Modulation Detection Method Research Articles

HetFCM: functional co-module discovery by heterogeneous network co-clustering.

Functional molecular module (i.e., gene-miRNA co-modules and gene-miRNA-lncRNA triple-layer modules) analysis can dissect complex regulations underlying etiology or phenotypes. However, current module detection methods lack an appropriate usage and effective model of multi-omics data and cross-layer regulations of heterogeneous molecules, causing the loss of critical genetic information and corrupting the detection performance. In this study, we propose a heterogeneous network co-clustering framework (HetFCM) to detect functional co-modules. HetFCM introduces an attributed heterogeneous network to jointly model interplays and multi-type attributes of different molecules, and applies multiple variational graph autoencoders on the network to generate cross-layer association matrices, then it performs adaptive weighted co-clustering on association matrices and attribute data to identify co-modules of heterogeneous molecules. Empirical study on Human and Maize datasets reveals that HetFCM can find out co-modules characterized with denser topology and more significant functions, which are associated with human breast cancer (subtypes) and maize phenotypes (i.e., lipid storage, drought tolerance and oil content). HetFCM is a useful tool to detect co-modules and can be applied to multi-layer functional modules, yielding novel insights for analyzing molecular mechanisms. We also developed a user-friendly module detection and analysis tool and shared it at http://www.sdu-idea.cn/FMDTool.

COMMO: a web server for the identification and analysis of consensus gene modules across multiple methods.

A variety of computational methods have been developed to identify functionally related gene modules from genome-wide gene expression profiles. Integrating the results of these methods to identify consensus modules is a promising approach to produce more accurate and robust results. In this application note, we introduce COMMO, the first web server to identify and analyze consensus gene functionally related gene modules from different module detection methods. First, COMMO implements eight state-of-the-art module detection methods and two consensus clustering algorithms. Second, COMMO provides users with mRNA and protein expression data for 33 cancer types from three public databases. Users can also upload their own data for module detection. Third, users can perform functional enrichment and two types of survival analyses on the observed gene modules. Finally, COMMO provides interactive, customizable visualizations and exportable results. With its extensive analysis and interactive capabilities, COMMO offers a user-friendly solution for conducting module-based precision medicine research. COMMO web is available at https://commo.ncpsb.org.cn/, with the source code available on GitHub: https://github.com/Song-xinyu/COMMO/tree/master.

Vibro-acoustic modulation-based bolt looseness detection method using frequency sweep probe waves

ABSTRACT Traditional vibro-acoustic modulation methods for bolt looseness detection often use a probe wave with single frequency. However, using a single frequency probe wave not only reduces the detection accuracy but also increases the difficulty of selecting the frequency of the probe wave. To overcome these drawbacks, this paper proposes a bolt looseness detection method based the vibro-acoustic modulation using a frequency sweep probe wave. For the low-frequency pump wave, the excitation frequency is set as the resonant frequency of the bolted joint to enhance the modulation effect. For the high-frequency probe wave, a frequency sweep excitation is used to replace the single frequency excitation, which improves the detection accuracy and overcomes the difficulty of selecting the probe frequency. A signal processing method is proposed to extract modulation information from the complex response signal obtained under a frequency sweep excitation. A nonlinear modulation index is defined using the extracted modulation information to quantify the loosening level of bolts. An experimental setup is designed to implement the vibro-acoustic modulation detection method on a bolted joint. Experimental results demonstrate that the proposed method is effective for detecting bolt looseness and has high detection sensitivity and accuracy for the early bolt loosening.

Photoacoustic spectrometry and resonant frequency tracking based dual-mode gas sensor

Photoacoustic spectrometry has excellent advantages in trace gas detection. However, due to the influence of gas saturation absorption, when the gas concentration spikes to a certain level, this technology is difficult to achieve accurate detection. Therefore, a dual-mode gas sensor based on photoacoustic spectroscopy and resonant frequency tracking is developed by the features of both detection technologies. A tunable DFB diode laser in the 1654 nm wavelength range and a loudspeaker are used for the photoacoustic detection, and the resonant frequency tracking, respectively. A resonant photoacoustic cell is used as the sensing element to work in dual-mode by time-division multiplexing technology. The wavelength-modulated photoacoustic detection is enabled in the methane concentration below 30%. When the gas concentration exceeds 30%, it will be automatically switched to the measurement-mode of resonant frequency tracking by the fast Fourier transform. The double-range gas detection is realized by a single sensor with dual-mode detecting functionality. With the wavelength modulation and lock-in harmonic detection method, the minimum detectable limit (1σ) of 3.5 ppm CH4 was demonstrated under the photoacoustic detection mode. The response time of the sensor is about 10 s within the measurement range of double-range.

