Suitability and limitations of portion-specific abattoir data as part of an early warning system for emerging diseases of swine in Ontario

<p>Abstract</p> <p>Background</p> <p>Abattoir data have the potential to provide information for geospatial disease surveillance applications, but the quality of the data and utility for detecting disease outbreaks is not well understood. The objectives of this study were to 1) identify non-disease factors that may bias these data for disease surveillance and 2) determine if major disease events that took place during the study period would be captured using multi-level modelling and scan statistics. We analyzed data collected at all provincially-inspected abattoirs in Ontario, Canada during 2001-2007. During these years there were outbreaks of porcine circovirus-associated disease (PCVAD), porcine reproductive and respiratory syndrome (PRRS) and swine influenza that produced widespread disease within the province. Negative binomial models with random intercepts for abattoir, to account for repeated measurements within abattoirs, were created. The relationships between partial carcass condemnation rates for pneumonia and nephritis with year, season, agricultural region, stock price, and abattoir processing capacity were explored. The utility of the spatial scan statistic for detecting clusters of high partial carcass condemnation rates in space, time, and space-time was investigated.</p> <p>Results</p> <p>Non-disease factors that were found to be associated with lung and kidney condemnation rates included abattoir processing capacity, agricultural region and season. Yearly trends in predicted condemnation rates varied by agricultural region, and temporal patterns were different for both types of condemnations. Some clusters of high condemnation rates of kidneys with nephritis in time and space-time preceded the timeframe during which case clusters were detected using traditional laboratory data. Yearly kidney condemnation rates related to nephritis lesions in eastern Ontario were most consistent with the trends that were expected in relation to the documented disease outbreaks. Yearly lung condemnation rates did not correspond with the timeframes during which major respiratory disease outbreaks took place.</p> <p>Conclusions</p> <p>This study demonstrated that a number of abattoir-related factors require consideration when using abattoir data for quantitative disease surveillance. Data pertaining to lungs condemned for pneumonia did not provide useful information for predicting disease events, while partial carcass condemnations of nephritis were most consistent with expected trends. Techniques that adjust for non-disease factors should be considered when applying cluster detection methods to abattoir data.</p

Equilibration through local information exchange in networks

We study the equilibrium states of energy functions involving a large set of real variables, defined on the links of sparsely connected networks, and interacting at the network nodes, using the cavity and replica methods. When applied to the representative problem of network resource allocation, an efficient distributed algorithm is devised, with simulations showing full agreement with theory. Scaling properties with the network connectivity and the resource availability are found.Comment: v1: 7 pages, 1 figure, v2: 4 pages, 2 figures, simplified analysis and more organized results, v3: minor change

Factors associated with whole carcass condemnation rates in provincially-inspected abattoirs in Ontario 2001-2007: implications for food animal syndromic surveillance

<p>Abstract</p> <p>Background</p> <p>Ontario provincial abattoirs have the potential to be important sources of syndromic surveillance data for emerging diseases of concern to animal health, public health and food safety. The objectives of this study were to: (1) describe provincially inspected abattoirs processing cattle in Ontario in terms of the number of abattoirs, the number of weeks abattoirs process cattle, geographical distribution, types of whole carcass condemnations reported, and the distance animals are shipped for slaughter; and (2) identify various seasonal, secular, disease and non-disease factors that might bias the results of quantitative methods, such as cluster detection methods, used for food animal syndromic surveillance.</p> <p>Results</p> <p>Data were collected from the Ontario Ministry of Agriculture, Food and Rural Affairs and the Ontario Cattlemen's Association regarding whole carcass condemnation rates for cattle animal classes, abattoir compliance ratings, and the monthly sales-yard price for various cattle classes from 2001-2007. To analyze the association between condemnation rates and potential explanatory variables including abattoir characteristics, season, year and commodity price, as well as animal class, negative binomial regression models were fit using generalized estimating equations (GEE) to account for autocorrelation among observations from the same abattoir. Results of the fitted model found animal class, year, season, price, and audit rating are associated with condemnation rates in Ontario abattoirs. In addition, a subset of data was used to estimate the average distance cattle are shipped to Ontario provincial abattoirs. The median distance from the farm to the abattoir was approximately 82 km, and 75% of cattle were shipped less than 100 km.</p> <p>Conclusions</p> <p>The results suggest that secular and seasonal trends, as well as some non-disease factors will need to be corrected for when applying quantitative methods for syndromic surveillance involving these data. This study also demonstrated that animals shipped to Ontario provincial abattoirs come from relatively local farms, which is important when considering the use of spatial surveillance methods for these data.</p

