Random walks on mutual microRNA-target gene interaction network improve the prediction of disease-associated microRNAs

Background: MicroRNAs (miRNAs) have been shown to play an important role in pathological initiation, progression and maintenance. Because identification in the laboratory of disease-related miRNAs is not straightforward, numerous network-based methods have been developed to predict novel miRNAs in silico. Homogeneous networks (in which every node is a miRNA) based on the targets shared between miRNAs have been widely used to predict their role in disease phenotypes. Although such homogeneous networks can predict potential disease-associated miRNAs, they do not consider the roles of the target genes of the miRNAs. Here, we introduce a novel method based on a heterogeneous network that not only considers miRNAs but also the corresponding target genes in the network model. Results: Instead of constructing homogeneous miRNA networks, we built heterogeneous miRNA networks consisting of both miRNAs and their target genes, using databases of known miRNA-target gene interactions. In addition, as recent studies demonstrated reciprocal regulatory relations between miRNAs and their target genes, we considered these heterogeneous miRNA networks to be undirected, assuming mutual miRNA-target interactions. Next, we introduced a novel method (RWRMTN) operating on these mutual heterogeneous miRNA networks to rank candidate disease-related miRNAs using a random walk with restart (RWR) based algorithm. Using both known disease-associated miRNAs and their target genes as seed nodes, the method can identify additional miRNAs involved in the disease phenotype. Experiments indicated that RWRMTN outperformed two existing state-of-the-art methods: RWRMDA, a network-based method that also uses a RWR on homogeneous (rather than heterogeneous) miRNA networks, and RLSMDA, a machine learning-based method. Interestingly, we could relate this performance gain to the emergence of "disease modules" in the heterogeneous miRNA networks used as input for the algorithm. Moreover, we could demonstrate that RWRMTN is stable, performing well when using both experimentally validated and predicted miRNA-target gene interaction data for network construction. Finally, using RWRMTN, we identified 76 novel miRNAs associated with 23 disease phenotypes which were present in a recent database of known disease-miRNA associations. Conclusions: Summarizing, using random walks on mutual miRNA-target networks improves the prediction of novel disease-associated miRNAs because of the existence of "disease modules" in these networks

Seeing the world from its words: All-embracing Transformers for fingerprint-based indoor localization

In this paper, we present all-embracing Transformers (AaTs) that are capable of deftly manipulating attention mechanism for Received Signal Strength (RSS) fingerprints in order to invigorate localizing performance. Since most machine learning models applied to the RSS modality do not possess any attention mechanism, they can merely capture superficial representations. Moreover, compared to textual and visual modalities, the RSS modality is inherently notorious for its sensitivity to environmental dynamics. Such adversities inhibit their access to subtle but distinct representations that characterize the corresponding location, ultimately resulting in significant degradation in the testing phase. In contrast, a major appeal of AaTs is the ability to focus exclusively on relevant anchors in RSS sequences, allowing full rein to the exploitation of subtle and distinct representations for specific locations. This also facilitates disregarding redundant clues formed by noisy ambient conditions, thus enhancing accuracy in localization. Apart from that, explicitly resolving the representation collapse (i.e., none-informative or homogeneous features, and gradient vanishing) can further invigorate the self-attention process in transformer blocks, by which subtle but distinct representations to specific locations are radically captured with ease. For that purpose, we first enhance our proposed model with two sub-constraints, namely covariance and variance losses at the Anchor2Vec. The proposed constraints are automatically mediated with the primary task towards a novel multi-task learning manner. In an advanced manner, we present further the ultimate in design with a few simple tweaks carefully crafted for transformer encoder blocks. This effort aims to promote representation augmentation via stabilizing the inflow of gradients to these blocks. Thus, the problems of representation collapse in regular Transformers can be tackled. To evaluate our AaTs, we compare the models with the state-of-the-art (SoTA) methods on three benchmark indoor localization datasets. The experimental results confirm our hypothesis and show that our proposed models could deliver much higher and more stable accuracy

Adherence to highly active antiretroviral therapy among people living with HIV and associated high-risk behaviours and clinical characteristics: A cross-sectional survey in Vietnam

