Compositional Intent: A Presentation of Original Music

This paper summarizes the inspiration, compositional techniques, and performance history of three works by composer Daniel Ryan Key: cling (2015), for euphonium, tuba, and piano; GAIA (2014), for two flutes; and Hours (2016), for chamber orchestra. Hours is a work in two movements, 0400 and 1700

An historical look at honors student characteristics in higher education

Having the opportunity to listen in on several Honors Committee Meetings at the University of Northern Iowa proved to be very thought provoking. The UNI Honors Committee had been discussing plans for a new honors program design when a question was asked, one that proved to be very important and needs serious consideration. What type of student are we looking for? What seemed to be a simple question with a simple answer gave rise to further questions and an issue which is the heart of any honors program. What characteristics will potential honors students possess? How will they be selected? What will their impact be on campus? What are these students looking for in education and in life? What can be learned from these students and the programs they demand

Bayesian joint inversion of controlled source electromagnetic and magnetotelluric data to image freshwater aquifer offshore New Jersey

Author Posting. © The Authors, 2019. This article is posted here by permission of The Royal Astronomical Society for personal use, not for redistribution. The definitive version was published in Geophysical Journal International 218(3), (2019): 1822-1837, doi: 10.1093/gji/ggz253.Joint inversion of multiple electromagnetic data sets, such as controlled source electromagnetic and magnetotelluric data, has the potential to significantly reduce uncertainty in the inverted electrical resistivity when the two data sets contain complementary information about the subsurface. However, evaluating quantitatively the model uncertainty reduction is made difficult by the fact that conventional inversion methods—using gradients and model regularization—typically produce just one model, with no associated estimate of model parameter uncertainty. Bayesian inverse methods can provide quantitative estimates of inverted model parameter uncertainty by generating an ensemble of models, sampled proportional to data fit. The resulting posterior distribution represents a combination of a priori assumptions about the model parameters and information contained in field data. Bayesian inversion is therefore able to quantify the impact of jointly inverting multiple data sets by using the statistical information contained in the posterior distribution. We illustrate, for synthetic data generated from a simple 1-D model, the shape of parameter space compatible with controlled source electromagnetic and magnetotelluric data, separately and jointly. We also demonstrate that when data sets contain complementary information about the model, the region of parameter space compatible with the joint data set is less than or equal to the intersection of the regions compatible with the individual data sets. We adapt a trans-dimensional Markov chain Monte Carlo algorithm for jointly inverting multiple electromagnetic data sets for 1-D earth models and apply it to surface-towed controlled source electromagnetic and magnetotelluric data collected offshore New Jersey, USA, to evaluate the extent of a low salinity aquifer within the continental shelf. Our inversion results identify a region of high resistivity of varying depth and thickness in the upper 500 m of the continental shelf, corroborating results from a previous study that used regularized, gradient-based inversion methods. We evaluate the joint model parameter uncertainty in comparison to the uncertainty obtained from the individual data sets and demonstrate quantitatively that joint inversion offers reduced uncertainty. In addition, we show how the Bayesian model ensemble can subsequently be used to derive uncertainty estimates of pore water salinity within the low salinity aquifer.We gratefully acknowledge funding support from National Science Foundation grants 1458392 and 1459035. We thank the captain and crew of the R.V. Marcus G. Langseth for a successful cruise and the Marine EM Lab at Scripps Institution of Oceanography for providing the instrumentation. We also thank Chris Armerding, Marah Dahn, John Desanto, Jimmy Elsenbeck, Matt Folsom, Keiichi Ishizu, Jeff Pepin, Charlotte Wiman and Georgie Zelenak for participating in the cruise. We gratefully acknowledge Alberto Malinverno for the idea to use a Monte Carlo scheme to estimate the distribution of pore fluid salinity, and William Menke for many constructive conversations and suggestions

