3D Distribution Research Articles

Magnetotelluric sampling theorem

We consider a general case of a magnetotelluric (MT) study to reveal three-dimensional (3D) distribution of the electrical conductivity within the Earth based on measurements of electromagnetic (EM) fields by a two-dimensional (2D) array. Such an MT array observation can be regarded as a spatially discrete sampling of the MT responses (impedances), and each observation site can be regarded as a sampling point. This means that MT array measurements must follow the Nyquist–Shannon sampling theorem. This paper discusses how the sampling theorem is applied to MT array studies and what kind of consideration is required in the application on the basis of synthetic model calculations, with special attention to spatial resolutions. With an aid of the EM scattering theory and the sampling theorem, we can show that an observation array resolves some features of the MT impedance but does not others. We call the resolvable and unresolvable features the MT signal and noise, respectively. This study introduces the spatial Fourier transform of array MT data (impedances) which helps us investigating sampling effects of lateral heterogeneity from a different angle (in the wavenumber domain). Shallow heterogeneities cause a sharp spatial change of impedance elements near structural boundaries. High wavenumber Fourier components are required to describe such a feature, which means the site spacing must be sufficiently short to be able to resolve such features. Otherwise, a set of array MT data will suffer from aliasing, which is one of the typical causes of MT distortion (MT geologic noise). Conversely, a signal due to a deep-seated conductivity anomaly will have more reduced amplitude at higher wavenumbers, which means focused imaging of such an anomaly is generally difficult. Finally, it is suggested to properly consider the sampling theorem in an observation array design, so as to have best performance in resolving MT signals.Graphical abstract

Cubitus varus deformity following paediatric supracondylar humeral fracture remodelling predominantly in the sagittal direction: A three-dimensional analysis of eighty-six cases

PurposeThree-dimensional (3D) capacity for remodelling in cubitus varus deformity (CVD) after paediatric supracondylar humeral fractures (PSHFs) remains unelucidated. This study investigated remodelling patterns after PSHFs by examining 3D deformity distribution over time after injury.MethodsComputed tomography (CT) data of 86 patients with CVD after PSHFs were analysed. The 3D deformity angles in the sagittal, coronal, and axial directions were assessed and correlated with the duration between the age at injury and CT evaluation. For the subgroup analysis, we performed the same correlation analysis in a younger (< 8 years old) and an older group (≥ 8 years old); we categorized the duration into early (< 2 years), middle (≥ 2 to < 5 years), and late periods (≥ 5 years) and compared the deformity angles of each direction among the three groups.ResultsSagittal deformity showed a moderate correlation with the duration of deformity (r = -0.54; P < 0.001), while coronal and axial deformities showed a negligible correlation. Sagittal deformity showed moderate correlations with the duration in the younger group (r = -0.62; P < 0.001) and weak correlations in the older group (r = -0.37; P = 0.091). In the sagittal direction, the deformity angle in the early period was significantly larger than those in the mid and late periods (P < 0.001). However, there were no significant differences among the three groups in the coronal and axial directions.ConclusionSagittal deformities in CVDs are capable of remodelling, especially in the early period and at a younger age, whereas coronal and axial deformities are less likely to undergo remodelling.

Three-Dimensional Quality Assessment of Urban Underground Space Resource Based on Multiple Geological Environmental Factors

With the rapid advancement of urbanization, the development and utilization of urban underground space resource (UUSR) has become one of the dominant features. However, in certain areas, the development of UUSR may cause disasters and accidents, such as ground collapse, settlements, and tunnel water gushing. Geological environmental factors (GEFs) are recognized as the fundamental constraining factor of UUSR development. In this paper, quality based on GEFs is defined to assess the development difficulty degree of UUSR. A 3D assessment framework is proposed based on 3D geological modelling and the interval continuous mathematical model (ICMM). The subjective and objective joint weight method of analytic hierarchy process and entropy weight method (AHP–EWM) is utilized to determine the weight of each indicator. The quality index (QI) of each spatial node of the 3D geological model is calculated by the ICMM mathematical model. A case study conducted in the Jiangbei New District of Nanjing, China, serves as a demonstration of the UUSR assessment. The results clearly illustrate the 3D distribution characteristics of the quality in the study area, offering valuable insights for future 3D urban underground space planning.

