• Food Science and Preservation
• /
• v.15 no.5
• /
• pp.774-781
• /
• 2008

was performed and Staphylococcus sciuri, Bacillus cereus, Escherichia coli, Proteus mirabilis, and Enterococcus faecalis were identified. No Salmonella strain, a typical contaminant of eggs, was found. The radiation sensitivities of isolated bacteria and Salmonella typhimurium, in an inoculated model system, were expressed in $D_{10}$ values. The ranges of $D_{10}$ values shown by S. typhimurium, S. sciuri, B. cereus, E. coli, P. mirabilis, and E. faecalis were 0.365-0.399 kGy, 0.418-0.471 kGy, 1.075-1.119 kGy, 0.280-0.304 kGy, 1.132-1.330 kGy, and 0.993-1.290 kGy, respectively. The growth of all six test bacteria in eggs (inoculated at $10^6-10^7\;CFU/g$) during 3 days of post-irradiation storage at ambient conditions ($25^{\circ}C$) was recorded. S. typhimurium was eliminated by irradiation at 3 kGy, and E. coli and S. sciuri were eliminated by irradiation at 5 kGy. The viable cell counts of B. cereus, P. mirabilis, and E. faecalis in eggs showed 4-6 log reductions after irradiation with 5 kGy.

• Parasites, Hosts and Diseases
• /
• v.57 no.4
• /
• pp.341-357
• /
• 2019

Acanthamoeba, one of free-living amoebae (FLA), remains a high risk of direct contact with this protozoan parasite which is ubiquitous in nature and man-made environment. This pathogenic FLA can cause sight-threatening amoebic keratitis (AK) and fatal granulomatous amoebic encephalitis (GAE) though these cases may not commonly be reported in our clinical settings. Acanthamoeba has been detected from different environmental sources namely; soil, water, hotspring, swimming pool, air-conditioner, or contact lens storage cases. The identification of Acanthamoeba is based on morphological appearance and molecular techniques using PCR and DNA sequencing for clinico-epidemiological purposes. Recent treatments have long been ineffective against Acanthamoeba cyst, novel anti-Acanthamoeba agents have therefore been extensively investigated. There are efforts to utilize synthetic chemicals, lead compounds from medicinal plant extracts, and animal products to combat Acanthamoeba infection. Applied nanotechnology, an advanced technology, has shown to enhance the anti-Acanthamoeba activity in the encapsulated nanoparticles leading to new therapeutic options. This review attempts to provide an overview of the available data and studies on the occurrence of pathogenic Acanthamoeba among the Association of Southeast Asian Nations (ASEAN) members with the aim of identifying some potential contributing factors such as distribution, demographic profile of the patients, possible source of the parasite, mode of transmission and treatment. Further, this review attempts to provide future direction for prevention and control of the Acanthamoeba infection.

• Journal of Life Science
• /
• v.24 no.6
• /
• pp.618-625
• /
• 2014

Rice flour is used in many food products. However, dough made from rice lacks extensibility and elasticity, making it less suitable than wheat for many food products such as bread and noodles. The high-molecular weight glutenin subunits (HMW-GS) of wheat play a crucial role in determining the processing properties of the wheat grain. This paper describes the development of marker-free transgenic rice plants expressing a wheat Glu-Dy10 gene encoding the HMG-GS from the Korean wheat cultivar 'Jokyeong' using Agrobacterium-mediated co-transformation. Two expression cassettes, consisting of separate DNA fragments containing Glu-1Dy10 and hygromycin phosphotransferase II (HPTII) resistance genes, were introduced separately into Agrobacterium tumefaciens EHA105 for co-infection. Each EHA105 strain harboring Glu-1Dy10 or HPTII was infected into rice calli at a 3: 1 ratio of Glu-1Bx7 and HPTII. Among 290 hygromycin-resistant $T_0$ plants, we obtained 29 transgenic lines with both the Glu-1Dy10 and HPTII genes inserted into the rice genome. We reconfirmed the integration of the Glu-1Dy10 gene into the rice genome by Southern blot analysis. Transcripts and proteins of the Glu-1Dy10 in transgenic rice seeds were examined by semi-quantitative RT-PCR and Western blot analysis. The marker-free plants containing only the Glu-1Dy10 gene were successfully screened in the $T_1$ generation.

