Journal articles: 'NAR model'

1

Lu, Fei. "Data-Driven Model Reduction for Stochastic Burgers Equations." Entropy 22, no. 12 (2020): 1360. http://dx.doi.org/10.3390/e22121360.

Full text

Abstract:

We present a class of efficient parametric closure models for 1D stochastic Burgers equations. Casting it as statistical learning of the flow map, we derive the parametric form by representing the unresolved high wavenumber Fourier modes as functionals of the resolved variable’s trajectory. The reduced models are nonlinear autoregression (NAR) time series models, with coefficients estimated from data by least squares. The NAR models can accurately reproduce the energy spectrum, the invariant densities, and the autocorrelations. Taking advantage of the simplicity of the NAR models, we investigate maximal space-time reduction. Reduction in space dimension is unlimited, and NAR models with two Fourier modes can perform well. The NAR model’s stability limits time reduction, with a maximal time step smaller than that of the K-mode Galerkin system. We report a potential criterion for optimal space-time reduction: the NAR models achieve minimal relative error in the energy spectrum at the time step, where the K-mode Galerkin system’s mean Courant–Friedrichs–Lewy (CFL) number agrees with that of the full model.

APA, Harvard, Vancouver, ISO, and other styles

2

Ma, Qingwen, Sihan Liu, Xinyu Fan, Chen Chai, Yangyang Wang, and Ke Yang. "A Time Series Prediction Model of Foundation Pit Deformation Based on Empirical Wavelet Transform and NARX Network." Mathematics 8, no. 9 (2020): 1535. http://dx.doi.org/10.3390/math8091535.

Full text

Abstract:

Large deep foundation pits are usually in a complex environment, so their surface deformation tends to show a stable rising trend with a small range of fluctuation, which brings certain difficulty to the prediction work. Therefore, in this study we proposed a nonlinear autoregressive exogenous (NARX) prediction method based on empirical wavelet transform (EWT) pretreatment is proposed for this feature. Firstly, EWT is used to conduct adaptive decomposition of the measured deformation data and extract the modal signal components with characteristic differences. Secondly, the main components affecting the deformation of the foundation pit are analyzed as a part of the external input. Then, we established a NARX prediction model for different components. Finally, all predicted values are superpositioned to obtain a total value, and the result is compared with the predicted results of the nonlinear autoregressive (NAR) model, empirical mode decomposition-nonlinear autoregressive (EMD-NAR) model, EWT-NAR model, NARX model, EMD-NARX model and EWT-NARX model. The results showed that, compared with the EWT-NAR and EWT-NARX models, the EWT-NARX model reduced the mean square error of KD25 by 91.35%, indicating that the feature of introducing external input makes NARX more suitable for combining with the EWT method. Meanwhile, compared with the EMD-NAR and EWT-NAR models, the introduction of the NARX model reduced the mean square error of KD25 by 78.58% and 95.71%, indicating that EWT had better modal decomposition capability than EMD.

APA, Harvard, Vancouver, ISO, and other styles

3

Saxby, Claire. "NAR launches new Advance Access publication model." Nucleic Acids Research 34, no. 14 (2006): 3833. http://dx.doi.org/10.1093/nar/gkl523.

Full text

APA, Harvard, Vancouver, ISO, and other styles

4

Huang, Shih-Feng, Hsin-Han Chiang, and Yu-Jun Lin. "A network autoregressive model with GARCH effects and its applications." PLOS ONE 16, no. 7 (2021): e0255422. http://dx.doi.org/10.1371/journal.pone.0255422.

Full text

Abstract:

In this study, a network autoregressive model with GARCH effects, denoted by NAR-GARCH, is proposed to depict the return dynamics of stock market indices. A GARCH filter is employed to marginally remove the GARCH effects of each index, and the NAR model with the Granger causality test and Pearson’s correlation test with sharp price movements is used to capture the joint effects caused by other indices with the most updated market information. The NAR-GARCH model is designed to depict the joint effects of nonsynchronous multiple time series in an easy-to-implement and effective way. The returns of 20 global stock indices from 2006 to 2020 are employed for our empirical investigation. The numerical results reveal that the NAR-GARCH model has satisfactory performance in both fitting and prediction for the 20 stock indices, especially when a market index has strong upward or downward movements.

APA, Harvard, Vancouver, ISO, and other styles

5

Wang, You, Ruxue Jia, Fang Dai, and Yunxia Ye. "Traffic Flow Prediction Method Based on Seasonal Characteristics and SARIMA-NAR Model." Applied Sciences 12, no. 4 (2022): 2190. http://dx.doi.org/10.3390/app12042190.

Full text

Abstract:

Traffic flow is used as an essential indicator to measure the performance of the road network and a pivotal basis for road classification. However, the combined prediction model of traffic flow based on seasonal characteristics has been given little attention at present. Because the seasonal autoregressive integrated moving average model (SARIMA) has superior linear fitting characteristics, it is often used to process seasonal time series. In contrast, the non-autoregressive dynamic neural network (NAR) has a vital memory function and nonlinear interpretation capabilities. They are suitable for constructing combined forecasting models. The traffic flow time series of a highway in southwest China is taken as the research object in this paper. Combining the SARIMA (0,1,2) (0,1,2)12 model and the NAR model with 15 hidden layer neurons and fourth-order delay, two combined models are constructed: the linear and nonlinear component combination method is realized by the SARIMA-NAR combination model 1, and the MSE weight combination method is used by the SARIMA-NAR combination model 2. We calculated that the prediction accuracy of SARIMA-NAR combined model 1 is as high as 0.92, and the prediction accuracy of SARIMA-NAR combined model 2 is 0.90. In addition, the traffic flow forecast under the influence of the epidemic is also discussed. Through a comprehensive comparison of multiple indicators, the results show that the SARIMA-NAR combined model 1 has better road traffic flow fitting and prediction effects and is suitable for the greater volatility of traffic flow during the epidemic. This model improves the effectiveness and reliability of traffic flow forecasting, and the forecasting process is more convenient and efficient.

APA, Harvard, Vancouver, ISO, and other styles

6

López-Almada, Gabriela, J. Abraham Domínguez-Avila, Rosario Maribel Robles-Sánchez, et al. "Naringenin Decreases Retroperitoneal Adiposity and Improves Metabolic Parameters in a Rat Model of Western Diet-Induced Obesity." Metabolites 15, no. 2 (2025): 109. https://doi.org/10.3390/metabo15020109.

