Detection Of Structural Changes Research Articles

Detection of structural changes in the cell membrane induced by electroporation with second-harmonic imaging microscopy

Detection of structural changes in the cell membrane induced by electroporation with second-harmonic imaging microscopy

Myocardial tissue and metabolism characterization in men with alcohol consumption by cardiovascular magnetic resonance and 11C-acetate PET/CT

BackgroundChronic alcohol consumption initially leads to asymptomatic left ventricular dysfunction, but can result in myocardial impairment and heart failure if ongoing. This study sought to characterize myocardial tissues and oxidative metabolism in asymptomatic subjects with chronic alcohol consumption by quantitative cardiovascular magnetic resonance (CMR) and 11C-acetate positron emission tomography (PET)/computed tomography (CT).MethodsThirty-four male subjects (48.8 ± 9.1 years) with alcohol consumption > 28 g/day for > 10 years and 35 age-matched healthy male subjects (49.5 ± 9.7 years) underwent CMR and 11C-acetate PET/CT. Native and post T1 values and extracellular volume (ECV) from CMR and Kmono and K1 from PET imaging were measured. Quantitative measurements by CMR and PET imaging were compared between subjects with moderate to heavy alcohol consumption and healthy controls, and their correlations were also analyzed.ResultsCompared to healthy controls, subjects with alcohol consumption showed significantly shorter native T1 (1133 ± 65 ms vs. 1186 ± 31 ms, p < 0.001) and post T1 (477 ± 42 ms vs. 501 ± 38 ms, p = 0.008) values, greater ECV (28.2 ± 2.2% vs. 26.9 ± 1.3%, p = 0.003), marginally lower Kmono (57.6 ± 12.1 min− 1 × 10− 3 vs. 63.0 ± 11.7 min− 1 × 10− 3, p = 0.055), and similar K1 (0.82 ± 0.13 min− 1 vs. 0.83 ± 0.15 min− 1, p = 0.548) after adjusting for confounding factors. There were no significant differences in CMR measurements and K1 between subjects with heavy and moderate alcohol consumption (all p > 0.05). In contrast, subjects with heavy alcohol consumption showed significantly lower Kmono values compared to those with moderate alcohol consumption (52.9 ± 12.1 min− 1 × 10− 3 vs. 63.7 ± 9.2 min− 1 × 10− 3, p = 0.012). Strong and moderate correlations were found between K1 and ECV in healthy controls (r = 0.689, p = 0.013) and subjects with moderate alcohol consumption (r = 0.518, p = 0.048), respectively.ConclusionAsymptomatic men with heavy alcohol consumption have detectable structural and metabolic changes in myocardium on CMR and 11C-acetate PET/CT. Compared with quantitative CMR, 11C-acetate PET/CT imaging may be more sensitive for detecting differences in myocardial damage among subjects with moderate to heavy alcohol consumption.

Using broadband signals for structural change detection in metal details

Using broadband signals for structural change detection in metal details

Experimental study of structural change detection using data-driven reduced-order models

Experimental study of structural change detection using data-driven reduced-order models

Experimental study of structural change detection using data-driven reduced-order models

This paper presents a comparison of the effectiveness of three different data-driven vibration-based approaches in detecting and locating structural changes in a 1/4 scale six-storey single-bay steel frame laboratory structure from measured experimental input-output data obtained from band-limited white-noise base-excitation tests. The implemented methodologies are based on reduced-order models obtained using three input-output system identification approaches: a system realisation algorithm using information matrices, a general time-domain least-squares identification method, and a non-parametric chain-like system identification approach. Variations in the estimated reduced-order models are then used to indicate the presence and infer the location of actual structural changes in the test structure. The results of this experimental study show that even though the changes introduced by the various levels of damage in the structure were robustly detected in the presence of modelling, measurement, and data processing errors using reduced-order representations, the identified change locations in the reduced-order model could not be, in some cases, reliably correlated with the actual damage location in the structure.

