DuDoCFNet: Dual-Domain Coarse-to-Fine Progressive Network for Simultaneous Denoising, Limited-View Reconstruction, and Attenuation Correction of Cardiac SPECT.

Single-Photon Emission Computed Tomography (SPECT) is widely applied for the diagnosis of coronary artery diseases. Low-dose (LD) SPECT aims to minimize radiation exposure but leads to increased image noise. Limited-view (LV) SPECT, such as the latest GE MyoSPECT ES system, enables accelerated scanning and reduces hardware expenses but degrades reconstruction accuracy. Additionally, Computed Tomography (CT) is commonly used to derive attenuation maps (µ-maps) for attenuation correction (AC) of cardiac SPECT, but it will introduce additional radiation exposure and SPECT-CT misalignments. Although various methods have been developed to solely focus on LD denoising, LV reconstruction, or CT-free AC in SPECT, the solution for simultaneously addressing these tasks remains challenging and under-explored. Furthermore, it is essential to explore the potential of fusing cross-domain and cross-modality information across these interrelated tasks to further enhance the accuracy of each task. Thus, we propose a Dual-Domain Coarse-to-Fine Progressive Network (DuDoCFNet), a multi-task learning method for simultaneous LD denoising, LV reconstruction, and CT-free µ-map generation of cardiac SPECT. Paired dual-domain networks in DuDoCFNet are cascaded using a multi-layer fusion mechanism for cross-domain and cross-modality feature fusion. Two-stage progressive learning strategies are applied in both projection and image domains to achieve coarse-to-fine estimations of SPECT projections and CT-derived µ-maps. Our experiments demonstrate DuDoCFNet's superior accuracy in estimating projections, generating µ-maps, and AC reconstructions compared to existing single- or multi-task learning methods, under various iterations and LD levels. The source code of this work is available at https://github.com/XiongchaoChen/DuDoCFNet-MultiTask.

Nicotinic acid availability impacts redox cofactor metabolism in Saccharomyces cerevisiae during alcoholic fermentation.

Anaerobic alcoholic fermentation, particularly in high-sugar environments, presents metabolic challenges for yeasts. Crabtree-positive yeasts, including Saccharomyces cerevisiae, prefer fermentation even in the presence of oxygen. These yeasts rely on internal NAD+ recycling and extracellular assimilation of its precursor, nicotinic acid (vitamin B3), rather than de novo NAD+ production. Surprisingly, nicotinic acid assimilation is poorly characterized, even in S. cerevisiae. This study elucidated the timing of nicotinic acid uptake during grape juice-like fermentation and its impact on NAD(H) levels, the NAD+/NADH ratio, and metabolites produced. Complete uptake of extracellular nicotinic acid occurred premid-exponential phase, thereafter small amounts of vitamin B3 were exported back into the medium. Suboptimal levels of nicotinic acid were correlated with slower fermentation and reduced biomass, disrupting redox balance and impeding NAD+ regeneration, thereby affecting metabolite production. Metabolic outcomes varied with nicotinic acid concentrations, linking NAD+ availability to fermentation efficiency. A model was proposed encompassing rapid nicotinic acid uptake, accumulation during cell proliferation, and recycling with limited vitamin B3 export. This research enhances the understanding of nicotinic acid uptake dynamics during grape juice-like fermentation. These insights contribute to advancing yeast metabolism research and have profound implications for the enhancement of biotechnological practices and the wine-making industry.

RAPID resistance to BET inhibitors is mediated by FGFR1 in glioblastoma.