Detection of Clostridium difficile infection clusters, using the temporal scan statistic, in a community hospital in southern Ontario, Canada, 2006–2011

BACKGROUND: In hospitals, Clostridium difficile infection (CDI) surveillance relies on unvalidated guidelines or threshold criteria to identify outbreaks. This can result in false-positive and -negative cluster alarms. The application of statistical methods to identify and understand CDI clusters may be a useful alternative or complement to standard surveillance techniques. The objectives of this study were to investigate the utility of the temporal scan statistic for detecting CDI clusters and determine if there are significant differences in the rate of CDI cases by month, season, and year in a community hospital. METHODS: Bacteriology reports of patients identified with a CDI from August 2006 to February 2011 were collected. For patients detected with CDI from March 2010 to February 2011, stool specimens were obtained. Clostridium difficile isolates were characterized by ribotyping and investigated for the presence of toxin genes by PCR. CDI clusters were investigated using a retrospective temporal scan test statistic. Statistically significant clusters were compared to known CDI outbreaks within the hospital. A negative binomial regression model was used to identify associations between year, season, month and the rate of CDI cases. RESULTS: Overall, 86 CDI cases were identified. Eighteen specimens were analyzed and nine ribotypes were classified with ribotype 027 (n = 6) the most prevalent. The temporal scan statistic identified significant CDI clusters at the hospital (n = 5), service (n = 6), and ward (n = 4) levels (P ≤ 0.05). Three clusters were concordant with the one C. difficile outbreak identified by hospital personnel. Two clusters were identified as potential outbreaks. The negative binomial model indicated years 2007–2010 (P ≤ 0.05) had decreased CDI rates compared to 2006 and spring had an increased CDI rate compared to the fall (P = 0.023). CONCLUSIONS: Application of the temporal scan statistic identified several clusters, including potential outbreaks not detected by hospital personnel. The identification of time periods with decreased or increased CDI rates may have been a result of specific hospital events. Understanding the clustering of CDIs can aid in the interpretation of surveillance data and lead to the development of better early detection systems

Non-parametric belief propagation for mobile mapping sensor fusion

© 2016 Wuhan University. Published by Informa UK Limited, trading as Taylor & Francis Group. Many different forms of sensor fusion have been proposed each with its own niche. We propose a method of fusing multiple different sensor types. Our approach is built on the discrete belief propagation to fuse photogrammetry with GPS to generate three-dimensional (3D) point clouds. We propose using a non-parametric belief propagation similar to Sudderth et al’s work to fuse different sensors. This technique allows continuous variables to be used, is trivially parallel making it suitable for modern many-core processors, and easily accommodates varying types and combinations of sensors. By defining the relationships between common sensors, a graph containing sensor readings can be automatically generated from sensor data without knowing a priori the availability or reliability of the sensors. This allows the use of unreliable sensors which firstly, may start and stop providing data at any time and secondly, the integration of new sensor types simply by defining their relationship with existing sensors. These features allow a flexible framework to be developed which is suitable for many tasks. Using an abstract algorithm, we can instead focus on the relationships between sensors. Where possible we use the existing relationships between sensors rather than developing new ones. These relationships are used in a belief propagation algorithm to calculate the marginal probabilities of the network. In this paper, we present the initial results from this technique and the intended course for future work

Dynamic Status Signal Reflects Outcome of Social Interactions, but Not Energetic Stress

Social defeat induces stress-responses in a wide array of vertebrates and can generate winner-loser effects. Dynamic condition-dependent signaling systems that reflect preparation for subsequent agonistic interactions, and thereby mediate winner-loser effects, should be more sensitive to competitive history than to non-social sources of stress. Bill color of female American goldfinches (Spinus tristus) is a dynamic condition-dependent ornament that functions as a signal of competitive status and mediates intrasexual agonistic social interactions. We tested the “social experience signaling hypothesis” in female goldfinches by (1) manipulating a non-social energetic stressor by experimentally elevating flight costs via wing-clipping in free-ranging birds, and (2) manipulating social experience by staging competitive interactions among captive birds. Bill color change did not differ between wing clipped and non-clipped females, even though stress-response, as measured by the heterophil to lymphocyte (H:L) ratio, increased significantly in clipped females relative to unclipped females. In contrast, winners and losers in the social experience experiment differed significantly in the degree and direction of bill color change following social contests, with bill color increasing in winners and decreasing in losers. These results suggest that dynamic bill color of female American goldfinches signals recent social history, but is less sensitive to some stressors stemming from non-social sources, and thereby suggest that signals can evolve sensitivity to specific types of processes relevant to the context in which they are used

