Hemozoin crystal formation is a major antimalarial drug target as it is essential for survival of Plasmodium parasites that cause malaria, one of the world’s most devastating infectious diseases. Hemozoin crystals rapidly tumble inside the parasite food vacuole, and the mechanism and significance of this motion have been mysterious. Using quantitative live-cell imaging and computational modeling, we found that hemozoin motion is driven by catalytic decomposition of hydrogen peroxide on the crystal surface, a mechanism analogous to synthetic nanomotors. Hemozoin crystals have been viewed as inert detoxification products. Our work reframes the physiological role of these crystals as catalytically active nanoparticles whose mechanism of propulsion helps to neutralize toxic hydrogen peroxide generated by parasite digestion of hemoglobin during blood-stage infection. Malaria parasites infect red blood cells where they digest host hemoglobin and release free heme inside a lysosome-like organelle called the food vacuole. To detoxify excess heme, parasites form hemozoin crystals that rapidly tumble inside this compartment. Hemozoin formation is critical for parasite survival and central to antimalarial drug activity. Although the static structural properties of hemozoin have been extensively investigated, crystal motion and its underlying mechanism have remained puzzling. We used quantitative image analysis to determine the timescale of motion, which requires the intact vacuole but does not require the parasite itself. Using single particle tracking and Brownian dynamics simulations with experimentally derived interaction potentials, we found that hemozoin motion exhibits unexpectedly tight confinement but is much faster than thermal diffusion. Hydrogen peroxide, which is generated at high levels in the food vacuole, has been shown to stimulate the motion of synthetic metallic nanoparticles via surface-catalyzed peroxide decomposition that generates propulsive kinetic energy. We observed that peroxide stimulated the motion of isolated crystals in solution and that conditions that suppress peroxide formation slowed hemozoin motion inside parasites. These data suggest that surface-exposed metals on hemozoin catalyze peroxide decomposition to drive crystal motion. This work reveals hemozoin motion in malaria parasites as a biological example of an endogenous self-propelled nanoparticle. This mechanism of propulsion likely serves a physiological role to reduce oxidative stress to parasites from hydrogen peroxide produced by large-scale hemoglobin digestion during blood-stage infection.

We investigate whether tactile charts support comprehension and learning of complex visualizations for blind and low-vision (BLV) individuals and contribute four tactile chart designs and an interview study. Visualizations are powerful tools for conveying data, yet BLV individuals typically can rely only on assistive technologies -- primarily alternative texts -- to access this information. Prior research shows the importance of mental models of chart types for interpreting these descriptions, yet BLV individuals have no means to build such a mental model based on images of visualizations. Tactile charts show promise to fill this gap in supporting the process of building mental models. Yet studies on tactile data representations mostly focus on simple chart types, and it is unclear whether they are also appropriate for more complex charts as would be found in scientific publications. Working with two BLV researchers, we designed 3D-printed tactile template charts with exploration instructions for four advanced chart types: UpSet plots, violin plots, clustered heatmaps, and faceted line charts. We then conducted an interview study with 12 BLV participants comparing whether using our tactile templates improves mental models and understanding of charts and whether this understanding translates to novel datasets experienced through alt texts. Thematic analysis shows that tactile models support chart type understanding and are the preferred learning method by BLV individuals. We also report participants' opinions on tactile chart design and their role in BLV education. 

A key challenge faced when preparing codes for Department of Energy (DOE) exascale systems was designing scalable applications for systems featuring hardware and software not yet available at leadership class scale. With such systems now available, it is important to evaluate scalability of the resulting software solutions on these target systems. One such code designed with the exascale DOE Aurora and DOE Frontier systems in mind is the Uintah Computational Framework, an open-source asynchronous many-task (AMT) runtime system. To prepare for exascale, Uintah adopted a portable MPI+X hybrid parallelism approach using the Kokkos performance portability library (i.e., MPI+Kokkos). This paper complements recent work with additional details and an evaluation of the resulting approach on Aurora and Frontier. Results are shown for a challenging benchmark demonstrating interoperability of 3 portable codes essential to Uintah-related combustion research. These results demonstrate single-source portability across Aurora and Frontier with scaling characteristics shown to 3,072 Aurora nodes and 9,216 Frontier nodes. In addition to showing results run to new scales on new systems, this paper also discusses lessons learned through efforts preparing Uintah for exascale systems.

