RNA-protein binding motifs mining with a new hybrid deep learning based cross-domain knowledge integration approach

BACKGROUND: RNAs play key roles in cells through the interactions with proteins known as the RNA-binding proteins (RBP) and their binding motifs enable crucial understanding of the post-transcriptional regulation of RNAs. How the RBPs correctly recognize the target RNAs and why they bind specific positions is still far from clear. Machine learning-based algorithms are widely acknowledged to be capable of speeding up this process. Although many automatic tools have been developed to predict the RNA-protein binding sites from the rapidly growing multi-resource data, e.g. sequence, structure, their domain specific features and formats have posed significant computational challenges. One of current difficulties is that the cross-source shared common knowledge is at a higher abstraction level beyond the observed data, resulting in a low efficiency of direct integration of observed data across domains. The other difficulty is how to interpret the prediction results. Existing approaches tend to terminate after outputting the potential discrete binding sites on the sequences, but how to assemble them into the meaningful binding motifs is a topic worth of further investigation. RESULTS: In viewing of these challenges, we propose a deep learning-based framework (iDeep) by using a novel hybrid convolutional neural network and deep belief network to predict the RBP interaction sites and motifs on RNAs. This new protocol is featured by transforming the original observed data into a high-level abstraction feature space using multiple layers of learning blocks, where the shared representations across different domains are integrated. To validate our iDeep method, we performed experiments on 31 large-scale CLIP-seq datasets, and our results show that by integrating multiple sources of data, the average AUC can be improved by 8% compared to the best single-source-based predictor; and through cross-domain knowledge integration at an abstraction level, it outperforms the state-of-the-art predictors by 6%. Besides the overall enhanced prediction performance, the convolutional neural network module embedded in iDeep is also able to automatically capture the interpretable binding motifs for RBPs. Large-scale experiments demonstrate that these mined binding motifs agree well with the experimentally verified results, suggesting iDeep is a promising approach in the real-world applications. CONCLUSION: The iDeep framework not only can achieve promising performance than the state-of-the-art predictors, but also easily capture interpretable binding motifs. iDeep is available at http://www.csbio.sjtu.edu.cn/bioinf/iDeep ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s12859-017-1561-8) contains supplementary material, which is available to authorized users

The Diphoton Excess, Low Energy Theorem and the 331 Model

We interpret the diphoton anomaly as a heavy scalar $H_3$ in the so-called 331 model. The scalar is responsible for breaking the $SU(3)_C\otimes SU(3)_L\otimes U(1)_X$ gauge symmetry down to the standard model electroweak gauge group. It mainly couples to the standard model gluons and photons through quantum loops involving heavy quarks and leptons. Those quarks and leptons, in together with the SM quarks and leptons, form the fundamental representation of the 331 model. We use low energy theorem to calculate effective coupling of $H_3gg$, $H_3\gamma\gamma$, $H_3ZZ$, $H_3WW$ and $H_3Z\gamma$. The analytical results can be applied to new physics models satisfying the low energy theorem. We show that the heavy quark and lepton contribution cannot produce enough diphoton pairs. It is crucial to include the contribution of charged scalars to explain the diphoton excess. The extra neutral $Z^\prime$ boson could also explain the 2 TeV diboson excess observed at the LHC Run-I.Comment: To appear in PR

Extended warfarin treatment versus rivaroxaban treatment for first episode of symptomatic unprovoked pulmonary embolism: A prospective cohort study

Purpose: To compare the benefits and risks of extra 6 months of warfarin therapy with those of rivaroxaban treatment in patients with initial unprovoked pulmonary embolism (PE) episode who completed 3- or 6-month of therapy on heparin/vitamin K antagonist standard regime.Methods: This prospective observational study included 212 patients with follow-up from July 2013 to July 2018. The primary endpoint was symptomatic recurrent venous thromboembolism (VT), composite of non-fatal symptomatic PE or deep vein thrombosis or fatal VT, and major bleeding (non-fatal/fatal) up to 6 months. Secondary endpoints were death not related to PE or major bleeding.Results: During the 6-month therapy period, the primary endpoint was seen in 3 out of 106 patients (2.83 %) in warfarin category, and in  rivaroxaban category, for a hazard ratio (HR) of 1.22 [95 % confidence interval (CI) = 0.09 - 11.18; p = 0.813]. With warfarin therapy, 2 patients (1.89 %) had recurrent VT, while 3 patients (2.83 %) had VT with rivaroxaban. Major bleeding was observed in 2 patients (1.89 %) on warfarin, and in one patient (0.94 %) on rivaroxaban. During the entire 18-month period, the primary endpoint was seen in 15 patients (14.15 %) treated with warfarin, and in 18 patients (16.98 %) treated with rivaroxaban (HR 0.84; 95 % CI = 0.47 - 1.84; p = 0.431). Major bleeding was observed in 5 patients (4.72 %) under warfarin (one fatal), relative to 3 patients (2.83 %) under rivaroxaban (R 1.67; 95 % CI = 0.62 - 5.95; p = 0.09).Conclusion: Rivaroxaban showed higher efficacy than warfarin in recurrent VT prevention, with lower risk of major bleeding. However, the extended therapeutic benefit was not maintained post-therapy. Keywords: Pulmonary embolism, Rivaroxaban, Warfarin, Heparin, Vitamin K, Hazard rati

