Enzymatic Properties and Mutational Studies of Chalcone Synthase from Physcomitrella patens

PpCHS is a member of the type III polyketide synthase family and catalyses the synthesis of the flavonoid precursor naringenin chalcone from p-coumaroyl-CoA. Recent research reports the production of pyrone derivatives using either hexanoyl-CoA or butyryl-CoA as starter molecule. The Cys-His-Asn catalytic triad found in other plant chalcone synthase predicted polypeptides is conserved in PpCHS. Site directed mutagenesis involving these amino acids residing in the active-site cavity revealed that the cavity volume of the active-site plays a significant role in the selection of starter molecules as well as product formation. Substitutions of Cys 170 with Arg and Ser amino acids decreased the ability of the PpCHS to utilize hexanoyl-CoA as a starter molecule, which directly effected the production of pyrone derivatives (products). These substitutions are believed to have a restricted number of elongations of the growing polypeptide chain due to the smaller cavity volume of the mutant's active site.     

Magnetic nanoparticles: preparation, physical properties, and applications in biomedicine

ABSTRACT: Finally, we have addressed some relevant findings on the importance of having well-defined synthetic strategies developed for the generation of MNPs, with a focus on particle formation mechanism and recent modifications made on the preparation of monodisperse samples of relatively large quantities not only with similar physical features, but also with similar crystallochemical characteristics. Then, different methodologies for the functionalization of the prepared MNPs together with the characterization techniques are explained. Theorical views on the magnetism of nanoparticles are considered.     

Transformation Mechanism of Preprecipitation NiMn Nanostructures

The transformation mechanism of constrained b.c.c NiMn (β phase) clusters coherently precipitated during isothermal aging of Fe-Ni-Mn maraging alloy at 753 K (480 °C) has been studied. Preprecipitation of β-NiMn phase was demonstrated using conventional and high-resolution transmission electron microscopy and confirmed by flanking of electron diffraction spots, X-ray diffraction sidebands, and the invariance of lattice parameter and peak breadths in the earlier stages of age hardening. The β-NiMn clusters were recognized with interfacial dislocation dipoles lying on {011} _α planes and Burgers vectors of a ₀ $ \left\langle {01\overline{1} } \right\rangle_{\alpha } $ type. During further aging, the β-NiMn clusters transformed martensitically into fct NiMn (θ phase) precipitates as envisaged by the characteristic microtwinning texture. Considering the anomalous elastic softness of β-NiMn alloy, the transformation was plausibly assumed to take place by the localized soft-mode mechanism in which the structural instability of clusters is stimulated by the strain field of the interfacial dislocations. Computerized calculations in accordance with the phenomenological theory of martensite crystallography indicated that microtwinning of martensitic θ precipitates occurs in a self-accommodating manner. Transformation of very small β clusters at earlier stages of aging is thermodynamically hampered by the interfacial energy barrier.     

Probabilistic approach for QoS-aware recommender system for trustworthy web service selection

We present a QoS-aware recommender approach based on probabilistic models to assist the selection of web services in open, distributed, and service-oriented environments. This approach allows consumers to maintain a trust model for each service provider they interact with, leading to the prediction of the most trustworthy service a consumer can interact with among a plethora of similar services. In this paper, we associate the trust in a service to its performance denoted by QoS ratings instigated by the amalgamation of various QoS metrics. Since the quality of a service is contingent, which renders its trustworthiness uncertain, we adopt a probabilistic approach for the prediction of the quality of a service based on the evaluation of past experiences (ratings) of each of its consumers. We represent the QoS ratings of services using different statistical distributions, namely multinomial Dirichlet, multinomial generalized Dirichlet, and multinomial Beta-Liouville. We leverage various machine learning techniques to compute the probabilities of each web service to belong to different quality classes. For instance, we use the Bayesian inference method to estimate the parameters of the aforementioned distributions, which presents a multidimensional probabilistic embodiment of the quality of the corresponding web services. We also employ a Bayesian network classifier with a Beta-Liouville prior to enable the classification of the QoS of composite services given the QoS of its constituents. We extend our approach to function in an online setting using the Voting EM algorithm that enables the estimation of the probabilities of the QoS after each interaction with a web service. Our experimental results demonstrate the effectiveness of the proposed approaches in modeling, classifying and incrementally learning the QoS ratings.     

Temperature insensitive conductance detection with surface-functionalised silicon nanowire sensors

The effects of suspension and functionalisation on electrical conduction of silicon nanowires (SiNWs) are experimentally investigated towards highly sensitive chemical and biological detection applications. The conductance is found to be affected by the suspension process, while it shows the similar trend against the temperature before and after the suspension. The reduction of conductance after the functionalisation with NH₂ self-assembled monolayer/glutaraldehyde/biotin can be explained based on the assumption that there is a charge transfer from the SiNW to the molecular layers. The temperature insensitive conductance found in the functionalised SiNWs experimentally for the first time is expected to be extremely useful for practical sensing applications.     

Effect of MgO particle size on the microstructure, mechanical properties and wear performance of ZTA-MgO ceramic cutting inserts

The mechanical properties, microstructure and wear performance of zirconia-toughened alumina (ZTA) cutting inserts with Magnesia (MgO) in different particle sizes as additives was investigated. The MgO particle sizes were varied from 80 nm to 7000 nm. The alumina (Al₂O₃), yittria stabilized zirconia (YSZ) and MgO powders were mixed, compacted and sintered at 1600 °C using the solid-state sintering method. The mechanical and physical properties of the samples such as wear resistance, Vickers hardness, fracture toughness, microstructure and density were analyzed. Commercially available stainless steel (316L) was used as the workpiece for the wear resistance study. It was observed that smaller MgO particle sizes induce better wear performance and mechanical properties for the cutting inserts. Wear resistance analysis showed that the cutting insert with nano-sized MgO (particle size 80 nm) had the lowest wear area of 0.019 mm². The same cutting insert also possessed the highest Vickers hardness value of 1740 Hv compared to the other samples. Furthermore, microstructural observations show that the Al₂O₃ grain size depends on the particle size of MgO, and is directly related to its hardness property.     

Design and construct optimum dosimeter to detect airborne radon and thoron gas: Experimental study

Aim of this work is to design and select optimum dimension of a radon and thoron dosimeter within the measure optimum value of the calibration factor, using CR-39 Nuclear Track Detectors (NTDs). The results show that the best dimension to detect and measure real values of airborne radon and thoron concentrations is 6 cm and 7 cm for diameter and height, respectively. Calibration factors (K) for radon and thoron at this dimension were 2.68 ± 0.03 cm and 0.83 ± 0.01 cm, respectively, and these factors relatively depend on the detector efficiency. Therefore, the efficiency of CR-39NTDs to register alpha particles and their effects on the calibration factor estimated. It is found that the calibration factor increased exponentially with detector efficiency. Moreover, detector efficiency was equal to 80.3 ± 1.23% at the optimum dosimeter.