Predictive impact of rare genomic copy number variations in siblings of individuals with autism spectrum disorders.

Identification of genetic biomarkers associated with autism spectrum disorders (ASDs) could improve recurrence prediction for families with a child with ASD. Here, we describe clinical microarray findings for 253 longitudinally phenotyped ASD families from the Baby Siblings Research Consortium (BSRC), encompassing 288 infant siblings. By age 3, 103 siblings (35.8%) were diagnosed with ASD and 54 (18.8%) were developing atypically. Thirteen siblings have copy number variants (CNVs) involving ASD-relevant genes: 6 with ASD, 5 atypically developing, and 2 typically developing. Within these families, an ASD-related CNV in a sibling has a positive predictive value (PPV) for ASD or atypical development of 0.83; the Simons Simplex Collection of ASD families shows similar PPVs. Polygenic risk analyses suggest that common genetic variants may also contribute to ASD. CNV findings would have been pre-symptomatically predictive of ASD or atypical development in 11 (7%) of the 157 BSRC siblings who were eventually diagnosed clinically

The Growing Threat of Agroterrorism and Strategies for Agricultural Defense

Due to the dynamic nature of human conflict, non-traditional terror tactics have evolved to undermine the socioeconomic stability of targeted societies. Considering the landscape in which terrorists operate, emphasis on more subversive methods of biological terror have become prominent in recent decades. Agroterrorism, or the use of plant pathogens to infect a nation’s cultivated crops, is an emerging topic due to its threat to global food security and economic stability. Although emergency preparedness objectives have been enacted at national, state, and even local levels, preemptive measures can no longer remain the sole responsibility of intelligence and law enforcement agencies. The agricultural and scientific communities are responsible for collaboration to improve security and pioneer new methods of disease resistance in susceptible crops. Plant immunology is an expanding field which explores the molecular defense mechanisms innately present within the plant kingdom and provides insight concerning novel methods of boosting the immunity of susceptible crops to existing and emerging pathogenic agents. This thesis serves to define the threat of agroterrorism from a national security and scientific perspective, identify notable plant pathogens, provide a brief survey of plant immunology, and discuss topics which can aid scientists, policymakers, and growers in efforts to secure the global food supply from those who would cause harm

Data Driven Inference in Populations of Agents

abstract: In the artificial intelligence literature, three forms of reasoning are commonly employed to understand agent behavior: inductive, deductive, and abductive.  More recently, data-driven approaches leveraging ideas such as machine learning, data mining, and social network analysis have gained popularity. While data-driven variants of the aforementioned forms of reasoning have been applied separately, there is little work on how data-driven approaches across all three forms relate and lend themselves to practical applications. Given an agent behavior and the percept sequence, how one can identify a specific outcome such as the likeliest explanation? To address real-world problems, it is vital to understand the different types of reasonings which can lead to better data-driven inference.   This dissertation has laid the groundwork for studying these relationships and applying them to three real-world problems. In criminal modeling, inductive and deductive reasonings are applied to early prediction of violent criminal gang members. To address this problem the features derived from the co-arrestee social network as well as geographical and temporal features are leveraged. Then, a data-driven variant of geospatial abductive inference is studied in missing person problem to locate the missing person. Finally, induction and abduction reasonings are studied for identifying pathogenic accounts of a cascade in social networks.Dissertation/ThesisDoctoral Dissertation Computer Science 201