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The functional heterogeneity of dentate gyrus mossy cells and its implications in psychiatric disorder
No full textHippocampus, Heterogeneity, Dentate gyrus, Mossy cells, Granule cells, TRAP-seq, PTSD, Fear generalization, Contextual discrimination, Memory ensemble, Pattern separation.치아이랑 모시세포의 기능적 이형성과 정신 질환에서의 역할
적당한 스트레스는 주의력, 기억 및 기타 인지 기능을 향상시켜 일상 생활에 이로운 영향을 미칠 수 있습니다. 그러나 외상성 스트레스는 개인의 대처 능력을 압도할 수 있으며, 주요 우울 장애와 외상 후 스트레스 장애(PTSD)의 중요한 위험 요소로 작용합니다. PTSD의 주요 특징 중 하나는 두려움의 일반화로, 이는 외상성 스트레스에 대한 부적응 반응으로, 위협적인 상황과 안전한 상황을 구별하는 능력을 저하시킵니다. 그러나, 이 결핍의 신경 기전은 아직 명확하지 않습니다. 본 연구에서는 외상성 스트레스 노출유도 모델인 학습된 무기력 모델(LH 모델)에서 위협과 안전 맥락 간의 맥락 구별을 저하시킨다는 것을 발견했습니다. 외상성 스트레스에 대한 반응으로 등측 모시세포는 억제되지만, 배측 모시세포는 그렇지 않습니다. 학습된 무기력 모델에서 배측 모시세포 활동의 양방향 조작은 모시세포 억제가 맥락 구별 장애와 인과적으로 연결되어 있음을 보여줍니다. 맥락 구별 장애에 대한 기전은 모시세포 억제에 따라 주어진 맥락에서 활성화된 과립 세포의 수를 증가시켜 맥락별 앙상블이 상당히 겹치게 합니다. 외상성 스트레스 후 등측 모시세포의 부적절한 억제가 두려움의 일반화와 맥락 구별 장애의 중요한 메커니즘임을 입증하였으며, 이는 PTSD의 인지 증상에 대한 잠재적인 치료 표적이 될 수 있음을 시사합니다.
모시세포의 외상성 스트레스에 대한 차별적 반응을 바탕으로, 쥐에서 모시세포의 구조적, 분자적 및 기능적 특성이 등측-배측 위치에 따라 어떻게 다른지를 조사하였습니다. 등측 모시세포가 배측 모시세포와는 다른 축삭 연결성을 가진다는 것을 발견했습니다. 또한, 번역 리보솜 친화성 정제(TRAP)를 사용한 세포 유형 특이적 RNA 시퀀싱을 통해 등측과 배측 모시세포 간의 독특한 분자 서명을 확인했습니다. 등측 모시세포는 신경 연결성과 시냅스 전송과 관련된 유전자가 유의미하게 증가하여 발현됩니다. 반면에, 배측 모시세포는 대사 및 세포 과정과 관련된 유전자의 높은 발현을 보입니다. 또한 등측 모시세포는 배측 모시세포와는 달리 과립 세포에 대한 억제 조절 기능을 수행하며, 빠른 맥락 구별에 중요하다는 것을 발견했습니다. 이는 모시세포의 등배축 간의 이질성은 기능적 차별화의 새로운 메커니즘을 제공하고 해마의 종축 길이에서의 뚜렷한 연관성을 나타낼 수 있습니다.
해마의 긴 곡선 구조가 설치류에서 배측-복측 축을 따라, 비인간 영장류에서 후방-전방 축을 따라 기능적으로 보존되어 있다는 증거를 바탕으로, 설치류의 모시세포의 등측-배측 이질성이 비인간 영장류에서도 보존되는지를 추가로 조사했습니다. 생쥐와 원숭이의 치아이랑에서 모시세포의 신경 해부학을 비교했습니다. 모시세포 표지자인 칼레티닌은 두 가지 하위 집단인 분절축과 측두축을 구별합니다. 이 하위 집단의 축삭 투사 패턴을 비교하기 위해 이중 형광 라벨링을 사용했습니다. 생쥐와 원숭이 모두에서 분절축 및 측두축 모시세포는 같은 축의 치아이랑의 종축을 따라 축삭을 투사하여 보존된 연관 투사를 나타냅니다. 그러나, 생쥐와 달리 원숭이의 모시세포 하위 집단은 반대측 치아이랑으로 연합 투사를 하지 않습니다. 원숭이에서는 측두축 모시세포가 오직 내분자층에만 연합 섬유를 보내는 반면, 분절축 모시세포는 여러 분자층에 걸쳐 넓은 축삭 투사를 합니다. 보존된 분절측두축에 따른 이형성에도 불구하고, 세포 조직의 배측 MC에서 각 분자층의 상대 축삭 밀도와 같은 종 간 차이가 관찰됩니다. 이러한 발견은 치아이랑의 분절측두축을 따라 기능적 차별화를 이해하는 데 기여하며, 포유류의 DG 회로의 해부학적 진화에 대한 지식을 넓히는 데 도움이 됩니다.
