Study of the effects of the Cannabinoids Anandamide and Cannabidiol on the feeding processes of Tetrahymena pyriformis

The regulation and physiological management of feeding behaviour, appetite and fullness in humans, and many other multicellular organisms, is governed by the pathways involved in the Endocannabinoid system (ECS). This complex system comprises lipid endocannabinoids e.g. Anandamide (AEA), that bind to cannabinoid receptors (e.g. CB1 and CB2), together with the enzymes involved in cannabinoid generation and hydrolysis. The ECS can also be stimulated by the plant cannabinoids (phytocannabinoids) such as Δ9-tetrahydrocannabidiol (Δ9-THC) and cannabidiol (CBD) which are found in Cannabis sativa. This study examined the effects exogenous of AEA and CBD on prey ingestion and food vacuole formation in the ciliate Tetrahymena pyriformis when feeding on an indigestible fluorescent cyanobacterium Synechococcus. Both AEA and CBD affected the ciliate feeding by inducing a lag; AEA having a shorter lag (ca. 36 min) in comparison to CBD (ca. 60 min). When ingestion resumed, AEA-treated cells fed at the same rate as the Control cells whereas CBD-treated cells had elevated ingestion rates (hyperphagia). The mechanism behind this is currently unknown but it does not appear to involve a cessation of food vacuole trafficking and defecation. It was also considered unlikely to be due to vacuole membrane recycling and the formation of phagocytic cups as the cellular machinery for this is very similar to that required for vacuole trafficking, which is unaffected by AEA and CBD. The study therefore hypothesised that: AEA and CBD completely stops prey capture but that pre-existing vacuoles are trafficked and defecated as normal and membrane is recycled to the cytostome where it accumulates. A lag of 60 min would allow the accumulation of more membrane than a 30 min lag and is the possible reason as to why ingestion rates after the former are higher. This study provides a basis for future research into the effects of CBD and AEA on the feeding capture processes of protists as well as their potential targets. Since protists do not possess the usual cannabinoid receptors associated with the human ECS future work might elucidate the ancestral targets of these cannabinoids together with their function in single cells

Identification of Ilarviruses in almond and cherry fruit trees using nested PCR assays

In this study nested PCR assays have been developed for the detection of Prune dwarf virus (PDV), Prunus necrotic ringspot virus (PNRSV) and Apple mosaic virus (ApMV) modifying a previously reported assay for the generic detection of ilarviruses. In all cases one generic upstream primer was used along with a virus-specific downstream primer in respective nested PCR assays. The application of the same thermocycling profile allowed all amplifications to run in parallel. Ilarvirus isolates from different hosts were used for the evaluation of the detection range of the assays, which were afterwards applied for screening almond and cherry plant material. In almond trees the incidence of PNRSV and PDV was 41% and 21.5%, respectively. In cherry orchards the opposite was observed with PDV (56.6%) being the prevalent virus followed by PNRSV (19.4%). Mixed infections with both viruses were also encountered in approximately 10 and 17% of cherry and almond trees, respectively. ApMV was not detected in any of the samples tested. This is the first extensive survey conducted in Greece in order to monitor the distribution of these viruses using molecular assays. Keywords: Prune dwarf virus, Prunus necrotic ringspot virus, Apple mosaic virus, cherry, almond, nested PC

Chapter 21 Artificial intelligence and data analytics for geosciences and remote sensing theory and application

To address the limitation of conventional statistics in dealing with hyperspectral data of satellite and airborne images, two contextual analyses are introduced in this chapter. The first case study presents the development of an artificial intelligence (AI) and data analytics algorithm capable of classifying hyperspectral data to support remote sensing and geographic information systems researchers in understanding and predicting changes in natural earth processes. The classification algorithm is based on a fuzzy approach combining a decision tree classifier with a fuzzy multiple-criteria decision analysis classifier. The second case study presents the development of an AI tool that extracts features from the hyperspectral data to transform a two-dimensional (2D) satellite and airborne picture to a pseudo-3D picture to improve complexity and produce multidirectional sun-shaded pictures and their edges. Such 3D images are useful in supporting the discovery of prospective ground for mineral exploration, extraction from the earth of precious minerals or other geological materials, usually from deposits of ore, veins, lodes, seams, reefs, or placer deposits, and overall to improve the efficiency and effectiveness of mineral exploration