Overcoming Data Inconsistencies and Insufficiencies

학위논문(석사) -- 서울대학교대학원 : 사범대학 협동과정 환경교육전공, 2023. 8. 송영근.Aim: To predict annual distribution patterns and reduction rates of insufficiently observed species by using co-occurrence pattern learning and devising filling-in strategy to overcome structural and temporal inconsistencies in multi-source noisy data. Idea: Although more than 10% of insects will face extinction in the coming decades, studies on their reduction rates that will form the basis for conservation strategies are still limited. This limitation is first due to the dominance of unstructured records available for invertebrates, secondly, to the inconsistencies among them, and thirdly, to the insufficiencies of them. While compelling to gather data across multiple sources, the small amount of data precludes deep filtering to handle structural and temporal inconsistencies among sources for time-series comparison. This is the first study to estimate annual reductions with machine learning from multi-sourced, presence-only, and small data, by overcoming its inconsistencies and insufficiencies. This study proposes and validates the following two novel strategies. (1) Co-occurrence pattern learning: By grouping low-quality, unreliable individual occurrence records into patterns, I validate that structural and temporal inconsistencies can be overcome without deep filtering. (2) Filling-in strategy: I propose a procedure for estimating population trends by filling in the prediction into the deficiencies of the collected yearly data to be evenly compared. Location: 51 states of the USA and 6 provinces of Canada Taxa: four ladybugs native to North America Methods: In chapter 2, seven performance scores were used to evaluate the predictions on presence versus absence in the following three situations: (1) learning unstructured data to predict structured data or low-efficiecy data to high-efficiency data; (2) learning data before a particular year to predict after that year and vice versa; (3) learning 70% of multi-source data to predict the rest. During both the evaluation and generalization phases, a comparison was made between the performance of the co-occurence pattern using models and the environmental information using models, as well as with the commonly accepted benchmark. In chapter 3, reduction rates and extinction status were estimated by ML's predicting the occupancy of species annually at all coordinates where species have appeared since 2007. In addition to that, the newly suggested approach's methodological reliability was verified, in comparison with pre-established methods. Furthermore, the reliability of the newly proposed method was validated by examining discrepancies in estimations under the following scenarios: variances in data extraction for pseudo-absence data points, variances in variable selection techniques, and the stochastic incorporation of missing or false information within the presence data. Results: 1) The COP models' performance surpassed acceptable criteria for all validation steps and all species. They also ouperformed over the ENV models. 2) Reduction rates were 36.4% for H. parenthesis (2007–2021; VU), 29.7% for A. bipunctata (2010–2019; NT), 23.7% for C. novemnotata (2009–2018; NT), and 14% for C. trasversoguettata (2007–2018; LC). Additionally, the newly proposed approach was confirmed to possess strong methodological validity when compared to pre-established methods. In terms of reliability tests, the range of estimations from the new method did not misrepresent IUCN conservation status to a significant extent. Conclusion: The combination of using co-occurrence patterns as variables and filling-in strategy enabled SDM to predict species' finer time scale distribution patterns and reduction rates by overcoming structural and temporal inconsistencies in multi-source data integrating considerable citizen science data. In North America, four native ladybug species have been declining steadily. This study suggests that ML developed with COP can integrate multiple-source data without filtering, allowing for the acquisition of more data, and that COP-based SDMs may be advantageous for predictions at finer temporal scales (and thus more precise than commonly used SDMs developed with environmental variables usually spanning over decades). This can aid in tackling the challenge in global conservation initiatives posed by rare and invertebrate taxa, which frequently face restricted data availability and are often underrepresented in conservation lists.Chapter 1. Introduction 1.1. General background of the study 1 1.2. Purpose of the study 6 1.3. Study history 7 Chapter 2. Co-Occurrence Patterns Overcome Structural and Temporal Inconsistencies in Multi-Source Datasets, Outperforming Environmental Variables 2.1. Materials and methods 14 2.1.1. Summary of materials and methods 14 2.1.2. Target species 14 2.1.3. Occurrence data 14 2.1.4. Psuedo-absence 15 2.1.5. Variables 16 2.1.6. Development and characterization of models 17 2.1.7. Generalization 20 2.1.8. Evaluation 22 2.2. Results 23 2.2.1. Biases in multi-source data 23 2.2.2. Structural and temporal generalization 24 2.2.3. Evaluation of the developed models 31 2.2.4. Importance and correlation among variables 31 2.3. Discussion 34 2.3.1. The strength of co-occurrence pattern learning 34 2.3.2. The interpretation of used variables 35 2.3.3. The incorporation of new variables 36 2.3.4. The limitation in application 38 2.4. Conclusion 38 Chapter 3. Filling Machine Learning Predictions In Temporal Data Gaps Can Estimate Annual Reductions Across Every Historical Distribution 3.1. Materials and methods 40 3.1.1. Summary of materials and methods 40 3.1.2. Target species 40 3.1.3. Occurrence data 40 3.1.4. Psuedo-absence 40 3.1.5. Variables 40 3.1.6. Development and characterization of models 40 3.1.7. Prediction on annual distributions and reduction rates 41 3.1.8. Validity Evaluation 41 3.1.9. Reliability Evaluation 42 3.2. Results 43 3.2.1. Estimated reduction rates and conservation status 43 3.2.2. Validity comparison with pre-established methodologies 45 3.2.3. Reliability analysis on filling-in approach 49 3.2.4. Predicted distribution 51 3.3. Discussion 52 3.3.1. The theoretical rationale for the ML reduction rates 52 3.3.2. Affect of temporal fluctuations of data on various models 53 3.3.3. Practical benefits of the filling-in approach 55 3.3.4. The filling-in approach in conjunction with data filtering methods 56 3.4. Conclusion 58 Bibliography 59 Abstract in Korean 73석