• Korean Journal of Breeding Science
• /
• v.42 no.5
• /
• pp.547-554
• /
• 2010

Low-molecular-weight glutenin subunit (LMW-GS) in common wheat (Triticum aestivum L.) is important for quality processing of bread and noodles. The objectives of this study were to clarify the composition of LMW-GSs and to identify their corresponding proteins. Using LMW-GS specific primers we cloned and characterized 43 LMW-GS genes in the wheat cultivar 'Jokyoung'. Some of these genes contain polypeptides different in size due to the presence of various deletions or insertions within repetitive and glutamine-rich domains. The comparison of deduced amino acid sequence of the LMW-GS genes in Jokyoung with that of 12 groups LMW-GSs of wheat cultivar Norin 61 showed that the deduced amino acid sequences were nearly the same to LMW-GS groups of 1, 2, 3/4, 5, 7, 10 and 11. All LMW-GS genes contain eight cysteine residues, which are conserved among all of the typical LMW-GS sequences. The relative positions of cysteine residues are also conserved, except those of the first and seventh. Based on phylogenetic analysis, the 43 sequences with the same N-terminal and C-terminal amino acid sequences were clustered in the same group. To identify the proteins containing the corresponding amino acid sequences, we determined the N-terminal amino acid sequence of 7 spots of LMW-GSs of Jokyoung separated by two-dimensional gel electrophoresis (2DE). Of them, Glu-B3 (LMW-m and LMW-s) and Glu-D3 (LMW-m) were detected in two and three spots, respectively and the others were not clear. Collectively, we classified diverse LMW-GSs and identified their corresponding protein products. These results will be helpful in breeding programs for improvement of wheat flour quality.

• Journal of Internet Computing and Services
• /
• v.15 no.3
• /
• pp.101-107
• /
• 2014

With the development of online service, recent forms of databases have been changed from static database structures to dynamic stream database structures. Previous data mining techniques have been used as tools of decision making such as establishment of marketing strategies and DNA analyses. However, the capability to analyze real-time data more quickly is necessary in the recent interesting areas such as sensor network, robotics, and artificial intelligence. Landmark window-based frequent pattern mining, one of the stream mining approaches, performs mining operations with respect to parts of databases or each transaction of them, instead of all the data. In this paper, we analyze and evaluate the techniques of the well-known landmark window-based frequent pattern mining algorithms, called Lossy counting and hMiner. When Lossy counting mines frequent patterns from a set of new transactions, it performs union operations between the previous and current mining results. hMiner, which is a state-of-the-art algorithm based on the landmark window model, conducts mining operations whenever a new transaction occurs. Since hMiner extracts frequent patterns as soon as a new transaction is entered, we can obtain the latest mining results reflecting real-time information. For this reason, such algorithms are also called online mining approaches. We evaluate and compare the performance of the primitive algorithm, Lossy counting and the latest one, hMiner. As the criteria of our performance analysis, we first consider algorithms' total runtime and average processing time per transaction. In addition, to compare the efficiency of storage structures between them, their maximum memory usage is also evaluated. Lastly, we show how stably the two algorithms conduct their mining works with respect to the databases that feature gradually increasing items. With respect to the evaluation results of mining time and transaction processing, hMiner has higher speed than that of Lossy counting. Since hMiner stores candidate frequent patterns in a hash method, it can directly access candidate frequent patterns. Meanwhile, Lossy counting stores them in a lattice manner; thus, it has to search for multiple nodes in order to access the candidate frequent patterns. On the other hand, hMiner shows worse performance than that of Lossy counting in terms of maximum memory usage. hMiner should have all of the information for candidate frequent patterns to store them to hash's buckets, while Lossy counting stores them, reducing their information by using the lattice method. Since the storage of Lossy counting can share items concurrently included in multiple patterns, its memory usage is more efficient than that of hMiner. However, hMiner presents better efficiency than that of Lossy counting with respect to scalability evaluation due to the following reasons. If the number of items is increased, shared items are decreased in contrast; thereby, Lossy counting's memory efficiency is weakened. Furthermore, if the number of transactions becomes higher, its pruning effect becomes worse. From the experimental results, we can determine that the landmark window-based frequent pattern mining algorithms are suitable for real-time systems although they require a significant amount of memory. Hence, we need to improve their data structures more efficiently in order to utilize them additionally in resource-constrained environments such as WSN(Wireless sensor network).