Use of Response Surface Methodology to Investigate the Effectiveness of Commercial Enzymes on Buckwheat Malt for Brewing Purposes

The influence of two factors, total concentration and fraction of three pairs of commercial enzymes, which showed statistical significance (Biocellulase W with Hitempase 2XL, Biocellulase W with Amylo 300 and Amylo 300 with Hitempase 2XL), were studied for their overall effect on buckwheat wort quality using response surface methodology (RSM). This study revealed that the addition of increasing levels of Hitempase 2XL to the buckwheat mash increased colour, extract levels, wort filtration, fermentability and total fermentable extract (TFE), along with decreasing viscosity values. Results also determined a high level of fermentability when an enzyme combination of 30% Biocellulase and 70% Hitempase was added to the mash. The addition of increasing levels of Amylo 300 to buckwheat mashes resulted in increases in fermentability and total fermentable extract (TFE), along with increases in total soluble nitrogen (TSN), free amino nitrogen (FAN) and Kolbach index (KI). With regard to the proposed optimal regime, although no synergistic effect was found when the three enzymes were used together, the optimum conditions for the production of buckwheat wort with lowest viscosity, highest extract and optimal fermentability were achieved using a joint model. Overall, the findings of this study demonstrate the feasibility of producing wort suitable for the brewing of gluten‐free beer from 100% malted buckwheat with careful optimisation of enzyme types and dosage levels.     

Pre-illumination to control the active trap density in asemi-insulating MQW device

The trap density in a photorefractive semi-insulating quantum-well (QW) device can be dynamically adjusted for optimum sensitivity by passivating some of its traps with photocarriers from a pre-illumination pulse. By reducing the effective trap density with pre-illumination, we demonstrate that the carrier drift length can increase, causing more than an order of magnitude increase in device sensitivity by increasing the number of QW's screened per carrier. Too much pre-illumination, on the other hand, decreases four-wave mixing diffraction efficiency, due to lateral drift. The optimum trap density enables higher sensitivity while maintaining high-resolution operation. Pre-illumination can also be used to speed up grating erasure by increasing the electron-hole recombination probability     

Clinical comparison of an enhanced-sensitivity branched-DNA assay and reverse transcription-PCR for quantitation of human immunodeficiency virus type 1 RNA in plasma

We investigated whether inhibition of the BCR-ABL tyrosine kinase by the CGP57418B compound would render chronic myeloid leukaemia (CML) cells susceptible to Fas (CD95, Apo-1)-mediated cell death. Only two (AR230 and SD1) out of 10 BCR-ABL positive cell lines were found to express the CD95 protein. No change in Fas expression was observed in any of the 10 cell lines after 48 h exposure to CGP57418B. AR230 cells were resistant and SD1 cells were partially resistant to Fas-mediated apoptosis induced by ligation of the Fas receptor to an anti-Fas IgM antibody. Pre-incubation with 1 microM CGP57418B did not change the susceptibility of these cell lines to Fas-mediated cell death. Similar results were observed in experiments with CD34+ cells from CML patients and from normal individuals. The data suggest that, in contrast to some cytotoxic drugs, the CGP57148B tyrosine kinase inhibitor utilizes a pathway other than the CD95 system in order to induce apoptosis in CML cells.     

Measurement of histamine release from human lung tissue ex vivo by microdialysis technique

OBJECTIVE AND DESIGN: Currently no method is available for measurement of mediator release from intact human lung. In this study, a microdialysis technique was used to measure histamine release from mast cells in human lung tissue ex vivo. MATERIAL: Microdialysis fibers of 216 microm were inserted into lung tissue and perfused with Krebs Ringer buffer at a rate of 3 microl/min. After a 15 min period of steady-state perfusion, anti-IgE and vehicle were injected into the lung tissue above individual fibers. Samples from each fibre were collected for 20 min at 2 min intervals. Histamine was assayed fluorometrically. RESULTS: Anti-IgE concentrations of 40-40,000 U/ml dose-dependently released histamine, significant histamine release being demonstrated with anti-IgE concentrations of 400 U/ml and greater. The kinetics of histamine release showed peak values 2-8 min after the injection. Great individual responses were observed but data could be reproduced within individual donors. Monocyte chemoattractant protein-1, a potent basophil secretagogue, did not induce histamine release in lung tissue which indicated mast cells to be the histamine source. Substance P did not release histamine in the lung tissue. CONCLUSIONS: The microdialysis technique allowed measurements of histamine release from mast cells in intact lung ex vivo. The method may prove useful since a number of experiments can be performed in a few hours in intact lung tissue without any dispersion or enzymatic treatment.     

