Modulation of the human gut microflora towards improved health using prebiotics: assessment of efficacy

There is increasing awareness that the human gut microflora plays a critical role in maintaining host health, both within the gastrointestinal tract and, through the absorption of metabolites, systemically. An 'optimal' gut microflora establishes an efficient barrier to the invasion and colonisation of the gut by pathogenic bacteria, produces a range of metabolic substrates which in turn are utilized by the host (e.g. vitamins and short chain fatty acids) and stimulates the immune system in a non-inflammatory manner. Although little is known about the individual species of bacteria responsible for these beneficial activities, it is generally accepted that the bifidobacteria and lactobacilli constitute important components of the beneficial gut microflora. A number of diet-based microflora management tools have been developed and refined over recent decades including probiotic, prebiotic and synbiotic approaches. Each aims to stimulate numbers and/or activities of the bifidobacteria and lactobacilli within the gut microflora. The aim of this article is to examine how prebiotics are being applied to the improvement of human health and to review the scientific evidence supporting their use.

Improved arthritic knee health in a pilot RCT of phytotherapy

Although practitioner-prescribed 'western' herbal medicine (phytotherapy) is a popular complementary therapy in the UK, no clinical studies have been reported on patient-orientated outcomes. The objective of this pilot study was to investigate the effects of phytotherapy on symptoms of osteoarthritis of the knee. A previous study of Chinese herbal medicine for the treatment of irritable bowel syndrome, published in the Journal of the American Medical Association,(1) acted as a model in the development of the protocol of this investigation. Twenty adults, previously diagnosed with osteoarthritis of the knee, were recruited from two Inner London GP practices into this randomized, double-blind, placebo-controlled, pilot study carried out in a primary-care setting. All subjects were seen in consultation three times by a herbal practitioner who was blinded to the randomization coding. Each subject was prescribed treatment and given lifestyle advice according to usual practice: continuation of conventional medication where applicable, healthy-eating advice and nutrient supplementation, Individualized herbal medicine was prescribed for each patient, but only dispensed for those randomized to active treatment - the remainder were supplied with a placebo. At baseline and outcome (after ten weeks of treatment), subjects completed a food frequency questionnaire and the Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC) knee health and Measure Yourself Outcome Profile (MYMOP) wellbeing questionnaires. Subjects completing the study per protocol (n = 14) reported an increased intake of wholegrain foods (p = 0.045) and oily fish (p = 0.039) compared to baseline, but no increase in fruit and vegetables and dairy products intakes. There was no difference in the primary outcome measure of knee health assessed as the difference in the mean response (baseline-week 10) in WOMAC score between the two treatment groups. However, there was, compared with baseline, improvement in the active group (n = 9) for the mean WOMAC stiffness sub-score at week 5 (p = 0.035) and week 10 (p = 0.060) but not in the placebo group (n = 5). Furthermore, for the active, but not the placebo group, the mean WOMAC total and sub-scores all showed clinically significant improvement (>= 20%) in knee symptoms at weeks 5 and 10 compared with baseline. Moreover, the mean MYMOP symptom 2 sub-score, mostly relating to osteoarthritis (OA), showed significant improvement at week 5 (p = 0.02) and week 10 (p = 0.008) compared with baseline for the active, but not for the placebo group. This pilot study showed that herbal medicine prescribed for the individual by a herbal practitioner resulted in improvement of symptoms of OA of the knee.

Improved fluorescent proteins for single-molecule research in molecular tracking and co-localization

Three promising variants of autofluorescent proteins have been analyzed photophysically for their proposed use in single-molecule microscopy studies in living cells to compare their superiority to other fluorescent proteins previously reported regarding the number of photons emitted. The first variant under investigation the F46L mutant of eYFP has a 10% greater photon emission rate and > 50% slower photobleaching rate on average than the standard eYFP fluorophore. The monomeric red fluorescent protein (mRFP) has a fivefold lower photon emission rate, likely due to the monomeric content, and also a tenfold faster photobleaching rate than the DsRed fluorescent protein. In contrast, the previously reported eqfp611 has a 50% lower emission rate yet photobleaches more than a factor 2 slowly. We conclude that the F46L YFP and the eqfp611 are superior new options for single molecule imaging and tracking studies in living cells. Studies were also performed on the effects of forced quenching of multiple fluorescent proteins in sub-micrometer regions that would show the effects of dimerization at low concentration levels of fluorescent proteins and also indicate corrections to stoichiometry patterns with fluorescent proteins previously in print. We also introduce properties at the single molecule level of new FRET pairs with combinations of fluorescent proteins and artificial fluorophores.

