Multiple Acquisition Research Articles

Abstract 4948: Automated tumor microenvironment analysis for multiple samples by image-based spatial transcriptomics on tissue microarray

Abstract Introduction. Tissue Microarrays (TMAs), widely utilized in the field of pathology, have now found a powerful ally in Image-based Spatial Transcriptomics (ST). By analyzing various gene expression data with high resolution, image-based ST data on TMA can provide the heterogeneous patterns of tumor microenvironment in multiple samples. However, an efficient method for processing multiple samples post-data acquisition is still under development. A streamlined process would expedite the discovery of spatial gene expression patterns, thereby enhancing our understanding of the tumor microenvironment and its implications for cancer diagnostics and treatment strategies. Methods. Our automated pipeline is initiated by automatic segmentation of every core in the whole TMA, derived from the processed output of the MERSCOPE or Xenium platform. Then, it subsequently removes QC failed cells unrelated to any core. Each core receives sequential naming and undergoes automated cell type mapping utilizing a reference single-cell RNAseq data. Additionally, the pipeline computes neighborhood enrichment between cell types, providing a nuanced comprehension of spatial relationships and interactions among diverse cell populations within the tissue microenvironment from multiple samples on the TMA. Results. The automated pipeline we've developed provides several key advantages. It enables the simultaneous analysis of multiple tissue cores, effectively minimizing batch effects and ensuring the reliability of results across diverse tissue samples. Furthermore, by automating core separation, labeling, and cell type mapping, our pipeline significantly streamlines the time and effort required for TMA data analysis. This cost-effective approach allows researchers to optimize resource allocation, making our automated pipeline a valuable tool in cancer research. It facilitates the exploration of gene expression patterns within specific tissue regions, ultimately contributing to the advancement of our understanding of cancer biology. Conclusion. By seamlessly integrating TMA data with image-based ST technologies such as Xenium and MERSCOPE, we can perform comprehensive spatial transcriptomics analyses and provide detailed statistical information on cell type proportions within the tumor microenvironment. This automated pipeline not only ensures robust and reliable results across diverse tissue samples but also optimizes resource allocation by minimizing batch effects and reducing analysis time. This cost-effective solution empowers researchers to delve deeper into spatial gene expression patterns, enhancing our comprehension of the tumor microenvironment. Citation Format: Dongjoo Lee, Seungho Cook, Yeonjae Jung, Myunghyun Lim, Jae Eun Lee, Hyung-Jun Im, Daeseung Lee, Hongyoon Choi. Automated tumor microenvironment analysis for multiple samples by image-based spatial transcriptomics on tissue microarray [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 4948.

Abstract 3800: Next generation of spatial biology: High-throughput multiplexed Imaging Mass Cytometry™ with whole slide modes

Abstract Gaining spatial insights into the cellular composition of tumor tissue has tremendous potential to inform clinical and translational researchers about mechanisms behind spatial predictors of immunotherapy success, disease etiology and progression. Imaging Mass Cytometry™ (IMC™) is a high-plex spatial biology imaging technique that enables deep characterization of the diversity and complexity of the tumor microenvironment (TME). IMC supports detailed assessment of cell phenotype and function using 40-plus metal-tagged antibodies simultaneously on a single slide without issues associated with fluorescence-based spectral overlap, tissue autofluorescence or implementation of multiple washing and acquisition cycles. Currently, IMC enables user-defined regions of interest (ROI) in tissues to evaluate cellular and structural composition. To enhance the IMC user experience, we developed two new whole slide imaging (WSI) modes which enable streamlined workflows using ultrafast preview mode (PM) and high-throughput tissue mode (TM). PM samples the entire tissue at predefined spacing to rapidly capture a low-resolution image of all expressed markers in the antibody panel. PM generates an image in minutes to enable informed ROI placements while leaving the stained tissue intact for higher-resolution imaging. PM and TM are designed so acquisitions can easily occur on the same slide without additional processing steps. TM rapidly acquires images of the whole tissue at lower resolution (7 μm pixel size) at a quality that can be used for quantitative analysis of the tissue spatial biology. Specifically designed for high-throughput applications, TM in combination with a newly available 40-slide loader for the Hyperion XTi™ Imaging System permits automated and continuous imaging of more than 40 large tissue samples (400 mm2 per tissue) per week. We showcase the application of WSI modes using the newly developed Maxpar® Human Immuno-Oncology IMC Panel Kit. The 31-marker panel was combined with catalog antibodies to create a 40-plus-marker panel that expands the ability to conduct comprehensive high-plex tumor and immune cell profiling. Tumor tissue microarrays (TMA) and whole tumor tissue sections were stained with the expanded panel. Single-cell analysis of selected ROIs, on whole tumor sections and TMAs, guided by PM data successfully provided quantitative analyses of spatial biology at single-cell resolution. In parallel, TM on whole tumor sections followed by pixel-based analysis provided a spatially resolved quantitative assessment of specific tumor and immune components of the TME. This work demonstrates the expanded capabilities of IMC and establishes it as a reliable high-plex spatial biology imaging platform with high-throughput imaging capabilities ideally suited for translational and clinical applications. For Research Use Only. Not for use in diagnostic procedures. Citation Format: Qanber Raza, Thomas D. Pfister, Jyh Yun Chwee, Liang Lim, David Howell, Nikesh Parsotam, David King, Christina Loh. Next generation of spatial biology: High-throughput multiplexed Imaging Mass Cytometry™ with whole slide modes [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 3800.