Surface treatments and functionalization of metal‐ceramic membranes for improved enzyme immobilization performance

AbstractBACKGROUNDEnzyme immobilization in porous membranes often improves enzyme performance. This work reports the preparation and characterization of robust and scalable asymmetric metal‐ceramic microfiltration membrane. The surface of the porous metal‐ceramic membrane was treated by impregnation with a ceramic oxide for enzyme adsorption and corrosion protection. Finally, enzyme immobilization in the support was investigated.RESULTSThe bilayer membrane was successfully fabricated by combining a ceramic microfiltration layer with a metal support by tape casting, lamination and co‐sintering. A pore size in the ceramic microfiltration layer of 0.4 μm resulted in high water permeability (12 000 L/(m2 h bar)). Two different surface treatments were compared: heat treatment and yttrium(III) oxide (Y2O3) impregnation. Corrosion stability tests under enzyme‐relevant conditions gave no detectable chemical or structural changes. Alcohol dehydrogenase (EC 1.1.1.1) was immobilized in the membrane by physical adsorption and by two covalent immobilization methods. Covalent immobilization significantly improved enzyme loading, activity, and recyclability. Membrane reuse by heat treatment removed fouling, but decreased immobilization performance.CONCLUSIONThe improved microstructure obtained by Y2O3‐impregnation had a significant effect on enzyme loading yield and activity. This indicates the potential of this surface modification method and of these metal‐supported ceramic membranes in enzyme immobilization. Covalent immobilization was superior. © 2019 Society of Chemical Industry

A Frequency-Based Approach for the Detection and Classification of Structural Changes Using t-SNE †.

This work presents a structural health monitoring (SHM) approach for the detection and classification of structural changes. The proposed strategy is based on t-distributed stochastic neighbor embedding (t-SNE), a nonlinear procedure that is able to represent the local structure of high-dimensional data in a low-dimensional space. The steps of the detection and classification procedure are: (i) the data collected are scaled using mean-centered group scaling (MCGS); (ii) then principal component analysis (PCA) is applied to reduce the dimensionality of the data set; (iii) t-SNE is applied to represent the scaled and reduced data as points in a plane defining as many clusters as different structural states; and (iv) the current structure to be diagnosed will be associated with a cluster or structural state based on three strategies: (a) the smallest point-centroid distance; (b) majority voting; and (c) the sum of the inverse distances. The combination of PCA and t-SNE improves the quality of the clusters related to the structural states. The method is evaluated using experimental data from an aluminum plate with four piezoelectric transducers (PZTs). Results are illustrated in frequency domain, and they manifest the high classification accuracy and the strong performance of this method.

Limnological effects of a large Amazonian run-of-river dam on the main river and drowned tributary valleys

Run-of-river dams are often considered to have lower environmental impacts than storage dams due to their smaller reservoirs and low potential for flow alteration. However, this has been questioned for projects recently built on large rivers around the world. Two of the world’s largest run-of-river dams—Santo Antônio and Jirau—were recently constructed on the Madeira River, a major tributary to the Amazon River in Brazil. Here we evaluate the effects of the creation of the Santo Antônio dam on the water chemistry and thermal structure of the Madeira River mainstem and back-flooded valleys of tributaries within the reservoir inundated area. In contrast to the mainstem river, some back-flooded tributaries periodically developed thermal stratification, which is associated with higher water residence times. Additionally, biochemical oxygen demand, partial pressure of CO2, and organic carbon all increased in the tributary valleys inundated by the reservoir, possibly due to increased input of allochthonous organic matter and its subsequent mineralization upon back-flooding—a common feature of newly flooded impoundments. The mainstem did not show detectable dam-related changes in water chemistry and thermal structure. Although the majority of the reservoir area maintained riverine conditions, the lateral valleys formed upon back-flooding—corresponding to ~30% of the Santo Antônio reservoir area—developed lake-like conditions akin to a typical reservoir of a storage dam.

Unveiling the structural features of nonnative trimers of human superoxide dismutase 1

