SangHyun Lee

Untangling amyloid-β, tau, and metals in Alzheimer's disease.

Protein misfolding and metal ion dyshomeostasis are believed to underlie numerous neurodegenerative diseases, including Alzheimers disease (AD). The pathological hallmark of AD is accumulation of misfolded amyloid-β (Aβ) peptides and hyperphosphorylated tau (ptau) proteins in the brain. Since AD etiology remains unclear, several hypotheses have emerged to elucidate its pathological pathways. The amyloid cascade hypothesis, a leading hypothesis for AD development, advocates Aβ as the principal culprit. Additionally, evidence suggests that tau may contribute to AD pathology. Aβ and tau have also been shown to impact each others pathology either directly or indirectly. Furthermore, metal ion dyshomeostasis is associated with these misfolded proteins. Metal interactions with Aβ and tau/ptau also influence their aggregation properties and neurotoxicity. Herein, we present current understanding on the roles of Aβ, tau, and metal ions, placing equal emphasis on each of these proposed features, as well as their inter-relationships in AD pathogenesis.

Insights into antiamyloidogenic properties of the green tea extract (−)-epigallocatechin-3-gallate toward metal-associated amyloid-β species

Despite the significance of Alzheimer’s disease, the link between metal-associated amyloid-β (metal–Aβ) and disease etiology remains unclear. To elucidate this relationship, chemical tools capable of specifically targeting and modulating metal–Aβ species are necessary, along with a fundamental understanding of their mechanism at the molecular level. Herein, we investigated and compared the interactions and reactivities of the green tea extract, (−)-epigallocatechin-3-gallate [(2R,3R)-5,7-dihydroxy-2-(3,4,5-trihydroxyphenyl)-3,4-dihydro-2H-1-benzopyran-3-yl 3,4,5-trihydroxybenzoate; EGCG], with metal [Cu(II) and Zn(II)]–Aβ and metal-free Aβ species. We found that EGCG interacted with metal–Aβ species and formed small, unstructured Aβ aggregates more noticeably than in metal-free conditions in vitro. In addition, upon incubation with EGCG, the toxicity presented by metal-free Aβ and metal–Aβ was mitigated in living cells. To understand this reactivity at the molecular level, structural insights were obtained by ion mobility-mass spectrometry (IM-MS), 2D NMR spectroscopy, and computational methods. These studies indicated that (i) EGCG was bound to Aβ monomers and dimers, generating more compact peptide conformations than those from EGCG-untreated Aβ species; and (ii) ternary EGCG–metal–Aβ complexes were produced. Thus, we demonstrate the distinct antiamyloidogenic reactivity of EGCG toward metal–Aβ species with a structure-based mechanism.

Rational Design of a Structural Framework with Potential Use to Develop Chemical Reagents That Target and Modulate Multiple Facets of Alzheimer's Disease

Alzheimers disease (AD) is characterized by multiple, intertwined pathological features, including amyloid-β (Aβ) aggregation, metal ion dyshomeostasis, and oxidative stress. We report a novel compound (ML) prototype of a rationally designed molecule obtained by integrating structural elements for Aβ aggregation control, metal chelation, reactive oxygen species (ROS) regulation, and antioxidant activity within a single molecule. Chemical, biochemical, ion mobility mass spectrometric, and NMR studies indicate that the compound ML targets metal-free and metal-bound Aβ (metal-Aβ) species, suppresses Aβ aggregation in vitro, and diminishes toxicity induced by Aβ and metal-treated Aβ in living cells. Comparison of ML to its structural moieties (i.e., 4-(dimethylamino)phenol (DAP) and (8-aminoquinolin-2-yl)methanol (1)) for reactivity with Aβ and metal-Aβ suggests the synergy of incorporating structural components for both metal chelation and Aβ interaction. Moreover, ML is water-soluble and potentially brain permeable, as well as regulates the formation and presence of free radicals. Overall, we demonstrate that a rational structure-based design strategy can generate a small molecule that can target and modulate multiple factors, providing a new tool to uncover and address AD complexity.

Computer vision techniques for construction safety and health monitoring

For construction safety and health, continuous monitoring of unsafe conditions and action is essential in order to eliminate potential hazards in a timely manner. As a robust and automated means of field observation, computer vision techniques have been applied for the extraction of safety related information from site images and videos, and regarded as effective solutions complementary to current time-consuming and unreliable manual observational practices. Although some research efforts have been directed toward computer vision-based safety and health monitoring, its application in real practice remains premature due to a number of technical issues and research challenges in terms of reliability, accuracy, and applicability. This paper thus reviews previous attempts in construction applications from both technical and practical perspectives in order to understand the current status of computer vision techniques, which in turn suggests the direction of future research in the field of computer vision-based safety and health monitoring. Specifically, this paper categorizes previous studies into three groups-object detection, object tracking, and action recognition-based on types of information required to evaluate unsafe conditions and acts. The results demonstrate that major research challenges include comprehensive scene understanding, varying tracking accuracy by camera position, and action recognition of multiple equipment and workers. In addition, we identified several practical issues including a lack of task-specific and quantifiable metrics to evaluate the extracted information in safety context, technical obstacles due to dynamic conditions at construction sites and privacy issues. These challenges indicate a need for further research in these areas. Accordingly, this paper provides researchers insights into advancing knowledge and techniques for computer vision-based safety and health monitoring, and offers fresh opportunities and considerations to practitioners in understanding and adopting the techniques.

