Gentle Masking of Low-Complexity Sequences Improves Homology Search

Detection of sequences that are homologous, i.e. descended from a common ancestor, is a fundamental task in computational biology. This task is confounded by low-complexity tracts (such as atatatatatat), which arise frequently and independently, causing strong similarities that are not homologies. There has been much research on identifying low-complexity tracts, but little research on how to treat them during homology search. We propose to find homologies by aligning sequences with “gentle” masking of low-complexity tracts. Gentle masking means that the match score involving a masked letter is , where is the unmasked score. Gentle masking slightly but noticeably improves the sensitivity of homology search (compared to “harsh” masking), without harming specificity. We show examples in three useful homology search problems: detection of NUMTs (nuclear copies of mitochondrial DNA), recruitment of metagenomic DNA reads to reference genomes, and pseudogene detection. Gentle masking is currently the best way to treat low-complexity tracts during homology search

Forty years on: Uta Frith's contribution to research on autism and dyslexia, 1966–2006

Uta Frith has made a major contribution to our understanding of developmental disorders, especially autism and dyslexia. She has studied the cognitive and neurobiological bases of both disorders and demonstrated distinctive impairments in social cognition and central coherence in autism, and in phonological processing in dyslexia. In this enterprise she has encouraged psychologists to work in a theoretical framework that distinguishes between observed behaviour and the underlying cognitive and neurobiological processes that mediate that behaviour

Mitigating Gender Bias in Machine Learning Data Sets

Artificial Intelligence has the capacity to amplify and perpetuate societal biases and presents profound ethical implications for society. Gender bias has been identified in the context of employment advertising and recruitment tools, due to their reliance on underlying language processing and recommendation algorithms. Attempts to address such issues have involved testing learned associations, integrating concepts of fairness to machine learning and performing more rigorous analysis of training data. Mitigating bias when algorithms are trained on textual data is particularly challenging given the complex way gender ideology is embedded in language. This paper proposes a framework for the identification of gender bias in training data for machine learning.The work draws upon gender theory and sociolinguistics to systematically indicate levels of bias in textual training data and associated neural word embedding models, thus highlighting pathways for both removing bias from training data and critically assessing its impact.Comment: 10 pages, 5 figures, 5 Tables, Presented as Bias2020 workshop (as part of the ECIR Conference) - http://bias.disim.univaq.i

Estimating Uncertainty in Long Term Total Ozone Records from Multiple Sources

Total ozone measurements derived from the TOMS and SBUV backscattered solar UV instrument series cover the period from late 1978 to the present. As the SBUV series of instruments comes to an end, we look to the 10 years of data from the AURA Ozone Monitoring Instrument (OMI) and two years of data from the Ozone Mapping Profiler Suite (OMPS) on board the Suomi National Polar-orbiting Partnership satellite to continue the record. When combining these records to construct a single long-term data set for analysis we must estimate the uncertainty in the record resulting from potential biases and drifts in the individual measurement records. In this study we present a Monte Carlo analysis used to estimate uncertainties in the Merged Ozone Dataset (MOD), constructed from the Version 8.6 SBUV2 series of instruments. We extend this analysis to incorporate OMI and OMPS total ozone data into the record and investigate the impact of multiple overlapping measurements on the estimated error. We also present an updated column ozone trend analysis and compare the size of statistical error (error from variability not explained by our linear regression model) to that from instrument uncertainty

NASA's Optical Program on Ascension Island: Bringing MCAT to Life as the Eugene Stansbery-Meter Class Autonomous Telescope (ES-MCAT)

In June 2015, the construction of the Meter Class Autonomous Telescope was completed and MCAT saw the light of the stars for the first time. In 2017, MCAT was newly dedicated as the Eugene Stansbery-MCAT telescope by NASA's Orbital Debris Program Office (ODPO), in honor of his inspiration and dedication to this newest optical member of the NASA ODPO. Since that time, MCAT has viewed the skies with one engineering camera and two scientific cameras, and the ODPO optical team has begun the process of vetting the entire system. The full system vetting includes verification and validation of: (1) the hardware comprising the system (e.g. the telescopes and its instruments, the dome, weather systems, all-sky camera, FLIR cloud infrared camera, etc.), (2) the custom-written Observatory Control System (OCS) master software designed to autonomously control this complex system of instruments, each with its own control software, and (3) the custom written Orbital Debris Processing software for post-processing the data. ES-MCAT is now capable of autonomous observing to include Geosynchronous survey, TLE (Two-line element) tracking of individual catalogued debris at all orbital regimes (Low-Earth Orbit all the way to Geosynchronous (GEO) orbit), tracking at specified non-sidereal rates, as well as sidereal rates for proper calibration with standard stars. Ultimately, the data will be used for validation of NASA's Orbital Debris Engineering Model, ORDEM, which aids in engineering designs of spacecraft that require knowledge of the orbital debris environment and long-term risks for collisions with Resident Space Objects (RSOs)

Music in advertising and consumer identity: The search for Heideggerian authenticity

