Fine Structural Details Research Articles

Use of structurally-accurate 3D plant models for estimating light interception and photosynthesis of sweet pepper (Capsicum annuum) plants

Plant structure is a significant factor for influencing the light interception and photosynthesis of plants. The light interception on the plant surface can be analyzed by three-dimensional (3D) plant model and optical simulation, but its accuracy is directly affected by the structural accuracy of the 3D model. This study aims to analyze and compare the effect of the accuracy of 3D structural models on light interception and photosynthesis. 3D-scanned plant models with different structural accuracies were constructed, and the light interception and photosynthetic rate were analyzed at single leaf and whole-plant scales. When using a low accuracy model that lacked the fine structural details of the plant, it was overestimated in light interception and photosynthetic rate compared to the 3D-scanned model that has high structural accuracy. At the single leaf scale, the light interception was higher in the low-accuracy model than that in the 3D-scanned model due to self-shadings from higher curvature in the leaf surface. At the whole-plant scale, the light interception and the subsequent photosynthetic rate in the low-accuracy model were 18% and 45 to 58% higher than those in the 3D-scanned model at light intensities of 700–2000 μmol m−2 s−1 at the upper canopy. The 3D-scanned plant model could accurately estimate the light interception and photosynthetic rate of the plants through optical simulation. The presented methodology can contribute to accurate analyses of plant light environment, plant physiological response, and plant growth modelling.

Statistical image-based material decomposition for triple-energy computed tomography using total variation regularization.

Triple-energy computed tomography (TECT) can obtain x-ray attenuation measurements at three energy spectra, thereby allowing identification of different material compositions with same or very similar attenuation coefficients. This ability is known as material decomposition, which can decompose TECT images into different basis material image. However, the basis material image would be severely degraded when material decomposition is directly performed on the noisy TECT measurements using a matrix inversion method. To achieve high quality basis material image, we present a statistical image-based material decomposition method for TECT, which uses the penalized weighted least-squares (PWLS) criteria with total variation (TV) regularization (PWLS-TV). The weighted least-squares term involves the noise statistical properties of the material decomposition process, and the TV regularization penalizes differences between local neighboring pixels in a decomposed image, thereby contributing to improving the quality of the basis material image. Subsequently, an alternating optimization method is used to minimize the objective function. The performance of PWLS-TV is quantitatively evaluated using digital and mouse thorax phantoms. The experimental results show that PWLS-TV material decomposition method can greatly improve the quality of decomposed basis material image compared to the quality of images obtained using the competing methods in terms of suppressing noise and preserving edge and fine structure details. The PWLS-TV method can simultaneously perform noise reduction and material decomposition in one iterative step, and it results in a considerable improvement of basis material image quality.

Noncontact super-resolution guided wave array imaging of subwavelength defects using a multiscale deep learning approach

Subwavelength defect imaging using guided waves has been known to be a difficult task mainly due to the diffraction limit and dispersion of guided waves. In this article, we present a noncontact super-resolution guided wave array imaging approach based on deep learning to visualize subwavelength defects in plate-like structures. The proposed approach is a novel hierarchical multiscale imaging approach that combines two distinct fully convolutional networks. The first fully convolutional network, the global detection network, globally detects subwavelength defects in a raw low-resolution guided wave beamforming image. Then, the subsequent second fully convolutional network, the local super-resolution network, locally resolves subwavelength-scale fine structural details of the detected defects. We conduct a series of numerical simulations and laboratory-scale experiments using a noncontact guided wave array enabled by a scanning laser Doppler vibrometer on aluminate plates with various subwavelength defects. The results demonstrate that the proposed super-resolution guided wave array imaging approach not only locates subwavelength defects but also visualizes super-resolution fine structural details of these defects, thus enabling further estimation of the size and shape of the detected subwavelength defects. We discuss several key aspects of the performance of our approach, compare with an existing super-resolution algorithm, and make recommendations for its successful implementations.

MRI Super-Resolution using Laplacian Pyramid Convolutional Neural Networks with Isotropic Undecimated Wavelet Loss.

