Spectrum Imaging Analysis Research Articles

Visible Spectrum Image Analysis For Estimation of Phenological Stages in Irrigated Bean Cropping

Visible Spectrum Image Analysis For Estimation of Phenological Stages in Irrigated Bean Cropping

Investigating the interaction of uranium(VI) with diatoms and their bacterial community: A microscopic and spectroscopic study

Diatoms and bacteria play a vital role in investigating the ecological effects of heavy metals in the environment. Despite separate studies on metal interactions with diatoms and bacteria, there is a significant gap in research regarding heavy metal interactions within a diatom-bacterium system, which closely mirrors natural conditions. In this study, we aim to address this gap by examining the interaction of uranium(VI) (U(VI)) with Achnanthidium saprophilum freshwater diatoms and their natural bacterial community, primarily consisting of four successfully isolated bacterial strains (Acidovorax facilis, Agrobacterium fabrum, Brevundimonas mediterranea, and Pseudomonas peli) from the diatom culture. Uranium (U) bio-association experiments were performed both on the xenic A. saprophilum culture and on the four bacterial isolates. Scanning electron microscopy and transmission electron microscopy coupled with spectrum imaging analysis based on energy-dispersive X-ray spectroscopy revealed a clear co-localization of U and phosphorus both on the surface and inside A. saprophilum diatoms and the associated bacterial cells. Time-resolved laser-induced fluorescence spectroscopy with parallel factor analysis identified similar U(VI) binding motifs both on A. saprophilum diatoms and the four bacterial isolates. This is the first work providing valuable microscopic and spectroscopic data on U localization and speciation within a diatom-bacterium system, demonstrating the contribution of the co-occurring bacteria to the overall interaction with U, a factor non-negligible for future modeling and assessment of radiological effects on living microorganisms.

Characterizing stalagmite composition using hyperspectral imaging

Stalagmites offer nearly continuous records of past climate in continental settings at high temporal resolution. The climatic records preserved in stalagmites are commonly investigated by examining compositional characteristics such as mineralogy, organic content, and lamination patterns. These proxies provide valuable insights into the environmental conditions during stalagmite formation. However, the methods used to obtain information about these proxies are relatively destructive. This study uses hyperspectral imaging, a non-contact technique, to identify mineral composition, organic matter content, and laminations in stalagmites. It is the first wide spectrum imaging analysis in speleothem research, using both visible–near infrared and shortwave infrared wavelengths. Results obtained from hyperspectral imaging were compared by point spectral analysis using an ASD spectroradiometer and a grayscale profile along the growth axis of a stalagmite. Petrographic observation of thin sections and X-ray diffraction (XRD) analyses on selected stalagmite layers were performed to cross-validate the hyperspectral data. A travertine sample was also used to replicate the method on calcite. To automate mineral identification, a machine learning algorithm was developed to map spatial distribution and quantify relative proportions of minerals across the sample. Our findings are in good agreement with traditionally used methods for mineral identification, i.e. XRD and petrography, aiding in the interpretation of paleoclimate proxies, and offer a spatial guide for U–Th dating analyses. It also provides insight for future investigations of stalagmites using hyperspectral data and classification through machine learning algorithms.

Investigations towards incorporation of Eu3+ and Cm3+ during ZrO2 crystallization in aqueous solution

Nuclear energy provides a widely applied carbon-reduced energy source. Following operation, the spent nuclear fuel (SNF), containing a mixture of radiotoxic elements such as transuranics, needs to be safely disposed of. Safe storage of SNF in a deep geological repository (DGR) relies on multiple engineered and natural retention barriers to prevent environmental contamination. In this context, zirconia (ZrO2) formed on the SNF rod cladding, could be employed as an engineered barrier for immobilization of radionuclides via structural incorporation. This study investigates the incorporation of Eu3+ and Cm3+, representatives for trivalent transuranics, into zirconia by co-precipitation and crystallization in aqueous solution at 80 °C. Complementary structural and microstructural characterization has been carried out by powder X-ray diffraction (PXRD), spectrum imaging analysis based on energy-dispersive X-ray spectroscopy in scanning transmission electron microscopy mode (STEM-EDXS), and luminescence spectroscopy. The results reveal the association of the dopants with the zirconia particles and elucidate the presence of distinct bulk and superficially incorporated species. Hydrothermal aging for up to 460 days in alkaline media points to great stability of these incorporated species after initial crystallization, with no indication of phase segregation or release of Eu3+ and Cm3+ over time. These results suggest that zirconia would be a suitable technical retention barrier for mobilized trivalent actinides in a DGR.

