Showing papers by "Concordia University published in 2020"

Worldwide contamination of food-crops with mycotoxins: Validity of the widely cited 'FAO estimate' of 25.

Abstract: Prior to 1985 the Food and Agriculture Organization (FAO) estimated global food crop contamination with mycotoxins to be 25%. The origin of this statement is largely unknown. To assess the rationale for it, the relevant literature was reviewed and data of around 500,000 analyses from the European Food Safety Authority and large global survey for aflatoxins, fumonisins, deoxynivalenol, T-2 and HT-2 toxins, zearalenone and ochratoxin A in cereals and nuts were examined. Using different thresholds, i.e. limit of detection, the lower and upper regulatory limits of European Union (EU) legislation and Codex Alimentarius standards, the mycotoxin occurrence was estimated. Impact of different aspects on uncertainty of the occurrence estimates presented in literature and related to our results are critically discussed. Current mycotoxin occurrence above the EU and Codex limits appears to confirm the FAO 25% estimate, while this figure greatly underestimates the occurrence above the detectable levels (up to 60-80%). The high occurrence is likely explained by a combination of the improved sensitivity of analytical methods and impact of climate change. It is of immense importance that the detectable levels are not overlooked as through diets, humans are exposed to mycotoxin mixtures which can induce combined adverse health effects.

Variability in the analysis of a single neuroimaging dataset by many teams

Abstract: Data analysis workflows in many scientific domains have become increasingly complex and flexible. Here we assess the effect of this flexibility on the results of functional magnetic resonance imaging by asking 70 independent teams to analyse the same dataset, testing the same 9 ex-ante hypotheses1. The flexibility of analytical approaches is exemplified by the fact that no two teams chose identical workflows to analyse the data. This flexibility resulted in sizeable variation in the results of hypothesis tests, even for teams whose statistical maps were highly correlated at intermediate stages of the analysis pipeline. Variation in reported results was related to several aspects of analysis methodology. Notably, a meta-analytical approach that aggregated information across teams yielded a significant consensus in activated regions. Furthermore, prediction markets of researchers in the field revealed an overestimation of the likelihood of significant findings, even by researchers with direct knowledge of the dataset2-5. Our findings show that analytical flexibility can have substantial effects on scientific conclusions, and identify factors that may be related to variability in the analysis of functional magnetic resonance imaging. The results emphasize the importance of validating and sharing complex analysis workflows, and demonstrate the need for performing and reporting multiple analyses of the same data. Potential approaches that could be used to mitigate issues related to analytical variability are discussed.

Obesity in adults: a clinical practice guideline

Abstract: KEY POINTS Obesity is a complex chronic disease in which abnormal or excess body fat (adiposity) impairs health, increases the risk of long-term medical complications and reduces lifespan.[1][1] Epidemiologic studies define obesity using the body mass index (BMI; weight/height2), which can stratify

UAV Trajectory Planning for Data Collection from Time-Constrained IoT Devices

Abstract: The global evolution of wireless technologies and intelligent sensing devices are transforming the realization of smart cities. Among the myriad of use cases, there is a need to support applications whereby low-resource IoT devices need to upload their sensor data to a remote control centre by target hard deadlines; otherwise, the data becomes outdated and loses its value, for example, in emergency or industrial control scenarios. In addition, the IoT devices can be either located in remote areas with limited wireless coverage or in dense areas with relatively low quality of service. This motivates the utilization of UAVs to offload traffic from existing wireless networks by collecting data from time-constrained IoT devices with performance guarantees. To this end, we jointly optimize the trajectory of a UAV and the radio resource allocation to maximize the number of served IoT devices, where each device has its own target data upload deadline. The formulated optimization problem is shown to be mixed integer non-convex and generally NP-hard. To solve it, we first propose the high-complexity branch, reduce and bound (BRB) algorithm to find the global optimal solution for relatively small scale scenarios. Then, we develop an effective sub-optimal algorithm based on successive convex approximation in order to obtain results for larger networks. Next, we propose an extension algorithm to further minimize the UAV’s flight distance for cases where the initial and final UAV locations are known a priori. We demonstrate the favourable characteristics of the algorithms via extensive simulations and analysis as a function of various system parameters, with benchmarking against two greedy algorithms based on distance and deadline metrics.