High spatial resolution of Magnetic Resonance images (MRI) provide rich structural details to facilitate accurate diagnosis and quantitative image analysis. However the long acquisition time of MRI leads to patient discomfort and possible motion artifacts in the reconstructed image. Single Image Super-Resolution (SISR) using Convolutional Neural networks (CNN) is an emerging trend in biomedical imaging especially Magnetic Resonance (MR) image analysis for image post processing. An efficient choice of SISR architecture is required to achieve better quality reconstruction. In addition, a robust choice of loss function together with the domain in which these loss functions operate play an important role in enhancing the fine structural details as well as removing the blurring effects to form a high resolution image. In this work, we propose a novel combined loss function consisting of an L1 Charbonnier loss function in the image domain and a wavelet domain loss function called the Isotropic Undecimated Wavelet loss (IUW loss) to train the existing Laplacian Pyramid Super-Resolution CNN. The proposed loss function was evaluated on three MRI datasets - privately collected Knee MRI dataset and the publicly available Kirby21 brain and iSeg infant brain datasets and on benchmark SISR datasets for natural images. Experimental analysis shows promising results with better recovery of structure and improvements in qualitative metrics.

STED nanoscopy of KK114-stained pathogenic bacteria.

Super-resolution microscopy techniques can provide answers to still pending questions on prokaryotic organisms but are yet to be used at their full potential for this purpose. To address this, we evaluate the ability of the rhodamine-like KK114 dye to label various types of bacteria, to enable imaging of fine structural details with stimulated emission depletion microscopy (STED). We assessed fluorescent labeling with KK114 for eleven Gram-positive and Gram-negative bacterial species and observed that this contrast agent binds to their cell membranes. Significant differences in the labeling outputs were noticed across the tested bacterial species, but importantly, KK114-staining allowed the observation of subtle nanometric cell details in some cases. For example, a helix pattern resembling a cytoskeleton arrangement was detected in Bacillus subtilis. Furthermore, we found that KK114 easily penetrates the membrane of bacterial microorganism that lost their viability, which can be useful to discriminate between living and dead cells.

Accelerating Super-Resolution and Visual Task Analysis in Medical Images

Medical images are acquired at different resolutions based on clinical goals or available technology. In general, however, high-resolution images with fine structural details are preferred for visual task analysis. Recognizing this significance, several deep learning networks have been proposed to enhance medical images for reliable automated interpretation. These deep networks are often computationally complex and require a massive number of parameters, which restrict them to highly capable computing platforms with large memory banks. In this paper, we propose an efficient deep learning approach, called Hydra, which simultaneously reduces computational complexity and improves performance. The Hydra consists of a trunk and several computing heads. The trunk is a super-resolution model that learns the mapping from low-resolution to high-resolution images. It has a simple architecture that is trained using multiple scales at once to minimize a proposed learning-loss function. We also propose to append multiple task-specific heads to the trained Hydra trunk for simultaneous learning of multiple visual tasks in medical images. The Hydra is evaluated on publicly available chest X-ray image collections to perform image enhancement, lung segmentation, and abnormality classification. Our experimental results support our claims and demonstrate that the proposed approach can improve the performance of super-resolution and visual task analysis in medical images at a remarkably reduced computational cost.

Velocity model estimation by means of Full Waveform Inversion of transmitted waves: An example from a seismic profile in the geothermal areas of Southern Tuscany, Italy

