Showing papers by "University of Guelph published in 2020"

TRY plant trait database : Enhanced coverage and open access

Abstract: Plant traits-the morphological, anatomical, physiological, biochemical and phenological characteristics of plants-determine how plants respond to environmental factors, affect other trophic levels, and influence ecosystem properties and their benefits and detriments to people. Plant trait data thus represent the basis for a vast area of research spanning from evolutionary biology, community and functional ecology, to biodiversity conservation, ecosystem and landscape management, restoration, biogeography and earth system modelling. Since its foundation in 2007, the TRY database of plant traits has grown continuously. It now provides unprecedented data coverage under an open access data policy and is the main plant trait database used by the research community worldwide. Increasingly, the TRY database also supports new frontiers of trait-based plant research, including the identification of data gaps and the subsequent mobilization or measurement of new data. To support this development, in this article we evaluate the extent of the trait data compiled in TRY and analyse emerging patterns of data coverage and representativeness. Best species coverage is achieved for categorical traits-almost complete coverage for 'plant growth form'. However, most traits relevant for ecology and vegetation modelling are characterized by continuous intraspecific variation and trait-environmental relationships. These traits have to be measured on individual plants in their respective environment. Despite unprecedented data coverage, we observe a humbling lack of completeness and representativeness of these continuous traits in many aspects. We, therefore, conclude that reducing data gaps and biases in the TRY database remains a key challenge and requires a coordinated approach to data mobilization and trait measurements. This can only be achieved in collaboration with other initiatives.

Multiple wheat genomes reveal global variation in modern breeding.

Abstract: Advances in genomics have expedited the improvement of several agriculturally important crops but similar efforts in wheat (Triticum spp.) have been more challenging. This is largely owing to the size and complexity of the wheat genome1, and the lack of genome-assembly data for multiple wheat lines2,3. Here we generated ten chromosome pseudomolecule and five scaffold assemblies of hexaploid wheat to explore the genomic diversity among wheat lines from global breeding programs. Comparative analysis revealed extensive structural rearrangements, introgressions from wild relatives and differences in gene content resulting from complex breeding histories aimed at improving adaptation to diverse environments, grain yield and quality, and resistance to stresses4,5. We provide examples outlining the utility of these genomes, including a detailed multi-genome-derived nucleotide-binding leucine-rich repeat protein repertoire involved in disease resistance and the characterization of Sm16, a gene associated with insect resistance. These genome assemblies will provide a basis for functional gene discovery and breeding to deliver the next generation of modern wheat cultivars.

Carbon release through abrupt permafrost thaw

Abstract: The permafrost zone is expected to be a substantial carbon source to the atmosphere, yet large-scale models currently only simulate gradual changes in seasonally thawed soil. Abrupt thaw will probably occur in <20% of the permafrost zone but could affect half of permafrost carbon through collapsing ground, rapid erosion and landslides. Here, we synthesize the best available information and develop inventory models to simulate abrupt thaw impacts on permafrost carbon balance. Emissions across 2.5 million km2 of abrupt thaw could provide a similar climate feedback as gradual thaw emissions from the entire 18 million km2 permafrost region under the warming projection of Representative Concentration Pathway 8.5. While models forecast that gradual thaw may lead to net ecosystem carbon uptake under projections of Representative Concentration Pathway 4.5, abrupt thaw emissions are likely to offset this potential carbon sink. Active hillslope erosional features will occupy 3% of abrupt thaw terrain by 2300 but emit one-third of abrupt thaw carbon losses. Thaw lakes and wetlands are methane hot spots but their carbon release is partially offset by slowly regrowing vegetation. After considering abrupt thaw stabilization, lake drainage and soil carbon uptake by vegetation regrowth, we conclude that models considering only gradual permafrost thaw are substantially underestimating carbon emissions from thawing permafrost. Analyses of inventory models under two climate change projection scenarios suggest that carbon emissions from abrupt thaw of permafrost through ground collapse, erosion and landslides could contribute significantly to the overall permafrost carbon balance.

