FIRE PROTECTION ALTERNATIVES FOR RURAL AREAS

Community/Rural/Urban Development,

Comparative Linkage Meta-Analysis Reveals Regionally-Distinct, Disparate Genetic Architectures: Application to Bipolar Disorder and Schizophrenia

New high-throughput, population-based methods and next-generation sequencing capabilities hold great promise in the quest for common and rare variant discovery and in the search for ”missing heritability.” However, the optimal analytic strategies for approaching such data are still actively debated, representing the latest rate-limiting step in genetic progress. Since it is likely a majority of common variants of modest effect have been identified through the application of tagSNP-based microarray platforms (i.e., GWAS), alternative approaches robust to detection of low-frequency (1–5% MAF) and rare (<1%) variants are of great importance. Of direct relevance, we have available an accumulated wealth of linkage data collected through traditional genetic methods over several decades, the full value of which has not been exhausted. To that end, we compare results from two different linkage meta-analysis methods—GSMA and MSP—applied to the same set of 13 bipolar disorder and 16 schizophrenia GWLS datasets. Interestingly, we find that the two methods implicate distinct, largely non-overlapping, genomic regions. Furthermore, based on the statistical methods themselves and our contextualization of these results within the larger genetic literatures, our findings suggest, for each disorder, distinct genetic architectures may reside within disparate genomic regions. Thus, comparative linkage meta-analysis (CLMA) may be used to optimize low-frequency and rare variant discovery in the modern genomic era

Biometric signature verification system

Signature Verification involves the checking for the authenticity of a signature. It is composed of a hardware system that acquires the data and a software algorithm that verifies the signature. There are many techniques in signature verification but such approaches rely on different basis. This thesis will present a technique in signature verification based on the dynamic characteristics of the signature, namely pressure and acceleration. A concept of acceleration in an invariant system will be serve as basis for the necessary acceleration data. A neural network will be used to find out the most likely set of pressure inputs and acceleration inputs for a particular signature. The personal computer will then disclose the authenticity of the signature

Klimasensibilitaet und Stabiltaet nicht regenerierbarer Oekosysteme: Kuestenduenen Schlussbericht

SIGLEAvailable from TIB Hannover: F01B506 / FIZ - Fachinformationszzentrum Karlsruhe / TIB - Technische InformationsbibliothekBundesministerium fuer Bildung, Wissenschaft, Forschung und Technologie, Bonn (Germany)DEGerman