Direct Reconstruction Research Articles

Lateral Ankle Ligaments: An Insight Into Their Functional Anatomy, Variations, and Surgical Importance.

Ankle sprains are prevalent injuries leading to functional impairment. The lateral ankle ligament complex (LLC), comprising the anterior talofibular ligament (ATFL), posterior talofibular ligament (PTFL), and calcaneofibular ligament (CFL), is weak and prone to injury. The morphometric data of these ligaments are essential for orthopedic practices, including techniques like direct repair or ATFL reconstruction with autograft/allograft, which are limited in the literature. The present study aims to document the anatomy and morphometry of the LLC. Fifteen adult Indian-origin embalmed cadavers were selected for the study. Ankles with antemortem or postmortem injuries or previous surgical interventions were excluded from the study. After precise dissection of the ankle's anterior and lateral aspects as per Cunningham's dissection manual, ligaments were exposed. Length and width were measured using a digital vernier caliper. Morphological attributes such as shape, orientation, and inter-fiber angles were documented. The most common shape in ATFL was a single band (53.33%). Inner ATFL fibers merged with the ankle joint capsule in 73.33%. ATFL mean length and width were 14 ± 2.4 mm and 7.6 ± 2.0 mm. The angle between the fibula's long axis and ATFL fibers was 107 ± 22°, and the angle between tibiotalar joint lines and parallel ATFL fibers was 30 ± 9.5°. A single band of CFL was predominant (73.33%). The mean length and width ofCFL were 18.4 ± 3.9 mm and 5.2 ± 1.3 mm; the angle between the anterior fibula border's long axes and parallel CFL line was 131°. PTFL length was 20.9 ± 3.3 mm and width was 6.2 ± 1.4 mm. The mean length and width of the anterior inferior talofibular ligament (AiTFL) were 11.7 ± 2.6 mm and 9.5 ± 1.6 mm, and of the posterior inferior talofibular ligament (PiTFL) were 12.8 ± 2.1 mm and 10.4 ± 2 mm. Comprehensive knowledge of these ligaments' anatomy and relationships is vital for clinical examination and ultrasonography. Understanding LLCdetails aids radiologists and orthopedic surgeons in graft selection, sizing, and precise anatomical structure placement during surgical reconstruction.

Metal‐Organic Frameworks: Direct Synthesis by Organic Acid‐Etching and Reconstruction Disclosure as Oxygen Evolution Electrocatalysts

AbstractMetal‐organic frameworks (MOFs) have emerged as promising oxygen evolution reaction (OER) electrocatalysts. Chemically bonded MOFs on supports are desirable yet lacking in routine synthesis, as they may allow variable structural evolution and the underlying structure‐activity relationship to be disclosed. Herein, direct MOF synthesis is achieved by an organic acid‐etching strategy (AES). Using π‐conjugated ferrocene (Fc) dicarboxylic acid as the etching agent and organic ligand, a series of MFc‐MOF (M=Ni, Co, Fe, Zn) nanosheets are synthesized on the metal supports. The crystal structure is studied using X‐ray diffraction and low‐dose transmission electron microscopy, which is quasi‐lattice‐matched with that of the metal, enabling in situ MOF growth. Operando Raman and attenuated total reflectance Fourier transform infrared spectroscopy disclose that the NiFc‐MOF features dynamic structural rebuilding during OER. The reconstructed one showing optimized electronic structures with an upshifted total d‐band center, high M−O bonding state occupancy, and localized electrons on adsorbates indicated by density functional theory calculations, exhibits outstanding OER performance with a fairly low overpotential (130 mV at 10 mA cm−2) and good stability (144 h). The newly established approach for direct MOF synthesis and structural reconstruction disclosure stimulate the development of more prudent catalysts for advancing OER.

Metal-Organic Frameworks: Direct Synthesis by Organic Acid-Etching and Reconstruction Disclosure as Oxygen Evolution Electrocatalysts.

