Interaction of heterocyclic thiols/thiones eliminated from cephalosporins with iodine and its biological implications

Hydrolysis of beta-lactam antibiotics by beta-lactamases (e. g., metallo-beta-lactamase, m beta l) is one of the major bacterial defense systems. These enzymes can catalyze the hydrolysis of a variety of antibiotics including the latest generation of cephalosporins, cephamycins and imipenem. It is shown in this paper that the thiol/thione moieties eliminated from certain cephalosporins by m beta l-mediated hydrolysis readily react with molecular iodine to produce ionic compounds having S-I bonds. While the reaction of MTT with iodine produced the corresponding disulfide, MDT and DMETT produced the charge-transfer complexes MDT-I-2 and DMETT-I-2, respectively. Addition of two equivalents of I-2 to MDT produced a novel cationic complex having an almost linear S-I+-S moiety and I-5(-) counter anion.However, this reaction appears to be highly solvent dependent. When the reaction of MDT with I2 was carried out in water, the reaction produced a monocation having I-5(-), indicating the reactivity of MDT toward I2 is very similar to that of the most commonly used antithyroid drug methimazole (MMI). In contrast to MMI, MDT and DMETT, the triazine-based compound MTDT acts as a weak donor toward iodine. (C)2010 Elsevier Ltd. All rights reserved.

ErbB-2 expression and its association with other biological parameters of breast cancer among Indian women

Overexpression of the epidermal growth factor receptor family genes, which include ErbB-1, 2, 3 and 4, has been implicated in a number of cancers. We have studied the extent of ErbB-2 overexpression among Indian women with sporadic breast cancer. Methods: Immmunohistochemistry and genomic polymerase chain reaction (PCR) were used to study the ErbB2 overexpression. ErbB2 status was correlated with other clinico-pathological parameters, including patient survival. Results: ErbB-2 overexpression was detected in 43.2% (159/368) of the cases by immunohistochemistry. For a sub-set of patients (n = 55) for whom total DNA was available, ErbB-2 gene amplification was detected in 25.5% (14/55) of the cases by genomic PCR. While the ErbB2 overexpression was significantly higher in patients with lymphnode (χ2 = 12.06, P≤ 0.001), larger tumor size (χ2 = 8.22, P = 0.042) and ductal carcinoma (χ2 = 15.42, P ≤ 0.001), it was lower in patients with disease-free survival (χ2 = 22.13, P ≤ 0.001). Survival analysis on a sub-set of patients for whom survival data were available (n = 179) revealed that ErbB-2 status (χ2 =25.94, P ≤ 0.001), lymphnode status (χ2 = 12.68, P ≤ 0.001), distant metastasis (χ2 = 19.49, P ≤ 0.001) and stage of the disease (χ2 = 28.04, P ≤0.001) were markers of poor prognosis. Conclusions: ErbB-2 overexpression was significantly greater compared with the Western literature, but comparable to other Indian studies. Significant correlation was found between ErbB-2 status and lymphnode status, tumor size and ductal carcinoma. ErbB-2 status, lymph node status, distant metastasis and stage of the disease were found to be prognostic indicators.

An intelligent approach using support vector machines for monitoring and identification of faults on transmission systems

Power system disturbances are often caused by faults on transmission lines. When faults occur in a power system, the protective relays detect the fault and initiate tripping of appropriate circuit breakers, which isolate the affected part from the rest of the power system. Generally Extra High Voltage (EHV) transmission substations in power systems are connected with multiple transmission lines to neighboring substations. In some cases mal-operation of relays can happen under varying operating conditions, because of inappropriate coordination of relay settings. Due to these actions the power system margins for contingencies are decreasing. Hence, power system protective relaying reliability becomes increasingly important. In this paper an approach is presented using Support Vector Machine (SVM) as an intelligent tool for identifying the faulted line that is emanating from a substation and finding the distance from the substation. Results on 24-bus equivalent EHV system, part of Indian southern grid, are presented for illustration purpose. This approach is particularly important to avoid mal-operation of relays following a disturbance in the neighboring line connected to the same substation and assuring secure operation of the power systems.

Tool wear monitoring using ant behaviour

In this paper we show the applicability of Ant Colony Optimisation (ACO) techniques for pattern classification problem that arises in tool wear monitoring. In an earlier study, artificial neural networks and genetic programming have been successfully applied to tool wear monitoring problem. ACO is a recent addition to evolutionary computation technique that has gained attention for its ability to extract the underlying data relationships and express them in form of simple rules. Rules are extracted for data classification using training set of data points. These rules are then applied to set of data in the testing/validation set to obtain the classification accuracy. A major attraction in ACO based classification is the possibility of obtaining an expert system like rules that can be directly applied subsequently by the user in his/her application. The classification accuracy obtained in ACO based approach is as good as obtained in other biologically inspired techniques.

Can biological motion be a biometric?

Biological motion has successfully been used for analysis of a person's mood and other psychological traits. Efforts are made to use human gait as a non-invasive mode of biometric. In this reported work, we try to study the effectiveness of biological gait motion of people as a cue to biometric based person recognition. The data is 3D in nature and, hence, has more information with itself than the cues obtained from video-based gait patterns. The high accuracies of person recognition using a simple linear model of data representation and simple neighborhood based classfiers, suggest that it is the nature of the data which is more important than the recognition scheme employed.

