Kinetic behaviour of muscle LDH immobilized in nylon tubing

Immobilization was carried out of the lactate dehydrogenase (LDH) from rabbit muscle (EC 1.1.1.27), cross-linked through the bifunctional reactive glutar-aldehyde on to nylon tubing (1 m long, 53cm² internal surface area). Immobilized LDH inactivation kinetics are of first order (t_1/2 = 3·6 years, k = 5·4,e^?4 day^?1 to 5°C). The smaller effect of pH on activity than in the case of LDH in solution can be explained on the basis of limitation to proton diffusion towards the support. A limiting effect to free external diffusion of the substrate towards and products from the support was also observed, an effect which seems to determine the effective kinetic behaviour of immobilized LDH. The apparent optimum temperature is centred around 40°C, observing a clear inactivation (thermal denaturation) above this temperature. In the temperature range studied (10–40°C), the co-existence was seen of a kinetic control accompanied by another control, involving diffusional transport of substrates and products, on the global activity of the immobilized enzyme. This makes the Arrhenius profiles curvilinear. Both graphic and statistical non-linear regression analysis of the kinetic data—rate, v, versus substrate concentration [S]—carried out under conditions in which the diffusional limitations can be considered negligible (high recirculation flow rate), permitted investigation of the intrinsic kinetic behaviour of immobilized LDH. In this sense, it can be deduced that the rate equation to which these data seem to be fitted is of the polynomial quotient type in [S] of minimum degree 2:2. Although the diffusional limitations have a marked effect on the type of global kinetics shown by immobilized LDH, temperature was not found to affect its v[S] behaviour. The experimental evidence obtained thus indicates that the rate equation in the 10-40°C temperature range continues to be a rational equation of at least degree 2:2 in [S].     

Optically generated electrical-modulation formats in digital-microwave link applications

We investigate a novel technique to generate the transmission signal in digital-microwave link applications. This technique is based on a photonic device that achieves both electrical modulation (QAM, PSK, ASK) and harmonic upconversion of the microwave carrier. Simulation results are provided for a seventh-harmonic 38.5-GHz transmission of a 2.5-Gbps signal through a 1-km fiber span. The impact of fiber chromatic-dispersion on the link bandwidth is also analyzed.     

Variability in the interpretation of thorax radiography in a primary care medical community and its reference radiologists

OBJECTIVE: To know the variability in the interpretation (normal/pathological) of posteroanterior view chest radiograph and the associated factors between a medical primary care community and a Gold Standard (GS) from 3 reference radiologists. DESIGN: Crossover study. SETTING: Primary care. Teaching health centers of Elche. METHODS: Interobserver percentage agreement (IPA) and Kappa statistic (K) were used to analyse the interobserver agreement (IA) between the GS and every of the participants in interpretation of 100 radiographs. Intraobserver variability was assessed with a second review. Using uni and multivariant analysis some possibly associated factors were studied. RESULTS: Overall IPA and K were 71.3% (CI 95%: 69.2-73.3) and 0.41 (CI 95%: 0.36-0.46) respectively. Time employed in radiographs reading and time passed after medicine studies finished were independently and significantly associated to higher and lower interobserver agreement results respectively, whereas recent professional experience, permanent job and age were not associated. Professional training is the variable that most explain the inter and intraobserver variability. CONCLUSIONS: Overall IA is acceptable. The IA showed by the participants trained under the MIR system is good but the IA achieved by the rest of participants is not acceptable. It is necessary a training which increase the clinical competence, as it does MIR training.     

Microbial community composition and reactor performance during hydrogen production in a UASB reactor fed with raw cheese whey inoculated with compost

This study investigated the microbial community developed in a UASB reactor for hydrogen production and correlated it to reactor performance. The reactor was inoculated with kitchen waste compost and fed with raw cheese whey at two organic loading rates, 20 gCOD/Ld and 30 gCOD/Ld. Hydrogen production was very variable, using an OLR of 30 gCOD/Ld averaged 1.0 LH(2)/Ld with no methane produced under these conditions. The hydrogen yield was also very variable and far from the theoretical. This low yield could be explained by selection of a mixed fermentative population with presence of hydrogen producing organisms (Clostridium, Ruminococcus and Enterobacter) and other non-hydrogen producing fermenters (Lactobacillus, Dialister and Prevotella). The molecular analysis of the raw cheese whey used for feeding revealed the presence of three predominant organisms that are affiliated with the genera Buttiauxella (a low-yield hydrogen producer) and Streptococcus (a lactic acid-producing fermenter). Although these organisms did not persist in the reactor, the continuous addition of these fermenters could decrease the reactor's hydrogen yield.     

