Monoclonal antibodies against human astrocytomas and their reactivity pattern

The establishment of hybridomas after fusion of X63-Ag8.653 mouse myeloma cells and splenocytes from mice hyperimmunized against human astrocytomas is presented. The animals were primed with 5 × 106 chemically modified uncultured or cultured glioma cells. Six weeks after the last immunization step an intrasplenal booster injection was administrated and 3 days later the spleen cells were prepared for fusion experiments. According to the specificity analysis of the generated antibodies 7 hybridoma products (MUC 7-22, MUC 8-22, MUC 10-22, MUC 11-22, MUC 14-22, MUC 15-22 and MUC 2-63) react with gliomas, neuroblastomas and melanomas as well as with embryonic and fetal cells but do not recognize non-neurogenic tumors. The selected monoclonal antibodies (McAbs) of IgG1 and IgG2a isotypes are not extensively characterized but these antibodies have been demonstrated to be reactive with a panel of glioma cell lines with varying patterns of antigen distribution. Using the McAbs described above and a series of cryosections of glioma biopsies and paraffin sections of the same material as well as glioma cultures established from these, variable antigenic profiles among glioma cell populations could be demonstrated. From these results it is evident that there is not only a distinct degree of antigenic heterogeneity among and within brain tumors, but also that the pattern of antigenic expression can change continuously. Some of the glioma associated antigens recognized by the selected antibodies persist after fixation with methanol/acetone and Karnovsky's fixative and probably are oncoembryonic/oncofetal antigen(s). The data suggest that the use of McAbs recognizing tumor associated oncofetal antigens in immunohistochemistry facilitates objective typing of intracranial malignancies and precise analysis of fine needle brain/tumor biopsies in a sensitive and reproducible manner

On the relation of nonanticipative rate distortion function and filtering theory

In this paper the relation between nonanticipative rate distortion function (RDF) and Bayesian filtering theory is investigated using the topology of weak convergence of probability measures on Polish spaces. The relation is established via an optimization on the space of conditional distributions of the so-called directed information subject to fidelity constraints. Existence of the optimal reproduction distribution of the nonanticipative RDF is shown, while the optimal nonanticipative reproduction conditional distribution for stationary processes is derived in closed form. The realization procedure of nonanticipative RDF which is equivalent to joint-source channel matching for symbol-by-symbol transmission is described, while an example is introduced to illustrate the concepts.Comment: 6 pages, 4 figures, final version submitted for publication at 12th Biannual European Control Conference (ECC), 201

Zero-Delay Rate Distortion via Filtering for Vector-Valued Gaussian Sources

We deal with zero-delay source coding of a vector-valued Gauss-Markov source subject to a mean-squared error (MSE) fidelity criterion characterized by the operational zero-delay vector-valued Gaussian rate distortion function (RDF). We address this problem by considering the nonanticipative RDF (NRDF) which is a lower bound to the causal optimal performance theoretically attainable (OPTA) function and operational zero-delay RDF. We recall the realization that corresponds to the optimal "test-channel" of the Gaussian NRDF, when considering a vector Gauss-Markov source subject to a MSE distortion in the finite time horizon. Then, we introduce sufficient conditions to show existence of solution for this problem in the infinite time horizon. For the asymptotic regime, we use the asymptotic characterization of the Gaussian NRDF to provide a new equivalent realization scheme with feedback which is characterized by a resource allocation (reverse-waterfilling) problem across the dimension of the vector source. We leverage the new realization to derive a predictive coding scheme via lattice quantization with subtractive dither and joint memoryless entropy coding. This coding scheme offers an upper bound to the operational zero-delay vector-valued Gaussian RDF. When we use scalar quantization, then for "r" active dimensions of the vector Gauss-Markov source the gap between the obtained lower and theoretical upper bounds is less than or equal to 0.254r + 1 bits/vector. We further show that it is possible when we use vector quantization, and assume infinite dimensional Gauss-Markov sources to make the previous gap to be negligible, i.e., Gaussian NRDF approximates the operational zero-delay Gaussian RDF. We also extend our results to vector-valued Gaussian sources of any finite memory under mild conditions. Our theoretical framework is demonstrated with illustrative numerical experiments.Comment: 32 pages, 9 figures, published in IEEE Journal of Selected Topics in Signal Processin