Metal-organic frameworks (MOFs) have emerged as promising oxygen evolution reaction (OER) electrocatalysts. Chemically bonded MOFs on supports are desirable yet lacking in routine synthesis, as they may allow variable structural evolution and the underlying structure-activity relationship to be disclosed. Herein, direct MOF synthesis is achieved by an organic acid-etching strategy (AES). Using π-conjugated ferrocene (Fc) dicarboxylic acid as the etching agent and organic ligand, a series of MFc-MOF (M=Ni, Co, Fe, Zn) nanosheets are synthesized on the metal supports. The crystal structure is studied using X-ray diffraction and low-dose transmission electron microscopy, which is quasi-lattice-matched with that of the metal, enabling in situ MOF growth. Operando Raman and attenuated total reflectance Fourier transform infrared spectroscopy disclose that the NiFc-MOF features dynamic structural rebuilding during OER. The reconstructed one showing optimized electronic structures with an upshifted total d-band center, high M-O bonding state occupancy, and localized electrons on adsorbates indicated by density functional theory calculations, exhibits outstanding OER performance with a fairly low overpotential (130 mV at 10 mA cm-2 ) and good stability (144 h). The newly established approach for direct MOF synthesis and structural reconstruction disclosure stimulate the development of more prudent catalysts for advancing OER.

ParaPET: non-invasive deep learning method for direct parametric brain PET reconstruction using histoimages

BackgroundThe indirect method for generating parametric images in positron emission tomography (PET) involves the acquisition and reconstruction of dynamic images and temporal modelling of tissue activity given a measured arterial input function. This approach is not robust, as noise in each dynamic image leads to a degradation in parameter estimation. Direct methods incorporate into the image reconstruction step both the kinetic and noise models, leading to improved parametric images. These methods require extensive computational time and large computing resources. Machine learning methods have demonstrated significant potential in overcoming these challenges. But they are limited by the requirement of a paired training dataset. A further challenge within the existing framework is the use of state-of-the-art arterial input function estimation via temporal arterial blood sampling, which is an invasive procedure, or an additional magnetic resonance imaging (MRI) scan for selecting a region where arterial blood signal can be measured from the PET image. We propose a novel machine learning approach for reconstructing high-quality parametric brain images from histoimages produced from time-of-flight PET data without requiring invasive arterial sampling, an MRI scan, or paired training data from standard field-of-view scanners.ResultThe proposed is tested on a simulated phantom and five oncological subjects undergoing an 18F-FDG-PET scan of the brain using Siemens Biograph Vision Quadra. Kinetic parameters set in the brain phantom correlated strongly with the estimated parameters (K1, k2 and k3, Pearson correlation coefficient of 0.91, 0.92 and 0.93) and a mean squared error of less than 0.0004. In addition, our method significantly outperforms (p < 0.05, paired t-test) the conventional nonlinear least squares method in terms of contrast-to-noise ratio. At last, the proposed method was found to be 37% faster than the conventional method.ConclusionWe proposed a direct non-invasive DL-based reconstruction method and produced high-quality parametric maps of the brain. The use of histoimages holds promising potential for enhancing the estimation of parametric images, an area that has not been extensively explored thus far. The proposed method can be applied to subject-specific dynamic PET data alone.

Greybox: A hybrid algorithm for direct estimation of tracer kinetic parameters from undersampled DCE-MRI data.

A variety of deep learning-based and iterative approaches are available to predict Tracer Kinetic (TK) parameters from fully sampled or undersampled dynamic contrast-enhanced (DCE) MRI data. However, both the methods offer distinct benefits anddrawbacks. To propose a hybrid algorithm (named as 'Greybox'), using both model- as well as DL-based, for solving a multi-parametric non-linear inverse problem of directly estimating TK parameters from undersampled DCE MRI data, which is invariant to undersamplingrate. The proposed algorithm was inspired by plug-and-play algorithms used for solving linear inverse imaging problems. This technique was tested for its effectiveness in solving the nonlinear ill-posed inverse problem of generating 3D TK parameter maps from four-dimensional (4D; Spatial + Temporal) retrospectively undersampled k-space data. The algorithm learns a deep learning-based prior using UNET to estimate the and parameters based on the Patlak pharmacokinetic model, and this trained prior was utilized to estimate the TK parameter maps using an iterative gradient-based optimization scheme. Unlike the existing DL models, this network is invariant to the undersampling rate of the input data. The proposed method was compared with the total variation-based direct reconstruction technique on brain, breast, and prostate DCE-MRI datasets for various undersampling rates using the Radial Golden Angle (RGA) scheme. For the breast dataset, an indirect estimation using the Fast Composite Splitting algorithm was utilized for comparison. Undersampling rates of 8 , 12 and 20 were used for the experiments, and the results were compared using the PSNR and SSIM as metrics. For the breast dataset of 10 patients, data from four patients were utilized for training (1032 samples), two for validation (752 samples), and the entire volume of four patients for testing. Similarly, for the prostate dataset of 18 patients, 10 patients were utilized for training (720 samples), five for validation (216 samples), and the whole volume of three patients for testing. For the brain dataset of nineteen patients, ten patients were used for training (3152 samples), five for validation (1168 samples), and the whole volume of four patients for testing. Statistical tests were also conducted to assess the significance of the improvement inperformance. The experiments showed that the proposed Greybox performs significantly better than other direct reconstruction methods. The proposed algorithm improved the estimated and in terms of the peak signal-to-noise ratio by up to 3 dB compared to other standard reconstructionmethods. The proposed hybrid reconstruction algorithm, Greybox, can provide state-of-the-art performance in solving the nonlinear inverse problem of DCE-MRI. This is also the first of its kind to utilize convolutional neural network-based encodings as part of the plug-and-play priors to improve the performance of the reconstructionalgorithm.

