Assessment of Potential Impacts to Surface and Subsurface Water Bodies Due to Longwall Mining

Ground movements due to longwall mining operations have the potential to damage the hydrological balance within as well as outside the mine permit area in the form of increased surface ponding and changes to hydrogeological properties. Recently, the Office of Surface Mining, Reclamation and Enforcement (OSMRE) in the USA, has completed a public comment period on a newly proposed rule for the protection of streams and groundwater from adverse impacts of surface and underground mining operations (80 FR 44435). With increased community and regulatory focus on mining operations and their potential to adversely affect streams and groundwater, now there is a greater need for better prediction of the possible effects mining has on both surface and subsurface bodies of water. With mining induced stress and strain within the overburden correlated to changes in the hydrogeological properties of rock and soil, this paper investigates the evaluation of the hydrogeological system within the vicinity of an underground mining operation based on strain values calculated through a surface deformation prediction model. Through accurate modeling of the pre- and post-mining hydrogeological system, industry personnel can better depict mining induced effects on surface and subsurface bodies of water aiding in the optimization of underground extraction sequences while maintaining the integrity of water resources

The codes and the lattices of Hadamard matrices

It has been observed by Assmus and Key as a result of the complete classification of Hadamard matrices of order 24, that the extremality of the binary code of a Hadamard matrix H of order 24 is equivalent to the extremality of the ternary code of H^T. In this note, we present two proofs of this fact, neither of which depends on the classification. One is a consequence of a more general result on the minimum weight of the dual of the code of a Hadamard matrix. The other relates the lattices obtained from the binary code and from the ternary code. Both proofs are presented in greater generality to include higher orders. In particular, the latter method is also used to show the equivalence of (i) the extremality of the ternary code, (ii) the extremality of the Z_4-code, and (iii) the extremality of a lattice obtained from a Hadamard matrix of order 48.Comment: 16 pages. minor revisio

PhdA catalyzes the first step of phenazine-1-carboxylic acid degradation in Mycobacterium fortuitum

Phenazines are a class of bacterially produced redox-active metabolites that are found in natural, industrial, and clinical environments. In Pseudomonas spp., phenazine-1-carboxylic acid (PCA)—the precursor of all phenazine metabolites—facilitates nutrient acquisition, biofilm formation, and competition with other organisms. While the removal of phenazines negatively impacts these activities, little is known about the genes or enzymes responsible for phenazine degradation by other organisms. Here, we report that the first step of PCA degradation by Mycobacterium fortuitum is catalyzed by a phenazine-degrading decarboxylase (PhdA). PhdA is related to members of the UbiD protein family that rely on a prenylated flavin mononucleotide cofactor for activity. The gene for PhdB, the enzyme responsible for cofactor synthesis, is present in a putative operon with the gene encoding PhdA in a region of the M. fortuitum genome that is essential for PCA degradation. PhdA and PhdB are present in all known PCA-degrading organisms from the Actinobacteria. M. fortuitum can also catabolize other Pseudomonas-derived phenazines such as phenazine-1-carboxamide, 1-hydroxyphenazine, and pyocyanin. On the basis of our previous work and the current characterization of PhdA, we propose that degradation converges on a common intermediate: dihydroxyphenazine. An understanding of the genes responsible for degradation will enable targeted studies of phenazine degraders in diverse environments

Analysis of telephone network traffic based on a complex user network

The traffic in telephone networks is analyzed in this paper. Unlike the classical traffic analysis where call blockings are due to the limited channel capacity, we consider here a more realistic cause for call blockings which is due to the way in which users are networked in a real-life human society. Furthermore, two kinds of user network, namely, the fully-connected user network and the scale-free network, are employed to model the way in which telephone users are connected. We show that the blocking probability is generally higher in the case of the scale-free user network, and that the carried traffic intensity is practically limited not only by the network capacity but also by the property of the user network.Comment: 17 pages, 9 figures, accepted for Physica

Applying the Transtheoretical Model of Change to Consumer Debt Behavior

The Transtheoretical Model of Change (TMM) provided the framework for developing a measure to assess readiness to get out of credit card debt with consumers who are having credit card debt troubles. Key constructs of TTM include stages of change, decisional balance, self-efficacy, and processes of change. The items for the measure were developed by qualitative interviews with experts in credit counseling and consumers with debt troubles. A survey was then completed with a reliability and validity of the measure. The results have potential for use by counseling practitioners, educators and researchers

Human norovirus infection and the acute serum cytokine response

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/115914/1/cei12681.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/115914/2/cei12681_am.pd

A motif-based approach to network epidemics

Networks have become an indispensable tool in modelling infectious diseases, with the structure of epidemiologically relevant contacts known to affect both the dynamics of the infection process and the efficacy of intervention strategies. One of the key reasons for this is the presence of clustering in contact networks, which is typically analysed in terms of prevalence of triangles in the network. We present a more general approach, based on the prevalence of different four-motifs, in the context of ODE approximations to network dynamics. This is shown to outperform existing models for a range of small world networks