A comparison of approximate reasoning results using information uncertainty

An Approximate Reasoning (AR) model is a useful alternative to a probabilistic model when there is a need to draw conclusions from information that is qualitative. For certain systems, much of the information available is elicited from subject matter experts (SME). One such example is the risk of attack on a particular facility by a pernicious adversary. In this example there are several avenues of attack, i.e. scenarios, and AR can be used to model the risk of attack associated with each scenario. The qualitative information available and provided by the SME is comprised of linguistic values which are well suited for an AR model but meager for other modeling approaches. AR models can produce many competing results. Associated with each competing AR result is a vector of linguistic values and a respective degree of membership in each value. A suitable means to compare and segregate AR results would be an invaluable tool to analysts and decisions makers. A viable method would be to quantify the information uncertainty present in each AR result then use the measured quantity comparatively. One issue of concern for measuring the infornlation uncertainty involved with fuzzy uncertainty is that previously proposed approaches focus on the information uncertainty involved within the entire fuzzy set. This paper proposes extending measures of information uncertainty to AR results, which involve only one degree of membership for each fuzzy set included in the AR result. An approach to quantify the information uncertainty in the AR result is presented

Toxic trauma: Household water quality experiences predict posttraumatic stress disorder symptoms during the Flint, Michigan, water crisis

We examined the relationship between perceptions of household tap water quality and posttraumatic stress disorder (PTSD) symptoms during the Flint, Michigan, water crisis in 2015–2016. The Speak to Your Health Community Survey is a community‐based participatory component of the health surveillance system in Genesee County, Michigan. Perceptions of household tap water quality was added to the 2015–2016 survey wave after inadequate official response to concerns over water quality after a change in Flint’s municipal water supply. Respondents (N = 786) also completed a brief PTSD screening tool. We examined the relationships of perceived household tap water quality to PTSD symptomatology and positive screening criteria for PTSD, controlling for sociodemographics. Perceived tap water quality predicted PTSD symptomatology and positive screening criteria for PTSD, independent of sociodemographics. The adverse mental health impact of municipal toxic contamination may generalize to other similar environmental contamination incidents.Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/138395/1/jcop21898_am.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/138395/2/jcop21898.pd

Assessing simulation ecosystem processes for climate variability research at Glacier National Park

Glacier National Park served as a test site for ecosystem analyses that involved a suite of integrated models embedded within a geographic information system. The goal of the exercise was to provide managers with maps that could illustrate probable shifts in vegetation, net primary production (NPP), and hydrologic responses associated with two selected climatic scenarios. The climatic scenarios were (a) a recent 12-yr record of weather data, and (b) a reconstituted set that sequentially introduced in repeated 3-yr intervals wetter–cooler, drier–warmer, and typical conditions. To extrapolate the implications of changes in ecosystem processes and resulting growth and distribution of vegetation and snowpack, the model incorporated geographic data. With underlying digital elevation maps, soil depth and texture, extrapolated climate, and current information on vegetation types and satellite-derived estimates of leaf area indices, simulations were extended to envision how the park might look after 120 yr. The predictions of change included underlying processes affecting the availability of water and nitrogen. Considerable field data were acquired to compare with model predictions under current climatic conditions. In general, the integrated landscape models of ecosystem processes had good agreement with measured NPP, snowpack, and streamflow, but the exercise revealed the difficulty and necessity of averaging point measurements across landscapes to achieve comparable results with modeled values. Under the extremely variable climate scenario significant changes in vegetation composition and growth as well as hydrologic responses were predicted across the park. In particular, a general rise in both the upper and lower limits of treeline was predicted. These shifts would probably occur along with a variety of disturbances (fire, insect, and disease outbreaks) as predictions of physiological stress (water, nutrients, light) altered competitive relations and hydrologic responses. The use of integrated landscape models applied in this exercise should provide managers with insights into the underlying processes important in maintaining community structure, and at the same time, locate where changes on the landscape are most likely to occur

\u3ci\u3eToxoplasma gondii\u3c/i\u3e requires its plant-like heme biosynthesis pathway for infection