BackgroundHuman SOD1 contains a single tryptophan residue (W32) which has been identified as a site of oxidative modification and a potentiator of aggregation involving in familial amyotrophic lateral sclerosis (fALS). In situ substitution of a tryptophan analog, 2,6-diazatryptophan ((2,6-aza)Trp) with its unique water-catalyzed proton transfer property, into proteins exhibits extraordinary sensitivity in the detection of subtle water-associated structural changes with only a few micro-molar concentration of samples. MethodsA combination of size-exclusion chromatography and water-catalyzed fluorescent emission was utilized to probe the structural features of metastable SOD1 nonnative trimers, the potential neurotoxic species in the fALS. ResultsThe monomer of apo-A4V SOD1 exhibits variable conformations and the fastest trimeric formation rate compared to that of wild type and I113T. The trimeric A4V SOD1 exhibits the least water molecules surrounding the W32, while I113T and the wild type appear to have more water molecules in the proximity of W32. A small molecule stabilizer, 5-fluorouridine, effects the structural conformation of SOD1 nonnative trimers. ConclusionsOur studies unveil new insights into water-associated structural changes of SOD1 nonnative trimers and demonstrate that in situ incorporation of (2,6-aza)Trp is a sensitive and powerful tool for probing subtle changes of water environments during protein aggregation. General significanceThe water-sensitive probe, (2,6-aza)Trp, demonstrates superior sensitivity for detecting modulation of water microsolvation, structural conformation during oligomer formation and 5FUrd binding to both wild type and mutant SOD1.

Application of superpixel segmentation and morphological projector for structural changes detection in remote sensing images

Detection of structural changes in images is one of the important tasks of remote sensing (RS) data thematic analysis. The effective way to solve it is applying the Pyt'ev’s morphological projector to the pair of images of the same scene acquired on different dates. The main advantage of this method is its invariance to global brightness transformations, which in the case of RS images correspond to different parameters of the atmosphere or the different values of the brightness-contrast ratio of the scene. However, the classical Pyt'ev’s morphological projector and its regularized versions do not take into account the spatial connectivity of image samples. As a result, they ignore the textural features of images. In this article, we suggest the algorithm of structural changes detection based on superpixel segmentation and Pyt'ev’s morphological projector that takes into account local characteristics of the image pixels. In the experimental research, we analyzed the accuracies of the proposed and classical Pyt'ev’s structural change detection methods using simulated and real RS images. The comparison of two algorithms showed that the proposed method is more robust to the additive white Gaussian noise (AWGN) at different values of signal-to-noise (SNR) ratio. Additionally, the experiments with nonlinear brightness distortions (vignetting) of one of the pair of images demonstrated that the proposed method has lower false positive rates than the classical one.

Spatial frequency domain correlation mapping optical coherence tomography for nanoscale structural characterization

Most of the fundamental pathological processes in living tissues exhibit changes at the nanoscale. Noninvasive, label-free detection of structural changes in biological samples pose a significant challenge to both researchers and healthcare professionals. It is highly desirable to be able to resolve these structural changes, during physiological processes, both spatially and temporally. Modern nanoscopy largely requires labeling, is limited to superficial 2D imaging, and is generally not suitable for in vivo applications. Furthermore, it is becoming increasingly evident that 2D biology often does not translate into the real 3D situation. Here, we present a method, spatial frequency domain correlation mapping optical coherence tomography (sf-cmOCT), for detection of depth resolved nanoscale structural changes noninvasively. Our approach is based on detection and correlation of the depth resolved spectra of axial spatial frequencies of the object which are extremely sensitive to structural alterations. The presented work describes the principles of this approach and demonstrates its feasibility by monitoring internal structural changes within objects, including human skin in vivo. Structural changes can be visualized at each point in the sample in space from a single image or over time using two or more images. These experimental results demonstrate possibilities for the study of nanoscale structural changes, without the need for biomarkers or labels. Thus, sf-cmOCT offers exciting and far-reaching opportunities for early disease diagnosis and treatment response monitoring, as well as a myriad of applications for researchers.

Controlling Near-Surface Ni Composition in Octahedral PtNi(Mo) Nanoparticles by Mo Doping for a Highly Active Oxygen Reduction Reaction Catalyst.