Abstract 2344: Federated image quality assessment of prostate MRI scans in a multi-institutional setting

Abstract Introduction: Reliability of machine and deep learning models on medical images can be compromised by the presence of image artifacts of variability in image acquisition. This is further exacerbated when validating model performance across multiple institutions, where multiple acquisition protocols and scanner equipment may result in significant numbers of outlier scans. We developed a privacy-preserving outlier identification algorithm via federated MR imaging quality evaluation of a distributed multi-institutional cohort of prostate MRI scans. Methods: This retrospective study utilized T2-weighted prostate MRI scans from 1 public collection (used as a template) and 3 federated institutions. An open-source MR image quality evaluation tool (MRQy) was implemented via Docker within the RhinoHealth Federated Computing Platform (FCP). This enabled remote extraction of 9 specific metadata values and remote computation of 15 quality measurements from each scan, while ensuring confidentiality of patient data by avoiding direct access to patient images and information. Centralized analysis of extracted image quality attributes utilized a rule-based classifier to determine whether individual metadata or measurement values for a given MRI scan fell within or outside predefined bounds: (i) ±20% of template metadata values, (ii) 5th - 95th percentile range of template measurement values. Scans were considered outliers if more than 60% of their image quality attributes fell outside predefined bounds. Results: In total, 630 MRI scans from 3 institutions were analyzed in a federated manner. 344 MRI scans from 1 public collection formed a benchmark cohort to calibrate the rule-based classifier and determine predefined bounds for outlier detection. Centralized analysis of federated image quality attributes showed marked differences in the number of outliers per institution (3%, 19%, 79% for Institutions 1, 2, 3, respectively). Further analysis of Institution 3 determined significant intensity and resolution differences compared to other institutions, requiring extensive post-processing prior to downstream analysis. Conclusion: Federated image quality assessment can allow for distributed, privacy preserving analysis of outliers and thus curate multi-institutional cohorts of consistent quality for downstream machine learning tasks. Further analysis of the impact of quality-based curation on federated machine learning applications is currently underway. Citation Format: Mohsen Hariri, Prathyush Chirra, Malhar Patel, Tal Tiano Einat, Ittai Dayan, Alex Tonetti, Yuval Baror, Tristan Barrett, Nikita Sushentsev, Joshua D. Kaggie, Shuaiyu Yuan, Dufan Wu, Baihui Yu, Zhiliang Lyu, Cheyu Hsu, Weichung Wang, Smitha Krishnamurthi, Satish E. Viswanath. Federated image quality assessment of prostate MRI scans in a multi-institutional setting [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 2344.

