Zernike Phase Contrast Cryo-Electron Microscopy and Tomography for Structure Determination at Nanometer and Subnanometer Resolutions

As cryo-electron microscopy (cryo-EM) and cryo-electron tomography (cryo-ET) continue to advance, the ability to visualize cellular and organismal structures with unprecedented clarity is redefining the landscape of structural biology. Breakthroughs in imaging technology, sample preparation and image processing now enable the detailed elucidation of cellular architecture, macromolecular organization, and dynamic biological processes at sub-nanometer resolution. Recent methodological advances have propelled the field to new frontiers, facilitating the investigation of complex biological questions across scales-from macromolecular complexes to organism-wide structural insights. This review explores rapidly emerging trends, highlights key innovations that are pushing the boundaries of in situ structural biology, and addresses persistent challenges in expanding the applicability of cryo-EM and cryo-ET across diverse biological systems.

Portal vein imaging is an important method for investigating portal venous disorders. However, the diagnostic requirements are not usually satisfied when using single imaging techniques. Diagnostic accuracy can be improved by combining different imaging techniques. Contrast agents that can be used for combined imaging modalities are needed. In this study, the feasibility of using microbubbles containing gadolinium (MCG) as contrast agents for both phase contrast imaging (PCI) and magnetic resonance imaging (MRI) are investigated. MCG were made by encapsulating sulfur hexafluoride (SF6) gas with gadolinium and lyophilized powder. Absorption contrast imaging (ACI) and PCI of MCG were performed and compared in vitro. MCG were injected into the main portal trunk of living rats. PCI and MRI were performed at 2 min and 10 min after MCG injection, respectively. PCI exploited the differences in the refractive index and visibly showed the MCG, which were not detectable by ACI. PCI could facilitate clear revelation of the MCG-infused portal veins. The diameter of the portal veins could be determined by the largest MCG in the same portal vein. The minimum diameter of clearly detected portal veins was about 300 µm by MRI. These results indicate that MCG could enhance both PCI and MRI for imaging portal veins. The detection sensitivity of PCI and MRI could compensate for each other when using MCG contrast agents for animals.

A quick localization of the tumor site to guide the therapeutic administration is important for an image guided cancer therapy to decrease both mortality and morbidity, especially in prolonged procedures, such as photodynamic and photothermal therapies. Consequently, the images have to be updated quickly enough to capture the patient motion and the movement of therapeutic device, which poses a significant challenge on functional imaging probes. Besides increasing the sensitivity of imaging detectors, advanced image processing methods may play an important role to overcome this challenge. In this work, we investigate the effectiveness of the spatiotemporal processing to shorten the acquisition time of each image frame to be able to capture the object motion with good image quality. The simulation study of beta imaging of a moving tumor phantom at different imaging dose levels demonstrates that the spatiotemporal processing can yield superior image quality compared to either a short acquisition or a prolonged acquisition with only spatial smoothing. The receiver operating characteristic curve analysis also reveals its advantage on the tumor detection, particularly at lower imaging dose or equivalently shorter acquisition time.

