Training Sequence Design for Efficient Channel Estimation in MIMO-FBMC Systems

This paper is focused on training sequence design for efficient channel estimation in multiple-input multiple-output filterbank multicarrier (MIMO-FBMC) communications using offset quadrature amplitude modulation (OQAM). MIMO-FBMC is a promising technique to achieve high spectrum efficiency as well as strong robustness against dispersive channels due to its feature of time-frequency localization. A salient drawback of FBMC/OQAM signals is that only real-field orthogonality can be kept, leading to the intrinsic imaginary interference being a barrier for high-performance channel estimations. Also, conventional channel estimations in the MIMO-FBMC systems mostly suffer from high training overhead especially for large number of transmit antennas. Motivated by these problems, in this paper, we propose a new class of training sequences, which are formed by concatenation of two identical zero-correlation zone sequences whose auto-correlation and cross correlation are zero within a time-shift window around the in-phase position. Since only real-valued symbols can be transmitted in MIMO-FBMC systems, we propose “complex training sequence decomposition (CTSD)” to facilitate the reconstruction of the complex-field orthogonality of MIMO-FBMC signals. Our simulations validate that the proposed CTSD is an efficient channel estimation approach for practical preamble-based MIMO-FBMC systems

IKKβ Suppression of TSC1 Links Inflammation and Tumor Angiogenesis via the mTOR Pathway

SummaryTNFα has recently emerged as a regulator linking inflammation to cancer pathogenesis, but the detailed cellular and molecular mechanisms underlying this link remain to be elucidated. The tuberous sclerosis 1 (TSC1)/TSC2 tumor suppressor complex serves as a repressor of the mTOR pathway, and disruption of TSC1/TSC2 complex function may contribute to tumorigenesis. Here we show that IKKβ, a major downstream kinase in the TNFα signaling pathway, physically interacts with and phosphorylates TSC1 at Ser487 and Ser511, resulting in suppression of TSC1. The IKKβ-mediated TSC1 suppression activates the mTOR pathway, enhances angiogenesis, and results in tumor development. We further find that expression of activated IKKβ is associated with TSC1 Ser511 phosphorylation and VEGF production in multiple tumor types and correlates with poor clinical outcome of breast cancer patients. Our findings identify a pathway that is critical for inflammation-mediated tumor angiogenesis and may provide a target for clinical intervention in human cancer

Selection of DDX5 as a novel internal control for Q-RT-PCR from microarray data using a block bootstrap re-sampling scheme

<p>Abstract</p> <p>Background</p> <p>The development of microarrays permits us to monitor transcriptomes on a genome-wide scale. To validate microarray measurements, quantitative-real time-reverse transcription PCR (Q-RT-PCR) is one of the most robust and commonly used approaches. The new challenge in gene quantification analysis is how to explicitly incorporate statistical estimation in such studies. In the realm of statistical analysis, the various available methods of the probe level normalization for microarray analysis may result in distinctly different target selections and variation in the scores for the correlation between microarray and Q-RT-PCR. Moreover, it remains a major challenge to identify a proper internal control for Q-RT-PCR when confirming microarray measurements.</p> <p>Results</p> <p>Sixty-six Affymetrix microarray slides using lung adenocarcinoma tissue RNAs were analyzed by a statistical re-sampling method in order to detect genes with minimal variation in gene expression. By this approach, we identified <it>DDX5 </it>as a novel internal control for Q-RT-PCR. Twenty-three genes, which were differentially expressed between adjacent normal and tumor samples, were selected and analyzed using 24 paired lung adenocarcinoma samples by Q-RT-PCR using two internal controls, <it>DDX5 </it>and <it>GAPDH</it>. The percentage correlation between Q-RT-PCR and microarray were 70% and 48% by using <it>DDX5 </it>and <it>GAPDH </it>as internal controls, respectively.</p> <p>Conclusion</p> <p>Together, these quantification strategies for Q-RT-PCR data processing procedure, which focused on minimal variation, ought to significantly facilitate internal control evaluation and selection for Q-RT-PCR when corroborating microarray data.</p

Body Temperature Monitoring and SARS Fever Hotline, Taiwan

In Taiwan, a temperature-monitoring campaign and hotline for severe acute respiratory syndrome (SARS) fever were implemented in June 2003. Among 1,966 calls, fever was recorded in 19% (n = 378); 18 persons at high risk for SARS were identified. In a cross-sectional telephone survey, 95% (n = 1,060) of households knew about the campaign and 7 households reported fever

Establishing a risk scoring system for predicting erosive esophagitis

SummaryObjectiveThis study aims to establish a noninvasive scoring system to predict the risk of erosive esophagitis (EE).MethodsFrom 2002 to 2009, a total of 34,346 consecutive adults who underwent health check-ups and upper gastrointestinal endoscopy were retrospectively enrolled. Of the participants, 22,892 in the earlier two-thirds period of examination were defined as the training set and the remaining 11,454 as the validation set. EE was diagnosed by upper gastrointestinal endoscopy. Independent risk factors associated with EE were analyzed by multivariate analysis using a logistic regression model with the forward stepwise selection procedure in the training set. Subsequently, an EE risk scoring system was established and weighted by β coefficient. This risk scoring system was further validated in the validation set.ResultsIn the training set, older age, male gender, higher body mass index, higher waist circumference, higher serum triglyceride, and lower high-density lipid cholesterol levels were independent risk factors for predicting EE. According to the β coefficient value of each independent risk factor, the total score ranging from 0 to 10 was established, and then low- (0–3), moderate- (4–6), and high-risk (7–10) groups were identified. In the validation set, the prevalence rates of EE in the low-, moderate-, and high-risk groups were 5.15%, 15.76% and 26.11%, respectively (p < 0.001).ConclusionThis simple noninvasive risk scoring system, including factors of age, gender, body mass index, waist circumference, triglyceride, and high-density lipid cholesterol, effectively predicted EE and stratified its incidence