Unsunnidhal, Lalu;Wasito, Raden;Setyawan, Erif Maha Nugraha;Warsani, Ziana;Kusumawati, Asmarani
Journal of Veterinary Science
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v.22
no.6
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pp.76.1-76.15
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2021
Background: The development of a vaccine for Jembrana disease is needed to prevent losses in Indonesia's Bali cattle industry. A DNA vaccine model (pEGFP-C1-tat) that requires a functional delivery system will be developed. Polylactic-co-glycolic acid (PLGA) may have potential as a delivery system for the vaccine model. Objectives: This study aims to evaluate the in vitro potential of PLGA as a delivery system for pEGFP-C1-tat. Methods: Consensus and codon optimization for the tat gene was completed using a bioinformatic method, and the product was inserted into a pEGFP-C1 vector. Cloning of the pEGFP-C1-tat was successfully performed, and polymerase chain reaction (PCR) and restriction analysis confirmed DNA isolation. PLGA-pEGFP-C1-tat solutions were prepared for encapsulated formulation testing, physicochemical characterization, stability testing with DNase I, and cytotoxicity testing. The PLGA-pEGFP-C1-tat solutions were transfected in HeLa cells, and gene expression was observed by fluorescent microscopy and real-time PCR. Results: The successful acquisition of transformant bacteria was confirmed by PCR. The PLGA:DNA:polyvinyl alcohol ratio formulation with optimal encapsulation was 4%:0.5%:2%, physicochemical characterization of PLGA revealed a polydispersity index value of 0.246, a particle size of 925 nm, and a zeta potential value of -2.31 mV. PLGA succeeded in protecting pEGFP-C1-tat from enzymatic degradation, and the percentage viability from the cytotoxicity test of PLGA-pEGFP-C1-tat was 98.03%. The PLGA-pEGFP-C1-tat demonstrated luminescence of the EGFP-tat fusion protein and mRNA transcription was detected. Conclusions: PLGA has good potential as a delivery system for pEGFP-C1-tat.
KSII Transactions on Internet and Information Systems (TIIS)
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v.13
no.4
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pp.1765-1794
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2019
Genetic Programming (GP) is an intelligence technique whereby computer programs are encoded as a set of genes which are evolved utilizing a Genetic Algorithm (GA). In other words, the GP employs novel optimization techniques to modify computer programs; imitating the way humans develop programs by progressively re-writing them for solving problems automatically. Trial programs are frequently altered in the search for obtaining superior solutions due to the base is GA. These are evolutionary search techniques inspired by biological evolution such as mutation, reproduction, natural selection, recombination, and survival of the fittest. The power of GAs is being represented by an advancing range of applications; vector processing, quantum computing, VLSI circuit layout, and so on. But one of the most significant uses of GAs is the automatic generation of programs. Technically, the GP solves problems automatically without having to tell the computer specifically how to process it. To meet this requirement, the GP utilizes GAs to a "population" of trial programs, traditionally encoded in memory as tree-structures. Trial programs are estimated using a "fitness function" and the suited solutions picked for re-evaluation and modification such that this sequence is replicated until a "correct" program is generated. GP has represented its power by modifying a simple program for categorizing news stories, executing optical character recognition, medical signal filters, and for target identification, etc. This paper reviews existing literature regarding the GPs and their applications in different scientific fields and aims to provide an easy understanding of various types of GPs for beginners.
Islam, Sk Injamamul;Mou, Moslema Jahan;Sanjida, Saloa;Tariq, Muhammad;Nasir, Saad;Mahfuj, Sarower
Genomics & Informatics
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v.20
no.1
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pp.11.1-11.20
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2022
Vibrio harveyi belongs to the Vibrio genus that causes vibriosis in marine and aquatic fish species through double-stranded DNA virus replication. In humans, around 12 Vibrio species can cause gastroenteritis (gastrointestinal illness). A large amount of virus particles can be found in the cytoplasm of infected cells, which may cause death. Despite these devastating complications, there is still no cure or vaccine for the virus. As a result, we used an immunoinformatics approach to develop a multi-epitope vaccine against most pathogenic hemolysin gene of V. harveyi. The immunodominant T- and B-cell epitopes were identified using the hemolysin protein. We developed a vaccine employing three possible epitopes: cytotoxic T-lymphocytes, helper T-lymphocytes, and linear B-lymphocyte epitopes, after thorough testing. The vaccine was developed to be antigenic, immunogenic, and non-allergenic, as well as having a better solubility. Molecular dynamics simulation revealed significant structural stiffness and binding stability. In addition, the immunological simulation generated by computer revealed that the vaccination might elicit immune reactions in the actual life after injection. Finally, using Escherichia coli K12 as a model, codon optimization yielded ideal GC content and a higher codon adaptation index value, which was then included in the cloning vector pET2+ (a). Altogether, our experiment implies that the proposed peptide vaccine might be a good option for vibriosis prophylaxis.