Turbulence-resilient differential-phase-shift-keying free-space optical communications using automatic multi-mode optoelectronic mixing

Free-space-optical (FSO) communications can benefit from power-efficient modulation formats and detection methods. In general, differential-phase-shift-keying (DPSK) tends to be more optical-power efficient when detecting both output arms in the receiver as compared to intensity modulation. Unfortunately, turbulence can induce power coupling from a Gaussian mode to higher-order spatial modes, which can produce high power loss in the differential receiver. Here we demonstrate a 2.25-Gbit/s turbulence-resilient DPSK FSO link under emulated turbulence effects with a strength of 2ω0/r0∼5.5. The turbulence-induced modal coupling loss is automatically compensated by optoelectronic (O/E) mixing of many higher-order spatial modes. The experimental results, based on 200 random turbulence realization measurements, indicate that compared with a single-mode coherent receiver, the O/E multi-mode mixing can reduce the average turbulence-induced mixing loss by ∼14.6 dB.

MDSN: A Module Detection Method for Identifying High-Order Epistatic Interactions.

Epistatic interactions are referred to as SNPs (single nucleotide polymorphisms) that affect disease development and trait expression nonlinearly, and hence identifying epistatic interactions plays a great role in explaining the pathogenesis and genetic heterogeneity of complex diseases. Many methods have been proposed for epistasis detection; nevertheless, they mainly focus on low-order epistatic interactions, two-order or three-order for instance, and often ignore high-order interactions due to computational burden. In this paper, a module detection method called MDSN is proposed for identifying high-order epistatic interactions. First, an SNP network is constructed by a construction strategy of interaction complementary, which consists of low-order SNP interactions that can be obtained from fast computations. Then, a node evaluation measure that integrates multi-topological features is proposed to improve the node expansion algorithm, where the importance of a node is comprehensively evaluated by the topological characteristics of the neighborhood. Finally, modules are detected in the constructed SNP network, which have high-order epistatic interactions associated with the disease. The MDSN was compared with four state-of-the-art methods on simulation datasets and a real Age-related Macular Degeneration dataset. The results demonstrate that MDSN has higher performance on detecting high-order interactions.

SUBATOMIC: a SUbgraph BAsed mulTi-OMIcs clustering framework to analyze integrated multi-edge networks

BackgroundRepresenting the complex interplay between different types of biomolecules across different omics layers in multi-omics networks bears great potential to gain a deep mechanistic understanding of gene regulation and disease. However, multi-omics networks easily grow into giant hairball structures that hamper biological interpretation. Module detection methods can decompose these networks into smaller interpretable modules. However, these methods are not adapted to deal with multi-omics data nor consider topological features. When deriving very large modules or ignoring the broader network context, interpretability remains limited. To address these issues, we developed a SUbgraph BAsed mulTi-OMIcs Clustering framework (SUBATOMIC), which infers small and interpretable modules with a specific topology while keeping track of connections to other modules and regulators.ResultsSUBATOMIC groups specific molecular interactions in composite network subgraphs of two and three nodes and clusters them into topological modules. These are functionally annotated, visualized and overlaid with expression profiles to go from static to dynamic modules. To preserve the larger network context, SUBATOMIC investigates statistically the connections in between modules as well as between modules and regulators such as miRNAs and transcription factors. We applied SUBATOMIC to analyze a composite Homo sapiens network containing transcription factor-target gene, miRNA-target gene, protein–protein, homologous and co-functional interactions from different databases. We derived and annotated 5586 modules with diverse topological, functional and regulatory properties. We created novel functional hypotheses for unannotated genes. Furthermore, we integrated modules with condition specific expression data to study the influence of hypoxia in three cancer cell lines. We developed two prioritization strategies to identify the most relevant modules in specific biological contexts: one considering GO term enrichments and one calculating an activity score reflecting the degree of differential expression. Both strategies yielded modules specifically reacting to low oxygen levels.ConclusionsWe developed the SUBATOMIC framework that generates interpretable modules from integrated multi-omics networks and applied it to hypoxia in cancer. SUBATOMIC can infer and contextualize modules, explore condition or disease specific modules, identify regulators and functionally related modules, and derive novel gene functions for uncharacterized genes. The software is available at https://github.com/CBIGR/SUBATOMIC.