The increasing demand for larger and higher fidelity simulations has made Adaptive Mesh Refinement (AMR) and unstructured mesh techniques essential to focus compute effort and memory cost on just the areas of interest in the simulation domain. The distribution of these meshes over the compute nodes is often determined by balancing compute, memory, and network costs, leading to distributions with jagged nonconvex boundaries that fit together much like puzzle pieces. It is expensive, and sometimes impossible, to re-partition the data posing a challenge for in situ and post hoc visualization as the data cannot be rendered using standard sort-last compositing techniques that require a convex and disjoint data partitioning. We present a new distributed volume rendering and compositing algorithm, Approximate Puzzlepiece Compositing, that enables fast and high-accuracy in-place rendering of AMR and unstructured meshes. Our approach builds on Moment-Based Ordered-Independent Transparency to achieve a scalable, order-independent compositing algorithm that requires little communication and does not impose requirements on the data partitioning. We evaluate the image quality and scalability of our approach on synthetic data and two large-scale unstructured meshes on HPC systems by comparing to state-of-the-art sort-last compositing techniques, highlighting our approach’s minimal overhead at higher core counts. We demonstrate that Approximate Puzzlepiece Compositing provides a scalable, high-performance, and high-quality distributed rendering approach applicable to the complex data distributions encountered in large-scale CFD simulations.

In applications where efficiency is critical, developers may examine their compiled binaries, seeking to understand how the compiler transformed their source code and what performance implications that transformation may have. This analysis is challenging due to the vast number of disassembled binary instructions and the many-to-many mappings between them and the source code. These problems are exacerbated as source code size increases, giving the compiler more freedom to map and disperse binary instructions across the disassembly space. Interfaces for disassembly typically display instructions as an unstructured listing or sacrifice the order of execution. We design a new visual interface for disassembly code that combines execution order with control flow structure, enabling analysts to both trace through code and identify familiar aspects of the computation. Central to our approach is a novel layout of instructions grouped into basic blocks that displays a looping structure in an intuitive way. We add to this disassembly representation a unique block-based mini-map that leverages our layout and shows context across thousands of disassembly instructions. Finally, we embed our disassembly visualization in a web-based tool, DisViz, which adds dynamic linking with source code across the entire application. DizViz was developed in collaboration with program analysis experts following design study methodology and was validated through evaluation sessions with ten participants from four institutions. Participants successfully completed the evaluation tasks, hypothesized about compiler optimizations, and noted the utility of our new disassembly view. Our evaluation suggests that our new integrated view helps application developers in understanding and navigating disassembly code. 

Adverse drug events represent a key challenge in public health, especially concerning drug safety profiling and drug surveillance. Drug-drug interactions represent one of the most popular types of adverse drug events. Most computational approaches to this problem have used different types of drug-related information utilizing different machine-learning algorithms to predict potential drug interactions. In this work, we focus on genetic information about the drugs, particularly the protein sequence and protein structure of protein targets in drug interaction networks, to predict potential drug interactions. We collected various drug information like drug-drug interaction (DDI), Drug attributes like drug active ingredients, protein targets, protein sequence, protein structure etc. We proposed a similarity-based Neural Network framework called protein sequence-structure similarity network (PS3N) and used this to predict novel DDI’s. The drug-drug similarities are computed using different categories of drug information based on multiple similarity metrics. Our method outperforms the state-of-the-art and achieves competitive results. Our performance evaluations on different datasets showed the predictive performance as follows: Precision 91%–98%, Recall 90%–96%, F1 Score 86%–95%, Area Under Curve (AUC) 88%–99%, and Accuracy 86%–95%. Our evaluation demonstrates the effectiveness of PS3N in predicting DDI’s, including the clinical significance of some new DDI’s discovered by the model.

Anatomy evaluation is crucial for understanding the physiological state, diagnosing abnormalities, and guiding medical interventions. Statistical shape modeling (SSM) is vital in this process, particularly in medical image analysis and computational anatomy. By enabling the extraction of quantitative morphological shape descriptors from medical imaging data such as MRI and CT scans, SSM provides comprehensive descriptions of anatomical variations within a population. However, the effectiveness of SSM in anatomy evaluation hinges on the quality and robustness of the shape models, which face challenges due to substantial nonlinear variability in human anatomy. While deep learning techniques show promise in addressing these challenges by learning complex nonlinear representations of shapes, existing models still have limitations and often require pre-established shape models for training. To overcome these issues, we propose Mesh2SSM++, a novel approach that learns to estimate correspondences from meshes in an unsupervised manner. This method leverages unsupervised, permutation-invariant representation learning to estimate how to deform a template point cloud into subject-specific meshes, forming a correspondence-based shape model. Additionally, our probabilistic formulation allows learning a population-specific template, reducing potential biases associated with template selection. A key feature of Mesh2SSM++ is its ability to quantify aleatoric uncertainty, which captures inherent data variability and is essential for ensuring reliable model predictions and robust decision-making in clinical tasks, especially under challenging imaging conditions. Through extensive validation across diverse anatomies, evaluation metrics, and downstream tasks, we demonstrate that Mesh2SSM++ outperforms existing methods. Its ability to operate directly on meshes, combined with computational efficiency and interpretability through its probabilistic framework, makes it an attractive alternative to traditional and deep learning-based SSM approaches. Github: https://github.com/iyerkrithika21/Mesh2SSMJournal