The use of computersComputer in cancerCancer diagnosisDiagnosis is increasing continually powered by new modalities of imaging and advanced image processing methods. The use of bioinformatics, chemoinformatics, mathematical models and artificial intelligenceArtificial Intelligence is some of the critical fields but not limited to employing computersComputer for drug design. Medical imagining remains to be notable factor in the management of cancerCancer in patient which is currently at the rise with the clinicians due to an increase in cancerCancer incidences. This increase availability of data with the medical practitioners has the potential for using computersComputer or algorithmsAlgorithm for cancerCancer diagnosisDiagnosis for elevation of patient outcomes, reduction in toxicity and lowering the clinical burdens. CancerCancer data used for diagnosisDiagnosis and treatment will always be on the rise due to the advanced treatment options such as adaptive radiotherapy (RT) planning and image mining of quantitative nature. Machine learningMachine Learning particularly is used to build statistical models based on the past data sets collected having the self-learning knowledge from collected data sets for making future predictions. It has been shown that a trained deep neural network is able to detect malignant cancerCancer lesionsLesions form skin lesionsLesions photographs giving a rivalry to well-trained dermatologists. MammographicMammography lesionsLesions can also be detected by trained deep neural networks producing rivals to certified radiologist which shows the immense accuracy and profitable use of computerComputer-aided methods in cancerCancer diagnosisDiagnosis. Designing new anti-cancerCancer therapeutic or helping in the process of developing such therapies to decrease the failure rate and approval time is the most exciting potential of CAD in cancerCancer such as autoencoders. Precise clinical and preclinical, which lead increased likelihood for clinical success, may be achieved by using computational methods to predict correct mechanism of action. Effective combinations of drugs, which remain to be a combinatorial, can also be solved using AI-based methods as the number of anti-cancerCancer drugs is increasing day by day. An increase in the growth of complex experimental data sets has lead researchers today to welcome sophisticated algorithmic and computational tools to analyse the results and study basic biological questions including oncologyOncology. Recently, some of the prominent applications of CAD in cancerCancer diagnosisDiagnosis include tumour detection through medical image analysis, histopathological characterization and quantificationsQuantification, clinical diagnosisDiagnosis through computersComputer, selection of treatments, planning treatment and using multimodal clinical data, anti-cancerCancer drug development and surveillance of cancerCancer in populations. These steps collectively help to achieve precision oncologyOncology which may increase the efficiency of early screening and improve treatment levels. In this chapter, computerComputer methods employed in most of the critical fields of oncologyOncology such as radiation oncologyOncology, diagnosisDiagnosis and detection of cancerCancer and optimization of cancerCancer treatment are discussed.

Medical imaging workstations need capabilities for defining a Region Of Interest (ROI) within an image, so that it can be processed for visualization or quantitative measurements. The purpose of this work was to develop a generic, object-oriented toolkit which allows creation, modification, querying and traversal of an ROI. In the object-oriented design, an ROI class is defined with associated methods. An ROI object consists of a set of 3-D points represented using interval- end-points. The ROI toolkit has been developed using C++, in conjunction with patient browser and image object navigation (display) toolkits, to serve as the foundation for thoracic and neuro-radiology workstations. Because of the object-oriented design and generic representation of the ROI, this toolkit can easily facilitate standard and advanced image processing methods.

Skin cancer is the most common cancer with several different types. According to current estimations, one in five Americans will develop skin cancer in their lifetime. Therefore, early diagnosis and treatment of it is of crucial significance. Several advanced image processing methods have been applied to predict skin cancer. However, few researchers utilize those methods to build an interactive application. In this work, we implemented an interactive skin cancer diagnosis website, combining the convolutional neural network (CNN) and natural language processing (NLP) technology. The neural network model uses four convolutional layers and dense layers respectively to improve the accuracy. Two max-pooling layers were used to reduce redundant information. To address the severe overfitting problem, we chose to utilize the batch normalization along with dropout layers. Based on our results, 0.9935 in accuracy and 0.0225 loss is realized for training data, and accuracy of 0.8393 and 0.6648 loss for testing data. Natural language processing (NLP) was used to implement a chatbot for interaction with users. We crawled skin cancer related questions and answers from Quora and used them to train our chatbot. Lastly, we combined CNN and NLP to build an interactive skin cancer diagnosis website. VUE.js and Django were used to build the front-end and back-end of our website. These results offer a guideline for combining artificial intelligence with not only medicine but also interactive network, which enables people to get medical care more easily.

Cryo-electron tomography is an imaging technique that provides 3D images (tomograms) in situ of cells with sub-nanometer resolution. Typically, the first step in the analysis is to classify the tomogram voxels into different structures, named semantic segmentation. However, the segmentation results are sets of voxels, hindering further quantitative analysis. In this paper, we define and implement algorithms to convert the semantic segmentation of the main structures in a cellular cryo-electron tomogram (membranes, filaments, cytosolic and membrane-bound macromolecules) into specific skeletons, preserving their topological and geometrical information. Additionally, we have defined a metric for comparing segmentations in cryo-ET coming from different methods more robust than the standard DICE. We also demonstrate how this approach can be used to trace cellular features by analyzing several in situ cellular cryo-electron tomograms.

