Geospatial analysis of food waste generation at the consumer-level in high-income regions, 2000-2023 - A scoping review.

Food waste (FW) is a growing problem globally, with around 13% of food lost from harvest to retail, and another 17% wasted by retailers, households, and the food service sector. In high-income countries, consumer-level FW, primarily originates from private households and the food service sector, forming the largest waste stream in the food supply chain. Despite extensive research on FW, there is still a lack of knowledge about its geographic distribution, sources, spatial locations, and volume, impeding effective waste management strategies.

A rapid on-site loop-mediated isothermal amplification technology as an early warning system for the detection of Shiga toxin-producing in water.

Shiga toxin-producing (STEC) is an important waterborne pathogen capable of causing serious gastrointestinal infections with potentially fatal complications, including haemolytic-uremic syndrome. All STEC serogroups harbour genes that encode at least one Shiga toxin ( and/or ), which constitute the primary virulence factors of STEC. Loop-mediated isothermal amplification (LAMP) enables rapid real-time pathogen detection with a high degree of specificity and sensitivity.

Development of a "big data" groundwater microbial contamination index and spatial comparisons with enteric infection rates in southern Ontario.

Big data have become increasingly important for policymakers and scientists but have yet to be employed for the development of spatially specific groundwater contamination indices or protecting human and environmental health. The current study sought to develop a series of indices via analyses of three variables: Non-E. coli coliform (NEC) concentration, E.

Classification of white blood cells (leucocytes) from blood smear imagery using machine and deep learning models: A global scoping review.

Machine learning (ML) and deep learning (DL) models are being increasingly employed for medical imagery analyses, with both approaches used to enhance the accuracy of classification/prediction in the diagnoses of various cancers, tumors and bloodborne diseases. To date however, no review of these techniques and their application(s) within the domain of white blood cell (WBC) classification in blood smear images has been undertaken, representing a notable knowledge gap with respect to model selection and comparison. Accordingly, the current study sought to comprehensively identify, explore and contrast ML and DL methods for classifying WBCs.