We report and study the translation of exceptionally high catalytic oxygen electroreduction activities of molybdenum-doped octahedrally shaped PtNi(Mo) nanoparticles from conventional thin-film rotating disk electrode screenings (3.43 ± 0.35 A mgPt-1 at 0.9 VRHE) to membrane electrode assembly (MEA)-based single fuel cell tests with sustained Pt mass activities of 0.45 A mgPt-1 at 0.9 Vcell, one of the highest ever reported performances for advanced shaped Pt alloys in real devices. Scanning transmission electron microscopy with energy dispersive X-ray analysis (STEM-EDX) reveals that Mo preferentially occupies the Pt-rich edges and vertices of the element-anisotropic octahedral PtNi particles. Furthermore, by combining in situ wide-angle X-ray spectroscopy, X-ray fluorescence, and STEM-EDX elemental mapping with electrochemical measurements, we finally succeeded to realize high Ni retention in activated PtNiMo nanoparticles even after prolonged potential-cycling stability tests. Stability losses at the anodic potential limits were mainly attributed to the loss of the octahedral particle shape. Extending the anodic potential limits of the tests to the Pt oxidation region induced detectable Ni losses and structural changes. Our study shows on an atomic level how Mo adatoms on the surface impact the Ni surface composition, which, in turn, gives rise to the exceptionally high experimental catalytic ORR reactivity and calls for strategies on how to preserve this particular surface composition to arrive at performance stabilities comparable with state-of-the-art spherical dealloyed Pt core-shell catalysts.

Longitudinal Mapping of Cortical Thickness Measurements: An Alzheimer's Disease Neuroimaging Initiative-Based Evaluation Study.

Longitudinal studies of development and disease in the human brain have motivated the acquisition of large neuroimaging data sets and the concomitant development of robust methodological and statistical tools for quantifying neurostructural changes. Longitudinal-specific strategies for acquisition and processing have potentially significant benefits including more consistent estimates of intra-subject measurements while retaining predictive power. Using the first phase of the Alzheimer's Disease Neuroimaging Initiative (ADNI-1) data, comprising over 600 subjects with multiple time points from baseline to 36 months, we evaluate the utility of longitudinal FreeSurfer and Advanced Normalization Tools (ANTs) surrogate thickness values in the context of a linear mixed-effects (LME) modeling strategy. Specifically, we estimate the residual variability and between-subject variability associated with each processing stream as it is known from the statistical literature that minimizing the former while simultaneously maximizing the latter leads to greater scientific interpretability in terms of tighter confidence intervals in calculated mean trends, smaller prediction intervals, and narrower confidence intervals for determining cross-sectional effects. This strategy is evaluated over the entire cortex, as defined by the Desikan-Killiany-Tourville labeling protocol, where comparisons are made with the cross-sectional and longitudinal FreeSurfer processing streams. Subsequent linear mixed effects modeling for identifying diagnostic groupings within the ADNI cohort is provided as supporting evidence for the utility of the proposed ANTs longitudinal framework which provides unbiased structural neuroimage processing and competitive to superior power for longitudinal structural change detection.

Graph-Based Change Detection for Condition Monitoring of Rotating Machines: Techniques for Graph Similarity

Detection of structural changes in machine state attracts increasing attention for the monitoring and prognosis of rotating machinery. Very recently, the graph model has been introduced and adopted for modeling of normal machine state so as to investigate the likelihood of potential changes by the use of Martingale-test. This paper expands the potential of the graph model toward correlation-analysis-based monitoring applications, where the problem of measuring graph similarity is the main challenge due to domain specificity. We first investigated six typical schemes taken from other areas for this purpose, and found that they show discriminative capacities when dealing with changes with different types in the machine condition monitoring scenarios. Subsequently, based on a procedure called “method of ranks,” we have chosen four schemes among them which are potentially promising for our usage. Meanwhile, based on these metrics, two machine learning based similarity metrics are proposed to further improve their practical values by combining them. At last, comprehensive theoretical interpretations and comparisons of presented methods are made both in simulated scenarios and real-engineering monitoring applications.

Pyrolytic Carbon Coating Effects on Oxide and Carbide Kernels Intended for Nuclear Fuel Applications

The coating of nuclear fuel kernels with pyrolytic carbon (PyC) is a well-understood practice dating back over half a century. In spite of decades of studies related to these coatings, no study has yet investigated the effect of the PyC deposition coating process on the kernels themselves. In this study, the composition and crystallographic phase of kernel materials were observed to change after exposure to the thermal and chemical environment of the PyC coating process. Specifically, the coating process increased the fraction of high carbon content phase within carbide microsphere kernels, with W2C containing microspheres driven toward WC, and UC containing microspheres driven toward UC2. Oxide microspheres consisted of a mixture of two crystalline phases. The monoclinic phase within yttria-stabilized zirconia microspheres was eliminated by the coating process resulting in a purely tetragonal phase. Hafnium oxide microspheres were more stable showing no detectable change in composition or crystal structure after coating.