Bromodomain and extra-terminal domain (BET) proteins are therapeutic targets in several cancers including the most common malignant adult brain tumor glioblastoma (GBM). Multiple small molecule inhibitors of BET proteins have been utilized in preclinical and clinical studies. Unfortunately, BET inhibitors have not shown efficacy in clinical trials enrolling GBM patients. One possible reason for this may stem from resistance mechanisms that arise after prolonged treatment within a clinical setting. However, the mechanisms and timeframe of resistance to BET inhibitors in GBM is not known. To identify the temporal order of resistance mechanisms in GBM we performed quantitative proteomics using multiplex-inhibitor bead mass spectrometry and demonstrated that intrinsic resistance to BET inhibitors in GBM treatment occurs rapidly within hours and involves the fibroblast growth factor receptor 1 (FGFR1) protein. Additionally, small molecule inhibition of BET proteins and FGFR1 simultaneously induces synergy in reducing GBM tumor growth in vitro and in vivo. Further, FGFR1 knockdown synergizes with BET inhibitor mediated reduction of GBM cell proliferation. Collectively, our studies suggest that co-targeting BET and FGFR1 may dampen resistance mechanisms to yield a clinical response in GBM.

High-resolution extracellular pH imaging of liver cancer with multiparametric MR using Deep Image Prior.

Noninvasive extracellular pH (pHe ) mapping with Biosensor Imaging of Redundant Deviation in Shifts (BIRDS) using MR spectroscopic imaging (MRSI) has been demonstrated on 3T clinical MR scanners at 8 × 8 × 10 $$ \times 8\times 10 $$ mm3 spatial resolution and applied to study various liver cancer treatments. Although pHe imaging at higher resolution can be achieved by extending the acquisition time, a postprocessing method to increase the resolution is preferable, to minimize the duration spent by the subject in the MR scanner. In this work, we propose to improve the spatial resolution of pHe mapping with BIRDS by incorporating anatomical information in the form of multiparametric MRI and using an unsupervised deep-learning technique, Deep Image Prior (DIP). Specifically, we used high-resolution T 1 $$ {\mathrm{T}}_1 $$ , T 2 $$ {\mathrm{T}}_2 $$ , and diffusion-weighted imaging (DWI) MR images of rabbits with VX2 liver tumors as inputs to a U-Net architecture to provide anatomical information. U-Net parameters were optimized to minimize the difference between the output super-resolution image and the experimentally acquired low-resolution pHe image using the mean-absolute error. In this way, the super-resolution pHe image would be consistent with both anatomical MR images and the low-resolution pHe measurement from the scanner. The method was developed based on data from 49 rabbits implanted with VX2 liver tumors. For evaluation, we also acquired high-resolution pHe images from two rabbits, which were used as ground truth. The results indicate a good match between the spatial characteristics of the super-resolution images and the high-resolution ground truth, supported by the low pixelwise absolute error.

KRAS mutation-selective requirement for ACSS2 in colorectal adenoma formation.

Oncogenic KRAS mutations are prevalent in colorectal cancer (CRC) and are associated with poor prognosis and resistance to therapy. There is a substantial diversity of KRAS mutant alleles observed in CRC. Emerging clinical and experimental analysis of common KRAS mutations suggest that each mutation differently influences the clinical properties of a disease and response to therapy. Although there is some evidence to suggest biological differences between mutant KRAS alleles, these are yet to be fully elucidated. One approach to study allelic variation involves the use of isogenic cell lines that express different endogenous Kras mutants. Here, we generated Kras isogenic Apc-/- mouse colon epithelial cell lines using CRISPR-driven genome editing by altering the original G12D Kras allele to G12V, G12R, or G13D. We utilized these cell lines to perform transcriptomic and proteomic analysis to compare different signaling properties between these mutants. Both screens indicate significant differences in pathways relating to cholesterol and lipid regulation that we validated with targeted metabolomic measurements and isotope tracing. We found that these processes are upregulated in G12V lines through increased expression of nuclear SREBP1 and higher activation of mTORC1. G12V cells showed higher expression of ACSS2 and ACSS2 inhibition sensitized G12V cells to MEK inhibition. Finally, we found that ACSS2 plays a crucial role early in the development of G12V mutant tumors, in contrast to G12D mutant tumors. These observations highlight differences between KRAS mutant cell lines in their signaling properties. Further exploration of these pathways may prove to be valuable for understanding how specific KRAS mutants function, and identification of novel therapeutic opportunities in CRC.

