3D Profile Research Articles

3D pre-stack inversion constrained by spatial reflection features using the Sylvester's equation

Reflection feature-constrained inversion (RCI) is a multichannel strategy that can effectively improve the stability and continuity of seismic inversion in complex geology, especially for pre-stack inversion with higher uncertainty. To address the limitations of conventional RCI to 2D profiles and high computational costs, a 3D pre-stack seismic inversion constrained by spatial reflection features was developed. First, a new approach to characterize seismic reflection features by decomposing seismic events along the vertical and lateral directions was proposed, which significantly reduces the size of the reflection feature operators. Second, we constructed reflection feature regularization terms and designed a fast reflection feature-constrained inversion (FRCI) for 2D seismic profiles. Finally, the proposed FRCI was extended to 3D by considering the spatial reflection features in two mutually perpendicular directions. Fast spatial reflection feature-constrained inversion (FSRCI) was proposed by constructing spatial reflection feature operators in a 3D coordinate system. The key to this method is that the parameter estimation is transformed into solving the 3D Sylvester equation such that the model parameters can be estimated directly from 3D seismic data. The inversion results of the overthrust model and field data showed that the proposed method can improve efficiency while ensuring spatial continuity. In summary, this study developed a fast pre-stack seismic inversion constrained by spatial reflection features based on the characterization of stratum spatial features, which is effective in complex geology, such as high-dipping and faults.

RETROFIT: Real-Time Control of Time-Dependent 3D Point Cloud Profiles

Abstract In modern manufacturing processes, ensuring the precision of 3D profiles of products is crucial. Nonetheless, achieving this accuracy is challenging due to the complex interactions between process inputs and the data structure of the 3D profile data. Our solution, a 3D profile-based control framework, addresses this challenge by actively adapting and controlling the manufacturing process to enhance 3D shape accuracy. 3D profile scans represent the ultimate measure of desired part quality. Therefore, utilizing them as the system responses for control purposes yields the most direct and effective feedback. We leverage recent advancements from Koopman operator theory to create an effective model-based control strategy. Initially, we estimate the process model by exploring the relationship between 3D profiles and heterogeneous process inputs. Then, we formulate an online model predictive control law. Challenges include dealing with unstructured, high-dimensional 3D point cloud data, capturing spatial and temporal structures, and integrating heterogeneous, high-dimensional process input data into the control model. To overcome these challenges, we introduce RETROFIT, a solution designed for the real-time control of time-dependent 3D point cloud profiles. Unlike traditional models, RETROFIT is not bound by linear assumptions and can handle unstructured 3D point cloud data directly. We demonstrate its effectiveness through a wire arc additive manufacturing case study, highlighting its potential to enhance 3D profile accuracy in manufacturing processes.

The inviscid incompressible limit of Kelvin–Helmholtz instability for plasmas

Introduction: The Kelvin–Helmholtz Instability (KHI) is an interface instability that develops between two fluids or plasmas flowing with a common shear layer. KHI occurs in astrophysical jets, solar atmosphere, solar flows, cometary tails, planetary magnetospheres. Two applications of interest, encompassing both space and fusion applications, drive this study: KHI formation at the outer flanks of the Earth’s magnetosphere and KHI growth from non-uniform laser heating in magnetized direct-drive implosion experiments. Here, we study 2D KHI with or without a magnetic field parallel to the flow. We use both the GAMERA code, which solves the compressible Euler equations, and the STRATOSPEC code, which solves the Navier-Stokes equations under the Boussinesq approximation, coupled with the magnetic field dynamics. GAMERA is a global three-dimensional MHD code with high-order reconstruction in arbitrary nonorthogonal curvilinear coordinates, which is developed for a large range of astrophysical applications. STRATOSPEC is a three-dimensional pseudo-spectral code with an accuracy of infinite order (no numerical diffusion). Magnetized KHI is a canonical case for benchmarking hydrocode simulations with extended MHD options.Methods: An objective is to assess whether or not, and under which conditions, the incompressibility hypothesis allows to describe a dynamic compressible system. For comparing both codes, we reach the inviscid incompressible regime, by decreasing the Mach number in GAMERA, and viscosity and diffusion in STRATOSPEC. Here, we specifically investigate both single-mode and multi-mode initial perturbations, either with or without magnetic field parallel to the flow. The method relies on comparisons of the density fields, 1D profiles of physical quantities averaged along the flow direction, and scale-by-scale spectral densities. We also address the triggering, formation and damping of filamentary structures under varying Mach number or Atwood number, with or without a parallel magnetic field.Results: Comparisons show very satisfactory results between the two codes. The vortices dynamics is well reproduced, along with the breaking or damping of small-scale structures. We end with the extraction of growth rates of magnetized KHI from the compressible regime to the incompressible limit in the linear regime assessing the effects of compressibility under increasing magnetic field.Discussion: The observed differences between the two codes are explained either from diffusion or non-Boussinesq effects.

