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451 results for “outbreaks”
Declining survival rates of red foxes (Vulpes vulpes) during the first outbreak of sarcoptic mange in Sweden
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Spatial locations of reported FMD outbreaks
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Data from: Comparing the efficacy of control strategies for infectious disease outbreaks using field and simulation studies
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Limited outbreak of highly pathogenic influenza A(H5N1) virus in a Herring Gull colony, Canada, 2022
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Clinical surveillance identifies SARS-CoV-2 outbreaks and emergence of novel variants in real-time
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A Twitter dataset for Monkeypox outbreak in 2022
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History of coronavirus naming during the three zoonotic outbreaks in relation to virus taxonomy and diseases caused by these viruses. According to the current international classification of diseases49, MERS and SARS are classified as 1D64 and 1D65, respectively. in The species Severe acute respiratory syndromerelated coronavirus: classifying 2019-nCoV and naming it SARS-CoV-2
History of coronavirus naming during the three zoonotic outbreaks in relation to virus taxonomy and diseases caused by these viruses. According to the current international classification of diseases49, MERS and SARS are classified as 1D64 and 1D65, respectively.
Data and code for changes in contact patterns shape the dynamics of the novel coronavirus disease 2019 outbreak in China
<p>Data and code for the paper "changes in contact patterns shape the dynamics of the novel coronavirus disease 2019 outbreak in China" published in Science.</p>
Data from: Immediate and carry-over effects of insect outbreaks on vegetation growth in West Greenland assessed from cells to satellite
Aim: Tundra ecosystems are highly vulnerable to climate change and climate-growth responses of Arctic shrubs are variable and altered by microsite environmental conditions and biotic factors. With warming and drought during the growing season, insect-driven defoliation is expected to increase in frequency and severity with potential broad-scale impacts on tundra ecosystem functioning. Here we provide the first broad-scale reconstruction of spatiotemporal dynamics of past insect outbreaks by assessing their effects on shrub growth along a typical Greenlandic fjord climate gradient from the inland ice to the sea. Location: Nuuk Fjord (64°30′N/51°23′W) and adjacent areas, West Greenland. Taxa: Great brocade (Eurois occulta L.) and grey willow (Salix glauca L.). Methods: We combined dendro-anatomical and remote sensing analyses. Time series of ring width and wood-anatomical traits were obtained from chronologies of > 40 years established from 153 individuals of S. glauca collected at nine sites. We detected anomalies in satellite-based Normalized Difference Vegetation Index (NDVI) related to defoliation and reconstructed past changes in photosynthetic activity across the region. Results: We identified outbreaks as distinctive years with reduced ring width, cell-wall thickness and vessel size, without being directly related to climate but matching with years of parallel reduction in NDVI. The two subsequent years after the defoliation showed a significant increase in ring width. The reconstructed spatiotemporal dynamics of these events indicate substantial regional variation in outbreak intensity linked to the climate variability across the fjord system. Main conclusions: Our results highlight the ability of S. glauca to cope with severe insect defoliation by changing carbon investment and xylem conductivity leading to high resilience and rapid recovery after the disturbance. Our multi-proxy approach allows us to pin-point biotic drivers of narrow ring formation and to provide new broad-scale insight on the C-budget and vegetation productivity of shrub communities in a widespread arctic ecosystem
Data from: Temporal variation in spatial genetic structure during population outbreaks: distinguishing among different potential drivers of spatial synchrony
Spatial synchrony is a common characteristic of spatio-temporal population dynamics across many taxa. While it is known that both dispersal and spatially autocorrelated environmental variation (i.e., the Moran effect) can synchronize populations, the relative contributions of each, and how they interact, is generally unknown. Distinguishing these mechanisms and their effects on synchrony can help us to better understand spatial population dynamics, design conservation and management strategies, and predict climate change impacts. Population genetic data can be used to tease apart these two processes as the spatio-temporal genetic patterns they create are expected to be different. A challenge, however, is that genetic data are often collected at a single point in time, which may introduce context-specific bias. Spatio-temporal sampling strategies can be used to reduce bias and to improve our characterization of the drivers of spatial synchrony. Using spatio-temporal analyses of genotypic data, our objective was to identify the relative support for these two mechanisms to the spatial synchrony in population dynamics of the irruptive forest insect pest, the spruce budworm (Choristoneura fumiferana), in Quebec (Canada). AMOVA, cluster analysis, isolation by distance and sPCA were used to characterize spatio-temporal genomic variation using 1370 SBW larvae sampled over four years (2012-2015) and genotyped at 3,562 SNP loci. We found evidence of overall weak spatial genetic structure that decreased from 2012 to 2015 and a genetic diversity homogenization among the sites. We also found genetic evidence of a long-distance dispersal event over > 140 km. These results indicate that dispersal is the key mechanism involved in driving population synchrony of the outbreak. Early intervention management strategies that aim to control source populations have the potential to be effective through limiting dispersal. However, the timing of such interventions relative to outbreak progression is likely to influence their probability of success.