Boosted Convolutional Neural Network Algorithm for the Classification of the Bearing Fault form 1-D Raw Sensor Data.

Renewable energy sources are a growing branch of industry. One such source is wind farms, which have significantly increased their number over recent years. Alongside the increased number of turbines, maintenance problems are growing. There is a need for newer and less intrusive predictive maintenance methods. About 40% of all turbine failures are due to bearing failure. This paper presents a modified neural direct classifier method using raw accelerometer measurements as input. This proprietary platform allows for better damage prediction results than convolutional networks in vibration spectrum image analysis. It operates in real time and without signal processing methods converting the signal to a time-frequency spectrogram. Image processing methods can extract features from a set of preset features and based on their importance. The proposed method is not based on feature extraction from image data but on automatically finding a set of features from raw tabular data. This fact significantly reduces the computational cost of detection and improves the failure detection accuracy compared to the classical methods. The model achieved a precision of 99.32% on the validation set, and 96.3% during bench testing. These results were an improvement over the method that classifies time-frequency spectrograms of 97.76% for the validation set and 90.8% for the real-world tests, respectively.

Bottom-up fabrication of FeSb2 nanowires on crystalline GaAs substrates with ion-induced pre-patterning

In recent decades, nanostructuring has become one of the most important techniques to design and engineer functional materials. The properties of nanostructured materials are influenced by the interplay of its instrinsic bulk properties and the properties of its surface - the relative importance of the latter being enhanced by the increased surface-to-volume ratio in nanostructures. For instance, nanostructuring of a thermoelectric material can reduce the thermal conductivity while maintaining constant electrical conductivity and the Seebeck coefficient, which would improve the thermoelectric properties. For that reason, this study investigated the possibility of preparing nanowires of iron antimonide (FeSb2), a thermoelectric material, on single-crystalline gallium arsenide GaAs (001) substrates with ion-induced surface nanoscale pre-patterning and characterized the structure of the prepared FeSb2 nanowires. The GaAs (001) substrates were pre-patterned using 1 keV Ar+ ion irradiation. By using an ion source with a broad, unfocused ion beam at normal incidence, the patterned area can be scaled to nearly any size. The self-organized surface morphology is formed by reverse epitaxy and is characterized by almost perfectly parallel-aligned ripples at the nanometer scale. For the fabrication of FeSb2 nanowires, iron and antimony were successively deposited on the pre-patterned GaAs substrates at grazing incidence and then annealed. They were characterized using transmission electron microscopy (TEM), in particular high-resolution TEM imaging for structure analysis and spectrum imaging analysis based on energy-dispersive X-ray spectroscopy for element characterization. With the presented fabrication method, FeSb2 nanowires were produced successfully on GaAs(001) substrates with an ion-induced nanopatterned surface. The nanowires have a polycristalline structure and a cross-sectional area which is scalable up to 22 × 22 nm2. Due to the high order nanostructures on the GaAs substrate, the nanowires have a length of several micrometer. This bottom-up nanofabrication process based on ion-induced patterning can be a viable alternative to top-down procedures regarding to efficiency and costs.

Espm: A Python library for the simulation of STEM-EDXS datasets

We present two open-source Python packages: “electron spectro-microscopy” (espm) and “electron microscopy tables” (emtables). The espm software enables the simulation of scanning transmission electron microscopy energy-dispersive X-ray spectroscopy datacubes, based on user-defined chemical compositions and spatial abundance maps of constituent phases. The simulation process uses X-ray emission cross-sections generated via state-of-the-art calculations made with emtables. These tables are designed to be easily modifiable, either manually or using espm. The simulation framework is designed to test the application of decomposition algorithms for the analysis of STEM-EDX spectrum images with access to a known ground truth. We validate our approach using the case of a complex geology-related sample, comparing raw simulated and experimental datasets and the outputs of their non-negative matrix factorization. In addition to testing machine learning algorithms, our packages will also help experimental design, for instance, predicting dataset characteristics or establishing minimum counts needed to measure nanoscale features.