High-performance computing (HPC) systems are becoming increasingly water-intensive due to their reliance on water-based cooling and the energy used in power generation. However, the water footprint of HPC remains relatively underexplored-especially in contrast to the growing focus on carbon emissions. In this paper, we present ThirstyFLOPS - a comprehensive water footprint analysis framework for HPC systems. Our approach incorporates region-specific metrics, including Water Usage Effectiveness, Power Usage Effectiveness, and Energy Water Factor, to quantify water consumption using real-world data. Using four representative HPC systems - Marconi, Fugaku, Polaris, and Frontier - as examples, we provide implications for HPC system planning and management. We explore the impact of regional water scarcity and nuclear-based energy strategies on HPC sustainability. Our findings aim to advance the development of water-aware, environmentally responsible computing infrastructures. 

Flow matching-based generative models have been integrated into the plug-and-play image restoration framework, and the resulting plug-and-play flow matching (PnP-Flow) model has achieved some remarkable empirical success for image restoration. However, the theoretical understanding of PnP-Flow lags its empirical success. In this paper, we derive a continuous limit for PnP-Flow, resulting in a stochastic differential equation (SDE) surrogate model of PnP-Flow. The SDE model provides two particular insights to improve PnP-Flow for image restoration: (1) It enables us to quantify the error for image restoration, informing us to improve step scheduling and regularize the Lipschitz constant of the neural network-parameterized vector field for error reduction. (2) It informs us to accelerate off-the-shelf PnP-Flow models via extrapolation, resulting in a rescaled version of the proposed SDE model. We validate the efficacy of the SDE-informed improved PnP-Flow using several benchmark tasks, including image denoising, deblurring, super-resolution, and inpainting. Numerical results show that our method significantly outperforms the baseline PnP-Flow and other state-of-the-art approaches, achieving superior performance across evaluation metrics. 

Legg-Calvé-Perthes Disease (LCPD) often results in a permanent residual hip deformity, with long-term impacts on hip function. In the clinic, decisions on how to manage LCPD deformity are based on 2D radiographic measures such as the Stulberg grades. It is unclear whether these measures accurately represent complex, patient-specific 3D deformity, making it difficult to determine the best treatment approach. Statistical shape modeling (SSM) provides an objective and compact description of variability, but there is currently no 3D SSM that focuses solely on describing shape variability in LCPD. In this study, we have constructed an SSM of femurs with LCPD deformity, which will provide a foundation for future research into how deformity is measured in the clinic.

Clinical magnetic resonance (MR) images (N=13 hips, 11 patients, 3 F/8 M) of affected hips were obtained in patients with stage IV LCPD (age range: 6–12 years, estimated Stulberg grades: II-V). MR volumes were resampled isotropically to the smallest voxel dimension (.47 – .63 mm), and two raters manually segmented the proximal femurs. ShapeWorks (SCI Institute, University of Utah, Salt Lake City, UT) was used to produce an SSM with 512 particles using an incremental optimization routine: corresponding particles were initially distributed and optimized on a subset of the five most similar femurs. The mean particle distribution of the initial model was then used to initialize the remaining eight shapes, followed by an additional optimization of the particle distribution. Differences in pose and scale between shapes were removed with generalized Procrustes analysis. Modes of shape variation were quantified using principal component analysis.

The first four shape modes described 87.5% of the population variability, and qualitatively captured clinically relevant variables such as femoral head width and articular-trochanteric distance (see Figure 1). A significant association (p=0.04) was observed between shape mode 4 and asphericity of the femoral head (rho=0.58). A leave-one-out cross-validation of the model demonstrated good generalizability to unseen shapes, with a mean point-to-point reconstruction error of < 1 mm if > 4 modes were included.

