Co-transcriptional R-loops are the main cause of estrogen-induced DNA damage.

The hormone estrogen (E2) binds the estrogen receptor to promote transcription of E2-responsive genes in the breast and other tissues. E2 also has links to genomic instability, and elevated E2 levels are tied to breast cancer. Here, we show that E2 stimulation causes a rapid, global increase in the formation of R-loops, co-transcriptional RNA-DNA products, which in some instances have been linked to DNA damage. We show that E2-dependent R-loop formation and breast cancer rearrangements are highly enriched at E2-responsive genomic loci and that E2 induces DNA replication-dependent double-strand breaks (DSBs). Strikingly, many DSBs that accumulate in response to E2 are R-loop dependent. Thus, R-loops resulting from the E2 transcriptional response are a significant source of DNA damage. This work reveals a novel mechanism by which E2 stimulation leads to genomic instability and highlights how transcriptional programs play an important role in shaping the genomic landscape of DNA damage susceptibility

Cast Glance Near Infrared Imaging Observations of the Space Shuttle During Hypersonic Re-Entry

High resolution calibrated infrared imagery of the Space Shuttle was obtained during hypervelocity atmospheric entries of the STS-119, STS-125 and STS128 missions and has provided information on the distribution of surface temperature and the state of the airflow over the windward surface of the Orbiter during descent. This data collect was initiated by NASA s Hypersonic Thermodynamic Infrared Measurements (HYTHIRM) team and incorporated the use of air- and land-based optical assets to image the Shuttle during atmospheric re-entry. The HYTHIRM objective is to develop and implement a set of mission planning tools designed to establish confidence in the ability of an existing optical asset to reliably acquire, track and return global quantitative surface temperatures of the Shuttle during entry. On Space Shuttle Discovery s STS-119 mission, NASA flew a specially modified thermal protection system tile and instrumentation package to monitor heating effects from boundary layer transition during re-entry. On STS-119, the windward airflow on the port wing was deliberately disrupted by a four-inch wide and quarter-inch tall protuberance built into the modified tile. In coordination with this flight experiment, a US Navy NP-3D Orion aircraft was flown 28 nautical miles below Discovery and remotely monitored surface temperature of the Orbiter at Mach 8.4 using a long-range infrared optical package referred to as Cast Glance. Approximately two months later, the same Navy Cast Glance aircraft successfully monitored the surface temperatures of the Orbiter Atlantis traveling at approximately Mach 14.3 during its return from the successful Hubble repair mission. In contrast to Discovery, Atlantis was not part of the Boundary Layer Transition (BLT) flight experiment, thus the vehicle was not configured with a protuberance on the port wing. In September 2009, Cast Glance was again successful in capturing infrared imagery and monitoring the surface temperatures on Discovery s next flight, STS-128. Again, NASA flew a specially modified thermal protection system tile and instrumentation package to monitor heating effects from boundary layer transition during re-entry. During this mission, Cast Glance was able to image laminar and turbulent flow phenomenology optimizing data collection for Mach 14.7. The purpose of this paper is to describe key elements associated with STS-119/125/128 mission planning and execution from the perspective of the Cast Glance flight crew that obtained the imagery. The paper will emphasize a human element of experience, expertise and adaptability seamlessly coupled with Cast Glance system and sensor technology required to manually collect the required imagery. Specific topics will include a near infrared (NIR) camera upgrade that was implemented just prior to the missions, how pre-flight radiance modeling was utilized to optimize the IR sensor configuration, communications, the development of aircraft test support positions based upon Shuttle trajectory information, support to contingencies such as Shuttle one orbit wave-offs/west coast diversions and then the Cast Glance perspective during an actual Shuttle imaging mission

The Next Generation of High-Speed Dynamic Stability Wind Tunnel Testing (Invited)

Throughout industry, accurate measurement and modeling of dynamic derivative data at high-speed conditions has been an ongoing challenge. The expansion of flight envelopes and non-conventional vehicle design has greatly increased the demand for accurate prediction and modeling of vehicle dynamic behavior. With these issues in mind, NASA Langley Research Center (LaRC) embarked on the development and shakedown of a high-speed dynamic stability test technique that addresses the longstanding problem of accurately measuring dynamic derivatives outside the low-speed regime. The new test technique was built upon legacy technology, replacing an antiquated forced oscillation system, and greatly expanding the capabilities beyond classic forced oscillation testing at both low and high speeds. The modern system is capable of providing a snapshot of dynamic behavior over a periodic cycle for varying frequencies, not just a damping derivative term at a single frequency

