Host Cellular Response to Multiple Stressors Using a Chicken in vitro Model

Heat stress (HS) is a major environmental stressor to chickens because chickens lack sufficient physical ability to mitigate heat. One of the potential results of heat stress is the “leaky gut syndrome”, which allows gut bacteria to escape into the host and release toxins including lipopolysaccharide (LPS). To model the chicken immune response to bacteria toxins under heat stress, a chicken macrophage-like cell line, HD11, was subjected to HS, LPS, or HS + LPS treatments. Expression of a gene panel of heat shock proteins, stress-related molecules, signaling molecules, and immune response molecules were measured and analyzed at 4 time points across the 3 conditions. As expected, heat shock proteins and immune response molecules increased in expression during HS and LPS challenge, respectively. Treatment of HS + LPS increased the expression of these responsive genes even more than either treatment alone. This suggests that heat stress proteins not only mitigate heat stress, but also trigger a higher level of immune response in chickens

Multiple Imputation For Interval Censored Data With Auxiliary Variables

We propose a nonparametric multiple imputation scheme, NPMLE imputation, for the analysis of interval censored survival data. Features of the method are that it converts interval-censored data problems to complete data or right censored data problems to which many standard approaches can be used, and the measures of uncertainty are easily obtained. In addition to the event time of primary interest, there are frequently other auxiliary variables that are associated with the event time. For the goal of estimating the marginal survival distribution, these auxiliary variables may provide some additional information about the event time for the interval censored observations. We extend the imputation methods to incorporate information from auxiliary variables with potentially complex structures. To conduct the imputation, we use a working failure-time proportional hazards model to define an imputing risk set for each censored observations. The imputation schemes consist of using the data in the imputing risk set to create an exact event time for each interval censored observation. In simulation studies we show that the use of multiple imputation methods can improve the efficiency of estimators and reduce the effect of missing visits when compared to simpler approaches. We apply the approach to cytomegalovirus shedding data from an AIDS clinical trial, in which CD4 count is the auxiliary variable

Liver transcriptome response to hyperthermic stress in three distinct chicken lines

Background High ambient temperatures cause stress in poultry, especially for broiler lines, which are genetically selected for rapid muscle growth. RNA-seq technology provides powerful insights into environmental response from a highly metabolic tissue, the liver. We investigated the effects of acute (3 h, 35 °C) and chronic (7d of 35 °C for 7 h/d) heat stress on the liver transcriptome of 3-week-old chicks of a heat-susceptible broiler line, a heat-resistant Fayoumi line, and their advanced intercross line (AIL). Results Transcriptome sequencing of 48 male chickens using Illumina HiSeq 2500 technology yielded an average of 33.9 million, 100 base-pair, single-end reads per sample. There were 8 times more differentially expressed genes (DEGs) (FDR \u3c 0.05) in broilers (n = 627) than Fayoumis (n = 78) when comparing the acute-heat samples to the control (25 °C) samples. Contrasting genetic lines under similar heat treatments, the highest number of DEGs appeared between Fayoumi and broiler lines. Principal component analysis of gene expression and analysis of the number of DEGs suggested that the AIL had a transcriptomic response more similar to the Fayoumi than the broiler line during acute heat stress. The number of DEGs also suggested that acute heat stress had greater impact on the broiler liver transcriptome than chronic heat stress. The angiopoietin-like 4 (ANGPTL4) gene was identified as differentially expressed among all 6 contrasts. Ingenuity Pathway Analysis (IPA) created a novel network that combines the heat shock protein family with immune response genes. Conclusions This study extends our understanding of the liver transcriptome response to different heat exposure treatments in distinct genetic chicken lines and provides information necessary for breeding birds to be more resilient to the negative impacts of heat. The data strongly suggest ANGPTL4 as a candidate gene for improvement of heat tolerance in chickens

Heat Stress Alters Immune Pathways in Liver of Divergent Chicken Lines

The liver plays a variety of roles in energy metabolism, digestion, and immune response. During the challenge by anenvironmental stressor, such as heat, the liver is one of the key organs that determines the chicken’s ability to cope with the hostile environment. Sequencing RNA extracted from liver samples from a heat-susceptible broiler line and a heat-resistant Fayoumi line, we have identifieda set of genes that were differentially expressed due to an acuteheat stress challenge. Analysis of these genes in context of the biological pathways showed opposite responsesby the broiler and Fayoumichickens,but there was activation of immune signaling pathways for both lines.This result suggests that despite mechanistic differences in heat stress response, selection for improvement in heat tolerance may also cause alteration to immune response

