Wideband Spectrum Sensing on Real-Time Signals at Sub-Nyquist Sampling Rates in Single and Cooperative Multiple Nodes

The authors would like to acknowledge the Engineering and Physical Sciences Research Council (EPSRC) in the UK for their support of this work with Grant No. EP/L024241/1. Mark D. Plumbley was partly supported by a Leadership Fellowship (EP/G007144/1) from the UK EPSR

Low-rank matrix completion based malicious user detection in cooperative spectrum sensing

In a cognitive radio (CR) system, cooperative spectrum sensing (CSS) is the key to improving sensing performance in deep fading channels. In CSS networks, signals received at the secondary users (SUs) are sent to a fusion center to make a final decision of the spectrum occupancy. In this process, the presence of malicious users sending false sensing samples can severely degrade the performance of the CSS network. In this paper, with the compressive sensing (CS) technique being implemented at each SU, we build a CSS network with double sparsity property. A new malicious user detection scheme is proposed by utilizing the adaptive outlier pursuit (AOP) based low-rank matrix completion in the CSS network. In the proposed scheme, the malicious users are removed in the process of signal recovery at the fusion center. The numerical analysis of the proposed scheme is carried out and compared with an existing malicious user detection algorithm

How to Make a Better-Informed Go-No-Go Decision in Oncology Trials Using Tumor Measurement Data and Beyond

In this paper, we discussed new methods to summarize clinical efficacy data. The method is called “Trajectory and their corresponding non-missing Proportions Plot (TPP). The method focuses on the conditional mean response, condition on that the data are non-missing, and the proportion of non-missing, overtime. This graph is proposed for all trials to assess the treatment effect and dropout pattern across all time points. These two parameters are jointly used to access the treatment effect to answer a particular question about effectiveness that reduces the ambiguity of phrasing in federal regulations. It will be seen that it requires both parameters to conclude that one treatment is better than other at a particular time point. The first parameter is obvious, while the second parameter is from the belief that the proportion of non-missing from the more effective treatment (A) should not be less than that of the less effective treatment (B) to conclude that A is better than B. Another way to put this is: to conclude A is better than B, we should not know less about A than B. From the parameters defined above, the missing proportions are an integral and intrinsic part of our data and are used in assessing non-missing probability. The non-missing proportion overtime reflects the reality in practice and is a very important information for stakeholders. The paper focuses on using the approach in making go-no-go decision in oncology proof-of-concept trials

Efficient compressive spectrum sensing algorithm for M2M devices

Spectrum used for Machine-to-Machine (M2M) communications should be as cheap as possible or even free in order to connect billions of devices. Recently, both UK and US regulators have conducted trails and pilots to release the UHF TV spectrum for secondary licence-exempt applications. However, it is a very challenging task to implement wideband spectrum sensing in compact and low power M2M devices as high sampling rates are very expensive and difficult to achieve. In recent years, compressive sensing (CS) technique makes fast wideband spectrum sensing possible by taking samples at sub-Nyquist sampling rates. In this paper, we propose a two-step CS based spectrum sensing algorithm. In the first step, the CS is implemented in an SU and only part of the spectrum of interest is supposed to be sensed by an SU in each sensing period to reduce the complexity in the signal recovery process. In the second step, a denoising algorithm is proposed to improve the detection performance of spectrum sensing. The proposed two-step CS based spectrum sensing is compared with the traditional scheme and the theoretical curves

Holographic two dimensional QCD and Chern-Simons term

We present a holographic realization of large Nc massless QCD in two dimensions using a D2/D8 brane construction. The flavor axial anomaly is dual to a three dimensional Chern-Simons term which turns out to be of leading order, and it affects the meson spectrum and holographic renormalization in crucial ways. The massless flavor bosons that exist in the spectrum are found to decouple from the heavier mesons, in agreement with the general lore of non-Abelian bosonization. We also show that an external dynamical photon acquires a mass through the three dimensional Chern-Simons term as expected from the Schwinger mechanism. Massless two dimensional QCD at large Nc exhibits anti-vector-meson dominance due to the axial anomaly.Comment: 22 page

Evaluation of retinal nerve fibre layer thickness as a possible measure of diabetic retinal neurodegeneration in the EPIC-Norfolk Eye Study

Background/aims: Markers to clinically evaluate structural changes from diabetic retinal neurodegeneration (DRN) have not yet been established. To study the potential role of peripapillary retinal nerve fibre layer (pRNFL) thickness as a marker for DRN, we evaluated the relationship between diabetes, as well as glycaemic control irrespective of diabetes status and pRNFL thickness. Methods: Leveraging data from a population-based cohort, we used general linear mixed models (GLMMs) with a random intercept for patient and eye to assess the association between pRNFL thickness (measured using GDx) and demographic, systemic and ocular parameters after adjusting for typical scan score. GLMMs were also used to determine: (1) the relationship between: (A) glycated haemoglobin (HbA1c) irrespective of diabetes diagnosis and pRNFL thickness, (B) diabetes and pRNFL thickness and (2) which quadrants of pRNFL may be affected in participants with diabetes and in relation to HbA1c. Results: 7076 participants were included. After controlling for covariates, inferior pRNFL thickness was 0.94 µm lower (95% CI −1.28 µm to −0.60 µm), superior pRNFL thickness was 0.83 µm lower (95% CI −1.17 µm to −0.49 µm) and temporal pRNFL thickness was 1.33 µm higher (95% CI 0.99 µm to 1.67 µm) per unit increase in HbA1c. Nasal pRNFL thickness was not significantly associated with HbA1c (p=0.23). Similar trends were noted when diabetes was used as the predictor. Conclusion: Superior and inferior pRNFL was significantly thinner among those with higher HbA1c levels and/or diabetes, representing areas of the pRNFL that may be most affected by diabetes

