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Abstract Coronavirus disease 2019 (COVID-19) virus is an infectious disease which has spread globally since 2019, resulting in an ongoing pandemic. Since it is a new virus, it takes some time to develop a vaccine against it. Until then, the best way to prevent the fast spread of the virus is to enable the proper social distancing and isolation or containment to identify potential patients. Since the virus has up to 14 days of the incubation period, it is important to identify all the social interactions during this period and enforce social isolation for such potential patients. However, proper social interaction tracking methods and patient prediction methods based on such data are missing for the moment. This paper focuses on tracking the social interaction of users and predict the infection possibility based on social interactions. We first developed a BLE (Bluetooth Low Energy) and GPS based social interaction tracking system. Then, we developed an algorithm to predict the possibility of being infected with COVID-19 based on the collected data. Finally, a prototype of the system is implemented with a mobile app and a web monitoring tool. In addition, we performed a simulation of the system with a graph-based model to analyze the behaviour of the proposed algorithm and it verifies that self-isolation is important in slowing down the disease progression.

Abstract Due to the spread of Coronavirus disease 2019 (COVID-19), the world has encountered an ongoing pandemic to date. It is a highly contagious disease. In addition to the vaccination, social distancing and isolation of patients are proven to be one of the commonly used strategies to reduce the spread of disease. For efficient social distancing, contact tracing is a critical requirement in the incubation period of 14-days of the disease to contain any further spread. However, we identify that there is a lack of reliable and practical social interaction tracking methods and prediction methods for the probability of getting the disease. This paper focuses on user tracking and predicting the infection probability based on these social interactions. We first developed an energy-efficient BLE (Bluetooth Low Energy) based social interaction tracking system to achieve this. Then, based on the collected data, we propose an algorithm to predict the possibility of getting the COVID-19. Finally, to show the practicality of our solution, we implemented a prototype with a mobile app and a web monitoring tool for healthcare authorities. In addition to that, to analyze the proposed algorithm’s behaviour, we performed a simulation of the system using a graph-based model.

COVID-19 pandemic is on a trajectory to cause catastrophic global upheaval with the potential to alter geopolitical and socio-economic norms. Many countries are frantically responding with staggering financial stimulus recovery initiatives. This opinion-paper reviews challenges, opportunities, and potential solutions for the post-COVID-19 era that focuses on intensive sustaining of agri-food supply chain in tandem with meeting the high demand for new green deal innovation. For example, the development of wet peatland innovation, known as Paludiculture, can intensively sustain and blend agri-food and green innovations that will help support COVID-19 pandemic transitioning. The future looks bright for the creation of new sustainability multi-actor innovation hubs that will support, connect, and enable businesses to recover and pivot beyond the COVID-19 pandemic. The nexus between first ‘Green Deal’ initiative supporting 64 selected European Startups and SMEs (European Innovation Council) and 43 Irish Disruptive Technology projects are addressed in the context of cross-cutting developments and relevance to COVID-19. Candidate areas for future consideration will focus on climate action, digitization, manufacturing, and sustainable food production, security, and waste mitigation. Recommendations are also provided to facilitate community transitioning, training, enterprise, and employment to low carbon economy. yes

This study aimed to describe the prevalence, severity and socio-demographic predictors of food insecurity in Australian households during the COVID-19 pandemic in 2020, from the perspective of women. A cross-sectional online survey of Australian (18–50 years) women was conducted. The survey collected demographic information and utilised the 18-item US Department of Agriculture Household Food Security Survey Module and the Kessler Psychological Distress Scale (K10). A multivariable regression was used to identify predictors of food security status. In this cohort (n = 1005), 19.6% were living in households experiencing food insecurity; with 11.8% experiencing low food-security and 7.8% very low food-security. A further 13.7% of households reported marginal food-security. Poor mental health status (K10 score ≥ 20) predicted household food insecurity at all levels. The presence of more than three children in the household was associated with low food-security (OR 6.24, 95% CI: 2.59–15.03). Those who were renting were 2.10 (95% CI: 1.09–4.05) times likely to experience very low food-security than those owning their own home. The COVID-19 pandemic may have contributed to an increased prevalence of household food insecurity. This study supports the need for a range of responses that address mental health, financial, employment and housing support to food security in Australia. Monash University National Heart Foundation Future Leader Fellowship National Health & Medical Research Council

The 29th International Joint Conference on Artificial Intelligence - 17th Pacific Rim International Conference on Artificial Intelligence (IJCAI-PRICAI-20), Yokohama, Japan, January 2021 (Conference postponed due to COVID-19 pandemic) Smart agriculture (SmartAg) has emerged as a rich domain for AI-driven decision support systems (DSS); however, it is often challenged by user-adoption issues. This paper reports a case-based reasoning system, PBI-CBR, that predicts grass growth for dairy farmers, that combines predictive accuracy and explanations to improve user adoption. PBI-CBR’s key novelty is its use of Bayesian methods for case-base maintenance in a regression domain. Experiments report the tradeoff between predictive accuracy and explanatory capability for different variants of PBI-CBR, and how updating Bayesian priors each year improves performance. Science Foundation Ireland Insight Research Centre