Abstract 7386: Multi-plane rectal tumor segmentation on pre-treatment MRI via human-in-the-loop deep learning

Abstract Introduction: Precise segmentation of rectal tumors on pre-treatment MRI is critical for in vivo clinical staging and treatment selection. While automated deep learning models can help alleviate laborious manual annotation, these require significant expertise and time commitment. Towards addressing these issues, we present initial results of an integrated human-in-the-loop refinement learning model for semi-automated segmentation of rectal tumors across multiple acquisition planes (axial, coronal) on pre-treatment MRI scans. Methods: This retrospective study accrued baseline MRI scans from rectal cancer patients at four different institutions. A deep learning (DL) nnUNet tumor segmentation model was first trained on both axial plane and coronal plane T2w MRIs from two institutions, with segregated training and internal validation cohorts based on available radiologist annotations. The DL model outputs tumor delineations on both axial and coronal MRI scans in a separate human-in-the-loop (HITL) optimization cohort. Two rounds of HITL refinement learning were then conducted, where in each round model-generated segmentations were refined by two novice radiology readers. Refined annotations were then used to re-optimize the nnUnet model to yield final tumor segmentations. Model-generated segmentations were evaluated before HITL as well as after each round of HITL against “ground truth” radiologist annotations obtained for an external validation cohort, with performance quantified via dice similarity coefficient (DSC) as well as time taken for human annotation of MRIs. Statistical significance was determined via Wilcoxon rank-sum testing. Results: A total of 381 MRI scans from 231 rectal cancer patients were considered. After one round of HITL refinement, DL-based segmentations yielded the best overall performance on the external validation cohort (N=20, two institutions) for both axial (DSC = 0.76) and coronal scans (DSC=0.75). A second round of HITL refinement did not yield significantly different model performance. By comparison, the pre-HITL DL segmentation model yielded significantly worse performance on both axial (DSC=0.60, p=0.0362) and coronal (DSC=0.62, p=0.0186) MRI scans. HITL refinement for rectal tumor annotation was also found to significantly improve reader efficiency by requiring only 18 minutes/scan compared to manual “ground truth” de novo annotations which took up to 38 minutes/scan (p<0.001). Conclusion: Human-in-the-loop deep learning models can help generate accurate rectal tumor segmentations on pre-treatment MRI scans across both axial and coronal acquisition planes. Improvements to model generalizability, model efficacy, and reader efficiency suggest the potential for HITL-based semi-automated segmentation schemes for other medical imaging tasks. Citation Format: Michael Kong, Thomas DeSilvio, Leo Bao, Brennan Flannery, Benjamin N. Parker, Stephen Tang, Gregory O'Connor, Amit Gupta, Emily Steinhagen, Andrei S. Purysko, Eric L. Marderstein, Aaron Carroll, Marka Crittenden, Michael Gough, Kristina Young, Satish E. Viswanath. Multi-plane rectal tumor segmentation on pre-treatment MRI via human-in-the-loop deep learning [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 7386.

Artifact-suppressing reconstruction of strongly interacting objects in X-ray near-field holography without a spatial support constraint.

The phase problem is a well known ill-posed reconstruction problem of coherent lens-less microscopic imaging, where only the squared magnitude of a complex wavefront is measured by a detector while the phase information of the wave field is lost. To retrieve the lost information, common algorithms rely either on multiple data acquisitions under varying measurement conditions or on the application of strong constraints such as a spatial support. In X-ray near-field holography, however, these methods are rendered impractical in the setting of time sensitive in situ and operando measurements. In this paper, we will forego the spatial support constraint and propose a projected gradient descent (PGD) based reconstruction scheme in combination with proper preprocessing and regularization that significantly reduces artifacts for refractive reconstructions from only a single acquired hologram without a spatial support constraint. We demonstrate the feasibility and robustness of our approach on different data sets obtained at the nano imaging endstation of P05 at PETRA III (DESY, Hamburg) operated by Helmholtz-Zentrum Hereon.

Design of smart home environment based on wireless sensor system and artificial speech recognition

In the context of the rapid development of intelligent technology, smart home has become the focus of attention. However, the traditional smart home system has some problems such as complicated operation and inconvenient information interaction. In order to solve these problems and improve the user experience of smart home environment, this study establishes a fast and efficient smart home environment design scheme by introducing wireless sensor system and artificial speech recognition technology. By arranging multiple wireless sensor nodes, the monitoring and data acquisition of environmental parameters are realized, and then the voice control and command interaction of smart home devices are realized by using artificial voice recognition technology. Through the effective combination of wireless sensor system and artificial voice recognition technology, users can realize the control of smart home environment through voice commands, and improve the convenience and intelligence of the system. The experimental results show that the design scheme can accurately and stably realize the recognition of user voice commands and the control of smart home devices, and users' evaluation of the degree of intelligence and user experience has also been significantly improved.