Full text

Abstract:

Background: Obesity is a multifactorial disease with detrimental effects on health and quality of life; unregulated satiety plays a crucial role in food intake and obesity development. Naringenin (NAR) has shown beneficial effects on lipid and carbohydrate metabolism, although its impact on adiposity and satiety remains unclear. This study reports a Western diet (WD)-induced obesity model in rats, wherein 100 mg/kg of NAR was administered as an anti-obesity agent for 8 weeks; oxidative stress, lipid profile, and satiety biomarkers were then studied, as well as in silico interaction between NAR and cholecystokinin (CCK) and ghrelin receptors. Results: NAR supplementation resulted in a significant decrease in retroperitoneal adipose tissue and liver weight, as compared to the untreated WD group (p < 0.05), potentially associated with a decreased feed efficiency. NAR also inhibited the development of dyslipidemia, particularly by reducing serum triglycerides (p < 0.05). NAR supplementation increased CCK serum levels in the basal diet group, an effect that was abolished by the WD (p < 0.05); likewise, no changes were determined on ghrelin (p > 0.05). In silico data shows that NAR is capable of interacting with the CCK and ghrelin receptors, which suggests a potential for it to modulate hunger/satiety signaling by interacting with them. Conclusions: We conclude that NAR has anti-obesogenic effects and may regulate CCK serum levels, although further research is still needed.

APA, Harvard, Vancouver, ISO, and other styles

7

Karr, Jonathan R., Jayodita C. Sanghvi, Derek N. Macklin, Abhishek Arora, and Markus W. Covert. "WholeCellKB: model organism databases for comprehensive whole-cell models." Nucleic Acids Research 41, no. D1 (2012): D787—D792. http://dx.doi.org/10.1093/nar/gks1108.

Full text

APA, Harvard, Vancouver, ISO, and other styles

8

Taherdangkoo, Reza, Alexandru Tatomir, Mohammad Taherdangkoo, Pengxiang Qiu, and Martin Sauter. "Nonlinear Autoregressive Neural Networks to Predict Hydraulic Fracturing Fluid Leakage into Shallow Groundwater." Water 12, no. 3 (2020): 841. http://dx.doi.org/10.3390/w12030841.

Full text

Abstract:

Hydraulic fracturing of horizontal wells is an essential technology for the exploitation of unconventional resources, but led to environmental concerns. Fracturing fluid upward migration from deep gas reservoirs along abandoned wells may pose contamination threats to shallow groundwater. This study describes the novel application of a nonlinear autoregressive (NAR) neural network to estimate fracturing fluid flow rate to shallow aquifers in the presence of an abandoned well. The NAR network is trained using the Levenberg–Marquardt (LM) and Bayesian Regularization (BR) algorithms and the results were compared to identify the optimal network architecture. For NAR-LM model, the coefficient of determination (R2) between measured and predicted values is 0.923 and the mean squared error (MSE) is 4.2 × 10−4, and the values of R2 = 0.944 and MSE = 2.4 × 10−4 were obtained for the NAR-BR model. The results indicate the robustness and compatibility of NAR-LM and NAR-BR models in predicting fracturing fluid flow rate to shallow aquifers. This study shows that NAR neural networks can be useful and hold considerable potential for assessing the groundwater impacts of unconventional gas development.

APA, Harvard, Vancouver, ISO, and other styles

9

WANG, K. W., C. DENG, J. P. LI, Y. Y. ZHANG, X. Y. LI, and M. C. WU. "Hybrid methodology for tuberculosis incidence time-series forecasting based on ARIMA and a NAR neural network." Epidemiology and Infection 145, no. 6 (2017): 1118–29. http://dx.doi.org/10.1017/s0950268816003216.

Full text

Abstract:

SUMMARYTuberculosis (TB) affects people globally and is being reconsidered as a serious public health problem in China. Reliable forecasting is useful for the prevention and control of TB. This study proposes a hybrid model combining autoregressive integrated moving average (ARIMA) with a nonlinear autoregressive (NAR) neural network for forecasting the incidence of TB from January 2007 to March 2016. Prediction performance was compared between the hybrid model and the ARIMA model. The best-fit hybrid model was combined with an ARIMA (3,1,0) × (0,1,1)12 and NAR neural network with four delays and 12 neurons in the hidden layer. The ARIMA-NAR hybrid model, which exhibited lower mean square error, mean absolute error, and mean absolute percentage error of 0·2209, 0·1373, and 0·0406, respectively, in the modelling performance, could produce more accurate forecasting of TB incidence compared to the ARIMA model. This study shows that developing and applying the ARIMA-NAR hybrid model is an effective method to fit the linear and nonlinear patterns of time-series data, and this model could be helpful in the prevention and control of TB.

APA, Harvard, Vancouver, ISO, and other styles

10

Song, Jintao, Yongchao Chen, and Jie Yang. "A Novel Outlier Detection Method of Long-Term Dam Monitoring Data Based on SSA-NAR." Wireless Communications and Mobile Computing 2022 (August 30, 2022): 1–11. http://dx.doi.org/10.1155/2022/6569367.

Full text

Abstract:

Outlier generally exists in dam monitoring data which may seriously affect the accuracy of dam safety evaluation results. Aiming at the outlier detection of dam monitoring data, a novel dynamic detection method of dam outlier data based on SSA-NAR is proposed. This combined method does not depend on the effect quantity and influence quantity relationship of traditional dam safety theory and only uses the time series of effect quantity to mine the variation, which can avoid the impact of missing or abnormal of the influence quantity. The Nonlinear Autoregression (NAR) is a classical time series neural network widely used in engineering field. However, the prediction accuracy of NAR is greatly affected by the selection of model parameters, the Sparrow Search Algorithm (SSA) which is a novel model parameter solution method and can be combined with NAR to derive the optimal parameters of NAR prediction model. The outlier is identified through the analysis of the residual distribution between the predicted data and the measured data. The case study shows that when the original data does not contain outliers, the prediction accuracy of the model is high. When the outlier is included, the proposed model has good robustness which the outlier has little influence on the prediction effect. It can effectively detect the outlier in the original dam monitoring data and provide a reliable data basis for dam safety evaluation.

APA, Harvard, Vancouver, ISO, and other styles

11

Dada, Anelize, Rita de Cássia Vilhena da Silva, Mariana Zanovello, et al. "Comparative Analysis of the Protective Effect of Naringenin on Cardiovascular Parameters of Normotensive and Hypertensive Rats Subjected to the Myocardial Infarction Model." Pharmaceuticals 17, no. 10 (2024): 1324. http://dx.doi.org/10.3390/ph17101324.

