Recently, it was reported that the role of mitochondria-reactive oxygen species (ROS) generating pathway in cisplatin-induced apoptosis is remarkable. Since a variety of molecules are involved in the pathway, a comprehensive approach to delineate the biological interactions of the molecules is required. However, quantitative modeling of the mitochondria-ROS generating pathway based on experiment and systemic analysis using the model have not been attempted so far. Thus, we conducted experiments to measure the concentration changes of critical molecules associated with mitochondrial apoptosis in both human mesothelioma H2052 and their ${\rho}^0$ cells lacking mitochondrial DNA (mtDNA). Based on the experiments, a novel mathematical model that can represent the essential dynamics of the mitochondrial apoptotic pathway induced by cisplatin was developed. The kinetic parameter values of the mathematical model were estimated from the experimental data. Then, we have investigated the dynamical properties of this model and predicted the apoptosis levels for various concentrations of cisplatin beyond the range of experiments. From parametric perturbation analysis, we further found that apoptosis will reach its saturation level beyond a certain critical cisplatin concentration.
The Journal of Asian Finance, Economics and Business
/
v.7
no.2
/
pp.181-193
/
2020
The midst competition makes a brand all together with its offering products and services is becoming a crucial element for company existence. This requires direct involvement from internal organizational to develop effective strategic branding. According to Asosiasi Penyelenggara Internet Indonesia (APJII), Indonesia's internet penetration is among the highest in Asia. The purpose of this research is to improve the strategic role of brand orientation within Internet Service Provider (ISP) for maximizing return on the company's financial and non-financial benefits by proposing organization change readiness variable. The data collection is taken using an online survey with a non-parametric sampling method and collected 68 qualified respondents for data analysis using SEM-PLS (Structural Equation Modeling with Partial Least Square). The result indicates partial hypotheses on the constructed model between variables brand orientation, brand commitment, and internal brand equity is acceptable. Another finding is stated hypotheses on organization change readiness as moderation is not accepted and means there is no significance to the constructed model. The main conclusion resumes associative human memory can shape up organization change readiness inside internal toward then brand. Relevant cues generate information received in the human brain then will create common associative and becoming social identity on internal brand equity.
Current water quality monitoring systems in Korea carried based on in-situ grab sample analysis. It is difficult to improve the current water quality monitoring system, i.e. shorter sampling period or increasing sampling points, because the current systems are both cost- and labor-intensive. One possible way to improve the current water quality monitoring system is to adopt a modeling approach. In this study, a modeling technique was introduced to support the current water quality monitoring system, and an artificial neural network model, the computational tool which mimics the biological processes of human brain, was applied to predict water quality of the river. The approach tried to predict concentrations of Total coliform at the outlet of the river and this showed, somewhat, poor estimations since concentrations of Total coliform were rapidly fluctuated. The approach, however, could forecast whether concentrations of Total coliform would exceed the water quality standard or not. As results, modeling approaches is expected to assist the current water quality monitoring system if the approach is applied to judge whether water quality factors could exceed the water quality standards or not and this would help proper water resource managements.
In medical imaging, three-dimensional (3D) display using Virtual Reality Modeling Language (VRML) as a portable file format can give intuitive information more efficiently on the World Wide Web (WWW). The web-based 3D visualization of functional images combined with anatomical images has not studied much in systematic ways. The goal of this study was to achieve a simultaneous observation of 3D anatomic and functional models with planar images on the WWW, providing their locational information in 3D space with a measuring implement using VRML. MRI and ictal-interictal SPECT images were obtained from one epileptic patient. Subtraction ictal SPECT co-registered to MRI (SISCOM) was performed to improve identification of a seizure focus. SISCOM image volumes were held by thresholds above one standard deviation (1-SD) and two standard deviations (2-SD). SISCOM foci and boundaries of gray matter, white matter, and cerebrospinal fluid (CSF) in the MRI volume were segmented and rendered to VRML polygonal surfaces by marching cube algorithm. Line profiles of x and y-axis that represent real lengths on an image were acquired and their maximum lengths were the same as 211.67 mm. The real size vs. the rendered VRML surface size was approximately the ratio of 1 to 605.9. A VRML measuring tool was made and merged with previous VRML surfaces. User interface tools were embedded with Java Script routines to display MRI planar images as cross sections of 3D surface models and to set transparencies of 3D surface models. When transparencies of 3D surface models were properly controlled, a fused display of the brain geometry with 3D distributions of focal activated regions provided intuitively spatial correlations among three 3D surface models. The epileptic seizure focus was in the right temporal lobe of the brain. The real position of the seizure focus could be verified by the VRML measuring tool and the anatomy corresponding to the seizure focus could be confirmed by MRI planar images crossing 3D surface models. The VRML application developed in this study may have several advantages. Firstly, 3D fused display and control of anatomic and functional image were achieved on the m. Secondly, the vector analysis of a 3D surface model was defined by the VRML measuring tool based on the real size. Finally, the anatomy corresponding to the seizure focus was intuitively detected by correlations with MRI images. Our web based visualization of 3-D fusion image and its localization will be a help to online research and education in diagnostic radiology, therapeutic radiology, and surgery applications.
