Time-of-flight secondary ion mass spectrometry (ToF-SIMS) is a well-established technique in material sciences but has not yet been widely explored for implementation in life sciences. Here, we demonstrate the applicability and advantages of ToF-SIMS analysis for the study of minerals and biomolecules in osseous tissue. The locally resolved analysis of fragment ions deriving from the sample surface enables imaging and differentiation of bone tissue and facilitates histology on non-stained cross sections. In a rat model, bilateral ovariectomy combined with either a multi-deficiency diet or steroid treatment was carried out to create osteoporotic conditions. We focused our study on the Ca content of the mineralized tissue and monitored its decline. Calcium mass images of cross sections show the progressive degenerative changes in the bone. We observed a decreased Ca concentration in the edge region of the trabeculae and a decline in the Ca/P ratio. Additionally, we focused on the non-mineralized matrix and identified fragment ions that are characteristic for the collagen matrix. We observed trabeculae with wide ranges of non-mineralized collagen for the diet group owing to an impaired mineralization process. Here, the advantage of coeval monitoring of collagen and minerals indicated an osteomalacic model rather than an osteoporotic one. 相似文献
DC-DC converters are widely used in power electronic systems where there is a need for stabilizing a given dc voltage to a
desired value. It has been reported that DC-DC converters exhibit different non-linear phenomena including bifurcations, quasi-periodicity
and chaos under both voltage mode and current mode control schemes. In this work, current mode controlled SEPIC converter
operating in continuous conduction mode is considered and by varying the reference current Iref, the converter exhibits chaos. It has been observed that the system changes from a stable buck-like operation to an unstable
boost-like operation by varying Iref. Bifurcation diagram is plotted for control signal and capacitor voltage with Iref as bifurcation parameter. Resonant parametric perturbation control technique has been applied to suppress chaos. Effects
of phase shift and frequency mismatch are also analyzed. With phase shift, control power required for suppressing chaos has
been reduced. Also intermittent chaotic stages are suppressed with the effect of frequency mismatch at the expense of increasing
control power. The stability analysis in SEPIC converter is performed by means of discrete model and is validated through
the simulated and experimental results. 相似文献
Yttria‐stabilized zirconia (YSZ) deposition by the solution precursor plasma spraying (SPPS) route has been of interest for potential thermal barrier coating (TBC) applications. It has been surmised that realization of unique microstructural features like vertical cracks, nanosized pores and fine splats in the TBCs can significantly enhance coating durability and performance. However, satisfactory control over the YSZ coating microstructure has been elusive in the absence of an adequate understanding of the mechanism responsible for coating deposition in SPPS. This study demonstrates the ability to tailor microstructure of deposited YSZ coatings over a wide range, from nano‐porous coatings to a vertically cracked microstructure. Varying of precursor flow rate has been shown to dictate the pyrolysis events occurring in situ and, adopting this approach, YSZ coatings with widely varying microstructural features have been developed. The coatings have been characterized in detail and the observations correlated with in‐flight particle generation and splat formation. These studies also provide useful insights into the possible origin of vertical cracks in the coating for which a mechanism is proposed. 相似文献
With the advent of nanotechnology, many methods of synthesis of nanoparticles have come into practice and the 'polymer mediated growth' technique is among them. In this route, ions of one of the reactants are allowed to diffuse from an external solution into a polymer matrix where the other reactant is complexed and bound. The exact role of ionic diffusion in the formation of nanoparticles was investigated in the current study by studying the patterns of kinetics of nanoparticle formation using UV vis spectroscopy. Typically, calcium carbonate nanoparticles were formed by the aforementioned technique using polyethylene glycol solution. The particle size was calculated using Scherrer's formula on x-ray diffraction plots and was reconfirmed with field emission scanning electron microscope and transmission electron microscope images. Energy-dispersive x-ray analysis was used to study the composition and purity of the nanoparticles formed. The reactant to polymer ratio, reaction temperature and molecular weight of polyethylene glycol affected the size of the particles formed. Through this knowledge we optimized these parameters to obtain particles as small as 20?nm and confirmed that this technique can be used to control the size of nanoparticles. 相似文献
In this study, an attempt has been made to differentiate Novel Coronavirus-2019 (COVID-19) conditions from healthy subjects in Chest radiographs using a simplified end-to-end Convolutional Neural Network (CNN) model and occlusion sensitivity maps. Early detection and faster automated screening of the COVID-19 patients is essential. For this, the images are considered from publicly available datasets. Significant biomarkers representing critical image features are extracted from CNN by experimentally investigating on cross-validation methods and hyperparameter settings. The performance of the network is evaluated using standard metrics. Perturbation based occlusion sensitivity maps are employed on the features obtained from the classification model to visualise the localization of abnormal areas. Results demonstrate that the simplified CNN model with optimised parameters is able to extract significant features with a sensitivity of 97.35% and F-measure of 96.71% to detect COVID-19 images. The algorithm achieves an Area Under the Curve-Receiver Operating Characteristic score of 99.4% with Matthews correlation coefficient of 0.93. High value of Diagnostic odds ratio is also obtained. Occlusion sensitivity maps provide precise localization of abnormal regions by identifying COVID-19 conditions. As early detection through chest radiographic images are useful for automated screening of the disease, this method appears to be clinically relevant in providing a visual diagnostic solution using a simplified and efficient model.
