Microbiome Data Analysis by Symmetric Non-negative Matrix Factorization With Local and Global Regularization.

A network is an efficient tool to organize complicated data. The Laplacian graph has attracted more and more attention for its good properties and has been applied to many tasks including clustering, feature selection, and so on. Recently, studies have indicated that though the Laplacian graph can capture the global information of data, it lacks the power to capture fine-grained structure inherent in network. In contrast, a Vicus matrix can make full use of local topological information from the data. Given this consideration, in this paper we simultaneously introduce Laplacian and Vicus graphs into a symmetric non-negative matrix factorization framework (LVSNMF) to seek and exploit the global and local structure patterns that inherent in the original data. Extensive experiments are conducted on three real datasets (cancer, cell populations, and microbiome data). The experimental results show the proposed LVSNMF algorithm significantly outperforms other competing algorithms, suggesting its potential in biological data analysis.

MHSNMF: multi-view hessian regularization based symmetric nonnegative matrix factorization for microbiome data analysis.

BACKGROUND: With the rapid development of high-throughput technique, multiple heterogeneous omics data have been accumulated vastly (e.g., genomics, proteomics and metabolomics data). Integrating information from multiple sources or views is challenging to obtain a profound insight into the complicated relations among micro-organisms, nutrients and host environment. In this paper we propose a multi-view Hessian regularization based symmetric nonnegative matrix factorization algorithm (MHSNMF) for clustering heterogeneous microbiome data. Compared with many existing approaches, the advantages of MHSNMF lie in: (1) MHSNMF combines multiple Hessian regularization to leverage the high-order information from the same cohort of instances with multiple representations; (2) MHSNMF utilities the advantages of SNMF and naturally handles the complex relationship among microbiome samples; (3) uses the consensus matrix obtained by MHSNMF, we also design a novel approach to predict the classification of new microbiome samples. RESULTS: We conduct extensive experiments on two real-word datasets (Three-source dataset and Human Microbiome Plan dataset), the experimental results show that the proposed MHSNMF algorithm outperforms other baseline and state-of-the-art methods. Compared with other methods, MHSNMF achieves the best performance (accuracy: 95.28%, normalized mutual information: 91.79%) on microbiome data. It suggests the potential application of MHSNMF in microbiome data analysis. CONCLUSIONS: Results show that the proposed MHSNMF algorithm can effectively combine the phylogenetic, transporter, and metabolic profiles into a unified paradigm to analyze the relationships among different microbiome samples. Furthermore, the proposed prediction method based on MHSNMF has been shown to be effective in judging the types of new microbiome samples.

Total Bilirubin Predicts Severe Progression of Diabetic Retinopathy and the Possible Causal Mechanism.

Early detection and treatment are key to delaying the progression of diabetic retinopathy (DR), avoiding loss of vision, and reducing the burden of advanced disease. Our study is aimed at determining if total bilirubin has a predictive value for DR progression and exploring the potential mechanism involved in this pathogenesis. A total of 540 patients with nonproliferative diabetic retinopathy (NPDR) were enrolled between July 2014 and September 2016 and assigned into a progression group (N = 67) or a stable group (N = 473) based on the occurrence of diabetic macular edema (DME), vitreous hemorrhage, retinal detachment, or other conditions that may cause severe loss of vision following a telephonic interview in August 2019. After further communication, 108 patients consented to an outpatient consultation between September and November 2019. Our findings suggest the following: (1) TBIL were significant independent predictors of DR progression (HR: 0.70, 95% CI: 0.54-0.89, p = 0.006). (2) Examination of outpatients indicated that compared to stable group patients, progression group patients had more components of urobilinogen and LPS but a lower concentration of TBIL. The relationship between bilirubin and severe DR was statistically significant after adjusting for sex, age, diabetes duration, type of diabetes, FPG, and HbA1c (OR: 0.70, 95% CI: 0.912-0.986, p = 0.016). The addition of serum LPS and/or urobilinogen attenuated this association. This study concludes that total bilirubin predicts an increased risk of severe DR progression. Decreasing bilirubin might be attributed to the increased levels of LPS and urobilinogen, which may indicate that the change of bilirubin levels is secondary to intestinal flora disorder and/or intestinal barrier destruction. Further prospective investigations are necessary to explore the causal associations for flora disorder, intestinal barrier destruction, and DR.