Nonreciprocity in acoustic and elastic materials

Abstract: The law of reciprocity in acoustics and elastodynamics codifies a relation of symmetry between action and reaction in fluids and solids. In its simplest form, it states that the frequency-response functions between any two material points remain the same after swapping source and receiver, regardless of the presence of inhomogeneities and losses. As such, reciprocity has enabled numerous applications that make use of acoustic and elastic wave propagation. A recent change in paradigm has prompted us to see reciprocity under a new light: as an obstruction to the realization of wave-bearing media in which the source and receiver are not interchangeable. Such materials may enable the creation of devices such as acoustic one-way mirrors, isolators and topological insulators. Here, we review how reciprocity breaks down in materials with momentum bias, structured space-dependent and time-dependent constitutive properties, and constitutive nonlinearity, and report on recent advances in the modelling and fabrication of these materials, as well as on experiments demonstrating nonreciprocal acoustic and elastic wave propagation therein. The success of these efforts holds promise to enable robust, unidirectional acoustic and elastic wave-steering capabilities that exceed what is currently possible in conventional materials, metamaterials or phononic crystals. Nonreciprocal acoustic and elastic wave propagation may enable the creation of devices such as acoustic one-way mirrors, isolators and topological insulators. This Review presents advances in the creation of materials that break reciprocity and realize robust, unidirectional acoustic and elastic wave steering.

Ageism and COVID-19: what does our society's response say about us?

Abstract: The goal of this commentary is to highlight the ageism that has emerged during the COVID-19 pandemic. Over 20 international researchers in the field of ageing have contributed to this document. This commentary discusses how older people are misrepresented and undervalued in the current public discourse surrounding the pandemic. It points to issues in documenting the deaths of older adults, the lack of preparation for such a crisis in long-term care homes, how some 'protective' policies can be considered patronising and how the initial perception of the public was that the virus was really an older adult problem. This commentary also calls attention to important intergenerational solidarity that has occurred during this crisis to ensure support and social-inclusion of older adults, even at a distance. Our hope is that with this commentary we can contribute to the discourse on older adults during this pandemic and diminish the ageist attitudes that have circulated.

Research gaps in knowledge of the impact of urban growth on biodiversity

Abstract: By 2030, an additional 1.2 billion people are forecast in urban areas globally. We review the scientific literature (n = 922 studies) to assess direct and indirect impacts of urban growth on habitat and biodiversity. Direct impacts are cumulatively substantial, with 290,000 km2 of natural habitat forecast to be converted to urban land uses between 2000 and 2030. Studies of direct impact are disproportionately from high-income countries. Indirect urban impacts on biodiversity, such as food consumption, affect a greater area than direct impacts, but comparatively few studies (34%) have quantified urban indirect impacts on biodiversity. The world is urbanizing. This Review assesses impacts of urban growth on habitat and biodiversity, finding direct impacts more in high-income countries while indirect impacts affect more land but are lesser studied.

Rare-earth metal-organic frameworks: from structure to applications.

Abstract: In the past 30 years, metal-organic frameworks (MOFs) have garnered widespread attention owing to their diverse chemical structures, and tunable properties. As a result, MOFs are of interest for a wide variety of potential applications spanning multiple scientific and engineering disciplines. MOFs have been synthesized using several elements from the periodic table, including those with metal nodes containing s-, p-, d-, and f-block elements. MOFs synthesized with rare-earth (RE) elements, which include scandium, yttrium and the series of fifteen lanthanides are an intriguing family of MOFs from the standpoint of both structure and function. While RE-MOFs can possess many of the same properties common to all MOF families (i.e., permanent porosity, tunable pore size/shape, accessible Lewis acidic sites), they can also display unique structures and properties owing to the high coordination numbers and distinct optical properties of RE-elements. In this review, we present the progress, and highlight several discoveries from research conducted on the topic of RE-MOFs. First, diverse structures of RE-MOFs are presented, divided into classes based on the composition of the RE-metal node being RE(iii)-ions, RE(iii)-chains, or RE(iii)-clusters. Then, several potential applications of RE-MOFs are presented, highlighting examples in the areas of chemical sensing, white light emission, biological imaging, drug delivery, near infrared emission, catalysis, gas adsorption, and chemical separations.