An integrated uncrewed aerial vehicle platform with sensing and sampling systems for the measurement of air pollutant concentrations

Abstract. In this study, an uncrewed aerial vehicle (UAV) platform with sensing and sampling systems was developed for three-dimensional (3D) measurements of air pollutant concentrations. The sensing system of this platform contains multiple microsensors and Internet of Things devices for determining the 3D distributions of four critical air pollutants and two meteorological parameters in real time. Moreover, the sampling system comprises remote-controllable gas sampling kits, each of which contains a 1 L Tedlar bag for the 3D measurement of volatile organic compound (VOC) concentrations according to the Toxic Organics-15 (TO-15) method of the US Environmental Protection Agency. The performance of the developed UAV platform was verified in experiments where it was used to detect air pollutant emissions from a large industrial zone in Taiwan that included a traditional industrial park, a precision machinery park, and a municipal waste incineration plant. Three locations were selected as field measurement sites according to the prevailing local wind direction. The vertical distributions of four critical air pollutants, the ambient temperature, and the relative humidity were determined from data gathered at the aforementioned sites in March and May 2023. A total of 56 and 72 chemical species were qualitatively and quantitatively analyzed in these two periods, respectively. The experimental results verified the feasibility of using the proposed UAV platform for accurately evaluating the air pollutant concentration distribution and transport in an industrial zone. The sampling system can be used as the sampling part of the TO-15 method, thus extending the method to measure the 3D distribution of VOCs in an area. The UAV platform can serve as a useful tool in the management of and decision-making process for air pollution in industrial areas.

Error detection for radiotherapy planning validation based on deep learning networks.

Quality assurance (QA) of patient-specific treatment plans for intensity-modulated radiation therapy (IMRT) and volumetric modulated arc therapy (VMAT) necessitates prior validation. However, the standard methodology exhibits deficiencies and lacks sensitivity in the analysis of positional dose distribution data, leading to difficulties in accurately identifying reasons for plan verification failure. This issue complicates and impedes the efficiency of QA tasks. The primary aim of this research is to utilize deep learning algorithms for the extraction of 3D dose distribution maps and the creation of a predictive model for error classification across multiple machine models, treatment methodologies, and tumor locations. We devised five categories of validation plans (normal, gantry error, collimator error, couch error, and dose error), conforming to tolerance limits of different accuracy levels and employing 3D dose distribution data from a sample of 94 tumor patients. A CNN model was then constructed to predict the diverse error types, with predictions compared against the gamma pass rate (GPR) standard employing distinct thresholds (3%, 3mm; 3%, 2mm; 2%, 2mm) to evaluate the model's performance. Furthermore, we appraised the model's robustness by assessing its functionality across diverse accelerators. The accuracy, precision, recall, and F1 scores of CNN model performance were 0.907, 0.925, 0.907, and 0.908, respectively. Meanwhile, the performance on another device is 0.900, 0.918, 0.900, and 0.898. In addition, compared to the GPR method, the CNN model achieved better results in predicting different types of errors. When juxtaposed with the GPR methodology, the CNN model exhibits superior predictive capability for classification in the validation of the radiation therapy plan on different devices. By using this model, the plan validation failures can be detected more rapidly and efficiently, minimizing the time required for QA tasks and serving as a valuable adjunct to overcome the constraints of the GPR method.

Comparison between 2D and 3D microstructures and implications for metamorphic constraints using a chloritoid–garnet-bearing mica schist

