Two key events associated with a transposable element burst occurred during rice domestication

Transposable elements shape genome evolution through periodic bursts of amplification. In this study we exploited knowledge of the components of the mPing/Ping/Pong TE family in four rice strains undergoing mPing bursts to track their copy numbers and distribution in a large collection of genomes from the wild progenitor Oryza rufipogon and domesticated Oryza sativa (rice). We characterized two events that occurred to the autonomous Ping element and appear to be critical for mPing hyperactivity. First, a point mutation near the end of the element created a Ping variant ( Ping16A ) with reduced transposition. The proportion of strains with Ping16A has increased during domestication while the original Ping (Ping16G) has been dramatically reduced. Second, transposition of Ping16A into a Stowaway element generated a locus ( Ping16A_Stow ) whose presence correlates with strains that have high mPing copies. Finally, demonstration that Pong elements have been stably silenced in all strains analyzed indicates that sustained activity of the mPing/Ping family during domestication produced the components necessary for the mPing burst, not the loss of epigenetic regulation

On Efficiency of AS Paths from Users to Content Servers: A Case Study of Netflix

The majority of the Internet traffic today is content delivery traffic. The performance of content delivery depends on the efficiency of the routing paths from users to content servers and from content servers back to the users. While content providers can control the paths from their servers to the users, they have no control over the paths from users to their content servers and the efficiency of such paths is generally unknown. In this work, we conduct a case study of Netflix to understand the efficiency of the AS paths from various access ISPs to Netflix servers deployed at IXPs in different regions of the world. We discover inefficient AS paths in Europe, North America, and South America. Paths in South America are especially inefficient as many of them leave the continent. We also analyze long paths in each region, explore their causes, and propose ways to avoid long paths

Computing Observed Autonomous System Relationships in the Internet

Autonomous Systems (ASes) in the Internet use BGP to perform interdomain routing. BGP routing policies are mainly determined by the business relationships between neighboring ASes, which can be classified into three types: provider-to-customer, peer-to-peer, and sibling-to-sibling. ASes usually do not export provider routes and peer routes to providers or peers. It has been proved that if all ASes conform to this common export policy then all AS paths are valley-free. Since AS relationships are not publicly available, several studies have proposed heuristic algorithms for inferring AS relationships using publicly available BGP data. Most of these algorithms rely on the valley-free property of AS paths. However, not all AS paths are valley-free because some ASes do not conform to the common export policy. As a result, inferred AS relationship are inaccurate. Instead of inferring AS relationships, we propose an algorithm for computing observed AS relationships based on transit relationships between ASes that are revealed by BGP data. We analyze the types of mismatches between observed AS relationships and actual AS relationships and show that the mismatches can be used to identify ASes that violate the common export policy

A Machine Learning Approach to Edge Type Prediction in Internet AS Graphs

The Internet consists of a large number of interconnected autonomous systems (ASes). ASes engage in two types of business relationships to exchange traffic: provider-to-customer (p2c) relationship and peer-to-peer (p2p) relationship. Internet AS-level topology can be represented by AS graphs where nodes represent autonomous systems (ASes) and edges represent connectivity between ASes. While researchers have derived AS graphs using various data sources, inferring the types of edges (p2c or p2p) in AS graphs remains an open problem. In this paper we present a new machine learning approach to edge type inference in AS graphs. Our method uses the AdaBoost machine learning algorithm to train a model that predicts the edge types in a given AS graph using two node attributes - degree and minimum distance to a Tier-1 node. We train a model for a BGP graph and validate the model using ground truth AS relationships and CAIDA\u27s inferred AS relationship dataset. Our results show that the model achieves over 92% accuracy on a number of BGP graphs

Assessing risk to fresh water resources from long term CO2 injection- laboratory and field studies

In developing a site for geologic sequestration, one must assess potential consequences of failure to adequately contain injected carbon dioxide (CO2). Upward migration of CO2 or displacement of saline water because of increased pressure might impact protected water resources 100s to 1000s of meters above a sequestration interval. Questions posed are: (1) Can changes in chemistry of fresh water aquifers provide evidence of CO2 leakage from deep injection/sequestration reservoirs containing brine and or hydrocarbons? (2) What parameters can we use to assess potential impacts to water quality? (3) If CO2 leakage to freshwater aquifers occurs, will groundwater quality be degraded and if so, over what time period? Modeling and reaction experiments plus known occurrences of naturally CO2-charged potable water show that the common chemical reaction products from dissolution of CO2 into freshwater include rapid buffering of acidity by dissolution of calcite and slower equilibrium by reaction with clays and feldspars. Results from a series of laboratory batch reactions of CO2 with diverse aquifer rocks show geochemical response within hours to days after introduction of CO2. Results included decreased pH and increased concentrations of cations in CO2 experimental runs relative to control runs using argon (Ar). Some cation (Ba, Ca, Fe, Mg, Mn, and Sr) concentrations increased over and an order of magnitude during CO2 runs. Results are aquifer dependant in that experimental vessels containing different aquifer rocks showed different magnitudes of increase in cation concentrations. Field studies designed to improve understanding of risk to fresh water are underway in the vicinity of (1) SACROC oilfield in Scurry County, Texas, USA where CO2 has been injected for enhanced oil recovery (EOR) since 1972 and (2) the Cranfield unit in Adams County, Mississippi, USA where CO2 EOR is currently underway. Both field studies are funded by the U.S. Department of Energy (DOE) regional carbon sequestration partnership programs and industrial sponsors. Preliminary results of groundwater monitoring are currently available for the SACROC field study where researchers investigated 68 water wells and one spring during five field excursions between June 2006 and July 2008. Results to date show no trend of preferential degradation below drinking water standards in areas of CO2 injection (inside SACROC) as compared to areas outside of the SACROC oil field.Bureau of Economic Geolog

