Application of artificial neural networks for prokaryotic transcription terminator prediction

Artificial neural networks (ANN) to predict terminator sequences, based on a feed-forward architecture and trained using the error back propagation technique, have been developed. The network uses two different methods for coding nucleotide sequences. In one the nucleotide bases are coded in binary while the other uses the electron-ion interaction potential values (EIIP) of the nucleotide bases. The latter strategy is new, property based and substantially reduces the network size. The prediction capacity of the artificial neural network using both coding strategies is more than 95%

Genetic programming assisted stochastic optimization strategies for optimization of glucose to gluconic acid fermentation

This article presents two hybrid strategies for the modeling and optimization of the glucose to gluconic acid batch bioprocess. In the hybrid approaches, first a novel artificial intelligence formalism, namely, genetic programming (GP), is used to develop a process model solely from the historic process input-output data. In the next step, the input space of the GP-based model, representing process operating conditions, is optimized using two stochastic optimization (SO) formalisms, viz., genetic algorithms (GAs) and simultaneous perturbation stochastic approximation (SPSA). These SO formalisms possess certain unique advantages over the commonly used gradient-based optimization techniques. The principal advantage of the GP-GA and GP-SPSA hybrid techniques is that process modeling and optimization can be performed exclusively from the process input-output data without invoking the detailed knowledge of the process phenomenology. The GP-GA and GP-SPSA techniques have been employed for modeling and optimization of the glucose to gluconic acid bioprocess, and the optimized process operating conditions obtained thereby have been compared with those obtained using two other hybrid modeling-optimization paradigms integrating artificial neural networks (ANNs) and GA/SPSA formalisms. Finally, the overall optimized operating conditions given by the GP-GA method, when verified experimentally resulted in a significant improvement in the gluconic acid yield. The hybrid strategies presented here are generic in nature and can be employed for modeling and optimization of a wide variety of batch and continuous bioprocesses

Search for stop and higgsino production using diphoton Higgs boson decays

Results are presented of a search for a "natural" supersymmetry scenario with gauge mediated symmetry breaking. It is assumed that only the supersymmetric partners of the top-quark (stop) and the Higgs boson (higgsino) are accessible. Events are examined in which there are two photons forming a Higgs boson candidate, and at least two b-quark jets. In 19.7 inverse femtobarns of proton-proton collision data at sqrt(s) = 8 TeV, recorded in the CMS experiment, no evidence of a signal is found and lower limits at the 95% confidence level are set, excluding the stop mass below 360 to 410 GeV, depending on the higgsino mass

Severe early onset preeclampsia: short and long term clinical, psychosocial and biochemical aspects

Preeclampsia is a pregnancy specific disorder commonly defined as de novo hypertension and proteinuria after 20 weeks gestational age. It occurs in approximately 3-5% of pregnancies and it is still a major cause of both foetal and maternal morbidity and mortality worldwide1. As extensive research has not yet elucidated the aetiology of preeclampsia, there are no rational preventive or therapeutic interventions available. The only rational treatment is delivery, which benefits the mother but is not in the interest of the foetus, if remote from term. Early onset preeclampsia (<32 weeks’ gestational age) occurs in less than 1% of pregnancies. It is, however often associated with maternal morbidity as the risk of progression to severe maternal disease is inversely related with gestational age at onset2. Resulting prematurity is therefore the main cause of neonatal mortality and morbidity in patients with severe preeclampsia3. Although the discussion is ongoing, perinatal survival is suggested to be increased in patients with preterm preeclampsia by expectant, non-interventional management. This temporising treatment option to lengthen pregnancy includes the use of antihypertensive medication to control hypertension, magnesium sulphate to prevent eclampsia and corticosteroids to enhance foetal lung maturity4. With optimal maternal haemodynamic status and reassuring foetal condition this results on average in an extension of 2 weeks. Prolongation of these pregnancies is a great challenge for clinicians to balance between potential maternal risks on one the eve hand and possible foetal benefits on the other. Clinical controversies regarding prolongation of preterm preeclamptic pregnancies still exist – also taking into account that preeclampsia is the leading cause of maternal mortality in the Netherlands5 - a debate which is even more pronounced in very preterm pregnancies with questionable foetal viability6-9. Do maternal risks of prolongation of these very early pregnancies outweigh the chances of neonatal survival? Counselling of women with very early onset preeclampsia not only comprises of knowledge of the outcome of those particular pregnancies, but also knowledge of outcomes of future pregnancies of these women is of major clinical importance. This thesis opens with a review of the literature on identifiable risk factors of preeclampsia

Counterpropagation neural networks for fault detection and diagnosis

This paper shows the application of a counterpropagation neural network (CPNN) to detect single faults and their magnitudes. The performance of CPNN has been evaluated by considering a variety of faults occurring in a nonisothermal continuous stirred tank reactor (CSTR). The results presented here indicate that CPNN provides an attractive alternative to error-back-propagation (EBP) networks due to its faster learning ability for fault detection and diagnosis

