University of Greenwich

About: University of Greenwich is a education organization based out in London, United Kingdom. It is known for research contribution in the topics: Population & Context (language use). The organization has 3749 authors who have published 9958 publications receiving 234340 citations.

Glacigenic clast fabrics: genetic fingerprint or wishful thinking?

Abstract: The interpretation of glacigenic diamictons is a subjective process, for which quantitative support is frequently sought from parameters such as clast shape and fabric. It has been widely suggested that different glacigenic diamicton facies possess distinct clast-fabric signatures. This paper examines this concept using a data set of 111 clast fabrics, and a synthesis of published results. Eigenvalues are calculated and compared for a variety sedimentary facies. It is concluded that clast fabric alone is not able to discriminate between different glacigenic facies, and it is argued that clast fabric offers little quantitative support in the interpretation of glacigenic sediments. It is suggested, therefore, that although clast fabric may continue to have a role as an indicator of relative strain at specific sites, its use in the discrimination of glacigenic facies is limited. Consequently, we should be much more selective in undertaking such analyses in the future. Copyright © 1999 John Wiley & Sons, Ltd.

Evidence for arginine vasopressin as the primary activator of the HPA axis during adjuvant-induced arthritis.

Abstract: 1. Adjuvant-induced arthritis (AA) is an experimental inflammation of the joints that results in chronic activation of the hypothalamo-pituitary-adrenal (HPA) axis. 2. In this study the role of hypothalamic corticotrophin-releasing factor (CRF) and arginine vasopressin (AVP) in the regulation of the HPA axis in this condition both in Sprague-Dawley (SD), and Piebald-Viral-Glaxo (PVG) rats has been further characterized. 3. The increase in AVP peptide content of portal blood (as early as day 11), just prior to the onset of arthritis is confirmed and further increases, peaking at day 16 are shown, coincident with the progression of inflammation in the PVG rats. 4. The increase in AVP is associated with a significant increase in the expression of AVP but not CRF mRNAs in the medial parvocellular division of the hypothalamic paraventricular nucleus (PVN) of arthritic SD rats. 5. In the presence of maximal inflammation of SD rats there was a significant decrease in the maximum binding of [125I]-Tyr-oCRF to anterior pituitary membranes, whereas AVP receptor concentration in anterior pituitary membranes from both PVG and SD rats showed a significant increase with respect to controls. 6. The basal adrenocorticotrophin (ACTH) secretion in vitro was similar in both control and arthritic SD rats but that from arthritic PVG rat pituitaries was significantly greater than the respective controls (436 +/- 91 v 167 +/- 23 pg/tube). The ACTH response of pituitaries of arthritic PVG rats to CRF or the combination of CRF and AVP was significantly higher compared with the controls, although the ACTH response of arthritic SD rat pituitaries was unchanged. 7. The results are consistent with the view that activation of the parvocellular vasopressin system has an important role in the adaptation of the HPA axis to experimentally-induced chronic stress of arthritis.

RNAi-mediated resistance to diverse isolates belonging to two virus species involved in Cassava brown streak disease

Abstract: Cassava brown streak disease (CBSD) is emerging as one of the most important viral diseases of cassava (Manihot esculenta) and is considered today as the biggest threat to cassava cultivation in East Africa. The disease is caused by isolates of at least two phylogenetically distinct species of single-stranded RNA viruses belonging to the family Potyviridae, genus Ipomovirus. The two species are present predominantly in the coastal lowland [Cassava brown streak virus (CBSV); Tanzania and Mozambique] and highland [Cassava brown streak Uganda virus (CBSUV); Lake Victoria Basin, Uganda, Kenya and Malawi] in East Africa. In this study, we demonstrate that CBSD can be efficiently controlled using RNA interference (RNAi). Three RNAi constructs targeting the highland species were generated, consisting of the full-length (FL; 894 nucleotides), 397-nucleotide N-terminal and 491-nucleotide C-terminal portions of the coat protein (CP) gene of a Ugandan isolate of CBSUV (CBSUV-[UG:Nam:04]), and expressed constitutively in Nicotiana benthamiana. After challenge with CBSUV-[UG:Nam:04], plants homozygous for FL-CP showed the highest resistance, followed by the N-terminal and C-terminal lines with similar resistance. In the case of FL, approximately 85% of the transgenic plant lines produced were completely resistant. Some transgenic lines were also challenged with six distinct isolates representing both species: CBSV and CBSUV. In addition to nearly complete resistance to the homologous virus, two FL plant lines showed 100% resistance and two C-terminal lines expressed 50–100% resistance, whereas the N-terminal lines succumbed to the nonhomologous CBSV isolates. Northern blotting revealed a positive correlation between the level of transgene-specific small interfering RNAs detected in transgenic plants and the level of virus resistance. This is the first demonstration of RNAi-mediated resistance to CBSD and protection across very distant isolates (more than 25% in nucleotide sequence) belonging to two different species: Cassava brown streak virus and Cassava brown streak Uganda virus.

A wavelet-based approach to abrupt fault detection and diagnosis of sensors

Abstract: This paper describes a novel wavelet-based approach to the abrupt fault detection and diagnosis of sensors. By the use of wavelet transforms that accurately localize the characteristics of a signal both in the time and frequency domains, the occurring instants of abnormal status of a sensor in the output signal can be identified by the multiscale representation of the signal. Once the instants are detected, the distribution differences of the signal energy on all decomposed wavelet scales of the signal before and after the instants are used to claim and classify the sensor faults. Synthetic data simulated by means of a computer using real-word data from a general-purpose pressure sensor have verified the effectiveness of the proposed method.