Inception module and deep residual shrinkage network-based arc fault detection method for pantograph–catenary systems

Inception module and deep residual shrinkage network-based arc fault detection method for pantograph–catenary systems

SNMRS: An advanced measure for Co-expression network analysis

The challenge of identifying modules in a gene interaction network is important for a better understanding of the overall network architecture. In this work, we develop a novel similarity measure called Scaling-and-Shifting Normalized Mean Residue Similarity (SNMRS), based on the existing NMRS technique [1]. SNMRS yields correlation values in the range of 0 to +1 corresponding to negative and positive dependency. To study the performance of our measure, internal validation of extracted clusters resulting from different methods is carried out. Based on the performance, we choose hierarchical clustering and apply the same using the corresponding dissimilarity (distance) values of SNMRS scores, and utilize a dynamic tree cut method for extracting dense modules. The modules are validated using a literature search, KEGG pathway analysis, and gene-ontology analyses on the genes that make up the modules. Moreover, our measure can handle absolute, shifting, scaling, and shifting-and-scaling correlations and provides better performance than several other measures in terms of cluster-validity indices. Also, SNMRS based module detection method results in interesting biologically relevant patterns from gene microarray and RNA-seq dataset. A set of crucial genes having high relevance with the ESCC are also identified.

Network-based prioritization of cancer biomarkers by phenotype-driven module detection and ranking

This paper describes an ensemble method with supervised module detection and further module prioritization for reliable network-based biomarker discovery. We design a module detection and ranking method called mRank to discover reliable network modules as cancer diagnostic biomarkers, with two procedures: (1) an iterative supervised module detection guided by phenotypic states in a specific network, (2) a block-based module ranking locally and globally via network topological centrality. We validate its effectiveness and efficiency by identifying hepatocellular carcinoma (HCC) network modules on a comprehensive gene regulatory network with specifying gene interactions by HCC RNA-seq data from the Cancer Genome Atlas (TCGA). These top-ranked modules by mRank get a mean AUC of 0.995 on TCGA HCC dataset with 371 tumor samples and 50 controls by cross-validation SVM. Based on the prior knowledge of cancer dysfunctions enriched in top-ranked modules, 69 genes are identified as HCC candidate biomarkers. They are further validated in independent cohorts with a classifier trained on TCGA HCC dataset. A mean AUC of 0.846 is achieved in distinguishing 976 disease samples from 827 controls. Moreover, some known HCC signatures such as AFP and SPP1 are also included in our identified biomarkers. mRank enables us to find more reliable network modules for cancer diagnosis. For a proof-of-concept study, we validate it in identifying HCC network biomarkers and it is generalizable to other cancers or complex disease. The overall results have demonstrated that mRank can find effective network biomarkers for cancer diagnosis which result in less false positives.

TPSC: a module detection method based on topology potential and spectral clustering in weighted networks and its application in gene co-expression module discovery

BackgroundGene co-expression networks are widely studied in the biomedical field, with algorithms such as WGCNA and lmQCM having been developed to detect co-expressed modules. However, these algorithms have limitations such as insufficient granularity and unbalanced module size, which prevent full acquisition of knowledge from data mining. In addition, it is difficult to incorporate prior knowledge in current co-expression module detection algorithms.ResultsIn this paper, we propose a novel module detection algorithm based on topology potential and spectral clustering algorithm to detect co-expressed modules in gene co-expression networks. By testing on TCGA data, our novel method can provide more complete coverage of genes, more balanced module size and finer granularity than current methods in detecting modules with significant overall survival difference. In addition, the proposed algorithm can identify modules by incorporating prior knowledge.ConclusionIn summary, we developed a method to obtain as much as possible information from networks with increased input coverage and the ability to detect more size-balanced and granular modules. In addition, our method can integrate data from different sources. Our proposed method performs better than current methods with complete coverage of input genes and finer granularity. Moreover, this method is designed not only for gene co-expression networks but can also be applied to any general fully connected weighted network.

Balanced Functional Module Detection in genomic data.

High-dimensional genomic data can be analyzed to understand the effects of variables on a target variable such as a clinical outcome. For understanding the underlying biological mechanism affecting the target, it is important to discover the complete set of relevant variables. Thus variable selection is a primary goal, which differs from a prediction criterion. Of special interest are functional modules, cooperating sets of variables affecting the target which can be characterized by a graph. In applications such as social networks, the concept of balance in undirected signed graphs characterizes the consistency of associations within the network. This property requires that the module variables have a joint effect on the target outcome with no internal conflict, an efficiency that may be applied to biological networks. In this paper, we model genomic variables in signed undirected graphs for applications where the set of predictor variables influences an outcome. Consequences of the balance property are exploited to implement a new module discovery algorithm, balanced Functional Module Detection (bFMD), which selects a subset of variables from high-dimensional data that compose a balanced functional module. Our bFMD algorithm performed favorably in simulations as compared to other module detection methods. Additionally, bFMD detected interpretable results in an application using RNA-seq data obtained from subjects with Uterine Corpus Endometrial Carcinoma using the percentage of tumor invasion as the outcome of interest. The variables selected by bFMD have improved interpretability due to the logical consistency afforded by the balance property. Supplementary data are available at Bioinformatics Advances online.

