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

Angiogenic gene expression and vascular density are reflected in ultrasonographic features of synovitis in early Rheumatoid Arthritis: an observational study.

INTRODUCTION: Neovascularization contributes to the development of sustained synovial inflammation in the early stages of Rheumatoid Arthritis. Ultrasound (US) provides an indirect method of assessing synovial blood flow and has been shown to correlate with clinical disease activity in patients with Rheumatoid Arthritis. This study examines the relationship of US determined synovitis with synovial vascularity, angiogenic/lymphangiogenic factors and cellular mediators of inflammation in a cohort of patients with early Rheumatoid Arthritis (RA) patients prior to therapeutic intervention with disease modifying therapy or corticosteroids. METHODS: An ultrasound guided synovial biopsy of the supra-patella pouch was performed in 12 patients with early RA prior to treatment. Clinical, US and biochemical assessments were undertaken prior to the procedure. Ultrasound images and histological samples were obtained from the supra-patella pouch. Histological samples were stained for Factor VIII and a-SMA (a-smooth muscle actin). Using digital imaging analysis a vascular area score was recorded. QT-PCR (quantitative-PCR) of samples provided quantification of angiogenic and lymphangiogenic gene expression and immunohistochemistry stained tissue was scored for macrophage, T cell and B cell infiltration using an existing semi-quantitative score. RESULTS: Power Doppler showed a good correlation with histological vascular area (Spearman r--0.73) and angiogenic factors such as vascular endothelial growth factor-A (VEGF-A), Angiopoietin 2 and Tie-2. In addition, lymphangiogenic factors such as VEGF-C and VEGF-R3 correlated well with US assessment of synovitis. A significant correlation was also found between power Doppler and synovial thickness, pro-inflammatory cytokines and sub-lining macrophage infiltrate. Within the supra-patella pouch there was no significant difference in US findings, gene expression or inflammatory cell infiltrate between any regions of synovium biopsied. CONCLUSION: Ultrasound assessment of synovial tissue faithfully reflects synovial vascularity. Both grey scale and power Doppler synovitis in early RA patients correlate with a pro-angiogenic and lymphangiogenic gene expression profile. In early RA both grey scale and power Doppler synovitis are associated with a pro-inflammatory cellular and cytokine profile providing considerable validity in its use as an objective assessment of synovial inflammation in clinical practice

Tumor origin detection with tissue-specific miRNA and DNA methylation markers

MOTIVATION: A clear identification of the primary site of tumor is of great importance to the next targeted site-specific treatments and could efficiently improve patient‘s overall survival. Even though many classifiers based on gene expression had been proposed to predict the tumor primary, only a few studies focus on using DNA methylation (DNAm) profiles to develop classifiers, and none of them compares the performance of classifiers based on different profiles. RESULTS: We introduced novel selection strategies to identify highly tissue-specific CpG sites and then used the random forest approach to construct the classifiers to predict the origin of tumors. We also compared the prediction performance by applying similar strategy on miRNA expression profiles. Our analysis indicated that these classifiers had an accuracy of 96.05% (Maximum–Relevance–Maximum–Distance: 90.02–99.99%) or 95.31% (principal component analysis: 79.82–99.91%) on independent DNAm datasets, and an overall accuracy of 91.30% (range 79.33–98.74%) on independent miRNA test sets for predicting tumor origin. This suggests that our feature selection methods are very effective to identify tissue-specific biomarkers and the classifiers we developed can efficiently predict the origin of tumors. We also developed a user-friendly webserver that helps users to predict the tumor origin by uploading miRNA expression or DNAm profile of their interests. AVAILABILITY AND IMPLEMENTATION: The webserver, and relative data, code are accessible at http://server.malab.cn/MMCOP/