3D LAND-MARINE PROPERTIES ADMINISTRATION IN THE CONTEXT OF SEA LEVEL RISE (SLR) – ISSUES & CHALLENGES

Abstract. The current scenario of understanding spatial objects with regards to land and marine environment seems lacking. Inevitably, the scenario in line with the global warming issues as being highly mentioned by world-bodies such as the United Nations (UN) on climate change, etc. Recently the statistics on earth surface temperature point towards high record temperatures that lead to sea-level rise and degradation of land surface area, in general. These situations caused other spatial objects (land and marine properties) to be affected by their physical properties’ changes (including geometries and locations). Thus, this paper attempts to review various aspects related to digital twins of land and marine spatial objects with respect to issues and challenges of multiple data acquisition techniques considering accuracy, data standard, seamless and integrated georeferenced data within the scenario as being reported in the 6th IPCC document. The situation warrants us to explore different types of land and marine properties (static and dynamic) where static properties are defined as immovable objects and vice versa for the dynamic environment. This paper also discusses on a brief experiment utilizing MERIT DEM (Multi-Error-Removed Improved-Terrain Digital Elevation Model), existing building footprints, simulated bathymetry, and land use datasets. The experiment reveals the prediction of the sea level rise according to the IPCC report. The gist of this paper to serve a motivation for our near-future research in the context of geospatial components and sea level rise (SLR).

Dynamic changes in perivascular space morphology predict signs of spaceflight-associated neuro-ocular syndrome in bed rest

During long-duration spaceflight, astronauts experience headward fluid shifts and expansion of the cerebral perivascular spaces (PVS). A major limitation to our understanding of the changes in brain structure and physiology induced by spaceflight stems from the logistical difficulties of studying astronauts. The current study aimed to determine whether PVS changes also occur on Earth with the spaceflight analog head-down tilt bed rest (HDBR). We examined how the number and morphology of magnetic resonance imaging-visible PVS (MV-PVS) are affected by HDBR with and without elevated carbon dioxide (CO2). These environments mimic the headward fluid shifts, body unloading, and elevated CO2 observed aboard the International Space Station. Additionally, we sought to understand how changes in MV-PVS are associated with signs of Spaceflight Associated Neuro-ocular Syndrome (SANS), ocular structural alterations that can occur with spaceflight. Participants were separated into two bed rest campaigns: HDBR (60 days) and HDBR + CO2 (30 days with elevated ambient CO2). Both groups completed multiple magnetic resonance image acquisitions before, during, and post-bed rest. We found that at the group level, neither spaceflight analog affected MV-PVS quantity or morphology. However, when taking into account SANS status, persons exhibiting signs of SANS showed little or no MV-PVS changes, whereas their No-SANS counterparts showed MV-PVS morphological changes during the HDBR + CO2 campaign. These findings highlight spaceflight analogs as models for inducing changes in MV-PVS and implicate MV-PVS dynamic compliance as a mechanism underlying SANS. These findings may lead to countermeasures to mitigate health risks associated with human spaceflight.

The Monitoring System of Soil PH Factor Using IoT-Webserver-Android and Machine Learning: A Case Study

In Indonesia, the agriculture industry has been more reluctant than other sectors to adopt IoT, IT, and AI technology. Utilizing this technology will enable precision agriculture. This research aims to make and implement an IoT-Webserver-Android and Machine Learning-based soil PH factor monitoring tool system. The steps for making the tool system are divided into three subsystems. The first is a multiple sensors data acquisition subsystem, consisting of sensors for soil PH-Moisture, Temperature-Humidity, and Sunlight. The sensors are connected to the Arduino Uno microcontroller for serial communication with the ESP 8266 microcontroller for the Wi-Fi module. The second part is the monitoring subsystem with the local web application, which contains a MySQL database and a local web page. The third part is the monitoring subsystem with the Android application, which includes a real-time Firebase database and the application for real-time and mobile data display. The results have been implemented and display the expected outcomes. It is clear from the performance of the three subsystems. The outcomes of the tool system's data evaluation provide precise statistical values. Then, Machine Learning analysis generates accurate soil PH prediction models. It has been demonstrated that the monitoring system is applicable and has a favorable impact on data soil PH factor. The implication for the future is that this monitoring system should be added with Nitrogen-Phosphorus-Potassium sensors to measure soil nutrients. Also, the system added edge-analysis to be integrated in monitoring and analyzing soil nutrients.