A homo-proline tetrazole as an improved organocatalyst for the asymmetric Michael addition of carbonyl compounds to nitro-olefins

A new homo-proline tetrazole derivative 7 has been prepared and shown to have improved properties for achieving asymmetric Michael addition of carbonyl compounds to nitro-olefins.

Organocatalysis with proline derivatives: improved catalysts for the asymmetric Mannich, nitro-Michael and aldol reactions

Tetrazole and acylsulfonamide organocatalysts derived from proline have been synthesised and applied to the asymmetric Mannich, nitro-Michael and aldol reactions to give results that are superior to the proline-catalysed counterpart.

Improved channel estimation in TDMA by reproduction with applications in GSM/GPRS under co-channel interference

Finding an estimate of the channel impulse response (CIR) by correlating a received known (training) sequence with the sent training sequence is commonplace. Where required, it is also common to truncate the longer correlation to a sub-set of correlation coefficients by finding the set of N sequential correlation coefficients with the maximum power. This paper presents a new approach to selecting the optimal set of N CIR coefficients from the correlation rather than relying on power. The algorithm reconstructs a set of predicted symbols using the training sequence and various sub-sets of the correlation to find the sub-set that results in the minimum mean squared error between the actual received symbols and the reconstructed symbols. The application of the algorithm is presented in the context of the TDMA based GSM/GPRS system to demonstrate an improvement in the system performance with the new algorithm and the results are presented in the paper. However, the application lends itself to any training sequence based communication system often found within wireless consumer electronic device(1).

Two-Pass Hexagonal algorithm with improved Hashtable structure for motion estimation

This paper presents an improved Two-Pass Hexagonal (TPA) algorithm constituted by Linear Hashtable Motion Estimation Algorithm (LHMEA) and Hexagonal Search (HEXBS) for motion estimation. In the TPA, Motion Vectors (MV) are generated from the first-pass LHMEA and are used as predictors for second-pass HEXBS motion estimation, which only searches a small number of Macroblocks (MBs). The hashtable structure of LHMEA is improved compared to the original TPA and LHMEA. The evaluation of the algorithm considers the three important metrics being processing time, compression rate and PSNR. The performance of the algorithm is evaluated by using standard video sequences and the results are compared to current algorithms.

Improved two-pass hexagonal algorithm with parallel implementation for video coding - art. no. 60630J

This paper presents an improved parallel Two-Pass Hexagonal (TPA) algorithm constituted by Linear Hashtable Motion Estimation Algorithm (LHMEA) and Hexagonal Search (HEXBS) for motion estimation. Motion Vectors (MV) are generated from the first-pass LHMEA and used as predictors for second-pass HEXBS motion estimation, which only searches a small number of Macroblocks (MBs). We used bashtable into video processing and completed parallel implementation. The hashtable structure of LHMEA is improved compared to the original TPA and LHMEA. We propose and evaluate parallel implementations of the LHMEA of TPA on clusters of workstations for real time video compression. The implementation contains spatial and temporal approaches. The performance of the algorithm is evaluated by using standard video sequences and the results are compared to current algorithms.

An improved distributed dynamic channel assignment scheme for dense WLANs

The popularity of wireless local area networks (WLANs) has resulted in their dense deployment in many cities around the world. The increased interference among different WLANs severely degrades the throughput achievable. This problem has been further exacerbated by the limited number of frequency channels available. An improved distributed and dynamic channel assignment scheme that is simple to implement and does not depend on the knowledge of the throughput function is proposed in this work. It also allows each access point (AP) to asynchronously switch to the new best channel. Simulation results show that our proposed scheme converges much faster than similar previously reported work, with a reduction in convergence time and channel switches as much as 77.3% and 52.3% respectively. When it is employed in dynamic environments, the throughput improves by up to 12.7%.