Incremental Genetic K-means Algorithm and its Application in Gene Expression Data Analysis

Background In recent years, clustering algorithms have been effectively applied in molecular biology for gene expression data analysis. With the help of clustering algorithms such as K-means, hierarchical clustering, SOM, etc, genes are partitioned into groups based on the similarity between their expression profiles. In this way, functionally related genes are identified. As the amount of laboratory data in molecular biology grows exponentially each year due to advanced technologies such as Microarray, new efficient and effective methods for clustering must be developed to process this growing amount of biological data. Results In this paper, we propose a new clustering algorithm, Incremental Genetic K-means Algorithm (IGKA). IGKA is an extension to our previously proposed clustering algorithm, the Fast Genetic K-means Algorithm (FGKA). IGKA outperforms FGKA when the mutation probability is small. The main idea of IGKA is to calculate the objective value Total Within-Cluster Variation (TWCV) and to cluster centroids incrementally whenever the mutation probability is small. IGKA inherits the salient feature of FGKA of always converging to the global optimum. C program is freely available at http://database.cs.wayne.edu/proj/FGKA/index.htm. Conclusions Our experiments indicate that, while the IGKA algorithm has a convergence pattern similar to FGKA, it has a better time performance when the mutation probability decreases to some point. Finally, we used IGKA to cluster a yeast dataset and found that it increased the enrichment of genes of similar function within the cluster

Inferring neutron star properties with continuous gravitational waves

Detection of continuous gravitational waves from rapidly-spinning neutron stars opens up the possibility of examining their internal physics. We develop a framework that leverages a future continuous gravitational wave detection to infer a neutron star's moment of inertia, equatorial ellipticity, and the component of the magnetic dipole moment perpendicular to its rotation axis. We assume that the neutron star loses rotational kinetic energy through both gravitational wave and electromagnetic radiation, and that the distance to the neutron star can be measured, but do not assume electromagnetic pulsations are observable or a particular neutron star equation of state. We use the Fisher information matrix and Monte Carlo simulations to estimate errors in the inferred parameters, assuming a population of gravitational-wave-emitting neutron stars consistent with the typical parameter domains of continuous gravitational wave searches. After an observation time of one year, the inferred errors for many neutron stars are limited chiefly by the error in the distance to the star. The techniques developed here will be useful if continuous gravitational waves are detected from a radio, X-ray, or gamma-ray pulsar, or else from a compact object with known distance, such as a supernova remnant.Comment: 10 pages, 4 figures. To be published in MNRA

Comparing treatment policies with assistance from the structural nested mean model

Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/142500/1/biom12391-sup-0001-SuppData.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/142500/2/biom12391_am.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/142500/3/biom12391.pd

No credible evidence supporting claims of the laboratory engineering of SARS-CoV-2

The emergence and outbreak of a newly discovered acute respiratory disease in Wuhan, China, has affected greater than 40,000 people, and killed more than 1,000 as of Feb. 10, 2020. A new human coronavirus, SARSCoV- 2, was quickly identified, and the associated disease is now referred to as coronavirus disease discovered in 2019 (COVID-19) (https://globalbiodefense. com/novel-coronavirus-covid-19-portal/)

Exploration of factors associated with perceived barriers to cervical cancer screening among Chinese American women

This study explored factors associated with perceived barriers to Pap smear testing among Chinese American women (CAW). A total of 121 CAW, ages 21–65, living in California and Nevada completed a self-report questionnaire. Data included demographics, prior screening behavior, risk factors, and perceived barriers to screening. Logistic regression models revealed that participants with less education, and who have never been screened were more likely to report (I) worry about getting a Pap smear, (II) expense of a Pap smear, and (III) not knowing where to get a Pap smear. Partner’s resistance to the women’s participation in screening was another barrier among the never screened. Uninsured women were more likely to worry about getting a Pap test and embarrassment associated with getting a Pap test. Women who had never been screened, those with lower education, and those who were uninsured reported more barriers. Attention to these common and unique barriers may help address health disparities in screening rates. These findings reinforce the importance of literacy- and culturally-appropriate educational interventions designed for improving knowledge of cervical cancer and improving screening rates