Effects of SUV39H1 and SUV420H1/H2 on Programmed Genome Rearrangement in \u3cem\u3ePetromyzon marinus\u3c/em\u3e

The sea lamprey (Petromyzon marinus), diverged from the vertebrate lineage roughly 550 million years ago, prior to the evolution of several major morphological features such as jaws and paired fins/appendages. Lamprey therefore provides a comparative perspective that can be used to study the evolution of differences in genome regulation, including epigenetics and programmed genome rearrangement (PGR). Programmed genome rearrangement is a unique regulatory mechanism wherein specific genes are effectively turned off by completely eliminating their sequences from the genome. Through PGR, lamprey delete approximately 20% of their genome from all somatic cells, with these specific sequences being only retained by germline cells. The mechanisms of PGR have yet to be fully understood; however, I hypothesized that two genes (SUV420H1/2 and SUV39H1) might be involved in the process. The gene SUV420H1/2 encodes a methyltransferase that trimethylates Histone 4 at Lysine 20, a site important for recruitment of factors necessary for DNA damage response and DNA repair, which could plausibly be involved in the elimination of DNA during PGR. The gene SUV39H1 transcribes a methyltransferase that is responsible for catalyzing di- and tri-methylation of Histone 3 at Lysine 9, a significant marker for heterochromatic DNA. Due to its function, it is suspected that PGR levels might decrease in CRISPR-mediated knockouts because the embryos will be lacking a marker for chromatin packaging and deletion. Results from light-sheet microscopy demonstrate that both SUV420H1/2 and SUV39H1 significantly affect levels of PGR. These results indicate that additional genes within the suppressor of variegation family should be further investigated for potential contributions to PGR

Climate Informatics: Accelerating Discovering in Climate Science with Machine Learning

The goal of climate informatics, an emerging discipline, is to inspire collaboration between climate scientists and data scientists, in order to develop tools to analyze complex and ever-growing amounts of observed and simulated climate data, and thereby bridge the gap between data and understanding. Here, recent climate informatics work is presented, along with details of some of the field's remaining challenges. Given the impact of climate change, understanding the climate system is an international priority. The goal of climate informatics is to inspire collaboration between climate scientists and data scientists, in order to develop tools to analyze complex and ever-growing amounts of observed and simulated climate data, and thereby bridge the gap between data and understanding. Here, recent climate informatics work is presented, along with details of some of the remaining challenges

Evidence that implementation intentions reduce drivers' use of mobile phones while driving

Implementation intentions are IF-THEN plans that have the potential to reduce mobile phone use while driving and thus contribute towards the prevention of road traffic crashes. We tested whether an intervention, designed to promote the formation of implementation intentions, could reduce drivers’ use of mobile phones. A randomized controlled design was used. The participants (N = 136) were randomised to an implementation or a control condition. Self-report questionnaires were administered to all participants at both pre- and one-month post-intervention to measure the use of mobile phones while driving, goal intentions and the theoretically derived motivational pre-cursors of goal intentions (attitudes, subjective norm and perceived behavioural control). Immediately following the pre-intervention questionnaire, the participants in the implementation intention condition (n = 67) were given a volitional help sheet, which asked them to form implementation intentions by specifying target driving situations that tempted them the most to use a mobile phone and linking them with goal-directed responses that could be used to resist the temptation. The participants in the control condition (n = 69) were asked to specify target situations that tempted them the most to use a mobile phone while driving and to generally try to avoid using a mobile phone in those situations. One-month post-intervention, the participants in the implementation intention condition reported using a mobile phone less often while driving in their specified target driving situations than did the participants in the control condition. As expected, no differences were found between the conditions in the reported frequency of mobile phone use in unspecified driving situations, goal intentions or any motivational pre-cursor of goal intentions. The implementation intention intervention that was tested in this study is a potentially effective tool for reducing mobile phone use while driving in target driving situations where behaviour-change is most needed

Spatial Predictive Modeling and Remote Sensing of Land Use Change in the Chesapeake Bay Watershed

