Modeling Censored Data Using Mixture Regression Models with an Application to Cattle Production Yields

This research develops a mixture regression model that is shown to have advantages over the classical Tobit model in model fit and predictive tests when data are generated from a two step process. Additionally, the model is shown to allow for flexibility in distributional assumptions while nesting the classic Tobit model. A simulated data set is utilized to assess the potential loss in efficiency from model misspecification, assuming the Tobit and a zero-inflated log-normal distribution, which is derived from the generalized mixture model. Results from simulations key on the finding that the proposed zero-inflated log-normal model clearly outperforms the Tobit model when data are generated from a two step process. When data are generated from a Tobit model, forecasts are more accurate when utilizing the Tobit model. However, the Tobit model will be shown to be a special case of the generalized mixture model. The empirical model is then applied to evaluating mortality rates in commercial cattle feedlots, both independently and as part of a system including other performance and health factors. This particular application is hypothesized to be more appropriate for the proposed model due to the high degree of censoring and skewed nature of mortality rates. The zero-inflated log-normal model clearly models and predicts with more accuracy that the tobit model.censoring, livestock production, tobit, zero-inflated, bayesian, Livestock Production/Industries,

An Evaluation of the Soda Tax with Multivariate Nonparametric Regressions

This research extends past work by Shonkwiler and Yen (1999) by allowing for distributional flexibility and nonlinear responses in the form of established semiparametric and nonparametric regressions. The proposed models are shown to outperform the parametric version typically used in demand analysis to characterize a system of censored equations in terms of model fit and prediction power. Using the developed models, we derive elasticities associated with different individual-specific scenarios with regard to the recently proposed “penny-an-ounce” tax on soft drinks sweetened with sugar.censoring, health taxes, nonparametric regressions, Research Methods/ Statistical Methods,

A Multivariate Evaluation of Ex-ante Risks Associated with Fed Cattle Production

The purpose of this study is to evaluate the risks faced by fed cattle producers. With the development of livestock insurance programs as part of the Agricultural Risk Protection Act of 2000, a thorough investigation into the probabilistic measures of individual risk factors is needed. This research jointly models cattle production yield risk factors, using a multivariate dynamic regression model. A multivariate framework is necessary to characterize yield risk in terms of four yield factors (dry matter feed conversion, averaged daily gain, mortality, and veterinary costs), which are highly correlated. Additionally, a conditional Tobit model is used to handle censored yield variables (e.g., mortality). The proposed econometric model estimates parameters that influence the mean and variance of each production yield factor, as well as the covariance between variables. Following the model fitting using a maximum likelihood approach, simulation methods allow for profits, revenue, and gross margins to be evaluated given different assumptions concerning volatility among other shocks. The profit function is composed of random draws, based on conditioning variables, as well as parameter estimates. Shocks to variability, yield factors, or prices allow for a visual representation of the vulnerability of cattle feeder profits to these shocks.Livestock Production/Industries,

Mapping the mammalian ribosome quality control complex interactome using proximity labeling approaches.

Previous genetic and biochemical studies from Saccharomyces cerevisiae have identified a critical ribosome-associated quality control complex (RQC) that facilitates resolution of stalled ribosomal complexes. While components of the mammalian RQC have been examined in vitro, a systematic characterization of RQC protein interactions in mammalian cells has yet to be described. Here we utilize both proximity-labeling proteomic approaches, BioID and APEX, and traditional affinity-based strategies to both identify interacting proteins of mammalian RQC members and putative substrates for the RQC resident E3 ligase, Ltn1. Surprisingly, validation studies revealed that a subset of substrates are ubiquitylated by Ltn1 in a regulatory manner that does not result in subsequent substrate degradation. We demonstrate that Ltn1 catalyzes the regulatory ubiquitylation of ribosomal protein S6 kinase 1 and 2 (RPS6KA1, RPS6KA3). Further, loss of Ltn1 function results in hyperactivation of RSK1/2 signaling without impacting RSK1/2 protein turnover. These results suggest that Ltn1-mediated RSK1/2 ubiquitylation is inhibitory and establishes a new role for Ltn1 in regulating mitogen-activated kinase signaling via regulatory RSK1/2 ubiquitylation. Taken together, our results suggest that mammalian RQC interactions are difficult to observe and may be more transient than the homologous complex in S. cerevisiae and that Ltn1 has RQC-independent functions

Specific disruption of hippocampal mossy fiber synapses in a mouse model of familial Alzheimer's disease.

The earliest stages of Alzheimer's disease (AD) are characterized by deficits in memory and cognition indicating hippocampal pathology. While it is now recognized that synapse dysfunction precedes the hallmark pathological findings of AD, it is unclear if specific hippocampal synapses are particularly vulnerable. Since the mossy fiber (MF) synapse between dentate gyrus (DG) and CA3 regions underlies critical functions disrupted in AD, we utilized serial block-face electron microscopy (SBEM) to analyze MF microcircuitry in a mouse model of familial Alzheimer's disease (FAD). FAD mutant MF terminal complexes were severely disrupted compared to control - they were smaller, contacted fewer postsynaptic spines and had greater numbers of presynaptic filopodial processes. Multi-headed CA3 dendritic spines in the FAD mutant condition were reduced in complexity and had significantly smaller sites of synaptic contact. Significantly, there was no change in the volume of classical dendritic spines at neighboring inputs to CA3 neurons suggesting input-specific defects in the early course of AD related pathology. These data indicate a specific vulnerability of the DG-CA3 network in AD pathogenesis and demonstrate the utility of SBEM to assess circuit specific alterations in mouse models of human disease

One-Component Order Parameter in URu2_2Si2_2 Uncovered by Resonant Ultrasound Spectroscopy and Machine Learning

The unusual correlated state that emerges in URu$_2$Si$_2$ below T$_{HO}$ = 17.5 K is known as "hidden order" because even basic characteristics of the order parameter, such as its dimensionality (whether it has one component or two), are "hidden". We use resonant ultrasound spectroscopy to measure the symmetry-resolved elastic anomalies across T$_{HO}$. We observe no anomalies in the shear elastic moduli, providing strong thermodynamic evidence for a one-component order parameter. We develop a machine learning framework that reaches this conclusion directly from the raw data, even in a crystal that is too small for traditional resonant ultrasound. Our result rules out a broad class of theories of hidden order based on two-component order parameters, and constrains the nature of the fluctuations from which unconventional superconductivity emerges at lower temperature. Our machine learning framework is a powerful new tool for classifying the ubiquitous competing orders in correlated electron systems

Superparamagnetic and metal-like Ru2TiGe: a propitious thermoelectric material

We report a study of structural, magnetic, heat capacity and thermoelectric properties of a Rubased Heusler alloy, Ru2TiGe. The magnetic measurements reveal that at higher temperatures, diamagnetic and Pauli paramagnetic contributions dominate the magnetic behaviour whereas, at lower temperatures (T<= 20 K), superparamagnetic interaction among clusters is observed. Effect of such magnetic defects is also evident in the electrical resistivity behaviour at lower temperatures. Though the temperature dependence of resistivity exhibits a metal-like nature, the large value of Seebeck coefficient leads to an appreciable power factor of the order of 1 mW/mK2 at 300 K. Large power factor as well as low thermal conductivity results in a value of ZT = 0.025 at 390 K for Ru2TiGe that is orders of magnitude higher than that of the other pure Heusler alloys and point towards its high potential for practical thermoelectric applications