Co-Activation of TGFβ and Wnt Signalling Pathways Abrogates EMT in Ovarian Cancer Cells

The aggressive property of ovarian cancer (OC) in terms of epithelialmesenchymal transition (EMT), proliferation and metastasis are of major concern. Different growth factors including TGFβ are associated with regulating these molecular events but the underlying mechanisms remain unclear. The aim of this report is to decipher the regulation of EMT by co-activation of TGFβ and Wnt signalling cascades in gaining malignancy. Methods: The expression of the different components of signalling events were analyzed by QPCR, Western blot, Immunofluorescence microscopy and flow cytometry. β-catenin promoter activity was checked by luciferase assay. Results: We observed reduced EMT in ovarian cancer cells upon co-activation with TGFβ1 and LiCl as shown by the expressions of epithelial/ mesenchymal markers and the EMT promoting factor, Snail1, accompanied by decrease in the invasion and migration of the cells compared to individual pathway activation. A detailed study of the mechanism suggested reduction in the β-catenin and p-GSK3b (Ser 9) levels to be the driving cause of this phenomenon, which was reversed upon co-activation with higher concentrations of LiCl. Conclusions: Therefore, tumourigenesis might be affected by the concentration of ligand/ growth factors for the respective signalling pathways activated in the tumour microenvironment and interaction between them might alter tumourigenesis

Timing Analysis of Body Area Network Applications

Body area network (BAN) applications have stringent timing requirements. The timing behavior of a BAN application is determined not only by the software complexity, inputs, and architecture, but also by the timing behavior of the peripherals. This paper presents systematic timing analysis of such applications, deployed for health-care monitoring of patients staying at home. This monitoring is used to achieve prompt notification of the hospital when a patient shows abnormal vital signs. Due to the safetycritical nature of these applications,worst-case execution time (WCET) analysis is extremely important

InkStream: Real-time GNN Inference on Streaming Graphs via Incremental Update

Classic Graph Neural Network (GNN) inference approaches, designed for static graphs, are ill-suited for streaming graphs that evolve with time. The dynamism intrinsic to streaming graphs necessitates constant updates, posing unique challenges to acceleration on GPU. We address these challenges based on two key insights: (1) Inside the $k$-hop neighborhood, a significant fraction of the nodes is not impacted by the modified edges when the model uses min or max as aggregation function; (2) When the model weights remain static while the graph structure changes, node embeddings can incrementally evolve over time by computing only the impacted part of the neighborhood. With these insights, we propose a novel method, InkStream, designed for real-time inference with minimal memory access and computation, while ensuring an identical output to conventional methods. InkStream operates on the principle of propagating and fetching data only when necessary. It uses an event-based system to control inter-layer effect propagation and intra-layer incremental updates of node embedding. InkStream is highly extensible and easily configurable by allowing users to create and process customized events. We showcase that less than 10 lines of additional user code are needed to support popular GNN models such as GCN, GraphSAGE, and GIN. Our experiments with three GNN models on four large graphs demonstrate that InkStream accelerates by 2.5-427$\times$ on a CPU cluster and 2.4-343$\times$ on two different GPU clusters while producing identical outputs as GNN model inference on the latest graph snapshot

Combined on-line lifetime-energy optimization for asymmetric multicores

In this paper we present an architectural and on-line resource management solution to optimize lifetime reliability of asymmetric multicores while minimizing the system energy consumption, targeting both single nodes (multicores) as well as multiple ones (cluster of multicores). The solution exploits the different characteristics of the computing resources to achieve the desired performance while optimizing the lifetime/energy trade-off. The experimental results show that a combined optimization of energy and lifetime allows for achieving an extended lifetime (similar to the one pursued by lifetime-only optimization solutions) with a marginal energy consumption detriment (less than 2%) with respect to energy-aware but aging-unaware systems