PhdA catalyzes the first step of phenazine-1-carboxylic acid degradation in Mycobacterium fortuitum

Phenazines are a class of bacterially produced redox-active metabolites that are found in natural, industrial, and clinical environments. In Pseudomonas spp., phenazine-1-carboxylic acid (PCA)—the precursor of all phenazine metabolites—facilitates nutrient acquisition, biofilm formation, and competition with other organisms. While the removal of phenazines negatively impacts these activities, little is known about the genes or enzymes responsible for phenazine degradation by other organisms. Here, we report that the first step of PCA degradation by Mycobacterium fortuitum is catalyzed by a phenazine-degrading decarboxylase (PhdA). PhdA is related to members of the UbiD protein family that rely on a prenylated flavin mononucleotide cofactor for activity. The gene for PhdB, the enzyme responsible for cofactor synthesis, is present in a putative operon with the gene encoding PhdA in a region of the M. fortuitum genome that is essential for PCA degradation. PhdA and PhdB are present in all known PCA-degrading organisms from the Actinobacteria. M. fortuitum can also catabolize other Pseudomonas-derived phenazines such as phenazine-1-carboxamide, 1-hydroxyphenazine, and pyocyanin. On the basis of our previous work and the current characterization of PhdA, we propose that degradation converges on a common intermediate: dihydroxyphenazine. An understanding of the genes responsible for degradation will enable targeted studies of phenazine degraders in diverse environments

Contamination of Fresh Produce by Microbial Indicators on Farms and in Packing Facilities: Elucidation of Environmental Routes

ABSTRACT To improve food safety on farms, it is critical to quantify the impact of environmental microbial contamination sources on fresh produce. However, studies are hampered by difficulties achieving study designs with powered sample sizes to elucidate relationships between environmental and produce contamination. Our goal was to quantify, in the agricultural production environment, the relationship between microbial contamination on hands, soil, and water and contamination on fresh produce. In 11 farms and packing facilities in northern Mexico, we applied a matched study design: composite samples (n � 636, equivalent to 11,046 units) of produce rinses were matched to water, soil, and worker hand rinses during two growing seasons. Microbial indicators (coliforms, Escherichia coli, Enterococcus spp., and somatic coliphage) were quantified from composite samples. Statistical measures of association and correlations were calculated through Spearman’s correlation, linear regression, and logistic regression models. The concentrations of all microbial indicators were positively correlated between produce and hands ( � range, 0.41 to 0.75; P � 0.01). When E. coli was present on hands, the handled produce was nine times more likely to contain E. coli (P � 0.05). Similarly, when coliphage was present on hands, the handled produce was eight times more likely to contain coliphage (P � 0.05). There were relatively low concentrations of indicators in soil and water samples, and a few sporadic significant associations were observed between contamination of soil and water and contamination of produce. This methodology provides a foundation for future field studies, and results highlight the need for interventions surrounding farmworker hygiene and sanitation to reduce microbial contamination of farmworkers’ hands. IMPORTANCE This study of the relationships between microbes on produce and in the farm environment can be used to support the design of targeted interventions to prevent or reduce microbial contamination of fresh produce with associated reductions in foodborne illness. KEYWORDS environmental microbiology, food-borne pathogens, produc

Testcrosses are an efficient strategy for identifying cis-regulatory variation: Bayesian analysis of allele-specific expression (BayesASE)

Allelic imbalance (AI) occurs when alleles in a diploid individual are differentially expressed and indicates cis acting regulatory variation. What is the distribution of allelic effects in a natural population? Are all alleles the same? Are all alleles distinct? The approach described applies to any technology generating allele-specific sequence counts, for example for chromatin accessibility and can be applied generally including to comparisons between tissues or environments for the same genotype. Tests of allelic effect are generally performed by crossing individuals and comparing expression between alleles directly in the F1. However, a crossing scheme that compares alleles pairwise is a prohibitive cost for more than a handful of alleles as the number of crosses is at least (n2-n)/2 where n is the number of alleles. We show here that a testcross design followed by a hypothesis test of AI between testcrosses can be used to infer differences between nontester alleles, allowing n alleles to be compared with n crosses. Using a mouse data set where both testcrosses and direct comparisons have been performed, we show that the predicted differences between nontester alleles are validated at levels of over 90% when a parent-of-origin effect is present and of 60%-80% overall. Power considerations for a testcross, are similar to those in a reciprocal cross. In all applications, the testing for AI involves several complex bioinformatics steps. BayesASE is a complete bioinformatics pipeline that incorporates state-of-the-art error reduction techniques and a flexible Bayesian approach to estimating AI and formally comparing levels of AI between conditions. The modular structure of BayesASE has been packaged in Galaxy, made available in Nextflow and as a collection of scripts for the SLURM workload manager on github (https://github.com/McIntyre-Lab/BayesASE)