In situ Structure of Rotavirus VP1 RNA-Dependent RNA Polymerase

Rotaviruses, like other non-enveloped, double-strand RNA viruses, package an RNA-dependent RNA polymerase (RdRp) with each duplex of their segmented genomes. Rotavirus cell entry results in loss of an outer protein layer and delivery into the cytosol of an intact, inner capsid particle (the “double-layer particle,” or DLP). The RdRp, designated VP1, is active inside the DLP; each VP1 achieves many rounds of mRNA transcription from its associated genome segment. Previous work has shown that one VP1 molecule lies close to each 5-fold axis of the icosahedrally symmetric DLP, just beneath the inner surface of its protein shell, embedded in tightly packed RNA. We have determined a high-resolution structure for the rotavirus VP1 RdRp in situ, by local reconstruction of density around individual 5-fold positions. We have analyzed intact virions (“triple-layer particles”), non-transcribing DLPs and transcribing DLPs. Outer layer dissociation enables the DLP to synthesize RNA, in vitro as well as in vivo, but appears not to induce any detectable structural change in the RdRp. Addition of NTPs, Mg2+, and S-adenosylmethionine, which allows active transcription, results in conformational rearrangements, in both VP1 and the DLP capsid shell protein, that allow a transcript to exit the polymerase and the particle. The position of VP1 (among the five symmetrically related alternatives) at one vertex does not correlate with its position at other vertices. This stochastic distribution of site occupancies limits long-range order in the 11-segment, double-strand RNA genome.

Application of Gray Level co-Occurrence Matrix Algorithm for Detection of Discrete Structural Changes in Cell Nuclei After Exposure to Iron Oxide Nanoparticles and 6-Hydroxydopamine.

The gray level co-occurrence matrix (GLCM) algorithm is a contemporary computational biology method which, today, is frequently used to detect small changes in texture that are not visible using conventional techniques. We demonstrate that the toxic compound 6-hydroxydopamine (6-OHDA) and iron oxide nanoparticles (IONPS) have opposite effects on GLCM features of cell nuclei. Saccharomyces cerevisiae yeast cells were treated with 6-OHDA and IONPs, and imaging with GLCM analysis was performed at three different time points: 30 min, 60 min, and 120 min after the treatment. A total of 200 cell nuclei were analyzed, and for each nucleus, 5 GLCM parameters were calculated: Angular second moment (ASM), Inverse difference moment (IDM), Contrast (CON), Correlation (COR) and Sum Variance (SVAR). Exposure to IONPs was associated with the increase of ASM and IDM while the values of SVAR and COR were reduced. Treatment with 6-OHDA was associated with the increase of SVAR and CON, while the values of nuclear ASM and IDM were reduced. This is the first study to indicate that IONPs and 6-OHDA have opposite effects on nuclear texture. Also, to the best of our knowledge, this is the first study to apply the GLCM algorithm in Saccharomyces cerevisiae yeast cells in this experimental setting.

Swept source-OCT and swept source-OCT angiography findings in posterior microphthalmos.

To describe swept source-OCT (SS-OCT) and swept source-OCT angiography (SS-OCTA) findings in eyes with posterior microphthalmos (PM). Twelve eyes (six patients) with PM were evaluated using SS-OCT and SS-OCTA. Structural changes, subfoveal choroidal thickness (SFCT), and perifoveal capillary changes with qualitative and quantitative assessments were analyzed. Twenty eyes served as control group. SS-OCT findings included elevated retinal papillo-macular fold (75%), retinal pigment epithelium folds (83%), macular cystoid spaces (42%), subretinal fluid (17%), and increased visibility of posterior vitreous cortex and hyaloid (42%). Mean SFCT in PM and in control eyes were 430.33 ± 157.48µm and 290.05 ± 52.87µm, respectively (p = 0.004). Perifoveal capillary changes on SS-OCTA included foveal avascular zone (FAZ) remodeling (100%), vessel tortuosity (67%), disorganization of the deep capillary network (67%), intraretinal cystoid spaces (42%), and areas of signal voids in the choriocapillaris (33%). FAZ area was significantly smaller in eyes with PM than in the control group in both the superficial (p < 0.001) and deep capillary plexuses (p = 0.001). Capillary vessel density (CVD) was significantly lower in the PM than in the control group in the deep capillary plexus (p = 0.004). Log MAR BCVA correlated negatively with axial length (r = - 0.929, p < 0.001), FAZ area in both the superficial (r = - 0.637, p < 0.001) and deep capillary plexus (r = - 0.561, p = 0002), and CVD in the deep capillary plexus (r = - 0.450, p = 0.016). Combined SS-OCT and SS-OCTA allow the detection of various retinal and choroidal structural and microvascular changes in eyes with PM. These findings can provide new insights onto this blinding ocular condition.

