Large Margin Object Tracking with Circulant Feature Maps

Structured output support vector machine (SVM) based tracking algorithms have shown favorable performance recently. Nonetheless, the time-consuming candidate sampling and complex optimization limit their real-time applications. In this paper, we propose a novel large margin object tracking method which absorbs the strong discriminative ability from structured output SVM and speeds up by the correlation filter algorithm significantly. Secondly, a multimodal target detection technique is proposed to improve the target localization precision and prevent model drift introduced by similar objects or background noise. Thirdly, we exploit the feedback from high-confidence tracking results to avoid the model corruption problem. We implement two versions of the proposed tracker with the representations from both conventional hand-crafted and deep convolution neural networks (CNNs) based features to validate the strong compatibility of the algorithm. The experimental results demonstrate that the proposed tracker performs superiorly against several state-of-the-art algorithms on the challenging benchmark sequences while runs at speed in excess of 80 frames per second. The source code and experimental results will be made publicly available

Incremental Processing and Optimization of Update Streams

Over the recent years, we have seen an increasing number of applications in networking, sensor networks, cloud computing, and environmental monitoring, which monitor, plan, control, and make decisions over data streams from multiple sources. We are interested in extending traditional stream processing techniques to meet the new challenges of these applications. Generally, in order to support genuine continuous query optimization and processing over data streams, we need to systematically understand how to address incremental optimization and processing of update streams for a rich class of queries commonly used in the applications. Our general thesis is that efficient incremental processing and re-optimization of update streams can be achieved by various incremental view maintenance techniques if we cast the problems as incremental view maintenance problems over data streams. We focus on two incremental processing of update streams challenges currently not addressed in existing work on stream query processing: incremental processing of transitive closure queries over data streams, and incremental re-optimization of queries. In addition to addressing these specific challenges, we also develop a working prototype system Aspen, which serves as an end-to-end stream processing system that has been deployed as the foundation for a case study of our SmartCIS application. We validate our solutions both analytically and empirically on top of our prototype system Aspen, over a variety of benchmark workloads such as TPC-H and LinearRoad Benchmarks

