Skin Mast Cells Contribute to Sporothrix schenckii Infection

Background: Sporothrix schenckii (S. schenckii), a dimorphic fungus, causes sporotrichosis. Mast cells (MCs) have been described to be involved in skin fungal infections. The role of MCs in cutaneous sporotrichosis remains largely unknown. Objectives: To characterize the role and relevance of MCs in cutaneous sporotrichosis. Methods: We analyzed cutaneous sporotrichosis in wild-type (WT) mice and two different MC-deficient strains. In vitro, MCs were assessed for S. schenckii-induced cytokine production and degranulation after incubation with S. schenckii. We also explored the role of MCs in human cutaneous sporotrichosis. Results: WT mice developed markedly larger skin lesions than MC-deficient mice (> 1.5 fold) after infection with S. schenckii, with significantly increased fungal burden. S. schenckii induced the release of tumor necrosis factor alpha (TNF), interleukin (IL)-6, IL-10, and IL-1β by MCs, but not degranulation. S. schenckii induced larger skin lesions and higher release of IL-6 and TNF by MCs as compared to the less virulent S. albicans. In patients with sporotrichosis, TNF and IL-6 were increased in skin lesions, and markedly elevated levels in the serum were linked to disease activity. Conclusions: These findings suggest that cutaneous MCs contribute to skin sporotrichosis by releasing cytokines such as TNF and IL-6

dMAPAR-HMM: Reforming Traffic Model for Improving Performance Bound with Stochastic Network Calculus

A popular branch of stochastic network calculus (SNC) utilizes moment-generating functions (MGFs) to characterize arrivals and services, which enables end-to-end performance analysis. However, existing traffic models for SNC cannot effectively represent the complicated nature of real-world network traffic such as dramatic burstiness. To conquer this challenge, we propose an adaptive spatial-temporal traffic model: dMAPAR-HMM. Specifically, we model the temporal on-off switching process as a dual Markovian arrival process (dMAP) and the arrivals during the on phases as an autoregressive hidden Markov model (AR-HMM). The dMAPAR-HMM model fits in with the MGF-SNC analysis framework, unifies various state-of-the-art arrival models, and matches real-world data more closely. We perform extensive experiments with real-world traces under different network topologies and utilization levels. Experimental results show that dMAPAR-HMM significantly outperforms prevailing models in MGF-SNC

The Alteration of Runner and Partial Vanes on a Fixed Blade Propeller Water Turbine Basing on the Full Passage Simulation

ABSTRACT Basing on the 3D-steady Navier-Stokes equations with standard k-ε turbulence closure models, non-structure mesh with fitted body coordinate and finite element based finite volume method, the internal flow on the full passage of the 6.5-meters head fixed blade propeller water turbine is analyzed. Numerical results show that the low output is caused by unsuitable full passage. The flow on the stay vanes isn't uniform and the circumferential velocity of the runner rim is too large, which leads to a high loss in the draft tube. So the runner and partial stay vanes in the concrete spiral casing are redesigned. The output of the full passage with new runner and new partial stay vanes under 6.5-meters head is 295KW larger than the old one with 240KW output, and the efficiency is 81%, which is larger than former 70%. The redesign of runner and stay vanes is successful

Sensing Aided Covert Communications: Turning Interference into Allies

In this paper, we investigate the realization of covert communication in a general radar-communication cooperation system, which includes integrated sensing and communications as a special example. We explore the possibility of utilizing the sensing ability of radar to track and jam the aerial adversary target attempting to detect the transmission. Based on the echoes from the target, the extended Kalman filtering technique is employed to predict its trajectory as well as the corresponding channels. Depending on the maneuvering altitude of adversary target, two channel models are considered, with the aim of maximizing the covert transmission rate by jointly designing the radar waveform and communication transmit beamforming vector based on the constructed channels. For the free-space propagation model, by decoupling the joint design, we propose an efficient algorithm to guarantee that the target cannot detect the transmission. For the Rician fading model, since the multi-path components cannot be estimated, a robust joint transmission scheme is proposed based on the property of the Kullback-Leibler divergence. The convergence behaviour, tracking MSE, false alarm and missed detection probabilities, and covert transmission rate are evaluated. Simulation results show that the proposed algorithms achieve accurate tracking. For both channel models, the proposed sensing-assisted covert transmission design is able to guarantee the covertness, and significantly outperforms the conventional schemes.Comment: 13 pages, 12 figures, submitted to IEEE journals for potential publicatio

Utilizing Longitudinal Chest X-Rays and Reports to Pre-Fill Radiology Reports

Despite the reduction in turn-around times in radiology reports with the use of speech recognition software, persistent communication errors can significantly impact the interpretation of the radiology report. Pre-filling a radiology report holds promise in mitigating reporting errors, and despite efforts in the literature to generate medical reports, there exists a lack of approaches that exploit the longitudinal nature of patient visit records in the MIMIC-CXR dataset. To address this gap, we propose to use longitudinal multi-modal data, i.e., previous patient visit CXR, current visit CXR, and previous visit report, to pre-fill the 'findings' section of a current patient visit report. We first gathered the longitudinal visit information for 26,625 patients from the MIMIC-CXR dataset and created a new dataset called Longitudinal-MIMIC. With this new dataset, a transformer-based model was trained to capture the information from longitudinal patient visit records containing multi-modal data (CXR images + reports) via a cross-attention-based multi-modal fusion module and a hierarchical memory-driven decoder. In contrast to previous work that only uses current visit data as input to train a model, our work exploits the longitudinal information available to pre-fill the 'findings' section of radiology reports. Experiments show that our approach outperforms several recent approaches. Code will be published at https://github.com/CelestialShine/Longitudinal-Chest-X-Ray

Optimal maintenance strategy for systems with two failure modes

This paper considers a single-unit system subject to two types of failures: a traditional catastrophic failure and a two-stage delayed failure. Periodic inspections are carried out to identify the defective stage of the two-stage failure process, whereas preventive replacements are implemented to avoid any potential failure due to the catastrophic failure mode. We construct a basic maintenance model and then extend it to the cases of imperfect inspections (i.e., inspections that do not always notice a defective state). We analyze the renewal process of the system and establish the expected long-run cost rate (ELRCR). The optimal inspection period and preventive replacement interval are determined by minimizing the ELRCR. A case study on infusion pumps is presented to illustrate the proposed model