LoVis: Local Pattern Visualization for Model Refinement

Linear models are commonly used to identify trends in data. While it is an easy task to build linear models using pre‐selected variables, it is challenging to select the best variables from a large number of alternatives. Most metrics for selecting variables are global in nature, and thus not useful for identifying local patterns. In this work, we present an integrated framework with visual representations that allows the user to incrementally build and verify models in three model spaces that support local pattern discovery and summarization: model complementarity, model diversity, and model representivity. Visual representations are designed and implemented for each of the model spaces. Our visualizations enable the discovery of complementary variables, i.e., those that perform well in modeling different subsets of data points. They also support the isolation of local models based on a diversity measure. Furthermore, the system integrates a hierarchical representation to identify the outlier local trends and the local trends that share similar directions in the model space. A case study on financial risk analysis is discussed, followed by a user study.     

The application of inductively coupled plasma mass spectrometry in clinical pharmacological oncology research

Metal-based anticancer agents are frequently used in the treatment of a wide variety of cancer types. The monitoring of these anticancer agents in biological samples is important to understand their pharmacokinetics, pharmacodynamics, and metabolism. In addition, determination of metals originating from anticancer agents is relevant to assess occupational exposure of health care personnel working with these drugs. The high sensitivity of inductively coupled plasma mass spectrometry (ICP-MS) has resulted in an increased popularity of this technique for the analysis of metal-based anticancer drugs. In addition to the quantitative analysis of the metal of interest in a sample, ICP-MS can be used as an ultrasensitive metal selective detector in combination with speciation techniques such as liquid chromatography. In the current review we provide a systematic survey of publications describing the analysis of platinum- and ruthenium-containing anticancer agents using ICP-MS, focused on the determination of total metal concentrations and on the speciation of metal compounds in biological fluids, DNA- and protein-adducts, and environmental samples. We conclude that ICP-MS is a powerful tool for the quantitative analysis of metal-based anticancer agents from multiple sample sources.     

A non-autonomous optimal control model of renewable energy production under the aspect of fluctuating supply and learning by doing

Given the constantly raising world-wide energy demand and the accompanying increase in greenhouse gas emissions that pushes the progression of climate change, the possibly most important task in future is to find a carbon-low energy supply that finds the right balance between sustainability and energy security. For renewable energy generation, however, especially the second aspect turns out to be difficult as the supply of renewable sources underlies strong volatility. Further on, investment costs for new technologies are so high that competitiveness with conventional energy forms is hard to achieve. To address this issue, we analyze in this paper a non-autonomous optimal control model considering the optimal composition of a portfolio that consists of fossil and renewable energy and which is used to cover the energy demand of a small country. While fossil energy is assumed to be constantly available, the supply of the renewable resource fluctuates seasonally. We further on include learning effects for the renewable energy technology, which will underline the importance of considering the whole life span of such a technology for long-term energy planning decisions.     

Recommendation of RILEM TC 238-SCM: determination of the degree of reaction of siliceous fly ash and slag in hydrated cement paste by the selective dissolution method

The performance of slag and fly ash in hydrated cementitious materials depends on the degree of reaction developed at the evaluated age. Several methods for the determination of the reaction degree of supplementary cementitious materials are available, among which the selective dissolution method is one of methods developed the earliest. This is a direct method that aims to quantify the amount of unreacted slag or fly ash in the sample by applying a selective acid attack. The degree of reaction is obtained from the comparison between the remaining unreacted SCM, which should not dissolve, and the total amount initially included in the mix. This recommendation indicates suitable procedures for computing the degree of reaction by selective dissolution of cement pastes containing slag and fly ash. Specific considerations are indicated for necessary corrections due to the imperfect selective dissolution when the procedure is applied to hydrated cement paste.     

Symbolic Intersect Detection: A Method for Improving Spatial Intersect Joins

Due to the increasing popularity of spatial databases, researchers have focused their efforts on improving the query processing performance of the most expensive spatial database operation: the spatial join. While most previous work focused on optimizing the filter step, it has been discovered recently that, for typical GIS data sets, the refinement step of spatial join processing actually requires a longer processing time than the filter step. Furthermore, two-thirds of the time in processing the refinement step is devoted to the computation of polygon intersections. To address this issue, we therefore introduce a novel approach to spatial join optimization that drastically reduces the time of the refinement step. We propose a new approach called Symbolic Intersect Detection (SID) for early detection of true hits. Our SID optimization eliminates most of the expensive polygon intersect computations required by a spatial join by exploiting the symbolic topological relationships between the two candidate polygons and their overlapping minimum bounding rectangle. One important feature of our SID optimization is that it is complementary to the state-of-the-art methods in spatial join processing and therefore can be utilized by these techniques to further optimize their performance. In this paper, we also develop an analytical cost model that characterizes SIDs effectiveness under various conditions. Based on real map data, we furthermore conduct an experimental evaluation comparing the performance of the spatial joins with SID against the state-of-the-art approach. Our experimental results show that SID can effectively identify more than 80% of the true hits with negligible overhead. Consequently, with SID, the time needed for resolving polygon intersect in the refinement step is improved by over 50% over known techniques, as predicted by our analytical model.