Exploring the Potential Application of an Innovative Post-Weld Finishing Method in Butt-Welded Joints of Stainless Steels and Aluminum Alloys.

The prerequisite of the weld bead finishing is intricately linked to the quality of the welded joint. It constitutes the final, yet pivotal, stage in its formation, significantly influencing the reliability of structural components and machines. This article delineates an innovative post-weld surface finishing method, distinguished by the movement of a specialized cutting tool along a butt weld. This method stands out due to its singular approach to machining allowance, wherein the weld bead height is considered and eradicated in a single pass of the cutting tool. Test samples were made of AISI 304L, AISI 316L stainless steels and EN AW-5058 H321, EN AW-7075 T651 aluminum alloys butt-welded with TIG methods. Following the welding process, the weld bead was finished in accordance with the innovative method to flush the bead and the base metal's surface. For the quality control of welded joints before and after the weld finishing, two non-destructive testing methods were chosen: Penetrant Testing (PT) and Radiographic Testing (RT). This article provides results from the examination of 2D profile parameters and 3D stereometric characteristics of surface roughness using the optical method. Additionally, metallographic results are presented to assess changes in the microstructure, the microhardness, and the degree of hardening within the surface layer induced by the application of the innovative post-weld finishing method.

Applying Deep Electrical-Resistivity Tomography Techniques for the Exploration of Medium- and Low-Geothermal Energy Resources

The growth of the geothermal industry demands the constant search of techniques with the aim of reducing exploration efforts whilst minimizing subsurface uncertainty. The exploration of geothermal resources is fundamental from the exploitation point of view, especially in those regions where this energy is not as widespread as the rest of renewable sources. This research shows how geoelectrical methods can contribute to the investigation and characterization of medium–low enthalpy geothermal resources until about 800 m of depth. A 2000 m long electrical-resistivity tomography profile was performed in a region of Southern Spain with previous evidence of moderate geothermal potential. Results of this geophysical campaign (together with a preliminary geological characterization) allowed for the obtainment of a 2D profile and a pseudo-3D model with extensive information about the subsoil in terms of geological composition and formations. The interpretation of geophysical results denotes the existence of a potential formation constituted by carbonate materials with thickness greater than 300 m, crossing different fractures. Once the ideal location for the geothermal exploitation is defined, the research evaluates the contribution of the possible energy source, deducing that the energy extraction in the potential fracturing area would be double that of the one in the vicinity of the site.

A protocol for annotation of total body photography for machine learning to analyze skin phenotype and lesion classification.

Artificial Intelligence (AI) has proven effective in classifying skin cancers using dermoscopy images. In experimental settings, algorithms have outperformed expert dermatologists in classifying melanoma and keratinocyte cancers. However, clinical application is limited when algorithms are presented with 'untrained' or out-of-distribution lesion categories, often misclassifying benign lesions as malignant, or misclassifying malignant lesions as benign. Another limitation often raised is the lack of clinical context (e.g., medical history) used as input for the AI decision process. The increasing use of Total Body Photography (TBP) in clinical examinations presents new opportunities for AI to perform holistic analysis of the whole patient, rather than a single lesion. Currently there is a lack of existing literature or standards for image annotation of TBP, or on preserving patient privacy during the machine learning process. This protocol describes the methods for the acquisition of patient data, including TBP, medical history, and genetic risk factors, to create a comprehensive dataset for machine learning. 500 patients of various risk profiles will be recruited from two clinical sites (Australia and Spain), to undergo temporal total body imaging, complete surveys on sun behaviors and medical history, and provide a DNA sample. This patient-level metadata is applied to image datasets using DICOM labels. Anonymization and masking methods are applied to preserve patient privacy. A two-step annotation process is followed to label skin images for lesion detection and classification using deep learning models. Skin phenotype characteristics are extracted from images, including innate and facultative skin color, nevi distribution, and UV damage. Several algorithms will be developed relating to skin lesion detection, segmentation and classification, 3D mapping, change detection, and risk profiling. Simultaneously, explainable AI (XAI) methods will be incorporated to foster clinician and patient trust. Additionally, a publicly released dataset of anonymized annotated TBP images will be released for an international challenge to advance the development of new algorithms using this type of data. The anticipated results from this protocol are validated AI-based tools to provide holistic risk assessment for individual lesions, and risk stratification of patients to assist clinicians in monitoring for skin cancer.

