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Erratum to “Development and Validation of the Islamic Work Exemplary Scale in Indonesia” [Islamic Guidance and Counseling Journal 6(2) 2023]
No full textRefers to:
Sumin, S., Retnawati, H., & Sayadi, W. (2023). Development and Validation of the Islamic Work Exemplary Scale in Indonesia. Islamic Guidance and Counseling Journal, 6(2). https://doi.org/10.25217/0020236392000
Erratum:
The authors requested a revision to the abstract in the original publication of this article. Specifically, at the behest of the corresponding author, the phrase "test validity and reliability" was amended to "Validity and Reliability checking". Subsequently, this correction has been implemented in the original article
공간 오믹스를 위한 자동 세포 분리 분석
Full text link학위논문(박사) -- 서울대학교대학원 : 공과대학 전기·정보공학부, 2023. 2. 권성훈.Spatial omics profiling technologies have been recognized recently for its ability to decipher the genetic molecules that are structurally relevant in pathology. Especially, in tumor biology, tumor is not the group of malignant tumor cells, but rather group of various cells such as tumor cells, immune cells, fibroblasts, etc. gathers together, constructing the tumor microenvironments. Technologies to analyze such microstructures have evolved from bulk sequencing, single cell sequencing to spatial omics profiling technologies. Spatial omics profiling technologies have highly influenced in decoding cancerous mechanisms by questioning the tumor heterogeneity, tumor microenvironment and spatial biomarkers.
Most of the spatial omics technologies focus on mapping the spatial omics landscape in a large scale. They rather introduces the spatially-barcoded capture probes or fluorescence labeled target probes to spatially locate the genetic molecules. The information depth and the scalability of the techniques varies according to the purpose of the spatial assay techniques. Such technologies are capable of discovering the spatial heterogeneity and the spatial landscape of the consisting cell types due to relatively low depth of the omics information. To effectively address the target molecules for therapeutics or diagnostics, higher depth of the omics information are required. To meet the needs, region of interest (ROI)-based spatial technologies isolated the target regions and applies chemistries for higher coverage omics data.
Conventional cell sorters utilizes microfluidic channels to sort cells of interest which requires cell dissociation in a solution phase. For instance, Fluorescence activated cell sorter (FACS) or Magnetic-activated cell sorting (MACS) uses fluorescence or magnetic particles, respectively, to designate the cells of interest in dissociated cell solutions. Spatially isolating techniques such as laser capture microdissection (LCM) is able to sort out the ROIs while preserving the spatial context, but it approximately takes an hour for isolating the targets. Also, it uses rather UV laser to dissect out cells or IR-activated melting of polymers to stick out cells which might cause damage to cells.
Here, I developed the automated spatially resolved laser activated cell sorter that isolates the cells in target per second while preserving the spatial context of the cells. Specific region of indium tin oxide (ITO) coated slide glass evaporates when illuminated by IR laser pulse, plunging the cells into the desired reservoir. The applicability of the suggested cell sorter are demonstrated in omics profiling chemistries such as DNA sequencing, RNA sequencing, mass spectrometry, etc.종양은 종양세포만이 원인이 되는 질병이 아닌, 면역세포와 혈관 구조, 표피 세포 등 다양한 세포들이 공동체를 이뤄 질병을 발전시키는 복합적인 질병이다. 이를 분석하기 위하여 차세대 시퀀싱 (NGS), 단일 세포 분석, 유세포 분석 등 다양한 기술이 활용되어 왔다. 특히 RNA sequencing 을 포함한 단일 세포 분석은 종양 내에서 성장, 전이, 진화, 약물의 내성과 관련된 타겟을 발굴하고 이를 조절할 수 있는 인자들을 개발하는 등 종양 연구의 혁신을 이끌었다. 그러나 이러한 접근 방법은 종양 미세 환경 내에서 세포가 존재하고 있는 위치 정보가 손실 되기 때문에 종양의 온전한 그림을 제공하지 못한다. 최근에는 종양 내 이형적으로 분포하고 있는 유전 물질들을 이해하면 종양의 이형성과 종양 미세환경의 탐구와 암의 진화 및 발전 메커니즘을 이해하여 이전에는 발견하지 못한 새로운 제약, 진단 타겟을 발굴 할 수 있을 것이라는 관점들이 제시되고 있다.
다중의 형광 프로브를 활용한 방식으로 DNA, RNA, 단백질 등의 위치를 표적하여 종양의 전체적인 그림을 파악하는 기술들이 최근 폭발적으로 개발되고 있고, 현장 바코딩 방식으로 위치 별로 발현하고 있는 유전 물질을 표적하여 정량화 할 수 있는 방법도 상용화 되어 종양 에서 새로운 발견을 이끌어내고 있다. 그러나 위의 제시된 기술들 모두 De novo로 새로운 제약 진단 타겟을 발굴하는 데에는 한계가 존재한다.