We propose an FWI strategy that makes use of transmitted waves as input data and utilizes both global and local optimization methods to estimate the P-wave velocity model of the subsurface. We envisage that our approach may be applicable to difficult seismic land data, like those from geothermal areas characterised by complex geological structures. As a test case, we considered the CROP/18A seismic reflection profile that crosses the geothermal field of Larderello (southern Tuscany, Italy). The aim is to estimate the P-wave velocity model down to a few kilometres depth below the surface that could be used as complementary information to the standard seismic reflection image which, in this case, does not show interpretable reflections in a range of depths accessible to industrial drillings.One innovative aspect of the inversion we propose with respect to conventional FWI approaches is its independence of a starting model that, ideally, should reproduce the true long wavelength velocity structure of the subsurface and that may be rather difficult to obtain in case of low quality data and complex geology. We lessen the dependence on knowledge of a suitable starting model by performing a sequence of two inversions. First, we employ a genetic-algorithm (GA) based inversion, a global optimisation method that does not require any specific starting model, resulting in a long wavelength, low-resolution velocity model. This model then becomes the starting model for a second FWI, driven by a local optimisation algorithm, aimed at bringing in the fine details of the subsurface velocity structure. The reliability of the final model is checked by comparing observed and predicted waves for many common shot gathers along the seismic line and through the matching between the velocities measured by check shots in two nearby wells and the FWI velocities in the same locations. Many details of the velocity field, likely related to metamorphic and igneous formations, become apparent and may complement the interpretation of the standard reflection image. From these results, it appears that the use of transmitted waves and of the FWI approach discussed here may effectively improve the information for geophysical interpretation of challenging seismic land data, such as those that characterise many areas of geothermal exploration.

Structure and Dynamics in the ATG8 Family From Experimental to Computational Techniques.

Autophagy is a conserved and essential intracellular mechanism for the removal of damaged components. Since autophagy deregulation is linked to different kinds of pathologies, it is fundamental to gain knowledge on the fine molecular and structural details related to the core proteins of the autophagy machinery. Among these, the family of human ATG8 proteins plays a central role in recruiting other proteins to the different membrane structures involved in the autophagic pathway. Several experimental structures are available for the members of the ATG8 family alone or in complex with their different biological partners, including disordered regions of proteins containing a short linear motif called LC3 interacting motif. Recently, the first structural details of the interaction of ATG8 proteins with biological membranes came into light. The availability of structural data for human ATG8 proteins has been paving the way for studies on their structure-function-dynamic relationship using biomolecular simulations. Experimental and computational structural biology can help to address several outstanding questions on the mechanism of human ATG8 proteins, including their specificity toward different interactors, their association with membranes, the heterogeneity of their conformational ensemble, and their regulation by post-translational modifications. We here summarize the main results collected so far and discuss the future perspectives within the field and the knowledge gaps. Our review can serve as a roadmap for future structural and dynamics studies of the ATG8 family members in health and disease.

Cytoarchitecture of the mouse brain by high resolution diffusion magnetic resonance imaging

MRI has been widely used to probe the neuroanatomy of the mouse brain, directly correlating MRI findings to histology is still challenging due to the limited spatial resolution and various image contrasts derived from water relaxation or diffusion properties. Magnetic resonance histology has the potential to become an indispensable research tool to mitigate such challenges. In the present study, we acquired high spatial resolution MRI datasets, including diffusion MRI (dMRI) at 25 ​μm isotropic resolution and quantitative susceptibility mapping (QSM) at 21.5 ​μm isotropic resolution to validate with conventional mouse brain histology. Diffusion weighted images (DWIs) show better delineation of cortical layers and glomeruli in the olfactory bulb than fractional anisotropy (FA) maps. However, among all the image contrasts, including quantitative susceptibility mapping (QSM), T1/T2∗ images and DTI metrics, FA maps highlight unique laminar architecture in sub-regions of the hippocampus, including the strata of the dentate gyrus and CA fields of the hippocampus. The mean diffusivity (MD) and axial diffusivity (AD) yield higher correlation with DAPI (0.62 and 0.71) and NeuN (0.78 and 0.74) than with NF-160 (−0.34 and −0.49). The correlations between FA and DAPI, NeuN, and NF-160 are 0.31, −0.01, and −0.49, respectively. Our findings demonstrate that MRI at microscopic resolution deliver a three-dimensional, non-invasive and non-destructive platform for characterization of fine structural detail in both gray matter and white matter of the mouse brain.

