The utility of leaf flavonoids as taxonomic markers for some Malaysian species of the tribe Shoreae (Dipterocarpaceae)

A flavonoid survey was carried out on 45 taxa from the genera Shorea, Hopea, Parashorea, Neobalanocarpus, and Dryobalanops of the tribe Shoreae in the Dipterocarpaceae. The study showed significant chemotaxonomic differences in leaf flavonoid aglycone patterns and the presence of tannins in these taxa. The flavonoid patterns are useful in the delimitation of some taxa. For example, the genus Parashorea is distinguished by the universal presence of kaempferol 3-methyl ether, and the monotypic genus Neobalanocarpus is unique in not producing ellagic and gallo tannins. The presence of chalcones and flavone C-glycosides supports the separation of the genus Hopea into two sections, section Dryobalanoides and section Hopea in Ashton's classification, which is based on the type of venation. The flavonoid distributions in this study show that they can be very useful for differentiating between the Balau group in the genus Shorea, and some scaly barked Hopea species, particularly H. helferi (lintah bukit), H. nutans (giam), and H. ferrea (malut). (C) 2008 The Linnean Society of London.

Kinetics of high pressure facilitated starch gelatinisation in Thai glutinous rice

The combined effect of pressure and temperature on the rate of gelatinisation of starch present in Thai glutinous rice was investigated. Pressure was found to initiate gelatinisation when its value exceeded 200 MPa at ambient temperature. On the other hand, complete gelatinisation was observed at 500 and 600 MPa at 70 degrees C, when the rice was soaked in water under these conditions for 120 min. A first-order kinetic model describing the rate of gelatinisation was developed to estimate the values of the rate constants as a function of pressure and temperature in the range: 0.1-600 MPa and 20-70 degrees C. The model, based on the well-known Arrhenius and Eyring equations, assumed the form [GRAPHICS] The constants k(0), E-a, and Delta V were found to take values: 31.19 s(-1), 37.89 kJ mol(-1) and -9.98 cm(3) mol(-1), respectively. It was further noted that the extent of gelatinisation occurring at any time, temperature and pressure, could be exclusively correlated with the grain moisture content. (c) 2006 Elsevier Ltd. All rights reserved.

Robust classification of SAR imagery

In this work the G(A)(0) distribution is assumed as the universal model for amplitude Synthetic Aperture (SAR) imagery data under the Multiplicative Model. The observed data, therefore, is assumed to obey a G(A)(0) (alpha; gamma, n) law, where the parameter n is related to the speckle noise, and (alpha, gamma) are related to the ground truth, giving information about the background. Therefore, maps generated by the estimation of (alpha, gamma) in each coordinate can be used as the input for classification methods. Maximum likelihood estimators are derived and used to form estimated parameter maps. This estimation can be hampered by the presence of corner reflectors, man-made objects used to calibrate SAR images that produce large return values. In order to alleviate this contamination, robust (M) estimators are also derived for the universal model. Gaussian Maximum Likelihood classification is used to obtain maps using hard-to-deal-with simulated data, and the superiority of robust estimation is quantitatively assessed.

Overall evaluation of the PETS2009 results

This paper presents the results of the crowd image analysis challenge, as part of the PETS 2009 workshop. The evaluation is carried out using a selection of the metrics available in the Video Analysis and Content Extraction (VACE) program and the CLassification of Events, Activities, and Relationships (CLEAR) consortium. The evaluation highlights the strengths of the authors’ systems in areas such as precision, accuracy and robustness.

Classification of lactose and mandelic acid THz spectra using subspace and wavelet-packet algorithms

This work compares classification results of lactose, mandelic acid and dl-mandelic acid, obtained on the basis of their respective THz transients. The performance of three different pre-processing algorithms applied to the time-domain signatures obtained using a THz-transient spectrometer are contrasted by evaluating the classifier performance. A range of amplitudes of zero-mean white Gaussian noise are used to artificially degrade the signal-to-noise ratio of the time-domain signatures to generate the data sets that are presented to the classifier for both learning and validation purposes. This gradual degradation of interferograms by increasing the noise level is equivalent to performing measurements assuming a reduced integration time. Three signal processing algorithms were adopted for the evaluation of the complex insertion loss function of the samples under study; a) standard evaluation by ratioing the sample with the background spectra, b) a subspace identification algorithm and c) a novel wavelet-packet identification procedure. Within class and between class dispersion metrics are adopted for the three data sets. A discrimination metric evaluates how well the three classes can be distinguished within the frequency range 0. 1 - 1.0 THz using the above algorithms.