Saccharomyces cerevisiae SC-2201 Attenuates AOM/DSS-Induced Colorectal Cancer by Modulating the Gut Microbiome and Blocking Proinflammatory Mediators.

Colorectal cancer is the third most common cancer in the world today, and studies have shown that the ratio of Candida to Saccharomyces cerevisiae increased, and the abundance of S. cerevisiae in the intestines of patients with colorectal cancer decreased, which suggests that there is an imbalance in the proportion of fungi in the intestines of patients with colorectal cancer. The objective of this study was to screen S. cerevisiae isolate from traditional Chinese fermentation starters and assess its ability to ameliorate dysbiosis and to alleviate the carcinogenic process of azoxymethane/dextran sodium sulfate-induced colorectal cancer in mice model. S. cerevisiae strain SC-2201 was isolated and exhibited probiotic properties, including the ability to survive in an acidic pH environment and in the presence of bile salts in the gastrointestinal tract, as well as antioxidant activities. Oral administration of S. cerevisiae SC-2201 not only alleviated weight loss but also reduced colonic shortening and histological damage in azoxymethane/dextran sodium sulfate-induced colorectal cancer in mice. Furthermore, the administration of S. cerevisiae SC-2201 suppressed the expression of proinflammatory mediators, such as interleukin-1ß, interleukin-6, cyclooxygenase-2, vascular endothelial growth factor, nucleotide-binding domain, leucine-rich repeat, and pyrin domain-containing protein 3. Specifically, the analysis of gut bacteriome showed a significant decrease in Bacteroidota and Campylobacterota levels, as well as an increase in Proteobacteria level in the colorectal cancer group, which was alleviated by supplementation with S. cerevisiae SC-2201. The analysis of the mycobiome revealed a significant increase in the levels of Basidiomycota, Apiosordaria, Naganishia, and Taphrina genera in the colorectal cancer group, which were alleviated after supplementation with S. cerevisiae SC-2201. However, the levels of Xenoramularia, Entoloma, and Keissleriella were significantly increased after administration with S. cerevisiae SC-2201. Overall, the findings of this study demonstrate that S. cerevisiae SC-2201 possesses potential probiotic properties and can effectively attenuate the development of colorectal cancer, highlighting its cancer-preventive potential. This is the first report of a S. cerevisiae strain isolated from traditional Chinese fermentation starters which showed good probiotic properties, and mitigated azoxymethane/dextran sodium sulfate-induced colorectal cancer by modulating the gut microbiome and blocking proinflammatory mediators in mice.

Grand Challenges at the Interface of Engineering and Medicine.

Over the past two decades Biomedical Engineering has emerged as a major discipline that bridges societal needs of human health care with the development of novel technologies. Every medical institution is now equipped at varying degrees of sophistication with the ability to monitor human health in both non-invasive and invasive modes. The multiple scales at which human physiology can be interrogated provide a profound perspective on health and disease. We are at the nexus of creating "avatars" (herein defined as an extension of "digital twins") of human patho/physiology to serve as paradigms for interrogation and potential intervention. Motivated by the emergence of these new capabilities, the IEEE Engineering in Medicine and Biology Society, the Departments of Biomedical Engineering at Johns Hopkins University and Bioengineering at University of California at San Diego sponsored an interdisciplinary workshop to define the grand challenges that face biomedical engineering and the mechanisms to address these challenges. The Workshop identified five grand challenges with cross-cutting themes and provided a roadmap for new technologies, identified new training needs, and defined the types of interdisciplinary teams needed for addressing these challenges. The themes presented in this paper include: 1) accumedicine through creation of avatars of cells, tissues, organs and whole human; 2) development of smart and responsive devices for human function augmentation; 3) exocortical technologies to understand brain function and treat neuropathologies; 4) the development of approaches to harness the human immune system for health and wellness; and 5) new strategies to engineer genomes and cells.