This SSM provides a continuous, accurate, and compact representation of 3D shape variation in LCPD. Limitations to this model include a small sample size, and thus the inability to control for age. In addition to expanding this model, future work will incorporate the acetabular side to describe joint congruence, and associate shape modes with radiographic parameters.

Increasing the fidelity of analyses of nuclear material is an important goal of technical nuclear forensics R&D. Many well-proven and mature analysis tools exist; some give visual representations of materials, while others offer clues to the chemical composition. Two such tools are Scanning Electron Microscopy (SEM) and X-ray diffraction (XRD). We present a machine learning framework for fusing these distinct modalities—themselves well-studied tools of nuclear forensics—to increase their specificity together. Early model fusion allows the use of correlations between data sources. We significantly improve accuracy over any single model alone, even over baseline methods that incorporate ground-truth information about the material. If XRD data is limited, we show that generating synthetic patterns from crystallographic information is viable for increasing available data. Comparing models trained on real and synthetic XRD pattern data, we conclude that our early fusion method trained on real XRD pattern data can utilize processing route information previously hidden in the XRD pattern, outperforming late fusion methods and models trained on synthetic data alone.

Motivation:
Hemodynamic analysis is crucial for diagnosing and predicting cardiovascular diseases. However, methods relying on fluid flow simulations or blood flow imaging are complex, time-consuming, and require specialized expertise, limiting their clinical use.

Goal:
This research aims to automate the enhancement of blood flow images, providing clinicians with a fast, accurate tool for hemodynamic analysis without requiring advanced expertise.

Objectives:
A software tool based on physics-constrained neural networks was developed to enable clinicians to easily select and process regions of interest (ROIs) in time-resolved three-dimensional phase contrast magnetic resonance imaging (4D-Flow MRI) blood flow images for quick, accurate analysis.

Methods:
The Input Parameterized Physics-Informed Neural Network (IP-PINN) was introduced to improve the spatio-temporal resolution of 4D-Flow MRI. IP-PINN mitigates noise, velocity aliasing, and phase errors. A convolutional neural network processes ROI data into latent vectors, which are then used to predict velocity, pressure, and spin density via a multi-layer perceptron. The method is trained with synthetic blood flow data using an innovative loss function that addresses noise and artifacts.

Results:
IP-PINN successfully enhanced image resolution, reducing noise and artifacts when tested on synthetic 4D-Flow MRI data derived from blood flow simulations of intracranial aneurysms. For data with 20 decibels (dB) signal-to-noise ratio, results closely matched the ground truth with less than 5.5% relative error. Processing took under two minutes. The method also has the potential to reduce data acquisition time by 25%.

Conclusions:
IP-PINN could significantly enhance the clinical use of 4D-Flow MRI for personalized hemodynamic analysis in cardiovascular diseases.

Spatial transformations that capture population-level morphological statistics are critical for medical image analysis. Commonly used smoothness regularizers for image registration fail to integrate population statistics, leading to anatomically inconsistent transformations. Inverse consistency regularizers promote geometric consistency but lack population morphometrics integration. Regularizers that constrain deformation to low-dimensional manifold methods address this. However, they prioritize reconstruction over interpretability and neglect diffeomorphic properties, such as group composition and inverse consistency. We introduce MORPH-LER, a Log-Euclidean regularization framework for population-aware unsupervised image registration. MORPH-LER learns population morphometrics from spatial transformations to guide and regularize registration networks, ensuring anatomically plausible deformations. It features a bottleneck autoencoder that computes the principal logarithm of deformation fields via iterative square-root predictions. It creates a linearized latent space that respects diffeomorphic properties and enforces inverse consistency. By integrating a registration network with a diffeomorphic autoencoder, MORPH-LER produces smooth, meaningful deformation fields. The framework offers two main contributions: (1) a data-driven regularization strategy that incorporates population-level anatomical statistics to enhance transformation validity and (2) a linearized latent space that enables compact and interpretable deformation fields for efficient population morphometrics analysis. We validate MORPH-LER across two families of deep learning-based registration networks, demonstrating its ability to produce anatomically accurate, computationally efficient, and statistically meaningful transformations on the OASIS-1 brain imaging dataset.

