A Survey on Continuous Time Computations

We provide an overview of theories of continuous time computation. These theories allow us to understand both the hardness of questions related to continuous time dynamical systems and the computational power of continuous time analog models. We survey the existing models, summarizing results, and point to relevant references in the literature

Genetics of Host Response to Leishmania tropica in Mice – Different Control of Skin Pathology, Chemokine Reaction, and Invasion into Spleen and Liver

Several hundred million people are exposed to the risk of leishmaniasis, a disease caused by intracellular protozoan parasites of several Leishmania species and transmitted by phlebotomine sand flies. In humans, L. tropica causes cutaneous form of leishmaniasis with painful and long-persisting lesions in the site of the insect bite, but the parasites can also penetrate to internal organs. The relationship between the host genes and development of the disease was demonstrated for numerous infectious diseases. However, the search for susceptibility genes in the human population could be a difficult task. In such cases, animal models may help to discover the role of different genes in interactions between the parasite and the host. Unfortunately, the literature contains only a few publications about the use of animals for L. tropica studies. Here, we report an animal model suitable for genetic, pathological and drug studies in L. tropica infection. We show how the host genotype influences different disease symptoms: skin lesions, parasite dissemination to the lymph nodes, spleen and liver, and increase of levels of chemokines CCL2, CCL3 and CCL5 in serum

Genetic Control of Resistance to Trypanosoma brucei brucei Infection in Mice

Trypanosoma brucei are extracellular protozoa transmitted to mammalian host by the tsetse fly. They developed several mechanisms that subvert host's immune defenses. Therefore analysis of genes affecting host's resistance to infection can reveal critical aspects of host-parasite interactions. Trypanosoma brucei brucei infects many animal species including livestock, with particularly severe effects in horses and dogs. Mouse strains differ greatly in susceptibility to T. b. brucei. However, genes controlling susceptibility to this parasite have not been mapped. We analyzed the genetic control of survival after T. b. brucei infection using CcS/Dem recombinant congenic (RC) strains, each of which contains a different random set of 12.5% genes of their donor parental strain STS/A on the BALB/c genetic background. The RC strain CcS-11 is even more susceptible to parasites than BALB/c or STS/A. In F2 hybrids between BALB/c and CcS-11 we detected and mapped four loci, Tbbr1-4 (Trypanosoma brucei brucei response 1–4), that control survival after T. b. brucei infection. Tbbr1 (chromosome 3) and Tbbr2 (chromosome 12) have independent effects, Tbbr3 (chromosome 7) and Tbbr4 (chromosome 19) were detected by their mutual inter-genic interaction. Tbbr2 was precision mapped to a segment of 2.15 Mb that contains 26 genes

Renal small cell oncocytoma with pseudorosettes: A histomorphologic, immunohistochemical, and molecular genetic study of 10 cases

10.1016/j.humpath.2011.01.022Human Pathology42111751-1760HPCQ

Finite-State Computation in Analog Neural Networks: Steps Towards Biologically Plausible Models?

Finite-state machines are the most pervasive models of computation, not only in theoretical computer science, but also in all of its applications to real-life problems, and constitute the best characterized computational model. On the other hand, neural networks ---proposed almost sixty years ago by McCulloch and Pitts as a simplified model of nervous activity in living beings--- have evolved into a great variety of so-called artificial neural networks. Artificial neural networks have become a very successful tool for modelling and problem solving because of their built-in learning capability, but most of the progress in this field has occurred with models that are very removed from the behaviour of real, i.e., biological neural networks. This paper surveys the work that has established a connection between finite-state machines and (mainly discrete-time recurrent) neural networks, and suggests possible ways to construct finite-state models in biologically plausible neural networks