Giant virus diversity and host interactions through global metagenomics

Abstract: Our current knowledge about nucleocytoplasmic large DNA viruses (NCLDVs) is largely derived from viral isolates that are co-cultivated with protists and algae. Here we reconstructed 2,074 NCLDV genomes from sampling sites across the globe by building on the rapidly increasing amount of publicly available metagenome data. This led to an 11-fold increase in phylogenetic diversity and a parallel 10-fold expansion in functional diversity. Analysis of 58,023 major capsid proteins from large and giant viruses using metagenomic data revealed the global distribution patterns and cosmopolitan nature of these viruses. The discovered viral genomes encoded a wide range of proteins with putative roles in photosynthesis and diverse substrate transport processes, indicating that host reprogramming is probably a common strategy in the NCLDVs. Furthermore, inferences of horizontal gene transfer connected viral lineages to diverse eukaryotic hosts. We anticipate that the global diversity of NCLDVs that we describe here will establish giant viruses-which are associated with most major eukaryotic lineages-as important players in ecosystems across Earth's biomes.

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.

Equivalency of the diagnostic accuracy of the PHQ-8 and PHQ-9: A systematic review and individual participant data meta-analysis

Abstract: BACKGROUND: Item 9 of the Patient Health Questionnaire-9 (PHQ-9) queries about thoughts of death and self-harm, but not suicidality. Although it is sometimes used to assess suicide risk, most positive responses are not associated with suicidality. The PHQ-8, which omits Item 9, is thus increasingly used in research. We assessed equivalency of total score correlations and the diagnostic accuracy to detect major depression of the PHQ-8 and PHQ-9. METHODS: We conducted an individual patient data meta-analysis. We fit bivariate random-effects models to assess diagnostic accuracy. RESULTS: 16 742 participants (2097 major depression cases) from 54 studies were included. The correlation between PHQ-8 and PHQ-9 scores was 0.996 (95% confidence interval 0.996 to 0.996). The standard cutoff score of 10 for the PHQ-9 maximized sensitivity + specificity for the PHQ-8 among studies that used a semi-structured diagnostic interview reference standard (N = 27). At cutoff 10, the PHQ-8 was less sensitive by 0.02 (-0.06 to 0.00) and more specific by 0.01 (0.00 to 0.01) among those studies (N = 27), with similar results for studies that used other types of interviews (N = 27). For all 54 primary studies combined, across all cutoffs, the PHQ-8 was less sensitive than the PHQ-9 by 0.00 to 0.05 (0.03 at cutoff 10), and specificity was within 0.01 for all cutoffs (0.00 to 0.01). CONCLUSIONS: PHQ-8 and PHQ-9 total scores were similar. Sensitivity may be minimally reduced with the PHQ-8, but specificity is similar.

The impact of COVID-19 pandemic upon stability and sequential irregularity of equity and cryptocurrency markets.

Abstract: We explore the evolution of the informational efficiency in 45 cryptocurrency markets and 16 international stock markets before and during COVID-19 pandemic. The measures of Largest Lyapunov Exponent (LLE) based on the Rosenstein's method and Approximate Entropy (ApEn), which are robust to small samples, are applied to price time series in order to estimate degrees of stability and irregularity in cryptocurrency and international stock markets. The amount of regularity infers on the unpredictability of fluctuations. The t-test and F-test are performed on estimated LLE and ApEn. In total, 36 statistical tests are performed to check for differences between time periods (pre- versus during COVID-19 pandemic samples) on the one hand, as well as check for differences between markets (cryptocurrencies versus stocks), on the other hand. During the COVID-19 pandemic period it was found that (a) the level of stability in cryptocurrency markets has significantly diminished while the irregularity level significantly augmented, (b) the level of stability in international equity markets has not changed but gained more irregularity, (c) cryptocurrencies became more volatile, (d) the variability in stability and irregularity in equities has not been affected, (e) cryptocurrency and stock markets exhibit a similar degree of stability in price dynamics, whilst finally (f) cryptocurrency exhibit a low level of regularity compared to international equity markets. We find that cryptos showed more instability and more irregularity during the COVID-19 pandemic compared to international stock markets. Thus, from an informational efficiency perspective, investing in digital assets during big crises as the COVID-19 pandemic, could be considered riskier as opposed to equities.

