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Mycoplasma bovis (Mbovis) was first detected in cattle in New Zealand (NZ) in July 2017. To prevent further spread, NZ launched a world-first National Eradication Programme in May 2018. Existing diagnostic tests for Mbovis have been applied in countries where Mbovis is endemic, for detecting infection following outbreaks of clinical disease. Diagnostic test evaluation (DTE) under NZ conditions was thus required to inform the Programme. We used Bayesian Latent Class Analysis on paired serum ELISA (ID Screen Mycoplasma bovis Indirect from IDvet) and tonsillar swabs (qPCR) for DTE in the absence of a gold standard. Tested samples were collected at slaughter between June 2018 and November 2019, from infected herds depopulated by the Programme. A first set of models evaluated the detection of active infection, i.e. the presence of Mbovis in the host. At a modified serology positivity threshold of SP %> = 90, estimates of animal-level ELISA sensitivity was 72.8% (95% credible interval 68.5%—77.4%), respectively 97.7% (95% credible interval 97.3%—98.1%) for specificity, while the qPCR sensitivity was 45.2% (95% credible interval 41.0%—49.8%), respectively 99.6% (95% credible interval 99.4%—99.8%) for specificity. In a second set of models, prior information about ELISA specificity was obtained from the National Beef Cattle Surveillance Programme, a population theoretically free—or very low prevalence—of Mbovis. These analyses aimed to evaluate the accuracy of the ELISA test targeting prior exposure to Mbovis, rather than active infection. The specificity of the ELISA for detecting exposure to Mbovis was 99.9% (95% credible interval 99.7%—100.0%), hence near perfect at the threshold SP%=90. This specificity estimate, considerably higher than in the first set of models, was equivalent to the manufacturer’s estimate. The corresponding ELISA sensitivity estimate was 66.0% (95% credible interval 62.7%-70.7%). These results confirm that the IDvet ELISA test is an appropriate tool for determining exposure and infection status of herds, both to delimit and confirm the absence of Mbovis.

Marine reserve networks must ensure the representation of important conservation features, and also guarantee the persistence of key populations. For many species, designing reserve networks is complicated by the absence or limited availability of spatial and life-history data. This is particularly true for data on larval dispersal, which has only recently become available. However, systematic conservation planning methods currently incorporate demographic processes through unsatisfactory surrogates. There are therefore two key challenges to designing marine reserve networks that achieve feature representation and demographic persistence constraints. First, constructing a method that efficiently incorporates persistence as well as complementary feature representation. Second, incorporating persistence using a mechanistic description of population viability, rather than a proxy such as size or distance. Here we construct a novel systematic conservation planning method that addresses both challenges, and parameterise it to design a hypothetical marine reserve network for fringing coral reefs in the Keppel Islands, Great Barrier Reef, Australia. For this application, we describe how demographic persistence goals can be constructed for an important reef fish species in the region, the bar-cheeked trout (Plectropomus maculatus). We compare reserve networks that are optimally designed for either feature representation or demographic persistence, with a reserve network that achieves both goals simultaneously. As well as being practically applicable, our analyses also provide general insights into marine reserve planning for both representation and demographic persistence. First, persistence constraints for dispersive organisms are likely to be much harder to achieve than representation targets, due to their greater complexity. Second, persistence and representation constraints pull the reserve network design process in divergent directions, making it difficult to efficiently achieve both constraints. Although our method can be readily applied to the data-rich Keppel Islands case study, we finally consider the factors that limit the method's utility in information-poor contexts common in marine conservation.

\"Birds of Australia covers all 714 species of resident birds and regularly occuring migrants and features more than 1,100 stunning color photographs, including many photos of subspecies and plumage variations never before seen in a field guide. Detailed facing-page species accounts describe key identification features such as size, plumage, distribution, behavior and voice ... The text relies on the very latest IOC taxonomy\"--Back cover.

Background Diagnostic testing using PCR is a fundamental component of COVID-19 pandemic control. Criteria for determining who should be tested by PCR vary between countries, and ultimately depend on resource constraints and public health objectives. Decisions are often based on sets of symptoms in individuals presenting to health services, as well as demographic variables, such as age, and travel history. The objective of this study was to determine the sensitivity and specificity of sets of symptoms used for triaging individuals for confirmatory testing, with the aim of optimising public health decision making under different scenarios. Methods Data from the first wave of COVID-19 in New Zealand were analysed; comprising 1153 PCR-confirmed and 4750 symptomatic PCR negative individuals. Data were analysed using Multiple Correspondence Analysis (MCA), automated search algorithms, Bayesian Latent Class Analysis, Decision Tree Analysis and Random Forest (RF) machine learning. Results Clinical criteria used to guide who should be tested by PCR were based on a set of mostly respiratory symptoms: a new or worsening cough, sore throat, shortness of breath, coryza, anosmia, with or without fever. This set has relatively high sensitivity (> 90%) but low specificity (< 10%), using PCR as a quasi-gold standard. In contrast, a group of mostly non-respiratory symptoms, including weakness, muscle pain, joint pain, headache, anosmia and ageusia, explained more variance in the MCA and were associated with higher specificity, at the cost of reduced sensitivity. Using RF models, the incorporation of 15 common symptoms, age, sex and prioritised ethnicity provided algorithms that were both sensitive and specific (> 85% for both) for predicting PCR outcomes. Conclusions  If predominantly respiratory symptoms are used for test-triaging,  a large proportion of the individuals being tested may not have COVID-19. This could overwhelm testing capacity and hinder attempts to trace and eliminate infection. Specificity can be increased using alternative rules based on sets of symptoms informed by multivariate analysis and automated search algorithms, albeit at the cost of sensitivity. Both sensitivity and specificity can be improved through machine learning algorithms, incorporating symptom and demographic data, and hence may provide an alternative approach to test-triaging that can be optimised according to prevailing conditions.

