Unearthing new learning opportunities: adapting and innovating through the 'Antibiotics under our feet' citizen science project in Scotland during COVID-19.

'Antibiotics under our feet' is a Scottish citizen science project that aimed to raise science capital in primary school learners and their teachers through measurement of microbial diversity in urban soil samples in the search for novel antimicrobial compounds. Resistance to antibiotics is rising, posing a global threat to human health. Furthermore, science, technology, engineering and mathematics (STEM) skills are in crisis, jeopardising our capacity to mobilise as a society to fight antimicrobial resistance (AMR). Originally conceived as a response to the AMR and STEM emergencies, our project was hit by the unprecedented challenge of engaging with schools during the COVID-19 pandemic. We describe how we adapted our project to enable remote participation from primary schools and youth groups, utilising COVID-19 response initiatives as opportunities for multi-level co-creation of resources with learners in primary, secondary, and higher education. We produced portable kit boxes for soil sample collection with learning activities and videos linked to the Scottish Curriculum for Excellence. We also addressed glaring project specific content gaps relating to microbiology on English and Simple English Wikipedia. Our hybrid model of working extended our geographical reach and broadened inclusion. We present here the inception, implementation, digital resource outputs, and discussion of pedagogical aspects of 'Antibiotics under our feet'. Our strategies and insights are applicable post-pandemic for educators to develop STEM skills using soil, microbes, and antibiotics as a theme.

AMCSMMA: Predicting Small Molecule-miRNA Potential Associations Based on Accurate Matrix Completion.

Exploring potential associations between small molecule drugs (SMs) and microRNAs (miRNAs) is significant for drug development and disease treatment. Since biological experiments are expensive and time-consuming, we propose a computational model based on accurate matrix completion for predicting potential SM-miRNA associations (AMCSMMA). Initially, a heterogeneous SM-miRNA network is constructed, and its adjacency matrix is taken as the target matrix. An optimization framework is then proposed to recover the target matrix with the missing values by minimizing its truncated nuclear norm, an accurate, robust, and efficient approximation to the rank function. Finally, we design an effective two-step iterative algorithm to solve the optimization problem and obtain the prediction scores. After determining the optimal parameters, we conduct four kinds of cross-validation experiments based on two datasets, and the results demonstrate that AMCSMMA is superior to the state-of-the-art methods. In addition, we implement another validation experiment, in which more evaluation metrics in addition to the AUC are introduced and finally achieve great results. In two types of case studies, a large number of SM-miRNA pairs with high predictive scores are confirmed by the published experimental literature. In summary, AMCSMMA has superior performance in predicting potential SM-miRNA associations, which can provide guidance for biological experiments and accelerate the discovery of new SM-miRNA associations.

SGAEMDA: Predicting miRNA-Disease Associations Based on Stacked Graph Autoencoder.

MicroRNA (miRNA)-disease association (MDA) prediction is critical for disease prevention, diagnosis, and treatment. Traditional MDA wet experiments, on the other hand, are inefficient and costly.Therefore, we proposed a multi-layer collaborative unsupervised training base model called SGAEMDA (Stacked Graph Autoencoder-Based Prediction of Potential miRNA-Disease Associations). First, from the original miRNA and disease data, we defined two types of initial features: similarity features and association features. Second, stacked graph autoencoder is then used to learn unsupervised low-dimensional representations of meaningful higher-order similarity features, and we concatenate the association features with the learned low-dimensional representations to obtain the final miRNA-disease pair features. Finally, we used a multilayer perceptron (MLP) to predict scores for unknown miRNA-disease associations. SGAEMDA achieved a mean area under the ROC curve of 0.9585 and 0.9516 in 5-fold and 10-fold cross-validation, which is significantly higher than the other baseline methods. Furthermore, case studies have shown that SGAEMDA can accurately predict candidate miRNAs for brain, breast, colon, and kidney neoplasms.