Abstract. Despite the fact that rock textures depend on the 3D spatial distribution of minerals, our tectono-metamorphic reconstructions are mostly based on a 2D visualisation (i.e. thin sections). This work compares 2D and 3D investigations of petrography and microstructures, modal abundances, and local bulk rock composition and their implication for P–T estimates, showing the pros and cons and reliability of 2D analysis. For this purpose, a chloritoid–garnet-bearing mica schist from the Dora-Maira Massif in the Western Alps has been chosen. In particular, for 2D a thin section scan has been combined with chemical X-ray maps, whereas for 3D the X-ray computerised axial microtomography (µCT) has been applied. Two-dimensional investigations are readily accessible and straightforward but do not consider the entire rock volume features. Conversely, the rise of 3D techniques offers a more comprehensive and realistic representation of metamorphic features in the 3D space. However, they are computationally intensive, requiring specialised tools and expertise. The choice between these approaches should be based on the research aims, available resources, and the level of detail needed to address specific scientific questions. Nevertheless, despite differences in the modal distribution, the estimated bulk rock compositions and relative thermodynamic modelled phase fields show similarities when comparing the 2D and 3D results. Also, since different thin section cut orientations may influence the results and consequent interpretations, three different cuts from the 3D model have been extrapolated and discussed (i.e. XZ, YZ, and XY planes of the finite-strain ellipsoid). This study quantitatively corroborates the reliability of the thin section approach for tectono-metamorphic reconstructions, still emphasising that 3D visualisation can help understand rock textures.

A Global-Local collaborative approach to quantifying spatial non-stationarity in three-dimensional mineral prospectivity modeling

The three-dimensional (3D) modeling of mineral prospectivity plays a pivotal role in the mineral exploration process, enabling explorers to identify, evaluate, and prioritize deep-seated targets more effectively.Accurately predicting the 3D distribution of mineral prospectivity relies on effectively quantifying correlations to mineralization. The ore formation process is extremely intricate, marked by spatial non-stationary correlations. While state-of-the-art 3D mineral prospectivity modeling methods use machine learning (including deep learning) to capture correlation between geological evidence and mineralization hidden in the geosciences data, limited attention has been given to quantifying the local non-stationary nature in ore formation. This paper proposes a novel method that adapts to local spatial non-stationarity in 3D mineral prospectivity modeling. The approach integrates a prospectively model with a local regression model, creating a global–local collaborative model to quantify spatial non-stationary associations with mineralization. Random Forest (RF) and Reset are employed to construct mineral prospectivity models that quantify the global associations with mineralization. Simultaneously, the geographically weighted regression (GWR) model serves as a local model to capture the local non-stationarity within mineralization association. An Expectation Maximization algorithm is tailored to bridge the gap between global and local models, resulting in an iterative framework that progressively fuses global spatial associations and local non-stationarity related to mineralization. The iterative process generates a mineral perspectivity model that adapts to local non-stationarity in predictive areas. A case study on the Dingjiashan Pb-Zn deposit validates the effectiveness of the proposed method. The results demonstrate that the global–local collaborative mineral perspectivity model significantly boosts prediction accuracy in predictive areas. Moreover, the regression parameters of GWR derived from the model construction process effectively reveal the spatial non-stationary impacts of different ore-controlling factors, thereby improving the interpretability and reliability of predicted mineral prospectivity.

Imaging 3D chemistry at 1 nm resolution with fused multi-modal electron tomography

Measuring the three-dimensional (3D) distribution of chemistry in nanoscale matter is a longstanding challenge for metrological science. The inelastic scattering events required for 3D chemical imaging are too rare, requiring high beam exposure that destroys the specimen before an experiment is completed. Even larger doses are required to achieve high resolution. Thus, chemical mapping in 3D has been unachievable except at lower resolution with the most radiation-hard materials. Here, high-resolution 3D chemical imaging is achieved near or below one-nanometer resolution in an Au-Fe3O4 metamaterial within an organic ligand matrix, Co3O4-Mn3O4 core-shell nanocrystals, and ZnS-Cu0.64S0.36 nanomaterial using fused multi-modal electron tomography. Multi-modal data fusion enables high-resolution chemical tomography often with 99% less dose by linking information encoded within both elastic (HAADF) and inelastic (EDX/EELS) signals. We thus demonstrate that sub-nanometer 3D resolution of chemistry is measurable for a broad class of geometrically and compositionally complex materials.