Purpose: To obtain regional blood flow and tissue-blood partition coefficient with time-activity curves from ${H_2}^{15}O$ PET, fitting of some parameters in the Kety model is conventionally accomplished by nonlinear least squares (NLS) analysis. However, NLS requires considerable compuation time then is impractical for pixel-by-pixel analysis to generate parametric images of these parameters. In this study, we investigated several fast parameter estimation methods for the parametric image generation and compared their statistical reliability and computational efficiency. Materials and Methods: These methods included linear least squres (LLS), linear weighted least squares (LWLS), linear generalized least squares (GLS), linear generalized weighted least squares (GWLS), weighted Integration (WI), and model-based clustering method (CAKS). ${H_2}^{15}O$ dynamic brain PET with Poisson noise component was simulated using numerical Zubal brain phantom. Error and bias in the estimation of rCBF and partition coefficient, and computation time in various noise environments was estimated and compared. In audition, parametric images from ${H_2}^{15}O$ dynamic brain PET data peformed on 16 healthy volunteers under various physiological conditions was compared to examine the utility of these methods for real human data. Results: These fast algorithms produced parametric images with similar image qualify and statistical reliability. When CAKS and LLS methods were used combinedly, computation time was significantly reduced and less than 30 seconds for $128{\times}128{\times}46$ images on Pentium III processor. Conclusion: Parametric images of rCBF and partition coefficient with good statistical properties can be generated with short computation time which is acceptable in clinical situation.
$N^1$-(2, 5-dimethoxyphenyl)-$N^8$-hydroxyoctanediamide (N25) is a novel SAHA cap derivative of HDACi, with a patent (No. CN 103159646). This invention is a hydroxamic acid compound with a structural formula of $RNHCO(CH_2)6CONHOH$ (wherein R=2, 5dimethoxyaniline), a pharmaceutically acceptable salt which is soluble. In the present study, we investigated the effects of N25 with regard to drug distribution and molecular docking, and anti-proliferation, apoptosis, cell cycling, and $LD_{50}$. First, we designed a molecular approach for modeling selected SAHA derivatives based on available structural information regarding human HDAC8 in complex with SAHA (PDB code 1T69). N25 was found to be stabilized by direct interaction with the HDAC8. Anti-proliferative activity was observed in human glioma U251, U87, T98G cells and human lung cancer H460, A549, H1299 cells at moderate concentrations ($0.5-30{\mu}M$). Compared with SAHA, N25 displayed an increased antitumor activity in U251 and H460 cells. We further analyzed cell death mechanisms activated by N25 in U251 and H460 cells. N25 significantly increased acetylation of Histone 3 and inhibited HDAC4. On RT-PCR analysis, N25 increased the mRNA levels of p21, however, decreased the levels of p53. These resulted in promotion of apoptosis, inducing G0/G1 arrest in U251 cells and G2/M arrest in H460 cells in a time-dependent and dose-dependent manner. In addition, N25 was able to distribute to brain tissue through the blood-brain barrier of mice ($LD_{50}$: 240.840mg/kg). In conclusion, our findings demonstrate that N25 will provide an invaluable tool to investigate the molecular mechanism with potential chemotherapeutic value in several malignancies, especially human glioma.
Alkyl hydroperoxide reductase subunit C from Pseudomonas aeruginosa PAO1 (PaAhpC) is a member of the 2-Cys peroxiredoxin family. Here, we examined the peroxidase and molecular chaperone functions of PaAhpC using a site-directed mutagenesis approach by substitution of Ser and Thr residues with Cys at positions 78 and 105 located between two catalytic cysteines. Substitution of Ser with Cys at position 78 enhanced the chaperone activity of the mutant (S78C-PaAhpC) by approximately 9-fold compared with that of the wild-type protein (WT-PaAhpC). This increased activity may have been associated with the proportionate increase in the high-molecular-weight (HMW) fraction and enhanced hydrophobicity of S78C-PaAhpC. Homology modeling revealed that mutation of $Ser^{78}$ to $Cys^{78}$ resulted in a more compact decameric structure than that observed in WT-PaAhpC and decreased the atomic distance between the two neighboring sulfur atoms of $Cys^{78}$ in the dimer-dimer interface of S78C-PaAhpC, which could be responsible for the enhanced hydrophobic interaction at the dimer-dimer interface. Furthermore, complementation assays showed that S78C-PaAhpC exhibited greatly improved the heat tolerance, resulting in enhanced1 survival under thermal stress. Thus, addition of Cys at position 78 in PaAhpC modulated the functional shifting of this protein from a peroxidase to a chaperone.