The residential real estate market is very important because most people’s wealth is in this sector, and it is an indicator of the economy. Real estate market data in general and market transaction data, in particular, are inherently spatiotemporal as each transaction has a location and time. Therefore, exploratory spatiotemporal methods can extract unique locational and temporal insight from property transaction data, but this type of data are usually unavailable or not sufficiently geocoded to implement spatiotemporal methods. In this article, exploratory spatiotemporal methods, including a space-time cube, were used to analyze the residential real estate market at small area scale in the Dublin Metropolitan Area over the last decade. The spatial patterns show that some neighborhoods are experiencing change, including gentrification and recent development. The extracted spatiotemporal patterns from the data show different urban areas have had varying responses during national and global crises such as the economic crisis in 2008–2011, the Brexit decision in 2016, and the COVID-19 pandemic. The study also suggests that Dublin is experiencing intraurban displacement of residential property transactions to the west of Dublin city, and we are predicting increasing spatial inequality and segregation in the future. The findings of this innovative and exploratory data-driven approach are supported by other work in the field regarding Dublin and other international cities. The article shows that the space-time cube can be used as complementary evidence for different fields of urban studies, urban planning, urban economics, real estate valuations, intraurban analytics, and monitoring sociospatial changes at small areas, and to understand residential property transactions in cities. Moreover, the exploratory spatiotemporal analyses of data have a high potential to highlight spatial structures of the city and relevant underlying processes. The value and necessity of open access to geocoded spatiotemporal property transaction data in social research are also highlighted.

Necrotizing enterocolitis (NEC) is a devastating condition of premature infants that results from the gut microbiome invading immature intestinal tissues. This results in a life-threatening disease that is frequently treated with the surgical removal of diseased and dead tissues. Epidermal growth factor (EGF), typically found in bodily fluids, such as amniotic fluid, salvia and mother's breast milk, is an intestinotrophic growth factor and may reduce the onset of NEC in premature infants. We have produced human EGF in soybean seeds to levels biologically relevant and demonstrated its comparable activity to commercially available EGF. Transgenic soybean seeds expressing a seed-specific codon optimized gene encoding of the human EGF protein with an added ER signal tag at the N' terminal were produced. Seven independent lines were grown to homozygous and found to accumulate a range of 6.7 +/- 3.1 to 129.0 +/- 36.7 μg EGF/g of dry soybean seed. Proteomic and immunoblot analysis indicates that the inserted EGF is the same as the human EGF protein. Phosphorylation and immunohistochemical assays on the EGF receptor in HeLa cells indicate the EGF protein produced in soybean seed is bioactive and comparable to commercially available human EGF. This work demonstrates the feasibility of using soybean seeds as a biofactory to produce therapeutic agents in a soymilk delivery platform.

Abstract CONTEXT Livestock are the primary source of greenhouse gas (GHG) emissions from agriculture in most African countries, but there is a paucity of baseline data and monitoring of GHG emissions from livestock in Africa, particularly for extreme or shock events. The COVID-19 pandemic represents a novels shock to livestock systems and may result in indirect effects on livestock emissions and other Sustainable Development Goals (SDGs). Due to the pandemic in 2020, extensive pastoralist livestock systems in Northern Kenya were subjected to restrictions on movement, increased costs of transportation, and closure of livestock markets. OBJECTIVE The objective of this study was to assess the indirect effects of the COVID-19 pandemic on GHG emissions from livestock systems in Northern Kenya using proxy data and a three-part framework based on changes in 1) herd size, 2) feed availability, and 3) livestock movement. METHODS We evaluated changes in GHG emissions from livestock systems in Northern Kenya due to the COVID-19 pandemic based on proxy data from crowd-sourced market data, household panel surveys, and remote sensing data on Normalized Difference Vegetation Index (NDVI). Proxy data were obtained before the pandemic in 2019 and after the pandemic in 2020 to compare between years and evaluate the indirect effects of the pandemic and associated restrictions on livestock GHG emissions using the three-part framework. RESULTS AND CONCLUSIONS Overall GHG emissions from livestock in Northern Kenya have decreased due to the pandemic and this was largely driven by reductions in herd size. This reduction in GHG emissions occurred despite an increase in GHG emissions from livestock associated with higher feed availability. Decreased livestock movement due to the pandemic contributed to reductions in GHG emissions from livestock, but such reductions were likely to be small due to limited need for livestock to travel longer distances under the prevailing conditions of high feed availability. SIGNIFICANCE This research shows that assessments of changes in GHG emissions from livestock systems due to shock events can be conducted successfully based on proxy data and the three-part framework developed here. We found that shock events, such as the COVID-19 pandemic, may lead to unexpected results with respect to the direction and magnitude of changes in livestock emissions depending on contextual factors and environmental conditions. Thus, we call for more spatially explicit and continued data collection to assess and monitor the consequences of shock events on GHG emissions from livestock and related SDGs in Africa.