Mixed-state ptychography for quantitative optical properties measurement of vector beam.

Recent advances in ptychography have extended to anisotropic specimens, but vectorial reconstruction of probes owing to polarization aliasing remains a challenge. A polarization-sensitive ptychography that enables full optical property measurement of vector light is proposed. An optimized reconstruction strategy, first calibrating the propagation direction and then performing faithful retrieval, is established. This method avoids multiple image acquisitions with various polarizer configurations and significantly improves the measurement accuracy by correlating the intensity and position of different polarization components. The capability of the proposed method to quantify anisotropic parameters of optical materials and polarization properties of vector probe is demonstrated by experiment.

Lipid Isobar and Isomer Imaging Using Nanospray Desorption Electrospray Ionization Combined with Triple Quadrupole Mass Spectrometry.

Mass spectrometry imaging (MSI) is widely used for examining the spatial distributions of molecules in biological samples. Conventional MSI approaches, in which molecules extracted from the sample are distinguished based on their mass-to-charge ratio, cannot distinguish between isomeric species and some closely spaced isobars. To facilitate isobar separation, MSI is typically performed using high-resolution mass spectrometers. Nevertheless, the complexity of the mixture of biomolecules observed in each pixel of the image presents a challenge, even for modern mass spectrometers with the highest resolving power. Herein, we implement nanospray desorption electrospray ionization (nano-DESI) MSI on a triple quadrupole (QqQ) mass spectrometer for the spatial mapping of isobaric and isomeric species in biological tissues. We use multiple reaction monitoring acquisition mode (MRM) with unit mass resolution to demonstrate the performance of this new platform by imaging lipids in mouse brain and rat kidney tissues. We demonstrate that imaging in MRM mode may be used to distinguish between isobaric phospholipids requiring a mass resolving power of 3,800,000. Additionally, we have been able to image eicosanoid isomers, a largely unexplored class of signaling molecules present in tissues at low concentrations, in rat kidney tissue. This new capability substantially enhances the specificity and selectivity of MSI, enabling spatial localization of species that remain unresolved in conventional MSI experiments.

Host association and intracellularity evolved multiple times independently in the Rickettsiales

The order Rickettsiales (Alphaproteobacteria) encompasses multiple diverse lineages of host-associated bacteria, including pathogens, reproductive manipulators, and mutualists. Here, in order to understand how intracellularity and host association originated in this order, and whether they are ancestral or convergently evolved characteristics, we built a large and phylogenetically-balanced dataset that includes de novo sequenced genomes and a selection of published genomic and metagenomic assemblies. We perform detailed functional reconstructions that clearly indicates “late” and parallel evolution of obligate host-association in different Rickettsiales lineages. According to the depicted scenario, multiple independent horizontal acquisitions of transporters led to the progressive loss of biosynthesis of nucleotides, amino acids and other metabolites, producing distinct conditions of host-dependence. Each clade experienced a different pattern of evolution of the ancestral arsenal of interaction apparatuses, including development of specialised effectors involved in the lineage-specific mechanisms of host cell adhesion and/or invasion.

