Chronic Sinusitis: The Empiric Treatment Strikes Back: Is CRS Directly Caused by Infectious Agent(s)?

Chronic sinusitis leads to unresolved infection and inflammation resulting in tissue remodeling, then further propagates the vicious cycle of deterioration and dysfunction of the sinuses’ natural defense mechanisms, and yet another cycle of infection and mucosal injury. Antibiotic therapy targeting pathogens classically implicated in sinusitis could augment the risk of therapeutic failure through the natural selection of resistant and/or virulent pathogens, especially in the presence of Gram-negative E. coli. Our recent demonstration of highly pathogenic E. coli, detected through intraoperative biopsy of sinus tissue, allowed the resolution of chronic sinusitis symptoms upon E. coli targeted therapy. The isolated E. coli carried three genes, each coding biofilm formation, which may, in part, account for the chronicity of E. coli sinusitis. We recommend that, patients with chronic sinusitis be considered for intraoperative biopsy for unusual pathogens, therefore allowing targeted therapy. In the future, use of vaccines and biofilm inhibitors might be an effective therapeutic consideration

Studies of Breakup Mechanisms in the Reaction of E/A = 25 MeV 6-Li Ions with 232-U

This research was sponsored by the National Science Foundation Grant NSF PHY 87-1440

Deformed Heisenberg algebra and minimal length

A one-dimensional deformed Heisenberg algebra $[X,P]=if(P)$ is studied. We answer the question: For what function of deformation $f(P)$ there exists a nonzero minimal uncertainty in position (minimal length). We also find an explicit expression for the minimal length in the case of arbitrary function of deformation.Comment: to be published in JP

Przyszłość teletransmisji. Przegląd Zagadnień Łączności, 1965, nr 6 (45)

Tłumaczenia artykułó

Fast divide-and-conquer algorithms for preemptive scheduling problems with controllable processing times – A polymatroid optimization approach

We consider a variety of preemptive scheduling problems with controllable processing times on a single machine and on identical/uniform parallel machines, where the objective is to minimize the total compression cost. In this paper, we propose fast divide-and-conquer algorithms for these scheduling problems. Our approach is based on the observation that each scheduling problem we discuss can be formulated as a polymatroid optimization problem. We develop a novel divide-and-conquer technique for the polymatroid optimization problem and then apply it to each scheduling problem. We show that each scheduling problem can be solved in O(Tfeas(n) log n) time by using our divide-and-conquer technique, where n is the number of jobs and Tfeas(n) denotes the time complexity of the corresponding feasible scheduling problem with n jobs. This approach yields faster algorithms for most of the scheduling problems discussed in this paper

Real-Time Onboard Object Detection for Augmented Reality: Enhancing Head-Mounted Display with YOLOv8

This paper introduces a software architecture for real-time object detection using machine learning (ML) in an augmented reality (AR) environment. Our approach uses the recent state-of-the-art YOLOv8 network that runs onboard on the Microsoft HoloLens 2 head-mounted display (HMD). The primary motivation behind this research is to enable the application of advanced ML models for enhanced perception and situational awareness with a wearable, hands-free AR platform. We show the image processing pipeline for the YOLOv8 model and the techniques used to make it real-time on the resource-limited edge computing platform of the headset. The experimental results demonstrate that our solution achieves real-time processing without needing offloading tasks to the cloud or any other external servers while retaining satisfactory accuracy regarding the usual mAP metric and measured qualitative performanc