A modified beam-to-earth transformation to measure short-wavelength internal waves with an acoustic Doppler current profiler

Author Posting. © American Meteorological Society 2005. This article is posted here by permission of American Meteorological Society for personal use, not for redistribution. The definitive version was published in Journal of Atmospheric and Oceanic Technology 22 (2005): 583–591, doi:10.1175/JTECH1731.1.The algorithm used to transform velocity signals from beam coordinates to earth coordinates in an acoustic Doppler current profiler (ADCP) relies on the assumption that the currents are uniform over the horizontal distance separating the beams. This condition may be violated by (nonlinear) internal waves, which can have wavelengths as small as 100–200 m. In this case, the standard algorithm combines velocities measured at different phases of a wave and produces horizontal velocities that increasingly differ from true velocities with distance from the ADCP. Observations made in Massachusetts Bay show that currents measured with a bottom-mounted upward-looking ADCP during periods when short-wavelength internal waves are present differ significantly from currents measured by point current meters, except very close to the instrument. These periods are flagged with high error velocities by the standard ADCP algorithm. In this paper measurements from the four spatially diverging beams and the backscatter intensity signal are used to calculate the propagation direction and celerity of the internal waves. Once this information is known, a modified beam-to-earth transformation that combines appropriately lagged beam measurements can be used to obtain current estimates in earth coordinates that compare well with pointwise measurements.A. Scotti was partially supported by ONR Grants N00014-03-1-0553 and N00014-01-1- 0172, B. Butman and P. Alexander by the U.S. Geological Survey, and R. Beardsley by the WHOI Smith Chair and ONR Grant N00014-98-1-0210. S. Anderson received partial support from ONR (Grant N00014-97- 1-0158). The Massachusetts Bay Internal Wave Experiment was jointly supported by ONR and USGS

Professional development and sustainable development goals

Professional development is defined as a consciously designed systematic process that helps professionals to attain, utilize, and retain knowledge, skills, and expertise. It is simply a process of obtaining skills, qualifications, and experience that help in advancement in one’s career. In the field of education, it is defined as the process of improving staff skills and competencies needed to produce outstanding performance of students. It also refers to a process of improving an organization’s staff capabilities through access to education and training opportunities for better output. Professional development can include a variety of approaches such as formal and informal education, vocational, specialized, or skill-based training, or advanced professional learning

2018 update to the HIV-TRePS system: the development of new computational models to predict HIV treatment outcomes, with or without a genotype, with enhanced usability for low-income settings

Objectives Optimizing antiretroviral drug combination on an individual basis can be challenging, particularly in settings with limited access to drugs and genotypic resistance testing. Here we describe our latest computational models to predict treatment responses, with or without a genotype, and compare their predictive accuracy with that of genotyping. Methods Random forest models were trained to predict the probability of virological response to a new therapy introduced following virological failure using up to 50 000 treatment change episodes (TCEs) without a genotype and 18 000 TCEs including genotypes. Independent data sets were used to evaluate the models. This study tested the effects on model accuracy of relaxing the baseline data timing windows, the use of a new filter to exclude probable non-adherent cases and the addition of maraviroc, tipranavir and elvitegravir to the system. Results The no-genotype models achieved area under the receiver operator characteristic curve (AUC) values of 0.82 and 0.81 using the standard and relaxed baseline data windows, respectively. The genotype models achieved AUC values of 0.86 with the new non-adherence filter and 0.84 without. Both sets of models were significantly more accurate than genotyping with rules-based interpretation, which achieved AUC values of only 0.55–0.63, and were marginally more accurate than previous models. The models were able to identify alternative regimens that were predicted to be effective for the vast majority of cases in which the new regimen prescribed in the clinic failed. Conclusions These latest global models predict treatment responses accurately even without a genotype and have the potential to help optimize therapy, particularly in resource-limited settings

2018 update to the HIV-TRePS system: the development of new computational models to predict HIV treatment outcomes, with or without a genotype, with enhanced usability for low-income settings

Objectives Optimizing antiretroviral drug combination on an individual basis can be challenging, particularly in settings with limited access to drugs and genotypic resistance testing. Here we describe our latest computational models to predict treatment responses, with or without a genotype, and compare their predictive accuracy with that of genotyping. Methods Random forest models were trained to predict the probability of virological response to a new therapy introduced following virological failure using up to 50 000 treatment change episodes (TCEs) without a genotype and 18 000 TCEs including genotypes. Independent data sets were used to evaluate the models. This study tested the effects on model accuracy of relaxing the baseline data timing windows, the use of a new filter to exclude probable non-adherent cases and the addition of maraviroc, tipranavir and elvitegravir to the system. Results The no-genotype models achieved area under the receiver operator characteristic curve (AUC) values of 0.82 and 0.81 using the standard and relaxed baseline data windows, respectively. The genotype models achieved AUC values of 0.86 with the new non-adherence filter and 0.84 without. Both sets of models were significantly more accurate than genotyping with rules-based interpretation, which achieved AUC values of only 0.55–0.63, and were marginally more accurate than previous models. The models were able to identify alternative regimens that were predicted to be effective for the vast majority of cases in which the new regimen prescribed in the clinic failed. Conclusions These latest global models predict treatment responses accurately even without a genotype and have the potential to help optimize therapy, particularly in resource-limited settings