Cryogenic electron tomography (cryo-ET) combined with subtomogram averaging, allows in situ visualization and structure determination of macromolecular complexes at subnanometre resolution. Cryogenic focused ion beam (cryo-FIB) micromachining is used to prepare a thin lamella-shaped sample out of a frozen-hydrated cell for cryo-ET imaging, but standard cryo-FIB fabrication is blind to the precise location of the structure or proteins of interest. Fluorescence-guided focused ion beam (FIB) milling at target locations requires multiple sample transfers prone to contamination, and relocation and registration accuracy is often insufficient for 3D targeting. Here, we present in situ fluorescence microscopy-guided FIB fabrication of a frozen-hydrated lamella to address this problem: we built a coincident three-beam cryogenic correlative microscope by retrofitting a compact cryogenic microcooler, custom positioning stage, and an inverted widefield fluorescence microscope (FM) on an existing FIB scanning electron microscope. We show FM controlled targeting at every milling step in the lamella fabrication process, validated with transmission electron microscope tomogram reconstructions of the target regions. The ability to check the lamella during and after the milling process results in a higher success rate in the fabrication process and will increase the throughput of fabrication for lamellae suitable for high-resolution imaging.

Advances in cryogenic electron microscopy (cryoEM) single particle analysis have revolutionized structural biology by facilitating the in vitro determination of atomic- and near-atomic-resolution structures for fully hydrated macromolecular complexes exhibiting compositional and conformational heterogeneity across a wide range of sizes. Cryogenic electron tomography (cryoET) and subtomogram averaging are rapidly progressing toward delivering similar insights for macromolecular complexes in situ, without requiring tags or harsh biochemical purification. Furthermore, cryoET enables the visualization of cellular and tissue phenotypes directly at molecular, nanometric resolution without chemical fixation or staining artifacts. This forward-looking review covers recent developments in cryoEM/ET and related technologies such as cryogenic focused ion beam milling scanning electron microscopy and correlative light microscopy, increasingly enhanced and supported by artificial intelligence algorithms. Their potential application to emerging concepts is discussed, primarily the prospect of complementing medical histopathology analysis. Machine learning solutions are poised to address current challenges posed by "big data" in cryoET of tissues, cells, and macromolecules, offering the promise of enabling novel, quantitative insights into disease processes, which may translate into the clinic and lead to improved diagnostics and targeted therapeutics.

Cryo-electron tomography (cryo-ET) enables the (noisy) observation of individual macromolecular complexes executing their biological functions within a near-native vitrified cellular environment of interest.Subsequent subtomogram averaging (STA) can reveal macromolecular structure at sub-nanometer resolution, though typically at the cost of coarsely discretizing or "averaging out" potentially important heterogeneity.We have previously introduced tomoDRGN, a deep learning tool to resolve ensembles of highly heterogeneous structures imaged via cryo-ET [1].Here we present updates to tomoDRGN aimed at facilitating its use and interaction with modern STA software, and illustrate the resulting insights gained from applying tomoDRGN to experimental cryo-ET datasets.

—Brain tumor detection is a critical aspect of neurooncology, where timely and accurate diagnosis significantly impacts patient outcomes. Traditional imaging techniques, while useful, are often limited by variations in image quality, illumination inconsistencies, and the complex nature of brain tumors themselves, which vary widely in size, shape, and texture. This paper presents a deep learning- based approach to brain tumor detection using Convolutional Neural Networks (CNNs) combined with advanced image processing methods. Our methodology involves preprocessing brain MRI scans using histogram equalization, morphological operations, and data augmentation to address illumination issues and enhance the tumor region. Following preprocessing, the images are fed into a CNN, where transfer learning from the Sequential model is applied to improve classification accuracy. Experimental results demonstrate high recall and precision rates, affirming the model’s robustness in detecting brain tumors in MRI images. This approach not only addresses the challenge of inconsistent MRI scan quality but also reduces the risk of misclassification, showing promise for clinical application. Keywords— Brain Tumor Detection, Computer-aided Diagnosis, Computer Vision, Convolutional Neural Networks, Deep Learning, Image Processing, Transfer Learning.

