QSAR Model Study of 2,3,4,5-tetrahydro-1H-pyrido[4,3-b]indole of Cystic-brosis-transmembrane Conductance-regulator Gene Potentiators

2021 ◽  
Vol 18 ◽  
Author(s):  
Yaru Si ◽  
Kang Ma ◽  
Yingfeng Hu ◽  
Hongzong Si ◽  
Honglin Zhai
Keyword(s):  
Gene Expression ◽  
Mean Square Error ◽  
Correlation Coefficient ◽  
Gene Expression Programming ◽  
Heuristic Method ◽  
Transmembrane Conductance Regulator ◽  
Mean Square ◽  
Regulator Gene ◽  
Transmembrane Conductance ◽  
The Mean

Background: Cystic fibrosis (CF) is a genetic disease, which has no effective treatment. Objective: The aim of this study is to predict the EC50 value of 2,3,4,5-tetrahydro-1H-pyrido[4,3-b]indole core as a novel chemotype of potentiators to establish a highly predicting quantitative structure-activity relationship model. Methods: 41 products were optimized, and a linear model was built by a heuristic method in CODESSA program. In this study, 3 descriptors were selected and utilized to build a nonlinear model in gene expression programming. Results: The square of the correlation coefficient of the heuristic method is 0.57, and the s2 is 0.30. In gene expression programming, the square of correlation coefficient and the mean square error for the training set are 0.74 and 0.13, respectively. The square of correlation coefficient and the mean square error for the test set are 0.70 and 0.27, respectively. Conclusion: The GEP model has stronger predictive ability to help develop the novel structure of 2,3,4,5-tetrahydro-1H-pyrido[4,3-b]indole of cystic-brosis-transmembrane conductance-regulator gene potentiators.

scholarly journals IL-6 and IL-8 increase the expression of glycosyltransferases and sulfotransferases involved in the biosynthesis of sialylated and/or sulfated Lewisx epitopes in the human bronchial mucosa

2008 ◽  
Vol 410 (1) ◽  
pp. 213-223 ◽  
Author(s):  
Sophie Groux-Degroote ◽  
Marie-Ange Krzewinski-Recchi ◽  
Aurélie Cazet ◽  
Audrey Vincent ◽  
Sylvain Lehoux ◽  
...  
Keyword(s):  
Gene Expression ◽  
Cystic Fibrosis ◽  
Early Death ◽  
High Molecular Mass ◽  
Transmembrane Conductance Regulator ◽  
Regulator Gene ◽  
Bronchial Mucosa ◽  
Sialyl Lewisx ◽  
Transmembrane Conductance ◽  
Cf Transmembrane Conductance Regulator

Bronchial mucins from patients suffering from CF (cystic fibrosis) exhibit glycosylation alterations, especially increased amounts of the sialyl-Lewisx (NeuAcα2-3Galβ1-4[Fucα1-3]GlcNAc-R) and 6-sulfo-sialyl-Lewisx (NeuAcα2-3Galβ1-4[Fucα1-3][SO3H-6]GlcNAc-R) terminal structures. These epitopes are preferential receptors for Pseudomonas aeruginosa, the bacteria responsible for the chronicity of airway infection and involved in the morbidity and early death of CF patients. However, these glycosylation changes cannot be directly linked to defects in CFTR (CF transmembrane conductance regulator) gene expression since cells that secrete airway mucins express no or very low amounts of the protein. Several studies have shown that inflammation may affect glycosylation and sulfation of various glycoproteins, including mucins. In the present study, we show that incubation of macroscopically healthy fragments of human bronchial mucosa with IL-6 (interleukin-6) or IL-8 results in a significant increase in the expression of α1,3/4-fucosyltransferases [FUT11 (fucosyltransferase 11 gene) and FUT3], α2-6- and α2,3-sialyltransferases [ST3GAL6 (α2,3-sialyltransferase 6 gene) and ST6GAL2 (α2,6-sialyltransferase 2 gene)] and GlcNAc-6-O-sulfotransferases [CHST4 (carbohydrate sulfotransferase 4 gene) and CHST6] mRNA. In parallel, the amounts of sialyl-Lewisx and 6-sulfo-sialyl-Lewisx epitopes at the periphery of high-molecular-mass proteins, including MUC4, were also increased. In conclusion, our results indicate that IL-6 and -8 may contribute to the increased levels of sialyl-Lewisx and 6-sulfo-sialyl-Lewisx epitopes on human airway mucins from patients with CF.