Early postoperative interventions in the prevention and management of thyroidectomy scars

Thyroidectomy scars, located on the exposed site, can cause distress in patients. Owing to the cosmetic importance of thyroidectomy scars, many studies have been conducted on its prevention and treatment. Scar formation factors mainly include inflammatory cell infiltration, angiogenesis, fibroblast proliferation, secretion of cytokines such as transforming growth factor (TGF)-β1, and mechanical tension on the wound edges. Anti-scar methods including topical anti-scar agents, skin tension-bearing devices, and local injections of botulinum toxin, as well as lasers and phototherapies, that target these scar formation factors have been developed. However, current studies remain fragmented, and there is a lack of a comprehensive evaluation of the impacts of these anti-scar methods on treating thyroidectomy scars. Early intervention is a crucial but often neglected key to control hyperplastic thyroidectomy scars. Therefore, we review the currently adopted early postoperative strategies for thyroidectomy scar reduction, aiming to illustrate the mechanism of these anti-scar methods and provide flexible and comprehensive treatment selections for clinical physicians to deal with thyroidectomy scars

On second-order combinatorial algebraic time-delay interferometry

Inspired by the combinatorial algebraic approach proposed by Dhurandhar {\it et al.}, we propose two novel classes of second-generation time-delay interferometry (TDI) solutions and their further generalization. The primary strategy of the algorithm is to enumerate specific types of residual laser frequency noise associated with second-order commutators in products of time-displacement operators. The derivations are based on analyzing the delay time residual when expanded in time derivatives of the armlengths order by order. It is observed that the solutions obtained by such a scheme are primarily captured by the geometric TDI approach and therefore possess an intuitive interpretation. Nonetheless, the fully-symmetric Sagnac and Sagnac-inspired combinations inherit the properties from the original algebraic approach, and subsequently lie outside of the scope of geometric TDI. We explicitly show that novel solutions, distinct from existing ones in terms of both algebraic structure and sensitivity curve, are encountered. Moreover, at its lowest order, the solution is furnished by commutators of relatively compact form. Besides the original Michelson-type solution, we elaborate on other types of solutions such as the Monitor, Beacon, Relay, Sagnac, fully-symmetric Sagnac, and Sagnac-inspired ones. The average response functions, residual noise power spectral density, and sensitivity curves are evaluated for the obtained solutions. Also, the relations between the present scheme and other existing algorithms are discussed.Comment: 22 pages, 4 figure

Inferring Disease-Associated MicroRNAs Using Semi-supervised Multi-Label Graph Convolutional Networks

Disease; Gene Network; Biocomputational Method; Computer ModelingMicroRNAs (miRNAs) play crucial roles in biological processes involved in diseases. The associations between diseases and protein-coding genes (PCGs) have been well investigated, and miRNAs interact with PCGs to trigger them to be functional. We present a computational method, DimiG, to infer miRNA-associated diseases using a semi-supervised Graph Convolutional Network model (GCN). DimiG uses a multi-label framework to integrate PCG-PCG interactions, PCG-miRNA interactions, PCG-disease associations, and tissue expression profiles. DimiG is trained on disease-PCG associations and an interaction network using a GCN, which is further used to score associations between diseases and miRNAs. We evaluate DimiG on a benchmark set from verified disease-miRNA associations. Our results demonstrate that DimiG outperforms the best unsupervised method and is comparable to two supervised methods. Three case studies of prostate cancer, lung cancer, and inflammatory bowel disease further demonstrate the efficacy of DimiG, where top miRNAs predicted by DimiG are supported by literature