Alcohol-dysregulated microRNAs in hepatitis B virus-related hepatocellular carcinoma

published_or_final_versio

Differential expression analysis with global network adjustment

<p>Background: Large-scale chromosomal deletions or other non-specific perturbations of the transcriptome can alter the expression of hundreds or thousands of genes, and it is of biological interest to understand which genes are most profoundly affected. We present a method for predicting a gene’s expression as a function of other genes thereby accounting for the effect of transcriptional regulation that confounds the identification of genes differentially expressed relative to a regulatory network. The challenge in constructing such models is that the number of possible regulator transcripts within a global network is on the order of thousands, and the number of biological samples is typically on the order of 10. Nevertheless, there are large gene expression databases that can be used to construct networks that could be helpful in modeling transcriptional regulation in smaller experiments.</p> <p>Results: We demonstrate a type of penalized regression model that can be estimated from large gene expression databases, and then applied to smaller experiments. The ridge parameter is selected by minimizing the cross-validation error of the predictions in the independent out-sample. This tends to increase the model stability and leads to a much greater degree of parameter shrinkage, but the resulting biased estimation is mitigated by a second round of regression. Nevertheless, the proposed computationally efficient “over-shrinkage” method outperforms previously used LASSO-based techniques. In two independent datasets, we find that the median proportion of explained variability in expression is approximately 25%, and this results in a substantial increase in the signal-to-noise ratio allowing more powerful inferences on differential gene expression leading to biologically intuitive findings. We also show that a large proportion of gene dependencies are conditional on the biological state, which would be impossible with standard differential expression methods.</p> <p>Conclusions: By adjusting for the effects of the global network on individual genes, both the sensitivity and reliability of differential expression measures are greatly improved.</p&gt

Classification tools for carotenoid content estimation in Manihot esculenta via metabolomics and machine learning

Cassava genotypes (Manihot esculenta Crantz) with high pro-vitamin A activity have been identified as a strategy to reduce the prevalence of deficiency of this vitamin. The color variability of cassava roots, which can vary from white to red, is related to the presence of several carotenoid pigments. The present study has shown how CIELAB color measurement on cassava roots tissue can be used as a non-destructive and very fast technique to quantify the levels of carotenoids in cassava root samples, avoiding the use of more expensive analytical techniques for compound quantification, such as UV-visible spectrophotometry and the HPLC. For this, we used machine learning techniques, associating the colorimetric data (CIELAB) with the data obtained by UV-vis and HPLC, to obtain models of prediction of carotenoids for this type of biomass. Best values of R2 (above 90%) were observed for the predictive variable TCC determined by UV-vis spectrophotometry. When we tested the machine learning models using the CIELAB values as inputs, for the total carotenoids contents quantified by HPLC, the Partial Least Squares (PLS), Support Vector Machines, and Elastic Net models presented the best values of R2 (above 40%) and Root-Mean-Square Error (RMSE). For the carotenoid quantification by UV-vis spectrophotometry, R2 (around 60%) and RMSE values (around 6.5) are more satisfactory. Ridge regression and Elastic Network showed the best results. It can be concluded that the use colorimetric technique (CIELAB) associated with UV-vis/HPLC and statistical techniques of prognostic analysis through machine learning can predict the content of total carotenoids in these samples, with good precision and accuracy.CAPES -Coordenação de Aperfeiçoamento de Pessoal de Nível Superior(407323/2013-9)info:eu-repo/semantics/publishedVersio

Evolution of Th2 responses : Characterization of IL-4/13 in sea bass (Dicentrarchus labrax L.) and studies of expression and biological activity

Acknowledgements This research was funded by the European Commission under the 7th Framework Programme for Research and Technological Development (FP7) of the European Union (Grant Agreement 311993 TARGETFISH). T.W. received funding from the MASTS pooling initiative (The Marine Alliance for Science and Technology for Scotland). MASTS is funded by the Scottish Funding Council (grant reference number HR09011) and contributing institutions.Peer reviewedPublisher PD

The non-coding landscape of head and neck squamous cell carcinoma

published_or_final_versio