The 3D Revolution in Cancer Discovery.

The transition from 2D to 3D spatial profiling marks a revolutionary era in cancer research, offering unprecedented potential to enhance cancer diagnosis and treatment. This commentary outlines the experimental and computational advancements and challenges in 3D spatial molecular profiling, underscoring the innovation needed in imaging tools, software, artificial intelligence, and machine learning to overcome implementation hurdles and harness the full potential of 3D analysis in the field.

Scanning ex situ solid-state magnetic resonance imaging on polymeric films using a static magnetic field gradient by an electromagnet.

A new technique for ex situ solid-state nuclear magnetic resonance imaging of polymeric films has been developed. This method uses the static magnetic field gradient generated by a water-cooled copper electromagnet. The imaging process involves scanning the sample in the plane of the film under a static magnetic field gradient. Two-dimensional (2D) 19F MRI measurements are attempted, where the fast Fourier transform (FFT) spectra of the second half of a Hahn echo peak is used for the depth (X-axis) direction, and the sample film is mechanically moved for the Y-axis, which is in-plane with respect to the film surface and normal to the inter-magnetic-pole line (Z-axis). The sample is a poly(tetrafluoroethylene) [PTFE] film with carved stripes on its surface. Furthermore, three-dimensional (3D) measurements of the film surface and thickness orientations were also performed for an analogous PTFE film. For 2D profiling on the film surface, 2D sample-moving was used. For depth profiling, FFT spectrum analysis was undertaken for each voxel situated at a specific (Y, Z) coordinate within the film. As a result, a 3D image of the sample and its corresponding geometry were obtained, although the scanning area was restricted to a part of the sample. Finally, numerical simulations of the spatial distribution of the static magnetic field were performed to confirm the validity of the present method.

The intake port of DI-engine type VIKYNO RV165 desinged using 3D-CAE software

The intake manifold profile in a marine diesel engine profoundly influences engine intake performance, which, in turn, significantly impacts the engine's power characteristics and exhaust emissions which leading to marine polution and challenging the marine sustainable development. Improving the performance of the appropriate geometric design of the intake manifold in order to optimize the study time is a key basis. This research aims to build a parametric model for the 3D profile of the VIKYNO RV165-2 engine intake line. By changing the value of parameter variables in the model through SolidWorks software, it is able to design and change the 3D profile design of the intake manifold. The 3D model of the intake line will be parameterized based on the 2D design drawings of this part provided by VIKYNO. The parameter variables used are upper inclination, lower inclination, curvature, intake valve diameter, input cross-section. Many different 3D models of intakes will be created with respect to the values of parameter variables changed by using SolidWorks software. The results of the study provided a parametric model for the 3D profile of the intake tract, linking the parametric variables of the model with Solidwork software as a basis for speeding up this detailed 3D design process. The 3D design of the intake by the parametric model compared to the 3D design of the intake manifold by direct rendering from the 2D drawing has a difference of no more than 2% in volume. This adaptability and precision are crucial for ensuring air intake efficiency for marine engines operating in diverse and challenging marine conditions.From the platform model, the change of input variables is displayed quickly on new designs, in accordance with the standard, and thereby the model is selected for the stage of researching the efficiency of air intake on the engine. Keywords: CAE, Diesel engine, intake port geometry, numerical design.