This project was focused on modeling the processes by which increasing demand for developed land uses, brought about by changes in the regional economy and the socio-demographics of the region, are translated into a changing spatial pattern of land use. Our study focused on a portion of the Chesapeake Bay Watershed where the spatial patterns of sprawl represent a set of conditions generally prevalent in much of the U.S. Working in the region permitted us access to (i) a time-series of multi-scale and multi-temporal (including historical) satellite imagery and (ii) an established network of collaborating partners and agencies willing to share resources and to utilize developed techniques and model results. In addition, a unique parcel-level tax assessment database and linked parcel boundary maps exists for two counties in the Maryland portion of this region that made it possible to establish a historical cross-section time-series database of parcel level development decisions. Scenario analyses of future land use dynamics provided critical quantitative insight into the impact of alternative land management and policy decisions. These also have been specifically aimed at addressing growth control policies aimed at curbing exurban (sprawl) development. Our initial technical approach included three components: (i) spatial econometric modeling of the development decision, (ii) remote sensing of suburban change and residential land use density, including comparisons of past change from Landsat analyses and more traditional sources, and (iii) linkages between the two through variable initialization and supplementation of parcel level data. To these we added a fourth component, (iv) cellular automata modeling of urbanization, which proved to be a valuable addition to the project. This project has generated both remote sensing and spatially explicit socio-economic data to estimate and calibrate the parameters for two different types of land use change models and has undertaken analyses of these models. One (the CA model) is driven largely by observations on past patterns of land use change, while the other (the EC model) is driven by mechanisms of the land use change decision at the parcel level. Our project may be the first serious attempt at developing both types of models for the same area, using as much common data as possible. We have identified the strengths and weaknesses of the two approaches and plan to continue to revise each model in the light of new data and new lessons learned through continued collaboration. Questions, approaches, findings, publication and presentation lists concerning the research are also presented

The Link Between Health Insurance Coverage and Citizenship Among Immigrants: Bayesian Unit-Level Regression Modeling of Categorical Survey Data Observed with Measurement Error

Social scientists are interested in studying the impact that citizenship status has on health insurance coverage among immigrants in the United States. This can be done using data from the Survey of Income and Program Participation (SIPP); however, two primary challenges emerge. First, statistical models must account for the survey design in some fashion to reduce the risk of bias due to informative sampling. Second, it has been observed that survey respondents misreport citizenship status at nontrivial rates. This too can induce bias within a statistical model. Thus, we propose the use of a weighted pseudo-likelihood mixture of categorical distributions, where the mixture component is determined by the latent true response variable, in order to model the misreported data. We illustrate through an empirical simulation study that this approach can mitigate the two sources of bias attributable to the sample design and misreporting. Importantly, our misreporting model can be further used as a component in a deeper hierarchical model. With this in mind, we conduct an analysis of the relationship between health insurance coverage and citizenship status using data from the SIPP

MEMS practice, from the lab to the telescope

Micro-electro-mechanical systems (MEMS) technology can provide for deformable mirrors (DMs) with excellent performance within a favorable economy of scale. Large MEMS-based astronomical adaptive optics (AO) systems such as the Gemini Planet Imager are coming on-line soon. As MEMS DM end-users, we discuss our decade of practice with the micromirrors, from inspecting and characterizing devices to evaluating their performance in the lab. We also show MEMS wavefront correction on-sky with the "Villages" AO system on a 1-m telescope, including open-loop control and visible-light imaging. Our work demonstrates the maturity of MEMS technology for astronomical adaptive optics.Comment: 14 pages, 15 figures, Invited Paper, SPIE Photonics West 201

Cost effective, experimentally robust differential-expression analysis for human/mammalian, pathogen and dual-species transcriptomics.

As sequencing read length has increased, researchers have quickly adopted longer reads for their experiments. Here, we examine 14 pathogen or host-pathogen differential gene expression data sets to assess whether using longer reads is warranted. A variety of data sets was used to assess what genomic attributes might affect the outcome of differential gene expression analysis including: gene density, operons, gene length, number of introns/exons and intron length. No genome attribute was found to influence the data in principal components analysis, hierarchical clustering with bootstrap support, or regression analyses of pairwise comparisons that were undertaken on the same reads, looking at all combinations of paired and unpaired reads trimmed to 36, 54, 72 and 101 bp. Read pairing had the greatest effect when there was little variation in the samples from different conditions or in their replicates (e.g. little differential gene expression). But overall, 54 and 72 bp reads were typically most similar. Given differences in costs and mapping percentages, we recommend 54 bp reads for organisms with no or few introns and 72 bp reads for all others. In a third of the data sets, read pairing had absolutely no effect, despite paired reads having twice as much data. Therefore, single-end reads seem robust for differential-expression analyses, but in eukaryotes paired-end reads are likely desired to analyse splice variants and should be preferred for data sets that are acquired with the intent to be community resources that might be used in secondary data analyses