Sequence and phylogenetic analysis of the gp200 protein of Ehrlichia canis from dogs in Taiwan

Ehrlichia (E.) canis is a Gram-negative obligate intracellular bacterium responsible for canine monocytic ehrlichiosis. Currently, the genetic diversity of E. canis strains worldwide is poorly defined. In the present study, sequence analysis of the nearly full-length 16S rDNA (1,620 bp) and the complete coding region (4,269 bp) of the gp200 gene, which encodes the largest major immunoreactive protein in E. canis, from 17 Taiwanese samples was conducted. The resultant 16S rDNA sequences were found to be identical to each other and have very high homology (99.4~100%) with previously reported E. canis sequences. Additionally, phylogenetic analysis of gp200 demonstrated that the E. canis Taiwanese genotype was genetically distinct from other reported isolates obtained from the United States, Brazil, and Israel, and that it formed a separate clade. Remarkable variations unique to the Taiwanese genotype were found throughout the deduced amino acid sequence of gp200, including 15 substitutions occurring in two of five known species-specific epitopes. The gp200 amino acid sequences of the Taiwanese genotype bore 94.4~94.6 identities with those of the isolates from the United States and Brazil, and 93.7% homology with that of the Israeli isolate. Taken together, these results suggest that the Taiwanese genotype represents a novel strain of E. canis that has not yet been characterized

Crimmigration and Refugees: Bridging Visas, Criminal Cancellations and ‘Living in the Community’ as Punishment and Deterrence

Australia’s status as the only state with a policy of mandatory indefinite detention of all unlawful non-citizens, including asylum seekers, who are within Australian territory is a fact that is both well-known and frequently cited. From its inception, mandatory immigration detention was touted as ‘the method of deterrence for those seeking asylum onshore’ and since then ‘mandatory detention has been at the forefront of a deterrence as control and control as deterrence discourse’2. The imagined subjects of deterrence are frequently asylum seekers presented as ‘bogus’ or as economic migrants, and the sites for control are Australia’s ‘immigration program’ and borders. While these dual factors have animated the implementation and continuation of the policy for over 25 years, the contemporary practice and enforcement of detention in Australia presents a much more complex picture

Air pollution exposure estimation using dispersion modelling and continuous monitoring data in a prospective birth cohort study in the Netherlands

Previous studies suggest that pregnant women and children are particularly vulnerable to the adverse effects of air pollution. A prospective cohort study in pregnant women and their children enables identification of the specific effects and critical periods. This paper describes the design of air pollution exposure assessment for participants of the Generation R Study, a population-based prospective cohort study from early pregnancy onwards in 9778 women in the Netherlands. Individual exposures to PM10 and NO2 levels at the home address were estimated for mothers and children, using a combination of advanced dispersion modelling and continuous monitoring data, taking into account the spatial and temporal variation in air pollution concentrations. Full residential history was considered. We observed substantial spatial and temporal variation in air pollution exposure levels. The Generation R Study provides unique possibilities to examine effects of short- and long-term air pollution exposure on various maternal and childhood outcomes and to identify potential critical windows of exposure

Automated time activity classification based on global positioning system (GPS) tracking data

<p>Abstract</p> <p>Background</p> <p>Air pollution epidemiological studies are increasingly using global positioning system (GPS) to collect time-location data because they offer continuous tracking, high temporal resolution, and minimum reporting burden for participants. However, substantial uncertainties in the processing and classifying of raw GPS data create challenges for reliably characterizing time activity patterns. We developed and evaluated models to classify people's major time activity patterns from continuous GPS tracking data.</p> <p>Methods</p> <p>We developed and evaluated two automated models to classify major time activity patterns (i.e., indoor, outdoor static, outdoor walking, and in-vehicle travel) based on GPS time activity data collected under free living conditions for 47 participants (N = 131 person-days) from the Harbor Communities Time Location Study (HCTLS) in 2008 and supplemental GPS data collected from three UC-Irvine research staff (N = 21 person-days) in 2010. Time activity patterns used for model development were manually classified by research staff using information from participant GPS recordings, activity logs, and follow-up interviews. We evaluated two models: (a) a rule-based model that developed user-defined rules based on time, speed, and spatial location, and (b) a random forest decision tree model.</p> <p>Results</p> <p>Indoor, outdoor static, outdoor walking and in-vehicle travel activities accounted for 82.7%, 6.1%, 3.2% and 7.2% of manually-classified time activities in the HCTLS dataset, respectively. The rule-based model classified indoor and in-vehicle travel periods reasonably well (Indoor: sensitivity > 91%, specificity > 80%, and precision > 96%; in-vehicle travel: sensitivity > 71%, specificity > 99%, and precision > 88%), but the performance was moderate for outdoor static and outdoor walking predictions. No striking differences in performance were observed between the rule-based and the random forest models. The random forest model was fast and easy to execute, but was likely less robust than the rule-based model under the condition of biased or poor quality training data.</p> <p>Conclusions</p> <p>Our models can successfully identify indoor and in-vehicle travel points from the raw GPS data, but challenges remain in developing models to distinguish outdoor static points and walking. Accurate training data are essential in developing reliable models in classifying time-activity patterns.</p