Dynamic estimation method for pulsar periods based on photon energy distribution folding and image template matching

The accuracy of the pulsar period estimation directly affects the restoration effect of the signal profile. A more accurate pulsar profile will help improve the accuracy of pulsar delay estimation and thereby improve the performance of X-ray pulsar navigation. This paper proposes a pulsar period estimation method based on photon energy distribution folding and image template matching (PETM). This method uses the probability distribution information of photon energy for weighted epoch folding. The one-dimensional (1D) profile information was converted into two-dimensional (2D) image information through reverse space-filling curve (SFC) encoding. Then, a feature matching was performed between the target structure and the template structure. At the same time, the criterion of Pearson correlation coefficient (PCC) was used to quantitatively evaluate the matching effect to estimate the optimal period. The simulation results show that the period estimation accuracy of the PETM method is significantly improved, as compared with the traditional \( method. This work also analyzes the folding effect based on the photon energy distribution model and conducts simulation experiments and comparisons on influencing factors, such as noise interference and data quality. At the same time, we also specifically demonstrated the effectiveness of the PETM method for the glitch phenomenon (i.e., a sudden change in period) of pulsar periods. Finally, we also used China's XPNAV-1 satellite to conduct experiments and analysis of the actual observation data of PSR B0531+21 pulsar within a fixed period of time. The results show that the period estimation accuracy of this method is 4.8190$ns$, which is 50.23<!PCT!> higher than the traditional \( method. The method proposed in this article has the advantages of high estimation accuracy, stable estimation performance, strong anti-interference ability, and excellent dynamic period estimation performance. Therefore, it can further improve the navigation performance of X-ray pulsars.

Bead Geometry Control in Wire Arc Additive Manufactured Profile — A Review

Wire arc additive manufacturing (WAAM) is a well-established additive manufacturing method that produces 3D profiles. A better deposition efficiency can be achieved by understanding the parameters that may influence the geometry of the bead. This paper provides a review that focuses on the factors that may influence the formation of the 3D profile. The included factors are the flow pattern of the molten pool after deposition, the built structure and orientation, the heat input and cooling conditions, the welding parameters, and other uncertainties. This review aims to facilitate a better understanding of these factors and achieve the optimum geometry of the 3D parts produced. According to the literature, the behavior of molten pools is identified as one of the major factors that can impact the deposition efficiency of a bead and govern its geometry. The review indicated that the flow behavior of the molten pool and the geometry of the deposited bead are significantly affected by most welding parameters, such as torch angle, wire travel speed, filler feed rate, and cooling conditions. Furthermore, this paper incorporates the technology utilized for comprehending the behaviors of the molten pool, as it constitutes an integral component of the control strategy. It has been concluded that automated planning and strategy are necessary to ensure efficient deposition by controlling those factors. The integration of artificial intelligence could bring benefits in planning to address the variation and complexity of shapes.

New Traveling Wave Solutions to the Simplified Modified Camassa–Holm Equation and the Landau-Ginsburg-Higgs Equation

Researchers are interested in the (1+1)-dimensional Camassa-Holm and Landau-Ginzburg-Higgs equations as they allow for the study of unidirectional wave propagation in shallow waters with a flat seabed, as well as nonlinear media exhibiting dispersion systems and superconductivity. This work has effectively developed exact wave solutions to the stated models, which may have significant consequences for characterising the nonlinear dynamical behaviour related to the phenomena. The extended -expansion technique is employed to procure a diverse array of progressive wave solutions characterized by hyperbolic, trigonometric, and rational functions. The solutions are shown as 3D profiles with a variety of shapes, including kink, singular kink, periodic, singular periodic, etc. The physical significance of the solutions is discussed by these plots, and the approach used in this study is considered efficient and capable of finding analytical solutions for the nonlinear models. Dhaka Univ. J. Sci. 72(1): 7-13, 2024 (January)

Scan-less 3D microscopy based on spatiotemporal encoding on a single-cavity dual-comb laser.

Dual-comb microscopy enables high-speed and high-precision optical sampling by simultaneously extracting both amplitude and phase information from the interference signals with frequency division multiplexing. In this Letter, we introduce a spatiotemporal encoding approach for dual-comb microscopy that overcomes previous limitations such as mechanical scanning, low sampling efficiency, and system complexity. By employing free-space angular-chirp-enhanced delay (FACED) and a low-noise single-cavity dual-comb laser, we achieve scan-less 3D imaging with nanometer precision and a 3D distance-imaging rate of 330 Hz, restricted only by the repetition rate difference of the dual-comb laser. Specifically, the FACED unit linearly arranges the laser beam into an array. A grating subsequently disperses this array transversely into lines, facilitating ultrafast spectroscopic applications that are 1-2 orders of magnitude quicker than traditional dual-comb methods. This spatiotemporal encoding also eases the stringent conditions on various dual-comb laser parameters, such as repetition rates, coherence, and stability. Through carefully designed experiments, we demonstrate that our scan-less system can measure 3D profiles of microfabricated structures at a rate of 7 million pixels per second. Our method significantly enhances measurement speed while maintaining high precision, using a compact light source. This advancement has the potential for broad applications, including phase imaging, surface topography, distance ranging, and spectroscopy.