The first section of this chapter concerns scattering factors for the diffraction of electrons by crystalline solids. An explanation of the theory of scattering by a perfect crystal is followed by a discussion of the kinematical, two-beam and phase-grating approximations. Relativistic and absorption effects are considered. Extensive tables of atomic scattering amplitudes for electrons for neutral and ionized atoms are presented. The second section of the chapter briefly discusses the parameterization of electron atomic scattering factors. Tables of useful parameters as a function of accelerating voltage and elastic atomic scattering factors for neutral atoms are given. Complex scattering factors for the diffraction of electrons by gases are discussed in the third section of the chapter. This section includes tables of scattering factors of interest for gas-phase electron diffraction from atoms and molecules in the keV energy region. In addition to the tables and a description of their uses, a discussion of the theoretical uncertainties related to the material in the tables is also provided. The tables give scattering factors for elastic and inelastic scattering from free atoms. The theory of molecular scattering based on these atomic quantities is also discussed. Electron energy-loss spectroscopy on solids is discussed in the fourth section of the chapter. Topics covered include: the use of electron beams; single and multiple scattering; the classification of the different excitations in a spectrum; instrumentation; and the excitation spectra of valence and core electrons. The fifth section of the chapter describes oriented texture patterns. Lamellar and fibre texture patterns are discussed and applications to metals and organic materials are mentioned. The computation of dynamic wave amplitudes in then described in the sixth section of the chapter. This section deals first with the multislice method. The numerical procedure is outlined and factors that influence the choice of thickness of the slice are discussed. Two checks that can be performed during a multislice calculation are noted. The Bloch-wave method is then described. The use of Bloch waves to describe electron diffraction and electron imaging in thin crystals is outlined together with the concept of the dispersion surface. These emerge as natural solutions of the Schrodinger equation with a periodic optical potential to generate the elastic scattering and also the loss of intensity from the coherent wave field due to thermal diffuse and inelastic scattering. The Bloch-wave approach is a useful complement to the multislice method and provides a clear picture of wave propagation in perfect and imperfect crystals. In the seventh section of the chapter, the measurement of structure factors and the determination of crystal thickness by electron diffraction are described. The use of convergent-beam electron diffraction to obtain integrated intensities is discussed and the relationship between intensity features and the dispersion surface is explained. The last section of the chapter concerns crystal-structure determination by high-resolution microscopy. This technique allows the arrangement of atomic columns in thin crystals to be observed directly. The resolution of the best instruments is now slightly below 0.1 nm. The images usually show a projection through a slice of crystal about 20 nm thick, however tomographic (three-dimensional) reconstruction is now possible at nanometre resolution. The images show the host of microphases, grain boundaries, twins, line and planar defects which broad-beam methods, such as X-ray diffraction, provide the average scattering from. These defects often control the properties of crystals, engineering materials and electronic devices. Individual nanostructures, such as carbon nanotubes and catalyst particles, may be imaged at atomic resolution. Fine twinning, polytypes, intergrowth of oxide phases etc. can be identified, and increasingly the detailed atomic structure of defects (such as oxide, superconductor and semiconductor interfaces) is being determined. Substitutional dopant atoms have recently been imaged for the first time. In biology the method is limited by radiation damage; however by summing many images of identical randomly oriented macromolecules, tomographic density maps can be reconstructed at subnanometre resolution from hydrated proteins which cannot be crystallized (e.g. membrane proteins). This section reviews the theoretical principles of high-resolution electron microscopy, including few-beam and structure image formation, effects of electron-optical lens aberrations, partial coherence, resolution-limiting factors, image-simulation methods, dynamical effects, and a summary of super-resolution schemes. Keywords: absorption; atomic scattering amplitudes; atomic scattering factors; Bethe theory; Bloch-wave method; core-electron spectroscopy; core-loss spectroscopy; crystal thickness; crystalline solids; dielectric description; dynamical diffraction; dynamical wave amplitudes; EELS; elastic scattering; electron diffraction; electron energy-loss spectroscopy; electron microscopy; electrons; free-electron gas; high-resolution electron microscopy; HREM; hyper-resolution; inelastic scattering; lamellar textures; lattice-fringe images; molecular scattering factors; multiple scattering; multislice method; oriented texture patterns; plasmons; real solids; relativistic effects; STEM; scanning transmission electron microscopes; scattering factors; solid-state effects; spectrometers; structure factors; surface plasmons; texture; wave amplitudes; X-ray diffraction

