On Achievable Rates of the Two-user Symmetric Gaussian Interference Channel

We study the Han-Kobayashi (HK) achievable sum rate for the two-user symmetric Gaussian interference channel. We find the optimal power split ratio between the common and private messages (assuming no time-sharing), and derive a closed form expression for the corresponding sum rate. This provides a finer understanding of the achievable HK sum rate, and allows for precise comparisons between this sum rate and that of orthogonal signaling. One surprising finding is that despite the fact that the channel is symmetric, allowing for asymmetric power split ratio at both users (i.e., asymmetric rates) can improve the sum rate significantly. Considering the high SNR regime, we specify the interference channel value above which the sum rate achieved using asymmetric power splitting outperforms the symmetric case.Comment: 7 pages, to appear in Allerton 201

Towards a scalable and efficient data classification technique.

Data Classification is a task that could be found in many life activities. In general, the term could be used for any activity that derives some decision or forecast based on the currently available information. Using a more accurate definition, a classification procedure is the construction of some kind of a method for making judgments for a continuing sequence of cases, where each new case must be assigned to one of pre-defined classes. This type of construction has been termed supervised learning, in order to distinguish it from unsupervised learning or clustering in which the classes are not pre-defined but are concluded from the available data. This thesis is divided into five chapters, analyzing three classification techniques, namely nearest neighbor technique, perceptron learning algorithm and multi-layer perceptrons with backpropagation, based on performance and scalability issues. Chapter one gives an introduction to the research topic of this thesis. In addition it states the problem that builds the core of this thesis and predefines the objective of this study, namely selecting the most efficient and scalable classification algorithm that suits a given classification task. Chapter two explores a historical review of the literature introduced in the classification domain. It focuses mainly on the topics that are related to this study and presents some of the new classification approaches. Chapter three introduces the way based on which this thesis is designed. The technical methodology used to analyze and investigate the three classification algorithms is clearly described. In this thesis different experiments are introduced to prove the findings. The datasets used here are considered to be real-life datasets that present sports players and cars classification tasks. Chapters four and five represent the main core of this thesis, as they contain the data analysis, main findings and conclusions that are derived from different experiments. The nearest neighbor classification technique is one of the lazy learners because before the classification process starts, it needs to store all of the training samples. But, although it takes more time to classify any unknown samples, it is considered the most efficient technique amont other classification techniques. A natural and future step would be using the single-layer perception algorithm that does not need to store the data samples to reach an acceptable convergence rate. Alternatively, it speeds the recognition or the learning process, because it learns and stores only the weights of the neural network used to implement the algorithm. This algorithm has a big deficiency: it only works for the linearly separable data samples. So, it is now a suitable phase to start working on a more scalable and efficient technique. It is the multi-layer perceptrons network with backpropagation that has the power of solving different complex and non-linearly separable classification tasks

Concordance of p16INK4a and E6*I mRNA among HPV-DNA-Positive Oropharyngeal, Laryngeal, and Oral Cavity Carcinomas from the ICO International Study