Impact of coronavirus disease 2019 (COVID-19) outbreak on radiology research: an Italian survey
<p>This article reports the results of a national survey, which had the purpose of understanding how COVID-19 pandemic has changed the scientific activity of Italian radiology researchers. A total of 327 Italian radiologists took part in the survey (mean age: 49±12 years). The majority of participants (245/327, 74.9%) was not working for or in agreement with a University and most of them were hospital staff radiologists (222/327, 67.9%). More than two-thirds of surveyed radiologists (231/327, 71%) was working in a public institution, which mostly was a general hospital (188/327, 57.5%); 86/327 (26.3%) participants declared to work in a university hospital. After national lockdown, the working-flow came back to normal in the vast majority of cases (285/327, 87.2%). Participants reported that a total of 462 radiological trials were recruiting patients at their institutions prior to COVID-19 outbreak, of which 332 (71.9%) were stopped during the emergency. On the other hand, 252 radiological trials have been started during the pandemic, of which 156 were non-COVID-19 trials (61.9%) and 96 were focused on COVID-19 patients (38.2%). Participants reported a significant increase of the number of hours per week spent for research purposes during national lockdown (mean 4.5±8.9 hours during lockdown vs. 3.3±6.8 hours before lockdown; p=.046), followed by a significant drop after lockdown (3.2±6.5 hours per week, p=.035). Notably, 60% of participants reported that they do not spend any time on research. During national lockdown, 15.6% of participants started new review articles and completed old papers, 14.1% completed old works, and 8.9% started new review articles. Ninety-six surveyed radiologists (29.3%) declared to have submitted at least one article during COVID-19 emergency. This study confirms the need to be prepared to future challenging scenarios like COVID-19 emergency in order to support radiology researchers, thereby allowing them to push forward their activity.</p>
Data from: Infection dynamics, dispersal, and adaptation: understanding the lack of recovery in a remnant frog population following a disease outbreak
Emerging infectious diseases can cause dramatic declines in wildlife populations. Sometimes these declines are followed by recovery, but many populations do not recover. Studying differential recovery patterns may yield important information for managing disease-afflicted populations and facilitating population recoveries. In the late1980s, a chytridiomycosis outbreak caused multiple frog species in Australia's Wet Tropics to decline. Populations of some species (e.g., Litoria nannotis) subsequently recovered, while others (e.g., Litoria dayi) did not. We examined the population genetics and current infection status of L. dayi, to test several hypotheses regarding the failure of its populations to recover: 1) a lack of individual dispersal abilities has prevented recolonization of previously occupied locations, 2) a loss of genetic variation has resulted in limited adaptive potential, and 3) L. dayi is currently adapting to chytridiomycosis. We found moderate to hig h levels of gene flow and diversity (Fst range: <0.01–0.15; minor allele frequency: 0.192–0.245), which were similar to previously published levels for recovered L. nannotis populations. This suggests that dispersal ability and genetic diversity do not limit the ability of L dayi to recolonize upland sites. Further, infection intensity and prevalence increased with elevation, suggesting that chytridiomycosis is still limiting the elevational range of L. dayi. Outlier tests comparing infected and uninfected individuals consistently identified 18 markers as putatively under selection, and several of those markers matched genes that were previously implicated in infection. This suggests that L. dayi has genetic variation for genes that affect infection dynamics and may be undergoing adaptation.
