Insights on boron impact on structural characteristics in epitaxially grown BGaN

Journal of Materials Science - It is shown that MOCVD growth allows to obtain BGaN epitaxial layers at growth temperature (Tgr) between 840 and 1090 °C. It is found that morphology of...     

Identification of bacteria using tandem mass spectrometry combined with a proteome database and statistical scoring

Detection and identification of pathogenic bacteria and their protein toxins play a crucial role in a proper response to natural or terrorist-caused outbreaks of infectious diseases. The recent availability of whole genome sequences of priority bacterial pathogens opens new diagnostic possibilities for identification of bacteria by retrieving their genomic or proteomic information. We describe a method for identification of bacteria based on tandem mass spectrometric (MS/MS) analysis of peptides derived from bacterial proteins. This method involves bacterial cell protein extraction, trypsin digestion, liquid chromatography MS/MS analysis of the resulting peptides, and a statistical scoring algorithm to rank MS/MS spectral matching results for bacterial identification. To facilitate spectral data searching, a proteome database was constructed by translating genomes of bacteria of interest with fully or partially determined sequences. In this work, a prototype database was constructed by the automated analysis of 87 publicly available, fully sequenced bacterial genomes with the GLIMMER gene finding software. MS/MS peptide spectral matching for peptide sequence assignment against this proteome database was done by SEQUEST. To gauge the relative significance of the SEQUEST-generated matching parameters for correct peptide assignment, discriminant function (DF) analysis of these parameters was applied and DF scores were used to calculate probabilities of correct MS/MS spectra assignment to peptide sequences in the database. The peptides with DF scores exceeding a threshold value determined by the probability of correct peptide assignment were accepted and matched to the bacterial proteomes represented in the database. Sequence filtering or removal of degenerate peptides matched with multiple bacteria was then performed to further improve identification. It is demonstrated that using a preset criterion with known distributions of discriminant function scores and probabilities of correct peptide sequence assignments, a test bacterium within the 87 database microorganisms can be unambiguously identified.     

Estimation of K distribution parameters using neural networks

The K distribution is an accurate model for ultrasonic backscatter. A neural approach is developed to estimate K distribution parameters. Accuracy and consistency of the estimates from simulated K and envelope data compare favorably with other techniques. Neural networks can potentially be used as a complementary technique for tissue characterization.     

Robust weighted averaging

Signal averaging is often used to extract a useful signal embedded in noise. This method is especially useful for biomedical signals, where the spectra of the signal and noise significantly overlap. In this case, traditional filtering techniques introduce unacceptable signal distortion. In averaging methods, constancy of the noise power is usually assumed, but in reality noise features a variable power. In this case, it is more appropriate to use a weighted averaging. The main problem in this method is the estimation of the noise power in order to obtain the weight values. Additionally, biomedical signals often contain outliers. This requires robust averaging methods. This paper shows that signal averaging can be formulated as a problem of minimization of a criterion function. Based on this formulation new weighted averaging methods are introduced, including weighted averaging based on criterion function minimization (WACFM) and robust epsilon-insensitive WACFM. Performances of these new methods are experimentally compared with the traditional averaging and other weighted averaging methods using electrocardiographic signal with the muscle noise, impulsive noise, and time-misalignment of cycles. Finally, an application to the late potentials extraction is shown.     

The effect of lamellar separation on the properties of a Ti–46Al–2Nb–2Cr intermetallic alloy

The relationships between the γ and ₂ lamellae apparent separation and hardnesses as well as the peak flow stresses estimated in hot compression tests obtained for specimens made of Ti–46Al–2Nb–2Cr alloy are presented. The lamellae separations were estimated using image analysis on the microstructure of the specimens. The procedure employing a fast Fourier transformation of secondary electron microstructural images for fully automatic lamellae separation measurements is described. It was found that the effect of the apparent lamellae separations of γ and ₂ on the peak flow stress is significant. For the microstructure with thick lamellae of γ and ₂ phases, the peak flow stress decreases. The hardness of the specimens decreases with an increase of the lamellae apparent separation as well.     

A computational framework for modelling solid tumour growth

The biology of cancer is a complex interplay of many underlying processes, taking place at different scales both in space and time. A variety of theoretical models have been developed, which enable one to study certain components of the cancerous growth process. However, most previous approaches only focus on specific aspects of tumour development, largely ignoring the influence of the evolving tumour environment. In this paper, we present an integrative framework to simulate tumour growth, including those model components that are considered to be of major importance. We start by addressing issues at the tissue level, where the phenomena are modelled as continuum partial differential equations. We extend this model with relevant components at the cellular or even sub-cellular level in a vertical fashion. We present an implementation of this framework, covering the major processes and treat the mechanical deformation due to growth, the biochemical response to hypoxia, blood flow, oxygenation and the explicit development of a vascular system in a coupled way. The results demonstrate the feasibility of the approach and its applicability to in silico studies of the influence of different treatment strategies (like the usage of novel anti-cancer drugs) for more effective therapy design.     