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.

In vitro evaluation of the microbiota modulation abilities of different sized whole oat grain flakes.

Epidemiological studies and healthy eating guidelines suggest a positive correlation between ingestion of whole grain cereal and food rich in fibre with protection from chronic diseases. The prebiotic potential of whole grains may be related, however, little is known about the microbiota modulatory capability of oat grain or the impact processing has on this ability. In this study the fermentation profile of whole grain oat flakes, processed to produce two different sized flakes (small and large), by human faecal microbiota was investigated in vitro. Simulated digestion and subsequent fermentation by gut bacteria was investigated using pH controlled faecal batch cultures inoculated with human faecal slurry. The different sized oat flakes, Oat 23’s (0.53–0.63 mm) and Oat 25’s/26’s (0.85–1.0 mm) were compared to oligofructose, a confirmed prebiotic, and cellulose, a poorly fermented carbohydrate. Bacterial enumeration was carried out using the culture independent technique, fluorescent in situ hybridisation, and short chain fatty acid (SCFA) production monitored by gas chromatography. Significant changes in total bacterial populations were observed after 24 h incubation for all substrates except Oat 23’s and cellulose. Oats 23’s fermentation resulted in a significant increase in the Bacteroides–Prevotella group. Oligofructose and Oats 25’s/26’s produced significant increases in Bifidobacterium in the latter stages of fermentation while numbers declined for Oats 23’s between 5 h and 24 h. This is possibly due to the smaller surface area of the larger flakes inhibiting the simulated digestion, which may have resulted in increased levels of resistant starch (Bifidobacterium are known to ferment this dietary fibre). Fermentation of Oat 25’s/26’s resulted in a propionate rich SCFA profile and a significant increase in butyrate, which have both been linked to benefiting host health. The smaller sized oats did not produce a significant increase in butyrate concentration. This study shows for the first time the impact of oat grain on the microbial ecology of the human gut and its potential to beneficially modulate the gut microbiota through increasing Bifidobacterium population.

Classification of instability modes in a model of aluminium reduction cells with a uniform magnetic field

A unified view on the interfacial instability in a model of aluminium reduction cells in the presence of a uniform, vertical, background magnetic field is presented. The classification of instability modes is based on the asymptotic theory for high values of parameter β, which characterises the ratio of the Lorentz force based on the disturbance current, and gravity. It is shown that the spectrum of the travelling waves consists of two parts independent of the horizontal cross-section of the cell: highly unstable wall modes and stable or weakly unstable centre, or Sele’s modes. The wall modes with the disturbance of the interface being localised at the sidewalls of the cell dominate the dynamics of instability. Sele’s modes are characterised by a distributed disturbance over the whole horizontal extent of the cell. As β increases these modes are stabilized by the field.

The north Atlantic marine oundary layer experiment (NAMBLEX): Overview of the campaign held at Mace Head, Ireland, in summer 2002