Mental health status of health care professionals during the Covid-19 outbreak The initial study in Qazvin province, Iran
<p>This study was conducted to examine the mental health status of health care workers fighting COVID-19 in Qazvin province, Iran.</p>
Modeled Atmospheric Optical and Thermodynamic Responses to an Exceptional Trans-Atlantic Dust Outbreak
<p>Nine WRF-Chem 3.8.0 hindcasts, each utilizing a different dust emission configuration, from 1 March – 31 May 2015, coinciding with a Saharan air layer (SAL) dust outbreak during the 2015 Caribbean drought. WRF-Chem modeled aerosol optical depth (AOD) and Gálvez-Davison Index (GDI), a convective forecasting parameter, are provided in the files. These data correspond to the results published in "Modeled Atmospheric Optical and Thermodynamic Responses to an Exceptional Trans-Atlantic Dust Outbreak" by Paul Miller, Marcus Williams, and Thomas Mote, published the <em>Journal of Geophysical Research: Atmospheres</em>.</p>
Data from: Invasive Drosophila suzukii facilitates Drosophila melanogaster infestation and sour rot outbreaks in the vineyards
How do invasive pests affect interactions between members of pre-existing agrosystems? The invasive pest Drosophila suzukii is suspected to be involved in the aetiology of sour rot, a grapevine disease that otherwise develops following Drosophila melanogaster infestation of wounded berries. We combined field observations with laboratory assays to disentangle the relative roles of both Drosophila in disease development. We observed the emergence of numerous D. suzukii, but no D. melanogaster flies, from bunches that started showing mild sour rot symptoms days after field collection. However, bunches that already showed severe rot symptoms in the field mostly contained D. melanogaster. In the laboratory, oviposition by D. suzukii triggered sour rot development. An independent assay showed the disease increased grape attractiveness to ovipositing D. melanogaster females. Our results suggest that in invaded vineyards, D. suzukii facilitates D. melanogaster infestation and, consequently, favours sour rot outbreaks. Rather than competing with close species, the invader subsequently permits their reproduction in otherwise non-accessible resources and may cause more frequent, or more extensive, disease outbreaks.
Data from: Avoidable errors in the modeling of outbreaks of emerging pathogens, with special reference to Ebola
As an emergent infectious disease outbreak unfolds, public health response is reliant on information on key epidemiological quantities, such as transmission potential and serial interval. Increasingly, transmission models fit to incidence data are used to estimate these parameters and guide policy. Some widely used modelling practices lead to potentially large errors in parameter estimates and, consequently, errors in model-based forecasts. Even more worryingly, in such situations, confidence in parameter estimates and forecasts can itself be far overestimated, leading to the potential for large errors that mask their own presence. Fortunately, straightforward and computationally inexpensive alternatives exist that avoid these problems. Here, we first use a simulation study to demonstrate potential pitfalls of the standard practice of fitting deterministic models to cumulative incidence data. Next, we demonstrate an alternative based on stochastic models fit to raw data from an early phase of 2014 West Africa Ebola virus disease outbreak. We show not only that bias is thereby reduced, but that uncertainty in estimates and forecasts is better quantified and that, critically, lack of model fit is more readily diagnosed. We conclude with a short list of principles to guide the modelling response to future infectious disease outbreaks.
Data from: Recurrent insect outbreaks caused by temperature-driven changes in system stability
Insect species often undergo regular outbreaks in population density, but identifying the causal mechanism for such outbreaks in any particular species has proven difficult. Here we show that outbreak cycles in the tea tortrix Adoxophyes honmai can be explained by temperature-driven changes in system stability. Wavelet analysis of a 51yr time series spanning over 200 outbreaks reveals a threshold in outbreak amplitude each spring when temperature exceeds 15°C, and a secession of outbreaks each fall as temperature decreases. This is in close agreement with our independently parameterized mathematical model that predicts the system crosses a Hopf bifurcation from stability to sustained cycles as temperature increases. These results suggest that temperature can alter system stability and provide an explanation for generation cycles in multivoltine insects.
Data from: Can novel pest outbreaks drive ecosystem transitions in northern-boreal birch forest?
1. The boreal biome exhibits distinct alternative ecosystem states with high and low levels of tree-cover. Insect outbreaks facilitated by climate warming could potentially drive transitions from high to low tree-cover states. We investigated whether two key premises for such outbreak-induced transitions – critical thresholds (tipping points) and positive feedbacks that could maintain alternative states – are present in the northern-boreal mountain birch forest of Fennoscandia. Here, climate warming has promoted range expansions of defoliating geometrid moths, resulting in novel, severe multispecies outbreaks, most recently during 2002-2010. 2. We conducted regional-scale field surveys of forest damage and recovery in 280 mountain birch stands in a northeast Norway immediately after the outbreak (2010) and six years later (2016). Satellite-derived time series of the normalized difference vegetation index (NDVI) provided an index of stand defoliation during the outbreak period. 3. The proportion of dead stems per stand displayed a bimodal distribution, with stands generally being either lightly or severely damaged. This was due to a critical threshold in the relationship between defoliation and stem mortality, with mortality rates increasing abruptly in stands experiencing a mean drop in NDVI of more than 4 % during the outbreak. The two key forest regenerative pathways – basal sprouting and sapling production – both displayed positive feedbacks with surviving stems and trees, so that regeneration success declined with increasing damage to the mature tree layer. These feedbacks imply that stands which have been forced across critical defoliation thresholds and suffered collapses of living tree cover may struggle to recover, especially if the loss of positive regenerative feedbacks is compounded by ungulate browsing on birch recruits. 4. Synthesis. The north Fennoscandian mountain birch forest displays critical thresholds and positive feedbacks that conform to theoretical expectations for a system that could be vulnerable to abrupt and persistent changes of state in the face of novel, climatically facilitated insect outbreaks. These findings deepen the understanding of the persistent losses of tree-cover that have occasionally been observed after outbreaks in this system in the past, and adds to the list of mechanisms that could help explain the bistability of tree-cover across the boreal biome.
