An Integrated Approach to Control and Manage Potato Black Dot Disease: A Review - Marta Sanzo-Miró; Daniel M. Simms; Faisal I. Rezwan; Leon A. Terry; M. Carmen Alamar (American Journal of Potato Research, 2023)
Potato black dot is a foliar and tuber blemish disease that has become an increasingly economic problem in recent years. Black dot is caused by the fungus Colletotrichum coccodes and is characterised by silver/brown lesions on the tuber skin leading to lower aesthetic quality of potatoes destined for the pre-pack market. Given the consumers’ growing demand for washed and pre-packed potatoes, skin blemish diseases (such as black dot and silver scurf), once considered of minor importance, are now serious challenges for the fresh potato industry. The management of C. coccodes is far from satisfactory at either pre- or postharvest stages: firstly, the disease symptoms have not been consistently described on potato plant foliage; and secondly, black dot disease is often confounded with other tuber blemishes during postharvest storage. Good field managing practices in combination with improved postharvest strategies and an accurate detection support tool can be a useful integrated approach to manage potato black dot disease. This review aims to evaluate and critically discuss different novel approaches for better management and detection of potato black dot disease.

Common, low-frequency, rare, and ultra-rare coding variants contribute to COVID-19 severity - Chiara Fallerini; Nicola Picchiotti; Margherita Baldassarri; Kristina Zguro; Sergio Daga; Francesca Fava; Elisa Benetti; Sara Amitrano; Mirella Bruttini; Maria Palmieri; Susanna Croci; Mirjam Lista; et al. (Human Genetics, 2022)
The combined impact of common and rare exonic variants in COVID-19 host genetics is currently insufficiently understood. Here, common and rare variants from whole-exome sequencing data of about 4000 SARS-CoV-2-positive individuals were used to define an interpretable machine-learning model for predicting COVID-19 severity. First, variants were converted into separate sets of Boolean features, depending on the absence or the presence of variants in each gene. An ensemble of LASSO logistic regression models was used to identify the most informative Boolean features with respect to the genetic bases of severity. The Boolean features selected by these logistic models were combined into an Integrated PolyGenic Score that offers a synthetic and interpretable index for describing the contribution of host genetics in COVID-19 severity, as demonstrated through testing in several independent cohorts. Selected features belong to ultra-rare, rare, low-frequency, and common variants, including those in linkage disequilibrium with known GWAS loci. Noteworthily, around one quarter of the selected genes are sex-specific. Pathway analysis of the selected genes associated with COVID-19 severity reflected the multi-organ nature of the disease. The proposed model might provide useful information for developing diagnostics and therapeutics, while also being able to guide bedside disease management.

Development of Zinc Chelating Resin Polymer Beads for the Removal of Cell-Free Hemoglobin - Kelli Simms; Elisabeth Rebholz; Robert M. Mayberry; Swati Basu; Andreas Perlegas; Martin Guthold; Daniel B. Kim-Shapiro; Elaheh Rahbar (Annals of Biomedical Engineering, 2019)

Improved estimates of opium cultivation in Afghanistan using imagery-based stratification - Daniel M. Simms; Toby W. Waine; John C. Taylor (International Journal of Remote Sensing, 2017)

Image segmentation for improved consistency in image-interpretation of opium poppy - Daniel M. Simms; Toby W. Waine; John C. Taylor; Timothy R. Brewer (International Journal of Remote Sensing, 2016)

Towards improving the accuracy of opium yield estimates with remote sensing - Toby W. Waine; Daniel M. Simms; John C. Taylor; Graham R. Juniper (International Journal of Remote Sensing, 2014)

The application of time-series MODIS NDVI profiles for the acquisition of crop information across Afghanistan - Daniel M. Simms; Toby W. Waine; John C. Taylor; Graham R. Juniper (International Journal of Remote Sensing, 2014)