Directorate for Computer & Information Science & Engineering

Introduction Ovarian cancer recurs in most High Grade Serous Ovarian Cancer (HGSOC) patients, including initial responders, after standard of care. To improve patient survival, we need to identify and understand the factors contributing to early or late recurrence and therapeutically target these mechanisms. We hypothesized that in HGSOC, the response to chemotherapy is associated with a specific gene expression signature determined by the tumor microenvironment. In this study, we sought to determine the differences in gene expression and the tumor immune microenvironment between patients who show early recurrence (within 6 months) compared to those who show late recurrence following chemotherapy. Methods Paired tumor samples were obtained before and after Carboplatin and Taxol chemotherapy from 24 patients with HGSOC. Bioinformatic transcriptomic analysis was performed on the tumor samples to determine the gene expression signature associated with differences in recurrence pattern. Gene Ontology and Pathway analysis was performed using AdvaitaBio’s iPathwayGuide software. Tumor immune cell fractions were imputed using CIBERSORTx. Results were compared between late recurrence and early recurrence patients, and between paired pre-chemotherapy and post-chemotherapy samples. Results There was no statistically significant difference between early recurrence or late recurrence ovarian tumors pre-chemotherapy. However, chemotherapy induced significant immunological changes in tumors from late recurrence patients but had no impact on tumors from early recurrence patients. The key immunological change induced by chemotherapy in late recurrence patients was the reversal of pro-tumor immune signature. Discussion We report for the first time, the association between immunological modifications in response to chemotherapy and the time of recurrence. Our findings provide novel opportunities to ultimately improve ovarian cancer patient survival.

The ubiquity of wireless connectivity between an ever-increasing number of devices has confirmed dire past predictions of widespread eavesdropping (among other security concerns) in wireless networks, further promoting the need for secure communications. Of particular interest are physical layer techniques which exploit error-correction coding intrinsic to digital transceivers and which offer two clear advantages: (i) no need for key distribution; and (ii) secrecy that is not conditioned on a computationally limited adversary. Present techniques, however, assume some knowledge of the channel connecting the transmitter to the eavesdropper; if the eavesdropper is truly passive, this critical parameter is unavailable. We therefore examine secure communication schemes that do not assume knowledge of the eavesdropper's channel, yet remain keyless in their operation. Two schemes are offered, with behavior intermediate between cryptographic and information-theoretic solutions.

This is an introduction to the special section of papers from DySPAN 2007 - the IEEE Symposium on New Frontiers in Dynamic Spectrum Access Networks that was held on April 17-20, 2007 in Dublin, Ireland.

The purpose of the special issue is to illustrate the complexity of radio communications standards by emphasizing the variety of technical topics that are being considered for beyond 3G (B3G) systems. All of the contributions are from the Virtual Centre of Excellence in Mobile and Personal Communications (Mobile VCE).

Reports on the mission and scope of the National Science Foundation's Computer and Information Science and Engineering (CISE) Core Solicitation (NSF20-6161 that was released in September 2021. The CISE Core encompasses the Communications and Information Foundations (CIF) program, the traditional home of Information Theory within NSF.

This paper explores strategies to predict and adapt to volatile financial markets. The goal of the project is to enhance investorspsila abilities to evaluate current market conditions and invest accordingly, maximizing future returns and minimizing the associated risk. By analyzing predictive indicators and constructing investment models, the project seeks to develop a reliable method to predict and adapt to market movements. Testing indicates there are benefits that come from using various indicators to qualify investment strategies based on market conditions. Incorporating specific factors into investment models allows for the enhancement of returns while simultaneously reducing risks. Given the nature of compound interest, this is a noteworthy improvement that will make a significant difference in the long run.