• International Journal of Industrial Entomology and Biomaterials
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• v.20 no.2
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• pp.37-43
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• 2010

The wild silkworm, Antheraea mylitta Drury (Lepidoptera: Saturniidae) is a polyphagous silk producing insect that feeds on Terminalia arjuna, T. tomentosa and Shorea robusta and is distributed in the forest belts in different states of India. Phenotypically distinct populations of the A. mylitta are called "eco-race" or "ecotypes". Genetic improvement of this wild silkworm has not progressed much due to lack of adequate information on the factors that control the expression of most of the economically important traits. Considering the amazing technological advances taking place in molecular biology, it is envisaged that it is now possible to take greater control on these intractable traits if a combination of genetic, molecular and bioinformatics tools are used. Linkage disequilibrium (LD) mapping is one such approach that has extensively been used in both animal and plant system to identify quantitative trait loci (QTLs) for a number of economically important traits. LD mapping has a number of advantages over conventional biparental linkage mapping. Therefore, LD mapping is considered more efficient for gene discovery to meet the challenge of connecting sequence diversity with heritable phenotypic differences. However, care must be taken to avoid detection of spurious associations which may occur due to population structure and variety interrelationships. In this review, we discuss how LD mapping is suitable for the dissection of complex traits in wild silkworms (Antheraea mylitta).

• Journal of Plant Biotechnology
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• v.45 no.4
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• pp.315-321
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• 2018

Molecular and functional characterization of proteins and their levels is of great interest in understanding the mechanism of diverse cellular processes. In this study, we report on the convenient Escherichia coli-based protein expression system that allows recombinant of soluble proteins expression and cytosolic domain of membrane-localised kinases, followed by the detection of autophosphorylation activity in protein kinases. This approach is applied to regulatory proteins of Arabidopsis thaliana, including 14-3-3, calmodulin, calcium-dependent protein kinase, TERMINAL FLOWER 1(TFL1), FLOWERING LOCUS T (FT), receptor-like cytoplasmic kinase and cytoplasmic domain of leucine-rich repeat-receptor like kinase proteins. Our Western blot analysis which uses phospho-specific antibodies showed that five putative LRR-RLKs and two putative RLCKs have autophosphorylation activity in vitro on threonine and/or tyrosine residue(s), suggesting their potential role in signal transduction pathways. Our findings were also discussed in the broader context of recombinant expression and biochemical analysis of soluble and membrane-localised receptor kinases in microbial systems.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.6
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• pp.3258-3279
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• 2019

Recently, ransomware has earned itself an infamous reputation as a force to reckon with in the cybercrime landscape. However, cybercriminals are adopting other unconventional means to seamlessly attain proceeds of cybercrime with little effort. Cybercriminals are now acquiring cryptocurrencies directly from benign Internet users without the need to extort a ransom from them, as is the case with ransomware. This paper investigates advances in the cryptovirology landscape by examining the state-of-the-art cryptoviral attacks. In our approach, we perform digital autopsy on the malware's source code and execute the different malware variants in a contained sandbox to deduce static and dynamic properties respectively. We examine three cryptoviral attack structures: browser-based crypto mining, memory resident crypto mining and cryptoviral extortion. These attack structures leave a trail of digital forensics evidence when the malware interacts with the file system and generates noise in form of network traffic when communicating with the C2 servers and crypto mining pools. The digital forensics evidence, which essentially are IOCs include network artifacts such as C2 server domains, IPs and cryptographic hash values of the downloaded files apart from the malware hash values. Such evidence can be used as seed into intrusion detection systems for mitigation purposes.

