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Global changes play today an important role in altering patterns of human, animal, and plant host-pathogen interactions and invasive pest species. With rapid development in sequencing technology, there is also an increase in pathogen and pest studies adopting a macroscale, biogeographical perspective, and we present the most recent elements on existing ecological and biogeographical trends. We also compare the results on the one hand on emerging infectious diseases of animals and humans, and on the other hand on plant pathogens and pests. International exchanges of people, animals, and plant products currently contribute to their geographical extension but with notable differences across disease and pest systems, and regions. This review highlights that the subject of pathogens and plant pests, traditionally rooted in agronomic approaches, lacks work on macroecology and biogeography. We discuss the research orientations to better anticipate their ecological and economic impacts in order to better achieve environmental sustainability.
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BACKGROUND: Zoonotic diseases account for more than 70% of emerging infectious diseases (EIDs). Due to their increasing incidence and impact on global health and the economy, the emergence of zoonoses is a major public health challenge. Here, we use a biogeographic approach to predict future hotspots and determine the factors influencing disease emergence. We have focused on the following three viral disease groups of concern: Filoviridae, Coronaviridae, and Henipaviruses. METHODS: We modelled presence-absence data in spatially explicit binomial and zero-inflation binomial logistic regressions with and without autoregression. Presence data were extracted from published studies for the three EID groups. Various environmental and demographical rasters were used to explain the distribution of the EIDs. True Skill Statistic and deviance parameters were used to compare the accuracy of the different models. RESULTS: For each group of viruses, we were able to identify and map areas at high risk of disease emergence based on the spatial distribution of the disease reservoirs and hosts of the three viral groups. Common influencing factors of disease emergence were climatic covariates (minimum temperature and rainfall) and human-induced land modifications. CONCLUSIONS: Using topographical, climatic, and previous disease outbreak reports, we can identify and predict future high-risk areas for disease emergence and their specific underlying human and environmental drivers. We suggest that such a predictive approach to EIDs should be carefully considered in the development of active surveillance systems for pathogen emergence and epidemics at local and global scales.
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Based on extensive new material, 2088 valves resulting from search sampling of ~500 kg of sediment, the Pliocene chiton biodiversity of the Mondego Basin (Portugal) is reassessed. Twelve species were identified, assigned to seven genera. Eight species are new for the Pliocene of Portugal, as well as two of the genera: Hanleya, Acanthochitona. Two taxa are described as new: Ischnochiton loureiroi n. sp. and Lepidochitona rochae n. sp. Until now, the polyplacophoran European Neogene record was too poorly known to be of help in generating a clear picture of the Miocene to present-day biogeography of the group. This new wealth of data from western Iberia, in conjunction with recent data from the Loire Basin Upper Miocene assemblages (France), allows clarification the Late Miocene to Recent eastern Atlantic and Mediterranean biogeography of the Polyplacophora. The northern range of warm-water northeast Atlantic and Mediterranean Polyplacophora experienced a sharp contraction since, at least, Late Miocene to Early Pliocene times. Warm-water chiton species represented in the Upper Miocene of the Loire Basin of NE France (European-West African Province) and the Pliocene of the Mondego Basin of central-west Portugal (Pliocene French-Iberian Province) are today confined to the southern Mediterranean-Moroccan Molluscan Province.UUID: http://zoobank.org/a2a550c0-caed-449a-aa45-9492c45f882e
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Managing wildlife populations in the face of global change requires regular data on the abundance and distribution of wild animals, but acquiring these over appropriate spatial scales in a sustainable way has proven challenging. Here we present the data from Snapshot USA 2020, a second annual national mammal survey of the USA. This project involved 152 scientists setting camera traps in a standardized protocol at 1485 locations across 103 arrays in 43 states for a total of 52,710 trap-nights of survey effort. Most (58) of these arrays were also sampled during the same months (September and October) in 2019, providing a direct comparison of animal populations in 2 years that includes data from both during and before the COVID-19 pandemic. All data were managed by the eMammal system, with all species identifications checked by at least two reviewers. In total, we recorded 117,415 detections of 78 species of wild mammals, 9236 detections of at least 43 species of birds, 15,851 detections of six domestic animals and 23,825 detections of humans or their vehicles. Spatial differences across arrays explained more variation in the relative abundance than temporal variation across years for all 38 species modeled, although there are examples of significant site-level differences among years for many species. Temporal results show how species allocate their time and can be used to study species interactions, including between humans and wildlife. These data provide a snapshot of the mammal community of the USA for 2020 and will be useful for exploring the drivers of spatial and temporal changes in relative abundance and distribution, and the impacts of species interactions on daily activity patterns. There are no copyright restrictions, and please cite this paper when using these data, or a subset of these data, for publication.
