Spectral beam combining of 2 μm Tm fiber laser systems

Output beams from three independently frequency-stabilized thulium master-oscillator power-amplifier fiber laser systems were spectrally combined using a plane-ruled metal diffraction grating. Two laser channels were frequency-stabilized with guided mode resonance filters and the third was stabilized using a plane-ruled metal diffraction grating. The systems had output wavelengths between 1984 and 2015 nm, each with a spectral width of 100-450 pm and output powers between 40-120 W. The combined beam had powers up to 49 W and was 32% efficient with respect to the launched pump power.     

Performance of conformal guided mode resonance filters

Guided mode resonance (GMR) filters are highly functional micro-optics capable of narrowband spectral filtering. GMR devices have previously been demonstrated on flat substrates using a wide range of materials and configurations. In this Letter, we apply a soft lithographic technique followed by the deposition of dielectric layers to generate GMR filters on a concave lens surface. Resonances of the resulting conformal GMR filters are experimentally measured and characterized, and the results are compared to the performance of similar GMR filters fabricated on flat surfaces.     

Quantitative combination of volumetric MR imaging and MR spectroscopy data for the discrimination of meningiomas from metastatic brain tumors by means of pattern recognition

The analysis of information derived from magnetic resonance imaging (MRI) and spectroscopy (MRS) has been identified as an important indicator for discriminating among different brain pathologies. The purpose of this study was to investigate the efficiency of the combination of textural MRI features and MRS metabolite ratios by means of a pattern recognition system in the task of discriminating between meningiomas and metastatic brain tumors. The data set consisted of 40 brain MR image series and their corresponding spectral data obtained from patients with verified tumors. The pattern recognition system was designed employing the support vector machines classifier with radial basis function kernel; the system was evaluated using an external cross validation process to render results indicative of the generalization performance to “unknown” cases. The combination of MR textural and spectroscopic features resulted in 92.15% overall accuracy in discriminating meningiomas from metastatic brain tumors. The fusion of the information derived from MRI and MRS data might be helpful in providing clinicians a useful second opinion tool for accurate characterization of brain tumors.     

(-)-Catechin in cocoa and chocolate: occurrence and analysis of an atypical flavan-3-ol enantiomer

Cocoa contains high levels of different flavonoids. In the present study, the enantioseparation of catechin and epicatechin in cocoa and cocoa products by chiral capillary electrophoresis (CCE) was performed. A baseline separation of the catechin and epicatechin enantiomers was achieved by using 0.1 mol x L(-1) borate buffer (pH 8.5) with 12 mmol x L(-1) (2-hydroxypropyl)-gamma-cyclodextrin as chiral selector, a fused-silica capillary with 50 cm effective length (75 microm I.D.), +18 kV applied voltage, a temperature of 20 degrees C and direct UV detection at 280 nm. To avoid comigration or coelution of other similar substances, the flavan-3-ols were isolated and purified using polyamide-solid-phase-extraction and LC-MS analysis. As expected, we found (-)-epicatechin and (+)-catechin in unfermented, dried, unroasted cocoa beans. In contrast, roasted cocoa beans and cocoa products additionally contained the atypical flavan-3-ol (-)-catechin. This is generally formed during the manufacturing process by an epimerization which converts (-)-epicatechin to its epimer (-)-catechin. High temperatures during the cocoa bean roasting process and particularly the alkalization of the cocoa powder are the main factors inducing the epimerization reaction. In addition to the analysis of cocoa and cocoa products, peak ratios were calculated for a better differentiation of the cocoa products.     

Pattern recognition system for the discrimination of multiple sclerosis from cerebral microangiopathy lesions based on texture analysis of magnetic resonance images

In this study, a pattern recognition system has been developed for the discrimination of multiple sclerosis (MS) from cerebral microangiopathy (CM) lesions based on computer-assisted texture analysis of magnetic resonance images. Twenty-three textural features were calculated from MS and CM regions of interest, delineated by experienced radiologists on fluid attenuated inversion recovery images and obtained from 11 patients diagnosed with clinically definite MS and from 18 patients diagnosed with clinically definite CM. The probabilistic neural network classifier was used to construct the proposed pattern recognition system and the generalization of the system to unseen data was evaluated using an external cross validation process. According to the findings of the present study, statistically significant differences exist in the values of the textural features between CM and MS: MS regions were darker, of higher contrast, less homogeneous and rougher as compared to CM.     

Enhancing the discrimination accuracy between metastases, gliomas and meningiomas on brain MRI by volumetric textural features and ensemble pattern recognition methods

Three-dimensional (3D) texture analysis of volumetric brain magnetic resonance (MR) images has been identified as an important indicator for discriminating among different brain pathologies. The purpose of this study was to evaluate the efficiency of 3D textural features using a pattern recognition system in the task of discriminating benign, malignant and metastatic brain tissues on T1 postcontrast MR imaging (MRI) series. The dataset consisted of 67 brain MRI series obtained from patients with verified and untreated intracranial tumors. The pattern recognition system was designed as an ensemble classification scheme employing a support vector machine classifier, specially modified in order to integrate the least squares features transformation logic in its kernel function. The latter, in conjunction with using 3D textural features, enabled boosting up the performance of the system in discriminating metastatic, malignant and benign brain tumors with 77.14%, 89.19% and 93.33% accuracy, respectively. The method was evaluated using an external cross-validation process; thus, results might be considered indicative of the generalization performance of the system to "unseen" cases. The proposed system might be used as an assisting tool for brain tumor characterization on volumetric MRI series.     

Automated sample preparation by pressurized liquid extraction-solid-phase extraction for the liquid chromatographic-mass spectrometric investigation of polyphenols in the brewing process

The analysis of polyphenols from solid plant or food samples usually requires laborious sample preparation. The liquid extraction of these compounds from the sample is compromised by apolar matrix interferences, an excess of which has to be eliminated prior to subsequent purification and separation. Applying pressurized liquid extraction to the extraction of polyphenols from hops, the use of different solvents sequentially can partly overcome these problems. Initial extraction with pentane eliminates hydrophobic compounds like hop resins and oils and enables the straightforward automated on-line solid-phase extraction as part of an optimized LC-MS analysis.