핵심어: 해마, 이형성, 치아이랑, 모시세포, 과립세포, RNA 시컨싱, 공포 일반화, 맥락 구분, 기억 엔그렘, 패턴분리력 |Moderate stress can enhance vigilance, memory, and other cognitive functions, offering benefits in daily life. However, traumatic stress can overwhelm an individual’s capacity to cope, acting as a significant risk factor for conditions such as major depressive disorder and post-traumatic stress disorder (PTSD). Traumatic stress induces major changes in brain circuits that can lead to maladaptive responses, often involving structural, molecular, and functional alterations that become detrimental over time. Understanding the neural mechanisms underlying maladaptive consequences of traumatic stress is critical for the elucidation of PTSD pathophysiology and the identification of new treatment strategies. Fear overgeneralization is a maladaptive response to traumatic stress that is associated with the inability to discriminate between threat and safety contexts, a hallmark feature of post-traumatic stress disorder. Despite its significance, the neural mechanisms underlying this deficit remain unclear. In this study, I found that traumatic stress exposure impairs contextual discrimination between threat and safety contexts in the learned helplessness (LH) model. Dorsal mossy cells (MCs) are suppressed in response to traumatic stress, but not ventral MCs. Bidirectional manipulation of dorsal MC activity in the LH model reveals that MC inhibition is causally linked to impaired contextual discrimination. Mechanistically, MC inhibition increases the number of active granule cells in a given context, significantly overlapping context-specific ensembles. I demonstrated that maladaptive inhibition of dorsal MCs after traumatic stress is a substantial mechanism underlying fear overgeneralization with contextual discrimination deficit, suggesting a potential therapeutic target for cognitive symptoms of PTSD. The functional heterogeneity of the hippocampus can be broadly segregated along the dorsoventral axis, which has distinct characteristics and specialized functions. These differences are evident at multiple levels, including connectivity and molecular level through distinct gene expression profiles. Understanding the heterogeneity is crucial for understanding how the hippocampus processes diverse types of information, and how disruptions in these processes might lead to neurological and psychiatric disorders. In this study, I found substantial heterogeneity of MCs in mice along the dorsoventral axis of the DG, in terms of structural and molecular and functional characteristics. I found that dorsal and ventral MCs display distinct axonal projections in the molecular layers of the DG along the dorsoventral axis. Furthermore, by cell-type-specific RNA sequencing using Translating Ribosome Affinity Purification (TRAP), I identified that dorsal and ventral MCs show distinct neurobiological molecular signatures. I found that dorsal MCs, but not ventral MCs exert inhibitory control on granule cells and are critical for rapid contextual discrimination. Collectively, dorsoventral heterogeneity of MCs may provide a novel mechanism for functional differentiation as well as distinct association along the longitudinal extent of the hippocampus. The long, curved structure of the hippocampus is conserved along the dorsal-to-ventral axis in rodents and the posterior-to-anterior axis in primates. I further examined whether the dorsoventral heterogeneity of MCs in rodents is conserved in non-human primates. I compared the neuroanatomy of MCs in the DG of mice and monkeys. The MC marker calretinin distinguishes two subpopulations: septal (dorsal in rodents, posterior in primates) and temporal (ventral in rodents, anterior in primates). Dual-colored fluorescence labeling is utilized to compare the axonal projection patterns of these subpopulations. In both mice and monkeys, septal and temporal MCs project axons across the longitudinal axis of the ipsilateral DG, indicating conserved associational projections. However, unlike in mice, no MC subpopulations in monkeys make commissural projections to the contralateral DG. In monkeys, temporal MCs send associational fibers exclusively to the inner molecular layer, while septal MCs give rise to wide axonal projections spanning multiple molecular layers, akin to equivalent MC subpopulations in mice. Despite conserved septotemporal heterogeneity, interspecies differences are observed in the topological organization of septal MCs, particularly in the relative axonal density in each molecular layer along the septotemporal axis of the DG. In summary, these findings have implications for understanding functional differentiation along the septotemporal axis of the DG and contribute to our knowledge of the anatomical evolution of the DG circuit in mammals. In conclusion, my research reveals the selective roles of MCs in stress-induced psychiatric disorders. Additionally, it highlights the structural and molecular heterogeneity of MCs in rodents, with evidence that this heterogeneity extends to non-human primates. These findings suggest that MCs possess unique characteristics with significant translational potential for mental health treatments. Keywords: Hippocampus, Heterogeneity, Dentate gyrus, Mossy cells, Granule cells, TRAP-seq, PTSD, Fear generalization, Contextual discrimination, Memory ensemble, Pattern separation.List of Contents
Abstract i
List of contents iii
List of figures viii
Chapter 1. Background 1
1. Hippocampus 1
1.1 Trisynaptic circuitry of the hippocampus 1
1.2 Microcircuit of the dentate gyrus 1
1.3 Pattern separation and the dentate gyrus 2
2. Post traumatic stress disorder 4
2.1 Natural Course and Risk Factors 4
2.2 Core of PTSD symptom of fear overgeneralization 5
2.3 Preclinical animal models of PTSD: Learned helplessness 5
Chapter 2. Maladaptation of dentate gyrus mossy cells
mediates contextual discrimination deficit after traumatic stress 7
2.1 Introduction 7
2.2 Materials and method 10
2.2.1 Animals 10
2.2.2 Viral constructs 10
2.2.3 Stereotaxic surgery 10
2.2.4 Histology and imaging 11
2.2.5 Cell counting 12
2.2.6 CNO administration 12
2.2.7 Behavioral procedures 13
2.2.8 Fluorescence in situ hybridization 17
2.2.9 catFISH assay 17
2.2.10 Quantification and statistical analysis 18
2.3 Results 19
2.3.1 Traumatic stress impairs contextual discrimination in the LH model 19
2.3.2 MCs are suppressed in response to traumatic stress exposure 23
2.3.3 Intact contextual discrimination is disabled by chemogenetic
Inhibition of MCs in stress-resilient mice 29
2.3.4 Selective activation of MCs is sufficient to restore
