Endoscopic sclerotherapy is useful in Dieulafoy''s disease

1.5% Capsaicin (Cap) or Vehicle was respectively used to treat the right or left sciatic nerve in 20 Sprague-Dawley rats. On the seventh day, the 20 rats were at random divided into electroacupuncture (EA) group and non-EA group, the spinal cord corresponding to the afferent segments of sciatic nerve was taken out for observing the changes of acetylcholinesterase (AChE) activity and [3H]-quinuelidinylbenzylate (QNB) binding sites in the spinal dorsal horn (SDH). The results were as follows: (1) EA "Huantiao" could enhance AChE activity in the SDH and decrease [3H]-QNB binding sites; (2) Cap treating sciatice nerve could weaken AChE activity in the SDH and merease [3H]-QNB binding sites; (3) Cap treatment could inhibit or partially inhibit the actions of EA as above. The results indicated that ACh participated in the primary afferent of acupuncture information and might exist in Cap-sensitive neurons.     

Our experience in brachytherapy of certain non-cancerous diseases

The Neurospora crassa Asm-1+ (ascospore maturation 1) gene encodes an abundant nucleus-localized protein required for formation of female structures and for ascospore maturation. Deletion mutants of Asm-1+ are "ascus-dominant," i.e., when crossed to wild type, neither Asm-1+ nor Asm-1 delta spores mature. To explain this behavior, we considered three models: an effect of reduced dosage of the gene product, failure of internuclear communication, and failure of transvection (regulation dependent on pairing of alleles). We found that for proper regulation of subsequent sexual sporulation, Asm-1+ must be in proximity, probably paired, to its allelic counterpart in the zygote: i.e., transvection must occur. Disruption of pairing causes failure of ascospore progeny to mature. Transvection in Neurospora, unlike in Drosophila, occurs immediately before meiosis, and can be demonstrated between wild-type alleles.     

Influence of serotonergic neurons and of pineal gland on the development of the neonatal androgen sterilization syndrome in the rat

The effect of the removal of pineal gland, performed in adult age or during perinatal life, was investigated in the neonatal androgen sterility (NA-CEA) syndrome, in combination with drugs acting on serotonergic neurons. Perinatal pinealectomy (Px) was more potent in preventing the development of NA-CEA state than Px performed in the adult age. These data indicate that the masculinized hypothalamus becomes less sensitive to pineal influences during the lifespan. Effect of Px was potentiated by drugs increasing the central serotonergic tone. The results lead to the assumption that pineal hormones are influential on the maturation of serotonergic neurons, which might interfere with the sterilizing property of neonatal androgen treatment during the "critical" period of sexual differentiation.     

Monoclonal antibodies against potato virus A -- immunoblot analysis

Monoclonal antibodies (MoAbs) against potato virus A (PVA) were examined in their reactivity with PVA and its denatured capsid protein (PVA-CP) bound to the nitrocellulose membrane. Five MoAbs reacted with native PVA, three of them also with PVA-CP. One MoAb gave no reaction in dot-blot test. In polyacrylamide electrophoresis in the presence of sodium dodecyl sulphate (SDS-PAGE) PVA-CP migrated as two major bands. In immunoblot analysis, two MoAbs reacted only with the slower band, one only with the faster one. We presume that those bands do not correspond to the intact CP but they do to truncated N- and C-terminal CP molecules, respectively, and that the corresponding epitopes reacting with MoAbs are localized near to both termini of CP molecules. After mild trypsinolysis of PVA particles no MoAb reacted with resulting "core" CP.     

An adaptive, real-time, traffic monitoring system

In this paper we describe a computer vision-based traffic monitoring system able to detect individual vehicles in real-time. Our fully integrated system first obtains the main traffic variables: counting, speed and category; and then computes a complete set of statistical variables. The objective is to investigate some of the difficulties impeding existing traffic systems to achieve balanced accuracy in every condition; i.e. day and night transitions, shadows, heavy vehicles, occlusions, slow traffic and congestions. The system we present is autonomous, works for long periods of time without human intervention and adapts automatically to the changing environmental conditions. Several innovations, designed to deal with the above circumstances, are proposed in the paper: an integrated calibration and image rectification step, differentiated methods for day and night, an adaptive segmentation algorithm, a multistage shadow detection method and special considerations for heavy vehicle identification and treatment of slow traffic. A specific methodology has been developed to benchmark the accuracy of the different methods proposed.     

A comparison of several neural networks to predict the execution times in injection molding production for automotive industry

In the industrial environment, specifically in the automotive industry, an accurate prediction of execution times for each production task is very useful in order to plan the work and to optimize the human, technical and material resources. In this paper, we applied several regression neural networks to predict the execution times of the tasks in the production of parts for plastic injection molds. These molds are used to make a variety of car components in automotive industry. The prediction is based on the geometric features of the mold parts to be made. The accuracy of the predicted times is high enough to be used as a tool for the design stage of the mold parts, e.g. guiding the design process in order to get the lowest production time.     

A new mathematical programming approach to multi-group classification problems

In this paper we introduce a goal programming formulation for the multi-group classification problem. Although a great number of mathematical programming models for two-group classification problems have been proposed in the literature, there are few mathematical programming models for multi-group classification problems. Newly proposed multi-group mathematical programming model is compared with other conventional multi-group methods by using different real data sets taken from the literature and simulation data. A comparative analysis on the real data sets and simulation data shows that our goal programming formulation may suggest efficient alternative to traditional statistical methods and mathematical programming formulations for the multi-group classification problem.