Directional growth and reconstruction of ultrafine uranium oxide nanorods within single-walled carbon nanotubes.

Understanding the atomic structures and dynamic evolution of uranium oxides is crucial for the reliable operation of fission reactors. Among them, U4O9-as an important intermediate in the oxidation of UO2 to UO2+x -plays an important role in the nucleation and conversion of uranium oxides. Herein, we realize the confined assembly of uranyl within SWCNTs in liquid phase and reveal the directional growth and reconstruction of U4O9 nanorods in nanochannels, enabled by in situ scanning transmission electron microscopy (STEM) e-beam stimulation. The nucleation and crystallization of U4O9 nanorods in nanochannels obey the "non-classical nucleation" mechanism and exhibit remarkably higher growth rate compared to those grown outside. The rapid growth process is found to be accompanied by the formation and elimination of U atom vacancies and strain, aiming to achieve the minimum interfacial energy. Eventually, the segments of U4O9 nanorods in SWCNTs merge into single-crystal U4O9 nanorods via structural reconstruction at the interfaces, and 79% of them exhibit anisotropic growth along the specific 〈11̄0〉 direction. These findings pave the way for tailoring the atomic structures and interfaces of uranium oxides during the synthesis process to help improve the mechanical properties and stability of fission reactors.

Prior Knowledge-guided Triple-Domain Transformer-GAN for Direct PET Reconstruction from Low-Count Sinograms.

To obtain high-quality positron emission tomography (PET) images while minimizing radiation exposure, numerous methods have been dedicated to acquiring standard-count PET (SPET) from low-count PET (LPET). However, current methods have failed to take full advantage of the different emphasized information from multiple domains, i.e., the sinogram, image, and frequency domains, resulting in the loss of crucial details. Meanwhile, they overlook the unique inner-structure of the sinograms, thereby failing to fully capture its structural characteristics and relationships. To alleviate these problems, in this paper, we proposed a prior knowledge-guided transformer-GAN that unites triple domains of sinogram, image, and frequency to directly reconstruct SPET images from LPET sinograms, namely PK-TriDo. Our PK-TriDo consists of a Sinogram Inner-Structure-based Denoising Transformer (SISD-Former) to denoise the input LPET sinogram, a Frequency-adapted Image Reconstruction Transformer (FaIR-Former) to reconstruct high-quality SPET images from the denoised sinograms guided by the image domain prior knowledge, and an Adversarial Network (AdvNet) to further enhance the reconstruction quality via adversarial training. Specifically tailored for the PET imaging mechanism, we injected a sinogram embedding module that partitions the sinograms by rows and columns to obtain 1D sequences of angles and distances to faithfully preserve the inner-structure of the sinograms. Moreover, to mitigate high-frequency distortions and enhance reconstruction details, we integrated global-local frequency parsers (GLFPs) into FaIR-Former to calibrate the distributions and proportions of different frequency bands, thus compelling the network to preserve high-frequency details. Evaluations on three datasets with different dose levels and imaging scenarios demonstrated that our PK-TriDo outperforms the state-of-the-art methods.

Direct visualization and 3D reconstruction of conductive filaments in aSiO2 material-based memristive device.