Characterization and classification of tumor lesions using computerized fractal-based texture analysis and support vector machines in digital mammograms

Objective: This paper presents a detailed study of fractal-based methods for texture characterization of mammographic mass lesions and architectural distortion. The purpose of this study is to explore the use of fractal and lacunarity analysis for the characterization and classification of both tumor lesions and normal breast parenchyma in mammography. Materials and methods: We conducted comparative evaluations of five popular fractal dimension estimation methods for the characterization of the texture of mass lesions and architectural distortion. We applied the concept of lacunarity to the description of the spatial distribution of the pixel intensities in mammographic images. These methods were tested with a set of 57 breast masses and 60 normal breast parenchyma (dataset1), and with another set of 19 architectural distortions and 41 normal breast parenchyma (dataset2). Support vector machines (SVM) were used as a pattern classification method for tumor classification. Results: Experimental results showed that the fractal dimension of region of interest (ROIs) depicting mass lesions and architectural distortion was statistically significantly lower than that of normal breast parenchyma for all five methods. Receiver operating characteristic (ROC) analysis showed that fractional Brownian motion (FBM) method generated the highest area under ROC curve (A z = 0.839 for dataset1, 0.828 for dataset2, respectively) among five methods for both datasets. Lacunarity analysis showed that the ROIs depicting mass lesions and architectural distortion had higher lacunarities than those of ROIs depicting normal breast parenchyma. The combination of FBM fractal dimension and lacunarity yielded the highest A z value (0.903 and 0.875, respectively) than those based on single feature alone for both given datasets. The application of the SVM improved the performance of the fractal-based features in differentiating tumor lesions from normal breast parenchyma by generating higher A z value. Conclusion: FBM texture model is the most appropriate model for characterizing mammographic images due to self-affinity assumption of the method being a better approximation. Lacunarity is an effective counterpart measure of the fractal dimension in texture feature extraction in mammographic images. The classification results obtained in this work suggest that the SVM is an effective method with great potential for classification in mammographic image analysis.

An improved reliability Cuk based solar inverter with sliding mode control

Many photovoltaic inverter designs make use of a buck based switched mode power supply (SMPS) to produce a rectified sinusoidal waveform. This waveform is then unfolded by a low frequency switching structure to produce a fully sinusoidal waveform. The Cuk SMPS could offer advantages over the buck in such applications. Unfortunately the Cuk converter is considered to be difficult to control using classical methods. Correct closed loop design is essential for stable operation of Cuk converters. Due to these stability issues, Cuk converter based designs often require stiff low bandwidth control loops. In order to achieve this stable closed loop performance, traditional designs invariably need large, unreliable electrolytic capacitors. In this paper, an inverter with a sliding mode control approach is presented which enables the designer to make use of the Cuk converters advantages, while ameliorating control difficulties. This control method allows the selection of passive components based predominantly on ripple and reliability specifications while requiring only one state reference signal. This allows much smaller, more reliable non-electrolytic capacitors to be used. A prototype inverter has been constructed and results obtained which demonstrate the design flexibility of the Cuk topology when coupled with sliding mode control.

Precipitation and latent heating distributions from satellite passive microwave radiometry. Part I: improved method and uncertainties

A revised Bayesian algorithm for estimating surface rain rate, convective rain proportion, and latent heating profiles from satellite-borne passive microwave radiometer observations over ocean backgrounds is described. The algorithm searches a large database of cloud-radiative model simulations to find cloud profiles that are radiatively consistent with a given set of microwave radiance measurements. The properties of these radiatively consistent profiles are then composited to obtain best estimates of the observed properties. The revised algorithm is supported by an expanded and more physically consistent database of cloud-radiative model simulations. The algorithm also features a better quantification of the convective and nonconvective contributions to total rainfall, a new geographic database, and an improved representation of background radiances in rain-free regions. Bias and random error estimates are derived from applications of the algorithm to synthetic radiance data, based upon a subset of cloud-resolving model simulations, and from the Bayesian formulation itself. Synthetic rain-rate and latent heating estimates exhibit a trend of high (low) bias for low (high) retrieved values. The Bayesian estimates of random error are propagated to represent errors at coarser time and space resolutions, based upon applications of the algorithm to TRMM Microwave Imager (TMI) data. Errors in TMI instantaneous rain-rate estimates at 0.5°-resolution range from approximately 50% at 1 mm h−1 to 20% at 14 mm h−1. Errors in collocated spaceborne radar rain-rate estimates are roughly 50%–80% of the TMI errors at this resolution. The estimated algorithm random error in TMI rain rates at monthly, 2.5° resolution is relatively small (less than 6% at 5 mm day−1) in comparison with the random error resulting from infrequent satellite temporal sampling (8%–35% at the same rain rate). Percentage errors resulting from sampling decrease with increasing rain rate, and sampling errors in latent heating rates follow the same trend. Averaging over 3 months reduces sampling errors in rain rates to 6%–15% at 5 mm day−1, with proportionate reductions in latent heating sampling errors.