Cecal Microbiome Characterization for Layers underHeat Stress

Animal microbiomeshavegainedattentionin recent years because oftheirrolesin avariety of physiological responses to disease and environment challenges. In this study, we subjected production white egg layers to a 4-week heatstress challenge to measure changes in the cecal microbiome. We found that heat stress altersthe cecal microbial compositionafter 2 weeks of cyclic heat exposure. To our knowledge, this is the first study to demonstrate changes in microbiome in heat-stressed layers, and provides an approximatetime course for the microbiome alterations

System Synthesis for Networks of Programmable Blocks

The advent of sensor networks presents untapped opportunities for synthesis. We examine the problem of synthesis of behavioral specifications into networks of programmable sensor blocks. The particular behavioral specification we consider is an intuitive user-created network diagram of sensor blocks, each block having a pre-defined combinational or sequential behavior. We synthesize this specification to a new network that utilizes a minimum number of programmable blocks in place of the pre-defined blocks, thus reducing network size and hence network cost and power. We focus on the main task of this synthesis problem, namely partitioning pre-defined blocks onto a minimum number of programmable blocks, introducing the efficient but effective PareDown decomposition algorithm for the task. We describe the synthesis and simulation tools we developed. We provide results showing excellent network size reductions through such synthesis, and significant speedups of our algorithm over exhaustive search while obtaining near-optimal results for 15 real network designs as well as nearly 10,000 randomly generated designs.Comment: Submitted on behalf of EDAA (http://www.edaa.com/

Conversation Effects on Driving: Neural Mechanisms Underlying Reaction Times to Visual Events

OBJECTIVES The purpose of this study was to determine the behavioral and neural correlates of conversation effects on driving using the same visual event detection paradigm in brain imaging, behavioral testing, and closed-road driving experiments. METHODS The “load” paradigm (Young et al., 2005b) assessed the effects of conversation on visual event detection during simulated driving in behavioral labs, fMRI and MEG imaging centers, and actual driving on a closed road. Behavioral and imaging data were collected. The primary task was to depress a foot pedal in response to a small red light presented to the left or below the driving scene at unpredictable times. The secondary task was to engage in a conversation. The participant pressed a button to answer a ring tone, and then answered simple auditory questions such as “What is your birthdate?” fMRI and MEG data were analyzed to examine the neural substrates of driving with and without conversation. The correlation, reliability and repeatability across experimental settings were analyzed using statistical procedures such as random effect ANOVA and multivariate regression models with repeated measure adjustment. RESULTS The behavioral results from all sites demonstrated that conversation had a small but consistent increase in reaction time (about 70-200 ms) with no effect on miss rates compared to the “no conversation” baseline. The random effect ANOVA and adjusted regression models confirmed the conversation effect in all settings, with good reliability and repeatability. The fMRI results showed that conversation activated not only language-specific areas as expected, but also increased activation in fronto-parietal pathways engaged in sensory-motor integration, attention modulation, and decision execution (Young et al., 2005a). Results of MEG imaging showed that in the “no conversation” baseline, behavioral RT was inversely related to changes in MEG brain activity in the right superior parietal lobe: more modulation in brain activity in the 200-300 ms range after light onset resulted in shorter RTs, and less modulation in longer RTs. A similar relation to RT was also seen in brain activity in the visual cortex in the 85-90 ms interval after red light onset. Conversation again activated language-specific areas in the MEG study, and resulted in less modulation in the right parietal and visual regions (Bowyer et al., 2006). Accordingly, conversation tended to increase mean behavioral RT slightly (no conversation 926 ms; conversation 993 ms). Further experiments are required to determine if the reduction in modulation due to conversation arises from inhibition, interference, or a removal of facilitation from top-down attentional processes. CONCLUSIONS Conversation slightly increases visual event reaction times in laboratory and closed-road driving experiments compared to a no-conversation baseline, with little or no effect on miss rates. Common fMRI and MEG imaging findings revealed fronto-parietal and visual-auditory-motor networks associated with sensory-motor integration, decision-making, and attention modulation during a driving-like scenario. Conversation appears to contribute to increased reaction times by reducing brain modulation to visual events in the right superior parietal region and visual cortices. These experimental findings should not be interpreted as if conversation increases the rate of crashes in real-world driving when compared to baseline driving without real-world validation and comparison of reaction time effects from other in-vehicle tasks. REFERENCES Bowyer, S., Moran, J., Hsieh, L., Manoharan, A., Young R.A., Malladi, K., Yu, Y-J., Chiang, YR., Hersberger, R., Genik, R., & Tepley, N. (2006). MEG localization of neural mechanisms underlying reaction time to visual events while watching a driving video: Effects of conversation. International Congress Series: New Frontiers in Biomagnetism. Proceedings of the 15th International Conference on Biomagnetism. Vancouver, BC Canada, August 21-25. D. Cheyne, B. Ross, G. Stroink and H. Weinberg (Editors). Young, R.A., Hsieh, L., Graydon, F.X., Genik II, R., Benton, M.D., Green, C.C., Bowyer, S.M., Moran, J.E., & Tepley, N. (2005a). Mind-on-the-Drive: Real-time functional neuroimaging of cognitive brain mechanisms underlying driver performance and distraction. Human Factors in Driving, Telematics and Seating Comfort 2005, SP-1934. Society of Automotive Engineering, Warrendale, PA, April. Young, R.A., Aryal, B., Muresan, M., Ding, X., Oja, S., & Simpson, S. (2005b). Road-to-lab: Validation of the static load test for predicting on-road driving performance while using advanced in-vehicle information and communication devices. Proceedings of the Third International Driving Symposium on Human Factors in Driver Assessment, Training and Vehicle Design, Rockport, Maine, July