Abstract 512: Cell-cycle inhibition may influence differences in 2D vs 3D tumor sensitivity profiles

Abstract Background: Traditional cell culture methods involve growing cells on flat surfaces, which do not fully replicate the human physiological conditions. In contrast, 3D cell cultures accurately reproduce aspects of the in-vitro malignant and non-malignant cell behavior. However, differences in tumor sensitivity platforms between 2D vs. 3D models might be relevant for some, but not for other drugs. Methods: The colorectal cancer cell line HCT-116 was cultured and plated in flat bottom well plates for the 2D condition and in Ultra Low Attachment round-bottom plates for the 3D condition. After plating, cells were treated with chemotherapies and targeted therapies for four days; cellular sensitivity was assessed through a viability assay. Four-parameter logistic (4PL) dose response curves were generated and IC50s were quantified to understand the differences in responses between 2D and 3D cellular environments. Results: HCT-116 exhibited similar responses to platinum drugs, taxanes, and several other tested drugs in both platforms, with the exception of gemcitabine (2D IC50 0.04 mcM; 3D IC50 12.8 mcM), lapatinib (2D IC50 23; 3D IC50 418), tucatinib (2D IC50 23.3; 3D IC50 79.3), and palbociclib (2D IC50 7.0; 3D IC50 24.0). Interestingly, these drugs share a common mechanism of action, which is the inhibition of cell cycle progression from G1 to S phase. Hence, we hypothesize that this shared mechanism may account for the observed differences in drug efficacy between the two environments. Conclusions: Similar dose responses have been observed with various drugs on both platforms, yet drugs that inhibit cell cycle progression from G1 to S phase, revealed higher sensitivity in 2D and more resistance in the 3D platform. Confirmatory cell-cycle results analyses are in progress. Further research is needed to understand if this applies to patient samples. Citation Format: Jahanvi Kumar, Chiara Maestri, Rajeshwar Nitiyanandan, Ivan Trus, Ricardo J. Parker, Brigitte Apfel, Christian Apfel. Cell-cycle inhibition may influence differences in 2D vs 3D tumor sensitivity profiles [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 512.

Abstract 3386: Changes in extracellular vesicle miRNAs from three-dimensional ovarian cancer cell models reflect physiological changes and cancer survival

Abstract Ovarian Cancer (OC) is the most common gynaecological malignancy and the eighth most diagnosed cancer in females worldwide. Currently, it is the fifth leading cause of cancer-related mortality among patients globally, largely due to delayed diagnosis, chemotherapy resistance, high metastasis rates, and subtype heterogeneity. Recent OC research highlights extracellular vesicles (EVs) as pivotal contributors to intercellular communication and disease progression, elucidating the intricacies of OC pathology. EVs, diverse membrane-derived vesicles released by most cells, carry molecular cargoes containing proteins and nucleic acids. Studies indicate that the biogenesis, packaging, and release of EVs are highly dependent and sensitive to the cellular microenvironment and depend on the in-vitro and in-vivo modelling systems. Labs use varied model systems—2D monolayers, animal models, and innovative 3D models—to investigate EVs' ovarian cancer roles. Therefore, in this study we aimed to compare the miRNA profiles associated with EVs in 3D ovarian cancer cell models, and to identify the pathophysiological relevance of the EVs isolated from these models to the patient derived EVs.In this study, two OC epithelial cell lines, SKOV-3 and OVCAR-, were initially cultured as 2D monolayers and embedded within Gelatin Methacryloyl hydrogels. Over nine days, multiple assays observed spheroid formation within the cell-laden hydrogels. EVs isolated from the cell-conditioned media were characterized per MISEV 2018 guidelines. Small RNA sequencing identified statistically significant miRNAs, subject to gene ontology and gene rank analyses. EVs from a cohort of 60 OC patients were used to identify survival-associated miRNA profiles.Our findings in the cell-laden hydrogels demonstrated OC cell growth, proliferation, and aggregation into spheroidal structures, establishing an ideal 3D model. The isolated EVs were characterized for size, concentration, morphology, and surface markers. Small RNA sequencing revealed 18 significantly different EV-associated miRNA species across 3D vs 2D models, influencing apoptosis, angiogenesis, migration, and proliferation in ovarian cancer. Notably, 3D model-derived EV-associated miRNAs mirrored patient-derived EV-associated miRNA profiles, indicating their pathophysiological relevance.This study establishes a robust 3D OC model in hydrogels, showcasing growth, proliferation, and aggregation capabilities. Differential miRNA profiles between 3D and 2D model EVs highlight the critical roles of these miRNAs in essential ovarian cancer processes. Moreover, similarities between 3D model and patient-derived EV-associated miRNA profiles emphasize the clinical relevance of our model. Citation Format: Nihar Godbole, Akhilandeshwari Ravichandran, Dominic Guanzon, Andrew Lai, Flavio Carrion, Priyakshi Kalita de Croft, Lewis Perrin, John Hooper, Laura Bray, Carlos Salomon. Changes in extracellular vesicle miRNAs from three-dimensional ovarian cancer cell models reflect physiological changes and cancer survival [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 3386.

