Impaired lipoprotein metabolism in obese offspring of streptozotocin-induced diabetic rats

The time course of changes in lipoprotein metabolism of obese offspring of mildly diabetic rats was studied with respect to serum lipoprotein composition as well as LCAT and tissue lipoprotein lipase (LPL) activities. Mild hyperglycemia in pregnant rats was induced by intraperitoneal injection of streptozotocin on day 5 of gestation. Control pregnant rats were injected with citrate buffer. At birth, obese pups had higher serum glucose, insulin, and lipoprotein (VLDL, LDL-HDL₁, HDL_2–3) levels than control pups. After 1 mon of life, all of these parameters in obese rats became similar to those of controls. However, LCAT, adipose tissue LPL, and hepatic triacylglycerol lipase activities were high. At 2 mon of age, VLDL-TAG levels were higher in obese females than in controls. By the age of 3 mon, obese offspring had developed insulin resistance with hyperglycemia, hyperinsulinemia, and higher serum lipoprotein concentrations. Indeed, qualitative abnormalities of lipoproteins were seen and were typical of obese and diabetic human beings. Fetal hyperinsulinemia should be considered as a risk factor for later metabolic diseases, including dyslipoproteinemia.     

Fish Oil and Microalga Omega-3 as Dietary Supplements: A Comparative Study on Cardiovascular Risk Factors in High-Fat Fed Rats

Dietary supplementation with marine omega‐3 polyunsaturated fatty acids (n‐3 PUFA) can have beneficial effects on a number of risk factors for cardiovascular disease (CVD). We compared the effects of two n‐3 PUFA rich food supplements (freeze‐dried Odontella aurita and fish oil) on risk factors for CVD. Male rats were randomly divided into four groups of six animals each and fed with the following diets: control group (C) received a standard diet containing 7 % lipids; second group (HF high fat) was fed with a high‐fat diet containing 40 % lipids; third group (HFFO high fat+fish oil) was fed with the high‐fat diet supplemented with 0.5 % fish oil; and fourth group (HFOA high fat+O. aurita) received the high‐fat diet supplemented with 12 % of freeze‐dried O. aurita. After 8 weeks rats fed with the high‐fat diet supplemented with O. aurita displayed a significantly lower bodyweight than those in the other groups. Both the microalga and the fish oil significantly reduced insulinemia and serum lipid levels. O. aurita was more effective than the fish oil in reducing hepatic triacyglycerol levels and in preventing high‐fat diet‐induced steatosis. O. aurita and fish oil also reduced platelet aggregation and oxidative status induced by high fat intake. After an OA supplementation, the adipocytes in the HFOA group were smaller than those in the HF group. Freeze‐dried O. aurita showed similar or even greater biological effects than the fish oil. This could be explained by a potential effect of the n‐3 PUFA but also other bioactive compounds of the microalgae.     

Detection of Electrical Fault in Medium Voltage Installation Using Support Vector Machine and Artificial Neural Network

Russian Journal of Nondestructive Testing - Infrared thermography plays an important role in the inspection of electrical installations allowing to avoid failures and breakdowns. Condition...     

Inferential sensor – Based adaptive principal components analysis for mechanical properties prediction and evaluation

This paper is concerned with a method for on-line quality prediction and evaluation of mechanical properties in metal testing. This method uses an Adaptive Principal Component Analysis (APCA) as a multi predictor of different sub-models defining the mechanical properties such as constraints limits and elongation. The PCA technique, characterized by its multivariate component, is strongly recommended to model a multi-input-multi-output system. The complex system is generally known as a non-linear and unsteady state process. The PCA method is a linear projection. To adapt it and to improve the prediction accuracy, a variant of this method is considered based on iteratively using a specific algorithm. This kind of approaches is applied for constructing an inferential model, which allows a reliable and accurate predictor. Simulation results, based on the measured and computed data using the above-cited method, show that the proposed approach is easily implementable and give an accurate prediction.     

Behavior of Listeria monocytogenes and Staphylococcus aureus in yogurt fermented with a bacteriocin-producing thermophilic starter

Streptococcus salivarius subsp. thermophilus B producing a bacteriocin active against Listeria monocytogenes ATCC 7644 and Staphylococcus aureus SAD 30 was isolated from bakery yeast. The bacteriocin was partially purified by an adsorption/desorption technique, and its spectrum of action was compared to that of a neutralized cell-free supernatant (CFS). Although the CFS inhibited a number of gram-positive and -negative bacteria of health and spoilage significance, the spectrum of action of the partially purified bacteriocin was limited to gram-positive bacteria. L. monocytogenes was the most sensitive to both preparations. The bacteriocin-producing streptococcal strain was used in combination with a Bac- Lactobacillus delbrueckii subsp. bulgaricus CY strain isolated from commercial yogurt to assess the effectiveness of the resulting thermophilic starter in controlling L. monocytogenes and S. aureus in yogurt during fermentation and storage at refrigeration (ca. 7 degrees C) or abuse (ca. 22 degrees C) temperature. Yogurt samples were contaminated with L. monocytogenes or S. aureus to the approximate levels of 10(3) and 10(6) CFU/ml of milk, respectively. The results showed that in situ bacteriocin production was more active against L. monocytogenes than against S. aureus in vitro and in contaminated samples. While L. monocytogenes leveled off below the detectable limit in a 1-ml sample of yogurt within 24 h of processing, S. aureus survived in Bac+ and Bac- samples during 10 days of storage at room temperature (ca. 22 degrees C). Use of a Bac+ starter resulted in a 5-day extension of the shelf life.     

Feature selection for fault detection systems: application to the Tennessee Eastman process

In fault detection systems, a massive amount of data gathered from the life-cycle of equipment is often used to learn models or classifiers that aims at diagnosing different kinds of errors or failures. Among this huge quantity of information, some features (or sets of features) are more correlated with a kind of failure than another. The presence of irrelevant features might affect the performance of the classifier. To improve the performance of a detection system, feature selection is hence a key step. We propose in this paper an algorithm named STRASS, which aims at detecting relevant features for classification purposes. In certain cases, when there exists a strong correlation between some features and the associated class, conventional feature selection algorithms fail at selecting the most relevant features. In order to cope with this problem, STRASS algorithm uses k-way correlation between features and the class to select relevant features. To assess the performance of STRASS, we apply it on simulated data collected from the Tennessee Eastman chemical plant simulator. The Tennessee Eastman process (TEP) has been used in many fault detection studies and three specific faults are not well discriminated with conventional algorithms. The results obtained by STRASS are compared to those obtained with reference feature selection algorithms. We show that the features selected by STRASS always improve the performance of a classifier compared to the whole set of original features and that the obtained classification is better than with most of the other feature selection algorithms.