Survival Analysis Using Auxiliary Variables Via Nonparametric Multiple Imputation

We develop an approach, based on multiple imputation, that estimates the marginal survival distribution in survival analysis using auxiliary variable to recover information for censored observations. To conduct the imputation, we use two working survival model to define the nearest neighbor imputing risk set. One model is for the event times and the other for the censoring times. Based on the imputing risk set, two nonparametric multiple imputation methods are considered: risk set imputation, and Kaplan-Meier estimator. For both methods a future event or censoring time is imputed for each censored observation. With a categorical auxiliary variable, we show that with a large number of imputes the estimates from the Kaplan-Meier imputation method correspond to the weighted Kaplan-Meier estimator. We also show that the Kaplan-Meier imputation method is robust to misspecification of either one of the two working models. In a simulation study with the time independent and time dependent auxiliary variables, we compare the multiple imputation approaches with an inverse probability of censoring weighted method. We show that all approaches can reduce bias due to dependent censoring and improve the efficiency. We apply the approaches to AIDS clinical trial data comparing ZDV and placebo, in which CD4 count is the time-dependent auxiliary variable

Prediction of Treatment Response to Neoadjuvant Chemotherapy for Breast Cancer via Early Changes in Tumor Heterogeneity Captured by DCE-MRI Registration.

We analyzed DCE-MR images from 132 women with locally advanced breast cancer from the I-SPY1 trial to evaluate changes of intra-tumor heterogeneity for augmenting early prediction of pathologic complete response (pCR) and recurrence-free survival (RFS) after neoadjuvant chemotherapy (NAC). Utilizing image registration, voxel-wise changes including tumor deformations and changes in DCE-MRI kinetic features were computed to characterize heterogeneous changes within the tumor. Using five-fold cross-validation, logistic regression and Cox regression were performed to model pCR and RFS, respectively. The extracted imaging features were evaluated in augmenting established predictors, including functional tumor volume (FTV) and histopathologic and demographic factors, using the area under the curve (AUC) and the C-statistic as performance measures. The extracted voxel-wise features were also compared to analogous conventional aggregated features to evaluate the potential advantage of voxel-wise analysis. Voxel-wise features improved prediction of pCR (AUC = 0.78 (±0.03) vs 0.71 (±0.04), p < 0.05 and RFS (C-statistic = 0.76 ( ± 0.05), vs 0.63 ( ± 0.01)), p < 0.05, while models based on analogous aggregate imaging features did not show appreciable performance changes (p > 0.05). Furthermore, all selected voxel-wise features demonstrated significant association with outcome (p < 0.05). Thus, precise measures of voxel-wise changes in tumor heterogeneity extracted from registered DCE-MRI scans can improve early prediction of neoadjuvant treatment outcomes in locally advanced breast cancer