Detecting breaking news rumors of emerging topics in social media

Abstract: Users of social media websites tend to rapidly spread breaking news and trending stories without considering their truthfulness. This facilitates the spread of rumors through social networks. A rumor is a story or statement for which truthfulness has not been verified. Efficiently detecting and acting upon rumors throughout social networks is of high importance to minimizing their harmful effect. However, detecting them is not a trivial task. They belong to unseen topics or events that are not covered in the training dataset. In this paper, we study the problem of detecting breaking news rumors, instead of long-lasting rumors, that spread in social media. We propose a new approach that jointly learns word embeddings and trains a recurrent neural network with two different objectives to automatically identify rumors. The proposed strategy is simple but effective to mitigate the topic shift issues. Emerging rumors do not have to be false at the time of the detection. They can be deemed later to be true or false. However, most previous studies on rumor detection focus on long-standing rumors and assume that rumors are always false. In contrast, our experiment simulates a cross-topic emerging rumor detection scenario with a real-life rumor dataset. Experimental results suggest that our proposed model outperforms state-of-the-art methods in terms of precision, recall, and F1.

Quantifying sources of variability in infancy research using the infant-directed-speech preference

Abstract: Psychological scientists have become increasingly concerned with issues related to methodology and replicability, and infancy researchers in particular face specific challenges related to replicability: For example, high-powered studies are difficult to conduct, testing conditions vary across labs, and different labs have access to different infant populations. Addressing these concerns, we report on a large-scale, multisite study aimed at (a) assessing the overall replicability of a single theoretically important phenomenon and (b) examining methodological, cultural, and developmental moderators. We focus on infants’ preference for infant-directed speech (IDS) over adult-directed speech (ADS). Stimuli of mothers speaking to their infants and to an adult in North American English were created using seminaturalistic laboratory-based audio recordings. Infants’ relative preference for IDS and ADS was assessed across 67 laboratories in North America, Europe, Australia, and Asia using the three common methods for measuring infants’ discrimination (head-turn preference, central fixation, and eye tracking). The overall meta-analytic effect size (Cohen’s d) was 0.35, 95% confidence interval = [0.29, 0.42], which was reliably above zero but smaller than the meta-analytic mean computed from previous literature (0.67). The IDS preference was significantly stronger in older children, in those children for whom the stimuli matched their native language and dialect, and in data from labs using the head-turn preference procedure. Together, these findings replicate the IDS preference but suggest that its magnitude is modulated by development, native-language experience, and testing procedure.

The Development and Validation of the Compassion Scale.

Abstract: This article presents a measure of compassion for others called the Compassion Scale (CS), which is based on Neff's theoretical model of self-compassion. Compassion was operationalized as experiencing kindness, a sense of common humanity, mindfulness, and lessened indifference toward the suffering of others. Study 1 (n = 465) describes the development of potential scale items and the final 16 CS items chosen based on results from analyses using bifactor exploratory structural equation modeling. Study 2 (n = 510) cross-validates the CS in a second student sample. Study 3 (n = 80) establishes test-retest reliability. Study 4 (n = 1,394) replicates results with a community sample, while Study 5 (n = 172) replicates results with a sample of meditators. Study 6 (n = 913) examines the finalized version of the CS in a community sample. Evidence regarding reliability, discriminant, convergent, construct, and known-groups validity for the CS is provided.