A Comparative Analysis of Three Major Transfer Airports in Northeast Asia Focusing on Incheon International Airport Using a Conjoint Analysis

Due mainly to the privatization and commercialization of airline companies and deregulation of the aviation rules, the demand for air transport has continuously been increasing. Airport authorities state that transfer passengers, who contribute to the large portion of the airports’ profits, are gaining much more importance, particularly in the Northeast Asia region where the air transport industry is very vital. Therefore, this study aims to investigate the competitiveness of IIA (Incheon International Airport) with other major airports located in Northeast Asia in passenger transfers made between Southeast Asia and China to North America using Conjoint Analysis. Results have indicated that airport brand is the most important attribute for the competitiveness of airport, followed by cost, connectivity and duty free shops. In further analysis focusing on brand value of the three airports measured by the use of transfer passengers, it was revealed that IIA needs more effort in developing their brand identity to become the leading transfer hub airport. Based on the results, recommendations for increasing the brand value have also been suggested

Rapid Detection Strategies for the Global Threat of Zika Virus: Current State, New Hypotheses, and Limitations

The current scenario regarding the widespread Zika virus (ZIKV) has resulted in numerous diagnostic studies, specifically in South America and in locations where there is frequent entry of travelers returning from ZIKV-affected areas, including pregnant women with or without clinical symptoms of ZIKV infection. The World Health Organization, WHO, announced that millions of cases of ZIKV are likely to occur in the United States of America in the near future. This situation has created an alarming public health emergency of international concern requiring the detection of this life-threatening viral candidate due to increased cases of newborn microcephaly associated with ZIKV infection. Hence, this review reports possible methods and strategies for the fast and reliable detection of ZIKV with particular emphasis on current updates, knowledge and new hypotheses that might be helpful for medical professionals in poor and developing countries that urgently need to address this problem. In particular, we emphasize liposome-based biosensors. Although these biosensors are currently among the less popular tools for human disease detection, they have become useful tools for the screening and detection of pathogenic bacteria, fungi and viruses because of their versatile advantageous features compared to other sensing devices. This review summarizes the currently available methods employed for the rapid detection of ZIKV and suggests an innovative approach involving the application of a liposome-based hypothesis for the development of new strategies for ZIKV detection and their use as effective biomedicinal tools

Tgif1 Counterbalances The Activity Of Core Pluripotency Factors In Mouse Embryonic Stem Cells