Full text

Abstract:

Background: Cardiovascular diseases rank as the top global cause of mortality, particularly acute myocardial infarction (MI). MI arises from the blockage of a coronary artery, which disrupts blood flow and results in tissue death. Among therapeutic approaches, bioactives from medicinal plants emerge as promising for the development of new medicines. Objectives: This study explored the effects of naringenin (NAR 100 mg/kg), a flavonoid found in citrus fruits, in normotensive (NTR) and spontaneously hypertensive (SHR) rats, both subjected to isoproterenol (ISO 85 mg/kg)-induced MI. Results: Post-treatment assessments indicated that NAR reduced blood pressure and minimized clot formation, particularly notable in the SHR group, which helps mitigate damage related to hypertension and ISO exposure. Additionally, NAR effectively restored KCl-induced contractility in the aortas of both NTR and SHR groups. NAR treatment reduced reduced glutathione (GSH) and lipid hydroperoxides (LOOH) values and recovered the activity of the antioxidant enzymes catalase (CAT) and glutathione-s-transferase (GST) in NTR groups. Moreover, myocardial damage assessed through histological analyses was reduced in groups treated with NAR. Conclusions: The results highlight significant pathophysiological differences between the groups, suggesting that NAR has protective potential against ISO-induced cardiac damage, warranting further investigation into its protective effects and mechanisms.

APA, Harvard, Vancouver, ISO, and other styles

12

Kopp, J. "The SWISS-MODEL Repository of annotated three-dimensional protein structure homology models." Nucleic Acids Research 32, no. 90001 (2004): 230D—234. http://dx.doi.org/10.1093/nar/gkh008.

Full text

APA, Harvard, Vancouver, ISO, and other styles

13

Karami, Yasaman, Julien Rey, Guillaume Postic, Samuel Murail, Pierre Tufféry, and Sjoerd J. de Vries. "DaReUS-Loop: a web server to model multiple loops in homology models." Nucleic Acids Research 47, W1 (2019): W423—W428. http://dx.doi.org/10.1093/nar/gkz403.

Full text

Abstract:

AbstractLoop regions in protein structures often have crucial roles, and they are much more variable in sequence and structure than other regions. In homology modeling, this leads to larger deviations from the homologous templates, and loop modeling of homology models remains an open problem. To address this issue, we have previously developed the DaReUS-Loop protocol, leading to significant improvement over existing methods. Here, a DaReUS-Loop web server is presented, providing an automated platform for modeling or remodeling loops in the context of homology models. This is the first web server accepting a protein with up to 20 loop regions, and modeling them all in parallel. It also provides a prediction confidence level that corresponds to the expected accuracy of the loops. DaReUS-Loop facilitates the analysis of the results through its interactive graphical interface and is freely available at http://bioserv.rpbs.univ-paris-diderot.fr/services/DaReUS-Loop/.

APA, Harvard, Vancouver, ISO, and other styles

14

Li, Li, Ru Liu, Jing He, et al. "Naringin Regulates Microglia BV-2 Activation and Inflammation via the JAK/STAT3 Pathway." Evidence-Based Complementary and Alternative Medicine 2022 (May 19, 2022): 1–10. http://dx.doi.org/10.1155/2022/3492058.

Full text

Abstract:

Objective. Microglial BV-2 cells are activated in the brain following insomnia. Naringin (NAR) is a polymethoxylated flavonoid that is also commonly found in citrus fruits and is known for its antioxidant potential. However, the effect of NAR on microglial cells has rarely been studied in the brain of an organism after insomnia. This study aimed to investigate the effects and potential mechanisms of action of NAR on microglial cell activation and inflammation. Methods. BV-2 cells were obtained from the China Center for Type Culture Collection and randomly divided into five treatment groups: control, model, NAR (10 μM), WP1066 (5 μM), and NAR + WP1066. With the exception of the control group, all groups were stimulated with LPS (1 μg/mL) for 6 h. CCK8 was used to quantify cell viability and a scratch test was performed to detect cell migration. The expression levels of interleukin 6 (IL-6), tumor necrosis factor-alpha (TNF-α), interleukin 1 beta (IL-1β), nterleukin 10 (IL-10), and insulin like growth factor (1IGF-1) were measured by ELISA. Western blotting was performed to determine the levels of p-STAT3 and p-JAK. The Focalcheck™ Thin-Ring Fluorescent Microspheres kit was used to detect cell phagocytosis. Immunofluorescence was used to observe the expression of iNOS and arginase1 in BV-2 cells. Results. Compared with the control group, cell migration, cell viability, and the expression of IL-1β, IL-6, TNF-α, and iNOS were significantly increased in the model group, whereas the expression levels of IL-10, IGF-1, and arginase 1, as well as cell phagocytosis were reduced. With the increase in NAR concentration, cell migration, cell viability, the expression levels of IL-1β, IL-6, TNF-α, and iNOS decreased, while the expression of IL-10, IGF-1, and arginase 1 increased. Compared with the control group, p-STAT3, and p-JAK expression in the model group were significantly increased (P<0.05). Compared with the model group, the expression of p-STAT3 and p-JAK in the NAR, NAR + WP1066, and WP1066 groups was significantly decreased ( P < 0.05 ). Conclusion. NAR treatment inhibited the proliferation, migration, and inflammation of BV-2 cells as well as the activation of microglia to the M1 phenotype. Conversely, NAR treatment promoted the activation of microglia to the M2 phenotype and enhanced the phagocytic function of BV-2 cells by regulating the activity of the JAK/STAT3 pathway.

APA, Harvard, Vancouver, ISO, and other styles

15

Liu, Sheng Peng, and Ye Zhang. "City Fire Forecasts and Analysis Based on Nonlinear Auto-Regressive Time-Series Model." Applied Mechanics and Materials 241-244 (December 2012): 1550–55. http://dx.doi.org/10.4028/www.scientific.net/amm.241-244.1550.

Full text

Abstract:

The forecasting to future developments of the city fire time series is a challenging task that has been addressed by many researchers due to the importance. In this paper, a Nonlinear Auto-Regressive (NAR) prediction model is applied to forecast the city fire data based on support vector regression. The performances of the NAR prediction model in city fire forecasting are compared with the BP neural network method. The experimental results show that the proposed model performs best.

APA, Harvard, Vancouver, ISO, and other styles

16

Zhang, Xueqing, Yibin Zeng, Hui Li, et al. "A Modified NAR Scoring Model Incorporating Immune Infiltration Characteristics to Better Predict Long-Term Survival Following Neoadjuvant Radiotherapy in Rectal Cancer." Life 13, no. 11 (2023): 2106. http://dx.doi.org/10.3390/life13112106.