scholarly journals Discussion on Criterion of Determination of the Kinetic Parameters of the Linear Heating Reactions

Minerals ◽  
2022 ◽  
Vol 12 (1) ◽  
pp. 81
Author(s):  
Kui Li ◽  
Wei Zhang ◽  
Menglong Fu ◽  
Chengzhi Li ◽  
Zhengliang Xue
Keyword(s):  
Kinetic Parameters ◽  
Mean Square Error ◽  
Correlation Coefficient ◽  
Linear Correlation ◽  
Euler Method ◽  
Kinetic Process ◽  
Mean Square ◽  
Heating Rates ◽  
Linear Heating ◽  
The Mean

Generally, the linear correlation coefficient is one of the most significant criteria to appraise the kinetic parameters computed from different reaction models. Actually, the optimal kinetic triplet should meet the following two requirements: first, it can be used to reproduce the original kinetic process; second, it can be applied to predict the other kinetic process. The aim of this paper is to attempt to prove that the common criteria are insufficient for meeting the above two purposes simultaneously. In this paper, the explicit Euler method and Taylor expansion are presented to numerically predict the kinetic process of linear heating reactions. The mean square error is introduced to assess the prediction results. The kinetic processes of hematite reduced to iron at different heating rates (8, 10 and 18 K/min) are utilized for validation and evaluation. The predicted results of the reduction of Fe2O3 → Fe3O4 indicated that the inferior linear correlation coefficient did provide better kinetic predicted curves. In conclusion, to satisfy the above two requirements of reproduction and prediction, the correlation coefficient is an insufficient criterion. In order to overcome this drawback, two kinds of numerical prediction methods are introduced, and the mean square error of the prediction is suggested as a superior criterion for evaluation.

scholarly journals Compressive Strength Prediction via Gene Expression Programming (GEP) and Artificial Neural Network (ANN) for Concrete Containing RCA

Buildings ◽  
2021 ◽  
Vol 11 (8) ◽  
pp. 324
Author(s):  
Ayaz Ahmad ◽  
Krisada Chaiyasarn ◽  
Furqan Farooq ◽  
Waqas Ahmad ◽  
Suniti Suparp ◽  
...  
Keyword(s):  
Neural Network ◽  
Gene Expression ◽  
Artificial Neural Network ◽  
Compressive Strength ◽  
Mean Square Error ◽  
Gene Expression Programming ◽  
Mix Design ◽  
Mean Square ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

To minimize the environmental risks and for sustainable development, the utilization of recycled aggregate (RA) is gaining popularity all over the world. The use of recycled coarse aggregate (RCA) in concrete is an effective way to minimize environmental pollution. RCA does not gain more attraction because of the availability of adhered mortar on its surface, which poses a harmful effect on the properties of concrete. However, a suitable mix design for RCA enables it to reach the targeted strength and be applicable for a wide range of construction projects. The targeted strength achievement from the proposed mix design at a laboratory is also a time-consuming task, which may cause a delay in the construction work. To overcome this flaw, the application of supervised machine learning (ML) algorithms, gene expression programming (GEP), and artificial neural network (ANN) was employed in this study to predict the compressive strength of RCA-based concrete. The linear coefficient correlation (R2), mean absolute error (MAE), mean square error (MSE), and root mean square error (RMSE) were evaluated to investigate the performance of the models. The k-fold cross-validation method was also adopted for the confirmation of the model’s performance. In comparison, the GEP model was more effective in terms of prediction by giving a higher correlation (R2) value of 0.95 as compared to ANN, which gave a value of R2 equal to 0.92. In addition, a sensitivity analysis was conducted to know about the contribution level of each parameter used to run the models. Moreover, the increment in data points and the use of other supervised ML approaches like boosting, gradient boosting, and bagging to forecast the compressive strength, would give a better response.