MAPPING 3D STRUCTURE OF URBAN TREES USING AIRBORNE LIDAR DATA FOR TOPOGRAPHIC SURVEY

Abstract. The designing of urban spaces utilizing trees is a promising approach to alleviate the severe thermal environment. Three-dimensional (3D) information on individual trees is important for their maintenance and for evaluating their microclimatic effects. Methods have been developed for estimating the 3D distribution of leaf area density (LAD) using high-density airborne light detection and ranging (LiDAR) point cloud. However, the coverage of high-density data is limited. This study sought to develop a method for estimating LAD distribution using existing low-density LiDAR data that was obtained for the nationwide topographic survey. A point cloud with the same density as the existing data was generated from the high-density data. LAD was estimated using the generated point cloud and was compared with LAD derived from the high-density data and terrestrial LiDAR to clarify the spatial scale of information obtained from the existing data. A method to improve the spatial scale was also examined. These methods were applied to the actual existing LiDAR data to generate a 3D information map at an urban scale.

Faraday tomography of LoTSS-DR2 data. II. Multi-tracer analysis in the high-latitude outer Galaxy

We conducted a follow-up study on the analysis of the LOw Frequency ARray (LOFAR) Two-metre Sky Survey (LoTSS) mosaic in the high-latitude outer Galaxy presented in the first paper of this series. Here, we focus on the search for alignment between the magnetic field traced by dust, HI filaments, starlight optical linear polarisation, and linear depolarised structures (depolarisation canals) observed in low-frequency synchrotron polarisation. This alignment was previously found in several smaller fields observed with LOFAR, offering valuable insights into the nature of the interstellar medium and the 3D spatial distribution of the diffuse ionised medium. We aim to determine whether the alignment of the interstellar medium (ISM) phases observed through multiple tracers is a common occurrence or an exception. Additionally, in areas where depolarisation canals align with the magnetic field, we use starlight polarisation to constrain the distance to the structures associated with the observed canals. We employed the Rolling Hough Transform (RHT) and projected Rayleigh statistics (PRS) to identify and quantify the alignment between the different tracers. We used these tools to detect linear features in the data and quantify the significance of the orientation trends between pairs of tracers. On the scale of the whole mosaic, we did not find any evidence of a universal alignment among the three tracers. However, in one particular area, the western region (Dec between $29^ and $70^ and RA between $7 ^h ^m $ and $9 ^h ^m $), we do find a significant alignment between the magnetic field, depolarisation canals, and HI filaments. Based on this alignment, we used the starlight polarisation of stars with known parallax distances to estimate that the minimum distance to the structures observed by LOFAR in this region lies within the range of 200 to 240 pc. We associate these structures with the edge of the Local Bubble.

Analysis and Compensation of Lorentz Force Magnetic Bearing Magnetic Flux Density Uniformity Error.

Aiming at the influence of the magnetic flux density uniformity error (MFDUE) of the Lorentz force magnetic bearing (LFMB) on the sensitivity accuracy of magnetically suspended control and sensing gyroscopes (MSCSGs) on the angular rate of a spacecraft, a high precision measurement method of the angular rate of a spacecraft based on the MFDUE compensation of LFMB is proposed. Firstly, the structure of MSCSG and the sensitivity principle of MSCSG to the spacecraft angular rate are introduced. The mechanism influencing the accuracy of MSCSG to spacecraft angular rate sensitivity is deduced based on the definition of magnetic flux density uniformity. Secondly, the 3D magnetic flux distribution of LFMB is analyzed using ANSYS. The relationship between the rotor tilt angle, tilt angular rate, and magnetic flux density is established. The induced current calculation model due to MFDUE is proposed, and the LFMB magnetic flux density error compensation is realized. Finally, the simulation results show that the estimation accuracy of the induced current by the proposed method can reach 96%, and the simulation and the experiment show that the error compensation method can improve the accuracy of MSCSG in measuring the spacecraft angular rate by 12.5%.

PdRu Bimetallic Nanoparticles/Metal-Organic Framework Composite through Supercritical CO2-Assisted Immobilization.