Full text

Abstract:

(1) Background: The neoadjuvant rectal (NAR) score has been developed as a prognostic tool for survival in locally advanced rectal cancer (LARC). However, the NAR score only incorporates weighted cT, ypT, and ypN categories. This long-term follow-up study aims to modify a novel prognostic scoring model and identify a short-term endpoint for survival. (2) Methods: The prognostic factors for overall survival (OS) were explored through univariate and multivariate analyses. Based on Cox regression modeling, nomogram plots were constructed. Area under the curve (AUC) and concordance indices were used to evaluate the performance of the nomogram. Receiver operating characteristic (ROC) analysis was conducted to compare the efficiency of the nomogram with other prognostic factors. (3) Results: After a long-term follow-up, the 5-year OS was 67.1%. The mean NAR score was 20.4 ± 16.3. Multivariate analysis indicated that CD8+ T-cell, lymphovascular invasion, and the NAR score were independent predictors of OS. The modified NAR scoring model, incorporating immune infiltration characteristics, exhibited a high C-index of 0.739 for 5-year OS, significantly outperforming any individual factor. Moreover, the predictive value of the nomogram was superior to the AJCC stage and pathological complete regression at 3-year, 5-year, and 10-year time points, respectively. Over time, the model’s predictions of long-term survival remained consistent and improved in accuracy. (4) Conclusions: The modified NAR scoring model, incorporating immune infiltration characteristics, demonstrates high accuracy and consistency in predicting OS.

APA, Harvard, Vancouver, ISO, and other styles

17

Zhou, Wei, Xiao Zhu, Jun Wang, and Yan Ran. "A New Error Prediction Method for Machining Process Based on a Combined Model." Mathematical Problems in Engineering 2018 (July 16, 2018): 1–8. http://dx.doi.org/10.1155/2018/3703861.

Full text

Abstract:

Machining process is characterized by randomness, nonlinearity, and uncertainty, leading to the dynamic changes of machine tool machining errors. In this paper, a novel model combining the data processing merits of metabolic grey model (MGM) with that of nonlinear autoregressive (NAR) neural network is proposed for machining error prediction. The advantages and disadvantages of MGM and NAR neural network are introduced in detail, respectively. The combined model first utilizes MGM to predict the original error data and then uses NAR neural network to forecast the residual series of MGM. An experiment on the spindle machining is carried out, and a series of experimental data is used to validate the prediction performance of the combined model. The comparison of the experiment results indicates that combined model performs better than the individual model. The two-stage prediction of the combined model is characterized by high accuracy, fast speed, and robustness and can be applied to other complex machining error predictions.

APA, Harvard, Vancouver, ISO, and other styles

18

Fujihara, C. K., D. M. Limongi, R. Falzone, M. S. Graudenz, and R. Zatz. "Pathogenesis of glomerular sclerosis in subtotally nephrectomized analbuminemic rats." American Journal of Physiology-Renal Physiology 261, no. 2 (1991): F256—F264. http://dx.doi.org/10.1152/ajprenal.1991.261.2.f256.

Full text

Abstract:

The Nagase analbuminemic rat (NAR), a mutant of the Sprague-Dawley (SD) strain, exhibits persistent hypercholesterolemia, thrombocytosis, and enhanced platelet aggregation, abnormalities possibly involved in the genesis of glomerular sclerosis (GS). Previous observations suggest that these rats never develop aging GS. We studied the development of GS in NAR after 5/6 nephrectomy (Nx). Fifteen days after Nx, marked glomerular hypertension was observed in NAR, compared with only mild elevations in SD rats. Glomerular hypertrophy was more marked in SD rats than in NAR. Enalapril normalized glomerular volume and partially reversed glomerular hypertension in NAR without altering platelet function or cholesterol levels. Glomerular endothelial injury and intraluminal fibrin deposition were seen only in NAR. Two months after Nx, severe GS and massive glomerular lipid deposition were seen in NAR, whereas only mild glomerular injury occurred in SD rats. Enalapril attenuated GS and prevented lipid deposition in NAR. Glomerular hypertension may be a key factor in the genesis of GS in this model in association with endothelial injury, intracapillary coagulation, and lipid accumulation.

APA, Harvard, Vancouver, ISO, and other styles

19

John, B. "Comparative protein structure modeling by iterative alignment, model building and model assessment." Nucleic Acids Research 31, no. 14 (2003): 3982–92. http://dx.doi.org/10.1093/nar/gkg460.

Full text

APA, Harvard, Vancouver, ISO, and other styles

20

Li, Yong, Yiyuan Pan, Lin Gao, et al. "Naringenin Protects against Acute Pancreatitis in Two Experimental Models in Mice by NLRP3 and Nrf2/HO-1 Pathways." Mediators of Inflammation 2018 (2018): 1–13. http://dx.doi.org/10.1155/2018/3232491.

Full text

Abstract:

Background. Naringenin (Nar) is a type of flavonoid and has been shown to have anti-inflammatory and antioxidative properties. However, the effects of Nar on acute pancreatitis (AP) have not been well studied. In this study, we aimed to investigate the function of Nar in a mouse model of AP. Methods. Mild acute pancreatitis (MAP) was induced by caerulein (Cae), and severe acute pancreatitis (SAP) was induced by L-arginine in mice. Nar was administered intraperitoneally at doses of 25, 50, or 100 mg/kg following MAP induction and at a dose of 100 mg/kg following SAP induction. The serum levels of cytokines, lipase, and amylase were determined, and pancreatic and pulmonary tissues were harvested. Results. The serum levels of amylase, lipase, and cytokines were significantly decreased in both MAP and SAP models after Nar treatment. The malondialdehyde (MDA) levels of the pancreatic tissue was significantly reduced in both MAP and SAP after Nar treatment. In contrast, glutathione peroxidase (GPx), glutathione reductase (GR), glutathione S-transferase (GST), total sulfhydryl (T-SH), and non-proteinsulthydryl (NP-SH) were markedly increased in both MAP and SAP after Nar treatment. The injury in pancreatic and pulmonary tissues was markedly improved as evidenced by the inhibited expression of myeloperoxidase, nod-like receptor protein 3, and interleukin 1 beta as well as the enhanced expression of nuclear factor erythroid 2-related factor 2/heme oxygenase-1 in pancreatic tissues. Conclusions. Nar exerted protective effects on Cae-induced MAP and L-arginine-induced SAP in mice, suggesting that Nar may be a potential therapeutic intervention for AP.

APA, Harvard, Vancouver, ISO, and other styles

21

Long, Bing, Xiangnan Li, Xiaoyu Gao, and Zhen Liu. "Prognostics Comparison of Lithium-Ion Battery Based on the Shallow and Deep Neural Networks Model." Energies 12, no. 17 (2019): 3271. http://dx.doi.org/10.3390/en12173271.