Toward addressing many neuroprosthetic applications, the Neurochip3 (NC3) is a multichannel bidirectional brain-computer interface that operates autonomously and can support closed-loop activity-dependent stimulation. It consists of four circuit boards populated with off-the-shelf components and is sufficiently compact to be carried on the head of a non-human primate (NHP). NC3 has six main components: (1) an analog front-end with an Intan biophysical signal amplifier (16 differential or 32 single-ended channels) and a 3-axis accelerometer, (2) a digital control system comprised of a Cyclone V FPGA and Atmel SAM4 MCU, (3) a micro SD Card for 128 GB or more storage, (4) a 6-channel differential stimulator with ±60 V compliance, (5) a rechargeable battery pack supporting autonomous operation for up to 24 h and, (6) infrared transceiver and serial ports for communication. The NC3 and earlier versions have been successfully deployed in many closed-loop operations to induce synaptic plasticity and bridge lost biological connections, as well as deliver activity-dependent intracranial reinforcement. These paradigms to strengthen or replace impaired connections have many applications in neuroprosthetics and neurorehabilitation.

We present a Bayesian statistical approach to estimate volumes for a series of eruptions from an assemblage of sparse proximal and distal tephra (volcanic ash) deposits. Most volume estimates are of widespread tephra deposits from large events using isopach maps constructed from observations at exposed locations. Instead, we incorporate raw thickness measurements, focussing on tephra thickness data from cores extracted from lake sediments and through swamp deposits. This facilitates investigation into the dispersal pattern and volume of tephra from much smaller eruption events. Given the general scarcity of data and the physical phenomena governing tephra thickness attenuation, a hybrid Bayesian-empirical tephra attenuation model is required. Point thickness observations are modeled as a function of the distance and angular direction of each location. The dispersal of tephra from larger well-estimated eruptions are used as leverage for understanding the smaller unknown events, and uncertainty in thickness measurements can be properly accounted for. The model estimates the wind and site-specific effects on the tephra deposits in addition to volumes. Our technique is exemplified on a series of tephra deposits from Mt Taranaki (New Zealand). The resulting estimates provide a comprehensive record suitable for supporting hazard models. Posterior mean volume estimates range from 0.02 to 0.26 km 3 . Preliminary examination of the results suggests a size-predictable relationship.

Australia is home to a spectacular diversity of birdlife, from parrots and penguins to emus and vibrant passerines.Birds of Australiacovers all 714 species of resident birds and regularly occurring migrants and features more than 1,100 stunning color photographs, including many photos of subspecies and plumage variations never before seen in a field guide. Detailed facing-page species accounts describe key identification features such as size, plumage, distribution, behavior, and voice. This one-of-a-kind guide also provides extensive habitat descriptions with a large number of accompanying photos. The text relies on the very latest IOC taxonomy and the distribution maps incorporate the most current mapping data, making this the most up-to-date guide to Australian birds. Covers all 714 species of resident birds and regularly occurring migrantsFeatures more than 1,100 stunning color photosIncludes facing-page species accounts, habitat descriptions, and distribution mapsThe ideal photographic guide for beginners and seasoned birders alike

The performance of a new point-of-care diagnostic (Mastatest), an on-farm test designed to identify bacteria and provide antibiotic sensitivity testing information from milk samples, was compared with standard microbiological culture methods. A total of 292 milk samples from clinical mastitis cases in dairy cows on New Zealand dairy farms were examined, and latent class analysis was used to estimate the performance characteristics of both tests. Two hundred and fifty-six samples (87.7%) demonstrated bacterial infection in standard culture, and 269 (92.1%) using the point-of-care diagnostic. The most common bacterial species detected was Streptococcus uberis , found in 195 samples (66.8%) using standard culture and 190 samples (65.1%) using the point-of-care diagnostic. Latent class analysis found no significant differences in test characteristics between the point-of-care diagnostic and standard culture. The estimated sensitivity and specificity of the point-of-care diagnostic against all targets combined were 94.6 and 72.1% respectively; the corresponding estimates for standard culture were 90.5 and 73.9%. Comparison of antibiotic susceptibility testing using the point-of-care diagnostic and the reference method showed similar trends and, in some cases, identical MIC 50 and MIC 90 values, with at most one antibiotic dilution difference.