at arbitrary concentrations is expected to make a significant contribution to the advancement of personalized medicine.Doctor1. Introduction 12
1.1. Heterogeneity and Personalized medicine 12
1.2. Computational methods for the personalized medicine and their limitations 15
1.3. Research Motivation 17
1.4. Outline of Dissertation 18
2. Monotherapy response prediction 19
2.1. Introduction 19
2.2. Materials and Methods 23
2.2.1. Workflow of HiDRA 23
2.2.2. Data Sets 24
2.2.3. Model Architecture 26
2.2.4. Training Scheme 30
2.2.5. Attention Score Analysis 34
2.2.6. Drug Repositioning 35
2.3. Results 36
2.3.1. Prediction Performance of HiDRA 36
2.3.2. Independent Validation 40
2.3.3. Analyzing Attention Result 43
2.3.4. Drug Repositioning Using HiDRA 49
2.4. Discussion 51
3. Combination therapy response prediction 54
3.1. Introduction 54
3.2. Materials and methods 58
3.2.1. Drug response datasets 58
3.2.2. Data representation 62
3.2.3. Monotherapy response prediction 63
3.2.4. Combination therapy response prediction 64
3.2.5. Training scheme 66
3.2.6. Performance evaluation 72
3.3. Results 74
3.3.1. Model performance evaluation 74
3.3.2. Performance comparison with related studies 81
3.3.3. Performance evaluation on external dataset 86
3.3.4. Large-scale evaluation of synergistic effects in drug combinations 88
3.3.5. Synergism by the cancer type 92
3.3.6. Prediction case study 96
3.4. Discussion 98
4. Discussion 100
4.1. Drug response Prediction using Biological Pathway information and Attention mechanism 100
4.2. Drug response Prediction in a Dosage-dependent manner and Combination therapy efficacy Prediction with Monotherapy information 101
5. Conclusions and Future works 102
5.1. Summary of Dissertation 102
5.2. Future Research Directions 103
6. Reference 104
7. Acknowledgement 109
8. Curriculum Vitae 11
A genotype-to-drug diffusion model for generation of tailored anti-cancer small molecules
No full textDespite advances in precision oncology, developing effective cancer therapeutics remains a significant challenge due to tumor heterogeneity and the limited availability of well-defined drug targets. Recent progress in generative artificial intelligence (AI) offers a promising opportunity to address this challenge by enabling the design of hit-like anti-cancer molecules conditioned on complex genomic features. We present Genotype-to-Drug Diffusion (G2D-Diff), a generative AI approach for creating small molecule-based drug structures tailored to specific cancer genotypes. G2D-Diff demonstrates exceptional performance in generating diverse, drug-like compounds that meet desired efficacy conditions for a given genotype. The model outperforms existing methods in diversity, feasibility, and condition fitness. G2D-Diff learns directly from drug response data distributions, ensuring reliable candidate generation without separate predictors. Its attention mechanism provides insights into potential cancer targets and pathways, enhancing interpretability. In triple-negative breast cancer case studies, G2D-Diff generated plausible hit-like candidates by focusing on relevant pathways. By combining realistic hit-like molecule generation with relevant pathway suggestions for specific genotypes, G2D-Diff represents a significant advance in AI-guided, personalized drug discovery. This approach has the potential to accelerate drug development for challenging cancers by streamlining hit identification.TRUEsciescopu
Bipolar Phase Shifts of Reconfigurable Intelligent Surface for Channel Estimation in RIS-Aided Multiuser mmWave Communications
No full textReconfigurable intelligent surface (RIS) is a key enabler for 6G wireless networks, reconfiguring wireless propagation to enhance the quality of signal transmission. In RIS-aided communications, channel estimation plays a crucial role because the performance improvement from RIS relies on the accuracy of channel state information. In this paper, we configure the phase shifts of RIS passive reflective elements, in an effort to improve the performance of cascaded channel estimation between a base station and users through the RIS in multiuser mmWave communications. We first apply the Golay complementary sequences to configure the bipolar phase shifts of RIS in training phase. Then, we study a theoretical recovery guarantee for compressed sensing based channel estimation with the Golay-based bipolar phase shifts of RIS. Simulation results demonstrate that the Golay-based phase shift configuration exhibits excellent performance for channel estimation, outperforming random and optimized phase shifts. The algebraic configuration of phase shifts contributes to improving the performance of channel estimation efficiently through a large-scale RIS with bi-phase resolution in RIS-aided multiuser mmWave communications. © 2025 Elsevier B.V., All rights reserved.FALSEsciescopu
Trio of human, old and new copilots: Collaborative accountability of human, manuals/standards, and artificial intelligence (AI)
No full textHumans have developed and employed manuals to systematically organize, standardize, and transfer knowledge for decision-making in organizations. These manuals and standards have served as a “conventional copilot” for humans’ intellectual activities, taking the form of collected references or operational procedures. Recently, artificial intelligence (AI) has emerged as a “novel copilot” that aids humans in organizations. Given the two non-human supports, this article aims to redefine the relational dynamics among the trio (human, manuals/standards, and AI). It analyzes and suggests that, rather than the new copilot (AI) making the old one (manuals/standards) obsolete, the trio needs to collaborate and complement one another to sustain accountabilities in terms of contingency, competence, and stewardship. © 2024 The AuthorsTRUEssciscopu
Bio-inspired projected gradient method for leader selection in minimum cost problem
No full textInspired by the independent and efficient synaptic connections in biological neural networks, this paper introduces the Diagonal Projected Gradient Method (DPGM), a bio-inspired approach to optimize leader selection in networks. Existing methods, such as the Trace-constraint-based Projected Gradient Method (TPGM) and the Orthonormal-constraint-based Projected Gradient Method (OPGM), impose constraints on the input matrix but suffer from redundancy and overlap in input connections, leading to distorted importance indices and suboptimal leader selection. DPGM employs a diagonal input matrix, ensuring that each input affects exactly one node, akin to how synapses independently influence specific neurons. By optimizing the input vector rather than a full matrix, this method simplifies the optimization process and mirrors the efficiency of biological systems, allowing the importance index to reflect the independent influence of each node accurately. We provide a convergence analysis of the proposed method and demonstrate through simulations on real-world networks that DPGM outperforms TPGM and OPGM in leader selection, resulting in improved control efficiency. © 2025 Elsevier B.V., All rights reserved.FALSEscopu