Toward fast and robust in vivo MR quantification of microvasculature

Department of Biomedical EngineeringMagnetic resonance imaging (MRI) assessments of microvascular anatomy and function in diseases, such as cancer and neurodegenerations are important for detecting abnormal vascular behavior and monitoring therapeutic progress in a noninvasive manner. In MRI, quantitative microvascular biomarkers such as vessel permeability, orientation, blood volume, vessel size index are actively being developed for in vivo applications. Firstly, quantitative vessel permeability information is typically measured by dynamic contrast enhanced (DCE) - MRI, which uses extravasating contrast agent (Gd-DOTA). Following pharmacokinetic modelling is usually applied to dynamical signal change curve after the administration of contrast agent to extract vessel-permeability related parameters. On the other hand, it is generally accepted that there are certain limitations in conventional DCE - MRI acquisitions in terms of its accuracy, and the acquisition speed due to the demanding spatio-temporal tradeoffs for dynamic studies. For example, gradient echo based sequence is typically used for DCE - MRI for high temporal resolution requirements, but induces T2* decay that we often neglect, but becomes significant for high contrast agent concentration regions such as artery or kidney. Tradeoff between spatial and temporal resolution also limits the desired spatial coverage or temporal accuracy of time intensity curves. Secondly, vessel orientation, blood volume, and vessel size index are usually measured by detecting transverse relaxation difference before and after the administration of intravascular T2 contrast agent, such as superparamagnetic iron oxide nanoparticles (SPION). However, transverse relaxation is well known to be affected by unwanted environmental conditions such as air-tissue interface and vessel orientation, which frequently causes severe error in the measurement of blood volume and vessel size index. The subjects and goals of this thesis can be categorized by two sub-sections. In the first section, fast and accurate DCE - MRI was achieved by applying compressed sensing (CS) algorithms, which mitigates the spatio-temporal resolution competition of dynamic acquisitions. Firstly, the optimization for the implementation of compressed sensing to conventional fast low-angle shot (FLASH) sequence which is generally used for DCE - MRI acquisition was performed. After optimization step, temporal or spatial resolution improvements were demonstrated by in vivo experiment, especially in the tumor model. Secondly, compressed sensing was implemented to turbo spin echo (TSE) sequence to minimize transverse artifact by replacing T2* to T2 without reducing temporal resolution and slice coverage. This minimized transverse artifact realized calibration-free T1 estimation from T1-weighted signal intensity. Finally, ultrafast 3D spin echo acquisition was developed by applying compressed sensing to multiple-modulation-multiple-echo (MMME) sequence. Improved enhancement in developed sequence was observed, compared to conventional FLASH sequence with 3D coverage. In the second section, alternative methods to improve accuracy in detecting vessel orientation, blood volume, and vessel size index were developed. Firstly, alternative way to measure blood volume, and vessel size index was suggested and demonstrated by using ultra-short echo time (UTE) sequence. UTE sequence realized the measurement of blood volume with the change of longitudinal relaxation before and after administration of contrast agent, not from that of transverse relaxation. Consequently, accurate blood volume measurement was achieved by longitudinal relaxation which is not sensitive to environmental conditions such as air-tissue interface and vessel orientation. Moreover, alternative vessel size index including longitudinal relaxation showed the potential to reduce the error from environmental conditions. Finally, the new concept of obtaining MR tractography with magnetic field anisotropy was introduced. Compared to the conventional way using susceptibility-induced anisotropic magnetic field inhomogeneity studies, this method doesn???t need re-orientation of the subject utilizing the interference pattern between internal and external field gradients. Developed several methodologies in this thesis for the fast and robust in vivo quantification of microvasculature such as vessel permeability, orientation, blood volume, and vessel size index demonstrated the potentials to improve not only the speed of acquisition but also the accuracy of the in vivo microvascular measurements via efficient sensing and reconstruction MR techniques.ope

Community of Learners: Ontological and non-ontological projects

Our analysis reveals two major types of "Community of Learners" (COL) projects: instrumental and ontological. In instrumental COL, the notion of community is separated from instruction in order to reach some preset endpoints: curricular or otherwise. We notice three main instrumental COL models: relational, instructional, and engagement. Ontological COL redefines learning as an ill-defined, distributed, social, multi-faceted, poly-goal, agency-based, and situated process that integrates all educational aspects. We will consider two ontological COL projects into: narrowly dialogic and polyphonic

Exploring the Relationship Between Online Comments Usage and Civic Engagement in South Korea

Using logistic and hierarchical regression analysis (N=798), the present study explored the relationship between civic participation and daet-geul, the online comments posted under online news articles as well as comments underneath other comments. Our analyses showed that individuals’ online media use (two types of online news use and online community use) principally predicted two types of daet-geul behavior. Also, writing daet-geul behavior increased the level of individuals’ community participation, whereas reading daet-geul has no effect on either political participation or community participation. The results also indicated that civic attitudes (trust in community, trust in individuals, and tolerance) significantly enhanced civic and political participation, but those effects on each kind of participation were varied. The implications and limitations of the study were also discussed

