Physicochemical changes upon micronization process positively improve the intestinal health-enhancement ability of carrot insoluble fibre

The influences of the particle size reduction and physicochemical changes by micron technology on the intestinal health-improvement ability of a promising carrot insoluble fibre was investigated. The feeding of the micronized fibres (10.3–20.9 μm), especially that prepared by the high-pressure micronization, could significantly (p < 0.05) improve some caecal and faecal parameters in the intestinal lumen by decreasing caecal ammonia concentration (−25.5%), increasing faecal output (137%) and moisture content (142%), and also reducing the activities of undesired β-d-glucosidase (−42.1%) and β-d-glucuronidase (−68.9%) in faeces. The relationships between the physicochemical properties and physiological functions of the micronized fibres have been discussed. The results also demonstrated that both the particle size and way of treatment were important factors affecting the physicochemical properties and physiological functions of fibres, and the consumption of micronized fibre at 5% level might exert a favourable effect on improving intestinal health.     

Single-symbol-decodable differential space-time modulation based on QO-STBC

We present a novel differential space-time modulation (DSTM) scheme that is single-symbol decodable and can provide full transmit diversity. It is the first known single-symbol-decodable DSTM scheme not based on orthogonal STBC (O-STBC), and it is constructed based on the recently proposed minimum-decoding-complexity quasi-orthogonal space-time block code (MDC-QOSTBC). We derive the code design criteria and present systematic methodology to find the solution sets. The proposed DSTM scheme can provide higher code rate than DSTM schemes based on O-STBC. Its decoding complexity is also considerably lower than DSTM schemes based on Sp(2) and double-symbol-decodable QO-STBC, with negligible or slight trade-off in decoding error probability performance     

Effects of micronisation on the characteristics and physicochemical properties of insoluble fibres

The effects of micronisation on the characteristics and physicochemical properties of (a) an insoluble fibre‐rich fraction (IFRF) prepared from orange peel and (b) cellulose were studied and compared. The results showed that micronisation techniques such as ball milling, jet milling and high‐pressure micronisation could effectively (P < 0.05) reduce particle sizes to microscale, decrease the bulk density and improve the physicochemical properties (water‐holding capacity, swelling capacity, oil‐holding capacity, cation exchange capacity and glucose adsorption capacity) of IFRF and cellulose to different extents (up to 25‐fold). After micronisation the inhibitory effects of these insoluble fibres towards α‐amylase and pancreatic lipase were significantly increased (up to 5.8‐ and 7.8‐fold respectively) and a redistribution of some fibre components from insoluble to soluble fractions was observed. It was concluded that these micronisation treatments, especially high‐pressure micronisation, could effectively improve the functionalities of IFRF and cellulose, which might then be used as potential fibre‐rich ingredients in functional food applications. Copyright © 2006 Society of Chemical Industry     

Nanoaggregate‐Embedded Beads as Novel Raman Labels for Biodetection

Novel Raman tags called nanoaggregate‐embedded beads (NAEBs) have been developed. NAEBs are silica‐coated, dye‐induced aggregates of a small number of metal nanoparticles. In this work, the Raman reporters used to induce aggregation of gold nanoparticles include strongly binding dyes such as XRITC, TRITC, and DTDC and weakly binding dyes such as R6G. Surface‐enhanced Raman scattering (SERS) signal from a single NAEB can be detected. This study also demonstrates that these SERS‐active beads can be used as Raman tags for bio‐detection.     

A sparse matrix approach to the solubilization of overexpressed proteins

Many biophysical experiments depend on large amounts of pure,soluble protein. Indeed, the revolution in structural biologyhas depended on molecular biology's potential to make experimentspossible by allowing the overexpression of normally rare proteinsin a heterologous host. All too often, however, overexpressedproteins are poorly soluble in buffers that attempt to mimicphysiological conditions. Often in such cases the overexpressedprotein is assumed to be present in inclusion bodies and hopesof obtaining the desired sample from the overexpression vectorare abandoned. We have developed a sparse matrix approach tothe solubilization of such proteins that is often successful.This approach relies on well accepted theories of protein solubilityand folding to build a sparse matrix that samples `solubilityspace' effectively. The buffers of the sparse matrix are usedto make crude extracts that are rapidly assayed for solubleprotein using gel electrophoresis. We describe our approachand give examples of its application.     

Optimizing the mixture of wood biomass for greenhouse heating

This paper presents the development of a linear programming model to determine the optimum fuel mix for greenhouse heating. The model is applied to two cases (a 7.5 hectare (ha) vegetable greenhouse and a 2 ha flower greenhouse) in British Columbia, which use a combination of wood pellets and wood residue to provide heat. The objective of the model is to minimize the annual total cost of combusting wood pellets and wood residue, while the heat demand is satisfied and all resources and emission limitations are not exceeded. The boiler system was enhanced with an electrostatic precipitator to limit the particulate matter emissions to acceptable levels. The results show that the optimal fuel mix for a 2 ha flower greenhouse is 641 tonne (t) of wood pellets and 381 t of wood residues, and it is 10 106 t of wood pellets and 3007 t of wood residues for a 7.5 ha vegetable greenhouse. The minimized annual total cost, including amortized capital cost, is estimated to be C$216 030 y^?1 and C$1 611 866 y^?1 for a 2 ha flower greenhouse and a 7.5 ha vegetable greenhouse, respectively. A sensitivity analysis indicated that these optimal solutions would not change even when the variable costs (material cost and emission fee) of wood pellets decreased by 58% or the variable costs of wood residue increased by 150%. Copyright © 2008 John Wiley & Sons, Ltd.     

Approximation techniques for response-time analysis of static-priority tasks

We consider sporadic tasks with static priorities and constrained deadlines to be executed upon a uniprocessor platform. Pseudo-polynomial time algorithms are known for computing worst-case response times for this task model. Some applications require to evaluate efficiently upper bounds of response times. For this purpose, we propose parametric algorithms that allow to make a tradeoff between quality of results and computational effort according to an input accuracy parameter. In this paper, we present a parametric polynomial-time algorithm for computing upper bounds of worst-case response times, that is based on an improved fptas (Fully Polynomial Time Approximation Scheme). Then, we show that our bound does not achieve constant error bound in comparison with the exact worst-case response time. However, using the resource augmentation technique, we obtain a performance guarantee that allows to define a compromise between our response-time bound and processor capacity requirements. The algorithm average behavior is then analyzed through numerical experimentations.     

Real-time classification of forearm electromyographic signals corresponding to user-selected intentional movements for multifunction prosthesis control

Pattern recognition-based multifunction prosthesis control strategies have largely been demonstrated with subsets of typical able-bodied hand movements. These movements are often unnatural to the amputee, necessitating significant user training and do not maximally exploit the potential of residual muscle activity. This paper presents a real-time electromyography (EMG) classifier of user-selected intentional movements rather than an imposed subset of standard movements. EMG signals were recorded from the forearm extensor and flexor muscles of seven able-bodied participants and one congenital amputee. Participants freely selected and labeled their own muscle contractions through a unique training protocol. Signals were parameterized by the natural logarithm of root mean square values, calculated within 0.2 s sliding and non overlapping windows. The feature space was segmented using fuzzy C-means clustering. With only 2 min of training data from each user, the classifier discriminated four different movements with an average accuracy of 92.7% +/- 3.2%. This accuracy could be further increased with additional training data and improved user proficiency that comes with practice. The proposed method may facilitate the development of dynamic upper extremity prosthesis control strategies using arbitrary, user-preferred muscle contractions.