Impact of template backbone heterogeneity on RNA polymerase II transcription.

Variations in the sugar component (ribose or deoxyribose) and the nature of the phosphodiester linkage (3'-5' or 2'-5' orientation) have been a challenge for genetic information transfer from the very beginning of evolution. RNA polymerase II (pol II) governs the transcription of DNA into precursor mRNA in all eukaryotic cells. How pol II recognizes DNA template backbone (phosphodiester linkage and sugar) and whether it tolerates the backbone heterogeneity remain elusive. Such knowledge is not only important for elucidating the chemical basis of transcriptional fidelity but also provides new insights into molecular evolution. In this study, we systematically and quantitatively investigated pol II transcriptional behaviors through different template backbone variants. We revealed that pol II can well tolerate and bypass sugar heterogeneity sites at the template but stalls at phosphodiester linkage heterogeneity sites. The distinct impacts of these two backbone components on pol II transcription reveal the molecular basis of template recognition during pol II transcription and provide the evolutionary insight from the RNA world to the contemporary 'imperfect' DNA world. In addition, our results also reveal the transcriptional consequences from ribose-containing genomic DNA

Individual Stellar Halos of Massive Galaxies Measured to 100 kpc at 0.3<z<0.50.3<z<0.5 using Hyper Suprime-Cam

Massive galaxies display extended light profiles that can reach several hundreds of kilo parsecs. These stellar halos provide a fossil record of galaxy assembly histories. Using data that is both wide (~100 square degree) and deep (i>28.5 mag/arcsec^2 in i-band), we present a systematic study of the stellar halos of a sample of more than 3000 galaxies at 0.3 < z < 0.5 with $\log M_{\star}/M_{\odot} > 11.4$. Our study is based on high-quality (0.6 arcsec seeing) imaging data from the Hyper Suprime-Cam (HSC) Subaru Strategic Program (SSP), which enables us to individually estimate surface mass density profiles to 100 kpc without stacking. As in previous work, we find that more massive galaxies exhibit more extended outer profiles. When this extended light is not properly accounted for as a result of shallow imaging or inadequate profile modeling, the derived stellar mass function can be significantly underestimated at the highest masses. Across our sample, the ellipticity of outer light profiles increases substantially as we probe larger radii. We show for the first time that these ellipticity gradients steepen dramatically as a function of galaxy mass, but we detect no mass-dependence in outer color gradients. Our results support the two-phase formation scenario for massive galaxies in which outer envelopes are built up at late times from a series of merging events. We provide surface mass surface mass density profiles in a convenient tabulated format to facilitate comparisons with predictions from numerical simulations of galaxy formation.Comment: Submitted to MNRAS; 23 pages, 8 figures, 2 appendix; Data will be made available here: http://massivegalaxies.com/ once the paper is publishe

Retrieval cues fail to influence contextualized evaluations.

Initial evaluations generalise to new contexts, whereas counter-attitudinal evaluations are context-specific. Counter-attitudinal information may not change evaluations in new contexts because perceivers fail to retrieve counter-attitudinal cue-evaluation associations from memory outside the counter-attitudinal learning context. The current work examines whether an additional, counter-attitudinal retrieval cue can enhance the generalizability of counter-attitudinal evaluations. In four experiments, participants learned positive information about a target person, Bob, in one context, and then learned negative information about Bob in a different context. While learning the negative information, participants wore a wristband as a retrieval cue for counter-attitudinal Bob-negative associations. Participants then made speeded as well as deliberate evaluations of Bob while wearing or not wearing the wristband. Internal meta-analysis failed to find a reliable effect of the counter-attitudinal retrieval cue on speeded or deliberate evaluations, whereas the context cues influenced speeded and deliberate evaluations. Counter to predictions, counter-attitudinal retrieval cues did not disrupt the generalisation of first-learned evaluations or the context-specificity of second-learned evaluations (Experiments 2-4), but the counter-attitudinal retrieval cue did influence evaluations in the absence of context cues (Experiment 1). The current work provides initial evidence that additional counter-attitudinal retrieval cues fail to disrupt the renewal and generalizability of first-learned evaluations

An ecosystem inspired framework for digital government transformation

Traditional e-government and Digital Government Transformation (DGT) initiatives are often focused on technology transformation and business-IT alignment with the attempt to make government more efficient, transparent and easier to operate within. However, billions of public funding has been spent on those initiatives with very few anticipated benefits yielded. This thesis asserts that DGT will bring about true government transformation when business, people and culture are considered together. Many existing business/industry frameworks, architectures and best practices in the literature for DGT projects address only one or two of those dimensions. To focus on the development and delivery of transformational changes in DGT initiatives, this thesis will propose a solution framework for an integrative approach that brings about business transformation, technology transformation, with people-stakeholder-leadership oriented cultural transformation to form a holistic methodology framework for DGT that is beyond technology alone and will illuminate the road to success in DGT executions. This thesis will provide an ecosystem inspired framework together with tools and maturity model framework to guide a government-wide successful execution of the DGT journey that is iterative, measurable and with consideration of all aspects of business, technology and people. This thesis will approach DGT journeys by: 1. Implementing of a holistic framework to guide the DGT; 2. Considering people and culture for an effective DGT; 3. Providing an integrated approach that can bring innovative knowledge and cultural transformation together with the technology transformation; and by 4. Providing a measurement framework and metrics to guide the maturity of DGT projects. This thesis will be evaluated through four case studies including public sector, the defence force, and health ecosystems and is aimed at supporting public entities for better utilisation of resources, modernising operations, displaying better use of public funds, keeping trust high, saving time, offering fast learning, and better engagement and services for its stakeholders both internally and externally

Detecting Changes in the Transmission Rate of a Stochastic Epidemic Model

Throughout the course of an epidemic, the rate at which disease spreads varies with behavioral changes, the emergence of new disease variants, and the introduction of mitigation policies. Estimating such changes in transmission rates can help us better model and predict the dynamics of an epidemic, and provide insight into the efficacy of control and intervention strategies. We present a method for likelihood-based estimation of parameters in the stochastic SIR model under a time-inhomogeneous transmission rate comprised of piecewise constant components. In doing so, our method simultaneously learns change points in the transmission rate via a Markov chain Monte Carlo algorithm. The method targets the exact model posterior in a difficult missing data setting given only partially observed case counts over time. We validate performance on simulated data before applying our approach to data from an Ebola outbreak in Western Africa and COVID-19 outbreak on a university campus