VerifyNet: Secure and Verifiable Federated Learning

Abstract: As an emerging training model with neural networks, federated learning has received widespread attention due to its ability to update parameters without collecting users’ raw data. However, since adversaries can track and derive participants’ privacy from the shared gradients, federated learning is still exposed to various security and privacy threats. In this paper, we consider two major issues in the training process over deep neural networks (DNNs): 1) how to protect user’s privacy (i.e., local gradients) in the training process and 2) how to verify the integrity (or correctness) of the aggregated results returned from the server. To solve the above problems, several approaches focusing on secure or privacy-preserving federated learning have been proposed and applied in diverse scenarios. However, it is still an open problem enabling clients to verify whether the cloud server is operating correctly, while guaranteeing user’s privacy in the training process. In this paper, we propose VerifyNet, the first privacy-preserving and verifiable federated learning framework. In specific, we first propose a double-masking protocol to guarantee the confidentiality of users’ local gradients during the federated learning. Then, the cloud server is required to provide the Proof about the correctness of its aggregated results to each user. We claim that it is impossible that an adversary can deceive users by forging Proof , unless it can solve the NP-hard problem adopted in our model. In addition, VerifyNet is also supportive of users dropping out during the training process. The extensive experiments conducted on real-world data also demonstrate the practical performance of our proposed scheme.

Skeletal muscle energy metabolism during exercise.

Abstract: The continual supply of ATP to the fundamental cellular processes that underpin skeletal muscle contraction during exercise is essential for sports performance in events lasting seconds to several hours. Because the muscle stores of ATP are small, metabolic pathways must be activated to maintain the required rates of ATP resynthesis. These pathways include phosphocreatine and muscle glycogen breakdown, thus enabling substrate-level phosphorylation ('anaerobic') and oxidative phosphorylation by using reducing equivalents from carbohydrate and fat metabolism ('aerobic'). The relative contribution of these metabolic pathways is primarily determined by the intensity and duration of exercise. For most events at the Olympics, carbohydrate is the primary fuel for anaerobic and aerobic metabolism. Here, we provide an overview of exercise metabolism and the key regulatory mechanisms ensuring that ATP resynthesis is closely matched to the ATP demand of exercise. We also summarize various interventions that target muscle metabolism for ergogenic benefit in athletic events.

The Impact of COVID-19 on Health Behavior, Stress, Financial and Food Security among Middle to High Income Canadian Families with Young Children.

Abstract: The COVID-19 pandemic has disrupted many aspects of daily life. The purpose of this study was to identify how health behaviors, level of stress, financial and food security have been impacted by the pandemic among Canadian families with young children. Parents (mothers, n = 235 and fathers, n = 126) from 254 families participating in an ongoing study completed an online survey that included close and open-ended questions. Descriptive statistics were used to summarize the quantitative data and qualitative responses were analyzed using thematic analysis. More than half of our sample reported that their eating and meal routines have changed since COVID-19; most commonly reported changes were eating more snack foods and spending more time cooking. Screen time increased among 74% of mothers, 61% of fathers, and 87% of children and physical activity decreased among 59% of mothers, 52% of fathers, and 52% of children. Key factors influencing family stress include balancing work with childcare/homeschooling and financial instability. While some unhealthful behaviors appeared to have been exacerbated, other more healthful behaviors also emerged since COVID-19. Research is needed to determine the longer-term impact of the pandemic on behaviors and to identify effective strategies to support families in the post-COVID-19 context.

Mathematical modelling of COVID-19 transmission and mitigation strategies in the population of Ontario, Canada.