본 논문에서는 공간 오믹스를 가능하게 하는 세포 분리 분석기를 개발하여 관심영역의 세포를 분리하고 이후 DNA, RNA, 단백질 등을 분석하는 어세이들과 연결하여 위치정보를 포함하면서도 높은 정보량으로 세포를 분석하고, 이전까지 발굴하지 못하였던 새로운 진단 제약 타겟을 탐색할 수 있는 기술에 대하여 설명한다. 레이저를 조사하면 희생층이 승화되며 조사 영역에 위치한 세포들을 회수할 수 있고, 현존하는 생화학 어세이를 수행 할 수 있다. 자동으로 이미지 프로세싱을 통하여 원하는 세포들을 특정할 수 있는 프로그램을 개발하였고, 이를 세포주와 임상 샘플에 적용하고 이후 생화학 어세이 적용을 입증하였다. 항원 항체 반응을 이용한 염색 방법 이외에도 높은 정보량으로 유전체 지도를 그리는 염색법 등 다양한 염색 이미지에 적용했으며 본 기술을 활용하여 향후 응용할 수 있는 연구를 제안하였다.CHAPTER 1. INTRODUCTION １
1.1. The Need for Spatial Profiling in Cancer Biology ２
1.1.1. Historical Review of Technologies to Address Cancer Research ２
1.2. Spatial Landscape Profiling Technologies ６
1.2.1. Spatial transcriptomics profiling technologies ６
1.2.2. Spatial Omics Technologies ９
1.3. Spatial ROI-profiling technology １５
1.3.1. Previous sorting systems for spatial omics １５
1.3.2. The need for the development of automated spatially-resolved cell sorter １８
1.4. Outline of the dissertation ２１
1.4.1. Previous work from our group ２１
1.4.2. Main concept: Automated cell sorting system for multi omics ２４
1.4.3. Outline of the dissertation ２４
CHAPTER 2. DEVELOPMENT OF THE SPATIALLY RESOLVED LASER-ACTIVATED CELL SORTING PLATFORM ２６
2.1. Development of the SLACS system ２７
2.1.1. Advantage of the SLACS system compared to other cell sorting technologies ２７
2.1.2. Workflow and design of the SLACS system ２９
2.2. The quality of the spatial omics assays after cell isolation via SLACS ３６
CHAPTER 3. AUTOMATED IMAGE-BASED CELL SORTING PLATFORM ４０
3.1. Validation of the automated targeting and transferring using the encoded microparticles ４１
3.1.1. Design of the encoded microparticles for the validation ４１
3.1.2. Neural net-based pattern recognition for decoding the encoded microparticles ４４
3.2. Automated cell sorting for targeting the rare cells ４９
3.2.1. The need for automated cell sorting in isolating the circulating tumor cells (CTCs) ４９
3.2.2. Development of the cell sorting algorithm ５２
3.2.3. The quality of the biomolecules from the isolated cells ５８
3.2.4. Application to isolating the circulating tumor cells (CTCs) ６０
3.3. Automated cell sorting for targeting the clinical tissue samples ６３
3.3.1. Need for sorting the tumor regions for specific markers ６３
3.3.2. Cell sorting of the target markers in clinical samples ６５
CHAPTER 4. INTEGRATED SPATIAL PROFILING TECHNOLOGIES ６７
4.1. Integration with the other spatial landscape profiling technologies ６８
4.1.1. In situ sequencing for profiling the spatial landscape ６８
4.1.2. Gene annotation and analysis of In situ sequencing data ７１
4.2. Multi-omics analysis in integrated spatial profiling ７２
4.2.1. In situ sequencing in two different groups ７２
4.2.2. Multi-omics profiling by integrating in situ sequencing to its genomic aberrations ７４
4.2.3. Combination with other spatial profiling technologies ７６
CHAPTER 5.CONCLUSION AND DISCUSSION ７７
5.1.1. Summary of the dissertation ７８
5.1.2. Limitation of the technology ７９
5.1.3. The impact of the suggesting technology ８０
BIBLIOGRAPHY ８２
국문 초록 ９７박
U of M Crookston Senior Sumin "Nicki" Gwak, Finds Campus Filled with Opportunities for International Students
Full text linkTollefson, Elizabeth. (2015). U of M Crookston Senior Sumin "Nicki" Gwak, Finds Campus Filled with Opportunities for International Students. Retrieved from the University Digital Conservancy, https://hdl.handle.net/11299/223578
A data-driven maximum likelihood classification for nanoparticle agent identification in photon-counting CT
No full textThe nanoparticle agent, combined with a targeting factor reacting with lesions, enables specific CT imaging. Thus, the identification of the nanoparticle agents has the potential to improve clinical diagnosis. Thanks to the energy sensitivity of the photon-counting detector (PCD), it can exploit the K-edge of the nanoparticle agents in the clinical x-ray energy range to identify the agents. In this paper, we propose a novel data-driven approach for nanoparticle agent identification using the PCD. We generate two sets of training data consisting of PCD measurements from calibration phantoms, one in the presence of nanoparticle agent and the other in the absence of the agent. For a given sinogram of PCD counts, the proposed method calculates the normalized log-likelihood sinogram for each class (class 1: with the agent, class 2: without the agent) using the K nearest neighbors (KNN) estimator, backproject the sinograms, and compare the backprojection images to identify the agent. We also proved that the proposed algorithm is equivalent to the maximum likelihood-based classification. We studied the robustness of dose reduction with gold nanoparticles as the K-edge contrast media and demonstrated that the proposed method identifies targets with different concentrations of the agents without background noise.1