Gait recognition has been considered as the emerging biometric technology for identifying the walking behaviors of humans. The major challenges addressed in this article is significant variation caused by covariate factors such as clothing, carrying conditions and view angle variations will undesirably affect the recognition performance of gait. In recent years, deep learning technique has produced a phenomenal performance accuracy on various challenging problems based on classification. Due to an enormous amount of data in the real world, convolutional neural network will approximate complex nonlinear functions in models to develop a generalized deep convolutional neural network (DCNN) architecture for gait recognition. DCNN can handle relatively large multiview datasets with or without using any data augmentation and fine-tuning techniques. This article proposes a color-mapped contour gait image as gait feature for addressing the variations caused by the cofactors and gait recognition across views. We have also compared the various edge detection algorithms for gait template generation and chosen the best from among them. The databases considered for our work includes the most widely used CASIA-B dataset and OULP database. Our experiments show significant improvement in the gait recognition for fixed-view, crossview, and multiview compared with the recent methodologies. 相似文献
Composite thin films of molybdenum disilicide-silicon carbide (MoSi2-SiC) have been deposited via rf magnetron sputtering onto molybdenum substrates. An intermediate layer was deposited in the presence of nitrogen gas and evaluated as a potential diffusion barrier layer. The composite films have been characterized using X-ray diffractometry, scanning electron microscopy, transmission electron microscopy, and Auger electron spectroscopy. The as-deposited films were amorphous but crystallized into nanometer-sized grains after annealing under vacuum at 1000°C for 30 min. There was a significant amount of interdiffusion between the film and substrate, which resulted in the formation of subsilicides such as Mo5Si3 and MoSi3, as well as Mo2C. The films that were deposited via reactive sputtering in a nitrogen ambient were amorphous in both the as-deposited and annealed conditions. Significantly fewer second phases were detected with the presence of the intermediate layer, which suggests the potential use of the nitrided (MoSi x N y C z ) layer as a high-temperature diffusion barrier layer for the silicon and carbon. 相似文献
An in silico protein model based on the Kauffman NK-landscape,where N is the number of variable positions in a protein andK is the degree of coupling between variable positions, wasused to compare alternative search strategies for directed evolution.A simple genetic algorithm (GA) was used to model the performanceof a standard DNA shuffling protocol. The search effectivenessof the GA was compared to that of a statistical approach calledthe protein sequence activity relationship (ProSAR) algorithm,which consists of two steps: model building and library design.A number of parameters were investigated and found to be importantfor the comparison, including the value of K, the screeningsize, the system noise and the number of replicates. The statisticalmodel was found to accurately predict the measured activitiesfor small values of the coupling between amino acids, K 1.The ProSAR strategy was found to perform well for small to moderatevalues of coupling, 0 K 3, and was found to be robust to noise.Some implications for protein engineering are discussed. Received January 2, 2003;revised May 13, 2003;accepted June 19, 2003.相似文献
Text data mining is a process of exploratory data analysis. Classification maps data into predefined groups or classes. It is often referred to as supervised learning because the classes are determined before examining the data. This paper describes the proposed k-Nearest Neighbor classifier that performs comparative cross-validation for the existing k-Nearest Neighbor classifier. The feasibility and the benefits of the proposed approach are demonstrated by means of data mining problem: direct marketing. Direct marketing has become an important application field of data mining. Comparative cross-validation involves estimation of accuracy by either stratified k-fold cross-validation or equivalent repeated random subsampling. While the proposed method may have a high bias; its performance (accuracy estimation in our case) may be poor due to a high variance. Thus the accuracy with the proposed k-Nearest Neighbor classifier was less than that with the existing k-Nearest Neighbor classifier, and the smaller the improvement in runtime the larger the improvement in precision and recall. In our proposed method we have determined the classification accuracy and prediction accuracy where the prediction accuracy is comparatively high. 相似文献