contextual fear overgeneralization in susceptible mice 39
2.3.5 MC inhibition enlarges active GC subpopulation in response to
a contextual stimulus 46
2.3.6 MCs modulate non-overlapping reactivation of
context-specific GC ensembles 50
2.4 Discussion 57
Chapter 3. Structural, molecular, functional
heterogeneity of MCs along the dorsoventral axis of the DG 66
3.1 Introduction 66
3.2 Materials and method 67
3.2.1 Animals 68
3.2.2 Dual fluorescence labeling of dMCs and vMCs 68
3.2.3 Immunohistochemistry 69
3.2.4 Fluorescence imaging of dMCs and vMCs 70
3.2.5 Three-dimensional imaging of MC projections 70
3.2.6 Quantification of dMCs and vMCs subpopulation 71
3.2.7 Transcriptional profiling of dMCs and vMCs 71
3.2.8 Gi-DREADD-dependent MC manipulation 74
3.2.9 Behavioral procedures 75
3.2.10 Data analysis and statistics 76
3.3 Results 77
3.3.1 Dorsoventral heterogeneity within MCs in their axonal projections 77
3.3.2 Spatial distribution and quantities of two distinct MC subpopulations 85
3.3.3 Transcriptional heterogeneity of MCs along the DV axis of the DG 87
3.3.4 Differential gene expressions between dorsal and ventral MCs 91
3.3.5 Distinct neurobiological properties between dorsal and ventral MCs 94
3.3.6 Net inhibitory control of dentate GCs by dMCs, but not by vMCs 98
3.3.7 Selective roles of dMCs in pattern separation 101
3.4 Discussion 106
Chapter 4. Comparative anatomy of the dentate mossy cells
in non-human primates: their spatial distributions and
axonal projections compared with mouse mossy cells 110
4.1 Introduction 110
4.2 Materials and method 112
4.2.1 Animals 112
4.2.2 Adeno-associated virus (AAVs) and stereotaxic surgery 113
4.2.3 Brain tissue preparation 114
4.2.4 Immunochemistry 115
4.2.5 Confocal imaging 115
4.2.6 Quantification of confocal images 116
4.2.7 Experimental design and statistical analysis 117
4.3 Results 118
4.3.1 Spatial segregation of two distinct MC subpopulations along
the septotemporal axis of the DG in mouse and monkey 118
4.3.2 Associational and commissural projections of septal and temporal MCs
In the mouse DG 112
4.3.3 Associational projections of septal and temporal MCs, but
Absence of commissural projections, in the monkey DG 112
4.3.4 Species difference in topological projections pattern of septal MCs
In the molecular layers of the DG 128
4.4 Discussion 134
Chapter 5. Conclusion 140
Reference 141
Summary in Korean 156
List of Figures
Figure 1. Traumatic stress impairs contextual discrimination in the LH model
Figure 2. MCs in the dorsal DG are suppressed after traumatic stress exposure
Figure 3. MCs in the dorsal DG are persistently suppressed after traumatic stress exposure
Figure 4. c-Fos immunoreactivity in the ventral MCs is not different between resilient and susceptible mice in the LH mode
Figure 5. Hippocampal subregion- and cell type-specific expression of control mCherry and hM4Di construct in AAV-injected Calcrl-Cre mice
Figure 6. Chemogenetic inhibition of MC during either training or testing session does not alter stress-susceptibility itself in the LH model
Figure 7. Intact contextual discrimination in resilient mice is disabled by chemogenetic inhibition of MCs
Figure 8. Chemogenetic inhibition of MCs in resilient mice did not alter basal behaviors
Figure 9. Chemogenetic inhibition of dorsal MCs does not affect contextual discrimination bet ween highly distinct context pair
Figure 10. Chemogenetic activation of dorsal MCs is sufficient to restore contextual fear overgeneralization in susceptible mice
Figure 11. Chemogenetic activation of Gq-DREADD expressing MCs
Figure 12. Chemogenetic activation of MCs in susceptible mice did not alter basal behaviors
Figure 13. Chemogenetic inhibition of dorsal MCs enlarges active GC subpopulations through PV+BC inhibition across the dorsoventral axis of the DG
Figure 14. MCs respond to contextual stimuli regardless of threat or safety contexts
Figure 15. Suppression of MC activation exacerbates overlap between context-specific GC ensembles
Figure 16. Calcrl-Cre mice displays a high levels of specificity of MCs
Figure 17. dMCs and vMCs extend distinct axonal projections along the longitudinal axis of the DG
Figure 18. Axonal fibers in MML of vDG are originated from dMCs
Figure 19. Holistic visualization of dMC and vMC projections in the DG
Figure 20. Two distinct MC subpopulations are spatially segregated along the DV axis
Figure 21. TRAP-based isolation of MC subpopulation-specific mRNA Transcripts from the hippocampus
Figure 22. RNA-seq DEGs analysis of MC-TRAP dorsal and ventral MCs
Figure 23. Distinct neurobiological properties between dorsal and ventral MCs
Figure 24. Acute inhibition of dMCs, but not of vMCs, results in hyperexcitation of GCs across the DV axis of the DG
Figure 25. Contextual fear memory acquisition is not interfered by MC inhibition along the DV axis
Figure 26. dMCs are crucial for contextual pattern separation
Figure 27. Two distinct MC subpopulations in mouse and monkey are spatially segregated along the septotemporal axis
Figure 28. Septal and temporal MCs in monkey make associational projections in the ipsilateral DG, but not commissural projections in the contralateral DG
Figure 29. Septotemporal heterogeneity of MCs in their axonal projections in the DG molecular layers in the mouse
Figure 30. Septotemporal heterogeneity of MCs in their axonal projections in the DG molecular layers in the monkeyDoctordCollectio
Molecular Regulatory Mechanisms of DNA Damage- and Stress-induced Leaf Senescence in Arabidopsis
No full textDNA-Protein Crosslink, WSS1A, Salt stress, ERF34, Leaf senescence, Arabidopsis thaliana|DNA-단백질 교차결합, WSS1A, 염 스트레스, ERF34, 잎 노화, 애기장대본 학위논문에서는 애기장대(Arabidopsis thaliana)에서 잎 노화에 영향을 미치는 DNA-단백질 교차결합(DNA-protein crosslink, DPC) 수리를 통한 유전체 안정성 유지 및 이를 기반으로 한 노화 조절 메커니즘과, 염 스트레스 반응 전사인자를 통한 노화 조절 메커니즘을 연구하였다. 본 연구는 유전체 안정성과 환경 스트레스 반응이 식물의 노화 조절에 기여하는 분자 기작을 규명하는 데 주안점을 두었다.
잎의 노화는 잎 발달 과정의 최종 단계로, 노화가 진행됨에 따라 잎의 광합성 능력이 점진적으로 저하되며, 식물의 생장기간 동안 축적된 영양분들이 분해되며 새로 발생하는 기관으로의 재분배된다. 이러한 과정은 호르몬 변화와 같은 내재된 유전 프로그램과 빛, 온도와 같은 외부 환경요인 간의 복합적 상호작용에 의해 정교하게 조절된다. 그러나 잎 노화를 조절하는 분자적 메커니즘은 그 중요성에도 불구하고 여전히 부분적으로만 밝혀져 있어 추가적인 연구가 필요하다.
모든 생명체는 생애 동안 내인성 및 외인성 스트레스 요인에 지속적으로 노출되며, 이는 심각한 DNA 손상을 초래할 수 있다. DNA 손상은 동물에서 노화 과정을 촉진하고 세포의 기능을 저하시키는 주요한 요인으로 인식되어 왔다. 특히 DNA-단백질 교차결합(DPC)은 DNA 복제 및 전사 과정을 방해하여 유전체 안정성을 저해하며, 다양한 생물에서 조기 노화를 유발하는 치명적인 손상 유형으로 알려져 있다. 그러나 DPC 의 생물학적 중요성과 잎 노화 조절에서의 역할은 아직 명확히 규명되지 않았다. 본 연구에서는 DPC 가 잎 노화에 과정에서 점진적으로 축적되며, 이러한 과도한 축적이 잎의 조기 노화를 유발한다는 사실을 입증하였다. 이는 미수선된 DPC 가 잎 노화를 유도하는 분자 신호로 기능할 가능성을 시사하며, DPC 수리 기작이 잎 수명 연장에 핵심적인 역할을 수행함을 보여주었다.