Full text

Abstract:

Prognostics of the remaining useful life (RUL) of lithium-ion batteries is a crucial role in the battery management systems (BMS). An artificial neural network (ANN) does not require much knowledge from the lithium-ion battery systems, thus it is a prospective data-driven prognostic method of lithium-ion batteries. Though the ANN has been applied in prognostics of lithium-ion batteries in some references, no one has compared the prognostics of the lithium-ion batteries based on different ANN. The ANN generally can be classified to two categories: the shallow ANN, such as the back propagation (BP) ANN and the nonlinear autoregressive (NAR) ANN, and the deep ANN, such as the long short-term memory (LSTM) NN. An improved LSTM NN is proposed in order to achieve higher prediction accuracy and make the construction of the model simpler. According to the lithium-ion data from the NASA Ames, the prognostics comparison of lithium-ion battery based on the BP ANN, the NAR ANN, and the LSTM ANN was studied in detail. The experimental results show: (1) The improved LSTM ANN has the best prognostic accuracy and is more suitable for the prediction of the RUL of lithium-ion batteries compared to the BP ANN and the NAR ANN; (2) the NAR ANN has better prognostic accuracy compared to the BP ANN.

APA, Harvard, Vancouver, ISO, and other styles

22

Tian, Kai, Yi Xu, Jihong Guan, and Shuigeng Zhou. "Network as Regularization for Training Deep Neural Networks: Framework, Model and Performance." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 04 (2020): 6013–20. http://dx.doi.org/10.1609/aaai.v34i04.6063.

Full text

Abstract:

Despite powerful representation ability, deep neural networks (DNNs) are prone to over-fitting, because of over-parametrization. Existing works have explored various regularization techniques to tackle the over-fitting problem. Some of them employed soft targets rather than one-hot labels to guide network training (e.g. label smoothing in classification tasks), which are called target-based regularization approaches in this paper. To alleviate the over-fitting problem, here we propose a new and general regularization framework that introduces an auxiliary network to dynamically incorporate guided semantic disturbance to the labels. We call it Network as Regularization (NaR in short). During training, the disturbance is constructed by a convex combination of the predictions of the target network and the auxiliary network. These two networks are initialized separately. And the auxiliary network is trained independently from the target network, while providing instance-level and class-level semantic information to the latter progressively. We conduct extensive experiments to validate the effectiveness of the proposed method. Experimental results show that NaR outperforms many state-of-the-art target-based regularization methods, and other regularization approaches (e.g. mixup) can also benefit from combining with NaR.

APA, Harvard, Vancouver, ISO, and other styles

23

Walther, Jürgen, Pablo D. Dans, Alexandra Balaceanu, Adam Hospital, Genís Bayarri, and Modesto Orozco. "A multi-modal coarse grained model of DNA flexibility mappable to the atomistic level." Nucleic Acids Research 48, no. 5 (2020): e29-e29. http://dx.doi.org/10.1093/nar/gkaa015.

Full text

Abstract:

Abstract We present a new coarse grained method for the simulation of duplex DNA. The algorithm uses a generalized multi-harmonic model that can represent any multi-normal distribution of helical parameters, thus avoiding caveats of current mesoscopic models for DNA simulation and representing a breakthrough in the field. The method has been parameterized from accurate parmbsc1 atomistic molecular dynamics simulations of all unique tetranucleotide sequences of DNA embedded in long duplexes and takes advantage of the correlation between helical states and backbone configurations to derive atomistic representations of DNA. The algorithm, which is implemented in a simple web interface and in a standalone package reproduces with high computational efficiency the structural landscape of long segments of DNA untreatable by atomistic molecular dynamics simulations.

APA, Harvard, Vancouver, ISO, and other styles

24

Castrignano, T. "The PMDB Protein Model Database." Nucleic Acids Research 34, no. 90001 (2006): D306—D309. http://dx.doi.org/10.1093/nar/gkj105.

Full text

APA, Harvard, Vancouver, ISO, and other styles

25

Kummong, Ratree, and Siriporn Supratid. "Long-term forecasting system using wavelet – nonlinear autoregressive neural network conjunction model." Journal of Modelling in Management 14, no. 4 (2019): 948–71. http://dx.doi.org/10.1108/jm2-11-2018-0184.

Full text

Abstract:

Purpose An accurate long-term multi-step forecast provides crucial basic information for planning and reinforcing managerial decision-support. However, nonstationarity and nonlinearity, normally consisted of several types of managerial data can seriously ruin the forecasting computation. This paper aims to propose an effective long-term multi-step forecasting conjunction model, namely, wavelet–nonlinear autoregressive neural network (WNAR) conjunction model. The WNAR combines discrete wavelet transform (DWT) and nonlinear autoregressive neural network (NAR) to cope with such nonstationarity and nonlinearity within the managerial data; as a consequence, provides insight information that enhances accuracy and reliability of long-term multi-step perspective, leading to effective management decision-making. Design/methodology/approach Based on WNAR conjunction model, wavelet decomposition is executed for efficiently extracting hidden significant, temporal features contained in each of six benchmark nonstationary data sets from different managerial domains. Then, each extracted feature set at a particular resolution level is fed into NAR for the further forecast. Finally, NAR forecasting results are reconstructed. Forecasting performance measures throughout 1 to 30-time lags rely on mean absolute percentage error (MAPE), root mean square error (RMSE), Nash-Sutcliffe efficiency index or the coefficient of efficiency (Ef) and Diebold–Mariano (DM) test. An effect of data characteristic in terms of autocorrelation on forecasting performances of each data set are observed. Findings Long-term multi-step forecasting results show the best accuracy and high-reliability performance of the proposed WNAR conjunction model over some other efficient forecasting models including a single NAR model. This is confirmed by DM test, especially for the short-forecasting horizon. In addition, rather steady, effective long-term multi-step forecasting performances are yielded with slight effect from time lag changes especially for the data sets having particular high autocorrelation, relative against 95 per cent degree of confidence normal distribution bounds. Research limitations/implications The WNAR, which combines DWT with NAR can be accounted as a bridge for the gap between machine learning, engineering signal processing and management decision-support systems. Thus, WNAR is referred to as a forecasting tool that provides insight long-term information for managerial practices. However, in practice, suitable exogenous input forecast factors are required on the managerial domain-by-domain basis to correctly foresee and effectively prepare necessary reasonable management activities. Originality/value Few works have been implemented to handle the nonstationarity, consisted of nonlinear managerial data to attain high-accurate long-term multi-step forecast. Combining DWT and NAR capabilities would comprehensively and specifically deal with the nonstationarity and nonlinearity difficulties at once. In addition, it is found that the proposed WNAR yields rather steady, effective long-term multi-step forecasting performance throughout specific long time lags regarding the data, having certainly high autocorrelation levels across such long time lags.

APA, Harvard, Vancouver, ISO, and other styles

26

Rueda-Bayona, Juan Gabriel, Juan José Cabello Eras, and Alexis Sagastume Gutiérrez. "Modeling Wind Speed with a Long-Term Horizon and High-Time Interval with a Hybrid Fourier-Neural Network Model." Mathematical Modelling of Engineering Problems 8, no. 3 (2021): 431–40. http://dx.doi.org/10.18280/mmep.080313.