Core pluripotency factors, such as Oct4, Sox2, and Nanog, play important roles in maintaining embryonic stem cell (ESC) identity by autoregulatory feedforward loops. Nevertheless, the mechanism that provides precise control of the levels of the ESC core factors without indefinite amplification has remained elusive. Here, we report the direct repression of core pluripotency factors by Tgif1, a previously known terminal repressor of TGF beta/activin/nodal signaling. Overexpression of Tgif1 reduces the levels of ESC core factors, whereas its depletion leads to the induction of the pluripotency factors. We confirm the existence of physical associations between Tgif1 and Oct4, Nanog, and HDAC1/2 and further show the level of Tgif1 is not significantly altered by treatment with an activator/inhibitor of the TGF beta/activin/nodal signaling. Collectively, our findings establish Tgif1 as an integral member of the core regulatory circuitry of mouse ESCs that counterbalances the levels of the core pluripotency factors in a TGF beta/activin/nodal-independent manner.Cancer Prevention Research Institute of Texas (CPRIT) R1106Molecular Bioscience

Trapping a Free-propagating Single-photon into an Atomic Ensemble as a Quantum Stationary Light Pulse

Efficient photon-photon interaction is one of the key elements for realizing quantum information processing. The interaction, however, must often be mediated through an atomic medium due to the bosonic nature of photons, and the interaction time, which is critically linked to the efficiency, depends on the properties of the atom-photon interaction. While the electromagnetically induced transparency effect does offer the possibility of photonic quantum memory, it does not enhance the interaction time as it fully maps the photonic state to an atomic state. The stationary light pulse (SLP) effect, on the contrary, traps the photonic state inside an atomic medium with zero group velocity, opening up the possibility of the enhanced interaction time. In this work, we report the first experimental demonstration of trapping a free-propagating single-photon into a cold atomic ensemble via the quantum SLP (QSLP) process. We conclusively show that the quantum properties of the single-photon state are preserved well during the QSLP process. Our work paves the way to new approaches for efficient photon-photon interactions, exotic photonic states, and many-body simulations in photonic systems

Mitogen-Activated Protein Kinases and Reactive Oxygen Species: How Can ROS Activate MAPK Pathways?

Mitogen-activated protein kinases (MAPKs) are serine-threonine protein kinases that play the major role in signal transduction from the cell surface to the nucleus. MAPKs, which consist of growth factor-regulated extracellular signal-related kinases (ERKs), and the stress-activated MAPKs, c-jun NH2-terminal kinases (JNKs) and p38 MAPKs, are part of a three-kinase signaling module composed of the MAPK, an MAPK kinase (MAP2K) and an MAPK kinase (MAP3K). MAP3Ks phosphorylate MAP2Ks, which in turn activate MAPKs. MAPK phosphatases (MKPs), which recognize the TXY amino acid motif present in MAPKs, dephosphorylate and deactivate MAPKs. MAPK pathways are known to be influenced not only by receptor ligand interactions, but also by different stressors placed on the cell. One type of stress that induces potential activation of MAPK pathways is the oxidative stress caused by reactive oxygen species (ROS). Generally, increased ROS production in a cell leads to the activation of ERKs, JNKs, or p38 MAPKs, but the mechanisms by which ROS can activate these kinases are unclear. Oxidative modifications of MAPK signaling proteins and inactivation and/or degradation of MKPs may provide the plausible mechanisms for activation of MAPK pathways by ROS, which will be reviewed in this paper

The resistance anomaly in the surface layer of Bi2_2Sr2_2CaCu2_2O8+x_{8+x} single crystals under radio-frequency irradiation

We observed that radio-frequency (rf) irradiation significantly enhances the $c$-axis resistance near and below the superconducting transition of the CuO$_{2}$ layer in contact with a normal-metal electrode on the surface of Bi$_{2}$Sr$_{2}$CaCu$_{2}$O$_{8+x}$ single crystals. We attribute the resistance anomaly to the rf-induced charge-imbalance nonequilibrium effect in the surface CuO$_{2}$ layer. The relaxation of the charge-imbalance in this highly anisotropic system is impeded by the slow quasiparticle recombination rate, which results in the observed excessive resistance.Comment: 8 pages, 5 figure