The use of three-dimensional model construction of virtual technology in orthopedic treatment

ObjectiveThe objective of this study is to explore the construction of a digital three-dimensional model of virtual technology that plays an auxiliary role in orthopedic treatment.MethodsThree fracture patients were selected, with no abnormality was observed in bone examination, no musculoskeletal disease in the past; and spiral CT scan of the spine and pelvis, upper limbs, and lower limbs was performed. The virtual technology was used to build a digital 3D model, mainly using the editing software Mimics10.0 software. In addition, the virtual three-dimensional model was verified by virtual surgery, data storage security, work efficiency of the model, model validity, three-dimensional characteristics of the model, the interaction mode of the model, and the data accuracy of the model were studied.ResultsThe digital 3D model was successfully established by Mimics10.0 software. The data fitting efficiency was very high. The data storage security of the 3D model was greatly improved compared with the 2D model, and the work efficiency was improved by at least 50%. There was also a significant change in the accuracy and interaction of data acquisition. Therefore, the detection of digital 3D model work through virtual surgery simulation fully demonstrated the positive auxiliary role of 3D model in orthopedic treatment.ConclusionThe digital 3D model based on Mimics10.0 software is efficient and accurate in obtaining data. It is very effective for subsequent adjuvant therapy in the field of orthopedics, reducing the probability of misdiagnosis by doctors, saving time and improving efficiency, reducing patient's physical pain and unnecessary economic expenses.     

Nonlinearity in interspecific interactions in response to climate change: Cod and haddock as an example

Climate change has profound ecological effects, yet our understanding of how trophic interactions among species are affected by climate change is still patchy. The sympatric Atlantic haddock and cod are co‐occurring across the North Atlantic. They compete for food at younger stages and thereafter the former is preyed by the latter. Climate change might affect the interaction and coexistence of these two species. Particularly, the increase in sea temperature (ST) has been shown to affect distribution, population growth and trophic interactions in marine systems. We used 33‐year long time series of haddock and cod abundances estimates from two data sources (acoustic and trawl survey) to analyse the dynamic effect of climate on the coexistence of these two sympatric species in the Arcto‐Boreal Barents Sea. Using a Bayesian state‐space threshold model, we demonstrated that long‐term climate variation, as expressed by changes of ST, affected species demography through different influences on density‐independent processes. The interaction between cod and haddock has shifted in the last two decades due to an increase in ST, altering the equilibrium abundances and the dynamics of the system. During warm years (ST over ca. 4°C), the increase in the cod abundance negatively affected haddock abundance while it did not during cold years. This change in interactions therefore changed the equilibrium population size with a higher population size during warm years. Our analyses show that long‐term climate change in the Arcto‐Boreal system can generate differences in the equilibrium conditions of species assemblages.     

Temperature and precipitation at migratory grounds influence demographic trends of an Arctic‐breeding bird

Anthropogenic climate disruption, including temperature and precipitation regime shifts, has been linked to animal population declines since the mid‐20th century. However, some species, such as Arctic‐breeding geese, have thrived during this period. An increased understanding of how climate disruption might link to demographic rates in thriving species is an important perspective in quantifying the impact of anthropogenic climate disruption on the global state of nature. The Greenland barnacle goose (Branta leucopsis) population has increased tenfold in abundance since the mid‐20th century. A concurrent weather regime shift towards warmer, wetter conditions occurred throughout its range in Greenland (breeding), Ireland and Scotland (wintering) and Iceland (spring and autumn staging). The aim of this study was to determine the relationship between weather and demographic rates of Greenland barnacle geese to discern the role of climate shifts in the population trend. We quantified the relationship between temperature and precipitation and Greenland barnacle goose survival and productivity over a 50 year period from 1968 to 2018. We detected significant positive relationships between warmer, wetter conditions on the Icelandic spring staging grounds and survival. We also detected contrasting relationships between warmer, wetter conditions during autumn staging and survival and productivity, with warm, dry conditions being the most favourable for productivity. Survival increased in the latter part of the study period, supporting the possibility that spring weather regime shifts contributed to the increasing population trend. This may be related to improved forage resources, as warming air temperatures have been shown to improve survival rates in several other Arctic and northern terrestrial herbivorous species through indirect bottom‐up effects on forage availability.     