Full text

Abstract:

The limited availability of local climatological stations and the limitations to predict the wind speed (WS) accurately are significant barriers to the expansion of wind energy (WE) projects worldwide. A methodology to forecast accurately the WS at the local scale can be used to overcome these barriers. This study proposes a methodology to forecast the WS with high-resolution and long-term horizons, which combines a Fourier model and a nonlinear autoregressive network (NAR). Given the nonlinearities of the WS variations, a NAR model is used to forecast the WS based on the variability identified with the Fourier analysis. The NAR modelled successfully 1.7 years of wind-speed with 3 hours of the time interval, what may be considered the longest forecasting horizon with high resolution at the moment.

APA, Harvard, Vancouver, ISO, and other styles

27

van der Meer, Dieudonne, Syd Barthorpe, Wanjuan Yang, et al. "Cell Model Passports—a hub for clinical, genetic and functional datasets of preclinical cancer models." Nucleic Acids Research 47, no. D1 (2018): D923—D929. http://dx.doi.org/10.1093/nar/gky872.

Full text

APA, Harvard, Vancouver, ISO, and other styles

28

Jin, Xuezhu, Xin Chen, Song Wang, and Yan Leng. "Investigation of narciclasine’s role in dietary interventions for nonalcoholic fatty liver disease." Italian Journal of Food Science 37, no. 1 (2025): 441–49. https://doi.org/10.15586/ijfs.v37i1.2835.

Full text

Abstract:

Nonalcoholic fatty liver disease (NAFLD), a condition caused by a buildup of fat in liver cells because of wrong choices and nutrition intake patterns over time, is a common chronic metabolic disorder among individuals today. Narciclasine (Nar), a compound present in Haemanthus coccineus L., has been noted for its strong anti-inflammatory effects. However, its impact on regulating metabolic imbalances associated with NAFLD requires further investigation. In this research investigation using mice with NAFLD induced by a high-fat diet (HFD) and a cell model stimulated with fatty acids (FFAs), the aim was to examine the impact of Nar on liver health outcomes. The findings indicated that Nar helped improve the changes in metabolic parameters caused by the HFD, such as metabolism and inflammatory markers. Moreover, the buildup of liver fat triggered by the HFD was lessened after Nar treatment. Similarly, the inflammatory reaction in liver tissues exacerbated by the HFD was notably reduced with the involvement of Nar. The results of lab tests showed that Nar stopped FFA, which effectively triggered production and cell death in liver cells. It was shown that Nar blocked the activation of the NF-κB signaling pathway, which plays a role in inflammation and metabolic imbalances. These discoveries indicate a potential for Nar as a supplement or treatment for handling NAFLD. It provides a method to cure patients by adjusting important metabolic and inflammatory routes.

APA, Harvard, Vancouver, ISO, and other styles

29

Eggenhofer, Florian, Ivo L. Hofacker, and Christian Höner zu Siederdissen. "RNAlien – Unsupervised RNA family model construction." Nucleic Acids Research 44, no. 17 (2016): 8433–41. http://dx.doi.org/10.1093/nar/gkw558.

Full text

APA, Harvard, Vancouver, ISO, and other styles

30

Kolker, Eugene, Roger Higdon, Winston Haynes, et al. "MOPED: Model Organism Protein Expression Database." Nucleic Acids Research 40, no. D1 (2011): D1093—D1099. http://dx.doi.org/10.1093/nar/gkr1177.

Full text

APA, Harvard, Vancouver, ISO, and other styles

31

Forties, Robert A., Justin A. North, Sarah Javaid, et al. "A quantitative model of nucleosome dynamics." Nucleic Acids Research 39, no. 19 (2011): 8306–13. http://dx.doi.org/10.1093/nar/gkr422.

Full text

APA, Harvard, Vancouver, ISO, and other styles

32

Milner, Joel J., Edi Cecchini, and Peter J. Dominy. "A kinetic model for subtractive hybridization." Nucleic Acids Research 23, no. 1 (1995): 176–87. http://dx.doi.org/10.1093/nar/23.1.176.

Full text

APA, Harvard, Vancouver, ISO, and other styles

33

Konishi, T. "A thermodynamic model of transcriptome formation." Nucleic Acids Research 33, no. 20 (2005): 6587–92. http://dx.doi.org/10.1093/nar/gki967.

Full text

APA, Harvard, Vancouver, ISO, and other styles

34

Cobb, Jennifer A., and Lotte Bjergbaek. "RecQ helicases: lessons from model organisms." Nucleic Acids Research 34, no. 15 (2006): 4106–14. http://dx.doi.org/10.1093/nar/gkl557.

Full text

APA, Harvard, Vancouver, ISO, and other styles

35

Agah, A. "A multi-enzyme model for pyrosequencing." Nucleic Acids Research 32, no. 21 (2004): e166-e166. http://dx.doi.org/10.1093/nar/gnh159.

Full text

APA, Harvard, Vancouver, ISO, and other styles

36

Zhang, Zhihan, Hui Liu, Xiang Hu, et al. "Heat Shock Protein 70 Mediates the Protective Effect of Naringenin on High-Glucose-Induced Alterations of Endothelial Function." International Journal of Endocrinology 2022 (August 1, 2022): 1–10. http://dx.doi.org/10.1155/2022/7275765.

Full text

Abstract:

Endothelial dysfunction plays a pivotal role in the development and progression of diabetic vascular complications. Naringenin (Nar) is a flavanone bioactive isolated from citrus fruits known to have in vitro and in vivo antidiabetic properties. However, whether Nar affects endothelial function remains unclear in diabetes or under high-glucose (HG) condition. Using an in vitro model of hyperglycemia in human umbilical vein endothelial cells (HUVECs), we found that Nar administration markedly attenuated HG-induced alterations of endothelial function, evidenced by the mitigation of oxidative stress and inflammation, the reduction of cell adhesion molecular expressions, and the improvement of insulin resistance. We also found that HG exposure significantly reduced the levels of intracellular heat shock protein 70 (iHSP70 or iHSPA1A) and the release of HSP70 from HUVECs. HSP70 depletion mimicked and clearly diminished the protective effects of Nar on HG-induced alterations of endothelial function. In addition, Nar treatment significantly enhanced iHSP70 protein levels through a transcription-dependent manner. These results demonstrated that Nar could protect HUVECs against HG-induced alterations of endothelial function through upregulating iHSP70 protein levels. These findings are also helpful in providing new therapeutic strategies that are promising in the clinical use of Nar for the treatment of diabetes and diabetic complications.

