Search for: All records

Disaggregated memory architecture decouples computing and memory resources into separate pools connected via high-speed interconnect technologies, offering substantial advantages in scalability and resource utilization. However, this architecture also poses unique challenges in designing effective index structures and concurrency protocols due to increased remote memory access overhead and its shared-everything nature. In this paper, we present DART, a lock-free two-layer hashed Adaptive Radix Tree (ART) designed to minimize remote memory access while ensuring high concurrency and crash consistency in the disaggregated memory architecture. DART incorporates a hash-based Express Skip Table at its upper layer, which reduces the round trips of remote memory access during index traversal. In the base layer, DART employs an Adaptive Hashed Layout within ART nodes, confining remote memory accesses during in-node searches to small hash buckets. By further leveraging Decoupled Metadata Organization, DART achieves lock-free atomic updates, enabling high scalability and ensuring crash consistency. Our evaluation demonstrates that DART outperforms state-of-the-art counterparts by up to 5.8X in YCSB workloads. 

Understanding the mechanisms that dictate the localization of cytoskeletal filaments is crucial for elucidating cell shape regulation in prokaryotes. The actin homolog MreB plays a pivotal role in maintaining the shape of many rod-shaped bacteria such asEscherichia coliby directing cell-wall synthesis according to local curvature cues. However, the basis of MreB’s curvature-dependent localization has remained elusive. Here, we develop a biophysical model for the energetics of a filament binding to a surface that integrates the complex interplay between filament twist and bending and the two-dimensional surface geometry. Our model predicts that the spatial localization of a filament like MreB with substantial intrinsic twist is governed by both the mean and Gaussian curvatures of the cell envelope, which strongly covary in rod-shaped cells. Using molecular dynamics simulations to estimate the mechanical properties of MreB filaments, we show that their thermodynamic preference for regions with lower mean and Gaussian curvatures matches experimental observations for physiologically relevant filament lengths of ~50 nm. We find that the experimentally measured statistical curvature preference is maintained in the absence of filament motion and after a cycle of depolymerization, repolymerization, and membrane rebinding, indicating that equilibrium energetics can explain MreB localization. These findings provide critical insights into the physical principles underlying cytoskeletal filament localization and suggest design principles for synthetic shape-sensing nanomaterials. 

Purpose of Review To provide an overview of human induced pluripotent stem cell (hiPSC)-derived cardiovascular lineages and describe their impact on drug testing in vitro. Recent Findings hiPSCs have garnered tremendous interest over the last decade due to their potential for unlimited proliferation and differentiation into cardiovascular lineages. Technologies using tissue engineering, 3D bioprinting, and organ-ona-chip platforms composed of hiPSC derivatives can produce cardiovascular tissue mimetics that enhance drug screening applications. Summary: hiPSC-derived cardiovascular lineages advance drug screening efforts by using autologous cells that are more therapeutically relevant. Established approaches to reproducibly generate hiPSC-derived cardiovascular lineages and their subsequent organization into 3D constructs more accurately mimic the physiological organization of cardiac tissue, leading to improved identification of potential drug targets for therapeutic testing. 

Deep learning models have strong potential for automating breast ultrasound (BUS) image classification to support early cancer detection. However, their vulnerability to small input perturbations poses a challenge for clinical reliability. This study examines how minimal pixel-level changes affect classification performance and predictive uncertainty, using the BUSI dataset and a ResNet-50 classifier. Two perturbation types are evaluated: (1) adversarial perturbations via the One Pixel Attack and (2) non-adversarial, device-related noise simulated by setting a single pixel to black. Robustness is assessed alongside uncertainty estimation using Monte Carlo Dropout, with metrics including Expected Kullback–Leibler divergence (EKL), Predictive Variance (PV), and Mutual Information (MI) for epistemic uncertainty, and Maximum Class Probability (MP) for aleatoric uncertainty. Both perturbations reduced accuracy, producing 17 and 29 “fooled” test samples, defined as cases classified correctly before but incorrectly after perturbation, for the adversarial and non-adversarial settings, respectively. Samples that remained correct are referred to as “unfooled.” Across all metrics, uncertainty increased after perturbation for both groups, and fooled samples had higher uncertainty than unfooled samples even before perturbation. We also identify spatially localized “uncertainty-decreasing” regions, where individual single-pixel blackouts both flipped predictions and reduced uncertainty, creating overconfident errors. These regions represent high-risk vulnerabilities that could be exploited in adversarial attacks or addressed through targeted robustness training and uncertainty-aware safeguards. Overall, combining perturbation analysis with uncertainty quantification provides valuable insights into model weaknesses and can inform the design of safer, more reliable AI systems for BUS diagnosis.