Metal-nanoparticle (NP)/metal-organic framework (MOF) composites have attracted considerable attention as heterogeneous catalysts. Compared with porous carbon, silica, and alumina, the charge-transfer interaction between the metal NPs and the MOF accelerated the catalytic activity. In this study, PdRu bimetallic NPs were successfully immobilized on MOFs such as MIL-101(Cr) by using supercritical carbon dioxide. The STEM-EDX images show a uniform 3D distribution of the PdRu bimetallic NPs on MIL-101(Cr). The resulting PdRu@MIL-101(Cr) catalyst exhibited higher CO oxidation than monometal/MOF composites such as Pd@MIL-101(Cr) and Ru@MIL-101(Cr). Furthermore, PdRu@MIL-101(Cr) exhibited higher catalytic activity than PdRu@SiO2. This is because the particle size of the PdRu bimetallic NPs in MIL-101(Cr) was within the range of 2-3 nm. The synergistic effects were based on the combination of two metals, Pd and Ru, small bimetal particle formation, and charge-transfer interactions between the bimetal NPs and the MOF. These factors enhance the catalytic activity of the bimetal/MOF composites.

Study of PRESAGE® dosimeter for end-to-end 3D radiotherapy verification using an anthropomorphic phantom with bespoke dosimeter insert.

Modern radiotherapy techniques have advanced and become more sophisticated. End-to-end 3D verification of the complex radiotherapy dose distribution in an anthropomorphic phantom can ensure the accuracy of the treatment delivery. The phantoms commonly used for dosimetry are homogeneous solid water phantom which lacks the capability to measure the 3D dose distribution for heterogeneous tissues necessary for advanced radiotherapy techniques. Therefore, we developed an end-to-end 3D radiotherapy dose verification system based on MAX-HD anthropomorphic phantom (Integrated Medical Technologies Inc., Troy, New York) with bespoke intracranial insert for PRESAGE® dosimeter. In this study, several advanced radiotherapy treatment techniques of various levels of complexity; 3D-CRT, IMRT and VMAT treatment, were planned for a 20mm diameter of a spherical target in the brain region and delivered to the phantom. The dosimeters were read out using an in-house developed optical computed tomography (OCT) imaging system known as 3DmicroHD-OCT. It was found that the measured dose distribution of the PRESAGE® when compared with the measured dose distribution of EBT film and Monaco TPS has a maximum difference of less than 3% for 3D-CRT, IMRT and VMAT treatment plans. The gamma analysis results of PRESAGE® in comparison to EBT film and Monaco TPS show pass rates of more than 95% for the criteria of 3% dose difference and 3mm distance-to-agreement. This study proves the capability of PRESAGE® and bespoke MAX-HD phantom in conjunction with the 3DmicroHD-OCT system to measure 3D dose distribution for end-to-end dosimetry verification.

A machine learning-driven approach to predicting thermo-elasto-hydrodynamic lubrication in journal bearings

Traditional methods of evaluating the performance of journal bearings, for example thermal-elastic-hydrodynamic- lubrication theory, are limited to simplified conditions that often fail to accurately model real-world components. Numerical models that include additional phenomena such as cavitation and fully coupled effects like deformation, temperature, pressure and viscosity can be more accurate but require a large amount of computational overhead, making analysis slower and more costly. To address this limitation, a novel machine learning-driven approach is developed to predict the 2D distribution of surface deformation, film thickness, temperature, and pressure across the bearing surface as a function of design variables such as load and speed. The training dataset, generated using a fully coupled Reynolds’ Equation solver implemented in OpenFOAM, contains a significantly extended range of conditions than in previous studies with approximately 39000000 points encompassing 4925 different test cases. Modelled bearing speeds range from 2000 to 10000rpm, while load values are varied between 1 and 30kN. Predicting surface deformation, film thickness, temperature and pressure across the bearing surface results in a mean absolute percentage error below 0.4% or better. The work also demonstrates that the trained models have a strong ability to generalise the prediction beyond the original training data range with only a 1% error at up to 200% of the highest trained speed. This work also demonstrates that machine learning-based processes are a practical alternative to physics-based numerical modelling, especially in cases where rapid performance evaluation is desired as real-time calculation is possible with significantly reduced computational cost. This has the potential to enable development of rapid design optimisation tools and real-time performance monitoring at high resolution and with low latency. Using consumer hardware, it is found that the neural network-based approach is faster than the existing numerical modelling technique by a factor of over 10000, enabling real-time predictions of lubrication systems.

