Simultaneous Matrix Diagonalization for Structural Brain Networks Classification

This paper considers the problem of brain disease classification based on connectome data. A connectome is a network representation of a human brain. The typical connectome classification problem is very challenging because of the small sample size and high dimensionality of the data. We propose to use simultaneous approximate diagonalization of adjacency matrices in order to compute their eigenstructures in more stable way. The obtained approximate eigenvalues are further used as features for classification. The proposed approach is demonstrated to be efficient for detection of Alzheimer's disease, outperforming simple baselines and competing with state-of-the-art approaches to brain disease classification

Foothill: A Quasiconvex Regularization for Edge Computing of Deep Neural Networks

Deep neural networks (DNNs) have demonstrated success for many supervised learning tasks, ranging from voice recognition, object detection, to image classification. However, their increasing complexity might yield poor generalization error that make them hard to be deployed on edge devices. Quantization is an effective approach to compress DNNs in order to meet these constraints. Using a quasiconvex base function in order to construct a binary quantizer helps training binary neural networks (BNNs) and adding noise to the input data or using a concrete regularization function helps to improve generalization error. Here we introduce foothill function, an infinitely differentiable quasiconvex function. This regularizer is flexible enough to deform towards $L_1$ and $L_2$ penalties. Foothill can be used as a binary quantizer, as a regularizer, or as a loss. In particular, we show this regularizer reduces the accuracy gap between BNNs and their full-precision counterpart for image classification on ImageNet.Comment: Accepted in 16th International Conference of Image Analysis and Recognition (ICIAR 2019

Selection of tuning parameters in bridge regression models via Bayesian information criterion

We consider the bridge linear regression modeling, which can produce a sparse or non-sparse model. A crucial point in the model building process is the selection of adjusted parameters including a regularization parameter and a tuning parameter in bridge regression models. The choice of the adjusted parameters can be viewed as a model selection and evaluation problem. We propose a model selection criterion for evaluating bridge regression models in terms of Bayesian approach. This selection criterion enables us to select the adjusted parameters objectively. We investigate the effectiveness of our proposed modeling strategy through some numerical examples.Comment: 20 pages, 5 figure

Manifold Elastic Net: A Unified Framework for Sparse Dimension Reduction

It is difficult to find the optimal sparse solution of a manifold learning based dimensionality reduction algorithm. The lasso or the elastic net penalized manifold learning based dimensionality reduction is not directly a lasso penalized least square problem and thus the least angle regression (LARS) (Efron et al. \cite{LARS}), one of the most popular algorithms in sparse learning, cannot be applied. Therefore, most current approaches take indirect ways or have strict settings, which can be inconvenient for applications. In this paper, we proposed the manifold elastic net or MEN for short. MEN incorporates the merits of both the manifold learning based dimensionality reduction and the sparse learning based dimensionality reduction. By using a series of equivalent transformations, we show MEN is equivalent to the lasso penalized least square problem and thus LARS is adopted to obtain the optimal sparse solution of MEN. In particular, MEN has the following advantages for subsequent classification: 1) the local geometry of samples is well preserved for low dimensional data representation, 2) both the margin maximization and the classification error minimization are considered for sparse projection calculation, 3) the projection matrix of MEN improves the parsimony in computation, 4) the elastic net penalty reduces the over-fitting problem, and 5) the projection matrix of MEN can be interpreted psychologically and physiologically. Experimental evidence on face recognition over various popular datasets suggests that MEN is superior to top level dimensionality reduction algorithms.Comment: 33 pages, 12 figure

Luminescent platinum(II) complexes with functionalized N-heterocyclic carbene or diphosphine selectively probe mismatched and abasic DNA

published_or_final_versio

Intracellular interferons in fish : a unique means to combat viral infection

Peer reviewedPublisher PD

Exercise referral for drug users aged 40 and over: results of a pilot study in the UK.