Simple Summary The utility of a diagnostic algorithm for the detection of HPV-driven oral cavity (OCC), oropharyngeal (OPC), and laryngeal (LC) carcinomas using HPV-DNA testing followed by p16(INK4a) immunohistochemistry, taking E6*I mRNA detection as the reference standard, was assessed in HPV-DNA-positive formalin-fixed paraffin-embedded samples from 29 countries. The concordance of p16(INK4a) and E6*I mRNA among 78, 257, and 51 HPV-DNA-positive OCC, OPC, and LC, respectively, was moderate to substantial in OCC and OPC but only fair in LC. A different p16(INK4a) expression pattern was observed in those cases HPV-DNA-positive for types other than HPV16, as compared to HPV16-positive cases. We concluded that the diagnostic algorithm of HPV-DNA testing followed by p16(INK4a) immunohistochemistry might be helpful in the diagnosis of HPV-driven OCC and OPC, but not LC. Our study provides new insights into the use HPV-DNA, p16(INK4a), and HPV-E6*I mRNA for diagnosing an HPV-driven head and neck carcinoma. Background: Tests or test algorithms for diagnosing HPV-driven oral cavity and laryngeal head and neck carcinomas (HNC) have not been yet validated, and the differences among oral cavity and laryngeal sites have not been comprehensively evaluated. We aimed to assess the utility of a diagnostic algorithm for the detection of HPV-driven oral cavity (OCC), oropharyngeal (OPC) and laryngeal (LC) carcinomas using HPV-DNA testing followed by p16(INK4a) immunohistochemistry, taking E6*I mRNA detection as the reference standard. Methods: Formalin-fixed paraffin-embedded OCC, OPC, and LC carcinomas were collected from pathology archives in 29 countries. All samples were subjected to histopathological evaluation, DNA quality control, and HPV-DNA detection. All HPV-DNA-positive samples (including 78 OCC, 257 OPC, and 51 LC out of 3680 HNC with valid HPV-DNA results) were also tested for p16(INK4a) immunohistochemistry and E6*I mRNA. Three different cutoffs of nuclear and cytoplasmic staining were evaluated for p16(INK4a): (a) >25%, (b) >50%, and (c) >= 70%. The concordance of p16(INK4a) and E6*I mRNA among HPV-DNA-positive OCC, OPC, and LC cases was assessed. Results: A total of 78 OCC, 257 OPC, and 51 LC were HPV-DNA-positive and further tested for p16(INK4a) and E6*I mRNA. The percentage of concordance between p16(INK4a) (cutoff >= 70%) and E6*I mRNA among HPV-DNA-positive OCC, OPC, and LC cases was 79.5% (95% CI 69.9-89.1%), 82.1% (95% CI 77.2-87.0%), and 56.9% (95% CI 42.3-71.4%), respectively. A p16(INK4a) cutoff of >50% improved the concordance although the improvement was not statistically significant. For most anatomical locations and p16(INK4a) cutoffs, the percentage of discordant cases was higher for HPV16- than HPV-non16-positive cases. Conclusions: The diagnostic algorithm of HPV-DNA testing followed by p16(INK4a) immunohistochemistry might be helpful in the diagnosis of HPV-driven OCC and OPC, but not LC. A different p16(INK4a) expression pattern was observed in those cases HPV-DNA-positive for types other than HPV16, as compared to HPV16-positive cases. Our study provides new insights into the use HPV-DNA, p16(INK4a), and HPV-E6*I mRNA for diagnosing an HPV-driven HNC, including the optimal HPV test or p16(INK4a) cutoffs to be used. More studies are warranted to clarify the role of p16(INK4a) and HPV status in both OPC and non-OPC HNC

Willingness to vaccinate against COVID-19 among healthcare workers: an online survey in 10 countries in the eastern Mediterranean region

BACKGROUND: Willingness of healthcare workers to be vaccinated is an important factor to be considered for a successful COVID-19 vaccination programme. This study aims to understand the willingness of health workers to receive a COVID-19 vaccine and its associated concerns across 10 countries in the Eastern Mediterranean Region (EMR). METHOD: A cross-sectional study was conducted in January 2021 among healthcare workers in EMR using an online survey. Data were analyzed using IBM SPSS software package version 20.0. RESULTS: A total of 2806 health workers (physicians, nurses and pharmacists) completed and returned the informed consent along with the questionnaire electronically. More than half of the respondents (58.0%) were willing to receive a COVID-19 vaccine, even if the vaccination is not mandatory for them. On the other hand, 25.7% of respondents were not willing to take COVID-19 vaccine while 16.3 % were undecided. The top three reasons for not willing to be vaccinated were unreliability of COVID-19 vaccine clinical trials (62.0%), fear of the side effects of the vaccine (45.3%), and that COVID-19 vaccine will not give immunity for a long period of time (23.1%). CONCLUSION: Overall, the study revealed suboptimal acceptance of COVID-19 vaccine among the respondents in the EMR. Significant refusal of COVID-19 vaccine among healthcare professionals can reverse hard-won progress in building public trust in vaccination program. The findings suggest the need to develop tailored strategies to address concerns identified in the study in order to ensure optimal vaccine acceptance among healthcare workers in the EMR