The North Atlantic Marine Boundary Layer Experiment (NAMBLEX), involving over 50 scientists from 12 institutions, took place at Mace Head, Ireland (53.32° N, 9.90° W), between 23 July and 4 September 2002. A wide range of state-of-the-art instrumentation enabled detailed measurements of the boundary layer structure and atmospheric composition in the gas and aerosol phase to be made, providing one of the most comprehensive in situ studies of the marine boundary layer to date. This overview paper describes the aims of the NAMBLEX project in the context of previous field campaigns in the Marine Boundary Layer (MBL), the overall layout of the site, a summary of the instrumentation deployed, the temporal coverage of the measurement data, and the numerical models used to interpret the field data. Measurements of some trace species were made for the first time during the campaign, which was characterised by predominantly clean air of marine origin, but more polluted air with higher levels of NOx originating from continental regions was also experienced. This paper provides a summary of the meteorological measurements and Planetary Boundary Layer (PBL) structure measurements, presents time series of some of the longer-lived trace species (O3, CO, H2, DMS, CH4, NMHC, NOx, NOy, PAN) and summarises measurements of other species that are described in more detail in other papers within this special issue, namely oxygenated VOCs, HCHO, peroxides, organo-halogenated species, a range of shorter lived halogen species (I2, OIO, IO, BrO), NO3 radicals, photolysis frequencies, the free radicals OH, HO2 and (HO2+Σ RO2), as well as a summary of the aerosol measurements. NAMBLEX was supported by measurements made in the vicinity of Mace Head using the NERC Dornier-228 aircraft. Using ECMWF wind-fields, calculations were made of the air-mass trajectories arriving at Mace Head during NAMBLEX, and were analysed together with both meteorological and trace-gas measurements. In this paper a chemical climatology for the duration of the campaign is presented to interpret the distribution of air-mass origins and emission sources, and to provide a convenient framework of air-mass classification that is used by other papers in this issue for the interpretation of observed variability in levels of trace gases and aerosols.

Advances in continuously profiling the thermodynamic state of the boundary layer: integration of measurements and methods

This paper describes advances in ground-based thermodynamic profiling of the lower troposphere through sensor synergy. The well-documented integrated profiling technique (IPT), which uses a microwave profiler, a cloud radar, and a ceilometer to simultaneously retrieve vertical profiles of temperature, humidity, and liquid water content (LWC) of nonprecipitating clouds, is further developed toward an enhanced performance in the boundary layer and lower troposphere. For a more accurate temperature profile, this is accomplished by including an elevation scanning measurement modus of the microwave profiler. Height-dependent RMS accuracies of temperature (humidity) ranging from 0.3 to 0.9 K (0.5–0.8 g m−3) in the boundary layer are derived from retrieval simulations and confirmed experimentally with measurements at distinct heights taken during the 2005 International Lindenberg Campaign for Assessment of Humidity and Cloud Profiling Systems and its Impact on High-Resolution Modeling (LAUNCH) of the German Weather Service. Temperature inversions, especially of the lower boundary layer, are captured in a very satisfactory way by using the elevation scanning mode. To improve the quality of liquid water content measurements in clouds the authors incorporate a sophisticated target classification scheme developed within the European cloud observing network CloudNet. It allows the detailed discrimination between different types of backscatterers detected by cloud radar and ceilometer. Finally, to allow IPT application also to drizzling cases, an LWC profiling method is integrated. This technique classifies the detected hydrometeors into three different size classes using certain thresholds determined by radar reflectivity and/or ceilometer extinction profiles. By inclusion into IPT, the retrieved profiles are made consistent with the measurements of the microwave profiler and an LWC a priori profile. Results of IPT application to 13 days of the LAUNCH campaign are analyzed, and the importance of integrated profiling for model evaluation is underlined.

Features for detection of Parkinson's disease tremor from local field potentials of the subthalamic nucleus

Deep Brain Stimulation (DBS) is a treatment routinely used to alleviate the symptoms of Parkinson's disease (PD). In this type of treatment, electrical pulses are applied through electrodes implanted into the basal ganglia of the patient. As the symptoms are not permanent in most patients, it is desirable to develop an on-demand stimulator, applying pulses only when onset of the symptoms is detected. This study evaluates a feature set created for the detection of tremor - a cardinal symptom of PD. The designed feature set was based on standard signal features and researched properties of the electrical signals recorded from subthalamic nucleus (STN) within the basal ganglia, which together included temporal, spectral, statistical, autocorrelation and fractal properties. The most characterized tremor related features were selected using statistical testing and backward algorithms then used for classification on unseen patient signals. The spectral features were among the most efficient at detecting tremor, notably spectral bands 3.5-5.5 Hz and 0-1 Hz proved to be highly significant. The classification results for determination of tremor achieved 94% sensitivity with specificity equaling one.