Measurement of prompt fission neutron and gamma spectra in the fast neutron induced fission of $$^{232}$$Th

Measurements of prompt fission neutrons and gamma rays spectra have been carried out in the fast neutron induced fission of $$^{232}$$Th at an average neutron energy of 2.6 MeV. The energy spectrum of neutrons is obtained from the time-of-flight spectrum measured using EJ301 detectors with respect to a twin section trigger fission chamber, while the energy spectrum of prompt gamma rays is obtained after unfolding the measured pulse height spectrum using the response matrix of the CeBr$$_{3}$$ detectors. The Maxwellian temperature obtained from the prompt fission neutron spectrum is found to be 1.22 ± 0.03 MeV. The low efficiency and limited resolution of the gamma set up, in addition to the low cross section of the reaction which result into poor statistics, made the reliable extraction of the detailed fine structure in the prompt fission gamma spectrum difficult. However, a reasonable estimate of the average gamma energy could be obtained as 0.91 ± 0.04 MeV. The extracted observables are then compared with the values predicted by the GEF model. It is found that, while the measured prompt fission neutron spectrum and the extracted Maxwellian temperature agree reasonably well when compared with the predictions of the GEF model, the average gamma ray energy from the measured prompt fission gamma spectrum is found to be lower by $$\sim $$ 17$$\%$$ from the predictions of the model. A rough estimate of average neutron and gamma multiplicities are also obtained from the measurements.

LA RIVOLUZIONE DELLA CRIO-MICROSCOPIA ELETTRONICA NELLO STUDIO STRUTTURALE DI PROTEINE E ACIDI NUCLEICI

Observing the fine details of molecular structures (e.g. in proteins and in nucleic acids) has been a central part of Structural Biology over the past 50 years. The recent advent of single particle cryo-electron microscopy brought a revolution in this field, that previously relied on X-ray crystallography and nuclear magnetic resonance. It is now possible to explore the structures of large subcellular assemblies, such as the ribosome, resolving details on the scale of amino acids and nucleotides, in favorable cases reaching the 2 Ã… resolution level.

A theoretical first principles computational investigation into the potential of aluminum-doped boron nitride nanotubes for hydrogen storage

Hydrogen storage remains a largely unsolved problem facing the green energy revolution. One approach is physisorption on very high surface area materials incorporating metal atoms. Boron nitride nanotubes (BNNTs) are a promising material for this application as their behaviour is largely independent of the nanoscopic physical features providing a greater degree of tolerance in their synthesis. Aluminum doping has been shown to be a promising approach for carbon nanotubes but has been underexplored for BNNTs. Using first principles density functional theory, the energetics, electronics and structural impacts of aluminum adsorption to both zigzag and armchair polymorphs of BNNTs was investigated along with their potential capacity to adsorb hydrogen. The fine atomic structural and electronic details of these interactions is discussed. We predicted that in an ideal situation, highly aluminum-doped armchair and zigzag BNNTs could adsorb up to 9.4 and 8.6 wt percent hydrogen, well above the United States Department of Energy targets marking these as promising materials worthy of further study.

Low-energy excitations in type-II Weyl semimetal Td−MoTe2 evidenced through optical conductivity

Molybdenum ditelluride, MoTe2, is a versatile material where the topological phase can be readily tuned by manipulating the associated structural phase transition. The fine details of the band structure of MoTe2, key to understanding its topological properties, have proven difficult to disentangle experientially due to the multi-band character of the material. Through experimental optical conductivity spectra, we detect two strong low-energy interband transitions. Both are linked to excitations between spin-orbit split bands. The lowest interband transition shows a strong thermal shift, pointing to a chemical potential that dramatically decreases with temperature. With the help of ab initio calculations and a simple two-band model, we give qualitative and quantitative explanation of the main features in the temperature-dependent optical spectra up to 400 meV.

SpineJ: A software tool for quantitative analysis of nanoscale spine morphology.

Super-resolution microscopy provides diffraction-unlimited optical access to the intricate morphology of neurons in living brain tissue, resolving their finest structural details, which are critical for neuronal function. However, as existing image analysis software tools have been developed for diffraction-limited images, they are generally not well suited for quantifying nanoscale structures like dendritic spines. We present SpineJ, a semi-automatic ImageJ plugin that is specifically designed for this purpose. SpineJ offers an intuitive and user-friendly graphical user interface, facilitating fast, accurate, and unbiased analysis of spine morphology.