Filter design using radial basis function neural network and genetic algorithm for improved operational health monitoring

The problem of denoising damage indicator signals for improved operational health monitoring of systems is addressed by applying soft computing methods to design filters. Since measured data in operational settings is contaminated with noise and outliers, pattern recognition algorithms for fault detection and isolation can give false alarms. A direct approach to improving the fault detection and isolation is to remove noise and outliers from time series of measured data or damage indicators before performing fault detection and isolation. Many popular signal-processing approaches do not work well with damage indicator signals, which can contain sudden changes due to abrupt faults and non-Gaussian outliers. Signal-processing algorithms based on radial basis function (RBF) neural network and weighted recursive median (WRM) filters are explored for denoising simulated time series. The RBF neural network filter is developed using a K-means clustering algorithm and is much less computationally expensive to develop than feedforward neural networks trained using backpropagation. The nonlinear multimodal integer-programming problem of selecting optimal integer weights of the WRM filter is solved using genetic algorithm. Numerical results are obtained for helicopter rotor structural damage indicators based on simulated frequencies. Test signals consider low order polynomial growth of damage indicators with time to simulate gradual or incipient faults and step changes in the signal to simulate abrupt faults. Noise and outliers are added to the test signals. The WRM and RBF filters result in a noise reduction of 54 - 71 and 59 - 73% for the test signals considered in this study, respectively. Their performance is much better than the moving average FIR filter, which causes significant feature distortion and has poor outlier removal capabilities and shows the potential of soft computing methods for specific signal-processing applications.

Filter design using radial basis function neural network and genetic algorithm for improved operational health monitoring

The problem of denoising damage indicator signals for improved operational health monitoring of systems is addressed by applying soft computing methods to design filters. Since measured data in operational settings is contaminated with noise and outliers, pattern recognition algorithms for fault detection and isolation can give false alarms. A direct approach to improving the fault detection and isolation is to remove noise and outliers from time series of measured data or damage indicators before performing fault detection and isolation. Many popular signal-processing approaches do not work well with damage indicator signals, which can contain sudden changes due to abrupt faults and non-Gaussian outliers. Signal-processing algorithms based on radial basis function (RBF) neural network and weighted recursive median (WRM) filters are explored for denoising simulated time series. The RBF neural network filter is developed using a K-means clustering algorithm and is much less computationally expensive to develop than feedforward neural networks trained using backpropagation. The nonlinear multimodal integer-programming problem of selecting optimal integer weights of the WRM filter is solved using genetic algorithm. Numerical results are obtained for helicopter rotor structural damage indicators based on simulated frequencies. Test signals consider low order polynomial growth of damage indicators with time to simulate gradual or incipient faults and step changes in the signal to simulate abrupt faults. Noise and outliers are added to the test signals. The WRM and RBF filters result in a noise reduction of 54 - 71 and 59 - 73% for the test signals considered in this study, respectively. Their performance is much better than the moving average FIR filter, which causes significant feature distortion and has poor outlier removal capabilities and shows the potential of soft computing methods for specific signal-processing applications. (C) 2005 Elsevier B. V. All rights reserved.

Synthetic Modifications of Estrogens and Androgens

Estrogens the female sex hormones have numerous biological actions. Estradiol is the most abundant estrogen in women before menopause. It influences the development, maturation and function of the female reproductive tract. It also plays a role in mammary cancer. Accordingly determinations of estradiol level in body fluids assist in the evaluation of ovarian function and diagnosis for malignancies. Estriol is the primary estrogen in pregnant women and secreted from the fetoplacental unit. Measurement of estriol in maternal body fluids is the basis of fetoplacental monitoring test. Concentration of estrogens in body fluids is determined by immunoassay. Accuracy of this measurement depends on the availability of a specific antibody. As estrogens are not antigenic, their derivatives (haptens) are coupled with a carrier and this hapten-protein conjugate is used to generate antibodies. Specificity of the generated antibody largely depends on the structure of hapten. Therefore the synthesis of a hapten with a right structure is crucial for the accurate measurement of a steroid. We have synthesised new haptens for estradiol and estriol by adding an alkyl or alkoxy side chain at the C-7 of estrane skeleton. The side chains carry a terminal amino group, which can be used for conjugation with a carrier molecule. Estrogens and their biosynthetic precursor androgens both exist as fatty acid esters. They are known to act as hormone storage but their physiological role is not completely known yet. Our collaborator is studying their effect in cardiovascular diseases. We synthesised fatty acid ester derivatives of several steroids in high yield by a very rapid procedure (in 1 min) under microwave irradiation in an ionic liquid (IL). An expedient regioselective hydrolysis at C-3 of estradiol diesters is also reported. 8-Isoestrogens are compounds of pharmaceutical interests, their synthesis, structure, conformation and biological activity studies are ongoing. 7-Hydroxy-8-isoestradiol and 7-alkyl ether of it were synthesised as well. During this study we have developed a selective O-debenzylation method. A mild route for selective removal of benzylic protection on phenol in presence of benzyl protected alcohol was explored.

The role of Acinetobacter sp. in biological phosphorus removal from forest industry wastewaters

Yhteenveto: Acinetobacter sp. metsäteollisuuden jätevesien biologisessa fosforinpoistossa

Use of adult male bonnet monkeys (Macaca radiata) for testing the biological activity of gonadotropin releasing hormone and its analogues

The effect of injecting agonistic and antagonistic analogues of gonadotropin releasing hormone analogues on serum testosterone levels was checked in adult and immature male bonnet monkeys. Of the agonistic analogues Buserelin, Ovurelin and D-Phe6 Gln8 GnRH were found to be most potent in increasing serum testosterone levels in the adult male bonnet monkeys. While 27-month-old monkeys responded well to des Gly10 GnRH, only marginal response was observed in the case of 15-month-old monkeys. Studies carried out with Ovurelin indicated that it was not effective in causing desensitization in adult monkeys. The antagonistic analogue was effective in blocking nocturnal surge of serum testosterone. Based on these studies it is suggested the adult male bonnet monkeys can be effectively used for testing the activity of GnRH analogues.