Impact of the quantity and flavonoid content of fruits and vegetables on markers of intake in adults with an increased risk of cardiovascular disease: the FLAVURS Trial

Purpose Limited robust randomised controlled trials investigating fruit and vegetable (F&V) intake in people at risk of cardiovascular disease (CVD) exist. We aimed to design and validate a dietary strategy of increasing flavonoid-rich versus flavonoid-poor F&V consumption on nutrient biomarker profile. Methods A parallel, randomised, controlled, dose–response dietary intervention study. Participants with a CVD relative risk of 1.5 assessed by risk scores were randomly assigned to one of the 3 groups: habitual (control, CT), high-flavonoid (HF) or low-flavonoid (LF) diets. While the CT group (n = 57) consumed their habitual diet throughout, the HF (n = 58) and LF (n = 59) groups sequentially increased their daily F&V intake by an additional 2, 4 and 6 portions for 6-week periods during the 18-week study. Results Compliance to target numbers and types of F&V was broadly met and verified by dietary records, and plasma and urinary biomarkers. Mean (±SEM) number of F&V portions/day consumed by the HF and LF groups at baseline (3.8 ± 0.3 and 3.4 ± 0.3), 6 weeks (6.3 ± 0.4 and 5.8 ± 0.3), 12 weeks (7.0 ± 0.3 and 6.8 ± 0.3) and 18 weeks (7.6 ± 0.4 and 8.1 ± 0.4), respectively, was similar at baseline yet higher than the CT group (3.9 ± 0.3, 4.3 ± 0.3, 4.6 ± 0.4, 4.5 ± 0.3) (P = 0.015). There was a dose-dependent increase in dietary and urinary flavonoids in the HF group, with no change in other groups (P = 0.0001). Significantly higher dietary intakes of folate (P = 0.035), non-starch polysaccharides (P = 0.001), vitamin C (P = 0.0001) and carotenoids (P = 0.0001) were observed in both intervention groups compared with CT, which were broadly supported by nutrient biomarker analysis. Conclusions The success of improving nutrient profile by active encouragement of F&V intake in an intervention study implies the need for a more hands-on public health approach.

Obesity and body fat classification in the metabolic syndrome: impact on cardiometabolic risk metabotype

Obesity is a key factor in the development of the metabolic syndrome (MetS), which is associated with increased cardiometabolic risk. We investigated whether obesity classification by body mass index (BMI) and body fat percentage (BF%) influences cardiometabolic profile and dietary responsiveness in 486 MetS subjects (LIPGENE dietary intervention study). Anthropometric measures, markers of inflammation and glucose metabolism, lipid profiles, adhesion molecules and haemostatic factors were determined at baseline and after 12 weeks of 4 dietary interventions (high saturated fat (SFA), high monounsaturated fat (MUFA) and 2 low fat high complex carbohydrate (LFHCC) diets, 1 supplemented with long chain n-3 polyunsaturated fatty acids (LC n-3 PUFAs)). 39% and 87% of subjects classified as normal and overweight by BMI were obese according to their BF%. Individuals classified as obese by BMI (± 30 kg/m2) and BF% (± 25% (men) and ± 35% (women)) (OO, n = 284) had larger waist and hip measurements, higher BMI and were heavier (P < 0.001) than those classified as non-obese by BMI but obese by BF% (NOO, n = 92). OO individuals displayed a more pro-inflammatory (higher C reactive protein (CRP) and leptin), pro-thrombotic (higher plasminogen activator inhibitor-1 (PAI-1)), pro-atherogenic (higher leptin/adiponectin ratio) and more insulin resistant (higher HOMA-IR) metabolic profile relative to the NOO group (P < 0.001). Interestingly, tumour necrosis factor alpha (TNF-α) concentrations were lower post-intervention in NOO individuals compared to OO subjects (P < 0.001). In conclusion, assessing BF% and BMI as part of a metabotype may help identify individuals at greater cardiometabolic risk than BMI alone.