FT-IR Spectroscopic Detection of Biochemical and Structural Changes Associated with Early Tumor Transformation in Hamster Oral Carcinogenesis

FT-IR spectroscopy is a vibrational spectroscopic technique that can be used to investigate the biochemical changes at the molecular level in various tissue transformation conditions [hyperplasia, dysplasia and well differentiated squamous cell carcinoma (WDSCC)] with respect to control tissues in DMBA-induced hamster oral carcinogenesis. The results revealed that a significant increase in the amount of proteins and nucleic acid contents and a decrease in the amount of lipids contents are observed in WDSCC when compared to control, hyperplasia, dysplasias. In addition, the detailed secondary structure of proteins in the control and various tissue transformation conditions is also presented. Further, the diagnostic algorithms based on PC-LDA achieved an overall sensitivity of 80-100 % and specificity of 81- 100 %. The present study further shows a great potential of FT-IR spectroscopy as a complimentary tool, which may provide a rapid screening method and have potential use in the diagnosis of dysplasia and early, non-invasive oral cancer.

Shear-Induced CCN1 Promotes Atheroprone Endothelial Phenotypes and Atherosclerosis.

Atherosclerosis occurs preferentially at the blood vessels encountering blood flow turbulence. The matricellular protein CCN1 is induced in endothelial cells by disturbed flow, and is expressed in advanced atherosclerotic lesions in patients and in the Apoe-/- mouse model. The role of CCN1 in atherosclerosis remains undefined. To assess the function of CCN1 in vivo, knock-in mice carrying the integrin α6β1-binding-defective mutant allele Ccn1-dm on the Apoe-/- background were tested in an atherosclerosis model generated by carotid artery ligation. Additionally, CCN1-regulated functional phenotypes of human umbilical vein endothelial cells, or primary mouse aortic endothelial cells isolated from wild-type and Ccn1 dm/dm mice, were investigated in the in vitro shear stress experiments under unidirectional laminar shear stress (12 dyn/cm2) versus oscillatory shear stress (±5 dyn/cm2) conditions. We found that Ccn1 expression was upregulated in the arterial endothelium 3 days after ligation before any detectable structural changes, and intensified with the progression of atherosclerotic lesions. Compared with Apoe-/- controls, Ccn1 dm/dm/ Apoe-/- mice were remarkably resistant to ligation-induced plaque formation (n=6). These mice exhibited lower oxidative stress, expression of endothelin-1 and monocyte chemoattractant protein-1, and monocyte homing. CCN1/α6β1 critically mediated flow-induced activation of the pleiotropic transcription factor nuclear factor-κB and therefore the induction of atheroprone gene expression in the mouse arterial endothelium after ligation (n=6), or in cultured human umbilical vein endothelial cells or primary mouse aortic endothelial cells exposed to oscillatory shear stress (n=3 in triplicate). Interestingly, the activation of nuclear factor-κB by CCN1/α6β1 signaling prompted more production of CCN1 and α6β1. Blocking CCN1-α6β1 binding by the Ccn1-dm mutation or by T1 peptide (derived from an α6β1-binding sequence of CCN1) disrupted the positive-feedback regulation between CCN1/α6β1 and nuclear factor-κB, and prevented flow-induced atheroprone phenotypic alterations in endothelial cells or atherosclerosis in mice. These data demonstrate a causative role of CCN1 in atherosclerosis via modulating endothelial phenotypes. CCN1 binds to its receptor integrin α6β1 to activate nuclear factor-κB, thereby instigating a vicious circle to persistently promote atherogenesis. T1, a peptide antagonist selectively targeting CCN1-α6β1, can be further optimized for developing T1-mimetics to treat atherosclerosis.