Heme, an iron-containing organic ring, is essential for virtually all living organisms by serving as a prosthetic group in proteins that function in diverse cellular activities ranging from diatomic gas transport and sensing, to mitochondrial respiration, to detoxification. Cellular heme levels in microbial pathogens can be a composite of endogenous de novo synthesis or exogenous uptake of heme or heme synthesis intermediates. Intracellular pathogenic microbes switch routes for heme supply when heme availability fluctuates in their replicative environment throughout infection. Here, we show that Toxoplasma gondii, an obligate intracellular human pathogen, encodes a functional heme biosynthesis pathway. A chloroplast-derived organelle, termed apicoplast, is involved in heme production. Genetic and chemical manipulation revealed that de novo heme production is essential for T. gondii intracellular growth and pathogenesis. Surprisingly, the herbicide oxadiazon significantly impaired Toxoplasma growth, consistent with phylogenetic analyses that show T. gondii protoporphyrinogen oxidase is more closely related to plants than mammals. This inhibition can be enhanced by 15- to 25-fold with two oxadiazon derivatives, lending therapeutic proof that Toxoplasma heme biosynthesis is a druggable target. As T. gondii has been used to model other apicomplexan parasites, our study underscores the utility of targeting heme biosynthesis in other pathogenic apicomplexans, such as Plasmodium spp., Cystoisospora, Eimeria, Neospora, and Sarcocystis

A global monthly climatology of oceanic total dissolved inorganic carbon: a neural network approach

Anthropogenic emissions of CO2 to the atmosphere have modified the carbon cycle for more than 2 centuries. As the ocean stores most of the carbon on our planet, there is an important task in unraveling the natural and anthropogenic processes that drive the carbon cycle at different spatial and temporal scales. We contribute to this by designing a global monthly climatology of total dissolved inorganic carbon (TCO2), which offers a robust basis in carbon cycle modeling but also for other studies related to this cycle. A feedforward neural network (dubbed NNGv2LDEO) was configured to extract from the Global Ocean Data Analysis Project version 2.2019 (GLODAPv2.2019) and the Lamont–Doherty Earth Observatory (LDEO) datasets the relations between TCO2 and a set of variables related to the former's variability. The global root mean square error (RMSE) of mapping TCO2 is relatively low for the two datasets (GLODAPv2.2019: 7.2 µmol kg−1; LDEO: 11.4 µmol kg−1) and also for independent data, suggesting that the network does not overfit possible errors in data. The ability of NNGv2LDEO to capture the monthly variability of TCO2 was testified through the good reproduction of the seasonal cycle in 10 time series stations spread over different regions of the ocean (RMSE: 3.6 to 13.2 µmol kg−1). The climatology was obtained by passing through NNGv2LDEO the monthly climatological fields of temperature, salinity, and oxygen from the World Ocean Atlas 2013 and phosphate, nitrate, and silicate computed from a neural network fed with the previous fields. The resolution is 1∘×1∘ in the horizontal, 102 depth levels (0–5500 m), and monthly (0–1500 m) to annual (1550–5500 m) temporal resolution, and it is centered around the year 1995. The uncertainty of the climatology is low when compared with climatological values derived from measured TCO2 in the largest time series stations. Furthermore, a computed climatology of partial pressure of CO2 (pCO2) from a previous climatology of total alkalinity and the present one of TCO2 supports the robustness of this product through the good correlation with a widely used pCO2 climatology (Landschützer et al., 2017). Our TCO2 climatology is distributed through the data repository of the Spanish National Research Council (CSIC; https://doi.org/10.20350/digitalCSIC/10551, Broullón et al., 2020)

Concert recording 2015-11-22a

[Track 01]. Saxophone quartet. Elegie ; [Track 02]. Finale / Paul Reade -- [Track 03]. Gallumphery from Diversions in denim / Carl Anton Wirth -- [Track 04]. Shepherd\u27s hey / Percy Grainger -- [Track 05]. He is good and handsome / Pierre Passereau -- [Track 06]. Chorale prelude and fugue / J.S. Bach ; arranged by Laycock -- [Track 07]. Fugue in D / Michael Hanna -- [Track 08]. Explorations / Ryan Key -- [Track 09]. Fugato in F / René Borel -- [Track 10]. Finale from Quartett, opus 109 / Alexander Glazunov