Newer Surveillance Data Extends Our Understanding of the Niche of \u3ci\u3eRickettsia montanensis\u3c/i\u3e (Rickettsiales: Rickettsiaceae) Infection of the American Dog Tick (Acari: Ixodidae) in the United States

Background: Understanding the geographic distribution of Rickettsia montanensis infections in Dermacentor variabilis is important for tick-borne disease management in the United States, as both a tick-borne agent of interest and a potential confounder in surveillance of other rickettsial diseases. Two previous studies modeled niche suitability for D. variabilis with and without R. montanensis, from 2002-2012, indicating that the D. variabilis niche overestimates the infected niche. This study updates these, adding data since 2012. Methods: Newer surveillance and testing data were used to update Species Distribution Models (SDMs) of D. variabilis, and R. montanensis infected D. variabilis, in the United States. Using random forest (RF) models, found to perform best in previous work, we updated the SDMs and compared them with prior results. Warren’s I niche overlap metric was used to compare between predicted suitability for all ticks and ‘pathogen positive niche’ models across datasets. Results: Warren’s I indicated \u3c 2% change in predicted niche, and there was no change in order of importance of environmental predictors, for D. variabilis or R. montanensis positive niche. The updated D. variabilis niche model overpredicted suitability compared to the updated R. montanensis positive niche in key peripheral parts of the range, but slightly underpredicted through the northern and midwestern parts of the range. This reinforces previous findings of a more constrained pathogen-positive niche than predicted by D. variabilis records alone. Conclusions: The consistency of predicted niche suitability for D. variabilis in the United States, with the addition of nearly a decade of new data, corroborates this is a species with generalist habitat requirements. Yet a slight shift in updated niche distribution, even of low suitability, included more southern areas, pointing to a need for continued and extended monitoring and surveillance. This further underscores the importance of revisiting vector and vector-borne disease distribution maps

LYMESIM 2.0: An Updated Simulation of Blacklegged Tick (Acari: Ixodidae) Population Dynamics and Enzootic Transmission of \u3ci\u3eBorrelia burgdorferi\u3c/i\u3e (Spirochaetales: Spirochaetaceae)

Lyme disease is the most commonly reported vector-borne disease in the United States, and the number of cases reported each year continues to rise. The complex nature of the relationships between the pathogen (Borrelia burgdorferi sensu stricto), the tick vector (Ixodes scapularis Say), multiple vertebrate hosts, and numerous environmental factors creates challenges for understanding and predicting tick population and pathogen transmission dynamics. LYMESIM is a mechanistic model developed in the late 1990s to simulate the life-history of I. scapularis and transmission dynamics of B. burgdorferi s.s. Here we present LYMESIM 2.0, a modernized version of LYMESIM, that includes several modifications to enhance the biological realism of the model and to generate outcomes that are more readily measured under field conditions. The model is tested for three geographically distinct locations in New York, Minnesota, and Virginia. Model-simulated timing and densities of questing nymphs, infected nymphs, and abundances of nymphs feeding on hosts are consistent with field observations and reports for these locations. Sensitivity analysis highlighted the importance of temperature in host finding for the density of nymphs, the importance of transmission from small mammals to ticks on the density of infected nymphs, and temperature-related tick survival for both density of nymphs and infected nymphs. A key challenge for accurate modeling of these metrics is the need for regionally representative inputs for host populations and their fluctuations. LYMESIM 2.0 is a useful public health tool that downstream can be used to evaluate tick control interventions and can be adapted for other ticks and pathogens