Performing high-fidelity computational fluid dynamics (HF-CFD) to predict the flow and heat transfer state of the coolant in the reactor core is expensive, especially in scenarios that require extensive parameter search, such as uncertainty analysis and design optimization. This work investigated the performance of utilizing a multi-fidelity reduced-order model (MF-ROM) in PWR rod bundles simulation. Firstly, basis vectors and basis vector coefficients of high-fidelity and low-fidelity CFD results are extracted separately by the proper orthogonal decomposition (POD) approach. Secondly, a surrogate model is trained to map the relationship between the extracted coefficients from different fidelity results. In the prediction stage, the coefficients of the low-fidelity data under the new operating conditions are extracted by using the obtained POD basis vectors. Then, the trained surrogate model uses the low-fidelity coefficients to regress the high-fidelity coefficients. The predicted high-fidelity data is reconstructed from the product of extracted basis vectors and the regression coefficients. The effectiveness of the MF-ROM is evaluated on a flow and heat transfer problem in PWR fuel rod bundles. Two data-driven algorithms, the Kriging and artificial neural network (ANN), are trained as surrogate models for the MF-ROM to reconstruct the complex flow and heat transfer field downstream of the mixing vanes. The results show good agreements between the data reconstructed with the trained MF-ROM and the high-fidelity CFD simulation result, while the former only requires to taken the computational burden of low-fidelity simulation. The results also show that the performance of the ANN model is slightly better than the Kriging model when using a high number of POD basis vectors for regression. Moreover, the result presented in this paper demonstrates the suitability of the proposed MF-ROM for high-fidelity fixed value initialization to accelerate complex simulation.
Targeted Alpha Therapy (TAT) is a new method of cancer treatment that protects normal tissues while selectively killing tumor cells using high cytotoxicity and short range of alpha particles, and target alpha therapy is a highly specific and effective cancer treatment strategy, and its potential has been proven through many clinical and experimental studies. This treatment method accurately delivers alpha particles by selecting specific molecules present in cancer tissue, which has an effective destruction and tumor suppression effect on cancer cells, and one of the main advantages of target alpha treatment is the physical properties of alpha particles. Alpha particles have a very high energy and short effective distance, interacting with target molecules in cancer tissues and having a fatal effect on cancer cells, which is known to cause DNA damage and cell death in cancer cells. TAT has shown positive results in preclinical and clinical studies for various types of cancers, especially those that resist or are unresponsive to existing treatments, but there are several challenges and limitations to overcome for successful clinical transition and application. These include the provision and production of suitable alpha radioisotopes, optimization of target vectors and delivery formulations, understanding and regulation of radiological effects, accurate dosage calculation and toxicity assessment. Future research should focus on developing new or improved isotopes, target vectors, transfer formulations, radiobiological models, combination strategies, imaging techniques, etc. for TAT. In addition, TAT has the potential to improve the quality of life and survival of cancer patients due to the possibility of a new treatment for overcoming cancer, and to this end, prospective research on more carcinomas and more diverse patient groups is needed.
In this paper, we propose an emotion recognition system that can discriminate human emotional state into neutral or anger from the speech captured by a cellular-phone in real time. In general. the speech through the mobile network contains environment noise and network noise, thus it can causes serious System performance degradation due to the distortion in emotional features of the query speech. In order to minimize the effect of these noise and so improve the system performance, we adopt a simple MA (Moving Average) filter which has relatively simple structure and low computational complexity, to alleviate the distortion in the emotional feature vector. Then a SFS (Sequential Forward Selection) feature optimization method is implemented to further improve and stabilize the system performance. Two pattern recognition method such as k-NN and SVM is compared for emotional state classification. The experimental results indicate that the proposed method provides very stable and successful emotional classification performance such as 86.5%. so that it will be very useful in application areas such as customer call-center.
KIPS Transactions on Software and Data Engineering
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v.11
no.1
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pp.41-50
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2022
In this paper, a method to directly calculate the major elements of skin color such as melanin and hemoglobin from the RGB signal of the camera is proposed. The main elements of skin color typically measure spectral reflectance using specific equipment, and reconfigure the values at some wavelengths of the measured light. The values calculated by this method include such things as melanin index and erythema index, and require special equipment such as a spectral reflectance measuring device or a multi-spectral camera. It is difficult to find a direct calculation method for such component elements from a general digital camera, and a method of indirectly calculating the concentration of melanin and hemoglobin using independent component analysis has been proposed. This method targets a region of a certain RGB image, extracts characteristic vectors of melanin and hemoglobin, and calculates the concentration in a manner similar to that of Principal Component Analysis. The disadvantage of this method is that it is difficult to directly calculate the pixel unit because a group of pixels in a certain area is used as an input, and since the extracted feature vector is implemented by an optimization method, it tends to be calculated with a different value each time it is executed. The final calculation is determined in the form of an image representing the components of melanin and hemoglobin by converting it back to the RGB coordinate system without using the feature vector itself. In order to improve the disadvantages of this method, the proposed method is to calculate the component values of melanin and hemoglobin in a feature space rather than an RGB coordinate system using a feature vector, and calculate the spectral reflectance corresponding to the skin color using a general digital camera. Methods and methods of calculating detailed components constituting skin pigments such as melanin, oxidized hemoglobin, deoxidized hemoglobin, and carotenoid using spectral reflectance. The proposed method does not require special equipment such as a spectral reflectance measuring device or a multi-spectral camera, and unlike the existing method, direct calculation of the pixel unit is possible, and the same characteristics can be obtained even in repeated execution. The standard diviation of density for melanin and hemoglobin of proposed method was 15% compared to conventional and therefore gives 6 times stable.