Data from: Tracing the effects of eutrophication on molluscan communities in sediment cores: outbreaks of an opportunistic species coincide with reduced bioturbation and high frequency of hypoxia in the Adriatic Sea
Estimating the effects and timing of anthropogenic impacts on the composition of macrobenthic communities is challenging because early 20th century surveys are sparse and the corresponding intervals in sedimentary sequences are mixed by bioturbation. Here, to assess the effects of eutrophication on macrobenthic communities in the northern Adriatic Sea, we account for mixing with dating of the bivalve Corbula gibba at two stations with high sediment accumulation (Po prodelta) and one station with moderate accumulation (Isonzo prodelta). We find that, first, pervasively bioturbated muds typical of highstand conditions deposited in the early 20th century were replaced by muds with relicts of flood layers and high content of total organic carbon (TOC) deposited in the late 20th century at the Po prodelta. The 20th century shelly muds at the Isonzo prodelta are amalgamated but also show an upward increase in TOC. Second, dating of C. gibba shells shows that the shift from the early to the late 20th century is characterized by a decrease in stratigraphic disorder and by an increase in temporal resolution of death assemblages from ~25-50 years to ~10-20 years in both regions. This shift reflects a decline in the depth of the fully-mixed layer from more than 20 cm to few centimeters. Third, the increase in abundance of the opportunistic species C. gibba and the loss of formerly abundant, hypoxia-sensitive species coincided with the decline in bioturbation, higher preservation of organic matter, and higher frequency of seasonal hypoxia in both regions. This depositional and ecosystem regime shift occurred in ~1950 AD. Therefore, the effects of enhanced food supply on macrobenthic communities were overwhelmed by oxygen depletion even when hypoxic conditions are limited to few weeks per year in the northern Adriatic Sea. Preservation of trends in molluscan abundance and flood events in sedimentary sequences was enhanced by eutrophication that reduced bioturbational mixing.
Data from: Growth and nitrogen uptake characteristics reveal outbreak mechanism of the opportunistic macroalga Gracilaria tenuistipitata
Macroalgae has bloomed in the brackish lake of Shenzhen Bay, China continuously from 2010 to 2014. Gracilaria tenuistipitata was identified as the causative macroalgal species. The aim of this study was to explore the outbreak mechanism of G. tenuistipitata, by studying the effects of salinity and nitrogen sources on growth, and the different nitrogen sources uptake characteristic. Our experimental design was based on environmental conditions observed in the bloom areas, and these main factors were simulated in the laboratory. Results showed that salinity 12 to 20 ‰ was suitable for G. tenuistipitata growth. When the nitrogen sources' (NH4+, NO3−) concentrations reached 40 µM or above, the growth rate of G. tenuistipitata was significantly higher. Algal biomass was higher (approximately 1.4 times) when cultured with NH4+ than that with NO3− addition. Coincidentally, macroalgal bloom formed during times of moderate salinity (~12 ‰) and high nitrogen conditions. The NH4+ and NO3− uptake characteristic was studied to understand the potential mechanism of G. tenuistipitata bloom. NH4+ uptake was best described by a linear, rate-unsaturated response, with the slope decreasing with time intervals. In contrast, NO3− uptake followed a rate-saturating mechanism best described by the Michaelis-Menten model, with kinetic parameters Vmax = 37.2 µM g−1 DM h−1 and Ks = 61.5 µM. Further, based on the isotope 15N tracer method, we found that 15N from NH4+ accumulated faster and reached an atom% twice than that of 15N from NO3−, suggesting when both NH4+ and NO3− were available, NH4+ was assimilated more rapidly. The results of the present study indicate that in the estuarine environment, the combination of moderate salinity with high ammonium may stimulate bloom formation.
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Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.