Review of life-cycle based methods for absolute environmental sustainability assessment and their applications

Abstract: In many regions and at the planetary scale, human pressures on the environment exceed levels that natural systems can sustain. These pressures are caused by networks of human activities, which often extend across countries and continents due to global trade. This has led to an increasing requirement for methods that enable absolute environmental sustainability assessment (AESA) of anthropogenic systems and which have a basis in life cycle assessment (LCA). Such methods enable the comparison of environmental impacts of products, companies, nations, etc., with an assigned share of environmental carrying capacity for various impact categories. This study is the first systematic review of LCA-based AESA methods and their applications. After developing a framework for LCA-based AESA methods, we identified 45 relevant studies in the existing literature through an initial survey, database searches and citation analysis. We characterized these studies according to their intended application, impact categories, basis of carrying capacity estimates, spatial differentiation of environmental model and principles for assigning carrying capacity. We then characterized all method applications and synthesized their results. Based on this assessment, we present recommendations to practitioners on the selection and use of existing LCA-based AESA methods, as well as ways to perform assessments and communicate results to decision-makers. Furthermore, we identify future research priorities intended to extend coverage of all components of the proposed method framework, improve modeling and increase the applicability of methods.

Anaerobic digestion of livestock manure in cold regions: Technological advancements and global impacts

Abstract: Anaerobic digestion (AD) is an effective process for waste management, pollution mitigation, renewable energy utilization, and greenhouse gas emissions reduction. However, low temperature is one of the most limiting factors for the application of AD in many cold regions. To expand the applications of AD to larger areas in the world, many studies have been conducted to explore its potential under low-temperature conditions. The purpose of this article is to present a comprehensive review on recent progresses and findings in this field. The generation and management of manure in cold regions are summarized to demonstrate the potential capacity of AD. Advancements in theories and technologies that can improve the performance of anaerobic digestion in cold regions are thoroughly reviewed. The benefits of AD applications in terms of emission reduction are evaluated at global scale.

Late-spring frost risk between 1959 and 2017 decreased in North America but increased in Europe and Asia.

Abstract: Late-spring frosts (LSFs) affect the performance of plants and animals across the world’s temperate and boreal zones, but despite their ecological and economic impact on agriculture and forestry, the geographic distribution and evolutionary impact of these frost events are poorly understood. Here, we analyze LSFs between 1959 and 2017 and the resistance strategies of Northern Hemisphere woody species to infer trees’ adaptations for minimizing frost damage to their leaves and to forecast forest vulnerability under the ongoing changes in frost frequencies. Trait values on leaf-out and leaf-freezing resistance come from up to 1,500 temperate and boreal woody species cultivated in common gardens. We find that areas in which LSFs are common, such as eastern North America, harbor tree species with cautious (late-leafing) leaf-out strategies. Areas in which LSFs used to be unlikely, such as broad-leaved forests and shrublands in Europe and Asia, instead harbor opportunistic tree species (quickly reacting to warming air temperatures). LSFs in the latter regions are currently increasing, and given species’ innate resistance strategies, we estimate that ∼35% of the European and ∼26% of the Asian temperate forest area, but only ∼10% of the North American, will experience increasing late-frost damage in the future. Our findings reveal region-specific changes in the spring-frost risk that can inform decision-making in land management, forestry, agriculture, and insurance policy.

A First Look at the Impact of COVID-19 on Commercial Real Estate Prices: Asset-Level Evidence

Abstract: This is the first paper to examine how the COVID-19 shock transmitted from the asset markets to capital markets Using a novel measure of the exposure of commercial real estate (CRE) portfolios to the increase in the number of COVID-19 cases (GeoCOVID), we find a one-standard-deviation increase in GeoCOVID on day t-1 is associated with a 0 24 to 0 93 percentage points decrease in abnormal returns over 1- to 3-day windows There is substantial variation across property types Local and state policy interventions helped to moderate the negative return impact of GeoCOVID However, there is little evidence that reopenings affected the performance of CRE markets

An integrated approach based on system dynamics and ANP for evaluating sustainable transportation policies

Abstract: Transportation systems are growing and complex systems. The presence of multiple, correlated, dynamically changing elements in this system with dependence and feedback add further complexity to the...

