Rhus verniciflua Stokes against Advanced Cancer: A Perspective from the Korean Integrative Cancer Center

Active anticancer molecules have been searched from natural products; many drugs were developed from either natural products or their derivatives following the conventional pharmaceutical paradigm of drug discovery. However, the advances in the knowledge of cancer biology have led to personalized medicine using molecular-targeted agents which create new paradigm. Clinical benefit is dependent on individual biomarker and overall survival is prolonged through cytostatic rather than cytotoxic effects to cancer cell. Therefore, a different approach is needed from the single lead compound screening model based on cytotoxicity. In our experience, the Rhus verniciflua stoke (RVS) extract traditionally used for cancer treatment is beneficial to some advanced cancer patients though it is herbal extract not single compound, and low cytotoxic in vitro. The standardized RVS extract's action mechanisms as well as clinical outcomes are reviewed here. We hope that these preliminary results would stimulate different investigation in natural products from conventional chemicals

Familial Correlation and Heritability of Hand Grip Strength in Korean Adults (Korea National Health and Nutrition Examination Survey 2014 to 2019)

Background The onset and progression of sarcopenia are highly variable among individuals owing to genetic and environmental factors. However, there are a limited number of studies measuring the heritability of muscle strength in large numbers of parent-adult offspring pairs. We aimed to investigate the familial correlation and heritability of hand grip strength (HGS) among Korean adults. Methods This family-based cohort study on data from the Korea National Health and Nutrition Examination Survey (2014 to 2019) included 5,004 Koreans aged ≥19 years from 1,527 families. HGS was measured using a digital grip strength dynamometer. Familial correlations of HGS were calculated in different pairs of relatives. Variance component methods were used to estimate heritability. Results The heritability estimate of HGS among Korean adults was 0.154 (standard error, 0.066). Correlation coefficient estimates for HGS between parent-offspring, sibling, and spouse pairs were significant at 0.07, 0.10, and 0.23 (p<0.001, p=0.041, and p<0.001, respectively). The total variance in the HGS phenotype was explained by additive genetic (15.4%), shared environmental (11.0%), and unique environmental (73.6%) influences. The odds of weak HGS significantly increased in the offspring of parents with weak HGS (odds ratio [OR], 1.69–3.10; p=0.027–0.038), especially in daughters (OR, 2.04–4.64; p=0.029–0.034). Conclusion HGS exhibits a familial correlation and significant heritable tendency in Korean adults. Therefore, Asian adults, especially women, who have parents with weak HGS, need to pay special attention to their muscle health with the help of healthy environmental stimuli

Compact and Cost-Effective Mobile 2.4 GHZ Radar System for Object Detection and Tracking

Various types of small mobile objects such as recreational unmanned vehicles have become easily approachable devices to the public because of technology advancements. The technology advancements make it possible to manufacture small, light, and easy to control unmanned vehicles, therefore the public are able to handily access those unmanned vehicles. As the accessibility to unmanned vehicles for recreational purposes, accidents or attacks to threat a person using those the unmanned vehicles have been arising and growing rapidly. A specific person could be a target of a threat using an unmanned vehicle in open public places due to its small volume and mobility. Even though an unmanned vehicle approaches to a person, it could be difficult to detect the unmanned vehicle before the person encounters because of the compact size and maneuverability. This research is to develop a radar system that is able to operate in open public areas to detect and track unmanned vehicles. It is not capable using existing radar systems such as for navigation, aviation, national defense, air traffic control, or weather forecasting to monitor and scan public places because of large volume, high operation cost, and danger to human health of the radar systems. For example, if electromagnetic fields emitted from high-power radar penetrate exposed skin surface or eyes, the energy from the electromagnetic fields can cause skin burns, eye cataracts, or more (Zamanian & Hardiman, 2005). Therefore, a radar system that can perform at the public place is necessary for monitoring and surveillance the area. The hardware of this proposed radar system is composed of three parts: 1) radio frequency transmission and receiver part which we will call RF part; 2) transmitting radio frequency control and amplifying reflected signal part which we will call electric part; and 3) data collection, data processing, and visualization part which we will call post-processing part. A transmitting radio frequency control and an amplifying reflected signal part are based on a research performed at a lecture and labs designed by researchers at Massachusetts Institute of Technology (MIT) Lincoln Lab, Charvat et al. (2012) and another lecture and labs designed by a professor at University of California at Davis, Liu (2013). The radar system designed at University of California at Davis is based on the system designed at MIT Lincoln Lab that proposed a design of a small, low cost, and low power consuming radar. The low power radar proposed by MIT Lincoln Lab is suitable to operate in any public places without any restrictions for human health because of it low power transmission, however surveillance area is relatively short and limited. To expand monitoring area with this proposed low power radar system, the transmit power of the radar system proposed in this study is enhanced comparing to the radar proposed by MIT Lincoln Lab. Additionally, the radar system is designed and fabricated on printed circuit boards (PCBs) to make the system compact and easy to access for use of various purposed of research fields. For instance, the radar system can be utilized for mapping, localization, or imaging