Community of Learners: Ontological and non-ontological projects

Our analysis reveals two major types of "Community of Learners" (COL) projects: instrumental and ontological. In instrumental COL, the notion of community is separated from instruction in order to reach some preset endpoints: curricular or otherwise. We notice three main instrumental COL models: relational, instructional, and engagement. Ontological COL redefines learning as an ill-defined, distributed, social, multi-faceted, poly-goal, agency-based, and situated process that integrates all educational aspects. We will consider two ontological COL projects into: narrowly dialogic and polyphonic

Label-aware Hard Negative Sampling Strategies with Momentum Contrastive Learning for Implicit Hate Speech Detection

Detecting implicit hate speech that is not directly hateful remains a challenge. Recent research has attempted to detect implicit hate speech by applying contrastive learning to pre-trained language models such as BERT and RoBERTa, but the proposed models still do not have a significant advantage over cross-entropy loss-based learning. We found that contrastive learning based on randomly sampled batch data does not encourage the model to learn hard negative samples. In this work, we propose Label-aware Hard Negative sampling strategies (LAHN) that encourage the model to learn detailed features from hard negative samples, instead of naive negative samples in random batch, using momentum-integrated contrastive learning. LAHN outperforms the existing models for implicit hate speech detection both in- and cross-datasets. The code is available at https://github.com/Hanyang-HCC-Lab/LAHNComment: Accepted to ACL 2024 Finding

KHAN: Knowledge-Aware Hierarchical Attention Networks for Accurate Political Stance Prediction

The political stance prediction for news articles has been widely studied to mitigate the echo chamber effect -- people fall into their thoughts and reinforce their pre-existing beliefs. The previous works for the political stance problem focus on (1) identifying political factors that could reflect the political stance of a news article and (2) capturing those factors effectively. Despite their empirical successes, they are not sufficiently justified in terms of how effective their identified factors are in the political stance prediction. Motivated by this, in this work, we conduct a user study to investigate important factors in political stance prediction, and observe that the context and tone of a news article (implicit) and external knowledge for real-world entities appearing in the article (explicit) are important in determining its political stance. Based on this observation, we propose a novel knowledge-aware approach to political stance prediction (KHAN), employing (1) hierarchical attention networks (HAN) to learn the relationships among words and sentences in three different levels and (2) knowledge encoding (KE) to incorporate external knowledge for real-world entities into the process of political stance prediction. Also, to take into account the subtle and important difference between opposite political stances, we build two independent political knowledge graphs (KG) (i.e., KG-lib and KG-con) by ourselves and learn to fuse the different political knowledge. Through extensive evaluations on three real-world datasets, we demonstrate the superiority of DASH in terms of (1) accuracy, (2) efficiency, and (3) effectiveness.Comment: 12 pages, 5 figures, 10 tables, the Web Conference 2023 (WWW

Electrochemical detection of mismatched DNA using a MutS probe

A direct and label-free electrochemical biosensor for the detection of the protein–mismatched DNA interaction was designed using immobilized N-terminal histidine tagged Escherichia coli. MutS on a Ni-NTA coated Au electrode. General electrochemical methods, cyclic voltammetry (CV), electrochemical quartz crystal microbalance (EQCM) and impedance spectroscopy, were used to ascertain the binding affinity of mismatched DNAs to the MutS probe. The direct results of CV and impedance clearly reveal that the interaction of MutS with the CC heteroduplex was much stronger than that with AT homoduplex, which was not differentiated in previous results (GT > CT > CC ≈ AT) of a gel mobility shift assay. The EQCM technique was also able to quantitatively analyze MutS affinity to heteroduplexes

Needle angle dynamics as a rapid indicator of drought stress in Larix kaempferi (Lamb.) Carrière: advancing non-destructive imaging techniques for resilient seedling production

Larix kaempferi (Lamb.) Carrière, a valuable species for timber production and reforestation, faces challenges in large-scale seedling propagation due to its slow growth cycle and high susceptibility to environmental stressors. Early detection of drought stress is critical for preparing seedlings for harsh field conditions and for optimizing irrigation strategies. This study aimed to detect drought stress at an early stage in L. kaempferi seedlings by integrating physiological traits with image-based phenotypic measurements, with a focus on needle angle dynamics under controlled drought and irrigation conditions. The apical needle angle of one-year-old seedlings was measured using ImageJ, while seedling-level analysis was conducted using PlantCV to collect data and extract relevant parameters. Statistical analyses were performed to evaluate temporal trends and to identify growth environment and physiological traits significantly influenced by drought stress. As a result, apical needle wilting and recovery, along with seedling-level image analysis (parameter: Center of Mass(y)), exhibited significant responses to drought stress as early as Day 2. This provides a non-destructive method for early detection, preceding observable changes in physiological traits such as chlorophyll fluorescence and needle temperature that responded to drought stress by Day 6, as well as before seedling mortality occurred. Multiple regression analysis indicated that, as drought stress progressed, solar radiation and thermal-related parameters (ФNPQ and needle temperature) emerged as key predictors of needle angle variation. Image-based approaches, including RGB and thermal imaging, proved effective for real-time stress monitoring, demonstrating their practical potential for nursery applications. In summary, this study lays the groundwork for needle-based phenomic approaches using imaging techniques in nursery systems and highlights the need for further research to optimize these methods for the large-scale, cost-effective production of high-quality, drought-resilient L. kaempferi seedlings