Hematoxylin and eosin (H&E) staining is a gold standard for microscopic diagnosis in pathology. However, H&E staining does not capture all the diagnostic information that may be needed. To obtain additional molecular information, immunohistochemical (IHC) stains highlight proteins that mark specific cell types, such as CD3 for T-cells or CK8/18 for epithelial cells. While IHC stains are vital for prognosis and treatment guidance, they are typically only available at specialized centers and time consuming to acquire, leading to treatment delays for patients. Virtual staining, enabled by deep learning-based image translation models, provides a promising alternative by computationally generating IHC stains from H&E stained images. Although many GAN and diffusion based image to image (I2I) translation methods have been used for virtual staining, these models treat image patches as independent data points, which results in increased and more diverse data requirements for effective generation. We present ImplicitStainer, a novel approach that leverages local implicit functions to improve image translation, specifically virtual staining performance, by focusing on pixel-level predictions. This method enhances robustness to variations in dataset sizes, delivering high-quality results even with limited data. We validate our approach on two datasets using a comprehensive set of metrics and benchmark it against over fifteen state-of-the-art GAN- and diffusion based models. Full Code and models trained will be released publicly via Github upon acceptance.

Obtaining large-scale medical data, annotated or unannotated, is challenging due to stringent privacy regulations and data protection policies. In addition, annotating medical images requires that domain experts manually delineate anatomical structures, making the process both time-consuming and costly. As a result, semi-supervised methods have gained popularity for reducing annotation costs. However, the performance of semi-supervised methods is heavily dependent on the availability of unannotated data, and their effectiveness declines when such data are scarce or absent. To overcome this limitation, we propose a simple, yet effective and computationally efficient approach for medical image segmentation that leverages only existing annotations. We propose BoundarySeg , a multi-task framework that incorporates organ boundary prediction as an auxiliary task to full organ segmentation, leveraging consistency between the two task predictions to provide additional supervision. This strategy improves segmentation accuracy, especially in low data regimes, allowing our method to achieve performance comparable to or exceeding state-of-the-art semi supervised approaches all without relying on unannotated data or increasing computational demands. Code will be released upon acceptance.

Deep learning models can generate virtual immunohistochemistry (IHC) stains from hematoxylin and eosin (H&E) images, offering a scalable and low-cost alternative to laboratory IHC. However, reliable evaluation of image quality remains a challenge as current texture- and distribution-based metrics quantify image fidelity rather than the accuracy of IHC staining. Here, we introduce an automated and accuracy grounded framework to determine image quality across sixteen paired or unpaired image translation models. Using color deconvolution, we generate masks of pixels stained brown (i.e., IHC-positive) as predicted by each virtual IHC model. We use the segmented masks of real and virtual IHC to compute stain accuracy metrics (Dice, IoU, Hausdorff distance) that directly quantify correct pixel - level labeling without needing expert manual annotations. Our results demonstrate that conventional image fidelity metrics, including Frechet Inception Distance (FID), peak signal-to-noise ratio (PSNR), and structural similarity (SSIM), correlate poorly with stain accuracy and pathologist assessment. Paired models such as PyramidPix2Pix and AdaptiveNCE achieve the highest stain accuracy, whereas unpaired diffusion- and GAN-based models are less reliable in providing accurate IHC positive pixel labels. Moreover, whole-slide images (WSI) reveal performance declines that are invisible in patch-based evaluations, emphasizing the need for WSI-level benchmarks. Together, this framework defines a reproducible approach for assessing the quality of virtual IHC models, a critical step to accelerate translation towards routine use by pathologists. 

Multiscale coupling between cell-scale biology and tissue-scale mechanics is a promising approach for modeling disease growth. In such models, tissue-level growth and remodeling (G&R) are driven by cell-level signaling pathways and systems biology models, where each model operates at different scales. Herein, we generate multiscale G&R models to capture the associated multiscale connections. At the cell-scale, we consider systems biology models in the form of systems of ordinary differential equations (ODEs) and partial differential equations (PDEs) representing the reactions between the biochemicals causing the growth based on mass-action or logic-based Hill-type kinetics. At the tissue-scale, we employ kinematic growth in continuum frameworks. Two illustrative test problems (a tissue graft and aneurysm growth) are examined with various chemical signaling networks, boundary conditions, and mechano-chemical coupling strategies. We extend two open-source software frameworks—febio and fenics—to disseminate examples of multiscale growth and remodeling simulations. One-way and two-way coupling between the systems biology and the growth models are compared and the effect of biochemical diffusivity and ODE versus PDE-based systems biology modeling on the G&R results are studied. The results show that growth patterns emerge from reactions between biochemicals, the choice between ODEs and PDEs systems biology modeling, and the coupling strategy. Cross-verification confirms that results for febio and fenics are nearly identical. We hope that these open-source tools will support reproducibility and education within the biomechanics community.