Heterogeneity–diversity relationships in sessile organisms: a unified framework

The hypothesis that environmental heterogeneity promotes species richness by increasing opportunities for niche partitioning is a fundamental paradigm in ecology. However, recent studies suggest that heterogeneity–diversity relationships (HDR) are more complex than expected from this niche‐based perspective, and often show a decrease in richness at high levels of heterogeneity. These findings have motivated ecologists to propose new mechanisms that may explain such deviations. Here we provide an overview of currently recognised mechanisms affecting the shape of HDRs and present a conceptual model that integrates all previously proposed mechanisms within a unified framework. We also translate the proposed framework into an explicit community dynamic model and use the model as a tool for generating testable predictions concerning how landscape properties interact with species traits in determining the shape of HDRs. Our main finding is that, despite the enormous complexity of such interactions, the predicted HDRs are rather simple, ranging from positive to unimodal patterns in a highly consistent and predictable manner.     

Vanishing islands in the sky? A comparison of correlation- and mechanism-based forecasts of range dynamics for montane salamanders under climate change

Forecasting the effects of climate change on species and populations is a fundamental goal of conservation biology, especially for montane endemics which seemingly are under the greatest threat of extinction given their association with cool, high elevation habitats. Species distribution models (also known as niche models) predict where on the landscape there is suitable habitat for a species of interest. Correlative niche modeling, the most commonly employed approach to predict species' distributions, relies on correlations between species' localities and current environmental data. This type of model could spuriously forecast less future suitable habitat because species' current distributions may not adequately represent their thermal tolerance, and future climate conditions may not be analogous to current conditions. We compared the predicted distributions for three montane species of Plethodon salamanders in the southern Appalachian Mountains of North America using a correlative modeling approach and a mechanistic model. The mechanistic model incorporates species-specific physiology, morphology and behavior to predict an annual energy budget on the landscape. Both modeling approaches performed well at predicting the species' current distributions and predicted that all species could persist in habitats at higher elevation through 2085. The mechanistic model predicted more future suitable habitat than the correlative model. We attribute these differences to the mechanistic approach being able to model shifts in key range-limiting biological processes (changes in surface activity time and energy costs) that the correlative approach cannot. Choice of global circulation model (GCM) contributed significantly to distribution predictions, with a tenfold difference in future suitability based on GCM, indicating that GCM variability should be either directly included in models of species distributions or, indirectly, through the use of multi-model ensemble averages. Our results indicate that correlative models are over-predicting habitat loss for montane species, suggesting a critical need to incorporate mechanisms into forecasts of species' range dynamics.     

Complementary strengths of spatially-explicit and multi-species distribution models

Species distribution models (SDMs) project the outcome of community assembly processes – dispersal, the abiotic environment and biotic interactions – onto geographic space. Recent advances in SDMs account for these processes by simultaneously modeling the species that comprise a community in a multivariate statistical framework or by incorporating residual spatial autocorrelation in SDMs. However, the effects of combining both multivariate and spatially-explicit model structures on the ecological inferences and the predictive abilities of a model are largely unknown. We used data on eastern hemlock Tsuga canadensis and five additional co-occurring overstory tree species in 35 569 forest stands across Michigan, USA to evaluate how the choice of model structure, including spatial and non-spatial forms of univariate and multivariate models, affects ecological inference about the processes that shape community composition as well as model predictive ability. Incorporating residual spatial autocorrelation via spatial random effects did not improve out-of-sample prediction for the six tree species, although in-sample model fit was higher in the spatial models. Spatial models attributed less variation in occurrence probability to environmental covariates than the non-spatial models for all six tree species, and estimated higher (more positive) residual co-occurrence values for most species pairs. The non-spatial multivariate model was better suited for evaluating habitat suitability and hypotheses about the processes that shape community composition. Environmental correlations and residual correlations among species pairs were positively related, perhaps indicating that residual correlations were due to shared responses to unmeasured environmental covariates. This work highlights the importance of choosing a non-spatial model formulation to address research questions about the species–environment relationship or residual co-occurrence patterns, and a spatial model formulation when within-sample prediction accuracy is the main goal.     

