Results 1 - 10 of 27
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[en] Highlights: • Molecular docking showed that BCB/PEA was bound at sub-domain IIA of HSA. • Fluorescence lifetimes indicated that the quenching was a static quenching. • CD spectra showed that BCB/PEA changed the conformation of HSA. • The competitive binding between site markers and BCB/PEA was studied. • The mutual influence on the two drugs binding HSA was studied. - Abstract: Using fluorescence quenching, fluorescence lifetime, (UV + vis) absorption, circular dichroism (CD) and molecular docking technique, the interactions of human serum albumin (HSA) with bromchlorbuterol-HCl (BCB) and phenylethanolamine A (PEA) were investigated. The quenching rate constants and binding constants for BCB/PEA with HSA were determined at T = (292.15, 302.15 and 312.15) K respectively, which were all decreased with the increase of the temperature, showing not a dynamic quenching. The fluorescence lifetime of HSA with BCB/PEA had changed little compared to that of HSA alone (τ_0), further confirming that BCB/PEA quenching of intrinsic fluorescence of HSA is a static quenching. The effects of K"+, Ca"2"+, Cu"2"+, Zn"2"+ and Fe"3"+ on the binding were studied. The analysis of the thermodynamic parameters for BCB/(PEA + HSA) showed that BCB/PEA could bind to HSA via hydrophobic force. The binding distances were determined as 2.90 and 4.11 nm for (BCB + HSA) and (PEA + HSA) based on the Förster’s non-radiative energy transfer theory (FRET). Synchronous fluorescence and CD spectra indicated that the conformation of HSA was changed by BCB/PEA. The competitive studies for the drug with site marker suggested that both BCB and PEA were bound at Sudlow’s sites I (sub-domain IIA, also known as indometacin binding site) in HSA, and the results of the study of molecular docking also leads to the same conclusion. The competitive binding experiments for the two drugs were also performed, which further indicates that PEA and BCB could share the same binding site, and PEA has a much stronger binding capacity than BCB.
[en] Magnetostrictive Cu_0_._1Co_0_._9RE_xFe_2_-_xO_4 (RE=Ho, Gd or Sm) was fabricated by a sol-gel auto-combustion technique using spent lithium-ion batteries as raw materials. X-ray diffraction analysis confirmed the spinel structure of the RE-incorporated samples with limited RE solubility. Field-emission scanning electron microscopy and Fourier transform infrared spectroscopy revealed a layered structure composed of particles and the cation distribution. Magnetic hysteresis loops and magnetostriction strain curves showed that the saturation magnetization, magnetostriction coefficient and strain derivative were significantly modified due to the substitution of larger ionic radius RE"3"+ ions for Fe"3"+ ions, influencing the interaction between the tetrahedral and octahedral sites. - Highlights: • Magnetostrictive Cu_0_._1Co_0_._9RE_xFe_2_−_xO_4 (RE=Ho, Gd or Sm, x=0.0–0.25) nanocomposites were fabricated via sol-gel auto-combustion route using spent lithium-ion batteries as raw materials. • The RE elements doping had limited solubility. • The saturation magnetization (M_s) and maximum magnetostriction (λ_m_a_x) were reduced and the lattice parameter (a) was increasing by increasing RE"3"+ substitution contents. • The relationship of maximum strain derivative (dλ/dH_m_a_x) after the incorporation of RE was Ho>Gd>Sm.
[en] A method is described for the determination of the polarity of mixed organic solvents by using the fluorescent probe Hostasol Red (HR) desposited on the outer surface of nanosized zeolite L. Organic solvents and their mixtures can be roughly classified according to their polarity with bare eyes and fluorometrically. Emission peaks range from 520 to 640 nm. Some solvents act as quenchers. The method is studied with series of protic and nonprotic solvents, and with selected mixtures of organic solvents.