Repetitive magnetic stimuli over the motor cortex impair long-term consolidation of a balance task by suppressing up-regulation of intracortical inhibition

Introduction
 Recent findings have demonstrated that low-frequency repetitive magnetic stimulations (rTMS) over the primary motor cortex (M1) impaired short-term consolidation of a balance task, underscoring the causal connection between M1 and the consolidation of balancing skills (Egger et al., 2023). However, the underlying neural mechanisms induced by rTMS and whether these adaptations endure over an extended period, encompassing multiple acquisition sessions, remain insufficiently elucidated (Censor & Cohen, 2011). So far, its is widely acknowledged that GABAergic processes play an important role for consolidation (Sanes & Donoghue, 2000), at the same time, are affected by learning balance skills (Mouthon & Taube, 2019; Taube et al., 2020). Therefore, the present study aimed to investigate the impact of rTMS on GABA-mediated short-interval intracortical inhibition (SICI) and to explore the role of M1 in the long-term consolidation of a balance task (i.e., across multiple acquisition sessions).
 Methods
 Thirty-one volunteers underwent six balance acquisition sessions on a rocker-board, each followed by either rTMS or sham rTMS based on group affiliation. During the first and last training session, SICI was measured twice; before the balance acquisition and after the application of rTMS or sham-rTMS to investigate potential short- and long-term adaptations in intracortical inhibition. Adaptations were assessed during the execution of the learned balance task and in a non-learning postural control task (i.e., stable upright stance).
 Results
 Regardless of group affiliation, all participants achieved comparable improvements within the balance acquisition sessions. However, consolidation varied between groups. In particular, between the third and the fourth acquisition session, as Tukey corrected post-hoc tests showed a significant decline in performance for the rTMS group (p = 0.006). Both short- (p = 0.014) and long-term (p = 0.038) adaptations in SICI were affected by rTMS: while the sham rTMS group upregulated SICI, rTMS led to reduced levels of inhibition. No neurophysiological effects were observed in the non-learning control task (upright stance).
 Discussion/Conclusion
 The interfering effect of rTMS on balance consolidation and on upregulation of SICI indicates that increased intracortical inhibition is an important mechanism to protect and engrave newly acquired motor memory. Importantly, adaptations in SICI were only apparent during the execution of the learned task.
 References
 Censor, N., & Cohen, L. G. (2011). Using repetitive transcranial magnetic stimulation to study the underlying neural mechanisms of human motor learning and memory. The Journal of Physiology, 589(1), 21-28. https://doi.org/10.1113/jphysiol.2010.198077
 Egger, S., Wälchli, M., Rüeger, E., & Taube, W. (2023). Short-term balance consolidation relies on the primary motor cortex: A rTMS study. Scientific Reports, 13, Article 5169. https://doi.org/10.1038/s41598-023-32065-x
 Mouthon, A., & Taube, W. (2019). Intracortical inhibition increases during postural task execution in response to balance training. Neuroscience, 401, 35-42. https://doi.org/10.1016/j.neuroscience.2019.01.007
 Sanes, J. N., & Donoghue, J. P. (2000). Plasticity and primary motor cortex. Annual Review of Neuroscience, 23, 393-415. https://doi.org/10.1146/annurev.neuro.23.1.393
 Taube, W., Gollhofer, A., & Lauber, B. (2020). Training-, muscle- and task-specific up- and downregulation of cortical inhibitory processes. The European Journal of Neuroscience, 51(6), 1428-1440. https://doi.org/10.1111/ejn.14538

Self-supervised multicontrast super-resolution for diffusion-weighted prostate MRI.

This study addresses the challenge of low resolution and signal-to-noise ratio (SNR) in diffusion-weighted images (DWI), which are pivotal for cancer detection. Traditional methods increase SNR at high b-values through multiple acquisitions, but this results in diminished image resolution due to motion-induced variations. Our research aims to enhance spatial resolution by exploiting the global structure within multicontrast DWI scans and millimetric motion between acquisitions. We introduce a novel approach employing a "Perturbation Network" to learn subvoxel-size motions between scans, trained jointly with an implicit neural representation (INR) network. INR encodes the DWI as a continuous volumetric function, treating voxel intensities of low-resolution acquisitions as discrete samples. By evaluating this function with a finer grid, our model predicts higher-resolution signal intensities for intermediate voxel locations. The Perturbation Network's motion-correction efficacy was validated through experiments on biological phantoms and in vivo prostate scans. Quantitative analyses revealed significantly higher structural similarity measures of super-resolution images to ground truth high-resolution images compared to high-order interpolation (p 0.005). In blind qualitative experiments, of super-resolution images were assessed to have superior diagnostic quality compared to interpolated images. High-resolution details in DWI can be obtained without the need for high-resolution training data. One notable advantage of the proposed method is that it does not require a super-resolution training set. This is important in clinical practice because the proposed method can easily be adapted to images with different scanner settings or body parts, whereas the supervised methods do not offer such an option.