Predictive First-Principles Modeling of a Photosynthetic Antenna Protein: The Fenna-Matthews-Olson Complex.

High efficiency of light harvesting in photosynthetic pigment-protein complexes is governed by evolutionary-perfected protein-assisted tuning of individual pigment properties and interpigment interactions. Due to the large number of spectrally overlapping pigments in a typical photosynthetic complex, experimental methods often fail to unambiguously identify individual chromophore properties. Here, we report a first-principles-based modeling protocol capable of predicting properties of pigments in protein environment to a high precision. The technique was applied to successfully uncover electronic properties of the Fenna-Matthews-Olson (FMO) pigment-protein complex. Each of the three subunits of the FMO complex contains eight strongly coupled bacteriochlorophyll a (BChl a) pigments. The excitonic structure of FMO can be described by an electronic Hamiltonian containing excitation (site) energies of BChl a pigments and electronic couplings between them. Several such Hamiltonians have been developed in the past based on the information from various spectroscopic measurements of FMO; however, fine details of the excitonic structure and energy transfer in FMO, especially assignments of short-lived high-energy sites, remain elusive. Utilizing polarizable embedding quantum mechanics/molecular mechanics with the effective fragment potentials, we computed the electronic Hamiltonian of FMO that is in general agreement with previously reported empirical Hamiltonians and quantitatively reproduces experimental absorption and circular dichroism spectra of the FMO protein. The developed computational protocol is sufficiently simple and can be utilized for predictive modeling of other wild-type and mutated photosynthetic pigment-protein complexes.

Simultaneous laser-driven x-ray and two-photon fluorescence imaging of atomizing sprays

In this Letter, we report for the first time, to the best of our knowledge, the possibility of visualizing an atomizing spray by simultaneously recording x-ray absorption and two-photon laser-induced fluorescence imaging. This unique illumination/detection scheme is made possible due to the use of soft x rays emitted from a laser-driven x-ray source. An 800 mJ laser pulse of 38 fs duration is used to generate an x-ray beam with up to 4 × 10 8 photons ranging from 1 to 10 keV, allowing projection radiography of water jets generated by an automotive port fuel injector. In addition, a fraction of the laser pulse ( ∼ 10 m J ) is employed to form a light sheet and to induce two-photon fluorescence in a dye added to the water. The resulting high-contrast fluorescence images provide fine details of the spray structure, with reduced blur from multiple light scattering, while the integrated liquid mass is extracted from the x-ray radiography. In this proof of principle, we show that the combination of these two highly complementary techniques, in both the visible and soft x-ray regimes, is very promising for future characterization of challenging spray, as well as for further understanding of the physics of liquid atomization.

First-principles study of the phonon replicas in the photoluminescence spectrum of 4H -SiC

The free-exciton photoluminescence (PL) spectrum of $4H$-SiC exhibits a detailed fine structure due to the different phonons involved in the indirect gap transition. Here a first-principles calculation of these phonons, their symmetry labeling and their contribution to the photoluminescence spectrum is presented. The calculation uses phonons and electron-phonon coupling matrix elements computed via density functional perturbation theory and energy bands and optical phonon matrix elements calculated in density functional theory. The results are in excellent agreement with the experiment for the phonon energies and the polarization dependence of the spectrum. The relative intensities are also in fair agreement if we allow for some phonons within a few meV to be interchanged. There is however a remarkable discrepancy that the experimental spectrum shows a distinct behavior for phonons with energy below $\ensuremath{\sim}55$ meV and above that energy, which is absent in the theory. The experimental PL lines corresponding to phonon energies below 55 meV are about a factor 5--10 smaller in intensity. This is not found in our calculations. The calculations show a similar peak distribution as the experiment in this range but with intensities comparable to those above 55 meV. This indicates that another mechanism outside the scope of the electron-phonon mediated transitions is operative for photon energies of the PL lines closer to the indirect exciton gap than this 55 meV cutoff, which reduces the overall intensity of these lines. We propose that this may result from competition between the phonon-assisted PL and trapping of the electron in the available unoccupied hexagonal site N-donor shallow level at 53-meV binding energy. As part of this study, we also present the phonon dispersions and density of states in $4H$-SiC and the electronic band structure including quasiparticle corrections.