[en] Highlights: • A flexible pyroelectric device composed of CNT/PVDF/CNT was prepared. • This pyroelectric device could convert waste heat from chemical process into electricity. • This pyroelectric device was also proved to be a self-powered temperature monitor. • Application of the pyroelectric device to power a small electronic was demonstrated. - Abstract: As one of the most important renewable and green sources, the development and utilization of waste heat have been received more and more attentions. A big part of waste heat comes from chemical process, which is ubiquitous in industry and laboratory. However, this form of waste heat is difficult to be utilized due to its low grade and easy dissipation. In this paper, we present a flexible pyroelectric device as a potential approach for effectively harvesting waste heat from chemical exothermic process. To achieve practical application, the pyroelectric device simply attaches to the outside of a beaker, in which various chemical exothermic processes happen. The output voltage (under input impedance of 100 M Ohm) and short-circuit current can be 9.1 V and 95 nA when the neutral reaction of sodium hydroxide and hydrochloric acid per amount-of-substance concentration proceeds in the beaker. The generated electricity can directly drive a liquid crystal display. Moreover, this pyroelectric device is also proved to be a self-powered temperature monitor reflecting chemical process in real time, as the calculated temperature variation of solution based on pyroelectric current well agrees with the measured one by thermometry reference. This work expands the development of pyroelectric device for harvesting chemical waste heat and opens up the potential applications on self-powered chemical process monitor.
[en] Although the viscosity behavior of bacteria and extracellular polymeric substances (EPS) in flocculent activated sludge (FAS) and aerobic granular sludge (AGS) has been investigated, no studies have explored the role of viscosity in microbial attachment in pure culture. This study investigated the viscosity behavior of bacteria and EPS. The results showed that bacteria and their EPS exhibited non-Newtonian fluid and shear-thinning behavior. The viscosity of bacteria and EPS was 1.55–3.80 cP and 1.10–2.40 cP, respectively, while the attachment of bacteria (optical density at 600 nm) was 0.1426–3.1015. Bacteria with high attachment secreted EPS with a higher viscosity (2.40 cP), whereas those with weak attachment expressed EPS with a lower viscosity (1.10 cP). Viscosity and microbial attachment or extracellular polysaccharide (PS) content were significantly positively correlated. PS content was the source of bacterial viscosity, and β-polysaccharide played a more important role in viscosity and microbial attachment than α-polysaccharide. Thus, viscosity plays a critical role in microbial attachment, and high viscosity and PS content result in high microbial attachment, which is beneficial to the granulation process of AGS.
[en] Purpose: Incorporating training into image segmentation is a good approach to achieve additional robustness. This work aims to develop an effective strategy to utilize shape prior knowledge, so that the segmentation label evolution can be driven toward the desired global optimum. Methods: In the variational image segmentation framework, a regularization for the composite shape prior is designed to incorporate the geometric relevance of individual training data to the target, which is inferred by an image-based surrogate relevance metric. Specifically, this regularization is imposed on the linear weights of composite shapes and serves as a hyperprior. The overall problem is formulated in a unified optimization setting and a variational block-descent algorithm is derived. Results: The performance of the proposed scheme is assessed in both corpus callosum segmentation from an MR image set and clavicle segmentation based on CT images. The resulted shape composition provides a proper preference for the geometrically relevant training data. A paired Wilcoxon signed rank test demonstrates statistically significant improvement of image segmentation accuracy, when compared to multiatlas label fusion method and three other benchmark active contour schemes. Conclusions: This work has developed a novel composite shape prior regularization, which achieves superior segmentation performance than typical benchmark schemes.
