Improved Computational Prediction of Function and Structural Representation of Self-Cleaving Ribozymes with Enhanced Parameter Selection and Library Design

Biomolecules could be engineered to solve many societal challenges, including disease diagnosis and treatment, environmental sustainability, and food security. However, our limited understanding of how mutational variants alter molecular structures and functional performance has constrained the potential of important technological advances, such as high-throughput sequencing and gene editing. Ribonuleic Acid (RNA) sequences are thought to play a central role within many of these challenges. Their continual discovery throughout all domains of life is evidence of their significant biological importance (Weinreb et al., 2016). The self-cleaving ribozyme is a class of noncoding Ribonuleic Acid (ncRNA) that has been useful for relating sequence variants to structural features and their associated catalytic activities. Self-cleaving ribozymes possess tractable sequence spaces, perform easily identifiable catalytic functions, and have well documented structures. The determination of a self-cleaving ribozyme’s structure and catalytic activity within the laboratory is typically a slow and expensive process. Most current explorations of structure and function come from these empirical processes. Computational approaches to the prediction of catalytic activity and structure are fast and inexpensive, but have failed both to achieve atomic accuracy or to correctly identify all base-pair interactions (Watkins et al., 2018). One prominent impediment to computational approaches is the lack of existing structural and functional data typically required by predictive models (Jumper et al., 2021). Using data from deep-mutational scanning experiments and high-throughput sequencing technology, it is possible to computationally map mutational variants to their observed catalytic activity for a range of self-cleaving ribozymes. The resulting map reveals important base-pairing relationships that, in turn, facilitate accurate predictions of higher-order variants. Using sequence data from three experimental replicates of five model self-cleaving ribozymes, I will identify and map all single and double mutation variants to their observed cleavage activity. These mappings will be used to identify structural features within each ribozyme. Next, I will show within a training tool how observed cleavage for multiple reaction times can be used to identify the catalytic rates of our model ribozymes. Finally, I will predict the functional activity for model ribozyme variants of various mutational orders using machine learning models trained only on functionally labeled sequence variants. Together, these three dissertation chapters represent the kind of analysis needed to further the implementation of more accurate structural and functional prediction algorithms

Determination of the critical current density in the d-wave superconductor YBCO under applied magnetic fields by nodal tunneling

We have studied nodal tunneling into YBa2Cu3O7-x (YBCO) films under magnetic fields. The films' orientation was such that the CuO2 planes were perpendicular to the surface with the a and b axis at 450 form the normal. The magnetic field was applied parallel to the surface and perpendicular to the CuO2 planes. The Zero Bias Conductance Peak (ZBCP) characteristic of nodal tunneling splits under the effect of surface currents produced by the applied fields. Measuring this splitting under different field conditions, zero field cooled and field cooled, reveals that these currents have different origins. By comparing the field cooled ZBCP splitting to that taken in decreasing fields we deduce a value of the Bean critical current superfluid velocity, and calculate a Bean critical current density of up to 3*10^7 A/cm2 at low temperatures. This tunneling method for the determination of critical currents under magnetic fields has serious advantages over the conventional one, as it avoids having to make high current contacts to the sample.Comment: 8 pages, 2 figure

Resistivity of Graphene Nanoribbon Interconnects

Graphene nanoribbon interconnects are fabricated, and the extracted resistivity is compared to that of Cu. It is found that the average resistivity at a given line-width (18nm<W<52nm) is about 3X that of a Cu wire, whereas the best GNR has a resistivity comparable to that of Cu. The conductivity is found to be limited by impurity scattering as well as LER scattering; as a result, the best reported GNR resistivity is 3X the limit imposed by substrate phonon scattering. This study reveals that even moderate-quality graphene nanowires have the potential to outperform Cu for use as on-chip interconnects.Comment: 10 pages, 3 figures, to be published in IEEE Electron Device Letter

Breakdown Current Density of Graphene Nano Ribbons

Graphene nanoribbons (GNRs) with widths down to 16 nm have been characterized for their current-carrying capacity. It is found that GNRs exhibit an impressive breakdown current density, on the order of 10^8 A/cm2. The breakdown current density is found to have a reciprocal relationship to GNR resistivity and the data fit points to Joule heating as the likely mechanism of breakdown. The superior current-carrying capacity of GNRs will be valuable for their application in on-chip electrical interconnects. The thermal conductivity of sub-20 nm graphene ribbons is found to be more than 1000 W/m-K

