13 research outputs found
Astrobiological Complexity with Probabilistic Cellular Automata
Search for extraterrestrial life and intelligence constitutes one of the
major endeavors in science, but has yet been quantitatively modeled only rarely
and in a cursory and superficial fashion. We argue that probabilistic cellular
automata (PCA) represent the best quantitative framework for modeling
astrobiological history of the Milky Way and its Galactic Habitable Zone. The
relevant astrobiological parameters are to be modeled as the elements of the
input probability matrix for the PCA kernel. With the underlying simplicity of
the cellular automata constructs, this approach enables a quick analysis of
large and ambiguous input parameters' space. We perform a simple clustering
analysis of typical astrobiological histories and discuss the relevant boundary
conditions of practical importance for planning and guiding actual empirical
astrobiological and SETI projects. In addition to showing how the present
framework is adaptable to more complex situations and updated observational
databases from current and near-future space missions, we demonstrate how
numerical results could offer a cautious rationale for continuation of
practical SETI searches.Comment: 37 pages, 11 figures, 2 tables; added journal reference belo
Laws and Explanations in Biology and Chemistry:Philosophical Perspectives and Educational Implications
Predicting intensity of white-tailed deer herbivory in the Central Appalachian Mountains
Anatomical Network Analysis Shows Decoupling of Modular Lability and Complexity in the Evolution of the Primate Skull
Distinguishing prehistoric human influence on late-Holocene forests in southern Ontario, Canada
Spatial factors of white-tailed deer herbivory assessment in the central Appalachian Mountains
Effects of Hierarchical Roost Removal on Northern Long-Eared Bat (Myotis septentrionalis) Maternity Colonies
Behavioral evidence suggests facultative scavenging by a marine apex predator during a food pulse
Lack of natural control mechanisms increases wildlife–forestry conflict in managed temperate European forest systems
A serial analysis of gene expression profile of the Alzheimer's disease Tg2576 mouse model
Serial analysis of gene expression (SAGE), a technique that allows for the simultaneous detection of expression levels of the entire genome without a priori knowledge of gene sequences, was used to examine the transcriptional expression pattern of the Tg2576 mouse model of Alzheimer’s disease (AD). Pairwise comparison between the Tg2576 and nontransgenic SAGE libraries identified a number of differentially expressed genes in the Tg2576 SAGE library, some of which were not previously revealed by the microarray studies. Real-time PCR was used to validate a panel of genes selected from the SAGE analysis in the Tg2576 mouse brain, as well as the hippocampus and temporal cortex of sporadic AD and normal age-matched controls. NADH dehydrogenase (ubiquinone) 1 alpha subcomplex 5 (NDUFA5) and FXYD domain-containing ion transport regulator 6 (FXYD6) were found to be significantly decreased in the Tg2576 mouse brain and AD hippocampus. PTEN-induced putative kinase 1 (PINK1), phosphatidylethanolamine binding protein (PEBP), crystalline μ (CRYM), and neurogranin (NRGN) were significantly decreased in AD tissues. The gene ontologies represented in the Tg2576 data were statistically analyzed and demonstrated a significant under-representation of genes involved with G-protein-coupled receptor signaling and odorant binding, while genes significantly over-represented were focused on cellular communication and cellular physiological processes. The novel approach of profiling the Tg2576 mouse brain using SAGE has identified different genes that could subsequently be examined for their potential as peripheral diagnostic and prognostic markers for Alzheimer’s disease.Amee J. George, Lavinia Gordon, Tim Beissbarth, Irene Koukoulas, R. M. Damian Holsinger, Victoria Perreau, Roberto Cappai, Seong-Seng Tan, Colin L. Masters, Hamish S. Scott and Qiao-Xin L