Comparison of some components of pigs kept in natural (free-range) and large-scale conditions

A new pig genotype of Hungarian Large White (75%) × Mangalica (pig with curly bristles, 25%) was used in the experiments. The experimental group was kept in a farming system under strictly natural circumstances until a body weight of 120 kg was attained. Neither antibiotics nor yield-increasing hormones were given to this group. The control group was kept in the usual large-scale farming conditions until body weight of 120 kg was attained. In most cases, protein, zinc, and copper levels and, in some cases, iron levels were significantly higher in some major cuts (neck-end (Boston-butt), loin, ham) of the 'free-range' pigs (n = 12) in comparison with the control animals kept in large-scale farming conditions. There was, on average, 15% less cholesterol in the muscles and 30% less cholesterol in the liver and less linoleic acid in all tissues of 'free-range' pigs than in the control group. There was less fat, but the amount of thiamin and riboflavin in the liver of the naturally kept pigs was twice that in the controls. In respect of TBA-reactive compounds, there were no significant differences between the two groups, but superoxide dismutase (SOD) showed significantly higher activities in all cuts from pigs kept in a large-scale farming system. This may be the effect of a higher level of linoleic acid in the fat.     

General distributed economic framework for dynamic spectrum allocation

The emergence of novel radio techniques enables the application of advantageous revolutionary spectrum policies. An important body of research has appeared about possible frequency management schemes, but none of them proposes solutions that meet every related criteria. In this paper we present our work on dynamic spectrum allocation and pricing that offers a distributed mechanism design, well-suited to practical employment issues. Our model handles interference effects without any restricting assumptions, provides universal scalable and incentive-compatible allocation and pricing mechanisms. We provide both analytical and numerical evaluation of the proposed framework, and in either case we prove this latter to be a suitable approach to efficient and flexible spectrum utilization.     

The behaviour of the multi-layer perceptron and the support vector regression learning methods in the prediction of NO and NO2 concentrations in Szeged, Hungary

The main aim of this paper is to predict NO and NO₂ concentrations 4 days in advance by comparing two artificial intelligence learning methods, namely, multi-layer perceptron and support vector machines, on two kinds of spatial embedding of the temporal time series. Hourly values of NO and NO₂ concentrations, as well as meteorological variables were recorded in a cross-road monitoring station with heavy traffic in Szeged, in order to build a model for predicting NO and NO₂ concentrations several hours in advance. The prediction of NO and NO₂ concentrations was performed partly on the basis of their past values, and partly on the basis of temperature, humidity and wind speed data. Since NO can be predicted more accurately, its values were considered primarily when forecasting NO₂. Time series prediction can be interpreted in a way that is suitable for artificial intelligence learning. Two effective learning methods, namely, multi-layer perceptron and support vector regression are used to provide efficient non-linear models for NO and NO₂ time series predictions. Multi-layer perceptron is widely used to predict these time series, but support vector regression has not yet been applied for predicting NO and NO₂ concentrations. Three commonly used linear algorithms were considered as references: 1-day persistence, average of several day persistence and linear regression. Based on the good results of the average of several day persistence, a prediction scheme was introduced, which forms weighted averages instead of simple ones. The optimization of these weights was performed with linear regression in linear case and with the learning methods mentioned in non-linear case. Concerning the NO predictions, the non-linear learning methods give significantly better predictions than the reference linear methods. In the case of NO₂, the improvement of the prediction is considerable, however, it is less notable than for NO.     

Application of Kernel-Based Feature Space Transformations and Learning Methods to Phoneme Classification

This paper examines the applicability of some learning techniques to the classification of phonemes. The methods tested were artificial neural nets (ANN), support vector machines (SVM) and Gaussian mixture modeling (GMM). We compare these methods with a traditional hidden Markov phoneme model (HMM), working with the linear prediction-based cepstral coefficient features (LPCC). We also tried to combine the learners with linear/nonlinear and unsupervised/supervised feature space transformation methods such as principal component analysis (PCA), independent component analysis (ICA), linear discriminant analysis (LDA), springy discriminant analysis (SDA) and their nonlinear kernel-based counterparts. We found that the discriminative learners can attain the efficiency of HMM, and that after the transformations they can retain the same performance in spite of the severe dimension reduction. The kernel-based transformations brought only marginal improvements compared to their linear counterparts.