[en] Multi-atlas based image segmentation sees great opportunities in the big data era but also faces unprecedented challenges in identifying positive contributors from extensive heterogeneous data. To assess data relevance, image similarity criteria based on various image features widely serve as surrogates for the inaccessible geometric agreement criteria. This paper proposes a general framework to learn image based surrogate relevance criteria to better mimic the behaviors of segmentation based oracle geometric relevance. The validity of its general rationale is verified in the specific context of fusion set selection for image segmentation. More specifically, we first present a unified formulation for surrogate relevance criteria and model the neighborhood relationship among atlases based on the oracle relevance knowledge. Surrogates are then trained to be small for geometrically relevant neighbors and large for irrelevant remotes to the given targets. The proposed surrogate learning framework is verified in corpus callosum segmentation. The learned surrogates demonstrate superiority in inferring the underlying oracle value and selecting relevant fusion set, compared to benchmark surrogates. (paper)
[en] Picking geometrically relevant atlases from the whole training set is crucial to multi-atlas based image segmentation, especially with extensive data of heterogeneous quality in the Big Data era. Unfortunately, there is very limited understanding of how currently used image similarity criteria reveal geometric relevance, let alone the optimization of them. This paper aims to develop a good image based surrogate relevance criterion to best reflect the underlying inaccessible geometric relevance in a learning context. We cast this surrogate learning problem into an optimization framework, by encouraging the image based surrogate to behave consistently with geometric relevance during training. In particular, we desire a criterion to be small for image pairs with similar geometry and large for those with significantly different segmentation geometry. Validation experiments on corpus callosum segmentation demonstrate the improved quality of the learned surrogate compared to benchmark surrogate candidates. (paper)
[en] Multi-atlas based image segmentation sees unprecedented opportunities but also demanding challenges in the big data era. Relevant atlas selection before label fusion plays a crucial role in reducing potential performance loss from heterogeneous data quality and high computation cost from extensive data. This paper starts with investigating the image similarity metric (termed ‘surrogate’), an alternative to the inaccessible geometric agreement metric (termed ‘oracle’) in atlas relevance assessment, and probes into the problem of how to select the ‘most-relevant’ atlases and how many such atlases to incorporate. We propose an inference model to relate the surrogates and the oracle geometric agreement metrics. Based on this model, we quantify the behavior of the surrogates in mimicking oracle metrics for atlas relevance ordering. Finally, analytical insights on the choice of fusion set size are presented from a probabilistic perspective, with the integrated goal of including the most relevant atlases and excluding the irrelevant ones. Empirical evidence and performance assessment are provided based on prostate and corpus callosum segmentation. (paper)
[en] Purpose: Fast growing access to large databases and cloud stored data presents a unique opportunity for multi-atlas based image segmentation and also presents challenges in heterogeneous atlas quality and computation burden. This work aims to develop a novel two-stage method tailored to the special needs in the face of large atlas collection with varied quality, so that high-accuracy segmentation can be achieved with low computational cost. Methods: An atlas subset selection scheme is proposed to substitute a significant portion of the computationally expensive full-fledged registration in the conventional scheme with a low-cost alternative. More specifically, the authors introduce a two-stage atlas subset selection method. In the first stage, an augmented subset is obtained based on a low-cost registration configuration and a preliminary relevance metric; in the second stage, the subset is further narrowed down to a fusion set of desired size, based on full-fledged registration and a refined relevance metric. An inference model is developed to characterize the relationship between the preliminary and refined relevance metrics, and a proper augmented subset size is derived to ensure that the desired atlases survive the preliminary selection with high probability. Results: The performance of the proposed scheme has been assessed with cross validation based on two clinical datasets consisting of manually segmented prostate and brain magnetic resonance images, respectively. The proposed scheme demonstrates comparable end-to-end segmentation performance as the conventional single-stage selection method, but with significant computation reduction. Compared with the alternative computation reduction method, their scheme improves the mean and medium Dice similarity coefficient value from (0.74, 0.78) to (0.83, 0.85) and from (0.82, 0.84) to (0.95, 0.95) for prostate and corpus callosum segmentation, respectively, with statistical significance. Conclusions: The authors have developed a novel two-stage atlas subset selection scheme for multi-atlas based segmentation. It achieves good segmentation accuracy with significantly reduced computation cost, making it a suitable configuration in the presence of extensive heterogeneous atlases