• Korean Journal of Remote Sensing
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• v.35 no.1
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• pp.137-149
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• 2019

Traffic emissions are the main cause of environmental pollution in cities and respiratory problems amongst people. This study developed a model based on an integration of support vector regression (SVR) algorithm and geographic information system (GIS) to map traffic carbon monoxide (CO) concentrations and produce prediction maps from micro level to macro level at a particular time gap in a day in a very densely populated area (Utara-Selatan Expressway-NKVE, Kuala Lumpur, Malaysia). The proposed model comprised two models: the first model was implemented to estimate traffic CO concentrations using the SVR model, and the second model was applied to create prediction maps at different times a day using the GIS approach. The parameters for analysis were collected from field survey and remote sensing data sources such as very-high-resolution aerial photos and light detection and ranging point clouds. The correlation coefficient was 0.97, the mean absolute error was 1.401 ppm and the root mean square error was 2.45 ppm. The proposed models can be effectively implemented as decision-making tools to find a suitable solution for mitigating traffic jams near tollgates, highways and road networks.

• Nuclear Engineering and Technology
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• v.53 no.4
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• pp.1199-1209
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• 2021

The detection of unexpected loose parts in the primary coolant system in a nuclear power plant remains an extremely important issue. It is essential to develop a methodology for the localization and mass estimation of loose parts owing to the high prediction error of conventional methods. An effective approach is presented for the localization and mass estimation of a loose part using machine-learning and deep-learning algorithms. First, a methodology was developed to estimate both the impact location and the mass of a loose part at the same times in a real structure in which geometric changes exist. Second, an impact database was constructed through a series of impact finite-element analyses (FEAs). Then, impact parameter prediction modes were generated for localization and mass estimation of a simulated metallic loose part using machine-learning algorithms (artificial neural network, Gaussian process, and support vector machine) and a deep-learning algorithm (convolutional neural network). The usefulness of the methodology was validated through blind tests, and the noise effect of the training data was also investigated. The high performance obtained in this study shows that the proposed methodology using an FEA-based database and deep learning is useful for localization and mass estimation of loose parts on site.

• Nuclear Engineering and Technology
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• v.53 no.7
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• pp.2277-2288
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• 2021

To regulate the safety protocols in nuclear facilities, radioactive aerosols have been extensively researched to understand their health impacts. However, most measured particle-size distributions remain at low resolutions, with the particle sizes ranging from nanometer to micrometer. This study combines the high-resolution detection of 500 size classes, ranging from 6 nm to 10 ㎛, for aerodynamic diameter distributions, with a regional lung deposition calculation. We applied the new approach to characterize particle-size distributions of aerosols generated during the plasma arc cutting of simulated non-radioactive steel alloy wastes. The high-resolution measured data were used to calculate the deposition ratios of the aerosols in different lung regions. The deposition ratios in the alveolar sacs contained the dominant particle sizes ranging from 0.01 to 0.1 ㎛. We determined the distribution of various metals using different vapor pressures of the alloying components and analyzed the uncertainties of lung deposition calculations using the low-resolution aerodynamic diameter data simultaneously. In high-resolution data, the changes in aerosols that can penetrate the blood system were better captured, correcting their potential risks by a maximum of 42%. The combined calculations can aid the enhancement of high-resolution measuring equipment to effectively manage radiation safety in nuclear facilities.

• The Journal of Information Systems
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• v.30 no.1
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• pp.21-44
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• 2021

Purpose The opportunities and approaches to big data have grown in various ways in the digital era. Business analytics is nowadays an inevitable strategy for organizations to earn a competitive advantage in order to survive in the challenged environments. The purpose of this study is to analyze the intellectual structure of business analytics literature to have a better insight for the organizations to the field. Design/methodology/approach This research analyzed with the data extracted from the database Web of Science. Total of 427 documents and 23,760 references are inserted into the analysis program CiteSpace. Author co-citation analysis is used to analyze the intellectual structure of the business analytics. We performed clustering analysis, burst detection and timeline analysis with the data. Findings We identified seven sub- areas of business analytics field. The top four sub-areas are "Big Data Analytics Infrastructure", "Performance Management System", "Interactive Exploration", and "Supply Chain Management". We also identified the top 5 references with the strongest citation bursts including Trkman et al.(2010) and Davenport(2006). Through timeline analysis we interpret the clusters that are expected to be the trend subjects in the future. Lastly, limitation and further research suggestion are discussed as concluding remarks.