Effect of Incomplete Cryoablation and Matrix Metalloproteinase Inhibition on Intratumoral CD8+ T-Cell Infiltration in Murine Hepatocellular Carcinoma.

Background Image-guided tumor ablation is the first-line therapy for early-stage hepatocellular carcinoma (HCC), with ongoing investigations into its combination with immunotherapies. Matrix metalloproteinase (MMP) inhibition demonstrates immunomodulatory potential and reduces HCC tumor growth when combined with ablative treatment. Purpose To evaluate the effect of incomplete cryoablation with or without MMP inhibition on the local immune response in residual tumors in a murine HCC model. Materials and Methods Sixty 8- to 10-week-old female BALB/c mice underwent HCC induction with use of orthotopic implantation of syngeneic Tib-75 cells. After 7 days, mice with a single lesion were randomized into treatment groups: (a) no treatment, (b) MMP inhibitor, (c) incomplete cryoablation, and (d) incomplete cryoablation and MMP inhibitor. Macrophage and T-cell subsets were assessed in tissue samples with use of immunohistochemistry and immunofluorescence (cell averages calculated using five 1-µm2 fields of view [FOVs]). C-X-C motif chemokine receptor type 3 (CXCR3)- and interferon γ (IFNγ)-positive T cells were assessed using flow cytometry. Groups were compared using unpaired Student t tests, one-way analysis of variance with Tukey correction, and the Kruskal-Wallis test with Dunn correction. Results Mice treated with incomplete cryoablation (n = 6) showed greater infiltration of CD206+ tumor-associated macrophages (mean, 1.52 cells per FOV vs 0.64 cells per FOV; P = .03) and MMP9-expressing cells (mean, 0.89 cells per FOV vs 0.11 cells per FOV; P = .03) compared with untreated controls (n = 6). Incomplete cryoablation with MMP inhibition (n = 6) versus without (n = 6) led to greater CD8+ T-cell (mean, 15.8% vs 8.29%; P = .04), CXCR3+CD8+ T-cell (mean, 11.64% vs 8.47%; P = .004), and IFNγ+CD8+ T-cell infiltration (mean, 11.58% vs 5.18%; P = .02). Conclusion In a mouse model of HCC, incomplete cryoablation and systemic MMP inhibition showed increased cytotoxic CD8+ T-cell infiltration into the residual tumor compared with either treatment alone. © RSNA, 2024 Supplemental material is available for this article. See also the editorial by Gemmete in this issue.

The ATCC genome portal: 3,938 authenticated microbial reference genomes.

The ATCC Genome Portal (AGP, https://genomes.atcc.org/) is a database of authenticated genomes for bacteria, fungi, protists, and viruses held in ATCC's biorepository. It now includes 3,938 assemblies (253% increase) produced under ISO 9000 by ATCC. Here, we present new features and content added to the AGP for the research community.

Does the injury pattern drive the surgical treatment strategy in multiply injured patients with major fractures?

