Nitric oxide synthase-immunoreactive neurons in human and porcine respiratory tract

The presence of nitric oxide synthase (NO-synthase), the enzyme responsible for the production of nitric oxide (NO) from L-arginine, is shown immunocytochemically in the intrinsic neurons of the human and porcine respiratory tract. NO-synthase immunoreactivity is demonstrated in a subpopulation of neurons of the microganglia present in the wall of the extra- and intrapulmonary bronchi as well as in the hilar region of the lung in relation to blood vessels. The immunostaining was also found in some nerve fibers of the respiratory nervous system. Human and porcine lung gave similar results. The possible involvement of NO in the nonadrenergic noncholinergic (NANC) nervous regulation of the lung is discussed.     

Autoimmune diabetes induced by the beta-cell toxin STZ. Immunity to the 60-kDa heat shock protein and to insulin

Administered at a suitably low dose, the toxin streptozotocin (STZ) can trigger an autoimmune process leading to destruction of the beta-cells of the pancreatic islets. In this study, we examined specific immunological reactions in mice before and during the development of STZ-induced autoimmune diabetes. We now report that the development of spontaneous autoantibodies to insulin can serve as a marker of susceptibility to a low dose of STZ. Susceptible male mice of the C57BL/KsJ strain manifested such anti-insulin antibodies, and resistant female mice did not. Administration of a low dose of STZ (five daily doses each of 30 mg/kg) induced transient hyperglycemia approximately 20-30 days later, which temporarily remitted but was followed by intractable diabetes approximately 2.5 months later. The diabetogenic process triggered by the low dose of STZ was associated with an increase in the level of anti-insulin antibodies bearing the Dana and Micha (DM) idiotype, later followed by the appearance of anti-idiotypic antibodies that peaked before the onset of diabetes. Antibodies and T-cells reactive to hsp60 (heat shock protein) were triggered by the low-dose STZ administration and persisted throughout the period that preceded clinical diabetes. T-cells reactive to the p277 peptide of hsp60 were also observed. Finally, active immunization to hsp60 caused transient hyperglycemia by itself and also aggravated the hyperglycemia induced by low-dose STZ. Thus, autoantibodies to insulin can indicate susceptibility to a toxic trigger of diabetes, and a low dose of a toxin can activate the insulin and hsp60 autoimmunity that has been detected previously in the spontaneous autoimmune diabetes of NOD strain mice.     

Method of calculating the endurance of specimens with a stress raiser. 2

Dependencies of theoretical values of the endurance of specimens with a stress raiser on the method of determining the theoretical stress concentration factor are normalized. A method is proposed of calculating the endurance of specimens with an allowance made for the gradient of actual stresses in the concentration zone up to the start of formation of a macroscopic fatigue crack. The actual stresses in cyclic elastoplastic loading were determined by the equivalent energy method. The calculated stresses are compared with experimental values.N. E. Zhukovskii Aviation Institute, Khar'kov. Prochnost Regional Scientific Research Institute. Institute of Physical Metallurgy, Czechoslovak Academy of Sciences, Brno. Translated from Problemy Prochnosti, No. 9, pp. 100–103, September, 1989.     

Synthesis and crosslinking properties of phenolphtalein dimethacrylate

Summary By reacting esterification of phenolphtalein with methacrylate chloride, phenolphtalein dimethacrylate was obtained. Based on the results of elemental analysis, FTIR, ¹H-NMR and ¹³C-NMR spectra, the structure of the product was proposed. The dimethacrylate was used as new crosslinker to crosslink poly(methyl methacrylate) (PMMA). The influence of the amount dimethacrylate on the crosslink density and some properties of the crosslinked polymers networks such as swelling, mechanical properties (tensile strength and elongation at break) and thermal stability were examined.     

Short-term prediction of traffic flow using a binary neural network

This paper introduces a binary neural network-based prediction algorithm incorporating both spatial and temporal characteristics into the prediction process. The algorithm is used to predict short-term traffic flow by combining information from multiple traffic sensors (spatial lag) and time series prediction (temporal lag). It extends previously developed Advanced Uncertain Reasoning Architecture (AURA) k-nearest neighbour (k-NN) techniques. Our task was to produce a fast and accurate traffic flow predictor. The AURA k-NN predictor is comparable to other machine learning techniques with respect to recall accuracy but is able to train and predict rapidly. We incorporated consistency evaluations to determine whether the AURA k-NN has an ideal algorithmic configuration or an ideal data configuration or whether the settings needed to be varied for each data set. The results agree with previous research in that settings must be bespoke for each data set. This configuration process requires rapid and scalable learning to allow the predictor to be set-up for new data. The fast processing abilities of the AURA k-NN ensure this combinatorial optimisation will be computationally feasible for real-world applications. We intend to use the predictor to proactively manage traffic by predicting traffic volumes to anticipate traffic network problems.     

Towards language-to-language transformation

This paper proposes a simplicity-oriented approach and framework for language-to-language transformation of, in particular, graphical languages. Key to simplicity is the decomposition of the transformation specification into sub-rule systems that separately specify purpose-specific aspects. We illustrate this approach by employing a variation of Plotkin’s Structural Operational Semantics (SOS) for pattern-based transformations of typed graphs in order to address the aspect ‘computation’ in a graph rewriting fashion. Key to our approach are two generalizations of Plotkin’s structural rules: the use of graph patterns as the matching concept in the rules, and the introduction of node and edge types. Types do not only allow one to easily distinguish between different kinds of dependencies, like control, data, and priority, but may also be used to define a hierarchical layering structure. The resulting Type-based Structural Operational Semantics (TSOS) supports a well-structured and intuitive specification and realization of semantically involved language-to-language transformations adequate for the generation of purpose-specific views or input formats for certain tools, like, e.g., model checkers. A comparison with the general-purpose transformation frameworks ATL and Groove, illustrates along the educational setting of our graphical WebStory language that TSOS provides quite a flexible format for the definition of a family of purpose-specific transformation languages that are easy to use and come with clear guarantees.

     

Properties of diagnostic data distributions

In applications of statistical methods to medical diagnosis, information on patients' diseases and symptoms is collected and the resulting data-base is used to diagnose new patients. The data-structure is complicated by a number of factors, two of which are examined here: selection bias and unstable population. Under reasonable conditions, no correction for selection bias is required when assessing probabilities for diseases based on symptom information, and it is suggested that these "diagnostic distributions" should form the principal object of study. Transformation of these distributions under changing population structure is considered and shown to take on a simple form in many situations. It is argued that the prevailing paradigm of diagnostic statistics, which concentrates on incidence of symptoms for given disease, is largely inappropriate and should be replaced by an emphasis on diagnostic distributions. The generalized logistic model is seen to fit naturally into the new framework.     

Reliability-based optimal design using sample average approximations

An algorithm for reliability-based optimal design is developed using sampling techniques for estimating the failure probability. The algorithm applies a new method for sensitivity calculations of the failure probability. Initially, the estimates of the failure probability are coarse. As the algorithm progresses towards an optimal design, the number of sample points is increased in an adaptive way leading to better estimates of the failure probability. The algorithm is proven to converge to an optimal design. The applicability of the algorithm is shown in an example from the area of highway bridge design.