scholarly journals The Lyme Borreliosis Spatial Footprint in the 21st Century: A Key Study of Slovenia

Author(s):  
Daša Donša ◽  
Veno Jaša Grujić ◽  
Nataša Pipenbaher ◽  
Danijel Ivajnšič

After mosquitoes, ticks are the most important vectors of infectious diseases. They play an important role in public health. In recent decades, we discovered new tick-borne diseases; additionally, those that are already known are spreading to new areas because of climate change. Slovenia is an endemic region for Lyme borreliosis and one of the countries with the highest incidence of this disease on a global scale. Thus, the spatial pattern of Slovenian Lyme borreliosis prevalence was modelled with 246 indicators and transformed into 24 uncorrelated predictor variables that were applied in geographically weighted regression and regression tree algorithms. The projected potential shifts in Lyme borreliosis foci by 2050 and 2070 were calculated according to the RCP8.5 climate scenario. These results were further applied to developing a Slovenian Lyme borreliosis infection risk map, which could be used as a preventive decision support system.

Author(s):  
Tyler K. Chafin ◽  
Marlis R. Douglas ◽  
Bradley T. Martin ◽  
Zachery D. Zbinden ◽  
Christopher R. Middaugh ◽  
...  

ABSTRACTChronic-wasting disease (CWD) is a prion-derived fatal neurodegenerative disease that has affected wild cervid populations on a global scale. Susceptibility has been linked unambiguously to several amino acid variants within the prion protein gene (PRNP). Quantifying their distribution across landscapes can provide critical information for agencies attempting to adaptively manage CWD. Here we attempt to further define management implications of PRNP polymorphism by quantifying the contemporary geographic distribution (i.e., phylogeography) of PRNP variants in hunter-harvested white-tailed deer (WTD; Odocoileus virginianus, N=1433) distributed across Arkansas (USA), including a focal spot for CWD since detection of the disease in February 2016. Of these, PRNP variants associated with the well-characterized 96S non-synonymous substitution showed a significant increase in relative frequency among older CWD-positive cohorts. We interpreted this pattern as reflective of a longer life expectancy for 96S genotypes in a CWD-endemic region, suggesting either decreased probabilities of infection or reduced disease progression. Other variants showing statistical signatures of potential increased susceptibility, however, seemingly do so as an artefact of population structure. We also showed marked heterogeneity across the landscape in the prevalence of ‘reduced susceptibility’ genotypes. This may indicate, in turn, that differences in disease susceptibility among WTD in Arkansas are an innate, population-level characteristic that is detectable through phylogeographic analysis.


2012 ◽  
Vol 79 (2) ◽  
pp. 434-448 ◽  
Author(s):  
Graham Wilkes ◽  
Norma J. Ruecker ◽  
Norman F. Neumann ◽  
Victor P. J. Gannon ◽  
Cassandra Jokinen ◽  
...  

ABSTRACTNearly 690 raw surface water samples were collected during a 6-year period from multiple watersheds in the South Nation River basin, Ontario, Canada.Cryptosporidiumoocysts in water samples were enumerated, sequenced, and genotyped by detailed phylogenetic analysis. The resulting species and genotypes were assigned to broad, known host and human infection risk classes. Wildlife/unknown, livestock, avian, and human host classes occurred in 21, 13, 3, and <1% of sampled surface waters, respectively.Cryptosporidium andersoniwas the most commonly detected livestock species, while muskrat I and II genotypes were the most dominant wildlife genotypes. The presence ofGiardiaspp.,Salmonellaspp.,Campylobacterspp., andEscherichia coliO157:H7 was evaluated in all water samples. The greatest significant odds ratios (odds of pathogen presence when host class is present/odds of pathogen presence when host class is absent) forGiardiaspp.,Campylobacterspp., andSalmonellaspp. in water were associated, respectively, with livestock (odds ratio of 3.1), avian (4.3), and livestock (9.3) host classes. Classification and regression tree analyses (CART) were used to group generalized host and human infection risk classes on the basis of a broad range of environmental and land use variables while tracking cooccurrence of zoonotic pathogens in these groupings. The occurrence of livestock-associatedCryptosporidiumwas most strongly related to agricultural water pollution in the fall (conditions also associated with elevated odds ratios of other zoonotic pathogens occurring in water in relation to all sampling conditions), whereas wildlife/unknown sources ofCryptosporidiumwere geospatially associated with smaller watercourses where urban/rural development was relatively lower. Conditions that support wildlife may not necessarily increase overall human infection risks associated withCryptosporidiumsince mostCryptosporidiumgenotypes classed as wildlife in this study (e.g., muskrat I and II genotype) do not pose significant infection risks to humans. Consequently, from a human health perspective, land use practices in agricultural watersheds that create opportunities for wildlife to flourish should not be rejected solely on the basis of their potential to increase relative proportions of wildlife fecal contamination in surface water. The present study suggests that mitigating livestock fecal pollution in surface water in this region would likely reduce human infection risks associated withCryptosporidiumand other zoonotic pathogens.