DPC 수리가 잎 노화 조절에 기여하는 분자적 메커니즘을 규명하기 위해, 본 연구는 Zinc-metalloprotease 인 효모 Wss1 및 포유류 SPRTN 과 상동성을 가지는 애기장대 WSS1A 에 주목하였다. Wss1 및 SPRTN 단백질은 DPC 의 단백질 부분을 분해하여 유전체 안정성을 유지하는 데 중요한 기능을 수행한다고 알려져 있다. WSS1A 가 결핍된 돌연변이체에서는 자연 노화, 암 처리, cis-Pt 처리에 의한 조기 노화 관찰되었으며, 과발현 식물에서는 노화 현상이 지연되었다. 더불어 WSS1A 는 SUMO3 와 상호작용하며, 액체-액체 상분리(LLPS)를 통해 응집체를 형성하였다. 이는 SUMOylation 혹은 SIM 을 통한 WSS1A 과 SUMO3 간 비공유적 상호작용이 잎 노화 동안 DPC 수리에 중요한 역할을 할 가능성을 시사하였다. 또한 WSS1A 의 상분리 특성은 DPC 수리에 관여하는 단백질들의 국소적인 공간적 밀집화 및 생화학적 반응속도 향상에 기여할 수 있음을 보여주었다.
또한 잎 노화는 고온, 가뭄, 염 스트레스와 같은 다양한 환경 스트레스에 의해 조절된다. 특히 염 스트레스는 잎 노화를 가속화하는 대표적인 환경 요인으로 알려져 있다. 그러나 염 스트레스 신호와 잎 노화 프로그램의 통합 메커니즘은 명확히 밝혀지지 않았다. 본 연구에서는 APETALA2/ERF 전사인자 계열에 ERF34 가 염 스트레스 하에서 잎 노화를 조절하는 역할을 수행함을 규명하였다. ERF34 는 다양한 노화 유도 조건에서 차등적으로 발현되었으며, 나이, 암 스트레스 및 염 스트레스에 의해 유도된 잎 노화를 억제하였다. 또한 ERF34 는 종자 발아 및 영양생장 단계에서 염 스트레스 내성을 촉진하였다. 전사체 분석을 통해 ERF34 의 과발현이 염 스트레스 반응 관련 유전자(COR15A, ERD10, RD29A)의 발현을 유의미하게 증가시키는 것을 확인하였다. ERF34 는 ERD10 및 RD29A 의 프로모터에 직접 결합하여 전사를 활성화함으로써 염 스트레스 반응 경로를 강화하였다. 이러한 결과는 ERF34 가 염 스트레스 반응과 잎 노화 프로그램의 융합에 중요한 조절 인자로 기능하며, 염 스트레스 내성을 강화할 수 있는 작물 개량의 유망한 후보임을 시사하였다.
본 연구는 잎 노화 조절에서 유전체 안정성 유지 및 염 스트레스 반응의 분자 기작에 대한 새로운 통찰을 제공하며, 이를 통해 식물 노화 과정에 대한 심층적 이해를 제시한다. 나아가 환경 스트레스 조건에서 작물 생산성과 내성을 향상시키기 위한 유전적 및 분자 생물학적 전략 수립에 기여할 수 있을 것으로 기대된다.|Leaf senescence, the final stage of leaf development, is essential for plant fitness and nutrient recycling. It is regulated by a complex interplay of intrinsic genetic programs and environmental cues. Despite its biological importance, our knowledge of the regulatory mechanisms underlying leaf senescence is still fragmentary.
All living organisms are inevitably exposed to various endogenous and environmental factors throughout their lifetimes, many of which can trigger potentially fatal DNA damage. DNA damage has been widely recognized as a primary driver of the aging process in animals, where it disrupts cellular homeostasis and accelerates a decline in cellular function. DNA-Protein Crosslinks (DPCs), one of the common types of DNA damage, are particularly harmful DNA lesions that obstruct essential processes such as replication and transcription, which threatens genome integrity and eventually leads to premature aging in diverse organisms including humans. However, the biological significance of DPCs and their
repair in the control of leaf senescence remain poorly understood. In this study, I found that DPCs progressively accumulated during age-dependent leaf senescence, and their hyperaccumulation led to premature senescence. This finding suggests that unresolved DPCs act as molecular triggers for leaf senescence, emphasizing the importance of efficient DPC repair in extending leaf longevity. To understand the molecular mechanisms by which DPC repair contributes to the regulation of leaf senescence, I focused on WSS1A, an Arabidopsis metalloprotease orthologous to yeast Weak suppressor of smt3 (Wss1) and mammalian SPRTN. Both Wss1 and SPRTN are known to play essential roles in DPC repair by degrading the protein component of DPCs, thereby maintaining genome stability. Plants lacking WSS1A exhibited premature senescence, while its overexpression delayed senescence under both age-dependent and darkness-induced leaf senescence conditions. Furthermore, WSS1A interacted with SMALL UBIQUITIN MODIFIER 3 (SUMO3) and formed condensates via liquid-liquid phase separation (LLPS), indicating a potential role of SUMOylation in DPC repair during leaf senescence. WSS1A formed nuclear condensates via liquid-liquid phase separation (LLPS), which suggests that the phase separation behavior of WSS1A may facilitate the organization of DPC repair machinery. These findings implicate that SUMOylation and LLPS of WSS1A play roles in facilitating DPC repair during leaf senescence, probably through enhancing WSS1A’s function and organizing DPC repair machinery.
As mentioned above, leaf senescence is regulated by diverse environmental stresses such as extreme temperature, drought, and salt stress. Salt stress is one of the most well-known environmental stresses that accelerate leaf senescence. However, the molecular mechanisms that integrate salt stress signaling with leaf senescence programs remain elusive. In this study, I characterized the role of ETHYLENE RESPONSIVE FACTOR34 (ERF3), a member of the APETALA2/ERF family, which modulates leaf senescence under salt stress. ERF34 was differentially expressed under various leaf senescence-inducing conditions, and negatively regulated leaf senescence induced by age, dark, and salt stress. ERF34 also promoted salt stress tolerance at different stages of the plant life cycle such as seed germination and vegetative growth. Transcriptome analysis revealed that the overexpression of ERF34 increased the transcript levels of salt stress-responsive genes including COLD-REGULATED15A (COR15A), EARLY RESPONSIVE TO DEHYDRATION10 (ERD10), and RESPONSIVE TO DESICCATION29A (RD29A). Moreover, ERF34 directly bound to ERD10 and RD29A promoters and activated their expression. These findings indicate that ERF34 plays a key role in the convergence of the salt stress response with the leaf senescence programs, and is a potential candidate for crop improvement, particularly by enhancing salt stress tolerance.