Reply to the 'Comment on "Extent of conjugation in diazonium-derived layers in molecular junction devices determined by experiment and modelling"' by R. L. McCreery, S. K. Saxena, M. Supur and U. Tefashe, Phys. Chem. Chem. Phys., 2020, 22, DOI: 10.1039/d0cp02412k.

Our paper [Phys. Chem. Chem. Phys., 2019, 21, 16762] is the subject of a comment that clarifies a flaw in analysis of STM data. We accept the comment in regard to the aspect ratio considerations, but we also further clarify the main conclusions of our paper, and provide a molecular scale schematic using the data presented in the comment to help refine the original conclusions. Taken together, this shows that models presented in the literature may benefit from including more fine structure details, as better understanding may emerge from such considerations. This was the intent of our original article, and we thank the comment authors for the chance to clarify these points.

Canalicular domain structure and function in matrix-free hepatic spheroids.

Liver is pivotal in organism metabolism. This organ is receiving nutriments from the portal vein and then storing, metabolizing, distributing in the circulation or excreting excess and xenobiotics in bile. Liver architecture and hepatocyte polarization are crucial to achieve these functions. To study these mechanisms in details, relevant cell culture systems are required, which is not the case with standard 2D cell culture. Besides, primary hepatocytes rapidly de-differenciate making them inefficient in forming physiological system. Herein, we used an hepatoma-derived cell line to produce matrix-free hepatic spheroids and developed an integrated structural cell biology methodology by combining light sheet fluorescence microscopy and 3D electron microscopy to study their function and structure. Within these spheroids, hepatocytes polarize and organize to form bile canaliculi active for both organics and inorganics excretion. Besides, live imaging revealed the high dynamic of actin networks in basal membranes compared to their high stability in the apical pole that constitutes bile canaliculi. Finally, the first structure of active bile canaliculi was solved at nm resolution and showed the very high density of microvilli coming from all cells constituting the canaliculus. Therefore, this study is the first comprehensive and in-depth functional and structural study of bile canaliculi in a physiological-relevant context.

Modeling of laser produced Zn plasma with detailed electron impact fine structure excitation cross-sections

An intricate collisional radiative (CR) model is developed including all the important processes for the laser induced zinc plasma. The electron impact excitation (EIE) and de-excitation of several fine structure levels of Zn play a dominant role in the laser induced plasma and their cross-sections are needed. In view of this, an extensive calculation is performed to obtain the EIE cross-sections of the large number of fine structure transitions of zinc atom. The cross-sections of Zn are calculated and presented for the excitations from its ground state 4s2 (J = 0) as well as from the 4s4p excited state to the various fine structure levels of different excited states using fully relativistic distorted wave (RDW) theory. In the calculation, relativistic multi-configuration bound target atomic wave functions are obtained through GRASP2K program using 4s2, 4s4p, 4s5s, 4s5p, 4s4d, 4s6s, 4s6p, 4s5d, 4s7s and 4s7p configuration state functions while the projectile incident and scattered electron distorted wave functions are obtained by solving the relativistic Dirac equations. Further, the calculated cross-sections are incorporated in the CR model for the plasma characterization as the various rate coefficients are linked through their corresponding cross-sections. The model gives the state population distribution of the fine structure energy levels of the neutral Zn atom considered in the model and from which the intensities of the line emissions are obtained. The calculated intensities from the CR model for the four emission lines (334.5, 468, 472, and 481 nm) are matched with the spectroscopic measurements (Smijesh et al 2013 J. Appl. Phys. 114 093301) and the plasma parameters viz electron density and electron temperature are evaluated. The plasma parameters are presented at different ambient pressures in the range of 0.05–10 Torr.