Abstract: Background: Physical-distancing interventions are being used in Canada to slow the spread of severe acute respiratory syndrome coronavirus 2, but it is not clear how effective they will be. We evaluated how different nonpharmaceutical interventions could be used to control the coronavirus disease 2019 (COVID-19) pandemic and reduce the burden on the health care system. Methods: We used an age-structured compartmental model of COVID-19 transmission in the population of Ontario, Canada. We compared a base case with limited testing, isolation and quarantine to scenarios with the following: enhanced case finding, restrictive physical-distancing measures, or a combination of enhanced case finding and less restrictive physical distancing. Interventions were either implemented for fixed durations or dynamically cycled on and off, based on projected occupancy of intensive care unit (ICU) beds. We present medians and credible intervals from 100 replicates per scenario using a 2-year time horizon. Results: We estimated that 56% (95% credible interval 42%–63%) of the Ontario population would be infected over the course of the epidemic in the base case. At the epidemic peak, we projected 107 000 (95% credible interval 60 760–149 000) cases in hospital (non-ICU) and 55 500 (95% credible interval 32 700–75 200) cases in ICU. For fixed-duration scenarios, all interventions were projected to delay and reduce the height of the epidemic peak relative to the base case, with restrictive physical distancing estimated to have the greatest effect. Longer duration interventions were more effective. Dynamic interventions were projected to reduce the proportion of the population infected at the end of the 2-year period and could reduce the median number of cases in ICU below current estimates of Ontario’s ICU capacity. Interpretation: Without substantial physical distancing or a combination of moderate physical distancing with enhanced case finding, we project that ICU resources would be overwhelmed. Dynamic physical distancing could maintain health-system capacity and also allow periodic psychological andeconomic respite for populations.

Co-occurrence is not evidence of ecological interactions

Abstract: There is a rich amount of information in co-occurrence (presence-absence) data that could be used to understand community assembly. This proposition first envisioned by Forbes (1907) and then Diamond (1975) prompted the development of numerous modelling approaches (e.g. null model analysis, co-occurrence networks and, more recently, joint species distribution models). Both theory and experimental evidence support the idea that ecological interactions may affect co-occurrence, but it remains unclear to what extent the signal of interaction can be captured in observational data. It is now time to step back from the statistical developments and critically assess whether co-occurrence data are really a proxy for ecological interactions. In this paper, we present a series of arguments based on probability, sampling, food web and coexistence theories supporting that significant spatial associations between species (or lack thereof) is a poor proxy for ecological interactions. We discuss appropriate interpretations of co-occurrence, along with potential avenues to extract as much information as possible from such data.

Large stocks of peatland carbon and nitrogen are vulnerable to permafrost thaw.

Abstract: Northern peatlands have accumulated large stocks of organic carbon (C) and nitrogen (N), but their spatial distribution and vulnerability to climate warming remain uncertain. Here, we used machine-learning techniques with extensive peat core data (n > 7,000) to create observation-based maps of northern peatland C and N stocks, and to assess their response to warming and permafrost thaw. We estimate that northern peatlands cover 3.7 ± 0.5 million km2 and store 415 ± 150 Pg C and 10 ± 7 Pg N. Nearly half of the peatland area and peat C stocks are permafrost affected. Using modeled global warming stabilization scenarios (from 1.5 to 6 °C warming), we project that the current sink of atmospheric C (0.10 ± 0.02 Pg C⋅y-1) in northern peatlands will shift to a C source as 0.8 to 1.9 million km2 of permafrost-affected peatlands thaw. The projected thaw would cause peatland greenhouse gas emissions equal to ∼1% of anthropogenic radiative forcing in this century. The main forcing is from methane emissions (0.7 to 3 Pg cumulative CH4-C) with smaller carbon dioxide forcing (1 to 2 Pg CO2-C) and minor nitrous oxide losses. We project that initial CO2-C losses reverse after ∼200 y, as warming strengthens peatland C-sinks. We project substantial, but highly uncertain, additional losses of peat into fluvial systems of 10 to 30 Pg C and 0.4 to 0.9 Pg N. The combined gaseous and fluvial peatland C loss estimated here adds 30 to 50% onto previous estimates of permafrost-thaw C losses, with southern permafrost regions being the most vulnerable.