Theoretical and experimental verification of wide-spectrum thermometry based on Taylor series de-integration method.

In response to the challenges encountered in solving the integral equations and the disadvantages of requiring additional calibration parameters in the existing three-channel wide-spectrum temperature measurement, a wavelength-based Taylor series de-integration method is proposed. By combining the coefficient of determination, which characterizes the approximation effect, the selection criterion of characteristic wavelength (optimal expansion wavelength, OEW) is constructed. In the influence analysis of spectral emissivity on the de-integration method, the insensitivity of OEW to spectral emissivity is revealed. The feasibility of using blackbody OEW for de-integration processing is proved when the spectral emissivity is unknown, which provides necessary theoretical support for the selection of characteristic wavelengths in practical application. Based on this integration method, algebraic temperature measurement equations in the forms of graybody, three-channel fusion, and two-color are derived, and the theoretical errors of each form are discussed from both horizontal and longitudinal perspectives. Furthermore, thermometry experiments with multiple acquisition parameters and diverse samples were conducted corresponding to three solution forms, the universality of acquisition parameters and sample applicability are proven.

Multifocal multiview imaging and data compression based on angular-focal-spatial representation.

Multifocal multiview (MFMV) is an emerging high-dimensional optical data that allows to record richer scene information but yields huge volumes of data. To unveil its imaging mechanism, we present an angular-focal-spatial representation model, which decomposes high-dimensional MFMV data into angular, spatial, and focal dimensions. To construct a comprehensive MFMV dataset, we leverage representative imaging prototypes, including digital camera imaging, emerging plenoptic refocusing, and synthesized Blender 3D creation. It is believed to be the first-of-its-kind MFMV dataset in multiple acquisition ways. To efficiently compress MFMV data, we propose the first, to our knowledge, MFMV data compression scheme based on angular-focal-spatial representation. It exploits inter-view, inter-stack, and intra-frame predictions to eliminate data redundancy in angular, focal, and spatial dimensions, respectively. Experiments demonstrate the proposed scheme outperforms the standard HEVC and MV-HEVC coding methods. As high as 3.693 dB PSNR gains and 64.22% bitrate savings can be achieved.

Detection of sub-nmol amounts of the antiviral drug favipiravir in 19F MRI using photo-chemically induced dynamic nuclear polarization

In biological tissues, 19F magnetic resonance (MR) enables the non-invasive, background-free detection of 19F-containing biomarkers. However, the signal-to-noise ratio (SNR) is usually low because biomarkers are typically present at low concentrations. Measurements at low magnetic fields further reduce the SNR. In a proof-of-principal study we applied LED-based photo-chemically induced dynamic nuclear polarization (photo-CIDNP) to amplify the 19F signal at 0.6 T. For the first time, 19F MR imaging (MRI) and spectroscopy (MRS) of a fully biocompatible model system containing the antiviral drug favipiravir has been successfully performed. This fluorinated drug has been used to treat Ebola and COVID-19. Since the partially cyclic reaction scheme for photo-CIDNP allows for multiple data acquisitions, averaging further improved the SNR. The mean signal gain factor for 19F has been estimated to be in the order of 103. An in-plane resolution of 0.39 × 0.39 mm2 enabled the analysis of spatially varying degrees of hyperpolarization. The minimal detectable amount of favipiravir per voxel was estimated to about 500 pmol. The results show that 19F photo-CIDNP is a promising method for the non-invasive detection of suitable 19F-containing drugs and other compounds with very low levels of the substance.