Observation of conductive filaments has greatly aided the development of theoretical models of memristive devices. In this work, we visualized and reconstructed the conductive filaments in a Cu/Cu-doped SiO2/W device employing a focused ion beam (FIB) as a milling technique. The SEM images taken from the device after 150 DC sweep cycles showed that Joule heat played a vital role in determining the morphology of a conductive filament, where the vaporization of the conductive filament resulted in the creation of defects, including particles, voids, and cavities. The competition between the formation and vaporization of conductive filaments generally induces a remarkable current fluctuation. Since Cu-doped SiO2 was utilized as the electrolyte, the vapors exfoliated adjacent single layers. FIB milling proceeded in top-down and front-back modes; thus, a 3D model of conductive filaments and defects was constructed according to a series of FIB-SEM images. This methodology is promising for a future failure analysis of memristive devices.

Super-Resolution Imaging of Tau Proteins in Isolated and Immobilized Brain Synaptosomes.

Tau protein has important physiological functions at both presynaptic and postsynaptic terminals. Pathological tau species are also associated with synaptic dysfunctions in several neurodegenerative disorders, especially Alzheimer's disease. To understand tau distribution inside synaptic compartments, super-resolution imaging is required. Here, we describe a facile protocol to immobilize and image brain synaptosomes without aggregation artefacts, by substituting the standard fixative paraformaldehyde with ethylene glycol bis(succinimidyl succinate) (EGS). Super-resolution imaging of tau proteins is achieved through three-color direct stochastic optical reconstruction microscopy (dSTORM). Tau protein is found to colocalize with synaptic vesicles as well as postsynaptic densities.

An Adaptive Fake Permanent Scatterer Removal Algorithm Based on Direct Signal Reconstruction of Multiparameter Estimation for GNSS-InBSAR

An Adaptive Fake Permanent Scatterer Removal Algorithm Based on Direct Signal Reconstruction of Multiparameter Estimation for GNSS-InBSAR

Spherical shock waveform reconstruction by heterodyne interferometry.

The indirect measurement of shock waveforms by acousto-optic sensing requires a method to reconstruct the field from the projected data. Under the assumption of spherical symmetry, one approach is to reconstruct the field by the Abel inversion integral transform. When the acousto-optic sensing modality measures the change in optical phase difference time derivative, as for a heterodyne Mach-Zehnder interferometer, e.g., a laser Doppler vibrometer, the reconstructed field is the fluctuating refractive index time derivative. A technique is derived that reconstructs the fluctuating index directly by assuming plane wave propagation local to a probe beam. With synthetic data, this approach is compared to the Abel inversion integral transform and then applied to experimental data of laser-induced shockwaves. Time waveforms are reconstructed with greater accuracy except for the tail of the waveform that maps spatially to positions near a virtual origin. Furthermore, direct reconstruction of the fluctuating index field eliminates the required time integration and results in more accurate shock waveform peak values, rise times, and positive phase duration.

Ultra-low temperature direct reconstruction of biomass fermentation of ethanol to higher alcohols in water over thermostable hcp-Ni

Near-ambient temperature direct reconstruction of biomass fermentation of ethanol to higher alcohols in water over thermostable hcp-Ni.

A Photoactive Supramolecular Complex Targeting PD-L1 Reveals a Weak Correlation between Photoactivation Efficiency and Receptor Expression Levels in Non-Small-Cell Lung Cancer Tumor Models.

Photo-immunotherapy uses antibodies conjugated to photosensitizers to produce nanostructured constructs endowed with targeting properties and photo-inactivation capabilities towards tumor cells. The superficial receptor density on cancer cells is considered a determining factor for the efficacy of the photodynamic treatment. In this work, we propose the use of a photoactive conjugate that consists of the clinical grade PD-L1-binding monoclonal antibody Atezolizumab, covalently linked to either the well-known photosensitizer eosin or the fluorescent probe Alexa647. Using single-molecule localization microscopy (direct stochastic optical reconstruction microscopy, dSTORM), and an anti-PD-L1 monoclonal antibody labelled with Alexa647, we quantified the density of PD-L1 receptors exposed on the cell surface in two human non-small-cell lung cancer lines (H322 and A549) expressing PD-L1 to a different level. We then investigated if this value correlates with the effectiveness of the photodynamic treatment. The photodynamic treatment of H322 and A549 with the photo-immunoconjugate demonstrated its potential for PDT treatments, but the efficacy did not correlate with the PD-L1 expression levels. Our results provide additional evidence that receptor density does not determine a priori the level of photo-induced cell death.