Deep Learning for Intelligent Transportation Systems: A Survey of Emerging Trends

Abstract: Transportation systems operate in a domain that is anything but simple. Many exhibit both spatial and temporal characteristics, at varying scales, under varying conditions brought on by external sources such as social events, holidays, and the weather. Yet, modeling the interplay of factors, devising generalized representations, and subsequently using them to solve a particular problem can be a challenging task. These situations represent only a fraction of the difficulties faced by modern intelligent transportation systems (ITS). In this paper, we present a survey that highlights the role modeling techniques within the realm of deep learning have played within ITS. We focus on how practitioners have formulated problems to address these various challenges, and outline both architectural and problem-specific considerations used to develop solutions. We hope this survey can help to serve as a bridge between the machine learning and transportation communities, shedding light on new domains and considerations in the future.

Visions of travel and tourism after the global COVID-19 transformation of 2020

Abstract: The future belongs to those who believe in the beauty of their dreams.― Eleanor RooseveltThis special issue is a reflection by tourism scholars on the initial impacts of the COVID-19 pandemic on th...

VIRIDIC—A Novel Tool to Calculate the Intergenomic Similarities of Prokaryote-Infecting Viruses

Abstract: Nucleotide-based intergenomic similarities are useful to understand how viruses are related with each other and to classify them. Here we have developed VIRIDIC, which implements the traditional algorithm used by the International Committee on Taxonomy of Viruses (ICTV), Bacterial and Archaeal Viruses Subcommittee, to calculate virus intergenomic similarities. When compared with other software, VIRIDIC gave the best agreement with the traditional algorithm, which is based on the percent identity between two genomes determined by BLASTN. Furthermore, VIRIDIC proved best at estimating the relatedness between more distantly-related phages, relatedness that other tools can significantly overestimate. In addition to the intergenomic similarities, VIRIDIC also calculates three indicators of the alignment ability to capture the relatedness between viruses: the aligned fractions for each genome in a pair and the length ratio between the two genomes. The main output of VIRIDIC is a heatmap integrating the intergenomic similarity values with information regarding the genome lengths and the aligned genome fraction. Additionally, VIRIDIC can group viruses into clusters, based on user-defined intergenomic similarity thresholds. The sensitivity of VIRIDIC is given by the BLASTN. Thus, it is able to capture relationships between viruses having in common even short genomic regions, with as low as 65% similarity. Below this similarity level, protein-based analyses should be used, as they are the best suited to capture distant relationships. VIRIDIC is available at viridic.icbm.de, both as a web-service and a stand-alone tool. It allows fast analysis of large phage genome datasets, especially in the stand-alone version, which can be run on the user’s own servers and can be integrated in bioinformatics pipelines. VIRIDIC was developed having viruses of Bacteria and Archaea in mind; however, it could potentially be used for eukaryotic viruses as well, as long as they are monopartite.

Changes to virus taxonomy and the Statutes ratified by the International Committee on Taxonomy of Viruses (2020).

Abstract: This article reports the changes to virus classification and taxonomy approved and ratified by the International Committee on Taxonomy of Viruses (ICTV) in March 2020 The entire ICTV was invited to vote on 206 taxonomic proposals approved by the ICTV Executive Committee at its meeting in July 2019, as well as on the proposed revision of the ICTV Statutes All proposals and the revision of the Statutes were approved by an absolute majority of the ICTV voting membership Of note, ICTV has approved a proposal that extends the previously established realm Riboviria to encompass nearly all RNA viruses and reverse-transcribing viruses, and approved three separate proposals to establish three realms for viruses with DNA genomes

An Energy-Efficient SDN Controller Architecture for IoT Networks With Blockchain-Based Security

