The chromosomal assessment of salt tolerant substituted tritipyrum using genomic fluorescent in situ hybridiization (FISH)

Wheat, although moderately tolerant to salt, can not be cultivated in many areas. However, in the triticeae tribe, some of the wild wheat relatives are highly tolerant, e.g. Thinopyrum bessarabicum, which grows on the sea shore. Eight primary hexaploid tritipyrum lines, amphiploids between Triticum durum and Thinopyrum bessarabicum have been produced which can set seed in at least 250 mM NaCl. These tritipyrums (2n=6x=42, AABBEbEb) due to reasons such as brittle rachis, continuous production of tillers, late maturity, tall stature and meiotic instability will not fulfill the requirements of a successful commercial salt tolerant crop. To overcome such problems the substituted tritipyrum, in which selected Eb chromosomes are replaced by D genome chromosomes of 6x wheat, was produced from 6x tritipyrum x 6x wheat hybrids (F1: 2n=6x=42, AABBDEb) followed by selfing and backcrossing with 6x tritipyrum. The fertile plants among the above progenies were screened by the genomic fluorescent in situ hybridization technique to identify their Eb and D chromosome constitution. This study showed that producing tritiprum with variable numbers of Eb and D genome chromosomes is feasible and that FISH is a useful technique for determining the number of Eb chromosomes present.

Formation of triple helical nanofibers using self-assembling chiral benzene-1,3,5-tricarboxamides and reversal of the nanostructure's handedness using mirror image building blocks

Intertwining triple helical nanofibers with an overall handedness have been formed from self-assembling chiral benzene-1,3,5-tricarboxamides 1, 2 and 3, whereas the achiralbenzene-1,3,5-tricarboxamide 4 upon self-association gives rise to straight nanofibers without any twist and transmission electron microscopy images of chiral compounds clearly demonstrate that the handedness of the triple helical nanofibers can be reversed by using the enantiomeric benzene-1,3,5-tricarboxamide building blocks.

Separation of lanthanides and actinides(III) using tridentate benzimidazole, benzoxazole and benzothiazole ligands

The ability of new hydrophobic tridentate ligands based on 2,6-bis(benziinidazol-2-yl)pyridine, 2,6-bis(benzoxazol-2-yl)pyridine and 2,6-bis(benzothiazol-2-yl)pyridine to selectively extract americium(III) from europium(III) was measured. The most promising ligand-2,6-bis(benzoxazol-2-yl)-4-(2-decyl-1-tetradecyloxy)pyridine L-9 was found to give separation factors (SFAm/Eu) of up to 70 when used to extract cations from 0.02-0.10 M HNO3 into TPH in synergy with 2-bromodecanoic acid. Six structures of lanthanide complexes with 2,6-bis(benzoxazol-2-yl)pyridine L-6 were then determined to evaluate the types of species that are likely to be involved in the separation process. Three structural types were observed, namely [LnL(6)(NO3)(3)(H2O)2], 11-coordinate only for La, [LnL(6) (NO3)(3) (CH3CN)], 10-coordinate for Pr, Nd and Eu and [LnL(6) (NO3)(3)(H2O)], L 10-coordinate for Eu and Gd. Quantum Mechanics calculations were carried out on the tridentate ligands to elucidate the conformational preferences of the ligands in the free state and protonated and diprotonated forms and to assess the electronic properties of the ligands for comparison with other terdentate ligands used in lanthanide/actinide separation processes.

Selective separation of the major whey proteins using ion exchange membranes

Synthetic microporous membranes with functional groups covalently attached were used to selectively separate beta-lactoglobulin, BSA, and alpha-lactalbumin from rennet whey. The selectivity and membrane performance of strong (quaternary ammonium) and weak (diethylamine) ion-exchange membranes were studied using breakthrough curves, measurement of binding capacity, and protein composition of the elution fraction to determine the binding behavior of each membrane. When the weak and strong anion exchange membranes were saturated with whey, they were both selective primarily for beta-lactoglobulin with less than 1% of the eluate consisting of alpha-lactalbumin or BSA. The binding capacity of a pure alpha-lactoglobulin solution was in excess of 1.5 mg/cm(2) of membrane. This binding capacity was reduced to approximately 1.2 mg/cm(2) when using a rennet whey solution (pH 6.4). This reduction in protein binding capacity can be explained by both the competitive effects of other whey proteins and the effect of ions present in whey. Using binary solution breakthrough curves and rennet whey breakthrough curves, it was shown that alpha-lactalbumin and BSA were displaced from the strong and weak anion exchange membranes by beta-lactoglobulin. Finally, the effect of ionic strength on the binding capacity of individual proteins for each membrane was determined by comparing model protein solutions in milk permeate (pH 6.4) and a 10 mM sodium phosphate buffer (pH 6.4). Binding capacities of beta-lactoglobulin, alpha-lactalbumin, and BSA in milk permeate were reduced by as much as 50%. This reduction in capacity coupled with the low binding capacity of current ion exchange membranes are 2 serious considerations for selectively separating complex and concentrated protein solutions.