Prediction of the Crystallinity of White Pine Using near Infrared Spectroscopy

2011 ◽  
Vol 183-185 ◽  
pp. 1215-1218
Author(s):  
Zhi Hua Qu ◽  
Li Hai Wang
Keyword(s):  
Infrared Spectroscopy ◽  
Mean Square Error ◽  
Correlation Coefficient ◽  
Near Infrared Spectroscopy ◽  
Near Infrared ◽  
Partial Least Square ◽  
Mean Square ◽  
White Pine ◽  
Pine Wood ◽  
The Mean

The crystallinity of wood is an important property of wood materials, it has an important effect on the physical, mechanical and chemical properties of cellulose fibers such as MOR, density, hardness increase, alpha-cellulose content, dimensional stability, moisture regain and dye sorption, chemical reactivity etc. The aims of this study were to investigate the ability of near infrared spectroscopy (NIR) to predict the crystallinity of white pine wood and the effect of spectra pretreatment on the prediction of crystallinity using NIR. Spectra were collected from wood powder a slowly rotating turntable and the crystallinity of wood was determined by X-ray diffractmeter (XRD) in this experiment. The results showed that NIR coupled with partial least square (PLS) method could be correlated with the crystallinity of white pine wood, and the ability of NIR prediction based on first derivative spectra was better than based on raw spectra or second derivative pretreated spectra. There was a significant correlation between NIR spectra and XRD determined crystallinity. The correlation coefficient for calibration (RC) was 0.932; the mean square error of calibration (RMSEC) was 0.022; the correlation coefficient for validation (RV) was 0.911; the mean square error of calibration (RMSEV) was 0.023. It was proved that NIR can rapidly and accurately predict white pine wood crystallinity.

The Bordeaux Automatic Meridian Circle

1978 ◽  
Vol 48 ◽  
pp. 227-228
Author(s):  
Y. Requième
Keyword(s):  
Mean Square Error ◽  
Mean Square ◽  
Meridian Circle ◽  
The Mean ◽  
Full Automation

In spite of important delays in the initial planning, the full automation of the Bordeaux meridian circle is progressing well and will be ready for regular observations by the middle of the next year. It is expected that the mean square error for one observation will be about ±0.”10 in the two coordinates for declinations up to 87°.

Limit tolerances for sums of measurement errors

2018 ◽  
Vol 934 (4) ◽  
pp. 59-62
Author(s):  
V.I. Salnikov
Keyword(s):  
Normal Distribution ◽  
Mean Square Error ◽  
Gaussian Distribution ◽  
Measurement Errors ◽  
Theory And Practice ◽  
Mean Square ◽  
The Mean ◽  
Limiting Values ◽  
Limiting Value

The question of calculating the limiting values of residuals in geodesic constructions is considered in the case when the limiting value for measurement errors is assumed equal to 3m, ie ∆рred = 3m, where m is the mean square error of the measurement. Larger errors are rejected. At present, the limiting value for the residual is calculated by the formula 3m√n, where n is the number of measurements. The article draws attention to two contradictions between theory and practice arising from the use of this formula. First, the formula is derived from the classical law of the normal Gaussian distribution, and it is applied to the truncated law of the normal distribution. And, secondly, as shown in [1], when ∆рred = 2m, the sums of errors naturally take the value equal to ?pred, after which the number of errors in the sum starts anew. This article establishes its validity for ∆рred = 3m. A table of comparative values of the tolerances valid and recommended for more stringent ones is given. The article gives a graph of applied and recommended tolerances for ∆рred = 3m.

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