582 research outputs found
Folding Mechanism of Small Proteins
Extensive Monte Carlo folding simulations for four proteins of various
structural classes are carried out, using a single atomistic potential. In all
cases, collapse occurs at a very early stage, and proteins fold into their
native-like conformations at appropriate temperatures. The results demonstrate
that the folding mechanism is controlled not only by thermodynamic factors but
also by kinetic factors: The way a protein folds into its native structure, is
also determined by the convergence point of early folding trajectories, which
cannot be obtained by the free energy surface.Comment: 11 pages, 4 figure
Extraction of hidden information by efficient community detection in networks
Currently, we are overwhelmed by a deluge of experimental data, and network
physics has the potential to become an invaluable method to increase our
understanding of large interacting datasets. However, this potential is often
unrealized for two reasons: uncovering the hidden community structure of a
network, known as community detection, is difficult, and further, even if one
has an idea of this community structure, it is not a priori obvious how to
efficiently use this information. Here, to address both of these issues, we,
first, identify optimal community structure of given networks in terms of
modularity by utilizing a recently introduced community detection method.
Second, we develop an approach to use this community information to extract
hidden information from a network. When applied to a protein-protein
interaction network, the proposed method outperforms current state-of-the-art
methods that use only the local information of a network. The method is
generally applicable to networks from many areas.Comment: 17 pages, 2 figures and 2 table
REPUTATION COMPUTATION IN SOCIAL NETWORKS AND ITS APPLICATIONS
This thesis focuses on a quantification of reputation and presents models which compute reputation within networked environments. Reputation manifests past behaviors of users and helps others to predict behaviors of users and therefore reduce risks in future interactions. There are two approaches in computing reputation on networks- namely, the macro-level approach and the micro-level approach. A macro-level assumes that there exists a computing entity outside of a given network who can observe the entire network including degree distributions and relationships among nodes. In a micro-level approach, the entity is one of the nodes in a network and therefore can only observe the information local to itself, such as its own neighbors behaviors. In particular, we study reputation computation algorithms in online distributed environments such as social networks and develop reputation computation algorithms to address limitations of existing models. We analyze and discuss some properties of reputation values of a large number of agents including power-law distribution and their diffusion property. Computing reputation of another within a network requires knowledge of degrees of its neighbors. We develop an algorithm for estimating degrees of each neighbor. The algorithm considers observations associated with neighbors as a Bernoulli trial and repeatedly estimate degrees of neighbors as a new observation occurs. We experimentally show that the algorithm can compute the degrees of neighbors more accurately than a simple counting of observations. Finally, we design a bayesian reputation game where reputation is used as payoffs. The game theoretic view of reputation computation reflects another level of reality in which all agents are rational in sharing reputation information of others. An interesting behavior of agents within such a game theoretic environment is that cooperation- i.e., sharing true reputation information- emerges without an explicit punishment mechanism nor a direct reward mechanisms
Anti-CRISPR Proteins: Applications in Genome Engineering
Clustered, regularly interspaced, short palindromic repeats and CRISPR-associated proteins (CRISPR-Cas) constitute a bacterial and archaeal adaptive immune system. The ongoing arms race between prokaryotic hosts and their invaders such as phages led to the emergence of anti-CRISPR proteins as countermeasures against the potent antiviral defense. Since the first examples of anti-CRISPRs were shown in a subset of CRISPR-Cas systems, we endeavored to uncover these naturally-occurring inhibitors that inactivate different types of CRISPR-Cas systems. In the first part of my thesis, we have identified and characterized Type II anti-CRISPR proteins that inactivate several Cas9 orthologs. We share mechanistic insights into anti-CRISPR inhibition and show evidence of its potential utility as an off-switch for Cas9-mediated mammalian genome editing. Although the RNA programmability of Cas9 enables facile genetic manipulation with great potential for biotechnology and therapeutics, limitations and safety issues remain. The advent of anti-CRISPR proteins presents opportunities to exploit the inhibitors to exert temporal, conditional, or spatial control over CRISPR. In the second part of my thesis, we demonstrate that anti-CRISPR proteins can serve as useful tools for Cas9 genome editing. In particular, we have demonstrated that anti-CRISPRs are effective as genome editing off-switches in the tissues of adult mammals, and we further engineered anti-CRISPR proteins to achieve tissue-specific editing in vivo. Taken together, my thesis research aimed to mine for natural anti-CRISPR protein inhibitors and repurpose these proteins to complement current Cas9 technologies in basic and clinical research
Transfer from ESL academic writing to first year composition and other disciplinary courses: An assessment perspective
Many American universities offer ESL academic writing courses to help international students prepare for written communication in other university courses. This study investigated the connections and/or disconnections across an ESL writing course, a first-year composition course, and content courses at Iowa State University from the perspective of writing assessment. As a longitudinal mixed-methods study, data were collected over one academic year from 108 international students, 14 instructors of an ESL writing course, 18 instructors of a first-year composition course, and four instructors of content courses. Quantitative data included grades on written assignments and survey responses, while qualitative data consisted of course documents such as assignment sheets and scoring rubrics, written assignments, instructorsâ written feedback on studentsâ drafts, interview recordings, and survey responses.
The study found that the ESL writing course was not very well connected to the first-year composition or content courses in terms of writing assessment. There was a close correspondence across the three writing contexts in terms of evaluation criteria, but not in terms of writing tasks and the grades that students actually received on their writing assignments. Also, studentsâ performance on the written assessment in the ESL writing course was not a reliable indicator of their readiness for writing in other courses. In addition, although students were positive about the learning transfer from the ESL writing to other courses, the first-year composition and content course instructors were negative about it. These findings have implications for revising writing assignments, modifying evaluation practice, and determining ways to enhance learning transfer across the three writing contexts. Although the study was conducted within the context of Iowa State University, the findings are expected to have relevance beyond this particular institution given that ESL academic writing courses in American universities are designed in a more or less similar way and share comparable goals as those offered at Iowa State University
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