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  • About
  • The Global ETD Search service is a free service for researchers to find electronic theses and dissertations. This service is provided by the Networked Digital Library of Theses and Dissertations.
    Our metadata is collected from universities around the world. If you manage a university/consortium/country archive and want to be added, details can be found on the NDLTD website.
1

Substituted Cysteine Accessibility Method Analysis of the C-terminal Half of Human Concentrative Nucleoside Transporter 3 (hCNT3)

Mulinta, Ras 06 1900 (has links)
Concentrative nucleoside transporter (CNT) proteins mediate active nucleoside transport using the electrochemical gradient of the coupling cation. The molecular mechanisms underlying interactions with both nucleosides and cations were investigated by heterelogous expression of recombinant CNT family members in Xenopus oocytes. Substituted cysteine accessibility method (SCAM) analysis in combination with radioisotope flux assays and electrophysiological studies revealed novel topological features within the C-terminal half of human (h)CNTs and identified residues of functional importance. The hCNT (SLC28) protein family is represented by three members. hCNT1 and hCNT2 are pyrimidine nucleoside- and purine nucleoside-selective, respectively, while hCNT3 transports both pyrimidine and purine nucleosides. hCNT1 and hCNT2 function exclusively as Na+-coupled nucleoside transporters and share a 1:1 Na+:nucleoside stoichiometry. Belonging to a CNT subfamily phylogenetically distinct from hCNT1/2, hCNT3 utilizes electrochemical gradients of Na+, Li+ or H+ to drive nucleoside transport and exhibits 2:1 Na+:nucleoside and 1:1 H+:nucleoside stoichiometries. Non-mammalian H+-coupled CNT family members that have been functionally characterized include NupC from Escherichia coli. Both Na+ and H+ activate CNTs through mechanisms to increase nucleoside apparent binding affinity. Multiple alignments of CNT family members reveal strong sequence similarities within the C-terminal halves of the proteins, and hCNT1/3 and other chimeric studies have demonstrated that this region determines both nucleoside and cation interactions with the transporter. In hCNT3, access of pchloromercuribenzene sulfonate (PCMBS) to introduced cysteine residues within putative transmembrane segments (TMs) 7, 8, 9 and 11A revealed novel discontinuous regions within -helical structures, whereas putative TMs 10, 11, 12 and 13 exhibited conventional -helical characteristics. Putative TM 11A, which contains the highly conserved CNT family motif (G/A)XKX3NEFVA(Y/M/F), was shown to be membrane associated and, most likely, membrane spanning, TMs 7-11 having a reversed orientation in the membrane compared to previous models of CNT topology. Furthermore, putative TMs 7, 8, 9, 11A and 12 were shown to contribute functional and structural elements to a common nucleoside/cation translocation pore. These studies, which were extended to TMs 7 and 8 of hCNT1 and to corresponding TMs of E. coli NupC, provide important structural and functional insights into the nature of CNT nucleoside/cation cotransport.
2

Substituted Cysteine Accessibility Method Analysis of the C-terminal Half of Human Concentrative Nucleoside Transporter 3 (hCNT3)

Mulinta, Ras Unknown Date
No description available.
3

Towards Algorithmic Identification of Online Scams

Badawi, Emad Mohammad Hussein 13 December 2021 (has links)
In “web-based scams”, scam websites provide fraudulent business or fake services to steal money and sensitive information from unsuspecting victims. Despite the researchers’ efforts to develop anti-scam detection techniques, the scams continue to evolve and cause online threats. State-of-the-art anti-scam research still faces several challenges, such as automatically acquiring a labeled scam dataset and providing early detection and prevention mechanisms to attacks that use cryptocurrency as a payment medium. In this thesis, we implement a data-driven model to detect and track web-based scams with a web presence. Given a few scam samples, our model formulates scam-related search queries and uses them on multiple search engines to collect data about the websites to which victims are directed when they search online for sites that may be related to the scam. After collecting a sufficient corpus of web pages, our model semi-automatically clusters the search results and creates a labeled training dataset with minimal human interaction. Our model proactively looks for scam pages and monitors their evolution over time rather than waiting for the scam to be reported. Whenever a new scam instance is detected, the model sends it automatically to the eCrime eXchange data warehouse in real-time. We have used the model to investigate and gain knowledge on two scams; the “Game Hack” Scam (GHS) and the “Bitcoin Generator Scam” (BGS). To the best of our knowledge, GHS and BGS have not been well studied so far, and this is the first systematic study of both scams. GHS targets game players, in which the attackers attempt to convince victims that they will be provided with free in-game advantages for their favorite game. Before claiming these advantages, the victims are supposed to complete one or more tasks, such as filling out “market research” forms and installing suspicious executable files on their machines. Over a year of crawling, we uncovered more than 5,900 unique domains. We estimate that these domains have been accessed at least 150 million times from 2014 until 2019. BGS is a simple system in which the scammers promise to “generate” new bitcoins using the ones sent to them. BGS is not a very sophisticated attack; the modus operandi is to put up some web page that contains the address to send the money and wait for the payback. Over 21 months of crawling, we found more than 3,000 addresses directly associated with the scam, hosted on over 1,200 domains. Overall, these addresses have received (at least) over 9.6 million USD. Our analysis showed that a small group of scammers controls the majority of the received funds. The top two groups have received around 6 million USD, which is more than half of the total funds received by the scam addresses.

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