Abstract: Internet of Things (IoT) is a disruptive technology in many aspects of our society, ranging from communications to financial transactions to national security (e.g., Internet of Battlefield / Military Things), and so on. There are long-standing challenges in IoT, such as security, comparability, energy consumption, and heterogeneity of devices. Security and energy aspects play important roles in data transmission across IoT and edge networks, due to limited energy and computing (e.g., processing and storage) resources of networked devices. Whether malicious or accidental, interference with data in an IoT network potentially has real-world consequences. In this article, we explore the potential of integrating blockchain and software-defined networking (SDN) in mitigating some of the challenges. Specifically, we propose a secure and energy-efficient blockchain-enabled architecture of SDN controllers for IoT networks using a cluster structure with a new routing protocol. The architecture uses public and private blockchains for Peer to Peer (P2P) communication between IoT devices and SDN controllers, which eliminates Proof-of-Work (POW), as well as using an efficient authentication method with the distributed trust, making the blockchain suitable for resource-constrained IoT devices. The experimental results indicate that the routing protocol based on the cluster structure has higher throughput, lower delay, and lower energy consumption than EESCFD, SMSN, AODV, AOMDV, and DSDV routing protocols. In other words, our proposed architecture is demonstrated to outperform classic blockchain.

Long-Term Evidence Shows that Crop-Rotation Diversification Increases Agricultural Resilience to Adverse Growing Conditions in North America

Abstract: Summary A grand challenge facing humanity is how to produce food for a growing population in the face of a changing climate and environmental degradation. Although empirical evidence remains sparse, management strategies that increase environmental sustainability, such as increasing agroecosystem diversity through crop rotations, may also increase resilience to weather extremes without sacrificing yields. We used multilevel regression analyses of long-term crop yield datasets across a continental precipitation gradient to assess how temporal crop diversification affects maize yields in intensively managed grain systems. More diverse rotations increased maize yields over time and across all growing conditions (28.1% on average), including in favorable conditions (22.6%). Notably, more diverse rotations also showed positive effects on yield under unfavorable conditions, whereby yield losses were reduced by 14.0%–89.9% in drought years. Systems approaches to environmental sustainability and yield resilience, such as crop-rotation diversification, are a central component of risk-reduction strategies and should inform the enablement of policies.

Technology readiness and overcoming barriers to sustainably implement nanotechnology-enabled plant agriculture

Abstract: Nanotechnology offers potential solutions for sustainable agriculture, including increasing nutrient utilization efficiency, improving the efficacy of pest management, mitigating the impacts of climate change, and reducing adverse environmental impacts of agricultural food production. Many promising nanotechnologies have been proposed and evaluated at different scales, but several barriers to implementation must be addressed for technology to be adopted, including efficient delivery at field scale, regulatory and safety concerns, and consumer acceptance. Here we explore these barriers, and rank technology readiness and potential impacts of a wide range of agricultural applications of nanotechnology. We propose pathways to overcome these barriers and develop effective, safe and acceptable nanotechnologies for agriculture. Nanotechnology holds great application potential in plant agriculture. This Review Article identifies the technological readiness, addresses the primary barriers to adopting nano-enabled technologies and proposes a roadmap to advance nanotechnology-enabled agriculture.

The New Scope of Virus Taxonomy: Partitioning the Virosphere Into 15 Hierarchical Ranks

Abstract: Virus taxonomy emerged as a discipline in the middle of the twentieth century. Traditionally, classification by virus taxonomists has been focussed on the grouping of relatively closely related viruses. However, during the past few years, the International Committee on Taxonomy of Viruses (ICTV) has recognized that the taxonomy it develops can be usefully extended to include the basal evolutionary relationships among distantly related viruses. Consequently, the ICTV has changed its Code to allow a 15-rank classification hierarchy that closely aligns with the Linnaean taxonomic system and may accommodate the entire spectrum of genetic divergence in the virosphere. The current taxonomies of three human pathogens, Ebola virus, severe acute respiratory syndrome coronavirus and herpes simplex virus 1 are used to illustrate the impact of the expanded rank structure. This new rank hierarchy of virus taxonomy will stimulate further research on virus origins and evolution, and vice versa, and could promote crosstalk with the taxonomies of cellular organisms.

Double layer charging driven carbon dioxide adsorption limits the rate of electrochemical carbon dioxide reduction on Gold.

