BackgroundThe primary objective of this cross-sectional study was to estimate the crude, seasonal and cull-reason stratified prevalence of Salmonella fecal shedding in cull dairy cattle on seven California dairies. A secondary objective was to estimate and compare the relative sensitivity (Se) and specificity (Sp) for pools of 5 and 10 enriched broth cultures of fecal samples for Salmonella sp. detection.MethodsSeven dairy farms located in the San Joaquin Valley of California were identified and enrolled in the study as a convenience sample. Cull cows were identified for fecal sampling once during each season between 2014 and 2015, specifically during spring, summer, fall, and winter, and 10 cows were randomly selected for fecal sampling at the day of their sale. In addition, study personnel completed a survey based on responses of the herd manager to questions related to the previous four month’s herd management. Fecal samples were frozen until testing for Salmonella. After overnight enrichment in liquid broth, pools of enrichment broth (EBP) were created for 5 and 10 samples. All individual and pooled broths were cultured on selective media with putative Salmonella colonies confirmed by biochemical testing before being serogrouped and serotyped.ResultsA total of 249 cull cows were enrolled into the study and their fecal samples tested for Salmonella. The survey-weighted period prevalence of fecal shedding of all Salmonella sp. in the cull cow samples across all study herds and the entire study period was 3.42% (N = 249; SE 1.07). The within herd prevalence of Salmonella shed in feces did not differ over the four study seasons (P = 0.074). The Se of culture of EBP of five samples was 62.5% (SE = 17.12), which was not statistically different from the Se of culture of EBP of 10 (37.5%, SE = 17.12, P = 0.48). The Sp of culture of EBP of five samples was 95.24% (SE = 3.29) and for pools of 10 samples was 100.00% (SE = 0). There was no statistical difference between the culture relative specificities of EBP of 5 and 10 (P > 0.99).DiscussionOur study showed a numerically higher prevalence of Salmonella shedding in the summer, although the results were not significant, most likely due to a lack of power from the small sample size. A higher prevalence in summer months may be related to heat stress. To detect Salmonella, investigators may expect a 62.5% sensitivity for culture of EBP of five, relative to individual fecal sample enrichment and culture. In contrast, culture of EBP of 10 samples resulted in a numerically lower Se. Culture of EBP of size 5 or 10 samples, given similar prevalence and limit of detection, can be expected to yield specificities of 95 and 100%, respectively.

Increased public health risk caused by pathogen contamination in streams is a serious issue, and mitigating the risk requires improvement in existing microbial monitoring of streams. To improve understanding of microbial contamination in streams, we monitored Escherichia coli in stream water columns and streambed sediment. Two distinct streams and their subwatersheds were studied: (i) a mountain stream (Merced River, California), which represents pristine and wild conditions, and (ii) an agricultural stream (Squaw Creek, Iowa), which represents an agricultural setting (i.e., crop, manure application, cattle access). Stream water column and sediment samples were collected in multiple locations in the Merced River and Squaw Creek watersheds. Compared with the mountain stream, water column E. coli concentrations in the agricultural stream were considerably higher. In both mountain and agricultural streams, E. coli concentrations in bed sediment were higher than the water column, and principal component analysis indicates that land use affected water column E. coli levels significantly (p < 0.05). The cluster analysis showed grouping of subwatersheds for each basin, indicating unique land use features of each watershed. In general, water column E. coli levels in the mountain stream were lower than the USEPA's existing water quality criteria for bacteria. However, the E. coli levels in the agricultural stream exceeded the USEPA's microbial water quality criteria by several fold, which substantiated that increased agricultural activities, use of animal waste as fertilizers, and combined effect of rainfall and temperature may act as potential determining factors behind the elevated E. coli levels in agriculture streams. Core Ideas Watershed land use influences in‐stream pathogen contamination and hence public health risk. The agricultural stream showed higher E. coli concentration than the mountain stream. Increased forest and grassland use showed lower E. coli concentrations in stream water. Increased agricultural land use showed higher E. coli concentrations in stream water. The agricultural stream water column E. coli concentrations far exceeded USEPA recommended values.

Biclustering techniques have become very popular in cancer genetics studies, as they are tools that are expected to connect phenotypes to genotypes, i.e. to identify subgroups of cancer patients based on the fact that they share similar gene expression patterns as well as to identify subgroups of genes that are specific to these subtypes of cancer and therefore could serve as biomarkers. In this paper we propose a new approach for identifying such relationships or biclusters between patients and gene expression profiles. This method, named biDCG, rests on two key concepts. First, it uses a new clustering technique, DCG-tree [Fushing et al, PLos One, 8, e56259 (2013)] that generates ultrametric topological spaces that capture the geometries of both the patient data set and the gene data set. Second, it optimizes the definitions of bicluster membership through an iterative two-way reclustering procedure in which patients and genes are reclustered in turn, based respectively on subsets of genes and patients defined in the previous round. We have validated biDCG on simulated and real data. Based on the simulated data we have shown that biDCG compares favorably to other biclustering techniques applied to cancer genomics data. The results on the real data sets have shown that biDCG is able to retrieve relevant biological information.

Patients with pre-existing chronic health conditions, including End-Stage Renal Disease (ESRD) requiring dialysis, are at high risk for hospitalization with COVID-19. Kidney failure leads to a poorly regulated immune system [1], and susceptibility to infections, which is the second most reported cause of death in the ESRD population.Studies of hospitalized ESRD patients early in the COVID-19 pandemic have reached different conclusions, with some finding that ESRD conveys an independent risk for in-hospital death [2], and others not finding this association [3]. We sought to examine ESRD as an independent risk factor for poor outcomes in individuals hospitalized with