The State Space Models Toolbox for MATLAB

State Space Models (SSM) is a MATLAB toolbox for time series analysis by state space methods. The software features fully interactive construction and combination of models, with support for univariate and multivariate models, complex time-varying (dy- namic) models, non-Gaussian models, and various standard models such as ARIMA and structural time-series models. The software includes standard functions for Kalman fil- tering and smoothing, simulation smoothing, likelihood evaluation, parameter estimation, signal extraction and forecasting, with incorporation of exact initialization for filters and smoothers, and support for missing observations and multiple time series input with com- mon analysis structure. The software also includes implementations of TRAMO model selection and Hillmer-Tiao decomposition for ARIMA models. The software will provide a general toolbox for time series analysis on the MATLAB platform, allowing users to take advantage of its readily available graph plotting and general matrix computation capabilities.

Automatic Morphological Subtyping Reveals New Roles of Caspases in Mitochondrial Dynamics

Morphological dynamics of mitochondria is associated with key cellular processes related to aging and neuronal degenerative diseases, but the lack of standard quantification of mitochondrial morphology impedes systematic investigation. This paper presents an automated system for the quantification and classification of mitochondrial morphology. We discovered six morphological subtypes of mitochondria for objective quantification of mitochondrial morphology. These six subtypes are small globules, swollen globules, straight tubules, twisted tubules, branched tubules and loops. The subtyping was derived by applying consensus clustering to a huge collection of more than 200 thousand mitochondrial images extracted from 1422 micrographs of Chinese hamster ovary (CHO) cells treated with different drugs, and was validated by evidence of functional similarity reported in the literature. Quantitative statistics of subtype compositions in cells is useful for correlating drug response and mitochondrial dynamics. Combining the quantitative results with our biochemical studies about the effects of squamocin on CHO cells reveals new roles of Caspases in the regulatory mechanisms of mitochondrial dynamics. This system is not only of value to the mitochondrial field, but also applicable to the investigation of other subcellular organelle morphology

An Overview of Regional Experiments on Biomass Burning Aerosols and Related Pollutants in Southeast Asia: From BASE-ASIA and the Dongsha Experiment to 7-SEAS

By modulating the Earth-atmosphere energy, hydrological and biogeochemical cycles, and affecting regional-to-global weather and climate, biomass burning is recognized as one of the major factors affecting the global carbon cycle. However, few comprehensive and wide-ranging experiments have been conducted to characterize biomass-burning pollutants in Southeast Asia (SEA) or assess their regional impact on meteorology, the hydrological cycle, the radiative budget, or climate change. Recently, BASEASIA (Biomass-burning Aerosols in South-East Asia: Smoke Impact Assessment) and the 7-SEAS (7- South-East Asian Studies) Dongsha Experiment were conducted during the spring seasons of 2006 and 2010 in northern SEA, respectively, to characterize the chemical, physical, and radiative properties of biomass-burning emissions near the source regions, and assess their effects. This paper provides an overview of results from these two campaigns and related studies collected in this special issue, entitled Observation, modeling and impact studies of biomass burning and pollution in the SE Asian Environment. This volume includes 28 papers, which provide a synopsis of the experiments, regional weatherclimate, chemical characterization of biomass-burning aerosols and related pollutants in source and sink regions, the spatial distribution of air toxics (atmospheric mercury and dioxins) in source and remote areas, a characterization of aerosol physical, optical, and radiative properties, as well as modeling and impact studies. These studies, taken together, provide the first relatively complete dataset of aerosol chemistry and physical observations conducted in the sourcesink region in the northern SEA, with particular emphasis on the marine boundary layer and lower free troposphere (LFT). The data, analysis and modeling included in these papers advance our present knowledge of source characterization of biomass-burning pollutants near the source regions as well as the physical and chemical processes along transport pathways. In addition, we raise key questions to be addressed by a coming deployment during springtime 2013 in northern SEA, named 7-SEASBASELInE (Biomass-burning Aerosols Stratocumulus Environment: Lifecycles and Interactions Experiment). This campaign will include a synergistic approach for further exploring many key atmospheric processes (e.g., complex aerosol-cloud interactions) and impacts of biomass burning on the surface-atmosphere energy budgets during the lifecycles of biomass burning emissions

Association analyses of East Asian individuals and trans-ancestry analyses with European individuals reveal new loci associated with cholesterol and triglyceride levels

Large-scale meta-analyses of genome-wide association studies (GWAS) have identified >175 loci associated with fasting cholesterol levels, including total cholesterol (TC), high-density lipoprotein cholesterol (HDL-C), low-density lipoprotein cholesterol (LDL-C), and triglycerides (TG). With differences in linkage disequilibrium (LD) structure and allele frequencies between ancestry groups, studies in additional large samples may detect new associations. We conducted staged GWAS meta-analyses in up to 69,414 East Asian individuals from 24 studies with participants from Japan, the Philippines, Korea, China, Singapore, and Taiwan. These meta-analyses identified (P < 5 × 10-8) three novel loci associated with HDL-C near CD163-APOBEC1 (P = 7.4 × 10-9), NCOA2 (P = 1.6 × 10-8), and NID2-PTGDR (P = 4.2 × 10-8), and one novel locus associated with TG near WDR11-FGFR2 (P = 2.7 × 10-10). Conditional analyses identified a second signal near CD163-APOBEC1. We then combined results from the East Asian meta-analysis with association results from up to 187,365 European individuals from the Global Lipids Genetics Consortium in a trans-ancestry meta-analysis. This analysis identified (log10Bayes Factor ≥6.1) eight additional novel lipid loci. Among the twelve total loci identified, the index variants at eight loci have demonstrated at least nominal significance with other metabolic traits in prior studies, and two loci exhibited coincident eQTLs (P < 1 × 10-5) in subcutaneous adipose tissue for BPTF and PDGFC. Taken together, these analyses identified multiple novel lipid loci, providing new potential therapeutic targets