Together, this study provides novel insights into the molecular mechanisms of genome maintenance and salt-stress responses in regulating leaf senescence. These findings advance our understanding of plant senescence and provide promising strategies to improve crop productivity and stress tolerance under environmental stressesAbstract i
List of Contents iv
List of Figures v
Chapter I. Introduction - 1 -
1.1 Leaf Senescence - 1 -
1.2 Leaf senescence and DNA damage - 1 -
1.3 Leaf senescence and environmental stresses - 3 -
1.4 Objectives - 5 -
Chapter II. WSS1A Negatively Regulates Leaf Senescence through DNA-Protein Crosslink Proteolysis Repair Pathway in Arabidopsis - 7 -
2.1 Introduction - 7 -
2.2 Materials and Methods - 11 -
2.3 Results - 19 -
2.3.1 Accumulation of DPCs induces premature leaf senescence in Arabidopsis - 19 -
2.3.2 WSS1A is the Arabidopsis ortholog of yeast Wss1 for DPC-proteolysis repair - 23 -
2.3.3 WSS1A is a key regulator of age-dependent and dark-induced leaf senescence in Arabidopsis - 26 -
2.3.4 WSS1A negatively regulates cis-Pt-induced leaf senescence in Arabidopsis - 26 -
2.3.5 WSS1A is SUMOylated as well as interacts with SUMOylated proteins - 30 -
2.3.6 WSS1A undergoes LLPS both in vitro and in vivo - 35 -
2.3.7 The IDR and N-terminal segment (NTS) of WSS1A mediate the phase separation in the nucleus - 37 -
2.4 Discussion - 40 -
Chapter III. ERF34 Mediates Stress-Induced Leaf Senescence by Regulating Salt Stress-Responsive Genes - 46 -
3.1 Introduction - 46 -
3.2 Materials and Methods - 49 -
3.3 Results - 54 -
3.3.1 ERF34 expression is altered under diverse leaf senescence-inducing conditions - 54 -
3.3.2 ERF34 functions as a negative regulator of salt stress-induced leaf senescence - 56 -
3.3.3 ERF34 mediates age-, dark-, ABA-, and ethylene-induced leaf senescence - 63 -
3.3.4 ERF34 promotes salt stress tolerance at diverse developmental stages - 66 -
3.3.5 ERF34 acts as a transcriptional activator in yeast - 70 -
3.3.6 ERF34 directly regulates the expression of ERD10 and RD29A - 72 -
3.4 Discussion - 77 -
국문요약 - 94 -
Acknowledgement - 97 -
Appendix - 99 -DoctordCollectio
Fear memory disorder model through selective regulation of IQSEC3 and manufacturing method thereof
No full textTHERMAL CROSS-LINKABLE HOLE TRANSPORT MATERIAL, LIGHT EMITTING DIODE INCLUDING SAME AND METHOD FOR MANUFACTURING THE SAME
No full text본 발명은 열 가교가능한(cross-linkable) 정공 수송 물질, 이를 포함하는 발광 소자 및 이의 제조 방법을 개시한다. 본 발명의 열 가교가능한(cross-linkable) 정공 수송 물질은 하기 화학식 1로 표시되는 것을 특징으로 한다. [화학식 1] (상기 화학식 1에서, 상기 X 및 Y는 O 또는 S이고, R은 각각 독립적으로 가교성 아릴아민계 화합물이며, 상기 Ar은 방향족 화합물이고, 상기 n은 1 내지 4임
long noncoding RNA CHLORELLA에 의한 노화조절 네트워크의 분자유전학적인 연구
No full textLeaf senescenceList of Contents
Abstract i
List of Contents ii
List of Tables v
List of Figures vi
I. Background introduction 1
1.1 Developmental transition during leaf lifespan 1
1.2 Chloroplast degeneration 3
1.3 Nucleus-Chloroplast communication in response to internal and external signal 6
1.4 Regulatory RNAs in plant biology 9
1.5 Multidimensional regulatory role of long noncoding RNA 11
1.6 Research goal and Specific aims 12
II. Chloroplast-Related LncRNA (CHLORELLA) mediates chloroplast functional
transition across leaf aging. 13
2.1 Background 13
2.2 Results 13
2.2.1 Co-expression analysis of differentially expressed mRNAs and LncRNAs during leaf
lifespan. 13
2.2.2 Chloroplast Related lncRNA (CHLORELLA) is canonical lncRNA. 19
2.2.3 CHLORELLA influences the transition of leaves to senescence 21
2.2.4 The transcriptome analysis in Col-0 and chlorella-1 reveals that CHLORELLA affects
chloroplast related gene expression 27
2.2.5 CHLORELLA interacts with chloroplast related proteins. 31
2.2.6 CHLORELLA modulates PEP accumulation and its activity. 35
2.2.7 CHLORELLA translocases from nucleus to chloroplast through cytosol 44
2.2.8 CHLORELLA interacts with PEP component FLN1 in chloroplast. 51
2.3 Discussion 55
III. Transcriptional and post-transcriptional regulation of CHLORELLA-From GLK1/2
to miRNA863-3p during leaf lifespan 59
3.1 Background 59
3.2 Results 59
3.2.1 Identification of transcriptional regulator of CHLORELLA 59
3.2.2 GLK1 and GLK2 directly regulate CHLORELLA transcription 64
3.2.3 Transiently overexpressed CHLORELLA in glk1 glk2 protoplast rescue the PEP
accumulation and PEP-dependent genes’ expression. 67
3.2.4 GLK1/2-CHLORELLA module regulates PEP accumulation and its activity along the
aging 69
3.2.5 miRNA863-3p positively regulates leaf senescence. 74
3.2.6 miRNA863-3p suppresses CHLORELLA abundance by direct binding. 78
3.2.7 Genetic analysis of CHLORELLA-miRNA863-3p module in leaf senescence 83
3.3 Discussion 85
IV. Appendix 87
4.1 Hidden role of CHLORELLA: age-related resistance 87
4.1.1 Background 87
4.1.2 Results 88
4.1.3 Discussion 99
V. Materials & Methods in this thesis 101
5.1 Plant materials, preparation of constructs and transgenic plants, and growth conditions
. 101
5.2 Analysis of leaf senescence 102
5.3 RNA extraction and gene expression analysis 102
5.4 Coding potential analysis 103
5.5 Transcriptome analysis in chlorella-1 104
5.6 Protein extraction and western blot analysis 104
5.7 RNA Pull-down assay 105
5.8 In-gel peptide preparation 107
5.9 LC-MS/MS 108
5.10 Mass spectrometry Database searching 108