A DCT/DST Based Fast OFDM Method in IM/DD Systems

Intensity modulation and direct detection (IM/DD) method is widely used in optical communications. IM/DD systems require the transmitted symbols to be real-valued. Fast orthogonal frequency division multiplexing (FOFDM) is a multi-carrier communication method which can transmit by all the subcarriers and produce real-valued samples at the output. Besides the advantages of FOFDM the single-tap equalizer is unfeasible to compensate for inter symbol interference because of linear convolution property cannot be fulfilled by the inverse discrete cosine and discrete cosine transform (IDCT/DCT) pair that extensively used in FOFDM systems. In this letter, a novel FOFDM method is proposed which uses inverse discrete sine and discrete sine transform (IDST/DST) and IDCT/DCT pairs together. The proposed method provides the use of single-tap equalizer with the advantage of lower peak to average power ratio (PAPR).

ModularBoost: an efficient network inference algorithm based on module decomposition

BackgroundGiven expression data, gene regulatory network(GRN) inference approaches try to determine regulatory relations. However, current inference methods ignore the inherent topological characters of GRN to some extent, leading to structures that lack clear biological explanation. To increase the biophysical meanings of inferred networks, this study performed data-driven module detection before network inference. Gene modules were identified by decomposition-based methods.ResultsICA-decomposition based module detection methods have been used to detect functional modules directly from transcriptomic data. Experiments about time-series expression, curated and scRNA-seq datasets suggested that the advantages of the proposed ModularBoost method over established methods, especially in the efficiency and accuracy. For scRNA-seq datasets, the ModularBoost method outperformed other candidate inference algorithms.ConclusionsAs a complicated task, GRN inference can be decomposed into several tasks of reduced complexity. Using identified gene modules as topological constraints, the initial inference problem can be accomplished by inferring intra-modular and inter-modular interactions respectively. Experimental outcomes suggest that the proposed ModularBoost method can improve the accuracy and efficiency of inference algorithms by introducing topological constraints.

Optimized Low Frequency Temperature Modulation for Improving the Selectivity and Linearity of SnO2 Gas Sensor

The disadvantages of poor selectivity, thermal instability and nonlinear output for metal oxide semiconductor (MOS) gas sensors restrict their detection accuracy for flammable and toxic gases. In this paper, a low frequency dynamic temperature modulation detection method using rectangular wave was designed and first proposed herein. The temperature modulation detection mechanism was also provided in detail. The flammable gases CH4 and CO were detected successfully using this method by self-made indirectly Pt/SnO2 sensors. The results show that the response time of the Pt/SnO2 sensors is 9 s and 8 s, and the recovery time is 25 s and 23 s for 100 ppm CH4 and CO, using the rectangular wave temperature modulation with a working temperature of 335-382 °C and an optimized frequency of 100 mHz. The temperature modulation has more advantages than steady-state constant temperature method in power consumption and working temperature. The temperature-modulated response for CH4 and CO, are highly linear with the gas concentration without using the log coordinates. The selectivity of the Pt/SnO2 sensors for 500 ppm CH4 and CO is enhanced from 2.03 using the traditional steady-state constant temperature detection to 2.56 using the temperature modulation method. Compared with the steady-state constant temperature method, both the selectivity and linearity of the response output for the Pt/SnO2 sensors are improved by this method, and an effective detection can be realized for a wide gas concentration range of 0–500 ppm CH4 and CO. It provides a new potential detection pathway for MOS-based gas sensors.

Identification of protein complexes and functional modules in E. coli PPI networks

