Evaluating corellations in Salmonella serotypes in swine in four longitudinal dataset

S. enterica serovars surveillance program have been established for many years in the United State of America (USA). Data from long running surveillance programs provides the opportunity to compare prevalence of serotypes within and across surveillance programs, observed patterns and generate hypothesis. To this end, the aim of this project was to estimate the correlation between changes in the yearly changes in serotypes proportions in concurrent years and lagged years from swine, beef and avian longitudinal datasets: (The Iowa State University Veterinary Diagnostic Laboratory (VDL), The National Antimicrobial Resistance Monitoring System ( NARMS animal-based isolates only), compared to data from The Centers for Disease Control (CDC) Laboratory-based Enteric Disease Surveillance (LEDS) Program. The lagged correlations were as follows: a) 1-year lag with the animal data preceding the human data and b) The correlation across a 2-year lag with the human data preceding the animal data

Encoding Enhanced Complex CNN for Accurate and Highly Accelerated MRI

Magnetic resonance imaging (MRI) using hyperpolarized noble gases provides a way to visualize the structure and function of human lung, but the long imaging time limits its broad research and clinical applications. Deep learning has demonstrated great potential for accelerating MRI by reconstructing images from undersampled data. However, most existing deep conventional neural networks (CNN) directly apply square convolution to k-space data without considering the inherent properties of k-space sampling, limiting k-space learning efficiency and image reconstruction quality. In this work, we propose an encoding enhanced (EN2) complex CNN for highly undersampled pulmonary MRI reconstruction. EN2 employs convolution along either the frequency or phase-encoding direction, resembling the mechanisms of k-space sampling, to maximize the utilization of the encoding correlation and integrity within a row or column of k-space. We also employ complex convolution to learn rich representations from the complex k-space data. In addition, we develop a feature-strengthened modularized unit to further boost the reconstruction performance. Experiments demonstrate that our approach can accurately reconstruct hyperpolarized 129Xe and 1H lung MRI from 6-fold undersampled k-space data and provide lung function measurements with minimal biases compared with fully-sampled image. These results demonstrate the effectiveness of the proposed algorithmic components and indicate that the proposed approach could be used for accelerated pulmonary MRI in research and clinical lung disease patient care

Metabolomics of sorghum roots during nitrogen stress reveals compromised metabolic capacity for salicylic acid biosynthesis

Sorghum (Sorghum bicolor [L.] Moench) is the fifth most productive cereal crop worldwide with some hybrids having high biomass yield traits making it promising for sustainable, economical biofuel production. To maximize biofuel feedstock yields, a more complete understanding of metabolic responses to low nitrogen (N) will be useful for incorporation in crop improvement efforts. In this study, 10 diverse sorghum entries (including inbreds and hybrids) were field-grown under low and full N conditions and roots were sampled at two time points for metabolomics and 16S amplicon sequencing. Roots of plants grown under low N showed altered metabolic profiles at both sampling dates including metabolites important in N storage and synthesis of aromatic amino acids. Complementary investigation of the rhizosphere microbiome revealed dominance by a single operational taxonomic unit (OTU) in an early sampling that was taxonomically assigned to the genus Pseudomonas. Abundance of this Pseudomonas OTU was significantly greater under low N in July and was decreased dramatically in September. Correlation of Pseudomonas abundance with root metabolites revealed a strong negative association with the defense hormone salicylic acid (SA) under full N but not under low N, suggesting reduced defense response. Roots from plants with N stress also contained reduced phenylalanine, a precursor for SA, providing further evidence for compromised metabolic capacity for defense response under low N conditions. Our findings suggest that interactions between biotic and abiotic stresses may affect metabolic capacity for plant defense and need to be concurrently prioritized as breeding programs become established for biofuels production on marginal soils

Review of advanced road materials, structures, equipment, and detection technologies

As a vital and integral component of transportation infrastructure, pavement has a direct and tangible impact on socio-economic sustainability. In recent years, an influx of groundbreaking and state-of-the-art materials, structures, equipment, and detection technologies related to road engineering have continually and progressively emerged, reshaping the landscape of pavement systems. There is a pressing and growing need for a timely summarization of the current research status and a clear identification of future research directions in these advanced and evolving technologies. Therefore, Journal of Road Engineering has undertaken the significant initiative of introducing a comprehensive review paper with the overarching theme of “advanced road materials, structures, equipment, and detection technologies”. This extensive and insightful review meticulously gathers and synthesizes research findings from 39 distinguished scholars, all of whom are affiliated with 19 renowned universities or research institutions specializing in the diverse and multidimensional field of highway engineering. It covers the current state and anticipates future development directions in the four major and interconnected domains of road engineering: advanced road materials, advanced road structures and performance evaluation, advanced road construction equipment and technology, and advanced road detection and assessment technologies

Statistical methods for microbiome data and antimicrobial resistance analysis

This thesis consists of three projects motivated by biological problems: (i) detecting differentially abundant taxa in multiple metagenomic samples (chapter 2), (ii) developing a two-stage causal mediation model for identifying taxa mediating the effect of environmental conditions on an outcome in the analysis of microbiome data (chapter 3), and (iii) analyzing temporal changes of the antimicrobial susceptibility (chapter 4). Although the emerging field of metagenomics has revolutionized our understanding of the microbial world, the analysis of metagenomic data raises some statistical challenges, including modeling high-dimensional overdispersed count data with excessive zeros. In the first project (chapter 2), we propose a hypothesis testing framework based on a Poisson Hurdle hierarchical model to address the considerable zeros issue in the metagenomic data, and a full Bayesian inference is performed to identify the differentially abundant taxa among multiple treatment groups. Simulation studies demonstrate our model outperforms the existing approaches in terms of false discovery rate control at desired level of significance and statistical power as well. In the second project (chapter 3), we develop a causal mediation model to investigate the effect of a treatment on an outcome transmitted through microbes. Considering the sparsity and high-dimensional overdispersed count natures of the metagenomic data, we propose a novel screening procedure to reduce the dimension to a moderate size. Then a Bayesian variable selection strategy with a shrink and diffuse prior is used to select the key taxa with mediation effects. The performance of the proposed method is illustrated via simulation studies. In the third project (chapter 4), we present a hierarchical Bayesian latent class mixture model to detect the temporal changes in antibiotic resistance using minimum inhibitory concentration (MIC) values. By taking the censorship into account, our proposed approach would achieve less bias in the estimation of mean MIC. We also apply this proposed method to the data from CDC NARMS program and show that evidence of temporal changes in mean MIC exist in spite of no changes or changes of adverse direction in the proportion of resistance.</p