This study discusses netnographic findings involving 472 YouTube postings categorized to identify themes regarding consumers’ experience of music in advertisements. Key themes relate to musical taste, musical indexicality, musical repetition and musical authenticity. Postings reveal how music conveys individual taste and is linked to personal memories and Heidegger’s coincidental time where moments of authenticity may be triggered in a melee of emotions, memories and projections. Identity protection is enabled as consumers frequently resist advertisers’ attempts to use musical repetition to impose normative identity. Critiques of repetition in the music produce Heideggerian anxiety leading to critically reflective resistance. Similarly, where advertising devalues the authenticity of iconic pieces of music, consumers often resist such authenticity transgressions as a threat to their own identity. The Heideggerian search for meaning in life emphasizes the significance of philosophically driven ideological authenticity in consumers’ responses to music in advertisements

Characterizing Debris in the Infrared with UKIRT

The United Kingdom Infrared Telescope (UKIRT) has been a major asset for the NASA Orbital Debris Program Office (OPDO) since March, 2014. With the UKIRT current contract coming to an end at the finish of FY15, there is a golden opportunity for this community to fund and gain access to UKIRT as an SSA asset through HCAR (Hawaii Center for Astronautics Research). UKIRT is the only telescope on Mauna Kea dedicated to infrared bands. Spectral coverage ranges from the near- (0.8-5m) to the mid- to far-infrared (8-25 micrometer) regime. To date, debris observations have been collected with three instruments. Near-Infrared photometry with ZYJHK filters has been obtained with the Wide Field Camera (WFCam). Near-Infrared (1-2.5 micrometer) spectra are the focus of observations taken with the UKIRT Imager SpecTrometer (UIST). And Michelle (Mid Infrared escCHELLE) is a thermal imager-spectrometer designed for the 8-25 micrometer regime. With 35% of the telescope time allocated to ODPO, a very steady stream of data has been collected on a variety of debris targets using all the above instrumentation. Initial results from WFCam were discussed at AMOS and NISOI including analyses on IDCSPs, the MSG cooler and baffle covers. The cylindrical HS-376 buses were the focus of recent WFCam runs. Summary analyses of these works will be presented. Focus will be given to initial results of the data collected with the Cassegrain instruments, UIST and Michelle. UIST spectra were collected in September 2014, March and April 2015. Targets included a suite of HS-376 buses, well suited to investigate the signatures of blue solar panels; several dead satellites with solar array wings; Titan 3C transtage debris; the CTA Array cover, and others. In addition, Michelle mid-IR photometry was collected on a select few objects during the April 2015 run. Using WFCam, UIST and Michelle the Lockheed Martin has been observing operational satellites in the near- mid and far-infrared regime in an attempt to understand the health and status of several satellites that are based on the Lockheed Martin A2100 bus. The potential insights into debris characterization using this range of assets, and early analyses will be discussed, as well as the opportunities possible for utilizing UKIRT as an SSA asset

Integrating Orbital Debris Measurements and Modeling - How Observations and Laboratory Data are used to Help Make Space Operations Safer

The NASA Orbital Debris Program Office has been statistically surveying human-made resident space objects (RSOs) in geocentric orbits for several decades, using optical and infrared telescopes. The prime goal has been to understand the evolving population and characteristics of debris generated by RSOs. The debris population includes any non-functioning RSO that no longer serves a useful purpose. Any object that cannot be purposely maneuvered, including non-functioning satellites, rocket bodies, and any object generated by a collision, explosion, or fragmentation event, may pose a future collisional threat to active satellites. Key questions immediately surface from this knowledge: What can we do to protect our precious functioning satellites from collisions? How do we design our satellites to prevent them from being future sources of debris? And what can we do as a society to protect the environment surrounding Earth to preserve it for future generations? To begin to address these questions, and to better understand this population as well as break-up events contributing to it, NASA has developed a suite of models and experimental laboratory data to work in tandem with observational and laboratory measurements of RSOs. These models include the Orbital Debris Engineering Model (ORDEM), the Standard Satellite Break-up Model (SSBM), and an evolutionary model of the environment from LEO to GEO (LEGEND). Ground-based data have been collected from the infrared telescope UKIRT (UK Infrared Telescope) in Hawaii, as well as the 1.3m Eugene Stansbery Meter Class Autonomous Telescope, ES-MCAT, historically called MCAT, on Ascension Island. MCAT will be tasked to collect GEO (Geosynchronous) survey data, scanning orbits to search for uncatalogued objects (e.g. fragmentation/break-up events (SSBM)), and targeted observations of catalogued objects for more intensive studies, e.g. when a break-up or anomalous event occurs. Laboratory experimental data includes DebriSat, a satellite impacted at ~6.9 km/s in an impact laboratory on Earth, and optical photometry from the Optical Measurements Center at NASA JSC. An integrated view will be discussed of how our telescopic observations and lab measurements interplay with models to understand the current (ORDEM) and future (LEGEND) environment, the evolution of satellite breakups (SSBM), and how this knowledge can help to promote an environment that is safer for operations