Predictive big data analytics for supply chain demand forecasting: methods, applications, and research opportunities

Abstract: Big data analytics (BDA) in supply chain management (SCM) is receiving a growing attention. This is due to the fact that BDA has a wide range of applications in SCM, including customer behavior analysis, trend analysis, and demand prediction. In this survey, we investigate the predictive BDA applications in supply chain demand forecasting to propose a classification of these applications, identify the gaps, and provide insights for future research. We classify these algorithms and their applications in supply chain management into time-series forecasting, clustering, K-nearest-neighbors, neural networks, regression analysis, support vector machines, and support vector regression. This survey also points to the fact that the literature is particularly lacking on the applications of BDA for demand forecasting in the case of closed-loop supply chains (CLSCs) and accordingly highlights avenues for future research.

Dis-ease Surveillance: How Might Surveillance Studies Address COVID-19?

Abstract: We are currently in the midst of a global pandemic with the spread of Coronavirus Disease 2019 (COVID-19) While we do not know how this situation will unfold or resolve, we do have insight into how it fits within existing patterns and relations, particularly those pertaining to sociocultural constructions of (in)security, vulnerability, and risk We can see evidence of surveillance dynamics at play with how bodies and pathogens are being measured, tracked, predicted, and regulated We can grasp how threat is being racialized, how and why institutions are flailing, and how social media might be fueling social divisions There is, in other words, a lot that our scholarly community could add to the conversation In this rapid-response editorial, we provide an introduction to the framing devices of disease surveillance and discuss how a surveillance studies orientation could help us think critically about the present crisis and its possible aftermath

Obesity and ageing: Two sides of the same coin

Abstract: Conditions and comorbidities of obesity mirror those of ageing and age-related diseases. Obesity and ageing share a similar spectrum of phenotypes such as compromised genomic integrity, impaired mitochondrial function, accumulation of intracellular macromolecules, weakened immunity, shifts in tissue and body composition, and enhanced systemic inflammation. Moreover, it has been shown that obesity reduces life expectancy by 5.8 years in men and 7.1 years in women after the age of 40. Shorter life expectancy could be because obesity holistically accelerates ageing at multiple levels. Besides jeopardizing nuclear DNA and mitochondrial DNA integrity, obesity modifies the DNA methylation pattern, which is associated with epigenetic ageing in different tissues. Additionally, other signs of ageing are seen in individuals with obesity including telomere shortening, systemic inflammation, and functional declines. This review aims to show how obesity and ageing are "two sides of the same coin" through discussing how obesity predisposes an individual to age-related conditions, illness, and disease. We will further demonstrate how the mechanisms that perpetuate the early-onset of chronic diseases in obesity parallel those of ageing.

Distributed Finite-Time Fault-Tolerant Containment Control for Multiple Unmanned Aerial Vehicles

Abstract: This paper investigates the distributed finite-time fault-tolerant containment control problem for multiple unmanned aerial vehicles (multi-UAVs) in the presence of actuator faults and input saturation. The distributed finite-time sliding-mode observer (SMO) is first developed to estimate the reference for each follower UAV. Then, based on the estimated knowledge, the distributed finite-time fault-tolerant controller is recursively designed to guide all follower UAVs into the convex hull spanned by the trajectories of leader UAVs with the help of a new set of error variables. Moreover, the unknown nonlinearities inherent in the multi-UAVs system, computational burden, and input saturation are simultaneously handled by utilizing neural network (NN), minimum parameter learning of NN (MPLNN), first-order sliding-mode differentiator (FOSMD) techniques, and a group of auxiliary systems. Furthermore, the graph theory and Lyapunov stability analysis methods are adopted to guarantee that all follower UAVs can converge to the convex hull spanned by the leader UAVs even in the event of actuator faults. Finally, extensive comparative simulations have been conducted to demonstrate the effectiveness of the proposed control scheme.