High valley-degeneracy electron gas at double perovskite - strontium titanate interface

Emergent phenomena such as two-dimensional electron gas (2DEG) and interfacial superconductivity and ferromagnetism are generally built on the interface between insulating oxide thin films and substrates, e.g., LaAlO3/SrTiO3, where the 2D profiles of these electronic states are precisely confined at the interface of two insulators. Herein we report a high-mobility electron gas state with unusual symmetry at the interface of the Sr2CrMoO6/SrTiO3 (110) heterostructures, the fermiology of which follows the cubic crystallographic symmetry rather than the two-dimensional interface itself, resulting in the identical Shubnikov-de Haas oscillations with applied magnetic field along all the twelve equivalent [110] crystallographic directions of SrTiO3, distinctly different from the 2D nature of the electron gas reported previously. Neutron diffraction verifies the predicted ferrimagnetic ordering between Cr and Mo moments. This, together with the magnetic hysteresis loops and negative magnetoresistance in low-field region, suggests possible spin polarization of itinerant electrons. Therefore, a quasi-3D profile, high mobility (up to 104 cm2 V−1 s−1) and possibly spin polarized electronic state is observed in the double-perovskite-based oxide heterostructures. This finding of the electronic properties in Sr2CrMoO6/SrTiO3 (110) heterostructure expands the knowledge of interfacial physics, as well as shines light on oxide-based electronics and spintronics research.

Image Processing Techniques for Improving Quality of 3D Profile in Digital Holographic Microscopy Using Deep Learning Algorithm.

Digital Holographic Microscopy (DHM) is a 3D imaging technology widely applied in biology, microelectronics, and medical research. However, the noise generated during the 3D imaging process can affect the accuracy of medical diagnoses. To solve this problem, we proposed several frequency domain filtering algorithms. However, the filtering algorithms we proposed have a limitation in that they can only be applied when the distance between the direct current (DC) spectrum and sidebands are sufficiently far. To address these limitations, among the proposed filtering algorithms, the HiVA algorithm and deep learning algorithm, which effectively filter by distinguishing between noise and detailed information of the object, are used to enable filtering regardless of the distance between the DC spectrum and sidebands. In this paper, a combination of deep learning technology and traditional image processing methods is proposed, aiming to reduce noise in 3D profile imaging using the Improved Denoising Diffusion Probabilistic Models (IDDPM) algorithm.

Prostate diffusion-weighted imaging (DWI) in MR-guided radiotherapy: Reproducibility assessment on 1.5 T MR-Linac and 1.5 T MR-simulator