BackgroundEscherichia coli always plays an important role in microbial research, and it has been a benchmark model for the study of molecular mechanisms of microorganisms. Molecular complexes, operons, and functional modules are valuable molecular functional domains of E. coli. The identification of protein complexes and functional modules of E. coli is essential to reveal the principles of cell organization, process, and function. At present, many studies focus on the detection of E. coli protein complexes based on experimental methods. However, based on the large-scale proteomics data set of E. coli, the simultaneous prediction of protein complexes and functional modules, especially the comparative analysis of them is relatively less.ResultsIn this study, the Edge Label Propagate Algorithm (ELPA) of the complex biological network was used to predict the protein complexes and functional modules of two high-quality PPI networks of E. coli, respectively. According to the gold standard protein complexes and function annotations provided by EcoCyc dataset, most protein modules predicted in the two datasets matched highly with real protein complexes, cellular processes, and biological functions. Some novel and significant protein complexes and functional modules were revealed based on ELPA. Moreover, through a comparative analysis of predicted complexes with corresponding functional modules, we found the protein complexes were significantly overlapped with corresponding functional modules, and almost all predicted protein complexes were completely covered by one or more functional modules. Finally, on the same PPI network of E. coli, ELPA was compared with a well-known protein module detection method (MCL) and we found that the performance of ELPA and MCL is comparable in predicting protein complexes.ConclusionsIn this paper, a link clustering method was used to predict protein complexes and functional modules in PPI networks of E. coli, and the correlation between them was compared, which could help us to understand the molecular functional units of E. coli better.

Miniaturized optical rotation detection system based on liquid crystal variable retarder in a K-Rb-21Ne gyroscope.

A novel method to control the light intensity stability and modulate the probe light polarization using a liquid crystal variable retarder (LCVR) to detect atomic spin precession simultaneously in a K-Rb-21Ne gyroscope is reported. A sinusoidal driving voltage is applied to drive the LCVR and is skillfully used to produce a high-frequency modulation for the probe light. The modulation helps to avoid electronic detection noise appearing at low frequencies and allows for phase-sensitive detection. The coefficient of rate ramp can be reduced from 1.31 (deg/h)/h to 0.05 (deg/h)/h (Allan deviation), and the bias instability of about 0.08 deg/h at the averaging time of 200 s is achieved. Therefore, the long-term stability of the angular velocity measurement can be improved and other optical modulators can be replaced to facilitate the miniaturization of the gyroscope by using this intensity modulation detection method. This optical rotation detection method also can be applied to other miniaturized atomic sensors, such as atomic magnetometers.

Enriching Human Interactome with Functional Mutations to Detect High-Impact Network Modules Underlying Complex Diseases.

Rapid progress in high-throughput -omics technologies moves us one step closer to the datacalypse in life sciences. In spite of the already generated volumes of data, our knowledge of the molecular mechanisms underlying complex genetic diseases remains limited. Increasing evidence shows that biological networks are essential, albeit not sufficient, for the better understanding of these mechanisms. The identification of disease-specific functional modules in the human interactome can provide a more focused insight into the mechanistic nature of the disease. However, carving a disease network module from the whole interactome is a difficult task. In this paper, we propose a computational framework, Discovering most IMpacted SUbnetworks in interactoMe (DIMSUM), which enables the integration of genome-wide association studies (GWAS) and functional effects of mutations into the protein–protein interaction (PPI) network to improve disease module detection. Specifically, our approach incorporates and propagates the functional impact of non-synonymous single nucleotide polymorphisms (nsSNPs) on PPIs to implicate the genes that are most likely influenced by the disruptive mutations, and to identify the module with the greatest functional impact. Comparison against state-of-the-art seed-based module detection methods shows that our approach could yield modules that are biologically more relevant and have stronger association with the studied disease. We expect for our method to become a part of the common toolbox for the disease module analysis, facilitating the discovery of new disease markers.

Symmetric >67 Gbps OFDM-IMDD based WDM access network for mitigating Rayleigh backscattering interference noise

In this paper, we propose a symmetric >67 Gbps/λ wavelength-division-multiplexing (WDM) access network with mitigation of Rayleigh backscattering (RB) interference noise by using the orthogonal frequency division multiplexing (OFDM) modulation and intensity modulation and direct detection (IMDD) method. According to the optimal OFDM bit-loading algorithm design, the fiber chromatic dispersion and RF fading can be improved after a length fiber connection of 20 km long. Moreover, the obtained OFDM modulation rate also can meet the forward error correction (FEC) target.

SNFM: A semi-supervised NMF algorithm for detecting biological functional modules.

Unraveling protein functional modules from protein-protein interaction networks is a crucial step to better understand cellular mechanisms. In the past decades, numerous algorithms have been proposed to identify potential protein functional modules or complexes from protein-protein interaction (PPI) networks. Unfortunately, the number of PPIs is rather limited, and some interactions are false positive. Therefore, the algorithms that only utilize PPI networks may not obtain the expected results related to functional modules. In this study, we propose a novel semi-supervised functional module detection method based on non-negative matrix factorization(NMF)(SNFM), which incorporate high-quality supervised PPI links from complexes as prior information.Our method outperforms all the other competitors with improvements on performance by around 15.4% in Precision, 28.9% in Recall, 27.1% in F-score (on DIP data set) by using PCDq as gold standards.