Eucalypt Dieback on the Northern Tablelands of New South Wales

This study was initiated in order to attempt to describe the nature and extent of eucalypt dieback on the Northern Tablelands. It was found that the typical symptoms of eucalypts exhibiting dieback involved general deterioration of the crown with thinning of foliage and progressive death of twigs and branches. Most of the declining trees were severely defoliated by a variety of native insects, in particular paropsine and scarabaeid beetles. Partial recovery usually occurred in the form of bursts of epicormic shoots, but the new growth was also liable to deterioration. The trees usually died, with a few intact leaves remaining, but occasionally trees wilted while still bearing a significant portion of their leafy crown. Wood decay was common in the affected eucalypts, but the rotting of live sapwood was only occasionally evident. Semi-quantitative scales were constructed to facilitate the assessment of tree vigour and foliage cover. A broadscale road survey of the Northern Tablelands was undertaken during 1980 to ascertain the extent of eucalypt dieback and which species were involved; 48 species of naturally occurring eucalypts were encountered. The stretch of country in which most dieback had occurred runs from Bendemeer and Yarrowitch in the south to Tenterfield in the north. ... It is argued that lignotuberous advanced growth is critical for the survival of eucalypt populations during periods of intense dieback

Monitoramento dos fluxos de gases de efeito estufa de plantio de pinus e mata nativa, Rio Negrinho, SC.

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Soil macrofauna in land use systems in Ponta Grossa-Paraná state, Brazil.

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Systematic review and meta-analysis appraising efficacy and safety of adrenaline for adult cardiopulmonary resuscitation

BACKGROUND: There is a beneficial effect of adrenaline during adult cardiopulmonary resuscitation (CPR) from cardiac arrest but there is also uncertainty about its safety and effectiveness. The aim of this study was to evaluate the use of adrenaline versus non-adrenaline CPR. METHODS: PubMed, ScienceDirect, Embase, CENTRAL (Cochrane Central Register of Controlled Trials) and Google Scholar databases were searched from their inception up to 1st July 2020. Two reviewers independently assessed eligibility and risk of bias, with conflicts resolved by a third reviewer. Risk ratio (RR) or mean difference of groups were calculated using fixed or random-effect models. RESULTS: Nineteen trials were identified. The use of adrenaline during CPR was associated with a significantly higher percentage of return of spontaneous circulation (ROSC) compared to non-adrenaline treatment (20.9% vs. 5.9%; RR = 1.87; 95% confidence interval [CI] 1.37-2.55; p < 0.001). The use of adrenaline in CPR was associated with ROSC at 19.4% and for non-adrenaline treatment - 4.3% (RR = 3.23; 95% CI 1.89-5.53; p < 0.001). Survival to discharge (or 30-day survival) when using adrenaline was 6.8% compared to non-adrenaline treatment (5.5%; RR = 0.99; 95% CI 0.76-1.30; p = 0.97). However, the use of adrenaline was associated with a worse neurological outcome (1.6% vs. 2.2%; RR = 0.57; 95% CI 0.42-0.78; p < 0.001). CONCLUSIONS: This review suggests that resuscitation with adrenaline is associated with the ROSC and survival to hospital discharge, but no higher effectiveness was observed at discharge with favorable neurological outcome. The analysis showed higher effectiveness of ROSC and survival to hospital discharge in non-shockable rhythms. But more multicenter randomized controlled trials are needed in the future

Galaxy classification: deep learning on the OTELO and COSMOS databases

Context. The accurate classification of hundreds of thousands of galaxies observed in modern deep surveys is imperative if we want to understand the universe and its evolution. Aims. Here, we report the use of machine learning techniques to classify early- and late-type galaxies in the OTELO and COSMOS databases using optical and infrared photometry and available shape parameters: either the Sersic index or the concentration index. Methods. We used three classification methods for the OTELO database: 1) u-r color separation , 2) linear discriminant analysis using u-r and a shape parameter classification, and 3) a deep neural network using the r magnitude, several colors, and a shape parameter. We analyzed the performance of each method by sample bootstrapping and tested the performance of our neural network architecture using COSMOS data. Results. The accuracy achieved by the deep neural network is greater than that of the other classification methods, and it can also operate with missing data. Our neural network architecture is able to classify both OTELO and COSMOS datasets regardless of small differences in the photometric bands used in each catalog. Conclusions. In this study we show that the use of deep neural networks is a robust method to mine the cataloged dataComment: 20 pages, 10 tables, 14 figures, Astronomy and Astrophysics (in press