Incremental Online Learning of Robot Behaviors From Selected Multiple Kinesthetic Teaching Trials
No full textThis paper presents a new approach to the incremental online learning of behaviors by a robot from multiple kinesthetic teaching trials. The approach enables a robot to refine and reproduce a specific behavior every time a new teaching trial is provided and to decide autonomously whether to accept or reject each trial. The robot neglects bad teaching trials and learns a behavior based on adequate teaching trials. The framework of this approach consists of the projection of motion data to a latent space and the description of motion data in a Gaussian mixture model (GMM). To realize the incremental online learning, the latent space and the GMM are refined incrementally after each proper teaching trial. The trial data are discarded after being used. The number of Gaussian components in the GMM is not initially fixed but is autonomously selected by the robot over the trials. The proposed method is more suitable for practical human-robot interaction. The experiments with a humanoid robot show the feasibility of the approach. We demonstrate that the robot can incrementally refine and reproduce learned behaviors that accurately represent the essential characteristics of the teaching trials through our learning algorithm and that it can reject erroneous teaching trials to improve learning performance
Prediction of highly imbalanced semiconductor chip-level defects using uncertainty-based adaptive margin learning
No full textIn semiconductor manufacturing, the package test is a process that verifies whether the product specifications are satisfied before the semiconductor products are finally shipped to customers. The packaged chips are classified as good or defective according to the verification results. To ensure high-quality products and customer satisfaction, it is important to detect defective chips during the package test. In this article, we consider the problem of predicting potential defects in advance using the wafer-test results data obtained from an earlier stage of the wafer test. There are several challenges in this problem. First, package-test data are highly class-imbalanced with a very low defect rate, and the imbalance level may vary due to the variability in manufacturing processes. Second, there is a complex relationship between package- and wafer-test results. Third, it is more important to increase the detection accuracy of defects than the overall classification accuracy. To address these challenges, we propose a Bayesian-neural-network-based prediction model. The proposed model adaptively considers unknown imbalance levels through the flexible adjustment of the decision boundary by using class- and sample-level prediction uncertainties and the relative frequency of each class. Using a real semiconductor manufacturing dataset from a global semiconductor company, we demonstrate that the proposed model can effectively predict defects even when the imbalance level of the test dataset differs from that of the training dataset.
Taxonomic review of the Genus Tautoneura Anufriev (Hemiptera: Auchenorrhyncha: Cicadellidae: Typhlocybinae) from Korea, with description of one new species
No full textOh, Sumin, Pham, Hong-Thai, Jung, Sunghoon (2016): Taxonomic review of the Genus Tautoneura Anufriev (Hemiptera: Auchenorrhyncha: Cicadellidae: Typhlocybinae) from Korea, with description of one new species. Zootaxa 4169 (1): 194-200, DOI: http://doi.org/10.11646/zootaxa.4169.1.1
A new species of the genus Eurhadina Haupt (Hemiptera: Auchenorrhyncha: Cicadellidae: Typhlocybinae) from Korea, with a key to Korean species
No full textOh, Sumin, Lim, Jongok, Jung, Sunghoon (2016): A new species of the genus Eurhadina Haupt (Hemiptera: Auchenorrhyncha: Cicadellidae: Typhlocybinae) from Korea, with a key to Korean species. Zootaxa 4103 (1), DOI: 10.11646/zootaxa.4103.1.
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