PurposeDiffusion-weighted imaging (DWI) holds promise for image-guided radiotherapy (MRgRT) in prostate cancer. However, challenges persist due to image distortion, artifacts, and apparent diffusion coefficient (ADC) reproducibility issues. This study aimed to assess DWI image quality and ADC reproducibility on both a 1.5 T MR-simulator and a 1.5 T MR-Linac, employing measurements from both an ACR MRI phantom and prostate cancer patients undergoing MRgRT. MethodsDW-MRI scans were conducted on 19 patients (mean age = 69 ± 8 years, with 23 MR-visible intra-prostatic lesions) and an ACR MRI phantom using a 1.5 T MR-simulator (b-values = 0, 800, 1400s/mm2) and a 1.5 T MR-Linac (b-values = 50, 500, 800 s/mm2). ADC homogeneity in the phantom was evaluated via 1D profile flatness (FL) in three directions. Image quality was assessed through qualitative 5-point Likert scale ratings and quantitative ADC and signal-to-noise ratio (SNR) measurements. Intra-observer reproducibility of image quality scores was evaluated using ICC(1, 2). Geometric distortion was measured by comparing landmark sizes on the ACR phantom against the ground truth. Mean ADC and reproducibility were assessed using Bland-Altman plots. ResultsBoth MR-simulator and MR-Linac demonstrated high ADC homogeneity (FL > 87.5% - MR-simulator: 97.23 ± 0.62%, MR-Linac: 94.75 ± 0.62%, p < 0.05) in the phantom. Image quality scores revealed acceptable ratings (≥3) for capsule demarcation, image artifacts, and geometric distortion in patients. However, intra-prostatic lesions were barely discernible in b800 images for both MR-simulator (average score = 2.37 ± 1.33) and MR-Linac (average score = 2.16 ± 1.28). While MR-Linac DWI scans exhibited significantly more severe geometric distortion than MR-simulator scans (p < 0.01), most phantom measurements fell within the image in-plane resolution of 3 mm. Significant differences were noted in MR-simulator ADC (CTV: 1.20 ± 0.14 × 10−3 mm2/s (MR-simulator) vs 1.06 ± 0.10 × 10–3 mm2/s (MR-Linac); GTV: 1.05 ± 0.21 × 10−3 mm2/s vs 0.91 ± 0.16 × 10 mm2/s, all p < 0.05), with a small non-zero bias observed in the Bland-Altman analysis (CTV: 12.3%; GTV: 14.5%). ConclusionThe significantly larger MR-simulator ADC and the small non-zero bias hint at potential systematic differences in ADC values acquired from an MR-simulator and an MR-Linac, both at 1.5 T. Although acceptable ADC homogeneity was noted, caution is warranted in interpreting MR-Linac DWI images due to occasional severe artifacts. Further studies are essential to validate DWI and ADC as reliable imaging markers in prostate cancer MRgRT.

Sensitive remote homology search by local alignment of small positional embeddings from protein language models

Accurately detecting distant evolutionary relationships between proteins remains an ongoing challenge in bioinformatics. Search methods based on primary sequence struggle to accurately detect homology between sequences with less than 20% amino acid identity. Profile- and structure-based strategies extend sensitive search capabilities into this twilight zone of sequence similarity but require slow pre-processing steps. Recently, whole-protein and positional embeddings from deep neural networks have shown promise for providing sensitive sequence comparison and annotation at long evolutionary distances. Embeddings are generally faster to compute than profiles and predicted structures but still suffer several drawbacks related to the ability of whole-protein embeddings to discriminate domain-level homology, and the database size and search speed of methods using positional embeddings. In this work, we show that low-dimensionality positional embeddings can be used directly in speed-optimized local search algorithms. As a proof of concept, we use the ESM2 3B model to convert primary sequences directly into the 3D interaction (3Di) alphabet or amino acid profiles and use these embeddings as input to the highly optimized Foldseek, HMMER3, and HH-suite search algorithms. Our results suggest that positional embeddings as small as a single byte can provide sufficient information for dramatically improved sensitivity over amino acid sequence searches without sacrificing search speed.

Cancer‐on‐Chip and Integrative Tumor Microenvironment Sensor Technologies for Progressing Precision Radiotherapy

AbstractPrecision radiotherapy, such as targeted radioligand therapy, accentuates the precise delivery of radiation to tumor cells while limiting radiation damage to surrounding normal cells. Although recent clinical trial data has shown targeted radioligand therapy to have significant patient survival benefit, it is still unavoidable that the cancer cells will eventually adapt and develop radioresistance. Thus, the study of radiotherapy‐induced changes in the tumor microenvironment (TME) is crucial for developing strategies to best overcome radioresistance. To this end, organ‐on‐chip (OOC) systems with integrative sensors represent cutting‐edge pre‐clinical models for miniaturized 3D modelling and profiling of the TME. This Review features OOC systems which have demonstrated feasibility for radiation‐associated studies, as well as showcased the progress of different OOC systems for profiling core components of the TME. Furthermore, this Review discusses the knowledge gap in cancer‐on‐chip systems with integrative TME sensors for precision radiotherapy applications. It is anticipated that this Review can kickstart the propagation of new concepts and approaches to drive a new era of miniaturized sensors on OOC systems for precision radiotherapy.