Current Perceptions of Sustainable Diets in Ireland and the Outlook of Circular Eating Practices.

Encouraging sustainable dietary practices is a positive step towards alleviating the negative impact of increasing food waste. This study explores consumers' perceptions of sustainable diets and factors influencing their adoption of sustainable practices, such as circular eating. Fifteen consumers in Ireland aged 18-65+ years were recruited for a qualitative study exploring their views on sustainable diets and their opinions on the use of peels, trimmings, and other by-products from fruit and vegetables as potential new ingredients or new circular food products. Age, gender, dietary preferences and educational background were important considerations during recruitment. Online interviews were conducted, and the data were analyzed using thematic analysis managed by NVivo 12. The results highlighted two overarching themes impacting circular eating, namely, food choice motives and awareness of food waste. These were linked to the participants' knowledge of and potential adoption of sustainable diets. Daily trade-offs were evident, linked to the product and the person who was also strongly influenced by their micro-environment. Consumer education from credible sources would be required to create awareness of circular eating. Most respondents were positively disposed to the concept of circular eating, as long as food safety concerns and affordability considerations were addressed through industry transparency.

Assessment of antidiabetic potential and phytochemical profiling of Rhazya stricta root extracts.

BACKGROUND: Diabetes mellitus is a chronic disease characterized by hyperglycemia that may occur due to genetic, environmental or lifestyle factors. Natural remedies have been used to treat diabetes since long and many antidiabetic compounds of varied efficacies have been isolated from medicinal plants. Rhazya stricta has been used for decades for the treatment of diabetes mellitus and associated ailments. Considering the folkloric use of R. stricta against diabetes, it was aimed to investigate the effectiveness of its root extracts against diabetes through in vitro assays and in vivo studies using animal model along with phytochemical profiling through GCMS. METHODS: Various fractions of Rhazya stricta obtained through column chromatography were evaluated for a variety of assays including α-glucosidase, Dipeptidyl peptidase-IV (DPP-IV), ß-secretase and Glucagon-like peptide-1 (GLP-1) secretion studies. For the in vivo studies the alloxan-induced diabetic mice were treated with root extracts and blood glucose levels, HbA1C, and other biochemical markers along with the histological study of the liver were done. The phytochemical identification was performed using an Agilent 7890B GC coupled to a 7010 Triple Quadrupole (MS/MS) system. GraphPad Prism software version 5.01 was used for statistical analysis. RESULTS: Majority of the extract fractions showed excellent results against diabetes by inhibiting enzymes DPP-IV (Up to 61%) and ß-secretase (Up to 83%) with IC50s 979 µg/ml and 169 µg/ml respectively with increase in the GLP1 secretion. The results of in vivo studies indicated a marked reduction in blood glucose and HbA1c levels along with positive effects on other parameters like lipid profile, liver functions and renal functions of extract-treated mice as compared to control. The histological examination of the liver demonstrated hepatoprotective effects against diabetes led changes and various classes of phytochemicals were also identified through GCMS in different fractions. CONCLUSION: The results revealed strong antidiabetic activity of R. stricta root with the potential to protect body organs against diabetic changes. Moreover, a variety of phytochemicals has also been identified through GCMS that might be responsible for the antidiabetic potential of Rhazya stricta root.

DeepWeeds: A Multiclass Weed Species Image Dataset for Deep Learning.

Robotic weed control has seen increased research of late with its potential for boosting productivity in agriculture. Majority of works focus on developing robotics for croplands, ignoring the weed management problems facing rangeland stock farmers. Perhaps the greatest obstacle to widespread uptake of robotic weed control is the robust classification of weed species in their natural environment. The unparalleled successes of deep learning make it an ideal candidate for recognising various weed species in the complex rangeland environment. This work contributes the first large, public, multiclass image dataset of weed species from the Australian rangelands; allowing for the development of robust classification methods to make robotic weed control viable. The DeepWeeds dataset consists of 17,509 labelled images of eight nationally significant weed species native to eight locations across northern Australia. This paper presents a baseline for classification performance on the dataset using the benchmark deep learning models, Inception-v3 and ResNet-50. These models achieved an average classification accuracy of 95.1% and 95.7%, respectively. We also demonstrate real time performance of the ResNet-50 architecture, with an average inference time of 53.4 ms per image. These strong results bode well for future field implementation of robotic weed control methods in the Australian rangelands.

In Vitro Protocols for Measuring the Antioxidant Capacity of Algal Extracts.

In the last decade a large amount of research has been directed at targeting algal resources for biologically active molecules. High-throughput in vitro antioxidant assays are routinely used to screen for biologically active compounds present in algal extracts when the requirement is to identify samples for progression to more detailed biological scrutiny. Whilst a myriad of antioxidant assays have been developed, this present chapter aims to give step-by-step practical guidance on how to carry out some of the most popular and biologically relevant assays at the bench.

Anti-inflammatory effects of wild Irish mushroom extracts in RAW264.7 mouse macrophage cells.

Mushrooms and mushroom extracts have traditionally been used as therapies for a wide variety of ailments, including allergy, arthritis, and other inflammatory disorders. However, more evidence is required on the mechanism by which mushrooms exert these effects. In the present study, the anti-inflammatory properties of ethanol and hot water extracts prepared from 27 fungal samples collected between October and November 2011 at various forest locations in the southwest of Ireland were investigated using the lipopolysaccharide (LPS)-stimulated mouse macrophage (RAW264.7 cells) model of inflammation. LPS-stimulated cells were incubated in the presence of mushroom extracts at nontoxic concentrations for 24 h and the production of interleukin-6 (IL-6) was quantified by ELISA. Seven ethanolic and one hot water extract that decreased IL-6 production were selected for further study. The extracts were then incubated with LPS-stimulated cells for 24 h and the production of IL-6, tumor necrosis factor-alpha (TNF-α), and nitric oxide (NO) was measured. Ethanolic extracts prepared from Russula mairei, Lactarius blennius, Craterellus tubaeformis, Russula fellea, and Craterellus cornucopioides demonstrated selective anti-inflammatory activity by decreasing the production of NO and IL-6 but not TNF-α in LPS-stimulated RAW264.7 cells. These findings support existing evidence of the anti-inflammatory potential of mushroom extracts.