A RG-II type polysaccharide purified from Aconitum coreanum and their anti-inflammatory activity

Korean mondshood root polysaccharides (KMPS) isolated from the root of Aconitum coreanum (Lévl.) Rapaics have shown anti-inflammatory activity, which is strongly influenced by their chemical structures and chain conformations. However, the mechanisms of the anti-inflammatory effect by these polysaccharides have yet to be elucidated. A RG-II polysaccharide (KMPS-2E, Mw 84.8 kDa) was isolated from KMPS and its chemical structure was characterized by FT-IR and NMR spectroscopy, gas chromatography–mass spectrometry and high-performance liquid chromatography. The backbone of KMPS-2E consisted of units of [→6) -β-D-Galp (1→3)-β-L-Rhap-(1→4)-β-D-GalpA-(1→3)-β-D-Galp-(1→] with the side chain →5)-β-D-Arap (1→3, 5)-β-D-Arap (1→ attached to the backbone through O-4 of (1→3,4)-L-Rhap. T-β-D-Galp is attached to the backbone through O-6 of (1→3,6)-β-D-Galp residues and T-β-D-Ara is connected to the end group of each chain. The anti-inflammatory effects of KMPS-2E and the underlying mechanisms using lipopolysaccharide (LPS) - stimulated RAW 264.7 macrophages and carrageenan-induced hind paw edema were investigated. KMPS-2E (50, 100 and 200 µg/mL) inhibits iNOS, TLR4, phospho-NF-κB–p65 expression, phosphor-IKK, phosphor-IκB-α expression as well as the degradation of IκB-α and the gene expression of inflammatory cytokines (TNF-α, IL-1β, iNOS and IL-6) mediated by the NF-κB signal pathways in macrophages. KMPS-2E also inhibited LPS-induced activation of NF-κB as assayed by electrophorectic mobility shift assay (EMSA) in a dose-dependent manner and it reduced NF-κB DNA binding affinity by 62.1% at 200µg/mL. In rats, KMPS-2E (200 mg/kg) can significantly inhibit carrageenan-induced paw edema as ibuprofen (200 mg/kg) within 3 h after a single oral dose. The results indicate that KMPS-2E is a promising herb-derived drug against acute inflammation.

The causal relationship between private and public investment in Zimbabwe

Aims: The study examines the relationship between private and public investment in Zimbabwe utilizing yearly time series data for the period 1967 to 2004. Study Design: Time Series Study. Place and Duration of Study: Zimbabwe, May 2011 to July 2011. Methodology: Emphasis is placed on the direction of causality and the long run and short run effect of the two types of investment on each other. The paper constructs empirical models for both private and public investment, based on the flexible accelerator theory. Private investment is found to be cointegrated with public investment. A cointergration and VEC models are employed to assess the long and short run relationship existing between public and private investment. Conclusion: The relationship between private and public investment is found to be insignificant and the direction of causality found to be unidirectional. The results support the notion that private investment precedes public investment.

A case study in identifying acceptable bitrates for human face recognition tasks

Face recognition from images or video footage requires a certain level of recorded image quality. This paper derives acceptable bitrates (relating to levels of compression and consequently quality) of footage with human faces, using an industry implementation of the standard H.264/MPEG-4 AVC and the Closed-Circuit Television (CCTV) recording systems on London buses. The London buses application is utilized as a case study for setting up a methodology and implementing suitable data analysis for face recognition from recorded footage, which has been degraded by compression. The majority of CCTV recorders on buses use a proprietary format based on the H.264/MPEG-4 AVC video coding standard, exploiting both spatial and temporal redundancy. Low bitrates are favored in the CCTV industry for saving storage and transmission bandwidth, but they compromise the image usefulness of the recorded imagery. In this context, usefulness is determined by the presence of enough facial information remaining in the compressed image to allow a specialist to recognize a person. The investigation includes four steps: (1) Development of a video dataset representative of typical CCTV bus scenarios. (2) Selection and grouping of video scenes based on local (facial) and global (entire scene) content properties. (3) Psychophysical investigations to identify the key scenes, which are most affected by compression, using an industry implementation of H.264/MPEG-4 AVC. (4) Testing of CCTV recording systems on buses with the key scenes and further psychophysical investigations. The results showed a dependency upon scene content properties. Very dark scenes and scenes with high levels of spatial–temporal busyness were the most challenging to compress, requiring higher bitrates to maintain useful information.