From Nonspecific DNA–Protein Encounter Complexes to the Prediction of DNA–Protein Interactions

©2009 Gao, Skolnick. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.doi:10.1371/journal.pcbi.1000341DNA–protein interactions are involved in many essential biological activities. Because there is no simple mapping code between DNA base pairs and protein amino acids, the prediction of DNA–protein interactions is a challenging problem. Here, we present a novel computational approach for predicting DNA-binding protein residues and DNA–protein interaction modes without knowing its specific DNA target sequence. Given the structure of a DNA-binding protein, the method first generates an ensemble of complex structures obtained by rigid-body docking with a nonspecific canonical B-DNA. Representative models are subsequently selected through clustering and ranking by their DNA–protein interfacial energy. Analysis of these encounter complex models suggests that the recognition sites for specific DNA binding are usually favorable interaction sites for the nonspecific DNA probe and that nonspecific DNA–protein interaction modes exhibit some similarity to specific DNA–protein binding modes. Although the method requires as input the knowledge that the protein binds DNA, in benchmark tests, it achieves better performance in identifying DNA-binding sites than three previously established methods, which are based on sophisticated machine-learning techniques. We further apply our method to protein structures predicted through modeling and demonstrate that our method performs satisfactorily on protein models whose root-mean-square Ca deviation from native is up to 5 Å from their native structures. This study provides valuable structural insights into how a specific DNA-binding protein interacts with a nonspecific DNA sequence. The similarity between the specific DNA–protein interaction mode and nonspecific interaction modes may reflect an important sampling step in search of its specific DNA targets by a DNA-binding protein

OxyCAP UK: Oxyfuel Combustion - academic Programme for the UK

The OxyCAP-UK (Oxyfuel Combustion - Academic Programme for the UK) programme was a £2 M collaboration involving researchers from seven UK universities, supported by E.On and the Engineering and Physical Sciences Research Council. The programme, which ran from November 2009 to July 2014, has successfully completed a broad range of activities related to development of oxyfuel power plants. This paper provides an overview of key findings arising from the programme. It covers development of UK research pilot test facilities for oxyfuel applications; 2-D and 3-D flame imaging systems for monitoring, analysis and diagnostics; fuel characterisation of biomass and coal for oxyfuel combustion applications; ash transformation/deposition in oxyfuel combustion systems; materials and corrosion in oxyfuel combustion systems; and development of advanced simulation based on CFD modelling

Detecting retinal neurodegeneration in people with diabetes: Findings from the UK Biobank

IMPORTANCE: Efforts are underway to incorporate retinal neurodegeneration in the diabetic retinopathy severity scale. However, there is no established measure to quantify diabetic retinal neurodegeneration (DRN). OBJECTIVE: We compared total retinal, macular retinal nerve fiber layer (mRNFL) and ganglion cell-inner plexiform layer (GC-IPL) thickness among participants with and without diabetes (DM) in a population-based cohort. DESIGN/SETTING/PARTICIPANTS: Cross-sectional analysis, using the UK Biobank data resource. Separate general linear mixed models (GLMM) were created using DM and glycated hemoglobin as predictor variables for retinal thickness. Sub-analyses included comparing thickness measurements for patients with no/mild diabetic retinopathy (DR) and evaluating factors associated with retinal thickness in participants with and without diabetes. Factors found to be significantly associated with DM or thickness were included in a multiple GLMM. EXPOSURE: Diagnosis of DM was determined via self-report of diagnosis, medication use, DM-related complications or glycated hemoglobin level of ≥ 6.5%. MAIN OUTCOMES AND MEASURES: Total retinal, mRNFL and GC-IPL thickness. RESULTS: 74,422 participants (69,985 with no DM; 4,437 with DM) were included. Median age was 59 years, 46% were men and 92% were white. Participants with DM had lower total retinal thickness (-4.57 μm, 95% CI: -5.00, -4.14; p<0.001), GC-IPL thickness (-1.73 μm, 95% CI: -1.86, -1.59; p<0.001) and mRNFL thickness (-0.68 μm, 95% CI: -0.81, -0.54; p<0.001) compared to those without DM. After adjusting for co-variates, in the GLMM, total retinal thickness was 1.99 um lower (95% CI: -2.47, -1.50; p<0.001) and GC-IPL was 1.02 μm lower (95% CI: -1.18, -0.87; p<0.001) among those with DM compared to without. mRNFL was no longer significantly different (p = 0.369). GC-IPL remained significantly lower, after adjusting for co-variates, among those with DM compared to those without DM when including only participants with no/mild DR (-0.80 μm, 95% CI: -0.98, -0.62; p<0.001). Total retinal thickness decreased 0.40 μm (95% CI: -0.61, -0.20; p<0.001), mRNFL thickness increased 0.20 μm (95% CI: 0.14, 0.27; p<0.001) and GC-IPL decreased 0.26 μm (95% CI: -0.33, -0.20; p<0.001) per unit increase in A1c after adjusting for co-variates. Among participants with diabetes, age, DR grade, ethnicity, body mass index, glaucoma, spherical equivalent, and visual acuity were significantly associated with GC-IPL thickness. CONCLUSION: GC-IPL was thinner among participants with DM, compared to without DM. This difference persisted after adjusting for confounding variables and when considering only those with no/mild DR. This confirms that GC-IPL thinning occurs early in DM and can serve as a useful marker of DRN