Burnout and Adverse Outcomes in Athletic Training Students: Why All Healthcare Educators Should Be Concerned

Background: Burnout is linked to various adverse outcomes (i.e., thoughts of dropout, depression, unprofessional behaviors) in healthcare students (i.e., nursing students, medical students). However, potential adverse outcomes associated with burnout in athletic training students, a subset of healthcare students, have yet to be identified. Objective: To adapt a previously tested theoretical model to explore relationships between student workload, burnout, and potential adverse outcomes in a sample of graduate athletic training students. Methods: An online survey assessing the variables of interest and study information was sent to program directors of graduate-level athletic training programs at their publicly accessible email addresses with a request to forward the opportunity to their students. This was a nationwide sample of graduate athletic training students with 320 graduate athletic training students completing the survey. Descriptive statistics and structural equation modeling was used in our analysis. Results: Structural equation modeling confirmed that our hypothesized model successfully described relationships between academic workload, burnout, and adverse outcomes in athletic training students. Specifically, academic workload predicted burnout, and burnout in turn predicted various adverse outcomes (i.e., thoughts of dropout, depression, unprofessional behaviors) in athletic training students. Educators should be aware of the potential adverse outcomes identified in this sample of athletic training students that have also been reported in other healthcare students. Conclusions: Methods to combat symptoms of burnout to enhance student well-being and avoid potential adverse outcomes should be identified. Future research should use the adapted theoretical model discussed in this article within other healthcare students\u27 samples to understand further the complex network of relationships between academic workload, burnout, and adverse outcomes in the educational environment

Preceding rule induction with instance reduction methods

A new prepruning technique for rule induction is presented which applies instance reduction before rule induction. An empirical evaluation records the predictive accuracy and size of rule-sets generated from 24 datasets from the UCI Machine Learning Repository. Three instance reduction algorithms (Edited Nearest Neighbour, AllKnn and DROP5) are compared. Each one is used to reduce the size of the training set, prior to inducing a set of rules using Clark and Boswell's modification of CN2. A hybrid instance reduction algorithm (comprised of AllKnn and DROP5) is also tested. For most of the datasets, pruning the training set using ENN, AllKnn or the hybrid significantly reduces the number of rules generated by CN2, without adversely affecting the predictive performance. The hybrid achieves the highest average predictive accuracy

Space Launch System Liftoff and Transition Aerodynamic Characterization in the NASA Langley 14- by 22-Foot Subsonic Wind Tunnel

A 1.75% scale force and moment model of the Space Launch System was tested in the NASA Langley Research Center 14- by 22-Foot Subsonic Wind Tunnel to quantify the aerodynamic forces that will be experienced by the launch vehicle during its liftoff and transition to ascent flight. The test consisted of two parts: the first was dedicated to measuring forces and moments for the entire range of angles of attack (0deg to 90deg) and roll angles (0 deg. to 360 deg.). The second was designed to measure the aerodynamic effects of the liftoff tower on the launch vehicle for ground winds from all azimuthal directions (0 deg. to 360 deg.), and vehicle liftoff height ratios from 0 to 0.94. This wind tunnel model also included a set of 154 surface static pressure ports. Details on the experimental setup, and results from both parts of testing are presented, along with a description of how the wind tunnel data was analyzed and post-processed in order to develop an aerodynamic database. Finally, lessons learned from experiencing significant dynamics in the mid-range angles of attack due to steady asymmetric vortex shedding are presented

Empirical Evaluation of the Difficulty of Finding a Good Value of k for the Nearest Neighbor

As an analysis of the classification accuracy bound for the Nearest Neighbor technique, in this work we have studied if it is possible to find a good value of the parmeter k for each example according to their attribute values. Or at least, if there is a pattern for the parameter k in the original search space. We have carried out different approaches based onthe Nearest Neighbor technique and calculated the prediction accuracy for a group of databases from the UCI repository. Based on the experimental results of our study, we can state that, in general, it is not possible to know a priori a specific value of k to correctly classify an unseen example