Nanostructure–Optical Property Relationship in CuInSe2/ZnS Core/Shell Quantum Dots Revealed by Multivariate Statistical Analysis of X-ray Spectrum Images

Modifying nanostructures of colloidal quantum dots (QDs), such as forming core/shell structures and alloying, is a common strategy for tuning their optical properties. However, the relationship between the actual nanostructures and optical properties of QDs remains poorly discussed owing to inherent difficulties in the structural analysis of QDs. Here, we investigated nanostructures of CuInSe2/ZnS core/shell QDs as a function of temperature during ZnS shell growth using X-ray energy-dispersive spectroscopy spectrum imaging combined with multivariate statistical analysis. Lower signal-to-noise ratios resulting from very weak characteristic X-rays emitted from QDs were overcome using an aberration-corrected transmission electron microscope equipped with a large solid-angle X-ray detection system (total solid angle of 1.6 sr). The nanoscale X-ray mapping elucidated that QDs with homogeneous cores and island-like shells surrounding core vertexes were formed at temperatures of 225 and 250 °C. Moreover, diffusion of Zn atoms from shells to cores was revealed by quantitative analysis of the core composition and verified by atomic diffusion simulations. These modified nanostructures should contribute to the decrease in surface traps and internal defects, enhancing the optical properties of QDs. The nanoscale X-ray mapping provides direct evidence of modified nanostructures of QDs.

Research on vocal sounding based on spectrum image analysis

The improvement of vocal singing technology involves many factors, and it is difficult to achieve the desired effect by human analysis alone. Based on this, this study based on spectrum image analysis uses the base-2 time-selection FFT algorithm as the research algorithm, uses the wavelet transform algorithm as the denoising algorithm, and combines comparative analysis to discuss the mechanism of vocal music, the state of vocalization, and the vocal quality of vocalists in vocal music teaching. Simultaneously, this study compares the singer’s frequency, pitch, overtones, harmonics, singer formants, etc., and derives the characteristics of vocal vocalization under different conditions, and can be extended to all music vocal studies. Research shows that this research method has certain practicality and can provide theoretical reference for related research.

A Novel Method for Automatic Determination of the Number of Meaningful Components in the PCA Analysis of Spectrum-Images

A Novel Method for Automatic Determination of the Number of Meaningful Components in the PCA Analysis of Spectrum-Images

The Superior Fronto-Occipital Fasciculus in the Human Brain Revealed by Diffusion Spectrum Imaging Tractography: An Anatomical Reality or a Methodological Artifact?

The existence of the superior fronto-occipital fasciculus (SFOF) in the human brain remains controversial. The aim of the present study was to clarify the existence, course, and terminations of the SFOF. High angular diffusion spectrum imaging (DSI) analysis was performed on six healthy adults and on a template of 842 subjects from the Human Connectome Project. To verify tractography results, we performed fiber microdissections of four post-mortem human brains. Based on DSI tractography, we reconstructed the SFOF in the subjects and the template from the Human Connectome Project that originated from the rostral and medial parts of the superior and middle frontal gyri. By tractography, we found that the fibers formed a compact fascicle at the level of the anterior horn of the lateral ventricle coursing above the head of caudate nucleus, medial to the corona radiate and under the corpus callosum (CC), and terminated at the parietal region via the lower part of the caudate nucleus. We consider that this fiber bundle observed by tractography is the SFOF, although it terminates mainly at the parietal region, rather than occipital lobe. By contrast, we were unable to identify a fiber bundle corresponding to the SFOF in our fiber dissection study. Although we did not provide definite evidence of the SFOF in the human brain, these findings may be useful for future studies in this field.

The usage of data compression for the background estimation of electron energy loss spectra

Quantitative analysis of noisy electron spectrum images requires a robust estimation of the underlying background signal. We demonstrate how modern data compression methods can be used as a tool for achieving an analysis result less affected by statistical errors or to speed up the background estimation. In particular, we demonstrate how a multilinear singular value decomposition (MLSVD) can be used to enhance elemental maps obtained from a complex sample measured with energy electron loss spectroscopy. Furthermore, the usage of vertex component analysis (VCA) for a basis vector centered estimation of the background is demonstrated. Arising computational benefits in terms of model accuracy and computational costs are studied.

Validating Spectrum Image Analysis of Lymph Nodes During Endobronchial Ultrasound-Guided Transbronchial Needle Aspiration in Patients With Lung Cancer

Validating Spectrum Image Analysis of Lymph Nodes During Endobronchial Ultrasound-Guided Transbronchial Needle Aspiration in Patients With Lung Cancer

Subcomponents and Connectivity of the Inferior Fronto-Occipital Fasciculus Revealed by Diffusion Spectrum Imaging Fiber Tracking.