5.11 Gene expression analysis in the chloroplast-enriched fraction 109
5.12 RNA immunoprecipitation (RIP) assay 110
5.13 Arabidopsis mesophyll protoplast isolation and transient expression analysis 112
5.14 Dual-luciferase assay 112
5.15 PEP assembly detection by Blue-Native electrophoresis (BN-PAGE) 113
5.16 Chromatin immunoprecipitation (ChIP)–seq analysis and ChIP-qPCR 114
5.17 Fluorescence microscopy using phage MS2 system 116
5.18 Fluorescence resonance energy transfer imaging 117
5.19 2D BN-PAGE/SDS-PAGE and identification of thylakoid proteins 118
5.20 Crosslinked RNA pull-down assay 120
5.21 5’ RNA Ligase Mediated Rapid Amplification of cDNA End ( 5’-RLM RACE) assay.. 121
5.22 Drawing schematic diagram 122
V.I References 126DoctordCollectio
Realization of a 2H–Si microneedle with an ultrafast growth rate of 6.7 × 104 Å·s−1
No full textNanomaterials have facilitated the development of innovative technologies in various industries. However, most research has been limited to nanoscale phenomena, and the effects of nanomaterials on microscale crystal growth remain obscure. In this study, we demonstrated a straight 2H-Si microneedle with a longitudinal growth rate of 6.7 × 104 Å·s−1, which could not be explained by conventional crystal growth mechanisms, through AlN nanowires. The AlN nanowires were grown using the hydride vapor-phase epitaxy method, which induced the formation of Al membranes when NH3 supply was ceased. At this time, an elliptical Al membrane was created within 0.166 s, in accordance with the principle of Plateau-Rayleigh instability. The average spacing of the Al membrane was 4 μm, and approximately 10 000 elliptical Al membranes absorbed SiCl almost simultaneously to form a 40 mm 2H-Si microneedle within 100 min of growth time. Therefore, we realized straight 2H-Si microneedles with a growth rate of 6.7 × 104 Å·s−1. Differing from the conventional growth mechanism, this new growth method sheds light on the mechanism by which nanoscale phenomena contribute to the growth of microscale crystals. © 2025 IOP Publishing Ltd. All rights, including for text and data mining, AI training, and similar technologies, are reserved.FALSEsciescopu
Proteomic analysis for biomarker discovery in autism spectrum disorder using mass spectrometry
No full textProteomics,Mass spectrometry,Autism,Biomarker자폐 스펙트럼 장애(ASD)는 광범위한 행동 및 인지적 증상을 특징으로 하는 복잡한 신경발달 질환으로, 조기 진단에 상당한 어려움을 겪습니다. 따라서 신뢰할 수 있는 바이오마커를 식별하는 것은 조기 발견 및 개입 전략을 개선하는 데 필수적입니다. ASD 연구의 최근 진전에도 불구하고 일관된 생물학적 마커의 부재는 여전히 중요한 한계로 남아 있으며, 강력한 바이오마커 발견의 필요성을 강조합니다. 이 연구는 질량 분석 기반 프로테오믹 분석을 통해 ASD에 대한 잠재적 바이오마커를 식별하고 검증하여 장애와 관련된 분자 패턴을 발견하고 근본적인 생물학적 메커니즘에 대한 통찰력을 제공하는 것을 목표로 합니다. 자폐 스펙트럼 장애에 대한 바이오마커를 식별하는 것은 조기 진단, 개인화된 치료 및 장애의 근본적인 메커니즘을 이해하는 데 매우 중요합니다. 바이오마커는 생물학적 과정에 대한 객관적이고 정량화된 지표로, 자폐 스펙트럼 내에서 다양한 표현을 감안할 때 매우 중요한 다른 신경발달 장애와 ASD를 구별하는 데 도움이 될 수 있습니다. 현재 ASD 진단은 주로 행동 평가에 의존하며, 이로 인해 진단 및 개입이 지연될 수 있습니다. 유전적, 신경 영상 또는 생화학적 마커와 같은 바이오마커는 더 빠르고 정확한 진단과 시간 경과에 따른 ASD 관련 신경 생물학적 변화를 추적할 수 있는 잠재력을 제공합니다. 신뢰할 수 있는 바이오마커를 식별하면 특정 생물학적 경로를 임상 증상과 연결하여 표적 치료 전략의 개발을 용이하게 할 수 있으며, 궁극적으로 ASD가 있는 개인의 결과와 삶의 질을 개선하는 데 기여할 수 있습니다. 혈장에서 고농도 단백질을 고갈시키는 것은 자폐 스펙트럼 장애의 바이오마커를 식별하는 데 중요한 단계입니다. 혈장은 잠재적 바이오마커의 풍부한 공급원이지만 광범위한 단백질 농도를 포함하고 있으며, 소수의 고농도 단백질(예: 알부민, 면역글로불린)이 총 단백질 함량의 90% 이상을 차지합니다. 이러한 우세한 단백질은 ASD 병리 생리학과 관련이 있을 수 있는 단백질을 포함하여 저농도 단백질의 존재를 가릴 수 있어 탐지가 어렵습니다. 고갈 기술을 사용하면 이러한 고농도 단백질을 줄이거나 제거할 수 있으므로 유익한 바이오마커 역할을 할 수 있는 저농도 단백질에 대한 혈장을 풍부하게 할 수 있습니다. 하류 분석 기법(예: 질량 분석법)의 민감도를 향상시킴으로써 고갈은 ASD와 관련된 새롭고 미묘한 분자적 시그니처의 발견을 용이하게 하여 진단 및 치료 전략을 개선할 수 있습니다. 이 논문의 첫 번째 목표는 건강한 개인과 자폐 스펙트럼 장애가 있는 환자의 혈장 프로테옴을 비교하여 질병 특이적 바이오마커의 조기 검출을 위한 잠재적 바이오마커로 사용할 수 있는 차별적으로 발현된 단백질을 식별하여 예후와 치료 결과를 크게 개선하는 것입니다. 잠재적 바이오마커 관련성이 있는 저농도 혈장 단백질을 검출하기 위한 샘플 준비 및 질량 분석 매개변수를 최적화하여 분석 프로세스의 민감도와 처리량을 개선합니다. 혈장에는 광범위한 단백질 농도가 포함되어 있어 저농도 바이오마커를 검출하기 어렵습니다. 최적화된 프로토콜은 임상적으로 관련 있는 바이오마커의 발견을 향상시킬 수 있습니다. 마지막으로, 표적 접근 방식 병렬 반응 모니터링(PRM)을 사용하여 발견 기반 질량 분석 연구에서 확인된 후보 바이오마커를 검증하여 재현성과 임상적 유용성을 보장합니다. 바이오마커의 검증은 발견 연구를 임상 응용 프로그램으로 전환하고 식별된 바이오마커가 진단 또는 치료 모니터링에서 신뢰할 수 있고 적용 가능한지 확인하는 데 중요합니다. 키워드: 바이오마커, 자폐 스펙트럼 장애, 비표적 프로테오믹스, 표적 프로테오믹스, 질량 분석법|Autism spectrum disorder (ASD) is a complex neurodevelopmental condition characterized by a broad range of behavioral and cognitive manifestations, presenting significant challenges for early diagnosis. Consequently, identifying reliable biomarkers is essential to improve early detection and intervention strategies. Despite recent advances in ASD research, the absence of consistent biological markers remains a critical limitation, underscoring the need for robust biomarker discovery. This study seeks to identify and validate potential biomarkers for ASD through mass spectrometry-based proteomic analysis, aiming to uncover molecular patterns associated with the disorder and provide insights into underlying biological mechanisms. The identification of biomarkers for autism spectrum disorder is critically important for advancing early diagnosis, personalized treatment, and understanding the underlying mechanisms of the disorder. Biomarkers objective, quantifiable indicators of biological processes can aid in distinguishing ASD from other neurodevelopmental disorders, which is vital given the diverse presentations