Author(s):  
A. Shah-Heydari pour ◽  
P. Pahlavani ◽  
B. Bigdeli

According to the industrialization of cities and the apparent increase in pollutants and greenhouse gases, the importance of forests as the natural lungs of the earth is felt more than ever to clean these pollutants. Annually, a large part of the forests is destroyed due to the lack of timely action during the fire. Knowledge about areas with a high-risk of fire and equipping these areas by constructing access routes and allocating the fire-fighting equipment can help to eliminate the destruction of the forest. In this research, the fire risk of region was forecasted and the risk map of that was provided using MODIS images by applying geographically weighted regression model with Gaussian kernel and ordinary least squares over the effective parameters in forest fire including distance from residential areas, distance from the river, distance from the road, height, slope, aspect, soil type, land use, average temperature, wind speed, and rainfall. After the evaluation, it was found that the geographically weighted regression model with Gaussian kernel forecasted 93.4% of the all fire points properly, however the ordinary least squares method could forecast properly only 66% of the fire points.


2019 ◽  
Vol 9 (2) ◽  
Author(s):  
Kim Ward ◽  
Chantal Larose

Objectives: This research brief explores literature addressing developmental education to identify successful interventions in first-year math courses in higher education. Our goal is to describe the relationship between students’ academic practices and their final course grade in their first-year math courses. Method: Data on 3,249 students have been gathered and analyzed using descriptive statistics and predicative analytics. We describe the Math program, which includes a supplemental support component, and the environment under which it was created. We then examine the behavior between students’ participation in supplemental support and their academic performance. Results: We used classification and regression tree algorithms to obtain a model that gave us data-driven guidelines to aid with future student interventions and success in their first-year math courses. Conclusions: Students’ fulfillment of the supplemental support requirements by specified deadlines is a key predictor of students’ midterm and final course grades.  Implications for Theory and/or Practice: This work provides a roadmap for student interventions and increasing student success with first-year mathematics courses. Keywords: First-year mathematics courses, supplemental support, higher education


1994 ◽  
pp. 7-11
Author(s):  
Gerolamo Bianchi ◽  
Laura Buffrini ◽  
Patrizia Monteforte ◽  
Vincenzo Garzia ◽  
Maria Clara Grignolo ◽  
...  

2022 ◽  
Author(s):  
Panagiotis Petsas ◽  
Aggeliki Doxa ◽  
Vasiliki Almpanidou ◽  
Antonios D. Mazaris

Abstract Shifting distribution to track suitable climate is a potential strategy for marine species to cope with ocean warming. Yet, the ability of species to successfully reach future climate analogs largely depends on the length of the paths that connect them, and on the exposure of these paths to extreme climates during this transition. Here, we evaluate marine climate connectivity for trajectories between climatic analogs on a global scale. We find that while movement between climatic analogs is more intense in the northern seas of the planet, they require longer trajectories to reach climatic analogs, with high climatic exposure to extreme conditions. On the contrary, the southern seas host areas that have closer climatic analogs, further subjected to a lower exposure to dissimilar climates. These patterns are mirrored in the connectivity properties of the global marine protected areas, highlighting sites which might fail to facilitate connectivity to future climates. Our results suggest that potential shifts between climatic analogs might be subjected to more limitations than those suggested by previous studies, with marine connectivity offering novel insights for the establishment of climate-wise conservation future networks.


2021 ◽  
Vol 884 (1) ◽  
pp. 012026
Author(s):  
AF Nugraha ◽  
BS Hadi

Abstract Information about evapotranspiration is very important in relation to vegetation because it can be used for planning both in urban planning and agriculture. Magelang Regency has a lot of vegetated green land, both agricultural and non-agricultural and has no information about evapotranspiration. The calculation of evapotranspiration uses the SEBAL (Surface Energy Balance Algorithm for Land) method and modeling uses the GWR (Geographiccaly Weighted Regression) model. Calculation and modeling assisted by QGIS 2.14, QGIS 3.6, SPSS 20, and GWR 4.09 applications. The results showed that (1) GWR evapotranspiration model with significance (sig.) 5% is divided into 3 sub-district groups according to the significant variables in the sub-district (2) NDVI and Surface Albedo variables have a small effect on a global scale and have a large effect on a local scale.


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