Country-level pandemic risk and preparedness classification based on COVID-19 data: A machine learning approach

In this work we present a three-stage Machine Learning strategy to country-level risk classification based on countries that are reporting COVID-19 information. A K% binning discretisation (K = 25) is used to create four risk groups of countries based on the risk of transmission (coronavirus cases per million population), risk of mortality (coronavirus deaths per million population), and risk of inability to test (coronavirus tests per million population). The four risk groups produced by K% binning are labelled as ‘low’, ‘medium-low’, ‘medium-high’, and ‘high’. Coronavirus-related data are then removed and the attributes for prediction of the three types of risk are given as the geopolitical and demographic data describing each country. Thus, the calculation of class label is based on coronavirus data but the input attributes are country-level information regardless of coronavirus data. The three four-class classification problems are then explored and benchmarked through leave-one-country-out cross validation to find the strongest model, producing a Stack of Gradient Boosting and Decision Tree algorithms for risk of transmission, a Stack of Support Vector Machine and Extra Trees for risk of mortality, and a Gradient Boosting algorithm for the risk of inability to test. It is noted that high risk for inability to test is often coupled with low risks for transmission and mortality, therefore the risk of inability to test should be interpreted first, before consideration is given to the predicted transmission and mortality risks. Finally, the approach is applied to more recent risk levels to data from September 2020 and weaker results are noted due to the growth of international collaboration detracting useful knowledge from country-level attributes which suggests that similar machine learning approaches are more useful prior to situations later unfolding

Beyond goal-rationality:Traditional action can reduce volatility in socially situated agents

Systems that pursue their own goals in shared environments can indirectly affect one another in unanticipated ways, such that the actions of other systems can interfere with goal-achievement. As humans have evolved to achieve goals despite interference from others in society, we thus endow socially situated agents with the capacity for social action as a means of mitigating interference in co-existing systems. We demonstrate that behavioural and evolutionary volatility caused by indirect interactions of goal-rational agents can be reduced by designing agents in a more socially-sensitive manner. We therefore challenge the assumption that designers of intelligent systems typically make, that goal-rationality is sufficient for achieving goals in shared environments

Explaining Evolutionary Agent-Based Models via Principled Simplification

Understanding how evolutionary agents behave in complex environments is a challenging problem. Agents can be faced with complex fitness landscapes derived from multi-stage tasks, interaction with others, and limited environmental feedback. Agents that evolve to overcome these can sometimes access greater fitness, as a result of factors such as cooperation and tool use. However, it is often difficult to explain why evolutionary agents behave in certain ways, and what specific elements of the environment or task may influence the ability of evolution to find goal-achieving behaviours; even seemingly simple environments or tasks may contain features that affect agent evolution in unexpected ways. We explore principled simplification of evolutionary agent-based models, as a possible route to aiding their explainability. Using the River Crossing Task (RCT) as a case study, we draw on analysis in the Minimal River Crossing (RC-) Task testbed, which was designed to simplify the original task while keeping its key features. Using this method, we present new analysis concerning when agents evolve to successfully complete the RCT. We demonstrate that the RC- environment can be used to understand the effect that a cost to movement has on agent evolution, and that these findings can be generalised back to the original RCT. Then, we present new insight into the use of principled simplification in understanding evolutionary agents. We find evidence that behaviour dependent on features that survive simplification, such as problem structure, are amenable to prediction; while predicting behaviour dependent on features that are typically reduced in simplification, such as scale, can be invalid

Fruit quality and defect image classification with conditional GAN data augmentation

Contemporary Artificial Intelligence technologies allow for the employment of Computer Vision to discern good crops from bad, providing a step in the pipeline of selecting healthy fruit from undesirable fruit, such as those which are mouldy or damaged. State-of-the-art works in the field report high accuracy results on small datasets

Behavioural Plasticity Can Help Evolving Agents in Dynamic Environments but at the Cost of Volatility

Neural networks have been widely used in agent learning architectures; however, learnings for one task might nullify learnings for another. Behavioural plasticity enables humans and animals alike to respond to environmental changes without degrading learned knowledge; this can be achieved by regulating behaviour with neuromodulation—a biological process found in the brain. We demonstrate that by modulating activity-propagating signals, neurally trained agents evolving to solve tasks in dynamic environments that are prone to change can expect a significantly higher fitness than non-modulatory agents and also achieve their goals more often. Further, we show that while behavioural plasticity can help agents to achieve goals in these variable environments, this ability to overcome environmental changes with greater success comes at the cost of highly volatile evolution

Triazole-derivatized near-infrared cyanine dyes enable local functional fluorescent imaging of ocular inflammation