BACKGROUND: The timing of definitive surgery in multiple injured patients remains a topic of debate, and multiple concepts have been described. Although these included injury severity as a criterion to decide on the indications for surgery, none of them considered the influence of injury distributions. We analyzed whether injury distribution is associated with certain surgical strategies and related outcomes in a cohort of patients treated according to principles of early and safe fixation strategies. METHODS: In this retrospective cohort study, multiple injured patients were included if they were primarily admitted to a Level I trauma center, had an Injury Severity Score of ≥16 points, and required surgical intervention for major injuries and fractures. The primary outcome measure was treatment strategy. The treatment strategy was classified according to the timing of definitive surgery after injury: early total care (ETC, <24 hours), safe definitive surgery (SDS, <48 hours), and damage control (DC, >48 hours). Statistics included univariate and multivariate analyses of mortality and the association of injury distributions and surgical tactics. RESULTS: Between January 1, 2016, and December 31, 2022, 1,471 patients were included (mean ± SD age, 55.6 ± 20.4 years; mean Injury Severity Score, 23.1 ± 11.4). The group distribution was as follows: ETC, n = 85 (5.8%); SDS, n = 665 (45.2%); and DC, n = 721 (49.0%); mortality was 22.4% in ETC, 16.1% in SDS, and 39.7% in DC. Severe nonlethal abdominal injuries (odds ratio [OR], 2.2; 95% confidence interval [CI], 1.4-3.5) and spinal injuries (OR, 1.6; 95% CI, 1.2-2.2) were associated with ETC, while multiple extremity injuries were associated with SDS (OR, 1.7; 95% CI, 1.4-2.2). Severe traumatic brain injury was associated with DC (OR, 1.3; 95% CI, 1.1-1.4). When a correction for the severity of head, abdominal, spinal, and extremity injuries, as well as differences in the values of admission pathophysiologic parameters were undertaken, the mortality was 30% lower in the SDS group when compared with the DC group (OR, 0.3; 95% CI, 0.2-0.4). CONCLUSION: Major spinal injuries and certain abdominal injuries, if identified as nonlethal, trigger definitive surgeries in the initial setting. In contrast, severe TBI was associated with delayed fracture care. Patients with major fractures and other injuries were treated by SDS (definitive care, <48 hours) when the pathophysiological response was adequate. The choice of a favorable surgical treatment appears to depend on injury patterns and physiological patient responses. LEVEL OF EVIDENCE: Therapeutic/Care Management; Level IV.

Unique vulnerability of RAC1-mutant melanoma to combined inhibition of CDK9 and immune checkpoints.

RAC1P29S is the third most prevalent hotspot mutation in sun-exposed melanoma. RAC1 alterations in cancer are correlated with poor prognosis, resistance to standard chemotherapy, and insensitivity to targeted inhibitors. Although RAC1P29S mutations in melanoma and RAC1 alterations in several other cancers are increasingly evident, the RAC1-driven biological mechanisms contributing to tumorigenesis remain unclear. Lack of rigorous signaling analysis has prevented identification of alternative therapeutic targets for RAC1P29S-harboring melanomas. To investigate the RAC1P29S-driven effect on downstream molecular signaling pathways, we generated an inducible RAC1P29S expression melanocytic cell line and performed RNA-sequencing (RNA-seq) coupled with multiplexed kinase inhibitor beads and mass spectrometry (MIBs/MS) to establish enriched pathways from the genomic to proteomic level. Our proteogenomic analysis identified CDK9 as a potential new and specific target in RAC1P29S-mutant melanoma cells. In vitro, CDK9 inhibition impeded the proliferation of in RAC1P29S-mutant melanoma cells and increased surface expression of PD-L1 and MHC Class I proteins. In vivo, combining CDK9 inhibition with anti-PD-1 immune checkpoint blockade significantly inhibited tumor growth only in melanomas that expressed the RAC1P29S mutation. Collectively, these results establish CDK9 as a novel target in RAC1-driven melanoma that can further sensitize the tumor to anti-PD-1 immunotherapy.

Multi-Task Learning for Motion Analysis and Segmentation in 3D Echocardiography.

Characterizing left ventricular deformation and strain using 3D+time echocardiography provides useful insights into cardiac function and can be used to detect and localize myocardial injury. To achieve this, it is imperative to obtain accurate motion estimates of the left ventricle. In many strain analysis pipelines, this step is often accompanied by a separate segmentation step; however, recent works have shown both tasks to be highly related and can be complementary when optimized jointly. In this work, we present a multi-task learning network that can simultaneously segment the left ventricle and track its motion between multiple time frames. Two task-specific networks are trained using a composite loss function. Cross-stitch units combine the activations of these networks by learning shared representations between the tasks at different levels. We also propose a novel shape-consistency unit that encourages motion propagated segmentations to match directly predicted segmentations. Using a combined synthetic and in-vivo 3D echocardiography dataset, we demonstrate that our proposed model can achieve excellent estimates of left ventricular motion displacement and myocardial segmentation. Additionally, we observe strong correlation of our image-based strain measurements with crystal-based strain measurements as well as good correspondence with SPECT perfusion mappings. Finally, we demonstrate the clinical utility of the segmentation masks in estimating ejection fraction and sphericity indices that correspond well with benchmark measurements.