OBJECTIVES: To test whether older drug users (aged 40 and over) could be recruited to an exercise referral (ER) scheme, to evaluate the feasibility and acceptability and measure the impact of participation on health. DESIGN: Observational pilot. SETTING: Liverpool, UK. PARTICIPANTS: (1) 12 men and 5 women recruited to ER. (2) 7 specialist gym instructors. OUTCOME MEASURES: Logistic feasibility and acceptability of ER and associated research, rate of recruitment, level of participation over 8 weeks and changes in health. RESULTS: 22 gym inductions were arranged (recruitment time: 5 weeks), 17 inductions were completed and 14 participants began exercising. Attendance at the gym fluctuated with people missing weeks then re-engaging; in week 8, seven participants were in contact with the project and five of these attended the gym. Illness and caring responsibilities affected participation. Participants and gym instructors found the intervention and associated research processes acceptable. In general, participants enjoyed exercising and felt fitter, but would have welcomed more support and the offer of a wider range of activities. Non-significant reductions in blood pressure and heart rate and improvements in metabolic equivalents (METs; a measure of fitness) and general well-being were observed for eight participants who completed baseline and follow-up assessments. The number of weeks of gym attendance was significantly associated with a positive change in METs. CONCLUSIONS: It is feasible to recruit older drug users into a gym-based ER scheme, but multiple health and social challenges affect their ability to participate regularly. The observed changes in health measures, particularly the association between improvements in METs and attendance, suggest further investigation of ER for older drug users is worthwhile. Measures to improve the intervention and its evaluation include: better screening, refined inclusion/exclusion criteria, broader monitoring of physical activity levels, closer tailored support, more flexible exercise options and the use of incentives

Phenoloxidase activity acts as a mosquito innate immune response against infection with semliki forest virus

Several components of the mosquito immune system including the RNA interference (RNAi), JAK/STAT, Toll and IMD pathways have previously been implicated in controlling arbovirus infections. In contrast, the role of the phenoloxidase (PO) cascade in mosquito antiviral immunity is unknown. Here we show that conditioned medium from the Aedes albopictus-derived U4.4 cell line contains a functional PO cascade, which is activated by the bacterium Escherichia coli and the arbovirus Semliki Forest virus (SFV) (Togaviridae; Alphavirus). Production of recombinant SFV expressing the PO cascade inhibitor Egf1.0 blocked PO activity in U4.4 cell- conditioned medium, which resulted in enhanced spread of SFV. Infection of adult female Aedes aegypti by feeding mosquitoes a bloodmeal containing Egf1.0-expressing SFV increased virus replication and mosquito mortality. Collectively, these results suggest the PO cascade of mosquitoes plays an important role in immune defence against arboviruses

Recursive regularization for inferring gene networks from time-course gene expression profiles

<p>Abstract</p> <p>Background</p> <p>Inferring gene networks from time-course microarray experiments with vector autoregressive (VAR) model is the process of identifying functional associations between genes through multivariate time series. This problem can be cast as a variable selection problem in Statistics. One of the promising methods for variable selection is the elastic net proposed by Zou and Hastie (2005). However, VAR modeling with the elastic net succeeds in increasing the number of true positives while it also results in increasing the number of false positives.</p> <p>Results</p> <p>By incorporating relative importance of the VAR coefficients into the elastic net, we propose a new class of regularization, called recursive elastic net, to increase the capability of the elastic net and estimate gene networks based on the VAR model. The recursive elastic net can reduce the number of false positives gradually by updating the importance. Numerical simulations and comparisons demonstrate that the proposed method succeeds in reducing the number of false positives drastically while keeping the high number of true positives in the network inference and achieves two or more times higher true discovery rate (the proportion of true positives among the selected edges) than the competing methods even when the number of time points is small. We also compared our method with various reverse-engineering algorithms on experimental data of MCF-7 breast cancer cells stimulated with two ErbB ligands, EGF and HRG.</p> <p>Conclusion</p> <p>The recursive elastic net is a powerful tool for inferring gene networks from time-course gene expression profiles.</p

Maspin expression in gastrointestinal stromal tumors

<p>Abstract</p> <p>Background</p> <p>To investigate the role of maspin expression in the progression of gastrointestinal stromal tumors, and its value as a prognostic indicator.</p> <p>Methods</p> <p>In the study 54 patients with GIST diagnosis were included in Uludag University of Faculty of Medicine, Department of Pathology between 1997-2007. The expression of maspin in 54 cases of gastrointestinal stromal tumor was detected by immunohistochemistry and compared with the clinicopathologic tumor parameters.</p> <p>Results</p> <p>The positive expression rates for maspin in the GISTs were 66,6% (36 of 54 cases). Maspin overexpression was detected in 9 of 29 high risk tumors (31%) and was significantly higher in very low/low (78.6%) and intermediate-risk tumors (63.6%) than high-risk tumors.</p> <p>Conclusions</p> <p>Maspin expression might be an important factor in tumor progression and patient prognosis in GIST. In the future, larger series may be studied to examine the prognostic significance of maspin in GISTs and, of course, maspin expression may be studied in different mesenchymal tumors.</p