Mechanistic insights into the role of large carnivores for ecosystem structure and functioning

Large carnivores can exert top–down effects in ecosystems, but the size of these effects are largely unknown. Empirical investigation on the importance of large carnivores for ecosystem structure and functioning presents a number of challenges due to the large spatio-temporal scale and the complexity of such dynamics. Here, we applied a mechanistic global ecosystem model to investigate the influence of large-carnivore removal from undisturbed ecosystems. First, we simulated large-carnivore removal on the global scale to inspect the geographic pattern of top–down control and to disentangle the functional role of large carnivores in top–down control in different environmental contexts. Second, we conducted four small-scale ecosystem simulation experiments to understand direct and indirect changes in food-web structure under different environmental conditions. We found that the removal of top–down control exerted by large carnivores (> 21 kg) can trigger large trophic cascades, leading to an overall decrease in autotroph biomass globally. Furthermore, the loss of large carnivores resulted in an increase of mesopredators. The magnitude of these changes was positively related to primary productivity (NPP), in line with the ‘exploitation ecosystem hypothesis’. In addition, we found that seasonality in NPP dampened the magnitude of change following the removal of large carnivores. Our results reinforce the idea that large carnivores play a fundamental role in shaping ecosystems, and further declines and extinctions can trigger substantial ecosystem responses. Our findings also support previous studies suggesting that natural ecosystem dynamics have been severely modified and are still changing as a result of the widespread decline and extinction of large carnivores.     

Winds at departure shape seasonal patterns of nocturnal bird migration over the North Sea

On their migratory journeys, terrestrial birds can come across large inhospitable areas with limited opportunities to rest and refuel. Flight over these areas poses a risk especially when wind conditions en route are adverse, in which case inhospitable areas can act as an ecological barrier for terrestrial migrants. Thus, within the east-Atlantic flyway, the North Sea can function as an ecological barrier. The main aim of this study was to shed light on seasonal patterns of bird migration in the southern North Sea and determine whether departure decisions on nights of intense migration were related to increased wind assistance. We measured migration characteristics with a radar that was located 18 km off the NW Dutch coast and used simulation models to infer potential departure locations of birds on nights with intense nocturnal bird migration. We calculated headings, track directions, airspeeds, groundspeeds on weak and intense migration nights in both seasons and compared speeds between seasons. Moreover, we tested if departure decisions on intense migration nights were associated with supportive winds. Our results reveal that on the intense migration nights in spring, the mean heading was towards E, and birds departed predominantly from the UK. On intense migration nights in autumn, the majority of birds departed from Denmark, Germany and north of the Netherlands with the mean heading towards SW. Prevailing winds from WSW at departure were supportive of a direct crossing of the North Sea in spring. However, in autumn winds were generally not supportive, which is why many birds exploited positive wind assistance which occurred on intense migration nights. This implies that the seasonal wind regimes over the North Sea alter its migratory dynamics which is reflected in headings, timing and intensity of migration.     

Temporal and spatial dynamics of grapevine anthracnose and its relationship to pathogen survival

Anthracnose, caused by Elsinoë ampelina, is an economically important grapevine disease in south and southeast Brazil. Control is achieved by lime sulphur application during grapevine dormancy and foliar fungicide sprays until the berries are half-grown. This study assessed the temporal and spatial progress of grapevine anthracnose under field conditions in order to describe the disease dynamics and its relationship to pathogen survival. The experiment was carried out in a vineyard of table grape Vitis labrusca in Brazil, during the 2014 and 2015 growing seasons. The incidence of vines with diseased leaves, stems and berries and the disease severity on leaves were recorded from bud break to veraison. Monomolecular, logistic and Gompertz models were fitted by non-linear regression to the incidence and severity data over time to characterize the temporal progress. Ordinary runs, dispersion index, modified Taylor's power law and spatial hierarchy analyses were used to characterize the spatial pattern of diseased plants. The monomolecular model showed the best fit for the incidence progress, with disease progress rates ranging from 0.051 to 0.136 per day. In both seasons, the incidence of diseased plants reached 100% 1 month after bud break. However, the incidence of diseased leaves per plant was around 60% and leaf disease severity was lower than 5% for both years. Ordinary runs and dispersion index analyses revealed that diseased grapevines were distributed randomly on the majority of the assessment dates. Meanwhile, a slight aggregation of diseased vines was observed in the modified Taylor's power law analysis. Our results suggested that the progress of anthracnose incidence and severity over time was governed mainly by the income of the primary inoculum, which survived in the vineyard. Therefore, anthracnose control measures in Brazilian vineyards should be focused on the reduction in inoculum within the vineyard.