Patient-Specific Heart Geometry Modeling for Solid Biomechanics Using Deep Learning.

Automated volumetric meshing of patient-specific heart geometry can help expedite various biomechanics studies, such as post-intervention stress estimation. Prior meshing techniques often neglect important modeling characteristics for successful downstream analyses, especially for thin structures like the valve leaflets. In this work, we present DeepCarve (Deep Cardiac Volumetric Mesh): a novel deformation-based deep learning method that automatically generates patient-specific volumetric meshes with high spatial accuracy and element quality. The main novelty in our method is the use of minimally sufficient surface mesh labels for precise spatial accuracy and the simultaneous optimization of isotropic and anisotropic deformation energies for volumetric mesh quality. Mesh generation takes only 0.13 seconds/scan during inference, and each mesh can be directly used for finite element analyses without any manual post-processing. Calcification meshes can also be subsequently incorporated for increased simulation accuracy. Numerous stent deployment simulations validate the viability of our approach for large-batch analyses. Our code is available at https://github.com/danpak94/Deep-Cardiac-Volumetric-Mesh.

RAPID BRAIN MENINGES SURFACE RECONSTRUCTION WITH LAYER TOPOLOGY GUARANTEE.

The meninges, located between the skull and brain, are composed of three membrane layers: the pia, the arachnoid, and the dura. Reconstruction of these layers can aid in studying volume differences between patients with neurodegenerative diseases and normal aging subjects. In this work, we use convolutional neural networks (CNNs) to reconstruct surfaces representing meningeal layer boundaries from magnetic resonance (MR) images. We first use the CNNs to predict the signed distance functions (SDFs) representing these surfaces while preserving their anatomical ordering. The marching cubes algorithm is then used to generate continuous surface representations; both the subarachnoid space (SAS) and the intracranial volume (ICV) are computed from these surfaces. The proposed method is compared to a state-of-the-art deformable model-based reconstruction method, and we show that our method can reconstruct smoother and more accurate surfaces using less computation time. Finally, we conduct experiments with volumetric analysis on both subjects with multiple sclerosis and healthy controls. For healthy and MS subjects, ICVs and SAS volumes are found to be significantly correlated to sex (p<0.01) and age (p ≤ 0.03) changes, respectively.

Kinome profiling identifies MARK3 and STK10 as potential therapeutic targets in uveal melanoma.

Most uveal melanoma cases harbor activating mutations in either GNAQ or GNA11. Despite activation of the mitogen-activated protein kinase (MAPK) signaling pathway downstream of Gαq/11, there are no effective targeted kinase therapies for metastatic uveal melanoma. The human genome encodes numerous understudied kinases, also called the "dark kinome". Identifying additional kinases regulated by Gαq/11 may uncover novel therapeutic targets for uveal melanoma. In this study, we treated GNAQ-mutant uveal melanoma cell lines with a Gαq/11 inhibitor, YM-254890, and conducted a kinase signaling proteomic screen using multiplexed-kinase inhibitors followed by mass spectrometry. We observed downregulated expression and/or activity of 22 kinases. A custom siRNA screen targeting these kinases demonstrated that knockdown of microtubule affinity regulating kinase 3 (MARK3) and serine/threonine kinase 10 (STK10) significantly reduced uveal melanoma cell growth and decreased expression of cell cycle proteins. Additionally, knockdown of MARK3 but not STK10 decreased ERK1/2 phosphorylation. Analysis of RNA-sequencing and proteomic data showed that Gαq signaling regulates STK10 expression and MARK3 activity. Our findings suggest an involvement of STK10 and MARK3 in the Gαq/11 oncogenic pathway and prompt further investigation into the specific roles and targeting potential of these kinases in uveal melanoma.