Comparison of driven equilibrium and standard spin-echo sequence in MR microscopy: Analysis of signal dependence on RF pulse imperfection and diffusion

Rapid MR imaging of slowly relaxing samples is often challenging. The most commonly used solutions are found in multi spin-echo (RARE) sequences or gradient-echo (GE) sequences, which allow faster imaging of such samples with multiple acquisitions of k-space lines per excitation or imaging with very short repetition times (TRs). Another solution is the use of a spin-echo (SE) sequence superimposed with a driven equilibrium Fourier transform (DEFT) method. Such a (DE-SE) imaging sequence has two refocusing RF pulses that produce two spin-echoes. In the first echo, the signal is acquired from the k-space line, and in the second echo, a 90° RF pulse is applied, typically 180° out of phase with respect to the excitation RF pulse. This last RF pulse allows almost complete magnetization reversal back to the longitudinal orientation with minimal magnetization loss. The DE-SE sequence and its RARE variant are widely used in clinical imaging, but its use in MR microscopy has some peculiarities related to the usually less perfect RF pulse flip angles and diffusion. In this study, their effects are first theoretically analyzed and later verified by experiments on test samples performed on a 9.4 T system for MR microscopy. Experiments on a water-filled tube for TE = 3.4 ms and TR = 25–200 ms showed that the DE-SE sequence produces about 10 times more signal than the SE sequence in this TR range. Finally, the performance of the DE-SE sequence compared to the SE sequence was demonstrated on a biological sample. The presented DE-SE sequence has been shown to be effective for rapid imaging of samples with long T1 relaxation times in MR microscopy and can also be considered as a suitable method for rapid proton density weighed imaging of materials.

Repeated horizontal acquisition of lagriamide-producing symbionts in Lagriinae beetles.

Microbial symbionts associate with multicellular organisms on a continuum from facultative associations to mutual codependency. In the oldest intracellular symbioses there is exclusive vertical symbiont transmission, and co-diversification of symbiotic partners over millions of years. Such symbionts often undergo genome reduction due to low effective population sizes, frequent population bottlenecks, and reduced purifying selection. Here, we describe multiple independent acquisition events of closely related defensive symbionts followed by genome erosion in a group of Lagriinae beetles. Previous work in Lagria villosa revealed the dominant genome-eroded symbiont of the genus Burkholderia produces the antifungal compound lagriamide, protecting the beetle's eggs and larvae from antagonistic fungi. Here, we use metagenomics to assemble 11 additional genomes of lagriamide-producing symbionts from 7 different host species within Lagriinae from 5 countries, to unravel the evolutionary history of this symbiotic relationship. In each host, we detected one dominant genome-eroded Burkholderia symbiont encoding the lagriamide biosynthetic gene cluster. However, we did not find evidence for host-symbiont co-diversification or for monophyly of the lagriamide-producing symbionts. Instead, our analyses support a single ancestral acquisition of the gene cluster followed by at least four independent symbiont acquisitions and subsequent genome erosion in each lineage. By contrast, a clade of plant-associated relatives retained large genomes but secondarily lost the lagriamide gene cluster. Our results, therefore, reveal a dynamic evolutionary history with multiple independent symbiont acquisitions characterized by a high degree of specificity and highlight the importance of the specialized metabolite lagriamide for the establishment and maintenance of this defensive symbiosis.

Novel evolutionary insights on the interactions of the Holosporales (Alphaproteobacteria) with eukaryotic hosts from comparative genomics.

Holosporales are an alphaproteobacterial order engaging in obligate and complex associations with eukaryotes, in particular protists. The functional and evolutionary features of those interactions are still largely undisclosed. Here, we sequenced the genomes of two members of the species Bealeia paramacronuclearis (Holosporales, Holosporaceae) intracellularly associated with the ciliate protist Paramecium, which resulted in high correspondence. Consistent with the short-branched early-divergent phylogenetic position, Bealeia presents a larger functional repertoire than other Holosporaceae, comparable to those of other Holosporales families, particularly for energy metabolism and motility. Our analyses indicate that different Holosporales likely experienced at least partly autonomous genome reduction and adaptation to host interactions, for example regarding dependence on host biotin driven by multiple independent horizontal acquisitions of transporters. Among Alphaproteobacteria, this is reminiscent of the convergently evolved Rickettsiales, which however appear more diverse, possibly due to a probably more ancient origin. We identified in Bealeia and other Holosporales the plasmid-encoded putative genetic determinants of R-bodies, which may be involved in a killer trait towards symbiont-free hosts. While it is not clear whether these genes are ancestral or recently horizontally acquired, an intriguing and peculiar role of R-bodies is suggested in the evolution of the interactions of multiple Holosporales with their hosts.