Near-infrared (NIR) chemical fluorophores are promising tools for in-vivo imaging in real time but often succumb to rapid photodegradation. Indocyanine green (ICG) is the only NIR dye with regulatory approval for ocular imaging in humans; however, ICG, when employed for applications such as labelling immune cells, has limited sensitivity and does not allow precise detection of specific inflammatory events, for example leukocyte recruitment during uveitic flare-ups. We investigated the potential use of photostable novel triazole NIR cyanine (TNC) dyes for detecting and characterising activated T-cell activity within the eye. Three TNC dyes were evaluated for ocular cytotoxicity in-vitro using a MTT assay and optimised concentrations for intraocular detection within ex-vivo porcine eyes after topical application or intracameral injections of the dyes. TNC labelled T-cell tracking experiments and mechanistic studies were also performed in-vitro. TNC-1 and TNC-2 dyes exhibited greater fluorescence intensity than ICG at 10 μM, whereas TNC-3 was only detectable at 100 μM within the porcine eye. TNC dyes did not demonstrate any ocular cell toxicity at working concentrations of 10 μM. CD4+T-cells labelled with TNC-1 or TNC-2 were detected within the porcine eye, with TNC-1 being brighter than TNC-2. Detection of TNC-1 and TNC-2 into CD4+T-cells was prevented by prior incubation with dynole 34-2 (50 μM), suggesting active uptake of these dyes via dynamin-dependent processes. The present study provides evidence that TNC dyes are suitable to detect activated CD4+T-cells within the eye with potential as a diagnostic marker for ocular inflammatory diseases

Food System Resilience: Concepts, Issues, and Challenges

Food system resilience has multiple dimensions. We draw on food system and resilience concepts and review resilience framings of different communities. We present four questions to frame food system resilience (Resilience of what? Resilience to what? Resilience from whose perspective? Resilience for how long?) and three approaches to enhancing resilience (robustness, recovery, and reorientation—the three “Rs”). We focus on enhancing resilience of food system outcomes and argue this will require food system actors adapting their activities, noting that activities do not change spontaneously but in response to a change in drivers: an opportunity or a threat. However, operationalizing resilience enhancement involves normative choices and will result in decisions having to be negotiated about trade-offs among food system outcomes for different stakeholders. New approaches to including different food system actors’ perceptions and goals are needed to build food systems that are better positioned to address challenges of the future. Expected final online publication date for the Annual Review of Environment and Resources, Volume 47 is October 2022. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates

SARS-CoV-2 susceptibility and COVID-19 disease severity are associated with genetic variants affecting gene expression in a variety of tissues

Variability in SARS-CoV-2 susceptibility and COVID-19 disease severity between individuals is partly due to genetic factors. Here, we identify 4 genomic loci with suggestive associations for SARS-CoV-2 susceptibility and 19 for COVID-19 disease severity. Four of these 23 loci likely have an ethnicity-specific component. Genome-wide association study (GWAS) signals in 11 loci colocalize with expression quantitative trait loci (eQTLs) associated with the expression of 20 genes in 62 tissues/cell types (range: 1:43 tissues/gene), including lung, brain, heart, muscle, and skin as well as the digestive system and immune system. We perform genetic fine mapping to compute 99% credible SNP sets, which identify 10 GWAS loci that have eight or fewer SNPs in the credible set, including three loci with one single likely causal SNP. Our study suggests that the diverse symptoms and disease severity of COVID-19 observed between individuals is associated with variants across the genome, affecting gene expression levels in a wide variety of tissue types

The impact of immediate breast reconstruction on the time to delivery of adjuvant therapy: the iBRA-2 study

Background: Immediate breast reconstruction (IBR) is routinely offered to improve quality-of-life for women requiring mastectomy, but there are concerns that more complex surgery may delay adjuvant oncological treatments and compromise long-term outcomes. High-quality evidence is lacking. The iBRA-2 study aimed to investigate the impact of IBR on time to adjuvant therapy. Methods: Consecutive women undergoing mastectomy ± IBR for breast cancer July–December, 2016 were included. Patient demographics, operative, oncological and complication data were collected. Time from last definitive cancer surgery to first adjuvant treatment for patients undergoing mastectomy ± IBR were compared and risk factors associated with delays explored. Results: A total of 2540 patients were recruited from 76 centres; 1008 (39.7%) underwent IBR (implant-only [n = 675, 26.6%]; pedicled flaps [n = 105,4.1%] and free-flaps [n = 228, 8.9%]). Complications requiring re-admission or re-operation were significantly more common in patients undergoing IBR than those receiving mastectomy. Adjuvant chemotherapy or radiotherapy was required by 1235 (48.6%) patients. No clinically significant differences were seen in time to adjuvant therapy between patient groups but major complications irrespective of surgery received were significantly associated with treatment delays. Conclusions: IBR does not result in clinically significant delays to adjuvant therapy, but post-operative complications are associated with treatment delays. Strategies to minimise complications, including careful patient selection, are required to improve outcomes for patients

Author Correction:Study of 300,486 individuals identifies 148 independent genetic loci influencing general cognitive function

Christina M. Lill, who contributed to analysis of data, was inadvertently omitted from the author list in the originally published version of this article. This has now been corrected in both the PDF and HTML versions of the article