The definitive structure and functional role of the inferior fronto-occipital fasciculus (IFOF) are still controversial. In this study, we aimed to investigate the connectivity, asymmetry, and segmentation patterns of this bundle. High angular diffusion spectrum imaging (DSI) analysis was performed on 10 healthy adults and a 90-subject DSI template (NTU-90 Atlas). In addition, a new tractography approach based on the anatomic subregions and two regions of interest (ROI) was evaluated for the fiber reconstructions. More widespread anterior-posterior connections than previous “standard” definition of the IFOF were found. This distinct pathway demonstrated a greater inter-subjects connective variability with a maximum of 40% overlap in its central part. The statistical results revealed no asymmetry between the left and right hemispheres and no significant differences existed in distributions of the IFOF according to sex. In addition, five subcomponents within the IFOF were identified according to the frontal areas of originations. As the subcomponents passed through the anterior floor of the external capsule, the fibers radiated to the posterior terminations. The most common connection patterns of the subcomponents were as follows: IFOF-I, from frontal polar cortex to occipital pole, inferior occipital lobe, middle occipital lobe, superior occipital lobe, and pericalcarine; IFOF-II, from orbito-frontal cortex to occipital pole, inferior occipital lobe, middle occipital lobe, superior occipital lobe, and pericalcarine; IFOF-III, from inferior frontal gyrus to inferior occipital lobe, middle occipital lobe, superior occipital lobe, occipital pole, and pericalcarine; IFOF-IV, from middle frontal gyrus to occipital pole, and inferior occipital lobe; IFOF-V, from superior frontal gyrus to occipital pole, inferior occipital lobe, and middle occipital lobe. Our work demonstrates the feasibility of high resolution diffusion tensor tractography with sufficient sensitivity to elucidate more anatomical details of the IFOF. And we provides a new framework for subdividing the IFOF for better understanding its functional role in the human brain.

Organization of cortico-cortical pathways supporting memory retrieval across subregions of the left ventrolateral prefrontal cortex.

Functional magnetic resonance imaging (fMRI) evidence indicates that different subregions of ventrolateral prefrontal cortex (VLPFC) participate in distinct cortical networks. These networks have been shown to support separable cognitive functions: anterior VLPFC [inferior frontal gyrus (IFG) pars orbitalis] functionally correlates with a ventral fronto-temporal network associated with top-down influences on memory retrieval, while mid-VLPFC (IFG pars triangularis) functionally correlates with a dorsal fronto-parietal network associated with postretrieval control processes. However, it is not known to what extent subregional differences in network affiliation and function are driven by differences in the organization of underlying white matter pathways. We used high-angular-resolution diffusion spectrum imaging and functional connectivity analysis in unanesthetized humans to address whether the organization of white matter connectivity differs between subregions of VLPFC. Our results demonstrate a ventral-dorsal division within IFG. Ventral IFG as a whole connects broadly to lateral temporal cortex. Although several different individual white matter tracts form connections between ventral IFG and lateral temporal cortex, functional connectivity analysis of fMRI data indicates that these are part of the same ventral functional network. By contrast, across subdivisions, dorsal IFG was connected with the midfrontal gyrus and correlated as a separate dorsal functional network. These qualitative differences in white matter organization within larger macroanatomical subregions of VLPFC support prior functional distinctions among these regions observed in task-based and functional connectivity fMRI studies. These results are consistent with the proposal that anatomical connectivity is a crucial determinant of systems-level functional organization of frontal cortex and the brain in general.

Femto-molar detection of cancer marker-protein based on immuno-nanoplasmonics at single-nanoparticle scale

We describe an in vitro biomarker sensor based on immuno-silver nanomarbles (iSNMs) and the nanoscattering spectrum imaging analysis system using localized surface plasmon resonance (LSPR). In particular, highly monodisperse SNMs with large figures of merit are prepared, and the sensing substrates are also fabricated using the nanoparticle adsorption method. The high sensitivity of the LSPR sensor based on an SNM is confirmed using various solvents that have different refractive indexes. For the sensitive and specific detection of epithelial cell adhesion molecules (EpCAMs) expressed on cancer cells, the surface of the SNM is conjugated with an anti-EpCAM aptamer, and molecular sensing for the EpCAM expression level is carried out using whole cell lysates from various cancer cell lines. Collectively, we have developed a biomarker-detectable LSPR sensor based on iSNMs, which allows for the sensitive and effective detection of EpCAMs at both the single-cell and femto-molar level.