동물모델을 이용한 치주염 타액/치은연구액 진단마커의 발굴과세균응집 특생을 이용한 치주염 동물모델

학위논문 (박사) -- 서울대학교 대학원 : 치의학대학원 치의과학과, 2021. 2. 최영님.배경 치주 질환은 사람의 구강에서 흔하게 자주 발생하는 질환으로 치은 조직에만 영향을 미치는 치은염과 치주 인대, 치조골 및 백악질을 포함하는 깊은 치주 조직에 영향을 미치는 치주염의 두 가지 주요 유형의 질병을 포함한다. 치주염은 다인성 질환이라는 사실이 알려져 있다. 그 중 치은 연하 생물막의 세균과 그 대사 산물은 치주염의 발생에 필수 인자이지만 충분하지는 않다. 부가적으로 숙주의 국소 자극 인자와 전신 인자가 세균인자와 서로 영향을 주고 받으며 작용한다. 치주염의 발병 기전을 명확히 하기 위해서는 다양한 요인 간의 상호작용에 대한 전반적인 이해가 필요하다. 이 논문은 치주염의 발병 기전에서 병원성 미생물의 지속적인 감염의 역할과 치주염을 진단하기위한 타액 및 치은열구액의 바이오마커 분석에 초점을 맞추고 있다. 방법 비글견 모델에서 총 15 마리의 비글을 대조군(결찰 없음), 제1군(6 개 치아에 결찰) 및 제2군(12 개 치아에 결찰)의 세군으로 나누었다. 실험 기간은 치주염 유도 8 주와 치료 4 주로 구성되었다. 치주염의 임상적 평가와 타액 채취는 4 주마다 수행하였다. 타액 및 치은열구액 내 S100A8, S100A9, S100A8/A9 및 매트릭스 메탈로프로테아제 (MMP)-9의 수준은 효소결합면역흡착분석으로 측정하였다. 세균 간의 응집은 침강 분석 및 공초점레이저스캐닝 현미경으로 분석하었다. 공초점레이저스캐닝 현미경과 3D 세포 탐색기를 사용하여 세균이 생쥐구강상피세포 IMOK (Murine Oral Keratinocyte) 에 침입하는 것을 관찰하였다. 생쥐모델은 6 주령의 C57BL/6 암컷 생쥐 80마리를 사용하였다. 모든 생쥐에 Streptococcus danieliae (Sd) 2 x 10 ^ 9 세포를 1회 경구투여 후 5개 그룹으로 나누었다. 이어서 Fusobacterium. nucleatum Subsp. animalis KCOM 1280 (Fna), Porphyromonas. gingivalis 33277 (Pg33277), P. gingivalis KUMC-P4 (PgP4), 또는 PgP4 + Fna를 2% 카르복시메틸 셀룰로스를 함유하는 100 μL PBS에 섞어 2 일 간격으로 6 회 구강 투여하였다. 대조군은 PBS만으로 2% 카르복시메틸셀룰로오스를 받았다. 첫 접종 후 5 주 또는 8 주 후에 생쥐를 안락사시키고 조직과 혈청을 채취하었다. 상악의 치조골 손실은 컴퓨터 단층촬영으로 측정하였고, 하악골은 헤마톡실린과 에오신으로 염색하고 P. gingivalis-, F. nuleatum-, S. danieliae- 특이적 탐침자를 사용하여 in situ hybridization을 수행하였다. 생쥐 구강에서 얻은 세균의 DNA를 이용하여, Sd, Fna, Pg의 양을 qPCR로 분석 하였다. 타액 및 혈청 내 박테리아에 대한 IgG 및 IgA 항체의 수준은 효소결합면역흡착분석을 사용하여 측정하었다. 결과 비글견 모델에서 실험군의 모든 동물과 대조군의 두 마리가 치주염을 일으켰고 성공적으로 치료 되었다. 본 연구에서 시험한 모든 타액 바이오마커(S100A8, S100A9, S100A8/A9, MMP-9)는 진단 능력이 높았으며(c index ≥ 0.944) 단일 치아에서 치주염이 발생한 동물도 식별할수 있었다. 시험한 치주염 P.gingivalis 균주 용 환자에서 분리된 균주 PgP4가 Sd와 가장 강한 공중합을 보였다. IMOK 세포로의 박테리아 침입은 Sd에 비해 PgP4, Pg33227 및 Fna에 의해 높게 관찰되었다. 생쥐모델에서 대조준과 비교해 PgP4 그룹과 Fna + PgP4 그룹이 유의하게 증가된 치조골 소실을 보였다. Fna와 Pg는 구강투여 후 5주차보다 8주차에 증가한 것으로 보아 생쥐 강에 성공적으로 집락하였고 향체 반응을 유도하였다. PgP4 그룹은 치은조직에 Pg뿐 아니라 Sd의 높은 침투를 보였다. 결론 타액 S100A8, S100A9, S100A8/A9 및 MMP-9는 개의 치주염 진단에 사용할 수 있지만, 이들의 타액 수치에 영향을 미칠 수 있는 다른 질환에 주의해야 한다. P. gingivalis KUMC-P4균주는 생쥐의 구강 상주균인 S. danieliae와 응집되어 IMOK 세포로 침투하는 능력이 강해 C57BL/6 마우스에서 상당한 치조골 파괴를 유도하였다.Background Periodontal diseases are highly prevalent in the human oral cavity. There are two major types of periodontal diseases: gingivitis that only affects gingival tissue and periodontitis that affects deep periodontal tissues, composing of cementum, periodontal ligament, and alveolar bone. Periodontitis is a multifactorial disease. Among the involved factors, the bacteria and their metabolites of dental biofilms are the indispensable initiating factors for periodontitis, but it is not sufficient. The occurrence of periodontitis is also affected by other factors, including local and systemic factors that positively or negatively interact with each other. To clarify the pathogenesis of periodontitis, it is necessary to grasp the interactions among various factors. This paper focuses on the role of persistent infection of pathogenic microorganisms in the pathogenesis of periodontal disease, and the power of biomarkers in saliva and gingival crevicular fluid to screen periodontitis. Methods In a beagle dog model, a total of 15 beagles were divided into three groups: the control group (without ligature), the first group (ligature on 6 teeth) and the second group (ligature on 12 teeth). The experimental period was consisted of 8 weeks of periodontitis induction and 4 weeks of treatment. Clinical measurements and saliva sampling were performed every 4 