Abstract: Electrochemical CO[Formula: see text] reduction is a potential route to the sustainable production of valuable fuels and chemicals. Here, we perform CO[Formula: see text] reduction experiments on Gold at neutral to acidic pH values to elucidate the long-standing controversy surrounding the rate-limiting step. We find the CO production rate to be invariant with pH on a Standard Hydrogen Electrode scale and conclude that it is limited by the CO[Formula: see text] adsorption step. We present a new multi-scale modeling scheme that integrates ab initio reaction kinetics with mass transport simulations, explicitly considering the charged electric double layer. The model reproduces the experimental CO polarization curve and reveals the rate-limiting step to be *COOH to *CO at low overpotentials, CO[Formula: see text] adsorption at intermediate ones, and CO[Formula: see text] mass transport at high overpotentials. Finally, we show the Tafel slope to arise from the electrostatic interaction between the dipole of *CO[Formula: see text] and the interfacial field. This work highlights the importance of surface charging for electrochemical kinetics and mass transport.

Food security and Canada's agricultural system challenged by COVID-19

Abstract: The effect of COVID-19 on Canadian food security is examined from two different perspectives. COVID-19 creates a unique “income shock” that is expected to increase the prevalence of household food insecurity. This food insecurity can be measured by utilizing the Canadian Community Health Survey (CCHS). More fundamentally, COVID-19 heightens household concern about the capacity of the Canadian food system to ensure food availability. Despite surges in demand and supply chain disruptions, we currently do not observe broad, rapid appreciation in food prices. This suggests that there is an adequate supply of food for the near term. There is less certainty over intermediate and longer time periods because so many factors are in flux, particularly the rate of increases in sicknesses and deaths across the country and globally. Data on these health factors and elements of the food supply chain are needed to predict beyond a short time frame. In this regard, we discuss three ongoing considerations—ease of capital flows, international exchange, and maintaining transportation—that will help ensure food availability in the longer run.

Resource Allocation Based on Deep Reinforcement Learning in IoT Edge Computing

Abstract: By leveraging mobile edge computing (MEC), a huge amount of data generated by Internet of Things (IoT) devices can be processed and analyzed at the network edge. However, the MEC system usually only has the limited virtual resources, which are shared and competed by IoT edge applications. Thus, we propose a resource allocation policy for the IoT edge computing system to improve the efficiency of resource utilization. The objective of the proposed policy is to minimize the long-term weighted sum of average completion time of jobs and average number of requested resources. The resource allocation problem in the MEC system is formulated as a Markov decision process (MDP). A deep reinforcement learning approach is applied to solve the problem. We also propose an improved deep Q-network (DQN) algorithm to learn the policy, where multiple replay memories are applied to separately store the experiences with small mutual influence. Simulation results show that the proposed algorithm has a better convergence performance than the original DQN algorithm, and the corresponding policy outperforms the other reference policies by lower completion time with fewer requested resources.

Decentralized Authentication of Distributed Patients in Hospital Networks Using Blockchain

Abstract: In any interconnected healthcare system (e.g., those that are part of a smart city), interactions between patients, medical doctors, nurses and other healthcare practitioners need to be secure and efficient. For example, all members must be authenticated and securely interconnected to minimize security and privacy breaches from within a given network. However, introducing security and privacy-preserving solutions can also incur delays in processing and other related services, potentially threatening patients lives in critical situations. A considerable number of authentication and security systems presented in the literature are centralized, and frequently need to rely on some secure and trusted third-party entity to facilitate secure communications. This, in turn, increases the time required for authentication and decreases throughput due to known overhead, for patients and inter-hospital communications. In this paper, we propose a novel decentralized authentication of patients in a distributed hospital network, by leveraging blockchain. Our notion of a healthcare setting includes patients and allied health professionals (medical doctors, nurses, technicians, etc), and the health information of patients. Findings from our in-depth simulations demonstrate the potential utility of the proposed architecture. For example, it is shown that the proposed architecture's decentralized authentication among a distributed affiliated hospital network does not require re-authentication. This improvement will have a considerable impact on increasing throughput, reducing overhead, improving response time, and decreasing energy consumption in the network. We also provide a comparative analysis of our model in relation to a base model of the network without blockchain to show the overall effectiveness of our proposed solution.