within the autism spectrum. Currently, ASD diagnosis relies primarily on behavioral assessments, which may lead to delayed diagnosis and intervention. Biomarkers, such as genetic, neuroimaging, or biochemical markers, offer the potential for earlier, more precise diagnosis and for tracking ASD related neurobiological changes over time. Identifying reliable biomarkers can also facilitate the development of targeted therapeutic strategies by linking specific biological pathways with clinical symptoms, ultimately contributing to improved outcomes and quality of life for individuals with ASD. Depleting high-abundance proteins in plasma is a critical step in identifying biomarkers for autism spectrum disorder. Plasma, while a rich source of potential biomarkers, contains a wide range of protein concentrations, with a small number of high-abundance proteins (e.g., albumin, immunoglobulins) comprising over 90% of its total protein content. These dominant proteins can mask the presence of low- abundance proteins, including those potentially linked to ASD pathophysiology, making their detection challenging. Depletion techniques allow for reducing or removing these high-abundance proteins, thus enriching the plasma for low-abundance proteins that may serve as informative biomarkers. By enhancing the sensitivity of downstream analytical techniques, such as mass spectrometry, depletion facilitates the discovery of novel and subtle molecular signatures associated with ASD, which may lead to improved diagnostic and therapeutic strategies. This thesis first aim is to compare the plasma proteome of healthy individuals and patients with an autism spectrum disorder to identify differentially expressed proteins that could serve as potential biomarkers for early detection of disease-specific biomarkers that can significantly improve prognosis and treatment outcomes. To optimize sample preparation and mass spectrometry parameters for the detection of low-abundance plasma proteins with potential biomarker relevance, improving the sensitivity and throughput of the analytical process. Plasma contains a wide dynamic range of protein concentrations, making it challenging to detect low-abundance biomarkers. Optimized protocols can enhance the discovery of clinically relevant biomarkers. Finally, validation of candidate biomarkers identified in discovery-based mass spectrometry studies using targeted approach parallel reaction monitoring (PRM) to ensure their reproducibility and clinical utility. Validation of biomarkers is critical for translating discovery research into clinical applications, ensuring that the identified biomarkers are reliable and applicable in diagnostics or therapeutic monitoring. Keywords: Biomarker, Autism spectrum disorder, untargeted proteomics, targeted proteomics, Mass spectrometryList of Contents
Abstract.. i
List of contents..iv
List of tables..vi
List of figures.vii
Ⅰ. Introduction
1.1 Autism spectrum disorder (ASD)....1
1.1.1 Protein biomarkers....3
1.1.2 Metabolite biomarkers....7
1.1.3 Genetic biomarker..8
1.1.4 Cytokines and Immune system...9
1.2 Plasma as a Source for Biomarker Discovery...10
1.2.1 Advantages of Plasma as a Biological Sample...11
1.2.2 Challenges Associated with Plasma-Based Analysis..11
1.2.3 Impact of Plasma Variability on Biomarker Identification..12
1.2.4 Pre-Analytical and Analytical Challenges in Plasma Studies...13
1.2.5 Current Approaches to Plasma-Based Biomarker Discovery..14
1.3 Mass spectrometry for the discovery of biomarkers..15
1.3.1 Mass spectrometry-based protein identification techniques..16
1.3.2 Mass spectrometry-based protein quantification method..17
1.4 Rationale and objectives of the study....18
1.5 Significance and Impact of autism biomarker duiscovery.20
II. Label-free proteomics analysis of autism family and control samples..22
2.1 Introduction..22
2.2 Results...23
2.2.1 Experimental design for label-free quantification..23
2.2.2 Label-free quantification for significant proteins...24
2.3 Discussion.25
2.4 Materials and Methods...26
III. Labeling analysis to identify low abundant protein biomarker...30
3.1 Introduction.....30
3.2 Results....31
3.2.1TMT labeling experimental design....31
3.2.2Quantification of biomarker proteins..32
3.3 Discussion.....34
3.4 Materials and Methods...34
IV. Targeted proteomics for validation of biomarker protein....40
4.1 Introduction....40
4.2 Results..41
4.2.1 Experimental design for PRM targeted proteomics analysis..41
4.3 Discussion.....42
4.4 Materials and Methods....43
V. Conclusion...46
Reference...49DoctordCollectio
질병 de novo 타겟 탐색을 위한 생성 모델 기반의 타겟 녹다운 시뮬레이션
No full textDisease target,Omics data,Generative artificial intelligence,Cancer,Alzheimer’s disease질병의 타겟 연구는 생명과학 및 약물개발의 중요한 과정입니다. 타겟의 구조 예측, 타겟-약물 상호작용 예측, 약물 전달 체계 등과 같은 타겟 탐색 이후 약물개발에서 적용가능한 혁신 기술들이 등장함에 따라 타겟 탐색의 중요성은 그 어느 때보다 높아졌습니다.
질병 타겟 연구에서의 혁신은 오믹스 데이터의 심층적 이해와 활용을 통해 이루어질 수 있습니다. 오믹스는 생명과학에서의 총체적인 접근 방식으로, 완전히 활용되지 않은 잠재적 정보를 기저에 보유하고 있습니다. 오믹스 데이터의 고차원적 특성은 기존 방법론과 인간의 직관만으로는 그 잠재력을 충분히 활용하고 질병 발생 과정과 같은 생물학적 상호작용을 완전히 해석하는 데 한계가 있습니다. 이러한 도전 과제를 해결하기 위해서는 새로운 방법론이 필요하며, 적절한 방법론은 데이터 활용의 규모와 효율성을 비약적으로 확장하여 생물학적 과정에 대한 심층적인 이해를 가능하게 합니다. 오믹스 데이터를 보다 잘 통합하고 해석할 수 있는 도구를 개발함으로써, 생명과학 연구 및 치료 혁신의 진보를 이끌어낼 수 있습니다.
