ESTIMATION AND CORRECTION OF THE UNCONTROLLED BEAM LOSS DUE TO THE ALIGNMENT ERROR IN THE LOW-ENERGY LINEAR ACCELERATOR OF RAON

Abstract RAON(Rare isotope Accelerator Of Newness) mainly consists of the front-end system, ISOL system, reaccelerator for ISOL system, charge stripper section and main linear accelerator(linac) for ECR ion sourc

Anti-Biofouling Features of Eco-Friendly Oleamide-PDMS Copolymers

The biofouling of marine organisms on a surface induces serious economic damage. One of the conventional anti-biofouling strategies is the use of toxic chemicals. In this study, a new eco-friendly oleamide-PDMS copolymer (OPC) is proposed for sustainable anti-biofouling and effective drag reduction. The anti-biofouling characteristics of the OPC are investigated using algal spores and mussels. The proposed OPC is found to inhibit the adhesion of algal spores and mussels. The slippery features of the fabricated OPC surfaces are examined by direct measurement of pressure drops in channel flows. The proposed OPC surface would be utilized in various industrial applications including marine vehicles and biomedical devices. © Copyright © 2020 American Chemical Society.1

RNAi Methodologies for the Functional Study of Signaling Molecules

RNA interference (RNAi) was investigated with the aim of achieving gene silencing with diverse RNAi platforms that include small interfering RNA (siRNA), short hairpin RNA (shRNA) and antisense oligonucleotides (ASO). Different versions of each system were used to silence the expression of specific subunits of the heterotrimeric signal transducing G-proteins, G alpha i2 and G beta 2, in the RAW 264.7 murine macrophage cell line. The specificity of the different RNA interference (RNAi) platforms was assessed by DNA microarray analysis. Reliable RNAi methodologies against the genes of interest were then developed and applied to functional studies of signaling networks. This study demonstrates a successful knockdown of target genes and shows the potential of RNAi for use in functional studies of signaling molecules

Scientific opportunies for bERLinPro 2020+, report with ideas and conclusions from bERLinProCamp 2019

The Energy Recovery Linac (ERL) paradigm offers the promise to generate intense electron beams of superior quality with extremely small six-dimensional phase space for many applications in the physical sciences, materials science, chemistry, health, information technology and security. Helmholtz-Zentrum Berlin started in 2010 an intensive R\&D programme to address the challenges related to the ERL as driver for future light sources by setting up the bERLinPro (Berlin ERL Project) ERL with 50 MeV beam energy and high average current. The project is close to reach its major milestone in 2020, acceleration and recovery of a high brightness electron beam. The goal of bERLinProCamp 2019 was to discuss scientific opportunities for bERLinPro 2020+. bERLinProCamp 2019 was held on Tue, 17.09.2019 at Helmholtz-Zentrum Berlin, Berlin, Germany. This paper summarizes the main themes and output of the workshop

Effects of mirror distortion by thermal deformation in an interferometry beam size monitor system at PLS-II

Extraction mirrors installed at the most upstream position of interferometry beam size monitor are frequently used for measuring the beam size in storage rings. These mirrors receive the high power synchrotron radiation and are distorted owing to the heat distribution that depends on the position on the mirror surface. The distortion of the mirror changes the effective separation of the slit in the interferometry beam size monitor. Estimation of the effects of the front-end mirror distortion is important for measuring the beam size accurately. In this paper, we present the result of the numerical simulation of the temperature distribution and thermal expansion of the front-end mirror using ANSYS code, the theoretical basis of the effects of mirror distortion and compare with experimental results from Pohang Light Source II (PLS-II) at the Pohang Accelerator Laboratory (PAL). The equipment in the beam diagnosis line in PLS-II and experimental set-up for measuring the distortion of the front-end mirror using a multi-hole square array Hartmann screen are described.11sciescopu

Development of high resolution linear-cut beam position monitor for heavy-ion synchrotron of KHIMA project

A beam position monitor with high precision and resolution is required to control the beam trajectory for matching to the injection orbit and acceleration in a heavy ion synchrotron. It will be also used for measuring the beta function, tune, and chromaticity. Since the bunch length at heavy ion synchrotron is relatively long, a few meters, a boxlike device with plates of typically 20 cm length is used to enhance the signal strength and to get a precise linear dependence with respect to the beam displacement. Especially, the linear cut beam position monitor is adopted to satisfy the position resolution of 100 amp; 956;m and accuracy of 200 amp; 956;m for a nominal beam intensity in the KHIMA synchrotron of amp; 8764;7 108 particles for the carbon beams and amp; 8764;2 1010 for the proton beams. In this paper, we show the electromagnetic design of the electrode and surroundings to satisfy the resolution of 100 amp; 956;m, the criteria for mechanical aspect to satisfy the position accuracy of 200 amp; 956;m, the measurement results by using wire test bench, design and measurement of a high input impedance pre amplifier, and the beam test results with long amp; 8764;1.6 amp; 956;s electron beam in Pohang accelerator laboratory PA

Cerebrospinal Fluid Metabolome in Parkinson’s Disease and Multiple System Atrophy

Parkinson’s disease (PD) and multiple system atrophy (MSA) belong to the neurodegenerative group of synucleinopathies; differential diagnosis between PD and MSA is difficult, especially at early stages, owing to their clinical and biological similarities. Thus, there is a pressing need to identify metabolic biomarkers for these diseases. The metabolic profile of the cerebrospinal fluid (CSF) is reported to be altered in PD and MSA; however, the altered metabolites remain unclear. We created a single network with altered metabolites in PD and MSA based on the literature and assessed biological functions, including metabolic disorders of the nervous system, inflammation, concentration of ATP, and neurological disorder, through bioinformatics methods. Our in-silico prediction-based metabolic networks are consistent with Parkinsonism events. Although metabolomics approaches provide a more quantitative understanding of biochemical events underlying the symptoms of PD and MSA, limitations persist in covering molecules related to neurodegenerative disease pathways. Thus, omics data, such as proteomics and microRNA, help understand the altered metabolomes mechanism. In particular, integrated omics and machine learning approaches will be helpful to elucidate the pathological mechanisms of PD and MSA. This review discusses the altered metabolites between PD and MSA in the CSF and omics approaches to discover diagnostic biomarkers

Development of Anticancer Peptides Using Artificial Intelligence and Combinational Therapy for Cancer Therapeutics

Cancer is a group of diseases causing abnormal cell growth, altering the genome, and invading or spreading to other parts of the body. Among therapeutic peptide drugs, anticancer peptides (ACPs) have been considered to target and kill cancer cells because cancer cells have unique characteristics such as a high negative charge and abundance of microvilli in the cell membrane when compared to a normal cell. ACPs have several advantages, such as high specificity, cost-effectiveness, low immunogenicity, minimal toxicity, and high tolerance under normal physiological conditions. However, the development and identification of ACPs are time-consuming and expensive in traditional wet-lab-based approaches. Thus, the application of artificial intelligence on the approaches can save time and reduce the cost to identify candidate ACPs. Recently, machine learning (ML), deep learning (DL), and hybrid learning (ML combined DL) have emerged into the development of ACPs without experimental analysis, owing to advances in computer power and big data from the power system. Additionally, we suggest that combination therapy with classical approaches and ACPs might be one of the impactful approaches to increase the efficiency of cancer therapy