DECOLORIZATION OF ORANGE 16 BY BACTERIA

Joint Research on Environmental Science and Technology for the Eart

Decolorization of Orange 16 by Mixed Culture of Bacteria

Joint Research on Environmental Science and Technology for the Eart

PURIFICATION AND PROPERTIES OF AN AZO-REDUCTASE FROM BACILLUS SP.

Joint Research on Environmental Science and Technology for the Eart

GENERATION OF BACILLUS SUBTILIS CLONE DISPLAYING METAL-BINDING POLYHISTIDYL PEPTIDE

Joint Research on Environmental Science and Technology for the Eart

DECOLORIZATION OF AZO DYES BY PURPLE NON-SULFUR BACTERIA

Joint Research on Environmental Science and Technology for the Eart

Quantification of tongue colour using machine learning in Kampo medicine

AbstractIntroductionThe evaluation of tongue colour has been an important approach to examine human health in Kampo medicine (traditional Japanese medicine) because the change in tongue colour may suggest physical or mental disorders. Several tongue colour quantification methods have been published to objectify clinical information among East Asian countries. However, reliable tongue colour analysis results among Japanese test persons are limited because of a lack of quantitative evaluation of tongue colour. We aimed to use advances in digital imaging processing to quantify and verify clinical data tongue colour diagnosis by characterising differences intongue features.MethodsThe DS01-B tongue colour information acquisition system was used to extract tongue images of 1080 Japanese test subjects. Evaluation of tongue colour, body and coating was performed by 10 experienced Kampo medicine physicians. The acquired images were classified into five tongue body colour categories and six tongue coating colour categories based on evaluations from 10 physicians with extensive Kampo medicine experience. K-means clustering algorithm was applied as a machine learning (the study of pattern recognition by computational learning) method to the acquired images to quantify tongue body and coating colour information.ResultsTongue body (n=550) and tongue coating (n=516) colour samples were classified and analysed. Clusters consisting of five tongue body colour categories and six tongue coating colour categories were experimentally described in the CIELAB colour space. Statistical differences were evident among the clinically primary tongue colours.ConclusionsClinically important tongue colour differences in Kampo medicine can be visualised by applying machine learning to tongue images taken under stable conditions. This has implications for developing globally unified, reliable tongue colour diagnostic criteria which could be used to explore the relevance between clinical status and tongue colour

Optimal Timing of Insecticide Fogging to Minimize Dengue Cases: Modeling Dengue Transmission among Various Seasonalities and Transmission Intensities

Dengue virus infection is a serious infectious disease transmitted by Aedes mosquitoes in the tropics and sub-tropics. Disease control often involves the use of insecticide fogging against mosquito vectors. However, the effectiveness of this method for reducing dengue cases, in addition to appropriate application procedures, is still debated. The previous mathematical simulation study reported that insecticide fogging reduces dengue cases most effectively when applied soon after the epidemic peak; however, the model did not take into account seasonality and population immunity, which strongly affect the epidemic pattern of dengue infection. Considering these important factors, we used a mathematical simulation model to explore the most effective time for insecticide fogging and to evaluate its impact on reducing dengue cases. Simulations were conducted with various lengths of the wet season and population immunity levels. We found that insecticide fogging substantially reduces dengue cases if conducted at an appropriate time. In contrast to the previously suggested application time during the peak of disease prevalence, the optimal timing is relatively early: between the beginning of the dengue season and the prevalence peak

Effectiveness of xylose utilization for high yield production of lactate-enriched P(lactate-co-3-hydroxybutyrate) using a lactate-overproducing strain of Escherichia coli and an evolved lactate-polymerizing enzyme

Xylose, which is a major constituent of lignocellulosic biomass, was utilized for the production of poly(lactate-co-3-hydroxybutyrate) [P(LA-co-3HB)], having transparent and flexible properties. The recombinant Escherichia coil JW0885 (pflA(-)) expressing LA-polymerizing enzyme (LPE) and monomer supplying enzymes grown on xylose produced a copolymer having a higher LA fraction (34 mol%) than that grown on glucose (26 mol%). This benefit of xylose was further enhanced by combining it with an evolved LPE (ST/FS/QK), achieving a copolymer production having 60 mol% LA from xylose, while glucose gave a 47 mol% LA under the same condition. The overall carbon yields from the sugars to the polymer were similar for xylose and glucose, but the ratio of the LA and 3HB units in the copolymer was different. Notably, the P(LA-co-3HB) yield from xylose (7.3 g l(-1)) was remarkably higher than that of P(3HB) (4.1 g l(-1)), indicating P(LA-co-3HB) as a potent target for xylose utilization. (C) 2012 Elsevier Inc. All rights reserved