이전 연구에서는 오믹스 생성 인공지능이 중간시점 데이터를 생성하여 오믹스 데이터의 잠재 정보를 포착할 잠재력을 보여주었으며, 그 결과는 기존의 생물학적 연구와 부합하면서도 전통적 분석 방식과 원활하게 통합될 수 있음을 시사하였습니다. 이를 바탕으로 우리는 질병 타겟 연구에 특화된 오믹스 생성 모델의 활용성과 응용 범위를 확장하는 고도화된 프레임워크를 제시합니다. 우리는 오믹스 생성 모델과 타겟 녹다운 시뮬레이션을 제안하며 데이터셋, 모델, 분석 각각의 영역에서 진보를 이루고자 하였습니다. 본 연구에서는 각 영역에서의 개선 사항을 구현하고 검증했으며, 다수의 질병 연구를 통해 이 방법의 효과성을 입증했습니다. 이러한 세부 연구에는 코카인 중독에서 중간시점 신호전달 경로의 발견, 전립선 암의 de novo 타겟 스크리닝 및 검증, 간암과 알츠하이머 병에 대한 새로운 타겟 기반 약물 재창출, 알츠하이머 병의 유전자형 특이적 전사체 생성이 포함되며, 모두 기존의 연구결과와 잘 일치하였습니다. 이러한 연구 결과는 본 방법의 정확성과 효과성을 입증하며, 향후 연구 응용 가능성에 대한 기대감을 높입니다.|The study of disease targets is an essential step for biological science, particularly in pharmaceutical processes. As innovative technologies emerge in each step that follows target screening - such as target structure prediction, target-drug interaction prediction, and drug delivery systems - the importance of target screening has reached unprecedented levels. Innovations in this area have the potential to accelerate the entire process of biological research and drug development. Innovation in disease target study can be driven by a deeper understanding and utilization of omics data— a holistic approach in biological science that contains vast, untapped information. The high-dimensional nature of omics data presents a challenge, as conventional methods and human perception struggle to fully harvest its potential and interpret the complex interactions of biological entities within processes such as disease pathogenesis. Addressing this challenge requires de novo methods that expand our analytical reach, enabling unprecedented scale and efficiency in data utilization. By developing new tools to better integrate and interpret omics data, we can significantly advance our insights into biological processes, driving progress in both research and therapeutic innovation. In previous study, omics generative AI demonstrated its potential for capturing underlying information of omics data by generating intermediate data, which was not only aligned with previous biological research but also could be seamlessly integrated with traditional approaches. Based on this, we present an advanced framework to expand the utility and application of omics generative models in biological science, specifically for disease target study. We introduce an omics generative model paired with a target knockdown simulation, focusing on three primary domains: the dataset, the model and the analytic method. We implemented and validated enhancements across these domains, demonstrating the method’s efficacy through multiple disease studies. This includes revealing discovering intermediate pathways in cocaine addiction, enabling de novo target screening and validation for prostate cancer, repositioning drugs based on novel targets for liver cancer and Alzheimer’s disease, and generating genotype-specific transcriptomes for Alzheimer’s disease, all of which aligned well with existing biological knowledge. These findings validate the method’s accuracy and efficacy, highlighting its promise for future research applications. Keywords: Disease target, Omics data, Generative artificial intelligence, Cancer, Alzheimer’s diseaseⅠ. INTRODUCTION 1
Ⅱ. METHODS 4
2.1 Data preparation for training 4
2.1.1 Six brain region transcriptome during cocaine self-administration 6
2.1.2 Single-cell transcriptome of thirteen prostate cancer patients 8
2.1.3 Single-cell transcriptome of primary and metastatic hepatocellular carcinoma 11
2.1.4 Single nucleus and bulk transcriptome of Alzheimer’s disease from eighteen human donor 14
2.2 Wasserstein generative adversarial networks with gradient penalty loss 18
2.3 Timepoint dependent and gene knock-down sample generation using vector and weight manipulation 20
2.4 Genotype specific generation using conditional generative adversarial networks 24
2.5 in vitro, in vivo validation for in silico generation and prediction 26
Ⅲ. RESULTS 27
3.1 Spatiotemporal description of cocaine addiction in six brain regions using GAN-WGCNA method· 27
3.1.1 Background 27
3.1.2 GAN-WGCNA enables spatiotemporal analysis and a calculation of correlation between gene module and behavioral data 28
3.1.3 Statistically significant gene correlated with addictive behavior in the intermediate modules screened by rescued DEG calculation 44
3.1.4 Conclusion 44
3.2 Gene knock-down simulation and its validation 48
3.2.1 Background 48
3.2.2 de novo target screening and validation for prostate cancer 51
3.2.3 de novo target-based drug repositioning for hepatocellular carcinoma 59
3.2.4 de novo target-based drug repositioning for Alzheimer’s disease 64
3.2.5 Genotype specific generation of astrocyte activation omics data in Alzheimer’s disease 69
3.2.6 Conclusion 72
Ⅳ. DISCUSSION 73
References 77DoctordCollectio
Biomechanical Parameters Estimation for Real-Time Gait Analysis Using a Compact Radar Sensor
No full textCompact radar sensors for the Internet of Things (IoT) applications can be used to analyze indoor human gait characteristics. Conventional human gait analysis methods typically involve generating 2-D high-resolution time-frequency images and employing image processing techniques to estimate the gait parameters of a walking human. However, these computations can be resource-intensive for compact radar sensors. To address this problem, we propose a new scheme for estimating gait parameters. Our method has four significant contributions: 1) utilization of 1-D phase modulation in a radar echo for efficient gait parameter estimation, as opposed to relying on 2-D time-frequency images; 2) decomposition of microphase modulations corresponding to the torso or pelvis and lower body parts (e.g., knee, tibia, and ankle) using dedicated filtering techniques to mitigate the interference between body components; 3) compensation for effects of nonlinear macrophase modulation caused by whole-body movements; and 4) robust estimation of gait parameters, including time-varying radial velocity, gait rate, step length, and the height of the lower body. In experiments performed using a 5.8-GHz continuous-wave (CW) Doppler radar, we observed that the proposed scheme can perform efficient and robust gait parameter estimation of indoor human walking. © 2025 IEEE.FALSEsciescopu