weeks. The levels of S100A8, S100A9, S100A8/A9 and matrix metalloproteinase (MMP)-9 were measured using enzyme-linked immunosorbent assay (ELISA). The coaggregation between clinical isolate bacteria was analyzed by a sedimentation assay and confocal laser scanning microscopy. The invasion of bacteria into Murine Oral Keratinocyte (IMOK) cells was observed using a confocal laser scanning microscop and a 3D cell explorer. In a murine model, a total of 80 female six-week-old C57BL/6 mice were randomly divided into five groups (n=16 per group). 2 x 10^9 cells of murine oral commensal bacteria Streptococcus. danieliae DSM 26621 were gavaged before grouping. Mice were orally gavaged with total 2 x 10^9 cells of F. nucleatum Subsp. animalis KCOM 1280 (Fna), P. gingivalis ATCC 33277 (Pg33277), P. gingivalis KUMC-P4 (PgP4), or PgP4+Fna in 100 µL PBS plus 2% carboxymethyl cellulose every 2 days total six times. The sham group received 2% carboxymethyl cellulose in PBS alone. Mice were euthanized five or eight weeks after the first inoculation. Alveolar bone loss in the hemi-maxillae were measured by micro-CT. The mandibular molars were stained with hematoxylin and eosin and in situ hybridization using P. gingivalis-, F. nuleatum-, S. danieliae-specific probes. Using the genomic DNA of bacteria obtained from the oral cavity of mice, the copy numbers, Sd, Fna, and Pg were analyzed by qPCR. The levels of IgG and IgA antibodies against bacteria in saliva and sera were measured by ELISA. Result All dog animals in the experimental groups and the two control animals suffered from periodontitis and were successfully treated. All salivary biomarkers of periodontitis had high diagnostic ability (Area under curve, AUC index ≥ 0.944) and could identify animals with periodontitis on a single tooth. The saliva S100A8/A9 levels returned to healthy states, while the levels of S100A8, S100A9 and MMP-9 in periodontitis stability were still significantly higher than healthy levels. The clinically isolated strain PgP4 showed the strongest coaggregation with S. danieliae. Increased bacterial invasion into IMOK cells were observed by PgP4, Pg33227, and Fna. The alveolar bone levels significantly decreased in the PgP4 group at both time points (weeks 5 and 8) and in the Fna+PgP4 group at week 8 compared with the Sham group. The bacteria invasion to gingival tissues were significantly increased in the PgP4 group. Conclusion Salivary S100A8, S100A9, S100A8/A9, and MMP-9 may be used for the screening of periodontitis in dogs, but with caution of other conditions that can affect their levels in saliva. Porphyromonas gingivalis KUMC-P4, which had strong ability to coaggregate with the major murine oral flora S. danieliae and invade into IMOK cells, induced significant alveolar bone destruction in C57BL/6 mice.Chapter Ⅰ. Background 1 1. Periodontal tissue and periodontitis 1 1.1. Periodontal tissue 1 1.2. Periodontitis: Definition, and epidemiology 3 2. Etiology of periodontitis 4 3. Pathophysiology 5 3.1. Bacterial communities in periodontitis 5 3.2. Immune response of periodontal host 9 3.3. Saliva and gingival crevicular fluid 12 4. Histopathology 13 5. Diagnosis and treatment 14 6. Salivary biomarker 16 7 Periodontitis animal models 17 Chapter Ⅱ. Periodontitis: beagle dog model 19 1. Introduction 20 2. Aim of study 23 3. Methods and materials 24 4. Results 32 5. Discussion 48 Chapter III. Periodontitis: murine model 55 1. Introduction 56 2. Aim of study 61 3. Methods and materials 62 4. Results 81 5. Discussion 102 Chapter IV. Conclusion 106 Chapter V. References 107Docto