The Multilayer Nature of Molecular Gas toward the Cygnus Region

We study the physical properties and 3D distribution of molecular clouds (MCs) toward the Cygnus region using the MWISP CO survey and Gaia DR3 data. Based on Gaussian decomposition and clustering for 13CO lines, over 70% of the fluxes are recovered. With the identification result of 13CO structures, two models are designed to measure the distances of the molecular gas in velocity crowding regions. The distances of more than 200 large 13CO structures are obtained toward the 150 deg2 region. Additionally, tens of the identified MC structures coincide well with masers and/or intense mid-IR emission. We find multiple gas layers toward the region: (1) the extensive gas structures composing the Cygnus Rift from 700 pc to 1 kpc across the whole region; (2) the ∼1.3 kpc gas layer mainly in the Cygnus X South region; and (3) the 1.5 kpc dense filament at the Cygnus X North region and many cometary clouds shaped by Cygnus OB2. We also note that the spatial distribution of young stellar object candidates is generally consistent with the molecular gas structures. The total molecular mass of the Cygnus region is estimated to be ∼2.7 × 106 M ⊙ assuming an X-factor ratio XCO=2×1020cm−2(Kkms−1)−1 . The foreground Cygnus Rift contributes ∼25% of the molecular mass in the whole region. Our work presents a new 3D view of the MCs' distribution toward the Cygnus X region, as well as the exact molecular gas mass distribution in the foreground Cygnus Rift.

Simultaneous reconstruction of 3D fluorescence distribution and object surface using structured light illumination and dual-camera detection

Fluorescence molecular tomography (FMT) serves as a noninvasive modality for visualizing volumetric fluorescence distribution within biological tissues, thereby proving to be an invaluable imaging tool for preclinical animal studies. The conventional FMT relies upon a point-by-point raster scan strategy, enhancing the dataset for subsequent reconstruction but concurrently elongating the data acquisition process. The resultant diminished temporal resolution has persistently posed a bottleneck, constraining its utility in dynamic imaging studies. We introduce a novel system capable of simultaneous FMT and surface extraction, which is attributed to the implementation of a rapid line scanning approach and dual-camera detection. The system performance was characterized through phantom experiments, while the influence of scanning line density on reconstruction outcomes has been systematically investigated via both simulation and experiments. In a proof-of-concept study, our approach successfully captures a moving fluorescence bolus in three dimensions with an elevated frame rate of approximately 2.5 seconds per frame, employing an optimized scan interval of 5 mm. The notable enhancement in the spatio-temporal resolution of FMT holds the potential to broaden its applications in dynamic imaging tasks, such as surgical navigation.

3D Geometry and Slip Distribution in the Long Beach Earthquake Gate, Newport–Inglewood Fault, Los Angeles, California

ABSTRACT We present a new, 3D representation of the Long Beach restraining bend system along the Newport–Inglewood fault (NIF), Los Angeles, California. The NIF is an active strike-slip system that cuts over 60 km through densely populated metropolitan Los Angeles and poses one of the greatest deterministic seismic hazards in the United States (California Division of Mines and Geology, 1988). Part of the NIF sourced the 1933 M 6.4 Long Beach earthquake, which claimed ∼120 lives and remains one of the deadliest events in California history (Barrows, 1974; Hauksson and Gross, 1991). The event is thought to have arrested at Signal Hill within the Long Beach restraining bend, which is formed by a left step in the NIF (Hough and Graves, 2020). Events that rupture through Signal Hill could generate larger (M ≈7) events that pose a significant hazard to urban Los Angeles. Our analysis integrates a diverse range of datasets, including over 4200 fault and horizon penetrations from 243 wells, 2D seismic reflection surveys, field maps, machine-learning-based tomography studies, and the U.S. Geological Survey QFaults surface traces. We show that the fault system in Long Beach has three main strike-slip segments connected by orthogonal reverse faults. The strike-slip faults are nonvertical and nonplanar, merge at depth, and extend through the seismogenic crust. The Long Beach restraining bend system presents numerous rupture pathways and arrest points that NIF earthquakes may follow. We apply a novel, map-based restoration to quantify how much total slip has passed through each of the fault segments. About 375 m of total slip is partitioned into the three main fault strands, including the Reservoir Hill fault (≈75 m), the Northeast Flank fault (≈120 m), and the Cherry Hill fault (≈209 m). This slip partitioning informs our understanding of the tendency of ruptures to involve different fault segments or arrest at specific junctures.