Engineering On‐Surface Spin Crossover: Spin‐State Switching in a Self‐Assembled Film of Vacuum‐Sublimable Functional Molecule

The realization of spin‐crossover (SCO)‐based applications requires study of the spin‐state switching characteristics of SCO complex molecules within nanostructured environments, especially on surfaces. Except for a very few cases, the SCO of a surface‐bound thin molecular film is either quenched or heavily altered due to: (i) molecule–surface interactions and (ii) differing intermolecular interactions in films relative to the bulk. By fabricating SCO complexes on a weakly interacting surface, the interfacial quenching problem is tackled. However, engineering intermolecular interactions in thin SCO active films is rather difficult. Here, a molecular self‐assembly strategy is proposed to fabricate thin spin‐switchable surface‐bound films with programmable intermolecular interactions. Molecular engineering of the parent complex system [Fe(H₂B(pz)₂)₂(bpy)] (pz = pyrazole, bpy = 2,2′‐bipyridine) with a dodecyl (C₁₂) alkyl chain yields a classical amphiphile‐like functional and vacuum‐sublimable charge‐neutral Fe^II complex, [Fe(H₂B(pz)₂)₂(C₁₂‐bpy)] (C₁₂‐bpy = dodecyl[2,2′‐bipyridine]‐5‐carboxylate). Both the bulk powder and 10 nm thin films sublimed onto either quartz glass or SiO_x surfaces of the complex show comparable spin‐state switching characteristics mediated by similar lamellar bilayer like self‐assembly/molecular interactions. This unprecedented observation augurs well for the development of SCO‐based applications, especially in molecular spintronics.     

The changes in the contents of 137Cs in bottom sediments of some Masurian lakes during 10-15 y observation (Poland)

The measurements of radioactive caesium contents in bottom sedimentswere carried out in four lakes. First samples (47) were takenin 1992–95. The repeat sampling (109) was performed fromthe same places in 2005. We examined eight chosen areas in theselakes. In six of them, we observed statistically significantdifferences in the level of radioactive caesium. It indicatesthe permanent decrease in the level of ¹³⁷Cs. The mean annualdecrease in the level of radioactive caesium, taking into considerationthe radioactive decay, was from 4.2 to 7.8%. In two areas ofthe profundal zone, we did not observe statistically significantdifferences in the level of radioactive caesium (lakes Garbasand Rogale Wielkie). Taking into consideration the radioactivedecay of caesium, it means about the appearance of the processof accumulation of ¹³⁷Cs in these areas.     

Comparative analysis of viscosity of complex liquids and cytoplasm of mammalian cells at the nanoscale

We present a scaling formula for size-dependent viscosity coefficients for proteins, polymers, and fluorescent dyes diffusing in complex liquids. The formula was used to analyze the mobilities of probes of different sizes in HeLa and Swiss 3T3 mammalian cells. This analysis unveils in the cytoplasm two length scales: (i) the correlation length ξ (approximately 5 nm in HeLa and 7 nm in Swiss 3T3 cells) and (ii) the limiting length scale that marks the crossover between nano- and macroscale viscosity (approximately 86 nm in HeLa and 30 nm in Swiss 3T3 cells). During motion, probes smaller than ξ experienced matrix viscosity: η(matrix) ≈ 2.0 mPa·s for HeLa and 0.88 mPa·s for Swiss 3T3 cells. Probes much larger than the limiting length scale experienced macroscopic viscosity, η(macro) ≈ 4.4 × 10(-2) and 2.4 × 10(-2) Pa·s for HeLa and Swiss 3T3 cells, respectively. Our results are persistent for the lengths scales from 0.14 nm to a few hundred nanometers.     

Multiple regression analysis of Jominy hardenability data for boron treated steels

The relations between chemical composition and their hardenability of boron treated steels have been investigated using a multiple regression analysis method. A linear model of regression was chosen. The free boron content that is effective for the hardenability was calculated using a model proposed by Jansson. The regression analysis for 1261 steel heats provided equations that were statistically significant at the 95% level. All heats met the specification according to the nordic countries producers Classification. The Variation in chemical composition explained typically 80 to 90% of the Variation in the hardenability. In the regression analysis elements which did not significantly contribute to the calculated hardness according to the F test were eliminated. Carbon, silicon, manganese, phosphorus and chromium were of importance at all Jominy distances, nickel, vanadium, boron and nitrogen at distances above 6 mm. After the regression analysis it was demonstrated that very few outliers were present in the data set, i.e. data points outside four times the standard deviation. The model has successfully been used in industrial practice replacing some of the necessary Jominy tests.