Although Vietnam has promoted the utilisation of highly active antiretroviral therapy (HAART) towards HIV elimination targets, adherence to treatment has remained under-investigated. We aimed to describe high-risk behaviours and clinical characteristics by adherence status and to identify the factors associated with non-adherence. We included 426 people living with HIV (PLWH) currently or previously involved in HAART. Most participants were men (75.4%), young (33.6 years), with low income and low education levels. Non-adherent PLWH (11.5%) were more likely to have a larger number of sex partners (p-value = 0.053), sex without condom use (p-value = 0.007) and not receive result at hospital or voluntary test centre (p-value = 0.001). Multiple logistic regression analysis showed that demographic (education levels), sexual risk behaviours (multiple sex partners and sex without using condom) and clinical characteristics (time and facility at first time received HIV-positive result) were associated with HAART non-adherence. There are differences in associated factors between women (education levels and place of HIV testing) and men (multiple sex partners). Gender-specific programs, changing risky behaviours and reducing harms among PLWH may benefit adherence. We highlight the need to improve the quantity and quality of HIV/AIDS services in Vietnam, especially in pre- and post-test counselling, to achieve better HAART adherence, working towards ending AIDS in 2030. © The Author(s) 2021. **Please note that there are multiple authors for this article therefore only the name of the first 5 including Federation University Australia affiliate “Huy Nguyen” is provided in this record*

Controlled Synthesis of Titania using Water-soluble Titanium Complexes: A Review

The development of human society has led to the increase in energy and resources consumption as well as the arising problems of environmental damage and the toxicity to the human health. The development of novel synthesis method which tolerates utilization of toxic solvents and chemicals would fulfill the demand of the society for safer, softer, and environmental friendly technologies. For the past decades, a remarkable progress has been attained in the development of new water-soluble titanium complexes (WSTC) and their use for the synthesis of nanocrystalline titanium dioxide materials by aqueous solution-based approaches. The progress of synthesis of nanocrystalline titanium dioxide using such WSTCs is reviewed in this work. The key structural features responsible for the successfully controlled synthesis of TiO2 are discussed to provide guidelines for the morphology-controlled synthesis. Finally, this review ends with a summary and some perspectives on the challenges as well as new directions in this fascinating research

A Murnaghan-Nakayama rule for Grothendieck polynomials of Grassmannian type

We consider the Grothendieck polynomials appearing in the K-theory of Grassmannians, which are analogs of Schur polynomials. This paper aims to establish a version of the Murnaghan-Nakayama rule for Grothendieck polynomials of the Grassmannian type. This rule allows us to express the product of a Grothendieck polynomial with a power sum symmetric polynomial into a linear combination of other Grothendieck polynomials.Comment: 10 pages, 7 figure

Super Bound States in the Continuum on Photonic Flatbands: Concept, Experimental Realization, and Optical Trapping Demonstration

In this work, we theoretically propose and experimentally demonstrate the formation of a super bound state in a continuum (BIC) on a photonic crystal flat band. This unique state simultaneously exhibits an enhanced quality factor and near-zero group velocity across an extended region of the Brillouin zone. It is achieved at the topological transition when a symmetry-protected BIC pinned at $k=0$ merges with two Friedrich-Wintgen quasi-BICs, which arise from destructive interference between lossy photonic modes of opposite symmetries. As a proof-of-concept, we employ the super flat BIC to demonstrate three-dimensional optical trapping of individual particles. Our findings present a novel approach to engineering both the real and imaginary components of photonic states on a subwavelength scale for innovative optoelectronic devices

Immersed boundary method combined with proper generalized decomposition for simulation of a flexible filament in a viscous incompressible flow

In this paper, a combination of the Proper Generalized  Decomposition (PGD) with the Immersed Boundary method (IBM) for solving  fluid-filament interaction problem is proposed. In this combination, a  forcing term constructed by the IBM is introduced to Navier-Stokes equations  to handle the influence of the filament on the fluid flow. The PGD is  applied to solve the Poission's equation to find the fluid pressure  distribution for each time step. The numerical results are compared with  those by previous publications to illustrate the robustness and  effectiveness of the proposed method

Neural Scene Decoration from a Single Photograph

Furnishing and rendering indoor scenes has been a long-standing task for interior design, where artists create a conceptual design for the space, build a 3D model of the space, decorate, and then perform rendering. Although the task is important, it is tedious and requires tremendous effort. In this paper, we introduce a new problem of domain-specific indoor scene image synthesis, namely neural scene decoration. Given a photograph of an empty indoor space and a list of decorations with layout determined by user, we aim to synthesize a new image of the same space with desired furnishing and decorations. Neural scene decoration can be applied to create conceptual interior designs in a simple yet effective manner. Our attempt to this research problem is a novel scene generation architecture that transforms an empty scene and an object layout into a realistic furnished scene photograph. We demonstrate the performance of our proposed method by comparing it with conditional image synthesis baselines built upon prevailing image translation approaches both qualitatively and quantitatively. We conduct extensive experiments to further validate the plausibility and aesthetics of our generated scenes. Our implementation is available at \url{https://github.com/hkust-vgd/neural_scene_decoration}.Comment: ECCV 2022 paper. 14 pages of main content, 4 pages of references, and 11 pages of appendi