In recent years, big data analysis has been expanded to include automatic control through reinforcement learning as well as prediction through modeling. Research on the utilization of image data is actively carried out in various industrial fields such as chemical, manufacturing, agriculture, and bio-industry. In this paper, we applied NASNet, which is an AutoML reinforced learning algorithm, to DeepU-Net neural network that modified U-Net to improve image semantic segmentation performance. We used BRATS2015 MRI data for performance verification. Simulation results show that DeepU-Net has more performance than the U-Net neural network. In order to improve the image segmentation performance, remove dropouts that are typically applied to neural networks, when the number of kernels and filters obtained through reinforcement learning in DeepU-Net was selected as a hyperparameter of neural network. The results show that the training accuracy is 0.5% and the verification accuracy is 0.3% better than DeepU-Net. The results of this study can be applied to various fields such as MRI brain imaging diagnosis, thermal imaging camera abnormality diagnosis, Nondestructive inspection diagnosis, chemical leakage monitoring, and monitoring forest fire through CCTV.
The spatial and temporal variations in radiative forcing (RF) and mean temperature changes of greenhouse gases (GHGs), such as $CO_2$, $CH_4$, and $N_2O$, were analyzed at urban center (Yeon-dong) and background sites (Gosan) on Jeju Island during 2010~2015, based on a modeling approach (i.e., radiative transfer model). Overall, the RFs and mean temperature changes of $CO_2$ at Yeon-dong during most years (except for 2014) were estimated to be higher than those at Gosan. This might be possibly because of its higher concentrations at Yeon-dong due to relatively large energy consumption and small photosynthesis and also the difference in radiation flux due to the different input condition (e.g., local time and geographic coordinates of solar zenith angle) in the model. The annual mean RFs and temperature changes of $CO_2$ were highest in 2015 ($2.41Wm^{-2}$ and 1.76 K) at Yeon-dong and in 2013 ($2.22Wm^{-2}$ and 1.62 K) at Gosan (except for 2010 and 2011). The maximum monthly/seasonal mean RFs and temperature changes of $CO_2$ occurred in spring (Mar. and/or Apr.) or winter (Jan. and/or Feb.) at the two sites during the study period, whereas the minimum RFs and temperature changes in summer (Jun.-Aug.). In the case of $CH_4$ and $N_2O$, their impacts on the RF and mean temperature changes were very small (an order of magnitude lower) compared to $CO_2$. The spatio-temporal differences in these RF values of GHGs might primarily depend on the atmospheric profile (e.g., ozone profile), surface albedo, local time (or solar zenith angle), as well as their mass concentrations.
Objective: In this study, we used resting-state fMRI data to map differences in functional connectivity between a comprehensive set of 8 distinct cortical and subcortical brain regions in healthy controls and Internet addicts. We also investigated the relationship between resting state connectivity strength and the level of psychopathology (ex. score of internet addiction scale and score of Barratt impulsiveness scale). Background: There is a lot of evidence of relationship between Internet addiction and impaired inhibitory control. Clinical evidence suggests that Internet addicts have a high level of impulsivity as measured by behavioral task of response inhibition and a self report questionnaire. Method: 15 Internet addicts and 15 demographically similar non-addicts participated in the current resting-state fMRI experiment. For the connectivity analysis, regions of interests (ROIs) were defined based on the previous studies of addictions. Functional connectivity assessment for each subject was obtained by correlating time-series across the ROIs, resulting in $8{\times}8$ matrixs for each subject. Within-group, functional connectivity patterns were observed by entering the z maps of the ROIs of each subject into second-level one sample t test. Two sample t test was also performed to examine between group differences. Results: Between group, the analysis revealed that the connectivity in between the orbito frontal cortex and inferior parietal cortex, between orbito frontal cortex and putamen, between the orbito frontal cortex and anterior cingulate cortex, between the insula and anterior cingulate cortex, and between amydgala and insula was significantly stronger in control group than in the Internet addicts, while the connectivity in between the orbito frontal cortex and insula showed stronger negative correlation in the Internet addicts relative to control group (p < 0.001, uncorrected). No significant relationship between functional connectivity strength and current degree of Internet addiction and degree of impulsitivy was seen. Conclusion: This study found that Internet addicts had declined connectivity strength in the orbitofrontal cortex (OFC) and other regions (e.g., ACC, IPC, and insula) during resting-state. It may reflect deficits in the OFC function to process information from different area in the corticostriatal reward network. Application: The results might help to develop theoretical modeling of Internet addiction for Internet addiction discrimination.
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