DIRECT IMAGE RECONSTRUCTION IN MULTI-ELEMENT INTERFEROMETRY

The Ukrainian VLBI system of decameter radio telescopes URAN successfully solves many scientific problems, but implementing aperture synthesis technology has a number of difficulties. One of them is significant phase distortions in this range caused by an inhomogeneous propagation environment. Therefore, the task arose to develop an alternative technology with the conventional name "Interferovision", which would allow us to restore radio images with a limited number of antennas, operate with broadband signals, not require flatness of the objects scene, and have an extended field of view. A method of direct image reconstruction when observing objects in space, using a multi-element interferometer, is proposed. This method is based on a physically based principle that is similar to holography. The wave front is registered by the antennas of the interferometer, and further processing is equivalent to its playback in reverse and registration of the resulting spatial interference image. There are no special requirements for the radiation of individual points of the source, except for its delta correlation. A finite frequency band is considered, and each point can be characterized by its own spectrum, that is, its own autocorrelation function of radiation. The resolution of the direct reconstruction method depends on its width. With the quasi-monochromatic approximation, the autocorrelation functions of the radiation of all the source points degenerate into sinusoids, and the restoration of the image becomes possible only with the use of the Fourier transformation. A theoretical justification of the method for spaces of different dimensions has been obtained. Interferometric systems of the same rank with many antennas are reduced to a same canonical form with fixed number of virtual antennas placed at the origin of the coordinates and at unit distances on the coordinate axes. The ambiguity of the obtained solution is eliminated by using an additional antenna. A simulation of the proposed method of direct image reconstruction for two-dimensional space was carried out. Despite the low conditionality of the system for estimating the distance, when it increases, the angular characteristics are preserved. Therefore, the method is promising for restoring radio images of space radio sources.

A data-driven framework for designing microstructure of multifunctional composites with deep-learned diffusion-based generative models

This paper introduces a novel integrated microstructure design methodology that replaces the common existing design approaches for multifunctional composites: 1) reconstruction of microstructures, 2) analyzing and quantifying material properties, and 3) inverse design of materials. The problem of microstructure reconstruction is addressed using the diffusion-based generative model (DGM), which is a state-of-the-art generative model formulated with a Markovian diffusion process. Then, the conditional formulation of DGM is introduced for guidance to the embedded desired material properties with a transformer-based attention mechanism, which enables the inverse design of multifunctional composites. Furthermore, a convolutional neural network (CNN)-based surrogate model is utilized to facilitate the prediction of linear/nonlinear material properties for building microstructure-property linkages. Combined, the proposed artificial intelligence-based design framework enables large data processing and database construction that is often not affordable with resource-intensive finite element method (FEM)-based direct numerical simulation (DNS) or iterative reconstruction methods. It is worth noting that the proposed DGM-based methodology is not susceptible to unstable training or mode collapse, which are common issues in generative models that are often difficult to address even with extensive hyperparameter tuning. An example case is presented to demonstrate the effectiveness of the proposed approach, which is designing mechanoluminescence (ML) particulate composites. The results show that the designed ML microstructure samples with the proposed generative and surrogate models meet the multiple design requirements (e.g., volume fraction, elastic constant, and light sensitivity). This assessment demonstrates that the proposed integrated methodology provides an end-to-end solution for practical material design applications.

Prior information-guided reconstruction network for positron emission tomography images.

Deep learning has recently shown great potential in medical image reconstruction tasks. For positron emission tomography (PET) images, the direct reconstruction from raw data to radioactivity images using deep learning without any constraint may lead to the production of nonexistent structures. The aim of this study was to specifically develop and test a flexibly deep learning-based reconstruction network guided by any form of prior knowledge to achieve high quality and high reliability reconstruction. We developed a novel prior information-guided reconstruction network (PIGRN) with a dual-channel generator and a 2-scale discriminator based on a conditional generative adversarial network (cGAN). Besides the raw data channel, an additional channel is provided in the generator for prior information (PI) to guide the training phase. The PI can be reconstructed images obtained via conventional methods, nuclear medical images from other modalities, attenuation correction maps from time-of-flight-PET (TOF-PET) data, or any other physical parameters. For this study, the reconstructed images generated by filtered back projection (FBP) were chosen as the input of the additional channel. To improve the image quality, a 2-scale discriminator was adopted which can focus on both the coarse and fine field of the reconstruction images. Experiments were carried out on both a simulation dataset and a real Sprague Dawley (SD) rat dataset. Two classic deep learning-based reconstruction networks, including U-Net and Deep-PET, were compared in our study. Compared with these two methods, our method could provide much higher quality PET image reconstruction in the study of the simulation dataset. The peak signal-to-noise ratio (PSNR) value reached 31.8498, and the structure similarity index measure (SSIM) value reached 0.9754. The real study on SD rats indicated that the proposed network also has strong generalization ability. The flexible PIGRN based on cGAN for PET images combines both raw data and PI. The results of comparison experiments and a generalization experiment based on simulation and SD rat datasets demonstrated that the proposed PIGRN has the ability to improve image quality and has strong generalization ability.