• Natural Product Sciences
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• v.27 no.4
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• pp.264-273
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• 2021

Simultaneous quantification of multiple marker compounds in herbal medicine by high performance liquid chromatography (HPLC) analysis is still a challenge due to the complexity in various parameters to be considered and co-existing multi-components. As a case study, a reliable HPLC method for simultaneous quantification of paeoniflorin from Paeoniae Radix and decursin from Angelicae Gigantis Radix in various commercial herbal medicine was developed based on analytical quality by design (AQbD) strategy. As a first step, risk assessment was performed to select the critical method parameters (CMPs) which were decided as organic mobile phase ratio and column oven temperature. In order to evaluate the effect of the CMPs on critical method attributes (CMAs) of peak resolution and tailing, central composite design (CCD) was employed. The final chromatographic conditions were optimized as follows: column- C₁₈, 4.6 × 250 mm, 5 ㎛ particle size; mobile phase- A: acetonitrile, B: 0.1% acetic acid water; detection wavelength- 235 nm for paeoniflorin, 325 nm for decursin; column oven temperature- 25℃; flow rate- 1.0 mL/min; gradient mobile phase system as Time (min) : % A, 0:14, 25:14, 30:50, 60:50, 61:100, 65:100, 66:14, 75:14. The method was successfully validated according to the International Conference on Harmonization (ICH) guidelines and piloted for ten commercial herbal medicines.

• International journal of advanced smart convergence
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• v.13 no.2
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• pp.61-68
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• 2024

In the quest for advancing diabetes diagnosis, this study introduces a novel two-step machine learning approach that synergizes the probabilistic predictions of Logistic Regression with the classification prowess of Random Forest. Diabetes, a pervasive chronic disease impacting millions globally, necessitates precise and early detection to mitigate long-term complications. Traditional diagnostic methods, while effective, often entail invasive testing and may not fully leverage the patterns hidden in patient data. Addressing this gap, our research harnesses the predictive capability of Logistic Regression to estimate the likelihood of diabetes presence, followed by employing Random Forest to classify individuals into diabetic, pre-diabetic or nondiabetic categories based on the computed probabilities. This methodology not only capitalizes on the strengths of both algorithms-Logistic Regression's proficiency in estimating nuanced probabilities and Random Forest's robustness in classification-but also introduces a refined mechanism to enhance diagnostic accuracy. Through the application of this model to a comprehensive diabetes dataset, we demonstrate a marked improvement in diagnostic precision, as evidenced by superior performance metrics when compared to other machine learning approaches. Our findings underscore the potential of integrating diverse machine learning models to improve clinical decision-making processes, offering a promising avenue for the early and accurate diagnosis of diabetes and potentially other complex diseases.

• Journal of Applied Biological Chemistry
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• v.66
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• pp.67-72
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• 2023

A simple and accurate method was developed for the quantitative analysis of oleanonic acid (OA) from mastic gum. The analysis was carried out using reverse-phase high-performance liquid chromatography combined with a photodiode array detector (HPLC/PDA). Our optimized method was validated by measuring various parameters, using an INNO C18 column fitted with a gradient elution system. The results revealed limits of detection and quantification of 0.34 and 1.042 ㎍/mL, respectively. The OA calibration curve exhibited excellent linearity over the concentration range of 0.0625 to 2.0 mg/mL, with r² =0.9996. Accuracy tests revealed a high recovery rate of 99.44-103.66%, with precision values below 0.15%. These results suggest that the present analytical method can identify and quantify OA in mastic gum with high precision. The HPLC approach developed in this study might be applied to routine analyses and large-scale extraction procedures for OA content quantification.