Co-bedding as a Comfort measure For Twins undergoing painful procedures (CComForT Trial)

<p>Abstract</p> <p>Background</p> <p>Co-bedding, a developmental care strategy, is the practice of caring for diaper clad twins in one incubator (versus separating and caring for each infant in separate incubators), thus creating the opportunity for skin-to-skin contact and touch between the twins. In studies of mothers and their infants, maternal skin-to-skin contact has been shown to decrease procedural pain response according to both behavioral and physiological indicators in very preterm neonates. It is uncertain if this comfort is derived solely from maternal presence or from stabilization of regulatory processes from direct skin contact. The intent of this study is to compare the comfort effect of co-bedding (between twin infants who are co-bedding and those who are not) on infant pain response and physiologic stability during a tissue breaking procedure (heelstick).</p> <p>Methods/Design</p> <p>Medically stable preterm twin infants admitted to the Neonatal Intensive Care Unit will be randomly assigned to a co-bedding group or a standard care group. Pain response will be measured by physiological and videotaped facial reaction using the Premature Infant Pain Profile scale (PIPP). Recovery from the tissue breaking procedure will be determined by the length of time for heart rate and oxygen saturation to return to baseline. Sixty four sets of twins (n = 128) will be recruited into the study. Analysis and inference will be based on the intention-to-treat principle.</p> <p>Discussion</p> <p>If twin contact while co-bedding is determined to have a comforting effect for painful procedures, then changes in current neonatal care practices to include co-bedding may be an inexpensive, non invasive method to help maintain physiologic stability and decrease the long term psychological impact of procedural pain in this high risk population. Knowledge obtained from this study will also add to existing theoretical models with respect to the exact mechanism of comfort through touch.</p> <p>Trial registration</p> <p>NCT00917631</p

Robust estimation of bacterial cell count from optical density

Optical density (OD) is widely used to estimate the density of cells in liquid culture, but cannot be compared between instruments without a standardized calibration protocol and is challenging to relate to actual cell count. We address this with an interlaboratory study comparing three simple, low-cost, and highly accessible OD calibration protocols across 244 laboratories, applied to eight strains of constitutive GFP-expressing E. coli. Based on our results, we recommend calibrating OD to estimated cell count using serial dilution of silica microspheres, which produces highly precise calibration (95.5% of residuals &lt;1.2-fold), is easily assessed for quality control, also assesses instrument effective linear range, and can be combined with fluorescence calibration to obtain units of Molecules of Equivalent Fluorescein (MEFL) per cell, allowing direct comparison and data fusion with flow cytometry measurements: in our study, fluorescence per cell measurements showed only a 1.07-fold mean difference between plate reader and flow cytometry data

Machines that understand music

Machine learning, signal processing and data mining are being combined to analyze audio content in a relatively new field of research called computer audition. This thesis develops and describes a number of computer audition methods and shows how they can be applied to solve challenges including automatic tagging, similarity and recommendation, search and discovery, and segmentation of music content. To achieve these advances in music understanding requires human guidance. A further contribution of this work is to pioneer game-powered machine learning that uses crowdsourced human intelligence to guide the training of machine algorithms. By leveraging human perception with machine automation, the work described in this thesis presents a comprehensive approach to computer audition that leads to the development of machines that understand musi

Crowdsourcing the world: solving real-world problems with data from the crowd

Presented at the National data integrity conference: data sharing: the how, why, when and when not to share held on June 2-3, 2016 at University of Colorado, Denver, Colorado. The National Data Integrity Conference is a gathering of people sharing new challenges and solutions regarding research data and integrity. This conference aims to provide attendees with both an understanding of data integrity issues and impart practical tools and skills to deal with them. Topics addressed will include data privacy, openness, policy, education and the impacts of sharing data, how to do it, when to do it, and when not to. Speakers and audience members come from diverse fields such as: Academic Research; Information Technology; Quality Assurance; Regulatory Compliance; Private Industry; Grant Funding; Government.Dr. Barrington is a startup founder, computer hacker, and business leader. He earned a Ph.D in Electrical and Computer Engineering from UC San Diego with a thesis that pioneered the use of online crowdsourcing to train smarter machine learning algorithms. Luke was a founder of Tomnod, an online crowdsourcing community where millions of contributors explore satellite images to find interesting and important information about our changing planet. He is currently leader of new products at DigitalGlobe, which acquired Tomnod in 2013. Luke is a public speaker, technology leader, and funk guitarist, sometimes all at once.PowerPoint presentation given on June 2, 2016. PDF includes video on page 2

Machines that understand music

Machine learning, signal processing and data mining are being combined to analyze audio content in a relatively new field of research called computer audition. This thesis develops and describes a number of computer audition methods and shows how they can be applied to solve challenges including automatic tagging, similarity and recommendation, search and discovery, and segmentation of music content. To achieve these advances in music understanding requires human guidance. A further contribution of this work is to pioneer game-powered machine learning that uses crowdsourced human intelligence to guide the training of machine algorithms. By leveraging human perception with machine automation, the work described in this thesis presents a comprehensive approach to computer audition that leads to the development of machines that understand musi