How neural networks learn to classify chaotic time series.

We tackle the outstanding issue of analyzing the inner workings of neural networks trained to classify regular-vs-chaotic time series. This setting, well-studied in dynamical systems, enables thorough formal analyses. We focus specifically on a family of networks dubbed large Kernel convolutional neural networks (LKCNNs), recently introduced by Boullé et al. [403, 132261 (2021)]. These non-recursive networks have been shown to outperform other established architectures (e.g., residual networks, shallow neural networks, and fully convolutional networks) at this classification task. Furthermore, they outperform "manual" classification approaches based on direct reconstruction of the Lyapunov exponent. We find that LKCNNs use qualitative properties of the input sequence. We show that LKCNN models trained from random weight initialization, end in two most common performance groups: one with relatively low performance (0.72 average classification accuracy) and one with high classification performance (0.94 average classification accuracy). Notably, the models in the low performance class display periodic activations that are qualitatively similar to those exhibited by LKCNNs with random weights. This could give very general criteria for identifying, a priori, trained weights that yield poor accuracy.

Differential CD38 Expression on Daratumumab-Naïve Vs. Resistant Multiple Myeloma - a Direct Stochastic Optical Reconstruction Microscopy ( dSTORM) Analysis in 53 Patients

Background: The anti-CD38 antibody Daratumumab (Dara) has become the new standard of care for the treatment of newly-diagnosed and relapsed/ refractory multiple myeloma (MM). At the same time, Dara resistance remains poorly understood. Potential resistance mechanisms have been proposed and range from intrinsic downregulation in CD38 dim MM cells to genetic alterations and splicing variants which may affect CD38 epitope expression (Adamia, ASH 2022). Here, we used super-resolution microscopy for high-precision quantification of CD38 in MM and piloted this approach in a large cohort of 53 MM patients with vs. without Dara resistance. Methods: Direct stochastic optical reconstruction microscopy ( dSTORM) is a novel imaging modality based on photoswitching of certain fluorophores coupled to antibodies for molecular representation of surface molecules at unprecedented levels of resolution (&amp;lt;20 nM). To correlate CD38 expression data with clinical outcome, we divided our cohort based on treatment status into two groups: Dara-naïve (n = 31) vs. Dara-resistant (n = 22) patients. Imaging data was complemented by phenotypic assessment using a well-established multi-epitope anti-CD38 antibody (Cytognos), as per standard-of-care. Results: Mean CD38 receptor density as measured with Dara via dSTORM was significantly higher in Dara-naïve (of 48.81 ± 29.65 receptors/µm²) vs. Dara-resistant patients (15.52 ± 3.9 receptors/µm², p = 0.016). This finding was corroborated by multi-epitope phenotypic analysis which revealed mean CD38 receptor expression levels of 32.09 ± 13.14 receptors/µm² in Dara-naïve vs. 21.65 ± 17.73 receptors/µm² in Dara-resistant patients ( p = 0.05). Lower CD38 expression via dSTORM vs. multi-epitope anti-CD38 staining was consistently observed in our cohort and may reflect ongoing blockage of the receptor binding site by Dara in cases with shorter time to last Dara application (median 53 days, range 1-189 days). As there was no correlation with the interval between Dara application and sampling however, splice-site mutations affecting CD38 receptor accessibility as proposed by Adamia et al. may depict a more relevant mechanism of resistance in our Dara-resistant cohort. In a next step, we therefore investigated alternative epitope binding in Dara-resistant patients. To this end, we investigated antibody binding by dSTORM imaging for isatuximab (Isa), an alternative CD38-directed monoclonal antibody that binds to a location in the C-terminal portion of the CD38 extracellular domain which is entirely different from the Dara epitope. Interestingly, there was a trend for increased detection rate with Isa for mean CD38 receptor expression in Dara-resistant patients (22.2 ± 4.6 receptors/µm²) vs. Dara (15.5 ± 3.9 receptors/µm², p = 0.1) and on par with CD38 receptor expression. No data was available to assess Dara binding in Isa pre-exposed patients. Conclusion: Innovative microscopy using dSTORM allows for high-resolution epitope quantification at unprecedented granularity. While this finding enables visual proof of differential epitope binding of Dara vs. Isa on CD38 in a clinically feasible setting, correlation studies to determine the clinical relevance of this observation remain ongoing.