Cytosolic Ca2+ levels ([Ca2+]c) change dynamically in response to inducers, repressors, and physiological conditions, and aberrant [Ca2+]c concentration regulation is associated with cancer, heart failure, and diabetes. Therefore, [Ca2+]c is considered as a good indicator of physiological and pathological cellular responses, and is a crucial biomarker for drug discovery. A genetically encoded calcium indicator (GECI) was recently developed to measure [Ca2+]c in single cells and animal models. GECI have some advantages over chemically synthesized indicators, although they also have some drawbacks such as poor signal-to-noise ratio (SNR), low positive signal, delayed response, artifactual responses due to protein overexpression, and expensive detection equipment. Here, we developed an indicator based on interactions between Ca2+-loaded calmodulin and target proteins, and generated an innovative GECI sensor using split nano-luciferase (Nluc) fragments to detect changes in [Ca2+]c. Stimulation-dependent luciferase activities were optimized by combining large and small subunits of Nluc binary technology (NanoBiT, LgBiT:SmBiT) fusion proteins and regulating the receptor expression levels. We constructed the binary [Ca2+]c sensors using a multicistronic expression system in a single vector linked via the internal ribosome entry site (IRES), and examined the detection efficiencies. Promoter optimization studies indicated that promoter-dependent protein expression levels were crucial to optimize SNR and sensitivity. This novel [Ca2+]c assay has high SNR and sensitivity, is easy to use, suitable for high-throughput assays, and may be useful to detect [Ca2+]c in single cells and animal models.
An inversion method is presented for the determination of the compressional wave speed, compressional wave attenuation, thickness of the sediment layer and density as a function of depth for a horizontally stratified ocean bottom. An experiment for estimating those properties was conducted in the shallow water of South Sea in Korea. In the experiment, a light bulb implosion and the propagating sound were measured using a VLA (vertical line array). As a method for estimating the geoacoustic properties, a coherent broadband matched field processing combined with Genetic Algorithm was employed. When a time-dependent signal is very short, the Fourier transform results are not accurate, since the frequency components are not locatable in time and the windowed Fourier transform is limited by the length of the window. However, it is possible to do this using the wavelet transform a transform that yields a time-frequency representation of a signal. In this study, this transform is used to identify and extract the acoustic components from multipath time series. The inversion is formulated as an optimization problem which maximizes the cost function defined as a normalized correlation between the measured and modeled signals in the wavelet transform coefficient vector. The experiments and procedures for deploying the light bulbs and the coherent broadband inversion method are described, and the estimated geoacoustic profile in the vicinity of the VLA site is presented.
Vanillin is one of the world's principal flavoring compounds, and is used extensively in the food industry. The potential vanillin production of the bacteria was compared to select and clone genes which were appropriate for highly productive vanillin production by E. coli. The fcs (feruloyl-CoA synthetase) and ech (enoyl-CoA hydratase/aldolase) genes cloned from Amycolatopsis sp. strain HR104 and Delftia acidovorans were introduced to pBAD24 vector with $P_{BAD}$ promoter and were named pDAHEF and pDDAEF, respectively. We observed 160 mg/L vanillin production with E. coli harboring pDAHEF, whereas 10 mg/L of vanillin was observed with pDAHEF. Vanillin production was optimized with E. coli harboring pDAHEF. Induction of the fcs and ech genes from pDAHEF was optimized with the addition of 13.3 mM arabinose at 18 h of culture, from which 450 mg/L of vanillin was produced. The feeding time and concentration of ferulic acid were also optimized by the supplementation of $0.2\%$ ferulic acid at 18 h of culture, from which 500 mg/L of vanillin was obtained. Under the above optimized condition of arabinose induction and ferulic acid supplementation, vanillin production was carried out with four different types of media, M9, LB, 2YT, and TB. The highest vanillin production, 580 mg/L, was obtained with LB medium, a 3.6 fold increase in comparison to the 160 mg/L obtained before the optimization of vanillin production.
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