APA, Harvard, Vancouver, ISO, and other styles

37

Jin, Hua, Yue Zhao, Yinlian Yao, et al. "Intratracheal Administration of Stem Cell Membrane-Cloaked Naringin-Loaded Biomimetic Nanoparticles Promotes Resolution of Acute Lung Injury." Antioxidants 13, no. 3 (2024): 282. http://dx.doi.org/10.3390/antiox13030282.

Full text

Abstract:

Cytokine storm and ROS overproduction in the lung always lead to acute lung injury (ALI) and acute respiratory distress syndrome (ARDS) in a very short time. Effectively controlling cytokine storm release syndrome (CRS) and scavenging ROS are key to the prevention and treatment of ALI/ARDS. In this work, the naringin nanoparticles (Nar-NPs) were prepared by the emulsification and evaporation method; then, the mesenchymal stem cell membranes (CMs) were extracted and coated onto the surface of the Nar-NPs through the hand extrusion method to obtain the biomimetic CM@Nar-NPs. In vitro, the CM@Nar-NPs showed good dispersity, excellent biocompatibility, and biosafety. At the cellular level, the CM@Nar-NPs had excellent abilities to target inflamed macrophages and the capacity to scavenge ROS. In vivo imaging demonstrated that the CM@Nar-NPs could target and accumulate in the inflammatory lungs. In an ALI mouse model, intratracheal (i.t.) instillation of the CM@Nar-NPs significantly decreased the ROS level, inhibited the proinflammatory cytokines, and remarkably promoted the survival rate. Additionally, the CM@Nar-NPs increased the expression of M2 marker (CD206), and decreased the expression of M1 marker (F4/80) in septic mice, suggesting that the Nar-modulated macrophages polarized towards the M2 subtype. Collectively, this work proves that a mesenchymal stem cell membrane-based biomimetic nanoparticle delivery system could efficiently target lung inflammation via i.t. administration; the released payload inhibited the production of inflammatory cytokines and ROS, and the Nar-modulated macrophages polarized towards the M2 phenotype which might contribute to their anti-inflammation effects. This nano-system provides an excellent pneumonia-treated platform with satisfactory biosafety and has great potential to effectively deliver herbal medicine.

APA, Harvard, Vancouver, ISO, and other styles

38

Zhang, Lei. "Chaotic System Design Based on Recurrent Artificial Neural Network for the Simulation of EEG Time Series." International Journal of Cognitive Informatics and Natural Intelligence 13, no. 1 (2019): 25–35. http://dx.doi.org/10.4018/ijcini.2019010103.

Full text

Abstract:

Electroencephalogram (EEG) signals captured from brain activities demonstrate chaotic features, and can be simulated by nonlinear dynamic time series outputs of chaotic systems. This article presents the research work of chaotic system generator design based on artificial neural network (ANN), for studying the chaotic features of human brain dynamics. The ANN training performances of Nonlinear Auto-Regressive (NAR) model are evaluated for the generation and prediction of chaotic system time series outputs, based on varying the ANN architecture and the precision of the generated training data. The NAR model is trained in open loop form with 1,000 training samples generated using Lorenz system equations and the forward Euler method. The close loop NAR model is used for the generation and prediction of Lorenz chaotic time series outputs. The training results show that better training performance can be achieved by increasing the number of feedback delays and the number of hidden neurons, at the cost of increasing the computational load.

APA, Harvard, Vancouver, ISO, and other styles

39

Lova, Stelly Martha, and Faisal Faisal. "Menjadi Guru Adaptif dengan Pendekatan C-NAR di Sekolah Dasar." Paedagogi: Jurnal Kajian Ilmu Pendidikan (e-journal) 9, no. 1 (2023): 149. http://dx.doi.org/10.24114/paedagogi.v9i1.48202.

Full text

Abstract:

Penelitian ini bertujuan untuk mendeskripsikan implementasi Collaborative Nested Action Research (C-NAR) sebagai upaya menghasilkan guru adaptif di Sekolah Dasar (SD). Penelitin ini merupakan penelitian tindakan kelas berkelanjutan dengan pendekatan C-NAR. Dalam pelaksanaannya, selain guru melakukan perbaikan perkelanjutan dalam pembelajaran, dosen dan guru pamong melakukan perbaikan berkelanjutan dalam pembimbingan. Teknik pengumpulan data dilakukan melalui observasi dan wawancara. Instrumen yang digunakan dalam penelitian adalah lembar observasi dan pedoman wawancara. Data penelitian dianalisis secara kualitatif dengan model alir mulai dari reduksi data, penyajian data, hingga pada penarikan simpulan. Hasil penelitian menunjukkan bahwa pendekatan C-NAR mampu menghasilkan guru yang adaptif di sekolah dasar sesuai dengan tuntutan perkembangan revolusi industri 4.0 dan society 5.0. Dengan demikian, pendekatan C-NAR layak dipertimbangkan sebagai pendekatan mutakhir dalam menghasilkan guru adaptif di SD.

APA, Harvard, Vancouver, ISO, and other styles

40

Sioofy Khoojine, Arash, Mahdi Shadabfar, Vahid Reza Hosseini, and Hadi Kordestani. "Network Autoregressive Model for the Prediction of COVID-19 Considering the Disease Interaction in Neighboring Countries." Entropy 23, no. 10 (2021): 1267. http://dx.doi.org/10.3390/e23101267.

Full text

Abstract:

Predicting the way diseases spread in different societies has been thus far documented as one of the most important tools for control strategies and policy-making during a pandemic. This study is to propose a network autoregressive (NAR) model to forecast the number of total currently infected cases with coronavirus disease 2019 (COVID-19) in Iran until the end of December 2021 in view of the disease interactions within the neighboring countries in the region. For this purpose, the COVID-19 data were initially collected for seven regional nations, including Iran, Turkey, Iraq, Azerbaijan, Armenia, Afghanistan, and Pakistan. Thenceforth, a network was established over these countries, and the correlation of the disease data was calculated. Upon introducing the main structure of the NAR model, a mathematical platform was subsequently provided to further incorporate the correlation matrix into the prediction process. In addition, the maximum likelihood estimation (MLE) was utilized to determine the model parameters and optimize the forecasting accuracy. Thereafter, the number of infected cases up to December 2021 in Iran was predicted by importing the correlation matrix into the NAR model formed to observe the impact of the disease interactions in the neighboring countries. In addition, the autoregressive integrated moving average (ARIMA) was used as a benchmark to compare and validate the NAR model outcomes. The results reveal that COVID-19 data in Iran have passed the fifth peak and continue on a downward trend to bring the number of total currently infected cases below 480,000 by the end of 2021. Additionally, 20%, 50%, 80% and 95% quantiles are provided along with the point estimation to model the uncertainty in the forecast.