Secondary Genetic Events Impact the Expression of Key Immunotargets on the Surface of Multiple Myeloma Cells

Background: Multiple myeloma (MM) is characterized by complex genomic aberrations including primary translocations of the immunoglobin heavy chain (IGH) locus and secondary mutations involving key oncogenic drivers. This temporal complexity represents a constant challenge for the clinical management of MM, but at the same time may provide new avenues for immunotherapy-based approaches including CAR-T cells and T-cell engaging antibodies. In this study, we investigated the impact of secondary genetic events on the surface expression of common targets for immunotherapy in MM. Methods: Cell models harboring two hotspot activating mutations in KRAS (G12A, A146T) and TP53 (R282W) were created using the MM cell lines OPM-2 and AMO1 via CRISPR-Cas9 technology. Direct stochastic optical reconstruction microscopy ( dSTORM) was used for high-resolution receptor density estimation and epitope quantification of common immunotherapeutic targets such as CD38, BCMA and SLAMF7. Results: dSTORM imaging of cell line models carrying patient-derived secondary genetic alterations revealed a distinct pattern linked to each mutation respectively. In KRAS G12A, we observed a reduction in BCMA density by 2-fold as compared to wild type cells (5.9 ± 0.3 vs. 10.8 ± 1.2 localization clusters/µm 2, p=0005). This reduction in BCMA surface expression was accompanied by a decrease in other key immunotargets, including SLAMF7 (6.0 ± 0.8 vs 11.8 ± 0.8 localization clusters/µm 2, p &amp;lt;0,0001) and CD38 (12.2 ± 1.5 vs 17.9 ± 1.6 localization clusters/µm 2, p=0,012). In KRAS A146, CD38 density increased, whereas mean BCMA and SLAMF7 localizations were consistently reduced as compared to wild type control. To ensure that BCMA downregulation is a consistent feature in genomic high-risk disease, we next simulated a TP53 double-hit situation by introducing an inactivating hotspot mutation R282W in a TP53 deletion background. Surfaceome profiling by dSTORM revealed a minor increase in CD38 levels for TP53R282W/Del as compared to wild type control (51.5 ± 5.7 vs. 36.4 ± 4.2 localization clusters/µm 2, p=0.06). Interestingly, the expression level of BCMA, known to be comparably low on WT tumor cells, further decreased in TP53 double-hit cells (0.6 ± 0.1 vs 1.1 ± 0.1 localization clusters/µm 2, p=0.002), whereas no major differences in the cluster density of SLAMF7 were observed. This leaves room for speculation whether these effects on biallelic TP53 inactivated tumor cells may underlie reduced susceptibility towards BCMA-targeting therapy. Of note, we also observed altered cell morphology in the TP53 double-hit model, characterized by elongated stress fibers enriched with CD138, potentially interfering with receptor accessibility of surface receptors (Figure 1). Recently, secondary genetic events have been reported in BCMA and GPRC5D following targeted immunotherapy inducing acquired drug resistance. We created cell models representing homo- and heterozygous genetic alterations in BCMA and GPRC5D, studies to assess their effects on the surfaceome are ongoing and will be presented at the meeting. Summary: Our data suggest that surface epitope expression in MM cells seems to be less agnostic to secondary genomic events than expected. These findings may provide a direct link between genetic high-risk disease and inferior efficacy of anti-BCMA immunotherapies in the context of e.g. TP53 double-hit MM.

Pauli transfer matrix direct reconstruction: channel characterization without full process tomography

We present a tomographic protocol for the characterization of multiqubit quantum channels. We discuss a specific class of input states, for which the set of Pauli measurements at the output of the channel directly relates to its Pauli transfer matrix components. We compare our results to those of standard quantum process tomography, showing an exponential reduction in the number of different experimental configurations required by a single matrix element extraction, while keeping the same number of shots. This paves the way for more efficient experimental implementations, whenever a selective knowledge of the Pauli transfer matrix is needed. We provide several examples and simulations.