Federated Transfer Learning for Low-dose PET Denoising: A Pilot Study with Simulated Heterogeneous Data.

Positron emission tomography (PET) with a reduced injection dose, i.e., low-dose PET, is an efficient way to reduce radiation dose. However, low-dose PET reconstruction suffers from a low signal-to-noise ratio (SNR), affecting diagnosis and other PET-related applications. Recently, deep learning-based PET denoising methods have demonstrated superior performance in generating high-quality reconstruction. However, these methods require a large amount of representative data for training, which can be difficult to collect and share due to medical data privacy regulations. Moreover, low-dose PET data at different institutions may use different low-dose protocols, leading to non-identical data distribution. While previous federated learning (FL) algorithms enable multi-institution collaborative training without the need of aggregating local data, it is challenging for previous methods to address the large domain shift caused by different low-dose PET settings, and the application of FL to PET is still under-explored. In this work, we propose a federated transfer learning (FTL) framework for low-dose PET denoising using heterogeneous low-dose data. Our experimental results on simulated multi-institutional data demonstrate that our method can efficiently utilize heterogeneous low-dose data without compromising data privacy for achieving superior low-dose PET denoising performance for different institutions with different low-dose settings, as compared to previous FL methods.

FedFTN: Personalized federated learning with deep feature transformation network for multi-institutional low-count PET denoising.

Low-count PET is an efficient way to reduce radiation exposure and acquisition time, but the reconstructed images often suffer from low signal-to-noise ratio (SNR), thus affecting diagnosis and other downstream tasks. Recent advances in deep learning have shown great potential in improving low-count PET image quality, but acquiring a large, centralized, and diverse dataset from multiple institutions for training a robust model is difficult due to privacy and security concerns of patient data. Moreover, low-count PET data at different institutions may have different data distribution, thus requiring personalized models. While previous federated learning (FL) algorithms enable multi-institution collaborative training without the need of aggregating local data, addressing the large domain shift in the application of multi-institutional low-count PET denoising remains a challenge and is still highly under-explored. In this work, we propose FedFTN, a personalized federated learning strategy that addresses these challenges. FedFTN uses a local deep feature transformation network (FTN) to modulate the feature outputs of a globally shared denoising network, enabling personalized low-count PET denoising for each institution. During the federated learning process, only the denoising network's weights are communicated and aggregated, while the FTN remains at the local institutions for feature transformation. We evaluated our method using a large-scale dataset of multi-institutional low-count PET imaging data from three medical centers located across three continents, and showed that FedFTN provides high-quality low-count PET images, outperforming previous baseline FL reconstruction methods across all low-count levels at all three institutions.

Resilience in Army STARRS: Evaluating psychometrics of a multi-dimensional resilience measure.

As policymakers and the U.S. military continue to place an emphasis on the resilience of servicemembers, it is critical to utilize psychometrically sound and valid scales to measure resilience. Using two independent samples of Army soldiers-in-training, this study explored the measurement of resilience in the Army Study to Assess Risk and Resilience among Servicemembers (Army STARRS) New Soldier Study Component (NSS). Exploratory factor analysis (EFA) was used to identify the factor structure of a measure of resilience within the Army STARRS NSS. Confirmatory factor analysis (CFA) was then used to confirm the factor structure, then internal reliability was assessed. Convergent validity of the identified resilience factors was examined using two-tailed bivariate correlations. The EFA identified a three-factor structure of a measure of resilience. The CFA confirm the first-order three-factor structure of stress tolerance, positive orientation, and social resources. Each factor was uniquely distinct from measures of the likelihood of generalized anxiety disorder and major depressive disorder, lifetime stressful events, and social network. Findings highlights the utility of a three-factor aggregate measure of resilience in the Army STARRS NSS and provide practitioners with a more nuanced picture of the role of resilience among soldiers-in-training.