Design and Analysis of Cost-Efficient Sensor Deployment for Tracking Small UAS with Agent-Based Modeling

Recently, commercial unmanned aerial systems (UAS) have gained popularity. However, these UAS are potential threats to people in terms of safety in public places, such as public parks or stadiums. To reduce such threats, we consider a design, modeling, and evaluation of a cost-efficient sensor system that detects and tracks small UAS. In this research, we focus on discovering the best sensor deployments by simulating different types and numbers of sensors in a designated area, which provide reasonable detection rates at low costs. Also, the system should cover the crowded areas more thoroughly than vacant areas to reduce direct threats to people underneath. This research study utilized the Agent-Based Modeling (ABM) technique to model a system consisting of independent and heterogeneous agents that interact with each other. Our previous work presented the ability to apply ABM to analyze the sensor configurations with two types of radars in terms of cost-efficiency. The results from the ABM simulation provide a list of candidate configurations and deployments that can be referred to for applications in the real world environment

Prediction of Solar Irradiance and Photovoltaic Solar Energy Product Based on Cloud Coverage Estimation Using Machine Learning Methods

Cloud cover estimation from images taken by sky-facing cameras can be an important input for analyzing current weather conditions and estimating photovoltaic power generation. The constant change in position, shape, and density of clouds, however, makes the development of a robust computational method for cloud cover estimation challenging. Accurately determining the edge of clouds and hence the separation between clouds and clear sky is difficult and often impossible. Toward determining cloud cover for estimating photovoltaic output, we propose using machine learning methods for cloud segmentation. We compare several methods including a classical regression model, deep learning methods, and boosting methods that combine results from the other machine learning models. To train each of the machine learning models with various sky conditions, we supplemented the existing Singapore whole sky imaging segmentation database with hazy and overcast images collected by a camera-equipped Waggle sensor node. We found that the U-Net architecture, one of the deep neural networks we utilized, segmented cloud pixels most accurately. However, the accuracy of segmenting cloud pixels did not guarantee high accuracy of estimating solar irradiance. We confirmed that the cloud cover ratio is directly related to solar irradiance. Additionally, we confirmed that solar irradiance and solar power output are closely related; hence, by predicting solar irradiance, we can estimate solar power output. This study demonstrates that sky-facing cameras with machine learning methods can be used to estimate solar power output. This ground-based approach provides an inexpensive way to understand solar irradiance and estimate production from photovoltaic solar facilities

Accessible Real-Time Surveillance Radar System for Object Detection

As unmanned ground and aerial vehicles become more accessible and their usage covers a wider area of application, including for threatening purposes which can cause connected catastrophe, a surveillance system for the public places is being considered more essential to respond to those possible threats. We propose an inexpensive, lighter, safer, and smaller radar system than military-grade radar systems while keeping reasonable capability for use in monitoring public places. The paper details the iterative process on the system design and improvements with experiments to realize the system used for surveillance. The experiments show the practical use of the system and configuration for a better understanding of using the system. Cyber-physical systems for outdoor environments can benefit from the system as a sensor for sensing objects as well as monitoring

\u3csup\u3e13\u3c/sup\u3eC-sialic acid labeling of glycans on glycoproteins using ST6Gal-I

Glycans that are either N-linked to asparagine or O-linked to serine or threonine are the hallmark of glycoproteins, a class of protein that dominates the mammalian proteome. These glycans perform important functions in cells and in some cases are required for protein activity. Nuclear magnetic resonance (NMR) spectroscopy is a powerful tool for studying glycan structure and interactions, particularly in a form that exploits heteronuclei such as 13C. Here an approach is presented that that uses α-2,6-sialyltransferase (ST6Gal-I) to enzymatically add 13C-N-acetylneuraminic acid (NeuAc or sialic acid) to glycoproteins after their preparation using nonbacterial hosts. ST6Gal-I is itself a glycoprotein, and in this initial application, labeling of its own glycans and observation of these glycans by NMR are illustrated. The catalytic domain from rat ST6Gal-I was expressed in mammalian HEK293 cells. The glycans from the two glycosylation sites were analyzed with mass spectrometry and found to contain sialylated biantennary structures. The isotopic labeling approach involved removal of the native NeuAc residues from ST6Gal-I with neuraminidase, separation of the neuramindase with a lectin affinity column, and addition of synthesized 13C-CMP-NeuAc to the desialylated ST6Gal-I. Chemical shift dispersion due to the various 13C-NeuAc adducts on ST6Gal-I was observed in a 3D experiment correlating 1H-13C3-13C2 atoms of the sugar ring. Copyright © 2008 American Chemical Society