The state space models toolbox for MATLAB

State Space Models (SSM) is a MATLAB toolbox for time series analysis by state space methods. The software features fully interactive construction and combination of models, with support for univariate and multivariate models, complex time-varying (dynamic) models, non-Gaussian models, and various standard models such as ARIMA and structural time-series models. The software includes standard functions for Kalman filtering and smoothing, simulation smoothing, likelihood evaluation, parameter estimation, signal extraction and forecasting, with incorporation of exact initialization for filters and smoothers, and support for missing observations and multiple time series input with common analysis structure. The software also includes implementations of TRAMO model selection and Hillmer-Tiao decomposition for ARIMA models. The software will provide a general toolbox for time series analysis on the MATLAB platform, allowing users to take advantage of its readily available graph plotting and general matrix computation capabilities

Implied distributions in multiple change point problems

A method for efficiently calculating exact marginal, conditional and joint distributions for change points defined by general finite state Hidden Markov Models is proposed. The distributions are not subject to any approximation or sampling error once parameters of the model have been estimated. It is shown that, in contrast to sampling methods, very little computation is needed. The method provides probabilities associated with change points within an interval, as well as at specific points

Implied distributions in multiple change point problems

A method for efficiently calculating marginal, conditional and joint distributions for change points defined by general finite state Hidden Markov Models is proposed. The distributions are not subject to any approximation or sampling error once parameters of the model have been estimated. It is shown that, in contrast to sampling methods, very little computation is needed. The method provides probabilities associated with change points within an interval, as well as at specific points

Adaptive Local Thresholding for Fluorescence Cell Micrographs

[[sponsorship]]資訊科學研究所[[note]]未出

Pattern Statistics in Time Series Analysis

本論文提出一個新的序列圖案架構，可以正確並快速的計算出在時間序列資料中圖案出現的機率。此架構利用自動機描述出現圖案的計量，並將這個自動機嵌入一個馬可夫鏈，而圖案計量的分佈即可由馬可夫鏈的分佈求得。這個架構可以用來分析連續或離散的序列資料，只要圖案是出現於資料模型中的一個馬可夫來源。透過這個新方法，可以簡單並有效率的取得圖案計量的共同分佈。這個新方法的應用範圍極廣，本論文中將示範如何將之應用於時間序列中變異點的估計。This thesis introduces a new pattern statistics framework, which enables exact and efficient calculation of probabilities of pattern occurrences in sequence data. Statistics of pattern occurrences in data are formulated in terms of finite automata state transitions embedded into a Markov chain. This enables the analysis of continuous or discrete sequence data where the underlying generation process is governed by a Markov source, and where occurrences of specific patterns in the Markov state sequence is of interest. Through this new methodology, the full joint distribution of pattern statistics can be obtained in a conceptually simple and computationally efficient way. This new methodology can be adopted for many applications, and is here applied to change point estimation problems as an example.1 Introduction 1.1 Problem Statement . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.2 Contributions and Recognition . . . . . . . . . . . . . . . . . . . . . 3 The History of Pattern Statistics 7.1 Early Works related to Runs . . . . . . . . . . . . . . . . . . . . . . 8.2 Runs Conditional on Composition . . . . . . . . . . . . . . . . . . . 17.3 Recurrent Events . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19.4 Distributions of Order k . . . . . . . . . . . . . . . . . . . . . . . . 27.5 Combinatorial Enumeration . . . . . . . . . . . . . . . . . . . . . . 38.6 Waiting Time for Finite Regular Languages . . . . . . . . . . . . . 41.7 System of Equations of Probability Generating Functions . . . . . . 50.8 Markov Chain Embedding . . . . . . . . . . . . . . . . . . . . . . . 58.9 The Final Word . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 60 Pattern Statistics Through Markov Chain Embedding 62.1 Markov Chain Embedding of Deterministic Finite Automata . . . . 62.2 Patterns as Regular Languages . . . . . . . . . . . . . . . . . . . . 65.3 Recent Advances . . . . . . . . . . . . . . . . . . . . . . . . . . . . 69.4 Applications of Pattern Statistics . . . . . . . . . . . . . . . . . . . 70.4.1 Pattern Detection . . . . . . . . . . . . . . . . . . . . . . . . 70.4.2 Pattern Searching . . . . . . . . . . . . . . . . . . . . . . . . 71.4.3 Sequence Segmentation . . . . . . . . . . . . . . . . . . . . . 75.5 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 82 Pattern Statistics Applied to Change Point Estimation 84.1 Motivation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 86.2 Models and Methodology . . . . . . . . . . . . . . . . . . . . . . . . 87.2.1 Hidden Markov Models of Change Points . . . . . . . . . . . 87.2.2 Waiting Time Distributions of Change Points . . . . . . . . 88.2.3 Markov Chain Embedding of Change Points . . . . . . . . . 90.3 Data Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 93.3.1 U.S. Gross National Product . . . . . . . . . . . . . . . . . . 94.3.2 U.S. Treasury Bill Data . . . . . . . . . . . . . . . . . . . . 98.3.3 fMRI Data from Anxiety-Inducing Experiment . . . . . . . . 104.4 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 109 Conclusion 111ibliography 11