Robust estimation of marginal regression parameters in clustered data

We develop robust methods for analyzing clustered data where estimation of marginal regression parameters is of interest. Inverse cluster size reweighting in the objective function to be minimized is incorporated to handle the issue of informative cluster size. Performance of the resulting estimators is studied by simulation. Large sample inference and variance estimation is carried out. The methodology is illustrated using a periodontal disease dataset

Bias in estimating the cross-sectional smoking, alcohol, obesity and diabetes associations with moderate-severe periodontitis in the Atherosclerosis Risk in Communities study: comparison of full versus partial-mouth estimates

To assess whether partial mouth protocols (PRPs) result in biased estimates of the associations between smoking, alcohol, obesity, and diabetes with periodontitis

Global communication part 1: the use of apparel CAD technology

Trends needed for improved communication systems, through the development of future computer-aided design technology (CAD) applications, is a theme that has received attention due to its perceived benefits in improving global supply chain efficiencies. This article discusses the developments of both 2D and 3D computer-aided design capabilities, found within global fashion supply chain relationships and environments. Major characteristics identified within the data suggest that CAD/CAM technology appears to be improving; however, evidence also suggest a plateau effect, which is accrediting forced profits towards information technology manufactures, and arguably compromising the industry's competitive advantage. Nevertheless, 2D CAD increases communication speed; whereas 3D human interaction technology is seen to be evolving slowly and questionably with limited success. The article discusses the findings and also presents the issues regarding human interaction; technology education; and individual communication enhancements using technology processes. These are still prevalent topics for the future developments of global strategy and cultural communication amalgamation

New functionalism and the social and behavioral sciences

AbstractFunctionalism about kinds is still the dominant style of thought in the special sciences, like economics, psychology, and biology. Generally construed, functionalism is the view that states or processes can be individuated based on what role they play rather than what they are constituted of or realized by. Recently, Weiskopf (2011a, 2011b) has posited a reformulation of functionalism on the model-based approach to explanation. We refer to this reformulation as ‘new functionalism’. In this paper, we seek to defend new functionalism and to recast it in light of the concrete explanatory aims of the special sciences. In particular, we argue that the assessment of the explanatory legitimacy of a functional kind needs to take into account the explanatory purpose of the model in which the functional kind is employed. We aim at demonstrating this by appealing to model-based explanations from the social and behavioral sciences. Specifically, we focus on preferences and signals as functional kinds. Our argument is intended to have the double impact of deflecting criticisms against new functionalism from the perspective of mechanistic decomposition while also expanding the scope of new functionalism to encompass the social and behavioral sciences.</jats:p

Third World gap year projects: Youth transitions and the mediation of risk

This is the post-print version of the final published article. The definitive, peer-reviewed and edited version of this article is available from the link below. Copyright @ 2008 Pion.In recent years in the UK there has been a great expansion in the number of young people travelling to Third World countries between school and university in order to participate as volunteers on structured gap year projects. Travel to such places is commonly perceived as ‘risky’, and takes young people outside the protective cocoon of UK health and safety legislation. One of the functions played by the providers of gap year projects is to mediate risk. On the basis of analysis of promotional literature, interviews with organisers of gap year projects, and focus groups of returned volunteers, in this paper I argue that the various strategies of risk mediation undertaken by gap year providers serve to reconcile modernising tendencies in UK society toward risk control and structure with postmodern inclinations towards individualisation and uncertainty

RNA Sequence to Structure Analysis from Comprehensive Pairwise Mutagenesis of Multiple Self-Cleaving Ribozymes

Self-cleaving ribozymes are RNA molecules that catalyze the cleavage of their own phosphodiester backbones. These ribozymes are found in all domains of life and are also a tool for biotechnical and synthetic biology applications. Self-cleaving ribozymes are also an important model of sequence-to-function relationships for RNA because their small size simplifies synthesis of genetic variants and self-cleaving activity is an accessible readout of the functional consequence of the mutation. Here, we used a high-throughput experimental approach to determine the relative activity for every possible single and double mutant of five self-cleaving ribozymes. From this data, we comprehensively identified non-additive effects between pairs of mutations (epistasis) for all five ribozymes. We analyzed how changes in activity and trends in epistasis map to the ribozyme structures. The variety of structures studied provided opportunities to observe several examples of common structural elements, and the data was collected under identical experimental conditions to enable direct comparison. Heatmap-based visualization of the data revealed patterns indicating structural features of the ribozymes including paired regions, unpaired loops, non-canonical structures, and tertiary structural contacts. The data also revealed signatures of functionally critical nucleotides involved in catalysis. The results demonstrate that the data sets provide structural information similar to chemical or enzymatic probing experiments, but with additional quantitative functional information. The large-scale data sets can be used for models predicting structure and function and for efforts to engineer self-cleaving ribozymes