Comparative immunoreactivity of CD-68 and HMB-45 in malignant melanoma, neural tumors and nevi

The monoclonal antibody CD 68 (KP 1) reacts with fibrohistiocytic and some epithelial neoplasms; its reactivity compared with that of HMB 45 in malignant melanoma (MM) and neural tumors needs further elucidation. Using a streptavidin-biotin-immunoperoxidase procedure, we examined the reactivity of 65 MM (46 conventional, 1 polypoid, 6 desmoplastic [DMM], and 12 metastatic), 21 neurofibromas, 1 neurofibrosarcoma, 10 schwannomas, 1 perineurioma, 2 neurothekeomas, and 14 blue and 26 other nevi for CD-68, HMB-45-defined antigen, S 100 and neurofilament protein. A positive staining for CD 68 was observed in 38 of 42 primary, 5 of 6 DMM, and 11 of 12 metastatic melanomas; 6 of 10 schwannomas; 5 of 10 nevi with junctional component and all 14 blue nevi. All 21 neurofibromas, 1 each neurofibrosarcoma and perineurioma, both neurothekeomas, and all 12 nevi with dermal component were CD 68-negative. HBM 45 was expressed by all 44 primary, none of 6 DMM, and 7 of 12 metastatic melanomas; by none of 10 schwannomas, 6 neurofibromas, 1 neurofibrosarcoma, 1 perineurioma and 2 neurothekeomas. Both junctional nevi, 8 of 10 nevi with junctional components, 1 of 10 dermal components of junctional nevi, and 11 of 13 blue nevi were also HMB 45 positive. Except for 1 perineurioma, S 100 decorated all tumors examined. NF was immunoreactive in 1 of 45 conventional melanomas, 2 of 21 neurofibromas, 2 of 10 schwannomas, and 3 of 10 blue nevi; it was non-reactive in all polypoid, desmoplastic and metastatic melanomas; neurofibrosarcoma, perineurioma, neurothekeoma and other nevi. We conclude that the CD-68-reactivity in primary melanomas, neurofibromas, neurofibrosarcomas, perineuriomas, and nevi was similar to that of HMB 45. The significantly higher CD 68-positivity than of HMB 45 in metastatic and desmoplastic melanomas and schwannomas may be of diagnostic value.     

Studies of distributed parameter systems: Decoupling the state-parameter estimation problem

The problem of state-parameter estimation is considered in terms of decoupling the estimation procedure. First, the theoretical preliminaries necessary for the mathematical statement of the problem are defined. Then using the extended Kalman filter (EKF) approach, the state and parameter are estimated by applying the solution techniques to a distributed parameter system. Next, the state estimation problem is decoupled from the parameter estimation problem and by using a numerical example, the advantage of this decoupling procedure is demonstrated. The numerical results show that convergence can be improved when this decoupling procedure is employed. The effect of the location of the measurements on the estimation problem is also analysed in this work. The results show that the convergence of the problem depends on the location as well as the number of measurements.     

Copy move forgery detection using DCT,PatchMatch and cellular automata

Multimedia Tools and Applications - Copy-move image forgery is a type of digital image forgery where content is copied and pasted within the same image, either by hiding foreground objects within...     

A new external sorting algorithm with no additional disk space

This paper is concerned with an external sorting algorithm with no additional disk space. The proposed algorithm is a hybrid one that uses Quicksort and special merging process in two distinct phases. The algorithm excels in sorting a huge file, which is many times larger than the available memory of the computer. This algorithm creates no extra backup file for manipulating huge records. For this, the algorithm saves huge disk space, which is needed to hold the large file. Also our algorithm switches to special merging process after the first phase that uses Quicksort. This reduces the time complexity and makes the algorithm faster.     

Uncertain impulsive differential systems of fractional order: almost periodic solutions

In this paper, we investigate the existence and stability of almost periodic solutions of impulsive fractional-order differential systems with uncertain parameters. The impulses are realised at fixed moments of time. For the first time, we determine the impact of the uncertainties on the qualitative behaviour of such systems. The main criteria for the existence of almost periodic solutions are proved by employing the fractional Lyapunov method. The global perfect robust uniform-asymptotic stability of such solutions is also considered. We apply our results to uncertain impulsive neural network systems of fractional order.     

Almost periodic solutions for impulsive fractional differential equations

In this paper, we consider the existence of almost periodic solutions for impulsive fractional evolution equations involving Caputo fractional derivative. The main results are obtained by means of the theory of operators semi-group, probability density functions, fixed point theorems and the techniques based on fractional calculus. An example is also discussed to illustrate the theory. Some known results are improved and generalized.     

A multinomial logit model-Bayesian network hybrid approach for driver injury severity analyses in rear-end crashes

Rear-end crash is one of the most common types of traffic crashes in the U.S. A good understanding of its characteristics and contributing factors is of practical importance. Previously, both multinomial Logit models and Bayesian network methods have been used in crash modeling and analysis, respectively, although each of them has its own application restrictions and limitations. In this study, a hybrid approach is developed to combine multinomial logit models and Bayesian network methods for comprehensively analyzing driver injury severities in rear-end crashes based on state-wide crash data collected in New Mexico from 2010 to 2011. A multinomial logit model is developed to investigate and identify significant contributing factors for rear-end crash driver injury severities classified into three categories: no injury, injury, and fatality. Then, the identified significant factors are utilized to establish a Bayesian network to explicitly formulate statistical associations between injury severity outcomes and explanatory attributes, including driver behavior, demographic features, vehicle factors, geometric and environmental characteristics, etc. The test results demonstrate that the proposed hybrid approach performs reasonably well. The Bayesian network reference analyses indicate that the factors including truck-involvement, inferior lighting conditions, windy weather conditions, the number of vehicles involved, etc. could significantly increase driver injury severities in rear-end crashes. The developed methodology and estimation results provide insights for developing effective countermeasures to reduce rear-end crash injury severities and improve traffic system safety performance.