Visualisation of gene expression within the context of tissues using an X-ray computed tomography-based multimodal approach

The development of an organism is orchestrated by the spatial and temporal expression of genes. Accurate visualisation of gene expression patterns in the context of the surrounding tissues offers a glimpse into the mechanisms that drive morphogenesis. We developed correlative light-sheet fluorescence microscopy and X-ray computed tomography approach to map gene expression patterns to the whole organism`s 3D anatomy. We show that this multimodal approach is applicable to gene expression visualized by protein-specific antibodies and fluorescence RNA in situ hybridisation offering a detailed understanding of individual phenotypic variations in model organisms. Furthermore, the approach offers a unique possibility to identify tissues together with their 3D cellular and molecular composition in anatomically less-defined in vitro models, such as organoids. We anticipate that the visual and quantitative insights into the 3D distribution of gene expression within tissue architecture, by multimodal approach developed here, will be equally valuable for reference atlases of model organisms development, as well as for comprehensive screens, and morphogenesis studies of in vitro models.

A novel framework for three-dimensional electrical impedance tomography reconstruction of maize ear via feature reconfiguration and residual networks.

Electrical impedance tomography (EIT) provides an indirect measure of the physiological state and growth of the maize ear by reconstructing the distribution of electrical impedance. However, the two-dimensional (2D) EIT within the electrode plane finds it challenging to comprehensively represent the spatial distribution of conductivity of the intact maize ear, including the husk, kernels, and cob. Therefore, an effective method for 3D conductivity reconstruction is necessary. In practical applications, fluctuations in the contact impedance of the maize ear occur, particularly with the increase in the number of grids and computational workload during the reconstruction of 3D spatial conductivity. These fluctuations may accentuate the ill-conditioning and nonlinearity of the EIT. To address these challenges, we introduce RFNetEIT, a novel computational framework specifically tailored for the absolute imaging of the three-dimensional electrical impedance of maize ear. This strategy transforms the reconstruction of 3D electrical conductivity into a regression process. Initially, a feature map is extracted from measured boundary voltage via a data reconstruction module, thereby enhancing the correlation among different dimensions. Subsequently, a nonlinear mapping model of the 3D spatial distribution of the boundary voltage and conductivity is established, utilizing the residual network. The performance of the proposed framework is assessed through numerical simulation experiments, acrylic model experiments, and maize ear experiments. Our experimental results indicate that our method yields superior reconstruction performance in terms of root-mean-square error (RMSE), correlation coefficient (CC), structural similarity index (SSIM), and inverse problem-solving time (IPST). Furthermore, the reconstruction experiments on maize ears demonstrate that the method can effectively reconstruct the 3D conductivity distribution.

On the consistency of three-dimensional magnetohydrodynamical lattice Boltzmann models

The lattice Boltzmann method (LBM) for magnetohydrodynamics (MHD) was first developed for two-dimensional (2D) flows. The magnetic component of the Lorentz force is obtained from the divergence of the Maxwell stress tensor, which is recovered by an appropriate modification to the LBM equilibrium distribution function for the solution of the velocity field. Although this method has subsequently been applied to three-dimensional (3D) flows, we show that several common modifications of the 3D equilibrium distribution function cannot simultaneously recover the Maxwell stress tensor and conserve mass exactly. Our study brings attention to this theoretical issue since such inconsistent models have been used in recent 3D LBM MHD simulations. Both requirements can be simultaneously satisfied by incorporating an additional model term to the 3D equilibrium distribution function. Three variants of Hunt's flow in a rectangular channel are considered to illustrate the undesirable consequences of using inconsistent models: 1) flow driven by a pressure difference, 2) flow driven by an external body force, and 3) inclusion of a streamwise gradient in magnetic pressure. While spurious errors were observed using inconsistent models due to the violation of mass conservation or inaccuracy in the Maxwell stress tensor, the consistent model always achieves the correct solutions in these cases and is recommended for 3D MHD simulations.