Binary geometric phase metasurface for ultra-wideband microwave diffuse scatterings with optical transparency.

Diffuse scatterings of electromagnetic (EM) waves by thin-thickness metasurfaces have promising prospects in many fields due to their abilities of significantly reducing the backward scatterings of targets. One of the major challenges is to further improve the working bandwidth. Here, we propose a binary geometric phase metasurface with high optical transparency to realize ultra-wideband backward scattering reduction through diffuse scatterings. A multi-layered reflective meta-structure is used as the basic building block while its out-of-phase counterpart is achieved through a geometric rotating operation. The proposed metasurface shows a polarization-insensitive wave-diffusion property with about 10 dB scattering reduction in an ultra-wide frequency band from 3.5 GHz to 16.6 GHz, reaching a fractional bandwidth of 130%. As the experimental demonstration, prototype is fabricated and measured that is in agreement with simulated results. The proposed metasurface provides an efficient way to tailor the exotic scattering features with simultaneously high optical transmittance, which can offer crucial benefits in many practical uses, for example, window stealth applications.

MultiSourcDSim: an integrated approach for exploring disease similarity.

BACKGROUND: A collection of disease-associated data contributes to study the association between diseases. Discovering closely related diseases plays a crucial role in revealing their common pathogenic mechanisms. This might further imply treatment that can be appropriated from one disease to another. During the past decades, a number of approaches for calculating disease similarity have been developed. However, most of them are designed to take advantage of single or few data sources, which results in their low accuracy. METHODS: In this paper, we propose a novel method, called MultiSourcDSim, to calculate disease similarity by integrating multiple data sources, namely, gene-disease associations, GO biological process-disease associations and symptom-disease associations. Firstly, we establish three disease similarity networks according to the three disease-related data sources respectively. Secondly, the representation of each node is obtained by integrating the three small disease similarity networks. In the end, the learned representations are applied to calculate the similarity between diseases. RESULTS: Our approach shows the best performance compared to the other three popular methods. Besides, the similarity network built by MultiSourcDSim suggests that our method can also uncover the latent relationships between diseases. CONCLUSIONS: MultiSourcDSim is an efficient approach to predict similarity between diseases.

Genome-Wide Identification and Expression Profiling of the TCP Family Genes in Spike and Grain Development of Wheat (Triticum aestivum L.).

The TCP family genes are plant-specific transcription factors and play important roles in plant development. TCPs have been evolutionarily and functionally studied in several plants. Although common wheat (Triticum aestivum L.) is a major staple crop worldwide, no systematic analysis of TCPs in this important crop has been conducted. Here, we performed a genome-wide survey in wheat and found 66 TCP genes that belonged to 22 homoeologous groups. We then mapped these genes on wheat chromosomes and found that several TCP genes were duplicated in wheat including the ortholog of the maize TEOSINTE BRANCHED 1. Expression study using both RT-PCR and in situ hybridization assay showed that most wheat TCP genes were expressed throughout development of young spike and immature seed. Cis-acting element survey along promoter regions suggests that subfunctionalization may have occurred for homoeologous genes. Moreover, protein-protein interaction experiments of three TCP proteins showed that they can form either homodimers or heterodimers. Finally, we characterized two TaTCP9 mutants from tetraploid wheat. Each of these two mutant lines contained a premature stop codon in the A subgenome homoeolog that was dominantly expressed over the B subgenome homoeolog. We observed that mutation caused increased spike and grain lengths. Together, our analysis of the wheat TCP gene family provides a start point for further functional study of these important transcription factors in wheat.

NDRC: A Disease-Causing Genes Prioritized Method Based on Network Diffusion and Rank Concordance.