APA, Harvard, Vancouver, ISO, and other styles

41

Rivoira, María A., Alfredo Rigalli, Lucía Corball, Nori Tolosa de Talamoni, and Valeria Rodríguez. "Naringin prevents bone damage in the experimental metabolic syndrome induced by a fructose-rich diet." Applied Physiology, Nutrition, and Metabolism 47, no. 4 (2022): 395–404. http://dx.doi.org/10.1139/apnm-2021-0473.

Full text

Abstract:

We analyzed the effect of naringin (NAR), a flavonoid from citric fruits, on bone quality and biomechanical properties, as well as the redox state of bone marrow in rats fed a fructose-rich diet (FRD), an experimental model to mimic human metabolic syndrome. NAR blocked the increase in the number of osteoclasts and adipocytes and the decrease in the number of osteocytes and osteocalcin (+) cells caused by FRD. Trabecular number was significantly higher in the FRD+NAR group. FRD induced a decrease in the femoral trabecular and cortical bone mineral density, which was blocked by NAR. The fracture and ultimate loads were also decreased in the FRD and FRD+NAR groups. NAR increased the number of nodes to terminal trabecula, the number of nodes to node trabecula, the number of nodes, and the number of nodes with 2 terminals and decreased the Dist (mean size of branches) value. FRD decreased bone marrow catalase activity, an effect that was prevented by NAR. In conclusion, FRD has detrimental effects on the long bones, which are associated with oxidative stress in the bone marrow. Most of these changes are prevented by NAR through its antioxidant properties and promotion of bone formation. Novelty: Fructose-rich diets have detrimental effects on long bones, which are associated with oxidative stress in the bone marrow. Most of these changes are prevented by naringin through its antioxidant properties and promotion of bone formation.

APA, Harvard, Vancouver, ISO, and other styles

42

Fuller, Thomas. "28 IOHA National accreditation recognition certification program development template and guideline." Annals of Work Exposures and Health 68, Supplement_1 (2024): 1. http://dx.doi.org/10.1093/annweh/wxae035.013.

Full text

Abstract:

Abstract The National Accreditation Recognition (NAR) Committee establishes a process and criteria to recognize national occupational hygiene associations who offer certification examinations for the profession. The goal is to promote global respect for and recognition of Occupational Hygiene Certification Programs which meet or exceed the IOHA Model Certification Program requirements. Although some preliminary guidance is provided to potential applicants on the NAR Committee webpage, the development of required procedures and documents can be a daunting endeavor. This session will provide a guideline or template for proposed policies and programs that can be used as a model or roadmap for organizations considering the creation of a certification exam and program. Even members of national credentialing organizations who are already NAR Committee recognized can benefit by attending this topical session where innovations and new methodologies for examination and program implementation will be presented and discussed.

APA, Harvard, Vancouver, ISO, and other styles

43

Reshetnikov, Roman V., Anastasia V. Stolyarova, Arthur O. Zalevsky, et al. "A coarse-grained model for DNA origami." Nucleic Acids Research 46, no. 3 (2017): 1102–12. http://dx.doi.org/10.1093/nar/gkx1262.

Full text

APA, Harvard, Vancouver, ISO, and other styles

44

Steinberg, Sergey V., and Lev L. Kisselev. "Mosaic tile model for tRNA-enzyme recognition." Nucleic Acids Research 21, no. 8 (1993): 1941–47. http://dx.doi.org/10.1093/nar/21.8.1941.

Full text

APA, Harvard, Vancouver, ISO, and other styles

45

Kiefer, F., K. Arnold, M. Kunzli, L. Bordoli, and T. Schwede. "The SWISS-MODEL Repository and associated resources." Nucleic Acids Research 37, Database (2009): D387—D392. http://dx.doi.org/10.1093/nar/gkn750.

Full text

APA, Harvard, Vancouver, ISO, and other styles

46

Carrera, Javier, Guillermo Rodrigo, and Alfonso Jaramillo. "Model-based redesign of global transcription regulation." Nucleic Acids Research 37, no. 5 (2009): e38-e38. http://dx.doi.org/10.1093/nar/gkp022.

Full text

APA, Harvard, Vancouver, ISO, and other styles

47

Benkert, Pascal, Michael Künzli, and Torsten Schwede. "QMEAN server for protein model quality estimation." Nucleic Acids Research 37, suppl_2 (2009): W510—W514. http://dx.doi.org/10.1093/nar/gkp322.

Full text

APA, Harvard, Vancouver, ISO, and other styles

48

Long, Manyuan, and Michael Deutsch. "Intron—exon structures of eukaryotic model organisms." Nucleic Acids Research 27, no. 15 (1999): 3219–28. http://dx.doi.org/10.1093/nar/27.15.3219.

Full text

Abstract:

Abstract To investigate the distribution of intron—exon structures of eukaryotic genes, we have constructed a general exon database comprising all available introncontaining genes and exon databases from 10 eukaryotic model organisms: Homo sapiens, Mus musculus, Gallus gallus, Rattus norvegicus, Arabidopsis thaliana, Zea mays, Schizosaccharomyces pombe, Aspergillus, Caenorhabditis elegans and Drosophila . We purged redundant genes to avoid the possible bias brought about by redundancy in the databases. After discarding those questionable introns that do not contain correct splice sites, the final database contained 17 102 introns, 21 019 exons and 2903 independent or quasi-independent genes. On average, a eukaryotic gene contains 3.7 introns per kb protein coding region. The exon distribution peaks around 30–40 residues and most introns are 40–125 nt long. The variable intron—exon structures of the 10 model organisms reveal two interesting statistical phenomena, which cast light on some previous speculations. (i) Genome size seems to be correlated with total intron length per gene. For example, invertebrate introns are smaller than those of human genes, while yeast introns are shorter than invertebrate introns. However, this correlation is weak, suggesting that other factors besides genome size may also affect intron size. (ii) Introns smaller than 50 nt are significantly less frequent than longer introns, possibly resulting from a minimum intron size requirement for intron splicing.

APA, Harvard, Vancouver, ISO, and other styles

49

Huang, L. C., E. A. Wood, and Michael MCox. "A bacterial model system for chromosomal targeting." Nucleic Acids Research 19, no. 3 (1991): 443–48. http://dx.doi.org/10.1093/nar/19.3.443.

Full text

APA, Harvard, Vancouver, ISO, and other styles

50

Huang, L. C., E. A. Wood, and M. M. Cox. "A bacterial model system for chromosomal targeting." Nucleic Acids Research 19, no. 8 (1991): 1978. http://dx.doi.org/10.1093/nar/19.8.1978.

Full text

APA, Harvard, Vancouver, ISO, and other styles

Journal articles on the topic 'NAR model'

Create a spot-on reference in APA, MLA, Chicago, Harvard, and other styles