Ann modeling of dna sequences: new strategies using dna shape code

Two new encoding strategies, namely, wedge and twist codes, which are based on the DNA helical parameters, are introduced to represent DNA sequences in artificial neural network (ANN)-based modeling of biological systems. The performance of the new coding strategies has been evaluated by conducting three case studies involving mapping (modeling) and classification applications of ANNs. The proposed coding schemes have been compared rigorously and shown to outperform the existing coding strategies especially in situations wherein limited data are available for building the ANN models

Artificial neural networks for prediction of mycobacterial promoter sequences

A multilayered feed-forward ANN architecture trained using the error-back-propagation (EBP) algorithm has been developed for predicting whether a given nucleotide sequence is a mycobacterial promoter sequence. Owing to the high prediction capability (≅97%) of the developed network model, it has been further used in conjunction with the caliper randomization (CR) approach for determining the structurally/functionally important regions in the promoter sequences. The results obtained thereby indicate that: (i) upstream region of -35 box, (ii) -35 region, (iii) spacer region and, (iv) -10 box, are important for mycobacterial promoters. The CR approach also suggests that the -38 to -29 region plays a significant role in determining whether a given sequence is a mycobacterial promoter. In essence, the present study establishes ANNs as a tool for predicting mycobacterial promoter sequences and determining structurally/functionally important sub-regions therein

Estimating diffusion coefficients of a micellar system using an artificial neural network

A three-layer feedforward artificial neural network (ANN) model, trained using the error-back-propagation algorithm has been developed to estimate the diffusion coefficient of sodium dodecyl sulfate (SDS) micellar system over a wide range of operating parameters such as temperature and concentrations of SDS and NaCl. The network model validates the experimentally observed qualitative and quantitative trends. The optimal model parameters in terms of network weights have been estimated and can be used for computing diffusion coefficients over wide-ranging experimental conditions

Kinetics of hydrogenation of o-nitrophenol to o-aminophenol on Pd/carbon catalysts in a stirred three-phase slurry reactor

Kinetics of the hydrogenation of o-nitrophenol to o-aminophenol over a Pd/carbon (4.82 wt % Pd) catalyst (particle size 30 &#956;m) in an agitated three-phase slurry reactor has been investigated in the chemical control regime at different temperatures (293-328 K), with initial concentration of o-nitrophenol (0.072-0.36 mol dm<SUP>-3</SUP> ) and H<SUB>2</SUB> pressures (442-1476 kPa), using methanol as a reaction medium. To confirm the absence of gas-liquid, liquid-solid, and intraparticle mass-transfer effects on the reaction, the effects of stirring speed (260-1290 rpm), catalyst loading (0.05-1.0 g dm<SUP>-3</SUP>), and catalyst particle size (30-165 &#956;m) on the initial reaction rate at the maximum temperature (328 K) and o-nitrophenol concentration (0.36 mol dm<SUP>-3</SUP>) have been thoroughly studied. For a catalyst particle size of &#8804; 45 &#956;m and a stirring speed of &#8804;850 rpm, the reaction rate is not influenced by the mass-transfer processes. Effective intraparticle diffusivity of o-nitrophenol has been determined from the effectiveness factor of the catalyst for its different particle sizes. The observed large tortuosity factor (&#964; = 22.9 av) and activation energy (28.9 kJ mol<SUP>-1</SUP>) for the diffusion indicated a strong influence of adsorption and surface diffusion of o-nitrophenol on the catalyst. From the power law analysis of the initial rate data, the reaction order is found to be 0.53 &#177; 0.03 for o-nitrophenol in its concentration below 0.18, 0.22, 0.23, and 0.25 mol dm<SUP>-3</SUP> at 293, 308, 318, and 328 K, respectively, and from 0.54 (at 293 K) to 1.0 (at 328 K) for hydrogen. However, the reaction is found to be zero-order for the higher o-nitrophenol concentration (&gt;0.25 mol dm<SUP>-3</SUP>). The reaction kinetic data (including the initial rate data) could be fitted well to a Hougen-Watson-type model on the basis of the mechanism involving single-site surface reaction control with all the reaction species molecularly adsorbed. The activation energy for the initial reaction obtained from the power law analysis (70.2 kJ mol<SUP>-1</SUP>) is found to agree with that (68.0 kJ mol<SUP>-1</SUP>) obtained from the Hougen-Watson model

Soft-sensor development for fed-batch bioreactors using support vector regression

In the present paper, a state-of-the-art machine learning based modeling formalism known as "support vector regression (SVR)", has been introduced for the soft-sensor applications in the fed-batch processes. The SVR method possesses a number of attractive properties such as a strong statistical basis, convergence to the unique global minimum and an improved generalization performance by the approximated function. Also, the structure and parameters of an SVR model can be interpreted in terms of the training data. The efficacy of the SVR formalism for the soft-sensor development task has been demonstrated by considering two simulated bio-processes namely, invertase and streptokinase. Additionally, the performance of the SVR based soft-sensors is rigorously compared with those developed using the multilayer perceptron and radial basis function neural networks. The results presented here clearly indicate that the SVR is an attractive alternative to artificial neural networks for the development of soft-sensors in bioprocesses