Disease-causing genes prioritization is very important to understand disease mechanisms and biomedical applications, such as design of drugs. Previous studies have shown that promising candidate genes are mostly ranked according to their relatedness to known disease genes or closely related disease genes. Therefore, a dangling gene (isolated gene) with no edges in the network can not be effectively prioritized. These approaches tend to prioritize those genes that are highly connected in the PPI network while perform poorly when they are applied to loosely connected disease genes. To address these problems, we propose a new disease-causing genes prioritization method that based on network diffusion and rank concordance (NDRC). The method is evaluated by leave-one-out cross validation on 1931 diseases in which at least one gene is known to be involved, and it is able to rank the true causal gene first in 849 of all 2542 cases. The experimental results suggest that NDRC significantly outperforms other existing methods such as RWR, VAVIEN, DADA and PRINCE on identifying loosely connected disease genes and successfully put dangling genes as potential candidate disease genes. Furthermore, we apply NDRC method to study three representative diseases, Meckel syndrome 1, Protein C deficiency and Peroxisome biogenesis disorder 1A (Zellweger). Our study has also found that certain complex disease-causing genes can be divided into several modules that are closely associated with different disease phenotype.

Tunable circular polarization selective surfaces for low-THz applications using patterned graphene.

This paper demonstrates a new type of frequency tunable polarization selective surface operating at low THz, which is devised by utilizing the unique features of graphene. The device is comprised of an infinite array of identical unit cells in three layers. Multiple graphene dipoles are placed on the top and bottom layers to form the vertical and horizontal electric field filters. Using this new configuration, the proposed device exhibits reflection for the incident Left-Hand-Circular-Polarization (LHCP) waves and becomes transparent to the incoming Right-Hand-Circular-Polarization (RHCP) waves. The excited localized surface plasmonic resonance mode on the graphene based unit cells significantly reduces the physical dimension of the device. The unit cell dimension of the proposed design is in the order of 0.18 wavelengths in comparison to conventional metallic structures, where it is of order a half a wavelength. In the full wave analysis, the graphene based polarization selective surfaces exhibit an isolation of 21 dB for LHCP waves and a transmission loss of around 5.1 dB for waves with RHCP characteristics. The performance has also been examined under oblique incidence. The results fully verify that the proposed planar device operates properly for incident angles up to 40°. The tuning effect of the described device is investigated by varying the chemical potentials of graphene. Significant frequency reconfiguration capability is achieved in the isolation of LHCP incident waves, and meanwhile, for RHCP incidence, the transmission rate remains reasonably high.

An edge-based protein complex identification algorithm with gene co-expression data (PCIA-GeCo).

Recent studies have shown that protein complex is composed of core proteins and attachment proteins, and proteins inside the core are highly co-expressed. Based on this new concept, we reconstruct weighted PPI network by using gene expression data, and develop a novel protein complex identification algorithm from the angle of edge (PCIA-GeCo). First, we select the edge with high co-expressed coefficient as seed to form the preliminary cores. Then, the preliminary cores are filtered according to the weighted density of complex core to obtain the unique core. Finally, the protein complexes are generated by identifying attachment proteins for each core. A comprehensive comparison in term of F-measure, Coverage rate, P-value between our method and three other existing algorithms HUNTER, COACH and CORE has been made by comparing the predicted complexes against benchmark complexes. The evaluation results show our method PCIA-GeCo is effective; it can identify protein complexes more accurately.

A novel protein complex identification algorithm based on Connected Affinity Clique Extension (CACE).

A novel algorithm based on Connected Affinity Clique Extension (CACE) for mining overlapping functional modules in protein interaction network is proposed in this paper. In this approach, the value of protein connected affinity which is inferred from protein complexes is interpreted as the reliability and possibility of interaction. The protein interaction network is constructed as a weighted graph, and the weight is dependent on the connected affinity coefficient. The experimental results of our CACE in two test data sets show that the CACE can detect the functional modules much more effectively and accurately when compared with other state-of-art algorithms CPM and IPC-MCE.