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COVID-19 , Animals , Animals, Wild , Birds , COVID-19/epidemiology , Humans , Mammals , Pandemics , United StatesABSTRACT
The research indicates the importance of learning about our natural environment from an early age to train scientifically literate and environmentally responsible citizens. The purpose of this study was to investigate the perceptions of a group of 120 primary school children regarding the biodiversity of their nearby environment (Andalusia, Spain) based on their drawings. For this, we used a qualitative methodology that focused on the analysis of the content. We analyzed the content of each drawing, complemented with written questions and an interview. The different environmental elements (natural, geological, and anthropic) of the children’s drawings were identified and analyzed. The results show how children perceive the biodiversity (plants and animals) of their nearby environment and their low knowledge of the concept of the environment as a system. Likewise, this study allowed for the definition of a series of emerging categories as an instrument of analysis that will serve as the basis for the design of appropriate strategies to improve initial teacher training in regard to science education.
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The article presents the prospects for the development of natural and recreational activities in the field of medical and health tourism within the ribbon pine forest, located in the southern outskirts of the West Siberian dry steppe lowland. The purpose of the study is to assess the biological and geographical potential of the forest landscape for the development of recreational areas. This is especially true for the Northern region of Kazakhstan, since industry is developed on the territory, it is characterized by technogenesis. Research methods include field reconnaissance, cartographic modeling using GIS technologies, and laboratory experiments to determine the phytoncide activity of the biological material of trees - Scots pine - collected at different intervals of the day. The results of the study are statistical data on laboratory research. For convenience, they are presented in the form of cartographic material, built based on spatial data of the territory. The paper considers and addresses the ecosystem's natural potential and the recreational infrastructure of the studied object.
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With the rapid growth in e-commerce and virtual markets due to the COVID-19 pandemic, customer satisfaction due to logistics activities has become more imperative than ever. The Traveling Repairman Problem (TRP), which is also known as the cumulative traveling salesman problem, the deliveryman problem and the minimum latency problem, is a special variant of the Traveling Salesman Problem (TSP). All these problems differ in their objective function criteria. In TSP, the total cost (distance or time) of the salesman is minimized. In the case of the TRP, the total latency (waiting time or delay time) of all customers is minimized. Ultimately, with TRP, customer satisfaction is maximized through minimizing the total latency. This paper focuses on a generalized version of TRP with multi depots and time windows, namely Multi Depot Traveling Repairman Problem with Time Windows (MDTRPTW). A group of homogeneous repairmen initiate and finish their visit tours at depots. Each customer must be visited exactly by one repairman within their provided earliest end latest times, defined as their time windows. To the best of our knowledge, there is no study in the literature on this problem. In this paper, in addition to posing the problem, we propose a mixed integer programming model for MDTRPTW with O(n2) integer decision variables and O(n2) constraints. MDTRPTW is an NP-hard challenging combinatorial optimization problem. In order to find near optimal solutions within a reasonable computational time for realistic sized dimensions, we also propose a biogeography-based optimization algorithm as a metaheuristic approach. The performance of our formulation and metaheuristic are analyzed by solving instances with time windows from other problems in the literature that are adapted for MDTRPTW. We observe that our proposed formulation is able to solve small and moderate size problems in reasonable times. The efficacy of the metaheuristic solution approach is evaluated in terms of solution quality as well as computation time. The developed metaheuristic approach is able to solve problems with 300 customers within seconds. Moreover, when contrasted with the exact solution methodology, the proposed metaheuristic algorithm represents a high performance to find good quality solutions within acceptable CPU times for large-size problems. The main contribution of this paper is to define and to present a mathematical model for the multi depot Traveling Repairman Problem with time windows. In addition, to propose a metaheuristic approach for this problem. © IEOM Society International.
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Recent pandemic outbreak of the corona-virus disease 2019 (COVID-19) has raised widespread concerns about the importance of the bioaerosols. They are atmospheric aerosol particles of biological origins, mainly including bacteria, fungi, viruses, pollen, and cell debris. Bioaerosols can exert a substantial impact on ecosystems, climate change, air quality, and public health. Here, we review several relevant topics on bioaerosols, including sampling and detection techniques, characterization, effects on health and air quality, and control methods. However, very few studies have focused on the source apportionment and transport of bioaerosols. The knowledge of the sources and transport pathways of bioaerosols is essential for a comprehensive understanding of the role microorganisms play in the atmosphere and control the spread of epidemic diseases associated with them. Therefore, this review comprehensively summarizes the up to date progress on the source characteristics, source identification, and diffusion and transport process of bioaerosols. We intercompare three types of diffusion and transport models, with a special emphasis on a widely used mathematical model. This review also highlights the main factors affecting the source emission and transport process, such as biogeographic regions, land-use types, and environmental factors. Finally, this review outlines future perspectives on bioaerosols.
