A novel locus for split-hand/foot malformation associated with tibial hemimelia (SHFLD syndrome) maps to chromosome region 17p13.1-17p13.3

Split-hand/foot malformation (SHFM) associated with aplasia of long bones, SHFLD syndrome or Tibial hemimelia-ectrodactyly syndrome is a rare condition with autosomal dominant inheritance, reduced penetrance and an incidence estimated to be about 1 in 1,000,000 liveborns. To date, three chromosomal regions have been reported as strong candidates for harboring SHFLD syndrome genes: 1q42.2-q43, 6q14.1 and 2q14.2. We characterized the phenotype of nine affected individuals from a large family with the aim of mapping the causative gene. Among the nine affected patients, four had only SHFM of the hands and no tibial defects, three had both defects and two had only unilateral tibial hemimelia. In keeping with previous publications of this and other families, there was clear evidence of both variable expression and incomplete penetrance, the latter bearing hallmarks of anticipation. Segregation analysis and multipoint Lod scores calculations (maximum Lod score of 5.03 using the LINKMAP software) using all potentially informative family members, both affected and unaffected, identified the chromosomal region 17p13.1-17p13.3 as the best and only candidate for harboring a novel mutated gene responsible for the syndrome in this family. The candidate gene CRK located within this region was sequenced but no pathogenic mutation was detected.

A Road Following Approach Using Artificial Neural Networks Combinations

Navigation is a broad topic that has been receiving considerable attention from the mobile robotic community over the years. In order to execute autonomous driving in outdoor urban environments it is necessary to identify parts of the terrain that can be traversed and parts that should be avoided. This paper describes an analyses of terrain identification based on different visual information using a MLP artificial neural network and combining responses of many classifiers. Experimental tests using a vehicle and a video camera have been conducted in real scenarios to evaluate the proposed approach.