Facile synthesis of SnO2-PbS nanocomposites with controlled structure for applications in photocatalysis.

Recent studies have shown that SnO2-based nanocomposites offer excellent electrical, optical, and electrochemical properties. In this article, we present the facile and cost-effective fabrication, characterization and testing of a new SnO2-PbS nanocomposite photocatalyst designed to overcome low photocatalytic efficiency brought about by electron-hole recombination and narrow photoresponse range. The structure is fully elucidated by X-ray diffraction (XRD)/Reitveld refinement, Raman spectroscopy, X-ray photoelectron spectroscopy (XPS), Brunauer-Emmett-Teller (BET) surface area analysis, and transmission electron microscopy (TEM). Energy-dispersive X-ray spectroscopy (EDX) spectrum imaging analysis demonstrates the intermixing of SnO2 and PbS to form nanocomposites. A charge separation mechanism is presented that explains how the two semiconductors in junction function synergistically. The efficacy of this new nanocomposite material in the photocatalytic degradation of the toxic dye Rhodamine B under simulated solar irradiation is demonstrated. An apparent quantum yield of 0.217 mol min(-1) W(-1) is calculated with data revealing good catalyst recyclability and that charge separation in SnO2-PbS leads to significantly enhanced photocatalytic activity in comparison to either SnO2 or PbS.

Semi-automation of Doppler Spectrum Image Analysis for Grading Aortic Valve Stenosis Severity.

Doppler echocardiography analysis has become a golden standard in the modern diagnosis of heart diseases. In this paper, we propose a set of techniques for semi-automated parameter extraction for aortic valve stenosis severity grading. The main objectives of the study is to create echocardiography image processing techniques, which minimize manual image processing work of clinicians and leads to reduced human error rates. Aortic valve and left ventricle output tract spectrogram images have been processed and analyzed. A novel method was developed to trace systoles and to extract diagnostic relevant features. The results of the introduced method have been compared to the findings of the participating cardiologists. The experimental results showed the accuracy of the proposed method is comparable to the manual measurement performed by medical professionals. Linear regression analysis of the calculated parameters and the measurements manually obtained by the cardiologists resulted in the strongly correlated values: peak systolic velocity's and mean pressure gradient's R2 both equal to 0.99, their means' differences equal to 0.02 m/s and 4.09 mmHg, respectively, and aortic valve area's R2 of 0.89 with the two methods means' difference of 0.19 mm. The introduced Doppler echocardiography images processing method can be used as a computer-aided assistance in the aortic valve stenosis diagnostics. In our future work, we intend to improve precision of left ventricular outflow tract spectrogram measurements and apply data mining methods to propose a clinical decision support system for diagnosing aortic valve stenosis.

Why Principal Component Analysis of STEM spectrum-images results in “abstract”, uninterpretable loadings?

Principal Component Analysis (PCA) can improve dramatically the treatment of large STEM spectrum-images by finding the directions (loadings) of highest data variance in the factor space and projecting the data on these directions. Loadings typically do not show clear physical meanings, thus the interpretation of PCA results is difficult. This work investigates the potential reasons for appearing such counterintuitive PCA outputs. The following reasons are identified: (i) missing the step of centering the data in the PCA pre-treatment, (ii) complexity of data variations inconsistent with the orthogonality restrictions of PCA, (iii) non-linearity caused either by chemical variations or by the peculiarities of the spectra formation, and (iv) inaccuracy in extracting major PCA components. In many cases, the PCA treatment can be altered in such a way that the intuitively clear loadings are delivered.

Scattering analysis of single polyaniline nanoparticles for acidic environmental sensing

Herein, we demonstrate an acidic environmental sensing technique based on polyaniline (PAni) nanoparticles and a nanoscattering spectrum imaging analysis (NSSIA) system. PAni nanoparticles (PNs) were formulated by a homogeneous coating process using Tween® 80 as a surfactant. Subsequently, PNs were immobilized to an aminated glass substrate to sense environmental pH conditions for single PNs, using the NSSIA system. In contrast to dark-field scattering imaging of the PN, the NSSIA system enabled us to obtain scattering spectra for a single PN to specifically identify the environmental pH conditions. Furthermore, the immobilized PN on the substrate exhibited excellent reversibility for the acquisition of the scattering spectra after repeated change of environmental pH.