MOTIVATION: Intracellular signaling pathways regulate essential cellular functions and orchestrate complex biological processes, yet their dynamic activity remains challenging to quantify with precision. Advances in single-cell omics enable pathway activity inference at the transcriptional level; however, existing computational tools often overlook mechanistic features of signaling networks, failing to formally treat the expected directionality of transcriptional change due to signal transduction. To address this technological gap, we have engineered PathwayEmbed, an R-based computational framework for estimating intracellular signal transduction states from single-cell transcriptomic datasets. RESULTS: PathwayEmbed integrates KEGG pathway information with perturbation-derived RNA sequencing data to assign directional coefficients that capture gene-specific transcriptional responses to pathway activation, repression, and/or signal transduction. These coefficients, in combination with the input data, are used to compute hypothetic ON/OFF range for each pathway. Each cell is then mapped to a specific location between these ON/OFF states, and activity scores are then computed based on the distances to these reference states, providing a continuous and interpretable measure of signaling activity at single-cell resolution. This framework enables robust visualization and quantitative comparison of pathway activity across cell populations. Applied to spatial transcriptomic data, PathwayEmbed captures spatial variation in signaling transduction states and allows comparisons at both temporal and spatial scale. The framework takes tabular data as input and is broadly compatible with established single-cell analysis workflows, supports user-defined pathway ground-truths, and offers a flexible, mechanistically informed approach for quantifying and comparing intracellular signaling activity in a wide variety of contexts. AVAILABILITY: PathwayEmbed is an open-source R software under academic free license, and it is available at https://github.com/raredonlab/PathwayEmbed. Use-case vignettes are available at https://raredonlab.github.io/PathwayEmbed/.

Magnetic Resonance Spectroscopic Imaging (MRSI) is a non-invasive imaging technique for studying metabolism and has become a crucial tool for understanding neurological diseases , cancers and diabetes. High spatial resolution MRSI is needed to characterize lesions, but in practice MRSI is acquired at low resolution due to time and sensitivity restrictions caused by the low metabolite concentrations. Therefore, there is an imperative need for a post-processing approach to generate high-resolution MRSI from low-resolution data that can be acquired fast and with high sensitivity. Deep learning-based super-resolution methods provided promising results for improving the spatial resolution of MRSI, but they still have limited capability to generate accurate and high-quality images. Recently, diffusion models have demonstrated superior learning capability than other generative models in various tasks, but sampling from diffusion models requires iterating through a large number of diffusion steps, which is time-consuming. This work introduces a Flow-based Truncated Denoising Diffusion Model (FTDDM) for super-resolution MRSI, which shortens the diffusion process by truncating the diffusion chain, and the truncated steps are estimated using a normalizing flow-based network. The network is conditioned on upscaling factors to enable multi-scale super-resolution. To train and evaluate the deep learning models, we developed a 1 H-MRSI dataset acquired from 25 high-grade glioma patients. We demonstrate that FTDDM outperforms existing generative models while speeding up the sampling process by over 9-fold compared to the baseline diffusion model. Neuroradiologists’ evaluations confirmed the clinical advantages of our method, which also supports uncertainty estimation and sharpness adjustment, extending its potential clinical applications.

IP networks have seen tremendous growth in not only their size and speed, but also in the volume of traffic they carry. Over-provisioning is commonly used to protect network performance against traffic variations, be they caused by failures or transient surges. This paper investigates the influence that increasing network size has on the efficacy of over-provisioning in absorbing a certain range of traffic variations and preserving performance guarantees. For that purpose, we develop a general model that accounts for network topology, base offered traffic, and traffic variations, and allows us to explore how their combination behaves as the network and the traffic it carries grow. The model's generality enables us to investigate several representative scenarios and to identify critical thresholds in the relation between network and traffic growth, which delineate regions where a given amount of over-provisioning provides increasingly better protection against traffic variations. The results offer insight into how to grow IP networks in order to enhance their robustness

Today's Internet carries an ever broadening range of application traffic with different requirements. This has stressed its original, one-class, best-effort model, and has been one of the main drivers behind the many efforts aimed at introducing QoS. Those efforts have, however, experienced only limited success because their added complexity often conflict with the scalability requirements of the Internet. This has motivated many proposals that try to offer service differentiation while keeping complexity low. This paper shares similar goals and proposes a simple scheme, BoundedRandomDrop (BRD), that supports multiple service classes. BRD focuses on loss differentiation, as although both losses and delay are important performance parameters, the steadily rising speed of Internet links is progressively limiting the impact of delay differentiation. BRD offers strong loss differentiation capabilities with minimal added cost. BRD does not require traffic profiles or admission controls. It guarantees each class losses that, when feasible, are no worse than a specified bound, and enforces differentiation only when required to meet those bounds. In addition, BRD is implemented using a single FIFO queue and a simple random dropping mechanism. The performance of BRD is investigated for a broad range of traffic mixes and shown to consistently achieve its design goals.