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Mário C. C. de Pinna, the winner of Robert H. Gibbs, Jr. Memorial Award for Excellence in Systematic Ichthyology, is featured. De Pinna's research interests run the gamut of systematic ichthyology, from phylogenetic relationships, taxonomy, and biogeography, to theoretical considerations of phylogeny reconstruction, species concepts, the history of comparative biology and its place within the sciences. And his contributions across each of these arenas have been profound and impactful. Indeed, while still a graduate student in the joint City University of New York and American Museum of Natural History program, they published a paper on "Concepts and tests of homology in the cladistic paradigm," a contribution that has been cited over 1,093 times since its publication and almost two decades later has been cited 90 times in the past five years alone. His professional career began while an undergraduate with a publication on a cave population of Trichomyctems, and in the ensuing years has published over 90 peer-reviewed papers. Most of these have focused on the broad sweep of catfish systematics from the resolution of basal relationships across the order to numerous analyses of the composition and inter- and intrarelationships of individual catfish families and genera.
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Recent pandemic outbreak of the corona-virus disease 2019 (COVID-19) has raised widespread concerns about the importance of the bioaerosols. Bioaerosols are an important part of atmospheric aerosols. Due to its physico-chemical properties and inherent biological characteristics, bioaerosols play a key role in global ecosystem, climate change, air quality and public health. A large number of studies have been focused on the sampling and detection techniques of bioaerosols, disinfection protection, effects on health and environment. However, little is known about the source characteristics of bioaerosols. Therefore, the research status of airborne microbes source was focused, and the progress on the emission characteristics of natural and anthropogenic source of bioaerosols in the past 20 years was comprehensively summarized. Combined with our study, the main factors affecting the source emission and transport process were also highlighted, such as biogeographic regions, land-use types, and environmental factors. Subsequently, the various source analysis methods of current bioaerosols were discussed. Finally, the future work prospects of bioaerosols source characteristics were prospected. It is expected to provide reference for in-depth understanding of the source and transmission change mechanism of bioaerosols, and to better evaluate the level of atmospheric microbial pollution and monitor the aerosol transmission of pathogens. © 2021, Editorial Department of Journal of Earth Sciences and Environment. All right reserved.
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AIM: By October 6, 2020, Coronavirus disease 2019 (COVID-19) was diagnosed worldwide, reaching 3,355,7427 people and 1,037,862 deaths. Detection of COVID-19 and pneumonia by the chest X-ray images is of great significance to control the development of the epidemic situation. The current COVID-19 and pneumonia detection system may suffer from two shortcomings: the selection of hyperparameters in the models is not appropriate, and the generalization ability of the model is poor. METHOD: To solve the above problems, our team proposed an improved intelligent global optimization algorithm, which is based on the biogeography-based optimization to automatically optimize the hyperparameters value of the models according to different detection objectives. In the optimization progress, after selecting the immigration of suitable index vector and the emigration of suitable index vector, we proposed adding a comparison operation to compare the value of them. According to the different numerical relationships between them, the corresponding operations are performed to improve the migration operation of biogeography-based optimization. The improved algorithm (momentum factor biogeography-based optimization) can better perform the automatic optimization operation. In addition, our team also proposed two frameworks: biogeography convolutional neural network and momentum factor biogeography convolutional neural network. And two methods for detection COVID-19 based on the proposed frameworks. RESULTS: Our method used three convolutional neural networks (LeNet-5, VGG-16, and ResNet-18) as the basic classification models for chest X-ray images detection of COVID-19, Normal, and Pneumonia. The accuracy of LeNet-5, VGG-16, and ResNet-18 is improved by 1.56%, 1.48%, and 0.73% after using biogeography-based optimization to optimize the hyperparameters of the models. The accuracy of LeNet-5, VGG-16, and ResNet-18 is improved by 2.87%, 6.31%, and 1.46% after using the momentum factor biogeography-based optimization to optimize the hyperparameters of the models. CONCLUSION: Under the same experimental conditions, the performance of the momentum factor biogeography-based optimization is superior to the biogeography-based optimization in optimizing the hyperparameters of the convolutional neural networks. Experimental results show that the momentum factor biogeography-based optimization can improve the detection performance of the state-of-the-art approaches in terms of overall accuracy. In future research, we will continue to use and improve other global optimization algorithms to enhance the application ability of deep learning in medical pathological image detection.
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Viruses of the subfamily Orthocoronavirinae can cause mild to severe disease in people, including COVID-19, MERS and SARS. Their most common natural hosts are bat and bird species, which are mostly split across four virus genera. Molecular clock analyses of orthocoronaviruses suggested the most recent common ancestor of these viruses might have emerged either around 10,000 years ago or, using models accounting for selection, many millions of years. Here, we reassess the evolutionary history of these viruses. We present time-aware phylogenetic analyses of a RNA-dependent RNA polymerase locus from 123 orthocoronaviruses isolated from birds and bats, including those in New Zealand, which were geographically isolated from other bats around 35 million years ago. We used this age, as well as the age of the avian-mammals split, to calibrate the molecular clocks, under the assumption that these ages are applicable to the analyzed viruses. We found that the time to the most recent ancestor common for all orthocoronaviruses is likely 150 or more million years, supporting clock analyses that account for selection.