Synthesis of galacto-oligosaccharide from lactose using beta-galactosidase from Kluyveromyces lactis: studies on batch and continuous UF membrane-fitted bioreactors

A study of galacto-oligosaccharides (GOS) synthesis from lactose with beta-galactosidase from Kluyveromyces lactis (Maxilact(R) L2000) was carried out. The synthesis was performed using various initial lactose concentrations ranging from 220 to 400 mg/mL and enzyme concentrations ranging from 3 to 9 U/mL, and was investigated at 40degreesC and pH 7, in a stirred-tank reactor. In the experimental range examined, the results showed the amount of GOS formed depended on lactose concentration but not on enzyme concentration. Galactose was a competitive inhibitor, while glucose was a non-competitive inhibitor. In a further study, a laboratory-scale reactor system, fitted with a 10-kDa NMWCO composite regenerated cellulose membrane, was used in a continuous process. The reactor was operated in cross-flow mode. The effect of operating pressures on flux and productivity was investigated by applying different transmembrane pressures to the system. The continuous process showed better production performance compared to the batch synthesis with the same lactose and enzyme concentrations at 40degreesC, pH 7. Comparison of product structures from batch and continuous processes, analyzed by HPAEPAD and methylation analysis, showed similarities but differed from the structures found in a commercial GOS product (Vivinal(R)GOS). (C) 2004 Wiley Periodicals, Inc.

Increasing detection rate of user-to-root attacks using genetic algorithms

An extensive set of machine learning and pattern classification techniques trained and tested on KDD dataset failed in detecting most of the user-to-root attacks. This paper aims to provide an approach for mitigating negative aspects of the mentioned dataset, which led to low detection rates. Genetic algorithm is employed to implement rules for detecting various types of attacks. Rules are formed of the features of the dataset identified as the most important ones for each attack type. In this way we introduce high level of generality and thus achieve high detection rates, but also gain high reduction of the system training time. Thenceforth we re-check the decision of the user-to- root rules with the rules that detect other types of attacks. In this way we decrease the false-positive rate. The model was verified on KDD 99, demonstrating higher detection rates than those reported by the state- of-the-art while maintaining low false-positive rate.

Estimation of Hidden Markov Models parameters using differential evolution

Hidden Markov Models (HMMs) have been successfully applied to different modelling and classification problems from different areas over the recent years. An important step in using HMMs is the initialisation of the parameters of the model as the subsequent learning of HMM’s parameters will be dependent on these values. This initialisation should take into account the knowledge about the addressed problem and also optimisation techniques to estimate the best initial parameters given a cost function, and consequently, to estimate the best log-likelihood. This paper proposes the initialisation of Hidden Markov Models parameters using the optimisation algorithm Differential Evolution with the aim to obtain the best log-likelihood.

Identification of Parkinson’s disease tremor onset using artificial neural networks

In this paper we consider the possibility of using an artificial neural network to accurately identify the onset of Parkinson’s Disease tremors in human subjects. Data for the network is obtained by means of deep brain implantation in the human brain. Results presented have been obtained from a practical study (i.e. real not simulated data) but should be regarded as initial trials to be discussed further. It can be seen that a tuned artificial neural network can act as an extremely effective predictor in these circumstances.

Comparison of extrasystolic ECG signal classifiers using discrete wavelet transforms

This work compares and contrasts results of classifying time-domain ECG signals with pathological conditions taken from the MITBIH arrhythmia database. Linear discriminant analysis and a multi-layer perceptron were used as classifiers. The neural network was trained by two different methods, namely back-propagation and a genetic algorithm. Converting the time-domain signal into the wavelet domain reduced the dimensionality of the problem at least 10-fold. This was achieved using wavelets from the db6 family as well as using adaptive wavelets generated using two different strategies. The wavelet transforms used in this study were limited to two decomposition levels. A neural network with evolved weights proved to be the best classifier with a maximum of 99.6% accuracy when optimised wavelet-transform ECG data wits presented to its input and 95.9% accuracy when the signals presented to its input were decomposed using db6 wavelets. The linear discriminant analysis achieved a maximum classification accuracy of 95.7% when presented with optimised and 95.5% with db6 wavelet coefficients. It is shown that the much simpler signal representation of a few wavelet coefficients obtained through an optimised discrete wavelet transform facilitates the classification of non-stationary time-variant signals task considerably. In addition, the results indicate that wavelet optimisation may improve the classification ability of a neural network. (c) 2005 Elsevier B.V. All rights reserved.