Spirochetes such as Treponema, Borrelia, and Leptospira species can rotate their bodies to swim in liquid environments by rotating periplasmic flagella or endoflagella, which are present inside the cell. Electron cryotomography (ECT) is an imaging technique that directly provides three-dimensional (3D) structures of cells and molecular complexes in their cellular environment at nanometer resolution. Here, I present a general protocol of ECT that covers the sample preparation, data collection, tilt series alignment, and tomographic reconstruction for visualization of intact periplasmic flagella in Leptospira spp. This protocol is capable of determining protein structures at resolutions high enough to visualize their individual domains and secondary structures in their cellular environment.

The growing world demand for metals necessitates the economic extraction of metals from ores of lesser grades and scheduled waste with potential for metal recovery. In which case, efficient skilled-beneficiation is required to achieve such demand. This research paper examines the potential of ore recovery from magmatic Ni-Cu-PGE waste rock in an open pit mine operation. The waste material contains a marginal cut-off grade between the ore and what has been scheduled to be produced as waste throughout its life of mine (LOM). The waste material has the potential for metal recovery to extend the LOM. The main aim was to investigate the appropriateness of the potential for metal recovery using the already generated waste as a baseline model for subsequent waste production. To achieve this aim, the primary objective was to use Multi-elemental, Geochemical and Petrographic Analysis (MEGPA) on carefully selected waste rock samples. While the specific objectives were to examine the liberation of metals and establish optimal cut-off grade based on suitable size fractions for ore recovery. Multi-elemental, geochemical and Petrographic Analysis were carried out on selected size fractions using various instruments such as Scanning Electron Microscope Energy Disperse X-ray (SEM-EDX), X-Ray Diffraction (XRD) and X-Ray Fluorescence (XRF), reflected and transmitted microscopes. Categorization of results shown the predominant bulk mineral abundance to be tremolite, a member of the amphibole group of silicate minerals with composition Ca2Si8O22·(OH)2. Moreover, the result revealed that the economic Ni and Cu sulphides are hosted in pentlandite and chalcopyrite within the waste samples. Liberation of mineral and recovery of metal are in the finer fractions size below 1.5 mm, and that optimum metal recovery is at ≤1 mm. It was concluded from the study that, there is potential for metal recovery from mines waste in finer fractions below 1.5 mm without posing significant metalliferous acid mine drainage (AMD) risk to the mining environment. It is recommended that further study about the influence on the metallurgical processing of the waste at the selected optimum fractions size, and energy requirement for screening the sample is carried out. Since the PSD (particle size distribution) is critical to both the physical separation processes of the waste and the prediction of metalliferous AMD risk, other methods like advanced UAV 3D photogrammetry and digital image processing method could be used to test for both underestimation and overestimation of PSD. This is important as the amount of mineral liberation, metal recovery and sulphur generation are PSD dependent.

Infrared thermography is a cost-effective nonintrusive testing approach to assess surface and near-surface distresses, such as asphalt pavement surface cracks. However, the raw data collected by thermal cameras cannot be directly used for pavement surface distress inspection. Thus, advanced thermal image processing methods are desirable for extracting indicators of hidden flaws. The objective of this research was to develop an integrated system that combines infrared imaging, high-resolution visual imaging, real-time image processing, and data-rich analytics for automated inspection to support decision making for pavement preservative maintenance. This work developed a code that can integrate the characteristics captured by both thermal and visual images to provide a quantitative identification of the surface cracks and their severity. Field-test data were collected and a statistical analysis was conducted to correlate temperature gradient to the surface crack profile of asphalt pavement. It was found that the surface temperature distribution pattern has a direct correlation with pavement crack profile, and can be used as an indicator of crack depth. The proposed real-time thermal imaging-based system was found to be feasible for field inspection, and the three-dimensional (3D) crack profiling method can help produce fairly accurate measurements to enable fast and easy decision support for pavement preservation practices.