The nonrandom distribution of meiotic recombination shapes heredity and genetic diversification. A widely held view is that individual hotspots — favored sites of recombination initiation — are always ephemeral because they evolve rapidly toward extinction. An alternative view, often ignored or dismissed as implausible, predicts conservation of the positions of hotspots if they are chromosomal features under selective constraint, such as gene promoters. Here we empirically test opposite predictions of these theories by comparing genome-wide maps of meiotic recombination initiation from widely divergent species in the Saccharomyces clade. We find that the frequent overlap of hotspots with promoters is true of the species tested and, consequently, hotspot positions are well conserved. Remarkably, however, the relative strength of individual hotspots is also highly conserved, as are larger-scale features of the distribution of recombination initiation. This stability, not predicted by prior models, suggests that the particular shape of the yeast recombination landscape is adaptive, and helps in understanding evolutionary dynamics of recombination in other species.


A recently developed membrane-mimetic model was applied to study membrane interaction and binding of the two anchoring C2-like discoidin domains of human coagulation factor VIIIa (FVIIIa), the C1 and C2 domains. Both individual domains, FVIII C1 and FVIII C2, were observed to bind the phospholipid membrane by partial or full insertion of their extruding loops (the spikes). However, the two domains adopted different molecular orientations in their membrane-bound states; FVIII C2 roughly was positioned normal to the membrane plane, while FVIII C1 displayed a multitude of tilted orientations. The results indicate that FVIII C1 may be important in modulating the orientation of the FVIIIa molecule to optimize the interaction with FIXa, which is anchored to the membrane via its γ-carboxyglutamic acid-rich (Gla) domain. Additionally, a structural change was observed in FVIII C1 in the coiled main chain leading the first spike. A tight interaction with one lipid per domain, similar to what has been suggested for the homologous FVa C2, is characterized. Finally, we rationalize known FVIII antibody epitopes and the scarcity of documented hemophilic missense mutations related to improper membrane binding of FVIIIa, based on the prevalent nonspecificity of ionic interactions in the simulated membrane-bound states of FVIII C1 and FVIII C2.


The main goal of this thesis titled "Packet Classification using Growing Hierarchical Self-Organizing Map" is to validate the possibility of using a new approach for a well know problem. The core idea is to present a possible implementation of a Growing Hierarchical Self-Organized Map (GH-SOM) for use in packet classification in the context of software-defined networking (SDN). SDN applications are characterized by frequent network configuration updates on-the-fly and a large number of rules thereby requiring a highly flexible packet classification mechanism. Moreover, as network bandwidths continue to grow, a high-performance packet classification is imperative.\par Today's technology adopted for packet classification does not scale well in throughput and power consumption as number of rules increase. The main change proposed in this implementation is to move all the complexity to an offline phase, train a modeled neural network and exploit its properties to provide a more efficient solution for packet classification during run time. Different input space and different parameters have been tested for training the network; multiple simulations have been performed in order to find the best solution to this problem. The work proposed consists of a trained neural network that classifies the rules of a given data-set, then input packets are applied and rule with highest priority is determined.\par Multiple simulation has been carried out for finding the best feature map for the different layer of the GH-SOM structure, in order to provide the best accuracy in every application. The MATLAB code was designed to give the possibility to the designer to easily adjust the training parameters of the neural network in order to provide a general implementation of the structure which can be adapted in multiple situations.