Binding of small molecules to DNA junctions

Previous results in our laboratory suggest that the (CG) 4 segments whether present in a right-handed or a left-handed conformation form distinctive junctions with adjacent random sequences. These junctions and their associated sequences have unique structural and thermodynamic properties that may be recognized by DNA-binding molecules. This study probes these sequences by using the following small ligands: actinomycin D, 1,4-bis(((di(aminoethyl)amino)ethyl)amino)anthracene-9,10-dione, ametantrone, and tris(phenanthroline)ruthenium (II). These ligands may recognize the distinctive features associated to the (CG)4 segment and its junctions and thus interact preferentially near these sequences. Restriction enzyme inhibition assays were used to determine whether or not binding interactions took place, and to approximate locations of these interactions. These binding studies are first carried out using two small synthetic oligomers BZ-III and BZ-IV. The (5meCG)4 segment present in BZ-III adopts the Z-conformation in the presence of 50 m M Co(NH3)63+. In BZ-IV, the unmethylated (CG)4 segment changes to a non-B conformation in the presence of 50 m M Co(NH3)63+. BZ-IV, containing the (CG)4 segment, was inserted into a clone plasmid then digested with the restriction enzyme Hinf I to produce a larger fragment that contains the (CG)4 segment. The results obtained on the small oligomers and on the larger fragment for restriction enzyme Mbo I indicate that 1,4-bis(((di(aminoethyl)amino)ethyl)amino)anthracene-9,10-dione binds more efficiently at or near the (CG)4 segment. Restriction enzymes EcoRV, Sac I and Not I with cleavage sites upstream and downstream of the (CG)4 insert were used to further localize binding interactions in the vicinity of the (CG)4 insert. RNA polymerase activity was studied in a plasmid which contained the (CG)4 insert downstream from the promoter sites of SP6 and T7 RNA polymerases. Activities of these two polymerases were studied in the presence of each one of the ligands used throughout the study. Only actinomycin D and spider, which bind at or near the (CG)4 segment, alter the activities of SP6 and T7 RNA polymerases. Surprisingly, enhancement of polymerase activity was observed in the presence of very low concentrations of actinomycin D. These results suggest that the conformational features of (CG) segments may serve in regulatory functions of DNA. ^

Mixed spatial and nonspatial problems in location based services

With hundreds of millions of users reporting locations and embracing mobile technologies, Location Based Services (LBSs) are raising new challenges. In this dissertation, we address three emerging problems in location services, where geolocation data plays a central role. First, to handle the unprecedented growth of generated geolocation data, existing location services rely on geospatial database systems. However, their inability to leverage combined geographical and textual information in analytical queries (e.g. spatial similarity joins) remains an open problem. To address this, we introduce SpsJoin, a framework for computing spatial set-similarity joins. SpsJoin handles combined similarity queries that involve textual and spatial constraints simultaneously. LBSs use this system to tackle different types of problems, such as deduplication, geolocation enhancement and record linkage. We define the spatial set-similarity join problem in a general case and propose an algorithm for its efficient computation. Our solution utilizes parallel computing with MapReduce to handle scalability issues in large geospatial databases. Second, applications that use geolocation data are seldom concerned with ensuring the privacy of participating users. To motivate participation and address privacy concerns, we propose iSafe, a privacy preserving algorithm for computing safety snapshots of co-located mobile devices as well as geosocial network users. iSafe combines geolocation data extracted from crime datasets and geosocial networks such as Yelp. In order to enhance iSafe's ability to compute safety recommendations, even when crime information is incomplete or sparse, we need to identify relationships between Yelp venues and crime indices at their locations. To achieve this, we use SpsJoin on two datasets (Yelp venues and geolocated businesses) to find venues that have not been reviewed and to further compute the crime indices of their locations. Our results show a statistically significant dependence between location crime indices and Yelp features. Third, review centered LBSs (e.g., Yelp) are increasingly becoming targets of malicious campaigns that aim to bias the public image of represented businesses. Although Yelp actively attempts to detect and filter fraudulent reviews, our experiments showed that Yelp is still vulnerable. Fraudulent LBS information also impacts the ability of iSafe to provide correct safety values. We take steps toward addressing this problem by proposing SpiDeR, an algorithm that takes advantage of the richness of information available in Yelp to detect abnormal review patterns. We propose a fake venue detection solution that applies SpsJoin on Yelp and U.S. housing datasets. We validate the proposed solutions using ground truth data extracted by our experiments and reviews filtered by Yelp.

Mixed Spatial and Nonspatial Problems in Location Based Services

With hundreds of millions of users reporting locations and embracing mobile technologies, Location Based Services (LBSs) are raising new challenges. In this dissertation, we address three emerging problems in location services, where geolocation data plays a central role. First, to handle the unprecedented growth of generated geolocation data, existing location services rely on geospatial database systems. However, their inability to leverage combined geographical and textual information in analytical queries (e.g. spatial similarity joins) remains an open problem. To address this, we introduce SpsJoin, a framework for computing spatial set-similarity joins. SpsJoin handles combined similarity queries that involve textual and spatial constraints simultaneously. LBSs use this system to tackle different types of problems, such as deduplication, geolocation enhancement and record linkage. We define the spatial set-similarity join problem in a general case and propose an algorithm for its efficient computation. Our solution utilizes parallel computing with MapReduce to handle scalability issues in large geospatial databases. Second, applications that use geolocation data are seldom concerned with ensuring the privacy of participating users. To motivate participation and address privacy concerns, we propose iSafe, a privacy preserving algorithm for computing safety snapshots of co-located mobile devices as well as geosocial network users. iSafe combines geolocation data extracted from crime datasets and geosocial networks such as Yelp. In order to enhance iSafe's ability to compute safety recommendations, even when crime information is incomplete or sparse, we need to identify relationships between Yelp venues and crime indices at their locations. To achieve this, we use SpsJoin on two datasets (Yelp venues and geolocated businesses) to find venues that have not been reviewed and to further compute the crime indices of their locations. Our results show a statistically significant dependence between location crime indices and Yelp features. Third, review centered LBSs (e.g., Yelp) are increasingly becoming targets of malicious campaigns that aim to bias the public image of represented businesses. Although Yelp actively attempts to detect and filter fraudulent reviews, our experiments showed that Yelp is still vulnerable. Fraudulent LBS information also impacts the ability of iSafe to provide correct safety values. We take steps toward addressing this problem by proposing SpiDeR, an algorithm that takes advantage of the richness of information available in Yelp to detect abnormal review patterns. We propose a fake venue detection solution that applies SpsJoin on Yelp and U.S. housing datasets. We validate the proposed solutions using ground truth data extracted by our experiments and reviews filtered by Yelp.