Modeling Framework and Architecture of Hybrid System Dynamics and Discrete Event Simulation for Construction

: Construction system modeling aims to improve construction work performance by tracking the dynamic behaviors of construction systems. More accurate system modeling can be achieved by considering the mutual effects of construction operations on the context level of the system. Hybrid models of discrete event simulation (DES) and system dynamics aim to capture these mutual effects to provide model developers with more precise system analysis. Although system dynamics models are used to capture the behavior of the system at the context level, DES models are utilized to capture construction operations. The potential benefits of utilizing hybrid models for complex systems have been argued and established, but there are still limited studies that have utilized this modeling approach for real construction systems. In this research, we have attempted to identify the challenging issues that have caused this problem and to confront this problem by proposing a hybrid framework and architecture, which address these challenges. To verify the effectiveness of the new model, the performance of the proposed hybrid modeling framework and architecture has been tested by applying the proposed model in a real-scale construction-related system.

Analyzing Schedule Delay of Mega Project: Lessons Learned From Korea Train Express

In 2004, Korea became the fifth country in the world to own and operate a high-speed railway called Korea Train eXpress (KTX). Numerous uncertainties and challenges during planning and managing phase resulted in schedule delays and cost overruns. The delay causes of each activity along the 412 km Korea high-speed railway route were very difficult to identify because KTX project consisted of 11 141 different activities. This paper evaluates challenges, obstacles, and performances of KTX project. First, critical sections in the railway route that influenced significant delays to project completion were identified. Then, delay causes of these critical sections were investigated thoroughly. The analysis discovered five major delay causes for KTX project. They are lack of owners abilities and strategies to manage hi-tech oriented mega project; frequent changes of routes triggered by conflicts between public agencies and growing public resistance from environmental concerns; the inappropriate project delivery system; a lack of proper scheduling tool tailored for a linear mega project; and redesign and change orders of main structures and tunnels for high-speed railway, which is fundamentally different from the traditional railway construction. Based on the in-depth analysis of KTX project, through which a conceptual framework was established to identify the various facets of mega projects, this paper suggests lessons learned for engineers to better prepare and respond to potential causes of schedule delays for mega projects.

Empirical assessment of a RGB-D sensor on motion capture and action recognition for construction worker monitoring

BackgroundFor construction management, data collection is a critical process for gathering and measuring information for the evaluation and control of ongoing project performances. Taking into account that construction involves a significant amount of manual work, worker monitoring can play a key role in analyzing operations and improving productivity and safety. However, time-consuming tasks involved in field observation have brought up the issue of implementing worker observation in daily management practice.MethodsIn an effort to address the issue, this paper investigates the performances of a cost-effective and portable RGB-D sensor, based on recent research efforts extended from our previous study. The performance of an RGB-D sensor is evaluated in terms of (1) the 3D positions of the body parts tracked by the sensor, (2) the 3D rotation angles at joints, and (3) the impact of the RGB-D sensor’s accuracy on motion analysis. For the assessment, experimental studies were undertaken to collect motion capture datasets using an RGB-D sensor and a marker-based motion capture system, VICON, and to analyze errors as compared with the VICON used as the ground truth. As a test case, 25 trials of ascending and descending during ladder climbing were recorded simultaneously with both systems, and the resulting motion capture datasets (i.e., 3D skeleton models) were temporally and spatially synchronized for their comparison.ResultsThrough the comparative assessment, we found a discrepancy of 10.7 cm in the tracked locations of body parts, and a difference of 16.2 degrees in rotation angles. However, motion detection results show that the inaccuracy of an RGB-D sensor does not have a considerable effect on action recognition in the experiment.ConclusionsThis paper thus provides insight into the accuracy of an RGB-D sensor on motion capture in various measures and directions of further research for the improvement of accuracy.

Importance of Operational Efficiency to Achieve Energy Efficiency and Exhaust Emission Reduction of Construction Operations

Construction operations generate significant air pollutant emissions, including carbon emissions and diesel exhaust emissions. Controlling operational efficiency is the most important strategy for reducing air pollutants emitted from construction operations. However, current practices to assess air pollutant emissions from construction operations tend to ignore the variability of the operational efficiency that results from different resource allotting and scheduling. In this context, this paper presents a methodology for incorporating the analysis of operational efficiency into quantifying the amount of exhaust emission from construction operations. Case studies are presented to examine how and to what extent planning decisions affect the amount of air pollutants emitted from construction operations and to identify the impact that possible alternatives have on the schedule and cost of projects. The findings of case studies indicate that considering environmental aspects in the planning stage could contribute to a project’s increased integrated value, which includes schedule, cost, and environmental impact.

Droplet jumping by electrowetting and its application to the three-dimensional digital microfluidics

We introduce droplet jumping by electrowetting (DJE), which stretches droplets to store energy for jumping by electrowetting. The capillarity-driven droplet jumping is effective to overcome the energy barrier, where the threshold for jumping is less than 100 V. We studied the detailed jumping mechanisms with regard to the jumping height and the energy conversion and demonstrated the transport of sessile droplets to upper surfaces under diverse electrode configurations. While the droplet jumping on the superhydrophobic surface is the primary focus of our research, DJE is also found to be possible on conventional Teflon surfaces, envisioning the three-dimensional droplet-based digital microfluidics.

Motion Data-Driven Biomechanical Analysis during Construction Tasks on Sites

AbstractWork-related musculoskeletal disorders (WMSDs) are one of the major health issues that workers frequently experience due to awkward postures or forceful exertions during construction tasks. Among available job analysis methods, biomechanical models have been widely applied to assess musculoskeletal risks that may contribute to the development of WMSDs based on motion data during occupational tasks. Recently, with the advent of vision-based motion capture approaches, it has become possible to collect the motion data required for biomechanical analysis under real conditions. However, vision-based motion capture approaches have not been applied to biomechanical analysis because of compatibility issues in body models of the motion data and computerized biomechanical analysis tools. To address this issue, automated data processing is focused on to convert motion data into available data in existing biomechanical analysis tools, given the BVH motion data from vision-based approaches. To examine the feas...