Theory of Double-Sided Flux Decorations

A novel two-sided Bitter decoration technique was recently employed by Yao et al. to study the structure of the magnetic vortex array in high-temperature superconductors. Here we discuss the analysis of such experiments. We show that two-sided decorations can be used to infer {\it quantitative} information about the bulk properties of flux arrays, and discuss how a least squares analysis of the local density differences can be used to bring the two sides into registry. Information about the tilt, compressional and shear moduli of bulk vortex configurations can be extracted from these measurements.Comment: 17 pages, 3 figures not included (to request send email to [email protected]

Classification of porcine reproductive and respiratory syndrome clinical impact in Ontario sow herds using machine learning approaches

Since the early 1990s, porcine reproductive and respiratory syndrome (PRRS) virus outbreaks have been reported across various parts of North America, Europe, and Asia. The incursion of PRRS virus (PRRSV) in swine herds could result in various clinical manifestations, resulting in a substantial impact on the incidence of respiratory morbidity, reproductive loss, and mortality. Veterinary experts, among others, regularly analyze the PRRSV open reading frame-5 (ORF-5) for prognostic purposes to assess the risk of severe clinical outcomes. In this study, we explored if predictive modeling techniques could be used to identify the severity of typical clinical signs observed during PRRS outbreaks in sow herds. Our study aimed to evaluate four baseline machine learning (ML) algorithms: logistic regression (LR) with ridge and lasso regularization techniques, random forest (RF), k-nearest neighbor (KNN), and support vector machine (SVM), for the clinical impact classification of ORF-5 sequences and demographic data into high impact and low impact categories. First, baseline classifiers were evaluated using different input representations of ORF-5 nucleotides, amino acid sequences, and demographic data using a 10-fold cross-validation technique. Then, we designed a consensus voting ensemble approach to aggregate the different types of input representations for genetic and demographic data for classifying clinical impact. In this study, we observed that: (a) for abortion and pre-weaning mortality (PWM), different classifiers gained improvement over baseline accuracy, which showed the plausible presence of both genotypic-phenotypic and demographic-phenotypic relationships, (b) for sow mortality (SM), no baseline classifier successfully established such linkages using either genetic or demographic input data, (c) baseline classifiers showed good performance with a moderate variance of the performance metrics, due to high-class overlap and the small dataset size used for training, and (d) the use of consensus voting ensemble techniques helped to make the predictions more robust and stabilized the performance evaluation metrics, but overall accuracy did not substantially improve the diagnostic metrics over baseline classifiers

Common Raven Impacts on Nesting Western Snowy Plovers: Integrating Management to Facilitate Species Recovery

The U.S. Pacific coast population of the western snowy plover (Charadrius nivosus nivosus; plover) has declined due to loss and degradation of coastal habitats, predation, and anthropogenic disturbance. The U.S. Fish and Wildlife Service listed the subspecies in 1993 as threatened under the Endangered Species Act due to the population declines and habitat loss. Predation of nests and chicks has been identified as an important cause of historic population declines, and thus, most predator management actions for this subspecies are focused on reducing this pressure. In recent years, common ravens (Corvus corax; ravens) have become the most common and pervasive predators of plover nests and chicks, especially in areas with subsidized food sources for ravens and sites without predator management. We compiled data from a variety of sources to document the impact of raven predation on plover nesting success. We discuss current raven management and suggest several tools and strategies to increase plover nesting success, including multi-state approval for the use of the avicide DRC-1339, the use of lures and new trap types, and an increase in funding for predator management. The lack of coordinated and integrated management continues to impede the recovery of the Pacific coast plover population

Translational Correlations in the Vortex Array at the Surface of a Type-II Superconductor

We discuss the statistical mechanics of magnetic flux lines in a finite-thickness slab of type-II superconductor. The long wavelength properties of a flux-line liquid in a slab geometry are described by a hydrodynamic free energy that incorporates the boundary conditions on the flux lines at the sample's surface as a surface contribution to the free energy. Bulk and surface weak disorder are modeled via Gaussian impurity potentials. This free energy is used to evaluate the two-dimensional structure factor of the flux-line tips at the sample surface. We find that surface interaction always dominates in determining the decay of translational correlations in the asymptotic long-wavelength limit. On the other hand, such large length scales have not been probed by the decoration experiments. Our results indicate that the translational correlations extracted from the analysis of the Bitter patterns are indeed representative of behavior of flux lines in the bulk.Comment: 23 pages, 1 figure (not included), harvmac.tex macro needed (e-mail requests to [email protected] SU-CM-92-01